No Priors Ep. 34 | With Ginkgo Bioworks Co-Founder and CEO Jason Kelly
Elad Gil (host), Jason Kelly (guest), Sarah Guo (host), Narrator
In this episode of No Priors, featuring Elad Gil and Jason Kelly, No Priors Ep. 34 | With Ginkgo Bioworks Co-Founder and CEO Jason Kelly explores programming Living Cells: AI, Synthetic Biology, and Future Biosecurity Infrastructure Jason Kelly, co‑founder and CEO of Ginkgo Bioworks, explains synthetic biology as treating DNA like code, while emphasizing how messy, physical, and fundamentally different biological systems are from digital computers.
Programming Living Cells: AI, Synthetic Biology, and Future Biosecurity Infrastructure
Jason Kelly, co‑founder and CEO of Ginkgo Bioworks, explains synthetic biology as treating DNA like code, while emphasizing how messy, physical, and fundamentally different biological systems are from digital computers.
He describes Ginkgo’s core model: a highly automated ‘foundry’ that runs large-scale biological experiments, generates proprietary data, and lets in‑house ‘DNA programmers’ design cells for customers across pharma, agriculture, and industrial applications.
Kelly outlines how foundation models for proteins, built on Ginkgo’s experimental data and partnerships like its Google deal, could let AI rapidly ‘speak’ biology and outperform humans far sooner in biological domains than in natural language tasks.
The conversation also covers pandemic preparedness and biosecurity, advocating a “cybersecurity for biology” model with global genomic surveillance, rapid vaccine response, and employee-controlled corporate governance for powerful bio platforms.
Key Takeaways
Treat DNA as code, but respect biology’s physical and unpredictable nature.
While DNA resembles digital code, biological systems are noisy, non-deterministic, and physically embodied, so engineering them requires tolerating uncertainty and leveraging probabilistic and evolutionary approaches rather than strict logical debugging.
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Scale and automation in wet labs are crucial to unlock AI for biology.
Ginkgo’s robotic ‘foundry’ turns manual experiments into high-throughput, standardized processes that generate large, consistent datasets—exactly what’s needed to train effective AI models for protein engineering and cell design.
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Foundation models that ‘speak protein’ could rapidly outpace human intuition.
Because humans did not evolve to read or design biological code, AI models trained across massive protein datasets may achieve superhuman performance in understanding and generating functional proteins much earlier than they do in human-language tasks.
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A horizontal “AWS for cells” model challenges today’s vertically integrated biotech.
Ginkgo is betting that many organism-engineering tasks share enough commonality to justify shared infrastructure and platforms—contrary to the prevailing belief in pharma and ag that every product requires its own bespoke tech stack.
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AI‑discovered drugs are bottlenecked more by data than by compute.
Large models are now feasible, but high-quality, sufficiently rich biological and clinical datasets—especially those tied to outcomes like efficacy and safety—remain scarce and fragmented, slowing full-stack AI drug discovery.
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Pandemic defense should mirror cybersecurity: continuous monitoring plus rapid patching.
Kelly advocates building global ‘bio-radar’ via wastewater and travel surveillance, combined with rapid-response vaccine and countermeasure platforms, to detect and contain outbreaks early—treating infectious disease like a constantly managed security risk.
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Embedding governance with workers, not just founders or capital, is a deliberate safety choice.
By giving all current employees super‑voting shares, Ginkgo aims to ensure that those closest to the work—and answerable to real-world social consequences—have ultimate control over how a powerful biological platform is used.
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Notable Quotes
“The core idea of synbio is that it runs on code, and then what can we bring over from programming into this world that actually sticks?”
— Jason Kelly
“In biology, sometimes you can run the bug to ground and sometimes it’s just a part of the biology we don’t understand well enough that’s broken—and you’ve gotta stomach that.”
— Jason Kelly
“We think these computer brains are gonna kick our ass a lot faster in this domain than they do in English.”
— Jason Kelly
“We would not allow our computers to be as exposed to viruses as we allow ourselves to be.”
— Jason Kelly
“Who should control the platform? Humans. Not divorced capital… the theory was that the workers, because they’re humans and actually work there, are a good group to give governance to.”
— Jason Kelly
Questions Answered in This Episode
How far can the analogy between programming DNA and programming software realistically be pushed before it breaks down, and what are the most important limits?
Jason Kelly, co‑founder and CEO of Ginkgo Bioworks, explains synthetic biology as treating DNA like code, while emphasizing how messy, physical, and fundamentally different biological systems are from digital computers.
Get the full analysis with uListen AI
What specific types of biological data would most accelerate AI-driven drug discovery and protein design if they were generated or shared at scale?
He describes Ginkgo’s core model: a highly automated ‘foundry’ that runs large-scale biological experiments, generates proprietary data, and lets in‑house ‘DNA programmers’ design cells for customers across pharma, agriculture, and industrial applications.
Get the full analysis with uListen AI
How could a global ‘bio-radar’ network be governed and funded in a way that respects privacy, sovereignty, and equity while still being fast and effective?
Kelly outlines how foundation models for proteins, built on Ginkgo’s experimental data and partnerships like its Google deal, could let AI rapidly ‘speak’ biology and outperform humans far sooner in biological domains than in natural language tasks.
Get the full analysis with uListen AI
If AI becomes superhuman at ‘speaking biology,’ how should we think about responsibility and control over designs that no human fully understands?
The conversation also covers pandemic preparedness and biosecurity, advocating a “cybersecurity for biology” model with global genomic surveillance, rapid vaccine response, and employee-controlled corporate governance for powerful bio platforms.
Get the full analysis with uListen AI
What risks and failure modes does Ginkgo see in its employee super‑voting governance model, and how will it adapt if those emerge in practice?
Get the full analysis with uListen AI
Transcript Preview
(instrumental music plays) Biology is undergoing a digital revolution as we build developer tools and production infrastructure for synthetic biology. How will it change industries? How does it intersect with AI, and how do we rethink biosecurity? This week, Sara and I are joined by Jason Kelly, co-founder and CEO of Ginkgo Bioworks, to discuss their goal of making cells as easy to work with as computers, their data strategy, and the tech keeping the next pandemic at bay, and in general, what cell programming will do for the future of food, medicine, and agriculture. Jason, thanks so much for joining us today.
Yeah. Thanks for having me on.
So I think there's a lot of talk about synthetic biology and how biology and DNA and proteins are effectively just code and you can manipulate them in different ways now and things like that. I'd love to just get your view of both what Ginkgo does as well as what does synthetic biology actually mean?
So the... I think the founding idea of synthetic biology is that DNA is code, right? And inside of cells are A, Ts, Cs, and Gs, essentially on, like, a tape, and it, and it is very, like, surprisingly analogous to zeros and ones, uh, you know, inside memory in a computer. Th- that's roughly where the similarities end. Okay? Like, once you get to the next step of what the cell does with that code, we are in a totally different world. It- it- it is not virtual, is the first thing, right? It is a physical thing. The code itself is literally physical, right? It is a polymer, uh, and it is going to use that to make proteins, which are basically little pieces of nanotechnology, and they're all gonna be bumping into each other, and it's all crazy. It's not physically isolated like you would imagine with a semiconductor chip. It's not built by humans. So you have this really interesting thing where the hook is there for people in tech to engage with biology, but then once they get in, they're like, "What the fuck?" Uh, and, and-
(laughs)
... and so, and so, like, I'm happy to talk about those pieces, but I, I think you're right. The core idea of synbio is that it runs on code, and then what can we bring over from programming into this world that actually sticks? And so I think what synthetic biology has been, you know, really since it got going, I met, um, you know, the founders of Ginkgo back when we met at MIT in 2002. That was, like, early days of synbio. It's about 20 years now. It's basically engineers asking the question of what can they bring over into biology that's actually gonna work? And some stuff has been left by the wayside and some things do work, and the, and the latest technology that's being tried now is AI.
C- can you walk us through what you actually think does transfer over and then where there are one or two unique challenges and then how does AI help to solve for s- some of those things?
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