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/ 
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

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

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
 

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