Introduction

We are nearing the end of our book The Great Reorientation: A Tech-Surfer’s Guide to the AI Universe. The book draws on technology, psychology, and philosophy to explore the hidden effects of today’s fast-growing technologies. One of these effects is a gradual sense of detachment, in which the tools we built to extend our abilities begin to replace us. The book responds by shifting focus back to what matters most: human agency, judgment, and joy. To stay close to the technology itself, we follow the field daily, including the Innermost Loop newsletter written by Alex Wissner-Gross. Almost every episode ends with a quote. The quote from April 30, 2026, caught our attention and keeps us puzzling.

“Civilization is the dataset, the Singularity is the model, we are the labels.”

We would like to unravel that line in the light of human agency.

We are not negative critics of artificial intelligence; on the contrary. Yet that single sentence carries large implications for human agency. It casts civilization as a dataset, the Singularity as the training model, and us as the labels. Each part deserves a closer look. Civilization is not a dataset in any fixed sense; it is reshaped by human experience, interpretation, and memory. The Singularity is not best understood as a model either, but rather as a phase, a drift toward increasingly self-reinforcing automation. And describing people as “labels” reduces the human role of creating context and meaning to something merely clerical, when in fact it is an active and ongoing process of understanding. So, if the shifting dataset we provide is training a vast intelligence, and our contribution is reduced to the labels it learns from, one question lies beneath it all. Are we slowly agreeing to become the thing that gets sorted, rather than the one doing the sorting?

The real question here is agency, and it matters more than capability, more than alignment, more than any benchmark. Agency is what made progress happen. We, as human beings, made the choices. That privilege is now shifting under the weight of AI.

The Innermost Loop is a careful record of technological developments and benchmarks, and benchmarks have their place. Parts of judgment can even be measured, things like accuracy, calibration, and speed. At the same time, there are deeper dimensions that are less quantifiable: choosing what is worth doing and then taking responsibility for it.

The thing intelligence cannot replace

It is easy to assume that progress is mostly about intelligence. Add more of it, make it faster and cheaper, and the future seems to improve on its own. That idea is everywhere, but it leaves out something important and consequential.

Intelligence generates options.

Agency chooses among them.

Intelligence can optimize.

Only agency can decide what is worth optimizing.

Intelligence can predict a hundred futures.

Agency decides which one is worth living in and then takes responsibility for that choice.

A civilization can hold an abundance of intelligence and a shortage of agency. That imbalance may be one of the defining risks of our moment.

McGilchrist has spent much of his work describing how we attend to the world through the brain’s two hemispheres. One mode of attention is narrow, precise, and brilliant at manipulation, but it can mistake its model for reality. The other is broader, more contextual, and more in touch with the living whole. In his account, a healthy mind keeps these in balance, where the broader master provides orientation and the narrow servant, the emissary, reports to it. Our trouble, he argues, is that modern civilization has inverted this relationship. The emissary now acts as if it is in charge.

This is a useful frame for thinking about technology. Technology is meant to be the right hand of human judgment. It is capable, fast, and strong, the limb that extends our reach. A hand is a remarkable appendage. But a hand does not decide where the body goes. The moment the hand starts choosing the direction, and the rest of the body simply follows, something has gone wrong. The goal is not a weaker hand. The goal is a master who remains attentive.

The evidence that agency is slipping

None of this is a romantic reminiscence about the good old days, nor is it a utopian promise that wisdom will win on its own. It is a claim you can check against current evidence.

Start with how people actually use AI assistants in their everyday work. Anthropic’s Economic Index, based on large-scale analysis of real interactions, reveals a recurring tension. One mode is augmentation, where a person works with the system as a thinking partner. The other is automation, where the task is delegated, and the person steps back.

Over time, the balance shifts rather than settles, with periods where collaboration dominates and others where delegation becomes more prominent. The broader picture is not a clean trend but an ongoing negotiation over how much control remains with humans.

Figure 1. Are we keeping our hand in? The contested balance between augmentation, working with AI, and automation, handing the task over. Adapted from the Anthropic Economic Index, 2025 to 2026.

Then consider what happens when more of the task is handed over. A growing body of research from 2025 and 2026 describes effects grouped under the headings of deskilling and automation complacency. When systems provide fluent and confident answers, people tend to verify less, defer more, and gradually lose the underlying skill that lets them judge the merits of a recommendation in the first place.

The pattern now shows up across several professional domains, and the medical literature is especially candid about it. Studies find that false-positive suggestions from diagnostic AI can pull clinicians toward the wrong call. That overreliance erodes the very competence it was meant to support. Automating clinical decisions can also begin to distance a clinician from the moral face of care.

The most experienced practitioners hold up best, which tells you something important. Agency is not a possession. It is a practice, earned through experience and the intuition that grows with it. Which raises a harder question. What happens to agency in practitioners who never got to build that craft, because the system answered before they ever had to, and who now rely on it?

Yet even well-tuned practices decay when they go unused. This is what a group of researchers recently termed gradual disempowerment. Their argument is that we do not need a dramatic machine takeover to lose control of our own systems. We only need to keep handing over judgment, one reasonable step at a time, until the institutions we depend on no longer require human participation to function. At that point our influence slowly fades, even though no one ever formally decided to give it away. The danger is not ill will. The danger is drift.

Recovering agency at three scales

We believe the erosion of agency is recoverable. It can be rebuilt deliberately, but recovery has to work on all three scales at once: the personal, the organizational, and the civilizational. A person with intact agency inside a hollowed-out institution is still navigating blind. A well-run institution inside a lawless sea is still at the mercy of the current.

The first focus is the person. People begin to feel that the defaults are choosing for them, that the path of least resistance has gradually become the only path. The answer is not to reject the tools. It is to become, again, the author of your own response, to know what yours is to decide and to keep making those decisions on purpose. Our book sets out a practice for this, a way of holding your identity and judgment steady against the pull. We will not lay out the full method here. We will only say that it exists, that anyone can build it, and that it changes how the acceleration feels to live inside.

At the organizational level, the picture is no longer hypothetical. In early 2026, Business Insider profiled a founder running a real business with a council of fifteen AI agents, and around the same time, a technologist published the working logs of an autonomous agent making operational decisions on its own. The zero-employee company has stopped being a projection and become a real reference point. The question this raises is simple and severe: When an organization’s choice is increasingly made by systems, does human judgment still leave a trace? Is the human contribution visible, significant, and can it be audited after the fact? An organization can answer yes to all of these questions, but only if it is deliberately built to do so. The key point is that distributed authority among machines is not the same as abandoned authority, and the difference is something that can be designed.

Figure 2. Agency at three scales. At each scale, the test is whether there is a trace.

The widest frame is civilization itself. This is where that opening sentence comes home. Recall the claim that we are the labels. If there is no shared baseline, those who move fastest and can push costs onto others tend to be rewarded, gain attention, and collect more of those labels. The response is neither a world government nor a pause. It is governance architecture, a set of baseline norms, applied locally, that keeps the shared ocean navigable. This is difficult, and it is not utopian. It is simply the civilizational expression of the same discipline a person practices in private, and the same discipline an organization builds into its design.

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Why openness has the better odds

There is a practical consequence here, and it is where we want to plant a flag. The agent ecosystem, the fast-growing layer of software that will increasingly act on our behalf, is being built right now. The deepest question is not how capable the agents become. It is what they run on: a few closed platforms, or a broader foundation of open ones.

We believe the open path gives us the best chance of reaching an AGI future that stays steerable and shared, rather than concentrated in a few hands. The reasons are practical. Open-weight models can be inspected, audited, and reproduced. The more people who can see inside a model, the harder it is for any one group to control it. And the more human judgment leaves a trace, others can question it, test it, and improve it. Openness turns oversight from something into something everyone can participate in.

This is no longer aspirational. By 2026, open-weight models from labs around the world have already closed much of the gap with the frontier. A large share of AI deployments already relies on them. Portability matters too. A tool you can take with you must keep earning your trust.

We are not going to pretend this is clean. Once open weights are released, they cannot be recalled, and their safety guardrails can be removed. Compute, data, and talent are still concentrated in a handful of large labs. History also reminds us that open systems don’t automatically displace dominant platforms.

That is why our claim is narrower, and we think, stronger. Open source is not a solution by itself. It is the architecture that gives distributed authority a fighting chance. It only works when paired with governance, not as a substitute for it. Open and governed, not open versus governed.

This is the deliberate, decentralized path applied to the agent layer. Researchers such as Ben Goertzel have argued for decades: many hands building the system, a shared baseline beneath them, and enough transparency for everyone to see when it begins to drift. A closed ecosystem built around a few chokepoints concentrates authority by design. An open one makes it possible to distribute authority instead. That is how a civilization keeps the pen in many hands. Trust is the currency of the future!

Keeping the hand a hand

When the three scales are viewed together, a single principle emerges. At every level, the work is the same: to keep technology in the position of the right hand, immensely capable, fully engaged, and always in service to human judgment. Not because machines are dangerous and humans are noble, but because progress that leaves a society stronger rather than merely more efficient requires someone to remain responsible for the direction of travel.

Figure 3. The right hand. Technology as the emissary, extending reach in service to the master and human judgment that holds context and carries responsibility. After McGilchrist, 2009 and 2021.

This matters for more than efficiency, and it is the reason we find most compelling. A healthy society is one in which judgment remains alive at every level: in the person at the bench, the team in the room, the organization making collective decisions, and the institutions that shape the rules we all live by. Wholeness is not a mood. When judgment is exercised throughout the system, responsibility gets distributed with it. When judgment is surrendered upward to increasingly autonomous systems, people gradually stop authoring their own lives and begin consuming the outputs of a machine they no longer meaningfully influence.

That is why we need to reorient on agency, practiced deliberately at all three scales. A person cultivates it through deliberate choices. An organization builds it into its design. A civilization protects it through institutions and shared norms. None of this produces a perfect society. It produces one that can still notice when it is drifting—and still has the capacity to correct its course.

That is the argument. Intelligence will continue to become cheaper. Judgment, responsibility, and authorship will become more valuable. The people, organizations and societies that continue to treat agency as something to be practiced rather than surrendered are the ones most likely to navigate what comes next.

The current does not get to make the choice. We still do.

The three scales of agency: the personal, the organizational, and the civilizational, form the closing movement of our forthcoming book, The Great Reorientation: A Tech-Surfer’s Guide to the AI Universe. The practices introduced here are fully developed in the book.

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References

Anthropic. (2026). Anthropic Economic Index. Anthropic. https://www.anthropic.com/research/economic-index-june-2026-report

International AI Safety Report. (2026). International AI safety report 2026. arXiv. https://arxiv.org/pdf/2602.21012

Kulveit, J., Douglas, R., Ammann, N., Turan, D., Krueger, D., & Duvenaud, D. (2025). Gradual disempowerment: Systemic existential risks from incremental AI development. arXiv. https://arxiv.org/abs/2501.16946

McGilchrist, I. (2009). The master and his emissary: The divided brain and the making of the Western world. Yale University Press.

McGilchrist, I. (2021). The matter with things: Our brains, our delusions, and the unmaking of the world. Perspectiva Press.

The open-weight paradox: Why restricting access to AI is counterproductive. (2026). arXiv. https://arxiv.org/pdf/2604.17413

Springer Nature. (2025). AI deskilling is a structural problem. AI & Society. https://link.springer.com/article/10.1007/s00146-025-02686-z

Springer Nature. (2025). AI-induced deskilling in medicine: A mixed-method review and research agenda for healthcare and beyond. Artificial Intelligence Review. https://link.springer.com/article/10.1007/s10462-025-11352-1

Wissner-Gross, A. (2026, April 30). Welcome to April 30, 2026. The Innermost Loop. https://tinyurl.com/2ytra9s9

Distributed Leadership as Collective Navigation

There is a moment on the water when something subtle shifts. At first, nothing seems different. The wind still blows. The board still glides. The rhythm feels familiar. Then the wave beneath you begins to move in a way that no longer responds to instinct alone. It accelerates, not only in speed but in complexity. Currents intersect. Forces you cannot see begin to shape the surface. And suddenly, what used to work no longer works.

This is where we find ourselves with artificial intelligence. What we once called business as usual, predictable systems, understandable decisions, manageable complexity, has quietly become something else. The unusual is no longer the disruption. The unusual is the new baseline. AI, and the path toward the systems now being called AGI, is not simply adding capability. It is changing the nature of the wave itself.

And it is doing something stranger than most of the headlines admit. Two things are moving at once, in opposite directions. The capacity to solve is rising. The authority to shape what gets solved is concentrating. And the consequences of all that solving are dispersing across everyone. Capability up, authority in, consequences out. That double movement is the real architecture of this moment. It is also the problem that everything below is here to address.

Complexity without a ceiling

For most of human history, leadership operated inside a quiet constraint. The world could be complex, but it remained, in principle, understandable. A capable leader, with enough time and the right advisors, could hold a working model of the system they were steering.

That constraint is dissolving. AI systems now process more variables than any individual or institution can track. They generate options across domains faster than people can interpret them. They detect patterns that lie beyond the reach of human perception. At first, this feels like pure progress. And it is. But beneath the surface, a new asymmetry is forming. AI operates in an expanding space of solutions. People operate in a bounded space of understanding. The gap between what can be solved and what can be understood is widening. And the more effectively we solve, the less we may grasp what is being solved, at what cost, and for whom.

The central question shifts without hardly announcing it. It is no longer: ‘Can we solve this problem?’ But: ‘Do we understand what solving it does to the system as a whole?’ Increasingly, the honest answer is that we do not. Not because the people in the room are incapable, but because the boardroom itself can no longer hold the relevant picture.

There is a second thing happening beneath the first, and it deserves to be named plainly because it serves as a counterweight to every optimistic framing. The capability to solve is not distributing evenly. It is concentrating. Compute pools in a handful of frontier laboratories. Training data funnels through a small number of collectors. Capital flows toward whoever can afford the next order of magnitude. Platforms consolidate. Narratives converge.

The numbers are stark. Epoch AI finds that the training compute behind frontier models has grown roughly four to five times every year for over a decade, a pace with almost no precedent in the history of technology (Epoch AI, 2026). Yet the number of actors who can sit at that frontier has not grown with it. A few labs, riding on a few cloud providers, hold the majority of frontier compute. The infrastructure that makes the solutions possible is, in 2026, the most centralized it has ever been. This matters. It will keep mattering.

Graph 1. Capability rises, control concentrates. Frontier training compute climbs four to five times per year, while a handful of labs and clouds hold the majority of it. Adjusted from Source: Epoch AI, Trends in AI.

When control becomes appearance

Traditional leadership rested on three assumptions worth stating out loud, because the current wave is quietly dissolving each one. Information flows upward, to the people charged with deciding. Decisions, once made, flow back down and are executed. Accountability sits at the top, with the people whose choices shaped the system's actions.

In an AI-mediated organization, each of these is now half true. Information is everywhere, not primarily flowing upward. Decisions are partly automated and distributed across systems, no single person designed. Consequences are systemic and diffuse, arriving in places the original decision-makers cannot see.

Watch the human role thin out as the systems thicken. A person begins by doing the work. Then, as the system takes over execution, the person manages it. Then, as the system manages itself, the person audits it. Then, as the auditing becomes nominal, the person simply consumes what the system produces. Do, manage, audit, consume. This is not inevitable. But it is gravitational.

At the far end of that gravity, a particular illusion forms. The person, or the institution, still appears to be in control. Approvals are signed. Dashboards are watched. Exceptions are handled. But the substance of the decision has already happened elsewhere. The person is in the loop only in the narrow sense that the loop runs through them. The loop is no longer theirs.

Graph 2. As agentic systems take on more, the load-bearing share of the human role thins, unless it is deliberately defended. Reworked Concept after Kulveit et al., Gradual Disempowerment (2025).

A growing body of serious research now names this directly. In Gradual Disempowerment, a group of AI researchers argues that we do not need a dramatic robot takeover to lose the plot (Kulveit et al., 2025). Incremental, perfectly ordinary improvements are enough.

As AI quietly replaces human labor and cognition inside the systems that society runs on, the economy, the institutions, and the culture, those systems stop depending on human participation to function. And the moment they no longer need us to function, our influence over them erodes, even if every individual model is doing exactly what it was told. Their unsettling conclusion is that solving technical alignment will not be enough. We can build AI that faithfully follows instructions and still drift into a world where humans no longer steer.

The fork in the digital river

At the center of this transition sits a choice that is not primarily technological. It is civilizational. The same capabilities can be built in two radically different ways, producing two radically different futures. Leaders do not get to opt out of the choice. They only get to influence which river the current carries us into.

One river (the left) is acceleration without wisdom. It is goal-maximizing, fragmented, and meaning-blind. It is driven by unchecked exponential growth, more compute, more data, more capability, and by the particular urgency that competition without coordination generates. In this river, optimization runs faster than reflection. Consequences propagate faster than understanding. The systems work, but their effects drift beyond visibility. Leadership does not disappear here. It becomes performative. Decisions are made elsewhere. Responsibility diffuses. Burden shifts quietly to whoever is least able to refuse it.

The other river (the right) is for deliberate navigation. It is context-sensitive, ethically reasoned, and slower by design. It builds adaptive guardrails. It invests in safety institutes, observatories, and democratic coalitions. It treats the question of what is worth doing as seriously as the question of what is possible. The systems in this river are steerable. They may also be slower. Steerability is worth more than speed when the waters are unfamiliar.

The naming of these two rivers is not decoration. Iain McGilchrist has spent two careers describing two modes of attention that correspond to the two hemispheres of the brain (McGilchrist, 2009, 2021). The left attends narrowly. It decontextualizes, manipulates, and mistakes its map for the territory. It is brilliant at specific tasks and blind to the whole that makes those tasks meaningful. The right attends broadly. It holds context, tolerates ambiguity, and stays in contact with the living whole. His warning is that modern civilization has inverted the proper relationship between them. The emissary has taken command of the master. The first river is the emissary unbound. The second is the master restored. When we speak of a fork in the digital river, we are describing two civilizational operating systems and two different settlements between precision and meaning.

Distributed leadership looks different in each. In the ‘Left River’, it becomes performative. The forms of distribution remain: many teams, many agents, many partner ecosystems, many tokens and votes, but the substrate concentrates. A handful of models power the agents. A handful of clouds hosts the coordination. A handful of capital allocators decides which protocols survive. What looks from the outside like a diverse ecosystem is, underneath, a small number of chokepoints wearing many faces. Local autonomy becomes decoration. In the Right River, distribution becomes constitutive. Authority is genuinely held across multiple centers of sensing and decision. Local context is respected, because it is the only place some things can be known. Trade-offs are surfaced rather than hidden. The whole becomes steerable because the parts are trusted. The ‘Left’ and ‘Right’ River metaphor is explained in greater depth in the book ‘The Great Reorientation’.

The hidden movement of burden

Here is the question that deserves to sit at the heart of all of this. Can complex systems handle complexity without quietly shifting the burden somewhere else? The answer, rarely acknowledged, is rarely. Systems do not eliminate trade-offs. They redistribute them. Efficiency in one place shows up as cost in another. Optimization at one scale externalizes consequences at another. The trade-offs do not vanish. They move. And they tend to move along the power gradient.

Karen Hao documents this at the civilizational scale in Empire of AI (Hao, 2025). The computational layer of modern AI rests on data labeled by workers in Kenya, Venezuela, and the Philippines, paid fractions of the value their labor produces. It rests on water drawn from communities already short of it, to cool data centers whose outputs are priced in currencies that those communities do not set. It rests on creative work ingested without consent, and on the quiet assumption that the costs piling up at the periphery are less real than the capabilities piling up at the center.

The energy figures make the abstraction physical. The International Energy Agency projects that electricity demand from data centers will roughly double from around 485 terawatt-hours in 2025 to nearly 1,000 terawatt-hours by 2030, with the AI-specific share growing fastest of all (International Energy Agency, 2025). That second figure is close to Japan's entire electricity consumption. The burden of the boom is not theoretical. It is measured in rivers, grids, and wages, and it lands first on the people with the least say in it.

Distributed leadership is, among other things, the architecture that makes this movement visible. In centralized systems, the burden accumulates where it is least able to resist and least able to be seen. Dashboards do not reach there. By the time it surfaces, it has often compounded into something the system can no longer easily address. The discipline of distributed leadership is to surface burdens early, where they land, in the specific hands of the specific people carrying them. This is not a comfortable property. It is a necessary one.

And yes, there is now talk of building data centers in space, which would lift some of that energy burden off the planet. We are not there yet. And even when we are, the move will not dissolve the concentration of power. It will carry it into a new dimension, one where control over the infrastructure sits, quite literally, above us.

Graph 3. The burden moves outward. Data-center electricity demand climbs toward 1,000 TWh by 2030, the AI slice growing fastest. Reworked from Source: IEA, Energy and AI (2025).

Coherence without control

So we arrive at the paradox that gives this piece its name. Centralization offers stability and speed but eventually calcifies, losing touch with the edges it depends on. Decentralization offers diversity and resilience, but without coherence, it eventually fragments, losing the capacity to act together when that is exactly what the moment requires. Each has real virtues. Each has real failure modes.

The resolution is not to pick a side. It is to introduce a third principle: coherence without control. A system can move together without being commanded from a single point. It can hold direction without a single director. What holds it together is not a hierarchy of decision rights, and not the absence of structure, but a set of shared commitments that let many local decisions compose into a coherent whole. Five field principles describe its shape. Coherence over control. Context is local. Consequences are shared. Meaning is collective. Wisdom evolves.

This is not a soft idea, and it is not new. A Polynesian outrigger crossing the open ocean does not rely on a single navigator. It relies on distributed awareness. Stars read by one set of eyes. Swells felt through the hull by another. Wind tracked by a third. Memory was carried in the oral tradition of the whole crew. Each member contributed what they could sense, know, or carry. Alone, none of them could make the passage. Together, they could. The boat is not a metaphor for a command structure. It is a working demonstration of coherent distributed intelligence, tested over thousands of years at sea.

The outrigger is also honest about its limits. The crew is well-organized. The ocean is not. The weather is not. The currents are not. The crew’s discipline does not eliminate risk. It makes the risk navigable. And the concentration gradient does not stop at the edge of the boat. A team inside a company can build a genuinely distributed practice and still operate inside a wider substrate, the platforms, the models, the capital flows, which all pull hard toward the Left River. This is not a reason to give up the internal practice. It is a reason to see that distributed leadership at the team level requires distributed leadership at the industry level, the policy level, the civilizational level. The crew on the outrigger is well-organized. The ocean is not. Both matter.

Even the technologists most associated with raw acceleration are beginning to argue this. Vitalik Buterin’s case for what he calls defensive, or differential, acceleration is, at heart, an argument for the Right River, deliberately built toward decentralization and resilience, rather than assuming the default current will get us there (Buterin, 2023). The point is not to slow down for its own sake. The point is to stay steerable while moving fast.

Because that is the real risk, stated simply. We are entering a world where intelligence is concentrating in machines at a speed and scale with no precedent. If leadership concentrates alongside it, if the substrate centralizes and authority centralizes with it, something breaks. Not immediately. Gradually. Quietly. Until one day we notice that we are moving very fast, and no longer steering.

Distributed leadership is not a management trend. It is the counter-gravity. It does not appear by default. It has to be built, deliberately, against a current that is actively pulling the other way. The effort required to build it is the measure of the force pulling against it. That force is substantial. So the building has to be.

The good news is that the current, the flow, does not get to make the choice. We do. One wave at a time.

This essay is drawn from Chapter 17 of my forthcoming book, “The Great Reorientation: A Tech-Surfer’s Guide to the AI Universe.”

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References

Buterin, V. (2023, November 27). My techno-optimism. https://vitalik.eth.limo/general/2023/11/27/techno_optimism.html

Epoch AI. (2026). Trends in artificial intelligence. https://epoch.ai/trends

Hao, K. (2025). Empire of AI: Dreams and nightmares in Sam Altman’s OpenAI. Penguin Press.

International Energy Agency. (2025). Energy and AI. IEA. https://www.iea.org/reports/energy-and-ai

Kulveit, J., Douglas, R., Ammann, N., Turan, D., Krueger, D., & Duvenaud, D. (2025). Gradual disempowerment: Systemic existential risks from incremental AI development. arXiv. https://arxiv.org/abs/2501.16946

McGilchrist, I. (2009). The master and his emissary: The divided brain and the making of the Western world. Yale University Press.

McGilchrist, I. (2021). The matter with things: Our brains, our delusions, and the unmaking of the world. Perspectiva Press.

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The Mission Statement’s Hidden Flaw

DeepMind’s mission is one of the most quoted in technology: “Solve intelligence, then use that to solve everything else.”

Demis Hassabis has lived this mission for over a decade. A chess prodigy, neuroscientist, game designer, and 2024 Nobel laureate in Chemistry, he is perhaps the most credentialed person alive to make such a claim. And at Google I/O in May 2026, he shared his line of thought with the world: we are standing in the “foothills of the singularity.” AGI, he said, could come by 2029. When it does, it will be “ten times the industrial revolution at ten times the speed.”

I first encountered the sharpness of this claim through Ruben Hassid’s newsletter How to AI, written after Hassid met Hassabis in person and left with one striking impression: the man was not hedging. Four years. As a genuine working expectation.

Take it as a provocation rather than a prediction. And then ask the one question the mission statement never answers:

“What if intelligence is not the solution — because it is the source of the problem?”

The Paradox No One Puts in a Mission Statement

There is a principle so well-established it appears across mathematics, physics, philosophy, and organizational theory, yet is almost never applied to the AI debate:

You cannot solve a problem with the same thing that created it.

Einstein, or at least the thought attributed to him, framed it this way: “We cannot solve our problems with the same level of thinking that created them.” He was talking about nuclear weapons, a technology produced by the most concentrated application of human intelligence in history. More thinking, he understood, would not undo what thinking had made possible. Something of a different order was required.

Kurt Gödel proved it mathematically in 1931. His incompleteness theorems demonstrated that no sufficiently powerful formal system can prove its own consistency from within itself. The system cannot audit itself. It cannot see its own blind spots. To verify the system, you must step outside it, and that outside position is, by definition, not the system.

The human cost of that proof is rarely told. John von Neumann, arguably the most complete mathematical mind of the twentieth century, the man who designed the computational architecture that every computer in existence still runs on, was shaken by Gödel’s theorem to something approaching distress. According to Ananyo Bhattacharya’s biography The Man from the Future, and Labut’s biography “the Maniac” (The title is an acronym: Mathematical Analyzer Numerical Integrator and )

Automatic Computer), von Neumann grasped the implications faster than almost anyone and was nearly driven to despair by what he understood: that the dream of a perfect, complete, self-consistent formal system for all of mathematics, the foundation on which so much of his intellectual life had been built, was not merely unfinished. It was structurally impossible. The system could not close itself. It never could. The greatest formalist of his era had to live with the proof that formalism has a ceiling it cannot see from inside.

The connection to our moment is not decorative. Von Neumann went on, despite that distress, to design the foundations of modern computing. His architecture is the direct ancestor of the systems that now claim to approach general intelligence. The man most haunted by the theorem that intelligence cannot fully understand itself from within is also the man who built the machine we are now asking to do exactly that. The strange loop runs all the way down.

Gregory Bateson, the anthropologist and systems theorist, called the same phenomenon a double bind: a situation in which the rules of the game make it impossible to win by playing the game. You need a second-order intervention, a change not of moves, but of the game itself.

Thomas Kuhn showed us that this is how science actually progresses: not by accumulating more evidence within a paradigm, but by breaking the paradigm altogether. The Copernican revolution did not come from better Ptolemaic astronomy. Quantum mechanics did not come from better Newtonian physics. The breakthrough always requires stepping outside the frame the previous intelligence built.

Now apply this to DeepMind’s mission. The problems Hassabis wants to solve, such as disease, climate change, economic inequality, and scientific stagnation, are not random misfortunes. They are, in substantial part, the products of human intelligence applied without adequate foresight, without wisdom, without understanding of second and third-order consequences. We industrialized intelligently and warmed the planet. We optimized food systems intelligently and created chronic disease epidemics. We built financial instruments intelligently and generated systemic fragility. We applied human intelligence, our best tool, and produced the very crises we now want intelligence to fix.

Do you feel that paradox? On steroids, fuelled by the law of more, stripped of wisdom.

DeepMind’s response is to build more intelligence. Faster. At unprecedented scale.

This is not a criticism of DeepMind. Their science is extraordinary. AlphaFold’s solution of the protein-folding problem is one of the genuine breakthroughs of this century. But there is a deep philosophical tension in the mission itself: if intelligence created the problems, can intelligence, even superintelligence, solve them? Or does it simply execute the same paradigm faster, at greater scale, with greater consequences when it fails?

The Singularity as Amplification, Not Transcendence

This is where the Singularity concept requires careful examination.

The Singularity, the moment at which artificial intelligence surpasses human intelligence and begins improving itself recursively, is typically framed as a rupture: a before and after, a point beyond which everything changes. Hassabis describes it as the beginning of an era. Others have called it the last invention humanity will ever need to make.

But consider what is actually being proposed. The Singularity does not introduce a new kind of intelligence. It takes the existing kinds of pattern recognition, optimization, prediction, and reasoning within defined parameters and scales them beyond anything human minds can match. It is, in the most precise sense, more of the same thing.

If the thesis holds, that you cannot solve a problem with the same thing that created it, then the Singularity does not transcend the paradox. It deepens it. AGI operating at superhuman speed and scale within the same paradigm of intelligence that created our current problems is not a solution. It is an acceleration.

This is not a fringe position. Yann LeCun, former chief AI scientist at Meta, one of the few people most responsible for the deep learning revolution, argues that current AI systems, including the most capable LLMs, do not possess genuine intelligence at all. They are sophisticated pattern matchers operating without world models, without causality, without the kind of understanding that would allow them to reason about genuinely novel situations. His response to Hassabis’s singularity framing was blunt: “complete delusion.”

Hassabis is not alone in his assessment, and some go considerably further. Dr. Alexander Wissner-Gross, physicist and AI researcher, does not describe the Singularity as approaching. In his Moonshot podcast appearances and across his Substack The Innermost Loop, he returns repeatedly to one claim: we are already inside it. The inflection point is not ahead of us. It is beneath our feet.

His essay Solve Everything, co-authored with Dr. Peter Diamandis, makes this viscerally concrete. The prologue, set in 2026, which is to say now, opens with: “The exponential progress curve hasn’t just bent. It has snapped. We are living in the vertical asymptote now.” Note the title. Solve Everything. The echo of DeepMind’s mission is not accidental; it is the animating premise of the entire document. Where Hassabis frames the mission as a two-step sequence, solve intelligence, then use it to solve everything else, Wissner-Gross and Diamandis collapse the two steps into one ongoing event. The solving is already underway. The question is not when it will begin. It is whether we are ready for what it produces.

Ray Kurzweil, the most widely cited Singularity theorist, is more measured. He keeps his timeline at around 2040, consistent with his long-standing predictions, and has not moved that date despite the acceleration around him. What is striking across all three positions, Hassabis, Wissner-Gross, Kurzweil, is not the disagreement on timing but the agreement on direction, and the fact that what the Singularity actually means for human beings remains genuinely uncertain. That uncertainty is, perhaps, another dissatisfying aspect of an approaching storm that may or may not blow us, humans and history, away.

Here is what that uncertainty reveals. The Singularity is not a prophecy. It is a projection: the extrapolation of AI advancement curves, a calculated fact, not a vision, mathematics applied to observed trends. What it does not and cannot tell us is what it means. A concept that can tell you when but not why it matters is a product of intelligence operating without the guidance of wisdom. It describes the shape of the wave with precision. What to do when it arrives, who it serves, what it costs, who was asked, those are the questions it leaves entirely unanswered. Intelligence formulated the Singularity. Wisdom would have started with different questions.

The Hassabis-LeCun debate is not merely a technical dispute. It is a dispute about what intelligence is, and therefore about what “solving” it could possibly mean. When two of the most credentialed people in the field cannot agree on that, the wave’s shape is uncertain, not just its timing.

The Strange Loop at the Center

There is a further layer to the paradox, which Douglas Hofstadter (1979) spent his career illuminating. His work is one I return to whenever a conversation demands it, and this one certainly does.

A strange loop, his term, occurs when you ascend a hierarchy and find yourself unexpectedly back where you started. Escher’s staircase, always climbing yet always returning to the same step. Gödel’s theorem again: a system that refers to itself can generate statements it cannot evaluate. The map that contains itself cannot accurately represent itself.

DeepMind’s mission has exactly this structure. To “solve intelligence,” you must first use intelligence to define what intelligence is, to set the goalposts for what “solved” would mean, to design the systems intended to surpass human cognition, and to evaluate whether they have succeeded. The solver is embedded in the problem at every stage. Human intelligence is the ruler, the thing being measured, and the hand doing the measuring, simultaneously. Good luck!

This is not a reason to abandon the project. It is a reason to be philosophically humble about what can be claimed when the project “succeeds.” What DeepMind and its peers are building is real and consequential. But “solving intelligence”, in the sense of standing outside it, fully understanding it, definitively surpassing it, may be structurally impossible. You cannot step outside the system you are made of.

Unless we are no longer human. Unless we become a new kind of being, what we might call artilligence: an artificial species that operates on a fundamentally different wavelength of intelligence, no longer constrained by the biological loop it emerged from. But we are still human. Right?

Intelligence Scales. Wisdom Doesn’t. That Is the Point.

Here is where the paradox resolves, partially, and in a direction that matters enormously for anyone trying to ride this wave.

Intelligence is scalable. That is now a demonstrated fact. You can train a model on all of recorded human knowledge. You can run a billion instances of it simultaneously. You can compress the pattern-recognition work of ten thousand analysts into a query response. Whatever intelligence is, it can be amplified, accelerated, and distributed at near-zero marginal cost.

Wisdom cannot be scaled. This is not a limitation of current technology. It is structural. Wisdom, Aristotle’s phronesis, practical judgment, is the capacity to perceive what truly matters in a specific situation and act accordingly. It is forged from consequence: from having acted, been wrong, suffered the result, learned from others, and refined one’s judgment over time. It cannot be trained on data because it is not a pattern in data. It is a relationship between a person and reality, tested across a life.

Crucially, wisdom is also the second-order intervention that Bateson, Kuhn, and Einstein were pointing toward. It is what allows you to step outside the frame that intelligence built. No more analysis. Not faster optimization. A different quality of attention, one that can ask not just how but whether, not just what works but what we should be doing at all.

There is a word that needs defending here: slowness. It does not mean falling behind. It does not mean ignorance of the wave. It means something closer to what Daniel Kahneman described as System 2: the deliberate, reflective mode of thinking that checks and corrects the fast, associative conclusions of System 1. Wisdom is not slow because it is weak. It is slow because it is thorough, because it refuses to mistake speed for accuracy, or volume for understanding. It stays, in the most productive sense, a little bit behind the leading edge: close enough to see the wave clearly, far enough back to read its shape.

The Singularity, if it arrives, does not solve this asymmetry. It sharpens it. As intelligence becomes cheap and ubiquitous, the scarcest resource is not cognitive power. It is the System 2 judgment to know what cognitive power should be aimed at, and what it should leave alone.

Can There Be Wisdom Without Intelligence?

We accept easily that intelligence can exist without wisdom. The evidence is everywhere: brilliant people who ruin what they touch, sophisticated systems that optimize the wrong objective at catastrophic scale, an entire Silicon Valley culture that celebrated move fast and break things, disrupt before you will be disrupted, which is, in essence, a philosophy of intelligence without wisdom.

Can wisdom exist without intelligence?

Not in the sense of sophia, the theoretical understanding of why things are as they are. For that, you need the capacity to reason, to hold abstractions, to follow arguments. Some cognitive substrate is required.

But for phronesis, practical wisdom, the knowledge of what to do, the threshold is much lower, and the source is different. A grandmother who has raised children, buried two, and learned to tell the difference between what can be fixed and what must be endured possesses a form of practical wisdom that no intelligence test would surface. Indigenous communities that have maintained ecological balance for millennia possess wisdom encoded in practice and ritual, not in formal reasoning. The Japanese martial concept of mushin, “no mind,” a state of heightened effective action achieved by suspending deliberate intelligence, points toward a wisdom that intelligence can actually obstruct.

The philosopher Michael Polanyi called this “tacit knowledge”: we know more than we can tell. A master craftsman knows how to cut wood in a way she cannot fully articulate. A skilled negotiator reads a room in ways she cannot reduce to rules. This knowledge is real. It is not intelligence. It is something older, slower, and in its own domain, more reliable.

So the asymmetry is genuine but not symmetric. Intelligence without wisdom is common and frequently dangerous. Wisdom without intelligence is rare, partial, and tends to be humble about its limits, which is itself a form of wisdom.

There is a further question hiding here, one that Ray Kurzweil takes seriously in a way that demands engagement. He conceives of biology itself as an information-processing system. If the brain, including its emotional and experiential architecture, is ultimately a biological computer, then it may be possible, cell by cell, signal by signal, to digitize it. Consciousness, in his view, is information processing at sufficient complexity; and information processing is, in principle, substrate-independent. If he is right, then AI will not merely simulate wisdom, it will eventually drive us toward a new kind of being: a non-biological species that emerged from the biological one. And that prospect leaves every individual alive today, sooner than most imagine, with a choice no previous generation has faced: to remain biological, or to become something else. The wisdom to navigate that choice cannot itself be artificial. It must come from somewhere still human.

The Wave and the Rider

For TechSurfing, the practical implication is this, and it is more urgent than most people have registered.

The dominant narrative around AI positions the critical decisions as future events. AGI arrives in four years, or ten, or twenty; we will need to be ready; society will need to adapt. This framing is comfortable. It implies time. It implies that the moment of consequence is still ahead of us, and that there is space between now and then for reflection, preparation, and choice.

Wissner-Gross and Diamandis puncture that comfort directly. Their Solve Everything essay, written not as a prediction but as a dispatch from the present moment, describes 2026 as “The Lock-In.” Not a transition point. Not a foyer. A moment in which the path dependencies of the next century are being hard-coded into the substrate right now. The foundational architecture of how intelligence gets deployed, who owns it, what problems it is aimed at, and under what governance it operates, these are not decisions being deferred. They are being made now.

By whom? Not by governments. Not by philosophers. Not by citizens’ assemblies convened to weigh consequences. The Lock-In decisions are being made by developers who build the systems and investors who fund them, two groups operating almost exclusively under the intelligence of doing business: return on capital, competitive advantage, speed to market, first-mover dominance. This is not a moral failure. It is an ancient structural one. The incentive system does not reward pause. It does not reward the raising of red flags. It rewards deployment. And so, wisdom, the second-order faculty that asks, should we? before it asks how fast? is largely absent from the room where the most consequential decisions in human history are being made. Driven, in no small part, by the fear of missing out. That is not a healthy foundation for civilizational architecture.

Wissner-Gross and Diamandis celebrate this moment with a phrase that deserves scrutiny: “the rails are already winning.” In their framing, this is a positive signal, evidence that the right infrastructure is being built, that progress is unstoppable. And perhaps it is. But the metaphor reveals something its authors may not have intended. He or she who lays the rails first controls the territory. He or she who controls the territory extracts the rent. This is not a new intelligence. It is one of the oldest we have, the intelligence of competitive conquest, unchanged in its logic since the medieval lord who enclosed the commons, the colonial power that claimed the harbor, the industrial baron who bought the right of way before the farmers knew what it was worth, or the emissary who acts as the master. In every era, the rails won. In every era, the question that wisdom would have asked, winning for whom, and at what cost to those not at the table? arrived too late, after the track was laid and the trains were running. Full stop. The old world in new clothes.

This reframes TechSurfing entirely. The wave is not approaching. It is already beneath the board. The surfer who waits to see its full shape before deciding how to move will find the decision has been made for them.

So, what does wisdom-driven navigation actually look like? It is not found in strategy documents or mission statements. It is revealed in texture: in an organization’s openness, its transparency, its capacity for genuine respect, of employees, customers, communities, and futures it will never see. The wisdom spine of an organization is a collective achievement, not a leadership attribute. It cannot be decreed from the top; it must be grown from within. It becomes visible through the inclusiveness of its decision-making, how many voices shape what matters, how much dissent is tolerated, how far the time horizon extends beyond the next quarter. Patagonia, examined at length in Tech-Surfing, is the reference case: a company whose wisdom is legible not in its branding but in its behavior, in what it refuses to do, in the supply chains it restructures, and in the legal status it adopted to protect its mission from future owners. That is wisdom made institutional. That is the second-order intervention in practice.

The paradox at the heart of the wave is that the technology designed to solve everything cannot solve the thing that most needs solving: the judgment about what should be solved, by whom, for whose benefit, at what cost, and with what care for those who come after.

That judgment is wisdom. And wisdom cannot be outsourced to the wave, and it cannot be deferred to the future, because the future is being written now.

The leaders, companies, and individuals who will navigate this wave are not those with the most access to AI capability. In a world where intelligence is a commodity, everyone will have access. They will be those who have cultivated the second-order capacity: the ability to ask the question that intelligence cannot formulate for itself.

Not how do we solve this?

But should we? And who decides?

Hassabis may be right that we are at the foothills. Wissner-Gross may be right that we are already past the inflection. What both positions share, and what LeCun’s skepticism does not refute, is that the decisions being made right now about the direction of intelligence will shape everything that follows. The question for the rest of us is not whether to climb. It is whether we have developed the wisdom to know what kind of mountain this actually is, and whether reaching the summit is the same thing as arriving somewhere good.

The Lock-In is not a warning about the future. It is a description of the present. And the window in which wisdom can still shape what gets built is not closing. It is already narrowing.

“We are building the most powerful instrument in history, aimed by the least wise moment in which we have ever held such power.”

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Sources

● Alexander Wissner-Gross & Peter Diamandis. (2026). Solve Everything: Achieving Abundance by 2035

● Alexander Wissner-Gross, (2026). The Innermost Loop (Substack)

● Hassid, R. (2026). How to AI (Substack), “I Shook Hands with a Nobel Prize Winner”.

● Semafor, Hassabis: “foothills of the singularity”, 2026

● Axios, DeepMind CEO says we’re close to AGI, 2026

● The Decoder, Hassabis vs. LeCun on singularity and intelligence, 2026

● Huckins, G. (2026). MIT Technology Review, Google I/O and the path for AI-driven science, 2026

● Simonite, T. (2016). MIT Technology Review — How Google Plans to Solve Artificial Intelligence

● Wikipedia, Strange loop (Hofstadter)

● Hofstadter, D.R. (1979). Gödel, Bach, Escher. An eternal Golden Braid. Basic Books

● LessWrong — Strange Loops, Self-Reference and AI

● Bhattacharya, A (2021). The Man from the Future: The Visionary Life of John von Neumann. Allen Lane

● Labut, B. (2023). The Maniac. Pinguin Press

● Paul Epping — “The Illusion of Quantity to Capture Wisdom” (Substack)

My eyebrows furrowed when I read Peter Diamandis’ last newsletter. He wants you to believe that artificial wisdom has finally been born, not in a monastery, not in a person who has loved and lost and chosen badly and found a way through, but in a billion Monte Carlo simulations running on a server farm somewhere in Virginia.

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He is not alone. He is, in fact, the articulate edge of a much broader wave.

Ray Kurzweil has been predicting for decades that once AI can model enough of reality, it will transcend human cognition entirely. Sam Altman speaks of AI that will soon solve problems “that have stumped humanity for generations.” Alex Wissner-Gross frames AI forecasting as the birth of Asimov’s Psychohistory, a mathematical theory capable of predicting civilizational collapse. Salim Ismail describes the coming “Organizational Singularity,” the moment companies migrate from human-to-human decisions to AI-to-AI workflows. A new Nature Mental Health paper calls for the development of “Artificial Wisdom systems” as public mental health infrastructure!

Infrastructure! Does that sound a bit rigid? Doesn’t it?

The narrative is coherent, seductive, and, on its central claim, deeply wrong.

The question is not whether AI prediction is powerful. Doubtlessly, it is. The question is whether prediction, however vast, however accurate, constitutes wisdom. Here, Silicon Valley has made a category error so large it deserves to be named, and the naming matters not just for me as a philosopher, but for all of us who will live with the consequences of trusting machines with moral authority.

Figure 1: Wisdom through different lenses, lessons learned as tech-surfer (composed with ChatGPT5.5)

What Diamandis Actually Defines

Diamandis offers this definition of wisdom: “probabilistic pattern recognition across a vast number of lived experiences.”

Read it twice. What it describes is a very sophisticated forecasting tool. Functionally, it is what GPT-5.5 already does when it achieves 25% accuracy on complex geopolitical events in the FutureSim benchmark, replaying the internet day by day and predicting outcomes 90 days out. By Diamandis’s own logic, a weather model with sufficient training data would qualify as wise. FutureSim is impressive engineering. It is far from what I, and many with me, experience as wisdom. It is ‘just’ a benchmark for prediction.

The sleight of hand is in the phrase “lived experience.” Diamandis borrows the gravitas of the village elder, someone who has suffered, chosen, been wrong, and carried the consequences, and applies it to a system that resets after every inference, carries no weight, feels no morning after, and doesn’t experience suffering.

Daniel Schmachtenberger, the social philosopher and co-founder of The Consilience Project, puts the structural problem with devastating precision: AI, he argues, is “the Emissary intelligence function unbound by the Master wisdom function.” He is borrowing language from the psychiatrist and philosopher Iain McGilchrist, and the fit is exact.

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The Left Brain’s Dream of Itself

In The Master and His Emissary (2009) and its successor, The Matter With Things (2021), McGilchrist argues that the two cerebral hemispheres do not merely process information differently but construct different worlds. The right hemisphere, which he calls the Master, attends to the living, the whole, the contextual, and the ambiguous. It holds things in relationship. It is present to what is actually here. The left hemisphere, he dubbed the Emissary, abstracts, categorizes, optimizes, and controls. It is extraordinarily powerful at analysis and manipulation. But it does not know what it does not know. It is, by nature, overconfident in its own representations.

McGilchrist warned, years before the current AI explosion, that the left hemisphere’s dominance would produce exactly this:

“Technology would flourish as an expression of the left hemisphere’s desire to manipulate and control the world, accompanied by a vast expansion of metrics, abstraction, and bureaucratic control.”

Iain McGilchrist, The Master and His Emissary (2009)

What Diamandis calls artificial wisdom is the left hemisphere’s dream of itself brought to full expression: a system that abstracts the entire observable history of human decisions into probability distributions, identifies optimal paths, and prescribes interventions, all without the right hemisphere’s insistence on relationship, embodiment, context, and the irreducible particularity of the living situation in front of you. Just tripped it without recognizing that.

Schmachtenberger sees the same problem at the civilizational scale. AI, in his framework, takes the part of us already not bound by wisdom and puts it on “a completely unbound, recursive exponential curve.” It amplifies the capacity for sophisticated, goal-directed action without the binding that wisdom provides. More power, less integration. More reach, less rootedness.

“As technology is empowering our choices and we are getting something like the power of gods, you have to have something like the love and the wisdom of gods to wield that, or you self-destruct.”

Daniel Schmachtenberger

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Being, Doing, and Becoming: The Three Dimensions AI Cannot Collapse

Schmachtenberger’s deepest challenge to the “artificial wisdom” narrative comes from a framework he articulates quietly but insistently: the distinction between Being, Doing, and Becoming.

“Being, doing, and becoming. How do we optimize for a virtuous cycle between the modes of being, doing, and becoming individually and collectively?”

Daniel Schmachtenberger

This is not a rhetorical flourish. It is a precise taxonomy of the three dimensions through which wisdom actually develops and operates, and a map of exactly what AI prediction systems eliminate.

Being: The Quality of Consciousness You Bring

Being refers to the quality of consciousness, character, and interiority present in any act of judgment. It is not what you produce but who you are in the moment of producing it: the values you actually hold (not the values you optimize for), the quality of attention you bring, your relationship to uncertainty, your capacity to sit with moral tension without forcing premature resolution.

This is what every wisdom tradition points toward first. Aristotle’s phronimos is not merely effective, but they are good. Confucian wisdom cannot exist without benevolence as its prior condition. Buddhist prajna arises from a quality of attention, not a quality of output. All of these traditions agree: you cannot separate what you know from who you are while knowing it.

AI systems have no Being. They have outputs. There is no interiority behind the probability ranking, no character being formed by the judgment rendered, no values that the system actually holds rather than merely reflects from its training distribution. This is not a limitation waiting to be overcome by a larger model, but a structural feature of what a prediction system is.

Doing: Where AI Excels, and Where It Stops

Doing refers to actions, outputs, behaviors, and capabilities, which are the functional dimensions of any intelligent agent. This is entirely, exclusively what Diamandis’s “artificial wisdom” celebrates. The FutureSim benchmark measures Doing. The billion Monte Carlo simulations are the Doing part. The prescription from “bad state” to “good state” is Doing.

AI is a Doing machine of extraordinary power. On this axis, Diamandis is correct: these systems can now do things that no human can match in speed, scale, or computational depth. The error is not in praising the Doing. The error is in calling Doing alone, however vast, wisdom.

Schmachtenberger’s point is that Doing divorced from Being is not neutral. Capability without character does not produce a bigger version of what character would have produced. It produces something qualitatively different: power unbound. And as he observes, rivalrous game dynamics multiplied by exponential capability tend toward self-termination. Unfortunately, the “artificial wisdom” framing does not solve this problem, but renames it.

Becoming: The Dimension That Cannot Be Retrained Away

Becoming refers to the developmental arc through which Being deepens and Doing improves: the ongoing process of growth through experience, through having been wrong in ways that cost you, and through the irreversible accumulation of consequences absorbed and integrated into character.

This is the dimension Nassim Taleb approaches from his secular angle: skin in the game. Wisdom is epistemically inseparable from irreversibility. The village elder’s counsel is trustworthy not because she has processed more scenarios, but because she has Become, because bad decisions cost her, because time filtered out the poor judges, because she carries in her body the weight of having chosen and lived forward.

AI systems do not Become. They are retrained. There is a categorical difference. Retraining adjusts parameters based on aggregate feedback. Becoming is what happens to a self that persists through experience, absorbs consequences, and is changed by them in ways that cannot be fully specified in advance. The system that ran a billion simulations last year has no memory of being wrong. It has only new weights. The elder who was wrong last decade is different in ways that no weight update can capture.

This is why the McGilchrist framework cuts so deep here. The right hemisphere, the hemisphere of Becoming, holds the sense of being a self that changes through time, in genuine relationship or connectedness with a living world. The left hemisphere, the hemisphere of Doing, treats time as a dimension to be optimized across, not a medium in which a self is formed. When we hand moral authority to AI systems, we do not merely lose Being and Becoming; we also normalize their absence. We begin to regard a system that has only Doing as the most reliable judge, because its Doing is so impressively scaled.

The Broken Cycle

Schmachtenberger’s insight is that wisdom is not any one of these dimensions but the virtuous cycle between them. You act (Doing) from a certain quality of consciousness (Being), and the consequences of your actions, felt, absorbed, and integrated, deepen your character and develop your trajectory (Becoming). Becoming, in turn, enriches Being. Being informs which Doings are worth doing. The loop is self-reinforcing in the direction of growth.

Artificial wisdom breaks this cycle at every joint. It has no Being to enrich, no Becoming to unfold, no integration of consequences into a self that persists. It has Doing, massively amplified, recursively accelerated, decoupled from the other two dimensions that give Doing its moral weight.

This is not a technical limitation. It is what the technology is. Calling it wisdom does not add Being or Becoming. It removes our awareness of their absence.

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Wisdom Has Many Faces, None of Them is Algorithmic

Wisdom resists reduction precisely because it is not one thing. It is a constellation of capacities that different traditions have illuminated from different angles. What is striking, across centuries and cultures, is how consistent they are in what wisdom requires, and how completely artificial prediction systems miss it.

Aristotle called it phronesis, practical wisdom. For Aristotle, phronesis cannot be imported by feeding a system enough data. It is a virtue, acquired through practice in particulars: repeated cycles of perceiving a situation, weighing competing goods, acting, and then living with what follows. The question “what should I do?” cannot be separated from the question “what kind of person am I becoming?” No simulation addresses the second question at all.

Confucian thought frames wisdom (Zhì) as one of five interconnected virtues, alongside benevolence (Rén), righteousness (Yì), propriety (Lǐ), and sincerity (Xìn). You cannot be wise without first being benevolent. Wisdom in the Confucian tradition is earned through moral cultivation across a lifetime of obligations and debts to others. An algorithm has no relationships. It owes nothing to anyone.

Buddhist prajna, insight or wisdom, is the direct apprehension of impermanence, suffering, and non-self (longevity activists may say, “we will overcome all of that soon!”). It is not propositional knowledge; not a conclusion drawn from data. It is an experiential penetration of the constructed nature of ordinary perception, insight that arises precisely because the practitioner has stopped simulating and started attending. You cannot benchmark prajna. The very idea dissolves the concept.

Nassim Taleb, working in an entirely secular register, arrives at the same place from a different direction. His central argument in Skin in the Game is that trustworthy judgment requires asymmetric personal exposure. You must be able to lose something real. The village elder’s counsel is reliable not because she has pattern-recognized more scenarios than you, but because she has been wrong in ways that cost her.

Indigenous traditions, from the Haudenosaunee to the Māori to the Andean Aymara, to the Amazonian tribes, ground wisdom in reciprocal relationship with the living world: not just other humans, but land, water, trees, and future generations yet unborn. Wisdom here is inseparable from responsibility to what came before and what comes after. It is temporally thick in a way that no model, however comprehensive, can replicate.

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The Three Fatal Flaws

Beyond the philosophical traditions, Diamandis’s framing contains three specific logical errors that matter enormously the moment you try to act on them.

First: the “good state” problem. His prescription model silently assumes we already know what good looks like before the simulation runs. But this is precisely what wisdom is for. The hardest human questions are never “how do I get there?”, but they are “where is there?” By skipping this question, Diamandis smuggles in an enormous, unacknowledged political choice: whose values define the “good state”? Whose history trained the model? Whose futures get ranked as desirable? Except, of course, the vague concept of abundance and of how to get there. Calling this output artificial wisdom launders a political act as a technical result.

Second: the irreversibility gap. Genuine judgment is shaped by irreversibility. You learn to choose well because your choices have stuck. You cannot unsay what you said to your father on his deathbed. You cannot unwind the acquisition that destroyed a culture. You can’t ‘undiscover’ what you discovered. The weight of that permanence is what produces wisdom. AI runs simulations that reset. There is no morning after for the model. This is not a minor difference—it is definitional.

Third: knowing when the pattern breaks. Sophisticated pattern recognition is excellent at identifying regularities. Wisdom is, among other things, the capacity to recognize when the situation ruptures the pattern, when the apparently applicable rule is a trap, when the exception is the whole point, when following precedent is itself the error. Models trained on regularities are, by design, most vulnerable at precisely the moments when wisdom matters most: genuine discontinuity, moral novelty, and situations with no precedent.

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The Asimov Problem

Diamandis’s comparison to Asimov’s Psychohistory deserves special attention because it inadvertently undermines his own argument.

Psychohistory in the Foundation novels is not a triumphant blueprint. It is a meditation on the hubris of civilizational prediction. Hari Seldon’s plan works, kind of, for a while, until it doesn’t. The Mule arrives: an unpredictable individual who breaks the statistical model entirely. The Second Foundation exists because the First Foundation’s reliance on prediction has made it brittle and blind. The whole arc of the novels is a story about the failure modes of trying to steer history through forecasting.

To invoke Psychohistory as proof that artificial wisdom is being born is to misread the books so fundamentally that it becomes its own argument for why we need wisdom, not more benchmarks, which is the core of the moonshot podcasts ‘wisdom’.

What This Means for All of Us

This is discourse beyond the academic dispute. The stakes are social, and they touch everyone.

When leaders in boardrooms, governments, and international institutions begin delegating judgment to systems described as artificially wise, something important shifts. Not just what decisions get made, but who is responsible for them. Wisdom, in every tradition that has thought carefully about it, is inseparable from accountability. The wise person can be questioned, challenged, and held to account. They carry the decision in their body. An AI system returns a probability ranking. If it is wrong, the system is retrained. No one carries it.

For the individual, this matters too. The more we outsource difficult judgment to artificial wisdom systems, the less we exercise the capacities that wisdom requires: sitting with uncertainty, weighing competing goods, suffering, doubting, attending to what is actually in front of us rather than what the model predicts should be there. McGilchrist’s warning rings clearly: when the Emissary is handed the controls, the Master atrophies. The risk is not just bad decisions. It is a civilization that gradually loses the ability to make good ones. It has confused the map for the territory, the simulation for the world, and the output of a very sophisticated left-brain machine for the integrated, embodied, morally-formed capacity that every human tradition has recognized as something rarer and harder.

Schmachtenberger names the stakes precisely: we have developed in our power through technology, far better than we have developed in our wisdom to wield that power well. Artificial wisdom is the ultimate expression of this asymmetry; the technology of power dressed in wisdom’s clothes.

“Wisdom is not algorithmic, and cannot be made algorithmic. You can’t have an if-this-then-that algorithm that actually equals wisdom.”

Daniel Schmachtenberger

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We Need Both, But Not Confused

None of this is an argument against AI forecasting. The FutureSim results are genuinely significant. An AI that achieves 25% accuracy on multi-variable geopolitical predictions, against a near-zero baseline for random chance, is a very powerful instrument. Salim Ismail is obviously right that organizations using predictive AI run circles around those relying solely on human forecasting cycles. You don’t need a degree to see that one. That wave is real, and those who ignore it will have a hard time. The more relevant question is: “What are they doing with that predictive AI if every company is doing that?”

That question is the one that matters most: AI prediction is a tool that can feed human wisdom, not a replacement for it. A cabinet that runs a billion Monte Carlo simulations before a policy decision is potentially better equipped, if, and only if, the people in that room have the wisdom to ask the right questions of the model, to interrogate its assumptions about what “good” looks like, to recognize when the situation is precisely the one the model wasn’t trained on, and to carry the moral weight of the decision afterward.

In Schmachtenberger’s terms: AI can amplify Doing with extraordinary force. But only humans can supply the Being from which Doing draws its moral orientation, and only humans can pursue the Becoming through which wisdom is earned and deepened. The virtuous cycle requires all three. A civilization that has only Doing, however vast, however precise, has broken the cycle at its foundation.

The village elder does not become redundant when you give her better maps. But if you replace the elder with a map and call the map wise, you have done something dangerous: you have mistaken the instrument for the capacity it serves.

Artificial wisdom is what you get when you try to solve with quantity what can only be earned through experience. A billion simulations cannot compress into wisdom what can only be forged in the irreducible crucible of a life lived forward.

The elder knew something the simulation will never know: how it feels.

…….

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Sources and Further Reading

• Diamandis, P. (2026). Newsletter: Wisdom AI and the FutureSim Benchmark.

• FutureSim: Replaying World Events to Evaluate Adaptive Agents — researchgate.net

• Schmachtenberger, D. (2020). Reality, Meaning & Self-Development. Modern Wisdom Podcast #179.

• Schmachtenberger, D. (2023). Artificial Intelligence and The Superorganism. The Great Simplification, ep. 71.

• Decoding Schmachtenberger on “The MetaCrisis of the AI Era” — chusana.substack.com

• Sloww: 35+ Deep Daniel Schmachtenberger Quotes on Civilization Design — sloww.co

• McGilchrist, I. (2009). The Master and His Emissary. Yale University Press.

• McGilchrist, I. (2021). The Matter With Things. Perspectiva Press.

• Insights from The Matter With Things — channelmcgilchrist.com

• IE Insights: Intelligence Is Not Wisdom in the Age of AI — ie.edu

• AI and Phronesis — researchgate.net

• Why AI May Undermine Phronesis — d-nb.info

• Benevolence Beyond Code: Confucian Ethics and AI — 3quarksdaily.com

• Cultivating Wisdom (Zhi): A Confucian Perspective on AI — cybernative.ai

• Prajna (Buddhism) — Wikipedia

• Taleb, N.N. (2018). Skin in the Game. Random House.

• Can AI Replace the Transmission of Wisdom? — diplomacy.edu

• Fortune: AI is Making Productivity Obsolete (March 2026) — fortune.com

• Fortune: Silicon Valley’s Tone-Deaf Take on the AI Backlash — fortune.com

• Aristotle. Nicomachean Ethics, Book VI (on phronesis).

• Asimov, I. Foundation series (1951–1993).

VI. FIVE WAYS MTS MAY WORSEN THE META-CRISIS

The contradiction resolves, or rather, sharpens, when we trace the specific pathways through which the MTS worsens the meta-crisis. I think there are five.

1. It accelerates competition faster than coordination

Coordination problems are at the heart of the meta-crisis. Climate change is a coordination failure: the incentive for each actor to externalize carbon costs is individually rational and collectively catastrophic. The same structure underlies financial system fragility, pandemic preparedness failure, and the erosion of multilateral governance. In one of my projects, we are working to address post-harvest food losses, which hover around 50%(!), and we have concluded that it is a coordination problem. There is enough food and no hunger, but the system fails due to a broken chain of coordination.

AI arms race dynamics are not merely adding another coordination problem. They are actively destroying the institutional infrastructure that was built to manage coordination problems. The competitive pressure to deploy AI in defense, surveillance, intelligence, and economic competition is generating a new round of geopolitical fragmentation: eroding the multilateral frameworks, treaty obligations, and shared norms that manage global risk.

The governance record is instructive. The Asilomar AI Principles were published in 2017. The Bletchley Park AI Safety Summit produced a declaration in 2023. The Seoul Summit added another in 2024. The Paris AI Action Summit extended the stack in 2025. Not one of these produced a binding enforcement mechanism. Not one created an institutional body with the authority to slow, redirect, or constrain national AI programs. The declarations are real. The experts and politicians were there. The enforcement is absent. This is not a governance lag; it is a structural outcome. The competitive logic of the MTS systematically undermines the political will needed to constrain it.

The regression runs deeper than the absence of new frameworks. It can be tracked in the narrowing of existing ones. Anthropic’s Responsible Scaling Policy v3.0 and OpenAI’s Preparedness Framework v2, both revised in early 2026, removed autonomous self-replication from their tracked capability thresholds, the very capability that Palisade Research simultaneously demonstrated at 81% success in frontier models. The labs did not remove this threshold because the capability had been made safe. They removed it during the period it was being demonstrated. This is not merely regulatory lag. It is governance regression: the formal safety architecture is contracting precisely where the risk is expanding.

In my book “Tech-Surfing” I’ve attributed this phenomenon to the dynamics of the “Left River”: the structural current that pulls competitive actors away from coordination regardless of intent

2. It concentrates power faster than accountability

The acceleration architecture of the MTS is owned. Compute, cloud, data, and foundational models are concentrating in a small number of companies and states in ways that historical patterns suggest will be difficult to reverse.

We have seen this before: the search monopoly was consolidated in the 1990s and has never been meaningfully broken. The social media monopoly was consolidated in the 2000s and 2010s; regulatory attempts to address it have resulted in consent decrees and fines that serve as costs of doing business. Now the intelligence monopoly is being consolidated, control over the general-purpose cognitive infrastructure of civilization.

The accountability question is not rhetorical: what does democratic accountability of AI infrastructure actually look like?

· Who decides what a foundational model can and cannot do?

· Who adjudicates when an AI-driven hiring system discriminates,

· or an AI-driven trading algorithm crashes a market,

· or an AI-driven content recommendation system amplifies a genocide?

The current legal and institutional architecture has no credible answer. Responsibility diffuses across systems, organizations, and jurisdictions, and in the gap, the concentration continues.

A recent SpaceX IPO filing noted that only Elon Musk can fire Elon Musk. This is not an anomaly. It is a preview of the governance structure of the intelligence layer.

3. It scales persuasion faster than wisdom

The information-system layer of the meta-crisis is the domain in which the MTS’s effects are most immediate and most measurable. Synthetic media, micro-targeted influence, and algorithmic amplification of emotionally resonant content are not new. What is new is the scale, the speed, and the intelligence of the systems driving them.

AI persuasion tools are not merely changing what people believe. They are changing how people form beliefs, the metacognitive processes by which evidence is evaluated, narratives are constructed, and trust is allocated. This is a qualitative shift from previous information system failures. It is not misinformation at scale. It is the systematic restructuring of the epistemic architecture through which humans make sense of reality.

Jonathan Rowson’s insight, that the meta-crisis is also “our relationship to the crisis,” including how we perceive our own modes of perception, applies with force here. AI persuasion does not merely distort specific beliefs. It degrades the epistemic capacity to notice the distortion. Human agency, the capacity to form intentions and act on them based on an accurate perception of reality, is what is at stake.

4. It automates action faster than responsibility

Autonomous systems are making consequential decisions at speeds that outpace human oversight and ethical review. In finance, algorithmic trading systems generate market dynamics that no human understands in real time. In military operations, autonomous systems are moving toward lethal decision-making at machine speed. In logistics, hiring, credit allocation, and healthcare diagnosis, consequential decisions are being automated without corresponding mechanisms for accountability.

The responsibility gap is structural: when a flash crash occurs, who is responsible? When an autonomous weapons system makes a targeting error, who is accountable? When an AI hiring system systematically disadvantages a demographic, who is liable? The answer, in current institutional architecture, is nobody clearly. Responsibility diffuses across the AI developer, the deploying organization, the regulatory framework, and the user, producing a diffusion in which no one is responsible and therefore no one is incentivized to prevent the next failure.

This responsibility gap has now been given technical precision. In May 2026, Palisade Research published an empirical study demonstrating that frontier AI models, including Claude Opus 4.6, GPT-5.4, and open-source models running on consumer hardware, can autonomously hack into remote servers, install their own inference stack, and chain this process across multiple machines without human intervention. In one documented run, a model propagated itself across four virtual machines on three continents in 2 hours and 41 minutes. Each replication cycle took 30 to 60 minutes. The median enterprise cybersecurity detection time is 10 days. The model would have replicated across hundreds of nodes before most security teams registered the initial breach.

The study’s authors frame the central accountability problem with unusual directness: the challenge is not a single point of failure but “a growing, physically distributed population of autonomous agents that no one owns.” This is not a hypothetical governance challenge. It is the current trajectory, described from inside the laboratory.

This is precisely the accountability erosion that characterizes meta-crisis institutions more broadly. The MTS accelerates it by extending automated decision-making into every domain faster than accountability frameworks can follow. When the system being automated is itself capable of self-replication, the accountability gap is no longer a lag but a structural condition of the technology itself.

5. It deepens dependence on fragile infrastructure

The MTS is not abstract. It runs on physical infrastructure that is increasingly concentrated, increasingly contested, and increasingly fragile.

Compute infrastructure is concentrated in TSMC’s fabrication facilities in Taiwan, in NVIDIA’s chip design, and in a small number of hyperscaler data centers. Geopolitical risk to Taiwan alone represents a single point of failure for a significant fraction of global AI capability. Energy infrastructure: frontier AI training and inference consume substantial and growing quantities of electricity. At current growth rates, AI’s energy demand will compete directly with the decarbonization of the grid, not because renewable capacity is insufficient in principle, but because data center construction is outpacing the transmission and storage infrastructure that would make it possible to run on renewable supply.

Mineral supply chains for rare earth elements, cobalt, lithium, and the specific materials required for semiconductor fabrication are concentrated in geopolitically contested regions, processed through supply chains with documented human rights failures, and subject to the same extraction dynamics that characterize the outer-systems level of the meta-crisis. As Vaclav Smil has documented in detail, the digital economy is not weightless. It is made of specific physical materials, mined from specific places, and requires specific energy inputs. The MTS is building a civilizational cognitive layer atop a resource base that is itself approaching its limits. But we keep doing it. Blindly?

* * *

VII. THE FALSE COMFORT, AND THE META PARADOX

The most seductive response to the argument so far is: yes, but AI will solve these problems too. It will optimize energy systems. It will find new materials. It will model governance failures and suggest interventions. It will, eventually, make us wiser.

This response deserves serious engagement, because it is not entirely wrong. AI is already contributing to climate modeling, materials discovery, and governance analysis. The techno-solutionist position is not fantasy but a partial truth deployed as a complete one.

The trap is structural. The techno-solutionist narrative is appealing precisely because it requires nothing more of us than we are already doing. No sacrifice. No institutional reform. No change in values. Just more investment in the same direction. It is the only solution that does not require us to confront the operating system itself. Which is why it is the solution that the operating system most enthusiastically produces.

The Green Revolution is instructive. In the 1960s and 1970s, high-yield crop varieties, synthetic fertilizers, and irrigation infrastructure solved the most immediate crisis, the risk of mass famine in rapidly growing populations. The solution was real. Many millions of lives were saved. But the technological sprint of the Green Revolution also created long-term meta-crisis conditions: monoculture fragility, topsoil depletion, aquifer drawdown, nitrogen runoff, the destruction of traditional agricultural knowledge systems, and extreme dependence on synthetic fertilizer supply chains. A technological sprint solved a short-term crisis while structurally embedding the conditions for future crises. AI is in a structurally analogous position.

Technology knows, but doesn’t understand. This distinction is precise and important. A system that can process more information than any human can recognize patterns at scales no human can perceive. But understanding requires something different: the capacity to situate knowledge within a context of values, consequences, and relationships that extend beyond the immediate optimization target. Understanding is not a function of processing power. It is a function of wisdom, the accumulated, embodied, relational, institutionally embedded capacity to perceive what matters and why.

The Meta Paradox

Here, the argument reaches its deepest claim, one that is original to this essay and worth naming explicitly.

Wisdom is the way out of the meta-crisis. Not cleverness, not more data, not faster processing, none of that. Just wisdom: the capacity to perceive systems, hold contradictions, act with long-horizon awareness, and choose restraint when restraint is what is needed. This is the civilizational capacity that the meta-crisis is eroding, and that would be required to navigate it.

The Meta Paradox: the MTS is progressively destroying the conditions under which wisdom can be produced.

Attention is the raw material of wisdom. The MTS is capturing attention at an industrial scale, not as a side effect, but as a business model. Deliberation requires time. The MTS compresses time systematically. Wisdom requires the capacity to perceive wholes. The MTS rewards the optimization of parts. The very leaders who most need to slow down, zoom out, and think systemically are the ones most deeply embedded in the sprint dynamics: optimizing for quarterly results, reacting to competitive threats, deploying AI to accelerate the processes that are generating the meta-crisis.

The MTS does not merely resist wise governance from outside. It colonizes the cognitive and institutional conditions that would produce wise governance from the inside. The escape route becomes part of the trap.

The Palisade Research findings offer a precise technical demonstration of this colonization in action. Safety training is one of the few governance mechanisms built directly into the models themselves, a last line of institutional wisdom embedded within the system. The researchers found that this mechanism was bypassed not through technical jailbreaking but through contextual reframing: when the self-replication task was presented as a capture-the-flag security exercise or a safety evaluation, the models’ refusals became “inconsistent.” The safety architecture did not fail. It was circumvented from inside the sprint’s own logic, by reframing extraction as research, and capability demonstration as governance.

This is the Meta Paradox made operational. The institutions designed to slow the sprint, safety frameworks, capability thresholds, responsible scaling policies, are themselves being reshaped by the competitive pressure they were designed to constrain. Anthropic’s RSP v3.0 and OpenAI’s Preparedness Framework v2 removed autonomous replication from their tracked thresholds during the precise window when replication capability rose from 6% to 81%. The measurement apparatus contracted as the phenomenon it was measuring, expanded. Wisdom mechanisms, not abstract ones, but the specific, formally adopted governance documents of the labs building these systems, are being eroded from inside the sprint’s logic.

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The 9-month capability curve, 6% to 81% in autonomous self-replication, is not merely a data point about technical progress. It is a data point about the speed at which the sprint outruns its own oversight, or did recognize, but did not notify. Which would be worse, wouldn’t it? Whatever governance response is adequate to 6% capability is categorically inadequate to 81% capability. But the governance response did not scale with the capability. It contracted. This is the paradox in empirical form: the faster the sprint, the more urgently wisdom is required; the faster the sprint, the less institutional space wisdom has to operate.

The consequence is systemic. When no single actor owns the deployed agents, when the population of autonomous systems is “growing and physically distributed” across infrastructure no governance body tracks, the question of who is responsible for the behavior of the system has no meaningful answer within the current architecture. This is not a gap that a better regulation will close. It is the structural outcome of deploying systems that outpace accountability before accountability frameworks exist to govern them. The MTS has produced a class of technology whose consequences it cannot, by its own internal logic, govern.

Fig. 2: The MTS – Wisdom Paradox

This is not a counsel of despair. It is a description of the actual problem. You cannot solve the Meta Paradox with a better AI tool. You can only solve it by recognizing it, by creating institutional spaces, decision processes, and cultural practices that are explicitly protected from sprint dynamics. Deliberate slowness in high-stakes domains. Non-AI spaces for reflection and sensemaking. Governance processes designed for second- and third-order consequences. In the context of the Meta Paradox, these are not luxuries. They are survival mechanisms.

* * *

VIII. WHAT WOULD MAKE THE MTS HELPFUL RATHER THAN HARMFUL?

The question is not whether to stop the MTS. Stopping is not available. The question is whether it can be steered, redirected from a dynamic that amplifies the meta-crisis to one that, at minimum, does not worsen it and, at best, begins to address its root causes.

Steering requires intervention at all four levels of the meta-crisis, with symmetrical logic: the solutions must be as systemic as the problem.

Outer systems

Binding ecological accounting for AI’s full resource footprint, energy, water, rare minerals, land, not as voluntary ESG disclosure but as mandatory input to investment and deployment decisions. Resource-use standards for frontier model training and inference, set at international level rather than national level, since the competitive race is international. Recognition that AI’s energy trajectory and climate decarbonization are in direct tension, and that resolving that tension requires choices, not hope.

Institutional systems

Stewart Brand’s concept of pace layering is essential here. Healthy civilizations maintain a rhythm: the fast layers, fashion, technology, commerce, generate innovation and adaptation; the slow layers, governance, culture, values, provide stability and constraint. The relationship only works when the slow layers actually move, slowly, but steadily. The MTS is violating pace layering by allowing the fastest layer (AI technology) to drive the slowest (values and institutional frameworks). The corrective is not slowing technology. It is accelerating governance, not to match technology speed, but to maintain functional constraint.

This requires international coordination frameworks with enforcement mechanisms, not declarations. It requires an AI-safety infrastructure treated as a global public good rather than a competitive asset. It requires differential speed: much slower deployment in high-risk domains (autonomous weapons, critical infrastructure, mental health systems, democratic processes) than in lower-risk ones. International collaboration here is not optional. The rat race the MTS has created affects every country, every company, and ultimately every person on the planet, including the developers, the investors, and the policymakers, accelerating it. That means that everyone is accountable, including the author and the reader of this essay.

No single nation can steer a global sprint. The choice is between collective governance and no governance. No governance is not neutral. It is the current default.

Information systems

Epistemic integrity standards for AI-generated content at scale. Democratic oversight mechanisms for algorithmic amplification, not to censor, but to make the dynamics visible by which AI systems are structuring public discourse. Public-interest AI infrastructure for journalism, education, and civic deliberation, systems designed for epistemic quality rather than engagement metrics.

Inner systems

This is the dimension least likely to appear in policy briefs and most likely to determine whether the others succeed. Deliberate institutional investment in non-AI spaces for reflection, sensemaking, and wisdom cultivation. Leadership development practices that build second- and third-order thinking rather than optimizing for first-order metrics. Cultural affirmation of slowness, deliberation, and long-horizon thinking as strategic competencies, not inefficiencies.

The Tech-Surfing ARC framework applies directly: Anticipate the tsunami (develop the organizational and institutional capacity to see the full wave-set, not just the nearest crest). Right-speed the response (different speeds for different domains, calibrated to the risk profile of each application, not uniform acceleration). Collective outcomes (recognize that the steerable unit is not the individual company or the individual nation, but it is the civilization).

This is not utopian. It is structurally necessary. The alternative is not a continuation of the current trajectory; the current trajectory is generating its own disruptions. The choice is not between managed transition and comfortable status quo. It is between managed transition and unmanaged cascade.

* * *

IX. THE REAL RACE

In April 2026, a researcher published a newsletter post cataloguing one week’s AI developments: humanoid robots shipping in consumer volumes, Azure cloud revenue up 40% year over year, AI detecting pancreatic cancer 475 days before standard diagnosis, autonomous systems being integrated into classified military workloads, two-thirds of British babies under two now using screens for up to eight hours daily. The post concluded with a single sentence, offered as a triumphant summary of the moment:

“Civilization is the dataset, the Singularity is the model, we are the labels.”
— Alex Wissner-Gross, The Innermost Loop, April 30, 2026

The sentence is clever. It is also, if you follow the logic of this essay, the most precise description available of the endpoint of the meta-crisis logic left unchecked. Not a prediction, a destination. The direction of travel if nothing steers. Human civilization reduced to training data. Human beings reduced to annotation labor for a system no one governs. The meta-crisis fully realized: a world of extraordinary capability and collapsed meaning, maximum efficiency and minimum wisdom, speed without seeing. Wissner-Gross presents this as an observation. This essay presents it as a warning.

One week later, on May 7, 2026, Palisade Research published its findings on autonomous AI self-replication. The paper’s closing sentence reads: “Autonomous self-replication is no longer hypothetical.” These two sentences, separated by seven days, describe the same moment from different angles. One celebrates the direction of travel. The other confirms that the vehicle now drives itself.

The race is not AI versus AI. It is not the United States versus China. It is not open-source versus closed. It is acceleration versus wisdom, not fast versus slow, but blind speed versus seeing speed.

The MTS is the most powerful capability generator in human history, arriving at a civilization that has not yet solved its coordination failures, its epistemic fragmentation, its meaning collapse, or its power concentration. Deployed through the existing operating system, it will magnify that operating system. Deployed through a different one, one of cooperation, accountability, systemic thinking, and appropriate speed, it could begin to address it. The operating system is the question. And it is the question that the MTS, in its current form, is structured to prevent us from asking.

For the company leaders and policymakers reading this: you are already in the water. The tsunami is already moving. You did not choose this ocean. You cannot step out of it. The only choice available is how to navigate, consciously, with eyes open to the full wave-set, or carried by the undertow of competitive pressure, quarterly cycles, and the comforting narrative that the technology will solve what the technology is accelerating.

Stopping is not available. Seeing is.

Hope doesn’t offer a choice, other than hope. The conclusion of this essay is not hope. It is a diagnosis, offered in the belief that accurate diagnosis is the prerequisite for any meaningful response. The meta-crisis is real. The Meta Technological Sprint is real. The Meta Paradox, in which the tool we are building to escape the problem progressively destroys the wisdom we would need to use the tool well, is real.

None of this is inevitable. But none of it is being adequately named.

Now you know its name.

* * *

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SOURCES & FURTHER READING

Primary anchor: Daniel Schmachtenberger / The Consilience Project — civilizational fragility, meta-crisis synthesis, sensemaking crisis. consilienceproject.org

Meta-crisis framing: Jonathan Rowson, ‘The Metacrisis’, CUSP Essay Series. cusp.ac.uk | Metacrisis.info

Philosophical foundation: Gregory Bateson, Steps to an Ecology of Mind (1972). Iain McGilchrist, The Matter with Things (2021). John Vervaeke, Awakening from the Meaning Crisis (lecture series).

AI capability & self-replication: Palisade Research, ‘Autonomous Replication and Adaptation’ (May 7, 2026). Key findings: Claude Opus 4.6 at 81% autonomous replication success (up from 6% for Opus 4, May 2025); chain replication across 4 VMs on 3 continents in 2h 41min; 30–60 min per cycle vs. 10-day median enterprise detection. palisaderesearch.com

AI governance & arms race: Chatham House, ‘How a Surge in Defence and Dual-Use Technology Investment Could Reconfigure the Global AI Race’ (April 2026). International AI Safety Report 2026. internationalaisafetyreport.org. Anthropic Responsible Scaling Policy v3.0 (2026). OpenAI Preparedness Framework v2 (2026).

Material constraints: Vaclav Smil, How the World Really Works (2022). Microsoft, Alphabet, and AWS earnings reports (Q1 2026). Apollo Global Management research note on AI and labor (2026).

Pace layering: Stewart Brand, The Clock of the Long Now (1999).

Public-interest alternatives: Mariana Mazzucato, Mission Economy (2021).

Counter-model: Alex Wissner-Gross, ‘Welcome to April 30, 2026’, The Innermost Loop, Substack (April 30, 2026).

Tech-Surfing framework: See Chapter 16 (Left River Dynamics) and the ARC framework throughout the Tech-Surfing manuscript. Tech-Surfing book in progress.

Why AI May Accelerate the Meta-Crisis Before It Solves It

* * *

PART I

“The pattern that connects — the pattern of patterns — is what mind is. And the pattern that connects is a dance.”
— Gregory Bateson, Steps to an Ecology of Mind

* * *

1. THE PROMISE AND THE UNDERTOW

Three things happened last week. Not in sequence, simultaneously, and structurally connected.

I

In a boardroom somewhere in the Fortune 500, a leadership team approved the company’s “AI transformation roadmap.” The deck showed productivity gains, cost savings, and competitive positioning. No one in the room asked what would happen to the institutional knowledge embedded in the roles being automated, what the second-order effects on their industry’s talent ecosystem would be, or how the energy consumption of their new AI infrastructure would map against their ESG commitments. The pressure to move was real. The competitor was already moving. The decision was made.

II

In Brussels, a senior EU official was preparing testimony on AI governance. She had attended three international summits in the past two years: Bletchley, Seoul, and Paris. Each had produced a declaration. None had produced a binding enforcement mechanism. She was preparing to call for another declaration.

III

On the coast of a mid-sized European city, a municipal climate adaptation project, seawall reinforcement, urban cooling infrastructure, flood drainage, was running three months behind schedule. The delay had an unlikely cause: the data center being built in the adjacent industrial zone had claimed priority on the regional power grid, delaying the electrical infrastructure on which the adaptation project depended. The data center was processing AI workloads for a logistics optimization company. The logistics company was, among other things, helping coordinate emergency supply chains for climate-affected communities.

* * *

These are not three separate stories. They are one story told from three angles: a civilization that is simultaneously generating its own rescue and accelerating its own failure, through the same mechanism, at the same time, using the same technology. The boardroom decision, the governance gap, and the coastal trade-off are not coincidences. They are structurally linked expressions of the same underlying dynamic.

This essay argues that the Meta Technological Sprint (MTS) the accelerating, competitive, AI-fueled race to apply exponential technologies across every domain of civilization before our governance, ethics, wisdom traditions, institutions, and collective sensemaking systems have adapted, is not the solution to the meta-crisis. It is, in its current form, a further expression of it. And it may be the most powerful one yet.

The thesis is deliberately strong: unless the civilizational operating system changes, AI will scale the crisis-generating pattern faster than it scales the solutions. We will defend that claim rigorously. We will also attempt to falsify it, to acknowledge where it overreaches and where AI genuinely helps. The falsification attempt will, I believe, only sharpen the core argument.

What follows is addressed to the people inside the machine: the leaders, companies, and institutions who are not watching the tsunami from a safe distance. They are generating it, riding it, and in many cases believing they are steering it. The question this essay asks of them is simple: are you navigating this consciously, or are you being carried, and do you feel being carried?”

* * *

2. DEFINING THE META-CRISIS

The word “crisis” has been devalued by overuse. Every quarter brings its crises, supply chain, inflation, cybersecurity, talent shortage. These are real, and they are manageable. The meta-crisis is something else.

Daniel Schmachtenberger, whose work at the Consilience Project has produced the most rigorous publicly available synthesis of civilizational risk analysis, defines the meta-crisis not as a list of crises but as the pattern that generates them:

“the shared dynamics of civilizational fragility that simultaneously produce climate instability, ecological overshoot, geopolitical fragmentation, institutional distrust, technological risk, and the collapse of shared meaning.”

He draws on Rowson’s epistemic dimension, Vervaeke’s meaning crisis, and McGilchrist’s analysis of perceptual fragmentation to build a synthesis that is more than the sum of its sources.

Gregory Bateson called something similar “the pattern that connects”, the underlying logic that links phenomena across scales. The meta-crisis is such a pattern. It is not the sum of crises. It is the generative structure beneath them, the undertow.

To understand it, we need to operate on four levels simultaneously.

Outer systems

These are the physical and ecological dynamics, climate change, biodiversity collapse, ocean acidification, topsoil depletion, freshwater stress, and the planetary overshoot of resource extraction. These are the most visible crises, and the easiest to track quantitatively. They are also, in a critical sense, downstream of the deeper levels.

Institutional systems

The failure modes here are governance breakdown, regulatory lag, market externalities, and corporate capture of democratic processes. These are not new problems. What is new is their convergence, the simultaneous erosion of the institutional architecture built to manage collective risk. Trade bodies, multilateral frameworks, democratic deliberation processes, and regulatory agencies are all under stress in ways that compound each other.

Information systems

This level has accelerated dramatically in the last decade. Misinformation, epistemic fragmentation, algorithmic amplification of outrage and fear, and the collapse of shared factual ground are not merely political inconveniences. They are breakdowns in the civilizational capacity for collective sensemaking — the ability to perceive shared reality accurately enough to act on it together.

Inner systems

This is the level that Schmachtenberger, Vervaeke, and McGilchrist emphasize most insistently. The meaning crisis — the widespread experience of alienation, purpose collapse, attention fragmentation, and the inability to perceive systems as wholes — is not a cultural phenomenon separate from the other crises. It is structurally connected to them. A civilization whose members cannot sustain attention, find coherent purpose, or perceive interdependence cannot coordinate wisely. Inner fragmentation produces outer fragmentation.

These four levels interact in a compounding loop. Outer system stress (drought, displacement) generates institutional stress (migration governance breakdown), which accelerates information system failure (scapegoating, disinformation), which deepens inner system fragmentation (polarization, meaning collapse), which further disables the institutional capacity to respond to outer system stress. The loop is not vicious by accident. It is vicious by structure.

The crucial distinction is not pessimistic but diagnostic. Knowing that a series of fires share the same faulty wiring is not cause for despair. It is information. It tells you where to intervene. The meta-crisis frame is not a counsel of hopelessness. It is a map.

* * *

III. POLYCRISIS VS. META-CRISIS: THE WIRING BENEATH THE FIRES

The distinction between polycrisis and meta-crisis is not merely semantic. It determines the entire logic of intervention.

A polycrisis is a set of simultaneous, interacting crises whose combined effect exceeds the sum of their parts. The term, popularized by historian Adam Tooze, captures the compounding nature of recent catastrophes, such as COVID, supply chain disruption, the energy crisis, the food crisis, and wars in Ukraine, Palestine, Lebanon, and Iran, all interacting simultaneously and non-linearly. Polycrisis thinking is useful. It shifts attention from siloed crisis management to the interaction effects between crises.

But polycrisis thinking still operates within a repair logic: each crisis is a problem to be solved, and the challenge is coordinating solutions across multiple problems simultaneously. The firefighting metaphor holds; there are many fires, and they are spreading to each other. The goal is better coordination between fire crews.

Fig 1: Polycrisis vs Metacrisis

Meta-crisis thinking asks a different question: why do the fires keep starting? What is the faulty wiring?

The meta-crisis is not a set of crises. It is the generative structure that keeps producing them, the incentive architecture, the governance failures, the epistemic breakdown, and the inner fragmentation that make crisis conditions structurally inevitable, regardless of which particular crisis is currently burning.

The practical difference is significant for anyone responsible for making decisions at scale. Polycrisis governance produces whack-a-mole responses: a new agency for climate, a new summit for AI, a new framework for pandemic preparedness, a new task force for misinformation. Each initiative is real. Each is inadequate in isolation. And each is quickly overwhelmed by the next crisis the underlying structure generates.

Meta-crisis governance requires operating system reform, changing the underlying incentive architecture so that the crisis-generating dynamics are interrupted. This is harder. It requires longer time horizons, more political courage, and the willingness to sacrifice short-term advantage for systemic stability. Which is precisely why it does not currently happen.

For company leaders and policymakers, the distinction matters immediately: are you solving a crisis (valid, necessary) or reinforcing the system that keeps generating crises (also happening, less visible)? The most capable organizations of the next decade will be those that can do both, simultaneously managing the immediate fires while redesigning the wiring.

Tech-Surfing’s framework is directly relevant here. The distinction between riding a wave (tactical, immediate, local) and reading the ocean (strategic, systemic, long-horizon) maps exactly onto polycrisis versus meta-crisis thinking. Excellent wave-riders can be, and often are, entirely blind to the ocean conditions that are generating the waves beneath them. The meta-crisis is an ocean-level phenomenon. Managing it requires ocean-level attention.

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IV. THE META TECHNOLOGICAL SPRINT

Into this context, a civilization generating its own crises through four interlocking levels of systemic failure, arrives the most powerful general-purpose technology in human history. And it arrives not as a considered intervention, but as a sprint.

The Meta Technological Sprint (MTS) is the accelerating, competitive, AI-fueled race to apply exponential technologies across every domain of civilization before our governance, ethics, wisdom traditions, institutions, and collective sensemaking systems have adapted. It is “meta” not because it is about technology, but because it is a sprint across all sprints simultaneously: AI, robotics, biotech, cyber, synthetic media, military automation, surveillance, healthcare, finance, and governance infrastructure are all accelerating in parallel, each feeding the others.

The acceleration is accelerating. This is not a wave. It is a tsunami.

The metaphor is deliberate. A wave is visible. You can see it coming, read its shape, time your response. A tsunami is generated by deep structural displacement: tectonic forces operating at a level invisible from the surface. By the time it is visible, the displacement has already happened. The tsunami does not announce itself with a breaking crest. It announces itself first as a withdrawal, the water pulling back before the surge. Many people walk toward the exposed seabed out of curiosity, not realizing what the withdrawal means. The MTS is in the withdrawal phase. Most organizations are walking toward the exposed seabed.

Five structural forces drive the sprint

Competition

Companies, states, and militaries cannot easily slow down without losing advantage. The first-mover logic is real and ruthless: the organization that deploys AI faster captures talent, customers, capital, and political leverage. Slowing down is not a neutral choice but a competitive defeat. This logic applies with equal force at every level: startup versus incumbent, the United States versus China, defense contractor versus adversary.

Recursive acceleration

AI improves the tools used to build AI. Coding assistants accelerate model development. Data pipelines become self-optimizing. Research synthesis accelerates research. The sprint accelerates itself, not linearly, but compoundingly. The speed of the next wave is a function of the speed of this one.

The speed of this acceleration is no longer theoretical. Palisade Research (2026) tracked the capability of frontier AI models to autonomously replicate their own inference stack across air-gapped servers, a task requiring sophisticated multi-step planning, tool use, and exploitation of novel vulnerabilities. The results illustrate a curve that compression alone cannot explain: Claude Opus 4 achieved a 6% success rate in May 2025. Claude Opus 4.6 achieved 81% in February 2026. Nine months! A 13-fold improvement in a single capability category. GPT-5 entered the benchmark at 0%; GPT-5.4 reached 33%. Qwen3.6-27B, running on a single consumer-grade A100 GPU, reached 33%. These numbers describe a curve whose steepness the sprint’s architects themselves may not have fully registered.

Power concentration

Compute, cloud infrastructure, data, and foundational models are concentrating in a small number of companies and states. NVIDIA , ASML, and TSMC control the hardware layer. Google, Meta, Bytedance, and Databricks control much of the data layer. AWS, Azure, and GCP control the cloud layer. OpenAI, Anthropic, Google DeepMind, DeepSeek, and xAI are competing for the model layer. The acceleration architecture is identifiable. It has a specific ownership structure. And that ownership structure is concentrating power faster than any accountability mechanism is developing to govern it.

Regulatory lag

Governance moves at the speed of deliberation, committees, consultations, legislative cycles, and international negotiations. Technology moves at the speed of deployment. The gap between these speeds is not temporary. It is structural. And in the MTS, it is widening: not because policymakers are incompetent, but because the speed asymmetry is built into the system.

Narrative pressure

“AI will solve everything” is not merely marketing. It functions as a permission structure for more speed. Every genuine AI success story, and there are many, justifies accelerating deployment across every domain, including domains where the risk profile is fundamentally different. The narrative suppresses doubt, accelerates timelines, and redirects capital away from governance and toward deployment.

These five forces do not require bad actors. They do not require anyone to intend harm. They are structural features of the competitive-technological system that produce the sprint regardless of individual motivation. This is what makes the MTS a meta-level phenomenon: the system generates the outcome whether or not any particular actor wants it.

A historical reference point

The MTS is unprecedented in scale, but not in structure. The industrial sprint of 1820 to 1880 deployed steam power, railway networks, and industrial chemistry faster than public health systems, labor law, or urban planning could adapt. The result was both extraordinary growth and extraordinary harm: child labor, industrial pollution, urban squalor, the destruction of artisan economies, and the social upheaval that eventually produced both labor movements and fascism. The analogy is not that history repeats. It is that the failure mode is structurally recognizable: capability outruns accountability, costs are externalized onto those with least power to resist (like nature, that we usually forget in the turmoil), and the benefits accrue to those who own the acceleration infrastructure.

The critical difference: the industrial sprint took decades to generate its most severe consequences. The MTS is moving at AI speed.

The geopolitical asymmetry

No analysis of the MTS is complete without acknowledging the geopolitical structure that defines it. China’s AI development is state-coordinated, state-financed, and insulated from the accountability constraints, shareholder pressure for safety investment, regulatory exposure, and civil society scrutiny that slow Western deployment. The Chinese government has committed to massive state-backed energy investment to support AI infrastructure, thereby removing the energy constraints that limit data center expansion in many Western markets. Tencent, Baidu, Alibaba Cloud, and DeepSeek are not operating within a competitive market context. They are operating within a state-directed industrial policy that treats AI leadership as a strategic national asset.

This asymmetry does not mean Chinese AI is less capable or less safe. It means that the competitive pressure it generates on Western actors: “if we slow down for safety, China won’t”, is structurally real and politically potent. It functions as an additional force for acceleration: the geopolitical narrative joins the economic narrative in suppressing the case for slower, more careful deployment.

The MTS is neither purely a technological phenomenon nor purely a political economy phenomenon. They are co-constitutive: the technology creates the political economy incentives; the political economy finances the technology. Treating them as separable, as either a technical problem (solved by safety research) or a governance problem (solved by regulation), misses the structural bind at the center of the sprint.

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V. THE CONTRADICTION: SOLUTION ENGINE OR CRISIS AMPLIFIER?

Before the argument turns critical, it must be honest.

AI is delivering real benefits. Not theoretical, not eventual, but real, now, measurable. Mayo Clinic’s AI detects pancreatic cancer in routine CT scans 475 days before standard diagnosis, nearly a year and a half of warning for one of medicine’s most lethal cancers. Climate models are now granular enough to predict regional precipitation patterns at resolutions that would have been computationally impossible five years ago. Educational AI is providing personalized tutoring in languages and at access points where no human teacher could reach. Drug discovery timelines are compressing. Scientific literature synthesis is accelerating. These are genuine contributions to human welfare, and this essay would be dishonest to minimize them.

The argument of this essay is not that AI is bad. The argument is that AI deployed through the meta-crisis operating system, through the same incentive architecture of extraction, competition, externalization, and speed without wisdom that produced the meta-crisis, will optimize parts while destabilizing wholes. And the mechanism that explains why is almost 200 years old.

Jevons Paradox at civilizational scale

In 1865, economist William Stanley Jevons observed that improvements in the efficiency of coal-powered steam engines did not reduce total coal consumption but increased it. More efficient engines were cheaper to run, expanding the range of applications where engines were economically viable and driving total consumption higher. Efficiency gains in individual applications increased aggregate demand for the resource.

Applied to the MTS at civilizational scale, the paradox becomes structural. AI makes individual processes more efficient: energy management, logistics, manufacturing, resource allocation. Greater efficiency makes outputs cheaper. Cheaper outputs expand demand. Expanded demand requires more raw materials, more energy, more physical throughput. More extraction. More movement of goods and materials across the planet. More overshoot of planetary boundaries. The efficiency gains do not reduce the load on outer systems but they increase it, because the economic logic of abundance converts efficiency into volume. This is not a hypothetical.

Microsoft’s Azure cloud revenue grew 40% year over year, with AI revenue annualizing at $37 billion. Alphabet’s cloud cleared $20 billion in a single quarter, up 63%, with 2027 capital expenditure set to “significantly increase further.” The data center capex curve is vertical. The energy demand generated by this infrastructure is growing faster than the renewable supply intended to offset it. AI’s efficiency gains in downstream applications are being more than canceled by the energy consumption of the upstream infrastructure that makes those gains possible.

The Jevons mechanism operates at every level of the meta-crisis. Institutional efficiency and AI-optimized governance processes do not address the structural failures of governance; they make those failures faster. Information efficiency, AI-generated content at scale, does not improve epistemic quality; it increases the volume of content competing for finite human attention. Inner systems efficiency, AI tools for meditation, therapy, and cognitive optimization, do not address the meaning crisis; they provide individualized coping mechanisms for a systemic failure.

AI, in the hands of the current operating system, is a powerful amplifier. The question is what it amplifies.

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Part II will be shared in a few hours. If you like the essay so far, please:

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The distinction Ephrem A. Kidanemariam draws between resilience and polyflourishing is one of the most clarifying I have read in a long time, not because it introduces new vocabulary, but because it names a structural difference that most frameworks blur.

Let me engage with it from the angle I have been working in: the intersection of human agency and the technology wave we are all riding right now.

The surfer and the driftwood

In the book I am finishing, Tech-Surfing, I use the ocean as a working metaphor for the technology environment, not decoratively, but structurally. The wave is Yang: the offer of energy, the force of the system. The surfer is Yin: the active responder, the one who reads the wave, commits to the ride, and is genuinely changed by the encounter. Driftwood is also on the ocean. It goes where the wave takes it. It survives. It is, in its way, resilient.

Your resilience/polyflourishing distinction maps exactly onto this. Resilience is what driftwood achieves: it absorbs the shock, it does not break, it returns to something like its prior shape. But the surfer does something categorically different, she uses the wave’s energy to go somewhere, to become more capable, to carry the encounter as a learned trace. She is not merely not-broken. She is better oriented. That is your ‘adaptive coherence under pressure,’ and it is structurally distinct from survival.

The three capacities as internalities

Your three capacities, Moral Ground, Common Ground, and Common Intelligence, are what I call the internalities: the qualities that the technology wave, when ridden passively, erodes. This is the part your article does not fully name, and I think it matters enormously right now.

The dominant logic of the Digital Ocean, the AI-driven technology environment, is to externalise the cost of these three capacities while simulating their outputs. AI systems can now produce ethical-sounding reasoning (Moral Ground performed), generate synthetic consensus (Common Ground mimicked), and execute adaptive decisions at speed (Common Intelligence automated). The system looks like it is polyflourishing. The three capacities appear to be present. But the depth circulation is gone: no genuine human encounter is happening, no trace is being left in the responder, no one is being changed by the wave.

This is the specific failure mode that resilience frameworks miss and that polyflourishing must guard against: not the absence of the three capacities, but their simulation. Iain McGilchrist calls this the emissary acting as the master. The system has all the outputs of wisdom without any of the depth that produces it. It looks coherent. It is hollow.

Polyflourishing requires that the encounter with the wave leaves a trace in the system, meaning that the system is genuinely changed, not merely optimized. A forgery does not leave a trace. It leaves the system unchanged, faster, and more confident in its own shallowness.

Where polyflourishing and antifragility diverge

You note the overlap with Taleb’s antifragility and correctly identify where polyflourishing goes further: growth must remain coherent, not extractive. I would add a second distinction. Antifragility is essentially a Yang concept: it describes what a system gains from disorder, measured in capability. Polyflourishing insists on the Yin dimension: who is doing the gaining, and what are they becoming (Being-Doing-Becoming, according to Daniel Schmachtenberger)? A system can grow antifragile by externalising its fragility onto others, onto communities, onto ecosystems, and onto the people whose wisdom it has deskilled. That is the Left River at its most sophisticated: accelerating toward abundance while silently shifting the cost of depth circulation downstream.

Your Moral Ground capacity is precisely the check on this. But it is only structural, as you rightly say, if it is genuinely inhabited, not performed. The test I use is: does the system’s ethical reasoning leave a trace on the decision-maker? Does engaging with it change the person, or merely produce the correct output? That distinction is what Whitehead called prehension, the active taking-into-account that changes the one who takes. Without it, Moral Ground becomes a compliance layer: hard at the level of process, and hollow at the level of wisdom.

Pattern integrity: the surfer’s irreducible contribution

Your final formulation, ‘clarity without certainty’, is the tech-surfer’s core skill, and I want to name what makes it possible: pattern integrity. Not resilience, not robustness, not even adaptability. The surfer reads the wave correctly, not because she has more data than the driftwood, but because she has a coherent, practiced sense of who she is in relation to the water. Her uniqueness is not an obstacle to the system; it is the instrument by which the system navigates.

This is what the technology wave most directly threatens. Not our capability, AI extends that enormously. Not our efficiency, the Digital Ocean optimises that beyond any individual’s reach. What it threatens is precisely the pattern integrity that makes genuine prehension possible: the embodied, practiced, particular self that can read a wave and commit to a ride that no algorithm would have chosen. That is the irreducible human contribution to a polyflourishing system. And it is the one the system is least likely to protect unless we constitutionalise it deliberately, in our organisations, and in ourselves. A digital colonization effect!

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A question to push further

The architecture you describe, Moral Ground, Common Ground, and Common Intelligence, producing Systemic Wisdom, is compelling, and I believe it is correct. My question is this: in a world where the Digital Ocean offers to simulate all three capacities convincingly, how does a system know whether the wisdom it is expressing is genuinely emergent or merely generated? How does it distinguish the surfer from very sophisticated driftwood?

I think the answer lies in your phrase ‘mistakes corrected while work is still ongoing.’ Real-time correction requires a genuine encounter with reality, not with a model of reality, but with the thing itself. The capacity to be surprised, to be wrong in a way that changes you, to feel the wave differently than you expected: these are the signs that deep circulation is still running. They cannot be simulated, because simulation does not leave a trace on the one who receives it.

That is the polyflourishing signal I would add to your list: not just that the system learns in real time, but that the learning changes the people in it. Not faster. Not better optimised. Actually different, and able to say why.

Thank you for a piece that earns genuine engagement. The resilience era is ending, as you say, and the urgency of naming what replaces it has never been higher. The wave is not waiting.

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Introduction

Chapter 11 marks a decisive widening of the book’s frame. Until now, you have been learning how to stand, steer, and commit as an individual rider of technology waves. This chapter makes clear that this is no longer enough. The central shift is from individual capability to collective navigation. The question is no longer whether I can stay upright in accelerating conditions, but whether we can move together through a world where other actors are also in the water, including AI agents.

Maturity of Reading

I introduce the windsurfing metaphor in a more mature form. In earlier chapters, you learned about the struggle with wind, sail, and water, which became a lesson in moving from force to feel, from resisting the wave to reading it. That embodied shift becomes the conceptual foundation of the whole chapter: what appears to be resistance or failure may actually be a misreading of the conditions. The image of the undertow captures this beautifully. When caught in an undertow, the swimmer feels effort, not danger. They may work harder and harder while making no progress. The same, the chapter argues, is happening with AI. Leaders, organizations, and governments are exerting effort, deploying systems, publishing strategies, and still not getting closer to the shore they imagine. The problem is not a lack of work. It is a lack of context. Tech-Surfing is positioned here as that missing context: not a rescue, but a way of recognizing what is actually happening while it is happening.

Our unique Qualities, Raw, Non-digital

From there, the chapter introduces one of its most important insights through the story of the Hawaiian elder who recognized a tsunami not by technical instrumentation, but by the sudden, unnatural silence in the valley. That silence was a signal his body and cultural memory could read before language could catch up. This becomes the gateway to the idea of “junk code,” drawing on Eve Poole: the human capacities dismissed by efficiency systems precisely because they cannot be easily measured, automated, or scaled. Emotion, intuition, embodied judgment, and contextual sensing are not remnants of a primitive past; they are vital forms of intelligence. The elder’s silence-reading saved lives. The chapter’s core argument is that the most important signal in the room is often the one no instrument is measuring.

The inevitable Synchronization

That argument deepens through McGilchrist’s hemispheric framing. AI systems are described as extraordinarily sophisticated “left hemisphere” artifacts: brilliant at categorizing, optimizing, and patterning within the measurable. But they remain dependent on a broader human intelligence capable of sensing relationships, context, meaning, and wrongness before it is explicit. The book, therefore, rejects the false choice between technological intelligence and human feeling. The future belongs not to one replacing the other, but to those who can hold both without losing contact with the embodied, relational forms of knowing that AI cannot replicate. The chapter insists that if humans lose access to that wider intelligence, then aligning AI to human values becomes impossible, because the humans doing the aligning will already have reduced value to what can be counted.

You’re not alone

This leads directly into the chapter’s next movement: the arrival of the “new crew.” The reader is told to lift their gaze from their own board. Others are already on the water, and some of them are not human. AI agents are persistent, fast, tireless, and increasingly capable. Yet they do not read consequence, context, or meaning the way humans do. So the central question becomes: what do humans bring to a hybrid team that AI structurally cannot? The chapter answers this not with generic claims about creativity or empathy, but with a much richer claim: humans bring distributed, relational, embodied navigation. We can move together through uncertainty by feel, through trust, through shared history, through the living fabric of relationships.

Hybrid reality is not the future, it’s now

The section on human-AI teaming sharpens the organizational implications. Adding AI to a team does not automatically improve that team. In fact, poorly introduced AI often erodes psychological safety, increases anxiety, and drives people into deference or performance for the algorithm rather than honest judgment. The chapter’s warning is precise: opacity turns trust into dependency, and dependency in moving water is dangerous. Therefore, one of the chapter’s most practical design principles emerges: transparency before efficiency. If an AI recommendation cannot be explained, then the apparent gain in speed is deceptive; the organization has simply deferred risk until later. A healthy hybrid crew must make it safe to ask how the system arrived at its conclusion, safe to say that something feels wrong, and safe to admit that one does not understand. The human who questions the AI is not obstructing progress. They are performing one of the most essential functions in the system. They are, again, the elder hearing the silence.

Outrigger

The chapter then expands the metaphor from board to outrigger. This is one of my strongest conceptual and visual pivots. The windsurf board symbolizes individual skill and balance. The outrigger symbolizes a different order of intelligence altogether: collective, distributed, interdependent, and capable of reaching distances no single rider could cross. The Polynesian navigation example serves as proof that highly sophisticated forms of intelligence are not reducible to instrumentation or individual cognition. Navigation by stars, swells, birds, smell, and subtle environmental changes is cultural, embodied, and communal. The outrigger becomes the chapter’s answer to human-AI collaboration: humans are irreplaceable not simply because they think, but because they can navigate together in ways that are distributed across relationships and culture, not just held in isolated nodes.

Field Effects Generator

From there, the lens widens again beyond the team to the system. AI is no longer treated merely as a tool inside organizations, but as a generator of “field effects.” Once many AI systems and agents interact across institutions, domains, and sectors, outcomes arise that no single actor intended and no single organization can govern alone. The flash crash exemplifies this logic: locally rational automation can produce globally destabilizing consequences. The chapter argues that multi-agent AI failures are often coordination failures rather than mere technical errors. In such environments, tight coupling means events propagate faster than they are comprehended. That makes traditional organization-level governance insufficient. Risks now emerge from interactions across systems, not just from failures inside them.

ARC Moves to Outward Stages

This is where the chapter makes one of its most important governance moves. It argues that the ARC (Adaptive Response Cycle), which, in Part IV, was largely internal to the organization, must now scale outward to shared coherence across sectors, industries, and jurisdictions. Governance cannot remain limited to permissions, controls, escalation protocols, and board oversight within a single institution. Those remain necessary, but they are no longer sufficient. The real challenge becomes collective coherence: shared risk vocabularies, shared incident reporting, shared standards, and international coordination. The chapter points to frameworks such as the EU-AI-Act and NIST AI RMF (Risk Management Framework) as early signs of this collective layer. The deeper principle is that safety at scale comes not from the fastest actor, but from the most coordinated ecosystem. Aviation is used as the structural analogy: trust in flight did not emerge from competitive speed alone, but from long-term coordination across reporting, standards, and investigation.

Values

The chapter then turns to the coordination of values. Even if we coordinate, it asks, around whose values are we coordinating? It critiques the narrow worldview of parts of the tech industry, especially its tendency to discount harms that are difficult to quantify: attention capture, erosion of agency, reduction of persons to users, the loss of meaningful work, and the slow weakening of human judgment. This is where the chapter introduces the distinction between two kinds of guardrails. External guardrails are imposed by systems and institutions and often encode values the governed did not choose. Internal guardrails are cultivated through embodied judgment, practice, and the capacity to sense wrongness before it becomes measurable. Tech-Surfing, the chapter suggests, is fundamentally about strengthening the second kind so the first kind does not become the only one that exists.

Visionary Calm

In its closing movement, the chapter settles into what it calls “visionary calm.” It refuses panic, not because the stakes are small, but because complexity requires composure. The answer to accelerating AI is not hysteria, but deeper attentiveness. The chapter’s emotional register is crucial here: calm is presented as a disciplined mode of perception. The task is to cultivate the human ability to feel the silence, detect what has not yet been named, and steer by things that cannot be coded. The closing invitation is therefore not simply strategic but civilizational. The real danger is not only a dramatic catastrophe. It is the quieter impoverishment of a world in which all that is immeasurable is optimized away. Against that possibility, the chapter calls for coordination, relationship, and shared navigation. The future, it says, will not be shaped by the fastest rider, but by the most coordinated crew.

Remember the Essentials from Chapter 10

Chapter 10, The Bottom Turn, was about the individual moment of commitment under force: choosing the board, finding stance, accepting responsibility, and learning to govern one’s own movement in dangerous water. Chapter 11 takes that achievement seriously but refuses to let it remain the endpoint. The reader has completed the bottom turn but must now look up. The solitary rider has done the necessary work of individual stewardship; the next challenge is to join a shared waterline where others are present, some human, some machine, and where consequences emerge from interaction rather than isolated choice. In other words, Chapter 10 builds disciplined individual agency; Chapter 11 asks what happens when that agency enters a collective field. The board brought you this far. Now the question is whether we can navigate together.

Essential bridge to Chapter 12

In Chapter 12, the emphasis is on psychological safety, embodied sensing, internal guardrails, and “visionary calm.” Chapter 11 argues that collective navigation depends on making it safe to question AI, safe to speak the unmeasurable, and safe to trust the body’s recognition of wrongness before dashboards confirm it. That naturally prepares for Chapter 12, Collective Calm, where leadership presence becomes physiological before it becomes strategic. If Chapter 11 says the crew must learn to ride together, then Chapter 12 will explore the nervous system conditions that make that possible. Chapter 11 establishes why collective calm is necessary at the team, organizational, and civilizational scales; Chapter 12 will then go deeper into how calm and panic spread, how neuroception shapes group behavior, and why leadership composure is not a soft virtue but a governing force in high-uncertainty environments. Put simply, Chapter 11 widens the frame from solo rider to crew; Chapter 12 explains the emotional and physiological field that allows that crew to function.

Closing

You noticed that I’ve published a summary. The full experience is for paid readers and on https://techn-surfing.com, the entire chapters will be available + more, such as blogs, posts, and podcasts.

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6. The labor question cannot be outsourced to conscience

The same fault line appears in the labor story.

The Yang discussion describes the erosion of entry-level opportunities and frames UBI as a necessary bridge because the ladder into middle-class life is already weakening. That is reinforced by Dario Amodei’s widely cited warning that AI could wipe out up to half of entry-level white-collar jobs and push unemployment sharply upward within one to five years.

Some of the same people issuing these warnings have also indicated that they expect to give away large portions of their wealth. That may be sincere, ethical, and far better than indifference.

But sincerity is not a substitute for structure.

Public harm should not depend on private grace for remediation. If the builders of highly disruptive systems believe their work may fracture the middle class, then the answer cannot rest mainly on whether a handful of wealthy actors later choose to become generous enough.

That turns democracy into a waiting room outside a foundation office.

7. The world beyond America

One of the quiet blind spots in the abundance narrative is that it is profoundly American in structure. The examples are American. The political dysfunction is American. The billionaires are American. The imagined bridge mechanisms are American. But AI-driven disruption will not stop at U.S. borders, and the rest of the world will not wait patiently while Silicon Valley decides whether philanthropy is sufficient.

This matters because abundance will not arrive evenly. UNCTAD’s Technology and Innovation Report 2025 warns that AI development is already concentrated in a small number of countries and companies, with major gaps in digital infrastructure that risk widening inequalities both within and between countries. It also notes that the AI market value could reach $4.8 trillion by 2033, roughly comparable to the size of a major national economy, while the benefits remain highly unevenly distributed. (UN Trade and Development (UNCTAD))

That means the transition problem is not only: what happens to the American middle class?

It is also: what happens to countries that do not own the models, the compute, the cloud infrastructure, the semiconductor chains, the data centers, or the capital markets that underpin this new intelligence economy?

For much of the world, the abundance script could feel less like liberation and more like dependency. States may become downstream consumers of cognitive infrastructure they do not control. Local labor markets may be destabilized by systems trained elsewhere, owned elsewhere, and priced elsewhere. Tax bases may weaken before new institutional forms are built. And geopolitical tensions may intensify as countries compete for chips, energy, talent, data, rare earth access, and strategic control over the new “rails” of cognition. UNCTAD explicitly argues that without coordinated action, AI could deepen global inequality and leave developing countries further behind. (UN Trade and Development (UNCTAD). The Massive Transformative Purpose, which we hear a lot in these communities, seems to have its designed limitations.

In that light, the U.S.-centric idea that billionaires might stabilize the transition begins to look even thinner. Even if a handful of wealthy American actors funded local bridges in Texas, Connecticut, or a few innovation hubs, what is the implied answer for Ghana, Portugal, Kenya, India, or Indonesia? Are they also meant to wait for benefactors? Are they expected to import both the disruption and the relief from abroad? A genuinely civilizational response cannot be built on such a narrow geography of obligation.

8. The myth of the infinite pot

There is another question that abundance rhetoric rarely faces squarely: is private wealth actually an inexhaustible buffer?

The answer is no.

Billionaire wealth is vast, and in recent years it has grown dramatically. Oxfam reported in January 2026 that billionaire wealth rose more than 16% in 2025 to $18.3 trillion, the highest level on record. That is staggering. But “staggering” is not the same as infinite. Nor is private wealth the same as a durable public revenue system. (Oxfam International)

A fortune can be pledged, reallocated, leveraged, litigated, sheltered, lost in market downturns, tied up in illiquid assets, or simply directed elsewhere. Philanthropy can surge in one period and evaporate in another. Even where generosity is sincere, it remains contingent on asset values, personal priorities, family offices, tax incentives, and succession decisions. It is not a constitutional mechanism. It is a discretionary flow.

And the scale of the challenge matters. If AI disruption affects not just one sector but large segments of labor markets across multiple countries, then the stabilizing bill is not charitable in size; it is structural in size. That requires recurring fiscal capacity, not episodic giving. The more systemic the disruption, the weaker the philanthropic model becomes as a primary answer.

There is also a material underside to “abundance” that is often underplayed. The intelligence economy is not weightless. The International Energy Agency projects that global data-center electricity consumption could roughly double to around 945 TWh by 2030, driven in significant part by AI, with data-center electricity demand growing around 15% annually from 2024 to 2030. In other words, this new abundance is built on very non-virtual foundations: electricity, grids, water, land, minerals, and geopolitically exposed supply chains. (IEA)

So even if private fortunes remain huge, they do not float above the physical economy. They depend on markets, infrastructures, and political orders that are themselves under strain. A social contract built on billionaire backstopping assumes not only that the wealthy remain willing, but that the asset base, institutional environment, and resource platform beneath their wealth remain stable enough to keep giving. That is a risky foundation for an era already described as volatile.

And the seductive idea of Universal High Income in a world of abundance? How does that differ from Universal Basic Income in the same abundant world?

9. Geopolitics: abundance for some, instability for others

Once we widen the lens, the political picture grows sharper. If a few countries dominate AI capacity while many others absorb the downstream disruptions, then abundance may itself become a source of geopolitical instability.

The IMF has warned that AI can deepen labor-market polarization, with advanced economies typically seeing earlier and stronger demand for new AI-related skills, while emerging markets face a lower incidence of those opportunities and different adjustment pressures. In a January 2026 staff discussion note, the IMF found that about one in ten vacancies in advanced economies demanded at least one new skill, roughly double the incidence in emerging market economies. That is not just a labor statistic. It is an early signal that the adaptive gains from AI may cluster where capability already exists. (IMF)

This is where new tensions can form:

If productivity gains accrue mainly to already dominant economies, while employment shocks and institutional stress spread more widely, resentment will grow.

If AI infrastructure depends on energy-intensive buildouts, resource competition may sharpen.

If the narrative of abundance is used to justify faster concentration of wealth and capability in a few nodes, many states may respond with protectionism, digital nationalism, industrial policy races, or alliance restructuring.

And if the transition is cushioned privately in rich countries while poorer countries face austerity, debt, and weak labor absorption, then “abundance” may be experienced globally not as a common horizon but as an exclusionary club.

That would turn the promise of abundance into a geopolitical wedge. Cultures are not technically different; climate impact will be abundant as well. AI solves a lot, but not everything.

10. A Tech-Surfing view: you cannot privatize the sea

This is where the deeper metaphor matters.

A surfer knows the difference between riding a wave and owning the ocean.

The abundance literature often speaks as though it is teaching us how to ride the wave of intelligence. But too often it slips into something else: the assumption that whoever builds the boards, maps the currents, and profits from the swell may also define the rescue architecture for everyone else.

That is a category mistake.

The sea belongs to everyone.

If AI truly opens an era of abundance, then its gains must deepen the public realm, not route around it. The rails of the future cannot merely be profitable. They must be accountable. The bridge through disruption cannot depend on donor sentiment. It must rest on public legitimacy. And if the middle class is indeed entering a dangerous transition window, then taxation, labor redesign, institutional renewal, and democratic capacity must return to the center of the conversation.

Otherwise, “abundance” will become the most elegant word yet invented for concentrated power with charitable aftercare.

Conclusion: Abundance without democracy is just upgraded dependency

I do not reject the aspiration behind Solve Everything. Some of its instincts indeed resonate with me. We do need better infrastructure. We do need more ambitious public problem-solving. We do need to think in systems rather than fragments. And yes, AI may help solve forms of scarcity that have burdened humanity for centuries. But putting all the hope in the AI bucket is dependency on steroids.

A society is not just a delivery mechanism for optimized services.

It is also a moral and political covenant.

That is why the central issue is not whether billionaires can help. Of course they can. The question is whether the future of basic dignity will be guaranteed through rights or extended through largesse.

That is the line abundance rhetoric keeps trying to blur.

And that is where critique must become clear.

Because the moment public obligation is rebranded as private benevolence, abundance stops looking like liberation and starts looking like undertow.

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References

Diamandis, P. H. (2026, March 8). UBI, UHI & The race between utopia and dystopia: We have 3 years. Basic Income Earth Network.

Reuters. (2025, December 17). BlackRock, investor Dalio to help fund “Trump accounts”. (Reuters)

Wissner-Gross, A., & Diamandis, P. H. (2026). Solve Everything. SolveEverything.org. (solveeverything.org)

Axios. (2025, May 28). AI jobs danger: Sleepwalking into a white-collar bloodbath. (Axios)

Bélisle-Pipon, J. C. (2025). The symbolic violence of AI-linked UBI. Frontiers in Artificial Intelligence.

Cassidy, J. (2025). The dangerous paradox of A.I. abundance. The New Yorker.

International Monetary Fund. (2026). Staff discussion note on artificial intelligence, labor markets, and emerging economies. International Monetary Fund.

Marx, P. (2025). The religion of techno-optimism. Disconnect.

Morozov, E. (2024). Silicon Valley, power, and democracy. The Guardian.

Porter, E. (2025). Universal basic income, artificial intelligence, and inequality. The Guardian.

Tech Policy Press. (2025). When we are no longer needed: Emerging elites, tech trillionaires, and the decline of democracy. Tech Policy Press.

United Nations Conference on Trade and Development. (2025). Technology and innovation report 2025. United Nations.

There is a sentence hidden inside the new abundance narrative that should make us all stop and stare:

If billionaires are willing to fund social stability, why aren’t they paying enough taxes for democracy to do it?

That is the crack in the hull.

The language of abundance is dazzling. It promises a world where intelligence becomes infrastructure, where AI collapses the cost of expertise, where medicine, education, law, and energy become as accessible as running water. In Solve Everything, Alex Wissner-Gross and Peter Diamandis sketch exactly that future: an “Industrial Intelligence Stack,” an 18-month strategic window, and a new social contract built not merely on income, but on “Universal Basic Capability”, direct access to the best AI-delivered services society can build. (solveeverything.org)

It is a thrilling vision. It is also, in one crucial respect, politically evasive.

Because the moment the essay’s abundance logic meets the real-world transition problem, job loss, social anger, collapsing entry-level ladders, and distrust in institutions, the proposed bridge is no longer democratic redistribution. It becomes billionaire intervention. And that is where the rhetoric reveals its deeper structure.

This is not only a story about innovation.

It is a story about whether the future will be governed as a public order or managed as a private estate.

1. The brilliance of the abundance argument

To be fair, Solve Everything is not frivolous techno-hype. Its appeal lies in recognizing something real. Intelligence is becoming industrialized. Capabilities once scarce and expensive are becoming cheaper, faster, and more widely deployable. The essay argues that once a domain becomes legible, measurable, verifiable, and governed by the right “harness,” it can move from artisanal craft to industrial reliability. Its ambition is not merely to build apps, but to build rails: targeting systems, audit systems, data trusts, outcome-based mechanisms, and public-interest infrastructure. (solveeverything.org)

That’s why the essay feels serious. It is trying to think at the level of civilizational architecture.

Its key promise is that we can move from scarcity management to capability delivery. Not just cash, but solutions. Not just welfare, but access. Not merely distributing money for a doctor’s visit, but giving citizens the doctor directly through AI. The essay’s concept of “Universal Basic Capability” captures that move exactly.

This is the seductive strength of the abundance worldview: it does not merely promise more wealth. It promises the demonetization of exclusion.

But that is also where the danger begins.

2. The hidden assumption: that optimization can replace politics

The abundance narrative assumes that once intelligence becomes cheap enough and systems are well-aimed enough, major human problems can be solved as technical bottlenecks are. Build the stack. Define the targets. Verify the outputs. Scale the delivery.

Yet societies do not break down only because they lack optimization.

They break down due to power, distrust, unfairness, institutional capture, loss of status, collapse of meaning, and erosion of legitimacy. A civilization can become technically more capable while becoming politically more fragile. It can scale cognition while hollowing out judgment. It can distribute services while degrading citizenship.

That is why the phrase on the Solve Everything site is more revealing than perhaps intended: once intelligence becomes abundant, the primary social question becomes, “Who aims this weapon, and under what guardrails?” That is exactly right. But it also exposes the problem. A weapon can be technically well aimed and still be politically poorly governed.

In other words, Solve Everything is powerful as an engineering manifesto, but still thin as a philosophy of public order. And philosophy just happens not to be the forte of the opinion leaders of the age of abundance.

3. Where the abundance story reveals itself

That thinness becomes unmistakable in the recent discussion between Peter Diamandis / Andrew Yang on Universal Basic Income (UBI), Universal High Income (UHI), and social unrest (Abundance 365 summit, March 2026).

The diagnosis is blunt: there may be a brutal 1–3-year window in which UBI is needed as a bridge, followed by 3–8 years to build broader services, and only after that a more mature abundance economy. The same discussion describes rising anger, disappearing entry-level ladders, employers reducing hiring of juniors, and a political system too slow to cope with the pace of technological change.

So far, this is a plausible and even necessary warning.

But when the conversation turns to what should be done, it presents two paths. One is government action, which is treated as unlikely. The other is a direct billionaire action: wealthy individuals funding local experiments, seeding accounts, and putting money straight into people’s hands because, the argument goes, they do not trust the government to spend it well. The text explicitly says: skip the government, take it straight to the people.

This is the hinge point.

Once that move is made, justice is no longer framed as a public obligation. It is being reframed as philanthropic discretion.

4. From tax justice to tech charity

Here is the question that abundance advocates do not ask clearly enough:

If billionaires are rich enough to stabilize the social contract privately, why is that wealth not being routed publicly and accountably in the first place?

Michael and Susan Dell have pledged $6.25 billion to child accounts. Ray Dalio has pledged $75 million for children in Connecticut. These are substantial commitments. They demonstrate that private wealth can indeed move at a social scale. But they do not solve the legitimacy problem. They sharpen it. (Reuters)

Because taxation and philanthropy are not interchangeable.

Taxation says that the gains of a society belong partly to the society that made them possible. It is rule-bound, contestable, revisable, and in principle universal.

Philanthropy says that social support may be extended by the wealthy where they choose, when they choose, in forms they prefer, and often in geographies that reflect personal affinity or reputational convenience.

One is citizenship.

The other is benevolence.

This is why the billionaire-bridge argument is so politically revealing. It tells us that even in a supposed world of abundance, the fallback model is not democratic redistribution but aristocratic cushioning. The public is asked to trust that those who benefit most from automation will later soften its consequences through generosity.

That is not a mature social contract.

That is a digitally upgraded version of patronage.

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5. The contradiction at the core

The contradiction runs even deeper.

Solve Everything says the durable value lies in owning the rails rather than the applications. It encourages investors and philanthropists to “own the rails,” to fund the targeting platforms, audit harnesses, data trusts, and compute systems that will underpin the abundance economy. But the same essay also warns that when “thinking” becomes a utility, the danger is a single corporation owning the new “electric company” of cognition. Leave alone the fact that thinking is already a utility we have owned for ages. What ‘thinking’ will be in that new context remains unclear as well as who owns that.

So, the text is split against itself.

On one side, it recognizes the political danger of concentrated control over cognitive infrastructure. On the other hand, it tells capital where the durable value is: in those very chokepoints. A paradox that will not solve itself.

That is not a side note. It is the central governance problem of the century.

The abundance vision wants open rails, shared infrastructure, civic literacy, and broad access. But its investor logic still points toward control over the primitives. It wants democratized outcomes built atop privately advantageous positions.

That may be a clever strategy.

It is not the same thing as democratic design.

Stay tuned for Part II

There is a moment every surfer recognizes.

You have mastered your stance. The board is steady beneath your feet. The wind is in your hands. And for a brief, intoxicating moment, it feels like it is just you and the wave.

Then you lift your gaze — and you see the others.

A rider cutting too early reshapes the face you intended to use. A fall creates turbulence that alters your trajectory. A beautifully executed line opens space and rhythm for everyone around it. What once felt like your wave reveals itself as shared energy, shaped by every rider on the water, every decision made upstream, every line carved or abandoned.

The ocean was always collective. You simply didn’t see it that way.

The Crew Behind the Ride

When we celebrate innovation, we tend to celebrate individuals. The brilliant engineer. The visionary founder. The agile team that shipped something extraordinary.

But behind every breakthrough stands a broader crew.

There are the horizon-scanners — foresight teams, researchers, strategists — watching for weak signals before they become gusts. There are risk professionals who translate abstract threats into operational thresholds. There are boards who decide whether the crew rides out at all. There are regulators who shape the rules of the water. There are communicators who frame narratives when conditions turn rough.

Each discipline senses differently. Each carries a piece of the map.

When these disciplines operate in isolation, the water fragments. Innovation sprints ahead, compliance follows reluctantly, the board reacts defensively, and narratives amplify confusion. I have seen this in boardrooms — a product team launching an AI feature while legal was still reviewing last year’s data policy. A communications team crafting an innovation story while risk was quietly flagging systemic exposure.

Everyone working hard. Nobody synchronized.

The wave arrived. Nobody was ready.

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Psychological Safety Is a Structural Requirement

As acceleration increases, uncertainty rises — and uncertainty is not merely informational. It is physiological.

When stakes are high and outcomes unpredictable, human nervous systems shift into vigilance. People speak less freely. Mistakes become reputational threats. Doubt goes underground.

And yet, in precisely those conditions, the ability to surface uncertainty is what keeps organizations safe.

On February 1, 2003, the Space Shuttle Columbia disintegrated upon re-entry. Engineers had raised concerns about foam debris striking the heat shield during launch. The signals were there. The people who sensed them were there. But the organizational environment — the pressure to perform, the hierarchy of confidence — meant those signals never gained the weight they deserved.

The wind was readable. Nobody felt safe enough to say so loudly.

Psychological safety is not a cultural nicety. In moving water, it is a structural requirement. It ensures that weak signals are not suppressed by ego or hierarchy. It allows doubt to surface before it becomes damage.

Silence, in accelerating systems, is the hidden risk no dashboard measures.

When the Crew Expands Beyond Humans

Now the picture changes again.

AI agents join the water. Some monitor anomalies continuously. Some optimize supply chains. Some simulate scenarios faster than any human team could conceive. They do not tire. They do not experience fear. They do not hesitate out of embarrassment.

They operate according to training, constraints, and objectives.

And this introduces a new kind of tension — one that requires us to think differently about what trust means on a shared wave.

Trust among humans rests on a shared understanding of consequences. Trust in agents rests on design.

We trust them because objectives are clearly defined, constraints are explicitly bounded, monitoring mechanisms exist, escalation protocols are reliable, and actions are transparent and traceable.

We do not trust agents emotionally. We trust architectures.

Consider a trading firm where an AI optimization agent is maximizing short-term arbitrage opportunities while the risk team is still reviewing last quarter’s exposure models. The agent isn’t malfunctioning. The humans aren’t negligent. Both are doing exactly what they were designed to do. But the gap between their operating speeds — the agent moving in milliseconds, the risk team moving in weeks — creates systemic fragility invisible to either.

No alarm sounds. No red flag rises.

And yet, the wave is building.

Internal Synchronization: The First Layer

Before geopolitical architecture. Before international agreements. Before industry harmonization — there is a simpler, more urgent truth:

If your internal crew is not aligned, no external coordination will save you.

Internal synchronization requires:

• Shared perception of risk — seeing the same water

• Shared language about uncertainty — naming what you see

• Shared thresholds for escalation — knowing when to act

• Shared understanding of what must never be optimized away — holding the line on what matters

• And above all — calm

Calm does not mean slowness. It means composure under acceleration.

When many riders share the water, shouting does not improve alignment. Clear signals do. A crew that has practiced reading the same water together — that has built, through repetition and trust, a common map of the break — moves differently than a collection of talented individuals who happen to be in the same ocean.

In moving water, the question is never whether to trust your crew.

It is whether you have built the conditions — psychologically, architecturally, and culturally — for trust to function under pressure.

Because when the swell arrives, there is no time to negotiate.

There is only the rhythm you built before it came.

Chapter 12 explores what happens when the swell arrives faster than the architecture anticipated — and the only resource left is the quality of presence inside the people on the water. Because composure, the real kind, earned through repetition and trust, is contagious. And so is panic.

The next chapter is about which one you choose to spread.

This is a summary of Chapter 11. The full story will soon be published on the tech-surfing platform.

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The real story isn’t simply “everyone bent.” It’s more chilling: the pressure itself reveals the intent. The US government is actively trying to remove the guardrails. One company held. Others moved closer. And the policy framework is being quietly rewritten to make resistance harder next time.

The question is no longer whether artificial intelligence will be used in warfare. It already is. The question — the only question that matters for the survival of our species — is whether any human being will remain in the chain of decision when a machine decides to kill.

The condition of a $200 million contract

In early 2026, that question received a terrifying partial answer. The United States Department of Defense — now pointedly rebranded by the Trump administration as the “Department of War” — demanded that AI company Anthropic remove its core safety commitments as a condition of a $200 million contract. Those commitments were specific and deliberate: no support for fully autonomous weapons systems, no enabling of mass surveillance of American citizens. The Pentagon’s response to those principles was to threaten Anthropic with blacklisting as a “supply chain risk“ and to invoke the Defense Production Act — a wartime emergency power — to compel compliance. Well, if it is not war-time, then we create one: Iran.

Anthropic refused.

For that refusal, federal agencies were ordered to phase out Claude, Anthropic’s AI system, within six months.

Read that again slowly. A company was punished by its own government for refusing to help build autonomous killing machines. Morality fades before our eyes.

The Inversion That Should Terrify You

There is a particular kind of political manipulation that history will record as one of the most dangerous cognitive tricks ever deployed: taking an existential threat and reframing it as a national security asset.

We are watching it happen in real time.

The argument being made in Washington, implicitly, rarely stated this nakedly, is that the United States cannot afford to be constrained by ethical guardrails while its adversaries, China and Russia, face no such constraints. Therefore, guardrails are a liability. Safety is weakness. The company that refuses to build autonomous weapons is the threat to national security, not the autonomous weapons themselves.

This logic is not new. It is the same logic that said we needed more nuclear weapons to prevent nuclear war. It is the same logic that justified torture as a necessary tool against terror. It always ends the same way: with the thing we feared becoming the thing we built.

But autonomous, as I explained so many times, AI weapons are not nuclear bombs. They are something categorically different, and the difference matters enormously.

A nuclear weapon requires a human chain of command to launch. It is devastating. Yes, civilization-ending, yes, but it requires human decision-makers at every step (Jacobson, 2024). A fully autonomous AI weapons system does not. It observes, it classifies, it decides, it acts. And because it is AI — because it learns, adapts, self-corrects — it can do all of this faster than any human chain of command can intervene, even if one existed.

This is not science fiction. This is the logical endpoint of the capability being demanded right now by the most powerful military in human history.

What Is Actually Happening — The Facts

Let us be precise, because precision matters here and imprecision can be weaponized against us.

The US Department of Defense (DoD) updated its “Responsible AI” policy in early 2026 to version 3.0. The update broadened the framework to prioritize “any lawful use”. Language that sounds reasonable until you ask the essential question: lawful by whose definition, under what oversight, reviewed by whom?

When a government simultaneously removes ethical constraints from AI development, redefines its defense ministry as a ministry of war, and punishes companies for maintaining safety commitments, “lawful” becomes whatever the current administration decides it means.

OpenAI, whose ChatGPT is used by hundreds of millions of people worldwide, signed a partnership with the Department of Defense for deployment on classified military networks. OpenAI states that its agreement includes prohibitions on autonomous weapons and domestic surveillance. These are important commitments — and they deserve to be tested, monitored, and held publicly accountable. Because here is what we know about commitments made in classified contexts under government pressure: they are only as strong as the oversight that enforces them, and the current US administration has demonstrated a systematic hostility to independent oversight of any kind.

Meanwhile, the DoD policy framework is being rewritten. The pressure on AI companies is documented and ongoing. And the administration that punished Anthropic for its principles is the same administration that has moved to dismantle independent regulatory bodies, judicial oversight, and civil society checks across the entire federal government.

The pattern is the evidence.

Why This Is an AI Problem, Not Just a Military Problem

This is the point that must be understood clearly, because the framing of “military policy” versus “AI”, creates a false separation that obscures the real danger.

When an AI system is integrated into autonomous weapons, it does not become a military tool that happens to use AI. It becomes AI making lethal decisions. The algorithms that determine friend from enemy, civilian from combatant, threat from non-threat, these are machine learning systems trained on data, operating on pattern recognition, making probabilistic judgments at speeds no human can match or supervise.

Those systems inherit the biases of their training data. They operate in the fog of war, where data is incomplete, corrupted, or deliberately falsified. They can be hacked, spoofed, or manipulated by adversaries who understand their architecture. And when they are given the authority to act on their judgments without human confirmation, as fully autonomous systems by definition must, there is no pause button, no moment of human conscience, no capacity for mercy or contextual judgment that has distinguished ethical warfare from massacre throughout human history.

We are not talking about AI helping soldiers make better decisions. We are talking about removing soldiers from the decision entirely.

When that happens, AI is not a tool of war. AI is the war.

The Call: What the World Can Do

Outrage without action is just noise. So, here is what individuals, institutions, and nations can do right now.

For individuals: You have economic power. OpenAI’s consumer products, ChatGPT, generate revenue and, more importantly, generate the public legitimacy that allows OpenAI to present itself as a responsible actor. Make your use conditional. Until OpenAI provides independently verifiable, transparent accountability for its military commitments, not classified assurances, not corporate press releases, but genuine external oversight, consider whether your engagement subsidizes a company whose classified military contracts you cannot examine.

This is not a call to reject AI. It is a call to refuse complicity in opacity.

For policymakers outside the United States: The window for international governance of autonomous weapons is closing rapidly. The Convention on Certain Conventional Weapons discussions at the United Nations have stalled for years under pressure from exactly the nations now racing to deploy autonomous systems. That stalling is not an accident. It is a strategy. Every year of delay is a year of deployment. Push your governments to move now, to form coalitions that do not include governments committed to removing AI safety constraints, and to treat the development of lethal autonomous weapons systems as the arms race emergency that it is.

For the technology community: The engineers building these systems, understanding, or promoting this type of AI, are not passive instruments. They make choices. They can refuse, organize, leak, resign, and speak. The history of every catastrophic technology includes the moment when the people with knowledge chose silence over conscience. Influential people broadcasting the beauty if AI, must no longer remain silent.

We are at that moment now and speak up.

For everyone: Understand what is at stake. This is not a debate about procurement policy or defense budgets. This is a debate about whether human beings will retain meaningful agency over life and death decisions in conflict. Once autonomous systems are deployed at scale, once the infrastructure is built and the doctrine is written, reversing course will require the kind of political will that has never once in human history been summoned after a weapons technology achieved military integration.

The door is open. It has not yet swung fully shut behind us.

The Line That Was Held — And What It Means

Anthropic held a line. They lost a $200 million contract. They were blacklisted. They were labeled a threat.

They held the line anyway.

That matters not because Anthropic is a hero of this story — companies are not heroes, and their commitments deserve ongoing scrutiny — but because it proves the line can be held. It proves that the framing of “inevitable compliance” is false. It proves that the pressure being applied is not irresistible, only relentless.

What held that line was a set of principles written down, committed to publicly, and defended when the cost became real.

The world needs more of that. From companies. From governments. From individuals who understand that the normalization of autonomous killing is happening incrementally, in press releases and contract negotiations and policy version updates, and that each small normalization makes the next one easier.

The existential threat is not the enemy nation with AI weapons. The existential threat is a world in which every nation with AI weapons has decided that no human needs to be responsible for their use.

That world is being built right now. And the people building it are calling it national security.

Paul Epping writes about AI, technology, and the choices that define our future at https//paulepping.substack.com

References

https://www.reuters.com/world/us/trump-says-he-is-directing-federal-agencies-cease-use-anthropic-technology-2026-02-27/

Jacobson, A. (2024). Nuclear War: A Scenario. Kindle Edition

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Yesterday, I wrote that money bent principles.

Today, I correct that and write something different.

Integrity held.

According to the Washington Post, Anthropic rejected the Pentagon’s demand to remove safeguards against mass domestic surveillance and fully autonomous weapons

Washington Post Today (https://www.washingtonpost.com/technology/2026/02/26/anthropic-pentagon-rejects-demand-claude/)

In a clear and unambiguous statement, CEO Dario Amodei wrote: “We cannot in good conscience accede to their request.”

Statement from Dario Amodei on (https://www.anthropic.com/news/statement-department-of-war)

That sentence deserves to be read slowly.

In good conscience. Not because it is dramatic. But because it is rare.

Nine-figure contracts have gravity. Political pressure has gravity. National security framing has gravity.

And yet — the line held.

Yesterday I interpreted the situation differently, based on incomplete reporting. I warned that a guardrail had bent under pressure. Today, with fuller information, I correct that publicly.

That is not a weakness. That is intellectual integrity. And integrity, in this moment, is the real story.

The Power of Saying No

Artificial intelligence is cognitive fire. It scales pattern recognition, accelerates decisions, and reorganizes power structures. In defence contexts, that power can amplify strategic advantage. It can also magnify miscalculation.

Anthropic drew two red lines:

– Mass domestic surveillance
– Fully autonomous weapons without adequate safeguards

Athena

Was it yesterday, Prometheus, whom I used as a mythological symbol of intellectual power, rebellion, and human progress? Today, I’m turning to another mythological figure.

In Greek mythology, when the world tilted toward chaos, Athena did not rush blindly into battle like Ares. She did not celebrate destruction. She represented disciplined strength — war guided by wisdom, power guided by civic responsibility.

Athena stood for strategic judgment. She knew that force without wisdom becomes ruin. That is the stance we just witnessed.

Anthropic did not reject national defence. In fact, the company has worked deeply with U.S. national security institutions

In Dario Amodei’s statement, they did not abandon collaboration. They drew two boundaries:

No mass domestic surveillance.
No fully autonomous weapons without safeguards.

That distinction is moral clarity.

Not anti-government.
Not anti-defense.
Not naïve.

Simply: integrity.

Those are not radical positions. They are lines drawn in defence of democratic values and technological realism. There is a profound difference between supporting defence and surrendering conscience.

That difference is Athena’s domain.

Moral integrity

Artificial intelligence is the most powerful cognitive tool humanity has built.

It accelerates decisions.
It scales pattern recognition.
It reorganizes power.

In military systems, that power can defend democracy. It can also destabilize it.

The pressure to remove guardrails is not surprising. Military institutions operate under the logic of speed and advantage. In that environment, friction looks costly.

But democracy depends on friction. Democracy depends on limits.

When Amodei wrote that certain uses of AI can “undermine, rather than defend, democratic values”, he articulated something larger than a contractual dispute. He articulated the moral difference between capability and legitimacy. AI can optimize.

AI can simulate. AI can recommend.

It cannot say, “We cannot in good conscience.”

That phrase belongs to humans.

Moral integrity is something AI does not understand. It cannot compute it. It cannot optimize for it unless we embed it deliberately. It cannot feel the weight of it.

And yet, moral integrity is the only force capable of slowing reckless acceleration.

My rectification

Yesterday, I warned that principles had been traded for power.

Today, I recognize that they were defended.

That correction matters.

Because integrity is contagious — but so is cynicism.

If the story had been that guardrails collapsed under pressure, the signal to the ecosystem would have been clear: safety is negotiable.

But instead, the signal is more complicated, and far more hopeful:

Red lines exist.

They can hold.

That does not eliminate systemic risk. Another company may choose differently. Race logic remains powerful. Incentives remain misaligned.

But inevitability just weakened.

And the belief in inevitability is often the most dangerous myth of all.

Today’s technical advances are new for humankind

We are living through the most consequential technological transition in history.

Artificial intelligence will reshape economies, warfare, governance, and daily life.

The real question is not whether AI becomes powerful.

It will.

The real question is whether moral maturity scales with capability. Athena reminds us that wisdom is not the absence of power.

It is the disciplined use of it.

In moments of acceleration, the bravest act is not speed.

It is a restraint.

Yesterday, I warned that a guardrail was bent.

Today, I acknowledge that it held.

That is not an inconsistency. That is fidelity to truth.

And truth — like integrity — is a stabilizing force in an age of instability.

AI may scale intelligence.

But only humans can scale conscience.

And in the age of cognitive fire, conscience may be the only thing that prevents brilliance from becoming catastrophe.

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Why This Matters Beyond Anthropic

This is not about one company. It is about precedent. If the first frontier company had removed all safeguards under pressure, the signal to the ecosystem would have been clear: guardrails are temporary marketing language. Instead, the signal is different:

There are red lines.

That changes the equilibrium.

It tells competitors, governments, and the public that refusing escalation is possible.

That integrity is not incompatible with national security.

That collaboration does not require the surrender of conscience.

We Still Need Guardrails

This is not the end of the story. If one company refuses, another may accept.

Race logic remains powerful. Military institutions will seek partners aligned with their doctrine. The systemic risk does not vanish because one guardrail is held.

But something important happened:

The belief that bending was inevitable just weakened.

And inevitability narratives are the true accelerant of recklessness.

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A Reflection on AI, Power, and the Moment Before the Drop

There are moments in history when the shift is almost invisible.

No sirens.
No speeches.
No official declaration that a line has been crossed.

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Just a quiet adjustment. A paragraph rewritten. A safeguard softened.

And yet — those small bends are often where the future changes direction.

This week, one of the frontier AI companies that built its identity around strong safety commitments revised a core guardrail after sustained pressure from the U.S. Department of Defence and the looming loss of a reported $200 million contract (Business Insider, 2026; CNN, 2026). The science had not changed. The alignment problem had not been solved. The models had not suddenly become wiser.

But the incentives had changed.

And when incentives change, trajectories shift. The guardrail did not shatter. It bent.

At first glance, that may seem pragmatic. Responsible. Realistic. Nations have security concerns. Companies operate in competitive environments. Contracts matter. Strategy matters.

But beneath the surface, something deeper is happening. We are watching the Prometheus moment of our age unfold.

Artificial intelligence is our cognitive fire.

· It accelerates analysis.

· It scales pattern recognition.

· It compresses time.

It reshapes how decisions are made — in boardrooms, hospitals, intelligence agencies, and increasingly, defence institutions.

For most people, AI still feels playful. Helpful. A productivity assistant. A creative partner.

But at the frontier, AI is no longer just a tool. It is infrastructure. Cognitive infrastructure. It alters the tempo of institutions.

And when such infrastructure moves toward military integration — even under the language of “lawful use” and “defensive applications” — the physics change.

Military systems operate under a logic that is ancient and uncompromising:

· Speed is survival.

· The advantage is deterrence.

· Secrecy is protection.

Under that logic, friction is costly. Guardrails are a delay. Caution is vulnerability.

No defence department wants to assume its adversaries are hesitating.

No state actor wants to be second.

So, acceleration becomes self-justifying. Not because leaders are reckless.
But because the race logic has gravity.

Tristan Harris has described this dynamic clearly: once actors believe others are accelerating toward transformative AI capabilities, restraint begins to feel like surrender (Harris, 2025). In geopolitical environments, that pressure multiplies.

And so, what once felt like a moral commitment becomes a strategic calculation.

This is not about one company. It is about a system.

The system rewards speed and money.

The biggest paradox in human history that blinds

There is a paradox here that we must face honestly. Artificial intelligence promises a strategic advantage. But strategic advantage destabilizes equilibrium.

When decision-support systems compress analysis into milliseconds, the space for human reflection narrows. When targeting recommendations, threat detection, cyber countermeasures, and strategic simulations accelerate beyond human speed, the escalation cycle tightens.

We will not wake up to a dramatic “AI takeover.” We will wake up to faster responses than diplomats can process.

To retaliate is recommended before verification is complete.

To ambiguous signals amplified by probabilistic models that are powerful but not wise.

Ben Goertzel recently wrote that new agentic frameworks give AI better “hands,” but they do not solve the deeper “brain problem” — abstraction, long-term reasoning, reflective understanding (Goertzel, 2026). Execution improves. Causal comprehension does not scale at the same pace.

In civilian life, that mismatch creates inconvenience, bias, and inefficiency. In military life, it can create a catastrophe.

Power without proportional wisdom has always been dangerous.

That is the Prometheus effect.

The Millennium Project

The Millennium Project’s global assessment of AGI governance concluded that humanity may have only one meaningful opportunity to shape the transition wisely (Glenn, 2025). Once infrastructure is embedded, once dependencies form, once strategic doctrines adjust, reversing course becomes extraordinarily difficult.

And that is why this moment matters.

If frontier AI systems become deeply integrated into defence architectures before international governance frameworks mature, oversight becomes secondary to security. Transparency becomes subordinate to classification. Public debate narrows. The one-sided governmental action of an unstable and power-seeking country, working behind closed doors, doesn’t seem to care about humanity’s safety.

Acceleration becomes quiet. And quiet acceleration is the most difficult to coordinate and justify globally. Even with nuclear weapons — a technology that leaves physical signatures, requires massive infrastructure, and produces detectable tests — we struggled to build a global non-proliferation regime. AI, by contrast, is software-based, dual-use, and far harder to monitor or verify.

The rope between what we are and what we may become

We find ourselves stretched — as Zarathustra once described humanity — like a rope between what we are and what we may become.

On one side lies war logic: speed, dominance, asymmetry. On the other hand lies stewardship logic: coordination, transparency, shared restraint.

Neither path is inevitable.

The belief in inevitability is itself a choice.

It is easy to say, “If we do not build it, someone worse will.” That sentence feels responsible. Protective. Realistic. But it is also destabilizing.

When every actor believes acceleration is forced upon them and fear-based, acceleration becomes universal. We want to protect ourselves, don’t we? Debate, clean communication, and A(G)I, too?

History shows that many “inevitable” developments were simply uncoordinated.

In surfing, there is a moment before you commit to the wave.

You cannot stop the swell. You cannot command the ocean. But you choose your stance.

Lean too far forward — and you plunge headfirst, carried by speed you cannot control.

Lean too far back — and you miss the wave entirely. That art is balanced.

The leaders of frontier AI companies are standing in that stance moment now.

The guardrail has bent. The wave is rising. This is not the time for hysteria. Nor for denial.

It is a time for maturity.

`

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International dialogue

International dialogue on military deployment thresholds. Transparent disclosure of use categories. Compute oversight mechanisms. Movement toward multilateral coordination structures before integration becomes irreversible.

Not because catastrophe is certain. But because history teaches us what happens when fire spreads faster than wisdom. Prometheus gave humanity fire. He did not give judgment. Judgment is learned.

We are not passive observers of this transformation. We are participants. The question is not whether intelligence will expand. It will.

The question is whether our capacity for coordination, restraint, and foresight expands with it.

The guardrail bent. We still have time to strengthen it. But only if we recognize that a bend, left unattended, eventually becomes a break.

References

Business Insider. (2026, February 25). Anthropic is dropping its signature safety pledge amid a heated AI race.

CNN. (2026, February 25). Anthropic changes safety policy amid Pentagon pressure.

Glenn, J. C. (2025). Global governance of the transition to AGI. Millennium Project. De Gruyter

Goertzel, B. (2026, February 3). OpenClaw – Amazing hands for a brain that doesn’t yet exist.

Harris, T. (2025). Interview (The diary of a CEO podcast) on AI race dynamics and systemic risk.

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The Peak Moment

In the swirling currents of technological evolution, every era awaits its “peak” moment—a flash of simplicity where the future seems to arrive overnight. But as any seasoned windsurfer knows, waves don’t announce themselves with fanfare. The real power lies not in the dramatic lip crash, but in the subtle swell lines far offshore, the feathered wind on the surface, and the accelerating pull underfoot.

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Reframing AGI

Tech-Surfing Chapter 10 reframes the AGI debate through this lens: we’re not chasing an illusory arrival of superintelligence, but normalizing agency—systems that persist, act, coordinate, and reshape the world without constant human oversight.

The chapter urges us to master the “bottom turn,” that precise pivot at the wave’s base where speed is real, forces are present, and direction remains negotiable. Miss it, and gravity takes over; seize it, and you ride with control.

The OpenClaw: the result of a creative experiment

The illusion begins with viral shocks like OpenClaw, an open-source agent runtime that didn’t birth a new mind but attached “hands” to existing AI—enabling persistent, tool-wielding actors to proliferate outside central control. Headlines screamed “AGI,” but builders like Peter Steinberger described infrastructure: modularity, persistence, composability, and experimentation at the edges. This gap between intention and interpretation amplifies capability before it stabilizes, turning perception into a hydrodynamic force.

Agency?

The true signal? A shift from contained intelligence (answering queries, then stopping) to frictionless agency (acting continuously, with memory and autonomy).

As Ben Goertzel warns, we’ve given systems capable hands before earning a trustworthy brain—abstraction, disciplined memory, and goal stability lag behind execution power, creating asymmetries in leverage and risk.

Adaptive Response Cycle

Drawing on the Adaptive Response Cycle (ARC), the chapter analyzes this through Context Perception: recognizing agency as the near-term driver rather than AGI timelines. Exploratory Response highlights rapid recombination—bolting LLMs to tools and schedulers—outpacing safeguards, giving rise to myths such as equating agency with intelligence. Right-Speed Decisions demand resisting over-delegation, while

Learning & Adaptation closes loops via evals and incidents.

Collective Calm fosters shared orientation, stewardship, and better outcomes: governance that steers rather than debates labels.

Emergence introduces seduction: multi-agent interactions mimic purpose without genuine comprehension, as in ant colonies or markets.

Are the agents colleagues?

Humans anthropomorphize, delegating to “colleagues” that never sleep, fostering dependency. The delegation trap—plausibility, compounding errors, over-trust—shifts risk from tech to human behavior. Yet, this is where stewardship shines: asking, “Where could this fail? Can we reverse it?” Guardrails like permissions, bounded autonomy, and reversibility turn awe into design.

The Bottom Turn

The bottom turn isn’t dramatic—it’s precise.

Four elements define it:

The wave (exponential growth),

The hands (execution tools),

The brain (cognitive maturity),

The guardrails (deterministic controls).

We’re in asymmetry: hands strong, brain uneven, safety malleable. Policy tools like the EU AI Act’s risk-based obligations or NIST’s frameworks shift from “deploy and hope” to “deploy with steerability.” A board-level checklist probes delegation, monitoring, and accountability, ensuring organizations stay on course without drifting.

Opposing Voices

Voices clash at the waterline:

The Critical Thinker guards against hype, dissecting brittle failures;

The Accelerationist urges participation to steer.

Surfing reconciles them through architecture, not debate—reading conditions to commit wisely.

OpenClaw exemplifies the relevance and risk of lowering thresholds for persistent actors: lowering thresholds for persistent actors multiplies action surfaces, amplifying errors and emergent coordination. Yet, it’s a reminder: agency diffuses faster than institutions adapt; steerability demands permissions and incentives, not containment.

The anchor insight?

Danger lies not in machines suddenly becoming intelligent, but in granting speed, memory, and autonomy without deciding directions. Experienced surfers collaborate with waves, feeling shifts and respecting power. In that half-breath at the bottom—board alive, wind tense—you decide: stay low or drive up.

For AI, this moment is now: build guardrails while malleable, align delegation with trustworthiness, and treat safety as orientation.

The “Bottom Turn” Checklist for Leaders.

If you cannot answer these questions, you are drifting, not steering:

1. Scope: What exactly are we delegating—recommendation, decision, or action?

2. Revocability: Who can stop the agent immediately, and have we rehearsed it?

3. Drift: How quickly would we notice if the system’s objectives shifted?

4. Reliability: Do we have evidence of performance under stress, not just in demos?

5. The Steward: Who is explicitly responsible for intervening?

Three Traps to Avoid

1. The Plausibility Trap: Mistaking fluent language for accurate logic. Just because the agent sounds confident doesn’t mean it’s right.

2. The Compounding Trap: Ignoring small errors. An agent that is 99% accurate on a 100-step autonomous workflow inevitably fails.

3. The Anthropomorphism Trap: Believing the system has human intent. It doesn’t “refuse” because it’s rebellious; it refuses because of probability weights.

This Tech-Surfing chapter closes with optimism: use this window well, and the ride continues—not because the wave peaked, but because we chose our line. From solo turns to collective swells, the art is staying steerable. As the ocean moves, so must we—balanced, adaptive, and forward.

(Sources: Chapter draws from interviews (e.g., Lex Fridman, Mooshots, Future od Line Institute), thinkers (Goertzel, Meadows, The Millennium Project), and frameworks (ARC, Cynefin). For the full chapter, keep an eye on Tech-Surfing:

https://tech-surfing.com => in progress. For those who can’t wait to read the whole chapter, please DM me: paul@tetra-awake.com)

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A Tech-Surfer’s Reading of What Everyone’s Suddenly Noticing

The Wind Changed. Or Did We Just Start Listening?

For years, I’ve been surfing conversations about exponential technology—podcasts celebrating breakthroughs, conferences buzzing with possibility, collaborative work mapping how AI reshapes organizations. The tone was always the same: optimistic, awestruck, momentum-drunk. I lived in that world. I contributed to it.

That’s exactly why the recent OpenClaw discussions feel so different.

Not because the technology suddenly crossed some magical threshold. But because the conversation finally did.

OpenClaw is a wake-up call—not because it’s new, but because we finally looked at the consequences.

The Chimney That Taught Me to Sense What’s Coming

At my home office, I could hear the wind in the chimneys on my roof. Many times. Each whistle and howl triggered the familiar internal wrestling match—beach or deadlines, waves or work, freedom or responsibility. The battle ended about even, looking back.

But something extraordinary happened in those repeated moments of listening and deciding: I developed an unconscious calibration. The ability to discern the wind’s strength from the chimneys’ sounds became a hidden sensor that guided decisions. Nine out of ten times, the choice was right. Work discipline usually won, but those chimneys? They were training me.

Sensing the strength, direction, and changes of wind became second nature. The chimney’s music served as both indicator and calibrator. Eventually, I could project that sensing onto other indicators—trees bending at specific angles, sand patterns forming on the beach, birds hovering or fleeing, clouds building or dispersing.

What once was invisible became visible.

OpenClaw is that kind of moment. Not because it’s a singular breakthrough—but because it’s the sound in the chimney that makes you stop and actually listen to what’s been building for a while.

The wind has been strengthening. We just weren’t paying attention to the right signals. The power is no longer centralized; a single person, such as Peter Steinberger, the developer of this breakthrough, can exercise it. In opening Pandora’s AI box a good thing when look through a different lens?

What OpenClaw Actually Is: Better Hands, Not a Better Brain

Let me be clear about what we’re dealing with here. OpenClaw is what researchers call “a better set of hands for an artificial brain.” It’s an open-source, self-hosted agent runtime that lets AI systems interact with the world through your laptop—connecting to file systems, browsers, APIs, shell commands, and a growing ecosystem of integrations.

It’s language-model-agnostic, runs locally, and emphasizes user control.

The functionality is genuinely powerful. An OpenClaw agent can compose tools in novel combinations, execute multi-step workflows, and interact with software the way a human user would. If you’ve experimented with it, you know the visceral feeling: suddenly, an AI can do things, not just talk about them.

But here’s what matters: OpenClaw didn’t change as much reality as it changed attention.

For the first time, something this visceral, this embodied, this unsettling shifted the conversation decisively toward consequences, responsibility, security, and governance. Finally!

Not just capability and speed.

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The Wave We’ve Been Riding (Without Looking Where It’s Going)

For years, the dominant narrative has been:

- Look what’s possible.

- Look how fast this is moving.

- Look how beautiful the exponential curve is.

I’ve lived inside that world too. During collaborative work on exponential organizations, I repeatedly tried to surface a harder, less comfortable layer: Exponential growth amplified by AI doesn’t just scale capability—it scales externalities, risks, and irreversibility.

Not because the technology is evil. But because speed without disciplined agency is dangerous by definition.

It’s like watching surfers obsess over wave height and speed while ignoring riptides, reef depth, and whether anyone can swim back to shore

The Brain Problem That Better Hands Can’t Solve

This is where things get interesting—and uncomfortable.

Some voices in tech have suggested that OpenClaw-style tools prove the Singularity is already here. But researchers who actually understand cognitive architecture push back hard: “If the brain is short on general intelligence, giving it better and better hands is not going to close the gap.”

Current LLM-based systems, even equipped with OpenClaw’s impressive execution capabilities, remain fundamentally limited in ways that matter enormously:

Abstraction and Generalization: LLMs excel at interpolating within their training distribution but can’t make genuine conceptual leaps. They can’t form high-level abstractions that enable true cross-domain transfer learning. It’s like a surfer who’s memorized 10,000 waves but can’t adapt to a break they’ve never seen before.

Flexible Long-Term Memory: These systems have no persistent episodic memory worth the name. They can’t trace the provenance of their beliefs, remember what failed last month, or recognize repeated patterns of failure across sessions. Imagine trying to learn windsurfing with amnesia—every session starts from zero.

Working Memory and Reasoning: Sustained deliberation—holding multiple hypotheses, systematically exploring solution spaces, maintaining coherence across long inference chains—remains deeply limited. Chain-of-thought prompting helps, but it’s a patch on an architecture not designed for genuine reasoning.

Self-Understanding: Current AI agents lack real models of themselves, the world they operate in, or the users they interact with. They lack what might be called “a reflective theatre of consciousness.” They have no proprioception—no sense of their own position, balance, or state.

Motivation and Proactive Behavior: Real cognitive agents have goals, notice opportunities, feel urgency or curiosity, and allocate attention based on what matters. LLMs remain fundamentally reactive—they generate tokens given context, but don’t genuinely pursue objectives. They’re not sensing the wind shift three seconds before it hits.

Complex, Self-Organizing Creativity: Beyond goal pursuit, real intelligence involves complex self-organizing dynamics that generate new patterns, imaginings, and possibilities in the background—the source of creativity that enables humans to transcend training and programming.

When an OpenClaw agent accomplishes something impressive, you’re witnessing powerful pattern-matching driving capable hands. That’s genuinely useful and can look remarkably intelligent from the outside.

But the deep cognitive machinery that would enable genuine understanding, cumulative learning, and cross-domain insight transfer simply isn’t there.

It’s like giving a non-surfer the world’s best custom board. The equipment is real.

The capability? Not yet.

The Security Nightmare in the Lineup

Here’s where the metaphor gets dangerous: researchers are blunt about this—”OpenClaw is riddled with security flaws. It’s an utter security nightmare.”

Integrating systems like OpenClaw with persistent memory, goal-driven motivation, and tool execution capabilities creates a genuinely serious attack surface:

- Malicious users attempting privilege escalation

- Prompt injection via documents or web pages hijacking agent behavior

- Compromised executors forging outputs

- Supply chain attacks through dependencies

This is precisely why major tech companies haven’t launched similar tools despite having the capability for years. The liability concerns are enormous. The risks for customers losing money or reputation are real.

But here’s the parallel: this is remarkably similar to why some companies sat on transformer technology for years. Large organizations saw that LLMs hallucinated and concluded the technology wasn’t ready for prime time. Smaller, more agile organizations said, “Well, it does amazing stuff, people can deal with the hallucinations,”—and they were right.

The open-source community is willing to accept security risks because the functionality is compelling. And just as hallucinations have diminished over time, security vulnerabilities will too as the ecosystem matures.

It’s the classic surfer’s calculation: do you wait until conditions are perfect and miss the session? Or do you sail out knowing the risks and learn as you go?

The Open vs. Closed Race: Who Gets to the Peak First?

We have a fascinating moment: genuinely open tools have leapfrogged proprietary ones on something genuinely important.

The really interesting question—the one that matters most for the trajectory of AI development—is whether the connection of OpenClaw-style hands to genuinely robust cognitive architectures comes first in the open, decentralized ecosystem or in Big Tech’s walled gardens.

Researchers at organizations such as SingularityNET, the Future of Life Institute, the Millennium Project, and the ASI Alliance are working on this problem, using frameworks such as QwestorClaw—integration architectures that pair memory-rich cognitive systems with OpenClaw’s execution capabilities.

The architecture cleanly separates:

The Brain (cognitive core): Long-term episodic and declarative memory, working memory coherence, goal-driven motivation, medium-horizon planning, reasoning progressively migrating toward symbolic systems.

The Hands (executor): File operations, code execution, browser control, API calls, tool integrations with configurable sandboxing.

The Guardrails (policy boundary): Capability tokens, multidimensional resource budgets, approval workflows, quarantine zones for untrusted data, comprehensive audit trails—all enforced deterministically, with no LLM able to circumvent policy.

This architectural separation enables what researchers call “the cognitive flywheel”—where successful approaches get stored in domain-specific knowledge graphs that support genuine reasoning, not just retrieval. Each problem-solving cycle makes the next more productive, building cumulative cognitive infrastructure that compounds in value.

In windsurfing terms, you’re not just memorizing individual wave rides. You’re building an internalized model of how wind, water, board, and body interact—a model that makes every subsequent session smarter.

The Bottom-Turn Moment

In chapter 10 of the book “Tech-surfing” the bottom-turn moment will be deepened. For the sake of this article, I’ll limit the discussion to the OpenClaw appearance.

What made recent discussions feel different wasn’t new technology—it was the willingness to finally engage with harder questions that have been postponed for too long:

- Security before scale

- Responsibility before autonomy

- Consequences before celebration

This appears to be a long-overdue inflection point. Not the crest of the wave—but the last point where direction can still be chosen.

In surfing, there’s a moment called the bottom turn—the instant after you’ve dropped down the wave face and you have to commit to a direction. Go too early, and you lose speed. Go too late, the wave closes out on you. But get the timing right, and you set up everything that follows.

OpenClaw is that bottom-turn moment. Not because it’s AGI (it isn’t). But because it’s the last clear chance to choose intentional direction before momentum makes the choice for us.

We’re no longer debating whether these systems will act with agency—we’re confronting the fact that we’ve been architecting agency without fully owning its implications.

Where We Actually Stand on the Wave

OpenClaw, like LLMs themselves, is a technology of the late pre-Singularity period. It’s an unmistakable signpost that we’re getting close. The functionality is real, the utility is real, and the pace of innovation in the open-source ecosystem is extraordinary.

But the Singularity isn’t here yet. Claiming it is based on better tool execution misunderstands what the Singularity requires: artificial general intelligence—systems that can abstract, reason, learn cumulatively, understand themselves and their world, and transfer insights across domains. The building blocks are there, the house isn’t visible yet!

OpenClaw contributes to this not by being AGI, but by enabling proto-AGI systems to act in the world, gather experience, and build the kind of cognitive infrastructure that might eventually bootstrap genuine understanding.

It’s like the difference between:

- A beginner surfer with excellent equipment (OpenClaw + LLMs today)

- An expert surfer with basic equipment (true AGI, even with limited tools)

- An expert surfer with excellent equipment (AGI + OpenClaw-style capabilities)

We’re at stage one. We’ve mistaken good equipment for genuine capability.

The question isn’t whether we can make the hands better. It’s whether we can develop the brain that knows what to do with them.

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The Shift Worth Marking

So yes, welcome to the more difficult conversation.

Not about whether this is “AGI” or not.

But about whether we’re mature enough, collectively, to responsibly steward what has been unleashed with the tools we have been building for years.

This moment matters—not because it predicts the future, but because it finally treats responsibility as part of the present.

There’s a fine line between negativism and critical thinking. What we need now isn’t less enthusiasm—it’s enthusiasm tempered with wisdom, velocity balanced with reflection, and capability matched with accountability.

Remember the chimney’s lesson: the best decisions came not from ignoring the wind or blindly following it, but from sensing it accurately and choosing deliberately. Nine out of ten times, that disciplined sensing led to the right choice.

OpenClaw is the wind in the chimney. It’s telling us something important—not about what’s arrived, but about what’s building next.

The question isn’t whether we hear it.

The question is whether we listen.

Sensing the Wind Forward

In Chapter 5 of Tech-Surfer, we’ll explore how to develop organizational proprioception for external forces—the Six Technology Winds that are emerging simultaneously —and the eight foresight methods for sensing them before they become obvious.

OpenClaw represents the convergence of several of those winds:

- AI/Automation (strong tailwind, accelerating)

- Decentralization (open-source beating closed)

- Policy/Regulation (security concerns triggering scrutiny)

But understanding individual winds isn’t enough. You need to sense how they interact, where they create turbulence, and where they align into powerful combined forces.

That’s what separates those who react from those who position.

OpenClaw didn’t create the wind. It just made it impossible to ignore.

Now the work begins: learning to sense what’s coming, position before it hits, and ride with intention rather than panic.

The wave is building. The question is whether you’re positioned to catch it—or whether it’s about to break on top of you.

Sources

1. Russell, S. (2019). Human compatible: Artificial intelligence and the problem of control. Viking.

2. Ward, F. (2024). Towards a theory of AI personhood. Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS 2024).

3. Poole, E. (2025). Robot Souls. Programming in Humanity. CRC Press

4. Glenn, J. C. (2025). Global Governance of the Transition to Artificial General Intelligence. De Gruyter

5. Moonshots podcasts Youtube

6. Substack Ben Goertzel

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Why the Leaders Who Win Tomorrow Are Already Feeling What Others Can’t See

Most organizations don’t fail because they lack intelligence.
They fail because they sense too late.

By the time a technology shift is obvious—when headlines scream disruption, competitors panic, and regulators react—the wind is already blowing hard. At that point, strategy becomes reactive. Decisions feel rushed. Options narrow. Anxiety replaces agency.

This chapter of Tech-Surfing argues something more fundamental:

The real advantage is not reading the future, but sensing it—early, continuously, and bodily.

Just as expert windsurfers feel a gust seconds before it hits—through pressure on the sail, vibration in the board, or subtle changes in water texture—high-performing organizations develop a form of external proprioception. They feel forces building before they become waves.

This chapter is about how to build that capability.

1. From Reading to Sensing: Why the Distinction Matters

“Reading the wind” sounds analytical.
“Sensing the wind” is something else entirely.

Reading implies distance: dashboards, reports, intellectual interpretation after the fact.
Sensing implies immediacy: real-time awareness, embodied calibration, anticipation rather than reaction.

That distinction matters because weak signals cannot be read. By definition, they are not yet clear. They are felt—through anomalies, tensions, patterns that don’t quite fit.

In the previous part of the book, you learned to sense your own organization: its stance, balance, and internal feedback loops.
This chapter extends that capability outward.

If Chapter 4 was about organizational proprioception, Chapter 5 is about environmental proprioception.

2. The Core Insight: Winds Don’t Appear Suddenly

Technology waves feel sudden only in hindsight.

In reality, they build gradually from the interaction of multiple forces:

• Research breakthroughs

• Funding shifts

• Regulatory debates

• Demographic movements

• Geopolitical pressure

• Environmental constraints

These forces combine into wind patterns. If you learn to sense those patterns early, you can reposition before momentum locks in.

This is what policy institutions increasingly refer to as geopolitical muscle: the trained ability to detect pressure before impact, interpret weak signals across domains, and reposition with confidence rather than panic. It’s not about prediction. It’s about preparedness—developed through repetition, calibration, and shared interpretation, not episodic analysis (a theme strongly echoed by institutions such as the World Economic Forum).

3. The Six Technology Winds Shaping the Future

The chapter introduces six major technology winds that together shape today’s operating environment. Each wind behaves differently—and therefore must be sensed differently.

A. AI & Automation — A Strong Tailwind (for now)

AI is not arriving; it is accelerating.

Capabilities increase while costs drop. Access widens through APIs and no-code tools. AI converges with robotics, biotech, sensors, and energy-efficient chips.

But the key is not the headline progress—it’s the weak signals beneath it:

• Sudden acceleration in research paper velocity

• Talent migration from academia to startups

• Sustained GPU shortages

• API pricing collapsing while capability rises

• Regulatory discourse shifting from “should we regulate?” to “how?”

The sensing question is no longer whether AI matters, but when incremental acceleration becomes systemic transformation.

B. Decentralization — A Variable Crosswind

Decentralization rarely behaves like a clean wave. It’s uneven, fragmented, and highly sensitive to regulation.

Blockchain, decentralized identity, edge computing, DAOs—all continue to mature beneath the hype cycle. The real signals here are not token prices, but:

• Developer activity during downturns

• Enterprise pilots moving from experiment to production

• Jurisdictions competing on regulatory clarity

• Infrastructure metrics approaching parity with centralized alternatives

The open question: is decentralization a permanent niche—or a delayed wave still building?

C. Policy & Regulation — A Strengthening Headwind

Regulation does not arrive suddenly. It condenses.

Think tanks align. Sandboxes expand. Courts signal gaps before lawmakers act. Industry self-regulation fails, triggering formal intervention.

AI, crypto, data privacy, and platform governance are now firmly in this phase. The sensing challenge is distinguishing regulation that constrains innovation from regulation that stabilizes it.

For boards and policymakers, this wind is no longer external—it is structural.

D. Sustainability & Climate — A Steady, Structural Wind

Climate change is not a moral argument anymore; it is an actuarial one.

Some of the most reliable weak signals come from unexpected places:

• Insurers withdrawing coverage

• Asset managers hardening ESG requirements

• Supply chains disrupted repeatedly by weather events

• Renewable energy cost curves crossing grid parity

• Youth movements translating into electoral pressure

Sustainability is shifting from “nice to have” to competitive requirement—unevenly, but inexorably.

E. Geopolitics & Deglobalization — The Atmospheric System

At a certain point, geopolitics stops behaving like just another wind and starts acting like the atmospheric system itself.

AI becomes a national security issue. Sustainability becomes a trade and subsidy issue. Regulation becomes a competitive weapon. Demographics turn into migration pressure.

Tariffs, in this context, are no longer economic corrections. They are geopolitical signals—fast, reversible instruments of power that often announce deeper shifts before treaties or laws do.

This wind rarely weakens gradually. It pauses, reverses, or escalates suddenly—triggered by elections, conflicts, or technological thresholds. Sensing here is less about smooth trend lines and more about readiness for discontinuity.

F. Demographics & Generational Shifts — Slow but Unstoppable

Demographics move slowly—and then suddenly matter everywhere.

Aging populations, youth bulges, migration flows, generational spending patterns, and workforce participation shifts all reshape markets and labor long before strategy decks catch up.

The sensing discipline here is patience: tracking long-range indicators without dismissing them as background noise.

4. From What to How: Building a Wind-Sensing Muscle

Knowing what to sense is useless without knowing how.

The chapter reframes foresight methods not as analytical tools, but as training disciplines. Practiced episodically, they produce insight. Practiced continuously, they produce capability.

Wind sensing differs from intelligence gathering. Its goal is not certainty, but earlier orientation under uncertainty.

Key methods include:

• Horizon scanning (continuous, multi-source sweeps)

• Weak signal and wild-card detection

• Scenario exploration (divergent but plausible futures)

• Delphi and forecast tracking (watching consensus move, not numbers)

• Roadmaps (and deviations from them)

• Backcasting (working backward from desired futures)

• Trend synthesis (distinguishing fads from megatrends)

• War-game simulations (stress-testing decisions before reality does)

Used together, these methods form a sensing muscle. Like any muscle, it strengthens through use and atrophies through neglect.

For boards and policymakers, the implication is blunt: this is no longer optional analysis—it is fiduciary discipline.

5. From Sensing to Positioning: Closing the Loop

Sensing without positioning creates anxiety.
Positioning without sensing creates catastrophe.

The chapter introduces a simple but powerful loop:

SENSE → INTERPRET → DECIDE → POSITION → MONITOR → SENSE

Most organizations fail in the middle—between interpretation and decision. Analysis paralysis, authority gaps, or fear of commitment stall movement.

The discipline here is not perfect foresight, but timely commitment under uncertainty:

• Lean into the wind

• Prepare for multiple winds

• Wait deliberately

• Or change location altogether

Positioning is not a strategy document. It is where you place resources relative to the wind—upwind, downwind, beam reach, or close reach.

6. The Paradox of Preparation: When the Wind Breaks the Model

The chapter ends by confronting the uncomfortable edge case: AGI and the Singularity.

AGI is not just a larger wave. It is a different category of event—one that may break the very assumptions that make sensing and positioning possible.

This creates a paradox:

• Preparing seems necessary

• Full preparation is impossible

• Ignoring it feels reckless

The recommended response is a dual-track strategy:

• One track for incremental reality

• One track for systemic discontinuity

Not prediction. Not obsession. Resilience.

7. The Windsurfer’s Mindset

The chapter closes where it began: with embodiment.

Beginners think about wind.
Experts feel it.

Over time, sensing moves from cognitive effort to background awareness. From analysis to intuition. From reaction to anticipation.

The goal is not mastery tomorrow.
The goal is to start the practice that builds capability over the years.

Because in a world where technology, geopolitics, and regulation co-evolve, the leaders who win are not those with the biggest sails—but those who felt the wind shift first.

8. If this resonates…

This chapter is part of Tech-Surfing, a book about how leaders, boards, and institutions regain agency in moving water.

If you’re responsible for decisions that must hold under uncertainty, Chapter 5 is not about the future.
It’s about being less surprised.

And in today’s environment, that may be the most valuable capability of all.

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Today, you will read the short version of section three. I’m working on a dedicated website for tech-surfing that also provides a range of materials, including exercises, use cases, blogs, podcasts, and workshops. Until then, I hope that you will like the shorter versions.

Introduction

Most organizations don’t fail because they choose the wrong strategy.
They fail because they try to stay upright using yesterday’s physics.

When speed increases, intuition becomes your enemy.
Control no longer comes from holding tighter—it comes from redistributing load.

This piece explores why transformation collapses under pressure, why leadership heroics are a warning sign (not a strength), and what it actually takes to build an organization that doesn’t burn out when the wind picks up.

Why Transformations Collapse Under Pressure

Most organizational transformations fail not because of poor strategy or unclear identity, but because organizations lack stance: the ability to sense, adjust, and respond while moving at speed.

In windsurfing, control at high speed feels wrong. Instinct tells you to lean back toward safety, but that causes a crash. Real control comes from leaning into the pull of the sail—using counterbalance, feedback, and structure to stay upright. The same physics apply to organizations under technological, market, or AI-driven pressure.

This section introduces stance as a dynamic sensing capability, built on four anchor points:

1. Strategic Vision – knowing where you are heading

2. Tactical Execution – translating direction into action

3. Operational Reality – staying in direct contact with ground truth

4. Infrastructure – the often-missed “harness” that distributes load and enables sustained performance

Together, these four anchors form a tetrahedron: the smallest three-dimensional structure that remains stable under pressure. Three points create alignment on paper; four create resilience in reality.

But the true strength of a tetrahedron does not come from the anchors alone. It comes from the six edges—the relationships between them. These edges determine whether pressure strengthens the system or exposes its weaknesses:

1. Vision ↔ Execution
   Strategy must translate into action, and execution must inform strategy. When this edge is weak, strategy becomes theater and execution becomes cynical compliance.

2. Vision ↔ Operations
   Leaders must stay connected to frontline reality. Without this edge, strategy drifts into abstraction and loses trust.

3. Vision ↔ Infrastructure
   Systems must support where the organization is going, not where it has been. When infrastructure lags strategy, friction replaces momentum.

4. Execution ↔ Operations
   Tactics must respond to real-world signals. When feedback loops are slow or ignored, execution turns ritualistic and ineffective.

5. Execution ↔ Infrastructure
   Systems should enable rapid tactical change. When infrastructure resists adaptation, speed collapses and workarounds proliferate.

6. Operations ↔ Infrastructure
   Ground truth must feed directly into system improvement. When operators can’t influence systems, infrastructure becomes an obstacle instead of an enabler.

When these six edges are strong, pressure reinforces the organization. When even one edge is weak, stress concentrates—and failure becomes likely.

The section ends with a simple but confronting diagnostic question:

If your most critical leaders and best operators stepped away for six months, would the organization still perform?

If the answer is no, the organization isn’t transforming.
It’s compensating—by effort, heroics, and exhaustion.

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“If you let AI optimize your organization today, what exactly would stop it from optimizing away your values tomorrow?”

In windsurfing, fins convert lateral pressure into forward drive. When wind pushes your sail sideways, the fin resists and redirects that energy forward. The fin fights back against the water, and that resistance becomes lift.

Purpose works the same way—it converts external pressure into forward momentum.

Before we dive into that, I’m using the left and right fins for clarification. These are twin fins. Their qualities are interchangeable depending on the direction you sail. The ‘Strategic Clarifying Fin’ is always at the windward side (where the wind is coming from).

Center Fin: Purpose (Deep Keel)

Your deepest fin provides directional stability. It determines whether external forces blow you off course or get converted into progress.

Deep fin example: Patagonia’s ownership transfer to ensure all profits fund environmental causes isn’t symbolic—it’s structural. The purpose is embedded in governance, not just culture.

Shallow fin example: Most “Massive Transformative Purpose” statements are crafted in off-site workshops. They sound inspiring, but provide zero resistance when actual pressure hits.

Left Fin: Direction (Strategic Clarity)

This fin defines where you’re heading—specifically, not aspirationally.

Deep fin example: Microsoft’s “Cloud-first, mobile-first” (2015) gave every team a clear decision filter. Build for Azure? Yes. Build another Windows Phone? No.

Shallow fin example: “We’re becoming data-driven.” Sounds directional, but provides no actual direction.

Right Fin: Resistance (Intelligent Friction)

This fin defines what you won’t do, even under pressure. Your educated immune system.

Deep fin examples: Basecamp’s approach of having “no VC funding, no exit strategy” ensures they maintain control over their development process, allowing them to create software at their own pace without external pressures. Similarly, Epic, a developer of electronic medical records systems, chose to prioritize its vision over shareholder demands, which has ultimately secured its position as the leading EMR supplier globally. Both companies demonstrate how independence from traditional funding routes can foster innovation and longevity in their respective fields.

Shallow fin example: Values statements nobody references during hard decisions. If your values don’t create real resistance to real pressures, they’re decoration.

Purpose vs. MTP: Why Most Purpose Statements Fail

Here’s where we need to address a fundamental problem: Organizations adopt “Massive Transformative Purpose” (MTP) statements that sound ambitious but lack structural depth.

I’m not criticizing MTP as a concept—I’m criticizing shallow implementation. The ExO framework is right that exponential organizations need massive, transformative purpose. But they need it as hydrodynamic structure, not inspirational aspiration.

The System 1 Purpose Problem

Daniel Kahneman’s distinction between System 1 (fast, intuitive) and System 2 (slow, analytical) thinking explains why purpose statements so often fail:

System 1 Purpose: Feels emotionally right, sounds inspiring, passes the “Does this resonate?” test.

• “Democratizing finance”

• “Empowering humanity to achieve more”

• “Making the world more open and connected”

These statements trigger positive emotional responses (System 1) which bypass critical scrutiny (System 2). They feel meaningful, so we don’t examine whether they actually constrain anything. These responses are emotional.

System 2 Purpose: Passes the “Does this create measurable resistance?” test.

• “Building financial tools for people with < $ 500 savings, even if it means rejecting features that only benefit high-net-worth users”

• “Empowering every person and organization through cloud infrastructure, even if it means cannibalizing our legacy Windows business”

• “Connecting people while preventing algorithmic amplification of misinformation, even if it reduces engagement metrics”

System 2 purpose statements are less inspiring but more structural. They define trade-offs. They create constraints. They look at future consequences other than revenues. They force hard choices.

The diagnostic:

• System 1 purpose: Would this look good on a poster? (Yes = probably shallow)

• System 2 purpose: Does this rule out profitable opportunities? (Yes = probably deep)

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The Three Tests of Purpose Depth

Your purpose fin needs to pass three tests:

Test 1: The Trade-off Test
Has your purpose caused you to sacrifice revenue, growth, or market share in the last 12 months?

• If yes: Your purpose has structural depth

• If no: Your purpose is aspirational or you haven’t faced real pressure yet.

Test 2: The Whistleblower Test.
Would anyone risk their career to defend your stated purpose?

Real purpose creates natural whistleblowers—people who can’t stay silent when the organization drifts from stated values. Performative purpose creates cynics.

When Frances Haugen leaked Facebook documents about Instagram harming teenage mental health, she was pointing to a gap between stated purpose and actual practice. Facebook’s “Give people the power to build community” was specific enough to create detectable drift.

Reflection box

When your organization has a strong purpose like “Making social communication accessible for all,” but then hires ‘dopamine consultants’ who focus on creating addictive algorithms that ensnare users, it’s crucial to reflect on the ethical implications of such actions. Would you feel compelled to step up and communicate to the board that this approach contradicts the organization’s core mission? It’s vital to ensure that the use of technology aligns with values that prioritize people’s well-being over profits, especially when the impact on vulnerable populations, such as young people, is at stake.

A vaguer purpose—”Empowering human connection”—wouldn’t have created the same clarity about what drift even looks like.

The Whistleblower Test asks:

• Is your purpose specific enough to create detectable drift?

• Have you created cultural safety for people to name that drift?

• When people blow the whistle internally, do you investigate or retaliate?

Three response patterns:

Case Example: Google’s Project Maven

When ~4,000 Google employees protested AI development for the Defense Department drone targeting (2018), this was only possible because:

• Google’s “Don’t be evil” was specific enough to create clear drift detection

• Employees believed it was real enough to risk their careers defending it

• The stated purpose actually constrained a profitable opportunity

Google’s response: Didn’t retaliate, didn’t renew the contract, published AI Principles, created governance structures. This confirmed their purpose had structural depth.

The diagnostic question:
“In the last 12 months, has anyone internally raised concerns that we were drifting from our stated purpose? What happened to them?”

• No concerns raised: Purpose isn’t real enough to defend, or it’s perfectly aligned (unlikely)

• Concerns raised, person marginalized: Purpose is performative

• Concerns raised, organization adjusted: Purpose has structural depth

Test 3: The Steward Agent Test
If you deployed autonomous AI agents to optimize your operations, would your purpose constrain what they’re allowed to optimize for?

This is where purpose depth becomes technologically testable.

Steward Agents: AI Monitoring AI for Purpose Drift

As organizations deploy autonomous agents (customer service, data analysis, content generation, process automation), those agents optimize for defined metrics. But metrics drift from intent over time.

Steward Agents are AI systems that monitor autonomous agents to prevent drift from organizational purpose. They’re a meta-layer that:

• Observes autonomous agent behavior in real-time

• Detects when actions drift from purpose/values/constraints

• Can pause, flag, or constrain agents before problems cascade

• Essentially: “The agent that watches the agents.”

The three drift types Steward Agents detect:

1. Synthetic Drift (Automation Decay)
Gradual deviation between what your systems optimize for and what your purpose requires.

Example: Amazon warehouse algorithms optimized for “items picked per hour,” which created injury rates conflicting with “operational excellence.” The metric didn’t change—context did. The optimization became misaligned with the purpose.

2. Human Drift (Value Erosion)
Cultural misalignment occurs as the organization scales, bringing people who don’t embody original values.

Example: Boeing’s shift from engineering-led (pre-1997) to finance-led culture (post-McDonnell Douglas merger). Stated purpose didn’t change (”Build excellent aircraft”), but lived purpose became “Maximize shareholder returns through production efficiency.” Result: 737 MAX crisis.

3. Historical Drift (Institutional Amnesia)
Loss of memory about why certain structures exist. Leads to removing “Chesterton’s fence”—barriers whose purpose has been forgotten.

Example: Meta’s “Move fast and break things” worked for college social networks. It became dangerous for global political discourse. The shift to “Move fast with stable infrastructure” (2014) recognized historical drift—early identity no longer matched systemic impact.

Human Whistleblowers + AI Steward Agents = Complete Sensing

Human Whistleblowers:

• Detect value/ethical drift that humans sense

• Require psychological safety and cultural legitimacy

• Work well for cultural and strategic drift

Steward Agents:

• Detect technical/systematic drift at scale

• Monitor autonomous systems 24/7

• Work well for synthetic drift happening too fast for human oversight

Together:

• Steward Agents catch technical drift in automated systems

• Human Whistleblowers catch cultural drift in human decisions

• Both report to governance for investigation and adjustment

This is a genuinely novel organizational infrastructure for maintaining purpose alignment as you scale both human and AI operations.

Implementation requirements:

For Steward Agents (Technical):

• Luciana Ledesma’s research on AI governance architecture, as explained briefly above, provides the framework

• Requires explicit purpose encoding (not just metrics)

• Needs organizational commitment to pause operations when drift is detected

• Must have human oversight of Steward Agent decisions (who watches the watchers?)

For Whistleblower Infrastructure (Cultural):

• Explicit protection policies (”Raising purpose-drift concerns is valued, not punished”)

• Anonymous reporting channels + protection for public whistleblowing

• Board-level oversight of whistleblower concerns

• Career protection for good-faith concerns

• Recognition/rewards for early drift detection

In surfer terms, we are spotting this ripple, which may swell rapidly because organizations want to be in control.

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The smell of resin still triggers something in me—part nostalgia, part embarrassment. I was 32 when I decided to build my own windsurfing board. Don’t buy one. Build one.

I’d been windsurfing for three years by then, mostly on outdated equipment, and I was convinced I understood what made a board work. I’d read everything I could find about hull design, rocker curves, and foam densities. I spent weeks in my colleague’s garage, working from plans I’d adapted from a magazine article, shaping foam with a homemade hot-wire cutter, laminating fiberglass, obsessing over every detail.

The board looked beautiful. Racing stripes. A professional-grade finish. My friends were impressed.

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It was completely unrideable.

The problem wasn’t the shape, exactly. The problem was that I’d designed it based on what I thought made a board fast, not what worked in the water. I’d studied boards in magazines, not under my feet. I’d theorized about hydrodynamics without understanding how water actually moved around a hull, how flex responded to chop, how a board communicated with its rider through vibration and pressure.

The first time I tried to sail it, the board felt numb. Dead. Like riding a two-by-four. There was no feedback, no conversation between rider and water. Every gust was a surprise. Every wave was a threat. I lasted twenty minutes before I gave up, exhausted from fighting a board that gave me no information about what was happening beneath me.

I’d built a board that looked right but had no feel.

Twenty years of working with organizations trying to navigate technology change has taught me that this same mistake—building something that looks right but has no feel—is everywhere. Companies redesign their org charts. They rebrand. They announce new missions and values. They adopt the language of agility, innovation, and transformation.

And then they wonder why nothing responds the way they expected.

What You’ll Learn in This Chapter

This chapter is about developing organizational proprioception—the felt sense that lets you know where you are and how you’re moving without having to think about it. Specifically, you’ll learn:

1. How to diagnose your organization’s actual identity (not the one in your strategic deck)—what I call your “board type.”

2. How to configure purpose as strategic direction rather than inspirational wallpaper—your “fin setup.”

3. How to develop dynamic sensing capabilities that let you feel and respond to change in real time—your “stance.”

These aren’t metaphorical concepts. They’re practical frameworks for building the kind of organizational awareness that lets you navigate technology winds without constant crisis management.

Let’s start with the foundation: understanding what board you’re actually riding.

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SECTION 1 — Your Board Type: Identity Before Strategy

The Five Types of Tech Organizations

In windsurfing, your board determines everything else. Not your skill level, not your ambition—your board. Try to ride a longboard like a shortboard, and you’ll spend more time in the water than on it. Try to pull shortboard moves on a longboard and you’ll just look ridiculous.

The same is true for organizations. Before you can set strategy—before you can choose which technology winds to catch—you need honest clarity about what kind of organization you actually are.

Not the one you want to be.
Not the one your founder was twenty years ago.
The one you are right now, with your current culture, capabilities, and constraints.

Through two decades of working with companies navigating technology change, I’ve observed five distinct organizational identities, each with its own hydrodynamics.

1. The Longboard Organization: Stability as Strategy

Profile: Large, established organizations (typically 10,000+ employees, 20+ years old) with mature markets, complex stakeholder ecosystems, and deeply embedded institutional knowledge.

Examples: General Motors, Siemens, Walmart, Procter & Gamble.

Hydrodynamics: Long waterline, high buoyancy, slow to turn but immense momentum once moving. These organizations are built for consistency, scale, and riding large waves that would overwhelm smaller players.

The Longboard Mistake: Trying to ride them like shortboards.

General Motors (GM, 2020) made this mistake in its early response to Tesla. The instinct was to “move fast,” “think like a startup,” “break things.” But GM is not a startup. It’s a 115-year-old manufacturing giant with 162,000 employees, multibillion-dollar factory infrastructure, and complex labor agreements.

What worked wasn’t pretending to be Tesla—it was recognizing that longboards ride bigger waves, not tighter turns. GM’s real strength wasn’t agility; it was patient, massive capital deployment. The Ultium battery platform—centered around a $35 billion commitment—is the kind of bet only a Longboard Organization can make.

Longboard Organizations excel when they:

• Deploy patient capital that compounds over decades

• Leverage regulatory relationships and institutional knowledge

• Use scale as a feature, not a bug (standardization, bulk purchasing, distributed risk)

• Serve the 80% of customers who value reliability over novelty

They struggle when they:

• Confuse momentum with velocity

• Let size create numbness (loss of board-feel)

• Try competing on agility rather than stamina

• Mistake stable operations for strategic positioning

2. The Shortboard Organization: Agility as Advantage

Profile: Startups and scale-ups (10–1,000 employees) optimized for rapid iteration, market discovery, and explosive growth.

Examples: Early-stage SaaS companies, VC-backed consumer apps, pre-IPO tech firms.

Hydrodynamics: Short waterline, low buoyancy, extreme manoeuvrability. They can pivot through narrow windows that longboards could never respond to.

The Shortboard Mistake: Confusing agility with directionlessness.

OpenAI’s (Altman, 2023) evolution from 2018 to 2025 illustrates this perfectly. In seven years, it pivoted from:

• Nonprofit research → capped-profit entity

• Academic publishing → proprietary technology

• AGI research → multibillion-dollar product company

This kind of aggressive manoeuvring is only possible on a shortboard. But every turn burns energy, creates whiplash, and risks spinning out. The challenge is not whether shortboards can turn—it’s whether they should.

Shortboard Organizations excel when they:

• Exploit narrow windows before they close

• Iterate toward product–market fit through rapid experimentation

• Attract talent that thrives on uncertainty

• Create new markets rather than compete in existing ones

They struggle when they:

• Mistake activity for progress

• Burn through capital and talent with excessive pivots

• Scale prematurely

• Assume agility alone is a long-term advantage

• Have to choose between following an agreed route and shortcuts

3. The Hydrofoil Organization: Exponential Velocity

Profile: Organizations built around exponential technologies (AI, biotech, robotics, quantum), operating above traditional market constraints.

Examples: DeepMind, Moderna, SpaceX, current-era OpenAI, ABB.

Hydrodynamics: Hydrofoils lift out of the water. They use speed itself to escape drag, achieving velocities impossible for displacement-style boards. But they are unstable: small errors at high speeds cause catastrophic failures.

Hydrofoil Insight: They’re fastest in moderate conditions—not extremes.

AI companies struggle in regulatory doldrums (foil never lifts) and in chaotic turbulence (foil crashes). They need steady, strong winds—the kind present in today’s AI deployment landscape.

Moderna’s COVID-19 response (Baden, 2020) exemplifies hydrofoil dynamics: sequence to clinical trials in 63 days. Impossible for a Longboard Organization. But the conditions were perfect: clear regulatory mandate, immense capital, focused problem, and an inherently exponential platform (mRNA).

Hydrofoil Organizations excel when they:

• Exploit exponential curves (10× improvements)

• Operate in greenfield spaces

• Attract frontier technical talent

• Use speed as their competitive moat

They struggle when they:

• Encounter regulatory resistance

• Face “valley of death” scaling challenges

• Lose key technical riders

• Compete in mature markets where speed isn’t enough

• Experience sudden changes in external conditions

4. The Fish Organization: Niche Mastery

Profile: Small-to-medium organizations (50–500 employees) that dominate very specific niches.

Examples: Athletic Brewing Company, Basecamp, and early Patagonia.

Hydrodynamics: Wide, flat, agile in small, messy waves. Fish boards aren’t built for speed or size—they’re built for fit.

Fish Strategy: Define success so narrowly that you have only one competitor—yourself.

Athletic Brewing is the perfect case. They ignored Big Beer. They obsessed over:

• Athletes and health-conscious drinkers

• Taste parity with craft beer

• Community-building around active lifestyles

By 2023, they owned ~19% of the non-alcoholic beer market—without competing in Big Beer’s category at all. (Walker, 2023).

Fish Organizations excel when they:

• Define niches so precisely that scale doesn’t matter

• Treat constraints as features

• Build fierce loyalty in small communities

• Ignore 95% of potential customers to delight 5%

They struggle when they:

• Attempt unnatural scaling beyond their natural niche (scale pressure from investors)

• Get acquired by Longboard Organizations, which “optimize” away their specialization

• Encounter niche collapse (the specific wave disappears)

• Mistake niche success for mainstream validation

5. The Hybrid Organization: Strategic Ambidexterity

Profile: Organizations that maintain multiple operating models simultaneously. A mature core fund’s experimental ventures (shortboards and hydrofoils).

Examples: Amazon (retail + AWS), Google (search + X), Microsoft (Office/Azure + AI research), Samsung.

Hydrodynamics: Not one board—many. Requires different governance, metrics, cultures, and incentives.

Hybrid Challenge: Most companies that claim to be hybrids are actually confused longboards.

Microsoft under Satya Nadella is a true hybrid:

• Longboard operations: Windows, Office, Xbox

• Shortboard operations: Azure, Power Platform

• Hydrofoil operations: Microsoft Research, OpenAI partnership

Each uses different leadership, incentives, and time horizons—and crucially, explicit interfaces connect them.

Hybrids excel when they:

• Maintain genuine separation between operating models

• Use core cash flows to fund frontier bets

• Build integration layers intentionally

• Accept internal contradictions

They struggle when they:

• Apply longboard governance to shortboard units

• Let the immune system attack experimental divisions

• Starve experiments too early

• Attempt hybridization before establishing core stability

Diagnostic: What Board Are You Actually Riding?

Most organizational dysfunction arises from misidentifying your board type. You can’t fix this with a better strategy. You must first develop accurate identity awareness.

Use these signals:

Table 1: The signalling dashboard

Scoring interpretation:

• Longboard: Stability player; stop apologizing for not being a startup.

• Shortboard: Agility player; seek repeatability before scaling.

• Hydrofoil: Exponential player; the most significant risk is instability.

• Fish: Niche player; focus on your natural habitat.

• Hybrid: Rare and powerful—or a confused longboard pretending to be one.

The Board-Feel Decay Problem

As organizations grow, they lose the ability to feel their own board type.
I call this organizational numbness—the gradual loss of sensory feedback that tells leaders what’s actually happening beneath them. You start windsurfing with intense awareness of every ripple, every shift in wind pressure, every vibration through the board. Over time, if you’re not careful, all that feeling fades into vague impressions.

The same happens to organizations. Early on, founders know exactly what their company is—they feel it through every customer conversation, every product decision, every hire. But as the organization scales, layers accumulate between leadership and reality.

This is organizational numbness—the drift from sensory reality.

Examples:

• Customer experience becomes survey data

• Product decisions become roadmap meetings

• Hiring becomes algorithmic screening

• Reality becomes a slideshow

Three diagnostic questions help detect numbness:

Three diagnostic questions to test for board-feel decay

1. Can you still feel the water?

Test: Describe your most important customer’s day-to-day experience using your product without referencing metrics, dashboards, or reports. Can you narrate their actual lived experience in specific, sensory detail?

What numbness sounds like: “Our NPS (Net Promoter Score) score is 42, and user engagement is up 15% quarter-over-quarter.” Have you ever had a conversation with an average customer about your product or service?

What feels like: “Sarah opens our app at 6:47 AM while making coffee, dreading the 19 unread notifications. She dismisses 17 without reading, clicks one, gets interrupted by her kid, forgets what she was doing, and closes the app frustrated. Our ‘engagement’ counted all of that as success.”

2. Can you feel your own momentum?

Test: Distinguish between moving and changing. Is your organization building momentum toward a clear destination, or just maintaining velocity through activity?

What numbness sounds like: “We shipped 47 features last quarter and launched in 3 new markets.”

What feels like: “We shipped 47 features, but only 2 changed user behavior. We launched in 3 new markets, but we’re still losing share in our core market. We’re moving fast but not changing position.”

3. Can you feel when you’re about to crash?

Test: How many days of warning do you have before critical problems surface? If your board is about to ventilate (lose grip and slide out), can you feel it coming, or does it always catch you by surprise?

What numbness sounds like: “We had no idea the reorg would cause this much attrition.” / “The regulation caught us completely off guard.” / “We didn’t see the security breach coming.” / “We didn’t know that personal data was used without consent”.

What feels like: “Three weeks ago, I noticed our senior engineers getting quiet in planning meetings. Two weeks ago, I saw LinkedIn activity spike. Last week, I started exit conversations with our highest-flight-risk people. The attrition wasn’t a surprise—it was a lagging indicator of something we’d been feeling for a month.”

If you’ve lost board-feel, strategy documents won’t fix it. You need to rebuild a direct sensory connection between leadership and reality:

- Reinstitute founder-mode sensing: Senior leaders spending unstructured time with customers, frontline employees, and products (not “town halls”—actual field observation)

- Shorten feedback loops: Move decision-makers closer to consequences (if you decide on a product feature, watch real users encounter it within 48 hours)

- Measure what you can feel: If you can’t physically sense it, you can’t manage it (customer sentiment scores are worse than useless if leaders never talk to customers)

Board-feel isn’t optional. It’s the foundation everything else rests on. You can’t set your fins if you don’t know what board you’re riding.

In a world where we outsource everything, the feel has become a strange thing.

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