History might remember 2026, not 2022, as the year artificial intelligence (AI) truly changed everything. ChatGPT’s launch was a product moment. What is happening now is an anthropological moment: AI is no longer merely answering questions. It is now taking initiative and learning from others to get things done, behaving less like software and more like a colleague.
The economic consequence is the rise of the one-person company — a structure anticipated in the 2024 book The Choices Amid Great Changes, which I coauthored. The real target of AI is not labor. It is hierarchy. When AI sharply reduces the cost of internal communication, the optimal size of many companies begins to shrink — in the extreme case, toward a single human supported by a team of AI agents.
This is not theory. In February, Block — the parent of Square and Cash App — dismantled its entire middle management layer. Founder Jack Dorsey and Sequoia managing partner Roelof Botha later described the goal as rebuilding the company “as an intelligence” rather than a hierarchy. The market rewarded the move with a sharp rally.
The largest AI productivity gain will not come from cheaper labor. It will come from the near-elimination of internal friction.
Anyone who has worked inside an organization understands that friction intuitively. A chairman conveys a strategy to the head of sales, who interprets it for production, who negotiates with design, who loops in finance. By the time the original intent reaches the edge, it has decayed. Endless meetings exist primarily to repair this decay.
Now imagine an agent that — like Neo downloading a kung fu module in The Matrix — can install expert-level skills on demand, and shares a common memory across every department at the speed of light. The intent of a founder is preserved, lossless, throughout the corporate machine. When a key employee leaves, the company no longer hits the reset button — the common memory carries on.
Once a founder begins working this way, the logic becomes dizzying. Time spent explaining an idea to a human colleague is time that could have been spent shipping 10 product features through an AI coding agent. The opportunity cost of human collaboration, once trivial, becomes catastrophic.
This paradoxically inverts a law that has held since the Industrial Revolution: Output rises with head count. If output rises as head count falls, gathering humans no longer creates value by default.
The objection is obvious: If every company has access to the same AI, would the gains not cancel out? They would not. The advantage compounds for those who adopt earlier and learn faster, because the gap between fast and slow adopters keeps widening. A 1 percent lead does not stay a 1 percent lead. It becomes a winner-take-most economy.
For Taiwan, the strategic imperative is simple: It does not need to dominate the world; it just needs to be ahead of its peers by a small margin, and AI compounds that small lead into nearly all of the new output.
To achieve this, the Taiwanese instinct to deliberate and wait for consensus must be overridden. We must stop chatting, start executing and urgently update our economic frameworks for the “Agentic Era,” well beyond the hardware dominance we currently enjoy.
The one-person company fits no existing category in Taiwanese tax codes or labor laws, and Taiwan lacks a national strategy to treat AI compute tokens as a primary economic input alongside capital and labor.
However, as Taiwan secures that economic edge, this structural shift also forces a profound question that society has not seriously asked: If far fewer people can produce far more, what remains of the reasons we gather? The older answers — family, neighborhood, and the search for faith and meaning — were once dismissed as soft. In an AI economy, they would become the hardest and most vital forms of infrastructure in society.
The true test is whether schools, churches and civic associations are prepared to anchor a society in which employment no longer provides a primary sense of identity or purpose.
Taiwan built its place in the world by being indispensable to other people’s machines. The next chapter asks something harder: whether we can still be indispensable to each other in the Age of AI.
James Lee is senior adviser at the Taiwan External Trade Development Council.
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