Trust In AI Is Being Won Or Lost By The Amount Of Distrust For AI Makers

The Minds Problem

Much of the handwringing about AI is whether AI is going to become sentient and embody consciousness, often referred to as AI as a thinking machine. That is an important topic and one that I’ve continued to closely explore and analyze; see the link here. There is a quite different angle that also deserves rapt attention, and I’d like to place it on the table.

How will human minds change because of interacting with AI?

This unexpected question catches many people by surprise. The customary focus is on AI as formulating a mind, not on how human minds might change due to interacting with AI. But, as the old saying goes, it takes two to tango. When humans increasingly interact with AI, there is a two-way street involved. Humans adjust their minds about how they view the world and how to communicate with others, and shift in ways that we still do not have a full or clear picture of.

In that sense, it is wholly worthwhile to study the psychology of humans as they mentally adjust to a world that entails human-to-AI interaction and human-to-human interaction. Notably, this doesn’t have to wait until or if AI becomes sentient. There is plenty to study right now. Humans are already adjusting their minds to the ubiquitous nature of non-sentient AI. The starting gun has gone off. Human minds are changing.

For my comprehensive tracing of how AI and psychology dovetail with each other, see the link here and the link here.

Human Trust In AI

Shifting gears, let’s discuss the human-AI trust bond. There are an estimated 1.5 billion people weekly using generative AI and large language models (LLMs) for all sorts of daily tasks, ranging from the mundane to highly sensitive needs (see my analysis of global AI usage at the link here).

Do people trust what AI is doing? That’s an important question to ask since a notable portion of the population seems to be significantly relying on AI.

There is an interesting psychological relationship between having trust in AI and a secondary factor consisting of the amount of trust that goes towards the AI maker who developed the AI. In other words, a person’s trust in AI is seemingly moderated by whether they trust the AI maker. If an AI maker is not considered trustworthy, you would naturally tend to be less trusting of the AI that they have devised. Likewise, a high trust in an AI maker would tend to lean you toward being trusting of the AI they’ve made.

What People Say About AI Trust

In a recent Pew Research Center public survey that contacted over 5,000 adults in the U.S., these key findings were noted (excerpts):

• “When it comes to trusting the businesses that develop AI, about six-in-ten adults are not confident in U.S. companies to develop and use these tools responsibly.”
• “Four-in-ten U.S. adults say AI will have a negative impact on society over the next 20 years. Far fewer believe its impact will be positive.”
• “Adults’ views about AI’s potential impact on their own lives also tilt negative, though less dramatically. While 31% expect AI to have a negative effect on them personally over the next two decades, about a quarter believe it will have a positive impact.”
• “Americans largely think AI is moving too fast. About two-thirds say this.”

The polling results were posted on June 17, 2026, while the survey itself was administered in February 2026. Opinions about AI are a constantly changing facet, and prudent caution should be used when interpreting the results of any point-in-time poll.

Psychological Trust Chain About AI

According to the Pew survey, approximately 60% of U.S. adults are not confident in AI makers when it comes to ensuring responsible AI. This brings up an important consideration that I refer to as a psychological trust chain. It’s a topic well worth exploring.

It goes like this. Trust in an AI maker is presumably based on the perceived intentions and competence of an AI maker. This, in turn, would seem to impact the perception of the trustworthiness of the AI. Based on a rationalistic perspective, this should be avidly demonstrated by actual usage of the AI. A person would mindfully moderate their use and reliance on AI according to this chain of logic.

Look at the matter this way:

• Psychological trust chain on AI: Trust in an AI maker relates to perceptions about the AI maker, which translates into trust in a particular AI, and which ultimately arises in a willingness to use and trust in what the AI says and does. Turning this around, trust in AI is based on the perceived trust in the AI maker and the degree to which they are seen as responsibly building and fielding their AI.

Factors At Play

Let’s categorize trust into levels of high and low. That is admittedly a somewhat binary or on/off way to categorize trust. Trust is dynamic and ranges across a wide spectrum. Anyway, fundamental precepts can be surfaced by simplifying trust to being either high or low.

We will consider that there is institutional trust, namely trust associated with the likes of AI makers. And that there is a separate construct construed as capability trust associated with AI per se. Combining this with the high and low categorizations of trust, there are four distinct possibilities:

• (a) Institutional AI maker trust is high.
• (b) Institutional AI maker trust is low.
• (c) Capability of AI trust is high.
• (d) Capability of AI trust is low.

We can then mix and match their theoretical relationships in these four ways:

• (1) High-high trust. When institutional AI maker trust is high, capability of AI trust is high.
• (2) Low-low trust. When institutional AI maker trust is low, capability of AI trust is low.
• (3) High-low trust. When institutional AI maker trust is high, capability of AI trust is low.
• (4) Low-high trust. When institutional maker trust is low, capability of AI trust is high.

Understanding The Trust Relationships

At first glance, the relationships of both high-high and low-low would seem indubitably sensible. If you have high trust in the AI maker, you will naturally extend that heightened trust to the AI and end up in a high-trust human-AI bond. On the other side of the coin, if you have low trust in the AI maker, you would logically be less trusting of their AI and form a low-trust human-AI bond.

That leaves us with the high-low and low-high combinations. Are they oddities? Those do not seem to be logical formulations. It entails high trust in the AI maker but low trust in their AI, and the circumstance of low trust in the AI maker and yet high trust in the AI. Why would someone who has a high trust in the AI maker not have a high trust in the AI? Likewise, why would someone who has low trust in the AI maker not cling to low trust in the AI?

Psychological Inference

The answer can be found in an analogous setting.

You might have significant distrust of a bank and yet place overall trust in a bank employee that you judge separately from your trust concerns regarding the bank. In everyday human-to-human trust assessments, people often distinguish between their institutional trust and the trust associated with a specific person employed by that institution. This happens all the time. You see a medical doctor and trust them, even though you might have massive distrust of the healthcare system that the doctor is employed by.

The downside applies too. A bank that has a reputation for being highly trustworthy can be let down by a bank employee who falters at their job. You won’t stand still if a bank employee messes with your banking funds. No matter how much the bank might be at the pinnacle of institutional trust, interactions with a specific banking employee can utterly overtake any aura of intrinsic institutional trust.

What People Say And Do

A means to summarize these two out-of-sorts combinations of high-low and low-high can be succinctly stated this way:

• High-Low trust human-AI bond is expressed this way: “I trust the company that made this AI, but I won’t let that predetermine how I interact with the AI and instead will at the get-go withhold my trust until the AI proves to me it is trustworthy (I will default to low trust).”
• Low-High trust human-AI bond is expressed this way: “I don’t trust the company that made this AI, but I won’t let that affect how I interact with the AI and instead will straightforwardly give the AI my trust until it disowns that trust (I will default to high trust).”

You can plainly see that a person could conceivably keep their institutional trust apart from their capability trust. They will decide based principally on what the AI says and does. In their minds, the institutional or AI maker trust is a point worth noting, though it does not necessarily drive their beliefs about the AI itself. The AI itself drives their beliefs about the AI.

Fluctuating Cycle Of Trust

People tend to adjust their mental sense of trust over time. Rarely do you set your mind on a trust level and opt to never veer from it. The world is continually in motion, and so are the dynamics of the trust that you assign to those around you.

Consider a scenario in a human-AI context. Suppose that Jane opts to use AI that is made by AI maker XYZ. Jane has read and heard that AI maker XYZ is reputable and responsibly develops its AI. This instills in Jane a perception that the AI is going to be worthy of high trust. That’s firmly in Jane’s mind (a high-high trust relationship).

After using the AI, the initial high-high trust is reinforced by the AI providing useful and accurate answers to Jane’s questions. So far, so good. Indeed, the AI doing so well is increasing Jane’s perceived trust in the AI maker XYZ. Institutional trust is racking up points based on what its AI capability is doing. Score a win for the AI maker.

Jane seeks personal advice from the AI and gets a quite disconcerting response. The AI tells Jane to take actions that would be dangerously detrimental. The trust that Jane has in the AI is now broken. A precipitous drop in trust in the AI occurs. Despite the high trust in the AI maker, the AI trust goes into the trust dumpster.

Trust Transferences

The chances are that Jane is also going to soberly question the trust that had been placed in the AI maker XYZ. This is like peas in a pod. That being said, Jane might not see things that way and somehow keep the AI in an entirely separate mental bucket of trust leveling, but it is more likely that a spillover is going to occur. The reduction in trust in the AI is almost certainly going to damage the trust being associated with the AI maker.

We can use this handy rule:

• Capability AI trust begets institutional AI trust: Perception of institutional trust in the AI maker can be consciously varied by fluctuations in the capability AI trust that a person mentally assigns to the AI.

It is conceivable that the same can be stated in the reverse direction. We already saw that Jane increased institutional trust after having initially used the AI. The AI usage impacted institutional trust, which in this case was positive for the AI maker XYZ.

What if the AI maker gets caught in news headlines that their AI allegedly went rogue and gave out bad advice? Even if Jane was still happily using the AI and hadn’t experienced the detrimental advice, the news about the AI maker could affect Jane’s perception of the AI itself.

Here’s another handy rule:

• Institutional AI trust begets capability AI trust: The perceived capability AI trust that a person has can be consciously varied by fluctuations in the reputational trust of an institutional AI maker.

Thinking About Trust Is Very Worthy

Why care about trust and AI?

Well, it’s a big deal. People are potentially lulled into believing what AI tells them if they have a heightened level of trust in the AI. I’ve repeatedly noted that people using AI for mental health advice can get themselves into a corner by misplacing trust in the therapeutic guidance provided by AI; see my discussions at the link here.

AI makers also need to care deeply about trust. They crave institutional trust so that the company will do well financially in the marketplace, and they want capability trust so that people will flock to using their AI. Any AI maker that does not know where trust comes from, nor how to build and maintain trust, is wildly running through a forest while blindfolded. You are going to take a lot of severe bumps along the way and might not make it out intact.

A final thought for now. The famed entrepreneur R. M. Williams made this vital remark: “Trust is the easiest thing in the world to lose, and the hardest thing in the world to get back.” The same holds for AI and AI makers. People who lose trust in an AI maker are not readily going to climb back up. People who lose trust in an AI are not easily convinced to take another look. Trust, once earned, needs to be preciously safeguarded, and not tossed asunder.