Will Price

Last week's musings. You'll notice some reoccuring themes... 𝟭. 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗿𝗼𝗯𝗼𝘁𝗶𝗰𝘀/𝗱𝗮𝘁𝗮 𝗳𝗼𝗿 𝗥𝗟 🍇 (𝗿𝗲𝗾𝘂𝗲𝘀𝘁 𝘁𝗼 𝗷𝗮𝗺)  -recurring idea i've been mulling -robotics is happening particularly in industrial setting  -still lots of problems, but the revolution is happening and there are  work arounds until the tech is really there -prices in theory are highest they will ever be (impact of tariffs?) -folks looking for pre-training data, but eventually will also need fine tuned data -what data fidelity is good enough? 𝟮. 𝗧𝗿𝗮𝗻𝘀𝗗𝗶𝗴𝗺 𝗮𝘀 𝗶𝗻𝗰𝗿𝗲𝗱𝗶𝗯𝗹𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 + 𝗲𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝗵𝗮𝗿𝗱 𝘁𝗲𝗰𝗵 🍇  -it's an absolute monster, $75B market cap and up 300% over the last 5 years. - aircraft parts, 90% proprietary but high margin - killer aftermarket business  - how many software biz have lifetime value of 50 years?! -has lots of things software investors look for (sticky revenue, high margin, defensibility in GTM and unique flywheel, focus, etc.)  -makes me think way more about hard tech viability 𝟯. 𝗧𝗿𝘂𝗲 𝗳𝗼𝗰𝘂𝘀 - from Neil Shah on Invest Like the Best - incredible listen - few people have maniacal focus and it's so important for founders 𝟰. 𝗦𝗽𝗲𝗿𝗺 𝗥𝗮𝗰𝗶𝗻𝗴 -WTF  𝟱. 𝗖𝗼𝗿𝗹𝗲𝗼 𝘁𝗵𝗲 𝗥𝗼𝗯𝗼𝘁 𝗛𝗼𝗿𝘀𝗲 -Kawasaki released prototype and it's wild! Substack for full thoughts: https://lnkd.in/gzNtkEHk

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💥 Metropolis Technologies Raises $𝟭.𝟲𝗕 💥 Funding comes in 2 rounds: $500M equity round led by LionTree and $1.1B from J.P. Morgan. Metropolis is now 𝘃𝗮𝗹𝘂𝗲𝗱 𝗮𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 $𝟱𝗕. With the fresh funding, the plan is to take their "recognition and payment-automation" tech beyond parking assets. This would include retail stores, drive-throughs, gas stations, hotels and partnerships with real estate/asset owner. Congrats to Alex Israel, Peter Fisher, Courtney Fukuda, Travis Kell, Ryan Hunt,Chris Sherman, Robert Bland, Paul Rooney, Lookman Olusanya and the rest of the Metropolis family. #fundraise #parkinginvestment #parking #parkingindustry #metropolois Parking Today Media Parking Network B.V. National Parking Association International Parking & Mobility Institute (IPMI) Tim Maloney Advisory Tim Maloney Reservations Lab

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The Headline family is experiencing incredible momentum, and today our portfolio company DeepIP is announcing a $25M Series B, bringing total funding to $40M.  This is the kind of company that doesn’t just add AI to a workflow, but it sets the standard for an entire category. The momentum speaks for itself: 1️⃣ 10x ARR growth in < 18 months 2️⃣ Trusted by 400+ law firms and leading corporate IP teams globally 3️⃣ 2M+ chats on the DeepIP platform We’ve had the chance to back FX (Francois-Xavier) L. and Edouard D., leading their Seed round with Headline. Watching them take on a problem as complex and high-stakes as patent work has been genuinely impressive. And working with them has been an absolute pleasure: these 2 are coupling very high ambition and humility. Tech built in Europe, but opening the US office very early and FX starting a life in NYC with his family with 0 hesitation in order to build a global category leader in the IP industry. What convinced me early is still what stands out today: patent teams don’t need another standalone AI tool. They need something that fits the way the work actually happens: documents, long-running matters, multiple stakeholders, and zero room for sloppy context handoffs. Big congrats to the whole DeepIP team + Korelya Capital, Balderton & Serena Proud that we’re doubling down at Headline and continuing the support for what’s next!

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We rebuilt Alpaca VC with AI. From the ground up. In production. Not talking about it. Not planning it. Actually running our entire firm on infrastructure we built ourselves. Private MCP server. RAG deployed. Knowledge base living in Supabase. Custom agents covering more surface area than our team ever could alone. Ryan Freedman internally talks about it as "riding on the back of the wave" - implementing tools as they ship, breaking things, rebuilding better. We're former builders. We know how to move fast without waiting for permission. Most VCs use AI to write memos faster. We use it to find founders others miss. Our agents don't just summarize pitch decks - they identify patterns across thousands of data points, surface non-obvious connections, track founder momentum in real-time. They're doing the work that used to take analysts weeks in minutes. But more importantly, they're freeing us to do what only humans can: build trust, make judgment calls, spot that spark in a founder's eye during a tough conversation. The real goal? Not just efficiency... Alpha generation through better sourcing, winning, and supporting. And in our humble opinion, this isn't about being cutting-edge. It's about accepting that the firms still doing venture the old way are already obsolete. They just don't know it yet. Alex Konrad did a deep dive into who is doing this best, and identified the folks alongside us at Union Square Ventures (Rebecca Kaden), Thrive Capital, Topology Ventures (Casey Caruso), DVC (Nick Davidov) + more. Which other VC firms are building with AI? 🙋‍♀️ https://lnkd.in/gDRBvaBz

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SF AI-Native Operator Takeaway #2: In AI-native PLG, the hard part isn’t conversion... it’s discovery. Many AI-native teams are still talking about PLG using a classic SaaS mental model, but based on operator conversations in SF, that model is starting to break down in fairly obvious ways. The biggest bottleneck right now isn’t conversion. It’s discovery. In traditional PLG, users generally understood the category before they ever signed up. The problem was obvious, the product’s value was legible from the homepage, and the “aha” moment tended to show up quickly in first use. In that world, PLG meant optimizing onboarding, reducing friction, and improving free-to-paid conversion because user intent already existed. AI changes that assumption. In AI-native products, users are often curious but unclear. They don’t yet know what’s possible, value depends heavily on workflow, context, data, and role, and the product can feel abstract until it’s applied directly to their job. As a result, many users stall not because the product isn’t valuable, but because they haven’t discovered how it fits into their world and how they can't live without it. This is the real distinction people kept coming back to. PLG conversion answers, “Is this worth paying for?” PLG discovery answers, “What problem does this solve for me, right now?” What’s working best in practice is less about funnel polish and more about clarity up front: role- or workflow-specific entry points, guided examples instead of blank states, and opinionated first actions that show users a concrete outcome before asking them to explore. This also explains a broader pattern across AI-native companies. Forward-deployed teams and services-heavy delivery aren’t just implementation tools; they’re discovery mechanisms. They translate abstract AI capability into concrete workflow value, observe real use cases users wouldn’t self-discover, and feed those learnings back into what eventually becomes productized. PLG isn’t going away, but in AI-native companies it’s being redefined. Self-serve no longer means self-explanatory. Education becomes part of the product, and discovery has to come before optimization. The teams making progress aren’t obsessing over conversion rates yet. They’re focused on whether users see themselves in the product, how quickly they reach a meaningful outcome, and whether the product helps users get to a meaningful outcome for themselves quickly, without too much guesswork. Bottom line: in AI, PLG is less about removing conversion friction early and much more about creating understanding first. Once they understand, they may be hooked. Tomorrow is my last SF AI operator takeaway focusing on everyone's favorite topic du jour: 996 work schedules.

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Seed VC isn’t just in a downturn—it’s facing extinction unless it evolves fast. Great read from Rob Go & David Beisel at NextView Ventures... - YC and mega-funds now dominate the early-stage landscape, squeezing out traditional seed investors - Power law thinking has gone mainstream—everyone’s chasing trillion-dollar outliers - AI isn’t helping seed VCs—it’s raising the stakes, crowding the field, and disrupting VC itself - The middle of the market has collapsed, and excess profits are gone - Hope isn’t a strategy—seed needs a new model, now Link here... https://lnkd.in/ek2MZGhm And if you like this topic, I wrote a piece on venture bifurcating into inception funds and mega funds here: https://lnkd.in/eJz5DmX6 #ai #venturecapital #vc

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Skipping your studio fundamentals? Prepare to struggle. We developed the 9point8 Collective Foundations program with a simple premise based on years of studio operations. 🏗️ Before a studio scales, raises, or launches publicly, its core assumptions need to be explicit and testable. Why it matters: 🚄 Venture studios accumulate decisions quickly. 🔹 Governance, economics, staffing, venture pacing. Each choice narrows future options. 🛑 When those choices are implicit or borrowed from other models, you lose adaptability without realizing it. Corrections later are possible, but they’re slower, costlier, and more disruptive. Go deeper: Foundations is designed to slow teams down at the right moment. The program focuses on structural clarity: 👉 what kind of studio this is 👉 how it actually creates value, 👉 what success looks like across ventures 👉 how decisions get made when tradeoffs appear. What’s been encouraging is the mix of participants. Early-stage teams who want to do this right from the start, but also more established studios who recognize the drag that unclear foundations have already introduced. Monthly cohorts are filling up and the signal is clear: This work is uncomfortable, but critical. If you’ve built, or are building, a studio, when did you realize foundations mattered more than speed? And comment or DM for the link to our sign-up page

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30+ VCs Backing Marketplace Startups Below is a cross-stage, cross-sector mix of firms that back marketplace and platform companies early and support them as they grow. Consumer and platform investors Upfront Ventures 1752vc (Formerly Pegasus) BAM Ventures Forerunner Brand Foundry Ventures Maveron BoxGroup Greycroft Alumni Ventures Marketplace natives and network effects specialists FJ Labs NFX Great Oaks Venture Capital RiverPark Ventures Left Lane Capital First Round Capital Lerer Hippeau Precursor Ventures Seed partners building two-sided platforms early Founder Collective NextView Ventures Uncork Capital Slow Ventures Bling Capital Gaingels Initialized Capital Felicis Multi-stage firms backing marketplace winners at scale Accel Bessemer Venture Partners Lightspeed General Catalyst New Enterprise Associates (NEA) Kleiner Perkins Bain Capital Ventures (BCV) Founders Fund

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I’ve spoken to over 2 dozen MDs at PE firms I can confidently say that the arb of figuring out how to implement Vertical AI at portfolio companies is very real right now It will fundamentally change underwriting for those who can do it predictably and unlock generational returns. Most are aware they need to act. Very few have.

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2 years ago today, A16Z announced they put $100M into Pinecone, as everyone and their mother tripped over each other to invest in vector databases. Given how little standalone vector DBs are valued now, the hype cycle is wild to look back on. Quick timeline below: Late 2022: • ChatGPT launches. • Early RAG architectures emerge. • First buzz around Pinecone, Weaviate, Milvus, Chroma. Q1 2023: • Massive demand surge for vector search. • Vector DBs seen as critical infra. • Heavy VC excitement (“new database category!”). Q2 2023: • Peak hype. • Pinecone raises $100M, Weaviate raises $50M. • Every AI infra diagram has a vector DB layer. Q3 2023: • Saturation hits. • Postgres, Redis, MongoDB add native vector search. • Clouds bundle it. Q4 2023: • Hype fades. • Vector search becomes a commodity. Early 2024: • Market tightens. • Attention shifts to agents, fine-tuning, orchestration.

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