[cm2187]

What I don't understand in this AI race is that the #2 or #3 is not years behind #1, I understand it is months behind at worst. Does that headstart really matter to justify those crazy comps? Will takes years for large corporations to integrate those things. Also takes years for the general public to change their habits. And if the .com era taught us anything, it is that none of the ultimate winners were the first to market.

[peterlk]

There is a group of wealthy individuals who have bought in to the idea that the singularity (AIs improving themselves faster than humans can) is months away. Whoever gets there first will get compound growth first, and no one will be able to catch up.

If you do not believe this narrative, then your .com era comment is a pretty good analysis.

[godelski]

  > There is a group of wealthy individuals who have bought in to the idea that the singularity is months away.

My question is "how many months need to pass until they realize it isn't months away?"

What, it used to be 2025? Then 2027? Now 2030? I know these are not all the same people but there are trends of to keep pushing it back. I guess Elon has been saying full self-driving is a year away since 2016 so maybe this belief can sustain itself for quite some time.

So my second question is: does the expectation of achievements being so close lengthen the time to make such achievements?

I don't think it is insane to think it could. If you think it is really close you'd underestimate the size of certain problems. Claim people are making mountains out of molehills. So you put efforts elsewhere, only to find that those things weren't molehills after all.

Predictions are hard and I think a lot of people confuse critiques with lack of motivation. Some people do find flaws and use them as excuses to claim everything is fruitless. But I think most people that find flaws are doing so in an effort to actually push things forward. I mean isn't that the job of any engineer or scientist? You can't solve problems if you can't identify problems. Triaging and prioritizing problems is a whole other mess, but it is harder to do when you're working at the edge of known knowledge. Little details are often not so little.

[overfeed]

> My question is "how many months need to pass until they realize it isn't months away?"

It's going to persist until shareholders punish them for it. My guess is it's going to be some near-random-trigger, such as a little-known AI company declaring bankruptcy, but becoming widely reported. Suddenly, investing in AI with no roadmap to profitability will become unfashionable, budget cuts, down-rounds, bankruptcies and consolidation will follow. But there's no telling when this will be, as there's elite convergence to keep the hype going for now.

[xorcist]

Indeed, this will continue as long as the market allows it to. There is a well known quote about how long markets can stay irrational, but I would like to remind where we are right now:

Telco capex was $100 billion at the peak of the IT bubble, give or take. There's going to be $400 billion investments in AI in 2025.

[tonyhart7]

why we acting like this group wealthy individuals don't know what happening???

they know it may be or not gonna happen because months its ridiculous, but they still need to do it anyway since if you not gonna ride it, you are gonna miss the wave

stock market has not been rational since??? forever??? like stop pumping and dumping happen all the time

[godelski]

What I don't understand is with such small of a gap why this isn't a huge boon for research.

While there's a lot of money going towards research, there's less than there was years ago. There's been a shift towards engineering research and ML Engineer hiring. Fewer positions for lower level research than there were just a few years ago. I'm not saying don't do the higher level research, just that it seems weird to not do the lower level when the gap is so small.

I really suspect that the winner is going to be the one that isn't putting speed above all else. Like you said, first to market isn't everything. But if first to market is all the matters then you're also more likely to just be responding to noise in the system. The noisy signal of figuring out what that market is in the first place. It's really easy to get off track with that and lose sight of the actual directions you need to pursue.

[storus]

LLaMA 4 is barely better than LLaMA 3.3 so a year of development didn't bring any worthy gains for Meta, and execs are likely panicking in order not to slip further given what even a resource-constrained DeepSeek did to them.

[godelski]

  > given what even a resource-constrained DeepSeek did to them.

I think a lot of people have a grave misunderstanding of DeepSeek. The conversation is usually framed comparing to OpenAI. But this would be like comparing how much it cost to make the first iPhone (the literal first working one, not how much each Gen 1 iPhone cost to make) with the cost to make any smartphone a few years later. It's a lot easier and cheaper to make something when you have an example in hand. Just like it is a lot easier to learn Calculus than it is to invent calculus.

Which that framing weirdly undermines DeepSeek's own accomplishments. They did do some impressive stuff. But that's much more technical and less exciting of a story (at least to the average person. It definitely is exciting to other AI researchers).

[p1esk]

If comparing Deepseek to OpenAI, sure. But OP is comparing DeepSeek to Meta: both are followers.

[godelski]

Yes, but there's also more to what I said than just leader-follower.

[wiseowise]

But muh China.

[IshKebab]

Yeah this makes zero sense. Also unlike a pop star or even a footballer who are at least reasonably reliable, AI research is like 95% luck. It's very unlikely that any AI researcher that has had a big breakthrough will have a second one.

Remember capsule networks?