Unlike human-driven growth, which is limited by population and waking hours, AI agents can operate, replicate, and call each other endlessly. This creates a potentially infinite demand for compute infrastructure, far exceeding previous models and leading to massive, unpredictable strains on providers.

Today's AI computing demand from millions of human users is just the beginning. The real explosion in demand will come from billions of AI "agents" working 24/7. This will double the workforce and create a relentless, round-the-clock need for inference computing, dwarfing current infrastructure requirements.

Leaders at frontier labs like OpenAI and Anthropic indicate that RSI—AI models that self-improve—is closer than anticipated. The arrival of RSI would trigger unprecedented demand for compute, as models consume vast resources to develop and improve themselves autonomously.

Unlike the dot-com bubble's finite need for fiber optic cables, the demand for AI is infinite because it's about solving an endless stream of problems. This suggests the current infrastructure spending cycle is fundamentally different and more sustainable than previous tech booms.

Ben Thompson argues the shift from simple chatbots to AI agents creates an exponential, non-speculative demand for compute. Agents automate complex, multi-step tasks, driving constant usage that justifies the massive capex investments by hyperscalers. This suggests the current spending is based on real demand, not bubble-fueled speculation.

The current compute crunch isn't just a supply issue. It's because new AI models are so much more capable that they unlock a total addressable market (TAM) of valuable tasks that grows exponentially, far outpacing the linear or geometric growth of compute supply.

The current AI data center arms race isn't about meeting today's demand for chatbots. It's fueled by companies like Meta betting on a future where personal AI agents run constantly, analyzing every interaction. This vision of persistent, parallel agents requires an exponential increase in compute, explaining why they will buy any available capacity.

The shift from simple query-based AI to agentic AI, where AI calls itself recursively to solve complex tasks, increases compute demand by orders of magnitude. Most people, especially non-coders, fail to grasp this exponential shift, leading them to consistently underestimate the scale and duration of the AI infrastructure build-out.

The largest driver of future energy consumption for AI won't be human-initiated queries on chatbots. Instead, it will be the massive, continuous "machine-to-machine" traffic generated by autonomous AI agents performing tasks, which will ultimately swamp human-AI interaction and create a runaway demand for compute power.