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Liam Karlsson had no clue why his SEO clients were losing traffic while rankings heldThen his 57-year-old mom asked ChatGPT for new running shoesNike answer. Bought the shoe. Was super happy. Google was never part of that customer journey...That was the seed of Rankad.aiHe pitched Co-Founder William Gyltman. They went all inTrack and grow brand visibility across ChatGPT, Perplexity, Gemini, and Copilot. Enter your domain, 30 seconds later you're in the app. AI agent optimizes your site directlyLiam Karlsson & William Gyltman, Co-Founders, Rankad.ai at The Residency Demo Day 🎙️ Fondo START pod w/ David J. Phillips (full ep in comments)00:25 “We’re helping companies earn more money in AI search” 02:34 AI search is moving fast: new models, algorithms, protocols 03:05 A new source of income beyond Google traffic 03:31 AI search is growing fast, but has not outrun Google yet 05:34 Enter your domain and get inside the app in about 30 seconds 05:52 Visibility, competitor, and company data inside one platform 06:07 AI gives optimization tasks based on scan data 06:15 The agent executes tasks on your site 09:41 SEO rankings held, but traffic still disappeared 09:47 Liam’s mom used ChatGPT to buy running shoes 10:00 “That was like the seed of Rankad”

Amazon sellers do not have a data problem.They have a decision problem.They already have the clicksThe conversionsThe impressionsThe keyword historyWhat they do not have is a system that knows what to do with it.So brands pay agencies $5,000 to $50,000 a month - and still lose money on adsMatthew Chen built Laurence to change that.A quantitative system for Amazon advertising.Built for continuous decision-making under profit constraints.Using existing ad copy, reinforcement learning, and custom models to run ads on autopilot.When confidence is high, it actsWhen data is sparse, it borrows signal from similar keywordsThe result: about 40% better performance for customers.Amazon is the wedge.Autonomous performance marketing is the bigger vision.🎧 START pod: Matthew Chen, Founder & CEO, Laurence "Autonomous performance marketing"00:23 What Laurence does02:12 How the company found the wedge02:47 The customer quote that changed the company03:15 The flaw in the standard Amazon ad model05:25 The move from Amazon to the wider internet06:12 How Laurence uses transformers today

Construction doesn't fail on the jobsite. It fails in the drawings. The jobsite just reveals itProject managers spend half their time scanning plans page by page for conflicts between disciplines. Plumbing through steel beams. Electrical into HVACThey still miss most of it. Millions in rework when caught in the fieldMilind Sagaram built Articulate to catch these issues before construction startsAI reads the PDFs. Finds clashes across architectural, structural, and MEP sheets. Generates draft issue reports automaticallyThe surprise: construction teams aren't resistant. They want it more than anyone expected🎙️ Milind Sagaram, Co-Founder & CEO, Articulate / Helonic.com on Fondo START pod 00:18 AI for finding drawing issues before construction starts02:24 The old workflow: manual plan review02:45 The consequence: rework and delays03:00 Small issues, massive downstream cost03:27 Copilot, not replacement04:04 Why AI belongs in construction04:39 What surprised him about selling into the industry05:59 Who Articulate sells to

Agents don’t need bigger models. They need better tools.Morph trains coding subagents.Not for humans. For frontier models.Fast Apply edits at 10,000 tokens/sec. WarpGrep handles code and log search.Both keep the main model’s context cleanBecause when context gets too large, performance drops.Now Morph is pushing coding subagents even faster.One newer model runs at 33,000 tokens/sec: https://docs.morphllm.com/sdk/components/compact🎙️ Tejas Bhakta, Founder & CEO, Morph01:30 Fast Apply + WarpGrep02:26 Context fills up around 100k02:38 Keep the main model context clean03:31 “You can’t scale human attention 100x”07:28 Founders are missing how to value human attention08:30 New model at 33,000 tokens/sec10:08 Better, faster, and cheaper than the frontier

Pamir Ehsas spent years as outside counsel serving startups. He saw the same problem on repeatSimple legal work took weeks. Pricing was opaque. Lawyers kept starting from scratch instead of using AISo he built Arcline. AI generates the first draft. Elite lawyers from the best firms, and schools (Harvard, Oxford etc.) do the final revisionUp to 80% of the work gone. Same-day turnaround (Try getting that from a traditional law firm) 50+ venture-backed startups already onboard when this ep was recordedMost legal AI tries to replace the lawyer. Pamir replaced the busywork02:09 “Fondo, but for legal” / AI-native legal for startups03:12 Sold AI to law firms first; then pivoted to end users04:52 Why Arcline uses experienced lawyers, not junior lawyers05:43 Faster, higher-quality work — including complex matters07:00 Big Law was slow; incentives were broken07:36 Why going direct creates the data loop to improve AI08:34 AI does the grunt work; lawyers create trust11:08 What startups actually come to Arcline for12:26 The long-term vision: legal advice at your fingertips

An employee used his corporate Gemini account to generate fake receipts for reimbursementsNot because he was lying. He just didn't have the real onesThat's when the CISO realized they needed OximyAsk an enterprise how many AI tools they use. They say 10. The real number is probably 40+Most of the risk isn't malicious. People just want to get things done faster. So customer lists end up on free tools with no DPAThe first instinct is to block everything. But people bypass restrictions anywayThe real question: how do you say yes to AI without losing control?That's Oximy. A control layer for enterprise AI adoption. Track usage, manage spend, enforce governance🎙️ Naman Ambavi, Founder & CEO, Oximy on Fondo START pod00:19 — What Oximy helps enterprises understand and manage02:24 — From India to the Bay, and the thesis behind Oximy03:29 — Track adoption, cost, and risk controls03:52 — “They say they’re using not more than 10… the number goes roughly over 40”04:22 — Compliance, retraining risk, and why oversight matters05:10 — Most misuse starts with harmless intent06:23 — The gap between Silicon Valley and corporate America07:35 — How to say yes to AI without losing control10:25 — Why the pain shows up most clearly at 1,000+ employees12:59 — Why banning AI is the first instinct — and why it doesn’t last

Attorneys are trained to spot issues. That’s literally what law school teaches.Show them your product, and the first thing they’ll say is: “the margin is off on this.”Every hour they spend learning software is an hour they’re not billing.Raffi Isanians knows that because he lived it.Kirkland. Gunderson. Years inside private equity and venture work.That’s why Mage Legal has a simple standard: if a lawyer opens the product with no instructions and can’t figure it out, "we’re failing"Comprehensive AI coverage across the entire data room:red and yellow flags, diligence memos, disclosure schedules, redline comparison. 1,500 documents in tens of minutes. All async.05:15 - Puts a TOS into ChatGPT 3.5, comes back 85-90% there06:20 - Every hour learning a tool is an hour not billed08:37 - The product worked, nobody wanted it09:00 - YC partner on the two-year window09:53 - AI is an F1 engine in a world of bicycles13:00 - Clients are pushing AI adoption, not the lawyers15:19 - The goal is zero behavior change17:00 - 1,500 documents, diligence memos, tens of minutes18:22 - One good associate now does the work of six30:56 - Engineers simplify, lawyers complicate33:00 - 11PM, picture of his daughter, back to work40:34 - Simple enough to use with zero instructions

The internet was the training set for intelligenceNobody has built the equivalent for the physical worldPreviously, Lucas Ngoo co-founded Carousell, scaled it past $1B Now at Cortex AI he's collecting the data robotics labs need to train foundation modelsCameras, VR headsets, glasses on factory workers, retail workers, everyday people. Recording real-world manipulation work (Maybe tens of millions, even hundreds of millions of hours)Not building the robot. Not training the model. Collecting what goes in2026: scale data in a big way 2027+: start rolling robots out, 90% teleoperation, 10% autonomyWhile humans step in, the system keeps learningThat's the flywheel toward full autonomy01:38 Cortex as the data layer for general-purpose robotics02:19 Why text data doesn’t transfer cleanly to the real world02:51 Recording day-to-day human manipulation work03:23 “Tens of millions” to “hundreds of millions of hours” of data06:37 Cortex’s role: “We are really the data piece”07:28 When humanoids become part of everyday life08:00 2026 as the year data scales08:13 90% teleoperation, 10% autonomy08:37 Data flywheel toward full autonomy08:55 Synthetic data + real-world data reinforcing each other

Some hotel software is still DOS-based. Sometimes pen and paper (that's why guests are waiting 45 mins to get towels)Gavin's dad has worked at Marriott for the last nine years. When he showed Gavin the old software, that was the sparkLance builds AI agents that answer calls, handle back office operations, run sales workflows & moreStarted with voice, got inside the hotels, and realized how much more they could automateWith coding agents one engineer acts like fiveBig contracts need custom solutions. Now they can deliver🎙️ Gavin Brennen, Co-Founder, Lance (YC W26) on Fondo START pod01:02 The reality of hotel software01:53 The insight that sparked the company02:01 A better way to handle guest requests02:46 Why Lance had to build desktop agents03:08 The centralized dashboard layer04:00 Why hotel problems are highly custom04:32 One engineer can now act like five05:51 “Go all in”

Direct sales reply rates are going down. AI spam is making it worseNikhil Reddy saw the shift early: as trust matters more, partnerships become a real revenue channelProblem is most partnership teams are still running on spreadsheets. They don't know where to focus. Attribution across emails, events, and co-marketing is a messArzule uses CRM data, market signals, and ecosystem signals to help partnership teams discover and prioritize the partnerships that actually drive revenueTwo people building it. Already working with companies generating over $400M in ARRApplied late to YC's fall batch, never even got a reply. Applied again for winter, got in, and their group partner told them to pivot the next day. That became Arzule🎙️ Nikhil Reddy, Co-Founder & CEO, Arzule on the Fondo START pod01:09 What Arzule does and who it's for02:24 Direct sales is dying. Trust is taking over02:43 Pivoting mid-batch from multi-agent coordination to partnerships03:12 Why partnership teams are finally getting their moment03:54 The data layer: CRM signals, ecosystem signals, company-specific inputs04:09 Why attributing revenue to partnerships is so hard05:24 Affiliate links, rev-share, bounties, and contracts in one platform06:03 What onboarding looks like for larger customers07:02 Start analyzing everything early, even if it feels relationship-driven07:48 Tracking signals like new partnership hires and market expansion08:28 Revenue attribution as the core of predictable partnerships09:26 Applied late, got no reply, applied again, pivoted the day after getting in10:02 Move fast and pick something you want to do for 10 years

Setting up a single trigger in Zendesk takes 30 clicksWith Pillar it takes one sentenceJJ Maxwell built an open source copilot you build into your app. Users talk to it in natural language and it drives the app for themThe problem: products can do a lot but users don't always know what's there. So they ask support. Or they churnBefore Pillar, JJ built a creator ad marketplace with about 40,000 creators. Then spent two years on another product through YC W24. About $30M on the platform, real users, decent growth. Pivoted anyway. As soon as he lost belief it was gonna work, he ripped the bandaid offWeb MCP is already rolling out. Companies trying to stop agents from taking actions are fighting a losing battle🎙️ JJ Maxwell, Founder & CEO of Pillar (trypillar.com) on the Fondo START pod01:56 What Pillar is: a copilot that’s easy to build into your app02:19 Why users ask support or churn when product complexity hides value03:05 “30 clicks” in Zendesk becomes a sentence03:39 Why AI can do this now: models are better at reasoning and chaining actions04:19 What implementation looks like: wrap existing frontend code and tool calls05:25 Why teams can often get Pillar working in about a day06:09 The Double journey, real traction, and the decision to pivot11:00 Web MCP and why agent-ready software is coming12:23 Why companies may not be able to stop agents from taking actions forever

The script has been the same for decades: find a co-founderInvestors demanded it. Accelerators screened for it. The narrative became so entrenched that founders started pairing up out of obligation, not alignment.Julian Weisser, founder of SOLO and ODF, has a name for this phenomenon: co-founders of convenienceAnd he's proving they're not just unnecessary-they're often the reason companies fail.This week, Julian released 'The State of Solo Founding' report: "Today, solo founding is considered odd. Soon it will be the default. This report features exclusive Carta data alongside commentary from solo founders who have raised over $250M and the investors who backed them."The comprehensive tracks solo founder rates across thousands of startups, the headline finding is historic: for the first time, over one-third of new startups are solo-founded (That's 36% in 2025, up from under 25% in 2019)👉 Download the report here: https://solofounders.com/report👉 Apply to the Solo Founders Program today. A three-month, in-person residency for 6 ambitious solo founders. Next cohort starts Jan. 23, 2026: solofounders.com/program(00:44) Why the report matters now(02:17) The data: 24% → 36%, first year over 1-in-3(04:32) 3 forces: AI, visible wins, collapsing narrative(06:00) Investor POV(07:30) Org design insight: cutting the middle layer(09:47) Download report: http://solofounders.com/report(11:01) ODF26: half the cohort flipped to solo(12:33) Inside SOLO(14:30) Why coworking doesn't work for startups(16:37) Outcomes: $1M ARR in 2 months & more(17:19) Follow @solofounding & @joinodfWhere to find Julian Weisser:‍X: https://x.com/julianweisserLinkedIn: https://www.linkedin.com/in/julianweisserWebsite: https://weisser.io‍Where to find SOLO: ‍X: https://x.com/solofoundingLinkedIn: https://www.linkedin.com/company/solo-foundersWebsite: https://solofounders.comWhere to find ODF:‍X: https://x.com/joinodfLinkedIn: https://www.linkedin.com/company/solo-foundersWebsite: https://joinodf.com‍Newsletters: ‍Texts with Founders: https://textswithfounders.comMultitudes: https://multitudes.weisser.io‍Where to find David Phillips:X: https://x.com/davjLinkedIn: linkedin.com/in/davjphillips‍Brought to you by:‍ Fondo — All-in-one accounting for startups @ fondo.com

Nate Matherson has spent 10+ years as a founder. He's built companies and even exited. After that first exit, he started angel investing in dozens of companies, then launched a fund.Numeral was one of his early bets. He sent Numeral's CEO Sam an email. By the end of the day, he was working there as Head of Growth. Now he's helping scale the YC-backed sales tax platform serving over 2,000 customers.Nate's seen what happens when founders don't think about sales tax. "They actually found out that they owed about a half million dollars in sales tax… the buyer subtracted that off what would have gone to the founders."When Nate was a founder, he'd log into Stripe and that sales tax dashboard was lit up like a Christmas tree. Numeral does free nexus studies and monitoring — takes five minutes to set up, plugs into Stripe and Rippling, then runs itself.They launched their SaaS product recently, and their whole concept is set it and forget it. They actually love it when customers don't log in. Some of Nate's new learnings? "When I was a founder, I was always pretty good at marketing. I was just marketing not-so-great products, which made marketing a lot harder." At Numeral, with the right product at the right time, everything clicked.And he knows: great products make marketing easy. Timing makes it effortless.‍Key Topics Covered[00:10] $500K sales tax bill found during due diligence[01:10] What is nexus and how it triggers[01:51] From founder to angel to fund manager to operator[04:02] 10+ years building, angel investments, vc fund[05:27] Numeral: e-commerce to SaaS journey[08:12] States that tax SaaS + global VAT complexity[08:40] "Set it and forget it"[10:37] Free nexus study + monitoring in 5 minutes[11:24] What's working in growth [13:28] Great products make marketing easy, timing makes it effortless[14:25] Nate's investing rule per batchFollow Nate Matherson:X: @NateMathersonLinkedIn: linkedin.com/in/natematherson‍Follow Numeral:X: @numeralYouTube: youtube.com/@numeraltaxWebsite: numeral.comLinkedIn: linkedin.com/company/numeralhq‍‍FollowDavid Phillips:‍X: https://x.com/davj‍LinkedIn: https://www.linkedin.com/in/davjphillips‍Brought to you by: Fondo — All-in-one accounting for startups: fondo.com

Jayden Clark didn’t abandon music. He re-scored it for distribution. After music school, a hedge fund tour, and a B2B SaaS sprint, he launched MOTS—short, sharp episodes designed to be both of the moment and built to last a quarter. His north star isn’t “go viral.” It’s “be clear.”The insight is disarmingly pragmatic: structure is not the enemy of creativity—it’s the amplifier. Lists compress cognition. A beginning–build–end gives every clip a runway and a landing. When a five-replies-deep roast on X unexpectedly detonated, MOTS podcast already had the scaffolding to catch the surge. That’s the signature move: follow a consistent weekly cadence, then publish “emergency episodes” when the culture pops. The result is a feed that feels alive without feeling random.‍Key Topics CoveredCareer path: SF Conservatory → hedge fund → B2B SaaS → MOTS + Atlas Media LabsJazz formulas = content formulas: two-five-one progressions, building blocks, beginning-middle-endWhy lists work: digestibility, structure, pattern-matching"Neither timely nor timeless": weekly SF tech culture + emergency current-thing episodesEmergency episode #1: Brian Chesky's bench press The viral ratio: Meta Ray-Bans clip → five-tweets-deep → Theo's reply → Seth's "roast" GIF"16 hours of screen time": the competitive advantageDistribution strategy across Twitter, YouTube, Apple, Spotify‍Timestamps:00:20) "neither timely nor timeless"(00:51) Keeping the music alive(02:10) SF Music → hedge fund → SaaS → pod(03:20) Jazz improvisation: a content strategy(03:42) Building blocks & two-five-one progressions(04:35) How to make content easily digestible(04:53) The @theo ratio backstory(06:46) @sethsetse viral quote-tweet(07:17) Emergency pods vs. weekly episodes(07:22) Emergency Pod #1: @bchesky's bench press(09:42) Where to find @mots_pod‍Where to find Jayden Clark:X: @creatine_cycleLinkedIn: linkedin.com/in/jayden-clark-75991a1aa‍Where to find MOTS Podcast:X: @mots_podYouTube: @motspodLinkedIn: linkedin.com/company/mots-podWhere to find Atlas Media Labs:Website: atlasmedialabs.com‍Where to find David Phillips:‍X: https://x.com/davj‍LinkedIn: https://www.linkedin.com/in/davjphillips‍Brought to you by: Fondo — All-in-one accounting for startups: fondo.com

Sky Yang (CEO) & Neo Lee (CTO) are Co-founders of Imagine AI, an AI-powered content engine that clones B2B founders—replicating their voice, context, and backstory to create scalable personal brands. Before Imagine AI, Sky was elected student body president at UCSD by 32,000 students at age 19, then secured $150 million in state funding for university housing through coalition-building and advocacy in DC, Sacramento and at the UC Board of Regents. He co-founded "Break the Outbreak," a nonprofit that delivered PPE across 18 states and 53 cities during COVID, earning commendations from Senator Dianne Feinstein and Congressman Eric Swalwell. Neo transferred from UCSD to Berkeley, then dropped out to build. He met Sky freshman year at a beach event—asking "Are you Skygodkingdom?"—before they went skydiving together and Neo cut Sky's hair in the woods after COVID.Their catalyst was realizing founders were building in public on X but deals were happening on LinkedIn. After meeting advisor Gustaf, they pivoted distribution strategy to focus on where B2B founders actually live and transact. Sky calls this "content-market fit"—a state where your content hits your target customer every single time, creating scalable, repeatable inbound motion. They were fully booked from their first week post-YC launch, landing Series B customers. One founder messaged urgently, jumped on a 15-minute call, and paid on Stripe immediately. They recruited over Halloween weekend instead of partying. They hosted a yacht party with $10 billion in collective GDP (320 capacity, 750+ on waitlist). Neo's philosophy: "The product is just amplifying what we already are. Just be authentic." Sky's vision references Westworld: "Your agents will interact with each other instead of humans."Key Topics Covered:· What Imagine AI is: a chat-first AI clone with high-fidelity persona creation, subject matter expert interviews, and content engineering to hit content-market fit· From X to LinkedIn: pivoting distribution to where B2B deals actually happen; Gustaf's advice on market selection· Sky's origin arc: Chengdu → LA → Bay Area → UCSD student body president → $150M state funding advocacy → Break the Outbreak nonprofit· Neo's journey: UCSD → Berkeley dropout → "Skygodkingdom" beach encounter → haircut in the woods → building startups pre-Imagine AI· Content-market fit framework: when your content hits your customer every single time—scalable, repeatable motion with high-intent top-of-funnel inbound· Week-one hypergrowth: fully booked post-YC launch, Series B customers, 15-minute Stripe close during conference, recruiting over Halloween· Authenticity over algorithm: amplification not fabrication; the product shapes around you, not the other way around· Building clones that replicate voice, context, backstory, heuristics, and cognition· The $10B GDPyacht party: 320 founders, 3 DJs, 750 waitlist—building community as cultural moment· The 'Westworld' thesis: AI agents interacting on your behalf· Building in public as 2025 narrative: why founders do great work but nobody knows; solving discovery through personal brand at scale· Design philosophy: one infinite content motion thread vs. scattered posts; AI handles artifacts, humans make strategic decisions‍Chapters:‍01:21 - The origin story: "Are you Skygodkingdom?"02:00 - Neo cuts Sky's hair in the woods02:36 - Sky's journey: Youngest student body president at UCSD04:20 - Securing $150M in state funding for student housing06:28 - The nonprofit during COVID06:40 - How Imagine AI started: solving their own problem07:15 - Launching on YC and getting booked solid08:00 - Using their own product for personal branding09:08 - What is "content-market fit"?10:08 - The future: AI clones11:09 - The $10 billion GDP yacht party in SF12:11 - Where to find Sky, Neo, and Imagine AIWhere to find Sky Yang:‍LinkedIn: https://www.linkedin.com/in/skyyang‍X: https://x.com/skygodkingdom‍Where to find Neo Lee:‍LinkedIn: https://www.linkedin.com/in/neo-lky‍X: https://x.com/neo_lky‍Where to find Imagine AI:‍Website: https://www.imagineai.me‍X: https://x.com/imagineagi‍LinkedIn: https://www.linkedin.com/company/ai-imagine‍Where to find David Phillips:‍X: https://x.com/davj‍LinkedIn: https://www.linkedin.com/in/davjphillips‍Brought to you by:Fondo — All-in-one accounting for startups: fondo.com

Rebecca Medina and Jeff Phillips built an AI-powered talent marketplace that's disrupting recruitment with transparent pricing, direct negotiation, and same-day PM hires for SMBs.Rebecca Medina had the network. She had decades of Big Tech experience. She had the credibility. But when she needed project management help on a client engagement as an independent consultant, none of it mattered."Even with my network of project managers, I couldn't find the right person fast enough," Rebecca recalls. "And it created a big problem for the company because we weren't able to scale as quickly as we wanted."That pain point became Talent Cheetah.Five years later, Rebecca and her co-founder Jeff Phillips have built an AI-powered talent marketplace connecting pre-vetted project managers with SMBs. They've scaled to 300 PMs across 34 US states. They've even partnered with the Project Management Institute. But the metric that matters most: the Bureau of Labor Statistics says it takes 90 days to hire a technical project manager. Talent Cheetah does it in minutes—with same-day hiring possible.In this episode, Medina and Phillips break down the recruitment model that turns recruiting on its head: transparent pricing that exposes hidden markups, lower take rates than traditional agencies, direct PM-to-company negotiation, and real-time hiring through AI matching.Their core unlocks: many traditional staffing firms charge companies a significantly higher rate than what PMs actually earn—often without disclosing the difference to either side; cultural fit matters just as much as credentials (project management exists on a broad spectrum — the skills needed vary widely across industries, company sizes, and stages of growth.); and past execution remains the strongest predictor of future performance. Their 25-point vetting process includes one pivotal test: candidates must be able to produce legitimate professional references—if you can't find even one after years in the field, you're not ready for the platform.‍In this conversation, they reveal just how much AI is automating routine PM artifacts (like meeting notes, risk logs, and timelines) while increasing the premium on leadership and communication; how their intentional U.S.-based strategy competes on quality and transparency in an industry racing to the bottom on cost; and how Talent Cheetah is opening doors for underrepresented groups in project management; why fractional engagements (such as part-time PM support for short durations) are suddenly viable when traditional agencies can't deliver them well.Key Topics CoveredThe pain point origin: Rebecca's consulting crisis when her network couldn't deliver PM talent fast enoughThe 90-day problem: Bureau of Labor Statistics average vs. Talent Cheetah's minutes-to-same-day matchingExposing the hidden markup: traditional agencies bill $x/hour, pay PMs $x/hour, keep $x secret from both partiesNo posting fees: free to post unlimited jobs (vs. ZipRecruiter/Indeed/LinkedIn pay-per-post), no sign-up fees for PMsThe 25-point vetting process: professional references, credential validation, and candidates who wait yearsThe reference test: some applicants can't find anyone to vouch for them after 12-24 monthsFour-year minimum: experience requirement (not just title) focused on herding cats and managing projectsUS-based strategy: competing on quality, transparency, and credential familiarity instead of global price competitionPMP vs. experience: why certification proves framework knowledge but not execution capabilityDirect negotiation: PMs and companies set rates transparently, eliminating hidden recruiter markupsAI-powered matching: real-time algorithm surfaces top 3 PMs, with 297 more to browseCultural fit dynamics: startup PMs vs. Big Tech PMs require different personalitiesExpanding beyond PMs: network architects, developers, product managers using same vetting frameworkPMI partnership: hiring bonanzas and visibility programs in San FranciscoWhite glove service: helping first-time contractors negotiate rates and structure engagementsAI's impact on PMing: automating artifacts while amplifying leadership and communication needsFractional engagements: 10-hour/week arrangements that traditional agencies can't serveTransparent pricing model: complete visibility vs. hidden markups, lower take rates than Robert Half/Adecco/Tech SystemsChapters:(01:55) Origin story: Talent Cheetah(03:16) What makes Talent Cheetah different: Speed as the #1 differentiator, same-day hiring possible(04:05) US-based strategy: competing on quality and credential familiarity(06:08) Supporting underrepresented groups: veterans (logistics → PM transitions) and women in tech(07:33) Serving both sides: job search help for PMs, FAANG-quality talent for clients(08:43) White glove service: flexible involvement based on needs, negotiation help included(09:16) How it works: 30-second account creation, under-5-minute posting, real-time AI matching(10:15) Platform scale: 300 PM

Julian Weisser is the Founder and CEO of Solo Founders, a three-month residency program in San Francisco where founders live and work together while maintaining full authorship of their companies. He's also the CEO of On Deck Founders (ODF), a program that over seven years and 26 cohorts has helped over 1,000 people start companies that have collectively raised more than $2 billion. As an angel investor with more than 150 portfolio companies including Levels, Astroforge, and MagicSchool, he's seen patterns in what actually predicts startup success versus what investors claim they're looking for. He writes the Texts with Founders newsletter sharing bite-sized practical wisdom for entrepreneurs and publishes Multitudes, a newsletter exploring founder psychology and startup strategy.In this episode, Weisser breaks down the denominator delusion: solo-founded companies were more likely to succeed than co-founded ones, but nobody talked about it because when you look at the total number of successful companies, co-founded businesses eclipse solo successes—while hiding how many unsuccessful co-founded companies exist in the denominator. His core unlocks: two-thirds of startups die from co-founder disputes before reaching product-market fit or running out of money, being solo is far better than 99% of potential co-founders, and authorship (the desire to express yourself and put your vision into the world) matters more than contortionism (twisting your company to match what investors want to see). The flippening already happened in ODF 26—over half chose solo. In this conversation, he breaks down why MagicSchool's Adil Khan (a former high school principal with no startup experience) succeeded solo, how "co-founders of convenience" kill companies before they reach potential, what makes the Solo Founders residency feel like having "five co-founders while building your own company," and why mimicking trends accrues value to memes instead of founders.Key Topics Covered:The denominator delusion: why solo success rates are higher but invisible in the narrative.Two-thirds die early: co-founder disputes kill startups before product-market fit or funding issues.Co-founders of convenience: rushing into partnerships because investors demand it.Invalid constraints: questioning beliefs (like needing school/work co-founders) that prevent great companies.ODF's evolution: expanding who can start companies and who they can start them with.The flippening moment: over 50% of ODF 26 chose solo after the program.Authorship vs. contortionism: building authentically vs. pattern-matching for investors.Solo Founders residency: six to seven founders per cohort, living/working together for three months.Chapters:(00:11) The denominator delusion: why solo-founded companies are more likely to succeed(02:53) How ODF expanded the co-founder search beyond school and work connections(07:05) The flippening: when solo becomes the default instead of the exception(09:27) Why two-thirds of startups die from irreconcilable co-founder disputes, not lack of product-market fit(10:02) Best practices for co-founding: avoiding assumptions and pre-mortems(14:03) How early founders decide to go solo (most don't even consider it initially)(17:42) ODF 26 results: over half chose to build solo(18:48) Founder characteristics across boom cycles: more mimicry and trend-chasing than ever(20:21) Mimicry vs. authorship(22:33) The growth narrative trap: why $100B outcome fixation from massive funds limits great companies(25:09) The Solo Founders residency: three months, "five co-founders"(30:13) The space: own rooms, common areas, office on ground floor, 6am-3am usage(33:27) Who applies: half bootstrapped and sold companies(36:46) Authorship as the defining trait‍Where to find Julian Weisser:‍X: https://x.com/julianweisserLinkedIn: https://www.linkedin.com/in/julianweisserWebsite: https://weisser.io‍Where to find SOLO: ‍X: https://x.com/solofoundingLinkedIn: https://www.linkedin.com/company/solo-foundersWebsite: https://solofounders.com‍Where to find ODF:‍X: https://x.com/joinodfLinkedIn: https://www.linkedin.com/company/'odf'Website: https://joinodf.com‍Newsletters: ‍Texts with Founders: https://textswithfounders.comNewsletter (Multitudes): https://multitudes.weisser.io‍Where to find David Phillips:X: https://x.com/davjLinkedIn: linkedin.com/in/davjphillips‍Brought to you by:‍Fondo — All-in-one accounting for startups: fondo.com

Allen Naliath is the Founder and CEO of Friday, a Chrome extension that integrates AI email management directly into Gmail. Two years ago at Stanford, he struggled with the confidence to ask for what he wanted. So he engineered a solution: a 30-day rejection challenge where he had to hear "no" once per day or start to ask for increasingly audacious requests. The problem: people kept saying yes. He escalated strategically—waiting by a golf cart to ask Sam Altman to sign his laptop, and cold-asking Garry Tan to add him on LinkedIn during a Stanford talk. Garry's response: "Is this a Psyop?" He added him anyway. That connection led to YC. Today, Friday processes emails via predicted action buttons—users press enter repeatedly to archive, reply, or unsubscribe. Allen personally onboards every user to inbox zero in 10 minutes, even with 18,000 unread emails.Naliath's catalyst was advice from a founder mentor: "If you want to work on startups when you graduate, don't even apply to Apple and Google. If you have no plan B, plan A has to work." His core insight: most people's win condition depends on the other person saying yes. He reframed it so yes and no are both wins—the win condition is in his control just by asking. That philosophy runs through Friday's design: it doesn't put email on full autopilot (which "induces anxiety"), it gets users 99% of the way. Friday started as a hackathon project, evolved into a mobile text assistant, then became a Chrome extension after realizing Gmail integration was faster than building feature parity. The average person spends two hours per day in email; Friday users get through 30 emails in 60 seconds.‍Key Topics Covered:- Rejection challenge: daily "no" requirement, mindset shift from fear to relief- Win condition reframe: "Yes and no are both wins. The win condition is in my control just by asking."- Cold approaches: Sam Altman golf cart ambush, Garry Tan LinkedIn add during Stanford talk- Friday evolution: hackathon project → mobile assistant → Gmail Chrome extension- Anti-autopilot philosophy: "That induces anxiety. It gets you to 99%—you stay in control."- Predicted action buttons: archive, reply, unsubscribe—all one-keystroke approvals- Voice matching: Friday drafts replies that sound like you, including dash preference- 10-minute inbox zero: personal onboarding using auto-archive rules for old emails- Chat feature: "Look him up online, find his email in my inbox, draft an intro."‍Chapters:(00:33) The rejection challenge that rewired his confidence(02:08) Sam Altman signed his laptop(03:35) Changing you win-condition to be in your control(04:25) Asking for things that are "hard to get"(05:20) Meeting Silicon Valley Legends(06:05) "Is this a Psyop?" - how a cold LinkedIn ask to Garry Tan led to YC(07:03) Dropping out of Stanford: "If you have no plan B, plan A has to work."(09:23) Friday DEMO: how enter-enter-enter clears 30 emails in 60 seconds(13:45) The inbox zero system: snooze what matters, archive the rest, empty daily(15:13) Why Friday stops at 99%: "Full autopilot induces anxiety—you need control."(17:36) Chat-powered bulk actions: "Look him up online, find his email, draft an intro."(19:21) Make every day feel like Friday‍Where to find Allen Naliath:X: https://x.com/AllenNaliathLinkedIn: https://www.linkedin.com/in/allennaliathWhere to find Friday:‍Company X: https://x.com/fridaymailCompany Website: https://www.friday.soCompany LinkedIn: https://www.linkedin.com/company/fridaymailWhere to find David Phillips:X: https://x.com/davjLinkedIn: linkedin.com/in/davjphillipsBrought to you by:Fondo — All-in-one accounting for startups: fondo.com

Lindsay Amos is the Founder of Amos Communications, a boutique firm for founder-led marketing and PR. From 2018 to 2024, she ran communications at Y Combinator, where she coached thousands of startups and wrote YC's handbook on startup PR. Before that, she worked in comms at Square and Meta, giving her a 360° view of how stories move from boardrooms to bylines to buyer behavior. Today, she advises founders on landing real news (not ads), building durable founder brands, and operating across a media landscape that's shifted from legacy gatekeepers to creator-led growth channels. She also co-created The To-Do List Summit, a workshop bootcamp teaching early-stage teams the tactical basics of comms, video, events, and community, and she writes a Substack on startup storytelling and strategy.Amos's catalyst was living both media eras: nine months shepherding a single Wired story about Square moving into a new office versus today's "algorithms plus authenticity" environment. Her core unlocks: lead with the what (then earn the why), tie every pitch to a macro trend your audience already cares about, and default to exclusives over embargoes until you're big enough to run a press gauntlet. New media isn't a replacement for traditional outlets; the best founders run both lanes—because audiences follow people first, products second. In this conversation, she breaks down how to pick the right channel, prep for tough interviews, avoid blacklist behaviors, and time transparency (share the "personal hell" after you've won, to teach—not spiral).‍Key Topics Covered:- What "news" actually is: a hook plus a macro trend your customer already thinks about.- Founder brand vs. company brand: why audiences follow people first (and how to use it).- Exclusive > embargo (early): how editors green-light stories and why timing matters.- Practical media ops: avoid Friday pitches, follow up once, don't text or Signal reporters.- Content that converts: entertaining, educational, or perspective—never just ads.- Cinematic launches: when video helps, when it's sizzle; why distribution still wins.- New media shift: reporters → Substack/podcasts; find where your audience actually is.- The To-Do List Summit: teaching founder-led marketing when agencies aren't the answer.‍Chapters:(01:58) Old media → new media(05:26) Why founder-on-camera works—and when it doesn't.(08:59) Playing the LinkedIn game, Substack, and sustaining the channels you'll keep.(11:10) "Personal hell" as narrative fuel—share it after the win.(21:58) Defining a real news hook; anchoring to macro trends (IRL + wellness example).(25:48) Exclusive vs. embargo: how reporters decide what to cover.(26:53) Pitch etiquette that keeps you off blacklists (days, follow-ups, warm intros).(32:32) Founder brand > company brand (early) and the three content modes.(36:33) The To-Do List Summit: workshops over thought leadership; hands-on playbooks.‍Where to find Lindsay Amos:X: https://x.com/lindsayaamosLinkedIn: https://www.linkedin.com/in/lindsayamos, https://www.linkedin.com/company/amoscommsWebsite: https://www.amoscomms.comSubstack: https://lindsayamos.substack.comTo-Do List Summit: https://x.com/todolistsummit‍Where to find David Phillips:X: https://x.com/davjLinkedIn: linkedin.com/in/davjphillipsBrought to you by:Fondo — All-in-one accounting for startups: fondo.com

Joe Holberg is the Founder & former CEO of Spring, a workplace financial wellness platform that began D2C, pivoted to employer-paid, and became a top-rated U.S. offering for three consecutive years, serving 25,000+ users. He bootstrapped from 2015 to 2018, raised a $1M seed, and sold Spring to Mariner Wealth Advisors in 2023, remaining through early 2025. Before Spring, he taught with AmeriCorps on Chicago’s West Side and built CS education at Google. A first-generation college graduate who once slept in his car to finish school, Joe is now a declared candidate for the 58th Mayor of Chicago.Holberg’s catalyst was seeing financial confusion across backgrounds—even among peers with professional-class parents. Early Spring had universal interest but low willingness to pay; the unlock was changing the buyer (HR) and making a firm pricing decision: “Pricing isn’t science—it’s a decision.” In this conversation, he discusses building Spring, the B2B pivot, lessons from pricing and sales, and his views on city governance, housing supply, business climate, and tech-literate leadership. This episode presents his perspective and experiences as a founder and candidate.Key Topics Covered:What Spring was: outcomes-oriented financial wellness delivered as a workplace benefit.D2C → B2B: universal desire vs. $20/mo friction; employers fund, employees benefit.Pricing lessons: fewer options, clearer value, faster decisions.Builder arc: bootstrapping (2015–2018), $1M seed, top-rated product, 2023 acquisition; stayed through early 2025.Sales scrappiness: writing a book to establish credibility with HR leaders.Entering politics: motivations, background across economic circumstances, and emphasis on tech literacy.Chicago context (as framed by the guest): population and business trends; collaboration vs. adversarial postures.Governance mechanics: mayor/city council dynamics; CPS school board changes; housing supply constraints.Campaign posture: outsider experience and how he frames his narrative as a candidate.Chapters:(00:36) Spring’s origin — addressing financial education gaps observed across income levels.(01:43) Early arc — glow-stick hustle; first-gen college; sleeping in the car; AmeriCorps; Google; leaving to build.(04:21) “Credibility book” — unconventional sales asset for HR conversations.(06:14) The pivot — strong demand, low D2C conversion; employer-paid model.(08:43) Building years — 2015 start, 2018 $1M seed, solo grind → top-rated 3 years, 25k+ users; 2023 acquisition; through early 2025.(12:39) Pricing "aha" — choosing and owning a price to accelerate qualified deals.(14:37) Why enter politics — empathy across the income spectrum; need for tech-aware governance.(20:02) Entering the arena — outreach, mentorship, and announcing candidacy.(24:23) Status quo (guest’s view) — resident/business trends; collaboration with builders.(27:22) How Chicago governance works — mayor vs. council; CPS board; housing supply.(30:55) Voter expectations — vision, ideas, results.(32:32) Closing themes — affordability, fiscal considerations, and civic participation.Where to find the Joe Holberg: X: @holbergjLinkedIn: linkedin.com/in/joeholbergWebsite: joeforchicago.comWhere to find David Phillips:X: @davjLinkedIn: linkedin.com/in/davjphillipsDisclosure / Non-Endorsement Note:The views expressed by the guest are their own and do not reflect the views of David J. Phillips, Fondo or the Startup Growth Podcast. Appearance on the podcast does not constitute an endorsement of any candidate, campaign, or policy proposal. This episode is provided for informational purposes only.

Jay Ram is Founder & CEO of Hud, the evaluation and RL platform for AI agents. Hud helps startups build RL environments, run fast reward loops, and plug into any RL backend—so teams can cut costs and push last-mile accuracy once they've hit PMF. Before Hud, Jay left a lucrative quant career, shipped an AI prank-calling app that briefly hit #1 on the App Store (≈500k calls), and decided he wanted harder problems and smarter customers. He's a YC W25 alum; Hud is already used by researchers at foundation labs and is expanding into enterprise environments.Jay's catalyst was realizing he didn't want to just talk weekends—he wanted to build. He and his co-founders first tackled computer-use evals for labs. Inside that work, the language shifted: labs asking for "evals" really needed environments—places where you design rewards, iterate, and actually improve model behavior. Today, Jay frames Hud as the "Next.js of RL environments": opinionated lifecycle, backend-agnostic training, and infra that returns signal fast. Early on, use a foundation model; post-PMF, train your own with SFT/RL—that's where environments matter. Looking ahead, he sees post-training speciation: domain-tuned models for finance, accounting, creative tooling, and more—because teams will own more of their stack again.Key Topics Covered:· What Hud is: tools to set up your agent for RL, define tasks, shape rewards, and plug into RFT/other RL backends.· From evals to environments: why scores measure but rewards improve—and how iteration loops change outcomes.· Where it fits: use foundation models early; post-PMF train your own for cost leverage + last-mile gains.· Design + infra: a new category needs opinionated UX and fast results; why lab researchers use Hud for computer-use evals.· Market timing: the "DeepSeek moment" pulled RL from hobbyists into enterprise interest in 2025.· Pre-train vs post-train: scale vs accuracy + domain depth—and why post-training is the real edge.· Future of work: enterprises will own more of the stack; model speciation by domain.· Reality check: agents ace toy DBs, struggle in production; modeling real environments is the unlock.· YC W25 arc: vision matched the original app more than mid-batch; enterprise demand is catching up now.· Finance stack aside: keep ops boring; focus cycles on shipping product (Fondo shoutout in-episode).Chapters:(00:15) Cold open — "We give you all the tools to set up your agent for RL."(00:59) Intro — Jay Ram, Hud, and the origin story(01:41) What Hud does — build RL environments; backend-agnostic (OpenAI RFT, Thinking Machines, etc.)(02:12) Where environments fit — early: foundation models; post-PMF: train for cost + accuracy(02:50) From quant to builder — leaving Wall Street to make things(03:30) The prank-calling app — #1 on App Store; ≈500k calls; why the customers weren't it(04:40) Evals → environments — labs' "eval" asks were really RL environments with rewards(05:40) Evals vs RL — scores vs rewarded steps; how updates happen(07:14) Hard parts — opinionated design + infra speed for researchers and teams(08:08) Before Hud — no toolkit/standards; emerging gymnasium-style efforts vs Hud's opinionated path(09:25) YC W25 — applying, partners (Aaron & Matt), why YC felt like "actual college"(11:05) Vision vs timing — market caught up; enterprises now exploring environments(12:20) Trend — teams rolling their own models post-PMF (SFT/RL)(13:01) Today's fragmented stack — hosting, inference, data; Hud's role in the loop(13:48) The "DeepSeek moment" — hobbyist RL → enterprise interest in 2025(15:57) Future of agents — own the stack, post-training speciation(18:26) Why end-to-end is hard — production data systems need real environments(19:29) Forward-deployed labs — domain hires and environments; how Hud plugs into RFT(20:15) Rapid wrap — it's early; the stack is shifting fast‍Where to find Jay Ram:X: @jayendra_ramLinkedIn: www.linkedin.com/in/jay-ram-29003b198/Where to find Hud:X: @hud_evalsWebsite: hud.aiWhere to find David Phillips:X: @davjLinkedIn: linkedin.com/in/davjphillips‍Brought to you by:‍Fondo — All-in-one accounting for startups: fondo.com

Kevin Xu is Founder & CEO of Alpha AI, your “AI money friend” that plugs into real-time markets and your portfolio to explain what just happened—and what matters next—inside a simple chat. Before Alpha, Kevin became a WallStreetBets folk hero as turning $35K in a 401(k) into $10M through high-conviction swing trades. He previously founded Fan Hero (YC S13), worked at Stripe (~#300) and Google/YouTube, and appeared in MSNBC Studios’ Diamond Hands on Peacock.Kevin’s catalyst was realizing the products he loved—Google, Wikipedia—were built by real people. That sent him to YC, then Stripe for world-class reps, then into the internet’s finance classroom: Reddit. He posted every win and loss, learned in public, and distilled trading into rules like “If it’s good enough to screenshot, it’s good enough to sell.” After building After Hour to socialize trading, he’s now productizing that edge with Alpha AI: a proactive, personable copilot designed to build money confidence for the next million millionaires.Key Topics Covered: • What Alpha AI is: a chat-first AI money friend with market context + your portfolio, proactive “what just happened” nudges, and customizable character. • From WSB to product: turning public receipts (35K→10M) into a system—floors, catalysts, concentration, disciplined exits. • Earnings humility: why reports are a coin flip; behavior, sizing, and timing are the real edges. • Founder arc: Stanford → YC pivot muscle → Stripe discipline → Google scale → After Hour → Alpha AI. • Culture shift: finance as entertainment/sport; people don’t need courses—they need context at the right moment. • Design over dashboards: one infinite chat thread > scattered tools; AI handles background work, humans make decisions. • Missed GME, learned anyway: thesis right, timing wrong—how to keep momentum without hero trades. • Distribution & trust: followable identities, real screenshots, timely alerts—how credibility compounds. • Building in 2025: attention-maxxing, shipping fast, leaning into new formats (e.g., Sora experiments). • Finance stack mindset: keep ops boring—Fondo for the back office, Brex for cash/cards—so you can ship product.Chapters (00:00) Cold open — “I wanted a cool dream”: realizing real people build the internet (00:59) Intro — Kevin Xu, Alpha AI, and the origin story (01:28) Stanford → YC S13 double-interview; pivot from Alpha Labs to Fan Hero (06:11) Stripe (#~300) → Google/YouTube: seriousness vs. internet-native play (09:00) WallStreetBets culture: memes, transparency, learning in public (12:26) The 401(k) stake: missed HR toggle → $35K starting gun (14:53) Early pandemic plays: APT, CODX; the floor + catalyst lens (17:33) Chasing pops: cruises, Chewy-era stories, and disciplined exits (20:11) The GME almost: all-in October, out in December; lessons on timing (23:33) Million-dollar swing days; detachment and the screenshot rule (25:10) Big 5 finale → $10M peak; why earnings are coin flips (27:15) After Hour: social finance, trust via receipts, real-time notifications (30:50) Alpha AI: proactive context, AI friends as the interface (32:52) Beyond investing: building money confidence; simple company finance stackWhere to find Kevin Xu:LinkedIn: https://www.linkedin.com/in/imkevinxu X: https://x.com/kevinxu Instagram: https://www.instagram.com/founderkevinWhere to find Alpha AI:Website: https://alpha.so X: https://x.com/alpha_ai Instagram: https://www.instagram.com/chatwithalphaWhere to find David Phillips:‍X: https://x.com/davjLinkedIn: https://www.linkedin.com/in/davjphillipsBrought to you by: Fondo — All-in-one accounting for startups: fondo.com

Daivik Goel is Co-founder & CEO of Shor, a global payroll platform for startups. Traditional EOR providers charge around $7,000 per year to manage an employee earning $20,000 per year. Shor uses automation to reduce costs and embeds payroll actions into Slack and WhatsApp through AI agents, so founders can request tax documents or payment updates without opening another dashboard.Daivik and co-founder Avi Konduru submitted their YC application at 7:59 PM, one minute before the deadline. After multiple prior rejections, they got an interview, then a follow-up call, then acceptance. They started YC with a crypto payment idea, pivoted five weeks before demo day to global payroll—a problem they'd worked on two years earlier—and shipped contractor payroll within a week. They've since raised funding and are scaling.‍Key Topics Covered:• What Shor is: global payroll/EOR rebuilt for startups; automation handles ops, AI teammates deliver docs/actions in Slack/WhatsApp.• From clever to sellable: pivoted inside YC from crypto/fiat rails to payroll where they had access and clear pain.• Cost math that breaks: why legacy EORs charging ~$7k/yr on a $20k salary fail SMB/unit economics—and how Shor attacks the middle.• Ship speed as strategy: prior fintech muscle let them launch contractor payroll in one week (KYC/KYB, payouts, tax flows).• Design → dashboards: move work to the user (chat interfaces), keep humans making decisions, let AI do the background jobs.• Distribution as a moat: serve the massive long tail priced out by incumbents; win on affordability + responsiveness.• YC pragmatism: plain-English interviews beat pitch theater; momentum over mockups.• Execution after Demo Day: demand first, fundraising next, delivery always—scaling compliance/country coverage without losing speed.• Founder operating cadence: daily inches over hype cycles; embrace “pivot hell,” but pick battles you can actually win with customers.• Finance stack mindset: reliability and support matter most when back-office tools fail—opt for vendors who show up.‍Chapters(00:00) Cold open — the 7:59 PM YC submission(00:37) Intro — Davik & what Shor is (affordable global payroll)(02:51) Waterloo → founder mindset and process discipline(06:05) YC journey and batch dynamics(08:26) First leap without an idea + early GTM lessons(11:56) Marketplaces are hard — takeaways that shaped Shor(14:56) The last-day YC rush & the crypto/fiat idea(24:49) Pivot hell inside YC → choosing global payroll(27:28) Shipping contractor payroll in one week + why now (AI/stablecoins)(29:33) Fundraising wrapped; AI teammates over dashboards; what’s next‍Where to find Daivik Goel:‍Multilink: https://bento.me/daivikLinkedIn: https://www.linkedin.com/in/daivikgX: https://x.com/DaivikGoelInstagram: https://instagram.com/daivikgoelYouTube: https://m.youtube.com/channel/UCzkRfrCXIrW1v60Wyasgq7QSubstack: https://daivikgoel.substack.comTikTok: https://tiktok.com/@daivikgoel‍Where to find Shor:‍Website: https://tryshor.comX: https://x.com/shor_payLinkedIn: https://www.linkedin.com/company/shorpayInstagram: https://www.instagram.com/shor.pay/YouTube: https://www.youtube.com/watch?v=OF1m1H0arYY‍Brought to you by:Fondo — All-in-one accounting for startups: https://fondo.com

Cody Schneider is the Founder & CEO of Graphed, an AI agent for marketing analytics. Graphed plugs into common data sources, manages the data warehouse, and lets marketers chat with their data to generate on-demand visuals—“stacked bar of new vs. total users week over week,” “add a line of best fit,” and similar prompts. It’s built to handle scale (Cody mentions onboarding ~25M rows of Facebook data) and to avoid rate limits and sluggish queries by owning the warehousing layer.In this episode, Cody outlines a practical path from data sprawl to decisions: skip steep BI learning curves and ticket queues; connect sources and ask in plain English for charts and basic analyses. He also talks about how creative volume now functions as targeting—ship lots of concepts, let algorithms find buyers—and positions Graphed as the way to see what’s working without waiting on a data team. For founders and marketers, it’s a clear primer on turning raw rows into faster feedback loops.‍Key Topics Covered:• What Graphed is: an AI agent for marketing analytics that connects sources, manages the warehouse, and lets you chat to generate charts and basic analyses. • From tickets to answers: why BI queues and tool learning curves slow teams—and how a chat interface shortens time-to-insight.• Scale as a requirement: handling large datasets (e.g., ~25M rows of ads data) and avoiding rate limits via a managed warehousing layer.• Roadmap preview: proactive weekly Slack briefs that summarize what changed and why (future functionality).• Creative = targeting: in 2025 paid acquisition, high-volume creative acts as the audience filter while algorithms find buyers.• Stacking S-curves: double down on the working channel, then layer the next before growth plateaus.• Arbitrage windows: underpriced media (e.g., creator CPMs ≈ $2; low-cost local streaming TV CPMs) and why illiquid channels create edge.• Unit economics discipline: CAC/ARPU/LTV/payback thinking—losing on month one can be rational if LTV justifies it.• Validation before build: use ads and landing pages to test demand—even before a product exists.• Founder ops stack: practical setup (e.g., Stripe Atlas, Mercury, Carta, Fondo) to keep focus on product and sales.Chapters(00:00) Introduction to Graphed.com(02:12) Cody's Journey at Rupa Health(05:36) Growth Strategies and Metrics(11:19) Paid Advertising Insights(15:10) Exploring Programmatic TV Advertising(18:57) The Vision Behind Graphed.com(21:57) Building a Financial Stack for StartupsWhere to find Cody Schneider:LinkedIn: https://www.linkedin.com/in/codyxschneiderX: https://x.com/codyschneiderxx Where to find Graphed:X: https://x.com/graphed Website: https://www.graphed.com‍Where to find David Phillips:‍X: https://x.com/davjLinkedIn: https://www.linkedin.com/in/davjphillipsBrought to you by:Fondo — All-in-one accounting for startups: https://fondo.com

Craig Lewis is the Founder & CEO Ogentic AI, builder of Zing—an AI-native enterprise browser that turns intent → action in a secure, workflow-native workspace. Before Ogentic, he founded Gig Wage (750k contractors paid, ~$1B moved, $25M+ raised) and learned payroll inside ADP. That operator muscle fuels Ogentic’s pace: incorporated in June, alpha in July, beta in August. He also serves on the governing board at MassChallenge and angels actively.In this episode, Craig shares velocity advice like: ship before perfect (feedback > stealth), build pro-human AI (human-in-the-loop), and treat fundraising like sales (expect 19 no’s, optimize investor–founder fit, when it’s right—TTFM). He outlines the back-office stack that keeps your startup in good shape and his board philosophy: offer perspective, not prescriptions. If you’re building enterprise AI—or just want to move in weeks, not quarters—this one’s for you.Key Topics Covered:Ogentic AI focuses on enterprise productivity and automation.Building a strong back office is crucial for startups.Fundraising is a numbers game; persistence is key.Feedback is essential for product development.AI will replace some jobs but also create new ones.Having a technical co-founder can accelerate growth.Navigating the fundraising landscape requires understanding investor fit.MassChallenge supports entrepreneurs in solving global challenges.The future of work will involve augmenting human capabilities with AI.Startups should find their niche in the AI market.Chapters(00:00) The Rise of Ogentic AI(13:37) Building a Strong Back Office(17:02) Navigating Fundraising Challenges(19:14) The Role of MassChallenge(23:12) AI and the Future of Work(27:40) Fundraising in the AI EraWhere to find Craig J. Lewis:Linkedin: https://www.linkedin.com/in/mrfutureofworkX: https://x.com/CraigJamalLewis Instagram: https://www.instagram.com/craigjlewisWhere to find Ogentic AI: Website: https://ogenticai.comLinkedIn: https://www.linkedin.com/company/ogenticaiX: https://x.com/ogenticai Instagram: https://www.instagram.com/ogenticai‍Where to find David Phillips:‍X: https://x.com/davjLinkedIn: https://www.linkedin.com/in/davjphillipsBrought to you by:Fondo — All-in-one accounting for startups: https://fondo.com

Grace Gong is the Founder & CEO of Smart Venture Media, podcast host, angel investor, and author. She’s interviewed 500+ founders, investors, and operators on her podcasts, then parlayed that network into a high-signal community: curated founder–VC dinners, conferences (including the Smart AI Summit), and rooms where intros turn into customers and checks. The flywheel started during the pandemic with 5 pm Friday Zooms—and evolved into tightly curated IRL events supported by sponsors and operators.In this episode, Grace outlines a practical approach to community-building: curate for outcomes, not optics (every seat should benefit from every other seat). Her angel filter doubles as her invite list. Online → IRL is the sequence: earn trust digitally, concentrate it offline. For founders aiming to stand out without burning cash, this is a clear primer on turning audience into deal flow.‍Key Topics Covered:Building community has to happen organically.Engaging with entrepreneurs can lead to unexpected opportunities.Sales and storytelling are crucial skills for success in VC.Offering value to others is key to building relationships.The people you meet at events can significantly impact your journey.Planning events requires meticulous attention to logistics.Creating a curated experience enhances networking opportunities.AI is transforming the media landscape and how we build companies.Networking is essential for both founders and investors.Continuous learning and adaptation are vital in the fast-paced tech world.Chapters(00:00) Building Community: The Organic Approach(02:50) Journey into Venture Capital: From Real Estate to VC(05:44) Insights from Interviews: Lessons Learned in VC(08:53) Angel Investing: Key Considerations(11:51) Creating Value: The Importance of Community(15:02) Event Planning: From Small Gatherings to Large Conferences(17:59) The Smart AI Summit: Curating Experiences(20:54) Future of Media: Building with AI(23:48) Final Thoughts and Online PresenceWhere to find Grace Gong & Smart Venture Media:Linktree: https://linktr.ee/gracegong115‍Where to find David Phillips:‍X: https://x.com/davjLinkedIn: https://www.linkedin.com/in/davjphillipsBrought to you by:Fondo — All-in-one accounting for startups: https://fondo.com

Collin Wallace is a partner at Lobby Capital with 20+ years as an engineer, inventor, operator, and investor. Before Lobby, he was Managing Director of Techstars Silicon Valley, launching the first two Bay Area accelerator programs with JPMorgan and eBay. He founded FanGo (Techstars S10)—acquired by Grubhub in 2011, where he became Head of Innovation (OrderHub + pre-IPO patents)—and later co-founded ZeroStorefront (YC W19), acquired by Thanx in 2022. Collin advises the Roelof Botha & Huifen Chan Innovation Program, co-teaches Startup Garage at Stanford GSB, has run two YC Demo Day Funds, and has invested in 80+ startups (e.g., Payjoy, Landed, Mosaic Voice, Postscript, Vellum).In this episode, Collin gives founders some great advice: you’re running two businesses (product for customers, equity for investors). Fund math in concentrated portfolios means ~2 of ~20 bets must carry returns; with dilution to ~10% at exit, winners need multi-billion-dollar potential. Sequence your proof: Pre-seed = prove value; Seed = prove people pay (repeatably); Series A = scale what’s already repeatable. Don’t scale misses (the Steph Curry test). And match capital to your vehicle - venture is rocket fuel: perfect for rockets, destructive for "pickup trucks".Key Topics Covered:Running a startup involves selling to customers and investors.Different VCs have varying expectations based on fund size and strategy.Founders should tailor their pitches to the specific needs of investors.Understanding investor dynamics can improve fundraising success.Successful founders diverge from conventional thinking in their industries.Ambition and hustle are key traits for founders.Expectations change significantly after receiving funding.Consistency and repeatability are crucial for scaling a startup.Community engagement can foster innovation and collaboration.The back office is essential but often seen as a distraction.Chapters(00:00) Introduction to Colin Wallace and His Journey(02:14) The Shift in Growth Expectations for Startups(05:03) Understanding Investor-Fit and Fundraising Dynamics(11:12) The Importance of Founder Attributes(17:15) Navigating the VC Landscape and Expectations(21:03) Post-Funding Realities for Founders(22:40) Understanding Seed Capital and Series A Expectations(25:19) The Evolution of Funding: Series B and C(29:05) Coaching the Next Generation of Founders(32:09) Building the Back Office: The Unsung Hero(35:42) Community Building and Inclusive EventsWhere to find Collin Wallace:‍Linkedin: https://www.linkedin.com/in/collin-wallace/ X: https://x.com/pithyprof Website: https://lobby.vc/people/collin-wallace/ ‍Where to find Lobby Capital:Linkedin: https://www.linkedin.com/company/lobby-capital/ X: https://x.com/lobby_vcWebsite: https://lobby.vc/‍‍Where to find David Phillips:X: https://x.com/davjLinkedIn: https://www.linkedin.com/in/davjphillipsBrought to you by:Fondo — All-in-one accounting for startups: https://fondo.com

Alessandro Chesser is the founder and CEO of Dynasty, a startup focused on making Qualified Small Business Stock (QSBS) trust stacking accessible to founders. Before launching Dynasty, he led sales at Carta from the early days to roughly $300M in ARR, gaining hands-on insight into equity workflows, 409A dynamics, and how distribution is built around real, recurring needs. Dynasty offers a subscription service—$1,500 per year for up to four family trusts—that includes trust creation, annual administration, and tax return filing, turning a traditionally bespoke, high-cost process into something founders can set up early in their journey.In this episode, we unpack the mechanics and timing that make—or break—QSBS outcomes. We cover the core tests (acquiring shares before $50M in assets, five-year hold, qualified C-corp status), state-level differences (New York recognizes QSBS; California does not), and why early planning can start both the QSBS and long-term capital gains clocks while avoiding later surprises. Chesser talks about trust stacking—gifting shares into multiple family trusts so each may pursue its own QSBS exclusion—and notes practical guardrails and expert advice for dong it right. Beyond the tax planning, Chesser shares go-to-market lessons from Carta and Dynasty: using the network effect (e.g., certificates signed), creating urgency with must-do workflows (like 409A), iterating growth levers monthly, hiring decisively, and using social + creator partnerships instead of traditional cold outbound. The result is clear: tactical advice for founders on when to exercise, when to gift, how to document, and how to avoid the common QSBS pitfalls discussed in the conversation.‍Key topics covered- QSBS allows startup shareholders to sell up to $15 million tax-free.- Most startups qualify for QSBS, but there are specific criteria.- Holding shares for at least five years is crucial for QSBS eligibility.- The new rules under the big beautiful bill change QSBS eligibility timelines.- Dynasty helps founders maximize QSBS benefits through trust stacking.- Early exercise of stock options can prevent alternative minimum tax issues.- Filing an 83B election is essential for QSBS qualification.- Social media is a powerful tool for startup growth and marketing.- Building partnerships with influencers can enhance visibility and credibility.- The cost of setting up trusts for QSBS is significantly lower with Dynasty.‍In This Episode, We Cover(00:00) Introduction to QSBS and Its Importance(06:35) Understanding QSBS Eligibility and Benefits(13:08) The Role of Dynasty in Maximizing QSBS Benefits(16:29) Alessandro's Journey and the Birth of Dynasty(18:36) Growth Strategies and Lessons from Carta(27:14) Leveraging Social Media for Growth‍Where to Find Alessandro Chesser:‍LinkedIn: https://www.linkedin.com/in/alessandro-chesser-84763748X: https://x.com/SandroChess‍Where to Find Dynasty:‍Website: https://www.getdynasty.comLinkedIn: https://linkedin.com/company/getdynastyX: https://x.com/getdynasty_com‍Where to Find David Phillips:X: https://x.com/davjLinkedIn: https://www.linkedin.com/in/davjphillipsBrought to you by:Fondo — All-in-one accounting for startups: https://fondo.com

Jeff ‘Jiho’ Zirlin is a co-founder of Sky Mavis, the team behind Axie Infinity and the Ronin blockchain. At the forefront of Web3's most groundbreaking experiments, Jeff helped transform Axie from a small crypto-native community into a cultural phenomenon that onboarded millions to blockchain technology. With over $4 billion in NFT trading volume - earning a Guinness World Record - Axie didn't just talk about bringing people to crypto; it actually did it. Beyond Axie, Jeff pioneered the Ronin blockchain, which now hosts 70+ games and has proven that purpose-built infrastructure can unlock exponential growth for crypto applications.In this episode, we trace the evolution of Web3 gaming from its origins in the CryptoKitties community to today's institutional adoption cycle. The conversation explores how manual onboarding and white-glove user acquisition laid the foundation for viral growth. Jeff shares the pivotal moments that shaped Axie's trajectory: tokenizing experience points, creating the "play-to-earn" model that democratized crypto mining, and the strategic decision to build their own blockchain when existing infrastructure couldn't scale. We also examine the current state of crypto gaming, the shift from retail mania to Wall Street adoption, and why the next wave of innovation might create entirely new cultural mediums rather than just new ways to make money.Key topics covered:The CryptoKitties Mafia: How a December 2017 viral game spawned the founders of Axie, OpenSea, and the modern NFT ecosystemManual onboarding at scale: From personally gifting Axies to Binance angels to hitting 2 million usersThe biological insight: Why CryptoKitties failed (no death = exponential breeding) and how ecosystem balance became Axie's core principle300 users was "#1": How being the largest crypto game with just 300 players became a marriage proposal line - and a growth trajectoryTokenizing the game economy: The moment players asked to buy experience points and accidentally invented play-to-earn"You can't build your startup on another startup": Why Loom Network's failure forced Sky Mavis to create Ronin blockchainThe Ronin Effect: Deploying at 30,000 users, scaling to 2 million in six months - and the infrastructure playbook now powering 70+ gamesThe Uniswap wealth effect: How every Axie player unexpectedly received $4,000, creating a growth catalyst nobody predictedWhy gaming onboards better than DeFi: More people game than trade - and nostalgia beats complexity when introducing scary new technologyFrom Binance to NYSE: This cycle's institutional meta and why crypto gaming hasn't figured out Wall Street yetThe new Renaissance: How fractional reserve banking created the actual Renaissance, and why crypto's lasting impact will be cultural, not financialLoyalty programs vs. helicopter money: Evolving from infinite money glitches to targeted behavioral incentives70+ economic experiments: From AI-powered tanuki battles to on-chain "Runescape" - why only one or two need to workThe cypherpunk optimism: Why crypto offers a more definite, grounded vision for the future than AI or roboticsWhere to find...Jeff 'Jiho' Zirlin:Instagram: https://www.instagram.com/axieinfinityX: https://x.com/Jihoz_AxieLinkedIn: https://www.linkedin.com/in/jeffzirlinSkymavis:Website: https://skymavis.comInstagram: https://www.instagram.com/axieinfinityX: https://x.com/skymavishqLinkedIn: https://www.linkedin.com/company/skymavisAxie Website: https://axieinfinity.com/Instagram: https://www.instagram.com/axieinfinityX: https://x.com/AxieInfinityLinkedIn: https://www.linkedin.com/company/axieinfinityRoninWebsite: https://roninchain.comInstagram: https://www.instagram.com/axieinfinityX: https://x.com/ronin_network LinkedIn: https://x.com/ronin_networkDavid Phillips:Website: www.fondo.comInstagram: https://www.instagram.com/axieinfinityX: https://x.com/davjLinkedIn: https://www.linkedin.com/in/davjphillipsIn This Episode, We Cover(00:00) From 300 to thousands of Users: The Binance Effect(17:41) Community-Driven Growth: The Role of Guilds(18:38) Experimentation as a Growth Strategy(19:52) Challenges and Advantages in Crypto Growth(20:03) Learning Through Gaming: Onboarding to Crypto(21:27) The Uniswap Airdrop: A Catalyst for Growth(22:18) Onboarding and Scaling in Crypto Gaming(23:17) The Ronin Network: A Solution for Scalability(24:40) The Evolution of Ronin and Its Community(25:10) Expanding the Ronin Ecosystem: New Games and Innovations(27:02) Economic Experiments in Crypto Gaming(28:56) The Cultural Renaissance of Crypto(30:14) Future Innovations in Web3 Gaming(31:36) Optimism for the Future of CryptoBrought to you by:Fondo — All-in-one accounting for startups: https://tryfondo.com

Parthi Loganathan is the founder and CEO of Letterdrop, a Y Combinator-backed startup that helps B2B companies build pipeline by focusing on the warmest leads and people who are actually in market. Since launching Letterdrop, he's helped companies move beyond saturated email and cold calling tactics to identify prospects who want to talk and send them highly tailored messaging. The platform analyzes public conversations, CRM data, and sales calls to segment buyers and enable personalized outreach without relying on high-volume approaches.In this episode, we explore the fundamental shift happening in B2B sales as traditional cold outbound becomes less effective and companies invest in higher-effort tactics to stand out. The conversation covers the evolution from Letterdrop's origins as an SEO tool to its current focus on conversation intelligence, driven by market changes from ChatGPT's emergence. Parthi shares insights about the three essential components of effective outbound messaging, why customer conversations represent untapped content goldmines, and his firsthand experience being demoed by an AI sales agent. We also examine his predictions about AGI's timeline and the philosophical question facing all founders: do you build for today's market or tomorrow's technological reality?Key topics covered:Why cold email reply rates dropped 40% in 2024 and the shift away from "spam your TAM" tacticsThe reality that only 2-3% of your market wants to purchase at any given timeThree components of effective outbound: solid observation, poking the P0 problem, and value-first offersLetterdrop's strategic pivot from SEO tools to conversation intelligence as ChatGPT emergedHow customer and prospect conversations contain unique content that competitors can't replicateThe founder journey from Google product manager through multiple micro-SaaS startups to YCReal experience with an AI AE conducting demos better than human salespeopleWhy "marinating" in a single problem space beats jumping between different marketsThe philosophical choice between building Cursor (for today) versus Anthropic (for the future)AGI timeline predictions and whether UBI will arrive before widespread job displacementWhere to find Parthi Loganathan:Linkedin: https://www.linkedin.com/in/parthiloganathan/X: https://x.com/parthi_loganWhere to find Letterdrop:Website: https://letterdrop.com/Linkedin: https://www.linkedin.com/company/letterdrop/X: https://x.com/letterdropcoPodcast: https://open.spotify.com/show/43bSCi3FcFaJ28H7qEK59X?si=2f6afe15cea342eaWhere to Find David Phillips:X: https://x.com/davjLinkedIn: https://www.linkedin.com/in/davjphillips/In This Episode, We Cover(00:00) Introduction to LetterDrop and Its Mission(02:52) The Evolution of Outbound Sales Strategies(06:11) Crafting Effective Outbound Messages(09:09) Parthi's Journey as a Founder(12:02) Leveraging Social Conversations for Sales(14:59) Creating Content from Customer Conversations(17:55) Back Office Operations for Startups(20:49) The Role of AI in Sales(23:38) The Future of Work: AGI and UBI(26:46) Closing Thoughts and Future QuestionsBrought to you by:Fondo — All-in-one accounting for startups: https://tryfondo.com

In this episode, I sat down with John Paul Mussalli, the co-founder and COO of CareSwift, a Y Combinator-backed startup building AI-powered software to streamline documentation for EMT workers. JP and his cofounders brings a unique blend of technical expertise and entrepreneurial drive to the healthcare technology space, having previously worked across diverse fields from real estate automation to web development. Since co-founding CareSwift, he's helped scale the platform to serve over 2,000 EMTs in New York City alone, generating more than 90,000 automated reports. Beyond product development, JP leads go-to-market strategy and is currently pursuing EMT certification himself to deepen his understanding of the industry's challenges.In this episode, we explore the journey from scrappy prototype to venture-backed startup and the critical lessons learned along the way. Key topics covered:How a ChatGPT prototype evolved into a venture-backed healthcare AI platformThe hidden costs of poor EMT documentation: $1,800 per error and 11% industry revenue lossWhy 25% of New York's EMTs organically adopted CareSwift without marketingCritical incorporation mistakes that can delay funding and how to avoid themThe strategic decision to expand from narrative reports to full documentation workflowWhy domain expertise matters when building AI for specialized industriesNavigating regulatory compliance and the founder stack for healthcare startupsThe reality of Y Combinator: 996 work culture and rapid iteration cyclesFrom 15-20 minute reports to 2-minute automated workflows saving hours per shiftWhy sometimes a bug in Apple Mail can redirect your entire startup journeyWhere to find John Paul Mussalli - Linkedin: https://www.linkedin.com/in/jpmussalli/- X: https://x.com/jpm1126Where to find CareSwift:- Website: https://careswift.ai/- Linkedin: https://www.linkedin.com/company/careswift/Where to Find David Phillips:- X: https://x.com/davj- LinkedIn: https://www.linkedin.com/in/davjphillips/In This Episode, We Cover(00:00) Introduction to CareSwift and Its Founders(02:54) The Birth of CareSwift: Addressing EMT Challenges(06:09) Impact of CareSwift on EMT Efficiency(09:01) Navigating the Startup Journey: Lessons Learned(09:35) Navigating Startup Structures and Legalities(12:14) The Journey Through Y Combinator(15:06) Daily Life as a Founder in Y Combinator(17:13) Building a Founder Stack: Tools and Resources(18:57) Future PlansBrought to you by:Fondo — All-in-one accounting for startups: https://tryfondo.com

Reuben Torenberg is a Senior Vice President at CBRE, the world's largest commercial real estate services firm. Reuben specializes in helping startups in San Francisco navigate the complex and rapidly changing office leasing landscape. Since joining CBRE in 2014, he's represented some of the biggest names in tech - including Airbnb, Coinbase, Cruise, and Dropbox - and is widely known as the go-to broker for early-stage startups and growth-stage companies alike. Beyond real estate, Reuben is also a community builder, having founded SF Hoops and SF Links, two of the city's most exclusive and founder-heavy social sports leagues.In this episode, we explore the dramatic transformation of San Francisco's commercial real estate market and the evolving dynamics between landlords, tenants, and the broader tech community. The conversation delves into current market trends in both office and retail spaces, examines how AI companies are reshaping demand patterns, and discusses the critical importance of community building in the tech industry through initiatives like SF Hoops. We also dive deep into pricing strategies, emerging market opportunities, and provide a comprehensive outlook for businesses seeking space in San Francisco.Topics covered:Why SF's commercial real estate recovery is finally here after 5 years of declineHow AI companies are driving massive demand and changing the market dynamicsWhere to find the best deals: neighborhood analysis and sweet spot sizing (10-20K sq ft)Why rents are rising and landlords are getting more confident by the dayBuildings selling at 80% discounts and what it means for new opportunitiesThe return-to-office mandate trend and its impact on space demandLower SoMa as the last frontier for deeply discounted office spaceRetail space conversion opportunities in Union SquareWhy you should secure space now vs. waiting for better dealsPricing breakdown: what 10, 25, and 50-person companies should budgetHow to navigate the search process and when to use a broker& Much moreWhere to Find Reuben Torenberg:CBRE: https://www.cbre.comX: https://x.com/RTorenberg021LinkedIn: https://www.linkedin.com/in/reuben-torenberg-b985b646Where to Find SF Hoops:https://sfhoopsleague.comhttps://x.com/SFHoopsleagueWhere to Find David Phillips:X: https://x.com/davjLinkedIn: https://www.linkedin.com/in/davjphillips/In This Episode, We Cover(00:00) Current Trends in San Francisco Commercial Real Estate(02:53) Navigating the Market: Opportunities and Challenges(05:54) The Shift in Office Space Demand(08:43) Retail Space and Its Transformation(11:55) Landlord Strategies and Market Dynamics(14:52) The Rise of SF Hoops: Networking Through Sports(17:59) Future Outlook: What to Expect in the Coming MonthsBrought to you by:Fondo — All-in-one accounting for startups: https://tryfondo.com

Stephen Llevano is the founder and CEO of Capabuild, a software platform designed for restoration contractors who work on insurance jobs. Capabuild helps these businesses manage compliance, streamline field documentation, and create accurate estimates — fast.In this episode, Stephen shares the full story behind Capabuild: how it started, what he got wrong early on, and the key insights that helped turn it into a real business. One of the biggest takeaways? The power of watching customers work in their real environment — instead of relying on what they say they need.We dive into how observing contractors in the field led to unexpected product decisions, how Capabuild evolved its pricing model after early pushback, and what it takes to build trust in a traditional, change-resistant industry.Stephen also shares his thoughts on building for overlooked markets, supporting local service businesses, and why long-term traction comes from delivering real operational value — not chasing trends or vanity metrics.If you’re building software for non-obvious industries or trying to unlock early traction, this episode is packed with practical, hard-earned wisdom.Check out Capabuild:https://www.capabuild.app/https://apps.apple.com/us/app/capabuild/id1642228115https://play.google.com/store/apps/details?id=com.capabuild.app&pcampaignid=web_shareThis episode is brought to you by:Fondo — Automate your accounting and unlock up to $500k from the IRS: tryfondo.comWhere to find Stephen LlevanoX: https://x.com/StephenLlevanoLinkedIn: https://www.linkedin.com/in/stephen-llevano/Where to Find David PhillipsX: https://x.com/davjLinkedIn: https://www.linkedin.com/in/davjphillips/TakeawaysConnecting directly with customers as a founder is crucial for product successObserving customers in their actual work environment reveals true needs beyond feedbackPricing strategies must evolve based on real customer insights and market dynamicsBuilding a startup requires more time and emotional investment than initially expectedThe insurance industry is shifting, creating new opportunities for adaptive contractorsPersonal relationships often drive initial customer acquisition and business developmentDeep market understanding is essential for navigating industry complexitiesOperational efficiency directly impacts a contractor's ability to serve clients effectivelySupporting local service businesses creates stronger community economic foundationsEvery entrepreneurial experience offers valuable learning opportunities worth embracingChapters(00:01) The Entrepreneur's Journey(03:03) Identifying Market Opportunities(05:55) Building the First Version of Capabild(08:51) Customer Acquisition and Pricing Strategies(11:57) The Evolution of Capabuild(20:56) Operational Challenges and Solutions(23:51) Future of the Industry and Capabild's Mission

Jake Wedig is the Director of Tax at Fondo, where he helps startups navigate complex tax legislation and maximize their tax benefits. With deep expertise in startup tax strategy, Jake specializes in R&D tax credits, Section 174 compliance, and helping growing companies optimize their tax positions while managing cash flow challenges.In this conversation, Jake breaks down the recent changes in tax legislation that every startup founder needs to know about, particularly the game-changing provisions in the One Big Beautiful Bill and how startups can leverage R&D tax credits to get substantial cash back on their development investments.We explore the challenges that Section 174 has created for startups and dive into practical strategies for navigating these changes, including when amending tax returns makes sense and how to leverage bonus depreciation and Section 179 deductions. Jake also explains the powerful long-term benefits of Qualified Small Business Stock (QSBS) for founder wealth optimization.Key topics covered:How the One Big Beautiful Bill creates new tax optimization opportunities for startupsMaximizing R&D tax credits for substantial cash returns on development investmentsNavigating Section 174's impact on R&D expense deductions and cash flow managementStrategic use of amended returns to recover from unexpected tax positionsLeveraging bonus depreciation and Section 179 for immediate equipment deduction benefitsUnderstanding QSBS benefits and the five-year holding period requirementsThe importance of proactive tax planning partnerships between startups and advisorsStaying ahead of evolving tax legislation to capture emerging opportunitiesAnd much moreBrought to you by:Fondo — Automate your accounting and unlock up to $500k from the IRS: https://tryfondo.comWhere to find Jake WedigLinkedIn: https://www.linkedin.com/in/jacob-wedigWhere to Find David PhillipsX: https://x.com/davjLinkedIn: https://www.linkedin.com/in/davjphillips/In This Episode, We Cover(01:42) Understanding the New Tax Bill(03:31) R&D Tax Credits and Their Importance(07:51) Impact of Section 174 on Startups(13:05) Amending Returns and Cash Flow Considerations(16:38) The Role of Tax Advisors for Startups(30:55) Bonus Depreciation and Section 179(35:23) Qualified Small Business Stock (QSBS) Benefits

Nathan Latka is the founder and CEO of Founderpath, a fintech platform that has deployed nearly $200 million in non-dilutive capital to 500+ software companies. He’s also the creator of GetLatka, a massive SaaS database built off the back of his top-ranked Latka podcast, where he’s interviewed thousands of founders. Nathan’s entrepreneurial journey began at 18 with the launch of Heyo, a Facebook fan page SaaS tool he bootstrapped to $2M in ARR before raising venture capital and eventually exiting.In this conversation, Nathan shares hard-earned lessons from building and exiting companies, explains why most founders don’t understand the true cost of raising VC, and offers a compelling case for why debt and secondaries can be a smarter option. We also explore:How he bootstrapped Heyo to $2M ARR before raising VCThe $6.5M exit offer he had to turn down (and regrets)What most founders misunderstand about venture capitalHow GetLatka became the #1 ranked SaaS benchmarking databaseWhy the future belongs to tiny teams with huge revenueThe three AI trends shaping SaaS company formationHow FounderPath prices startup equity daily—instantly enabling secondariesWhy hooks and attention matter more than everAnd much moreBrought to you by:Fondo — Automate your accounting and unlock up to $500k from the IRS: https://TryFondo.comWhere to Find Nathan LatkaX: https://x.com/NathanLatkaInstagram: https://www.instagram.com/nathanlatka/LinkedIn: https://www.linkedin.com/in/nathanlatka/Podcast: Latka PodcastSaaS Database: https://getlatka.comBook: How to Be a Capitalist Without Any CapitalWhere to Find David PhillipsX: https://x.com/davjLinkedIn: https://www.linkedin.com/in/davjphillips/In This Episode, We Cover(00:00) Intro to Nathan and FounderPath(01:30) The story behind Heyo and bootstrapping to $2M ARR(03:50) Raising venture and losing optionality(05:30) The $6.5M offer and why his board said no(07:10) Lessons from a slow death and how to move on(08:15) Why Nathan started the Latka podcast(09:30) Building GetLatka to 3,000+ daily organic clicks(11:00) The power of hooks and attention in modern SaaS(12:45) Inside FounderPath: non-dilutive capital for SaaS(14:30) How venture debt differs from traditional VC(16:15) Why secondaries are healthy, not harmful(18:00) How FounderPath prices startup equity daily(20:10) Big trends: tiny teams, chat-based dashboards, attention > techReferencedGetLatka – https://getlatka.comFounderPath – https://founderpath.comHeyo (archived) – https://en.wikipedia.org/wiki/HeyoHow to Be a Capitalist Without Any Capital (Book) – https://www.amazon.com/How-Be-Capitalist-Without-Capital/dp/0525534440ZoomInfo – https://www.zoominfo.comTechstars – https://www.techstars.comG2 Crowd – https://www.g2.com

Ajith Govind and Avinash Joshi are the co-founders of Cactus, an AI copilot for solopreneurs such as private chefs and caterers, helping them streamline admin tasks and grow their business. Brian Kuan, Community Manager at Vanta, hosted the conversation. Together, we explore early-stage sales, building trust, and the YC network's unique power to catalyze startup momentum. In this episode, we discuss:Why founder-led sales is irreplaceableLeveraging Bookface and social media for early tractionBuilding trust with SMBs and solopreneurs outside your networkCold outreach tactics that actually workedWhy you should launch even a half-baked productThe underestimated power of urgency in early salesStories behind onboarding first customers at Fondo and CactusThe emotional moments that proved they were building something impactfulHow fundraising and selling are deeply intertwinedMuch moreBrought to you by:Fondo — All-in-one accounting for startups: https://fondo.comWhere to Find David PhillipsX: https://x.com/davjLinkedIn: https://www.linkedin.com/in/davjphillips/Where to Find the Guests:Brian Kuan (Vanta, W18)LinkedIn: linkedin.com/in/briankuanAjith Govind (Cactus, X25)X: x.com/itsajith747LinkedIn: linkedin.com/in/ajith-govindAvinash Joshi (Cactus, X25)X: x.com/avinashjoshiLinkedIn: linkedin.com/in/joshi-avinashWhere to Find the CompaniesCactus: x.com/oncactusai | LinkedInFondo: fondo.comVanta: x.com/TrustVanta | LinkedInIn This Episode, We Cover(00:00) Introductions and what each company does(03:12) How Fondo found its first customer through a cold DM to Sam Parr(05:40) How Cactus began by solving a personal need with personal chefs(09:05) Using Bookface to unlock growth(13:00) Tips for selling to startups and SMBs(16:45) How product-led growth and founder empathy drive traction(20:18) Balancing building with selling as a founder(25:12) Founder-led sales vs. traditional sales teams(28:00) How trust is built—especially outside your network(32:40) Lightning Round: first big sales wins, boldest cold DMs, best YC perks(38:35) Sales tactics they wish they knew on Day 1(41:00) Fundraising urgency and investor psychologyReferencedSam Parr (The Hustle): https://www.thehustle.coPostHog: https://posthog.comBookface (YC Internal Network): https://www.ycombinator.comBrex: https://www.brex.comChristina Cacioppo (CEO, Vanta): LinkedInMarketplace Capital: https://marketplacecapital.vcYC Demo Day: https://www.ycombinator.com/demo-day

Reuben Torenberg is a First Vice President at CBRE, the world’s largest commercial real estate services firm. Reuben specializes in helping startups in San Francisco navigate the complex and rapidly changing office leasing landscape. Since joining CBRE in 2014, he's represented some of the biggest names in tech — including Airbnb, Coinbase, Cruise, and Dropbox — and is widely known as the go-to broker for early-stage startups and growth-stage companies alike. Beyond real estate, Reuben is also a community builder, having founded SF Hoops and SF Links, two of the city’s most exclusive and founder-heavy social sports leagues.In this episode, we dive into the state of commercial real estate for startups in 2025, including:Why SF is now a tenant’s market — and what that means for startupsHow to find cheap, high-quality office space (and avoid costly mistakes)How much space your startup really needs at each stageWhy brokers are free for tenants — and why every founder should use oneWhere the best startup neighborhoods are in SF right nowHow coworking has evolved — and why it's a smart move for teams <10What landlords are offering in TI (tenant improvement) allowances todayHow much to offer below list — and why you should always send multiple proposalsThe return of Class A space and what’s happening in Mission Bay, Hayes Valley, and Jackson SquareMuch moreBrought to you by:Fondo — All-in-one accounting for startups: https://tryfondo.comWhere to Find Reuben TorenbergCBRE: https://www.cbre.comX: https://x.com/RTorenberg021LinkedIn: https://www.linkedin.com/in/reuben-torenberg-b985b646/Where to Find David PhillipsX: https://x.com/davjLinkedIn: https://www.linkedin.com/in/davjphillips/In This Episode, We Cover(00:00) Intro to Reuben and CBRE(01:30) From sports to real estate: Reuben’s career path(03:15) Lessons from Custom Spaces and early startup deals(04:50) Major tenants Reuben’s worked with: Airbnb, Coinbase, Cruise(06:30) How SF Hoops became a startup founder hub(08:05) SF Links and the evolution of social networking for tech(09:40) How startups actually find space in SF(11:00) What tenant brokers do and why they’re free(13:00) Square footage per employee and planning for growth(15:00) COVID's impact: SF market shift explained(17:30) Class A space demand and the "flight to quality"(19:45) Leasing terms, TI allowances, and negotiation tips(23:00) Market insights from Q1 2025: 2.9M sq ft leased(25:15) Hottest neighborhoods: Mission Bay, Jackson Square, and beyond(28:00) Best advice for founders raising and scaling: flexibility, furnished space, subleases(30:00) Budgeting by headcount: ballpark lease costs for 5, 10, and 25-person teams(32:00) Final takeaways and next stepsReferencedCBRE San Francisco Office Market Report Q1 2025 (search: “CBRE SF Q1 2025 report”)LoopNet — Office space listingsIndustrious — Premium coworkingMindspaceCanopyWeWorkOpenAI HQ in Mission BayMission Rock Development — Home to Visa and the Warriors

Roger Dickey is a serial entrepreneur and prolific angel investor with over 130 startup investments under his belt. From humble beginnings coding games as a kid to building Mafia Wars at Zynga—a game that reached a $300 million annual run rate—Roger has scaled multiple companies and exited to giants like Zynga, Home Depot, and private equity. He's also pioneered the "search lab" approach to company building, a structured yet high-velocity process for launching and validating startup ideas. In this episode, we cover:Roger’s early obsession with coding and gamesHow Dope Wars turned into a breakout Facebook game successThe origin and explosive growth of Mafia Wars at ZyngaBuilding a startup that scaled to $100K/day in revenueThe matrix method for startup idea generationWhy distribution, not code, is today’s biggest moatWhy he believes in going deep on one growth channelLessons learned from two successful search labsThe importance of knowing when a product isn't workingWhat he's exploring now across SaaS, games, and socialMuch moreBrought to you by:Fondo – Accounting for startups: https://www.tryfondo.comFind the transcript at: https://www.tryfondo.com/podcast (or wherever you're hosting it)Where to Find Roger DickeyX: https://x.com/rogerdickeyLinkedIn: https://www.linkedin.com/in/rogerdickey/Essay – Lessons from 2 Search Labs: https://medium.com/@rogerdickey/lessons-from-2-search-labs-fe07d0bc0fb4Where to Find David PhillipsX: https://x.com/davjLinkedIn: https://www.linkedin.com/in/davjphillips/In This Episode, We Cover(00:00) Intro and Roger's startup credentials(01:04) Roger’s coding origin story and first “viral” product(05:01) How Dope Wars exploded on Facebook(10:45) Building and scaling Mafia Wars at Zynga(16:00) Product-led growth mechanics and viral loops(21:00) Inventing and reinventing distribution(25:35) How Roger structured his “search lab” process(31:00) Metrics and mindset for early-stage validation(34:55) CAC, LTV, and cracking the S-curve of growth(38:47) Scaling a $100K/day construction tech startup(44:10) The role of deep focus vs. testing across channels(48:05) What Roger’s building next and how he’s thinking about itReferencedLessons from 2 Search Labs (Roger Dickey on Medium): https://medium.com/@rogerdickey/lessons-from-2-search-labs-fe07d0bc0fb4Mafia Wars: https://en.wikipedia.org/wiki/Mafia_WarsDope Wars (original inspiration): https://en.wikipedia.org/wiki/Drug_Wars_(video_game)Y Combinator Demo Day: https://www.ycombinator.comDropbox Growth Story: https://www.dropbox.com/business/resources/growth-storyStripe Founder Story: https://stripe.com/blog/stripe-series-a

Travis Hedge is the co-founder and Chief Revenue Officer of Vouch, an insurance platform purpose-built for high-growth technology companies. After growing up around a family-owned insurance agency in Columbus, Ohio, Travis spent his early career at Nationwide Insurance and SVB Capital, where he saw firsthand the gaps in insurance for startups. He co-founded Vouch in 2018, and in just a few years, the company has scaled to nearly 6,000 customers. In our conversation, we dive into:How Travis’s third-grade dream of becoming an insurance agent turned into a mission-driven startupThe critical moment that pushed him to found VouchThe importance of founder-led sales and getting your first 20 customersWhy partnerships alone won't get you early tractionHow Vouch built a full-stack insurance platform versus being a digital brokerThe go-to-market lessons learned from Utah to nationwide expansionHow early hiring mistakes shaped Vouch’s sales strategyHow Travis thinks about demand generation and balancing inbound and outboundWhy domain expertise is essential in evaluating AI vendorsThe inflection points that changed how Vouch scaledHow AI will reshape insurance but not eliminate the human elementMuch moreBrought to you by:Fondo — All-in-one accounting for startups: https://tryfondo.comWhere to Find Travis HedgeWebsite: https://vouch.usX: https://x.com/The_HedgeFundLinkedIn: https://www.linkedin.com/in/travishedge/Where to Find David Phillips (Host)X: https://x.com/davjLinkedIn: https://www.linkedin.com/in/davjphillips/In This Episode, We Cover(00:00) Introduction to Travis and Vouch(01:20) Travis’s early inspiration from his family's insurance business(05:00) Lessons from Nationwide and SVB Capital(10:30) The painful moment that sparked Vouch’s creation(14:00) Building a startup insurance platform from scratch(19:00) Getting the first 20 customers without relying on partners(23:00) Lessons in early hiring and go-to-market team building(27:00) How demand gen strategy evolved(33:00) AI’s real role in insurance and go-to-market(38:00) Big revenue and customer milestones(43:00) How Vouch now serves both seed-stage startups and late-stage scale-ups(47:00) How insurance mistakes can cost startups millions(51:00) The best time for founders to get insurance(56:00) Closing thoughts and Travis’s advice to foundersReferencedY Combinator (YC): https://www.ycombinator.comRibbit Capital: https://www.ribbitcap.com/SVB Capital: https://www.svb.com/svb-capitalOpendoor: https://www.opendoor.com/Root Insurance: https://www.joinroot.com/Nationwide Ventures: https://nationwideventures.com/Amplemarket (AI Sales tool): https://amplemarket.com/Goodhart's Law: https://en.wikipedia.org/wiki/Goodhart%27s_law

Roy Lee is the 21-year-old founder and CEO of Interview Coder a breakout startup that has taken the internet by storm. In one year, Roy went from having his Harvard acceptance rescinded to building an AI tool used by thousands of aspiring developers to land jobs at companies like Amazon, Meta, and TikTok. His story — marked by risk-taking, resilience, and relentless building — has captivated millions on social media and sparked a firestorm of controversy in academia and Big Tech alike.In this episode, we cover:Why Roy's Harvard acceptance was rescinded, and how he bounced backHow a year of isolation turned into a coding bootcamp of oneWhy community college is underrated — and how it shaped Roy’s founding teamHow Interview Coder went from MVP to $10K MRR in a few monthsThe Amazon interview video that triggered a firestorm at ColumbiaHow going viral led to threats of expulsion — and Roy’s strategic responseWhy Roy believes controversy is essential for attentionHow the Z Fellows program changed his trajectoryA sneak peek into Roy’s new startup: PikeMuch moreBrought to you by:Fondo — All-in-one accounting platform for startups. Bookkeeping, taxes, and cash back from the IRS: https://trifondo.comWhere to Find Roy LeeX: https://x.com/im_roy_leeLinkedIn: https://www.linkedin.com/in/roy-lee-swe/Website: https://www.interviewcoder.co/Where to Find David PhillipsX: https://x.com/davjLinkedIn: https://www.linkedin.com/in/davjphillips/In This Episode, We Cover(00:00) Welcome and intro(01:05) Roy's Harvard saga and forced gap year(03:00) Learning to code in isolation(05:10) Attending community college and meeting co-founders(07:00) Transferring to Columbia and launching Interview Coder(08:00) Building a viral-first product in 4 days(10:20) The MVP, tech stack, and early traction(12:00) Using the tool to land offers from Amazon, Meta, and more(13:30) Turning open source into $10K MRR(15:00) The infamous Amazon video and Columbia’s response(17:30) Leveraging virality to fight institutional pressure(19:30) Controversy and creator growth strategy(20:40) Getting into Z Fellows and its impact(22:00) Roy’s new startup: Pike(23:30) What's next and where to follow alongReferencedInterview Coder: https://www.interviewcoder.coZ Fellows: https://zfellows.comRoy’s Amazon interview post: Roy’s X ProfileLeetCode: https://leetcode.comCursor (AI code editor): https://www.cursor.shClaude (AI assistant by Anthropic): https://www.anthropic.com/index/claude

Jay Reno is the founder and CEO of PointHound, a free platform helping hundreds of thousands of people earn and redeem credit card points for maximum value—often unlocking free business class flights. But Jay’s journey started long before PointHound. He previously founded Feather, a furniture subscription startup that redefined how millennials furnish their homes. Under Jay’s leadership, Feather scaled to $15M in annual recurring revenue, raised over $70M in funding, and was ultimately acquired in 2022.In this conversation, Jay and David dive deep into the full founder arc—from early failures to scaling a venture-backed operation—and everything he's applying to his new startup. They discuss:How Jay lost his life savings on his first company—and what he learnedThe origin story behind Feather and the cold email that changed his lifeHow Feather scaled from a duct-taped operation to $15M in ARRThe operational challenges of running a semi-vertical logistics businessWhy the pandemic forced a complete shift in strategyWhat it’s like raising capital when everything looks perfect—but still isn’t easyHow to build loyalty and change consumer behavior with timeWhy most people are wasting their credit card points—and what to do insteadHow PointHound helps anyone fly business class for freeThe cards Jay wishes he used while running FeatherWhat Jay learned going through YC… twiceMuch moreBrought to you by:Fondo — Your all-in-one accounting platform for startups. Bookkeeping, taxes, and R&D credits, on autopilot. https://tryfondo.comPointHound — Redeem credit card points for free flights. No guesswork, just travel. https://pointhound.comFind the transcript at: Startup Growth PodcastWhere to Find Jay RenoX: @jayjrenoLinkedIn: linkedin.com/in/jayjrenoWebsite: https://pointhound.comWhere to Find David PhillipsX: @davjLinkedIn: linkedin.com/in/davjphillipsIn This Episode, We Cover(00:00) Introduction and welcome(01:22) Jay’s first founder punch with his grocery delivery startup(03:06) Early lessons from failure and jumping into Feather(04:43) Starting Feather and how the West Elm deal happened(06:55) Applying to YC from a pizza shop Wi-Fi(08:59) Feather’s scrappy early operations—manual delivery and DIY logistics(13:14) Raising a $3.5M seed and hitting 7% week-over-week growth(19:46) Operating out of a chaotic Dumbo retail space(22:34) Discovering a B2B growth channel and building a team(26:48) The surprising difficulty of raising a Series A(30:22) Closing a $30M warehouse line to unlock scale(33:20) How COVID froze growth and forced strategy shifts(36:02) Selling Feather to Vesta in 2022(37:03) Jay’s time in VC and why he returned to building(38:53) The pain of points mistakes and the birth of PointHound(40:20) The best (and worst) credit cards for startups(41:51) Making points redemption 10x easier and smarter(43:40) Getting PointHound’s first users through Reddit and Bookface(45:32) Final thoughts and where to find JayReferencedFeather: https://feather.comY Combinator (YC): https://www.ycombinator.com645 Ventures: https://645ventures.comBrex Card: https://www.brex.com/cardAmex Business Gold: https://www.americanexpress.com/en-us/business/credit-cards/business-gold-cardCapital One Spark Miles: https://www.capitalone.com/small-business/credit-cards/spark-milesBookface (YC network): https://bookface.ycombinator.comReddit r/Churning: https://www.reddit.com/r/churning/