Developer on Habr left an international company over its refusal to actively adopt AI

The developer said he voluntarily left an international company with a good salary not because of fear of AI, but because of his employer’s unwillingness to use new tools. According to him, after mature LLM appeared, the job stopped feeling like growth, and his interest shifted toward agents and knowledge management systems.

Why He Left

The author describes his experience as a division into before and after the era of large language models. Before that, work at the company looked stable and predictable: clear tasks, an international team, decent compensation, and a familiar flow of web projects.

Once LLM became useful enough in real-world development, especially Anthropic models, he saw a new layer of technologies, products, and career paths forming around them. Against that backdrop, his previous job stopped seeming like a point of growth and started feeling like a repetition of things he had already mastered.

The turning point, he says, was a conversation about the practical use of AI inside the business. Instead of experiments and attempts to integrate new tools into processes, he heard the logic of preserving the current model: the company makes money from sites, so what it needs first and foremost is sites.

To the author, this sounded like a refusal to look at the market beyond the limits of its own niche. He directly compared such a position to reactions from the past, when a new technology seems unnecessary simply because the old model is still making money.

“We make money from small sites.

What we need is small sites.”

What Drew Him In

What became the main magnet for the author was not abstract talk about an “AI revolution,” but very applied things he had already begun building with his own hands. For several years he had been developing his own knowledge management system, and after integrating LLM, it turned, in his words, into something far more alive: not just an archive of notes, but a working environment where knowledge helps make decisions and launch actions. At that point, the technology stopped being a toy and became a personal tool.

Then, as he describes it, one personal agent began to acquire other agents, and the whole setup started to resemble a distributed network of assistants. This is where he saw a new area for growth — not another web project, but a combination of memory, automation, and language models that can already change day-to-day work. That became his alternative to the standard career track in service development.

His interest shifted from classical development to a broader set of tasks:

• creating agent scenarios on top of a knowledge base
• automating everyday work actions
• linking several assistants into one system
• searching for new AI products instead of standard web orders

In essence, this is a familiar shift for the industry: value is starting to be created not only by code as such, but also by the ability to quickly assemble useful working setups on top of models. For some developers, this looks like the natural next step, while for companies with an established service business it looks like a risk that is easier to postpone. But it is precisely from such postponed decisions that technological lag often grows later.

Why It Struck a Nerve in the Market

The story resonated not only because of the resignation itself, but also because it hit a nerve across the entire industry. Many teams reason in much the same way today: AI is interesting, but the main income still comes from the old set of services, so there is no need to make large-scale changes.

That approach is rational in the short term, but it also often pushes away specialists who want to work not only for stability, but for future competence.

A separate thread in the text is the author’s disappointment in the professional community and in the format of discussion around the AI topic. He writes that he received a wave of negativity after his previous publication and sees less and less value in a platform where discussion is replaced by crowd reaction.

But even against that background, his main message is not about resentment, but about looking for people who are still interested in building new technological combinations, exchanging projects, and shaping the desired future together.

What It Means

This story shows that the turn toward AI is already affecting not only products, but career decisions as well. If a company treats LLM and agents as an optional fad, it risks losing employees for whom this is precisely where the next point of professional growth lies.

For the market, this is another signal: an AI strategy has become a matter of talent retention, not just efficiency gains.