“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....
