Nicholas Arcolano on why 10x the tokens buys only 2x the output

Jellyfish’s analysis of AI coding-agent use across roughly 300,000 developers suggests that high token consumption correlates with greater coding throughput but also sharply diminishing efficiency, as the top 10% of engineers use about 10 times the tokens of median users to achieve roughly twice the productivity. Research presented by Head of Research Nicholas Arcolano at the AI Engineer World’s Fair 2026 characterizes adoption as three distinct operating regimes, ranging from autocomplete-style assistance to supervised multi-agent workflows and highly autonomous systems, with different engineering domains benefiting unevenly. While companies are increasingly concerned about AI spending, Jellyfish reports that developers generally favor the strongest available models and that token costs remain low relative to developer salaries for most users. The discussion argues that the main constraint is shifting from headcount and coding capacity to organizational bottlenecks such as product planning, code review, infrastructure, access controls, governance, and the ability to safely deploy autonomous agents. Moving beyond supervised agent use requires sandboxed environments, orchestration, context engineering, and permissioning, while organizational adoption is most likely to accelerate when teams can demonstrate customer value and financial returns.

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