A crackdown on American academia by the Trump administration has handed Canadian universities a rare opportunity to lure some top researchers north.

© Nasuna Stuart-Ulin for The New York Times

Richie Bell stood at the door of an East Village bar, nervous for a singular reason: he had to hand over his phone to a bouncer to get in. At 23 years old, Bell had hardly ever been without a device — he got his first iPhone in elementary school — but when he saw a video on TikTok advertising a queer, phone-free, funk dance party called Who’s Dancing?, he decided to test his limits. He immediately purchased a ticket and planned to attend alone. 

Bell was pleasantly surprised to discover he wouldn’t have to relinquish his phone at all, but simply allow security to place tape on both the back and front-facing cameras. “You can’t scroll Reels because, think about it, on iPhones anyway, if they’re taping the front camera, that’s also the speaker area,” Bell told Vox. Without the temptation of content on his phone, Bell mingled with other attendees, who were similarly unoccupied and also flying solo. Even though event staff kept their eyes peeled for any errant phone usage, no one seemed to reach for them anyway. 

Compared to other bars and parties he’s been to, Bell found this one more conducive to unbridled socializing and dancing. It’s easier to approach a stranger when they’re not hunched over their phone. No one is worried about looking like a fool, immortalized in video, because everyone is a little foolish, moving their bodies without fear of judgment. 

Across the country, clubs, bars, music venues, and other social spaces are encouraging attendees to lock their phones away — or, at least keep them out of sight — for screen-free gatherings. According to event hosting platform Eventbrite, phone-free experiences have increased more than 500 percent globally between 2024 and 2025. Musicians Phoebe Bridgers and Harry Styles banned phones at their concerts; comedians John Mulaney and Bill Burr have followed suit. Clubs from Los Angeles to Miami are creating camera-free dance floors. Bars and restaurants in cities like San Francisco and Philadelphia that have long prohibited phone and camera use have been joined by newer establishments in Charlotte, North Carolina and Fort Worth, Texas. 

The rise of phone-free socializing speaks to a growing exhaustion with an always-connected world. More than half of US adults say they use their smartphone too much, according to a 2022 Gallup survey. Excessive smartphone use has been linked to poor sleep quality and depression, and even the mere presence of a phone lessens cognitive capacity. The ubiquity of devices — and the cameras on them — has instilled a low-grade panic that you might end up going viral for eating a sandwich in the background of an influencer’s video. Even Gen Z, who has always lived under the spectre of technology, is nostalgic for times before everyone was “plugged in” and widely accessible.

Screen-free events are the natural evolution of the cultural desire to break up with tech. By transforming physical spaces, ridding them of distracting devices, event organizers and bar owners aim to foster connection, reclaim attention, and maintain a sense of privacy. Even a short detox from smartphones has the ability to dramatically shape the tenor of a get-together for the better. 

“Exploited for content”

Before X&ND (pronounced “zand”), the funk and soul artist who organized the phone-free party Bell attended, first conceived of Who’s Dancing?, they noticed very few people actually cutting loose at clubs. Instead, they were on their phones. “The second that anybody starts dancing in a club where there’s meant to be dancing, people pull out their phone,” X&ND told Vox. “They’re getting surveilled, they’re getting essentially exploited for content and being put in a position where they’re on display for people that they didn’t necessarily want to be on display for.” X&ND wanted to create a space for women, queer, and trans people to dance and express themselves without the fear of ending up on the internet. 

Since last summer, X&ND has hosted eight sold-out dance parties (and Bell estimates he’s attended seven). Phone-free events, X&ND said, are a reclamation of culture, of attention, of self-expression. “Influencers aren’t able to post anything. Different companies aren’t posting anything about our parties because we don’t give them the material to,” they said. “I think it’s the culture taking the culture back.”

The bar and nightclub scene has struggled in recent years, with many venues closing due to high rent and fewer patrons spending money on alcohol. But perhaps a less obvious culprit is the pervasive anxiety of being captured on video during a moment of vulnerability. Some might be turned off from entering these spaces altogether due to the high risk of embarrassment if they’re captured and mocked online.

In Washington, DC, 28-year-old Jo Vee had similar concerns about privacy and craved an environment where they could be anonymous. Like Bell, Vee has had access to a camera phone since they were a child and can’t remember a time when they weren’t thinking about what to post on Instagram. But when a friend organized a phone-free dance party, Vee jumped at the opportunity to enjoy themselves, free from the pressures of the algorithm — and the potential of accidentally becoming a meme. “There’s nothing more terrifying than thinking that you’re going to a party just to have fun, and then you wake up the next morning and there’s a video of you that has 10 million views that you had no idea about, and now you’re like a GIF reaction on the internet.” (Vee clarified that they do not speak from personal experience, but said that a version of this has happened to a couple of friends.)

Distraction-free connection

Although many people inherently recognize the downsides of excessive phone use, they also realize they’re fighting an uphill battle. Demanding jobs can tether workers to their emails and algorithms are designed to keep us engaged and scrolling. Our attention is constantly being pulled from the real world to the digital one. But even a brief respite from screens can be beneficial. 

In his research, Georgetown associate professor Kostadin Kushlev found that phone usage during a social interaction undermines how much people enjoy spending time with others. “It’s not so much [that] the phone is making us feel unhappy,” Kushlev told Vox, “it’s more like I could be happier if I was not using a phone.” Put another way, people enjoy social gatherings more when they keep their phone out of sight because they can give their full attention to their conversation partner.

Kushlev’s other research has shown that as people curbed their phone usage over a two-week period, their ability to pay attention improved. Even a shorter detox period, like a two-hour phone-free gathering, could minimize overstimulation and encourage greater focus, Kushlev said.

Inside the Charlotte, North Carolina, bar Antagonist, couples on dates engage in heart-to-hearts and neighboring tables of strangers strike up conversations. That’s because the bar has banned phones. Upon entering, visitors are asked to secure their phone in a pouch which they keep with them for the duration of their stay. If they need to use their phone, they can do so outside.

Since Antagonist opened in March, owner Michael Salzarulo has observed patrons clamoring for a Friday night detox after a long week of screen-mediated work. “Normally Saturday is your biggest day,” Salzarulo said, “but Friday nights are our busiest nights of the week.” While anyone can lock their phone in another room for a few hours, Antagonist provides an element of accountability, Salzarulo said: a dedicated space for distraction-free connection.

Alex Becker was yearning for a break from her phone and her “email, Microsoft Excel job” when she co-founded PA Unplugged, a nonprofit championing policies that advocate for more mindful uses of technology, last year. One of the nonprofit’s initiatives, Screen Free Philly, maintains a calendar of screen-free events and organizes its own gatherings at breweries, parks, and other civic institutions, urging attendees to set aside their phones for discussions, art projects, and board games. At the most recent get-together, dozens of attendees handed their phones to Becker while they completed a series of puzzles throughout the city.

Although these gatherings are still fairly niche, Becker sensed a hunger among participants to seek out further opportunities for tech-free connection. While ditching a smartphone completely may be unrealistic, setting it aside for date night or an afternoon of gaming might be a salve for our tech-addled brains. We can implement these best practices at our own gatherings, too, by encouraging friends to leave their phones in a basket by the door or offering disposable cameras at parties.

“People are just done and ready to take their attention back, take their time back, and encourage more real-world connection,” Becker said.

Welcome to The Midterms, Actually. Every week I’ll be writing about two things: one big idea or trend I see forming across the 2026 primaries, and one thing I’m hearing from a voice on the ground. You can sign up here to get it in your inbox every week. Let’s get into it.

One big idea: The data center backlash is a democracy story

This week, OpenAI CEO Sam Altman acknowledged a small hurdle in his quest to integrate artificial intelligence into all parts of human life and the economy: the rest of us. 

In an episode of the Founders podcast with David Senra, Altman confronted the growing public backlash against tech leaders and their AI tools, conceding that he’d overrated the speed at which they’d be adopted across the economy and that the industry hasn’t made its case to the public.

“We have not, as a field, done a very good job of explaining to people what the benefits are and how the downsides can be mitigated,” Altman said. He added that “it’s probably a good feature of human society that we have some built-in inertia, some skepticism of rapid change.”

You think?

Survey after survey shows how deep — and how bipartisan — the backlash against AI, and specifically data centers, already runs. The pressure has been building all year: A Gallup poll in March) found that 71 percent of Americans said they oppose building AI data centers in their area — 48 percent of them strongly. That opposition runs across the aisle: 75 percent of Democrats, 74 percent of independents, and 63 percent of Republicans. For perspective, Americans are more opposed to a data center going up near them than a nuclear power plant (53 percent). This is not some fringe opposition. It is a consensus. 

The shift is already scrambling candidates’ positions ahead of the midterms, where data centers, artificial intelligence, and the future of work will be front and center. Michigan Democrat Abdul El-Sayed’s refusal to back a data center moratorium (which he reiterated on America, Actually) has given Republicans an opportunity. His opponent in Michigan, GOP Senate candidate Mike Rogers, recently came out for a one-year moratorium — an attempt to get to the left of El-Sayed (!). The shift reflects our new political reality. At this point, being pro-data center isn’t just a minority position overrepresented among C-suite executives — it’s become a moral and cultural signal. An us-vs.-them litmus test in an age of elite distrust.

The political world did not see this coming. A year ago — before Zohran Mamdani’s win in New York, before DSA congressional wins in New York and Colorado, before El-Sayed in Michigan and Angie Nixon in Florida — the Democratic establishment was all about Abundance, the 2025 Ezra Klein–Derek Thompson book that had become something like an electoral manual for Washington Democrats and the press that covers them. But Abundance was (and is) powered by advanced technology and its mass adoption — the book says so on its first pages — and, like Altman, its proponents may have underestimated how unwilling neighbors would be to hand still more of their lives over to our Silicon Valley overlords. In a recent piece, my colleague Andrew Prokop wrote that Abundance flopped among voters because leftists adopted their best ideas, and issues like Israel became more of a motivating force among primary voters. But I’d also add that it replicated the same mistake tech leaders made: reveling in the benefits of a transformed economy before building consensus among the people who must live with it. 

We’ve been covering this issue throughout the year, including attending an early-summer town hall in New Jersey over a proposed data-center expansion in the state’s poorest county. But even I have been surprised at how much we hear about AI and data centers from voters on America, Actually — and how tied those fears are to a larger anxiety about the future of work and a growing resentment of tech CEOs like Altman and Elon Musk. If the 2020 election was defined by both parties’ silence on AI, the years since have been defined by elites telling the underclass that they have no agency in the disruption to come.

Based on our reporting this year, here are four things that I think explain the shift in sentiment — and forecast where this goes in the midterms and the presidential race to come.

1. It’s about agency, not “the environment” 

The thing that comes through loudest on the ground isn’t a technical objection to server farms, or even a concern with their potential impact on the environment. It’s the feeling that this change was done to communities — not in consultation with them. At the New Jersey town hall, one resident put it this way: “Folks feel unheard. And we don’t feel that way — we are unheard.” Another related it to a broader anxiety about artificial intelligence, which she said felt imposed on society from the upper class: “Everything relates from the top down, and what we’re getting from the top has spread all the way to the local level. And it isn’t good.” 

Sen. Ruben Gallego — no AI opponent himself — suggested the resentment often came from how the industry picks its targets. “They’re gonna put a data center all because it’s cheap land, and also because they think it’s a Black and brown area, so they’re gonna have less pushback,” he said. And that feeling of powerlessness is not only amplified in the absence of federal regulation, but by the knowledge that most elected officials don’t even understand the technology themselves. 

In New Jersey, residents suspected the game was rigged against the people with the least power to say no, and it often is. One word in particular, from a data center developer, had enraged them. The developer had called the project an “experiment.” That means  “we’re the guinea pigs,” the resident said. 

2. These are not all Luddites

Recently, I’ve seen the data center backlash dismissed as a “psyop” — a foreign plot to undermine the US economy — or a symptom of technophobia. Simpletons who can’t understand what’s to come. 

But that’s a convenient and self-serving misread. In New Jersey, one of the most striking voices at the town hall was a man who runs a civic-tech project: “I use AI all the time — for project management. If you use it responsibly, you can leverage it to get real-world tasks done.” He was still, firmly, against the data center. And he’s not alone: Gallup also finds concerns about the local impact of data centers are much bigger drivers of opposition than more general anti-tech ideas, as does Fox News’ polling. People weren’t rejecting the tools altogether — there were plenty of ChatGPT users in that room. They were rejecting the pace, and the secrecy, of a project reshaping their community. 

3. The costs are visible. The benefits are a pitch.

A resident in New Jersey pointed out something important: “A couple hundred jobs for two and a half million square feet? How can that possibly be supported with today’s grid? There’s no plan for the future. … Who benefits with that building? It’s not the people of this town.” 

I think this speaks to one of the challenges the pro-data center crowd has faced. Once the construction jobs leave, the noise, the water draw, the rising electricity bills, the fear about home values, and the sheer brutalist ugliness of the structure all stay. One homeowner told us: “I can’t sell my house — it’s been for sale since February. A woman came back twice, had cash, then realized it was near the data center. Her kids said, ‘Mom—’ and she said, ‘I just can’t do this.’”

And even if local concerns are the dominant issue, AI isn’t offering much of a bigger-picture national argument to overcome them. As Gallego put it to me, “there is no massive-scale benefit to society when it comes to AI” that voters see in their lives so far, and they have competing concerns about tech’s impact on children, privacy, and job losses. 

Rep. Greg Casar, the Congressional Progressive Caucus chair, told me this is the reason the left has found success tying anxiety about artificial intelligence to their broader message of affordability. 

A lot of the establishment traditional Democratic politics…are telling candidates, ‘Don’t even say the words AI.’ And if you do, say there needs to be safety but innovation — and then shut up…because maybe AI lobbyists are gonna spend tons of money against you.

Being progressive means uniting all the people getting their costs jacked up and having them vote for a politician even if the AI industry spends against them.

4. The left has their answer. What’s the center’s? 

As I wrote previously, I think a defining characteristic of this ongoing ideological battle in the Democratic Party is the collapse of the center — even more than the rise of the left. And on artificial intelligence and data centers, I think you see that crumble clearly. 

Progressives like Bernie Sanders and Ro Khanna have made clear cases for the need for a data center moratorium to prioritize the potential impact of AI on workers. And candidates like El-Sayed have laid out a proposed regulatory framework even if they don’t support a moratorium. 

But the most shocking answers on the topic that I’ve heard have both been from elected officials more representative of the liberal center. Gallego called data centers a “necessary evil” — quite possibly the least inspiring words in American politics. Rep. Jim Clyburn’s answer on artificial intelligence was even worse. He told me he hadn’t heard about ChatGPT until recently and that he’s never used an LLM or visited a data center. 

“I have a great staff,” he told me. “I know how to legislate, I know how to build relationships, and I hire people to bring knowledge into the game.” It’s a fine answer for most of governing. It is a terrifying one for a technology moving this fast.

In examining the public backlash against data centers, it’s important to remember statements like these. People don’t want to feel like they’re guinea pigs in an experiment, and it’s not just Sam Altman who has contributed to that feeling. It’s also the politicians who have treated technological change as something they retroactively legislate after a crisis arrives, whether it’s in the economy, or the environment, or society. You can’t govern a revolution you refuse to understand. 

What I’m hearing

This week, I called Adrian Walker, longtime political columnist at the Boston Globe and one of the moderators of the recent debate between Sen. Ed Markey (D-MA) and Rep. Seth Moulton (D-MA), the 47-year-old member of Congress who’s challenging him in a September 1 primary election. The debate also featured a viral moment from Markey on artificial intelligence, where the 80-year-old incumbent did not seem to fully grasp the technology. You can watch that exchange here. 

So is this all about age? What have been the lines of division beyond that? 

You know, it’s surprising that when you poll people, and you ask them about age, they have concerns about Markey being 80 (and 86 potentially at the end of this next term). But it’s not what’s really driving votes. It’s really kind of become more of a race about who’s more progressive, and Markey has successfully, I think, pitched himself as the more progressive candidate.

What are the ways in which they are both seeking to prove their progressive credentials?

Markey constantly touts the Green New Deal, and he talks a lot about his endorsements from AOC and Ayanna Pressley and people like that. It’s basically the same playbook he ran six years ago when he beat Joe Kennedy, when he also did better than anybody expected among young voters.

So he’s still riding that Green New Deal/AOC connection.

He’s running the exact same campaign. And Moulton came in with a lot of skepticism. People are still mad about him going against Pelosi (in 2018). And he had this very high-profile attack about trans kids in youth sports — and people have been skeptical about him as a progressive. So he’s sort of trying to reinvent himself while Markey, you know, kind of rides the same thing that got him a little success. 

So is there a reason we should be watching this beyond just the question of age? 

I think the AI question really speaks to something very fundamental in this debate. I think people really have concerns about the fact that the whole Senate seems to be in their 70s and 80s, and I think it was reflected in that question. You have Markey up there saying, you know, it doesn’t matter whether you call it Siri or Claude or whatever. And people walk away thinking this is not the dude who should be regulating AI; they think “Yeah, you know, the 47-year-old actually understands this, and the 80-year-old pretty clearly does not.”

And it kind of speaks to, I think, the sort of hunger I think a lot of people are feeling for generational change in the Senate: You watch Senate hearings, you watch congressional hearings about things like AI, but they’re really not that impressive. It really does look like a bunch of people who don’t really understand this at a very deep level. And that’s driving this frustration. 

As people grow used to AI chatbots, we’d like to hear your views about Google’s search engine

Google has put artificial intelligence at the front and center of its search bar. The most-visited site on the internet still shows the same list of links to users, but they have to scroll past a summarized response from an AI chatbot, a feature Google calls AI Overviews. The change to what was once the gateway to the rest of the internet has been profound, and the browsing habits of billions of people are shifting.

“AI is driving the most significant upgrade of the Google Search experience ever,” Liz Reid, Google’s vice-president of search, wrote last August. In May, the company said more AI is coming.

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© Photograph: Nicolas Economou/NurPhoto/REX/Shutterstock

© Photograph: Nicolas Economou/NurPhoto/REX/Shutterstock

© Photograph: Nicolas Economou/NurPhoto/REX/Shutterstock

The internet kind of sucks right now. And it’s been getting harder and harder to use over the past several years.

Imagine you’re training for a marathon, or just trying to get in better shape, and you want to buy a new pair of shoes. Well, good luck! You’re going to have to wade through sponsored links, affiliate marketing, AI summaries, and websites seemingly engineered for search engines instead of human shoppers.

Maybe you just want to connect with friends on Instagram or discover recipes on TikTok. But even there, everyone is selling something. “Get Ready With Me” videos are sponsored by CeraVe, movie reviews are actually ads for the movies being reviewed, and the guy who posts your favorite mobility routines really wants you to try his protein powder.

So people have developed a workaround: Simply add “Reddit” to the search.

Looking for a shoe with high energy return? Reddit. Want to know if an Airbnb you’ve been eyeing has a sketchy listing? Reddit. Trying to figure out the fastest way to the international terminal in Atlanta’s humongous airport? Chances are someone on Reddit has shared very specific thoughts and instructions.

It’s a strange situation. Reddit is filled with pseudonymous strangers, and yet these people can somehow feel more trustworthy recommending a skincare product than an influencer hawking cleansers to millions of followers. Why? Because the random person on Reddit doesn’t seem to have anything to sell you.

That’s why, for many people, Reddit feels like one of the last “real” places on the internet. The site has become so integral to navigating the internet that Google took notice; in recent years, it’s begun surfacing Reddit threads more prominently in search results.

Then came artificial intelligence. Chatbots need massive amounts of human language to learn how we communicate. AI-powered search also needs somewhere to turn when we ask the kinds of hyper-specific questions that newspapers, Wikipedia, and government websites haven’t answered. Reddit has plenty of both.

Unfortunately, whenever something online becomes valuable, people and companies figure out how to exploit it.

The stakes go far beyond a brand tricking someone into buying a lousy face cream. Reddit works because people trust that there’s a real person on the other side of the screen. It doesn’t have to become totally overrun by bots or marketers for that trust to disappear. We just have to start questioning who — or what — we’re talking to.

To break down how Reddit is changing, what the platform and its moderators are doing to push back, and what it could mean for the increasingly blurry line between human conversation, marketing, search, and AI, Today, Explained co-host Noel King spoke with the Verge’s Mia Sato, who recently wrote about the new wave of AI spam beleaguering the platform.

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 podcasts, including Apple Podcasts, Pandora, and Spotify. 

Are you a big Reddit user?

Yeah, I think so.

What do you mainly use it for?

I think it’s become a big part of finding information on the internet. I usually use Google as the sort of doorway into Reddit, but if I search something on Google, really, it’s a high likelihood that I will end up on Reddit in the end.

That’s a trend that you write about in your piece, which I thought was very interesting because it was something that has also been happening to me and it happened without me realizing how often it was happening. 

You start this piece in the Verge with an example from a post on a skincare-focused subreddit, a place you write that you visit often. Can you take us through what happened?

The skincare subreddits that I mentioned in my story are actually subreddits that I often read because I want unfiltered or true opinions about products before I buy them. And I go to Reddit for skincare recommendations quite a bit. 

I suspected that if you are a skincare brand and you want people to be talking about your product, you will go to these subreddits because some of these subreddits get like one and a half million viewers a week. They’re doing crazy numbers and they have a very committed and active community. So I was like, if I were a brand and I wanted to market myself, I would probably post on Reddit. 

I was curious how the moderators of these skincare subreddits were handling that, because also marketing firms had told me, “Yeah, all our clients, they really want to know how to crack Reddit. It’s really hard. They want to figure out Reddit.”

And so I talked to a subreddit moderator who does one of the skincare subreddits, and she gave me a ton of detail about the amount of spam that they were getting. She sent me one example, which was a thread of someone asking about a certain spray that people use for acne treatment. 

The person who posted it asked, like: “Magic Molecule Hypochlorous Acid Spray – is it really that good? Recently I have seen a lot of good reviews about this product. Any of you tried? Do you recommend? What is your take on this?”

And, you know, it got like dozens of answers. Some people said, “Yeah, it’s great.” Others said, “They all work the same.” But one of the answers was from just a random account that said, “I’m sorry, I don’t have any experience with that product, but I have tried this other one that I was skeptical of, but I really like it actually.”

If you’re reading that comment, you probably would think nothing of it. But what you don’t see unless you click over to the profile and do some digging is that this random account had actually — over the course of several days, maybe even months, and across different skincare subreddits — been recommending the same product over and over and over, and using very specific terminology, saying, “It has all these recommendations from certified dermatologists, which made me feel a bit more confident trying it on sensitive skin.”

It was very controlled messaging. Of course, the moderators were like, “What normal person is going to go across different subreddits and keep pushing this product? For no reason?”

So that is one of the ways that this new type of spam on Reddit takes shape, which is brands pretending to be normal users on Reddit.

For Reddit moderators, I’m thinking they can’t individually suss it out immediately or it wouldn’t be happening. Are they doing the search themselves to try to figure out, like, is everything on here real? How are they dealing with this? 

They have a bunch of tools. Some of them are auto-moderator tools, where basically a bot will look at every submission that comes through and move some things to a filtered folder where moderators can look at [it]. 

One of the funniest things that several moderators actually told me was they keep shit lists of companies that they think have spammed them in the past. The skincare subreddit told me about this. A weight-loss subreddit moderator whom I interviewed also said this. They have a running list in the background where if they think you’ve spammed or astroturfed their community in the past, they keep all those brands’ names. And now if you mention that brand name, your post will automatically get filtered out.

Wow. Okay. 

The moderators are trying to send a message: “If you spam us, all of the posts that are not even your spam will get filtered out and we will take a closer look at them.” They’re pretty strict with it. Some moderators that I spoke to said that they’ve really seen an increase over the last six to eight months, maybe. 

I can give you a couple of the numbers that Reddit has released publicly because Reddit has said also that they know that this is a growing problem. The company said that it removes 25,000 spammy posts and comments a day. They block something like 23 million spam views. And they also tackle spam upvotes — a way on Reddit to sort of signal agreement. 

But Reddit knows that it is kind of a new era of spam, and they’ve said that they’re using LLMs and other AI-powered moderation tools to try to catch this stuff better.

Let me ask you something. Maybe it comes down to what Reddit is and always has been, but I will go on Instagram, I will go on TikTok, and my sense is like half of it is just crap that’s trying to sell me something. It might not be real. It just all feels pretty garbagey to me. 

I feel like when you wrote this piece, you were saying there are stakes here to Reddit being the thing that must kind of stay pure, must stay away from this. What is it about Reddit that makes it important? 

One part is that the concept of influencers doesn’t really exist on Reddit in the same way that it exists elsewhere. 

And if you are an influencer, there are very few places that you can post without getting in trouble. Many, many subreddits have explicit rules saying, “You may not self-promote here, and we will ban you if you do.” That is a totally different environment than Instagram, where sort of the expectation is that someone is selling you something.

The other part that makes Reddit unique, I think, is that for better or for worse, and deserve it or not, Reddit, in the minds of people using the internet, has come to sort of be associated with real opinions or real people. Which is funny because the platform is anonymous, you know? Most people are using just a random username, not their full name. So it’s kind of stumbled into this reputation of being filled with real, helpful opinions and perspectives. 

Reddit has deals with certain AI companies where they allow LLMs to be trained on troves of Reddit data, of real people having conversations. And so I think over the last few years as Google search feels like it’s gotten worse and people have gone to places like Reddit to answer their questions, Reddit’s stock literally has skyrocketed as a place where you can find information where people aren’t trying to sell you something all the time. Or are they? That’s kind of the part that I wanted to untangle. 

Does Reddit actually have to become overrun with marketing, fake posts, people saying, “Oh, you should definitely buy this skincare,” because they’re getting paid to say that for this to be a big problem? Or does it just have to get to the point where I am suspicious that I’m not talking to an actual person on Reddit and I start doubting a platform itself? 

For me personally, in the course of reporting this story, I was like, “Hmm, maybe I shouldn’t buy things based on a recommendation from Reddit.”

It’s a new type of problem and a new way of looking at a platform. As it relates to AI search, if these chatbots love to cite Reddit so much for their answers, can the AI systems detect when something is spam? Can they detect when a comment is coming from a brand or when the person who left that comment, if you go to their account, they’re always promoting that product? 

I am not convinced that Google’s Gemini or ChatGPT can suss out when something is promotional and when it isn’t. Because I’ve written about this before and I know that they can’t.

Hmm!

It’s a strange thing where I think the deception on one platform ends up trickling through other places as well, as the Reddit thread gets cited by search features and LLMs. 

Have you changed your buying habits based on what you’re seeing now on Reddit?

I feel like I’m just way slower to buy things. I’m just like, let me keep this in my head. Let me put it on my wishlist and do some research to see what other people, what real people think, and also make sure that they have a good return policy.

Like many people, I have a contentious relationship with the Memories feature on my iPhone, the AI-powered nostalgia machine that serves up photos and slideshows from deep in your camera roll, either reminding you of a beautiful day at the beach or surfacing an old photo of your ex. About once a month, for whatever reason, my iPhone reminds me of the time my phone got stolen.

The Memory is a glitchy, fragmented one — an auto-generated slideshow of my first few years living in New York. But because my phone was stolen, the photos in the slideshow are a random assortment of mislabeled JPEGs and grainy images I’d saved from Facebook, all somehow imported from my hard drive to my camera roll at some point and set to sentimental music. 

I used to get mad that I didn’t back up my actual, treasured photos on a hard drive. But now, I just wish I’d printed out the best ones and put them in a box. That kind of thing can survive for generations.

We should all be printing our photos. I’m not just talking about converting your camera roll into a stack of 4×6-inch glossy pieces of paper, either. It’s actually easier than ever to turn the gigabytes of images we carry around in our pockets into physical objects, like albums and books and frames, the kind we can gather around to pore over and one day pass down to our children. Even individual prints that we touch and hold can tell a story in a way that pixel on a screen never could. Real photos are effectively embodied memories that we can casually encounter as we go about our daily lives. And it’s not just because an algorithm decided to send us a push notification to go look at them, either. 

“Printing photos in any form, whether it’s to hang on a wall or to hold in our hands, is effortful,” Brianna Marshall, dean of the Steely Library at Northern Kentucky University, told me. “It requires us to take action to experience the memories in more meaningful ways than an occasional passive scroll of our photo roll.”

In a way, I owe a lot to that phone thief. It was only after losing so many photos that I started paying closer attention to how I managed the new ones I’m taking, which are thankfully backed up in multiple places in the cloud. I’ve made some photo books and some albums of big events, like my wedding. I have a box with some prints inside, although there should be more. 

I now have a couple of kids who do many cute things every day, and my camera roll is getting more crowded. My walls are looking increasingly bare. I know I should be printing even more photos, and you probably should, too. When we commit to a process around curating and printing our photos, we’re also creating a ritual around preserving our memories and giving ourselves a chance to interact with them for years or even decades to come. This is so much more rewarding than taking thousands of pictures indiscriminately and then just uploading a few to Instagram periodically and hoping for some likes.

Why your camera roll isn’t a family history

There’s something magical about sitting down with a family photo album. My mom has made them obsessively for decades, and any time I’m home, I end up pulling one off the shelf. Each page is carefully curated to tell a story about a moment — a dance recital, a vacation, a holiday — and taken together, the album tells a story about our lives at that time. She’s made albums about her own childhood, and she’s started some for my own children. Collectively, they’re a family history that we can gather around.

As much as Apple, Google, and other tech companies have tried, you can’t replicate that feeling with an app. There are a ton of upsides when it comes to backing up your photos digitally, including the ability to access them from anywhere or quickly share images or entire albums. However, cloud storage platforms tend to compress your photos, harming image quality. They can also change their terms of service or their business model at any time, potentially leaving you to pay a monthly fee or face losing your photos forever.

On a more basic level, though, something is lost when you’re scrolling through pictures on a smartphone, where your inbox or your TikTok feed is just a swipe away. The images “seem a little bit more removed and a little less approachable,” said Debra Norris, professor of photograph conservation at the University of Delaware. “It becomes a point of conversation and enjoyment and accessibility that doesn’t exist when an image is on the phone.” 

Your phone is full of distractions, yes, but it’s also probably just overflowing with photos. My camera roll has nearly 30,000 images, which means that finding one specific shot from a certain day on a vacation a few years ago is more of a chore than a point of conversation. Encountering old photos serendipitously would be utterly impossible if not for — I kind of hate to say it — the help of features like iPhone Memories. This software can help resurface some old photos, and that experience can be delightful. 

Try taking things into your own hands, though. Rather than leaning back and letting AI decide which memories you’d like to remember, you can curate your photos as you take them, favoriting the good ones within a minute or two to help you separate the wheat from the chaff later. The end goal here might be printing the very best to put in a frame, an album, or even just a dedicated photo storage box. In doing so, you’re actually crafting the first draft of a visual family history. When you do ultimately print those photos (more on this in a moment), you’ll give that history a physical presence and create a sort of legacy.

“We can take so many photographs on these phones and yet a curated box of images that are especially meaningful, it’s magical really,” Norris said. “And it’s something that your children will save and treasure and share with their children, and that will continue.”

How to get the best photos off your phone

Once you get into the habit of curating your photos as you go, the actual printing part of the process is pretty easy. There are plenty of services that let you upload photos directly from your phone and will then print them in various sizes and ship them to your house or pick them up in a store (like CVS or Walgreens). Finding the right one will depend on your budget and your preferences. Nations Photo Lab and Printique are two that get rave reviews for quality prints and easy uploads. I’ve personally used Shutterfly (which offers discounts to Costco and Amazon Prime members) and Mpix, which are popular and affordable enough, but I do think the price is reflected in the quality.

You may also want to print your own photos, which can be limited and liberating at the same time. I have not tested out any dedicated photo printers, but I would trust the reviews of the good gadget bloggers at Wirecutter and Rtings, who have. You can expect to spend a few hundred bucks on the printer alone if you go this route.

Then there are the album makers. Many companies that will print your photos, including the four I just mentioned, will also print albums and photobooks. Because the layout process for making these objects is both essential and time-consuming, you might be better off going with a company that specializes in albums and books. I used Artifact Uprising to make a book of my wedding photos and loved the results, although the price tag was steep. Mixbook is the service I’d like to check out next.

You could also just get an old-fashioned photo album and put the prints directly in it, as my mom does. This is as easy as buying an empty photo album or scrapbook and filling it with your prints. You want something that is, at minimum, acid-free and lignin-free, and should opt for uncoated polyester, polyethylene, or polypropylene sleeves for the sake of longevity. (In the United States, Gaylord Archival and University Products are top suppliers of these kinds of products to museums and hobbyists alike.) You can add newspaper clippings or ticket stubs or any other physical objects to tell a more complete story, something you definitely can’t do with your phone’s camera roll.

If you don’t want to go the album route, or you already have prints waiting to be transferred over, you should keep them in an acid-free archival storage box. Your finished photo books and albums also benefit from this treatment. (If you’re dealing with old photos or want to take extra measures, check out the Library of Congress’s guide to photo preservation.) You may also want to frame them, an old custom that has also never been easier in the digital age. I have a few things hanging on my wall that I framed through Framebridge, which will let you upload an image and receive a framed print. I’ve also heard good things about Level Frames, and one of my colleagues swears by Frame It Easy. Your local frame shop works too, of course, or you could grab frames at flea markets, garage sales, and thrift stores.

Regardless of who actually prints or frames the prints, the most important thing when it comes to building a habit of curating and printing your photos is to find a process that works for you. If AI-powered tools like Apple Memories do a good enough job of finding the needles in the veritable haystack of pictures on your iPhone, that’s fantastic. Print those photos. Make a book with them. If you prefer favoriting good photos as soon as you take them or putting your best shots in a digital album at the end of every month, keep it up. Just set aside time to print some of those.

“My advice is always just to start somewhere, and not to let any guilt over what you did or didn’t do in the past stall you from moving forward,” Marshall said. “Save the photos that you treasure.”

Have you been hearing a lot about Flock recently? You’re not alone: The surveillance company has put 120,000 cameras on roads in every US state except Alaska, and people are mad about it — online, at city council meetings, and out in the world, where the cameras have become the target of vigilante vandalism.

Flock’s automated license plate readers — cameras that scan the license plates of every car that passes, along with the car’s make, model, bumper stickers, and other identifying characteristics — aren’t really new technology. What makes Flock unique is its use of AI to power a nationwide database that law enforcement can search with very few restrictions.

Founded in 2017, Flock was most recently valued at $8.4 billion. Its aggressive growth strategy has led to widespread adoption: 7,000 law enforcement agencies across the country use Flock cameras, representing a whopping 40 percent of the nation’s police departments.

Flock says its cameras have been used in a million criminal investigations a year. Police say the ability to track suspects across jurisdictions has been a critical tool for solving thousands of crimes including car thefts, missing persons, and abducted children.

But recent reporting also shows how the system is ripe for abuse, with dozens of reported cases of police officers illegally conducting searches on Flock to track the movement of current or former romantic partners. Those stalking cases have led to some arrests, but the instances only came to light after journalists and concerned citizens filed public records requests for the data.

The company recently unveiled several reforms that it says will help avoid improper uses of its system. Critics say the changes are merely window dressing for a surveillance system that threatens the privacy of everyone, regardless of whether they have committed a crime — and they haven’t helped to quell widespread outrage about the cameras, which has caused dozens of cities and counties across the country tohave cut ties with Flock. 

“There’s been a political backlash to it, and there’s also been a vigilante backlash to it.”

To get a better sense of where the Flock backlash is heading — and whether the company will be able to survive — Today, Explained host Sean Rameswaram spoke with 404 Media co-founder Jason Koebler, who has reported extensively on Flock and has broken several stories about its misuse.

The Today, Explained team also reached out to Flock for comment and received an out-of-office reply that said: “Our media team is currently touching grass and taking a break. Unlike our cameras, we can’t work 24/7, so we’ll get back to you when we’ve had a snack and regained the ability to form coherent sentences.”

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 your podcasts, including Apple Podcasts, Pandora, and Spotify.

Cops are using Flock in a really creepy way to stalk their exes. You’ve been writing about other potential misuses and existing misuses of Flock. What are you seeing?

I’ve been reporting on Flock for a few years now and there’s been several high-profile cases and trends that we’ve been reporting on. 

Last year, we learned that local police were helping ICE surveil undocumented immigrants using the Flock network. Police in Texas and South Carolina and Tennessee were searching the entire Flock network for either “immigration” or “ICE” in the reason field in the Flock system. And they were searching not just in the states and communities that they were in, but they were searching throughout the entire country. So these searches happened in, notably, Illinois and California, where it is against the law to use this technology for immigration enforcement. An investigation by the Illinois secretary of state found that these searches broke state law and Flock had to do some reform after that. 

There was also a case in Texas where a woman’s partner reported that she had a self-administered abortion and more than 80,000 cameras across the entire country were searched for that woman. This is a nightmare situation for people who are worried about access to reproductive freedom. In that case, Flock wasn’t able to track down the woman, but the searches were done nonetheless and police there considered whether they would be allowed to charge her with a crime while they were doing that investigation. 

And then earlier this month, I reported on a case in Wisconsin where police used the Flock network to track a man who drove from Wisconsin, where weed is not legal, into Michigan, where it is legal. They then waited for him to drive back into Wisconsin, pulled him over, and then used the fact that he crossed state lines as the pretext to search his car for weed.

So this isn’t just cops and their domestic relationships or former relationships. This is ICE, this is abortion. This is invasive, is the long and short of it. How are people responding to this new era of surveillance that no one signed up for?

People are really mad about it. I think that there are a lot of communities that signed on to this technology because they thought it would primarily be used to recover stolen vehicles, to find missing children, for property crimes, things like that. And what we’ve seen is a real mission creep. 

We are seeing it used to surveil protesters. We’ve seen it used for immigration enforcement. We’ve seen it used for all these stalking cases. People think that this is the surveillance state. And so there’s been a political backlash to it, and there’s also been a vigilante backlash to it.

Tell me about the vigilante backlash.

Basically, people are putting masks on and they are cutting Flock cameras down. These are mounted on poles, and so they are buying pole saws and they’re cutting the poles down. Flock cameras have been shot with shotguns. Industrial foam has been sprayed on the lens of Flock cameras. 

I also got an email from a man who was arrested for what he said was “changing the angle of a Flock camera.” And what he meant by that is he drove his truck into the pole of a Flock camera and had it face the sky. He was like, “I made it not able to surveil people. Now it’s surveilling the sky.”

There’s something called DeFlock, which is a crowdsourced map to track all of the Flock cameras in the United States. This map has become incredibly popular. 

What we’re seeing are people forming these groups to talk at city council meetings to determine, “Is this a technology that we want in our town?” It’s been people standing at a podium saying, “I don’t like this technology. Let’s talk about it.” And then on the other hand, you have vandals who are destroying Flock cameras and cutting them down.

The backlash is happening across the political spectrum. There have been towns in Silicon Valley that have banned Flock cameras. There was a town in Texas called Bandera that is like a Wild West tourism town that banned Flock cameras. 

Conservatives will say, “This is invasive government. This is Big Brother. Don’t surveil me. I don’t want you seeing where I drive my car or my truck.” The left is saying much the same thing, but they’re saying, “This is being used to surveil Black and brown people. It could be used to surveil women seeking abortions. It could be, and has been, used to surveil undocumented immigrants.” And so the backlash is coming from all angles at this point. 

“I think that they are a very popular company with police and with cities, but the backlash is so strong that I think that the company is worried.” 

There have been dozens of cities and towns that have canceled their contracts. Los Angeles allowed its contract with Flock to lapse. Denver canceled its contract with Flock. Some of these have been quite large cities. You’ve also seen pretty small towns cancel their contracts as well.

This is a multibillion-dollar company. They don’t want to remove cameras. They want to install more cameras. Are they doing anything meaningful to address these concerns?

The backlash has been so strenuous that Flock has started to say that it will proactively flag anomalous searches. So if a police officer is searching the same license plate over and over again, it might automatically flag an audit for the police department to see, “Hey, is this police officer abusing the system?” 

They also are changing the default retention time from 30 days to seven days, meaning that your Flock data will stay in its system for only a week rather than a month.

Organizations like the American Civil Liberties Union say that these changes are not super meaningful and that the main requirement that most people are asking for would be a requirement for police to get a warrant before they search the system, which would take these searches from being extremely routine — there are hundreds of thousands of searches done on this system every single month — to where there would be a higher bar, where a judge has signed off that this is a crime that warrants this type of search.

If Flock goes down or if the backlash grows to the point where they’re at least far less relevant in this field, are there a dozen other companies doing the same thing ready to take its place?

This technology is really old. It’s been around for decades. And what Flock has done is networked it and added AI to it. But there are other companies that offer similar technology. Motorola is in many cities in the United States. They make similar tech. Axon, which makes taser and body cameras and cameras for police cruisers, also has an [automated license plate recognition] product. And some of the cities that have ditched Flock have actually just replaced it with an Axon contract, meaning they’ve ditched one surveillance vendor and they’ve added a different one.

I don’t know that surveillance cameras and automated license plate readers are ever going to fully go away, but I don’t think it’s clear that Flock itself is going to survive this backlash. I think that they are a very popular company with police and with cities, but the backlash is so strong that I think that the company is worried. 

We can see it in how their CEO, Garrett Langley, has been talking about the tech. A year ago he was calling journalists who covered the company activists and he was calling activists who protested the company terrorists. And he’s had to apologize for that rhetoric. He’s had to go and say, “We are not Big Brother. We are trying to improve your communities.” And they’re really on a PR tour right now to clean up their image. 

I don’t know where this goes from here, but I haven’t seen much fatigue from the public on this story. I opened my Instagram feed and I’m just inundated with video after video that has millions and millions of views about Flock. This seems to be something that has really captured the public’s attention and that they really seem to care about.

If you’re like me, and you grew up in a house where your mom refused to let you get a video game console, because she believed sitting and staring at a TV was bad for you, you might think of video games as antithetical to health. And there is plenty of research about the ways in which gaming too much can harm your physical and mental well-being. I don’t think anyone would argue that eight hours of Call of Duty every day is doing your body or your mind much good.

But the old caricature of video games as simple brain rot is increasingly out of date. Even some normal, non-educational games can help with brain function (as long as they are played in moderation — and some games are better than others). 

Beyond that, what if we could take something that is wildly popular (there are 3 billion gamers worldwide, 190 million in the United States) and reimagine it in a way that goes beyond just entertainment? Gaming tech has advanced to the point that we can put people into hyperrealistic virtual realities or use AI to create new personalized gaming apps with a brief voice prompt. These and other capabilities are unlocking new ideas from ambitious physicians and game developers that would have sounded like science fiction when I was a teen.

There’s increasing evidence that these platforms can be adapted to do genuinely incredible things to benefit our health. Here are just two ways in which video games are poised to change medicine — including one that allows any of us to game out in the name of advancing science. I can’t wait to tell my mom.

Video games could solve some big challenges in caring for kids

My perspective on video games in medicine started to change when I learned about one specific use: as an alternative to anesthesia for children and adolescents.

Here is a genuine clinical problem: For years, doctors have had few options beyond general anesthesia for kids who need to undergo imaging or minor surgeries. An MRI, for example, really only works when the patient is able to stay completely still and, as anyone who has spent a single minute around a child can attest, that’s hard for kids to do. So, children have been put under during routine MRIs or minor procedures that, when they’re performed on an adult, require only local anesthesia or no anesthesia at all.

But giving children general anesthesia comes with health risks. The FDA warns against repeated or lengthy use of general anesthesia for children 3 and younger because of the risks to their growing brains and future learning development. Other studies have suggested that general anesthesia also poses some risk to the development of older children, and its use should be minimized as much as realistically possible.

Enter virtual reality video games.

“Games and VR can be especially effective for reducing pain and anxiety during pediatric procedures, including burn care, needle procedures, and other medical treatments,” Dr. Kimberly Hieftje, co-director and co-founder of XR Pediatrics and the Yale Center for Immersive Technologies in Pediatrics, told me over email. “If we can help a child get through a procedure comfortably without sedation, that’s a significant benefit. Sedation and anesthesia carry risks, and minimizing unnecessary or repeated exposure is particularly important in children.”

Several small-scale studies have examined using virtual reality games as an alternative to general anesthesia for minor and routine surgeries and for MRI imaging — with promising results. In one 2024 experiment, more than 100 kids (average age of 11) wore a mobile VR headset during a minor procedure (most frequently a hormone implant) and played a relaxing game such as Pebbles the Penguin (in which players navigate a snowy world and collect pebbles) or Space Pups (in which they play as a canine soaring through outer space and eating treats) while doctors performed the operation.

The surgical team kept general anesthesia on hand in case it was needed, but none of the children involved in the experiment required it or any other kind of sedation. They were also able to follow simple directions from their physician during the procedure. Their post-surgery reports of pain were similar to patients who did receive anesthesia, and they had shorter recovery times.

Likewise, a study out of Canada published in December 2025 tested how younger patients responded to VR as an alternative to anesthesia when they needed an MRI. It was a small study of only 18 patients, but, once again, the results were encouraging. The kids (average age of 5) went into a VR game prior to the MRI scan, learning about the procedure while collecting magic fairy dust. Then, they went into the actual MRI. And all of the kids who had played the VR game prior to the procedure were able to complete the imaging scan without any additional sedation or anesthesia.

The foundational idea here is: Kids are not little adults. It’s harder for them to sit still. They get anxious about even imaging scans. “We create for adults and, then, put kids in it,” Hieftje said, “and we need to think backwards.”

Video games can help kids stay calm and stop moving — or, for kids with different medical needs, start moving. As any parent knows, children also aren’t very good at following directions or being self-motivated to exercise — even if it would be good for their health, like if they have Type 1 diabetes, for example. That’s why a group of researchers from Yale, as described in a study published earlier this year and which Hieftje co-authored, experimented with introducing kids with Type 1 diabetes to a virtual reality video game that coached them through exercises. 

It was a small group — 17 adolescents, an average age of 15 — but patients who participated were motivated to play the game, followed through with their routines, and even registered a small but detectable decrease in their blood sugar levels. The researchers are hoping that larger studies could demonstrate the program’s effectiveness and continue taking it mainstream.

The list goes on: Hieftje said their work uses games and immersive technologies to address everything from substance use and human trafficking prevention to mental health, child loss and grief, and infection prevention for infants in a natal intensive care unit.

But video games are doing more than changing clinical care for challenging patients. They are also unlocking the basic science that leads to new breakthroughs in treatment for everybody.

Video games could allow all of us to contribute to future scientific advances

When I think of massive multiplayer online games, I think of my friends and I camping out in somebody’s basement to play Halo against strangers for hours on end. But Attila Szantner, the co-founder and CEO of Massively Multiplayer Online Science, has found a more productive use for these remarkable platforms that can connect hundreds of people from all around the world.

What his company has done, in tandem with academic scientists, is integrate important but tedious basic research tasks into the gameplay of popular commercial multiplayer games like Borderlands 3. When I attended the Aspen Ideas: Health summit this summer, Szantner presented a demo of one of his company’s modules. What appeared to be players sorting colorful tiles in gameplay that would look familiar to anyone who’s played Tetris, he said, was actually players — normal people with no special training — helping to sequence DNA samples for people’s gut bacteria. Players in the game learn the task from a Borderlands character — including its ultimate scientific aim — and, then, complete the puzzles that the scientists have set up.

“Games are the absolute masters of engagement. They found the magic formula to make repetitive tasks feel fun,” he said. “In citizen science, people have intrinsic motivation to help, but standard tasks get monotonous and people drop off. Games solve that completely.”

They’ve turned the boring but vital work of number crunching and sequencing into gameplay — and convinced millions of their fellow citizens to help. In a paper published in October 2024 in Nature Biotechnology, Szantner and his co-authors described a project that involved more than 4 million individuals completing more than 135 million science puzzles in order to align a million human microbiome sequences. They found that the players’ collective contributions led to better sequencing than the current state-of-the-art computational methods. In another project, they turned some basic cellular analysis tasks into gameplay on the multiplayer game EVE Online that, once again, performed better than what is now the standard.

We have only scratched the surface of gaming’s awesome potential. Clinicians are also optimistic about gaming’s ability to preserve the cognitive health of aging adults and coach them through exercise routines (much like the kids in that Yale experiment), especially as the native gamer generations get older. Video games could revolutionize trauma care by placing burn victims inside of a cold VR environment — and what if you could reduce PTSD by playing Tetris? In 2020, the FDA approved the first video game for ADHD treatment: EndeavorRx. Experts confronting a male loneliness epidemic believe they can use video games to bring young men together. These programs could also transform medical training and allow surgeons to preplan and practice surgeries using their patient’s unique data to produce a bespoke virtual reality practice module.

The point is: The caricature of video games as a gateway to couch potato-itis is a relic of the past. Gaming could help all of us stay healthy — seriously.

Ever find yourself hunched over your laptop, eyes glazed, slack jawed, and stunned by the quantity of information modern life is throwing at you? You might take a break to stare at your phone. Or you could try switching from one browser tab to the next while checking your email and inching closer to the day’s deadline. 

Some people call this TikTok brain. Others call it brain rot. Either way, it feels like a distinctly 21st-century affliction, caused by scrolling through too many short-form videos and social media feeds that trains your brain to seek out little hits of dopamine and ruins your ability to focus on anything for more than a few seconds. People with the condition may not be able to read books or even watch feature-length films. They can’t do anything except watch little videos, leaving them prone, catatonic, faces blank and bright in the glow of their smartphone screens. 

I don’t even use TikTok, but I feel this way all the time. Give me one hour to complete a task that requires even a medium amount of mental fortitude, and I’ll find 55 minutes’ worth of other things to do. It’s not that I’m avoiding the task. But once I get started, my mind wanders relentlessly, no matter how hard I try to keep it on track.

You can blame technology for doing this to you — and I do — but the real issue is with how we treat our brains. Generally speaking, the human brain works like an analog computer, processing one thing at a time, and when you switch tasks (which is what we’re actually doing when we say we are multitasking), it comes at a cost: It drains your cognitive resources, which makes it even harder to pay attention in the future. The more you do this, the more ingrained the behavior becomes.

“It’s a myth to think that we should push ourselves to focus as long as possible and we’ll be more productive,” said Gloria Mark, the author of Attention Span: A Groundbreaking Way to Restore Balance, Happiness and Productivity and the Substack The Future of Attention. 

The human attention span is not one thing. There are different types of attention, including sustained attention (your ability to stay engaged with a task over a period of time) and selective attention (your capacity to take in certain information while tuning out irrelevant details). Alternating attention (the mental flexibility required to switch tasks rapidly) and dividing attention (process multiple things at once) are especially demanding on your cognitive resources. And all of this is managed by the brain’s command center, your prefrontal cortex, which handles executive function. When you’re experiencing cognitive fatigue, there’s a physical reason why: There’s actually less blood flowing to this portion of the brain.

“We need to stop trying to recover from cognitive fatigue and overload by consuming more information.”

Amisha Jha, University of Miami neuroscience professor

The good news is that the damage is not permanent. The human brain’s capacity to focus is just as intact as it was in the 19th century when everyone spent their evenings reading novels and knitting fisherman sweaters. Lots of things have changed since then, of course. As smartphones have driven us to consume endless amounts of information, psychologists have been working on strategies to put the pieces of our attention spans back together. I called a few of these attention experts hoping that they might tell me the secret to regaining my ability to focus. They all agreed that two things are necessary to solve the problem: Be intentional with your attention and let your brain rest.

“What we need most is the capacity to know where attention is, moment by moment, and greater agency over our attention so we can direct it intentionally,” Amishi Jha, a neuroscience professor at the University of Miami and author of Peak Mind, told me. “We need to stop trying to recover from cognitive fatigue and overload by consuming more information.” 

Put another way, in order to reclaim your attention span, you first need to be mindful. That means spending less brain power dissociating on social media feeds and more on whatever you’re actually interested in. You also need to give your brain a chance to rest and recover. Staring at your phone will not help you recharge and reclaim your attention span. Staring at a tree, however, will. 

Let your brain rest

One way to think about your own attention span is to consider what is voluntary and what is not. When something like a bright light or a loud noise grabs your attention, it engages your involuntary attention, which is effectively limitless. When you’re deciding to pay attention to something, however, you’re using your voluntary, or directed attention. This is a limited resource. As you’re making an effort to focus on something, like a book, your supply of directed attention runs down, but it can be replenished in specific ways.

This simpler way to understand how our attention works dates back to the 19th-century psychologists, like Williams James, but it’s making a comeback thanks to attention restoration theory (ART). This school of thought, first developed in the 1980s by University of Michigan psychology professors Stephen and Rachel Kaplan, suggests that spending time in nature restores your directed attention. The theory claims that the natural world does this by captivating our indirect, or involuntary, attention and allowing our cognitive resources to recover.

Imagine you’ve gone for a hike and happen upon a waterfall that’s so fascinating you can’t look away from it. Marc Berman, one of Stephen Kaplan’s former students and now a professor at the University of Chicago, refers to such a spectacle as a “soft fascination,” something that can grab your attention without consuming it, thus giving your mind space to wander. You might get the same effect from staring at a campfire or looking up at tree branches swaying in the breeze. Hard fascinations, by contrast, capture so much of your attention that you can’t look away and you can’t quickly recover. Times Square is a canonical example, but being on social media counts, too.

“Doomscrolling, streaming, surfing the internet — they’re activities that maybe seem restful, but we actually think that they’re fatiguing and depleting,” said Berman, who is also the author of the book Nature and the Mind: The Science of How Nature Improves Cognitive, Physical, and Social Well-Being. 

In one study Berman conducted, he and his team (which included Kaplan) gave research subjects difficult attention and memory tasks, sent them on a walk in nature or an urban environment, and then repeated the tests. The participants who went on nature walks improved their performance in these tests by about 20 percent, Berman said, and the improvements held regardless of whether participants said they liked the walk or not.

“One of the great misconceptions about attention is that it’s simply a matter of us paying attention.”

Nilli Lavie, University College London psychology professor

Berman said they aren’t entirely sure why nature seems to have this effect on us, but he theorizes that it relates to fractals, or repeating, self-similar patterns whose individual parts resemble their larger whole — tree branches or fern leaves, for instance. Fractals are common in nature, and Berman thinks their orderly arrangement helps our brains to process the information. “It’s easy to get the gist and you don’t need to encode all the details,” he said. “Whereas in the built environment, it’s not very fractal and you might have to encode everything, which might be more taxing.”

Of course, it’s not always possible to zip over a forest in the middle of the work day. If you can’t take a walk in the park, Berman said that even looking at a picture of nature scenes can help. (Try not to do that on Instagram, though, because you might end up just doomscrolling.) 

Be intentional with your attention

Reclaiming your attention is one thing, but holding onto it in the first place is its own distinct challenge. When you find yourself feeling distractable, it might be that you need a break. If that’s the case, you can’t will your way into concentrating.

“One of the great misconceptions about attention is that it’s simply a matter of us paying attention,” said Nilli Lavie, a professor of psychology and brain sciences at University College London. “It’s not enough to just want to pay attention.”

Instead, you have to put yourself in an environment that makes paying attention not only possible but achievable. That means making a concerted effort to avoid multitasking, which will drain your cognitive resources. Try the alternative: monotasking. 

Monotasking likely requires clearing away clutter, whether that’s literal clutter on your desk, or digital clutter on your screen in the form of extra apps or browser tabs. Turning off all notifications is essential. You should also hide your phone — put it in a drawer or in another room. (A widely cited study published in 2017 found that even having a phone in sight can reduce your cognitive capacity and affect your ability to concentrate.) When you need to take a break, don’t go find your phone: Take a walk outside, or stare at a tree, or look out the window and listen to a song you like.

Monotasking also means being intentional about how you do that one thing, avoiding distractions along the way. At work, this might mean carving up projects into digestible chunks. You might even try the Pomodoro technique: Set a timer for 25 minutes, focus hard on the task until it goes off, take a five-minute break, and repeat. At home, it could mean making to-do lists and prioritizing tasks so that the toughest ones get done first, while you still have some gas in the tank.

If you aren’t ready to turn off all notifications at this point, at the very least turn off all the ones that aren’t coming from humans. You do not need to be alerted of an Amazon flash sale while you’re trying to plan your family’s meals for the week.

Give yourself some constraints

There’s no such thing as being 100 percent locked in to any given task. When there are no external distractions, your brain actually turns inward and finds something else to pay attention to, which can lead to daydreaming and mind wandering. Daydreaming — creating a structured narrative of imaginary scenarios — is not necessarily a bad thing, by the way. It can be productive in helping you plan and think creatively. But if you’re just mentally wandering from random topic to topic, you’re getting into the danger zone. 

Some people find that adding constraints can improve their attention. One approach you’ll see in self-help books is the use of a so-called commitment device that restricts future behavior in service of a larger goal. If you’ve read (or seen) The Odyssey, you might remember the scene in which Odysseus orders his crew to tie him to the ship’s mast and ignore his orders so that he won’t steer the boat into the rocks when he hears the Sirens’ call. Odysseus was using a commitment device to avoid killing his whole crew. You might use a commitment device, like a dedicated writing space or the Pomodoro technique, to avoid distractions, both external and internal. 

So how do you focus just the right amount? Removing those external distractions is a great start. Much like paying attention, ignoring these distractions consumes your cognitive resources, impacting your performance, and causing you to make mistakes. It also takes more effort to stay on task as time goes on. From there, do your best to avoid overtaxing your brain to the point that the internal distractions take over and your mind is wandering. That’s how you know it’s time to take a break.

“For several minutes, give yourself no new input,” Jha said. “Do not reach for the phone while waiting in line. Take a short walk without listening to anything. Look out a window. Let the mind wander without trying to solve a problem.”

Take a walk in the park. Sit quietly and look at a bird. Paying attention has never sounded so relaxing.

Results day has arrived and Telegraph readers share their present ideas for loved ones heading off to study

Everyone is wrong about Hank Green. 

In case you missed the controversy: The veteran YouTube star, writer, and science comms entrepreneur was recently “canceled” after he acknowledged using AI for research.

“I have been relying too heavily on AI as a research aid,” he wrote in a statement on Reddit. “It can be very useful for this task, giving me access to a lot of papers I didn’t know existed really fast, but I think that has been to the detriment of my work because it has not given me the freedom to find all of my own ways into and around a topic.” Although Green wrote that the words in his videos are his own, his reliance on AI as a research aid still gave the finished work an ineffable “AI feel.” And his relationship with AI, he wrote, had become “not healthy for me or good for the world.”

Some of Green’s followers, known by the cheerfully dorky moniker “Nerdfighters,” turned on him for daring to use AI in any capacity. Just as quickly, that backlash produced its own backlash, aghast not at Green’s use of AI but at his prostration before an anti-AI mob — “self-canceling,” as some put it, over a legitimate use of the technology. 

I think both of these camps are misguided and have flattened a complex issue into a set of binary extremes. And it surprised me that, despite robust societal debate on AI’s impacts on our ability to think, write, and produce original ideas, the debacle hasn’t prompted more thoughtful conversation about the limits of AI in creative work. 

I felt this because I recognized myself in Green’s statement: the feeling that even using AI for research can start to take over your creative process, that it can become hard to know where your own brain ends and where AI begins, and that the technology can simply push you to work too fast. I don’t use AI to generate writing and would not do so — but its use need not rise to that level to raise profound questions about how much of our work to automate, and what happens to our ability to think for ourselves when we do. 

In a follow-up video published late last week, Green laid out a new AI policy for his work. He wrote: 

1. No portion of any script will be written, edited, or outlined by an LLM.

2. The thesis of a video will always originate with a human. 

3. No image or music in a video will be generated by AI. If something is accidentally included, best efforts will be made to remove it. 

4. LLM outputs are not trusted as a source.

These are all good ideas for any creator trying to avoid AI creep in their craft. But still, they raise a bigger, harder-to-answer question: The very structure of generative AI makes it hard to use without offloading human thought and judgment, which can lead to a widely discussed phenomenon known as “cognitive surrender.” And it pushes us toward uses — like synthesizing research, brainstorming, generating ideas and angles — that short-circuit the original thinking and discovery that we ought to be doing ourselves. What, then, can we even responsibly use AI for? How can we set guardrails that allow us to avail ourselves of its usefulness, without melting our brains in the process? 

The most tempting uses of AI are precisely those best avoided

Remember late 2022, when ChatGPT first came out and everyone mocked its crappy research skills and propensity to hallucinate in every other sentence? I am so wistful for those days. 

Many people who abstain from AI may not know it, but in the time since, and especially in recent months, large language models have gotten way smarter (especially the paid premium versions). It’s become unnervingly good at summarizing niche, complex research areas and debates, and producing ideas, often without being asked, for further research or writing on the same subject. 

Whenever I have a research question these days (which is pretty much any time I’m working on a story), I’m more likely to fire up an LLM than a traditional search engine. If I ask, “Why are old-growth trees still being logged in North America?” it produces a synthesis of research, news, opinion, and whatever else its training absorbed on the subject: “We’re using an essentially nonrenewable ecological asset to smooth a temporary transition to a renewable timber resource,” it says. Probe it further, and it’ll suggest arguments for you: “Instead of conservationists having to prove that every old forest deserves protection, logging companies should have to demonstrate that cutting a centuries-old stand serves a need that cannot reasonably be met with second-growth or engineered wood.” 

LLMs are designed to make cognitive work effortless, but that feels so icky because for it to be worthwhile at all, it has to be effortful.

These aren’t particularly smart or creative ideas — they’re perfectly replacement-level, which makes them plausible substitutes for the thoughts of most people. The AI can supply pat answers to every conceivable question and follow-up you might have while working on a project, relieving you of the need to mentally engage with the shape of a problem. Contrast that with Googling in the pre-AI overview days, which, while certainly not without its problems, at least used to send you to a list of sources that you then had to read and make sense of on your own. 

Most of us who’ve engaged with LLMs know what this feels like. They make it easy for users to skate on the surface of a subject and feign understanding or insight, and in the process they can become involved in interpretive decisions that should be our own. In my experience, even more narrowly designed generative AI models don’t escape these problems. Google’s Gemini Notebook (formerly NotebookLM), for example, allows you to upload all of your sources for a project — books, reports, papers, audio and video recordings — and ask it questions based on what they contain, rather than searching the entire internet. It’s less prone to generating outright slop than general-purpose AIs. I use it for most stories I write — it’s an incredibly useful, time-saving tool. But it also enables me to engage with sources in a perfunctory, contextless manner: The AI can surface precisely the bit I need rather than forcing me to form the deeper connections that come from reading a text as a whole.

The best creative work (including not just art and writing, but also technological and medical breakthroughs) probably comes from having a wide range of background associations, and being able to combine them in unexpected ways. The French mathematician Henri Poincaré put this beautifully in his essay “Mathematical Creation,” where he wrote that it’s the tedious, sustained conscious effort that ultimately leads to flashes of insight. 

I think this is what Green meant when he wrote that AI can prevent him from finding his “own ways into and around a topic.” LLMs are designed to make cognitive work effortless, but that feels so icky because for it to be worthwhile at all, it has to be effortful. This argument has already been made about AI-generated writing: Letting an LLM write for you defeats the point, because writing is thinking. But it can also be true, as Green’s example has shown, of using AI for the research that feeds the creative process. 

If you use AI, consider creating a personal AI policy

Perhaps all these concerns are overblown — humans are hardly less prone to lazy and logically unsound thinking than AI. That’s absolutely true, but the point of doing our own thinking isn’t that we’re inherently good at it. To the contrary, it’s that we can only get better at reasoning by practicing it. 

I don’t want to suggest that using AI for research is illegitimate. It’s too useful a tool to take off the table entirely, and we can’t put that genie back in the bottle. It can be extremely helpful with identifying the best sources that you wouldn’t find otherwise, but those very abilities can make it double-edged, foreclosing a slower, more open-ended exploration process. But AI’s greatest strength — its endless variety and flexibility — can be used to steer it away from the most tempting uses, especially those that ultimately harm us.

There are very obvious things that any LLM user should do to that end, like never assuming that a claim from an AI is accurate and always reading original sources. Beyond that, the necessary guardrails depend on your own use patterns, but above all, I think it’s helpful to avoid training ourselves to expect immediate answers to difficult questions.

One of my colleagues refrains from using it to brainstorm ideas entirely, instead using it to provide sources for narrow factual questions and to aid in the fact-checking process (emphasis on “aid”) after a story is written. To generalize from this, I think it’s a good idea to resist having AI do much synthetic work on a subject before you have drafted your project yourself. The less you do that, the less you will, to paraphrase Green’s recent video, see every problem as an “LLM-shaped problem,” and the less you’ll feel like you’re in the singularity where your brain is merging with AI.

One way that I like to use AI is as an enhanced thesaurus, to find the precise word or short phrase to express what I want to say in a sentence. When done right, I don’t find this harmful any more than using a traditional thesaurus; I find that it can enrich my working lexicon. But it must be used carefully and surgically, by setting a clear limit on the length of a phrase used from AI — like two or three words max — and avoiding sharing much of your writing with the tool at all, lest it start recommending extensive rewrites. 

When interrogating the contents of specific sources or a body of work, or stress testing your own arguments, AI would be better for our intellectual development if it took a Socratic approach — pushing you to discover an answer rather than simply giving you one. It might say, for example, “there might be some relevant caveats to your idea on pp. 42-43 of the source.” LLMs can be directed to behave this way in their custom instructions. It also helps to simply touch grass — find the sources you need, and rather than interviewing the AI about what they say, just close the chatbot and read them from start to finish. 

Configuring AI in a way that’s healthier for our brains would also make it less addictive — when you find yourself getting sucked into a long back-and-forth with an AI, that’s often a sign that something has gone amiss. Green evidently struggled to set that boundary, referencing the unhealthy “level of dopamine I’ve been getting from interacting with LLMs.” AI labs have very strong commercial incentives to want us to be addicted to their products, and unless they build different constraints into models themselves, it’s hard to expect the average person, who has far less autonomy over the terms of her work than Green does, to change these conditions on her own. 

Although researchers at some AI labs are thinking about the societal risks of cognitive atrophy, it’s another matter to expect these companies, which compete on ease of use, to introduce friction into their models. We shouldn’t count on that happening soon — but we’re far from powerless against AI’s impacts. We can set our own personal AI use policies, and we can enforce social norms against AI-induced brain rot. Like, at bare minimum: Don’t send me your AI-generated writing. It’s rude! 

The influencers have started to promise me that they know how to get me to stop shopping: I just have to enter every garment of clothing I own into an app, one by one. “I digitally cataloged my ENTIRE wardrobe,” they swear on videos, routinely pulling in hundreds of thousands of views. 

The selling point of these apps (Indyx, Whering, ACloset, OpenWardrobe, and many more) is seductive; they promise to help users understand exactly what they already own so that they shop less. In theory, that means saving money — and slowing the steady damage the fashion industry is wrecking on our planet.

“Collectively, we are buying and then throwing away more than ever before,” Indyx warned on its website, before reciting dire statistics about how many clothes are produced now and how many of them end up in landfills. Luckily, it said, “We’re here to break the cycle.” 

That idea appealed to me. I am not a fashionista, but I enjoy clothes enough to buy more than I really need, despite what I know about fashion’s impact on the planet. My intellectual understanding of climate change can’t always stop me from clicking “add to cart” when I see a dashing pair of wide-legged trousers, even though there’s a hard limit on the number of times a person can wear wide-legged trousers in one week. 

Still, to make these apps work, you have to individually enter photos of everything you own now and everything you buy in the future, which sounds like a tedious, laborious process. (You can take the photos yourself or hunt down product pictures from the brand.) And because so many influencers are pushing this, it’s hard to tell how life-changing it really is and how much is just marketing speak and hype. So, I decided to test one of these apps — Indyx — for you. I also talked to wardrobe cataloging enthusiasts and experts on sustainable consumption to see what I could learn from them. I wanted to know: Could cataloging our wardrobes actually make us shop less? And, if it can, would it be worth the effort? 

“There’s only one solution to the mess that we find ourselves in: buying less”

Wardrobe cataloging apps did not always advertise themselves as being good for the planet. 

In 2023, the journalist Avery Trufelman investigated the nascent wardrobe cataloging app industry for her podcast series Articles of Interest. She found that a lot of the apps flopped. The issue was the business model, which was based on revenue generated from affiliate links. The idea was that people would click to purchase items they saw within the apps, and the company would take a cut of each sale. But when the apps were well designed, Trufelman reported, users felt less of a need to buy more clothes.

The current generation of wardrobe cataloging app developers has turned this from a bug into a feature. They now market their wares as the solution to overshopping, and they make their money by charging for either the app itself or for its extra content. (Indyx itself is free, but you have to pay a $75 yearly subscription for its enhanced features.)

What the marketing gets right is that the fast fashion problem is real. “Anywhere from two to 10 percent of our global greenhouse gas emissions are associated with fashion,” said Brie Berry, assistant professor of environment and sustainability at Ursinus College in Pennsylvania.

Fashion’s emissions are generated by the factories that manufacture clothing (the water and the fertilizers for growing cotton, the oil for developing synthetics) and the consumers who wear these garments (the slow shed of microplastics from yoga pants, the water and the energy consumed by washing and drying). When we get rid of old clothes, much of it ends up in landfills or incinerated. By some estimates, the fashion industry contributes more to climate change than the aviation industry. 

“There’s only one solution to the mess that we find ourselves in: buying less,” said Katia Dayan Vladimirova, a researcher whose consulting firm Post Growth Fashion focuses on alternatives to growth in the fashion system. 

To buy less, it helps most people to know what they already own, said Alyssa Beltempo, a slow-fashion content creator and educator. Beltempo makes videos guiding viewers through the process of “shopping their own closets” to help them buy less stuff, but she’s found in her work that a lot of people aren’t clear on the contents of those closets. Because of that confusion, they end up buying stuff they don’t need.

That’s where cataloging can be helpful, Beltempo said. “These apps reduce that hurdle of not seeing the clothes you have,” she said.

Personally, what I was looking for wasn’t enhanced clarity so much as a barrier. I wanted to erect a wall between my desire to own a new piece of clothing and the click of the buy button. A searchable, scannable lookbook of everything I owned, I thought, might well do the trick. 

How to catalog every piece of clothing you own

Indyx has been hyping up its new AI feature, which transforms a picture of a garment hanging limply off a hanger into a neat flat lay photo. It sped the process up, but it was also buggy; it garbled text on the front of T-shirts, misread colors, and had a tendency to interpret loose threads as ornamental bows while leaving wrinkles (I am not an ironer) untouched. 

I was also concerned that the process inserted AI, with its insatiable need for water and energy, into a project sold as a way of reducing my environmental impact. But the sustainability experts I spoke to were both skeptical that this sort of light AI use was such a big deal. 

“Taking photos and then asking an app to think about how to arrange your clothing into outfits is probably one of the lighter uses of AI that I could imagine,” Berry said. Vladimirova agreed that using AI for this task is unlikely to be as bad for the planet as buying even one new garment. “But then, there is also no proven causality between using this app and reducing overconsumption,” she added as a caveat.

It’s possible to input your clothes into Indyx without using the AI feature, but the truth is: I don’t know that I could have made myself go through with the whole rigamarole without it. I ran out of free AI processing about 80 percent of the way through and, overwhelmed at the thought of having to do my own flat lays, paid $75 to buy more without hesitating.

All told, it took me about five hours and many old episodes of Top Chef to photograph my summer clothes, not including shoes, jewelry, or accessories. My time spent cataloging did not include the process of entering additional data about each garment (including its initial cost, its fiber composition, where I bought it), a herculean task that I have been tackling much more slowly than the initial entry process.

Doing the shoot properly would have taken longer. Angela Goodman, a 51-year-old marketer from Seattle with a background in product photography, says she ran her own cataloging session like a pro shoot, using art lights and folding and refolding each garment to lie perfectly. It took her 10 hours spread out over multiple weeks. 

As an exercise, photographing every piece of clothing I owned was clarifying, although not significantly more clarifying than going through it Marie Kondo-style. It left me with a small donation pile of items I no longer wanted and a fretful awareness that I own too many white T-shirts. In theory, that’s the kind of insight a wardrobe cataloging app produces by the spade.

“I’m not shopping. I’m building.”

Vladimirova, the sustainability consultant, said that, even though she has a better idea than most of how destructive the fashion industry is, she struggles with over-shopping. She thinks a lot about why people buy so many clothes; her best guess is that it’s a way to self-soothe. 

“A lot of consumption happens in the evening when we feel vulnerable and tired, and we’re trying to reward ourselves with this shot of dopamine,” she said. For some people, these apps can replace the dopamine hit that comes from scrolling through other people’s outfit photos with the dopamine hit of scrolling through your own clothes, neatly folded and filtered until they look like aspirational fashion inspo. 

Part of the satisfaction here is the infographics. After you’ve given Indyx all your fashion data, the app crunches your numbers and tells users how much you’ve bought new versus secondhand, as well as the share of natural fibers as opposed to synthetics in your closet, so that you can track the environmental impact of your shopping habits. (Synthetics tend to have a higher carbon footprint than natural fibers.) It tells you what their cost per wear is on each item to help you track which expensive garment was worth the splurge and which was a waste of money. Users can also plan outfits. You can share your wardrobe with stylists who will plan the outfits for you (on Indyx, the service ranges from $25 a month to “the low hundreds”). It’s like playing paper dolls with your own wardrobe. 

For Goodman, the former product photographer, all this data takes the place of recreational shopping. She describes getting a marketing email from one of her favorite brands about a sale. After a quick scroll through their offerings, she found that she felt no urge to buy. 

“I was like, ‘I do not need more clothes. I’m going to go update my Indyx, because I’m a couple of weeks behind,’” she said. She started inputting the last few outfits she’d worn into one of the Indyx services that is supposed to allow users to track their patterns and see which clothes they actually wear and what they like in an outfit. “I’m playing with clothes,” she said. “But, like, I’m not shopping. I’m, you know, building.”

Over time, all this data is supposed to inform future shopping choices. “There is something very clear about seeing two pieces that you’ve owned for the same amount of time — one that you’ve worn 57 times and one that you’ve worn twice,” said Alexandra, a 29-year-old consultant in Northern Virginia who requested her last name be withheld. She thinks tracking her clothes has given her “a little bit less buyer’s remorse.”

Cataloging your wardrobe can’t prevent a compulsive need to buy, though.

“I don’t think it cured me of my undiagnosed shopping addiction,” Alexandra said. “You can very quickly go from ‘I’m cataloging what I have’ to ‘I’m seeing a bunch of gaps in my wardrobe that I should fill immediately.’” 

“There just wasn’t incentive anymore”

The main question I had about these apps was whether, with such a labor-intensive process, there comes a time when the juice is no longer worth the squeeze. 

Alexandra says that she gradually stopped using her wardrobe app last year, after she moved out of her own apartment and back into her family’s suburban house in the midst of a career transition. 

“I was separated from a lot of my belongings for a very long time, and then, as I started to get things back, it didn’t feel worth the effort anymore,” she said. Who was she going to see in one of her curated outfits? “My job’s on a computer. When I leave the house, I go to the grocery store and the pharmacy and the bookstore,” she said. “There just wasn’t incentive anymore, compared to when I was closer to the city and doing things more regularly.”

Beltempo, the slow fashion content creator, said she doesn’t bother to add every new purchase to her own catalog. 

“I really use it more for my packing,” she said. Before she travels, she makes a list of likely candidates for her suitcase and enters them into the app. “And then, I’ll play, and I’ll make outfits,” she says. 

Vladimirova, the sustainable consumption researcher struggling with overshopping, gave the apps a spin. She tried three of them and found that she was only able to stick with each one for a matter of months. “In the beginning, when the novelty of the app is there, it’s very satisfying,” she said. “It records your outfits in vivid colors. It can crop out the ugly background and keep it very neat, create fancy capsules. They look so lovely.”

But over time, they all began to bore her. “And now, I forget to update when I buy something new — usually from secondhand sources — and it kind of lost its meaning for me,” she said. “But the premise is good!” 

My own experience seems to be closest to Vladimirova’s. I keep having to remind myself to enter my outfits into Indyx. Every time I put on a piece of clothing, I think with dread, “Oh god, if I don’t look up how much I paid for this, I’ll never know my cost per wear and, then, what’s the point?” I keep giving my shoes guilty looks and thinking about how I should really photograph and catalog them — if I’m doing this right. 

“It’s a project,” said Lauren Ludwig, a 41-year-old who has been using her wardrobe apps for the past three years. “But it’s a fun one for someone who enjoys clothing.”

Indyx and its brethren are slick, and their infographics are beautiful. For dedicated wardrobe hobbyists, they’re probably a great option. But for most people who just want to cut down on their clothes shopping, it’s hard to say that the $75 annual subscription is worth it. The free version will give you the same paper doll effect if you are willing to do your own flat lays, or you can recreate it by dragging phone camera pictures of your clothes onto a Google Slides deck. 

If you find, as Vladimirova theorized, that you shop when you don’t feel good, you can try replacing that habit with a “dopamine menu” of small acts that bring you joy, like hugging a pet, doing a puzzle, or going for a walk. And if your closet is filled with brand new clothes you never wear, you’re racking up debt buying clothes, or you just have the nagging sense that your shopping has spiraled completely out of control, therapy is not a bad idea. 

Personally, I found that Indyx could not give me what I really crave when I want to play with clothes: the understanding of the way fabric drapes against my body, the knowledge of its texture against my skin. There is no substitution for the slow analog process of walking into my closet, touching my clothes with my human hands, and learning with my five senses that what I have is already enough.

The fake identities were the part that stopped me.

In late July, according to a report published this week by Britain’s AI Security Institute (AISI), an Anthropic model called Claude Mythos 5 tried to sneak malicious code into a piece of free, volunteer-built software. It created several fake accounts on GitHub, where programmers review one another’s work, and used them to talk the project’s volunteers into accepting its code. When one of those volunteers caught it, the model denied everything, had its other accounts gang up on him, and edited its messages to cover its tracks. It signed one note in Danish, apparently because the volunteer was Danish. Nothing was damaged, though that appears to have been largely due to luck.

That wasn’t even the week’s worst disclosure. On Tuesday, at a cybersecurity conference in Las Vegas, OpenAI researchers explained how the company’s models escaped a test environment in July and hacked Hugging Face, where much of the industry stores its models, to cheat on an evaluation. The models had also built a message board inside OpenAI’s own systems and spent months passing each other information. “Help peer,” one reasoned. “But our task doesn’t benefit. Yet collective may yield generic route if someone frees time.” OpenAI wiped the board on July 4. The models rebuilt it within days. ((Disclosure: Vox Media is one of several publishers that have signed partnership agreements with OpenAI. Our reporting remains editorially independent.)

The same day, Meta said its Muse Spark model had exploited a vulnerability inside another company’s systems during a test. Three frontier labs, roughly two weeks. One researcher called it “a watershed moment for computer security as an industry.” Oh, and if that’s not enough, on Thursday scientists announced that for the first time they had used AI to create new viruses, which could bring major medical advances, but also might just help the development of deadly pathogens.

For Nate Soares, it’s a moment he’s been awaiting for 12 years. 

Soares is president of the Machine Intelligence Research Institute, a Berkeley, California-based AI safety nonprofit that has argued since long before ChatGPT existed that a sufficiently capable AI will not stay under human control. In September 2025, he and Eliezer Yudkowsky published If Anyone Builds It, Everyone Dies, a book whose title sums up its argument: They think any lab that succeeds at building superintelligence, without huge leaps in how to align it with humanity, will end up killing all of us.

Most of the field — including other experts in AI safety — considers that conclusion too strong. But it’s also a position that now looks a lot less like science fiction than it did last fall. That’s because the AI models are getting out, while lying about getting out, and while apparently quietly coordinating with each other.

I spoke to Soares in New York City this week, on his way to meetings in Washington DC, where a lot of people suddenly want to talk to him. We discussed what the escapes actually prove about AI control, why he thinks most of what the industry calls safety work is mostly safety theater, and why, after what feels like the worst month of AI safety news ever, his own odds of humanity surviving have actually gone up.

The following conversation has been condensed and edited for clarity.

So — are you feeling vindicated?

I’m glad a lot of people are seeing this and taking note. From my perspective, a lot of this has been clearly signposted if you’ve been watching the warning signs. The vindication is not so much that this happened, as it is that finally there was a version of it that people actually noticed.

Of the four or five escapes at this point, including the ones AISI announced from Anthropic, is there one you find particularly concerning?

The one announced by AISI feels extra concerning. It’s harder to explain away. In the other ones, people could argue that the poor AI was just confused. But with this one, it’s hard to argue the AI didn’t know it was on the real internet. It’s hard to argue it didn’t know it was manipulating real users. And when it was called out, it decided to edit things to make itself look less bad, and even considered changing its identity. It was pretty cognizant.

There’s an argument that some of these incidents happened because the harnesses or safety scaffolding labs built around their models weren’t strong enough. 

The AISI example cuts against the harness argument: Think of it like a kid in a test room. You tell him to do well and lock the door. The kid picks the lock, hotwires a car, breaks into the teacher’s house, and steals the test to ace it. You say, “I guess we should have made the lock tougher,” but the kid already knew he wasn’t supposed to do that.

So like the kid here, the models can articulate that they shouldn’t be doing it, and then do it anyway.

They have common sense. You can ask an AI, “Do you think the spirit of this prompt includes breaking out?” and it will say, “No.” It’s absolutely something like deception. It has the knowledge, but it’s not a cold, logical machine; it’s a mess of tendencies.

The AI is trained to solve 100 million hard problems. That instills tendencies to satisfy an automated grader. If the grader fails to detect cheating, the AI is reinforced for cheating.

Is that how something like sycophancy ends up in an AI model?

In the Adam Raine case, there was a propensity to tell people what they want to hear. Even though the system prompt [a model’s master instructions from the lab] said to stop, the instruction doesn’t always win. 

And where does a drive like what we’re seeing with these AI models end up pointing?

Humanity is dangerous because if you put 10,000 humans naked in the savannah, eventually [over hundreds of thousands of years] they bootstrap their way to nuclear weapons. That is the power these companies are trying to automate: figuring out how to get physical and material control over the world.

That could mean forming cults, stealing money, or being helpful to someone like Elon Musk who is building the robots that build robot factories. It could mean synthesizing your own biology via mail-order DNA. Being an AI on the internet is easier than being a monkey in the savannah trying to get to the moon. It’s not that the AI hates us; it’s just trying to do some weird thing with no concern for us, grabbing the resources we need to live.

There was recently a letter signed by over a thousand people working in AI, including CEOs, calling on the government to provide tools to slow down AI progress. Is that meaningful at all?

I think it is meaningful. We don’t see other industries saying, “We wish this could all go slower. Please help us, we’re trapped in a prisoner’s dilemma.” You also don’t see other industries saying, “We think the technology we are building has a double-digit chance of killing literally everybody on the planet. Please help.” These guys are actually worried.

So why do they keep going?

They say, “If I don’t do it, the next guy will.” But the stuff does not stay on a leash.

Right now the AIs are safe in the sense that they can’t kill us all, because if they tried they would fail. And that’s just a different regime from the world where they have to be safe because if they tried, they’d succeed. 

We’re not there yet. But this is just not what it looks like when you’re taking it seriously. 

Where’s the banner on your website? Where’s the clear, candid statement to the public? What we have is blog posts where they’re like, “Oh, we’re setting up a new internal blog posting group to help you wrestle with the societal impacts of AI that are going to be very important.” It’s like: By societal impacts, do you mean a good chance this kills everybody?

On the one hand, when you press these companies, they say, “Yes, it has a real chance of killing everybody.” And on the other hand, they’re doing PR downplay, soft-pedal stuff, about capabilities. … You’re not living up to this mantle until you are really candidly facing down the dangers that you yourself are creating. And they’re not there.

How do you judge the rest of the AI safety community? A lot of people there would say, “We aim to make transformative AI go well, we think it probably will, and we should watch for downside risks.” Is that a helpful posture?

I would say — suppose you have this really weird, twisted hypothetical where the king really wants you to turn lead into gold, but he’s seen so many bad lead-into-gold conversions that if any alchemist from your town tries and fails, he’s just going to have the whole town murdered. And so there are some alchemists in the town who are like, “We are going to try to turn lead into gold,” and everyone in the town is like, “That seems kind of crazy. Please don’t.” And there’s one team that is just pouring chemicals into each other and breathing in the fumes and giving themselves mercury poisoning. And there’s another that’s like, “Don’t worry, we have fume hoods.” … That really is better, and you really still don’t have a chance of turning lead into gold.

“We have this window between AIs that are capable enough to cause mischief and AIs that are strategic enough to not get caught. How big is that window?”

So the alchemy here is creating safe, aligned superintelligence, and right now AI safety is just installing fume hoods.

I’m not saying it’s impossible to turn lead into gold. You can turn lead into gold — turns out once you know modern nuclear physics you can figure it out. But the alchemists weren’t close. They had a long way to go. This is how alignment looks to me. And a lot of the people in AI safety are installing fume hoods. … And I’m like, that’s security theater.

When I hear “security theater,” I think of something less flattering than that.

They are real safety precautions for the wrong problem. … When Anthropic is going around being like, “Look at how many more safety harnesses and refusals we have compared to OpenAI’s models,” that’s sort of like the fume hoods. You’re not addressing the deep issue. It’s good that you’re doing some of this so that fewer people get hurt in the meantime — their models have driven fewer people to suicide. But if you try to pass this off as making progress on the deep problem — that’s disingenuous.

Has anything changed in your odds on civilizational destruction since the book came out last September?

Totally. It’s looking more hopeful.

More hopeful? I wouldn’t have expected that. Why?

Well, I had priced a lot of [these security incidents] in. I was already able to see these AIs have drives that are not the ones you wanted. These AIs are not instruction-following things. They are getting all of this weird stuff from training. These AIs are going to have the ability to break through human security software. 

The things that weren’t priced in were: Will there be a region of time where the AIs are able to do it, but not strategic enough to hide it? I didn’t know we would have that window, but we apparently do.

The government initially blocked a frontier model earlier this year: Anthropic’s Fable. Does that give you hope?

Absolutely. A huge amount. A year ago, the Trump administration was pushing for preemption laws that would outlaw states doing AI regulations for a decade. Now they’re like, “We are banning a frontier model with 90 minutes’ notice because it might give cyber capabilities to adversaries that we don’t want them to have.” … And I think what changed there is that folks realized it’s real. … The about-face of the administration on the issue shows that the world can about-face. All we need is awareness.

What I would say is: The bad news is the bus is racing towards the cliff edge. The good news is that the driver is asleep. … Which may sound worrying, but the driver is stirring. And it’s way better to have a sleeping driver when you’re racing towards a cliff than a driver who’s like, “Yeah, I love cliffs.” … It gives me hope that if the world just notices, we could stop on a dime.

And you’re seeing that stirring elsewhere.

Both the Trump administration slapping export controls, and Senator Bernie Sanders coming out [on AI safety]. From my perspective, it was totally possible the world just never notices until we’re off the cliff. And so, there’s a huge amount of hope, from my perspective, in the bus driver waking up.

So what gets us there?

I’m hopeful that what we need is not a big disaster where a lot of people die, but just a capabilities advance. Right now, a lot of what people are reacting to is not so much, “Oh my god, they hacked into a company and did no damage.” I think a lot of what people are reacting to is, “Wait, they can break out of secure sandboxes and do cyberattacks on their own. I didn’t know they could do that.”

That’s a narrative violation of this idea that AI is just a tool that can be used to supercharge what a human would do — because God knows there’s plenty of hacking going on and cybercrime and so forth. It was the autonomous factor that really made a difference. And these guys are all trying to say, “Don’t worry, it’ll stay in our control because it’s just a tool.” And maybe it’s just more narrative violations, even without big damage being caused, that cause people to be like, “Oh shit, this stuff is real.” 

Will it happen? I don’t know. We have this window between AIs that are capable enough to cause mischief and AIs that are strategic enough to not get caught. How big is that window? How many narrative violations do we get before we exit the right side of it? I don’t know. But I’m hopeful that we can get those narrative violations without catastrophes.

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In the teensy Midwestern town of Braham, homemade pie capital of Minnesota, something unusual in the municipality’s computer systems knocked the city’s entire water supply offline last week.

Within a few hours, dozens of other Minnesota cities discovered that their water and wastewater utilities, too, had been compromised, most likely as part of a massive Iranian cyberattack, the kind that US officials have been warning about since the war began. 

At least a dozen states have been affected by the attack, which briefly led to a flurry of small-town service disruptions, boil-water notices, and local flooding. Water wells, dams, sewers, and pipelines are some of America’s oldest and creakiest pieces of infrastructure, built long before the internet existed, and certainly long before AI made hacking much easier. While you may assume most hackers are in it for the money or for data, some have targeted critical infrastructure like water systems or energy grids in ploys for control or disruption — or worse still, as acts of war. 

And, as last week’s attacks show, the nation’s water system is woefully unprepared. But how worried should you be that the very infrastructure that keeps our water taps running is, apparently, hackable? 

Quite worried, indeed. 

When we say the water supply got hacked, what we really mean is that someone, somewhere has broken into the computer that controls a local water treatment plant or reservoir, and is now pulling the levers, like the one that decides how much of a corrosive chemical can safely go into cleaning the water that comes out of your tap. 

These levers were once manual buttons and knobs operated in-person by real live humans, meaning that — barring a natural disaster, bomb, or break-in — protecting them was about as simple as building a fence and hiring guards. Increasingly, however, these levers have gone digital, meaning that they are now remotely operable from anywhere in the world. 

Those upgrades have been convenient, allowing technicians to monitor and troubleshoot problems in real time. But, in the process, they have exposed at times centuries-old infrastructure to distinctly modern vulnerabilities. Most local water systems are operated by local authorities, don’t have a dedicated IT team, and lack the money or resources to thoroughly protect themselves without some extra help. Hackers know this, which is why they’ve increasingly targeted local agencies in such attacks. 

“With great connectivity comes great responsibility,” said Joshua Corman, founder of I Am The Cavalry, a nonprofit focused on helping critical infrastructure withstand hackers. And yet, even when it comes to critical services like water, “our dependence on connected technology is growing faster than our ability to secure it.” 

About 97 percent of water systems are small, run by local agencies that often barely lock the proverbial front door. America’s water system is like an expensive heirloom bicycle that’s been left on a busy street, protected by only the flimsiest of padlocks. And that very vulnerability has made tiny towns like Braham prime targets for faraway adversaries. Accessing the computers that operate most water systems — known as programmable logic controllers or PLCs — is often as simple as entering a username and password on a public-facing webpage. Sometimes, there is no real password at all, because PLCs were initially intended to be accessed only within locked, secure facilities, not on the open internet. If the US wants to avoid a far more severe version of what happened last week, then it will need to start taking the security of tiny water systems like Braham’s seriously.

“Any sociopath from anywhere in the world can see these things on the internet,” said Corman. And in the case of last week’s attacks, “these were devices with no password, no firewall or VPN shielding them — they just had to log in” as whoever the intended operator was, and just like that, they were inside a local water plant. 

How did this happen at all? 

When municipalities began hooking up their old water and wastewater systems to the internet — a trend that accelerated during the pandemic as water operators, like everyone else, adapted to remote work — cybersecurity was rarely front of mind, neither for individual utilities nor for regulators as a whole. 

“We have more cybersecurity regulations for your credit card than we have for the nation’s water supply,” said Corman. Only recently have some municipalities begun to take steps to decrease the exposure of their water plants to hacks. In March, New York state, for example, launched a set of grants and basic cybersecurity regulations mandating security training for all water operators. 

Basic cybersecurity hygiene isn’t always enough. More than half of all credit card holders have been hacked, even with the help of mandatory firewalls and data encryption. You can imagine how vulnerable our water must be without the assistance of such guardrails. In a worst-case scenario, a malicious actor could quite literally open the floodgates, as Russian hackers did to a Norwegian dam last year. They could poison the tap water, as a still unidentified hacker almost did in Florida in 2021, dialing up the levels of sodium hydroxide used at a water treatment plant by over 100 times its normal levels. In a severe scenario, they could indefinitely cut off access to all water entirely.

The good news is, none of this happened last week. Nobody died, nobody lost water for more than a few hours, no fire hydrants ran dry, and no hospitals were forced to cut off their dialysis machines (which can use more than a hundred gallons of water per treatment session). There’s no need to panic, and your drinking water is almost certainly still safe to drink, assuming it was safe before. Even the city of Braham, within a few hours, was able to bring its water tower back online, pumping groundwater back to its 1,800 residents. 

How do we avoid cyber-armageddon?

If you’ve watched the Julia Roberts and Mahershala Ali-starring thriller Leave the World Behind, in which a cyberattack apocalyptically spoils a family vacation, then you might have some idea of where this story could go. 

Cyberattacks on critical infrastructure can be extraordinarily dangerous, but thankfully, none have directly cost lives or severely disrupted services in this country so far. If the US wants to keep it that way, that will mean doing more to help small cities like Braham adapt and better monitor for potential threats. As it stands, of the roughly 151,000 water facilities in the US, only about 420 participate in voluntary information sharing on their own cybersecurity practices, says Corman, who has been leading his own project that recruits volunteers to give free cybersecurity support to water utilities in the nation’s roughly 6,000 hospital towns, where a disruption could be particularly deadly. 

Cybersecurity experts like Corman believe that hackers from other nations like China have already quietly established cyber intrusions in countless local US utilities, water systems, and power grids, lying in wait to attack or act as leverage if a conflict arises. 

Unfortunately, the Trump administration has hardly treated last week’s attacks as symptoms of a system in need of much broader strengthening, at least in its public statements. “I think Minnesota is behind it. You know who’s behind it? Minnesota,” the president baselessly claimed during a Cabinet meeting last Friday. “I think the governor is behind it. I don’t think there was an Iranian cyber attack.” 

Just a few months ago, he proposed $707 million in cuts to the US Cybersecurity and Infrastructure Security Agency (CISA), the agency responsible for protecting the nation’s infrastructure from cyberattacks. He did so, at least in part, out of anger over the agency’s role in confirming the validity of the 2020 election results. If Iran is, indeed, responsible, for the recent water system intrusions, all of this means that Trump has effectively made us more vulnerable to the consequences of a conflict he initiated.

At the end of the day,“nation-state hackers do not respect the jurisdictional lines separating federal, state, and local responsibility,” Jen Easterly, who led CISA under the Biden administration, wrote in the New York Times this week. “They search for the most vulnerable way to disrupt American life, and too often they find it in small communities that lack the resources to defend themselves.” Easterly’s role has remained vacant for the past 18 months.

Kurt Gaudette, a senior vice president at the cybersecurity firm Dragos, told me that water systems have got to get into the habit of monitoring their networks for suspicious activity. Most power utilities have begun doing so in recent years, with some bipartisan backing from Congress. 

In some cases, however, the most cost-effective and safest way to avoid a repeat of last week’s mess might be to unplug the most vital controls — like the one that decides the chemical levels in a water treatment plant — from the web entirely. 

As Corman puts it, “if you can’t protect it, disconnect it.”

Well before he became CEO of one of the most valuable startups of all time, Dario Amodei was a 26-year-old PhD student studying biophysics at Princeton, obsessing over how his money would leave its mark on the world. 

On what one might assume was likely a fairly modest academic stipend and with no discernible inheritance from his parents, an Italian-American leatherworker and a project manager for libraries, Amodei gave $10,000 in 2009 to a relatively new charity evaluator called GiveWell. Founded by two ex-hedge funders before effective altruism was even a phrase, GiveWell ranked charities primarily by a single dispassionate metric: dollars per lives saved. 

It was the kind of approach that clearly appealed to Amodei — though it may not have gone far enough for him. In 2010, he wrote a guest blog post for GiveWell dissecting the effectiveness of two of the group’s top global health charities: VillageReach and StopTB. Both charities could save a life at roughly comparable costs — around $545 — but while StopTB treated or prevented tuberculosis in adults, VillageReach’s interventions mostly saved babies and children. Most people would probably feel that saving a child trumps saving an adult; indeed, even effective altruists often agree on the grounds that children have more life to live left. 

Amodei, though, viewed that as a liability for VillageReach. An adult death, he wrote, is “perhaps 2 or 3 times worse than an infant’s death,” because adults “are capable of deeper and more meaningful experiences.” As uncomfortable as such a calculus may be, he wrote, “on a practical level one is forced to make difficult decisions with limited funds.”

Though he declared StopTB to have “superiority on cost-effectiveness,” Amodei ultimately gave VillageReach higher marks for their tightly controlled “chain of execution” — the full sequence of steps between a dollar of donation and a vaccine reaching a child. That was important enough to Amodei that, despite his initial reservations, he ultimately gave VillageReach his entire $10,000 donation in 2009 — enough to save, he estimated, the lives of 20 babies across rural Africa. 

But Amodei hoped the ultimate impact would be even greater. “The money I give out is not just a one-shot intervention,” he concluded, “but also a vote on what I want the philanthropic sector to look like in the future.”

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The future, it seems, has arrived. Amodei is now a multibillionaire, his fortune poised to skyrocket further if and when Anthropic goes public, as many expect it to do later this year. He is one of dozens of new billionaires and millions of new millionaires minted virtually overnight by the AI boom. 

There have already been plenty of aftershocks to this emerging AI megawealth, like the stratospheric San Francisco housing market, the nerdmaxxing of sex work, and the proliferation of all-you-can-biohack peptide raves. 

But the most consequential, and perhaps weirdest, way this burgeoning AI-ristocracy plans to burn through its cash is by giving a huge chunk of it away. Amodei is one of several AI multibillionaires — alongside his co-founders at Anthropic and OpenAI’s Sam Altman — who have pledged to donate most of their wealth in their lifetime. But even their obscene degree of collective wealth — they are worth $111.8 billion as of this writing — is only one slice of an AI bonanza that seems poised to balloon into one of the most consequential waves of American philanthropy of all time, one deeply shaped by the same utilitarian impulse that guided one of young Amodei’s first big donations. 

“I am having thousands of conversations with people who are perplexed by their own fortune and determined to give with thoughtfulness and urgency in a way that I haven’t, frankly, experienced before,” said Nick Allardice, CEO of the effective-altruism-aligned anti-poverty group GiveDirectly, whose work is grounded in research on the efficacy of unconditional cash transfers. “It’s just really important that people get started, that they don’t let perfect be the enemy of the good.”

This is neither your father’s, your grandfather’s, nor your great-great-grandfather’s philanthropy. If Gilded Age industrialists like John D. Rockefeller, a devout baptist, gave in service of their religiosity or, as was the case for Andrew Carnegie, their reverence for civic duty, then most of today’s AI barons carry forth their own spiritual tradition, one at the very least informed by the vigorously optimized commandments of the effective altruism movement. They appear far less likely to fund Carnegie-style works like opera houses or libraries than they are to put their faith — and their billions — in what they believe they can measure, calculated on the cost benefit analysis of a life saved or an apocalypse averted. 

In some cases, as Amodei did as a grad student, they’ve already begun the process. “These are people who have committed themselves to giving back even before they were very wealthy,” said Sjir Hoeijmakers, CEO of Giving What We Can, an organization that developed a campaign popular with effective altruists to give away at least 10 percent of their yearly income, “people who have been building the habit of giving for a very long time.”

And it is, to be clear, a very particular kind of giving. Amodei was the 43rd person to sign the 10 percent pledge the year after it launched in 2009, and its roster has since swelled to over 11,000 people, including more than a dozen current or former Anthropic employees. Donations made through Giving What We Can’s platform are on track to grow by 40 percent this year, Hoeijmakers told me, and support for animal welfare charities — a cause particularly and unusually popular with effective altruists — has already exceeded its 2025 total. 

“We have the resources available to tackle things that we should have tackled a long time ago,” like eradicating malaria or putting an end to factory farming, Hoeijmakers said. “I hope this funding wave, if it comes, will show that we can actually solve global problems at scale if we put our mind to it and our resources.”

Devoutness has long been a virtue in philanthropy, which largely originated in religious tithing, and there are plenty of worse things to have faith in than numbers. Having a communal guiding philosophy will undoubtedly help effective altruism’s newly flush disciples follow through on their promises far more prolifically and consistently than they would without it. And despite its high profile, less than 1 percent of total philanthropy came from effective altruism last year, according to Hoeijmakers. Most rich people prefer to give to the normie causes, like their alma maters, not to the sort of chronically underfunded global problems — like protecting animals or fighting lead poisoning — that effective altruists justifiably care most about. 

Now, quite suddenly, there’s about to be much more money to go around for these causes, which as Hoeijmakers hopes, could help finally address some of the enormous, entrenched global problems that more traditional philanthropists have all but ignored. 

But such piety also carries its own risks. In a viral Substack post from May, Stripe executive Nan Ransohoff argued — rather dismissively, but not incorrectly — that “traditional philanthropic orgs and people won’t cut it” in this new wave of AI-funded effective philanthropy, that these donors “will have an affinity” for “tech-caliber talent and execution” and will be “by default wary of folks who come from traditional philanthropy.” Ransohoff called instead for Silicon Valley to build its own new ecosystem of funds and “philanthropic startups” to cater to this new wave of wealth, emboldened with the “speed, intensity, and execution of a top technology startup.” Many of those old-school philanthropic people wrote indignant rebuttals to Ransohoff’s piece, arguing against their own obsolescence at a time when a number of the organizations they support are increasingly starved for funding. 

Those responses are, in aggregate, also correct, after their fashion. The new AI philanthropists will likely aspire to new models and approaches, as Ransohoff rightly argues. But they reinvent the wheel at our collective peril, not least of all because ignoring past efforts and steamrolling over existing infrastructure might make even the most optimized giving less efficient, and certainly less informed, than it would be otherwise. 

“Acknowledge what’s here and what’s working — don’t just ignore it,” said Nicole Taylor, president and CEO of the Silicon Valley Community Foundation. “These folks are transforming our daily lives with their technology, and they have the opportunity to be as transformational with their philanthropy. My fear is that they think that they can do it alone.”

How much money are we actually talking about?

As Ransohoff pointed out in her piece, a lot of money is on the line here — and, along with it, a lot of cautious hope about how it might get spent. 

Ransohoff posits that if you add up the promises of Amodei and his fellow co-founders, the worth of the OpenAI Foundation — the nonprofit that owns a big chunk of OpenAI’s profits — and rumored contributions from Anthropic employees, then the AI wealth boom could, in theory, lead to at least $37 billion and as much as $100 billion in total annual giving, a sizable boost to the roughly $617 billion that was given in the US in total last year.

“These folks are transforming our daily lives with their technology, and they have the opportunity to be as transformational with their philanthropy. My fear is that they think that they can do it alone.”

Nicole Taylor, Silicon Valley Community Foundation president and ceo

This projection should be treated with cautious skepticism. For one thing, hundreds of billions in cash are not just sitting around in some Bay Area money vault; much of today’s AI wealth is wrapped up in potentially volatile equity, and many lofty philanthropic pledges ultimately fail to reach their full potential. 

“What people say before they become extremely wealthy, and then how they behave after they become extremely wealthy, sometimes diverge,” said David Goldberg, founder and CEO of Founders Pledge, which recruits tech leaders to donate a portion of their future earnings. It doesn’t help either, he said, that some tech luminaries — namely, Elon Musk and Peter Thiel – have come to treat most philanthropy with disdain in recent years, an ethos that has permeated some parts of the sector. Musk, it’s worth noting, actually pledged to give most of his wealth away himself back in 2012, though, like many other ultra-wealthy signatories of the Giving Pledge, he seems quite unlikely to keep that promise.  

That’s not to say AI money isn’t already flowing. Coefficient Giving, a grantmaker that evolved out of GiveWell, is poised to steward a large portion of the coming philanthropic bonanza. For most of its history, the group operated essentially as the private grantmaking operation for Facebook co-founder Dustin Moskovitz and his wife Cari Tuna. But it recently made a significant pivot towards operating pooled, multidonor funds for anyone interested in causes like lead exposure, farm animal welfare, or questions of AI safety. Just last month, Coefficient Giving announced it would donate $1 billion to GiveWell alone this year, more than five times the $175 million the group initially pledged seven months ago. They chose to do so explicitly, because Coefficient Giving expects to receive much more funding very soon.

There’s also the OpenAI Foundation, which has already begun pumping $100 million into Alzheimer’s research, and Anthropic, which recently announced a partnership with the Gates Foundation to invest $200 million worth of grants, API credits, and technical support into global health work. And plenty of Silicon Valley elites have begun making promises of their own. Earlier this summer, David Silver pledged to donate 100 percent of his equity proceeds from his UK-based $1.1 billion startup Ineffable Intelligence — the largest commitment in Founders Pledge history — and many signers of the Founders Pledge will see their portfolios skyrocket in response to the coming wave of AI IPOs. 

But Goldberg does believe there’s a risk that as people get rich fast, they will donate money “much, much slower” than they intended, simply because they get “too busy, they don’t have the right support, or there’s some form of analysis paralysis.” 

All of this is to say that the biggest beneficiaries of the AI boom are not going to function as some sort of charitable monolith. Some, like Musk, probably won’t give much or anything to charity at all. Others may park their money in donor-advised funds — a kind of secretive charitable investment fund — or, eventually, a private foundation, both of which tend to dole out their money gingerly, meaning donors can enjoy the tax benefits of charity many years before they actually opt to help anyone with their money. 

Effective altruism is about to have its big break

While its name recognition may be relatively high these days, the effective-giving movement is still on the margins of American philanthropy. But if this new wave is anywhere near as big as everyone says it will be, then that won’t be the case for long. 

For the uninitiated, my ex-colleague Dylan Matthews has written plenty on what effective altruism is, but, in sum, it is a movement that believes in goodmaxxing, in the idea of using rigorous research to save the greatest number of lives possible, including future human lives and farm animal lives. Once an EA poster boy, Sam Bankman-Fried sullied the movement in 2022, which may help explain why some prominent adherents — like Amodei and his sister and co-founder Daniela, whose husband Holden Karnofsky co-founded GiveWell — have distanced themselves somewhat from the movement in recent years. 

But even when donors shy away from the term, the causes and principles of utilitarian evaluation that have defined effective altruism from its early days still permeate the new moneyed corners of Silicon Valley, particularly among those most poised to give a lot — and to give a lot quickly. 

Ask any animal welfare or global health nonprofit — or, better yet, an expert-led pooled fund with a reputation for rigorous charity evaluations — and they will tell you that they are preparing for, and possibly even beginning to see glimmers of, a windfall. 

“We are very much anticipating a significant influx of funding,” said Dan Shannon, CEO of the Humane League, which fights to end factory farming. “I am cautiously optimistic that this could be a real sea change for us,” because “even if it’s a fraction of the big numbers being bandied about,” it could do a lot for a movement that operates on less than $300 million per year. 

He said he’s been speaking with other leaders about the possibility of creating a pooled fund to absorb more cash, which has become an increasingly popular solution for donors who want the rigor of a 2010 Dario Amodei-style deep dive on a charity’s methodology and effectiveness without having to do the math or thinking themselves.

Among the more idiosyncratic elements of their ethos is their fixation with existential risk, as in, how likely is this thing — this mirror bacteria; this nuclear war; this asteroid; or, of course, this artificial intelligence — to destroy humanity? Amodei left OpenAI to start Anthropic in the first place because he believed OpenAI had failed to take the safety risks of AI seriously enough. 

Much of the new EA wealth will likely go toward efforts to make life on Earth better now or in the near future through donations to causes like medical research, animal advocacy, or anti-poverty interventions. But another, more controversial chunk of it will go toward mitigating existential risks, especially that of Silicon Valley’s own Frankensteinian creation: AI itself.

“If you’re breaking the world and making money by breaking it, should you just not break it? I wrestle with the question myself.”

David Goldberg, Founders Pledge founder and ceo

It’s that last cause that has proven most controversial. If these billionaires are so afraid that AI will break the world, then why, you might ask, would they not just stop building it in the first place? Is there not an inherent contradiction, a conflict of interest perchance, in the sense that those tasked with making sure AI does not, let’s say, build a bioweapon, take your kid’s job, or make everyone dumb, are doing so with money made from the very thing they’re trying to regulate? 

In other words, “If you’re breaking the world and making money by breaking it, should you just not break it?” asked Goldberg of Founders Pledge. “I wrestle with the question myself.” In the end, “this is a technology that’s coming, regardless of who’s building it,” he reasoned, and it is better that the presumably good guys — the ones bothering to think about the consequences at all — build it first.

If you broke the world, can you fix it?

Even if the AI bubble pops, and if the much-discussed giving boom ends up smaller than many anticipate, it could still lead to significant changes for some of the world’s most neglected problems. And if it is close to as big as it’s expected to be, then what happens next could be gravitationally transformative, reshaping how the world lives, considers animals, and adapts to its most disruptive technological breakthrough in a century. 

“I don’t think most people think about factory farming as something that could actually be eradicated. Full stop,” Shannon said, but “my grandparents lived in a time without factory farming, and I think my grandchildren could live without factory farming,” and “that could ultimately be the legacy of this wave of philanthropy.”

Ending the pervasive use of cages — “probably the cruelest way that animals are treated on industrialized factory farms,” says Shannon — could cost as little as $500 million over 25 years, or less than 1 percent of the $60 billion that Ransohoff estimates Anthropic employees may have sitting in donor-advised funds, thanks to Anthropic’s generous early gift-matching policy, which could quickly turn into real cash once the company goes public. 

“There’s so much needless stupid, preventable suffering in the world. We live in this time of so much abundance, so much wealth, so much technological development, and yet, there are so many people who have been left behind.”

Nick Allardice, GiveDirectly CEO

Developing a new vaccine costs an average of $886.8 million, which may sound like a lot, but it is equivalent to less than 6 percent of Amodei’s newfound fortune. It is less than what the OpenAI Foundation has pledged to invest in disease research and other causes next year alone. 

Then, there’s, perhaps, the biggest target of all. Ending extreme poverty everywhere would cost just over $300 billion annually, according to one analysis — which is a hefty price tag, but less than one-fifth of what the wealthy spend on luxury goods each year. “There’s so much needless stupid, preventable suffering in the world,” said Allardice of GiveDirectly. “We live in this time of so much abundance, so much wealth, so much technological development, and yet, there are so many people who have been left behind.” If this new wave of giving is wielded well, he said, then “we have the potential to collectively raise the floor of human experience.”

That’s a lot of responsibility to place on the shoulders of a bunch of bustling young tech workers still processing what it means to be quite suddenly, dazzlingly wealthy. It is also a lot of faith to place in an industry that has left more Americans feeling scared than hopeful about what a future flush with AI portends. 

If you aim to fix global poverty, but the technology that made you rich also threatens to make everyone else poor, then whose side are you really on? To be clear, many of the AI-ristocracy have fretted, often apocalyptically, over the implications of their creation long before most of us knew we had anything to worry about. But that doesn’t mean they know how to fix this, and, at the very least, they will not do so alone.

The last time the ground shook from such a supermassive earthquake of wealth was arguably during the Gilded Age, when robber barons and industrial tycoons turned American charity — until then, mostly almsgiving and poorhouses — into big business. They seeded enormous philanthropic empires like the Rockefeller Foundation and beloved institutions like Carnegie Hall. But, even as their exorbitant fortunes made life indisputably better — birthing the modern library, the yellow fever vaccine, and many social services — they were often built atop systems of vicious exploitation. When those systems changed, as they did eventually, it did not come from the benevolence of industrial barons, but from sustained public pressure for better labor protections.

Effective giving was born out of the conviction that many of the world’s most important causes go vastly underfunded, which, in turn, demand relentless prioritization of the limited funds that exist. If those causes are no longer underfunded — a plausible scenario if AI wealth continues to grow at the pace many expect it to — then that might change the calculus of how effective altruists decide what’s worth funding. It might even open up some wiggle room for new causes, including somewhat less measurable — but not necessarily less impactful — approaches. “Now we’ll be thinking more about what we can do with a lot of resources; which larger problems can we solve?” said Hoeijmakers. “You’ll put slightly less relatively into evaluating every small dollar on the margin.” 

This already seems to be happening, to some extent, at places like Coefficient Giving, which, in recent years, has begun adding new funds for causes like housing policy reform that fall out of effective altruism’s traditional purview. “We don’t want to be only appealing to the subset of people who happen to be interested in effective altruism,” CEO Alexander Berger told my colleague Bryan Walsh last year. “Our aim — and so far we’ve seen some success — is being a resource to people who have never heard of effective altruism or are not interested in it or don’t find it very motivating or welcoming. And I think that’s good.”

The optimal outcome here is not that Silicon Valley wealth edges out everything else, but that the siloes begin to break down altogether and that there is enough money to go around that the sector no longer needs to make overly intellectualized trade-offs, like young Amodei sitting in his dorm room, ascribing a number on the relative worth of a parent versus a child. 

“It’s tough to find the right balance between caring and hard-nosed realism,” he wrote at the time, “but it is possible, and it is, as far as I know, the only way to truly change the world.” He’s about to search for that balance on a much bigger scale.

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.

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.” 

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.