Why is AI usage rising even as trust in AI is falling?

Several forces push usage upward regardless of trust: competitive and social pressure to adopt AI tools, employer mandates, the convenience of AI-powered products that people use without consciously choosing them, and the fact that many everyday AI interactions (autocomplete, recommendations) are low-stakes enough that distrust does not translate into avoidance. Trust, in this context, measures something different from habit or necessity. People can use a tool routinely while still doubting whether they can rely on it for important decisions.

What is shadow AI, and why does it matter for trust?

Shadow AI refers to the use of AI tools at work without organizational knowledge, approval, or governance oversight. Research from the University of Melbourne and KPMG found that half of U.S. workers used AI tools at work without knowing if it was permitted. This matters for trust because unmonitored AI use means errors go uncorrected and unlearned from, accountability is unclear when something goes wrong, and the organization cannot build the institutional knowledge needed to deploy AI responsibly at scale.

Are AI hallucinations ever going to be fully solved?

Current mathematical research suggests complete elimination of hallucinations is not achievable given how large language models fundamentally work. Because these systems generate text by predicting statistically probable sequences from learned data, they will sometimes produce outputs that are not grounded in fact. The practical goal is reducing hallucination rates in high-stakes domains through techniques like retrieval-augmented generation, multi-model verification, and human oversight, and being transparent with users about where uncertainty exists.

Does more experience with AI lead to higher trust?

The relationship is complicated. At the aggregate level, usage has increased while average trust has declined. But at the individual level, people who use AI and find it genuinely helpful in low-stakes contexts tend to report higher trust. The Edelman Flash Poll found that helping users understand complex ideas through AI produced large trust gains. The implication is that how someone's first experiences with AI are structured matters enormously. Positive, well-supported first encounters can build trust; high-stakes failures without context can destroy it.

How does the EU AI Act address the trust problem?

The EU AI Act takes a risk-based approach: the higher the potential harm from an AI application, the more stringent the requirements. It bans certain AI practices outright (social scoring, certain biometric surveillance), requires transparency and human oversight for high-risk systems, and imposes governance obligations on providers of general-purpose AI models. The theory is that mandatory accountability structures, enforced by financial penalties that exceed even GDPR levels, will create external pressure for AI systems to be built and deployed in ways that are actually trustworthy rather than merely claimed to be.

What is the most important thing to watch to predict whether AI trust recovers?

Watch the rate of consequential public failures versus the rate of demonstrably beneficial outcomes in high-visibility domains. Trust in AI is primarily shaped by stories, and the stories people encounter about AI in healthcare, law, finance, and education are disproportionately influential. If the dominant narrative shifts from hallucination-caused harm to AI-assisted diagnosis or AI-enabled research breakthroughs, the trust trajectory will likely improve. If high-profile failures continue to outpace compelling success stories, skepticism will deepen even as usage continues to grow.

AI Policy & Safety

The AI Trust Paradox: Usage Surged While Skepticism Grew

By The Agent5 founder·July 1, 2026·How we fact-check

More people are using AI than ever before, yet global surveys consistently show trust in the technology is declining. Understanding why these two trends move in opposite directions is one of the most important questions in AI policy today.

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Key takeaways

Global AI usage is at record highs, but multiple large surveys show trust in AI systems declining simultaneously, producing a persistent paradox that researchers call the AI trust gap.
Hallucinations and consequential errors, particularly in legal, medical, and financial contexts, are a primary driver of eroding trust, and current research indicates they cannot be fully eliminated from large language model architectures.
Shadow AI (ungoverned, unsanctioned use) is a direct consequence of the trust paradox and creates accountability gaps that are arguably riskier than open, monitored deployment.
Geographic, generational, and income divides mean the trust deficit is not evenly distributed and will not self-correct uniformly across populations as AI usage grows.
The EU AI Act and similar frameworks represent a regulatory forcing function that may gradually close the gap between usage and genuine accountability, with the most consequential enforcement dates arriving in 2026 and beyond.

More people are using AI tools than at any point in history. Yet the more they use them, the less they seem to trust them. That contradiction sits at the heart of the current AI moment, and it matters enormously for anyone trying to reason clearly about where AI goes from here.

Why the Two Lines Are Moving Apart

The natural instinct is to assume that more experience with a technology leads to more confidence in it. For most technologies, that is exactly what happens. You learn the quirks, develop mental models, build routines, and familiarity breeds comfort. AI is behaving differently, and there are several reasons why.

The first is exposure to failures. When someone uses a navigation app for years and it occasionally gives a suboptimal route, the consequence is minor and forgiving. When someone uses an AI system for legal research, a medical question, or a financial decision and receives a confidently stated wrong answer, the stakes are different. High-profile hallucination incidents have moved from being niche technical complaints to mainstream news. A Canadian tribunal forced Air Canada to honor a discount after its AI customer-service chatbot confidently cited a nonexistent bereavement fare policy. A database of hallucinated legal citations maintained by researchers at HEC Paris and Sciences Po had logged over 1,300 instances of fabricated or incorrect citations appearing in legal proceedings by early 2025. A 2024 study at the University of Mississippi found that nearly half of AI-generated citations submitted by students had incorrect titles, dates, or authors. These are not abstract risks. They are accumulating in public awareness.

The second reason is a mismatch between what AI promises and what it reliably delivers. The global KPMG-Melbourne study found that 66 percent of users rely on AI output without properly evaluating its accuracy, and 56 percent report making mistakes in their work due to AI. These users are not being careless. Many of them are simply encountering the gap between a system that sounds authoritative and a system that actually is. When you encounter that gap enough times, trust erodes.

The third factor is context sensitivity. The Statista and KPMG data both show that people make sharp distinctions based on what AI is being asked to do. Using AI to summarize a document or draft a first email is one thing. Using it to make healthcare decisions, issue legal advice, or take autonomous action on someone's behalf is quite another. As AI expands its scope from informational to consequential, it runs into much sharper skepticism.

What Probably Happens Next

This is where reasoning in probabilities becomes genuinely useful. The current pattern, widespread use combined with declining or stagnant trust, is not a stable long-term equilibrium. Something has to give. Here is how to think about the most likely directions.

The highest-probability scenario over the next three to five years is a gradual bifurcation rather than a single clean resolution. In sectors with strong governance structures, clear accountability, and well-calibrated AI deployment (certain financial services, some healthcare applications, enterprise productivity tools with human-in-the-loop oversight), trust is likely to stabilize and gradually recover as track records accumulate and failures are corrected in visible ways. In sectors where AI is deployed quickly without adequate oversight, where hallucinations carry serious consequences, and where users have no mechanism to distinguish reliable outputs from unreliable ones, trust is likely to continue eroding.

Regulation will act as a forcing function in the EU and, increasingly, in other jurisdictions that adopt similar frameworks. As the EU AI Act's high-risk provisions activate in August 2026 and enforcement begins at the national level, companies that have genuinely built governance infrastructure will have an advantage, not just a compliance checkbox but a credibility signal to users who have learned to be skeptical.

The Edelman finding that familiarity with AI correlates with higher trust, combined with the finding that most AI resistance is anticipatory rather than experience-based, suggests a plausible trust recovery mechanism: structured, well-supported first experiences with AI that demonstrate genuine value before they expose users to high-stakes failure modes. Employers who run serious AI training programs, provide honest framing about limitations, and deploy AI in low-risk contexts first are essentially manufacturing the conditions under which the trust loop can close.

For people who want to think clearly about AI rather than just react to it, the trust paradox is a useful frame. It reveals that adoption rates are not the same as acceptance rates, that widespread use does not guarantee widespread confidence, and that the gap between the two is exactly where both the risks and the opportunities concentrate. Getting smart about AI means understanding not just what the technology can do, but which conditions allow it to do those things in ways that people can actually rely on.

The probability that trust and usage realign quickly and smoothly is low. The probability that governance, transparency, and accumulated experience slowly close the gap is considerably higher, but only if the institutions building and deploying AI treat trust as an engineering problem rather than a communication one.

The AI Trust Paradox: Usage Surged While Skepticism Grew

The Numbers Are Stark and Consistent

Why the Two Lines Are Moving Apart

The Geography of Distrust

The Shadow AI Problem

What Hallucinations Are Actually Teaching People

Regulation Is Racing to Catch Up

Familiarity Is the Underappreciated Variable

What Probably Happens Next

Sources