The AI boom’s biggest payoff could be better medicines

The AI boom has rapidly expanded from the digital to the physical. Chips, data centers, cloud infrastructure, and new sources of power are being built at extraordinary speed. But what valuable work will this capacity enable?

At the World Economic Forum’s 2026 Annual Meeting in Davos, Nvidia CEO Jensen Huang described AI as a five-layer stack: energy, chips, cloud infrastructure, models, and applications. The first four layers create the capacity. The application layer turns that capacity into products, services, and measurable outcomes.

We think the value AI capacity will enable in two ways: The potential financial and societal return on invested capital. No single industry needs to carry the entire economics of the AI buildout. Returns will emerge across industries and through thousands of products. Yet few fields offer a clearer path from computational progress to economic value and human benefit than medicine.

THE OPPORTUNITY FOR HEALTHCARE

U.S. healthcare spending reached $5.3 trillion in 2024, or 18% of GDP, including $467 billion in prescription drugs. Only a fraction of that total is directly relevant to drug discovery, but the figures show how much money, labor, and human attention are already devoted to treating disease.

A medicine that materially changes the course of a common or serious disease can create value in several directions at once. It can improve patients’ lives, reduce the need for other forms of care, help people remain healthy and productive, and generate substantial returns for the company that develops it and the investors who underwrote it.

Access, affordability, and real-world outcomes determine how widely that value is shared. At its best, however, a successful medicine allows commercial success and social benefit to reinforce one another. Few products have comparable potential to support a durable business while giving people more healthy years of life.

The recent growth of tirzepatide illustrates the scale involved. In 2025, Eli Lilly reported approximately $23 billion in sales for Mounjaro and $13.5 billion for Zepbound, bringing the annual sales of the two brands, based on the same molecule, to roughly $36.5 billion.

Those sales illustrate the commercial scale of a medicine that changes the treatment of a widespread condition. While they say nothing about whether AI will discover the next one, the consequential question is whether AI can make such successes more frequent by reducing unproductive experiments and improving decisions along the way.

There are good reasons to believe it can. Drug discovery consists of a long sequence of decisions under uncertainty, including which chemical scaffold or biological mechanism to pursue, which experiment to run next, which safety signals matter, and which patients are most likely to benefit. AI can help researchers rank possibilities, identify relationships across large and varied datasets, and design experiments that produce more useful information.

THE ROLE OF AI IN HEALTHCARE

AI does not make weak biology or poor data disappear. Scarce data, inconsistent measurements, and the complexity of living systems remain formidable obstacles. The technology is most promising when computational models are connected to high-quality experimental data and laboratories capable of testing predictions quickly.

The most effective AI systems create a learning loop in which each experiment sharpens the next decision, making the quality of that loop far more important than the sheer number of hypotheses a model can generate. Perhaps more excitingly, AI can enable entirely new ways of thinking about the entire drug discovery process and where new medicines might come from.

For example, the advent of AI models for mass spectrometry has enabled our scientists to understand an organism’s chemical code more comprehensively, allowing them to discover a new hormone that might capture the benefits of exercise and develop it into a candidate medicine with positive Phase 1 trial results in just four years. 

The field is also nascent. In an August 2026 Nature Reviews Drug Discovery piece, the leading authors assessed progress over the past decade and concluded that, despite extensive model development and benchmarking, evidence of clinically relevant impact remains disappointingly limited. Among their recommendations was a shift toward evaluating whether AI improves real drug-discovery decisions.

The field should welcome that challenge. It gives AI-enabled drug discovery the right scoreboard: translation.

FINAL THOUGHTS

Drug development has a stubborn failure mode. Results that look compelling in a model or laboratory often do not hold up in people. Generating more molecules and hypotheses at greater speed does little to solve that problem if the resulting candidates simply enter the same attrition funnel.

For executives and capital allocators, the central question is whether AI improves the odds of eventual clinical success at each stage, from early discovery through clinical trials. That requires evidence that models enable better choices of biology or chemistry, that candidates with a higher probability of success advance to the clinic, and that those candidates go on to produce stronger trial results and better patient outcomes. AI can help scientists search chemistry and biology more intelligently, learn faster from every experiment, and direct human judgment toward the questions that matter most.

Few outcomes would better justify the scale of today’s AI investment than medicine because breakthroughs that arrive more often and reach patients sooner give millions of people more healthy years of life, hope, and meaning.

Viswa Colluru is the founder and CEO of Enveda. Bigyan Bista is head of capital formation at Enveda.