the ai reckoning being discussed is bound to happen imo when the current approaches plateau and leave the gen pop disappointed. in terms of openai vs real, ope

the ai reckoning being discussed is bound to happen imo when the current approaches plateau and leave the gen pop disappointed. in terms of openai vs real, open initiatives, there are two things in favour of the former: - the startup playbook of burning vc/investor funds till you make it - the use of human-enriched and/or private datasets, supported by the previous point i see a couple bottlenecks to current efforts in reaching the best results: - hardware/software architechture limitations - the levels of abstraction needed for training the largest models are sacrificing efficiency for achieving the newest targets. in an ideal world, all gpus in the pool would be fully utilized, and each underlying hardware used to its fullest. currently i'd wager we are only halfway there. software stack hegemony doesn't help here either. - model architecture approach vs goal - it is amazing how much can be achieved from vectorizing everything and predicting the next block in the sequence. however, it can only do so much from what we can see thus far. i don't have any answers to what can replace it, but i can at least assert that the current approach does not fully leverage the raw digital data that exists out there. while we ride this wave, i am glad money is being spent in research and tooling here, and i am bullish about people doing more with their devices going forward.