about 3 years ago, micro1 lost 60% of its revenue overnight and almost went bankrupt. today, i'm proud to share we've surpassed $1B annual run rate while remaining historically profitable. it’s been a special journey. we’re grateful for every struggle, moment, and person who has been part of it. we've taken a very untraditional path to get here. our core team is only ~180 people, spanning 14+ countries, and fully remote. many of our decisions sacrifice hundreds of millions of revenue and slow us down short term. but we believe they compound into advantages that are impossible to replicate long term. the long term is now. these are the 8 principles that made micro1 a leader in the data industry and will soon make us number one: 1.) human 1st AI: treat the tens of thousands of experts helping train models with absolute best-in-class care. our goal is to make AI training one of the largest, highest paying, and most enjoyable job sectors in the world. 2.) U.S. first: we refuse to work with adversarial nations, or export data that improves models against the interests of Americans. 3.) long-term research over short-term ARR: invest in deep research, products, and infrastructure for years from now, not just the next customer pipeline. 4.) proper incentives for intensely hard work: no arbitrary requirements like 996. inspire people to work extremely hard with outlier incentive structures instead of forcing weekend work. we’ve maintained close to 100% core team retention as a result. 5.) stay lean and global: we’ve reached $1B run rate with ~180 people, and intend to slow team growth significantly from here. exceptional talent is found around the world, and pedigree is the wrong thing to over-index on. 6.) execution alignment: company to retain a streamlined board so we can optimize for the next several decades, remain light-weight and dynamic, and take bold & calculated risks. 7.) abundant intelligence improvement: invest in building data generation models to make high-quality datasets and RL environments dramatically less expensive and more abundant. 8.) scale data on realism and horizon: realism brings the complexity of the real world into training models, while horizon extends how long models can work and how much complexity they can handle. both have enormous room to scale, and we’re pushing the limits of both. additionally: after surpassing $500M run rate this summer, we priced our Series B at a $4B valuation, alongside a ~$25M tender to give our early employees liquidity as we build for the long term. the round was led by Dream Ventures, a longtime supporter and one of our earliest investors, alongside Microsoft, Javelin, Flybridge, G Squared, Up Partners, and LG. we’re also grateful to be backed by incredible industry leaders, including Guodong Zhang (co-founder of xAI), Andy Fang (CTO of DoorDash), Igor Babuschkin (co-founder of xAI), Behnam Neyshabur (founder of Mirendil), Jerry Tworek (ex-VP of Research at OpenAI), and many more. data is on its way to becoming the largest industry humanity has ever seen, and we're certain this is just the beginning. best, Ali

Peter Thiel in his lectures talks about how the railroads were an amazing public good, but because there was no monopoly, the railroad building companies kept competing until they tore each other apart, so could not extract the economic value, and they went bankrupt. It was obvious from the beginning that that was AI. The big AI companies are screaming doom and safety because they know they are the 21st century railroad companies and know the only way to avoid that fate is a monopoly from government regulation. It really is that simple. So I just want to say, sorry frontier labs, u knew what u were getting into, don't hold us hostage. Ur going bankrupt, but thanks so much for what u r doing before u do.

Today, with a heavy heart, I have decided to resign from my role at Anthropic. While I was concerned that we might destroy humanity by training superintelligent AI, the real reason I resigned is because they stopped serving samosas in the cafeteria.

Private gains and socialise losses…What is wrong with this?

Gavin Baker: "AI models will go to zero. Data centers are commodity. Energy is commodity. Only two things in the whole stack have real value: data moats and reinforcement learning." His survival list is short: - Google — already multi-trillion, safe - xAI — his call: worth over $1T combined with X - Anthropic — the other closed model that survives Microsoft, OpenAI, Meta — chose open source. Baker treats them as commoditized. What he actually buys: things that increase GPU utilization. Take a GPU from 30% utilized to 60%, you've doubled the output of the AI factory. The math he keeps quoting: GPUs got 50x faster in 4 years. Nvidia sells for $50,000 what Taiwan makes from $700 of sand. Baker's frame: don't rent intelligence. Own the thing it runs on. Full breakdown — in the article below.