Odd to see this the day after the appearance of what's supposed to be a "leaked" memo from inside Google advocating the opposite course -- claiming that the out

Odd to see this the day after the appearance of what's supposed to be a "leaked" memo from inside Google advocating the opposite course -- claiming that the outsider crowd playing with (mostly) LLaMa derivatives is getting good results, and that the best thing for Google might be to try to engage. It claims, for example, that the open-source crowd, trying to find cheaper training methods using smaller, more targeted data sets -- because they have to -- has found ones that work as well, by some metrics, as anything that the groups with huge compute clusters. And that because they work as well, with less resources, they can subsequently explore alternatives a whole lot quicker than anything involving training new base models from scratch. The (supposed, unsourced) leak: https://www.semianalysis.com/p/google-we-have-no-moat-and-neither https://www.semianalysis.com/p/google-we-have-no-moat-and-ne...