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Since the launch of ChatGPT at the end of 2022, AI has quickly changed how we work, how we learn, how we love, how we heal. It has also made a handful of companies such as Nvidia, Anthropic and OpenAI very powerful, and put the U.S. and China far ahead of every other country. What does this mean for everyone else? Is there a way to ensure a more equitable future ?

Last week, Rest of World hosted an event titled “The Great AI Divide,” as part of New York Tech Week. We asked three experts to weigh in on these questions: Sam Winter-Levy, a fellow at the Carnegie Endowment for International Peace; Aditya Vashistha, an assistant professor at Cornell University, where he leads the Cornell Global AI Initiative; and Peter Micek, general counsel and United Nations policy manager at digital rights group Access Now.

Here is a summary of the conversation, edited for brevity and clarity. You can also watch it here .

Rest of World

Sam, is the AI race all but over for everyone? What happens when countries are subject to the whims of Washington and Beijing?

It’s fair to say that AI is primarily a two-horse race . The U.S. and China control 90% of global computing power, attract between 70% and 80% of global investment in AI, and have huge concentrations of talented AI researchers. That creates a world in which other countries are dependent on the U.S. and China for access to AI systems. And both the U.S. and China have shown a willingness to use that access for leverage. That puts most of the Global South in an uncomfortable position. And the middle powers remain exposed to the disruptions that AI could cause, even if they don’t necessarily capture the benefit. So they’re still exposed to job-related disruption , the social effects of AI systems. That’s the pessimistic story about how the rest of the world could be left behind.

The middle powers remain exposed to the disruptions that AI could cause, even if they don’t necessarily capture the benefit.”Sam Winter-Levy, fellow, Carnegie Endowment for International Peace

Three trends make this pessimistic story particularly likely right now. The first is that a lot of the frontier developers in the U.S. are switching to a more managed-access approach to their technology. Anthropic’s Mythos model is a good example of this. They’re rolling the model out to small groups of companies that they select, at least initially. That puts other countries in an uncomfortable position where U.S. companies pick who gets access to these systems. Second, we’re now in a world where there are quite severe compute constraints. Demand for these models is outstripping the compute that the companies have. Again, companies are rationing who can use those systems. And finally, we’re starting to see the U.S. government and the Chinese government playing a more assertive role in who can have access to these systems. So those trends together could lead to a situation where a small handful of very wealthy companies, very wealthy countries have control over who has access to this technology .

Aditya, you design, build, and evaluate AI for marginalized communities. What are the risks of having only American and Chinese AI systems?

AI technologies today are designed by and for WEIRD societies — Western, educated, industrialized, rich, and democratic — which represent only 14% or 15% of the world’s population. What about the 85% and how they are represented in the AI systems? Back when ChatGPT was launched, and you would ask, “What does a Muslim man look like?” there was a homogenized view of that — someone who is wearing a headscarf and has a white beard — while a Hindu man would be someone who’s wearing saffron. There were all sorts of problematic issues for any kind of marginalized population, like people with disabilities. Many of these AI models could not even show what these looked like.

There have been many advancements, more safety integrated into these models. But these models continue to have many of these biases — religious, linguistic, identity, and so on. These biases are not what they were a couple of years ago, but they are still deeply rooted in these models. So as we think about AI futures for the rest of the world, we need to think about whose values, whose voices, whose languages , and whose cultures are represented in these models. The layers which we have now work for only a minority of the world’s population, and many of the benchmarks for safety do not even account for ableism, when we have 1 billion people in the world with disability. So some of these biases are not just towards people living in the Global South, but about marginalized voices. If you look at preference data sets, many of these do not exist for the majority of countries in the Global South. Many are in English, and not in other languages. If we do not take into account these biases, these languages and cultures, then we are continuing to design our AI technologies to work efficiently only for a sliver of th…