The co-founder of the face of the face Thomas Wolf simply provoked the vision of the anthropic executive director for the future of the AI ​​industry of $ 130 billion notices notice

Thomas WolfCo -founder of AI Company HugHe issued a great challenge to the most optimistic visions for the artificial intelligence of the technology industry, arguing that today’s AI systems are the basis of unable to provide the scientific revolutions that their creators promise.

Provocative Blog post Posted on his personal website this morning, Wolf is directly confronted with the widespread vision of the Anthropian CEO Dario AMDI, who predicts that Advanced AI will delivercompressed 21st century“Where decades of scientific progress can only develop in years.

“I am afraid that AI will not give us a” compressed 21st century, “Wolf wrote in his post, arguing that current AI systems are more likely to produce”Server“Instead of from”Genius“This provides for Amade.

The exchange emphasizes the growing division in how AI leaders think about the potential of technology to transform the scientific discovery and solution of problems, with major consequences for business strategies, research priorities and political solutions.

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The wolf founded his criticism in personal experience. Although he is a student who has visited MIT, he describes, finding that he is a “rather medium, subordinate, mediocre researcher” when he begins his doctoral work. This experience formed his opinion that academic success and scientific genius require radically different mental approaches – the first rewarding correspondence, with the second requiring a rebellion against established thinking.

“The main mistake that people usually make is to think that Newton or Einstein is just a large -scale students,” Wolf explains. “A real breakthrough of science is that Copernicus offers, against all knowledge of his days – in ML terms we would say,” Despite his entire set of data, ” – the Earth can go around the sun, not the other way around.”

AModei’s vision published last October in his “Machines of loving grace“Essay, presents a radically different perspective. He describes a future in which AI operating with the “10x-100x human speed” and with an intelligence exceeding that of the Nobel Prize winners can achieve progress in biology, neuroscience and other areas within five to 10 years.

Amodei provides “reliable prevention and treatment of almost all natural infectious diseases”, “elimination of most cancer”, effective treatments for genetic disease and potentially doubling human life, all accelerated by AI. “I think the return of intelligence is high for these discoveries and that everything else in biology and medicine is mostly followed by them,” he writes.

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This fundamental tension in Wolf’s criticism reveals a commonly neglected reality in the development of AI: our indicators are designed mainly to measure convergent thinking, not different thinking. These AI systems are distinguished by the production of answers, which are aligned with the existing consensus of knowledge, but are struggling with the type of counterria, transparent insights that stimulate scientific revolutions.

The industry has invested seriously in measuring how well AI systems can answer questions with established answers, solve problems with some solutions, and fit into the existing framework for understanding. This creates a systematic bias for systems that correspond, not a challenge.

The wolf specifically criticizes current AI assessment indicators as ”Humanity’s last exam“And”Frontier Math“Who tests AI systems of difficult questions with known answers, not their ability to generate innovative hypotheses or cause existing paradigms.

“These indicators test if AI models can find the right answers to a set of questions we already know,” Wolf wrote. “However, true scientific breakthroughs will come not from the answer to known questions, but from asking a challenge for new questions and asking common concepts and previous ideas.”

This criticism indicates a deeper question in how we conceptize artificial intelligence. The current focus on the number of parameters, the volume of the training and the performance of the standard can create the AI ​​equivalent of excellent students, not revolutionary thinkers.

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This intellectual division is essential for the AI ​​industry and the wider business ecosystem.

Companies aligned with AModei’s vision can prioritize large -scale AI systems to unprecedented size, expecting interrupted innovations from increased computational power and wider integration of knowledge. This approach is at the heart of companies’ strategies as Anthrop., OPENAI and other border AI laboratories that collectively raised Tens of billions of dollars In recent years.

On the contrary, the Wolf perspective suggests that more return can come from the development of AI systems specifically designed to challenge existing knowledge, examine counter -facts and generate new hypotheses – opportunities that do not necessarily emerge from current training methodologies.

“We are currently building many obedient students, not revolutionaries,” Wolf explains. “This is ideal for today’s primary goal in the field of creating great assistants and too compatible helpers. But until we find a way to encourage them to question their knowledge and to offer ideas that potentially contradict the past data on learning, they will not yet give us scientific revolutions. “

For the leaders of enterprises that rely on II to stimulate innovation, this debate raises decisive strategic issues. If WOLF is correct, organizations investing in current AI systems with the expectation of revolutionary scientific breakthroughs may need to hook their expectations. True value may be more relevant to the improvements to existing processes or in the placement of a person’s joint work approaches, in which people provide intuitions that cause the paradigm while AI systems deal with computing heavy lifting.

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This exchange comes at a main moment in the evolution of the AI ​​industry. After years of explosive AI growth, possibilities and investment, both public and private stakeholders, are increasingly focusing on the practical return on these technologies.

Recent data from the Pitchbook risk -risk analysis company shows AI funding has been reached $ 130 billion worldwide in 2024With health care applications and scientific discoveries, attracting particular interest. Still, questions about tangible scientific breakthroughs from these investments have become more urgent.

The Wolf-Amodei Debate is a more in-depth philosophical division in the development of AI, which boils below the surface of discussions in the industry. On the one hand, there are scaling optimists, who believe that continuous improvements to the size of the model, the volume of data and learning techniques will eventually give systems capable of revolutionary insights. On the other hand, architectural skeptics, who claim that the main restrictions on how the current systems are designed can prevent them from making the type of cognitive jumps that characterize the scientific revolutions.

What makes this debate particularly important is that it happens between two respected leaders, who are both at the forefront in the development of AI. None of them can be rejected as simply uninformed or resistant to technological progress.

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The tension between these perspectives indicates a potential evolution in the way AI systems are designed and evaluated. Wolf’s criticism does not propose to abandon the current approaches, but rather increase them with new techniques and indicators, specifically aimed at promoting counter -thinking.

In his post, Wolf suggests that new indicators need to be developed to verify that the scientific models of AI can “challenge their own knowledge of training data” and “take bold counter -phasic approaches”. This is a call not for less investment in AI, but for a more thoughtful investment, which takes into account the full range of cognitive opportunities needed for scientific progress.

This nuanced view recognizes the huge potential of AI, while acknowledging that current systems can be distinguished by certain types of intelligence while struggling with others. The ahead probably involves developing additional approaches that use the strengths of current systems while finding ways to deal with their restrictions.

For enterprises and research institutions navigated in the II strategy, the consequences are essential. Organizations may need to develop evaluation frameworks that evaluate not only how well AI systems answer existing questions, but also how effectively they generate new ones. It may be necessary to design models for cooperation between a person who pair the coincidental models and computing AI with the intuition of paradigms that relate to the paradigm of human experts.

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Perhaps the most precious result of this exchange is that it pushes the industry to a more balanced understanding of both AI’s potential and its restrictions. The vision of Amodei It offers an overwhelming reminder of the transformative impact that AI can be at the same time in multiple domains. Wolf criticism Provides the necessary counterweight, emphasizing the specific types of cognitive possibilities necessary for true revolutionary progress.

As the industry moves forward, this tension between optimism and skepticism, between the scale of existing approaches and the development of new ones, will probably lead to the next wave of innovation in the development of AI. Understanding both perspectives, organizations can develop more strategies that maximize the potential of current systems while investing in approaches that deal with their restrictions.

So far, the question is not whether WOLF or Amodei are right, but more recently how their contrasting visions can inform a more comprehensive approach to the development of artificial intelligence, which is not only distinguished by the answers to the questions we already have, but helps us to find the questions we have not yet thought of asking.