Three AI pioneers argue for keeping big models open as safety worries grow

Initiatives like Pacing the Frontier aim to ensure AI safety by partnering with major labs, yet open-source models remain a contentious issue within the industry. Due to their free distribution and lack of usage control, open-weight models are difficult to regulate, causing some organizations to view them with significant apprehension.

However, at last week’s Ai4 conference in Las Vegas, three prominent AI experts—Nobel laureate Geoffrey Hinton, World Labs CEO and co-founder Fei-Fei Li, and Coursera co-founder Andrew Ng—addressed this topic. Although they differed on specific strategies, all three strongly advocated for maintaining an open AI ecosystem.

For these speakers, the primary concern was preventing a small group of major AI firms from controlling technological progress. When a few companies dominate access to a technology, similar to how Apple and Google control mobile operating systems, innovation can stagnate, and platform controllers can dictate what gets developed.

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

Companies are incentivized to protect their competitive edges, including by shaping industry regulations. This could create a scenario where only the largest, best-funded firms with the resources to build advanced AI systems survive.

Ng’s solution was to maintain multiple providers, ensuring that models and companies compete rather than allowing a few players to dominate. “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.”

Not everyone agreed that open-weight models would preserve this competitive balance. Hinton, in particular, distinguished between open-source software, which makes 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 explained. “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.”

Despite his reservations, Hinton acknowledged that open-weight models are now a permanent fixture in 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 did not mean ignoring the risks. Hinton’s position was clear: AI would continue to advance, and he believed this was largely beneficial. He noted 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 offered a different perspective. He argued that the question was not whether open models were risky, but who controlled access and who would win the market. The entity that builds the cheaper model gains 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 encounter 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 challenged this 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 does not have to be an all-or-nothing choice. Different layers of the ecosystem can operate at varying 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.”

However, 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.”