Into the Gnar

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/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

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
 

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.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

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

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

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

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

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

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

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