As AI agents become more sophisticated, they will autonomously seek out and use the cheapest decentralized services for tasks like storage and processing. This creates a relentless, 24/7 market pressure that will continuously drive down the fundamental costs of computing for everyone.

Contrary to fears of a monopoly, the AI market is heading toward a diverse ecosystem. The proliferation of open-weight models and specialized tooling allows companies to build and control their own differentiated AI systems rather than simply renting intelligence token-by-token from a handful of large labs.

The 'Andy Warhol Coke' era, where everyone could access the best AI for a low price, is over. As inference costs for more powerful models rise, companies are introducing expensive tiered access. This will create significant inequality in who can use frontier AI, with implications for transparency and regulation.

The current focus on building massive, centralized AI training clusters represents the 'mainframe' era of AI. The next three years will see a shift toward a distributed model, similar to computing's move from mainframes to PCs. This involves pushing smaller, efficient inference models out to a wide array of devices.

The PC revolution was sparked by thousands of hobbyists experimenting with cheap microprocessors in garages. True innovation waves are distributed and permissionless. Today's AI, dominated by expensive, proprietary models from large incumbents, may stifle this crucial experimentation phase, limiting its revolutionary potential.

Fears of a single AI company achieving runaway dominance are proving unfounded, as the number of frontier models has tripled in a year. Newcomers can use techniques like synthetic data generation to effectively "drink the milkshake" of incumbents, reverse-engineering their intelligence at lower costs.

If AI makes intelligence cheap and universally available, its economic value may collapse. This theory suggests that selling raw AI models could become a low-margin, utility-like business. Profitability will depend on building moats through specialized applications or regulatory capture, not on selling base intelligence.

While the internet has consolidated around major platforms, AI presents a counter-force. By drastically lowering the cost and complexity of building mobile apps, new tools could enable a 'Cambrian explosion' of personalized applications, challenging the one-size-fits-all model.

The idea that one company will achieve AGI and dominate is challenged by current trends. The proliferation of powerful, specialized open-source models from global players suggests a future where AI technology is diverse and dispersed, not hoarded by a single entity.