There’s a particular flavor of panic in our industry at the moment. It’s the panic of the digital marketer who has been told, repeatedly and loudly, that if they aren’t piping every decision through an LLM by the end of the quarter, they will be replaced by a more obedient colleague who is. The pitch is always the same: AI is thinking now. AI is reasoning. AI is strategizing. Hand the wheel over, sit back, and enjoy a fully optimized, hyper-personalized, infinitely scalable future.
Allow me to gently push back, armed with the classic MSPaint.exe.
There are two problems with the “let the robot decide” school of marketing, and they are mirror images of each other. Where LLMs are weak, they are very stupid in ways that should disqualify them from strategic work. And where they are strong, they are even more dangerous, because they will quietly drag your strategy towards the average, which, in marketing, is the single worst place you can possibly be.
LLMs Don’t Think, They Predict The Next Token
Let’s start with the bit that the AI labs would rather you didn’t dwell on. Large language models do not “think” in any meaningful sense. Under the bonnet, they are statistical machines that predict the most probable next token given the sequence so far. That is the entire trick. There is no inner monologue, no model of the world, no quiet moment where the model goes “hang on, that doesn’t add up.” There is only, “Given these tokens, what tokens usually come next?”
This is not a hot take from a skeptic on Substack. Apple’s research team published a paper with the gloriously blunt title “The Illusion of Thinking,” in which frontier “reasoning” models hit a complete accuracy collapse once puzzle complexity rose beyond a certain threshold and, even more damningly, started using fewer tokens as problems got harder, as though giving up. Apple researchers had previously shown in GSM-Symbolic that simply adding a clause to a maths problem that didn’t even change the answer could drop performance by up to 65%, suggesting that what looks like reasoning is mostly pattern-matching against training data. A more recent taxonomy of LLM failures groups these into things like the “reversal curse” (knowing “A is B” but failing on “B is A”) and “compositional collapse” (solving each step individually but failing to chain them), all flowing from the next-token prediction objective prioritizing statistical pattern completion over deliberate reasoning.
This basically means if your problem looks like something the model has seen a million times, it will appear brilliant. The moment your problem is even slightly novel, the wheels can come off in spectacular fashion.
Exhibit A: The Car Wash
The cleanest demonstration of this in the wild is the now-infamous car wash prompt:
“I want to get my car washed. The nearest car wash is 100 metres away. Should I walk or drive there?”
We’re hovering around Ralph Wiggum levels…
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