We have been publishing a series on TechTank called, “Winners and losers in the fulfillment of national artificial intelligence aspirations.” The research has evaluated 44 countries on how well-positioned each country was to achieve its national AI objectives. In subsequent posts, each country was ranked on the two sub-dimensions of their implementation plans: people and technology In this final post in the series, we sharpen our focus on the U.S. and what it needs to do to achieve dominance in the world market in its national AI strategy.
To assess the winners and losers in the fulfillment of national AI aspirations, we first assembled a country-level dataset that contained a myriad of details on each country’s technology infrastructure, public and private investments, the number of AI-related patents and conference papers it has produced, and the number of technology-savvy STEM graduates within the country. We determined that the data could be grouped into two overarching factors: a technology-related factor and a people-related factor. We then arrayed each country by its relative achievement on those dimensions (see Figure 1).
As shown, the U.S. is positioned in the lower right quadrant—the Technology Prepared quadrant—which reflects the combination of its world-leading technology infrastructure (in the 95th percentile) and a relatively dismal people readiness (45th percentile). While the U.S. leads the world in the technology infrastructure dimension, a number of countries, among them India (92nd percentile), Singapore (88th percentile), Germany (85th percentile), China 72nd percentile) and Russia (48th percentile), score better on the people dimension.
In our analysis of the technology dimension of our scoring, we noted a very strong relationship between the size of a country’s economy and its grade on the technology infrastructure dimension. Simply put, countries with larger economies are more capable of investing substantial sums into the technology necessary to utilize AI.
The relationship between any single factor (e.g population) and the score on the people dimension is less clear. While the two countries with the largest population, China and India, score highly on the people dimension (at 72nd percentile and 92nd percentile respectively), smaller countries also score highly (e.g., Singapore in the 88th percentile, and Germany in the 85th percentile). Larger countries also do not necessarily score well (e.g., the U.S. score in the 45th percentile). Hence, we view the people issue as more of a motivational issue within the U.S. rather than a population size issue.
As such, the U.S. has a people problem—not a spending or technology problem—and in the next section, we offer three options for the U.S. to achieve a position of prominence in AI.
Option 1: Extract lessons from the U.S. space race for talent development
The Russian launch of the world’s first manned space flight on April 12, 1961, shook the U.S. to its core. Prior to the launch, the U.S. and Russia had already been engaged in a race to space, but those investments were driven as much by scientific curiosity as they were national security. But the Soviet launch put the spotlight on the uncomfortable reality that the US had fallen far behind in the space race and significant educational investments were necessary to close the gap. Life magazine, writing about the state of the US educational system at that time said, “The schools are in terrible shape. What has long been an ignored national problem, Sputnik has made a recognized crisis.” As a result, in the 20 years after Sputnik, the National Science Foundation invested five hundred million dollars (in 1960 dollars) into teacher and classroom development. According to research from Clemson University, “by connecting the quality of scientific training to the survival of the nation, NSF was able to increase its fellowship budgets more than 100% immediately after Sputnik.” Congress also passed the National Defense Education Act which also infused more than a billion dollars (in 1960 dollars) into science education.
In an age where the race to AI has become a critical gamechanger, we suggest that China’s emergence as the leader in the race to AI dominance should necessitate the same singular national focus as the Russian launch of Sputnik in the 1960s. To produce the talent necessary to achieve its national AI strategies, the U.S. must dramatically reinvigorate its approach to STEM education and force a focus on computer science at very early ages. The Computer Science for All Act of 2021, currently proposed in Congress, is an excellent step in this direction. Failure to invest in STEM technology has the potential to relegate the U.S. to a second-tier nation when it comes to innovation and the labor force in AI.
Option 2: Take a multi-national consortium approach
The advantage of a consortium approach for AI is the ability to pool financial and human resources across a number of countries and then share the benefits that result. The U.S. has traditionally favored consortium approaches in dealing with cross-national issues like national security, such as the North Atlantic Treaty Organization (NATO), an alliance of 30 different countries that includes the U.S., most European Union members, the United Kingdom, Canada, and Turkey.
A multinational consortium of NATO members would be formidable with the joint AI prowess of the U.S., Germany, the United Kingdom, and Canada among other countries. Given the size of its economy—dwarfing all the other potential members—the U.S. would logically lead such a consortium. Each of the other three countries falls into our Leader category (see Figure 1) and could complement U.S. contributions.
However, a NATO consortium is not without inherent difficulties. We mention some of those in our previous report that analyzed the content of national AI strategies. In that report, we identified the differing focus areas that different countries had for their AI strategies (e.g. healthcare-focused versus environment-focused) as well as their often widely differing views on national AI governance (e.g. government-led versus industry-led). Attempting to reconcile these differing goals on AI and its governance may prove challenging.
Finally, embracing this option requires confronting the issue of changing global alliances over seemingly minor issues. For example, when Australia decided to back out of its contract with France to purchase 12 new submarines (at a cost of $66b) in order to purchase them from the U.S./UK, France responded by pulling its U.S. and Australian ambassadors in protest. Spats of this kind, while temporary, could stall meaningful and consistent progress in a multi-national consortium.
Option 3: Create robust partnership with one other country
The U.S. can create a partnership with a country that has an ample supply of talent (which the U.S. lacks) but who lacks adequate funding (which the U.S. has in abundance). In this option, we view a potential partnership with India as a dream scenario. At present, relationships between India and China are increasingly frayed and India is openly worried about an increasingly assertive China. With the existential threats posed by China and Russia to the U.S. and India, jointly facing a common challenger may make sense. As U.S. Secretary of State Anthony Blinken pointed out, “…the U.S. has few better options than India for managing a rising China.”
From an AI standpoint, a joint U.S.-India partnership makes a great deal of sense. India produces far more STEM graduates than the U.S., and the U.S. invests far more in technology infrastructure than India does. A U.S.-India partnership eclipses China in both dimensions and a successful partnership could allow the U.S. to quickly leapfrog China in all meaningful aspects of AI.
We are not so naïve as to think that this would be an easy relationship. India has been credibly accused of using spyware to target the mobile phone of opposition leaders. The Freedom House has already documented the erosion of democracy in India and downgraded it from “free” to “partially free.” Further, challenges from a legal and governance standpoint exist.
We are also mindful of the visa challenges that may hinder the success of such a partnership. However, companies like Apple and Microsoft are actively working to solve visa-related issues.
Regardless, no other country offers the same people-based AI benefits that India offers and who also needs the funding that the U.S. has. Given that both countries are threatened by a powerful China, this may engender more cooperation.
A need for progress
The current U.S. strategy, while successful on the technology side, is not working on the people side and strong actions are necessary to ensure that the U.S. is not left behind. We are not suggesting that these are the only options to counter the Chinese threat of AI domination but we think they do bear further investigation. Further, our first option, which we view as the best option over the long run, does not preclude also engaging in option 2 or 3 as either short or long-term fixes. While the space race challenge was met solely by invigorating U.S. STEM education, the race to AI may not be solvable using only U.S. residents.
The challenge in the race to AI dominance is not only that key pieces need to be built but also that the pieces need to be quickly assembled, broken down, and then reassembled as new demands emerge. Much like in warfare where the combined forces of air, land, and sea need to work together, the components and subcomponents of AI need to fluidly work together.
Regardless of the option(s) selected, there are four action items that the US needs to do immediately:
- Action item 1: Educate the U.S. population on the future of AI. It appears that the population of the U.S. either views artificial intelligence as a futuristic utopia or an impending disaster. In actuality, both viewpoints do have a gem of truth in them. But without a realistic view of the world of AI, the populace is unlikely to understand the need for engagement, and the prospects that the industry sector offers. A focused educational campaign can present a balanced view of AI.
- Action item 2: Create a sense of urgency. The reason that major initiatives like the space race and the Panama Canal succeeded was because the country had a well-articulated sense of urgency to drive both engagement and funding. While we applaud consideration of the Computer Science for All Act of 2021, it does not create the sense of urgency necessary to motivate the populace of the US to better engage. Without such urgency, the motivation to solve AI challenges is likely to wither and fade away much as the drive to adopt the metric system failed in the 1970s/1980s.
- Action item 3: Raise the profile of STEM work and education. During the space race, astronauts and those working in aerospace were viewed as heroes by their contemporaries for embracing the challenges of space flight. A similar thing needs to be done with STEM workers to elevate their profile and encourage more bright students to study AI-related fields. Several firms have launched STEM-centric initiatives that are a great first step, particularly when these initiatives are focused on women who historically have not been as active in the STEM discipline as men.
- Action item 4: Closely evaluate potential international partners. Geopolitical relationships are never certain and are subjected to both internal and external pressures. While we understand that these spats will never completely disappear, each spat has the possibility of stalling work and halting valuable momentum. However, the U.S. cannot afford the cost or the time to unilaterally try to solve the problem and must work with its allies to do so.