The coming knowledge-work supply-chain crisis

Reliability, Confidence, and “Calculator” Analogies

• Many see the core flaw as: LLMs are confidently, unpredictably wrong; you must review every output.
• Calls for “confidence scores” run into: token probabilities don’t map to truth, only to “looks like human text.”
• Models can still hallucinate obviously wrong things (e.g., glue on pizza, fake command-line flags, imaginary hardware).
• Unlike calculators (single right answer, rarely fail, no one re-checks), language is inherently probabilistic and multi‑valid; people fear we’ll treat LLMs like calculators anyway and stop checking.

Review Burden and Hypervigilance

• You can get 9 good PRs then a catastrophic 10th, so reviewers must treat all LLM code like risky intern work.
• Passive oversight is cognitively exhausting; parallels to self‑driving cars and self‑checkout: “monitoring” is a bad human task.
• Senior engineers report burnout from reviewing growing volumes of often‑opaque AI code, with little mentoring payoff.

Juniors, Learning Ladders, and Labor Structure

• Concern: if LLMs do “junior” work, how do humans gain the experience needed to become seniors?
• Counter: LLMs mostly replace “copy‑from‑StackOverflow” coders; serious juniors still read docs, reason, and learn.
• Some foresee law/accounting–style pyramids: layers of juniors and seniors iteratively editing AI output.
• Others argue LLMs don’t learn from feedback today, so “tutoring the model” yields no compounding return.

Testing, Specs, and Viable Use Cases

• Strong theme: rely less on trust, more on tests and (ideally) formal methods.
• Proposed workflow: generate tests (reviewed), then iterate LLM‑generated code until tests pass.
• LLMs are viewed as very useful for: bug‑finding, code search, low‑risk utilities, info retrieval with human fact‑checking, and ambient dictation in medicine.

Meaning-Making and Decision Work

• The article’s claim that “meaningmaking” is uniquely human is contested: ML can score options given criteria, but humans must define those criteria and beat other humans (e.g., in trading).
• Others argue the hardest part is externalizing tacit expert judgment into explicit frameworks models (and juniors) can use.

Organizational, Data, and Job-Quality Concerns

• Fear of complacency once models feel “95% right,” enabling subtle errors, manipulation, or prompt‑injection–style attacks.
• Worries that future training data will degrade (enshittified web, AI‑generated noise), reducing model quality.
• Many dislike a future where skilled people mostly validate stochastic parrots, analogous to self‑checkout supervisors or outsourced body‑shops.
• Several commenters think “decision velocity” and exponential productivity are overstated; real bottlenecks are prioritization, strategy, user adoption, and maintaining quality.