Links to specific chapters
- Ecosystem & Biodiversity Science
- Climate & Earth System Modeling
- Biological Inference
- Neural Systems, Cognition, & Human Behavior
- Genomics & Human Variation
Artificial intelligence is reshaping the practice of scientific discovery.
The development of AlphaFold—an artificial intelligence system capable of predicting the three- dimensional structures of proteins with unprecedented accuracy—marked a watershed moment in this trajectory. Beyond recognizing the tool’s contributions to the understanding of life, the awarding of the 2024 Nobel Prize in Chemistry to AlphaFold’s creators signaled the arrival of AI as a powerful engine of scientific discovery.
Building on this success, society must now determine: (1) which scientific challenges now stand to benefit the most from AI and (2) how AI itself will evolve using breakthroughs across scientific domains?
Researchers understand these questions better than anyone and deserve to actively inform the technologies, research infrastructure, and investments that support their discoveries. With this purpose in mind, we at the Aspen Institute Science & Society Program hosted ten closed-door roundtables with researchers working across biodiversity, climate and Earth science, biological inference, genomics and human variation, and neuroscience and human behavior. Our program has a proud history of convening leaders across disciplines and sectors to address questions that no single community can answer alone. These discussions were no exception; participants represented universities, research institutes, industry, philanthropy, and nonprofit organizations across North America, Europe, Africa, Asia, and Australia.
Comparing perspectives across these five scientific fields revealed shared themes and opportunities, as well as common obstacles.
Each discussion kept scientific inquiry at the forefront. Participants described major unanswered questions, barriers to progress, and opportunities where artificial intelligence could accelerate discovery. Conversation naturally shifted from AI capabilities to the practical work of science: scoping the literature, testing ideas in the lab, and interpreting evidence.
Researchers repeatedly described AI as accelerating the process of discovery, often emphasizing contributions to model development, hypothesis generation, and experimental design. Participants also described exciting opportunities to synthesize diverse evidence streams and to reveal patterns that would otherwise remain hidden to human researchers alone.
The conversations also reinforced enduring scientific standards such as experimental validation and reproducibility. Moving from prediction to mechanistic understanding remained the goal across every discipline. Participants repeatedly returned to the foundations that make AI-enabled science possible, including high-quality data, shared infrastructure, and scientific workforce development.
The conversations presented in this report identify priorities that can help guide future research, investment, and collaboration. Scientists developing AI methods, organizations supporting scientific research, and institutions building shared infrastructure all face choices that will shape future discovery (and perhaps Nobel Prizes to come!). We hope this report provides a roadmap for those decisions—
because better decisions begin with deeper understanding offered by those at the center of scientific research.
This work is supported by Google.org.
