Raman Malik

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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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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Product-market fit genuinely feels different...and it'll show up in the data Demo to close rates are the easiest thing to look at for product-market fit right now. Vertical AI startups in our portfolio are seeing 50%+ demo to close rates, even with brand new account executives selling the product. It's happening in every market from AI takeoffs to AI CSR/Voice software to AI procurement solutions. When you see a demo to close rate over 50% chances are you have a jaw dropping customer experience and you should put your foot on the gas. If you're seeing something dramatically lower in mid-market or SMB ACV categories, it's worth revisiting your product and/or your pitch.

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The hottest role in AI startups right now isn’t Forward Deployed Engineers. It isn't GTM Engineers. It’s Deployment Strategists. Decagon calls it an “Agent Product Manager.” Harvey calls it a “Solutions Architect.” Palantir Technologies has had versions of this role for years. And the salaries are climbing fast: - Decagon: $200k–$285k - Palantir Technologies: $120k–$200k - Figma: $150k–$260k - Ramp: $100k–$180k - Harvey: $190k–$260k So who are these people? They are usually pseudo-technical -- CS or engineering majors, or folks with technical work experience. Many come from 2 years in consulting, IB, or PE, then jump into startups to get their hands dirty. They are young, hungry, polished, and comfortable being in front of customers. What do they actually do? They make sure enterprise AI deployments succeed. A $100k+ deal does not survive on a nice pitch or a self-serve onboarding flow. It survives if the customer sees value in the pilot. That means: - Embedding directly with the customer - Designing prompt logic for specific workflows - Working with engineering to align integrations and data flow - Helping exec teams define their AI roadmap - Running feedback loops into product and GTM Why does this role matter so much? Because enterprise AI is messy. Integrations, data transfer, and adoption make or break a deal. Most buyers are using AI for the first time, and each has unique workflows. Deployment Strategists bridge that gap. They own the outcome. They are accountable for making pilots successful, which often means millions in revenue down the line. At Conversion, Sam Bochner has been leading this work for us. We are now thinking about scaling it into a full team. Because a few successful pilots can fund an entire department, and the cost of failed deployments is too high to ignore. Is this just a rebrand of customer success? Not really. Success is about answering tickets and renewals. Deployment Strategy is about going deep with a few enterprise accounts, extracting maximum value, and ensuring the pilot closes into a multi-year contract. Call it Agent PM, Solutions Architect, or Deployment Strategist. Whatever the title, this is becoming one of the most important roles in AI SaaS.

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Orange Health Labs has always been committed to six-hour reporting. Not "as fast as possible" but specifically six hours, no exceptions. This one constraint made them build everything differently. They couldn't use standard labs designed for average daily volume - they had to build for peak hourly capacity. They couldn't have doctors at each location, so they built remote pathology, where one doctor reviews slides from multiple cities. They couldn't rely on traditional logistics - so they created dedicated networks covering four times the area of competitors. Now incumbents can't copy it without scrapping their existing infrastructure. They have hundreds of labs built the old way, doctors hired locally, and established logistics. Retrofitting would cost more than starting from scratch, and starting from scratch means abandoning their existing business. I like how Orange Health's edge is that matching their model means incumbents must treat their current infrastructure as sunk cost. This is the kind of advantage that compounds.

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Most accelerators give you a pitch coach and a playbook. Black Flag gives you access to Palantir. 🏴‍☠️ Every Black Flag startup now gets free Palantir credits to build directly on Foundry and AIP! That means early-stage critical technology startups can now move faster, ship better product, and plug into infrastructure trusted by the U.S. government. This isn’t a startup perk. It’s a national advantage. Black Flag exists to support the next generation of mission-driven companies. Palantir has spent two decades solving the hardest problems in national security. Now, we’re teaming up to help new founders do both at once. If you’re building for the warfighter, the analyst, or the operator—we want to hear from you. 🏴‍☠️🏴‍☠️🏴‍☠️

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Most delivery companies focus on speed. DoorDash is chasing inches. The secret to dominating a brutal industry? Obsess over the “last 100 feet”: Most tech founders talk strategy from a whiteboard. Tony Xu puts on a Dasher jacket and delivers meals himself. He does it because of one belief: every second counts. A late dessert or the wrong entrance wastes time. Multiply that by millions of orders, and you’ve lost the edge. So DoorDash built its own mapping tech. It tells drivers exactly where to park. Which entrance to use. Even when to drop an order that’s running late so others aren’t at risk. Tiny moves. Massive outcome. Today, the company owns 60% of the U.S. food delivery market. Double its next competitor. You won’t get there copying the big players. You get there by noticing what they miss. Ask yourself: What’s your “last 100 feet”? Find it. Own it. That’s how you win.

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🚨NEW EPISODE: Recorded live at FUTURE TITANS 2026 - Jeff Perry of Carta sat down with the iconic Seth Levine, co-founder of Foundry. Seth has been in venture for 25 years, built Foundry from scratch as an emerging manager himself, and has backed about 50 emerging manager funds through his fund of funds. He has genuinely seen every side of this table. They went deep on building Foundry, why VCs are in the influence business, not the decision business, and why the concentration problem in venture is not only bad for LPs, but also for the innovation ecosystem overall. And why Seth's new book, Capital Evolution, is so important for the future of America. 🎧 Links to listen... Apple: https://lnkd.in/ehQUQ2EM Spotify: https://lnkd.in/eU4FExpg

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I always like when nuance goes viral. Ashu Garg and Jaya Gupta wrote a fantastic post and state upfront that agents vs systems of record is the wrong debate. My 🌶️🌶️ spicey 🌶️ 🌶️ take is that this is THE question for #verticals for 2026, and the answer is "it depends". Lame, I know. I disagree with them in spots, and the primary place is the (dis)advantage of incumbent systems of records (SOR). Some are VERY well positioned to build the next context graph. Not in all verticals, maybe just in a few, but the issue is not what part of the customers data the incumbent SOR owns, but the RELATIONSHIP the incumbent SOR has with the customer. Are they used across multiple functions? Are customers increasing their spend with the incumbent? Is the incumbent SOR a verb in your organization? An incumbent SOR can easily layer on the "context graph" if they are these things, and keeping up with technology. That's a bad place for startups to be because you can't wedge yourself in. But in many verticals, the incumbent system of record is loathed by the employer and the employee base. Data is strategically withheld from the system, decisions are made elsewhere, and the ability for the incumbent to capture that context is insurmountable. Those are the places where founders will want to build and live!

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A pilot with the wrong partner is worse than no pilot at all. In healthtech, not all early customers add value. Some pilots erode investor confidence, stretch your sales cycle, and quietly damage your valuation. In this week’s HealthVC newsletter, I break down: • Why misaligned pilots kill momentum • How to structure pilots for credibility and conversion • Scripts to set milestones and decision points • The “Do and Do Not” framework for pilot success Done right, a pilot becomes a powerful sales and fundraising asset. Done wrong, it can stall your growth for months. 📩 Read the full issue at HealthVC on Substack

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An emerging VC manager's fundraising lessons... the first two funds are a grind. Now, we’re on our third fund, it feels like we’re almost over the hump, but fundraising never gets easy for most of us. I want to congratulate the emerging VC firms presenting at this year’s RAISE Global conference. As former RAISE presenters, and (soon-to-graduate) emerging managers, we thought we’d share this LP Translator, a lighthearted guide to decoding what LPs really mean during the fundraising process. Fellow GPs, which ones did I miss? 👇 The LP Translator 📣 "Let’s stay in touch.” Translation: We’re not interested. “We want to see your track record develop.” Translation: Either we don't believe in your strategy, or we’re focused on managers with more buzz. “We’re not allocating to new managers right now.” Translation: We’re not allocating to you right now. "Show us your deals so we can get to know you.” Translation: We’d like free co-invests if you get something hot. “We need to see more DPI before we commit.” Translation: We don’t really understand VC, but we’re pretending to. “We’re fully allocated for this year; check in early next year.” Translation: Next year we’ll still be fully allocated (just not to you). “Call us before final close.”  Translation: I’m too polite to say no at this time, so I’m kicking the can down the road. “Your fund is too small.”  Translation: Okay, that one might actually be true (for some LPs). “We went through your data room and want to meet face-to-face.” Translation: We’re genuinely interested, keep going! “Can you send us your LPA for signature?” Translation: Let’s go! 🚀 -------------------------- Fundraising is a long game, longer than we ever expected. We're now seeing how LP relationships are built across several funds, not several months. If they’re investing time to learn about you and your strategy, that’s your best signal of real interest. #emergingmanager #venturecapital #LP #RAISEGLOBAL

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AI isn’t just another technology wave, it’s reshaping how value is created, priced, and captured across software and services. As AI systems take on human-emulating tasks/ roles, pricing, margins, go-to-market strategies, and even valuation frameworks must evolve accordingly. We are excited to share our thinking in this three part series and would love to engage with founders and investors alike in this topic.

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Great piece by Alex Konrad on what this week’s OpenAI and Anthropic healthcare announcements really signal for founders and the ecosystem. Thanks for including my perspective. As I shared with Alex: healthcare has always been a massive opportunity, but what feels different now is ecosystem readiness. Patients and providers are finally prepared to adopt AI in ways that fit real workflows, with serious enterprise partnerships acknowledging that healthcare change doesn’t happen in isolation. These launches feel less like an overnight disruption and more like a formal declaration of intent—raising the bar for startups while underscoring how critical trust, privacy, and deep domain relationships remain. AI will increasingly be a primary interface for analysis and decision-making in health, even if it doesn’t ultimately create a single “master” access point for patients. Worth a read: https://lnkd.in/ebYp_U7D

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What kind of mindset does it take to go from building data systems at Palantir, to leading engineering at Komodo Health, to becoming CEO of Particle Health – and suing one of healthcare’s largest incumbents? Jason Prestinario is a First Builder through and through. As CEO of Particle Health, his mission is to fix patient data interoperability, so your primary care doctor, specialist, and surgeon can all make decisions using the same complete health record. What a concept, right? And yet, it’s far from reality. That’s why Jason and the team at Particle are now suing Epic, one of the industry’s largest incumbents, to push for real change. We talked about how he approaches challenges of this scale, and what keeps him grounded in the process. We sat down with Jason to talk about this and much more. Hope you'll enjoy this spicy and insightful episode! Link in comments 🌶️ 👇

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Retention is the most underestimated growth lever in SaaS. Most teams pour energy into acquisition, then act surprised when churn quietly erodes the business underneath them. In this In Revenue Capital blog, I break retention down into three systems that actually move the needle: - Strong customer relationships built on real understanding, not reactive support - Onboarding that drives time-to-value instead of feature exposure - Upsell and cross-sell motions rooted in customer outcomes, not quota pressure Retention is not a customer success problem. It is a company-wide discipline. When customers win consistently, growth follows. Full breakdown in the blog, link in comments.

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Big milestone for Firmable. We’ve raised $14m Series A led by Airtree. Sales has moved through a few big waves: intuition-led, CRM-led, data-led. We’re now entering the next one – intelligence-led sales. The opportunity isn’t just better data. It’s turning that data into clear direction and action, without adding more work for sales teams. That’s what we’re building at Firmable: a foundation of trusted external data, layered with intelligence that helps sellers know who to focus on and when. Led by Airtree, this round supports our expansion across Asia and into the US – and accelerates the build-out of AI agents that take the admin work off sales teams so they can focus on what they do best. Proud of the team, grateful to our customers and investors. We’re just getting started. Read the exclusive in the AFR. https://lnkd.in/gr66uknb Leigh Jasper | Tara Salmon | Karthik Venkatasubramanian| Chester Thompson| Chath Widanapathirana

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I had a great time chatting with Patrick O'Shaughnessy on Invest Like The Best. I've known Patrick since college, and this is the first time we've talked markets and investing at this much depth. The fundamentals of company building haven’t changed: people, products, and markets matter. But obviously, private markets have evolved substantially over my career: there are now ~6x more private unicorns than public companies with a $1b+ market cap. And at the end of 2010, just 2 public technology companies were among the top 10 in market cap; today it’s 8 of 10. AI (alongside software eating everything more generally) is clearly driving a lot of this. But it’s instructive to look at everything from the steam engine, to the early days of Facebook and Google user monetization, to real-time success stories like Databricks, Anduril, OpenAI and Waymo, to get a clear picture of where the opportunities lie. It was a pleasure to go deep on all this and more!

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