I made a list of great startups to join. It's called the Breakout List. The list has 92 companies. These are the 20 with 25 or fewer employees: - Hone (@moritz_stephan, @CarloWillem, @oqbrady) - Normal (@ansonyuu, @hudzah) - Standard Intelligence (@G413N, @devanshpandey) - Tacit Labs (@ninklefitz, @AmDroste) - American Terawatt (@atroyn, @rslparker, @aranibatta) - Conduit (@clemvonstengel, @riopopper) - Convergent (Omkar Savant, Vivek Katara, @debnilsur) - Core Automation (@MillionInt, @_arohan_) - Engram (@dan_biderman, @EyubogluSabri, @realJessyLin) - Instinct (@noahrshinn) - Keenable (@styskin, Matthias Petri) - Lumaril (Mark Elliot, Ben Duffield) - Neion Bio (@Dimkell, Sam Levin) - Pangram Labs (@max_spero_, @bradley_emi) - Quadrillion (@echinaceous) - Re (@karnsaroya, @AnandDhillon, @thecliffwhite, @benaneesh) - Ricursive (@annadgoldie, @Azaliamirh) - Sail Research (@neilmovva, @blintzbase) - Trajectory (@rronak_, @michaelelabd, @QuantumArjun) - Watney Robotics (Sean Cheong, Ryan Gannon) Picks from Elad Gil, Charlie Songhurst, Keith Rabois, Mike Vernal, Alana Goyal, Sonya Huang, Ramtin Naimi, Marc Bhargava, Cory Levy, Aashay Sanghvi, Konstantine Buhler, John Luttig, Varun Gupta, Ray Tonsing and Avichal Garg. Disclosure: I'm a small investor in American Terawatt, Convergent, Standard Intelligence and Trajectory (in this post), and in Factory, Physical Intelligence and SF Compute (elsewhere on the list). I didn't vote. The full list is on Breakout List.

The Artificial Intelligence Underwriting Company has raised $55M to audit and insure frontier AI models. Insurance produces audits the public can trust and understand. The mechanism is simple: insurers need audits to price the risk. They pick auditors they believe will help them price the risk most accurately. Insurers then turn the audits into a price signal that cuts through political debates. Insurers are aligned with the public: if they get the audits wrong, they go out of business. That’s different from today, where labs pick their own auditors, creating public distrust. Insurance only works if we scale up auditing, and we are building the infrastructure to do that. Millions of agents are being deployed; they leave terabytes of traces and will create thousands of incidents across dozens of risk categories. To scale auditing, we need reliable agents to surface evidence traces for human review. History guides us. Take cars: auto insurers funded crash tests to price risks; crash tests led us to airbags, lowering the risk. Take electricity: home insurers funded lightbulb tests to price fire risks; today, every lightbulb carries a safety mark. Nuclear safety works similarly. We’ve been learning by doing.. In the last 6 months, we’ve audited and insured frontier agent builders like Cursor, ElevenLabs, Lovable and Harvey. For ElevenLabs to get the world’s first agent insurance coverage, a Lloyd’s of London insurer required quarterly technical audits by an auditor of the insurer’s choice. We’ve built infrastructure for quarterly technical audits of any agent, working alongside firms like Gray Swan (technical evaluations) and Schellman (audit). In the coming weeks, we’ll share our vision for how an insurance ecosystem can work for the frontier, and open source the world’s first actuarial model for frontier AI catastrophic risk. We’re hiring across all roles in San Francisco. Come do your life’s work with us. Rune & Rajiv

The Artificial Intelligence Underwriting Company has raised $55M to audit and insure frontier AI models. Insurance produces audits the public can trust and understand. The mechanism is simple: insurers need audits to price the risk. They pick auditors they believe will help them price the risk most accurately. Insurers then turn the audits into a price signal that cuts through political debates. Insurers are aligned with the public: if they get the audits wrong, they go out of business. That’s different from today, where labs pick their own auditors, creating public distrust. Insurance only works if we scale up auditing, and we are building the infrastructure to do that. Millions of agents are being deployed; they leave terabytes of traces and will create thousands of incidents across dozens of risk categories. To scale auditing, we need reliable agents to surface evidence traces for human review. History guides us. Take cars: auto insurers funded crash tests to price risks; crash tests led us to airbags, lowering the risk. Take electricity: home insurers funded lightbulb tests to price fire risks; today, every lightbulb carries a safety mark. Nuclear safety works similarly. We’ve been learning by doing.. In the last 6 months, we’ve audited and insured frontier agent builders like Cursor, ElevenLabs, Lovable and Harvey. For ElevenLabs to get the world’s first agent insurance coverage, a Lloyd’s of London insurer required quarterly technical audits by an auditor of the insurer’s choice. We’ve built infrastructure for quarterly technical audits of any agent, working alongside firms like Gray Swan (technical evaluations) and Schellman (audit). In the coming weeks, we’ll share our vision for how an insurance ecosystem can work for the frontier, and open source the world’s first actuarial model for frontier AI catastrophic risk. We’re hiring across all roles in San Francisco. Come do your life’s work with us. Rune & Rajiv

Introducing @PoeticHQ: a new AI system that executes complex multi-hour tasks with 99%+ accuracy and 10x fewer tokens than agents. We raised $50M at $500M from Kleiner Perkins, Founders Fund, First Harmonic, and Genius Ventures to build AI that does complex work inside Fortune 500 companies without hallucination. While code is too brittle, agents are too unpredictable. The work that runs the global economy - anti-money laundering, fraud investigations, underwriting - needs extreme accuracy. So we built a new kind of software that pairs the flexibility of AI with the predictability of code. When the world stays the same, Poetic runs fixed code: fast, cheap, identical every time. When the world changes, Poetic uses AI to regenerate its approach and find its way back to the objective. In one year, we went from zero to an eight-figure run rate as a team of four. Since then, we’ve scaled the team and executed the highest-stakes processes at AIG, SoFi, and Chime. At SoFi, a large US bank, Poetic reached 99%+ quality on fraud investigations in five weeks.

📣 We just raised our $45M Series B from @TCVTech and @firstharmonic, with participation from @BainCapVC, @firstround, and @AlkeonCapital. Sales is the most expensive function in most companies — and one of the hardest. Hundreds of accounts, different context, different stakeholders, different dynamics, and a different path to close. No one can do that perfectly. Critical things get missed. So we gave every account its own AI agent — working 24/7, maintaining full context, progressing it through the funnel, and guiding reps and leaders on what to do next. The greatest privilege has been building alongside companies like @Samsara @tryramp @ironclad_inc and @attentiveHQ — enterprises with thousands of sellers who are already living in the future. There are only two unbounded upside problems out there: building and selling. Every company sells. and @useactively is building the system they'll all run on. We’re excited to pull every revenue team into the future we know is inevitable.

We just raised a $45M Series B at a $500M valuation led by @a16z and @SalesforceVC to build the knowledge infrastructure for AI

EXCLUSIVE: startup Simple AI has raised $14M to help customers like Omaha Steaks boost sales with AI phone reps. Is AI ushering in a voice revival? We talked to founders @catheryn_li and @Zach_Kamran -- both former YC staffers -- about what's going on. upstartsmedia.com/p/simple-a…

Announcing Artie’s $12M Series A. When Robin Tang and I started Artie, the idea was simple: make it easy to deploy real-time streaming pipelines. Streaming pipelines used to be something only a few companies could afford to build. Guaranteeing transactional integrity, handling schema evolution, recovering from failures, and keeping latency low at scale is hard. Most teams limp along with brittle systems. But today, with AI systems that make decisions, trigger workflows, and interact with customers, stale data isn’t just slow - it’s incorrect. Real-time data is becoming non-negotiable. Today, companies like ClickUp, Substack, and Alloy, are processing 700B+ rows of data annually with Artie to power AI/ML workloads, customer-facing analytics, and operational systems. @artie_labs is a fully managed real-time data streaming platform. We move data across systems in real time - so teams can rely on fresh, accurate data to power AI products - without building or maintaining streaming infrastructure. We raised this round to double down on a simple belief: the next generation of data infrastructure will be streaming-first. Huge thank you to @daltonc and @Standard_Cap for leading the round, with continued support from @ycombinator, @PathlightVC, and incredible angels including @arashf, @bennstancil, @chrisbest, @charleshearn, and @lennysan. I also want to thank our team, customers, and partners for supporting our mission. And thank you to Chris Metinko and @axios for capturing the exclusive story. P.S. - If you want the same streaming infrastructure that Netflix and DoorDash have, but want to skip the multi-year build - reach out.