31–40 of 168 posts
That said, all of these LLMs are interchangeable, there are no moats, and the profit will almost entirely be in the "last mile," in local subject matter experts applying this technology to their bespoke business processes.
I think the author's take is overly bleak. Yes, he supports his claim that AI businesses are currently money pits and unsustainable. But I don't think it's reasonable to claim that AI can't be profitable. This whole thing is moving so extremely fast. Models are getting better by the month. Cost is rapidly coming down. We broadly speaking still don't know how to apply AI. I think it's hubris to claim that, in the wake…
We are pretty much plateauing in base model performance since gpt4. It's mostly tooling and integration now. The target is also AGI so no matter your product you will get measured on your progress towards it. With new "sota" models popping up left and right you also have no good way of user retention because the user is mostly interested in the models performance not the funny meme generator you added. looking at you…
Probably very expensive to run of course, probably ridiculously so, but they were able to solve really difficult maths problems.
Are we in a bubble that's going to pop and take a large part of the economy with it? Almost certainly. Does it mean that the AI is a scam? Not really. After all, the Internet did not disappear after the dotcom burst, and, actually, almost everything we were promised by the dotcoms became reality at some point.
"doing everything on the internet" definitely worked out, but I don't see why that implies "GPU accelerated LLMs will replace large swathes of human labor" will also be true
You can bet that even if the specific forms attempted in this interval don't take hold, they will eventually.
You and I are too expensive, and have had too much power.
I believe this is a "good" bubble in the sense that the 19th century railroad bubble and original dot com bubble both ended up invested in infrastructure that created immense value. That said, all of these LLMs are interchangeable, there are no moats, and the profit will almost entirely be in the "last mile," in local subject matter experts applying this technology to their bespoke business processes.
how can massively buying hardware that will have to be thrown away in a few years be a "good" bubble in the sense of being a lasting infrastructure investment?
Earlier quoted context omitted.
We are pretty much plateauing in base model performance since gpt4. It's mostly tooling and integration now. The target is also AGI so no matter your product you will get measured on your progress towards it. With new "sota" models popping up left and right you also have no good way of user retention because the user is mostly interested in the models performance not the funny meme generator you added. looking at you…
So, how do you feel about the recent IMO stuff? Don't they cause a consistency problem for your view that we've plateaued-- to me at least, I felt we were something like two years away from this kind of thing. Probably very expensive to run of course, probably ridiculously so, but they were able to solve really difficult maths problems.
Earlier quoted context omitted.
"doing everything on the internet" definitely worked out, but I don't see why that implies "GPU accelerated LLMs will replace large swathes of human labor" will also be true
If there's anything that can be reliably predicated to be true over multiple decades it's that capitalism will continually seek to reduce labour costs and automate everything. You can bet that even if the specific forms attempted in this interval don't take hold, they will eventually. You and I are too expensive, and have had too much power.
what about improved life quality? what about an explosion of types of jobs?
> You and I are too expensive, and have had too much power.
do you think the average citizen (or the collective) have MORE power or LESS power than 100 years ago, than 200 years ago?
I think the author's take is overly bleak. Yes, he supports his claim that AI businesses are currently money pits and unsustainable. But I don't think it's reasonable to claim that AI can't be profitable. This whole thing is moving so extremely fast. Models are getting better by the month. Cost is rapidly coming down. We broadly speaking still don't know how to apply AI. I think it's hubris to claim that, in the wake…
"Cost is rapidly coming down" but capital expenditures are still high. They'll have to charge for this eventually, no?
We could compare it to the railroad boom, and the telecom boom - in both cases vast sums capital expenditures were made, and reasonable people might have concluded that eventually these expenses would have to be reimbursed through higher prices. However, in both cases, many firms simply went bankrupt and all that excess infrastructure went time to serve humanity for decades at lower cost.
I think the author's take is overly bleak. Yes, he supports his claim that AI businesses are currently money pits and unsustainable. But I don't think it's reasonable to claim that AI can't be profitable. This whole thing is moving so extremely fast. Models are getting better by the month. Cost is rapidly coming down. We broadly speaking still don't know how to apply AI. I think it's hubris to claim that, in the wake…
I believe this is a "good" bubble in the sense that the 19th century railroad bubble and original dot com bubble both ended up invested in infrastructure that created immense value. That said, all of these LLMs are interchangeable, there are no moats, and the profit will almost entirely be in the "last mile," in local subject matter experts applying this technology to their bespoke business processes.
> "good" bubble in the sense how can massively buying hardware that will have to be thrown away in a few years be a "good" bubble in the sense of being a lasting infrastructure investment?
AI-optimist or not, that's just shocking to me.