AI Research
47 articles · page 6 of 6
The Next Bottleneck in AI Is Measurement, Not Model Size
For a decade, frontier AI advanced by making models bigger, data piles deeper, and hardware clusters wider. The harder problem now is proving what these systems can actually do, where they fail, and whether their reasoning can be trusted when they operate beyond the toy benchmarks that made them famous.
The Next AI Bottleneck Is Not Model Size. It Is Evaluation Drift.
For two years, the industry treated benchmark gains like a scoreboard for intelligence. The harder problem is now visible inside real deployments: models change, tasks change, and the evaluation frame quietly stops matching the work.