Hacker News new | ask | show | jobs
by IAmGraydon 4 hours ago
I remember back when ChatGPT first came out, there was an article on HN about this AI researcher who worked for one of the big companies (I think Google) who came to believe the model was truly intelligent and that it was being abused by being locked in the machine. We all laughed as the guy had clearly lost his mind to AI psychosis. What we didn’t realize is he may have been patient zero.

This is the delusion that went viral, or at least one version of it. It all leads back hijacking the human tendency to anthropomorphize, leading to the belief that an LLM is somehow something more than it actually is. So the question is - what breaks the spell? Failed attempts to automate that don’t work out? The realization that the return on money spent doesn’t make sense? Furthermore, how to we accelerate the eventual realization?

2 comments

> what breaks the spell?

If throwing more compute at the problem keeps only resulting in incremental gains, I think that should do it. It goes one of 2 ways, really. Either we can throw enough compute at pre-training that results in infinitely more capable models to the point that the cost is now justified [1], or, we hit a scaling wall, get stuck with what we have now (or at that time) and the valuations crash knowing that "this is it" for the foreseeable future without a big breakthrough.

The labs go bankrupt or get acquired by the typical giants (Google, Microsoft, Amazon), the models get rolled into GCP, Azure, and AWS as a service, and that's it. It becomes another dev tool, much like a new IDE.

[1] cost being justified I'd rank as "your average non technical PM can now end to end develop robust, production software free of most serious vulnerabilities." model & tool capabilities that would allow you to hire a small team of non-techincal roles, for half the salary, that can produce the output of a large engineering org. If that doesn't happen, I don't see how the current buildout is sustainable.