The PR Shipped. Did the Engineer Grow?
The Long Commit
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The PR Shipped. Did the Engineer Grow?<br>AI can improve engineering output while weakening the signals managers use to evaluate growth.
Juan Cruz Martinez<br>Jul 21, 2026
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The merged PR can be excellent while the source of judgment remains unclear.<br>The project is a success. The pull request is clean, the design document is thorough, and the change shipped faster than expected. Yet at the six-month review, the manager still cannot answer the question that matters: what can this engineer decide now that they could not decide six months ago?<br>Consider a project to add automatic retries to an event-processing service. An LLM proposes a plausible design: exponential backoff, capped attempts, sensible alerts, and clean tests. The engineer turns it into a polished pull request.<br>During review, a staff engineer asks whether the downstream handler is idempotent. It is not. Under the wrong failure conditions, the proposed design could create duplicate side effects. The team revises the approach, and the change ships safely.<br>The review worked: the system is safe, and the team can operate it. What the merged pull request does not reveal is whether the engineer learned to recognize the risk, or whether the relevant judgment remained with the reviewer.<br>A recent discussion on X framed this as managing engineers who “don’t want to think anymore” because they delegate judgment to LLMs. The replies described polished documents with shallow reasoning, entirely AI-written pull requests with obvious flaws, and engineers appealing to model authority during disagreement: “Cursor thinks you’re wrong.”<br>These are practitioner anecdotes, not evidence that engineers everywhere are losing the ability or desire to think. That framing mistakes a measurement problem for a motivation problem. The same tool that removes boilerplate can recommend an architecture, weigh a tradeoff, and produce a persuasive explanation of why the decision makes sense. It never announces when assistance has become substitution.<br>AI may also accelerate learning by exposing engineers to alternatives and feedback they would not otherwise encounter. The management problem is not that growth cannot happen. It is that a polished artifact can hide how much of the underlying judgment developed in the engineer who submitted it.<br>A manager now needs to separate three outcomes:<br>Did the work deliver?
Is the decision understood and safely owned?
Did the engineer’s judgment develop?
AI can improve the first. Review and senior oversight can protect the second. The third can remain unresolved even when the project looks entirely successful.
Delivery, ownership, and development are different management questions.<br>Output is becoming a weaker signal of growth
This problem is not entirely new. Reviewers have always corrected one another, and finished artifacts have never revealed every step of the reasoning behind them. Engineers have always borrowed judgment from colleagues, documentation, previous designs, and Stack Overflow.<br>AI changes the signal. It can generate both the proposed decision and a convincing account of how that decision was reached. Borrowed judgment can now arrive with the polish of independent reasoning.<br>If AI can help you deliver more is out of question now. Randomized field experiments involving 4,867 software developers estimated that access to GitHub Copilot increased completed tasks by 26.1 percent. The experiments did not measure understanding, code quality, or long-term capability, but they provide strong evidence that AI assistance can create real leverage.<br>Other research suggests that the cognitive work is moving. A Microsoft Research and Carnegie Mellon study asked 319 knowledge workers about 936 examples of using generative AI at work. Participants described critical thinking shifting toward verification, integration, and oversight. A 2026 longitudinal preprint about professional software engineers similarly describes a shift from creation toward directing, evaluating, and correcting AI output.<br>None of these studies shows that engineers are learning less. They measured completed work or workers’ descriptions of how their work was changing, not long-term judgment. Together, they explain why completed work is becoming a weaker proxy for individual development.<br>A correct answer does not, by itself, demonstrate engineering growth. Growth becomes visible when someone forms a view, encounters evidence or disagreement, updates that view, and carries the lesson into a different problem. If a model proposes the approach and a senior reviewer repairs its weak assumptions, the system may be safe while the author’s mental model remains unchanged.<br>A model can also produce the design rationale, the rejected alternatives, and the explanation for why the eventual decision was correct. The work may look thoughtful without showing whether the engineer can reproduce the thinking when the...