In this Tech & Drugs episode, I sit down with Laura Matz, Chief Science and Technology Officer at Merck KGaA Darmstadt, Germany, to explore how AI, data, automation, and digital technologies are reshaping science at scale.Laura brings a rare perspective across chemistry, semiconductors, life science, healthcare, and advanced materials. We discuss what pharma can learn from the semiconductor industry, why AI is changing how scientists design experiments, and what it takes to move from pilots to real impact inside a large organization.The conversation goes beyond generic “AI in pharma” claims. Laura shares a practical view of how AI can help scientists make better experimental decisions, why data governance and infrastructure matter, how autonomous labs are being built, and why leadership in the AI era requires both speed and responsibility.We also explore the future of foundation models for chemistry, biology, and physics, the role of Europe in global science and technology, and what young scientists should do to prepare for careers at the intersection of science and technology.Key themes discussed:- How AI is changing the role of science and technology leadership- What pharma can learn from Moore’s law and the semiconductor ecosystem- Bayesian optimization and smarter experimental design- Human intuition vs machine-guided discovery- Scaling AI beyond successful pilots- AI-ready data, governance, and secure access- Autonomous labs and the connection between physical and digital science- How leaders can balance speed, stability, and experimentation- Europe’s role in global science and technology competitiveness- Foundation models for chemistry, biology, and physics- Career advice for scientists entering an AI-enabled worldWhy this matters:AI will not transform pharma, biotech, or R&D through models alone. The real challenge is connecting data, infrastructure, scientific expertise, leadership, and operating models in a way that helps scientists move faster while preserving rigor, safety, and trust.Chapters:00:00 Introduction01:10 Laura Matz’s background and early scientific curiosity03:25 Basketball, teamwork, and leadership05:05 From pre-med to chemistry08:00 What a Chief Science and Technology Officer does10:00 How the CSTO role changed after ChatGPT11:25 What pharma can learn from semiconductors14:35 Is pharma truly more complex than other industries?16:20 Biology, engineering, and the tension between tech and life sciences18:40 Where AI is already impacting R&D workflows19:15 Bayesian optimization and better experiment design21:15 Pairing AI experts with scientific experts22:25 Human intuition vs machine-driven discovery25:20 Scaling AI pilots beyond the “messy middle”28:15 Adoption friction in large organizations29:45 AI-ready data and the foundations of AI transformation35:50 Data access, governance, and security37:25 Building an autonomous chemistry lab in Boston39:25 Decision-making in the AI era40:15 Why leaders need to experiment with AI themselves41:10 Balancing speed and stability in AI transformation47:30 AI as augmentation, not replacement50:00 Europe, innovation, and global competitiveness52:00 Foundation models for chemistry, biology, and physics52:45 Career advice for young scientists54:00 Closing thoughtsGuest information:Guest: Laura MatzRole: Chief Science and Technology OfficerCompany: Merck KGaA Darmstadt, GermanyLinkedIn: https://www.linkedin.com/in/laura-m-matz/Company website: https://www.merckgroup.com/enTech & Drugs explores how data, AI, and technology are changing pharma, biotech, and drug R&D. Hosted by Thibault Geoui, the podcast brings together leaders, scientists, technologists, and builders working at the interface of science and technology.If you enjoyed this conversation, subscribe to Tech & Drugs for more discussions on AI, data, and the future of pharma and biotech.#AIinPharma #DrugDiscovery #AutonomousLabs #Biotech #TechAndDrugs