#red-teaming
3 articles
Benchmarks Are Quietly Becoming the Interface to Frontier Models
The most important design work in AI is moving away from the demo and into the evaluation stack. The way labs measure models increasingly determines what the rest of us experience as product quality, safety, and trust.
The Benchmark Era Is Ending, and That’s Good for AI
For years, benchmark gains offered a clean story about progress: one number, one leaderboard, one direction. That story is getting harder to believe as models move into messy settings where the real failures are specific, social, and expensive.
The Hard Part of AI Research Now Is Measurement, Not Generation
Model capability is still climbing, but the bottleneck has shifted. The real contest is no longer who can produce the next flashy demo; it is who can measure systems well enough to trust where they break, where they generalize, and where they should never be deployed.