Omri Mor

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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💰 Most retail startups struggle to raise $10M. eyewa just closed $100M. Here's what caught my attention about this Series C round: eyewa isn't just another e-commerce play. They're building something bigger—a tech-enabled eyewear empire across the Middle East. The numbers tell the story: ✅ 150+ stores already operational ✅ Plans for 100+ new locations in 2025 alone ✅ Targeting 250 total stores ✅ Launching their own production facility in Saudi Arabia But here's the real insight: General Atlantic doesn't just write $100M checks for retail expansion. They're betting on eyewa's omnichannel approach and supply chain innovation. This isn't about opening more stores—it's about revolutionizing how an entire region buys eyewear. The Middle East retail market is exploding, and eyewa is positioning itself as the dominant player before the competition even realizes what's happening. Smart money follows smart strategy. And this move shows exactly why the GCC is becoming a hotbed for retail innovation. What's your take—is omnichannel retail the key to winning emerging markets?

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San Diego-based Manifold (AI Detection and Response platform) has raised $8 million in seed funding; lead backer Costanoa Ventures, along with several institutional investors including Cherry Ventures, Rain Capital, Modern Technical Fund, and angel investors.

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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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Here's how Clay used "Reverse Demos" to find product-market fit and build a self-serve motion from scratch: When we first launched Clay on Product Hunt in February 2022, we got hundreds of signups overnight. We demo’d our product to them like we knew best: by running 30-minute presentations like college professors on Zoom. Unfortunately, few retained -- most likely because no one was actually learning during the demo. Potential customers were usually impressed when they saw me use Clay, but as soon as they tried to use it themselves, they got stuck and churned. Enter: the reverse demo. Instead of running canned demo meetings, we started emailing potential customers before meetings, asking them to come prepared with a specific data enrichment problem to solve. If they didn’t come with an idea, we’d use the first 5 minutes to come up with a personalized use case for them. Once they were in the meeting, we’d ask them to share their screen, give them a Clay signup link, and use Zoom annotations to show them exactly where to click to complete their workflow. Our goal on every reverse demo was simple: solve the customer’s stated problem within the 30 minutes -- and try to blow their minds in the process. Initially, we had to walk people through multiple calls before they had their "a-ha" moments. Through reverse demos, we were able to get it in under one call. Our goal was to build product habits: by solving any customer’s unique business problem within 30 minutes, we helped them build confidence with Clay and made it more likely for them to use the product to solve their future problems. The obvious benefit of the reverse demo was better customer conversion and retention. After 100+ reverse demos, we were getting people to “a-ha” moments within just a few minutes and most would convert after that call alone. But—much more importantly—the reverse demo helped us quickly improve Clay’s product. There’s no tighter feedback loop than getting your ideal customers on Zoom and watching them use the product for the first time. There were dozens of times that I noticed something on a reverse demo, whether bad UX, bad copy, or a bug, and asked Eric Engoron on our engineering team to ship a change the same day. Each reverse demo was a UX masterclass. That helped us go from 7 calls to convert a customer to just 1 in less than a year. Eventually this improvement gave us the confidence to remove our waitlist and launch a successful self-serve motion. This feedback loop was foundational for our growth as we navigated product-market fit and evolved our GTM motion.

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