Kontur engineer compared D&D wizards and warlocks to the LLM era on Habr

Alexander Ovchinnikov, a senior testing specialist at Kontur, published an essay on Habr titled "From Wizard to Warlock: Magic in D&D as a Metaphor for the LLM Era," in which he uses the change of character classes in the tabletop game Dungeons & Dragons to describe what is happening to people's work after the emergence of large language models.

What Do Wizards and Warlocks from D&D Have to Do With It

In the tabletop role-playing game Dungeons & Dragons, a wizard spends years studying spells from books and thoroughly understands the mechanics of each one, whereas a warlock receives magical power from an external source — a patron — and can wield powerful effects without mastering all the details of how they work. Based on the article title, it is this difference between "magic from knowledge" and "magic from an external source" that the author uses as a metaphor for what is happening in the LLM era, although the full essay text beyond the introduction is not provided in the source.

• Author — Alexander Ovchinnikov, senior testing specialist at Kontur
• The material was published on Habr under the title "From Wizard to Warlock: Magic in D&D as a Metaphor for the LLM Era"
• According to the author himself, he has been actively using LLMs for personal tasks for several years

Kontur (SKB Kontur) is a major Russian IT company based in Yekaterinburg that develops services for business: electronic reporting, electronic document management, and accounting systems. The fact that an article about LLMs comes from the practice of a testing specialist at a company of this profile is itself instructive: it is not about the developers of AI products themselves, but about an adjacent technical specialty where large language models are becoming a working tool alongside conventional testing and automation software.

Why the

Author Considers Demonstrating LLMs to Others More Important Than Personal Use

Ovchinnikov writes that his most interesting experience was not using LLMs in his own projects, but rather starting to show these tools to other people. This phrasing underscores a gap that often arises around new technologies: some people already work freely with LLMs as a working tool, while others have not yet seen what these models can do in practice and need someone to show them a concrete example from their own task rather than an abstract demonstration.

If we interpret this metaphor, it is less about the technology itself and more about people's attitude toward the result it produces: some want to understand how it works internally before they start using it, while others are ready to apply the tool as it is if it solves their problem here and now.

What It Means

The essay captures a theme that increasingly appears in professional communities: LLMs change not only what specialists do, but also how they themselves explain what is happening to colleagues — through metaphors, analogies, and demonstrations using live examples rather than dry instructions. For technical specialists who are introducing such tools to their teams, personal example and clear demonstration often prove more convincing than any formal training.

It is also telling that such materials come not from the developers of AI models themselves, but from adjacent technical professions like testing — this indirectly suggests how widely LLMs have already spread throughout work processes far beyond the narrow AI community.