Over the last few years, AI moved from the sidelines into the middle of software development. At first, it was almost always the same scene: a question, an open chat, a pasted code snippet, and the feeling that you could keep moving a little faster.
Before that, the workflow was different. We thought about the solution, aligned expectations, refined the backlog, and only then sat down to write. Code was still the most visible part of the delivery.
The fact is that this changed. First came ChatGPT, and the whole thing became almost a habit: ask here, paste there, adjust, and keep working. Then came more aggressive IDEs, agents, and files being created on their own, with code being rewritten almost before you noticed.
Then came another jump. Tools like Cursor started building part of the path by themselves, creating files, rewriting classes, and even spitting out entire pages in a few minutes. Yes, it saved time. But it also started to become clear that speed did not necessarily come with quality.
Today, it feels like this stopped being a novelty and became part of the work itself. Many companies still resist it, but more and more developers are writing less code manually and thinking more about how to guide the process. The focus is no longer just knowing how to use the tool, but knowing when it is actually helping and when it is only accelerating chaos.
when the shortcut becomes a habit
For a long time, being fast at writing code seemed like a huge advantage.
Many people built part of their productivity around that. Keyboard, editor, vim motions, autocomplete, snippets, reusable abstractions, all of that helped turn common tasks into something increasingly automatic. But AI messed with that ruler.
When a tool can build a large part of an implementation in a few minutes, the question is no longer only who writes faster. It becomes who understands better what needs to be done, what can be delegated, and what needs to be reviewed carefully.
The shortcut is still useful. The problem starts when it becomes the only way to move.
in 2026, AI became a commodity
Jumping to 2026, using AI no longer feels like such a special choice. It has almost become a commodity. There are models, agents, harnesses, entire IDEs built around it, and the question is no longer who uses AI. The question now is who can get real work out of it without turning the project into a mess.
Because if everyone has access to the same tools, the filter starts moving somewhere else. Before, it could be knowing a language, a framework, a stack, or even being able to write concise code faster than average. Now, much of that has become cheaper.
And then a strange question appears: if using AI became a commodity, what exactly still differentiates a developer?
If learning the syntax of a language became cheaper, if understanding the basics of a library became faster, if creating a CRUD or a simple MVP no longer impresses like it used to, then maybe the filter is moving somewhere else.
Maybe it is less about knowing one specific tool and more about knowing what to do with all of them. About understanding architecture, context, trade-offs, product, maintenance, cost, and impact. About being able to use AI without giving up technical depth.
But that also leaves a more uncomfortable question: if a senior developer can now deliver alone something that used to look like the work of a team, is that an evolution of the workflow or just more workload disguised as productivity?
who will still train the next developers?
After AI becomes a commodity, the discussion stops being only about tools and becomes about the reorganization of work. And then one of the most important consequences appears: the path into the industry becomes more confusing.
For a long time, there was an implicit progression. Someone studied logic, learned a language, built a few projects, created CRUDs, assembled MVPs, took smaller tasks, made mistakes inside a relatively controlled scope, and, over time, built repertoire. It was not an easy path, but there was a kind of work that served as a training ground. That was where a developer learned to deal with real code, real demand, real deadlines, real business rules, and review from someone more experienced.
But a large part of that space started to shrink. The CRUD that used to be an entry-level task can now be generated in minutes. The simple MVP that used to be an opportunity for a junior developer or freelancer to prove themselves can now be guided by a more experienced professional using AI.
The small task that taught workflow, code reading, responsibility, and delivery starts to look too expensive when compared with the promise of automation. And that creates a complicated contradiction: the industry keeps saying it needs better professionals, with stronger fundamentals, more critical thinking, and more autonomy, while at the same time reducing the spaces where those people could mature. The bar goes up before the person can even get in.
The person who is truly committed to entering technology, who chooses to learn fundamentals, logic, data structures, databases, architecture, and core concepts, may be taking the right path. But the filter became harder. What used to take months or a few years to become an opportunity now seems to demand a level of maturity that used to come only after real experience.
On the other side, the pressure on specialists also changes. Seniors, staff engineers, specialists, technical references, are no longer evaluated only by depth, consistency, and good decisions. Now there is a new expectation, often implicit, of speed.
If AI exists, if agents exist, if autocomplete on steroids exists, if there are tools promising to multiply productivity, then why has delivery not multiplied too?
And then the conversation leaves engineering and enters the territory of economic justification. It is not enough to deliver well. You need to justify salary, impact, return, metrics, efficiency, backlog burned down, features in production, visible results in the application. Delivery stops being evaluated only by technical consistency and starts being measured by how much it seems to convert into speed and return.
The problem is that this reasoning can easily become an accumulation of responsibilities. The specialist remains responsible for hard decisions, complex bugs, architecture, mentoring, review, and technical alignment. But now they also carry trivial tasks that used to be distributed, because "with AI it is fast."
What looks like productivity may hide a silent redistribution of work.
And there is another important point: this pressure does not come from nowhere. It is fed by the marketing of the companies selling AI. There is an aggressive narrative saying everything can be accelerated, automated, simplified, reduced. The pitch sells models, agents, platforms, IDEs, and workflows as if they removed a large part of the cost of producing software.
But software is not just producing code. Code is a visible part of the process. The complexity is still there: understanding the problem, business context, maintenance, trade-offs, integration with existing systems, security, quality, evolution, communication, support, technical debt.
AI accelerates a lot of things, but it does not erase responsibility for what is being built.
So maybe the question is not only "how do we hire junior developers in 2026?". Maybe it is: how do we form professionals in an industry that is removing exactly the tasks that taught people how to become professionals?
That is a much harder question, because if all trivial work is automated or absorbed by specialists using AI, the industry may gain speed in the short term, but it risks weakening the very base that forms the next experienced professionals.
crossing the bubble without losing talent
Maybe it is too early to define exactly what the new model for developer formation will be, but it seems clear to me that it will need to change.
The industry is changing, and we will have to absorb the consequences of this new industrial revolution that hit technology with force. But absorbing it does not mean accepting everything passively, as if every increase in speed were automatically progress.
Critical systems, highly specialized tasks, and problems that truly demand technical depth will always exist. But in recent years, technology also became a promise of mobility, education, and work for many people. Many people entered this journey because they saw programming as a real possibility to build a career, change their lives, and participate in a market that seemed open to whoever was willing to study.
That needs to be taken seriously. If the bar rises, if entry-level tasks disappear, if productivity pressure increases, and if the AI discourse turns every delivery into a dispute over efficiency, we need to be careful not to confuse natural selection with wasted talent.
Because good professionals do not appear ready-made. They are formed in context, with room to make mistakes, review, ask questions, deliver small things, understand larger systems, and slowly develop judgment.
Maybe crossing this AI bubble with mastery is not only about learning how to use the models, agents, and tools that are emerging. Maybe it is also about rethinking how we form people in this new scenario, so the industry does not lose good talent exactly when it most needs people capable of thinking well.
In the end, maybe the question is not how far AI can accelerate development, but what happens to the industry when accelerating becomes more important than forming.