National Power After AI

National Power After AI
AUTHORS
Matthew Daniels
Ben Chang JULY 2021

Center for Security and Emerging Technology 2Established in January 2019, the Center for Security and
Emerging Technology (CSET) at Georgetown’s Walsh
School of Foreign Service is a research organization fo-
cused on studying the security impacts of emerging tech-
nologies, supporting academic work in security and tech-
nology studies, and delivering nonpartisan analysis to the
policy community. CSET aims to prepare a generation of
policymakers, analysts, and diplomats to address the chal-
lenges and opportunities of emerging technologies. CSET
focuses on the effects of progress in artifi cial intelligence,
advanced computing, and biotechnology.
CSET.GEORGETOWN.EDU | CSET@GEORGETOWN.EDU
National Power
After AI JULY 2021
AUTHORS
Matthew Daniels
Ben Chang
ACKNOWLEDGMENTS
We would like to thank Igor Mikolic-Torreira, James Baker, Jack Clark,
Remco Zwetsloot, Teddy Collins, Helen Toner, Jack Corrigan, Jeff Alstott,
Maura McCarthy, Alex Friedland, Lynne Weil, David Lasker, Jared
Dunnmon, Matt Mahoney, and Greg Allen for their comments on earlier
drafts. We would also like to thank Andrew Imbrie for many thoughtful
discussions and reading suggestions and Melissa Flagg for early input and
ideas. This work benefitted directly from the early Office of Net Assessment
summer study on AI in 2016. Of course, the authors are solely responsible
for the views expressed in this publication and for any errors.
AUTHORS
Matthew Daniels was a Senior Fellow at CSET, where Ben Chang is an
Andrew W. Marshall Fellow.
Since authoring this paper, Matthew Daniels has taken a position in the
U.S. government. He completed his contributions to this paper prior to
departing CSET. The views expressed herein are the authors' and do not
necessarily reflect those of the U.S. government. Inquiries about this report
should be directed to Ben Chang.
PRINT AND ELECTRONIC DISTRIBUTION RIGHTS
© 202 1 by the Center for Security and Emerging Technology. This work
is licensed under a Creative Commons Attribution-NonCommercial 4.0
International License.
To view a copy of this license, visit:
https://creativecommons.org/licenses/by-nc/4.0/.
DOCUMENT IDENTIFIER
doi: 10.5 1 593/202 10016
Cover photos: (Top left) https://www.flickr.com/photos/
ywcausa/8527933591(Top right) https://unsplash.com/photos/-
7Xb4TmVTN8 (Bottom) https://unsplash.com/photos/jHZ70nRk7Ns.
Center for Security and Emerging TechnologyiINTRODUCTION
1 | AN EVOLUTIONARY THEORY OF TECHNOLOGICAL
 COMPETITION
2 | POWER AFTER AI: NEW ELEMENTS, CHANGED FACTORS,
 AND ALTERED GOALS
3 | CONCLUSIONS AND KEY POINTS
APPENDIX
ENDNOTESIII
1
5
17
21
23Contents
Center for Security and Emerging Technologyiv
Center for Security and Emerging Technologyiiiow will artificial intelligence affect long-term U.S.-China
competition?\* Many analyses approach this question by focus-
ing on how AI technologies may enhance each side’s current ca-
pabilities–making aircraft, businesses, and nations, say, 10 percent faster
and stronger. This perspective essentially suggests a broad race: the side
that leverages modern AI technologies the most and soonest wins.
This view can mislead American strategists in two ways. First, it ignores
the new vulnerabilities, costs, and accident risks associated with adopting
modern AI technologies in certain settings.1 Second, and most importantly,
over the longer term, AI technologies will likely alter great power competi-
tions in foundational ways, changing both how nations create power and
their motives for wielding power against one another. In short, strategists
are trying to create strategies for a game while the field, the players, the
ball, and the rules could be changing.
Why? Major innovations themselves not only generate value for lead-
ing states, but also change the relative value of previously acquired assets,
organizational practices, and defense strategies. Germany’s development
of Blitzkrieg during the interwar period, which represented a new way of
using motorized vehicles and radios, is often cited as a military-specific
example of important new organizational approaches. The German mili-
tary effectively adapted its operational practices to suit new technologies.
But the impact of major innovations can also be broader. For instance, the
Industrial Revolution made new factors central to national power, includ-Introduction
H
\*In this work, we use “artificial intelligence” to mean, as per the Defense Innovation
Board, “a variety of information processing techniques and technologies used to perform
a goal-oriented task and the means to reason in the pursuit of that task.” See Appendix.
Center for Security and Emerging Technologyiving access to certain resources and the industrial capacity to leverage them. These
broader effects take more time to appear, but their impact can be enormous: indus-
trialization was not “revolutionary” because of the rapidity of change, as it unfold-
ed in waves over decades, but because of its ultimate magnitude of change. With
AI technologies, progressive substitution of machines for human cognitive labor may
eventually have economic and social implications on a scale comparable to the
Industrial Revolution. And like the Industrial Revolution, this AI revolution will change
some fundamental elements of national power.
Of course, these foundational shifts can render some of the current processes
and resources of a state obsolete, but they can also make what states are already
doing, or already possess, more valuable. For example, the invention of railroads
was a boon for those rich in steel precursors.2 With AI, data-hungry algorithms may
advantage authoritarian states, which already surveil and catalogue their own pop-
ulations with little regard for human rights.3
We suggest an “evolutionary” view of technological change: major, wide -
ly diffused innovations are akin to environmental shifts, affecting the competitive
capacity of states based on their existing trends in population, resources, institutions,
character, and policies. Some previously “maladaptive” factors may become advan-
tageous, and vice versa; states will adapt their institutions, organizations, and policies
to the new environment in different ways and to varying degrees, and consequently
gain or lose relative power as a result. Nations that primarily focus on AI technologies
as offering marginal improvements in existing capabilities (“helping to build better
mousetraps”) will eventually miss larger opportunities to adapt. This paper is a first
step into thinking more expansively about AI and national power. In what follows, we
first explain this evolutionary view in greater detail before applying it to AI.
Ultimately, we seek pragmatic insights for long-term U.S. competition with au -
thoritarian governments like that of China. For the foreseeable future, China’s pop-
ulation and total economic size will very likely exceed those of the United States,
even as its per capita GDP lags. This new challenge differs fundamentally from the
United States’ Cold War competition with the Soviet Union, and success will require
thoughtful and timely diagnosis of modern environmental shifts in how states can
produce power. These insights can guide our own investments as well as our ap -
proach to alliances. The United States has many sources of advantage and strength,
and as Joseph Nye rightly observed, “Our greatest mistake in such a world would
be to fall into one-dimensional analysis and to believe that investing in military
power alone will ensure our strength.”4 This paper is a first step, intended to provoke
new questions and provide a framework for assessing the relationship between AI
and national power.\*
\*This work benefitted directly from the early Office of Net Assessment summer study on AI in 2016.
Center for Security and Emerging Technology1WHY STATIC, UNIVERSAL MEASURES OF POWER (THAT
ARE USEFUL) DO NOT EXIST
Power is simply the relative capability of a state to achieve what it wants
in international affairs. Power depends on one state’s favorable factors
relative to another. In one of the founding works of international relations,
Hans Morgenthau proposed distinguishing nine elements of national
power: geography, resources, industrial capacity, military preparedness,
population, national character, national morale, quality of diplomacy,
and quality of government.5
Since Morgenthau’s writing, generations of analysts have sought a
definitive way to measure national power that would, finally, allow ac-
curate judgment of relative strength without fighting a war.6 The search
has included dozens of books and hundreds of journal articles offering
competing methodologies and metrics.7 For example: Should measures of
useful access to resources include both steel and oil, or only steel? How
should “soft power” be measured? What about the “latent” power that a
state could theoretically draw from its population?8
Were such a universal, “objective” measure obtainable, the benefits
would be enormous. We could easily answer questions such as, “who’s
ahead?” and “if it’s not us, what should we do about it?” This quest, how-
ever, has not borne fruit. Proposed measures have tended to perform
poorly when generalized.9 History is full of surprises where states have An Evolutionary
Theory of
Technological
Competition1
Center for Security and Emerging Technology2achieved victory even when “objective” metrics would predict their defeat: the Unit-
ed States owes its national existence to victory in the Revolutionary War over the
British Empire, the superpower of the time.
Why? First and foremost, power is always contextual.10 This is especially clear in
military matters. A large military’s skill at high-intensity conflict may not translate to
skill at counterinsurgency; and factors that provide one country advantage relative
to another can change. The world offers no “power particle” to measure objectively
alongside other characteristics of nature–what we intuitively mean by “power” is
mostly a generalization from particular observations.11 Elements of power can also
combine in surprising ways. Andrew Marshall offered the reminder that countries
with relatively smaller populations and GNPs can pose substantial challenges to
larger competitors: in 1938, Japan had roughly half the population and one-tenth
the GNP of the United States,12 but it built a navy that challenged the United States
in wartime.13 In part because of these issues, history is rife with leaders who have
had a large gap between their beliefs and the reality of military capabilities.14
Each competition should be analyzed carefully on its own, distinguishing elements
of power, identifying key areas of competition, and working to diagnose the most
important problems and opportunities in each area of competition.
MAJOR INNOVATIONS CHANGE THE SECURITY
ENVIRONMENT, CHANGING WHAT GIVES RISE TO POWER
Military leaders throughout history are sometimes faulted for preparing to fight
the previous war instead of the next one. We should likewise avoid strategizing
to “win the previous competition.” Just as changing adversaries from the Soviet
Union to insurgents in Iraq and Afghanistan represented a new security environ-
ment and revealed the non-fungibility of power, major innovations also change
the security environment as they are widely adopted. Such innovations do this in
part by changing what assets, practices, and strategies give rise to power.
Differential impacts of emerging technologies often bring shifts in relative capa-
bilities of individual countries.15 Thinking about long-term competition in periods of
rapid technological change therefore requires assessing how innovations change
factors related to military and national power.
Major innovations can change the estimations of power in three ways:
• First, innovations introduce new elements of power. Major innovations,
in changing how states generate power, can create new factors that must be
considered in characterizing power. For example, the advent of railroads,
internal combustion engines, and nuclear weapons dramatically increased
Center for Security and Emerging Technology3the importance of a state’s access to steel, oil, and uranium, respectively.16
New factors, however, are not only limited to materials. They may also
encompass characteristics of a society’s culture, organizations, or economic
activities.17
• Second, innovations change the importance of existing elements of
power. Major innovations also change the “coefficients” of existing ele -
ments of power, causing them to matter more or less than before. For exam-
ple, Mongol light cavalry, modern navies, and ballistic missiles all changed
how geographic barriers affected one’s balance of power with geographic
neighbors, eroding the effectiveness of simple remoteness, oceans, and
armies still in the field, respectively, as shields against coercive power.18 In-
dustrialization meant the inventiveness of a nation’s scientists and engineers
became more important.
• Finally, innovations alter states’ intermediate goals. Perhaps least
obviously, major innovations sometimes broadly alter what policies states
pursue, by making certain kinds of behavior more valuable or less costly.
While states retain the same ultimate ends, such as securing survival and
prosperity, the intermediate, instrumental goals they pursue to reach those
ends may shift. This can drive dramatic changes in state goals and policies.
For example, before the Industrial Revolution, potential productivity gains in
areas like agriculture and manufacturing were small and stable; this made
conquering territory a primary means by which one group could increase
its wealth and security.19 During and after the Industrial Revolution, modern
states could also pursue substantial military and economic growth by apply -
ing new technologies to increase productivity.
The next section discusses how these three changes manifest in the context of AI. Perhaps least obviously, major innovations sometimes
broadly alter what policies states pursue, by making
certain kinds of behavior more valuable or less costly.
Center for Security and Emerging Technologyviii
Center for Security and Emerging Technology5e offer early thinking about potential changes caused by
AI: new elements of power, shifting importance for exist-
ing elements of power, and shifting intermediate goals for
states. These are not definitive or complete results, but a starting place for
broader thinking.
NEW ELEMENTS
One of the most familiar examples of new elements of power is as-
sociated with the Industrial Revolution, when machines began to help
humans with physical labor in new and organized ways. The Industrial
Revolution led to dramatic changes in the character of war and military
power. A simple approximation is that, before the Industrial Revolution,
any group’s military power correlated most closely with its quantity of
fieldable humans under arms, a measure of both taxable population
and military potential. After the Industrial Revolution, any estimate of
military power had to include a society’s industrial capacity and access
to resources to enable that capacity, which are measures of a society’s
ability to produce useful military hardware, such as ships, tanks, planes,
and submarines.
It is useful to see AI technologies today as part of another large-scale
transition: machines are increasingly helping humans with certain kinds
of cognitive labor in new and organized ways.20 This transition will span
decades, with potential economic and social implications on a scale
comparable to those of the Industrial Revolution. Today, as then, there are Power After AI: New
Elements, Changed
Factors, and Altered
Goals 2
W
Center for Security and Emerging Technology6large questions about the future of economic production, human labor, and military
capabilities. These future trends will define new elements of power.
U.S. defense leaders believe the rapidly growing military applications of AI
technologies will be critical for the years ahead.21 State power will increasingly
hinge on the new factors required to effectively adopt AI. Four such factors often
identified by existing literature include data, AI scientists and engineers (“AI talent”),
computational power (“compute”), and AI-adapted organizations. Below, we ex-
plore the latter two in greater detail.
Ability to Access and Leverage Compute
The United States has historically used large-scale compute capabilities for
analysis of nuclear weapons detonations and cryptanalysis.22 More recently the
U.S. government’s uses have grown to include climate modeling and a variety of
scientific applications. In the years ahead, the United States may also use large
compute resources for creating and countering new AI capabilities.
For decades, cutting-edge AI systems have used steadily increasing quantities
of compute resources, making improvements in compute capabilities a key driver of
AI progress. This usage appears to have accelerated across the last decade: the com-
pute used in the largest AI training runs has doubled every 3.4 months since 201 2,
growing more than 300,000 times from AlexNet in 201 2 to AlphaGo Zero in 201 8.23
OpenAI researchers have shown that the 2010s appear to be the beginning of a new
computing era for AI technologies, distinct from the preceding 40-50 years.24
For military applications where limited real-world data is available, techniques
leveraging computer simulations instead of large quantities of data may further
increase demand for compute.25 Cloud compute may become vital for rapidly
processing and fusing intelligence across platforms, while edge compute will be
necessary for autonomous systems deployed in the field tasked with assessing and
outthinking adversaries’ equivalent systems.
As such, a nation’s ability to leverage large quantities of computational power
could become a new primary term feeding into its ability to influence international U.S. defense leaders believe the rapidly growing military
applications of AI technologies will be critical for the
years ahead. State power will increasingly hinge on the
new factors required to effectively adopt AI.
Center for Security and Emerging Technology7affairs. For example, the key technical precursors required to manufacture cut-
ting-edge AI chips are currently concentrated in the United States and allied coun-
tries–though semiconductor manufacturing capabilities more broadly, beyond just
the most cutting-edge chips, may further grow the importance of Taiwan and South
Korea as international trading partners.26
Importantly, compute resources must be configured in ways useful for modern
AI capabilities. High-performance computing (HPC) systems currently maintained
within the U.S. Government, such as in the Department of Energy, tend to be both
specialized for non-AI functions and subject to system-specific security measures,
posing challenges for broad, standardized utilization by other organizations. Con-
sequently, commercial cloud compute resources may better serve the U.S. Govern-
ment in deploying certain kinds of AI technologies, although potentially promising
efforts to improve the use of U.S. HPC assets for AI are also underway.27 Effective
use will depend, too, on accessible software tools for using cloud compute systems–
which may prove to be comparable to process and tooling approaches developed
to make factories effective during industrialization in the United States.28
Compute resources can flow more easily than many traded goods. As comput-
ing infrastructure continues to grow, new ways of sharing access to large, regional-
ly-concentrated quantities of compute, including through space internet constella-
tions, may create new opportunities and incentives for international partnerships.
Ability to Manage Data and AI Safety & Security
Even when states possess the raw resources required to adopt some major in-
novation, they still must undertake the often-difficult process of institutional and
organizational adaptation. Bureaucratic factors in organizations matter greatly: in
militaries, competing civilian, interservice, and intra-service actors may promote
or resist adoption of new technologies.29 Resistance can include parochial forces
that attempt to stymie adoption: for example, only direct pressure from Eisenhower
moved the Air Force to adopt ICBMs instead of focusing solely on less survivable
crewed bombers.30 Organizational culture also has significant impacts: because
mass armies threatened the pre-existing hierarchical power structure within many
European militaries, many states failed to adopt Napoleon’s innovation even after
his dramatic string of victories.31 During periods of rapid change, medium-sized
powers may have opportunities to adopt innovations more speedily than larger
powers.32
With AI, demands for organizational adaptations will be significant. Two factors
are especially important: effective data pipelines and the effective management of
security issues associated with modern AI technologies.
Center for Security and Emerging Technology8The ability to deploy cutting-edge AI applications will increasingly depend on
the quality of each organization’s data pipeline. Modern machine learning methods
are notoriously data-hungry, but simply possessing large quantities of data-collect-
ing sensing platforms will be insufficient—for supervised learning applications, data
must be structured, labeled, and cleaned; fusing data from many platforms, sources,
and formats will represent its own herculean challenge for many militaries. Finally,
these data pipelines must also be dynamic: data management itself must be moni -
tored, in part to detect attacks, because “data poisoning” attacks can manipulate
AI behavior by changing what lessons it learns.33 Consequently, it will be increas-
ingly important for military leaders to successfully implement organizational reforms
to create and maintain effective data pipelines.
Military leaders must also learn to effectively manage the novel security issues
associated with AI technologies. Relying on modern AI systems for safety- or mis-
sion-critical tasks carries challenges because many deep learning models are ex-
ceptionally hard to interpret.34 Michael Jordan at UC Berkeley has analogized the
creation of early large-scale AI models to building bridges before civil engineering
was a rigorous discipline: “While the building blocks are in place, the principles
for putting these blocks together are not, and so the blocks are currently being put
together in ad-hoc ways. … Just as early buildings and bridges sometimes fell to
the ground—in unforeseen ways and with tragic consequences—many of our early
societal-scale inference-and-decision-making systems are already exposing seri-
ous conceptual flaws.”35 A more developed engineering discipline for AI is needed
to manage the risk of accidents from relying on opaque machines in the field.36 In
near-term military settings, effectively integrating new AI technologies will require
special investment in test, evaluation, validation and verification (TEVV) processes
by competent organizational leaders.37
More widely, many modern AI systems are not designed to work in the pres-
ence of malevolent actors. Potential security issues for deep learning systems in-
clude adversarial examples and model inversion, in addition to data poisoning and
more traditional computer network and software attacks.38 Adversarial examples
refer to “inputs” (such as visual or audio patterns) to an AI system that cause the
system to malfunction; model inversion refers to an ability to reverse-engineer the
data used to train an AI system, which may include private or classified information.
Despite these challenges, modern machine learning capabilities will be increasingly
woven into G20 societies, economies, and military systems.\* The U.S. position with
\*For example, AI technologies will intersect with 5G and networking trends in cities as autonomous
systems (like vehicles) in urban areas begin to have large quantities of interactions with other
intelligent agents—working on everything from traffic coordination to utilities management and
financial investments. The ability for intelligent systems to interact on large scales, safely and securely,
will be critical.
Center for Security and Emerging Technology9AI technologies for the next two or three decades appears analogous to the future
that faced IT technologies in the 1990s: AI technologies are so valuable that they
will be used despite substantial design and security issues.
What might the future look like given these vulnerabilities? We can only specu-
late: in direct military settings, there may be new sub-competitions that resemble the
emergence of electronic warfare after the invention of radar.39 In economic systems,
in addition to the potential for the novel security risks discussed previously, there is
risk of physical manifestations of the kinds of problems currently seen in high fre -
quency trading systems, such as rapid, unanticipated interactions among automated
agents managing services in cities.40 These issues may open new vulnerabilities to
both individual rogue actors and state adversaries. Organizations that are able to
adapt early to manage these new security issues will be advantaged.
Since states vary in their access to compute, data, AI talent, and useful orga-
nizational adaptations, they will also vary in their ability to benefit from modern
AI technologies. Any national rankings based on these factors will be debatable,
but the nations that generally lead in these metrics is unsurprising, and include: the
United States, China, Japan, South Korea, the UK, Canada, Taiwan, Israel, France,
Germany, and Russia. Advanced economies should be increasingly expected to
focus their own investments and policies on improving their positions in these areas.
CHANGED FACTORS
Industrialization meant that a nation’s stock of productive scientists and engineers
counted more than it had in the past. With the arrival of AI, various previously
recognized elements of national power will become more important, while others
may become gradually less so. For illustrative purposes below, we discuss popu-
lation size and scientific talent as contrasting examples: population size becoming
less important, scientific talent becoming more important.The U.S. position with AI technologies for the next two
or three decades appears analogous to the future that
faced IT technologies in the 1990s: AI technologies are
so valuable that they will be used despite substantial
design and security issues.
Center for Security and Emerging Technology10Population Size
As AI technologies increasingly substitute for human labor, total population size
may become less important for national military and economic capacity. 41 Just
as machines took over rote physical labor during industrialization, AI technol-
ogies will automate rote cognitive labor, from diagnosing maintenance needs
to exploiting imagery intelligence. This may reduce the total quantity of human
labor needed to maintain a military’s operational capacity. In major wars, par-
tially or fully autonomous AI platforms may further reduce a country’s need to
field humans in combat. As militaries rely more on autonomous systems for mili-
tary operations, defense planners may come to count autonomous systems and
their available domestic supply of AI chips the way they once counted soldiers
and the available domestic recruiting pool of military-age adults.42 Downstream,
this could help technologically advanced nations compensate for demographic
challenges, such as aging populations and low birth rates, a situation the United
States, China, Japan, Western Europe, and Russia all face to varying degrees.43
Population trends continue to matter for national power—but AI technologies,
like many other technologies of the past century, may further erode this importance.
Industrious Scientists and Engineers
Harnessing new technologies, both by developing technologies and accessing in-
novations created elsewhere, is an important means of growing power. Applica-
tions of AI can help in both areas, serving as a force multiplier on, and therefore
increasing the importance of, productive scientists and engineers.
Recently, for example, DeepMind’s AlphaFold achieved breakthrough rates of
accuracy comparable to experimental methods in the protein-structure prediction
challenge known as CASP.44 By obviating the need for experimental protein struc-
ture assessment, a skill-demanding and time-intensive procedure, AlphaFold rep-
resents a large augmentation of human scientists’ biosciences research. In a differ-
ent domain of research, modern AI applications are able to help with chip design.45
Researchers have demonstrated a deep learning system capable of designing the
physical layout of computer chips more effectively than human engineers.46 Google
has used this system to design its next generation of Tensor Processing Units (TPUs),
the company’s specialized AI chips.
Likewise, rapid progress in machine translation, automatic literature review,
and related tools means a given scientific discipline’s state-of-the-art will become
increasingly accessible and useful to well-organized groups of human scientists and
engineers. Just as the printing press alleviated the need to travel from country to
country to accumulate knowledge from different libraries, AI applications can lower
the costs for researchers to access state-of-the-art knowledge in any field.
Center for Security and Emerging Technology11 Center for Security and Emerging TechnologyThere are three ways that modern AI applications will contribute on a large
scale to scientific discovery and engineering invention: they will contribute directly
to new discoveries and engineered systems, especially in areas that involve search-
es over large spaces in data or design;47 automate the physical work of science and
engineering, such as “self-driving laboratories” that robotically automate experi-
mental laboratory work;48 and make global scientific knowledge more accessible
to humans, such as by extracting knowledge from millions of articles as well as from
articles in many different languages.49
Finally, there is an old debate about whether science advances most because
of new ideas or new tools;50 AI technologies appear able to contribute both. In
the longer-term, AI may enable new and more creative forms of knowledge-gen-
eration that function as “pathfinders” for human brains, unlocking otherwise diffi-
cult-to-reach innovations. When AlphaGo beat Lee Sedol, its 37th move in the sec-
ond game surprised human professionals. In the words of Go master Fan Hui, “It’s
not a human move. I’ve never seen a human play this move. So beautiful.”51 When
AI behavior surprises us, we learn something new. Looking ahead, modern and
future AI systems may be able to solve scientific puzzles that have thus far stumped
humanity’s best minds.52
Just as railways advantaged nations with access to steel, it appears that AI tools
capable of augmenting science and engineering work will favor nations with the
best existing “resources” of industrious scientists and engineers. This trend appears
likely to deepen the advantages of nations that host, or can attract, a disproportion-
ate fraction of the world’s best in those fields.53
ALTERED GOALS
Finally, major innovations can alter state strategies, as different instrumental goals
become more appealing for achieving a state’s ultimate ends.
The Industrial Revolution again provides a clear example. Before industrial-
ization, conquering territory was a primary way that one group could increase its
wealth and security relative to others.54 During and after the Industrial Revolution,
in contrast, states have been able to pursue these ends effectively by increasing
productivity—as well as by gaining access to international trading networks and
new technologies to enable further military and economic growth. Territorial con-
quest by states in the modern era is rarer for many reasons—but not simply because
states have become more beneficent, instead because changes in technology
have reshaped how they can best achieve their goals.55 In short, major innovations
can alter what long-term competitions in each era are fundamentally about. In the
standard “ends, ways, means” trichotomy, this corresponds to ways. States have the
Center for Security and Emerging Technology12same ends (security, wealth, prestige, influence, sovereign action), but the ways that
competition is best pursued can change, such as through participation in globalized
production chains instead of territorial conquest.
With AI technologies, there are two worrying possibilities: a broad movement
toward authoritarianism and the greater use of advanced forms of population- and
economy-targeting information warfare.
Social Control Temptations
A technological innovation rarely tilts intrinsically toward “freedom” or “author-
itarianism.” It is possible, however, to try to discern how new technologies may
affect current social and economic systems in the future. Especially in authoritar-
ian states like China, AI technologies may provide elites with tools that reduce
contradictions between maintaining power and promoting economic growth
through free markets. By making authoritarianism appear more feasible, this may
generate an “authoritarian temptation” for the many states with malleable gover-
nance systems.
First, AI technologies are likely to reduce the costs of controlling populations
under authoritarian rule. Automating mass collection, processing, and analysis of
data is likely to decrease the marginal cost of controlling additional citizens, thus
reducing the resources required to indefinitely sustain totalitarianism. With access to
hundreds of millions of cameras, social media postings, bank accounts, automated
analysis of emotions and sentiment, and other data streams, AI-empowered algo-
rithms can perform much of the work previously done by secret police in pre-AI
authoritarian states.56 Automated surveillance methods are likely to scale more ef-
fectively than manual surveillance, which requires some amount of human labor per
citizen to be controlled. For example, Lichter et al. analyzed official Stasi records
from East Germany, finding that more than 1.5 percent of the population was either
officially employed or unofficially used as informers by the secret police.57 Beyond
the quantity of people involved in human surveillance operations, automated sur-
veillance may impose lower economic costs on a society than human surveillance.58
On this matter, China appears poised to benefit from feedback cycles between
AI deployment and data aggregation—the Chinese government is already using
AI technologies to enhance population control, as well as to profile and control
its ethnic minorities.59 In these early efforts, the Chinese government is collecting
large quantities of data, from facial scans to DNA; COVID-19 has only deepened
PRC data collection on its citizens.60 This data will help fuel new AI development
for social control in Chinese firms. Future AI applications could, in turn, help China
manage its data and drive more expansive collection, continuing the cycle.
Center for Security and Emerging Technology13China will likely export versions of these capabilities to authoritarian govern-
ments globally in the 2020s and 2030s, as it has already begun to do. According
to recent CSET research, since 2008, over 80 countries have adopted Chinese sur -
veillance technologies.61 These tools will help authoritarian governments worldwide
deepen their holds on power.62
Second, and more speculatively, AI progress may benefit authoritarian states by
reducing the costs and consequences of state interventions into internal markets. The
classic critique of centrally planning complex economies is that attempting to do so
poses intractable optimization problems.63 For many practical reasons, from human
organizational factors to corruption, AI technologies are unlikely to change this.
However, AI technologies could reduce, to some degree, the negative consequenc-
es of state interventions in markets.
For example, AI applications may help gather and interpret the volumes of
information necessary for more effective economic controls. An analogous effect is
visible inside large firms in both China and the United States today: companies like
eBay, Taobao, Amazon, and Uber apply machine learning to mine large volumes
of sales data to better match demand and supply. Modern machine learning tools
enable automatic pattern analysis, improved forecasting, and natural language
processing for predicting demand and performing sentiment analysis. Google’s
“Smart Bidding,” for example, uses machine learning to optimize conversions for
ads; California uses AI to predict electricity demand, more effectively controlling
the power grid and reducing blackouts.64 Walmart’s internal logistical management
has analogs to a centrally planned micro-economy.65 There are many challenges to
using analogous tools effectively for state economic policy, perhaps most of all the
variable goals of planners themselves. But these trends suggest national-level stra-
tegic planning may be able to benefit from better information by applying modern
machine learning tools to data accessible by states.
Leaders of authoritarian states like China may find themselves facing lower
costs for sustaining domestic political and economic control; leaders of authoritari-
an-leaning states may find themselves handed these tools by China.
The effects of AI on population control and state interventions in markets are not
certain. In the near term, however, it seems likely that Chinese elites at least believe
that AI may help them better control their society, and so too may elites in other states.
Information Warfare
Besides increasing the fitness of authoritarian governments more generally,
AI-enhanced information warfare may lower the costs of both influencing foreign
populations and pursuing economic warfare policies at scale. If mass opinion can
Center for Security and Emerging Technology14be decisively influenced by the clash between AI influence systems, for example,
China may determine its best bet for reabsorbing Taiwan is heavy investment in
AI-empowered propaganda.
Information attacks can also target economic systems and financial markets, es-
pecially AI systems associated with managing equities investments. An unintentional,
early demonstration of this possibility occurred in 201 3, when U.S. trading algo-
rithms responded to disinformation posted by the AP’s Twitter account after it was
hacked.66 Information warfare may be increasingly linked to economic warfare, not
just political disruptions.
Higher-end, AI-empowered information warfare is a more speculative, lon-
ger-term capability. Chris Wiggins has characterized current technical trends as
enabling “reality jamming”: the potential for synthetic, targeted, and optimized dis-
information at web-scale.67 In this future, current computational propaganda con-
cerns are just the tip of the iceberg. The bigger issue is the potential for large-scale
machine-generated information that is highly targeted at particular individuals or
subpopulations, evolved to maximally shape particular behaviors, and potentially
able to affect anyone with web access.68
Leveraging these developments, governments may attempt to shape perceptions
of other populations more frequently than in the past.69 OpenAI self-censored full
publication of its GPT-2 language-generation model in 2019, for example, because
it was concerned that generating close-to-human text would enable nefarious ac-
tors to proliferate disinformation. It is easy to imagine states pursuing similar capa-
bilities for their own ends.70 According to recent CSET research, GPT-2’s successor,
GPT-3, may be especially potent at generating disinformation at scale when steered
by a skilled human operator and editor, opening up the possibility of highly effec-
tive human-machine teaming.71
These trends may pose challenges for democratic societies, though it is still too
early to make clear judgments. Three unresolved questions exist today: First, if a
long-term risk in authoritarian systems is intellectual conformity, an analogous effect
in democracies may be mob majoritarianism.72 This inherent challenge in democrat-
ic societies could turn out to be exacerbated by modern information technologies
and make organizational reforms even more difficult. Second, more research is
needed to understand the balance between democracies’ ability to use disagree -
ments and diverse information to advance new explanations and solutions, and the
potential for information attacks to undermine political stability.73 And third, most
fundamentally, Western democracies, and particularly the U.S. system of govern-
ment, are based on a foundation of individual freedom where individuals are the
Center for Security and Emerging Technology15best judges of their own interests. It is not yet obvious how Western institutions will
adapt to machines that can anticipate—or shape—individuals’ own preferences,
states, and choices better than the individuals themselves can.74
In the context of international competition, leveraging AI technologies to alter
target states’ national priorities or political stability through information warfare
would represent “winning without fighting” par excellence.
Center for Security and Emerging Technology12
Center for Security and Emerging Technology17In this evolutionary theory of technological competition, AI’s effects
on national power fall into three categories: new elements of pow-
er, changed factors, and altered goals. Exploring new elements
required for successful AI adoption, such as compute and organizational
adaptations, helps us understand when, how, and why some societies
may be better positioned than others to benefit from major innovations.
Similarly, the idea of changed factors helps focus on how existing ele -
ments of national power may have changing importance, such as pop-
ulation size and industrious researchers. Finally, thinking about altered
goals of states in competition shows how major innovations can reshape
the ways that states engage in competition, such as enacting new do-
mestic political and economic controls and leveraging AI-enabled
information attacks on other states’ social and economic systems. This
research offers a way to start thinking about these issues together, and
hopes to spur new, wider thinking and work.
Creating new conceptual tools for U.S. decision-makers and analysts
to make sense of AI technologies’ effects is vital to American prosperity.
Over the long term, these technologies will create significant changes in
U.S.-China competition.
From this research, we see three early sets of insights into opportunities
for U.S. leaders:
• Thinking of long-term competitions in an evolutionary framework
makes large, broadly-diffused technology changes akin to envi -
ronmental shifts. Like a volcanic eruption or the start of an ice age, Conclusions and
Key Points3
I
Creating new con-
ceptual tools for U.S.
decision-makers and
analysts to make
sense of AI technolo-
gies’ effects is vital to
American prosperity.
Over the long term,
these technologies
will create significant
changes in U.S.-China
competition.
Center for Security and Emerging Technology18broad adaptations are valuable and some states will be better at adapting
than others. It is useful to begin thinking about how AI technologies can
create new elements of power, change the importance of existing elements
of power, and alter the goals of states in competition. Getting a better sense
of AI’s effects in each of these factors will be critical for major powers.
The United States has a number of opportunities: studying the approaches
of other countries, especially U.S. competitors and medium-sized, quick-
ly-changing countries;75 developing strategies for global leadership in
producing, using, and sharing compute resources; supporting development
of AI engineering as a rigorous discipline in the United States and lever-
aging humans trained in it; continuing to push DOD and IC organizational
reforms for how data is managed and leveraged; and leveraging AI tools,
cross-training between AI and other disciplines, and high-skilled STEM
immigration to access new breakthroughs in science and engineering more
widely.
• AI technologies may change not only what states can do, but also what
they want. Major innovations can broadly alter intermediate, instrumental
objectives that states pursue by making certain kinds of behaviors more
valuable or less costly. This can drive dramatic changes in state goals and
policies. The United States may look for new opportunities in technolo -
gy-related democracy promotion; shaping AI technologies themselves to
favor democracies, such as by supporting development of AI technologies
with less dependence on centralized data; 76 and developing approach-
es to more rapidly adapt social and economic institutions to “information
attacks” by AI systems.
• Finally, effects of technological change can be highly asymmetric: new ele -
ments, changed factors, and altered goals may have very different manifes-
tations in different countries. For the United States, this means learning from
its competitors without mirror imaging them and sharing insights with allies
before assuming they should symmetrically match U.S. policies. Perhaps
most significantly, it may also mean looking ahead to how AI technologies
may affect the aims and interests of U.S. allies and partners.
The scale of possible impacts from major technologies is obvious: the United
States benefitted greatly from growth connected to technological and economic
changes in the 40 years from 1 880 through 1920; and China has also already
benefitted from a mix of technological and economic changes in its resurgence from
1980 through 2020.77 Recent history demonstrates that getting technology right is
Center for Security and Emerging Technology19critical for long-term national flourishing—and determining trajectories for the Unit-
ed States and China over the next 20 to 30 years.
Can we sketch the longer-term future? Only speculation is possible today:
Broad historical examinations tend to suggest that more successful societies
present fewer obstacles to long-term change and, especially, limit the costs of intel-
lectual conformity. They seek to maximize the benefits of pluralism, competition, and
mechanisms to share, challenge, and supplement new knowledge.78
A key challenge for China will be limiting the long-term costs of intellectual con-
formity induced by an authoritarian government. A favorable factor for China will
be the dynamic organizations it has built over the last 20 years, which may remain
able to adapt and benefit from organizational learning as the world continues to
change over the next 10 to 20 years. In the longer term, however, continued evolu-
tion seems increasingly challenging for China under the CCP and absent substantial
pluralism; many of its main challenges for net economic-technological growth are
likely to persist, while the benefits of its dynamic organizations are likely to decline
over time.
A likely challenge for the United States will be institutional and organizational
sclerosis, which will make organizational learning and adaptation challenging over
the next decade. Interactions between AI technologies and democratic institutions
increase uncertainty and may exacerbate these challenges. Weighing against these
factors is Samuel Huntington’s reminder of the United States’ multidimensional sourc-
es of power and ability for self-renewal.79 The most favorable factors for U.S. vitality
and competition with authoritarian governments coincide with its enduring strengths:
areas such as its cultural values and pluralism, overall approach to governance, and
access to global talent.80 In the longer term, the United States’ central challenges
appear more temporary, and its greatest advantages more enduring—a favorable
outlook achievable with thinking and work today.
Center for Security and Emerging Technology16
Center for Security and Emerging Technology21Appendix
In 1948, after John von Neumann gave a talk on computing machines in Princeton, a member of the audience asked
the canonical question: Of course, machines can’t really think, can they? Von Neumann replied, “You insist that there
is something a machine cannot do. If you will tell me precisely what it is that a machine cannot do, then I can always
make a machine which will do just that!”81 Part of the challenge of defining AI has been that defining intelligence and
thinking in humans continues to be difficult.
This paper uses the definition of AI from the Defense Innovation Board: a variety of information processing techniques
and technologies used to perform a goal-oriented task and the means to reason in the pursuit of that task.82 More col-
loquially, AI can be thought of as a broad discipline and set of technologies centered on creating machines that can
make decisions relatively well under uncertainty.83
It is useful to distinguish AI from autonomy. The former is defined above; the latter is best thought of as some degree of
delegation of decision-making agency to another entity, which could be a human or a machine.84 Systems can have
neither, both, or one of these two things. For example, an autonomous military system can be unintelligent, as in the
case of a landmine, or an intelligent system can support humans without autonomy, as in the case of an information
system for a pilot.
The 2010s were the third period of global excitement about AI. The first period occurred in the 1960s, centered in the
United States and the UK, and the second period occurred in the 1980s, centered in the United States and Japan. Both
periods were associated with significant investment and optimism for cascading breakthroughs in machine intelligence.
Both periods were followed by “AI winters”: periods of widespread divestment from AI R&D and the belief that earlier
expectations had far exceeded reality.85 The current period will probably be remembered as being centered in the
United States and China, though with substantial activity in the UK, Europe, Canada, Japan, Israel, and South Korea.
Since the 2010s, most excitement about AI has focused on machine learning (ML), and, within ML, mostly on applica-
tions of neural networks (deep learning). ML is a broad subfield of AI that centers on inference from data and overlaps
substantially with statistics and optimization. “Neural networks” refers to a family of statistical models for extracting
patterns from large quantities of data, originally inspired by the behavior of biological neurons.
While the rediscovery and improvement of neural nets started the current AI wave in the late 2000s, specific trends over
the last 20 to 30 years enabled the success of recent applications: global growth and diffusion of compute resources;
large quantities of digital data globally; and the connection of these two by the global internet. For this reason, the
foundation of modern AI advancements is often called the “triad” of new algorithms, compute resources, and data.86
Center for Security and Emerging Technology26
Center for Security and Emerging Technology23Endnotes
1. On AI-specific vulnerabilities, see Andrew Lohn, “Hacking AI: A Primer for Policymakers on Machine
Learning Cybersecurity,” CSET, December 2020, https://cset.georgetown.edu/research/hacking-ai/. On
AI-specific accidents, see Tim G. J. Rudner and Helen Toner, “Key Concepts in AI Safety: An Overview,”
CSET, March 202 1, https://cset.georgetown.edu/research/key-concepts-in-ai-safety-an-overview/.
2. Emily O. Goldman and Richard B. Andres, “Systemic effects of military innovation and diffusion,” Security
Studies 8 (1999), 1 16.
3. Dahlia Peterson, “Designing Alternatives to China’s Repressive Surveillance State,” CSET, October 2020,
https://cset.georgetown.edu/research/designing-alternatives-to-chinas-repressive-surveillance-state/; Tim
Hwang, “Shaping the Terrain of AI Competition,” CSET, June 2020, https://cset.georgetown.edu/research/
shaping-the-terrain-of-ai-competition/; and Andrew Imbrie, Ryan Fedasiuk, Catherine Aiken, Tarun Chhabra,
and Husanjot Chahal, “Agile Alliances: How the United States and Its Allies Can Deliver a Democratic Way
of AI,” CSET, February 2020, https://cset.georgetown.edu/research/agile-alliances/.
4. Joseph Nye, The Paradox of American Power: Why the World’s Only Superpower Can’t Go It Alone, (New
York: Oxford University Press, 2002), p. 1 2.
5. Morgenthau carefully distinguished that some of these are relatively stable over time, whereas others are
more subject to frequent change. Some also have useful sub-elements to consider, making the complete
set: geography, access to resources (including food and raw materials), industrial capacity, military
preparedness (including technology, leadership, and quantity and quality of armed forces), population
(including distribution and trends), national character, national morale, quality of diplomacy, and quality of
government. See: Hans Morgenthau, Politics Among Nations: The Struggle for Power and Peace (New York:
Alfred A. Knopf, 1948), pp. 102-1 52.
6. Indeed, for some political scientists, the lack of such a consensus measure in fact is the cause of all wars,
as if states were to agree who would win in advance, there would be no reason to pay the costs of war at
all, instead of simply bargaining. See James D. Fearon, “Rationalist Explanations for War,” International
Organization 49, no. 3 (1995), 38 1, 390-401; and Bernard Brodie, War and Politics (New York: MacMillan
Publishing Co., Inc., 1973), 35-6, 63.
7. One 201 1 survey of the literature catalogued 69 different proposals of equations for measuring national
power. See Karl Hohn, “Geopolitics and the Measurement of National Power,” PhD diss., (University of
Hamburg, 201 1). Some selected examples:
Joseph Nye distinguished military power, economic power, and soft power. He noted that “power resources”
for the United States in the 20th Century included economic scale, scientific and technological leadership,
location, military forces and alliances, universalistic culture and liberal international regimes; for the 2 1st
century he suggested the corresponding elements as technological leadership, military and economic scale,
soft power, and being a hub of transnational communications. See: Joseph Nye, The Paradox of American
Power: Why the World’s Only Superpower Can’t Go It Alone, (New York: Oxford University Press, 2002),
pp. 4-1 2.
Robert Gilpin focuses power simply as the “military, economic, and technological capabilities of states,” and
notes that he intentionally leaves out “important and intangible elements that affect the outcomes of political
actions, such as public morale [and] qualities of leadership.” See: Robert Gilpin, War & Change in World
Politics, (New York: Cambridge University Press, 198 1), p. 1 3-1 4.
The Correlates of War project, a widely used quantitative database for studying warfare, defines a
“Composite Index of National Capability” (CINC) in terms of a country's share of world population, urban
Center for Security and Emerging Technology24population, iron and steel production, energy consumption, military expenditure, and military personnel.
(See: Singer, J. David, Stuart Bremer, and John Stuckey, “Capability Distribution, Uncertainty, and Major
Power War, 1 820-1965,” in Bruce Russett (ed.) Peace, War, and Numbers, (Beverly Hills: Sage, 1972), pp.
1948, as well as. https://correlatesofwar.org/data-sets/national-material-capabilities .)
More recently, Michael Beckley has argued that traditional measures of power conflate gross resources
with net resources, and thus fail to account for a country's burdens in addition to its assets. Thus, he proposes
the use of “GDP \* GDP per capita.” See “The Power of Nations: Measuring What Matters,” International
Security 43.2 (201 8): 7-44.
8. The best overview of this quest is Ashley J. Tellis, Janice Bially, Christopher Layne, and Melissa McPherson,
“Measuring National Power in the Postindustrial Age,” RAND Corporation, 2000.
9. This statement includes whether such measures are used quantitatively to predict who wins a war, or whether
war will occur, or whether settlement terms will favor one side or another. See: Ibid., 17.
10. For various discussions of this, see: Stephen Biddle, Military Power: Explaining Victory and Defeat in Modern
Battle (Princeton: Princeton University Press, 2004); David A. Baldwin, “Power Analysis and World Politics:
New Trends versus Old Tendencies,” World Politics 161 (1979): 161-94; Jeffrey Hart, “Three Approaches to
the Measurement of Power in International Relations,” International Organization 30 (1976), 289-305.
11 . Almond and Genco (1977) most famously made this point about how to think about political phenomena in
general. See Gabriel A. Almond and Stephen J. Genco, “Clouds, Clocks, and the Study of Politics,” World
Politics 29.4 (1977): 489-522.
1 2. Paul Kennedy, The Rise and Fall of the Great Powers, (New York: Random House, 1987), p. 199.
13 . Andrew Marshall, “RMA Update,” Memorandum for the Record, 2 May 1994.
1 4. For many examples collected in one place, see: Herbert Goldhamer, “Reality and Belief in Military Affairs,”
RAND Corporation, 1977.
15. For others who have offered ways of thinking about this, all with substantial detail, see: George Modelski
and William R. Thompson, Leading Sectors and World Powers (Columbia: University of South Carolina
Press, 1996); Paul Kennedy, The Rise and Fall of the Great Powers (New York: Random House, 1987);
Robert Gilpin, War and Change in World Politics (UK: Cambridge University Press, 198 1).
16. On steel, see Goldman and Andres, “Systemic effects of military innovation and diffusion,” 1 16. On
the internal combustion engine and oil, see W. G. Jensen, “The Importance of Energy in the First and
Second World Wars,” The Historical Journal 1 1 (1968): 538-54. On uranium, see R. Scott Kemp, “The
Nonproliferation Emperor Has No Clothes: The Gas Centrifuge, Supply-Side Controls, and the Future of
Nuclear Proliferation,” International Security 38 (201 4): 39-78, especially 41-4.
17. Michael C. Horowitz, The Diffusion of Military Power: Causes and Consequences for International Politics
(Princeton: Princeton University Press, 2010).
1 8. On the Mongols, see Goldman and Andres, “Systemic effects of military innovation and diffusion,” 102, 88-
9. On modern power projection and the loss of American “free security,” see C. Vann Woodward, “The Age
of Reinterpretation,” The American Historical Review 66 (1960): 1-19. On nuclear weapons, see Thomas C.
Schelling, Arms and Influence (New Haven: Yale University Press, 2008), 30-1.
19. Gilpin, p. 23.
20. This is an old idea about artificial intelligence, but was brought to our attention in the modern context by
Richard Danzig in July 2016, during discussion as part of the review board of a DOD Summer Study.
21. A voluminous literature discusses AI’s military applications. See an overview at Daniel S. Hoadley and Kelley
M. Sayler, “Artificial Intelligence and National Security,” Congressional Research Service, November 10,
2020, https://fas.org/sgp/crs/natsec/R45 178.pdf. For further reading, see Robert O. Work and Shawn
Brimley, “20YY: Preparing for War in the Robotic Age,” CNAS, January 201 4, https://s3.amazonaws.com/
files.cnas.org/documents/CNAS\_20YY\_WorkBrimley.pdf?; Luttwak (ONA, 2020); Christian Brose, The Kill
Chain: Defending America in the Future of High-Tech Warfare (New York: Hachette Books, 2020).
22. National Research Council, “Getting Up to Speed: The Future of Supercomputing,” (Washington, DC: The
National Academies Press, 2005), https://www.nap.edu/catalog/1 1 1 48/getting-up-to-speed-the-future-
of-supercomputing.
Center for Security and Emerging Technology2523. Dario Amodei and Danny Hernandez, “AI and Compute,” OpenAI, 16 May 201 8, https://openai.com/
blog/ai-and-compute/.
24. Girish Sastry, Jack Clark, Greg Brockman, and Ilya Sutskever, “Addendum: AI and Compute,” OpenAI, 7
November 2019, https://openai.com/blog/ai-and-compute/.
25. Xue Bin Peng, Lerrel Pinto, Alex Ray, Bob McGrew, Jonas Schneider, Josh Tobin, Marcin Andrychowicz,
Peter Welinder, Pieter Abbeel, and Wojciech Zaremba, “Generalizing from Simulation,” OpenAI,
October 19, 2017 , https://openai.com/blog/generalizing-from-simulation/. For a recent study
substituting simulated for real-world data in a military context, see Li Ang Zhang, Jia Xu, Dara Gold, Jeff
Hagen, Ajay K. Kochhar, Andrew J. Lohn, and Osonde A. Osoba, “Air Dominance Through Machine
Learning – A Preliminary Exploration of Artificial Intelligence–Assisted Mission Planning,” RAND, 2020,
https://www.rand.org/pubs/research\_reports/RR43 1 1.html.
26. CSET has a line of research both explaining and advising on how to maintain this state of affairs. See Saif
M. Khan, “Securing Semiconductor Supply Chains” (Washington, DC: Center for Security and Emerging
Technology, January 202 1); Saif M. Khan, "The Semiconductor Supply Chain: Assessing National
Competitiveness," CSET, January 202 1, https://cset.georgetown.edu/research/the-semiconductor-
supply-chain/; Saif M. Khan, “U.S. Semiconductor Exports to China: Current Policies and Trends” CSET,
October 2020, https://cset.georgetown.edu/wp-content/uploads/U.S.-Semiconductor-Exports-
to-China-Current-Policies-and-Trends.pdf; Saif M. Khan and Carrick Flynn, “Maintaining China’s
dependence on democracies for advanced computer chips,” Brookings, April 2020, https://www.
brookings.edu/research/maintaining-chinas-dependence-on-democracies-for-advanced-computer-
chips/; Saif M. Khan, "AI Chips: What They Are and Why They Matter," CSET, April 2020, https://cset.
georgetown.edu/research/ai-chips-what-they-are-and-why-they-matter/.
27. E. A. Huerta, Asad Khan, Edward Davis, Colleen Bushell, William D. Gropp, Daniel S. Katz, Volodymyr
Kindratenko, Seid Koric, William T. C. Kramer, Brendan McGinty, Kenton McHenry, and Aaron
Saxton, "Convergence of artificial intelligence and high performance computing on NSF-supported
cyberinfrastructure," Journal of Big Data 88 (2020).
28. This suggestion offered by Jack Clark in early comments on this paper.
29. The innovation literature is large. Seminally, see Barry R. Posen, The Sources of Military Doctrine: France,
Britain, and Germany Between the World Wars (New York: Cornell University Press, 1984); Stephen P.
Rosen, Winning the Next War: Innovation and the Modern Military (New York: Cornell University Press,
1991); Theo G. Farrell and Terry Terriff, The Sources of Military Change: Culture, Politics, Technology
(Colorado: Lynne Rienner Publishers, 2002).
30. Edmund Beard, Developing the ICBM: A Study in Bureaucratic Politics (New York: Columbia University
Press, 1976).
31. Goldman and Andres, “Systemic effects of military innovation and diffusion.”
32. Andrew Marshall, “RMA Update,” Memorandum for the Record, 2 May 1994; Horowitz, The Diffusion of
Military Power.
33. Marcus Comiter, “Attacking Artificial Intelligence: AI’s Security Vulnerability and What Policymakers Can
Do About It,” Belfer Center for Science and International Affairs, August 2019, https://www.belfercenter.
org/sites/default/files/2019-08/AttackingAI/AttackingAI.pdf.
34. As Alan Turing wrote in 1950, “An important feature of a learning machine is that its teacher will often
be very largely ignorant of quite what is going on inside.” (Alan Turing, “Computing Machinery and
Intelligence,” Mind, Volume LIX, Issue 236, October 1950, p. 458.) With deep learning, this problem
is especially acute due to the scale of statistical models involved – for example, ResNet, a commonly
used image classification architecture, uses around 5\*107 parameters. What is layer 27 of a hundred-
layer neural network doing? (See, for example: Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha
Bajwa, Michael Specter, and Lalana Kagal, “Explaining Explanations: An Overview of Interpretability of
Machine Learning,” arXiv, February 3, 2019, https://arxiv.org/pdf/1 806.00069.pdf.) Intuitively, it is
difficult for a human being to understand the inner workings of the model with any precision. For a very
good effort see: Chris Olah, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine
Center for Security and Emerging Technology26Ye, and Alexander Mordvintsev, “The Building Blocks of Interpretability,” Distill, 201 8, https://distill.
pub/201 8/building-blocks/.
35. Michael Jordan, “Artificial Intelligence—The Revolution Hasn’t Happened Yet,” Harvard Data Science
Review, July 2019, https://hdsr.mitpress.mit.edu/pub/wot7mkc1 .
36. Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané, “Concrete
Problems in AI Safety,” arXiv, July 25, 2016, https://arxiv.org/pdf/1606.06565.pdf.
37. Michèle A. Flournoy, Avril Haines, and Gabrielle Chefitz, “Building Trust through Testing: Adapting DOD’s
Test & Evaluation, Validation & Verification (TEVV) Enterprise for Machine Learning Systems, including
Deep Learning Systems,” CSET / WestExec Advisors, 2020, https://cset.georgetown.edu/wp-content/
uploads/Building-Trust-Through-Testing.pdf.
38. For modern machine learning systems specifically, these issues are introduced in places like: Nicolas
Papernot et al., “SoK: Towards the Science of Security and Privacy in Machine Learning,” Nov 2016,
arXiv:161 1.038 1 4v1; Gamaleldin Elsayed, Ian Goodfellow, and Jascha Sohl-Dickstein, “Adversarial
Reprogramming of Neural Networks,” June 201 8, arXiv:1 806. 1 1 1 46v1; and Nicholas Carlini et al., “On
Evaluating Adversarial Robustness,” February 2019, arXiv:1902.06705v2.
39. A companion reading for thinking about this might be: R. V. Jones, The Wizard War: British Scientific
Intelligence, 1939-1945, (New York: Coward, McCann & Geoghegan: 1978).
40. Perhaps the best introduction to this was provided by Tim Hwang et al.: “For a heart-stopping few minutes
on May 6, 2010, the Dow Jones Industrial Average dropped a staggering 1,000 points—and inexplicably
proceeded to recover all of those losses within the following few minutes. The Flash Crash, as it was later
dubbed, remains the biggest one-day point decline in Dow Jones history. After a five-month investigation,
the SEC reported that the sudden loss and gain that day was the result of an unusually large number of
contracts being sold by a mutual fund, which triggered a wave of aggressive sell-off activity from untold
numbers of firms running automated high frequency trading programs. No human agency was at the heart
of the momentary crash. Instead, it appears that unanticipated interactions among multiple automated
scripts designed to buy and sell stock produced the precipitous fall and rise in prices. Financial robots may
also be behind the otherwise inexplicable correlations between mentions of the actor Anne Hathaway in
the news and increases in the stock price of Warren Buffet’s Berkshire Hathaway fund.” See: Tim Hwang,
Ian Pearce, and Max Nanis, “Socialbots: Voices from the Fronts,” Interactions, March-April 201 2. More
recently, and closer to a direct example, in 201 3 trading systems responded to information from the AP’s
twitter feed after it had been hacked by (apparently) Syrian dissidents, causing a temporary drop of
$1 30B. See: Max Fisher, “Syrian hackers claim AP hack that tipped stock market by $1 36 billion. Is it
terrorism?” Washington Post, 23 April 201 3, https://www.washingtonpost.com/news/worldviews/
wp/201 3/04/23/syrian-hackers-claim-ap-hack-that-tipped-stock-market-by-1 36-billion-is-it-terrorism/.
41. Daron Acemoglu and Pascual Restrepo, “Demographics and Automation,” NBER, March 201 8, https://
www.nber.org/papers/w2442 1 .
42. Paul Scharre, Army of None: Autonomous Weapons and the Future of War (New York: W. W. Norton &
Company, 201 8).
43. Todd Schneider, Gee Hee Hong, and Anh Van Le, “Land of the Rising Robots,” IMF, June 201 8, https://
www.imf.org/external/pubs/ft/fandd/201 8/06/japan-labor-force-artificial-intelligence-and-robots/
schneider.pdf.
44. Ewen Callaway, “‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein
structures,” Nature, November 30, 2020, https://www.nature.com/articles/d41 586-020-03348-4.
45. For a useful overview: Jeffrey Dean, “The Deep Learning Revolution and Its Implications for
Computer Architecture and Chip Design,” arXiv, 1 3 Nov 2019, https://arxiv.org/ftp/arxiv/
papers/191 1/191 1.05289.pdf.
46. Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan, et al. “A graph placement methodology for fast chip
design,” Nature 594, 207–2 1 2 (202 1). https://doi.org/10. 1038/s41 586-02 1-03544-w.
47. For example, suggesting valuable hypotheses to test or engineering design configurations. This goal has
had a resurgence in the 2010s: in 2016 Hiroaki Kitano, creator of Robocup, proposed a grand challenge
for AI systems capable of making Nobel-worthy scientific discoveries: Hiroaki Kitano, “Artificial intelligence
Center for Security and Emerging Technology27to win the nobel prize and beyond: Creating the engine for scientific discovery.” AI magazine 37 , no. 1
(2016): 39-49. A recent data brief by CSET also surveys how AI technologies have been accelerating
growth of new science and engineering research clusters across a broad span of disciplines, see: Matthew
Daniels, Autumn Toney, Melissa Flagg, and Charles Yang, “Machine Intelligence for Scientific Discovery and
Engineering Invention,” CSET, May 202 1, https://cset.georgetown.edu/publication/machine-intelligence-
for-scientific-discovery-and-engineering-invention/.
48. For example: Kevin Williams, Elizabeth Bilsland, Andrew Sparkes, Wayne Aubrey, Michael Young, Larisa N.
Soldatova, Kurt De Grave et al. “Cheaper faster drug development validated by the repositioning of drugs
against neglected tropical diseases.” Journal of the Royal Society Interface 1 2, no. 104 (201 5): 201 41 289.
49. For example, see: Volodymyr Kuleshov, Jialin Ding, Christopher Vo, Braden Hancock, Alexander Ratner, Yang
Li, Christopher Ré, Serafim Batzoglou, and Michael Snyder, “A machine-compiled database of genome-wide
association studies,” Nature Communications 10, 3341 (2019), https://doi.org/10. 1038/s41 467-019-
1 1026-x.
50. See, for example: Freeman Dyson, “Is Science Mostly Driven by Ideas or by Tools?” Science, Vol. 338
(December 201 2): 1 426-1 427.
51. Cade Metz, “In Two Moves, AlphaGo and Lee Sedol Redefined the Future,” Wired, March 16, 2016,
https://www.wired.com/2016/03/two-moves-alphago-lee-sedol-redefined-future/.
52. Iain M. Cockburn, Rebecca Henderson, and Scott Stern, “The Impact of Artificial Intelligence on Innovation:
An Exploratory Analysis,” in The Economics of Artificial Intelligence: An Agenda, eds. Ajay Agrawal, Joshua
Gans, and Avi Goldfarb (Chicago: University of Chicago Press, 2017), 1 1 5-46.
53. Other work has made analogous arguments, though from a different perspective, see: Remco Zwetsloot and
Zachary Arnold, "Foreign Brains Help America Compete," Wall Street Journal, January 30, 2020; Remco
Zwetsloot and Dahlia Peterson, "The US-China Tech Wars: China’s Immigration Disadvantage," The Diplomat,
December 3 1, 2019; Remco Zwetsloot, Roxanne Heston, and Zachary Arnold "Strengthening the U.S. AI
Workforce," CSET, September 2019, https://cset.georgetown.edu/publication/strengthening-the-u-s-ai-
workforce/.
54. Gilpin, p. 23.
55. Stephen Brooks, Producing Security: Multinational Corporations, Globalization, and the Changing Calculus
of Conflict (Princeton: Princeton University Press, 2005).
56. Ross Andersen, “The Panopticon Is Already Here,” The Atlantic, September 2020, https://www.theatlantic.
com/magazine/archive/2020/09/china-ai-surveillance/61 4197/; Ben Angel Chang, “AI and US-China
Relations,” in Nicholas D. Wright, ed., AI, China, Russia, and the Global Order: Technological, Political,
Global, and Creative Perspectives (DOD SMA: December 201 8).
57. Andreas Lichter, Max Löffler, and Sebastian Siegloch, “The long-term costs of government surveillance:
Insights from Stasi spying in East Germany,” SOEPpapers on Multidisciplinary Panel Data Research 865
(2016): 1-60. Available online: https://www.econstor.eu/bitstream/10419/1 46890/1/869045423.pdf.
58. This remains speculative. For example, some evidence suggests surveillance itself depresses economic
activity by eroding social trust, causing individuals to reduce their productive activity. (See: Lichter, Löffler,
and Siegloch, “The long-term costs of government surveillance: Insights from Stasi spying in East Germany,”
p. 22). Other studies have found interpersonal trust to correlate with entrepreneurship and innovation.
(See: Stephen Knack and Philip Keefer, “Does Social Capital Have an Economic Payoff? A Cross-Country
Investigation,” The Quarterly Journal of Economics 1 1 2 (1997): 1 25 1-88.) This effect was particularly acute
because Stasi informants retained their normal roles as colleagues, family, and friends, and so the knowledge
of Stasi presence caused widespread doubt and fear. Automated technological surveillance would plausibly
avoid these effects. (See: Lichter et al., “The long-term costs of government surveillance,” p. 22.)
59. Paul Mozur, “One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority,” New York
Times, April 2019; Josh Chin and Liza Lin, “China’s All-Seeing Surveillance State Is Reading Its Citizens’
Faces,” Wall Street Journal, June 2017.
60. Liza Lin and Shan Li, “Chinese Citizens Must Scan Their Faces to Register for New Mobile-Phone Service,”
Wall Street Journal, December 2019; Sui-Lee Wee, “China Uses DNA to Track Its People, With the Help of
American Expertise,” New York Times, February 2019. Shan Li, “Made-in-China Censorship for Sale,” Wall
Center for Security and Emerging Technology28Street Journal, March 2020; Paul Mozur, Raymond Zhong, and Aaron Krolik, “In Coronavirus Fight, China
Gives Citizens a Color Code, With Red Flags,” New York Times, March 2020. CSET has also considered
possible future trends based on COVID-driven increases in surveillance: https://www.cset-foretell.com/
blog/surveillance-creep.
61. Dahlia Peterson, “Designing Alternatives to China’s Repressive Surveillance State,” CSET, October 2020,
https://cset.georgetown.edu/research/designing-alternatives-to-chinas-repressive-surveillance-state/.
62. Jessica Chen Weiss, “Understanding and Rolling Back Digital Authoritarianism,” War on the Rocks,
February 17 , 2020, https://warontherocks.com/2020/02/understanding-and-rolling-back-digital-
authoritarianism/; Jessica Chen Weiss, “An Ideological Contest in U.S.-China Relations? Assessing China’s
Defense of Autocracy,” forthcoming in Security and US-China Relations: Differences, Dangers, and
Dilemmas, eds. Avery Goldstein and Jacques deLisle. Available online: https://papers.ssrn.com/sol3/
papers.cfm?abstract\_id=3427 1 8 1 .
63. Ludwig von Mises, Human Action (Chicago: Contemporary Books, Inc., 1963), 678-80; More colloquially,
see Cosma Shalizi, “In Soviet Union, Optimization Problem Solves You,” Crooked Timber, May 30, 201 2,
http://crookedtimber.org/201 2/05/30/insoviet-union-optimization-problem-solves-you/.
64. Paul R. Milgrom and Steve Tadelis, “How Artificial Intelligence and Machine Learning Can Impact Market
Design," forthcoming in The Economics of Artificial Intelligence, eds. Ajay K. Agrawal, Joshua Gans, and Avi
Goldfarb (Chicago: University of Chicago Press, 2019), 1-24. Available online: https://www.nber.org/
books/agra-1 .
65. Leigh Phillips and Michal Rozworski, “The People’s Republic of Walmart: How the World's Biggest
Corporations are Laying the Foundation for Socialism” (New York: Verso, 2019).
66. Max Fisher, “Syrian hackers claim AP hack that tipped stock market by $1 36 billion. Is it terrorism?”
Washington Post, 23 April 201 3, https://www.washingtonpost.com/news/worldviews/
wp/201 3/04/23/syrian-hackers-claim-ap-hack-that-tipped-stock-market-by-1 36-billion-is-it-terrorism/.
67. Chris Wiggins, “Reality Jamming: technology-enabled misinformation at scale,” independent abstract, 2017;
Susan McGregor, Chris Wiggins, Joan Donovan, Matt Jones, Jonathan Albright, and Sam Thielman, “Reality
Jamming: The Future of Information Online,” Tow Center, December 1 1, 2017 , https://medium.com/tow-
center/reality-jamming-the-future-of-information-online-3ad5cb0d932e.
68. See also: Matt Chessen, “The MADCOM Future,” The Atlantic Council, 2017 , https://www.atlanticcouncil.
org/wp-content/uploads/2017/09/The\_MADCOM\_Future\_RW\_0926.pdf.
69. The Office of Net Assessment sponsored early work on this. For example, see: Michael J. Mazarr, Abigail
Casey, Alyssa A. Demus, Scott W. Harold, Luke J. Matthews, Nathan Beauchamp-Mustafaga, James
Sladden, “Hostile Social Manipulation: Present Realities and Emerging Trends,” RAND Corporation, 2019,
https://www.rand.org/pubs/research\_reports/RR27 1 3.html.
70. Alec Radford, Jeffrey Wu, Dario Amodei, Daniela Amodei, Jack Clark, Miles Brundage, and Ilya Sutskever,
“Better Language Models and Their Implications,” OpenAI, February 1 4, 2019, https://openai.com/
blog/betterlanguage-models/; Dipayan Ghosh and Ben Scott, “Digital Deceit: The Technologies Behind
Precision Propaganda on the Internet,” New America, January 23, 201 8, https://www.newamerica.org/
public-interest-technology/policypapers/digitaldeceit/, 26-8; Sarah Kreps and Miles McCain, “Not
Your Father’s Bots: AI Is Making Fake News Look Real,” Foreign Affairs, https://www.foreignaffairs.com/
articles/2019-08-02/not-your-fathers-bots.
71. Ben Buchanan, Andrew Lohn, Micah Musser, and Katerina Sedova, "Truth, Lies, and Automation: How
Language Models Could Change Disinformation," CSET, May 202 1, https://cset.georgetown.edu/
publication/truth-lies-and-automation/.
72. For example, Tocqueville’s admonition: “If, in place of all the diverse powers that hindered or slowed beyond
measure the rapid development of individual reason, democratic peoples substituted the absolute power
of a majority, the evil would only have changed character. Men would not have found the means to live
independently; they would only have discovered, a difficult thing, a new face of servitude. I cannot say it
enough: for those who see liberty of the mind as a holy thing, and who hate not only the despot but also
despotism, there is in that something to make them reflect deeply. For me, when I feel the hand of power
pressing on my head, knowing who is oppressing me matters little to me, and I am no more inclined to put
Center for Security and Emerging Technology29my head in the yoke, because a million arms present it to me.” (Alexis de Tocqueville, Democracy in
America, Edited by Eduardo Nolla, Translated by James T. Schleifer (Indianapolis: Liberty Fund, 201 2),
Vol. II, Section 1, Chapter 2.)
73. Henry John Farrell and Bruce Schneier, “Common-Knowledge Attacks on Democracy,” Berkman Klein
Center Research Publication No. 201 8-7 , Available online: https://ssrn.com/abstract=3273 1 1 1
7 4. Richard Danzig, “An Irresistible Force Meets a Moveable Object: The Technology Tsunami and the
Liberal Order,” Lawfare Research Paper Series 5. 1 (2017), https://assets.documentcloud.org/
documents/3982439/Danzig-LRPS1.pdf, 4-7.
75. For learning from U.S. competitors, see: Peter Westwick, “Lessons from Stealth for Emerging
Technologies,” CSET, March 202 1, https://cset.georgetown.edu/publication/lessons-from-stealth-for-
emerging-technologies/, pp. 25-26.
76. Tim Hwang, “Shaping the Terrain of AI Competition,” CSET, June 2020, https://cset.georgetown.edu/
publication/shaping-the-terrain-of-ai-competition/.
77. The United States emerged as a major power following industrialization of its economy and society
over the half-century from 1 875 to 1925. This period was particularly unstable for the international
system, with rapid technological change and uneven growth—the U.S. fraction of global manufacturing
output more than doubled from 1 4.7 percent in 1 880 to 39.3 percent in 1928. (See: Paul Kennedy,
The Rise and Fall of the Great Powers, (New York: Random House, 1987), p. 202.) As industrialization
transformed the U.S. economy and society, population growth allowed the United States to harness
these changes into national power. U.S. population increased from 44M in 1 87 4 to 1 1 4M in 1924. (See:
Hans Morgenthau, Politics Among Nations, (New York: Knopf, 1956), p. 1 1 4.) This was well above
the populations of Germany, Japan, France, Britain, and Italy. (Paul Kennedy, The Rise and Fall of the
Great Powers, (New York: Random House, 1987), p. 199.) The United States in this period became
both industrialized and populous relative to other countries and, by 1920, was the strongest power
on the planet. A century later, China is undergoing an analogous shift, but with still-uncertain results.
China began instituting major economic reforms in 1979. In the period from approximately 1980-
2020, China transformed its economy, society, and military, partly by harnessing modern information
technologies. In this period, China’s fraction of global GDP (by PPP) increased from 2.3 percent in 1980
to 1 8.3 percent in 2017 , while the United States declined from 24.3 percent to 1 5.3 percent in the same
period. (Wayne Morrison, “China’s Economic Rise: History, Trends, Challenges, and Implications for the
United States,” Congressional Research Service, June 2019, p. 10. https://www.everycrsreport.com/
files/20190625\_RL33534\_088c5467dd1 1 365dd4ab5f72 1 33db289fa10030f.pdf) China already had
the population needed to harness economic reforms for growth. If U.S. growth in economic power came
from industrialization and population growth, China’s could be described as coming from large-scale
capital investment and productivity growth—the latter due to both resource reallocations and imported
technologies and processes. (Morrison, “China’s Economic Rise”, p. 6-7) China’s trajectory for the next
20-30 years, however, remains highly uncertain. Whether China can continue to sustain substantial
economic growth depends in significant part on the degree to which it can make new technology and
innovation a source of future growth. (Morrison, “China’s Economic Rise”, p. 7-8.) China also faces
enormous demographic, environmental, public health, and peripheral security challenges that will impose
large costs on its government. (See, for example: Michael Beckley, Unrivaled: Why America Will Remain
the World's Sole Superpower, (New York: Cornell University Press, 201 8), pp. 1 20-1 34.)
78. See, for example: Joel Mokyr, A Culture of Growth: The Origins of the Modern Economy, (Princeton:
Princeton University Press, 2017). Paul Kennedy, Rise and Fall of the Great Powers (New York: Random
House, 1989).
79. Samuel Huntington, “The US-decline or renewal.” Foreign Affairs 67 (1988): 76.
80. A modern reflection on these strengths is presented in Richard Danzig et al., “A Preface to Strategy: The
Foundations of American National Security,” JHU Applied Physics Laboratory, 201 8.
81. E.T. Jaynes was in the audience and noted the exchange. E.T. Jaynes, Probability Theory: The Logic of
Science, (St. Louis, MO: Washington University, 1996), p. 4.
Center for Security and Emerging Technology3082. Defense Innovation Board, “AI Principles: Recommendations on the Ethical Use of Artificial Intelligence by
the Department of Defense,” https://media.defense.gov/2019/Oct/3 1/2002204458/-1/-1/0/DIB\_
AI\_PRINCIPLES\_PRIMARY\_DOCUMENT.PDF.
83. For example, Nils Nilsson, a pioneer of AI research, writes: “artificial intelligence is that activity devoted to
making machines intelligent, and intelligence is that quality that enables an entity to function appropriately
and with foresight in its environment. According to that definition, lots of things — humans, animals, and
some machines — are intelligent. Machines, such as 'smart cameras,’ and many animals are at the primitive
end of the extended continuum along which entities with various degrees of intelligence are arrayed. At
the other end are humans, who are able to reason, achieve goals, understand and generate language,
perceive and respond to sensory inputs, prove mathematical theorems, play challenging games, synthesize
and summarize information, create art and music, and even write histories.” See: Nils Nilsson, The Quest for
Artificial Intelligence (New York: Cambridge University Press, 2010).
84. This was most recently reiterated by the Defense Science Board. See: Defense Science Board, “Summer
Study on Autonomy,” U.S. Department of Defense, June 2016.
85. The first AI winter was actually initiated by a government report in the UK, commonly referred to as the
Lighthill Report.
86. Ben Buchanan, “The AI Triad and What It Means for National Security,” CSET, August 2020: https://
cset.georgetown.edu/wp-content/uploads/CSET-AI-Triad-Report.pdf. Further, many primers on modern
AI and ML systems now exist and are not reproduced here. See, for example: Mark Halpern, “No Ghost
in the Machine,” The American Scholar, Spring 2020, https://theamericanscholar.org/no-ghost-in-the-
machine/#.Xnq96G4pCu6; John Launchbury, “A DARPA Perspective on Artificial Intelligence,” Defense
Advanced Research Projects Agency (DARPA), March 2017 , https://www.darpa.mil/about-us/darpa-
perspective-on-ai; Ben Buchanan and Taylor Miller, “Machine Learning for Policymakers: What it is and
why it matters,” Harvard Belfer Center for Science and International Affairs, June 2017 , https://www.
belfercenter.org/sites/default/files/files/publication/MachineLearningforPolicymakers.pdf; Andrej
Karpathy, “AlphaGo, in context,” https://medium.com/@karpathy/alphago-in-context-c477 1 8cb95a5;
Michael Jordan, “Artificial Intelligence—The Revolution Hasn’t Happened Yet,” Harvard Data Science
Review, https://hdsr.mitpress.mit.edu/pub/wot7mkc1 .
Center for Security and Emerging Technology39

CSET.GEORGETOWN.EDU | CSET@GEORGETOWN.EDU