For me opus-4.8 was the most useful coding model. Sure fable is better at planning but is expensive to use as a daily driver. But even fable, when it fails, fails in such strange ways that I am now convinced this intelligence is an illusion and path to AGI lies elsewhere. I was willing to buy the whole emergent intelligence claim till last year. Now, not so much.
As for calling for slow down, sure the two companies kept parroting each other's lines but they were not slowing down the cash burn or gpu purchases. Why would they? The prize was too high. Now, with open Ai needing trillion dollar valuation to IPO and private funding possibly showing signs of slowing down (only for these labs because the valuation is too high to begin with for most prudent investors: My speculation) they have no option but to slow down. Then would you rather say, slowed down because we are running out of cash or that we are slowing down because national security? It really cannot be that they can't solve alignment but otherwise it is really powerful and improving. Simply because airgap exists and we know how to do it. If all else fails power off the freaking gigawatt cluster. More likely this recursive self improvement is an unstable loop and the model is likely degrading with self improvement effort. At 100's of millions per experiment this is going to be unsustainable.
Ah, now having the best model be expensive to use, and thinking the cash burn of both companies is insane and unsustainable I completely agree with. This, I think is likely the biggest reason they’re asking for government intervention and regulation: to help them weather the coming investment/cash plateau, not because the models are nearing any asymptotic limits. To be fair, there is an argument to be made that the improvement in models is tied to the cash burn; if they can’t train bigger models or acquire more data or research more effective harnesses, then the improvement of their models might slow down - while GLM or other models continue to improve.
I’ve never thought LLMs were on the AGI path, but I have to admit it’s surprising how far it’s come with no end in sight yet. There is something important to be said about how ‘intelligence’ is embedded in language, and it suggests that intelligence isn’t exactly what we thought it was. The language component of intelligence also goes a long way to explaining technology’s progress in human civilization; how language and the printing press and mail and radio/tv and the internet have each ushered in accelerations in the pace of progress. Biologically and evolutionarily speaking, it’s unlikely that humans have become any smarter in the last two thousand years, but technology (among other things) has exploded.
