The one about the socio-technical model applied to AI.

Much of today’s AI conversation centers on tangible constraints — chip shortages, compute capacity, and the growing energy demands of model training. Those are real limits, and they’ll shape what’s technically possible for years.

But anyone who has lived through an EHR rollout or a “clinical decision support” pilot knows that the harder part often begins after the technology is installed. Once the silicon is humming, we still have to integrate it into the living fabric of care — how clinicians think, how teams coordinate, how patients experience the system.

That’s where the carbon-based agents come in. Not as obstacles, but as the essential medium through which any digital innovation actually becomes care.

The Sittig and Singh (2010) socio-technical model still provides one of the clearest guides here. It lays out eight interdependent dimensions — from technical infrastructure and clinical content to workflow, organizational culture, and external environment. It’s a reminder that safety, quality, and adoption emerge not from the technology itself, but from how these layers interact in practice.

The same logic applies to AI. A model can perform flawlessly in validation but fail to add value at the bedside if it doesn’t align with clinical priorities, decision rhythms, or accountability structures. These systems don’t just plug into existing teams; they subtly reshape how roles are defined, how authority flows, and how judgment is shared.

So yes, the field will need more chips, more power, and more scalable infrastructure. But the real breakthroughs will come from designing AI that supports the people who deliver care and ultimately the carbon-based agents central to their mission: the patient.