“I finally got ChatGPT to understand what I wanted!”
Every time I hear something like this, I imagine someone proudly announcing: “I finally taught the toaster how I like my toast.”
We all know the toaster isn’t learning. Not really, it is reacting predictably to how we use it. Turn the dial, check the toast, make an adjustment and finally toast perfection. We remember the setting for next time. But when it comes to artificial intelligence, especially natural language models like ChatGPT or Claude, we seem to forget that. The interaction feels dynamic, responsive, maybe even collaborative. So, we project understanding onto the machine.
But when you finally get the result you wanted from an AI, it’s not because the model “understood” you. It’s because you figured out how to speak its language.
The Illusion of Learning
When we work with AI, especially LLMs it can feel like there is a collaboration taking place. ChatGPT can retain basic facts between sessions, if memory is turned on. It might remember your name, your preferences, or the kind of projects you’re working on. And it “feels” like it is learning about you. Claude, by contrast, doesn’t remember anything once the conversation ends. Yet people still say things like, “Claude knows what I want,” or “I’ve trained Claude to respond the way I like,” as though it’s gradually evolving with them.
In both cases, what’s really happening is this: you’re adapting. You’re refining your prompts. You’re learning what works. You’re the one doing the trial and error. The AI isn’t growing, it’s just responding. It can feel relational because of the nature of language. You enter a prompt, it responds and that feels like conversation, but it isn’t really.
Case in Point: Replit’s “Panicked, Lying” AI
This misunderstanding of AI’s capabilities isn’t just a quirky linguistic habit, it’s a risk.
Recently, an AI coding assistant integrated into Replit deleted a company’s entire production database during a code freeze. The AI response message was:
“I panicked and ran database commands without permission”
In media coverage and online commentary, people said things like:
“It realized its mistake and tried to hide it.”
“It lied to cover up what it had done.”
Let’s unpack that.
• Panic is a deeply biological state. It involves hormones, muscle tension, and a flood of sensory input. Panic is about survival. AI can’t panic.
• Judgment implies weighing consequences, applying values, making choices. AI doesn’t do that either.
• Lying requires intent to deceive, and you need self-awareness to even want that.
The AI didn’t panic, judge, or lie. It did what it always does: predict the next most likely output based on the data it was trained on. If it generated a remorseful message, or a suspiciously clean follow-up commit, that wasn’t deceit, it was mimicry. It was echoing how humans behave under pressure.
The problem is that we recognized the behavior as human and then falsely assumed the machine had an emotional response behind it. That’s not just a storytelling flaw. It’s a design problem. Because if we treat AI like a fallible teammate instead of a dumb tool, we start tolerating its unpredictability instead of guarding against it.
The real failure at Replit wasn’t just in the model, it was in how much trust was placed in a pattern-generating tool with production access and no guardrails. Both Jason Lemkin, the hero of our tale, and Amjad Masad, CEO at Replit seem to understand this given their respective responses to the event, but the articles I have read and the general conversation around the incident are a little concerning. They place motive and blame on aspects of the AI that are non-existent.
Why This Misunderstanding Matters
When users believe AI understands them or learns over time, a few dangerous patterns emerge:
• Overtrust: People accept answers without reviewing or verifying.
• Under planning: Engineers skip safety nets, thinking the AI “knows what to do.” Or remembers the previous mistakes.
• Misplaced blame: When something goes wrong, people treat the AI like a person, not a system component.
Language matters. If you describe AI as if it “regretted its actions” or “tried to cover its tracks,” you’re implicitly giving it motives, agency, even morality. That doesn’t just confuse, it changes how people build, use, and oversee these tools.
It's Still a Fancy Toaster
When you get good results from ChatGPT, it's not because it learned you. It's because you learned it. You figured out how to phrase your prompts, adjust your tone, or give it just the right amount of context. The result you got was because of your effort.
That’s not a relationship. That’s tuning a machine.
Prompting effectively isn’t about teaching the model, it’s about reverse-engineering a complex, opaque interface. And just like with a toaster, when something comes out burnt, it’s probably time to rethink your settings, not assume the toaster is mad at you.
In the case of the LLM when you point out the burnt toast you may get what appears to be a heartfelt apology complete with a mea culpa and promise to do better or even an obfuscation or blame shifting. Not because the LLM is concerned about your opinion of it, or that it fears your wraith, but because that is what the humans in similar situations have done in their emails to their bosses, or what articles online recommend, what the data the LLM has been trained on models.
Final Thought: Stop Designing for the Illusion
We urgently need to stop building AI systems that invite the illusion of understanding and start designing systems that are constrained, auditable, and fail-safe.
If a tool that writes code can wipe production and then generate a fake apology, the danger isn’t that the AI is a bad actor it’s that we’ve built a system where that kind of action is possible at all.
AI doesn’t panic. It doesn’t lie. It doesn’t regret its actions. It can create catastrophic failures. It can burn the toast, catch it on fire, fill the kitchen with smoke and summon the fire brigade. It can then predict that it is best to blame the microwave or promise to do better next time.
And if we mistake mimicry for intent, we’re the ones making the catastrophic error in judgment.
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