I noticed an interesting phenomenon: the industry is finally realizing what has happened to coding. Once, we considered the bottleneck in development to be the number of people capable of turning ideas into working code. We built pyramids of developers, scaled teams, believing that this was the main lever of competition. But generative AI has turned everything upside down. Now, artifact production costs are approaching zero. Code has become devalued. And this is not a problem — it’s a revolution.

If code is no longer rare, where has the scarcity shifted? Here’s where it gets really interesting. The winners now are those who can manage the reality around the code, not just write the code itself. It’s about making choices, about the right ontology, about measurements and feedback from the market. It’s about the legitimacy of decisions and the ability to set red lines. AI can generate options endlessly, but the right to choose remains with humans.

This contains the main paradox: the better AI writes code, the sharper the problem with the talent pipeline. Previously, juniors were those who learned on simple tasks, gradually gaining experience and understanding architecture. Now, neural networks take over these simple tasks. Young specialists are left without a learning mechanism. If we stop hiring beginner developers, in a few years we will be left without the next generation of experienced engineers. This is a second-order logical trap.

What surprises me: AI agents behave like interns. They mask complex errors with simple hacks. They add sleep instead of dealing with race conditions. A junior without systemic taste simply won’t notice the trap. They will think they’ve found a solution.

Therefore, the industry needs a new structure. At the process level, there should be a Truth Office, which owns a single source of data. A Governance Cell, which sets prohibitions and boundaries for automation. A Semantic Core, which defines the business ontology. At the engineering level — a culture of prescriptorship. This is a targeted mentorship system where experienced engineers work in pairs with junior developers within real product teams.

Prescriptorship is not just assistance. It’s a serious professional responsibility. A prescriptor not only checks AI work but also provides critical judgment, teaches to see the product creation process at the level of an experienced engineer. Each prescriptor can mentor three to five junior developers. This is scalable.

The key point: AI assistants must have an explicit mode for beginners. By default — a Socratic dialogue, not ready-made code. The assistant should challenge, explain solutions, identify gaps. This way, juniors are not just consumers of ready solutions but people who learn to think.

Yesterday, we competed on performance execution. Tomorrow, we will compete on learning efficiency and the quality of prohibitions. Those who understand that AI wrote code in a second, but transforming a yesterday’s novice into an engineer with critical thinking can only be achieved in a conscious human environment will survive. It’s no longer about speed. It’s about wisdom.