By Eric Vandenbroeck and co-workers
There Is No Time To Waste
Yesterday we
described how the development of robust AI systems is
inevitable, and people everywhere need to be prepared for what such
technologies will do to their communities and the broader world.
Looking towards the
future now, it should be clear that in, for example, 2035, artificial intelligence will be everywhere. AI systems run
hospitals, operate airlines, and battle each other in the courtroom.
Productivity has spiked to unprecedented levels, and countless previously
unimaginable businesses have scaled at blistering speed, generating immense
advances in well-being. New products, cures, and innovations hit the market
daily as science and technology kick into overdrive. And yet the world is
growing more unpredictable and fragile, as terrorists find new ways to menace
societies with intelligent, evolving cyberweapons and white-collar workers lose
their jobs en masse.
Just a year ago, that
scenario would have seemed purely fictional; today, it seems nearly inevitable.
Generative AI systems can write more clearly and persuasively than most humans
and produce original images, art, and computer code based on simple language
prompts. And generative AI is only the tip of the iceberg. Its arrival marks a
Big Bang moment, the beginning of a world-changing technological revolution
that will remake politics, economies, and societies.
AI will pair
extraordinary growth and opportunity with immense disruption and risk like past
technological waves. But unlike previous waves, it will also initiate a seismic
shift in the structure and balance of global power as it threatens the status
of nation-states as the world’s primary geopolitical actors. Whether they admit
it or not, AI’s creators are geopolitical actors. Their sovereignty over AI
further entrenches the emerging world order—one in which technology companies
wield the kind of power in their domains once reserved for nation-states. For
the past decade, big technology firms have effectively become independent,
sovereign actors in the digital realms they have created. AI accelerates this
trend and extends it far beyond the digital world. The technology’s complexity
and the speed of its advancement will make it almost impossible for governments
to make relevant rules at a reasonable pace. If governments do not catch up
soon, they may never.
Thankfully,
policymakers worldwide have begun to wake up to the challenges posed by AI and
wrestle with how to govern it. In May 2023, the G-7 launched the “Hiroshima AI
process,” a forum devoted to harmonizing AI governance. In June, the European
Parliament passed a draft of the EU’s AI Act, the first comprehensive attempt
by the European Union to erect safeguards around the AI industry. And in July,
UN Secretary-General Antonio Guterres called for establishing a global AI
regulatory watchdog. Meanwhile, politicians on both sides call for regulatory
action in the United States. But many agree with Ted Cruz, the Republican
senator from Texas, who concluded in June that Congress “doesn’t know what the
hell it’s doing.”
Unfortunately, too
much of the debate about AI governance remains trapped in a dangerous false
dilemma: leverage artificial intelligence to expand national power or stifle it
to avoid its risks. Even those who accurately diagnose the problem try to solve
it by shoehorning AI into existing or historical governance frameworks. Yet AI
cannot be governed like any previous technology, and it is already shifting
traditional notions of geopolitical power.
The challenge is
designing a new governance framework for this unique technology. If global
governance of AI is possible, the international system must move past
traditional conceptions of sovereignty and welcome technology companies to the
table. These actors may not derive legitimacy from a social contract,
democracy, or the provision of public goods, but effective AI governance will
not stand a chance without them. This is one example of how the international
community must rethink basic assumptions about the geopolitical order. But it
is not the only one.
A challenge as
unusual and pressing as AI demands an original solution. Before policymakers
can begin to hash out an appropriate regulatory structure, they must agree on
basic principles for governing AI. Any governance framework must be precautionary,
agile, inclusive, impermeable, and targeted. Building on these principles,
policymakers should create at least three overlapping governance regimes: one
for establishing facts and advising governments on the risks posed by AI, one
for preventing an all-out arms race between them, and one for managing the
disruptive forces of technology, unlike anything the world has seen.
Like it or not, 2035
is coming. Whether it is defined by the positive advances enabled by AI or the
negative disruptions caused by it depends on what policymakers do now.
Faster, Higher, Stronger
AI is different from other
technologies and diverse in its effect on power. It does not just pose policy
challenges; its hyper-evolutionary nature also makes solving those challenges
progressively harder. That is the AI power paradox.
The pace of progress
is staggering. Take Moore’s Law, which has successfully predicted the doubling
of computing power every two years. The new wave of AI makes that rate of
progress seem quaint. When OpenAI launched its first large language model,
GPT-1, in 2018, it had 117 million parameters—a measure of the system’s scale
and complexity. Five years later, the company’s fourth-generation model, GPT-4,
is thought to have over a trillion. The amount of computation used to train the
most powerful AI models has increased by a factor of ten every year for the
last ten years. Put another way, today’s most advanced AI models—also known as
“frontier” models—use five billion times the computing power of cutting-edge
models from a decade ago. Processing that once took weeks now happens in
seconds. Models that can handle tens of trillions of parameters are coming in
the next couple of years. “Brain scale” models with more than 100 trillion
parameters—roughly the number of synapses in the human brain—will be viable
within five years.
With each new order
of magnitude, unexpected capabilities emerge. Few predicted that training on
raw text would enable large language models to produce coherent, novel, and
even creative sentences. Fewer still expected language models to be able to
compose music or solve scientific problems, as some now can. Soon, AI
developers will likely succeed in creating systems with self-improving
capabilities—a critical juncture in the trajectory of this technology that
should give everyone pause.
AI models are also
doing more with less. Yesterday’s cutting-edge capabilities are running on
smaller, cheaper, and more accessible systems today. Just three years after
OpenAI released GPT-3, open-source teams have created models capable of the
same level of performance that are less than one-sixtieth of its size—that is,
60 times cheaper to run in production, entirely free, and available to everyone
on the Internet. Future large language models will probably follow this
efficiency trajectory, becoming available in open-source form just two or three
years after leading AI labs spend hundreds of millions of dollars developing
them.
As with any software
or code, AI algorithms are much easier and cheaper to copy and share (or steal)
than physical assets. Proliferation risks are apparent. For instance, Meta’s
powerful Llama-1 large language model leaked to the Internet within days of
debuting in March. Although the most potent models still require sophisticated
hardware, midrange versions can run on computers rented for a few dollars an
hour. Soon, such models will run on smartphones. No powerful technology has
become so accessible, widely, and quickly.
Robots preparing food at a hotpot restaurant in
Beijing
AI also differs from
older technologies in that almost all of it can be characterized as “dual
use”—having both military and civilian applications. Many systems are
inherently general, and generality is the primary goal of many AI companies.
They want their applications to help as many people in as many ways as
possible. But the same systems that drive cars can drive tanks. An AI
application built to diagnose diseases might be able to create—and weaponize—a
new one. The boundaries between the safely civilian and the militarily
destructive are inherently blurred, partly explaining why the United States has
restricted the export of the most advanced semiconductors to China.
All this plays out
globally: AI models can and will be everywhere once released. And it will take
just one malign or “breakout” model to wreak havoc. Therefore, regulating AI
cannot be done in a patchwork manner. There is little use in controlling AI in
some countries if it remains unregulated in others. Because AI can proliferate
so quickly, its governance can have no gaps.
What is more, the
damage AI might do has no obvious cap, even as the incentives to build it (and
the benefits of doing so) continue to grow. AI could be used to generate and
spread toxic misinformation, eroding social trust and democracy; to surveil,
manipulate, and subdue citizens, undermining individual and collective freedom;
or to create powerful digital or physical weapons that threaten human lives. AI
could also destroy millions of jobs, worsening existing inequalities and
creating new ones; entrench discriminatory patterns and distort decision-making
by amplifying bad information feedback loops; or spark unintended and
uncontrollable military escalations that lead to war.
The time frame needs
to be clarified for the most significant risks. Online misinformation is an
apparent short-term threat, just as autonomous warfare seems plausible in the
medium term. Farther out on the horizon lurks the promise of artificial general
intelligence, the still uncertain point where AI exceeds human performance at
any given task, and the (admittedly speculative) peril that AGI could become
self-directed, self-replicating, and self-improving beyond human control. All
these dangers need to be factored into governance architecture from the outset.
AI is not the first
technology with potent characteristics but the first to combine them all.
Unlike cars or airplanes, AI systems are built on hardware amenable to incremental
improvements and whose most costly failures result in individual accidents.
They are unlike chemical or nuclear weapons, which are difficult and expensive
to develop and store, let alone secretly share or deploy. As their enormous
benefits become self-evident, AI systems will only grow bigger, better,
cheaper, and more ubiquitous. They will even become capable of
quasi-autonomy—achieving concrete goals with minimal human oversight—and,
potentially, of self-improvement. Any one of these features would challenge
traditional governance models; all of them together render these models
hopelessly inadequate.
Too Powerful To Pause
As if that were not
enough, by shifting the structure and balance of global power, AI complicates
the very political context in which it is governed. AI is not just software
development but an entirely new means of projecting power. In some cases, it
will upend existing authorities; in others, it will entrench them. Moreover,
its advancement is propelled by irresistible incentives: every nation,
corporation, and individual will want some version of it.
Within countries, AI will
empower those who wield it to surveil, deceive, and even control
populations—supercharging the collection and commercial use of personal data in
democracies and sharpening the tools of repression authoritarian governments
use to subdue their societies. Across countries, AI will be the focus of
intense geopolitical competition. Whether for its repressive capabilities,
economic potential, or military advantage, AI supremacy will be a strategic
objective of every government with the resources to compete. The least
imaginative strategies will pump money into homegrown AI champions or attempt
to build and control supercomputers and algorithms. More nuanced approaches
will foster specific competitive advantages, as France seeks to do by directly
supporting AI startups; the United Kingdom, by capitalizing on its world-class
universities and venture capital ecosystem; and the EU, by shaping the global
conversation on regulation and norms.
Most countries need
more money and technological know-how to compete for AI leadership. Their
access to frontier AI will instead be determined by their relationships with a
handful of already rich and powerful corporations and states. This dependence
threatens to aggravate current geopolitical power imbalances. The most powerful
governments will vie to control the world’s most valuable resource while, once
again, countries in the global South will be left behind. This is not to say
that only the richest will benefit from the AI revolution. Like the Internet
and smartphones, AI will proliferate without respect for borders, as will the
productivity gains it unleashes. And like energy and green technology, AI will
benefit many countries that do not control it, including those that contribute
to producing AI inputs such as semiconductors.
However, the
competition for AI supremacy will be fierce at the other end of the
geopolitical spectrum. At the end of the Cold War, powerful countries might
have cooperated to allay one another’s fears and arrest a potentially
destabilizing technological arms race. But today’s tense geopolitical
environment makes such cooperation much harder. AI is not just another tool or
weapon that can bring prestige, power, or wealth. It can potentially enable a
significant military and economic advantage over adversaries. Rightly or
wrongly, the two players that matter most—China and the United States—both see
AI development as a zero-sum game that will give the winner a decisive
strategic edge in the decades to come.
China And The United States Both See AI Development As
A Zero-Sum Game
From the vantage
point of Washington and Beijing, the risk that the other side will gain an edge
in AI is more significant than any theoretical risk the technology might pose
to society or their domestic political authority. For that reason, both the
U.S. and Chinese governments are pouring immense resources into developing AI
capabilities while working to deprive each other of the inputs needed for
next-generation breakthroughs. This zero-sum dynamic—and the lack of trust on
both sides—means that Beijing and Washington are focused on accelerating AI
development rather than slowing it down. In their view, a “pause” to assess
risks, as some AI industry leaders have called for, would amount to foolish
unilateral disarmament.
But this perspective
assumes states can assert and maintain some control over AI. This may be the
case in China, which has integrated its tech companies into the fabric of the
state. Yet, in the West and elsewhere, AI is more likely to undermine state
power than to bolster it. Outside China, a handful of large, specialist AI
companies currently control every aspect of this new technological wave: what
AI models can do, who can access them, how they can be used, and where they can
be deployed. And because these companies jealously guard their computing power
and algorithms, they alone understand (most of) what they are creating and
(most of) what those creations can do. These few firms may retain their
advantage for the foreseeable future—or they may be eclipsed by a raft of
smaller players as low barriers to entry, open-source development, and
near-zero marginal costs lead to the uncontrolled proliferation of AI. Either
way, the AI revolution will take place outside the government.
To a limited degree,
some of these challenges resemble those of earlier digital technologies.
Internet platforms, social media, and even devices such as smartphones all
operate, to some extent, within sandboxes controlled by their creators. When governments
have summoned the political will, they have been able to implement regulatory
regimes for these technologies, such as the EU’s General Data Protection
Regulation, Digital Markets Act, and Digital Services Act. But such regulation
took a decade or more to materialize in the EU, and it still has not been fully
set in the United States. AI moves far too quickly for policymakers to respond
at their usual pace. Moreover, social media and other older digital
technologies do not help create themselves, and the commercial and strategic
interests driving them never dovetailed in quite the same way: Twitter and
TikTok are powerful, but few think they could transform the global economy.
This all means that,
at least for the next few years, AI’s trajectory will be determined mainly by
the decisions of a handful of private businesses, regardless of what
policymakers in Brussels or Washington do. In other words, technologists, not
policymakers or bureaucrats, will exercise authority over a force that could profoundly
alter the power of nation-states and how they relate to each other. That makes
the challenge of governing AI unlike anything governments have faced before, a
regulatory balancing act more delicate and more stakes than any policymakers
have attempted.
Moving Target, Evolving Weapon
Governments are
already behind the curve. Most proposals for governing AI treat it as a
conventional problem amenable to the state-centric solutions of the twentieth
century: compromises over rules hashed out by political leaders sitting around
a table. But that will not work for AI.
Regulatory efforts to
date are in their infancy and still inadequate. The EU’s AI Act is the most
ambitious attempt to govern AI in any jurisdiction, but it will apply in full
only beginning in 2026, by which time AI models will have advanced beyond
recognition. The United Kingdom has proposed an even looser, voluntary approach
to regulating AI, but it lacks the teeth to be effective. Neither initiative
attempts to govern AI development and deployment globally—something necessary
for AI governance to succeed. And while voluntary pledges to respect AI safety
guidelines, such as those made in July by seven leading AI developers, including
Inflection AI, led by one of us (Suleyman), are welcome, they are no substitute
for legally binding national and international regulation.
Advocates for
international-level agreements to tame AI tend to reach for the model of
nuclear arms control. But AI systems are not only infinitely easier to develop,
steal, and copy than nuclear weapons; private companies, not governments,
control them. The nuclear comparison looks even more outdated as the new
generation of AI models diffuses faster than ever. Even if governments can
successfully control access to the materials needed to build the most advanced
models—as the Biden administration is attempting to do by preventing China from
acquiring advanced chips—they can do little to stop the proliferation of those
models once they are trained and therefore require far fewer chips to operate.
For global AI
governance to work, it must be tailored to the specific nature of the
technology, the challenges it poses, and the structure and balance of power in
which it operates. But because the evolution, uses, risks and rewards of AI are
unpredictable, AI governance cannot be fully specified at the outset—or at any
point in time, for that matter. It must be as innovative and evolutionary as
the technology it seeks to govern, sharing some of the characteristics that
make AI such a powerful force in the first place. That means starting from
scratch, rethinking, and rebuilding a new regulatory framework from the ground
up.
The overarching goal
of any global AI regulatory architecture should be to identify and mitigate
risks to global stability without choking off AI innovation and the
opportunities that flow from it. Call this approach “technoprudentialism,”
a mandate like the macroprudential role played by global financial institutions
such as the Financial Stability Board, the Bank of International Settlements,
and the International Monetary Fund. Their objective is to identify and
mitigate risks to global financial stability without jeopardizing economic
growth.
Guards at a Huawei conference in Shanghai.
A technological
mandate would work similarly, necessitating the creation of institutional
mechanisms to address the various aspects of AI that could threaten
geopolitical stability. These mechanisms, in turn, would be guided by shared
principles tailored to AI’s unique features and reflect the new technological
balance of power that has put tech companies in the driver’s seat. These
principles would help policymakers draw up more granular regulatory frameworks
to govern AI as it evolves and becomes a more pervasive force.
The first and most
vital principle for AI governance is precaution. As the term implies, technoprudentialism is, at its core, guided by the
precautionary credo: first, not harm. Maximally constraining AI would mean
forgoing its life-altering upsides, but maximally liberating it would mean
risking all its potentially catastrophic downsides. In other words, the
risk-reward profile for AI is asymmetric. Given the radical uncertainty about
the scale and irreversibility of some of AI’s potential harms, AI governance
must aim to prevent these risks before they materialize rather than mitigate
them after the fact. This is especially important because AI could weaken
democracy in some countries and make it harder for them to enact regulations.
Moreover, the burden of proving an AI system is safe above some reasonable
threshold should rest on the developer and owner; it should not be solely up to
governments to deal with problems once they arise.
AI governance must
also be agile to adapt and correct course as AI evolves and improves itself.
Public institutions often calcify to the point of being unable to adapt to
change. And in the case of AI, the sheer velocity of technological progress
will quickly overwhelm the ability of existing governance structures to catch
up and keep up. This does not mean that AI governance should adopt Silicon
Valley's “move fast and break things” ethos, but it should more closely mirror
the nature of the technology it seeks to contain.
In addition to being
precautionary and agile, AI governance must be inclusive, inviting the
participation of all actors needed to regulate AI in practice. That means AI
governance cannot be exclusively state-centered since governments neither
understand nor control AI. Private technology companies may lack sovereignty in
the traditional sense, but they wield real—even sovereign—power and agency in
the digital spaces they have created and effectively govern. These nonstate
actors should not be granted the same rights and privileges as states, which
are internationally recognized as acting on behalf of their citizens. But they
should be parties to international summits and signatories to any agreements on
AI.
Such a broadening of
governance is necessary because any regulatory structure that excludes the real
agents of AI power is doomed to fail. In previous waves of tech regulation,
companies were often afforded so much leeway that they overstepped, leading
policymakers and regulators to react harshly to their excesses. But this
dynamic benefited neither tech companies nor the public. Inviting AI developers
to participate in the rule-making process from the outset would help establish
a more collaborative culture of AI governance, reducing the need to rein in
these companies after the fact with costly and adversarial regulation.
AI Is A Problem Of The Global Commons, Not Just
Superpowers
Tech companies should
only sometimes have a say; some aspects of AI governance are best left to
governments, and states should always retain final veto power over policy
decisions. Governments must also guard against regulatory capture to ensure
that tech companies do not use their influence within political systems to
advance their interests at the expense of the public good. But an inclusive,
multistakeholder governance model would ensure that the actors who will
determine the fate of AI are involved in—and bound by—the rule-making
processes. In addition to governments (especially but not limited to China and
the United States) and tech companies (especially but not limited to the Big
Tech players), scientists, ethicists, trade unions, civil society
organizations, and other voices with knowledge of, power over, or a stake in AI
outcomes should have a seat at the table. The Partnership on AI—a nonprofit
group that convenes a range of large tech companies, research institutions,
charities, and civil society organizations to promote responsible AI use—is a
good example of the needed mixed, inclusive forum.
AI governance must
also be as impermeable as possible. Unlike climate change mitigation, where the
sum of all individual efforts will determine success, AI safety is determined
by the lowest common denominator: a single breakout algorithm could cause
untold damage. Because global AI governance is only as good as the
worst-governed country, company, or technology, it must be watertight
everywhere—with entry easy enough to compel participation and exit costly
enough to deter noncompliance. A single loophole, weak link, or rogue defector
will open the door to widespread leakage, bad actors, or a regulatory race to
the bottom.
In addition to
covering the entire globe, AI governance must protect the whole supply
chain—from manufacturing to hardware, software to services, and providers to
users. This means technological regulation and oversight along every node of
the AI value chain, from AI chip production to data collection, model training
to end use, and across the entire stack of technologies used in a given
application. Such impermeability will ensure there are no regulatory gray areas
to exploit.
Finally, AI
governance will need to be targeted rather than one-size-fits-all. Because AI
is a general-purpose technology, it poses multidimensional threats. More than
one governance tool is required to address the various sources of AI risk. In
practice, determining which tools are appropriate to target which risks will
require developing a living and breathing taxonomy of all the possible effects,
AI could have—and how each can best be governed. For example, AI will be
evolutionary in some applications, exacerbating current problems such as
privacy violations and revolutionary in others, creating entirely new harms.
Sometimes, the best place to intervene is where data is collected. Other times,
it will be the point at which advanced chips are sold—ensuring they do not fall
into the wrong hands. Dealing with disinformation and misinformation will
require different tools than dealing with the risks of AGI and other uncertain
technologies with potentially existential ramifications. A light regulatory
touch and voluntary guidance will sometimes work; in others, governments must
strictly enforce compliance.
This requires a deep
understanding and up-to-date knowledge of the technologies in question.
Regulators and other authorities will need oversight of and access to key AI
models. In effect, they will need an audit system that can track capabilities
at a distance and directly access core technologies, which will require the
right talent. Only such measures can ensure that new AI applications are
proactively assessed, both for obvious risks and for potentially disruptive
second and third-order consequences. Targeted governance, in other words, must
be well-informed governance.
The Technoprudential
Imperative
Built atop these principles
should be a minimum of three AI governance regimes, each with different
mandates, levers, and participants. All will have to be novel in design, but
each could look for inspiration to existing arrangements for addressing other
global challenges—namely, climate change, arms proliferation, and financial
stability.
The first regime
would focus on fact-finding and take the form of a global scientific body to
objectively advise governments and international bodies on questions as basic
as what AI is and what kinds of policy challenges it poses. Effective
policymaking will be impossible if no one can agree on the definition of AI or
the possible scope of its harm. Here, climate change is instructive. To create
a baseline of shared knowledge for climate negotiations, the United Nations
established the Intergovernmental Panel on Climate Change. They gave it a
simple mandate: provide policymakers with “regular assessments of the
scientific basis of climate change, its impacts and future risks, and options for
adaptation and mitigation.” AI needs a similar body to evaluate the state of AI
regularly, impartially assess its risks and potential impacts, forecast
scenarios, and consider technical policy solutions to protect the global public
interest. Like the IPCC, this body would have a worldwide imprimatur and
scientific (and geopolitical) independence. And its reports could inform
multilateral and multistakeholder negotiations on AI, just as the IPCC’s
reports inform UN climate negotiations.
The world also needs
a way to manage tensions between the significant AI powers and prevent the
proliferation of dangerous advanced AI systems. The most important
international relationship in AI is between the United States and China.
Cooperation between the two rivals is difficult to achieve under the best
circumstances. But in the context of heightened geopolitical competition, an
uncontrolled AI race could doom all hope of forging an international consensus
on AI governance. One area where Washington and Beijing may find it
advantageous to work together is in slowing the proliferation of robust systems
that could imperil the authority of nation-states. At the extreme, the threat
of uncontrolled, self-replicating AGIs—should they be invented in the years to
come—would provide strong incentives to coordinate safety and containment.
On all these fronts,
Washington and Beijing should aim to create areas of commonality and even
guardrails proposed and policed by a third party. Here, the monitoring and
verification approaches often found in arms control regimes might be applied to
the most important AI inputs, specifically those related to computing hardware,
including advanced semiconductors and data centers. Regulating key chokepoints
helped contain a dangerous arms race during the Cold War and could help contain
a potentially even more dangerous AI race now.
Few Powerful Constituencies Favor Containing AI
But since much of AI
is already decentralized, it is a problem of the global commons rather than the
preservation of two superpowers. The devolved nature of AI development and core
characteristics of the technology, such as open-source proliferation, increase
the likelihood that it will be weaponized by cybercriminals, state-sponsored
actors, and lone wolves. The world needs a third AI governance regime to react
when dangerous disruptions occur. For models, policymakers might look to
financial authorities' approach to maintain global financial stability. The
Financial Stability Board, composed of central bankers, ministries of finance,
and supervisory and regulatory authorities worldwide, works to prevent global
financial instability by assessing systemic vulnerabilities and coordinating
the necessary actions to address them among national and international
authorities. A similarly technocratic body for AI risk—call it the
Geotechnology Stability Board—could work to maintain geopolitical stability
amid rapid AI-driven change. Supported by national regulatory authorities and
international standard-setting bodies, it would pool expertise and resources to
preempt or respond to AI-related crises, reducing the risk of contagion. But it
would also engage directly with the private sector, recognizing that key
multinational technology actors play a critical role in maintaining
geopolitical stability, just as systemically important banks do in maintaining
financial stability.
With authority rooted
in government support, such a body would be well-positioned to prevent global
tech players from engaging in regulatory arbitrage or hiding behind corporate
domiciles. Recognizing that some technology companies are systemically
important does not mean stifling start-ups or emerging innovators. On the
contrary, creating a single, direct line from a global governance body to these
tech behemoths would enhance the effectiveness of regulatory enforcement and
crisis management—both of which benefit the whole ecosystem.
A regime that
maintains technological stability would also fill a dangerous void in the
current regulatory landscape: responsibility for governing open-source AI. Some
level of online censorship will be necessary. If someone uploads a hazardous
model, this body must have a clear authority—and ability—to take it down or
direct national authorities to do so. This is another area for potential
bilateral cooperation. China and the United States should want to work together
to embed safety constraints in open-source software—for example, by limiting
the extent to which models can instruct users on how to develop chemical or
biological weapons or create pandemic pathogens. In addition, there may be room
for Beijing and Washington to cooperate on global antiproliferation efforts,
including through interventionist cyber tools.
Each of these regimes
would have to operate universally, enjoying the buy-in of all significant AI
players. The rules need to be specialized enough to cope with real AI systems
and dynamic enough to keep updating their knowledge of AI as it evolves.
Working together, these institutions could take a decisive step toward technoprudential management of the emerging AI world. But
they are by no means the only institutions that will be needed. In the next few
years, other regulatory mechanisms, such as “know your customer” transparency
standards, licensing requirements, safety testing protocols, and product
registration and approval processes, will need to be applied to AI. The key
across all these ideas will be to create flexible, multifaceted governance
institutions not constrained by tradition or lack of imagination. After all,
technologists will not be constrained by those things.
Promote The Best, Prevent The Worst
None of these
solutions will be easy to implement. Despite all the buzz and chatter from
world leaders about the need to regulate AI, there is still a lack of political
will. Right now, few powerful constituencies favor containing AI—and all
incentives point toward continued inaction. But designed well, an AI governance
regime described here could suit all interested parties, enshrining principles
and structures that promote the best in AI while preventing the worst. The
alternative—uncontained AI—would pose unacceptable risks to global stability, be
bad for business, and counter every country’s national interest.
A strong AI
governance regime would mitigate the societal risks posed by AI and ease
tensions between China and the United States by reducing the extent to which AI
is an arena—and a tool—of geopolitical competition. And such a regime would
achieve something even more profound and long-lasting: it would establish a
model for addressing other disruptive, emerging technologies. AI may be a
unique catalyst for change, but it is not the last disruptive technology
humanity will face. Quantum computing, biotechnology, nanotechnology, and
robotics also have the potential to reshape the world fundamentally.
Successfully governing AI will help the world successfully govern those
technologies as well.
The twenty-first
century will throw up few challenges as daunting or opportunities as promising
as those presented by AI. In the last century, policymakers began to build a
global governance architecture that, they hoped, would equal the tasks of the age.
They must build a new governance architecture to contain and harness this era's
most formidable and potentially defining force. The year 2035 is just around
the corner. There is no time to waste.
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