As AI models achieve human-level capabilities in valuable roles like software engineering, they can generate significantly more revenue from the same hardware. This increased monetization potential will cause the rental price of GPUs to skyrocket, potentially by over 15x, to match the economic value they produce.
The Alkin-Allen effect suggests that as a fixed cost (expensive compute) dominates, users will pay a large premium for the highest quality good (the most efficient model). This allows top labs to charge much higher margins for models that economize on costly compute by using fewer tokens to achieve the same result.
The current 3x annual growth in AI compute is not easily accelerated and may be unsustainable. It is fundamentally constrained by the slowing of Moore's Law (1.4x), the fixed production rate of ASML's EUV machines for new fabs (1.2x), and the near-total absorption of leading-edge wafer capacity from other sectors (1.8x).
As compute costs rise, driven by AI's ability to perform high-value tasks like automating scientific research, many current AI applications will be priced out. AI labs will be willing to outbid consumer use cases to allocate scarce compute for their own R&D, shifting the landscape of viable AI services.