If we want to make progress toward AGI, we need a clear definition of intelligence—and a way to measure it.
In this episode, Hugo talks with Greg Kamradt, President of the ARC Prize Foundation, about ARC-AGI: a benchmark built on Francois Chollet’s definition of intelligence as “the efficiency at which you learn new things.” Unlike most evals that focus on memorization or task completion, ARC is designed to measure generalization—and expose where today’s top models fall short.
They discuss:
🧠 Why we still lack a shared definition of intelligence
🧪 How ARC tasks force models to learn novel skills at test time
📉 Why GPT-4-class models still underperform on ARC
🔎 The limits of traditional benchmarks like MMLU and Big-Bench
⚙️ What the OpenAI O₃ results reveal—and what they don’t
💡 Why generalization and efficiency, not raw capability, are key to AGI
Greg also shares what he’s seeing in the wild: how startups and independent researchers are using ARC as a North Star, how benchmarks shape the frontier, and why the ARC team believes we’ll know we’ve reached AGI when humans can no longer write tasks that models can’t solve.
This conversation is about evaluation—not hype. If you care about where AI is really headed, this one’s worth your time.
LINKS
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