April 15, 2026
Bring your VPN and wizard hat
The Future of Everything Is Lies, I Guess: New Jobs
Wizards, “Meat Shields,” and a UK Block: Comment Section Meltdown
TLDR: An AI essay proposes quirky new roles—prompt “incanters,” quality-control fixers, and even “meat shields”—and the comments explode. Readers blast the dehumanizing language, mock the witchy vibe as “magical thinking,” and rage about UK blocking and archive links, turning a jobs piece into a culture clash over words and access.
The author pitched a world of new AI-adjacent jobs—think “incanters” who whisper prompts to chatbots, process engineers who catch AI goofs, statistical engineers who tame weird model behavior, plus model trainers, “meat shields” to take the blame when AI messes up, and haruspices to interpret the machine’s moods. But the comments? Pure fireworks.
One reader slammed the term “meat shields” as dehumanizing, dropping a Jeffrey Dahmer reference and asking if this is “normal” now. Others rolled their eyes at the mystical job names, with one regular saying the whole taxonomy leans into “magical thinking” and the author’s witchy vibe. Cue jokes about wizard hats and AI entrails—because yes, haruspices literally read guts in ancient times.
Then came the UK drama. Multiple folks reported the post is blocked with a “Unavailable Due to the UK Online Safety Act” wall unless you’re on a VPN. The ritual continues: someone posts an archive link, another asks why a static site needs archiving, and a veteran points out this is part 9 of a 10-part daily drop—and every day, it’s the same chorus. Love it or roast it, the crowd can’t stop talking. The real action isn’t the new jobs—it’s the comment section’s battle over language, vibes, and access.
Key Points
- •The article outlines emerging roles at the human–ML interface: incanters, process engineers, statistical engineers, model trainers, accountability roles, and interpreters of model behavior.
- •Incanters specialize in crafting inputs and managing context to improve LLM performance, accounting for prompt sensitivity and degradation with long inputs.
- •A legal-focused quality-control workflow is proposed where deliberate errors are seeded and subsequently verified by editors, supported by provenance tracking and tools like LexisNexis.
- •Process engineers would design, tune, and measure such QC workflows, train personnel, and calibrate automation levels versus manual work.
- •Statistical engineers would model and control variability in ML systems (e.g., order effects), drawing on psychometrics-like methods for domain-specific performance evaluation.