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Mind the gap: Closing the AI trust gap for developers

Developer trust is synonymous with a willingness to deploy AI-generated code to production systems with minimal human review, as well as assurance that AI tools aren’t introducing unacceptable risks and technical debt that will burden you down the line.

February 18, 2026

Mind the gap: Closing the AI trust gap for developers

Credit: Alexandra Francis*

Stack Overflow’s 2025 developer survey revealed a puzzle: Developers’ use of AI rose, with more than 84% of respondents using or planning to use AI tools in 2025. But their trust in those tools dropped sharply: Only 29% of 2025 respondents said they trust AI, down 11 percentage points from 2024.

At the risk of stating the obvious, the trustworthiness of a tool is important. Trust determines whether organizations can realize the productivity, scalability, and innovation potential of AI. It impacts whether AI-generated code makes it to production or needs to be rewritten by humans. It affects whether organizations scale AI adoption or keep it confined to low-stakes experiments. And it influences whether the next generation of developers learns to work effectively with AI or not.

To be clear, when we talk about trust, we mean confidence that AI outputs are accurate, reliable, and rooted in relevant context. Developer trust is synonymous with a willingness to deploy AI-generated code to production systems with minimal human review, as well as assurance that AI tools aren’t introducing unacceptable risks and technical debt that will burden you down the line.

The gap between usage and trust spotlighted by our survey reveals something important about this moment in software development. Developers are neither reflexively change-resistant nor overly eager to integrate AI into their workflows without first ensuring that it adds value. They're professionals trying to navigate a paradigm shift that calls into question core aspects of how they've been trained to think about their work.

For everyone involved in building software, it’s important to understand why this gap exists, what it reveals about the culture and practice of software development, and how we might close it. Let's get into it.

Survey data reveals a trust gap

Stack Overflow has been tracking developer sentiment around AI since 2023, asking consistent questions to illuminate how developers’ attitudes are evolving over time.

In 2023, roughly 70% of developers reported using or planning to use AI tools. Trust levels hovered around 40%: not great, but understandable for a new category of tech. In 2025, we saw usage rise to 84% even as trust dropped to 29%, a counterintuitive dip in trustworthiness that correlates with higher adoption rates.

A typical technology adoption curve shows the opposite relationship. Familiarity breeds confidence. You learn the scope and quirks of a piece of tech; develop best practices; understand through experience what it can and can’t do. But the more devs use AI, it seems, the less they trust it. What’s going on here?

Part of the answer lies in who software developers are at the population level. Software engineers are trained for deterministic thinking: write the same code twice, get the same result twice. Their professional identity hinges on craftsmanship, on elegant solutions written to solve hard problems. Approaching AI coding tools from that perspective, developers are primed to note every inconsistency, every failure, every instance when the tool falls short of the high standards they’ve set for themselves.

Why devs lack trust in AI tools

The determinism problem

Developers are trained to think in terms of reproducible outcomes. They write a function, they test it, and it behaves predictably. Same input, same output. There's something deeply satisfying about this: "I'm going to do this; as a result, I'm going to get that." It's dependable and easily comprehensible, like following a recipe that reliably produces a delicious meal. It's what makes the field software engineering rather than software hoping-for-the-best.

Fundamentally, AI operates on different principles. It's probabilistic rather than deterministic, meaning that if you ask the same question twice, you’ll probably get two different answers. Both are potentially correct, but they might be structured differently, using different approaches, making different tradeoffs. There's no single "right" output; just a distribution of possibilities weighted by probability.

Many devs find this jarring because it violates their foundational expectations about how coding tools should work. Working effectively with AI requires accepting variability as an inherent feature of the system. This can be a major mental shift for developers trained to prioritize precision and reproducibility.

Instead of assessing whether AI is “better” or “worse” than conventional coding practices, we should acknowledge that it’s different in significant ways that create cognitive friction. Understanding and adjusting to those differences takes time, and during that adjustment period, trust can falter.

The hallucination reality

AI hallucinates, and not always in ways that are apparent or easy to catch. Developers report encountering plausible-looking code that simply doesn't work, confidently wrong explanations of what code does, references to APIs that don't exist or methods that were deprecated years ago, and subtle security vulnerabilities that slip past casual review because the surrounding code looks polished.

This creates a discernment burden. When every piece of AI-generated code requires verification, you can't just accept it and move on. Instead, you have to read it carefully, understand what it's doing, test it thoroughly, and check for edge cases. If that verification takes as long as it would have taken you to write the code yourself, what exactly have you gained?

If you're building financial systems, healthcare applications, or any software that handles user data, one undetected hallucination can have serious consequences. Pushing unvetted AI code into important systems is a bad idea, even if it seems to save time and effort in the short term. Developers know this, which is why fear of hallucinations keeps them from placing more trust in AI.

The newness factor

Another factor in the trust deficit is simple: People just aren’t used to AI coding tools yet. As we said above, AI tools are simply different, and they require a different skill set from conventional coding tools. You need to learn effective prompting: how to communicate intent clearly to a system that doesn't think like you do. You need to develop evaluation frameworks for assessing outputs. You need to figure out validation workflows that catch problems without creating bottlenecks.

There's a competence-confidence gap here too. Many developers recognize that they don't fully understand how to use AI tools. They're uncertain about their own skills: "Am I prompting this correctly? Would a better prompt have given me better code? Is this the tool's limitation or my limitation?" That uncertainty comes across as a lack of trust in the tool, but it's also an uncertainty about one's own ability to use the tool well. It's a learning curve masquerading as a trust issue.

The job security question

Finally, there's the elephant in the room. Developers contemplating AI coding tools often wonder if they’re using (and, thereby, improving) tools that will ultimately replace them.

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