Announcements
The Map and the Territory: Why Capability Is Not the Same as Reliability
Capability is advancing faster than reliability. LLMs model language—a lossy map of reality—so fluency scales, but truth doesn’t. The key constraint isn’t compute, but grounding, objectives, and stable learning dynamics.
The Mapping Problem: Why AI's Biggest Bottleneck Isn't the Technology
A field experiment across 515 startups reveals AI's real bottleneck: not the technology, but discovering where it creates value. Firms that learned to map AI across their production chains grew 1.9× faster, while asking for 39.5% less capital.
The Depreciation Trap: Why the AI Bubble Is Real, Inevitable, and Irrelevant
AI’s core risk isn’t validity but timing: massive capex assumes demand grows fast enough. If supply outpaces real usage, GPU economics collapse, exposing fragile financing. Depreciation doesn’t cause the correction; it determines when it becomes visible.
The Verification Gap: From Low Energy To Proven Correct
Generation is cheap. Verification is what creates trust. The systems that matter won't be the ones that generate plausible outputs—they'll be the ones that can prove their outputs are right.
The Software Paradox: Bigger Industry, Fewer Winners
AI will produce the most valuable software companies in history while destroying most that exist today. The industry gets larger. The winners get fewer. Software creation is becoming cheap. Distribution and trust are becoming more valuable. That's it.