Eli Lilly’s rise to a $1 trillion market valuation marks a defining moment in the global pharmaceutical industry. No healthcare company had crossed that threshold before. Lilly reached it in a year shaped by surging demand for its GLP-1 medicines, expanding AI partnerships, new internal computing infrastructure, and a growing network of research collaborations.
Lilly’s valuation milestone follows unprecedented commercial activity around its GLP-1 portfolio. The company’s weight-loss therapy Zepbound and diabetes treatment Mounjaro continue to experience exceptional demand across markets. These medicines sit within one of the fastest-growing therapeutic categories worldwide, and their uptake has transformed Lilly into the most valuable pharmaceutical company in history.
The company’s record valuation also places renewed attention on the Lilly Endowment, its largest shareholder. The foundation holds more than 92 million shares, valued at roughly $102 billion based on mid-week trading. With that asset base, the Endowment remains the largest private foundation in the United States and continues to fund education, community development and religious initiatives, especially in Indiana.
In the same period, Novo Nordisk, Lilly’s closest GLP-1 competitor has experienced a 45% decline in share price, driven in part by disappointing results from an Alzheimer’s trial. Lilly’s stock, meanwhile, has climbed more than 37% this year. Analysts expect ongoing expansion in global obesity and diabetes markets, positioning GLP-1 therapies among the industry’s most significant revenue generators.
Transformation in Infrastructure
As Lilly’s GLP-1 portfolio accelerated, the company began reshaping its scientific and technological infrastructure. On Oct. 28, 2025, Lilly announced that it is building what it describes as the most powerful supercomputer owned and operated by any pharmaceutical company. Developed with NVIDIA, the system is designed to support a full “AI factory”—a computing environment capable of handling data ingestion, training, fine-tuning and high-volume inference across Lilly’s research operations.
“Today that requires excellence not just in science but also in technology,” said Diogo Rau, executive vice president and chief information and digital officer at Lilly. “As a 150-year-old medicine company, one of our most powerful assets is decades of data.”
The supercomputer is the world’s first NVIDIA DGX SuperPOD built with DGX B300 systems. It includes more than 1,000 B300 GPUs connected through a unified high-speed network that links compute resources, storage and related systems. According to Lilly, this architecture enables rapid experimental learning and model training at scales previously unavailable inside the organization.
Scientists will be able to run millions of experimental simulations, supporting the development of new AI-driven models for drug discovery. Many of these models feed into Lilly TuneLab, the company’s federated AI and machine-learning platform designed to expand access to advanced discovery tools. TuneLab is already used by external research partners. Its workflows will now incorporate selected NVIDIA Clara open-source models as part of the platform’s ongoing evolution.
AI Extends Beyond Discovery Into Development and Manufacturing
Lilly’s AI strategy spans multiple phases of drug development. According to the company, the new supercomputer will be used to shorten development timelines, create new scientific reasoning agents equipped to support researchers, and expand the use of advanced medical imaging to study disease progression more precisely.
Lilly also outlines plans to integrate digital twins and robotic technologies into manufacturing environments using the new computing power. These tools are intended to improve production efficiency and reduce downtime. The system operates on 100% renewable electricity and uses the company’s existing chilled-water infrastructure for liquid cooling as part of its sustainability commitments.
“The AI industrial revolution will have its most profound impact on medicine,” said Kimberly Powell, vice president of health care at NVIDIA.
“By embedding intelligence into every layer of our workflows, we’re opening the door to a new kind of enterprise,” added Thomas Fuchs, Lilly’s senior vice president and chief AI officer.
Expanding AI-Driven Drug Discovery Partnerships
While constructing its internal computing backbone, Lilly also expanded its external AI collaborations. On Nov. 10, the company signed a deal with Insilico Medicine worth more than $100 million in upfront, milestone and tiered royalty payments. The agreement gives Lilly access to Insilico’s Pharma.AI platform, which provides end-to-end capabilities for pharmaceutical R&D.
Insilico will design and generate compounds for undisclosed targets, delivering candidate molecules as part of the collaboration. The new partnership builds on a 2023 software licensing relationship between the two companies.
“Lilly has been a valued user of our Pharma.AI software suite,” said Insilico founder and co-CEO Alex Zhavoronkov, Ph.D. “This expanded collaboration further recognizes Insilico’s AI-driven drug discovery capabilities.”
Insilico has raised $110 million this year to advance its AI-powered drug design programs and has announced a humanoid laboratory robot designed to learn scientific workflows. Between 2021 and 2024, the company nominated 20 preclinical candidates, with its most advanced compound moving into pivotal trials for idiopathic pulmonary fibrosis following phase 2a results. Insilico lists fibrosis, oncology, immunology, pain, obesity and metabolic disorders among its areas of focus. It also counts Sanofi, Pfizer, Menarini Group and Boehringer Ingelheim as partners.
As Lilly expands its AI capabilities, CEO David Ricks has noted that the industry remains in the early stages of applying advanced models to drug development. “AI still has a way to go when it comes to helping drug development,” Ricks said. “Probably we need to create the equivalent of what got created with human language, which is a more complete repository of biological knowledge to train against before the machines get a lot better. And today, I don’t know, I would estimate we might know 10 to 15% of human biology, so the machine is not going to be good at all until we get way above 50%.”
Ricks added that reaching that level will require major advances in automated experimentation. “To even reach that point,” he said, “there would need to be a significant investment in robotics to create the training data needed to teach AI.”
Lilly TuneLab Opens AI Models to Early-Stage Biotechs
Lilly has been simultaneously scaling TuneLab, its AI and machine-learning platform for early-stage biotechs. On Sept. 9, the company announced the platform’s formal launch, offering access to models trained on research data that Lilly says cost more than $1 billion to generate.
“Lilly TuneLab was created to be an equalizer so that smaller companies can access some of the same AI capabilities used every day by Lilly scientists,” said Daniel Skovronsky, chief scientific officer.
Circle Pharma and Insitro are among the participating companies. Circle will use TuneLab to develop cancer therapies, while Insitro will build new AI models for small molecule discovery.
TuneLab operates on datasets representing experimental results across hundreds of thousands of unique molecules. In return for access, biotech partners contribute training data that can be used to improve the platform’s capabilities.
Eli Lilly ends the year as the first healthcare company to reach a $1 trillion valuation, a period defined by record demand for its GLP-1 medicines and a series of AI initiatives that expand its research and technology infrastructure. The company has announced the construction of a large-scale NVIDIA DGX SuperPOD supercomputer, the launch of an “AI factory” to support data processing and model training, and new collaborations through TuneLab, its federated machine-learning platform.
Thomas Fuchs, Lilly’s chief AI officer, described the company’s approach in clear terms: “Lilly is shifting from using AI as a tool to embracing it as a scientific collaborator. By embedding intelligence into every layer of our workflows, we’re opening the door to a new kind of enterprise: one that learns, adapts and improves with every data point.”
