> it seems fairly apparent now that AI has largely hit a brick wall in terms of the benefits of scaling

I don't know about the rest, but I spoke up because I didn't want to hit a brick wall, I want to keep going! I still want to keep going! But if accurate predictions (with good explanations) aren't a reason to shift resource allocation then we just keep making the same mistake over and over. We let the conmen come in and people who get too excited by success that they get blind to pitfalls.

And hey, I'm not saying give me money. This account is (mostly) anonymous. There's plenty of people that made accurate predictions and tried working in other directions but never got funding to test how methods scale up. We say there's no alternatives but there's been nothing else that's been given a tenth of the effort. Apples and oranges...

You need to model the business world and management more like a flock of sheep being herded by forces that mostly don't have to do with what actually is going to happen in future. It makes a lot more sense.

It's all a big hype bubble and not only is no one in the industry willing to pop it, they actively defend against popping a bubble that is clearly rupturing on its own. It's endemic of how modern businesses no longer care about a proper 10 year portfolio and more about how to make the next quarter look good.

There's just no skin in the game, and everyone's ransacking before the inevitable fire instead of figuring out how to prevent the fire to begin with.

  > mostly don't have to do with what actually is going to happen

I'm saying we should have memory. Look at predictions people make. Reward accurate ones, don't reward failures. Right now we reward whoever makes the craziest predictions. It hasn't always been this way, so we should go back to less crazy

But if you had been wrong and we would now have had superintelligence, the upside for its owners would presumably be great.

... Or at least that's the hypothesis. As a matter of fact intelligence is only somewhat useful in the real world :-)

Those people always do that. Shouting about cryptocurrencies and NFTs from the rooftops 3-4 years ago, now completely gone.

I suspect they're the same people, basically get rich quick schemers.

After their success, I definitely saw a ton of blog posts and general "AI chatter" that to get to AGI all you really needed to do (obviously I'm simplifying things a bit here) was get more data and add more parameters, more "experts", etc. Heck, OpenAI had to scale back it's pronouncements (GPT 5 essentially became 4.5) when they found that they weren't getting the performance/functionality advances they expected after massively scaling up their model.

This is an improvement for sure, but LLMs themselves are definitely hitting a wall. It was predicted that scaling alone would allow them to reach AGI level.

This is a genuine attempt to inform myself. Could you think to those sort of claims from experts at the top?

There were definitely many other prominent researchers who vehemently disagreed, e.g. Yann LeCun. But it's very hard for a layperson (or, for that matter, another expert) to determine who is or would be "right" in this situation - most of these people have strong personalities to put it mildly, and they often have vested interests in pushing their preferred approach and view of how AI does/should work.

A year ago I expected a golden age of local model intelligence integrated into most software tools, and more powerful commercial tools like Google Jules to be something used perhaps 2 or 3 times a week for specific difficult tasks.

That said, my view of the future is probably now wrong, I am just saying what I expected.