To add a single example here (feel free to chime in with your own):
Problem: editing code is sometimes tedious because external APIs require boilerplate.
Solutions:
- Use LLM-generated code. Downsides: energy use, code theft, potential for legal liability, makes mistakes, etc. Upsides: popular among some peers, seems easy to use.
- Pick a better library (not always possible).
- Build internal functions to centralize boilerplate code, then use those (benefits: you get a better understanding of the external API, and a more-unit-testable internal code surface; probably less amortized effort).
- Develop a non-LLM system that actually reasons about code at something like the formal semantics level and suggests boilerplate fill-ins based on rules, while foregrounding which rules it's applying so you can see the logic behind the suggestions (needs research).
Obviously LLM use in coding goes beyond this single issue, but there are similar analyses for each potential use of LLMs in coding. I'm all cases there are:
1. Existing practical solutions that require more effort (or in many cases just seem to but are less-effort when amortized).
2. Near-term researchable solutions that directly address the problem and which would be much more desirable in the long term.
Thus in addition to disastrous LLM effects on the climate, on data laborers, and on the digital commons, they tend to suck us into cheap-seeming but ultimately costly design practices while also crowding out better long-term solutions. Next time someone suggests how useful LLMs are for some task, try asking yourself (or them) what an ideal solution for that task would look like, and whether LLM use moves us closer to or father from a world in which that solution exists.
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