But is this pattern of small, cheap, and fast models profitable for the companies making them? Open-source models capable enough to solve user needs and small enough to run on-device: they’re great for users, but it means there’s no one company to take profit. Everybody and every product becomes more valuable and more capable, which is hard to extract differential profit from.
Yes I do. I expect chip makers such as TSMC and Nvidia to massively profit from them because people will need a lot more chips now and in the future.
I expect companies who integrate well with them to increase their value. For example, companies with proprietary data to integrate an LLM with. Another example could be a company that develops very capable agents.
Wallstreet cares about stocks. If models reach a point where it’s cheap, capable, and fast, why wouldn’t the S&P500 take off because of the huge boost to productivity? And some companies will take off more. That’s normal.
Sure, Cisco benefited a lot during the boom - as did ISPs who laid down internet infrastructure. Those were the shovel makers. Eventually, the people who bought the shovels significantly outclassed the shovel makers. Google and Amazon are both 10x larger than Cisco in 2024. Meta is 10x. Microsoft is 15x. Hell, Uber was bigger than Cisco for most of the last decade.
These shovel maker comments get tossed around a lot in any AI "bubble" talk. Yet, the shovel makers did not even come close to being the most profitable from the dotcom boom.
Demos Habassis from DeepMind have already spun off a drug discovery company based on AlphaFold v3 which is proprietary.
Others have developed Deep Learning based weather forecast models which much cheaper (orders of magnitude less compute), faster and more accurate than conventional physics simulation based models which costs billions of dollar to run.
There might exists companies which have already enough private, unique and valuable data that if used to develop models that can bring huge benefits.
