The Nat'l Security Commission on Emerging Biotech (@biotech_gov) just put out a report on US leadership in BCIs. Some of the bottlenecks are very real and in need of centralized/national efforts, e.g. lack of US strategy & coordination, outdated/slow regulation & reimbursement systems. Others like the technical challenges section overfit to a narrow set of problems facing MEAs/DBS/etc. and don't provide vision on the myriad directions that will/are disrupting the field, (some of which are present in the China 5 year plan).

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

talked to a guy from anthropic for a long time last night. fascinating conversation. & believe it or not, one of their biggest problems is still hiring. there simply aren’t enough exceptional ppl, esp for long term high conviction bets where the stuff is not obvious yet. i.e. people who can independently generate good judgment under extreme ambiguity.