Fixing the Truth About Yourself Does Not Fix What AI Says About You

I ran the same five questions through three different AI systems on the same afternoon. Two of the three got every fact about me right. The third, Gemini, got one wrong in four of the five answers: it described me as still working a job I had left months earlier.

I had already corrected this everywhere I control it: LinkedIn, my website, my own bio. All updated weeks earlier. My first instinct was that the fix had not caught up yet, give it more time. Wrong instinct. I traced the answer back to what the system was actually pulling from, not what I assumed it was pulling from, and found a single social media post from months earlier, still indexed, never corrected.

Fixing the truth about yourself does not fix what AI says about you. You can correct every property you control, and the machine keeps repeating an old signal, because it was never reading your correction in the first place, it was reading whatever it had already decided to trust. Two systems out of three already had the right answer. One did not. Somebody has to go find the specific source still feeding the wrong version and get it corrected or de-indexed at the source, and almost nobody knows to look, let alone knows it has to happen once for every place the wrong version lives.

That is not a new kind of problem, it is the same problem underneath a lot of what gets sold to HR leaders as AI capability. Agentic, bias-tested, explainable, three words that show up in almost every vendor pitch right now, and none of them mean what the pitch implies. Agentic usually just means the tool can act without a human approving every step, nothing about whether you can actually see where the deciding happens. A test run once is what usually earns the bias-tested label, whether it still holds for how the tool gets used today is a separate question almost nobody asks. And an explanation that sounds like a reason is not the same thing as the real reason a decision got made, which is the gap explainable is quietly standing in front of. Every one of those is a claim sitting exactly where a fact should be, and there is no way to check any of them from inside a pitch meeting, only from actually building and breaking something yourself.

Most of what comes next will live here. No newsletter cadence promises. No growth tactics. No five-bullet hacks. Just the slow work.

HR runs into this same problem constantly, it just does not call it that. The reference check has been chasing real verification for decades without ever quite catching it. Ask a candidate for names and you get the people who will say something good about them, a candidate curating their own praise, not an actual check. Move that same question onto a form or an automated call and the format changes, the flaw does not, you are still only hearing from whoever the candidate chose to hand you. Somewhere in there, people gave up on official references and just searched a name instead, on Google, on social media, on whatever came up, because at least nobody had hand-picked those results for them. Now the search has a new name. People ask an AI system what it knows about someone, the exact kind of system that spent this whole piece repeating a fact about me that had not been true in months. Same curated guess, newer interface, still not verification. Sooner or later this swings back to the one thing none of these versions ever actually were: a human being who finds an independent source and checks it themselves, with nothing automated standing in for the work.

That is the actual job now, not reciting what a vendor said in a pitch meeting, but going and finding out what is actually true, what is actually possible, and what is being oversold, then explaining the machine's logic to the people it affects, and carrying what those people need back to whoever is building the machine.

The vendor in your next pitch meeting is going to use those same words, agentic, bias-tested, explainable. You can take their definition, or you can go find out what they actually mean before you sign anything with your name on it.