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As projects like Pacing the Frontier look to major labs as a way to keep AI research safe, open-source models have become a sore spot for the industry. With free distribution and little control over how they’re used, open-weight models aren’t easily controlled, leading some labs to treat them as downright scary.

But at the Ai4 conference in Las Vegas last week, three of the world’s most respected AI researchers — Nobel Prize winner Geoffrey Hinton, World Labs CEO and co-founder Fei-Fei Li, and Coursera co-founder Andrew Ng — spoke out on the issue. And while they disagreed on particular tactics, all three made a powerful case for keeping AI open.

For the three speakers, the core concern was allowing a handful of major AI companies to control the pace of progress. When a few companies control access to a technology, as Apple and Google do with mobile operating systems, innovation can slow and the companies that control the platforms can influence what gets built on them.

Andrew Ng said that he worried about a similar dynamic emerging in AI. “I don’t want there to be gatekeepers,” Ng said. “That limits how all of us can access AI.”

Companies have an incentive to protect their competitive advantages, including by influencing the rules that govern the industry. That could create a dynamic where only the largest, best-capitalized firms with the resources to build the most advanced AI systems.

Ng’s solution was to maintain multiple providers, with models and companies competing rather than allowing a handful of of players to dominate the field. “If I were to try to give one prescription, it would be to promote openness,” Ng said, “because AI is amazing technology and I want it to be in everyone’s hands.”

But not everyone agreed that open-weight models would help preserve that state of play. Hinton, in particular, drew a distinction between open-source software, which makes the underlying code available for inspection and modification, and open-weight models, which release the parameters of a trained AI model to the public.

“Open source is great. You show people the code, and lots of people look at the lines of code and say, ‘Oh, there’s a bug.’ Open weights means you train a big model and then you give people the weights. That’s very different,” Hinton said. “I was against open [weights] because it makes it so easy for people to take these big foundation models, which are very expensive to train, and for much less money train them to do bad things like cyber attacks.”

But whatever his reservations, Hinton acknowledged that open-weight models are already a permanent fixture of AI. “I think that battle’s been lost. We now have open-weight models, so the barrier to lots of people getting these big models, which was the cost of training foundation models, that barrier has disappeared. It’s too late.”

Yet accepting reality didn’t mean ignoring the risks. Hinton’s position was clear: AI would continue to advance, and he thought that was largely a good thing. He said it would boost productivity and improve education and healthcare. “Worrying about the possible bad effects of AI and the things that intelligent beings might do when they’re smarter than us. I don’t think that’s unfair. I think it is unfair to label anybody who thinks like that as a fear-monger,” Hinton added.

Ng took a different view. The question, he argued, wasn’t whether open models were risky, but who controlled access and who would win the market. Whoever built the cheaper model would have the advantage. If China’s open-weight models gained widespread adoption across Asia, Africa, and/or the developing world, he warned, they could influence how billions of people encountered ideas about democracy, freedom, and human rights.

“One thing I hope we do is encourage American competitiveness and open-source AI. It turns out that AI is a tremendous source of soft power. You can see the way China’s model has tremendous accomplishment with Africa, for example,” Ng said. “But my worry is because of all the lobbying in the U.S. and the fear-mongering, building open-source AI in America is struggling to compete with open-weight models coming out of China, and my worry is that if China figures out a fundamentally more cost-efficient way to build AI, then things that are more cost-efficient have a fundamental business adoption advantage.”

Li pushed back on that framing. “It’s very dangerous to make this a dichotomy between complete openness all the way to complete closedness,” she said. “In complex software systems as well as scientific systems it’s much more nuanced.”

Li used nuclear physics as an example: scientific papers are published openly, but uranium is regulated, while laboratory work falls somewhere in between. The lesson, she explained, was that openness doesn’t have to be an all-or-nothing choice. Different layers of the ecosystem can operate at different levels of openness.

She also highlighted collaborations between public and private institutions, such as the Human Genome Project. The resulting knowledge became a platform that others could build on, she said, allowing pharmaceutical companies to profit, scientists to advance their work and society to benefit.

“So I think we have to use [AI] as that kind of infrastructure,” Li said. “We need some levels of openness, both in scientific discovery, in education, in global partnership, as well as lucrative business models for entrepreneurs. But we also will accept closed-source systems. This debate, especially at the sweeping level of ‘we can only tolerate one,’ is a false debate. We need to get to a level of nuance.”

But everyone agreed that some level of regulation would be necessary to keep AI on the right track. “What we want to do is develop AI in a direction that helps people, and regulation will help us do that,” Hinton said. “You can’t leave it to people like Elon Musk and Mark Zuckerberg to decide how AI should be done.”