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

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.

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 👇