Key takeaways

• AI coding tools accelerate dependency intake faster than traditional governance processes can keep pace with.
  
  
• Remediation debt compounds over time, creating unplanned work that increasingly lands on engineering teams.
  
  
• More capable AI increases the need for software supply chain governance, rather than eliminating it.
  
  
• Moving dependency governance upstream helps teams spend less time fixing vulnerabilities and more time shipping features.

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Artificial intelligence (AI) coding tools have made software development faster. A lot faster. And on the one hand they’re delivering exactly what engineering teams want, including faster iteration cycles and less time spent on repetitive coding tasks.

On the other hand, AI is introducing more dependencies into modern applications, and the governance processes surrounding those dependencies haven’t accelerated alongside them.

The result is a quickly multiplying form of tech debt: remediation debt.

It’s important to note that AI didn’t create this problem. All it did was accelerate it, and it’s a software supply chain issue that engineering teams are increasingly being asked to absorb. Unless the way dependencies enter the environment changes, the productivity gains promised by AI may ultimately be consumed by the cleanup waiting downstream.

In this article, we explore why AI coding tools are creating a new kind of remediation debt, and why the issue isn’t AI itself, but the lack of governance keeping pace with it.

AI simply removed the friction that used to contain the problem

Open source dependency selection used to involve a moment of human judgment. This moment was exactly that, too: a moment. Engineering teams, for example, performed a quick security review on every import, searching for a package, reading documentation and checking maintenance activity. And this created a natural pause in production.

Today, the advent of AI in coding has cost us this momentary pause.

Instead, what happens is that AI coding tools surface dependency recommendations directly in the developer workflow, dramatically increasing the speed at which new packages can enter the environment.

As velocity increases, so too do dependencies, and they enter a codebase without the small moments of evaluation that used to happen naturally. 

For what it’s worth, engineering teams didn’t suddenly become less disciplined with their open source security checks and balances, and they certainly aren’t doing anything wrong, so to speak. However, the workflow has changed faster than the processes surrounding it, and organizations that were already struggling to govern human-speed dependency decisions are now trying to keep pace with machine-speed ones.

The result is more remediation work waiting patiently in the future, accumulating interest until it eventually lands in a sprint no one has planned for.

Who owns these consequences? (Hint: the engineering team)

When a dependency surfaces a critical issue, scanners generate findings and security tools raise alerts. It's the engineering teams, though, who are left to investigate the problem, evaluate the tradeoffs, test upgrades and manage the inevitable breaking changes. In fact, 60% of those working for the largest enterprises spend 50% or more of their time on maintenance and bug fixes instead of new feature development.

This department is forced to context switch away from product development momentum to deal with problems they often didn’t know existed in the first place.

‍Remediation debt behaves a lot like tech debt

Like traditional tech debt, remediation debt is deceptively easy to accumulate.

Sure, one or two unplanned fixes can be relatively easy to solve. Over time, though, these interruptions compound and can create a whole host of downstream issues. For example, an incidental dependency upgrade may introduce a breaking change to foundational infrastructure. Or a transitive library might create new consequences that don’t just delay a sprint, but throw off entire roadmap plans. 

The challenge is compounded by the fact that high and critical vulnerabilities take an average of 54.8 days to remediate, meaning engineering teams can spend weeks addressing issues that weren’t part of the original plan. We’re seeing this across the industry, and engineering teams are consistently struggling to predict roadmap capacity. 

The consequences extend well beyond engineering, too. As remediation work becomes increasingly unpredictable, delivery timelines become harder to trust. This means that product teams struggle to plan releases with confidence, or marketing campaigns lose their launch dates. Over time, your software supply chain problem morphs into an organizational trust problem, where teams across the business can no longer rely on engineering to deliver when they say they will.

‍This is the hidden cost of AI-assisted development. Ironically, it’s creating an administrative burden issue, and engineering teams are the ones being asked to foot the bill.

‍Better AI won’t solve the problem

It’s tempting to assume this problem will eventually fix itself.

After all, AI coding assistants are improving rapidly and nearly half of all developers now use them daily. As models become more capable and agentic workflows become more sophisticated, it’s safe to predict that AI will lead to better outcomes for everyone involved in application development. 

‍That’s not necessarily true.

Yes, AI coding tools excel at identifying patterns and suggesting solutions based on what they’ve seen before. They don’t, however, understand software supply chain governance, nor can they determine whether a package maintainer’s account was compromised last month or last year. Most importantly, AI coding tools are not built to  carry the responsibility when something goes wrong.

‍More capable AI requires more capable governance

As AI agents become increasingly autonomous, dependency intake will continue to accelerate. We know this because human oversight cannot scale at the same rate that AI coding is.

The more organizations rely on AI to accelerate software development, the more important it becomes to have an automated governance layer underneath it. More capable AI doesn’t eliminate the need for oversight, it increases it.

‍How ActiveState helps teams get ahead of remediation debt

Remediation debt won’t be solved with more work. It needs a foundational, structural solution.

ActiveState helps teams move governance earlier in the software lifecycle by providing a curated catalog of open source components: built from verified source code, with a contractual remediation SLA. 

The result is no surprises, no remediation work, and your team’s time back to shipping features.

Explore the ActiveState Curated Catalog

Remediation debt is the hidden tax on AI productivity

AI coding tools have delivered on their promise of faster software development. But speed without governance comes with a cost, and the faster dependencies enter an environment, the more surprise work engineering teams inherit downstream. 

When all is said and done, AI isn’t making engineering teams careless, but it is making dependency decisions happen faster than most organizations were designed to govern. And unless that governance moves upstream, remediation debt will continue to compound until the productivity gains AI promised are consumed by the time it takes to continuously clean up dependencies.

To learn more about how ActiveState makes AI-assisted development safer to scale, explore our secure container catalog.