A 10x increase in compute may only yield a one-tier improvement in model performance. This appears inefficient but can be the difference between a useless "6-year-old" intelligence and a highly valuable "16-year-old" intelligence, unlocking entirely new economic applications.

The massive capital investment in AI infrastructure is predicated on the belief that more compute will always lead to better models (scaling laws). If this relationship breaks, the glut of data center capacity will have no ROI, triggering a severe recession in the tech and semiconductor sectors.

The AI era is not an unprecedented bubble but the next phase in a recurring pattern where each new computing cycle (mainframe, PC, internet) is roughly 10 times larger than the last. This historical context suggests the current massive investment is proportional and we are still in the early innings.

As long as every dollar spent on compute generates a dollar or more in top-line revenue, it is rational for AI companies to raise and spend limitlessly. This turns capital into a direct and predictable engine for growth, unlike traditional business models.

Unlike previous tech bubbles characterized by speculative oversupply, the current AI market is demand-driven. Every time a major player like OpenAI 3x-es its compute capacity, the new supply is immediately consumed. This sustained, unmet demand indicates real utility, not just speculative froth.

OpenAI's CFO argues that revenue growth has a nearly 1-to-1 correlation with compute expansion. This narrative frames fundraising not as covering losses, but as unlocking capped demand, positioning capital injection as a direct path to predictable revenue growth for investors.

Unlike the dot-com era where capital built unused "dark fiber," today's AI funding boom is different. Every dollar spent on GPUs is immediately consumed due to insatiable demand. This prevents a supply overhang, making the "circular funding" model more sustainable for now.

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.

Unlike traditional software, AI model companies can convert capital directly into a better product via compute. This creates a rapid fundraising-to-growth cycle, where money produces a superior model with a small team, generating immediate demand and fueling the next, larger round.