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.

Reports that OpenAI hasn't completed a new full-scale pre-training run since May 2024 suggest a strategic shift. The race for raw model scale may be less critical than enhancing existing models with better reasoning and product features that customers demand. The business goal is profit, not necessarily achieving the next level of model intelligence.

Current AI models resemble a student who grinds 10,000 hours on a narrow task. They achieve superhuman performance on benchmarks but lack the broad, adaptable intelligence of someone with less specific training but better general reasoning. This explains the gap between eval scores and real-world utility.

Instead of relying solely on massive, expensive, general-purpose LLMs, the trend is toward creating smaller, focused models trained on specific business data. These "niche" models are more cost-effective to run, less likely to hallucinate, and far more effective at performing specific, defined tasks for the enterprise.

OpenAI operates with a "truly bottoms-up" structure because it's impossible to create rigid long-term plans when model capabilities are advancing unpredictably. They aim fuzzily at a 1-year+ horizon but rely on empirical, rapid experimentation for short-term product development, embracing the uncertainty.

Building a single, all-purpose AI is like hiring one person for every company role. To maximize accuracy and creativity, build multiple custom GPTs, each trained for a specific function like copywriting or operations, and have them collaborate.

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.

The AI arms race will shift from building ever-larger general models to creating smaller, highly specialized models for domains like medicine and law. General AIs will evolve to act as "general contractors," routing user queries to the appropriate specialist model for deeper expertise.

The key to successful open-source AI isn't uniting everyone into a massive project. Instead, EleutherAI's model proves more effective: creating small, siloed teams with guaranteed compute and end-to-end funding for a single, specific research problem. This avoids organizational overhead and ensures completion.