Every job i’ve gotten in tech has been through X and I’ve always wanted a way to track/refer my network on here. Excited about @cosign and what @david__booth @eriktorenberg and co are building. Check out the companies I think you should join: cosign.co/lists/sonofalli/co…

One of the best bubble indicators from @pmarca -- where Harvard and Stanford MBAs go after graduation. If they go into tech → market’s overblown. If they go back to banking → great time to invest in startups. Social status of MBAs is a good bubble indicator. Cool trivia!

The evolving landscape of drug targets nature.com/articles/s41573-0… rdcu.be/22o1FPpNJ3Ga In the past 25 years, advances in areas such as genomics and the diversification of therapeutic modalities have expanded the drug target landscape, which now includes ~700 targets mapped here

New York City is rolling out public toilets that only give you 10 minutes to finish An announcer warns if time is running out before the door slides open, potentially exposing users to the public

>be me >discover effective altruism >apparently normal charity is inefficient >why donate to random sad thing when spreadsheet can tell you optimal sad thing >fair enough >buy mosquito nets >save lives >numbers look good >feel powerful >couple years later >someone asks an innocent question >why only count people alive today >huh >future people matter too >obviously >my grandchildren shouldn't matter less just because they haven't spawned yet >reasonable.jpg >keep following logic >what about their grandchildren >also yes >what about people in 500 years >sure >5000 years >why not >500 million years >starting to get weird but morality is morality >open calculator >humanity could survive for an astronomically long time >could colonize galaxy >could have trillions upon trillions of descendants >maybe digital people too >maybe simulated civilizations >maybe dyson spheres full of happy uploaded minds >calculator starts smoking >realize currently living humans are rounding error >8 billion people suddenly looking extremely beta >future contains potentially 10^something people >can't even fit beneficiaries in google sheets >new moral priority unlocked >protect the long-term future >stop thinking in units of "people helped" >start thinking in "fraction of cosmic endowment preserved" >malaria? >terrible >but only kills existing humans >AI extinction could delete the entire light cone >nuclear war could permanently derail civilization >bad institutions could lock in terrible values for ten million years >someone invents wrong constitution in 2140 >quadrillions suffer >better fund governance workshop now >friend says maybe we should improve hospitals >explain opportunity cost >friend says hospitals are full of actual sick people >explain scope sensitivity >friend stops inviting me to dinner >need to decide what to fund >easy >expected value >suppose project has one in a million chance of preventing extinction >sounds tiny >but extinction destroys 10^50 future lives >multiply >mother of god >$10 million project has expected value of several galaxies >charity evaluation complete >someone asks where the one-in-a-million number came from >expert judgement >which expert >us >how calibrated >extremely thoughtfully >reduce estimate to one in ten million to be conservative >still beats curing cancer by 38 orders of magnitude >epistemic robustness achieved >someone says maybe project doesn't work >assign 20% chance >still astronomical >maybe project makes problem worse >assign 5% chance >still astronomical >why 5 >because 30 felt pessimistic >publish 46-page report >contains seventeen sensitivity analyses >every sensitivity analysis begins after assuming intervention has positive sign >critic says you're multiplying enormous hypothetical stakes by extremely uncertain probabilities >yes >that's literally why it's important >critic says the uncertainty might be structural rather than numerical >make probability smaller >critic says no, I mean maybe your model is wrong >make probability smaller again >critic begins rubbing temples >discover AI safety >perfect longtermist cause >AI might kill everyone >or create utopia >or seize galaxy >or tile universe with paperclips >or create billions of conscious software minds >finally a problem with numbers big enough for me >start AI safety nonprofit >mission: prevent dangerous AI >hire smartest people available >smartest people immediately start building better AI to understand dangerous AI >interesting >we must understand capabilities to understand safety >we must scale models to study alignment >we must race ahead so less responsible actors don't get there first >we must deploy systems to learn how deployment can go wrong >we must build the thing quickly because building the thing quickly is dangerous >outsider asks why the people most worried about AI apocalypse all work at AI companies >complicated field >company releases stronger model >very concerned >company begins training even stronger model >extremely concerned >company raises $14 billion >concern reaches unprecedented levels >need to influence government >future is at stake >normal democratic process too slow >politicians don't understand exponential curves >public doesn't understand x-risk >experts must guide them >who counts as expert >people who understand x-risk >who understands x-risk >our friends >someone objects that this seems politically convenient >explain we're representing future generations >future generations unavailable for comment >develop concept of value lock-in >terrifying possibility that one ideology controls civilization forever >therefore extremely important that civilization adopts correct values before lock-in >whose values >let's circle back >begin with impartial morality >end with small group of people deciding what quadrillions of hypothetical beings would want >beautiful arc >meanwhile actual humans keep doing annoying things >voting wrong >having parochial attachments >loving family more than strangers >caring about local community >getting upset when told their suffering is cosmically negligible >evolutionary biases everywhere >explain that moral intuition cannot be trusted >except intuition that future digital people count >and intuition that extinction is uniquely bad >and intuition that our probability estimates are sane >and intuition that our institutional choices improve the future >those intuitions survived peer review >someone donates $5k to local homeless shelter >inefficient >could have funded 0.0000000000003% of an AI governance researcher >think of all the simulated people you just killed >okay maybe don't phrase it that way publicly >PR team says "future generations deserve a voice" >much better >journalist asks what longtermism means >say "future people matter" >everyone agrees >great >journalist asks what follows from that >well technically we should redirect enormous resources toward low-probability interventions affecting astronomical futures >journalist raises eyebrow >return to "future people matter" >motte has entered the chat >critic: of course future people matter >me: glad we agree >critic: I don't agree that your institute knows how to help them >me: why do you hate our grandchildren >eventually notice uncomfortable implication >if future value dominates everything >then helping people today mostly matters through effects on future >education matters because future institutions >health matters because future productivity >democracy matters because future trajectory >human beings slowly become instrumental variables in their own moral philosophy >see starving child >feel compassion >check spreadsheet >child's direct welfare contribution negligible >but perhaps childhood nutrition improves national institutional quality >compassion restored >tell myself this is impartial altruism >one day assistant asks obvious question >"how do you know your intervention actually improves the far future?" >silence >open spreadsheet >increase column width >add confidence interval >assistant asks again >"no, I mean how do you know the sign is positive?" >stare into cosmic light cone >10^50 people staring back >none of them exist >none of them can tell me >none of them can falsify my assumptions >realize I have invented the perfect constituency >infinitely important >completely silent >and always represented by me

I made a list of great startups to join. It's called the Breakout List. The list has 92 companies. These are the 20 with 25 or fewer employees: - Hone (@moritz_stephan, @CarloWillem, @oqbrady) - Normal (@ansonyuu, @hudzah) - Standard Intelligence (@G413N, @devanshpandey) - Tacit Labs (@ninklefitz, @AmDroste) - American Terawatt (@atroyn, @rslparker, @aranibatta) - Conduit (@clemvonstengel, @riopopper) - Convergent (Omkar Savant, Vivek Katara, @debnilsur) - Core Automation (@MillionInt, @_arohan_) - Engram (@dan_biderman, @EyubogluSabri, @realJessyLin) - Instinct (@noahrshinn) - Keenable (@styskin, Matthias Petri) - Lumaril (Mark Elliot, Ben Duffield) - Neion Bio (@Dimkell, Sam Levin) - Pangram Labs (@max_spero_, @bradley_emi) - Quadrillion (@echinaceous) - Re (@karnsaroya, @AnandDhillon, @thecliffwhite, @benaneesh) - Ricursive (@annadgoldie, @Azaliamirh) - Sail Research (@neilmovva, @blintzbase) - Trajectory (@rronak_, @michaelelabd, @QuantumArjun) - Watney Robotics (Sean Cheong, Ryan Gannon) Picks from Elad Gil, Charlie Songhurst, Keith Rabois, Mike Vernal, Alana Goyal, Sonya Huang, Ramtin Naimi, Marc Bhargava, Cory Levy, Aashay Sanghvi, Konstantine Buhler, John Luttig, Varun Gupta, Ray Tonsing and Avichal Garg. Disclosure: I'm a small investor in American Terawatt, Convergent, Standard Intelligence and Trajectory (in this post), and in Factory, Physical Intelligence and SF Compute (elsewhere on the list). I didn't vote. The full list is on Breakout List.

A new industry report shows 90% of major brand-name biologic drugs losing exclusivity over the next decade have no low-cost competitors in development. on.wsj.com/4gTivTi

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

You cannot become a “hard tech” company without becoming a manufacturing company. You cannot become a great manufacturing co without becoming exceptional at physical operations. Technology alone is not enough to win in the space. If your investor does not understand what this really means be wary. 🚩🚩🚩

Producing the 3 to 5 grams of a biologic needed for IND-enabling studies costs millions of dollars using traditional CHO manufacturing. A single egg, meanwhile, contains 6 grams of protein, costs just 10 cents, and requires only chicken feed and water as inputs. Neion Bio is taking advantage of this incredible technology to turn chickens into drug factories. And they’ve already signed a commercial deal with a drugmaker to produce biosimilars. With AI leaders promising to unleash countless new therapies, we’re going to need to scale up our manufacturing. That sci-fi future may be one where we stop producing drugs in huge steel tanks, and instead start farming them like we do our food. We sat down with co-founders Dimi Kellari and Sam Levin to discuss this future. [Disclosure: I’m an investor, though I wasn’t at the time of recording.] Watch the full episode below, or look up Free Radicals (@FreeRadicalsBio) on your favorite podcasting platform. Thank you to @SynBioBeta for hosting us! 0:00 Intro 1:07 What Neion Bio does 4:53 How the founders arrived at synthetic biology and chickens 10:21 Biosimilars as Neion’s first commercial market 13:30 Why farming drugs is a viable path to reshoring biomanufacturing 18:38 Neion’s technical moat & why engineering birds is difficult 25:50 Advantages of chicken-based manufacturing 30:06 Why CHO manufacturing became the industry standard 33:21 What it takes to disrupt CHO manufacturing 37:57 What it means to program biology 42:48 Skepticism around this new manufacturing approach 47:00 Applying tech principles to biotech 55:34 Natural intelligence vs artificial intelligence 57:14 The future of farming drugs & programmable chickens

as Amca matures, I'm excited to start peeling back what we've been up to. New software, a LOT more hardcore engineering - some of which hasn't been done in decades, and hopefully happy customers. Exciting news coming over the next few months. More to follow soon...

I am so clucking eggcited that Neion Bio is finally coming out of its shell. 🐣 Today, pharma uses Chinese hamster ovary (CHO) cells to produce biologics like Keytruda and Humira in huge stainless steel bioreactors. Merck spent $1B on a single Keytruda facility. In early 2024, Elliot Hershberg wrote that chicken eggs are much more efficient bioreactors. They run on grain and water, produce six grams of protein per unit, and we already farm them at massive scale. Sam Levin, who we previously backed at Melonfrost, and Dimi Kellari explored the frontiers of this research, which they could get their arms around because the cutting edge stuff is happening in a very small number of labs around the world. They realized that the time was right to build a company that uses nature's bioreactors to produce drugs at a fraction of the cost. In today's NYT article, Sam predicts that the cost can be 1/10th or even 1/100th of the current cost, and that just 3,900 hens could meet global Humira demand. I'm proud to back Sam, Dimi, and the Neion Bio team as they work to hatch the balk of the world's biologics and dramatically lower the cost to produce critical drugs, right here in NYC. And I'm sure they're happy that I can share my chicken puns with all of you instead of just replying to investor updates with them. Check out what they're up to in the NYT article below.