Comment by probably_wrong
17 hours ago
Speaking for myself: because if the hype were to be believed we should have no relational databases when there's MongoDB, no need for dollars when there's cryptocoins, all virtual goods would be exclusively sold as NFTs, and we would be all driving self-driving cars by now.
LLMs are being driven mostly by grifters trying to achieve a monopoly before they run out of cash. Under those conditions I find their promises hard to believe. I'll wait until they either go broke or stop losing money left and right, and whatever is left is probably actually useful.
The way I've been handling the deafening hype is to focus exclusively on what the models that we have right now can do.
You'll note I don't mention AGI or future model releases in my annual roundup at all. The closest I get to that is expressing doubt that the METR chart will continue at the same rate.
If you focus exclusively on what actually works the LLM space is a whole lot more interesting and less frustrating.
> focus exclusively on what the models that we have right now can do
I'm just a casual user, but I've been doing the same and have noticed the sharp improvements of the models we have now vs a year ago. I have OpenAI Business subscription through work, I signed up for Gemini at home after Gemini 3, and I run local models on my GPU.
I just ask them various questions where I know the answer well, or I can easily verify. Rewrite some code, factual stuff etc. I compare and contrast by asking the same question to different models.
AGI? Hell no. Very useful for some things? Hell yes.
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