Chris Hicken

While there has never been a more exciting time to be a founder building dev tooling or next-gen infra, it has also never been less investable at seed/pre-seed. I'm either really missing something or a lot of my peers are lost. As someone who has not just written, but also SHIPPED, about 75k lines of code in the past 6 months I can tell you that the evolution of how to build products has changed as much in the past year as it did in the entirety of 2007-2017. The complete rise and fail of frameworks, platforms, methodologies, etc... paved over and forgotten... that is of course except for the 1 company that gets a 1000x return from a wildly overvalued hyper-scaler or drunken growth stage investor obsessed with compounding at scale. Imagine a world where any seed investor in trends like Openstack, Hadoop, PaaS, etc all took a full loss on their investment. That's what we're looking at right now. I personally know of over a dozen well-funded seed-stage companies building in these spaces, with years of runway, scrambling to get acquired for a return of capital + several million personally while they're still relevant. If you're not seeing this unfold in front of you, you either aren't paying attention or you're satisfied playing the lottery instead of investing.

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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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Here's a test we run on every studio thesis that comes through Foundations: compress it to 50 words. Not an elevator pitch. Not a tagline. A complete statement covering what the studio builds, for whom, and what structural advantage makes the studio the right builder. Most can't do it. Not because the thesis is bad, but because it hasn't been forced through the filter that reveals what's load-bearing versus what's decoration. 🎯 The test exposes three failure modes instantly. Scope creep: claiming three sectors and two stages. Missing advantage: describing a market but not why this team wins in it. Founder confusion: operators who can't distinguish their thesis from their personal interests. Fifty words is roughly one breath. If you can't articulate the whole thing in one breath, every stakeholder conversation takes three times longer than it should. LPs, founders, follow-on investors -- they all make fast decisions about relevance. Clarity earns attention. Ambiguity loses it. Here's the lesson: if your thesis can't survive compression, it probably can't survive an LP conversation either. We use the 50-Word Test in Week 1 because it sets the floor for everything that follows. 📉 New cohorts launch roughly each month. I'll drop a link in the comments for those interested. ⚡

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"Speed is the only moat" Our friends at Genspark just announced they crossed $100M in ARR in just 9 months. Only a few months ago I interviewed COO Wen S. when they had just crossed $50M in ARR. Founders love to talk about product, brand, community. All of that matters... a little bit. But in today's fast evolving AI native world, none of it saves you if someone ships faster than you for long enough. Everyone has access to the same foundation models. The winner is who can turn them into useful shipped features and revenue the fastest. Swipe this carousel and ask yourself one question as a founder. Are you moving faster than the rate of innovation in your own market? If not, give this episode a listen for the real tactics you can apply today that take your company into hypergrowth mode.

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Founders make dozens of decisions every week. Make sure you're one of the founders who go back to check how those decisions played out. It's easy to remember the wins and explain away the misses. If you don't actively work against that, you end up with a growing confidence in your own judgment that's never actually been tested. I was in a session with a founding team that had been running for years. Experienced, sharp, deep domain knowledge. When I asked how they make product and prioritization decisions, the answer was honest: "From experience. But we don't always revisit those critically." Their experience had created assumptions that felt like facts. Hardwired beliefs about customers, pricing, and priorities that hadn't been questioned since they were first formed. In another company, a founder had a real "lightbulb" moment when she realized they'd been tracking customer data for billing and sales, but never for learning. The data to evaluate their own assumptions was sitting right there. They just hadn't thought to look at it that way. This isn't really about "data vs. gut." Most founders use both, in some mix, all the time. The deeper question is whether you have any feedback loop on your own decision-making. When you made a bet on a customer segment six months ago, do you remember what you assumed? Did it turn out to be true? If not, what did that teach you? The founders who build the best companies hold themselves accountable for their own calls. They go back, check what happened, and adjust their compass. That's how intuition actually develops. Not through experience alone, but through experience that's been honestly examined. Every correction makes the next decision a little sharper. Don't let your ego or the pace of the business stop you from developing the one asset that compounds over time: your judgement.

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You don't need funding, revenue, or a single paying customer to become a target. You just need "Founder" in your LinkedIn headline. Nick Goodman found that out building Everyday Security: fake invoices, fake trademark notices, "investors" who want your cap table before they've asked your name, and consulting pitches that read like a phishing kit with a Canva template. Scammers target founders because founders move fast, answer their own email, and are trained to say yes to anyone who might write a check. Not because founders have money -- most pre-seed founders don't. Nick is running a session on the specific scams hitting startups right now and how to spot them before they cost you time, data, or worse. I'll be there - hope you will be, too. Thursday, September 3, 10-11am PDT, over Google Meet. Register: https://luma.com/voi9aud9

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