Summary: The Department of Government Efficiency (DOGE), backed by Elon Musk, is reportedly working on automating mass government workforce reductions through a repurposed tool called AutoRIF. Internal sources and documents suggest that DOGE engineers are modifying this decades-old software, originally designed by the Department of Defense, to speed up federal employee terminations. While the first round of firings was conducted manually, the next phase could be heavily influenced by AI, raising concerns about large-scale job losses. Employees have already been receiving emails pressuring them to justify their work, hinting at an algorithm-driven review process. This article explores how DOGE’s actions may reshape the federal workforce and the role that automation could play in these changes.
DOGE’s Push for Automated Workforce Cuts
Mass government layoffs have traditionally been bureaucratic, labor-intensive processes. Under the leadership of Musk-aligned officials, the Department of Government Efficiency (DOGE) is working to accelerate terminations with the help of software. Documents indicate that this involves modifying AutoRIF, a tool originally developed decades ago by the Department of Defense to handle workforce reductions in military agencies. Now, with updates and AI enhancements, the software could be used for much broader applications across multiple government agencies.
Modifying an Old System for a New Purpose
Internal repositories within the Office of Personnel Management (OPM) show that AutoRIF has been accessed and potentially modified by engineers linked to Musk’s network. Code changes have been made within OPM’s GitHub system, with recent updates still appearing over the weekend. One key figure involved is Riccardo Biasini, a former Tesla engineer currently with The Boring Company, whose name is attached to the AutoRIF repository.
The precise nature of these modifications remains unclear. However, sources within OPM speculate that rather than merely using AutoRIF in its original form, DOGE may be developing its own automation tools based on its framework. If true, this could allow for more aggressive, algorithm-driven mass terminations.
Current Manual Layoffs and AI-Driven Decisions
Without the full-scale deployment of AutoRIF, agencies have so far been relying on traditional, manual processes for terminations. Human resources (HR) departments have been tasked with reviewing employee lists and identifying staff, particularly probationary workers, for termination. Employees who have recently been promoted, transferred, or newly hired are more vulnerable since they lack the civil service protections of longer-tenured workers.
Despite this, a more data-driven approach appears to be emerging. Federal employees have recently been receiving emails from OPM directing them to list their job accomplishments from the past week. This information, according to NBC News reports, is being fed into an AI system, likely to assess an employee’s necessity to the agency.
Federal Agencies Pushed to Ignore OPM Emails
Not all agencies are cooperating with DOGE’s efforts. Some, such as the FBI, have instructed their employees not to respond to OPM’s performance reporting emails. Additionally, internal communications suggest that OPM has also advised agencies that they are not required to comply with these requests. This introduces a potential roadblock to the AI-driven termination process, but it remains unclear how much resistance agencies can realistically provide.
Automation Overriding Human Decision-Making
Previous attempts to reduce government staffing have involved some level of human judgment—particularly from managers familiar with employee performance. At agencies such as the Centers for Disease Control and Prevention (CDC), supervisors were asked to classify probationary employees as either “mission critical” or expendable. This was supposed to help determine who should remain employed.
However, according to a CDC insider, these assessments were ultimately ignored. HR departments were simply issued lists of names and directed to implement immediate terminations, with little room for managerial discretion. A fully automated system could make this impersonal process even more widespread, sidelining any remaining human input.
What Comes Next?
If DOGE’s modifications to AutoRIF continue, and if automation becomes central to the next round of federal layoffs, employees could see mass firings happen more rapidly. The lack of transparency surrounding the AI selection process fuels concerns about fairness and job security. Are performance metrics being weighed fairly, or is this simply a cost-cutting exercise with little room for appeal?
For now, government workers remain in a state of uncertainty. If AI-driven termination tools like AutoRIF evolve further, the concept of job security in the federal workforce could fundamentally change.
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