If you're a researcher looking to: → conduct rigorous studies on how multiple models can outperform the frontier → leverage data from the largest LLM marketplace (150 trillion tokens processed per month) ... DM me with your work! We have an exciting role coming in the future, but might fill it opportunistically.

Game theory from here is super interesting: Original Mags (Google, Amazon, Microsoft, Meta) now have a serious non-zero opportunity to tank the frontier labs. Go to the government, kneecap the labs’ motion of putting the latest models out in the wild, become the trusted gatekeeper between the labs and the public at large (including internationally) by having the labs go through their clouds (AWS, GCP, Azure) and implement strict KYC to seal the deal. The frontier labs should have seen this coming years ago and implemented a robust KYC for just this moment. The fact they didn’t is kind of concerning. Why did they not do it? Best guess is because it would have changed the run-rate revenues (downward) which would have then changed funding dynamics - lower valuations, more dilution, less secondary. A valuation reset may happen now anyways, except the labs may end up with less control and more restrictions at the end of it. At the same time, everyone is already clamoring about token prices of the old models from the labs anyways… This couldn’t be a better setup for open source and neoclouds. Big question is can they meet the moment? There are too few of them and their progress seems sporadic at best.

One piece of advice we got during YC was to explain our company using verbs instead of nouns. Early on, I walked into a meeting and did the opposite: “We’re building a cloud platform for AI” No one knew that that meant, their eyes glazed over. Then I started saying this instead: “We containerize your code and run it on GPUs in the cloud so you don’t have to manage the infra yourself” That clicked way more. Our brains understand verbs because they’re more concrete. If you describe your company using nouns, you risk people not understanding you. And no one buys or invests in things they don’t understand.

“don’t train your own model” is common ai advice. it's wrong. your token bill's the proof. today, we’re excited to launch castform into open preview. castform is the easiest way for you to train your own model, on your own data. open-weights models are performant and much cheaper. when trained on your task & proprietary data, they beat closed models. the thing standing between you and that was weeks of plumbing & years of ml expertise. with castform, model training is as simple as prompt engineering. @castformai bring your agent traces or raw corpora. castform turns it into training data, picks the right algorithmic recipes, manages gpus, and gives you an ide to watch and chat with your model as it learns. see what you can build with castform👇

We are incredibly excited to announce River AI. Our mission is to create personal AI that is owned and shaped by you. Today’s best AIs are controlled by a few large corporations. We are building the alternative: a new, personal stack for AI that works entirely for you, shares your values, and operates on your terms.

Co-sign.

The best diligence question is usually not "how big can this get?" It is "what has to be painfully true for this tiny wedge to keep expanding?" That question exposes the actual company.

i feel like every conversation with a VC is actually so funny b/c they’re so obviously tryna alphafarm u but then ur also tryna alphafarm them

🚨 GREG BROCKMAN JUST CONFESSED UNDER OATH Q: You have an ownership interest in this cap profit company. Brockman: That is accurate. Q: And you invested $0 in order to acquire that interest. Correct? Brockman: That is also accurate. Q: Your ownership interest in this for-profit is valued today at more than $20 BILLION Correct? Brockman: Yes. Q: In fact, it may be closer to $30 BILLION. Correct? Brockman: I think that may be true. Yes. Brockman invested $0. Walked away with $20–30 billion. Musk donated $38 million plus the office rent. Got $0 personally. This is unjust enrichment, captured in his own testimony.

“When founders are both formidable and earnest, they're as close to unstoppable as you get.” Paul Graham on earnestness: paulgraham.com/earnest.html

From a portco founder selling his co and hitting generational wealth. "I'm buying a minivan after this and having a 3rd kid." cc @ericbahn

Jane Street made ~$40B in 2025 with 3,500 employees, a ~2x from the year before. At ~65-70% profit margin, that's $8M profit / employee, the highest for a 1000 ppl company. High-frequency trading continues to be the most efficient money making engine. I want to share an old story about my Jane Street interview in 2014. Jane Street was known for hiring a lot of math, physics and CS olympiad winners from top universities and putting them through many rounds - including, for trading roles, a gauntlet of mental math. It was my 6th interview and my final round and I recall being asked "What is the next day after today in DD/MM/YYYY where all the digits are unique?" They'd toy with you and say "You can use a pencil and paper, if you want" but you knew that was an instant no. Painstakingly and as quickly as I could, I came to an answer. "How confident are you that this is correct on a 0-1 probability scale?" the interviewer said. "0.95", I blurted out, not fully knowing how to answer that. "Are you sure?" After thinking harder for a few more seconds, I realized I could've flipped the digits around to get a closer date. I gave the interviewer my answer. It was correct. "0.95 huh?" he chuckled. That's when I knew I failed. Note: fwiw, other companies that come close in efficiency are - Tether ($90M profit/emp) - Hyperliquid ($80M profit/emp) and on revenue: - Valve ($50M/emp) - OnlyFans ($37M/emp) - Craigslist ($14M/emp) - Anthropic ($12M/emp, run rate) - OpenAI ($8M/emp, run rate) For comparison, Nvidia is very efficient at scale and is $4.4M/emp.