The frenzy over Mac Minis to run Moltbot is a "sideshow." The true economic impact is the massive increase in GPU/TPU demand for inference. Each user running a persistent personal agent is effectively consuming the output of a dedicated data center chip, not just a local machine.

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.

The new multi-agent architecture in Opus 4.6, while powerful, dramatically increases token consumption. Each agent runs its own process, multiplying token usage for a single prompt. This is a savvy business strategy, as the model's most advanced feature is also its most lucrative for Anthropic.

A paradox exists where the cost for a fixed level of AI capability (e.g., GPT-4 level) has dropped 100-1000x. However, overall enterprise spend is increasing because applications now use frontier models with massive contexts and multi-step agentic workflows, creating huge multipliers on token usage that drive up total costs.

While the growth of new consumer AI users is slowing into an S-curve, the compute consumption per user is still growing exponentially. This is driven by the shift from simple queries to complex, token-intensive tasks like reasoning and agents, sustaining massive demand for GPU infrastructure.

The future of compute demand is a tale of two opposing forces. Enterprises will use AI to compress redundant data and streamline operations, reducing compute costs. Consumers, however, will demand generative AI for entertainment and personalization (e.g., 'Star Wars with my face'), creating massive new compute needs.

While the cost to achieve a fixed capability level (e.g., GPT-4 at launch) has dropped over 100x, overall enterprise spending is increasing. This paradox is explained by powerful multipliers: demand for frontier models, longer reasoning chains, and multi-step agentic workflows that consume exponentially more tokens.

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.

While the cost for GPT-4 level intelligence has dropped over 100x, total enterprise AI spend is rising. This is driven by multipliers: using larger frontier models for harder tasks, reasoning-heavy workflows that consume more tokens, and complex, multi-turn agentic systems.