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  1. Ignite Startups: The Startup Giving Brands an Edge in AI-Powered Search with Max Sinclair | Ep213

    23시간 전

    Ignite Startups: The Startup Giving Brands an Edge in AI-Powered Search with Max Sinclair | Ep213

    Imagine you wake up tomorrow and Google quietly matters 50% less to your business. Your customers still search, but they’re not typing into a box—they’re asking a bot: “What’s the best B2B payments platform for marketplaces?” or “Which laundry detergent is safest for kids?” Whoever wins that answer wins the customer. That’s the world Max Sinclair is building for with Azoma, and the heart of this Ignite Startups episode. If you don’t have time (or patience) for a full podcast listen, this is the executive-summary-plus-nerdy-commentary version. Who’s Max, and what is Azoma? Max went straight from university into Amazon and hit the fast track: * Early team member at Amazon Business in the UK * Worked on search & browse for Amazon Singapore * Ran parts of EU Grocery So he’s lived inside the machine that decides what products you see, in what order, and why. Then two big things happened: * He joined Entrepreneur First (EF), a 4-month “founder bootcamp” that mashes up The Apprentice with Love Island—except the prize is a cofounder and a term sheet. * ChatGPT launched in late 2022 and made traditional e-commerce search look… kind of dumb. Max connected those dots and built Azoma: a platform that helps big brands show up as the recommended answer when people ask AI agents and answer engines what to buy. Not just on ChatGPT, but across marketplaces and emerging “answer engines” that sit between customers and products. From SEO to AEO: The shift Max is betting on For the last 20 years, the game was SEO: * Keywords * Backlinks * “Top 10” listicles * Hoping Google sends you traffic But as Max points out, that model assumes: People search with keywords.Google returns a list of blue links.You fight for position #1–3. That’s already breaking. LLMs and “answer engines” flip the script: * Users ask a question (“What’s the best X for Y?”) * The model synthesizes across multiple data sources * It returns one or a small handful of recommended products or brands You’re no longer optimizing pages for keywords.You’re optimizing your product data, content, and presence so that AI systems feel confident naming you in their answer. Max calls this Answer Engine Optimization (AEO). Roughly: SEO = “How do I get Google to show my page?”AEO = “How do I get AI systems to pick my product when users ask a question?” It’s a subtle but brutal shift. Instead of getting a tiny slice of lots of searches, you either show up in the answer—or you don’t exist. How Azoma fits into this new stack At a simple level, Azoma does three big things for brands: * Simulates how AI systems “see” your productsThey run ChatGPT-style prompts and agent-like flows against your catalog and category.Example: “What’s the best dog food for sensitive stomachs?” * Does your brand show up? * Does the model understand what your product is good for? * Is the answer actually accurate and on-brand? * Finds gaps and weaknesses in your product data and contentAI models don’t just read your product title. They use: * Attributes (ingredients, materials, use cases) * Reviews * FAQ content * Category and comparison data If your product is technically perfect but your data is vague, the AI will favor a competitor with clearer signals. * Helps generate and optimize content to fix those gaps at scaleNot fluffy blog posts—structured, machine-readable, grounded content that makes answer engines confident:“This product is specifically great for X scenario for Y user.” It’s like user research, product marketing, and technical SEO had a baby, and that baby only cares about what AI agents think of you. Why this is hard (and why that matters) Max doesn’t pretend this is just “SEO but with more AI”. A few hard truths come through: * Most brands are still optimizing for yesterday’s channel.Budgets, agencies, and dashboards are glued to Google Ads and SEO rankings. Very few teams track: “How often do we get recommended by LLMs when someone asks about our category?” * Fragmented ownership kills progress.Who owns AEO? * Search & merch teams? * Brand marketing? * Performance marketing? * E-com / marketplace teams?Usually: everyone a bit, so no one really. * Data quality is an unsexy moat.It’s easier to talk about “AI” than to fix messy product attributes, inconsistent descriptions, or half-baked category taxonomies. Yet that’s exactly what LLMs feed on. Azoma’s edge is partly timing (they started before the hype cycle caught up) and partly this willingness to grind through all the boring bits—data standardization, structured content, edge-case behavior—so the “AI magic” actually works. The founder story: Leaving Amazon for an ambiguous problem The career risk part is worth pausing on, especially if you’re an operator thinking about founding. Max left a prestigious, well-paid, high-upside Amazon track and jumped into EF with: * No idea yet what company he’d build * No guaranteed cofounder * No clear category Just a directional conviction: search is broken, and the way people find products will change as models improve. What stands out: * He didn’t leave for a specific startup idea. He left for a space. * He used EF as a forcing function to: * Meet a technical cofounder * Filter ideas based on real customer pain, not just hype * Get early capital and momentum There’s a nice realism underneath: he openly admits the first couple of years felt early and hard. Brands didn’t fully get it. Budgets lagged. The terms “AEO” and “GEO” (Generative Engine Optimization) weren’t even in circulation yet. They kept going because a small group of forward-thinking customers did get it, and the underlying trend—LLMs eating search—was only accelerating. GTM in a weird, emerging category One of the most interesting parts of the episode is how Azoma actually grew despite: * Selling into big, slow-moving enterprises * Inventing language for a category that barely existed * Competing with agencies and internal teams who claimed they “already do AI” A few key moves: 1. Obsessively inbound, not outbound Instead of brute-forcing cold outbound, they: * Built nerdy, specific content around AI search, answer engines, and practical AEO * Turned early customer experiments into stories and playbooks * Let “what the hell is AEO?” curiosity pull people in In other words: they treated their own brand like a case study in AEO/AI search, not like a typical SaaS doing generic thought leadership. 2. Start narrow, then expand They didn’t try to boil the ocean. They focused on: * Categories where AI answer quality really matters (CPG, healthcare-adjacent, complex consumer goods) * Brands with high stakes and budget, as opposed to SMBs who can’t pay for deep integrations Once they proved it in a few lighthouse accounts, it became easier to generalize the story: “We help brands win in AI-powered search, across marketplaces and answer engines.” 3. Sell the future with receipts from the present Max isn’t just waving hands about “AI will change everything”. He walks customers through: * What’s already live today (Rufus, ChatGPT plugins, marketplace answer modules, etc.) * Internal experiments they’re seeing across retailers and marketplaces * Concrete deltas: “Here’s how often you’re recommended now vs. after we fix your data and content.” This is the key: AI hype opens the door; measurable change in recommendations closes the deal. So… what should founders and operators actually do? If you’re running a B2B SaaS, marketplace, or consumer brand, here’s how to translate the episode into action without becoming an Azoma customer tomorrow. 1. Audit your “AI surface area” Ask a brutally simple question: “If someone asks an AI agent what to buy in my category, do we show up at all?” Try: * ChatGPT / Claude-style models * Retailer search where LLM features are rolling out * Marketplaces experimenting with AI assistants Don’t over-interpret a single query—but get directional signal. 2. Fix your product and content fundamentals Before “AI strategy”, fix: * Clear, structured product attributes (not just “great quality” fluff) * Specific use cases (“great for…” with real contexts) * Rich, accurate FAQs and comparisons LLMs reward clarity and context. If your data is a mess, no model will save you. 3. Decide who owns “answer engine visibility” Someone on your leadership team should explicitly own: “Do we show up when AI systems answer questions in our category?” That person likely needs to collaborate across: * Product / merchandising * Brand & content * Data / engineering But if it’s truly “everyone’s job”, it will quietly be no one’s priority. 4. Treat AEO as a long-term compounding bet, not a quick hack This isn’t a “change a meta tag, get traffic next week” move. It’s closer to: * Building a data moat * Training an entire category of models that your product is the safest, most relevant answer for specific jobs You start now because compounding works in your favor: the earlier you become the “default” in AI answers, the harder it is for competitors to dislodge you later. Lessons from Max’s journey (beyond AI) Zooming out from search, there are a few founder principles embedded in Max’s story: * Career safety is often the biggest trap.The Amazon career path looked perfect on paper. But the ceiling on impact per decision felt lower than what he wanted. Leaving wasn’t logical; it was directional. * Being early hurts… until it doesn’t.For a while, customers didn’t care about AEO. Then the world snapped to where Azoma already was. Founders underestimate how long that “too early” valley can feel. * Category language matters.“Answer engine optimization” gives people a mental model. Once a term exists, budgets and OKRs can attach to it. * Tech alone isn’t the moat; distribution and insight are.Running

    32분
  2. Ignite LP: Cracking the Code of Accredited Investing with Leyla Kunimoto | Ep212

    4일 전

    Ignite LP: Cracking the Code of Accredited Investing with Leyla Kunimoto | Ep212

    Imagine you’re at a buffet where half the dishes are behind a velvet rope. You can see them—private equity, venture funds, secondaries, slick CRE deals—but there’s a sign: “Accredited Investors Only.” Most people either walk away or hop the rope blindly. Both are bad strategies. In this conversation, Leyla Kunimoto—co-founder of Accredited Investor Insights—opens the rope, explains what’s actually on the table, and hands you a fork and a checklist. If you’re curious about alternatives but allergic to hype, this is your field guide. The Big Picture: Access Is Rising, Asymmetry Still Rules Over the next decade, more retail capital will be invited into private markets through friendlier wrappers (target-date funds, interval funds, feeder platforms). That’s the access story. The catch? Information remains uneven. Pros look beyond glossy decks: position sizing, manager selection, reporting quality, and exit pathways matter more than any single “hot” thesis. Translation: It’s becoming easier to get in; it’s not getting easier to be good. What Leyla’s Learned the Hard Way * Diligence ≠ Googling. Real diligence feels like investigative journalism: triangulate sources, verify track records, and separate marks from money. * Illiquidity bites twice. You pay a time tax (can’t exit) and a behavioral tax (panic when you want out but can’t). Price that in. * Secondaries aren’t a magic escape hatch. A $50k LP slice is often too small and too fussy to move quickly at a fair price. * Tokenization is plumbing, not pixie dust. Digital rails help with settlement and fractionalization, but they can’t fix bad underwriting or poor reporting. * Concentration is earned. Start diversified while you learn. Concentrate only where you have repeatable edge. The LP Quickstart Playbook 1) Pick Your Arena (Before Your Deals) * Define your why: return profile, time horizon, risk appetite. * Choose 1–2 core arenas (e.g., early-stage software funds, private credit) and ignore the rest for now. * Draft a 3-year pacing plan: small, regular commitments > sporadic, oversized bets. 2) Underwrite the Manager, Not the PDF Ask the questions GPs don’t expect: * Sourcing edge: Where do your best deals come from that others can’t access? * Decision evidence: Show me call notes and pre-investment memos from winners and losers. * Portfolio construction: Target # holdings, check sizes, reserves, follow-on policy; what breaks this? * Exit math: Walk me through 3 realized outcomes—how did cash actually return? * Data room reality check: What’s missing and why? 3) Underwrite the Vehicle * Liquidity: Is there any structural liquidity (intervals, periodic tenders)? If not, assume zero. * Fees & waterfall: Where can value leak? Simple beats clever. * Reporting cadence & KPIs: What shows up quarterly, and can I reconcile marks to cash? 4) Underwrite Yourself * Position sizing: Size so a zero doesn’t derail your plan. * Behavioral guardrails: Pre-commit rules for adding, holding, or pausing commitments. * Documentation discipline: Keep a one-pager per commitment: thesis, risks, “kill switches.” The Diligence Stack (A Simple, Repeatable Flow) * Screen90-second filter: strategy fit, team coherence, portfolio construction sanity. * CorroborateReference calls (LPs and portfolio founders), cross-check bios, verify realizations. * Stress the modelRebuild returns with conservative exit timelines, lower multiples, real dilution. * Mark-to-cashAsk: “How do these marks turn into distributions? What would slow or block that?” * Decision memoTwo pages, max: why buy, why pass, what would change your mind. Pro tip: If you can’t summarize the edge in two sentences, you don’t understand it yet. Illiquidity: Feature, Bug… or Both? * Feature: Can protect you from yourself; forces long-term compounding. * Bug: Blocks rebalancing when you need it most; amplifies regret. * Answer: Treat illiquidity like a mortgage—carry it only if the asset pays you for the inconvenience and you can service it during storms. Secondaries & Tokenization (Sans Fairy Dust) * Retail-sized secondaries often fail to clear because trade costs, verification, and buyer diligence don’t scale for small tickets. * Tokenization improves transfer mechanics but doesn’t conjure buyers or fix messy cap tables. Think “better pipes,” not “better water.” Your move: If liquidity really matters, prefer structures with planned windows (interval funds/tenders) or allocate to strategies with shorter duration (e.g., certain private credit). Concentrate vs. Diversify (The Grown-Up Version) * Early days: Diversify to learn—vintages, managers, and sub-strategies. * Later: Concentrate where you’ve demonstrated edge (domain knowledge, access, or analytic process). * Rule of thumb: Concentration without evidence is just hope with better branding. The Reporting That Actually Helps Ask for: * Cohort analysis by vintage and check size. * Reserves & follow-on map (who gets more capital and why). * Attribution: selection vs. post-investment value-add. * Cash reconciliation: capital calls, fees, distributions, net IRR/TVPI explained in plain English. If a manager can’t produce this, assume they manage stories better than portfolios. A 30-Minute “First Pass” Checklist * Strategy fit with your plan * Team continuity and decision record * Portfolio construction math (targets, reserves, loss rate assumptions) * Realized case studies → cash paths * Reporting samples you can actually analyze * Liquidity expectations you can live with * Fee stack you can explain to a friend * Your position size ≠ your ego size Print it. Use it. Update it. Common Traps (And How to Step Around Them) * Shiny objects: Co-invests and SPVs with unclear risk controls—pass until your base is built. * Mark-driven FOMO: Quarterly marks don’t equal distributions; celebrate wires, not PDFs. * Process drift: Saying yes because you’ve “done so much work already.” Sunk costs don’t improve outcomes. * One-deal heroics: Diversify first; earn concentration. If You Remember One Thing Access is widening, but outcomes still reward process over theater. Build a simple, repeatable system for evaluating managers, vehicles, and yourself. Then stick to it when markets get loud.👂🎧 Watch, listen, and follow on your favorite platform: https://tr.ee/S2ayrbx_fL 🙏 Join the conversation on your favorite social network: https://linktr.ee/theignitepodcastChapters:00:01 Welcome & what Accredited Investor Insights does00:31 Leyla’s path: public to private markets02:18 The “real estate gateway” to alternatives04:24 Why accreditation limits exist05:36 The likely future: a knowledge-based accreditation test06:28 Alternatives inside retirement accounts (via target-date funds)08:59 Why $50k secondaries rarely clear10:17 Tokenizing LP interests: promise vs. verification14:54 Misconceptions to unlearn about private markets16:57 Illiquidity explained (vs. REITs)21:03 The retail LP diligence gap25:42 Concentration vs. diversification: earning the right to concentrate Transcript Brian Bell (00:01:03): Hey, everyone. Welcome back to the Ignite podcast. Today, we’re thrilled to have Layla Kunimoto on the mic. She’s a co-founder of Accredited Investor Insights, a platform helping LPs and high net worth investors navigate private markets with more clarity. Layla writes and speaks about how accredited investors can better understand private placements, commercial real estate, and venture funds. Pretty relevant for this podcast, cutting through the jargon to focus on what really matters. Thanks for coming on. Leyla Kunimoto (00:01:28): Thank you for having me, Brian. Brian Bell (00:01:29): So I’d love to get your backstory. What’s your origin story? Leyla Kunimoto (00:01:31): Yeah, so I have a background in finance. I have been investing since I was 19. I’ve been investing in public markets for a very long time. And then in 2020, about five years ago, I discovered private placement. I kind of stumbled into it, and it captured my attention. It was very interesting to see how differently things are presented to investors, how big of a gap there is between what investors know and the information they get. I dove head first and had a lot of conversations. I’ve had thousands of conversations with other LPs, hundreds with GP service providers. In early 2024, a friend of mine and I decided to take this information public and share what we had learned. The friend has since departed and gone on to other things, so I inherited Accredited Investor Insights. What we started doing was writing about our experience. What’s been interesting is to see this grow and capture a lot of different audiences. I thought the biggest audience would be limited partners, and it was for a while. And now our newsletter is read by fund managers, wealth advisors, RIAs, investors—anybody in the ecosystem. Brian Bell (00:02:55): That’s amazing. What was that aha moment where you’re like, private placements, let’s look into this? Leyla Kunimoto (00:03:01): My gateway drug was real estate. In 2020, real estate prices were high. A lot of things didn’t pencil, but I saw that economies of scale in private placement allowed GPs to go out and buy much bigger properties, syndicate them, raise investor capital, and bring that to retail investors. Instead of owning something 100%, an investor can own a small slice of a much bigger pie. The bigger pie has very different economics versus a smaller one. It was interesting, and I thought there was an opportunity to exploit that inefficiency. Of course, it’s less efficient than public markets. In public markets, you have thousands of eyes looking at financials—let’s say, publicly traded REITs. In private markets, you do not. The downside of private markets is the lack of information, and that’s what creates that imbalance. Brian Bell (00:04:12): There’

    28분
  3. Ignite Startups: Building SportsVisio and the Future of AI Sports Tech with Jason Syversen | Ep211

    6일 전

    Ignite Startups: Building SportsVisio and the Future of AI Sports Tech with Jason Syversen | Ep211

    Imagine arguing about who scored the last bucket in your rec league—and instead of replaying grainy footage, your phone instantly shows you a perfectly tracked highlight reel, complete with stats and player tags. That’s the world Jason Syversen, founder and CEO of SportsVisio, is building. But his path to this moment didn’t start courtside. It began deep in the world of defense tech—inside DARPA, the Pentagon’s secretive R&D arm—where he helped design systems that could see, predict, and react faster than humans. Now he’s bringing that same precision to your local gym. From Defense Labs to Pickup Games Before SportsVisio, Jason wasn’t thinking about basketball stats. He was leading DARPA programs, hacking Wi-Fi cameras, and running a company that built cutting-edge cyber and vision systems for government and defense. But after selling his previous company, he started coaching and playing again. One night, a classic rec-league argument broke out: “Who scored what?” That’s when he saw a Pivo ad—one of those rotating tripods that follow players automatically—and had the thought every founder knows too well: why doesn’t this exist for us? That question turned into SportsVisio, an AI platform that captures games using ordinary phones or cameras and automatically generates highlights, stats, and analytics. No expensive hardware. No human tagging. Just algorithms and data doing the work. The Big Idea: Democratizing Sports Analytics SportsVisio’s real play isn’t just automation—it’s access. Pro teams have had vision-based analytics for years; youth and amateur players haven’t. Jason’s bet: every athlete deserves pro-level insights. And every game—from high school tournaments to adult rec leagues—should produce data that players can own and learn from. The key insight? You don’t need fancy gear. SportsVisio is hardware-agnostic. Any phone can become your camera. Their AI handles occlusion (when players overlap), lighting differences, and the chaos of multi-court gyms. That’s what makes the moat deep: solving for messy, real-world footage. Timing Is Everything Jason says timing, not tech, makes this moment possible. Five years ago, cloud costs and mobile GPUs were too expensive to make the math work. Today, AI inference has gotten cheap enough that you can process entire games affordably and even start approaching real-time feedback. That means leagues can finally make sense of hours of footage without spending thousands on analysts. And it means SportsVisio’s unit economics—unlike many AI startups—actually pencil out. Go-to-Market: Leagues, Not Parents Instead of chasing individual subscriptions (a death trap in youth sports), SportsVisio sells directly to leagues and tournaments. One deal gets them hundreds of teams, predictable data collection, and scalable distribution. It’s the same lesson Jason learned in defense: start where the data is dense. By owning the league pipeline, SportsVisio also gets a compounding advantage—thousands of labeled games that continuously improve the model. Each new upload trains the next generation of AI to recognize more sports, more contexts, and more subtle plays. The Tech Moat: Thousands of Games, One Big Brain SportsVisio’s vision AI has seen a lot. Thousands of games across different sports, camera angles, lighting conditions, and skill levels. Every correction from a coach or player feeds back into the system. That loop—data → learning → accuracy → adoption—is the compounding engine behind the business. And because the input (user footage) comes from real customers, the dataset is defensible in a way scraped or synthetic data isn’t. The Future: Robo-Refs, Smart Scoreboards, and Beyond SportsVisio’s roadmap reads like the natural evolution of AI in sports: * AI-assisted refereeing, helping reduce disputes and improve accuracy * Real-time stat overlays and auto-generated highlights for livestreams * Instant scoreboards powered entirely by computer vision Imagine walking into any gym, setting up two phones, and seeing a real-time scoreboard and highlight reel by the time you leave. That’s not five years away—it’s already here. Why It Matters SportsVisio isn’t just another AI startup—it’s a glimpse into how invisible AI will become. You won’t “use” it; it’ll just quietly handle the boring stuff. For sports, that means fewer arguments, fairer calls, better data, and—most importantly—more kids and amateurs feeling like pros. For founders, it’s a masterclass in taking deep-tech expertise and finding a new, emotional application for it. Jason didn’t just port his skills—he repurposed them for joy, not defense. Key Takeaways * Technical Moats Matter: Real-world data is the new barrier to entry. * Timing Beats Genius: Cloud and compute costs finally make vision AI viable. * Distribution Is Strategy: Selling to leagues compounds reach and data. * Mission Over Market: Turning rec players into “pro-level” athletes is both noble and scalable. 👂🎧 Watch, listen, and follow on your favorite platform: https://tr.ee/S2ayrbx_fL 🙏 Join the conversation on your favorite social network: https://linktr.ee/theignitepodcast Chapters:00:01 From DARPA to the Driveway03:10 Growing Up with Grit06:45 Hacking for the Pentagon11:36 The Spark: A Rec-League Argument13:34 Building an AI Sports Platform15:34 The Timing Advantage18:40 Go-to-Market That Works20:38 Cameras, Courts, and Chaos22:20 Scaling Beyond Basketball24:00 The A-Team Behind the Tech26:18 The Hardest Problem in Vision31:26 Learning from Thousands of Games34:40 The Future: AI Scoreboards and Robo-Refs37:10 Speed, Cost, and Scale40:05 Lessons from Building Deep Tech Startups45:15 What’s Next for SportsVisio47:40 Closing Reflections Transcript (00:01:07) Brian BellHey everyone, welcome back to the Ignite Podcast. Today, we’re thrilled to have Jason Syversen on the mic. He is the founder and CEO of SportsVisio, a former DARPA program manager, repeat entrepreneur, investor, philanthropist. He’s now on a mission to democratize sports analytics with AI at my basketball league. That’s how I found out about him. Thanks for coming on. (00:01:26) Jason SyversenThanks for having me, Brian. (00:01:27) Brian BellSo we’d love to get your background. What’s your origin story? (00:01:30) Jason SyversenYeah, so I was born in the New York suburbs. Some guy in a pickup truck tried to abduct a girl on our street and circled the neighborhood looking for her, which I thought was pretty exciting because I got to go to the police station, meet the detectives. My parents did not share my enthusiasm over the experience and decided to relocate our family to Maine. I bought a house up on the ocean. My dad was an engineer working for the paper mills. And then the paper mill started shutting down and he lost the job and we ended up being very poor for the next, for a long period of time after that. Bounced around, rented. We were actually on food stamps, welfare. We were homeless one summer, lived in a pop-up camper in someone’s backyard. I still have like toes that are like bent in because I wore shoes that were too small and fun stuff like that. Got a free ride for college, went to University of Maine for computer engineering, and then worked at Lockheed Martin’s electronic warfare division in New Hampshire, about an hour north of Boston. Did a leadership program, rotations every six months. Ended up going to grad school at Worcester Polytechnic Institute in electrical engineering, focused on cryptography and security. You know, I was very fortunate to be in middle class. I was super excited about having like food in the fridge and a car that worked and all those fun things and started getting into hacking pretty much first week on the job. I couldn’t get some software I needed to do my job and the guy wasn’t around to install it for a week. I was like, I’m allowed to install the software? Like, yeah, yeah, but you know, Bob is away. I’m like, but it’s okay for me to install it. Like, yeah, but you can’t because you know, it’s not here. I’m like, well, let’s see about that. So I started poking around and figured out how to bypass the protections and install the software. And that led to a new hobby that led to playing pranks on coworkers and taking over the corporate network with permission and HR visiting my desk occasionally gave me a lecture. I ended up starting a cyber warfare group inside the company with an older guy. Really cool projects came out of that. My favorite was the James Bond Ocean 11 speed thing where we hacked a Wi-Fi camera surveillance network, intercepted the video, updated the timestamp, and injected the recorded video back in the target. I got to demo that at San Clemente Island off the coast of San Diego for Special Forces guys, which led to SEAL Team 6, DevGuru, recruiting me to join their team in Virginia Beach. I turned that down actually, but a couple of years later, I was recruited to go to DARPA, which is this building over here. I ran a $100 million portfolio of classified programs for the Defense Department. I transitioned those to Air Force, Army, Navy, CIA, NSA, and another classified DoD element. Left, I commuted from New Hampshire every week for the two years I was there. It’s a two-year government appointment. And then I started my own company, CH Technologies, which was a cyber warfare R&D company. Started in 2009, did a million in revenue in 2010, two million the year after that, kind of kept growing. Ended up selling to a private equity-backed firm in 2016. My wife and I had an eight-digit exit from that. I took enough to live on and donated the rest to a charitable foundation. So I guess I’ll stop there for the origin series that moves into the more current times. (00:10:09) Jason SyversenWhat an amazing journey. And I also grew up poor and welfare, the whole sad story. So I

    48분
  4. Ignite Startups: Designing the Future of AEC Software with Amar Hanspal | Ep210

    11월 10일

    Ignite Startups: Designing the Future of AEC Software with Amar Hanspal | Ep210

    Imagine if the software shaping our cities still thought it was 1998. Layers, files, and workflows built for floppy disks—but running in a world of real-time collaboration and generative AI. That’s the world Amar Hanspal wants to rewrite. He’s done it before. As a long-time Autodesk executive, Amar helped drag the CAD giant from boxed software into the cloud era. Then he led Bright Machines, bringing software-defined intelligence to manufacturing. Now, as co-founder and CEO of Motif, he’s tackling one of the last analog frontiers: the architecture, engineering, and construction (AEC) stack. Below is a deep dive into his conversation on Ignite Startups, for anyone who won’t catch the full episode—but still wants the ideas that matter. From Support Calls to Software Vision Amar’s career began not in the corner office, but on the phones. He spent his early days fielding 100 customer calls a day, absorbing frustration, context, and unfiltered truth. That experience hardwired one of his core beliefs: “Empathy scales better than features.” It also shaped how he later led product at Autodesk—anchoring on what users feel, not what they say. The Early CAD Days: Falling in Love with Tools that Think In college, Amar stumbled on CAD software running on a microVAX, a machine the size of a small fridge. Watching lines snap into geometry felt like magic. That fascination eventually brought him to Autodesk—then a scrappy upstart turning technical drawing into digital art. Over the years, he climbed from small product lines to leading the company’s transition to the cloud and subscription models, one of the boldest business shifts in design software history. First Startup Lessons: The Price of Timing Before that success, there was failure. Amar’s first startup came during the dot-com wave—and crashed just as quickly. He laughs about it now, but it taught him the kind of humility founders don’t forget: * Talk to customers early * Ask for money before you ask for feedback * Be paranoid about timing When he returned to Autodesk, those scars became pattern recognition. Bright Machines and the Allure of the Solvable Problem After years in design software, Amar turned his attention to manufacturing—a world full of messy processes, but tractable with computer vision and robotics. That led to Bright Machines, where he explored “software-defined factories.” But while industrial automation was moving fast, AEC was stuck—a trillion-dollar sector still battling file-based tools and 20-year-old workflows. That itch became Motif. Motif: Rethinking Design for the AI Era Motif’s founding question was simple: What if building design worked like modern software collaboration? Think Figma for architects, Cursor for engineers, and Miro for context—all rolled into one, web-native canvas. The team’s first challenge wasn’t fancy AI—it was real-time collaboration. Architects, engineers, planners, and clients all work across disciplines and data models. Keeping that live, in 3D, in the browser, is harder than it sounds. Once the foundation was stable, Motif layered in intelligence: contextual suggestions, code-aware design, and constraint-based automation. Not replacing designers—but giving them superpowers. Embrace, Then Replace Amar’s go-to-market philosophy is refreshingly pragmatic: “You can’t disrupt what you don’t first embrace.” Instead of demanding users abandon legacy tools like Revit or Rhino, Motif plays nice with them—then quietly replaces the brittle parts over time. It’s the Trojan Horse strategy of platform shifts: meet users where they are, then show them what they’ve been missing. Founder vs. Executive: The Identity Shift After decades running large organizations, Amar had to relearn the founder’s grind. As he puts it: “At a startup, your title is just your to-do list.” The skills that matter most now? * Relentless execution * Emotional resilience * Recruiting people smarter than you He calls his team “the real product.” AI in AEC: Automate the Boring, Not the Brilliant When asked about AI, Amar doesn’t gush about futuristic design bots. He’s more grounded: “The biggest wins won’t be sexy—they’ll kill the drudgery.” Think: * Auto-filling permit forms * Running code compliance checks * Translating model data between tools In other words, using AI to clear creative bandwidth—not replace it. Clean Sheets and Big Swings Why start from scratch when you could build on existing BIM tools? Because the architecture stack is built on geometry-first assumptions, not constraint- or code-first logic. Amar’s team believes a clean sheet is the only way to make design truly adaptive, collaborative, and intelligent. He compares it to the leap from AutoCAD to Onshape or Figma—a switch from files to flows. Defining Success For Motif, early success isn’t measured in ARR. It’s measured in love. Do architects feel faster? Do projects move smoother? Do users want to work in Motif again tomorrow? That user obsession, Amar says, is what carried him through every chapter—from Autodesk’s reinvention to his new startup. 👂🎧 Watch, listen, and follow on your favorite platform: https://tr.ee/S2ayrbx_fL 🙏 Join the conversation on your favorite social network: https://linktr.ee/theignitepodcast Chapters: 00:01 — Early hustle: empathy learned through 100 customer calls a day 00:27 — Discovering CAD on a microVAX and joining Autodesk’s early wave 02:57 — From listening to choosing: the evolution of product judgment 04:50 — First startup lessons: timing, humility, and asking customers to pay 05:19 — Rejoining Autodesk and learning to scale leadership 07:00 — Dot-com detour: building, raising, and shutting down a venture 07:31 — Leading Autodesk’s cloud transformation and move into construction 08:45 — Why computer vision led to Bright Machines 10:46 — The birth of Motif: designing for carbon, complexity, and collaboration 12:49 — Tackling the hardest problems first: scale and real-time teamwork 15:00 — Starting with architects: where Motif begins 15:40 — Product #1: a collaborative 3D workspace for context sharing 16:44 — Embrace, then replace: winning users from legacy tools 18:07 — “Figma meets Cursor” — AI as a co-designer for buildings 19:26 — GTM strategy: human-in-the-loop now, product-led later 21:29 — Founder vs. executive mindset: execution, resilience, and team 24:57 — Real AI value: automating the boring, not the brilliant 27:46 — Why Motif starts from a clean sheet (not geometry-first) 30:40 — Measuring speed-ups and early user feedback 31:37 — Defining success: love from users before scale 32:50 — Lessons for AEC founders: problem-first, tech-second 33:19 — Mythbusting BIM: it doesn’t have to be painful 33:37 — Pride point: leading Autodesk’s cloud shift 34:27 — Adobe vs. Autodesk: two paths to reinvention 35:26 — Regrets and reflections: taking bigger swings sooner Transcript Amar Hanspal (00:00:00): If you start something, you better care about the problem you’re trying to solve. Amar Hanspal (00:00:03): Otherwise, you’ll get bored and you’ll drop out after a while. Amar Hanspal (00:00:06): That’s one. Amar Hanspal (00:00:07): Second thing I’ve learned is that the art of all of this is it’s all about execution. Amar Hanspal (00:00:11): I hear so many people come and tell me, Amar Hanspal (00:00:13): this competitor is doing this, Amar Hanspal (00:00:15): the competitor is doing that. Amar Hanspal (00:00:16): None of that actually matters. Amar Hanspal (00:00:17): What really matters is the thing that you’re trying to do. Amar Hanspal (00:00:20): Are you able to keep iterating? Amar Hanspal (00:00:23): Whether you listen to customers, the people who succeed very well, Amar Hanspal (00:00:27): The example you gave about the warmly gentleman, like you open, close. Amar Hanspal (00:00:30): That is the art of building, which is like you listen. Amar Hanspal (00:00:33): You try something. Amar Hanspal (00:00:35): You figure it out. Amar Hanspal (00:00:35): Does it work? Amar Hanspal (00:00:36): You iterate. Amar Hanspal (00:00:37): Execution is a huge amount of the challenge that you need to take on. Amar Hanspal (00:00:41): It’s not the original idea. Amar Hanspal (00:00:43): And then the third one is that the team is everything. Amar Hanspal (00:00:46): Startups to me, the team you build is the product you build is the company you build. Amar Hanspal (00:00:50): It’s so true. Brian Bell (00:01:12): Hey, everyone. Brian Bell (00:01:13): Welcome back to the Ignite podcast. Brian Bell (00:01:14): Today, we’re thrilled to have Amar Hanspal on the mic. Brian Bell (00:01:17): He is a veteran of design software, Brian Bell (00:01:19): former co-CEO and CPO at Autodesk, Brian Bell (00:01:21): founder of Bright Machines, Brian Bell (00:01:23): and now leading the charge at Motif, Brian Bell (00:01:24): a startup reimagining tools for architecture, Brian Bell (00:01:27): engineering, Brian Bell (00:01:27): and construction. Brian Bell (00:01:29): Amar, thanks for coming on. Brian Bell (00:01:30): Hey, Brian. Brian Bell (00:01:30): Thanks for having me. Brian Bell (00:01:31): Good to be here. Brian Bell (00:01:32): Yeah, super excited to sit down with you today. Brian Bell (00:01:34): I’d love to get your origin story for the audience. Brian Bell (00:01:36): What’s your background? Amar Hanspal (00:01:37): I grew up as a mechanical engineer. Amar Hanspal (00:01:38): That’s what I was trained to do. Amar Hanspal (00:01:40): And I know it’s kind of sounds quaint that, Amar Hanspal (00:01:43): you know, Amar Hanspal (00:01:43): I would talk about the eighties and stuff like that, Amar Hanspal (00:01:45): but I kind of got introduced to using computers for engineering back in the Amar Hanspal (00:01:50): eighties. Amar Hanspal (00:01:50): And that kind of

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  5. Ignite VC: Jeffrey Fidelman on Building a Repeatable Capital Engine & Fundraising Wins | Ep209

    11월 6일

    Ignite VC: Jeffrey Fidelman on Building a Repeatable Capital Engine & Fundraising Wins | Ep209

    Jeffrey Fidelman—Founder & CEO of Fidelman & Company—breaks down how early-stage founders and emerging managers can run a repeatable, data-driven fundraising process. His team “productizes” investment banking: compliant and licensed advisory, a staffed outbound motion, and a new platform (Fundex) that systematizes sourcing, personalization, and follow-through. Big themes: stop hunting for silver bullets, design a weekly workflow, communicate like a pro, and ask for the sale. Who is Jeffrey Fidelman? Jeffrey started in traditional banking (Morgan Stanley, HSBC), helped operate a nine-figure venture fund, and now runs Fidelman & Company—an investment bank built for startups and emerging managers. He’s scaled a 40-person team that blends institutional rigor with hands-on execution, working across founder rounds and funds (typically Fund I–III/V). Why listen: He sits at the intersection of venture and banking, translating Wall Street process into fundraising outcomes for people who don’t have time to reinvent the wheel. The Big Idea: Productize Fundraising Most teams approach fundraising as a one-off sprint. Jeffrey treats it like a product and a pipeline. What “productized” looks like: * Compliance + transparency: Licenses, disclosures, real diligence—versus “Rolodex-for-rent.” * Staffed engine: Analysts and operators who do the unglamorous work (research, qualification, personalization, sequencing). * Clear incentives: A base fee to fund sustained effort, plus success economics aligned to capital raised. * Platform leverage (Fundex): A banker-backed, self-serve system for building targeted lists, managing outreach, and tracking what converts. Who They Serve (and Who Shouldn’t Hire Them) * Startups raising institutional seed–Series A where traction matters more than vibes. * Emerging managers (often Fund I–III/V, ~$20M–$300M) who need consistent, qualified LP conversations. * When they say “no”: Unrealistic timelines, expectations misaligned with market reality, or founders who want magic, not process. The Weekly Workflow That Moves Money Forget the fantasy of a single intro unlocking a round. Jeffrey’s team runs a repeatable weekly rhythm: * Lead Generation * Build/refine a tight investor list by stage, sector, check size, geography, and thesis. * Prioritize based on recency of activity and fit (spearfishing > spray-and-pray). * Qualification * Confirm mandate (stage, ownership targets, reserve strategy for funds; revenue, margins, LTV/CAC for companies). * Track disqualifiers ruthlessly to avoid wasting cycles. * Personalization * Use actual signals: portfolio patterns, recent memos, partner backgrounds. * One reason to care + one clear next step. No generic “circling a round” language. * Sequenced Outreach * Multi-touch cadence (email, LinkedIn, warm paths, occasionally a thoughtful call). * 5–6 touches is normal; design them up-front. * Follow-through * Treat every “maybe later” as a specific follow-up task with a date and a reason. * Log objections; update copy and filters from data, not vibes. * Newsletter/Update Layer * A monthly note to keep the pipeline warm and prime closes. Core belief: There are no silver bullets—only consistent systems. Pricing & Incentives (Why It Matters) Jeffrey advocates for a transparent base fee (to fund the real work of research and outbound) plus success-based economics tied to capital raised. The structure mirrors how an internal analyst + data subscriptions would be paid—just purpose-built for fundraising. Tools & Team Design * People: Former BDRs/SDRs often outperform traditional bankers in outbound because they think in funnels, not favors. * CRM: Move from generic tools to purpose-built workflows; Fundex grew out of internal needs for source-of-truth investor data and campaign analytics. * Experiment windows: Run 60–90-day experiments before changing copy/targeting; otherwise you mistake randomness for insight. LP & Investor Communication Use a simple three-part monthly update: * Last month: What happened (pipeline, wins, key metrics). * This month: What’s in motion (meetings, diligence, product/research). * Next month: What’s planned (targets, launches, hires) + optional ask (intros, talent, data). Why it works: It’s predictable, compounding, and lets prospects “ride along” until the timing is right. Founder & GP Pitfalls (and Fixes) Pitfall: Waiting for perfect decks. Fix: “Good enough” materials + more qualified conversations. Iterate from live objections. Pitfall: Not asking for the money. Fix: Use explicit closes: “If the rest of diligence checks out, are you comfortable taking a $X–$Y allocation?” Pitfall: Boiling the ocean. Fix: Start with the highest-probability 50–100 names. Earn the right to expand. Pitfall: Over-automating. Fix: Let AI assist research and drafting, but keep humans on qualification, nuance, and final sends. Market Reality Check * “Venture winter” ≠ zero capital: Dollars still flow, but standards are higher and timelines longer. * Seed expectations have shifted: Revenue or sharp leading indicators help. Narrative alone is fragile. * What’s next: Infrastructure for AI and energy/compute constraints are shaping both venture theses and LP interest. Valuation & Storytelling * Frameworks: Use comps and multiples as guardrails, not gospel; avoid outlier benchmarks to justify price. * Narrative: Investors buy a believable path—origination advantage, diligence advantage, or distribution advantage. Decks don’t raise money—founders and their narrative do. 30–60–90 Day Action Plan For Founders Days 1–30 * Define ICP investors (stage, check size, sector, geo). * Draft “why now/why us” narrative and a one-page. * Build first 75–100-name list; research 3 relevance signals per target. Days 31–60 * Launch a 6-touch sequence; log every objection. * Start monthly update cadence; add 10–15 “ride-along” prospects. * Book 10–15 first meetings; convert 3–5 to diligence. Days 61–90 * Tighten targeting from conversion data; refresh copy. * Formalize diligence packet (metrics, cohorts, references). * Ask for allocations explicitly; stack soft-circled commitments. For Emerging Managers Days 1–30 * Clarify mandate (check size, ownership, geography, reserves). * Build LP map (funds of funds, family offices, HNW, endowments). * Draft a one-page with sourcing+diligence edge. Days 31–60 * Start a monthly LP note (three-part format). * Run 50–75 targeted outreaches with true personalization. * Line up 3–5 reference calls (founders, co-investors). Days 61–90 * Share pipeline quality and “how we pass.” * Test 1–2 small events/webinars to compress discovery. * Push for anchor/lead soft-circles; set a first-close target date. What to Steal for Your Next Raise * A weekly pipeline review with real conversion metrics. * A tight investor ICP you can defend. * A 6-touch multi-channel sequence you actually finish. * A monthly update that compounds attention. * A clear close: amount, timing, and next steps. Resources Mentioned * Book: Productize by Aisha Armstrong. * Concepts: Spearfishing > spray-and-pray; no silver bullets; “good enough → learn in the market.” Final Word Fundraising isn’t a moment—it’s a machine. Build the machine, feed it every week, and let data (not drama) tell you what to do next. If you want this distilled into show-notes, chapter markers, or a LinkedIn post template for your episode page, I can spin that up too. 👂🎧 Watch, listen, and follow on your favorite platform: https://tr.ee/S2ayrbx_fL 🙏 Join the conversation on your favorite social network: https://linktr.ee/theignitepodcast Chapters: 00:01 Intro & guest background 00:37 Career path: banking to VC 02:56 Productizing fundraising 04:16 “Do it for us” inflection point 06:58 Licenses, transparency, pricing 09:42 Fundex launch & vision 10:42 Saying no & realistic timelines 12:29 Weekly lead-gen workflow 14:54 Multi-touch outreach cadence 15:49 Targeted spearfishing approach 17:03 Team design for outbound 19:26 CRM evolution to Fundex 20:39 Fee structures by vehicle 28:57 Monthly LP/GP update format 31:48 60–90 day testing windows 35:48 Market shift & venture winter 41:05 What LPs value most 43:06 Personal brand over company page 47:07 Valuation frameworks 52:00 AI infra & energy thesis 58:50 Fundex recap & close Transcript (00:01:12) Brian BellHey, everyone. Welcome back to the Ignite podcast. Today, we’re thrilled to have Jeffrey Fiddleman on the mic. He’s the founder and CEO of Fiddleman & Co., naturally, a firm that builds investment banking for startups and funds. Before that, Jeffrey worked in investment banking at Morgan Stanley, HSBC. He did some early venture work, co-founded a few startups, and now helps founders raise capital, shape their decks, valuations, and runs fundraising as a service. He knows both the polished financial model world and what it’s like to hustle for your first dollar. Let’s dive in. Thanks for coming on. (00:01:40) Jeffrey Fidelman Thank you so much for having me, Brian. Well, Jeffrey, I’d love to get your origin story. What’s your background? From the Northeast originally, born and raised in New York on Long Island. I ended up going to school, graduating from Harvard, and then spent the better part of a decade in banking. First at Morgan Stanley, as you mentioned, and then at HSBC. Really cut my teeth there. Although everyone around me was English speaking, when I started, it was totally foreign language to me. And I really was able to learn how institutional processes and structured works. I then was recruited by a family office to help them run a venture fund. We were investing in early stage tech and tech enabled investments. The fund was about $120, $25 million. And we invested in about four dozen companies, leaving some capital on for dry powder and follow-on rounds.

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  6. Ignite Startups: Max Greenwald on Precision Demand Gen with AI Agents | Ep208

    11월 2일

    Ignite Startups: Max Greenwald on Precision Demand Gen with AI Agents | Ep208

    If you only have time to read one deep-dive this week on modern B2B growth, make it this one. In our latest Ignite Podcast episode, we sat down with Max Greenwald, co-founder and CEO of Warmly.ai—a platform building AI agents that warm your total addressable market (TAM) and route only the hottest leads to sales. A former Google PM (yes, the “Where’s Waldo” Maps prank that ~100M people played) who has steered six pivots before breaking through, Max brings a rare, field-tested view of how demand is actually generated in 2025. Below is a full written breakdown for readers who may not catch the episode—complete with frameworks, playbooks, and practical takeaways you can put to work immediately. TL;DR (Skimmable Takeaways) * Warm demand beats cold outreach. Use AI agents to initialize relationship and intent before your team ever emails or calls. * Multi-signal > single-signal. Blend website behavior, firmographics, tech stack, channel touchpoints, and recency to score buying readiness, not just MQLs. * Open/Close for PMF. Deliberately open to explore new segments/solutions; close to double down on a narrow wedge once you see real pull (inbound, referrals, fast sales cycles). * Dogfood relentlessly. Warmly sources a big chunk of its own pipeline by running its product on itself—shortening feedback loops and sharpening the scoring logic. * Omnichannel or bust. The modern buyer pinballs across channels. Orchestrate consistent, helpful moments wherever they surface. The Origin Story (and Why It Matters) Max’s path: Princeton CS → Google PM → founder. The early venture years weren’t linear: rejections, co-founder turbulence, and six pivots—without changing the company name. The lesson isn’t “pivot more”; it’s pivot intentionally. Max’s team learned to separate idea vanity from signal quality, and to treat PMF as a sequence of experiments rather than a single epiphany. “Signals, not vibes. Inbound interest, shorter sales cycles, and word-of-mouth are the scoreboard—not the story you tell yourself.” — Max Greenwald From Anonymous Traffic to Booked Meetings Warmly’s evolution mirrors how demand gen has changed: * De-anonymize & observe: Who’s on your site, which pages matter, and how often do they return? * Turn conversation on: Live chat → AI chat to capture and qualify in real time. * Retarget with brains: Not just pixel-chasing—use context and timing to surface relevant prompts. * Score with multiple signals: Stitch behavior, firmographics, timing, and engagement into a single readiness score. * Route instantly: Push only the best leads to AEs with context, while nurturing the rest automatically. Outcome: Less spam, more meetings. Sales trusts marketing because they feel the difference in their calendars. The Open/Close Framework for PMF (and Go-To-Market Fit) Most founders oscillate between wandering and tunnel vision. Max’s answer: * Open Phases: Widen inputs (segments, use cases, price points). Look for surprising pull: unsolicited demos, referral chains, faster closes. * Close Phases: Narrow everything (ICP, message, channels, packaging). Write “not-for” rules. Instrument the funnel and operationalize what works. * Cadence: Time-box each phase and define the exit criteria (e.g., 3 straight weeks of >30% demo-to-opportunity conversion in a target segment). This rhythm protects teams from infinite exploration and from premature locking. The Demand Gen Playbook (2025 Edition) 1) Warm the Market Before You Pitch * Use AI agents to greet, guide, and qualify visitors—on site and across channels. * Offer contextual value (micro-audits, fast answers, tailored content) before asking for time. Quick win: Deploy an AI concierge on your high-intent pages (pricing, integrations, case studies) to offer a 90-second “fit check” and one-click meeting booking. 2) Build a Multi-Signal Lead Score Single-event triggers (e.g., one ebook download) are noisy. Combine: * Firmographics: Size, industry, territory * Technographics: Key tools in their stack * Behavioral: Pages viewed, depth, recency, return visits * Channel: Email replies, social touches, events/webinars * Context: Problem-specific pages, integration interest, pricing views Rule of thumb: If your “hot lead” definition fits in one sentence, it’s too shallow. 3) Orchestrate Omnichannel Moments Buyers hop between website, LinkedIn, G2, webinars, and email. Meet them with consistent narrative and next step wherever they show up. * Awareness: Short problem clips, proof snippets * Consideration: Integration demos, ROI calculators, teardown posts * Decision: Customer stories that mirror ICP pain, live Q&A, fast trials Guardrail: Every touch should make the next step painfully obvious. 4) Route with Context, Not Just Priority AEs need more than “hot lead.” Deliver a one-sheet: why now, what they looked at, similar customers, suggested opener, and a 3-bullet discovery plan. The handoff is part of demand gen. 5) Dogfood to Learn Faster Warmly runs Warmly on Warmly—capturing real conversations, false positives, and timing cues. Internal usage produces the sharpest tuning for scoring, prompts, and routing. Try this: Weekly “signal review” between RevOps and PM. Pick five wins and five misses; update rules and prompts that week. Inside the Stack: Where AI Adds Lift * Code generation: Faster iteration on experiments and internal tools. * Sales assistance: Summaries, email drafting, call prep—grounded in the multi-signal profile. * Support enablement: Faster, more relevant help so prospects don’t stall pre-demo. This isn’t “AI replaces SDRs.” It’s AI reduces waste and raises the floor on every touch. Metrics That Actually Matter * Meeting rate from high-intent pages (and time to book) * Demo-to-opportunity conversion (per ICP) * Opportunity velocity (days stage-to-stage) * Pipeline sourced by owned demand (vs. rented channels) * AE acceptance of routed leads (qualitative trust + quantitative accept rate) If these move, the MQL count can be… background noise. What’s Changing in MarTech (and How to Adapt) * Lean in-house teams, more agencies: Keep strategy and data close; outsource execution sprints where needed. * Content goes atomic: Short, high-signal pieces repurposed across channels with AI assists. * Agentic workflows: Helpful, brand-safe agents that learn over time and feel like teammates. “In a few years, agents won’t just answer—they’ll own outcomes with the same playbooks your best reps use.” — Max 90-Day Action Plan Weeks 1–2: Instrument * Identify high-intent pages and set up session recording + basic de-anonymization. * Define current “hot lead” criteria (even if imperfect). Weeks 3–6: Pilot * Launch an AI concierge on top pages with a short “fit check”. * Create the AE one-sheet template and automate the handoff. Weeks 7–10: Score & Route * Roll out multi-signal scoring; test 2–3 thresholds for “instant route” vs. nurture. * Start weekly signal reviews; adjust rules promptly. Weeks 11–12: Scale * Layer in retargeting with context (use the pages they touched). * Publish 3 proof assets that match your top ICPs; wire them into agent prompts. Founder Notes: Leading Through the Messy Middle * Narrative discipline: Keep a running doc of “not-for” segments to avoid silent scope creep. * Team rituals: Monthly gratitudes, weekly “what surprised us” reviews—low-friction ways to stay human and curious. * Capital efficiency: Brute-forcing channels is out; precision and sequencing are in. If You Only Do One Thing After Reading This… Add an AI concierge to your top two high-intent pages with a 90-second fit check and one-click booking. Measure meeting rate and time to book for two weeks. The clarity you gain on true demand will change how you score, route, and plan content. 👂🎧 Watch, listen, and follow on your favorite platform: https://tr.ee/S2ayrbx_fL 🙏 Join the conversation on your favorite social network: https://linktr.ee/theignitepodcast Chapters: 00:01 Intro and guest setup 01:12 Princeton to Google path 02:45 Leaving Big Tech to found Warmly 04:10 Early rejections and resilience 05:30 Co-founder dynamics and roles 06:50 The Open/Close framework 08:05 Defining ICP and not-for list 09:18 Six pivots, one mission 10:40 From de-anonymization to conversations 12:00 AI concierge on high-intent pages 13:22 Multi-signal lead scoring 14:45 Routing with context to AEs 16:10 Dogfooding and feedback loops 17:35 GTM systems and repeatability 18:50 Omnichannel orchestration 20:15 Content atoms and proof assets 22:00 Metrics that actually matter 23:30 Pipeline quality over volume 25:05 Hiring the first great AE 27:20 RevOps cadence and signal reviews 29:10 Buyer intent vs interest 31:00 Demand gen without waste 33:05 Agents as always-on SDRs 34:40 Near-term roadmap and vision 36:06 Closing takeaways Transcript Max Greenwald (00:00:00): It’s called the death triangle. It’s called the Bermuda triangle of you’re trying to convince investors to give you money. You’re trying to convince customers to buy a product or to find an idea. And you’re trying to convince co-founders to come join you. So I think in the very earliest score, it’s like co-founders, investors, you know, idea. And each leg of that triangle wants the other two to be perfect, right? It’s like a VC wants to know that you have an idea and you have co-founders. A co-founder wants to know you have funding and you have an idea. And for your idea to work, you’re going to need, you know, co-founders and VC funding. So it’s like just that brutal triangle. Brian Bell (00:00:53): Hey everyone, welcome back to the Ignite Podcast. Today we’re thrilled to have Max Greenwald on the mic. He’s the co-founder and CEO of Wormly.ai, former Google PM and founder who’s navigated multiple pivots in pursuit of product market fit and someone deeply invested in how AI is reshaping G

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  7. Ignite VC: The Truth About Product-Market Fit and Founder Grit with Gabriel Jarrosson | Ep207

    10월 29일

    Ignite VC: The Truth About Product-Market Fit and Founder Grit with Gabriel Jarrosson | Ep207

    Most founders believe that if they just build a great product, customers will come.But as Gabriel Jarrosson, General Partner at Lobster Capital, puts it — “Build it and they will come is the biggest myth in startups.” A seven-time founder with three exits and now an investor in over $25 million of early-stage deals, Gabriel has lived both sides of the startup grind. From coding alone as a computer science engineer to backing YC’s top performers, his journey reveals what truly separates startups that survive from those that don’t. From Founder to Investor Gabriel’s story begins where many do — building products that nobody wanted. After several failed ventures (and a few modest wins), he realized the hard truth: success isn’t about product perfection but about early traction. This realization led him to angel investing. But with limited capital, he started small — writing checks as low as $2,000. That grew into an angel syndicate and, later, into Lobster Capital, a fund that invests exclusively in the top 2% of each Y Combinator batch. His syndicate’s early wins and a viral YouTube channel in France helped him scale both his visibility and access. By 2023, Lobster Capital was born — a professionalized VC fund known for its traction-first approach. The Philosophy Behind “Traction-Driven” Investing Gabriel’s investment thesis is deceptively simple: “If you don’t have traction, what are we even talking about?” Having built seven startups, he learned that momentum and customer validation are the only real signals of product-market fit. He avoids “build it and they will come” fallacies and looks for founders who’ve proven demand — not just coded features. For Lobster Capital, this means focusing on startups with strong early revenue growth. In YC terms, that’s: * Good: Around $100K ARR * Better: $100K–$500K ARR * Best: $500K+ ARR (often growing 10–15% week over week) Those numbers may sound high for early-stage founders, but they’re exactly what separates the exceptional from the average. The Discipline of Saying No One of Gabriel’s biggest challenges as a former syndicate leader turned fund manager was learning to say no.As he puts it, “In a syndicate, you can say yes to everything. With a fund, you can’t.” He now filters ruthlessly — even rejecting deals he personally likes if they don’t meet the traction bar. Still, he leaves room for exceptions — especially in deep tech or moonshot bets where traction takes longer. About 10% of his portfolio is reserved for these high-risk, high-upside plays. The Meaning Behind “Lobster Capital” Ever wondered why “Lobster”? It’s not just a quirky brand.Gabriel chose the name for its symbolism: * Lobsters never stop growing, just like great startups. * Blue lobsters — ultra-rare — represent unicorns. * Their reddish-orange color mirrors YC’s branding. * And, as Jordan Peterson famously noted, lobsters have existed longer than trees — a reminder that power laws are as old as nature itself. It’s a memorable identity that ties humor, philosophy, and brand storytelling together — and it’s one reason founders remember his fund long after the pitch. Inside YC’s Top 2% Lobster Capital invests in only 3–5 companies per YC batch — a level of selectivity that forces precision. While other funds chase volume, Gabriel’s approach is concentrated conviction. He relies on YC’s internal data, founder networks, and pattern recognition from hundreds of past calls to identify which startups are breaking through. Over time, Lobster Capital has become so well-known inside YC that founders now pitch him first, reversing the traditional investor–founder dynamic. How AI Is Transforming Venture and Founding Gabriel is optimistic but grounded about AI’s role in both venture and entrepreneurship.He sees AI not as a threat but as a tool that lowers the barrier to entry — the latest in a long line of democratizing technologies: “First you needed a factory to start a company, then just a laptop, then cloud servers. Now, you can have AI build your app and write your code.” AI, he believes, will accelerate every part of the startup cycle — from ideation to exits — and even shorten fund lifecycles by speeding up liquidity events. Still, he’s skeptical that AI can replace human investors anytime soon. Venture capital, at its core, is a trust-based craft — part art, part intuition, and part psychology. Founders Who Win When it comes to evaluating founders, Gabriel looks for what he calls “relentless resourcefulness.”He’s less concerned with Ivy League pedigrees or fancy backgrounds and more focused on resilience, grit, and adaptability — the founders who find a way through the wall, not around it. “The best founders never stop. Close the door, they come in through the window. Close the window, they come through the chimney.” Key Takeaways * Early traction is everything. Ideas are easy; execution and revenue prove reality. * Saying no is a superpower. Discipline defines good investors. * AI won’t replace VCs — it will empower them. * The best founders are relentlessly resourceful. * Growth without traction is a mirage. Final Thought Gabriel’s story is a rare look at someone who’s seen the startup world from every angle — founder, failure, builder, angel, and VC. His philosophy distills a decade of lessons into one principle: traction is truth. Whether you’re raising your first round, launching a startup, or evaluating deals, this episode offers a blueprint for thinking like a founder—and investing like one, too.👂🎧 Watch, listen, and follow on your favorite platform: https://tr.ee/S2ayrbx_fL 🙏 Join the conversation on your favorite social network: https://linktr.ee/theignitepodcastChapters:00:02 Early life in Vilnius and family influence in finance 03:00 Starting career as a trader in Amsterdam 04:10 Transition from public markets to family office investing 05:30 Discovering venture capital and building early networks 06:25 Joining Willgrow and professionalizing the venture journey 07:45 Lessons from angel investing and early portfolio wins 09:45 Evolution of Willgrow from transport and real estate to investments 11:50 Growth into a diversified global investment platform 13:00 Five core investment areas at Willgrow 14:10 Why Willgrow focuses on funds over direct deals 16:30 Building a “bulletproof” fund portfolio strategy 18:10 Strategic asset allocation and balancing risk across classes 20:40 How Willgrow sets portfolio weights and long-term targets 22:40 Shifts in venture returns and performance assumptions 23:50 Managing liquidity and duration through secondaries 25:55 Currency exposure between U.S. and European investments 28:20 Sourcing fund managers and building LP networks 30:40 Partnering with fund-of-funds and global advisors 31:00 GP–strategy fit as a key selection criterion 32:30 Evaluating track records and past success indicators 33:00 Investing in first-time GPs and assessing credibility 34:20 Soft referencing and network-based validation 35:00 From concentrated bets to diversified fund portfolios 37:20 Navigating a more competitive venture landscape 38:20 Specialist vs. generalist fund approaches 40:40 How Willgrow evaluates sector focus and adaptability 44:30 Benchmarking and monitoring fund performance 46:50 Lessons learned after four years of venture investing 48:50 Ticket sizing, pacing, and realistic growth assumptions 49:30 Risks of first-time teams in emerging managers 51:00 Case study: when a first-time GP team collapses 52:00 Evaluating fund valuations and markup practices 53:00 Due diligence and data room best practices 54:20 What LPs look for in deal memos and documentation 54:37 End of main discussion Transcript Brian Bell (00:01:11–00:01:36): Hey, everyone. Welcome back to the Ignite podcast. Today, we’re thrilled to have Gabriel Gerson on the mic. He’s a seven-time founder of Three Exits, now crafting early stage magic as GP at Lobster Capital, where he’s backed more than 25 million, plowed 25 million into YC startups. That’s amazing. He’s bootstrapped to 1 million ARR solo, survived four failed ventures, and now writes in podcasts about the bleeding edge of investing in AI-powered high-traction startups. Thanks for coming on. Well, thank you so much for having me. Gabriel Jarrosson (00:01:37): We’ve been close in the ecosystem for a long time, so I’m very excited about this conversation. Brian Bell (00:01:42–00:02:01): Yeah, no, we’ve been kind of collaborating kind of at a distance for years. And then we finally connected on a panel at the Decile event a month ago. So it’s cool. It was just kind of serendipitous that you were going to come on the pod a month later. So I’m excited to finally like sit down and get to know a little bit about you. Would love to know kind of like, you know, what’s your origin story? Where did it all start? Gabriel Jarrosson (00:02:02–00:04:50): Well, I mean, you touched on it briefly. originally a computer science engineer and turned founder. So my origin story is really with building startups on my own. That’s how I came to investing after building so many startups and failing so many, succeeding a few, not massive successes, but some stuff did work. And I was seeing myself in other founders. I was saying, oh, many times I was saying to myself, oh, they’re doing exactly the same mistakes that I did. The beginner’s mistake. I can recognize that now. And sometimes I was saying to myself, oh, they’re not doing those mistakes. They’re much better than me, which I guess the bar is not very high. And so I thought, how can I profit from that? I just understood probably before anyone else that those people are somehow on the right track. And that’s how I became an angel investor in 2013. been some years now. And I just try to convince them, be like, hey, you

    1시간 2분
  8. Ignite VC: Scaling With Discipline and Growth Equity Without the Hype with Jim Ferry | Ep206

    10월 27일

    Ignite VC: Scaling With Discipline and Growth Equity Without the Hype with Jim Ferry | Ep206

    Most founders know the extremes: early-stage venture bets and late-stage buyouts. But what about the “middle”—where the product works, the market is real, and the challenge is disciplined scaling? That’s the world of growth equity. In this episode, Jim Ferry, Partner at Volition Capital, breaks down how founders can grow with focus, avoid valuation traps, and build durable companies in an AI-accelerated market. Below is a full recap for anyone who won’t catch the audio—complete with frameworks, questions to ask your team, and actionable takeaways. Why Growth Equity Is Different Growth equity invests after product–market fit but before a company is fully optimized at scale. You still have execution risk—go-to-market, hiring, operations—but you’re not betting on whether the product should exist. It’s less about “zero to one,” more about “one to ten.” What that means for founders: * You need evidence PMF is real (cohort retention, usage, expansion), not just a few big customers. * Dollars are aimed at repeatability—sales coverage, channel strategy, pricing, and ops. Defining Real Product–Market Fit (Beyond Vanity Metrics) Jim stresses PMF is a pattern in data and behavior, not a revenue milestone. Look for: * Consistent retention across cohorts * Expansion (upsell/cross-sell) without spiking churn * Sales efficiency that stays healthy as you add reps * Pipeline sources that are repeatable (not just founder-led or word of mouth) Founder self-check: If you paused paid channels for 30–60 days, would new logos and expansion still happen? If the engine stalls, you might have traction—not PMF. TAM vs. SAM: Right-Sizing Opportunity Yes, the TAM slide matters, but Jim argues serviceable and attainable market (SAM) is where strategy gets real. A focused SAM helps you: * Prioritize ICPs you can win now * Design a go-to-market motion that compounds * Choose adjacencies deliberately (not as a distraction) Try this exercise: Define your top three ICPs by pain, urgency, and willingness to pay. Map which channels predictably reach each one. Kill or pause everything else for two quarters. Capital Efficiency in Practice Capital efficiency isn’t austerity—it’s sequencing. Spend where the playbook is proven; protect burn where it’s not. Jim’s signals of efficient execution: * Payback periods within target (and not deteriorating as you scale) * Gross margin improving with volume or mix * Sales productivity consistent across tenured reps * GAAP discipline (not just adjusted metrics) Rule of thumb: Before you hire the next 10–20 go-to-market seats, prove the current motion is repeatable outside founder heroics or special discounts. Valuation Sanity: Start with Public Comps In choppy markets, private headline multiples can mislead. Jim’s approach: * Anchor on public comps (growth, margins, rule of 40) * Adjust for scale and risk (earlier stage = wider discount) * Reality-check with unit economics (LTV/CAC, payback, net dollar retention) Negotiation tip: If the bid–ask spread is wide, focus the conversation on business levers (pricing, mix, channel, margin) that—if improved—justify your target multiple. Where AI Helps—and Where It Doesn’t AI is accelerating adoption curves, but durability matters. What looks exciting today can be copied tomorrow. Jim looks for moats in: * Workflow integration (embedded in multi-step processes) * Proprietary data loops (improving model outcomes over time) * Distribution power (channels that competitors can’t match) Ask yourself: If a well-funded competitor replicated your model, what would be hardest to copy—your data, your seat in the workflow, or your distribution? Minority Investing, Major Impact Volition typically invests as a minority partner. That means support is “pull, not push.” Expect: * Help building talent pipelines (execs, operators, board members) * Access to portfolio playbooks and founder communities * A sounding board for pricing, packaging, and channel experiments * Prep for banker processes and clean data rooms when it’s time to exit Founder takeaway: If you want a partner, be clear on the 2–3 needles you want help moving in the next 12 months. Set that agenda early. Founder Evolution: Builder → Scaler → Operator Many companies stall because the role outgrows the original job description. Jim’s pattern: * Builder: Finds PMF, ships fast, sells vision * Scaler: Hires leaders, installs process, drives repeatability * Operator: Manages complexity, portfolio of bets, quality of earnings Honest moment: If you’re spending 70% of your time on tasks someone else should own, you’re probably late to hire that leader. Deal Structures 101 (Without the Jargon) When markets wobble, structures appear. Used well, they bridge valuation gaps and align incentives. Used poorly, they create headaches. * Liquidation preferences and dividends: Can protect downside but must fit growth plans * Participating preferred: Understand how it impacts founder outcomes * Earnouts / performance kickers: Make sure metrics are under management’s control Pro tip: Model the cap table under multiple exit scenarios. If the path to a great founder outcome requires a perfect landing, renegotiate. Where Others Aren’t Looking Some of Jim’s favorite hunting grounds are “unsexy” categories—supply chain, logistics, facilities, even parking. Why? Clear pain, willing buyers, and less noise. If your category isn’t hot on Tech Twitter, that might be your edge. Common Pitfalls Jim Sees * Confusing traction with PMF (one big customer ≠ repeatability) * Over-hiring GTM before the motion is proven * Top-down TAM theater without a credible SAM plan * Arbitrage theses (cheap CAC channels that decay quickly) * AI features without durable moats A Simple Blueprint to Apply This Week * PMF audit: Chart cohort retention, payback, and NDR by segment. Identify where the pattern is strongest. * Focus your SAM: Pick one ICP to win for the next two quarters. Write the “why us, why now” in one page. * Valuation reality check: Build a quick public comps table (growth, margins, rule of 40). Know your band. * Moat map: List your workflow integrations, data advantages, and distribution assets. Choose one to deepen. * Team design: Identify the next 1–2 leadership hires that unlock your builder → scaler transition. Who Should Read This * Founders with working products who need to scale with discipline—not just money. * Operators tasked with turning early wins into a repeatable growth engine. * Finance leaders navigating valuation, structures, and board expectations in a volatile market. Final Thought Growth equity isn’t about spray-and-pray or financial engineering. It’s about evidence-backed scaling—picking the right customers, proving the motion, and compounding with discipline. If that’s the game you’re playing, Jim Ferry’s frameworks are a sharp place to start. Enjoyed this summary? Share it with a founder who’s moving from builder to scaler—or drop your biggest question in the comments and we’ll tackle it in a follow-up. 👂🎧 Watch, listen, and follow on your favorite platform: https://tr.ee/S2ayrbx_fL 🙏 Join the conversation on your favorite social network: https://linktr.ee/theignitepodcast Chapters: 00:01 Welcome & episode setup 00:39 Jim’s origin story (Rhode Island → investing → Volition) 02:45 What growth equity really is—between early VC and buyout 06:17 Defining real product–market fit vs. vanity metrics 08:41 AI’s impact: speed, durability, and building moats 10:16 Anti-portfolio & misses: lessons from deals that got away 13:13 Volition’s check sizes, minority focus, and underwriting lens 16:47 TAM vs. SAM: why the attainable market (and share) matters 18:04 Capital efficiency in practice: burn multiple, alignment, pacing 19:25 Value-add after the check: talent, community, and playbooks 21:58 Minority investing approach: “pull, not push” support 23:40 Partnership model & Jim’s focus on internet business models 25:57 Team underwriting: founder traits that correlate with outperformance 29:00 Founder evolution: builder → scaler → operator (and when to hire) 31:16 Exit readiness: banks, data rooms, and a ButterflyMX example 33:42 Today’s valuation reality: wide bid–ask spreads; start with publics 36:50 Deal structures 101: prefs, dividends, participating preferred 39:44 Where others aren’t looking: logistics, supply chain, parking 40:59 Lessons from failed theses: why arbitrages and DTC CACs decay 42:20 Founder grit vs. market size; winning a disproportionate share 43:33 Underrated founders to watch (e.g., Kinetics, Rounds) Transcript Brian Bell (00:01:13): Hey, everyone. Welcome back to the Ignite podcast. Today, we’re thrilled to have Jim Ferry on the mic. He is the partner at Volition Capital. It’s a Boston-based growth equity firm that backs high growth founder on businesses across software, internet, and consumer sectors. He’s been at Volition for over a decade, leading investments in companies like Attitude. Did I say that right? Doing Things, Kinetics, Butterfly MX, and many others. We’re going to dive into his journey, what he’s seeing in the growth equity landscape. I’m pretty interested in this. And where he thinks the next wave of breakout companies will come from. Thanks for coming on. Jim Ferry (00:01:42): Yeah. Thank you for having me, Brian. Appreciate it. Brian Bell (00:01:44): Yeah. So I’d love to get your origin story. What’s your backstory? Jim Ferry (00:01:47): My origin story from Rhode Island, small state, my backyard backed up to the Massachusetts border. So I was only like 45 minutes outside of Boston. That’s where I reside today. That’s where the entire team is here. And actually went to school in Providence, Rhode Island. Didn’t want to go there. It felt too close to home. If anyone knows it, it’s a small state. You can drive acr

    45분
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