AI mania isn't just a buzzword problem — it's quietly corrupting the quality of decisions being made at every level, from boardrooms to government policy, and knowing how to spot it is now a core AI literacy skill.
The Decision-Making Crisis Nobody's Naming
A widely-shared essay by writer Ludic argues that the current wave of AI enthusiasm isn't just producing bad software — it's producing bad thinking. Organisations are outsourcing judgement to AI tools before they've established what good judgement even looks like in their domain.
The pattern is consistent: a decision-maker encounters a complex problem, reaches for an AI tool, gets a confident-sounding output, and stops interrogating it. The AI didn't fail. The process did.
This isn't anti-AI pessimism. It's a practical warning: AI amplifies whatever reasoning process you feed into it. Weak input framing plus a powerful model equals a very convincing wrong answer.
The Practical Fix: Audit Your AI Decision Workflow Today
The good news is that this is a solvable problem, and you can start solving it right now. Before you use any AI tool for a consequential decision, run it through three quick checks.
1. Define the decision first, without AI. Write one sentence describing what you're actually deciding and what a good outcome looks like. If you can't do this, the AI definitely can't do it for you.
2. Ask the AI to argue the opposite. After getting your AI-generated recommendation, prompt it explicitly: "Now give me the strongest case against this conclusion." This forces the model out of confirmation mode and surfaces hidden assumptions.
3. Check what the AI didn't say. AI outputs are shaped by what's in training data. Ask: "What information would change this answer that you might not have access to?" It sounds simple. It catches a surprising number of errors.
These three steps take under five minutes and dramatically raise the quality of AI-assisted decisions. They're not workarounds — they're the workflow.
What This Means for Learners
The essay's core insight maps directly onto a skill gap most AI users haven't addressed: understanding how AI systems actually produce outputs, and therefore where they silently fail. If you're using AI tools professionally, understanding the mechanics behind language model behaviour isn't optional anymore — it's risk management.
Our course How Neural Networks Really Work gives you the mental model to understand why AI produces confident-sounding nonsense, which is the first step to catching it. For those working in environments where AI-assisted decisions carry real stakes, Leading AI Assurance covers exactly how to build oversight processes that don't slow teams down but do catch the failures that matter.
The readers who thrive in the next few years won't be the ones who use AI most — they'll be the ones who use it most critically.
