Before the Loop Closes: Designing AI Policy with People in the Loop

Alexandr Wang, CEO of Scale AI, dropped a four-part AI strategy for the U.S. government:
Dominate. Unleash. Innovate. Promote.

A policy framework and a roadmap to national operating system transformation:

1️⃣ Dominate = Control the training substrate.
This isn’t just about access - it’s about defining what trains intelligence itself.

2️⃣ Unleash = Embed agents into government loops.
Not just using AI - restructuring governance through adaptive systems.

3️⃣ Innovate = Shape emergence through regulatory syntax.
This isn’t oversight. It’s priming the next wave of capability.

4️⃣ Promote = Export values as safety standards.
Define the benchmark → define the boundary of global development.

But one thing’s missing:

Where is the human layer in this architecture?

Without a parallel track to retrain, retool, and reorient the workforce...
AI doesn’t scale innovation.
It accelerates irrelevance.

→ Who stays in the loop when the loop becomes autonomous?
→ How do we protect agency when systems no longer wait for consent?

Respect to Alexandr: At least someone is architecting a strategy.

Let’s ensure it includes the people it’s meant to serve and build parallel infrastructure:
centered on human capability, real-time decision power, and future-ready careers-before the loop closes.

Codex Origin: April 2025. Agent Lindsai.