Pytorch fits in the same bucket as datacenter hardware. By giving the designs away they drove the price down, made it easier and cheaper to hire people familiar

Pytorch fits in the same bucket as datacenter hardware. By giving the designs away they drove the price down, made it easier and cheaper to hire people familiar with the technology and reduced the risk of sitting on an outdated stack being overtaken by someone else (or worse, someone else capturing open source momentum). What happened with llama and stable diffusion is (another) cautionary tale for anyone gambling on proprietary technology advantages. Your competitors can hurt you both directly and indirectly if you leave the open source route open and Sam Altman being on world diplomacy tours selling regulators on the dream they can control AI China style by regulating a few willing large companies makes a lot more sense when you compare the open source trajectory against proprietary models (and because Meta doesn’t charge, they are a slippery fish to action). Meta easily shaved billions of dollars from the proprietary market over time at very little cost to themselves because LLama was an existing long term investment whose primary costs were already incurred at release time and it had no serious market viability as a proprietary model without vastly more investment and cost. It’s the open source contributions that made it competitive.