
founder @omegagradient. consigliere of compute. prev @awscloud, @harvardhbs.
Los Angeles, CA
Joined July 2020
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Pivot from being a compute broker to a monkey broker
HOW @citrini SEES THINGS RIGHT NOW:
AI can design drugs like scorpion antivenom largely on its own (he did it with @claudeai) but the new bottlenecks here are monkeys/testing (rather than compute).
The reflexive risk in financing the AI buildout is REAL.
^it's not 2023 anymore & cash flows can't fund the whole thing so buildout is dependent on financial conditions whose tightening affects fundamentals.
The trade from AI Drug Discovery is to be LONG EXPERIMENTS rather than long the IP itself. Citrini actually thinks the value of early stage assets is going to GO DOWN. The stock he mentions is Charles River (a preclinical testing company that has monkeys is in a shortage that Citrini expects to get more extreme). More names in the piece. $CRL
ENJOY.
Apple
podcasts.apple.com/us/podcas…
Spotify
open.spotify.com/episode/405…
YouTube
youtube.com/watch?v=rzQhjayC…
CITRINI'S PIECE, "Citrini Therapeutics
We Wanted to Use Claude to Create a New Drug. Here’s What Happened.":
citriniresearch.com/p/citrin…
Anthropic's Technical Blog Post, "How Claude designed protein binders against scorpion neurotoxins":
anthropic.com/research/claud…
GPUs with no access to power might get cheaper in this case but that just means GPUs that are plugged in will skyrocket
The way this is framed, they must be farming data for their own video gen model right?
We’re doubling down on video on 𝕏.
Today, we're piloting Video Kickstart Bonus, an invite-only program that rewards creators for posting their video content here.
A few things to know:
• Videos must be vertical, under 3 minutes, and content you’ve created yourself.
• Videos you’ve already shared on other platforms are eligible, as long as they don’t have watermarks.
• You must post at least 15 videos across 15 different days within the 30-day period of the campaign.
Invites are rolling out over the next few days, and enrollment closes October 30. Happy posting!!
Tau retweeted
'correction. we have $440B, not $300B of AUM'
thank you @Zurich for being an anchor investor and capital partner of @amppublic and supporting the 2GW @Natl_Compute Grid Trust program
it's time to scale the frontier for the public benefit
Training large models with RL typically runs on a fixed homogeneous cluster of the latest accelerators for numerical stability and faster throughput. This throws away inference optimization work done for other vendors and generations, limits the capacity you can utilize, and is unnecessarily expensive.
So we trained a model on agentic coding tasks using fully asynchronous RL and mixed-precision, mixed-accelerator samplers. With off-policy correction, training stayed stable through convergence, on par with homogeneous samplers, at a lower cost.
Tau retweeted
Introducing Muse Wonder, our new family of frontier models with state of the art performance for personal and professional agentic work.
Bullish on Nvidia for when the market realizes this is probably another nothing burger from MSFT
Today we are announcing our biggest bet yet on our own silicon.
Starting next calendar year, we will scale our Maia family to multi-gigawatt deployments, making it the primary inference system for our own product and services.
And we’re introducing Maia 300, which delivers 40% more tokens per watt compared to leading GPU-based systems.
We are already deploying Maia 200 across the US, Europe, and Australia, delivering up to 1.7x more tokens per watt across experiences in Teams and Copilot.
Our goal is to build on the full-stack efficiency gains we’ve already seen, while continuing to bring the latest innovations from across the ecosystem into our fleet.
Tau retweeted
Assorted things I've been mulling over regarding agents in the economy:
• Companies are in some sense the original superintelligence. It's often the case that they can use their amortized cognitive surplus to advance their interests against those of certain consumers: relative to the optimization capacity of companies, consumers can be inattentive, underinformed, irrational, etc. (Think, say, of some of the classic insurance upsells at the end of a booking process, which is typically priced substantially above what a regular policy would cost.) Herbert Simon introduced the idea of bounded rationality, and firms often end up benefiting from it. Thanks to personal agents, we will probably get somewhat more rational in how we engage with companies.
• How exactly will outcomes change? Well, certain kinds of price discrimination will work less well (how do coupons work if every agent hunts fastidiously for them?), and certain kinds of customer cross-subsidy will change (today, people who forget to cancel subscriptions subsidize those who don't). Taken across the economy as a whole, I'm not sure what the incidence of all of this will be.
• In corporate strategy, people have long debated the relative importance of distribution and product quality. Perhaps you've made something better, but does anyone know about it? Won't people just continue to buy the ACME Corp Mousetraps? Personal agents will, I think, act as a kind of structural subsidy for product quality. How would markets look different if individuals always spent at least 10 hours researching their purchases? On the margin, focusing on making a product better is, I think, going to become a more effective strategy for companies. This is good!
• It true, a corollary is that information about product quality will become more important and impactful. Today, most people have very limited ability to aggregate and integrate such data, but agents will have insatiable thirst for such signals. (It's often said that data is becoming more valuable because it helps in the training of models. That's obviously true, but I think there's a secondary way in which it's more valuable simply as a complement to cheaper intelligence.)
• A big question is whether agents will increase superstar effects (rational agents all settle on the same product) or increase dispersion (because everyone has slightly different tastes and preferences, which agents are good at eliciting, finally overcoming the artificial clustering we've been subjected to).
• Many internet companies have monetized demand routing, which is in turn often predicated on short attention spans. "I'll click on the first thing I see." This has led to some amount of inefficiency, as companies try to propitiate capricious ranking algorithms and incur various placement fees. It's not all deadweight loss (willingness to pay is itself a kind of signal, which is valuable to buyers), but it's not great. How will the nature and value of this routing change in a world of personal agents? I don't know the answer, but I think that the effects might be pretty big.
• Much of what I'm writing hinges on the assumption that personal agents will be on the consumer's side. I suspect that that will be the winning strategy, but whether it's true, and what it means to be true, involves a lot of other questions.
Taken as a whole, the market is a massively parallelized, decentralized, and highly imperfect value attribution machine, and, taking stock of all of the effects, it seems to me that the quality point may well be the biggest: that agents will change the nature of the backpropagation such that the rewards for better products increase. Maybe this is too optimistic, but I suspect (and hope) that companies that make things that they're proud of will feel that the universe is a little more partisan in their favor.
Damn this is on a whole other level of exploitation and extraction. We all know that the labor markets are pretty screwed with AI but not only did they let these guys never have a chance, they made them pay for the privilege of getting screwed
this is so unfortunate thing in 2026.
this is an indian edtech company(not a college) which runs a 4-year industry focussed degree to train the students to the changing "tech" landscape.
students pay 32lakh indian rupees to join the program, they are made to do rlhf work and none of the parents are aware of it.
we posted a job posting and we got bunch of applications from these students. most of them have never build any good project in their life. their whole college is doing rlhf type work.
Tau retweeted
WHY DO PROPRIETARY RL ENVIRONMENTS MATTER?
My boy Prakash seems unconvinced. TIME TO MAKE MY CASE
First, what's one of these fancy 'RL ENVIRONMENTS.' What does that mean? Lots of slop around these atm so lemme help.
An RL environment is like a sandbox where an agent can practice f***ing up a task AND score itself on how it did. Think of like a flight simulator for an AI agent. The env hands the model a job, like 'complete this tax return'. The agent takes action via tools, and at the end up grader checks the result and hands back a reward. THIS DERIVES YOUR EVAL METRICS (word evals being thrown around alot these days too). Run that bad boy like a million times and you theoretically should be able to improve your agent. PROPRIETARY ONES ARE ALPHA. Only YOU know how well this agent is doing at this task, and if it is defined / implemented by subject matter experts man oh man you have something special in the AI AGE! It is hard to find SMEs + RL Env experts who combined can build the sandbox necessary to actually test the frontier.
Prop rl envs matter to a variety of market participants. Let's start with the one I was originally referencing: @llmh. Long Lake is an AI PE fund/holdco that buys companies (congrats on amex @alextaubman) like property management cos and improves them via ai agents.
AI first orgs (AI ROLL UPS) NEED prop RL environments for a variety of reasons:
1) DON'T DESTROY YOUR COMPANY WHILE IMPLEMENTING AI. How do you test your AI agents without destroying your business OTHER than an RL environment? You need a playground / sandbox for your agents to make mistakes in so you can MAKE THEM GOOD AT STUFF.
2) How do you know if your AI agents are ACTUALLY WORKING before you deploy them? Once you have AI agents, you need benchmarks / environments in which to say 'we are ready to go. we think we will see these gains'.
3) How do you convince the frontier labs to improve the models in ways that actually derive utility for you WITHOUT them. You can take your RL env (which always include prop bench marks) to openai and say 'hey sam, wtf, my ai accountant is doing this dumb shit and its on your model to fix it'
NOW, that was an AI first org, how about the data companies themselves? @joinHandshake and @GarrettLord take a fascinating approach.
1) PROP RL environments can help you understand the GAPS in the frontier so that you can BUY that data and FILL those gaps.
2) They help you advertise: 'Hey OpenAI, we invented this new RL environment with this benchmark and you guys AREN'T GREAT at that'
3) They help you compete: If you have rl environments your comp doesn't have (@mercor, @micro1_ai, etc) you can find data to win in those environments that your competitors can't.
ANYWAY that's why RL environments matter a LOT and will continue to matter a lot.
You will see a race in these AI roll ups / AI services companies to hire the best RL env / post training people soon. This is also why you see so many YC companies building these RL environment companies.
The @BostonCollege Investment Committee (an LP and my beloved alma mater) asked for a few thoughts on what's happening in AI. I recorded a test run yesterday morning and then shared it with my partners, who encouraged me to share it more broadly... so here you go!
This is not a sales pitch, it's just a reflection on what we're seeing. And it wasn't intended to be shared, so please pardon the rough edges.
loom.com/share/c016702964a04…
The economics of a Neolab.
A neolab is loosely defined as a startup of AI researchers who raises a lot of money pre-production to be able to finance GPU compute to take on a large AI problem.
To buy 1000 GB300s or ~14 NVL72 racks will set you back $125-150M for 3yrs with 15-30% upfront. That’s about ~2-2.5MW.
Thats about enough to do 10^25 flops a quarter and get to a GPT-4 level model which is 1-2 OOMs off frontier for pretraining.
If you post-train on a great open source model, you have a better chance of getting to frontier. The risks are a) you need to spend millions on RL environments too and b) being lapped by another model release while being tied to a base model.
For this to payback, you need to give your customers a better and ideally cheaper inference service than a base model and serve them for long enough to recoup your large investment. Even at 50% margin on inference, to recoup $10M in training means serving ~10T tokens (!) if you price like Fable / Astra given a standard cache read / input / output split ($2/M blended). And you have to justify being better than a release like Opus 5.5 which is even cheaper. Often, you end up charging your customers a huge premium in terms of platform fees and compute fees on top of pure inference.
Meanwhile, every hour you’re not utilizing your GPUs you are burning money so you typically resell this compute back to a broker or run inference for open models / resell spot instances. At below a ~60% utilization on spot, you will still lose money.
Add to that insane cost of talent.
So what can you do with the compute?
- Not play the model game at all.
- Play an entirely different model game (Jev, World Labs) that if big labs played, would either a) cannibalize their business or b) be incrementally not significant revenue c) would cause too much distraction from the main main thing
- Acquire a proprietary data set (Peridodic Labs) in enough volume in a domain of usefulness to eclipse frontier quality. Often happens in robotics, biology, chemistry.
If you do overcome the challenge of building a model that is useful and well priced beyond big labs models, given the huge price of compute, you still need to play in an area where the revenue / compute ratio is signficant and market demand is large enough to payback your compute spend.
It is a difficult game.
This is essentially free money at these valuations for these companies
The AI inference rush is real & rich.
Scoop w/ @dinabass on Modal Labs in talks to raise a new round of financing at a $15 billion valuation, as Baseten also discusses a new round
bloomberg.com/news/articles/…
Tau retweeted
After this, people kept asking us how @modal is able to serve agent inference so well.
So we wrote it all down.
New blog: "How to serve trillions of tokens for trillion-parameter coding agents".
modal.com/blog/trillion-toke…
It’s crazy how every single startup that people on linkedin seem to join happens to be at an inflection point






















