By Martín Piqueras.
Professor of Obsiness School and member of Gartner (consulting company and research of information technology based in Stamford, Connecticut, United States).
When the AI occupied the first chaatgpt headlines, Sam Ullman himself said the models they had used to develop it were not scalable. That is, the AI had cost a lot of money and a lot of computing capacity, and there would not be enough of either of the two things to take it to the next level. It would be more than a gross force model.
Since then multiple organizations have worked thoroughly to develop AI models in a much more frugal way. This is not only desirable for economic reasons, but also for reasons of sustainability; For example, there are not enough raw materials to manufacture as many chips as it would be desired.
If we add to all this that Asian giants also have great tech in constant innovation (digital dragons such as Tencent, Alibaba and others), we have the perfect formula so that, while some develop IAS with a muscle and talonary coup, others try it in a more frugal way and, from there, of the results we are seeing.
What can happen to investments in the US? Will both models coexist or will one win? In my opinion, there is space for different types of AI: on the one hand, those of muscle, which will need very high computing capabilities and that, perhaps, in the future they rely on quantum computing; And, on the other, the most affordable and sustainable AIs that can solve smaller problems, for example, operate autonomously on a smartphone or on the computer of a car.
It is possible that, in the short term, investments as large as have been made in the US are difficult to sustain, but they will also be necessary for certain types of computing based on brute force, as in the discovery of new medicines or the design of New molecules.