AI Got Good at Language. Now It’s Learning the Language of Life. (Eric Nguyen, Co-Founder and CEO of Radical Numerics)

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

—

Thank you to the partners who make this possible

Ahrefs Brand Radar: Find your brand in AI results.

Brex: The intelligent finance platform.

Guru: The AI source of truth for work.

—

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

X: https://x.com/exnx

Website: https://erictnguyen.com

—

Resources and episode mentions: https://www.generalist.com/p/ai-got-good-at-language-now-its-learning

—

Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.