velocity and confidence over accuracy. the incentive structure of social media rewards the wrong things when it comes to ai information.
there is more content about ai on social media than there has ever been about any technology in history. linkedin is drowning in it. twitter moves three hundred posts about claude or chatgpt for every one that's worth reading. tiktok has entire creator economies built around "ai tips" that were accurate eight months ago and haven't been updated since. youtube has a whole ecosystem of thumbnails with shocked faces and titles like "this changes everything."
some of it is good. most of it isn't. and the thing that separates them is almost never the quality of the writing.
the incentive structure of social media rewards velocity and confidence over accuracy and nuance. a post that says "here are five prompts that will change how you work" gets ten times the engagement of a post that says "here's why this works in some contexts and not others." the first post is a dopamine delivery system. the second one requires the reader to think.
the result is an information environment where the most widely distributed ai content is systematically the least useful. the people who know the least post the most confidently. the people who actually understand the tools post less, because they know their knowledge is partial and context-dependent.
"the loudest voices in any new technology ecosystem are usually the people who learned just enough to feel certain."
it's specific. it leads with a number — "7 prompts," "3 things," "10 ways." it promises to save time without specifying time doing what. it presents a single use case as if it generalizes to all situations. it was accurate when it was written and hasn't been updated. it treats capability as a trick you can copy rather than a practice you develop. it optimizes for saving the post rather than applying it.
the most insidious version is content that's technically correct but missing critical context. "you can use claude to write your emails" is true. "here's why you should and exactly what context to give it and how to review what comes back" is useful. the first gets saved. the second gets done.
it's specific about what failed, not just what worked. it acknowledges that the tools change — what was true of gpt-4 is not necessarily true of claude 3.5 sonnet, and what's true today may not be true in three months. it gives you the framework, not just the output. it tells you what conditions have to be true for something to work.
the best ai content is written by people who are actively using the tools in real contexts and reporting back honestly. not as a brand exercise. not to build a following. because they're trying to figure something out and writing about it as they go.
the practical move isn't to stop consuming ai content. it's to develop a filter. when you encounter a piece of ai content, ask three things: does this person show what didn't work? does this generalize, or is it specific to one tool in one context? when was this written, and has the tool changed since then?
most content fails at least one of those questions. the content that passes all three is worth your time. there's less of it than the volume would suggest — but it exists, and it's worth finding.
the other move is to spend more time with the tools than with the content about the tools. no amount of social media consumption replaces an hour of actual use. the feed can point you toward something worth trying. it can't replace the trying.