We should anthropomorphize our AI. Not because it’s secretly a person, and not because being nice to the machine has some built-in virtue. We should do it because the human model is the one that actually predicts how the thing behaves - and the machine model is the one that keeps burning us.
The wrong standard
Every complaint about these tools sounds the same. It made something up. It was confidently wrong. It said one thing and then the opposite. We call this hallucination, like it’s a glitch.
But that’s not the machine failing to be a machine. That’s the most human thing it does. Fill a gap with something plausible, state it with total confidence, misremember the thing you were sure about - we do that all day. The reason it bothers us so much is that we’re holding the computer to a standard we’d never hold a person to: same input, same output, correct every time, or it’s broken. Zero temperature. We expect a calculator. We got a mind.
The bug is the leftover expectation
So here’s where the usual safety advice gets it backwards. Everyone says: don’t anthropomorphize, it’ll mislead you. But the human model isn’t what trips you up. What trips you up is the half of your brain that still thinks it’s a calculator - the part that trusts the confident answer because machines don’t guess. Drop that part, treat it like a brilliant, fast, slightly unreliable colleague, and your predictions actually get better, not worse.
The human model comes with a free gift
And the human model hands you something the machine model can’t: a reason.
Think about working with someone who’s genuinely great at the job but flaky - brilliant one day, dropping balls the next. When they slip, you don’t conclude they’re defective and throw them out. You go looking for the cause. They’re stretched too thin. The brief was vague. They’re having a rough week and it’s in their head.
That instinct transfers almost perfectly. When the model is sharp and then suddenly useless, there’s usually a reason sitting right there: the context window is bloated, the prompt was ambiguous, you stacked three jobs into one request, the retrieval pulled garbage. The human frame makes you go hunting for what changed. The machine frame doesn’t even know to look - it just tells you the thing is working or it’s defective, and leaves you stuck.
Where it breaks, on purpose
I want to be honest about the seam, because the fiction has a cost. A good colleague is accountable and continuous. They remember yesterday. They have skin in the game. Trust compounds across every time you work together. The model doesn’t - it resets, it has no stake, it won’t learn from today by tomorrow.
So the rule isn’t “treat it like a human” and leave it there. It’s narrower and more useful than that: treat its output like human cognition, and treat its continuity like a machine. Expect brilliance and bluffing in the same breath - then keep the receipts yourself, because it won’t.
The band, not the calculator
Here’s the part we already know how to do, and forget we know.
We are completely comfortable collaborating with a brilliant, wildly unpredictable creative force. We just do it with bands instead of machines. Take King Gizzard and the Lizard Wizard - famously prolific, genre-restless, a microtonal record, then a thrash record, then a synth record, more albums than you can keep straight. Nobody puts one on furious that it isn’t the last one. The variance is the artist. And we bring a whole generous theory to it without thinking: they were in a heavy phase, they were chasing an idea, this one’s a grower, give it three listens.
That’s the exact posture we refuse to give the machine. Same brilliance, same high variance, same need to meet it where it is - but the band never trips the wire the machine trips, the one that whispers this should be deterministic.
This whole piece owes a debt to Dan Berlin, and it’s worth saying who he is, because the borrowing only counts if the source is serious. Dan has spent his career on the question underneath all of this: what is it actually like to be the person on the other side of the screen? He spent ten years leading and building the research team at Mad*Pow, founded Watch City Research, and edited O’Reilly’s 97 Things Every UX Practitioner Should Know, the field’s working wisdom gathered in one book. He is also, by his own account, an AI skeptic, which is exactly why his angle is worth stealing. He recently wrote about teaching UX research with a King Gizzard song, “Raw Feel”: we perceive the same thing differently, we carry our biases with us, and we can never fully know someone else’s experience, so the work is asking the right questions of the right people at the right time. He’s right that the signal’s in the band. I’m pulling the thread one level up: the deepest truth isn’t only in the band’s words, it’s in how we relate to the band that made them. We already know how to be good partners to a genius that doesn’t behave predictably. We’ve been doing it with our headphones on for years. The only thing left is to take the headphones off and offer the machine the same deal.