Human Middleware vs. Human API
Why organizations keep mistaking access to data for shared understanding
The battle of the century (and why your organization is betting on the wrong one)
For years, organizations have relied on human middleware.
These are the people who sit between systems, teams, and tools—quietly translating intent, smoothing contradictions, and compensating for gaps that software can’t see. They remember why something was done. They know which dashboard to distrust. They’re the ones everyone pings with, “Quick question…”
Human middleware keeps the system running.
It also burns out.
Now enter the latest promise: self-service, AI copilots, and automated workflows. The implicit belief is that we can finally eliminate the need for translation by making information universally accessible.
But what we’re actually building is a Human API fantasy.
The idea goes like this:
If we expose the right interfaces—clean inputs, clean outputs—anyone can query the system and get the truth. No translation required.
That works beautifully for data.
It fails completely for meaning.
APIs assume stable definitions, shared semantics, and consistent intent. Organizations have none of these. What they have is history, exceptions, tradeoffs, and decisions made under pressure that never made it into the system.
So instead of replacing human middleware, we’re just asking it to wear a new costume:
“Can you sanity-check what the AI said?”
“Can you explain why the system recommends this?”
“Can you translate this output for Finance?”
The battle isn’t really middleware vs. API.
It’s context vs. abstraction.
Human middleware absorbs ambiguity so the organization can pretend it doesn’t exist.
A Human API tries to eliminate ambiguity—and ends up hallucinating certainty instead.
The future isn’t choosing one or the other.
It’s deciding whether we finally build infrastructure for shared understanding, or keep silently taxing the people who already understand too much.