AI and Technical Debt

Introduction

AI coding assistants can produce working software astonishingly quickly. That speed is one reason many technologists see programming as an early sign of a broader AI-enabled future in which intelligence becomes abundant and complex work becomes dramatically cheaper. But mature software systems expose a harder reality: generating code is not the same thing as maintaining a reliable, understandable, long-lived system.

In large codebases, AI tools can increase technical debt even while teams appear more productive in the short term. Engineers may ship more features, but leave behind duplicated logic, inconsistent abstractions, fragile integrations, and code that future maintainers struggle to understand. Several recent studies suggest that this hidden maintenance burden is already measurable. Research from METR found experienced open-source developers working in familiar repositories were actually slowed down by AI tools, largely because reviewing, correcting, and integrating generated code consumed substantial time. [metr.org]metr.org2025 07 10 early 2025 ai experienced os dev studyMeasuring the Impact of Early-2025 AI on Experienced…10 Jul 2025 — We conduct a randomized controlled trial (RCT) to understand how ea… Meanwhile, large-scale analyses from GitClear and academic researchers have linked AI-assisted coding to higher code churn, more duplication, and persistent maintainability issues. [gitclear.com]gitclear.comAI Copilot Code Quality: 2025 Data Suggests 4x Growth in…The focus was to understand how AI Assistants influence the type of code bein… [gitclear.com]gitclear.comOver the same period, copy/pasted (duplicated) code rose from 8% of changes to 18%…

That tension matters well beyond software engineering. If advanced AI is supposed to help civilisation flourish over decades and centuries, then the systems it helps build must remain understandable, repairable, and governable over long periods of time. Mature codebases are one of the first real-world tests of whether AI-generated abundance scales cleanly or whether hidden complexity accumulates faster than humans can manage it.

Why more code can mean more future work

Technical debt is often misunderstood as merely “bad code”. In practice, it refers to the future cost created by present shortcuts: design decisions that make systems harder to modify, debug, secure, or extend later.

AI systems can increase this debt because they dramatically lower the cost of producing code without equally lowering the cost of understanding it.

In mature systems, the bottleneck is rarely typing speed. Experienced engineers usually spend more time reasoning about architecture, tracing dependencies, evaluating trade-offs, reviewing changes, and maintaining conceptual coherence than writing raw code. Research cited by software engineering researchers regularly estimates that understanding systems consumes the majority of engineering time. [arXiv]arxiv.orgarXivMeasuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity…

AI tools help most with the cheapest part of the process: generating plausible-looking implementations.

That changes team incentives. When code becomes easy to produce, organisations may tolerate larger systems, more experimental features, and more local patches instead of careful simplification. The result can resemble a city that expands faster than its infrastructure can support.

Several recurring patterns appear in AI-heavy repositories:

GitClear’s analysis of hundreds of millions of changed lines of code found rising duplication and declining refactoring activity in the AI era. [gitclear.com]gitclear.comFree Code Quality Report and DORA MetricsGitClear's latest research, AI-Generated Code Exerts Downward Pressures on Code Quality has been… [gitclear.com]gitclear.comcoding on copilot data shows ais downward pressure on code qualityCoding on Copilot: 2023 Data Suggests Downward…Code churn – the percentage of lines that are reverted or updated less than two weeks… Refactoring matters because it is one of the main ways engineers pay down debt: reorganising systems so they remain coherent as they grow. If code generation accelerates while refactoring declines, long-term maintainability can deteriorate even when short-term delivery metrics improve.

This creates a deceptive productivity effect. Teams may appear faster because they produce more code and close more tickets. But future developers inherit a larger and less coherent system that takes longer to reason about.

Mature systems punish shallow understanding

AI coding tools often perform best in environments with limited context: isolated functions, standard frameworks, or greenfield prototypes.

Mature systems are different.

Large production repositories contain years or decades of accumulated decisions, undocumented assumptions, backward compatibility requirements, historical bugs, regulatory constraints, operational workarounds, and organisational conventions. Much of this knowledge exists only tacitly inside experienced maintainers’ heads.

The METR study on experienced open-source developers highlighted this problem clearly. Developers using AI tools spent less time writing code directly, but more time reviewing outputs, correcting misunderstandings, and steering the models back toward the repository’s actual architecture. metr.org [Reuters The issue was not that the generated code was obviously nonsensical. Often it was superficially reasonable. That can be more dangerous.]reuters.comDespite initially believing AI would speed up their work by 24%, developers took 19% longer to complete tasks with AI assistance. The res…

In mature systems, incorrect code frequently looks plausible because the failure lies in subtle interactions:

Human experts often avoid these traps through contextual memory accumulated over years. AI systems generally lack this embedded organisational understanding.

This is one reason experienced developers sometimes report that AI-generated code “feels expensive”. The output must be interrogated carefully because the reviewer cannot safely assume the system understands the repository’s deeper logic.

That review burden becomes especially severe in infrastructure-heavy environments such as finance, aviation, healthcare, telecoms, or industrial systems where reliability matters more than raw feature velocity.

What AI-authored commits reveal about maintainability

Early empirical research is beginning to measure these effects directly rather than relying on anecdotal impressions.

A 2026 large-scale study examining more than 300,000 verified AI-authored commits across thousands of GitHub repositories found that AI-generated changes frequently introduced code smells, bugs, and security issues. [arXiv]arxiv.orgarXivMeasuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity… Code smells are not necessarily immediate failures, but structural weaknesses associated with future maintenance difficulty.

The study found that roughly a quarter of identified AI-introduced issues still remained unresolved in the latest repository revisions. [arXiv]arxiv.orgarXivMeasuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity… That persistence matters because technical debt compounds over time. Small maintainability problems become embedded assumptions around which future systems are built.

Researchers studying “GenAI-induced self-admitted technical debt” have also found developers explicitly leaving comments acknowledging uncertainty about AI-generated code. [arXiv]arxiv.orgarXivMeasuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity… Some comments effectively amount to warnings from maintainers to future maintainers: this works for now, but nobody fully trusts it.

That resembles older forms of technical debt, but with an important difference. Traditional debt usually emerged because humans knowingly cut corners under time pressure. AI-assisted debt can emerge even when engineers believe they are following best practice because the generated solution appears complete and polished.

This creates a dangerous illusion of correctness.

Generated code is often highly fluent. It can look cleaner than hurried human code while still embedding architectural problems that become visible only later. A junior engineer may feel reassured by the confidence and completeness of the output. A rushed reviewer may approve changes faster because the code “looks professional”.

But readability is not the same as maintainability.

Some engineering leaders now worry that AI systems optimise for local plausibility rather than long-term coherence. The model’s goal is usually to generate a convincing next solution, not preserve conceptual integrity across years of future modifications.

The hidden labour shifts onto maintainers

One of the most important organisational effects of AI coding may be the redistribution rather than elimination of labour.

Generated code still requires human review, integration, testing, debugging, and maintenance. In practice, the burden often shifts toward senior engineers and maintainers.

This can create a widening asymmetry inside engineering organisations:

Recent industry surveys already suggest that many developers spend substantially more time reviewing AI-generated work. [IT Pro]itpro.comThe widespread integration of AI is causing developers to spend significantly more time on manual code reviews and bug fixes, with 81% re… That review work is often difficult to measure because traditional productivity metrics emphasise outputs like commits, tickets, or delivery speed rather than future maintainability.

Maintainers of long-running open-source projects have described related problems for years even before modern AI assistants: contributors submitting large volumes of superficially useful patches that increase long-term stewardship costs.

AI can intensify this dynamic because contribution volume rises faster than experienced maintainers can evaluate architectural consequences.

The hidden cost appears later:

At scale, this can produce a paradoxical outcome where software creation accelerates while software evolution becomes slower and more brittle.

Why prototypes and production systems diverge

One reason public discussion around AI coding often becomes confused is that different people are describing different environments.

AI tools can genuinely transform early-stage prototyping.

For startups, internal tooling, experimental products, or temporary automations, the ability to generate acceptable code quickly is enormously valuable. In these settings, shipping speed often matters more than architectural perfection. Some software may only need to work for months rather than decades.

Mature systems operate under different constraints.

Banks cannot casually rewrite transaction systems. Hospitals cannot tolerate opaque failures in clinical infrastructure. Governments depend on legacy software whose continuity matters more than novelty. Industrial control systems may remain operational for decades.

In these environments, maintainability becomes part of safety and institutional stability.

The distinction matters for broader AI abundance arguments. Demonstrating that AI can rapidly generate software prototypes does not automatically prove it can sustain civilisation-scale technical systems over long time horizons.

A flourishing future depends not merely on accelerating output but on preserving reliability, institutional memory, and human comprehensibility as complexity grows.

The broader lesson for AI abundance

The technical debt problem reveals something deeper about intelligence and human flourishing.

Many optimistic visions of advanced AI assume that once intelligence becomes cheap, progress accelerates smoothly across science, engineering, medicine, and governance. Software development partially supports this view: more people can indeed create useful tools than before.

But mature codebases suggest that intelligence alone may not eliminate coordination and maintenance constraints.

Civilisation depends heavily on systems that persist over time:

These systems accumulate complexity faster than any individual can fully understand them. AI may help extend human capability, but it may also increase the total volume of complexity civilisation must manage.

That creates a central tension inside the broader AI bloom debate.

The optimistic case is that AI eventually helps humanity manage complexity better than humans alone ever could: automatically refactoring systems, documenting infrastructure, preserving institutional memory, and coordinating vast technical ecosystems.

The pessimistic version is that AI accelerates system growth faster than governance, understanding, and maintenance capacity improve. In that world, societies become increasingly dependent on opaque, fragile infrastructures that few humans can fully oversee.

Software engineering may therefore function as an early warning signal for larger civilisational questions:

can advanced AI help humanity build systems that remain understandable and governable over generations, or will complexity compound faster than institutions can absorb it?

How teams try to prevent AI-driven debt

Engineering organisations are already adapting their workflows around this risk.

Many teams increasingly restrict where AI-generated code is acceptable, distinguishing between low-risk acceleration and high-risk architectural work.

Common defensive practices include:

Some organisations are also experimenting with using AI defensively rather than expansively: employing models to improve documentation, identify duplication, explain legacy systems, or propose validated refactorings instead of merely generating more features. [arXiv]arxiv.orgarXivMeasuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity…

That distinction may become increasingly important.

If AI mainly increases code volume, technical debt may grow faster than productivity. But if AI increasingly helps humans simplify, document, and maintain complex systems, the long-term balance could shift in a more sustainable direction.

The broader outcome may depend less on whether AI can generate code and more on whether institutions reward maintainability, restraint, and long-term stewardship instead of measuring success purely through short-term output metrics.

Endnotes

1. Source: metr.org
   Title: 2025 07 10 early 2025 ai experienced os dev study
   Link: https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/
   Source snippet

   Measuring the Impact of Early-2025 AI on Experienced...10 Jul 2025 — We conduct a randomized controlled trial (RCT) to understand how ea...

2. Source: arxiv.org
   Link: https://arxiv.org/abs/2507.09089
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   arXivMeasuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity...

3. Source: gitclear.com
   Link: https://www.gitclear.com/ai_assistant_code_quality_2025_research
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   AI Copilot Code Quality: 2025 Data Suggests 4x Growth in...The focus was to understand how AI Assistants influence the type of code bein...

4. Source: gitclear.com
   Link: https://www.gitclear.com/recent_ai_developer_productivity_code_quality_research
   Source snippet

   Over the same period, copy/pasted (duplicated) code rose from 8% of changes to 18%...

5. Source: arxiv.org
   Link: https://arxiv.org/abs/2603.28592

6. Source: arxiv.org
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   arXivACE: Automated Technical Debt Remediation with Validated Large Language Model RefactoringsJuly 4, 2025...

   Published: July 4, 2025

7. Source: reuters.com
   Link: https://www.reuters.com/business/ai-slows-down-some-experienced-software-developers-study-finds-2025-07-10/
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   Despite initially believing AI would speed up their work by 24%, developers took 19% longer to complete tasks with AI assistance. The res...

8. Source: arxiv.org
   Link: https://arxiv.org/abs/2601.07786
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   arXiv"TODO: Fix the Mess Gemini Created": Towards Understanding GenAI-Induced Self-Admitted Technical DebtJanuary 12, 2026...

   Published: January 12, 2026

9. Source: gitclear.com
   Link: https://www.gitclear.com/free_code_quality_2024_dora_report
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   Free Code Quality Report and DORA MetricsGitClear's latest research, AI-Generated Code Exerts Downward Pressures on Code Quality has been...

10. Source: gitclear.com
    Title: coding on copilot data shows ais downward pressure on code quality
    Link: https://www.gitclear.com/coding_on_copilot_data_shows_ais_downward_pressure_on_code_quality
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    Coding on Copilot: 2023 Data Suggests Downward...Code churn -- the percentage of lines that are reverted or updated less than two weeks...

11. Source: gitclear.com
    Link: https://www.gitclear.com/blog/gitclear_q1_2024_updates_copy_paste_line_counts_better_industry_stats_per_developer_stats
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    Copy/Paste Line Counts, Better Industry Stats, Per...13 Feb 2024 — GitClear customers can download our new research at no cost: it analy...

12. Source: gitclear.com
    Title: How much more productive are AI-powered developers?
    Link: https://www.gitclear.com/research/ai_tool_impact_on_developer_productive_output_from_2022_to_2025
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    1 Oct 2025 — GitClear's previous code quality research has shown that 2024 was the first-ever year where "Copy/pasted" code was more prev...

13. Source: gitclear.com
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    GitClear First-Party Research & StudiesAnalysis of 153 million lines of code authored between 2020 and 2023 suggest that the proportion o...

14. Source: gitclear.com
    Link: https://www.gitclear.com/help/code_quality_moved_and_copy_pasted_percents
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    Moved and Copy/Pasted (Duplicated) line percentageAs described in GitClear's research into the impact of AI-generated code on code qualit...

15. Source: gitclear.com
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    GitClear: Measure AI ROI with Research-Backed Developer...GitClear is a developer-friendly Software Engineering Intelligence platform th...

16. Source: metr.org
    Title: 2026 02 24 uplift update
    Link: https://metr.org/blog/2026-02-24-uplift-update/
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    We are Changing our Developer Productivity Experiment...Feb 24, 2026 — METR previously published a paper which found the use of AI tools...

17. Source: github.com
    Link: https://github.com/marketplace/gitclear
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    GitClear · GitHub MarketplaceGitClear helps every member of an engineering team understand their team's code and velocity with the clarit...

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    Debt Behind the AI Boom: A Large-Scale Empirical Study...30 Mar 2026 — Previous studies have shown that AI-generated code contains code...

19. Source: arxiv.org
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21. Source: letsdatascience.com
    Title: developers thought ai made them faster the data said otherwise
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    AI Coding Tools Made Developers 19% Slower: METR StudyFeb 20, 2026 — A gold-standard scientific study found AI coding tools made experien...

22. Source: itpro.com
    Link: https://www.itpro.com/software/development/ai-might-help-speed-up-software-development-but-81-percent-of-devs-now-spend-more-time-reviewing-code-and-its-creating-an-invisible-work-trend-thats-pushing-teams-to-the-limit
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    The widespread integration of AI is causing developers to spend significantly more time on manual code reviews and bug fixes, with 81% re...

23. Source: Wikipedia
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    DeveloperComputers · Software developer, a person or organization who develop programs/applications · Video game developer, a person o...

24. Source: dictionary.cambridge.org
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    | definition in the Cambridge English Dictionarya person or company that makes money from buying land, building new houses, offices, etc...

Additional References

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   AI Code Deletes Production Stacks, Creates $61B Tech...AI generated code passes the test suite, it'll ship, and then six months later so...

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   AI slows down developers on large codebases, study findsA study from METR found AI makes developers 19% slower. Developers thought they w...

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   Humans do it better: GitClear analyzes 153M lines of code:...Recent trends in how code changes point to an “add it and forget it”-type p...

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   AI-Generated Code Is Creating a Technical Debt StormRecent large-scale codebase analysis shows: Duplicated code blocks have increased up...

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