Frontier AI labs have begun publishing remarkably detailed reports on how their products are used. Reports from Anthropic, OpenAI, and Microsoft have documented not only overall usage, but also insights on how AI might interact with labor markets, by linking requests to job-related tasks, and differentiating between requests for help and for automating tasks. These statistics help policymakers grapple with one of the most important questions facing countries that have many knowledge workers: how AI will affect employment.
However, employment is not the most important question for billions of the lowest income people around the world, who for the most part are not knowledge workers and predominantly live in low- and middle-income countries. Interviews conducted by Anthropic found that low- and middle-income countries’ residents were much less concerned that AI would displace jobs, and were much more optimistic about AI. For the world’s poorest residents, AI may improve access to advice on health, business, education, and countless other needs.
The labs have made strides in reaching these communities, from developing language benchmarks to country-specific reports to flood forecasting for billions. But poor countries still use AI far less than rich countries. While this gap appeared to be shrinking for OpenAI’s ChatGPT from 2024-25, it was expanding in Microsoft’s more recent data. Understanding impacts on the poorest would both help target these companies’ efforts and help societies that are wary of downsides of AI assess the degree to which its benefits will accrue beyond Silicon Valley and the Fortune 500.
Because the world’s poorest have different needs, labs should release tailored statistics to understand them. I describe here simple, feasible analyses that would help the AI industry, governments, and social sector organizations better serve the poor. I also predict what we might find.
1. Are the world’s poorest using AI?
Anthropic, Microsoft, and OpenAI document that in low- and middle-income countries, a smaller share of the population uses AI than in high-income settings. However, some patterns appear puzzling. On average, users of AI in lower-adoption countries are more likely to use Anthropic’s Claude for programming, and countries that are less educated on average are submitting requests that AI more successfully completes. These counterintuitive patterns likely result because early adopters in low-income countries are wealthier people.
Wealthy people in low-income countries live like residents of rich countries: they may be knowledge workers and speak international languages like English, Chinese, or French. However, the poorest people in low-income countries have lives that are very different from anyone in wealthy countries: they may have income reliant on risky agricultural harvests with no safety net, have limited access to clean water, see medical providers who give incorrect information and counterfeit drugs, and send their children to packed classrooms where they learn little.
Because of the large difference between the wealthy and poor, country-level averages do not tell us whether AI is providing opportunities for the poor. There are early signs that some of the poor are using AI: my team analyzed usage of a custom AI chatbot, finding that a group of teachers in Sierra Leone used AI more often than web search, mostly to look up facts. The labs have data on many more users, and thus a correspondingly larger opportunity to illuminate the extent to which AI is reaching people in need.
There is not a single definition of poverty, but there are several indicators in the data the labs collect that could both identify the poor and assess barriers that limit their access to AI.
Devices
Lower-income people more commonly access the internet from smartphones, not computers, and typically use cheaper handsets. Many of the poorest have only basic phones that cannot access the internet. Low-literacy people may prefer interacting with voice rather than text.
What could be computed: What proportion of users have inexpensive versus expensive handsets? How does their usage compare? What proportion of consumer use is via smartphone apps or voice/SMS (possibly by third parties using the labs’ APIs)?
What we might find: In low-income countries, a large share of access is from wealthier people, using computers or higher priced smartphones. People with lower priced handsets have lower usage. Some custom apps tailored to local needs may be seeing substantial uptake, such as health or education apps by governments or NGOs. One large question is whether the poorest are better served by third-party solutions; if so, that may suggest that partnerships may be more important than improving the consumer app experience.
Language
Many of the world’s poorest residents primarily speak local languages like Tamil, Kannada, or Yoruba, even in countries where the wealthy speak major global languages. In India, if 95 percent of Claude requests are in English, that would indicate a very different kind of diffusion than if 40 percent of requests are in local languages.
What could be computed: What proportion of queries are written in each language, in each country? How does that compare to the breakdown of first languages in that country? Additionally, what are the failure or abandonment rates by language?
What we might find: A substantial fraction of AI usage is likely to be in major global languages, even in countries where the population predominantly speaks local languages. That may indicate a combination of lack of access and lower model capabilities in local languages. However, we are likely to find exceptions: broad take up in some languages, which can illuminate what is working and where additional language data need be collected. These measures may be stronger evidence about how AI performs in diverse languages than benchmarks, because they reflect whether models meet real world needs.
Remoteness
Cities contain a mix of rich and poor people, but in many countries rural areas are poorer. Additionally, people in rural areas may have less access to the internet and be less connected.
What could be computed: What proportion of use is rural versus urban, by country.
What we might find: There is substantial use in cities around the world. Some countries likely have substantial rural use, but for some it is almost entirely absent. That could indicate access barriers, that the models are less useful for the problems faced by rural residents, or lack of awareness.
2. What do the poor use AI for?
After the first step, we are likely to find a group of the poorest who are in fact successfully using AI. There is limited market research on these populations, and many do not often use web search, so their needs are absent from web query data. AI usage can provide a new window into the needs of the poor, analogous to Google Trends. This can help AI labs and a variety of organizations better serve these populations. We saw early examples of this among teachers in Sierra Leone, who submitted requests for not only facts and lesson plans, but also on handling reports for insurance claims and navigating interpersonal situations with students and supervisors. Another study found that one of the top uses of ChatGPT among gig workers in India and Brazil was for health queries.
What could be computed: An easy start would be to take the standard categorization of requests already reported by the labs (such as writing, technical help, or mapping to industries) and report them specifically for the subset of users in marginalized groups (defined by having cheaper devices, speaking local languages, or using from remote areas). However, these taxonomies are built around knowledge work, and may systematically undercount the ways poor people find the technology useful. Thus, it would be helpful to develop new taxonomies to understand poverty-specific needs, including particular uses within agriculture, health, navigating government bureaucracy, and business advice.
What we might find: The poor are likely to use AI differently from the wealthy: almost no software development, some use for navigating bureaucracy and social problems, more for help with homework, and less for writing assistance. Anthropic has already reported that Claude users in lower-income countries are more likely to request help with coursework. A further breakdown will help us understand if AI is being used only in the wealthiest schools or broadly, and help school systems ensure it is used in ways that support learning.
The governments and social sectors of low-income countries are all deciding how to respond to the spread and potential of AI. At the same time, AI labs are being asked whether their products will enrich a small few—or widely benefit humanity. These simple analyses are feasible. They would transform our understanding of whether AI is reaching people who may benefit most.
This piece is cross-posted to Daniel Björkegren’s blog.
This piece has benefited from feedback from Jeff Berens, Han Sheng Chia, and Markus Goldstein.
