“The potential to create or manipulate life with AI could reinvent nearly all of biology, but it also carries inherent risk. We’ve seen just a taste of this on the natural-language and chatbot side. Giving AI the power to generate and create life carries a certain level of responsibility.”
— Eric Nguyen, Co-founder and CEO, Radical Numerics
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Eric Nguyen is the co-founder and CEO of Radical Numerics, an AI research lab that has raised $50 million to train models directly on biological data. Before starting the company, Eric helped develop Evo and Evo 2, large-scale genome language models trained on unlabeled DNA sequences. Radical Numerics is now building models that can connect information across DNA, RNA, proteins, epigenetics, and other parts of biology, rather than treating each as a separate problem. Researchers have already used Evo to generate viable bacteriophage genomes, and Eric says Radical Numerics’ newer model, Omnii, matched key findings from two years of Alzheimer’s wet-lab research in a matter of days. He also believes these tools could make it easier to create dangerous pathogens, which is why the company is working on both biological design and biodefense.
In our conversation, we explore:
What AI models can learn by treating DNA as a language
Why reading scientific papers is not the same as learning directly from biological data
How Eric’s unusually free-range childhood shaped the way he follows his curiosity
Why biology may have more useful data than researchers know how to use
How Radical Numerics plans to connect information across DNA, RNA, proteins, and other biological systems
Where the company sees early opportunities in drug discovery, diagnostics, synthetic biology, and biodefense
Why testing AI-generated biology in the lab is still slow and difficult
How models that design biological systems could also help detect dangerous or manipulated pathogens
How to make powerful biology models safer without eliminating the capabilities that make them valuable
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Explore the episode
Timestamps
(00:00) Intro
(03:35) An overview of Radical Numerics
(06:35) From protein models to modeling all of biology
(11:08) Why they started with DNA
(15:04) The process of mapping DNA as a language
(19:47) What’s unknown, and how we learn from novelty
(26:24) The limits of language models in biology
(31:15) Eric’s free-range upbringing and path to his PhD program
(41:20) Applying long-context models to DNA and meeting his co-founders
(46:36) Biology’s untapped data opportunity
(49:02) Why biology needs multimodal AI
(55:30) How better general LLMs benefit Radical Numerics
(57:19) The challenges of biological verification
(1:02:05) Making biology more concrete
(1:04:51) Radical Numerics’ strategy and early use cases
(1:07:26) Balancing safety with capable AI models
(1:15:47) What success in biodefense looks like
(1:18:09) Final meditations
Follow Eric Nguyen
LinkedIn: https://www.linkedin.com/in/nguyenstanford
Website: https://erictnguyen.com
Resources and episode mentions
Books
Biohazard: The Chilling True Story of the Largest Covert Biological Weapons Program in the World--Told from Inside by the Man Who Ran It: https://www.amazon.com/dp/0385334966
Blitzed: Drugs in the Third Reich: https://www.amazon.com/Blitzed-Norman-Ohler/dp/1328915344
People
Greg Brockman on LinkedIn: https://www.linkedin.com/in/thegdb
Eric Horvitz on LinkedIn: https://www.linkedin.com/in/erichorvitz
Other resources
Radical Numerics: https://www.radicalnumerics.ai
Genome modelling and design across all domains of life with Evo 2: https://www.nature.com/articles/s41586-026-10176-5
AlphaFold Protein Structure Database: https://alphafold.ebi.ac.uk
A new frontier in generative genomics with Omnii: https://www.radicalnumerics.ai/blog/omnii-health-preview
On the Red Circle: What it’s really like to give a TED talk: https://erictnguyen.substack.com/p/on-the-red-circle-what-its-really
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