The rivalry between Sam Altman and Elon Musk is entering its Apprentice era. Both men have the ambition to redefine how the modern world works—and both are jockeying for President Donald Trump’s blessing to accelerate their plans.
Altman’s company, OpenAI, as well as Musk’s ventures—which include SpaceX, Tesla, and xAI—all depend to some degree on federal dollars, permits, and regulatory support.
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Elon Musk and his Department of Government Efficiency appear to have wide-reaching plans to remake the government with AI. Thomas Shedd, a former Tesla engineer who is now the head of the Technology Transformation Services, a federal IT division, invoked an “AI-first strategy” at a recent staff meeting.
Over the past few weeks, Donald Trump has positioned himself as an unabashed bull on America’s need to dominate AI. Yet the president has also tied this newfound and futuristic priority to a more traditional mission of his: to go big with fossil fuels. A true AI revolution will need “double the energy” that America produces today, Trump said in a recent address to the World Economic Forum, days after declaring a national energy emergency.
The internet is growing more hostile to humans. Google results are stuffed with search-optimized spam, unhelpful advertisements, and AI slop. Amazon has become littered with undifferentiated junk. The state of social media, meanwhile—fractured, disorienting, and prone to boosting all manner of misinformation—can be succinctly described as a cesspool.
It’s with some irony, then, that Reddit has become a reservoir of humanity.
One week ago, a new and formidable challenger for OpenAI’s throne emerged. A Chinese AI start-up, DeepSeek, launched a model that appeared to match the most powerful version of ChatGPT but, at least according to its creator, was a fraction of the cost to build.
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Earlier this week, Donald Trump unveiled perhaps the most ambitious infrastructure project in history—one that may rival the costs of the first moon missions—and all but dedicated it to Sam Altman.
Late yesterday afternoon, the president of the United States transformed, very briefly, into the comms guy for a new tech company. At a press conference capping his first full day back in the White House, Donald Trump stood beside three of the most influential executives in the world—Sam Altman of OpenAI, Larry Ellison of Oracle, and Masayoshi Son of SoftBank—and announced the Stargate Project, “the largest AI infrastructure project, by far, in history.
Yesterday morning, donning his new signature fit—gold chain, oversize T-shirt, surfer hair—Mark Zuckerberg announced that his social-media platforms are getting a makeover. His aggrievement was palpable: For years, Zuckerberg said, “governments and legacy media have pushed to censor more and more.” No longer. Meta is abolishing its third-party fact-checking program, starting in the U.S.; loosening its content filters; and bringing political content back to Facebook, Instagram, and Threads.
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Earlier this week, OpenAI launched the full version of its video-generating model, Sora. Hype has been building around this release since the startup teased the program nearly 10 months ago, but the final product doesn’t quite meet expectations.
For more than two years, every new AI announcement has lived in the shadow of ChatGPT. No model from any company has eclipsed or matched that initial fever. But perhaps the closest any firm has come to replicating the buzz was this past February, when OpenAI first teased its video-generating AI model, Sora.
This week, OpenAI launched what its chief executive, Sam Altman, called “the smartest model in the world”—a generative-AI program whose capabilities are supposedly far greater, and more closely approximate how humans think, than those of any such software preceding it. The start-up has been building toward this moment since September 12, a day that, in OpenAI’s telling, set the world on a new path toward superintelligence.
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Google is taken for granted as a dominant force in the generative-AI market—so it’s easy to forget that, in the initial frenzy following the release of ChatGPT, the search giant was caught flat-footed. The company raced to catch up with OpenAI, and its early models made some basic and highly publicized errors.
The images and videos were hard to miss in the days leading up to November 5. There was Donald Trump with the chiseled musculature of Superman, hovering over a row of skyscrapers. Trump and Kamala Harris squaring off in bright-red uniforms (McDonald’s logo for Trump, hammer-and-sickle insignia for Harris). People had clearly used AI to create these—an effort to show support for their candidate or to troll their opponents. But the images didn’t stop after Trump won.
Democrats will spend the next four years debating why the party suffered a sweeping defeat last week. Maybe it was inflation, or the culture wars, or Joe Biden’s hubris, or podcasts, that drove voters in every swing state to the Republican presidential nominee. At least one theory, however, can already be put to rest: Elon Musk did not “steal” the election for Donald Trump.
If the presidential election has provided relief from anything, it has been the generative-AI boom. Neither Kamala Harris nor Donald Trump has made much of the technology in their public messaging, and they have not articulated particularly detailed AI platforms. Bots do not seem to rank among the economy, immigration, abortion rights, and other issues that can make or break campaigns.
But don’t be fooled.
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Among the most tangible and immediate effects of the generative-AI boom has been a total upending of English classes.
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Even for Silicon Valley, where executives have spent the past two years likening their chatbots to fire, electricity, and nuclear weapons, the past few months have been extraordinary.
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The list of Nobel laureates reads like a collection of humanity’s greatest treasures: Albert Einstein, Marie Curie, Francis Crick, Toni Morrison. As of this morning, it also includes two physicists whose research, in the 1980s, laid the foundations for modern artificial intelligence.
When the Swedish inventor Alfred Nobel wrote his will in 1895, he designated funds to reward those who “have conferred the greatest benefit to humankind.” The resulting Nobel Prizes have since been awarded to the discoverers of penicillin, X-rays, and the structure of DNA—and, as of today, to two scientists who, decades ago, laid the foundations for modern artificial intelligence.
Terence Tao, a mathematics professor at UCLA, is a real-life superintelligence. The “Mozart of Math,” as he is sometimes called, is widely considered the world’s greatest living mathematician. He has won numerous awards, including the equivalent of a Nobel Prize for mathematics, for his advances and proofs. Right now, AI is nowhere close to his level.
But technology companies are trying to get it there.
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A disaster is brewing on dark-web forums, in messaging apps, and in schools around the world: Generative AI is being used to create sexually explicit images and videos of children, likely thousands a day.
When the Three Mile Island power plant in Pennsylvania was decommissioned in 2019, it heralded the symbolic end of America’s nuclear industry. In 1979, the facility was the site of the worst nuclear disaster in the nation’s history: a partial reactor meltdown that didn’t release enough radiation to cause detectable harm to people nearby, but still turned Americans against nuclear power and prompted a host of regulations that functionally killed most nuclear build-out for decades.
After weeks of speculation about a new and more powerful AI product in the works, OpenAI today announced its first “reasoning model.” The program, known as o1, may in many respects be OpenAI’s most powerful AI offering yet, with problem-solving capacities that resemble those of a human mind more than any software before. Or, at least, that’s how the company is selling it.
As with most OpenAI research and product announcements, o1 is, for now, somewhat of a tease.
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Today’s generative-AI tools can concoct stunning designs and playful prose with the push of a few buttons. That, in turn, has bred fears about how the technology could hurt human artists and writers, and led many, in their defense of humanity, to a well-intentioned but confused claim.
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Year three of AI college is about to begin, and instructors across the country still seem to have no clue how to handle the technology: no good way to stop students from using ChatGPT to write essays, and no clear way to instruct students on how AI might enhance their work.
At a rally this past April in Michigan, surrounded by a cadre of law-enforcement officials, Donald Trump suddenly began railing against electric cars. President Joe Biden’s decision to support EVs, he decried, “is one of the dumbest I’ve ever heard.” Minutes later, he was back to praising the sheriffs behind him: “We have to get law and order back. These are the best people in the world,” he said to a smattering of applause.
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For the past two years, the biggest tech firms have begun spending historic amounts of money to develop generative-AI products and spread them across the web. The build-out may demand a trillion dollars or more of investment across the economy this decade—more than the Apollo missions or the interstate-highway system.
Every second of every day, people across the world type tens of thousands of queries into Google, adding up to trillions of searches a year. Google and a few other search engines are the portal through which several billion people navigate the internet. Many of the world’s most powerful tech companies, including Google, Microsoft, and OpenAI, have recently spotted an opportunity to remake that gateway with generative AI, and they are racing to seize it.
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Yesterday OpenAI made what should have been a triumphant entry into the AI-search wars: The start-up announced SearchGPT, a prototype tool that can use the internet to answer questions of all kinds. But there was a problem, as I reported: Even the demo got something wrong.
Whenever AI companies present a vision for the role of artificial intelligence in the future of searching the internet, they tend to underscore the same points: instantaneous summaries of relevant information; ready-made lists tailored to a searcher’s needs. They tend not to point out that generative-AI models are prone to providing incorrect, and at times fully made-up, information—and yet it keeps happening.