Not because AI replaced experienced engineers. But because the market stops nurturing new ones
For the past few years, IT has repeated an almost reassuring phrase: AI won't replace developers, it will become their assistant.
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In 2026, this formulation increasingly fails to describe reality.
AI tools no longer just autocomplete lines in the IDE. Codex, Claude Code, and other agentic environments increasingly take on the full implementation cycle: reading the codebase, editing files, running commands, writing tests, and preparing changes for review. The human remains in the process, but their role shifts from being the author of every line to being a result controller.
I would put it this way:
Human participation in development is becoming critical yet minimal.
Critical—because someone must understand the task, make architectural decisions, verify the result, and be responsible for production.
Minimal—because an ever-growing volume of direct code production shifts to the model.
At Cloud Next 2026, Google Cloud's flagship event for developers, companies, and partners, Google publicly stated that 75% of new code within the company is already generated by AI and approved by engineers. The important thing is not just the number itself, but the wording: code is generated by AI, and humans approve it.
And here emerges a deeper problem than "AI will replace juniors."
The problem is that the market still wants seniors, leads, and architects, but increasingly fails to support the path through which they used to emerge.
AI doesn't take the entire profession at once. It takes the bottom layer
When people say "AI will replace developers," the debate often goes to extremes.
Some imagine complete disappearance of the profession. Others respond that complex systems still require people, so nothing fundamentally changes.
But the real shift happens between these extremes.
AI doesn't need to replace the entire profession at once to radically change the market. It only needs to automate the bottom layer of tasks:
- typical CRUD scenarios;
- simple components;
- migrations;
- basic integrations;
- tests;
- documentation;
- simple bug fixes;
- initial refactoring;
- explanation of others' code;
- quick prototypes.
Beginning developers used to learn on exactly these tasks.
An intern joined the team. They were given a small task. They did it slowly, made mistakes, received code review, rewrote it, asked questions, broke the local environment, fixed it again, gradually understood the codebase, and learned to see the consequences of their decisions.
It wasn't the most efficient way to close tasks here and now. But it was a way to grow engineers.
Now the company looks at the same task differently: why give it to a junior for several days if a mid-level or senior with Codex, Claude Code, Copilot, or another agent can close it faster, cheaper, and with fewer organizational risks?
The problem with juniors isn't that they got worse. The problem is that their learning tasks became too easy to automate.
Code production is separating from the profession of development
In the Sonar State of Code Developer Survey 2026, developers estimate that 42% of their current code is already AI-generated or substantially AI-assisted. They expect this to grow to 65% by 2027. Among those who've tried AI development tools, 72% use them daily.
You can argue about the accuracy of each individual figure. You can say that "AI-assisted" isn't the same as fully generated code. You can rightfully note that autocomplete, chat, an IDE agent, and autonomous PR work represent different levels of model involvement.
But the direction is clear: code production is becoming an increasingly less manual process.
In the Agentic Coding Trends 2026 report, Anthropic describes a similar shift: developers increasingly don't write every line themselves, but rather manage agents that handle implementation, tests, documentation, and codebase work. However, an important caveat: according to Anthropic's internal research, developers use AI in roughly 60% of their work, but can fully delegate only a small portion of tasks—typically 0–20%.
I discussed this Anthropic report in more detail on my Telegram channel.
Development looks less and less like manual code production. And more and more like managing a stream of solutions that need to be reviewed, constrained, and connected to real systems.
Circle of Responsibility
Here comes the first important term.
Circle of Responsibility—this is everything that remains with the human, even if the machine writes the code:
- understand the task;
- clarify requirements;
- choose the architectural approach;
- identify system constraints;
- assess security;
- verify performance;
- anticipate edge cases;
- organize testing;
- conduct review;
- accept risk;
- be responsible for production.
AI can generate code.
But AI is not responsible for a broken payment scenario, data leaks, service outages, increased technical debt, or an architectural decision that will render the system unmaintainable in six months.
So the new role of a strong developer isn't simply to write code faster.
The new role is to maintain the circle of responsibility.
This sounds abstract, but in practice it looks very concrete.
Old process:
разработчик получил задачу → написал код → написал тесты → открыл PR → получил ревью → поправил → смерджилОбъяснить с
New process:
разработчик сформулировал задачу для агента → агент написал код→ агент открыл PR → разработчик проверил diff → нашёл неверную абстракцию → усилил тесты → проверил безопасность → принял ответственность за mergeОбъяснить с
In the first process, humans produce most of the code.
In the second process, humans may write less code, but still bear responsibility for 100% of the consequences.
This is the new asymmetry of development: less operational participation, more responsibility.
A human may write 5% of the code but be responsible for 100% of the outcome.
What this changes in team work
Imagine a typical task: add a new endpoint, save data, return a response, cover with tests.
This used to be a normal task for a junior. Not too complex, but useful: figuring out routing, DTOs, validation, the data access layer, tests, the local environment, and team conventions.
Now the same task can be given to an agent.
After some time, the agent opens a PR. It will contain an endpoint, migration, tests, and possibly updated documentation.
Business is happy: the task is completed faster.
The senior is happy: less routine work.
But this win has a hidden cost: no one went through a learning cycle.
- No thoughtful reading of others' code;
- No mistakes in migrations;
- No questions during review like "why did you put this logic here?";
- No independent attempt to figure out why the test fails locally;
- No understanding of where the boundaries of responsibility lie in this system.
Task closed. Engineer didn't grow.
If this happens once—no big deal.
If the entire funnel into the profession is structured this way—the market begins to impoverish itself.
Engineering Reproduction Gap
A senior developer isn't someone who just wrote code for longer.
It's someone who went through mistakes, code reviews, others' legacy code, poor architectural decisions, incidents, deadlines, controversial compromises, production bugs, and responsibility.
Seniority can't be fully read in documentation. It can't be obtained only through pet projects. It can't be learned only through prompts. It forms through practice. But if the lower layer of practice is automated, an engineering reproduction gap emerges.
Engineering Reproduction Gap—this is a situation where the market still needs strong developers but stops supporting the rungs through which these developers used to grow.
Companies want mid-levels, seniors, team leads, architects.
But fewer and fewer want to hire people who need to progress from simple tasks to complex ones. For business, this becomes a difficult investment: a junior requires onboarding, review, and mentorship, with returns that may take time or never come. Against the backdrop of relatively cheap AI tools, this investment increasingly looks less obvious.
Stanford AI Index 2026 captures an alarming signal: employment of software developers aged 22–25 has dropped nearly 20% since 2024. Meanwhile, AI's effects on the labor market are uneven, concentrating more in hiring and among the youngest workers in AI-exposed professions.
This doesn't prove that AI alone "killed juniors." The market is affected by interest rates, post-COVID correction, layoffs, bootcamp market saturation, hiring geography, and overall corporate caution.
But AI amplifies an already existing shift.
If a newcomer used to be an investment in a future mid-level developer, they now increasingly look like an expensive and slow way to close tasks that can be given to a model under an experienced engineer's supervision.
The AI Ceiling for Juniors
So here comes the second term—the AI Ceiling for Juniors.
The AI Ceiling for Juniors—this is a barrier between entering the profession and real engineering practice.
A newcomer no longer competes only with another newcomer.
They compete with a combination of:
experienced developer + AI tools + existing infrastructure + team's accumulated context.
And it's unfair competition. A junior doesn't lose because they're lazy or didn't study well.
They lose because the market compares them not to a person of the same level, but to an AI-augmented experienced developer.
That's why the headline "Senior Developers as an Endangered Species" is more important than "Juniors are disappearing." Because a junior isn't a separate type of employee. A junior is a future senior in the first stage of formation.
If the normal path for juniors disappears, the stream of new seniors will start disappearing in a few years.
Cognitive Ceiling: Juniors Don't Have Time to Build a Foundation
The AI ceiling has not only a market side but also a cognitive one.
Today's junior doesn't just enter the profession of development. They enter a profession that is restructuring faster than they can build a foundation.
They need to simultaneously learn the language, frameworks, Git, databases, testing, architecture, security, working with legacy code—and on top of that, another layer of AI tools: Cursor, Claude Code, Copilot, Codex, agentic workflows, context, prompts, AI code review rules, new verification modes, and new risks.
For an experienced engineer, this can be an amplifier. They already have an internal map: they understand where the model errs, what needs to be checked, and which solutions are risky.
For a beginner, that same layer of tools often becomes overwhelming. When the task is unclear, delegating to the model seems rational. The model will faster explain the error, write a function, suggest tests, assemble a prototype.
But here emerges a cycle of cognitive abdication:
unclear task:
→ overwhelm
→ delegating to AI
→ quick result
→ weak understanding
→ the next task seems even harder
→ even more delegating.
The person wins the task but loses the skill.
Anthropic in a recent study on the impact of AI assistance on the development of programming skills showed a similar risk. In a randomized experiment, participants with AI completed the task slightly faster, but then showed weaker understanding: the average score of the AI group on the verification test was 50% versus 67% for the group that wrote code manually. Researchers separately note that aggressive adoption of AI in the work environment can provide a productivity gain but harm the development of skills necessary for reviewing AI code.
This doesn't mean that beginners can't use AI. On the contrary, they will have to use it. But the way they use it becomes critical.
There is a big difference between:
"do it for me"
and
"explain the principle, show me the options, help me find the error, but I myself must understand the solution".
In the first case, AI closes the understanding gap.
In the second, it helps fill it.
That's why the main question for a junior in 2026 doesn't sound like this:
"do I know how to use AI?"
But rather like this:
"am I getting stronger after using AI?"
If after each task a person gets a result but doesn't gain understanding, they don't grow as an engineer. They're just learning to manage someone else's thinking.
In this sense, the AI ceiling for a junior is not just a hiring problem. It's a training problem.
Even if a beginner formally enters the profession, it's increasingly difficult for them to follow the path of deep understanding: the temptation to close gaps in understanding with generation is too great.
The market is already showing the first symptoms
Separately, it's worth talking about the job market.
In Russia, this is already felt not just as an abstract conversation about the future. For beginners, the market has become noticeably harsher.
Yes, there are still many vacancies. On hh.ru, Habr Career and other platforms you can find job postings for juniors and interns. But the presence of a vacancy on the site no longer always means real, active demand.
Some postings hang for months. Some collect resumes "for the future". Some close with internal candidates. Some get stuck due to frozen hiring or long approvals.
For a junior, the difference is small. They see a vacancy, apply, pass the test, wait for an answer — and meet silence.
The phenomenon of "ghost vacancies" is already being discussed in the HR community: this is what they call postings that create the appearance of growth or collect a database of candidates, but don't necessarily lead to actual hiring right now.
Meanwhile, public analyses of the Russian IT market in 2026 show another problem: in some stacks and fields, the imbalance between vacancies and active resumes has become so severe that a small number of vacancies can receive a huge stream of responses. One analysis on Habr directly formulates it as "2 vacancies, 1,000 responses per day". This is not an academic study of the entire market, but a good symptom of how the market feels from the inside. Based on personal experience, I encountered huge waves of responses to junior/intern positions when vacancies were posted 5-6 months ago; the situation is even sharper now.
There is also a broader trend: the labor market is shifting from hypersupply and mass hiring to retention, internal rotation, and professional development of already-hired employees. In hh's February 2026 report, it's clear that the average number of active vacancies has decreased year-over-year, while active resumes have grown.
That's why the phrase "the market needs IT specialists" has become too general.
What kind of specialists are needed?
Strong ones — yes.
Experienced ones — yes.
Rare ones — yes.
People who can quickly take responsibility for a system — yes.
But whether the market needs the same number of junior specialists as it did during the growth era of 2020-2021 is a big question.
This is precisely where AI amplifies the painful shift. And in this new economy, a junior ends up in the most vulnerable position.
- They don't yet bring the speed of a senior;
- They don't yet hold a circle of responsibility;
- They still require onboarding;
- They still make mistakes;
- They still need review;
- And the tasks they could learn from are increasingly being automated.
The main question now is not whether AI will replace developers
AI doesn't cancel development.
But it does cancel the old economy of development, in which a beginner could gradually grow on simple tasks.
AI amplifies those who already know how to design, review, and take responsibility. But at the same time, it automates the layer of tasks through which people used to learn.
Seniors are not disappearing now. On the contrary, strong engineers are becoming even more valuable.
But if the market stops developing new specialists, in a few years the problem will return at a different level. Companies will complain not about the lack of juniors, but about the lack of people capable of managing complex systems.
And here a new challenge arises for IT companies: what to do with junior specialists?
Not with olympiad winners and rare talents — they almost always find a way.
Not with those who are already writing complex infrastructure projects at 20.
But with ordinary juniors who could have become good mids through a few years of practice.
Where will they go if the business needs them less and less?
This is not a question of morality. Business is not obligated to hire people just because they need to learn somewhere.
But if the entire industry starts optimizing only for short-term efficiency, it could break its own staffing cycle.
- There will be more code
- There will be more AI agents
- There will be more automatic PRs
- The speed of feature delivery will be higher.
But there could be fewer people who understand how all this works.
That's why the main question for the coming years doesn't sound like this:
"Will AI replace developers?"
And not even like this:
"Will juniors be needed?"
The main question is harsher:
"Who will develop engineers if simple tasks no longer require people?"
If IT companies don't build a new career ladder, the market will get a strange structure: at the top — expensive seniors and AI agents, at the bottom — a crowd of people who want to enter the profession, and between them — fewer and fewer living rungs.
Senior developers as a disappearing species is not about current seniors being no longer needed. It's about the industry still wanting mature engineers but increasingly failing to understand how to create the conditions for them to emerge.
And perhaps this will become one of the main HR problems in IT in the age of AI.
A channel with guides and content on Claude Code, we post news (when they cut limits by 10 times) and what tools we implement through Claude for projects, channel: https://t.me/claudedevolper
