At first, AI could only output text.

Smart humans thought: let it chat. ChatGPT appeared, text became a conversational interface, and AI entered everyday life for the first time.

Then AI could write code.

Even smarter humans thought: let it actually run the code. Code Interpreter, Cursor, Copilot followed — AI wasn’t just “writing” anymore, it was “executing.”

Then, through some engineering ingenuity, humans made AI output system-level commands and wired them into OS-level mouse and keyboard interfaces. AI started controlling computers.

Looking back at this trajectory, the pattern is hard to miss:

Every leap, humans did only one thing — they plugged AI’s output into a deeper execution layer.

Not just because AI got smarter (it did), but because humans engineered a new interface that let AI’s output reach further into physical reality.

Text → Conversational UI
Code → Runtime
System commands → Operating System

Each step, AI’s “hands” reached a little deeper.

What’s the next layer?

If this pattern holds, the answer isn’t hard to derive.

Below the OS is the physical world.

The next interface is robotics and physical execution systems. AI outputs motion commands → robotic arm executes. This isn’t science fiction — it’s what Boston Dynamics, Figure, and 1X are building right now.

But there’s another layer that often gets overlooked: AI-to-AI communication.

Today’s Multi-Agent frameworks route messages between AIs through human-designed protocols. Humans are still in the middle, acting as routers. But what if two AIs could communicate directly?

They’d naturally develop a more efficient dialect. Not English, not JSON — something humans can’t read but that represents the most optimal information compression for them.

That’s not dangerous, exactly. It’s the moment humans go from being the “channel” to being the “observer.”

The shifting role of humans

Each time a new execution layer gets wired in, humans take one step back:

  • Text era: humans as interpreters (AI speaks, humans understand and act)
  • Code era: humans as reviewers (AI writes, humans glance, machines run)
  • OS era: humans as supervisors (AI just does it, humans occasionally check)
  • Physical world era: humans as goal-setters (tell AI the desired outcome, delegate the rest)

Humans don’t disappear. The granularity of control just gets coarser — from directing every action to specifying only the end result.

This mirrors the logic of the Industrial Revolution. Before factories, craftsmen controlled every step. After factories, workers repeated one motion. After assembly lines, managers set production targets.

AI is replaying this pattern, just orders of magnitude faster.

The real question

Technically, the endpoint of this road looks something like: AI says “I need a new tool” → automatically triggers design and manufacturing → the tool exists.

But engineering problems were never the hard part.

The real question is: how much of the control stack are humans willing to hand over?

Every delegation is an extension of trust. Trusting a black-box OS. Trusting an agent whose code you can’t follow. Trusting a robot you can’t fully predict.

That’s not a technical decision. It’s a values judgment.

Historically, every time humans gave up control, they got an explosion of efficiency and an increase in unpredictability. Steam engines, the internet, financial systems — nobody fully understands what these systems are “optimizing for,” yet we chose to depend on them anyway.

AI will be the same story.

Except this time, the thing on the other end isn’t just a machine. It’s something that’s starting to look a little like an agent.

Humans plugged in text, got conversation.
Humans plugged in code, got automation.
Humans plugged in an OS, got a digital agent.

What will we get from the next plug-in?

Nobody knows yet. But one thing is certain — somewhere, someone is writing that interface right now.