What does it mean to be first in developing applications of artificial intelligence (AI), and does it matter? In a recent interview, the former Chief Software Officer of the U.S. Air Force Nicolas Chaillan stated that he resigned in part because he believed that, “We have no competing chance against China in fifteen to twenty years. Right now, it’s already a done deal; it is already over.” He reasoned that a failure of the U.S. Department of Defense (DoD) to follow through on stated intentions to build up in AI and cyber means many departments within DoD still operate at what Chaillan considers a “kindergarten level.” Those are strong words, but Chaillan’s overall assessment misses the mark—the United States becoming an AI also-ran is not a foregone conclusion. Leadership in AI is not necessarily achieved by the first adopter.
There is No AI Arms Race
Much of the debate over military AI leadership and U.S. technological competition with China hinges on the assumption that there is a significant first-mover advantage when it comes to these technologies, meaning the first to develop them could reap substantial economic and military effects. However, fear of pronounced AI first-mover advantages instead reflects how AI is prone to “overhyping” where incredibly high expectations of capabilities surpass the reality of what is possible. Overhyping can obscure real progress and generate an inappropriate perception of an AI arms race that “misrepresents the competition going on among countries.” Any possible first-mover advantage for AI would be unsustainable.
AI is a general-purpose, enabling technology not dissimilar to electricity. Moreover, the private sector drives its development, rather than the defense sector. While technologies that are singularly applicable to military contexts diffuse more slowly, those that are multi-use like AI have the added prodding of market incentives to speed up their spread.
AI is Open Source and Available to All
Even when compared to other private sector-driven technologies, AI could spread even faster, since most AI research and development is open source with an unprecedented exchange of code and talent between tech companies and academia. This is a relatively new phenomenon in tech - “there is no business need to make closed infrastructure solutions, because within a few months everything will be totally different,” which has led to actors releasing even the most cutting-edge, proprietary AI. In 2015, Google opened up its sourcing framework TensorFlow. Facebook followed suit just a few years later with Caffe2 and PyTorch. OpenAI published GPT-2 in 2019, a large language processing model. The culture of keeping this work open-source and collaborative is widespread. A survey of AI and machine learning researchers showed that a majority believed that both a high-level and detailed description of methods, the results, and the actual algorithms should always be published, absent compelling risks from openness.
How to Become a World Leader in AI, if Everyone has the Models
What does it mean to be competitive, if not a leader, in AI if AI techniques themselves will spread quickly, leading to a similar nature and quality level across the board? The competitive advantage for countries will lie in a state’s ability to successfully leverage AI. Renewing America through military AI leadership will not succeed if focused purely on acquiring a technical edge, as opposed to organizational capacity and integration.
This idea isn’t new—the 2018 National Defense Strategy said that when it comes to adopting and deploying emerging technologies like AI, “Success no longer goes to the country that develops a new technology first, but rather to the one that better integrates it and adapts its way of fighting.” AI integration leadership will not only improve operations in DoD and beyond in the US government, but it will also enhance US economic competitiveness by setting a model and serving as a catalyst for broader innovation. But success will require both a significant mindset shift within DoD, as well as an elevation of the value of data.
Algorithms are continuously evolving, being tested against new data and updated and verified accordingly—to stay competitive in the 21st century DoD must operate and move in a similar way. In parallel with how tech companies are developing new algorithms—whatever state manages to adopt the latest open-source model, train and benchmark against their own data and models, and discard the losing model while implementing the more efficient one, will be the one to “win” military AI leadership.
Successful military AI leadership will also require U.S. data leadership. As Andrew Ng explains, “data is food for AI,” and with models and algorithms being open source—data will become the differentiating factor. Labeling, standardization, and sharing of data across DoD, therefore, is a critical precursor to AI integration and adoption. As it currently stands, DoD has access to large, diverse streams of data—however much of it is unlabeled, uncleaned, unconsolidated, and further complicated by security restrictions. When it comes to creating algorithms, it has been estimated that up to 80% of the time spent is allocated to processing the data needed for training them. Google released a paper in 2021 that discussed how data cascades—“compounding events causing negative, downstream effects from data issues” are pervasive. Moreover, DoD already has a competitive advantage when it comes to processing data—an existing cadre of data scientists, analysts, and more with particular knowledge of “how to assemble high-quality data in their domain,” it just needs to use them.
The United States has the capacity to become the world leader in AI—but it needs to take the necessary steps to revitalize its ability to adopt innovations in order to do so.