After AI takes everything
Engineers and readers react to a long essay on “what’s left for humans after AI,” focusing less on abstract philosophy and more on concrete issues of code quality, jobs, and economic power. Many argue current AI tools churn out “slop” that managers and weak developers can’t reliably evaluate, threatening to accelerate already-poor software practices and deskill the profession, while others see them as acceptable “good enough” accelerants. Underneath the technical angst runs a larger worry: if AI really does automate much knowledge work, who will capture the resulting wealth, and can society avoid a repeat of past industrial revolutions where efficiency gains mainly deepened inequality.
AI-generated code quality and “slop”
- Many commenters report that LLM-written code is often subtly wrong or structurally poor, even on simple tasks (e.g., a byte-based LRU implemented as entry-count-based).
- Review cost frequently outweighs generation speed; experts say it’s often faster and safer to write the code themselves.
- Others reply that human code is also buggy and duplicated; they argue LLM output only needs to reach “good enough,” not elegant.
- Concern that managers can’t recognize slop, but will still drive adoption, accelerating software “enshittification.”
Taste, craftsmanship, and responsibility
- Several people frame the difference as “having taste” or “giving a shit”: clear mental models, consistent structure, and willingness to clean up.
- LLMs in unskilled hands magnify bad taste; some see this as a deserved existential crisis for shallow practitioners.
- Others counter that elegance has always taken a back seat to shipping; AI just makes this more visible.
Timelines and hype skepticism
- Strong pushback on claims that AI will “take everything” in 18–24 months; predictions are seen as unfalsifiable hype.
- Some argue model improvements are now incremental, not paradigm shifts.
Jobs, skills, and economic impact
- Debate over whether most work disappears vs. shifts to new roles (skilled trades, healthcare, deeper-stack engineering).
- Worry that even “AI-proof” jobs will be flooded by displaced white‑collar workers, depressing wages.
- Core anxiety: not just losing a job, but losing perceived worth and the ability to support a family.
Who benefits and how to respond
- Repeated question: if AI generates massive wealth, who captures it—few elites, or society broadly?
- Some see parallels to past tech revolutions; best case is intense competition driving AI costs down and benefits to consumers.
- Others stress organizing and policy (copyright reform, mandatory licensing, redistribution) over individual “hustle” narratives.
Data, ownership, and paying humans
- Idea that AI is parasitic on human-created data; suggestions that models should pay individuals for content usage or fund large pools of creators.
- Counterpoint that trying to assign per-person value becomes equivalent to taxation/redistribution problems.
Meta: AI-written essays and discourse fatigue
- Several think the linked essay (or its English version) shows LLM hallmarks: bloated length, repetitive slogans, over-styling.
- Frustration with long, philosophical “how to adapt” pieces that seem to skip over concrete, material questions.