TL;DR. More and more often, a text reaches its reader through two converters: the AI the author writes with, and the AI that summarizes it for the reader. In industrial instrumentation this design is called a measurement channel, and it has a well-known property: the errors of each link add up, so you have to check the whole channel, not just its parts. AI passes knowledge through this channel well. Context, it does not. Below I look at which context gets lost, why an error shared by all readers of one model is more dangerous than ordinary misunderstanding, and how authors and readers can verify this channel.
Two converters between author and reader
Not long ago, a text went from author to reader almost directly. The author wrote, the reader read. People understood it differently, but they read the same thing.
Now there are more and more often two converters between them. The first one is on the author's side: AI helps put together a draft, choose the wording, cut it down. The second one is on the reader's side: AI retells a long text, filters the feed, and produces a summary tuned to one person's interests. Fewer and fewer people see the original text.
The idea that texts now pass through AI on both ends is not new. I don't see it as a disaster or as progress. It is just a new signal path. And signal paths are what I have worked with for 18 years in industrial instrumentation. My contribution here is to look at it through the eyes of a metrologist.
How a measurement channel works
In a plant, an operator almost never sees a physical quantity directly. Pressure in a pipe takes this path:
- A sensor turns pressure into an electrical signal.
- A signal converter brings it to a standard form.
- A cable carries it to a controller.
- The controller converts the signal back into pressure units.
- A screen shows the operator a number.
Each link adds its own error, and the errors build up. That is why metrology has two ways to check such a system: link by link, verifying each element separately, and end to end, verifying the whole channel from the sensor input to the number on the screen. The second way catches what the first one misses: the effect of the cable, interference, a mistake in unit conversion.
There is one more rule that looks like bureaucracy. A measurement result is not just a number. It is a value, a unit, an uncertainty, and the conditions it was obtained under. "Twenty degrees" without conditions means nothing: in a workshop it is normal, in a furnace it is a failure.
Knowledge passes through the channel, context does not
Text now has the same kind of channel: the author's thought → the author's converter → the text → the reader's converter → the summary. And the same rule applies. A value without measurement conditions is meaningless, and what AI passes best is exactly the value.
A model has a huge stock of knowledge. It often knows the subject better than the author. But knowledge is the value. The conditions that give it meaning come from a human. There are three kinds of context that hardly pass through the channel at all.
Unspoken context. What you know but don't think to say: who your audience is, what has already been said in this discussion, what is not discussed here, what specialists consider common knowledge. The model won't ask, because it doesn't know that it doesn't know.
Context of consequences. What you risk, not the model. Reputation, relations with a client, standing on a platform. The model writes with the same calm a text that will earn you praise and a text that will get you torn apart.
Changing context. What happened after you set the task. A new comment in the thread, fresh news, a shift in the audience's mood.
The obvious objection: you can put the context in the prompt. You can, if you are aware of it. The problem is that the first two kinds of context usually stay unnoticed until something goes wrong.
An error shared by all readers
People understand the same text differently even without AI. You could ask: so what has changed? The nature of the error has changed.
Every person has stable perception biases; psychology calls them cognitive biases. But different people have different ones. A hundred readers err in a hundred different directions, and in a discussion the right meaning usually comes through.
If many people read through the same model, its tendencies are the same for everyone. In metrology this is called a correlated error: it is shared by many channels and does not shrink when you average. However many summaries made by one tool you compare, the shift they share stays invisible.
In a workshop it looks like this: if ten thermometers were calibrated against the same reference, and that reference is itself half a degree off, all ten will read half a degree high. Comparing them with each other won't reveal the error; they all agree. You can find it only by checking against another, independent reference.
Personal tuning adds one more layer. By the very logic of personalization, the filter shifts the meaning for each reader in their own direction, most likely toward topics and views the person already knows. This is my hypothesis, but the mechanism suggests it. The reader does not see the shift: the summary looks smooth and logical. The reference here is the original text. The only question is when it was last opened.
A case from practice
Recently I left a comment in a discussion outside my field. AI helped me put the draft together. The analogy in it was elegant. The comment got downvoted.
Looking into it showed a double error. The first was in knowledge: one of the statements was inaccurate for that field; the specialists knew the subject more deeply than the comment suggested. The second was in context: the AI did not know the discussion — what had already been said above, what people there have long considered common knowledge, how strictly they treat analogies from outsiders.
The second error hid the first. Had I known the platform's context, I would have doubted the statement before posting. That is exactly unspoken context: the context I did not pass on because I was not aware of it.
Comments in my own field, put together the same way, went fine. There I had the context: I could see at once what was inaccurate in the draft and what the audience already knew. One case is not an experiment, it is an illustration. But it shows the main point: without context, an author cannot verify even the knowledge.
This channel had a verification link: me, before posting. That time it failed. In a field outside my own, my knowledge was not enough to spot the inaccuracy, and I did not check the platform's context.
Verifying the channel
If the channel now has two converters, it has to be verified from both ends. Here is the procedure I use myself.
| Side | What to verify | How |
|---|---|---|
| Author | Will the main idea survive compression | State it in one sentence and put it at the start. If it can't be stated, the summary will build it for you, in its own way |
| Author | Unspoken context | Before posting, read the discussion itself: what has been said, what is considered common knowledge here. If you write with AI, put this in the task explicitly |
| Author | Context of consequences | Check every claim you might be asked about against the original source and link to it. Especially outside your field |
| Author | Is it your field | Outside your field, show the draft to a specialist or don't publish |
| Reader | Filter shift | From time to time, compare the summary with the original: a few control points instead of blind trust |
| Reader | Important decisions | Make them based on the original text, not the retelling |
| Reader | One-sidedness | Sometimes ask the filter not for what interests you, but for what contradicts your view |
There is nothing complicated here. It is the same logic as in a plant: every link is verified, the whole channel is verified, and the result comes with the conditions it was obtained under.
The case against
You can pass context in the task. Yes, and that is the main remedy. But you can only pass the context you are aware of. The most expensive mistakes come from context you didn't think about.
People distort meaning without AI too. Also true. Retellings, quotes out of context, headlines — all of this existed long before models. The difference, in my view, is scale and the nature of the error: different people's distortions differ, while readers of one model share the same ones.
The metaphor is imprecise. The links of a measurement channel have a specified accuracy. An AI summarizer has no specification, and its behavior changes from version to version. So the analogy explains why errors build up, but does not let you calculate them. Metrology has an answer to this too: instruments without a specification are characterized experimentally, against reference samples. For a text, the reference is the original.
Summaries save time. Of course, and there is no reason to give them up. An instrument also saves time compared to measuring by hand. But you use an instrument together with verification, not instead of it.
Models are getting better. They are. They will have more knowledge and larger context windows. But unspoken context and the context of consequences stay with the human until the human passes them on. A better model does not receive what the person never said.
Instead of a conclusion
AI has knowledge; humans have context. Knowledge is the value. Context is the conditions that give the value its meaning. In metrology, a measurement without conditions is not accepted. A text without context now passes through two converters and reaches the reader smooth, confident, and shifted.
The path by which meaning reaches the reader has already changed, and going back to direct transmission is unlikely. Meaning can drift along this path, so the path itself has to be verified from both ends, and always against the original: a shift shared by everyone can't be caught by comparing summaries with each other.
How do you check what AI writes for you or summarizes for you? Have you had a case where a summary was smooth, but the meaning in it had shifted?
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