Musk Engineers Update Mass Layoff Tool for Government Use
Engineers working for Elon Musk's Department of Government Efficiency (DOGE) are modifying software designed to assist with mass firings of federal workers, according to WIRED's investigation.
The software, called AutoRIF (Automated Reduction in Force), was originally developed by the Department of Defense over twenty years ago. DOGE operatives have accessed the software and appear to be editing its code in the Office of Personnel Management's GitHub system.
Screenshots reviewed by WIRED show Riccardo Biasini, a former Tesla engineer and director at The Boring Company, working with the AutoRIF repository. Biasini has also been listed as the main contact for the government-wide email system soliciting resignation emails from federal workers.
Federal agency firings have so far been conducted manually, with HR officials reviewing employee registries and lists from managers. Probationary employees have been targeted first since they lack certain civil service protections. Thousands of workers have already been terminated across multiple agencies in recent weeks.
The CDC experienced this firsthand. Managers carefully identified "mission critical" probationary employees to protect them from termination. "None of that was taken into account," a CDC source told WIRED. "They just sent us a list and said, 'Terminate these employees effective immediately.'"
Government workers recently received another email demanding they detail their accomplishments from the past week. NBC News reported this information would be fed into a large language model to assess employee necessity.
Why this matters:
- The marriage of AI and automated firing systems threatens to accelerate government workforce reductions without human oversight.
- Civil service protections built over decades could be systematically undermined through technological automation.
- This represents a shift from targeted cuts to algorithm-driven terminations, potentially transforming how government operates.
Read on, my dear:
