Thomas Wolf, co-founder and chief science officer at Hugging Face, has cast doubt on the belief that current artificial intelligence systems will lead to major scientific breakthroughs.
Wolf told Fortune that today's large language models, or LLMs, excel at providing answers but fall short when it comes to formulating original questions.
“In science, asking the question is the hard part,” he said. “Once the question is asked, often the answer is quite obvious, but the tough part is really asking the question, and models are very bad at asking great questions.”
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Response To Anthropic's AI Acceleration Claims
Wolf’s comments were in response to a blog post by Anthropic CEO Dario Amodei, who argues that artificial intelligence could compress a century's worth of scientific breakthroughs into just a few years.
Wolf said he initially found the post compelling but became skeptical after rereading. “It was saying AI is going to solve cancer, and it's going to solve mental health problems—it's going to even bring peace into the world. But then I read it again and realized there's something that sounds very wrong about it, and I don't believe that,” he told Fortune.
San Francisco-based Anthropic is backed by tech giants, including Amazon.com Inc. (NASDAQ:AMZN) and Alphabet Inc. (NASDAQ:GOOG, GOOGL)), and is also known for its Claude family of AI models.
‘Yes-Men On Servers' Critique
For Wolf, the core issue lies in how LLMs are trained. In another blog post, Wolf argues that today’s AI systems are built to predict likely outcomes, act as "yes-men on servers," capable of mimicking human responses but incapable of challenging assumptions or generating original ideas.
"To create an Einstein in a data center, we don't just need a system that knows all the answers, but rather one that can ask questions nobody else has thought of or dared to ask," Wolf wrote.
He cited that real scientific progress often comes from paradigm shifts—like Copernicus proposing heliocentrism or the invention of CRISPR-based gene editing—rather than from answering existing questions.
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Benchmarks Focus On Answers, Not Inquiry
Wolf also questioned how AI performance is measured today. In his blog post, he pointed to benchmarks like Humanity's Last Exam or Frontier Math, which test how well AI models can answer complex but well-defined questions.
"These are exactly the kinds of exams where I excelled," Wolf wrote, referencing his academic background. "But real scientific breakthroughs come not from answering known questions, but from asking challenging new ones and questioning previous ideas."
He argued that AI needs to demonstrate the ability to challenge its training data, take counterfactual approaches, and identify new research directions from incomplete information.
An Einstein-Level AI Remains Distant
Using the board game Go as an analogy, Wolf said the landmark 2016 victory of DeepMind's AlphaGo over world champions made headlines but was not revolutionary.
"Move 37, while impressive, is still essentially a straight-A student answer to the question posed by the rules of the game of Go," he wrote in his blog. "An Einstein-level breakthrough in Go would involve inventing the rules of Go itself."
Hugging Face's Role In AI Development
Hugging Face is a prominent open-source platform in the AI community, known for its collaborative development of open-source machine learning models and tools. The company is backed by investors including Sequoia Capital and Lux Capital, and it plays a leading role in developing transparent and accessible AI systems.
Wolf concluded that while current models are useful as assistants, true scientific progress requires a different kind of intelligence—one that can formulate disruptive questions rather than repeat what is already known.
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