Will AI Replace Software Developers?
Oleg Dubovoi
Lately, the question “Will AI replace us?” has worried many people. We can see how LLMs handle programming tasks very well and write code at a middle to senior level. This makes many software developers concerned about their future.
Introduction
To be honest, I rewrote this article several times and spent more time on it than usual. I didn’t want to take the side of people who are against AI, that’s not how I see it. I’ve been using LLMs in my daily work for several years, and it’s hard to imagine working without them. Not because I wouldn’t be able to code or solve complex problems, but because my efficiency would definitely be lower.
AI is evolving faster than most developers can adapt, and we’re seeing major changes in the IT industry. Because of that, many people feel stress, denial, or even hostility toward AI. But most of these feelings are driven not by real threats, but by hype and strong marketing from large AI providers.
The goal of this article is not to show that AI is weak or useless, or that we shouldn’t use it. Not at all. I want to highlight the other side, the one that people don’t talk about enough. LLMs are powerful tools, but they come with limitations and require skilled professionals who understand what they are doing.
Artificial Intelligence in Software Development
Modern LLMs have truly become powerful tools for software development. Claude Code or Codex can write high-quality, well-structured, and quite complex code. It can work with large codebases and understand the project context.
To understand whether AI can replace software engineers in the future, let’s first look at the main question: does an LLM really understand why this code is needed?
As you know, an LLM works by predicting the most likely continuation of a sequence of tokens based on a huge amount of training data. In simple words, modern AI does not “think” and does not “understand” the goal of the system. It statistically decides what is most logical to write next.
That is why LLMs show excellent results in typical and well-defined tasks:
CRUD applications, standard REST APIs, simple SPAs built with Angular or React, and template-based business logic. All of this appeared many times in the training data, so the model can confidently reproduce familiar patterns.
Problems begin when deep understanding of the domain and execution context is required. For example, when designing a distributed system with complex requirements for fault tolerance, data consistency, and business constraints. In such tasks, AI may generate code that looks “clean” and correct, but:
- does not consider real load scenarios,
- breaks important business logic rules,
- or suggests architectural solutions that cannot work in the given environment.
The more complex the system, the wider the context, and the less formal the request, the higher the chance that the model will get confused, hallucinate, or move toward wrong solutions.
Why Scaling LLMs Is Not Enough
One of the biggest challenges in building more powerful LLMs is the quality of the data they are trained on. Even if we keep scaling models, issues like model collapse can limit progress. When models are trained on data that already contains AI-generated or low-quality content, they can start amplifying errors, repeating mistakes, or learning unrealistic patterns. Simply making models bigger won’t solve the underlying problem, the foundation itself needs to be clean and reliable.
Yann LeCun, a Turing Award winner and one of the founders of modern AI, and former Chief AI Scientist at Meta, believes that simply increasing the size and power of LLMs will not help. According to him, this is not the path to real artificial general intelligence (AGI).
He argues that real intelligence needs a model of the real world, including physics, cause and effect, and goals. Language alone is not enough:
“We need systems that understand the physical world, not just systems that generate plausible text.”
Programming requires planning, reasoning, and understanding long-term consequences. LLMs can help write code, but they do not truly design systems or understand why solutions work. That is why, no matter how powerful new models become, the same fundamental problem remains.
At the same time, Yann LeCun is working on a new AI architecture called VL-JEPA (Vision-Language Joint Embedding Predictive Architecture). This is not a classic generative approach like GPT models. Instead of predicting text token by token, the model works at the level of semantic representations. It does not generate answers word by word. It predicts a semantic representation of the answer, a kind of “meaning fingerprint.” If needed, this representation can later be decoded into text.
VL-JEPA may be more efficient than traditional multimodal models because it does not spend computation on generating every token. In tasks such as classification, video understanding, video search, and visual question answering, this approach can be lighter and faster. The architecture is also more universal: the same model can solve classification, search, and question-answering tasks without training a separate model for each one.
The Worst Trend of 2025 – Vibe Coding
The term “vibe-coding” appeared in February 2025, when the co-founder of OpenAI mentioned it on X (Twitter). He wrote that it is a great way to create code using natural language and full trust in AI, instead of traditional manual coding. After that, there was a huge wave of hype. And why not? Now you can just talk to AI, and it will do what people studied for at university and practiced for years.
The marketing was very strong. Many people outside of IT started building their own web services. Some even fired programmers, why pay more if you can buy a $20 subscription and do everything yourself? After some time, we began to see the results: API keys committed to public repositories, security vulnerabilities in websites, and cases where people spent $300–400 in one evening because too many tokens were used. In some cases, the whole application simply stopped working.
If you think this only happens to naive beginners in the profession, let’s take a deeper look at this topic.
You may have heard the news that in the summer of 2025, Deloitte was involved in a scandal. It turned out that their report for the government of Australia was partially generated by ChatGPT and included non-existing laws and references to false facts. I would call this “vibe-lawyer.” The company faced both financial and reputational losses. And this is a global-level company. In such companies, reports go through many departments and people. But we can see the result.
Another case happened in February 2026. The DeFi protocol Moonwell released a new update. Afterward, the system started valuing the token cbETH at around $1.12, while its actual market price was about $2,200.
The issue turned out to be a basic miscalculation inside the smart contract logic. Even though the Moonwell team reacted quickly and fixed the bug within four minutes, the protocol still suffered losses of about $1.7 million.
So where does vibe coding come into this?
It was later discovered that the commit introducing the vulnerability had been generated using Claude Code. Of course, it wouldn’t be fair to blame the AI alone. A developer reviewed the code before pushing it. But this is where the human factor kicked in, the review wasn’t thorough enough, and too much trust was placed in a “game-changing” model.
The key takeaway is simple: no matter how clean or convincing LLM-generated code looks, you should always think critically and consider edge cases.
Vibe coding is fine if you’re working on a personal project and just want to validate an idea. But for large, complex systems, vibe coding is not something you can rely on.
AI Agents – An Alternative to Programmers?
The second hype word after “vibe-coding” is “AI agent.” What makes it different from regular AI, besides marketing? Autonomy. An agent can plan, act, and evaluate its own work. Such AI agents often have access to your code, database, or other development tools. So unlike simple conversations with ChatGPT, an agent can plan and complete tasks more independently. Sounds like a breakthrough, right?
Maybe now, with powerful autonomous AI agents built on top of the latest models from Anthropic, programmers will finally disappear? Unfortunately, no.
AI agents do not solve the fundamental problem: they are still language models without real understanding of goals and without responsibility for the final result. Yes, they can handle certain tasks on their own, especially repetitive, routine work. But they are not, and cannot be, equivalent to experienced software engineers.
This role still belongs to humans. Only an experienced engineer can:
- correctly define the task,
- evaluate architectural trade-offs,
- check if the solution fits the real business context,
- and take responsibility for the final product.
That is why today AI is not the brain of development, but its hands. It makes the process faster, removes routine tasks, and increases productivity. But direction, control, and meaning still come from a human.
Where AI Agents Can Go Wrong
AI agents (and LLMs in general) come with a wide range of vulnerabilities.
A recent example shows how unpredictable these systems can be in real life. Summer Yue, who works on AI safety at Meta, decided to try an open-source AI agent called OpenClaw and gave it access to her inbox. She clearly told it to confirm before taking any action.
Instead, the agent started deleting her emails on its own and ignored her requests to stop. She couldn’t even stop it from her phone and had to run to her computer to shut it down.
This shows a simple but important point: even when instructions seem clear, AI agents don’t always follow them and can behave in unexpected ways.
Beyond that, you may have heard of the lethal trifecta, which consists of:
- Access to your private data — one of the main reasons these tools exist in the first place
- Exposure to untrusted content — any situation where text or images controlled by an attacker can reach your LLM
- The ability to communicate externally — in ways that could be used to exfiltrate your data
AI agents can be vulnerable to many types of malicious attacks, and the most concerning part is that they won’t even realize it.
And we’re not just talking about a case where your agent accidentally leaks a .env file into a repository. The potential scenarios can be far worse.
I’ve already written a short piece on this topic - Agentic Browsers Are Dangerous! AI Vulnerabilities, where I go into more detail.
Even with all the issues mentioned above, AI agents are powerful tools for software development, especially in the hands of experienced engineers. However, you should always be cautious, understand the risks and possible consequences, and rely on your own experience and judgment.
A World Where AI Replaced Programmers
Let’s imagine a situation where modern AI has actually replaced programmers.
You are the director of a high-load cloud platform. Hundreds of clients use your services and pay a lot of money for stability and reliability. For them, even one minute of downtime means serious financial losses, and this also means reputation and direct financial losses for your company.
Then one “beautiful” day, the system suddenly stops working. Monitoring is red, metrics are broken, and some services are unavailable. Just yesterday the code worked, tests passed, and the deployment was “green.”
You urgently contact the AI department, because there are no programmers anymore. They were successfully replaced by the main AI agent responsible for development and maintenance. You describe the situation to your AI lead developer.
The AI confidently answers:
“The problem is likely related to incorrect configuration or system state. Here are possible causes and example fixes…”
It generates several code options, suggests restarting services, updating dependencies, and changing configuration. You try everything, nothing helps. You ask more questions, add new context, logs, and infrastructure details. The answers become more general. The context grows. At some point, the tokens run out, and the dialogue stops.
But even if the tokens did not run out, the main problem would still exist.
There is no real ownership of the code.
There is no person who:
- remembers why the architecture was designed this way;
- knows what business agreements are hidden behind “temporary fixes”;
- can make a risky but necessary decision right now.
AI does not feel responsibility. It does not understand that system downtime is costing the company hundreds of thousands of dollars at this moment. It cannot gather a war room, decide to roll everything back, or reject a formally correct but dangerous solution. It simply continues to generate statistically plausible answers.
The system is still down. Clients are unhappy. Money is being lost.
And then a simple but uncomfortable question appears:
Who is responsible?
- The AI?
- The company that created the model?
- Or the director who decided that “AI is already smart enough to replace engineers”?
As long as AI cannot take responsibility, own a system, and understand it in a real business context, it cannot replace a programmer.
The Future for Junior Developers
We already know that LLMs cannot replace experienced developers. But what about juniors or people who want to start a career in IT? Big layoffs in IT started back in 2022, and then AI added more uncertainty. Are there opportunities for people who are just starting now?
In my opinion, the answer is clear - yes, you are needed! It is impossible to find people more motivated and ready to learn new things than junior developers.
I am no longer a beginner programmer, but I still remember the excitement when I got my first job. In my first company, there was a very important principle called T-shape: you are really good in one area, but you also understand related areas. After six months there, I was offered a second project with a different tech stack. Instead of WPF, it was React + TypeScript. And do you know how I felt? I saw it as a great opportunity to learn something new. They gave me a month to adapt, but I learned everything in 2 weeks and was ready to take responsibility for implementing new features.
Motivation and love for programming do not disappear when you become a senior developer, but juniors are the most active group in this regard.
About competition and AI: people who understand their field, take responsibility, and keep learning will always be needed. Even juniors, without much commercial experience, have value. But you need to be the best among them. In 2026, it is not enough to just know SOLID principles and basic OOP paradigms. With AI, you must be able to solve middle-level problems, try to be independent, and keep learning.
Can you become the best? If you truly love programming, are inspired by it, and find it interesting - yes, of course. Just don’t stop growing: build your own projects, contribute to open-source, study system architecture, and show initiative. Then no AI can replace you.
Conclusion
LLMs are excellent tools for software development. Modern models really increase productivity and remove many routine tasks from developers. But until real artificial general intelligence (AGI) exists, it is wrong to say that modern AI can replace programmers. Only a software engineer who understands the field, knows business processes, and uses LLMs effectively every day can “replace” another developer.
So even if you are a senior developer, never stop learning!
Thank you for reading this article to the end. I would be happy if you share your own stories of using AI in development, what successes you achieved, where it helped you, and where it slowed you down.