alt Hacker News1. Mainstream users don't care about benchmarks or whether some open weight model has technically surpassed GPT-X on a leaderboard. They care about whther GPT does what they want it to do. Capable Open source models already exist, and that hasn't caused ordinary chatGPT users to abandon chatGPT for them. Hell Anthropic exists, and that didn't cause that either. OpenAI still dwarfs Anthropic in the consumer space. Obviously, sufficiently large differences in capability can eventually matter like when Anthropic blew everyone away in coding at one point, but that's very different from saying OpenAI has to train a frontier model every few months or inference margins go to zero.
2. Nobody said anything about a fixed target. Not sure why you interpreted 'slow down' as 'freeze current models forever'.
>And what happens to their valuations if they abandon the goal of building AGI?
The capabilities these companies already have, combined with their growing userbases, revenue and distribution are plausibly enough to sustain trillion dollar businesses already. OpenAI is a company with a billion active users that has started running ads that reached ARR of $1 billion in the first 2 months and Anthropic is a company that hit $11B+ in revenue last quarter after a pretty massive jump.
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.