What Happens When the AI Boom Slows Down

Martin Casado argues that AI is not another software cycle but an industrial shift that changes what capital, engineering, and infrastructure can do. He says the biggest constraint is no longer technical capability but creativity, because the models are already powerful enough to make entirely new products and experiences that software teams could not build before. He also frames venture investing as a choice among four mindsets: traditional finance, productive investing, frontier investing, and speculation, and he says confusion happens when investors mix those frameworks without realizing it.

A major theme is that most people misuse concepts like network effects and moats. Casado says network effects are often invoked imprecisely, and that investors should focus instead on whether a moat is superlinear, linear, or sublinear in practice. In his view, defensibility matters less in fast-growing markets because growth creates white space; when growth slows, the market consolidates anyway. That logic leads him to think many current AI arguments are being overread through a public-markets lens too early.

He also argues that AI is reshaping infrastructure from compute and storage to databases, networking, and models themselves. On frontier models, he says the key question is what happens if market growth slows: today’s leaders have pricing power, but if model lifecycles stay as short as three to six months, value could shift quickly. He sees capital as increasingly usable in AI, but says the real bottlenecks are power, chips, and physical supply, with debt markets tightening for smaller players.