How Diffusion Will Win The AI Race

Not just R&D but tech diffusion is critical for long term national competitiveness

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.

Mercatus’ Matthew Mittelsteadt and I co-authored a new piece in Noema, where we argue that facilitating AI diffusion will be key to maintaining American pre-eminence in the coming decade.

I hope you read the entire piece. The following are some excerpts that illustrate our points.

We discuss:

• Why the US shouldn’t bank on its firms on the AI frontier to sustainably outperforming Chinese models;

• Why AI diffusion will be the 21st century’s driver of national competitiveness;

• What strategies China may deploy to overcome hardware hurdles in the coming decades;

• What regulators should do to manage public opinion while encouraging AI adoption;

• And why it’s okay for policymakers to “drive in the dark” when governing emerging technologies.

The West’s AI Lead Won’t Hold

Computing power, algorithms, and data form the core triad for developing AI. We start by arguing that neither hardware nor software are reliable long-term leads for the West. On the algorithmic front:

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.

Not to mention the impacts of open source:

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.    

As for hardware, Western firms like NVIDIA are intentionally designing chips that skirt export controls to supply China. And that’s only the legal side:

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 into China.

As I argue in last week’s piece below, China is perhaps a year or two off from making homegrown chips roughly competitive with NVIDIA’s. 

The Time for Diffusion is Now

The key to AI impact is putting technologies into action across a range of sectors. Policy institutions have always played a key role in technology diffusion:

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. 

In the case of AI, we believe that American policymakers should prioritize clarity and adaptability:

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. 

We can learn from Beijing, in this case:

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

Planning Ahead of Backlash

Forces in society from incumbent firms to change-resistant voters may seriously dampen the speed of AI diffusion. Lobbying from multiple industries can lead to protectionism, which then cedes the market to ambitious Chinese firms. The public has reason to be wary of the social disruptions advanced AI can potentially cause; in response and in order to ensure progress in diffusion, governments should look to policy solutions that minimize harm.

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. 

Education is one specific field where AI has the potential to exponentially improve outcomes.

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.

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 also 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.

How to Change A Culture

Beyond the public, bureaucratic culture is often the main roadblock to effective technology-diffusion policies.

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.

How do we break out of this bind? In the spirit of former Secretary of the Navy Richard Danzig’s view that “policymakers will always drive in the dark,” we look to broad applications and flexibility as guiding principles for governance.

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.

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