The Industrialization of Intelligence and Transformation of Human Identity
For several years, much of the conversation around artificial intelligence has revolved around a deceptively simple question. When will AI become as intelligent as humans? There are variations of the same question everywhere. When will we reach AGI? When will AI replace programmers? When will it replace knowledge workers? When will it discover new science? When will it become superintelligent? These are fascinating questions. They are also, in some ways, the wrong questions. Not because they don’t matter. They do. But because they make AI appear to be a destination. A point on a timeline. A moment when something called “AGI” arrives and history changes. I suspect the reality will be less dramatic. The transformation may already have begun. It may not have a single starting date. There may be no morning when we wake up and discover that artificial general intelligence has arrived.
Instead, capability will continue to move into places where it previously did not exist. A machine will write software. Then manage software. A machine will assist a scientist. Then design an experiment. A machine will analyze a factory. Then operate one. A machine will help a company make decisions. Then become part of the company itself. None of these events, taken individually, necessarily looks like the beginning of a new civilization. Together, they may be.
This is why I have become less interested in predicting the exact arrival of AGI and more interested in understanding what happens when intelligence itself becomes increasingly engineered, scalable and deployable. That is a different question. And perhaps a much bigger one. The forecasts already tell us something important. They disagree. The OECD’s 2026 exploration of possible AI trajectories describes four broad possibilities through 2030, progress could stall, slow, continue along current trends, or accelerate substantially. The UK’s 2026 AI Scenarios exercise similarly develops five plausible and stretching futures rather than presenting one prediction.
Even the forecasting community that produced AI 2027 has since revised its timelines as new evidence arrived. Its newer AI Futures Model places its median superintelligence timeline substantially later than the original AI 2027 model. This is not a failure of forecasting. It is a reminder of something more fundamental. The future of AI is uncertain. And uncertainty does not mean that we know nothing. It means that we need a better way of thinking. Rather than asking which forecast will turn out to be correct, perhaps we should ask, what transformations would matter across several different futures?
If AI progress slows, some transformations may still occur. If progress continues, they may accelerate. If progress becomes extraordinary, entirely new ones may appear. The job, then, is not to predict one future. It is to understand the structure beneath many possible futures. That is what this work attempts to do. There is another reason. AI is often discussed as a technology sector. That makes sense if we are looking at companies, models and investment. But artificial intelligence is becoming difficult to contain within the boundaries of a sector.
It touches energy because computation requires electricity. It touches semiconductors because intelligence requires hardware. It touches manufacturing because increasingly capable AI can control machines. It touches science because researchers can use AI to search enormous spaces of possible discoveries. It touches finance because decisions can be automated. It touches education because teaching can become personalized. It touches defense because intelligence has always been strategic. It touches government because institutions themselves depend on information and decision-making. And eventually it touches something much harder to measure. Human identity.
If a machine can perform a task that once gave a person expertise, status or purpose, the consequence is not captured by productivity statistics. Something changes inside the person as well. This is where the AI conversation becomes much more interesting to me. Because the industrial and human dimensions are not separate. They are connected. A change in technology changes the economy. The economy changes the organization. The organization changes work. Work changes society. Society changes the way people understand themselves. And eventually those changes feed back into the technology itself. AI is therefore not simply a new tool entering an existing system. It is a new capability entering a system that will reorganize around it.