Famous tech leaders claim programmers are done in 2026. That’s not quite right. AI won’t replace great engineers, but it will make them even more valuable. It will also widen the gap between those who thrive and those who struggle.

AI-assisted coding is mind-blowing, but it’s a double-edged sword. Used carelessly, it accelerates the production of low-quality code. Used wisely, it amplifies what skilled engineers can accomplish. This post breaks down how AI is reshaping the industry and what you need to do to stay relevant—or better yet, become irreplaceable.

The Productivity Paradox

Every engineer I know is using AI daily. It’s just stupid to pretend otherwise. But the numbers tell an interesting story. According to CodeRabbit’s State of AI vs Human Code Generation Report:

• AI enables 40-50% faster initial code generation
• AI-generated code has 1.7× more issues and 1.4–1.7× more critical/major issues than human-written code
• Security vulnerabilities were up to 2.74× higher in AI-authored pull requests

This creates a productivity paradox: the time saved writing code gets eaten up by review and debugging. Humans are still very much in the loop, and they need to be.

In practice, AI suggests something every day that would significantly decrease product quality if left untouched. It cannot make good design decisions based on business needs. It’s excellent at being a CRUD monkey, but that kind of engineer was never hard to find. AI can help you solve hard problems, but it can just as easily distract you from thinking with your own brain—the only irreplaceable power you have as an engineer.

Most people miss this: AI makes you fast at dropping quality. Moving fast with quality is actually harder now, not easier.

Why Quality Still Matters

Software quality isn’t abstract—you feel it every day. When you launch certain apps, something just feels off. That’s quality, or the lack of it.

Quality has three dimensions: user experience, developer experience, and security. Unsupervised AI coding—vibe coding—threatens all three.

It’s amazing how fast AI can produce working toy versions of products. But then quality drops rapidly, and by the time you need to go live, the codebase becomes nearly impossible to work with. AI tends to ignore failures, remove tests, and create patches and hacks instead of building extensible patterns.

Most vibe-coded projects never reach the scaling phase—which is exactly where successful businesses operate. They need quality products, not just working ones. Fast and cheap is a solved problem now. You have to decide if that’s the game you want to play.

The Industry Is Changing

For a long time, software engineering was a unique profession—you didn’t need to be a master to make good money. That era is ending.

Programming is maturing into a normal industry where top talent captures most of the value and “average” struggles to compete. This is how it works everywhere else. The best doctors, lawyers, and salespeople command premium compensation. Companies hunt them down and pay whatever it takes. Everyone else fights for scraps.

This shift is hitting tech because AI gives senior engineers a massive leverage boost. If you can drive significant revenue, you’ll capture a big chunk of it. But average engineers will struggle to demonstrate unique value. Competition at that level is about to get brutal.

Becoming Irreplaceable

If you don’t have a clear plan, this external pressure will erode your career. Start by asking yourself: are you learning or executing? If you consider yourself mid-level or below, you’re learning—even on the job.

When Executing: Use AI Wisely

Use AI to avoid typing, not thinking. Nobody talks about it, but AI-assisted coding makes you dumber if you don’t actively fight it. To stay relevant, you cannot afford to get worse.

Own your output. AI is a tool, and you bear responsibility for everything you produce with it. You can’t let AI make decisions that could damage your company.

Translate business intent yourself. AI is bad at converting business requirements into design decisions. You understand how things should be structured. You know what’s coming next. It doesn’t.

The New Definition of “Great”

The image of a great software engineer has fundamentally changed. Even if you’re talented, you need to match industry expectations to be seen as valuable.

Specialists used to be gold. Dedicated QA engineers, Postgres specialists, WordPress developers—those roles still exist, but demand has shrunk. Great engineers now need both depth and width.

Depth means a solid understanding of CS fundamentals: how programming languages and databases actually work under the hood, architectural patterns, and best practices for different situations. Your knowledge must go deep because you’re reviewing three times as much code now. You need sharp judgment to distinguish good from bad.

Width means communication, presentation skills, and business awareness. This matters more than ever because AI has effectively promoted all engineers to leadership roles—but most of us aren’t ready for it.

When Learning: Ditch AI

People keep saying that “prompting skills” will differentiate good engineers from bad ones. What skill are they even talking about?

We’re software engineers. We know how to write assembly. Typing commands into a prompt bar isn’t a skill—anyone can do that. You are part of the bleeding edge of technology that has shaped the world for over 60 years. Typing prompts is not a differentiator. Your judgment is.

If you’re still building your foundation, ditch AI and write code by hand. Work on interesting, complicated projects. Build real understanding. No matter how much AI can multiply your productivity, zero times anything is still zero.

The less truly great software engineers there are, the more demand there is for each of them.