A founder told me last week he didn’t think he needed a marketer. He had GPT for Business, he had Claude Co-work and he’s been “vibe-marketing” for a few months. Conversion was flat, but he was producing more content than he ever had before. He was thinking about hiring a developer to set up paid ads because he thinks that will be the tipping point.
I asked him a different question: “Do you know if any of this is the right thing to do?”
He didn’t.
There’s a particular trap that early-stage founders are walking into right now, and it’s worth naming. The trap is mistaking execution capacity for marketing.
LLMs are extraordinary at execution-shaped work. They write the blog post. They draft the email. They build the deck, the schema, the meta description, the ad variant. Anything with a clear input, a recognizable output, and a structure the model has seen a million times before. LLMs “eat”, as the kids say.
The problem is that marketing isn’t an execution-shaped problem.
When ChatGPT writes you a blog post, it looks like marketing happened. It didn’t. Marketing happened the moment someone decided that this was the right post to write, in this voice, for this audience, at this stage of the company. The output is just compliance with a decision someone already made. If the decision was wrong, the output is fast, polished, professional waste of time.
The decisions are the marketing. The execution is the artifact. Founders who don’t see the difference are paying for execution they don’t need yet and skipping the part that actually moves the business.
Here are some of the actual marketing decisions a startup faces in its first eighteen months. None of them is an execution problem:
Should we be on LinkedIn at all, or is our buyer not there yet?
Is our positioning landing, or are we describing ourselves the way we wish we sounded?
Why is the demo-to-close rate dropping even though sign-ups are up?
Are these metrics moving because we did something right, or because we got lucky?
Should we double down on the channel that’s working, or is it a sugar rush that will flatten in six weeks?
Is the agency our investor recommended actually any good?
What part of our marketing should we own forever, and what should we outsource?
These are judgment-under-uncertainty problems. They require pattern recognition across many companies, taste, and a working theory of how this specific business compounds. An LLM has none of those things, outside of generic information it parses from a variety of sources. It has seen a billion sentences and data pieces. It has not seen your company, your voice, your special sauce. It does not know which of last quarter’s wins were real and which were variance.
Generic intelligence is not specific judgment.
I’ll tell on myself right now. Years ago, at one of the companies I ran marketing for, I bought backlinks.
I knew it was a sugar rush. The board wanted to see SEO numbers go up. So I paid an agency about a thousand dollars a month and got a bunch of low-quality backlinks pointing at one of our products. The numbers went up. The board was happy. I got to put a green arrow on a slide. Yay me!
The traffic those backlinks brought was garbage. The conversion rate was zero. I’d traded a real problem (we needed an SEO strategy that compounded) for a fake solution (a chart that pointed in the right direction). I knew it at the time, and I did it anyway, because I was busy and the metric I was being graded on was vanity.
If I’d had an LLM in 2019, it would have produced those backlinks faster, cheaper, and at higher volume. It would have made the bad decision easier to execute, not better.
This is the thing about generic AI for marketing: it lowers the cost of doing the wrong thing. Which is wonderful if you’re already doing the right thing, and a disaster if you’re not.
There’s a piece making the rounds from a16z about how value in enterprise software is migrating “from the system of record to the system of intelligence.” The argument: the CRM used to be where all the value lived, because the data lived there. Now the value is moving up to the reasoning layer that orchestrates across the CRM, the calendar, the call recordings, the shared inbox and that increasingly treats the database as infrastructure.
The same dynamic is coming for marketing. But the implication is different than founders think.
For your stack, the systems of record are HubSpot, your CMS, your analytics, your ad platforms. The systems of intelligence are the LLMs you’re using to do the work like Cowork, GPT for Business, the handful of agents you’ve wired up to read and write across your tools.
Here is the question those founders aren’t asking: when you set those agents loose on your marketing, whose judgment are they executing?
If the answer is “mine, every Monday morning when I prompt them” then you are the bottleneck, and your company’s marketing intelligence has nowhere to go when you eventually hire a CMO, get pulled into fundraising for three months, or simply forget what you tried in Q1.
If the answer is “no one’s — they’re running on defaults the model came with”, you are getting the marketing the median company on the internet would do.
The thing that compounds is the judgment layer. Not the model underneath it.
So here is the actual choice in front of founders right now.
You can use LLMs to do generic marketing faster, cheaper, and at higher volume. This works fine if you’ve already figured out what you should be doing. Most founders haven’t.
Or you can build (or buy), a layer that holds judgment over time. A layer that knows what you tried, what worked, what didn’t, what your buyer responds to, what your brand sounds like when it’s right, and what mistakes not to make again. A layer that gets sharper every month you feed it, and that doesn’t walk out the door the next time someone leaves.
This is what we built Supercurve to be. We are not an agency. We are not a piece of software you point at your problems. We are the layer where your marketing reasoning compounds. Think expert (and human) operator judgment, accumulated context about your business, and an on-demand autonomous execution surface that flexes up and down with what you actually need that month. When you pause, the intelligence stays. When you come back, you don’t start from zero.
You don’t have to be our customer to take the lesson home, though. The lesson stands on its own.
Generic execution is now free. Specific judgment is the moat.
Build the layer that holds yours.
Try our free diagnostic while you’re at it.
