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.

AI companies subsidize compute costs to attract users, much like Uber and Lyft subsidized rides. This creates impressive top-line growth but hides the true cost-to-serve and user churn. The industry faces a reckoning when capital dries up and profitability becomes paramount.

While an AI bubble seems negative, the overproduction of compute power creates a favorable environment for companies that consume it. As prices for compute drop, their cost of goods sold decreases, leading to higher gross margins and better business fundamentals.

The perceived constraint on AI compute isn't a true supply issue, but a consequence of VC-funded companies pricing their services below cost to fuel growth. This creates artificial demand that masks the true, profitable market size until unit economics are forced.

The narrative of "off the charts" AI demand is misleading. Major AI providers like OpenAI are "burning tens of billions of dollars," indicating they are not charging the true cost for their services. A realistic picture of demand will only emerge once they are forced to price for profitability, which could significantly cool the market.

Contrary to the idea that technology always gets cheaper, building on AI is less expensive now. The current phase is characterized by abundant venture capital and intense competition among AI tool providers, which subsidizes costs for developers. As the market consolidates, these costs will rise.

While AI dramatically lowers the capital needed to build software, it creates a new significant expense: compute costs. Venture capital remains essential, but its purpose has shifted from funding initial development to covering substantial cloud and AI service bills as companies scale.

AI companies like OpenAI are losing money on their popular subscription plans. The computational cost (inference) to serve a user, especially a power user, often exceeds the subscription fee. This subsidized model is propped up by venture capital and is not sustainable long-term.

The common goal of increasing AI model efficiency could have a paradoxical outcome. If AI performance becomes radically cheaper ("too cheap to meter"), it could devalue the massive investments in compute and data center infrastructure, creating a financial crisis for the very companies that enabled the boom.