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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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E-Commerce
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