AI Is Here. The Hard Part Is Surviving the Capex Hangover
A Google Doc roundtable throws cold water and rocket fuel on the same question: are we watching a genuine platform shift—or a historic misallocation of capital? The optimism case is simple: the 2017 “tabula rasa agents” era gave way to Transformers + scaling laws, producing general-purpose models—and now agents are back, riding on pretrained foundations (“this is the worst it will ever be”).
The skepticism case is even simpler: economies don’t grow at AI-speed. If GDP expands ~2–4% annually, the spending base is arithmetically constrained—and the application layer still looks small next to the infrastructure binge. One sharp mismatch: roughly $400B in Nvidia chip sales versus < $100B in end-user AI product revenue, plus the reminder that total SaaS spend is < $1T. Capex can become the “escalator” problem—everyone must spend just to avoid falling behind, with no durable margins to show for it.
Meanwhile, the productivity evidence is muddy: a widely-cited METR result suggests coding tools can slow experienced developers on familiar codebases, even as internal surveys report big self-perceived gains.
The most actionable consensus lands somewhere else: energy. If AI demand does not stop climbing, infrastructure—especially power—becomes the real bottleneck, and the real policy story.