alt Hacker NewsUltimately this isn't "AI's fault". As the article points out, clueless executives who don't understand the capabilities and limitations of specific LLMs push the technology on workers so they can say they are "using AI to improve efficiency".
Replies
Which clueless executives are we blaming? The ones who purchase products from vendors like OpenAI or AI-powered products that run API calls through OpenAI et al., or the vendors themselves who make bombastic claims about capabilities?
As an analogy, I don’t know if I fully blame my executives for buying Microsoft Teams when Microsoft Teams turns out to suck. Microsoft promised to my company’s executives that Microsoft Teams doesn’t suck just like Sam Altman promised that AI is so powerful and so capable of exceeding the capabilities of humans that it’s dangerous.
Maybe at some point some of the B2B customers of AI products will be suing their vendors for the kind of grossly exaggerated marketing we are seeing right now. I think that SaaS companies would never get away with it.
I don’t know that the executives at my company suddenly lost their mind and decided that hundreds of dollars of AI spend per employee is suddenly justified even though approving a simple $##/month/employee SaaS tool was previously a rigorous slog. The only reason the executives at my company suddenly changed their mind on frugality is becausey they were sold a lie by a huge industry.
We could consider them clueless for falling for that, or an alternate viewpoint is that they are victims of widespread fraud.
Don't underestimate the ability of workers to completely misunderstand the capabilities and drive it from the bottom up too.
Half my colleagues are besotted with the technology and unaware that it is shaping their decision making, not the other way around.
But isn’t this also just bad implementations of AI? “It got a stupid simple answer wrong”, seems like a gpt-4o on a buggy RAG implementation from 2 years ago problem.
Hard to fathom a modern model would get any of this basic stuff wrong. I know accountants using it for (a lot more complex but similar problem) tax law parsing and it going through flawlessly.
There seems to be some bidirectional media gaslighting happening here where the doomers and glazers are talking past each other a bit.
Given how the AI industry lies about capabilities and is pushing the rollout with all their weight, the blame doesn’t go fully on their customers
I don't know, it feels like "reading a claims summary and classifying the type of claim" should be the bread and butter LLM use case? Not that executives are blameless for pushing AI everywhere, but we should also be able to "blame" AI for doing poorly at a task it's supposed to be good at.