The Moment the Model Got It Wrong
It started in the most unremarkable way possible. I was halfway through reorganising a deck, waiting for a file to upload, the kind of dead minute where your brain wants a distraction. So I typed a quick question into one of the generative engines, basically: “How would you describe this brand?” Not a test, not research. Just curiosity mixed with boredom.
The answer looked fine until it did not. A couple of details were off, not dramatically wrong, but wrong in that familiar way where you think, hang on, who still talks about us like that? One phrase in particular felt like it had been copy-pasted out of a 2022 review. I reread it, convinced I had misremembered something or clicked the wrong prompt history.
I tried again with a slightly different wording. Same vibe. Different sentences, but the same outdated idea sitting in the middle like old furniture no one wants to throw out.
And because I am stubborn, I switched to another model entirely. Surely one of them would get it right. Except this one invented a detail we killed ages ago. Not maliciously; just… confidently incorrect in a way that did not match any user feedback I had seen in months.
At this point, I was not annoyed so much as confused. Users misunderstand things all the time. That is normal. But models? They usually fail in loud, bizarre ways, not in this quiet, oddly consistent drift. This felt like they were all remembering an older version of the company, one we had already outgrown, even though no actual user I had spoken to in ages would describe it that way.
And that is what made me pause. If two completely separate systems are repeating the same stale narrative, then the problem is not the prompt. It is not me. It is not even the brand.
It is the memory those systems are pulling from.
I caught myself wondering something I had never asked before:
“When did the machines start misremembering us before the people did?”
The Part Nobody Warns You About
Once you notice this kind of drift, it does not slide quietly back into the background. Especially in fintech and growth, where small misunderstandings can snowball into real problems, the source of confusion matters as much as the confusion itself. The subtle shift happening now is that people no longer begin with your website or a search bar. A surprising number start with whatever their AI assistant tells them. A national survey earlier this year even highlighted how common it has become for users to run quick “what does this company do” checks directly through AI tools instead of opening a new tab.
It sounds trivial until you think about how early that moment is. The model becomes the unofficial narrator of your identity. When it is working off an outdated imprint, the drift begins inside the model’s memory long before it appears in user behavior or customer feedback. Most teams are not tracking this yet because it does not resemble any traditional metric. But the shift is already underway: brand perception begins in model memory now, not human memory.
When Drift Starts in the Machine Instead of the Market
I ran the same test across several other companies I follow, competitors, partners, and a few whose messaging shifts I watch out of habit. The pattern was eerie in its consistency. A fintech app that had evolved into a sophisticated platform was still framed as a student budgeting tool. A crypto wallet known for its tightened security protocols was described as if it were still in its experimental phase. A SaaS product that had spent years humanizing its narrative was still labelled automation-first.
These were not hallucinations. They were artifacts, snapshots preserved from older versions of the internet. The models were remembering a past the companies had already outgrown.
And the trend is gaining momentum. Mckinsey reports last year showed generative AI being woven directly into everyday research habits, with information-seeking becoming one of the fastest-growing uses across both consumers and enterprise teams.
If people increasingly let a model carry the weight of their early understanding, whatever that system remembers, accurate or not, becomes the story’s starting point.
The Experiments I Ran to Understand What Was Happening
Out of curiosity, more than anything, I turned this into a small personal ritual. Every few days, I asked the same handful of questions and compared what moved and what refused to budge.
Some misunderstandings were remarkably persistent. A retired tagline resurfaced again and again, even after the brand had scrubbed it everywhere. A competitor comparison the company had long outgrown kept slipping back into the summaries. One model confidently described a feature that a company never launched, almost certainly borrowed from a speculative Reddit thread years ago.
Patterns emerged. Some updates propagated instantly, some decayed immediately, and some older ideas regenerated as if they were foundational. These systems were never designed to track the evolution of a brand. They hold impressions the way people hold early memories, loosely and with no built-in refresh cycle.
The Realisation: Nobody Owns Model Accuracy Inside a Company
Detecting drift is one thing. Figuring out who is supposed to fix it is something else entirely. Inside companies, every team cares about perception, but none of them has responsibility for how generative engines describe the organisation. Marketing watches sentiment. SEO watches search surfaces. Comms monitors narrative consistency. Product pays attention to comprehension gaps. Growth follows funnel behavior. Yet the model layer, the one that now influences the earliest stage of discovery, sits completely unowned.
There is no audit. No owner. No cadence. No shared sense that this event is a surface worth monitoring. And yet it is increasingly the first one people encounter.
What Happens When You Audit the Models Before You Audit the Market
Once I understood the gap, I started checking model recall before I checked anything else. Not as a formal system, just a habit. Before new messaging goes live, or before a repositioning experiment rolls out, I ask the same basic questions a casually curious person might ask.
The answers tell you where the model’s memory is stuck, which parts of the story it has absorbed, and which updates have not landed at all. It becomes an informal early-warning system. If the summaries feel misaligned, you know the drift is upstream, not in the funnel, not in the copy, not in the positioning.
Once this habit enters your workflow, it becomes almost impossible to imagine operating without it.
Why This Will Matter More Over the Next 2–3 Years
Generative engines are on track to become the first opinion layer in consumer and enterprise decision-making. They shape expectations before anyone reaches your homepage or reads your documentation. They influence which comparisons seem reasonable, which features people assume you offer, and how much trust they extend before they interact with anything real.
A user misunderstanding you can be corrected. A model misunderstanding you becomes a multiplier, because it affects everyone who starts their research with the system’s memory, not your own.
A Quiet Warning for Founders and Marketers
If there is one habit worth adopting, it is simply this: before you look at the market, look at the model. Before you assume users are confused, check which version of your company is circulating inside the engines that now introduce you to the world. Most teams skip this entirely, not out of negligence, but because they do not realise how influential this layer has become.
Models introduce you long before you show up. If that introduction is outdated, everything downstream becomes a repair job. The drift does not start with users. It begins with systems that quietly misremember a version of you that no longer exists.