Plus: Hackers have disrupted the US Federal Election Commission's website. When Keelan Miskell began winding down his crypto startup, he didn't think its data was worth much. But he's since turned the company's digital archives into tens of thousands of dollars, selling its codebase and workspace data. He's part of a growing market in which companies that help shut down startups sell their data to AI labs. Demand has grown as training labs hit the limits of public and synthetic datasets, with buyers seeking real-world records to help train AI agents. But the sales raise legal and ethical questions.

Plus: Anthropic's listing could determine the future of the AI investment boom. Will the real Anthropic please stand up? After weeks of frenzied speculation, Anthropic has filed its IPO prospectus. The 335-page document (100 pages or so are focused on risks) lays out the company's unique view of the technology it is building and how it believes it will change the world. But Anthropic has been honing its storytelling skills for years. Its public messaging is a master class in narrative capture, presenting it as different things to different people: an AI safety organization, a profit-driven megacorp, and a group hell-bent on creating superhuman technology.

Physical AI brings business intelligence to physical processes, turning knowledge into decisions and decisions into action. Every manufacturer has its own industrial intelligence. Some is documented, and some resides in the knowledge of experienced workers. Consider an experienced operator preparing to restart a line after a changeover. The machine settings matter, but so does knowing how a particular material behaves, what happened during the previous shift, and when an apparently minor variation deserves a closer look. A procedure can describe the sequence, but successful execution depends on the judgment that comes from experience and the organization's accumulated know-how.

Transformers are a crucial but nearly invisible part of our grid. The devices help efficiently move power across long distances, but they're complicated and in short supply. And given that their basic design hasn't changed much for about a century, they could use an upgrade. As the world races to meet rising electricity demand from EVs, data centers, and more, researchers and companies are looking to modernize transformers. New, solid-state versions could help relieve some of the supply concerns and make sure the grid can handle the growing pressure. Conventional transformers generally rely on huge amounts of specialized steel alloys and miles of copper wire to do their work of transferring electricity between circuits. Solid-state models, in comparison, combine magnetic components with high-power semiconductors, making them more compact and allowing for more control over the flow of electricity. These devices are still in development, and can't replace all traditional transformers today, especially the largest ones that work at the high voltages necessary for long-distance transmission. But a new generation of transformer technology could be a crucial part of our future grid.

The company has been masterful at storytelling for years. Will the IPO reveal the company behind the mask or lean deeper into the myth-making? After weeks of frenzied speculation, Anthropic has filed its IPO prospectus. The 335-page document (100 pages or so are focused on risks) lays out the company's unique view of the technology it is building and the ways it believes it will change the world. It also warns that there's a fair chance that its tech could "erode trust, harm society and destabilize the geopolitical order" and, if things don't go to plan, "pose catastrophic or existential risks to humanity." "We encourage you, as a prospective owner of our stock, not to think of your purchase in purely transactional terms," the company writes.

Plus: Ukraine has regained high-speed Starlink and Starshield connectivity. We have historically allowed debates over technology to be defined by the worst thing a product might do, with remarkably little discussion of the insidious damage it does every day. A third of teens now choose to discuss serious matters with AI companions instead of other people. Students are relying on chatbots to write their papers, while social media feeds engineered to provoke envy keep users watching. These products can meet narrow safety requirements yet still erode how we think, learn, and interact with one another. As we enter an era defined by powerful AI products, we should demand that the tech industry do more than address only catastrophic harms.

Lawmakers are passing industry-backed bills to guard against the worst outcomes, rather than addressing more common problems. Consider what passes for success in technology policy. An AI company promises that its chatbot will not encourage suicide . A social media platform makes it more difficult for children to buy fentanyl . These are important protections, and they should have existed all along. But companies can clear these low bars while continuing to make products that leave young people dependent on simulated friendships or trapped in feeds built to keep them scrolling.

The Download: testing six tech promises and biotech's blind spots Plus: Palmer Luckey's new startup plans to genetically enhance military dogs. Some seemingly impossible feats are too good to give up on. Sounds great, but how close are we, really? In the new Impossible issue of, we ran progress reports on six potentially transformative technologies and ideas that have been "just around the corner" for years, or even decades. From exercise in a pill to nuclear-powered spaceships, we asked experts what's holding them back and how far they've come. Check out our stories on how they all stacked up .

Earlier this week, I attended a screening of, the buzzy new documentary about the disgraced former biotech CEO Elizabeth Holmes. I went in not really knowing what to expect. I'd avoided spoilers (and won't be giving anything away here, either!). I was wondering what new information this film could reveal, given how familiar many of us are with the Theranos story thanks to years of media coverage, the best-selling book, and the Hulu series, starring Amanda Seyfried. Like many others who have seen the film, I came away feeling unsettled.

The engineering system surrounding an AI model increasingly determines whether its intelligence can be delivered reliably, economically, and at scale. The next competitive advantage in enterprise AI has less to do with which model you choose than with what you build around it. For the past several years, much of the AI conversation has centered on model capability: bigger models, better reasoning, larger context windows, higher benchmark scores. Those advances have been extraordinary. But as AI moves from experimentation to production, the question facing enterprise leaders has changed--it's no longer, "what can the model do?"--it's now, "Can we deliver that intelligence reliably, economically, and at scale?" Increasingly, the answer depends on the serving harness--the engineering system surrounding the model, including the architecture, memory management, scheduling, and optimization decisions that determine how efficiently model intelligence becomes a production capability. For technical executives, that changes where AI investment should go--and what they should demand from the architecture supporting it.