What Most Get Wrong About the "AI Arms Race"

This is an excerpt from my personal blog, Machine Yearning, a collection of essays on AI, high technologies, and the future of economics and society. To read it in full, subscribe via Substack for future posts and get them in your inbox as soon as they're published.

?? How Technology Changes Societies

“First to AI supremacy?” Not so fast. When it comes to artificial intelligence, it doesn’t matter who’s first because it's not a product in the way most pundits understand it.

“AI is the new electricity,” but electricity isn’t a product. Being first to have electricity doesn’t matter without the infrastructure to deliver it, the human capital to manage and improve upon it, and the standards to commercialize it. Benjamin Franklin famously conducted his kite experiment in 1752, and Thomas Edison patented the lightbulb in 1879, but it wasn’t until the 1920s that even half of American homes had electricity.

Like electricity, AI is a general-purpose technology - one with vast potential for nearly all sectors of the global economy. Talking about “AI supremacy” as if it were a zero-sum game is a fundamental misunderstanding of both how AI works and where AI power comes from. If institutions truly want to harness AI for large-scale transformations, then we need to create environments suitable for innovating upon it. We need to cultivate our societies into “innovation gardens,” instead of planting our flag on the moon and never going back.

A Tale of Two Theories

There are two competing theories for understanding technological advancement in societies.?

Most westerners subscribe to the leading sector theory of technological advancement, which emphasizes first mover advantages, immediate impacts, and concentration of power in single actors. The product life cycle curve visualizes this theory rather well.

An alternative theory, proposed by Stanford researcher Jeffrey Ding, is the diffusion theory of general purpose technologies. This theory highlights longer, drawn-out trajectories of improvements to general-purpose technologies that diffuse across the entire economy overtime. The impact comes later and is more dispersed, so first-mover advantages are much less defensible.

• Like electricity, AI did not have a singular commercial application when first introduced
• The open-source nature of most AI research means that state-of-the-art performance is not restricted to first movers
• It is highly pervasive, meaning it has applications for many sectors of the economy. To oversimplify, anywhere a decision must be made using data or intuition, AI can augment or replace the decision-maker

Solely inventing a new general-purpose technology does not guarantee a competitive moat. Instead of focusing on building innovation monopolies from one-off products, Ding suggests societies that see the greatest impact from technology diffusion are those which intentionally cultivate environments suitable for innovation, so they can lead the cutting edge of the diffusion curve.

?? “AI is the New Electricity”

Andrew Ng famously coined the phrase “AI is the new electricity.” If, like electricity, AI adoption follows a diffusion theory trajectory, what practical investments are required for institutions and countries to cultivate leading innovation gardens?

I would argue for a cohesion of 5 focus areas of public-private partnerships:

1. Clear Research and Development Goals: Clear research and development objectives at the national level signal strategic interests which may not emerge from the commercial sector on their own. Take a look at China's Next Generation Artificial Intelligence Development Plan for some great examples.
2. Synergistic Infrastructure: Institutions should focus on building and supporting synergistic frameworks between open-source hardware, software, and cloud infrastructure, to avoid concentrating power and to build anti-fragility. You see a lack of this today in semiconductor supply chains, where nearly every industry is competing for the same general purpose chipsets from the same suppliers. Serious consideration of new computing architectures, like neuromorphic computing or quantum computing, is in order.
3. Human Capital Upgrades: The US is in dire need of an upgrade to its human capital. While American universities still attract the best international talent, the rest of the US population suffers from incredibly low literacy rates on fundamental AI concepts. By some estimates, fewer than half of US high schools teach any computer science at all. Of those that do, the curricula have remained more or less unchanged for 15 years.
4. Commercial Standardization: Like Agile software processes which formalized mechanical and software engineering workflows, MLOps is standardizing ML engineering in the workplace. Through MLOps, engineers and product managers are learning to tackle development of AI products in virtuous closed loops rather than linear progressions. Startups like WhyLabs and Arize are formalizing these practices, making model development a core business process at many institutions, instead of a data science side project.
5. Ethics and Explainable AI (XAI): We need accepted rubrics for explainable AI (XAI) and the ability to audit model decision-making. This is especially relevant for “black box” models in mission-critical applications, such as loan applications or the criminal justice system, where hidden biases may have drastic and immediate impacts on humans’ well-being. Startups like Credo AI are paving the way for XAI frameworks, guaranteeing safe model deployment to critical sectors.

?? In 100 words or fewer…

Institutional AI supremacy will not be the result of one-off killer products, but a concerted, holistic series of investments in resource infrastructure, human capital upgrades, and standards-setting to create environments that nurture and encourage innovation. These innovation gardens do not exist de facto for any one system of governance, but are deliberate in their construction… without them, any first mover advantages will quickly wane, and the world’s primary AI innovation center may converge elsewhere.

Thanks for reading! What are your thoughts on the so-called "AI Arms Race?" Drop a comment below to discuss. To support more content like this, subscribe to Machine Yearning via Substack and get new posts in your inbox as soon as they're published.