Recently, I noticed an interesting paradox in how AI is flipping the entire logic of development. For years, we thought the bottleneck was a lack of hands capable of turning requirements into code. We built pyramids of developers, scaled "feature factories." But generative AI has broken all that. Now, code is generated almost for free — it’s no longer a competitive advantage. When coding becomes commoditized, the volume of lines and commit speed turn into noise. They simply stop meaning anything. This raises the question: if code is getting cheaper, where is the real scarcity now?

The first thing that comes to mind — maybe AI will just replace people in management positions? But there’s a fundamental problem here. AI is excellent at generating solution options, it can be a brilliant advisor. But decision-making isn’t a computational task. Management hinges on things that AI simply cannot do: defining values, (what is acceptable), taking responsibility through personal risk, managing conflicts via social contracts, working with unknown variables that aren’t in the training data. Humans remain the bearers of mandate and responsibility — that doesn’t change.

But what really concerns me is this: while we’re grappling with it, a quiet crisis is unfolding at the development level. AI acts as a technological shift toward seniority. Experienced engineers get a huge boost — their productivity multiplies. But AI puts junior developers in a tough spot. They lack the context to verify neural network results, they can’t see hidden errors like race conditions that AI masks as simple hacks. And a new hiring logic is emerging: we hire seniors, automate juniors. It sounds logical, but it’s a trap.

Traditionally, organizations hired novices for simple tasks — they gained experience, studied architecture, and became the future generation of seasoned engineers. If we stop hiring beginners, the talent pipeline will simply collapse. And in five years, the company will be left without the next generation of professionals. Juniors cease to be an investment in the future; they become a burden in the logic of “speeding up code release.” But that’s a short-term strategy.

If the ability to implement features is no longer rare, then competition shifts to entirely different layers. The winner is the one who can translate chaos of desires into clear alternatives, who controls the business ontology before writing code, who builds the right feedback from the market. This is the layer of choice, the layer of world models, the layer of measurements. It’s the layer of legitimacy — who grants the mandate for change. It’s the layer of prohibitions — who defines the boundaries of automation. And it’s the data layer — infrastructure becomes a political-technical asset.

To avoid drowning in this, new structures are needed. At the process level, a Truth Office appears — the owner of a single source of data and measurements. Governance Cell — those who control risks and have the authority to halt the pipeline. Semantic Core — ontology architects.

But most importantly — a culture of prescriptorism at scale is required. This is not just mentorship. It’s a targeted program where novice developers work in pairs with experienced mentors within real product teams. The goal isn’t speed of code release but developing critical thinking, passing on “systemic taste.” AI assistants should have a mode for beginners that uses Socratic dialogue, challenges the learner, explains decisions, and identifies gaps in knowledge.

Yesterday, we competed on execution performance. Tomorrow, we will compete on learning performance and the quality of prohibitions. Those who understand that AI can write code in a second, but only a conscious human environment can turn yesterday’s junior into an engineer with critical thinking will survive.