Sber explains why AI-generated code looks reliable but breaks under real load

Sber has drawn attention to a problem that teams making heavy use of AI in development are already running into: generated code often looks tidy and convincing, but starts to fall apart under real load. The cause is not only the limits of the models. Developers also tend to judge such code by its surface plausibility rather than the depth of its logic.

Why the code is convincing

Models reproduce familiar patterns well: properly structured functions, tidy variable names, standard error-handling patterns and even tests that look professional. That is why AI code passes the first visual filter easily. It resembles the work of a strong engineer because it is assembled from a huge body of existing solutions.

But resembling quality code does not mean the model has actually checked all the preconditions, constraints and failure scenarios. This is the idea behind so-called LLM intelligence: the model does not understand a system the way a human does. It predicts the most likely continuation based on text, code and context. When the task is typical, this approach works surprisingly well. When business rules, non-obvious dependencies, race conditions, rare inputs or complex integrations appear, the gaps show.

Hence the main thesis of the piece: it is not enough for a developer to write prompts; they need to understand exactly how the model goes wrong.

Where the logic breaks

Problems most often appear not in syntax but in hidden assumptions. A model may build a database query correctly but miss a transaction problem. It may write validation that passes on demo data but falls apart on boundary values. It may generate tests that cover the main scenario and skip timeouts, partial failures or concurrent access. While the load is small, everything looks stable. Once the code reaches a real service, the cost of such simplifications quickly becomes visible.

"AI does not understand code until the developer understands its 'thinking'."

This leads to another problem: overestimating quality. If a team treats the model's answer as an almost finished engineering solution, review becomes a formality. Code review starts to look at style rather than invariants. Testing confirms basic functionality, but not resilience. In this context, AI literacy is not the ability to quickly get a long code fragment. It is the ability to recognize where the model filled gaps with logic that is attractive but unreliable.

How to work with the model

Sber suggests treating AI as an accelerator of engineering work, not a replacement for engineering thinking. In practice, this means a team should have a process in which the model generates a draft, and a human checks the assumptions, the links between components and the behavior under load. The more complex the system, the more dangerous it is to settle for the impression that the code looks reasonable. Separate steps are needed that expose logical holes, not just formatting and linter compliance.

• Ask the model to explicitly list the assumptions and weak points of its solution
• Break the task into small parts instead of generating a large block of code at once
• Test edge cases, integrations and error behavior with separate tests
• Compare the generated code with patterns already working in the project
• Separate the readability of the answer from the engineering reliability of the implementation

A useful practice is to treat every model response as a hypothesis. If the AI proposes an architectural move, run it through the same questions as a human solution: what happens when traffic grows, where consistency breaks, how the code behaves on empty input, how the process recovers after a failure. This approach improves not only AI-assisted development but the whole engineering discipline of the team, because it forces people to formalize what used to live "in their heads".

What this means

Mass adoption of AI in development does not remove basic engineering responsibility; it makes it stricter. The winners will not be the teams that generate the most code, but those that better understand the models' limits and can turn their strengths into a controlled workflow.