by Brandon Roberts, Vernal Coleman and Eric Umansky

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As the Trump administration prepared to cancel contracts at the Department of Veteran Affairs this year, officials turned to a software engineer with no health care or government experience to guide them.

The engineer, working for the Department of Government Efficiency, quickly built an artificial intelligence tool to identify which services from private companies were not essential. He labeled those contracts “MUNCHABLE.”

The code, using outdated and inexpensive AI models, produced results with glaring mistakes. For instance, it hallucinated the size of contracts, frequently misreading them and inflating their value. It concluded more than a thousand were each worth $34 million, when in fact some were for as little as $35,000.

The DOGE AI tool flagged more than 2,000 contracts for “munching.” It’s unclear how many have been or are on track to be canceled — the Trump administration’s decisions on VA contracts have largely been a black box. The VA uses contractors for many reasons, including to support hospitals, research and other services aimed at caring for ailing veterans.

VA officials have said they’ve killed nearly 600 contracts overall. Congressional Democrats have been pressing VA leaders for specific details of what’s been canceled without success.

We identified at least two dozen on the DOGE list that have been canceled so far. Among the canceled contracts was one to maintain a gene sequencing device used to develop better cancer treatments. Another was for blood sample analysis in support of a VA research project. Another was to provide additional tools to measure and improve the care nurses provide.

ProPublica obtained the code and the contracts it flagged from a source and shared them with a half dozen AI and procurement experts. All said the script was flawed. Many criticized the concept of using AI to guide budgetary cuts at the VA, with one calling it “deeply problematic.”

Cary Coglianese, professor of law and of political science at the University of Pennsylvania who studies the governmental use and regulation of artificial intelligence, said he was troubled by the use of these general-purpose large language models, or LLMs. “I don’t think off-the-shelf LLMs have a great deal of reliability for something as complex and involved as this,” he said.

Sahil Lavingia, the programmer enlisted by DOGE, which was then run by Elon Musk, acknowledged flaws in the code.

“I think that mistakes were made,” said Lavingia, who worked at DOGE for nearly two months. “I’m sure mistakes were made. Mistakes are always made. I would never recommend someone run my code and do what it says. It’s like that ‘Office’ episode where Steve Carell drives into the lake because Google Maps says drive into the lake. Do........

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