Taehan Yoon

Occasional thoughts, updates, and writing.

1. The System We Take for Granted

March 14, 2026

Most of us live in a world that is at least partially capitalist. The underlying rules of this system feel so obvious that we rarely stop to question them: if we want something, we need money; if we want money, we need to work.

These rules feel almost natural. In reality, they are not universal truths but outcomes of a particular economic structure built to solve a specific set of constraints. To understand why AI may disrupt this system so profoundly, we first need to understand why the system works the way it does today.

Trade and Specialization

At its most basic level, an economy exists because individuals cannot easily satisfy all their needs on their own. Even if it were theoretically possible, it would be extremely inefficient. Imagine trying to grow your own food, build your own house, weave your own clothes, and manufacture every tool you need. A single person attempting to do everything would produce very little of anything.

Instead, societies evolved around specialization. Individuals focus on tasks they perform relatively well, and trade with others who specialize in different areas. This allows everyone to benefit from greater overall productivity for the following reasons:

  1. Capability: Individuals often lack the specific resources, climate, or inherent skills to produce everything they need.
  2. Efficiency: Even if we could produce something ourselves, it is usually far more productive to focus on tasks where we hold a comparative advantage and trade the surplus for goods produced more efficiently by others.

This division of labor drives economies of scale, allowing a society to overcome individual limitations and significantly increase its total standard of living. The same dynamic applies to organizations, where companies focus on particular industries, technologies, or stages of production. Entire global supply chains exist because specialization increases efficiency at nearly every step.

Even academia reflects this pattern. Earlier periods of history produced well-known polymaths who made significant contributions across many different fields. Today, the body of human knowledge has expanded so dramatically that researchers often spend their careers advancing extremely narrow areas of study.

Why Money Exists

With the emergence of specialization and trade, we now face another challenge: coordination.

Before the widespread use of money, exchange relied on barter. However, barter requires a rare coincidence where each party must want exactly what the other is offering at the same time. A farmer who wants shoes must find a shoemaker who simultaneously wants crops. Such matches are difficult to find, which makes large-scale trade inefficient.

Money solves this problem by acting as a universal medium of exchange. Instead of requiring a single perfect trade, transactions can be split into two steps:

  1. You provide a good or service and receive money.
  2. You use that money to obtain the goods or services you actually want.

Money therefore does more than represent value. It allows complex networks of exchange to function without requiring every trade to be directly matched.

The Central Role of Labor

Once money exists, another pattern emerges: most people earn money by selling their labor. Individuals work for organizations or clients, receive wages or income, and use that income to purchase goods and services produced by others. Firms, in turn, hire labor and combine it with machinery, resources, and capital to produce products that can be sold for profit.

The system creates a continuous cycle of "labor → production → wages → consumption" that forms the backbone of modern economies. As long as labor remains necessary for production, individuals can earn income through work, and markets continue to function.

The Hidden Assumption

All of these structures, trade, money, labor markets, corporations, and specialization, are responses to the same underlying condition: productive capability, especially human intelligence, labor, and time, is scarce.

When intelligence is scarce, societies must carefully allocate it. Education systems train specialists. Companies compete for skilled workers. Individuals build careers around narrow expertise.

But if that scarcity begins to disappear, many of these arrangements may stop making sense. And this is precisely where AI enters the picture. What will happen if the assumption that intelligence and productive capability are limited stops being true?

0. The End of Pre-AI Thinking

February 14, 2026

For years, our thinking about AI has been dominated by visions of the post-AI world. Much of this imagination has been shaped by fiction like The Matrix and similar narratives, which present AI as a singular endpoint: a dramatic rupture after which society is fundamentally transformed. These stories have quietly become the reference points by which we judge AI’s influence, risks, and promises.

This framing has misled us.

While we speculate about a post-AI world, we fail to notice that the foundational premises of the current world are already breaking down. Traditional economic models, most notably the assumption that resources are fundamentally scarce, were not built for systems in which intelligence, coordination, and production scale at near-zero marginal cost. As AI advances, these assumptions do not bend; they fail.

Equally overlooked is the fact that this transformation will not occur in a single leap. There is no clean jump from “pre-AI” to “post-AI.” Instead, we are moving through a series of transitional stages, each with its own economic logic, social tensions, and policy failures. Treating AI as a future event blinds us to the changes already unfolding and leaves us unprepared for what comes next.

This blog marks the beginning of a longer exploration. My goal is to examine what these intermediate stages actually look like: how AI reshapes incentives, labor, capital, and power before any hypothetical endpoint is reached; which assumptions collapse first; and how individuals, institutions, and societies might prepare. The only uncertainty is whether this series will conclude before ever-accelerating AI makes all this effort obsolete.