January 29, 2026
Ctrl‑C, Ctrl‑Feelings
The Value of Things
Speed vs soul: coders call it ‘generative slop’ while others chase convenience
TLDR: The essay asks whether AI-made things have value beyond usefulness. Commenters split: some feel coding lost its soul and fear “generative slop” will stunt craft, while others argue AI can improve outputs but human process still matters—making this a broader fight over speed versus meaning.
An anxious essay asks a simple, unsettling question: if generative AI (think large language models like ChatGPT) makes digital stuff faster, does it truly have value—or just utility? The author steelmans AI, focusing on usefulness, while confessing sleepless nights and a craving for meaning in the work.
The comments lit up. One reader, burned out by AI-churned coding, says they’re ready to ditch software and pick up a soldering iron, chasing craft over autocomplete. Another warns that if we normalize “generative slop,” we’ll lose the messy practice that turns beginners into masters. A counterpoint: an AI might draft a better screenplay, but the human’s abandoned ideas still shape the final movie—there’s magic in the process. Fans praised the piece for drawing a line between where AI helps and where it hollows out.
Cue drama: a government example got roasted for calling people “journey-level developers,” spawning snarky memes about bureaucratic buzzword bingo. Threads spiraled into jokes like “LLM = Large Loss of Meaning” and “AI is the Red Delicious of code”—technically fine, rarely delicious. The vibe: split between speed and soul. Everyone agrees utility matters; the fight is whether faster also means emptier. And yes, people are losing sleep over it.
Key Points
- •The article defines its focus on generative AI (LLMs) and distinguishes it from other machine learning applications.
- •It proposes evaluating AI’s contribution by whether its outputs provide real utility (usefulness), separate from usability.
- •Digital information can deliver utility comparable to traditional sources, exemplified by learning DSP to program FM synthesis.
- •Assuming a best-case (reliable) generative AI, the piece asks how to apply it responsibly to produce valuable outcomes.
- •Technology is framed as anything that enables more efficient generation of utility, foundational to human progress.