APPLE WAITED 7 YEARS TO GET FOLDABLES RIGHT, THEN CAME THE CRACK
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AI can improve the work a team delivers without proving that its people are becoming better at the job. New research on professional judgment and AI-intensive workplaces asks how companies can capture productivity gains while developing the experts they will still need.
Apple spent years watching its competitors refine foldable smartphones, promising to enter the market only when the technology met its standards. Days after the iPhone Duo launched, nearly identical screen failures and troubling evidence from its manufacturing supply chain have raised an uncomfortable question: what exactly did Apple learn from waiting?
AI shopping is moving beyond recommendations. New moves from Walmart, Commerce, Cloudflare and Visa show why agents need reliable store interfaces, current product data and verifiable transactions, with plans, previews and pilots kept distinct.
Google’s expanded agreement with Voltus puts a different part of AI’s energy strategy in focus: paying homes and businesses to adjust demand when the grid needs it. The practical test is how much dependable flexibility gets delivered, where it helps and who benefits.
Google’s AMIE research offers an early look at AI conversations before a doctor’s visit. The promising results also reveal why medical AI should be judged by the whole appointment: the information preserved, the work shifted, the oversight required and the patient’s ability to stay in control.
A new wave of home robots promises companionship, folded laundry and general-purpose help. Those are very different products, even when they share a humanlike shape. As new launches arrive, the practical test is what a robot can take off a household’s hands, how much attention it still requires, and which capabilities remain on the roadmap.
Microsoft’s latest warning about agentic insiders highlights a practical challenge for enterprise AI: an agent can understand its assignment and still have too much authority. Useful autonomy depends on permissions, independent enforcement and recovery plans that remain effective when a model makes a bad decision.
AI can help predict flash floods, but first it needs a reliable record of where flooding actually happened. Google's Groundsource shows how language models can turn local reporting into scientific data, while exposing the gap between a promising forecast and a warning people can use.