The tech industry is betting on AI coding assistants to boost developer productivity or even replace coders entirely. Non-technical leaders assume engineering teams waste hours on code development, debugging, testing and re-writing. So, if AI can write that code in a fraction of the time, it seems like a no-brainer.
The reality is that developers don’t lose time writing code, they lose it gathering information. I’ve worked at Uber Eats as an engineer for years, and it was more detective work than anything else. Which version of the API is in production? Why did this pipeline fail last week? Where is the documentation for this legacy service?
I often found myself sifting through outdated documentation or pursuing colleagues for answers. The actual time spent coding, when I could manage it, was the most productive part of my day (and the part I enjoyed the most).
The Fundamental Problem With AI Code Assistants
AI coding assistants might induce gains in productivity, but they don’t solve the core problem. The average engineer only codes for about an hour a day. The rest of the time is consumed by information discovery — finding the relevant documentation, understanding system dependencies and overcoming technical roadblocks. Every time an engineer is interrupted to track down a missing piece of information, it breaks focus and delays progress.
Consider a developer trying to implement a feature — they might spend 15 minutes writing code and the rest of the hours researching how their changes could impact other systems, reading outdated documentation, searching for the owner of previous features and writing new documentation. This fragmentation means that their overall productivity is still low even when developers are technically coding. The mental energy spent piecing together context often leads to more time spent on tasks that could have been completed faster with the right information upfront.
The problem compounds with modern architecture. Each microservice adds exponential complexity to the information maze. At Uber, deploying a simple feature meant understanding dependencies across dozens of services, each with its own documentation scattered across multiple tools and teams.
Where AI for Devs Can Make a Difference
AI can help increase developer productivity; however, focusing on speeding up coding alone isn’t the answer. The real opportunity lies in changing the way we approach information discovery. Rather than fixating on how quickly code can be written, we should focus on tools that make it easier to find, understand and share information. Improving documentation, building better knowledge-sharing platforms and creating tools that map out system dependencies can significantly reduce the time spent searching for answers.
Imagine AI as a seasoned technical advisor who knows your entire system inside out. Rather than generating code, it could help map which services need your attention, surface relevant documentation during code reviews and proactively flag potential issues before deployment. When you ask, “How will this change impact our payment system?”, it would instantly provide architecture diagrams, recent incidents and deployment patterns, saving hours of manual investigation. The AI assistant can bridge fragmented knowledge systems, transforming the current scattered information landscape to provide an accessible, unified view of your technical ecosystem.
AI can transform engineering productivity, albeit not through automating code writing alone. These tools will be most effective when developers already have a clear understanding of what they are building and how it fits into the overall system. Without this context, AI-generated code may only add to the confusion, increasing technical debt and complexity.
To unlock real productivity gains, the industry needs to shift its focus from simply accelerating coding time to improving the way engineers access and manage the information they rely on to do their jobs. That’s when the true potential of AI can be realized.