Proprietary labs argue against 'distillation' (using their model outputs for training) while they have built their own models on vast amounts of copyrighted data. This opposition is an anti-competitive tactic, as model outputs are not copyrightable and distillation helps smaller, open players to compete.

Large, centralized AI models are vulnerable to 'distillation attacks,' where a smaller model can be trained cheaply by querying the larger one. This technical reality, combined with the moral hypocrisy of creators restricting copying after scraping the internet, strongly suggests a future dominated by decentralized, open-source models.

While techniques like model distillation can reduce costs for near-frontier AI capabilities, this hasn't dampened demand for the absolute best models. The market shows very little desire for the third-best model, but exceptional demand for the top-performing one for any given task, demonstrating a winner-take-all dynamic.

The common practice of model distillation suggests that AI capabilities will eventually be commoditized. As smaller models can cheaply mimic larger ones, differentiation will shift away from raw performance to product integration and price, likely triggering a massive price war among providers.

The intense competition and personal rivalries among AI lab leaders, while seemingly petty, serve as a structural safeguard. This prevents the formation of a monopoly on frontier AI. The resulting diversity in model weights and ownership makes a unilateral takeover by a single entity's AI far less likely than in a world with a unified development effort.

The AI industry is not a winner-take-all market. Instead, it's a dynamic "leapfrogging" race where competitors like OpenAI, Google, and Anthropic constantly surpass each other with new models. This prevents a single monopoly and encourages specialization, with different models excelling in areas like coding or current events.

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.

The current oligopolistic 'Cournot' state of AI labs will eventually shift to 'Bertrand' competition, where labs compete more on price. This happens once the frontier commoditizes and models become 'good enough,' leading to a market structure similar to today's cloud providers like AWS and GCP.

Despite billions in funding, large AI models face a difficult path to profitability. The immense training cost is undercut by competitors creating similar models for a fraction of the price and, more critically, the ability for others to reverse-engineer and extract the weights from existing models, eroding any competitive moat.