AI models can get the facts right and still miss what matters for sensitive U.S. Government use cases. We tested nine frontier models on government mission reporting: only 3 of 540 reports met every criteria. Introducing MissionReport, a benchmark built by @micro1_ai with current and former U.S. government and military professionals to test whether AI can identify, prioritize, and communicate what commanders and senior leaders need to know for mission critical matters. More info in thread 🧵

We have two additions to PersonalAgentBench: @hark_labs and @OpenAI's Dots! Dots has taken the lead, with Hark close behind while Gemini Spark moves to third, followed by Instinct, Grok Bot, and Muse. We also doubled the number of tasks this week, adding research, image understanding, editing, and everyday communication. The wider set changed the picture. Gemini Spark was strongest on research but weaker on image and editing tasks. Hark and Gemini tied on tasks involving user authority. Dots was the most consistent across task types, although it was weaker when it had to infer personal context. No assistant led every category; the overall ranking rewarded consistent performance across the full set. This is still an early snapshot, and we’re excited to see how results evolve as we add more agents, expand the tasks, and re-evaluate after product updates. If you’re using any of these six personal agents, follow along by visiting the link in the original post.

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.

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.

in the past 24 hours alone, micro1 has paid out $5.8M to 9 businesses for their enterprise data. that’s an average of over $600k per business. frontier AI needs training data that captures the complexity of real-world work. real businesses hold decades of decisions, exceptions and learnings that make the next breakthroughs possible. realism is a new dimension of scale & arguably the most important ingredient for model training data. if your company is interested in a data partnership, reach out to us.

Why we bid $12.5 million for Spirit Airlines’s data: As you may have seen in the news, we are aiming to acquire the Spirit operational data. We'd like to transparently & directly explain why. The future of AI is a bet on two things: the messiness of the real world and the brilliance of the humans working inside it. The combination of these two things results in a new dimension of scale called realism. Realism is the most significant thing that has ever happened to model training. It’s what lets RL environments and tasks match the conditions a model is deployed into. The limit is the real world itself. But the real world is also dynamic. It grows with a company and shifts as operations evolve. There is not a binary scaling of entering the real world. Realism keeps scaling as long as real work keeps happening. As a data lab, we want this data to benefit the entire AI ecosystem, while ensuring labs that we partner with for this data, agree to strict privacy obligations. This includes keeping the data away from labs outside of U.S. and U.S-allied countries. Our mission is to push the frontier in novel ways, with a human & privacy first approach. To do that, we've bid on a portion of Spirit’s Airline’s non-sensitive operational data. Effectively scaling realism requires de-identification as a core competency. Very few organizations can do this. We believe the few that can have an obligation to act transparently. That’s why we’ve made binding commitments. We will prohibit re-associating the data and will not profile any former employee. We’re paying for an independent data ombudsman to review the data and how we use it. Furthermore, our offer aims to create net new opportunities for as many as we can from the 17,000+ former Spirit employees. We’re providing former Spirit employees preferential access to paid human data roles on our platform, along with free access to our AI training and reskilling curriculum. These commitments bind us to spending at least $1M. That is just the minimum. It’s entirely possible we spend orders of magnitude more than $1M working with and training former Spirit employees to help define the frontier. We think this transaction will set the terms for how data like this changes hands from here. Those terms should be worth copying.

we’re hiring 10,000 robotics trainers in the next 7 days. $50–$90/hour, accepting applicants globally. you’ll review and label videos of robots performing tasks to help them improve. no prior AI experience required. an entirely new category of work is emerging around teaching robots how to interact with the world. application link in the comments below.

Excited to share that micro1 has been selected to support the U.S. Department of Energy’s Genesis Mission. Genesis is a Manhattan Project-scale national effort to accelerate scientific discovery with AI and build the energy foundation needed to power America’s AI leadership. micro1 will support the mission by developing the data and evaluations that enable frontier AI to tackle some of the country’s most difficult scientific and engineering challenges. We’re proud to play a role in an effort this important to the future of American science, energy, and AI. More on our work with the DOE linked in the comments. 🇺🇸

The future of AI belongs to the humans behind it. There’s a common fear that as AI gets better, people get pushed out of the picture. We believe the opposite is happening. AI is creating entirely new categories of work, and an entirely new economy around the people whose knowledge, judgment, and experience are helping these systems improve. Today, tens of thousands of experts are actively contributing to AI training projects through micro1. Over time, we believe this will grow to tens of millions+. And if humans are going to play such a critical role in building the future of AI, the companies they work with should raise the standard for how they’re supported. Today, I’m proud to announce the micro1 Expert Support Program. We’re building a new set of protections, resources, and support for our expert community, starting with: -Expert Bill of Rights: a clear set of commitments outlining what experts can expect when working with micro1. -Rest Credits: paid time away from projects when experts need or want a break. -Expert Emergency Fund: financial support for experts facing emergencies. -Mental Health & Coaching: new resources to support expert wellbeing and growth. -Confidential Support Line: a confidential way to ask questions, seek support, or raise concerns related to pipelines. Within the next two weeks, every expert currently active with micro1 will receive an email with the full program details and launch dates. AI is going to keep getting more capable. The opportunity in front of us is to make sure the people helping build it benefit from that progress too.

more than 1,000 companies have signed up to get paid for their anonymized data to train models just in the last few weeks. a massive TAM opportunity for the entire economy has emerged almost overnight.

Regarding the last topic @DavidSacks : With respect, the data being used to train frontier models in the U.S. is not a commodity. It is American intelligence, paired with anonymized operational data from U.S. enterprises. Put simply, we have top doctors, scientists, lawyers, and physicists in the U.S. using their knowledge to create detailed rubrics that train these models. Each individual data point and its corresponding rubric can take an expert anywhere from 10 to 30 hours to create. This is not preference labeling or drawing bounding boxes. It is highly complex, structured human judgment from leading experts here in the West—people who deeply understand and directly contribute to the latest American innovations in their respective fields. That expertise is then converted through highly specific data structures and RL environments (developed collaboratively by U.S. AI labs and data labs) from raw human intelligence into high-signal rewards that improve frontier models. On top of that, any AI advancement, even something that begins as a simple chatbot designed to improve operations within a defense agency, can create a major competitive advantage in adversarial situations and may have dual-use applications. Lastly, many datasets today are seeded with anonymized, real-world operational data from U.S. companies to build highly realistic environments. When those datasets are sold to China, we are not simply exporting “labeling.” We are exporting proprietary American intelligence, structured for machine learning and delivered directly to China at scale. It is very easy to categorize this work as “labeling” and ignore what it actually represents. However, as @altcap suggested: “Then these things will get a lot more scrutiny than they’re getting today. I think the only reason they pass muster today is because we’re still leading the race.” If China catches up to U.S. labs, this will become much harder to ignore. In retrospect, the role of data as the root cause will become very clear — and by that point, it may be too late. The race is tight. I suggest looking into this now.

Shameful act to optimize for short term revenue increases and serve an adversarial nation in the most important race of our lifetime. The only way models improve is through data. if you have the recipe on what data pushes the frontier and send that to China, you are doing a disservice to U.S. AI Labs as well as the U.S. government. Beyond that, a large portion of datasets today require anonymized real operational data from U.S. companies. Selling such environments to Chinese labs means exporting U.S. company data directly to China at a large scale. This must stop.

there are two dimensions that data demand is scaling on tremendously: horizon and realism of tasks/data points. environments built on top of anonymized real operational data is step function change in scaling on both of these dimensions. and as we do this, companies that choose to contribute can make data a very meaningful portion of their revenue while accelerating their path towards becoming more AI native. thanks for covering Stephanie!

The more we think about robotics, the more it seems the field is moving toward increasingly specialized datasets. Better foundation models increase the value of task/environment-specific datasets. As base models become more capable, annotation becomes more about data structuring. The challenge shifts toward adapting data to the environments, objectives, and edge cases a model will encounter. Over the past several months, our robotics team has been running a large number of experiments around the question of how do you maximize the training signal from the same raw data? One conclusion we've become increasingly convinced of is that there isn't a universal annotation pipeline for robotics. Different tasks require different combinations of models, verification, and human expertise to produce the highest-quality datasets. The great @AndrewLeeMaas and Mitali Potnis from our team have put together a paper walking through the ideas and experiments behind this approach. The full paper showcasing examples of how we have applied these annotations is in the comments.