Today, I'm excited to welcome Andy Lambert to Valar Atomics as Chief Production Officer. Andy has led an extraordinary career in manufacturing. As VP of Production at SpaceX, Andy took Falcon 9 from a fledgling single-use rocket to the world's most reliable launch vehicle and first orbital-class self-landing system, while also industrializing Falcon Heavy production and leading quality for America's primary human-rated spacecraft, Crew Dragon. Those who know me best know my particular style of hiring executives; that is, not hiring them until I've found the best in the world. It has taken me three years to find somebody that I feel is capable of executing on our mission to turn nuclear energy from a construction industry to one based on vertically integrated mass-manufacturing and rapid deployment. If you prick Valar Atomics in any place, you will find that we bleed scale. Every decision in our architecture has been meticulously and ruthlessly honed for building not tens of reactors, but thousands. Nuclear reactors are simple machines, but real scaled production is very hard. I wanted to find somebody who has lived through production hell of a novel system where the safety of human life hangs in the balance. Andy started his career working on the Harrier for the Royal Air Force, then moved into production at BMW. At SpaceX, Andy was responsible for the production of Falcon 9 and the quality of Crew Dragon, two of humanity's most challenging and safety-sensitive products. After SpaceX, Andy built complex and novel energy systems at Heliogen, then ran production at the innovative and vertically integrated hypercar company Czinger. This mix of novel environments, unprecedented pace, deep vertical integration, and life-safety quality is exactly what Valar Atomics needs to bring humanity into the atomic age and make energy cheaper than ever before. But more than all of that, I found Andy and his wife Carrie to be incredibly kind and genuine people who are incapable of resting on their laurels. They are hungry to help build the future. My reference checks on Andy were all consistent: he fights hard to win, but he does so in a way that brings everyone along. If you've worked with Andy in the past, or have worked in organizations that have been fortunate enough to have him at the helm, this is your chance to see him in peak form. The mission of Valar Atomics is to make energy cheaper and more abundant in the universe than ever before. We are doing this by becoming the first company in history to build thousands of nuclear fission reactors. This will require the absolute best people in the United States working harder than they've ever worked before. But our mission is worth your efforts. Join us!

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

Today micro1 is announcing a commitment to spend $1B over the next 12 months on the acquisition & licensing of enterprise data for AI training, with financial backing from Citi and Hercules. micro1.ai/blog/micro1-commit…

Which personal agent is the best life assistant? Today we're releasing the micro1 PersonalAgentBench to help answer that, and we're paying real personal agent users to contribute to the benchmark. Personal agents can now book your flights, answer emails, and move your meetings around. That’s great, until one messages the wrong person or makes a purchase without confirming. Capability is not the same as trust. So we put four personal agents to the test: Instinct, Muse, Grok Bot, and Gemini Spark. We found that even when agents found the right information, they still often miss what matters, overshare, or make things up. To find out for yourself how your agent compares, you can join our benchmark. We’re paying the first 100 users to run our prompts and share their results.

over the last few weeks, we've been getting over 100 companies sign up every day to license their data for model training. our data partnerships team of 5 couldn't handle the volume, so we built a sales agent to handle all the calls named Kai. today Kai closed a $585k deal and it did so all in one day (from first call to human confirmation and contract signature). the average sales cycle without Kai is 9+ days.

W @RBDreamer! A great guy and a world class business partner of mine!! RB is crushing it with investments in @valaratomics, Dream Fund I, and @micro1_ai RB has made me a lot of money and we have had a lot of fun together: thanks RB! #POSITIVITY

PrivacyBench v2 was recently released, and flow-transform 1.0 by micro1 now leads the expanded benchmark by a wider margin, reaching 95.84% combined detection + synthesis accuracy and finishing 9.64 points ahead of the next-best model. The latest version expands the benchmark to full synthetic enterprise workspaces with native documents and attachments. Congrats to Tonic on the release. PrivacyBench v2 is a meaningful step forward, but it still measures only part of the end-to-end transformation problem. In our report, we introduced the Enterprise De-Identification Bench and its Transformation Quality Index (TQI) to measure the final transformed dataset across privacy, utility, fidelity, coverage and coherence. We believe this is the better way to evaluate enterprise de-identification because a single combined detection + synthesis score can hide important failure modes. It doesn’t account for the severity of what was missed, unnecessary transformations, uneven performance across files, or whether the final dataset remains coherent and usable.

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.

Rainmaker has raised $100M to become the world’s best lab for weather modification and atmospheric science. Thank you @noavctech @upfrontvc @DCVC @lowercarbon @DreamVenturesVC and other visionary partners. We are aggressively hiring and scaling our research org to create a flywheel of atmospheric data collection, synthesis, understanding, and intervention. We will solve the weather and we will set the stage for the terraformation of Earth. More in my essay below.