Dave RicksChair & CEO

I chair and run Eli Lilly, steering a 150-year-old drug maker toward the next diseases. My job is choosing where to aim research, how much risk to take, and balancing investors with patients waiting for treatments. Right now that means guiding the GLP-1 surge and rebuilding our science with large-scale computing.

GLP-1 drugs began as diabetes treatments and proved powerful for weight loss. They act on brain and gut pathways for appetite, glucose, and energy. For many people they bring sustained double-digit weight loss with health benefits, so demand exploded. Our job is to scale factories, supply chains, and long-term evidence to match that responsibility.

We may spend around $14B in a year and tens of billions over a few years. It sounds extreme, but most ideas fail and winners take a decade. To keep a flow of new medicines you fund large, diverse pipelines through uncertainty and aim them at problems where a breakthrough truly changes lives.

Biology is now a data problem. With NVIDIA we are building a system that can simulate and rank billions of potential interactions across protein structures, genomics, clinical histories, and imaging. The goal is not for the machine to invent drugs, but to narrow the search so scientists start from higher-probability ideas.

I use AI as an assistant: summarizing reports, comparing scenarios, stress-testing arguments, and translating technical language for broader audiences. It compresses the time between seeing a problem and forming a view. It does not replace my team or judgment, but it speeds the work.

AI can find targets, propose molecules, and flag some toxicity earlier. It makes the front end faster. But you still have to test drugs in real people across many countries over long periods. Most cost and time sits in rigorous clinical trials, so AI improves odds but does not erase the need for careful testing.

One successful drug can represent several billion dollars when you include all the failed programs. Estimates around $3.5–$4B feel directionally right. Costs are not just science; they include global trials, manufacturing scale-up, safety monitoring, and post-approval commitments. Pricing discussions need that whole picture.

We go outside the U.S. for speed and cost. Many countries let you enroll patients and get approvals faster while keeping high ethics and safety standards. U.S. trial infrastructure can be slower and pricier. We still run pivotal studies here, but we cannot ignore places that let us move faster without losing quality.

LillyDirect is a digital path that combines online prescribing partners, pharmacy services, and support programs. It is not meant to replace doctors or local pharmacies, but to make it easier for people to start and stay on therapy, especially in areas like obesity. It also shows us where access friction lives so we can fix it.

AI will sit alongside our best scientists. It will propose options, simulate scenarios, and surface patterns, while humans choose risks, design experiments, and balance trade-offs. I expect it to shrink early discovery timelines and design smarter trials, but people will still make the hard calls for patients trusting us.