The Pinnacle of Enshittification, or Large Language Models
The Pinnacle of Enshittification, or Large Language Models
By Michał Górny Published: April 5, 2026
A Gentoo developer's comprehensive case against LLMs covering data acquisition ethics, environmental costs, copyright erosion, open-source community fracturing, labor displacement, and psychological dependency. Argues LLMs are fundamentally unethical regardless of utility.
In Pursuit of Nonhuman Intelligence
Humans have historically sought nonhuman intelligence through animal research, SETI, and science fiction. Our conception of intelligence is anthropocentric: we recognize intelligence in creatures that communicate like us. LLMs exploit this through sophisticated language mimicry. Society has embraced "bullshit" — meaningless but plausible discourse — and LLMs trained on such content excel at generating more of it.
How LLMs Actually Work
LLMs operate through next-token prediction. "You can train a parrot to respond to some questions, but said parrot won't comprehend the questions." They lack genuine understanding; they remix training data.
The Data Problem
Major LLM companies employ ethically questionable acquisition: destructively scanning physical books, torrenting pirated media, processing open-source repos, aggressively scraping websites with unthrottled bots ignoring robots.txt. Services like Gentoo Bugzilla experience server strain from scrapers — "like a constant DDoS attack" — harming genuine users.
Environmental and Economic Costs
Hardware scarcity (video cards, memory prices skyrocketing), rapid model obsolescence generating e-waste, massive energy and water consumption, companies "quietly removing" climate pledges for AI expansion.
Copyright and Attribution
Chatbots can output near-verbatim copies of published works with no attribution mechanism. "Copywashing" emerged: using LLMs to recreate projects while removing attribution. Backfired when Claude Code's leaked source was copywashed to prevent DMCA takedowns.
Fracturing FLOSS
Three camps: projects rejecting LLM contributions, projects requiring human review, projects relying on LLM code. Dormant projects suddenly releasing massive changes. LLM-generated bug analyses "sound plausible at first but are entirely wrong." Curl abandoned bug bounty due to LLM-generated low-quality reports. Trust erosion: formal writing treated with suspicion.
Malicious Applications
Sophisticated spam, automated deception (phone calls indistinguishable from legitimate), search pollution, industrial-scale disinformation, accountability evasion through decision-deferral to machines.
Labor and Creativity Loss
Explicit worker replacement marketing. Job losses, artist displacement, translation studio closures, increased workload for remaining staff, "bleak repetitive slop" replacing human work.
Psychological Dependency
Users experience distress when services go down, anthropomorphize chatbots. Dependencies are precarious: companies can raise prices, discontinue, or manipulate.
Conclusion
LLMs are "fundamentally unethical regardless of utility." Compared to asbestos — useful but poisonous. Widespread adoption isn't inevitable. "Getting left behind" might be beneficial.