Gillian O'Brien

Founders doing $5M+ don’t lose by picking the wrong channel. They lose by running too many at once. At that stage, complexity is the tax. Every channel can work. The mistake is spreading focus instead of scaling what already converts. The real question isn’t what’s new. It’s what’s predictable. If you had to cut everything but one channel tomorrow, which one survives?

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At Eniac Ventures we’re investing in companies built for a world of abundant intelligence. At the agentic layer, that means leaning into regulated industries like healthcare, government/defense, and financial services, where data friction is the moat, not the obstacle. The companies that will win here aren’t just building smart products. They’re willing to take on liability, to stand behind the output, own the outcome, and put skin in the game alongside their customers. At the tooling layer, it means things like security, privacy, and compliance. The model companies can’t be the ones solving this. You don’t grade your own homework. These need to be independent layers, and the need only grows as agents move deeper into mission critical environments. In the age of abundant intelligence, the hardest areas to build in will likely be where the most durable companies emerge.

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Consolidation is creating compounding chaos. Every acquisition adds another tech stack. Another CRM. Another portfolio system. Another layer of complexity. Most aggregators just absorb the mess and move on. Jim Dickson didn't want to repeat that cycle. When he launched Elevation Point, he built an accelerator—not just another roll-up. That means taking control of data from day one and putting it to work in a compounding, impactful way for partner firms. We're honored to be their data infrastructure partner. Already deep in the build—connecting Practifi, Salesforce FSC, Addepar, Orion, Goldman Sachs FICC and Equities and everything in between into one unified layer and a world class application and workflow layer. Real-time dashboards. Automated workflows. Data that compounds value instead of chaos. This is what intentional growth looks like. Excited for what's ahead with Jim and the entire Elevation Point team. 🔗 ThinkAdvisor coverage in comments

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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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The easiest way to find VC roles before they’re even posted is.... Most people apply when a VC job goes public. By then, hundreds of applicants are already competing. The smarter way is to track fund lifecycles. VC firms usually hire when: 1️⃣ They are in the 3rd–4th year of their fund: That’s when portfolio companies mature and firms need more support. 2️⃣ They are raising or have just closed a new fund: More capital = more deals = more hiring. So instead of randomly applying, track which funds are deploying capital. That’s where opportunities appear before they’re announced. Ways to find this information: • Fund announcements and press releases • Platforms like Crunchbase, PitchBook, Tracxn • LP announcements and portfolio updates • LinkedIn activity of partners VC recruiting is less about job portals and more about understanding how funds operate. If you want a list of active investors and VC funds, comment “INVESTORS.” If you want advanced career playbooks, check this out: https://lnkd.in/gE9wy_sQ

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What do small-business loans, satellites and clinical trials have in common? A founder who helped build Robinhood. For Issue 06 of Specter’s MAFIA SERIES, we mapped the companies built by Robinhood’s founders and former employees. The nine below have raised $1.67B combined. → Parafin · $562M Sahill Poddar, Vineet Goel & Ralph Furman ML engineering, risk & fraud, and data science. → Cowboy Space Corporation · $325M Baiju Bhatt Robinhood co-founder. → Harmonic · $295M Vlad Tenev Robinhood co-founder and still its CEO. → Lightspark · $175M Christina Smedley Former Chief Marketing & Communications Officer. → Vial · $111M Simon Burns Former founding GTM. → Orkes · $89.3M Boney Sekh Former engineering. → Lassie · $47M Steijn Pelle Former Product Manager. → Sei Labs · $35M Jayendra Jog Former Software Engineer. → Casap · $33.5M Shanthi Shanmugam Former Senior Product Manager. And there’s another group worth getting to know. Jason Scharff · Xuan Zhao · Ryan McCaffrey · Jasmine Gao · Mac Liu · Joshuah Touyz · Bhupendra Ubeja · Ariana D. All building in stealth, with 35 more on Specter’s radar. Their backgrounds span engineering, research, product, operations and M&A. That’s what makes these maps interesting: the next founder can come from almost anywhere inside the company. If you worked at Robinhood, whose company are we missing?

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Seth Levine nailed it! The concentration problem isn't just a GP pain point... it's choking innovation. When capital pools around the same 20 funds, emerging managers and founders get shut out. Love that he's using his platform to call this out. Foundry has backed 50+ emerging managers. That's the diversification the ecosystem needs. This is exactly why Carta exists — making capital allocation visible and accessible. Capital Evolution hits at exactly the right moment. Thanks for having us Daniel Dart. Team Carta loves the community of FUTURE TITANS you have built 🚀

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Most founders walk out of a VC meeting saying: “It went great.” But did it? Super excited to release my conversation with my dear friend Lia Cromwell , Partner at UpWest, She unpacked something simple but uncomfortable: If there’s no real next step. it probably wasn’t a great meeting. We talked about identity. About instinct. About what happens when you trust your gut before the spreadsheet makes sense. Lia grew up in New York She experienced being the outsider. She learned how culture shapes communication and how directness can be misunderstood. Years later, that cultural tension became one of her greatest strengths as an investor. We also unpacked something every founder needs to hear: Most pitch meetings that feel “great”... aren’t. If there’s no next step, no growing excitement, no pull toward a second conversation, that’s your signal. VCs don’t always say no. Sometimes they just don’t lean in. Thank you Lia Cromwell for an amazing conversation and your friendship Full episode in the comments 👇 https://lnkd.in/gsfCj4zf

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