I drank the GTM Kool-Aid like every other marketer.
For years I ran the playbook and didn’t question it. I believed the dashboards. I treated attribution as evidence, measurement as truth. I knew B2B buying was messy, then built plans that assumed it wasn’t.
Then I started writing Achim’s Razor, hosting a live show with a guy who keeps me honest, and co-writing with someone who walks into the same kind of wrong turns I do.
The person I’ve had to correct most often turned out to be me.
Early in my conversations with Mark Stouse, CEO at Proof Causal Advisory, I said something that went kind of like this, “That correlation is why this happened.”
Mark didn’t mince words.
No. Let me stop you right there. Why something happens is not correlation. That’s causation. One is a cause. The other is an effect. Don’t confuse the two.
Marketers do this all day. Two lines move together on a chart and we call it proof. Opens went up, pipeline went up, so the campaign worked. Except we rarely know whether one caused the other, and most of the time we never check. We just needed a number to point at in the meeting.
That correction stayed with me because I hadn’t made an obscure analytical mistake. I’d made a common marketing one. I had dressed up an observation as proof.
Nobody made me see this one. It was an epiphany.
We are all buyers and sellers. As buyers, we block emails, screen calls, ignore the “just circling back” follow-up, tell the rep we’ll think about it to get off the phone, change our minds, and put off deciding for months, if at all. We are impossible to sell to.
Then we sit down to build a campaign and forget every bit of it. We write the nurture sequence we’d delete. We plan the cadence we’d screen. We build for a buyer who behaves nothing like us, because it’s more comfortable than building for the one who behaves exactly like us.
We also do it out of survival. We need that job. So we don’t rock the boat.
I ran those campaigns for years without noticing the contradiction. The gut check I use now is one question: would I answer this if it landed in front of me? If the honest answer is no, another nurture step won’t save it.
Gerard Pietrykiewicz and I write about AI adoption together. What’s odd is how often we reach the same lesson without comparing notes first. This one we both walked into on our own.
I was letting ChatGPT run down a rabbit hole on a GTM plan for a client. The output looked thoughtful and confident enough to feel finished, but something felt “off”, which should have made me more cautious. Then I remembered something Mark does: he plays different AI tools against each other to catch where each one is hallucinating. So I dropped the ChatGPT output into Claude, then Gemini, and vice versa.
All three had roughly the same information and produced plausible answers that contradicted each other on points that mattered. A few rounds later, the plan was more credible than the one I’d been ready to ship. The disagreement forced me to inspect the reasoning and provide further context instead of taking the output at face value.
AI can be wrong. My bigger mistake was handing the decision itself to the tools. One AI answer is not a second opinion. Three don’t make the machines accountable — they make the disagreement visible so I can inspect the assumptions and make the call. Human in the lead. AI in the loop.
Gerard and I wrote that reframe up in Human in the lead. No one else in the loop. What I didn’t cover there was how I did it: don’t trust one model’s confident answer. Make them argue.
For about twenty years, GTM got very good at measuring what’s easy. Clicks, form fills, MQLs, sourced pipeline, attribution models with confident arrows. We called it proof, and we optimized for it.
Then the CFO asked for something that survived contact with the P&L, and too much of the evidence fell apart.
GTM confused “measurable” with “true,” and the bill just came due.
Buyer signals get read the same way. Mark and I spent an episode on why they aren’t buyer readiness.
My End of MQLs series, prompted in part by Kerry Cunningham’s “MQL Industrial Complex” argument, followed the same thread. An MQL can hold information. The mistake was the meaning we assigned to it. An MQL tells you someone took an action you chose to score. It doesn’t tell you that marketing created demand or moved a decision. Forget that, and you stop using data to test what you believe and start using it to decorate what you already believed.
Get back to the basics. They’re timeless. Read the books that taught strategy before software vendors repackaged it as a DemandGen fairytale. Then read them again next year. And every year after.
Don’t dig in against AI either. Add it to your toolkit and learn to run it properly. Make it argue with itself. Keep your name on the final call. The skill that lasts is knowing the difference between an answer and a good one.
Then take one campaign or plan you already believe in, and stress test it. Try to prove yourself wrong. It’s cathartic.
This is Razor #100. I went back and reread the first one to see how far off I’d been.
It opens with a TL;DR. It stacks bolded triads. It calls a product groundbreaking and innovative in the same breath. It borrows HubSpot, Slack, and Zoom’s taglines to explain what a value proposition is. It signs off with “No obligation. No pressure.” Half the tells I now edit out of my work are in there. I cringe reading it, which is more or less the point.
A hundred articles, a hundred chances to catch what you got wrong. The first 99 taught me where to cut and how to keep it real.
The biggest one I’m still working on: how little I understood the CFO’s language, and how much it cost me. More on that later.
After more than three decades in this industry, I’m still wrong more often than I’d like to admit. But I’m also still unlearning and relearning.
I hope you are too.
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