Global Deaths from TB, Malaria, and HIV

Most forecasters express pessimism that AI-discovered treatments will be a key factor in reducing deaths from these maladies in the near term, citing inadequate health systems in the developing world and poverty as the real reasons high death levels persist: “We have near-cures for all of these now. The problem is health delivery, not medical capability.”; “The core of the problem is poverty-driven barriers to healthcare and prevention, and AI is unlikely to solve these issues.” Several optimists, however, point to the potential for AI to alleviate poverty, and by extension death from disease, in the regions most afflicted: “AI makes the developed world wealthier, and (even if AI does not result in new treatments), more resources would probably be spent on frontline treatments and prevention”; “The decline of malaria and TB is driven less by new drugs/vaccines than by rising income and the associated improvements in public health, sanitation and nutrition. These diseases are essentially a function of development.”

Optimists frequently anchor on the long downward trend and expect AI to bend it further, noting that deaths “have continued to fall since 2000, even without AI interventions.” Pessimists tend to read the same trend as decelerating and unlikely to persist, with many citing “the growing challenge of drug-resistant tuberculosis” and others pointing to climate change’s potential to “expand the range of malaria, causing it to spread significantly.” Several pessimists also note that deaths from these maladies are predominantly in regions experiencing robust population growth. One writes, “Africa’s population is expected to increase by nearly 1 billion people by 2050 [and] the pool of potential victims is highly relevant to the total number of deaths.”

For many pessimists, the recent cuts to global aid is the dominant factor, particularly when considering the 2030 horizon: “The thing that actually moves my 2030 number isn’t AI, it’s the 2025 foreign-aid shock. The Lancet modeling on the PEPFAR [funding decline] runs anywhere from 0.77M to 2.93M additional HIV deaths through 2030”; “By 2030, any possible impact of AI...will still be vastly outweighed by the curtailing of US aid programmes globally in 2025.”; “IHME projected for 2025 that child mortality under 5 had reached an estimated 4.8 million deaths in 2025, an increase of 200,000 preventable deaths compared with 2024, after child mortality had decreased every other year since 2000.” A few optimists treat the shock as likely to be transient, with one arguing that funding crises "tend to resolve (budget reversals, other donors, domestic substitution),” and another arguing that it is likely to be outweighed by “GDP improvements in hotspot regions.”

Many pessimists express near certainty that little will change for the better by 2030, citing the long timelines typically needed to develop and release drugs and the inability for AI to change this: “Even if AI can create custom drugs by 2029, there's no time for testing, manufacturing, dissemination.”; “Clinical trials cannot be done on GPUs and cannot be sped up by bigger clusters.” Several optimists, however, argue that AI could act via domains in which much faster progress is possible, such as “mapping and containing outbreaks, deploying doctors and medical professionals, [and] improving logistics of treatment deliveries.” Others conclude that while AI-discovered treatments are unlikely to have much effect on the 2030 outcome, on a longer timeline, “AI will accelerate the medical advances that will address these diseases, including prevention and treatment.” This argument is frequently cited by forecasters considering 2050 under a rapid progress scenario, with one optimist predicting, “AI-discovered medicines and cures will plausibly cut combined deaths by 90% or more.”

A recurring theme among pessimists is that health gains are unlikely given that gains from AI progress are unlikely to be evenly distributed, with one writing that gains are “more likely going to wealthy individuals in wealthy countries,” and another predicting that “there will be a global underclass left behind...for decades to come.” Several pessimists expect this dynamic to materialize even in a rapid progress world, with one noting that “a rapid progress scenario refers to the capabilities of AI, not to the funding priorities of global health needs.” A few optimists point to the potential for philanthropists, newly wealthy as a result of AI, to contribute more to global health initiatives: “There are a bunch of people who are likely to get rich from worlds with rapid progress who care a lot about global health interventions...I expect that there will just be much more money going towards these causes.”