While generative tools significantly increase development speed, engineering teams must ensure rapid delivery doesn't lead to disorganized architecture, hidden technical issues or a lack of deep codebase understanding.
AI coding assistants have altered software delivery timeframes, allowing developers to generate boilerplate, touch code components they’d long forgotten about and ship functional features quickly. Yet speed without scrutiny creates a silent, compounding liability (or “technical debt”) across libraries and repositories.
When syntactically correct code floods a codebase faster than engineers can review it, technical debt often balloons. It stops showing up as simple syntax errors and becomes systemic architectural fragmentation that's extremely difficult to track.
Navigating this reality requires rethinking code provenance, task and codebase ownership and reviewing workflows. To understand how developers and engineering leaders can keep delivery high without losing control of their repositories, we spoke with three veteran engineering leaders.
Fast Code Creates Rot at the Root
Generative AI produces syntactically correct code in seconds, but high output often short-circuits planning. When developers bypass architectural planning in favor of instant generation, code quality degrades at the system level.
"AI has increased delivery velocity, but it also accelerates debt creation when teams merge code faster than they can understand it," explains Nick Chapsas, founder of online learning platform DomeTrain and former senior engineering manager at Checkout.com. "The debt is less about isolated bad code and more about bad architectural patterns. Going faster usually means that you spend less time planning and thinking about your code."
This shifts technical debt from edge cases to root fragmentation. Because generative models produce lines of code far faster than humans can manually inspect, repositories quickly fill with inefficiencies.
"Generative models can produce hundreds of lines of syntactically correct code per second, and the amount of code being produced far exceeds the ability to have engineers manually review all of it," says Darryl Stevens, founder of the web design firm Digitech and algorithmic investment trading firm Chrysos Wealth. "Not only does this structural debt represent non-optimization and missing documentation; it represents extremely fractured, disconnected logic blocks that resemble a clean architecture, but do not contain coherent systemic intent."
Stevens notes that AI assistants routinely introduce outdated library versions and redundant functions, creating repo-bloat that overwhelms standard analysis tools.
At the same time, AI enables individual developers to cross boundaries that once required dedicated specialists. While this removes cross-team friction, it can also introduce architectural blind spots.
"The change that previously required multiple specialists can now be handled by a single person with minimal communication overhead," Stan Misiurev, Head of Technology in Engineering at Clutch, a B2B marketplace ratings and reviews platform, tells Dice. "Of course, this leads to suboptimal solutions that a specialist would avoid. AI-generated debt tends to be plausible-looking and locally correct but globally inconsistent, making it harder to spot."
Establish Review Guardrails and Automation Checks and Balances
To catch inconsistencies before they hit production, engineering teams must establish strict review standards and automation checks and balances.
Stevens argues that organizations must enforce strict provenance rules for any machine-generated contributions. "Engineering departments need to develop stringent 'justification for existence' and 'provenance' requirements prior to merge," Stevens says. "If a block of code is generated or significantly altered by an AI copilot, there needs to be a written explanation for why that particular piece of code was created."
Stevens also emphasizes updating automated toolsets to automatically halt builds when they detect unvetted packages, countering AI models' tendency to pull in unnecessary dependencies for simple tasks. "Reviews must shift from simply reviewing syntax toward validating functionality and ensuring proper integration, so development team members clearly indicate acceptance of responsibility for all code created by AI tools."
Automated tooling should handle baseline scans so humans can focus on architectural integrity.
"Dependency-graph linters, duplication and dead-code detection tools have existed for years and should be enabled in every project—AI assistants shorten the time to wire them into the pipeline," Misiurev points out. "On the review side, senior engineers should keep pull requests small enough to reason about and be able to explain the change in their own words, rather than making the reviewer reverse-engineer what the AI assistant produced."
Chapsas agrees that human oversight remains the ultimate line of defense, even when teams build custom AI instructions. "There are a lot of skills that we can use with AI agents to instruct the AI to code in a certain way," Chapsas notes. "Companies can create their own, as it is not very hard to do so. But unless a human actually code reviews the AI-generated code, anything can go wrong."
Measuring Debt and Reclaiming Code Ownership
The most dangerous consequence of unvetted AI generation is "cognitive debt" – a condition where developers ship working software that they do not genuinely understand or know how to maintain. The old “if it compiles, ship it” mentality is alive and well.
"Track how often developers ship code they cannot confidently explain, maintain or extend without AI assistance," Chapsas advises. "If you ask a basic question and you cannot get a clear response, then ownership does not exist within the team."
Because cognitive debt is qualitative, tracking it requires looking past delivery metrics. Stevens suggests monitoring downstream friction, such as spikes in Mean Time to Repair (MTTR) and code ownership churn.
"Management can determine if the organization has cognitive debt when there is an unusually high spike in MTTR during bug fix resolutions, or significant increases in code ownership churn where several developers struggle to modify one area of the codebase," Stevens explains. "If after developing an application using an AI tool, developers spend twice as much time debugging it as they spent writing it, the codebase carries substantial cognitive debt that will hinder future iterations."
Measuring output volume or pull request counts can actively incentivize bad behavior. Instead, Misiurev recommends tracking review overhead as a signal of architectural degradation.
"An engineering leader should not measure developers' productivity by the amount of changes they make in a codebase, and measuring pull request volume is discouraged as well," Misiurev explains. "Instead, measure the number of comments every pull request gets and the number of changes requested—the bigger the number, the worse the architecture. There must be a culture in which developers fully understand the changes their AI assistant produces and own the architectural decisions."
Turn AI into a Refactoring Engine
While AI can accelerate code bloat, those same capabilities can be directed to remediate architectural debt and streamline maintenance.
Historically, large-scale refactoring required heavy budget allocation and meeting-room maneuvering. Misiurev believes AI has fundamentally changed that dynamic.
"The premise of pitching massive cleanups is becoming obsolete," Misiurev says. "Massive codebase cleanup used to be a large, disruptive project that had to be pitched, budgeted and bundled with a broader overhaul. AI has simplified that, so debt that once demanded its own business case is now absorbed into ordinary, ongoing maintenance."
When leaders need to justify dedicated refactoring time, Stevens recommends framing code bloat as a financial drag.
"Senior technologists need to translate delivery friction into operational and financial risks," Stevens says. "Uncontrolled growth in code acts like an invisible tax on the business. Educate leaders on how poorly managed code bloat causes teams to lose the very speed advantages that drew them to AI in the first place, citing product stability risks, degrading delivery timelines and rising cloud infrastructure costs."
To safely execute refactoring tasks with AI, teams must provide guardrails, regression safety nets and clear boundaries.
"Use AI as a refactoring accelerator, not an autonomous architect," Chapsas emphasizes. "Let it detect duplication, propose consolidation paths, generate tests, map dependencies and modernize patterns—but don’t let it do whatever it wants. AI needs guidance, otherwise you get a result based on assumptions it makes."
Stevens highlights using context-specific models as analytical engines to identify repeated patterns across legacy modules, map dependencies and write unit tests. Misiurev adds that verification must precede generation: "Before letting an AI assistant refactor, make sure regression tests cover the affected behavior. Give the assistant access to documentation for context to minimize mistakes and keep changes small and incremental so every step remains reviewable."
AI coding assistants can be force multipliers, but they don’t replace architectural rigor. When teams prioritize sheer line generation over comprehension, they trade short-term velocity for long-term fragility.
By enforcing provenance, keeping pull requests tidy and using AI as a refactoring tool rather than an autonomous decision-maker, engineering teams can capture the benefits of velocity while protecting the health of their codebases.
