For decades, three computer scientists built their careers around an idea about the limits of what computers can do.
Artificial intelligence may now have shown it to be wrong: OpenAI and Anthropic each said on Monday one of their AI models had disproved the "exponential time hypothesis" (ETH).
The hypothesis holds that some problems are so hard that computers can solve them only by trying nearly every possible answer. Hundreds of studies in computer science and biology rely on it.
The claim is the latest in a string of AI results that have shaken academia, setting off soul-searching, most notably among mathematicians, over the value of human expertise and the future of their profession.
Russell Impagliazzo and Ramamohan Paturi of the University of California San Diego first proposed the hypothesis around 25 years ago.
Ryan Williams of MIT, a leading researcher in the field, had long publicly doubted it.
The three are now helping organize an independent review of the claims from the AI companies.
They shared their initial reactions with AFP. The interview has been edited for length and clarity.
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