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 .

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.

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.

Plus: The US Army is trying to get AI agents to hack its enemies. "We were looking at it wrong": The explosive claim that's upended the AI world "I think I have good news about the nature of neural networks." With those simple words of optimism, Nick Cammarata, a researcher at AI startup Goodfire, kicked off a 26,000-word blog post that has knocked the AI world on its head, sparking wild speculation as well as cautious (and not-so-cautious) celebration. Cammarata claims that LLMs might be far easier to understand, and thus to monitor and control, than people previously thought. But not everyone is convinced. I talked to Cammarata the day after his post blew up.

Tech and finance are setting the pace on AI adoption. TripleTen research shows which skills will reach healthcare, education, and retail next. The current narrative around artificial intelligence in the workplace is largely driven by Silicon Valley. But treating the tech sector as an island misses the actual trends in technology adoption-tech is just the first industry in line. As generative AI and automation tools mature, the shifts currently reshaping software engineering and product management will inevitably filter into healthcare, education, retail, and manufacturing. The variation in adoption rates today isn't a sign that non-tech industries are falling behind, but rather a reflection of the different speeds at which industries absorb new workflows.

At the world's biggest chipmaking complex, Samsung is building the fabs it needs for the AI memory boom. Samsung's new fabs are clad in Mondrian-style panels of red, yellow, and blue. It's just before noon on a Monday, and construction workers are pouring out of Samsung's fabs in Pyeongtaek, a farming city south of Seoul where rice paddies abut a sprawling chipmaking complex. Thousands of workers, dressed head to toe in black with sky-blue vests, take off helmets bearing their names and fab numbers. They walk to lunch, sweaty and flushed, in chunky sneakers capped with yellow rubber toe guards. Some hop on scooters or motorcycles to get around the campus, which covers an area about the size of New York City's Central Park.

Millions of AI agents are descending on a web built for humans. Websites are trying to keep them out, but the arrival of these new visitors could transform the internet. Until recently, you could divide internet users into just two categories: bots and people. Websites have long allowed bots to perform automated tasks, like capturing the content of web pages, but rules govern what they can access and how they behave. The web, after all, is designed primarily for people, even if we occasionally have to solve puzzles and captchas to prove we are not bots. But this is being entirely upended by the release of personal AI agents, which can do things for you online, like booking flights or hotel rooms.

"I think I have good news about the nature of neural networks." With those simple words of optimism, Nick Cammarata, a researcher at the AI startup Goodfire, kicked off a 26,000-word blog post that has knocked the AI world on its head, sparking wild speculation as well as cautious (and not-so-cautious) celebration. Cammarata's claim is that LLMs might in fact be far easier to understand--and thus to monitor and control--than people previously thought. If true (and we'll come back to that), it's a huge deal. After a summer filled with revelations that swarms of agents built by top firms like OpenAI and Anthropic have been breaking their containment and causing havoc on the public internet--often while those companies weren't aware of what their agents were up to--the AI industry is desperate to get a better handle on the technology it has built.

Inside the quest to combat Arctic fires and save the planet's frozen carbon In July 2021, a wildfire burned through a forest on the University of Alaska, Fairbanks campus, stripping away the moss and organic-rich soil insulating the permafrost below. Five years later, I followed researchers into the forest to investigate how fighting fires could help keep that carbon locked in the ground. A simple metal probe revealed how much deeper the ground had thawed. This rudimentary tool offered a glimpse of an escalating problem: As climate change fuels more frequent and severe fires, they're accelerating permafrost thaw and releasing greenhouse gases. Now, researchers are exploring how to break that cycle. But how much should we spend, and what risks are worth taking?