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.

A scalable AI system requires a strong foundation. Data, engineering, platforms, operating models, and governance must converge to work as one. This session from MIT Technology Review's EmTech Future conference explores what it takes to build that foundation in a highly regulated environment and how a responsible, trustworthy implementation can turn emerging capabilities, including agentic systems, into lasting value for investors. Jing Wang leads Vanguard's Center for AI & Insights (CAI), advancing the firm's AI, machine learning, generative AI, and behavioral science capabilities. Her team drives enterprise-wide innovation, research, and delivery, enabling Vanguard to translate emerging technologies into trusted solutions that enhance client experiences, improve investor outcomes, and create measurable business value. Before her current position, Jing served in a range of leadership roles across Vanguard's Financial Services, Workplace Solutions, International, and Corporate Services divisions.

Plus: AI has uncovered a decade-old flaw threatening much of the web. How far can we push computing? On a December evening in 1959, the physicist Richard Feynman gave a lecture called "There's Plenty of Room at the Bottom." There is no physical law stopping us from writing the entire on the head of a pin, he argued. We simply had not built the tools yet. He was right, and for decades, computing took us into that space almost for free, as transistors repeatedly halved in size.

Moving AI beyond experimentation requires connecting agents to operational context and identifying the business processes where they can deliver the greatest value. Enterprise AI can generate recommendations, automate tasks, and help organizations make decisions faster. But without an understanding of how a business actually operates, even powerful models can miss the constraints and relationships that determine whether a decision is the right one. As companies look to move AI from experimentation into everyday operations, the challenge ahead is giving technology the context to act usefully within complex business processes. For Devavrat Shah, chief scientist of enterprise AI at Celonis, that context is central to what he calls "decision intelligence." AI agents can support individual tasks within larger decision-making processes, he argues, but they need to understand the role each task plays and the business conditions surrounding it. "You can't just do that in a vacuum," he says.

How much do you know about permafrost? Maybe, like me, you've been aware that this layer of frozen soil is thawing, that it's important in the global puzzle of climate change and that's about the extent of it. The great news for both of us is that my colleague James Temple just published two in-depth stories about permafrost. His reporting took him to the boreal forests of Alaska, where he helicoptered into the wilderness to learn more about how this material could affect our climate both now and in the future. I'd recommend you give both of James's stories a read. There's this feature about efforts to tackle the intensifying fires that are threatening permafrost, as well as this look at how researchers are trying to understand the complicated tangle of potential climate effects. Here are three things I took away.

AI's hidden storage bill: What inference leaves behind Enterprises need economically scalable capacity to keep pace with the data created by AI inference. Ask most executives to picture the cost of AI and they'll likely see GPUs--racks of accelerators with gigawatts of power--and a waitlist for silicon. But that picture leaves something out: Data storage costs. What happens to data after a model responds? Much of the back-end data that's generated--from billions of daily prompts, images, and videos--needs to be stored. But data storage is one of the harder infrastructure costs to plan for, and it's often overlooked in initial AI infrastructure planning.

Plus: Elon Musk reportedly ordered the recent Starlink slowdown in Ukraine. We wanted our last print edition of 2026 to include a slate of stories that check in on impossible ideas. Because it's fun to dream about living on other planets or uploading our brains. But also because scientists and engineers who won't give up on daring endeavors remind us what technology is supposed to do: make life better, safer, healthier, even more joyful. We explore bold attempts to make synthetic blood, build cities on the moon, and attain cognitive immortality . We investigate AI's ability to say no, a bionic eye that can restore sight, and high hopes for blended meat.

A new era of spaceflight could save astronauts time and cut exposure to cosmic radiation as they rocket to Mars--and beyond. Just before the Artemis II crew began its historic slingshot around the moon this past April, NASA announced plans to build Space Reactor-1 Freedom. SR-1, the first-ever nuclear-reactor-powered interplanetary spacecraft, is meant to fly an uncrewed mission to Mars by the end of 2028, arriving in 2029. A successful mission would herald a new era of more efficient, potentially faster spaceflight--saving astronauts time and cutting their exposure to harmful cosmic radiation as they travel to Mars and beyond. "You wake up to that announcement, and it puts a big smile on your face," says Simon Middleburgh, co-director of the Nuclear Futures Institute at Bangor University in Wales. Running a nuclear reactor in space may sound like lunacy, but it's not.