Unlike human-driven growth, which is limited by population and waking hours, AI agents can operate, replicate, and call each other endlessly. This creates a potentially infinite demand for compute infrastructure, far exceeding previous models and leading to massive, unpredictable strains on providers.

Leaders at frontier labs like OpenAI and Anthropic indicate that RSI—AI models that self-improve—is closer than anticipated. The arrival of RSI would trigger unprecedented demand for compute, as models consume vast resources to develop and improve themselves autonomously.

Escalating compute requirements for frontier models are creating a new market dynamic where access to the best AI becomes restricted and expensive. This shifts power to the labs that control these models, creating a "seller's market" where they act as "kingmakers," granting massive competitive advantages to the highest corporate bidders.

According to research from Anthropic, the most likely future scenario involves AI agents creating massive productivity multipliers. This will enable small, agile teams to compete with large enterprises by doing the work of organizations 100x to 1000x their size, revolutionizing knowledge work.

Decentralized power has been a key driver of capitalist growth. However, AI exhibits immense economies of scale in training, data, and R&D. This suggests a future where hyper-centralized "AI economies" within a few firms could grow much faster than the broader, decentralized market, inverting a core economic principle.

Contrary to the idea of AI for all, the most powerful models will likely be restricted to a few high-paying clients to prevent distillation and maximize revenue. This creates a future where competitive advantage is defined by exclusive AI access, potentially allowing large incumbents to crush smaller competitors.

As demand for AI far outpaces compute supply, costs will rise. Only labs with the most lucrative algorithms, like OpenAI and Anthropic, can afford it. They reinvest massive revenues into the next training run, creating a self-reinforcing loop that raises the barrier to entry for any potential competitor, solidifying their duopoly.

In the current AI landscape, economic value is overwhelmingly created by companies possessing the highest ratio of utilized GPUs per employee. This trend suggests that access to and efficient use of computational power, rather than human capital alone, is the primary driver of value, at least at the infrastructure layer.

The two leading AI labs are acquiring compute at a faster rate than the rest of the world. Their share of new compute is projected to rise from 30% this year to over 50% by 2028, dramatically accelerating the concentration of AI power and capabilities.