![Vivodyne started as an idea between co-founders Andrei Georgescu (left), now Vivodyne’s CEO, and Dan Huh, PhD, professor at UPenn (right), now Vivodyne’s CSO, while Andrei performed his Ph.D. graduate studies in Huh’s bioengineering research lab. [Penn Engineering]](https://www.genengnews.com/wp-content/uploads/2026/08/6a5156e97215b1f019eebbae_65c1501946fecb29765d655f_Dan-Huh-and-Andrei-Georgescu_sm.jpeg "6a5156e97215b1f019eebbae_65c1501946fecb29765d655f_Dan-Huh-and-Andrei-Georgescu_sm")
Vivodyne reports that it has launched “the world’s largest human biological datacenter,” with 12 robotic HIVE laboratories and the annual capacity to perform controlled trials on 3.1 million large human tissues per year—estimated at twice the scale of every clinical trial in the U.S. combined.
![A row of automated human-tissue testing machines in Vivodyne’s Human Datacenter. [Vivodyne]](https://www.genengnews.com/wp-content/uploads/2026/08/A-row-of-automated-human-tissue-testing-machines-in-Vivodynes-Human-Datacenter-1024x683.jpg)
Eight of the world’s largest pharmaceutical companies have paid for early access to the platform, according to Vivodyne, to discover and test new medicines “in humans” before ever testing in people, notes Andrei Georgescu, PhD, CEO and co-founder of Vivodyne.
Together, the TissueDisk and Vivodyne’s robotic HIVE laboratories create something that neither pharmaceutical research nor artificial intelligence has possessed before: a large-scale experimental environment in which the same reinforcement learning technique that has driven the explosion in AI language models can finally be harnessed to learn the workings of our physiology, explains Georgescu.
For pharmaceutical companies, that means learning how actual human tissue responds to a drug while decisions about targets, chemistry, dosing, and safety can still be made, he continues adding that Vivodyne’s approach allows millions of therapeutic interventions to be introduced into living human tissues, and their causal biological consequences measured directly with the most advanced, paired-data modalities available today: 3D scanning, transcriptomic sequencing, and deep proteomic analysis.
Vivodyne’s platform provides the foundation of the first world model of human biology, claims Georgescu. Previously, the controlled experiments required to reveal complex, physiological cause-and-effect could not be performed safely in patients or at nearly the needed scale, and Vivodyne makes those experiments possible in living human tissue outside the body, he says.
“Superintelligence in biology is needed more than ever, because we’re running out of diseases that can be cured with the simple, single-target medicines of today,” states Georgescu. “You cannot fix a car by turning a single screw, and the idea that the complex malfunctions in cancer, fibrosis, autoimmune disorders, or neurological disease can be fixed with a conventional single-target drug is wishful denial. To create AI that understands our intricate human biology, we need to continuously generate and train on huge amounts of human data, and we can’t get that by risking people. So, we grow these functional human tissues by the millions instead; large living tissues that grow their own blood vessels and immune cells and all the structures of native tissues. They mature, get diseases, bleed, scar, and, at huge scale, we learn how to make them heal.
“Every human response gives our AI something it cannot learn from a paper or a simulation: a living substrate to poke so that it can learn, from richer data than has ever been gathered, how it pokes back. At Vivodyne’s scale of automated human-tissue trials, all those learned consequences together become the training landscape for a world model of the human body, and the physical evidence that a pharmaceutical company needs before a drug is brought to patients.”
22 human organ systems grown
Vivodyne grows over 20 types of different human organ tissues, both healthy and with patient-linked diseases, including liver, lungs, gut, bone marrow, pancreas, kidney, eyes, lymph nodes, and more, with disease-specific versions spanning fibrosis, site-specific solid tumors, inflammation, metabolic disorders, vascular disease, and countless others. The company trains causal, multimodal AI models on the experiments conducted within each organ type, alone and combined.
Connecting those models across organ systems builds a world model of the human body that can answer what happens when a pair of receptors is drugged, a biological pathway is interrupted, a therapy causes an unexpected side effect, how cells respond and communicate, and whether disease is aggravated, stopped, or reversed, points out a company official. These predictions can then be real-world tested at scale to confirm what actually happens in human tissue, and then refined and advanced.
