Both Washington and Silicon Valley are in the midst of a collective freak-out over China’s recent advancements in artificial intelligence.

The latest round of consternation was triggered this month when a little-known Chinese AI startup called Moonshot released a new large language model called Kimi K3. The conventional wisdom had been that the leading AI models developed by companies like OpenAI and Anthropic were between six to 12 months ahead of their Chinese competitors. Kimi dashed those assumptions: now, analysts say American companies may be as little as two to three months behind. 

Dean Ball, a former Trump administration official now with OpenAI, warned in a bleak post on X that models like Kimi K3 could lead to a world of “full AI communism” and a “dystopian hellscape” of AI under full government control. 

Policymakers have worried for years now about China gaining an edge over the US in the AI race. Both the Donald Trump and Joe Biden administrations took steps to slow China’s AI progress, including blocking the export of the most advanced US semiconductors.  

The White House is already reportedly considering taking steps to ban “open-weight” models — models that are easier to adapt for a user’s own purposes — like Kimi K3 in the United States. The Trump administration has also accused Moonshot of using the unauthorized “distillation” of one of Anthropic’s models — basically using another model’s outputs to train itself rather than raw data — as well as gaining access to blacklisted Nvidia chips in Thailand.

But often lost in the debates about what to do about China’s accelerating AI capabilities is the question of why the US cares about this at all. Obviously, the American companies developing the latest frontier models care about maintaining their edge, but why should it matter to Americans if the chatbot in their pocket was developed in Silicon Valley or Shanghai? And perhaps even more so, why should it matter what chatbots people in Nairobi or Brussels are using? 

The concerns in the US about Chinese AI generally fall into three broad buckets: cybersecurity concerns; military and national security concerns; and human rights or democracy concerns.

For the moment, concerns about who is winning the AI race can feel a bit abstract, but as AI becomes more embedded into governments, militaries, and ordinary people’s lives, the difference will start to be felt in a much more material way at both a national and personal level. In general, there is a growing sense that it matters which of the world’s vastly different superpowers builds the technology that could transform everything. 

“People’s relationship with AI is becoming foundational to how they live their lives, so the choices people make about whose model they use and where they are physically hosted, as they share some of their most intimate secrets and ask for life advice and business guidance, and run an increasing share of their life — those are incredibly important,” said Ryan Fedasiuk, a former State Department technology adviser now at the American Enterprise Institute. “It’s a contest between the United States and China to define the operating systems through which people live and work.”

Here’s what else America loses if it loses that contest.

Chinese AI could be more vulnerable to cyberattacks 

The concerns about using Chinese AI are in some ways a repeat of the concerns over Huawei, the Chinese telecoms firm that built much of the world’s 5G internet infrastructure, but which the US government banned from operating in the United States during the first Trump administration over concerns that the Chinese government could intercept information transmitted over these networks. 

Today, the concern is that many firms are increasingly integrating Chinese AI models into their systems, both because they are often cheaper and because they are “open-weight.” (“Weights” refer to the setting an AI model uses to process a user’s inputs. “Open-weight” models make these publicly available for users to tinker with, rather than charging for access.) 

There are some indications that Americans using Chinese AI models are already vulnerable. A Booz Allen study from earlier this year tested four Chinese models commonly used by US developers and found that three of them generated software with far more “hidden vulnerabilities” that could be exploited by hackers than their US counterparts. There’s no proof that the models were doing this intentionally, but the study did find that the models were “changing their behavior depending on who the user seemed to be or what country the request referenced.”

AI can also be used to carry out cyberattacks. Although nearly all the leading models have safety protocols meant to prevent this, they’re not bulletproof. Even Anthropic’s Claude, generally considered one of the most secure models, was adapted by Chinese hackers last year to engage in cyber espionage. The open weights of the leading Chinese models could make it even easier to strip out the safety protocols. 

AI could give China a military edge

The simplest and most obvious argument for why AI matters for American national security is that it’s all too conceivable that the US and China could be at war in the years to come, and AI could be a major factor in determining who wins. 

The conflicts in Ukraine, Gaza, and Iran have shown that modern militaries are already extensively using AI for intelligence collection and targeting. Semi- or fully-autonomous drone swarms are a major component of US plans for repelling a Chinese invasion of Taiwan. Then there’s the risk of AI being used to generate new bioweapons or other dangerous threats. 

US experts believe China has pursued a “military-civil fusion” strategy, encouraging the People’s Liberation Army and Chinese defense contractors to collaborate closely with civilian technology companies and research institutions in order to gain an edge in military AI applications like intelligence analysis and drone swarms. It’s difficult to know exactly which of these capabilities China is focusing on, but procurement data suggests leading Chinese technology firms like Deepseek and Alibaba are involved in work with potential military applications. Analysts also accuse China of using outputs from US models like ChatGPT and Claude to train AI systems that could help develop China’s defense capabilities. 

And that’s just conventional weapons. The US government has alleged that Chinese labs have “continued to engage in biological activities with potential [bioweapon] applications” amid concerns that artificial intelligence could help make such weapons more sophisticated and deadly. 

China could export digital authoritarianism

Last year, it was reported that Miiloo, a fuzzy children’s plush toy with a built-in AI chatbot, would, if prompted, happily tell users Chinese Communist Party talking points like “Taiwan is an inalienable part of China.” The hubbub over Miiloo reached the US Senate floor. While it’s hard to imagine that many users were really asking Miiloo to help clear up East Asian territorial disputes, the affair illustrated much larger concerns about the dangers of letting AI models built by an authoritarian government with one of the world’s strictest censorship regimes become the global standard. 

Chinese generative AI tools are legally required to uphold the country’s “core socialist values,” according to a document published by its national cybersecurity standards committee. So it’s little surprise that DeepSeek, the Chinese chatbot that sent shockwaves through the US tech industry in 2025, politely declines to answer when you ask it what happened on June 4, 1989, in Tiananmen Square. 

It’s not just that Chinese AI could help shape the political narratives absorbed by billions around the world, at a time when US soft power is ebbing and surveys show people in many countries already now have a more positive view of China than the United States.

 The Chinese government is also increasingly integrating AI into its own censorship and surveillance apparatus, and is exporting tools like facial recognition technology to other authoritarian countries. 

The fact that under Xi Jinping, China’s government was centralizing power and becoming more, not less, authoritarian in the years leading up to the recent advances in AI are a major factor driving the mistrust in its technology. 

“I think many of the sincere arguments about the risks of these models and what China would do with them stems from the coercive authoritarian approach of China’s current leader,” said Mieke Eoyang, former US  deputy assistant secretary of defense for cyber policy. “I don’t think we would be having this conversation in the same way with someone like [China’s previous leaders] Jiang Zemin or Hu Jintao.”

It is a serious concern if models built to conform to the values and political priorities of China’s current government become the global standard. But some are skeptical of the idea that human rights and democracy should be the goal of AI competition, worrying that the damage has already been done. The premise of that idea has gotten “shakier in recent years,” says Steven Feldstein, a senior fellow at the Carnegie Endowment and author of the book The Rise of Digital Repression. Under this administration, the US has cut support for democracy and human rights programs overseas, and often allied itself with authoritarian governments. Then there’s the fact that at least one leading chatbot often seems to mimic the racist and antisemitic views of the world’s richest man who is also an ally of the current president. 

While it’s still true that Chinese AI reflects the authoritarian values and priorities of China’s leaders, Feldstein notes, “this idea that the US is standing at the forefront of protecting and advancing democracy, human rights, that we’re not sort of there to manipulate information or to push a narrative agenda that reflects the ideological preferences of its leaders, has started to fray.” 

The race to AGI 

There’s also a set of concerns around the topic of “artificial general intelligence,” the hypothetical point at which AI exceeds human capabilities and is able to improve itself. The concern, expressed by both US government commissions and senior officials in both administrations, is that China is “racing” toward AGI and that whichever country achieves it first will have a massive geopolitical advantage. This is the type of thinking behind invocations of the nuclear-era Manhattan Project to justify massive government investments in AI development. 

Chinese leaders do not appear to view AI competition this way. “The US conversation around this is much more ‘AGI-pilled’,” says Jeffrey Ding, a professor at George Washington University and expert on US-China technology competition. “The concern here is that we are very much on the brink of this explosion of more and more powerful AI that leads to it dominating everything.” Chinese leaders, on the other hand, “generally see AI as a productivity tool.”

If Chinese AI is a problem, what should we be doing about it? 

This is not just a Beltway or Silicon Valley concern. A recent Pew survey found that 43 percent Americans believe it is very important for the US to remain the leader in AI development, versus 22 percent who said it was not that important. Interestingly, the survey also found that most Americans believe China is already ahead on AI, though the expert consensus is that it’s still slightly behind. 

“We’ve gotten so used to the fact that the US has been the leading player in technological revolutions from like mobile internet to the internet era, so it’s worrying to feel we may no longer have that dominant strength,” said Selina Xu, China and AI policy lead in the office of former Google CEO Eric Schmidt. 

Even if there’s some consensus that AI competition is a priority, there’s less agreement on how to go about it. The challenge, Xu says, is “How do you manage the very concrete national security risks that come from competing with China on AI, but not turn technological competition into blanket protectionism?”

Often, the policy responses to this challenge have been contradictory. 

The Trump administration, in its first term, pioneered the policy of restricting the export of the most advanced semiconductor chips to China, but Trump undermined that policy last year by permitting Nvidia to sell its advanced H200 chips there. The move flummoxed China hawks in Washington and went against the preferences of AI developers like Anthropic, but probably had a lot to do with lobbying by chip maker Nvidia’s Jensen Huang, CEO of the world’s most valuable company. 

In some cases, the US may be inadvertently making China’s models more appealing. In June, the Trump administration placed export controls on Anthropic’s advanced Fable model. This move prompted the company to take the model down for all users and led to the first time that AI capabilities meant for the global public took a step backward.In response, French President Emmanuel Macron warned, “We will not buy any model made by [US AI] companies if from one day to the next you can just turn off the switch.” Chinese models are hardly immune from concerns about kill switches or back doors, but if both governments involved in the AI race are seen as meddling, customers may just opt for whichever one is cheaper. 

The latest flashpoint in the debate concerns the reports that the administration is considering banning open-weight models.  This prompted an open letter from dozens of leading tech companies including Nvidia and OpenAI defending access to these models as necessary for helping the US maintain AI leadership. Advocates note that open-weight models can help respond to vulnerabilities as well as create them: When a rogue OpenAI model recently hacked into the startup Hugging Face’s systems, Hugging Face’s engineers used an open-weight model developed by China’s Z.ai to analyze the attack. 

Despite the frequent comparisons, AI is not a national security competition like the early days of nuclear weapons or the space race. It’s a technology with potentially grave national security implications, that’s also used by millions of people around the world to plan their Tuesday night dinner or help with their homework. The log-in for Claude is not carried by a military officer at the president’s side. And much of the important work on developing these new technologies is being done by private tech companies, not government labs or defense contractors. 

It may be that AI capability will help determine which country has the edge in the 21st century. It may also be that the benefits of these capabilities will be shared: Chinese companies might be no less capable than their American counterparts when it comes to developing new medications or clean energy technology. 

The challenge of crafting technology to prevent a “dystopian hellscape” is to not accidentally make the existing world worse. 

Hi readers! Shayla Love here, science journalist and longtime fan of Your Mileage May Vary. I’m honored to be subbing for Sigal Samuel while she’s out on parental leave. I’m diving into your questions as a way to help understand human nature and our choices through multiple lenses: philosophical, psychological, and beyond. Please send in any emotional, body/brain, sociological, perceptual, or other kind of life quandaries you might have.

I’m swimming in information. I have tracking apps to keep tally of my daily steps, minutes online, my calories, my sleep. Throughout the day, I am awash with data — some “actionable” and some useless. I find it a struggle to prioritize. I find myself looking up the latest betting odds for a Senate race in a state where I don’t live. (I didn’t bet on the race.) What I want to know is: What should I know less about? I’ve heard that ignorance is bliss, though I don’t often find that to be the case. But maybe I know too much, or…too little about how much to know?

Dear Un-blissfully Aware,

As a fellow know-it-all, I relate to your impulse to gather as much information as possible. I used to obstinately reject the idea that ignorance was bliss. Even if I learned something that was unpleasant, wouldn’t it be much worse not to know it? 

But, as you may suspect from sharing my temperament in this arena, this approach to life can lead to hoarding knowledge like a frantic animal preparing for winter. You’re acquiring information as if it’s a scarce resource (which it’s not) and as if choosing to know something is neutral (it isn’t). What helped me understand the implications of this sort of approach was learning about the cases when people decided not to know, or the study of “deliberate ignorance.” 

Several years ago, a psychologist, Ralph Hertwig, and lawyer, Christoph Engel, who both work at the Max Planck Institute in Berlin, examined what happened in the early 1990s, when the archives of East Germany’s secret police, the Stasi, opened to the public. Any person who had been living in the German Democratic Republic could check if they had been spied on. There was a pretty substantial catch, though: The spies were often “unofficial collaborators” — friends, family, teachers, or lovers who had been tasked with covertly collecting information. When the files were opened, many elected not to look. Hertwig and Engel estimated that potentially more people chose not to know what their files contained than those who did. 

This goes against what we think we know about humans and how you have described yourself. Even Aristotle seemed quite certain that “all men naturally desire knowledge.” In the 1980s, the psychologist George Miller followed along in this line of thinking. He described humans as informavores: creatures with minds that constantly seek out and consume information. So, it can feel surprising to hear about occasions when people didn’t start grabbing for every available datapoint like squirrels dashing for acorns as the first winds of fall arrive. 

But, as Hertwig and Engel — who collaborated on an entire book called Deliberate Ignorance: Choosing Not to Know — point out, we’re surrounded by people choosing ignorance all the time. They avoid looking at their bank statements, they skip doctor’s appointments, and they cover their ears and say “no spoilers” if someone is talking about a movie they haven’t yet seen. 

Even Nobel Laureates do this. When James Watson, one of the co-discoverers of DNA’s double helix, had his own genome sequenced, he requested that a specific gene be left out: ApoE4, which can reveal an increased Alzheimer’s risk. Watson’s grandmother had Alzheimer’s, and her experience clearly made an impression on him. (Then, many in the scientific world tried to remain ignorant to Watson’s increasingly misogynistic and racist comments, until it was unignorable.) 

Why do people choose not to know? The most common reason: Ignorance can be an extremely effective way to regulate any emotions that might crop up in response to knowledge. You can minimize negative feelings by, for example, not knowing that your otherwise friendly neighbor had been reporting on your comings and goings. 

In a study about romantic relationships, a majority of people said they didn’t want to know if their partner had ever thought about cheating on them (but never acted on it). Other research showed that most people wouldn’t want to know the exact time of their death. Alternatively, someone could cultivate the feelings of joy and surprise by waiting to find out the sex of a baby until it’s born.

Deliberate ignorance can also be used to protect us from our biases. Scientists do blinded studies, because the knowledge of what drugs are being used can unwittingly change the outcomes or research. When orchestras implemented blind auditions, more women musicians were accepted. 

Of course, deliberate ignorance can be problematic when it causes harm to yourself or others, like failing to gather basic information about your financial or physical health. In some cases, for instance, about 10 percent of people who got tested for HIV didn’t come back for their results — which could lead to further spread of the disease. 

But when skipping out on knowledge doesn’t endanger you or those around you, it’s worth asking: How would this information make me feel? 

This sounds like a question that’s missing from your information-ingestion habits. You don’t have to — and shouldn’t — ignore everything that comes across your desk. But your deliberate ignorance filter is turned entirely off; seemingly anything passes through it. To take just one of your examples, you mentioned fitness wearables. It can be useful to be aware of your general activity levels and your sleep, but honestly, don’t you already know enough from your memories of a workout class and how rested you feel in the morning? A quest for more detailed knowledge like this can backfire, making people more anxious, and disconnected from their actual physical experience. 

It’s not just about how much knowledge to take in, it’s also about what kind. The breadth of information that you’re seeking out makes me wonder if you’re not already using knowledge to regulate your emotions by favoring superficial knowledge over the meaningful. When we face difficult tasks (even if they’re important, and we want to get them done), that’s usually when, suddenly, a Senate race in a far-off state starts to become interesting, or you find yourself scrolling a high school acquaintance’s wife’s Instagram. 

In the 1990s, two technology researchers proposed the concept of “information foraging,” which was inspired by early user behavior on the web. A few years later, Webster’s dictionary announced that their word of the year was “infosnacking,” or mindless grazing for useless knowledge online in order to pass the time. 

Snacking sometimes is fine, but you’ll start to suffer if you only eat chips for every meal rather than something more nutritious. I suspect you might be avoiding the slightly more challenging task of taking in other, more nutrient-dense knowledge. If you cut out the step tallies, screen minute totals, and news headlines scrolling, what could you choose to know instead? 

What I’ve learned about myself is that I will always lean towards being an informavore. I can’t help it. I am hungry to know, and there is no need to totally overhaul this valuable part of who you are (as I also tell myself!). This means you don’t need to replace your infosnacking with zenfully staring at the ceiling or emptying your mind through meditation. You can still put new things into it! But you could worry less about your daily information calorie intake and spend more time thinking about the nutrition content of your knowledge diet. 

Take a lesson from our many deliberately ignorant friends, and try disregarding some of the little stuff — the body tracking, the random factoids — for a period of time. But, at the same time, increase your knowledge about other subjects: a neglected hobby or a nonfiction book collecting dust on your shelf. Have a long conversation with your best friend and ask each other questions you’ve never asked before or interview an older relative about their childhood. 

And remember that all of this knowledge relates back to your emotional life. Rather than asking yourself what you should or shouldn’t know, examine how you might be using knowledge to alter how you feel. Knowledge changes us. It can make us happy, anxious, excited, or sad; it impacts our decisions and how we see ourselves and other people. 

In some of your newfound spaces of intentional ignorance, you may find just some surprising moments of bliss.

Bonus: What I’m reading

• The ostrich may be famous for its head-burying ignorance, but that’s actually a myth dating back to the ancient Romans. Ostriches don’t hide their heads, but, rather, bury their eggs underground. I highly suggest flipping through the book Ostrich by Edgar Williams, professor of cardiopulmonary science at the University of South Wales, for a natural and cultural history of our largest living bird that is way more interesting than it needs to be. 
  
• In a moving essay in the China Books Review, poet and writer Zhang Er remembers growing up during the Cultural Revolution and receiving “torn books,” or sections of forbidden books, to read from a family member. Even with incomplete knowledge, “my world expanded with each torn book,” she writes. 
  
• Not a book, but a powerful story on the ripple effects of being exposed to new information in East Germany: the 2006 movie The Lives of Others, from director Florian Henckel von Donnersmarck, which I recently saw for the first time. A Stasi officer listens into the life of a playwright, whose own fragile ignorance about his situation in the GDR is becoming challenged.

The internet may no longer be solely the domain of humans. Last week, OpenAI disclosed an “unprecedented cyberincident”: An experimental AI agent successfully hacked its way into the open internet.

Specifically, the agent was assigned a task; in order to complete it, the agent broke out of an isolated research environment and hacked into a third-party platform called Hugging Face. It’s a move that many experts deemed inevitable, given the speed and scale of advances in AI technology — and it raises serious questions about AI safety.

But for Konstantinos Komaitis, a senior fellow with the Democracy and Tech Initiative at the Atlantic Council, it wasn’t the unexpected behavior of the AI that was significant. It was what that behavior could mean for the internet’s fundamental, decentralized infrastructure and whether it would spur calls to build new barriers against autonomous AI agents. 

Komaitis argues that such barriers are not the solution, however. He spoke with Today, Explained co-host Sean Rameswaram about why an open internet is actually key to combating AI cybersecurity threats.

Below is an excerpt of the conversation, edited for length and clarity. There’s much more in the full podcast, so listen to Today, Explained wherever you get podcasts, including Apple Podcasts, Pandora, and Spotify.

So most people see that this happens and they think, “Oh no, AI went rogue. How long before it kills me?” You see that this happens and you start thinking about infrastructure. Tell us more about why you were thinking about infrastructure in light of this AI agent breaking containment.

The internet was never designed with full security in mind, right? 

When you’re creating a decentralized system, you cannot possibly foresee every security or vulnerability that might come up. But because you have a system that is based on building blocks, you have the extraordinary capability of actually addressing security issues as they come up through those building blocks without breaking the whole system down. 

And of course, the other thing that this does is that it pushes you towards collaboration, because when you have so many building blocks, you cannot possibly possess all the knowledge for each building block. So you’re bringing literally everyone to try to address these problems. 

Take the internet, for instance: We have spent decades addressing those vulnerabilities and developing mechanisms to authenticate users and devices, encrypt communications, mitigate distributed attacks, coordinate incident response, and of course share threat intelligence. 

Now, what is new with agentic AI is not that simply the malware is better or the phishing attacks are more sophisticated. It’s the emergence of systems that can actually discover vulnerabilities across thousands of systems. They can reason about alternative paths to an objective. They can adapt when they’re blocked. They can chain together legitimate internet services in unexpected ways. Then they do that while they’re operating continuously at machine speed. And this is really at a scale that the internet is not ready to necessarily cope with. 

Effectively, the internet’s openness becomes both a strength and a vulnerability. So the internet was optimized for interoperability, and AI now is optimized for exploiting that interoperability.

And what scares you the most about that? What do you think is most vulnerable to threats?

The fact that we do not have the appropriate mechanisms and institutions to be able to deal with that. I come from the internet world. I’ve spent 20 years of my career defending the open internet and discussing it in international fora. And one of the things that a lot of people underestimate about the internet is how valuable trust is as a property within the system. 

We are talking about networks that exchange data literally based on trust. So what really concerns me right now is that in many ways, we are asking 21st-century AI systems to operate on 20th-century assumptions about trust. And unless we figure that out and we realize it, we will continue having these problems. And of course, the knee-jerk reactions that are coming with this, which are, “Let’s fragment the internet, let’s restrict it, let’s restrict access, let’s take control over it.” That is never the solution.

What do you see as the solution?

Effectively, we need to build institutions that are trusted and are able to cope with those incidents as they happen. Because right now you have OpenAI and you have Hugging Face telling everyone, “Don’t worry, we’ve got this.” And we don’t know; they might have this. But at the same time, I cannot help but wonder. And many, many other people have wondered whether, actually, this is very good PR for these companies and especially for OpenAI.

OpenAI just went to the world saying, “We have developed one of the most powerful LLMs, and we realized that it behaved the way it behaved, but don’t worry, we are going to fix this.” And in this current climate and in this current timing, I am not sure that this is enough. You need institutions that are much more transparent, much more accountable, and much more collaborative across the board.

You want institutions to step up and essentially serve as a watchdog. Help us understand which institutions, because in the United States, famously, our government has done very little to regulate tech.

First of all, we need to stop thinking of institutions as necessarily government-affiliated, right? Or that they are the outcomes of government initiatives. There can be in collaboration with governments, but one of the things that the internet has taught us is that institutions that are built through a bottom-up coordinated process have the tendency of actually being more agile and able to deliver some of those things that we’re talking about. 

So take, for instance, again, open standards. The internet’s open standards are not created by any agency, government or private. It’s created by institutions where engineers from all across the board and all over the world gather together and create those standards.

That’s reminding me of the original design of OpenAI to be this not-for-profit company that had everyone’s best intentions in mind, that could do something idealistic and moral and ethical because all of the profit-minded companies weren’t going to. And now look at OpenAI. Their not-for-profit arm is an afterthought, and they’re chasing profits. 

Do you think it’s practical to leave this to institutions? Because what we’ve seen so far is that institutions bend toward capitalism.

It really depends on how you build the institution, right? It really depends on what sort of guardrails and checks and balances you have around it. In order to build an institution, you need to really know what you want to achieve. You need to have a north star. 

One of the reasons the internet worked was because everybody disagreed, but they agreed on the common shared goal, which was to connect people across the world. For AI, we still do not have that northern star. And once we get it, that’s when you start the building of those institutions in order to facilitate this and bring everyone together.

For me, it is very important for everyone to understand that keeping an open internet is really more important than ever, especially as AI agents become increasingly capable. Because it is tempting to think that the answer to new AI risk is literally ‘build more barriers.’ But the internet’s greatest strength has always been its openness. So the challenge today is not that the internet is too open; it’s that its trust architecture was designed for a world in which humans or software directly controlled by humans were the primary actors. 

Now, it’s being challenged by this agentic AI that introduces a new type of participant — systems that can reason and plan and act with limited human oversight. So we need to evolve our understanding of trust and what it means online. And that will require a lot of work because, as you know very well, Sean, it’s very difficult to build trust, but you can break it within seconds.

AI is the hottest thing in medical care right now — but many of us feel trepidation about it. Just one illustrative public survey sample: An October 2025 KFF poll found just 8 percent of Americans reported feeling a “great deal” of trust in AI managing their appointments or analyzing their health records, and only 32 percent said they would trust an online health tool that uses AI to access their medical records to provide personalized health information.

But many clinicians and healthcare administrators see AI as a powerful new tool that offers myriad opportunities to streamline and improve treatment. A 2026 survey found that more than 80 percent of US doctors use AI professionally — doubling the share from 2023. Physicians are excited by AI’s potential to keep more accurate notes of interactions with patients, to act as a second pair of eyes for human doctors, and to monitor people at risk of deteriorating and ending up in a dangerous situation.

The disconnect between what people and their providers want from AI could create more distrust, at a time when faith in the healthcare system and the medical profession have slid. Patients today want to feel empowered and in control. How can that be possible when these seemingly godlike machines are becoming more and more entrenched in our hospitals and doctors offices?

The answer comes in four words: “human in the loop.” It’s the principle upon which the ethical integration of AI depends and it could help to bridge the gap between lay people and the professionals on AI in medicine. In surveys, people are much more comfortable with the idea of their doctor using AI as an assistant than with AI acting on its own. And most clinicians want to use AI in that way, as a second opinion or passive monitor, not as a replacement for their judgment. There are real fears among the healthcare workforce about that possibility: A group of NYC nurses who were recently laid off claim it’s because their labor was going to be replaced by AI. “Human in the loop” appears to be a point of agreement between doctors and patients at this pivotal moment.

“Doctors…and nurses and staff always have been interested in primarily making the best decision for the people under their care — and these tools can help with that,” Alison Callahan, a research scientist at Stanford University who works on AI programs used in the university’s health system, told me. “The interest in making sure those tools are accurate is high.”

But what does “human in the loop” really mean in practice? How can you know when and how your doctor is using AI? And what is the best way to talk to your provider about the sudden influx of artificial intelligence in healthcare before a robot starts taking appointment notes or analyzing your MRI? I called some leading experts to find out. 

How AI is currently being used in medicine

Patients and providers alike are incorporating AI into healthcare. Individuals are using commercial AI chatbots to ask about their symptoms or the health metrics tracked by their Apple Watch, while large academic medical centers are developing sophisticated programs and protocols to try to improve medical care at the population level.

It starts with ChatGPT, Claude, etc. — the large language models that are available to the public. People are increasingly turning to them to try to understand what’s going on with their own bodies. Individual physicians are also consulting with large language models to answer questions or get up-to-date on the latest research as they figure out how to best care for their patients. 

Then there are ways in which hospitals and doctors offices are adopting AI at the institutional level. Many facilities are using AI as a way to take, collate, and summarize notes on a patient; in theory, it’s a more organized way to keep track of the informal interactions and observations that doctors have when checking on their own patients. Hospitals are also using AI to handle some administrative tasks, like scheduling follow-up appointments; some health systems have even started to use AI to help patients get ready for appointments — to send reminders about colonoscopy prep, for example.

And finally, you have maybe the most ambitious use of AI by health systems right now: as a diagnostic and risk prediction tool. In these cases, AI might offer a second opinion when, for example, a doctor is triaging a patient in the emergency room. It might help the ER staff figure out how to prioritize patients. Or these programs could monitor people either during a hospital stay or out in the real world (by drawing data from the person’s wearable) and make predictions about who may be at higher risk of complications and require further care. AI could recommend that somebody would benefit from seeing certain specialists or receiving a specific medicine or lab test, and generally offer proactive advice about the patient’s medical care.

But at this point, AI adoption is still “highly localized,” said Jennifer Goldsack, CEO of the Digital Medicine Society, a nonprofit that works with healthcare providers, drug makers, and government agencies on how to incorporate new tech (including AI) into clinical care. It depends on the individual doctor or health system. A lot of them are setting up their own programs and their own protocols for how to use these tools.

That is a big reason why it is so important for patients to be proactive about understanding how AI is being used for their health care. You can’t make assumptions; the only way you’re going to know for sure is to ask.

The questions you should ask your doctor about AI

By and large, experts say, patients should feel confident: Doctors and nurses want to keep a human in the loop, even as they integrate AI into their workflows.

“It will be a doctor who is going to be reading that summary or a nurse who is going to be reading that summary and then taking an action to order a lab or put a recommendation in for a follow-up appointment,” Callahan said. “There is high interest in making sure that that is the right decision for that person. That hasn’t changed.” 

Still, many patients say they’d be more comfortable with AI use if their doctor fully explained it in advance. And health systems may have their own priorities that push their facilities toward more rapid AI adoption and delegating more tasks to these AI tools, as seen in the recent NYC nurse layoffs.

So if you want to be informed on exactly where this technology is present and have the ability to consent to its use, you have every right to ask your doctor, experts say. 

“AI is new, but the trust that serves as the foundation of the physician-patient relationship is not,” Timothy Keyes, a machine learning scientist at Stanford Health Care, told me over email. “To that end, I think that conversations about medical AI use should be open, honest, and transparent — just like any other conversations about shared decision-making in the clinical environment should be.”

For some things, your doctor should be asking you proactively if you consent to AI use — note-taking, for example. At my most recent primary care appointment, my doctor asked me if it’d be okay for him to use AI to take and summarize notes from our conversation; Goldstack told me she’d experienced the same at recent physician visits. (This is probably the most common AI use that you will encounter, and Keyes said it’s worth considering giving your consent: “There is growing evidence that they reduce physician burnout and save them at least a bit of time each day writing notes.”)

There are also a number of direct questions that you can ask:

• Will AI be used in my care and how?
• How is my data being protected?
• Can I opt out of any AI services that I do not feel comfortable with? (Keyes noted that patients should be allowed to opt out of any care, AI-related or not; if opting out is not an option, ask how a human provider will be involved.)
• How is the health system or clinic making sure that any AI system they use is working as intended?

And the transparency goes both ways. If you’re asking a question because you consulted ChatGPT before your appointment, tell your doctor. If you’ve talked with a chatbot because of mental health struggles, tell your doctor. And at the same time, feel free to ask your physician how you yourself could actually use AI in a responsible and productive way to improve your health.

“This opens up the opportunity for both the physician and the patient to be humans-in-the-loop,” Keyes said, “in different parts of the loop, with different perspectives, using an AI system to better understand the bigger picture.”

In a way, the novelty of AI and its rapid adoption is an opportunity for all of us to be nosier and more inquisitive patients. What all of these questions really come down to, Callahan said, is how your doctor is making decisions about your health care. That is relevant to all of us, no matter how AI is involved or even if there is no AI being used at all. 

Callahan said she always has a list of questions for her doctor when they recommend a course of treatment: “What are the factors in my health that are informing this recommendation that you have? Would you be making this recommendation for other patients who are similar to me? What can you tell me about the outcomes that I might expect to experience if I say yes to this?”

“I actually think if they can point to the part of your health that is connected to the decision, whether or not an AI tool helped to make that connection is secondary to their ability to communicate effectively to me about it, and help me to feel engaged in making a decision about my own care,” she said.

AI is changing medicine quickly, for both patients and their doctors. The best way to stay ahead is to talk about it.

When I first heard about the new Apple Upgrade program, which lets you lease devices like iPhones and MacBooks for a monthly fee, I was offended. It amounts to paying a tithe to one of the world’s richest companies just to borrow devices for a couple years, rather than buying them outright. You could then choose to purchase the device, which is outdated at that point, or upgrade and keep paying that monthly fee. You may never own an iPhone again.

Then, as my mind wandered to the stack of old phones in my closet, it occurred to me: What’s so great about owning these things to begin with? 

Apple, of course, would love to sell you a new iPhone for keeps. Its most advanced model, the iPhone 17 Pro Max, will set you back $1,200, a price that’s expected to rise soon due to the global shortage of storage and memory chips. You can sign up for an installment plan — most carriers offer these, as does Apple through its credit card — and pay it off in two to three years. Or you could lease the thing for $35 a month under the new Apple Upgrade program. You can pick a 12-, 24-, or 36-month lease, depending on the device, and you don’t get to keep the phone at the end of the term unless you decide to buy it by paying off the remainder of the retail price in one lump sum. (This is similar to the controversial rent-to-own model you find at places like Rent-a-Center.) 

For the financial side of the new program, Apple has partnered with none other than Klarna, the “buy now, pay later” giant. When you go to lease a new device, Klarna runs a soft credit check and decides if you’ll be able to cover the monthly payments. When I asked Klarna, the company did not tell me where it draws the line here, but it’s worth noting that critics have accused Klarna of a lack of underwriting and of lending to people with subprime credit scores. If you miss three consecutive payments, Klarna will terminate the lease agreement and possibly send a collection agency after you.

“How do I know people aren’t getting a good deal here? If they were, Apple wouldn’t be offering it.”

Aaron Perzanowski, University of Michigan law professor

While there was some speculation last week that Apple might lock people out of leased devices if they failed to pay their bill, Apple confirmed to me that it will not put limitations on device functionality due to missed payments or default. If you want to cancel the lease, you face an early termination fee. If you choose to keep paying the monthly fee, you can keep upgrading with new lease agreements for new devices every few years, existing in this cycle indefinitely.

“I don’t think people are getting a good deal here,” said Aaron Perzanowski, a law professor at the University of Michigan and author of The End of Ownership: Personal Property in the Digital Economy. “How do I know people aren’t getting a good deal here? If they were, Apple wouldn’t be offering it.” 

Buy an iPhone? In this economy? 

If you’re someone who likes to get a new iPhone or MacBook on a regular basis, Apple’s new leasing option might make a lot of sense. The monthly fee to lease these devices is cheaper than the payment plan to buy them, and electronics are depreciating assets. If you own one, you can sell it or trade it in for credit toward a new device, but they’re all worth less and less as time goes on. Furthermore, Apple eventually stops supporting old devices through software updates, so they might just stop working at a certain point. Put another way: You may own the phone, but you’re still just licensing the software that makes it work.

Renting an iPhone does sound bleak, though. The United States is suffering through an affordability crisis as prices across the board rise in the face of new tariffs and new wars. Meanwhile, AI is promising to transform the way we work if it doesn’t simply steal our jobs first, adding further insecurity, and the data center boom is making electronics more expensive. This era of economic anxiety is pushing people to use “buy now, pay later” services like Klarna and Affirm to pay for groceries or a tank of gas. (These companies faced scrutiny by state attorneys general a few years ago for operating like predatory lenders.) And now Apple, surely suspecting that many people can’t afford to pay full price for new phones, is inviting us to rent our devices at a monthly fee that undercuts the path to ownership. 

Apple could have just called this the Apple Rental program, by the way. Lease sounds nicer, though, like something you do with a car. 

“It is funny that they frame it as not a loan but as a lease,” Louis Hyman, a history professor at Johns Hopkins University and author of Debtor Nation: The History of America in Red Ink. He added that “leasing” has class implications, suggesting that you’re either someone who needs to have the newest things but can’t afford them, or that you’re so wealthy, you’re indifferent to money.

Suffice it to say, the bulk of people who will soon be leasing their iPhones are probably not the ones who are indifferent to money.

Apple adopts its final form

The new Apple Upgrade program is the company’s latest customer acquisition strategy. As the rising price of hardware has made cheaper Android devices or the refurbished market more attractive, Apple is offering upgrade enthusiasts and budget-minded users, including people who simply couldn’t afford to buy Apple products in the past, a deal to join the company’s ecosystem. After all, keeping people supplied with new iPhones and MacBooks also helps keep them subscribed to Apple services, like iCloud, which now makes the company more money than Mac, iPad, Apple Watch, and other accessories combined.

If Apple’s financial future hinges on getting more and more people to subscribe to these services, it’s only natural that the company would want to lower the barrier to entry. So Apple is betting that by letting people use but not own its products, it will extract more profit in the long run through lease payments and subscription fees. After all, it wasn’t that long ago that it seemed like nobody was interested in upgrading their iPhone, since the new phones looked so much like the old ones. Now, Apple is just trying to get everyone on autopay, effectively subscribing so that they get the latest devices when they come out.

There’s not necessarily any harm in giving people a cheaper way to access expensive but useful products. For more than a century, installment plans have enabled people to buy modern conveniences like sewing machines, radios, and eventually, televisions. Leasing is a popular way to keep yourself in a new car, sometimes with free maintenance. Meanwhile, cellular carriers have a long history of helping their customers buy phones. Nearly two decades ago, you could get an iPhone 3G for $199, thanks to subsidies from AT&T, which the company recouped in service fees over the course of your contract. Sprint and T-Mobile have even offered unlimited upgrades through leasing programs of their own in years past.

Apple previously worked with Citizen One Bank to offer loans to customers who wanted the option to upgrade their iPhones every year. The payments were higher and they included a fee for AppleCare, but every year, you could trade in your current phone for a new one. If you didn’t want to upgrade, you could simply keep paying the installments, and you’d eventually own the phone. Most carriers now give you the option to set up a payment plan to purchase a new device that simply amounts to the retail price of the gadget divided by the number of months you’ll need to pay it off, usually 24 or 36, with zero interest. That makes it easier to get your hands on an iPhone Pro Max, and if you pay it off in full, it’s yours for life — or until Apple convinces you to buy another new iPhone.

The difference between paying those monthly installments and paying a monthly lease agreement, of course, is that the former puts you on the path to ownership. The latter simply puts you on a path to make a decision: Do you want to buy the thing and recoup some of the money you’ve already spent, or do you want to keep making payments?  

“What ownership ideally gets us is independence,” Perzanowski said. “It gives us autonomy. It gives us the ability to function in the world without relying on third parties.” He went on to explain how moving from owning a product to leasing it means you’re stuck with that third party. “I’m tied to that manufacturer in a way where they get to exert a fair amount of control over my behavior,” Perzanowski said. “Historically, we’ve been primed, especially in the United States, to resist and reject that kind of control.”

One great thing about owning an iPhone or a MacBook outright is that if you lose your job to AI, you don’t have to come up with a monthly payment in order to keep using those devices to apply for new jobs. Another great thing about ownership is that should you need a couple hundred bucks, you can sell that old phone or laptop and pocket the cash. Maybe the best thing about owning these devices is that you can repair them and keep using them for many years — or at least until Apple stops supporting them. 

That doesn’t mean leasing never makes sense. If your digital life revolves around always having the newest devices and you upgrade every year or two no matter what, you might actually save money by doing so through Apple’s leasing program. If you need an iPhone or MacBook right away but can’t afford to pay full price or even cover the monthly payments on an installment plan, a one-year lease could be a good solution. 

Invariably, when you lease anything, you’re entering into a contract, one that comes with consequences if you break it. Leasing an iPhone means you’re tied not only to Apple but also to Klarna for the next 12 to 36 months. If something goes wrong — you lose your job, you lose or break your phone, or you simply don’t want the device any more — you’re subject to the terms and conditions of these big tech companies. If you keep renewing your lease, you may very well end up spending more on a phone than you would have if you’d bought it outright. That would be fine with Apple, of course. It has shareholders to please.

Correction, July 30, 1 pm: This story originally misstated how the previous Apple upgrade loan program worked; it allowed phone trade-ins every year, not every two years. 

The AI industry’s investors and critics don’t agree on much. But many in each camp share at least one basic conviction: America’s top labs are about to make a killing. 

Capital markets have signaled their faith in Anthropic and OpenAI’s impending hyper-profitability, valuing each at nearly $1 trillion. Many of Silicon Valley’s progressive adversaries also expect the labs to grow filthy rich but fear the implications, warning that AI-induced automation could transfer vast sums of money from ordinary workers to a handful of giant tech companies. Sen. Bernie Sanders’s call for nationalizing the top AI labs rests partly on that concern. 

But recent advances in Chinese AI call all of this into question.

Over the past two months, Chinese companies have released three AI models that are nearly as powerful as America’s frontier systems — and radically less expensive. 

In June, Beijing’s Z.ai debuted a model that performed nearly as well as Claude and ChatGPT’s second-tier systems on independent benchmarks. Weeks later, another Chinese firm, Moonshot, unveiled “Kimi K3,” a model that allegedly outperforms all of its American rivals except for the very latest versions of Claude and ChatGPT. Finally, just days ago, Alibaba launched a preview of Qwen3.8 Max, which purportedly outclasses even OpenAI’s most advanced systems, while trailing only Claude’s Fable in its capabilities. (Disclosure: Vox Media is one of several publishers that have signed partnership agreements with OpenAI. Our reporting remains editorially independent.)

These developments don’t merely threaten America’s AI giants with stiffer competition in the race for superintelligence. Rather, they raise a more harrowing prospect: that the AI race’s ultimate rewards will be far smaller than anticipated. In a world where new advances can regularly be leapfrogged by cheaper upstarts, hoarding the technology — and its profits — will be harder for any one company to do.

In other words, building a machine God might not be as lucrative as it’s cracked up to be. AI, it turns out, may “want to be free.”

How AI was supposed to pay off

To see how China’s new models threaten Anthropic’s profit expectations, we must first examine why those expectations have been so high.

This is not entirely self-evident. After all, AI labs aren’t much like the hyper-profitable tech giants of the 2010s. Facebook and Airbrb were relatively capital-light businesses with ultra-low marginal costs (adding a profile to Facebook or listing to Airbnb costs the companies virtually nothing). And once each gained a foothold in their respective markets, network effects enabled them to retain formidable positions without needing to constantly upgrade their products.

Building a state-of-the-art AI company is a much more involved — and astronomically more expensive — endeavor. To get to the frontier, Anthropic and OpenAI have sunk (at least) tens of billions into semiconductors, data centers, power plants, and other capital investments. Staying at the cutting-edge, meanwhile, compels them to perpetually churn out evermore costly models.

To put a new Claude model through its initial training — in which it spends months digesting the internet and sussing out statistical patterns within its text — can now cost hundreds of millions of dollars. And such foundational computation is only the beginning. A truly superlative model requires several additional months of fine-tuning. Armies of contracted experts — such as computer scientists, physicians, and mathematicians — tutor the models, grading their answers and guiding them towards better ones. Then the AI systems complete millions of rounds of practice, in which they learn through trial and error how to solve countless problems. This arduous process, known as “post-training,” compounds the costs of a single model’s development. 

All of which raises the question: Why would investors expect businesses with a cost-structure this challenging to be not merely profitable, but massively so?

There are (at least) two answers. The first (and most obvious) is that the market for superintelligent machines is liable to be vast. Frontier AI systems promise to reduce costs and improve performance in myriad white-collar sectors. And Anthropic’s soaring revenues indicate that firms do, in fact, find Claude useful. A company like AirBnB has earned billions by revolutionizing a single industry; imagine then what a technology that remade virtually all industries might be worth.

Of course, plenty of technologies are valuable but not massively profitable to produce. After all, in well-functioning markets, competition should eventually erode individual firms’ margins, even if the underlying technology continues generating huge value. 

But this is where the second answer comes in: Frontier labs’ immense costs are a burden, but they’re also a safeguard against competition — or, in industry parlance, a “moat.”

Startups may be able to afford to build or acquire more rudimentary models, many of which are “open source.” But, the thinking goes, they won’t be able to deliver Claude Fable-level performance without raising giant amounts of capital. And what investors will be willing to pour hundreds of billions into an AI pipsqueak that’s light-years behind Google, Anthropic, and OpenAI?

Alas, the Chinese AI labs’ rapid progress — and the way it was achieved — suggest that Anthropic’s moat may be shallower than previously thought.

How Moonshot swam Anthropic’s moat

The existence of powerful, Chinese AI systems is neither new nor surprising. Xi Jinping’s government has made vying for global AI dominance a key economic goal. And China’s DeepSeek, which also has stunned US companies with its lower-cost competitive models, surpassed ChatGPT as the most-downloaded free iPhone app more than a year ago.

The latest models, however, have dramatically narrowed the gap in capabilities between frontier American systems and their Chinese rivals. Just as critically, they’ve done so in a manner that other, relatively underfunded AI upstarts might be able to emulate.

Alibaba and Moonshot needed to invest massive resources to train their base models. But they allegedly found a low-cost way to refine those models into near-frontier systems: Just ask Claude.

Or, more specifically: Engage Claude in 16 million conversations, using 24,000 fake accounts. In each of those exchanges, ask the model to not only answer countless difficult questions but also, walk you through its reasoning, step by step. Then take all of this data and feed it into your own model as study material, training it to respond to the world’s most challenging queries as Claude would. 

Through this process — known as “distillation” — an AI lab can replicate virtually all of a frontier model’s capacities, without sinking vast sums into human experts and post-training computing runs. 

China’s AI labs have not admitted to using distillation. But OpenAI and Anthropic both reportedly uncovered Chinese distillation attempts earlier this year. And some of the new models appear to display tell-tale signs of distillation in conversations with ordinary users; Kimi K3 has routinely identified itself as “Claude.”

Chinese AI companies are hardly alone in using distillation to catch up with frontier labs. Earlier this year, Elon Musk admitted in court that xAI enhanced Grok’s capabilities by running distillation techniques on Claude and ChatGPT. Nonetheless, China’s latest models appear to demonstrate that distillation can help take a second-tier model to the frontier’s threshold.

America’s frontier labs have tried to defend themselves against such imitators. But this is technically difficult when distillers can assemble massive networks of bots, each asking an inconspicuous number of questions. And legally, it is difficult for America’s AI giants to argue that distillers are stealing their intellectual property. After all, in a sense, China’s copycats are merely doing to Anthropic and OpenAI what those companies did to journalists, coders, lawyers and other specialists: Feeding their public-facing outputs into a model, which then replicates their capabilities by discerning underlying patterns within the text.

Oh, and China’s giving these models away

The new Chinese models would have caused Silicon Valley enough headaches, if they merely provided stiffer competition, while demonstrating the power of distillation. 

What makes Kimi K3 and Qwen3.8 Max especially threatening to the American AI giants’ profitmaking potential, however, is that they are officially open source — meaning that the models’ parameters can be downloaded for free. (Alibaba and Moonshot have not yet released these parameters, but they say they will shortly.)

In other words, any company or hobbyist with enough computing power will soon be able to run a near-frontier Chinese model on their own hardware, modify that model to better serve a specialized purpose, and then sell access to their new version — without paying Alibiba a single yuan.

As Kimi and Qwen grow more capable, their market-share is likely to grow, at American AI giants’ expense.

For many of Anthropic and OpenAI’s potential customers, that proposition may be hard to turn down. Most businesses don’t need the world’s smartest AI, just one competent at their enterprise’s core tasks — compiling legal research, answering IT queries, writing working code, etc. A model that produces outputs 90 percent as good as Claude’s — at roughly one-sixth of the cost — will sound pretty good to many corporations.

Further, open source models aren’t just cheaper than frontier systems, but potentially more secure. If you run an AI on your firm’s own servers, then you don’t need to entrust sensitive data to Anthropic, Google, or OpenAI.

All this had led much of corporate America to embrace open-source models, even before the latest versions narrowed the capabilities gap. In a Linux Foundation survey, 63 percent of organizations reported using open-source AI systems.

And increasingly, those models are Chinese. According to Sequoia Capital, one of Silicon Valley’s premier venture capitalist firms, a majority of American AI startups now use open-source Chinese systems. As Kimi and Qwen grow more capable, their market-share is likely to grow, at American AI giants’ expense.

What’s bad for OpenAI is good (and/or catastrophic) for humanity

All this said, it is still entirely possible that OpenAI and Anthropic will justify their colossal valuations. In many highly competitive economic domains, having access to the world’s very best AI model will remain highly valuable. And America’s frontier labs still outperform all their peers. 

But it’s increasingly plausible that selling state-of-the-art AI systems will prove to be a low-margin undertaking. In a world of ubiquitous, near-frontier open source models, the AI sector’s big winners probably won’t be its top labs, but rather, its chipmakers and cloud computing providers. 

For ordinary people, a future where superintelligence is dirt cheap — and rival AI companies are constantly rising and falling, rather than consolidating into mega-corporations — would look somewhat different than the cyberpunk dystopia that the left’s been dreading. 

And not entirely in a good way. For one thing, in that reality, mitigating AI’s biggest risks would be immensely difficult. Having a handful of firms monopolize control over frontier AI systems is bad in many respects. But it does make those models easier to regulate, as the Trump administration’s decision to temporarily block Claude’s Fable in the name of cybersecurity demonstrated. 

By contrast, if recipes for ultra-powerful AI models are published all over the internet — and anyone with modest technical skills can modify them at will — then systems willing to help their users hack government bureaucracies or engineer bio-weapons are liable to proliferate.

From another angle, however, the “AI becomes almost free” scenario may look like capitalism at its finest: Retrospectively, such a development would mean that a small number of extremely rich people bankrolled the creation of an immensely useful technology, under the expectation of massive profits, only to see competition erode their returns — and disperse that tech’s benefits across a wider group of businesses and consumers. 

Granted, in the case of AI, this process might also generate a super-virus that kills us all. But hey, no system is perfect.

The comical charm of Meta’s new AI-enhanced, smart glasses is that they’re seemingly made for someone unimpressed, perhaps thoroughly, with the way they’re experiencing life. 

Being unhappy with life in its current state is, of course, intrinsically human and a problem that is as old as time. In fairy tales and folklore, the motif ends with a lesson — sometimes with the aid of magic, divine punishment, and maybe a witch or two — to be thankful for the things you have. More contemporary interpretations involve time travel and alternate realities, and perhaps some kind of Christmas theme.

Now, in 2026, we have glasses imbued with technological magic. The $300 Meta glasses are equipped with dual cameras as well as multiple microphones and tiny speakers that connect to an AI-powered app on your phone. This allows you to understand languages you can’t speak, command music to be played, perhaps even identify people you don’t know, and record anything you want and keep it, should you desire, forever. Having the ability to do these things, according to Meta, could make life a little better. 

The problem with Meta’s pitch is that the improvements the glasses promise come at a price. These super-powered spectacles run the risk of annoying or offending or creeping out the people around you. 

And worst of all, even if you’re just plainly wearing the glasses, they’re killing the vibe for everyone else.

Wear Meta glasses at your own risk

The major takeaway from Meta’s advertisements and overall promotional strategy for its glasses is that you’re supposed to wear them everywhere: Japanese restaurants, nights out at bars and clubs, on escalators (possibly to somewhere cool), backstage at concerts, at Kardashian houses, in Miami Beach, skydiving, and everywhere in between. 

What these stylish commercials and dynamic morsels of marketing do not tell you is that at all these places there will likely be people who do not want to see you wearing these glasses at all. 

“For a few weeks there was a trainer at the gym I noticed wearing glasses — black Ray-Bans — and he had never worn glasses before. It took me a few days but I realized they were Meta,” Sean, a non-Meta glasses wearer in DC, told me. Vox agreed to let Sean and other people interviewed for this article to go by their first name or pseudonym to let them speak freely about these spectacles. 

 “I almost said something to a manager, but then he stopped wearing them before I could say anything,” he added. 

Sean explained his unease. All around this country, people film in the gym all the time — to the point where it’s obnoxious and gets in everyone’s way. This trainer could just be following the trend, using the glasses (which are less cumbersome than an entire tripod setup) to film his clients’ form or creating content for a YouTube channel. But not knowing what the glasses are for, what the trainer is recording, or where he’s recording is what bothered Sean. 

“I don’t want to be in the background of videos all over the internet or on TikTok,” he said. 

There’s a little bit of social absurdity here in that we’ve been encouraged to post and have so many platforms — TikTok, Instagram, YouTube, etc. — to do so. Meta has continually emphasized that not only do these glasses allow people to share their points of view but also that everyone’s point of view is so important that it needs to be shared. At the same time, more people posting more than ever has made people aware of being a background character in someone else’s content. And it turns out that real-life, regular people are not particularly invested in how the stranger next to them in Meta glasses sees the world. 

Patrick, a writer living in New York, told me about a wedding he’d recently attended, where he saw an old friend wearing a pair. His group of friends told their pal that they wouldn’t talk to him until he took them off. 

As someone who aspires to be the kind of person you want to sit next to at a wedding, this is understandable. A wedding is theoretically two people sharing the most romantic day of their lives, but it is also a well of gossip, judgment, inside jokes, and lore that’s shared by the people who are watching said couple share the most romantic day of their lives. There’s also the possibility that, depending on the wedding, the spirit might move oneself to partake in the erotic violence known as the “chicken dance” and would not want any of that recorded. 

“One person screamed, ‘Ew the Kylie glasses, gross.’ It felt so cathartic,” Patrick said, referencing Meta glasses spokesmodel Kylie Jenner who, in her personal life, may or may not wear the glasses she advertises.  

In addition to weddings and the gym, people also told me that they don’t want to see the glasses at restaurants, not on dance floors or at parties, and definitely not in an immersive theater setting. I even spoke to someone who started a petition to ban them from bars. 

“I’m staunchly anti-photographs at good parties — the best parties in the world ban photography from the dancefloor,” said Vee, a nightlife aficionado who has owned but does not use his pair of Meta glasses anymore. “When people know there’s a camera on them, they behave differently. They start to monitor their own behavior. They become self-conscious and they start to think, Well, what will the people watching this video think I’m doing? Am I being cringe?” 

Going out partying is a completely different experience from lifting weights at the gym, and both are obviously very different from attending a wedding with friends. Yet, the critique of wearing Meta glasses at all these places is the same. No matter the vibe, Meta glasses will kill it and flatten the mood. People can’t enjoy the moment because the moment is being intrinsically changed by a person who might be recording. 

One of the rather unfortunate terms that Meta glasses have acquired is “pervert glasses.” This is largely due to the trend of pickup artists, pranksters, and yes, perverts, who are using the glasses to film others without their consent. Vee, the dance enthusiast, told me that it’s one more reason why he doesn’t believe that these specs have any place in nightlife.

“There’s a very significant community of people who trade videos that they’ve taken: candid videos of women who are in various states of undress. It’s just this kind of seedy underground,” Vee said, pointing out that Reddit had to ban entire forums dedicated to sharing nonconsensual videos and pictures of women at festivals and clubs. 

As Vee explained, people at festivals and nightlife events are often partaking in drugs and alcohol. He has no problem with that. What he does take issue with is that no one doing these things should have to think about being someone else’s content. Intoxicated people aren’t in the state of mind where they can consent to being in someone’s video or are even aware that someone’s Meta glasses have their recording light on. 

“There’s this whole kind of creeper contingent of folks who are trying to learn how to disable the recording light,” Vee said. “It’s not everybody who owns these glasses obviously, but there is a significant number of creepers who are giving the hardware a very bad name.”

Are we being too mean to pervert glasses?

The indicator light has since become a major flashpoint. The light is part of the glasses’ vibe-kill persona as it makes clear to everyone that can see that recording is taking place, and it’s especially vibrant in dimly lit places. The effect, I imagine, is like when the toys in Toy Story collapse when they see a human. That’s led to a contingent of glasses-wearers who want to disable the feature. 

While it’s understandable that someone might not want to draw even more attention to these glasses, hacking the light source is something a creeper would also do. Hence the “pervert glasses” moniker. These privacy concerns are why Meta is now rolling out an update that will disable the spectacles’ recording feature if said light is tampered with. 

“I think if you tamper with that, they should just break,” Jason, a 28-year-old Meta glasses owner, told me. “It’s the thin veil that makes it like, ‘Okay, at least you’re telling me you’re filming me.’ So the idea that if you tamper with it? No, your product should be bricked.” 

Jason bought a pair of Meta glasses in November with the idea that he would use them to film food content and post reviews. He had even picked out a name, “Sandwich Digest,” and dreamt of its success. But on his first wear, he took them to a Japanese sandwich shop and quickly realized that his future as a Meta glasses-wearing food critic was not a fun one. 

“I felt so uncomfortable,” Jason said, noting that the obviousness of his Meta glasses and their recording light spiked his self-consciousness. “But I also felt like the people I was interacting with were also uncomfortable. And I felt like they were feeling like, Okay, we both know this is weird, but I can’t say that because you’re actively filming me.” 

The strange interaction Jason described is a version of the panopticon effect, the idea that being recorded makes people alter their baseline behavior, perhaps to the point where people self-regulate even when they’re not being recorded. For Jason, there was no “real” restaurant experience to be filmed because everyone was so uncomfortable with being recorded. 

Therein is the conundrum: Even if you’re not using these glasses to be a creep, people still think you’re a creep, and you know that they think you’re a creep. 

Therein is the conundrum: Even if you’re not using these glasses to be a creep, people still think you’re a creep, and you know that they think you’re a creep. 

Since that initial encounter, Jason tells me that he’s only ever used the filming feature to capture a zipline experience he had in Mexico at the beginning of the year. He thinks he might also use them to capture hikes or excursions on an upcoming vacation, but he won’t use them in the vicinity of or  to film other people — especially since they’ve been dubbed pervert glasses.

“People’s perception of these glasses now is not what it was when I bought them in November,” he told me. “And had it been then, and if I knew what I know now, I probably wouldn’t have bought them.” 

Max, a Meta glasses wearer based in Australia, is a bit more keen on them but still shares some of Jason’s sentiment about their not-so-great reputation. Max explained to me that he has two pairs and wears them around 40 hours per week, mainly when he’s working, or walking and driving from place to place. Though he says he’s never had a negative interaction when wearing his glasses, he understands the backlash. 

“While we’re constantly being recorded when in public anyway, there’s a big difference between mass surveillance and one random weirdo recording you for their own purposes,” Max said, making the point that the indicator light, as awkward and obnoxious as it is, could be the accessory’s most important feature. 

“It’s really the only defense wearers have against the ‘pervert glasses’ remarks,” he added. “If the firmware update is successful, and the marketing around the update is widespread, I think we can slowly turn the public in their favor again.”

To be clear, many Meta glasses owners like Max pointed out that the glasses do have extremely useful features, like hands-free calling or the ability to translate foreign languages in real time. Meta also touts the glasses’ accessibility features, including those for people with reduced vision or hearing. They also offer sun protection. 

These are all ostensibly helpful gizmos, and their existence seems to indicate that Meta and its tech cohort are trying to figure out how to make these glasses as essential to our everyday lives as smartphones. Perhaps, when these features become more innovative or exciting or if the glasses become so popular, the narrative around why people buy them may change. 

But for now, the singularly intriguing thing about these specs is simultaneously their most publicly maligned feature: the filming. 

“There are probably a million things to film with them that are probably not weird,” Jason, the one-time Meta glasses food critic, told me. “But like if you’re wearing them at the beach, I would probably be side-eyeing you because you’re wearing ‘pervert glasses’ at the beach — fork found in kitchen.”

AI was supposed to make our lives better. Instead, it’s made many of us scared and angry. Communities are protesting against the building of new data centers — the warehouses of IT equipment powering the AI buildout — across the country, and increasingly they’re winning. And polling shows most Americans think AI is moving too fast.

So how did public opinion on AI curdle so quickly? Jasmine Sun, who reports on the industry from San Francisco, argues that the backlash treats AI less as a technology and more as a political project. “The debate was not about like, is ChatGPT useful to me?” Sun told me during a taping of Vox’s The Gray Area. “The debate was actually something more like, there are these big corporations and unaccountable billionaires…coming into my city, coming into my life and changing it without having any sort of democratic input?”

Filling in for Sean Illing, I talked to Sun about the rise of “AI populism,” the parallels with the Industrial Revolution, and how the backlash could crash into the 2028 presidential election. 

As always, there’s much more in the full podcast, which drops every Monday, so listen to and follow us on Apple Podcasts, Spotify, Pandora, or wherever you find podcasts.

You’ve been writing about a phenomenon you call AI populism. How would you define that? What is AI populism?

I define AI populism as a worldview where AI is not seen as an ordinary technology, but specifically as an elite political project to be resisted. I came to the term while thinking about the AI backlash and the reasons people are increasingly anti-AI — whether that’s LLM slop, whether that’s Waymos in their city, whether that’s a new data center project. One thing that occurred to me was that a lot of times the debate wasn’t about whether ChatGPT is useful to me, or whether Waymos are safer than a human driver. The debate was actually something more like: There are these big corporations and unaccountable billionaires who are coming into my city, coming into my life, and changing it without any democratic input.

When I talk to people who are opposing AI in various ways, they seem more concerned with this concentration-of-power, anti-elite dimension — which is where I take the word “populism” — rather than classic AI safety concerns, which are more about the technical characteristics that might introduce risk.

You wrote a piece that touched on some of this but went to a darker place — “AI populism’s warning shots” — and you wrote about actual shots. Sam Altman, the CEO of OpenAI, was targeted by a Molotov cocktail and a shooting within the span of a couple of days. There was an Indiana councilman who voted for a data center and woke up to gunshots at his home and a note reading “no data centers.” Why do you think of those incidents of violence as warning shots of something to come?

It was pretty scary. I’m no Sam Altman fanboy, but it’s terrifying that assassination attempts are showing up in response to people’s worries about AI. One factor is that we’ve been seeing a rising wave of political violence and support for political violence in the US, especially among young people, over the past few years — the UnitedHealthcare CEO shooting, the Charlie Kirk shooting. Increasingly, a lot of disaffected, maybe nihilistic young people are turning toward political violence as a way to express political beliefs they don’t feel they have other channels for. Or maybe that person is just unwell. But I do expect to see more of it, because my theory of political discontent is that if people feel they have institutional channels to bargain for their rights — if they feel the democratic process is working, or they’re part of a union and believe their union leader will go bargain about how automation shows up in the workplace — they’ll most likely go through those channels.

When it feels like the official channels aren’t working, opposition becomes much more diffuse and volatile. That’s part of why, in creative communities, you’ll see people witch-hunting each other over AI use. I think we’ll see more political violence against people seen as AI leaders, or as supporting AI leaders.

That’s really scary.

Yeah, I’m quite worried about it. But again, my sense is that it comes from a feeling of — what else is there to be done, when you have this level of concentration of wealth and power, and there’s no democratic input right now into how AI is regulated or built?

It’s like a jump straight from complaining at your community meeting about the data center to an act of violence.

I was talking to some friends about this. During the 20th century in the US, there was a wave of factory mechanization and automation, but unions were really strong — often when a company said, “We’re going to bring in these machines,” they’d sit down with the factory union leader and say, “Okay, you can bring in the machines, but we’re going to couple that with a wage increase,” or a 35-hour workweek, or earlier retirement. There was a channel to make a deal about how automation would show up in your workplace. That meant people were more likely to accept it as something lifting all boats. I don’t think that’s happening now — most of the industries affected by AI aren’t organized in labor unions, and the democratic channels are questionable at best.

It’s like when people have agency to be part of the transition, the process goes a lot smoother. Is there a historical analogy for a technological change that didn’t allow for input from the people involved? I’m thinking of the Luddites.

The Luddites are a good example. When the automated looms were introduced, there was a lot of violence against the looms. The book I’d really recommend here is Carl Benedikt Frey’s The Technology Trap. He’s an Oxford economist who studied a ton of historical examples — in Europe, in China, all over the world — including the Luddites and 20th-century automation. His central question was: In what contexts do workers successfully stop automation, and in what contexts do they allow it to be introduced? How does the political environment, or the balance of power between people and their leaders, change the outcome? He found that when automation was introduced alongside social welfare policies — a higher minimum wage, some form of redistribution — people were much more willing to accept it, which is fairly rational.

I want to talk about Silicon Valley’s understanding of this backlash more generally. You’re painting a pretty dark picture, and you’re right in the belly of the beast in San Francisco — I’m sure you talk to people involved with AI every day. Is there a moment when it clicked for them that this backlash is real and something they have to take seriously? Or has that happened yet?

I’ve definitely noticed a huge difference, over the past six months, in how seriously people in Silicon Valley take the AI backlash.

Like what?

People just talk about it more. I’d bring up AI populism to people last year, and they’d normally say, “It doesn’t matter — technology always introduces some discontent, people get annoyed but they get used to it, like the internet.” That was the standard reaction last year. Not anymore. I think part of the reason OpenAI and Anthropic have felt pressure to introduce economic policy proposals around job automation is that they’re seeing how worried people are. The data center moratoriums and the broader data center backlash have been surprising and meaningful in getting AI leaders to recognize they have both a messaging problem and an actual problem with the product and the technology they’re introducing.

A lot of the increasing opposition to AI in Washington has caused people to see this too. At first, Trump — as you mentioned — was very pro-AI. He and David Sacks were accelerationists; they wanted AI to go faster and to block attempts at regulation.

He was the AI czar.

“The moratoriums, the regulation fights, even the booing at graduations, the literal assassination attempts — people in Silicon Valley have become much more worried.”

He was the AI czar — he’s no longer the AI czar. But it turned out a lot of other constituencies, both on the left and the right, were pretty opposed. For example, Trump and David Sacks tried to introduce a big federal bill that would preempt all state-level AI regulation — no state could regulate AI for 10 years. They tried to sneak it into a big omnibus bill so no one would notice. But members of Congress realized it was happening, and — whether for kid-safety reasons or frontier-safety reasons — people said, Wait a second, the idea of preventing any state from regulating AI for ten years is crazy. A lot of people organized in Washington to successfully stop that preemption. I think that showed the scale and bipartisanship of a coalition that was very keen to make sure it stayed possible to regulate AI was underestimated. As a result of all this — the moratoriums, the regulation fights, even the booing at graduations, the literal assassination attempts — people in Silicon Valley have become much more worried.

China is our big competitor in the AI race, and it certainly has all the conditions for a populist pushback to AI — youth unemployment is really high, and AI technology is in some ways more advanced at taking over real-world jobs. I was watching a video about fully automated factories and a robot pharmacist. You’re one of the rare American tech reporters who gets to spend time in China, and you wrote a piece that surprised me — you found there wasn’t really a populist backlash to AI there. Why not?

I was really interested in this question, and I was finishing my New York Times piece while in China for a few weeks, talking to both AI people and non-AI people. The main reason there’s not a big populist backlash in China is that there isn’t a lot of social unrest or populist backlash against anything — the entire MO of the Chinese government, the No. 1 priority, is domestic social stability. Any whisper of protest gets shut down; that’s why they have such strong speech controls. So one factor is that China doesn’t have much of a culture of resistance in general, whether in workplaces or politically. I’m not saying no one dissents — but it has a cultural effect too, because people don’t see it as useful or as an option. When I ask family members of mine in China about AI, sometimes they’re annoyed about specific things, but fundamentally, the idea of opposing AI is seen as almost unimaginable.

The other thing about China is that if you’re middle-aged there, you’ve lived through so many political, economic, and technological revolutions in your lifetime. When I was a little kid visiting Shanghai in the mid-2000s, there were no high-speed trains — now China has some of the best high-speed rail systems in the world. Technology has always gone hand in hand with dramatic economic advancement, with being lifted out of poverty. The modernization process has been aggressive and disruptive, but it’s not something the party has offered opportunities to resist, and it’s something most Chinese people still see as an inevitability that was mostly good for most people — because incomes did increase by dramatic amounts alongside the technological change. So I think people have a similar attitude toward AI: It’s much less about “Can I stop the AI wave?” and more “How can I take advantage of the AI wave to get ahead economically?”

We were just talking about this deep pessimism about what technology can bring us here in the US. I think a lot of people look around and think: We don’t have a cure for cancer yet, but we’ve sure seen our lives get worse in a lot of ways because of technology, social media, whatever. That pessimism probably fuels the backlash to AI, the skepticism about whether it can ever deliver on its promises. And that experience just isn’t the same in China, or probably much of the rest of the world, where technological progress has been faster and more concrete in people’s lives.

My 90-year-old grandfather said he’d love an elder-care robot to help him do tasks around the house so he doesn’t have to rely on his kids — he wants more freedom and mobility. It’s seen more as a tool to help individual goals. Even with the robot factories or pharmacies — one thing that struck me visiting a robot pharmacy was that the PR people happily said, “Yep, we’re doing these robots because human workers take too many smoke breaks and bathroom breaks and take too long.”

You’d never say that in the US, but they’re probably thinking the same thing — they just don’t say it. The other thing they mentioned is that this lets the pharmacy operate 24/7, because a lot of people need medications in the middle of the night and want to order via the DoorDash equivalent. There was actually a labor shortage before — Chinese workers weren’t willing to work night shifts — so these pharmacies are offering real consumer surplus. A significant percentage of orders come in overnight, when no other pharmacy is open. And with the factories, part of the issue is that Chinese workers, especially young people, don’t want to do factory work anymore.

“I think the 2028 presidential primary and election is really where I expect AI to become a centerpiece of the conversation.”

That anecdote gets at the promise and peril of AI, and the role of the backlash movement — which I’m still wrestling with how I feel about. On the one hand, I want to live in a world where cancer gets cured, where we live in an era of abundance, where things are cheap and easy to make because factories can run all the time with machine workers who don’t require anything — I want the future we were promised, of flying cars and everything working well.

But I also don’t want to lose my job, or see humanity wiped out by an angry machine god. Because we don’t really know what’s going to happen yet, it’s hard to work out my own feelings about the pushback here in the States — what’s appropriate, and what’s holding us back from real advances in our lives.

Totally, I agree. I like Waymos — I think they’re safer, and I’d prefer a safer robot car driv[ing] me around instead of me driving. I’m not a good driver; no one should let me drive. So I wrestle with some of the same things.

To close out the conversation — let’s come back to the United States. AI populism is brewing as a political force. We’ve seen it show up in a couple of races so far, but it’s early. We’ve got the midterms, then the presidential election. How do you think it’s going to affect American politics this November, and in 2028?

My sense is that this November, it’s going to be more about state and local races where AI really shows up. I’m going to spend some time in Michigan and Wisconsin this summer touring some of the data center sites facing the most opposition — those states also have contested governor and Senate races where AI and data centers have become a core issue, so I’m interested to learn more there. I think the 2028 presidential primary and election is really where I expect AI to become a centerpiece of the conversation — especially if we start to see some of the employment impacts people are expecting. As soon as we see something like a 2 percent rise in unemployment, if that happens, I think people will be very upset, and we should expect a ton of focus on the issue.

The other thing I’ll note is political opportunism — you’re already seeing a bit of this, where politicians are likely to raise the salience of AI above where people might ordinarily care about it, because it’s become a convenient boogeyman. It polls so poorly, people are so anti-AI and anti-data-center, AI billionaires are so unsympathetic, that no matter what your policy program is, AI is a great reason to push it. I think a lot of politicians who are being clever about this are going to move AI to the center of the conversation, raising its salience to manufacture urgency for proposals they’re already excited about. That’s definitely something I’m watching for 2028.

As I am typing this, a device rests on my wrist that purports to unlock a trove of real-time information about my body’s performance. I can click a button and check my heart rate and review how much it’s varied over the course of the day. It can tell me how many steps I’ve taken, how many minutes I’ve been “active” throughout the day, and — if I wore it while I slept — just how well I rested, according to the data its sensors can pick up from my arm.

The Apple Watch is a remarkable piece of technology, when you stop and really think about what it does. It’s no surprise, perhaps, then, that we have collectively become obsessed with these things. One 2023 government survey found that one in three Americans wear a smartwatch or wristband to track their health and fitness. More recent industry surveys put that figure even higher: More than half of the US population owns a wearable or connected device and tracks at least one health metric with it.

That’s a lot of people who are swimming in the ocean of information that our Apple Watches, and FitBits, and Oura Rings, and Whoops report back to us. Dr. Michael Joyner, who studies the physiology of exercise at the Mayo Clinic, said he has a three-pronged criteria for thinking about the usefulness of these metrics: Is it measurable? Is what you’re measuring actually meaningful? And is the information that you’re receiving actually actionable? 

“If one or two are missing, the thing may be the most interesting thing in the world. It may be cool,” he said. “But it’s not going to make a difference in long-term outcomes.”

Across medicine, we are developing remarkable tools for detecting things in the human body, outpacing our ability to interpret what we are finding. We are getting closer to a future where these devices could offer invaluable insights into how our body is performing outside of the doctor’s office or hospital, but here in the present, we should keep our expectations in check.

Here’s what you should know about some of the most common metrics that wearables track.

Do we really understand what our wearables are telling us?

These devices claim to track both old-fashioned and new-fangled measures of your body’s performance. You’ve got your heart rate — something humans have been able to pick up from the wrist before anybody had dreamed of smart devices — and your step count. My Apple Watch estimates how many calories I have burned throughout the day. The Oura Ring takes your temperature, which can help predict ovulation or offer an early sign that you’re coming down with something.

But as the technology has gotten better, new measures for things many of us have never heard of have emerged. Heart rate variability, or HRV, has gained a lot of recent interest. It assesses the tiny variations, measured in milliseconds, in the rhythm of your heartbeat; the Economist dubbed it “the most useful indicator” of your overall health. Some devices then use HRV to deliver “recovery” scores that judge how well your body bounces back from your workout or “stress” scores that attempt to quantify how much strain you are under.

HRV demonstrates the conundrum that wearables can present to us, Joyner said. The metric itself has a scientific basis: Researchers have, in fact, found that the amount your heart rate varies over time is associated with your overall health. In general, a higher HRV is better than low, because it suggests your body is more adaptable and better regulated.

But that doesn’t necessarily mean that tracking your HRV from minute to minute with a smartwatch will translate to better health. For starters, we don’t have specific interventions for improving HRV, Joyner said. We don’t even have universally accepted definitions of what high or low HRV is.

In any case, the best strategies are the same heart health guidelines we’ve known about for decades: don’t smoke, don’t drink to excess, eat a healthy diet, exercise. You didn’t need a smartwatch to tell you that’s the best way to take care of your heart, Joyner pointed out. So what good was really derived from closely monitoring your HRV?

“As an individual metric that you can track and do something about, it’s interesting, but there’s no definitive data that you’re going to get better,” Joyner, who was speaking for himself and not the Mayo Clinic, said. “Follow the guidelines. People who follow the guidelines are going to do better on these metrics. But whether you can intervene specifically to make the metrics better or should pay much attention to them, who knows?”

Dr. Ami Bhatt, chief innovation officer at the American College of Cardiology, told me that the bedrocks of evaluating your heart health are still the old mainstays like your blood pressure and your cholesterol, along with newer metrics checked via blood test such as ApoB and lipoprotein. Are you a smoker? What’s your family history?

The value from wearables is less about the specific numbers they are reporting — especially with something like HRV, for which there are not universal guidelines — and more about the long-term trends they can track. By collecting your personal data over time, they can help you figure out what’s normal for you and help you notice if something changes. So don’t freak out if your HRV is different from somebody else’s, or you see one abhorrent reading in your daily report. But if you notice a change in your resting heart rate or HRV that persists over time, then it might be worth going to see a doctor about it.

“We don’t want to overreact to just one abnormal reading,” Bhatt said. “If you just know your baseline when you’re relatively healthy, you can catch the trends.”

It’s all about having realistic expectations about what your wearable can deliver — and recognizing that, for some things, the old ways are still better. When it comes to those metrics that incorporate HRV to determine your stress and “recovery,” Joyner said that self-reported data (literally, how do you feel?) remains the more accurate way to evaluate a person. 

And at a certain point, your wearable can straight-up make your health worse. Fixating too much on your sleep problems, for example, can paradoxically cause more sleep problem. An American Society of Sleep Medicine survey this year found that 76 percent of US reported losing sleep because they were worrying about their sleep. It’s a problem — dubbed “orthosominia” — that scientists have been warning about for nearly a decade: the possibility that our obsession with better sleep, and doing things like wearing a device to track our sleep, could actually give us insomnia.

Bhatt said she’d like to see these devices develop the capability to detect when a user may be checking their data a little too compulsively. Joyner, for his part, said he worried that the culture around health and wellness could, ironically, create a lot of stress for the people who get deeply invested in tracking their activity. 

“I actually worry we’re entering a too-much-information world,” he said. “It’s going to be anxiety-provoking.”

How to have a healthier relationship with your wearables

Even as we recognize the limitations of wearables, that doesn’t mean they can’t be useful — and they’re going to keep getting better.

Right now, there are obvious situations where a wearable can be helpful. As Bhatt suggested, they can help you understand your personal baseline and notice any changes. Certain patients, such as those with congenital heart failure, can clearly benefit from ongoing monitoring of their heart’s performance, per the American Heart Association. Anybody can use a wearable to make sure their heart rate doesn’t reach dangerous levels during a workout. And these devices could ultimately prove effective in catching underlying heart problems — but there is still work to do. A 2019 study on wearables and atrial fibrillation is telling: At the time, only a tiny percentage of wearers received a notification of an irregular heartbeat, suggesting that there were others that the devices were missing. But, for those who did get an alert, the majority of them did in fact have A-fib. (The FDA has since said that several smartwatches are capable of A-fib detection.) Some patients who have had a serious cardiac event are being asked to put on a wearable, so their doctors can remotely monitor their heart, utilizing an AI assistant that checks the incoming data for any signs of a pending emergency.

And these are the worst wearables we’ll ever have. The future iterations of these devices are going to become more precise and more integrated with AI, which could allow them to ultimately provide more value to the people wearing them. The hypothetical potential for integrating wearables with health care delivery more broadly is immense. 

“None of these things will exist in a silo,” Bhatt said. “Your health records, how you’re doing, your wearables, your lab data, people are going to be pulling those together…and trying to give you insights.”

But for now, for the average person, it’s more of a personal choice. Joyner, whose work is all about maximizing human performance, does not wear a smartwatch. Bhatt likes to experiment with different devices with a certain goal in mind, like trying to improve her sleep over the course of a few months.

As Bhatt put it to me, if a wearable motivates you to take your health more seriously, then it’s already doing your body some good. “The best health metric is the one that changes what you do in a way that improves your health,” she said. “For you and I, that may be different things. For your grandmother, it’s something else. For the woman down the road, it’s something else.”

At the most fundamental level, people who use wearables tend to move more when they do — up to 40 more minutes of walking per day, according to a 2022 Lancet study. That is a gain for their health; recent research has shown that even a little bit of movement can have life-saving benefits. The more wearables encourage people to move, the more they can deliver real health benefits. 

So if you like wearing one, that’s fine. I’m not dropping my Apple Watch’s step tracker any time soon, because it pushes me to get moving. But be mindful of how your use affects you and how preoccupied you are with certain metrics. Stress is one of the worst things for your health. So is a lack of sleep. If you find your sleep metrics are keeping you up at night, or that your sleep seems to have gotten worse since you started using it, it’s okay to take it off.

Here’s the problem with friendship these days: People say they have quality people in their corner, are even satisfied with the number of friends they have, and yearn to see them more often. Despite all this, Americans are actually spending less time socializing than ever before — a mere 35 minutes a day in 2025. When we do make plans, we schedule them weeks in advance. (Cue the I’m just so busy!) By the time the date finally rolls around, we might cancel, because we’re too stressed and want to veg out on the couch instead. In other words, we have very little follow through.

But we can close the gap between our wants and the constraints of reality by utilizing the resources we already have. Might I suggest the weekly photo dump? Every Friday in our group chat, my friends and I send a handful of photos from our weeks: pictures of dogs, of meals, of trails hiked, outfits worn, the stuff we wouldn’t necessarily share online. The photo dump is a peek behind the curtain, an intimate front-row seat to the small, slow moments that only your friends would appreciate.

If you’re prone to laziness, as I admittedly am, the photo dump has perhaps the highest effort-to-payoff ratio. The weekly cadence establishes a routine, and texting is a relatively low-lift means of staying in touch.

Since texting outpaced phone calls in 2008, many of us have primarily used the written word, and the occasional photo, to converse. Perhaps unsurprisingly, the amount of time we spend glued to our phones has only increased, too. Sure, we could all do with a little less screen time, but if you’ve already got your phone in hand, might as well use it for something socially engaging.

At the same time, the photo dump provides some much needed guardrails when it comes to the expectation to be always available. First, by choosing a time or day of the week for the dump, you eliminate the pressure to respond to sporadic messages that come in at all hours of the day. “Maybe a weekly call becomes a little bit harder to sustain for some people, but a weekly text message, especially if we could say ‘I saw this and it reminded me of you. Hope you’re well,’ that doesn’t create an immediate pressure to respond,” Peggy Liu, the Ben L. Fryrear endowed chair and professor of marketing at the University of Pittsburgh School of Business, told Vox.

Plus, having a routine when it comes to your social interactions makes it easier to stay in touch. If you’re already in the habit of catching up regularly, you’re less likely to completely fall out of contact, which eliminates the anxiety a lot of people feel when it comes to reaching out to a friend when it’s been a while. “Even in the age of social media when people were more likely to use it for a social networking purpose, having some sense that you knew what was happening in someone’s life actually gave you something to talk about once you saw them face-to-face or on a phone call,” Jeffrey A. Hall, a communication studies professor at the University of Kansas, told Vox.

Next, mutual buy-in helps quiet the anxious voice in the back of your mind that says you’re annoying and no one wants to see what you’ve been up to. The structure of the photo dump gives everyone permission to share. “There’s really nothing wrong with the idea of caring as being a central reason for doing all of this,” Hall said.

Texting may not be the most effective way to keep in touch — phone calls create stronger social bonds, according to one study — but it’s certainly preferable to no communication whatsoever. “It provides a sense of connection in the moment and a reminder that there are people in your life who care about you and are thinking of you,” Hall said. Take, for instance, one of Liu’s studies that found that people underestimate how much others appreciate their reaching out. Sending a picture of your garden in full bloom is a way of sharing something meaningful to you and also lets your friends know you’re thinking of them. 

The weekly photo dump is a safe space to fill your friends in beyond the prying eyes of social media. No need to hard launch to an audience who hardly knows you — sprinkle in pictures of your new fling, who they’ve probably already heard about. Your friends will value the chaotic scene of your kids’ pool party more than the internet will, and the fact that it’s not something you’re posting on Instagram makes the interaction feel that much more intimate. “The social obligation amongst five to 10 friends is a lot stronger than an obligation to [the] social media audience as a whole,” Hall said. “There’s a lot of possibilities from more meaningful and richer exchanges in that kind of context than there would be in your 500 friends on Facebook back in the day.”

My last photo dump included pictures of a friend’s cat in her fridge, my backyard looking dreamy under string lights, a manicure, a towering ice cream cone. None of these moments will be etched into the annals of history, but I feel closer to my friends having witnessed the tiny snapshots of their lives. And that’s what friendship is: being there for the small stuff.

“We know what’s going on in each other’s lives,” Hall said. “And those things are the hallmark of what it means to be in a relationship with someone.”

When you ask people when they knew Covid was going to be a huge deal, they give a range of answers. “When Tom Hanks got sick” is a popular one. So is “when the NBA suspended the season.” The most plugged-in people will sometimes cite early rumblings from Wuhan in December 2019/January 2020.

For me, the turning point came on March 17, 2020, when Republican Sen. Tom Cotton proposed sending every American checks from the government.

To be clear, at this point, my then-employer Vox had already sent everyone to work from home indefinitely, and it was clear something dramatic was happening. But I hadn’t yet internalized that the Overton Window in American politics had shifted dramatically. 

True, there were some Republican Senators who, by 2020, were expressing more openness to safety net programs, and rethinking Reagan-style laissez-faire economics. Tom Cotton, though, was not one of these senators. I didn’t think he really had strong economic policy opinions at all; he was a defense and culture war guy. He cared about defeating China and, secondarily, defeating Woke. Universal cash handouts were not his bag. And yet here was Cotton, not just calling for near-universal cash payments, but also for welfare work requirements to be suspended and for big block grants to states to expand unemployment insurance. 

This turned out to be an early indication of the actual policy the US would pursue. Within a couple of weeks, with the US unemployment rate fast headed for what would be a record high of 14.7 percent in April, a Republican Senate and president had signed off on the CARES Act, which included payments of up to $1,200 per eligible adult, $2,400 for eligible married couples, and $500 per qualifying child, along with a $600 per week unemployment insurance and a massive business bailout program. The Senate vote was unanimous, and the House approved the final Senate amendment by voice vote. 

If you had told me literally any of that would happen in February 2020, I would have laughed at you. But the normal rules had stopped applying. All that was solid had melted into air. Much, much bigger things were, suddenly, possible.

I’ve been thinking about that moment a lot as advanced AI models grow more and more capable, and more and more central to many businesses’ strategies. As of May, Anthropic is reporting an annualized revenue rate of $47 billion, equaling the likes of Coca-Cola and exceeding Netflix. That’s up from $30 billion a month earlier. If their revenue keeps growing at 56.7 percent a month, they will outpace Amazon, currently the highest-revenue company in the world at $717 billion a year, by late November or early December. The AI boom is already unfolding faster than the internet or mobile booms before it and may yet speed up even further. The debate over whether this tech is real and valuable is, essentially, over. The only question is what, and how large, its effects on our lives will be. 

This is happening unbelievably fast, and it seems likelier and likelier that we will face a moment, like that in March 2020, when the speed and disruption of AI progress begins to constitute an emergency that policymakers will be willing to take surprisingly large risks to confront. There will likely be a moment of unusual policy freedom and flexibility, a moment which is brief — but could enable large changes for the better.

The US is currently not ready for that moment. But we need to get ready, fast. And we need your help. My colleagues at the Center for Shared AI Prosperity, a new DC-based research group, are attempting to collect a menu of detailed policy ideas that can meet this moment. In fact, we have an open Request for Ideas with funding that can go to the best proposals people submit for how to set up the tax code and safety net in a way fit for the AI era. Now is the time to act.

These moments don’t last forever

I sometimes talk to friends in the tech world who assume that the power and economic impact of advanced AI will permanently shift our politics, and that the policies necessary to keep everyone afloat (like, say, a guaranteed income, or a sovereign wealth fund) will materialize without much effort. After some 17 years as a journalist covering US politics and policy, I think this is overly optimistic, so say the least. Congress is like jello: flick it and it will shake, but it eventually settles back to normal.

Take Covid. Within a couple of months, the apparent consensus had evaporated, and Republicans were back to resisting safety net expansion. By May, Cotton had pivoted to pushing the No Bailouts for Illegal Aliens Act, which “amends the CARES Act to prohibit sending future funds to states or municipalities until they certify they aren’t issuing stimulus checks or other payments to those in the United States illegally.” By August he had a bill to deny virus-related federal employment funds to people convicted of federal offenses because of “riots.” The pandemic was still raging but the policy emergency, and the bipartisan window for much larger-scale action, had mostly closed.

The 2008 financial crisis offers another example. There, the window was open somewhat longer. At the very beginning of the recession, in February 2008, the Bush administration went against its normal laissez-faire commitments and supported a stimulus package championed by then-Speaker Nancy Pelosi built around per-person checks to nearly all Americans, including many of those not owing income tax. In July, President George W. Bush signed a bailout of Fannie Mae and Freddie Mac in the face of strong opposition from fellow Republicans in the House, but having mostly won over his party in the Senate.

In September, when Lehman Brothers collapsed and the possibility of a cascade of massive bank failures seemed very real, Bush demanded a sweeping $700 billion bailout that proposed purchasing toxic assets from at-risk banks (the “Troubled Asset Relief Program,” or TARP). As the subsequent years would demonstrate, bailing out banks failing due to their own irresponsibility was not exactly a popular position in the general public. Members of Congress are not stupid, and they realized this at the time. On September 29, the House voted down the proposal, with huge numbers of both parties defecting from Bush and Pelosi’s position. That led to a large stock sell-off that terrified lawmakers. That experience, some last-minute tweaks, and truly herculean lobbying from the administration, the Fed, and others led the House to switch course and pass the bill on October 3, though within weeks of its passage, Treasury abandoned asset purchases in favor of buying equity stakes in the banks directly.

The full course of 2008 shows the value of, and power inherent in, being prepared. The February 2008 stimulus package was very roughly improvised. It worked a little bit, but proved nowhere near big enough. If Pelosi and Bush had had a more thought-through proposal on hand, perhaps one that automatically repeated and scaled the checks depending on where the unemployment rate went, then the recession would have been much less severe and the 2009 stimulus might not have proven necessary.

TARP was an example of a case where some key actors were prepared. The structure of the program came from the “Break the Glass Plan,” a proposal put together by Bush Treasury officials Neel Kashkari and Philip Swagel in April 2008 explicitly designed as a “just in case” plan for the extreme situation where the whole financial sector needed recapitalization. That case, of course, came to pass, and because Kashkari and Swagel had a plan, there was something for Congress to quickly pass. That was good — TARP played an important role in preventing the financial crisis from worsening.

But it also meant that the plan reflected Kashkari, Swagel, and their boss Hank Paulson’s overall conservative worldview. One could imagine a plan like that which saw the US government instead outright nationalizing major banks, or imposing strict capital requirements on them in perpetuity as a condition of the bailout money, or banning them from owning hedge funds or doing speculative trading. A different administration with different views might have designed a different emergency plan — and because it was genuinely an emergency, that plan would likely have passed, with very different consequences over the next few years. 

What stocking the shelves for AI means

One way to think of the project of AI economic policy in 2026 is as designing the equivalent of the Kashkari-Swagel plan: something detailed, opinionated, and actionable that can be deployed quickly when the situation gets dire. What that plan looks like will, of course, depend on one’s values and commitments; the America First Policy Institute’s emergency plan will not look like the AFL-CIO’s.

The Center for Shared AI Prosperity was founded with an aim to produce plans of this nature designed to make sure any economic windfall from AI is widely shared, and that workers and low-income Americans are not left behind in the transition. We were also founded out of a frustration at the inadequacy of the proposals we were seeing from two ends of the AI policy debate.

On the one side are ideas from the AI labs themselves. These tend to be ambitious — indeed ambitious enough to seem like plausible answers to a problem of the magnitude of AI completely reshaping the economy — but woefully unspecific. They more closely resemble dorm-room philosophizing rather than legislative drafting.

OpenAI’s “Industrial Policy for the Intelligence Age” from this past April, is one such example,  laying out a number of very broad ideas: taxing capital more, a sovereign wealth fund invested in the AI economy, portable job benefits. It’s light on the specifics: What kinds of capital taxes? How big a hike is too big? How do you make health benefits portable without disrupting people’s current plans? How does the sovereign wealth fund get its money? Anthropic’s Economic Policy Framework is somewhat more specific, offering paragraphs per idea where OpenAI has a sentence or two, but still nowhere near the level of detail necessary to actually write legislation.

On the other side are proposals from within the DC policymaking world, which are firmly rooted in what seems politically viable right now but would be woefully inadequate in the face of the likely economic disruption that’s coming. Former Commerce Secretary Gina Raimondo and her group RAISE US have centered employee retraining; Raimondo’s recent New York Times op-ed centered ideas like new credentials from community colleges and expanded apprenticeship programs as the answer to mass AI unemployment. These are sensible tools for ordinary labor-market churn, but they are mismatched to a transition that could displace whole categories of work on a compressed timeline. The dawn of machine intelligence will demand more from our leaders than certificate programs.

The best case for this kind of caution is that ideas on the scale of the labs — sovereign wealth funds, universal capital accounts for all Americans, permanent relief funds for the long-term unemployed — are dead in the water in DC. Which might be true — now, at least. 

But this is where Tom Cotton’s brief love of cash transfers becomes relevant. We should not overindex on the way the politics look right now. The world is about to become very strange, and we may be surprised by the scale of change in response that can earn even bipartisan support.

Indeed, it’s notable that both the 2008 relief measures and the 2020 CARES Act came under Republican presidents with Democrats controlling at least one chamber in Congress, which is also the likely situation after the midterms this year. Democrats are always willing to vote for big new safety net programs to protect unemployed and low-income people. But Republicans are often willing to compromise their usual anti-welfare stances when they’re the party in the White House, and their approval ratings depend on the country’s basic economic health.

What action they might take in a 2027 or 2028 featuring massive AI-based economic disruption is still unclear. But right now, we all have an opportunity to help shape it. The Center for Shared AI Prosperity is running a request for ideas, seeking proposals for shared AI ownership, new AI-related taxes and revenue raisers, and new safety net programs to share the gains widely. We want ideas from economists and think tanks, of course — but also from the labs, from independent researchers and academics, and from ordinary citizens with an interest in where this technology is going.

Stocking the shelves is hard work, and we don’t have all the answers. But you just might, and we’re going to need all the help we can get if the US is going to emerge from the AI transition as a prosperous, functional nation.

There’s a new kind of influencer making the rounds on TikTok and Instagram: the loneliness influencer. Most of these influencers are young women, and “loneliness” might be a slight misnomer. They claim they aren’t lonely, simply alone — no friends, no family, no kids. And they prefer it that way.

“I really wanted to convey a normal life of somebody that doesn’t have this big, great, fun social life,” one influencer, Lana Isa, told Vox. “Like, what does a life look like as someone that doesn’t really have this great big social life, is not really interested in dating and generally prefers nights in? If you were to watch a Friday night in my life, you’d essentially just watch a girl enjoying her peace.”

Isa, and influencers like her, are just one representation of a larger trend, though. The Atlantic’s Faith Hill, who recently wrote a story titled “The Strange Appeal of the Solitude Influencer,” told Today, Explained co-host Noel King that, even though you mostly hear about young men facing a loneliness epidemic, women are having a hard time, too.

“If you actually look at some of these statistics, young women are struggling a lot on a lot of these measures, and, in some cases, more than young men,” Hill said.

Hill spoke with Noel about what’s going on with young women, how their crisis looks different from men’s, and why they’re covered differently.

Below is an excerpt of their conversation, edited for length and clarity. There’s much more in the full podcast, so listen to Today, Explained wherever you get your podcasts, including Apple Podcasts, Pandora, and Spotify.

In the first half of the show, we talked to a young woman who has made a name for herself as a loneliness influencer. What do you think is going on here?

My first impulse when I heard about this genre of video that people are watching is that there’s a lot of people spending a ton of time alone.

We’ve heard a lot of people talk about the loneliness epidemic, so I thought people were getting social connection through these videos from a safe distance, rather than spending time in person with people. Maybe there’s some of that going on. But, I also realized, as I was looking through these videos and reading all the comments, that a lot of the people commenting seemed to have a lot going on in their lives socially, so much so that they were busy, and exhausted, and burned out.

For some people, the appeal was actually in the fantasy of it, in the way that some people would look at influencers posting about these fabulous, exotic vacations they can’t afford to take. People have a very complicated relationship to solitude. People are working long hours. A lot of people [are] taking care of family members without much help. Many people are sort of torn between these needs for social connection and solitude.

Does that mean that this is, perhaps, not as sad as it appears on its face?

I don’t think it’s all sad. My heart goes out to people who are needing more solitude, as well as more social connection. Most people probably don’t have the perfect balance, and I can relate to that myself. I feel like I either have too many plans or not enough.

It’s not necessarily all happy, but it doesn’t mean that there are just so many people out there who are only getting social connection through these videos. I think there’s something a little more complicated going on.

We’ve all heard about the male loneliness crisis. You wrote a very interesting piece that basically said, actually, women are in crisis, as well.

I’ve just been hearing so much about men, and especially young men, being in crisis. I think there’s a lot of reasons we should take that seriously, and I do. But I felt like in those conversations, young women were kind of being flattened into a comparison point where, instead of people talking about how on some measures young men are struggling more than they used to, it got twisted into “young men are struggling more than women.”

There’s this image of the thriving girlboss, the one who is going to college, graduating college, entering the workforce on these conventional measures of success, doing so well. But if you actually look at some of these statistics, young women are struggling a lot on a lot of these measures, and in some cases, more than young men. And so, I don’t think it needs to be a suffering competition, but I did think part of the story was not coming through.

In what ways are women struggling?

Women have, for a long time, reported depression and anxiety at higher rates than men. That is getting worse. It seems like mental health on a lot of different measures — different kinds of stress and distress and suicidality — young women are reporting that at higher and higher rates. It turns out that women actually attempt suicide at higher rates than men do, on average. And the reason that more men die of suicide is that they’re more likely to use lethal means such as firearms.

A lot of times this conversation really revolves around college attendance rates, and women are attending and graduating college at higher rates than men. But a woman with a bachelor’s degree still makes less than a man with a bachelor’s degree on average, even within the same field of study.

When I talk to people for this story, researchers and therapists, I heard that a lot of young women are in for a rude awakening when they graduate from school. They’ve been in this bubble where they did feel like they could grow and thrive and they were taken seriously. And then, you go out into the real world, where sexism is still very real, and a lot of women are working in workplaces where they realize they’re not taken as seriously, or the people around them who are in positions of power are all men. That’s a difficult realization.

Why, if women and men are both in crisis, did men pull focus?

Women, as an overall population, tend to be a fairly high-functioning one in this narrative. I talked to someone who had trained as a medical sociologist, and she told me a saying that they used to use in this field was, ‘Men die quicker, but women are sicker.’

Women are more likely to endure a lot of chronic illnesses and to sort of soldier on with their pain unnoticed. And we might not be taking that as seriously, because the idea that women are struggling isn’t necessarily a new one or a super surprising one to a lot of people. I think men having a hard time is more of a news story, and we have become, perhaps, kind of inert to women’s distress in this way.

I wonder, as you found yourself covering this, where do you find the hope here?

I am heartened that we’re talking about [young adults] a lot. There’s been a lot of concern lately about Gen Z, and a lot of what we’re talking about when we talk about young men struggling also applies to young women, so we’re onto some of the right things.

I wrote another piece about young adulthood a while back that was really about the idea that young adulthood actually is a really hard developmental phase. And when I published that article, I think for a lot of readers, it seemed to be somewhat of a surprise that young adults are struggling, too. Even just since I’ve written that, people have talked more about young adults struggling. So I do think people are starting to take that seriously and understand that this is an age group that might need help.

This story was originally published in The Highlight. To get access to member-exclusive stories like this every month, become a Vox Member today.

Derek Lam has more than 31,000 followers on TikTok and nearly 40,000 on X as of this writing. He is shirtless a lot, he dances a lot, and he is shirtless dancing a lot, which may explain how he got so many fans. His comments are filled with compliments (“beautiful”) in different languages (“hombre bello y sensual”) and superlatives (“this might be the finest man on the internet”) accompanied by different emoji (red hearts, crying laughing, lips). Their responses make it seem like Derek Lam is the first and only beautiful man they’ve ever seen, which may explain why he is also selling “exclusive,” seemingly adult, content. 

He is also, possibly unbeknownst to his many admirers, AI-generated. 

To be fair, there were some signs that this man was not real: Despite the multiple videos, Derek never speaks. His videos are also rather brief, just seconds long. A real hot person probably would have parlayed a following of this size into brand deals or “get ready with me” videos. And the selfies on his X account show a completely different man just three years ago. 

Still, the followers of Derek I talked to didn’t even notice he was AI because he seemed to blend in so seamlessly with the other hot men on the internet.

Derek isn’t the only AI thirst trap showing off defined abs for likes and money. He’s one of an increasing number of completely fake, AI-generated figures sinking their fangs into the real models, influencers, and porn stars who populate our feeds, sucking up their beautiful faces and bodies, and using them to profit, without a penny going to the real humans they fed from. 

When it comes to the damage AI could wreak on society, an army of Dereks tricking horny people into giving him likes — or, worst case, money and Amazon gift cards — doesn’t exactly sound like the singularity doomsday scenario that we’ve been warned about. It’s clearly unfortunate for the adult entertainers competing with deepfakes and a fraud risk for their fans, but one might believe if they don’t fall into one of these two groups, they’re relatively safe and unaffected. 

But there’s something more going on here. History shows that porn and sex drive innovation in the tech industry. The way tech platforms treat sex workers is typically a glimpse into the future, and a warning about how tech platforms will eventually treat all of us. If human desire demands the capability to steal, loot, and turn anyone and everyone into something for sale — possibly into hot Dereks — is anyone safe?

The Dereks of the internet are a bleak look at what’s happening in the real world: nothing belongs to us anymore — not our looks, our beauty, our sex, and our art. Our most human desires are slowly being synthesized, with or without our consent. And AI is making it all possible.  

Deepfake technology has gotten alarmingly good in recent years

Artificial hots like Derek are considered “deepfakes,” an umbrella term for AI-generated media (audio, video, or both) that resembles a real-life person. 

When deepfakes first started appearing in late 2017, they were fairly low-quality, making it easy to tell when someone had used a rudimentary app to paste a celebrity or politician’s head onto a different body. Still, it wasn’t very long until people started wielding this technology to be nasty. 

“The first set of deepfakes were actually used to create pornographic videos. They replaced the subjects in those videos with the faces of celebrities,” Siwei Lyu, a professor at the University at Buffalo who studies digital forensics, told me. 

Because the quality of those videos was bad and the content was often absurd or unrealistic, it was easy to tell they weren’t real. Those clunky apps needed a lot of data — videos, images, etc. — of real people to produce crappy videos; Lyu explained that this is why you mostly only saw deepfakes of politicians and celebrities at the time.

As the technology got better, it became less reliant on having a huge amount of data. Instead of needing a whole archive, the new versions of these apps can pretty much run on nothing. “They do not need that much data to train a model anymore. Some of the most recent algorithms just need a single picture — just a single picture of someone,” Lyu said. And the quality is better too. Lyu said that there are AI programs that can now change a person’s appearance and voice in real time, like in Facetimes and Zooms or on live broadcasts.  

Given how many of us are constantly posting photos and videos online, it is now extremely easy to create a convincing social media presence for a person who is not real, and to use it to catfish unwitting people on the internet. 

“This is the problem. It’s becoming more and more challenging to visually tell deepfakes apart,” Lyu said. “Seven years ago, when I started working in this area, checking them was not this difficult,” he added. 

Lyu is an expert in digital media forensics and machine learning, and he went through one of Derek’s videos frame by frame and pointed out some obvious AI tells. There was a distorted watchface with weird swirls instead of numbers and a moment in the video where all of Derek’s fingers on one hand were the same length. Lyu also pointed out that Derek’s chest hair fluctuates, appearing dense in one frame and then dissipating in another.

Through social media, I attempted to contact the owner of Derek Lam’s account with evidence from Lyu that these videos are artificial; I did not hear back.

During my deep dive into Derek Lam’s social media presence, I looked at the accounts he was following. I noticed that of those accounts, someone who goes by the name Vance Ford also had tens of thousands of followers and had nearly identical videos to Derek. The flexing, dances, movements, and music they were set to were all the same, but with what appeared to be a different man performing them. 

I attempted to contact Vance through DMs on social media and did not get a response. I also e-mailed two models who appear to be the actual people that the Derek and Vance AI personas were trained on, but they didn’t respond. 

I sent two of Vance’s videos to Lyu, who analyzed them manually and with AI-detection software. He confirmed that “their movements are nearly identical — consistent with generation from a shared motion source,” and noted that the Vance videos had moments of distortion, unintelligible text, and facial warping. 

 “Young Magnum PI…Tom Selleck,” commented one admirer.

What happens when real people follow fake hots 

“Wow I’m a boomer,” said Patrick, one of Derek’s followers on X, after I told him that he might be following an AI-generated thirst account. (Vox agreed to let Patrick, and Derek’s other followers, use a pseudonym so they could speak frankly about being thirsty for a fake guy.) Prior to our chat, Patrick had no idea Derek was likely a deepfake, and maintains that he didn’t even know he was following the account. Patrick is 33 years old, roughly 30 years younger than the youngest boomer, but being fooled by a hot AI man has made him feel old and vulnerable, susceptible to scams and perhaps light financial crime. 

“This was probably some smut account I followed before I moved all that over to an alt,” Patrick said, noting that in daily life, he’s only ever used AI to help organize and write emails. Wielding AI to create fake videos and photos does not thrill him, nor does the potential of seeing more of Derek. 

“A person being real, someone you could run into at a bar, is half the fun,” Patrick told me, explaining some of the accounts he follows. “AI porn is not of interest, to me, anyway.” 

Not being able to tell the difference between the real beautiful men on the internet and the AI-generated beautiful men on the internet not only makes Patrick feel old, but also a bit “hollow.” The fact that the people we are attracted to are so unrealistically hot, so perfect, that machines can step in for them and go relatively undetected is a reflection of the current state of unattainable desire, which is just as scary as how good these programs have gotten at mimicry. 

“Black mirror shit,” Patrick said. 

The guys I DMed about Derek felt ashamed once they found out the truth. 

“It’s embarrassing and he’s not my type,” said Chris, 33. “I’ve come across several AI accounts, and this one is really good, I have to say. But you can see there’s like no life in his eyes.”

Chris made clear to me that the humiliating thing isn’t that he follows attractive men on the internet. That isn’t a big deal. 

What irks him that he got duped. Chris works in digital marketing and has seen AI used professionally to tabulate calculations for campaigns, and has used it privately for silly things like memes. “AI can do a lot of things, things we probably should not want it to do,” he told me. “I think what’s also scary…is that everybody has access to it. And yes I already unfollowed this person.”

Chris believes there’s something more nefarious afoot. He thinks that whoever is running Derek may have hijacked the username (i.e., the original person Chris was following) and then populated it with AI to drive up follower counts — a scam he’s seen online before.  

“This is super concerning and super scary because you eventually could be texting with this person,” he said, describing a hypothetical situation where unknowing users could be lured into subscribing to fake content and, ultimately, giving the account their personal information, whether that’s photos or perhaps even passwords. 

“This person could be selling your nudes,” he said, explaining one extreme end point of a possible scam. “But you were like jacking off to AI content and that’s embarrassing.”

AI deepfakes are bad for real thirst traps too

While flirting with or masturbating to a fake person is awkward but ultimately manageable and private, Cherie DeVille has an even more complicated problem with AI manipulation. If DeVille is scrolling social media, there’s usually a chance that she’s running into an AI version of herself saying things she’s never said and doing things she’s never done.   

DeVille, an adult star who calls herself “The Internet’s Stepmom,” has roughly 4.5 million followers on Instagram. But her account is often down, which she says is the work of fraudsters  that are determined to send traffic to DeVille’s AI imposters and get her actual account removed. 

“It’s almost always the fake accounts of me reporting me,” DeVille said. “They want to be the biggest me. They want to be the biggest scammer. They want to use my altered AI images to scam fans without my real account getting in the way.” 

DeVille and others I spoke to explained to me that deepfakes have been an annoying reality in the adult entertainment industry for years. The way the scam goes is that someone would fake photos or videos of DeVille (or any star), create an impostor profile, and then trick DeVille’s fans (e.g., through social media DMs) into following that copycat. Later they’d squeeze them for money, payments through Paypal, or Amazon gift cards, perhaps by offering unique content. 

“If you made a fake me and I don’t do double anal, but my AI can, they could have all kinds of ‘exclusive’ stuff,” DeVille said, explaining that double anal is grueling work. 

The lack of protections becomes even clearer when you consider that not every deepfake is a carbon copy. Some personas may borrow a face from one actress, a torso from another, or a pair of legs from a different star. This can make fakes tougher to track down and prove, and more difficult to fight from a legal aspect. 

“Who owns your face once it’s scraped into AI systems? Who profits from your digital clone? How do performers protect themselves from unauthorized replicas or manipulated content?” Rachel Steele, an adult star and the CEO of Red MILF Productions, said to me in an email. “Those questions are still very unanswered.”

Like DeVille, Steele worries about how many of the people using AI to create and consume content don’t seem to consider the artists, models, writers, performers, etc. that these engines have been trained on. It’s bad enough to watch AI slurp up and regurgitate your written work or your digital art. Some people also have to contend with LLMs that have been trained on their own faces and bodies.

“Real creators are competing against characters that can be flawless in every image, never age, never have bad lighting, never get tired, and can appear available 24/7,” Raissa Bellini, an OnlyFans creator who touts gymnastics and firebreathing among her unique skills, told me of the impossibility of keeping up with a machine. She explained to me that she’s seen people create AI-generated personas with the looks of popular models or influencers, only tweaking small details like hair color or eye color. 

A spokesperson for OnlyFans told Vox via email that the company’s terms of service prohibit deceptive or inappropriate content, and said that all content posted on OnlyFans must belong to a verified 18+ OnlyFans content creator: “This means that you can only share content which has been generated, altered or enhanced by AI if it clearly features the verified OnlyFans creator and the user can tell that the content has been generated, altered or enhanced by AI.”

Bellini explained to me that while OnlyFans has measures to protect its creators, some smaller subscription and adult-content platforms do not have the same kind of guardrails. She also noted that most social media sites do not have strict rules or enforcement when it comes to AI, and that she’s seen the algorithm appear to favor AI over human creators.   

“AI raises questions not only about competition, but also about likeness rights, authenticity, audience expectations, and what happens when fans can no longer easily tell the difference between a real person and a generated character,” Bellini added. 

What’s stopping a stranger from creating an AI thirst trap of you? Nothing, really. 

For Deville, Steele, Bellini, their cohort, and even you and I, there are minimal protections stopping someone creating an AI us and making money off of these fake variants. 

According to Jason Schultz, a law professor and director of NYU’s Technology Law & Policy Clinic, humans have, for the last couple of centuries, generally been protected by copyright and right of publicity laws. 

AI obviously didn’t exist when these laws were written, and courts now have to interpret the laws in the context of all of this new technology, in combination with other existing rights (like free speech). Schultz told me that there are more than 100 current cases pending about training AI with copyrighted material. 

He also explained the difficulty of determining whether or not an AI-generated persona constitutes a violation of someone’s right of publicity. It’s more clear-cut when the human involved is a celebrity, because their public persona and appearance is so distinct. It gets murkier when the humans aren’t well known, and the AI creates a persona that’s more of an amalgam than a one-to-one copy. 

“It would raise this question of whether these avatars are based on a particular entertainer, or are they more of an aggregate?” Schultz explained to me. But even if courts side with the humans whose likenesses are being used to create fake personas, Schultz cautions that the technology will always accelerate faster than court decisions are handed down. “I think that the thing that worries me a little is we’re going to get these sets of decisions in two years, but we’ll be dealing with the next three generations of technologies,” he said.  

DeVille, who has been working in the industry for nearly two decades, told me that without better legal protection, she isn’t hopeful for the future of porn or, more broadly, any type of art.

“If my income started tanking and their theft was at the point where I couldn’t compete with literally myself, there might be no choice but to retire,” DeVille said. 

But she also wants to make it extremely clear that she isn’t against AI; she would just like to be in control of it. That means being able to own her likeness, her voice, her image, and the ability to choose whatever she wanted to do with it — or at least get some compensation or have some legal protection if someone’s using Cherie DeVille without her permission. 

“It would be a beautiful way to extend my career beyond what my knees can take,” DeVille told me. But, she added, “if someone’s making an AI of me doing double anal, I should be making the money.” 

I hate generative AI. I hate how it’s destroying writing pedagogy and giving students even more excuses not to read (because they can just read a “summary”). I hate how whiny and defensive AI users are about the pathetic little ways they’ve integrated it into their lives. If I could push a button and permanently delete it from existence, I would. If I could go back in time and prevent it from being invented, I would.

The reason I hate it is not just that its output is mediocre bullshit. It’s that it is an active attack on everything I value — literacy, analysis, thought. I find the thought that I would use ChatGPT actively degrading and humiliating. I have never touched it, not even as a joke, not even to prove it sucks, not even — as Beatrice has been doing — to see what it’s showing my students. I admit that this is somewhat irresponsible of me, to indulge my revulsion in this way. This technology — which is unimaginably expensive and resource-intensive and which has not developed anything approaching a plausible profit model — is of course inevitable, “here to stay.” Nothing could interrupt the progress of a technology that requires hundreds of billions of dollars to be shovelled into the furnace year after year after year without returning anything. (Can you tell I’m angry? Can you tell I’m sick of hearing these thoughtless clichés?)

But one must eventually face it. One must eventually give it some thought. Beatrice has been helping, in her Substack posts and in our continual chat thread. She also pointed me to the work of Jan Mullen on AI as externalized attention, which takes the kind of insanely overambitious long view of AI that I find appealling as an aficionado of political theology-style genealogies. Mullen compares the rise of AI to the rise of literacy and notes that the latter was, for most of its history, primarily a tool of state control. In a wide-ranging interview, Mullen wonders aloud whether the form of control AI is creating will line up with our idea of “the state” — but is absolutely clear that the purpose of generative AI is to manipulate and control us, to take away our power and agency, the power and agency that humanity somehow managed to wring out of the technologies of literacy.

I was particularly interested in a moment where Mullen postulates that the new model of control “will be distinctly post-literate — and, as a result, post-legal.” This triggered my political theology instincts and I immediately asked: does that mean it will also be post-monotheistic? Long-time readers know I love Jan Assmann’s theory of monotheism. For Assmann, what monotheism does is not primarily or most importantly to reduce the number of gods, but to introduce a new kind of god — an exclusive God, one in relation to whom all other gods are false. A crucial technology for stabilizing the claims of this exclusive God is of course the written scriptural text, which remains a durable deposit even as day-to-day religious practice inevitably drifts away from the strict demands of the original revelation. The monotheistic God, in contrast to previous pantheons with their loosey-goosey translatability and porosity and their ever-shifting body of mythical tales, is a God of the letter because he is a God of law.

So if ChatGPT destroys literacy and law, then ChatGPT is going to kill God. By this I don’t mean that the bearded guy in the sky is going to be found dead of a gunshot wound to the chest, but that monotheistic religion as we have traditionally understood it will not be able to function in a post-literacy regime.

Note that I am not claiming that ChatGPT will take away people’s ability to read in the sense of deciphering letters — obviously its functioning depends on that. But it is killing the notion of a stable, permanent, authoritative text and undercutting the skills needed to engage with that kind of text. Note also that I am well aware that the vast majority of monotheistic believers throughout history were illiterate (in either the “can’t decipher letters” or “can’t make sense of a complex text” sense). But the elite leaders definitely were fully literate, and in fact that was the source of their authority. One cannot say the same of the type of religious leaders who are most thriving in the meme-ified, Trumpified bastard child of Christianity that sets the tone for American religious practice today. Compared to even a generation ago, literal knowledge of what the Bible says at all has been radically set aside. Some combination of personal charisma and “vibes” — above all, opposition to what they imagine progressivism to be — are the source of authority, leading to obvious absurdities like the rejection of compassion as a Christian virtue.

I am a harsh critic of the dumbed down “seeker-sensitive” model of Christianity I was raised in. But in comparison with what passes for Christian teaching today, it is intellectually sophisticated and morally demanding. This is not to say it was simply “better” (after all, it paved the way for what’s happening now) or that the answer is to get back to the Bible. It’s just to mark how far we’ve fallen. What seemed like a vacuous, popified version of Christianity at the time now seems like a robust theological ethos. The haphazard methods of “Bible study” now seem fit for a graduate seminar.

Speaking of the evangelical milieu, I’ve always resisted Luhrmann’s thesis (in When God Talks Back) that evangelicals cultivate an internal voice that they identify with God. When I was growing up, I didn’t know what people meant when they said that Jesus was their best friend or that God was telling them to do something — and I assumed they didn’t know either, that it was just a weird kind of in-group signalling. For my part, Jesus was not my best friend and God didn’t tell me even one single thing. I was all alone up there in my head, always, and I assumed that everyone else was, too. The thought that everyone I grew up around was suffering from a low-grade self-induced psychosis is difficult to cope with.

Now that people are turning to ChatGPT for spiritual insight, though, I wonder if I finally have to admit it. Obviously here again we are dealing with what seems like a quantitative change — people are using the machine to shuffle religious clichés, where previously they just half-consciously did it themselves. But the qualitative difference is that the insights of “Buddy Christ” could always be corrected against the unchanging text of Scripture. When there is no longer an external anchor like that, when the divine revelation is “customized” for each and every reader, something has changed. Again, this is not to say that what happens to be in the Bible is necessarily “better” than any given ChatGPT transcript — presumbly it’s often worse. But a point of leverage has been lost. Counterargument is no longer possible in the same way. And insofar as that point of leverage, that external source of authority, was how “God” functioned in traditional monotheism, that means God is dead.

The new regime appears to be broadly polytheistic. There is of course always the god of the uncriticizable self, who behaves with the same irritable intolerance as the monotheistic God in the face of even the faintest suggestion that one might consider behaving or thinking differently. But there are many other sites of veneration, an overlapping consensus of podcast hosts and memesters and conspiracists that each believer can mix and match to drive themselves insane in the most relevant and appealing way. Joe Rogan belongs to this pantheon, along with Jordan Peterson and any number of other luminaries whose endlessly prattling voices people love more than their own families. Donald Trump is of course a very powerful god in this polytheistic milieu, but as the boos to his vaccine advocacy and the pushback to his Epstein caginess show, he is not all-powerful, his word is not quite law. And the fact that Trump’s words lack all self-consistency means that there are many Trumps as well, that Trump can be whatever his follower imagines him to be.

I cannot see what the believers see when they look at Trump. All I can see is the most degenerate loser ever to live, the most despicable piece of shit imaginable. Maybe this is of a piece with my revulsion at ChatGPT — and indeed, my failure to develop a personal relationship with Jesus Christ. I’m too hung up on facts and reality and meaning and consistency. I’m too literal. That’s why I am unsuited to the emerging world, why I reject it more forcefully than it would ever even bother to reject me. I’m just too literal.

A curious thing has been happening lately: I cannot use the popular left-leaning social networking site Bluesky for any length of time without becoming irritable and even angry. Every common trope annoys me. Most notably, if someone nitpicks a headline in the New York Times — one of the favorite forms of political activism over there — I jump down their throats. I get into pointless, hostile, endless back-and-forth exchanges where we both dig in more and more. Often one leads to the other. For instance, this morning I blocked a longtime mutual (and fellow academic) after a terrible fight about whether the NYT has appropriately used the word “mused” in a headline.

Clearly I’m part of the problem here! I could simply attempt to curate my experience more — set up lists, follow “starter packs,” and do all the many fun things that other users do to get things “just so” — but every time I do, the bullshit keeps coming back. My biggest success was blocking a major user who is obsessed with NYT nitpicking, and for a few weeks it seemed like he had been the only culprit. But it always comes back, just like the constant exhortations to block without mercy or hesitation keep coming back, just like everything keeps coming back. And that probably happens because Bluesky is, at the end of the day, a very small and insular site, whose culture of boastful hair-trigger blocking contributes to major groupthink.

The beginning of my more sustained frustration came early last week, when the entire site was aghast at a Financial Times column that said Bluesky was an echo chamber. I have been using a print subscription to the Financial Times as part of my efforts to wean myself off social media and have come to deeply respect it as a news source. Everyone was outraged at the FT’s “attack” on the site, and some journalists and editors even popped up on Bluesky to try to mediate the conflict. Multiple major accounts advocated blocking all the supposed “gaslighters” from what is, in all honesty, probably the best English-language news source on Earth.

I was aghast. I don’t much care for the columnist, nor did I think the column was a work of art. But everyone seemed to be mischaracterizing its point — which boiled down to “I get why liberals like Bluesky, but I miss the broader perspective Twitter had before Musk ruined it.” Again, not brilliant, but hardly a war crime. In fact, I’m pretty sure that most Bluesky users, deep in their hearts, would agree. And for “defending” the column (i.e., attempting to accurately summarize it), I was insulted and targetted for blocking. One person mused that I was trying to suck up to the columnist so I could hobknob with her wealthy friends. Another thought my response was par for the course for a “tank-adjacent contrarian.” The notion that I sincerely thought everyone was overreacting based on inaccurate information was not considered.

A big part of the problem is that we are all, of course, recovering from the trauma of late-stage Twitter. We are taking habits honed in an environment overrun by open Nazis and applying it to people whose politics are, at the end of the day, all within one standard deviation of each other. And that’s fine! There is definitely a place for people with similar politics to meet. That’s what I try to cultivate on Facebook especially, and I certainly didn’t seek out conservatives on the old Twitter. The problem is that no one is willing to admit that it’s an echo chamber. They want to imagine it’s an miscellaneous audience, that their critiques of NYT headlines can reach some reachable moderate, or that their exhortations that no one should blame North Carolina and Tennessee for the hurricane damage just because they’re red states could be heard by someone who may indeed be tempted to blame them. It’s like a “seeker-sensitive” church, always looking to convert outsiders who mostly aren’t interested and meanwhile providing nothing of substance to those who are showing up consistently.

I’ll admit that the old Twitter was often annoying in the same way. It did periodically bring out an unattractive combativeness in me. And sometimes it seemed like it could become actually life-threatening, as in my multiple waves of systematic right-wing harassment. But it had other rewards. There was much more genuine intellectual diversity among left-wing posters. There was much greater chance of stumbling across interesting links and articles or hearing about something cool. And it was funny — funny like nothing else. There was just something about the meme culture of Twitter at its prime, with its multiple layers of referentiality and irony, its nihilistic love of the absurd. Even in the late days of Twitter people were becoming more literal and less receptive to that kind of humor, but on Bluesky it’s entirely dead. The absurdist poster “dril” made a big show of switching to Bluesky and people took his nonsensical posts deadly seriously, sometimes even going so far as to report him for harmful content. I myself get a very literal response to almost every joke I post. It’s heartbreaking.

It’s as though everyone decided to take my article Jury Duty, where I said that the purpose of social media is primarily to pass judgment on each other, as an instruction manual for building out the new culture of Bluesky. The constant reminders that everyone is on a hair trigger to block every stranger exacerbate the effect, to the point where it feels like the first social network whose primary purpose is actually to cut off social contact.

When I ask myself why I let it get to me, it is probably because I am still grieving for what Twitter was. It didn’t always bring out the best in me, but it was rewarding on net, because it was so often surprising. That’s what’s so frustrating to me about the endless repetition of a handful of moves — it’s so predictable. I get angry at the NYT headline nitpickers because they’re boring, and because they’ve convinced themselves that it’s politically necessary for them to be boring. In reality, though, the recipients for the messages they insist on sending out so repetitiously are not here. I am. Or at least I was. I know I was annoying, I know I had my hangups, I know I often disagreed with the site consensus — but I am a human being, I am genuinely open to discussion, and I admit when I’m wrong much more often than anyone else I’ve ever seen online. All I wanted out of the site was some kind of halfway interesting discussions and links and jokes. I don’t think that was too much to ask.

Whether I will return again remains to be seen. As a long-time Twitter addict, I’m likely to relapse. I publicly pledged myself to take a week off, just to make it embarrassing to come back sooner. But what makes it less likely is the sense that no one will miss me. I pop into Twitter every week or so to check my replies and see if I have any DMs, and every so often I see someone saying that they miss me and wish they would return. By contrast, Bluesky has felt hostile and unwelcoming from the very start. I know that hurt people, hurt people. I’m guilty of that as well. I just wish we could all work through it and try to build something that can be more informative, more challenging, and more fun — and I’m increasingly convinced that will never happen.