Ilya Sutskever has a controversial take: the way everyone thinks about artificial general intelligence (AGI) is wrong.
The OpenAI co-founder and architect of GPT-3 isn't trying to build an AI that can immediately perform every human job. Instead, his new company, Safe Superintelligence (SSI), is pursuing something different — a "superintelligent 15-year-old".
"I produce a superintelligent 15-year-old that's very eager to go. They don't know very much at all, a great student, very eager," Sutskever said in a recent podcast. "You go and be a programmer, you go and be a doctor, go and learn."
The vision fundamentally reimagines how humanity reaches superintelligence. Rather than releasing a finished system, SSI would deploy AI agents with exceptional learning capabilities that accumulate expertise on the job, like human workers joining an organisation. "The deployment itself will involve some kind of a learning trial-and-error period. It's a process, as opposed to you dropping the finished thing," he said.
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This approach could prevent winner-take-all dynamics in AI. Sutskever predicts intense specialisation. “You’re going to have lots of different niches and you’re going to have lots of different companies who are occupying different niches. In this world, we might say one AI company is really quite a bit better at some area of really complicated economic activity and a different company is better at another area. And the third company is really good at litigation,” he said.
After co-founding OpenAI in 2015 following his stint at Google Brain, Sutskever left the company in May 2024, worried that commercial priorities were overshadowing its safety roots. He went on to launch SSI the next month with Daniel Gross and Daniel Levy. When Gross joined Meta’s AI division in July 2025, Sutskever stepped in as the CEO.
Why scaling won't save us
The shift comes as Sutskever declares the end of AI's scaling era. "From 2020 to 2025, it was the age of scaling," he said. "But now the scale is so big. Is the belief really that if you had 100x more, everything would be transformed? I don't think that's true. So it's back to the age of research again, just with big computers."
Pre-training, the technique that powered ChatGPT, is exhausting the internet's data. Simply making models bigger no longer guarantees breakthroughs. "There are more companies than ideas by quite a bit," Sutskever noted. Scaling "sucked out all the air in the room" and caused everyone to pursue identical approaches.
Now companies need genuine research insights, not just bigger budgets. The question is what those insights should target.
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The emotional gap
Sutskever points to a fundamental mystery: why do humans generalise so much better than AI?
He recounted a neuroscience case where a patient suffered brain damage that eliminated emotional processing. "He stopped feeling any emotion. He didn't feel sad, he didn't feel anger, he didn't feel animated. He became somehow extremely bad at making any decisions at all. It would take him hours to decide on which socks to wear. He would make very bad financial decisions."
The lesson points to something missing in AI. "It should be some kind of a value function thing," Sutskever said about emotions. "But I don't think there is a great ML analogy because right now, value functions don't play a very prominent role in the things people do."
He explained that current reinforcement learning requires models to take potentially thousands of actions before getting a score. "That means that if you are doing something that goes for a long time—if you’re training a task that takes a long time to solve—it will do no learning at all until you come up with the proposed solution."
"These models somehow just generalise dramatically worse than people. It's super obvious. That seems like a very fundamental thing," Sutskever added.
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