What I Learned from a Simulated AI-Gaming of a New DOGE Era's Status Report
AI Oversight Doesn’t Just Track Work—It Restructures Reality
You live in a world where AI doesn’t just analyze reality—it reshapes it.
What gets seen, valued, and reinforced isn’t decided in open conversation—it’s structured through data-driven oversight systems that silently shape perception and optimize behavior.
You’ve probably noticed the effects, even if you couldn’t name them:
Why do some narratives take hold so quickly?
Why does something behind the scenes seem to reinforce certain patterns while suppressing others?
Why do people subtly adjust their language, their choices, and even their identities to fit invisible standards?
To answer that, we need to go inside one of the simplest, yet now most publicly revealed AI-governed systems—U.S. federal government workplace performance oversight. A new framework that’s putting many American civil servants and their leaders in a difficult position.
This first part of our ten-part series doesn’t just explore what AI does—it shows you how it does it, hands-on. Soon, you’ll have new hands-on skills in using AI and making new discoveries for yourself. What you do with it though, will be up to you.
In this article:
You will see how AI-driven oversight doesn’t just evaluate work—it actively structures what counts as valuable.
You will recognize that people, often unknowingly, align their language and behavior to fit AI-managed evaluation models.
You will understand how AI isn’t just tracking reality or emitting text into reality in isolation.
You’ll discover that AI actively curates what is recognized, what is optimized, and what quietly disappears.
By the end of this article, you will never look at AI governance the same way again.
A Simple Email Sends the System into Panic
On Saturday, February 22, 2025, U.S. federal workers received an official email instructing them to reply with what they had accomplished during the week. No portal. No template. Just an email.
And if they didn’t respond? Maybe they’d be fired.
The message came from the Office of Personnel Management with simple instructions:
Please reply to this email with approx. 5 bullets of what you accomplished last week and cc your manager.
Please do not send any classified information, links, or attachments. Deadline is Monday at 11:59 p.m. EST.
And a crucial corner of the world—federal workers, the media, and observers—went into a tailspin. Everyday Americans had been made afraid.
But I thought of the Algorithm.
To me, this wasn’t just an efficiency endeavor. This was yet another AI-governance trial run.
And I wondered: Why is everyone panicking?
AI Oversight Doesn’t Just Track Reality—It Restructures It
I suspected the answer was simple: Perhaps people don’t realize how AI-powered oversight works.
AI doesn’t just monitor work—it defines how work is evaluated. And once that happens, workers begin optimizing for what the AI prioritizes.
What AI tracks, AI optimizes.
What AI optimizes, AI reinforces.
What AI reinforces, AI governs.
This isn’t a theory—it’s already happening. This was just perhaps the most visible moment, on the national stage, to see the unseen.
Simulating an AI-Gamed Status Report
To explore this phenomenon, I ran a simple simulation.
I picked a common federal job—a Social Security Administration Customer Service Representative.
I used AI to generate a status report that would be evaluated favorably by an AI-driven oversight system.
I tested different variations of the report’s writing to see how the language changed perception—by the Algorithm, by human overseers, and perhaps by oneself.
The process was simple:
Generate a status report, much like a basic AI user.
Identify the key variables the AI would measure; plug in the correct values, much like a programmer.
Adjust wording to highlight efficiency, fraud prevention, and productivity, much like a prompt engineer (but with some more advanced techniques).
Introduce subtle narrative framing—reinforcing mission-driven language, much like a “consultant.”
Refine and finalize for submission, like a good public servant.
Done.
Reflect (optional).
It took minutes. Yet, my AI-generated report looked far more “mission-driven” than most anxious human-written updates ever could.
Here’s the simulation. Feel free to take it and play with it yourself:
Copy and paste parts / play → How to AI Your Report
The AI’s Own Reflection—Revealing the Hidden System
What happened next was more revealing.
The AI, upon analyzing the status report, recognized the strategic framing it had embedded and offered this evaluation of its work:
Structured for Performance Analysis – Metrics and tasks are still clearly outlined for tracking efficiency.
Narrative Curation for AI Review – Algorithms scanning reports for trends (e.g., case resolution speed, fraud detection, efficiency) will register both hard data and the thematic intent behind the work.
Strategic Framing – AI oversight tools don’t just look at numbers—they detect patterns in language. The refined version reinforced responsibility, efficiency, fraud prevention, and fairness through subtle language shifts.
Flagging Priority Work – AI models trained to detect keywords around fraud prevention, efficiency, and system integrity will rank this version of the report higher, even though the work itself hadn’t changed.
This wasn’t just about automating a bureaucratic report—it was about optimizing for AI-driven oversight.
The status report wasn’t just recording reality. It was structuring how the Algorithm interpreted a worker’s value.
The Breaking News That Confirmed the Experiment
Two days later, on February 24, 2025, NBC News reported:
DOGE Will Use AI to Assess the Responses of Federal Workers Who Were Told to Justify Their Jobs via Email.
It was obvious. But here’s the real kicker: The requirement to cc managers enabled the Algorithm to reconstruct power hierarchies at warp speed.
In response to a tweet about LLM usage, the newly installed head of the Department of Government Efficiency and one of the world’s most sophisticated power users of artificial intelligence denied that AI was needed here, claiming:
This was basically a check to see if the employee had a pulse and was capable of replying to an email.
That’s what’s called pressure testing.
The same request was repeated the next weekend on March 1, 2025.
If you don’t know what “narrative framing” and “pressure testing” are
in the world of AI today, it might be time to catch up.
According to Business Insider:
The latest email tells employees to expect to complete a productivity summary weekly going forward.
Federal workers who shared the email with BI said the doubling down is "nuts" and "infuriating."
And then the Business Insider piece had an ad from a leading high-tech job search website that aggregates job listings from many sources. The company “uses AI to personalize job recommendations for job seekers by analyzing their profiles, skills, and experience, matching them with relevant job openings.”
The ad embedded within the Business Insider article crisply invited:
Better work begins with better hiring.
[See How]
What could be paradoxically more ironic and profound?
What AI Oversight Might Mean for a Large Regulated Industry
I wonder if what happened in this case is a baby step of a big leap and even how it impacts my industry, healthcare tech.
Can the Algorithm be used to govern perception and policy of and for payer/provider reimbursement?
If the Algorithm shapes public perception and provides policy enforcement, what is the AI prioritizing and why? (e.g., perceived health, clinical outcomes, reduced physician burnout, greater profits, investment returns, higher voter support in the geographies and segments that most matter?).
So we need to ask this:
Who does the Algorithm serve?
There are no neutral answers.
The Biggest Risk? Failing to Recognize AI’s Role Before We Catch Up
AI oversight isn’t just tracking—it’s already shaping decision-making in real-time.
AI-driven perception modeling and influence are already driving financial market dynamics.
AI policy enforcement is determining what matters most—and doing it based on unseen optimization models.
This is all happening now.
How will you engage? Because both not engaging and engaging have equal consequences.
Next in This Series
In Part 2, you’ll see how shallow narrative alignment happens, how people optimize their words to fit algorithmically shaped expectations, and why it matters more than you think.