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Using the 2011 film 'In Time' as a metaphor, this piece argues that AI token costs are creating a new class divide among developers. Those with large budgets can run agents overnight, iterate freely, and accumulate institutional knowledge from production failures. Those watching a $20 API key cannot afford the failure budget needed to learn how to decompose problems for agents. Cheaper inference won't close this gap because the real inequality is experiential: accumulated scar tissue from production failures, the ability to handle the 'stop signal' problem in multi-agent systems, and observability infrastructure that catches silent burns. The post draws on real examples including Karpathy's home automation agent, Calacanis's $300/day surprise spend, and Perplexity Computer's 19-model orchestration to argue the structural inequality compounds even as token prices fall.

The Token Economy

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<p>I hope this image didn’t take too many tokens.</p>
