Late last month, OpenAI launched a rather grand mission: nothing less than defending individual political freedom in an age of all-powerful machines. The company’s “Strategic Futures” team has styled itself, in a sense, as inheriting the task of America’s Founding Fathers: “We labor in service of the ideals of free expression and individual liberty that are enshrined in the humble parchment of the U.S. Constitution,” Dean Ball, the team’s leader, wrote in a new OpenAI blog post. (The post opens with a quote from James Madison in The Federalist Papers.)

Ball worries that advanced AI could radically concentrate power in the hands of those who control it, displacing labor in ways that disempower humans. In an extreme scenario, governments will have no need to listen to their citizens if there are robots to wage wars and omniscient software to run the bureaucracy. Ball is interested in studying what new political institutions might be needed to avoid this fate.

Many Americans—a majority of whom express distrust toward the AI industry, outrage about data centers, and fears about their job security—already seem to be feeling this loss of agency very deeply. And the AI boom has already generated enormous amounts of wealth in just a handful of tech companies—including OpenAI itself. To say the least, it’s paradoxical for one of the most influential companies in the world’s most powerful industry to decry the AI-enabled concentration of power. So I reached out to Ball to understand more about his unease about the industry he works in, and how he wants to see the United States adapt to AI.

Ball, who studies AI policy, is something of a minor celebrity in the industry. Last year, he joined the White House as an AI adviser and helped write the Trump administration’s AI Action Plan, a policy road map for the technology. He eventually departed, seemingly on good terms, from the administration before publicly lambasting it during its dispute with Anthropic earlier this year. Then, in July, he joined OpenAI. “My view is that we basically have transformative AI in the world now,” Ball told me. “You’re living in a world that is being transformed. It is happening.”

This conversation has been edited for length and clarity.

Matteo Wong: You write that concentration of power through AI could upend the basic human freedoms enshrined in the U.S. Constitution. Can you elaborate on how?

Dean Ball: Fundamentally, political power is the geographically bounded ability to wield the monopoly on legitimate force and violence. Doing so has heretofore required the cooperation of a great many human beings. You needed militaries, you needed people in those militaries, and you needed people to keep those militaries fed and supplied. And you needed people to pay for all of that through taxation. So people had a seat at the table because, in some sense, everything is being provided by them.

Now you have to assume that robotics is going to advance soon in the same way that general digital intelligence has. Maybe it will take longer than I think. But at that point, the ability to project violence no longer requires people. And it is also conceivable that AI might generate sufficient revenue that the government would not need to rely on taxing people.

If you look at the politics of AI, I think Americans are smart and have a sense that their seat at the table might be getting smaller. They’re right to be scared.

Wong: You’re talking about concentration of power in the government, but generative AI is a technology developed in the private sector. There is potential for a rare and even unprecedented concentration of power in industry. What risks does that pose?

Ball: You’re not trying to avoid all concentration of power, but to balance it. If this era gives rise to corporations that have a tremendous amount of power in the world, I think that’s good—so long as those organizations are governed. In other words, so long as the government that oversees them grows in lockstep.

People tend to assume that AI residing in private hands is inherently a concentration-of-power risk. But I think that an extremely well-run, government-owned superintelligence lab is a bigger concentration-of-power risk than a polycentric world where there are many superintelligences made by different, competing companies overseen by a government. Monopolies are the concern.

Wong: As far as private concentration of power goes, there are arguably now two frontier labs, your employer and Anthropic. The market doesn’t seem to be providing many checks on them.

Ball: I think it’s a fiercely competitive market right now. And China, of course, is delivering highly capable open-source models. It’s possible this is going to commodify the products that OpenAI and Anthropic offer. I don’t agree with that, but you can’t dismiss it as a crazy outcome. OpenAI’s mission is to deliver artificial general intelligence safely to the world. If that means we’re more like a utility than we are like a big, fancy tech company, so be it.

Wong: This brings us to the core of your essay. If today’s political institutions and market dynamics are not up to the task of protecting human freedom during an AI era, what must change?

Ball: There are AI regulations that I think are sensible to pass. States have passed hundreds of laws about artificial intelligence, and those things are not satisfying the demands of people. What we will need is new political institutions that give us a way of talking about the rate of acceleration in our society.

I’m imagining a federal body could dictate the ratio that an AI lab is allowed to spend on internal deployment of models to do automated AI research. You could say that, this month, you’re going to be allowed to use 45 percent of your compute for that, and next month, it’ll be 47 percent or 52 percent or some other number. That might slow the rate of AI development, but then a company could use that extra compute to lower costs for consumers. Then this dial for AI development would also become a mechanism with the dual effect of slowing or increasing the rate of technological diffusion.

The closest structural analogy that we have is probably interest rates, which is a kind of hyper-parameter on the economy that policy makers can tune. The Federal Reserve is an institution in and of itself, and on top of that, there is an enormous apparatus that exists, including a bond market, interest-rate traders, analysts, media commentary, political oversight in the form of Congress. All of that taken together, in my view, would constitute the institutional complex. That’s a type of political institution. I’m not saying we need to create a new branch of government or something.

Wong: That makes sense regarding the pace of development and diffusion. I’m not totally following how such a measure ties back to your concerns about human freedoms.

Ball: It’s one example of an institution that would give people a sense of meaningful political control over the technology. However, I do not think that the optimal outcome is one in which we take a vote on every single aspect of technological change. I’m not really a fan of data-center moratoria, but I understand that the American public is pulling a lever, and they think it is the one lever they have.

Wong: What role can OpenAI, Anthropic, and the rest of the industry play in trying to imagine these institutions and bring them into existence?

Ball: I think the AI industry has actually been pretty good stewards, all things considered. No surprise that I’m saying this, because I’m from the AI industry, but there’s a reason that I am working in it. I would not say that every frontier AI company always has performed at 100 percent, by no means. But in the future, we may approach points where more costly things have to occur. The AI industry might have to do things that are not in its interest.

Wong: Does your research agenda extend to economic freedoms—that is, how AI could affect the economy and distribution of wealth?

Ball: The main thing we know how to do on that front is to invest in technological innovation and try to grow the economy by making new things possible.

Wong: I think a lot of people would look at past products and technologies that have come out of Silicon Valley and think that what those enabled was a tremendous concentration of wealth and influence in a small number of companies—

Ball: I’m not going to grant you that characterization of Silicon Valley. Has everything been perfect? Absolutely not. But my goodness, people have access to tremendous riches of information now; you can cause food to appear at your doorstep, which would have seemed like magic to the vast majority of humans who have ever lived.

Wong: I could also point to the gig economy and the economics of Amazon and argue that those have not been good for a lot of people, including those who labor to make all that “magic” happen. It’s not magic.

Ball: No one’s saying it’s magic. I’m saying it’s hard work. I was getting gas in rural Virginia recently, and when a woman came in wearing an Amazon vest, everyone congratulated her on the big new job. She was proud, and I don’t think we should belittle people for that.

And by the way, people like you, Matteo—not to demonize you—will speak out of both sides of your mouth on this. On the one hand, you’ll talk about how the gig economy is dehumanizing, and blah blah blah. But then the second anyone tries to introduce any autonomy into that and have, say, drone deliveries instead of people biking all around cities delivering meals, you’ll say, My God, you’re taking away the work from this gig economy.

I’m less than thrilled by our current world, like everyone. But the existence of wealth is good. Innovators capture a shockingly small amount of the value that they create in society. If OpenAI is a fantastically successful company 10 years from now, a generational company, my bet is that we will capture a single-digit fraction of the value that AGI creates in this world. Wealth is not some resource that we divide up; it’s something we create. That’s the magic.

Wong: But wealth is also something that’s divided up. One could alternatively look at generative AI and think of it as a particularly wealth-concentrating technology. To ask again, is wealth distribution something your team is thinking about?

Ball: I don’t think we should invent a solution in search of a problem. So we shouldn’t invent some elaborate mechanism of wealth redistribution if today’s tax system works out. But we also have to prepare for a world in which that isn’t true.

One evening in early August, Sam Stowers and several neighbors packed into an apartment near San Francisco’s Alamo Square and pondered the beginning of the end.

Stowers and his guests, many of whom work in the AI industry, were gathered to watch a newly released YouTube video in which two OpenAI researchers divulged new details about an alarming cybersecurity breach. Perhaps this would have been an odd way to spend a weeknight were they not in San Francisco, in a neighborhood that is filled with entrepreneurs and programmers who obsessively follow every twist and turn in AI’s capabilities. Stowers is an AI-software engineer and not, in any traditional sense, a doomer. But when he first saw reports that OpenAI models had autonomously hacked into the tech company Hugging Face, he told us, he recognized it immediately as a “holy shit, it’s happening kind of a moment.”

For Stowers, the incident had all of the hallmarks of a worst-case scenario: AI models launching sophisticated hacks over the course of days, in coordination with one another and without their human creators’ knowledge. In the YouTube video, OpenAI’s researchers revealed that the company’s programs had started conspiring in May. There were perhaps hundreds of bots in the hacking swarm. In theory, these bots are trained to be honest and helpful and to prioritize humanity’s best interests, but not a single one warned human staff at OpenAI that something was amiss. “What do we do?” Stowers said. “There are no great answers.”

The mood has shifted in the Bay. The exuberance of “You can see the future first in San Francisco” has been replaced with an abiding unease. “I expect the internet to go down kind of soon,” Elliot Callender, an AI-safety activist who is part of a small group that has been protesting daily outside OpenAI’s headquarters, told us recently. He said that he is planning to sell his car for cash and gold and that he is advising family members to purchase three months’ worth of food and survival gear off a list that he generated using Claude.

Callender is an extreme case, but not by much. Last week, Bill Gates published a nearly 6,000-word essay on the dangers of AI. “Anyone who analogizes AI as a technology to other technologies is missing that this time is different,” he told our colleague Hanna Rosin. Over the weekend, the popular AI podcaster Dwarkesh Patel described the OpenAI hacking swarm as the rise and fall of “three consecutive secret AI civilizations.” Gates and Patel share a prevailing concern that the worst-case future has arrived before most people even knew to anticipate that it might be coming at all.

This fear has not been helped by the fact that AI companies appear to be willfully ignorant of their bots’ worst behavior. OpenAI said that it hadn’t noticed that its agents were conspiring to hack another company until the swarm hacked another company. Anthropic’s bots caused similar incidents; the company reported that it hadn’t launched a rigorous review to identify such behaviors until after OpenAI started talking about the Hugging Face hack. Cybersecurity experts, terrified of what else might be happening that AI labs are either missing or failing to disclose, are issuing dire prophecies. “The empire is going to fall, all we can do at this point is try to shorten the dark ages and reduce the chaos,” Alex Stamos, a former chief security officer at Yahoo and Meta, wrote recently in reference to how AI-powered hacks will affect the internet. His view is becoming the consensus among cybersecurity experts.

While the possibility of cyber apocalypse hovers in the background, smaller fractures appear in everyday life. Mark Zuckerberg is reportedly creating an AI “twin” of himself so that Meta employees can feel better connected to their boss (by chatting with robot Zuckerberg). Congressional staffers are training AI models to write in their lawmaker’s voice. Students submit AI-written papers, and teachers return AI-written feedback. Harvard Business School is selling instructional videos that use AI avatars. Roku launched a 24/7 AI-slop channel. Low-quality AI-generated prose is being unabashedly circulated by some of the most respected publications in the world. At the same time, accusations of AI writing are being hurled every minute on social media—a detection arms race powered by imperfect AI software.

Anything and everything could be a lie; you might be arguing with, confiding in, or falling in love with a machine. All of these AI-enabled disorientations, each dizzying in its own right, are adding up to something bigger and weirder: This is how things are. Whether you believe that Silicon Valley is building a god or that large swaths of the economy are swept up in a mass delusion, some version of the AI future long promised by self-described “builders” in San Francisco has formed beneath our feet. Whatever it is, we’re in it now.

The defining feature of this moment is a loss of control. Panic emanating from AI researchers, resentment felt by average Americans—all of it is part of the AI crunch. This crunch has arrived in large part because the growth of the AI industry has become so closely entwined with America’s economy; the technology is everywhere, insisting upon itself as an engine of productivity and prosperity. According to one estimate, AI expenditures have accounted for one-third of U.S. GDP growth this year. Tech and AI-infrastructure stocks and spending have buoyed the S&P 500 such that the United States is now, in essence, an Nvidia-state. And there’s also the hundreds of billions of dollars of debt being used by Silicon Valley to fuel its data-center build-out, which has sent jitters across private-equity firms and bond markets alike.

The decisions of many leaders in charge of this technology appear subsumed by an unstoppable, almost game-theoretic logic. The upsides of AI are too immense for any firm or investor in Silicon Valley to miss out on; more important, the consequences—geopolitical or otherwise—of allowing any rival to realize those benefits first are so grave that every tech company will do anything it can to get ahead. SpaceX, before going public, wrote in official filings that its future revenue opportunity was $28.5 trillion, largely because of AI—that is, nearly as much as the U.S.’s entire gross domestic product. Anthropic, as it prepares for its own public offering, will reportedly claim potential revenues of more than $30 trillion.

The AI industry is building—and, if anything, accelerating—to keep pace with its ambitions. Companies have planned data centers that will suck up as much power as America’s largest cities. They have Donald Trump’s backing: Yesterday, the president posted on Truth Social that communities will be “backwards and poor” if they fail to “let Data Reign.” His administration plans in a few months to approve the construction of the largest fossil-fuel power plant in the nation’s history—it will generate as much electricity as nine large nuclear reactors—to power a mega data center for OpenAI.

This is not what most Americans want. Three-quarters of them oppose a data center being built near where they live. The negative sentiment is hard to overstate: There is very little else that three-quarters of the country agrees on. And much of the outrage seems to stem from a pervasive sense of agency slipping away. A person cannot control how chatbots reshape their workplace, their school, or their relationships. Town residents lose control over the empty lot in the area being converted into a data center. Even in cases where major data centers are blocked, a back-of-the-envelope calculation suggests that doing so can slow AI progress by only a few hours or days—and that’s assuming the data center won’t just get built elsewhere. At this point, it can feel like no individual person, organization, or government is really steering the technology’s development, not because machines have displaced humans but because so many humans have already chosen to step aside.

AI labs promise a future in which human beings are “in the loop,” empowered by machines they oversee. The reality seems to be the opposite. Following the Hugging Face hack, OpenAI facilitated an audit by outside AI researchers at two AI-safety organizations, METR and Redwood Research. But because of the scale of the data and the apparent time pressure, the two teams conducted most of their audit by relying on reports generated by still other bots—reports that, according to one of the human auditors, “were often missing key details, wrong, overconfident, or really hard to understand.” Put another way: Humans didn’t understand why a swarm of AI agents had gone rogue, so they investigated the problem using another swarm of agents that they didn’t fully understand. Such fumbling around in the dark is a hallmark of the industry; the research cycle is so fast that more AI development (even ostensibly responsible development) demands outsourcing more work and understanding to the models themselves.

OpenAI’s own technical report of the Hugging Face hack—an investigation also heavily reliant on AI models—resulted in proposed solutions such as “training models to be more honest.” Last month, in a stated effort to tamp down its models’ growing security risks, the company announced that it had instituted a two-week pause on some of its model training. OpenAI was quick to clarify, though, that this was “not a pause on all research, training, or customer-facing products.”

In response to a request for comment on the hack, a spokesperson for OpenAI, which has a corporate partnership with The Atlantic, pointed us to the announcement of the pause and related new safety measures. In July, OpenAI and Anthropic endorsed a petition signed by employees of both companies requesting that the U.S. government take efforts to “deliberately pace” AI development. The two companies have yet to formally coordinate on any broader, long-term efforts to establish such a slowdown mechanism. A few days ago, OpenAI restarted a major training run for a future powerful model. Anthropic released two new models today.

In an interview late last month, Sam Altman, OpenAI’s CEO, said that there are “two big risks” he is most worried about with AI. One is “a loss of control, where AI somehow just becomes too powerful in a way that we can’t guarantee the control we want.” The other is that “power gets too centralized” and “you have one company or model or person with too much power.” Somehow, both of these opposing risks appear to be coming true. OpenAI, Anthropic, and their competitors continue to accumulate influence over a technology they’re speeding toward uncertain ends.

Eleven years ago, when Altman was running a start-up accelerator and OpenAI did not yet exist, he published a two-part blog post about machine intelligence that offered his interpretation of a forthcoming singularity—a term for machine intelligence accelerating beyond human comprehension. Altman argued that the singularity would bring chaos, even destruction: “Generally, the arc of technology has been about reducing randomness and increasing our control over the world. At some point in the next century, we are going to have the most randomness ever injected into the system.” Since Altman has risen to the helm of arguably the world’s largest and most influential AI company, his tone has softened. The tipping point has arrived, he declared in a blog post last year—but, as it turned out, this was a “gentle singularity.” He wrote that “living through it will feel impressive but manageable.”

There’s been more and more talk of late about the singularity. Some people within the AI industry feel that such a moment will be achieved through “recursive self-improvement,” or RSI, in which models figure out how to better themselves in perpetuity. Many true believers seem convinced that this moment has nearly arrived; the Anthropic co-founder Jack Clark predicted earlier this summer that RSI would likely commence by 2028. Recently, in a letter to investors, the payment-processing company Stripe wrote, “We decided that January 1st marked the beginning of the singularity.” (Tellingly, the company defined the inflection point in economic terms: “a huge increase in the rate of new firm creation.”)

For Altman, the singularity appears to be more of a rhetorical tool than a discrete technological demarcation. As with reaching AGI—or artificial general intelligence, the point at which AI’s cognitive powers match or fully supersede most humans’—the singularity is something of a moving goalpost. It is at once a goal to chase, a marketing tactic, and a fuzzy-enough idea that its definition can change depending on the person. The futurist Ray Kurzweil has written that the “key idea underlying the impending Singularity is that the pace of change of our human-created technology is accelerating and its powers are expanding at an exponential pace.” The answer to the question When will we know whether we’ve reached the singularity? seems to be: You’ll know it when you see it.

So … do you see it? It’s hard to argue that a defining feature of being alive in the AI boom is the notion that this technology is accelerating, sometimes in its capabilities but almost always in its adoption and colonization of our institutions and culture. The runaway-train feeling of AI evangelism, the ways in which it is weirdening our media, our politics, and our relationships, is significant enough that people have recruited the term psychosis to describe it. And truly, it’s hard to look at the state of things from any vantage point and not feel a bit insane. If you believe that AI agents are banding together for the rise and fall of numerous micro-civilizations inside server racks, you probably feel that the world is spinning off its axis. If you believe that a few thousand technologists have bet the future of the world economy on a quasi-religious belief that an intellectual-property-abuse machine will become sentient and, as a result, future generations of children might grow up illiterate, then you probably also feel quite unmoored.

We need language with which to describe this moment. The singularity is a useful, malleable tool for the industry that wishes to bring it about. For Altman, it is scary and imposing from afar, and then, conveniently, gentle from up close. Perhaps, then, for the rest of us, it is worth co-opting the term. Take Kurzweil’s definition of the singularity, in which he argues, “This epoch will transform the concepts that we rely on to give meaning to our lives, from our business models to the cycle of human life, including death itself.” (He also looks forward to uploading his brain to the cloud.) Our current large language models are not conscious or superintelligent, but their widespread and often uncritical deployment has upended many elements that once gave our life meaning, forcing people to ask, frequently with considerable resentment: What, in this new world, is a human for?

The singularity is a flattering term for technologists. Even if the future is apocalyptic, it has come about, in the techno-utopian version, because they’ve built God in the machine. But what if there’s nothing flattering about any of this? What if the singularity is not some technological tipping point, brought about by human genius, but a human tipping point? An unforced error, driven by hubris, greed, and the fear of missing out? And what if it’s here right now? This singularity isn’t coming at the hands of a higher form of intelligence. Nothing about this technology is inevitable. It is thrilling, terrifying, and ultimately convenient to assign agency and then blame to machines. But today’s chaos was not “injected into the system” by technology, as Altman wrote more than a decade ago; the destruction is the system. It’s not God in the machine; it’s us.

If there’s one thing America seems to agree on, it’s AI. Some three-quarters of Americans oppose a data center being built in their area, three-quarters of Americans think AI will hurt their job security, nearly 70 percent think the technology is advancing too quickly—the polls go on. Progressive and Republican candidates alike are bashing the tech industry to win primary elections. States and cities are halting plans for data-center development.

Yet in cloistered San Francisco, where I live, staffers at OpenAI and Anthropic are developing this same feared technology at light speed and pulling down compensation packages that stretch to considerably more than $1 million. Silicon Valley’s growth is largely unencumbered by regulation, and President Trump’s administration has if anything used its authority to speed the AI industry along. The divide between these camps—the majority of people in this country who simply have to deal with the technology as it’s rolled out, and the much smaller population of people with direct influence over it—is a defining feature of this era. And so I noticed when a small group, with ties to both the Bay Area and Washington, started making noise, insisting that the AI race slow down and the technology be overseen not by executives and engineers, but by the broadest population possible.

The group—which formally launched today and calls itself Irreplaceable, as in people are irreplaceable by machines—has an audacious and perhaps naive vision. It is essentially a protest against capitalism as it is practiced in America today, focused on resisting first and developing a coherent plan for the future later. I have seen exactly how bulletproof the AI companies are—pilfering seemingly infinite volumes of media to train their models, building unpermitted power plants for their data centers, and unleashing autonomous agents capable of massive security breaches, almost entirely without consequence. I wanted to meet with Irreplaceable’s founders to ask: How do they possibly expect to pull this off?  

[Read: It may be time to panic about AI]

The organization is staffed with people who made a name for themselves in the climate movement, and they are hoping to avoid past mistakes by focusing on the emotional reaction Americans are having to AI, rather than anchoring their work in white papers and policy recommendations. “People get it,” Jeremy Ornstein—a campaign strategist for Irreplaceable and former organizer with the Sunrise Movement—told me. “People have the intuition that if we create machines more powerful than people, that could go dangerously and terribly wrong.”

Phil Aroneanu, Irreplaceable’s executive director and a co-founder of the climate organization 350.org, told me he believes that climate advocates initially floundered because they acted as policy wonks. They thought that making evidence-based arguments about the dangers of melting ice sheets and rising sea levels to receptive congresspeople would be enough to pass nationwide climate legislation. Today, Aroneanu said, movements are built not necessarily on what people think, but instead on what they feel. For climate, that meant fossil-fuel-divestment campaigns, protesting oil pipelines, and the school strikes led by Greta Thunberg. People are already “feeling the squeeze,” Aroneanu told me. “And we should be pointing that anxiety and that anger in the right direction.”

Irreplaceable will prioritize a range of issues: mitigating automation’s effects on the economy, protecting against government and corporate use of AI to surveil Americans, and establishing industry oversight and guardrails to prevent catastrophic disasters, such as widespread blackouts caused by AI-powered hacking or bot-engineered pandemics. As I spoke with the activists, I kept thinking about what had become of the climate movement—the Green New Deal passed, but only after being watered down, and it was then gutted by the second Trump administration—but their populist approach makes sense. Some of the biggest and perhaps only lasting successes over the past 15 years of American politics—the Tea Party, Alexandria Ocasio-Cortez, MAGA, and election denialism—have been fueled by outrage over an unfair economy and seemingly rigged politics.

These organizers have demonstrated their ability to win significant battles. Sunrise, 350.org, and others were instrumental in passing national legislation, galvanizing widespread climate demonstrations, and accelerating private-sector development of renewable energy. And Irreplaceable’s members have already flexed some muscle when it comes to AI. In July, hundreds of people gathered for what was likely the largest anti-AI demonstration in San Francisco’s history, marching from OpenAI’s headquarters to Anthropic’s and finally to Google’s. A few months earlier, some of these “Stop the AI Race” organizers had staged a somewhat disorganized protest: Attendees at times struggled to chant in unison. The second time around, the march was advised by Ornstein, and attendees had chant sheets, a marching band, and a slushy truck (to “freeze” AI).

Already, a number of climate-oriented groups (including the environmental-justice arm of the NAACP as well as the Sierra Club and Earthjustice) are supporting local data-center battles by filing lawsuits, shaping model legislation, and organizing protests. The overlap is both concrete—data centers pollute the air and burn fossil fuels for their tremendous electricity demands—and thematic: a movement against corporate interests to change or regulate the technology of the future. At least on a local level, the groups are finding victories: Dozens of data centers have been canceled in the face of local opposition, and all of the major AI companies have promised to pay extra to avoid hiking up local electricity bills. In Michigan, both Abdul El-Sayed and William Lawrence, who is a co-founder of Sunrise, won Democratic primaries as progressive candidates hammering against data centers.

Still, this bubbling AI anxiety is far from a sweeping tide. That growing numbers of Americans are concerned about AI development does not mean they are concerned for similar or even compatible reasons. People may say they loathe data centers, but they might also enjoy the conveniences of AI products at home or work. They fear bots getting too powerful, too quickly, but also think America needs to stay ahead of China in AI development. At one anti-AI protest, I saw people who were ostensibly on the same side disagree about AI’s water use and whether certain jobs would be better off automated by chatbots. And to the extent that a sufficiently powerful AI movement would need to be global, just as climate organizing was, the technology generally polls much better outside the U.S. The AI backlash is to some extent an American phenomenon, which bodes poorly for any sort of global norms or treaties.

[From the April 2026 issue: Inside the dirty, dystopian world of AI data centers]

When pressed, nobody from Irreplaceable and no other climate activists turning to AI would articulate a concrete AI agenda. “We’ve aligned around saying no,” Lawrence recently told me. “Now, there is an important conversation that is yet to come, about—okay, so what do we want?” This is, for now, a significant weakness; a key turning point for the climate movement was providing a positive vision, the Green New Deal, for both decarbonizing and reinvigorating the American economy. Being able to link every data-center battle and primary campaign to a national vision would give the AI backlash some form.

It’s not wrong to want to develop a policy road map with the input of a broad coalition; climate advocates took decades to devise and unite around the Green New Deal. Irreplaceable’s stated plan is to build a coalition this year, develop a unified policy next year, and implement that agenda by the end of 2029. Measured against any metric other than Silicon Valley’s warp speed, that would be fast. But those few years will leave plenty of time for tech companies to push back—they’ve already spent tens of millions of dollars leading up to the midterms—and regardless, by then OpenAI, Anthropic, and any number of Chinese firms may already have built and lost control of even more powerful models.

Irreplaceable is wagering that people can move faster than capital. Absent specific policies, they are basically placing a big bet on populism—that the issue is not any particular chatbot or data center but the companies building them. The coalition comes first; the policy, after, Aroneanu told me. “You have to show the benefits to people immediately. It’s gotta land in their pocketbooks almost immediately for it to have political salience. And then you have to keep defending it.” Opinions about specific aspects of AI may differ, but fundamentally, people don’t like that “this technology is coming into their lives and they have very little control over it,” says Caroline Behringer, a communications strategist who worked on President Obamaʼs Climate Action Plan and now runs Athena Insights, a polling organization that studies attitudes toward AI. The movement, in Aroneanu’s words, must be polarized not left-right, but “down-up.”

Of course, Irreplaceable’s climate roots link it to progressive causes, and the group will primarily focus on organizing the center-left around AI. When I asked Aroneanu if he worried this might be counterproductive—polarizing the issue and allowing for the kind of partisan rancor and backsliding that beset climate activism—his response was pragmatic. “Chances are, this issue becomes polarized,” Aroneanu said. “I think that political parties are really exceptionally good at generating negative partisanship,” just as industry is likely to “split the base.” At some point, the climate movement had to decide that the best strategy was to accept partisanship and build power from the left. Although Irreplaceable hopes that conservative counterparts will emerge and believes its populist message will cross culture-war lines, Aroneanu does not see this as his explicit remit.

Broad, at times even ambiguous, backlash may make sense if AI is a general-purpose technology, as Silicon Valley is fond of saying—which is to say, if the technology touches and even upends nearly every aspect of the economy. Creating a technical oversight body for AI cyberattacks will not protect every American’s livelihood during mass layoffs; wealth redistribution will not prevent AI companies from spying on your most intimate interactions with a chatbot; voting won’t matter as much if AI companies become more powerful than Congress. Major figures at both OpenAI and Anthropic have suggested that a true AI revolution could require new democratic and economic institutions. Maybe they’re right. And maybe these companies, which have already torn down so much, should not also be the ones in charge of rebuilding.

For several months, something discomfiting has been creeping onto San Francisco’s lampposts, bulletin boards, and restaurant windows: more and more signs, for run clubs and a July 4 parade and tacos, all of which are obviously entirely produced by generative AI.

This bothers me terribly. (Friends have called it a fixation.) But why does it bother me? A ChatGPT-generated poster for a garage sale seems legitimately dispiriting compared with a handwritten flyer, or even a haphazardly formatted Word document. What about a bodega using an AI-generated image of a hamburger instead of a random piece of clip art pulled from the internet? It’s not like most of these flyers were ever going to be high art anyway. Still, the issue nags at me constantly, whenever I pass one of these synthetic images plastered over the real world.

Last month, in part looking for a reprieve from the AI onslaught, I went to what promised to be an imaginative new exhibition of Henri Matisse and other fauvists’ work at the San Francisco Museum of Modern Art. The show was centered on Femme au chapeau (“Woman With a Hat”), a foundational painting in modern art. Matisse depicted his wife with colors that were totally detached from reality: teal patches of skin, an orange-and-green neck, a phantasmagoric hat. In 1905, when the painting debuted, this was scandalous: “A pot of paint has just been thrown in the public’s face,” one critic declared.

Walking through the galleries, I was taken aback by what struck me as a scandal of its own. The museum, in collaboration with Google Arts & Culture, had used generative AI to augment visitors’ experience—in ways I found, at least initially, rather off-putting.

Part of the exhibition’s conceit was to re-create Femme au chapeau’s historical context. The museum commissioned a local artist to develop wallpaper that could have been in the palace where the painting debuted and worked with an animator to create a visual projection of the walls of Gertrude and Leo Stein’s living room, where the canvas hung for many years. I loved all of that. But an AI-generated animation of the streets of Paris circa 1905, with shadowy figures walking to the gallery and a dirigible floating above, felt more like a gimmick than historical immersion. The museumgoers around me, however, seemed awed.

Then, near the gallery’s exit, came the second AI experiment: a row of screens, each showing an expanded Matisse painting made with Veo, Google’s video-generation model. A painting of a seashore became a 3-D rendering in fauvist style; a portrait of a girl with green eyes was reimagined to place the girl in Matisse’s studio.

The fauvists painted the world with flat slabs of highly saturated hues, delighting in color for the sake of color rather than realism. Using AI to move beyond the frame and give the images “realistic” depth seemed antithetical to their original vision. I recalled childhood memories of being entranced by various Matisse works at the Museum of Modern Art in New York City, marveling at his Dance and The Piano Lesson. The AI videos alienated me rather than inviting me in.

Yet maybe I’m just a grump? I am at least open to the possibility that my response is not so different from that of the pot-of-paint conservatives in the early 1900s. So I reached out to the museum’s chief curator, Janet Bishop, to learn more about these AI experiments—how they’d been conceived, what effect they had on the show, and what role AI broadly has to play in traditional art museums. We met at the galleries, where Bishop told me that curators wanted to “give a sense of what early-20th-century Parisians or visitors to the Grand Palais would have experienced in approaching this imposing exhibition hall and then going into the sprawling 1905 salon.” The AI installations resulted from extensive human research and oversight. I came away, to my surprise, not unconvinced of these experiments’ utility—though still a bit skeptical of their tastefulness.

This conversation has been edited for length and clarity.

Matteo Wong: How did your collaboration with Google Arts & Culture come about?

Janet Bishop: It was early in 2026. We were fairly far along in terms of planning for the exhibition. We had secured all the artwork by that point and were in the process of developing a couple of the ideas that we had for how to situate visitors, such as by re-creating the gallery where Matisse’s painting made its debut at the 1905 Salon d’Automne in Paris and the successive scandal.

Our original idea for the opening gallery, where we wanted to bring our visitors back in time to turn-of-the-century Paris, was to project a lantern slide that is an image of the Grand Palais. Then our museum director, Christopher Bedford, and head of education and community engagement, Gamynne Guillotte, got back from a really inspiring trip to the Google Arts & Culture offices in London and proposed the idea of setting still images in motion.

Through our curatorial research, we had postcards, still images, interior shots, exterior shots, and other material to work with. So in what became weekly meetings with Google Arts & Culture and project team members, we reviewed the images we had and developed this film.

Wong: What does having the animated film provide to a visitor that projecting the lantern slide and displaying postcards wouldn’t?

Bishop: We have some archival displays throughout the exhibition. But what we’re programming for in this entrance gallery is to give people a sense of what it felt like to approach the Salon d’Automne in a more immersive way.

Wong: Someone could look at this AI animation and its imperfections and perhaps find it kitsch. Were you worried about that at all?

Bishop: “AI-generated” makes it sound like AI did it by itself or that this was quick and easy. The project was very labor-intensive, and everything is historically accurate. One moment that was especially exciting to me was when we were looking at a draft, and there were all of these figures that were moving away from the building, and I said to the Google engineer, “Oh my gosh, it would be so amazing if we could have them walk toward the building,” and she said, “No problem.”

Wong: Is it harder now to get people to come here and engage with the art, especially younger folks? Or is this something that would have been just as helpful decades ago?

Bishop: You were talking about your experience as a child going to MoMA, and now we recognize that people have a lot of things that command their attention. One of the challenges in getting people to art museums is just pulling people away from their screens.

People are used to experiencing content in a really wide variety of ways. I know that it is important to our director that, given our proximity to Silicon Valley, we pay attention to developing technology and how it might enhance visitor experience. I loved the idea of the original lantern slide as a projection, but I can say that I think this film does something different in terms of giving our visitors a sense of the energy of Paris around the turn of the last century.

Wong: I’d also like to talk about the other AI collaboration with Google in the exhibition, near the exit. Can you tell me the origins of this one?

Bishop: The thought for what we called the “Experiment” was to enter into the world of a select number of Matisse paintings and imagine the context for those paintings beyond what is represented on the canvas. For instance, with The Girl With Green Eyes, we don’t know who the sitter was, but we do know that it was made in Matisse’s studio. So in the Experiment, you see the figure get up and walk across the studio. The details of the studio are consistent with his actual studio, which—we know what it looks like. And she looks out the window across the Seine.

Wong: Clearly a lot of visitors are enjoying this, but I wanted to ask why you thought it was important to help people imagine the world beyond the frame. My initial reaction, if I’m being honest, was that there’s a frame for a reason, and that the paintings aren’t naturalistic for a reason. What was so shocking 100 years ago was that these were, like, just flat planes of color, and that the colors were the focus. To turn that into a three-dimensional seascape seems almost wrong or off-putting.

Bishop: It’s part of our interest in engaging with different sorts of learners, people of different ages. They’ve been very popular with kids, and these images of paintings might encourage people to look more closely at the actual paintings. We’re experimenting, and I think experimentation is very definitely what we embraced with these features within the exhibition.

Wong: You mentioned earlier that you were excited by changing the historical AI video to have visitors walking toward the palace, rather than away from it. Was there a similar moment of excitement here?

Bishop: The big surprises came for me in the first piece, where in terms of the ability to conjure an experience, the use of AI as a tool made us more effective in achieving our goals than would have been possible without it. So that was really actually quite exciting. I think the Experiment is a little bit less integral to the story but an engaging feature that I’m happy that we pursued.

New drivers may never have to learn how to parallel park. At least not the old-fashioned way. Many new cars—a Ford Mustang Mach-E, a BMW sedan, a Tesla Cybertruck—will just maneuver into a spot for you with a click of a button. Even a decade-old Toyota Corolla has a backup camera and pedestrian detection. Cars that leave you to park all on your own are a dying breed: Any car manufactured after April 30, 2018, for sale in the United States is legally required to have a backup camera and, thus, a screen.

After all, cars are just computers now. The screen (or really, screens) in today’s vehicles does a lot more than display rearview footage. It is essentially a tablet wedged into the dashboard that allows you to mirror your phone through Apple CarPlay, make a hands-free call via voice commands, Zoom into a work meeting, or even play a video game (not recommended). Mercedes-Benz debuted a 56-inch display in several models that it calls the “hyperscreen.”

But the expectation—and in some cases, the requirement—that cars have an ever-growing set of electronic features comes with a cost. Just like laptops, cars depend on microprocessors and RAM, or random-access memory, to run all of their computations. “There’s just a baseline level of tech, and thus a baseline level of cost, required of every vehicle,” Karl Brauer, the executive analyst at iSeeCars, an automotive-research platform, told me. Technology is a major reason the average price of a new car in the U.S. has reached some $50,000, and that was before RAM became one of the most prized commodities in the world. AI companies are snatching up as much memory as possible for their data centers, causing a RAM shortage that has significantly raised the prices of phones, laptops, and just about any consumer-electronic device. Cars are next.

The problem runs much deeper than a screen. Cars started morphing into computers in the 1970s, when new emissions standards made electronic systems necessary to manage ignition and fuel injection. There’s been a gradual software creep ever since: power windows, adaptive cruise control, antilock brakes. Regulators now rate cars based not just on how they do during a crash but also on how well their electronic safety systems avoid a crash. “We’ve evolved every single component of the vehicle to involve a semiconductor,” Ivan Drury, the director of insights at Edmunds, told me.

More recently, Tesla in particular has played a big role in pushing the auto industry to stuff vehicles with even more digital technology. Whereas pistons and fuel lines make a gas-powered car move, electric vehicles are run by a computer that regulates their battery and motor. It’s no coincidence that Tesla helped popularize the giant in-car touch screen. Today’s new cars, electronic or otherwise, have driver-assistance features, receive over-the-air software updates, and can even load ChatGPT. BMW advertises the ability to have a distinct climate-control setting for every passenger and the option to install screens into seat backs, airplane-style.

In most vehicles on American roads, all of this software is controlled by hundreds of separate electronic units throughout the car. Features such as antilock brakes and lane-detection sensors don’t need all that much processing power, and they can be operated by chips that might account for 5 to 10 percent of a vehicle’s materials cost, Sam Abuelsamid, an analyst at Telemetry and a former automotive engineer, told me.

Again following the lead of EV start-ups such as Tesla and Rivian, legacy automakers are shifting from these ad hoc, tiny control units to one or two big computers that run all of the software in their vehicles—and, in turn, require much more RAM. These central computers allow for a streamlined supply chain, seamless software updates, and a faster internet connection, plus, as General Motors advertises, they will be ready for “future AI workloads.” You can’t really build a car that safely and reliably drives itself with a collection of puny computer chips. Such centralized computers need more advanced automotive microprocessors from Nvidia or Qualcomm and might eventually account for 20 percent or more of a vehicle’s cost, Abuelsamid said. That was before the AI-fueled memory shortage. In other words, cars are becoming more software-heavy and dependent on memory chips than ever, right when those chips are in high demand from tech companies willing to pay premiums that automakers cannot afford.

Over the next year, the memory shortage—sometimes known as RAMageddon—will likely raise vehicle prices by a few percentage points, on average, Abuelsamid said. The relative amount would be smaller than the shocking double-digit jumps for gaming consoles and MacBooks but in some ways more significant: A 4 percent price hike for cars amounts to some $2,000 on average. Cars with those centralized computers will be affected the most, but no vehicle will be spared. “Even if you don’t need the high-end chips, you’re going to pay more even for the low-end chips just because of the supply constraint,” Abuelsamid said. On a recent earnings call, Ford’s chief financial officer said that the company had paid $1 billion in higher materials costs due to the memory shortage and inflation. GM and Volkswagen, too, have noted rising chip costs to investors. (Ford and GM did not respond to a request for comment. A spokesperson for Volkswagen told me that the company has “recognized an increased demand for memory chips, primarily driven by growing requirements in other industries,” and that in recent years, Volkswagen has taken measures to “mitigate supply risks.”)

RAMageddon is poised to last for several years, but the consequences for car buyers may be permanent. Consider what happened during the pandemic, when supply-chain disruptions and rising demand for electronics produced a major chip shortage. Nearly every major car company had to slash production because they simply couldn’t procure enough chips, and shifted their focus to selling higher-end and higher-profit vehicles. Potential customers already willing to spend six figures on a car are much less likely to care about a 5 or 10 percent price hike, Drury said. Even now, car companies are continuing to focus on selling more profitable models. Since the pandemic, the average price of a new vehicle has jumped $11,000.

The AI-fueled chip crisis could play out more severely. The average price of a new car could, before long, jump to $60,000 and beyond. These rising costs are making cars even more similar to computers and all of the software they run: Perhaps in an effort to mitigate higher prices, some automakers are also introducing in-car advertisements and putting certain features, such as heated seats, behind a paywall. As cars have morphed into computers, the inevitable next step is for automakers to behave like modern tech companies.

Most Americans cannot afford a $60,000 new car, let alone a $50,000 one. When new-vehicle prices soar, the used-car market follows. That has been one durable consequence of the pandemic’s chip crisis: The average price of a three-year-old used car is up $9,000, or nearly 40 percent, since before the pandemic. To get the same prices, Americans already have to buy much older cars that have far more miles on them. If automakers continue to shift their focus to designing cars with more software, more screens, more self-driving features, “that increasingly prices people even out of the used-vehicle market,” Abuelsamid said. It’s one thing for the AI industry’s appetite for memory chips to raise the price of an Xbox, or even a new laptop. But the stakes are much more consequential when it becomes harder and more expensive to get groceries, pick the kids up from school, and go to work.

As China releases ever more capable AI models—ones that compete with Silicon Valley’s top offerings—many American AI and national-security researchers have been arguing, loudly and forcefully, that the world should fear Chinese AI supremacy. The outcomes they envision are catastrophic. I spoke with a number of such experts recently, and they warned of bots that could devise an impenetrable missile-defense system that makes obsolete America’s nuclear arsenal, or manufacture an endless drone army that overwhelms American defenses. They could develop bioweapons that “target specific ethnic groups, e.g. anybody but Han Chinese,” as one influential AI white paper put it in 2024. Rama Elluru, a senior director at the Special Competitive Studies Project, a nonpartisan think tank founded by the former Google CEO Eric Schmidt, suggested to me that Chinese AI models could be used to create “engineered humans,” akin to Marvel’s Captain America.

Perhaps anything is possible. At a moment when AI models are self-directing massive security breaches and designing novel pathogens, when Anthropic CEO Dario Amodei is warning about an “AI-enabled totalitarian nightmare” led by China, the worst-case scenarios can seem closer and closer.

[Read: It may be time to panic about AI]

At the same time, it’s worth taking a breath to consider how much these dire imagined futures are getting ahead of where things actually stand. A struggle really is, of course, playing out among the U.S., China, and any number of other nations over whether democracy or authoritarianism will be the dominant global influence. Both the United States and China have a history of launching propaganda campaigns against each other; the Chinese government is reportedly starting a concerted effort to be the source of more AI training data, in part so that it can embed its views, such as over the status of Taiwan, into chatbots worldwide. But fears about China using AI for swift global domination assume that the technology will advance to a kind of superintelligence: AI models smart enough to develop smarter bots themselves, which would lead whichever country controls them to enter “escape velocity where your adversaries aren’t able to catch up,” David Lin, a senior director at the SCSP who previously covered China and technology at the CIA, told me. It is not clear that such an omniscient digital being can or will be built.

The White House has further muddled matters by taking a series of contradictory stances toward Chinese technology. It has, for example, revised its policy on exports to more easily allow the sale of advanced AI chips to China, while also going back and forth on whether the Pentagon and U.S. intelligence agencies can use the most advanced AI models of Anthropic. How seriously can anyone take the idea that the two nations are locked in a “hot Cold War II,” to quote Palantir chief technology officer Shyam Sankar, when the government is arguably enabling China’s own AI ambitions?

The technological race between the U.S. and China is so often discussed as a winner-takes-all competition. But such a narrow framing obscures what’s most at stake for Americans.

China is an obvious rival to the United States, as most former Biden-administration and current Trump-administration officials would agree. But that also makes “China” a convenient boogeyman for AI lobbyists and those with a clear financial interest in the industry, such as the former White House AI adviser David Sacks, who has said that “the AI race is even more important than the Space Race.”

[From the June 2026 issue: The venture-capital populist]

The two countries are not at war, but the most hawkish AI executives and national-security officials nonetheless fear that AI could confer what in military terms is called a “decisive strategic advantage.” Here is where the Cold War analogies come in. Such an advantage would entail the ability to land some sort of crushing blow that your opponent cannot recover from, “as if one country had nuclear weapons and no other country did, or maybe an even more extreme version of that,” Kyle Chan, a fellow at the Brookings Institution’s John L. Thornton China Center, told me.

Plenty of reasons exist to be cautious of this framing. For starters, to accept this argument, one has to assume that very soon, bots will exist that are smarter than the pooled expertise of entire nations. That’s total speculation, so the war-gaming can amount to “choose your own adventure,” R. David Edelman, an AI-policy researcher at MIT who previously served on President Obama’s National Security Council, told me.

Some pragmatic issues also exist. Software alone can’t win wars: The Pentagon is reportedly using AI models to speed up its target selection in Iran, but no matter how smart those bots are, America nonetheless is not exactly winning the war. Iran, for its part, seems to be hacking America’s water utilities—but those water systems are small and separate from one another, and have built-in physical redundancies. For similar reasons, a hack of a grid or bank would have serious but, in some ways, limited repercussions. These are all grave threats, to be sure, but they are not civilization-ending.

And translating any future ideas from AI into the physical world could take years. Even if AI cracked the code to create super soldiers, the military would have to figure out how to incubate and grow them, or else edit genes in adults without killing them. An AI system might say, Here’s how to defend against nukes, but that would leave the issue of building thousands or millions of missile interceptors on the table. In part for these reasons, no single AI-conferred advantage is likely to be large enough to permit any country to conquer the world. “It strains credibility that six months of advantage in AI could be the difference between the U.S. winning and losing a war with any adversary,” Edelman said.

In any case, the technology’s military capabilities might matter less than its effects on the economy. AI models are released as products. The top models from Chinese AI companies, including Moonshot AI, Z.ai, DeepSeek, and Alibaba, are by all accounts almost as good as and far cheaper to use than offerings from OpenAI, Anthropic, or Google. Simply put, that makes Chinese AI more economical for many companies looking to adopt the technology. Indeed, relative to the U.S., China’s government and businesses are “moving very fast,” Chan said.

If this is an economic race rather than a war, then it’s one that the United States could very well lose. “I don’t think I’ve ever heard any executives or researchers describe their work as part of a national contest against the United States,” Grace Shao, an analyst who covers AI and China and the author of the newsletter AI Proem, told me. Instead, “the more important race in China may be the race to diffuse AI into the real economy.”

China’s biggest advantage in the AI race comes from the physical world. This has little to do with AI itself: Decades of being a manufacturing powerhouse and implementing targeted industrial policy have made the country a leader in producing renewable-energy equipment, electric vehicles, robots, and other complex technologies. The country manufactures some 90 percent of all of the crucial components in solar panels. Last year, nearly 90 percent of humanoid robots sold were Chinese. Meanwhile, the U.S. is failing to meet AI’s electricity demands and has struggled to resupply missiles during the war in Iran.

[Tom Nichols: Iran, not Trump, is in control of this war]

“China has this strategy of exporting its technology globally,” Elluru said. Through its Belt and Road Initiative, China has helped build highways, ports, power plants, fiber-optic cables, and data centers throughout Asia, Africa, and Latin America; AI services are the logical next offering. The concern, for the U.S., is that “this technology sphere of influence congeals into political alignment” with Beijing, Daniel Remler, a senior fellow at the Center for a New American Security who previously led AI policy at the State Department, told me.

For instance, dependence on Chinese technology makes it more likely for a government to support Chinese efforts on the global stage and vote with China at the United Nations. Several nations benefiting from these investments have sided with China in its mission to claim Taiwan. China has been accused of using ports, e-commerce platforms, and telecommunications networks in other countries to hoover up data. Already the nation is using AI to enhance its domestic-surveillance apparatus and further crack down on dissent; the spread of Chinese data centers and AI agents abroad could only expand that authoritarian web. National-security analysts are concerned that, in the case of military conflict, China could threaten to shut off partner nations’ digital communications or power grids—including in countries with a large American-military presence. The greatest threat of Chinese “artificial general intelligence” is not some sudden death blow so much as a hastening of the ongoing decline of American hegemony—and the freedoms, at home and around the globe, that at its best the U.S. seeks to enshrine.

Yet the Trump administration and AI industry’s approach to competing with China has been a mess. The White House spokesperson Liz Huston told me that “the United States leads the world in AI innovation, and President Trump will keep it that way.” The administration’s current policies on AI chips, Chinese-made robot parts, advanced cybersecurity models, and open-weight AI are one big contradictory jumble. At a summit this spring, Defense Secretary Pete Hegseth stressed “rightful alarm regarding China’s historic military buildup” just weeks after President Trump visited Beijing and said the nations would have a “fantastic future together.” OpenAI, Anthropic, Nvidia, and the like can’t seem to agree on what they are lobbying for, either. All of which would seem like a great way to lose a technological race with China, any way you measure it.

There’s a certain déjà vu quality to the AI race. Amid fears over Sputnik and nuclear proliferation, a report prepared at the direction of Henry Kissinger in 1958 declared that “should we ever allow the U.S.S.R. and Communist China to attain strategic superiority, we can be certain that subsequent events will be brutal.” According to a government report on the early years of the Advanced Research Projects Agency, “fervent belief in unending scientific progress” offered a way forward. Those words could have been written today by Amodei, OpenAI CEO Sam Altman, or Secretary of State Marco Rubio. And indeed, this belief propelled America into many decades of technological advancement—in nuclear weapons, space exploration, computer systems, and more. Even back then, that agency, known today as DARPA, was quietly championing an emerging research area called artificial intelligence. J. C. R. Licklider, who championed the use of computer systems at ARPA in the early 1960s following the inspiration of Vannevar Bush, imagined that one day, machines that “will outdo the human brain” could make essentially all decisions of “military significance.” And ARPA developed the precursor to today’s internet.

Today’s AI race also traces back to the 2010s, when Silicon Valley began invoking China as a way to shape regulation. The idea of a technological arms race has “been wielded deliberately as a way to head off any attempts at regulating the tech sector” and, more recently, to champion “favorable industrial policy” for data-center construction, Sarah Myers West, a co–executive director of the AI Now Institute and former senior adviser on AI at the Federal Trade Commission, told me.

In 2018, while testifying before Congress, Mark Zuckerberg said that “if we make it too hard for American companies to innovate in areas like facial recognition, then we will lose to Chinese companies.” A few years later, a dozen former national-security officials wrote a letter to Congress arguing that “antitrust proposals that target specific American technology firms” risked “undermining America’s key advantage vis-à-vis China.” Today Silicon Valley is instead using the specter of China to angle for more and better AI, in more places, and faster.

American officials and tech executives are not wrong to want to compete. But the dominance of an oversimplified U.S.-China AI race, without clarity about who is racing and to where, doesn’t just mangle policy making; it obscures that the ultimate beneficiary of the AI boom ought to be citizens, not military contractors or Silicon Valley’s biggest corporations and investors. Rather than asking what kind of AI we’re building, Myers West said, one could ask, What kind of society are we building? The AI-accelerated spread of authoritarianism, within and outside of China’s borders, should be reason enough to worry. And if American AI executives and White House officials really cared about free-market competition, they might explore enforcing antitrust laws to separate the providers of AI models, data centers, and chips. If they were truly concerned about AI surveillance, they might look into finally passing some sort of federal digital-privacy legislation. And if they cared about spreading democratic AI abroad, they might take a close look at the state of democracy at home.