[hn_throwaway_99]

Thank you so much for this comment. I don't really understand the need for people to go straight to semi-conspiratorial hypotheses, when the simpler explanation makes so much more sense. All the evidence is that this model is much larger than previous ones, so they must charge a lot more for inference because it costs so much more to run. OpenAI were the OGs when it came to scaling, so it's not surprising they went this route and eventually hit a wall.

I don't at all blame OpenAI for going down this path (indeed, I laud them for making expensive bets), but I do blame all the quote-un-quote "thought leaders" who were writing breathless posts about how AGI was just around the corner because things would just scale linearly forever. It was classic "based on historical data, this 10 year old will be 20 feet tall by the time he's 30" thinking, and lots of people called them out on this, and they either just ignored it or responded with "oh, simple not-in-the-know peons" dismissiveness.

[bee_rider]

It is weird because this is a board for working programmers for the most part. So like, who’s seen a gram conspiracy actually be accomplished? Probably now many. A lackluster product that gets released even though it sucks because too many people are highly motivated not to notice that it sucks? Everybody has experienced that, right?

[glenstein]

Exactly. Although I wouldn't even say they have blinders, it seems like OpenAI understands quite well what 4.5 can do and what it can't hence the modesty in their messaging.

To your point, though, I would add not only who has seen any grand conspiracy actually be accomplished, who has seen one even attempted and kept under wraps? Such that the absence of corroborating sources was more consistent with an effectively executed conspiracy theory than the simple absence of such a plan.

[danielbln]

It works until it doesn't and hindsight is 20/20.

[hn_throwaway_99]

> It works until it doesn't

Of course, that's my point. Again, I think it's great that OpenAI swung for the fences. My beef is again with these "thought leaders" who would write this blather about AGI being just around the corner in the most uncritical manner possible (e.g. https://news.ycombinator.com/item?id=40576324). These folks tended to be in one of two buckets:

1. "AGI cultists" as I called them, the "we're entering a new phase of human evolution"-type people.

2. People who had a motive to try and sell something.

And it's not about one side or the other being "right" or "wrong" after the fact, it's that so much of this just sounded like magical thinking and unwarranted extrapolations from the get go. The actual experts in the area, if they were free to be honest, were much, much more cautious in their pronouncements.

[danielbln]

Definitely, the grifters and hypesters are always spoiling things, but even with a sober look it felt like AGI _could_ be around the corner. All these novel and somewhat unexpected emerging capabilities as we pushed more data through training, you'd think maybe that's enough? It wasn't and test time compute alone isn't either, but that's also hindsight to a degree.

Either way, AGI or not, LLMs are pretty magical.

[snovv_crash]

If you've been around long enough to witness a previous hype bubble (and we've literally just come out of the crypto bubble), you should really know better by now. Pets.com, literally an online shop selling pet food, almost IPOd for $300M in early 2000, just before the whole dot-com bubble burst.

And yeah, LLMs are awesome. But you can't predict scientific discovery, and all future AI capabilities are literally still a research project.

I've had this on my HN user page since 2017, and it's just as true as ever: In the real world, exponentials are actually early stage sigmoids, or even gaussians.

[baxtr]

Well that's only because YOU don't understand exponential growth! No human can! /s

[Kye]

In fundamental science terms, it also proves once and for all that more model doesn't mean more better. Any forces within OpenAI pushing to move past just growing the model for gains now have a strong argument for going all-in on new processes.