
The Next Great AI Companies Won't Live Entirely in the Cloud
AI's first wave was built where data was cheap to collect. The next one has to go where the data is born: on machines, at the far end of slow and unreliable networks.
Company announcements, product notes, research and perspectives on intelligence for the physical world.

AI's first wave was built where data was cheap to collect. The next one has to go where the data is born: on machines, at the far end of slow and unreliable networks.

Industrial adoption, field reliability and labeled failure data all take years to build. Why infrastructure intelligence rewards patience, and what it takes.

What connected machine intelligence must get right on security: device identity, encryption, signed updates, network segmentation and the whole lifecycle.

A practical checklist for the plant behind the IT load: cooling, electrical distribution, pumps, backup power and environment, and what to watch in each.

After the data center boom comes a quieter build-out: sensing, edge inference, connectivity, fleet management and action. A layer by layer look at physical AI.

Why AFFERENT plans in decades: the service life of industrial assets, the slow work of earning trust, and the principles that guide how we intend to build.

A monitoring system's value lies in what it can safely discard. How trillions of samples a day become one or two decisions, and what makes a finding useful.

Lending scores made financial risk legible. How a machine health score could work for maintenance, insurance and asset finance, and what it must prove first.

Similar devices can sit inside very different businesses. What separates selling measurements from selling decisions, and how buyers can tell them apart.

Industrial buyers judge monitoring on reliability, not models. How they decide, why pilots stall, and how to build an honest ROI case with your own numbers.