ARC-AGI is redefining how to measure progress on the path to AGI - focusing on reasoning, generalization, and adaptability instead of memorization or scale. During this month's NeurIPS 2025 conference, YC's Diana Hu sat down with ARC Prize Foundation President Greg Kamradt to find out why most AI benchmarks fail, how ARC-AGI reveals the limits of today’s models, and why measuring intelligence may be harder than building it. Apply to Y Combinator: https://www.ycombinator.com/apply Chapters: 00:11 — What ARC Prize is and why it exists 00:38 — François Chollet’s definition of AGI 01:48 — What ARC-AGI Actually Tests 02:25 — When LLMs Failed the ARC Benchmark 02:44 — The Reasoning Breakthrough 03:38 — ARC-AGI Becomes the Standard 04:20 — Vanity Metrics 04:49 — False Positives in AI Progress 06:06 — The Evolution of ARC-AGI 07:05 — Inside ARC-AGI v3 08:55 — Measuring Intelligence beyond just accuracy 10:25 — What happens if a model solves ARC-AGI?