I’m hiring a Chief of Staff to help run @ZeroPrimeVC & @AICouncilConf. It's a mini-COO role for an unusually capable generalist. Most of your time will go to operating and growing AI Council. The rest goes to increasing my leverage across both AI Council and Zero Prime: turning priorities into action, preparing decisions, protecting focus, and making sure important threads don't drop. The right person is a high-agency generalist - comfortable owning a P&L, fully AI-pilled in how you work, and low ego about the job. You should move comfortably between commercial work, marketing, events, finance, executive communication, and operational detail. The scope is intentionally broad and expected to grow. The right person will earn ownership quickly and can develop toward broader operating leadership over time. Evidence of ownership matters more to me than a matched resume. $150–180K base, bonus, carry eligibility. Full time, in person, San Francisco. To apply, send resume and LinkedIn profile to [email protected], with short answers to two questions: 1. What's the most ambiguous cross-functional problem you've personally owned through execution? 2. Why does building both an AI community business and a day-zero venture platform appeal to you? Specific examples beat a polished cover letter. If a name came to mind while reading this, please tag them. A repost also helps this find them.

The Stone Age lasted ~3.3 million years. The transistor is only 80 years old and we already have flying cars. xitter.cf/elonmusk/status/211091…

Modal, CuspAI, LanceDB and 5 other Zero Prime portcos are hiring for technical roles across AI/infra right now. Here are the freshest, all posted in the last 3 weeks: @modal | cloud infra for AI ➔ Analytics Engineer (NYC / Stockholm / SF) jobs.ashbyhq.com/modal/a518f… ➔ Member of Technical Staff, Storage (Stockholm) jobs.ashbyhq.com/modal/34858… ➔ Member of Technical Staff, SDK (Stockholm) jobs.ashbyhq.com/modal/a65df… ➔ Member of Technical Staff, Inference Runtime (SF) jobs.ashbyhq.com/modal/6195f… @cusp_ai | AI-powered materials discovery ➔ Research Engineer, Materials Foundation Models (Cambridge UK / Amsterdam / London / Berlin) jobs.ashbyhq.com/cuspai/db0d… @hexanalytics | the analytics platform for humans and agents ➔ AI Engineer Intern (SF) jobs.ashbyhq.com/hex/f2562cd… ➔ Engineering Director, AI Engineering (SF / NYC) jobs.ashbyhq.com/hex/1377177… @lancedb | multimodal lakehouse for AI ➔ Documentation Engineer (US / Canada) jobs.ashbyhq.com/lancedb/ec9… ➔ Senior System Test Engineer (US / Canada) jobs.ashbyhq.com/lancedb/0ec… @LightningAI | full-stack AI infrastructure platform ➔ Senior Infrastructure Operations Engineer, EMEA (London) job-boards.greenhouse.io/lig… @runware | unified AI inference API ➔ Creative Engineer / AI Creative Technologist (UK / Spain / France) apply.workable.com/j/CE2C2FF… ➔ Senior / Lead Cooling Systems Design Engineer (Bucharest) apply.workable.com/j/FF7C5B8… @Freshpaint_app | healthcare marketing performance & compliance platform ➔ Enterprise Implementation Engineer (US, remote) jobs.ashbyhq.com/freshpaint/… @higgsfield | AI-native creative suite ➔ Data Scientist, Antifraud (Kazakhstan) jobs.ashbyhq.com/higgsfielda… ➔ Data Analyst, Antifraud (Kazakhstan) jobs.ashbyhq.com/higgsfielda… We're hiring too, for @ZeroPrimeVC and @AICouncilConf (SF, in person): ➔ Growth Marketer zeroprime.notion.site/Growth… ➔ Chief of Staff zeroprime.notion.site/Chief-…

From Spotify Engineering Intern to co-founder and CEO of a $15.75B company. @Bernhardsson joined Spotify in 2008 while writing his master’s thesis on music recommendation. He worked his way up to Engineering Manager, and put together the Discovery team behind Spotify's early recommendation system. Our paths first crossed in 2013 when Erik joined us our DataEng Meetup in NYC, and gave an excellent talk on data processing at Spotify. He introduced Luigi - an open-source Python framework that powered Spotify's recommendations, top lists, and several internal tools. He also put up the slide in the picture: "Plumbing sucks" In his words, “you want to implement the algorithms, you don’t want to deal with... what happens if something crashes.” 13 years later @Modal is that slide turned into a company. It gives engineers building blocks for running AI and ML in the cloud, so they write the code and Modal deals with the plumbing. Today Modal announced it crossed $1B in annualized run rate and raised a $750M Series D led by Accel at a $15.75B valuation. Usage has tripled since their Series C 5 months ago. Congrats to Erik, Akshat and the entire Modal team! We've been proud supporters at @zeroprimevc since the Seed round.

Know a great growth marketer? I’d love an intro. I'm hiring one for @AICouncilConf and @ZeroPrimeVC to own top-of-funnel growth and channel execution. This is a hands-on, individual-contributor role for someone who wants to build the growth engine of an ecosystem surrounding the frontier builders of AI. To apply, send resume and LinkedIn profile to [email protected], with short answers to two questions: 1. What is a growth channel or campaign you built from zero, and what did the numbers look like before and after? 2. Where do technical builders actually discover things today, and how would you reach them? Specific examples beat a polished cover letter. If a name came to mind while reading this, please tag them. A repost also helps this find them. The opportunity: zeroprime.notion.site/Growth…

Drop by anytime between 8am and noon. We'd love to see you. RSVP: partiful.com/e/D8y8XoMl52Wgw… xitter.cf/Theoryvc/status/210723…

Literally every week in SF is Tech Week

Next Wednesday morning @TheoryVC & @ZeroPrimeVC are taking over one of SF's best-kept matcha secrets in SoMa as part of SF @Techweek_. Drop by anytime between 8am and noon, get caffeinated with us, and meet other founders and builders across AI and software. We'd love to see you. RSVP Required: partiful.com/e/D8y8XoMl52Wgw…

.@Modal is hosting an invite-only AI/ML engineering event this Thursday at the Midway in SF. Looking forward to this lineup! Who will I see there? If you haven't gotten your invite, apply to join: modal.com/runtime

.@typesafeai coined "System One models" on September 15. The name comes from Daniel Kahneman's 'Thinking, Fast and Slow'. Fast thinking is System One. Quick calls like reading the mood on a face. Slow is System Two. Thoughtful work like multiplying 17 by 24 in your head. In TypeSafe's framing, LLMs are System Two. They write every answer one token at a time, and reasoning models spend extra tokens thinking before they commit. A System One model skips the slow parts. You send it some text (like a support ticket) plus questions whose answers are fixed in advance: question: "Which team owns this?" options: ["billing", "technical", "sales", "account"] question: "Does the customer want a refund?" options: ["yes", "no"] It sends back a pick for each question with a probability attached, something like "billing, 0.94" and "refund, 0.95." Because every possible answer is known before the model starts, it can score them all in one pass and answer in well under a second. TypeSafe named Jev after the Jevons paradox. When something gets cheaper to use, people end up using far more of it. Here's a very small fraction of what's been published so far by teams testing TypeSafe's first System One Model: ➔ @MotherDuck ran it over 100,000 labeled news articles. Jev got 89% right in 40 seconds for $0.50. The most accurate LLM they tested got 88% in 32 minutes for $37.58. ➔ @LangChain used it to grade an agent's work. It matched the expected verdict on all 500 decisions for $0.34 total, against $28.17 for Claude Sonnet 4.6. ➔ @Every had it judge writing in a median 0.35 seconds per passage, against 8.83 seconds for Fable 5.1. It caught 6 of 7 planted defects; Fable caught all 7. ➔ @archestra_ai ran it on 100 tool calls from Claude Code sessions. It flagged 7 of the 9 dangerous ones to Sonnet 5's 4, (though Sonnet scored higher overall). When a decision costs a fraction of a cent and comes back in half a second, you can stop rationing it. Checks that used to run on a sample can run on every row and every agent step.

Reminder that you can pay a frontier model to solve a problem, then let a far cheaper model reuse what it learned. xitter.cf/petesoder/status/20955…

I’m hiring a Chief of Staff to help build the next chapter of @AICouncilConf & @ZeroPrimeVC. Most of your time will go toward running and growing AI Council. You’ll also work closely with me across both orgs to get important work done. This is for an unusually capable generalist. The scope is intentionally broad and expected to grow. The right person will earn ownership quickly and can develop toward broader operating leadership over time. Full time, in person in San Francisco (office near Jackson Square). Base salary of $150,000–$180,000, based on experience. Role and application details: zeroprime.notion.site/Chief-… If someone comes to mind, send this their way.

3 months ago, an anonymous account commented "Y'all are sleeping on the most important talk of this conference". The speaker was @CompleteSkeptic and his talk was on RLHF's deal with the devil, and the argument behind Jev. Diogo argued that training AI to produce answers people prefer comes with a trade-off. You get polished, coherent responses. You lose the unusual alternatives through mode collapse. The problem is that the unusual alternative can be the correct one. Three months later, @typesafeai's Jev, a model designed for decisions, is now generally available to everyone. If Jev has caught your attention over the weekend (the internet is on fire with it after all!), Diogo’s @AICouncilConf talk is worth watching. It lays out the argument behind what his team is building and why they chose this direction. Diogo's talk: youtube.com/watch?v=o-y1HJ6b…

In January, humans still ran most of the queries on @motherduck. Last month, agents ran 29× more than people did. MotherDuck looked at their own query history (UI = human, MCP = agent) and discovered - perhaps not surprisingly - that agents are now the dominant consumers of the data platform. The average org has twice as many agents as human users. The workload shape is different too: ➔ Median gap between agent queries: 4 seconds. Humans: 60. ➔ Median agent query scans ~7,000 rows. Humans: ~200,000. ➔ Agents fire bursty, small, experimental queries then go quiet. Every assumption baked into warehouse design (latency budgets, concurrency, pricing, warm caches) was made for a user who thinks for a minute between queries. The new user thinks for four seconds and then probes with ten more small queries and loops again. It isn't just MotherDuck who is seeing agent activity skyrocket. OpenAI published a report this week showing a huge climb in internal coding agent usage over the past few months. Cursor cloud agents went from ~10% of merged PRs in December to 56% by July, and Cursor now says it’s more than 60%. Internal agents are exploding and the primary user of data infra has flipped. MotherDuck's report is a useful view into what that feels like for a data platform: motherduck.com/blog/agents-d…

.@AgnoHQ just showed you can pay a frontier model to solve a problem, then let a far cheaper model reuse what it learned and nearly match its score. ARC-AGI-3 drops an agent into a game it has never seen, without instructions or goal. The agent has to experiment its way towards understanding the game, and it's scored on how efficiently it learns. GPT-5.6 + Agno's Learning Machines scored 100 on the public set (183 levels across 25 games). As it played, it saved what it learned into a per-game manual. Then Agno handed those manuals to Gemini-3.7-Flash, which had scored 37.33 starting cold. With the manuals it scored 96.42, above the 95.4 human baseline. And because nothing was retrained and no weights changed, the whole gain came from the smaller model reading what the bigger one had learned. The learning transfer made the much cheaper, small model viable for a task you'd normally trust only to the frontier. Significant, because agents burn a lot of tokens rediscovering lessons an earlier run already paid for. Pay frontier prices for discovery once, and the repeat work runs on cheaper models. Full write-up: agno.com/articles/learning-m…

Building a company in Europe is hard mode. Which is why we spend a lot of time across the pond hunting for startups! By the time a European founder has product in front of customers, they've already cleared obstacles a Delaware C-corp founder never meets. 2 of my early European deals were co-investments alongside @YTR4N_, then at the European seed fund Speedinvest. Yang built his career in Europe, and when I asked him to join me as partner at @ZeroPrimeVC, his European roots were part of the plan. Some of the European companies we've backed: ➔ @cusp_ai (Cambridge): AI-powered materials breakthroughs ➔ @pydantic (London): the end-to-end AI engineering stack ➔ @runware (London): screaming fast inference for video & image gen ➔ DataLinks (Zurich): your data, ready for agents in minutes ➔ @sodadata (Brussels): data contracts at scale ➔ QontextAI (Berlin): a brain for the AI-native company And our conference @AICouncilConf puts European teams on stage every year, like: @duckdb (Amsterdam), @UnikraftCloud (Heidelberg), LogicStar AI (Zurich), @Doubleword_ (London), Datalinks (Zurich, from our own portfolio). Then there's companies like n8n (Berlin), @Spotify (Stockholm), Mistral (Paris), @Lovable (Stockholm), Klarna (Stockholm), and DeepMind (London, now part of Google). We'll keep making the trip.

OpenAI measured 17M messages inside 1,500+ companies. Per active user, analysts out-message every job title class, engineers included. So the most intense AI use in the enterprise is landing on the work @isidoremiller calls "the cursed domain": "Easy questions look hard. Hard questions look easy. Many questions are impossible to answer; to even try is to fail. Bugs are usually silent and subtle. Innocuous assumptions (LLM’s favorite!) make or break analyses. There are no linters, no test suite, no formalization language. There is almost no realistic public data to train on or build environments from, and there is a surplus of unrealistic tutorial-slop jamming up the pretrain. Everyone’s data warehouse is out of distribution. For every right answer, there are ten plausible but subtly incorrect wrong answers, and no way to verify or validate the result." TL;DR: analytics is a hard domain, fraught with booby traps inviting confidently incorrect answers, and public benchmarks mostly test text-to-SQL on clean, self-contained datasets. Izzy's team at @_hex_tech published a new benchmark called DataBench. DataBench runs 100 analytical tasks inside a fake $129M company with 6 years of deliberately broken data, built around the conditions that make analytics hard in practice. A few highlights: > Opus 5 at high effort caught every one of the 10 signature traps. > The traps are what separate the models. Scores there run from 100% down to 30%, against a much tighter 91% to 61% range on straightforward Q&A. > Fable 5 Max leads on Q&A at 91% and is the only model that keeps improving as you spend more. If you operate in analytics, the report is worth reading. Hex: hex.tech/blog/databench-agen… OpenAI: cdn.openai.com/pdf/how-organ…

Running a technical conference is a strange thing for a venture fund to do. Every year, we sell sponsorships, tickets and welcome a mass of attendees and AI/infra thought leaders for a week's worth of events where mistakes are very visible. The ops are pUnIsHiNg. For over a decade I've built the community behind @AICouncilConf, formerly Data Council, and it remains one of our strongest sources of deal flow for @ZeroPrimeVC. Here's the recipe: speakers turn into -> founders -> and then we back them. We invest at the earliest stage in companies like Modal, Runware, Hex, Pydantic and Higgsfield. It's time for the next stage of growth - so I'm hiring a Chief of Staff in San Francisco to help me run it all. → Own the AI Council operating plan and the numbers under it. Sponsorship and ticket sales, marketing, programming, event production, budgets, P&L. → Direct the teammates and partners who already run those functions, our event agency included. You won't be building it solo. → Take some off my plate across both businesses, which mostly means turning priorities into action and keeping threads from dropping. → Occasional special projects on the Zero Prime side, usually portfolio support or fund ops. You'll build a sponsorship forecast in the morning, then spend the afternoon figuring out whether the boat for the evening happy hour community cruise has power. It's full-time and in person in SF. You should have an MBA and want to put it to use. VC, PE, consulting, or fast-growing startup experience helps. Send me an email w/ the most ambiguous cross-functional problem you've personally owned through execution, and why running a community business and a day-zero fund at once appeals to you. Know someone you'd bet on? Send them my way. Details here: zeroprime.notion.site/Chief-…