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An analysis of why running more than three parallel agents in Claude Code hits a bottleneck, revealing a duty-cycle problem where the developer becomes the primary latency source, and the 'join' process of merging parallel outputs is the biggest time cost.
The article discusses the primary challenges hindering the widespread adoption of AI agents, focusing on key bottlenecks.
Discusses how the bottleneck for AI development is shifting from GPU availability to electricity and grid capacity, as data centers expand faster than power infrastructure can support.
This essay argues that evaluation is the hardest problem in production AI, not generation, and decomposes AI self-knowledge into calibration, discrimination, and expression, with implications for system design.