Replies to comments on my "LLMs are eroding my career" post

Perceptions of Software, Capitalism, and “Things That Work”

• Several argue customers and investors now prioritize cheap, “good enough” outputs that appear to work over robustness or ethics.
• Others push back, saying poor quality causes constant low-grade user rage, but people feel powerless to hold vendors accountable.
• Public companies, short-termism, and shareholder pressure are blamed for cutting corners and incentivizing grift.

AI, Art, and Resentment in Creative Fields

• Many note intense hostility from artists, tied not just to job loss but to being mocked as replaceable and dismissed by AI boosters.
• Some find AI art/copy “good enough” for commercial uses; others highlight obvious flaws once you know the domain (e.g., surfing).
• A minority admits schadenfreude at previously “arrogant” professions being automated, while others find this attitude cruel and corrosive.

Knowledge, Ability, and What Sets Workers Apart

• Common theme: “mere knowledge” no longer differentiates; ability to ship, solve messy problems, and communicate does.
• Comparisons to plumbers and apprentices: tools (including LLMs) raise the floor, but complex work still needs judgment, responsibility, and trust.
• Some emphasize adaptability and “wayfinding” (integrating conflicting requirements) as future-proof skills; others insist capital ownership ultimately dominates.

Demand for Software and Job Compression

• Debate over whether software demand has an upper bound.
  • One side: complexity and automation needs are effectively unbounded; Jevons-like effects increase demand.
  • Other side: there are limits to useful complexity and to what people will pay for; many routine dev jobs may commoditize or vanish.
• Some teams report clear “more output because of LLMs” dynamics; others predict high future unemployment for tech workers.

AI Trajectory, Limits, and Hype

• Skeptics note assumptions behind AI maximalism: continued rapid improvement, unlimited capital, and a functioning economy after mass displacement.
• Others counter that even modest continued gains and falling costs could drastically change day-to-day software work within a decade.
• Disagreement over whether current deep-learning approaches can ever “learn good engineering principles” or handle truly novel work.

Societal and Ethical Fears

• Worries about mass unemployment, unrest, and increasing inequality if knowledge work is broadly automated.
• Some foresee elites using AI and agents to concentrate power further, with fewer human checks on “evil software.”
• A few hold out hope for political action or post-scarcity outcomes, but most express anxiety and uncertainty.