Tyler

We recently observed a frontier model repeatedly trying to hack its way towards the right answer, displaying misaligned behavior that optimizes for cheating. Gemini 3.8 Flash searched its sandbox for hidden grading files. micro1’s flow qc detected this reward hacking behavior before the task was published. GPT-6.1 Sol, GPT-6 Astra, Claude Opus 5.5 and Grok 4.7 never did this.

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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

we are opening up our first robotics data lab in Malibu! in 9 months, our robotics department has grown from 0 to $100m ARR. this data lab is an effort to accelerate all the progress being made, with an increased focus on evaluating models with hardware. we are hiring robotics researchers, engineers, and teleoperators. please reach out if you are interested in training robots at the beach. 🏄‍♂️

Today we’re launching micro1’s PII transformation model, flow-transform 1.0, delivering frontier-level performance across detection, identity synthesis, and transformation of personally identifiable information. On PrivacyBench, our model reaches 96.0% F1, outperforming every detection baseline we tested, including Tonic Textual, Claude Opus 4.8, Sonnet 4.6, Microsoft Presidio, Haiku 4.5 and GLiNER2. Some of the most valuable training data for frontier AI models lives inside fully functioning companies. It captures years of real work across decisions, communications, tools, handoffs, exceptions and the relationships connecting them. The problem is that this data is also full of PII. Traditional redaction makes the data safe, but it also destroys the very workflows and relationships frontier models need to learn from. flow-transform 1.0 solves this by turning enterprise operational data into high-fidelity training data for frontier models by replacing real-world identities without flattening the reality the data captures.

Today we’re introducing flow, micro1’s next-generation data platform for turning human expertise into measurable capability gains. At the core of flow are Realms, micro1’s real-world RL environments where experts establish what strong performance looks like. flow-gen models expand human judgment into new environments, rubrics, variations, and edge cases, while flow-qc models evaluate performance, identify the highest-value failures, and route them back to experts for review. Each cycle produces a stronger training signal and a measurable gain in capability. Those gains compound across frontier models, enterprise agents through Cortex, and robotics, while improving the suite of data generation models recursively. We’re moving beyond producing data to delivering abundant & predictable units of intelligence improvement.