Coding expertise is going to collapse from AI reliance

TEXT START: "We see a future where intelligence is a utility like electricity or water and people buy it from us on a meter and use it for whatever they want to use it for" -

The Dissection

This is a warning about apprenticeship collapse masquerading as a defense of craft. It correctly identifies the mechanism: AI removes deliberate practice, produces confidence without comprehension, and hollows out the junior-to-expert pipeline.

Then it retreats to individual discipline—preserve friction, ask better questions, use AI pedagogically. That is not a systemic solution. It is a maintenance manual for human cognition inside a system whose incentives reward removing that cognition from production.

The Core Fallacy

The text treats expertise as if it remains the decisive economic bottleneck. Under DT, expertise can remain necessary for reliable software while ceasing to be widely valuable labor. The relevant question is whether owners can combine better models, tests, telemetry, reusable architectures, and a thin layer of expert supervision to produce more output with fewer humans. If they can, the expertise pipeline can collapse without software production collapsing.

“AI needs expertise” describes the transition. It does not establish the destination. Human friction is a training technology; once automation performs enough of the resulting work, preserving that training process becomes culturally admirable and economically dispensable. Competitive firms will not preserve slow human-only practice at scale against P1 and P2.

Hidden Assumptions

• Mass demand for human programmers survives.
• Expertise scarcity continues to generate bargaining power.
• Firms will tolerate deliberate inefficiency to cultivate juniors.
• Human verification scales as fast as machine production.
• AI remains a brittle pattern interpolator rather than becoming an integrated production system.
• Individual discipline can override mandates, pricing, and access to capital.
• The main danger is bad learning rather than labor substitution and ownership concentration.
• Keeping humans competent preserves productive participation.

Social Function

Partial truth, transition management, and ideological anesthetic. Its warning about illusion of competence is valid. Its prescription tells individuals to preserve the very friction employers are pricing out. That converts structural dispossession into a personal conduct problem: become disciplined enough and you may remain relevant.

The approach may preserve a Servitor minority—architects, auditors, incident commanders, and domain specialists—but it presents that narrow survival channel as a future for the profession.

The Verdict

The article is right that unrestricted AI coding can produce incompetent developers and a gutted expertise pipeline. It is wrong about the strategic implication. The pipeline is not merely failing to produce the next generation of programmers; it is being rendered unnecessary for the majority of production.

Friction-first learning can create survivable human competence, but it cannot restore mass demand for it. Under DT, coding expertise becomes a controlled asset for Sovereigns and indispensable Servitors, while everyone else rents intelligence by the meter and mistakes access to output for participation in production. The article diagnoses the corpse’s loss of muscle memory; it does not recognize that the economic organism is already replacing the muscle.