🫡 to @micro1_ai for leading the way on protecting users' privacy while keeping training data useful. Some data labs talk the talk on privacy but micro1 and @aliansarinik are walking the walk.

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.

100% support @aliansarinik and @micro1_ai on only working with U.S. AI labs and our allies.

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.

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. 🇺🇸

Surging demand for AI training data is driving rapid growth for the startup and its rivals. spr.ly/6017B1c0SB

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.

Huge congrats to @bekacru and @better_auth! We committed on the first call with Bereket and it has been a joy watch him execute at such a high level ever since. @vercel is a wonderful home for Better Auth and I look forward to seeing what they build together!

ICYMI: I chatted with @bekacru - Bereket, founder of Better Auth: one of the fastest-growing, developer-first auth products with 6M+ weekly downloads. They power companies the leading AI companies like OpenAI, Anthropic, Exa, Vercel, Databricks, Neon etc. We spoke about: - How AI agents will reshape auth + security - Why AI-native applications need a new auth stack - Why open-source and developer-first approach wins - Better Auth's company journey piped.video/watch?v=Xe6UTqFO…

VCs marked safe from frontier models

better-npm. Every npm package with 50k+ weekly downloads gets analyzed by AI and static analysis before it hits your node_modules - prevents typo squatting - blocklist pkgs you don't want agents installing - open source one cmd: ~ npx @better-npm/cli enjoy!

Today we're announcing Agent Auth Protocol An open standard for agent authentication, capability based authorization and service discovery ⇃read more ⇂

🎉 50 episodes of @InvestNStartups! We’re marking the milestone with a special episode packed with the biggest insights and hardest lessons I’ve learned from hosting the show. We dive into the game-changing impact of AI on founders and investors, the fiercest debates shaking up venture, what I’ve completely reversed my thinking on, and my most contrarian take on VC. Here's the longer breakdown of my conversation with good friend and guest host @ChrisHillALX, host of @MoneyUnpluggedP: AI has been the defining force in venture since the show started in 2024. AI has changed how startups get funded, how fast they grow, and how lean they can be, helping revive venture after the post-2021 downturn. The rebound has been real, but highly concentrated to the biggest winners. AI is also driving growth and efficiency at once. Startups can scale revenue much faster without scaling headcount the same way, pushing more value toward companies that pair strong demand with small teams. One of venture’s biggest debates is around portfolio construction. I talked about the tension between ultra-diversified investing, which improves the odds of catching a unicorn at the expense of diluting its impacts to returns, and concentrated investing, which can raise the quality bar and leave more time to support founders. Another major split is hot deals versus non-consensus deals. Some investors want the most in-demand founders regardless of price, while others hunt for overlooked opportunities in less fashionable markets and sectors. Several guest conversations changed my thinking. One example was @MacConwell on reserves: instead of setting aside large amounts for future rounds that may never materialize, I now favor a “fast follow” approach when conviction rises. Another lesson, from @nikiscevak, was the importance of finding your tribe with fundraising. Rather than trying to change minds, it is more effective to build a clear identity and attract founders and LPs who already share your worldview. My most contrarian take is that ownership targets are overrated. What really matters to LPs and fund performance is not what percentage of a winner a fund owns, but what percentage of the fund’s capital is invested in the winner. We also talked about emerging managers and the insights from guests on why they're able to outperform. Namely, that smaller, newer funds are hungrier, more focused, and more flexible. Lastly, we talked about AI's role in VC. AI will increasingly help investors analyze startups, move faster, and operate with fewer people, even if judgment remains difficult to automate. AI will also narrow the gap between small and large venture firms by giving smaller managers better tools without requiring a large internal team. Thanks to Chris for guest hosting this one, to all our listeners, and to the 50 amazing guests who have come on the show!

Excited to introduce micro1 Cortex, a contextual evaluation, visibility, and improvement platform for enterprise AI agents. Foundational models are trained for general intelligence, but enterprises need agents that perform reliably inside their unique context: workflows, policies, data environments, and edge cases. Cortex brings trust to enterprise AI by leveraging domain experts and real-world scenarios for any use case to test, diagnose, and improve how agents behave in production.

We've been busy - soon

Excited to announce Better Auth Infrastructure Get started: better-auth.com

My first interview with @aliansarinik, Founder & CEO of @micro1_ai. 1:10 Continuous model improvement 2:42 Starting with an AI recruiting tool 5:42 Moving into the human data space 7:19 The Human Happiness Index 13:32 Creating a real world robotics dataset 17:03 Hiring hundreds of doctors & lawyers in a week 20:15 Providing a world class white glove service 22:15 Hiring for agency & risk taking 26:03 High velocity on two way decisions 27:35 Going all-in on data 28:52 Structuring incentives 38:18 Leadership & staying in the details 40:10 Losing their biggest customer right before an investor pitch 43:24 Becoming risk averse after an existential crisis 50:45 Taking action as fast as possible when you feel the urge to act 56:12 What it felt like growing revenue 30x in a year 1:00:48 Staying focused & limiting new projects 1:02:30 Predicting what the big AI labs will want 1:06:50 Creating a model to predict timelines 1:12:23 Hiring as a last resort 1:18:25 Taking big bold bets 1:28:49 Long-horizon tasks

We spun out of the #1 hacking team in the US and built AI that finds what even the best hackers miss. During one engagement, it found 6 different ways to take over any user's account on a popular webapp. Completely autonomously. Then suggested fixes for every single one. Today we're announcing @verialabs' $3.2M seed, backed by @ycombinator, @gokulr, @paulg, and @woloski (co-founder of Auth0), and many other great investors. DM me if you want to know what we'd find in your app.