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On Full-Stack Development in the Age of AI

A student recently asked me a question I’m sure many others are wondering about:

How important is full stack development given that AI coding tools can now generate full stack applications fairly reliably? If it’s still worth pursuing, which stack would you recommend learning?

It’s a fair question. I teach a course called Full-Stack JavaScript, and I’d be concerned too if I were a student watching AI agents generate complete applications. Let me give you an honest answer, even if it’s not entirely reassuring.

The short version: Yes, I still think learning full-stack development is valuable. But the landscape is changing in ways that are uncomfortable to acknowledge.

Why This Course Exists

First, let’s be clear about something. I didn’t create Full-Stack JavaScript out of love for JavaScript or passion for full-stack development. I created it to help students be job ready. That was my sole intention—to give you the skills needed to work as software developers, particularly building applications that businesses actually need.

The question is whether that preparation still makes sense when AI can do much of what we teach in that course.

The Complexity Spectrum

To understand where AI fits, think about software projects along multiple axes of difficulty:

The simpler a project is on each axis, the better AI coding agents perform. As complexity increases along any of these dimensions, AI struggles more. This is exactly how human developers work too—simple problems are easier than complex ones.

Where AI Stands Today

Here’s the uncomfortable truth: AI coding agents are excellent at the simpler-to-moderate levels of this spectrum. They can do very well what a junior software developer can do. Everything I teach in Full-Stack JavaScript—setting up servers, creating REST APIs, building React frontends, connecting to databases, deploying applications—AI can do with flying colors, much better than students just learning these skills.

But—and this is the crucial point—AI hasn’t bridged the gap to become completely autonomous. It’s not yet at the “build me X and it shall be” stage. You still need a human co-pilot to guide it, validate its decisions, catch its mistakes, and handle the ambiguous parts.

Current evidence suggests that AI copilots work best with senior developers. Senior + AI is more productive than Senior + Junior. This creates an obvious incentive: replace junior developers with AI.

The Market Reality

This isn’t speculation. The data (see [1] and [2]) is already showing the shift:

Decline in Junior Roles: Studies across 250,000 firms indicate that junior roles are decreasing. Data for software developers shows that headcount for early-career employees (ages 22-25) has been consistently dropping, coinciding with the proliferation of AI coding tools.

Increase in Senior Roles: In contrast, the number of senior positions continues to grow. Headcount for developers aged 31 and up is expanding. Companies struggle to find enough senior engineers, leading them to poach talent or acquire entire companies just to secure capable developers.

Hiring Strategy Shift: The sudden acceleration of AI capabilities has prompted firms to reduce junior roles they anticipate will be automated. Companies view this as less costly than future layoffs—cut the hiring now rather than lay people off later.

The Paradox

Here’s the problem: you can’t create a course that directly turns someone into a senior developer. Senior developers are made through years of experience, starting as juniors and growing through mistakes, projects, and mentorship.

This creates two possible futures:

Scenario 1: You start learning now, but before you develop senior-level skills, AI closes the gap. It becomes fully autonomous, handles complex projects, and junior-to-mid-level developers are no longer needed. You’re out of luck.

Scenario 2: You start learning now, but AI fails to close the gap. The industry suffers from its reduction in junior roles. As current seniors retire, there aren’t enough people to replace them because we stopped training juniors. You become extremely valuable.

I hope the second scenario happens. Unfortunately, major companies seem to be betting on the first.

So Should You Take the Course?

Despite all this, my answer is still yes—but with clear eyes about what you’re getting into.

You need to understand the fundamentals. Even if AI is writing the code, someone needs to know whether that code makes sense, whether the architecture is sound, whether the security implications have been considered. You can’t effectively guide an AI copilot if you don’t understand what you’re asking it to build.

The complexity spectrum matters. The full-stack development we teach is intentionally at the simpler end. But understanding these fundamentals is necessary before you can tackle the complex problems that AI still can’t handle autonomously. You need to crawl before you walk.

Speed matters. If you’re going to beat the AI gap, you can’t take the leisurely four-year path. You need to learn intensively, build real projects, contribute to meaningful work, and accelerate your growth from junior to mid-level as quickly as possible. The clock is ticking.

Depth over breadth still applies. Don’t try to learn every framework and stack. Pick one, learn it deeply, understand the underlying principles, and focus on getting good at solving real problems. The ability to think through complex technical challenges is what will differentiate you from AI. (Think about this the next time you’re signing up for another humanities course!)

What About Which Stack?

Here’s another paradox you might be considering: since AI coding agents are better at popular stacks (trained on more data, better documentation, more Stack Overflow answers), maybe you should learn something more esoteric to gain an edge. This is a mistake. You’re trying to compete with AI, and you can’t win that game. AI will eventually cover the esoteric stacks too, and you’ll have spent your time learning technologies with smaller job markets and fewer resources. You’ll have optimized for being different from AI rather than being good at building software.

Put AI aside. Don’t make your technology choices based on what AI can or can’t do. Make them based on what you need to learn to build real applications and get real jobs. If you want to build web applications, learn JavaScript and its popular frameworks and libraries—that’s where the jobs are. If you want to work on machine learning or data science, learn Python and its ecosystem. Pick something practical with real market demand, go deep, and build things that matter.

And here’s the crucial part: whatever stack or domain you choose, learn to leverage AI effectively within it. The future isn’t about competing with AI—it’s about working alongside it. Learn the fundamentals deeply enough that you can guide AI tools, validate their output, and catch their mistakes. Learn to use AI to accelerate your development, not replace your understanding. The developers who thrive won’t be the ones who avoid AI or try to outrun it; they’ll be the ones who understand their craft well enough to make AI a force multiplier.

But here’s the tricky part: using AI while you’re still learning can actually hinder that learning if you’re not careful. There’s a real tension between learning to use AI and using AI to learn. I’ll write more about how to navigate this in a future post, but for now, be mindful of the difference between using AI as a tutor versus using it as a ghost writer.

The Honest Bottom Line

I can’t promise that learning full-stack development will help you get a job in five years. The industry is changing too rapidly, and the AI capabilities are advancing too quickly for anyone to make that promise honestly.

What I can say is this: understanding how to build software systems, even if AI is doing the typing, remains valuable. The question is whether that value translates to employment opportunities at the junior level, or whether you’ll need to accelerate past that tier before the market for junior developers disappears entirely.

It’s a gamble. But it’s a more informed gamble than going in blind.

If you’re going to make this bet, make it count. Don’t just complete the assignments. Build real things. Contribute to open source. Work on projects that challenge you. Aim to be mid-level capable by the time you graduate, not entry-level.

The industry is changing. How you adapt to that change will matter more than which stack you choose to learn.

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