Democrats won control of both chambers of the US Congress in a midterm election landslide that amounted to a rejection of President Donald Trump following widespread voter discontent with the economy and the war in Iran. Across the country, Democrats scored victories in GOP strongholds, flipping at least seven Senate seats and winning House races in districts Republicans easily carried in 2024. Several news organizations projected that Democrats would win the House, while Decision Desk HQ projected they took the Senate.
I have no doubt that Iran and the economy played significant roles in this election; what stood out to me, however, was very much Stratechery fodder. Specifically, I watch a lot of sports, and particularly this fall, watched a lot of baseball. My Milwaukee Brewers — excuse me, my World Champion Milwaukee Brewers — demanded my full attention, which also meant I saw a lot of campaign ads, and by far the most common topic, at least here in Wisconsin, was data centers. Moreover, it was never entirely clear which way it cut politically: both parties were anti-data center, accusing their opponent of being the opposite.
A strong showing by Democrats in Texas swept Gina Hinojosa into office as the state’s first Democratic governor since 1990 in a contest closely watched by Big Tech companies. The state is considered a bellwether for government restrictions on AI data centers…Hinojosa, Texas’ first Latina governor, has pledged to impose a moratorium on data center construction in Texas, which accounts for more than 25% of planned US capacity by 2030.
Republican incumbent Greg Abbott, I would note, already declared a moratorium on new data-center permits in September; that’s how much of a hot-button issue this is, including in the state that is the biggest opportunity and beneficiary of the data center buildout. So yeah, this is a Stratechery topic of concern.
There really wasn’t widespread opposition to data centers when they were being built for the Internet generally; indeed, local governments would compete for them, eager for the economic benefits that came during their construction and the long-term tax benefits once they were operational. In other words, it seems pretty clear that the current wave of opposition can’t be divorced from AI — you know, the technology that in the best case scenario is going to take everyone’s jobs, and in the worst case scenario end the human race.
I do not, I don’t think, need to explain to you why this is not a very compelling message, and, in the very long run, I do think that humans will have economic value. At the same time, it’s hard to explain the huge amount of investment that is going into AI, not just at the model level but also in terms of data centers, without admitting that a big part of the thesis is that AI can not just increase the top line for companies, but also increase the bottom line — i.e. replace the jobs of some number of humans. Again, I’m generally an optimist here, but this already happened with the first wave of IT.
That leads to an interesting contrast to globalization: when companies were closing down American factories and laying off workers and moving operations to China, none of the affected towns or workers had a say. They just suddenly no longer had a job, and a huge number of cities across the Rust Belt no longer had a reason to exist. People simply had to move, or worse, retreat to things like alcohol or drugs. AI, however, is the opposite: building data centers requires permission, which is to say that people actually have a say. Again, I am not at all saying that these people are well informed about data centers, or about the economic impact on their communities, much less the economic impact of AI generally; what I am noting is that people who didn’t have a say in globalization are suddenly finding they do have a say about AI, and it’s not a surprise they are expressing their disapproval by blocking data centers.
That message was loud and clear last spring; now it is deafening, and it’s tempting to say that tech, particularly the frontier labs, have no one to blame but themselves. In fact, however, some of the biggest culprits may, in the long run, be some of the biggest beneficiaries of the anti-data center wave.
My thesis — driven not just by national results and the Texas governorship, but also the fact that, as this thread documents, nearly every relevant local ballot measure opposing data centers won or is winning (the only exception so far was in North Dakota) — is that the constraint on AI dispersion has shifted once again. First we were short on compute, but that ramped; now we are short on memory, but that will ramp; we were coming close to being short on power, but that will be rectified. The final constraint is political, and that may be the most imposing constraint of all. Not only is it harder to solve, but it actually interferes with solving the other constraints. Many of the theories about AI assume that it becomes a commodity; I think it’s just as likely we are looking at scarcity for much longer than we expected, which will have ripple effects on everything.
Ten Takes
To that end, here — with the caveat that nothing leads to hot takes you regret like an election — are ten perspectives on what a permanently impaired ability to build might mean. To be clear, I’m not declaring permanency — look at how our elections swing back and forth — but it’s worth considering what the implications might be if Americans don’t change their minds about data centers.
1. The AI Trade Got It Right
The most obvious reason why the stock market has been rocky over the last few weeks is the tightening of credit markets, which I wrote about this summer. We are, needless to say, full on into the equity funding era (and while I’m here, I haven’t written about Anthropic’s updated S-1, but it strongly affirms my initial analysis). At the same time, it seems clear that this outcome is very bad for the AI trade in the short term, but much of the pain may have already been endured.
2. Neoclouds on the Edge
The Neoclouds have been at the forefront of credit concerns, and from a certain perspective, being unable to build data centers has a silver lining in that they couldn’t get the money to pay for them anyways. Moreover, Neoclouds with facilities that are already permitted now have an asset that is going to be scarcer than expected. At the same time, it’s Neoclouds that need new sites and new permits, even as they are likely to have much less political sway than the hyperscalers — who already have more sites they can repurpose or rebuild. It was already getting harder to get capital, relative to the hyperscalers; now you have to layer on that it’s going to be harder to get permission, too.
3. Hyperscalers Are Winners
The downside for the neoclouds is upside for the hyperscalers, who are more likely to grow their share of AI compute than they were before. Moreover, investors may be pleased if the hyperscalers’ ambitions are somewhat curtailed; political opposition is an excuse to moderate spending, knowing that everyone else is in the same boat.
4. Nvidia’s Mixed Bag
Nvidia will almost certainly be a short-term loser. Fewer data centers mean fewer Nvidia chips generally, and a weakened Neocloud set is particularly harmful not only because Neoclouds almost exclusively use Nvidia full-rack systems, but also because Nvidia is back-stopping a huge number of them. It’s the hyperscalers, meanwhile, who are Nvidia’s biggest long-term threat, given their scale and ability to invest in their own chips.
At the same time, a constrained world makes Nvidia’s offerings more compelling. CEO Jensen Huang actually spent an entire keynote making this point back in 2025; I wrote in an Update:
When Huang says “iso power” he means that power is the constraint; the question is how many tokens can you generate given a fixed power envelope, and Blackwell can generate a lot more. These were the numbers Huang gave for a 100 MW data center:
What this means is that the first order of business for an ASIC maker is to match that level of efficiency. That, though, means keeping up with Nvidia’s product development cadence; The Information has a post about Amazon offering instances using its latest Trainium ASIC at a significant discount to the H100. Huang, though, spent this keynote insulting the H100! You can almost hear him speaking to Amazon directly: you spent all this money, and are offering these huge discounts, just to entice developers to lock themselves into an inferior chip, even as reasoning models are going to swamp your available power generation…
Huang is issuing a challenge to every cloud provider tempted to make an ASIC: do you really think that you can keep up with this, and risk running out of power capacity because your chips aren’t efficient enough? Don’t even try.
A world without power or data center constraints is one where it is, in the very long run, very difficult to maintain Nvidia’s margins; once those constraints start to bite, however, an advantage in tokens/megawatt is worth a lot of money. Go back to my point about the hyperscalers: one way to generate more tokens is to build a new data center; another way is to rebuild a data center with new Nvidia chips.
5. Advantage China
China is the inverse of the scenario that Huang laid out: the country has plenty of power and the ability to build a lot more, but they don’t have leading-edge chips. Some of the advantages of leading-edge chips, however, can be overcome by simply throwing tons of electricity at the problem. From a 2025 Update about the Huawei CloudMatrix 384 supercomputer:
The CloudMatrix 384, which is a supercomputer consisting of 384 chips, takes up dramatically more space, uses dramatically more power, is almost certainly dramatically more expensive to make, and is dramatically more brittle than Nvidia’s NVL72; there is no one in the world who would choose the CloudMatrix 384 over the NVL72 if they had the choice.
That’s the thing, though: China doesn’t have the choice, because of U.S. export controls. The country’s saving grace is that they are operating on different constraints than the U.S.…China — which actually builds power generation — is not iso power; they are iso compute: Huawei’s goal with the CloudMatrix 384 is to get as much compute as possible, power efficiency be damned.
In the fullness of time — i.e. as China figures out leading-edge chips — this may be an insurmountable advantage.
6. Data Centers in Space
When SpaceX released their S-1, I explicitly pointed to political opposition to data centers as one of the company’s most significant upside scenarios:
The third question about data centers in space is if there is a stick. Specifically, while I think that racks-in-space are both a lot more viable than people think, and a lot more relevant to agentic inference than current modes of compute, it is at the end of the day cheaper and easier to build on earth, all things being equal.
All things are not equal, however: right now we are at the very beginning of the AI buildout and already one of the biggest constraints is not just power (expected), but zoning (unexpected). I wrote in an Update last week:
I already posted the relevant excerpt above. Continuing:
In that Update I made the case that data center builders — and by extension the companies that use them — should straight up pay people for permission to build data centers in their communities. At a minimum, however, that increases the costs of terrestrial data centers. What seems very plausible in the long run is that the demand for compute ends up being so large that there eventually is nowhere left to build, making the vast expanses of space not just an alternative but in fact the only choice.
This is where I come back to my experience watching these campaign ads: this isn’t a partisan issue, where the pendulum will swing back in two years. Both parties see opposition to data centers as a winning cause, and not just nationally: that thread has multiple examples of local officials straight up recalled for approving data centers in their localities. Space will be incredibly difficult, but difficulty is all relative! Moreover, to the extent opposition is permanent, the more this actually reduces the risk of data centers in space, because investors will have more confidence in there being enduring demand.
7. The Frontier Labs Benefit
If the hyperscalers had a red queen problem, the frontier labs have it even worse: the biggest revelation from Anthropic’s S-1 was that their inference margins were very high (good!), which meant that their massive compute costs were actually in R&D; this is bad because that R&D spend is governed by competition with the other frontier labs, and absent constraints, that spend looked set to surpass revenue sooner rather than later. Now, however, it is the public that is effectively demanding that the frontier be paced, which is great for the frontier labs’ financial outlook.
The upside goes further than that: the point about efficiency being more important in a world of constraints doesn’t just matter for chips, but also for models. Devoting limited compute to inefficient and costlier-to-serve Chinese models is a poor trade if there isn’t enough compute. Moreover, open models may be at greater regulatory risk, as Democrats seem much more likely to pass laws that ultimately favor the frontier labs.
There are some counter-arguments. First, if compute is constrained, and thus more expensive, then that will increase the drive to right-size models for jobs, which is a big argument in favor of open models; the fact that compute won’t be a commodity means that the frontier labs’ lower cost to serve will never come to bear on the low end of the market. And, it’s worth noting, it will be harder for the frontier labs to build their own compute, leaving them dependent on the hyperscalers; big tech wins again.
8. Apple Gains
The most obvious advantage for Apple is that in a world of constrained compute, inference on the edge will be that much more important. Another advantage is that any slowdown in the dispersion of AI is good for a company that dominated the previous paradigm.
The biggest advantage of all, however, is that any slowdown in the AI buildout offers hope that the memory crunch will ease. This will take a while, to be sure, but no company would be a bigger beneficiary of the world running out of places to put AI chips than Apple.
9. Startups Suffer
AI has drawn a great many battle lines, not just in terms of data centers, but also within AI, and between AI and the rest of tech. Specifically, anything that strengthens the frontier labs is likely bad for the rest of the startup ecosystem. In a world with fewer data centers, startups will have less access to cheap compute, even as a slowdown in capabilities both means it takes longer to build compelling products and also gives the frontier labs time to actually build products that secure their ownership of the end user.
10. The Human Factor
One major fear that investors have had about the data center buildout is that there would be too much capacity, causing prices to plummet and investors and creditors to lose their shirts; that scenario, however, would be great for users, who would suddenly have access to incredibly cheap compute. Of course most users aren’t going to use AI directly; what they will miss out on is the equivalent of the Web 2.0 wave of free Internet services that were predicated on compute being free. Now there’s a good chance that AI is going to have meaningful marginal costs for a long time to come.
At the same time, while I have been an optimist about humans in an AI future, there is no question that we were and are facing a wrenching change as intelligence becomes a commodity. It follows, however, that if it doesn’t become a commodity — or if that change takes longer than expected — that humanity will have more time to adapt. Maybe this is a break we needed; what is increasingly clear is it’s the break Americans wanted.
That noted, there is a long history of citizens demanding the ends and opposing the means, and it’s the demand that wins in the end. The big difference today is that, unlike the first few waves of tech, the industry is going to need to do some convincing, and break the habit of assuming everything it does will get the benefit of the doubt. AI wasn’t technically on the ballot, but it was implicitly, and will be forevermore.
Presented by
I spend a lot of time trying to figure out what is happening to businesses on the internet. What I often find I’m missing is a view across the economy itself. With more than 1.7% of global GDP running on Stripe, working with them means I can bring some of what they’re seeing to Stratechery. They are, in a sense, a barometer of the economy.
Take the “SaaSpocalypse”. Earlier this year, nearly a trillion dollars in enterprise software value disappeared amid fears of AI eating its lunch. Stock prices only tell us what investors think will happen, and not necessarily what is happening. In this case, Stripe recently built a SaaS Index using payments data from 72,000 non-AI SaaS businesses. Looking at their actual performance, we can see that while SaaS stocks were selling off, revenue growth was actually accelerating.
Year-over-year growth is now over 30%, with some of the fastest growth coming from the youngest SaaS companies. I suspect there are a lot more interesting things hiding in Stripe’s data, and I’m excited about having an excuse to go look for them.
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