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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.