Paul McDonald

Today, we’re thrilled to publicly launch Phoebe and announce $17M in seed funding, in rounds led by GV and Cherry Ventures. We are building the immune system for software: An AI agent platform that continuously investigates live data, diagnoses emerging issues and generates preemptive fixes. As AI accelerates how software is built, reliability must keep pace. Phoebe started with faster troubleshooting, and our aim is to prevent the overwhelming majority of emergent problems before becoming customer-impacting failures. Huge thanks to the hundreds of early users at customers such as Trainline and PPRO. Thanks to our investors, including 20 CTOs and tech veterans joining as angels. And of course, thanks to the small and mighty Phoebe team! This is just the beginning! 🚀 Shoutout to Mark Kleinman at Sky News for breaking the news this morning (linked in the comments) Some of the many backers we're grateful for 🤗 🙏 GV (Google Ventures), Cherry Ventures, Anthony Danon, Carmen Alfonso Rico 🍫, William Gaybrick, Shola Akinlade, Yaron Shaer, Christian Dorffer, Tom Stafford, Thomas Wolf, Andrew Tan, Victor Riparbelli, Harsh Sinha, Jeanne DeWitt Grosser and more

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Where are seed-stage AI companies finding real, outlier traction—think multi-million ARR within months or 10× traffic in a year? We looked at ~30 startups with breakout revenue or traffic growth, and the data points to four themes where customer demand is exploding right now: Agentic Workflows are replacing SaaS Startups are building AI "teammates" and agents that own entire workflows, from SEO to recruiting. The ROI is immediate and the market is rewarding it with real revenue. Generative Tools becoming Pro-Creative Engines    GenAI is moving from a fun toy to a serious creative and marketing platform. Startups are seeing big user growth by empowering both pros and a massive long-tail of creators to build high-quality content and businesses. Dev Stack, Observability & Runtime As LLM apps move from pilot to production, developers need the same primitives they expect for traditional software: repeatable deploys, cost and latency telemetry, and notebook-grade experimentation.  Compute & Inference Infrastructure GPU scarcity and inference costs create urgency for elastic, pay-as-you-go capacity. Teams that package raw compute into a “one-click deploy” experience are becoming a starting point for AI workloads. The big question?  Which of these opportunities will be sustaining, long term independent categories? Personally, I'm most bullish on agentic workflows in the form of AI Teammates being significant long term opportunities. Would love your thoughts. If you’re building in one of these lanes—or see a new one emerging—I’d love to compare notes.

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the last few weeks feels like a mini breakthrough for AI apps hiding in plain sight - partial autonomy, high agency models, and context eng vs prompt eng. are all incredibly important concepts a lot of these principles were apparent but were just recently articulated (mostly by Karpathy - legend!!). one is the concept of partial autonomy apps - autonomy sliders, app specific UI, and “keeping the AI on a leash” two is the idea of small, high agency models that know how to solve problems vs knowing answers via tool-use / reasoning / multimodality (“maximally sacrifices encyclopedic knowledge for capability”) three is context engineering vs prompt engineering - putting the right stuff into working memory, building around the “jagged intelligence” of models etc these should probably be the starting point for founders thinking through AI native apps Refs: Partial autonomy apps: [Karpathy @YC startup school] High agency mini models: https://lnkd.in/gthCXtMe Context Eng: https://lnkd.in/gha2i7BX

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Excited to share episode 5 of the Slow Hunch Podcast, with Zoe Weinberg, founder of ex/ante . Zoe makes a compelling case for why technology isn't neutral - it's purpose-built, and architectural choices matter enormously. We explored how the next wave of founders have the potential to build tech that enhances human agency rather than diminishing it. A key insight for me was that the tradeoff between user convenience and user control is not inevitable. We have the potential to build technology that's both magical to use AND preserves individual autonomy. Full episode here: https://lnkd.in/eaGc2sah

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⚠️ Founders - consider the below reasons to not use Forward Deployed Engineers (FDEs) to acquire your *first* customers. This signals (1) lack of PMF; (2) miscalculation b/w development and sales timeframes; and (3) mischaracterization of Palantir Technologies timing when they deployed their FDE model. 💡 At the early-stage, it's crucial to demonstrate ability to repeatedly acquire customers with limited resources - capital, team, & time - that supports the narrative of 'more funding will amplify my traction.' Bespoke solutions for one company demonstrates zero repeatability. These custom solutions will not carry over to other customers within the same cohort. 💡 AI accelerates engineering but sales, system integration, and stakeholder buy-ins are perpetually slow. This imbalance results in concentrating all your engineering resources into one company, ignoring other opportunities, and still not going live on an accelerated timeline. 💡 Palantir didn't start using FDEs until year 3 when they got their first *gov't* customer, not their first customer. Gotham was already built and had strong PMF. Gotham allowed Palantir to take GTM risk by using FDEs b/c if it didn't work, Gotham was humming in the background. TLDR: At the early-stage, repeatable customer acquisition is the holy grail. Don't let AI deceive you that it can accelerate human behavior like it has with development cycles. It C.A.N.N.O.T.

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