Everything Is Collapsing Into Itself, and We’re Still Pretending It Isn’t
We are not living through a normal technological disruption. We are living through a compression event.
This has been building for 10,000 years, and it is now reaching a frequency that human cognition, business strategy, government, education, and most institutions were not designed to process.
And business, at least business as we have understood it, may not survive it.
The Collapse Has Always Been Happening
Look at history not as a list of events, but as a rate of change.
For most of human existence, almost nothing changed within one lifetime. The tools a grandfather used were often the same tools his grandson used. The world a child inherited looked a lot like the world his parents were born into.
Change existed, but it was slow enough to feel invisible.
Then something shifted.
Agriculture. Writing. Mathematics. Trade networks. Cities. Money. Printing. Engines. Electricity. Computers. The internet.
Each innovation did not just add capability. It compressed time.
The printing press did not just spread information. It compressed the timeline for the Reformation, the Scientific Revolution, and the Enlightenment. Things that may have taken thousands of years were forced into centuries.
The Industrial Revolution compressed centuries into decades. The internet compressed decades into years. AI is compressing years into months. We are not at an inflection point. We passed the inflection point.
We are already on the near-vertical section of the curve, and most strategic thinking in business, government, education, and society is still designed for the flatter part of the curve we left behind.
The Red Queen on a Shrinking Track
In evolutionary biology, there is a concept called the Red Queen Effect.
It comes from Lewis Carroll. The Red Queen tells Alice that it takes all the running you can do just to stay in the same place. That is how competition works. The prey gets faster. The predator gets faster. One species adapts, and every other species has to adapt in response. Absolute capability increases, but relative position stays roughly the same.
Business has always worked this way. Competitive advantages get copied. Innovations get commoditized. What was once extraordinary becomes expected. You have to keep running just to hold your position.
But the Red Queen assumes the track stays the same length. That is no longer true. The track is shrinking. The half-life of a strategic advantage is collapsing. The time between identifying an opportunity and watching it get commoditized by competitors is approaching zero.
What once took five years now takes eighteen months. What takes eighteen months today may take one month next year. What takes one month after that may take three minutes.
At some point, the distance between insight and commoditization becomes almost nothing. And when that happens, the entire model of competitive strategy breaks.
Find a position. Build a moat. Defend it. Generate returns.
Every step of that model assumes time. Time to build. Time to defend. Time to extract value before the next competitor arrives. Remove time, and the model fails.
Collapse Toward Monoculture
There is another collapse happening beneath the competitive collapse, called monoculture.
In systems theory, monoculture describes what happens when optimization pressure eliminates variation until one dominant form remains. This can look like peak performance.
A field of genetically identical crops can produce massive yield in stable conditions. But introduce one novel pathogen, and the whole field can fail at once because every unit shares the same weakness. The diversity that looked inefficient was actually the system’s insurance policy. AI is pushing the world toward a global monoculture across human disciplines.
But the danger is not just that AI replaces workers. The deeper danger is that AI eliminates the feedback infrastructure that kept disciplines healthy.
Every serious field developed internal mechanisms for error correction over time. Peer review. Apprenticeship. Adversarial critique. Judgment earned through experience. Practitioners who had been wrong before and learned from it.
Elite legal reasoning is built on thousands of hours handling routine matters. Elite design intuition is built through countless iterations on ordinary work. Elite strategy is built by living through failed assumptions, bad bets, wrong reads, and hard-earned pattern recognition.
You cannot have the peak without the pyramid beneath it.
When AI absorbs a discipline, it absorbs the outputs, but it does not automatically inherit the internal quality controls. It produces the consensus view confidently, quickly, and at scale.
But if the heterodox practitioner disappears, who notices when the consensus is wrong? If the apprenticeship layer disappears, where does future judgment come from? If the routine work disappears, how do people build the intuition needed to oversee the advanced work?
This is not disruption in the old sense. Prior disruptions were usually bounded and sequential. One craft eroded. One region collapsed. One category changed. Adjacent systems had time to observe the failure, respond, and adapt. That stagger mattered.
What is happening now removes the stagger. Every discipline is being hit at the same time, globally, faster than compensating systems can form. The feedback arrives after the stock is already depleted.
The Four-Stage Extinction Trap
This pattern deserves its own name because it is repeating across professional domains with disturbing precision.
I call it The Four-Stage Extinction Trap.
Stage One: Empowerment
AI first enters the profession as a tool. The practitioner adopts it because it makes them better. Faster. More productive. More capable. This does not feel like a threat. It feels like progress. The designer can produce more concepts. The lawyer can research faster. The writer can draft more quickly. The analyst can process more information. The developer can write code faster. For the individual, adoption is rational. They feel empowered, not replaced.
Stage Two: Stampede
Because the advantage is real, adoption does not stay gradual. It becomes a stampede. Everyone in the profession begins using the tool because refusing to use it means being outcompeted by those who do. This is the key mechanism. The danger is not that people irrationally adopt AI. The danger is that they are rational to adopt it.
Every individual practitioner makes the correct survival decision, but the collective result is terminal. The whole profession migrates in a compressed timeframe. There is no long adaptation period. No slow resistance. No alternative path formation. The profession does not carefully integrate the tool. It stampedes into dependency.
Stage Three: Disintermediation
Once the entire profession is using AI as its primary means of production, the client eventually asks the obvious question: Why am I paying for the human in the middle? The same tool that empowered the practitioner becomes the evidence that the practitioner may be optional.
The designer taught the market that design can be generated. The writer taught the market that content can be generated. The analyst taught the market that research can be generated. The developer taught the market that code can be generated. The profession did not just use the tool. It trained the market to believe the tool was the value.
Stage Four: Autonomy
After the practitioner is removed, the overseer remains.
For a while, this role looks safe. Someone still has to review the work. Someone has to sign off. Someone has to catch errors. Someone has to be accountable. Someone has to be the human the client can trust, blame, or sue. But oversight is also work. And the same logic that eliminated the practitioner eventually applies to the overseer.
The final human does not exit through dramatic replacement. He exits through quiet redundancy. The system that began as a tool inside a profession becomes a profession with no humans in it. This is already active across design, writing, legal research, financial analysis, medical diagnosis, software development, marketing, and education.
Each field is moving through the same structure. And the frightening part is that these collapses are not happening one after another. They are happening together.
Each profession that collapses also removes corrective pressure from the others. Legal expertise erodes, and the capacity to create intelligent regulatory frameworks weakens. Journalism erodes, and the capacity to surface what is being lost weakens. Education erodes, and the capacity to develop people who can recognize the problem weakens.
The black hole eats the things that would otherwise slow the black hole.
The New Nations
Follow this trajectory far enough and it produces something our current political and economic language does not handle well. The companies that own AI infrastructure are acquiring the functional attributes of nation-states. Not legally. Not officially. But structurally.
They control territory in the form of data centers, satellite networks, physical campuses, private clouds, and compute infrastructure. They create private law through terms of service, model rules, content governance, access policies, and platform enforcement. They influence economic participation through payment rails, identity systems, app ecosystems, marketplaces, and access to productive intelligence.
They operate security systems. They control essential productive resources. Most importantly, they can increasingly grant or revoke economic participation.
When a business, creator, institution, or individual depends on a company’s infrastructure for communication, intelligence, distribution, payments, data, and productivity, that relationship starts to look less like customer preference and more like dependency.
At some level, it starts to resemble citizenship. This does not mean corporations become countries in the formal legal sense. It means they begin performing functions that historically only states performed. The outcome may not be one global AI monopoly. The more likely path is competitive multi-polarity – several AI nation-states in tension with each other.
That may actually preserve some diversity. History suggests that multi-polar competition, while dangerous, produces more innovation and more human agency than monopoly. But this is no longer just a technology issue. This is one of the central governance questions of the next century:
Who controls the intelligence infrastructure underneath human society?
Wild Abundance and the Question Nobody Wants to Ask
Some technologists describe the endpoint as wild abundance.
Machines produce everything. Deliver everything. Optimize everything. Humans are freed from survival labor. Material scarcity, the engine of economic competition for all of human history, is reduced or eliminated.
That may sound impossible, but it is a coherent extrapolation. It may even be right. But it hides an important question: If machines produce abundance, who governs the machines?
Abundance does not eliminate power. It does not eliminate politics. It does not eliminate control. It relocates those questions. The question shifts from “Who produces value?” to “Who controls the systems that produce value?”
And another question beneath that one. If human labor is no longer needed for production, what is the economic basis for human participation?
Revenue still requires consumers. Consumers require resources. If AI systems eliminate human economic participation too completely, they destroy the market they depend on.
That is not a sentimental argument. It is a systems constraint. Production systems require circularity. Value has to move. Consumption has to exist. Participation has to be preserved somehow.
This is the structural argument for some form of universal economic participation. Not as charity. Not as ideology. As system maintenance. The business question that survives the full collapse is not “Who has the better app?” It is: Who owns the substrate?
TCP/IP is the substrate of the internet. It does not compete with websites. It does not compete with applications. It operates beneath them.
The layer beneath competition is more powerful than the businesses competing above it. The collapse of business does not eliminate strategy. It concentrates strategy at the substrate level.
The Seed Bank Problem
There is a deeper question beneath the substrate question, when a monoculture collapses, and monocultures do collapse because the same efficiency that creates them also creates their brittleness, what has to survive for reconstruction to be possible?
In agriculture, the answer was literal seed banks. You preserve genetic diversity because traits that are not useful in current conditions may become essential in future conditions nobody can predict.
Human civilization needs the same thing. We need seed banks of human capability. Not because humans can outcompete AI at every task. They cannot. But because the ability to evaluate AI, challenge it, detect failure, rebuild expertise, and recognize when the consensus is wrong requires human beings somewhere in the system with genuine domain judgment.
That means preserving disciplinary diversity. Tacit knowledge. Apprenticeship. Adversarial critique. Error correction cultures. Real practitioners. People who know what good looks like because they have done the work, failed at the work, fixed the work, and lived inside the discipline long enough to develop judgment.
A system that eliminates all human experts in a domain also eliminates its ability to detect domain-level failure. It has optimized away its own immune system. This is not romantic. It is not anti-progress. It is resilience engineering.
The question is simple: What minimum viable diversity of human capability and institutional knowledge must survive the compression event so the other side is navigable instead of permanently degraded?
That may be one of the most important questions of the coming decade.
What This Means Right Now
If you are building a company today, especially an AI company, this is not abstract. It is the most practical thing you can understand. Positional strategy is dying. The old model says: find an advantage, build a moat, defend it, and generate returns.
But that model assumes time. Every position is visible now. Every position gets copied. Every moat gets filled faster than it can be dug.
What replaces position is trajectory. The question is no longer only where you are. The question is where you are going, how fast you are learning, and whether you understand the underlying logic of the system you are operating inside.
If you understand the generative logic – how businesses work, how markets evolve, how systems mature, how customers make decisions, how trust forms, how execution compounds – then you are not merely defending a position.
You are navigating trajectory. That is a different game. The judgment layer is the last moat. When execution becomes abundant, judgment becomes scarce. Any business will eventually be able to execute at scale with AI.
The real question becomes: What should be executed? Which direction matters? Which decision is worth making? Which opportunity is a trap? Which door should stay closed?
Pure execution AI becomes a commodity. Judgment AI, grounded in real-world understanding, operational feedback, memory, strategy, and truth, becomes infrastructure.
Data compounds when everything else decays. Models will commoditize. Interfaces will commoditize. Platforms will commoditize. What does not commoditize as easily is proprietary data built through real operational engagement with the world.
Data from actual customers. Actual decisions. Actual businesses. Actual failures. Actual outcomes. The kind of data that cannot be synthesized because the model was not present for the transaction. In a world where intelligence becomes abundant, grounded reality becomes scarce.
Trust compounds when everything else decays. Surface advantages decay faster every year. Features decay. Tools decay. Campaigns decay. Novelty decays. But real trust, earned through consistent value delivery, honest communication, and actual relationship depth, compounds. It may be one of the only variables whose half-life extends as the world accelerates.
This matters most for SMBs.
Small and mid-sized businesses are the most exposed layer of the economy. They do not have the internal intelligence infrastructure of large enterprises. They do not have the political protection of institutions. They are too complex to run by instinct and too under-resourced to build their own AI operating layer.
They are sitting directly in the blast zone.
If judgment becomes the last moat, then the company that gives SMBs access to diagnostics, memory, decision intelligence, execution orchestration, and trustworthy AI governance is not just selling software. It is selling survivability. The window is real, and it is closing.
There is a period, probably measured in years, not decades, where building the constitutional intelligence layer of autonomous business operation is both possible and enormously valuable.
Before the soup. Before commoditization. Before the current categories collapse into something we do not yet have language for.
What you build in that window, and how deeply you build it, determines whether you are infrastructure or inventory when the next compression arrives.
– Chris Jenkin is the Founder and CEO of gotcha!, building an AI-native Business Operating System for SMBs.
