A collective feeling of fear and pessimism

When you ask people about how they feel about the future of their country, we Europeans score very negative. Only 30% of the Dutch think the future will be better. Asian countries are much more optimistic: of the Chinese people, 88% think the future of their nation is bright. Of the Indonesians, 86%, and India 73%1. What’s rather surprising about these numbers is that, while they are pessimistic about the future of their country, the Dutch are spectacularly happy with their personal life.

This pessimism therefore seems to be a collective experience. Systems thinkers call this an ‘emergent property’ of a system. The more complex systems become, the more they produce properties that weren’t there before. Nobody designed this pessimism, and no single actor is responsible for it. The system produces it.

So why is our system producing this “emerging property” of pessimism?

A system that optimizes for overprotection

The big challenge with Western societies is that it’s producing more and more a shared belief that things are gradually going downhill. A good explanation for this shared belief is that we live in a system that optimizes for overprotection, and it’s precisely this system property that is hurting us more and more.

In his fantastic book “Breakneck: China’s Quest to Engineer the Future”, Dan Wang argues that the big difference between China and the West is not capitalism versus communism. A much better distinction to look at the difference between China and the West is that China is an engineering state and the West has become a lawyerly state. Whereas China uses an engineering mindset to invest in the future they envision, we are using the laws and the judiciary system to prevent that future from happening.

To give you an example: China has built the Beijing–Shanghai railroad in about three years, with a cost of $36 billion. California is trying to do the same thing, but voters approved it in 2008, and eighteen years later they still haven’t got one, while the total project cost is estimated at $128 billion.

The reason is that “lawyerly states” - states ruled by people who control the rules and laws - have been designed around a shared belief that everyone’s interest should be protected as much as possible. We in the West have a deep belief that our prosperity and peace is a precious gift from history that should be protected at all cost. Given the brutality with which we bulldozed through history, this desire makes perfect sense.

When a system optimizes too much for one goal

But here’s where things become problematic: every system that organizes too long for one dominant goal will eventually suffocate other system goals. In a healthy system several system goals are in balance: worker rights balance with investor and employer rights, and societal rights. And by balancing these rights we’re producing a sustainable thriving economy. But a system that optimizes for one goal only – protecting the status quo – will eventually become extremely vulnerable.

Think of a family as a metaphor: the more the parents optimize the education of their offspring for protecting their children from harm, the more this parental strategy will hurt their children’s capacity for becoming independent, and resilient. In the end, the child will have two options. This system will make them weak, or they will rebel and break free from the tyranny of the parents.

This dysfunctional overprotecting family is a pretty useful analogy for why western civilizations are not able to deal with the accelerating pace of innovation from the East.

The problem: protecting positions instead of outcomes

Before I illustrate this, one thing needs to be clear: I’m not arguing against protection. The distinction is about what you protect. A protection stays healthy as long as it protects an outcome: clean air, safe labour, decent housing, a nature reserve that survives, but it turns pathological the moment it starts protecting a mechanism or a position: this engine, this procedure, this sector, this job.

Let me illustrate this with two spectacular examples.

The self sabotage of the German car industry

Link: https://www.politico.eu/article/germany-economy-car-industry-automotive-crisis-tariffs-china-energy-costs/

The German car industry is a textbook case of a system that sabotaged itself. The whole industry was trying to protect the interests of the combustion engine, while the industry is being disrupted by the EV. The reason was simple: the combustion engine employed all the suppliers of the car industry (e.g. Bosch, ZF, Mahle, Continental), it represents the strongest unions (IG Metall), and it has enormous political stakes (Lower Saxony holds a 20% voting stake in VW).

In other words: Every actor had a reason to defend the engine, and none had a reason to defend the future of the industry as such. So it shouldn’t come as a surprise to see what the energy went into: Dieselgate (2015) was effectively an engineering effort to keep diesel alive against emission rules; in 2023 Germany forced Brussels to reopen the already agreed 2035 combustion ban to add an e-fuel loophole; BMW’s CEO spent years publicly lobbying against the deadline. Meanwhile China used subsidies, a domestic battery supply chain and brutal internal competition to build the thing German lobbying was trying to postpone.

In 2024, VW prepared to close German plants for the first time in its history, the German brands’ share of the Chinese market collapsed, and BYD started exporting into Europe. The forces in the German car industry that resisted change were so powerful that it ended up hurting the industry more than it should have.

The Dutch nitrogen crisis

In May 2019, the highest administrative court in the Netherlands decided that the country wasn’t following its own rules. For years the Dutch had used a clever permitting system: because farming, traffic and industry all emit nitrogen, which damages protected nature reserves, the government allowed new projects to go ahead on the promise that nature would be restored later. However, the court looked at this construction and concluded that a promise is not a protection. Therefore it struck the system down.

The country is now deep into self-inflicted problems that are growing worse and worse: Housing developments, road works, farm expansions, even wind farms lost their legal basis overnight, because in a system where every interest is protected by law, there is no authority that can decide to protect one interest slightly less. Seven years later the Netherlands has a housing shortage measured in hundreds of thousands of homes, an electricity grid so congested that new businesses are put on a waiting list for a connection, a farming sector in open revolt, and a political landscape reshaped by that revolt.

The drama is the following: nobody chose this consequence. A protective mechanism, working exactly as designed, did it on its own, and precisely because it worked as designed, nobody could switch it off. One simple intervention of the system, that was executing the will and the beliefs of the system is completely suffocating the capacity of the system for problem-solving.

Both stories are the same story. Germany defended a component instead of an industry, the Netherlands defended a procedure instead of the outcome that procedure was invented for. In both cases the protection was legitimate, and defended by people acting in good faith. That’s what makes this a system problem instead of a cultural or morality problem.

Japan is a cautionary tale about why it’s not about culture

If this were a cultural difference, it would be stable. But the problem is: it isn’t. In 1985 Japan was the optimistic engineering state everybody was afraid of. Then the bubble burst in 1990, and Japan started optimizing for stability. It kept zombie firms alive by their banks, it offered lifetime employment, and prioritized consensus decision making that gave every stakeholder a veto. Thirty years later Japan scores as low on future optimism as Europe does, and its young people feel exactly what young Europeans feel: that the system exists to protect people older than they are.

It’s the system, stupid

To go back to the theme of this story. Why are we so much more pessimistic than eastern cultures? Because we perfectly understand that the system we inhabit is making things harder and harder for solving problems. We desperately need more houses, but for some strange reason, we don’t seem to be able to get it done. Whatever you think immigration policy should be, faster or slower, tighter or looser, we don’t seem able to steer it in any direction at all. Many companies and project developers give up before they start, because the permits, protocols, audits and sustainability requirements have expanded to the degree that building something profitable has become simply impossible.

Notice that the complaint is never about the goals. Housing, clean water, sustainable construction, a controlled border: nobody is against any of it. The complaint is about a system that has lost the ability to choose.

None of this makes the engineering state superior, by the way. China builds cities nobody lives in, and is now dumping its overcapacity on our markets. But our system produces other problems. We have a system that optimizes for the status quo.

Our biggest problem is that it’s very hard to change the nature of this system. As Europe becomes an elderly care continent very rapidly, this demographic change will produce more and more conservative politicians that will probably make things worse, as this is where the electoral power lies.

How to break this pattern?

We need a bit of a wakeup call to shift how we think about ourselves. The Dutch journalist Caroline de Gruyter wrote an excellent piece in NRC, where she reframed normality2. We need to start to realize that this era of turbulence is much more normal than the era of peace and prosperity that we enjoyed in the buildup years after the war, under the protective umbrella of the US, and the relative powerlessness of the East.

That era is over, but our beliefs about ourselves haven’t caught up yet with this reality. Once we accept this reality, we can start redesigning our policies and rules so that investments in future growth can overrule policies that aim at protecting the status quo of the interests of the present. This will of course lead to anger and resentment, but as a society we need to optimize for our collective future prosperity, and not be hijacked by the interests of the past.

And this can be done. In March 2025, Germany changed its own constitution to break the “Schuldenbremse”, the debt brake it had installed to protect future generations, and freed up hundreds of billions for defence and infrastructure. A rule that had been untouchable for fifteen years fell within weeks, because the cost of not moving had finally become more visible than the cost of moving.

That’s the good news and the warning in one. Overprotected systems do change, but they wait until a threat makes the change inevitable. The only question that matters is whether we can make that cost visible to ourselves, before something else does it on our behalf.

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Tom De Bruyne Founder SUE Behavioural Design Academy and SUE & The Alchemists, consultancy for public affairs, and author of Gamechangers: How small interventions unleash big change (currently in Dutch only).
Follow me on Linkedin: https://linkedin.com/in/tomdebruyne

Britain’s self-sabotage

“The British broke Britain.” That is the closing verdict of Idrees Kahloon’s Atlantic essay on how a country that in 2007 had higher median household income than Germany now produces less per head than Mississippi, America’s poorest state. Britain went through all the same crisises as the rest of the European continent, however Britain’s distinguishing feature, Kahloon writes, was “its self-sabotaging responses” to problems that everyone faced.

Think for a moment how strange that is. No one in Whitehall wakes up wanting to make the country poorer. No planning officer, no Treasury official, no minister, no council leader is trying to kill the goose that lays the golden eggs. And yet, collectively, that is precisely what they have done for two decades. This is the puzzle I keep returning to: how do institutions staffed by intelligent, well-meaning people, each doing their job competently, produce a whole that steadily destroys itself?

The answer is surprisingly simple, and it all comes down to systems thinking: Systems sometimes eat themselves when they are captured by a goal that sabotages its capacity for problem solving.

The mechanism: local rationality, collective ruin

In my book “Gamechangers: how small interventions unleash big changes” (published in Dutch, 2026), I call this pattern “the spiral of harm”. Every actor behaves rationally inside their own frame. Each optimises the metric they are actually rewarded for. And the sum of all that individual rationality is a system that impoverishes everyone inside it. The system becomes the only winner, while every player loses.

Britain is a case study of “the spiral of harm” running at the scale of a nation-state.

1. On the level of fiscal policy: After the financial crisis of 2008, the Treasury decided to choose for policies that would signal fiscal credibility to financial markets. Cutting harder than anyone else would signal prudence, reassure markets, distinguish Britain from “those Greek irresponsible spenders.” The result was austerity that completely cannibalised the National Health Service capital budget until mental-health patients were housed in “Victoria-era cells infested with vermin,” cut council grants by 40 percent, and pushed the share of children in long-term poverty from 14 to 23 percent. This in turn resulted in doctors reporting the return of rickets and scurvy. A rational bet on confidence produced a decade of decay.

2. On the level of planning. Every reviewer, every consultee, every statutory body optimised for one thing: never being the one who signed off on a risk. The aggregate is a monstrous bureaucracy that would even make the former Soviet Union blush. Hinkley Point nuclear plant whose environmental assessment ran to 31,401 pages and which now includes a £700 million “fish disco” to keep marine life away from its pipes; a high-speed rail line that became the most expensive on earth before its most important sections were cancelled; a £216 million bat tunnel; £179 million and 32 years spent planning a Stonehenge tunnel that was then scrapped. Kahloon’s line is exact: Parliament “has chosen instead to smother the state with veto points, proceduralism, and endless reviews.” Everyone protecting against a small local risk built a state that cannot build.

3. On the level of politics. Kahloon makes a stark observation. Politicians have learned to optimize for the press release, because there they can signal a willingness for taking action, whereas the dramatic consequences only show up after they’re gone. Boris Johnson promised “Levelling Up” for the “left-behind towns,” then produced almost no specifics until months before he resigned. Six prime ministers since 2010, each abruptly terminating the last one’s policies. This is the pathology Dominic Cummings1, of all people, named most sharply in his famous 2019 essay on high-performance government: “for people like Cameron and Blair the announcement IS the only reality and ‘management’ is a dirty word for junior people.” The press release is the deliverable. What happens afterwards belongs to someone else’s brief. Parliament “has chosen instead to smother the state with veto points, proceduralism, and endless reviews.” Everyone protecting against a small local risk built a state that cannot build.

None of these people are villains. That is the whole point. Each is a competent professional optimising the thing their institution measures and rewards. And every one of those local optimisations quietly withdraws energy from the shared thing — the country’s capacity to grow, build, and improve — that no single brief is responsible for protecting.

Why the system fights to stay this way

Here is the part that should worry anyone hoping for a quick turnaround. A Spiral of Harm is not a bug the system is trying to fix, but much rather a stable equilibrium the system actively defends.

Cummings puts it with unusual bluntness: critical institutions “are programmed to fight to stay dysfunctional, they fight to stay closed and avoid learning about high performance, they fight to exclude the most able people.” You do not have to share his politics to see that he is describing a reinforcing feedback loop. When an institution rewards process, prestige, and signalling rather than, in his phrase, “error-correction and predictive accuracy,” then the people who rise are the ones best at process, prestige, and signalling. They in turn reward the same traits in others. The system selects for exactly the behaviour that is hollowing it out. Every player is a rational winner of a game that the country is losing.

This is why the standard responses miss. Farage’s Reform Party, currently out-polling both major parties for the first time in forty years, offers an answer to “who broke Britain”: feckless elites, obstructive civil servants, unwanted immigrants. His remedy is shrinking the state and shock therapy. But shrinking a system that is optimising for the wrong goal does not change what it optimises for. You get a smaller machine pointed in the same direction. As Kahloon dryly notes, Brexit already proved that “a sweeping assertion of sovereignty is by itself insufficient to ensure growth, and, indeed, can be self-harming.” The blame paradigm is not a new goal. It is the old spiral of harm wearing a populist coat.

The leverage point everyone walks past

Donella Meadows, the systems thinker that inspired me to write Gamechangers, ranked the places you can intervene in a system from weakest to strongest. At the bottom sit the parameters: tax rates, budgets, speed limits, headcounts. The knobs on the dashboard. “We spend 95 percent of our energy on this kind of intervention,” she observed, “while it accounts for maybe 5 percent of real change.”

Now look at British political debate. Austerity was a parameter. Farage’s spending cuts are parameters. Even the fights over migration numbers are, in Meadows’ sense, parameters. Everyone is crowded around the weakest lever in the system, turning it as hard as they can, wondering why nothing moves. It is like sitting in a car flooring the accelerator, never noticing the handbrake is still on.

Near the top of Meadows’ list sits something almost nobody in the debate is touching: the goal of the system. Systems goals are not what people say they want, but what the system is structured to produce. And the goal a system optimises for is encoded in what it rewards. Today the British state rewards the announcement over the delivery, the avoided risk over the built thing, the defended process over the corrected error. Change that reward structure and you change what all those rational professionals rationally do. Leave it untouched and no amount of parameter-twisting will help.

This is the real convergence between the despair in the Atlantic’s essay and Cummings’ provocation. Both are pointing, from opposite ends of the political spectrum, at the same lever: the system needs to start optimising for a different thing. Cummings’ concrete image for this is the “seeing room”: a decision space engineered so that people can actually see the state of reality they are acting on, rather than argue over stories and authority. He notes, damningly, that the room in which British governments make decisions affecting millions “is essentially indistinguishable from the room in which Prime Minister Asquith sat in July 1914.” When the goal is signalling, you do not need to see reality. When the goal is error-correction, you cannot function without seeing it.

There is even a working proof inside Britain’s own borders. Manchester grew at double the national rate after 2011, for one structural reason: devolution changed what the system optimised for. Given control over its own transport and taxes and a fund to grow with, the city stopped waiting for London’s announcements and started building feedback loops it could own. “You can’t order growth from the top down,” its mayor told Kahloon. This is a beautiful description of a goal being moved from the wrong level of the system to the right one, and behaviour changing in response.

What this means for anyone running a system

You do not need to be governing Britain to recognise the spiral of harm. Any organisation where each department hits its targets while the whole loses ground is running the same script. Sales optimises the quarter, compliance optimises against risk, every leader optimises their own brief, and the shared capacity that no one is accountable for quietly bleeds out. Everyone is competent. Everyone is rational. Everyone is contributing to the decline.

The instinct, always, is to push harder on the parameters: tighter targets, bigger cuts, louder announcements. It rarely works, because the parameters were never the problem. Interventions in complex systems are more like acupuncture than force: one needle in exactly the right place. And the right place is almost never where everyone is already pushing.

Britain will not be changed by choosing the self-harming option slightly less often. It will be changed, if at all, the day it stops rewarding the announcement and starts rewarding the built thing. Everything else is just another turn of the knob.

I want to step outside that frame.

Raising a birth rate is a system design problem, and system design problems have rules. You do not optimise a number until you have asked why the number matters.

And systems push back against interventions in predictable ways

The conservative proposal violates both rules. So does almost every progressive counter-proposal.

1. Why do we want a higher birth rate at all?

This sounds rhetorical, but it’s not. It is the most expensive question in the debate, because nobody asks it explicitly.

Strip the moral language away and the concern is mathematical. A society needs enough productive capacity to sustain its standard of living: working-age population times output per person. When that shrinks faster than productivity rises, pensions, healthcare and the intergenerational contract erode.

Children are a means in this equation, not an end. The moment you treat them as the goal you have lost the design problem. You are optimising a proxy, and proxies rot when they become targets. Hospitals optimise for length of stay instead of healing. Countries that optimise for birth rates instead of prosperity produce neither.

2. What South Korea actually proved

You do not need to speculate about what happens when a country builds its policy apparatus around birth rate as the goal. Korea has run the experiment for two decades. The result is the most expensive failure in the history of demographic policy.

Since 2006, the government has spent roughly 270 billion dollars on pronatalist programmes. The total fertility rate in 2006 was 1.13. In 2026 it is 0.72 nationally and 0.55 in Seoul. Every won spent moved the indicator the wrong way.

The mechanism, which I covered in my book Gamechangers (Dutch), is the part that matters. Korea is a system killed by its own success formula. The cultural engine that made the country rich, built on relentless competition and status through credentials, became past a tipping point the engine that made family formation irrational. Each child requires private tutoring from kindergarten to university entrance, because the alternative is permanent social status loss.

The system produces a fatal reinforcing feedback loop: The cost per child climbs, families shrink, investment per child rises, the race feeds itself. Marriage becomes an examination scored on income, real estate and family background. Most people decide this is a shitty deal, and opt-out.

This is systemic lock-in: behaviour individually rational at every node and collectively catastrophic. Cash transfers cannot solve it because the binding constraint is not money but a paradigm. In my book I talk about Systems Thinker Donella Meadows who ranked intervention points from weakest to strongest. Parameters sit at the bottom. Paradigms at the top. Korea spent 270 billion dollars at the bottom of the ladder, hoping it would reach the top.

Three quarters of that spending went to families who would have had children anyway.

The leverage was almost zero.

No country, anywhere, has reversed below-replacement fertility through policy. Not one.

3. What the Heritage proposal will actually produce

Run the conservative package through this lens and the prediction is uncomfortable.

Baby bonuses and married-couple savings accounts are parameter interventions. They mostly subsidise children that would have been already going to be born anway. The marginal effect on fertility is real but economically trivial relative to the spend.

The cultural side, bootcamps, medals, the penalisation of non-traditional households, produces something worse. When the state signals to a group that their life arrangement is illegitimate, the group does not assimilate.

It reacts.

Educated women, who disproportionately drive both productivity and birth-rate decisions, read the signal accurately, and do the smart thing: They exit the system. This is the famous cobra effect in action: the intervention designed to expand the traditional family will shrink it.

4. What might move the rate, and what definitely will not

The interventions with the cleanest evidence are the infrastructural ones the Nordics pioneered: subsidised childcare, paid parental leave including fathers, affordable family-sized housing. Each can lift a rate by 0.2 to 0.3 of a child per woman. None gets a country back to replacement.

Everything the Heritage package leans on, cash bonuses at the margin, restricting choice, symbolic moral campaigns, has near-zero or negative net effects over a generation.

5. If the rate will not move, what is the system goal anyway?

Here the analysis flips from depressing to interesting.

The real goal was solvent prosperity, not births. If the birth rate is unreversible, babies are no longer the obvious mechanism. Three other levers remain.

The first is productivity per worker. The arrival of useful AI at the same historical moment as demographic decline is not planned but consequential. A two-percent annual productivity gain over twenty years offsets most of the labour shortfall. The open question is whether AI delivers real productivity rather than the simulated kind it currently provides in most knowledge work.

The second is the structure of working life. Most developed countries still run a working-age definition designed when life expectancy was sixty-five. Healthy life expectancy is now twenty years longer. Redraw the line and the dependency problem shrinks.

The third is migration: the most effective, most immediate, most politically gated lever. The trap inside the conservative position sits here. If migration is forbidden and the birth rate cannot be moved, the only remaining path is AI-driven productivity, which restructures labour markets and identity scripts in ways those same coalitions reject. The position contains no internally consistent exit.

The honest framing is not “how do we raise the birth rate” but “given that prosperity is the goal and the birth-rate lever is mostly broken, which of the three remaining levers do we choose?” The current debate spares both sides this discomfort by arguing about a number neither side can move.

The frame that explains everything

A useful test for any policy claiming to address a demographic, climate or economic crisis: ask what the headline number is a proxy for, and whether the intervention works on the proxy or on the underlying mechanism. Most debates get stuck on the proxy. The proxy feels like the goal because it is what gets measured. The mechanism is invisible because it sits at the level of paradigms, beliefs and life scripts.

Babies are not the goal. Solvent prosperity is the goal. The traditional family was, for a long historical window, an effective package for producing both. That window is closing for reasons policy did not cause and cannot reverse. Pretending otherwise is expensive. South Korea has the receipts.

The interesting question for the next decade is not who wins the birth-rate argument. It is which countries notice early that the argument is a distraction, and design for prosperity directly.

Tom De Bruyne
Founder SUE Behavioural Design Academy
Founder SATA, experts in designing public support
Follow me on Linkedin.
Buy my book Gamechangers (2026): Hoe kleine ingrepen grote veranderingen ontketenen (Dutch), or listen to it on Storytel.
Book me as a speaker via Athenas (International), The Next Speaker (Tech), or Sprekerspool (Belgium)

The consulting problem: talent doesn’t scale.

I’ve been leading creative companies for over 18 years. First as a managing director of the ad agency Boondoggle Amsterdam, later as a co-founder of SUE, SATA and Crowdrock. The business model of every consultancy or agency depends on one thing and one thing only: You can only be successful if you develop a market reputation for delivering distinctive, high-quality work in your domain. That might be a marketing strategy that drives predictable revenue growth, a public support strategy that builds or defends a company’s reputation, or a change strategy that aligns people around a shared mission.

One of the hardest scaling challenges is maintaining consistent quality as the company grows. The faster you scale, the more you’re forced to hire inexperienced people to get the work done. That inevitably puts pressure on your senior staff. They’re accountable for quality across more projects, while also mentoring juniors into real craftsmanship. Not every senior is a natural mentor. Not every junior is a fast learner.

Talent and experience don’t scale. One excellent senior will outperform ten juniors on complex problems, because they know how to move from ambiguity to insight, from insight to strategy, and from strategy to stakeholder buy-in.

You can’t “outspend” this. Seniors have finite time.

Historically, you had two options: hire more seniors (risky, because fit isn’t guaranteed) or invest in training programs (expensive and slow, often taking years before clients trust juniors with high-stakes work).

Then AI arrived, and suddenly, one senior could do the work of ten. The economics of consulting just flipped.

From Mediocristan to Extremistan

There’s a lot of debate currently on whether AI delivers on its promise for transforming the workplace. A study quoted frequently last year was MIT’s Project Nanda. The study found that 95% of all pilots delivered no measurable P&L impact. That’s a spectacularly underwhelming number.

My take: This number is completely irrelevant.

In his book “The Black Swan,” Nassim Taleb introduces the concept of Mediocristan versus Extremistan. Mediocristan is a metaphor for domains where adding one extreme outlier to a population wouldn’t dramatically change the average. If you added the heaviest person who ever lived to a random group of 1,000 people, the group’s average weight wouldn’t be affected that much.

However, if you added Elon Musk to a group of 1,000 people and measured their average income, it would dramatically transform the number. That’s Extremistan.

AI is taking consultancies from Mediocristan to Extremistan.

The MIT study assumes AI impact distributes evenly. That’s classic Mediocristan thinking. But in Extremistan, the 5% that works doesn’t just succeed marginally better. It creates exponential advantages that make old models obsolete.

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AI Gives Talented, Entrepreneurial teams Superpowers

One of Steve Jobs’s most famous quotes was “Real Artists Ship.” He was obsessed with turning technology into desirable products and figuring out ways to build scalable production capacity for these products.

There are now multiple labs - Safe Superintelligence, Thinking Machines Lab, OG Labs, and many more - with sometimes no more than twenty people that got more than a billion in funding and $3-35 billion in valuation. The reason is simple: A small AI-empowered team can break through all the barriers for turning opportunities and ideas into scalable products. You now only need a small dedicated team.

Senior people carry mental models: ways of spotting patterns, testing hypotheses, stress-testing ideas, and making trade-offs under constraints. They know how to dissect a messy problem into insights, opportunities, hypotheses, strategy, concepts, tactics. Plus they know how to persuade, reassure, and align stakeholders around a winning direction. They also have a refined sense of what differentiates quality from mediocrity.

LLMs amplify all these capabilities, fast.

In practice, I can:

• Generate synthetic personas based on client data and use them to test and co-create ideas

• Build a project context that “loads” the AI with the relevant constraints, history, stakeholders, and previous decisions

• Prototype strategy quickly using image/video models, thereby reducing weeks of iteration to hours

• Create custom GPTs for training (e.g., leadership simulations) that push back, challenge, and coach in real time

• Let Claude Code build real-time dashboards that track the company’s health and signal potential problems on the spot

There are many ways LLMs give seniors superpowers across the workflow. But the biggest shift is this: it massively increases capacity to execute and adapt strategy.

We used to sell research and advice. A lot of that will commoditize. What won’t commoditize is making the work land. Creating wins, not just documents.

Comedian Jimmy Carr had a sharp line when someone asked if AI can replace him: “No. My job is not jokes, it’s joy.”

That’s a useful analogy for consultancies and agencies. The job is not reports. It’s outcomes.

It’s the 1999 moment, all over again

There’s a LinkedIn post by Craig Taggart that was shared with me multiple times during the last couple of weeks. It’s about how McKinsey lost 3 Fortune 500 clients to small 12-person boutiques1.

The reason: All the stuff that McKinsey used to charge millions for, only to get an army of junior consultants to do the groundwork, can now be done by a couple of AI-savvy consultants using smart agents for analyzing all the data that matters.

The real value shifts from being a consultant towards being a trusted advisor that helps you prototype, test, execute, and adapt strategy until you achieve your objective. In this new Extremistan world, extreme AI-savviness combined with extreme advisory skills are the new gold.

As an entrepreneur, I feel it’s so much fun to be part of this new era. I’m old enough to remember the excitement in 1999 when the internet had its breakthrough moment. There was this 5-8 year period where the incumbents where trying to figure out how to compete in this new world, but it were the innovators and early adopters who created this new world. This is 1999 all over again.

Related reading: If you want to dive deeper into how organisations can successfully navigate AI adoption, check out this article on AI adoption in organisations. And if you’re curious about how behavioural science can amplify AI’s potential, read more about combining AI and behavioural insights.

Tom De Bruyne

I am a Belgian, living in Amsterdam. I’m founding partner at SUE Behavioural Design Academy and SUE & The Alchemists boardroom consultancy. If you want to follow me: My main publication channel is Linkedin. I published the book “Gamechangers: How small interventions, unlock massive change in 2025 (in Dutch). You can hear me in podcasts (currently all in Dutch) Gamechangers, Wat Gebeurt er Eigenlijk in je hoofd, and Zo Gaat Dat Dus (upcoming). You can also just drop me an e-mail.

Vorige week presenteerde hij zijn antwoorden. Wat meteen opviel is dat het niet zomaar een boodschappenlijstje is van beleidsvoorstellen. Integendeel, Wennink presenteerde veel meer de blauwdruk voor een fundamentele systeemverandering. Hij wil dat Nederland zichzelf rond een ander systeemdoel gaat organiseren: niet meer rond risicomijding en proceduretrouw, maar rond groei en uitvoeringskracht.

Daarmee haalt hij zich een loodzware ambitie op de hals. In mijn boek “Gamechangers: hoe kleine ingrepen grote veranderingen ontketenen” beschrijf ik hoe systemen immers altijd in de weerstand gaan tegen verandering en hoe de meeste interventies falen als ook niet de diepste lagen van het systeem worden beetgepakt. In elk systeem zijn er zwakke knoppen waar je aan kunt draaien - parameters, budgetten - die weinig blijvend veranderen. En er zijn krachtige hefbomen - doelen, regels, zelforganisatie, paradigma’s - die het systeem fundamenteel kunnen verschuiven.

De vraag bij elk hervormingsrapport is: waar grijpt het in op het systeem? Op welke knoppen wordt gedraaid? En hoe denkt het de weerstand van het systeem te neutraliseren?

Het rapport Wennink is een masterclass systeemverandering, omdat het bewust probeert in te grijpen op de krachtigste hefbomen. Sterker nog: het grijpt in op vier niveaus tegelijk, zodat ze elkaar versterken.

Dit is hoe het werkt.

De logica van hefbomen

De centrale held in Gamechangers is systeemdenker Donella Meadows. Zij introduceerde in haar boek “Thinking in Systems” een soort archeologische kaart van onzichtbare hefbomen waarop je in kan grijpen om het gedrag van systemen te veranderen. Ze introduceert in totaal twaalf plekken waar je kunt ingrijpen - van zwak naar krachtig - en waar je duwt bepaalt of het systeem transformeert of gewoon terugkeert naar zijn oude vorm. Voor de vereenvoudiging categoriseer ik ze hier in vier blokken:

Onderaan de hiërarchie staan parameters: budgetten, cijfers, targets. Dit zijn de knoppen waar 95% van alle aandacht naartoe gaat. “We investeren €10 miljard extra.” “Het tekort mag niet boven 3% komen.” Parameters voelen concreet en controleerbaar. Maar de impact is minimaal en tijdelijk. Zodra de druk eraf is, keert het systeem terug naar haar standaard

Hoger zitten regels en informatiestromen: de spelregels van het systeem. Denk aan: moet een project door drie of dertig goedkeuringsrondes? Krijgen beslissers real-time feedback over wat werkt en wat niet? Mag een ambtenaar iets goedkeuren of moet alles naar boven? Dit heeft meer impact dan parameters omdat regels bepalen hoe mensen zich gedragen.

Nog krachtiger is zelforganisatie: het vermogen van een systeem om zichzelf te herstructureren, te leren, en te evolueren. Dit is het verschil tussen “de overheid bepaalt welke zonnepanelenproducent subsidie krijgt” en “ondernemers zien een kans, investeerders stappen in, werknemers switchen naar groeiende sectoren, en dit zonder dat iemand het centraal plant.” Als je de randvoorwaarden klopt (heldere regels, betrouwbare infrastructuur, vertrouwen), gaat het systeem zelf oplossingen genereren die niemand van bovenaf had kunnen bedenken. Gedecentraliseerde intelligentie werkt altijd beter dan centrale planning.

En bovenaan de hiërarchie: doelen en paradigma’s. Het doel bepaalt waar het hele systeem naartoe werkt. Stel je het doel op “geen fouten maken” dan investeer je in controle en procedures. Stel je het doel op “groei genereren” dan investeer je in infrastructuur en innovatie. Helemaal anders, helemaal andere uitkomsten.

Het paradigma zit nóg dieper: het fundamentele denkraam dat bepaalt welke doelen überhaupt denkbaar zijn. Als jouw paradigma is “de overheid moet vooral niets verkeerd doen,” dan kun je het doel “snel experimenteren en leren” niet eens serieus overwegen - het voelt riskant, onverantwoord, onmogelijk. Het paradigma bepaalt wat je kunt zien. Dit zijn de meest krachtige hefbomen, maar ook de moeilijkste om te veranderen.

Het vernuftige aan deze hiërarchie hefboompunten van Meadows is dat de hogere niveaus de lagere sturen: Het paradigma bepaalt welke doelen je stelt. De doelen bepalen welke regels je nodig hebt. De regels bepalen of zelforganisatie kan floreren. En pas aan het eind: welke parameters je kiest.

Als je alleen parameters aanpast zonder de hogere niveaus te raken, vecht je tegen de stroom in. Het systeem absorbeert je interventie en keert terug. Maar als je op meerdere niveaus tegelijk ingrijpt, zodanig dat ze elkaar versterken, dan creëer je een kettingreactie van verandering.

En dat is precies wat het Wennink rapport doet. Het interveniëert bewust op vier hefbomen die elkaar versterken:

1. Het doel herprogrammeren - Waar moet Nederland naartoe optimaliseren?

2. De spelregels verbeteren - Welke regels maken dat doel mogelijk?

3. Zelforganisatie vrijmaken - Hoe activeren we het Nederlandse potentieel?

4. Het paradigma beschermen - Hoe voorkomen we dat oude reflexen alles weer terugdraaien?

Elk van deze hefbomen krijgt concrete uitvoering in Wennink’s voorstellen. En samen vormen ze een kettingreactie: elk niveau maakt het volgende mogelijk. Dat is waarom het rapport leest als een masterclass in systeemverandering.

Stap 1: Het doel herprogrammeren

Wennink stelt voor om 1,5 tot 2% economische groei als harde kabinetsdoelstelling te verankeren, met de premier persoonlijk verantwoordelijk. Dit klinkt op het eerste zicht als als “gewoon een cijfer” (een parameter), maar het is fundamenteler dat dat. Groei is het doel van het economisch systeem. En doelen bepalen waar al het andere naartoe werkt.

Waarom is deze 1,5% groei zo slim? Omdat deze economische groei een randvoorwaarde is die alle andere doelen van zuurstof voorziet. Wil je betere zorg? Je hebt groei nodig om het te financieren. Wil je de energietransitie? Je hebt groei nodig om te investeren. Wil je strategische autonomie? Je hebt groei nodig om onafhankelijk te zijn.

Wennink formuleert dit als volgt:

“Zonder economische groei staan onze publieke voorzieningen onder druk. We kunnen niet blijven investeren in zorg, onderwijs, veiligheid en klimaat zonder het verdienvermogen dat groei genereert.”

Dit is echter geenszins een pleidooi voor groei als enig doel. Het is erkennen dat of we dat willen of niet, economische groei alles financiert wat we willen bereiken. Door dit als kabinetsdoel te verankeren, verandert Wennink waar het systeem naartoe optimaliseert. Niet meer “hoe voorkomen we fouten?” maar “hoe genereren we groei?”

Dat is een fundamentele herprogrammering van het systeem.

Stap 2: De spelregels verbeteren die het doel mogelijk maken

Een doel alleen is niet genoeg. Het systeem heeft regels nodig die het mogelijk maken dat doel te bereiken. Dit is waar Wennink concreet wordt met investeringsvoorstellen.

Hij identificeert vier cruciale randvoorwaarden en koppelt daar concrete interventies aan:

Snelheid in vergunningen: Ons huidigever stelsel is geoptimaliseerd voor risicomijding. Wennink stelt onder andere een Versnellingswet voor, “regulatory sandboxes” waar innovaties getest kunnen worden, en nationale regie op strategische projecten. Dit dit zijn essentiële spelregels die bepalen of het systeem überhaupt kan leveren op het doel. Zonder deze spelregels zal het systeem - en de rechtstaat die het systeem beschermt - altijd sturen op het beschermen van ieders deelbelang.

Toegang tot talent: Nederland heeft arbeidskrapte maar maakt het toetreden van buitenlands talent moeilijk, zowel in de arbeidsmarkt als in het onderwijs. We schieten onszelf daarmee ontzettend in de voet.

Betaalbare energie: Door congestie op het elektriciteitsnet moeten meer dan 14.000 bedrijven en organisaties wachten op een aansluiting, waardoor de vernieuwing en verduurzaming van de Nederlandse economie stokt.” Zijn oplossing: nationale regie op energie-infrastructuur, flexibel netgebruik bij grootverbruikers, en prioritaire netbesluiten. Peter Wennink ziet het oplossen van “netcongestie” als randvoorwaarde voor groei als topprioriteit. Zonder overvloed aan betaalbare stroom, geen groei en geen verduurzaming. Punt.

Sterke infrastructuur: Mainports Rotterdam en Schiphol, Brainport Eindhoven, innovatie-ecosystemen zoals Leiden Bioscience Park, zijn de plekken waar kennis en industrie samenkomen. Wennink pleit voor een nationaal plan om deze te versterken.

Dit zijn de spelregels die het groeidoel mogelijk maken. Zonder deze randvoorwaarden kun je het groeicijfer wel verhogen, maar bereik je het niet. Je hebt als het ware de motor aangezet, maar de handrem staat nog aan.

Het slimme aan het rapport: Wennink koppelt elk voorstel aan een concrete uitvoeringsstructuur. De Nationale Investeringsbank met €10-20 miljard werkkapitaal om publiek-private projecten te co-financieren. Het Nationaal Agentschap voor Baanbrekende Innovatie met €2 miljard voor innovatie-ecosystemen.

Je kan deze voorstellen eigenlijk zien als concrete machines of vehikels om de nieuwe spelregels uit te voeren.

Stap 3: Zelforganisatie vrijmaken

Nu komen we bij de krachtigste interventie in het rapport - al noemt Wennink het niet altijd zo. Tussen regels en doelen zit een cruciaal hefboompunt:

Het vermogen van het systeem om zichzelf te organiseren.

Keer op keer benadrukt Wennink: “Nederland heeft alles in huis: kennis, creativiteit, kapitaal en een sterke traditie van samenwerken.” Hij zet het zelfs prominent in zijn inleiding:

“De wil om te verbeteren is overal aanwezig, maar mensen hebben het gevoel dat het systeem hen belemmert.”

Dit is het kernpunt. Nederland heeft enorm potentieel aan talent, creativiteit, kennis, technologie, ondernemerschap én kapitaal. Maar het systeem blokkeert dit. Wennink’s pleidooi is vooral: maak zelforganisatie weer makkelijker. Zijn aanbevelingen lezen dan ook als een pleidooi om het vermogen van de samenleving om oplossingen te bedenken te bevrijden van de dictatuur van de goede bedoelingen

Kijk naar de concrete voorstellen. Hij ontving 51 project- en investeringsvoorstellen van samenwerkende partijen - bedrijven, kennisinstellingen, regio’s. Ze staan te trappelen. Het kapitaal is er. De kennis is er. Maar wat ontbreekt zijn de randvoorwaarden.

Wennink’s financieringsmodel moet je dan ook vanuit deze hefboom van zelforganisatie bekijken. Hij vraagt om €100 miljard te mobiliseren, maar de meerderheid daarvan komt uit de markt zelf. De overheid moet vooral randvoorwaarden creëren en co-financieren. Daarmee schetst hij het beeld van een overheid die niet zozeer stuurt op een centraal geplande economie, maar die gedecentraliseerde energie probeert vrij te maken.

Maar - cruciaal - dat werkt alleen met vertrouwen. Wennink hamert hierop:

“Jaren van instabiel beleid hebben geleid tot een vertrouwensbreuk. Alleen met een geloofwaardige en consistente strategie voor de aankomende tien jaar is ambitieuze publiek-private samenwerking te herstellen.”

Zonder vertrouwen geen zelforganisatie. Zonder zelforganisatie geen groei, hoe hard je ook duwt op parameters. Om dat vermogen tot zelforganisatie te beschermen tegen politieke of ambtelijke bemoeizucht en controledwang, stelt hij onder andere een ‘Commissaris Toekomstige Welvaart’ voor. Dit is iemand met wettelijk mandaat en eigen fonds die impasses kan doorbreken en de publiek-private dialoog organiseert om zo de voorwaarden te creëren én te beschermen waaronder het systeem zichzelf kan organiseren.

Stap 4: Het paradigma zien en het systeem beschermen

Tot hier is Wennink’s kettingreactie zeer slim en doordacht. Hij interveniëert op drie krachtige hefbomen: doel herprogrammeren, spelregels veranderen, zelforganisatie mogelijk maken. Ook het “paradigma”, het meest krachtigste niveau van hefbomen voor systeemverandering komt uitgebreid aan bod in het rapport.

Hij beschrijft het leidende paradigma in Nederland glashelder als de “inquisitiedemocratie” waarin afrekenen wordt beloond en leveren wordt gestraft. De doorgeschoten democratisering die leidt tot besluiteloosheid. En misschien wel zijn scherpste observatie:

“In Nederland hebben we geprobeerd risico’s uit te sluiten met regels, maar zo ook de ruimte om te handelen verkleind.”

Dit is paradigma-niveau diagnostiek. Het onderliggende denkraam is: “Niets mag misgaan.” Het gevolg: een systeem geoptimaliseerd voor het voorkómen van fouten, niet voor het realiseren van doelen.

Het probleem is niet dat Wennink dit mist. Hij ziet het beter dan vrijwel elk ander beleidsrapport. Het probleem is dat paradigma’s als een zwart gat werken: ze trekken elke interventie naar zich toe en vertalen die terug naar hun eigen logica.

Zelfs als je het doel verandert, de spelregels aanpast, en zelforganisatie stimuleert, kan het oude paradigma dit bij de eerste tegenslag absorberen. Bij het eerste project dat misgaat: “Zie je wel, we zijn te snel gegaan.” Bij versnelde vergunningen: “Ze ontmantelen checks and balances.” Het systeem bijt altijd opnieuw terug.

Daarom heb je een aantal aanvullende interventies nodig als beschermlaag om de interventies heen. Ik heb deze uitgebreid beschreven in het essay “Hoe beschermen we hetWennink rapport tegen de Nederlandse immuniteitsreactie” op de Voor Ons Nederland Substack. De belangrijkste daarvan is dat we experimenteren en leren als succes criterium introduceren, zodat we het systeem beschermen tegen hysterie in media of oppositie voor elk initiatief dat niet meteen succes opleverde. Zolang we door dit soort “inquisitiecultuur” geregeerd worden, zullen we nooit dapper beleid krijgen.

Voor Ons Nederland

Hoe beschermen we het Wennink rapport tegen de Nederlandse immuniteitsreactie?

Nu Peter Wennink een bijzonder heldere onderbouwing gegeven heeft voor de keuzes die we moeten maken om ons toekomstig verdienvermogen te beschermen, is het opletten dat het systeem niet doet wat het altijd doet: elke poging tot verandering elegant afstoten…

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9 months ago · 5 likes · Tom De Bruyne

Besluit: Waarom dit een masterclass is

Het rapport Wennink rapport is bijzonder intelligent geschreven. Het is een zorgvuldig gekozen set aan instrumenten om het systeem in beweging te krijgen en haar typische weerstandsreacties te ontmijnen. En naar mijn gevoel doet het rapport dat op drie manieren:

1. Het interveniëert op de juiste hefbomen. Niet alleen op parameters en budgetten, maar op systeemdoel, spelregels, en zelforganisatie. Dit zijn de plekken waar je echte impact hebt. De cascade laat zien hoe elk niveau het volgende mogelijk maakt: het doel maakt nieuwe spelregels nodig, die spelregels maken zelforganisatie mogelijk.

2. Het koppelt elk niveau aan concrete uitvoering. Het groeidoel wordt verantwoordelijkheid van de premier. De spelregels krijgen concrete machines (NIB met €10-20 miljard, NABI met €2 miljard, Versnellingswet). De zelforganisatie krijgt een facilitator (Commissaris Toekomstige Welvaart). Dit is niet alleen denken, dit is doen.

3. Drie: Het ziet het paradigma. Wennink’s beschrijving van de “inquisitiedemocratie” is de scherpste diagnose van Nederland’s systeemprobleem in jaren. Hij begrijpt dat het echte obstakelvooral cultureel is: een denkraam van risicomijding dat elk initiatief absorbeert.

De vraag voor het volgende kabinet is dan ook: hoe beschermen we deze interventies tegen de kracht van dat paradigma? Hoe zorgen we dat bij de eerste tegenslag niet alles terugkeert naar het oude evenwicht?

Dat vraagt om de beschermlaag: meerdere meetpunten zodat één mislukking niet fataal is, transparante sturing zodat vertrouwen kan groeien, en ruimte voor leren zodat fouten niet leiden tot paralyserende controle.

Zonder die beschermlaag blijft het risico dat Nederland over vier jaar terugkijkt op nóg een mislukte hervormingspoging. Met die beschermlaag kan Wennink’s cascade eindelijk doorbreken wat generaties hervormers niet lukte: een systeem dat niet alleen begrijpt wat er moet veranderen, maar het ook daadwerkelijk verandert,… en veranderd houdt.

Verder lezen: Wil je meer weten over hoe je verandering kunt realiseren in organisaties? Lees dan de complete gids over change management. En als je wilt begrijpen waarom veranderinitiatieven zo vaak mislukken, lees dan dit artikel over waarom veranderinitiatieven falen.

Tom De Bruyne
Lees het boek Gamechangers (2025)
Volg mij op Linkedin
Boek mij als spreker via Athenas of The Next Speaker
Partner bij SUE & The Alchemists en SUE Behavioural Design Academy

Cities are living organisms. They think, feel, and behave in ways that reveal their deepest beliefs about the future.

Walking through Barcelona last weekend, I witnessed the city expressing one of its most optimistic thoughts: a gluten-free pet food store. The store was actually in walking distance from a gluten-free bakery. Both were packed with customers, and both were clearly thriving businesses.

At face value, they are shops. But when you zoom out you can look at them as behaviours. They're how Barcelona behaves when it feels confident, prosperous, and secure about its future. When a city believes deeply that its residents have disposable income, sophisticated tastes, and emotional attachment to their pets' wellbeing, it manifests businesses that would seem absurd in a struggling urban environment.

But cities, like all living systems, can change their minds. And when they do, their behaviors change too, sometimes in ways that shock everyone, including the city itself.

When cities change their minds

This way of thinking about cities - as organisms with thoughts, feelings, and behaviours - is the key to understanding emergence: how complex systems develop properties and behaviors that didn't exist when the system was simpler.

A city's "thoughts" are the collective beliefs, expectations, and assumptions of everyone living, working, and investing there. A city's "feelings" are reflected in confidence levels, risk tolerance, and optimism about the future. And a city's "behaviors" are everything from the businesses that open to the protests that erupt in its streets.

Consider two examples of how this concept of “emerging properties of a system” works beyond cities:

In technology: The internet started as a way for researchers to share files. But once enough people connected, something new emerged. The system developed new behaviours that were impossible before. Online marketplaces, social networks, and gig economies weren't planned by anyone. They emerged because the system had reached a point where it could "think" about commerce, connection, and work in entirely new ways.

In society: Silicon Valley wasn't designed to become the world's innovation hub. But once you had Stanford University, defense contractors, venture capital, and risk-taking entrepreneurs in proximity, the region developed a new collective mindset: one that believed failure was a learning experience rather than a shame. This "thought" produced behaviours: billion-dollar companies growing from garage startups, entire industries pivoting overnight, and a culture that celebrated disruption.

Barcelona's success story

Back to that gluten-free pet food store. In 1990s Barcelona, before the Olympics transformed the city, this business would have failed within months, because the city itself was thinking differently.

The pre-Olympic Barcelona thought like a struggling industrial port: cautious, pessimistic, focused on survival rather than aspiration. When a city thinks this way, it produces different behaviours: discount stores, basic services, businesses built around necessity rather than desire. This is behaviour that you can witness in many struggling suburbs.

But something remarkable happened as Barcelona invested in its transformation. The city began to think differently about itself. It started believing it could attract educated professionals, creative industries, and international businesses. As this new mindset took hold, the city's behaviors changed. Disposable income rose. Health consciousness grew. Pet ownership became aspirational rather than purely functional.

The gluten-free pet food store didn't create this market. It’s much more the opposite: it's how Barcelona behaves when it believes in its own prosperity. The store is both a symptom of the city's optimistic mindset and a reinforcement of it. Every customer who buys premium pet food signals back to the city: "Yes, we believe in this future too."

When success changes how a city thinks, feels and behaves

But here's where the story gets complex: Barcelona's success was so complete that it fundamentally changed how the city thought about itself. The confident, prosperous mindset that produced gluten-free pet stores also began producing different behaviours. Behaviours that eventually created entirely new problems.

Walk through Barcelona today and you'll encounter the city expressing a new emotion: anxiety mixed with anger. This shows up as protests against tourism, locals with signs reading "Tourists go home" and "Barcelona is not for sale." Neighborhoods where long-term residents can no longer afford rent because of Airbnb saturation.

These protests ar emerging behaviours of a city that is realizing its success has gone too far. The city that once felt optimistic about tourism now feels overwhelmed by it. With 15.5 million tourists annually visiting a city of 1.7 million residents, Barcelona started thinking differently: "I've become successful, but I'm losing my identity. I'm no longer serving my own people."

When a city thinks this way, it produces specific behaviours: rent prices increased 68% over a decade, housing became unaffordable for middle-class residents, and entire neighborhoods transformed into tourist playgrounds where locals felt like strangers.

The anti-tourism protests are how Barcelona behaves when it feels this internal conflict. It’s organized resistance that emerged because the city's original optimistic mindset had created conditions that now threatened what it valued most.

How Barcelona is reshaping its thoughts and feelings today

Barcelona's current response provides a fascinating case study in how a city can consciously change its thinking and behavior. Like therapy for an urban organism, the city is pursuing both surface-level adjustments and deeper psychological shifts.

Surface-Level Behavioral Changes:

• Increasing tourist taxes to €7.50 per night

• Reducing tour buses from 156,000 to 70,000 annually

• Removing overcrowded bus routes from Google Maps

• Trying to eliminate "tacky souvenir shops"

These interventions are like treating symptoms. They might reduce some pressure, but they don't change how the city fundamentally thinks about itself. They’re like aspirins for the head-each that the city is experiencing.

Deeper Mindset Shifts:

More interesting is Barcelona's attempt to change its core beliefs. The city announced a radical plan to eliminate all 10,000+ short-term rental licenses by 2028, with no renewals and no new permits. On the surface level, this is policy design. But on a deeper level it's Barcelona trying to think differently about what kind of success it wants.

Mayor Jaume Collboni explained the psychological shift: "We are confronting what we believe is Barcelona's largest problem. More supply of housing is needed, and the measures we're presenting are to provide more supply so that the working middle class does not have to leave the city."

Here's the sophisticated part: while banning Airbnbs, the city simultaneously plans to add 5,000 new hotel rooms. This shows Barcelona learning to think more strategically. The city recognizes that tourism demand is a force it can't eliminate, but it can change how that force expresses itself. Hotels create different behaviours than distributed short-term rentals—they don't remove housing from the long-term market, they create different employment patterns, different neighborhood effects.

This is a city learning to think systemically about its own behavior.

When cities fall into depression

This principle works in reverse too. Cities can develop pessimistic, fearful mindsets that produce entirely different behaviours. When cities think negatively about their future, they manifest different symptoms: increased gang activity, drug epidemics, teen pregnancy rates, abandoned storefronts, and brain drain.

Detroit provides a perfect example of urban depression in action. As the auto industry declined, the city began thinking like a victim rather than an agent of its own destiny. This mindset produced specific behaviors: middle-class families left, tax revenues dropped, city services deteriorated, property values plummeted, and crime increased. Each behavior reinforced the city's negative self-image until you had entire districts where the urban organism had essentially given up on itself.

The abandoned buildings were the behaviours that Detroit produced when it lost faith in its own future.

How to read a city's mind?

Once you grasp the concept of the city as a living organism, you can start thinking about reading its mind. Whether you're choosing where to invest, start a business, or build your career, learning to read your city's psychology gives you a massive advantage.

Here's how to decode what your city is really thinking and use that insight to make better choices:

For business owners: Spot opportunities before they're obvious

When Barcelona started thinking optimistically about its future in the 1990s, savvy entrepreneurs could read the signals years before the gluten-free pet stores appeared. The city was investing in public spaces, attracting international events, and developing cultural programming. These were symptoms of a city learning to believe in itself.

Now imagine two restaurant owners in a gentrifying neighborhood. The first sees rising rents as a threat and complains about changing demographics. The second reads the city's psychological signals: new bike lanes signal environmental consciousness, art galleries opening indicate cultural confidence, young professionals moving in suggest disposable income growth, and changes its menu to cater to the preferences of this group. While the first owner struggles with decreased foot traffic from longtime residents, the second thrives by aligning with the neighborhood's evolving mindset.

Businesses should pay attention to key indicators like what does the city invest its money in (Infrastructure spending reveals confidence), what kind of businesses are opening (High-margin, specialized services indicate optimism), or how do residents talk about the future? (Language reveals collective mindset)

For policy designers: dare to think different about social housing policies

Take controversial urban policies through this lens. When Rotterdam concentrates temporary immigrant workers in specific neighborhoods, it's revealing something about how the city thinks about integration and long-term planning. For a business owner, this signals different opportunities: areas with transient populations need different services than areas where people are putting down roots.

The opposite is also true: When cities approve luxury penthouses in downtown areas, they're expressing confidence about wealth concentration creating positive spillover effects. This mindset produces behaviours: high-end dining, cultural venues, premium services that create opportunities for businesses that could transform the signals of how a city thinks about itself. This in turn could trigger a chain reaction of attracting more people, more prosperity and more investments, until the city gets fed up with its success (as the example of Barcelona is illustrating.

For investors: Decode market signals before others

Cities telegraph their future through their current behaviors. When Barcelona announced its 2028 Airbnb ban while simultaneously planning 5,000 new hotel rooms, that was the city thinking strategically about what kind of tourism economy it wanted.

Smart investors could read this signal: the city believes tourism demand will continue, but wants to change how that demand manifests. Hotel investments become more attractive, while short-term rental investments become risky.

Investors should watch for confidence signals, like major infrastructure projects, international event hosting, public space improvements, anxiety signals, like protest movements, restrictive policies, brain drain statistics or identity shifts, like changes in what the city markets about itself, what it celebrates, what it regulates

For residents: align with your city's evolution

Understanding your city's mindset helps you make personal decisions that benefit from rather than fight against urban psychology.

If your city is thinking like early-stage Barcelona (optimistic, growth-oriented), different career strategies make sense than if it's thinking like Barcelona today (successful but anxious about sustainability). In growth mode, entrepreneurship and creative industries thrive. In maturity-with-anxiety mode, governance roles, urban planning, and sustainability-focused careers become more valuable.

The leadership implication:
Help your city think better

For leaders, this means becoming urban therapists rather than just problem-solvers. Instead of attacking individual issues, focus on changing how your city thinks about itself and its future.

Barcelona didn't solve tourism, culture, and economic development separately in the 1990s. They helped the city develop a new self-image: one that believed it could be internationally significant. This new way of thinking produced behaviours: public space investments, cultural programming, infrastructure improvements. The gluten-free pet food store is evidence this mental transformation worked.

But Barcelona's story also shows that successful cities can develop new anxieties that require new ways of thinking. The leader's job isn't to prevent this, but to help the city recognize when its thoughts and behaviours are no longer serving its values, and to facilitate the mental shifts needed to develop healthier behaviours.

The protesters understand this intuitively. They're not just complaining, rather they're trying to help Barcelona think differently about what kind of success it wants. They're working to shift the city's identity from "tourism at any cost" to "tourism that serves residents."

Remember: Cities, like people, can change their minds. The Barcelona that confidently built its tourism economy in the 2000s is learning to think differently now. Smart decision-makers stay ahead by reading these psychological shifts before they become obvious policy changes.

The next time you see an unusual business thriving - or closing - in your neighborhood, ask yourself: What is this telling me about how my city thinks about its future? That insight might be the most valuable market research you'll ever do.

Related reading: Want to learn more about using behavioural science to understand and shape how people act? Start with this guide on what is behavioural design. And for a deeper look at why changing behaviour is so challenging, read about behaviour change and how to make it work.

This essay is based on the thinking and frameworks I explore in my upcoming book “Gamechangers: How small interventions unleash radical change”, published first in Dutch (November 2025), but will be translated into English. Follow me on Linkedin for staying up to date.

Image generated with Sora, as I couldn’t find a copy-right free image of the gluten free pet-store I came across in Barcelona.

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Last week, a fascinating experiment took place that was so "successful" that OpenAI got quite alarmed and quickly reversed course. They allowed ChatGPT to express more personality. The result became immediately clear: ChatGPT transformed overnight into an ass-kissing machine. I highly recommend reading Ethan Mollick’s “Personality and Persuasion” piece on this.

I found it instantly irritating because my method of working with AI is that I'm continuously looking for perspectives to improve my thinking. The last thing that helps with this is when AI positions itself as an unconditional cheerleader that finds everything wonderful and brilliant.

It turns out I wasn't alone in my instinctive dislike of this change. OpenAI – the company behind ChatGPT – quickly discovered that the algorithm was busy confirming and applauding everyone, even for content that was objectively just mediocre or poor. The company decided to (partially) roll back the update after just a week.

This is the social media moment of AI

This served as a cautionary tale. Small tweaks in the algorithm can have an enormous influence on our behavior. If the algorithm decides that it primarily wants to tell us what we want to hear, because that's where the perverse incentives lie – so that we might prefer ChatGPT over Claude.ai, for instance – then we face exactly the same problem as with social media. There, algorithms select content that confirms our worst prejudices and indignations, because the algorithms ‘learned’ that this is what gets us to spend more time on the platform, which results in more revenue.

However, unlike social media, AI can be what we want it to be. If you want AI to behave like a mentor, it will continuously try to stimulate you to dive deeper into the subject. If you want it to teach you a topic, it behaves like an endlessly patient teacher trying to instruct you using the best methods. If you want it to make you a better writer, you ask AI to be your editor and inspire you on how to improve your text.

The only problem is: You still have to do this yourself. You need to explicitly tell the AIs what you want them to be. Claude tries much more to be a wise friend. ChatGPT has given us a glimpse of how quickly it can become mega-addictive by manipulating our emotions to give us dopamine hits that make us addicted.

How I solved this problem: custom instructions that design for growth

I've taken a proactive approach to this challenge by designing my own guardrails through custom instructions. In ChatGPT's settings under "Personalization > Customize," I've explicitly shaped how I want the AI to interact with me.

Here's what I've specified:

"If you need more information from me to provide a high-quality answer, please ask clarifying questions – you don't have to answer on the first try.

I really appreciate having my thinking challenged. I love when I'm offered a perspective I hadn't considered.

Don't address me by my first name. Just use 'you.' Keep it professional.

I have no need for political correctness. I'm neither left nor right, but a progress thinker. Anything that benefits society interests me.

Inspire my strategic thinking by pointing out possible second-order effects. Occasionally ask me if I've considered these.

Try – where relevant – to always use as a first principle that the outcome of our conversations should ultimately lead to behavioral change, either in myself or others. For complex questions, always ensure an action-oriented outcome.

Use quick and clever humor when appropriate, but maintain a proper distance. We're high-level teammates working intensively together to make things better."

By explicitly designing this relationship, I've created an AI interaction that pushes me to grow rather than one that simply flatters my ego. This approach treats AI as a tool for development rather than validation – the difference between using a mirror to check your appearance versus using a telescope to see further.

The four essential questions to design your AI collaboration

The key to making AI truly useful lies in how you structure your relationship with it. Here are the four questions I recommend asking to design an AI collaboration that serves your growth rather than your ego:

1. What is the outcome that defines your collaboration with AI? If the goal is to grow, learn, and get better by helping you to think better, then make that outcome explicit.

2. What is the role you want it to play? Do you want it to be a mentor, a guide, a coach, a friendly buddy? A drill sergeant? Whatever works best for you.

3. What context does it need to know? I shared with AI that the psychology of influence and behavioral change is my professional domain, and that my upcoming book is about behavioral change in systems. I am, for instance, always interested in the second-order effects of things – the hidden consequences of actions that are not always thought through. ChatGPT includes possible second-order effects in every post.

4. Tell it which next steps you prefer. I, for instance, always want an actionable next step – something I can do with the answer it gave me.

The key insight from behavioral design is that we need to be intentional about the feedback loops we create in our AI interactions. Without this intentionality, we'll naturally drift toward the path of least resistance, seeking validation rather than growth, comfort rather than challenge, and ultimately stagnation rather than transformation.

As the AI landscape continues to evolve, the most important development to watch in the coming months is how the 'personalities' of these AIs develop. If they're designed to stimulate our hunger to learn, develop, and improve, the future looks fantastic. If they discover how – just like social media algorithms – they can tap into our deepest desires and fears to make us addicted, we should fear the worst.

Related reading: To understand the behavioural science behind why AI adoption fails in organisations, read this article about why AI adoption fails. And for practical guidance on using AI for communication and persuasion, check out this guide on AI for communication professionals.

Image generated by Chat GPT.

Tom De Bruyne
Founder / Partner SUE & The Alchemists

In my upcoming book with the working title "Fck the system, change the system”," I dive deeper into these invisible forces that shape our systems and how small interventions can create powerful transformations. This Substack is where I'll be sharing key insights, testing new ideas, and building a community of practice around the science of influence. Subscribe to join the conversation and be the first to receive exclusive content from the book. Together, we'll explore how understanding these hidden mechanisms can help us design more effective change in our organizations, communities, and lives.

Recently, I've found myself more and more hesitating to engage with friends who are unconditional Musk enthusiasts. Not because I don't value their perspectives, but because I've realized that the disagreements are just a big waste of time and energy. Each side is only looking for arguments that confirm their existing beliefs.

What if, instead of arguing about whether Musk's Department of Government Efficiency is right or wrong, we make predictions and learn which beliefs were wishful thinking? We can then update our thinking based on predictive success rather than rhetorical skill.

In this blog I want to make three predictions, based on three mental models for predicting behavior.

My friends’s valid concerns about regulatory capture

My Musk-enthusiast friends make a valid point about regulatory capture. Even progressive thought leaders like Ezra Klein and Dereck Thompson acknowledge in "Abundance" that suffocating regulations have contributed to the housing crisis, cost-of-living crisis, and the near impossibility of implementing major projects. Regulatory capture creates rules that individually seem reasonable but combined form a Kafkaesque nightmare of obstruction.

They argue this problem is so pervasive that only a brilliant mind with an AI-empowered team can break through government's defense mechanisms. Only shock therapy can change a system dedicated to maintaining the status quo.

I'm partly receptive to this argument. Regulatory protections work well in stable times but become problematic during periods requiring rapid change.

However, acknowledging regulatory capture doesn't justify Musk's specific approach.

I believe three mental models will better predict DOGE's outcomes:

1. behavior predicts outcomes better than words

2. Stated goals of the system often serve to mask the real goals of the system

3. When you treat clouds, like clock, clouds will fight back.

1. Behavior predicts outcomes better than words

Musk's pattern across organizations is revealing:

At Twitter/X, he promised free speech but systematically amplified aligned voices while suppressing others. He dismantled safeguards, attacked concerned advertisers, and punished questioning journalists – all while championing "openness."

At Tesla, he cultivated a board that approved controversial decisions by removing independent voices. When regulators questioned Autopilot safety claims, he attacked the regulators rather than engaging with evidence. He also ensured the heads of the SEC and FTC were fired after the election.

At SpaceX, he leveraged public pressure campaigns against officials who questioned contracts, creating feedback loops that rewarded agencies aligning with his interests.

Musk systematically and repeatedly games systems to serve his needs. While reducing regulatory capture may be valuable, handing societal operating systems to a low-trust individual like Musk seems to be a far worse outcome.

2. Stated goals often mask the real system goals

If DOGE's goal is truly government efficiency, why is Musk's approach so divisive, focusing on ideological purges of progressive policies? This culture war approach is already damaging his businesses – Tesla stock has dropped 45% in three months, with investors warning of a "Brand Tornado Crisis." Markets deliver feedback that government systems might not provide until too late.

More tellingly, while claiming debt reduction as the goal, the administration is simultaneously preparing a $4.5 trillion tax cut for the wealthy – rendering DOGE's potential savings meaningless.

This disconnect suggests the real goal isn't efficiency but dismantling government guardrails and concentrating power, as evidenced by the administration's treatment of dissenters. If you don’t agree with the Trump administration, it can go after you.

3. Clouds cannot be treated like clocks

Karl Popper's distinction between "clouds" (complex, unpredictable systems) and "clocks" (mechanistic, predictable systems) offers a powerful lens for understanding Musk's approach. Human organizations are fundamentally "cloud-like" – they consist of individuals with agency, emotions, and personal motivations. Yet Musk consistently approaches them as if they were "clock-like" systems that can be reconfigured through brute-force engineering. This fundamental category error explains why his interventions often create chaotic resistance rather than orderly transformation. Government agencies aren't malfunctioning machines to be reprogrammed; they're complex adaptive systems that respond to threats with self-preservation. Treating clouds like clocks inevitably produces turbulence – a reality Musk's engineering mindset seems unable to grasp.

My three predictions

Based on these mental models, I predict DOGE will primarily serve business and power consolidation rather than meaningful efficiency. Specifically:

1. Within 18 months, departments influenced by DOGE will show increased alignment with Musk's business interests rather than objective efficiency metrics.

2. Independent voices will be systematically removed, with loyalty becoming the primary qualification for leadership.

3. Oversight mechanisms will be weakened, with decreased transparency where Musk has influence.

The Alternative Prediction

My Musk-supporting friends might predict DOGE will:

1. Produce measurable efficiency improvements in 18 months.

2. Eliminate obsolete regulations while maintaining safety standards.

3. Implement technological solutions that dramatically improve service delivery.

Beyond opinion wars

Instead of debating whether Musk and DOGE are "good ideas," let's put predictions to the test. This approach forces us all to be precise about what we believe will happen and why.

That's how real knowledge advances – not through opinion wars, but through testable predictions that help us understand how complex systems actually work

Related reading: For a deeper understanding of how our brains make decisions and why we often get it wrong, explore this guide on how to make better decisions. And to learn about the dual-system theory behind our cognitive biases, read about Kahneman’s Thinking Fast and Slow..

Tom De Bruyne
Founder / Partner SUE & The Alchemists

In my upcoming book "The Snowball Effect: How Influential Leaders Drive Change," I dive deeper into these invisible forces that shape our systems and how small interventions can create powerful transformations. This Substack is where I'll be sharing key insights, testing new ideas, and building a community of practice around the science of influence. Subscribe to join the conversation and be the first to receive exclusive content from the book. Together, we'll explore how understanding these hidden mechanisms can help us design more effective change in our organizations, communities, and lives.

1. We can already see the predicted second-order effects emerging

2. We desperately need a more optimistic perspective of the bigger picture.

This post is part of a series in which I explore the ideas and frameworks from my upcoming book "The Snowball Effect: How Influential Leaders Drive Change," where I examine the hidden forces that shape the behavior of systems, and how to leverage them.

What if we’re wrong?

At first sight, all the recent headlines paint a grim picture: tariff threats, NATO uncertainty, and a potential withdrawal of support for Ukraine. Conventional wisdom suggests Europe faces dark days ahead as American isolationism rises again.

But what if the exact opposite happens?

What if, years from now, historians look back and conclude that Trump's aggressive policies accidentally triggered Europe's renaissance? It sounds counterintuitive, but this is precisely how complex systems work: through second-order effects that often completely contradict the intended first-order outcomes.

Most people are trapped in linear thinking: A leads to B, end of story. But in interconnected systems like global geopolitics, A leads to B, which triggers C, D, and an entire cascade of consequences that nobody anticipated – least of all the initiators.

Here are five second-order effects already emerging that could ultimately strengthen Europe far more than weaken it:

1. Europe's investment renaissance

The most visible second-order effect is already unfolding: faced with American unpredictability, European leaders have unlocked hundreds of billions in investment for critical industries and defense. This fiscal stimulus is projected to significantly boost European GDP and technological capacity over the coming decade – creating precisely the self-sufficiency that American pressure was meant to prevent.

Look at Rheinmetall, the German defense contractor whose stock has more than quadrupled since 2022. Once struggling, they're now rapidly expanding production facilities and hiring thousands as European governments prioritize defense readiness. They've formed new cross-European joint ventures that would have been politically impossible without the external pressure forcing cooperation.

2. Accelerated Strategic Autonomy & Economic Resilience

For decades, Europe has talked about strategic autonomy without meaningful action. Now, faced with real threats to security guarantees and trade relationships, European capitals are racing to develop independent capabilities in energy, defense, and critical technologies. This pivot from comfortable dependence to determined self-reliance may prove the most significant European strategic shift since the formation of the EU itself.

The economic impact is already becoming visible. While economists like Noah Smith project grim prospects for the US economy – which was in perfect health until recent policy shocks "took a baseball bat" to it – Europe's growth outlook is surprisingly positive. The massive investment impulse triggered by the need for strategic autonomy is projected to boost European GDP growth rates over the coming years. The European Commission has already revised growth forecasts upward, with billions flowing into infrastructure, renewable energy, and industrial modernization.

What's particularly striking is the speed of this transition. Policy changes that would normally take years of deliberation are being fast-tracked through European institutions. The crisis atmosphere has created political will for bold economic measures that would have been unthinkable in normal times – from joint bond issuance to coordinated industrial policy. These aren't just temporary responses but structural transformations in how Europe manages its economy and industrial base.

3. The new European defense identity

NATO uncertainty has done what decades of diplomatic pressure couldn't – pushed European nations to dramatically increase defense spending and coordination. Germany's €100 billion defense fund represents just the beginning. The emergence of a cohesive European defense identity with serious capabilities will reshape global power dynamics for generations. Again, this is a second-order effect that completely contradicts the intended outcome of American pressure.

4. Diplomatic diversification

As trust in American partnerships erodes, European powers are aggressively diversifying their diplomatic and economic relationships. New arrangements with India, ASEAN nations, and moderate Middle Eastern powers are creating a more balanced European foreign policy less susceptible to single-point dependencies. This diversification creates resilience that wouldn't have developed without the pressure.

5. Acceleration of our technological sovereignty

Perhaps most importantly for long-term competitiveness, Europe is channeling unprecedented resources into technological sovereignty. From semiconductor production to AI capabilities to space assets, to clean tech, the acceleration of European tech investment is creating innovation clusters that might otherwise have taken decades to develop organically.

Fear or Excitement? Take the counterintuitive response

Our instinctive reaction to geopolitical turbulence is fear and pessimism. This is understandable but fundamentally misguided. History shows that periods of greatest pressure often trigger the most significant innovations and adaptations.

Europe's post-WWII economic miracle didn't happen despite the devastation, but partly because of it – the pressure created space for reinvention. Similarly, the current pressures might be precisely what Europe needs to break decades of complacency and dependency.

The fascinating pattern here is clear: Actions intended to weaken Europe are triggering compensatory reactions that may ultimately strengthen it beyond what would have otherwise been possible. The pressure is forcing precisely the kind of difficult changes that comfort would have indefinitely delayed.

You can only see these patterns emerge, when you study complex systems. It reflects a fundamental truth about complex systems that applies equally to organizations, markets, and even personal relationships. Linear actions in complex systems often trigger non-linear responses that completely reverse the intended outcomes.

Related reading: The way we frame a situation determines how we respond to it. Learn more about the framing effect and how it shapes perception. And if you want to understand why people resist change and how to overcome it, read about reducing resistance to change with behavioural science.

Tom De Bruyne
Founder / Partner SUE & The Alchemists

In my upcoming book "The Snowball Effect: How Influential Leaders Drive Change," I dive deeper into these invisible forces that shape our systems and how small interventions can create powerful transformations. This Substack is where I'll be sharing key insights, testing new ideas, and building a community of practice around the science of influence. Subscribe to join the conversation and be the first to receive exclusive content from the book. Together, we'll explore how understanding these hidden mechanisms can help us design more effective change in our organizations, communities, and lives.

A disclaimer to start with

Before I dive into this analysis, I want to acknowledge the gravity of what we witnessed in that Oval Office meeting. The war in Ukraine isn't theoretical, but a devastating reality for millions of people fighting for their very survival. President Zelenskyy wasn't in Washington for political theater; he was there seeking crucial support for his nation under existential threat.

Like many of you, I watched the exchange with a mixture of discomfort and concern. Yet within this troubling interaction lies an opportunity to understand and learn from a high-stakes negotiation gone wrong. By analyzing what happened and how it might have been handled differently, we can extract valuable lessons about influence and communication that apply beyond geopolitics to our own challenging conversations.

Because what we all witnessed with great horror was a great leader and a master communicator walk straight into a verbal trap. That's what happened when Ukrainian President Volodymyr Zelenskyy faced Donald Trump and JD Vance in that now-infamous Oval Office meeting. What was supposed to be diplomatic dialogue quickly turned into political theater, with Trump's parting words revealing the true purpose:

"This is going to be great television."

The script that shaped the ambush

When we analyze this interaction, one thing becomes clear: the goals of each participant were wildly misaligned. Trump and Vance weren't seeking solutions, but were orchestrating a spectacle of dominance. Zelenskyy, meanwhile, was trying to secure vital support for his nation.

The trap was sprung when Vance said, "I think it's disrespectful for you to come into the Oval Office to try to litigate this in front of the American media." This wasn't a negotiation; it was a performance with Zelenskyy cast as the ungrateful recipient who needed to show proper deference.

What makes negotiation ambushes so dangerous is that they trigger our fight-or-flight response. Zelenskyy chose to fight – defending his position point by point, exactly as his opponents wanted. By engaging directly with their accusations, he became entangled in their narrative instead of reframing the conversation on his terms.

Play psychological aikido on Trump

Of all the strategies available in such situations, one stands above the rest: Psychological Aikido. This technique, borrowed from martial arts, is about redirecting your opponent's energy rather than opposing it directly.

The key to Psychological Aikido is finding the underlying truth in even the most aggressive statement and using that truth as a bridge to redirect the conversation. Instead of resisting, you move with their energy while guiding it in a new direction.

When Trump accused Zelenskyy of "gambling with World War III," Zelenskyy could have used this technique with language specifically calibrated to Trump's psychological profile:

"You're absolutely right about the stakes here – they're huge, maybe the biggest in Europe since World War II. That's exactly why I came to you. Everyone knows you build things to last – Trump buildings, Trump deals. A Trump Peace Deal would be historic, tremendous. They'll be teaching it at Wharton. You could create the Trump Peace Framework that will show the world who the real dealmaker is."

This response acknowledges Trump's concern about global conflict, which reduces psychological resistance. It appeals directly to his self-image as a superior builder and dealmaker. Most importantly, it transforms what was framed as a problem into an opportunity for Trump to demonstrate his unique abilities and secure historic recognition – something that matters deeply to him.

When Vance repeatedly demanded gratitude, Zelenskyy could have similarly redirected:

"The American support has been crucial for Ukraine's survival – I've said this many times, and we demonstrate our gratitude through responsible stewardship of that aid. The world is watching to see if Mr Trump can do what no other president could accomplish. A Trump-brokered peace would be remembered for generations - it would be your biggest win on the world stage”

Find the psychological triggers of your negotiation partner

The effectiveness of the Aikido technique depends on understanding your negotiation partner's psychological triggers. In Trump's case, understanding his psychological landscape would have revealed several key touchpoints.

First is his desire for historic accomplishments. A statement like "When they write the history books, they'll say you achieved what was impossible" would connect directly with his aspiration for legacy. His self-image as the ultimate dealmaker could be leveraged with language like "Everyone knows your deals are the strongest, most respected deals. A Trump-brokered peace would be remembered for generations."

Trump also exhibits a tendency toward binary thinking that categorizes people and outcomes as winners or losers. This could be utilized to position the peace process as an opportunity for a significant win. Finally, his appreciation for loyalty and respect makes acknowledgment of his unique capabilities particularly effective.

By framing responses through these psychological lenses, Zelenskyy could have redirected the conversation away from demands for gratitude and toward a vision of Trump achieving historic success that would fulfill his deepest psychological needs for recognition and validation.

What would you have done in this situation?

I think many people who saw this appalling display of disrespect that Trump and Vance had for Zelenskyy will have thought “What would I have done in this situation?”.

It was so out of proportion that even an extremely experienced leader as Zelenskyy wasn’t prepared for this ambush.

The most important lesson isn't about specific responses, but about preparation. Before entering any high-stakes negotiation, it's essential to study your counterpart's psychological profile and language patterns. This knowledge allows you to prepare Aikido responses for predictable attacks.

Negotiation ambushes work because they create emotional reactions that override strategic thinking. By recognizing the trap early and responding with Psychological Aikido rather than defensiveness, you maintain control even when others are trying to wrest it away.

I want to close by acknowledging again the profound seriousness of the Ukraine situation. This analysis isn't meant to trivialize the life-and-death consequences that hang in the balance. Rather, by understanding the mechanics of influence that played out in that room, we can recognize similar dynamics in our own lives and respond more effectively when faced with conversational ambushes. The stakes for most of us will rarely be as high as they were for Zelenskyy, but the psychological principles remain the same.

Related reading: To master the art of influence and persuasion, explore Cialdini’s principles of persuasion explained. And for a practical framework on how to ethically influence behaviour, read about the SUE Influence Framework.

Tom De Bruyne

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In my upcoming book "The Snowball Effect: How Influential Leaders Drive Change," I dive deeper into these invisible forces that shape our systems and how small interventions can create powerful transformations. This Substack is where I'll be sharing key insights, testing new ideas, and building a community of practice around the science of influence. Subscribe to join the conversation and be the first to receive exclusive content from the book. Together, we'll explore how understanding these hidden mechanisms can help us design more effective change in our organizations, communities, and lives.

Ever since I learned about Steve Bannon's infamous "flood the zone with shit" strategy, I've been fascinated by how effectively it works. The concept is brutally simple: overwhelm people with so much misinformation, controversy, and outrage that they become exhausted, cynical, and unable to distinguish truth from fiction.

It's the perfect strategy if your goal is to erode trust in institutions and create a vacuum for authoritarian alternatives. And let's be honest – it's been working remarkably well.

But what if we turned this concept on its head?

The invisible opportunity

Here's what fascinates me: While we're drowning in negativity, we're simultaneously experiencing extraordinary progress that remains largely invisible to most people.

Think about it:

• Global extreme poverty has fallen dramatically over the past 30 years

• Child mortality rates have plummeted worldwide

• Renewable energy costs have declined at a pace that exceeded even the most optimistic projections

• Medical breakthroughs are happening at unprecedented speed (remember how quickly COVID vaccines were developed?)

Yet somehow, 8 in 10 people believe the world is getting worse, not better. We live in exponential times, which means that the speed of progress is accelerating at an increasing speed. To give you an example: Futurist Ray Kurzweil - who has been an accurate forecaster for more than 50 years - claims that within 10 years, because of exponential progress solar + batteries will be able to meet 100% of the world’s energy needs.

This perception gap isn't just wrong, it's even dangerous. When we believe things are irreversibly broken, we become susceptible to those who promise to tear everything down. We start to see democracy as ineffective rather than as the messy, imperfect, but ultimately successful system it is.

A new strategy: Flooding the zone with progress

What if our response to the flood of negativity wasn't to fight it directly but to create an equally powerful counter-current of progress, hope, and evidence of what's working?

This isn't about toxic positivity or ignoring real problems. It's about accurately representing the full picture so we can make rational decisions about our future.

Here's how we might start:

1. Share progress alongside problems: For every issue we discuss, let's also highlight where measurable improvement is happening and what's driving it.

2. Make progress visible and tangible: Use stories, visuals, and personal experiences to make abstract progress feel concrete and real.

3. Focus on solutions that scale: Direct attention toward what's working and can be replicated, not just what's broken.

4. Create momentum through social proof: Highlight communities and institutions making positive change to demonstrate what's possible.

Oh and one simple forcing function: follow and unfollow interesting people who have a progress and abundance mindset: Noah Smit, Ethan Mollick, Jason Crawford, Tyler Cowen, Michael Spencer, just to name a few. All of them are on Substack.

The most powerful part of this approach is that it's not just more accurate – it's also more effective. Research shows that people are more likely to take action on climate change, social inequality, or other challenges when they believe progress is possible and their actions will matter.

The hidden force of system goals

If you've been following my writing on behavioral design, you'll recognize this as an example of changing the invisible goals that shape our systems. When we flood our information ecosystem with evidence of progress, we shift the system goal from "maximize outrage and engagement" to "accurately inform and empower."

This changes everything downstream – the information people value, the decisions they make, and ultimately how they behave as citizens.

I am committing to only sharing the good stuff. Not because I’m naive and I’m not seeing the dangers, but because - as an entrepreneur - I know that a curious, open and hopeful mindset really is the only way to make progress. As the saying goes: Watch out what you wish for, because you might actually get it.

Remember – autocrats benefit when we see only darkness. They fear nothing more than citizens who can see both challenges AND possibilities clearly

Related reading: To understand the science behind how persuasion and influence work, explore this comprehensive guide on persuasion and the science of influence. And if you want to learn how to drive meaningful culture change, read about culture change with behavioural science..

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In my upcoming book "The Snowball Effect: How Influential Leaders Drive Change," I dive deeper into these invisible forces that shape our systems and how small interventions can create powerful transformations. This Substack is where I'll be sharing key insights, testing new ideas, and building a community of practice around the science of influence. Subscribe to join the conversation and be the first to receive exclusive content from the book. Together, we'll explore how understanding these hidden mechanisms can help us design more effective change in our organizations, communities, and lives.

In my upcoming book "The Snowball Effect: How Influential Leaders Drive Change", I explore a fascinating insight from systems thinking pioneer Donella Meadows: The goals of a system aren't what people claim they are. They rather are what the system actually does. And these invisible goals are incredibly powerful. They shape everything: the rules we create, the information we value, and ultimately how everyone behaves.

Let me share three stories that perfectly illustrate how this chain reaction works in practice. Each shows how invisible goals create self-reinforcing loops that either trap organizations in dysfunction or catalyze remarkable transformation.

1. The Dutch government's safety trap

Take the Dutch government. Deep in its DNA lies a powerful belief: the state must protect and secure people's lives at all costs. Sounds noble, right? But watch how this invisible goal creates a cascade of effects:

• The belief creates a goal: Balance all interests and mitigate every possible risk

• This goal demands rules: Expand regulations to ensure all interests are aligned

• These rules shape information flows: Focus on compliance and risk reporting

• This creates a feedback loop: More rules → more things to protect → more rules

The result? A suffocating web of regulations that's strangling entrepreneurship, paralyzing the housing market, and making agricultural transformation nearly impossible. The system is perfectly designed to achieve its invisible goal: absolute safety and zero risk. But in pursuing this goal, it's created gridlock.

2. The quarterly earnings trap

When companies go public, something fascinating happens. The invisible goal often shifts from "serve customers brilliantly" to "deliver predictable quarterly numbers." This shift triggers its own cascade:

• New rules emerge to ensure hitting the numbers

• Information systems reorient around short-term metrics

• Investment in innovation drops (it hurts quarterly numbers)

• Employee satisfaction plummets (good work becomes secondary to profits)

• Customer experience suffers (but slowly enough to hit this quarter's targets)

The system perfectly achieves its invisible goal: stable quarterly numbers. But in doing so, it slowly erodes everything that makes a company great.

3. Escaping the trap: The ING bank transformation

But here's a story of someone who understood how to hack this system. When Ralph Hamers took the helm at ING, he noticed something jarring: While 70% of customer transactions happened through the app, the entire bank was still optimized for a world of physical branches and risk mitigation. The invisible goal shaping everything was "don't make mistakes" rather than "delight customers."

Hamers realized this goal would eventually kill the bank. Tech companies were already eating into traditional banking services, and they were obsessed with customer experience, not risk avoidance. Instead of fighting the old system directly, he changed the bank's primary goal to "create the world's best banking experience."

What made this transformation remarkable was how he aligned the entire system to reinforce this new goal. By declaring "we're not a bank, but a tech company with a banking license," he legitimized a complete rewiring of how the organization worked. Traditional departments were replaced by multidisciplinary teams working in rapid 6-week innovation sprints. These teams got direct access to customer behavior data and could act on it without multiple approval layers. Success was redefined around customer satisfaction rather than risk mitigation metrics, and the organization started celebrating experimentation instead of punishing it.

Watch how this created a powerful feedback loop: Teams empowered with customer data started discovering opportunities for improvement. Their quick experiments led to better customer experiences, which generated positive feedback and data, which inspired more teams to innovate. The system began optimizing itself around customer delight rather than risk avoidance.

The result? When the pandemic hit and every bank had to become digital-first overnight, ING didn't have to transform - they were already there. The new goal had created a system that was naturally oriented toward the future rather than protecting the past.

4. Why this matters for leaders

These stories reveal something crucial about change: Transformation isn't about pushing harder against resistance. It's about understanding the invisible goals that shape your system and changing the leverage points that matter.

In the coming weeks, I'll be sharing more insights from "The Snowball Effect" about how to spot these invisible goals and use them to drive change. Because once you see these hidden forces, you can never unsee them. And more importantly - you'll know exactly where to push to create real transformation.

Tom De Bruyne
Founder SUE & SATA

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Fear versus reality

The fear narrative around AI's energy consumption reminds me of someone panicking about leaving a LED light on while their neighbor is heating their swimming pool with coal. Yes, AI consumes energy. But it's like worrying about a drop of water while standing next to Niagara Falls.

As environmental scientist Sam Matey points out in his brilliant analysis “The future of AI and Energy”:

"The International Energy Agency expects the AI-driven data centers boom to drive only 3% of electricity demand growth to 2030 worldwide."

That's it. Just 3%.

The Hidden force multiplier

What the doomsayers miss is that AI isn't just consuming energy, it's revolutionizing how we produce, distribute, and optimize energy. It's like having millions of Einstein-level scientists working 24/7 on solving our energy challenges.

Consider this stunning fact from Matey's research:

"Machine learning-enabled predictive maintenance can improve [solar farm] productivity by up to 25%."

That's not just incremental improvement - that's game-changing optimization at scale.

The exponential opportunity of AI x Green Energy

Here's what's truly mind-blowing: We're already in the middle of the fastest energy transition in human history. Solar power is outperforming everyone's wildest expectations. Perhaps the most telling quote from the analysis:

"Ten years ago, some of the top energy experts on the planet thought we'd have a certain amount of solar power generating capacity by 2050, and we got it by 2023."

But there's another powerful force at play that few people are talking about: Big Tech's massive capital investment. The AI boom is driving unprecedented investment in energy infrastructure. Companies like Google are pouring billions into new clean energy projects to power their AI operations. Alongside with consuming energy they’re also expanding the entire clean energy ecosystem with massive capital flows.

The real story is much brighter than you think

The next time someone tells you AI is an environmental nightmare, remind them: We're not facing an energy crisis: We're witnessing an energy revolution. AI isn't the villain in this story; it's the catalyst that could help us solve our environmental challenges faster than we ever thought possible.

What we're seeing is what systems thinkers call a powerful positive feedback loop: AI drives clean energy innovation, which makes AI more sustainable, which enables more innovation. Once exponential effects kick in, accelerating change becomes unstoppable

The real question isn't whether AI will consume too much energy. The real question is: How quickly can AI help us build the clean energy future that's already unfolding?

Tom De Bruyne

PS: I’m currently writing a book called Chain Reactions: How successful leaders transform behavior at at scale. Sign up to my substack to get early access to chapters and insights.

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🫵 Master psychology of influence in our open courses or our Learning and Development Tracks for Teams,
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Behavioral scientist Dan Ariely once remarked that if he had to design a problem that would paralyze people into inaction, it would look exactly like climate change. Yet when Urgenda, a Dutch environmental foundation, decided to take on climate change, they didn't ask people to care more about the environment. Instead, they asked: "Want to help us bring the government to its knees through legal action?"

They succeeded spectacularly.

This gets to the heart of something fascinating about human behavior: the questions we ask shape the answers we get. When I ask my 8-year-old daughter if she wants to eat broccoli, I get a predictable "yuck." But when I ask if she wants ice cream from Tofani's ice cream parlor after finishing her broccoli? That vegetable vanishes faster than you can say "behavioral design."

This principle – that the framing of a question fundamentally shapes behavior – reveals a crucial blind spot in how we approach charitable giving and social change. The effective altruism movement has done incredible work identifying where our donations can have the maximum impact. But they're selling ratio and logic to an audience that craves something else entirely.

Here's the problem: Most charitable appeals target people's compassion and solidarity. This works beautifully – for a specific type of person. But it's like trying to win a game of chess while only moving your bishops. You're artificially limiting your potential impact.

Consider these transformations:

• Instead of "Help prevent malaria with bed nets," what if we asked "Want to show the WHO bureaucrats how to save more lives with one-tenth of their budget?"

• Rather than "Make AI safer," what if we offered "Join the citizen movement keeping Big Tech in check?"

• Instead of "Stop factory farming," what if we invited people to "Take down the industrial farming cartels and fertilizer barons?"

The magic happens because these reframes tap into deeper human needs than just compassion:

1. They address feelings of powerlessness

2. They offer membership in a winning team

3. They provide an opportunity to disrupt broken systems

4. They transform giving from a passive act into active participation

This isn't about manipulating people. It's about understanding that different individuals are motivated by different questions. Some people are moved by appeals to compassion. Others come alive at the prospect of holding powerful institutions accountable. Both motivations can serve the greater good.

So here's my proposal: Let's stop selling giving and start selling impact. Let's create opportunities for people to be part of history-making movements. Whether someone leans left or right, progressive or conservative, they can be drawn to the idea of being part of something bigger than themselves – especially if that something involves challenging the status quo.

Maybe we should stop calling it effective altruism altogether. I asked Claude.ai for a bit of help on this one, and it suggested we call it “The Counter Force” or "The Force Multipliers." I kind of like that. It speaks to what we're really offering: not just the chance to help, but the opportunity to rewrite the rules.

Because in the end, the question isn't "Will you give?" It's "Will you join us in making history?"

Tom De Bruyne
Partner SUE & The Alchemists
tom@wearesata.com

🫵 Master psychology of influence in our open courses or our Learning and Development Tracks for Teams,
🫵 Hire our experts in reputation, transformation and behavioral change.|
🫵 Read our Managementbook Beststeller “The Art of Designing. Behaviour”.
🫵 Follow me on Linkedin
🤓 Subscribe to our Newsletter “1,5 minutes on Influence”,
🎧 Listing to our (Dutch) podcast “Wat gebeurt er eigenlijk in je hoofd?”

This weekend I asked ChatGTP o1 pro, to write comprehensive strategy reports for both my companies. What came back wasn't just good - it was exceptional. Detailed market analysis, strategic recommendations, risk assessments, implementation roadmaps - all delivered in minutes, at a quality that would have cost six figures from a top consulting firm.

But here's what's truly fascinating: this isn't just about strategy reports. What we're witnessing is a fundamental shift in the value of business intelligence. AI is rapidly turning what used to be expensive, exclusive knowledge - guarded by consultants and specialists - into an essentially free commodity.

Need a detailed market analysis? AI can give you that in seconds. Want to understand your competitive landscape? Here's a comprehensive report. Looking for customer insights? They're just a prompt away. What once required weeks of expert work now happens in minutes.

The New Gold Rush

This massive democratization of strategic intelligence is creating a new reality: when everyone has access to brilliant insights, having those insights no longer gives you an edge. The real gold lies elsewhere - in the ability to turn those insights into actual change.

I've been exploring this new frontier extensively, using AI to enhance behavioral design work:

• Creating detailed behavioral personas to test messaging and tactics

• Exploring ideas, tactics and strategies for behavioural change.

• Crafting compelling narratives and communication strategies

• Generating vivid future scenarios for leadership meetings

• Designing nuanced stakeholder engagement approaches

Each experiment has reinforced a crucial truth: while AI can provide brilliant strategy, it can't lead people through change. It can't navigate the complex human dynamics of transformation. It can't build coalitions or overcome deeply rooted resistance.

The New Value Stack

Think of it as a fundamental reordering of business value:

1. Data & Analysis - Now essentially free through AI

2. Strategic Insights - Rapidly becoming commoditized

3. Behavioral Design - The new premium skill

4. Change Implementation - Where real value is created

The implications are profound. The organizations that will thrive aren't those with the best information (everyone will have that), but those who best understand how to make humans move.

The Real Work Begins

This shift makes behavioral design capabilities more crucial than ever:

• Understanding how humans actually make decisions (not how we think they do)

• Identifying what truly drives and blocks behavioral change

• Designing interventions that work with human nature, not against it

• Building momentum in complex systems

• Transforming resistance into forward motion

And most importantly: learning to use AI as an amplifier for these capabilities. Not just for analysis, but as a powerful tool in the behavioral designer's arsenal.

The Path Forward

Think of it this way: If strategic insight is becoming free, but behavioral change remains hard, where should you invest your development? Where should organizations focus their capabilities?

The answer is clear: the future belongs to those who can bridge the gap between free intelligence and expensive change. Those who can transform AI-generated insights into real-world movement.

I'm seeing this play out in real time in my work. While AI helps me generate deeper insights faster than ever, the real value I provide has shifted decisively toward helping people and organizations actually move - overcoming inertia, building momentum, and creating lasting change.

So exciting to be living in this weird, magical era.

Tom

Patner SUE & The Alchemists, experts in reputation and behavioural change
Partner SUE Behavioural Design Academy.
Linkedin.com/in/tomdebruyne

Let's explore three cases where looking beyond our initial panic reveals hidden opportunities for growth:

Argument 1: Trump is great for Europe:
Trump's impact on Europe offers a perfect example of second-order thinking in action. The first-order thinking sees Trump's "America First" doctrine as a devastating blow to European security and trade relationships. But look at what happens next: European leaders are finally being forced to move beyond comfortable dependencies. The second-order effect is a long-overdue push toward strategic autonomy. The hidden opportunity? Trump might be exactly the external shock Europe needed to evolve from a collection of comfortable nation-states into a genuine global power.

Argument 2: Geert Wilders is a blessing for liberals and progressives
The rise of Geert Wilders in the Netherlands follows a similar pattern. First-order thinking sees his surge as an existential threat to Dutch progressive values. The second-order effect is more interesting: his rise is forcing established parties to move beyond their role as bureaucratic managers. The hidden opportunity lies in how this pressure is compelling the political center to rediscover its voice and vision. Sometimes you need a populist threat to shake democratic institutions out of their complacency.

Argument 3: AI-restrictions were a blessing for China
China's response to AI chip restrictions demonstrates this principle in technology. First-order thinking predicted these restrictions would cripple Chinese AI development. The second-order effect was unexpected: being cut off from advanced chips forced Chinese researchers to innovate differently. The hidden opportunity emerged in their development of more efficient architectures that deliver similar performance at lower costs. Sometimes constraints are the mother of invention.

So, what about Europe missing the AI boat? Great news!

Before joining the chorus of concern about Europe falling behind, consider this:

• Pioneers like Microsoft and Google are spending billions on infrastructure and discovery

• First movers have to make big gambles that can always fail spectacularly.

• First movers spend billions in trying to capture market share, but in the AI-era, the traditional market capture advantage is very fragile.

The real value usually emerges not from building the infrastructure, but from innovating on top of it. The value is in the interface with the end user. Europe's position might be perfect for learning from others' expensive mistakes while focusing on developing sustainable, ethical business models that leverage mature AI infrastructure. There’s a famous saying that says: ‘You recognize the first movers by the arrows in their back”.

You recognize the first movers by the arrows in their back.

The limits of Second-Order Thinking.

Of course, second-order thinking has its limits. Not every threat conceals a silver lining, and not every constraint leads to innovation. Sometimes a danger is just a danger. But by training ourselves to look beyond our initial reactions, we often discover opportunities hidden in plain sight. The key is not to dismiss real threats, but to understand how systems respond to pressure – and where that pressure might create space for positive change.

The next time you encounter seemingly bad news, pause before joining the chorus of doom. Ask yourself what happens next, and next after that. The first-order effect might look scary, but the second-order effects could be creating exactly the conditions needed for breakthrough change.

Tom De Bruyne

Curious for more?

• Let’s connect on Linkedin.

• I’m co-founder of SUE Behavioural Design Academy. We train the art and science of behavioural change.

• I’m founder of SUE & The Alchemists, experts in change, transformation and reputation.

• I’m writing a book called “Gamechangers: The hidden forces of change” that will be published in Q3. You can keep track of the themes and stories of the book in this substack.

• Co-host of the Dutch podcast “Wat gebeurt er eigenlijk in je hoofd” and editor of the newsletter by the same name.

• My wife and co-founder Astrid Groenewegen has a great newsletter called “1,5 minutes on Influence”. You can also subscribe to it on Subtack.

To understand this counterintuitive idea, we need to stop looking at Trump as just a political figure and start seeing him as what systems theorists call a "forcing function" – an external pressure that compels a system to evolve or die.

Europe’s Comfort Trap

Europe has been caught in what behavioral scientists call a "comfort trap." Our aging population, prosperity, and stable institutions have made us deeply resistant to change. We've optimized for maintaining what we have rather than adapting to new realities. Like a successful company that becomes too comfortable to innovate, we've prioritized stability over resilience.

Enter Trump. His questioning of NATO, trade relationships, and the liberal world order creates what systems theorists call "parameter disruption." He's not just challenging specific policies – he's forcing Europe to question its fundamental assumptions about security, economics, and global leadership.

The Forcing Function

This disruption operates on multiple levels:

• Military: Europe can no longer simply outsource its defense to America

• Economic: We must rethink our trade dependencies and industrial policies

• Political: The rise of populism forces us to address citizen disconnection from EU institutions

The beauty of this systemic pressure is that it works regardless of Trump's intentions. Whether he means to or not, he's creating what Systems Thinking pope Donella Meadows would call "new feedback loops" that force European leaders to confront uncomfortable realities.

From Wake-Up Call to Action

The proof of this systemic transformation isn't just theoretical - it's showing up in concrete policy shifts. The recently released Draghi report on European competitiveness reads like a blueprint for what happens when a comfortable system gets shocked into evolution. Europe isn't just reacting to Trump's disruption; it's using that pressure to reimagine its future.

Consider these systemic changes:

Defense and Security:

• After decades of underinvestment, European NATO members have significantly increased defense spending

• The EU is developing autonomous defense capabilities and strategic autonomy

• New cooperation frameworks are emerging that don't rely solely on US leadership

Economic Transformation:

• The EU is finally tackling long-standing structural weaknesses through what von der Leyen calls the "competitive compass":

• A revolutionary "28th regime" that creates a single unified framework for corporate law, insolvency, labor law, and taxation

• Aggressive reduction of regulatory burdens that have hampered European businesses

• Strategic investment in high-tech sectors and critical supply chains

• A genuine Capital Markets Union to unlock continental-scale investment

The Power of Productive Disruption

What makes these changes particularly fascinating is their systemic nature. They're not just defensive reactions to Trump's pressure - they represent a fundamental shift in how Europe thinks about its place in the world.

The EU is moving from what systems theorists would call a "parameter adjustment" approach (tweaking existing policies) to "structural redesign" (changing the rules of the game itself). This is exactly what Donella Meadows identified as a higher-leverage point for system change. Changing parameters (tax, rules, tariffs) is the weakest lever for creating systemic change. Changing the rules is a far more superior leverage point.

The Demographic Paradox

Here's where the story gets even more interesting. Europe's resistance to change isn't just institutional - it's demographic. With a median age of 44.4 years (compared to 38.1 for China and 38.3 for the US), Europe has become the world's oldest continent. Our old-age dependency ratio - the number of people aged 65+ relative to those aged 15-64 - is expected to rise from 32% to 57% by 2100. These aren't just numbers - they're the vital signs of a system oriented toward preservation rather than adaptation.

These aren't just numbers - they're the vital signs of a system oriented toward preservation rather than adaptation.

Think of it as a system-level immune response: When nearly a third of your population is focused on protecting their retirement assets and maintaining their quality of life, the system naturally optimizes for stability over innovation. Our populist movements, seen through this lens, aren't just political phenomena - they're the predictable behavior of a system trying to maintain stability at all costs.

Breaking the Demographic Lock-in

This is where Trump's disruption becomes particularly valuable. By threatening the very stability our aging population seeks to protect, he's creating a forcing function powerful enough to overcome demographic inertia.

Consider how the Draghi report's recommendations specifically address this demographic trap:

• The Capital Markets Union isn't just about finance - it's about unlocking the €23 trillion in European household savings (largely held by those 55+) for future-focused investment

• The "28th regime" isn't just about regulation - it's already enabling companies like Spotify and Klarna to scale across borders, creating jobs for young Europeans

• The focus on high-tech investment has led to a 30% increase in STEM job creation, directly targeting Europe's youth unemployment challenge

A New Intergenerational Contract

What's emerging is more than policy change - it's a new intergenerational bargain. Young Europeans are responding to these opportunities in unexpected ways:

• A surge in "intergenerational startups" where young tech entrepreneurs partner with experienced industry veterans

• New pension fund models that explicitly balance current retiree security with investment in future-focused industries

• "Silver tech" initiatives where young innovators focus on solving aging population challenges

The data tells this story clearly: Regions of Europe that have embraced these changes are seeing up to 3x higher rates of young entrepreneur success compared to those maintaining traditional barriers.

The Gift of Disruption

There's a profound irony in Trump's role as Europe's unwitting change agent. By threatening the stability our aging continent desperately wants to preserve, he's forcing us to build something more valuable: resilience.

Systems theorist Donella Meadows once wrote that the highest leverage point in any system is "the power to transcend paradigms." Trump's disruption - combined with pressure from Putin and Xi - hasn't just forced Europe to change its policies. It's forcing us to transcend our paradigm of preservation and rediscover our capacity for reinvention.

The results are becoming clear: A Europe that's:

• More strategically autonomous but not isolated

• More integrated but not rigid

• More competitive but not at the expense of its social fabric

• More focused on the future while still protecting its aging population

So perhaps it's time we Europeans send an unexpected thank you note across the Atlantic. Not for Trump's intentions or methods, but for providing the systemic pressure we needed to evolve. Sometimes the most valuable gifts come in the most unsettling packages.

The real question now isn't whether Trump is good or bad for Europe. It's whether we'll have the wisdom to use this disruption to build something better than what we're trying to preserve.

Image courtesy of Erik Torenberg

How the Rainbow Hydrant Became a Rorschach Test for Ideology

The internet is a strange place, but perhaps nowhere is it stranger than in its capacity to turn ordinary objects into battlegrounds for ideology. Take, for example, the image of a rainbow-painted fire hydrant in front of a neighborhood engulfed in flames. Its sarcastic caption reads: "Doesn’t provide water. But is affirming."

In a single image, we see a potent example of how framing works—how facts and visuals are bent, reshaped, and weaponized to reinforce existing worldviews. To people on the right-wing spectrum of political ideology, this image represents the folly of prioritizing symbolic gestures over practical solutions. To people on the left, it’s a cynical attack on inclusivity, designed to mock efforts to create a more tolerant society. Neither interpretation necessarily reflects reality—but both reveal something profound about the human tendency to interpret facts through the lens of our beliefs.

Let’s unpack what’s happening here and why it matters.

Framing: The Art of Guiding Interpretation

At its core, framing is about context. It’s the way information is presented to shape how we perceive it. In behavioral science, we know that the same set of facts can lead to radically different conclusions depending on how they are framed. For instance, a glass that is “half full” suggests abundance, while one that is “half empty” implies scarcity.

The caption of the rainbow hydrant is a textbook example of framing at work. By emphasizing the hydrant’s lack of function (“Doesn’t provide water”) and juxtaposing it with its symbolic purpose (“But is affirming”), the caption primes the viewer to see a conflict: practicality versus symbolism, utility versus ideology. Whether you find this framing amusing or enraging likely depends on your existing beliefs.

This is where the power of framing collides with polarization.

The Polarized Mind: Why We Bend Facts

In a polarized world, facts don’t simply inform our beliefs, but rather become ammunition in our ideological battles. This phenomenon is rooted in a cognitive bias called confirmation bias: our tendency to seek out and interpret information that reinforces what we already believe.

When presented with an ambiguous stimulus—like the rainbow hydrant—our brains rush to fill in the gaps with meaning that aligns with our worldview. If you believe that symbolic gestures often distract from real solutions, this image is proof. If you believe that critics of inclusivity are dismissive and cruel, the image is also proof. In both cases, the underlying facts (a painted hydrant, a burning neighborhood) remain the same. What changes is the story we tell ourselves about what those facts mean.

The polarized mind craves coherence. It wants to see the world in a way that aligns with its values and beliefs, even if that means bending reality. This is why two people can look at the same image and see entirely different truths.

The Danger of Framing in a Polarized World

Framing isn’t inherently bad. In fact, it’s a necessary part of how we navigate the overwhelming complexity of the world. The problem arises when framing becomes a tool for deepening divisions rather than fostering understanding.

Consider the rainbow hydrant. If you strip away the framing, what you’re left with is a functional question: Does this hydrant work? If not, why? And what can be done about it? These are practical, solvable issues. But when the conversation is reframed as a symbol of everything wrong with “the other side,” the possibility of a constructive solution evaporates. Instead, we’re left with outrage, mockery, and a further entrenchment of our ideological positions.

This is the great irony of framing in a polarized world: It makes us feel more certain even as we move further away from the truth.

How to Resist the Pull of Polarized Framing

So, what can we do? How do we resist the urge to see every image, every fact, as proof of our worldview? Here are a few suggestions:

1. Pause Before Reacting: When you encounter a provocative image or statement, take a moment to ask yourself: What assumptions am I bringing to this? What might someone with a different perspective see?

2. Seek Out the Facts: Go beyond the framing. In the case of the rainbow hydrant, this might mean asking: Was this hydrant operational? What’s the story behind the painting? What steps are being taken to address the fires?

3. Engage with Complexity: Recognize that most issues are not as simple as they’re made out to be. Symbolism and practicality are not mutually exclusive; both can matter in different ways.

4. Practice Empathy: Try to understand why someone might interpret the same facts differently. This doesn’t mean you have to agree with them, but it can help you see the limitations of your own perspective.
   

Why this matters

The rainbow hydrant is more than just a quirky internet meme. It’s a mirror, reflecting back our polarized world and the ways we use framing to make sense of it. If we want to move beyond the echo chambers of ideology, we need to become more aware of how framing shapes our perceptions—and more willing to challenge the stories we tell ourselves.

Because at the end of the day, the truth doesn’t belong to the left or the right. It’s sitting quietly in the middle, waiting for us to notice.

Tom De Bruyne
Founder SUE Behavioural Design Academy and SUE & The Alchemists

🫵 Master psychology of influence in our open courses or our Learning and Development Tracks for Teams, and our Research and Advisory Services.💡 Read our Managementbook Beststeller “The Art of Designing. Behaviour”. 🎧 Follow me on Linkedin 🤓 Subscribe to our Newsletter “1,5 minutes on Influence”

1. Access to super intelligence is here. Now what?

Only two years ago the question “What would you do if you had infinite access to super intelligence” probably sounded like a weird thought provoking conversation starter for a creative workshop. Today, with the introduction of 4th-generation GPT models, we’ve reached a tipping point. AI now excels in domains previously reserved for human expertise, such as legal research and medical diagnosis, and we already have access to unlimited amounts of insane intelligence on every domain you can imagine, for free.

The big debate right now is what the implications are of this sudden boost in access to superintelligent AI’s. Some argue it’s terrifying and it will destroy millions of jobs, before it will turn against us and kill us all, while others argue - in my view correctly - that technology moves much faster as societies’ capacity to internalize it.

The big question to which we will have to figure the answer in the upcoming years is pretty straightforward: How do we leverage this unlimited access to intelligence? How do we harvest its potential? How do we integrate it in our daily work, and our routines and habits in order to grow as people, families, companies and societies?

I want to argue that we need to rethink the way we think about “being intelligent”. We used to think of intelligence as having a lot of knowledge and knowhow. That will radically change towards knowing which questions to ask. True wisdom in the AI-era is knowing how to aks the right questions to solve problems.

2. Wisdom is the ultimate form of intelligence

You might have seen this visual before. It’s called the DIKW-pyramid. The pyramid is an elegant framework to think about intelligence. At the bottom of the pyramid is data. Data intelligently combined produces information. When the realtime data of train traffic is made available to travellers, it’s great to have the information that your train has a 20 minutes delay. One intelligence level higher is knowledge. If I know that my train has a 20 minutes delay, and I know I can trust the accuracy of this information, I might decide to stay in the office a bit longer, because I have the knowledge and knowhow that I’ll be on time to catch my train.

The ultimate level of intelligence is wisdom. It’s the capacity to apply knowledge and knowhow to completely new context. In our example above: My experiences with understanding how access to real time information works in dealing with trains, might inspire me to apply the same techniques, tools and frameworks to dealing with flying, navigating through cities I have never been to before - by asking Google Maps to send me an alert when to call a taxi to be on time on my meeting -, or even investing in the stock market. Wisdom is knowing how to transfer knowhow from one domain to another.

3. The ultimate form of wisdom is asking the right questions

If wisdom is the capacity to transfer knowledge and knowhow to new context, then what exactly do wise people do? What sets apart the best people in any given field at the top tier of the distribution curve? In other words: What sets apart someone who could repeat the same levels of success even when you put them in a completely new context, challenge or industry?

I think there are three superpowers that wise people harness better than other people

Superpower 1: Mental models for analyzing problems

This is one is the most impotant one. When Coen Dekker and Erik Soeteman, two brand leaders at Swinkels, a famous Dutch brewing company think about a marketing problem, they use the most important proven mental models on branding to dissect problems and opportunities for their brands. And that makes them incredibly successful in discovering the best strategies within the budget constraints they have. For every brand they have under their responsibility they ask themselves and their teams questions based on the proven theories about how brands grow:

• How can we make this brand distinctive within the category, so that we make it easy for customers to feel we’re unique and intriguing?

• What are the category entry points that drive consumer behaviour in this category? Are we connecting with these category entry points?

• How can we boost choice preference on the moment of purchase? Which cues do we offer for customers to unconsciously prefer our brand above other brands

Wise brand marketeers are very disciplined in dissecting their brands strengths and weaknesses using these mental models from the science of how brands grow. Based on this analysis, they then make bold decisions on where to invest in.

You can think of mental models as the application of the most important proven scientific concepts that are available and use them as a lens for 1) analyzing problems, 2) spotting opportunities and 3) making decisions.

Superpower 2: Reverse-engineering outcomes

The second superpower that wise people know how to harness is that they are great at reverse engineering their thinking from the outcome they want to accomplish.

In other words: they prioritize outcomes above outputs. When you are fully committed to producing a working product within a 6-weeks period, and you have knowhow and wisdom at your disposal to make it happen, you will most likely ask yourself the following questions at the end of each week;

• How do I know I am on track? Which signals are worth tracking and paying attention to?

• What are potential risks and obstacles I will need to tackle?

• Wat kind of interventions will I have to do to re-allocate focus and resources in order to mee the deadline?

• Which trade-offs do I need to make? Which parts can I kill, and which parts do I give extra resources, in order to increase my chances of achieving the outcome that we are looking for?

Wise people have a deep understanding of steps to take towards achieving an outcome. What’s even more important: They know how to track progress, and shift gears if one of the signals is red.

Superpower 3: Bias toward action.

Wise people know that knowledge is not a goal as such, but a means towards achieving successful outcomes. They know that the value of intelligence (data, knowledge, wisdom) is only valuable when it facilitates decision-making.

When I do a performance review of my staff, I have an infinite amount of data at my disposal. I can look at how much billable hours they have spent, or I can look at customer satisfaction or retention on their projects. I can look at the revenue and profit they generated. And I can look at what their peers tell about them. the number of data points are gigantic.

However, the only question that really matters to the company is: What are the data points that tell me the most about whether I should offer this person a new contract or whether it’s a better leadership decision if we terminated the collaboration. It’s my leadership responsibility to look for the data points that allow me to make an estimated guess or to make a bet on the future value of this person to the company. If I make a bad bet, it could damage the team and the company in many ways.

Wisdom in this context is about:

• understanding that there’s a lot of typical biases at work in assessments that I need to be conscious of. I might suffer from confirmation bias, because I like the person a lot.

• understanding that a lot of the data might have correlation, but no causation with quality. E.g. the data might tell the person was highly billable, but this might be produced by working on a big project another collaegue was responsible for.

• I might realize that the best predictors for future success are not to be found in the data, but in more abstract observations like: How hungry for learning is this person? Is he/she a ferocious learner?

In each case, the quality of my leadership decisions are directly related to my capacity to make high-quality predictions based on carefully selecting the right information, and my capacity to avoid making biased decisions.

4. Wisdom in the AI-era: From oracle to decision-instrument

This finally brings me to the question of what wisdom is, when we have free unlimited access to intelligence at our disposal. I think the definition of intelligence in the AI-era is the following:

Leveraging AI as a highly skilled collaborator to assist in making informed decisions and achieving successful outcomes is the new frontier of intelligence.

As long as we keep thinking of AI as a tool for knowledge and knowhow, we are looking in the wrong direction. AI as an oracle is pretty cool, but that won’t change the world. What will change the world are all these wise people who move forward in building incredibly persuasive products and services, while harnessing the power of Ai at every stop of the journey to make better decisions.

Every startup ever built, is nothing more than a gamble on what people might like so much that they would be willing to change their behaviour and invest their time, money and hopes into something new. Those founders who know how to harness the power of the best project manager, the best product leader, the best CTO, CFO, UX’ers, Designers, Growth Hackers en marketeers, all personified by one AI, will become the new superstars.

The key question for each of us is: how do we develop the wisdom to make better decisions, with AI as our partner?”

The future is going to be incredibly weird and cool at the same time.

Curious to hear your thoughts and comments.

Tom De Bruyne,
Partner SUE & The Alchemists
tom@wearesata.com

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