America has a commanding lead in AI compute. Whether that becomes a lasting economic advantage is a separate question.
There is a growing argument that America loses the AI race to China if we don’t build data centers faster. I think there is a real concern behind it. Compute is a constraint, power takes years to build, and China is investing heavily in energy and industry.
But building more data centers is not a strategy by itself. So, are we measuring the wrong things? Partly, yes. We keep count of capacity: chips, megawatts, data centers. We count results far less.
I find it easier to think about three separate questions.
1. How much capacity does America need?
2. Who pays for it?
3. And how well can our economy turn it into productive activity?
The third one gets the least attention in the public debate.
America is ahead. By a lot.
The Lowy Institute’s November analysis concludes that America is winning. It estimates that US AI data centers have roughly 40 times China’s computing power and US AI chips 31 times, although the report doesn’t spell out how those multiples were built. American models draw more than 90% of combined web traffic to US and Chinese AI services. Lowy also says its model rankings are consistent with claims that the US is six to nine months ahead, a figure it cites but does not measure itself.
That strengthens the case for keeping our compute capacity growing.
Now put two numbers from the 2026 Stanford AI Index next to each other. US private AI investment in 2025 was $285.9 billion, against $12.4 billion in China. And in March 2026 the best American model led the best Chinese model by 2.7% on the benchmark Stanford highlights.
These aren’t like-for-like. Chinese state funding isn’t in the private investment figures, a benchmark covers a narrow slice of what models do, and capital spending pays back over years.
Still, it makes me ask how much capability and value we get from each additional dollar invested.
China’s progress doesn’t show compute is unimportant. Chip export controls have made it a real constraint for Chinese developers. But Chinese labs have shown that efficiency can close part of the gap. We should care about how well we use compute, not only how much we have.
Open models
China is strong in open-weight models. Per Lowy, Chinese models were downloaded about 1.8 billion times on Hugging Face in the third quarter of 2026, against 500 million for US models. That is 78% of the two countries’ combined total.
Downloads aren’t deployments, though. Many are small models and automated pulls, and American models still dominate consumer use.
Lowy also reports that Chinese models handled 55% of text requests on OpenRouter during one week in late October, against 38% for American models. That is one developer marketplace for one week, and Lowy itself notes that most people reach AI through apps and websites, where US models dominate. It is one signal of where developers are choosing to build.
A company that can run an open model on its own hardware and tune it may get good results at much lower cost than a large proprietary model would give. That is worth supporting, along with independent testing of provenance, security and behavior.
Neither open weights nor a model’s country of origin tells you whether it can be trusted.
The real constraint is speed to power
Most large training clusters don’t need to sit near customers. Some inference does. Developers already look at remote sites and their own generation.
The bottleneck is how fast they can get power, and new transmission in advanced economies can take four to eight years.
FERC moved in June, ordering six regional grid operators to justify or reform their rules for connecting large loads. The first responses are due November 16. Those will be proposals. The real test is whether connection times fall once new rules are in force.
In the meantime, dedicated generation may be the fastest option for some projects, even if it is more expensive or less environmentally attractive. Modernizing the grid is necessary, but it won’t solve the next two years of demand.
I’d like to see published connection timelines, utilities saying what each new large load requires, and a transparent split of incremental costs. Grid upgrades help manufacturing too, so we shouldn’t treat them as a subsidy for AI.
Water gets a lot of attention. Closed-loop cooling cuts water use but can raise electricity use, so the trade-off has to be checked site by site. In some places the power bill will matter more. In others, water availability will be the bigger concern.
Who pays if the forecasts are wrong?
Companies are committing hundreds of billions of dollars based on forecasts of demand, prices and hardware performance. Some of those forecasts will be wrong.
Cheaper inference could shrink the compute each task needs, or it could grow total demand. Both are plausible.
If a utility builds for a data center that gets cancelled, or that uses half the power it forecast, existing customers shouldn’t pick up the bill.
Ohio’s large-load tariff uses minimum payments. State commissions should look at long-term commitments, financial guarantees and exit charges, without piling on so much that good projects die.
Investors should keep the upside and carry a fair share of the downside.
The constraint nobody is measuring
Gartner expects cloud spending on running AI models ($23.3 billion) to pass spending on training them ($19 billion) this year.
The 2026 McKinsey survey says 80% of respondents report personal productivity gains, but only 37% report any positive effect on enterprise EBIT, and about 6% qualify as high performers, attributing at least 5% of EBIT to AI with significant overall impact.
The last two numbers are flat from 2025, even though adoption and spending went up.
Spending on running models is climbing, yet the share of surveyed companies reporting an enterprise profit effect isn’t. That doesn’t prove infrastructure is being overbuilt. Returns may be lagging the investment, or may be concentrated in a small group of companies. Either way, making intelligence more available isn’t automatically producing broader financial returns.
McKinsey also found that nearly three-quarters of high performers rebuilt workflows around AI, against about a quarter of everyone else. The survey can’t show cause, but it matches kind of what I’ve seen in enterprise work.
A bank automates document review and leaves credit approval alone. An insurer speeds up claims and keeps decision rights where they were. The AI works. The organization hasn’t changed enough to capture the value.
That is the argument in Migrating Scarcity. When intelligence gets abundant, scarcity moves to the next constraint, which is our ability to absorb it, authorize its use and redesign work around it.
Is China better at this? I don’t have the evidence to say. Stanford reports that China installed about 295,000 industrial robots in 2024, 54% of the world total, against about 34,000 in the US. That mostly reflects the size of its manufacturing base, but it is a reminder that deployment matters as much as model quality.
What I’d check in 2028
Enterprise returns. Move the share reporting any EBIT gain from current baseline of 37% that McKinsey reports to say 50%, and double the high performers from 6% to say 12%. These are targets I’d use to judge progress, not predictions. McKinsey’s sample is global, so I’d also want a US-only cut.
Grid timing. A one-third cut in the median time from a complete application to energization, measured against a published baseline. Alongside it, publish cancellations and any costs shifted onto existing customers.
Cost per task. Not the price per token. The cost of finishing a defined set of tasks at fixed quality, reliability and security, at least halved from today. This needs to be baselined quickly – I am surprised this hasn’t been done yet.
New things. New products, markets and companies that couldn’t have existed before AI. This is harder to count, but I’d start with whether business formation in AI-related sectors is growing faster than business formation overall.
What would worry me
If we miss the enterprise-returns targets while capacity keeps expanding, our ability to use AI isn’t keeping up with our ability to produce it. That wouldn’t necessarily mean we built too much. But it would make me question where the next dollar of investment should go.
I’d also want to know whether data center owners are earning adequate returns. Enterprise productivity alone doesn’t guarantee they made good investments.
America should build the compute it needs, modernize the grid and protect ratepayers. None of that removes the need to change the institutions that will use the intelligence.
As I argued in AI and the Next American Economy, we also need new products and industries, not only cheaper versions of today’s work.
Compute is the lead we already have. Using it productively is how we keep it.
