LLM

LLM Agents Boost Speed—And Technical Debt: Evidence from Cursor

Kari Jaaskelainen

07 Nov 2025 — 1 min read

Speed now, debt later? What a study of Cursor finds

Do AI coding agents really make teams more productive? This large-scale study estimates the causal impact of adopting Cursor across GitHub projects using a modern difference-in-differences design.

Fast start: Projects see a significant, large—but short-lived—jump in development velocity.
Quality costs: Static analysis warnings and code complexity rise significantly and persist.
Long-run drag: Panel GMM links those quality hits to later slowdowns.

The takeaway: AI agents can feel like a turbo boost, but without guardrails they accumulate technical debt that slows teams over time.

Practical tips: budget time for refactoring, keep code review strict, automate linting/CI gates, and track complexity and warning trends—not just PR counts. Toolmakers should bake in quality-aware defaults and prompts.

Paper: http://arxiv.org/abs/2511.04427v1

Paper: http://arxiv.org/abs/2511.04427v1

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Speed now, debt later? What a study of Cursor finds

Read more

Automating GDPR Compliance: A Roadmap for Companies and Law Firms

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Teaching chatbots to stop contradicting themselves (DECODE)