Nathan L. argues that open and closed models are developing along different exponential curves. The key question is whether marginal gains in model intelligence translate into practical value. Some use cases may reward small capability improvements, while others may not benefit proportionally from additional intelligence.
Nathan Lambert argues that 2026 AI progress is becoming higher-stakes, with model capabilities, work patterns, economics, and real-world risks all escalating. He says open models still lack a true Claude Code and Opus 4.5-style agent moment, and Gemini has no clear competitor to Claude Code or Codex yet. The essay also tracks Mythos, American open-model momentum, frontier-lab competition, and mounting intervention from governments and other power structures.