Nvidia and Eli Lilly commit up to $1B to AI drug discovery lab

Nvidia’s newest lab partner isn’t a chipmaker or a cloud giant. It’s a drugmaker.

On January 12, 2026, Nvidia and Eli Lilly announced plans to co-invest up to $1 billion over the next five years in an AI co-innovation lab in the San Francisco Bay Area. The goal is to use AI to change how new medicines are discovered and how biomedical research gets done.

The headline figure grabs attention. The structure matters more. This is a joint commitment from two companies, and “up to” signals a ceiling rather than a check already cashed.

What the money is supposed to buy

The core idea is proximity. Teams from both companies will work in the same space instead of trading emails across organizational lines.

Those co-located teams plan to generate large datasets. They will use that data to train foundation models for biology and chemistry.

A foundation model is a large AI system trained on broad data and then adapted for specific jobs. ChatGPT learned language. These models are meant to learn molecules, proteins and the chemistry that connects them.

The training will run through Nvidia’s BioNeMo platform. It will also use Nvidia’s upcoming Vera Rubin architecture, the chip design that follows the company’s current generation of hardware.

The lab also plans to bring robotics and what Nvidia calls physical AI into the mix. Those tools are aimed at three areas: experimentation, manufacturing and supply chain operations.

Physical AI refers to AI systems that act in the real world rather than only on a screen. Picture robotic lab equipment that runs experiments, feeds the results back to a model and lets the model choose what to test next.

That loop sits at the center of the plan. The lab aims to build continuous learning systems for AI-assisted drug discovery that run 24/7, with work set to begin in early 2026.

Nvidia CEO Jensen Huang and Eli Lilly Chair and CEO David Ricks both framed the venture as a bet on AI’s power to transform life sciences. Their pitch combines three ingredients: data, computing power and domain expertise.

Not their first date

This partnership builds on earlier work. In 2025, the two companies set up a supercomputer for Lilly featuring over 1,000 Grace Blackwell GPUs.

The Lilly deal also isn’t Nvidia’s only science play in the US. The company is working with the US Department of Energy on the Genesis Mission, a public-private initiative launched through an executive order in late 2025.

The Genesis Mission aims to double the productivity and impact of American science. Nvidia’s role centers on strengthening AI infrastructure and open-source models across scientific fields, including energy and national security.

The collaboration spans several fronts. These include supercomputers at national labs, digital twins, robotics and materials science.

A digital twin is a detailed virtual copy of a physical system. Researchers can stress-test a reactor, a material or a manufacturing line in simulation before touching the real thing.

The $1 billion commitment belongs mainly to the Lilly lab, not the DOE program. Taken together, though, the two efforts show Nvidia pushing deeper into scientific research on both the corporate and government sides.

What this means for Nvidia, Lilly and AI-driven science

For Nvidia, the deal pushes the company further up the value chain. Selling chips to whoever needs them is a fine business. Embedding engineers inside a pharmaceutical giant’s research process is a stickier one.

If BioNeMo and Vera Rubin become the default tools for training biology and chemistry models at a company of Lilly’s scale, that sets a reference design.

The 24/7 continuous learning setup is the most ambitious piece. If models can design experiments, robots can run them and results can flow straight back into training, the research cycle could compress.

The “up to” wording deserves attention too. Spending commitments framed as ceilings can flex depending on results, priorities and budgets over a five-year stretch.

There are several milestones worth tracking. The first is whether the continuous learning systems actually go live in early 2026 as planned.

The second is the rollout of Vera Rubin, since the lab’s model training depends partly on the arrival of that architecture.

The third is the Genesis Mission. Its goal of doubling the productivity and impact of American science is enormous, and progress on national lab supercomputers and open-source models will show whether the public side of Nvidia’s science strategy keeps pace with the private one.