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.

Jensen Huang argues the "AI bubble" framing is too narrow. The real trend is a permanent shift from general-purpose to accelerated computing, driven by the end of Moore's Law. This shift powers not just chatbots, but multi-billion dollar AI applications in automotive, digital biology, and financial services.

The AI industry is hitting data limits for training massive, general-purpose models. The next wave of progress will likely come from creating highly specialized models for specific domains, similar to DeepMind's AlphaFold, which can achieve superhuman performance on narrow tasks.

Despite powering the AI revolution, Jensen Huang's strategy of selling GPUs to everyone, rather than hoarding them to build a dominant AGI model himself, suggests he doesn't believe in a winner-take-all AGI future. True believers would keep the key resource for themselves.

Jensen Huang forecasts that the next major AI breakthrough will be in digital biology. He believes advances in multimodality, long context models, and synthetic data will converge to create a "ChatGPT moment," enabling the generation of novel proteins and chemicals.

OpenAI CEO Sam Altman now publicly hedges that winning requires the best models, product, *and* infrastructure. This marks a significant industry-wide shift away from the earlier belief that a sufficiently advanced model would make product differentiation irrelevant. The focus is now on the complete, cohesive user experience.

A key strategic difference in the AI race is focus. US tech giants are 'AGI-pilled,' aiming to build a single, god-like general intelligence. In contrast, China's state-driven approach prioritizes deploying narrow AI to boost productivity in manufacturing, agriculture, and healthcare now.

Jensen Huang suggests that established AI players promoting "end-of-the-world" scenarios to governments may be attempting regulatory capture. These fear-based narratives could lead to regulations that stifle startups and protect the incumbents' market position.

The true commercial impact of AI will likely come from small, specialized "micro models" solving boring, high-volume business tasks. While highly valuable, these models are cheap to run and cannot economically justify the current massive capital expenditure on AGI-focused data centers.