Panos Panay, Amazon's SVP of Devices & Alexa, said at TechCrunch Disrupt that personal computing is heading toward ambient computing devices where sensors and AI work proactively in the background — the next generation of users won't open an app for everything but will expect devices to understand their needs, though smartphones will remain important.
Semiconductor Engineering examines how open-weight AI models affect chip architecture, noting the focus is shifting from peak TOPS to memory bandwidth and capacity, sustained throughput, numerical format flexibility, and deployment security.
The Verge senior reviewer Victoria Song spent about 100 days using Twin Health's digital twin service, which combines AI with Dexcom G7 CGM data to predict her blood sugar responses to different foods. The service is not publicly available for download and can only be accessed through employers or private health plans, mainly targeting people with chronic metabolic conditions, and requires continuously uploading body composition, blood pressure and other data to feed the digital twin. She said logging fatigue is a common complaint, and while her behavior changed, her goals of losing weight and reducing medication did not pan out.
Cerebras CEO Andrew Feldman said at TechCrunch Disrupt 2026 that the era of at-home AI devices is not far off. He predicted personal AI assistants will run locally, splitting work between devices and the cloud like phones do, and could eventually shop on users' behalf. He cited Meta's Muse and Instinct, a startup valued at $10 billion, and said he already uses a set of agents to manage his schedule and look up who is calling him.
At MIT Future Fest, Tony Fadell said 'Gen 1' AI devices like the Rabbit R1, Humane Ai pin, and Limitless pendant failed because they didn't solve real needs—they were just tech that was fun for geeks.