The progression from early neural networks to today's massive models is fundamentally driven by the exponential increase in available computational power, from the initial move to GPUs to today's million-fold increases in training capacity on a single model.

The relationship between computing power and AI model capability is not linear. According to established 'scaling laws,' a tenfold increase in the compute used for training large language models (LLMs) results in roughly a doubling of the model's capabilities, highlighting the immense resources required for incremental progress.

Over two-thirds of reasoning models' performance gains came from massively increasing their 'thinking time' (inference scaling). This was a one-time jump from a zero baseline. Further gains are prohibitively expensive due to compute limitations, meaning this is not a repeatable source of progress.

Today's AI is largely text-based (LLMs). The next phase involves Visual Language Models (VLMs) that interpret and interact with the physical world for robotics and surgery. This transition requires an exponential, 50-1000x increase in compute power, underwriting the long-term AI infrastructure build-out.

The shift from simple chatbots (one user request, one API call) to agentic AI systems will decouple inference requests from direct user actions. A single user request could trigger hundreds or thousands of automated model calls, leading to an exponential increase in compute demand and cost.

The focus in AI has evolved from rapid software capability gains to the physical constraints of its adoption. The demand for compute power is expected to significantly outstrip supply, making infrastructure—not algorithms—the defining bottleneck for future growth.

Contrary to the idea that infrastructure problems get commoditized, AI inference is growing more complex. This is driven by three factors: (1) increasing model scale (multi-trillion parameters), (2) greater diversity in model architectures and hardware, and (3) the shift to agentic systems that require managing long-lived, unpredictable state.

AI's computational needs are not just from initial training. They compound exponentially due to post-training (reinforcement learning) and inference (multi-step reasoning), creating a much larger demand profile than previously understood and driving a billion-X increase in compute.

Countering the narrative of insurmountable training costs, Jensen Huang argues that architectural, algorithmic, and computing stack innovations are driving down AI costs far faster than Moore's Law. He predicts a billion-fold cost reduction for token generation within a decade.