This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Introducing: the Creativity issue The university computer lab may seem like an unlikely center for creativity. We tend to think of creativity as happening more in the artist’s studio or writers’ workshop. But throughout history, very often our greatest creative leaps—and I would argue that the web and its descendants represent one such leap—have been due to advances in technology. But the key to artistic achievement has never been the technology itself. It has been the way artists have applied it to express our humanity.This latest issue of our magazine, which was entirely produced by human beings using computers, explores creativity and the tension between the artist and technology. We hope you enjoy reading it as much as we enjoyed putting it together. —Mat Honan, editor in chief
Here’s just a taste of what you can expect: + AI is warping our expectations of music. New diffusion AI models that make songs from scratch are complicating our definitions of authorship and human creativity. Read the full story.+ Meet the researchers testing the “Armageddon” approach to asteroid defense. Read the full story.
+ How the federal government is tracking changes in the supply of street drugs. A new harm reduction initiative is helping prevent needless deaths. Read the full story.+ How AI is ushering in a new era of co-creativity, laying the groundwork for a future in which humans and machines create things together. Read the full story. + South Korea’s graphic artists are divided over whether AI will immortalize their work or threaten their creativity. + A new biosensor can detect bird flu in just five minutes. Read the full story. MIT Technology Review Narrated: Quantum computing is taking on its biggest challenge—noise For a while researchers thought they’d have to make do with noisy, error-prone systems, at least in the near term. That’s starting to change.This is our latest story to be turned into a MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. Join us today to chat about brain-computer interfaces

Brain-computer interfaces are electrodes implanted into the brain to send neural commands to computers, primarily to assist paralyzed people, and our readers recently named them as the 11th Breakthrough Technology of 2025 in our annual list. So what are the next steps for companies like Neuralink, Synchron, and Neuracle? And will they be able to help paralyzed people at scale?Join our editor at large David Rotman and senior editor for biomedicine Antonio Regalado today for an exclusive subscriber-only Roundtable discussion exploring the past, present, and future of brain-computer interfaces. Register here to tune in at 1pm ET this afternoon! The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 OpenAI is interested in buying Chrome from Google ChatGPT’s head of product Nick Turley said folding its tech into Chrome would improve it greatly. (Bloomberg $)+ It would be just one of many prospective buyers. (Insider $)+ Turley would also be happy with a distribution deal with Google. (The Information $) 2 Instagram’s founder says Meta starved it of resourcesKevin Systrom believes Mark Zuckerberg saw the app as a threat to Facebook. (NYT $)+ It sounds as if the pair had a strained relationship. (The Verge) 3 Elon Musk will step back from DOGE next month In his absence, Tesla’s profits have plummeted. (WP $)+ But he’ll still spend a day or so a week working on US government matters. (CNBC)+ There’s no denying that his political activities have damaged Tesla’s brand. (WSJ $)+ DOGE’s tech takeover threatens the safety and stability of our critical data. (MIT Technology Review) 4 Chinese scientists and students are under scrutiny in the USIt’s a repeat of the China Initiative program launched under Trump’s first Presidency. (WSJ $)+ US universities are starting to push back against government overreach. (Ars Technica)+ The FBI accused him of spying for China. It ruined his life. (MIT Technology Review)
5 Rare earth elements aren’t so rare after allWhich is bad news for China. (Wired $)+ But China’s export curbs are harming Tesla’s Optimus robot production. (Reuters)+ This rare earth metal shows us the future of our planet’s resources. (MIT Technology Review) 6 How to wean yourself off fossil fuelsMassive home batteries are an intriguing energy alternative. (Vox)
7 A new mission to grow food in space has blasted offScientists are investigating creating food from single cells in orbit. (BBC)+ Future space food could be made from astronaut breath. (MIT Technology Review) 8 It’s time to bid farewell to SkypeRIP to the OG video calling platform. (Rest of World)  9 Analysts are using AI to psychologically profile top soccer players ⚽And also to spot bright young talent. (The Guardian) 10 Saving the world’s seeds is a tricky business 🌱They’re the first line of defense against extinction. (Knowable Magazine)+ The weeds are winning. (MIT Technology Review) Quote of the day
“Stuffing Chrome with even more AI crap is one way to spur browser innovation, I guess.” —Tech critic Paris Marx isn’t convinced that OpenAI buying Chrome would improve it, in a post on Bluesky. The big story
How gamification took over the worldIt’s a thought that occurs to every video-game player at some point: What if the weird, hyper-focused state I enter when playing in virtual worlds could somehow be applied to the real one?Often pondered during especially challenging or tedious tasks in meatspace (writing essays, say, or doing your taxes), it’s an eminently reasonable question to ask. Life, after all, is hard. And while video games are too, there’s something almost magical about the way they can promote sustained bouts of superhuman concentration and resolve.For some, this phenomenon leads to an interest in flow states and immersion. For others, it’s simply a reason to play more games. For a handful of consultants, startup gurus, and game designers in the late 2000s, it became the key to unlocking our true human potential. But instead of liberating us, gamification turned out to be just another tool for coercion, distraction, and control. Read the full story. —Bryan Gardiner We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.) + Succession creator Jesse Armstrong’s new film Mountainhead looks intriguing.+ Domestic cats have a much more complicated history than we previously realized.+ If you enjoyed the new vampire flick Sinners, you’ll love these Indian folk horrors.+ This hispi cabbage side dish looks incredible.

A new play about OpenAI I recently saw Doomers, a new play by Matthew Gasda about the aborted 2023 coup at OpenAI, here represented by a fictional company called MindMesh. The action is set almost entirely in a meeting room; the first act follows executives immediately after the firing of company CEO Seth (a stand-in for Sam Altman), and the second re-creates the board negotiations that determined his fate. It’s a solid attempt to capture the zeitgeist of Silicon Valley’s AI frenzy and the world’s moral panic over artificial intelligence, but the rapid-fire, high-stakes exchanges mean it sometimes seems to get lost in its own verbosity. Themed dinner parties and culinary experiments The vastness of Chinese cuisine defies easy categorization, and even in a city with no shortage of options, I often find myself cooking—not just to recapture something closer to home, but to create a home unlike one that ever existed. Recently, I’ve been experimenting with a Chinese take on the charcuterie board—pairing toasted steamed buns, called mantou, with furu, a fermented tofu spread that is sharp, pungent, and full of umami. Sewing and copying my own clothes I started sewing three years ago, but only in the past year have I begun making clothes from scratch. As a lover of vintage fashion—especially ’80s silhouettes—I started out with old patterns I found on Etsy. But recently, I tried something new: copying a beloved dress I bought in a thrift store in Beijing years ago. Doing this is quite literally a process of reverse-engineering—­pinning the garment down, tracing its seams, deconstructing its logic, and rebuilding it. At times my brain feels like an old Mac hitting its GPU limit. But when it works, it feels like a small act of magic. It’s an exercise in certainty, the very thing that drew me to fashion in the first place—a chance to inhabit something that feels like an extension of myself.

The reason you are reading this letter from me today is that I was bored 30 years ago.  I was bored and curious about the world and so I wound up spending a lot of time in the university computer lab, screwing around on Usenet and the early World Wide Web, looking for interesting things to read. Soon enough I wasn’t content to just read stuff on the internet—I wanted to make it. So I learned HTML and made a basic web page, and then a better web page, and then a whole website full of web things. And then I just kept going from there. That amateurish collection of web pages led to a journalism internship with the online arm of a magazine that paid little attention to what we geeks were doing on the web. And that led to my first real journalism job, and then another, and, well, eventually this journalism job.  But none of that would have been possible if I hadn’t been bored and curious. And more to the point: curious about tech.  The university computer lab may seem at first like an unlikely center for creativity. We tend to think of creativity as happening more in the artist’s studio or writers’ workshop. But throughout history, very often our greatest creative leaps—and I would argue that the web and its descendants represent one such leap—have been due to advances in technology. 
There are the big easy examples, like photography or the printing press, but it’s also true of all sorts of creative inventions that we often take for granted. Oil paints. Theaters. Musical scores. Electric synthesizers! Almost anywhere you look in the arts, perhaps outside of pure vocalization, technology has played a role.   But the key to artistic achievement has never been the technology itself. It has been the way artists have applied it to express our humanity. Think of the way we talk about the arts. We often compliment it with words that refer to our humanity, like soul, heart, and life; we often criticize it with descriptors such as sterile, clinical, or lifeless. (And sure, you can love a sterile piece of art, but typically that’s because the artist has leaned into sterility to make a point about humanity!)
All of which is to say I think that AI can be, will be, and already is a tool for creative expression, but that true art will always be something steered by human creativity, not machines.  I could be wrong. I hope not.  This issue, which was entirely produced by human beings using computers, explores creativity and the tension between the artist and technology. You can see it on our cover illustrated by Tom Humberstone, and read about it in stories from James O’Donnell, Will Douglas Heaven, Rebecca Ackermann, Michelle Kim, Bryan Gardiner, and Allison Arieff.  Yet of course, creativity is about more than just the arts. All of human advancement stems from creativity, because creativity is how we solve problems. So it was important to us to bring you accounts of that as well. You’ll find those in stories from Carrie Klein, Carly Kay, Matthew Ponsford, and Robin George Andrews. (If you’ve ever wanted to know how we might nuke an asteroid, this is the issue for you!)   We’re also trying to get a little more creative ourselves. Over the next few issues, you’ll notice some changes coming to this magazine with the addition of some new regular items (see Caiwei Chen’s “3 Things” for one such example). Among those changes, we are planning to solicit and publish more regular reader feedback and answer questions you may have about technology. We invite you to get creative and email us: [email protected]. As always, thanks for reading.

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Hyperscale data warehouse vendor Ocient announced today that it has raised $42.1 million as the second extension of its series B funding to accelerate the development and delivery of energy-efficient solutions for costly and unwieldy operational data and AI workloads. The funding infusion doesn’t just add to the Chicago startup’s already hefty war chest; it sharpens a mission to make hyperscale analytics radically cheaper and greener at the very moment enterprises fear ballooning data‑center power bills.  The new round increases the company’s total funding to $159.4 million. The latest round was led by climate-savvy backers such as Blue Bear Capital and Allstate Strategic Ventures — a signal that investors now view data-platform efficiency as a climate issue as much as a performance one.  Ocient CEO Chris Gladwin told VentureBeat that Ocient’s architecture already delivers “ten‑to‑one price‑performance gains” on multi‑petabyte workloads, and plans are underway to carry that advantage into new verticals from automotive telemetry to climate modeling. The startup has doubled its revenues for three consecutive years and appointed Henry Marshall, formerly CFO at space-infrastructure firm Loft Orbital, to steer its financial operations, signaling that Ocient is entering a formal growth stage. A funding round framed by climate economics The $42.1 million top‑up follows the $49.4 million raise in March 2024 that lifted Ocient’s invested capital to $119 million and marked 109 percent year‑over‑year revenue growth. Alongside its new investors, the company retains support from Greycroft and OCA Ventures, with Buoyant Ventures backing the extension for its “differentiated approach to delivering energy‑efficient analytics.” Gladwin linked the round to a broader mission: “Enterprises are grappling with complex data ecosystems, energy availability, and the pressure to control costs while proving business value,” he

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Coding with the help of AI models continues to gain popularity, but many have highlighted issues that arise when developers rely on coding assistants.  However, researchers from MIT, McGill University, ETH Zurich, Johns Hopkins University, Yale and the Mila-Quebec Artificial Intelligence Institute have developed a new method for ensuring that AI-generated codes are more accurate and useful. This method spans various programming languages and instructs the large language model (LLM) to adhere to the rules of each language. The group found that by adapting new sampling methods, AI models can be guided to follow programming language rules and even enhance the performance of small language models (SLMs), which are typically used for code generation, surpassing that of large language models. In the paper, the researchers used Sequential Monte Carlo (SMC) to “tackle a number of challenging semantic parsing problems, guiding generation with incremental static and dynamic analysis.” Sequential Monte Carlo refers to a family of algorithms that help figure out solutions to filtering problems.  João Loula, co-lead writer of the paper, said in an interview with MIT’s campus paper that the method “could improve programming assistants, AI-powered data analysis and scientific discovery tools.” It can also cut compute costs and be more efficient than reranking methods.  The researchers noted that AI-generated code can be powerful, but it can also often lead to code that disregards the semantic rules of programming languages. Other methods to prevent this can distort models or are too time-consuming.  Their method makes the LLM adhere to programming language rules by discarding code outputs that may not work early in the process and “allocate efforts towards outputs that more most likely to be valid and accurate.” Adapting SMC to

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Researchers from Stanford University and Google DeepMind have unveiled Step-Wise Reinforcement Learning (SWiRL), a technique designed to enhance the ability of large language models (LLMs) to tackle complex tasks requiring multi-step reasoning and tool use.  As the interest in AI agents and LLM tool use continues to increase, this technique could offer substantial benefits for enterprises looking to integrate reasoning models into their applications and workflows. The challenge of multi-step problems Real-world enterprise applications often involve multi-step processes. For example, planning a complex marketing campaign may involve market research, internal data analysis, budget calculation and reviewing customer support tickets. This requires online searches, access to internal databases and running code. Traditional reinforcement learning (RL) methods used to fine-tune LLMs, such as Reinforcement Learning from Human Feedback (RLHF) or RL from AI Feedback (RLAIF), typically focus on optimizing models for single-step reasoning tasks.  The lead authors of the SWiRL paper, Anna Goldie, research scientist at Google DeepMind, and Azalia Mirhosseini, assistant professor of computer science at Stanford University, believe that current LLM training methods are not suited for the multi-step reasoning tasks that real-world applications require. “LLMs trained via traditional methods typically struggle with multi-step planning and tool integration, meaning that they have difficulty performing tasks that require retrieving and synthesizing documents from multiple sources (e.g., writing a business report) or multiple steps of reasoning and arithmetic calculation (e.g., preparing a financial summary),” they told VentureBeat. Step-Wise Reinforcement Learning (SWiRL) SWiRL tackles this multi-step challenge through a combination of synthetic data generation and a specialized RL approach that trains models on entire sequences of actions.  As the researchers state in their paper, “Our goal is to teach the model how to decompose