You have to earn scale

You have to earn scale

Hey friends 👋 Every founder wants the J-curve — up and to the right, forever. But that chart has a boring flat part at the beginning that most founders try to skip. That’s the part where you figure out what actually works, prove it can happen again, and then pour gas on it. Skip it and you’re not scaling a growth engine. You’re just spending money faster. This week we dig into one of our core frameworks: predictable, repeatable, scalable. In that order. Always in that order. The key question: can you tell the difference between what worked and what just happened to work that one time? Because your entire engine rests on that answer. Three founders are convinced they’re ready to scale. One’s averaging two new customers a month (a number that might be doing some heavy lifting to hide the real variance). One has a genuinely tight LinkedIn playbook — but now wants to hand it to an SDR and go explore a new segment. And one had three TikTok videos go viral and wants to triple the content team to make the magic happen again. We rate all three on our conviction scale, and nothing gets above a six. Cameron recommends a detective show. JDM withdraws a previous recommendation entirely. As always, thanks for listening. —Cameron and JDM Timestamps 00:00 Introduction 02:15 Predictable, Repeatable, Scalable 05:30 Scenario 1: SaaS returns processing platform 12:45 Scenario 2: Warehouse safety compliance 25:00 Scenario 3: Consumer fintech / TikTok 40:00 Frivolous Thoughts This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit zerototraction.substack.com [https://zerototraction.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Don’t avoid your customers.

Hey friends 👋 You know the pattern. * January: idea. * February: market research. * March: wireframes, logo, landing page. * April: four customer convos. Two said routing is a pain point. Ouch — that’s just a museum of artifacts nobody bought a ticket to. This week, we break down Time to Customer: the single most important velocity metric for early-stage founders, and the one most likely to make you squirm. It’s a simple measurement — how long from “I have an idea” to “I tested it with a real customer”? Turns out, that gap is where most founders hide. Cameron and JDM dig into why we delay, what “progressivity” looks like in the wild, and what it actually means to have a bias toward getting in front of customers. We run three scenarios through the TTC lens — from a dog grooming app that spent three months in pre-production before a single customer call, to a churn prediction startup that got a prospect to ask “what would it cost to get this every month?”, to a compliance platform that built an entire outbound playbook before sending their first cold email. tbh… conviction scores were grim, but we left every founder with a clear path forward. We hope. And in frivolous Thoughts: * Cameron caught the Buffalo Traffic Jam live at a 200-person DC venue (Bands in Town is the app you didn’t know you needed), and * JDM makes a strong case for Sofia Isella as the reigning queen of dark pop. As always, thanks for listening. —Cameron and JDM Timestamps 00:00 Introduction 02:15 Time to Customer 05:30 Scenario 1: Mobile dog groomer routing app 12:45 Scenario 2: SaaS churn prediction platform 25:00 Scenario 3: Home healthcare compliance training 40:00 Frivolous Thoughts This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit zerototraction.substack.com [https://zerototraction.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Are you pivoting or just stacking assumptions?

Hey friends 👋 Here’s what a pivot is not: changing your messaging, tweaking your pricing, or trying a new ad channel. It’s also not torching the company and starting over. A pivot is what happens when data invalidates a core assumption — and there are really only three categories worth changing: your audience, your product, or how you distribute and capture value. That’s the framework this week. We map the three pivot categories, explain how the 10 named pivot types actually fit inside them, and then do what we do — put the theory through three real startup scenarios that are very much not doing it right. From an AI writing tool that quietly discovered its best customers were using it for something completely different, to a yoga marketplace simultaneously pivoting its customer, its product, and its monetization model (we both gave it a one), to a SaaS dashboard whose founders accidentally built the right feature inside the wrong product — each one gets rated on our conviction scale. In Frivolous Thoughts: Cameron barely survives a security line in Salt Lake City, and jdm goes deep on the agentic AI wild west — including a tool that lets you hire a zero-human CEO for your startup. Please don’t do that for your actual startup. As always, thanks for listening. —Cameron and JDM Timestamps 00:00 Introduction 02:15 What is a pivot? (and the three categories) 07:45 Scenario 1: AI writing tool — customer need vs. segment pivot 15:15 Scenario 2: Yoga marketplace — the danger of stacking pivots 25:00 Scenario 3: SaaS review dashboard — the zoom in pivot 34:00 Frivolous Thoughts This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit zerototraction.substack.com [https://zerototraction.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Clean your pipeline: polite nos vs real yeses

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 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit zerototraction.substack.com [https://zerototraction.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

How we evaluate traction

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 [https://luma.com/0jte03oe] * Traction Lab Venture School [https://tlvs.link] 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 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit zerototraction.substack.com [https://zerototraction.substack.com?utm_medium=podcast&utm_campaign=CTA_1]