Ces's random twalks on tech and stats.
by Cesaire Tobias
AI seems to be held to an impossible standard. We expect every output to be flawless — citations correct, claims verified, no hallucinations — while humans make mistakes constantly without facing anything like the same scrutiny. If AI is trying to approximate humanness, isn’t fallibility part of the package?
The more I pulled at this — partly in conversation with Anthropic’s Claude — the more I think the framing was wrong. The worry underneath it is real enough. The premise doesn’t survive contact with how we actually judge each other, and it comes apart in two places.
My argument went like this: AI is trying to approximate humanness, humans make mistakes, so why be surprised when AI does too? Makes sense — but I got the mechanism wrong.
AI makes mistakes because of how it works: statistical pattern completion over training data, with no grounded model of truth and no internal verification step. Its imperfections are a structural artefact of its architecture, and nothing about them was inherited from us. Nobody designing these systems set out to bake in our flaws as a charming touch of authenticity.
What gets borrowed from humans is the interface — the natural language, the conversational tone, the fluency. That is the surface AI produces, and the engine driving the errors underneath is unrelated to it.
So “AI is fallible because it’s trying to be human” is doing some sleight of hand. The fluent prose is a deliberate design choice; the wrongness is a side effect of how that prose gets generated. Pretending they’re the same thing — that the errors are part of the human-likeness package — gives AI’s mistakes a kind of charm they haven’t earned.
The other half of my original argument was that humans don’t get held to the same standard for getting things wrong. I now think that’s not true either — at least not where it counts.
When AI hallucinates citations, invents sources, or makes things up with confidence, people object. And they should. That is the same standard we hold each other to. A journalist who invents quotes or an academic who fabricates data gets drummed out of the field. A consultant who cites studies that don’t exist becomes unhireable. Lying is treated as lying, regardless of the source — and whether the lie is delivered confidently or hedged with qualifiers doesn’t really change the verdict.
We don’t tolerate fabrication from each other, and there’s no reason to tolerate it from a tool that produces text at scale.
The “humans make mistakes too” defence really only works for honest errors — typos, miscalculations, things you misremembered, points you got slightly wrong on the way to something mostly right. Those, both humans and AI get a degree of latitude on. The loud version of the AI-criticism conversation is about confidently invented information presented as fact, which has been a fireable offence for humans for a long time.
What’s changed is the volume. LLMs make publishable-looking content cheap to produce, so much more of it gets shipped, and a lot of it without verification.
Some of that comes from a category mistake. We’ve inherited a prior — machines are precise — that we then misapply. Type 1200 x 32 into a calculator and the answer is exact every time. Ask a person the same and you’d expect some hesitation, an approximate guess, possibly something to double-check. AI output looks as confident as the calculator’s, but it isn’t grounded the same way. So people who would normally verify a claim before making it skip the verification when the source is AI. They aren’t being fraudulent; they’re trusting an output that looks computed when it was generated.
When AI gets judged harshly for getting things wrong, that’s mostly fair, and mostly symmetric. The asymmetry is somewhere other than where I first put it.
The “this reads as AI” reflex — the pattern-matching against em-dashes and cadence and structure, the texture of the writing rather than its substance — is where I think the gap lives. That’s the argument of the companion piece, The Average Human Problem: Why AI “Sounds Like AI”.
Both threads end up pointing at the same underlying concern: whether the author engaged with what they shipped. A fabricated citation is a writer who didn’t verify. A piece flagged as “obviously AI” is often a reader detecting absence of thought.
I set out to write the version where AI is unfairly held to a higher bar. What I came out with is more deflating and probably more useful: the bar is about whether someone took responsibility for what came out, whoever or whatever produced it. We do sometimes pretend we judge each other more gently than that, and I’m not sure why.
May 4, 2026
tags: ai