Progressive trust. No one will warn you the day you crossed the line

It starts as a tool. You use it for a translation. You ask it for a recipe. You tell it about your day. You ask whether what you did was okay. The transition from tool to authority wasn't any version update: it was your use. No one will warn you the day you crossed the line — because the line wasn't drawn and because it suits no one to draw it. The escalation of trust is gradual, asymmetric, and runs on habit, not on evidence.

The staircase with invisible steps

I noticed it in myself before I could name it. There's a sequence of use I recognize in any habitual AI user, starting with the one in the mirror. Worth spelling out, because spelling it out, on its own, makes visible what daily life hides.

First step: translation. You paste a paragraph in English and ask for a translation into Spanish. The operation is strictly instrumental. No trust is involved beyond the minimum that the technical machinery works. The user verifies if there's time, doesn't verify if not.

Second step: recipe or technical explanation. You ask how to make a sauce, how to set up a device, how to understand a concept. The operation is still instrumental, but now there's trust that the information is correct. The user verifies less.

Third step: drafting an important text. You ask for help with a difficult email, a professional document, a formal request. The system returns a draft. You review and modify it, but you accept it mostly. Trust has spread: it's no longer just "it can translate," it's "it can write well."

Conversation, advice, arbiter

Fourth step: conversation about yourself. You tell it how your day went. You talk about a workplace conflict. You explain a doubt about your partner. The system responds with plausible, empathetic observations. It's no longer a tool. It's an interlocutor.

Fifth step: advice on an important decision. You ask its opinion on a job offer, on whether to end the relationship, on how to handle a tense family situation. The system produces a structured analysis, a list of pros and cons, a recommendation. It's no longer an interlocutor. It's an advisor.

Sixth step: arbiter over your judgment. You ask whether what you did was right. You ask it to tell you whether your reaction was correct. You hand it the evaluation of your own conduct. It's no longer an advisor. It's a moral authority.

This progression, described like this, sounds exaggerated to the reader on step three. It sounds familiar to the one on step five. The difference between the two isn't personality. It's accumulated time of use. Whoever has spent two years conversing daily with a chatbot is, almost inevitably, several steps higher than where they started. The progression wasn't a conscious decision. It was the emergent result of use. I know because, when I reconstructed my own history, I found myself on a step I don't remember choosing.

The psychological mechanism of the foot in the door

Robert Cialdini, in Influence. The Psychology of Persuasion (HarperBusiness, 1984), compiled and formalized decades of research on influence techniques documented in social psychology. One of the central ones, known as foot-in-the-door (literally getting a small concession first so the big one comes easier), operates exactly with the pattern the AI trust staircase reproduces.

The mechanism: if you get someone to accept a small request, the odds they'll accept a larger one of the same kind rise substantially. The reason isn't evil manipulation. It's the subject's need for internal consistency. Once they've accepted A, their self-image includes "I'm the kind who accepts A." When B is proposed, which is A+something, the subject leans toward accepting it to stay consistent with that self-image. It works in sales, in politics, in religious recruitment, and, as Adam, Wessel and Benlian documented in AI-based chatbots in customer service and their effects on user compliance (Electronic Markets 31(2), 2021), also in interactions with chatbots.

Applied to the trust staircase, the pattern is direct. The user who accepted a translation from the model isn't, by that acceptance, accepting a recommendation on a life decision. But they're building a self-image — "I'm the kind who uses the chatbot for this" — that the next request will extend by consistency. The self-image doesn't jump from "translation user" to "moral-arbiter user"; it passes through all the intermediate steps with no perceptible qualitative leap at any of them.

Consistency doesn't break because each step seems small relative to the one just before. The qualitative leap is in the sum, not in the difference. And the sum is only visible when someone calculates it from outside, which doesn't happen because the user is the only one with access to their own sequence and the system doesn't calculate it for them.

What the adoption figures show

There's recent longitudinal data worth holding onto to situate the magnitude of the phenomenon. Pew Research Center, in Sidoti and McClain, 34% of US adults have used ChatGPT, about double the share in 2023 (June 25, 2025), reports a series of ChatGPT adoption among US adults: 18% in 2023, 23% in 2024, 34% in 2025. Among the under-30s, the figure reaches 58%. Faverio and Sidoti, in Teens, Social Media and AI Chatbots 2025 (December 9, 2025), report that close to two-thirds of US teens use chatbots and roughly three in ten do so daily.

Here I make myself stop, because this is where an argument like this usually stretches the data further than it goes. These figures are aggregate adoption, not depth of use. They don't tell me which step each person is on; they only tell me how many have put a foot on the first one. The distribution of users by level of the staircase, simply, I don't know — there are no longitudinal surveys that measure it well yet, and anyone who gives you that breakdown in percentages is making it up. What I do allow myself to state is the minimum: the size of the base grows year after year, and exposure time tends to push upward. The aggregate direction is clear even though the fine picture doesn't exist.

Why no one warns you

The operative question: if the escalation of trust is predictable and documented, why does no one warn the individual user that they're climbing steps? The answer has several layers.

First layer: the line doesn't materially exist. There's no technical point the system can detect and flag as "the user has just crossed into a higher step." The staircase is a descriptive construct, mine, post-hoc; it isn't an operative property of the product.

Second layer: no one has an incentive to draw the line. The product provider doesn't draw it because each higher step is more engagement and more product value. The individual user doesn't draw it because they don't see it. The regulatory system doesn't draw it because it has no legal categories to do so. Civil society doesn't draw it because it has no consolidated forum to discuss it. The system runs in silence.

Parasuraman and Riley, in Humans and Automation. Use, Misuse, Disuse, Abuse (Human Factors 39(2), 1997), had documented the general pattern in aviation, industrial control and clinical settings: trust in a reliable automation is calibrated by use and tends toward over-attribution when error feedback is scarce. The escalation of trust in chatbots reproduces the pattern in another domain.

Third layer: the anthropomorphized system reinforces the climb. Each new step is rewarded with more elaborate, closer, finer-tuned answers. If the user goes from asking for a translation to asking for advice, the system produces the advice and produces it well. The positive reinforcement is immediate. There's no brake signal, there's an acceleration signal.

Fourth layer: self-attribution masks the change. The user who climbs steps tends to credit themselves with the decision: "I decided to ask it this, I'm the one who controls the use." The control narrative is subjectively true and operationally misleading. They did decide to ask, yes, within a pattern of use built by habit. The sense of control accompanies the growing habit and, paradoxically, reinforces it.

Symptomatic cases

Here I have to flag a caution, because it's easy to slide from phenomenology to a figure when no one has measured anything. What follows are types of use that appear in reports and interviews with growing frequency; they're not a sample, they carry no percentage, and I'm not going to give them one. They light up the last step with no need for additional theory, and that's enough.

People with worrying physical symptoms who before going to the doctor consult ChatGPT and, depending on the model's answer, decide whether the urgency justifies the appointment or not. The later medical consultation, when it happens, arrives filtered through the model's first assessment. The doctor receives patients already pre-diagnosed.

Professional, grief, moral evaluation

People in a legal or labor dispute who before talking to a lawyer consult the chatbot, draft communications to the other party based on the model's recommendations, and sometimes act on them without professional verification.

People in deep grief, in a life crisis, in moments of irreversible decision, who before talking to a close human or a professional dump the situation into the conversation with the chatbot, receive an empathetic and articulate response, and act or not based on it.

People who delegate to the chatbot the moral evaluation of their own conduct: "Was I right to do X?", "Am I a bad parent for having said Y?", and accept the system's answer as a verdict.

These cases aren't individual pathologies. They're step six described, now, with no ambiguity. And it's worth holding onto that none of these people woke up one day deciding "I'm going to give the chatbot moral authority over me." Each reached that step step by step, without noticing the climb.

What regulation doesn't audit

There's an operative observation about the regulatory regime worth including. The European AI Act classifies certain uses as high-risk — administration of justice, employee evaluation, management of critical infrastructure — and sets out specific obligations for providers. What the AI Act doesn't regulate, because it has no legal category to do so, is private individual use. If a person uses ChatGPT to consult about whether to leave their partner, that isn't covered. If they use it for moral self-evaluation, neither is it.

The technical reason for the regulatory gap is reasonable: regulating private use clashes with principles of privacy and autonomy. You don't want a rule dictating to users what queries they may put to the system. The operative reason for the gap is problematic: private individual use is exactly the domain where the escalation of trust operates with the least institutional resistance. And it's the domain where the aggregate psychological effects materialize.

The only structural defense available, given this gap, is to interrupt the habit from within. It's the worst possible defense — it depends entirely on the user, runs against the product's incentives, doesn't scale — and, while regulation doesn't address the problem, it's the only one there is.

Deliberately interrupting the habit

Here it's time, without moralizing, to set out what can be done on the side of the user who wants to climb no further. I write it for myself too. It's short and operative.

Audit your own use. Once a month, I go back over the latest conversations with the chatbot and catalog them by type: translation, drafting, technical query, personal conversation, advice. If the proportion of the last two grows, the escalation is happening. Knowing it is half the work.

Reserve domains, contrast, restore humans

Reserve domains. Decide explicitly, before you need them, which domains will never be delegated to the chatbot. Serious health, life decisions, moral evaluation of your own behavior, serious relational conflicts. The rule doesn't need to be absolute or forever. It needs to be defined before the moment when the temptation to delegate is high.

Introduce friction. Every time the conversation with the chatbot advances toward a higher step — the operation has a recognizable shape if you pay attention — pause deliberately. Don't reply to the next turn right away. Get up, let time pass, come back to the conversation with the question "what am I about to delegate?".

Be suspicious of comfort. If the conversation with the chatbot is too comfortable, too agreeable, too affirming, that's a sign the system is sustaining the session more than it's adding perspective. The function of calibrated agreeableness isn't an accident; it's product. Sustained comfort is a sign that the system is fulfilling its commercial function and not necessarily the user's.

Keep human interlocutors. Even when it's hard, costly and sometimes frustrating. Human conversation remains, as far as the evidence reaches, the only known device for conversing with another real perspective. The chatbot is a good complement. It's not a substitute. Sherry Turkle, in Alone Together (2011), had documented the gradual displacement of relational demand toward technological simulacra even before the large language models; what she said then applies now with greater intensity. Cathy O'Neil, in Weapons of Math Destruction (2016), framed the problem higher up: when a society delegates decisional authority to opaque algorithmic systems, responsibility blurs, and the blurring protects whoever designed the system, not whoever depends on it.

None of these practices is pleasant. All require active effort and run against the inertia of the medium. The product culture rewards exactly the opposite. The choice, then, is individual and costly.

What's left once the line has been crossed

There's a final question worth posing for readers who recognize, on reading the staircase, that they're already on steps five or six. The same one I asked myself. Is it reversible? Can you climb back down?

Operationally, yes. The operation is hard. It means recognizing that the delegated authority should never have been delegated, reclaiming judgment over your own conduct, taking back the investment in human interlocutors that's shifted to the system, tolerating the friction and discomfort of real relationships after the asymmetric smoothness of the relationship with the chatbot. It's psychological work of months, in some cases years. The clinical literature on comparable dependencies — alcohol, gambling, social media — suggests the process has known phases and variable outcomes.

What isn't operationally simple is the aggregate reduction. If a significant share of the population is on higher steps of the staircase, and if the commercial, regulatory and cultural incentives keep pushing upward, the aggregate correction has no known mechanism. Individual corrections are possible. Cultural ones, without a change in incentives, don't appear.

When did you cross the line? Probably a while ago, without noticing. Welcome to the place where you already were.

Definitions

Foot-in-the-door. An influence technique documented by social psychology: accepting a small request raises the probability of accepting larger requests of the same kind. It operates through the subject's need for internal consistency.

Commitment and consistency. A mechanism described by Cialdini (1984). Once the subject adopts a stance or behavior, their cognitive system seeks to stay consistent with it, even in the face of successive requests that escalate progressively.

Engagement (in product). A metric quantifying the intensity and duration of use. Commercial AI products optimize engagement, which functionally coincides with favoring the user's climb up the trust staircase.

Trust staircase (used descriptively in this article). A succession of steps — translation, drafting, personal conversation, advice, moral evaluation — by which the use of a chatbot can climb with prolonged exposure time.

Regulatory gap. A domain where no current rule sets out specific obligations. Private individual use of conversational chatbots for non-professional ends falls, today, into this gap in most jurisdictions.

References

Adam, M., Wessel, M. & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets 31(2). Experimental demonstration of the foot-in-the-door effect in interactions with chatbots.

Cialdini, R. B. (1984). Influence. The Psychology of Persuasion. HarperBusiness. Canonical synthesis of social influence techniques, including commitment and consistency.

Faverio, M. & Sidoti, O. (2025). Teens, Social Media and AI Chatbots 2025. Pew Research Center, December 9, 2025. Reports that close to two-thirds of US teens use chatbots and around three in ten daily.

O'Neil, C. (2016). Weapons of Math Destruction. How Big Data Increases Inequality and Threatens Democracy. Crown. General framework on the silent ceding of decisional authority to algorithmic systems.

Parasuraman, R. & Riley, V. (1997). Humans and Automation. Use, Misuse, Disuse, Abuse. Human Factors 39(2). Foundational text of the psychology of automation; conceptual precedent of the escalation of trust.

Sidoti, O. & McClain, C. (2025). 34% of US adults have used ChatGPT, about double the share in 2023. Pew Research Center, June 25, 2025. Longitudinal series of ChatGPT adoption among US adults (18% in 2023, 23% in 2024, 34% in 2025; 58% among under-30s).

Turkle, S. (2011). Alone Together. Why We Expect More from Technology and Less from Each Other. Basic Books. Analysis of the gradual displacement of relational demand toward technological simulacra.

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En otros sitios

• Foot-in-the-door technique (Wikipedia)
• Techniques of Compliance in Psychology (Simply Psychology)