MTS: the first architectural decisions in AI made today set constraints for decades ahead

An MTS text on Habr AI proposes looking at the development of artificial intelligence not as a race of models, but as an accumulation of architectural decisions that then live for decades. The main idea is simple: the first layers of code, data, and processes today can become as impenetrable a foundation for future AI systems as the Tech-Center became in "Hyperion".

Metaphor from "Hyperion"

The author draws from Dan Simmons' novel "Hyperion", where artificial intelligences spent centuries building their own systems on top of code written by humans. Over time, the architecture became so complex that neither humans nor the AIs themselves understood why key mechanisms were arranged the way they were. For science fiction of the late 1980s, this was an effective image.

For today's AI industry, it's an almost literal description of how technical debt accumulates in large AI platforms. Importantly, this metaphor is not about distant machine rebellions, but about a very real engineering problem. The more generations of teams, services, and models layer on top of each other without a common design, the higher the risk of ending up with a system that cannot be confidently developed.

It continues to work, but the reasons for its behavior become increasingly unclear. And where architecture's explainability disappears, both the cost of changes and the probability of errors grow rapidly.

How Complexity Grows

The problem is not just models. Any AI product is built in layers: data pipelines, filters, vector databases, orchestrators, system prompts, security rules, interfaces for humans, integrations with external services. Each layer typically emerges as a response to an urgent business task: speed up release, reduce request costs, improve answer quality, or mitigate risk.

Individually, such solutions look reasonable, but after a few years they assemble into a structure that is hard to explain, test, and safely modify. Because of this, early-stage architectural mistakes prove especially expensive. If a team initially didn't describe dependencies, didn't document invariants, and didn't agree where model responsibility ends and product responsibility begins, then all of this turns into systemic confusion.

A model can be retrained or replaced, but a chaotically grown contour around it—logging, routing, escalation rules, manual patches—outlives the neural network itself and causes more problems than the model does.

What Teams Should Do

This is why the conversation about AI's future increasingly shifts from model sizes to the quality of engineering foundations. This is not about freezing experiments and endlessly designing a perfect system. It's about minimal discipline, without which the AI stack quickly becomes opaque even to its own team. In MTS's column, this sounds like a warning: today the market is focused on speed, but real advantage tomorrow will go to those who are already designing clear and verifiable architecture today.

• Clearly separate what the model does from what the product's business logic does
• Document the reasons for key architectural decisions, not just the resulting code
• Limit hidden dependencies between data, prompts, retriever, and interface
• Build in observability: logs, tracing, prompt versions, and quality control
• Regularly remove temporary patches before they become permanent infrastructure

This concerns not just engineers. Product teams, managers, and leaders also influence future complexity when they demand adding one more flag, temporarily bypassing a limitation, or quickly joining two contours. In AI systems, such compromises are especially treacherous: they hide not only in code, but in data, model settings, hidden instructions, and manual support operations. Externally, the product may work, but internally it's already losing manageability.

What This Means

For teams building AI services today, the main takeaway is not that complexity needs to be stopped—that's impossible. The conclusion is different: every quick solution leaves a mark on the foundation. And if these layers aren't managed from the start, in a few years it will be them, not model quality, that determines the ceiling for the product's speed, reliability, and safety.