You are about to hear a lot more about Claude Code. 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, 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,,, and --he's an guy. Another listed people he had ghosted.

The Trump administration's radical changes to United States fiscal policy, foreign relations, and global strategy--combined with mass firings across the federal government--have created uncertainty around US cybersecurity priorities that was on display this week at two of the country's most prominent digital security conferences in Las Vegas. "We are not retreating, we're advancing in a new direction," Cybersecurity and Infrastructure Security Agency chief information officer Robert Costello said on Thursday during a critical infrastructure defense panel at Black Hat. As in other parts of the federal government, the Trump administration has been combing intelligence and cybersecurity agencies to remove officials seen as disloyal to its agenda. Alongside these shifts, the White House has also been hostile to former US cybersecurity officials. In April, for example, Trump specifically directed all departments and agencies to revoke the security clearance of former CISA director Chris Krebs.

It's late morning on a Monday in March and I am, for reasons I will explain momentarily, in a private bowling alley deep in the bowels of a 65 million mansion in Utah. Jesse Armstrong, the showrunner of HBO's hit series Succession, approaches me, monitor headphones around his neck and a wide grin on his face. "I take it you've seen the news," he says, flashing his phone and what appears to be his X feed in my direction. Everyone had: An hour earlier, my boss Jeffrey Goldberg had published a story revealing that U.S. national-security leaders had accidentally added him to a Signal group chat where they discussed their plans to conduct then-upcoming military strikes in Yemen. "Incredibly fucking depressing," Armstrong said.

Is it important that your lover be a biological human instead of an A.I. or a robot, or will even asking this question soon feel like an antiquated prejudice? This uncertainty is more than a transient meme storm. If A.I. lovers are normalized a little--even if not for you personally--the way you live will be changed. Does this notion disturb you? In the tech industry, we often speak of A.I. as if it were a person and of people as if they might become obsolete when A.I. and robots surpass them, which, we say, might occur remarkably soon.

The tech world is abuzz over a new open-source reasoning AI model developed by DeepSeek, a Chinese startup. Its success is remarkable given the constraints that Chinese AI companies face due to US export controls on cutting-edge chips. DeepSeek's approach represents a radical change in how AI gets built, and could shift the tech world's center of gravity. Hear from MIT Technology Review news editor Charlotte Jee, senior AI editor Will Douglas Heaven, and China reporter Caiwei Chen as they discuss what DeepSeek's breakout success means for AI and the broader tech industry.

In 2023, the world has felt like it was balanced on a precipice. A United States presidential election looms, with a resurgent candidate that threatens to bring with him all the chaos of 2016 and 2020. Artificial intelligence developed so quickly that it seemed to have suddenly sprung into being, heralding vast societal promise and disruption just around the bend of its exponential curve. And the world's richest man continued to use his power to push for a more reckless tech world, from free-for-all social media and oversold assisted-driving features to AI with a "rebellious streak." In the midst of that uncertainty, a new war between Israel and Hamas added more atrocities alongside the slow-burning horrors of Russia's invasion of Ukraine.

The techies are at it again--only this time, they're not looking for kombucha on tap or Patagonia vests, but all-inclusive "hacker houses" in Hayes Valley. Artificial intelligence workers are now forming co-living and coworking communities, where like-minded founders and developers can eat, sleep and breathe their work. These communities are often operated out of historic Victorians near Alamo Square, just a stone's throw from Souvla and a Cotopaxi outlet. The hacker house craze has grown quickly in recent months, so much so that some in the industry are now calling the neighborhood around them "Cerebral Valley." With catchy community names like Genesis House (or its Hillsborough iteration, Neogenesis House) and an ethos that promises to optimize work via play, these communities might sound like just another Silicon Valley fad.

On Wednesday the Chartr office party was in full swing, but instead of heading for drinks -- as originally planned -- we found ourselves still in the office, writing increasingly funny prompts into ChatGPT, a chatbot from OpenAI. Built on the architecture of GPT-3, with some 175 billion parameters, the key innovation of ChatGPT relative to other AI breakthroughs is that it's super easy to interact with. "Tell me a joke", "write a recipe for pecan pie in the style of a pirate", "explain long division to a ten-year-old"... ChatGPT has a -- pretty convincing -- response for all. That functionality has gone viral, with OpenAI reporting that ChatGPT had hit 1 million users in just 5 days. Searches for ChatGPT rocketed, surpassing those for "lensa", another AI app making waves this week, but ChatGPT is undoubtedly the much, much bigger story.

I am sure you've seen many doom and gloom stories. Tech companies that grew during the pandemic are now cutting back and laying off workers in various tech sectors. Tech companies' earnings are down. PC demand is off by 20% this year, and earliest estimates for tech growth looks like in early 2024.

The history of deep learning goes back as far as 1943, when Walter Pitts and Warren McCulloch created a computer model based on the neural networks of the human brain. Today, if we asked a language model like GPT-3 to write an article about the history of deep learning, it might begin with that sentence. Many changes led from Pitts and McCulloch's early neural network to what we now call "deep learning": the addition of backpropagation (Yann LeCun and others), and the creation of "deep" networks with many "hidden layers" (Geoff Hinton and others) are perhaps the most important. And while early neural networks couldn't be programmed effectively (if at all) on the computers of their day, deep learning has now become commonplace. What was once couldn't even be implemented on the largest supercomputers run comfortably on your laptop.