Autonomous science. Founder and CEO at Potato (@readysetpotato). Former neuro at Brown, NIH, UCSD.

San Diego, CA
Joined October 2012
We're building agents for autonomous science. Closed-loop, faster iterations, more discovery, less time. Massive human scientist + AI scientist collaboration.
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Nick Edwards retweeted
I have a lot of respect for @nickcammarata and Goodfire, but I will add some thoughts from brains/neuroscience. First, polysemanticity and distributed representations are ~widely~ seen in biological brains. (In some ways this is what I have spent my career on.) 1/
interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works already-clean.goodfire.com/
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🥔
Emma's is the right take. The interesting question is not "can lab automation replace literally everything we do by hand?" The interesting question is "what new experiments can we perform with automation that could never be done by hand?" And for my money the best automation-only experiments are not going to be the ones that simply require lots of scale and replicates. I'm more curious about the way AI enables rapid real-time adjustment of experimental conditions in response to data. I want to see tight automation loops where an AI is making significant changes to the protocol 10 times a day. Not just changing the pipetting volumes on a few experimental inputs - totally refactoring a protocol the way that a human would.
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It’s happening
New announcements by @mkratsios47 $2.4B in compute commitments from Genesis Mission partners $100M investment from @NSF & @ENERGY for autonomous science ready instrumentation BioHub / @ENERGY / @NIH partnership for data Too many more to keep track this quickly. It’s a lot!
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Nick Edwards retweeted
New announcements by @mkratsios47 $2.4B in compute commitments from Genesis Mission partners $100M investment from @NSF & @ENERGY for autonomous science ready instrumentation BioHub / @ENERGY / @NIH partnership for data Too many more to keep track this quickly. It’s a lot!
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Karl Deisseroth is also REALLY good at ping pong. Sometimes the world is unfair
BREAKING NEWS The 2026 #NobelPrize in Physiology or Medicine has been awarded to Karl Deisseroth, Peter Hegemann and Georg Nagel “for their discoveries concerning light-gated ion channels and optogenetics.”
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Been waiting for this for years. Well deserved. Wish Ed Boyden was on this list, though.
BREAKING NEWS The 2026 #NobelPrize in Physiology or Medicine has been awarded to Karl Deisseroth, Peter Hegemann and Georg Nagel “for their discoveries concerning light-gated ion channels and optogenetics.”
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Nick Edwards retweeted
Replying to @gregisenberg
Make sure you talk to @Nick___Edwards if you haven't! Also, @AaronBlotnick is a fantastic resource for anything AI + Bio
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Nick Edwards retweeted
More details on our plans in the linked interview. But, this is the gist:
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“AI can’t possibly know everything about biotech because some of it is tacit knowledge you can only experience first hand.” The tacit knowledge:
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I’m a scientist. I have worked in chemical industry R&D for >20 years. People talk about automated wet labs as if they’re the next great AI revolution. But there is a problem. They’re not new. Industry has been automating experiments for decades. Automated analytical chemistry: 1950s. Laboratory robots: 1980s. High-throughput screening: 1990s. Today we automate liquid handling, synthesis, biological testing, plant phenotyping and much more. Pharma and chemical companies have spent decades building the machines, protocols and infrastructure to generate experimental data at scale. So what does AI actually change? Potentially something much more interesting: Which experiment we run next. Generate 1,000 hypotheses. Choose the most informative experiment. Run it. Measure reality. Feed the result back. Choose again. Repeat. That loop could get dramatically faster. And here’s the irony: AI could make decades of investment in wet-lab infrastructure more valuable, not less. Because no matter how intelligent the model becomes, look at what never disappears from the loop: The experiment. AI can accelerate the thinking. Automation can accelerate the testing. But reality still gets the final vote.
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I've spent the past two years thinking about scientific superintelligence, and talking to founders. Dario's post yesterday, and the reactions to it, made me write it all up...
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Every lab will become at least semi-autonomous. Massive opportunity for scientists
In 12 weeks, we built a research facility that is run entirely by AI. AI designs, executes, and observes experiments end-to-end across biology, chemistry, and materials science. We’re introducing SciUniverse: a benchmark that measures AI’s ability to do real-world scientific research.
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I’m a scientist. I need to say this because the AI hype is getting ridiculous. AI can design a molecule in seconds. That doesn’t mean it discovered a drug. It discovered something we scientists have never been short of: Something to test. Someone still has to make it. Run the experiment. Measure whether it works. Check whether it’s toxic. And ultimately prove it works in the real world. AI hype tells us: “Prediction is discovery.” “Simulation is experimentation.” “Generating a molecule is developing a drug.” It isn’t. AI is making ideas incredibly cheap. But every new idea creates something AI cannot generate: Evidence. And the more hypotheses AI produces, the more experiments we’re going to need. That’s the irony nobody seems to be talking about. AI may not make laboratories obsolete. It may make them more valuable than ever. You can speedrun the thinking. You can’t speedrun reality.
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How does one habituate to the taste of glass?
Being a founder is like "chewing glass" "When you spend six months recruiting a candidate and they don't join, that's chewing glass. When you spend a bunch of time working on a product that gets completely washed over by the next model and makes you feel like an idiot for expending all that time and capacity on something that was the wrong call, chewing glass. When you get rejected by 40 investors in a row before you're able to raise capital, chewing glass. My experience of startup building is it's like a rollercoaster where you have to feel the extreme highs and feel the extreme lows, and I'm a super emotional guy. I will feel on top of the world at the high and like everything is cataclysmic at the low. But then if I look back at the journey, the lows get lower, the highs get much higher. And I look back three months ago at the low I was dealing with, I was like, 'What a joke.' I could do that in my sleep now."
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Pan’s labyrinth
can we make robots that look like wall-e and not this demonic shit
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Nick Edwards retweeted
Our cofounders @AnnaMarieWagner and @rwegrzyn sat down with @Nick___Edwards on the Once A Scientist Podcast to chat reproducibility and the business model of science. 🎬youtube.com/watch?v=dGZcbQ80…
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Science is full of art and even some superstition. All the little things we do because we don’t really know what matters for an experiment and what doesn’t. On the new Once a Scientist... episode, Renee Wegrzyn (@rwegrzyn) @transfyrai told me spending hours on-end doing tetrad dissections. Then seeing a postdoc do it with a single, gentle tap of a needle. That's the whole tacit-knowledge problem. Fun conversation: youtube.com/watch?v=dGZcbQ80…
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Nick Edwards retweeted
"There really is no reason that we can't transfer that to scientific domains like biology and make breakthroughs on questions we've struggled with for a long time." Disagree. Math isn't biology. Biology is a complex physical system and we are data-limited, not idea-limited.
Replying to @hilbertspaess
The people building AI earnestly believe that it could kill us all by the end of the decade. This is not a marketing stunt. If anything, many executives and senior researchers will couch their phrasing in the press to sound sensible - but I hear the same people express fear privately. No other human activity poses this level of danger.
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Nick Edwards retweeted
🚀 The Definitive #SFTechWeek Bio-Economy List 2026 is here 🚀 🔗docs.google.com/spreadsheets… 114 bio events. October 5 to 11. @Techweek_ has 1,500+ events. 213 of them are about AI agents. 114 are about biology. I know which ones I'd rather go to. 🫠 Under 8% of the calendar, but it's where the real value is. Like @jpmorgan, @SFClimateWeek, @IAmBiotech, and @SynBioBeta, the side events pile up. Breakfasts, lab tours, hack nights, rooftop launches, pizza, and a 7:30am run. It's enough to make your head spin 💫 But I've got you covered 👍 27 Clinical & Care Delivery 21 Capital & Dealmaking 12 Neuro & BCI 12 Women's Health & Fertility 11 Genomics, Proteins & Omics 11 Human Performance & Wellness 10 Drug Discovery & Trials 6 Industrial & Environmental Bio 4 Longevity & Healthspan Congrats to @KatiaAmeri, @andrewchen, @speedrun and the @Techweek_ team on the biggest one yet. Thanks to the sponsors: @FenwickWest, @HSBCInnovation, @IBM, @Adobe, @awscloud, @Cloudflare, @Google, @TriNet. And to the ones carrying the bio side: @NucleateHQ, @GrailBio, @NFX, @ThatMrE, @BiopunkLab, @merve_isler, @biopioneers, @LexiVentures, and the SF bio community at large.
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