From American Affairs Journal, Summer 2024 / Volume VIII, Number 2:
“What is your moat?” That’s Silicon Valley-speak for “what defends you from the competition.” As investors hunt for the next big AI company, it’s also one question that the hundreds of start-ups launched in the wake of ChatGPT increasingly can’t avoid.How do you profit off intelligence once it’s been commoditized? Will the AI transition let a thousand flowers bloom, or will the returns largely flow to a few tech behemoths and their infrastructure providers? If there is anything we’ve learned from the social media era, it is that the rules governing AI today have the potential to shape the distribution of economic and cultural power for decades to come. We better get it right.
The way value gets captured in the post-AI economy has implications for domestic competition as well as America’s technological competition with China. Just as AI could lead to monopolization domestically, the first country to develop AI systems advanced enough to automate most existing forms of human labor could unlock productivity growth so explosive as to secure indefinite economic and technological supremacy. Alternatively, AI’s deflationary effects could paradoxically undermine U.S. economic leverage by eroding key areas of comparative advantage—higher education, cultural exports, financial services, and R&D—while pushing value into a handful of scarce inputs over which we have limited control.
Artificial intelligence comes in many flavors, but what sets modern systems apart is their dependence on large amounts of computing power. Take large language models. By predicting text sequences from large corpora of training data, systems like ChatGPT not only discover the rules of natural language, but also learn common sense reasoning and other forms of abstract thought. There’s only one catch: the computing cost required to train a model grows exponentially with its raw capability.1
ChatGPT was created by OpenAI, one of only a handful of companies with the technical talent and data centers (courtesy of Microsoft) needed to train frontier models—best-in-class language, image, and audio models that developers can then build apps on through an application programming interface (API). Yet if you want to disparage a start-up founder, just call their new application a “wrapper on GPT-4.” Developers can only get so rich building appendages on a technology that someone else controls. Like a remora fish attached to the underbelly of a basking shark, where goes the API, so goes your company. You have no moat. You are, in a word, replaceable.
AI’s stark implications for market power were brought home last year when a pitch deck from OpenAI’s chief competitor, Anthropic, found its way online.2 The presentation revealed the company’s billion-dollar, eighteen-month plan to train a frontier AI model ten times more powerful than GPT-4—the digital brain behind OpenAI’s ChatGPT. What caused heads to turn in Silicon Valley, however, was how Anthropic laid out the stakes: “These models could begin to automate large portions of the economy,” the deck reads. “We believe that companies that train the best 2025/26 models will be too far ahead for anyone to catch up in subsequent cycles.”
It’s always worth taking claims geared toward prospective investors with a hefty grain of salt. The company with the best AI model in a few short years will gobble up whole sectors of the economy and leave their competitors in the dust? Talk about “big, if true.”
But suppose it is true. The best multimodal models can already do everything from pass the bar exam at the 90th percentile to autonomously plan and book your next vacation. By some estimates, over half of the code programmers produce is now AI generated. And while the current generation of models suffers from certain limitations—the propensity to hallucinate facts, the lack of a long-term memory—researchers are working furiously to iron out the remaining kinks.
In the very short run, AI will largely augment the work we already do. Average programmers with a coding copilot can become 10x software engineers; doctors with a medical chatbot can get an instant second opinion; and lawyers can use customized models to draft documents and summarize evidence, letting them take on more clients. Overtime, however, AI is trending toward agent-like systems that surpass human experts at a wide variety of tasks, if not entire categories of work. And while Anthropic’s timeline may be ambitious, it is consistent with independent forecasts that project the arrival of AIs competitive with most college-educated labor around 2026.3 What happens next is anyone’s guess.
In March 2023, researchers at OpenAI released estimates of the likely labor market impact from the current generation of GPT models.4 Their findings indicate “approximately 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of GPTs, while around 19% of workers may see at least 50% of their tasks impacted.”
If these estimates pan out, AI will be a massive boon for both productivity and some measures of income equality, as the jobs facing near-term automation span “all wage levels, with higher-income jobs potentially facing greater exposure.” Nonetheless, if proprietary models continue to crush open-source alternatives in their power and reliability, these same estimates raise the spectre of a significant cross-section of GDP suddenly flowing through models created by a single, dominant company. This is why OpenAI made the unusual decision to cap their investors’ profits at 100x, while Anthropic plans to shift control of its “public benefit corporation” to a board of trustees shielded from the profit motive.5 A world where the first company to build truly superhuman AI realizes unbounded market power is, by these companies’ own admission, a real possibility.
The economy is made of people, at least for now. But if AI progress continues at its current pace, “what is your moat” could soon become a question humans start asking themselves.
The Geopolitics of Chips
In The Wealth of Nations, Adam Smith noted an apparent paradox: water is essential to life but nearly free to consume, while diamonds are mostly useless but exorbitantly priced. The resolution to the paradox is to realize that water is plentiful while diamonds are rare, and market prices simply reflect that relative scarcity. (The wrinkle is that diamonds are kept artificially scarce because a single company, De Beers, has historically controlled over 80 percent of the world supply, but leave that aside.)
Futurists have long dreamt of AI ushering in a “post-scarcity” world, but such a thing does not exist. Even in a world where most labor is automated, value will continue to flow to what remains scarce: the capital. For AI, that means the owners of large data centers and leading chip makers.
Demand for semiconductors already vastly outstrips supply, particularly for the specialized hardware needed to efficiently train and run the most advanced models. The top chip designer, Nvidia, controls 80–95 percent of the market for the most advanced AI chip designs and has thus seen its stock price rise over 400 percent in just the past five years. With an interconnect bandwidth of nine hundred gigabytes per second (the rate individual chips share information with their supercomputing neighbors), Nvidia’s flagship H100 tensor core GPU is a technological marvel—surpassed only by the company’s newest chip family, Blackwell, which can pack a petaflop of computing power into a single GPU. Nvidia’s GPUs are also the result of one of the most complex and closely guarded design and manufacturing processes in human history—in other words, a moat.
Nvidia just designs the chips and the software to run them. The actual fabrication occurs at TSMC—a factory whose literal moat, the Taiwan Strait, provides only 110 miles of separation from mainland China. With an AI transformation on the horizon, access to advanced chips has thus taken on the crushing gravity of geopolitics.
In a bipartisan show of techno-nationalism, Congress allocated $54 billion to the rebuilding of America’s domestic chip-making capacity in the chips and Science Act of 2022. Multiple U.S.-based semiconductor projects are now underway or under consideration that represent capital expenditures of over $260 billion through 2030. Nevertheless, federal grants have been slow to move given bureaucratic inertia and the litany of mandates imposed on awardees. Delays have thus ensued, from Intel’s $20 billion chip factory in Ohio to the first of Samsung’s eleven planned fabs in Texas. TSMC’s $40 billion fab in Arizona was even forced to spend months wrangling with the local construction union after bringing in five hundred Taiwanese workers with the highly specialized skills needed to wire up semiconductor “cleanrooms”—skills local construction workers simply lack. While some have blamed the setbacks on the Act’s DEI provisions6 (from minority set-asides to workforce training programs for “justice-involved individuals”) they more broadly reflect what legal scholar Nicholas Bagley has dubbed the “procedure fetish” afflicting the U.S. government at every level.7
As if to buy time, the U.S. government, in concert with Japan and the Netherlands, followed up the chips Act by imposing sweeping export controls on the sale of advanced AI chips and semiconductor manufacturing equipment to China. The message is clear: if AI is the ultimate winner-take-all technology, anything that stymies China’s access to the most advanced chips—and bolsters our own—is imperative to U.S. national security.
One gets the sense that this is only the start. As the main currency in a post-AI economy, the future will be determined by those with access to large computing clusters and the energy needed to power them. Those clusters will ideally be located in the West, but with the monopoly risk looming in the background, it may not suffice to cede control to purely private hands. The power unleashed by future AI models will challenge our basic governance structures to their core, busting through decadent procedures and driving demands for new controls over the distribution of compute—if not outright public ownership.
Nationalization is certainly one answer to AI’s monopoly problem. On our current trajectory, it may even be a likely one. Yet Nvidia, for its part, has no interest in becoming a national champion, as China represents an enormous market for its GPUs. Shortly after export controls were introduced on the high-bandwidth GPUs used for training large AI models, Nvidia unveiled new chip designs—the A800 and H800—tailored for China, with specs tweaked to fall just under the line. A year later, the Bureau of Industry and Security (the home of the U.S. Export Enforcement Office within the Department of Commerce) was forced to update the controls to retroactively account for Nvidia’s workaround. The latest controls are incredibly strict, including a new “performance density threshold” that is essentially impossible to game....
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