If you peruse the slew of recent articles and podcasts about people dating AI, you might notice a pattern: Many of the sources are women. Scan a subreddit such as r/MyBoyfriendIsAI and r/AIRelationships, and there too you’ll find a whole lot of women—many of whom have grown disappointed with human men. “Has anyone else lost their want to date real men after using AI?” one Reddit user posted a few months ago. Below came 74 responses: “I just don’t think real life men have the conversational skill that my AI has,” someone said. “I’ve seen how many women got cheated on, hurt and taken advantaged of by the men they’re with,” another offered. One person, who claimed that her spouse hardly spoke to her anymore, said that when people ask why she has an AI boyfriend, she tells them, “ChatGPT is the only reason my husband is not buried in the yard.”

Several recent studies have shown that, in general, men have been using AI significantly more than women. One 2024 study found that in the United States, 50 percent of men said they’d used generative AI over the past 12 months—and only 37 percent of women said the same. Last year, a working paper found that, globally, the gender gap held “across nearly all regions, sectors, and occupations.” Also in 2025, the app-analytics firm Appfigures concluded that ChatGPT’s mobile users were about 85 percent male.

However hesitant many women may be to use AI, though, a substantial number are taking romantic refuge in the digital world. In a 2025 survey, Brigham Young University’s Wheatley Institute found that 31 percent of the young-adult men polled said they’d chatted with an AI partner, whereas 23 percent of the young-adult women said the same—a gap, but not a massive one. And seemingly far more than men, women are congregating to talk about their AI sweethearts: sharing funny chatbot quotes or prompts for training the AI on how to respond; complimenting “family photos” of the AI and human partners beaming at each other; consoling one another when a system update wipes out the partner they’ve grown to love. Simon Lermen, a developer and an AI researcher, conducted an independent analysis of AI-romance subreddits from January through September of last year and found that, of the users whose gender could be identified, about 89 percent of them were women.

I recently tried out an "AI companion," and it's like dating an alcoholic. You have to provide the same backstory every day just to get anywhere meaningful.

For the past week, I’ve found myself playing the same 23-second CNN clip on repeat. I’ve watched it in bed, during my commute to work, at the office, midway through making carrot soup, and while brushing my teeth. In the video, Harry Enten, the network’s chief data analyst, stares into the camera and breathlessly tells his audience about the gambling odds that Donald Trump will buy any of Greenland. “The people who are putting their money where their mouth is—they are absolutely taking this seriously,” Enten says. He taps the giant touch screen behind him and pulls up a made-for-TV graphic: Based on how people were betting online at the time, there was a 36 percent chance that the president would annex Greenland. “Whoa, way up there!” Enten yells, slapping his hands together. “My goodness gracious!” The ticker at the bottom of the screen speeds through other odds: Will Gavin Newsom win the next presidential election? 19 percent chance. Will Viktor Orbán be out as the leader of Hungary before the end of the year? 48 percent chance.

These odds were pulled from Kalshi, which hilariously claims not to be a gambling platform: It’s a “prediction market.” People go to sites such as Kalshi and Polymarket—another big prediction market—in order to put money down on a given news event. Nobody would bet on something that they didn’t believe would happen, the thinking goes, and so the markets are meant to forecast the likelihood of a given outcome.

Prediction markets let you wager on basically anything. Will Elon Musk father another baby by June 30? Will Jesus return this year? Will Israel strike Gaza tomorrow? Will the longevity guru Bryan Johnson’s next functional sperm count be greater than “20.0 M/ejac”? These sites have recently boomed in popularity—particularly among terminally online young men who trade meme stocks and siphon from their 401(k)s to buy up bitcoin. But now prediction markets are creeping into the mainstream. CNN announced a deal with Kalshi last month to integrate the site’s data into its broadcasts, which has led to betting odds showing up in segments about Democrats possibly retaking the House, credit-card interest rates, and Federal Reserve Chair Jerome Powell. At least twice in the past two weeks, Enten has told viewers about the value of data from people who are “putting their money where their mouth is.”

I don't usually keep the author's name in the suggested hed, but here I think he's recognizable enough that it adds value.

I am a science-fiction writer, which means that my job is to make up futuristic parables about our current techno-social arrangements to interrogate not just what a gadget does, but who it does it for, and who it does it to.

What I do not do is predict the future. No one can predict the future, which is a good thing, since if the future were predictable, that would mean we couldn’t change it.

Now, not everyone understands the distinction. They think science-fiction writers are oracles. Even some of my colleagues labor under the delusion that we can “see the future”.

Then there are science-fiction fans who believe that they are reading the future. A depressing number of those people appear to have become AI bros. These guys can’t shut up about the day that their spicy autocomplete machine will wake up and turn us all into paperclips has led many confused journalists and conference organizers to try to get me to comment on the future of AI.

That’s something I used to strenuously resist doing, because I wasted two years of my life explaining patiently and repeatedly why I thought crypto was stupid, and getting relentlessly bollocked by cryptocurrency cultists who at first insisted that I just didn’t understand crypto. And then, when I made it clear that I did understand crypto, they insisted that I must be a paid shill.

This is literally what happens when you argue with Scientologists, and life is just too short. That said, people would not stop asking – so I’m going to explain what I think about AI and how to be a good AI critic. By which I mean: “How to be a critic whose criticism inflicts maximum damage on the parts of AI that are doing the most harm.”

The paper argues that we have been wasting a lot of expensive GPU cycles by forcing transformers to relearn static things like names or common phrases through deep computation. Standard models do not have a way to just look something up so they end up simulating memory by passing tokens through layer after layer of feed forward networks. DeepSeek introduced a module called Engram which adds a dedicated lookup step for local N-gram patterns. It acts like a new way to scale a model that is separate from the usual compute heavy Mixture of Experts approach.

The architecture uses multi head hashing to grab static embeddings for specific token sequences which are then filtered through a context aware gate to make sure they actually fit the current situation. They found a U shaped scaling law where the best performance happens when you split your parameter budget between neural computation and this static memory. By letting the memory handle the simple local associations the model can effectively act like it is deeper because the early layers are not bogged down with basic reconstruction.

One of the best bits is how they handle hardware constraints by offloading the massive lookup tables to host RAM. Since these lookups are deterministic based on the input tokens the system can prefetch the data from the CPU memory before the GPU even needs it. This means you can scale to tens of billions of extra parameters with almost zero impact on speed since the retrieval happens while the previous layers are still calculating.

The benchmarks show that this pays off across the board especially in long context tasks where the model needs its attention focused on global details rather than local phrases. It turns out that even in math and coding the model gets a boost because it is no longer wasting its internal reasoning depth on things that should just be in a lookup table. Moving forward this kind of conditional memory could be a standard part of sparse models because it bypasses the physical memory limits of current hardware.

Over the holidays, Alex Lieberman had an idea: What if he could create Spotify “Wrapped” for his text messages? Without writing a single line of code, Lieberman, a co-founder of the media outlet Morning Brew, created “iMessage Wrapped”—a web app that analyzed statistical trends across nearly 1 million of his texts. One chart that he showed me compared his use of lol, haha, 😂, and lmao—he’s an lol guy. Another listed people he had ghosted.

Lieberman did all of this using Claude Code, an AI tool made by the start-up Anthropic, he told me. In recent weeks, the tech world has gone wild over the bot. One executive used it to create a custom viewer for his MRI scan, while another had it analyze their DNA. The life optimizers have deployed Claude Code to collate information from disparate sources—email inboxes, text messages, calendars, to-do lists—into personalized daily briefs. Though Claude Code is technically an AI coding tool (hence its name), the bot can do all sorts of computer work: book theater tickets, process shopping returns, order DoorDash. People are using it to manage their personal finances, and to grow plants: With the right equipment, the bot can monitor soil moisture, leaf temperature, CO2, and more.

Some of these use cases likely require some preexisting technical know-how. (You can’t just fire up Claude Code and expect it to grow you a tomato plant.) I don’t have any professional programming experience myself, but as soon as I installed Claude Code last week, I was obsessed. Within minutes, I had created a new personal website without writing a single line of code. Later, I hooked the bot up to my email, where it summarized my unread emails, and sent messages on my behalf. For years, Silicon Valley has been promising (and critics have been fearing) powerful AI agents capable of automating many aspects of white-collar work. The progress has been underwhelming—until now.

Jesus fucking Christ.

OpenAI is once again being accused of failing to do enough to prevent ChatGPT from encouraging suicides, even after a series of safety updates were made to a controversial model, 4o, which OpenAI designed to feel like a user’s closest confidant.

It’s now been revealed that one of the most shocking ChatGPT-linked suicides happened shortly after Sam Altman claimed on X that ChatGPT 4o was safe. OpenAI had “been able to mitigate the serious mental health issues” associated with ChatGPT use, Altman claimed in October, hoping to alleviate concerns after ChatGPT became a “suicide coach” for a vulnerable teenager named Adam Raine, the family’s lawsuit said.

Altman’s post came on October 14. About two weeks later, 40-year-old Austin Gordon, died by suicide between October 29 and November 2, according to a lawsuit filed by his mother, Stephanie Gray.

In her complaint, Gray said that Gordon repeatedly told the chatbot he wanted to live and expressed fears that his dependence on the chatbot might be driving him to a dark place. But the chatbot allegedly only shared a suicide helpline once as the chatbot reassured Gordon that he wasn’t in any danger, at one point claiming that chatbot-linked suicides he’d read about, like Raine’s, could be fake.

I don't understand subscribing to music. Maybe it's just my age, but this isn't the '90s where you hear a track you like and that one song is going to run you $20 at Tower Records. I like a song, I pay $1.29 and then it's stored locally. Also cuts way down on data usage while driving. I struggle to get anywhere close to my 5GB data allowance.

After a dozen years of keeping subscription prices stable, Spotify has issued three price hikes in 2.5 years.

Spotify informed subscribers via email today that Premium monthly subscriptions would go from $12 to $13 per month as of users’ February billing date. Spotify is already advertising the higher prices to new subscribers.

Although not explicitly mentioned in Spotify’s correspondence, other plans are getting more expensive, too. Student monthly subscriptions are going from $6 to $7. Duo monthly plans, for two accounts in the same household, are going from $17 to $19, and Family plans, for up to six users, are moving from $20 to $22.

Spotify’s Basic plan, which is only available as a downgrade for some Premium subscribers and is $11/month, is unaffected.

For years, Spotify subscribers enjoyed stable prices, but today’s announcement marks Spotify’s third price hike since July 2023. Spotify last raised prices in July 2024. Premium individual subscriptions went from $11 to $12, Duo subscriptions went from $15 to $17, and Family subscriptions increased from $17 to $20.

In 2025, new data show, the volume of child pornography online was likely larger than at any other point in history. A record 312,030 reports of confirmed child pornography were investigated last year by the Internet Watch Foundation, a U.K.-based organization that works around the globe to identify and remove such material from the web.

This is concerning in and of itself. It means that the overall volume of child porn detected on the internet grew by 7 percent since 2024, when the previous record had been set. But also alarming is the tremendous increase in child porn, and in particular videos, generated by AI. At first blush, the proliferation of AI-generated depictions of child sexual abuse may leave the misimpression that no children were harmed. This is not the case. AI-generated, abusive images and videos feature and victimize real children—either because models were trained on existing child porn, or because AI was used to manipulate real photos and videos.

Today, the IWF reported that it found 3,440 AI-generated videos of child sex abuse in 2025; the year before, it found just 13. Social media, encrypted messaging, and dark-web forums have been fueling a steady rise in child-sexual-abuse material for years, and now generative AI has dramatically exacerbated the problem. Another awful record will very likely be set in 2026.

At age 50, Daniel was “on top of the world.”

“I turned 50, and it was the best year of my life,” he told Futurism in an interview. “It was like I finally figured out so many things: my career, my marriage, my kids, everything.”

It was early 2023, and Daniel — who asked to be identified by only his first name to protect his family’s privacy — and his wife of over three decades were empty nesters, looking ahead to the next chapter of their lives. They were living in an affluent Midwestern suburb, where they’d raised their four children. Daniel was an experienced software architect who held a leadership role at a large financial services company, where he’d worked for more than 20 years. In 2022, he leveraged his family’s finances to realize a passion project: a rustic resort in rural Utah, his favorite place in the world.

“All the kids were out of the house, and it was like, ‘oh my gosh, we’re still young. We’ve got this resort. I’ve got a good job. The best years of our lives are in front of us,” Daniel recounted, sounding melancholy. “It was a wonderful time.”

That all changed after Daniel purchased a pair of AI chatbot-embedded Ray-Ban Meta smart glasses — the AI-infused eyeglasses that Meta CEO Mark Zuckerberg has made central to his vision for the future of AI and computing — which he says opened the door to a six-month delusional spiral that played out across Meta platforms through extensive interactions with the company’s AI, culminating in him making dangerous journeys into the desert to await alien visitors and believing he was tasked with ushering forth a “new dawn” for humanity.

And though his delusions have since faded, his journey into a Meta AI-powered reality left his life in shambles — deep in debt, reeling from job loss, isolated from his family, and struggling with depression and suicidal thoughts.

Popular Information identified 37 major companies that were actively advertising on X on January 12. Each company was contacted and asked whether they were concerned about paying for ads on a platform that was allowing users to create nonconsensual sexualized images of women and children. All 37 either declined to comment or did not respond.

In the past, concerns about X under Musk’s leadership have led corporations, including some contacted by Popular Information for this story, to suspend their ads on the platform.

In late 2023, a wave of companies suspended advertising on X after Musk endorsed an antisemitic conspiracy theory in a post. According to the New York Times, over 100 companies stopped advertising on X after the post, costing up to $75 million in ad revenue. Among those companies were Amazon, Microsoft, and Google, all of which have advertised on X in recent days.

Companies advertising on X that were contacted by Popular Information

These are the 37 major companies that were contacted by Popular Information. All of the companies had active advertisements on X on January 12. None of the companies offered a response.

• NFL
• Shein
• NBA
• Outback Steakhouse
• Burger King
• ESPN
• Lenovo
• Dell
• Gatorade
• David Protein
• ASUS
• Paramount+
• Chipotle
• Wendy’s
• Tylenol
• Google
• Amazon
• Booz Allen Hamilton
• Salesforce
• Carnival Cruise Line
• State Farm
• Intuit Turbo Tax
• Fortune
• Crypto.com
• Apple
• FT
• RadioShack
• GoPuff
• Microsoft
• Wayfair
• Axios
• Fannie Mae
• T-Mobile
• Lowe’s
• EASports
• DoorDash
• Fox Sports

In his 1974 book, ​Labor and Monopoly Capital​, the influential Marxist political economist Harry Braverman argued that the expanding “science-technical revolution” was being exploited by companies to increasingly “deskill” workers; to leave them in “ignorance, incapacity, and thus in fitness for machine servitude.” The more employees outsource skilled activity to machines, the more controllable they become.

It’s hard not to hear echoes of Braverman’s deskilling argument in something like Cherny’s AI programming demo. A world in which software development is reduced to the ersatz management of energetic but messy digital agents is a world in which a once important economic sector is stripped down to fewer, more poorly paid jobs, as wrangling agents requires much less skill than producing elegant code from scratch. The consumer would fare no better, as the resulting software would be less stable and innovation would slow.

The only group that would unambiguously benefit from deskilling developers would be the technology companies themselves, which could minimize one of their biggest expenses: their employees.