The Past and Future of AI Scientists
Paper · 2026 · Ross D. King
Summary
This survey by Ross D. King reviews how AI has been used to automate science and outlines where the field is heading. It introduces no new dataset, model or benchmark. Instead, it offers a history, a taxonomy of scientific AI systems, and a research and governance agenda. The author separates four overlapping categories: AI scientific assistants, agentic scientific systems, self-driving laboratories, and full AI Scientists that close the loop from hypothesis through experiment to belief revision. The history runs from DENDRAL and BACON, through literature-based discovery and statistical QSAR modelling, to the robot scientists Adam and Eve and foundation-model agents. The core claim is that individual research steps can already be automated, so the real bottleneck is integration. Neural learning must be joined with logic, probability, causal reasoning, simulation, experiment design, robotics and formal records. Science is described as a favourable domain for AI because its problems are narrow and the physical world does not try to deceive. The paper serves as a roadmap toward the Nobel Turing Challenge. It also argues that such systems need provenance, accountability and safety controls.
Key points
- Adam and Eve: Adam is credited as the first machine to make novel discoveries by cycling between hypothesis generation and physical experiments. Eve is described as the template for today's self-driving laboratories.
- Nobel Turing Challenge: the goal is AI capable of Nobel-level discoveries by 2050, and the author judges progress to be ahead of schedule.
- DENDRAL/Meta-DENDRAL: developed at Stanford in the 1960s–70s and led by Joshua Lederberg with Feigenbaum, Djerassi and Buchanan. It inferred molecular structures from mass-spectrometry data and is presented as the first expert system.
- Other milestones: BACON (Langley, Bradshaw, Żytkow, Simon) rediscovered scientific laws through what is now called symbolic regression. Lenat's AM and EURISKO explored concept invention. Swanson (1986) linked fish oil to Raynaud's syndrome via unconnected literature.
- Stated risks: concentration of scientific power, errors repeated automatically at scale, and direct coupling of AI reasoning to hazardous lab actions. The proposed remedies are rigorous evaluation, full provenance and governance.
Institutions
Fields, topics and concepts
📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 19,503 worksArtificial Intelligence · 18,805 worksMaterials Science · 58 worksMaterials Chemistry · 58 works
Cite
Cite as Ross D. King. 2026. The Past and Future of AI Scientists. arXiv:2608.14407.
@misc{king2026past,
title = {{The Past and Future of AI Scientists}},
author = {Ross D. King},
year = {2026},
doi = {10.48550/arxiv.2608.14407},
eprint = {2608.14407},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2608.14407}
}References
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