one year on
Blogger's anti-AI screed says the next person to mention AI gets a 'complimentary chiropractic adjustment'
A viral blog post argues that most AI initiatives are grifts and that companies should fix basic operations instead of chasing hype
A software engineer and former data scientist has published a blistering essay titled “I Will Fucking Piledrive You If You Mention AI Again,” arguing that most corporate AI initiatives are grifts perpetrated by incompetent leaders. The author, writing under the pseudonym Ludicity, claims that the vast majority of companies have no genuine use case for AI and should instead focus on fixing basic operational failures like database backups and documentation.
“Unless you are one of a tiny handful of businesses who know exactly what they’re going to use AI for, you do not need AI for anything,” the post states. It points to Scale’s 2024 AI Readiness Report and mocks the chart suggesting only 8% of companies have seen failed AI projects. It also notes that friends at FAANG have revealed faked AI demos.
On Hacker News, the post has accumulated 968 points and 563 comments. Users broadly agree that the term “AI” has been diluted by marketing. One commenter notes that “the concept of AI itself increasingly becomes more muddled until it becomes indistinguishable from a word like ‘technology.’” Another laments that “the ML part is amazing” and “the generative side is not even a solution to any particular problem.”
The commenter surfingdino further criticizes companies like Adobe for using their dominant position to override legal frameworks and grab content to train models they resell.
The record
Comments that the name 'AI' is being diluted by marketing and will fade as a trend, leaving only specific enhancements under other names.
Agrees that genAI has stolen attention from ML and is a problem forced on users by companies like Adobe.
Wonders what happened to the term 'ML', saying it is a more apt description of the current wave than 'AI'.
Suggests OpenAI bet on scaling leading to AGI but the math doesn't work that way; next-token prediction is not intelligence.
Argues that emergence is real and with enough recursive setup, limitations may become less important, though it may take time.
Compares expecting AGI from larger models to locking a mouse in a library and expecting it to become super intelligent.
Countered that transformers do learn things, unlike a mouse, so the comparison is flawed.
Questions whether gen AI truly learns or is just efficient compression and search, like WinRAR or Google.
One year later — open only if you can handle spoilers
The post became a touchstone for AI skepticism in engineering circles, frequently referenced in discussions about hype cycles. The author's prediction that most AI initiatives would fail was partly borne out by subsequent studies showing high failure rates for enterprise AI projects.
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