If you’d like to talk, just hit reply.

I’m dedicating this week’s Off Kilter to a hypothesis about a potential future for the marketing organization I’m calling an ‘advantage ecosystem.’

Not because this is a prediction, but because our capacity to imagine and then hold different potential futures in our head at once is one of the most important tenets of strategy. Which is why hypothesis development, built from different assumptions and interpretations of available facts, data, and information, is at the very core of strategy as a craft.

While none of the hypotheses we might create may come to pass, that’s often not the point. What they offer are different perspectives on futures that might significantly inform the decisions we choose to make in the present.

With that in mind, let’s explore the hypothesis for an ‘advantage ecosystem.’

The last gasp of efficiency dogma?

Spend any time listening to the AI rhetoric, and you’d be forgiven for thinking we’re entering a powerful new efficiency cycle, as the enterprise conversation revolves around the promise of increased productivity, fewer people, and lower costs.

But what if that isn’t actually the case, and we’re not at the beginning of an efficiency cycle at all, but at the end of one?

If we zoom out far enough, corporate history roughly alternates between unstable, high-uncertainty periods of innovation and exploration and stabler, more certain periods of optimization and exploitation. While every industry experiences these cycles on different timeframes, they’re typically precipitated by a significant technological breakthrough. A pattern we’ve seen roughly repeat from the Industrial Revolution onward.

Today, I’d argue that two things might be true:

1. We’re at the tail end of the digital cycle’s efficiency optimization and exploitation phase.

2. We’re at the beginning of the AI cycle’s innovation and exploration period.

At the leading edge of this emerging innovation cycle, model companies, chip manufacturers, and infrastructure providers aren’t efficiency optimizers; they’re possibility maximizers.

Whether the AI bubble continues forever, or pops with a whimper, or with a bang, what has already been built will almost certainly be less an infrastructure for greater productivity and efficiency than a means of future innovation and exploration.

If this is the case, then the most meaningful strategic questions we should be asking about the future of marketing are not simply about increasing the efficiency of existing practice, but about what we can now create that wasn’t possible before.

And, even more importantly, where the rare sources of competitive advantage are shifting to as formerly scarce knowledge and capabilities become freely available.

What is marketing actually for?

If we’re exiting a period of relative stability focused on efficiently optimizing and exploiting existing sources of advantage, and entering a period of instability focused on innovation and the exploration of new sources of advantage, what role marketing?

My frame of reference is that marketing should be understood as the business discipline of future customer value creation and subsequent value capture.

The word future is critical. Marketing isn’t simply responsible for efficiently converting today’s products into today’s demand. At its strategic best, it figures out what customers will value next, what the company might therefore create, which capabilities it needs, where new demand might emerge, and how that value will, in turn, be captured.

Under this definition, marketing acts as a source of differentiated competitive advantage for the firm. And if the role of marketing is to be a source of differentiated competitive advantage, then the marketing organization itself cannot be universally modeled. By definition, a marketing organization that acts as a source of differentiated advantage must itself be differentiated.

Of course, there can be things almost everybody should know how to do. There can be empirical generalizations, technical skills, and commodity capabilities. But Nike will not organize its marketing like P&G. Apple will not organize its marketing like Costco. And a luxury business won’t organize it the way a B2B software company would.

Different businesses possess different histories, customer permissions, capabilities, economics, ambitions, risk profiles, competitors, and theories of how they create value, which means there will be no universally optimal “AI-native marketing organization” just waiting to be revealed by McKinsey, OpenAI, Adobe, Google, or whoever. (Although we should expect exactly this claim in the coming months and years because the economics of intermediating client business processes are intoxicating. A topic for a future edition).

Instead, the important question is one of where advantage accrues to this particular organization, under its particular circumstances, within its particular market context, with its particular customers, competitors, and capabilities.

As the costs of coordination collapse...

If we look back twenty years, we see an interesting paradox. The digital revolution massively expanded the universe of things marketers could do, but we ended up narrowing it down to a fairly generic bundle of capabilities. Why?

It wasn’t that this bundle of capabilities was intrinsically optimal. Rather, our capacity to affordably manage and coordinate all of these new possibilities didn’t increase at anything like the same rate they were emerging. Managing a myriad of specialist partners is difficult. Connecting dozens of technologies is difficult. Moving information between systems is difficult. Maintaining clarity of vision and context across teams, agencies, platforms, and workflows is difficult, especially when they’re all measured differently. And all of that difficulty is expensive in terms of management time, financial resources, stability, and the risk of failure.

So we built larger internal departments made up of more specialist skills, often vendor-defined. We hired or built giant integrated agencies to take on some of the coordination burden. We bought pre-packaged martech suites. We consolidated vendors. And increasingly we ran marketing activity through a small number of giant platforms capable of coordinating complexity at scale.

The result was that, while digital expanded our theoretical universe of choices, the economics of coordination collapsed much of that choice back into standardized bundles. Over time, these bundles became the foundation for modern marketing as it was redefined and rebuilt for the digital era, where we drew a line around what technology could efficiently optimize for, labeled it “marketing,” and then allowed much of what fell outside of that line to slowly wither.

By contrast, in his book Reshuffle, Sangeet Paul Choudary argues that one of AI’s biggest impacts will be its alteration of the economics of coordination. Moving information, passing context, routing work, sequencing tasks, connecting specialists, invoking agents, integrating outputs, and maintaining state across complicated workflows.

Meaning that things that have traditionally been complicated, expensive, and thus hard to manage, especially across firm boundaries, would instead become cheap and easy. The direct implication is that if AI can dramatically reduce the cost and complexity of such coordination, management will gain much greater freedom over which capabilities to draw from, when to draw from them, and how to dynamically configure and reconfigure them.

In other words, the richness of choice promised by the digital revolution twenty years ago might finally become economically viable, which potentially changes the strategic equation significantly. Why? Because lower coordination costs don’t remove the need for strategic choice; instead, they dramatically expand the choice set, rendering strategic and creative judgment more important, not less.

If we assume that AI will make competent execution more abundant and more affordable across more activities and more channels, which it appears to be on the path to delivering, then deciding what is worth executing becomes increasingly consequential.

My main observation here isn’t that cheaper coordination will make giant marketing organizations easier to run, although that might also be true. It’s that it has the potential to free us to do a lot more, more differentiated things.

Advantage Ecosystems.

As knowledge, execution, analysis, production, and parts of expertise become increasingly commoditized and widely available under AI, capabilities that once represented sources of distinct competitive advantage will instead become table stakes. But it would be entirely wrong to conclude that just because many of today’s marketing activities are commoditizing that the marketing organization itself should be treated as a commodity.

Why? For two big reasons:

1. An increasingly abundant supply of generic competence will almost certainly make genuinely scarce capabilities and sources of valuable variance even more valuable.

2. Cheap coordination expands the choice set, allowing us to combine and recombine internal talent, external specialists, partners, technologies, proprietary knowledge, and machines to create and capture value in ways that can produce new sources of competitive advantage.

In other words, such a marketing organization will have the capacity to exploit two forms of differentiation. First, access to differentiated components (exceptional people, proprietary data, specialists, IP, bespoke agents, etc.); second, the capacity to configure and coordinate both differentiated and commodity components into uniquely differentiated systems of value.

This is what I mean by treating the marketing organization as an advantage ecosystem.

Not a looser organizational boundary, or a network of outsourced capabilities, but a deliberately cultivated ecosystem through which a company discovers, combines, and regenerates sources of differentiated value. Some components of that ecosystem will be commodities, while others will be genuinely scarce.

Advantage therefore resides both in the capabilities themselves and in the particular configuration through which they are combined.

This is AI making commodity activities easier and cheaper to access while simultaneously making it economically viable to construct increasingly bespoke systems around uncommon sources of value. These systems need not be fixed. Some components might be deeply institutional, others temporary. Some internal, some external. Some human, others machine. Some rare, others deliberately commoditized.

The point isn’t flexibility for flexibility’s sake. It’s reconfigurability relative to the strategic choices you make and the new choices that subsequently emerge, which shifts the marketing organization's strategic stance away from periodic planning cycles and toward dynamic reconfigurability.

As a result, the role of the marketing organization would stop being defined principally by downstream operational tactics, many of which will be automated or augmented by AI anyway, and increasingly flow downward from upstream questions like:

Where are rare sources of advantage moving toward, and what combination of capabilities gives us the best opportunity to pursue them?

Of course, such a shift would have potentially profound second-order effects. For example, on talent.

What are juniors actually for?

The most obvious implication of AI for entry-level employment has so far been to do less of it. If AI can do much of the work historically handed to junior marketers, why should companies continue to hire junior marketers?

Yet this framing mistakes their output for the purpose of hiring them in the first place.

The work done by entry-level employees has never been the purpose of hiring them; it’s the training ground from which they develop specialist skills, judgment, domain expertise, and leadership capabilities. In other words, junior hires are an investment in your own future capability, which seems pretty important if we’re defining marketing as the business discipline of future customer value creation.

Under an advantage ecosystem mindset, where sources of advantage are rare and therefore difficult to find, and where the range of affordable choices expands dramatically, such human roles will become more important rather than less.

As a result, the job of machines would be to remove low-value drudgery while helping provide younger employees with greater exposure to consequential problems, useful on-the-job feedback, simulation, strategic choices, and work across a wider range of contexts. Rather than being a low-cost means of execution, junior work would aim to develop people with the judgment to discover, coordinate, and regenerate future sources of advantage.

Therefore, eliminating junior roles because a machine can produce the output more economically might create a false sense of efficiency: improving the economics of today’s work while significantly degrading the quality of the capabilities and choices the organization has available to it tomorrow.

Your best talent doesn’t have to work for you.

The same logic extends beyond the walls of the firm. If the objective is to find and cultivate the highest-value sources of differentiated competitive advantage, cheap coordination means management should care considerably less about whether those capabilities are employees, agencies, specialists, partners, or something else entirely, as long as they can be woven into our coordination system.

A company might combine an exceptional internal brand team with a brilliant independent creative agency, a niche cultural researcher, a specialist technologist, an architect, a world-class data scientist, a manufacturing partner, several AI agents, and someone whose role doesn’t yet have a title. Not because we want to outsource more, but because we want to broaden the range and quality of possibilities available to us.

Of course, this would likely lead to a significant shift in our partners. Today, many large integrated agencies, consultancies, and other sizable intermediaries are valuable not because of the quality of any individual capability, but because their scale allows clients to outsource some of the cost and complexity of coordination.

As AI lowers that cost and complexity, clients will gain greater freedom to assemble the best capabilities regardless of partner size, organizational boundaries, or traditional agency definitions.

Meanwhile, because AI also commoditizes many of today’s marketing activities, genuinely distinctive capabilities will become significantly more valuable. This means advantage ecosystems will need very different working relationships with the partners who become a part of them. Put simply, it won’t be enough to want the best people; the best people will have to want to work with you.

This means that rather than demanding a 20% discount because AI makes vendors ‘20% more productive,’ if a particular external specialist or agency materially contributes to your advantage and has competition for its services, our job can’t simply be to extract as much value from them as possible. Instead, it’s to remain sufficiently important, interesting, profitable, and strategically valuable to them that they continue providing privileged access to their best people, best thinking, best technology, and best effort.

In this world, protecting the economics of advantage-creating partners isn’t philanthropy; it’s enlightened self-interest. If an unusually valuable agency’s margins allow it to maintain excellence, that isn’t inefficiency to be eliminated. It’s part of the mechanism that funds the differentiation through which that partner remains unusually valuable to you.

Which, of course, means significant second-order effects on procurement.

Traditional procurement is exceptionally good at reducing supplier variance through standardized scopes, comparable rates, benchmarking, and consolidated spend. That works brilliantly when the thing being purchased is genuinely substitutable, and the costs of coordination are high.

But an advantage ecosystem deliberately seeks out capabilities precisely because they aren’t substitutable and is no longer worried so much about the costs of coordination, which means a system designed to make suppliers interchangeable will eliminate exactly the valuable variance the organization is trying to acquire.

Now, this is far from an impossible thing to change, even if it will be hard. After all, procurement only seeks standardization because that’s the objective it’s been given by management. If, instead, that objective were to change in an attempt to procure differentiated capability, it would require management to set a new direction. Not “How cheaply can we acquire this input?” but “How much value can this relationship create, and what conditions will maximize that value over time?”

As with junior talent, an apparently efficient decision in the present will likely degrade the quality of the differentiated capabilities and choices available tomorrow.

Coordination is not management.

None of this means AI eliminates the need for management. Improved coordination reflects how work moves through the system; it doesn’t define the work the system chooses to undertake.

Management would still be required to align people, capabilities, incentives, objectives, time horizons, and competing ideas of what value should be created.

Equally, marketing will still need to mediate between product and sales organizations that disagree, finance functions that value certainty while innovation emerges from ambiguity, creative partners who believe the client is solving the wrong problem, and two entirely plausible futures that demand opposite actions.

And if cheaper coordination dramatically expands the number of economically viable choices, then marketing management will face more consequential decisions, more often, which means management itself will need to be of a higher caliber.

Which brings me back to the reason for developing junior talent being to give you more sources of better judgment in the future. This is because somebody still has to decide what matters. And the best marketing organizations will be those that empower more people to exercise better judgment, more accurately, more often, across a broader range of choices.

From efficiency to advantage.

So, this brings us back to where we started.

The current AI narrative is intensely focused on efficiency, but maybe this isn’t because we’re at the beginning of a powerful new efficiency cycle. Maybe it’s because we’re at the end of one. After a long period in which optimization and exploitation have been unusually well rewarded by shareholders, managers are naturally inclined to look for the same now. Which means it’s entirely rational for AI vendors to pursue such a narrative, because it offers them the shortest, clearest path to revenue.

But that doesn’t mean it’s necessarily correct, which sets up an interesting strategic thought experiment.

Let’s say you assume the efficiency thesis, where AI lets us optimize the existing system. Your assumption is that we’re at the start of a powerful new efficiency cycle and use AI to set up your marketing organization for maximum efficiency, automating existing processes and workflows, eliminating junior hiring and development, and optimizing today’s sources of competitive advantage.

Meanwhile, I assume the advantage thesis, where AI changes the economics sufficiently that we should reconsider the system itself. My assumption is that we’re at the beginning of a powerful new innovation cycle. As a result, I choose to use AI to reformulate my marketing organization as an advantage ecosystem: choosing to double down on future talent rather than eliminating it, creating the capacity to seamlessly combine internal and external sources of advantage, cultivating privileged relationships with scarce partners, and redesigning procurement to identify and protect valuable variance.

Who wins?

I don’t know. Maybe both of us. Maybe neither. But, ultimately, it depends on which of us made the right strategic bet based on superior assumptions about the future we might shape for ourselves.

But winning or losing isn’t the point of this edition. The point is the value of having differentiated strategic hypotheses to debate, pick apart, and argue about in the first place. They allow us to challenge received wisdom and common assumptions so that we can consider the implications if we, or those we compete with, were to act in unexpected ways.

Subscribe now

Share

Leave a comment

If you’d like to talk, just hit reply.

In last week’s edition, The Availability Trap, I referenced Roger Martin and his strategy choice cascade, which has two important questions at the top level: where to play? and how to win?

I then mentioned that for a small number of exceptional brands, another possibility exists. Over time, they earn the right to redefine what “where to play” even means. These are brands that create their own competitive arena, what I call a brand-defined domain. Today, I want to dive a little deeper into this topic.

Spend any time looking at brands like Apple, Amazon, Disney, LEGO, or Red Bull, and you can observe that a coherent worldview, clear customer permission, and a sequence of differentiated, interdependent business bets can enable a business to escape its pre-defined category limits.

The result isn’t a competitive category shared with others, but an apex outside-of-category strategic position where the brand re-draws the competitive arena it exists within. Of course, this isn’t something you can just declare. You first have to build the necessary ecosystem to deliver it, and only then will customers tell you whether the resulting domain is real. In other words, this isn’t a strategic option available to everyone; it’s competitively rare, hard to achieve, and it only becomes fully operative when customers recognize it as such. Rather than a reason not to pursue it, this difficulty and rareness is what renders this approach more strategically valuable as a source of differentiated competitive advantage.

What matters for our purposes in this Off Kilter edition, though, isn’t that competing in a brand-defined domain is rare. It’s that it’s a legitimate strategic possibility at all.

This is useful for a very simple reason. Once we know such a goal is possible, it’s the kind of knowledge that can change the futures business leaders are willing to imagine, and therefore the bets they’re willing to make on getting there.

First, stop defining brand narrowly.

Before we can consider creating a brand-defined domain as a strategic possibility, we need to clarify what we mean by brand. This is because, over the past two decades, we’ve progressively defined it in smaller, narrower, and weaker ways.

At one level, this has uses. Branding did accumulate some oddly mystical baggage over the years, which needed to be stripped away. As I mentioned last week, the empirical work of marketing science has provided us with valuable, generalizable insights: brands need to be easy to recognize, easy to remember, and easy to buy. Distinctive assets matter. Mental and physical availability matter.

The problem, however, is what happens when a useful description of one thing a brand does becomes conflated with everything a brand is. Extend the work of Ehrenberg Bass too far, and at its most reductive, brand becomes little more than a purchase trigger that has little or no relation to the proposition itself. Do the same with someone from Brand Finance, and it becomes an intangible financial asset. Do the same with an identity designer from Pentagram, and it becomes a visual identity. With a brand strategist from BBDO, it becomes brand advertising. You get the gist.

What I’m saying is that the biggest problem with the term brand these days isn’t its fabled mystical powers; it’s that we’ve allowed ourselves to fragment its definition through whatever lens we happen to be looking at it.

Why this matters isn’t just that it’s confusing; it’s that the narrower our definition of brand, the smaller the strategic possibilities we can imagine for it, and thus the more limited our actions will be.

Which is all to say that if we can’t define our brand in a bigger and more business-centric fashion, then the brand-defined domain becomes impossible to imagine, let alone achieve.

Business out and customer back.

The most useful way to understand brands is from the business out and the customer back, rather than through whatever professional lens we happen to be using.

Your worldview is what lies at the center, either explicitly or implicitly. This reflects the mindset behind what you stand for and thus your philosophy of customer value creation. It’s adjacent to, but not quite the same thing as, Roger Martin’s idea of a winning aspiration. While a winning aspiration describes what winning looks like, your worldview defines its shape. For example, any viable choices Disney might have regarding a winning aspiration will be shaped by its worldview’s perspective on family, story, and character. As a result, it reflects what the company believes about customers, value, and the world, which makes certain ways of winning more relevant, achievable, and attractive than others.

Over time, such a worldview gets encoded into distinctive business systems as you make your own unique choices about product, experience, pricing, distribution, innovation, service, operations, partnerships, and communications. The more cohesive these choices become under the governance of a distinct worldview, the less they look like independent functional decisions and the more they look like interdependent manifestations of the same underlying logic.

I’ve previously described this progression as:

Worldview → Distinctive Systems → Believable Brand Universe

The believable brand universe is how the offer and value generated by your systems are presented to the customer. What Sharp calls distinctive assets help identify your universe; communications dramatize it and give it further meaning; availability makes it easier to experience the product proposition; and the product proposition itself extends and deepens our experience.

The key point is that it’s the cumulative effects of upstream business choices that give all these downstream signals something to signify.

This directly contrasts with how most businesses compete. Rather than focusing on their own distinct relationship with the customer, their thinking is bounded by externally defined categories, such as cars, software, toys, insurance, beer, and energy drinks.

If the category rather than your worldview acts as the boundary, the job of strategy becomes deciding where within that category we intend to compete and how we intend to win.

This is also whjat distinguishes a brand-defined domain from simple brand extension. Rather than extending from one category to another and each time treating the category as the boundary condition for your offerings, the brand-defined domain is built through value created via cross-category interdependence. This creates hard-to-compete-with differentiation by forming a meaningful system of value that transcends conventional category boundaries.

What is Apple? A phone company, a computer company…

Consider Apple. In 2001, Apple was explicitly describing the Mac as the center of its Digital Hub strategy. Then came iPod, tightly integrated with iTunes. Then came the iTunes Music Store, which Apple described as part of a complete solution for buying, organizing, and listening to digital music. Then came the iPhone. Then came apps, tablets, watches, headphones, payments, services, custom silicon, and so on until we get to where we are today. A uniquely integrated and interdependent ecosystem of hardware, software, and services.

Viewed through a conventional category lens, this looks like a computer company repeatedly diversifying. However, if we jettison category as our lens, we can see that what actually happened is that a unique system of customer value was formed around the the underlying beliefs of theApple brand itself.

Each successful move increased the value of other parts of the system. Hardware made services more useful. Services made hardware more useful. Developers increased device value. Devices increased developer opportunity. Silicon allowed deeper integration. Customers’ acceptance of such cross-category behavior made subsequent moves easier to scale.

Apple controls that ecosystem and the business, design, product, and marketing communication choices that built it. But the domain it now uniquely competes in exists only because customers changed their consumption choices in response to it.

What Apple’s brand-defined domain has done is shift consumer questions from things like “Which headphones are best?” (High competitive intensity) to “Which headphones work best with my iPhone?” (Low competitive intensity). And, as you know, if you can shift customer choice away from an arena that many competitors can satisfy toward one that only a few, or in this case, one can satisfy, then what you’ve done is fashion a source of distinct competitive advantage.

Samsung can compete with iPhone. Spotify can compete with Apple Music. Garmin can compete with Apple Watch. Dell can compete with Mac. But competing with Apple increasingly requires competing with something larger than any one of those categories, because Apple’s strategic choices have systematically changed the customer’s frame of reference in its favor.

As a result, Apple has been able to uniquely redefine its own “where to play” choice-set.

Amazon too.

Amazon is a revealing example because the apparent category leap is so extreme. Retail-to-cloud computing makes little sense under conventional category-adjacency or brand-extension theory.

AWS launched in 2006 on an operating and economic model that made computing infrastructure easier to access and pay for based on usage rather than large upfront commitments. While the offering was wildly different from Amazon retail, its governing philosophy was exactly the same: Internet scale. Customer obsession. Reliability. Low friction. Infrastructure hidden behind a simple interface. Relentless pressure on cost. And something else Amazon brought to the table: customer permission.

Not because executives were equating reliability at package delivery with reliability at compute infrastructure. Instead, it was that the Amazon brand carried a set of accumulated beliefs that reduced the uncertainty barrier associated with the move: It operated at extraordinary scale, was deeply associated with tech innovation, was expected to remove friction, was expected to be highly reliable, and was expected to make expensive things cheaper.

While the categories were miles apart, the worldview that connected them was not, which is a useful distinction, because category adjacency matters less than worldview adjacency when the brand possesses sufficient clarity to make the leap credible.

Three Hurdles

None of this means that even a very strong brand can casually wander into any category it likes. Quite the reverse. Research on brand extension and category spanning suggests that crossing established boundaries can be costly when customers can’t figure out why you’re doing it and/or what you’re doing isn’t very good.

As a result, there are three hurdles any brand-defined domain-enabling strategy must cross:

1. Systemic coherence: Does this belong in our brand, and does its presence make the whole system more valuable?

2. Local competitiveness: Is it good enough to win on the terms of this arena, and does its interdependence with our broader system change the nature of its competitveness?

3. Customer permission: Does our existing brand materially lower the barrier to belief, trial, or adoption?

Brand permission is not merely affection or awareness. It’s accumulated customer belief that allows a new offering presented in a new category to begin somewhere other than zero.

The net, net is that cross-category activities must create additional value for one another, which category-bound competition cannot easily replicate. This is what separates a brand-defined domain as a distinct competitive arena from a branded portfolio of discreet offerings operating across categories.

Domains are discovered via strategic bets.

This brings us back to asset allocation. In 1997, Steve Jobs didn’t sketch out today’s Apple ecosystem on the back of a napkin and then have the company execute it flawlessly for decades. That would be ridiculous.

Instead, its history contains failures, experiments, and intermediate moves. The Newton came, went, and failed. Apple’s Digital Hub? Bet you can’t even remember that. Then iPod and iTunes created new opportunities. Opening iTunes to the PC created others. The Motorola Rokr was an experiment that preceded the iPhone.

The point is, it was far from a straight-line move into the future, and each move changed what became possible next, either by learning what didn’t work, or from the new opportunities that emerged from what did.

In other words, Apple’s journey toward the apex position of defining its own competitive domain was built upon a series of path-dependent choices. While it wasn’t gambling at random, Apple was making a portfolio of strategic bets under a thesis. Each bet created learning, added capabilities, impacted customer permission, and revealed strategic options that did not previously exist.

This is what makes the resulting domain unusually hard to copy. While a competitor might attempt to copy each individual component in retrospect, it cannot copy the path to the system itself. This is why we see competitors to Apple building phones, launching streaming services, opening stores, creating watches, and designing headphones. Yet, none have been able to reproduce decades of accumulated capabilities, customer meaning, installed relationships, permission, and the interdependence of Apple’s assets.

It’s also a reinforcement, if any were needed, that we should be wary of cost accounting as a governance framework for key marketing decisions. If every uncertain investment must first be justified by a predictable standalone ROI before it is made, then the cumulative strategic differentiation delivered through emergent and path-dependent bets would never occur.

A rare opportunity.

Most businesses will never earn their way to a brand-defined domain. Most probably shouldn’t even try. That doesn’t make it less valuable. Instead, its rarity and lack of competitor replicability render it strategically much more valuable.

The point isn’t to automagically announce you’ve created a new domain. It’s to recognize that, for some brands under certain conditions, the category boundary can become a strategically valuable variable. Not something you accept, but a boundary you choose to reshape.

This is why definitions that reduce brand to a purchase signal, an intangible asset, a visual identity, or advertising have consequences far beyond esoteric arguments between adjacent functions. It limits your strategic choices.

If brand were only a purchase trigger, our strategic ambition would be limited to building a better trigger. If it were solely an intangible financial asset, our ambition would be limited to maintaining that asset. You get the gist.

Meanwhile, if we instead view brand as a customer-oriented system that helps us organize distinctive sources of customer value, greater possibilities become visible.

You can build distinctive business systems. You can make path-dependent bets under a thesis. You can establish an interdependent ecosystem; you can earn customer permission. And if you do all that well, then you earn the opportunity, but not the right, to frame your own competitive arena. One nobody else gets to share.

Subscribe now

Share

Leave a comment

If you’d like to talk, just hit reply.

Last week I was talking to an executive at a large, mature brand that commands a considerable price premium over its competition and is experiencing double-digit volume growth. At one point, he said the following:

“If all we did was follow Sharp, we’d be screwed.”

It struck me because there’s an important point buried in that statement. He was neither elevating the How Brands Grow (HBG) theories of Professor Byron Sharp nor discounting them. He was merely saying they’re not enough on their own to create success.

Contrast this with the tedious LinkedIn battles being fought over HBG, where both sides dance atop the head of the same tiny pin. On one side we have HBG acolytes and self-styled ‘evidence-based marketers,’ brandishing Dirichlet distributions, double jeopardy, mental and physical availability, and distinctive assets as rhetorical weapons. On the other, we have those the acolytes dismiss as “Sharp baiters,” determined to knock it all down because they think he reduces the richness of brands and human behavior to little more than a purchase trigger.

Thing is, they’re both answering the wrong question. What they should be asking is what his work is useful for, and at what altitude within the marketing organization it should operate.

This is because HBG gets a lot right, just not everything. If our brand is hard to find, difficult to remember, looks different every six months, and our media strategy involves hyper-targeting a handful of people and a dog, we have a lot to fix.

What matters is less that we need to fix it, and more why we need to fix it. What HBG demonstrates are operational necessities of competent marketing. What we should be careful not to do is to mistake such operational competence for strategy.

Make this mistake, and we risk falling into the Availability Trap. At the company level, this pushes competitors toward increasingly similar ways of competing on the basis of mental and physical availability. And, when that happens, something more consequential happens. You commoditize the CMO.

Across recent Off Kilter editions, I’ve been making the opposite argument. First, that strategic marketing represents one of the best training grounds for future CEOs. Second, that high-performing marketing organizations should think like asset allocators rather than cost accountants, managing known returns alongside asymmetric bets on future customer value.

The focus of today’s edition is that any CMO who might plausibly become a CEO cannot spend their career elevating remedial competence into enterprise strategy. They need to understand what that competence is, ensure their marketing organization executes it brilliantly, and then use that as a foundation to operate at a much higher strategic altitude.

With that said, let’s dive into these murky and decidedly shark-infested waters.

How marketing got so lost it needed Sharp’s remedial wisdom.

To understand why HBG became so influential, we need to remember what happened during marketing’s chaotic shift to digital. With blindingly perfect 20:20 hindsight, the big mistake wasn’t adopting new tools. It was assuming these new tools meant marketing itself now worked according to radically new laws.

Sure, I mean, I get it. I was there. It was a confusing time. New channels, platforms, data, dashboards, metrics, and a veritable army of vendors loudly proclaiming the past was a corpse. What this led to was an epistemological free-for-all (epistemological meaning how we know what is true), where any charismatic technology or individual could credibly lay claim to the future. And they did. The problem is that most of it turned out to be spurious flim-flam. (Note to marketers. Beware the exact same thing happening right now with AI).

Onto this battlefield of spuriousness charged marketing science, like White Knights atop their mighty Australian destriers, shielded against flim-flam with their holy lance of empiricism.

And much of their prescription was necessary. Known patterns of consumer behavior didn’t cease to exist just because we were now face-down in an infinite scroll. Instead, marketing had become so thoroughly confused that being reminded of basic fundamentals somehow felt revolutionary.

This isn’t to disparage Sharp, Ehrenberg-Bass, or the wider marketing science community. They performed a genuine service by reintroducing empirical discipline to a function that was quickly disappearing up its own backside.

No, the problems came later, when a necessary correction to marketing’s epistemological chaos became a worryingly limiting epistemology of its own.

Operational competence is not strategy.

To understand where the limits of HBG sit, it’s worth going back to the academic work it’s built on. Among other empirical generalizations, the Dirichlet model is foundational and worth examining. In a 2004 paper in the Journal of Business Research, Andrew Ehrenberg and his co-authors were clear about what that model is and is not.

First, on what it covers: “The model is defined for steady state and unpartitioned markets where market shares are stationary and there is no clustering of particular brands.” Deciphering academic-speak: It describes markets where shares are broadly stable and where you don’t see distinct groups of customers tending to buy different sets of brands.

Second, on what it claims: “The model purports only to describe what markets are like when they are near steady and nonpartitioned.” Again, deciphering: It describes a benchmark against which to assess what happens; it does not pretend to tell you which strategic actions you should choose. The authors are actually quite specific that it includes no explicit ‘decision variables.’

Third, the paper notes that competing brands are often similar, using Ehrenberg’s memorable phrase ‘the commodity with a name.’

Why Dirichlet matters, and why I singled it out, is this: Empirical generalizations can tell us a lot about how markets behave, establish useful benchmarks, and help us judge the results of action. They can even help guide operations. But benchmarking a strategic intervention is very different from selecting what it should be.

This distinction applies to HBG more generally. It tells us a lot about how brands tend to grow and compete. Yet, on its own, it cannot define the wider strategic choices that decide where we should compete, what customer value we should create, or which uncertain future(s) we should bet on.

This tells us something important. Not whether HBG is right or wrong, but where its boundaries lie.

Over time, these have become progressively less visible. First, Sharp translated a large body of empirical marketing science into HBG, an unusually accessible business book by academic standards. Then content marketers and the ensuing online discourse further simplified it, and now LLMs uncritically recirculate the simplified version at scale. Net, net, useful empirical regularities are now being treated as universal laws.

Of course, it’s highly problematic to take empirical regularities observed within known market boundaries and then turn them into a universal prescription for what the company should do about the boundaries themselves.

By contrast, Michael Porter provides a useful frame for understanding where this work belongs. Through a Porterian lens, much of what HBG prescribes sits closer to operational effectiveness than strategy. Everyone should be easy to buy. Everyone should be easy to remember. Every brand should be recognizable. Everyone should reach enough prospective buyers. Everyone should understand the situations in which people might buy from them.

While not being operationally excellent at these things absolutely can cause underperformance, if every competitor must do these things, they cannot simultaneously be every competitor’s strategy for winning. They can be strategically important without being strategically sufficient.

As a result, the question inevitably has to shift from: how do we stop losing through operational weakness? to how do we win by creating something truly worth a customer’s choice?

And this is where the Availability Trap rears its ugly head.

Once we internalize descriptive steady-state patterns as universal laws, the result is an asymmetric burden of proof. Known action inside the boundary is “evidence-based,” while any new action designed to change that boundary is just a thesis about a future that doesn’t yet exist.

Under this frame, availability has evidence; reach has evidence; penetration has evidence; brand assets have evidence. Meanwhile, a new product cannot prove customers will buy it before it exists. A new proposition cannot prove how much people will value it before they experience it. A new competitive arena cannot provide complete data describing its dynamics before you enter it.

For illustration, let’s present this through the lens of different strategic scenarios. Imagine four different brands. A small challenger, a mature leader, one facing disruption, and a growth pursuer entering a new market. None of these share the same strategic problem, and none will share the same strategic appetite for risk. So rationally, none of them should be allocating their scarce marketing resources in the exact same way. Yet elevate a universal operating prescription to the status of strategy, and, as if by magic, all four will converge around exactly the same class of answer.

Strategy can redraw boundaries.

A more pressing strategic issue is that HBG itself largely analyzes brands in the context of a category. And before you can analyze a brand inside a category, somebody first has to decide what that category is.

Why this matters is illustrated by Roger Martin’s strategy choice cascade, where he places Where to Play immediately alongside How to Win as two of strategy’s most foundational choices. In other words, choosing your competitive arena isn’t an administrative step that happens before strategy can begin. It is strategy.

Rita McGrath extends this further. In her work on transient advantage, she explicitly argues that corporations should think in terms of competitive arenas, rather than assuming traditional industry boundaries will explain who to compete against. Her reasoning is that technology blurs category lines, while customers pursue outcomes that can be satisfied by others.

Put simply, customers don’t buy through the analytical abstraction of our category definitions. They buy what they need and want and don’t care how we categorize it.

Looked at through the taxonomy of categories, Apple moving from computers to music players to phones to watches to services looks like repeated category jumping, where it presents differently in each category it’s analyzed within. However, to the customer, it’s not jumping at all because Apple established permission to solve a broad array of technology-adjacent problems in a uniquely Apple way.

When Sharp analyzed the iPhone against the double jeopardy law, he found exactly the pattern he’d predict. But such a category-defined analysis cannot see that Apple’s competitive strength in each arena is impacted by its presence in others. The competitive strength of AirPods isn’t simply that they’re great headphones. It’s that they also integrate unusually well with the iPhone. The iPhone, in turn, becomes more valuable because of the broader Apple ecosystem. Competitive advantage is being generated across the system, not independently inside each category.

This exposes a deeper limit of using HBG to define strategy. While it can show us how brands tend to compete within an arena, category-level analysis cannot fully explain competitive advantage generated by the relationships between arenas, nor decide which combination of arenas the corporation should construct in the first place.

So, while McGrath is right that technology reduces structural barriers, there’s another strategic effect marketers have in their armory. Strong brands can create customer permission that drops the adoption barrier when crossing categories, enabling them to redraw competitive boundaries into brand-defined domains. (More on this in an upcoming edition).

CPG. A canary in the availability coal mine.

This brings us to CPG (FMCG to all my non-American friends), because while it would be completely ridiculous to blame the sector’s problems on a book, what’s happening there is directionally interesting in a canary-in-the-coal-mine kind of way, since many of its problems mirror what a strategist’s interpretation of HBG’s prescriptions might predict.

Large CPG incumbents possess tremendous physical availability, mature brand assets, significant advertising budgets, and decades of accumulated mental availability. Yet they’re being attacked from two directions and are facing real pressure from both.

From below comes private label. NIQ reports that branded CPG products globally are often sold at a 26% premium over private-label equivalents. At the same time, 68% of global consumers in its survey regard private label as a good alternative to name brands and 69% say they offer good value.

This creates a profoundly simple question for name brand incumbents: What makes my brand worth 26% more than the store-brand substitute?

Mental and physical availability cannot answer this on their own. Sure, they can carry and signal value, but neither can indefinitely act as a substitute for the value being signaled.

Meanwhile, from the other direction come insurgent brands with a fraction of incumbent scale. Bain's latest analysis found that 113 insurgent US consumer brands, holding less than 2% of market share, captured about 36% of market growth in 2025. Their volumes grew approximately 55% in a market where overall volumes were essentially flat. Paraphrasing, Bain attributes their success to enhanced performance, delivered via differentiated propositions. AKA the creation of new consumer value.

So private label is exposing whether the consumer value behind incumbent availability is sufficient to justify the premium, while insurgents are capturing disproportionate growth with propositions built around new sources of value.

Meanwhile, we see huge name brands expending a significant proportion of their scarce marketing resources fighting other incumbents on the exact same availability dimensions they all already share.

Again, this doesn’t disprove Sharp’s findings. It demonstrates something more useful: while availability can carry value into a market, it cannot tell you what value to create.

How not to commoditize the CMO.

The Availability Trap isn’t a flaw in Sharp’s evidence. It’s what happens when managers mistake empirical constraints for a complete strategy.

The implications of elevating availability (mental and physical) from an operational practice to the governing worldview of the CMO are, then, obvious. The CMO becomes responsible for reach, salience, brand assets, penetration, buying situations, and maintaining existing demand. Great. That matters.

Meanwhile, the bigger questions, by definition, will become somebody else’s job. Where should we play? How should we win? What customer value should we create? What should we make? What should we charge? Which boundaries should we cross? Which uncertain opportunities deserve resources? What can we do that competitors cannot easily reproduce?

This raises a very simple executive-level question: why would any CEO or board consider such a CMO to be a strategically valuable member of their executive team?

By falling into the Availability Trap, such CMOs voluntarily define themselves as an operator within an existing system, which metaphorically leaves them struggling away in the basement rather than elevating themselves to the boardroom.

Now, it would be entirely naive and foolish for me to suggest that CMOs should personally seek to seize product, innovation, pricing, corporate strategy, and everything else in some kind of marketing coup. I mean, I don’t even know how you’d do that. Maybe challenge your peers to an arm wrestle?

No, what I’m suggesting is more impactful than trying to bounce your face off a brick wall. Rather than claiming ownership, the role of a high-performing CMO should be participation in making fundamentally strategic choices alongside the CEO and relevant members of the executive team, with visibility from the board when the choices are consequential enough.

This is less about ownership and more about ensuring the CMO's uniquely outside-in perspective keeps customer value at the heart of the company's future.

This, rather than remediating availability, is what makes the marketing function strategically valuable. It’s also the very thing that develops the muscles required of a future CEO. If the CMO is already viewed internally as a key voice in customer-driven value creation, as well as the enabler of external coherence, they’re naturally putting themselves in the window for the top job.

As a result, in any medium-to-large corporation, there should be a capable team beneath the CMO ensuring the company never loses because people cannot remember it, recognize it, or buy it. They should understand category entry points, monitor availability, protect important brand assets, and ensure the operational fundamentals remain healthy.

But the CMO has different responsibilities: working alongside the rest of the executive team to help decide where the company will compete, what customer value it will create, why it deserves to win, and where scarce corporate resources should be placed against a future nobody can fully predict.

To finish, Sharp’s work helps ensure we don’t lose through a lack of operational excellence in marketing. It cannot, on its own, tell us what we should do to win, because that second question belongs to strategy, not operations.

And if the CMO really is one of the best training grounds for the future CEO, this is a distinction with a very meaningful difference.

Subscribe now

Share

Leave a comment

This week, I had lunch with an AI entrepreneur. As you do, we got to chatting about the state of the category, what’s likely coming next, and the unanswered question of whether OpenAI is going to be more like Google or more like Excite.com.

The thing both of us were most excited about is the dynamism of “application layer” innovation that open-weight models free up, especially with open-weight providers now trailing so close behind frontier capabilities at a fraction of the price.

With that in mind, I figured it might be useful to frame out some broad strategic implications for marketers and explore two potentially opposing strategic choices. Not because I’m making the case for either, but because I think most of us will operate somewhere within these two poles, so framing what they are can be extremely useful when making strategic decisions.

The trillion-dollar question.

As I’ve said many times in Off Kilter, when something commoditizes, value shifts elsewhere. More specifically, the commoditizing impact of technology tends to follow a specific pattern: It makes what was slow fast. What was hard easy. And what was expensive cheap. And when something shifts from slow, hard, and expensive to fast, easy, and cheap, there tends to be an explosion of differentiated and confusing innovation on top, which then settles over time as the wheat gets separated from the chaff and we enter a new phase of maturity.

With that in mind, we’re now seeing a specific thing happening in the AI space. Model makers are making upstream moves. In May, OpenAI launched the Deployment Company and bought a consulting firm to staff it, capitalizing the venture with more than $4 billion. Its new agent product isn’t sold through a website; it’s sold through this consulting arm. Anthropic, meanwhile, took its Cowork product to general availability and signed an enterprise alliance with PwC. And OpenAI has started selling ads, with Target, Albertsons, and Williams-Sonoma among the launch partners.

The way to read these shifts is as a warning light. Back in the day, IBM didn’t pivot to services because mainframes were thriving. Then-CEO Lou Gerstner made the move because hardware margins had compressed to almost nothing. The lesson is that tech firms generally move up the value stack only when the product below stops being profitable. And right now, the frontier labs are flashing neon red.

The competitive threat of open-weight models, most of them out of China, is the reason. Right now, they trail frontier capabilities by roughly four months at an order-of-magnitude lower cost. This puts a hard ceiling on what anyone can charge for LLM intelligence, which is bound to give you heartburn if you’re committing hundreds of billions in CapEx against the assumption that raw intelligence is the product. In June, Sequoia’s David Cahn called it the $600 billion question. Today it’s looking more like a trillion-dollar question.

As a direct result, anyone sitting on hundreds of billions of dollars of CapEx repayment is now scrambling to exploit higher-value opportunities, and because the sums are so huge, they’re all converging on the same high-dollar-value markets: Enterprise consulting, agentic solutioning (starting with coding, expanding into other functions soon), advertising...and broader marketing budgets next. Why? Because they’re a sweet spot for anyone seeking CapEx recoupment. Marketing budgets add up to a huge market; they’re more fungible than most corporate budgets, and tech has spent the past twenty years learning how to capture them.

But because marketing budgets are so attractive, the frontier models aren’t going to have everything to themselves. Quite the opposite. As open-weight models are readily available and act as a price floor, we’re seeing an explosion in “application layer” AI solutions built for marketers that’s only going to accelerate.

A quick whizz through some of the big players.

First up, Accenture is playing the outsourcing game it knows and loves, but this time with agents. McKinsey is doing the same thing under an outcomes banner. Adobe is re-framing its platform and solutions as application layer AI. Google is playing both sides as it monetizes its consumer products via advertising while selling marketers on AI tooling and media planning. Meanwhile, the advertising holdcos (other than Publicis) are destroying their last source of competitive advantage (client relationships + tacit knowledge in case you’re wondering), as they fire talent to dally with AI solutioning and outcome-based pricing in an effort to maintain their economics.

As an aside, outcome-based pricing is almost certainly going to be a competitive buzzsaw for the holdcos, because the likes of Accenture and McKinsey are better at it, are more experienced in it, and have the balance sheet strength to make it work. If, like me, you treat anything Sir Martin Sorrell has to say as strategically dubious (nothing personal, he just has a stellar track record of value destruction), then the fact he’s currently boosting outcome-based pricing should be the only warning you need.

So, to sum this all up, the model makers aren’t going to get it all their own way, and we’re about to see a vendor knife fight break out over marketing budgets. It’s not going to be a model-level fight, because the model layer is converging as capabilities compress and prices collapse toward commodity. Instead, this commoditization means the layer above it is doing the opposite and will explode with differentiated innovation as everyone jockeys for position against a market that didn’t exist three years ago.

So, convergence at the model level, divergence above. It’s exciting, but it’s also going to be as confusing as hell. Here’s a way of thinking about it.

Prix fixe versus à la carte AI.

While there’s a ton of noise out there, if we squint hard enough, we can just about make out the shape of the opposites that are emerging. Think of them as poles. Most marketing functions will end up somewhere in the middle, but knowing the edges can be very useful as a navigation tool as you make decisions.

At one end of the spectrum sits what I’m calling the prix fixe menu.

At the extreme end of this pole, the marketing function, or most of it, will be outsourced to a suitably scaled AI vendor, so they can run it. It might arrive labeled as outsourcing itself, or dressed as outcome-based pricing, but the net effect is the same. The corporation is handing over responsibility for the majority of its marketing activities and outcomes to a third party.

Why would you do this, you might ask? Well, under certain conditions, this is a strategically rational choice:

• You believe that AI will push all marketing performance toward the mean, which means there isn’t any competitive advantage inherent to treating it as a core competence.

• You don’t see marketing as a differentiated capability. You just need to be able to run best-in-class playbooks that deliver known performance.

• You’ve already fragmented responsibility for the 4Ps of the marketing mix, leaving marketing with an operations focus on the promotional P.

• You view the mean of performance as acceptable to you, and intend to use the capital you free up via outsourcing more effectively elsewhere.

• Your existing marketing function delivers below-average performance, so an outsourced solution that brings it up to the mean would be a net performance gain. (I guarantee you that Accenture has already run these numbers)

If I were to guess, I’d say a lot of small businesses would benefit from a lift to the mean, as would a range of non-marketing-oriented B2B businesses, especially those in lower value-add commodity or industrial categories.

Something to consider here is that if a large enough share of buyers converge on the same few vendors running the same optimization plays, the mean of performance is likely to decline over time as delivery costs (e.g., media) get bid up and consumer saturation diminishes returns. Knowing this is possible, or even likely, such outsourcing can still be a strategically rational decision, but only if the cost side decreases far enough to make up for the performance losses, and there are other, higher value, uses for any freed up capital.

At the opposite end of the spectrum is what I’m calling the à la carte approach.

At the extreme end of this pole, you’ll see organizations use AI to build highly differentiated marketing capabilities. This will be less about who you buy from, and more about what you choose to configure and how you choose to configure it. This will be a blend of built, bought, and partnered solutions designed to deliver against the corporation’s own thesis of customer value creation. This is what David Teece refers to as dynamic capabilities. This is a bet that a durable source of advantage can be created not from any single asset but from the unique configuration of assets you develop, that are built over time through path-dependent choices. (Meaning you don’t predict the outcome, but make bet 3 because of the outcomes of bets 1 and 2).

Why would you do this, when it’s so much harder and more complicated? Again, it’s the strategically rational choice under certain conditions:

• You believe that AI as a generalized technology won’t solely drive marketing performance to the mean, but that if utilized differently, will open up new opportunities for differentiated and above-average performance.

• You view marketing as a differentiated capability. Meaning the way you treat your marketing function enables you to differentiate yourself from your competition in order to perform more highly.

• You did not collapse the marketing mix down to the promotional P, or you intend to reintegrate, and view marketing as a strategic, rather than operational, capability.

• You do not view the mean of performance as acceptable. You intend to build an overperforming marketing function.

• Your existing marketing function overperforms the mean, so any outsourcing would lead to a net reduction in performance, and what you seek is a means of maintaining and extending this position.

Here, I can see ambitious consumer brands seeking above-average growth pursuing this path, as would larger, older brands known for their marketing expertise and differentiated brand meaning, as well as any business pursuing cross-category growth, through what I call a brand-defined-domain. In the B2B arena, I’d see those who view brand as a competitive differentiator following this path.

Whichever path you choose, the choice is strategic.

What the extreme nature of these poles demonstrates is that whichever path you go down is a distinct strategic choice rather than a procurement decision.

The ROI/cost efficiency promises of outsourcing and outcome-pricing are the right decision frame only if you operate under the strategic assumptions outlined above for the prix fixe approach. If, however, you operate under the strategic assumptions outlined for the à la carte approach, then the cost efficiency promises of outsourcing and outcome-pricing are the wrong frame entirely.

This is important, because most marketing organizations are going to come under cost pressures that lean them toward the prix fixe approach. This is because the cost-accounting governance layer that sits atop most marketing organizations cannot price what is being surrendered by reverting to the mean because it’s illegible to cost accounting governance.

And because the value of the second path can’t be read from an efficiency ratio, the argument you’d make for it cannot be made on the basis of ROI alone. Instead, the case for the à la carte approach needs to be made via a different set of evidence entirely. Things like maintenance of your price premium, price elasticity, margin quality, demand created rather than harvested, innovation capacity, customer permission, brand meaning, strategic optionality, and how much the corporation values its own unique thesis of customer value.

Average versus non-average.

Of course, most won’t be making such binary choices of all-outsourced, or all à la carte. But knowing the poles helps us frame which elements of the marketing function might be better off outsourced, versus which elements absolutely should not be.

Overall, if we step back far enough, it appears highly likely that achieving the mean of marketing performance is becoming cheaper and easier than ever to buy. Meanwhile, because of the commoditization of the model layer, and booming innovation in the “application layer,” differentiated capabilities have never been more available to buy, build or partner your way to.

The ensuing battle for supremacy, however, will be less about what costs more or less, or what delivers more or less ROI. Instead, it’ll be a question about which businesses are satisfied with the mean of performance, versus which decide to build differentiated capabilities in pursuit of above-mean performance.

And that’s a strategic choice that needs to be made well before it’s an accounting choice that needs to be justified.

Subscribe now

Share

Leave a comment

Marketers were told to learn the language of finance, and that all would be well. So we learned finance, and all is far from well. We believed ROI to be the defining language of boardroom credibility, yet the harder we ROIMAXX, the more we lose. Budget, capability, credibility, and eventually, the battle for the customer itself.

It’s not because we didn’t learn the language of finance; it’s because we learned the wrong language of finance. We learned the language of cost accounting rather than the language of asset allocation.

Where cost accounting asks, “how efficiently can we spend against outcomes we already understand?” asset allocation asks, “how should we allocate our scarce resources across opportunities that have different risks, levels of uncertainty, and potential returns?”

The bad news is that cost accounting is now the dominant governance frame through which marketing decisions get made.

The good news is that the highest performing marketers already operate like asset allocators, even if they’d never dream of explaining it the way I’m about to.

Alpha and beta.

Picture an investment index, any index. Let’s call it the S&P 500. To achieve the average return of the S&P 500, you’d buy an index fund built to replicate it. Investors often describe the return you’d receive as beta, meaning the average return at the average risk of a known benchmark that’s available to everyone. (OK finance propellerheads, I know this isn’t definitionally precise, but it’s a common usage of the term, and it helps make the point I’m about to make).

Sometimes this might be referred to as a “free return,” not because it is free of cost, but because it’s free of differentiated effort. You don’t need to do anything different; you just need to buy the index.

Now picture a venture capital fund, any VC fund. To achieve a return greater than beta, this fund actively makes a series of high-risk bets on individual startups. The idea is that while most will fail, its investment thesis and disciplined portfolio management will lead to enough successes for the aggregate return to exceed that of the available-to-anyone index. The VC fund operates on the logic of asymmetry. Each individual startup investment combines a capped downside (you can only lose what you invested) with a potentially unlimited upside (winners return exponentially more than you invested).

In finance terms, if the index fund delivers beta, the VC fund is pursuing alpha: a return greater than the benchmark that cannot be explained by the benchmark alone.

Now picture two different marketing organizations.

The first is a cost optimizer, governed by cost accounting. It isn’t judged on the basis of the total return it creates; it’s judged on how efficiently it spends the money it’s given. This tells us the return produced per dollar spent. What it doesn’t tell us is whether we maximized profit, growth, customer value, or enterprise value in the process.

By focusing on cost efficiency, this organization can only make bets where future returns fall within a predictable distribution (meaning activities that have been run enough times to statistically predict what a ‘normal’ risk/return looks like). Meanwhile, anything genuinely unpredictable is off the table, not because it’s a bad idea, but because everything unpredictable is rendered illegible when cost accounting is your governance framework.

Since everything this marketing organization does must have a predictable and known range of outcomes, anything it returns can be replicated by any other marketing organization that’s familiar with these same distributions.

As a result, such a marketing organization looks a lot like an index fund. It achieves a market-average return, at a market-average risk, and what it delivers is available to anyone in the market. Think of this as marketing’s beta return, or the “free return” on marketing. (Again, not free of cost, but free of differentiated effort).

The second marketing organization is a return maximizer. It’s governed by the asset-allocation question of how we should allocate our scarce resources across opportunities with different risks, levels of uncertainty, and potential returns.

Rather than relying solely on what’s predictable and legible based on past activities, it follows the finance logic of asset allocation, which accepts that unpredictable bets, especially those that are asymmetric, can be excessively valuable.

As a result, such a marketing organization is less of an optimization machine and more of a portfolio manager. It manages a series of activities, some of which are predictable and some of which are not. Where the cost optimizer views predictable performance as the ceiling, the asset allocator views it as the floor, with its unpredictable bets on future uncertainty defining the ceiling.

A marketing organization that seeks to lift the ceiling in this way is pursuing marketing’s alpha return. Meaning a return greater than the free return available to everyone that cannot be explained by known distributions alone.

A mono-strategy is no strategy at all.

The divide between these two organizations boils down to a simple yet profound philosophical question:

Do you believe that everything valuable in marketing lives within a known distribution, or do you not?

Cost accounting can only operate within the realm of known distributions because it needs to know what happened in the past before it can predict what will be efficient in the future. The result is that any marketing function governed by cost accounting is confined, by the nature of its governance, solely to the optimization of known activities with predictable future outcomes. (Of course, this does nothing to eliminate any future uncertainty the marketer might face; it merely renders the marketing organization unresponsive to it, and thus less capable of navigating it.)

Since strategy is, by definition, the art of making choices under the conditions of future uncertainty (meaning non-predictable by design), and cost-accounting governance removes the permission to make such choices, any marketing organization governed by cost accounting isn’t just less prepared for what it cannot model; it cannot have a differentiated strategy at all. Its only choice is optimization, which, when everyone is doing it, means it effectively has no strategy at all. And since every competitor has access to the same activities, platforms, data, and expected returns, we see exactly the converged performance we’d expect, which leaves us with what so many marketing organizations are today: a commodity optimization function rather than a source of distinct competitive advantage.

While this may be a surprise to some, it shouldn’t be. We’ve known since 1954 that maximizing return on marketing investment does not equal profit maximization. Meanwhile, the late, great, Professor Tim Ambler warned over 20 years ago what would happen to marketing under its misplaced obsession with ROI.

What he warned against is now directly visible in how modern marketing organizations are structured.

The fragmented P’s.

Where the marketing function once reflected the four Ps of the marketing mix (product, price, place, and promotion), this is rarely the case today.

As cost accounting took over marketing, the marketing mix migrated toward whichever function rendered the individual elements most legible to the corporation. Product, which has the highest variance and therefore the highest alpha ceiling, moved toward innovation, engineering, and operations because its inherent uncertainty couldn’t be managed through efficiency ratios alone. Price, a firm’s most certain profit lever, moved to finance because it was deemed too valuable to be entrusted to a cost center. Place fragmented across sales, distribution, logistics, e-commerce, retail, and platform management. This left marketing with the promotional P, which also happens to be the one that behaves most purely as a cost: a discretionary budget with measurable efficiency that can be cut on demand.

While this fragmentation kept each element legible to the corporation, it also made the whole less legible to the customer. Customers don’t experience four separate Ps, each managed by different executives against different targets, metrics, and governing logics. They experience a single proposition: what it is, what it costs, where and how they receive it, the experience of receiving it, and what the corporation tells them it means.

So, by fragmenting these decisions internally, corporations fragmented their capacity to create customer value externally. At best, they weakened their capacity to generate customer-driven alpha. At worst, they rendered it incoherent: products designed according to one logic, priced according to another, distributed through a third, and then promoted through a fourth.

The net, net is that while each of the four Ps of marketing has differing alpha and beta potential, reducing marketing to the promotional P as an artifact of cost-accounting governance, rather than as a considered asset-allocation choice, has made the pursuit of integrated marketing alpha harder rather than easier. (Having experienced these ill-effects firsthand, this is why some corporations are now re-integrating the Ps under marketing again).

It’s also why the loudest “strategy” debates in marketing, like Binet and Field’s 60:40 split versus Sharp’s '“laws” of growth, aren’t strategic arguments at all. Instead, they’re optimization arguments conducted under cost accounting governance inside a shrunken operating envelope.

AI. A beta machine accelerating declining performance.

The promise of AI to marketers is that it will commoditize the delivery of known distributions.

In the language of finance, this means AI will enable marketers to more fully optimize their beta return while driving down the cost of doing so. Among cost optimizers, this will decrease the gap between underperforming and overperforming marketing organizations. Those that used to underperform will be lifted to the mean, while those who used to execute the known playbook better than anyone else will be dragged back toward that same mean. Their edge will now be available to anyone with a well-enough-trained model and the right agentic tools. In other words, everyone.

Rapidly, the performance delta between the best and worst executors will compress until all that is left is the cost of delivery, and reducing this will then become the primary focus of management attention. If you think this sounds like what’s already happening, that’s because it is. As demonstrated above, we’ve spent so long chopping up and redefining marketing to make what’s left of it governable by cost accounting that we’re already well down the cost-engineering path.

The challenge with AI enhanced optimization of historically defined beta is simple, though. When everyone does it, delivery costs bid up, customers saturate, and returns diminish.

This means AI won’t just optimize beta at today’s level, it’ll optimize it into ongoing decline over time.

Alpha is THE strategic choice.

As AI closes the execution gap and turns marketing into an even more converged commodity, what will separate high-performing marketing organizations from the declining average?

It cannot be execution alone, but a differentiated answer to the philosophical question I asked above. Put simply, the high-performing marketing organizations of the future will understand that not all that is valuable in marketing exists on a known distribution.

As a result, while they will optimize where appropriate, what separates high performers from the mass average will be the alpha bets they choose to make, how they choose to make them, and how much of their total resources they choose to allocate to them.

Ask a day trader, and they’ll tell you that any idiot can lose an infinite amount of money making random bets in pursuit of alpha. But that’s not investing; it’s gambling.

What separates the strong from the weak isn’t that they make bets. It’s what I mentioned earlier about venture capitalists: the quality of the investment thesis and their ensuing portfolio-management discipline.

For marketers, this means strategy will be the primary difference between overperformance and the easily available average. Specifically, the thesis you build that guides the bets you choose to make, and the discipline with which you then choose to manage them.

Since so many marketing organizations have already become what finance people refer to as closet indexers, the coming divide is less a contest between differing strategies and more a question of who has a strategy at all versus who remains strategyless, optimizing by default.

Marketing strategy as an evolving thesis of customer value.

If strategy reflects decisions made under future uncertainty, what should marketers be deciding under uncertainty?

In simple terms, it boils down to this. What value do we intend to create, for whom, that they cannot get elsewhere?

This is because all value to the corporation starts with the value it creates for the customer. Paraphrasing the doyen of business strategy, Professor Michael Porter of Harvard Business School, value begins with what customers are willing to pay, while profit comes from creating that value at a cost below the price we can capture.

While the cost side of that equation has a hard floor, the customer value we can create and what they’re willing to pay for it has no equivalent fixed ceiling.

And since what customers will value in the future cannot be fully inferred from a known distribution, because the most valuable opportunities have a niggling habit of changing the distribution itself, the customer is where marketing alpha lives.

This means that any marketing strategy designed to pursue alpha must first begin with a thesis about customer value that cannot be explained by the free return, or beta, that’s available to everyone.

Taking this further, another giant of strategic thinking, Rita McGrath, helps explain marketing portfolio discipline. Paraphrasing her work on transient advantage, the alpha bets that pay today will eventually be copied, absorbed, and flattened into the category’s beta. As a result, we should manage them through a lifecycle from emergence, through scaling, and onward through maturity and eventual obsolescence.

What’s beautiful for marketers, however, is that while individual alpha bets may be copied, absorbed, and flattened into the category’s beta, an evolving portfolio of informed bets, made under a coherent thesis of customer value that’s proprietary to us, has the potential to produce the kind of differentiated systems Professor Porter describes as strategically durable sources of competitive advantage.

Applying this logic practically, while copying any single feature may be easy, copying the interdependence of the whole becomes almost impossible. For example, it’s trivially easy for competitors to copy LEGO kits because the brick patents ran out in the 1980s, yet it’s impossible to copy the totality of the LEGO play system and everything it means to people, because that system emerged as the product of multiple interdependent bets that were made and then built upon over time, which no competitor can now replicate.

Taken to its logical conclusion, the result of such uncopyable systems of value is that they begin acting as a private source of alpha return that only you can benefit from, yet have the predictability and known distributions of beta. Putting this crudely, it’s an outcome any professional investor would gladly give their right arm and left bollock for.

As a result, a portfolio of bets made under a coherent thesis isn’t a series of gambles; it’s a customer-centered exploration. You don’t design the finished system in advance, because you don’t yet know what it can become. Instead, you begin with a value thesis, make disciplined bets against it, and allow the evidence generated by these bets to reveal possibilities that weren’t previously available. This enables you to experiment your way into systems of value you could not have designed in advance because they emerge from the sequence of bets you make.

In strategy terms, this is called path dependency. It means, for example, that your third bet became available because of what your first two revealed or created. A competitor starting today cannot make your first two bets because the world that made them available to you no longer exists. Copying the system you’re creating requires them to replay your history, which is impossible.

Think Apple as a highly differentiated hardware, software, and services ecosystem. Back when it was the Mac computer company, such an outcome was unimaginable. Yet, at each point in its development, it made bets from which it learned. Those bets informed the next bet, which created new learning, which created the option to make further bets. Today, while anyone can (and does) copy Apple at a device-level, copying the totality of the functional and emotional value of the interdependent Apple ecosystem is impossible.

This is why marketing strategy among return maximizers is an evolving thesis. While a plan developed against known distributions might only need writing down once, a thesis that governs live bets evolves because each bet generates new learning that influences the next bet you make.

As a result, any strategy that’s treated as a finished object is an artifact of optimization thinking, which unfortunately includes far too much of my own field of brand strategy, and the crap that commonly masquerades as it.

Anyway, while a durable thesis may remain coherent for decades at the top level, how it is expressed through choices, bets, systems, and resource allocation must evolve as the organization learns and the market changes.

To summarize, return-maximizing marketers, either deliberately or intuitively, pursue marketing alpha by managing a portfolio of bets based on a strategic thesis of customer value. This thesis evolves, and competitive advantages emerge, as bets are made and successes and failures are learned from.

The training ground of the future CEO.

Last week, I argued that marketers who become CEOs make different, and more appropriate, leadership choices for the world we’re heading into, and that boards which treat marketing as a cost center are unwittingly destroying a valuable succession pathway toward the corner office.

This is because, at its core, the CEO role is resource-allocation.

At a fundamental level, CEOs get paid the big bucks because they’re responsible for allocating the firm’s scarce resources under the conditions of future uncertainty. They’re responsible for what value to create, for whom, and how to out-position competitors against a future that cannot be modeled.

Outside of marketing as I have described it, few C-suite roles provide a training ground upon which to practice discovering, creating, and capturing uncertain future value. Corporate finance is a capital function, principally trained to price, protect, and allocate capital, normally through models anchored in historical distributions. Operations is an optimization function by definition. Neither typically requires the formation and testing of an evolving thesis about where value will come from next.

Yet, a strategic marketer running a portfolio of marketing bets is doing exactly that. They’re managing the full loop of value creation through capture at survivable stakes. Developing the discipline of reading ambiguous signals. Learning how to kill losers, back winners, and double down on what emerges to create differentiated systems of durable competitive advantage.

So, yes, this is what will separate the overperforming marketing organization from its rapidly commoditizing peers. More importantly, a marketer who genuinely manages a portfolio of customer-value bets on alpha is not merely learning how to efficiently spend a marketing budget. They’re rehearsing the central task of the CEO: allocating scarce resources across uncertain possibilities, learning faster than competitors, and converting what works into a durable system of advantage in pursuit of return.

Ultimately, no matter what your governance model is capable of recognizing, the future will be no less uncertain. All that changes is how well prepared you are to meet it. And the leaders best prepared to navigate uncertainty will be those who’ve spent their whole careers making disciplined strategic choices under precisely these conditions.

Subscribe now

Share

Leave a comment

Marketers should have bigger ambitions. Not to see a CMO or equivalent title as peak possibility, but to view it as a natural stepping stone on the way to becoming a CEO. And they should begin demanding a media, thought-leadership, and conference-speaking circuit that raises the bar on achieving this reality, rather than one that damns them with low expectations.

Why? Because marketers who become CEOs make very different strategic decisions compared to their peers who come up through finance. Decisions that are much more in keeping with the world as it is becoming rather than the world as it has been.

Over the past two editions, I first focused on why industry events, such as Cannes, prioritize the wrong conversations (tactical because vendor incentives) and what happens when marketers internalize tactics over strategy (dependence and loss of self-determination). This final part in the trilogy does the opposite. It demonstrates what’s at stake when marketing acts strategically, and thus where the real prize should sit.

Contrasting theories of value. Engineered versus created.

When marketers become CEOs, they make very different decisions compared to CEOs who came up through finance. Not a little bit different, but different enough that you can decode contrasting theories of value creation from their choices.

Where a finance-trained CEO is more likely to understand value as something that can be engineered through financial mechanisms, capital structure, and the balance sheet. A marketing-trained CEO is more likely to understand value as something that must be created through market mechanisms that run through the customer.

The financial engineering approach taken by CEOs with a finance background has been studied to death. The definitive study by Custódio and Metzger in the Journal of Financial Economics found that firms led by financial-expert CEOs hold less cash, carry more debt, and repurchase more shares. As a theory of value, it says this: optimize what already exists, borrow against proven sources of return, and distribute back to shareholders rather than seek reinvestment opportunities. These are leaders who seek to engineer returns in market environments they believe to be stable and mature, where there is little opportunity to create something new. Or, more accurately, such CEOs are hired by boards that believe there to be little opportunity to create something new.

This approach brings known costs to the corporations that hire such leaders. When Graham, Harvey, and Rajgopal surveyed 401 financial executives, 78% said they’d sacrifice real economic value to keep earnings smooth, and 55% would kill a project they themselves believed created value rather than miss the quarter. Mizik and Jacobson showed what this trade-off buys: a temporary market reward, followed by years of underperformance as cuts to functions such as marketing and innovation are reflected on the income statement. And fifteen years into the “Big 4” accounting firms’ attempt to turn CFOs into the strategic owners of future value creation, their own trade survey finds that only 25% are leading investment decisions with uncertain returns or long-term horizons. Oops, since “uncertain” and “long-term” are the very definition of a strategic domain.

By contrast, CEOs from a marketing background make very different decisions. A study of 5,438 CEOs across S&P 1500 firms found that CEO marketing experience improves investment efficiency. This is because they mitigate underinvestment in areas such as innovation and marketing, especially in categories where demand uncertainty runs highest. Parallel work finds that the same executives generate more patents and higher innovation efficiency. And this difference isn’t just limited to having a marketer in the corner office; it extends to boards too. Having just one board director with marketing experience is associated with nearly 6 additional points of revenue growth. Yet only 2.6% of board directors have any marketing experience today. (Cough, arbitrage opportunity, cough cough to any board governance chairs reading this). As an aside, this also puts paid to the myth of “data-driven decision-making.” The data says to put a marketer on your board. That 97.4% of directors aren’t says, “hold my beer.”

Anyhoo, innovation, R&D, and demand creation are all bets on there being long-run value-creating opportunities in the market that exceed those that can be engineered from the balance sheet alone.

And it makes perfect sense. While executives with a finance background have spent their careers optimizing for commitments already made, those with a marketing background have spent theirs committing capital to creating desire and demand that don’t yet exist.

Only one of these theories has a consistent upside.

Boil down the strategy literature, and value (to the firm) is defined as ‘what a customer pays, minus the cost of provision.’ In Competitive Advantage, professor Michael Porter of Harvard Business School states that “value is what buyers are willing to pay” and that competitive advantage arises from the value a firm creates for buyers in excess of the cost of creating it. Strip that down to its essence, and all value stems from the customer.

Relative to this definition, CEOs with a finance background tend to primarily work the cost side of the equation. Problem is, you can only cut so far before cutting into muscle, and then the competition has a bad habit of scuppering your plans. This means the cost side has a hard floor, no matter how much you may seek to financially engineer a different outcome.

By contrast, CEOs with a marketing background tend to focus more on the customer willingness side of the equation. And, unlike the cost side, which has a hard floor, there is no hard ceiling on possibility.

This is why the marketer’s theory of value is inherently more strategic in the oldest and strictest sense. If all value stems from a customer deciding something is worth paying for, then the art of strategic leadership lies in understanding that everything else the corporation does is downstream of that decision, including the cost of providing it.

Brittleness versus resilience.

Choosing to hold less cash, take on more debt, and cut sources of future value in the interests of short-term return isn’t just a capital structure choice; it has direct implications for the business’s ability to meet future needs. Specifically, it thins the buffer against unanticipated events and precommits future cash flows to debt service. The net effect, if taken too far, is a machine tuned so precisely for past conditions that anything unexpected in the future might break it. This is why the financial engineering approach tends to build inherently brittle organizations.

By contrast, an approach that focuses first on the customer because that’s where all value stems, with its sustained focus on innovation and desire generation, creates more resilience. By not optimizing the business around past conditions and by assuming the real competitive fight is for future cash flows, such organizations gain resilience and a greater capacity to succeed even as volatility and uncertainty increase. In essence, this is what the findings on marketers-turned-CEOs point to. By not underinvesting in areas such as R&D, innovation, and marketing, they’re buying more and better options on the future, which makes the corporations they run more resilient to uncertainty.

What’s important to note is that I’m not saying one is necessarily good and the other bad. From a pure shareholder-value perspective, a highly leveraged, highly efficient, yet brittle organization might be an optimal choice. But only if the market remains stable and predictable, with volatility and uncertainty low.

And that’s my primary point in this. The conditions we are emerging from were historically stable. Generally speaking, markets were predictable, volatility was constrained, and we had more than a decade of zero percent interest rates, during which financial engineering delivered real returns.

It’s just that the world doesn’t look like this moving forward, so the playbook needs to change.

Fumbling our way toward an unknown future.

The Economic Policy Uncertainty Index hit its highest level this century in 2025, above both its 2008 financial crisis and more recent COVID peaks. Trade policy uncertainty ran to roughly ten times its 2015–2024 average. The World Uncertainty Index tells the same story across 143 countries. Geopolitical and societal tension is higher than at any point since the end of the Cold War.

Correspondingly, market volatility is also elevated, although the VIX has not spiked as high as it did in 2008 or 2020; the record turbulence is concentrated in the policy and structural layer, including tariffs, fragmentation, and industrial policy. The very rules of the game themselves.

Yet, that spike might be coming soon. AI is now compounding market uncertainty, and not in the way the efficiency narrative might suggest. Its first-order effect is to commoditize execution, and when execution commoditizes, the value doesn’t disappear…it moves. Which means AI doesn’t just add noise to the environment; it risks invalidating the target. Something you spent a decade optimizing stops being worth optimizing, while no amount of efficiency engineering can restore value that has moved.

Don’t lull yourself into thinking this is a far-away concern; it’s already happening. The “SaaSpocalypse” is exactly what happens when financial engineering slams headfirst into value migration.

Nor is this a rough patch before a return to normal. The deeper pattern, as Rita McGrath has argued for over a decade, is that competitive advantage itself has become transient. Positions erode faster, category boundaries dissolve as technology lets adjacent players cross them more easily, and the periods of stability between disruptions keep shrinking. Markets are becoming more dynamic as a structural condition. Planning for a reversion to stability is itself a bet, and increasingly a bad one.

Finally, the interest-rate environment has repriced the financial engineering playbook. Leverage amplifies return only when debt costs less than the business earns. As rates go up, the spread collapses. Debt-funded buybacks stop being accretive. Multiple expansion, which was the silent partner in a decade of engineered returns, begins to run in reverse as discount rates rise. Financial engineering’s golden run was far more about free money than leadership skill, and money isn’t free anymore.

The result of all of this is that one of the central questions facing every corporation has flipped from “how do we become more efficient?” to “what should we become more efficient at?”

And the only way to decide what to become more efficient at when we no longer know is to start with the source of all value: The customer. And who’s more in tune with the customer, a marketer with decades spent trying to understand them, or a CFO with decades spent looking at their spend on a spreadsheet?

The board’s cheap succession option

Ultimately, this is a governance matter.

Boards devote enormous attention to CEO succession, and almost none to the question upstream of it. Namely, what is the optimal executive background for cultivating the thinking that a future CEO will need?

This is why every decision about the marketing function and its scope is actually a decision about CEO succession in disguise. If you scope marketing as an executional and operational communications function under finance governance, you’ll develop excellent tactical operators with exactly zero capacity to become future CEOs. In strategy terms, this is option-eliminating behavior. On the other hand, scope marketing around customer value, competitive choice, and long-term resource allocation toward competitive advantage, and you’ve created a CEO development pathway. You don’t have to make your CMO your next CEO, but in strategy terms, you’re buying the option to make that choice.

This is why it should be a board-level demand to have marketing leaders who could plausibly become CEOs in the future, and the function should be scoped to attract such talent. The option it buys is insanely cheap. The downside is the employment of a very good marketer. The upside is increasing the breadth of potential within your succession candidate pool.

Yes, CFO-to-CEO promotions hit a decade-high in 2025, while those with genuine marketing backgrounds account for just 4–10% of big-company CEOs. But the consensus running hard the other way is the very definition of a strategic opportunity when external evidence points so strongly in the opposite direction.

What you were told versus the language you were taught.

For the marketer reading this, the prize demands something of you too. It starts with having higher ambitions for yourself and bigger demands of those you look to for thought leadership. Not accepting the limits that have been handed to you, but agitating for change.

Chief among these is the following. For decades, marketers have been told that if they learn the language of finance, all will be well. But all is not well. Why? Mostly it’s because marketing didn’t internalize the language of finance at all; it internalized the language of cost accounting.

Marketing is expensive. Corporations needed to know the money was being allocated efficiently. Without a credible baseline to measure return against, the discipline defaulted to what could be measured: cost. From this default, tremendous dysfunction followed. Not least, an entire management logic built on what marketing costs to deliver rather than what it exists to create.

If you are governed solely or primarily by cost, the best outcome marketing can hope for will be a maintenance return. This is because cost discipline converges everyone toward the same efficient sameness while stripping away strategic optionality precisely where returns are largest: namely, where risk is elevated and variance is high.

When a throughput logic (finance) governs an output function (marketing), it doesn’t create a virtuous circle as promised; instead, it creates a vicious cycle. Marketing is judged on cost, so it optimizes for cost efficiency. Cost efficiency means it can’t generate better than maintenance returns. Maintenance returns are then used as proof that marketing is incapable of delivering anything greater, so its costs should be screwed down further.

Of course, this isn’t a given; it’s not a law. It’s simply a cost-driven governance regime operating exactly as predicted. You can’t squeeze a return from something that’s been architected for the opposite.

It also explains the why behind the last two Off Kilter editions. Why does the industry’s biggest stage default to the executional? Because a disciplined cost center buys efficiency, and vendors sell to the existing budget. Why did dependency set in so deeply? Because a cost center doesn’t own strategy, it rents what it wasn’t resourced to build. When Mercado Libre’s CMO stood on the Cannes stage and said marketers “created our own language” and lost the boardroom’s trust, he was half right. Marketers didn’t create the language of cost accounting. They were sold it. (Neither did they create the language of engagement, impressions, CPMs, ROAS, last click attribution, or a myriad of other nonsensical terms no CEO gives a shit about. They were sold those, too. By the tech platforms rather than the accountants).

The flip is not to ignore cost but to do what the asset allocating investor side of finance already does. Judge returns first and costs second. To start thinking in terms of cost as a ratio relative to return, rather than an absolute to be reflexively minimized.

In other words, to assess marketing the way any investor would assess it: By the size, risk, and shape of what it returns, rather than the efficiency of what it consumes. Of course, this demands something cost control never required. What asset allocators call an investment thesis, and what you or I would call…a strategy.

What such a thesis looks like, and why the more interesting language of finance that marketers should internalize is alpha, not ROI, is next week’s edition.

Subscribe now

Share

Leave a comment

I’d love to join in; I really would. But it’s naive to think everyone will pursue this path, or even that it’ll become the dominant expression, as some seem to believe. Worse, it’s not a strategically cogent path for every corporation to pursue.

It’s not enough to solely think about the impact of AI on consumer discovery in a vacuum. Instead, we must zoom out and consider the implications of AI as a general-purpose technology within the context of today’s marketing orthodoxy.

If we look at it this way, AI isn’t merely creating a new consumer discovery problem that must be solved via a positioning renaissance. It exposes a strategy vacuum.

In the same way I highlighted last week that Cannes was having the wrong conversation about marketing, marketers keep asking the wrong questions about AI. Instead of asking which new strategic options AI opens up and how radically AI changes strategic possibility, the focus is overwhelmingly on its executional potential and its ability to speed up the generation, personalization, optimization, testing, automation, and scaling of the artifacts that marketing already executes.

At the heart of this is a real problem. The true promise of AI isn’t that it will speed up marketing execution; it’s that it will commoditize it. And we know from previous cycles that once a technology commoditizes something, the value shifts elsewhere. When AI chatbots commoditized routine customer service at IKEA, the company retrained 8,500 call center workers as remote interior design advisers — and the resulting advisory channel generated €1.3 billion in revenue in its first full year. The machine commoditized routine queries, enabling value to migrate to an advisory service.

However, if we singularly focus on AI as execution, it assumes the big strategic questions have already been answered. Yet, all too often, they have not.

What customers are we trying to win? What demand are we trying to build rather than rent? Which innovations do we pursue? What value do we create? What value do we have permission to capture? Which bets on the future are we making? What does the brand enable the enterprise to do? What should we refuse to become?

These aren’t executional questions. They’re boardroom questions. Yet the money, the machinery, the vendors, the dashboards, and now the AI systems are all pressuring upstream decisions from downstream under the guise of “catching up” to a new executional reality. Accelerating FOMO around answers to questions with implications most corporations haven’t yet thought through.

Here’s what I think is going to happen next. Over the next eighteen to thirty-six months, advances in AI execution will reveal which corporations treat marketing as a strategic competence and which treat it as a set of executional tactics. The more AI increases the speed and volume of what marketers can execute, the more we’ll see a lack of judgment exposed in what they should be executing.

This is because, over the past twenty years, during the flight to digital and the ensuing platform era, executional complexity obscured the difference between tactics and strategy: media fragmentation, martech stacks, performance dashboards, and agency ecosystems placed so much emphasis on activity that it inevitably passed for progress.

As complexity and risk accelerated, the platform value propositions began centering on ease and certainty. You didn’t need to create desire; you simply needed to harvest demand from their platforms. As a result, the deeper cost of the last twenty years wasn’t just that marketing became performance-ad obsessed or that brands didn’t get built or that attribution was shady. These were merely symptoms. It was that strategic muscle was lost and never rebuilt to meet the needs of a changed environment as companies learned to rent demand instead of creating their own desire. By making marketing a game of dashboard optimization, platforms made it easy and (apparently) riskless. Just follow their playbook toward certainty. Then, as the platforms own growth pressures mounted, they slowly shifted from business partners to drug dealers, extracting ever more value from the dependence they’d created, with marketers as dashboard addicts without the wherewithal to pursue other options.

Now, along comes AI with a promise to render this execution layer faster, cheaper, and available to all. As a result, dashboard competence will no longer be a differentiator. Instead, the differentiation becomes the quality of the choices being executed. To borrow from Warren Buffett, the tide is going out, and AI is about to show who’s been swimming naked. Not because the machines are replacing marketing strategy, but because they’re removing the costume of execution that disguised its absence.

This is where the positioning renaissance story begins to fray, because the most likely outcome for most will be a default toward AI platform dependency. Here’s how this is likely to unfold.

Few corporations are set up with the kind of Miele level of positioning to operating model coherence required to genuinely navigate consumer discovery organically. Few are set up to deal with a world where their claims of excellence now sit alongside their 1-star reviews in a AI dialogue that passes judgment. Even if they were, few beyond the very largest have the decades of proof LLMs seem to prefer behind them. And in recent years, corporate incentives have tolerated, if not encouraged, a widening say/do gap, in which margins could be gained at little or no cost in customer frustration. Switching all of this on a dime isn’t just hard; it’ll be a transformation too far for many.

More specifically, the evolutionary shift of the marketing function into an engine of tactical execution actively fights it, because such marketing organizations are not internally positioned to be the orchestrators of change.

As a result, every company now faces a three-way resource allocation choice when it comes to AI mediated consumer discovery, each of which has very different strategic implications.

Option A is to do what my peers and I would prefer. Do what you say, say what you do. Create clear customer value. Ensure positioning to operational coherence. Then present it so semantically uniquely that the machine cannot help but infer the brand correctly. This gives you the most strategic optionality and it’s the path toward building a long-term, accelerating brand asset. It’s also the most difficult to pull off if you don’t already operate this way because it’s a multi-year boardroom commitment rather than a CMO mandate. (Being difficult, of course makes it harder to copy and thus more valuable, but the importance of doing the hard things well is a topic for another Off Kilter).

Option B is to put your thumb on the scale of the information that LLMs draw from. Seed the corpus, manipulate the sources, and manufacture the appearance of consensus as best you can. This is sold as a way of optimizing the semantic layer to surface your brand first in the same way an earlier generation optimized search to do the same thing. This arbitrage path is readily purchasable right now; the CMO has complete responsibility, and it even comes with pretty dashboards. A certain amount of this is clearly necessary, for hygiene if nothing else. And doing a lot of it without doing anything more substantive may well work to differentiate and increase your likelihood of choice. At least in the short term until the platforms choke and then intermediate it.

Option C is to pay the platform that stands between the customer and the choice. Ads, sponsored answers, verified feeds, transaction tolls, agentic commerce. Whatever language the platforms choose to create for it. This is the path toward platform rents, and it’s coming whether we like it or not. OpenAI began testing ads in ChatGPT in February and had a self-serve ads manager with cost-per-click bidding by May.

My fear is the delta between the excitement CMOs show for the potential of AI as a paid channel and their lack of concern about its longer term rent-dependency implications. Option C is attractive because it layers the glow of innovation and revenue potential atop monetization mechanisms that are familiar, safe, and internally legible to finance. But it is not without risk.

The problem is that an overwhelmingly executional marketing environment will not treat these as distinct resource allocation choices with risk/reward calculations that require a long-term strategic perspective, but rather as a singular game of executional optimization. Which means A will be subsumed. First under path B, and then path C.

Option B is already growing quickly, as it’s sold as optimization under the guise of AEO, GEO, and AI visibility. But where SEO games salience, an LLM makes assertions. It represents the world in its own voice, with its own authority. At its worst, this means you inject untrue claims into the corpus, and they come out the other side stripped of their origin, wearing the machine’s credibility as truth. This isn’t optimization; it’s semantic hacking. Manufacturing the appearance of accumulated truth, then laundering it through tools the consumer treats as trusted narrators.

This is not hypothetical. Last month, the moderators of r/biohackers, one of Reddit’s largest health communities, restricted peptide and hormone posts entirely because companies selling those compounds were seeding threads to steer what ChatGPT and Google’s AI tell users, exploiting Reddit’s data partnerships with both. Meanwhile, 76% of peptide users have asked an AI tool for dosing instructions for these injectable compounds, some of which have almost no human safety record.

However, it’s likely the extremes of semantic hacking will have a half-life. It works now because the corpus is porous, the systems are young, and the people gaming them are moving faster than institutional protections. Over time, it’s unlikely the platforms will allow their answer engines to become so obviously polluted consumers no longer trust them. Not least, because there’s no money in it for them (more on that in a bit). So, the models will get better at detecting such activity until it becomes endemic whack-a-mole background noise.

What comes next is what will matter most, because the platforms will quickly shift from policing a bad corpus to selling a clean one. The black-hat tactic will justify the white-hat product: verified facts, canonical brand data, warranted claims, indemnified product information. All to be paid for. Not ads, necessarily, but something more boardroom-palatable: protection, provenance, and consumer trust. A toll on your truth, sold as a verification-layer cure for polluted platforms.

This is why GEO isn’t an alternative to platform monetization. It’s the market-making event that will drive the shift from GEO to generative engine monetization, GEM. (They’ll call it marketing, but that was always code for monetization).

We’ve seen this exact sequence before. In the social media era, platforms created a layer of apparently free reach. Brands invested, adapted, and grew dependent. Then organic reach was capped, and what looked like access became inventory. Search sold access to intent. Social sold access to attention. Marketplaces sold access to comparison. Retail media sold access to the shopper.

AI platforms will aggregate all of this and sell access to the choice itself, with each step moving the apparent value closer to the moment customer preference converts into cash, because that’s the kind of attribution the platforms can charge the most for.

In the same way that executional folks will inevitably default to option C, I fear my peers are too binary in assuming option A is the only answer. Instead, the real difference lies in whether you are making a strategic choice or if you end up in one of these positions by default.

For example, it’s not that platform rent never works. Sometimes rent is the strategy. In the search era, Booking.com built a machine designed for it based on high-intent aggregation, conversion discipline, and a business model capable of carrying the toll. It didn’t stumble into dependency; it engineered search as an integral part of doing business. Meanwhile, Airbnb made the opposite choice and built consumer preference around the brand instead. Both worked because both had made clear, economically viable, strategic choices.

Drifting into rent by default isn’t so much a platform problem as a management failure. Most companies become dependent on rented demand when they stop taking desire creation seriously enough. If the product is not meaningfully better, the experience not meaningfully more trusted, the brand not meaningfully more desirable, then demand must be acquired repeatedly from someone else’s intermediation. For most, rather than being a Booking, which is designed for it, platform rent fills a gap where long-term customer value should have been.

This brings us to a broader point about AI and technology more generally: it only becomes an advantage when subordinated to a broader set of strategic choices.

To use an analogy from an entirely different field, Ukraine’s battlefield effectiveness is not just about drones. It comes from a clear doctrine or set of strategic constraints that governs how it uses them. Scarce manpower, the need to disrupt logistics, the impossibility of matching Russia symmetrically. Here, their rapid tactical experimentation matters because it sits inside, and directly informs, a larger logic and set of doctrinal goals. The technology — in this case, the drone — should not be mistaken for the strategy. It enables it. Without the drone, the strategy cannot be effective. Without the strategy, the drone cannot be effective. You need both.

This distinction is what I fear marketing keeps missing. AI may be marketing’s drone moment, but only in a superficial sense. In a world where everyone can execute more, faster, better, a key question becomes one of the overall strategy that guides such tactics and shapes how they’re used and to what end: which demand the company intends to own, which it can afford to rent, which transient advantages it will exploit while they last, and which dependencies it must refuse before they become structural. This doctrinal layer of strategy is what separates purposeful experimentation toward a goal from running around like headless chickens. A condition the modern marketing function euphemistically labels “agile.”

The real preparation for a coming era of AI platform rent is to set the strategic context within which it must operate. Some demand should be owned: created through product truth, service experience, reputation, memory, and a worldview strong enough to survive synthetic mediation. Some demand should likely be rented, but only where the margin structure can carry the toll. Some demand should be experimented with. Seeking to exploit transient advantages while they work, then abandoning them before they become diminished-return dependencies.

And, yes, some demand should be refused. Even when it performs, because you see that today’s apparent efficiency will become a future constraint that will be hard to exit.

Different corporations will choose a different balance of each of these choices. The key being that they’re chosen, rather than defaulted to.

So no, AI isn’t as simple as the positioning renaissance we branding people want to tell ourselves it is, nor is it about gaming a semantic algorithm, nor is it about paying rent for transactions. Instead, it makes strategy matter more by raising the price of not having one.

If the problem in last week’s Off Kilter was a wrong conversation, this week it’s the challenge of unmade decisions happening by default. While execution may become cheap, fast, and abundant, strategic judgment will not. As a result, high-judgment marketing organizations will learn how to deliberately use AI within the frame of their own strategic imperatives, while low-judgment marketers will be controlled by it. Dependent on others for their own destiny.

This is how strategy will reveal itself. Not in the decisions brands make, but in the decisions they don’t.

Subscribe now

Share

Leave a comment

I’ve been in the branding business for 25+ years, and while my name is on a Cannes Lion somewhere, I’ve never been, nor had any interest, in going.

Cannes was always a bit like a reverse Purge with pink wine. If advertising used to be 51 weeks of the year spent stabbing each other in the back, Cannes was the one week they slapped each other on the back instead.

Today, while the backslapping continues, there are far fewer backs left to slap as ad-world slowly implodes. This put Cannes itself in a bit of a pickle, so a few years ago it invited the big-tech foxes into the henhouse in a repositioning revamp. This forever changed the flavor of the event, from creative backslapping to the triumphant celebration of the industrialization of advertising.

While the pink wine remains, the focus shifted from the artisanal fever dreams of ambitious creative directors that ran only once on late-night TV in New Zealand toward technology, data, AI, creators, and the kind of industrial-scale hype you can only buy when you’re worth a trillion dollars or more. And make no mistake, the tech firms’ embrace of creativity this year exists only because they’re so secure in their dominance.

Anyway, the result is a resurgently modern Cannes that now bills itself as the most sophisticated forum for the world’s most sophisticated global marketers.

Great positioning; completely lost in execution.

Whether he meant to or not, Sean Summers, of Mercado Libre, hit the nail on the head when he stood on stage and observed that CMOs have been their own worst enemies when it comes to boardroom influence, stating that “I think it’s on us,” and “We created our own language, which by definition reduced credibility. We lost trust from business.”

To put this in context, let’s look at the numbers because they’re ugly. In Boathouse’s latest study of CEOs, 57% see their CMO as an execution leader rather than a strategic one, and just 8% think the CMO actually leads strategy. Sixty percent now describe marketing as a cost center, up from 35% a year earlier. And in the past year, fourteen percent have considered scrapping the CMO role altogether. Meanwhile, Spencer Stuart finds that a third of Fortune 500 corporations don’t have one at all. The CMO already holds the shortest tenure in the C-suite, and the money is headed in the same direction. Marketing budgets have fallen from a pre-pandemic norm of around 11% of company revenue to 7.7% today; half of CMOs are now running on 6% or less, and 59% say that isn’t enough to execute their strategy, in what a Gartner analyst labeled “a story of privation.” Oh, and the path forward is automation with AI, not because it’s going to make marketing better, but because corporations want to cut costs even further.

With such ugliness in mind, you’d be forgiven for thinking the Cannes stages would’ve been filled with urgency from people like Mr. Summers forcefully making the case for CMOs in the boardroom. You know, focusing on how marketing is a strategic discipline, how its value-creating capacity starts far upstream of the choice to advertise, how creativity exists to create advantages rather than merely defend and steward them, how marketing must lead innovation rather than resist it. That kind of thing.

But nope. The main stage keynote was a masterclass in how to patronize your audience, as Mark Ritson and Byron Sharp teased conflict before breezing through a grab-bag of the basics of effective advertising. Meanwhile, just down the street, the tech firms and consultancies freely hyped their next grift generation of rent-seeking tollbooths, without any discernible counter-narrative. McKinsey, peddling agentic ops, wants to extract value by sitting between you and your operations; OpenAI, peddling its ad platform, wants to extract value by sitting between you and your customers; Cannes says that’s AOK as long as they’re paying for the rosé.

Why the extreme disconnect? Well, that, my dear Watson, is elementary. Financial incentives. More specifically, misaligned financial incentives, of which Cannes is a veritable canary in the coal mine.

Today’s global advertising business is a trillion-dollar land grab, while boardroom-level marketing strategy is a rounding error. As a result, there’s almost zero vendor incentive to help the CMO become more upstream strategic and advantage-creating, and an overwhelming tsunami of “creating our own language” rhetoric focused on execution, because that’s where the TAM lives.

This is why festivals like Cannes, paid for by vendors, never ask the really difficult questions, like, oh, I dunno, why maybe we shouldn’t be getting into bed with vendors hell-bent on intermediating us into dependency? Or why no boardroom in the land should care about what Ritson and Sharp agree on.

Instead, the financial incentives all point in the opposite direction. They seek to frame executional and operational innovations as strategic revolutions, while burying genuinely strategic concerns under hype.

Which neatly brings me back to Sharp and Ritson. Not because of them per-se, but because they’re a direct illustration of what goes wrong when you present executional, operational factors as if they’re strategic brilliance. In the case of their talk, there were two sleights of hand on display. First, what they agreed on was implicitly presented as a highest common factor, when it was really a lowest common denominator. Second, and far more importantly, they referred to their talk as marketing truths, while what they discussed were advertising tactics. This wove an illusion of strategy around concepts that, in their telling, were clearly executional.

To illustrate the point, they both agreed that purpose is nonsense. But this only holds true at a certain altitude. If what you mean by purpose is slapping a social narrative onto an ad, they’re correct. Nobody wants Gillette lecturing them on what it takes to be a man. However, bring that altitude up a few notches and things change quickly. If the claim is that purpose as a guiding OS for your business is nonsense, they couldn’t be further from the truth. Try telling Patagonia its environmental worldview has zero to do with its business success, and prepare to be laughed at. And for any Sharpian dogmatists out there who claim that Patagonia is just quality pants with a distinctive logo attached, let’s flip that. Quality pants are the opening ante. There are loads of quality outdoor pants out there. Without that, you don’t have a business. It doesn’t explain success. Nor does the distinctive logo. Not on its own, anyhow. It doesn’t explain what happened when a colleague once left his favorite fleece behind in a hotel, lamenting that “I loved that fleece. It made me feel like I was in the mountains when I wore it.” That isn’t just distinctiveness; it’s the meaning the distinctiveness represents. It’s what Patagonia stands for, being internalized and played back as emotional surplus by a customer. And that meaning is defined way upstream of advertising. It’s the company’s literal OS.

This, folks, begets a bigger problem, which explains why CMOs have drifted so far from the C-Suite. Something festivals like Cannes do nothing to check and everything to exacerbate. When you become overwhelmed by operational rhetoric masquerading as strategic revolution, you risk treating it strategically, often with disastrous consequences.

To illustrate this point, let’s dial in on CPG (FMCG for everyone outside the US) for a second.

Over the past decade and a half, since the book How Brands Grow was published, CPG corporations have most fully embraced Sharp’s “laws” of brand growth as a strategic marketing model. Yet during that exact period, CPG corporations have flipped from stock-market over-performers to perennial underperformers. Might there be a connection?

Now, just to be clear, I’m not suggesting CPG underperformance is solely down to mistaking Sharp’s theoretical model of advertising for a set of laws governing marketing strategy. That would be stupid. But the blind spots of doing so are showing up in exactly the places you’d expect them to, so I’m confident it’s at the very least a direct contributor.

Here’s the basic challenge. What Sharp’s empirical research focuses on, and what he and Ritson agreed upon on stage, is what happens in a mature market, where innovation has slowed to a crawl, and everyone is competing on availability. It describes an equilibrium, not what it will take to break it. And while rhetorical flourishes such as the “laws” of double jeopardy and Dirichlet patterns make it all seem very sophisticated, the prognosis it boils down to is simplistic in the extreme: activate light buyers through mental and physical availability, which is driven by distinctive assets as memory triggers for purchase and recall, which in turn necessitates mass ad-spend to increase your ESOV (excess share of voice) so you get noticed and remembered. Fine, as far as it goes. But that’s exactly the problem.

Let’s contrast strategic thinking with executional thinking about the above. If you’re thinking strategically, you’ll understand that an equilibrium exists not to be accepted, but to be broken. You’ll know the path toward asymmetric returns in a mature, stable market is paved by innovation that injects dynamism, which can sometimes be disruptive. This is because you know that the largest and most sustainable returns come from innovation, and that market stability is largely a myth. You’ll also understand that what Sharp has described is a mechanism for defending the value created by an innovation once it’s in the wild. As a result, you’d split your resources and capabilities between offensive innovation on the one hand and defensive mental and physical availability building on the other. The first allows you to explore your way into new possibilities; the second optimizes for known patterns in customer behavior. For the nerds out there, and I am one, it’s the combination of complexity thinking in experimenting your way into new advantages and optimization thinking in extending the value of those advantages until they’re exhausted, governed by a consistent worldview that people will discover, buy into, and come to trust over time as you continually reinforce and refresh it.

On the other hand, if you were to mistake Sharp’s executional prognostications for strategy, you’d follow a very different path. You’d assume market maturity and stability as a given. You’d accept equilibrium, which means small shifts in market share become acceptable targets. You’d under-resource newer, smaller, “underscale” brands in favor of larger ones, before eventually killing them off altogether. And you’d deliberately shift resources away from innovation and into ad budgets to drive a more concentrated focus on building and maintaining mental and physical availability. And you wouldn’t bother standing for anything, because your positioning is, to paraphrase Sharp, “whatever your last ad said it was.”

Does any of this sound familiar? It should, because this is exactly the underperformance hole into which large CPG corporations have fallen, with ugly consequences. All that growth Sharp promised? It went to just about everyone except them.

Over the past decade, category-leading CPG brands captured barely a quarter of their categories’ growth. Small and mid-sized brands took nearly half, and private label took the rest. The incumbents weren’t just growing slowly; they were being beaten from both ends at once. Store brands captured the value shopper, while challenger and niche brands captured premium growth, with customers willing to pay more for products they found healthier, more meaningful, and more desirable. This left the big brands duking it out over a constrained middle with increasingly irrelevant propositions.

This left them with a single lever as volume growth disappeared: price. Having spent a decade moving money out of innovation and into availability (genuinely new products fell to a record low, from roughly half of all launches in 2007 to about a third by 2024), there was no new value to sell, so they charged more for the old.

It worked for a while. During the pandemic and post-pandemic periods, higher prices propped up profits, while volume growth gradually declined. But you can only raise prices so far on products that haven’t changed in years. Just ask PepsiCo, which aggressively raised prices until North American volume fell for five straight quarters as shoppers bought less, or bought the store brand instead. Elasticity curves aren’t infinite. Sooner or later, customers walk.

And cutting innovation was only half of it. The same scale-wins logic that says big brands win on availability also says small brands are a distraction. So, the giants took a knife to their portfolios, pruning the tail to pile everything behind a handful of mega-brands. After the 2015 merger, Kraft Heinz became a masterclass in cost discipline and brand concentration…right up until 2019, when the company wrote down the value of Kraft and Oscar Mayer by $15.4 billion.

By 2025, the stock had lost two-thirds of its value since the merger, and by early 2026, a new CEO quietly shelved a planned breakup to pour $600 million back into R&D and brand, the exact investment the company had starved for a decade. He even admitted Kraft Heinz had been charging more without giving customers…more.

Now, this isn’t me randomly Sharp bashing. The problem isn’t so much his theory as the altitude at which CMOs have chosen to apply it. P&G, for example, is a direct contrast to Kraft Heinz. A decade ago, it took a flamethrower to its brand portfolio, but the savings weren’t sent back to shareholders as profits, nor were they used to shore up availability. Instead, P&G strategically reallocated resources into innovation and what it calls “superiority” across product, packaging, and performance in categories where it believed it could create, rather than capture, value.

P&G treated Sharp’s theory exactly as it should be treated: the thing you do to defend advantages you’ve built by other means, not what you do to make up for not creating them in the first place.

The prize for doing it right? P&G, the bluest of blue-chip stocks, more than doubled in value from 2010 to today, while almost the entirety of its CPG peers flatlined.

The biggest problem, which Sharp obscures under the rhetoric of “laws,” is that his theory only holds for execution within the stable conditions he describes. While that stability might have held true in the 1950s, it’s temporary these days because once a market is viewed as “stable,” it reads as “ripe for disruption.” Which is why treating such theories as a strategic frame is so dangerous.

So, let’s rewind a little to Ritson and Sharp at Cannes patronizing the world’s most sophisticated marketers on the main stage. The problem wasn’t that anything they said was unentertaining (it was quite entertaining), or wrong (it was largely correct, given the right altitude), or even that it was overly basic (although it was). In fact, my issues have almost nothing to do with them and everything to do with the Cannes organizers themselves. Why? Because of the opportunity cost of what could have been on that stage instead.

This was the opportunity for Cannes to be the most sophisticated forum for the world’s most sophisticated marketers. And it fumbled the football. Badly.

As a result, the world’s busiest, most sophisticated, most under-pressure marketers flew to the south of France for guidance and inspiration and instead got a keynote refresher on the basics of a playbook that no longer holds up in the one category that built its whole f’ing marketing strategy around it.

Meanwhile, the tech firms and consultancies were left to freely hype the next phase of their capture of the marketing function for their own benefit, not the benefit of any CMO in attendance, not for the benefit of any of the corporations employing them, and most certainly not for the benefit of any of us paying consumers.

Instead, imagine an alternate universe where the keynote had been about the role of creativity in delivering marketing strategy for the boardroom? Navigating the complexity modern marketers face? Rethinking marketing as a strategic discipline? Finding sustainable opportunities amid uncertainty? Building asymmetric return machines? Blending the disciplines of complexity management and optimization? How to see through the tech hype? Lessons from the platform era that now apply to AI? Or even, God forbid, creating new customer value?

This was the real cost of having the wrong conversation. Not a wasted week in France, but a wasted opportunity to talk about the stuff that really matters, which the LinkedIn warriors would’ve reliably amplified. Instead, they’re abusing us with asinine debates around whether it’s called mental availability or brand salience. FFS, who cares? No CEO, that’s for sure.

Rome, fiddles, anyone?

Ah well, there’s always next year.

Subscribe now

Share

Leave a comment

The biggest problem with the AI discourse is the same problem with all discourse these days: it’s binary. Put bluntly, there’s only money to be made at the extreme ends of the spectrum, leaving little room for nuance. So we’re left trying to navigate the gulf between “AI is bigger than the industrial revolution” and “the AI bubble is about to catastrophically collapse.”

Personally, I’ve struggled to articulate my own position, because it’s neither of these things. In the past couple of months, I’ve advised “AI-native” startups while also advising large brands on taking a slower, more measured approach. Evidence, if any were needed, that context is the first victim of dogma.

Fortunately for me, Dr. Suzanne Livingston (a former colleague from many moons ago, with a PhD in AI) is far more eloquent than I’ll ever be. To paraphrase her post: we’re entering a period where we’ll need to be more different, more consistently.

In her words:

“More friction, more oddity, more experiment, more push back. More offshoots and subversions. Less sinking in to the average…Maybe we will get there. I think it’s a case of engaging more with AI, not less.”

Now, I’ve butchered the order of that quote, which changes its meaning (sorry, Suzanne). But it captures my take pretty well. Commercial success will increasingly depend on preserving and sharpening our capacity for difference, and AI is a powerful tool that can help us get there. Or hurt us if we use it wrongly.

Which neatly brings me to the subject of synthetic insights. A topic all marketers are now dealing with, which has real uses, and also the potential to go very wrong indeed.

So, since marketers are currently being overwhelmed by synthetic rhetoric, let’s dig into its issues instead.

Section 1: Issues of accuracy

Weaponized stereotypes

Let’s start with what we actually mean by synthetic research, insights, panels, and respondents, because how they work explains much of what we need to be aware of.

Synthetic research is a language model trained to role-play a customer cohort by generating the most statistically likely answer a person in that cohort would give, based on patterns in its training data, which may include a database of your own proprietary research, but probably does not. Underneath lies a foundation model trained on the public internet, with a few that fine-tune using proprietary survey archives or platform access, though there’s little transparency here; all vendors treat their method as some kind of secret sauce.

This means a synthetic profile draws largely from what’s written about people rather than anything these people might say about themselves. Poll a synthetic CFO, and its persona is assembled from what others have written about CFOs. Think Big Four CFO studies, consulting reports, and trade-press features, not what actual CFOs have said. CFO discourse online is sparse, and what exists is often gatekept behind walls the model can’t see through. Anything posted in a closed community, said in a messaging app, or spoken aloud in a meeting simply isn’t in the training data. If the model can’t reach it, it doesn’t exist.

As a result, academics are finding these models mischaracterize groups through out-group imitation rather than in-group description. This produces, in the authors’ words, “flattened, oversimplified portrayals.” In other words, stereotypes.

This risk of stereotyping runs deeper than an accidental flattening because the source material isn’t neutral. Using our CFO example, Big Four CFO studies have commercial intent baked in; they’re biased by design because they’re built to sell an engagement.

Widen the aperture, and it’s not hard to consider what this means for marketing more generally: Put bluntly, we risk building strategies atop an internet’s biased caricature of our audience. A caricature that bleeds through even when we fill the synthetic model with our own proprietary research because, by definition, “the internet” is a much larger source of signal than anything we might feed it.

As a result, what’s being delivered isn’t really insight at all; it’s recall. And if you don’t know the sources that it’s recalling from, you don’t know how biased that recall will be.

Invisible failure

As a direct consequence of how these models work, synthetic insights will be most accurate where the data is thick, because the model has clearer patterns to match, and least accurate where the data is thin, because it has almost nothing to go on. Where the data is thin, an AI system won’t say “I don’t know.” Instead, it will act as designed and predict the most plausible-sounding text, even if there’s very little underlying signal from which to make that prediction. We call these inaccuracies “hallucinations,” but in reality, they’re just wrong.

This is why vendor accuracy claims cluster around data-rich use cases, leading you to assume that accuracy is linear across all use cases, when it isn’t.

The result is what we might label invisible failure. We take a tool validated on data-rich questions and point it at data-poor ones. A confidently wrong answer then looks identical to a right one.

What makes this dangerous is that insight from thin data is often the value proposition. The headline proposition of many synthetic vendors is access to populations that are difficult or expensive to reach. The very cohorts for which the least real discourse exists, and therefore the very cohorts the model knows least about. The tool produces a confident, statistically plausible answer that might bear zero relation to reality. And because the real population is hard or costly to sample, nobody checks. We just assume it’s right and blithely move on.

The net, net is that cohorts commanding the highest prices are often the ones where the product is most inaccurate, and also where this inaccuracy is least likely to be noticed. You can make up your own mind as to the ethics of this.

Fine-tuned sycophancy

Finally, there’s a third accuracy problem. The models behind synthetic respondents aren’t just trained on data; they’re further tuned using reinforcement learning based on human feedback to be helpful, agreeable, and pleasant to deal with.

It’s a tuning that’s great for a chatbot you want to drive engagement with, and poison for research, because it weakens the ability to reflect true attitudes, fails to highlight non-dominant views and edge cases, and introduces a user-pleasing filter.

As a result, the models drift toward a homogeneous profile: high in agreeableness, low in confrontation, “too neutral, detached, and nonjudgmental.”

Again, this fine-tuning bleeds through even if you’ve added masses of your own insights to its knowledge base. This is because it’s set at the factory to smooth away the very frictions that you’re looking for, while being careful not to tell you things it thinks you might not want to hear.

Pleasing, yes. Useful, not really.

Section 2: Issues of advantage

Commoditization

The commoditization problem isn’t technical; it’s strategic. If you can query a model, everyone else can too. You’re all sampling the same distribution, so whatever synthetic insight you pull is, by construction, available to your competitors too. It simply cannot be a competitive edge. At best, it raises the baseline.

This isn’t the first time we’ve been to the commoditization rodeo. As corporations mass-hired designers during the digital era, corporate design dramatically improved… and then stopped being a competitive advantage as everyone leveled up to the same average. As I frequently joke, good design became the new bad design. Table stakes rather than differentiation. Synthetic insight is on the exact same path, only it’ll happen faster, because there’s no hiring or training lag. It’s a capability arriving for everyone at the exact same time.

Now, I know what you’re thinking. So what if the insight is a commodity? Interpretation is the real edge. OK, fine. Except we’re increasingly handing interpretation to the exact same models. So, if everyone draws on the same source material and then runs it through the same interpretation engine, the outputs will inevitably converge, inadvertently erasing and reducing our differences rather than enhancing them. Let’s take a real example. It’s not uncommon right now for advertisers to use synthetic insight to inform campaigns, synthetic tools to generate campaigns, and synthetic panels to act as pre-screening for campaigns. It’s not hard to predict the outcome, and that outcome definitely won’t be a gorilla drumming along to Phil Collins.

Now, I’m not saying that synthetic insight can’t confer any advantage, ever. It’s that as synthetic insights commoditize differences, advantage will migrate to those areas where proprietary knowledge and genuine human judgment make the biggest impact.

Error % might be where the value lies

Human beings share circa 90% of their DNA with cats, but you’d never ask a cat what a CFO thinks. This is the inherent challenge when a synthetic vendor makes statements like “90% accurate.” What’s hidden in that error % isn’t evenly distributed noise; it’s often where the value lies.

Independent research is unambiguous. Language models reproduce the documented average while flattening the variance, and they become unstable on novel questions. Change the wording slightly, and the answer can change, sometimes quite significantly.

While reproducing the average can be useful at times, it’s precisely the wrong skill you seek in a good researcher. Good research exists to uncover the non-average: the dissenters, the emergent behaviors, the weird subgroup that becomes the market. An instrument that regresses to the consensus risks rendering you blind to what matters.

Behind the cultural eight-ball

A consensus machine will, by definition, be structurally late to cultural shifts. And being late to culture might be fatal if it’s your job to ride it.

This is because models are blind to what they can’t see. The group chats, the Discord groups, the private forums, and everything that isn’t happening online. Synthetic models learn from texts written about cultural subgroups rather than from the groups themselves. This means a trend becomes legible to the machine only after enough people have begun discussing it openly, and by then it will already have entered the mainstream. This means synthetic culture-sensing arrives late and risks reaching peak confidence at the exact point a trend has already crested. Imagine jumping on the Labubu brandwagon at the exact moment it became deeply uncool to do so, over and over again. It’s the stuff of CMO nightmares.

There’s a deeper concern here for brands that believe they’re in the business of manufacturing culture rather than chasing it. A machine that reflects the arithmetic mean of every viewpoint cannot produce the kind of viewpoints culture-shaping requires.

Put simply, you cannot average your way toward a stance.

Section 3: Issues of contamination

The well is being poisoned

A Cornell preprint study shows that as few as thirteen words planted in a Reddit or Quora comment can reliably bend what AI search tells people, because the systems treat sounding like the question as a proxy for being a true answer to it. There’s now an exponentially growing answer engine-optimization industry seeding the sources these tools scrape for exactly this reason.

Since synthetic research draws from the same well, using the same underlying foundation models, it’s also impacted.

This risks a Yossarian situation straight out of Catch-22, where a brand that seeds the discourse to flatter itself might run a synthetic panel that drinks from the now-poisoned well and reports the flattery it seeded back to itself as a consumer truth.

Now, the Cornell work is a preprint, run in a sandbox rather than on the live web, so some caution is warranted. But the root cause is what matters: a system that can’t tell true from plausible will always be at risk of being poisoned by the plausible. And, as the AEO industry explodes, this problem is likely to grow exponentially.

It’s also likely that, as online subgroups recognize the mechanism as a feedback loop, they’ll poison the well for fun. This already happened to Sony Pictures in 2022, where what looked like a groundswell of demand for a re-release of the truly horrendous Morbius movie turned out to be an internet meme. This is exactly the kind of plausibility LLMs are unlikely to see through.

Today, peptide hawkers are among the most aggressive AEO seeders. As a thought exercise, I’d be curious to see how this seeding manifests in synthetic sources as consumer insight.

There is no auditor

Today, the emerging synthetic insight category is grading its own homework.

Even Qualtrics, which might be viewed as the careful, responsible end of the market, develops and validates its synthetic methodology internally, against criteria it sets itself, and the only independent audits it conducts are for data-center security, not for whether its simulated humans genuinely resemble real ones. I’m not saying anyone is cheating, but algorithmic drift is a real phenomenon, and there’s no independent audit body certifying that a synthetic panel reflects reality. As a result, the vendor that builds the model gets to define “accurate” on its own terms, and we have to trust that they’re right.

Now, this isn’t a new pattern. Commercial innovation always outruns the creation of an independent trust infrastructure. But that doesn’t change the fact that it systemically advantages the vendor over the client. Nor does it change the fact that trust infrastructures usually emerge only after a massive failure forces their creation. Two examples are worth keeping in mind. First, the independent auditing of public companies wasn’t mandatory until the Securities Acts of 1933 and 1934 were passed after the crash that led to the Great Depression. Second, during the 2008 financial crisis, a trust infrastructure existed but was captured by market participants. Both warnings apply to synthetic research: there’s no referee yet, and you should be wary of whichever referee eventually emerges if the vendors are the ones paying for it.

Eyes wide open

I’m not doubting the potential for synthetic insights to democratize and improve many aspects of what businesses do. Even if they’re primarily recall machines, rather than insight machines, recall can still be hugely valuable, especially when you can wire it into decisioning systems in real-time. This isn’t a “synthetic insight bad” kind of rant.

However, we do need to be aware that synthetic insights have structural issues that mean they’re far from the panacea they’re being presented as, no matter how compelling their outputs might appear at first glance.

The winning brands of the next decade won’t be built upon the slickest synthetic engines. Everyone will have these. Instead, the winners will be those who recognize that proprietary insight into real human beings is becoming far more valuable as we move forward, not less.

Yes, synthetic insights will likely wipe out the commodity end of the research and insights spectrum, but anyone offering a novel, valuable, and, most importantly, genuinely insightful understanding of consumers and their foibles will almost certainly be well positioned for success. Even if it takes clients a while to figure that out.

My final advice is this. Keep your own connection to real customers. Do the hard work of seeking insights from real people, even if that means running against the tide. Don’t run away from synthetic - it has its place. But where you’re using it, be prepared to run your own periodic audits to ensure accuracy and guard against drift. And be especially careful about trusting synthetic insights in arenas where the underlying data is thin. The promise is most likely a mirage.

Subscribe now

Share

Leave a comment

Obligatory AI slop image from the prompt - “an executive in a Brioni suit with Nascar livery.”

Recently, Benedict Johnson, Ian Whittaker, and Rory Sutherland published a great piece on brand for Private Equity (PE) owners over at Aha Partners. You should read it.

For the purposes of this Off Kilter, here’s the gist: brand is the psychological advantage that lets a business hold market preference, trust, and price. It’s among the largest assets private equity buyers pay for when they acquire businesses, but they treat it as a cost during the hold period, and then act surprised when its value hasn’t grown at exit. Whittaker’s explanation is that brand isn’t ignored, it’s unmodelled. The model PE owners use measures EBITDA impact this year, which leaves no room for a return occurring years into the future.

While it’s great, I wish they’d used their argument to make the case for a different kind of PE firm entirely. As a result, the rest of this edition is my attempt to build on their work and do that. Here are the load-bearing observations I’m building on:

• What looks like financial discipline is a category error. Brand is not a line item waiting to be optimized. It is the option a business holds on being valuable in circumstances it cannot foresee. Optionality, not optimality.

• Brand returns are fat-tailed, and a system that can’t model fat-tailed returns only sees variance rather than return.

• In a PE firm, decisions that fail conventionally are survivable, while those that fail unconventionally are career-ending. Capital allocation inside these businesses is less a search for the highest probable return than a social system for mitigating institutional embarrassment.

Private Equity. Stuck in a trap of its own making.

Let’s begin by explaining why creating a different kind of private equity company is particularly relevant right now. (Please forgive any overly broad generalizations in the interests of clarity.)

Excluding the vulture funds and breakup specialists, PE has been broadly built on three engines of return, which emerged roughly sequentially as competition eroded the excess returns that preceded them:

• Engine 1: financial engineering through cheap debt and the multiple on equity.

• Engine 2: operational excellence. The hundred-day plan, the margin program, and the craft of running a business harder than its previous owner did.

• Engine 3: the rollup, enhancing margins via market power.

Because each engine began by delivering excess returns, each attracted competing firms underwriting the same thesis, ultimately bidding against one another to pay more for businesses that would then return less. Competition-driven commoditization, exactly as theory predicts.

The Aha piece references this as a professional monoculture. I’d go further. It’s an epistemic monoculture with a singular worldview, in which everyone looks at the world in pretty much the same way. (Think of this as Ivy League averaging rather than AI averaging.)

The resulting numbers are stark. Distributions to investors have sat near financial-crisis lows for four straight years. Close to four trillion dollars of unrealized value is parked in more than thirty thousand unsold companies. These are assets owners are unwilling to sell at prices buyers are willing to pay. Yes, some of this is rate-cycle-driven, but the rate cycle is accelerant, not cause. The convergence driving lower returns is structural.

Want proof? Look at how the category’s own advisers describe value creation. Alvarez & Marsal’s 2024 value creation survey lists technology, AI, operational transformation, and organic growth as the primary levers. Brand doesn’t appear anywhere. KPMG’s 2025 value creation framework doesn’t include brand investment as a positive lever at all. In fact, the only time “brand” shows up in the KPMG report is as “brand erosion and cultural drag.” In other words, as a risk of cutting costs too aggressively.

So, coming full circle back to the Aha piece: the buyers paying rich goodwill premiums for the brands they buy don’t view the source of that premium as having anything to do with value creation.

What if we inverted this?

A 4th engine. Differentiated value.

If the trap is sameness, the way out is difference. Not a better mousetrap, but a completely different approach. Put more simply, a fourth engine of return. One with potential that others haven’t yet seen.

While there could easily be others, brand is one such engine. This is for three reasons:

• At the operational level, brand strength is a powerful driver of differentiated value if treated as such.

• At the capital level, the PE monoculture that exists today doesn’t value brand, which creates arbitrage opportunities for those who do.

• At an epistemological level, brand centers on demand-side leverage rather than supply-side, which PE firms have already optimized.

What follows is an attempt to describe this opportunity in the language of a PE operator. I’m not claiming it as a complete thesis, but hopefully it’s enough to make the point.

Brand is market leverage, and what that leverage looks like is an options portfolio.

Here’s a different way of looking at it. Brand isn’t a single option on your future, as the article states. Instead, brand is a form of market leverage that manifests as a portfolio of options on future activities. The broader and deeper this options portfolio, the more valuable the brand.

When I say options, I mean it in the following sense:

• A brand option means having the right, but not the obligation, to make a particular customer move in the future.

• The premium for these options is hidden inside the goodwill you pay when buying a business.

• The asset underlying these options is the accumulated customer permission the brand has built. This is trust that transfers forward to things the company hasn’t yet done.

• Exercising a brand option means making a move that your brand’s permission supports. Whether that be a new price point, a new channel, entry into a new category, or even a new growth-inducing campaign.

• Like any option, brand options expire. Cultural windows close. Adjacencies get occupied. Permission erodes when it sits unused.

This is a very different way of thinking compared to the current PE monoculture. Their supply-side mentality treats a portfolio of brand options as worthless relative to greater efficiency, whereas a market-leverage lens views these options as critical sources of differentiated value to be exploited through innovation.

The exploitation of differentiated value is manifest not in greater efficiency but in how brands de-risk innovation. The accumulated permission a brand has built increases the probability that a new move will be adopted and scaled successfully. It’s why Apple never has to be first. It enters late, lets competitors de-risk the category, make mistakes, and educate the market, then proceeds to suck up the majority of available profits. The options value of the Apple brand converts lateness from a penalty into an advantage, because people are willing to wait and then pay more for what they believe will be a more resolved solution.

Permission also has a shape. Apple runs deep into a single ecosystem, while Virgin runs broadly as an iconoclastic alternative to the status quo, irrespective of category.

This means the diligence question changes. No longer an abstract question of “how valuable is this brand,” but instead, “what does the brand’s permission look like, and how valuable are the options this unlocks for the future?”

Off Kilter 217: The Capstone-to-Advantage, based on Rita McGrath’s work, delves deeper into this. Under her theories, advantages are transient, so sustainable advantage depends on how well you manage a portfolio of opportunities (options) you launch, exploit, and then retire as their value expires. My addition is that the brand should be the capstone, or governance layer, directing the portfolio, because exercising options well earns more customer permission, which generates more valuable options. Managed successfully, this loop is why such businesses consistently outperform the competition.

This isn’t about seeking a precise mathematical value for the options created by your brand; it’s not a re-hash of “real options” theory. Instead, I’m using the term relative to a PE environment that values a brand’s value-creating potential at zero. You don’t need to price an option precisely to know that pricing it at zero is wrong; success only requires you to have an approximation that is less wrong.

Variance is where return lives.

Thinking this way allows us to shift Rory’s observation of fat-tailed returns from problem to desired mechanism, and to shift unconventional failure from embarrassment to strategic choice. The trick is to accept high variance in pursuit of asymmetrical returns while capping the cost of any individual failure, which is exactly what brand strength combined with operating discipline enables.

This works through three connected mechanisms:

• Brand permission raises the odds. The accumulated permission increases the probability that any given bet, once exercised, will be successful.

• Operating discipline caps the cost. Every option exercised is a market experiment with a pre-agreed budget and clear kill/scale criteria. The downside of any individual failure is bounded by design.

• The portfolio supplies the ammunition. Across enough capped-cost bets, return asymmetry asserts itself. You don’t need every bet to pay off; you just need enough of them to.

Meanwhile, at the asset level, this adds up to something different: reduced risk. Whittaker makes this point from the equity research side. Strongly branded businesses trade at lower betas, achieve higher multiples, and recover faster from drawdowns. This is observable across two decades of market data.

So, the operating thesis of a differentiated value PE firm is this: high variance encouraged at the individual market-bet level; low beta delivered at the asset level. Brand investments reduce the house’s risk while increasing the odds of each wager.

By contrast, today’s PE monoculture looks at the fat-tailed profile of a single-brand bet and treats it as a high-variance cost to avoid, while failing to consider the asymmetrical upside. As a result, it far overstates the risk of building a brand while far understating the costs of not doing so. As proven by KPMG talking about brand erosion, without calling it what it really is, value destruction. Worse, this value destruction might even be booked as profit! Cut brand investments and liquidate your option value; meanwhile, the EBITDA calculation shows you created value. Grrr.

So, what might this look like in practice?

Any business exiting a conventional PE holding period has likely had its brand options portfolio degraded to somewhere around zero. Yet deep permission is durable. It can survive years, even decades, of indifferent ownership. So, where the consensus looks at a secondary buyout target and sees a tired asset that’s already been squeezed, the brand options-literate buyer might ask a different question: did the permission outlast the previous owner’s neglect?

Birkenstock is a great example of what’s possible through exactly this kind of rethinking. In February 2021, L Catterton and Financière Agache paid around four billion euros for a majority stake, and the PE commentariat’s reaction was entirely predictable: it’s a rich price for a sandal maker.

What the skeptics missed was that Birkenstock’s acquisition price included an undervalued portfolio of brand options. This included a price premium with room to widen, a wholesale-heavy channel mix with room for additional direct sales, and permission that ranged from high-fashion to category adjacency. What L Catterton did was exercise some of these brand options as value-creative moves, rather than “protect the brand” by doing nothing. Prices moved up and then held; direct-to-consumer sales were added. And collaborations were scaled from a novelty to a real engine of growth, through partnerships with Dior, Manolo Blahnik, and Rick Owens. Each of these moves represented a capped-cost experiment against brand permission and market leverage, which could easily be walked back if they failed. Meanwhile, each success expanded the aperture for the next.

Five years on, Birkenstock has achieved a rare success for a PE-owned firm, a liquidity event via IPO. It is now worth roughly double what it was bought for based on delivered earnings. Revenue grew 16% to over two billion dollars in fiscal 2025, earnings grew more than 80%, and management is confident enough in the trajectory that it’s buying back a quarter-billion dollars of its own stock. The exercised options converted directly to earnings.

Beyond Birkenstock and specifically outside of PE, there are powerful examples of adjacent innovation where value realization has been genuinely asymmetric. In January 2017, Liberty Media bought Formula One for $8 billion. Today, the Formula One Group trades somewhere around three times that. The standard story credits a commercial overhaul and the good fortune of a decade’s worth of sports-rights inflation. While both are true, they’re not enough on their own. Let’s, for a second, zoom in on the Netflix Drive to Survive documentary. This isn’t an ad in the classical sense. It’s media property innovation. A profit center that modernized the world’s perception of the sport while also making it relevant in the United States. The Formula One brand already had the option to become this character-driven entertainment franchise. That option remained unexercised until Liberty chose to exercise it. And by exercising it successfully, Liberty didn’t just increase some hand-wavy, academically derived value of an abstract asset called “the brand”; it directly increased the value of the entire franchise.

Red Bull built its entire contemporary success atop a similar thesis. After years spent marketing its energy drink through sports sponsorships, the brand earned the option to become a media and sports business itself. Red Bull exercised this option by purchasing Formula One teams and soccer clubs across three continents, as well as establishing a standalone media house to produce its own content. Exercising options in this way allowed Red Bull to do two things simultaneously. First, these sports and media assets became the advertising surface for Red Bull, paid for by their own economics, and far more impactful than any ad-buy alone. Simultaneously, the brand secured permission to successfully operate these assets in categories where values have been climbing faster than almost any other. The Ross-Arctos Sports Franchise Index shows that sports franchises across major leagues have returned something like thirteen percent annualized over two decades, outpacing nearly every other asset class. This means an energy drink bought its way into the era’s highest-performing asset class through a brand that earned the permission to do so, and then used that ownership to 10X the marketing surface area of the core drinks brand itself. Making money to make money. This may be one of the smartest business moves in decades.

While neither of these examples involves PE ownership, Liberty and Red Bull represent brand-as-options-portfolio thinking in action. Identifying brand options with asymmetric upside. Making staged bets on market leverage. Concentrating on winners and treating the brand itself as the edge.

The shape of the bet.

I’d be lying if I claimed this to be a fully worked-out thesis. I mean, I haven’t even mentioned hold periods (happy to if anyone really wants to dive down that hole). But the shape is clear enough. In a PE world where the conventional wisdom delivers ever-lower returns, there’s a bet out there for a new kind of firm focused on a very different worldview. One that sees brand value not as an abstract, lagging asset but as a portfolio of market-leverage options on the corporation’s future. Options with the capacity to unlock value that PE firms currently do not price.

The competitive advantage is that this can’t be bolted on. It isn’t about hiring a brand strategist, a creative director, a design-thinking innovator, or an over-the-hill CMO, and then wheeling them out like a novelty clown troupe downstream of a deal where all the big decisions were already made months ago by a very different group of operators, with a very different lens on success. Fully capturing the upside means having divergent thinkers with domain expertise who can embed a brand’s future directly into the investment thesis, with a vote on capital.

I mean, shit, if you’re going to be a PE firm creating differentiated value, you yourself must be differentiated. It’s a governance change far more than a hiring one. And because copying a differentiated worldview takes a monoculture far longer than copying a playbook does, this engine 4 advantage should hold up for longer than engines 1 through 3 did.

Show me the money.

Now, while all of what I’ve just said is nice and all, the real value isn’t about how well you’d manage brands as portfolios of options, or how you might fund ongoing brand innovation throughout your ownership. It’s how it changes the deals you’d choose to do, the diligence process you’d go through, and your ability to find hidden arbitrage.

If the PE field prices a brand’s option portfolio at or near zero, then it’s systematically mispricing any business with options that are large relative to its current earnings. You don’t need a precise valuation model to see this, because brand permission has clear signals. Pricing tolerance. Inbound collaboration demand. Adjacency fit. Online discourse. Fandom. The signals are there for anyone who knows what they’re looking for.

This strongly suggests that valuable arbitrage opportunities exist within the modern PE environment, because there’s no way a monocultural PE landscape has exhausted them all.

The differentiated-value thesis is a bet that valuable pockets of market leverage are currently attached to mispriced assets hidden in plain sight.

Maybe I’m wrong. Maybe I’m just thoroughly bored with the steadily diminishing returns of decades of efficiency dogma, but after 25 years in the brand business and having watched numerous PE firms blindly destroy forward-looking value without even realizing it, I’m confident the bet is good.

Subscribe now

Share

Leave a comment

This week’s edition began as a LinkedIn post. Afterward, I thought it was too important not to expand into a full Off Kilter. The picture above is a very average ChatGPT averaging of what an all-knowing AI looks like.

AI-induced Dunning-Kruger Syndrome.

Spend any time at all on LinkedIn, and it’s hard to avoid the conclusion that a growing majority of the posts represent what we might label “AI-induced Dunning-Kruger.” The mechanism is simple. The LLM hands you an authoritative-sounding answer within a domain you don’t know well, and the platform rewards you for posting it. The goal is marketing yourself; your confidence is machine-manufactured, and your reach is algorithmically dictated. Ultimately, this is less about a descent into charlatanism and more about people falling en masse into the trap of performative expertise without having the understanding necessary to recognize what’s happening.

In my own domain, I see a constant barrage of posts presenting the work of Professor Byron Sharp and the Ehrenberg-Bass Institute of Witches and Wizardry as some form of universal marketing “truth,” when, in reality, it’s a single theory among many. And, like all theories, it has blind spots and requires significant adaptation before becoming commercially useful rather than academically interesting.

Now, let’s be clear. Worshipping at the altar of Sharp isn’t a new phenomenon that arrived solely with the LLM. In fact, if there wasn’t already a large online corpus of information reinforcing the perceived credibility of Sharp and EB theory, the LLMs wouldn’t be presenting it to us as fact in the first place.

What is new is the speed, scale, and low cost at which AI systems are averaging out knowledge in exactly the fashion I described above, and the implications of this averaging run far deeper than just marketing theory.

Synthetic amplification.

Contrary to the confidence with which they present their answers, LLMs don’t, in fact, deal in truth; they deal in probabilities. Their answers regress to the mean of whatever they find most legible within the corpus of their training data. Put more simply, they seek out a coherent pattern of consensus, treat this average as “truth,” and ignore outlying signals unless explicitly told not to.

This consensus averaging then feeds on itself synthetically. The more the internet fills with LLM-generated posts positing any single theory as truth, the more the next generation of AI models learns to treat this as the default, creating a self-reinforcing loop that becomes very difficult to break.

Ultimately, such synthetic feedback loops form an epistemic monoculture, which is a fancy way of saying that a single, averaged perspective becomes inevitable when everyone sees and repeats the exact same thing.

This matters far beyond marketing, because the mechanism isn’t specific to marketing. It’s a property of how every LLM works. The same averaging is happening to anyone whose thinking starts as an AI first draft: the lawyer, the analyst, the doctor, the designer. The AI-generated posts on LinkedIn are simply the canary in the coal mine for the rapid collapse of every thinking category toward the mean. What’s interesting about this isn’t how AI raises the bar on average, which I think it does; it’s about who will choose to go beyond average to create advantage because, as every thinking category collapses around the mean, the only sustainable source of advantage will be the capacity to think beyond.

The AI of design.

Reverting to the mean is one way of looking at the trap Ferrari fell into with the Luce. I’d argue the biggest problem with the Luce has nothing to do with what it looks like, well, not directly. Instead, it was Ferrari’s decision to hire Jony Ive to design it.

Ive is a singularly successful designer. But his vanishingly rare superpower is setting the bar for average across an entire category. Not as a compromise, but as the most legible and resolved design experience, the one that sets an expectation others must at the very least match. It’s a skill worth literal billions if you’re operating at the volume scale of, say, Apple. However, the entire point of Ferrari is not to be average and not to operate at anything like Apple’s scale. Ferraris are exclusive, deliberately not for everyone, deliberately polarizing.

While Mark Ritson views the Luce problem as Ferrari walking away from its brand codes, that’s only partly correct. Ive didn’t abandon the brand codes of Ferrari, he translated them into an EV-specific form that, on average, will be maximally legible and thus attractive to as many people as possible. But, oops, doing so means it’s no longer a Ferrari; instead, it’s an EV setting the bar for others to match.

This illustrates the inherent challenge of using AI to dictate how we should think, rather than working the other way round. In this tale, Jony Ive is the AI of design. Sure, he’s setting the bar for average. But setting the bar for average is no good if you’re Ferrari, and all of your value rests on being something far beyond it.

Guiding the machine, rather than being guided by it.

This is why domain expertise and critical thinking are more important than ever in an AI world. Training yourself, learning continuously, cultivating curiosity by reading widely, building your expertise, and developing the capacity to interrogate new ideas while challenging received wisdom won’t lead to replacement by AI. Instead, these are the exact qualities necessary to enable something far more important: the capacity to guide and dictate the lens through which we want AI to “think.” Not to have it dictate our thinking, but for us to guide it in how it should think.

Here’s a direct example. After correcting Claude once too often for spouting EB theory as settled fact, I built an empirical marketing skill within it that wasn’t grounded in “How Brands Grow” but in a more complete yet far less-cited work, “Empirical Generalizations About Marketing Impact,” edited by Dominique Hanssens. This is the same LLM, but with very different guidance. I then asked it to critique Sharp’s theories through this new lens. Here’s what it had to say:

“Sharp is mostly right about the thing he studied and overconfident about how far it travels. The core findings he popularized aren’t really his and aren’t controversial: double jeopardy, the Dirichlet patterns, the duplication-of-purchase law, the leakiness of loyalty. These replicate across decades, categories, and geographies.

Where I part company is the move from ‘these patterns are robust’ to ‘these are laws.’ What Sharp actually documented is an equilibrium — the steady state of mature, repertoire, low-involvement categories where products have reached rough parity and competition has collapsed onto availability. That’s a huge and important slice of the economy, but it’s a slice. The patterns weaken or invert in subscription, services, luxury, B2B, and genuinely differentiated or disrupted categories. The universalism is the error, not the findings.

The deeper issue is that Sharp’s framework is descriptive, dressed as prescriptive. It tells you how brands grow within an equilibrium without asking whether the equilibrium is worth accepting. So you can follow it flawlessly and optimize yourself into a low-value corner: lots of cheap reach, mental availability maxed, and nothing that compounds into margin or pricing power. Useful as a floor, dangerous as a ceiling.”

Thinking beyond the average.

When everyone is fed and then accepts the same set of averaged answers, convergence becomes inevitable. That’s true whether you’re a marketer, a lawyer, a CEO, a strategist, or a designer. However, this convergence isn’t inevitable if we choose not to accept it.

What I want to leave you with has nothing to do with Ehrenberg-Bass theory or Byron Sharp, per se. Instead, it’s to observe that there’s a clear source of competitive advantage that emerges as LLMs collapse all thinking domains toward the mean.

A principle that’s easy to say and hard to do: In an AI-mediated world, advantages will accrue to those who are most willing to think beyond the averages their competition is settling for.

Subscribe now

Share

Leave a comment

If you’ve been living under a rock, the watch in question is The Royal Pop, a collaboration between Audemars Piguet and Swatch that launched globally on May 16. Eight plastic bioceramic pocket watches with Pop Art colorways, a lanyard, and a hand-wound mechanical movement. By Sunday, verified resale prices on Chrono24 were approaching $6,000 (although they’ve been dropping since). One idiot buyer reportedly paid over $25,000 for the full set of eight on StockX.

Within hours of the launch, watch influencers loudly proclaimed that AP had made a catastrophic error. They’d Hublot’d themselves. Brand dilution. Cheapening the iconic Royal Oak model. The beginning of a catastrophic end for the brand.

I dunno. It’s hard to agree with them, to be honest. AP has been on an incredible run of success and isn’t prone to making catastrophic strategic errors. So let’s take a clearer-eyed and less algorithmically hyped perspective and try to unpack why this collaboration, why now, and what's next.

Let’s start with what the data says about Audemars Piguet.

In 2025, AP grew revenue 9% to CHF 2.6 billion ($3.3bn at current exchange rates) on the sale of 53,000 watches. The average price exceeded CHF 51,000 ($65,000) per piece. It overtook Omega in the Swiss watch rankings for the first time and has quadrupled revenue since 2012, mostly on the strength of a single design — the Royal Oak — which has barely changed since 1972. The entry-level Royal Oak Selfwinding sells for circa $40k at retail and trades at a substantial premium on the secondary market because the waitlist to get one is years-long.

This is not a brand in trouble. It’s one of the best-performing luxury brands on earth. So why collaborate with Swatch on a $385 product?

The AP/Swatch collaboration is the first time Swatch has done a high-low crossover with a brand outside its own portfolio. When Swatch launched the MoonSwatch with Omega in 2022, it was an internal transaction. Swatch owns Omega. The Scuba Fifty Fathoms with Blancpain, same deal. Swatch owns Blancpain. These collaborations were essentially a single conglomerate playing with its own portfolio.

AP, by contrast, is independent. Family-owned since 1875. Nobody upstairs told them they had to do this. They chose it. And they donated 100% of their proceeds to watchmaking education and the preservation of the craft. So, it’s not a revenue play. Well, not directly, anyway. More on that in a sec.

CEO Ilaria Resta referred to it as “a megaphone.” A device to ignite collective desire for mechanical watchmaking. A singular, one-off event. Done for the industry, not for itself.

Maybe. But, I’m not convinced. Taken at face value, that seriously smacks of hubris. AP may be a highly aspirational brand, but is it really going to broadly “ignite collective desire for mechanical watch making” against a backdrop of watch wearing collapsing from circa 46% of wrists in 2020 to just 26% today?

What’s more interesting is why a winning, family-owned, and genuinely independent brand that expertly wields scarcity as a value mechanism would choose to voluntarily put its most iconic design on a $385 plastic pocket watch on a lanyard sold in a Swatch store. I don’t think altruistic megaphone is what’s really going on at all.

To get at what might actually be happening, let’s start with Swatch, because it needed this far more than AP did.

In 2024, Swatch Group operating profit fell 75% to CHF 304 million. Net income collapsed from CHF 1.19 billion to CHF 219 million. In 2025, things got worse. Greater China, which once accounted for over a third of group sales, is down over 30%. Activist investors are howling. The current board faces genuine governance pressure for the first time in decades.

Swatch needed another hit like the MoonSwatch. And they could bring something AP almost certainly needed to do this deal: a proven playbook.

MoonSwatch data is compelling. Sales of the real-deal Omega Speedmaster moonwatch rose more than 50% after the March 2022 launch, while the full Speedmaster lineup grew sales by double digits. Morgan Stanley estimates roughly 2 million MoonSwatches have been sold, helping jump Swatch back into the top 15 of the Swiss brand league table. Critically, and this is the number the AP board probably loved the most, Omega prices went up after the MoonSwatch launch, not down. The Speedmaster became more desirable, enabling Omega to make it more expensive.

I’m guessing the argument made to AP was pretty simple: here’s what happened to Omega. Here’s what that means for AP.

The odd part, though, is that AP isn’t Omega. The aspiration-to-conversion ladder for Omega is a lot more obvious: a meaningful percentage of two million people queuing up to buy a $260 MoonSwatch will plausibly be able to own a $7,800 Speedmaster within a few years of career progression. AP’s mechanical entry point, on the other hand, is circa $30k and rising. Its distribution is boutique-only. Its waitlists are relationship-based. The Royal Oak’s entire cultural logic is that you cannot simply buy one. The scarcity is the product.

This is almost certainly why the watch-world engaged in a collective tantrum, and where the risk in this move appears to lie. Within hours of launch, the watch forums, Reddit, Hodinkee comment sections, and YouTube were full of very serious people explaining with great solemnity why AP had gone insane and destroyed itself. Comparisons to Hublot were everywhere.

The thing is, though, insiders always oppose change because they have the most to lose, and they appear to fundamentally misunderstand AP’s position.

The Hublot comparison is particularly sloppy. Hublot’s collector problem has nothing to do with collaborative democratization of a price point. It’s about perceived design plagiarism of the Royal Oak and Nautilus, about claims of five-figure watches running modified commodity movements, and about marketing itself as a celebrity-event brand rather than watchmaker. AP, by contrast, enters this collaboration with 150 years of in-house manufacture, unimpeachable horological credibility, and the strongest independent brand narrative in Swiss watchmaking. If Royal Pop is genuinely a one-off and Royal Oak supply stays constrained, Hublot is the wrong comparison entirely.

Here’s a better one. If Stella McCartney does a collaboration with H&M, it doesn’t catastrophically destroy the value of her bespoke pieces. Why? Because consumers aren’t stupid. They know Stella McCartney designs made in partnership with H&M aren’t really Stella McCartney. They’re H&M. The Royal Pop is exactly the same. It’s not an AP. It’s a Swatch with an AP logo, designed in partnership with AP.

We really need to stop treating consumers as if they’re stupid. They might be greedy for resale value, but they’re not stupid; in fact, when it comes to brands, consumers are pretty damned sophisticated.

Here’s my bet. This has less to do with democratization and more to do with pricing. Selling thousands of plastic pocket watches will probably do nothing to devalue a real Royal Oak and everything to make it more desirable. The most likely result? Prices go up, while the waitlist increases. The Royal Pop creating greater salience among a younger generation of wealthy individuals because owning the real thing now confers, rather than removes, credibility among their peers.

What the watch intelligentsia seems to entirely miss in their howls of insanity is that looking a bit nuts is entirely in keeping with AP’s institutional character. The Royal Oak itself was considered borderline insane upon launch in 1972. A stainless steel watch priced higher than a gold Patek Philippe, with exposed screws and an industrial bracelet, at a moment when the watch world was retreating into conservative elegance. While the establishment hated it, it went on to define an entirely new category and save the company. AP has form here. Making moves counter to the received wisdom isn’t a departure from its DNA. It is its DNA.

And here’s something else the hand-wringers are missing. AP can afford to be wrong. They’ve already sold every watch they’ll make until 2030. If Royal Pop turns out to be a dud, it’ll be a footnote by the time the last Royal Oak on that waitlist is delivered. When you can afford to fail, you naturally make bolder bets. In this context, the Royal Pop isn’t reckless. It’s the opposite. It’s a calculated gamble from a brand so secure in its position that it can afford to look insane for a week.

The timing is also fascinating, as the luxury status of analog, “dumb” objects is about to explode.

Let’s contrast a Royal Oak to an Apple Watch. In practical terms, the Royal Oak is utterly obsolete. No step counts. No sleep tracking. No dentist appointment reminder. Just 18th-century engineering wrapped around your wrist as an act of deliberate, expensive uselessness. And the uselessness is the point.

Someone wearing a $40,000 Royal Oak communicates that they’re so rich, so successful, that they’re liberated from the tyranny of optimization. They don’t need to be alerted. They’re not managed by their schedule. They own time rather than being owned by it. A watch that can’t connect to anything is rapidly becoming a powerful status signal. Because the people rich enough and free enough to wear one are rare.

Put more simply, the more smart devices that appear, the more they’re imbued with AI and intelligence, and the more they optimize the wearer’s life for maximum productivity, the scarcer and more desirable the sheer uselessness of a mechanical movement becomes.

We’re entering a world where intelligence is a commodity. Within five years, AI-driven optimization, personalization, prediction, and automation will be ambient, surrounding us in every object, every surface, every interaction, at minimal marginal cost.

In this world, things that aren’t automated will become scarce and valuable. A watch assembled by hand in a Vallée de Joux workshop by someone with thirty years of experience finishing micro-components that are invisible to the naked eye. The shoe made by a cordwainer in Northampton who knows your last from memory. The dinner where a maître d’ knows your name and decides what you’re having tonight, because they know you well enough to know what you like better than you know yourself.

Against the backdrop of ambient intelligence, the embrace of the analog won’t just be a signal of wealth but also of an elevation above the plebeian drudgeries of the modern world.

AP almost certainly understands this, even if it hasn’t articulated it in these terms. A brand that has been owned by the same two families for 150 years thinks in generations, not quarters. So, the strategic question it’s answering isn’t “what do our buyers want in 2026?” Because they’ve already sold every watch they’ll make until 2030. Nope, it’s “who’s going to be wearing a mechanical watch in 2040, and how do we make sure they’re AP buyers?”

The Royal Pop has tapped into latent desire on a global scale among a generation that largely has no prior exposure to mechanical watchmaking. The riots, the resale premiums, the sold-out colorways, the global media coverage, all communicate one thing: this object has cultural gravity. The Royal Oak octagon means something to people who will never own one.

And that meaning, the breadth of desire, is exactly what will justify the next price increase on the real thing. And the one after that. Scarcity becoming more valuable the wider the gap between desire and access becomes. Royal Pop doesn’t close this gap. It widens it by expanding the desire side of the equation. Meanwhile, access to the real thing remains constrained. This is the opposite of democratization.

And it’s strategically savvy. In truth, a collaboration with Swatch insulates AP from cries of commoditization and going downmarket, while simultaneously increasing the surface area of its desirability among a new generation of potential buyers (Note that nobody was rioting outside an AP boutique). The more people who wear a Royal Pop, the more desirable the exclusivity of the real-deal Royal Oak becomes, because suddenly an entire generation knows what a Royal Oak is. And getting that generation to notice is hard.

Meanwhile, In Crewe.

In striking contrast to AP, Bentley just announced a collaboration that almost nobody is talking about. It’s a fascinating strategic comparison.

The collaboration in question is Bentley’s unveiling of an Audi Q7 in drag bespoke Bentayga EWB Chalet Edition. Strictly limited edition. Hand-painted in Light Tudor Grey with alpine flower embroidery. Done in partnership with the Gstaad Guy, an insider social media satirist of the wealthy.

Here’s the problem. The Gstaad Guy’s entire satirical premise is that true luxury is quiet, understated, and needs no announcement. Bentley then announces a car embodying these values to millions of people on social media, with attendant press releases and marketing apparatus. But the medium isn’t just contradicting the message; it’s eroding the brand. It doesn’t matter who the influencer is; this is how DTC companies sell $50 frying pans.

It also reflects a deeper issue. Unlike AP, Bentley is under real pressure. In 2024, revenue fell 10% to pandemic-era levels, operating profit dropped 37%, and unit sales fell 21.5%. In 2025, deliveries fell another 5%. Meanwhile, Ferrari grew, Lamborghini hit a record, and Rolls-Royce held steady. Every major ultra-luxury automotive brand is chasing the same pool of rich buyers, and Bentley is the one losing ground. The brand has quietly stopped disclosing unit sales as its core markets contract and an expensive electric transition looms. This is not a marketing team making a bold move from a position of strength. It's a team that can't afford a mistake, reaching for the safe and defensible choice. The kind nobody gets fired for.

And that’s the real tragedy here. Bentley almost certainly knows the Gstaad Guy collaboration is an expensive gamble that’s unlikely to move the needle by much. It’s a boringly forgettable contradiction — luxury car brand loudly peddles discreet wealth to millions of people by partnering with luxury satirist to gently mock how luxury brands talk about themselves. Christ, only an advertising creative director could’ve come up with something so convoluted.

The contrast, then, is stark. AP, from a position of strength, is willing to look a bit nuts to get the result it wants. Bentley, from a position of weakness, isn’t willing to look nuts at all, just oh-so-clever. The painful irony is that Bentley probably needs a Royal Pop moment far more than AP does. It needs something to make people engage with a brand gone stale. But that move requires a confidence you don’t have when volume is falling, margins are compressing, and an electric transition needs funding.

Safe moves often feel responsible when the numbers are going south. What’s harder to see is that just as often, taking no risk is the biggest risk of all.

Subscribe now

Share

Leave a comment

The short answer is a Volvo XC60 in case you’re interested.

As we speak, there’s a cottage industry of consultants and strategists burning Claude tokens on breathless LinkedIn posts claiming that “everything has changed” as AI becomes a ubiquitous customer discovery channel.

In some ways, they’re right. I mean, B2B buyers are ignoring LinkedIn for a start ¯\_(ツ)_/¯. In other ways, they’re wrong. I mean, is anyone surprised that Claude recommended a Volvo?

While it’s true that AI is becoming an increasingly important discovery channel, the rubber really hits the road in navigating what this means. There’s a genuine incentive challenge here. On the one hand, as salespeople, we consultants are incentivized to be maximal-change activists. It’s what catches a prospect’s attention and gets you into the room with a torn-in-a-million-directions-and-just-needs-to-be-told-what-to-do CMO. However, as strategists, we’re paid to see through the “everything is different” hype. Putting the strategy hat on, our job is to help our clients navigate the waters of uncertainty. To understand what’s changing, what isn’t changing, and help clients develop the kind of future options where they can be successful across multiple scenarios.

So, here’s the truth. Right now, we don’t know what consumer use of AI will look like in 2027 or 2028, let alone in 2030 or 2035. Anyone selling certainty is selling snake oil.

As a result, the real strategic posture should be one of emergence under uncertainty: experimentation, cheap bets, and optionality (a fancy strategy term for keeping your options open) combined with a refusal to get locked into things you might struggle to get out of. Oh, and a sharp nose for bullshit.

To help, what follows is my researched attempt at a commonsense guide to consumer use of AI as a discovery channel, organized around four questions I’m being asked by clients right now:

• The current state of play

• The emerging tablestakes

• Where advantage resides

• The bets that increase future options

None of this is settled. All of it might change tomorrow. I’m not trying to predict what’s coming. I’m trying to provide a framework to help you navigate it.

The current state of play

LLMs are already a major channel for consumer discovery. Right now, ChatGPT, Claude, and Gemini are the top three most-downloaded apps on the Apple App Store. ChatGPT alone has around 900 million weekly active users, more than double a year earlier. About 34% of US adults have used it, close to 60% under the age of 30. Most credible projections put weekly LLM use at two to three times current levels by 2027.

While LLM use is multi-faceted, meaning a lot of other things sit alongside product and brand discovery, the discovery slice is real, growing, and material to marketers.

The reason isn’t that consumers see themselves as early adopters. It’s that they consistently find LLM responses better than alternatives: Shoppers in the UK using AI spend an average of £118 more per basket, and 69% of B2B buyers say AI surfaced information led them to a different vendor than expected. Meanwhile, Microsoft Research describes the consumer/AI relationship as a thinking partner rather than a search tool.

The net, net is that buyers trust LLM answers more than classical search, act on them more directly, and this trust appears to increase with use.

There are two important reasons I think LLMs feel better:

• First, LLM authority works differently. Search ranks pages by backlinks, click signals, and SEO. It rewards marketers for gaming an algorithmic graph. LLMs synthesize answers from branded and third-party content, expert citations, and community discussion. They skew authority toward what they regard as credible sources, and reward coherence across them, rather than optimizing against a ranking signal. This consistently appears to produce higher-quality answers for the consumer. (How the logic of what an LLM deems an authoritative source changes as commercial pressures build is an open question worth tracking.)

• Second, there’s no adversarial marketing layer in LLMs. Well, not yet anyway. By contrast, two decades of online performance marketing innovation built an environment buyers never asked for: ad links masked as search results, commodity MFA, gated PDFs, retargeting that follows you for weeks, demo walls, pay-to-play comparison sites, social feeds filled with slop, and content marketing masquerading as thought leadership.

This has a material impact on the consumer experience. Ask traditional search for a recipe, and you first have to scroll through a novel about some rando’s grandmother’s kitchen before you reach the ingredients. Ask an LLM, and you get the recipe. The friction the marketing apparatus created around search algorithms simply doesn’t exist in the LLM. Buyers can route around all of it.

A direct implication is one I don’t think most marketers have yet absorbed. As AI eats consumer discovery, it’s not only routing around today’s performance marketing channels, but the tactics themselves are largely invisible to the LLM because performance ads are deemed non-authoritative sources. Double whammy.

B2B is an acute case. Why? Well, first, B2B buyers have been subjected to much shittier and more aggressive “lead generation” marketing experiences for longer, and second, they’re more likely to have already integrated LLMs into their professional workflows, which directly extends to their use in vendor search. In 2024, Forrester found 89% of B2B buyers used generative AI at some point in their purchase process. Meanwhile, G2 found 51% of B2B software buyers start their research in AI chatbots rather than Google. We all know by now that circa 70%+ of the B2B buyer journey occurs before speaking to a salesperson. Now it’s happening before they even use the web. Meanwhile, CRM systems incorrectly log inbound leads driven by an LLM as direct or organic search.

So, what’s the net, net? Simple. AI is a new consumer discovery layer; it’s growing fast toward ubiquity; it synthesizes authority differently from search; it’s more trusted than search; it doesn’t (yet) have an adversarial marketing layer baked in; and (so far) it largely routes around digital performance tactics.

This has significant implications for marketers.

Tablestakes

Before we get into specifics, let’s touch on the elephant in the room. There’s a thriving industry of AEO and GEO grifters pitching AI marketing transformation that’s tablestakes dressed up in transformation language. This includes genuine bottom-feeders you need to look out for. These are the agencies that pursue unsavory tactics: Reddit seeding at scale through synthetic accounts, llms.txt manipulation, listicle stuffing for citations, and even prompt injection schemes, all wrapped up in too-good-to-be-true ROI and performance claims. It’s basically the same playbook used by the link-farm SEO operators of the past, dressed in new clothes and sold to the unsuspecting. The problem is simple. The model builders will quickly figure it out, and those who bought into such tactics will rapidly have their citations zeroed out in an update, while the agency that charged good money to game the LLM has already moved on to its next mark.

Moral of the story? Do the work, don’t be a mark.

Anyway, onto the tablestakes. Something we often forget is that the reason something is tablestakes in the first place is that it’s so important that you have to have it. So, when looking at the below, the best way to think about it isn’t that these things will give you an advantage, but that if you don’t do them, you’ll almost certainly be at a competitive disadvantage.

1. Make the buying situations your brand credibly serves machine legible. Whatever frame you prefer, whether jobs to be done, problem statements, or category entry points, the underlying work is the same. Identify your most credible consumer buying situations clearly and ensure they appear consistently across your owned content, third-party presence, and earned coverage. An LLM matches user queries to situational claims. So, if yours aren’t in the corpus the LLM draws from, you risk missing out.

2. Make your reputation footprint machine-readable. Ungate substantive content. Product specs, comparisons, customer cases, expert content. Make all of it crawlable, structured, and consistently findable and readable by LLMs. That gated PDF is now gated against discovery, not just unqualified leads.

3. Be present, authentically, in the places LLMs treat as authoritative. Wikipedia. Third-party review sites in your category. Expert publications. Reddit communities where you can contribute something meaningful. The emphasis is on earned value. Synthetic presence is what the bottom-feeders are selling; it’s what the platforms will continue to filter out, and has little long-term value.

4. Track LLM citation share as a diagnostic, not a KPI. It tells you when something has changed. If your competitors are surfacing in shortlist responses and you aren’t, that’s a problem worth investigating. This should be viewed through a diagnostic and learning lens, not as an optimization challenge. Don’t let anyone pitch you on a roadmap to “win” citations when the models are changing so rapidly. Just watch out for pattern changes and be ready to respond accordingly.

5. Audit the funnel for adversarial friction. Demo walls, gated content, pay-to-play comparison sites, cold outreach saturation. The economics of friction under LLMs as a discovery channel are inverting. What used to be a qualifying mechanism is now a discovery barrier you need to remove.

6. Don’t anchor your citation footprint to a single platform. When ChatGPT rebalanced its Reddit weighting in September 2025, brands that had built their entire AI visibility strategy around their Reddit presence watched citations collapse. The better option is to diversify across different sources, just as you might diversify across different publishers.

Sustainable advantage might not lie where you think: LLMs are word-of-mouth on steroids.

The non-intuitive part of achieving an advantage in an AI-mediated world is that it doesn’t appear to be about AI at all. It’s more prosaic than that. It’s about delivering value to the customer. The clue as to why is in the name. The two Ls in LLM reflect human language at scale, which means they’re trained on the patterns of what people say and the context in which they say it. And when people talk about products and brands, they don’t talk about the gobbledygook on a landing page designed for SEO, or the discount offer in a performance ad. They’re more likely to brag, complain, talk about how well it solves their problem, discuss its fit with their lifestyle, what it means to them, talk about what it’s worth, and how it differs from the alternatives.

Strictly speaking, the corpus the LLM reads includes everything that’s been published, not just genuine human conversation. But sustained consistency across credible third parties and expert citation is consistently highly weighted in LLM responses.

A simple way to think about this is that an LLM is word-of-mouth on steroids. It takes everything you and others say about your brand online, synthesizes it, and feeds it back to new prospects in response to their questions. While AI might be new, the advantages it surfaces are the same advantages that have always mattered for word of mouth. It’s just synthesizing them into a powerful new discovery channel.

In practice, this means that most of what we know about building strong brands applies, just more so. In this sense, LLMs are less about transforming marketing into something new than rediscovering something evergreen. The difference primarily lies in how an LLM synthesizes these strengths for new prospects. Brands whose substance, consistency, and credibility hold up well are magnified. Brands that lipstick the pig of a thin proposition are exposed.

This is why the LLM is disorienting for so many modern marketers. For thirty years, the marketing function has been pushed in the opposite direction. Toward accountability to short-term metrics rather than long-term value. Toward a narrowing of marketing’s scope to comms and demand generation, toward a disconnection from product, pricing, and customer experience. Toward a lack of influence over the activities where substance is built. And toward a concept of brand itself that’s been pushed from substantive meaning to the surface recognition of distinctive assets.

None of that produces what an LLM rewards:

1. Substantive positions over surface distinctiveness.
   In considered-purchase categories, which include most categories worth a strategic conversation, including pretty much all B2B, LLMs are synthesizing based on expert authorship, original research, customer evidence, third-party reviews, and credible press. Brands that already earned that footprint are pre-advantaged. Brands that built their marketing apparatus around bid optimization and lead capture, the exact system that defined the career path for today’s generation of marketing leaders, have very little for the LLM to match against. While distinctive assets still matter as buyer recognition cues, they aren’t enough for an LLM to draw from. It’s reading what real people and credible sources say. Put simply, if the substance isn’t there, the surface isn’t going to save you.

2. Buying situations must be claimed and earned, not just identified.
   The tablestakes version of this work is making your buying situations machine-readable. The advantage version is staking distinctive claims to situations you can credibly own better than your competitors, substantiated by genuine differentiation in product, service, or expertise, and validated by third parties over time. Just like the Volvo example I led with. This is harder than it may seem, because it means positioning at a level deeper than messaging and comms. It requires genuine strategic clarity about what your brand stands for, who it’s for, what it does better than anyone else, and why people should care. Brands that have done this work show up as the credible answer when an LLM matches a user query to a situational claim. Brands that haven’t, don’t.

3. Brand-product coherence.
   LLMs synthesize across everything in the corpus — paid messaging, expert content, customer reviews, third-party coverage. Where the corpus contains both your brand's claims and contradicting evidence from customers, experts, or reviewers, the contradiction surfaces in the answer. Brands whose products, services, pricing, and customer experience align with their stated position are reinforced. Brands where marketing claims compensate for product or service issues get exposed, often directly. While coherence might be today’s buzzword, it’s always been one of the structural advantages of strong brands. What changed is that LLMs are rapidly increasing the cost of incoherence.

4. A rebalancing toward whole-brand thinking.
   For almost everyone, performance channels are experiencing increased costs and diminishing returns while producing less of the brand signal LLMs amplify. Meanwhile, whole-brand investments that improve the experience, create customer meaning, and increase customer preference receive 2nd-order amplification by the LLM. The implication is a rebalancing of investment toward the total brand experience as AI-mediated discovery grows. While there are devils in the details regarding category and company maturity in executing this rebalancing, the directional argument is obvious.

5. Organizational capability to produce substance, not just buy it.
   Over the past decade-plus, most marketing organizations have been built around digital comms, platform-provided audiences, performance channels, and short-term metrics. LLMs, by contrast, reward desire creation among human audiences, product and service strength, original research, expert authorship, genuine customer evidence, sustained category presence, and meaningful participation in the communities where your buyers gather. These are very different capabilities, and closing the gap is a long-term investment proposition. Spend any time doing customer queries in an LLM environment, and you’ll quickly see the brands that already operate this way are visibly ahead. The best time to start is now, because the longer you wait, the more you risk being left behind.

In sum, the bets that appear to create advantage in the LLM era look strikingly similar to the bets that long-term value-creating brand, marketing, and corporate competitive strategies have always required us to make. Yes, there is new work to be done, as highlighted above, but don’t be fooled by people dressing these activities up in fancy, new, transformative language.

What optionality looks like

Optionality means making bets that pay off across a range of plausible futures rather than committing to specific predictions. For example, AI-mediated discovery might accelerate, plateau, or be partially displaced by something else we don’t yet know about. Agent-mediated commerce might explode in two years, seven, or never. Advertising will almost certainly get more aggressive in LLM environments, though competitive pressure on the major platforms creates real guardrails. In particular, Apple has both the silicon roadmap and the business-model incentives to position itself directly against CapEx-recouping, advertising-driven enshittification.

The point of optionality isn’t to predict what will happen. It’s to make bets that pay off most strongly if AI-mediated discovery develops roughly as it currently is, retain value in adjacent scenarios, and won’t leave you stranded if the picture changes.

With that in mind, here are some suggestions. Some will be harder to deliver than others, and they're ranked in the order I thought of them, not by their importance.

1. Build proprietary data as a strategic asset.
   In a world where AI synthesizes from publicly available signals, the company with proprietary data has something an LLM can’t manufacture. Proprietary customer data, transaction histories, usage patterns, longitudinal preference data, behavioral signals, and first-party research. All of it gets more valuable as everything publicly available becomes more synthesizable. Proprietary data also enables the creation of substantive, original content that LLMs love to synthesize. Original research, customer evidence, benchmarks, and real category insight. This is a bet that will likely pay off under any future scenario.

2. Clean up the data infrastructure that makes proprietary data usable.
   Most companies have proprietary data they can’t do anything with because it sits in fragmented systems with inconsistent definitions, missing context, and no governance. This is the boring data hygiene work you didn’t get around to before because it was a political and technical nightmare and the ROI wasn’t clear. Across multiple AI futures, having a clean data spine gives you a proprietary asset that can be used in multiple ways. That unclear ROI status is rapidly sharpening into a must-deliver priority.

3. Create a brand surface area you control, and direct customer relationships you own.
   The first means owned publishing in areas such as entertainment, original research, customer evidence, expert research, community participation, etc. This increases your LLM surface area, giving the models something specific to synthesize when answering questions about you. The second means email lists, direct buyer relationships, app installs, customer accounts, and community memberships. Anything that constitutes a direct relationship rather than a platform-mediated audience. Neither is a bet on disintermediation. This isn’t a DTC strategy. The point is narrower: own enough surface area and enough of the customer relationship that you have something consistent for the LLM to draw from, no matter what happens across any other channel you don’t control.

4. Build trust capital deliberately.
   The trust hierarchies the LLM exposes are the ones that have always governed considered purchase. Brands that are meaningfully differentiated, create desire, earn trust through substantive behavior, keep promises, accept accountability, behave consistently with stated values, and produce genuine expert content rather than content marketing slop, accumulate an asset that will be valuable across any plausible future. While a trust-crisis scenario will reward this the most, it’s never going to go out of fashion.

5. Avoid a blinkered outlook.
   Whatever you do for AI-mediated discovery, do it so that it works across all the models consumers use. Today, in the US, that’s mostly ChatGPT, Claude, and Gemini, with on-device assistants developing fast. Optimizing your reputation footprint for one of them before there’s stable maturity simply doesn’t make sense. Until things become clearer, focus on all the channels consumers use, monitor how the business models are changing, and be vigilant about fast-growing newcomers. In particular, watch out for what Apple does. Unlike the hyperscalers, it has clear economic incentives to create an experience that embeds privacy and avoids rapid CapEx recoupment via advertising.

6. Plan for geographic divergence rather than convergence.
   A US-derived playbook for AI-era discovery shouldn’t be assumed to transfer cleanly to Europe or Asia and vice versa. The EU AI Act, GDPR, and growing public skepticism about AI-mediated information all point to structural differences that don’t appear likely to fully converge, while China runs its own models entirely. The bet here is on retaining the capacity to operate different playbooks across geographies, not committing to a single global approach.

7. Bank resources for the unknown.
   The single most useful thing you can do is keep discretionary budget, attention, and organizational capacity unallocated against any current bet. The highest-value play in 18 months is probably something that doesn’t exist today. The marketing organization that’s fully committed to and optimized for today’s playbook won’t be able to respond as conditions shift.

So, there you have it. If we cut through the hype and the “everything is changing” rhetoric, the one thing that stands out is just how profoundly the basics of good brand-building and customer value creation shine through in LLMs. Yes, we have work to do to make everything consistent and machine-readable and ensure we have the right surface area for the LLMs to draw from. But the bigger insight is that this is mostly a game of rediscovering the substance of great brand-building: having a well-defined strategic position, treating marketing as value delivery, not just value capture, creating great experiences, meaning something to your customers, and clearly stating what you do, while doing what you say.

Maybe this is just my bias showing through. Brand stuff is what I do for a living after all. But if I had to bet, I wouldn’t bet against it.

This summer, my son is climbing Mt. Kilimanjaro to raise money for clean water in a Kenyan community where 2 in 3 people lack access. I know this is a tough time for many of you, so I hesitate to ask, but if anyone would like to contribute to something as essential as clean water, you can do so here.

Subscribe now

Share

Leave a comment

Where’s the optimism? Where’s the incredible new future for all of us? Where’s the innovation? Where’s the raft of new entrepreneurialism? Where’s the creatively destructive energy of capitalism? Where’s the competitive advantage? Put more simply, where’s the promise? Nowhere. That’s where.

Even professional contrarian and arch provocateur Mark Ritson has fallen into doomerism, based on a lab report from Anthropic, one of the world’s largest purveyors of LLMs, stating that 65% of marketing jobs could be automated, not will be automated, amid a reality that is most decidedly not a lab. But a report from a purveyor of LLMs isn’t destiny. It’s an ad. Specifically, it’s the Chick-fil-A ad where the cows tell us to “eat mor chikin.”

Let’s cut to the chase. The AI automation narrative is an investment thesis. As nihilistic as it may be, cutting trillions from global operating costs is way easier to fund than a new technology whose impact is, as yet, unarticulated.

But there’s a major corporate challenge nested within this narrative. Efficiency gains from technology are always fleeting. Once greater efficiency is achieved, it gets rapidly competed away, and everyone moves back to parity. This is why the more interesting question about any new technology is always how it creates new competitive advantages, never how it makes you more efficient.

With that in mind, and because I’m exhausted by the automation automatons, I figured it would be more interesting to talk about the conditions under which AI might revolutionize marketing for competitive advantage, rather than displace it for efficiency.

As with all revolutions, it’s not enough to lead with potential. We must first describe the dissatisfying conditions that precipitate radical change. And to do that, we must address the problem directly: Marketing as it exists today is doing the wrong job, measured against the wrong outcomes, based on the wrong assumptions, in service to the wrong stakeholders. Namely, tech platform shareholders.

Why? Because a handful of extremely rich technology platforms have fully captured the modern marketing function and redefined it in their own image.

This wasn’t done through malicious conspiracy but the single-minded pursuit of growth. Capture was achieved by selling marketers on digital prophecies at a loss until the platforms became indispensable, then shifting gears to create monopolistic super-profits from that indispensability. A process Corey Doctorow oh-so-eloquently christened “enshittification.” The evidence of this enshittification is all around us, amid rising costs (CPC up 40% over 3 years), declining efficacy (CVR down 10% YoY), and worsening consumer experiences. (Consumer experience satisfaction now at lowest ever recorded levels.)

From Differentiation to Commoditization.

If we think about the marketing function in evolutionary terms, natural selection doesn’t require intent. It requires differential survival in any given environment. Against this, the platform era created an environment with very specific selection pressures. It rewarded speed over depth, optimization over enquiry, and certainty over curiosity. The marketer who asked hard questions and who wanted to understand why before optimizing for how became maladapted to the environment they were now in. Those who were naturally inclined toward strategic deliberation, those who were uncomfortable with false precision, and those who wanted to interrogate the clean narratives the dashboards provided were passed over, pushed out, and eventually replaced.

As these evolutionary pressures intensified, they systematically eroded the marketing function's capacity to meaningfully differentiate from the competition. It wasn’t the result of stupidity, but through the adoption of identical technological infrastructure, metrics, and optimization targets, leading to identical marketing models. When you share infrastructure, metrics, targets, and models, the inevitable result is shared thinking, and shared thinking creates shared outcomes. And when marketing became a game of shared outcomes, commoditization and underperformance became inevitable.

Chewing the Platform Cud.

During this process, the platforms didn’t just change the selection environment. They domesticated the marketing function. Like all successful domestications, it was achieved not through force but through dependency. Give an animal reliable food, shelter, and predictable conditions, and it will surrender the instincts it no longer needs. Give marketers reliable dashboards, pre-built audiences, and attributable results, and they will surrender the curiosity, judgment, creativity, and strategic instinct they no longer need.

We’re now in the second generation of platform marketer. They know no other environment. They believe their job is to optimize the platform metrics in front of them, like cows chewing cud. Their training is on platform certifications, and they’ve been promoted for their platform fluency, not their understanding of how marketing works. The result is that platforms now farm marketers far more efficiently than marketers can deliver their own mission using the platforms’ tools.

Put bluntly, it’s an evolutionary dead end. And it’s already failing.

Marketing Model Innovation. The Revolutionary Opportunity.

If, on average, marketing were demonstrably driving economic value, nobody would be talking about automating it for efficiency; they’d be demanding that it scale. Functions that drive value aren’t optimized; they’re funded.

The CFO is not wrong. Marketing, as Silicon Valley has redefined and then commoditized it, underdelivers. Cutting budgets by a third over four years wasn’t an irrational just-for-the-hell-of-it undertaking. It was a natural corporate response to failure.

These are exactly the conditions of dissatisfaction that precipitate revolution, not automation. Why automate a model that’s already failing? And why buy it from the exact same providers who sold you these failure conditions in the first place?

Instead, there’s a bigger opportunity worth pursuing. We’ve spent the last decade talking about business model innovation as the primary source of competitive advantage. The AI era will show that marketing model innovation, namely, how uniquely we understand, reach, and create value for customers, will be just as strategically important.

As in the platform era, when everyone had access to the same tools, the advantage won’t come from how efficiently we use the technology. Unlike the platform era, the AI advantage will come from the distinctiveness and differentiation of the marketing models we can now build with it.

Here’s what I mean. Here are three things AI makes possible that will drive innovation in marketing models. None is net automation; all are net-new. All might be revolutionary. (And, like the best predictions, they might also be completely and utterly wrong.)

1. See what everyone else is missing.

The platform era didn’t just give us bad metrics. It fundamentally impoverished our understanding of the customer. We became dependent on structured data predefined by platforms and interpreted through their dashboards in service to their business models. The richness of what was really happening in our markets, the cultural signals, the emerging behaviors, the unarticulated needs, the weak patterns that precede big shifts, became structurally invisible because they didn’t fit platform decision logic.

But the deeper trick wasn’t just controlling the metrics. It was controlling the questions. When the platform owns the experimental infrastructure, it decides what can be tested, which variables are available, which outcomes are measurable, and which time horizons are visible. This meant your experiments were really their experiments, run on their terms, answering their questions, in service to their business model.

AI changes this at a foundational level. For the first time, we have a technology that can process unstructured data such as customer service conversations, social media in all its messy richness, behavioral patterns, and qualitative intelligence, and return structured, actionable outputs at a speed and scale human teams can’t match. And critically, you get to decide what the technology looks for. Rather than importing a platform’s logic, you can program it with your own theory of customer value. Your definition of what matters, not theirs. Your experiments, informed by your theory of how your market works, surfacing results that the platforms never would.

L’Oréal already created an early version of this. It built an AI system called TrendSpotter that detects emerging beauty trends six to eighteen months before they go mainstream. Not by using a platform’s algorithm, but by programming AI with a proprietary theory of what constitutes an emerging trend. It then scans social feeds, blogs, and academic papers for atypical terms, using its own models to assess staying power.

This points to something the automation narrative ignores. AI doesn’t eliminate the need for human judgment in marketing; it dramatically increases its value. The platform era told marketers they didn’t need curiosity, instinct, or interpretive skill. All they had to do was read the dashboard. AI inverts this. Yes, it can surface patterns across an ocean of unstructured signals that no human team can process, but deciding what these patterns mean for your brand, your competitive position, your theory of customer value, and your innovation priorities remains irreducibly human work. In fact, it will rapidly become the highest-value work.

In the same way that the data revolution didn’t mean fewer scouts in professional sports, corporations are going to need more marketing scouts than ever. They’ll just be seeing across a much bigger and richer landscape than the platform era allowed.

2. Make smarter bets, not (fake) safer ones.

Tech platforms are structurally positioned at the point of value capture, not value creation, and are thus incentivized to shift budget allocation away from desire-creating activities (which they don’t sell) toward value-capturing activities (which they do). When your business model sits at the conversion end of the funnel, you’ll naturally build tools that make it seem like all the value resides there. Take last-click attribution as a perfect encapsulation. Awarding excess weight to the last click before purchase systematically starves all upstream investment that generated the desire that drove that click, while pushing excess profits into the pockets of the platform serving it. To quote Erich Joachimsthaler, this led platforms to “extract value from assets they did not build.”

This creates an intractable intelligence problem. When you can only see what happens at the point of conversion, you have no way of knowing which upstream actions generated the downstream result. This was by design. By focusing their measurement insights solely on the conversion end of the funnel, the platforms created the illusion of certainty (spend X to get Y), while also positioning themselves as the antidote to the perceived risks of brand investments that are slow, diffuse, risky, and hard to attribute. Greater certainty and lower risk. Who wouldn’t want that?

However, the platforms weren’t eliminating marketing risk at all. Instead, by pushing clients to overspend on demand capture while ignoring desire creation, they increased risk under the illusion of its elimination.

Nike and Starbucks reflect the most striking examples of the destructive effects of platform logic. Over decades, each painstakingly built a world-class and highly desirable brand. Then both pursued strategies that systematically eliminated desire creation in favor of demand capture, and watched demand soften before catastrophically collapsing and destroying circa 1/3 of each company's market capitalization ($100bn+ combined). The platforms hadn’t magically eliminated marketing risk at all; they’d just obscured it behind an illusion of certainty.

AI enables us to invert platform logic. Not by eliminating uncertainty, but by making uncertainty visible and navigable. It enables something that has genuinely never existed at scale before: directional models of how desire creation and value capture relate in your specific market. Not deterministic predictions, markets are too complex for that, but probabilistic models that tell you where to look, what’s working, and what isn’t, across a much broader and richer set of signals than just paid platform media. Combine this with the ability to run scenario analyses across multiple possible futures and to design your own marketing science experiments at speed and low cost, and you develop something the platform era systematically destroyed: competitive advantage from an enhanced understanding of where opportunity and risk actually reside.

Think of it like the best sports analytics operations. Sports analysts never treated data as truth the way marketers did. They viewed it as a way to extend the surface area of insight gathering. It gave their scouts better questions to ask and more precise places to look within a much bigger haystack. In his excellent book, “How to Win the Premier League,” Ian Graham makes the case: “Your scouts can’t watch every professional match played in every professional league, everywhere in the world. But data can.”

The result of a more calibrated understanding will be seemingly paradoxical. Organizations with a better understanding of risk versus reward will make marketing decisions that were dismissed in the platform era. Bigger and more original brand bets. More upstream investment in desire creation. Greater focus on critical experiences, like customer service. Better product innovation connected to the brand narrative.

When you get past the illusion of certainty and can see where risk and reward actually lie, you will pursue calibrated bets on advantage creation, with different corporations, with different appetites for risk, pursuing different bets.

3. Compound learning into something uncopyable.

Every experiment you run creates data. That data improves your next decision. Better decisions generate better outcomes, which generate richer data, which in turn improve the next decision. This is the flywheel AI enables. And this flywheel is by definition proprietary, because it’s built atop your questions, your data, your theory of customer value, and your accumulated understanding of your specific market.

Netflix doesn’t have a content advantage because it has better AI than its competitors. Every streaming service now has sophisticated recommendation algorithms. Netflix’s advantage is the accumulated understanding of its audience built over years of running experiments on its own terms, encoding its own theory of what audiences value, and iterating with each decision. That accumulated intelligence is why 80% of content watched on Netflix is discovered through its recommendation engine. A system no competitor can replicate just by licensing the same algorithm.

This is the deeper point about AI and competitive advantage. Technology is never an advantage in and of itself. What you do with it is. P&G’s advantage isn’t its AI; it’s decades of proprietary consumer research that its AI is now compounding.

What AI can do is help you accelerate the distinctiveness of the marketing model you already have, or help you innovate a new one. But the asset is the system you build, not the technology that enables it.

Instead of the platform-driven commoditization of marketing models, AI will drive innovation and differentiation. The result will be marketing model innovation that becomes as strategically valuable as business model innovation has been. And, as models fragment under the conditions of innovation, it will drive proprietary competitive advantages that cannot be easily copied.

And, when these innovative new models perform, they will attract more investment, not less. CFOs might be ignorant about how marketing works, but they’re not stupid. They’re experts at following the money.

Now, is this all a pipe dream? Maybe. Probably. Like anyone making predictions about any revolutionary technology, I will most likely be spectacularly wrong.

But here’s the important thing that I’m almost certainly not wrong about. The automation of existing processes has never been the primary impact of any truly revolutionary technology. All have led to net-new value we simply couldn’t have imagined before they happened. Johannes Gutenberg thought his printing press was for more efficiently printing Bibles. It did that, but it also led directly to science, democracy, and the nation-state. Oh, and the fiction novel, which may have brought more joy than the others combined.

If automation of today’s jobs is to be the overwhelming outcome of AI, it will be the first and only time a revolutionary technology has generated that outcome. It’s not a prophecy, it’s an investment narrative. Tell investors that your shiny new technology increases productivity by X%, thus removing $Y billion from corporate costs, and money will pour in. Tell them you have a revolutionary technology whose true impact hasn’t yet been articulated, and, well, you’ve got a mall cop on a Segway.

AI is here. It isn’t going anywhere. But its true value will not materialize from chewing the platform cud of automation. Instead, we must unleash our curiosity, creativity, and strategic instincts to make it do what we want it to do. Meaning we must reject the nihilistic vision of human capital destruction being sold to venture investors, and instead focus our human capital on figuring out how to use it to create amazing new value.

I’ll leave you by paraphrasing the late, great, Sly Stone RIP. When describing his first-in-history use of a drum machine on a hit single, he said that if you used it as intended, then yes, it was fake and boring. But if you beat the shit out of it until it feels right — well, that’s another thing entirely. This was the moment contemporary pop music was born. Not because Sly accepted the Rhythm King’s automated Cha-Cha preset, but because he pursued a different path entirely.

Let’s do the same with AI.

Subscribe now

Share

Leave a comment

So, Omnicom bought Interpublic, WPP hired a former Microsoft executive as CEO, Publicis continues to clean up from a new business perspective, and Accenture is presenting marketing as an outsourcing opportunity. At least it’s not boring.

Here’s the state of play. Omnicom scaling up is John Wren's retirement plan. It’s not a long-term play with any meaningful strategic rationale. It’s a Hail Mary pass that greater scale means buying more time to figure something out before terminal decline kicks in. They’re probably correct. Very large organizations rarely collapse spectacularly; they’re much more likely to enter a period of steady decline toward irrelevance and then obsolescence. As a result, we should probably view Omnicom as the Kodak-meets-digital moment for HoldCos. Once great, now steadily fading into the background.

By contrast, WPP is a basketcase. Its new strategy is, well, Publicis’s principal media-buying strategy with an AI sheen. Here’s the problem. When you have a large company in what looks like secular decline, the kind of people you need to become your CEO don’t want to touch you with a ten-foot pole. This isn’t a particular diss on Cindy Rose; I’ve never met her. It’s just that the WPP strategy, as publicly stated, is uninspiringly bad. Cut costs by integrating operations and getting into bed with tech platforms. Align behind a new principal buying platform. Magically achieve turnaround status in year three.

Getting into bed with tech platforms as an advertising Holdco is literally insane. The CapEx payback requirements of their AI investments mean they’re highly incentivized to put the Holdcos out of business entirely rather than support WPP’s turnaround. And integration cost-out that relies on an as-yet-unproven technology is really organizationally risky. The most likely outcome is organizational chaos destroying client relationships faster than AI can be figured out, deployed, and optimized. The turnaround likelihood? Vanishingly small because it’s a strategy with so little strategy at its heart.

As an aside, WPP is so wiped-out value-wise that there’s an obvious alternative strategy. It’s currently dragging its knuckles along the floor of the stock market at a $3.5bn-ish valuation. This is so low that we could easily rethink it through the lens of option value. Forget turning the current operations around; instead, assume they’re in terminal decline and manage them as such. This is your bad bank. Then use your direct ties to clients, global infrastructure, and deep institutional knowledge to spin up a slew of AI-enabled option bets focused on specific pain points and opportunities. Treat it like a portfolio of ventures. To put this in context, a single unicorn idea would raise WPP's value by 1/3. Shit, if GameStop can use an option bet to shore up its stock price, why can’t WPP? Don’t answer that, I think we know the answer.

Publicis is winning big right now, but primarily because of its competitors' missteps. It’s the cream of the crap, and eventually, the easy money being made from principal media buying is going to bite it in the ass as clients get angry and start taking the whole lot in-house. It should be focused more intently on the transformation side of its business, but when you’re addicted to the easy money, that’s hard.

This neatly brings me to Accenture Song. It was only a matter of time before Accenture’s outsourcing logic overwhelmed the creative DNA of the agencies it gorged itself on over the past decade and a half. I’m sure it’s going to be a compelling proposition on the surface. Vertically integrated marketing and creative capabilities connected via agentic AI that executes marketing activities at lightspeed, where you only pay for the outcomes delivered. Brilliant…or is it?

Here’s the problem. Outsourcing the marketing function doesn’t just hand your operations to Accenture; it hands them your understanding of customers, what matters to them, and what your brand means to them. If you’ve been reading this newsletter for long, you’ll know I’ve been arguing that marketing models are not universal; they’re contextual, which means owning your own marketing truth is a powerful form of competitive advantage. Accenture’s pitch is to surrender exactly this in exchange for a commodity model delivered as a variable-cost line on the P&L. And the incentive misalignment is stunning because Accenture will use customer data to create lock-in and dependence, while clients need exactly this data to shape their own destiny. Outsourcing your customer interface isn’t just operationally risky. It’s strategically suicidal. This means the corporations most likely to take the deal are those that are the worst at marketing. And if you’re bad at marketing, you’re probably bad at a lot of other things too, so what is Accenture really getting itself into? (Hello, Intel, how ya’ doin’? Oh, life is still a bit shit. Too bad. Sorry you missed every chip wave of the past twenty years. Maybe next time.)

So, where does this leave us?

Well, the harsh reality is that it leaves us with the following conundrum. The world’s largest marketing services corporations are either falling over as we speak, or are about to do so, slowly, maybe quickly, over the coming few years.

As a result, they’re firing thousands of people. Some of you, reading this newsletter, may have lost your job as a result. Suffice it to say that if you worked at a Holdco agency and you lost your job, it almost certainly had nothing to do with you and everything to do with the management incompetence of those steering the ship. The same people steering those ships straight into the rocks.

So screw ‘em. This story isn’t about them. It’s about you.

In 2000, when the dot-com bubble burst, thousands of Silicon Valley engineers lost their jobs and either started new businesses or joined the survivors. The likes of Meta, Google, Salesforce, and AWS then went on to wipe out the previous generation of tech companies and build an entirely new generation in their own image.

In 2008, when the financial crisis wiped out banks left, right, and center, thousands of bankers lost their jobs. They then created the Fintech category, quant trading, and went on to build an entirely new generation of alternative finance businesses in their own image.

I think you see the pattern. For all the talk of AI disrupting jobs, we’re not paying enough attention to recent history and the power of human ingenuity. When you see a widespread dispersal of talent with meaningful institutional knowledge, it creates a new entrepreneurial landscape, from which new categories emerge. Categories we cannot yet imagine.

By laying off thousands, the advertising Holdcos aren’t going to get more efficient and turn themselves around. Instead, they’re inadvertently seeding the market with the entrepreneurialism that’s going to accelerate their demise.

I ran the numbers (with a crayon and a rubber band in case you’re interested). Strip out media pass-throughs, apply a conservative assumption that the platforms will absorb the majority of what remains, and discount further for the work that simply ceases to exist as AI eliminates production and adaptation categories and, well, we still arrive at a revenue floor of circa $8bn in the US and $25bn globally that will potentially transfer to smaller independents as the Holdco model collapses.

While I reserve the right to be completely wrong, here’s how I think this might break out. The three paths aren’t independent of each other. Instead, they create a new ecosystem. The first creates the conditions for the second, and together they reveal the opportunities for the third.

1. Transforming the in-house model

2. Small, specialized, AI-enabled, and networked ecosystem specialists

3. Domain-expert driven technology solutions

Let’s walk through each in turn.

Transforming the in-house model.
First, being an entrepreneur, or working for one, isn’t right for everyone. Right now, there’s a huge talent pool that knows how to run agency operations and deliver marketing and creative services at scale within an enterprise environment that just happens to be called an agency. Many of them will end up on the client side. Five years ago, agency holding companies controlled nearly five times as many US ad dollars as brand-direct advertisers. Today, the two groups control nearly equal portions. Imminently, the field will tilt decisively in favor of brand-direct activity, as the Holdcos continue to hollow out, quality drops, conflicts of interest intensify, and client relationships become increasingly untenable.

However, the bigger shift isn’t talent leaving the Holdcos for brands, which is a long-term trend. It’s how internal agencies are about to transform themselves. In the past, in-house agencies were efficiency plays — cheaper and faster, treated almost exactly like an external agency, except everyone worked for a single brand.

This is about to change. The slop era we’re currently in won’t last for long, which means marketing execution is about to get harder and require better skills, not less. Brands will quickly realize that slop at AI speed is still slop, and that genuine quality is now necessary to cut through all the shit being fired from the AI cannon. Meanwhile, AI will level the efficiency playing field in areas like production, meaning efficiency will no longer be the driving force. There will simply be too few gains left to capture. Instead, ideas, quality, effectiveness, and excellence will become the new currency.

This means the in-house agency will transform from an internal agency focused on more efficiently delivering what external agencies used to do to instead become an integrated center of excellence. People with years of agency domain expertise will be brought into upstream conversations much sooner, directly shaping what gets done way earlier than would have been the case with a traditional agency brief. This will transform what that brief then becomes. Why not an OTT video property rather than a 30-second spot? Why not a new retail concept? To cut through, the mechanisms of marketing delivery will become more creative, not just the messages.

Now, the talent required to run a genuine center of excellence is not necessarily the same as that needed to deliver more efficiently. Fortunately, this transformation requires exactly the people the Holdcos are firing as we speak. People who know how to think boldly and strategically about brands and creativity, and know how to spot the best strategic and creative talent when they see it. Which brings me to the second thing that’s about to happen.

An ecosystem of small, specialized, AI-enabled specialists.
As in-house marketing talent shifts from efficiency-focused agencies to more deeply integrated centers of excellence, they’ll naturally seek new partners. As their authority and influence increase internally, they’re going to bypass the hollowed-out Holdcos and instead seek specialist talent with specific skills and expertise to tackle specific needs. Some refer to this as the Hollywood showrunner model; I think they’re correct.

This will spawn an ecosystem of small, highly specialized, expert players that will build deep partner relationships across multiple brands. They’ll use technology, including AI, to scale their internal capabilities and better connect with their clients' and other specialist partner systems. This blurring of boundaries between agency and client, already enabled by collaborative technologies such as Figma, Slack, and Google Docs, will accelerate as AI enables faster, better sharing of customer data and insight that shared project teams work on together.

This will likely create more reward for specialist talent than currently exists. Today, valuable specialist skills are buried within generalist agencies, paid for out of the pool of money clients hand over for all services, whether by retainer, project fee, or arcane FTE formula. Tomorrow, as the Holdco model collapses, these skills will fragment into specialist agencies who can charge a more appropriate fee for the value they create, without having to subsidize the loss-making activities and bloated overhead of the Holdco. This is the next generation of entrepreneurial talent being built now. It will further accelerate the transformation of in-house agencies into centers of excellence, as outputs from these specialist skills raise the in-house agency's internal profile, and the broader organization begins to view them as an exciting and desirable part of the brand’s extended skill set. It’s not just the CMO who likes shiny objects, especially shiny objects that move the merch.

Domain-expert driven technology solutions.
In the past, software engineering represented a hard limit on what could be built. No matter how much institutional knowledge someone had, how much domain expertise, how strong their creative instincts, nothing could be built without a software engineer’s say-so. And that has always been a communication, language, and understanding divide. Someone might have a brilliant idea that could be built by software, but unless they could first translate it into engineer-speak, the idea wouldn’t lead anywhere.

That divide is collapsing. For the first time, people with deep domain expertise and tacit institutional knowledge can create functional software that directly leverages it.

Now, I’m not naïve enough to suggest that we’re going to see enterprise-grade software being created by people who are clueless about software. Instead, we’re going to see entrepreneurial domain experts prototyping real solutions to the point where they can get funded, attract the interest of real software engineers, and demonstrate actual value to clients. And that’s a huge shift.

Think about what this means in practice. An experienced media strategist who understands the dynamics of attention and value might prototype a planning tool that embodies that understanding. A brand planner who spent a career developing an intuition for cultural resonance might build a research instrument that operationalizes this tacit knowledge. A creative director who understands the difference between production efficiency and creative excellence might build workflow tools to protect the latter while embracing the former.

The people who built careers developing tacit knowledge inside agencies. The kind of knowledge that can’t be written down in a process document, and the kind that the Holdcos never valued because it couldn’t be put on a rate card, can now encode it into software. This is a genuine democratization.

Of course, encoding institutional knowledge into defensible technology is exactly what the Holdcos should have been doing for the past decade. Instead, they wasted billions acquiring commodity digital agencies at absurd multiples, juniorizing their talent base, and eliminating the very institutional knowledge they should have been building from.

Now, in a delicious irony, the domain-expert technology companies that emerge from this great dispersal of Holdco talent will be built atop the exact institutional knowledge the Holdcos are throwing away.

So, yeah. Maybe the future isn’t so bleak after all.

For all the talk of AI disrupting jobs, I don’t think we’re paying enough attention to recent history. When there is a widespread dislocation of talent with specialized skills in valuable sectors of economic activity, it drives waves of new entrepreneurialism that both create new categories and disrupt the old guard.

My prediction is that the advertising Holdcos are structurally fragile, and through their own mismanagement, have entered a period of secular decline that will ultimately prove terminal. They deserve no mourning, for their demise will transform in-housed talent, while simultaneously freeing up billions in market potential for a new wave of small specialist agencies and domain-specific marketing and creative technology solutions.

The future won’t be built by failed dinosaurs that are actively hollowing themselves out. It’ll be built by the people they discarded. People whose institutional knowledge, domain expertise, and creative instincts were never valued by the institutions that employed them.

Subscribe now

Share

Leave a comment

Yet, the audience wasn’t buying it. Meltwater’s real-time analysis found nearly half of all social media mentions of AI ads were sharply negative. Guides to disabling Ring’s AI “Search Party” feature exploded days after it was showcased, as viewers connected the cuteness of a lost puppy to the scariness of neighborhood-wide surveillance. Google, OpenAI, Anthropic, Microsoft, Amazon. They all made their case, and the collective response was best captured by one viewer: “We are in hell.”

Now, while it might be fun to decode why Anthropic, in particular, seemed to whiff so badly (hint: it positioned its ads against an enshittification problem that hasn’t yet occurred, and strategically the ads made zero sense relative to its enterprise positioning. Double whammy!), there’s a deeper story here than simple AI fatigue in advertising.

The ads didn’t fail because people didn’t like the ads. The ads failed because people don’t believe AI’s promise. And they don’t believe AI’s promise because technology firms no longer have the permission to sell what they’ve been selling for the past two decades: Optimism.

The Under-Appreciated Power of Optimism

Being the carrier of societal optimism is enormously valuable. It’s not a branding exercise in the shallowest sense; it’s an apex strategic position that creates pricing power, attracts talent, builds regulatory goodwill, provides the cultural permission to innovate, and carries the attention-grabbing narratives that capture the consumer imagination. For roughly twenty years, the technology industry held this franchise almost unchallenged…before frittering it away.

To understand how they captured the optimism franchise, let’s head all the way back to 2008. The financial system had just detonated. The East Coast financial institutions trusted to steward the economy were caught strip-mining it for profit. The sheer scale of their hubris was so great that the government had to bail them out lest the global economy collapse. Yet while the bankers collected bonuses paid for by taxpayers, millions of these same taxpayers were losing their homes and their jobs. Fear, anger, and distrust were palpable.

Into this trust vacuum stepped West Coast techno-utopianism with a genuinely compelling counter-narrative: we’re building the future, and it’s going to be amazing. The iPhone was democratizing computing. Facebook was connecting people who’d lost touch with each other. Google was all about 20% time, not being evil, and organizing the world’s information. The sharing economy had landed from the future. Uber was shaking up the staid world of taxis, Airbnb was changing how we think about travel. And Tesla was making EVs cool. These weren’t just advertising or PR claims; they were real value propositions people were experiencing. People used these products, and their lives were meaningfully improved.

The optimism was both earned and accelerated amid a backdrop of crisis. It felt like Silicon Valley was creating an off-ramp from a bitter present and an on-ramp to a brighter future. And millions were there for it.

Edelman’s Trust Barometer captured the moment: a decade ago, 73% of Americans trusted technology companies. Tech wasn’t just another industry. It was fixing what the bankers had broken.

Today, that number is down to 63% and falling fast. Pew’s most recent data shows only 11% of Americans say they’re more excited than concerned about AI, while 51% are more concerned than excited. A figure that’s been climbing steadily since 2021. YouGov found that 77% of Americans are concerned AI could pose a threat to humanity. Only 32% of Americans trust AI, versus 72% of Chinese citizens.

What happened? The same thing that always happens. The optimism of creation gave way to the pessimism of extraction.

From Creation to Extraction

As it grew, Facebook became a conflict machine, as its algorithms learned that more ads are sold by outrage than by unity. Google quietly dropped “don’t be evil,” as it became an extractive monopolist. Airbnb became a housing crisis accelerant. And Amazon’s “customer obsession” now sits alongside warehouse conditions that would make a Victorian mill owner squirm. The products that once liberated people from real constraints are now extracting their time, attention, money, and increasingly, their mental health.

As a result, the societal permission to be the carriers of optimism died as the underlying value propositions enshittified.

This is why the Super Bowl AI ads failed. They asked for an emotional investment in an industry that’s no longer creating a better future, but extracting from the present. No longer optimistic, but cynical.

People aren’t stupid. They can smell a meta-narrative that no longer adds up.

AI compounds the disconnect. Think about it for a second. AI will supercharge GDP growth and eliminate half of all white-collar jobs. It possesses near-divine intelligence and confidently makes shit up that simply doesn’t exist. It will democratize creativity and is controlled by three companies with more compute power than most nations. Its creators warn that it could end civilization and that you’d be deeply irresponsible not to adopt it.

Any narrative that requires you to hold this many contradictions simultaneously isn’t a vision of the future. It’s a sales pitch. But selling what, exactly?

Here’s the part that rarely gets said aloud: the AI inevitability narrative has less to do with technological destiny than balance sheet arithmetic. The hyperscalers have committed hundreds of billions in CapEx to AI infrastructure. GPUs depreciate in two to three years. The data centers being built today will be obsolete the moment they’re turned on. This stunning level of investment must be recouped. Fast. And the only way these economics work is if AI replaces vast swathes of expensive human labor.

This means the narrative of inevitability isn’t, in fact, inevitable; it’s a permissioning system. The narrative that AI will take your job exists because AI needs to take your job for the economics to work. Tech needs CEOs to believe that AI-driven automation is inevitable in order for it to happen. Whether the technology can actually live up to this promise is irrelevant compared to the stark reality of the all-in debt these firms are now loaded with and the huge growth in market cap they need to justify.

If you’re wondering, you’re right. There’s a good chance the West Coast techno-utopian visionaries are now headed toward the exact same debt rocks that the East Coast bankers floundered on a generation ago. Just this time, the collateralized debt obligations are attached to data centers rather than homes.

People sense this. Not always consciously, perhaps, although the narrative is so strident it would be hard not to. But when 77% of the country tells pollsters they’re worried that AI threatens humanity, this isn’t a technical view of risk; it’s a trust assessment. And right now, tech is failing it. Badly.

The Mechanisms of The Optimism Franchise

Something lost in the current discourse is that technology wasn’t always the carrier of societal optimism. In fact, it’s a relatively new phenomenon. Throughout the post-WWII period, before the iPhone, before Facebook, before the West Coast techno-utopian narrative took hold, the cultural infrastructure of optimism was largely built and maintained through the commercial activities of brands. Not just tech brands, but brands more broadly. And not just through advertising, although that’s easiest for us to look back on with perfect 20:20 hindsight.

If we ignore shitstalgia and instead look at the mechanisms, we can see what the great brand-builders of the 1990s and early 2000s actually did. Orange didn’t just run optimistic ads—it built an optimistic value proposition that simplified mobile telephony at a time when the category was intimidating and exclusionary. “The Future’s Bright” wasn’t just a tagline; it was the articulation of a business model that genuinely believed making communication accessible was a worthy organizing principle. Easyjet didn’t advertise democratic values—it democratized air travel, stripping away the class markers that made flying a status signal. Nike’s “Just Do It” wasn’t a slogan bolted onto sneakers. It was a worldview about human potential that shaped product development, athlete partnerships, and retail experiences. PlayStation’s counter-cultural positioning wasn’t just tonal—it was a genuine strategic bet against the family-friendly Nintendo hegemony, opening gaming to an older, more culturally adventurous audience. Apple’s “Think Different” wasn’t just a campaign; it was a declaration of strategic intent that would take a decade to fully manifest, culminating in the very iPhone that would eventually transfer the optimism franchise to tech.

This mechanism matters because it’s a mechanism that’s been lost: brands that encoded optimism into their value propositions, their pricing, their business models. Brands where the advertising was an expression of a worldview already built into the product, not a disconnected ‘campaign.’ The optimism was real because the value creation was real. Consumers experienced it; they didn’t just watch it. And critically, these weren’t niche players. These were mass-market brands generating mass-market returns by betting that customers would reward genuine value creation. And they were right.

Why Brands Stopped Being Optimistic

So what happened?

Optimism didn’t disappear because brands ran out of creative talent or because culture shifted. It stopped because the incentive structures of business shifted from creation to extraction.

The numbers are stark. Average corporate markups rose from 21% above marginal cost in 1980 to 61% by 2016. Between 2003 and 2012, S&P 500 firms returned 91% of earnings to shareholders through buybacks and dividends, leaving virtually nothing for productive reinvestment. The American Customer Satisfaction Index flatlined even as net profit margins climbed 3-4 percentage points. Profits up. Value down. And customers noticed.

Meanwhile, marketing, the function historically responsible for championing the customer within the organization, was systematically demoted. Stripped of pricing (only 20% of organizations report giving marketing control), stripped of product development (26%), stripped of distribution (13%). Ultimately, marketing was reduced to the last P, promotion, which was then subdivided into channel-specific execution roles optimizing for CPCs within platform ecosystems. Over the past four years, as what remained of marketing activity was optimized to the point of no longer working, budgets collapsed by 30%, to just 7.7% of revenue. Only 40% of Fortune 500 marketing leaders even carry the CMO title. The organizational capacity to champion customer value and turn optimism into something customers actually experience has been structurally dismantled.

I’ve written before about Measureship, the brutalist management orthodoxy that replaced leadership with dashboard optimization across the economy. Measureship is what brand decay looks like at the operational level: optimize what you can measure, deprecate what you can’t, then strip mine customer relationships for profit and quarterly efficiency gains.

But something even more insidious happened within the Measureship environment. When brands adopted Google Analytics, they thought they were making a neutral technology decision, but they were really embedding Google’s worldview into their systems. When they implemented Salesforce, they thought they were buying better customer relationships, but they were really restructuring them around someone else’s pipeline metrics. When they built their marketing operations on Meta’s ad platform, they thought they were buying reach, not replacing their understanding of customers with Meta’s audience segments. But that’s what was happening.

Each decision felt rational, but none was neutral. The platforms didn’t just intermediate the advertising relationship between brands and customers. They intermediated brands’ very understanding of customers and what it meant to serve them successfully. Instead of creating genuine value, marketers were brainwashed into optimizing for metrics that make tech platforms look good on dashboards they designed, for the benefit of their shareholders. Click-through rates, cost-per-acquisition, ROAS. These aren’t measures of customer value creation. They’re measures of platform value extraction. But because they’re the only “understanding” layer available to most marketing teams, they became the de facto definition of what success is measured by. Meanwhile, the customer was abstracted into whatever the dashboard said they were.

Piece by piece, the same engineering-optimization worldview that hollowed out the optimism of tech has been embedded into how brands understand their customers, govern their operations, and evaluate their successes. All without anyone consciously choosing to do so. Whatever unique worldview a brand may have had wasn’t replaced from the top via strategic intent; it was hollowed out from below. By the time marketing capability and governance became fully subsumed under platform logic, their worldviews were left...empty. A mission statement on a wall that nobody’s operating system actually delivered on.

The result is this. Brands still say optimistic things, but no longer do optimistic things. The language remains the same, but optimism was systematically stripped from brands by the infrastructure they adopted to make themselves more efficient.

Same Disease, Different Host

Here’s the pattern, stated plainly. Optimism is earned by genuine value creation. It dies when extraction replaces creation. This is what happened to brands through the 2010s and into the 2020s as platform dependency and financialization replaced value-proposition innovation with cost optimization. And this is what is now happening to tech. Surveillance capitalism, monopoly economics, and now the CapEx-driven AI-will-replace-us-all inevitability narratives have replaced the genuine liberation that early tech products once delivered.

Same disease. Different host. Same outcome.

Big tech can’t fix this. Its economic model requires it to be extractive. The CapEx commitments demand it. The shareholder expectations enforce it. The debt load a Sword of Damocles hanging above its head. The AI narrative depends on the promise of replacing human labor, which is a fundamentally extractive proposition, no matter how cute the lost puppies in an ad might be.

Filling The Chasm

All of this leaves a chasm. The optimism franchise. The strategic position of being the first choice people turn to when they want to make things genuinely better has opened up for the first time in almost two decades.

This isn’t nostalgia. I have no interest in relitigating the golden age of advertising or pretending the answer is a better Super Bowl spot. The answer isn’t advertising at all.

The answer lies in innovation that creates more value for customers than we capture. The same mechanism that built brand optimism in the first place, genuine democratization of access, genuine improvement of experience, genuine creation of value that customers experience rather than watch, applied at a moment when society is so desperately hungry for it.

The evidence that people are hungry is everywhere. ACSI data shows satisfaction flatlined for a decade. Shrinkflation awareness is at 81% among grocery shoppers. Trust in business’s use of AI sits at 32% in the U.S. The cultural mood is one of suspicion, extraction fatigue, and a deep yearning for somebody, or something, that actually gives a shit.

The Cascade

When working with clients, I use a simple marketing model with four layers: Worldview, Understanding, Capabilities & Governance, and Execution. They’re connected by a downward flow of definition and an upward flow of emergent learning. It matters here because optimism should cascade through this architecture.

It starts with your worldview. A genuine belief about how value is created, and then works down through understanding, capabilities/governance until it reaches the customer as the lived experience we execute. The customer’s experience feeds back up through the system as emergent learning, refining the worldview, deepening our understanding, and sharpening our capabilities. That’s the flywheel. That’s what the great brand-builders of the 1990s ran intuitively and what the extraction economy systematically destroyed by colonizing the middle layers with platform logic.

The diagnosis tells us where the cascade broke: not at the worldview level, but at the understanding and capabilities layers. That’s where Google, Salesforce, and Meta inserted their logic for their own gain. Which means rebuilding optimism isn’t a brand campaign or a mission statement exercise. It’s an operational rebuild. Worldview first, not dashboard first; that then cascades into a genuine theory of customer value creation across everything the organization does.

What does that look like in practice?

Look at the brands already doing it. Costco’s member-first economics. Patagonia’s Worn Wear. Trader Joe’s refusal to play the shrinkflation game. None is optimistic because of its advertising. The optimism is inherent to the business architecture. The value proposition is the optimism. Costco’s membership renewal rate sits above 90%. That’s the market telling you that value creation still wins.

Now imagine this logic applied more broadly. A health insurer that prices transparency into its model rather than burying exclusions in footnotes. A bank that builds its product around the financial health of its customers rather than the extraction of fees they hope will go unnoticed. A retailer that uses AI not to optimize its own margins but to genuinely reduce the cost and friction of shopping for its customers. None of these requires heroic altruism. They require the strategic recognition that in an extraction economy, creation is the differentiated position.

Being successful in this next era requires us to read the cultural mood correctly. Not as a hollow “AI changes everything” promise or a creative brief for “funner” advertising, but as a market signal for the reinvention of value delivered. When an entire economy has tilted so far toward extraction, the strategically contrarian position—the only genuinely differentiated position—is to tilt just as hard toward value creation. When every competitor is optimizing for cost, optimizing for customer value becomes a structural advantage. When the dominant cultural narrative is the nihilistic inevitability of automation, humanity's optimism becomes a scarce resource with enormous market power.

And optimism compounds. Rebuild the cascade. Encode a genuine worldview into how you understand customers, govern capabilities, and deliver experiences, and create the emotional surplus that generates pricing power, innovation permission, and talent magnetism. That surplus funds more value creation, which deepens optimism and widens the moat. The 1990s brand-builders ran this flywheel without naming it. We can name it now. And we can rebuild it.

Tech filled the optimism vacuum created by the financial crisis. Today, tech is the vacuum. The chasm of optimism is wide open, and the only question that matters is this: which brands have the strategic ambition and the operational courage to fill it?

Subscribe now

Share

Leave a comment

Here’s the irony. They’re trying to build the future atop a growth playbook from the past that’s now obsolete. They’re raising hundreds of millions to discover what works in AI markets that are still forming while hiring marketers credentialed in fully decayed SaaS playbooks that do the opposite, having long ago excised the discovery capabilities market formation requires under the guise of “efficiency.”

It’s a fundamental mismatch: diminished-return optimization applied to market discovery.

The Product Marketing Decay Cycle

Between 2012 and 2021, modern-day tech product marketing (PMMs, growth marketers, demand gen, PLG specialists, etc.) emerged, creating a genuine competitive advantage. As SaaS matured and digital platforms became essential infrastructure, product marketing rapidly professionalized. It translated complex products into compelling narratives, ran sophisticated growth plays across fragmenting digital channels, and enabled sales with positioning that resonated. Tech companies with strong product marketing capabilities outperformed those without.

Yet the resulting commoditization and diminished returns toward obsolescence were as systematic as they were predictable. Pioneering playbooks became industry standards. Platform certifications became universal requirements. Everything became optimized for financial efficiency rather than strategic advantage.

Research reveals the depth of what happened: almost complete overlap in frameworks across product marketing methodologies, with roughly 75% of job postings requiring identical platform credentials as baseline qualifications.

Rita McGrath’s transient advantage framework, introduced in Off Kilter 217, explains the cycle: over time, advantages launch, ramp up, get exploited, and then decay. Product marketing followed this arc perfectly. Success drove professionalization, which in turn drove commoditization, which then led to obsolescence.

Platforms like Google, Meta, and LinkedIn deliberately accelerated this pattern for their own advantage by using certification programs to encode platform dependency as professional competence.

For a significant period, ZIRP-era economics masked an accelerating decay. Abundant capital meant metrics looked fine while differentiation eroded. When cheap money disappeared in 2021, the diminished returns became impossible to ignore. But the diagnosis of the problem was wrong. Companies assumed execution failure requiring optimization, not competitive decay requiring disengagement. Finance governance intensified: optimize harder, measure more, automate with AI. But applying efficiency discipline to an already-decayed advantage doesn’t make you win; it just accelerates failure.

The result is straightforward. The modern SaaS organization is squeezing a growth lemon that no longer has any juice.

Look at the numbers if you don’t believe me. The share of SaaS companies stuck in low or flat growth has surged as median and even top‑quartile growth rates have compressed materially since 2021. Growth rates have slowed sharply across all revenue bands. Customer acquisition costs have increased, while payback periods stretch to and beyond 3 years for less efficient companies.

Why would any AI company want to replicate this?

What Decay Looks Like

The decayed state of product marketing manifests in three recognizable patterns I consistently see in the tech clients I work with:

The Platform Tax Collector. Because of the certification trap laid by platforms and its subsequent encoding into the professional psyche, product marketers have become experts at feeding platform algorithms, optimizing for click-through rates and marketing-qualified leads, while these same platforms extract more and deliver less. Unintentionally, they’ve become platform tax collectors rather than growth drivers for the companies they work for.

The Product Delusion. Because product marketing is directly adjacent to product, it’s structurally incentivized to obsess over feature differentiation and battlecards. This is what the LinkedIn B2B Institute calls “The Product Delusion.” The engineering-led belief that the best specs always win. (Plot spoiler: they don’t) Meanwhile, buyers satisfice, setting “good enough” thresholds for functionality before deciding based on other factors such as brand familiarity, trust, support, innovation philosophy, roadmap alignment, and who they deem most likely to be a long-term “partner” rather than a short-term “vendor.”

Research consistently shows that 70-80% of the B2B buying journey occurs before any vendor contact. Yet product marketing exquisitely optimizes for the final 20%, crowded into the bottom of the funnel alongside sales, where it’s measured by metrics such as MQLs, sales enablement effectiveness, and revenue generation. This is akin to every player on a kindergarten soccer team chasing the ball instead of maintaining positional discipline. As we speak, product marketing teams across entire categories are fighting feature wars for a tiny fraction of in-market customers while ignoring the vast majority who need a compelling brand narrative before they’ll even pay attention.

Fragmentation at Scale. Since each product gets its own marketing team that handles its positioning, messaging, and customer acquisition motions, while paying lip service to the brand, it results in corporations marketing products atomistically (each trying to out-feature competing products, no matter how minutely) instead of marketing products in a way that reinforces the brand cohesively (where the brand becomes a halo that creates permission across all products). As a result, customers experience fragmented noise. Multiple voices, confusing value propositions, copycat claims, and featuritis that blur into incoherence.

In large organizations with genuine scale advantages, a proliferation of product marketing often leads to competing as if they’re a loose federation of small businesses rather than a market-leading, scaled player. Paradoxically, this benefits no one but smaller competitors, which can quickly replicate product features and product marketing playbooks, but lack the benefits of scale. The result of such weakness is rarely to elevate the brand, but rather to perfectly optimize each individual product “tree,” while the market ignores the confusion of the ensuing forest.

Combine these challenges, then multiply them by every competitor running identical playbooks in identical ways, using identical tactics down to the pixel level, and the outcome becomes clear: everything same, instantly forgettable brands, commoditized products, weak funnels, slower growth, poorer leads, and increasingly extreme pressure on sales to overperform in winning the leads they do get. Typically, while wondering why a competitor with an “inferior product” is winning. (Hint: not everyone is set up to act like a kindergarten soccer team all chasing after the ball at the same time.)

The Strategic Mirage

Of course, product marketing isn’t stupid. It knows something is wrong. It just isn’t acknowledging that it’s structural. As a result, its members are scrambling upstream, claiming ownership of revenue, win rates, and strategy. Some are even being relabeled with senior titles and leadership access.

But this is largely a mirage. True strategic authority requires portfolio lifecycle management, decision rights over disengagement, and capital reallocation power. Meanwhile, even strategic product marketers are limited to a product-level scope, with optimization mandates that are subordinate to sales metrics. Research shows that 44% are 1-2-person teams managing 5+ products with little or no budget. More responsibilities under a fancier title isn’t strategic empowerment, it’s tactical overload masquerading as elevation.

AI companies don’t have to follow this path. They have a different option: deliberately replace a SaaS-era product marketing function with a new infrastructure designed for AI-era conditions.

Experimentation is Non-Negotiable.

AI companies face two external realities that reshape how they should think about marketing.

The markets you will serve are still forming. Preferences haven’t yet crystallized. No decisive selection factors have been identified. No one knows which marketing activities create a real advantage yet. You can’t simply slap the old model onto a new paradigm and expect it to work. Instead, you must discover what does work through systematic experimentation.

This creates an uncomfortable truth: discovery requires inefficiency. Most experiments will fail. Many tactics you try won’t work. Significant capital will be spent learning what doesn’t create advantage before finding what does. This isn’t optional, it’s the necessary cost of navigating uncertainty. You can’t afford to view it as a failure of execution, instead it’s a necessary investment in learning.

As SaaS-era playbooks calcified, product marketing systematically eliminated its discovery capacity in the name of efficiency. Every tactic measured against backward-looking ROI. Every channel justified by the proven performance of the past. Every playbook certified only after it reduced waste. As a result, it’s a function structurally incapable of tolerating discovery-phase inefficiency, which is exactly what new market formation demands.

Advantages will be copied instantly. When SaaS-era growth playbooks were forming, the world was relatively stable. Since then, marketing’s advantage lifecycles have been permanently compressed. Today, the copying of specific tactics is happening at AI speed, and platform certification programs are commoditizing new approaches within months.

This means your primary advantage source can’t be inherent to any single set of tactics or channels. Instead, it must be inherent in your marketing organization’s capacity to continuously discover, leverage, and disengage from tactics that offer an advantage before competitors saturate and commoditize them. All while maintaining customer-focused coherence. Unfocused experiments without an organizing logic create chaos. What matters is how fast you cycle your learning while maintaining meaningful coherence of the customer experience.

This requires fundamentally different capabilities.

What Replaces Product Marketing

An experimentation infrastructure requires a new organizing principle and four core capabilities, all designed to discover and exploit advantage rather than execute playbooks.

The organizing principle: brand-as-worldview.

Not a distinctive logo and identity (though this certainly helps), but a coherent philosophy of what the company stands for and how it creates value. This is what centers the business. It determines which experiments run, which advantages fit, and which transitions make sense. Every product decision, channel experiment, and tactical pivot filtered through questions like: Does this reinforce what we stand for? Does it create customer value consistent with our story? Is it creating preference before engagement?

Brand-as-worldview represents the coherence layer that provides stability, allowing you to then operate with extreme agility. As such, it’s the capstone above the portfolio of transient marketing advantages that sit below. Its coherence enables rapid experimentation without decline into fragmented chaos.

Four new capabilities:

Strategic Intelligence. Not just traditional thin-data (surveys, platform statistics, reported preferences) but thick-data (observed behavior, emotional context, unarticulated needs). A deep understanding of how customers decide, especially in the currently invisible 70% of the buyer journey, where preferences form. What drives decisions? What creates consideration before comparison? What are their fears, aspirations, and barriers?

This intelligence must become infrastructure. Every decision and experiment informed by the same customer reality, not platform vanity or sales pressure.

Experimental Rigor. Hypothesis testing must be scientifically rigorous and measured against the creation of competitive advantage. Are we creating preference before comparison begins? Are we discovering advantages that competitors haven’t yet saturated? Not guided by platform efficiency or sales support metrics. Instead, guided by the curiosity and discipline necessary to test systematically and learn how to rapidly shift investment when necessary.

Portfolio Discipline. You must build organizational capacity to recognize when optimization reaches natural limits and provide the authority to disengage from diminishing returns before they become a drain on resources. This means killing what might be fully optimized and working, but is in rapid decay, while competitors still think it’s effective. Here’s the hard part. Your next sources of advantage must already be in development, ready for the moment of capital reallocation. Without this, “disengage” becomes “switch off and panic.” This is why the inefficiency of discovery is so important. It’s not a cost, it’s the process through which future advantage is created.

Worldview Coherence. This means treating the brand as an organizing principle that prevents product-centric fragmentation, while guiding real choices. “We stand for X, therefore we measure Y, invest in Z, organize around A, and say no to B.” This means developing the capability to require that every product, test, channel, tactic, playbook, and message be filtered through the brand: Does it reinforce what we stand for? Does it cohere with everything else? Does it mean what we want it to mean to customers? This prevents the chaos of random tactics while enabling the necessary velocity to discover advantages before competitors do.

AI will enable this infrastructure. Not by automating commodity tactics, but by helping find advantages, exploit them, and then flag when they’re approaching decay before competitors saturate.

The CEO Mandate

None of this will work without CEO level commitment. It’s non-negotiable. This isn’t twiddling around the edges. It’s the underlying hypothesis for a new model of marketing designed specifically to discover your way to success under the constraints of uncertainty:

1. First, establish Worldview Clarity. The CEO/CMO defines the worldview. What the brand stands for, your organizing philosophy, and your theory of customer value creation grant permission for everything else. This is the strategic layer above all tactical execution. This must be stable.

2. Subordinate marketing to worldview. All marketing activities operate under this layer of brand coherence rather than being chaotically and independently optimized product by product.

3. Build the experimental capabilities necessary to execute rapidly. Invest in a strategic customer intelligence infrastructure, establish experimental rigor measured against advantage creation, develop portfolio discipline with lifecycle authority, and rigorously utilize worldview coherence as the organizing filter.

This means accepting two things. First, products no longer center marketing activities; the brand, as an organizing principle for the customer, does. Second, in an AI era where preferences are still forming and advantages decay quickly, experimental inefficiency is a strategic investment in discovery and the creation of future advantage, not operational waste.

The AI Choice

Nobody knows what the AI future holds, although plenty are positioning themselves for profit by pretending they do. That’s exactly my point. What I’m suggesting here isn’t a playbook or a framework. I have no crystal ball. It’s a hypothesis for how AI firms can experiment their way to success through uncertainty.

The specifics, which worldview to choose, which experiments to run, which channels to rely on, which tactics to double down on, must be discovered.

What works in cybersecurity AI won’t work in creative AI. What works now won’t work tomorrow. What works within one corporate culture won’t work in all. What works for one audience won’t work for others.

AI companies have a unique opportunity. You’re raising massive capital without pre-hardened systems or orthodoxy. You have a window before these factors harden.

And you have a choice.

You could install a commoditized SaaS-era growth infrastructure and compress a decade of past decay into the next year or two.

Or you could build a new model for a new era. An experimentation infrastructure under worldview coherence, governed by competitive advantage discovery, that maintains the discipline to disengage from decaying advantages before competitors realize they’re dead, and that always has new advantages lined up to exploit.

The old world is dead. Time to build a new one.

Subscribe now

Share

Leave a comment

Yet, in reading the report, I couldn’t help but notice a striking contradiction. While CMOs rank brand as their #1 priority and Gen AI #17 of 20, what does McKinsey focus on? Yup, you guessed it, those CMOs capturing efficiency gains through AI, while lambasting CMOs en masse for their “massive undervaluation” of the technology. Sigh.

Here’s what McKinsey ignores: CMOs aren’t prioritizing AI efficiency. If they were, they’d have said so. Instead, they’re prioritizing brand. And if they’re prioritizing brand, it means they’re prioritizing growth and long-term value creation, because that’s what a brand focus delivers.

Now, McKinsey isn’t dumb; it’s assuming the CMOs reading the report are. The AI efficiency spiel has nothing to do with CMO priorities and everything to do with its own revenues. It’s pouring a fortune into Quantum Black, and the pressure is on to make back that money somehow. Especially when all the big AI players now view consulting as the margin moat amid a sea of model commoditization.

Unfortunately, this myopia means McKinsey isn’t answering the real question CMOs face when prioritizing brand: finding the signal amid the noise of how they should be thinking about it. Especially when treating brand as a genuine priority means impacting the business more deeply than hiring some designers to refresh your identity, bringing in an agency to run a brand campaign, or outsourcing it all to McKinsey’s Quantum Black AI agents.

So, what follows is my attempt to write the report McKinsey should have written: How to think about brand when it’s your #1 priority.

1. The Sharp Trap. Why The Theory Du Jour Probably Won’t Save You. Again.

It’s exhausting how many theories of brand advantage one must suffer over the course of a single career. Over the past few years, just as brand purpose began to fall out of fashion because mostly it didn’t work, Byron Sharp’s laws of brand growth rapidly fell in. Why? Not because they’re right, or even particularly actionable, but because it’s a theory that’s reductively simplistic, institutionally attractive, and perfectly designed for low-engagement social media scrolling. (This, in case you’re wondering, is why we now have to suffer the tediously boring distinctiveness disciples over on LinkeyLoo peddling brand growth truthiness according to Sharp. Have ChatGPT, will pontificate, I guess.)

Anyway, as a framework, what Sharp offers is narrow and measurable, which finance loves; it limits transformation primarily to media buying, which procurement loves; and it justifies a marketing function reduced to tactically managing the promotional P, which doesn’t upset any apple carts in the modern C-suite.

Win, win, ring-a-ding.

“So what. Will it make me successful?” I hear you asking. Weeeeelllll, that depends on how you define success and the kind of category you play in. If you’re Diageo, operating in low-innovation, low-growth, highly mature categories where you’re already huge and your ambitions are single-digit modest, then yeah, Sharp is the man. Knock yourself out. If you’re not Diageo, let’s step back for a second.

Among many issues I have with the Sharpian view is that he assumes markets are static. This complete lack of temporal context leads him to dismiss innovation as a strategic driver and disruption as a threat. Assumptions the economic evidence soundly disproves.

BCG’s analysis shows that innovation-driven companies generate three times the total shareholder returns of the S&P 1200 through margin expansion, business model transformation, and improvements in valuation multiples, not just volume growth. McKinsey (haha, why couldn’t they have put this in the CMO report?) also identifies innovation as the difference-maker among firms that consistently outperform their peers in shareholder value creation.

And the evidence of our own eyes is readily apparent. Tide Pods versus Tide liquid is a format innovation that still commands a roughly 20-50% price premium per load, over a decade after its introduction. Not through any Sharpian “law,” but through genuine product advancement.

Put simply, Sharp misses the transformative effects of innovation because his theories rely on an assumption of fixed markets, which means the entire basis of his thinking is tactical (what we do in the moment) rather than strategic (how we create future advantage). Yes, of course, how we execute tactics within the constraints of existing markets is important. But if we’re thinking strategically about future advantage, Sharp has very little to say.

So, if real value is delivered by innovation breakthroughs over time, rather than status-quo thinking in the moment, what does this mean for how we should think about brands?

Houston, we need a new theorist.

Fortunately, I have one I prepared earlier. Rita McGrath of Columbia Business School. While she has almost nothing to say about either brands or marketing, we shouldn’t hold that against her. It may even be an advantage.

What McGrath does bring is a career spent studying successful, long-lasting, and—gasp—innovative corporations. Not those fighting over points of share, but those winning battles for entire markets. Those who create new advantages rather than defend old positions. How refreshing.

2. McGrath’s Framework. And Why Brand is The Missing Capstone.

McGrath’s theory of transient competitive advantage begins with a fundamentally different assumption of reality from Sharp's: In dynamic markets, sustainable advantages have been replaced by temporary ones. Success requires managing a portfolio of advantages across different lifecycle stages—emerging through experimentation, strengthening through scaling, exhausting as markets shift.

Here, innovation isn’t dismissed; it’s front and centre. Companies must constantly experiment to discover new advantages before current ones erode. This is offensive innovation designed to create new sources of value, not the defensive kind designed to maintain parity, which Sharp grudgingly acknowledges.

However, such portfolios require discipline: Where to find new advantages? How to create them? How to know we’re on the right path? Which experiments deserve scaling? Which advantages should be abandoned? When should we abandon them?

While McGrath is rigorous about describing the stages of her model and identifies the overarching need for discipline as critical, referring to it as “sources of stability,” she does little to define how this works in practice. And without a clear guiding lens, portfolio management tends to slide rapidly into portfolio chaos. The kind of endless experimentation without direction that should send a shiver up the spine of any CMO who survived the last decade, and the utter misuse of the term agile by digital gurus grifters.

This is where brand becomes the missing capstone to her thinking. Not brand as logo or messaging, but the clarity of what we stand for, what we mean to people, and the decision logic this necessitates. This isn’t brand as a source of status quo equilibrium (Sharpian logic), but brand as a customer permission system through which advantage-creating innovation is brought to market (McGrathian logic).

There's a virtuous cycle here that McGrath hints at but doesn't fully articulate: innovation creates the permission for more innovation, as long as these innovations are coherent with each other. Over time, each successful innovation increases customers' willingness to try your next one. Apple can overnight dominate the watch and headphone markets because decades of previous iPhone and iPod success created the brand permission. Red Bull can launch new events and media properties and successfully run sports teams because its cultural innovation has built the brand's permission for such experiences. This is how brands de-risk innovation and earn the right to pursue new advantages that would be impossibly risky without this accumulated permission. (Conversely, this is why startups typically favor starting with a “wedge”). As a result, successful innovation drives, reinforces, and refreshes what the brand means rather than diluting it, thereby increasing future possibilities. (Looking at you, all you “must protect the brand at all costs" limiters of possibility).

In other words, brand can be thought of as the capstone atop McGrath's framework—an unnamed source of stability, coherence, and discipline. The virtuous cycle, or flywheel effect, this addition creates is why it’s the capstone. Brand brings coherence to innovation; innovation brings essential new vitality to the brand.

The good news is that brand strategy practitioners have been handling the definitional part of this capstone work for decades. Questions like “What do we stand for?” “What do we mean to people?” and “Why should anybody care?” aren’t marketing fluff. They’re exactly the coherence McGrath is looking for. The gap isn’t in the questions themselves; it’s that we’ve done a poor job of connecting the answers to a CEO-level framework for competitive advantage.

As a result, this potentially crucial definitional work ends up being underutilized when its:

1. Buried on page 22 of the design guidelines, and is skipped over by everyone

2. When not buried, rarely gets unpacked into a strategic choice logic. “We stand for X” is purely philosophical unless cascaded into “therefore we measure Y, invest in Z, organize around A, and say no to B.” Without such translation, it makes no difference.

3. Finally, if there is a choice logic, it’s usually focused on marcomms. Guiding what we look like, sound like, and message like. But this isn’t a capstone to advantage we’re architecting the future of the business around.

However, shift your mindset the tiniest bit and employ a McGrathian lens, and this definitional work shifts from lofty statements buried in guidelines to an explicit source of strategic intent that cascades into CEO- and CMO-level choices about competitive advantage.

This transforms it into an organizing principle that separates the strategic management of advantage from scattered activity.

3. Different Approaches, Different Advantages

If Sharp’s theories were truly universal as he posits, every company would pursue the Diageo approach to brand strategy. They don’t, because different relationships between what brands stand for and the innovation they undertake reveal distinctly different advantages to exploit.

Diageo brands stand for continuity over time. Its strategy centers on mental and physical availability in mature, low-differentiation categories. Its brand decision logic prioritizes distribution, distinctive assets, and penetration metrics. It minimizes innovation because its competitive context doesn’t reward it. By accepting equilibrium, it caps potential returns in exchange for reduced risk. Annual organic revenue growth of 1.8% reflects a deliberate choice—stable, predictable, unambitious. This works when your categories are mature and your aspirations modest.

P&G brands stand for superiority through product innovation. Its brand decision logic invests in R&D to improve performance. Tide Pods’ price premium came from solving real problems: convenience, dosing, and mess. Each superior product strengthens a P&G brand's category leadership while creating permission for the next innovation. P&G knows copycats will erode past advantages, exactly as McGrath predicts, so it’s always seeking to create more. P&G isn’t embracing return-capping equilibrium; it accepts innovation risk in pursuit of margin expansion. This only works if your brands give you permission to innovate continuously and your organization can systematically capture those advantages before competitors copy.

Red Bull stands for cultural meaning through media innovation. It doesn’t prioritize product or distribution. It scales the infrastructure for cultural participation. Stratos, Rampage, and sports team ownership aren’t traditional brand activations. They’re a different business model entirely. Here, innovation occurs in narrative, experience, and meaning, which exploits cultural advantages unavailable to energy drink competitors. Red Bull embraces cultural and media risk in pursuit of excess return to both margin and volume. This only works if you can continually create cultural moments that reinforce what the brand means without diluting it.

These aren’t just tactical differences in brand execution. They’re fundamentally incompatible strategic orientations. Each of these businesses stands for completely different things and thus has very different approaches to innovation and advantage.

P&G’s continuous product innovation would bankrupt Diageo’s efficiency-focused model. Diageo’s penetration logic would starve P&G of the innovation that justifies its premium pricing. Red Bull’s media infrastructure investment would destroy P&G’s margin structure entirely. Try applying Diageo’s “minimal innovation” logic to P&G’s portfolio and watch brand permission erode as competitors outflank you with superior products. Apply P&G’s innovation cadence to Red Bull and watch cultural meaning collapse as you pivot from authentic participation to product features nobody asked for.

All three are successful. None is universal.

What matters is how you frame the relationship between what you stand for and how you innovate, relative to the risk you are willing to take in return for commercial advantage. This determines the path you will take, and the portfolio of advantages you will, in turn, set yourself up to pursue.

4. The Strategic Implications

At its core, this is already how transformative corporate brand work gets done. The most successful brand transformations aren’t solo CMO projects; they’re upstream CEO-CMO partnerships. The CEO brings the business strategy, ambition, risk tolerance, and commensurate authority over innovation resources. The CMO brings customer intelligence, understanding of the brand's existing permission, and synthesis capability. Together, they define the relationship between brand and innovation, which serves as the capstone to the portfolio of advantages.

What’s been missing is our ability to connect this work to McGrath’s framework. Here’s what changes when we make that connection explicit:

• Brand becomes the organizing principle for innovation decisions.
  Not “should we innovate?” but “which innovations align with what we stand for?” P&G pursues product superiority innovations. Red Bull pursues cultural participation innovations. Diageo pursues distribution innovations while deliberately choosing not to innovate products. The brand answers which types of transient advantages you’re positioned to capture.

• Brand becomes the lens for product portfolio choices.
  Whether creating new products, amplifying existing ones, or deprecating exhausted advantages. Apple deprecated the iPod because the iPhone captured that advantage more completely while better reinforcing the meaning of “seamless experience.” Microsoft deprecated independent Windows and Office brands because, in a cloud-based world, these advantages had exhausted and were fragmenting the meaning of “productivity platform.” Your brand logic determines how you think about emerging versus exhausting advantages.

• Brand becomes the filter for partnership and M&A strategy.
  Which partnerships reinforce what you stand for versus dilute it? Microsoft began partnering with competitors like Apple and Salesforce because removing customer constraints strengthened Microsoft's “empowerment” focus under Nadella. P&G acquires innovation capabilities that enable continuous product superiority. Red Bull partners with athletes and events that amplify cultural participation. The brand determines which external relationships capture advantages rather than waste resources.

• Brand becomes the discipline for letting exhausted advantages go.
  McGrath’s portfolio management requires knowing when to abandon advantages before they become resource traps. The brand provides that lens—not “is this still profitable?” but “does this still reinforce what we stand for and mean?” When the answer is no, you begin disengaging and moving on to new sources of advantage regardless of current profitability, because defending yesterday’s advantages prevents the capture of tomorrow’s.

This shifts brand strategy work from periodic refresh projects to the coherence layer that continuously sits atop advantage management. Not “what should our new campaign say?” but “which advantages should we pursue this year, and which should we abandon?”

Ending as I began.

Let’s end where we began: with McKinsey.

What I’ve attempted to explain is what the survey actually revealed. When CMOs rank brand as their #1 priority, I’m pretty sure they aren’t asking for an isolated logo refresh or audits of their distinctive assets, or even a shiny new brand campaign.

No, what I think they’re seeking is what’s been lost amid the shift to digital and our current lurch toward all things AI: an organizing principle that enables them to identify and manage sources of competitive advantage in fast-changing markets. A framework that separates strategic portfolio decisions from scattered activity. And a lens through which innovation choices can be made confidently and coherently rather than chaotically.

Not a glorified brochure selling McKinsey’s AI efficiency services. And not Byron Sharps’ equilibrium of mediocrity. But clarity on how to strategically think about brands in an environment where advantages emerge and exhaust, and the hardest job is figuring out where, when, and how to shift from one to another.

So, I'll leave you with this McKinsey-inspired decision matrix:

• If you prioritize brand and care about winning in dynamic markets, think like McGrath.

• If you prioritize brand and care about surviving in mature, undifferentiated markets, think like Sharp.

• If you care about neither winning nor surviving, McKinsey has just the AI efficiency report for you.

Subscribe now

Share

Leave a comment

Over the past decade, sophisticated criminals didn’t just steal your digital media budgets; they stole your growth. And now they’re using AI to come for more.

For every dollar fraudsters steal from the digital advertising supply chain, they intercept roughly $8-12 in expected lifetime value—the return that justified the spend in the first place. This isn’t media waste; it’s customers who’ll never be acquired, relationships that’ll never be formed, and lifetime revenue that’ll never be generated. (For the numerate among you, this math is always going to be a bit fuzzy based on chosen multipliers. Think of it as a best guess directional signal rather than a hard number. The principle remains the same).

This is why spend goes up, while growth flattens.

Here’s what happened. Between 2014 and 2024, there was a catastrophic side-effect as digital became the dominant form of advertising. As digital went from around 25% to circa two‑thirds of media spend globally, and a greater 78% in the US, it became subject to a 22% fraud rate, compared to an estimated low single digits in traditional channels.

As a result, every dollar that shifted from traditional media (circa 2-4% fraud) to digital (22% fraud) now carries an additional 18-20 cents of fraud exposure. Risk that didn’t exist in the channels it replaced.

The consequences are direct. Criminals operating in an opaque supply chain systematically steal the lifetime value of customers you’ll never acquire. Meanwhile, financial controls to mitigate this were never deployed because the growth of digital channels outpaced the institutional capacity for adaptation.

Put simply, this is your future being stolen, not just your media budget.

The Vicious Cycle Accelerating Theft.

Here’s what happens repeatedly:

Marketing is tasked with delivering growth → criminals steal this growth via digital media fraud → finance, lacking visibility into supply chain theft, sees flat performance despite increased spend → finance cuts total marketing budget while pushing remaining dollars deeper into digital’s “measurable” channels → marketing becomes even more exposed to fraud → criminals steal more growth → rinse/repeat.

This explains why marketing budgets have collapsed by almost a third from 11% of revenue pre-pandemic to 7.7% in 2024 (Gartner), with 64% of CMOs reporting they now lack the budget to execute effectively, while being told they must “do more with less.” Yet even as total marketing spend declines, digital’s share of these shrinking budgets increases because finance sees digital as measurable and thus superior. As a result, marketing is simultaneously budget-starved and channel-constrained into the most fraud-heavy medium. Finance loves digital’s apparent measurability. The problem is its lack of visibility into the 22% of spend that’s fraudulent.

Under pressure to be measurably efficient, marketers optimize for cheaper inventory and better metrics. Cheaper inventory and better metrics mean more digital and more fraud. The Association of National Advertisers found that the average programmatic campaign runs on tens of thousands of domains, whereas “a few hundred” could reach 95% of the same audience. This sprawl creates systematic vulnerability—fraudsters gaming the metrics marketers are told to optimize for, delivering spectacular-looking results from bot traffic.

However, while campaign performance looks great, commercial outcomes deteriorate. Bots can easily fake engagement, but they can’t fake sales. As a result, seemingly high-performing campaigns don’t deliver value, so finance cuts total budgets further while pushing even more dollars into digital’s measurable channels, under the assumption that the underperformance must have come from somewhere less measurable. Marketing, facing greater pressure, is driven toward even more fraudulent inventory. Each budget cut accelerates the process.

The Finance Shift: From Internal Referee to Aggressive Offensive Line.

Here’s a metaphor to help understand what needs to change. Suppose marketing-driven growth is a game of football. The CMO is the quarterback tasked with running offensive plays to drive the ball up the field.

Today, without visibility into supply chain fraud, finance acts as an internal referee ruling on the efficiency and cost metrics they do see. As performance deteriorates, they flag the CMO’s plays, cut budgets, and demand better ROI. It’s destructive, but it’s rational given the available information.

But what finance doesn’t see is that while they’re busy flagging their own team, the CMO is being repeatedly sacked by transnational criminal enterprises running an unopposed blitz.

Finance isn’t being unreasonable. Without visibility, cutting budgets when metrics underperform is entirely rational. The problem is they’re responding to fraudulent data. They’re calling a flag on what looks like inefficiency when much of it is actually theft. The outcome—not the intent—is one-sided refereeing that simultaneously punishes marketing, limits what it can do, and leaves it with no offensive line to protect it, unlike every other spend category in the company.

Here’s the part that matters. You’d never expect a quarterback to execute a winning passing game while also blocking a repeated blitz by themselves. That’s not a quarterback problem. It’s a team problem.

Marketing doesn’t need finance to be a one-sided referee calling flags on the home team while the quarterback gets blitzed on every play. Marketing needs an aggressive offensive line that’s going to block for it. This is the protection finance should be offering so the CMO can focus on the offense they’ve been hired to run.

Finance has the nasty, the heft, the authority, and the institutional expertise to be this offensive line. CFOs already protect every other dollar the company spends by verifying vendors, auditing transactions, and preventing fraud in procurement, accounts payable, and vendor payments.

Doing the same in digital advertising is complex, yes, but the stakes are far too high for this not to be a top 3 priority for the finance organization. Unlike other fraud that steals only your money, digital ad fraud literally steals your future.

While execution may be fiendishly difficult due to the opaque nature of digital advertising itself, the mental shift required is extremely straightforward: Apply the same vendor verification standards to digital advertising that finance already applies everywhere else. Stop one-sided refereeing the CMO and start aggressively blocking the real opposition: organized crime.

Why Others Can’t or Won’t Fix It.

Platforms won’t fix this. They profit from supply chain opacity. Transparency would reveal fraud, reduce inventory, and force them to reject billions in revenue from questionable sources. Their problem is that fraud scaled alongside digital’s explosive growth. By the time its true impact became apparent, it was too embedded in the business model to control without a material impact on revenue. As a result, no platform will unilaterally disarm until there’s a revolt among advertisers that makes them all change simultaneously. Expecting otherwise is like expecting a casino to make its slot machines less profitable. It won’t happen.

Marketing can’t fix it. Marketers are measured against the exact metrics fraudsters game. That’s why they game them. You can’t fix a fraud problem when your job security depends on fraudulent output. Marketing also lacks the procurement authority and board representation necessary to elevate the problem to the level it needs to be elevated to. Only 26 CMOs serve on Fortune 1,000 boards. Fraud at the scale seen in digital advertising requires board-level authority, and marketing simply doesn’t have it.

But finance does.

Ad fraud is invisible to finance because it looks identical to non-fraud in reporting dashboards. A bot clicking an ad generates the same metrics as a human clicking an ad: impression served, click recorded, cost incurred. The difference only becomes visible at conversion, and by then, you’re diagnosing “poor campaign performance” rather than “systematic supply chain fraud.” This invisibility persists because the vendor verification and audit controls that work everywhere else have never been adapted to digital’s unique supply chain complexity.

Here’s what finance has to play with to fix this:

Board Authority: 25% of Fortune 500 CFOs hold board seats. The SEC mandates financial expertise on audit committees. When massive, systemic fraud and the necessary verification to stop it require boardroom authority, finance has it.

Capabilities & Mindset: The same capabilities and process-control mindset that have locked down procurement fraud to circa 4% ($40 million per billion in spend) could be adapted to digital advertising, which currently bleeds at 22% ($220 million per billion), over 5X worse. This isn’t because digital is inherently more fraudulent—attempted procurement fraud is rampant too—but because comparable standards don’t exist. Vendor verification, audit trails, and fraud detection aren’t new or bleeding-edge capabilities finance needs to build. They’re existing approaches that need to be aggressively adapted toward a new target.

The procurement comparison shows the opportunity. Today, we call 22% ad fraud an “industry standard,” shrug, move on, and expect marketing to fix it alone while being measured against fraudulent metrics. The one stakeholder with both the incentive and the authority to fix this is finance, because this isn’t a marketing problem. This is an institutionalized cap on future growth.

Competitive Advantage or Coordinated Action. You decide.

First-moving finance teams who view their role not as internal referees calling flags on their own growth, but as aggressive offensive lines blocking criminals will unlock asymmetric competitive advantage.

You’ll be reaching and acquiring real customers while competitors focus on bots. You’ll have more accurate attribution models, and you’ll be able to see marketing’s genuine signal without the noise of fraud. Your teams will learn which publishers perform and which verification signals matter. This is the kind of institutional knowledge that grows asymmetrically while competitors continue to waste 22 cents of every dollar optimizing for bot engagement. And, by the time they catch up, you’ll have already captured the growth that’s still being stolen from them.

Even better. Coordinate and demand new platform standards globally. It’s all of your futures being stolen, and the only winners today are criminals and platform shareholders.

What It Will Take.

Building a league-leading offensive line doesn’t come for free. Deploying such protection requires three commitments from leadership:

First, accept metric declines. CTRs will drop. CPMs will rise. This is a feature, not a bug. Bots offer great metrics, real people don’t. This is a short-term reality that needs to be accepted before things can improve.

Second, provide air cover from the board. The CEO and CFO need to give marketing 6-12 months, or more, while strategies and activities shift and metrics reset. This also provides an opportunity to begin judging marketing performance against meaningful long and short-run business outcomes rather than engagement theater.

Third, recognize that protection costs money. Verified inventory costs more than fraudulent inventory. Finance understands this in every other context: verified vendors cost more than unverified ones, quality costs more than fraud. And robust verification processes don’t come cheap. You can’t lowest-cost your way through an ongoing battle against sophisticated criminals with everything to gain and little to lose. The payoff is recapturing $180 million per billion spent, which compounds via the lifetime value multiplier that’s currently being stolen from you.

What Should Scare Us All: AI Is The Accelerator.

The growth you promised the board over the past decade wasn’t mismanaged by marketing. Criminal enterprises stole it as budgets shifted to digital. That additional 18-20 percent fraud exposure, which represents the gap between what was spent and what had a chance to reach real customers, is the growth that was stolen via the lifetime value those never acquired customers would have generated.

This theft continues unabated. For companies spending $100 million annually with industry-average fraud rates, criminals are intercepting in the region of $200+ million in expected future value—more than double the media budget itself.

Here’s what should terrify every CFO, though: future projections of fraud growth are wrong. In 2016, the World Federation of Advertisers projected that ad fraud would reach $50 billion by 2025. The actual figure in 2023 was already $84 billion—68% higher than projected and arrived at two years early. This pattern will accelerate. Why? Because AI is collapsing the barriers to entry. Click farms required devices, people, and infrastructure. AI-generated synthetic humans cost nearly nothing to produce at an unlimited scale. The asymmetry you’re already beginning to face is brutal: cost of fraud approaching zero while verification costs remain high or increase.

Every forecast assuming historical growth rates is almost certainly obsolete. The 22% fraud rate isn’t decreasing; AI is about to make it exponentially worse. The fraudsters who stole the last decade of growth are now armed with technology that makes their operations cheaper, faster, and harder to detect.

Left unopposed, rising fraud rates will eventually create a death spiral where low advertising-driven growth becomes no growth becomes de-growth, even while spend increases.

The opportunity is for finance to shift its mindset now. To protect growth by deploying an aggressive offensive line in support of the CMO, so they can run growth plays without being flagged by their own side and repeatedly sacked by the opposition. To apply the same verification expertise to digital advertising that already protects every other spend category.

The question isn’t whether you can afford to clean up your digital advertising supply chain.

It’s whether you can afford to let AI-enabled fraud steal the next decade of growth in the exact same way criminals stole the last one.

Subscribe now

Share

Leave a comment

However, this is merely a convenient inversion: reframing tactical outputs as a key strategic input without changing what these agencies actually do.

The problem isn’t that culture doesn’t matter. It absolutely does. The problem is a causal logic that’s running backwards.

Put more simply, cultural authority doesn’t drive strategy. It emerges from it.

The actual mechanism works in reverse: Systematic coherence creates cultural authority as a byproduct. The causal chain runs: Worldview → Distinctive Systems → Believable Brand Universes → Cultural Currency.

Note that cultural influence sits at the end of this sequence, not the beginning. This distinction matters because pursuing cultural authority directly feeds the wrong economic system.

Platform Economics Aren’t Brand Economics

Platform algorithms flatten culture for their own economic ends. As a result, they create simultaneous exposure to the same signals.

When Charli XCX’s “brat” album dropped in June 2024, everyone became overwhelmed by the ensuing ‘brat summer’ at the exact same time. This is how algorithmic mediation of culture guides us toward “conforming to each other with a homogenization of culture where we all accept the average of what everyone else is doing.” In other words, when brands jumped on the bratwagon and slapped lime green on everything from bratwurst billboards to toothbrush promotions, it became homogenized noise rather than cultural authority.

This flattening of culture equals sameness. It’s boring. It’s the shit we all scroll past because we’ve seen it a thousand times before: music following the same hooks, movies recycling the same plots, content optimized for engagement metrics rather than meaning. The basic rule is clear: “the popular becomes even more popular, and the obscure becomes even less visible.” The problem is that the popular becoming even more popular isn’t a natural phenomenon. It’s being artificially enhanced by algorithmic steroids, which is why it has a half-life measured in days to weeks, rather than months or years.

However, the reality is that while platform amplification homogenizes culture, what brands need is the earned right to celebrate its differences.

This is why a third of consumers view brands jumping on viral trends as “embarrassing,” while at the same time believing brands should understand culture. This isn’t a tautology; they’re just signaling the difference between meaningful engagement and opportunistic brandwagoning.

Put more simply, the exact sameness that algorithmic amplification drives is not good for brands seeking to be somehow different or distintive.

So, while platform economics might benefit from homogenization, brand economics benefit from the opposite.

From Brandwagoneers to Obscurantists.

The push to make culture a key strategic input spans a predictable high/low spectrum, but everyone makes the same fundamental error of treating platform-visible signals as strategic inputs rather than recognizing them as lagging outputs that primarily benefit the platform.

At the bottom are the brandwagoneers. Those agencies monitoring “cultural moments” and advising brands to “participate in conversations.”

When brat summer went viral, they called it a cultural moment worth pursuing. In reality, it was an algorithmic artifact. Just a few weeks later, these same agencies were warning clients the moment was now over due to the saturated scale of the brandwagon they’d themselves created. Rinse/repeat.

Because such moments have half-lives measured in days to weeks, and because of the startling degree of brandwagon homogeneity that ensues, value rarely compounds into anything worthwhile. In fact, it’s more likely the opposite when consumers view such behavior as an embarrassment ¯\_(ツ)_/¯.

At the opposite end of the food chain lie the futurists obscurantists who employ complexity to disguise intellectual thinness. These are the agencies diagnosing ‘meaning vacuums’ and ‘collective liminalities,’ while urging brands to address ‘cultural tensions’ through ‘potency,’ ‘metamorphosis,’ and ‘semiotic triangles.’ Wut?

Most often, these seemingly profound anthropological insights are just observations of the obvious dressed up in better graphic design, impenetrably obtuse language, and charismatic delivery.

Underneath it all, however, lies an incredible irony: when thousands of agencies all claim cultural understanding as their primary strategic differentiator, it no longer differentiates. It just extends the vicious cycle of commoditization. The brandwagoneers chasing brat summers and the obscurantists diagnosing meaning vacuums share an identical positioning: “We understand culture, therefore we should govern brand strategy.” Scale this across the industry, and an inversion designed to escape commodity competition becomes the very thing that locks them all into it.

Earn it, Don’t Claim it.

Here’s what the earned right to celebrate cultural difference actually looks like in practice.

Carhartt made workwear for a century. When skaters and hip-hop culture discovered it in the 1980s-90s, it wasn’t something Carhartt had pursued; it was consumer recognition of a systematic coherence that was meaningful to them. This coherence earned cultural authority rather than claiming it. Work In Progress then launched in 1994 to adapt to the permission Carhartt had already earned: ‘cultural immersion, not advertising.’ As a result, its cultural authority was a valuable and desirable byproduct of doing other things well rather than an input.

Equally, Crocs’ polarizing clog invited participation. In the summer of 2005, mom Sheri Schmelzer started decorating her kids’ Crocs with whatever cute items she could find around the house, and then turned it into a business. By August 2006, orders had flooded in, and by October, Crocs had acquired Jibbitz for $10 million. Product coherence multiplied by emergent consumer ingenuity bought the permission for personalization. By 2017, the ‘Come As You Are’ campaign rehabilitated the now-faded brand by articulating and focusing the cultural permission it had already earned a decade earlier, starring celebrities who already loved it. Not because Crocs was homogeneously jumping on a trend, but for the exact opposite reason.

Systematic coherence doesn’t just seek to buy cultural attention; it earns opportunities to celebrate cultural differences in ways that matter.

You don’t start there. Instead, you earn the right to participate creatively by first building a coherent and valuable identity over time. Not borrowing relevance or chasing algorithmic signals, but deploying authority you’ve systematically earned.

This is why it’s not about brandwagoneers commoditizing you into embarrassment, or obscurantists baffling you with bullshit you can’t understand. It’s far simpler and more profound than that.

Cultural authority emerges from clarity: be clear in what you stand for, be clear in your worldview, be clear in the distinctiveness of the systems you build, and then understand that it’s the cohesion of these systems that earns you the permission to be culturally participative, not the other way round.

The Big Question

As a result, when evaluating any “cultural” brand activity, ask the following: Has my brand earned the right to participate through systematic coherence, or is it just trying to borrow or claim what platforms make visible?

In 2017, Pepsi hadn’t earned the right to participate in social justice discourse. As a result, its Kendall Jenner ad was pulled within 48 hours. Marketing analyst Mike Jackson diagnosed the failure precisely: “The problem was that Pepsi did not have a history of promoting social justice causes.”

Meanwhile, Nike had earned the right. Thirty-plus years of building athlete-centric systems meant the Kaepernick campaign emerged from a well-established coherence rather than borrowed relevance. Commercially, it was essentially the same tactic, yet there were radically different outcomes. The difference was the systematic foundation.

This mechanism isn’t at all mysterious. It’s just inconvenient for agency business models that depend on making tactically borrowed relevance look like strategic capability.

Byproduct Not Input

Understanding that cultural authority is a byproduct rather than an input changes how marketers should act.

The work that matters isn’t monitoring TikTok for trending signals or hiring semioticians to decode platform-generated patterns. The work that matters is developing worldviews that inform every decision, building coherent systems that create value for customers, and earning cultural authority through systematic distinctiveness.

Chasing culture isn’t a strategy. It’s just feeding the platform economics of algorithmic amplifiers and praying it will create what only coherence can deliver.

The brands that matter in five years won’t have hired trend-spotters to read flattened signals. They’ll have built systems that create value, maintained coherent identities that invite participation, and earned the permission to creatively celebrate cultural differences rather than chase platform-homogenized sameness.

Cultural authority can’t be pursued directly. It emerges when you get other things right. Build systematic coherence first, and cultural currency, and the opportunities it creates will follow.

Not because you pursued it, but because you earned it.

Subscribe now

Share

Leave a comment