AI Coding Tools: Why Faster Doesn’t Necessarily Mean More Productive
AI-powered coding assistants promise to help developers write code faster, and in many cases they deliver on that promise. But does that automatically translate into increased productivity for the whole software development process? Not necessarily.
To understand why, it’s useful to look at what software development actually involves. A simplified lifecycle typically includes these steps:
Define requirements – Identify what the software should do, what problems it needs to solve, and what success looks like.
Write and functionally test code – Developers write code to meet those requirements, then check that their new code works as intended at the local level.
Test the overall system – The new code is combined with the rest of the system. Teams verify that the new functions behave correctly and—crucially—that nothing else has been broken.
Release the software – The tested software is deployed into production, where it creates real value for users and customers.
Each step depends on the successful completion of the one before it. Unlike a brainstorming session, these steps can’t usually run in parallel — you can’t reliably test or release software that hasn’t been written or integrated yet.
Where AI Tools Fit In
Large language model (LLM)-based coding tools are most effective in step 2: writing and functionally testing code. They can draft code snippets, generate boilerplate, and even help with debugging. This can significantly reduce the time a developer spends at the keyboard.
But here’s the important question: does making step 2 faster automatically accelerate the whole process?
A Manufacturing Analogy
Imagine a car factory. Cars move through a series of assembly steps: building the chassis, installing the engine, adding the body, fitting the interior, painting, and so on. Each step must be completed before the next can begin.
Now suppose we double the speed of the robots that install the engine, but we don’t change anything about painting, fitting interiors, or quality checks. Has the factory increased its overall output?
The answer is no. All we’ve done is free up capacity at one stage of the line. The rest of the plant — the true bottleneck — still limits the number of finished cars that roll off the line.
The Software Parallel
Software development works the same way. Accelerating the coding phase doesn’t necessarily mean software reaches users faster. Unless testing, integration, and release are also improved, developers may simply find themselves waiting for downstream processes to catch up.
That doesn’t mean AI tools are useless. The extra time developers gain can be redirected toward valuable activities such as:
Improving system architecture
Enhancing security and compliance
Paying down technical debt
Experimenting with new approaches
But in terms of throughput — the number of features or fixes actually tested and deployed to production — the improvement may be far smaller than advertised. In fact, whilst very useful, the work above will only increase WIP (work in progress) that is not released to production and therefore, not creating any customer value.
Where the Real Value Lies
Real productivity gains will come when AI is applied across the entire delivery pipeline:
Requirements: AI tools that help capture, clarify, and even simulate requirements can reduce costly misunderstandings.
Testing: AI-assisted test generation, automated regression testing, and anomaly detection can drastically reduce bottlenecks after coding.
Release: AI-driven deployment orchestration, monitoring, and rollback strategies can make releases faster and safer.
Only by addressing these stages can organizations transform faster coding into faster delivery — and that’s when customers feel the impact.
Final Thought
AI coding tools are impressive, and they certainly help developers. But we should be cautious about equating “faster coding” with “faster value.” The real opportunity is broader: using AI to improve every stage of software delivery. Organizations that take this end-to-end view will not just produce more code — they’ll deliver more value.
