GoKawiilSoftware engineer warns AI agents inherit 'bad priors' from non-expert training feedback
An experienced software engineer argues that AI agents perform well in domains their operators understand deeply, but operators are blindly trusting model judgment in countless other areas they cannot personally evaluate. The author points to 'slop'—technically functional but poor-quality code patterns—as evidence that models were rewarded during training by non-experts, embedding flawed defaults into the model's behavior.
GoKawiil's interpretation of the reporting above, not reported fact.
This matters because as agentic AI spreads into finance, law, and operations, the same reward-shaping problems seen in coding could be silently baking bad habits into decisions nobody is qualified to catch. The piece also flags that models lack long-term coherence or 'regret,' meaning errors can compound across stacked agentic actions without correction.
- Expertise reveals model flaws invisible to non-experts, who instead reinforce bad behaviors during training.
- 'Slop'—technically correct but poor-quality output—reflects misaligned reward signals from evaluators, not just engineers.
- AI agents lack mechanisms for long-term coherence, so errors can compound unnoticed across sequential actions.
The Alignment Problem by Brian Christian — If this piece left you chewing on "aligned to whom?", Brian Christian's The Alignment Problem is the natural next read — it digs into how machine learning systems absorb goals and priors that nobody explicitly specified. It's a great companion for engineers shipping agents who want vocabulary for the unknown-unknowns they can't personally evaluate.
See The Alignment Problem by Brian Christian on Amazon → Affiliate link — we may earn a commission on purchases, at no extra cost to you. Product picked by AI based on this article; it is not a tested recommendation.Source: hyperbo.la — Ryan Lopopolo, 2026-09-13
Published there as: “Aligned to whom?”
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