Chollet on X: o3's ARC-AGI Score and What It Means
AI Summary
Chollet's landmark X thread reacting to OpenAI's o3 model achieving 87.5% on ARC-AGI with heavy test-time compute. He frames this as significant but context-dependent — o3 achieves the score using what he estimates as roughly $10,000-$100,000 of compute per task, which is not the efficiency that intelligence implies. He draws a distinction between the result being impressive and the method being efficient, arguing that the path from o3 to genuine AGI still requires combining the core capability with much greater efficiency. The thread is the most shared piece of Chollet's writing in 2024 and shaped the broader discourse around o3.
Original excerpt
The most-shared Chollet thread of 2024. Key nuance: o3 achieves the score but at compute costs that preclude calling it "efficient intelligence" in the ARC paper's formal sense.
The thread clarifies: "high ARC score via expensive search" is meaningful progress, but not equivalent to "high ARC score via efficient learning." The distinction matters for understanding the path to AGI.
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Chollet's landmark X thread reacting to OpenAI's o3 model achieving 87.5% on ARC-AGI with heavy test-time compute. He frames this as significant but context-dependent — o3 achieves the score using what he estimates as roughly $10,000-$100,000 of compute per task, which is not the efficiency that int…
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"Chollet on X: o3's ARC-AGI Score and What It Means" was written by François Chollet. It is curated in the François Chollet vault on Burn 451, which covers agi evaluation & arc-agi.
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