Silicon-Driven Health: AI Digital Twins And The US$1 Billion Pharma Deal

The digital twin: From concept to US$1 billion reality

Dr. Michael Grieves first introduced the conceptual model of digital twins at a Society of Manufacturing Engineers conference in Michigan in 2002. He originally called it the “Information Mirroring Model”.

The phrase was coined by NASA technologist John Vickers in 2010. He was collaborating with Dr. Grieves and suggested the name for a NASA technical roadmap to describe the virtual replicas of spacecraft used for simulation and safety.

NVIDIA CEO Jensen Huang is currently the concept’s most well-known advocate after he used it to describe a cornerstone of NVIDIA’s Omniverse and industrial AI strategy at the GTC 2021 keynote. He later expanded this vision at CES 2026, where he declared that “the future of heavy industries starts as a digital twin.”

In a move that expands digital twins’ use cases, NVIDIA and Eli Lilly recently announced a first-of-its-kind, five-year partnership to build a co-innovation lab in the San Francisco Bay Area. This US$1 billion investment focuses on moving drug discovery away from traditional trial-and-error toward a high-speed engineering model.

Under the terms of the collaboration, the lab will utilize NVIDIA’s Vera Rubin chips, the successor to the Blackwell architecture, to provide the massive computational power required for large-scale biological models.

Researchers will use NVIDIA’s BioNeMo AI platform to simulate vast chemical and biological spaces in silico before a single physical molecule is created in a lab.

The collaboration extends into manufacturing, using NVIDIA Omniverse to create digital twins of production lines. This allows Lilly to stress-test supply chains and optimize the manufacturing of high-demand medications, such as GLP-1s and next-generation weight loss drugs.

Twin Health: Reversing chronic disease with digital twins

While NVIDIA and Lilly focus on creating new drugs, Twin Health is using AI to help patients wean off chronic injections.

Twin Health is a precision health company focused on reversing chronic metabolic diseases, specifically type 2 diabetes and related conditions like obesity and hypertension, using AI and digital twin technology. The company was founded by Jahangir Mohammed, a serial entrepreneur who previously founded Jasper, an Internet of Things (IoT) pioneer, which was later acquired by Cisco.

The core of Twin Health’s whole body digital twin technology is creating a dynamic, virtual map of a patient’s unique metabolism by gathering over 3,000 daily data points, including blood sugar, heart rate, sleep and physical activity.

Users wear continuous glucose monitors and smartwatches at home to capture real-time data, paired with a provided smart scale and blood pressure cuff for daily vitals. AI takes this data to build a digital replica of the user’s body’s unique metabolic responses.

No routine clinic visits are required for data collection, though periodic lab work and tele-coaching support the program. Through a mobile app, the AI provides real-time nudges; for example, it might tell the wearer that a 15-minute walk now will stabilize a blood sugar spike from their lunch.

On January 12, the company rang the Nasdaq opening bell as new clinical and economic data were released that highlighted the platform’s efficacy in high-cost patient populations. Central to this milestone was the Cleveland Clinic-led Randomized Controlled Trial (RCT), originally published in the New England Journal of Medicine Catalyst on August 20, 2025.

The study demonstrated that 71 percent of participants achieved type 2 diabetes reversal, defined as a level of hemoglobin A1C below 6.5 without the use of insulin or other glucose-lowering medications, with the exception of metformin, a low-cost, common first-line drug. Crucially for today’s market, the data showed that 85 percent of users were able to eliminate high-cost GLP-1 medications, such as Ozempic and Wegovy, while maintaining optimal blood sugar levels.

Market analysis: The payer revolt and the shift to value

The GLP-1 drug class rapidly transitioned from niche diabetes medications to multi-billion dollar blockbusters for obesity. From 2018 to 2023, researchers found that spending on GLP-1s in the US rose by more than 500 percent to reach US$71.7 billion. Sales are projected to reach US$100 billion by 2030.

In a fierce race to meet skyrocketing demand that outstripped production capacity, Eli Lilly and its main competitor in this space, Novo Nordisk (NYSE:NVO), committed massive investments. Lilly invested US$9 billion into API production, while Novo Nordisk matched this with a US$11 billion investment in facilities across Denmark and North Carolina.

Now, both companies are chasing affordability via direct-to-consumer deals and 2026 oral pills as payers raise plan costs or restrict access. AON’s Global Medical Trend Rates Report for 2026 projects 9.8 percent hikes in employer plan costs from GLP-1s and utilization surges, as Mercer’s Survey on Health and Benefit Strategies for 2026 shows 77 percent of large employers targeting GLP-1 costs, with coverage growth stalling amid restrictions.

The payer revolt is fueling Twin Health’s rise, marked by its US$53 million August 2025 raise for Fortune 500 expansion. Twin Health’s performance model pays on outcomes, delivering an estimated US$8,000 savings per high-cost member.

Big Pharma is betting on AI not just to sustain blockbuster growth but to reinvent the discovery engine amid exploding development costs. At Davos, Nvidia CEO Jensen Huang illustrated this shift bluntly: “Three years ago, most of their R&D budget…was probably wet labs,” Huang said. “Notice the big AI supercomputer that they’ve invested in, the big AI lab. Increasingly, that R&D budget is going to shift towards AI.”

This comes as the pharmaceutical sector comes under pressure to justify hundreds of billions in R&D spending, where Phase I candidates still face a roughly 90 percent failure rate before approval. Eli Lilly could lower the cost of drug failure by embedding NVIDIA’s Vera Rubin chips into a 24/7 learning loop.

The divergence between NVIDIA’s pharmaceutical supercomputer and Twin Health’s metabolic reversal tech captures 2026’s market trend pivot from AI experimentation to proven ROI. Deloitte’s 2026 US Health Care Outlook emphasizes that the industry is moving away from theoretical models in favor of scaling AI to realize measurable financial impact.

Investor outlook

Payers requiring measurable ROI are pushing healthcare innovators to prove value, whether by improving drug discovery or reversing chronic disease.

This tension shapes investment strategies, too. Paul MacDonald, CIO at Harvest ETFs, welcomes AI’s momentum while highlighting GLP-1’s staying power in the firm’s HHL ETF.

“AI in healthcare is very exciting, and we see practicable applications being deployed across many fields, most notably in the diagnostics areas, but increasingly in biopharma research and medical devices.

“As exciting as technology like wearables and designing more personalized lifestyle plans is, we continue to believe that the broader obesity drug classes and markets will continue to grow significantly in the coming years.

MacDonald points to expanding Medicare access and oral formulations as key drivers, even as payers tighten restrictions.

“The systemic benefits and significant health benefits beyond weight loss from the drugs (are) resulting in expanding adoption, and broader coverage affording larger patient cohorts to access the drugs. Currently, there are pilot plans to expand access for Medicare (enrollees) in the USA later this year, which (will expand) the prescription volume potential significantly.

“In addition to the traditional subcutaneous injection, oral options are increasing in availability, and that not only increases the potential for broader adoption but also improves the overall cost structure and margins for the companies with established production facilities.”

MacDonald’s balanced allocation of AI excitement alongside GLP-1 conviction captures a new reality: in 2026, life sciences investors are navigating a complex landscape defined by more variables than ever before.