Nick Edwards

We’ve acquired Orchestra Bio, a platform that helps biopharma teams coordinate the complex process of drug development. Together, we’ll build on Rosalind, our AI models and tools for life sciences research, to streamline those operations and help new treatments reach patients sooner. Welcome @ashokaraj_ and team! t.co/vi51OhznQn

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.

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!

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.”

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.”

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.

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."

can we make robots that look like wall-e and not this demonic shit x.com/israeldfirzt/s…

I was avoiding weighing in on this, but does anyone at OpenAI dispute that: (1) They heard rumors of a Millennium Prize breakthrough (the rumors I heard were that it was about Naiver-Stokes specifically, from Anthropic, and under external review. Who knows if they knew this, the blog posts suggests they didn't, but even so...) (2) They then spent millions of dollars in a week-long sprint to... scoop the (unknown) authors who made the breakthrough? Or at least push hard in that direction knowing there was a chance they would do so? (3) They then learned that Levant/Tristan were the authors in question and intended to offer them co-authorship? For a result that was presumably already achieved? Then they learned Levant/Tristan only had a resolution of the forced Euler problem (i.e. a key step on the way to a Navier-Stokes solution, but not the full milestone) so they published alone? If all the above is true, then sure. OpenAI independently found a solution to the Naiver-Stokes equation. But they seemingly did it for maximally icky reasons. I don't know the professional norms in math but something about this reeks of... silicon valley mindset/zero-sum thinking. Why not spend the millions of dollars pursuing a totally new problem, rather than directly competing with progress on a problem that you know is about to be announced, even if you don't know the exact shape or origin of the announcement? Even if you want to explore problems in the region of "millennium prize," why not give it two weeks to see what the pending result is so you can build off of it, not scoop someone, not waste millions on potentially duplicative efforts, etc. Seems like a waste, unless the goal is to beat someone else to the announcement, i.e. "I don't want to live in a world where someone else solves millennium prize problems better than we do."

asked gpt6 to draw a portrait in google calendar

I’ve successfully run the full retained MaleCNS v1.0 fruit fly connectome, all 166,700 neurons, inside Minecraft, with its simulated neural activity driving a fly’s movement. V1 Currently in development. Built with the help of GPT-6 Astra. Props to the @OpenAI team and @thsottiaux for this release. Code and mod coming soon!

New episode with @AdamMarblestone, CEO and co-founder of @Convergent_FROs. Adam is an extraordinary catalyst and accelerant for science: he has influenced or routed billions in funding, he helped start significant companies and scientific subfields, and he is the world’s go-to person for neuroscience roadmapping. This year, the NSF launched a $1.5B X-Labs initiative inspired by the FRO model Adam pioneered. Though he started with neuroscience, today he helps organize scientific endeavors across many fields, including AI, math, biology, climate, astrophysics, and more. Adam proposes that fields like neurotech, whole-brain emulation, cryo, and nanotech are severely limited by capital and coordination — not ideas. Connectomics is a clear case. He argues that mapping the brain's full wiring diagram is the approach most poised-to-scale and still extremely neglected. Costs for a molecularly annotated mouse connectome have come down orders of magnitude, from an estimated $10B to $100M–200M, Adam suggests, with a human connectome perhaps costing around $1B–2B. The cost curve of connectomes is similar to transistors and gene sequencing: once it is low enough, we get extraordinary outcomes for humanity. Near-term applications could pay for it: new drug targets for brain disease, insights for AI development, emulations, and even “control knobs” for mood and focus we haven’t identified yet. In this episode, we go deep on many topics in neurotech and beyond: what the brain can do that computers still can't; what neuroscience could teach AI; how you'd actually map a whole mammal brain; how far today's fly-brain simulations really get toward an upload; what it would take to move mind uploading beyond the fringe or science; where brain-computer interfaces go next; nanotech, reversible cryonics, AI doing its own ML research, and even predictive, agent-based economics. I'm very excited to have Adam on the podcast. Hope you enjoy! Other links to this episode and references below. Chapters 00:00:00 Introduction 00:01:40 What are the grand challenges of neurotech? 00:04:50 What the brain does that computers still can't (on a few bananas a day) 00:14:43 The neuroscience overhang: why brains learn from so little data 00:26:29 How to record and map the brain: DNA "ticker tape," ultrasound, and the unexplored map 00:40:29 Why connectomics is the area most poised to scale 00:47:54 How whole-brain connectomics works, end to end 00:55:27 Simulating the fly brain: how far does it actually get us? 00:57:51 What would it cost to map a mammal's brain? 00:59:55 What will be connectomics' ChatGPT moment? 01:04:35 What a connectome unlocks: disease, drug targets, and the brain's control knobs 01:14:20 Scaling up to the human brain — a faster timeline than expected 01:17:34 Mapping activity, and what the fly connectome has revealed 01:27:55 What you could build with today's connectome 01:35:29 Why uploading may be one of civilization's great cornerstones and transitions 01:42:43 Optimistic visions of a future with uploading 01:49:12 Beyond neurotech: virtual cells, nanotech, and AI-driven science 02:15:29 BCIs today: semi-invasive devices, ultrasound, and what's on the horizon 02:22:44 Why science is capital-and coordination-limited, not idea-limited

Biology's verification loop is months, not seconds. The tight RL feedback that drove LLM progress doesn't exist in bio - every reward signal costs a wet-lab cycle. @genophoria @Mattmcpartlon1 @timshi_ai on what to do about it 👇