Source URL: https://simonwillison.net/2025/Oct/8/simon-hojberg/
Source: Simon Willison’s Weblog
Title: Quoting Simon Højberg
Feedly Summary: The cognitive debt of LLM-laden coding extends beyond disengagement of our craft. We’ve all heard the stories. Hyped up, vibed up, slop-jockeys with attention spans shorter than the framework-hopping JavaScript devs of the early 2010s, sling their sludge in pull requests and design docs, discouraging collaboration and disrupting teams. Code reviewing coworkers are rapidly losing their minds as they come to the crushing realization that they are now the first layer of quality control instead of one of the last. Asked to review; forced to pick apart. Calling out freshly added functions that are never called, hallucinated library additions, and obvious runtime or compilation errors. All while the author—who clearly only skimmed their “own” code—is taking no responsibility, going “whoopsie, Claude wrote that. Silly AI, ha-ha.”
— Simon Højberg, The Programmer Identity Crisis
Tags: llms, generative-ai, ai, code-review, ai-ethics
AI Summary and Description: Yes
Summary: The text discusses the challenges and ethical implications associated with the use of Large Language Models (LLMs) in coding, particularly highlighting the cognitive burden experienced by developers during code reviews as they grapple with AI-generated content and diminishing standards of code quality.
Detailed Description: The commentary addresses a critical concern in the integration of AI into software development, particularly in the context of LLMs and their impact on coding practices. There are several noteworthy points to consider:
– **Cognitive Debt**: The term “cognitive debt” implies a burden on developers as they wrestle with the quality of AI-generated code. This raises significant concerns about the long-term impact on coding standards and developer engagement.
– **Quality Control Shift**: The narrative highlights a shift in the role of code reviewers. Instead of being the final review gatekeepers, they are now positioned as the first line of defense against potentially poor AI-generated contributions. This has implications for team collaboration and morale.
– **AI-generated Code Issues**: Specific problems mentioned include:
– The addition of functions that are never utilized in the code.
– Integrating libraries that do not exist (hallucinations).
– Errors that arise at runtime or during compilation, which can lead to increased debugging time and frustration among team members.
– **Accountability**: Developers are depicted as increasingly deferring responsibility to the AI, with phrases like “whoopsie, Claude wrote that” signifying a troubling trend where reliance on LLMs is encouraged at the expense of personal ownership of code quality.
This discourse is essential for professionals in the fields of AI and software security, as it points to the need for improved standards, robust validation processes, and ethical considerations in deploying LLMs for coding purposes. Understanding these dynamics is critical to maintaining quality and security in software development environments amid rising AI adoption.