The best deals aren't competitive. That's the view of my friend and early-stage investor, @alexrubalcava. We talked on @InvestNStartups about VC groupthink, the evolution of moats, embracing technical risk, only investing in greenfield markets, and more. Technology risk creates opportunity. Many VCs avoid technically difficult startups, leaving room for investors able to assess and underwrite those risks. As AI makes software easier to build, Alex sees more potential in frontier tech and physical-world applications. AI is redefining software moats. Faster development weakens traditional defensibility. Alex favors software in regulated or high-stakes settings where accuracy, auditability, accountability, and human oversight still matter. Sales and product judgment matter more. Alex evaluates founders on whether they can ship, sell, and hire. With building easier, sales velocity and deciding what to build—and when—have become more important. Venture suffers from groupthink. His public-markets background shapes a contrarian approach grounded in valuation discipline, independent judgment, and finding opportunities overlooked by consensus-driven investors. Small funds can deliver big returns. Exceptional LP outcomes don't require trillion-dollar exits or mega-funds. An overlooked multibillion-dollar exit can be transformative for a smaller venture fund. Capital efficiency reduces funding risk. Long runways give startups time to prove their case, especially when follow-on investors favor AI narratives. Alex cites profitable, fast-growing non-AI companies struggling to raise despite strong fundamentals. Greenfield markets offer an edge. Alex prefers startups serving needs no existing technology addresses over crowded VC-backed categories. Shipping products, winning early customers, and showing demand turn unknown markets into investable opportunities. Investing expertise must evolve. Alex highlights secondary share sales as a path to LP liquidity, Amplify's founder network as a diligence resource, and the importance of steadily expanding ones circle of competence. Persistence needs experimentation. Productive founders test new products and sales tactics rather than repeating failed approaches. Alex recalls a founder who shut down a struggling startup and returned half its capital instead of grinding on. Conviction beats allocation. Alex argues the best opportunities may have just one term sheet. At Stage VP, 42 of his 45 investments were noncompetitive, showing how leading overlooked rounds can create an edge. Alex is very talented and a great investor. I hope you enjoy this one! investinginstartups.com/post…

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

Our AI found a bug that let anyone mint infinite XRP, with $94B at risk. It had been hiding in one of the most audited codebases in crypto for nearly a decade, and earned us the maximum $250K bounty. Here's how it went unnoticed, and how we found it.

"There are about 150 companies signing up every single day." Ali shut down a $7M run-rate startup to build what's now a $4B company. Solo Founders Podcast ep 31 is live with @aliansarinik of @micro1_ai. 04:10 The first thing he ever sold online 17:30 Three signals it's time to move on 24:40 The all-in Loom that deleted the revenue 31:45 "Only the founding team can inject risk" 35:50 He never knew co-founders existed 43:40 Threads, final actions, zero meetings 53:30 Inside the AI data wars 1:01:50 Why micro1 won't sell to America's adversaries 1:07:30 The case for and against going solo

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.

deeply concerning that some American data companies are selling AI training data to labs in adversarial nations. this also includes operational data from U.S. businesses turned into AI training environments, and without telling the business owners. operational data captures years of how a company works, makes decisions and solves problems. when that knowledge becomes an RL environment, AI models practice those workflows and learn from the experience that business spent years building here in the U.S. signing a deal with an American company shouldn’t mean unknowingly helping train AI for a foreign adversary. at micro1 we only work with U.S. AI labs and our allies. we won’t ever sell proprietary American business knowledge (or any other data driving the frontier of AI) to adversarial nations.