"I think it's long past time I start discussing "artificial intelligence" ("AI") as a failed technology. Specifically, that large language models (LLMs) have repeatedly and consistently failed to demonstrate value to anyone other than their investors and shareholders. The technology is a failure, and I'd like to invite you to join me in treating it as such. I'm not the first one to land here,"
"And just to be clear, describing "AI" as a failure doesn't mean it doesn't have useful, individual applications; it's possible you're already thinking of some that matter to you. But I think it's important to see those as exceptions to the technology's overwhelming bias toward failure. In fact, I think describing the technology as a thing that has failed can be helpful in elevating what does actually work about it. Heck, maybe it'll even help us build a better alternative to it."
"The first is that as a product class, "AI" is a failed technology. I don't think it's controversial to suggest that LLMs haven't measured up to any of the lofty promises made by their vendors. But in more concrete terms, consumers dislike "AI" when it shows up in products, and it makes them actively mistrust the brands that employ it."
Large language models have repeatedly failed to deliver promised product value, producing benefits mainly for investors and shareholders while imposing widespread costs. Consumers often dislike integrated "AI" features and lose trust in brands that deploy them. The platforms carry significant environmental impacts, disregard copyright, and exploit labor and data sources. LLM outputs remain error-prone, hallucinate, and perform best only in narrow tasks, limiting real-world utility. The technology concentrates power and profit with a few firms and investors, sustaining hype cycles. Treating LLMs as a failed technology can clarify what genuinely works and motivate alternatives.
Read at Ethanmarcotte
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