There are 2 key factors at play here.
First, while smartphone growth is slowing down globally, IoT represents a different story. As of 2018, there were at least 7 billion IoT devices (with other estimates putting this number significantly higher), posed to grow to 21.5 billion by 2025, surpassing all the other categories combined. Perhaps more important than a specific number of devices is the fact that there is no natural limit to the number of IoT devices that can be put out there: it’s quite possible to imagine the world where there are dozens or even hundreds of devices per every living person, measuring everything from the traffic on the roads to the temperature in our apartments (and this is even before accounting for the IoT devices used by enterprises).
Second, the amount of existing data is to a significant extent defined by our willingness and ability to collect, share and store it (be it temporarily, or permanently). And here, the choices we make around what types of data we are willing to collect and retain are becoming crucial — any data that’s not captured today is by definition lost, and this effect is compounding over time.
Imposing restrictions on data collection out of concern for people’s privacy and to prevent potential abuses might be a reasonable thing to do, but in the narrow context of machine learning, those choices affect the amount of data available to train the models on. This, in turn, means that countries less concerned about privacy (with China being a prime example — for instance, see its experiments with AI-powered security cameras to catch criminals) will likely gain an edge when it comes to data.
That being said, it’s also important to recognize that privacy concerns aren’t applicable to every single problem, and there are some fields (such as driverless cars, or machine translation — see some interesting expert opinions here) where the West would actually have better datasets.
People represent the second crucial building block, as it is they who define the approach used to tackle any problem that could be addressed with machine learning.
Here, the situation is somewhat opposite of what we’ve seen in Data — the West, and the U.S. in particular, has a natural advantage, stemming from the fact that it remains one of the most desirable locations to work and live in, and thus has an easier time attracting people from all over the world. It could also be more tolerant towards unorthodox ideas, which provides for a more creative environment and helps to find and nurture innovative ideas.
In fundamental research, the U.S. has also historically had an advantage, thanks to its established system of research universities, not to mention its ability to attract top talent from all over the world. Still, in recent years, China has established a system of top-tier research universities and continues to aggressively invest in it. Today, China is already conferring more doctoral degrees in natural sciences & engineering and produces more articles in peer-reviewed journals than the U.S., according to the Economist. Moreover, in AI-specific research, the U.S. lead is even less certain, as was mentioned before (see CB Insights report for details).
Finally, when it comes to the practitioners who are focusing on implementation (rather than pure research), both the U.S. and China have some unique strengths; two possible proxies to evaluate those are the number of startups founded in each respective country, and the number of professionals joining the field.
The U.S. has the highest number of startups and also an established ecosystem of big tech companies such as Google, Microsoft, and Facebook investing in the field. Still, China is #2 here (#3, if looking at Europe as a whole); moreover, it receives an unprecedentedly high amount of investments (more on that in the section below), and is also a home to select few companies that could rival the biggest players in the U.S. (namely, Alibaba, Tencent & Baidu).