Summary: Elon Musk’s Department of Government Efficiency (DOGE) quietly adapted AI models for classifying federal employee responses to a controversial loyalty and resignation email—while laying foundational work for a centralized government email system. The tool behind the curtain? Meta’s Llama 2, repurposed to scan, sort, and analyze government worker reactions, reminiscent of Musk’s earlier tactics at Twitter. This isn’t a tech experiment—it’s a controlled reshuffle of federal workforce norms using AI to measure compliance and herd behavior at scale. And it’s forcing people to ask: Who owns your inbox when you work for the government?
Meta’s Llama 2 Enters the Federal HR Department
When people talk about AI in government, most imagine reducing red tape. Few expect it to be used as a litmus test for loyalty. Yet that’s exactly what happened under the Department of Government Efficiency—DOGE—a low-key outfit that, according to material reviewed by WIRED, deployed Meta’s Llama 2 AI model to scan email replies from federal employees.
The context? A blunt “Fork in the Road” email blasted to thousands across federal agencies in January. It offered an unusual exit ramp. The terms were especially eyebrow-raising: if you disagreed with the Trump administration’s return-to-office mandate, planned staffing cuts, or the new loyalty requirement, simply reply with “resign.” No courtroom drama. No HR process. Just hit reply.
This tactic mirrors Elon Musk’s infamous email to Twitter employees during his takeover—stay if you’re hardcore, reply “no” to walk. The difference? This time, the experiment took place within the U.S. government through a cleverly built information triage machine.
The Backdoor System Built by DOGE
DOGE staff, freshly integrated into the Office of Personnel Management (OPM), set up a pipeline to handle this heavy influx of email replies. A key player: Riccardo Biasini, a former Tesla engineer. The infrastructure wasn’t merely functional—it was scalable. This wasn’t just about sorting out resignation emails. It was about normalizing a surveillance-oriented HR model with an AI filter as the gatekeeper.
Llama 2 ran locally, according to the documents. That detail matters. It suggests that the AI worked inside government systems without transmitting sensitive data to Meta’s servers. From a security standpoint, that’s a win. From a privacy angle? Still murky.
What challenge does this present to ordinary civil servants? The AI isn’t just reading email. It’s estimating allegiance, triaging dissatisfaction, and accelerating administrative consequences. Can you guess how quickly a one-word “resign” reply could be misused in a mass analysis? And what signals does it send to those who stayed silent?
Email as a Loyalty Test: What’s the Precedent?
The Fork email was only round one. Within weeks, a follow-up message went to federal employees instructing them to summarize their weekly accomplishments in five bullet points. Thousands of workers scrambled to prepare sanitized updates, cautious not to trip any internal red lines.
Although there’s no direct proof that DOGE used Llama 2 to examine these “five-point” emails, sources say it would take little effort to apply the same logic. Volume-based sorting, classification by run-on themes, sentiment analysis—they’re all standard features in commercial AI language models. What would be your reaction if your weekly productivity notes were fed into a political loyalty heat map?
The Reuse of Corporate Playbooks Inside Government
If this sounds familiar, it should. The Musk/Twitter comparison isn’t accidental—it’s procedural. Artificial intelligence is increasingly being used not to discover human insights but to enforce organizational order. In this case, the kind of order favored by a political regime deeply skeptical of federal bureaucracies.
Grok, Musk’s proprietary AI from xAI, was notably absent from this round of government integrations. At the time, Grok wasn’t open enough for government tech stacks. But things might change. Just this week, Microsoft announced Grok 3 would be hosted inside its Azure AI Foundry—making Grok available in familiar ecosystems like those used by OPM. That’s not just a technical sidebar. It’s a sign that politically flavored AI might soon have the same ease-of-use as Excel inside the halls of governance.
Is this government modernization? Or private-sector command and control thinking repainted into public service? What happens when the tools for managing dissent are coded into infrastructure? And how will federal workers respond next time—knowing their words may be grist for the AI engine?
Hard Questions That Demand Real Answers
Let’s not simplify the ethical math here. AI classification isn’t “neutral” just because it’s scalable. When DOGE used Llama to flag resignation emails, the action had intent—reduce friction, extract order, filter dissent. When the five-point weekly report framework was injected into agencies, the effect was the same: systemic behavioral measurement fueled by automated interpretation.
This isn’t surveillance driven by curiosity. This is oversight calibrated for silent discipline. And it raises legitimate questions: Who owns public communication inside a federal job? Is administrative compliance being redefined through implicit loyalty scoring? And do we now need AI literacy just to understand when we’re being watched—or judged—by machines?
The deployment of Llama wasn’t just technical. It was strategic. And in a workplace shaped by political volatility, the strategic use of AI can become indistinguishable from political enforcement by proxy.
What’s your take? Does this kind of usage build a smarter, leaner bureaucracy—or are we watching loyalty tests dressed as digital transformation?
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