Gabriel Martinez argues that AI can generate plausible codeCode generation uses AI or another automated system to create source code from instructions, examples, schemas, or higher-level specifications. faster than teams can judge whether it should exist. Gabriel Martinez treats code as an ongoing maintenance liability, distinguishing an impressive prototype from software that fits an organisation's real requirements and can be supported.
Gabriel Martinez describes incentives that encourage developers to merge large changes and create extensive documentation without resolving underlying choices. More output can hide decisions, increase a colleague's review burdenCode review examines proposed software changes for correctness, clarity and risks before accepting them, including changes produced by an AI agent. and make it harder to evaluate whether a system is correct.
Gabriel Martinez recommends smaller reviewable changes, explicit reasoning, diagrams that compress the relevant system structure and conventions that reduce unnecessary variation. Framework conventions such as those in Rails illustrate the wider principle: AI-generated workAI-generated code is software source or configuration produced substantially by an AI model from prompts, context, examples, or tool feedback. should make human judgement easier, not merely produce more material.
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