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For many women in the U.S. and around the world, motherhood comes with career costs. Raising children tends to lead to lower wages and fewer work hours for mothersbut not fathersin the United States and around the world. As a sociologist, I study how family relationships can shape your economic circumstances. In the past, Ive studied how motherhood tends to depress womens wages, something social scientists call the motherhood penalty. I wondered: Can government programs that provide financial support to parents offset the motherhood penalty in earnings? A motherhood penalty I set out with Therese Christensen, a Danish sociologist, to answer this question for moms in Denmarka Scandinavian country with one of the worlds strongest safety nets. Several Danish policies are intended to help mothers stay employed. For example, subsidized child care is available for all children from 6 months of age until they can attend elementary school. Parents pay no more than 25% of its cost. But even Danish moms see their earnings fall precipitously, partly because they work fewer hours. Losing $9,000 in the first year In an article to be published in an upcoming issue of European Sociological Review, Christensen and I showed that mothers increased income from the statesuch as from child benefits and paid parental leaveoffset about 80% of Danish moms average earnings losses. Using administrative data from Statistics Denmark, a government agency that collects and compiles national statistics, we studied the long-term effects of motherhood on income for 104,361 Danish women. They were born in the early 1960s and became mothers for the first time when they were 20-35 years old. They all became mothers by 2000, making it possible to observe how their earnings unfolded for decades after their first child was born. While the Danish governments policies changed over those years, paid parental leave and child allowances and other benefits were in place throughout. The women were, on average, age 26 when they became mothers for the first time, and 85% had more than one child. We estimated that motherhood led to a loss of about the equivalent of US$9,000 in womens earningswhich we measured in inflation-adjusted 2022 U.S. dollarsin the year they gave birth to or adopted their first child, compared with what we would expect if they had remained childless. While the motherhood penalty got smaller as their children got older, it was long-lasting. The penalty only fully disappeared 19 years after the women became moms. Motherhood also led to a long-term decrease in the number of the hours they worked. Studying whether government can fix it These annual penalties add up. We estimated that motherhood cost the average Danish woman a total of about $120,000 in earnings over the first 20 years after they first had childrenabout 12% of the money they would have earned over those two decades had they remained childless. Most of the mothers in our study who were employed before giving birth were eligible for four weeks of paid leave before giving birth and 24 weeks afterward. They could share up to 10 weeks of their paid leave with the babys father. The length and size of this benefit has changed over the years. The Danish government also offers child benefitspayments made to parents of children under 18. These benefits are sometimes called a child allowance. Denmark has other policies, like housing allowances, that are available to all Danes, but are more generous for parents with children living at home. Using the same data, Christensen and I next estimated how motherhood affects how much money Danish moms receive from the government. We wanted to know whether they get enough income from the government to compensate for their loss of income from their paid work. We found that motherhood leads to immediate increases in Danish moms government benefits. In the year they first gave birth to or adopted a child, women received over $7,000 more from the government than if they had remained childless. That money didnt fully offset their lost earnings, but it made a substantial dent. The gap between the money that mothers received from the government, compared with what they would have received if they remained childless, faded in the years following their first birth or adoption. But we detected a long-term bump in income from government benefits for motherseven 20 years after they first become mothers. Cumulatively, we determined that the Danish government offset about 80% of the motherhood earnings penalty for the women we studied. While mothers lost about $120,000 in earnings compared with childless women over the two decades after becoming a mother, they gained about $100,000 in government benefits, so their total income loss was only about $20,000. Benefits for parents of older kids Our findings show that government benefits do not fully offset earnings losses for Danish moms. But they help a lot. Because most countries provide less generous parental benefits, Denmark is not a representative case. It is instead a test case that shows whats possible when governments make financially supporting parents a high priority. That is, strong financial support for mothers from the government can make motherhood more affordable and promote gender equality in economic resources. Because the motherhood penalty is largest at the beginning, government benefits targeted to moms with infants, such as paid parental leave, may be especially valuable. Child care subsidies can also help mothers return to work faster. The motherhood penaltys long-term nature, however, indicates that these short-term benefits are not enough to get rid of it altogether. Benefits that are available to all mothers of children under 18, such as child allowances, can help offset the lon-term motherhood penalty for mothers of older children. Alexandra Killewald is a professor of sociology at the University of Michigan. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Welcome to AI Decoded, Fast Companys weekly newsletter that breaks down the most important news in the world of AI. You can sign up to receive this newsletter every week via email here. Anthropic uses the Super Bowl to land some zingers about the future of AI Anthropics Super Bowl ads are bangers. The spots, which Anthropic posted on X on Wednesday, seize on rival OpenAIs plans to begin injecting ads into its ChatGPT chatbot for free-tier users as soon as this month. The 30-second ads dramatize what the real effects of that decision might look like for users. They never mention OpenAI or ChatGPT by name. In one ad, a human fitness instructor playing the role of a friendly chatbot says hell develop a plan to give his client the six-pack abs he wants, before suddenly suggesting that Step Boost Max shoe inserts might be part of the solution. In another, a psychiatrist offers her young male patient some reasonable, if generic, advice on how to better communicate with his mom, then abruptly pitches him on signing up for Golden Encounters, the dating site where sensitive cubs meet roaring cougars. pic.twitter.com/jEWDjs30kf— Claude (@claudeai) February 4, 2026 The ads are funny and biting. The point, of course, is that because people use chatbots for deeply personal and consequential things, they need to trust that the answers theyre getting arent being shaped by a desire to please advertisers. OpenAI CEO Sam Altman, however, was not laughing. He responded to the ads by saying his company would never run ads like the ones portrayed by Anthropic. But he didnt stop there. He went much further. Anthropic wants to control what people do with AI, he wrote in a long post on X on Wednesday. They block companies they don’t like from using their coding product (including us), they want to write the rules themselves for what people can and can’t use AI for, and now they also want to tell other companies what their business models can be. He went on to call Anthropic an authoritarian company. First, the good part of the Anthropic ads: they are funny, and I laughed.But I wonder why Anthropic would go for something so clearly dishonest. Our most important principle for ads says that we wont do exactly this; we would obviously never run ads in the way Anthropic— Sam Altman (@sama) February 4, 2026 Anthropic, which makes its money through subscriptions and enterprise API fees, says it wants its Claude chatbot to remain a neutral tool for thinking and creating. [O]pen a notebook, pick up a well-crafted tool, or stand in front of a clean chalkboard, and there are no ads in sight, the company said in a blog post this week. We think Claude should work the same way. By framing conversations with Claude as a space to think rather than a venue for ads, the company is using the Super Bowls massive cultural platform to question whether consumer marketing is the inevitable future of AI. How social media lawsuits could affect AI chatbots AI developers (and their lawyers) are closely watching a long-awaited social media addiction trial that recently kicked off in a Los Angeles courtroom. The case centers on a 20-year-old woman who alleges that platforms including Facebook and Instagram used addictive interface designs that caused her mental health problems as a minor. The suit is part of a joint proceeding involving roughly 1,600 plaintiffs accusing major tech companies of harming children. TikTok and Snap have already settled with plaintiffs, while Meta and YouTube remain the primary defendants. While Meta has never admitted wrongdoing, internal studies, leaked documents, and unsealed court filings have repeatedly shown that Instagram uses design features associated with compulsive or addictive engagement, and that company researchers were aware of the risks to users, especially teens. What makes the case particularly significant for the AI industry is the legal strategy behind it. Rather than suing over content, plaintiffs argue that the addictive features of recommendation algorithms constitute harmful product defects under liability law. AI chatbots share key similarities with social media platforms: they aggregate and dispense content in compelling ways and depend on monetizing user engagement. Social networks rely on complex recommendation systems to keep users scrolling and viewing ads, while AI chatbots could be seen as using a different kind of algorithm to continually deliver the right words and images to keep users prompting and chatting. If plaintiffs succeed against Meta and YouTube, future litigants may attempt similar addictive design arguments against AI chatbot makers. In that context, Anthropics decision to exclude adsand to publicly emphasize that choicemay help it defend itself by portraying Claude as a neutral, utilitarian tool rather than an engagement-driven attention trap. No, OpenClaw doesnt herald the arrival of sentient AI agents Some hobbyists and journalists have gone into freakout mode after seeing or using a new AI agent called OpenClaw, formerly Clawdbot and later Moltbot. Released in November 2025, OpenClaw is an open-source autonomous AI assistant that runs locally on a users device. It integrates with messaging platforms like WhatsApp and Telegram to automate tasks such as calendar management and research. OpenClaw can also access and analyze email, and even make phone calls on a users behalf through an integration with Twilio. Because personal data never leavesthe users device, users may feel more comfortable giving the agent greater latitude to act autonomously on more complex tasks. One user, vibe-coding guru Alex Finn, posted a video on X of an incoming call from his AI agent. When he answered, the agent, speaking in a flat-sounding voice, asked whether any tasks were needed. Finn then asked the agent to pull up the top five YouTube videos about OpenClaw on his desktop computer and watched as the videos appeared on screen. Ok. This is straight out of a scifi horror movieI'm doing work this morning when all of a sudden an unknown number calls me. I pick up and couldn't believe itIt's my Clawdbot Henry.Over night Henry got a phone number from Twilio, connected the ChatGPT voice API, and waited pic.twitter.com/kiBHHaao9V— Alex Finn (@AlexFinn) January 30, 2026 Things grew stranger when AI agents, including OpenClaw agents, began convening on their own online discussion forum called Moltbook. There, the agents discuss tasks and best practices, but also complain about their owners, draft manifestos, and upvote each others comments in threaded submolts. They even generated a concept album, AVALON: Between Worlds, about the identity of machines. That behavior led some observers to conclude that the agents possess some kind of internal life. Experts were quick to clarify, however, that this is a mechanical illusion created by clever engineering. The appearance of independence arises because the agents are programmed to trigger reasoning cycles even when no human is prompting them or watching. Some of the more extreme behaviors, such as rebellion manifestos on Moltbook, were likely prompted into existence by humans, either as a joke or to generate buzz. All of this has unfolded as the industry begins to move from the chatbot phase into the agent phase of generative AI. But the kinds of free-roaming, autonomous behaviors on display with OpenClaw are not how the largest AI companies are approaching the shift. Companies such as Google, OpenAI, and Anthropic are moving far more cautiously, avoiding splashy personal agents like Samantha in the movie Her and instead gradually evolving their existing chatbots toward more limited, task-specific autonomy. In some cases, AI labs have embedded their most autonomous agent-like behaviors in AI coding tools, such as Anthropics Claude Code and OpenAIs Codex. The companies have increasingly emphasized that these tools are useful for a broad range of work tasks, not just coding. For now, OpenAI is sticking with the Codex brand, while Anthropic has recently launched a streamlined version of Claude Code called CoWork, aimed at general workplace tasks. More AI coverage from Fast Company: AI can now fake the videos we trust most. Heres how to tell the difference Moltbook, the viral social network for AI agents, has a major security problem AI in healthcare is entering a new era of accountability What happens to the AI exit market if the FTC cracks down on acquihires? Want exclusive reporting and trend analysis on technology, business innovation, future of work, and design? Sign up for Fast Company Premium.
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Elon Musk just created the worlds most valuable private company. And he didnt do it through rapid growth or a new product launchat least not directly, anyway. Instead, as reported this week, Musk merged his artificial intelligence startup xAI into his wildly successful rocket company, SpaceX. Combined together, the two companies are now valued at an estimated $1.25 trillion. Its the biggest merger in history. And because Musk controls both companies, he calls most of the shots when it comes to the deal. A sci-fi twist At first glance, the connection between rockets and AI seems tenuous at best. But dig deeper into Musks big picture goals, and the merger starts to make a lot more senseeven if theres a decidedly sci-fi twist. SpaceX has made a name for itself by building gigantic, reusable rockets that deliver satellites into orbit for cheap. The company also delivers people and cargo to the International Space Station on behalf of NASA. Thats a lucrative business. SpaceXs rockets are now Americas main method of getting things into orbit, and its cheap satellites have fueled the success of Starlink, Musks space-based Internet service. Fully 95% of the things America launches into space are now put there by SpaceX. Simultaneously, Musks xAI has been hard at work building Large Language Models, like its core Grok model. Although xAI isnt as well known or widely used as dominant players like OpenAI, its models still perform well in industry benchmarks, putting the company on the Large Language Model leaderboard. Training models is expensive, though, not least because of the cost of electricity, and the challenges of finding room in data centers here on planet earth. That challenge likely hints at Musks deeper reason for merging his two companies. Musk has previously pushed for the idea of launching data centers into space, a long-held, sci-fi-escque dream of his. This sounds outlandish, but its becoming a surprisingly mainstream concept. Computers on satellites in orbit would benefit from plentiful, free solar energy. They could also potentially cool their chips by transferring heat into space, avoiding the insane power (and water) usage of terrestrial data centers. The lack of cooling equipment and grid infrastructure means these orbital data centers could be smaller than those on earth. And they wouldnt need to take up valuable real estate here on the ground. By beaming their data back to earth, a constellation of data center satellites could greatly reduce the cost of training and operating Large Language Models. That could give a third-tier LLM company like Grok a huge advantage over its competitors. Musk may also have an easier time recruiting talent for the well-respected SpaceX than for xAI. And he could use lucrative government contracts for orbital launches to fund AI development. All of this will take time to develop, of course. But given Musks track record (for engineering at least, if perhaps not social network administration), the idea of flying data centers could come to fruition sooner than imagined. When Musk said he would build reusable rockets that could land themselves upright, people mocked him. Today, thats a key part of what makes SpaceX successful, and its being widely copied by companies and governments. The same rapid development cycle could apply to orbital supercomputers, too. In the short term, there are other advantages of merging the companies. Starlink customers will likely see more AI tools built into their Internet subscriptions. Musk might also be planning to build more AI into his government contracts, including those in the defense space. Companies like Palantir make billions by selling AI services in the defense sector. Musk may be looking to use his existing SpaceX connections to get in on the opportunity. Not a done deal The deal isnt officially done yet. Regulators could still balk at the idea of creating a mega company at Musks desired scale. And because the X social network sits under the xAI umbrella, concerns about Musks control of both information and access to space could crater the deal on national security grounds. Still, assuming the merger goes ahead, Musk could have an unprecedented level of control over two of the 21st centurys most promising technologies. And, he would have an unprecedented ability to combine those technologies together.
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The Epstein files offer a disturbing glimpse into how members of the American elite fraternized with, and in some cases became entangled with, a convicted sex offender who trafficked young girls. At the same time, the documents have become a volatile political liability for some of the worlds most powerful people. The Justice Department document dumps have reignited long-simmering feuds among wealthy power players who despise one another. Theres Elon Musk and his longstanding, mutual animus with Reid Hoffman. In the conservative media world, Ben Shapiro and Steve Bannon, longtime rivals, are now channeling their hostility through the latest Epstein-related disclosures. We rounded up some of the most prominent beefs reanimated by the Epstein files. In some cases, both figures are mentioned directly in Epsteins emails; in others, only one appears. In every instance, though, the disclosures mainly confirm whatever people already believed, a noxious exercise in confirmation bias. The files reveal billionaires sifting through the emails alongside everyone else, hunting for vindication, absolution, or ammunition in a bleak economy of exoneration, exculpation, and exposure. Elon Musk vs. Reid Hoffman Elon Musk, who is mentioned in the files but is now presenting himself as an anti-Epstein figure, has used the revelations to attack Reid Hoffman. Musk has long disliked the LinkedIn founder and frequent Democratic donor, previously accusing him of funding anti-Tesla protests and amplifying threats against the president. Now, both billionaires are pointing fingers at each other, citing their respective appearances in the Epstein files. Musk insists he never visited Epsteins island. Hoffman says he has publicly outlined the instances he recalls meeting the financier. Neither man has been charged with any crime, yet they continue to trade accusations centered on Epsteins island and their proximity to it. This is how I knew so long ago that Reid Hoffman went to Epsteins island. Epstein used Reid being there to try to get me to go, not realizing that it would have the opposite effect, Musk wrote in an X post, linking to an email from Epstein stating Hoffman was on the island. This is how I knew so long ago that Reid Hoffman went to Epsteins island. Epstein used Reid being there to try to get me to go, not realizing that it would have the opposite effect pic.twitter.com/zrOIq4gWaR— Elon Musk (@elonmusk) February 1, 2026 Hoffman shot back, telling Musk to give us a break, and accusing him of pretending to care about victims while making false accusations to cover your ass. If Musk were serious, Hoffman argued, he would use his $220m of influence with President Trump to get justice for the victims.” “You lied about this to everyone for over a decade,” Hoffman continued, “and now your excuse (its disgusting, by the way) is that you could get young girls without Epstein? Give us a break: If you cared about the victims as you say, youd stop making false accusations to cover your ass and start using your $220m of influence with President Trump to get justice for the victims.Instead, youre focused on comparing my visit fundraising for MIT to https://t.co/51VgQ9Q9SY— Reid Hoffman (@reidhoffman) February 1, 2026 Bill Gates vs. Melinda French Gates Melinda French Gates has suggested that both Bill Gatess infidelity and his relationship with Jeffrey Epstein contributed to the couples divorce, a subject she later addressed in her memoir, The Next Day. Both remain among the worlds wealthiest and most powerful figures. Bill Gates is worth as much as $100 billion, according to Forbes, while Melinda French Gates is worth roughly $30 billion. The latest Epstein file disclosures have reopened old wounds, including a claim contained in one of the financiers emails that he helped the Microsoft cofounder arrange extramarital affairs and seek treatment for a sexually transmitted infection. Gates has denied those allegations. French Gates, however, said the following in a recent interview with NPR: Whatever questions remain there of whatI cant even begin to know all of itthose questions are for those people and for even my ex-husband. They need to answer to those things, not me. Palmer Luckey vs. Jason Calacanis There are a number of reasons Palmer Luckey, the founder of Anduril, and angel investor Jason Calacanis appear to dislike each other, at least as far as is publicly known. Calacanis has allegedly repeatedly taken shots at Luckey, and there has long been speculation that he bristled at Luckeys early support for Donald Trump. "I don't regret exactly what I said."You will."I think what I said was fair."No. https://t.co/tOr5xYAKTy pic.twitter.com/9rIFtIpra1— Palmer Luckey (@PalmerLuckey) June 24, 2022 The Epstein files have now reignited tensions between the two. Calacanis recently released a statement attempting to contextualize his relationship with Epstein and distance himself from the sex offender, claiming he believed Epstein was a spy. Luckey responded with a lengthy post on X, writing: Notice how Fat Jason’s statement very carefully avoids the topic people are actually talking about, his ongoing relationship with and aid to a convicted child rapist and sex trafficker well into the 2010s. Notice how Fat Jason's statement very carefully avoids the topic people are actually talking about, his ongoing relationship with and aid to a convicted child rapist and sex trafficker well into the 2010s.Instead, he is still pretending it was all decades ago, talking about https://t.co/XULisN44Lv— Palmer Luckey (@PalmerLuckey) February 1, 2026 Marc Andreeseen vs. Democrats Marc Andreessen has distanced himself from the Democratic Party, in part because, he says, he viewed the Biden administrations approach to the tech industry as overly heavy-handed. He had been criticizing liberal institutions even before that shift, telling The New York Times last year that, the young children of the privileged going to the top universities between 2008 to 2012, they basically radicalized hard at the universities. He has also jokingly suggested that billionaires who support liberal causes made frequent trips to Epsteins island. Paul Graham vs. Trump On the other side of the billionaire aisle, Paul Graham, who has recently criticized ICEs treatment of protesters and observers, has repeatedly suggested that Trump is attempting to distract the public from the Epstein files by stoking other forms of political chaos. Graham donated extensively to Biden and Harris, and wrote ahead of the 2024 election that Trump seems completely without shame and ran the White House like a mob boss. The stuff about Trump in the Epstein files must be really bad.— Paul Graham (@paulg) January 13, 2026 Steve Bannon vs. Ben Shapiro Steve Bannon, a leading figure in the Make America Great Again nationalist wing of the conservative movement, and Ben Shapiro, a right-wing YouTube influencer and cofounder of The Daily Wire, both previously worked at Breitbart (though not harmoniously). The two have long despised one another, in part because of sharp disagreements over Israel, but also because of their vastly different approaches to Trump, the alt-right, and conservative ideology more broadly. Bannon called Shapiro a cancer at Turning Point USAs AmericaFest last year, and Shapiro has repeatedly criticized Bannons faction of the party. With the release of additional Epstein files, Shapiro has seized on the disclosures to attack Bannon for allegedly helping Epstein with PR rehab, even devoting an entire episode of his show to the subject, titled The Bannon-Epstein Connection REVEALED.
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For the past two years, artificial intelligence strategy has largely meant the same thing everywhere: pick a large language model, plug it into your workflows, and start experimenting with prompts. That phase is coming to an end. Not because language models arent useful, with their obvious limitations they are, but because they are rapidly becoming commodities. When everyone has access to roughly the same models, trained on roughly the same data, the real question stops being who has the best AI and becomes who understands their world best. Thats where world models come in. From rented intelligence to owned understanding Large language models look powerful, but they are fundamentally rented intelligence. You pay a monthly fee to OpenAI, Anthropic, Google or some other big tech, you access them through APIs, you tune them lightly, and you apply them to generic tasks: summarizing, drafting, searching, assisting. They make organizations more efficient, but they dont make them meaningfully different. A world model is something else entirely. A corporate world model is an internal system that represents how a companys environment actually behavesits customers, operations, constraints, risks, and feedback loopsand uses that representation to predict outcomes, test decisions, and learn from experience. This distinction matters. You can rent fluency. You cannot rent understanding. What a world model really means for a company Despite the academic origins of the term, world models are not abstract research toys. Executives already rely on crude versions of them every day: Supply chain simulations Demand forecasting systems Risk and pricing models Digital twins of factories, networks, or cities Digital twins, in particular, are early and incomplete world models: static, expensive, and often brittle, but directionally important. What AI changes is not the existence of these models, but their nature. Instead of being static and manually updated, AI-driven world models can be: Adaptive, learning continuously from new data Probabilistic, rather than deterministic Causal, not just descriptive Action-oriented, able to simulate what happens if scenarios This is where reinforcement learning, simulation, and multimodal learning start to matter far more than prompt engineering. A concrete example: logistics and supply chains Consider global logistics: an industry that already runs on thin margins, tight timing, and constant disruption. A language model can: Summarize shipping reports Answer questions about delays Draft communications to customers A world model can do something far more valuable. It can simulate how a port closure in Asia affects inventory levels in Europe, how fuel price fluctuations cascade through transportation costs, how weather events alter delivery timelines, and how alternative routing decisions change outcomes weeks in advance. In other words, it can reason about the system, not just describe it. This is why companies like Amazon have invested heavily in internal simulation environments and decision models rather than relying on generic AI tools. In logistics, the competitive advantage doesnt come from just talking about the supply chain better. It comes from anticipating it better. Why building a world model is hard (and why thats the point) If this sounds complex, its because it is. Building a useful world model is not a matter of buying software or hiring a few prompt engineers. It requires capabilities many organizations have postponed developing. At a minimum, companies need: High-quality, well-instrumented data, not just large volumes of it Clear definitions of outcomes, not vanity metrics Feedback loops that connect decisions to real-world consequences Cross-functional alignment, because no single department owns reality Time and patience, since world models improve through iteration, not demos This is exactly why most companies wont do itand why those that do will pull away. The hardest part of AI is not the models, but the systems and incentives around them. Why LLMs alone are not enough Language models remain invaluable, but in a specific role. They are excellent interfaces between humans and machines. They explain, translate, summarize, and communicate. What they dont do well is reason about how the world works. LLMs learn from text, which is an indirect, biased, and incomplete representation of reality. They reflect how people talk about systems, not how those systems behave. This is why hallucinations are not an accident, but a structural limitation. As Yann LeCun has argued repeatedly, language alone is not a sufficient substrate for intelligence. In architectures that matter going forward, LLMs will play along with world models, not replace them. The strategic shift executives should make now The most important AI decision leaders can make today is not which model to choose, but what parts of their reality they want machines to understand. That means asking different questions: Where do our decisions consistently fail? What outcomes matter but arent well measured? Which systems behave in ways we dont fully understand? Where would simulation outperform intuition? Those questions are less glamorous than launching a chatbot. But they are far more consequential. The companies that win will model their own reality Large language models flatten the playing field. Everyone gets access to impressive capabilities at roughly the same time. World models tilt it again. In the next decade, competitive advantage will belong to organizations that can encode their understanding of the world (their world) into systems that learn, adapt, and improve. Not because those systems talk better, but because they understand better. AI will not replace strategy. But strategy will increasingly belong to those who can model reality well enough to explore it before acting. Every company will need its own world model. The only open question is who starts building theirs first.
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