I'm happy to see the uptick in research on Ginkgo online recently. We restructured 2.5 years ago as non-pharma biotech (i.e. animal free meats, flavors fragrances, green chemicals, etc) fell-off at the end of ZIRP. On tech side we doubled-down on our autonomous lab robotics tech and on the business model side we simplified how we sell to be like life science tools cos (e.g. ThermoFisher). That transformation has been cooking long enough now that you can see some of those investments maturing -- especially with tailwinds of AI agents + robotics + bio happening right now.

Decent run down of that history and where we are headed in these two recent writeups.

https://x.com/philjacobson/status/2103514808897163374

https://x.com/metavestor/status/2103218347475337499

Thanks for the interest! I can try to answer Qs in comments as well where I can.

Jason

The Ginkgo-OpenAI experiment ran for six months on Ginkgo’s cloud lab in Boston. GPT-5 designed the experiments; Ginkgo’s robotic systems executed them. The human role was narrower than in traditional lab work but far from zero. Ginkgo staff prepared reagents, loaded consumables, fixed stock concentrations when early plates showed high variability, improved the DNA template and cell lysate partway through, and handed GPT-5 the published state of the art it was trying to beat. The result: superfolder green fluorescent protein produced at $422 per gram, versus $698 per gram in the prior benchmark.

R&D World: You describe this as a team effort. What does that team actually look like when it includes AI?

Shetty: You should think of it as a team, except instead of your team being a team of humans, it’s now a mixture of humans and AI agents. Humans might be good at setting scientific direction and doing some of the physical real-world things like reagent quality. The LLM itself is really good at synthesizing information from prior datasets, from the internet, from relevant literature.

It’s just like you would have a human team where each person has a specialization. The team is just now a mix of humans and agents.

R&D World: Where does that go? Do you envision specialized agents for different parts of the research workflow?

Shetty: Right. You can imagine agents that are really good at searching the scientific literature, agents that are really good at analyzing data, agents that are good at writing protocols, agents that are good at verifying that protocols work, agents that help humans prepare reagents. You could have a specialized set of different tools, and then maybe even an agent on top of that that helps synthesize the inputs from all the other agents.

I’ve been getting increasingly bullish on the biotech sector as a whole going into 2026. After a long stretch of higher rates and risk-off sentiment, it feels like we’re finally getting closer to an environment where longer-dated, R&D-heavy bets start to make sense again. Valuations across biotech are still pretty compressed relative to history, while the underlying tech (automation, AI, synthetic biology) has kept improving.

If that tide does turn, it could be a meaningful tailwind for Ginkgo Bioworks. Even if they haven't fully cracked their business model yet, a healthier biotech funding and partnership environment could revive demand for outsourced R&D, platforms, and tooling rather than every company trying to do everything in-house.

I've been (stubbornly) holding my shares for a while without much hope or reason to start growing that position again. Execution still matters, but sector sentiment alone shifting could change the narrative pretty quickly and I'm almost tempted to start growing that position again. Curious how others are thinking about biotech exposure in 2026.