Into the Gnar

About Into the Gnar

Into the Gnar is for the CEO, COO, or operator who keeps getting burned when it comes to building software. Hosted by Mike Stone and Nick Maloney, co-founders of The Gnar Company - a 100% US-based software development agency in Boston - we cover real builds, real decisions, and real consequences. New episodes every other week. thegnar.com

Publisher

The Gnar Company

Category

technology

Language

en

Explicit

No

First episode

20 May 2026

Latest episode

5 Oct 2026

Latest episodes

13 episodes in the feed.

1. 

   5 Oct 2026

   AI Readiness: How to Organize Company Data for AI | Samantha Maxim on Into the Gnar Ep 13

   Not sure if your team is actually ready for AI? We built a free 2-minute assessment that gives you a straight answer → ai-assessment.thegnar.com/ Your company rolled out AI. The answers still come back wrong, generic, or contradicting your own documents. The problem probably isn't the AI. Samantha Maxim runs the AI strategy practice at The Gnar. She's spent her career in product roles, including running a learning platform portfolio at Boston Consulting Group (and she landed in QA by accident at the very start). She's also the first person from inside The Gnar we've brought on as a guest. Her take: "You can't just automate a bad process, and you can't just automate something that isn't working." AI only works as well as the information behind it. At most companies, that information is spread across drives, inboxes, and a few people's heads. Samantha breaks down information architecture in plain language: where your information lives, how it's organized, and which source wins when two of them disagree. She shares the worst shared drive she's ever opened, at a 150-person company with unencrypted database backups sitting next to weekly meeting notes. She walks through how she tells Claude which systems to trust first, and why a comment from a meeting shouldn't beat your requirements doc. She also gives mid-size companies a way to start without a year-long cleanup. Set a document retention policy. Move anything older than 10 years into a "to be deleted" bucket. Make each department review the files it owns. Plus, the news: ChatGPT shows up inside Microsoft Word, and an OpenAI agent gets unauthorized access to an Australian government portal. Samantha has a lot of feelings about that second one. In this episode, we cover: What ChatGPT inside Microsoft Word means for your file permissions and access controls Why the "one AI app for everything" future looks more like streaming than cable What an OpenAI agent getting into Australia's Medicare portal teaches anyone building agents Why your AI gives bad answers (usually the prompt or the context window) Information architecture, explained for people who don't design websites How to set a source of truth your AI tools can trust Quick wins for cleaning up company data without a big-bang project Why turning on Copilot across the company is a form of AI theater If your company has the AI tools but your data still lives in five places and three people's heads, start here. Take our free two-minute AI readiness assessment at ai-assessment.thegnar.com. New episodes every other Monday. TIMESTAMPS:00:00 Why We Hired Out the Lawn 03:03 The Worst Shared Drive Ever 05:16 Meet Samantha Maxim 06:12 What an AI Strategy Lead Does 07:01 You Can't Automate a Bad Process 11:46 Why AI Gives Bad Answers 14:10 LLMs Don't Actually Think 19:31 ChatGPT Comes to Microsoft Word 23:36 Will One AI App Replace Them All? 27:31 OpenAI Agent Breaches Medicare Portal 30:55 Who's Responsible When AI Goes Rogue 32:09 How to Test AI Agents 33:40 AI Agent Permissions and Guardrails 37:08 Why AI Regulation Is Coming 39:24 What Is Information Architecture? 44:59 How to Set a Source of Truth 49:00 Quick Wins for Messy Company Data 54:18 MCPs and Choosing the Right AI Tools 01:00:10 Rapid Fire Hosts: Mike Stone, Co-Founder of The Gnar Company https://www.linkedin.com/in/michaelvstone/ Nicholas Maloney, Co-Founder of The Gnar Company  https://www.linkedin.com/in/nicholasmaloney/ Guest: Sam Maxim, AI Strategy Manager at The Gnar Companyhttps://www.linkedin.com/in/samanthabmaxim/ Links Free two-minute AI readiness assessment: ⁠https://ai-assessment.thegnar.com⁠ The Gnar Company: ⁠https://thegnar.com⁠ If you've run a software project that went sideways, or one that finally went right, drop it in the comments. We read all of them. Watch/listen/Subscribe: Spotify: https://open.spotify.com/show/0JoqOMNJf8szkisHWebLwg?si=03a6d10a765146ef Apple Podcasts: https://podcasts.apple.com/au/p

2. 

   21 Sept 2026

   AI Made Improvement Cheap. Most Businesses Ignore It. | Brian Felker on Into the Gnar Ep 12

   Not sure if your team is actually ready for AI? We built a free 2-minute assessment that gives you a straight answer → ai-assessment.thegnar.com/ If you've bought the AI tools, rolled them out, and still can't say what actually got better, this one is for you. Brian Felker spent 15 years selling enterprise technology and watched most of it get bought and never really used. Now he runs The Growth Nerd, an advisory firm out of Nashville that goes into mid-market companies and fixes how the work actually moves. Two weeks before this recording, he published a book on exactly that problem: Operational Modernization: Closing the Continuity Gap. His line on it stuck with us: "AI can automate work. Automation and improvement are not the same thing." Automate a broken process and all you've built is a faster broken process. Brian walks through the three ideas at the center of his book. The continuity gap: technology moves continuously, but companies only transform once every couple of years. Human middleware: people doing manual work that software should be doing for them. Operational debt: what piles up in the gap between those two. He tells the story of a commercial real estate insurance company drowning in spreadsheets and email chains, and how consolidating their data saved up to 10 hours a week per person. He also names "AI theater" directly, the seats you bought that nobody's actually using for anything that moves the business. Brian and Mike get into who inside a company should own this work, and why Brian thinks the COO's job and the operational modernization job aren't the same one. Find Brian at The Growth Nerd and pick up his book, Operational Modernization: Closing the Continuity Gap. In this episode, we cover: Why the frontier AI models converged on the exact same price within two days of each other What Apple's near-billion-dollar bet on renting Google's models says about build versus buy The continuity gap, human middleware, and operational debt, defined in plain language A real client story: how one company saved 10 hours a week per person by fixing how their data moved The most common form of AI theater companies mistake for real transformation Why nobody currently owns operational modernization inside most companies, and who should Why the economics of continuous improvement finally work now, when they never did before If you've told your board you're leaning into AI and can't point to what's actually improved, this is the episode. New episodes every other Monday. TIMESTAMPS: 00:00 Why AI Automation Fails 01:23 Meet Brian Felker 04:21 AI Models Become a Commodity 10:33 Apple Rents AI From Google 15:36 The Continuity Gap 21:06 Why Improvement Is Cheap Now 26:56 Where AI Actually Works 34:05 Spotting AI Theater 40:25 Who Owns AI at Your Company 43:48 Rapid Fire Hosts: Mike Stone, Co-Founder of The Gnar Company https://www.linkedin.com/in/michaelvstone/ Nicholas Maloney, Co-Founder of The Gnar Company  https://www.linkedin.com/in/nicholasmaloney/ Guest: Brian Felker,Founder & Managing Partner of The Growth Nerd https://www.linkedin.com/in/brianwfelker/ https://www.thegrowthnerd.com/ Pick up his book, Operational Modernization: Closing the Continuity Gap. Links Free two-minute AI readiness assessment: ⁠https://ai-assessment.thegnar.com⁠ The Gnar Company: ⁠https://thegnar.com⁠ If you've run a software project that went sideways, or one that finally went right, drop it in the comments. We read all of them. Watch/listen/Subscribe: Spotify: https://open.spotify.com/show/0JoqOMNJf8szkisHWebLwg?si=03a6d10a765146ef Apple Podcasts: https://podcasts.apple.com/au/podcast/into-the-gnar/id1896780159 Youtube: https://www.youtube.com/@thegnarcompany Subscribe so the next episode finds you. Produced in partnership with OBEY Creative, building podcast-driven content engines for B2B founders. Learn more at⁠ ⁠⁠https://obeycreative.com⁠

3. 

   7 Sept 2026

   The Second Wow: What AI Actually Costs Once You Deploy It | Into the Gnar Ep 11

   Not sure if your team is actually ready for AI? We built a free 2-minute assessment that gives you a straight answer → ai-assessment.thegnar.com/ If you've told your board "we're leaning into AI" and couldn't say what any of it is actually costing you. Matt Crane runs MGMT Boston, a media company built around covering venture-backed startups across Greater Boston, more than 1,600 pieces of coverage and counting. He also runs a private community of almost 950 operators. Which means he hears two versions of every AI story: the one companies post about, and the one they admit over coffee. Those two versions rarely match. Ramp's data on more than 70,000 businesses shows Anthropic and OpenAI running neck and neck for corporate card spend, flipping back and forth every time either one ships a new model. 56% of businesses now pay for some AI product. That leaves 44% paying for none of it, even though usage inside those companies is almost certainly higher than the invoices show. Copilot rides along in the Microsoft license and nobody sees a line item. Matt's heard the other side of that curve too. One company, he says, blew through $500 million in tokens in six months and torched its budget for the year. That's the second wow. The one where finance sees the bill. We also get into why every new AI product now gets measured against a single question ("can't I just do this in Claude?"), why Klarna's attempt to build its own CRM in-house didn't survive contact with reality while Salesforce made itself agentic and got paid for it, and why Matt still writes every single event invite by hand. Then we flip the mic. Matt spends the back half of the episode interviewing us, and we don't hide from it: how The Gnar Company rebuilt its client base, its pricing, and its own AI stack around this exact gap. We're still building the reporting to track token spend at the project level. Ask us again in six months. Also settled, definitively: orange is the best Starburst. Find Matt at MGMT Boston and on his show Code and Cobblestones with Will Lehmann at Step Function Ventures. In this episode, we cover: Why 44% of businesses still don't pay for any AI product, and why the real usage number is higher What Ramp's data on 70,000+ businesses says about actual AI vendor loyalty Why Claude has become the bar every new AI product gets measured against What Klarna's reversal and Salesforce's headless bet say about which software survives AI Why Matt still writes every event invite by hand, no exceptions How MGMT runs 1,600+ interviews and a 950-person community with a tiny team What The Gnar Company changed about its clients, pricing, and stack this year What a CEO should actually ask for before their next AI renewal If you want to know what your peers are really doing with AI instead of what they're posting about it, this is the episode. New episodes every other Monday. TIMESTAMPS:00:00 Who Is Matt Crane 02:04 Wall Street To Startups 05:50 Building MGMT Boston 06:37 AI Hearing Glasses Raise 09:37 Boston Startup Trends 12:53 Claude Vs OpenAI Spend 18:26 AI Renewal Red Flags 19:48 MGMT's Interview Flywheel 24:12 Why Events Burn Out 25:47 How CEOs Buy Software 27:31 Vaporware Vs Claude 28:26 Claude Raises The Bar 29:22 Salesforce Vs Klarna AI 31:56 Lovable Plus Claude Stack 35:21 AI Dead Ends 36:58 Human In The Loop 38:14 Why Matt Writes By Hand 40:00 Matt Interviews The Hosts 40:39 Gnar's Big Pivot 42:54 Gnar's Claude Token Spend 46:54 Gnar's Next Chapter 50:54 Rapid Fire Questions 53:05 Free AI Assessment Hosts: Mike Stone, Co-Founder of The Gnar Company https://www.linkedin.com/in/michaelvstone/ Nicholas Maloney, Co-Founder of The Gnar Company  https://www.linkedin.com/in/nicholasmaloney/ Guest: Matt Crane, Founder of MGMT Boston https://mgmtboston.com/ https://www.linkedin.com/in/matthewhcrane/ Links Free two-minute AI readiness assessment: ⁠https://ai-assessment.thegnar.com⁠ The Gnar Company: ⁠https://thegnar

4. 

   24 Aug 2026

   AI Costs More Than It Saves for Most Companies Right Now | Into the Gnar Ep 10

   Not sure if your team is actually ready for AI? We built a free 2-minute assessment that gives you a straight answer → ai-assessment.thegnar.com/ If you've bought AI in the last eighteen months and couldn't tell your CFO what it's costing you per month, this one's for you. We've felt this gap ourselves. You know the shape of it. A Copilot license here, a personal ChatGPT account on the company card there, a vendor who just raised your renewal because they bolted on an AI feature nobody asked for. The bill keeps climbing. Nobody can point to the line that's causing it. This week, we go solo and go straight at the number almost nobody in our audience has: what AI is actually costing our business, and why almost nobody can answer that with a straight face. Not the vendor pitch version. The invoice version. A study of 150 business leaders found that not one of them reported a cost reduction from AI. Two thirds said costs went up. Sixty percent are running seven or more separate AI platforms with no one person managing any of it. And by one economist's math, the companies selling AI models and apps are running negative margins funded by investors, not customers, while the hardware makers are pocketing over forty percent. We also get into two stories that landed the same week: insurance carriers quietly writing AI liability out of standard business policies, and private equity circling old-school "systems of record" like Workday, because the boring backend software turns out to be the one thing AI can't easily replace. The main point is simple: you can't manage what you never measured. Most companies didn't instrument anything before they started spending, so now the bill shows up as a surprise instead of a line item. Find the leaks. Time one job, before and after. Match the model to the task instead of running a frontier model on something a cheap one could handle. Name one owner. In this episode, we cover: Why nobody can tell you what AI is actually costing their business How subsidized subscriptions are hiding your real per-project spend Why insurance carriers are quietly excluding AI from standard coverage What Silver Lake's Workday bid says about which software actually survives AI Why "systems of record" are becoming more valuable, not less How to tell a real AI cost increase from a vendor's price hike in disguise Why matching model size to task size is the cheapest fix most companies skip How to build an AI spend baseline in one afternoon, no platform required If you've been buying AI on faith, this is the week to go find the invoices. New episodes every other Monday. TIMESTAMPS:00:00 The AI Spend Problem 01:49 Claude Cost Telemetry 03:27 Subscription vs Token Pricing 08:51 Stripe Acquires OpenRouter 11:58 Frank and the 80/20 Trap 16:06 Building Around Your ERP 19:12 Insurance Drops AI Coverage 25:46 Workday Buyout Explained 31:03 AI Costs More Than It Saves 39:21 Why AI Spend Is Invisible 41:40 No Telemetry? Start Here 44:10 Build vs Buy AI Tools 47:23 Key Takeaways and Next Steps Links Free two-minute AI readiness assessment: ⁠https://ai-assessment.thegnar.com⁠ The Gnar Company: ⁠https://thegnar.com⁠ If you've run a software project that went sideways, or one that finally went right, drop it in the comments. We read all of them. Subscribe so the next episode finds you. Produced in partnership with OBEY Creative, building podcast-driven content engines for B2B founders. Learn more at⁠ ⁠⁠https://obeycreative.com⁠

5. 

   10 Aug 2026

   I Had a Cheaper Offshore Quote. I Turned It Down. | Ep 9

   Frank Granara runs General Insulation Company, a building materials supplier with 50 branches getting insulation to commercial job sites by 6 AM. Ask him what the business runs on and he'll tell you straight: a legacy ERP, spreadsheets, and tribal knowledge. Ten years ago he tried to fix that with a new ERP, watched the project run toward twice its budget, and pulled the plug. This is the conversation about what he did instead. Frank sits down with Gnar co-founders Mike Stone and Nick Maloney to talk about buying software when you are not a software company: why the best off-the-shelf option still leaves a 20% gap, why he turned down a cheaper offshore build after getting burned once, and why a fixed price mattered more to him than any AI feature. He also explains why he compressed a ten-year technology roadmap into a two-year one, and where he thinks the AI conversation is real versus noise for a 50-branch distributor. What you'll learn: Why "this one gets us 80% of the way there" is a more expensive decision than it looks What actually happens when an ERP migration fails, and how to know when to walk away Why Frank stopped trying to replace his ERP and started building around it How to evaluate enterprise software on whether you can get your own data out of it The one question that decided GIC's e-commerce strategy: can a customer transact faster than they can pick up the phone? Why the software your customers need is often something they never asked you for How to split a manager's week into high-leverage and low-leverage work, and which half to automate Why a firm price changes the risk math for a company with no R&D budget Where AI in customer service is real and where it's noise TIMESTAMPS: 00:00 The business nobody makes documentaries about 00:40 Welcome to Into the Gnar 01:26 What General Insulation actually does 03:06 Why construction lags every other industry on technology 04:00 News: only 26% of companies have AI governance aligned with adoption 05:17 Shadow AI across 50 branches 07:35 Replacing spreadsheets with working software 09:32 News: AI writes code in minutes, but can it keep it secure? 10:56 The 80% off-the-shelf trap 12:30 Why a firm price changed the math 14:24 Supply Chain 2025, a ten-year roadmap written in 2015 14:52 The barometer: can you beat a phone call? 15:57 The portal customers never asked for 18:56 What stayed the same and what had to change at scale 20:10 The leadership academy 26:29 The life of an order, from bid to delivery 30:32 Legacy ERP, spreadsheets, tribal knowledge 31:29 The ERP migration they walked away from 33:38 Burned once: why he said no to offshore 36:05 From a ten-year roadmap to a two-year roadmap 39:05 What's real and what's noise 39:49 The 50/50 branch manager 41:18 Staying human while getting faster 42:01 The second-order AI benefit nobody talks about 43:56 Rapid fire 47:26 Wrap In this episode: Frank Granara, Chief Executive Officer, General Insulation Company Mike Stone, Co-founder, The Gnar Company Nick Maloney, Co-founder, The Gnar Company Disclosure: General Insulation Company is a Gnar client. We work with Frank's team on their modernization and integration efforts. Links Free two-minute AI readiness assessment: https://ai-assessment.thegnar.com The Gnar Company: https://thegnar.com If you've run a software project that went sideways, or one that finally went right, drop it in the comments. We read all of them. Subscribe so the next episode finds you. Produced in partnership with OBEY Creative, building podcast-driven content engines for B2B founders. Learn more at https://obeycreative.com

6. 

   27 Jul 2026

   The New Rules of Growth Equity in the Age of AI | Cam Salem on Into the Gnar EP.8

   Not sure if your team is actually ready for AI? We built a free 2-minute assessment that gives you a straight answer → ai-assessment.thegnar.com/ Software stocks have been sliding for a month. IBM's down 20%. Microsoft, ServiceNow, Salesforce all lost 5 to 10% in a week. But what does that selloff actually look like from the seat of someone deciding which B2B software companies get funded? That's where this conversation goes. This week, we sat down with Cam Salem, founder of Headlight Partners, and someone who's been close with the Stone family since a 2007 Facebook photo got him and Mike's brother in trouble. He's spent his whole career in growth equity, and he's here to talk about the SaaSpocalypse, the scramble over AI costs, and what actually separates B2B software companies that keep growing from the ones that stall out. Quietly. In the multiples nobody's paying for feature parity anymore. In the deals where seat-based pricing is starting to look shaky. In the founders who get funded and the ones who get passed on. Headlight isn't trying to be Tiger Global. It's trying to make one, maybe two investments a year and actually roll up its sleeves. Which founders have real domain expertise? Which businesses have a moat beyond good code? What does it take to get a company from ten million dollars of revenue to forty? Some of this is already playing out through nOps, a Headlight portfolio company that built its business managing AWS and cloud costs and is now getting begged to solve the same problem for AI spend. Some of it is still messy. Sierra billing customers per completed task instead of per seat might be the start of something bigger. Or it might not be. Nobody, including Cam, has a clean answer yet on where AI pricing actually lands. Which feels about right. We also get into where Cam is more careful. Founders who are all product and no commercial instinct. CEOs who never learn to set a longer-term vision once the business is past product-market fit. AI and cheap capital can get a company moving fast. But someone still has to notice when the moat everyone thought they had, feature and functionality, stopped mattering months ago. In this episode, we cover: Why the SaaSpocalypse selloff hit software so hard, and why Cam thinks the fundamentals still hold up How Tiger Global's 2020-21 buying spree changed growth equity enough that Cam left to start his own fund Why nOps went from managing cloud bills to fielding panicked calls about AI costs What RepSpark's move from a subscription fee to transaction-based pricing actually looks like The real difference between founders who scale past $10M and the ones who get stuck there The one red flag Cam can't get past, no matter how good the product looks Why "growth is a mentality" starts with the CEO and has to show up in everyone below them Cam's rapid-fire take on ChatGPT vs. Claude, and why he's only just switching after years of loyalty And yes, Cam's actual dream project: a digital platform for kids trading basketball cards, inspired by getting ripped off trading his Gary Payton autograph as a kid If you're building a software company, raising a round, or just trying to figure out what actually holds up when the market gets scared, this one's worth a listen. New episodes every other Monday. TIMESTAMPS: 00:00 Show Intro & Pre-Show Banter 01:54 Meet Cam Salem, Headlight Partners Founder 02:43 Cam Salem's Path Into Growth Equity 04:16 Why Cam Left to Start Headlight Partners 07:28 IBM Earnings Miss & the SaaSpocalypse Selloff 11:20 AI Spend, Cloud Costs & FinOps Explained 16:33 Outcome-Based Pricing vs Seat-Based Pricing in SaaS 24:22 What Personalized Growth Equity Really Means 30:44 B2B Founder Red Flags & Green Flags 40:07 AI Strategy Tips & Growth Equity Myths 43:30 Rapid Fire: ChatGPT vs Claude & More 52:49 Closing Thoughts & Farewell

7. 

   13 Jul 2026

   How Traditional Businesses Should Actually Adopt AI Without Breaking Operations | Into the Gnar EP.7

   Not sure if your team is actually ready for AI? We built a free 2-minute assessment that gives you a straight answer → ai-assessment.thegnar.com/ If you run a profitable business with spreadsheets everywhere, an ERP nobody likes, and critical knowledge living in three people’s heads, this one’s for you. We’ve seen this company before. Orders come in by phone. Then email. Somehow, still fax. Pricing rules live in people’s heads. Inventory is technically tracked, but everyone has their own workaround. And Dave, the part-time IT guy, is doing his best. This week, Mike and Nick use a fictional $75 million industrial distributor to talk through what AI adoption looks like in a real operating business. Not the keynote version. The messy version. They get into vertical AI, why some industry-specific tools are useful and some are just expensive wrappers, and why ROI is harder to measure than most vendors want to admit. They also talk about governance, but not just who can access which model. The bigger problem is what gets created once everyone in the company starts building their own little AI tool. We’ve made that mess before. It compounds fast. The episode moves through order entry, quoting, pricing, inventory, legacy ERPs, and the uncomfortable build-versus-buy calls operators have to make before a “small AI project” turns into software nobody owns. And the main point is simple: before you build anything, understand the business. Find the leaks. Clean the data. Map the work. Talk to the person who knows how the weird process actually works. In this episode, we cover: Why vertical AI is getting more interesting for traditional businesses How to spot tools that look useful but won’t hold up Why AI ROI dies when nobody owns the workflow Where order entry automation can save real money How AI can help with quoting without removing human judgment Why replacing the ERP is usually the wrong first move How to decide when to buy, connect, or build Why “earn the complexity” is becoming a serious operating rule If you run a boring, profitable business and you’ve been wondering where AI should actually start, start here. New episodes every other Monday. TIMESTAMPS:00:00 Recap: What We Learned From Johnny-O 02:24 The AI Teardown Game: How Operators Should Think About AI 04:04 Vertical AI Is Coming for Traditional Software 05:12 Vertical AI vs. Generic SaaS: When Industry-Specific Tools Win 12:04 The AI Agents ROI Problem Most Companies Aren’t Measuring 16:15 AI Governance: The Risk Beyond Access Control 22:01 Meridian Supply: AI Teardown of a $75M Industrial Distributor 23:10 Start With Discovery Before Buying AI Tools 27:06 Automating Order Intake Across Phone, Email, and Fax 32:14 How AI Can Improve Quoting, Pricing, and Sales Rep Training 35:04 Legacy ERP Systems, Inventory Forecasting, and Dead Stock 38:04 Build vs. Buy: How to Avoid Becoming an Accidental Software Company 40:44 The Big Takeaway: Find the Leaks, Clean the Data, Own What You Ship 41:50 AI for Operators Events and Next Episode Preview

8. 

   29 Jun 2026

   Johnnie-O and the Future of AI in Premium Apparel | Matt Ferrer on Into the Gnar EP.6

   Not sure if your team is actually ready for AI? We built a free 2-minute assessment that gives you a straight answer → ai-assessment.thegnar.com/ AI is in every pitch deck right now. But what does it actually look like inside a company that sells shirts, polos, pants, and a very specific point of view? That’s where this conversation goes. This week, we sat down with Matt Ferrer, President of Johnnie-O and an old friend of Mike's, to talk about how AI is starting to show up inside a premium apparel brand. Quietly. Behind the scenes. In the data. In e-commerce. In the tools people are already using before anyone has written the grand master plan. Johnnie-O isn’t trying to become an AI company. It’s trying to answer better questions faster. Which stores create lift online? Which customers come back? What actually changes customer lifetime value? What can the team learn in seconds that used to take half an afternoon? Some of this is already happening through Claude, Shopify, Copilot, Snowflake, and the data team. Some of it is still messy. Employees are experimenting. Governance is catching up. Nobody has a perfectly clean answer yet. Which feels about right. We also get into the parts of the business where Matt is more cautious. Product. Taste. Creative direction. The feeling of the brand. AI can help move faster. But someone still has to know when the output feels wrong. In this episode, we cover: How Johnnie-O is starting to think about its AI roadmap Why retail data gets a lot more useful when the questions get faster The connection between stores, e-commerce, and customer behavior What customer lifetime value tells you about brand growth Why agentic commerce could change how people shop Where AI helps creative teams, and where it gets weird Why brand authenticity still matters in a more automated world How AI moves people from task-doers to reviewers and problem-solvers Why data security and accuracy have to come before the fun stuff And yes, Matt somehow makes a case for building SpaceX. If you’re building a brand, running a team, or still trying to figure out where AI belongs outside the tech bubble, this one’s worth a listen. New episodes every other Monday. TIMESTAMPS: 00:00 Welcome and Guest Introduction with Matt Ferrer 00:56 Matt Ferrer’s Journey to President of Johnnie-O 04:56 How Johnnie-O Is Using AI in the Business Today 10:11 AI Wins in Retail Analytics and Faster Data Insights 12:44 Customer Data, Customer Lifetime Value, and AI Segmentation 17:18 Johnnie-O’s AI Roadmap and the Future of “Johnny AI” 19:48 Agentic Commerce, Buyer Behavior, and Brand Authenticity 23:06 AI in Creative Services: Efficiency vs. Brand Identity 26:14 Rapid Fire: AI Tools, Software, and Big Ideas 29:03 Polo vs. Button-Up: Johnnie-O Product Design Details 31:23 Where Brands Should Start with AI Adoption 33:31 Where to Find Johnnie-O and Closing Thoughts

9. 

   15 Jun 2026

   How AI Helps You Rescue Legacy Codebases: Rebuild vs. Refactor Explained | Into the Gnar EP.5

   Not sure if your team is actually ready for AI? We built a free 2-minute assessment that gives you a straight answer → ai-assessment.thegnar.com/ If you’ve inherited a codebase, opened it up, and immediately felt your stomach drop, this one’s for you. The offshore build that went sideways. The internal tool nobody owns. The product the previous team left behind. We’ve seen all of it. And honestly, we used to dread rescue projects. Not anymore. This week we get into what actually happens when a messy, undocumented, half-understood codebase lands in your lap and you have to decide what to do with it. Do you fix what’s there? Do you rebuild from scratch? Do you stay with the current vendor? Do you call in a second opinion? We also talk about comprehension debt, and why the gap between the code being shipped and the code being understood is becoming a real problem. But there’s a flip side too: with the right tools and the right process, ramping up on an unfamiliar codebase is faster than it’s ever been. AI-powered bug hunting is surfacing years of buried vulnerabilities. GitHub is moving Copilot toward credits-based billing. Developers are using AI to navigate poorly documented legacy systems, but the code still has to be checked, understood, and grounded in the actual business context. And that’s really the point of this episode: you can’t make a good decision about a codebase until you honestly understand what you’re holding. In this episode, we cover: Why rescue projects went from something we dreaded to something we actually look forward to What comprehension debt is and why it matters for legacy software How AI is making buried bugs easier to find — and easier to patch Why token management is about to become a day-one engineering conversation What we look for when auditing an inherited codebase How to decide whether to refactor or rebuild without relying on gut feeling Why rebuilding from scratch is usually the last decision you should make What vendor red flags founders and operators tend to notice too late Why the audit comes before the rescue If you’ve got a system you inherited and have been quietly avoiding, this episode is for you. New episodes every other Monday. TIMESTAMPS:00:00 Comprehension Debt and the New AI Development Tradeoff 04:04 Why Rescue Projects Matter for Legacy Software Teams 06:00 AI-Powered Bug Hunting and the Technical Debt Warning 10:11 GitHub Copilot Credits, AI Pricing, and Token Management 15:16 Developer Survey: AI Toil, Legacy Systems, and Risky Code 17:34 How to Audit an Inherited Codebase 21:40 Refactor vs. Rebuild: How to Make the Right Call 27:01 Vendor Trust, Communication, and Software Project Red Flags 32:26 Why the Audit Comes Before the Rescue

10. 

    1 Jun 2026

    The $700 Billion AI Bet and What It Means for Founders Building Right Now | Cort Johnson on Into the Gnar EP.4

    Not sure if your team is actually ready for AI? We built a free 2-minute assessment that gives you a straight answer → ai-assessment.thegnar.com Everyone is talking about AI. Most companies aren't actually using it well. This week we brought in Cort Johnson, founder of Terrible Labs and a Boston tech operator who has spent the last few years going deeper on AI than just about any non-engineer we know. We skipped the hype and went straight to what actually matters when you're trying to build something. We get into the $700 billion wave of capex flooding into AI infrastructure and what it means if you're a founder shipping today. The chip wars, AMD's run at Nvidia, and whether any of that matters when your job is just to get product out the door. But the real conversation is about how building has changed. Your engineers aren't writing code anymore, and offshore development is losing the cost advantage it used to have. The founders winning right now are the ones who figured out how to use AI as a force multiplier before everyone else did. Cort also walks us through his personal stack (Claude, Obsidian, Gemini, Notion) and how he used Gemini to write a pop song that gets his kids ready for bed and loving him at the same time. We're not joking. He sings it to them. They sing it back. Here's what we got into: The $700B AI capex wave and what it means for startup founders in 2026 AMD vs. Nvidia: is the AI chip monopoly ending? The one question Cort asks before every AI decision (and most founders forget to ask) Why your developers stopped writing code and got promoted in the process Why offshore development is losing its cost advantage to AI tools Claude vs. ChatGPT vs. Gemini for non-technical founders How Cort uses Obsidian to build a personal AI knowledge base that sounds like him The Gemini-generated bedtime song that made his kids love him more If you're a founder trying to figure out how to actually build with AI right now, this is the episode to start with. What's in your AI stack? Drop it in the comments. TIMESTAMPS: 00:00 - The $700B AI Capex Wave: What It Means for Startup Founders in 2026 07:35 - AMD vs. Nvidia: Is the AI Chip Monopoly Finally Ending? 11:49 - From Consulting Shop to Autodesk Acquisition: The Terrible Labs Story 21:17 - How to Use AI to Build and Operate a Startup in 2026 32:52 - Claude, Obsidian, Gemini, and Notion: A Non-Technical Founder's AI Stack 38:32 - Rapid Fire: The One AI Tool You Can't Run Your Business Without

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    20 May 2026

    What Tech Stack Would We Use for B2B SaaS in 2026? | Into the Gnar EP.3

    Not sure if your team is actually ready for AI? We built a free 2-minute assessment that gives you a straight answer → ai-assessment.thegnar.com/ If your team is about to pick a tech stack and nobody can agree on what to build on, this one's for you. We've been getting this question constantly since the AI coding wave hit. If you were starting a B2B SaaS company today, what would you actually build on? Not what sounds impressive. Not what worked five years ago. What would you ship on right now. This week we talk about why the tool is almost never the problem, why boring technology is still the right call in 2026, and why the LLM your team codes with is starting to matter as much as the framework you pick. We also get into something founders don't talk about enough: the difference between a tech stack that gets you to launch and a stack that quietly kills you six months later. We cover the AI coding tool wars — Claude Code, Cursor, Copilot, Codex — and what the data actually says about who's winning and why. Spoiler: developers find the better tool. They always do. Here's what we got into: The worst B2B SaaS tech stack decision we've ever seen and what it actually cost Why 84% of developers use AI coding tools daily but only 29% trust what ships to production Claude Code vs. Copilot vs. Cursor in 2026 — what we actually use and why The exact tech stack we'd choose to build a B2B SaaS startup today Why annual AI tool licenses are a trap and what to do instead What "AI-ready architecture" actually means in practice If you're sitting on a stack decision right now, or someone on your team is pushing for the shiny new framework, send this one to your CTO before your next architecture conversation. New episodes every other Monday. TIMESTAMPS: 00:34 Worst Tech Stack Decisions and What They Cost 03:15 Claude Code, Cursor and Codex: Are They Merging Into One Stack? 04:25 Should You Standardize on One LLM for Your Dev Team? 06:46 Best Programming Languages for AI Coding Tools in 2026 08:09 Annual vs Monthly AI Tool Licenses: What Founders Get Wrong 10:15 How to Trust AI Generated Code in Production 15:42 How to Evaluate an AI Dev Shop Before You Hire Them 20:12 Claude Code vs GitHub Copilot: What the Adoption Data Says 26:39 How to Choose a Tech Stack With Limited Runway 29:52 Why Boring Technology Is Still the Right Call in 2026 32:46 What AI Ready Architecture Actually Means 34:39 The Exact B2B SaaS Tech Stack We Use at NAR 38:42 Key Takeaways: What to Build Your SaaS On Today

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    20 May 2026

    What is Vibe Coding and Why It's Becoming a Major Problem for Companies in 2026 | Into the Gnar EP.2

    Not sure if your team is actually ready for AI? We built a free 2-minute assessment that gives you a straight answer → ai-assessment.thegnar.com/ If someone on your team just shipped something that now runs payroll reconciliation and nobody knows what's inside it, this one's for you. We've been wanting to make this episode since we started to think about launching the show. Vibe coding. You've probably heard the term. You may already have an employee doing it. And if you haven't heard about it yet, you will soon, because the bill is coming due for a lot of companies. This week we get into what vibe coding actually is, why it's showing up inside companies that never thought of themselves as tech companies, and what happens when that AI-generated code becomes load-bearing infrastructure with no one to maintain it. We also draw a line that we think matters a lot right now: vibe coding and agentic development are not the same thing. One is a tool. The other is a process. And the difference between them is the difference between a fun prototype and a ticking time bomb. Gartner predicts that 75% of technology decision-makers will face serious technical debt by 2026. A new concept called comprehension debt is getting traction in the developer community, and honestly, it's scarier than technical debt because it means nobody in the building understands what the code does or how to fix it when it breaks. And if you're using tools like Cursor, Replit, or Lovable to build internal tools, this episode will tell you exactly what to watch out for. In this episode, we cover: What vibe coding actually is and why it's exploding in 2025 and 2026 The difference between vibe coding and agentic development What comprehension debt is and why it's worse than technical debt Why Gartner predicts a 2500% increase in software defects by 2028 What AI-generated code actually looks like when you open it up What to do when a vibe coded app becomes a critical business infrastructure If this one hits close to home, send it to your ops leader, your COO, or whoever on your team just said, "Look what I built in Cursor." New episodes every other Monday. TIMESTAMPS:00:58 — Nick vibe codes a full security camera system from scratch 03:43 — Can non-technical people actually do this? 05:20 — When vibe coding saves a client meeting 07:37 — The numbers: technical debt, defects, and Gartner's ugly predictions 13:38 — Vibe coding vs agentic development: what's the actual difference 16:33 — Comprehension debt: worse than technical debt and harder to fix 23:39 — The internal tool nobody owns that's running your business 26:30 — What companies should actually do about this 30:22 — Inside a vibe-coded codebase: what we actually see 36:15 — The doom loop: every new feature breaks something 41:13 — How Into the Gnar builds with AI differently

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    20 May 2026

    If You Run a Business and Software Keeps Going Wrong, Start Here | Into the Gnar EP.1

    Not sure if your team is actually ready for AI? We built a free 2-minute assessment that gives you a straight answer → ai-assessment.thegnar.com/ If you run a real business and software keeps going wrong, you're not alone, and it's probably not the developers' fault. Into the Gnar is where we talk about what actually happens when software gets built. The real decisions, the real consequences, and the moments where things went sideways and how they got back on track. No theory. No consultants. Just two people who've been in the trenches for ten years and have seen enough to know what nobody's telling you. We're Mike Stone and Nick Maloney, co-founders of The Gnar Company, a software development agency based in Boston. We started this show because the conversations we keep having with operators, founders, and executives deserve to be louder. The gap between how software gets talked about and how it actually gets built is where most of the damage happens. We're here to close that gap. In this first episode, we get into it: why Claude Code just displaced GitHub Copilot and what that means if you're hiring a dev partner right now, why MCP's 97 million installs don't mean your company should default to it, and why the thing that actually matters in software development was never the code to begin with. Ten years of hard lessons. Forty-five minutes at a time. New episodes every other Monday. If this resonated with you, subscribe, share it with someone who needs to hear it, and leave us a comment telling us what you want us to tackle next. What decision is keeping you up at night? What question do you wish someone would just answer plainly? That's exactly why we're here. TIMESTAMPS:00:00 Welcome to Into the Gnar 01:22 Team Autonomy Moment 03:26 AI Fluency Program 05:20 News Claude Code Wins 09:46 News MCP Hits Scale 15:04 Why We Started Thenar 18:14 Best Practices in AI Era 22:09 US-Based Product Shaping 26:41 Biggest Software Lies 31:24 Moving Fast with Guardrails 36:04 Who This Podcast Is For 38:38 Rapid Fire Questions 42:08 Why a Human Podcast 43:25 Wrap Up and Next Steps

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