The AI market is becoming "polytheistic," with numerous specialized models excelling at niche tasks, rather than "monotheistic," where a single super-model dominates. This fragmentation creates opportunities for differentiated startups to thrive by building effective models for specific use cases, as no single model has mastered everything.

When evaluating AI startups, don't just consider the current product landscape. Instead, visualize the future state of giants like OpenAI as multi-trillion dollar companies. Their "sphere of influence" will be vast. The best opportunities are "second-order" companies operating in niches these giants are unlikely to touch.

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.

Public focus on capital-intensive LLMs from companies like OpenAI obscures the true market landscape. A bigger opportunity for venture investment lies in the "long tail"—a vast ecosystem of companies building specialized generative models for specific modalities like images, video, speech, and music.

In an unusual strategy, OpenAI provides its latest models to direct competitors. The company believes that a more competitive market accelerates learning and pushes them to improve faster. This long-term view prioritizes the overall distribution of intelligence over short-term competitive moats.

The concentration of AI power in a few tech giants is a market choice, not a technological inevitability. Publicly funded, non-profit-motivated models, like one from Switzerland's ETH Zurich, prove that competitive and ethically-trained AI can be created without corporate control or the profit motive.

Initially, even OpenAI believed a single, ultimate 'model to rule them all' would emerge. This thinking has completely changed to favor a proliferation of specialized models, creating a healthier, less winner-take-all ecosystem where different models serve different needs.

For many companies, 'AI sovereignty' is less about building their own models and more about strategic resilience. It means having multiple model providers to benchmark, avoid vendor lock-in, and ensure continuous access if one service is cut off or becomes too expensive.

To escape platform risk and high API costs, startups are building their own AI models. The strategy involves taking powerful, state-subsidized open-source models from China and fine-tuning them for specific use cases, creating a competitive alternative to relying on APIs from OpenAI or Anthropic.