Could human tissue models help researchers spot a drug’s risks and benefits before clinical trials? And can AI learn to interpret the physical world across factories, machines, and sensors? This episode explores both questions through conversations with Vivodyne’s Andrei Georgescu and Archetype AI’s Nick Gillian.
Georgescu describes Vivodyne’s approach to vascularized tissues, robotic experiments, multi-omic measurement, and foundation models. Gillian explains how Archetype AI’s Newton combines data from radar, cameras, lidar, and time series to detect anomalies, anticipate safety events, and support real-time control. The hosts connect these technical questions to broader debates about product safety, alignment, AI agents, CRISPR research, and the prospect of useful biological breakthroughs before complete scientific understanding.
Show Notes
Vivodyne’s Andrei Georgescu explains how vascularized human tissue models, robotics, multi-omic measurement, and foundation models could improve drug testing before clinical trials. Archetype AI’s Nick Gillian discusses Newton, a Physical AI model that combines sensor data to understand and predict events in the real world; the hosts also examine AI product safety, OpenAI and Anthropic’s alignment philosophies, CRISPR research, and whether AI can deliver useful advances in biology before it is fully understood.
Chapters
(0:00) AI safety has two different meanings.
(0:15) Why drug testing needs human tissue.
(3:20) Physical AI is bigger than robots.
(5:01) Biology’s bar is lifesaving progress.
(11:17) Opening and AI news
(11:38) Jensen Huang’s safety argument
(13:24) The “just software” debate
(15:31) Why AI companies must mature
(21:12) Muse and product safety
(29:25) Testing AI agents in the real world
(29:35) Product safety versus existential risk
(29:45) The alignment philosophy problem
(38:34) OpenAI versus Anthropic
(40:36) When safety concerns converge
(41:55) Human tissue models
(44:12) Why organoids need realism
(47:35) Measuring tissue responses
(54:03) Mapping causal biology
(1:03:40) Improving drug development
(1:09:08) Foundation models and experiments
(1:12:18) TissueDisk and robotic labs
(1:12:28) Before clinical trials
(1:30:37) What is Physical AI?
(1:32:25) Beyond robots and cars
(1:35:33) Training on physical data
(1:38:04) Cleaning and aligning sensor data
(1:40:34) Human and machine outputs
(1:49:30) A river construction case study
(1:53:28) How Newton finds hidden patterns
(1:57:33) Zero-shot sensor adaptation
(2:01:16) Industrial impact and expert knowledge
(2:05:22) From machines to ecosystems
(2:14:10) Physical agents and superintelligence
(2:15:34) The big AI-biology question
(2:21:55) Specialists versus generalists
(2:23:39) Distilling frontier models
(2:33:33) AI discovers a CRISPR sequence
(2:39:22) Useful biology before theory
(2:45:10) From job displacement to progress
(2:46:44) AI diffusion and economic growth
Guests:
Andrei Georgescu — CEO, Vivodyne (𝕏 | LinkedIn)
Nick Gillian — CTO, Cofounder, Archetype AI (𝕏 | LinkedIn)
