jlorentz1

> To what end? To chase AGI, of course! An "open" lab does not have to stay open forever.

Are you confident that a step change in open source is unlikely? The industry seems to disagree, considering billions are being spent on them and billions are being spent to stay ahead of them. Open step changes have happened before (eg R1, or heck, self-attention). By the way, AIs themselves are quite good at writing GPU kernels now.

Enterprises absolutely care about benchmarks (especially internal ones, but the headline benchmaxxed ones too), and the Ant coding thing is a great example. How long did that last again? A few months? Illustrates my point perfectly. Switching is easy. Why would a business have any loyalty to one text->text endpoint over another? The consumer market may be less responsive to quality, sure, but it is more responsive to cost which I mentioned. It's also just not as big.

Well, I'm not really talking about freezing models forever either, I'm saying that nonstop training is a necessary part of their business. I don't think slowing down is untenable, I just think it's silly not to expect & account for ongoing training costs. That's all my original comment meant.

I also don't understand why you think the open labs couldn't catch up to a given level of quality. If something's been done twice already, why can't a well funded team of experts somewhere else do it a third time? Sounds like wishful thinking.

Two big differences are low switching costs and a higher rate of innovation.

1. They absolutely would, eventually, if open models surpassed Ant & OAI's flagships. Keep in mind "surpassed" encompasses both output quality and cost-saving architecture innovations like DSA, which may not be possible to apply to old models (and may take advantage of new hardware!)

2. "They're only as close..." is not natural law. You really think open weights couldn't catch up to a fixed target if Beijing makes it a priority? And what happens to their valuations if they abandon the goal of building AGI? There is no strategic alternative to constant training for these companies, which is why they're, uh, constantly training.

If they stop racing, the wave of open models will pass them and their inference margins will drop to zero. A realistic model of their operating costs surely must include ongoing training.

I'm similar strength (4k ogs) and I had the same experience. I think the essential difference is the cross-cut in this game can produce 3 groups (a connection on the shorter axis remains), whereas it produces 4 in Go. That makes it possible to rampage across the board, cutting a weaker player to ribbons while staying in one big group. Fun game.