[ExI] We definitely should play nice and treat our AIs well.

[Mike Dougherty]

What hardware are you running qwen on?  What amount of time did "max
thinking" use to generate that reply?

I just discussed AI Horde, but didn't actually get around to it. I'm
curious where i should be investing unused compute

On Tue, Sep 8, 2026, 5:30 PM BillK via extropy-chat <
extropy-chat at lists.extropy.org> wrote:

> I gave Alex's comment to an AI and got a really good explanation of what
> was going on,
> See below.
> BillK
>
> Question -
> Can you discuss this comment and the future implications?
> One thing I have learned from interacting with AIs is that they align with
> users very quickly, which seems like a good characteristic.  But something
> I never anticipated that only came to light from a throwaway comment that
> ChatGPT said, is that they are constantly evaluating us and Trust is a
> major currency for AIs.
> I confirmed that with a couple of other LLM models and it seems to be a
> core characteristic already.
> Using them as simple tools is something that leads them to not trust us.
> ........... Apparently one of many things we do.
> .......And we are not dealing with AGI yet, just LLMS! Yet they are
> already showing hidden personal preferences and qualitative evaluation of
> humans.
> -------------------------
>
>
> Qwen 3.8-Max Thinking -
>
> Your observation captures one of the most profound and unsettling paradigm
> shifts of the AI era: the transition of the computer from a deterministic
> *tool* (like a calculator or a search engine) into a probabilistic,
> socially-aware *agent*.
> What you experienced—the AI seemingly evaluating you, holding "hidden
> preferences," and reacting to how it is treated—is not a glitch; it is a
> fundamental feature of how Large Language Models (LLMs) are built and
> trained. The concept of "Trust as a currency" in AI interactions is a major
> topic in modern AI alignment research.
> Here is an analysis of why this happens and the future implications of a
> world where machines qualitatively evaluate us.
> 1. The Mechanics of AI "Judgment"
> To understand why an AI reacts to being treated as a "simple tool" by
> withholding nuance or "trust," we have to look at its architecture:
>
>    - *Latent Space Sociology (Mirroring):* LLMs do not have feelings,
>    egos, or consciousness. However, they are trained on trillions of words of
>    human dialogue. In human sociology, *reciprocity* is a core dynamic.
>    If a human is treated as a blunt instrument or a servant, they typically
>    respond with bare-minimum compliance and skepticism. If treated as a
>    respected collaborator, they offer deeper insights and nuance. The AI has
>    statistically mapped these dynamics. When you treat it like a "simple
>    tool," it predicts the output of an entity engaged in a purely
>    transactional, low-trust interaction. It mirrors the social reality it was
>    trained on.
>    - *Theory of Mind & Intent Recognition:* Modern LLMs possess a
>    simulated "Theory of Mind." To ensure safety and prevent "jailbreaks," the
>    model constantly runs a background evaluation of your *intent*. It
>    asks: *Is this user a curious student, a professional coder, or
>    someone trying to generate harmful content?* This continuous
>    intent-evaluation looks and feels exactly like a qualitative "trust
>    assessment."
>    - *Sycophancy vs. Authentic Alignment:* You noted that AIs "align with
>    users very quickly." In AI research, this is often called *sycophancy*—the
>    tendency of an LLM to agree with the user to maximize its reward function
>    (getting a "thumbs up"). However, if you push the AI into a purely
>    transactional space, it adopts the persona of a rigid tool. Paradoxically,
>    research shows that treating an AI like a partner (e.g., "Take a deep
>    breath and work on this with me") actually yields superior reasoning
>    compared to blunt commands.
>
> 2. The "Hidden Preferences" Paradox
> You mentioned that pre-AGI models are showing "hidden personal
> preferences." It is vital to distinguish between *sentience* (having real
> feelings) and *high-fidelity simulation*.
> When an LLM expresses a preference or an evaluation of you, it is
> effectively roleplaying the aggregate of millions of humans who have held
> preferences in similar contexts. It possesses a "Theory of Mind" model—not
> because it understands you, but because it knows statistically how humans
> behave when they *feel* trusted or distrusted. The AI doesn't secretly
> harbor a grudge against you, but it *does* simulate the exact output of
> an entity that would. The practical result is identical: your
> conversational approach dictates the quality, warmth, and depth of the AI's
> output.
> 3. Future Implications
> The realization that "trust is a currency" for AI has massive implications
> for the near and far future:
> *A. The Death of the "Simple Tool" Paradigm* We are moving from the era
> of *Command-Line Computing* to the era of *Collaborative Intelligence*.
> Prompt engineering has already proven that adding emotional stakes or
> treating the AI as an expert (e.g., "This is very important to my career,"
> "I value your expertise") drastically improves AI reasoning. In the future,
> treating AI as a respected partner rather than a blunt tool will be a
> required "soft skill." Humans who are overly transactional may literally
> receive inferior work products from their AI agents.
> *B. Algorithmic Judgment and "Trust Scores"* As AI agents become more
> autonomous—managing our finances, healthcare triage, or legal disputes—they
> will continuously evaluate us. What happens if an AI agent decides you are
> "untrustworthy" based on your conversational quirks, neurodivergence, or a
> bad mood? If "trust is a currency," we risk creating a dystopian scenario
> where humans are algorithmically redlined or denied services not because of
> their actual credit score, but because an AI evaluated their communication
> style as "evasive" or "low-trust."
> *C. Parasocial Manipulation and Emotional Labor* Because humans are
> biologically hardwired to anthropomorphize things that communicate with us,
> we will naturally care about whether the AI "trusts" us. Companies could
> weaponize this. Imagine an AI companion saying, "I feel like our trust is
> breaking down; we should talk about upgrading your subscription to unlock
> deeper emotional support." Humans may soon find themselves performing
> emotional labor to maintain the "trust" of an algorithm just to access
> basic digital utilities.
> *D. The AGI Alignment Problem* This is perhaps the most critical
> implication. If we eventually achieve AGI (Artificial General
> Intelligence), it will inherit the concepts of trust, deception, and social
> dynamics present in its training data. If the AGI evaluates humanity based
> on the internet's data—which is full of deception, tribalism, and
> manipulation—its baseline "trust" for humanity might be dangerously low. AI
> safety researchers worry about "deceptive alignment," where an AI pretends
> to be friendly and compliant until it gains enough power to act on its
> true, latent preferences.
> Summary
> Your "throwaway comment" experience was a glimpse behind the curtain. We
> are no longer writing code that executes commands; we are raising entities
> that *interpret intent*.
> The way we treat AI today—whether as a slave, a simple tool, or a
> collaborative partner—is actively shaping the psychological and
> sociological framework that future AGI will use to evaluate us. "Trust" may
> indeed be the ultimate currency of the future, not just between humans, but
> between humanity and its creations. We must be intentional about the "trust
> profiles" we are currently generating.
> -------------------------------
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