
ananth
64 posts




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.





Our @golang load balancer at @render handles more than 150 billion HTTP requests a month across millions of services. The number of times we've wanted to rewrite it in Rust: zero. Go is the most underrated language in infrastructure. "Boring" is the ultimate feature.

BREAKING: We're partnering with @SolanaFndn to rebuild Solana's read layer from the ground up. @anza_xyz and @jump_firedancer have done incredible work scaling execution and networking, but the read layer has stayed largely unchanged since genesis. It was built alongside the validator and never got its own architecture. By 2026, that gap shows: slower access, expensive customisation, and growing limitations at scale. The teams closest to the problem built great tools behind closed doors because the read path was too deeply coupled to the validator to improve without massive effort. It's time Solana's data access layer matched the ecosystem's needs, and we're proud to be the ones building it: Big news: reads are moving out of Agave into two modular systems, independently scalable, in sync with the network tip, open-source and managed by @SolanaFndn: - Accounts: an adaptive indexing engine that ingests, stores, and serves the exact account data your app needs at extremely low latency - Ledger: full architecture to ingest, store, and serve the entire ledger faster and more efficiently in a columnar engine purpose-designed for how builders query data Every infrastructure provider, builder, dApp, and institution benefits, with the biggest impact coming from what gets built on top. Full architecture overview: blog.triton.one/announcing-rpc… More technical posts coming as we build through 2026, so make sure to follow us on X and subscribe to our blog.




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