Why Infrastructure Intelligence Is a Long Game
Industrial adoption, field reliability and labeled failure data all take years to build. Why infrastructure intelligence rewards patience, and what it takes.
Perspectives
AFFERENT's point of view on physical AI, predictive maintenance and the future of industrial infrastructure.
Perspectives articles
The Physical AI Stack: What Comes After Today's AI Infrastructure Boom?
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.
From a Million Signals to One Useful Decision
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.
The Difference Between a Sensor Company and an Intelligence Platform
Similar devices can sit inside very different businesses. What separates selling measurements from selling decisions, and how buyers can tell them apart.
The Case for a Machine Health Foundation Model
Could one model learn how machines behave and fail, then adapt to a new asset with few labels? The case for a machine health foundation model, and the hurdles.
Why the Next Great Infrastructure Company May Start with Hardware
Conventional advice says avoid hardware. In infrastructure, an installed base on real assets often drives the recurring value. What Otis and others show.
The Next Data Moat Is Physical
The last wave of AI trained on public data. Data on how machines behave and fail is scarce, private and slow to gather, which is why it may be the next moat.
Resilience Begins Before the Outage
Most outage hours come from major events, but how equipment fares in them is decided weeks earlier. Why grid resilience starts with knowing asset condition.
Why Energy Infrastructure Needs an Intelligence Layer
Grid digitization has focused on electrical state and customers, not equipment health. Why energy networks need an intelligence layer, and its ground rules.