ProductLed Podcast

Hosted by Wes Bush

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316

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Oct 2026

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The ProductLed Podcast is a weekly interview series with both product-led growth leaders and practitioners who have real knowledge to share on what it takes to use their product to grow a business.

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October 5, 2026Episode 31018 min

Day 55 of 100: Halfway Through Building an AI Product

Day 55, and I'm past the halfway point of building an AI product in 100 days. This one looks back at the first 50 days and lays out the plan for the last 45. The biggest lesson so far: I thought building the app would be the hard part. It isn't. Figuring out what to build, how it fits into the business, and what the next offer should be is still way harder. The PLG analyzer now gets about 8 out of 10 recommendations right. I'm not willing to trade quality for cost or speed, so getting that to 99% comes first. The money side hasn't worked yet. The analyzer was meant to lead to working with a ProductLed operator, but most of the people using it run small companies, and they're hard to serve at that price point. Meanwhile the free product costs about $1,300 a month to run, developer included. So the plan for the next 45 days is one new enterprise client, won through blind calls: analyses of companies I already want to work with. Every one of them also trains the analyzer to give you better recommendations. IN THIS EPISODE 00:00 Day 55, and what I'm building 02:09 Your feedback, from "about as good as ChatGPT" to three scans in a row 03:05 Building the app was never the hard part 04:01 Highlights: shipping, Matt Pocock's skills, new friends 05:20 Lowlights: why 8 out of 10 isn't good enough 06:58 Building an app has no training plan 08:33 The game plan for the last 45 days 09:36 Why the operator offer hasn't worked 10:45 A free product that costs $1,300 a month 11:22 Pay for itself, fit the business, fix quality 13:08 How my advisory work actually runs 14:24 Blind calls: account-based marketing that trains the AI 15:29 One enterprise client and 99% good outputs 16:36 Next: turning internal workflows into products 17:54 What's your take? MENTIONED Run the free PLG analyzer: https://productled.com Matt Pocock's skills: https://github.com/mattpocock/skills Wes Bush on LinkedIn: https://www.linkedin.com/in/wesbush/ What's the biggest challenge you've hit building an AI product?

September 19, 2026Episode 30913 min

Day 39 of 100: I Said Ship It and Nothing Shipped

In the last episode I told you I'd hit a wall building an AI product in 100 days. This is what happened after I asked for help. So many of you replied, and a handful of those replies changed how I build. One pointed me to Matt Pocock's skills on GitHub, which exposed an embarrassing habit: session after session I'd been telling Claude "ship it" and assuming the change went live. It didn't. Others told me I was trying to boil the ocean. So the PLG analyzer now finds one opportunity worth your week instead of three, and a judge agent decides what matters most rather than a formula weighing everything the same. My own rating went from a 4 out of 10 to a 6. Next up is cost and speed. A free assessment still costs 40 to 60 cents and takes about two minutes, and I want it under 60 seconds. I close on something that has little to do with the build: using AI to write without ending up sounding 80% like yourself. IN THIS EPISODE 00:00 Day 39, and why updates are now every two weeks 00:47 Stuck, and what happened when I asked for help 02:01 Matt Pocock's skills, and the 11 I use all the time 02:37 I said ship it, and nothing shipped 03:41 Stop trying to boil the ocean 04:16 Why a formula misses the biggest problem on your site 04:57 From three opportunities to one worth your week 05:46 Why an LLM product needs a judge agent 06:58 Eric's idea: a playbook behind every recommendation 08:14 The next two weeks: cost and speed 09:20 From a 4 out of 10 to a 6 10:08 What I learned 10:42 Sound like yourself when AI writes for you MENTIONED Run the free assessment: https://productled.com Matt Pocock's skills: https://github.com/mattpocock/skills Wes Bush on LinkedIn: https://www.linkedin.com/in/wesbush/ What's one skill or tool someone recommended that changed how you build?

September 8, 2026Episode 3088 min

Day 27 of 100: My AI Product Is a 4 out of 10

Day 27 of 100. There's no win to report in this one. I'm building an AI product in public, and right now I'd rate it a 4 out of 10. That's my own score, and I'm hard on it, because I advise companies on this stuff for a living. The product is simple to describe. Give it your website URL, and it should hand back recommendations that blow your socks off. Sales-led company? It should spot what you could be giving away for free. Already product-led? It should look at your pricing page and your signup flow and find the real opportunities. All of it in under 60 seconds, because that's one of my success criteria. Getting there has been a tour of every AI building tool there is. Lovable got me the first 80% fast and then fought me for every point after that. I ported it to GitHub and opened a codebase I couldn't reason about, so I started over. Claude Code produced good recommendations but took 10 minutes to run. Cursor handled the file structure better, so that's where the real app got built, and we shipped it live on Render. Faster. Still not good enough. And now I get to watch what every free analysis costs us in model calls. None of that is the actual problem. The actual problem is codifying expertise. If you've spent years building pattern recognition in your head, getting it out of your head and into a product is the billion dollar question. I thought it was a rubric problem. I started with 30 questions and eventually cut my way down to the 13 that really matter. One of them: on your pricing page, can someone understand what they'll be charged in five seconds or less? That one is easy to write down. Then the nuance eats you alive. A sales-led company doesn't have a pricing page at all, so what's the recommendation now? Multiply that by every edge case and you start to see the shape of it. I've built a second app whose only job is to be the brain. I feed it tech websites and it finds the opportunities. It's the closest I've gotten, and I still haven't cracked how to break that thinking down and train the AI on it properly. Which is why this episode is also an ask. IN THIS EPISODE (00:48) What the analyzer does, and why I score it a 4 out of 10 (02:09) Lovable gets you 80% there, then it fights you (03:09) Cursor, Render, and the cost of every free analysis (04:16) The real wall: how do you codify what you know? (04:40) From 30 questions to 13, and the five second pricing test (05:24) The edge cases that break the rubric (05:45) The second app I built to be the brain (06:17) Calling in a favor (07:09) What we actually plan to monetize MENTIONED Lovable, Claude Code, Cursor and Render, the tools behind the three rebuilds Wes Bush on LinkedIn: https://www.linkedin.com/in/wesbush/ Have you tried turning your own expertise into a product? Tell me where you got stuck.

August 14, 2026Episode 30727 min

Day 5 of 100: Going Full Ironman Mode on an AI Product

The podcast has been quiet for a couple of months. This episode is why. I'm building a new AI-first product at ProductLed, in public, over 100 days. It launches November 19th. You'll be able to use it while I build it, and rip it apart, and I'm sharing everything as I go, including the revenue. Before any of that, I wanted to talk about how I'm going to attack it. I recently finished a full Ironman. It ends with a marathon, and that's after the 3.8km swim and the 180km bike. Training for it changed the way I take on anything large, and I'm running the same structure on this build. I call it full Ironman mode, and this episode walks through all 16 parts of it. Some of it is obvious. Most of it is not. The part that surprised me most was standards, because when you look at the standards you set for a goal, they should make the goal inevitable. That is the difference between hoping you finish and knowing you will. Whatever your next 100 days hold, this should be useful. IN THIS EPISODE (04:15) Get crystal clear on your vision (04:47) Name the core problem you're actually solving (05:28) Define the end game, and my three success criteria for this build (07:42) Tap your network for people who have already done it (08:54) The identity shift, and the line I write every morning (10:35) Pick the date (11:39) Why one why is never enough (12:53) Get a coach or an advisor (14:37) Create space, and audit what pulls you away (17:40) Find peers in the trenches (18:50) Name the price you're willing to pay (19:54) Resources are accelerants (20:48) A daily plan you don't have to think about (22:09) Standards that make the goal inevitable (23:05) A reward you only get if you finish (24:57) Write your own rules MENTIONED Conquer 100, the documentary about the Iron Cowboy Mickey Allen, CEO at Foldspace, advising on this build What's your next 100 days going to be? Let me know.

June 16, 2026Episode 30652 min

From 10 Failed Products to a $1M/Month SaaS Portfolio

After spending years building unvalidated products that went nowhere, Tibo Louis-Lucas completely changed how he approached startups. In this episode of the ProductLed Podcast, he shares how those early failures pushed him toward a faster, revenue-first way of building, one that eventually led to the success of Tweet Hunter and Taplio, and now powers a growing portfolio of product-led SaaS businesses. Tibo breaks down why revenue is the only validation that really matters, how Tweet Hunter stood out in a crowded market by going deep on a single platform, and the unusual distribution playbook that helped it take off. That included giving a major profit share to a creator-partner and building a network of “creative investors” who amplified the product from day one. The conversation also dives into why selling a company was far less glamorous than it sounds, and why Tibo now prefers building and holding long term. He shares how he thinks about creating an “indie hacker stack” for a specific persona, how AI has changed his day-to-day workflow, and why he now spends less time coding and more time reviewing, iterating, and building systems. One of the biggest takeaways is his operating style: no calls, fast feedback loops through DMs, and a strong focus on staying close to paying users. For founders building product-led companies, this episode is packed with practical lessons on validation, distribution, focus, and building with speed in the AI era. Key Highlights: 02:21 - Why Two Failed Startups Changed Everything Tibo shares the painful lesson of spending years on unvalidated ideas, and how that pushed him to become relentlessly validation-driven. 05:38 - Revenue Is the Only Validation That Counts Why free users can be misleading, how Tibo evaluates startup ideas today, and what made Tweet Hunter feel different almost immediately. 09:47 - How Tweet Hunter Won a Crowded Market The strategy behind focusing on one platform deeply, serving creators instead of enterprises, and building something clearly better for a narrower use case. 12:11 - The Distribution Deal That Fueled Growth How Tibo partnered with influencers using profit share and exit incentives, and why aligning distribution with the product was such a powerful lever. 15:25 - The Creative Investors Growth Engine Why he gave small ownership stakes to 17 creators, how that amplified launches and updates, and what made the model work. 19:31 - Why Selling Wasn’t the Dream Outcome Tibo opens up about the pressure of earnouts, platform risk, and why the acquisition experience made him want to build and hold instead. 23:46 - Building an Indie Hacker Software Stack Why Tibo organizes his portfolio around a specific persona instead of a single vertical, and how he thinks about expanding from five products to more. 34:54 - No Calls, More DMs, Better Feedback A look at his no-meeting policy, why DM-based customer conversations work so well for him, and how staying close to users improves product decisions. 37:18 - How AI Changed the Way He Builds Tibo explains how AI emptied his backlog, turned him into a QA-first builder, and created a new challenge: resisting feature creep. Resources: 🚀 Revid AI: https://www.revid.ai/ 💼 Connect with Tibo Louis-Lucas on LinkedIn: https://www.linkedin.com/in/tibo-the-maker/ 💼 Connect with Wes Bush on LinkedIn: https://www.linkedin.com/in/wesbush/ 💼 Connect with Esben Friis-Jensen on LinkedIn: https://www.linkedin.com/in/esbenfriisjensen/ 🧠 Sign up for the ProductLed Newsletter: https://www.productled.com/newsletter

May 26, 2026Episode 30553 min

No Sales Call Required: Roeland Delrue on Scaling Aikido to a Cybersecurity Unicorn

In this episode of the ProductLed Podcast, Wes Bush and Esben Friis-Jensen sit down with Roeland Delrue, CEO and co-founder of Aikido Security, to unpack how the company reached $40M+ ARR in just three and a half years in one of the most sales-heavy categories in software. Roeland shares how his team entered cybersecurity without a traditional security background, simply by living the problem themselves. After juggling eight different security tools and watching a security engineer quit from the sheer pain of triaging endless false positives, they decided to build the product they wished existed. The conversation digs into why Aikido took a radically product-led path in a market dominated by demos, gated trials, and opaque pricing. Roeland explains how transparent pricing, fast time-to-value, and a no-nonsense buying experience helped Aikido win trust with developers and security teams alike. They also get into the bigger growth story behind the business: why product-led motions scale so well, how compliance trends like SOC 2 create strong tailwinds, and why Aikido chose to build a multi-product platform from day one instead of another point solution. Toward the end, Roeland shares his view on AI in cybersecurity, where AI pen testing is already replacing human work, and where humans will still matter for a long time. It is a candid look at building a category-defining security company without following the usual playbook. Key Highlights: 01:46 - The Pain That Sparked Aikido How Roeland and his co-founders went from frustrated security-tool buyers to building their own solution. 04:40 - Why Cybersecurity Needed a PLG Rethink A sharp breakdown of why traditional sales-led security buying feels broken and expensive. 10:11 - Trust in Security Without Heavy Sales How Aikido built trust through product quality, compliance, transparency, and social proof. 15:24 - What Drove Aikido’s Fast Growth Why self-serve foundations, fast setup, and faster time-to-value helped the company scale quickly. 18:06 - Compliance and AI Fueling Demand How SOC 2, ISO requirements, open source risk, and AI-driven software growth are expanding the market. 20:15 - Building a Security Platform Day One Why Aikido bet on an all-in-one platform instead of a narrow point solution, and how they keep quality high. 27:08 - Brownfield vs Greenfield Growth Roeland explains why Aikido started by replacing existing tools and is now moving into faster AI-driven markets. 34:16 - A Practical View of AI in Security Why Roeland believes the future is hybrid, with deterministic scanners and AI working side by side. 36:31 - Can AI Replace Human Pen Testing? Where AI pen testing already works today, where it still falls short, and what adoption barriers remain. Resources: 🚀 Aikido Security: https://www.aikido.dev/ 💼 Connect with Roeland Delrue on LinkedIn: https://www.linkedin.com/in/roelanddelrue/ 💼 Connect with Wes Bush on LinkedIn: https://www.linkedin.com/in/wesbush/ 💼 Connect with Esben Friis-Jensen on LinkedIn: https://www.linkedin.com/in/esbenfriisjensen/ 🧠 Sign up for the ProductLed Newsletter: https://www.productled.com/newsletter

May 15, 2026Episode 30449 min

The Mutiny Pivot: Why Jaleh Rezaei Shut Down an 8-Figure SaaS to Go All-In on AI

In this episode of the ProductLed Podcast, Wes Bush and Esben Friis-Jensen sit down with Jaleh Rezaei, co-founder and CEO of Mutiny, to unpack one of the boldest founder moves you’ll hear this year. After building Mutiny into an eight-figure ARR SaaS company, Jaleh made the rare decision to shut down most of the original business and rebuild around AI agents. She shares why trying to run both a traditional SaaS company and an AI-native company at the same time created constant friction, slowed the team down, and made it impossible to move at the pace the market demanded. Jaleh walks through how she made the call, what gave her confidence to follow through, and what the first 90 days of the pivot actually looked like. That includes shrinking the team, moving to a smaller in-person setup, carefully migrating customers, and rebuilding company culture around speed, customer obsession, and founder-level context. The conversation also dives into why Mutiny shifted from sales-led to product-led growth, how self-serve products expose weaknesses faster, and why “showing” value beats explaining it, especially in AI. Jaleh also shares her view on what still counts as defensible in AI, why experience generation and analytics matter more than basic data movement, and how she personally uses AI across recruiting, meeting prep, and writing support. It’s a candid look at conviction, timing, and what it really takes to rebuild for the next wave. Key Highlights: 01:41 - Why She Left 8-Figure ARR Behind Jaleh explains why combining a SaaS business with an AI-native business created roadmap, pricing, and execution conflicts that made a harder pivot inevitable. 05:01 - The Gut Check Behind a High-Stakes Pivot How she built conviction for a risky decision, what made “moving as fast as possible” the real north star, and the advice she gives founders facing the same choice. 11:13 - Reframing the Pivot as Mission, Not Failure Why walking away from a successful product did not feel like giving up, and how first-principles thinking helped her reconnect the company to its original vision. 15:05 - The First 90 Days of the Transition A behind-the-scenes look at shrinking the team, getting back to a small in-person setup, and creating the conditions needed to find product-market fit again. 17:01 - How Mutiny Migrated Customers Gracefully The detailed playbook for protecting customer trust during the transition, from partner selection and pricing negotiations to white-glove migration support. 23:03 - Building a Team for Startup Intensity Again How Jaleh thought about team size, in-office culture, and the level of intensity required to compete in the current AI market. 25:58 - What Founders Must Stop Delegating Pre-PMF Why founders need direct exposure to customer calls, onboarding, pricing conversations, and product friction if they want to move fast and make better decisions. 32:12 - Why the New Mutiny Had to Be Product-Led Jaleh shares why self-serve makes products better, how AI products benefit from instant hands-on proof, and why PLG also improved the sales-led motion. 40:22 - What a Real AI Moat Looks Like Her take on defensibility in AI, why simple data workflows will get commoditized, and why Mutiny is focused on experience generation, analytics, and self-improving systems. 45:15 - Jaleh’s Highest-Leverage AI Workflows The practical ways she uses AI today across recruiting, meeting prep, and writing optimization, plus why she still believes strong writing needs a human point of view. Resources: 🚀 Mutiny: https://mutinyhq.com 💼 Connect with Jaleh Rezaei on LinkedIn: https://www.linkedin.com/in/jalehr/ 💼 Connect with Wes Bush on LinkedIn: https://www.linkedin.com/in/wesbush/ 💼 Connect with Esben Friis-Jensen on LinkedIn: https://www.linkedin.com/in/esbenfriisjensen/ 🧠 Sign up for the ProductLed Newsletter: https://www.productled.com/newsletter

May 8, 2026Episode 30354 min

$5M ARR, 2 People, $100M Exit — How Jeremy Clarke Did it, and What He is Building Next

Most founders hear stories about lean SaaS companies and assume they are the exception. Jeremy Clarke lived one. In this episode of the ProductLed Podcast, Wes Bush and Esben Friis-Jensen sit down with Jeremy Clarke, founder of WebMerge and now the builder behind Quin, to unpack what it really took to grow WebMerge into a multi-million dollar business with an incredibly small team. Jeremy shares how WebMerge started as a simple PDF generation tool, why integrations became the growth engine that unlocked scale, and how a strategic hire helped expand distribution without bloating the company. He also gets honest about what has changed in today’s AI market: thinner margins, tougher distribution, less generous free plans, and far more noise. The conversation also dives into the founder mindset behind building highly effective companies. Jeremy explains why staying close to support made WebMerge stronger, why he delayed hiring for as long as possible, and what drove his decision to eventually sell. From there, he opens up about building Quin, what it means to compete in a crowded AI category, and why word of mouth and customer trust still matter more than ever. If you want to build a meaningful software business without defaulting to a big team or venture funding, this episode is packed with practical insight. Key Highlights: 00:43 - From WebMerge to Quin Jeremy shares what he’s focused on today, why Quin is a much harder business to build than WebMerge, and how AI margins change the game. 07:36 - The WebMerge growth playbook How WebMerge evolved from a simple PDF tool into an integration-driven platform, and why partnerships became a major distribution engine. 12:40 - How WebMerge really got off the ground The early days of the business, the first customer outreach, and how a slow trickle of traction compounded into millions in revenue over time. 16:36 - Why Jeremy bootstrapped from day one Jeremy talks through his decision to stay self-funded, avoid outside control, and build on his own terms. 19:14 - How to reach $5M with almost no team A candid discussion on why Jeremy delayed hiring, what work he kept for himself, and when he finally saw the need for a strategic hire. 21:51 - Why founders should stay close to support Jeremy and Esben discuss support as a product advantage, how it tightens the customer feedback loop, and why speed matters so much. 25:28 - Why he sold a highly profitable business Jeremy shares the reasoning behind selling WebMerge, including risk, lifestyle, hiring pressure, and the chance to join a larger story. 47:18 - What still wins in a crowded AI market Jeremy explains what has changed in his new playbook, what has not changed, and why customer trust and word of mouth still matter most. Resources: 🚀 Quin : AI assistant for busywork and follow-through: 💼 Connect with Jeremy Clarke on LinkedIn 💼 Connect with Wes Bush on LinkedIn 💼 Connect with Esben Friis-Jensen on LinkedIn 🧠 Sign up for the ProductLed Newsletter

April 24, 2026Episode 30236 min

Built on a Crisis: Jeff Wang on Winning Enterprise AI Coding with Windsurf

When Jeff Wang stepped into the CEO role at Windsurf, it was not part of some long-term succession plan. It happened in the middle of a full-blown crisis. In this episode of the ProductLed Podcast, Wes Bush and Esben Friis-Jensen sit down with Jeff to unpack the wild chain of events that followed the collapsed OpenAI acquisition, the founders leaving for Google, and the intense 72-hour window Jeff had to help save the company and protect 250 jobs. He shares how Windsurf navigated that moment, how the Cognition deal came together, and what it has been like leading one of the most closely watched teams in AI coding ever since. Jeff also gets into what made Windsurf so strategically valuable in the first place, from shipping early breakthroughs in autocomplete, chat, context engineering, and agent workflows, to building one of the first generally available coding agents on the market. Beyond the origin story, the conversation goes deep on go-to-market strategy, why free products worked early on, how token economics changed the game, and why enterprise AI adoption takes far more than handing teams a tool. They also explore Windsurf 2.0, the shift toward managing multiple agents at once, how Jeff uses AI in his own CEO workflows, and why founders need to obsess over painful problems, customer conversations, and product-market fit instead of flashy demos. Key Highlights: 00:00 - The 72-Hour Crisis That Changed Everything Jeff shares the short version of the OpenAI, Google, and Cognition saga, and what it was like stepping into the CEO role during a company-defining emergency. 01:40 - Why Big Tech Wanted the Windsurf Team A look at the execution speed, product breakthroughs, and agent innovations that made Windsurf one of the most valuable teams in AI coding. 04:10 - The Future of Coding Is Multi-Agent Jeff explains why developers are moving from one-on-one AI assistance to managing many agents at once, and how Windsurf 2.0 is built for that shift. 08:54 - How Free Became Their Growth Wedge From free autocomplete to on-prem enterprise deals, Jeff walks through Windsurf’s early PLG motion and how it created awareness and pipeline. 13:10 - The Hard Truth About AI Pricing A candid discussion on token costs, self-serve subsidies, pricing pressure, and why raising prices can reveal whether you truly have product-market fit. 16:13 - Why Enterprise AI Sales Are Top-Down Jeff shares how Windsurf sells into large companies by focusing on transformation, adoption, security, and measurable outcomes instead of seat counts. 20:51 - What It Takes to Drive Real AI Adoption Why playbooks, training, and solving a meaningful first use case matter more than just rolling out a shiny new tool to an engineering team. 24:40 - Jeff’s AI Workflows as CEO Jeff reveals how he uses AI and custom playbooks for go-to-market research, outreach preparation, and spotting product trends before opening dashboards. 32:32 - Jeff’s Advice for Every Product Founder Build around painful problems, talk to hundreds of prospects, and learn to enjoy rejection because that is often where the real insight comes from. Resources: 🚀 Windsurf 💼 Connect with Jeff Wang on LinkedIn 💼 Connect with Wes Bush on LinkedIn 💼 Connect with Esben Friis-Jensen on LinkedIn 🧠 Sign up for the ProductLed Newsletter

April 9, 2026Episode 30134 min

From Feature Flags to AI Runtime Control: The LaunchDarkly Story

In this episode of the ProductLed Podcast, Wes Bush and Esben Friis-Jensen sit down with Edith Harbaugh, CEO and co-founder of LaunchDarkly, the feature management platform used by more than 5,000 customers, including 25% of the Fortune 500. Edith shares how her experience at TripIt led to the insight behind LaunchDarkly, and why feature management became such a critical part of modern software delivery. She explains what it actually took to create a category in the early days, when many companies were still shipping software only a few times a year, and why listening to customer pain mattered more than trying to force a new movement on the market. The conversation also dives into LaunchDarkly’s unusual balance of product-led and enterprise sales, why the company kept its free tier even as it grew upmarket, and the story behind its first real enterprise deal. Edith also opens up about returning as CEO, how AI is reshaping software delivery, and why she now sees LaunchDarkly as runtime control for the AI era. One of the biggest themes throughout the episode is Edith’s leadership philosophy: work should be fun. For her, that means helping teams reduce toil, build better software, and stay connected to the real impact they have on customers. Key Highlights: 01:59 - What Feature Management Actually Does Edith breaks down feature management in simple terms, from beta rollouts and experimentation to location-based access and safe runtime control. 03:09 - The TripIt Insight Behind LaunchDarkly How constant mobile and backend releases at TripIt revealed a problem most software teams still had not solved. 04:47 - How to Create a Category People Want Edith explains why category creation was much harder than it looked, and how meeting customers where they were helped LaunchDarkly gain traction. 07:00 - Why Early Customers Chose Buy Over Build A look at how teams with homegrown flagging systems became some of LaunchDarkly’s best early customers. 08:50 - Market Pull Matters More Than Pushing Why category creation only works when buyers already feel the pain, and how Edith looked for real pull instead of forcing the message. 12:23 - The Free Tier That Survived Enterprise Sales Edith shares why LaunchDarkly kept its free motion, even after realizing the company was becoming an enterprise sales business. 13:57 - The First Enterprise Deal Changed Everything The story of a customer who refused to buy on a credit card, and how that revealed the buying behavior that shaped the company’s go-to-market. 21:02 - Why Edith Came Back as CEO Edith talks about stepping away, returning to the role, and why AI created the kind of moment that called for founder-led leadership again. 22:11 - LaunchDarkly as Runtime Control for AI As AI accelerates code production, Edith explains why launch, measurement, and control are becoming even more important. 27:11 - Why Founders Should Make Work Fun Edith shares her leadership philosophy on reducing toil, helping teams enjoy the craft, and building in markets you genuinely care about. Resources: 🚀 LaunchDarkly 💼 Connect with Edith Harbaugh on LinkedIn: 💼 Connect with Wes Bush on LinkedIn 💼 Connect with Esben Friis-Jensen on LinkedIn 🧠 Sign up for the ProductLed Newsletter