How Did Developers Miss the Hidden Downside of AI Code Helpers?

AI coding assistants have revolutionized the software industry by offering faster development and smarter workflows. But new research suggests that these tools may not be the productivity boosters many developers believe them to be.

Researchers found that experienced programmers using AI code helpers, despite their widespread adoption, completed tasks 19% slower in familiar codebases, contradicting their perceptions of increased efficiency.

Why do developers believe AI tools make them faster?

Developers consistently report feeling more productive with AI assistants. Surveys show up to 88% of users believe these tools help them work faster and smarter, especially with code generation and boilerplate tasks.

However, self-reported productivity doesn’t always match objective outcomes. Studies measuring actual task completion times often reveal smaller or even negative productivity changes, particularly for experienced developers working on complex projects.

Did you know?
In a recent survey, only 6% of engineering leaders reported significant productivity gains from AI coding assistants, despite nearly 90% of users feeling more productive.

What explains the gap between perception and reality?

The disconnect may stem from cognitive factors. When developers invest time crafting prompts and integrating AI suggestions, they may overvalue the tool’s impact, a phenomenon known as the effort justification paradigm.

AI assistants also excel at making code more understandable, which creates a sense of progress that doesn’t always translate to faster delivery. When developers use the tool for sensemaking rather than direct code output, it can lead to inflated perceptions of productivity.

ALSO READ | Can AI Hallucinations Really Drive Product Innovation in the Music Tech Industry?

Developers overestimate productivity gains from AI assistants

Large-scale surveys show that only a small fraction of engineering leaders, just 6%, report significant productivity boosts from AI coding tools. Most see minor gains of 1–10%, while many experienced developers see little to no improvement.

Despite this, developers often believe they are working 20% faster, highlighting a striking perception-reality gap. This overestimation is more pronounced among those who invest greater cognitive effort in using AI tools.

Cognitive load and workflow disruption play a hidden role

For seasoned programmers, integrating AI suggestions can disrupt established workflows. Rather than saving time, the process of reviewing, editing, and contextualizing AI-generated code can increase cognitive load and slow progress.

AI coding assistants are most effective for less experienced developers or when tackling unfamiliar code, but in mature projects, their benefits may be limited or even counterproductive. As AI tools evolve, understanding these hidden downsides will be crucial for teams aiming to truly boost productivity.