Habr: AI is already moving beyond IT and shaping a distinct professional field

On Habr, a column was published about how AI is already closely linked with IT, but no longer fits within the boundaries of a typical digital product. The author proposes viewing neural networks as a separate professional environment with its own roles, market, and value criteria.

Why

It's Already a Sphere The key idea of the text is simple: a specific neural network can be an IT product, but AI as a phenomenon has become broader than one class of services. Its development requires not only programmers, but also mathematicians, linguists, biologists, researchers, and specialists in applied domains. That's why the author places AI alongside IT in terms of scale of influence, rather than inside it as another module.

The logic here is not that AI will replace the development industry, but that an independent layer of professions and practices has already grown around it. The starting point for this conclusion was the job market. According to the author's observation, vacancies and real work tasks already show a shift: access to results has gone to a much wider circle of people than before.

A person can build a bot, automate a process, or produce content with the help of AI without being able to write code at a professional level at all. Hence the main thesis: the finished result itself no longer proves that we are dealing with a programmer, even if the work looks technical.

Who

Works with AI Against this background, the author proposes a new term — "aishnik" (an AI specialist). Under it is understood not a model developer and not necessarily a prompt engineer, but a specialist who professionally solves applied tasks through AI and is embedded in processes where neural networks have become the main tool. The meaning of the term is to separate a person who can work systematically with AI from a casual user and from the familiar category of "IT person".

In the text, this is presented as an attempt to bring order to a rapidly changing market of roles. a specialist in image generation for brand or marketing tasks an AI developer who assembles products through models and no-code tools an analyst or editor who accelerates work through neural networks, rather than through manual pipelines an operator of applied AI systems, responsible for results, rather than writing code from scratch Separately, the author argues with the popular word "vibe-coder". According to his version, it too roughly mixes different levels of competence: one person simply gets acceptable output from a model, another builds a sustainable process and is accountable for business results.

This is an important distinction because AI is increasingly used not for the sake of experimentation, but as a working system for producing text, images, bots, and simple software.

Automation and

Quality The most practical part of the column — contrasting automation with profession. The author believes that AI solves well the task of rapid mass production of "good enough" results: from content to simple CRUD services. But deep engineering, architecture, reliability, and non-standard solutions do not disappear from this. On the contrary, when the basic level becomes cheaper, qualified manual work begins to be valued more highly, as has already happened in other industries between factory and craft production.

"AI is not another convenient service.

It's an infrastructural shift." This thesis also contains a warning for the market. If companies begin to evaluate everyone who uses AI by old IT labels, even more confusion will appear in hiring, grades, and candidate expectations. Quickly assembling a product with the help of a model is not the same as designing a system that will survive growing load, user errors, and real business constraints. That's why the author doesn't talk about the death of IT: rather, it's about the emergence of another large industry nearby, associated with automation.

What

It Means For the market, this is a signal to stop measuring AI only by familiar development categories. Business will have to separately distinguish between creating models, applied work through AI, and classical engineering expertise. If this view takes hold, the market will see new role names, different skill requirements, and a more accurate assessment of where a strong developer is needed and where a well-structured AI process is sufficient.