Into the Gnar

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:

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:

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

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 Company

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

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:

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/

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

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:

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:

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:

If you've been buying AI on faith, this is the week to go find the invoices.

New episodes every other Monday.

TIMESTAMPS:

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:

TIMESTAMPS:

00:00 The business nobody makes documentaries about

In this episode:

Frank Granara, Chief Executive Officer, General Insulation 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.

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:

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:

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:

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:

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:

If you’ve got a system you inherited and have been quietly avoiding, this episode is for you.

New episodes every other Monday.

TIMESTAMPS:

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:

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:

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