In this Tech & Drugs episode, I sit down with Mathieu Bourdenx, Group Leader at the UK Dementia Research Institute / UCL, to explore how AI agents and AI co-scientists are beginning to change neuroscience research.We discuss brain aging, Alzheimer’s disease, dementia, systems biology, Kosmos, Open Scientist, CBrain, and what it means to work with AI tools that can search literature, analyze data, generate hypotheses, and support scientific discovery.Mathieu’s research focuses on brain aging and the mechanisms that may increase the risk of dementia. In this conversation, we start with the biology: why Alzheimer’s disease is so difficult, why patient heterogeneity matters, why biomarkers are essential, and why preventing or delaying dementia could have a major impact on public health.We then move into the changing role of data and AI in neuroscience. Mathieu explains why biology is not a simple linear pathway, why modern scientists need to understand data science, and how high-dimensional, multimodal data can help us understand complex biological systems.A major part of the episode focuses on AI co-scientists. Mathieu shares his experience working with Kosmos, an AI system designed to support parts of the scientific process: forming hypotheses, searching literature, analyzing data, and iterating across evidence. He explains how the system generated a hypothesis from single-cell transcriptomic data, how the team tested signals across independent datasets, and why wet-lab validation remains the bottleneck.We also discuss CBrain and Open Scientist, including the importance of open-source tools, trusted research environments, patient data protection, and the future of AI agents that can support dementia research at scale.Finally, we explore what this means for the future of science: AI agents in the daily routine of researchers, scientific claim verification, hallucinations, code review, agent-to-agent critique, faster research output, pressure on scientific publishing, and the idea of “digital brains” for scientists and labs.Key themes discussed:- Alzheimer’s disease, biomarkers, and patient heterogeneity- Why prevention may matter as much as treatment- The limits of N-of-1 longevity experiments- Systems biology and high-dimensional data in neuroscience- Why scientists need data science and AI skills- Kosmos and the rise of AI co-scientists- I-generated hypotheses and experimental validation- CBrain, Open Scientist, and open-source AI for dementia research- Trusted research environments and protected patient data- AI agents in everyday scientific workflows- Verification, hallucinations, and scientific trust- How AI may change lab notebooks, publications, and institutional memoryWhy this matters:AI in science is often discussed in broad and abstract terms. This episode grounds the conversation in the real work of neuroscience: reading papers, analyzing data, testing hypotheses, validating findings, and deciding what is worth pursuing in the lab. For pharma, biotech, AI, data, and R&D leaders, it is a practical look at how agentic AI could reshape scientific work without removing the need for biological expertise, experimental validation, and careful judgment.Guest: Mathieu BourdenxRole: Group LeaderInstitution: UK Dementia Research Institute / UCLLinkedIn: https://www.linkedin.com/in/mathieu-bourdenx-21995546/ Website: https://www.ukdri.ac.uk/team/mathieu-bourdenx#AICoScientist #Neuroscience #DementiaResearch #DrugDiscovery #TechAndDrugs