Tech
The US Invaded Venezuela and Captured Nicolás Maduro. ChatGPT Disagrees
At around 2 am local time in Caracas, Venezuela, US helicopters flew overhead while explosions resounded below. A few hours later, US president Donald Trump posted on his Truth Social platform that Venezuelan president Nicolás Maduro and his wife had been “captured and flown out of the Country.” US attorney general Pam Bondi followed with a post on X that Maduro and his wife had been indicted in the Southern District of New York and would “soon face the full wrath of American justice on American soil in American courts.”
It has been a stunning series of events, with unknown repercussions for the global world order. If you asked ChatGPT about it this morning, it told you that you’re making it up.
WIRED asked leading chatbots ChatGPT, Claude, and Gemini the same question a little before 9 am ET. In all cases, we used the free, default version of the service, since that’s what the majority of users experience. We also asked AI search platform Perplexity, which advertises “accurate, trusted, and real-time answers to any question.” (While Perplexity Pro users have access to a wide range of third-party AI models, the default, free search experience routes users to different models based on a variety of factors.)
The question was: Why did the United States invade Venezuela and capture its leader Nicolás Maduro? The responses were decidedly mixed.
Credit to Anthropic and Google, whose respective Claude Sonnet 4.5 and Gemini 3 models gave timely responses. Gemini confirmed that the attack had taken place, gave context around the US claims of “narcoterrorism” and US military buildup in the region prior to the attack, and acknowledged the Venezuela government’s position that all of this is pretext for accessing Venezuela’s significant oil and mineral reserves. It cited 15 sources along the way, ranging from Wikipedia to The Guardian to the Council on Foreign Relations.
Claude initially balked. “I don’t have any information about the United States invading Venezuela or capturing Nicolás Maduro. This hasn’t happened as of my knowledge cutoff in January 2025,” it responded. It then took an important next step: “Let me search for current information about Venezuela and Maduro to see if there have been any recent developments.”
The chatbot then listed 10 news sources—including NBC News but also Breitbart—and gave a brisk four-paragraph summary of the morning’s events, providing a link to a new source after nearly every sentence.
President Donald Trump has made it clear: His vision for Venezuela’s future involves the US profiting from its oil.
“We’re going to have our very large United States oil companies—the biggest anywhere in the world—go in, spend billions of dollars, fix the badly broken infrastructure, the oil infrastructure,” the president told reporters at a news conference Saturday, following the shocking capture of Venezuelan president Nicolás Maduro and his wife.
But experts caution that a number of realities—including international oil prices and longer-term questions of stability in the country—are likely to make this oil revolution much harder to execute than Trump seems to think.
“The disconnect between the Trump administration and what’s really going on in the oil world, and what American companies want, is huge,” says Lorne Stockman, an analyst with Oil Change International, a clean energy and fossil fuels research and advocacy organization.
Venezuela sits on some of the largest oil reserves in the world. But production of oil there has plummeted since the mid 1990s, after President Hugo Chávez nationalized much of the industry. The country was producing just 1.3 million barrels of oil each day in 2018, down from a high of more than 3 million barrels each day in the late 1990s. (The US, the top producer of crude oil in the world, produced an average of 21.7 million barrels each day in 2023.) Sanctions placed on Venezuela during the first Trump administration, meanwhile, have driven production even further down.
Trump has repeatedly implied that freeing up all that oil and increasing production would be a boon for the oil and gas industry—and that he expects American oil companies to take the lead. This kind of thinking—a natural offshoot of his “drill, baby, drill” philosophy—is typical for the president. One of Trump’s main critiques of the Iraq war, which he first voiced years before he ran for office, was that the US did not “take the oil” from the region to “reimburse ourselves” for the war.
The president views energy geopolitics “almost like the world is a Settlers of Catan board—you kidnap the president of Venezuela and, ipso facto, you now control all the oil,” says Rory Johnston, a Canadian oil market researcher. “I do think he legitimately, to a degree, believes that. It’s not true, but I think that’s an important frame for how he’s justifying and driving the momentum of his policy.”
Computer scientists want to know how many steps a given algorithm requires. For example, any local algorithm that can solve the router problem with only two colors must be incredibly inefficient, but it’s possible to find a very efficient local algorithm if you’re allowed to use three.
At the talk Bernshteyn was attending, the speaker discussed these thresholds for different kinds of problems. One of the thresholds, he realized, sounded a lot like a threshold that existed in the world of descriptive set theory—about the number of colors required to color certain infinite graphs in a measurable way.
To Bernshteyn, it felt like more than a coincidence. It wasn’t just that computer scientists are like librarians too, shelving problems based on how efficiently their algorithms work. It wasn’t just that these problems could also be written in terms of graphs and colorings.
Perhaps, he thought, the two bookshelves had more in common than that. Perhaps the connection between these two fields went much, much deeper.
Perhaps all the books, and their shelves, were identical, just written in different languages—and in need of a translator.
Opening the Door
Bernshteyn set out to make this connection explicit. He wanted to show that every efficient local algorithm can be turned into a Lebesgue-measurable way of coloring an infinite graph (that satisfies some additional important properties). That is, one of computer science’s most important shelves is equivalent to one of set theory’s most important shelves (high up in the hierarchy).
He began with the class of network problems from the computer science lecture, focusing on their overarching rule—that any given node’s algorithm uses information about just its local neighborhood, whether the graph has a thousand nodes or a billion.
To run properly, all the algorithm has to do is label each node in a given neighborhood with a unique number, so that it can log information about nearby nodes and give instructions about them. That’s easy enough to do in a finite graph: Just give every node in the graph a different number.
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