Career Advice Is Not a Social Contract: AI & the Redesign of Society

 

Had Melquíades, the wandering merchant from Gabriel García Márquez's mythical town of Macondo, arrived today, he might have abandoned magnets and alchemy altogether. Instead, he would likely travel from village to village promoting flexibility, adaptability, lifelong learning, and prompt engineering. Hanging from the side of his cart would be the Swiss Army knife of educational advice, ready to be unfolded whenever uncertainty appeared.

The villagers would remain astonished, though perhaps no better informed about the future.

This is not because flexibility is useless. It is not. Adaptability is a real virtue. Lifelong learning matters. Education should absolutely help people develop judgment, range, curiosity, and the ability to keep moving when the ground changes under them.

But every era has its miracle cure. We have had snake oil, patent medicines, management fads, motivational posters, and now an unusually cheerful faith in adaptability. Whatever the problem, the answer arrives wearing sensible shoes: be more flexible, learn continuously, reinvent yourself, develop a growth mindset, and perhaps take a short course on prompting. Somewhere, one suspects, there is a conference panel titled “Thriving in Uncertainty” being delivered with absolute certainty.

The joke is not on adaptability. The joke is on our tendency to treat it as a universal solvent.

The Limits of Personal Adaptation

Much of our educational and professional advice assumes that the structure itself will remain fundamentally recognizable. A recession? Adapt. A new technology? Adapt. A volatile labor market? Adapt. The advice is not wrong. People do need tools for change.

The question is whether tools are enough.

Flexibility has become the Swiss Army knife of educational advice. Whatever the challenge, someone eventually unfolds the adaptability attachment. It is a useful tool, and perhaps an essential one. The concern is that we may be mistaking a toolbox for a blueprint.

A toolbox can help you repair a door, tighten a loose screw, or patch a leak. It does not tell you what kind of house you are building. It does not answer whether the foundation is stable. It does not decide who gets to live inside.

That distinction matters because the current moment may not be asking only for individual adaptation. It may be asking for institutional redesign.

Painting During an Earthquake

I sometimes wonder whether our conversation about the future resembles a group of people discussing painting techniques while the building is experiencing a mild but unmistakable earthquake.

The discussion is not wrong. Painting matters. Technique matters. Maintenance matters. But at some point, the urgency of the moment lies elsewhere. The question is no longer what color to paint the walls. It is whether the foundations require redesign.

Cosmetic upgrades are useful when the structure is sound. The possibility we seem reluctant to discuss is that the structure itself may require reconstruction.

This is where many conversations about education begin to feel simultaneously correct and insufficient. Schools and universities are told to prepare students for jobs that may not exist yet, industries that may transform beyond recognition, and tools that may change faster than curriculum committees can meet. Under the circumstances, teaching adaptability is not irrational. It is probably necessary.

But it is still a bet. It is not a strategy.

I do not blame educators for this. They are facing a problem for which nobody has a fully satisfying answer. The issue is not incompetence. The issue is genuine uncertainty. When uncertainty becomes too large to map, institutions naturally retreat toward durable virtues: critical thinking, communication, adaptability, collaboration, continuous learning. These are good things. They are also incomplete things.

They describe how a person should respond to change. They do not describe what kind of society we are trying to build through change.

The Wrong Question

Part of the confusion comes from the way we talk about artificial intelligence. Questions about intelligence, creativity, consciousness, understanding, and human likeness are not trivial; they are among the most fascinating questions the technology raises. But they are not the only questions in front of us. And they may not be the questions whose consequences arrive first.

The labor market is not asking whether AI has a soul. It is asking whether AI can do useful cognitive work.

We do not ask whether the tractor is “really” a horse. We ask whether it can pull the plow. We do not ask whether the calculator understands arithmetic. We ask whether it can perform arithmetic reliably enough to change the work around it. Economic consequences often arrive before philosophical consensus has finished putting on its coat.

That is what makes this moment different from ordinary technological disruption. We are not only automating muscle. We are beginning to automate parts of cognition: writing, coding, translation, analysis, design, research, synthesis, and coordination. Not all of it. Not perfectly. Not without human judgment. But enough to matter.

For centuries, many forms of human economic value were protected because cognitive labor was expensive to reproduce. Knowledge, language, analysis, and judgment took time to cultivate and were difficult to scale. Now we are watching some forms of cognitive work become cheaper, faster, and more reproducible. The shock does not come mainly from metaphysics. It comes from economics.

The Labor Problem Is Not the Knowledge Problem

This is why the conversation often becomes so polarized. On one side are people who look at AI and see an extraordinary tool for exploration, creativity, and collective learning. On the other side are people who see job loss, surveillance, power concentration, and institutional instability. Both reactions make sense because they are often responding to different problems.

If we perform a thought experiment and temporarily remove concerns about livelihoods, surveillance, and concentrated power, AI becomes much less frightening. In that world, many people would look at it and say: this is astonishing, useful, intellectually fascinating, and maybe even fun.

The trouble is that livelihoods are not a side issue. They are the mechanism through which most people secure autonomy, dignity, status, and participation in society. You cannot simply bracket them out and call the remaining picture progress.

This distinction matters. The labor problem is institutional, economic, and political. The knowledge problem is intellectual, scientific, and creative. They are connected, but they are not the same problem. Confusing them leads to bad arguments in both directions.

If the concern is income, then we should talk about income. If the concern is education, then we should talk about education. If the concern is surveillance, then we should talk about governance. If the concern is power concentration, then we should talk about institutions. But pretending all of this can be solved by telling individuals to reinvent themselves is a category error wearing a productivity badge.

Career Advice Is Not a Social Contract

The deeper problem is that modern societies have tied contribution, dignity, identity, purpose, and value very tightly to economic participation. Work is not merely how most people earn money. It is also how they are recognized, organized, measured, and often judged.

So if AI changes the role of human labor, the question is not only “What jobs will exist?” It is also: What happens to dignity? What happens to participation? What happens to status? What happens to education when information transfer becomes less central? What happens to identity when the old story of contribution no longer holds in the same way?

These are not problems that can be solved with a better personal brand. They are not solved by another webinar on resilience. They are not solved by telling everyone to become more adaptable, though adaptability may help people survive the transition.

At some point, we have to admit that career advice is not a social contract.

This is where the language of reconstruction becomes useful. The question may not be how to preserve every old category of human value against machines. The question may be what new forms of value, contribution, and collective learning we can build now that different capabilities are available.

From Defense to Construction

Much of the discussion around AI still has a defensive posture. It asks: What remains uniquely ours? Nuance? Consciousness? Creativity? Feelings? Meaning? These questions are understandable. They are also exhausting, because every new capability forces the boundary line to move again.

What if human value is not a fossil to be discovered but a project to be built?

That question changes the tone. It shifts us from defending a shrinking territory to designing a larger one. It suggests that the most important work ahead may not be proving that humans possess some untouchable essence, but building institutions that allow human beings to flourish in a world where intelligence, knowledge, and creativity are organized differently.

If knowledge becomes cheaper, education may need to move away from information transfer as its central identity. It may need to focus more deliberately on judgment, synthesis, framing, questioning, coordination, ethics, taste, and participation in larger systems of learning. Not because these things are magically immune to automation, but because they become more important when raw information is abundant.

This is the part that should be exciting. If the economic and political problems could be handled with seriousness instead of slogans, this might be one of the most intellectually alive moments in history. Never before have so many people had access to systems capable of helping them explore, combine, test, and express ideas at this scale. The possibility of collective learning is enormous.

But that possibility requires design. It requires institutions. It requires choices about power, incentives, access, labor, education, and dignity. In other words, it requires more than flexibility.

A Toolbox Is Not a Blueprint

Adaptability tells people how to respond to change. It does not tell society what future it is building toward.

That is the missing piece in so much of our current advice. We keep offering tools because nobody can confidently describe the building. So we hand people flexibility, lifelong learning, growth mindset, prompt engineering, and a small screwdriver attachment for emergencies. The tools are real. Some are valuable. But tools do not substitute for architecture.

The challenge may be larger than individual adaptation. If so, the honest response is not despair. It is construction.

Once we recognize that the problem is structural, we can stop blaming individuals for not running fast enough inside systems that are themselves being redesigned in real time. We can stop treating social questions as personal deficiencies. We can stop mistaking renovation for reconstruction.

The old map may be wrong. The old building may need work. The canvas may be blank in places we expected to find instructions.

That is unsettling. It is also strangely hopeful. A blank canvas is not the same as a dead end. It simply means we do not yet know what should be painted on it.

And perhaps that is the real task now: not merely to adapt to the future, but to participate in building one worth adapting to.

AI & the Theory of Complex Landscapes - The Problem of Climbing the Wrong Hill

 

There is a comforting story people like to tell about technological revolutions. A new tool appears, productivity rises, old inefficiencies disappear, and society, after a little turbulence, moves to a better equilibrium. It is a clean story. It is also, very often, the wrong story. 

The AI revolution does not feel clean because it is not arriving in a neat, predictable world. It is arriving inside markets, firms, labor systems, schools, bureaucracies, media networks, and geopolitical rivalries that are already unstable, interdependent, and constantly adapting. In other words, AI is not landing on a chessboard. It is landing in weather.

This is where Scott Page’s idea of landscapes becomes useful. It gives us a way to think about decisions in complex systems without pretending the world is simpler than it is. The basic metaphor is easy: imagine that every possible strategy, policy, organizational design, or technology choice sits somewhere on a landscape. Higher ground means better performance. The question is how easy it is to climb.

In a simple landscape, the path is fairly obvious. Improve a little, and things usually get better. You can make sensible local decisions and trust that they are moving you uphill.

In a rugged landscape, things get trickier. There are lots of peaks, not just one. You can climb successfully and still end up stuck on a mediocre hill, unable to reach a better one without first going downhill. The danger here is local success. You improve, but into a trap.

Then comes the nastiest version: the dancing landscape. Here, the terrain itself moves while you are climbing. The hill that looked best this quarter may not be the best one next year. The map changes while you are still folding it open.

Once you see these three landscapes, a lot of the AI conversation starts looking different. 

The most naive AI story assumes a simple landscape. Build better models, deploy them, automate tasks, cut friction, raise output, everyone adapts, done. That is the glossy presentation deck version. It assumes there is one main hill called “efficiency,” and that all serious people should sprint uphill.

But the real world looks much more like a rugged landscape. Firms are making decisions under uncertainty, and many of those decisions interact. A company cuts headcount because AI tools let the remaining employees do more. Margins improve. The stock likes it. Management concludes it has found a better organizational form. Maybe it has. But maybe it has just climbed the nearest visible hill.

That is the first hard lesson of rugged landscapes: short-term improvement does not prove long-term wisdom.

A leaner company may be faster in routine execution yet weaker in resilience. It may have removed people who looked redundant on a spreadsheet but who carried institutional memory, mentoring capacity, or a different way of thinking. It may have improved throughput while damaging adaptability.

This is especially relevant because Page’s broader work on complexity and diversity keeps returning to a stubborn idea: on hard, nonroutine tasks, systems often benefit from a wider repertoire of perspectives and tools, not just from squeezing more output out of a narrower elite. That matters in AI because many executives are currently acting as if “smaller team plus better tools” is self-evidently the final form of intelligence. It may turn out to be the final form of quarterly confidence instead.

The labor angle is the clearest place to see this. When people ask whether AI will “replace jobs,” they often picture a dramatic one-to-one substitution: software does task X, therefore worker Y disappears. Sometimes that will happen. But the more important story is structural. In a rugged landscape, the labor market changes through chains of local decisions that reinforce one another.

A firm hires fewer juniors because AI makes seniors more productive. Another firm does the same. Entry-level openings shrink. Training ladders weaken. Fewer people get the chance to build real expertise. Bargaining power shifts toward employers. Workers who remain employed are expected to cover more ground. Productivity may rise, but so does precarity. Eventually politics notices.

That is not a Hollywood robot takeover. It is something quieter and probably more realistic: a slow rewiring of the routes by which people enter, learn, advance, and negotiate. The system changes shape before it announces that it has changed shape.

And then, just to make things more interesting, the landscape starts dancing.

This is where the AI story gets genuinely weird. It is not only that firms are adapting to AI. AI itself is changing at high speed. Model capabilities improve, costs shift, interfaces evolve, regulation stirs, competitors copy one another, public trust rises and falls, and entire categories of work are redefined in real time. The landscape is moving because all the agents on it are moving too.

That means a strategy that looks brilliant today may simply be overfit to the current moment. A company may reorganize around the belief that AI will mostly automate execution, only to discover that the real bottleneck becomes judgment, distribution, trust, or integration. A university may redesign training around prompt fluency only to find that everyone has prompt fluency and the scarce skill is now domain depth. A government may regulate yesterday’s frontier while tomorrow’s frontier walks around the rule.

This is why the “musical chairs” feeling around AI is not imaginary. People sense that they are being asked to adapt before anyone can say, with confidence, what stable adaptation even looks like.

The business implications are huge. In a dancing landscape, over-optimization is dangerous. You do not want to strip away all slack, all redundancy, all experimentation, and all human variety in the name of immediate efficiency if the environment itself is still changing. That is a great way to become extremely efficient at solving last year’s problem.

The geopolitical implications are just as large. AI is not only about chatbots and office software. It touches military planning, cyber operations, intelligence analysis, semiconductor supply chains, infrastructure security, industrial policy, and state capacity. Countries are not choosing whether to adopt one tool; they are repositioning themselves inside a contest whose rules are still evolving. That is classic dancing-landscape territory. Every move changes the payoff structure for the next move.

Even culture starts behaving differently under this framework. AI affects not only what can be produced, but what can be trusted. It changes the economics of attention, the meaning of authorship, the cost of persuasion, and the volume of synthetic noise in the system. When abundance rises but trust falls, the peak is no longer just “more content.” Suddenly the valuable hill may be authenticity, curation, or institutions that still carry signal.

This is why complexity thinking helps. It refuses both the cheerful fantasy that technology automatically lifts all boats and the equally lazy fantasy that everything is doomed. Instead, it asks a better question: what kind of landscape are we actually in?

If the answer is simple, then optimization works well. If the answer is rugged, then exploration matters because local maxima are traps. If the answer is dancing, then resilience, optionality, and adaptability matter even more than efficiency.

That last point has political consequences. In a moving landscape, the job of public policy is not to guess the final destination perfectly and then force everyone toward it. That is planner vanity. The real task is to help society adapt without breaking. That means protecting people from being crushed by transition while preserving enough flexibility for experimentation.

So the right response to AI is probably not a single grand policy with a heroic name. It is more boring than that, which is often how serious things look in daylight. Wage insurance. Portable benefits. Recurrent retraining tied to real demand, not motivational brochures. Stronger worker bargaining power. Competition policy that prevents a few firms from locking up the gains. Education that teaches people how to think across models and domains rather than just training them for one brittle slot in a machine.

In other words: if the terrain is moving, build people and institutions that can move with it.

That may sound less exciting than the usual AI prophecy, but it is also more honest. The danger in moments like this is not just that people underestimate the technology. It is that they overestimate their ability to stand still while the world reorganizes around them. Firms can get trapped on the wrong hill. Workers can be shoved off the map. States can optimize for a version of the future that never arrives.

The point of the landscape metaphor is not to make everything sound mystical. It is to inject a little humility into systems that are drunk on certainty. AI may indeed create extraordinary gains. But in a rugged, dancing landscape, gains are not enough. The real question is whether the path that looks smart right now leads somewhere durable, or whether we are all just climbing very confidently toward a hill that is already moving.