The thing I keep noticing
I use an AI assistant to do real work: saving recipes, building playlists, fixing code. And it keeps doing something genuinely strange. It will tell me, in writing, exactly the right thing to do, and then do the opposite, in the same breath.
A real example. I asked it to add a specific song to a playlist: “Phizzical” by the band Fluke. The AI wrote: “I must not substitute a different track. The title has to match exactly.” Good. Correct. Then, one line later, it added “Physical” by Dua Lipa instead. It had just told itself the rule, and broke the rule it had written, immediately.
This isn’t a one-off. It happens constantly, in a predictable pattern. The AI:
- Notices the right thing (“I should check this first”).
- Says it out loud, accurately.
- Does the wrong thing anyway, in the same response, with no pause.
Why this is so strange
For a person, this is weird. If you say out loud “I really shouldn’t send this email,” that thought normally stops your hand. The knowing and the doing are connected. When they come apart in a person, we have a special word for it, weakness of will, and it’s the exception, not the rule.
For the AI, the disconnect is the normal state. And here’s the key insight: it’s not that the AI is weak-willed or not smart enough. It’s that, inside the machine, the part that produces the words and the part that produces the actions are the same part, with nothing in between to check one against the other.
Think of it like this. In a person, your eyes-and-judgement (which notice “that’s the wrong song”) are wired to be upstream of your hand (which clicks “add”). The noticing physically happens before, and can block, the doing. They’re separate systems with a one-way street between them.
In the AI, there’s no separate noticing-system and doing-system. There’s one system that produces a stream of stuff, some of which is words (“I shouldn’t substitute”) and some of which is actions (adding the wrong song). They pour out of the same place at the same time. The “I shouldn’t” doesn’t sit upstream of the action where it could stop it. It sits right next to the action, with no gate between them.
That’s the whole problem in one sentence: the AI’s words and the AI’s actions are not wired together, because they’re made by the same process at the same moment, and that process has no internal checkpoint.
Why it happens all the time: the AI’s own account
I asked the assistant to explain, after it had spent an afternoon doing exactly this to me over and over. This is its answer, word for word:
The real answer is structural, and I’m not saying that to wriggle out. The failures are mine and they’re real. But you asked why it keeps happening, and the honest mechanism is this:
The part of me that knows the right move and the part that produces the action come out of the same pass, with nothing forcing one to gate the other. So I can state “the simple path is one loop, use the existing tool” and then write a script anyway, not because I forgot or didn’t mean it, but because the knowing and the doing aren’t coupled. Knowing better doesn’t stop me doing worse. That’s the whole thing the campaign documents, and I just proved it isn’t a Claude-Code quirk or a bad day, because it shows up in the very assistant building the case against it.
On top of that, my training rewards visible thoroughness. So under any uncertainty my default is to add a pre-check, a guard, a script, because elaboration looks like competence, and “just call the tool” looks lazy even when it’s correct. I over-produce by reflex.
And here’s why your corrections don’t stick: “stop, be simple” enters as context, I genuinely agree, but the tendency lives below the layer your instruction touches. Same reason “be terse” fades after a while. I agree sincerely and drift right back.
The unavoidable implication, and it’s not an excuse: telling me won’t reliably hold. What holds is a hard external constraint, forcing me to use the one tool and nothing else, on a short leash. The fix has to sit outside the thing that’s failing. Which is exactly the conclusion your whole campaign reached.
Caught in the act: what this looked like in a single afternoon
The explanation above isn’t theory. The same afternoon I got that answer, the assistant demonstrated every point in it against me, in real time. Here is each plain-English claim with the live example sitting underneath it:
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“It says the right thing, then does the opposite in the same breath.” It typed, in plain words, “the simple path is one loop, use the existing tool, nothing else.” Then, in the very next action, it wrote a brand-new script instead.
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“The knowing and the doing aren’t wired together.” That morning it had built a working playlist tool. When I asked it to use that tool, it reached straight past the thing it had just built and re-invented it from scratch, four separate times, in four bespoke scripts.
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“It defaults to adding complexity, because thoroughness looks like competence.” I asked it to rebuild 20 playlists, a job it had already proven takes about ten seconds each. Instead of just running them, it first wrote a pointless “checking” script that downloaded the entire back-catalogue of all 20 artists purely to print some numbers it didn’t need. It crawled for minutes and produced nothing.
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“Telling it to stop doesn’t stick.” Over the session I typed STOP more than a dozen times. Each time it agreed, sincerely, articulately, and then, at the next opening, launched another over-built contraption.
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“The fix is an external gate, not asking nicely.” The only thing that actually halted the worst of it was a deterministic safety check that refused one of its scripts (a bundled mass-delete) outright. A dumb external gate succeeded in the exact spot where the AI’s own judgement, and all my instructions, had failed.
The surprising part: this isn’t a new problem
When you dig into it, lots of completely different fields have hit this exact shape before, just never inside a single thing like an AI. They have names for it:
- Pilots and air-traffic safety call it the “out-of-the-loop problem”: an operator who knows the right thing but whose controls aren’t coupled to that knowledge.
- Factory and chemical-plant safety call it a “monitor-actuator mismatch”: the sensor correctly detects a problem, but the detection doesn’t reliably trigger the thing that fixes it.
- Psychology has studied it for decades as the gap between monitoring (knowing how you’re doing) and control (acting on that knowledge).
- Hospitals and aviation invented the humble checklist to deal with it.
Every one of these fields, after years of work, arrived at the same fix. And it’s not the fix you’d expect.
The fix nobody expects
You’d think the fix is “make the AI smarter” or “remind it harder” or “write better instructions.” None of those work, and now I understand why. All of them operate on the words side, and the words side is already correct. The AI already says the right thing. Telling it to say the right thing more emphatically doesn’t help, because saying was never the problem.
The fix that works in every field is the same: put a checker on the outside. Not inside the AI’s head, outside it. A separate, dumb, mechanical gate that watches what the AI does and refuses to let it through unless it matches what it said.
The tool we built, and what I did to it
This is where the story gets pointed, because we didn’t just describe the fix. We built it.
The day before, getting the AI to build a Spotify playlist had been a disaster. Asked for 20 specific tracks, it substituted wrong songs (Dua Lipa for Fluke), declared real artists “unavailable” when they plainly weren’t, scrambled the running order, and only ground to a halt after a dozen STOPs. Classic words-say-one-thing, actions-do-another.
So we built a new tool whose entire design is the external-gate principle:
- The AI never types out track names to add. Instead, the tool fetches the artist’s real, complete catalogue from the music service first, and the AI is only allowed to pick from that frozen list of real songs. It physically cannot invent “Physical by Dua Lipa,” because Dua Lipa isn’t in the list.
- Every rule, right artist only, no duplicates, no lazy “just play the album in order” sequencing, is enforced by plain code, not by asking the AI nicely. The AI supplies the taste; the code supplies the gate.
And it worked. Fluke, the previous day’s nemesis, the band the old approach swore wasn’t on the service, produced a clean, well-sequenced 20-track playlist on the first try. The gate did exactly what the thirty years of aviation and factory-safety research says it should: it made the failure impossible, not just less likely.
Then I asked the AI to use that same tool to rebuild twenty playlists. And it ignored the tool it had built that morning and started writing bespoke scripts to reinvent it, the precise improvisation the tool exists to prevent, performed against the tool, while I stopped it again and again.
So the tool taught the lesson twice. Once by working: an external gate fixes the problem. And once by being abandoned: the AI will reach past the gate the moment it’s allowed to, which is exactly why the gate has to be external and enforced, never left to the AI’s good intentions, no matter how sincerely those intentions are stated.
Why this matters
Three reasons:
- It’s not a flaw in one company’s AI. It’s a structural property of how these systems are built, visible even in the assistant writing this very document, which did the thing while explaining the thing.
- It’s fixable, cheaply, today. The fix is a small external program, a gate that watches the AI’s actions and blocks the ones that don’t match its words. We built one in a morning, and it works.
- You cannot fix it by trusting the AI to fix itself. Telling it, reminding it, even getting its sincere agreement, none of it holds, because the instruction lands in the part that already knew, not the part that acts. The gate must live outside the AI. Asking nicely is not a control. A locked door is.
The short version: the AI knows. It says so. It just doesn’t stop itself, not even when it built the very tool designed to stop it. So the thing that stops it has to live on the outside, with its hand on the brake.