The future of AI
As models grow more capable, human judgment becomes more specialized, not less.
Fluent is not correct
Outputs can read perfectly and still fail in practice. The gap between sounding right and being right is where expert review matters most.
Evaluation becomes the moat
Models are converging. What stays scarce is the rubrics, edge-case catalogs and verified corrections behind them. Those are built by people who know the field.
Physical AI raises the stakes
When a model controls a robot, a wrong plan becomes a wrong action. Safety review by practitioners moves from nice to have to essential.
What we are watching
- →Agents that run for hours: how do you review a long trajectory?
- →Reviewing the reviewers: calibrating human and automated judges together
- →Domain depth: why general raters hit a ceiling
- →Evaluation for robots and embodied systems
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