Hey friends 👋

You’ve got 22 prospects in the pipeline. Eight firms said they’ll start after tax season. Five want a follow-up next quarter. Three need a case study first. And you’re feeling really good about it.

You shouldn’t.

“Sounds great” isn’t a yes — it’s a soft no with a future date attached. The say-do gap is real, and most founders are snorting hopium instead of closing it. This week, Cameron and JDM dig into the TEAM Framework: Time, Effort, Access, Money. It’s one of Traction Lab’s most-used tools, built specifically to separate genuine intent from enthusiasm that evaporates the moment you ask for a commitment.

From an accounting startup coasting on good demo vibes (combined score: one) to an insurance SaaS asking all the right questions but still afraid to ask for money, we run three startup scenarios through the conviction gauntlet and rate each one. The third founder learned from failure and corrected course — which earns real respect, even with some method questions still on the table.

In frivolous thoughts: Cameron’s deep in Justified season six, and JDM’s recommending Honey Dijon’s The Nightlife for the next time your AI agents are doing your work for you.

As always, thanks for listening.

—Cameron and JDM

PS: A mea culpa. This episode was supposed to drop on Saturday, but we had technical difficulties. We blame our AI agents for not doing our jobs better.

Timestamps

00:00 Introduction

02:00 The TEAM Framework (Time, Effort, Access, Money)

06:30 Scenario 1: Workflow automation for accounting firms

12:45 Scenario 2: Insurance agent SaaS — asking right, but dodging money

19:30 Scenario 3: D2C analytics dashboard — learning from failure

29:30 Frivolous Thoughts

Hey friends 👋

Six paying customers. $4,800 MRR. The founder already knows which two to discount. Same evidence. Cameron gives it an 8. JDM gives it a 6. That’s the conversation the Strength of Evidence Matrix is built for.

This week, we dig into the Strength of Evidence Matrix — one of the core Traction Lab tools. It evaluates your traction on two axes: how robust is the signal (from interest to intent), and how independent is the source (from affiliated to cold). Most founders count their evidence. This framework grades it — and most of the time, the grade is a lot worse than it looks.

From a pet emergency app drowning in warm surveys, to a background check SaaS with real revenue but one cold customer keeping the whole signal honest, to a B2B platform that made it from their rolodex all the way to a cold annual contract — we run three scenarios through the matrix and rate each on the conviction scale. There’s even a JDM-Cameron split on the last one.

JDM’s three-year Lord of the Rings marathon is almost over. Cameron just watched the GuLP team take fifth place and $10K in Minneapolis.

As always, thanks for listening.

—Cameron and JDM

Resources:

* High Conviction Happy Hour, August 27th

* Traction Lab Venture School

Timestamps

00:00 - Introduction

02:15 - Strength of Evidence Matrix

05:30 - Scenario 1: Pet emergency vet app

12:45 - Scenario 2: Background check SaaS

25:00 - Scenario 3: B2B CPG broker platform

40:00 - Frivolous Thoughts

Hey friends 👋

You’ve had the good conversations. Coffee chats that ended with “this is really interesting.” Followers who DM you to say they can’t wait. And when you go to investors, they pass.

Because interest and evidence are two completely different things.

This week, JDM and Cameron are joined by Nikki Sims — operator, VC fund veteran, and someone who’s actually seen what happens when founders confuse the two. In a special format, we invited founders from the Traction Lab Venture School to join us on the livestream to ask their questions.

We covered traction signals that matter in historically offline industries, what happens to your roadmap when you land a 227-location enterprise deal, why your TAM slide is probably lying to investors without you realizing it, what to do when your market has a literal expiration date, and how to think about funding a hard goods business when VC math doesn’t apply.

Nikki didn’t hold back. Neither did the guys. It’s the kind of honest investor feedback you usually only get if you already know someone.

As always, thanks for listening.

—Cameron and JDM

Timestamps

00:00 - Introduction & Nikki Sims

05:00 - Traction in offline markets (AgTech / game birds)

18:00 - When your market has an expiration date

24:30 - The enterprise trap: SMB to 227 locations

32:00 - Funding a hardware business (not a SaaS)

51:00 - TAM slides: bottoms-up or bust

58:30 - Biotech buy vs. build decisions

1:02:30 - Channel strategy for construction tech

Hey friends 👋

You know the pattern — founder shows up with a deck, flips to the TAM slide, and there it is: $53 billion… $67 billion… any enormous number with a B at the end. And everyone in the room nods like it means something.

It usually doesn’t.

This week JDM goes solo (Cameron’s out representing the ecosystem and cheering at a pitch competition — we’ll forgive him) to dig into one of his all-time founder pet peeves: the TAM.

Not the concept — the lazy, hopium-laced, percentage-game version that shows up in almost every pitch deck. The one that mistakes the market your customers are in for the market your product is in. The one that picks 0.1% because it makes the math look reasonable, not because it means anything.

From a Slack bot claiming a slice of the wrong pie, to a procurement platform that actually did the math (mostly), to an AI tutoring app that averaged its way to a $67B fantasy, JDM runs the conviction scale on three real TAM claims and calls out every shortcut along the way.

For his lone frivolous thought, he recommends Ben McKenzie’s documentary Everyone Is Lying to You for Money — a deep dive into crypto with strong opinions and receipts to match.

As always, thanks for listening.

—JDM & Cam

Timestamps

00:00 - Introduction

02:15 - TAM: what the slide is supposed to answer (and doesn’t)

05:30 - Scenario 1: The Slack status bot

12:45 - Scenario 2: Food manufacturer procurement platform

25:00 - Scenario 3: AI tutoring app for K-12

40:00 - Frivolous Thoughts

Hey friends 👋

You’ve seen the email before:

Subject: “Huge month!!”

Body: 10,000 users, engagement through the roof, three new features shipped, featured in a newsletter. No revenue. No retention breakdown. No ask. And somehow, you think that’s an investor update.

This week, JDM and Cameron break down what investor updates are actually for — and why the founders sending the flashiest ones are often the ones with the least to show.

A good update isn’t a highlight reel. It’s a signal:

* Here’s the one metric that matters right now;

* Here’s what’s working and what isn’t; and

* Here’s exactly what we need from you.

That’s it.

Do that consistently, and you’re building trust long before anyone writes a check.

Then comes the game! Three realistic scenarios, scored on the conviction scale:

* A SaaS tool drowning in vanity metrics and no ask (a very generous two).

* A dental practice management startup with actual numbers, actual churn honesty, and a specific ask that almost got there (a strong eight).

* A consumer mental health app with 150K downloads, no paying customers, and a premium tier with results too “encouraging” to share yet (another two).

And it’s a conviction sandwich!

But a Keto edition… the good stuff is in the middle.

Frivolous thoughts this week: JDM got a YouTube Music recommendation he didn’t ask for and absolutely needed; and Cameron walked out of a Sacramento record shop with some Cat Stevens vinyl.

Turns out the thread between grunge, riot-grrl, punk, and folk peace anthems is stronger than you’d think.

As always, thanks for listening.

—Cameron and JDM

Timestamps

00:00 Introduction

02:15 What is an investor update (and why most are facades)

05:30 What to actually include: the five elements

13:30 Scenario 1

23:15 Scenario 2

33:15 Scenario 3

37:30 Frivolous Thoughts

Hey friends 👋

You’ve got 43 customers using your product for free, a pilot converting at 4 out of 11, and a franchisor who just announced they’re building your product themselves—for free.

This week’s office hours was a live collision of founders at very different stages, all running into the same underlying problem: they’re optimizing for the wrong thing at the wrong time.

JDM and Cameron tackle five questions live from YouTube and LinkedIn:

* a funeral home consolidator mid-pricing negotiation

* a B2C wellness app chasing a launch before knowing who they’re selling to

* a CrossFit gym SaaS getting sherlocked by its own franchisor

* a baby sleep consultant product that discovered its pilot structure too late

* a surf school booking tool trying to solve seasonality before it’s solved willingness to pay.

We break down enterprise pricing psychology, the trap of logo-hunting, why “stoned beach dudes” is not a customer segment problem, and how a transaction-based revenue model might untangle a seasonal billing headache.

We recorded in person again, the rain is visible through the window, Cameron’s deep into Justified, and JDM is watching Harrison Ford gracefully exit Shrinking.

And, as always, thanks for listening.

—Cameron and JDM

Timestamps

00:00 Intro

02:15 Live Q: When to hold firm vs. concede on pricing

10:30 Q2: Building momentum before MVP launch

20:00 Q3: Getting sherlocked by the franchisor

25:00 Q4: 4 out of 11 pilot conversions and what the split actually means

30:30 Q5: Union workers who don’t buy their own software

36:00 Q6: Seasonality, and when to charge

43:30 Frivolous Thoughts: the future of TV (which is now)

Hey friends 👋

You have the domain expertise. You have the product. You might have even used AI to vibe-code the whole thing in a weekend. But here’s where most technical founders hit a wall — not because the product is wrong, but because getting your first paying customer is a completely different skill set than building the thing. And it’s harder than it looks.

This week, we dig into Tech Timmy — Traction Lab’s name for the technical or domain-expert founder who builds first and asks “now what?” later. We talk through what makes this archetype fascinating, the cruel irony of their situation, and why the very channels they gravitate toward (SEO, Reddit, Product Hunt launches) are almost guaranteed to fail them at this stage.

Then we run three Tech Timmy scenarios through our conviction scale — a browser extension for Slack productivity, a niche inventory tool for specialty coffee roasters, and an AI cover letter generator with some genuinely alarming freemium math. One gets an enthusiastic eight. One gets a swift and unapologetic one. Cameron earns the crown.

We also coined the Conviction Chasm — the space between “we’re somewhat in” and “we’re fully in” — which is immediately more threatening than it probably needs to be.

Frivolous thoughts this week: Artemis II sent humans further from Earth than anyone has ever been, which is objectively incredible. And Cameron watched Better Off Ted.

As always, thanks for listening.

—Cameron and JDM

Timestamps

00:00 - Introduction

02:15 - The Tech Timmy: who they are and why it matters

09:15 - The cruel irony of the technical founder

12:45 - Scenario 1: Slack summarizer browser extension

28:00 - Scenario 2: Inventory tool for coffee roasters

40:00 - Scenario 3: AI cover letter generator

50:00 - Frivolous Thoughts

Hey friends 👋

You’ve heard the feedback. You nodded along. You maybe even wrote some of it down. But are you actually listening — or are you running it through a filter that was already biased toward your conclusion?

This week, we got nerdy about the cognitive machinery that causes smart founders to ignore the data right in front of them: confirmation bias, motivated reasoning, and cognitive dissonance. Three overlapping traps that compound each other in ways that’ll genuinely make your skin crawl once you see it.

The uncomfortable part is that your brain runs them before you consciously evaluate anything. You’re not choosing to ignore the warning signs — you just don’t see them. And the smarter you are, the more sophisticated your rationalizations get.

Yay…

We put these ideas to the test across three scenarios — a real estate CRM with 11% monthly churn blaming “price-sensitive agents,” a project management tool ignoring a 67% feature request because it might “bloat” the product, and a B2B sales platform insisting it has an “education problem” when the market is already full of incumbents. We rated each on our conviction scale and called out the survivorship bias and say-do gaps hiding in the data.

In frivolous thoughts:

* JDM recommends a definitely-not-political SNL sketch.

* Cam watched Forrest Gump for the first time as an adult. It hits different.

As always, thanks for listening.

—Cameron and JDM

Timestamps

00:00 - Introduction

02:45 - Confirmation bias, motivated reasoning, and cognitive dissonance

16:15 - Scenario 1: Real estate CRM, 11% churn, blaming the customer

30:15 - Scenario 2: Agency PM tool ignoring 67% feature requests

41:30 - Scenario 3: B2B sales platform with an “education problem”

51:00 - Frivolous Thoughts

Hey friends 👋

Welcome to our weekly AMA! Every Friday, we go live on YouTube and LinkedIn to answer real questions from founders at every stage. We’re now adding that to this podcast feed every Wednesday so you can catch it wherever you listen.

This week, we dug into four questions that each hit a different flavor of the same core problem: are you solving the right job for the right person?

When your market feels price-sensitive, the move isn’t to race to the bottom—it’s to find the 24 people who did pay and figure out what makes them different from everyone else. That cluster is your wedge. We break down how to build that hypothesis without running to a spreadsheet first, and why “there are no facts inside the building, but there are sure as hell hypotheses” is your operating principle.

From a documentary producer wrestling with per-project pricing to a med-tech founder staring down the gap between a validated idea and an actual clinical product, the through-line is always the same: price is a signal, not the problem.

We also probably spend an irresponsible amount of time talking about cold brew coffee, Muppets, and which Muppet should run JDM’s coffee AI agent. And Cameron is in Las Vegas, about to run a marathon downhill from 7,500 feet. What was he thinking?!

If this episode stirs up a question for you, submit it in advance at the link below (we answer every one), or join us live.

See you on Saturday for our regular episode.

As always, thanks for listening.

—Cameron and JDM

Links & Resources

* 📅 Submit a question for next week’s AMA

* 📺 Join us live every Friday at noon Pacific on YouTube or LinkedIn

* Substack newsletter (tools + frameworks)

* Traction Lab Venture School

Timestamps

00:00 Introduction

05:15 Q1: Are your pilots actually signal?

13:30 Q2: Pricing a project-based customer

22:00 Q3: Taking MedTech from idea to institution

31:00 Q4: Mobile mechanic — pivot or persevere?

40:00 Frivolity

Hey friends 👋

Minimum Viable Product. Three words every founder knows, and almost nobody uses correctly. The “V” isn’t about whether the product exists—it’s about whether you can capture value back. Whether the market will actually pay. Whether you’re testing the riskiest assumption sitting between you and a working business model. Build without that framing, and you’re just... building. Optimizing something that may never have a buyer.

This week, Cameron’s recording from a hotel room in Vegas (yes, really—there’s a marathon involved), and we dig into what an MVP actually is, why the “minimum lovable product” crowd is missing the point entirely, and what it looks like when founders get the test right—then fumble the follow-through anyway.

We rate three scenarios on our conviction scale: an AI meal-planning app drowning in vanity metrics, a manual marketplace test with a very uncomfortable disintermediation signal, and a fraud-detection tool that had us fully on board until the last sentence. One of them earns an 8. One earns a 2. You’ll know which is which by the end.

Cameron closes with his Mt. Charleston marathon prep (7,000 feet of downhill—his knees, but not his problem), and jdm gives a very late recommendation for Hijack on Apple TV. Better late than never.

As always, thanks for listening.

—Cameron and JDM

Timestamps

00:00 Introduction

02:00 What an MVP actually is (and why MLP is a cope)

10:30 Scenario 1: AI meal planning app

18:15 Scenario 2: Manual gym-trainer matchmaking marketplace

26:00 Scenario 3: E-commerce fraud detection SaaS

34:30 Frivolous Thoughts