The momentum of AI development remains strong, and it has become an unavoidable topic in our daily lives. Since AI entered our work routines, discussions about whether it might replace humans have been ongoing. In software development, this question is particularly relevant: is AI a productivity booster, or does it present new challenges for software engineers?
AI in Real-World Development Scenarios
Earlier this year, my organization purchased GitHub Copilot licenses for our team. Naturally, any investment comes with an expectation of return, so the question of “how to leverage AI to improve work efficiency” quickly became a topic on the table.
Efficiency is often measured directly: a feature that used to take a full day to develop—can it now be completed in half a day? Many imagine AI as a “magic tool” that can instantly turn any developer into an experienced engineer. Online stories often emphasize speed—people with little programming experience have used AI to quickly build a small product and even launch it.
Before seeing these cases, my view of AI was mostly positive. I enjoyed using AI to help write code because it reduces repetitive work and frees me from thinking about trivial details. But when efficiency becomes the main focus, I began to hesitate: can AI really help me deliver more within the same amount of time?
The Cost of Speed
When discussing AI-assisted coding, the first thing people mention is speed: features launch faster, iterations progress quicker, and delivery cycles shorten. But in software engineering, is speed the only metric that matters?
In a business context, software engineers typically work as part of a team, which is very different from an individual building a product from scratch. Can AI truly integrate into an existing codebase? While enjoying the benefits AI brings, we also need to consider potential risks.
- Consistency: Different developers may use prompts differently, resulting in AI-generated code with inconsistent styles. Time saved in the short term may lead to long-term maintenance costs.
- Dependency: Over-reliance on AI, even for simple tasks, may actually reduce overall efficiency.
- Quality: AI can generate large volumes of code quickly, but these outputs still require human review. If reviews fall behind, potential issues may go unnoticed.
- Accuracy: When generated code doesn’t match expectations, humans still need to refactor or regenerate it.
- Maintainability: Seemingly completed features may later demand extra time for maintenance due to messy structure or poor readability.
Speed has its value, but if the focus is solely on being fast, the result may ultimately become unmanageable.
Value Beyond Speed
Many articles discussing AI-assisted development focus on rapid iteration and delivery speed. I believe it’s more important to recognize AI’s ability to help developers focus on complex problems and improve accuracy.
As noted by IBM in their AI in Software Development article:
Gen AI assists in code generation and automates repetitive coding tasks. Gen AI-powered tools help developers focus on complex problems, while AI-driven autocompletion and real-time suggestions improve speed and accuracy.
In other words, AI is not simply about “writing code faster.” Its true value lies in freeing developers from repetitive work and allowing them to focus on truly complex and creative tasks.
A Broader Perspective
Software development involves more than just the engineering team. Requirements gathering, analysis, UI/UX design, maintenance support, and product documentation can all leverage AI to improve efficiency. These areas are often overlooked, yet they represent some of the most untapped potential for AI to add value.
Defining Efficiency in Practice
So, can AI truly improve software development efficiency? This is a question that can only be answered through practice.
If we start with the assumption that “AI saves time” and work backward from there, teams may face risks. Efficiency is not just speed; it is stable delivery, consistent style, reliable quality, and maintainability over time.
Coding is not only about being “fast”; it also requires coordination and rhythm within the team. The value of AI may not lie in compressing a week’s work into three days, but in allowing developers to focus their limited energy where it matters most.
This is not the first time a method or tool has challenged team efficiency. The previous wave was agile development. Many companies, in adopting agile, focus on speed, but often simply increase pressure on the team without fully understanding agile principles. AI is similar: on the surface, it promises efficiency, but its true value lies in understanding the underlying principles and applying them thoughtfully.
AI is not a simple accelerator—it is an opportunity for us to redefine what efficiency truly means.
Reference:
IBM: AI in Software Development
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