NewsBite analysis · AI, DevOps, Programming
The New Stack
AI coding got faster. Why didn’t engineering?
Recent developments in AI have made coding significantly faster, showcasing the technology's potential to improve programming tasks. However, this increase in coding efficiency hasn't translated into enhanced engineering productivity. The article highlights the gap in measuring productivity within engineering disciplines and the complexities of applying AI tools effectively in real-world projects. The persistent challenges indicate that while AI can accelerate certain aspects of the development process, structural and procedural bottlenecks in engineering teams may continue to hinder overall productivity gains.
MonitorConfidence: MediumMid term horizon
Published 9 Aug 2026, 14:45 UTC1 min read
Visit original article ↗Lead image — The New Stack
Summary
TL;DR
- AI coding speed has significantly improved.
- Engineering productivity has not seen similar gains.
- Measurement of productivity in engineering is complex.
- AI tools face implementation challenges.
- The tech industry's productivity gap continues to be a concern.
Interesting facts
- AI tools could automate repetitive coding tasks, but engineering involves more than just writing code.
- Different engineering teams may adopt AI at varying rates, affecting overall productivity metrics.
- Cultural and organizational factors play a significant role in productivity beyond technological improvements.