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.
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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?
