LLMs Have a Different Shape of Intelligence Than Humans
ShakeelHashim · x · 2026-07-10
The author argues that the reason LLMs and human intelligence "look different" comes down to this: models excel at tasks where training processes and reward functions are clearly defined, but their generalization of these capabilities is inferior to humans. Therefore, one cannot infer their full spectrum of real-world abilities solely from benchmarks.
The author further speculates two underlying reasons:
- AI's reward functions are much simpler than human learning systems, making them more susceptible to the Goodhart effect.
- There is currently no clear equivalent to human "collective intelligence."
They add that someone from a decade ago, looking only at today's benchmarks, would likely assume these capabilities would unfold smoothly across all tasks just like human intelligence. However, the author doubts this "divergence in intelligence shapes between humans and AI" will disappear within the next 10 years.
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