AI Won't Replace Developers. It Will Make It Obvious Who Really Is One.

There’s a phrase I’ve been hearing with increasing insistence over the past few months: “AI will replace programmers.” It’s said by managers who have never written a single line of code, amplified by journalists chasing clickbait headlines, and feared by juniors who have just started their journey. And every time I hear it, I think the same thing: whoever says this has absolutely no idea what it really means to develop software.

Because developing software is not — and has never been — just “writing code.”

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Writing Code Is the Easy Part

I’ve been saying this for over twenty years of hands-on experience, and I repeat it every chance I get when talking with colleagues, clients, or students: writing code is a fraction of a developer’s work. Often it’s not even the hardest part.

Try asking an LLM to negotiate requirements with a client who doesn’t know what they want but knows perfectly well what they don’t want. Try asking Claude or ChatGPT to decide whether it’s worth introducing an extra layer of abstraction in the architecture, knowing that in six months the team will double in size. Try having it manage the trade-off between delivering an incomplete feature on Friday or risking losing the contract.

They can’t. Not because they’re “stupid,” but because these decisions require something no statistical model possesses: human context, responsibility, and real consequences.

A real developer isn’t a translator of specs into code. They are a problem solver who operates in an ecosystem of technical constraints, business constraints, and human constraints. And AI, however powerful, operates in a completely different space.

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The Real Impact: Different for Every Experience Level

One of the things that strikes me most in the AI debate is how it’s treated as a monolithic phenomenon. “AI will change software development” — yes, but how it will change it depends enormously on who you are and how much experience you have.

The Junior Developer

For those just starting out, AI is an always-available tutor. And that’s fantastic. When I started, to understand how a pattern worked I had to buy a book, read it, try it, fail, and try again. Today a junior can ask an LLM to explain the Observer pattern with an example in Delphi and get a reasonable answer in three seconds.

But there’s a huge risk that too many people underestimate: AI doesn’t know when it’s teaching something wrong. And a junior doesn’t have the tools to notice. Without a human mentor acting as a filter, the risk is building fragile foundations on code that “works” but is architecturally a disaster. AI accelerates learning, but it can also accelerate the acquisition of bad habits. It’s no coincidence that, according to the Stack Overflow 2025 survey, 45% of developers cite as their main frustration “AI solutions that are almost right, but not quite” — an “almost” that for an inexperienced junior is nearly impossible to spot.

The Mid-Level Developer

This is where AI shines brightest. The developer with 3-7 years of experience has enough competence to critically evaluate an LLM’s output, but gains enormous benefit from support in writing boilerplate, generating tests, and exploring unfamiliar APIs. AI reduces the cognitive load on repetitive tasks, freeing mental energy for the decisions that matter.

The Senior Developer

For a senior, AI is a force multiplier. It doesn’t change what they decide, but it drastically compresses the time between an architectural decision and its implementation. I’ve personally seen the difference: tasks that used to require half a day of scaffolding now get completed in an hour. But the fundamental decisions — which pattern to use, where to draw the boundaries between modules, how to handle data migration — remain firmly human.

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From “Vibe Coding” to “Context Engineering”

In February 2025, Andrej Karpathy — former AI director at Tesla and co-founder of OpenAI — coined the term “vibe coding”: “There’s a new kind of coding where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.” You write a vague prompt, the AI generates code, you copy-paste it into the project and hope it works. It’s the software equivalent of cooking by “vibes” instead of following the recipe.

The problem? It works for demos, it collapses in production.

The interesting thing is that Karpathy himself, a year later, declared vibe coding outdated, introducing the concept of “agentic engineering”: no longer random prompts, but structured orchestration of AI agents with human oversight, quality gates, and rigorous review. In practice, even the person who coined the term realized that without engineering discipline, vibe coding is a dead end.

In parallel, the concept of “context engineering” emerged — a term that Tobi Lutke, CEO of Shopify, defined as “the art of providing all the context necessary so that the task is solvable by the LLM”. It’s no longer about writing clever prompts, but about providing the model with precise specifications, architectural context, project constraints, and codebase conventions.

In practice, everyone is discovering what I’ve been saying for a while: the better you are as a developer, the better you can use AI.

It’s only an apparent paradox. To write an effective prompt that generates quality code, you need to know exactly what you want to achieve. You need to know the patterns, the best practices, the trade-offs. You need what I call “architectural vision” — the ability to see the system as a whole, not just the individual function.

Those who lack these skills end up doing permanent “vibe coding,” producing an accumulation of technical debt that will eventually come due. With interest.

And here we arrive at a point I consider crucial: stop for a second and think about it. If you don’t have the skills to evaluate what an AI tells you, you’re trusting a probabilistic system. A system that is probably telling you something true. Probably. Are you sure you can accept that? When the code you generate handles financial transactions, healthcare data, or critical infrastructure, “probably correct” isn’t good enough. You must have the skills to critically evaluate what an AI produces — otherwise you’re not programming, you’re playing roulette with someone else’s software. Martin Fowler, in a 2025 article, observed that LLMs can confidently assert that all tests pass when they actually fail — and asks: “If that was a junior engineer’s behavior, how long would it be before H.R. was involved?” Well, if your “AI colleague” has this level of reliability, maybe you shouldn’t give it unconditional access to the production codebase.

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The Question of Responsibility

There’s a point that often gets glossed over in the debate, and one that I consider fundamental: responsibility.

When a system goes to production and something doesn’t work — a data breach, an incorrect tax calculation, an interface that causes medical errors — who is accountable? The AI? The prompt you wrote? The model that “hallucinated” a nonexistent validation?

No. You are accountable. The developer. The team. The company.

AI can suggest security best practices, it can generate code that looks secure. But it takes on no legal, ethical, or professional responsibility. Every line of code generated by an LLM that ends up in production becomes your responsibility. And that means you must be able to:

• Read that code and understand it thoroughly
• Evaluate whether it’s secure, performant, maintainable
• Decide whether it’s appropriate for the specific context
• Answer when something goes wrong

Delegating the generation of code is possible. Delegating the responsibility for code is not. And it won’t be for a long time.

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The Job Market: Between Opportunities and Real Risks

It would be naive to deny that AI will have an impact on the developer job market. It will, and in part it already is. But not in the catastrophic way that certain media outlets would have us believe.

Let’s start with the data: the U.S. Bureau of Labor Statistics projects a 15% growth in software developer employment between 2024 and 2034 — five times faster than the average for all occupations (3%). An estimated 129,000 new positions per year are expected. That’s not exactly the “mass replacement” scenario we keep being told about. The Stack Overflow 2025 survey, conducted with over 49,000 developers, confirms: 64% don’t see AI as a threat to their jobs. At the same time, one figure struck me: trust in AI accuracy dropped from 40% to 29% year over year. 80% use AI tools, but they use them with growing awareness of the limitations.

That said, the demand for “code monkeys” — developers who mechanically translate specs into code — will decrease. This is almost certain. If your main added value is writing CRUDs without syntax errors, you have a problem. Not because AI does it better than you, but because it does it well enough to make your cost unjustifiable.

But the demand for professionals who know how to design systems, make architectural decisions, manage complexity, and take responsibility — that will not only not decrease, but will grow. Because the more code gets generated faster, the more you need someone who can evaluate it, integrate it, maintain it, and evolve it. Not coincidentally, according to the Robert Half 2026 report, AI/ML positions grew by 163% in 2025 and cybersecurity ones by 124% — we need more people who know how to build with AI and protect against AI, not fewer.

It’s the same dynamic we’ve seen with every technological leap: compilers didn’t eliminate programmers, frameworks didn’t eliminate architects, the cloud didn’t eliminate system administrators. It changed what they do, not whether they’re needed.

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The Subtle Danger: Quality Sacrificed at the Altar of Speed

There’s a risk that worries me more than the replacement of programmers, and it’s the commoditization of quality. Managers and non-technical decision-makers see AI generating code in seconds and think: “Why are we paying senior developers when AI can do the same job?”

The answer is simple: because it’s not the same job.

AI-generated code tends to be “correct on average” — it works for common cases, follows the most frequent patterns in the training set. But quality software lives in the details: in edge case handling, in error resilience, in long-term maintainability, in security by design.

It’s not just my experience confirming this. The numbers are damning. The Veracode 2025 report, which tested over 100 LLMs on 80 real-world tasks in Java, Python, C#, and JavaScript, found that 45% of AI-generated code samples introduce OWASP Top 10 vulnerabilities. For Java the figure is even worse: 72%. 86% of generated code doesn’t defend against cross-site scripting, 88% is vulnerable to log injection. The authors’ conclusion is blunt: “The models have improved at generating syntactically correct code, but not at generating secure code.”

A Stanford and NYU study (“Asleep at the Keyboard?”), presented at the IEEE Symposium on Security and Privacy, adds a detail that should give everyone pause: analyzing 89 scenarios and 1,689 programs generated by Copilot, approximately 40% contained security vulnerabilities. But the most disturbing finding is another: developers who used the AI assistant were more likely to believe they had written secure code compared to those who worked without it. In practice, AI not only introduces security bugs, but also generates a false confidence in those who use it without the right critical mindset.

I’ve seen this dynamic with my own eyes in recent consulting engagements: management deciding to “speed up” development by heavily relying on AI without adequate technical oversight. The result? Code that passed functional tests but was a security nightmare. SQL injections hidden in queries built with string concatenation. Authentication tokens stored where they shouldn’t be. Validation logic that appeared correct but was bypassable with crafted input.

AI doesn’t do these things out of malice. It does them because it optimizes for the most probable answer, not the most secure one. And the difference between “probable” and “secure” is exactly where the value of a competent developer comes into play.

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So, What Should You Do?

If you’re a developer wondering how to position yourself in this new landscape, my advice is direct and without beating around the bush:

1. Learn to use AI, now. It’s not optional. Ignoring these tools today is like having ignored the Internet in 1998. You don’t need to become a machine learning expert, but you do need to integrate LLMs into your daily workflow and understand where they excel and where they fail.

2. Invest in skills that AI cannot replicate. Software architecture, system design, requirements analysis, stakeholder communication, complexity management. These are the skills that make you irreplaceable, not your typing speed.

3. Never stop understanding the “why.” AI gives you the “how” — sometimes well, sometimes poorly. But the “why” behind every technical decision is what separates a professional from an operator. Why that pattern and not another? Why that architecture? Why that trade-off?

4. Think of AI as a very fast junior. You are the senior. It’s the metaphor I use most often and find most effective: an AI is like an incredibly fast junior developer at reading documentation and writing code, but one who has no project experience, doesn’t know the business context, and sometimes takes questionable shortcuts. Your role is that of the senior who guides, directs, and reviews. I’m not the only one who thinks this way: Addy Osmani, engineering lead at Google Chrome, describes AI as exactly “a fast but unreliable junior developer who needs constant oversight”. If you’re not doing code review on what AI produces with the same attention you’d give to code from a newly hired junior, you’re abdicating your professional responsibility. AI is powerful, but it needs direction. That direction is you.

5. Build real experience, not simulated. Working on real projects, with real users, real constraints, real deadlines — that’s what builds the competence AI can’t give you. No tutorial and no prompt can replace the experience of a deploy gone wrong at 3 AM and the ability to figure out, under pressure, what happened and how to fix it.

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Conclusion

Artificial intelligence isn’t here to replace developers. It’s here to amplify what we already are. If you’re a competent professional, AI will make you faster, more productive, more effective. If you’re not — if your only value was writing code mechanically — AI will make that gap impossible to hide.

This is a moment of professional natural selection, and like every selection, it rewards those who adapt and penalizes those who stand still. The good news is that adapting doesn’t require starting from scratch: it requires doing what every good developer has always done — keep learning, stay curious, never settle.

Quality software will continue to need competent human beings. The only question is: will you be among them?

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– Daniele Teti