Why AI Isn’t Always Faster: Surprising Study Reveals How Automation Can Slow Down Top Developers!

This recent study by non-profit METR (Model Evaluation & Threat Research) finds that experienced open-source developers actually took 19% longer to finish real coding tasks when using top AI tools—defying common expectations of dramatic speed-ups. Despite forecasting a productivity gain, developers spent more time with AI due to integration overhead, adherence to high code standards, and time spent refining AI-generated outputs. These findings reveal that AI may not automatically improve productivity for expert teams working on sophisticated software, especially in complex, quality-sensitive environments.

Why Might AI Slow Down Expert Developers?

• Quality and Context: AI generates code that often needs substantial review, refactoring, or additional documentation to pass muster in high-quality, mature codebases.
• Human-AI Collaboration Overheads: Time spent prompting, iterating, and validating AI output can offset (or even outweigh) speed gains in drafting. This is especially evident when developers are deeply familiar with the domain.
• Perception vs. Reality: Developers consistently overestimate the productivity benefits of AI, suggesting a gap between perceived and actual impact.
• Benchmarks vs. Practice: Benchmarks showing rapid AI gains often don’t capture the subtle but crucial requirements of real-world coding—such as maintainability, style, and implicit team norms.

ACL Digital: How We Help Clients Achieve Real Productivity Gains with AI

As part of the ALTEN Group, ACL Digital brings global scale, deep engineering expertise, and a proven track record of deploying AI across the entire software development life cycle (SDLC). Here’s how ACL Digital assures clients get the productivity results they expect when using AI:

1. Strategic AI Integration Across the SDLC

ACL Digital evaluates where AI and automation bring genuine value—design, coding, testing, DevOps, code review, and production support—so clients apply AI in the most impactful ways. We guide teams on what tasks AI accelerates and where human expertise remains essential for quality and context-specific decisions.

2. Customizable AI Workflows and Training

We help teams select and tune AI tools (including model fine-tuning, prompt engineering, and workflow design) to meet quality standards, maintain productivity, and minimize code debt—especially in high-bar settings like open-source or complex enterprise systems.

3. Quality Engineering and Continuous Measurement

By combining robust software QA practices and ongoing productivity analytics, ACL Digital ensures that AI deployment actually delivers time and cost savings—rather than hidden overheads or unseen risks. Our focus includes software quality, security, and maintainability in every automated workflow.

4. Collaborative Change Management

ACL Digital helps organizations calibrate expectations, educate teams, and develop an incremental adoption roadmap so productivity outcomes are tracked, measured, and refined throughout the transformation journey.

In summary, not all AI adoption leads to immediate productivity gains—especially for expert developers in complex settings. But with ACL Digital as a strategic software engineering partner, organizations can confidently harness AI throughout the SDLC, customize their approach for real-world contexts, and reliably achieve the time and quality results they expect.

Reference

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