The question lands in my inbox at least once a week: “With AI getting so good at coding, are software engineers going extinct?” Usually, it’s from a worried junior developer or an excited product manager who just discovered ChatGPT can write React components. After spending countless hours working with every major AI coding tool available, I have a clear answer:

Not in the near future. Maybe in 20 years. But probably not even then.

Let me explain why the reality is far more nuanced—and interesting—than the hype suggests.

The Benchmark Illusion

On paper, the latest AI models look unstoppable. Models like o3 Pro and GPT-5 are crushing competitive programming challenges that would make seasoned developers sweat. They’re solving LeetCode problems faster than humans, generating complex algorithms on demand, and even passing technical interviews at top tech companies.

Impressive? Absolutely. Game-changing for real-world software development? Not quite.

Here’s the disconnect that benchmarks don’t capture: competitive programming problems are self-contained puzzles with clear inputs, outputs, and success criteria. They’re the coding equivalent of chess problems—challenging but bounded. Real-world software development is more like conducting an orchestra while the musicians are writing the music, the venue is being renovated, and stakeholders keep changing what genre they want.

My Reality Check with o1 and Its Successors

When o1-preview dropped, I was genuinely excited—and for good reason. It delivered. I immediately integrated it into my workflow and saw real improvements on complex business problems—the kind that involve legacy codebases, complex integrations, ambiguous requirements, and the technical debt that accumulates like dust in forgotten corners. o1-preview was a legitimate step forward.

But here’s what’s surprising: the improvement since o1-preview has been marginal at best.

Despite all the hype around newer releases, when I test them on real-world challenges, they’re not significantly better than what o1-preview already delivered. Some newer models I’ve tested actually performed slightly worse than o1-preview on complex business and technical challenges. It’s as if we’ve hit a temporary plateau where making models “smarter” in abstract reasoning doesn’t necessarily translate to better performance on the messy, context-heavy problems that dominate real software development.

That said, there’s an important exception: Claude models (including Claude Sonnet and Opus) were and continue to be the best choice for coding entire applications and platforms. While o1-preview excels at specific problem-solving and algorithmic challenges, Claude’s models have consistently shown superior performance when it comes to building complete, production-ready systems—understanding project structure, maintaining consistency across files, and generating code that feels more architecturally sound.

The Real Revolution: It’s the Tools, Not the Models

Here’s what the headlines miss: the biggest leap forward in AI-assisted coding isn’t coming from better language models—it’s coming from better tooling around those models.

Tools like Claude Code and Cursor have genuinely transformed how I write code. But not for the reasons you might think. The magic isn’t in having access to a smarter AI. It’s in:

These tools have turned AI from a sophisticated autocomplete into something approaching a junior pair programmer. That’s revolutionary. But it’s a revolution in UX and systems integration, not in raw AI capability.

What the Next Few Years Actually Look Like

Based on my hands-on experience and conversations with engineers across the industry, here’s what I see unfolding:

Software Engineers Become AI-Augmented Powerhouses

The engineers who thrive will be those who master AI tools as extensions of their capabilities. I’m already 3-4x more productive on certain tasks using AI assistance. But—and this is crucial—I’m only able to leverage these tools effectively because I understand what good code looks like, how systems should be architected, and when the AI is leading me astray.

Non-Technical People Still Can’t Ship Complex Systems

Despite what some breathless LinkedIn posts claim, PMs and other non-technical folks aren’t suddenly becoming full-stack developers. Yes, they can now prototype simple features or create basic scripts. That’s valuable! But there’s a vast chasm between “generating code that runs” and “building production systems that scale, maintain, and evolve.”

I’ve watched non-technical colleagues try to build “simple” features with AI assistance. They invariably hit walls when:

Software Design and Architecture Become MORE Valuable

Counter-intuitively, as AI handles more of the implementation details, high-level design skills become more critical. Someone needs to decide:

AI can suggest patterns, but it can’t make these judgment calls that require understanding business context, technical constraints, and long-term implications.

Code Understanding and Debugging Remain Critical

Here’s an uncomfortable truth for the “everyone can code with AI” crowd: when AI-generated code breaks (and it will), you need to understand programming to fix it. Debugging is an exercise in mental modeling—understanding what the code should do, what it actually does, and why there’s a discrepancy.

I regularly see AI generate code that looks plausible but contains subtle bugs—race conditions, memory leaks, security vulnerabilities, or just plain logic errors. Catching these requires not just knowing syntax, but understanding the underlying principles of computation, data structures, and system behavior.

The Fundamental Truth No One Wants to Admit

You can’t excel at system design without hands-on coding experience.

This isn’t gatekeeping—it’s reality. The intuition for good architecture comes from having built things, having seen them break, having refactored them, having scaled them. It comes from the battle scars of production incidents and the hard-won lessons of technical debt.

Similarly, you can’t effectively prompt an AI to build complex systems without understanding what you’re asking for. It’s like trying to direct a movie without understanding cinematography—you might get something that technically works, but it won’t be good.

Learning to code isn’t just about memorizing syntax or understanding algorithms. It’s about developing the mental models needed to:

These skills remain irreplaceable, even in an AI-augmented world.

The Future Belongs to the Hybrid

The engineers who will thrive in the next decade aren’t the ones who resist AI tools, nor are they the ones who blindly depend on them. They’re the ones who understand both traditional software engineering fundamentals AND how to leverage AI effectively.

This means:

My Prediction: Evolution, Not Revolution

Software engineering as a profession will evolve dramatically, but it won’t disappear. We’ll see:

  1. Productivity Explosion: Individual engineers will output what previously required teams
  2. Skill Shift: Less time on syntax, more on architecture and system design
  3. Quality Elevation: AI handling routine tasks means humans can focus on the hard problems
  4. Accessibility Improvement: More people can build simple tools and prototypes
  5. Specialization Deepening: As simple tasks get automated, the remaining challenges become more complex

The result? Software engineers become more valuable, not less. The demand for software continues to grow faster than AI can replace human engineers. And the complexity of systems continues to require human judgment, creativity, and expertise.

Conclusion: Embrace the Tools, Master the Fundamentals

If you’re a software engineer worried about being replaced, stop worrying and start learning. Master the AI tools that are available today. Use them to amplify your capabilities, not replace your thinking. Most importantly, double down on the fundamentals—system design, architecture, debugging, and problem-solving—that will remain valuable regardless of how good AI gets.

If you’re a PM or non-technical person excited about AI making you a programmer overnight, temper your expectations. These tools are powerful, but they’re tools, not magic wands. Consider learning the basics of programming not to become an engineer, but to better collaborate with them and understand what’s possible.

The future of software development isn’t “humans vs. AI” or “everyone becomes a programmer.” It’s a future where human expertise and AI capability combine to build things we can barely imagine today.

And honestly? That future sounds a lot more exciting than one where we’re all replaced by robots.

What’s been your experience with AI coding tools? Are you seeing similar patterns in your work? I’d love to hear your perspective—whether you’re an engineer, PM, or anywhere in between.