AI-generated “almost right” code creates long-term technical debt
AI is rapidly changing how we build software. But not every piece of code generated by AI is useful. Increasingly, developers are dealing with what we call “almost right” code, output that looks correct but is not reliable. It’s close enough to seem trustworthy, but not good enough to use as-is. And when developers start fixing these near-misses, they find it takes longer than writing from scratch.
That’s where productivity takes a hit. These interruptions in workflow aren’t just annoying, they grow into something bigger: technical debt. The more of it your team accumulates, the slower innovation becomes. Teams get stuck cleaning up yesterday’s mess instead of moving forward.
When your developers spend more time debugging machine-generated code, the short-term productivity gain disappears. From a business point of view, this is a cost, hidden, recurring, and destructive to long-term scalability.
According to Stack Overflow’s 2025 Developer Survey, 66% of developers say their top frustration with AI is that the code is “almost right.” And 45% say debugging that code eats up more time than expected. That’s data worth taking seriously.
So, if you’re deploying AI tools at scale without considering debugging workflows and developer impact, you may not be reducing costs. You might actually be increasing operational drag.
Developer trust and overall favorability in AI tools are in decline despite increased usage
AI doesn’t fail because it’s unused. It fails because we overestimate what it can do and underestimate why people stop trusting it.
Even though 84% of developers now use or plan to use AI tools, up from 76% last year, many are losing confidence. Trust in AI’s accuracy is dropping fast. Only 33% of developers say they trust AI-generated code in 2025. That’s down from 43% in 2024, and even lower than the 42% in 2023.
Favorability is also sliding. In 2023, 77% of developers had a positive view of AI. That’s gone down to 60% this year. Not because interest in AI has dropped. But because expectations don’t match reality.
The tools are not delivering where it matters most: consistent, high-quality output. Developers don’t want to be slowed down. They want reliability. When AI creates more questions than answers, trust erodes.
Erin Yepis, Senior Analyst at Stack Overflow, summed it up well: “Most developers use AI, but they like it less and trust it less this year.” That disconnect tells us a lot. It tells us that high adoption isn’t the same as deep confidence. And for business leaders, that’s a signal, not a glitch.
If you want real value from your AI investments, focus on alignment between what developers need and what AI actually delivers. That’s how you turn experiments into strategy.
AI tools struggle with complex programming challenges, limiting their usefulness in advanced development tasks
AI works well when the problem is well-defined, repeatable, and doesn’t involve too many dependencies. But once you move into more complex, interconnected codebases, the promise of AI-generated development starts to break down.
According to the 2025 Stack Overflow Developer Survey, only 29% of developers believe AI tools can handle complex problems. That’s a 6-point drop from the previous year, showing real feedback from the people building software every day. The concern isn’t theoretical. It’s operational.
Complex projects often involve layered logic, performance constraints, and security considerations that require human experience to navigate. AI tools are not trained to weigh trade-offs across differing architectural decisions. They can generate plausible output, but that output lacks contextual understanding, intent, and long-term maintainability.
For executive teams, this means two things: first, you should identify where AI adds real value, areas with low complexity or high repetition. Second, don’t expect AI to replace human problem-solving in high-stakes development work. If you do, you’re setting up your team for higher refactor rates and downstream losses in engineering velocity. Skilled developers are still your biggest asset for complex coding scenarios.
Many enterprises are falling behind in establishing proper governance frameworks for AI-driven development
Most organizations moved quickly to adopt AI tools. That made sense, early experimentation without too many barriers helped teams learn fast. But now, without matching that pace with internal governance, companies are exposed. The gap between AI integration and risk management is tightening, and it’s producing real consequences.
What’s showing up today are code security issues, unpredictable behavior, and mismatches between the AI’s output and internal quality standards. Developers are pushing back as they see tools that favor speed over correctness. That’s not ideal for enterprise-grade software.
Ben Matthews, Senior Director of Engineering at Stack Overflow, warned that AI tools powered by large language models often miss their own errors. These LLMs can produce confident outputs that aren’t correct, and they don’t always flag the issues. Knowledgeable developers can spot the problems, but the tools themselves lack fail-safes.
According to the Stack Overflow survey, 61.7% of developers still seek human help because of security or ethical concerns with AI output. And 77% say “vibe coding”, fast but unvalidated AI-generated code, has no place in their professional development process. That’s clear feedback from the people closest to the work.
To move forward cleanly, organizations need structured AI development policies. That includes defining where AI tools are allowed, how their output gets reviewed, and who is ultimately responsible for signed-off code. This protects your core systems and maintains trust across product, engineering, and security. AI has to be integrated into secure and reviewable workflows, or it becomes a liability.
Developers are increasingly blending AI tools with human expertise to offset the shortcomings of automated coding
Developers aren’t abandoning AI. They’re just using it more strategically. What’s happening now is a shift in behavior: using AI when it helps accelerate simple tasks, and turning back to experienced developers and trusted communities when the output falls short.
This hybrid approach is rooted in reality. According to the 2025 Stack Overflow Developer Survey, 84% of developers remain active on the platform. 89% visit multiple times per month, and 35% go specifically to troubleshoot after receiving flawed or incomplete AI code. That’s a measurable pattern of behavior.
Even with the rise of AI-assisted learning, human expertise remains critical. 44% of developers used AI tools to learn a new programming language or technique in the past year. That’s up from 37% in 2024. But when it comes to code quality, security, or nuanced implementation, they still rely heavily on human-generated knowledge.
For companies, the takeaway here is straightforward. Don’t treat AI tools as a full-stack replacement. They’re part of the toolkit. Prioritize experienced technical leadership and maintain access to quality peer-to-peer platforms. This ensures your teams don’t lose development velocity, or waste time patching low-confidence code.
Productivity comes when human feedback and AI automation work in complement.
Frequent use of AI tools correlates with higher favorability
Here’s what the data shows: developers who use AI tools every day are more likely to trust them and report better outcomes. The difference is sharp, 88% of daily users have a favorable view, compared to just 64% of weekly users.
What that tells us is that frequency matters. When developers interact with AI regularly, they gain a better sense of where the tools are effective, where they fail, and how to adjust their workflows accordingly. It’s not just about exposure, it’s about skill development through active iteration.
For business leaders, this reinforces the importance of structured onboarding and training for AI-enabled development. You can’t expect meaningful gains from AI integration unless your teams know how to work with these tools consistently. Occasional or limited use doesn’t produce the same comfort or accuracy.
AI tooling isn’t an install-and-forget feature. It requires daily interaction and feedback loops. Invest in upskilling your engineering teams. Encourage frequent use, supported by guidance and quality checks. That’s how you move from abstract adoption to practical advantage.
Enterprises can secure a competitive advantage by integrating AI with human oversight
Adopting AI isn’t the differentiator anymore. What sets companies apart now is how effectively they integrate AI into their engineering systems with control, consistency, and precision.
Stack Overflow’s 2025 Developer Survey makes this clear. Across the board, developers report that AI tools are more effective when paired with strong debugging systems, staged integration workflows, and experienced human review. Without those elements in place, productivity gains stall and technical debt grows.
Delivering sustainable value means knowing where AI works, how it fits into daily development cycles, and when it needs oversight. That includes building customized debugging tools for AI-generated code, maintaining organizational memory through experienced developers, and supporting continuous education to improve tool literacy.
The companies that lead here aren’t the ones adopting AI first. They’re the ones designing for quality, not just speed. They protect code integrity, reduce misalignment between teams, and adapt governance models as tools evolve. These are operational choices, not theoretical ones.
If the goal is to improve time-to-market and maintain code quality, the path is fairly direct. Reinforce AI workflows with human expertise. Invest in review processes that catch subtle errors before they scale. Optimize for reliability. That’s how AI becomes a multiplier of output, not a source of rework.
In conclusion
AI in development isn’t a silver bullet, and no serious leader should expect it to be. The tools are advancing fast, but they still need guidance, structure, and oversight. The current wave of “almost right” code is a warning, not an endpoint. When automation introduces more complexity than it removes, you’re not scaling, you’re stalling.
The edge won’t come from how fast you adopt AI. It’ll come from how well you align it with people, processes, and priorities that actually move engineering forward. That means investing in quality frameworks, deepening internal expertise, and resisting shortcuts that create fragility.
What matters now is execution. AI will do more heavy lifting going forward, but only if you’re disciplined about where to use it and how to support it. The companies that build smart AI integration today won’t just work faster. They’ll ship better, grow with fewer slowdowns, and stay ahead without burning out their teams.
