Dylan Patel says OpenAI and Anthropic are taking a rapidly growing share of new AI infrastructure. He expects the two laboratories to consume roughly half of incremental compute by the end of 2027, with their newer hardware delivering more useful processing per watt than the older global stock.
The economic engine is a widening gap between the cost of infrastructure and the revenue frontier models can produce. As inference margins rise, the laboratories can fund more training and internal research, while cloud providers, chipmakers and independent infrastructure owners raise prices to capture part of the value.
Dwarkesh Patel and Dylan Patel examine constraints that could slow this trajectory. Semiconductor equipment, power generation, data centers and credit markets cannot expand instantly, while higher borrowing costs and political resistance could redirect capital away from AI or limit where new capacity is built.
The discussion ends with a governance problem. Economies of scale, learning from deployment and the ability of advanced models to improve later systems all reward the leading laboratory. Without a credible decentralizing force, a small number of companies could control both the largest compute fleets and a rapidly growing population of capable AI workers.
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