Why engineers lose trust in AI-coding tools

2026-06-02 · Source: LeadDev · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, short

Summary

Engineers' trust in AI-coding tools is highly susceptible to early negative experiences and social contagion, often overriding objective performance data. Alyson van Hardenberg, engineering director at Honeycomb.io, and Michael Tweed, principal software engineer at Skyscanner, highlight that initial impressions, whether from demos or hearsay, are difficult to reverse. A bad early experience, perceived as the tool leading an engineer astray, spreads rapidly through word-of-mouth, causing engineers to stop recommending it. This phenomenon is exacerbated by a "herd mentality," where peer discussions and external social media narratives on platforms like Hacker News or Bluesky heavily influence opinions, often aligning with a respected engineer's anchored view, as demonstrated by Solomon Asch's 1951 conformity experiments. Leaders introducing new AI tools must manage the gap between perception and reality by being transparent about trade-offs, avoiding overhyping, and simplifying initial adoption, as exemplified by Netflix's approach of offering one tool and one way to start.

Key takeaway

For AI/ML Directors evaluating new AI-coding tools, prioritize managing initial perceptions over raw performance metrics. You should avoid overhyping capabilities and instead be transparent about trade-offs, focusing on specific, small use cases to build confidence. Simplify the adoption process by offering limited choices, like "one tool, one way to start," to prevent early friction from eroding trust and hindering long-term integration.

Key insights

Early negative experiences and social influence significantly erode engineers' trust in AI-coding tools, outweighing performance data.

Principles

• First impressions are nearly irreversible.
• Perception spreads faster than performance data.
• Overselling tools sets up engineers for failure.

Method

Leaders should manage perception by being transparent about trade-offs, focusing on small use cases, and simplifying initial tool adoption.

In practice

• Offer one tool, one way to start.
• Share personal learning curves publicly.
• Provide weekly AI roundups.

Topics

• AI-coding Tools
• Trust Management
• Engineering Adoption
• Perception Bias
• Social Contagion
• AI Rollouts

Best for: CTO, VP of Engineering/Data, Executive, AI Engineer, Software Engineer, Director of AI/ML

Related on AIssential

• Trustworthy AI Software Engineers — Trustworthy AI software engineers must integrate into human-AI teams, extending beyond coding to socio-technical tasks and ethical alignment.
• A uni professor admitted using AI to write an opinion piece. Here’s what it revealed about trust in the technology — Undisclosed AI use erodes public trust and undermines industry credibility.
• People are adopting AI because it is available, convenient, and increasingly embedded in default workflows, not because they feel confident in it. Adoption is rising, but consent is thin. — AI adoption is rising due to convenience, but trust is low because institutions lack clear rules and accountability.
• Why AI Systems Struggle With Truth, Trust, and Reliability — AI systems prioritize believable language over verified truth, creating significant trust and reliability challenges.
• What the AI trust gap means for enterprise SaaS — Developer adoption of AI tools is rising while trust in their accuracy is falling, creating an "AI trust gap."
• 😺 Someone firebombed Sam Altman's house — A widening perception gap between AI experts and the public fuels distrust, alongside emerging AI-driven risks and disruptions.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by LeadDev.