Sahil Lavingia, who wrote the code, told it to cancel, or in his words “munch,” anything that wasn’t “directly supporting patient care.” Unfortunately, neither Lavingia nor the model had the knowledge required to make such determinations.

“I think that mistakes were made,” said Lavingia, who worked at DOGE for nearly two months, in an interview with ProPublica. “I’m sure mistakes were made. Mistakes are always made.”

It turns out, a lot of mistakes were made as DOGE and the VA rushed to implement President Donald Trump’s February executive order mandating all of the VA’s contracts be reviewed within 30 days.

ProPublica obtained the code and prompts — the instructions given to the AI model — used to review the contracts and interviewed Lavingia and experts in both AI and government procurement. We are publishing an analysis of those prompts to help the public understand how this technology is being deployed in the federal government.

The experts found numerous and troubling flaws: the code relied on older, general-purpose models not suited for the task; the model hallucinated contract amounts, deciding around 1,100 of the agreements were each worth $34 million when they were sometimes worth thousands; and the AI did not analyze the entire text of contracts. Most experts said that, in addition to the technical issues, using off-the-shelf AI models for the task — with little context on how the VA works — should have been a nonstarter.

Lavingia, a software engineer enlisted by DOGE, acknowledged there were flaws in what he created and blamed, in part, a lack of time and proper tools. He also stressed that he knew his list of what he called “MUNCHABLE” contracts would be vetted by others before a final decision was made.

Portions of the prompt are pasted below along with commentary from experts we interviewed. Lavingia published a complete version of it on his personal GitHub account.

Problems with how the model was constructed can be detected from the very opening lines of code, where the DOGE employee instructs the model how to behave:

This part of the prompt, known as a system prompt, is intended to shape the overall behavior of the large language model, or LLM, the technology behind AI bots like ChatGPT. In this case, it was used before both steps of the process: first, before Lavingia used it to obtain information like contract amounts; then, before determining if a contract should be canceled.

Including information not related to the task at hand can confuse AI. At this point, it’s only being asked to gather information from the text of the contract. Everything related to “munchable status,” “soft-services” or “DEI” is irrelevant. Experts told ProPublica that trying to fix issues by adding more instructions can actually have the opposite effect — especially if they’re irrelevant.

The models were only shown the first 10,000 characters from each document, or approximately 2,500 words. Experts were confused by this, noting that OpenAI models support inputs over 50 times that size. Lavingia said that he had to use an older AI model that the VA had already signed a contract for.

This portion of the prompt instructs the AI to extract the contract number and other key details of a contract, such as the “total contract value.”

This was error-prone and not necessary, as accurate contract information can already be found in publicly available databases like USASpending. In some cases, this led to the AI system being given an outdated version of a contract, which led to it reporting a misleadingly large contract amount. In other cases, the model mistakenly pulled an irrelevant number from the page instead of the contract value.

“They are looking for information where it’s easy to get, rather than where it’s correct,” said Waldo Jaquith, a former Obama appointee who oversaw IT contracting at the Treasury Department. “This is the lazy approach to gathering the information that they want. It’s faster, but it’s less accurate.”

Lavingia acknowledged that this approach led to errors but said that those errors were later corrected by VA staff.

Once the program extracted this information, it ran a second pass to determine if the contract was “munchable.”

Again, only the first 10,000 characters were shown to the model. As a result, the munchable determination was based purely on the first few pages of the contract document.

The above prompt section is the first set of instructions telling the AI how to flag contracts. The prompt provides little explanation of what it’s looking for, failing to define what qualifies as “core medical/benefits” and lacking information about what a “necessary consultant” is.

For the types of models the DOGE analysis used, including all the necessary information to make an accurate determination is critical.

Cary Coglianese, a University of Pennsylvania professor who studies the governmental use of artificial intelligence, said that knowing which jobs could be done in-house “calls for a very sophisticated understanding of medical care, of institutional management, of availability of human resources” that the model does not have.

The prompt above tries to implement a fundamental policy of the Trump administration: killing all DEI programs. But the prompt fails to include a definition of what DEI is, leaving the model to decide.

Despite the instruction to cancel DEI-related contracts, very few were flagged for this reason. Procurement experts noted that it’s very unlikely for information like this to be found in the first few pages of a contract.

These two lines — which experts say were poorly defined — carried the most weight in the DOGE analysis. The response from the AI frequently cited these reasons as the justification for munchability. Nearly every justification included a form of the phrase “direct patient care,” and in a third of cases the model flagged contracts because it stated the services could be handled in-house.

The poorly defined requirements led to several contracts for VA office internet services being flagged for cancellation. In one justification, the model had this to say:

The contract provides data services for internet connectivity, which is an IT infrastructure service that is multiple layers removed from direct clinical patient care and could likely be performed in-house, making it classified as munchable.

Despite these instructions, AI flagged many audit- and compliance-related contracts as “munchable,” labeling them as “soft services.”

In one case, the model even acknowledged the importance of compliance while flagging a contract for cancellation, stating: “Although essential to ensuring accurate medical records and billing, these services are an administrative support function (a ‘soft service’) rather than direct patient care.”

Shobita Parthasarathy, professor of public policy and director of the Science, Technology, and Public Policy Program at University of Michigan, told ProPublica that this piece of the prompt was notable in that it instructs the model to “distinguish” between the two types of services without instructing the model what to save and what to kill.

The emphasis on “direct patient care” is reflected in how often the AI cited it in its recommendations, even when the model did not have any information about a contract. In one instance where it labeled every field “not found,” it still decided the contract was munchable. It gave this reason:

Without evidence that it involves essential medical procedures or direct clinical support, and assuming the contract is for administrative or related support services, it meets the criteria for being classified as munchable.

In reality, this contract was for the preventative maintenance of important safety devices known as ceiling lifts at VA medical centers, including three sites in Maryland. The contract itself stated:

Ceiling Lifts are used by employees to reposition patients during their care. They are critical safety devices for employees and patients, and must be maintained and inspected appropriately.

This portion of the prompt attempts to define “soft services.” It uses many highly specific examples but also throws in vague categories without definitions like “non-performing/non-essential contracts.”

Experts said that in order for a model to properly determine this, it would need to be given information about the essential activities and what’s required to support them.

This section of the prompt was the result of analysis by Lavingia and other DOGE staff, Lavingia explained. “This is probably from a session where I ran a prior version of the script that most likely a DOGE person was like, ‘It’s not being aggressive enough.’ I don’t know why it starts with a 2. I guess I disagreed with one of them, and so we only put 2, 3 and 4 here.”

Notably, our review found that the only clarifications related to past errors were related to scenarios where the model wasn’t flagging enough contracts for cancellation.

This section of the prompt provides the most detail about what constitutes “direct patient care.” While it does cover many aspects of care, it still leaves a lot of ambiguity and forces the model to make its own judgements about what constitutes “proven efficacy” and “critical” medical equipment.

In addition to the limited information given on what constitutes direct patient care, there is no information about how to determine if a price is “reasonable,” especially since the LLM only sees the first few pages of the document. The models lack knowledge about what’s normal for government contracts.

“I just do not understand how it would be possible. This is hard for a human to figure out,” Jaquith said about whether AI could accurately determine if a contract was reasonably priced. “I don’t see any way that an LLM could know this without a lot of really specialized training.”

This section explicitly lists which tasks could be “easily insourced” by VA staff, and more than 500 different contracts were flagged as “munchable” for this reason.

“A larger issue with all of this is there seems to be an assumption here that contracts are almost inherently wasteful,” Coglianese said when shown this section of the prompt. “Other services, like the kinds that are here, are cheaper to contract for. In fact, these are exactly the sorts of things that we would not want to treat as ‘munchable.’” He went on to explain that insourcing some of these tasks could also “siphon human sources away from direct primary patient care.”

In an interview, Lavingia acknowledged some of these jobs might be better handled externally. “We don’t want to cut the ones that would make the VA less efficient or cause us to hire a bunch of people in-house,” Lavingia explained. “Which currently they can’t do because there’s a hiring freeze.”

The VA is standing behind its use of AI to examine contracts, calling it “a commonsense precedent.” And documents obtained by ProPublica suggest the VA is looking at additional ways AI can be deployed. A March email from a top VA official to DOGE stated:

Today, VA receives over 2 million disability claims per year, and the average time for a decision is 130 days. We believe that key technical improvements (including AI and other automation), combined with Veteran-first process/culture changes pushed from our Secretary’s office could dramatically improve this. A small existing pilot in this space has resulted in 3% of recent claims being processed in less than 30 days. Our mission is to figure out how to grow from 3% to 30% and then upwards such that only the most complex claims take more than a few days.

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 not drive into the lake.”

Though Lavingia has talked about his time at DOGE previously, this is the first time his work has been examined in detail and the first time he’s publicly explained his process, down to specific lines of code.

Lavingia has nearly 15 years of experience as a software engineer and entrepreneur but no formal training in AI. He briefly worked at Pinterest before starting Gumroad, a small e-commerce company that nearly collapsed in 2015. “I laid off 75% of my company — including many of my best friends. It really sucked,” he said. Lavingia kept the company afloat by “replacing every manual process with an automated one,” according to a post on his personal blog.

Lavingia did not have much time to immerse himself in how the VA handles veterans’ care between starting on March 17 and writing the tool on the following day. Yet his experience with his own company aligned with the direction of the Trump administration, which has embraced the use of AI across government to streamline operations and save money.

Lavingia said the quick timeline of Trump’s February executive order, which gave agencies 30 days to complete a review of contracts and grants, was too short to do the job manually. “That’s not possible — you have 90,000 contracts,” he said. “Unless you write some code. But even then it’s not really possible.”

Under a time crunch, Lavingia said he finished the first version of his contract-munching tool on his second day on the job — using AI to help write the code for him. He told ProPublica he then spent his first week downloading VA contracts to his laptop and analyzing them.

VA press secretary Pete Kasperowicz lauded DOGE’s work on vetting contracts in a statement to ProPublica. “As far as we know, this sort of review has never been done before, but we are happy to set this commonsense precedent,” he said.

The VA is reviewing all of its 76,000 contracts to ensure each of them benefits veterans and is a good use of taxpayer money, he said. Decisions to cancel or reduce the size of contracts are made after multiple reviews by VA employees, including agency contracting experts and senior staff, he wrote.

Kasperowicz said that the VA will not cancel contracts for work that provides services to veterans or that the agency cannot do itself without a contingency plan in place. He added that contracts that are “wasteful, duplicative or involve services VA has the ability to perform itself” will typically be terminated.

Trump officials have said they are working toward a “goal” of cutting around 80,000 people from the VA’s workforce of nearly 500,000. Most employees work in one of the VA’s 170 hospitals and nearly 1,200 clinics.

The VA has said it would avoid cutting contracts that directly impact care out of fear that it would cause harm to veterans. ProPublica recently reported that relatively small cuts at the agency have already been jeopardizing veterans’ care.

The VA has not explained how it plans to simultaneously move services in-house, as Lavingia’s code suggested was the plan, while also slashing staff.

Many inside the VA told ProPublica the process for reviewing contracts was so opaque they couldn’t even see who made the ultimate decisions to kill specific contracts. Once the “munching” script had selected a list of contracts, Lavingia said he would pass it off to others who would decide what to cancel and what to keep. No contracts, he said, were terminated “without human review.”

“I just delivered the [list of contracts] to the VA employees,” he said. “I basically put munchable at the top and then the others below.”

VA staffers told ProPublica that when DOGE identified contracts to be canceled early this year — before Lavingia was brought on — employees sometimes were given little time to justify retaining the service. One recalled being given just a few hours. The staffers asked not to be named because they feared losing their jobs for talking to reporters.

According to one internal email that predated Lavingia’s AI analysis, staff members had to respond in 255 characters or fewer — just shy of the 280 character limit on Musk’s X social media platform.

Once he started on DOGE’s contract analysis, Lavingia said he was confronted with technological limitations. At least some of the errors produced by his code can be traced to using older versions of OpenAI models available through the VA — models not capable of solving complex tasks, according to the experts consulted by ProPublica.

Moreover, the tool’s underlying instructions were deeply flawed. Records show Lavingia programmed the AI system to make intricate judgments based on the first few pages of each contract — about the first 2,500 words — which contain only sparse summary information.

“AI is absolutely the wrong tool for this,” said Waldo Jaquith, a former Obama appointee who oversaw IT contracting at the Treasury Department. “AI gives convincing looking answers that are frequently wrong. There needs to be humans whose job it is to do this work.”

Lavingia’s prompts did not include context about how the VA operates, what contracts are essential or which ones are required by federal law. This led AI to determine a core piece of the agency’s own contract procurement system was “munchable.”

At the core of Lavingia’s prompt is the direction to spare contracts involved in “direct patient care.”

Such an approach, experts said, doesn’t grapple with the reality that the work done by doctors and nurses to care for veterans in hospitals is only possible with significant support around them.

Lavingia’s system also used AI to extract details like the contract number and “total contract value.” This led to avoidable errors, where AI returned the wrong dollar value when multiple were found in a contract. Experts said the correct information was readily available from public databases.

Lavingia acknowledged that errors resulted from this approach but said those errors were later corrected by VA staff.

In late March, Lavingia published a version of the “munchable” script on his GitHub account to invite others to use and improve it, he told ProPublica. “It would have been cool if the entire federal government used this script and anyone in the public could see that this is how the VA is thinking about cutting contracts.”

According to a post on his blog, this was done with the approval of Musk before he left DOGE. “When he asked the room about improving DOGE’s public perception, I asked if I could open-source the code I’d been writing,” Lavingia said. “He said yes — it aligned with DOGE’s goal of maximum transparency.”

That openness may have eventually led to Lavingia’s dismissal. Lavingia confirmed he was terminated from DOGE after giving an interview to Fast Company magazine about his work with the department. A VA spokesperson declined to comment on Lavingia’s dismissal.

VA officials have declined to say whether they will continue to use the “munchable” tool moving forward. But the administration may deploy AI to help the agency replace employees. Documents previously obtained by ProPublica show DOGE officials proposed in March consolidating the benefits claims department by relying more on AI.

And the government’s contractors are paying attention. After Lavingia posted his code, he said he heard from people trying to understand how to keep the money flowing.

“I got a couple DMs from VA contractors who had questions when they saw this code,” he said. “They were trying to make sure that their contracts don’t get cut. Or learn why they got cut.

“At the end of the day, humans are the ones terminating the contracts, but it is helpful for them to see how DOGE or Trump or the agency heads are thinking about what contracts they are going to munch. Transparency is a good thing.”

The week before President Donald Trump unveiled bruising new tariffs that sent the stock market plummeting, a key official in the agency that shapes his administration’s trade policy sold off as much as $30,000 of stock.

Two days before that so-called “Liberation Day” announcement on April 2, a State Department official sold as much as $50,000 in stock, then bought a similar investment as prices fell.

And just before Trump made another significant tariff announcement, a White House lawyer sold shares in nine companies, records show.

More than a dozen high-ranking executive branch officials and congressional aides have made well-timed trades since Trump took office in January, most of them selling stock before the market plunged amid fears that Trump’s tariffs would set off a global trade war, according to a ProPublica review of disclosures across the government.

All of the trades came shortly before a significant government announcement or development that could influence stock prices. Some who sold individual stocks or broader market funds used their earnings to buy investments that are generally less risky, such as bonds or treasuries. Others appear to have kept their money in cash. In one case unrelated to tariffs, records show that a congressional aide bought stock in two mining companies shortly before a key Senate committee approved a bill written by his boss that would help the firms.

Using nonpublic information learned at work to trade securities could violate the law. But even if such actions aren’t influenced by insider knowledge, ethics experts warn that trading stock while the federal government’s actions move markets can create the appearance of impropriety. The recent trades by government officials, they said, underscore that there should be tighter rules on how, or if, federal employees can trade securities.

“The executive branch is routinely engaged in activities that will move the market,” said Tyler Gellasch, who, as a congressional aide, helped write the law on insider trading by government officials and now runs a nonprofit focused on transparency and ethics in capital markets. “I don’t think members of Congress and executive branch officials should be trading securities. To the extent they have investment holdings, it should be managed by someone else outside their purview. The temptation to put their own personal self-interest ahead of their duties to the country is just too high.”

There is no evidence that the trades by government officials identified by ProPublica were informed by nonpublic information. Still, when government officials trade stock at opportune times, Gellasch said, even if it was based on luck and not inside information, it undermines trust in government and the markets

“It then becomes a thing where our markets look rigged,” he said.

In response to questions from ProPublica, the officials who made the trades either said they had no insider information that would help them time their decisions or did not respond to questions about the transactions.

Questions about trades based on nonpublic information have swirled around Congress for years and began anew after Trump’s tariffs announcements led to wild swings in the market. Lawmakers’ trades are automatically posted online and, after multiple congressional stock-trading scandals, are widely scrutinized as soon as they become public.

But less attention is paid to the trades of executive branch employees and congressional aides whose work could give them access to confidential information likely to influence markets once made public.

Last week, ProPublica reported that Attorney General Pam Bondi sold between $1 million and $5 million worth of shares of Trump Media, the president’s social media company, on April 2. After the market closed that day, Trump unveiled his “Liberation Day” tariffs, sending the market reeling. Bondi’s ethics agreement required her to sell by early May, but why she sold on that date is unclear. She has yet to answer questions about the trades, and the Justice Department did not respond to requests for comment.

Earlier this week, ProPublica reported that Sean Duffy, Trump’s transportation secretary, sold shares in almost three dozen companies on Feb. 11, two days before Trump announced plans to institute wide-ranging “reciprocal” tariffs. A Transportation Department spokesperson said Duffy’s account manager made the trades and that Duffy had no input on the timing.

Using insider government information to buy or sell securities could violate the Stop Trading on Congressional Knowledge, or STOCK, Act. But no cases have ever been brought under the law, and some legal experts have doubts it would hold up to scrutiny from the courts, which in recent years have generally narrowed what constitutes illegal insider trading.

Thousands of government employees are required to file disclosure forms if they sell or buy securities worth more than $1,000. In many cases, the records are available only in person in Washington, D.C., or through a records request. The documents do not include exact amounts bought or sold but instead provide a broad range for the totals of each transaction.

ProPublica examined hundreds of records for trades shortly before major tariff announcements or other key government decisions. Trump, of course, repeatedly said on the campaign trail that he intended to institute dramatic tariffs on foreign imports. But during the first weeks of his term, investors were not panic selling, seeming to assume that his campaign promises were bluster. Several tariff announcements by Trump early on shook the markets, but it wasn’t until he detailed his new tariffs on April 2 that stocks dived.

Among those who sold securities before one of Trump’s main tariff announcements was Tobias Dorsey. Dorsey, a lawyer in the executive branch since the Obama administration, was named acting general counsel for the White House’s Office of Administration in January, when Trump was inaugurated. The division provides a range of services, including research and legal counseling across the president’s staff, including the Office of the United States Trade Representative, which helps craft trade policy. In his LinkedIn bio, Dorsey describes his duties since 2022 as giving “expert advice on a wide range of legal and policy matters to help White House officials achieve their policy goals.”

On Feb. 25 and 26, disclosure records show, Dorsey unloaded shares of an index fund and nine companies, including cleaning products manufacturer Clorox and engineering firm Emerson Electric. The total dollar figure for the sales was between $12,000 and $180,000. (He purchased one stock, defense contractor Palantir, which was selling for a bargain after recently plummeting on news of Pentagon budget cuts.)

At the time of Dorsey’s trades, investors were still largely in denial that Trump was going to go through with the massive tariffs he had promised during the campaign. But the next morning, Trump posted on social media that significant tariffs on Mexico and Canada “will, indeed, go into effect, as scheduled” in several days, and that “China will likewise be charged an additional 10% Tariff on that date.”

The S&P 500, a stock index that tracks a wide swath of the market, fell almost 2% that day alone and ultimately dropped nearly 18% in six weeks.

In an interview, Dorsey said the sale was made by his wife from an account belonging to her. He said she decided to sell around $20,000 worth of shares so they could make tuition payments and that he had no nonpublic information on the impending tariff announcements. The kind of work he does as a career employee, he said, focuses not on public policy, but on how the White House operates, including personnel, workplace technology, contracts and records issues.

“I’m not advising Stephen Miller or Peter Navarro,” he said, referring to top policy advisers to the president. “I’m advising the people running the campus. … I don’t have access to any sensitive political information.”

Another well-timed set of transactions was made by Marshall Stallings, the director of intergovernmental affairs and public engagement for Trump’s Trade Representative. The office helps shape the White House’s trade policy and negotiates trade deals with foreign governments.

On March 25 and 27, Stallings sold between $2,000 and $30,000 of stock in retail giant Target and mining company Freeport-McMoRan. The sales appear to have been an abrupt U-turn. He had purchased the shares less than a week earlier. Days after Stallings’ sales, Trump unveiled his most dramatic tariffs. Target stock fell 17%. Freeport-McMoRan fell 25%.

Stallings and the Trade Representative’s office did not respond to multiple requests for comment.

A longtime State Department official, Stephanie Syptak-Ramnath, who until April was ambassador to Peru, also appeared to make a bet against the stock market. On March 24 and 25, she sold between $255,000 and $650,000 in stocks, and bought between $265,000 and $650,000 in bond and treasury funds (along with $50,000 to $100,000 in stocks). Then, on March 31, two days before Trump’s “Liberation Day” announcement, she sold between $15,000 and $50,000 of a broad-based stock fund. When the market started to plummet, she bought back the same dollar range in another stock fund. Syptak-Ramnath said she did not have any information about the administration's decisions beyond what was publicly available. The trades, she said, were “undertaken as a result of family obligations” and in “response to a changing economy.”

A second longtime State Department official, Gautam Rana, who is now ambassador to Slovakia, sold between $830,000 and $1.7 million worth of stock on March 19, a week before Trump declared new tariffs on cars and two weeks before his “Liberation Day” announcement. The shares he sold were largely broad-based index funds. Rana declined to comment for this story.

Virginia Canter, a former government ethics lawyer, said executive branch employees who don’t have nonpublic information and want to trade stock should consult with ethics officials before doing so, thereby allowing an independent third party to assess their actions.

“If you trade and you don’t seek advice in advance, you kind of do it at your own risk, and if you’re asked about it, you have to hope there aren’t factors that make someone question your motivations,” Canter said. “If you seek ethics official advice, you have some cover.”

Executive branch employees are barred from taking government actions that would narrowly benefit them personally, and some are required to sell stock in companies and industries they have purview over in their jobs. But like members of Congress, they are allowed to trade securities.

Since Trump’s tariff announcements and walkbacks began causing fluctuations in the market, questions have been raised about whether anyone has profited off advance notice of the moves. After Trump unexpectedly rolled back some of his tariffs in early April, causing stocks to surge, Rep. Alexandria Ocasio-Cortez warned on social media that “any member of Congress who purchased stocks in the last 48 hours should probably disclose that now.”

Rep. Marjorie Taylor Greene bought between $21,000 and $315,000 of stock the day before and the day of the announcement. The Georgia Republican has not said what motivated the trades but in the past said a financial adviser manages her investments without her input.

ProPublica’s review of disclosures also found trades by congressional aides that took place before the market tumbled.

Michael Platt, a veteran Republican staffer who served in the Commerce Department during Trump’s first term and now works for the House committee that handles administrative matters for the chamber, restructured his portfolio in March. An account under his wife’s name sold off between $96,000 and $390,000 in mostly American companies, and purchased at least $45,000 in foreign stocks and at least $15,000 in an American and Canadian energy index fund. Some stock forecasters considered international markets a relatively safe haven if Trump went through with his tariffs. Platt did not respond to requests for comment.

Stephanie Trifone, a Senate Judiciary Committee aide, sold stock in mid-March and bought at least $50,000 in treasuries. A spokesperson for the committee’s Democratic minority said Trifone had no nonpublic information about the tariffs and her trades were conducted by a financial adviser without her input. Kevin Wheeler, a staffer for the Senate Appropriations Committee, made a similar move. In late February, he and his spouse offloaded between $18,000 and $270,000 in funds composed almost entirely of stocks and bought between $50,000 and $225,000 in bonds. A spokesperson for the Appropriation Committee’s Republican majority said Wheeler had no nonpublic information about Trump’s tariff plans and that a financial planner made the trades after advising Wheeler to take a more conservative approach with his portfolio.

Another staffer, Ryan White, chief of staff to Sen. James Risch, R-Idaho, bought shares worth between $2,000 and $30,000 in two precious metals mining companies two days before Trump’s “Liberation Day” announcement. He continued buying more shares in the companies, Hecla Mining and Coeur Mining, in the following days.

Precious metals can be a safe haven during a bear market turn, but those stocks, like the rest of the market, declined after Trump’s tariff announcements.

Two days after White’s last purchase in April of the mining companies’ shares, however, the firms got some good news. A bill White’s boss introduced to make it easier for mining companies like Hecla and Coeur to operate on public lands was approved by a Senate committee, an important step in passing a bill. (White added to his Hecla shares earlier this month and sold his stake in Coeur.)

White told ProPublica that “all required reporting and ethics rules were followed.” Any suggestion that the committee passing the bill played a role in his stock purchases “is a stretch and patently false,” he said, adding that the legislation “has not become law and even if it does, would take decades to have any appreciable impact.”