Test 2: Does Your Organization Actually Agree?

Ask your product/tech, sales, and marketing leads, separately, to describe your current best-fit or target customer. E.g. The specific one they picture building for, marketing to and closing next quarter.

At a public, multiple lines-of-business company I worked at, I frustratingly watched their three divisions land on three different answers to that question. In isolation or individually, each one seemed reasonable. One website builder team had focused its roadmap around very small, typically non-tech savvy, primarily US-based businesses wanting to get online and establish a presence on the web and on social networks. The Commerce team was betting on “power sellers”, businesses already doing six figures or more in online revenue. Partners focused on intermediaries: the agency world, dev shops and marketing studios reselling hosting and WordPress to their own clients. Multiple overlapping and competing views of the world . . . each incentivized to retain onto their view . . . rightly so. And little to no active push to make the other two let go of their ICP, roadmap(s), products/tech and GTM motions.

What made it expensive wasn’t the disagreement itself. Each division represented a real chunk of revenue, so there was never enough leadership/organizational will to de-invest from any one of them and concentrate behind the segment with the best opportunity. I call this playing to win vs. playing to participant. The inevitable tension was insidious and the company paid for the indecision on multiple fronts (e.g. investments, people/skills, product/tech, competitive strength, sustainable growth, etc.)

Even if you have a single line-of-business or product company, if your three leads give you three different answers, that’s not a communication problem. It’s the diagnosis: fit hasn’t just drifted in the pipeline, it’s fragmented in the org’s mental model, and no amount of sales execution repairs that.

Test 3: Is There Still a Market Left?

The third test is less about people and more about math. In early or developing markets it can be difficult to identify and painful for founders/CEOs to recognize.

A noteworthy version of this I heard from a founder who built a location-services infrastructure company to roughly $35M before growth stalled. I wrote about a version of this story in a guest piece on how companies lose product-market fit without noticing. Revenue growth plateaued (both existing clients and new acquisition) as mobile platforms absorbed the basic capability the company sold, and the original ICP of fast-growing apps needing their capability got served. What didn’t make it into that version is the founder’s own read on it . . . in retrospect, the company may have already been serving somewhere between 60-90% of the realistically addressable market. Nobody knew it until growth hit the ceiling and stopped.

That’s a different situation than ICP drift, and requires a different conversation. Drift is a correction: narrow back, get disciplined as the market’s still there. A market ceiling presents a dilemma requiring different decisions:

• Double down on a smaller opportunity than the plan assumed.

• Incubate a 2nd product to serve the existing ICP.

• Pivot to an adjacent market/customer.

• Run the business for cash instead of growth, or

• Return capital to investors and call it what it is.

Getting co-founders, investors, board members on the same page can be difficult. And I’m not surprised if leadership teams are slow in assessing/recognizing the problem and have difficulty aligning around the options that conflict with their original company thesis and the growth trajectory they appeared to be on. Because this is uncomfortable, many leaders avoid it (sometimes for quarters) and often treat the ceiling as an execution problem. Real numbers point to the need to find the truth and real explanation. Avoidance is a choice, but not the right decision.

Running Both

Neither test is complicated to run. Test 2 costs three short conversations. Test 3 costs an honest look at how much of the market is already reflected inside the funnel. Neither shows up in a dashboard, and both can get treated as a sales or marketing problem for months without an accurate diagnosis.

My next article will explore the fork these two tests create:

• drift you can correct versus a ceiling you have to decide about, and

• lays out what leadership actually does differently once that distinction is named instead of argued about.

If you’ve run either test and landed somewhere unexpected, I’d love to hear more.

A few explanations always come up, and each sounds reasonable by itself. Sales cycles stretched a bit, sure, but the process is more rigorous that previously. A recent win or two needed a feature that wasn’t on the roadmap, which reads as opportunism and/or ordinary customization. None of it gets flagged in the weekly deal review.

The instinct at that point is to go looking for an execution problem: is the team closing efficiently, is the messaging landing, is the process working well enough? Worth checking, though it’s rarely where the answer actually turns out to be.

What the Deals Are Actually Saying

Here’s a better question, and it isn’t about how the deals are closing. It’s who they’re closing with, and why, measured against a year ago instead of against last quarter’s number.

I laid out a version of this test a few weeks back in a guest piece for on how companies lose product-market fit without noticing it’s happening, which a reader later described, more precisely than I had, as watching drift compound like debt. That piece also lays out the three ways PMF quietly erodes: ICP drift, product signal decay, and messaging drift. It is worth a look for the fuller picture in addition to running this test.

The test itself: pull your last twenty to twenty-five closed deals. For each one, who bought, what problem, what triggered it. Now do the same for deals from nine to eighteen months back, and put the two lists side by side.

Almost nobody does this. The dashboard tells you deals closed. It doesn’t tell you who closed, or why, and a quarter can look completely healthy while the buyer has already quietly moved somewhere the team hasn’t caught up to yet.

If the buyer, the problem, and the trigger have all shifted between the two lists, this isn’t a sales execution problem. The customer base has moved, deal by deal, without anyone actually deciding it should.

Why It’s Hard to Catch From the Inside

Every deal on the newer list was a good call on its own terms . . . a rep working a real opportunity with real budget behind it. Nobody’s going to point to a single bad decision in the group. The pattern only shows up once you put all twenty side-by-side, not by scrutinizing any one of them harder.

The reverse mistake happens too. Teams panic and narrow the ICP back down before they’ve actually run the comparison, which just trades one guess for another one (dressed up as discipline).

A founder put it to me more bluntly than I would have: “What got us to ten won’t get us to world domination.” He wasn’t saying the team had gotten worse at selling. He was saying the customer type who contributed the first ten million wasn’t automatically the customer for the next stretch, and nobody had actually sat down and decided who that next customer should be.

The Question That Actually Changes

Once the two lists exist, the question in the room stops being about closing more deals like the recent ones. It becomes whether the recent ones are the business anyone actually meant to build.

Most teams never get there, mostly because nobody builds the twenty-deal list in the first place. So the drift stays a feeling instead of a documented pattern, something that comes up in hallway conversation but never makes it into a deck, meeting or strategy.

Two more of these diagnostics are coming over the next few weeks, whether your own team can even agree on who the best-fit customer is, and whether what you’re seeing is drift at all or just a buyer pool that’s gotten smaller. In the meantime, if your last twenty deals look meaningfully different from the twenty before them, on buyer, problem, or trigger, put it in front of the team before a missed forecast forces the conversation instead. And if you’ve already run this test and it didn’t hold, I’d like to hear where it broke down.

Last week I attended Seattle Tech Week for the first time, as a person, not as an operator running point for a company. That shift in vantage let me follow my nose: listen deeply, embrace some randomness in who I met, and skip the urge to walk in with an agenda or quantifiable outcomes.

The framework that stuck

The clearest piece of thinking all week came from @Nizar t (PitchBook) and (Madrona), during a live podcast recording on AI durability. Their frame: think of AI as a vertical stack of sorts.

• Infrastructure and models

• The harness (middleware tailored for non-deterministic agent workflows)

• Applications

Each layer carries its own investment logic and its own likely who-wins-and-who-loses dynamics. It’s a useful lens for cutting through a lot of hype-cycle noise, and for evaluating companies with more nuance than a single AI narrative allows.

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The ecosystem behind the hype

The rest of the week kept circling the same underlying question from different angles: how much of the AI narrative is real, and how much is compensation for old playbooks that stopped working. A stat from the JP Morgan startup showcase stuck with me, that Seattle now claims roughly 23 percent of the country’s AI engineering talent (their number, not independently verified, but directionally consistent with what I saw in the room). A GTM Day session made the case that buyers have largely stopped Googling and started asking AI directly, which changes what “being found” even means. And a fundraising panel with iSpot’s Sean Muller and Len Jordan, a Madrona Venture Partner, was a reminder that diligence, vetting, and negotiation fundamentals haven’t changed at all, regardless of what layer of the AI stack you’re pitching.

Underneath the AI framing, the fundamentals still decide outcomes: distribution discipline, real diligence, and an honest read on what’s differentiated versus borrowed.

The real value was off stage

Every formal session added something. None of them were the best part of the week. That was the serendipitous stuff: GeekWire co-founder Todd Bishop; founders like Stefan Kaehler testing low-lift, high-impact GTM plays; founder/CEOs Sean Muller (iSpot) and Court Lorenzini (Docusign), each offering hardened truths from the twists of scaling a company; venture investors comparing notes; a genuinely fascinating tangent on nuclear fission and fusion; and a connection with an Apple cloud engineer over drinks and snacks at The Pink Door.

In-person may be the best antidote we have to online noise.

What it clarified about my own next chapter

Since stepping away from senior operating roles late last year, the pull toward advising, consulting, investing, mentoring, and writing keeps showing up as real, not just a nice idea for later. I met Priebe, my new C100 partner and entrepreneur-in-residence at AI House. I compared notes with people already doing fractional and advisory work. There’s real value in synthesis, in taking what a week like this surfaces and making it legible for someone else.

The other thing worth naming: it was good just to be present, and to start feeling connected to the Seattle tech community. Not chained to a demanding operating role, able to actually be in a room, after battling Seattle traffic like in the old days.

Lesson for next year: register early, find opportunities to proactively contribute, get more days on the calendar, and build visiting family time around the week instead of despite it.

If you’re building in Seattle and want a second opinion on how your growth model actually holds together, or you’re navigating your own next chapter and want to compare notes, I’d enjoy the conversation.

That’s a better metaphor than the one I used. A “close enough” deal, a softened pitch, a feature scoped for a customer who isn’t quite the ICP: none of it registers as a mistake, because none of it is one in isolation. Each is a small loan against future clarity. Debt compounds the same way drift does: quietly, and mostly out of view of whoever signed for it.

The same reader flagged the timeline, and it’s worth taking seriously as a number rather than a warning. Twelve to eighteen months of unnoticed drift is real runway handed to competitors who aren’t drifting. That’s not an abstraction. That’s a clock running while the balance grows.

Another commenter offered the sharper diagnosis, the one that actually explains why this happens to well-run teams: it isn’t usually a vigilance failure. It’s an incentive structure. Sales carries a quota. Marketing carries lead-generation targets. Product carries roadmap velocity. , who hosted the original piece on his newsletter Focused Chaos, put it plainly: “Sales has quotas, they hit them by targeting non-ICP clients, Marketing has lead generation targets and goes after non-ICP clients, etc.”

Nobody’s asleep at the wheel. Everyone’s hitting their number. That’s precisely what makes the debt so hard to see from any single seat at the table - each function’s ledger looks fine.

I’ve watched a version of this play out directly: a sales team hitting quota by closing deals just outside the ICP, a product team shipping features to support them, and six months later no one able to say with real confidence who the company actually served. Nobody made a bad call. The company just financed its way into a customer base it didn’t design.

Debt has to be serviced or paid down on purpose. It doesn’t resolve itself just because every transaction that created it was defensible on its own terms. The leadership move isn’t finding who made the mistake. It’s opening the books.

Where’s the debt already sitting in your org, and who’s currently paying the interest without knowing it?

This continues the reaction to How Companies Quietly Lose Product-Market Fit Without Noticing, originally written as a guest post for Ben Yoskovitz’s Focused Chaos — worth a follow if you don’t already get it.

I recently met with a B2B software founder about the challenges he’s facing. The company hit $12M ARR and he believed they had achieved initial Product-Market Fit (PMF) 18 months prior (e.g. net revenue growth of 120%+, shortening sales cycle, rapid growth among “look-a-like” ideal/target customers).

But over the past two quarters, something shifted. The roadmap debates that used to resolve in a single meeting now drag across three. His sales team is requesting features that serve customer profiles he didn’t used to close. When a new hire asks him to describe the ideal customer, he finds himself hedging in ways he didn’t before.

I asked him what he thought was happening, his answer was immediate: “It’s hard to tell if this is normal friction or something more structural.”

That ambiguity is usually the first signal. He doesn’t have an execution problem. His product-market fit is eroding.

Product-Value Fit and Product-Market Fit Aren’t the Same

Ben Yoskovitz has written extensively on Focused Chaos about what PMF actually requires: a product that creates genuine value, customers who pay in some meaningful form, and a repeatable way to acquire more of those customers at scale. That third element is the one most companies underweight.

Product-Value fit (the sense that existing customers love what you’ve built) can look a lot like PMF. It isn’t the same thing.

Consider what the full picture looks like when everything is working. This company (offering a B2B infrastructure solution for location services for mobile applications) hit all the right signals: net revenue retention above 120%, sales cycles that collapsed because buyers already had budget and urgency, API usage climbing as existing customers scaled their own products. By any reasonable measure, the system was working.

Then net new customer acquisition stalled. Not because the product declined. Because the company had worked through the initial Ideal Customer Profile (ICP) of fast-growing apps that already needed location services. When that cohort was exhausted, sales cycles expanded again. It wasn’t the team’s fault; they now had to educate cold prospects rather than convert warm ones.

The underlying market also shifted. As Android and iOS embedded basic location capabilities natively, the addressable market for a premium/custom API quietly narrowed. Product-Value fit was intact. The distribution engine needed to reach and acquire new customers at scale had never fully developed.

This is only one pattern.

Market exhaustion and platform shifts are real causes of growth stalls, but so are ICP drift, channel saturation, pricing compression, and competitive entry.

What the diagnostics below are designed to catch isn’t any specific cause…it’s the moment the system starts losing coherence.

Where PMF Erosion Shows Up First

PMF erosion doesn’t announce itself in revenue or retention. By the time it appears there, it’s been compounding for quarters. The earlier signals show up across three dimensions.

1. Ideal Customer Profile (ICP) drift. As I’ve written about, ICP expansion at this stage is rarely a strategic move. When the original segment starts producing less reliably, the instinct is to cast wider. Sales closes deals with profiles that are “close enough.” Marketing broadens the message to stay relevant. Each individual decision has logic behind it. Together, they represent the original fit quietly giving way to a more diffuse version of the customer the company was built for.

2. Product signal decay. This one is less discussed and harder to see. When a company has genuine PMF, the product team receives a coherent signal from customers: the same pain points, the same use cases, the same feature requests clustering around a clear job to be done (JTBD). As fit erodes and the customer base diversifies, that coherence breaks down. Feature requests start arriving from multiple directions at once because different customer types now need meaningfully different things. The roadmap debates that once had a clear anchor (e.g. what does our core customer need?) start generating multiple legitimate answers. The product is still shipping. What’s decaying is the clarity of what to build and why.

3. Messaging drift. The value proposition that used to close deals gets softer at the edges. Not because the team is less prepared, but because no single explanation covers the range of customers’ needs the company is now selling to. The pitch broadens to stay relevant. Specificity gives way to optionality. Deals still close, but the team finds itself making slightly different arguments to slightly different buyers, with slightly different framings of what the product does. Each version is defensible. None of them is as sharp as the original.

Why PMF Drift Looks Like an Execution Problem

These three signals compound quietly. By the time they show up visibly - longer sales cycles, softer conversion rates, board questions that are harder to answer with confidence - they read as execution problems. Leadership responds with execution solutions: more pipeline, tighter process, a new hire. Each response is reasonable. None of them addresses the underlying cause.

That’s what makes this stage expensive. As one marketplace founder put it to me directly: “If you’re only selling to promoters, your net promoter score is going to be really good.” The surface holds. The erosion happens underneath it. Companies operating in that ambiguity tend to address symptoms for several quarters before realizing and naming what’s actually happening.

The Diagnostic

Three questions cut through the noise faster than any metric.

1. Deal Assessment

Pull your last 20-25 closed deals. For each one, ask:

• Who actually bought it?

• What problem were they solving?

• What triggered the decision to purchase?

Then run the same exercise on the cohort you closed 9-18 months ago. If the buyer profile, problem, and trigger have shifted meaningfully - not dramatically, but meaningfully - the center of gravity has moved.

2. Internal Consistency

Ask your product, sales, and marketing leads separately to describe your best-fit customer today. If you get three substantially different answers, fit has already fragmented across the organization’s mental model, not just the pipeline.

3. Primary Acquisition Channels

Are conversion rates holding or declining? Is the cost to reach a qualified prospect rising? If existing customers are healthy and expanding while new customer acquisition is getting harder, the issue may not be ICP drift at all. It may be that the pool of ready buyers has narrowed and the company hasn’t built the distribution engine to reach beyond it.

None of these is definitive alone.

Together, they tell you whether the original fit is intact, where the erosion is coming from, and whether you’re dealing with:

• a customer-definition problem,

• a product-clarity problem,

• or a distribution problem.

That distinction determines what comes next.

What To Do When You Find Drift

The first and most important step is distinguishing between two very different situations, because they require different responses.

1. Accidental Drift

The ICP or product focus shifted without a deliberate decision to go there. If that’s what the diagnostic reveals, the path forward is to narrow back. Say no to edge cases. Recommit to the original customer profile. Tighten the product roadmap around the core JTBD.

This feels counterintuitive when growth is the goal, but it’s often what restores the coherence that makes growth repeatable. The companies that do this well treat narrowing as a strategic choice, not a retreat. They get more specific about who they serve, sharpen the value proposition around that customer, and let the clarity do the work that activity couldn’t.

2. Deliberate Recalibration

Where the original market is genuinely exhausted, has shifted structurally, or was never as large as it appeared. This is harder, and riskier. The companies that navigate it well make an explicit choice about the next customer: who they are, what job they’re hiring the product to do, and why this company is the right solution. The ones that struggle continue treating the new segment as a slightly adjusted version of the old one, which means the same erosion pattern repeats in a different direction.

In either case, the organizational move is the same: name it explicitly.

When the leadership team can’t agree on whether the drift is accidental or structural, the company will continue operating under different assumptions and every debate from roadmap to Go-To-Market (GTM) to hiring will be harder to resolve than it should be.

And what happened to the startup I referenced at the beginning of the post?

They chose the “deliberate recalibration” path. The core organization stayed focused on serving and expanding existing customers and contracts, while the original founding team was carved out to rapidly iterate to discover the next $50M product and market. Structurally, it was the right call. It still produced real tension between the two founders over authority, resourcing, and whose mandate now defined the company, but at least they made a decision and pushed towards it. Time will tell if it was the right one.

Thank you Ben for his input on this article. Check out his newsletter and this post (which includes some wonderful infographics).

Two weeks later, sales brings a deal to the table. It needs one feature. The customer sits just outside the core Ideal Customer Profile (ICP): close enough to be tempting, different enough to matter. It is, without exaggeration, the exact kind of decision that created the drift the team just spent an afternoon diagnosing.

Why Leaders Think the Hard Part Is Over

The natural assumption is that the hard part is behind them. They agreed on the diagnosis. Now it’s a matter of discipline: hold the line, stay focused, don’t repeat the old pattern. That assumption feels reasonable. It’s also where most teams quietly start drifting again, usually within a month of the offsite that felt like a turning point.

Vocabulary Is Not a Decision Rule

Naming the phase gives a leadership team something real: a shared way to talk about what’s happening. Anyone can now reference the Growth Recalibration Phase in a roadmap meeting without re-litigating whether the model has actually shifted. That’s progress. It’s also incomplete.

What naming doesn’t do is answer the next ordinary question: what happens when a deal, a feature request, or a roadmap item shows up that looks exactly like the pattern the team just diagnosed? A shared word for the problem is not a shared rule for handling it. Most teams discover that gap only once they’re already inside a live decision, under the same pressure that created the drift the first time.

A CPO at a venture-backed software company described the moment after her own team’s version of this offsite plainly: “We agreed on what was happening. Nobody agreed on what we’d do the next time this exact deal showed up.” Three weeks later, it showed up.

The Question That Has to Change

Before naming the phase, the leadership question is diagnostic: is this genuine model drift, or is it the normal work of running a company? A logical product extension. An opportunistic revenue call that sales is right to chase. A routine adjustment to a competitor or a shifting market. None of that is a red flag on its own. Companies are supposed to adapt to their environment, and most of what looks like deviation is healthy, not a symptom.

After naming the phase, the question has to change, and this is the step most teams skip. It stops being “do we agree on the diagnosis” and becomes “what is our actual rule for telling the healthy adaptation apart from the deal that repeats the old pattern.”

If that rule doesn’t exist yet, the offsite produced a good meeting, not a change in how decisions get made. The team keeps recognizing the pattern in hindsight, deal after deal, without building the muscle to make the right call in the moment, not just diagnose the wrong one after it has already closed.

The Diagnostic

Pull every consequential decision the leadership team has made since naming the phase: every deal, feature commitment, and roadmap call. For each one, ask a single question: would this have moved through the exact same process, with the exact same scrutiny, before anyone named the phase at all?

If the answer is yes for more than one, the team has a name for what’s happening and not yet a rule for what to do about it. That gap is worth closing before the next deal arrives, not after.

I wrote about the naming moment itself a couple of weeks ago in If Growth Feels Harder to Explain, You’re Likely in a Recalibration Phase. This is the chapter that comes right after it, and it’s usually the one that decides whether the naming actually changed anything.

That moment, when explaining growth requires more from you than driving it, is one of the clearest signals that something structural has shifted. Not broken. Shifted. It’s a later-stage version of what I described in When You Stop Trusting the Forecast, Something Deeper Has Already Broken: confidence declines before the numbers do, and the narrative gets harder before either one does.

Most leadership teams don’t read it that way. The instinct is to treat it as a board management problem, e.g. a narrative gap, a communication challenge that better data or a crisper deck will resolve. The update gets refined. The metrics story gets tightened. And the next quarter’s update has the same problem, now better disguised.

What the Explanatory Burden Is Actually Measuring

When a growth model is working well, the narrative writes itself. The system produces consistent outcomes, and describing those outcomes is mostly an act of observation. Leaders don’t have to work at the story because the business is generating a clear one.

When the explanatory burden increases - when the same results require more qualification, more internal debate about how to characterize them, more time spent on framing - the system has stopped producing a clean signal. The work of explaining is compensating for signal the model used to generate automatically.

This is what I’d call a Growth Recalibration Phase. Not a crisis. Not a plateau. A specific inflection point where the growth model that got you here has started to drift from where growth is actually coming from, and the gap shows up first in how hard it is to talk about the business clearly.

What Naming It Does

Something specific happens when leadership teams stop treating this phase as a communication problem and start treating it as a model problem.

The first shift is in what gets examined. When the explanatory burden looks like a narrative issue, the response is to refine the narrative. When it’s recognized as a model issue, the response is to interrogate the model . . . which assumptions about the customer still hold, which motions are producing results that compound versus results that merely appear, where the team has been quietly optimizing for something that no longer reflects where the strongest growth is actually coming from.

Those questions tend to produce a different kind of leadership conversation: shorter in some ways, harder in others. Fewer debates about how to frame the story. More direct engagement with what’s actually true.

The second shift is in how decisions get prioritized. When a leadership team has a shared name for the phase they’re in, the evaluation logic changes. Roadmaps get constrained rather than expanded. GTM focus sharpens rather than broadens. Initiatives in flight get assessed not just on their own merits, but on whether they serve the specific work this phase requires. Clarity, in this case, is less about knowing the answer than about agreeing on the question.

What This Phase Is Not

Worth naming directly, because the instinct to pathologize is strong: Growth Recalibration is not a signal that something went wrong with the team or the strategy.

The companies that enter this phase are typically ones that executed well enough to reach meaningful scale, and then encountered the natural limits of the model that produced that growth. The original Ideal Customer Profile (ICP), the original motion, the original product thesis did its job. What’s required now is building the next version, not fixing the previous one.

The companies that struggle here are almost never the ones that lacked effort or talent. They’re the ones that applied effort and talent to optimizing a model that had already started to drift and kept optimizing it past the point where it could respond. That’s the pattern I described in Why Pushing Harder Stops Working at This Stage of Growth: more effort through an incoherent system doesn’t compound. It amplifies noise.

The Question Worth Asking Before the Next Planning Cycle

If growth is becoming harder to explain than to drive, one question tends to clarify the picture faster than any dashboard review or strategy offsite:

What would it take to make the growth story simple again?

Not simpler messaging. Simple underlying mechanics - the kind where outcomes are predictable enough that the narrative writes itself. The answer is almost never “better communication.” It’s almost always something more structural. And that’s usually where the real work begins.

If this is the moment you’re in - growth still happening, but harder to explain than it should be - I work with founders and CEOs to diagnose what’s shifted and define what needs to change.

Nothing is obviously broken. No single function is failing. But leadership conversations are getting longer, alignment is taking more meetings, and the forecast keeps requiring more explanation. The instinct is to double down on execution . . . more rigor, tighter OKRs, better cross-functional process.

That instinct is usually wrong.

When Functions Optimize Independently

In the early stages, growth is mostly a function of momentum. A sharp Ideal Customer Profile (ICP), a repeatable motion, strong product-market fit, etc. These create a system where individual execution compounds. Leaders optimize their functions and growth responds.

But at a certain inflection point, the functions start optimizing independently. Sales tightens its process around deals that close fastest, which aren’t always the deals the product roadmap is built for. Product prioritizes what the loudest customers want, which aren’t always the customers marketing is trying to reach. Go-To-Market (GTM) strategy expands to capture more of the market, which diffuses the signal that made the original motion work.

Each leader is making reasonable calls. But the system they’re operating inside has lost coherence.

What This Looks Like From The Inside

The easiest tell is what leadership team conversations are actually about. In a coherent growth system, the debates are mostly tactical, e.g. how to execute better against a shared model. When the system has lost alignment, the debates shift to the model itself, often without anyone naming it directly.

Sales and Product are fighting about priority. Marketing and Sales disagree on ICP. Finance is asking questions about unit economics that nobody can answer cleanly. These aren’t execution failures. They’re symptoms of a system that’s no longer pointing in the same direction.

Optimized for Nobody explored what happens when clarity breaks down at the leadership level. This is what happens before that - the moment when functional optimization quietly becomes the obstacle.

The Decision That Actually Needs To Change

Most leadership responses at this stage focus on improving coordination: more alignment meetings, clearer prioritization frameworks, better cross-functional rituals. These aren’t wrong, but they treat the symptom.

The decision that needs to change is upstream: what is the system optimizing for? When that answer is clear and specific, the Sales vs. Product tension mostly dissolves - not because the relationship improved, but because both teams are making decisions constrained by the same model. GTM choices get simpler. Roadmap debates get shorter. The forecast gets easier to defend.

Clarity doesn’t just reduce friction. It changes which decisions get made.

A Diagnostic Question Worth Witting With

If your leadership team is spending significant time debating priorities across functions, ask a harder question: do we have a shared, specific answer to what the growth model is optimizing for right now?

Not a mission statement. Not a set of values. A crisp answer to: who is the customer, what do they value most, and how does our model deliver that better than any alternative? If that answer requires a meeting to produce, the system has drifted further than most teams realize.

A VP of Product at a $12M B2B company makes three defensible calls in a single quarter. She expands the ICP to capture an adjacent segment the sales team has been requesting. She accelerates a feature to close a strategic enterprise deal. She shifts roadmap priority toward infrastructure after a competitor’s feature launch. The board agrees with each decision individually. Twelve months later, growth is harder to explain and the roadmap debates are exhausting.

No single call was wrong. But the pattern they formed was.

Ibrahim Bashir has written about how Amazon layers product choices to compound value over time - good decisions building on each other with the logic of compound interest. The Power of Layering Product Choices is a useful frame, because its inverse is also true. Individual good decisions, made under pressure from different directions, can compound into a product that’s optimized for nobody in particular.

This isn’t technical debt. It’s something harder to see: strategic drift. The product stops reflecting a clear growth thesis and starts reflecting the last six months of inbound pressure instead.

Why the Drift Happens

Three forces tend to drive it, and none of them look like mistakes in the moment.

1. Sales pressure. A significant deal needs a feature. It gets built. The customer type that deal represents gets another one in the next cycle. The Ideal Customer Profile (ICP) quietly shifts toward them without a deliberate decision to go there. By the time the pattern is visible, the roadmap has already followed.

2. Competitive response. A competitor ships something. The team responds. Roadmap priority shifts to close the gap. But the customer who benefits most from that response isn’t always the core segment - it’s often a different buyer at a different stage. The team moves fast and the signal about who they’re actually building for gets muddier.

3. Market optionality. An adjacent segment looks large. Expanding toward it feels like a strategic move. And sometimes it is. But each expansion dilutes the signal about what’s working in the core. When the expansion doesn’t convert as cleanly, the response is often more features, which compounds the original problem rather than resolving it.

The thread connecting all three is that no single decision triggers a reassessment of the whole. Each one is evaluated on its own merits, approved, and shipped. The pattern only becomes visible in the aggregate.

What It Looks Like in Practice

By the time strategic drift is obvious, it’s already expensive. But there are earlier signals worth watching.

• Deal cycles lengthen even as win rates hold. The product is closing, but it’s working harder for each customer - which usually means different value propositions are attractive to different buyers, yet none of them are fully served.

• Feature usage starts to fragment across customer segments. The core customers are using one part of the product, the newer segments are using another, and the roadmap is somehow trying to serve both.

• Roadmap debates become harder to resolve. The reason is usually that there’s no longer a clear center of gravity to adjudicate from. Everyone has a reasonable argument. Collectively, there is no shared thesis.

What Changes When Leaders Get This Right

The product leader’s job at this stage isn’t just to build a great product. It’s to ensure that the accumulation of product decisions is reinforcing a coherent growth thesis, not quietly undermining one.

That requires a few specific shifts in how decisions get made:

1. Name the center of gravity and protect it. The most effective product leaders I’ve seen in growth-stage companies have a stated growth thesis: who is the core customer, what job are they solving, and what does success look like for them specifically? Every significant roadmap decision gets evaluated against it. Not just “is this a good feature?” But “does this customer type represent where our best growth is actually coming from?” When that question is asked consistently, any pattern of accommodation decisions shows up quickly.

2. Treat ICP creep as a product signal, not just a Go-To-Market (GTM) one. When sales starts consistently closing deals outside the core segment, that’s not only a pipeline observation - it’s a product signal. If the roadmap is responding to those deals without a deliberate choice to shift direction, the product is drifting before the strategy has decided it should. The discipline is noticing the drift at the product decision level, before it becomes a structural realignment problem.

3. Separate the accommodation from the strategic bet . . . and label it clearly. When a feature gets built to close a deal, that can be the right call. The failure mode isn’t building it. It’s letting it quietly shape the roadmap narrative going forward. Labeling it explicitly, e.g. “this is a one-time accommodation, not a signal about where we’re heading,” sounds simple, but most teams skip it. Accommodations become strategy by default when they don’t get labeled.

4. Build a coherence question into quarterly reviews. Not just: what did we ship? But: across the last 90 days of product decisions, what customer type did we optimize for and does that match where our strongest growth is actually coming from? The disconnect surfaces almost immediately when you ask it directly. Most teams don’t ask it.

The Part Most Teams Skip

The decisions that quietly break growth don’t look like mistakes. They look like good judgment under pressure. A capable, responsive team doing their job.

The difference between a product team that compounds value - the way Ibrahim describes it - and one that compounds drift often comes down to a single discipline: whether the coherence question gets asked before decisions accumulate, or only in the post-mortem after they have.

My recent post on why pushing harder stops working at this stage of growth covers the downstream version of this problem - what it looks like when the system loses coherence and execution stops compounding value. But the upstream version, the one worth catching earlier, is here: the pattern of individually defensible product decisions that nobody thought to evaluate as a whole.

Ibrahim Bashir, aka @ibscribe, is currently the SVP of Product at Ontra, where he leads product management, user experience, data science, and operations. Before that, he built product teams and scaled product lines at Amplitude (flagship Analytics), at Box (Platform business), at Twitter (Engineering org), and at Amazon (Kindle ecosystem).

The instinct in that moment is almost universal: push harder. Add headcount. Run more experiments. Accelerate the roadmap. The assumption underneath is that the engine is sound . . . it just needs more fuel.

That assumption is usually what’s worth examining.

When Execution Stops Compounding

In the early stages of a growth company, execution compounds. Each improvement reinforces the next. Sales cycles shorten, win rates improve, the product gets stickier, and the funnel gets more efficient. Effort and outcome feel directly connected.

At some point - and it rarely announces itself - that relationship changes. Teams keep working harder, but incremental effort stops producing incremental return. A CPO I spoke with recently put it plainly: “What we were doing to get here isn’t going to get us where we need to go.” A founder described it differently: “The things that worked then don’t work now.”

Both were talking about execution. But the problem wasn’t execution.

The Misread

The natural interpretation is a skills gap, a hiring mistake, or a process failure. Leaders audit the team. They redesign the sales motion or restructure the product org. These interventions often produce activity without producing clarity.

What’s actually happening is that the system execution operates inside has started to lose coherence. Not dramatically . . . there’s no single point of failure. But over time, the Ideal Customer Profile (ICP) has quietly drifted. The roadmap reflects competing priorities rather than a clear center of gravity. The GTM motion is optimized for segments that no longer represent the core growth path. Effort is high, but it’s distributed across a system that no longer pulls in a single direction.

More execution through an incoherent system doesn’t compound. It amplifies the noise.

Why This Is Hard to Diagnose

The inputs still look right. The team is solid. Metrics are moving, just not enough. And the instinct to push harder, built over months/years where that genuinely worked, is difficult to override.

“When Nothing Is Broken But Everything Feels Harder.” Last week I wrote about the stage where growth gets heavier without anything being clearly broken. This is what’s underneath that feeling. It’s not a motivation problem or a talent shortfall. It’s a coherence problem that presents as an execution deficit. The distinction matters because one responds to pressure, and the other doesn’t.

What Changes

The leaders who navigate this phase well do something counterintuitive: they slow down before they accelerate. Not operationally - they don’t reduce activity across the board. But they step back from the execution layer and ask a different set of questions.

Not: How do we run this faster?

But: What is this system actually optimized for right now . . . and is that still where growth is coming from?

The answer often surfaces decisions that looked right at the time: an ICP expansion that made sense when a new segment seemed accessible, a roadmap pivot that responded to real customer pressure, a GTM change that chased a short-term signal. Each one individually defensible. Collectively, they’ve pulled the system in enough directions that execution has stopped compounding.

The Diagnostic

If your team is working harder but outcomes feel harder to predict, the question worth sitting with isn’t “what are we missing?” It’s: What has our system quietly optimized for over the last two or three quarters . . . and does that still match where growth is actually coming from?

That’s a harder question. It requires honesty about past decisions rather than a diagnosis of current effort. But it’s the question that changes the trajectory.

Execution stops working when the system it operates inside loses coherence. More effort won’t fix that. Clarity will.

If this pattern is familiar - the effort is there but growth feels harder to explain - you may be entering a recalibration phase. That’s usually where the conversation starts.

But growth suddenly feels heavier than it should.

Decisions that once felt crisp now require more coordination. Deals that should close slip without a clear explanation. More effort produces roughly the same output. Leaders start using the word “friction” more often — and struggling to say exactly where it comes from.

A co-founder at a venture-backed SaaS company in the $10–30M revenue range described it plainly in a recent conversation: “It’s hard to tell if this is normal friction or something more structural.” That distinction — normal versus structural — is precisely what makes this phase so difficult to navigate. It feels like execution is the variable. Usually, it isn’t.

The Execution Instinct

When growth starts to feel heavier, the first instinct is to look for an execution explanation. Maybe the team needs more urgency. Maybe the sales process has gotten sloppy. Maybe the board needs a cleaner narrative to stay confident in the plan.

These instincts are understandable. Earlier in the company’s life, applying more pressure often worked. When things slowed, pushing harder moved them again. Leaders who built the early momentum have direct experience of that cause-and-effect relationship, and it is reasonable to reach for what worked before.

But at this stage, execution pressure rarely changes the trajectory. The system absorbs the additional effort without producing the expected output. Leaders push, activity increases, and the results stay roughly the same. That gap, between effort invested and impact returned, is one of the most important signals available. Most companies don’t read it correctly.

What’s Actually Happening

The growth model (e.g. the set of assumptions about who buys, why they buy, how they convert, and what expands reliably) has started to lose coherence at the edges. Not dramatically, and not in a way that shows up cleanly on a dashboard. But the internal logic that once made the engine work is loosening.

This is different from execution failure. Execution failure shows up as missed steps, low accountability, or poor judgment in specific decisions. What’s happening here is more systemic. The model’s underlying assumptions are drifting out of alignment with the current reality of the market, the customer, and the organization.

The friction tends to appear in three places simultaneously:

1. Sales cycles lengthen, even as the process improves. More stages, better decks, stronger qualification . . . and deals still slip.

2. Product output increases, but customer impact stagnates. The roadmap is shipping, but the needle isn’t moving.

3. Spend efficiency declines. More pipeline investment, more marketing activity, lower returns per dollar.

No single signal is alarming in isolation. Together, they describe a system that is working harder to produce the same result . . . and gradually losing ground.

The Leadership Shift

This is where the leadership question has to change.

The instinct is to ask: “How do we execute better?” That question produces answers about process, accountability, and urgency. It keeps the team oriented toward activity. And in this phase, it is almost always the wrong question.

The more useful question is: “What has actually changed?”

• Which assumptions about the customer still hold?

• Which segments are converting at a repeatable rate?

• Which motions are producing results, and which are producing the appearance of results?

This is the same dynamic I described in When Motion Replaces Clarity The instinct to add more initiatives when the model starts to feel unstable. The difficulty is that the instinct looks responsible. More pipeline, more features, more process. But activity is not the same as coherence, and in this phase, adding motion often increases friction rather than resolving it.

The Diagnostic Signal

One question that consistently clarifies the picture: if the team stopped all discretionary initiatives tomorrow, would growth get better, worse, or roughly the same?

If the answer is “roughly the same,” the motion isn’t compounding. Something more fundamental has shifted, and the activity is masking the signal rather than addressing it.

A VP of Product at a scaled marketplace company, reflecting on a phase her company had moved past, put it plainly: “Nothing was really changing it, however much we improved the product.” That’s the recognition moment. Product quality wasn’t the constraint. The model had shifted in ways that product output alone couldn’t address.

Growth rarely breaks. It gets heavier first.

If this phase feels familiar, you may already be in a growth recalibration period. This is where leadership decisions begin to change.

The second question feels like ambition. It often signals something else.

In the phase after initial traction, when early PMF is established but the growth model is still being stress-tested, many companies begin expanding their ICP. The sales team starts closing deals with profiles that don’t quite match the original. Marketing adjusts messaging to cast a wider net. Product begins fielding requests from new segments and adding features to serve them. Each individual decision has internal logic.

One founder described the moment of recognition clearly: “You go from knowing all your customers to knowing none of them.”

What This Usually Looks Like

The pattern often starts in the sales org. A deal closes with a company that’s slightly outside the original profile . . . different size, different use case, different vertical. The team celebrates the new logo. Then another edge deal closes. Then another.

Marketing watches the expansion and adjusts. Messaging broadens to avoid excluding potential buyers who don’t match the original profile. The ICP document gets quietly revised, or quietly stops being referenced altogether. Product inherits feature requests from the new segments and begins building for them.

The leadership team typically frames this as market development: learning where else the product creates value, expanding the addressable market, not narrowing prematurely. None of these explanations are wrong on their own. The problem is what they obscure.

What’s Actually Happening

When original fit is strong, companies don’t typically expand the ICP deliberately . . . they narrow toward it. They say no to outliers because the core converts predictably, with lower CAC, faster cycles, and stronger retention. There is enough signal in the original segment to trust.

ICP expansion at this stage is rarely a strategic move. It is a pressure release.

When the original segment starts producing less reliably - not catastrophically, but inconsistently - the instinct is to cast wider. More profiles, more use cases, more verticals. The expansion gets framed as opportunity. It is more often a response to weakening fit in the original market.

This is one of the most common forms the pattern takes that I described in last week’s post When Motion Replaces Clarity: simultaneous change can look like innovation, but it also signals the growth model has begun to drift, where leadership teams add motion when clarity drops. ICP expansion is that motion with a market-development narrative attached to it.

The Leadership Shift

The question that matters isn’t “which new segment should we enter?” It is “what is actually happening in the segment we already understand?”

When the original ICP was working, that question had a clean answer. The profile was clear, the motion was repeatable, and the team could describe the ideal customer without significant disagreement. When that question stops having a clean answer, expansion gets proposed as the alternative.

But expansion doesn’t restore fit. It dilutes the system. Every edge case that closes teaches the sales team a slightly different motion. Marketing tests a slightly different message. Product inherits a slightly different set of requirements. Over several quarters, the growth system that was once coherent now serves multiple overlapping profiles — none of them deeply enough to produce consistently repeatable outcomes.

Why This Matters

The cost of ICP drift compounds slowly, which is part of why it’s difficult to catch early. In the short term, expansion looks like progress. Pipeline grows. New logos appear. The team has new conversations and new energy.

Over time, conversion rates begin to soften . . . not uniformly, but inconsistently, which makes diagnosis harder. Sales cycles lengthen. The ideal customer profile becomes harder to defend in hiring conversations. New reps take longer to ramp because there is no single profile to anchor around.

By the time the cost shows up clearly in the numbers, the organization has usually been absorbing the friction for several quarters. The expansion that felt like acceleration was, in many cases, the growth model losing coherence one edge case at a time.

The Diagnostic Signal

One question often clarifies the picture:

If you asked your sales team to describe your best-fit customer today, would they give the same answer they would have given twelve months ago?

If yes and the profile is still converting cleanly, the model is intact. If the answers diverge, or if different functions give meaningfully different answers, ICP drift has likely already begun. That divergence is usually downstream of something the original segment stopped doing reliably, not upstream of the growth the expansion is meant to create.

Broadening the ICP before diagnosing the original fit is not market development. It is, in most cases, growth model drift moving forward under a different name.

If your team is spending more time debating who the customer is than serving them, you may already be in a growth recalibration phase. This is where leadership decisions begin to change.

Several things start changing at once. The roadmap is being reworked. Pricing is in motion. Sales territories are being redrawn. Marketing is testing a new Ideal Customer Profile (ICP). Product bets are widening rather than narrowing.

From the inside, this can feel like momentum. Leaders describe it as iterating, leaning in, staying agile. Boards reward the appearance of decisiveness.

One founder described it this way: “It feels like we’re doing more, but I can’t tell what’s compounding.”

That instinct is easy to rationalize. It often gets framed as the natural response to a harder growth environment.

In practice, it is usually one of the clearer signals that the growth model itself has begun to drift.

What This Usually Looks Like

At this stage, most leadership teams interpret simultaneous change as a sign of progress. Each initiative has its own internal logic. Sales is reorganizing for better coverage. Marketing is sharpening positioning. Product is testing new bets. Pricing is being adjusted to reflect what the market seems to support.

Each individual change is defensible. The problem is the combination.

Roadmap shifts layered on pricing changes layered on GTM redesigns rarely produce compounding effects. They produce friction. The leadership team often cannot name which lever is doing the work, which means they also cannot tell which lever has stopped.

Activity rises across the system, and so does perceived intensity. What does not return is clarity.

What’s Actually Happening

This is the same dynamic I described in When You Stop Trusting the Forecast, Something Deeper Has Already Broken, one layer up.

When leadership loses internal conviction about the growth model, the natural response is to expand the surface area of bets. New ICPs get explored. Pricing gets tested. Product bets multiply. The organization pulls many levers at once because no single lever feels reliable enough to anchor on.

In the product team context, Marty Cagan describes this pattern as the feature factory. The leadership-team version is broader. It is a strategy factory: a system producing initiatives faster than it produces learning.

Complexity becomes a substitute for conviction. Motion compensates for missing signal.

This is also why the response often looks decisive even when the underlying model is weakening, a pattern related to what I covered in How Growth Models Actually Break.

The Leadership Shift

This is where the leadership question changes.

It stops being “what should we change next?” and becomes “what do we actually still know?”

When the second question can be answered cleanly, meaning which segment is working, which motion converts, which value proposition is repeatable, most of the initiatives currently in flight begin to look optional. Some get cut. Some get sequenced. The conversation changes shape.

When the question cannot be answered cleanly, additional initiatives rarely close the gap. They widen it.

In many companies, this shift does not happen explicitly. It shows up in how conversations evolve. Strategy reviews get longer. Disagreements between functions sharpen. Leaders begin defending initiatives they cannot fully connect to the model.

Execution continues. Coherence does not.

Why This Matters

Simultaneous change is often treated as a sign of an ambitious organization. In practice, it is one of the earliest operational signals that the system is compensating for something it cannot yet name.

By the time the cost of the complexity shows up in the numbers, the leadership team has usually been absorbing the friction for some time. Cycle times stretch. Roadmaps get rewritten more often than they get executed. Hiring is layered onto problems that were structural to begin with.

The cost is not the individual initiatives. It is the loss of the system’s ability to learn.

The Diagnostic Signal

One question often clarifies the picture for CEOs and Heads of Product/Technology navigating this pattern:

If we had to cut half of the changes currently in flight, would the team be able to agree on which half to keep?

If the answer is yes, the motion is probably coherent. If the answer is no, or if the conversation immediately fragments along functional lines, the system is likely compensating for something it cannot yet name.

That is the moment the work changes. Not more initiatives. Fewer, sharper ones, anchored to what is actually still working.

When growth becomes unclear, leaders add motion instead of clarity.

If this pattern feels familiar, you may already be in a growth recalibration phase. This is where leadership decisions begin to change.

Growth hasn’t stopped. Pipeline coverage appears sufficient. On the surface, the system still looks intact.

But internally, something begins to shift. Forecasts take longer to build. Assumptions require more debate. Leaders find themselves relying more on judgment than on the underlying mechanics of the business.

One founder described it simply: “Every forecast feels right until it doesn’t.”

That tension is easy to rationalize. It often gets attributed to normal variability in the system or to execution gaps that can be fixed with more discipline.

In practice, it’s usually one of the earliest signals that something deeper is changing.

What This Usually Looks Like

At this stage, most leadership teams interpret what’s happening through an execution lens.

Sales sees a pipeline quality issue. Marketing looks at acquisition efficiency. Product focuses on positioning or differentiation. Each explanation is plausible, and each suggests a different fix.

The instinct is to respond accordingly. Tighten qualification, increase pipeline coverage, add more rigor to forecasting. Activity increases across the system, and so does pressure.

What doesn’t return is predictability.

Instead, a more subtle pattern begins to emerge. Deals slip, but not for consistent reasons. Sales cycles stretch unevenly. Pipeline appears sufficient on paper, yet behaves unpredictably in practice.

Nothing is obviously broken, but the system stops behaving reliably.

What’s Actually Happening

By this point, the issue is rarely sales execution in isolation. The company’s growth model has started to lose coherence, a pattern I explored more deeply in How Growth Models Actually Break.

When the model is working, forecasts feel mechanical. Pipeline converts in expected ways, segments behave consistently, and leaders spend less time explaining outcomes.

As the model begins to weaken, those patterns degrade. Not all at once, and not in a way that points to a single cause. Instead, variability increases across the system.

Similar deals close differently. Conversion rates depend more on context than on structure. Outcomes become harder to explain, even when they remain acceptable.

Forecasting shifts from a reflection of the system to an exercise in interpretation.

This is also why confidence often declines before performance does, a pattern I explored in Confidence Declines Before Growth Does.

The Leadership Shift

This is where the leadership question changes.

It moves from improving forecast accuracy to understanding what in the growth model is no longer behaving consistently.

In many companies, this shift is not made explicitly. It shows up in how conversations evolve. Forecast discussions get longer. Assumptions require more justification. Leaders begin layering judgment on top of inconsistent signals.

Execution continues, but conviction does not.

At that point, leaders are no longer operating a predictable system. They are trying to interpret one.

Why This Matters

Forecast instability is often treated as a late-stage symptom. In practice, it is one of the earliest operational signals that the growth model is starting to drift.

Before growth slows, the system loses its ability to produce consistent outcomes. Before results decline, confidence erodes.

That sequence matters because by the time performance clearly drops, the underlying issue has usually been compounding for some time.

The Diagnostic Signal

One of the simplest ways to recognize this phase is straightforward:

You spend more time explaining the forecast than trusting it.

Confidence shifts from system to people, and from structure to judgment. That shift is subtle, but it has meaningful implications for how decisions get made.

If growth is still happening, but becoming harder to explain, you may already be in a recalibration phase.

This is where leadership decisions begin to change.

I owe a great deal of my career to him.

There are people who teach you a skill. There are people who help you get a job, make a connection, or understand a market. And then there are people who quietly alter the lens through which you see your work. Dan was that kind of person for me.

He helped me understand that software was never merely software. It was always a set of choices. Choices about what to make easier. Choices about what to amplify. Choices about who gets included, who gets ignored, and what kinds of human behavior are rewarded.

Those lessons have stayed with me.

Dan was brilliant, but what I admired most was not only his intellect. It was the moral seriousness he brought to his work.

As the internet grew, many people saw only possibility, scale, speed, and opportunity. Dan saw those things too. But he also saw something else taking hold: fanaticism, hatred, racism, and the darker currents of human behavior that could spread faster and farther through digital systems than most of us were prepared to admit.

And instead of looking away, he turned toward it. That takes courage.

It is one thing to build technology when the story is all optimism. It is another thing to stay engaged when the technology you understand so well begins to reveal the shadows of society back to itself. Dan chose to spend his working energy trying to understand those shadows, expose them, minimize their impact, and prevent them from becoming dominant or vibrant forces online.

That is not just technical work. That is moral work. And our society needs more of it.

The tech industry, especially, needs more minds, hearts, and souls like Dan’s. People who can hold complexity without becoming cynical. People who can see clearly without losing compassion. People who understand that intelligence without conscience is not wisdom. People who believe that building the future also means protecting the vulnerable, resisting dehumanization, and asking hard questions about the systems we create.

Dan made people better. He made the work better. He made the room better.

I am also deeply grateful that I was able to reconnect with him last summer in Chapel Hill, North Carolina. During that visit, he gave me ’s AOL.com as a nod to our early connection in the post-Netscape era. At the time, it was a spontaneous and thoughtful gift. Now, reading it cover to cover carries a much deeper and more lasting meaning for me.

Objects become sacred not because they are rare, but because love and memory attach themselves to them. That book will always remind me of Dan: of where we came from, of what we were trying to understand, and of the strange, consequential, beautiful, and sometimes dangerous world that technology helped create.

The news of Dan’s illness is devastating. Creutzfeldt-Jakob Disease is cruel in its speed and severity, and I am grieving alongside so many others who knew him, learned from him, worked with him, and loved him.

But even in grief, gratitude remains. Gratitude for his kindness. Gratitude for his brilliance. Gratitude for his courage. Gratitude for his example. Gratitude for the way his life and work continue to shape people like me.

My prayers and reflections are with Dan, his wife, his family, and everyone who is walking with him through these final days.

Dan, thank you for helping so many of us see more clearly. Thank you for reminding us that technology should never beseparate from humanity. Thank you for showing what it looks like to bring both intellect and conscience to work.

I hope you know how deeply you are loved, how widely your life has mattered, and how much of your influence will keep living on in the people you helped shape.

But inside the system, something starts to shift. Forecasts take longer to explain. Conversion becomes less consistent. Sales cycles stretch in ways that are hard to attribute to a single cause. Different leaders begin offering different explanations for what’s happening, each one plausible on its own.

One founder described it to me this way: “It’s hard to tell if this is normal friction or something more structural.”

That ambiguity is the signal. Not because any one metric has failed, but because the system is no longer producing outcomes that feel coherent or repeatable. Growth starts to feel less like a process the company understands and more like a series of outcomes it is reacting to.

At this point, most leadership teams default to a familiar conclusion. It looks like an execution issue. Pipeline needs to increase, product needs to move faster (e.g. we need more AI), marketing needs to generate more demand. The instinct is to push harder across the board.

And to be fair, that instinct is rational.

So activity increases. More initiatives are launched, more features are shipped, more campaigns are tested. The organization gets busier.

But predictability doesn’t return. The gap between effort and clarity widens.

The System Most Companies Are Actually Running

What’s often missed in this moment is that the problem isn’t just execution. It’s the system that execution is operating within.

Most venture-backed companies at this stage are not running a clearly articulated growth model. Instead, they are operating inside a feature-driven system where progress is measured by what gets built and shipped.

From the outside, this looks like momentum. There is visible activity and forward motion.

Inside the system, however, something more subtle is happening. Work is being optimized locally rather than globally. Each function is improving its part of the business. Product works to increase engagement, marketing focuses on acquisition, sales pushes on pipeline and conversion.

None of these efforts are wrong. In isolation, many of them produce results.

The issue is that they don’t necessarily connect.

Marty Cagan made this distinction years ago between the project model and the product model. In the project model, teams are asked to deliver output. In the product model, they are expected to own outcomes. At the time, this was largely a product conversation. What’s changed is the consequence.

In today’s venture-backed growth-stage companies, operating in a project model doesn’t just slow teams down. It breaks the company’s ability to sustain predictable growth.

The organization gets better at producing activity, but not necessarily better at producing results.

Why This Breaks Growth

A feature-driven system can sustain progress for a period of time. It can generate wins, improve metrics, and create the appearance of momentum.

What it struggles to do is create compounding.

Sustainable growth depends on a system where acquisition, conversion, expansion, and retention reinforce each other over time. When that system is working, the business becomes easier to understand, and outcomes become more predictable.

In a feature-driven system, improvements are typically localized. A new feature may lift conversion. A campaign may increase pipeline. A pricing change may improve margins.

These changes can be meaningful, but they don’t reinforce one another.

So the company experiences growth as a sequence of efforts rather than the output of a coherent system. One quarter, growth is driven by pipeline. The next, by product engagement. The next, by expansion.

Each gain requires fresh effort. Very little carries forward.

Growth doesn’t break because teams stop executing. It breaks because the system they’re executing against no longer compounds.

Over time, this creates a second-order problem. Leaders begin to lose confidence in what is actually driving results. The same data produces different interpretations across the leadership team.

This is the pattern behind what I described in Why Every Fix Creates a New Bottleneck. Pressure rotates because the system itself is no longer stable.

At that point, growth becomes difficult to explain. And when growth cannot be clearly explained, it becomes very difficult to repeat.

So the organization responds the only way it knows how. It increases output.

But more output does not restore coherence. It produces more signals inside a system that no longer connects.

The AI Acceleration Problem

This dynamic is becoming more pronounced as the cost and speed of building products continues to decline. Teams can now ship faster than ever, and the volume of output most organizations can generate is increasing rapidly.

On the surface, this looks like an advantage. In practice, within a feature-driven system, it often amplifies the underlying problem.

More output creates more data, more signals, and more movement across the business. But without a clear model to interpret those signals, it does not create clarity. It creates noise.

As Marty Cagan has noted, many organizations are effectively accelerating a system that was already misaligned. They become more efficient at building, without becoming more effective at understanding what actually drives results.

The result is a company that is moving faster, but understanding less.

The Real Shift

This is why simply improving execution rarely restores predictable growth. The issue isn’t speed. It’s clarity.

Companies that successfully work through this phase step back and make the underlying logic of the business explicit again. They clarify who they win with, why they win, and how growth compounds across the system.

Most importantly, they align on a shared explanation of how growth works.

That coherence changes how decisions get made. It reduces ambiguity, increases confidence, and allows execution to build on itself again.

What Actually Changes

When this shift happens, it rarely looks dramatic from the outside.

What changes is the feel of the company. Decisions become easier to make. Forecasts become easier to defend. Teams begin operating from the same understanding instead of competing interpretations.

Clarity doesn’t create growth. It restores the logic that allows growth to compound.

Over time, that clarity translates into momentum. Not because the company is doing more, but because what it is doing is finally connected.

Most companies never make this shift.

The ones that do regain something more valuable than speed. They regain predictability.

If growth is becoming harder to explain, you’re likely entering a recalibration phase. This is where leadership decisions matter most.

References

• Marty Cagan, Inspired: How to Create Tech Products Customers Love.

  • Silicon Valley Product Group — Product Fail: The Consequences of the Project Model.

  • Silicon Valley Product Group — The Product Operating Model: An Introduction.

• Marty Cagan + Jared Molton — The New Standard for Product Managers. Webinar discussion on product models, AI acceleration, and delivery systems, Apr 23rd, 2026.

• Prior essays: Why Every “Fix” Creates a New Bottleneck, Why Most Senior Executives Misunderstand Product Teams . . . And How to Fix It.

This article builds on those ideas through the lens of growth model breakdown and recovery in venture-backed companies.

Growth hasn’t stopped. The team is still executing. On the surface, things look stable.

But predictability starts to weaken.

Forecasts require more explanation. Different leaders describe different problems. Decisions that once felt straightforward begin to carry more weight.

One founder put it simply:

“It’s hard to tell if this is normal friction or something more structural.”

That ambiguity is the real signal.

What This Usually Looks Like

At this stage, most leadership teams interpret what’s happening in familiar ways.

Sales sees a pipeline problem. Marketing sees an acquisition problem. Product sees a positioning or differentiation problem.

Each explanation is plausible. Each points to a different fix.

So the instinct is to push harder on execution. Increase pipeline, ship more features, expand into adjacent segments.

Activity increases across the system. But predictability does not return.

This is typically the point where venture-backed founders and CEOs realize the model that drove early growth is no longer producing the same results.

This is also why confidence often declines before performance does, a pattern I wrote about in Confidence Declines Before Growth Does.

What’s Actually Happening

By this point, the issue is rarely execution in isolation. The company’s underlying growth model has started to drift.

The original customer converts less reliably. Differentiation becomes harder to explain. Acquisition efficiency becomes inconsistent. Expansion no longer follows a clear pattern.

Individually, these shifts are easy to rationalize. Together, they change how growth works.

What used to feel repeatable now feels situational. What used to compound now requires effort to sustain.

When the system stops compounding, predictability is the first thing to go.

This is rarely visible in a single metric. It shows up as inconsistency across many.

The Leadership Shift

This is where the leadership question changes.

It moves from: What should we do more of?

To something more fundamental: What actually drives growth here now?

In many companies, this question is not asked directly. It shows up in how conversations evolve.

Debates get longer. Alignment takes more effort. Teams begin solving for different versions of the problem.

Execution continues. But conviction does not.

At this point, leaders are no longer optimizing a system. They are trying to interpret one.

When Clarity Returns

In companies that work through this phase, the change is not dramatic from the outside.

There is no single breakthrough. No obvious inflection point.

Instead, a set of quieter shifts begin to happen.

Leadership teams converge on a shared explanation of what is happening and why. Debates narrow, not because tension disappears, but because the underlying model is clearer.

Roadmaps regain credibility. Product and GTM decisions connect more directly to revenue outcomes. Sales teams can explain not just what is coming, but why it matters.

Forecasts simplify. Fewer caveats are needed, because the assumptions behind the numbers are more explicit and better understood.

Execution pressure drops. Not because expectations are lower, but because direction is clearer. Teams spend less time second-guessing and more time following through.

As one operator described it:

“Growth starts to feel repeatable, not heroic.”

What Stops Happening

The change is also visible in what disappears.

Fewer reactive pivots. Less chasing of isolated wins. Fewer debates that end without resolution.

Leaders stop over-explaining every result. Teams stop interpreting the same signals in conflicting ways.

The organization becomes quieter in a specific sense.

Not less active. But less conflicted.

This is the inverse of what I described in How Growth Models Actually Break, where inconsistency spreads across the system before it becomes visible in outcomes.

Why This Matters

Nothing about the market necessarily improves.

What changes is the company’s internal understanding of how growth works.

The ICP is clearer. Differentiation is more specific. The path from acquisition to expansion is more coherent.

Most importantly, these elements connect into a shared growth logic that leadership actually believes.

That coherence changes how decisions get made.

It reduces ambiguity. It increases confidence behind commitments. It allows execution to compound again.

The Real Outcome

When growth models start working again, it doesn’t feel like acceleration at first.

It feels like relief.

Decisions feel grounded instead of provisional. Forecasts feel defensible instead of fragile. Leadership teams feel aligned instead of fragmented.

Clarity doesn’t create growth. It restores the conditions for growth to compound.

Over time, that clarity restores momentum.

Not because execution suddenly improves. But because the system it operates inside makes sense again.

If growth is starting to feel less predictable, you’re likely entering a recalibration phase.

This is where leadership decisions matter most.

Revenue may still be growing. Execution may still be improving. The team may still be expanding.

But something starts to change.

Growth becomes harder to explain. Forecasts require more judgment. Different teams describe different problems. Decisions that once felt obvious begin to feel heavier.

Nothing is clearly broken. Yet predictability begins to weaken.

This is usually the moment when a growth model starts to erode.

Why This Is Hard to See

Growth models rarely break in one place. They weaken across multiple dimensions at the same time.

The original Ideal Customer Profile (ICP) converts less reliably. Differentiation becomes harder to articulate. Acquisition efficiency declines. Expansion becomes inconsistent across customers.

Individually, none of these signals look like failure. Each can be explained away as execution, market noise, or temporary friction. But together, they reduce coherence.

When coherence weakens, growth becomes harder to repeat. When growth becomes harder to repeat, predictability declines.

This is often why leaders sense the shift before the data confirms it, a pattern I wrote about last week in Confidence Declines Before Growth Does.

Growth models don’t break all at once. They lose coherence.

Where Growth Models Start to Drift

In most companies, this erosion shows up in four areas.

1. ICP Drift

The original customer segment begins converting less reliably. Deals take longer. Sales cycles vary more than before. Pipeline coverage increases, but forecast confidence declines.

To compensate, sales expands into adjacent segments. Marketing broadens messaging. Product adds features to support edge use cases.

Short-term pipeline improves.
Long-term clarity deteriorates.

What looks like growth often masks weakening fit in the original segment.

2. Differentiation Weakens

As companies grow, competitors catch up and buyer expectations evolve. What once felt distinctive becomes increasingly commonplace.

Leaders often respond with incremental improvements: new features, repositioning, messaging changes. These actions can help temporarily, but they rarely restore the original clarity.

Over time, sales conversations require more effort. Win reasons become less consistent. Deals depend more on relationships and less on differentiation.

The company is still winning. But the reason for winning becomes less repeatable.

3. Acquisition Efficiency Declines

Channels that once worked predictably begin to vary. Customer acquisition cost (CAC) increases. Conversion becomes inconsistent. Marketing performance fluctuates more than expected.

This is usually interpreted as an execution problem. Teams adjust campaigns, change messaging, or add new channels.

Some improvements follow. But the underlying variability remains.

When acquisition efficiency declines across multiple channels, it often signals structural change, not execution gaps.

4. Expansion Becomes Inconsistent

In early growth, expansion often feels predictable. Customers adopt more use cases. Revenue compounds naturally.

As the growth model weakens, expansion becomes uneven. Some customers grow rapidly, while others stall. Usage increases without consistent revenue expansion. Customer behavior becomes harder to recognize or segment cleanly.

Nothing is clearly wrong. But the expansion engine loses reliability.

When multiple parts of the system weaken at once, growth becomes harder to repeat.

Why Leaders Miss This

Because each function sees a different symptom.

• Sales sees pipeline variability.

• Marketing sees acquisition challenges.

• Product sees differentiation pressure.

• Finance sees forecast instability.

Each interpretation is plausible. Individually, none capture the full picture.

So leaders fix locally. They push for more pipeline, more features, more campaigns, and more initiatives. Activity increases across the system, but clarity does not.

These execution improvements can create movement without restoring predictability, something I explored in Why Execution Improvements Often Make Growth Less Predictable.

Over time, the organization becomes busier, yet growth becomes harder to explain.

This is often where companies mistake motion for momentum.

The Quiet Breakdown

Growth models don’t always fail dramatically. They can erode quietly.

ICP clarity weakens. Differentiation softens. Acquisition becomes inconsistent. Expansion loses predictability.

None of these feel like failure. But together, they change how growth works.

By the time growth slows, the model has often been weakening for weeks or even months.

This is why leaders often sense the shift before they can fully explain it. Confidence declines. Forecasts require more explanation. Decisions become harder.

If growth still looks healthy but feels less predictable, the growth model itself may be starting to weaken.

This is usually when the next leadership decisions matter most.

Growth is still happening. Execution looks strong. The team is larger than it was six months ago.

But confidence starts to decline.

Forecasts require more explanation. Sales feedback becomes inconsistent. Roadmaps feel less connected to revenue outcomes. Board conversations shift from momentum to scrutiny.

Nothing is clearly broken. But everything becomes harder to explain.

This is often when founder/CEOs feel the problem before the metrics show it.

What Leaders Notice First

At this stage, performance rarely deteriorates immediately. Instead, predictability starts to weaken.

Sales results vary more than expected. One segment performs well while another stalls. Marketing campaigns generate leads, but conversion becomes less consistent. Product improvements increase engagement, but expansion feels uneven.

Individually, none of these signals look alarming. Together, they create ambiguity.

One founder described this moment simply:

“Nothing is obviously wrong… but every decision feels harder.”

That’s usually the first sign that something deeper is changing.

Confidence usually declines before growth does.

Why This Happens

Metrics lag structural change.

When the growth model begins to weaken, the first impact is not performance, but predictability. Outcomes become harder to repeat. Explanations become more complex. Leaders rely more on judgment and less on pattern recognition.

This is subtle. The company is still growing. Teams are still executing. The organization often appears healthier than it did six months earlier.

But the connection between effort and outcome becomes less reliable.

This is why founder/CEOs often sense the shift before the data confirms it. They’re closer to the decision pressure. They feel the friction before it appears in metrics.

Why Teams Often Miss It

Most teams are trained to look for performance problems: missed targets, slowing growth, declining conversion. But growth model pressure rarely starts there.

Instead, it shows up as ambiguity:

• Forecasts require more narrative.

• Different teams describe different problems.

• Priorities become harder to defend.

• Confidence in plans starts to weaken.

Because nothing is clearly broken, the instinct is to push harder on execution. More pipeline. More features. More campaigns.

But as discussed last week, stronger execution doesn’t always restore predictability. Sometimes it increases volatility.

This is where the confusion deepens. Execution improves, but clarity does not.

Why This Matters

This is the stage where leadership decisions become riskier.

When confidence declines, companies often respond by reorganizing, expanding segments, accelerating hiring, or shifting strategy. These decisions can be rational, but they become dangerous when the underlying issue is not yet understood.

Because performance hasn’t clearly deteriorated, leaders move forward without a shared explanation for what’s changing. Execution continues, but alignment weakens.

Over time, this increases the risk of committing to the wrong path.

Founders often sense growth model pressure before they can prove it.

Growth models rarely fail dramatically. They weaken quietly: confidence declines, predictability erodes, and decisions become harder.

By the time performance clearly deteriorates, the shift has usually been underway for months.

If growth still looks healthy but feels harder to explain, you may be entering a recalibration phase.

This is often where leadership decisions matter most.

And often, execution does improve. Pipeline expands. Engagement increases. Campaigns generate more leads. Activity rises across the organization.

But something else begins to change at the same time. Outcomes become less consistent. Results improve in some areas while weakening in others. Forecasts require more explanation. Confidence separates from performance.

This is where many teams become confused. Execution is improving, yet the system becomes harder to understand.

When Better Execution Increases Variability

Leaders generally expect stronger execution to produce clearer outcomes. When the growth model is stable, that expectation usually holds. Better inputs lead to more reliable results, and improvements compound across functions.

When the growth model starts to weaken, the opposite can occur. Execution amplifies whatever dynamics already exist in the system. If those dynamics are becoming unstable, stronger execution increases variability rather than restoring predictability.

This shows up in familiar ways . . . .

Sales increases outbound and pipeline improves, but deal behavior becomes less consistent. Some segments convert well, others stall unexpectedly. Forecasts require more judgment in order to engender confidence.

Marketing launches new campaigns and lead volume rises, but deal quality becomes uneven. Messaging resonates strongly with certain audiences and weakly with others, making performance harder to interpret.

Product ships new capabilities and engagement improves, but expansion remains inconsistent across customers. Usage may increase but without a clear, repeatable path to revenue.

Each function improves execution. The system, however, becomes harder to explain.

Execution amplifies the system it operates inside.
If the system is unstable, better execution increases volatility.

Why This Is Easy to Misinterpret

Execution improvements create visible progress. Local metrics improve. Activity increases. Leaders see signs that the organization is moving in the right direction. The natural response is to double down.

But as variability increases, predictability quietly deteriorates. Forecasts rely more on judgment. Results become harder to repeat. Local wins appear, but they do not translate or expand universally.

One founder described this moment simply:

“We’re hitting more numbers . . . but I trust the forecast less.”

This creates a subtle but important shift. The organization is working harder and often performing better, yet confidence in the system begins to weaken. Leaders sense the inconsistency, even if they cannot yet explain it.

Because execution improvements produce real gains, this phase rarely triggers immediate concern. Instead, teams expand initiatives, invest more aggressively, and pursue additional opportunities. Each decision makes sense in isolation. Collectively, they increase motion without restoring coherence.

When Growth Becomes Inconsistent

Growth rarely breaks suddenly. It becomes inconsistent first.

One quarter outperforms expectations. The next becomes harder to explain. Certain segments accelerate while others weaken. New initiatives show promise but fail to compound. No single issue explains the shift, but the pattern emerges across the system.

This is often the moment when leadership teams over-index on local wins. A successful campaign becomes a strategic pivot. A strong segment becomes the new focus. A handful of large deals become evidence of sustained momentum.

The underlying volatility, however, remains.

When execution improves but predictability declines, it is often a sign that the growth model is under pressure. The system is still producing results, but the logic behind those results is becoming less reliable.

Why This Matters

This is the stage where companies make expensive decisions. Execution improvements create enough confidence to justify larger bets: hiring accelerates, new segments are pursued, pricing evolves, product investments expand.

These decisions are rational when the growth model is stable. They become risky when the system is already becoming less predictable.

Execution improves. Confidence in the model weakens. Leaders often sense this shift before they can fully articulate it.

That is usually the moment when something deeper is changing, before growth slows and before the metrics make it obvious.