BMS expands AI footprint with Nvidia, aiming for ‘most powerful AI factory in life sciences’

In July 2026, Biopharmaceutical leader Bristol Myers Squibb scaled NVIDIA’s compute capacity to create the life sciences industry’s most powerful privately owned AI system. Bristol Myers Squibb (BMS) has scaled its NVIDIA-powered AI infrastructure to create what it describes as the ‘most powerful AI factory in life sciences’.

BMS possesses one of the life science industry’s most powerful AI systems, which is also powered by advanced NVIDIA technology, in order to support its research and development program. This second system will deliver up to ten times greater performance per megawatt than its predecessor. The system will also deploy an NVIDIA DGX SuperPOD with DGX Vera Rubin NVL72 systems, creating the life sciences industry’s most powerful and energy-efficient privately owned NVIDIA infrastructure.

AI-enabled drug discovery is one of the leading use cases for gen AI in healthcare. BMS has always been at the forefront of this movement since building its first NVIDIA-powered DGX SuperPOD three years ago. AI agents that automate target identification and validation save researchers weeks of manual work. This allows them to focus on higher-value scientific decisions. The company’s ‘Predict First’ approach uses AI to guide experimental design before laboratory testing. This informs every small-molecule program and most large-molecule programs as part of an integrated drug development strategy.

The expanded agreement has also supported the company’s research vision of ‘hybrid intelligence’, where AI co-scientists work alongside human researchers to improve workflows and outcomes. AI systems at BMS handle complex, data-intensive tasks. This allows scientists to focus on strategy, interpretation, and decisions that require human expertise. The cluster also provides the computing power behind this approach, supporting foundation models trained on decades of proprietary data. It also facilitates AI-driven workflows that allow researchers to test hypotheses at a scale previously impossible.

BMS’s expanding AI infrastructure also follows its strategic partnership with Anthropic, announced in May 2026. The AI firm’s Claude agent is set to be deployed across research, clinical development, manufacturing, commercial, and corporate functions. Aiming to transition AI use from conversational tools to agentic capabilities built into day-to-day workflows, the BMS-Claude partnership aims to accelerate software engineering with Claude Code.

Impact on the Healthcare Market

AI is set to address healthcare’s major challenges, which include the increasing demand for complex care, rising rates of chronic illness, a shortage of healthcare workers, and the explosion of data and technology tools. Integrating AI into clinical workflows can improve patient outcomes by early disease detection and speeding up drug discovery. AI's capacity to analyze complex datasets enables the identification of subtle disease indicators, allowing for timely intervention. 

Additionally, AI-driven drug discovery can help accelerate the development of new treatments, bringing them to patients more rapidly. As AI continues to develop, its potential uses in healthcare are also set to grow. From predictive analytics to personalized medicine, AI's transformative potential is just the start. However, the successful integration of AI into healthcare will depend on ongoing collaboration among technology developers, healthcare providers, and policymakers alike. 

Impact on the Smart Healthcare Market

The global smart Healthcare market size accounted for USD 360.02 billion in 2025 and is projected to reach USD 1,695.90 billion by 2035, growing at a CAGR of 16.76% between 2026 and 2035.

According to Precedence Research, healthcare organizations are increasingly adopting smart technologies to enhance diagnosis, streamline workflows, and reduce treatment costs. Rising demand for connected healthcare services and growing use of artificial intelligence and Internet of Things technologies are contributing to the market expansion.

Healthcare artificial intelligence is revolutionizing medicine by improving diagnosis, treatment, and efficiency. It aids in disease detection by interpreting medical images and analyzing patient data, thus facilitating the formulation of personalized treatment plans. It optimizes workflows by taking away administrative burdens and streamlining clinical operations so that one can have cost-effective remedial approaches toward patient care, including remote monitoring, risk alerting, and virtual assistance. AI also speeds up drug discovery, cutting drug development costs and introducing innovations that lead to cost reduction and improved outcomes for the healthcare network.

Impact on Artificial Intelligence (AI) in the Precision Medicine Market

The global artificial intelligence (AI) in precision medicine market size was accounted for USD 3.15 billion in 2025 and is anticipated to reach around USD 60.24 billion by 2035, growing at a CAGR of 34.33% from 2026 to 2035.

According to Precedence Research, the market is experiencing significant growth due to the increasing demand for personalized treatments. Moreover, the increasing prevalence of various life-threatening diseases and advancements in genomics and multi-omics technologies contribute to market expansion.

The ever-increasing need and demand for tailored medications, the growing number of people living with various types of diseases, such as respiratory illnesses and heart diseases, and rapidly increasing R&D spending globally, combined with the large number of companies that are progressively adopting collaborations and partnerships as key market strategies, are key factors driving the global market growth.

Expert Opinion

Robert Plenge, Executive VP and Chief Research Officer at Bristol Myers Squibb, says:

“Drug discovery is a sequence of decisions made under uncertainty, and better decisions come from better evidence, faster.

“This infrastructure lets us learn from every experiment and every clinical readout to sharpen the next hypothesis, allowing BMS scientists to spend less time on manual work and more time on the questions that require human judgment.

“The goal isn’t speed for its own sake; it’s raising the probability that each program we advance is the right one.”