Scaling Scientific R&D with AI Supercomputing Infrastructure — with Thomas Fuchs of Eli Lilly

**Daniel Faggella** (0:12)
Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Thomas Fuchs, Chief AI Officer at Eli Lilly. Thomas joins Emerge's Matthew DeMello to explain how Eli Lilly is building an AI-ready supercomputing platform to strengthen discovery, development and manufacturing. He describes how large-scale compute lets researchers work with bigger models, use decades of experimental results and explore a wider range of potential molecules. He also outlines where this is already changing scientific work, from cutting down unnecessary lab experiments to improving early prediction of molecular properties and speeding key manufacturing steps. Just a quick note for our audience that the views expressed by Thomas Fuchs on today's program do not reflect that of Eli Lilly or its leadership. Do you sell AI products or services? Emerj gives you access through trusted content and real conversations. They are now leading AI brands like NVIDIA and Google Cloud work with Emerj to reach Fortune 500 AI buyers. Download our media kit at go.emerj.com/partner.
That's go.emerj.com/p-a-r-t-n-e-r.
Now the conversation with Thomas.

**Matthew DeMello** (1:38)
Thomas, thank you so much for being with us on today's show.

**Thomas Fuchs** (1:41)
Thank you for having me, Matt. It's really a pleasure to be with you.

**Matthew DeMello** (1:44)
Absolutely. We're seeing pharmaceutical leaders really pushing AI in a lot of different spaces. We've seen things like digital twins at places like Pfizer.
I think these are moving from not just what's front-facing for customers, but now the thought is moving more towards the infrastructure of the very company itself. And a lot of pharmaceutical enterprises have proven that AI can generate value in research and development in very, very, very incremental ways. But scaling those gains has exposed a much harder problem. Most legacy infrastructure was never designed to support that large-scale parallel scientific discovery. For Eli Lilly, and a great pleasure having you on the show to give us an inside look at this, but you're launching a new AI supercomputing platform meant for solving for performance, security and organizational alignment all at the same time while ensuring the technology could support real scientific rigor, not just faster experiments. We're talking today about how Lilly approached those different challenges and why AI infrastructure is becoming a core strategic capability rather than a background IT decision. But just to start off, how is Eli Lilly defining the strategic purpose of the supercomputer within its broader digital and AI transformation roadmap?

**Thomas Fuchs** (3:01)
So Matt, as you know, of course, we are in very interesting times because AI by now is in everybody's language and everybody's mind. I do have a PhD in machine learning from a time when it was not cool yet, so it's very, very nice to see the world change. And to that end, of course, it touches everything in the pharmaceutical value chain. You already mentioned discovery, which is of course a big part, and there we place bets for AI on small molecules, large molecules and genetic medicines. But the beauty is, it of course goes far beyond that. If you think in the clinical space, we would have large language models for medical writing or answering regulatory questions and so forth. And then in manufacturing, we're also building digital twins, but in these cases, it's digital twins of manufacturing processes or machines or robots and so forth. And then you go into discovery and finance. In all these areas, AI plays a big role. And to that end, if you really want to level up in these spaces, you of course need to compute to drive all of that, to drive the exploration, to build larger models, to build meaningful models and so forth, and take advantage of all the data you already have. And Lilly is a very old company. We are 150 years this year, and so there is at least decades of data we can bring to bear to really be at the forefront of building foundation models, frontier models in the discovery space, of very dedicated physical model manufacturing. And the supercomputer is going to allow us to do that in all this space.

**Matthew DeMello** (4:34)
Yeah, the supercomputer itself and your partnership with NVIDIA, which we're going to get to, I think really is part and parcel of these trends that we're seeing, especially from those centuries-old Fortune 100 enterprises, that there's a certain way of going about this, where we can take the benefits that we have from this legacy infrastructure, the data that's there, but also not just have it stand on stilt technology, really get to the heart of these infrastructure systems and modernize them for capabilities in business goals that we'll need today. And we've had folks from Microsoft come on the show and talk about their partnerships with NVIDIA.