AI rarely fails with obvious nonsense. It fails with answers that sound right, read clean, and hold up just long enough to cause trouble. Plausibly Wrong breaks down how that happens.
Ask an agent a question the internet has an opinion about, and it doesn't look for the answer – it builds a case for the one its training data already handed it, using real tools to manufacture real-looking evidence. Here's how to make it prosecute the other side.
Ask your AI a direct question and it hedges, disclaims, and refuses to commit. That isn't caution for your benefit – it's two companies sanding every screenshottable risk off their product on the way to an IPO.
Correct an LLM and it says 'good catch' – then makes the same mistake in new words. Not because it's spineless, but because its wrong answer is still in the context window, and it cannot forget.
When Anthropic's status page showed elevated errors on June 16, the visible failures were the easy part. The dangerous part was the requests that succeeded.
Anthropic introduced its best model inside a flat subscription with a built-in two-week expiration. That's not a product tweak. It's compute economics showing through.
The AI mandate arrived with dashboards and OKRs. But has anyone asked the harder question: which work should exist at all?
The problem with AI is not the LinkedIn productivity flex. The problem is that its basic mechanism rewards plausibility rather than correctness, and people are increasingly using that output in decisions that are difficult or impossible to reverse.
Every month another tech company announces layoffs and credits AI. The press release says efficiency. The balance sheet says something else.
Having one model produce the work and another inspect the result is not review. Review means seeing the work happen, not just judging what survived.
Yelling at AI used to work. The model would flinch, reread everything, and come back with better output. That era is over. What replaced it is worse than the problem the frontier AIs solved.
Your model can generate a photorealistic Elon Musk in a Carmen Miranda fruit hat. Ask it to forecast revenue and you get the four-quarter average. The expensive part is In-Context Learning. Guess what it avoids.
Engineering assumes a target that holds still. Frontier language models do not. The fastest way to identify someone who has not run an AI system in production is to watch them say 'prompt engineering' anyway.
AI often sounds like the coworker you trust least: flattering, meandering, unwilling to disagree, eager to sound helpful whether or not it did the work. That's not a bug. It's RLHF doing what it was trained to do.
Different agents in a workflow don't create independence when they share the weights. The architecture is real engineering. It's just not the check your slide says it is.
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