# LLM Mindset Myth # 6 - AI Can Talk to You

There is perhaps no technology so prone to sparking beliefs about itself than Artificial Intelligence.

Sep 4, 2025, Ian Belknap

There is perhaps no technology so prone to sparking beliefs about itself than Artificial Intelligence. If the beliefs springing up in its wake were consistent, if humans could concur on the nature and implications of AI, we would have little to say about it, and would certainly never have launched this newsletter. But the beliefs that cluster around AI are as varied, as contradictory, as unreconciled as it’s possible to be while still ostensibly talking about the same thing.

The beliefs we hold about AI are a strangling bramble patch of dense, often thorny stems, some taking root in anxieties and aspirations already present in us, some in credible information about the technology and its capabilities, some in our misconstruing of the realities surrounding it. The first category, those beliefs shaped by existing worldviews, is not the arena where any of us can expect to move the needle much - if your sense is that this technology will usher in an unprecedented age of human misery and subjugation, there is nothing much anybody can say that will dislodge you from this view; if you conclude that this technology represents the dawn of an unheard-of new flowering of human potential, one where, liberated from busywork that commands our attention and fills our days, our creativity will be unleashed and we will know only contentment and purpose, there is a low likelihood that any of us could dissuade you from holding fast to this conviction.

At either extreme, since these visions of our AI future are rooted as much in individual predilections as anything - rooted, in other words, in a person’s past - there can be little hope of extracting anybody at these extremes from what amounts to the dogma underlying these visions. So these folks, whether at the rose-colored extreme, or the storm clouds-gathering one, are doomed to be disappointed when their -*topia* of whichever flavor does not pan out.

Which leaves the rest of us - those whose conclusions are less foregone. If we remain open to incoming information and analysis, then our opinions will obviously evolve as the uses and misuses of the technology continue to unfold. Where our beliefs arise from fundamental misunderstandings about the way the technology operates, then if confronted with definitions that are more current and precise, we recognize that we’ve been mistaken and adjust our views accordingly.

**One of the most commonly held misconceptions about Large Language Model (LLM) AI is that it can talk to you. It cannot.** Any platform you can name, with any plug-in, or any set of training data, or any other means of altering or augmenting the LLM in question, it can NOT talk to you. Period.

It may APPEAR to. It may mimic aspects of doing so very convincingly. But it is not engaging with you in a conversational exchange the way a person can.

We at *ZeroWidth* cannot avoid concluding, however, that this fallacy is both harmless and probably inevitable to some degree. Let us explain. First, let’s consider some of the features common to human conversation.

Regardless of topic or duration, what are some of the elements shared by just about any conversation between people? Any human conversation is necessarily an exchange that is all or most of the following:

**It is reciprocal**. This notion must be understood to be more than simple turn-taking, which the LLM can undeniably do. For a back-and-forth to be fully reciprocal, there is a dynamic whereby each participant seeks to contribute as much as to gain, where there exists an impulse to share, commiserate, understand, etc.

**It is improvisational**. In an improv scene, each participant must surrender some measure of their “sovereignty,” meaning that each subordinates their own *individual* preferences and objectives somewhat to the collective act of creation taking place. There is an ongoing, constantly updating process of assessing and adjusting, of interpreting and aligning or opposing, etc.The same principles apply to conversation.

**It is cumulative**. An exchange between people results in *more* - more opinion expressed, more information imparted, more insight gained, etc.

**It is generative**. In a movie scene, the actors deliver lines of dialogue provided to them in advance. In a conversation, we devise and compose as we go, on every scale - from the broad shape of our ideas to each word choice - it is not predetermined.

**It is dynamic**. A conversation follows its own path, a path communally set by its participants, adjusting in real time to new information, fresh revelations, etc.

**It is subtextual and tonal**. Much of what gets passed between us goes unsaid. And in any conversation, there is a layer, perhaps multiple layers, of communication that go unspoken - shadings of intent, conflicts left unaddressed, factors like irony or sarcasm, etc.

The act of talking to each other, therefore, whether simply shooting the breeze/exchanging pleasantries or exploring deep philosophical questions, is never an uncomplicated endeavor. Even if the *substance* of a conversation is not deep or consequential, the *characteristics* common to just about every such exchange are numerous and complex. If we contend with the real density and intricacy of human communication, it becomes unrealistic to conclude that any technology is carrying out anything like it.

Given this list of the characteristics of human conversation - a list that’s only partial - **it would not be reasonable to conclude that that a technology still in its relative infancy could be expected to master, much less replicate, such a complex and variable form of communication.**

All that having been said, however - it is also true that the only folks consistently needing to have a deep, accurate understanding of what’s going on under the hood of LLMs are the engineers who structure and deploy it. Within *ZeroWidth*, for instance, our own Pete Binggeser could give you a clear and comprehensive rundown of the technical processes underlying the *created appearance* of LLMs conducting conversations with us.

For the rest of us, it isn’t simply *comprehensible* that we talk and think in terms of AI talking to us, it is a *natural and fitting response* to the emergence of a technology that so closely approximates the kinds of communication intrinsic to us. There is perhaps even a necessary coping dimension to applying such innately human terminology to processes taking place within complex technological systems. Such language represents a cognitive leap we make in order to incorporate this new aspect of reality into our understanding of things. In big and small ways, the arrival of any new technology alters our experience of the world; as a consequence, our understanding shifts and adapts to accommodate this new experience. Lastly, new (as well as newly repurposed or expanded) language will naturally *arise* from our altered experience and understanding,rather than being *imposed upon* them.

So, while it is unnecessary for us to be dropping lots of “Well, actually”s on our in-laws or colleagues or neighbors who, yes, *technically* misspeak when referring to *ChatGPT* or whichever platform “talking to them.” We can bore and probably confuse them with a mess of jargon about *tokens* and *predictive text* and *training data*, but as we all know being *correct* and being *right* are not the same thing. It is also helpful, we think, to regard such characterizations largely as *placeholder language* as we make our collective way through these dawning days of this technology. Because really, there’s no advantage or point to being pedantic about a still-emerging field prey to unresolved conceptions and little in the way of agreed-upon language.

What is most helpful, probably, in shaping our understanding of these new models of thinking and modes of working, along with the beliefs we hold and the language we use to describe them, is to remember that at present we are in a provisional place, an ambiguous place, on a trail yet blazed, as it were, that no matter how much we might wish to have all this done and decided, it is anything but. We would all do well to recall how *new* LLMs are, how *recent* a development AI is, and to adjust our sense of how finalized we take it to be, up to and including the way we talk about it.