Plus: Almost $2.5 trillion has been wiped off tech stocks. I watched a humanoid robot clean my apartment. It felt like a taste of the future. When I saw an X post about a new humanoid-robot cleaning service in San Francisco, I immediately signed up for the waitlist. I wanted my apartment to feel like, even if just for an hour. The device that arrived at my building belongs to Tau Robotics, a local startup.

I'm not the sort of person who typically pays for a house cleaner. I'm too frugal, and I kind of enjoy putting on a podcast and going to town on my tub with a Magic Eraser. But when I saw an X post about a new humanoid-robot cleaning service in San Francisco, I immediately signed up for the waitlist. I wanted my apartment to feel like, even if just for an hour. I was also motivated by journalistic interest.

Finding New Drugs Is Laborious. Potential Sciences, a startup backed by the UK government, is betting that training AI models on computer-generated biochemical data can speed up the plodding process of developing new drugs. Drug discovery is a needle in a haystack problem. This AI startup wants to simulate the haystack to speed the process up.Photo-Illustration: Jobanny Cabrera; Getty Images A new British startup believes that artificial intelligence is the key to speedrunning the slow and temperamental process of drug design. Potential Sciences plans to build large-scale chemical datasets by using basic principles of physics to simulate interactions between atoms, then small molecules, then complex amino acids and proteins.

We see it every single day," he says. "People who can't afford their medications, people who can't get appointments, you know, the care being denied, we're dealing with it. So I think having that experience in Washington gives a different perspective."

Join a subscriber-only conversation exploring possible paths to keep your mind going. In an effort to reach an elusive continuity of the self, some far-thinking technologists are working on ways to keep your brain going--even beyond death. Join science editor Mary Beth Griggs and senior editor for biomedicine Antonio Regalado for a conversation exploring possible paths to keep your mind going, including gradual brain replacement and uploading our memories to the cloud. Technology that lets us "speak" to our dead relatives has arrived. "We were looking at it wrong": The explosive claim that's upended the AI world Will Douglas Heaven Don't be fooled by this summer of AI hype Timnit Gebru Emily M. Bender An AI "mind-reading" tool can reconstruct what you're looking at from a brain scan Jessica Hamzelou An AI "mind-reading" tool can reconstruct what you're looking at from a brain scan Scientists hope it could be used to reconstruct a person's inner thoughts, mental images, or even dreams. New studies pour cold water on a prominent rejuvenation idea.

Data lineage is the new security perimeter, providing the visibility needed for governance, accountability, and trust, without impeding AI adoption at scale. A sales representative asks an AI agent for customer pricing, and the agent pulls data from multiple sources: restricted pricing systems, internal communications, and archived quotes. The output looks accurate to the agent, so a second agent uses the data to draft the proposal for the sales rep. When it's discovered that the proposal includes unapproved, confidential, and sensitive information, no one can trace where the problematic data entered the workflow or how it spread across systems, files, and agents. This scenario illustrates why AI governance needs to evolve, rapidly and dynamically. As more enterprises begin to use agentic AI in daily workflows, they're realizing that they must manage more than static files; they must manage moving and fragmented data that is continuously ingested, copied, summarized, transformed, reused, and redistributed in new forms.

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The top reason analytics agents produce substandard insights is a lack of business context, prompting investment in semantic layers and governance, rather than bigger models. In just a few years, generative AI has changed the face of enterprise analytics, notably by making analytics accessible to users across the organization. Agentic AI promises to do even more, by automating entire workflows and taking actions based on agent insights. But there is a problem: The quality of insights that agents currently generate leaves much to be desired. Fewer than half of agent-generated insights at the organizations surveyed for this report lead to business improvement. And while most respondents praise the quality of their agents' descriptive insights, reviews of their prescriptive insights--those on which actions are based--are decidedly mixed.

Why rationing agentic coding backfires, and what to measure instead. When a developer gives an AI agent a coding task, the agent does more than answer once. It plans, generates code, runs tests, and repeats the process to correct errors. A single task can use 10 to 50 times more tokens than a basic chat prompt. The more useful the agent becomes, the more calls it makes. With per-token pricing, the workflows creating the most value can also become the most expensive.

Plus: A $1 trillion tech selloff has deepened fears of an AI bubble. In 2001, a decade after inventing the World Wide Web, Tim Berners-Lee articulated a new vision: an internet navigated by autonomous software agents that address our every need, without requiring us to muck about with browsers and search engines ourselves. At the time, the idea seemed far-fetched. But a quarter-century on, AI agents are beginning to make it a reality. They can triage your inbox, do your grocery shopping, and even access your bank accounts. This new era is being called the agentic web.