Unverified50% confidenceOpinionExact time
When generation is no longer the bottleneck, the room for improving engineering efficiency shifts from "getting the AI to do more work" to "letting people see faster and more accurately" — this is not a tooling problem but a way-of-working problem.
1
Sources
50%
Confidence
Long-term
Relevance
10/20/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedReal productivity gains often come not from improving the efficiency of individual steps, but from eliminating friction between steps.77% similarUnverifiedThe pace of economic growth is often determined by the hardest-to-improve segments rather than the easiest-to-automate ones; even if AI can automate 90% of tasks, the remaining 10% that cannot be automated may still become the ceiling on output.74% similarUnverifiedThe technical community has not reached a unified conclusion on the AI energy consumption issue: the optimistic side believes that efficiency improvements will create new tiers of demand and that advances in new energy and chip energy efficiency will ease the pressure; the cautious side believes that the AI energy consumption curve is steeper than past efficiency revolutions, and that the window for infrastructure to catch up is shorter.74% similarUnverifiedWhen building complex tasks or enterprise-level agents, automatic building alone is insufficient and manual building is required.74% similarUnverifiedFor engineering positions, being able to run models stably and economically in production environments is often scarcer than knowing how to call APIs.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1077754API
curl https://kongchang.com/api/v1/knowledge/claims/1077754MCP
get_claim(id=1077754)