Why Normal People Aren’t Using AI Agents
THE SO WHAT
Agent teams that optimize for what models can do instead of what consumers actually want are burning cycles on the wrong constraint. If you’re building agents, ruthlessly narrow to 1–2 repeatable, high-friction jobs and design around trust, recovery, and clear value — not generalized autonomy.
READ THE SOURCE
MORE FROM THE WIRE
Applied AIThe Company Behind Ugly Gold Phones Is Now Making Ugly Gold Smart Glasses
Luxury reskinning of Ray-Ban Meta AI glasses is a reminder that AI wearables will fragment into status objects as well as utilities. If you’re building around these platforms, design for a world where the same core capability shows up in very different form factors and social contexts.
Applied AIAMD acquires Toronto-based Taalas, which integrates model weights directly into silicon to boost inference performance, for an undisclosed sum
Baking model weights into silicon for up to 17,000 tokens/second is a bet that some AI workloads will be stable enough to harden, not endlessly re-train. If you own a large, relatively fixed model in production, start asking vendors about model-specific silicon roadmaps and lock-in tradeoffs.
Applied AIGoogle Maps Adds Agentic Feature That Orders Food and Books Hotels for You
Handing Google Maps your entire schedule so agents can book food and hotels is the clearest move yet to turn a utility app into a transaction engine. If you’re in travel, local commerce, or delivery, treat Maps as an increasingly opinionated intermediary—not just a traffic source.
Applied AISources: OpenAI's new device, slated for 2027, is a hockey puck-sized smart speaker with moving parts that help give it personality, likely costing $300+
A $300+ puck-sized speaker with moving parts is a bet that embodied personality—not just voice quality—will differentiate AI hardware. If you build for consumer assistants, start thinking in terms of continuous, ambient relationships rather than discrete app sessions.