Predictions about the end of software developers (or the “death” of software developers) have become a familiar cycle in the news. New predictions about the role of developers emerge daily. Every new breakthrough in artificial intelligence brings a loud and exciting wave of confident claims that developers will soon be obsolete and replaced by systems that can generate code faster and cheaper than any human ever could.
Yet when you look at how software is actually built in the real world today, those predictions feel disconnected from reality and I feel like they have been overblown every time so far. Teams are still wrestling with unclear requirements, evolving business constraints, and systems that must survive long after the initial implementation is written. AI has absolutely changed the way in which we write software, but it has not removed the need for developers. Instead, it has clarified what the job has always truly been about.
Some of the best developers I know right now only use AI for the most mundane tasks. Some of the best developers I know use AI to describe exactly how they want to solve a problem to save up code typing time. Some of the best developers I know barely, use, AI, at, all.
If software development were primarily about typing syntax, then AI would already have won. Modern language models can generate working code in seconds, often in multiple languages and styles, and they do so with an ease that would have seemed impossible just a few years ago. The Composer model in Cursor, for example, can generate code faster than my eyes can keep up. It may be a little bit slop at times, but if you keep on top of it, it will give you what you need most of the time.
But, writing code has never been the core value of software development. The real challenge has always been understanding what should be built in the first place, how it should behave under real-world conditions, and how it will evolve over time. Developers spend far more energy reasoning about trade-offs, constraints, and failure modes than they do choosing the correct syntax for a loop or an API call. I’ve always told the developers I’ve had the privilege of managing that their jobs are not about learning new languages or writing code, but about understanding problems and solving them. They’re problem solvers more than anything.
AI can generate an answer, but it does not understand the context in which that answer must live. It does not know your organization’s priorities, your team’s strengths and weaknesses, or the operational realities of the systems you maintain. Those factors are not edge cases; they are the substance of development work. They can be incorporated into prompts, but adding all that to the context will eat up valuable tokens.
The value of AI
When used thoughtfully, AI is an extraordinary tool for software developers. It removes friction from many parts of the development process that previously consumed time without adding much insight. Think about how much time you’ve spent generating boilerplate, scaffolding projects, summarizing unfamiliar codebases, and exploring multiple implementation options and then think about how much faster you can do that now.
This acceleration is meaningful because it allows developers to spend more time thinking about architecture and design and less time struggling with reading existing code or performing repetitive tasks. When used responsibly, it also enables faster experimentation, which can lead to better solutions.
When judgment is sound, AI-assisted development can dramatically improve development results. When judgment is weak, AI can just as easily produce code that is difficult to maintain and reason about.
Judgment and experience
As AI reduces the effort required to write code, it increases the need to decide whether that code should exist at all, whether it’s safe, whether it meets business needs, and whether it will stand the test of time. Developers are now spending less time asking how to implement a properly defined feature and more time evaluating whether a particular abstraction, architecture, or approach is appropriate for their system and whether they’ll stand the test of time. The best software developers I’ve worked with can reason about the level of abstraction required to make code require as little refactoring as possible in the future.
This kind of discernment cannot be automated away because it requires understanding long-term consequences, recognizing hidden assumptions, and anticipating how a system will behave under stress. These are skills developed through experience and deep engagement with real systems, often learned from senior developers and mentors and not through prompt optimization.
AI can propose patterns and solutions, but it can’t determine whether those patterns align with the realities of your domain. That responsibility remains firmly with the real-life human developer.
Experience matters
We think about AI as a tool that levels the playing field for all software developers and gives people who have never written code the chance to take matters into their own hands. In reality, it tends to magnify differences between developers and expose people who have never had to write code before. Experienced developers know when to trust AI output and when to challenge it. They’re not asking it to solve a problem. Instead, they’re describing the problem and defining how they want to approach it. They’re collaborating with AI. They recognize subtle issues that a solution may be fragile or misleading, even if it appears to function correctly on the surface. Metaphorically, your house may look good on the outside, but it’s foundation can be brittle.
Less experienced developers can also move faster with AI, but speed without context can lead to systems that accumulate technical debt quickly over a short period of time. This doesn’t mean that AI is harmful, it just means that guidance, review, and mentorship matter more than ever.
Senior developers, architects, and technical leaders play an increasingly important role as stewards of quality. Their value lies not in producing code faster than a model, but in ensuring that what gets built is coherent, maintainable, and aligned with long-term goals.
Software developer In 2026?
The role of the software developer is evolving very quickly, at a pace I’ve never seen before. Writing code is becoming a smaller part of the job, while reviewing, reasoning, and designing are becoming more crucial. Developers are acting more as translators and interpreters between human intent and machine execution.
AI will continue to reshape how software is built, and its influence will only grow stronger. But the developers who thrive in this environment will not be those who chase every new tool or attempt to offload responsibility to models. They will be the ones who understand systems deeply, ask better questions, and apply judgment where automation falls short. If experience has taught me nothing else, it’s that the foundations and fundamentals of software development never change.
The future of software development is not less human. It is more human, supported by tools that amplify our ability to think, reason, and build responsibly.