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THE KEY TO WINNING THE GLOBAL AI RACE

It’s essential to ensure that AI is shared beyond the leading labs to other
firms, schools and even the government itself.

Andrei Cojocaru for Noema Magazine
EssayGeopolitics & Globalization
By Jordan Schneider and Matthew Mittelsteadt September 19, 2023
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Credits

Jordan Schneider is the founder of the ChinaTalk podcast and newsletter.

Matthew Mittelsteadt is a research fellow and technologist for the AI and
Progress Project at the Mercatus Center at George Mason University.

In times of technological tumult, great powers rise and fall. In the past, it
was not just leading in the cutting-edge technologies that proved critical for
national advancement; the nations that spread the benefits of the technologies
most effectively, rather than innovated first, have been able to grow faster
over time and ultimately define the trajectory of their era. 

The age of AI may prove to be another of these moments. Alongside dramatic
societal and economic implications, AI could drive differences in national
trajectories. Facilitating AI’s diffusion will be key to national
competitiveness for the coming decades.

Alongside supporting frontier research, American policymakers should do their
utmost to ensure that not just leading-edge labs, but also firms, schools and
government bureaucracies themselves are able to make the most out of AI.


AMERICA SHOULDN’T BANK ON ITS FRONTIER TECH LEAD

The three components of the AI triad that comprise the building blocks for
developing AI are computing power, algorithms and data. OpenAI, Google,
Anthropic and Meta today all have models that significantly outperform their
Chinese rivals, U.S. tech firms’ main competitors on AI technology. For both
algorithms and hardware, however, it’s not difficult to imagine this competitive
edge fading.

Thanks to talent migration, industrial espionage, and open-source advancements,
the algorithmic gap between Western and Chinese firms is likely to narrow. With
China’s proven tech strengths and the global momentum of open-source AI, a more
level playing field in AI innovation may well emerge.

Unlike past technological advances, which favored government and firm control,
individual AI developers enjoy unique competitive autonomy today. Today’s
researchers can read up on computer science discoveries and get close to the
frontier of knowledge just through open-access publications. But the global
shortage of talent means top AI researchers can be enticed from across the
Pacific by top Chinese firms offering salaries in the millions, or even to
Chinese firms’ Bay Area research labs, which is where Anthropic’s Dario
Amodei got started in the field.

Industrial espionage adds additional competitive pressure and may further erode
the research gap. In China, Xi Jinping has personally emphasized that the state
must give “attention to the development of general artificial intelligence”;
espionage incentives are particularly high. Models will be stolen. Meanwhile,
Western defenses are tenuous. Amodei, perhaps the most outwardly
security-conscious leader of a top AI lab, stated that, despite his firm’s
commitment to keeping his models safe, “Could we resist if it was a state
actor’s top priority to steal our model weights? No. They would succeed.” 

“Our current lead is not as stable as many presume and should not be relied on
as the basis of a long-term, sustainable competitive advantage.”
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The espionage threat to AI algorithmic advances is uniquely elevated compared to
previous competitive technologies, which were often manufacturing-dependent.
While there certainly is a fair amount of “secret sauce” involved in training
frontier models, manufacturing at the technological frontier is fundamentally
much harder to grok than software. Chinese firms, even with the benefit of
sustained and generous state support, have struggled to compete in
some industries that require precision manufacturing like aerospace and internal
combustion engine vehicles. Unlike physically engineered systems where China has
struggled to transition from taking blueprints to, say, building a commercial
jet, learning from algorithms is simple. In software-dominant industries
like chip design and platform social media, where practitioners can directly
piggyback off global innovation, Chinese computer scientists have shown they
have the talent and drive to compete with anyone on the planet. 

These industrial espionage and talent transfer possibilities only matter,
however, if firms and labs lead the way. In recent years, the Western AI edge
has faced a potent third threat: open source. Today, the AI open-source hive
mind is collectively far larger than any single AI company, rapidly developing
innovations that are increasingly competitive with the latest and greatest
coming out of the top private AI labs. If the best models are to come from
open-source development, the best models will be equally accessible in both
China and the West. The success of open source may mean the elimination of any
software-related geopolitical technological gaps.    

The potential impact of open-source innovation in the future of AI cannot be
overstated. A now-famous leaked Google memo suggests that the rapid crumbling of
barriers to entry in AI technology has unleashed a “flurry of ideas and
iteration” from “ordinary people,” potentially rendering even the best IP
protections moot. 

Compute shortages add further innovative pressure. Both open-source researchers
and large Chinese firms are bottlenecked by a lack of computing power, driving
innovation to reduce the cost to train and deploy models, in turn further
democratizing access to top-tier AI across borders. 

Meanwhile, firms like Meta have added fuel to the open-source fire
by openly licensing their cutting-edge models to Chinese players. So long as
this backing continues, the open-source community won’t lack access to frontier
tech. An open-source-fueled future is indeed possible, and combined with
espionage and talent transfer, in the coming years, any AI algorithmic edge will
be fleeting at best. 


THE HARDWARE LEAD ALSO SHOULDN’T BE RELIED ON

If the Western algorithmic lead erodes, can we still succeed by leading in AI
chips and hardware? AI algorithms are only useful once they’re trained into
models and deployed at a large scale. To do so, firms around the world
are investing billions in acquiring the computing power necessary for training,
banking on a hardware-derived AI competitive edge. Currently, as demonstrated
through last year’s export controls, the U.S. has a seemingly dominant position
in this critical AI hardware space.

This position, however, may be less sustainable or relevant to long-term
national competitiveness than these policies presume. Restrictions on Chinese
players’ ability to procure cutting-edge AI chips are actually quite porous. For
instance, NVIDIA has intentionally designed a chip to skirt export control
restrictions, freeing them to reap $5 billion in orders from Chinese firms so
far this year.

These sales are augmented by a range of additional workarounds. Smuggling banned
AI chips, with hundreds of thousands manufactured each year, is a relatively
trivial task. While illicitly acquiring enough chips to graduate from
“GPU-poor” status may be difficult, using global cloud service providers or
even Chinese cloud service providers’ overseas server farms, Chinese companies
can access top-of-the-line chips that can’t be imported into China. What’s more,
there’s nothing on the books in the U.S. stopping the likes of Google or
Anthropic from selling API access in China.

“The most promising path forward is structural: a diffusion-centric AI policy
laying the groundwork for long-term productivity growth.”
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Beyond the reach of most Western export controls are Chinese firms trying to
develop leading-edge domestic semiconductor capabilities. The recent release of
Huawei’s Kirin 9000S chip manufactured by Chinese semiconductor champion SMIC
shows how, absent tighter export controls, China is perhaps a year or two off
from making chips roughly competitive with NVIDIA’s. 

In the medium term, as Moore’s Law slows down, it will be increasingly difficult
for the G7 to push their chips forward in capability relative to the advances
China can make along an already de-risked technological trajectory. There is
also the potential for paradigmatic shifts on the horizon like quantum or even
biological computing that may end up leveling the global playing field rather
than increasing the gap between China and the West. 

Hardware is certainly less prone to a level playing field; however, our current
lead is not as stable as many presume and should not be relied on as the basis
of a long-term, sustainable competitive advantage. 


A DIFFUSION-CENTRIC STRATEGY 

The challenges of maintaining long-term technical leadership in AI demand
policymakers consider alternatives to policies focused on maintaining, as
National Security Advisor Jake Sullivan put it, “as large of a lead as possible”
in frontier AI technology. The most promising path forward is instead
structural: a diffusion-centric AI policy laying the groundwork for long-term
productivity growth.

AI success requires breaking the tech out of the lab and putting it into
people’s hands. For AI to provide real economic and political advantages, this
is true no matter what future path the technology takes. As a catalyst for
productivity — a key driver of both general prosperity and geopolitical
strategic success — emerging evidence suggests today’s frontier AI technologies
have great productive potential if put into practical use and diffused. However,
the rapid diffusion and impact of this technology is no guarantee.

“AI success requires breaking the tech out of the lab and putting it into
people’s hands.”
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Diffusion, not development, is the bottleneck for success and today’s true
public policy challenge. Policymakers must shift strategic thinking away from
R&D and toward policies that can help AI avoid the slow, decades-long diffusion
process that delayed and inhibited electricity — a similar general-purpose
technology — in the 20th century.

While technical diffusion may sound amorphous from a policy lens,
as the Princeton scholar Helen Milner argues and the OECD has since empirically
validated, the process is significantly influenced by regulatory and
institutional design factors. The implication is that policymakers potentially
hold the tools and levers needed to speed up the process and to come out ahead. 


START WITH A LIGHT REGULATORY TOUCH 

Alongside technical advances, impactful diffusion will require inspired
organizational leadership beyond just the release of increasingly powerful
models. For instance, as the George Washington University scholar Jeff Ding has
written: “More than five decades passed before key innovations in electricity,
the quintessential GPT [general purpose technology], significantly transformed
manufacturing productivity. … Like other GPT trajectories, electrification
required a protracted process of workforce skill adjustments, organizational
adaptations, such as changes in factory layout, and complementary innovations
like the steam turbine, which enabled central power generation in the form of
utilities.” 

Like innovation itself, diffusion can be a protracted creative process,
characterized by trial and error and experimentation. For this process to play
out, regulation cannot act as an excessive burden. As a first step in any
diffusion-centric strategy, regulators should aim to do no harm. That requires
analyzing regulations and determining what rules and processes may inhibit AI
success for little societal gain. 

One piece of this task will be regulatory clarity. Today, the American AI
regulatory environment remains hazy: Agencies have yet to analyze their existing
statutes and determine how emerging AI products and technologies might interact
with standing law. To provide clarity, the Biden administration should begin the
process of identifying statutes and compiling them into a comprehensive “AI
regulatory map.” For the private sector, such clarity will promote risk-taking
and confident innovation. For U.S. AI strategy, this effort would help Congress
and regulators easily identify needless regulatory barriers, overlaps and
contradictions that may forestall competitive success. 

Successful AI regulation must also be adaptable to match and service the unique
needs of AI technology. Traditional Food and Drug Administration (FDA) approval
processes for medical devices, for example, were designed with assumptions of a
slow pace of innovation and static device design; AI innovation, on the other
hand, is proceeding at breakneck speed with dynamic devices whose designs
improve and change over time.

“Diffusion, not development, is the bottleneck for success and today’s true
public policy challenge.”
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To accommodate AI in the medical industry, the FDA has proposed statutory
changes to create a software pre-certification program, which
approves organizations that build medical software rather than discrete devices.
Such changes would allow regulatory room for the continuous updates, security
patches and changes safe and effective AI systems demand, rather than a
cumbersome case-by-case review. 

Across the government, similar changes are no doubt needed; in addition to
mapping relevant AI statutes, the Biden administration should also identify
which statutes may not match the needs and dynamics of AI technology. Agencies
and research organizations can then consider alternatives that may ease device
approval and deployment.

Read Noema in print.



In China, where many observers initially figured that large language models
would be unwelcome in a meticulously controlled information space, Beijing seems
to have internalized that AI is too important to be strangled in the cradle.
Baidu’s ERNIE release has proven it’s feasible for a large language model to be
good enough at filtering sensitive content to appease regulators. 

In this year’s draft regulations for generative AI released by the Cyberspace
Administration of China, regulators initially drew a hard line, which would have
significantly impacted innovation. What followed was a lively public debate in
which both firms and academics complained that mandates that require all
training data and AI outputs be “true and accurate” were unrealistic. In the
final ruling, regulators loosened their demands so that rules would not apply to
internal company R&D and only asked that firms “take effective measures to
increase the truth and accuracy of the training data and the outputs.”

Beijing is embracing an unexpectedly light regulatory touch; if Western
regulators fail to do the same, our AI diffusion may well fall behind.  


SOFTEN THE INEVITABLE BACKLASH

The best forcing function for AI diffusion will be the American capitalist
system’s tolerance for creative destruction. Firms that come up with the best
ways to improve their productivity should be empowered to outcompete their
rivals. With disruption, however, comes backlash and change-resistant incumbents
often try to use exsisting power to slow, stop or even reverse change. 

Policymakers should be wary of attempts by incumbent firms and workers who won
in the pre-AI paradigm to shield themselves from the knock-on changes necessary
to diffuse this technology and reap AI’s productivity gains.

AI licensure is one prominent regulatory idea to avoid and offers an
illustration of the growing backlash. According to an Obama-era Treasury
Department report, “most research does not find that licensing improves quality
or public health and safety.” Licensure discourages and limits market
participation, decreasing the very competition needed to ensure AI safety and
innovation. In exchange for these questionable safety benefits, such policies
simultaneously reduce competitiveness at a national level. 

Mercatus Center economists have also found licensure regulations tend to both
decrease labor supply and increase prices. OECD data from this year already
suggests that high-cost AI products supported by a limited pool of IT talent are
the primary factors limiting AI adoption. Exacerbating these adoption
chokepoints through licensure would slow growth and likely cede global market
share to less hidebound Chinese competitors.

Beyond the industry-by-industry regulatory risk of lobbying leading to
protectionism, there is the possibility of a broader societal AI backlash that
could choke off an even wider swath of potential productivity gains. Recent AI
Policy Institute polling shows that 86% of voters believe AI could cause a
catastrophic event, and as a result, 72% favor slowing down AI progress. 

The public is increasingly wary of AI tech, and not entirely without cause: If
AI does end up delivering substantial productivity gains, that will, like every
past industrial revolution, inevitably come alongside social disruption and a
new risk matrix. While the juice will be worth the squeeze (we doubt many
readers would like to exchange lives with those born before past industrial
revolutions, and the same will almost certainly be the case going forward),
governments would be wise to invest in certain technical and societal
interventions that could provide downside risk protection.

“Beijing is embracing an unexpectedly light regulatory touch; if Western
regulators fail to do the same, our AI diffusion may well fall behind.”
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In the U.S., to prevent anti-AI sentiments from translating into net-negative
regulation, regulators should invest in AI safety efforts. Transparency offers a
good starting point. 

Clarity about training data sets, ethical principles, foundation models used and
intended applications would help consumers navigate opaque AI markets while
allowing agencies like the Cybersecurity and Infrastructure Security Agency to
flag models or data sets that carry known security vulnerabilities. The
government could pair these reforms with other efforts to shore up institutions
likely to be impacted by certain AI risks. 

With the rise of AI-generated election disinformation, Congress should consider
restrictions on AI-generated content in political ads. In the face of novel AI
cyber threats, the government should invest in much-needed critical
infrastructure. Finally, governmental AI R&D support should target research that
prizes AI detection, including efforts to watermark AI outputs.

While this list is by no means comprehensive, and novel risks are sure to emerge
in the future, modest steps can help tame some of the most probable AI risks and
build the social trust needed for the technology to be developed and diffused
unimpeded.

More speculatively, if AI does perform surprisingly well, perhaps officials
should begin laying the groundwork for the equivalent of a Trade Adjustment
Assistance, a federal program that helps workers in professions most adversely
impacted by AI adjust to new industries so that society doesn’t throw the baby
out with the bathwater in a regulatory overcorrection. 


HOW TO SPEED UP DIFFUSION

Beyond measures to defend AI diffusion from those seeking to slow down
technological change, the government can also be proactive in laying the
groundwork for America to take advantage of the opportunities AI opens for
productivity growth.

Education offers the most promising path toward catalyzing technical
diffusion. Today, we cannot say with confidence what skills will be key to
future national competitiveness. What we can guarantee is continued change. To
diffuse the technological advancements of past industrial revolutions, entire
new disciplines will likely develop. The same will likely be the case for AI.

Historically, as Jeffrey Ding illustrates in his forthcoming book, America has
shined in adapting education to new technological paradigms and professional
disciplines. In the first industrial revolution, American home-taught mechanical
engineers and innovative institutions like MIT helped the U.S. take advantage of
British breakthroughs. In the late 19th century, the U.S. was able to overcome
an invention deficit relative to Germany because of its ability to produce more
chemical engineers to apply European breakthroughs to different industries. 

Most recently, the U.S. was able to outcompete Japan in the information and
communication technologies revolution thanks to its ability to develop human
capital. The U.S. was able to train hardware and software engineers and attract
top global talent, allowing a diffusion across the economy to reap the benefits
of digitization. 

As Alexandr Wang, the CEO of Scale.AI, put it, “Software engineering as a job
was invented in the 1960s with the Apollo program. Fast forward to today, it’s
viewed as the best job in America, with 1.6 million jobs.” While it’s too early
to say exactly what professions will be key to unlocking AI’s productive
potential, the government should be on the lookout for emerging disciplines and
incentivize universities to experiment, continuing this historical trend of
educational adaption.

“Local and national governments should view AI not as a threat to today’s
education system, which fails so many, but rather as a historic opportunity to
provide every student with superhuman levels of attention and training.”
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Beyond identifying new directions, how might education itself adapt? Programs
that promote technical adoption and diffusion may hinge on the idea of
educational flexibility and experimentation — qualities that match the guarantee
of change in coming years. Moving forward, education should deprioritize
predicting what technical skills might be needed for the future in favor of
broad-based adaptation and technical ability. 

Through proper curricular design, the workforce can be trained to flexibly adapt
to uncertain technical needs and prepare to continuously pivot and adopt
cutting-edge systems throughout their careers. The flexibility demands of
today’s computer science programs offer a promising conceptual direction. 

In CS, technical change is constant. From semester to semester, students must
learn new programming languages, techniques and architectural paradigms from the
ground up. The technical skills required are ever-shifting, forcing students to
avoid over-investing in one technical skill in favor of the ability to quickly
upskill in technologies they’ve never seen before whenever necessary.
Policymakers should prioritize a similar model across education, avoiding
overinvestments in technology-specific trade programs while promoting the very
confidence and comfort with technical change needed for fast diffusion and
adoption.

Simultaneously, policymakers should resist calls to unduly restrict the use of
AI in classrooms. The sort of teacher who can’t accept that phonics is a
superior way to teach reading because of misplaced romanticism about the
classroom can’t be expected to embrace AI out of the gate. But a decade from
now, we’ll look back at the curriculum of 2023 and see modules as outdated as
penmanship and calls to block ChatGPT as akin to the push to ban calculators. 

AI holds tremendous promise in being able not only to address a national teacher
shortage currently numbering in the hundreds of thousands but to scale the sort
of one-on-one tutoring-style education that’s far too expensive to roll out at a
societal level but has proven to be effective. Truly personalized education has
the pleasant side effect of being more likely to produce Einsteins. Local and
national governments should view AI not as a threat to today’s education system,
which fails so many, but rather as a historic opportunity to provide every
student with superhuman levels of attention and training.

“AI presents the U.S. government with a tremendous opportunity to improve the
way it does business.”
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Education, however, is not the only possibility — AI also presents the U.S.
government with a tremendous opportunity to improve the way it does business.
Without action, government “waste, fraud and abuse” may end up being primarily
tasks public-sector workers still do manually well after the private sector
figured out how to automate or accelerate them with AI. Already we can predict
what some of these tasks may be. From a public services perspective, perhaps AI
agents can process tax returns and conduct consular interviews. Meanwhile,
painful backend tasks from supply-chain monitoring to Department of Defense
auditing, could be accelerated. Finally, perhaps the Census Bureau could
supplement its forms with open-ended conversations between AI agents and
citizens to paint a richer, more nuanced portrait of the nation. 

Unfortunately, new approaches to applying technology within the government often
“die in the iron cage of outdated bureaucracy.” According to Code For America
founder Jennifer Pahlka, no matter the strength or quality of policy decisions,
“culture eats policy.” Today’s bureaucratic culture is often structured like a
waterfall: Policymakers at the top of the falls make one-way decisions that flow
down on top of developers and project managers who, operating under rigid
strictures, struggle to build successful systems. The result is a culture of IT
inflexibility, where developer ingenuity is bound by rules written by often
inaccessible decision-makers. Rather than succeed at policy outcomes, systems
are instead designed to follow rules. 

While there is no easy fix to such cultural challenges, small steps toward
improvement can be taken. At the State Department, leaders are considering
“designated technology tours” where diplomats are assigned to engage and study
critical technologies for several years in exchange for employment record
credits and possible preference toward advancement. While hardly a cure-all,
favoring the advancement of staff with technical competencies would counteract
waterfall structures. Managers who understand technology and developer needs may
be more likely to engage with implementation and build policies that respect
innovation. Across agencies, a version of this model could sow the seeds of a
technically inclined culture, one that not only has greater IT development
success but which also encourages AI adoption.


IT’S ALRIGHT TO DRIVE IN THE DARK

The former Secretary of the Navy Richard Danzig was right to note that
unpredictable technological futures mean “policymakers will always drive in the
dark.” Indeed, we don’t know what AI will look like in 2030, and we don’t know
what future innovations may come. Strategies that focus on maintaining an
often-tenuous technical lead therefore are insufficient. Uncertainty about
America’s long-term frontier AI advantage, however, needn’t be paralyzing. A
better guarantee is to focus on policies that enable these uncertain AI
innovations of the coming years to quickly shift from development into broad
application. 

If we take care to design today’s AI policy decisions around the need for
technical diffusion and the assumption of unpredictable change, we can ensure
those decisions flex and adapt to uncertain technical tides while setting the
table to maximize productivity, growth and competitive advantage. The result
will be confident, effective policy well matched to ensure American competitive
success while managing whatever changes the AI of the future may bring.

Enjoy the read? Subscribe to get the best of Noema.
Reassuringly, the converse also applies. China will find it exceedingly
difficult to develop a sustainable edge over the West. This makes it all the
more important to lean into diffusion.
Even core norms and structures of the scientific community such as academic
journals, peer review and the relationship between personal reputation and
journal publications, have failed to contain the rapid rate of AI/ML discoveries
published in unrestricted, free-access repositories such as Arxiv.

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