micro1 CEO @aliansarinik explains why blacking out PII destroys the relationships AI needs to learn, and how FlowTransformer preserves them in a synthetic digital twin: "Every ingredient within the environment, we aim to make more realistic because it matches the distribution that models need to learn on. That's the distribution they're gonna act in whenever they're deployed into enterprises." "We've been partnering with hundreds of companies, licensing their data, anonymizing it, and then using it for training" "The default is you just redact all the PII, you draw a black box around it. The problem is you lose the consistency of the identities and the relationships that allow you to train." "Instead of redaction, it does transformation. It creates a digital twin of any given enterprise. It changes all the names and identities into synthetic versions, keeps the relationships intact, but keeps privacy as the core." @micro1_ai

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 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.

We’re sharing a deeper look at flow, our next-generation data platform for delivering predictable units of intelligence improvement at scale. This article transparently lays out the full flow architecture including its data generation models, expert workflows, and models for quality control and evaluation. At its core is a flywheel where human expertise continuously compounds. Experts set the standard for models to generate and evaluate training data, then their corrections feed back into both the data and the models producing it. Each round strengthens the next, making intelligence gains more predictable while driving costs substantially lower. Explore the full framework below. micro1.ai/blog/introducing-f…

the hardware embodiment of frontier models like Claude and GPT is the most urgent AI safety problem in front of us today. we simulated two very simple use cases using claude both in simulation and using robot arms. in one, claude spilled toxic liquids in a lab. in another, the force it used to place an animal toy into a basket was strong enough that it could have physically harmed sensitive material—or anything else in its path. these are simple experiments, using models out of the box today. as researchers increasingly give frontier models arms, legs, and access to the physical world, we need to urgently build and assess guardrails around what these systems can and cannot do. models escaping sandboxes or compromising enterprise security infrastructure are serious concerns. however, hardware embodiments introduce something fundamentally different: an AI system can make a mistake in the physical world, and the consequences may not be reversible. this is not a future safety problem. the capabilities exist today. we’ve released a report detailing this & solutions we propose. link in comments below.

We were offered like $600k to sell our company data to Micro1. 100% not doing it. But it does interest me like crazy. What a crazy business model and idea. If you don't know, here's all I know about them: they go to private companies. They ask for your slack, notion, email info. Offer you 6-7 figures. Somehow anonymize that data and sell it to Openai, etc. Seems like Micro1 has grown to 9 figures in revenue very very fast. What else am I missing here. I get the value of this. But doesn't seem right to me.

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.

i think you're right. many trillions is very likely.