# Coefficient Giving RFP: Global Health and Wellbeing in an Era of Transformative AI > Source: > Published: 2026-06-19 18:12:25+00:00 *By **Deena Mousa**, Program Officer, Global Health & Wellbeing Cause Prioritization* AI is progressing rapidly and could have profound implications for the health and wellbeing of people around the world. While global health philanthropy has historically assumed relative continuity with the past, we would like to engage seriously with the potential implications of transformative AI. This RFP supports rigorous and cost-effective research, policy development, field-building, and implementation focused on improving health and economic outcomes for individuals in measurable ways that are responsive to both the challenges and opportunities that a world transformed by AI might bring. Please see the example projects below. This RFP is responsive to the possibility that AI reshapes which global health problems are tractable and where the highest-impact opportunities lie. That said, we think the questions core to global health work remain the same: who is helped, by how much, at what cost, and compared to what alternative? **A "compressed 21st century" in biomedicine**. AI [ could compress](https://www.darioamodei.com/essay/machines-of-loving-grace) many decades of biological progress into a few years, delivering cures or effective treatments for a wide range of diseases. The binding constraint would then shift from scientific discovery to regulatory approval and distribution, particularly to ensure new treatments reach the global poor. **A shift in where R&D bottlenecks are**. AI may [ accelerate stages of the biomedical pipeline unequally](https://www.nber.org/papers/w35143) — for example by dramatically speeding up literature synthesis, target identification, and molecular design, while leaving wet-lab work, clinical trials, regulatory review, and manufacturing scale-up much less affected. Some investments important in this world have long lead times, like clinical trial infrastructure in low- and middle-income countries (LMICs), regulatory approval capacity, and data infrastructure that represents underrepresented populations. **AI drives explosive economic growth**. If AI [ automates](https://www.nber.org/papers/w31815) R&D and a substantial share of labor, rapidly eliminating global poverty could become technically possible, but the distribution of those gains would not be guaranteed. **AI erases a rung of the canonical development ladder.** AI could [ widen](https://www.imf.org/-/media/files/publications/wp/2025/english/wpiea2025076-print-pdf.pdf) disparities between LMICs and advanced economies through vectors like accelerating reshoring, reducing foreign direct investment, and closing off service exports as remote work is automated. The demographic dividend in places like South Asia and sub-Saharan Africa risks becoming a liability as labor tax bases shrink and the financial foundations of health and welfare systems weaken. We think there’s significant uncertainty about which world we actually end up in, and what the second- and third-order consequences look like. We don’t expect to resolve that uncertainty (or for applicants to do so either!). But we believe that seeding a field of work that engages with this range of possibilities is valuable. We think LMICs will likely be especially vulnerable to the downsides and especially poorly positioned to capture the upsides. That said, we are fully open to work focused on high-income countries, provided it is responsive to the broader orientation above. We are excited about funding projects along two complementary axes: identifying the preparatory steps GHW-oriented funders and actors should be taking (research, policy analysis, empirical mapping), and executing on preparatory steps that look high-value (infrastructure, relationships, field-building, piloting). We're particularly interested in seeding new organizations or bringing new people into this area. And because there is significant uncertainty about how transformative AI scenarios could develop, we're more interested in getting people working on key aspects of the problem than in funding prespecified research projects. Examples of grants we might fund include: We’re *not *planning to fund the following through this RFP: For more information about how we evaluate potential funding decisions, please see our post on [ Importance, Neglectedness, and Tractability](https://coefficientgiving.org/research/strategic-cause-selection/). You can also Because we assess grants through their expected impact on health and economic prosperity, **research or writing proposals should clearly articulate how the work leads to direct real-world impact** (for example, a project may be very likely to inform a particular stakeholder making a particular decision where the wrong decision would have significant negative impact). We care about this beyond the strength of the research proposal itself and are unlikely to fund research without a clear path to impact. Grants are tiered by size, and the process differs accordingly: By default, grants will usually last two years. Renewability will depend on the nature of the project. We're open to even larger proposals, though these would be considered outside this RFP process. **Eligibility**: Open globally to individuals, academic institutions, think tanks, nonprofits, and (where structured appropriately) for-profit entities. We welcome applicants based in, or with deep working knowledge of, LMICs, and applicants who have not previously worked on AI futures. Proposals to spin up new organizations are in scope. **Confidentiality:** If we’re unable to provide support but think your project is promising, we may share your application materials with other funders who may be interested. Let us know if you’d prefer we not do this. We plan to accept applications until **August 21**. You can apply through [ this form](https://fillout.coefficientgiving.org/t/5g9yss4Q9eus?campaign=rec2QQivL6qAvnuwQ).