Each time a major discovery arrives in the world, the center of gravity shifts dramatically from the elite researchers who made such a discovery to experts around the world who will seek to implement these advances in solving different types of problems. This time, experts have an additional advantage: rapid access to new information. Unlike the Industrial Revolution, when inventions did not quickly cross the borders of the country of origin, AI researchers often quickly share their discoveries as it is likely that more people are working on the same topic. This healthy competition within the scientific community benefits everyone.
On October 12, 2016, the administration of then-President Barack Obama published a long-term plan on how to address the challenges posed by AI development. To do this, it proposed increasing research funding and boosting civil-military cooperation. Unfortunately for the United States, when former President Donald Trump took office, he proposed cutting AI research funding at the National Science Foundation[150].
China’s plan, on the other hand, was developed at the highest levels of its central government and executed to the letter by local authorities who did not hesitate to position their cities as development hubs for AI and other technologies, offering capital to innovators, being the first to buy the product of a local startup, and filling the area with tech incubators.
In this sense, the very bureaucracy of a government prevents the United States from directing more millions of dollars in investment and subsidies to AI development, due to the inability to accept that some of it will end up wasted. Yes, there will be companies that will go bankrupt and people trying to take their own cut, which we will address later. There will also be companies that will be absorbed by others. There will also be incubators and accelerators with many resources available without knowing what to do with their capital, and there could also be empty dormitories on university campuses and technological equipment purchased from one of these companies that will rarely be used by the government that acquires it. All of this can happen.
Public officials in the West are not used to exposing themselves to this type of risk, whereas for Chinese authorities, this is a fair price to pay if they want to use brute force, as synonymous with the technology employed by IBM’s Deep Blue against Kasparov[151], to improve the local economy and technology, changing the rules of the system and injecting it with as much money and information as if it were anabolic steroids. The very idea of using government funds to invest in technological improvements is a risky business for Western politicians. While success stories are usually ignored or minimized, every failure becomes a state matter where some head must roll.
Another profound difference is seen when analyzing the dynamics or strategies of companies in these two countries. At this point, we are used to seeing American companies launching to conquer the global market with their products and services, with a low tolerance for competition and the pretense of destroying it.
Chinese companies, on the other hand, apply a different technique in practice compared to their Western rivals. Instead of trying to conquer all markets with their products or services, like Facebook, Amazon, Google, or Uber do, Chinese companies aim to integrate with local startups in each region and maintain these brands along with their solutions targeted at that particular audience, rather than trying to create a single user model around the world. This is how the mobility company Didi forced Uber out of China. Then Didi invested in Lyft, the alternative to Uber in the United States, doing the same with 99 in Brazil or Ola in India, thus forming a global anti-Uber alliance, and providing a model built on cooperation rather than destruction and conquest. Technological disruptions will alter the political and economic order of all countries, impacting how they embrace or abandon the idea of digital globalization.
[150] Lauterbach, A & Bonime-Blanc, A. (2018). The Artificial Intelligence Imperative: A Practical Roadmap for Business. ABC-CLIO. p. 101.
[151] Press, G. (2018). The Brute Force Of IBM Deep Blue And Google DeepMind. Forbes. Retrieved on February 5, 2023, from https://www.forbes.com/sites/gilpress/2018/02/07/the-brute-force-of-deep-blue-and-deep-learning.