Comment by r_lee
1 day ago
I knew that there was no real moat from the very start, I mean, these things were close enough from the very start, how could it not result in a race to the bottom, especially as you can't really prevent distillation reliably?
I support and use open models as much as possible, but I'm not totally convinced that OAI or Anthropic have no moat, even as open models catch up to the frontier. Serving and inference are still hard problems when you're talking about a 2 trillion parameter model. Fine-tuning, if that remains a realistic need for businesses, is also a difficult infra problem at that scale. In the most bearish case, where there is no competitive advantage to using their models, big labs still have an advantage in this area.
Maybe there is some threshold where the price/quality math for your standard business tips in favor of smaller models and self-hosting the entire stack. I'd certainly love that.
Also people are people and they will get emotionally attached to claude :)
Or ready to get a divorce... The load-bearing seam of the relationship is affection versus annoyance.
2 replies →
I bet many people who use Claude at their workplaces still call it "chatgpt" because chatgpt is getting the kleenex treatment and they have no idea what Claude is
OK, but somehow there won't be companies who will sell you appropriate hardware and a turnkey system to serve inference? Or companies that will help you fine tune popular models?
It's not about money, it's about control. Companies have lots of money and want control over their key technology.
There will be companies doing this. I'm saying the labs are well positioned to be those companies, as they effectively are those companies right now.
The same dynamics that define the public cloud ecosystem are at play here. What AWS sells you is access to appropriate hardware and turnkey infra for your needs. Looking at the cloud industry over the last 20 years, I find it hard to believe that it is impossible to build a moat or a huge business around this.
True.
The problem is serving is a skill readily mastered by the hyperscalers. That's their MO.
All they need is weights to serve. And the open models provide that.
OpenAI is relatively well placed in that they have inference chips they've designed and they own compute.
A race to the bottom is where you lower standards, wages, or regulations to cut costs and attract business. What's actually happening is the opposite: a race to the top. Every model is trying to get better. Simultaneously they also happen to be getting more cost effective, but it's sort of a coincidence. Companies still require very good models, but they are not picking the "absolute best at any cost" anymore, because it turns out "any cost" isn't worth it.