Love and Deceit: Work-From-Home Era Spawns ‘Pillow Talk’ Insider Trading
Lawyers say recent securities-fraud cases have a new twist: they are the product of the daily humdrum of two adults doing their jobs remotely
4 min
Steven Teixeira’s use of his girlfriend’s laptop began innocently enough when she asked him to keep an eye on her work email while she went to fitness classes and ran errands.
As they weathered the pandemic from their apartment in Queens, N.Y., he gave in to temptation. His sweetheart worked as an executive assistant at Morgan Stanley, and her calendar invites included meetings about planned mergers and acquisitions that involved the investment bank.
Teixeira, a compliance executive at a payment-processing company whom she intended to marry, used the information to trade in advance of the deals. It netted him thousands in profits, promises of Rolex watches from friends he tipped off, and the scrutiny of federal officials probing insider trading. He pleaded guilty to a dozen fraud charges in June.
There is a rich history in securities fraud of “pillow talk” cases, in which insider traders glean confidential information from romantic partners. The Covid era offered a twist: Secrets weren’t spilled in the bedroom or over a bottle of wine, but during the humdrum routine of two adults working from home.
“During Covid, there was an uptick in brazen conduct,” said Edward Imperatore, a defence lawyer at law firm Morrison & Foerster. “In a work-from-home environment, people acted with more impunity.”
Another recent case snared a boyfriend who was training to work for the Federal Bureau of Investigation. Seth Markin pleaded guilty in December to trading on information he purloined from his lawyer girlfriend, an associate in the Washington office of law firm Covington & Burling.
In 2021, she was working on a pharmaceutical acquisition from her one-bedroom apartment, where Markin spent days at a time. According to prosecutors, she trusted him because he told her he had a security clearance, was going to be an FBI agent, and wanted to marry her.
Prosecutors said Markin passed on tips that led to at least 20 people trading based on confidential information. “I knew that my behaviour was wrong,” Markin told the judge during his plea hearing. He is scheduled to be sentenced in March.
Representatives for the FBI and Covington declined to comment.
In Teixeira’s case, he was aided by a mouse-jiggler he bought that ensured his girlfriend’s laptop wouldn’t lock when she wasn’t using it, according to court documents. He has been cooperating with prosecutors and is scheduled to testify this year at the trial of his former friend, Jordan Meadow, who at the time was a stockbroker with Spartan Capital Securities. Meadow made more than $700,000 trading on Teixeira’s information and used the tips to advise clients who made millions, prosecutors allege.
“Yo you see UFS,” Meadow texted Teixeira, referencing the stock symbol of a company involved in a $3 billion deal, according to the indictment. He then asked for more nonpublic information, texting, “Feed me.”
Meadow has pleaded not guilty to the eight charges he is facing. Lawyers for Meadow and Spartan declined to comment, as did a spokeswoman for Morgan Stanley, and a lawyer for Teixeira didn’t respond to requests for comment.
In 2022, the Securities and Exchange Commission settled with a New Jersey man who it accused of illegally trading on inside information he heard when his domestic partner, who worked in marketing for an IT company, participated in calls from home, including an 11:30 p.m. videoconference in a home-office adjacent to their bedroom. Although he typically discussed his trades with her, in this case he hid them, executing the transactions from his work office, the SEC said. The man, who didn’t admit wrongdoing, paid $180,000.
One thing hasn’t changed since the earliest days of pillow talk: It is usually the men who can’t resist the urge to take advantage of their confidential information.
“Insider trading is an equal opportunity crime,” said Dixie Johnson, a partner at law firm King & Spalding who advises companies on how to avoid such situations. “But the cases we see usually have involved men doing the trading.”
Not that female romantic partners have always been innocent bystanders. In 2002, adult-movie actress Kathryn Gannon, known on screen as Marylin Star, pleaded guilty to trading on tips from an investment bank CEO with whom she was having an affair. She was sentenced to three months in prison.
A decade later, former beauty queen turned hedge-fund consultant Danielle Chiesi pleaded guilty to securities fraud for her role in a sprawling insider-trading ring. In a sentencing submission, she blamed a toxic relationship with her boss—and lover of 20 years—who urged her to get inside information.
One challenge for prosecutors is determining whether the partner who is privy to the information was in on the crime, said former federal prosecutor Brendan Quigley.
“Do they say, ‘Oh, my God, I would never give information to my spouse or significant other?’ It depends not only on what actually happened, but also on the nature of their relationship,” said Quigley, who prosecuted insider-trading cases in Manhattan.
For defense lawyers, pillow-talk cases can be difficult to handle at trial, particularly if one partner testifies against another. “To a juror, this is the bad boyfriend,” said Imperatore, the defense attorney. “He’s acting badly in a relationship in a way that goes beyond the four corners of insider trading.”
Not surprisingly, many such relationships don’t survive. Teixeira and his girlfriend split up, as did Markin and his.
Former Playboy CEO Christie Hefner and her husband, William Marovitz, divorced about a year after the SEC accused him of illegally trading Playboy stock based on information gleaned from his wife—despite her explicit instructions not to. Marovitz didn’t admit wrongdoing in a 2011 settlement.
One woman whose husband recently settled insider-trading charges involving confidential information related to her employer said coping with the allegations strengthened their bond.
“It felt like an injustice,” said the woman, who wasn’t identified in court papers. “It brought us closer together.”
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Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
4 minA sharp technology sell-off has exposed a new anxiety on global markets: whether the enormous borrowing behind the artificial-intelligence infrastructure boom can generate sufficient returns as interest rates rise and Chinese competition intensifies.
The artificial-intelligence investment boom has powered technology shares, semiconductor manufacturers and data-centre operators to extraordinary heights. Last week, investors were given a reminder that much of this expansion is being built with borrowed money.
The technology-heavy Nasdaq Composite fell 4.5 per cent over the week ended October 16, its weakest performance since late June. Semiconductor shares suffered an even deeper correction, with the Philadelphia Semiconductor Index losing about 11.4 per cent.
The selling spread quickly through global markets. Micron Technology fell 14.6 per cent over the week, while South Korea’s Samsung Electronics and SK Hynix dropped approximately 18 per cent and 24 per cent respectively.
This was more than a routine bout of profit-taking. Investors were reassessing three interconnected risks: higher interest rates, the rapidly expanding debt burden attached to AI infrastructure and the prospect of stronger competition from Chinese memory-chip manufacturers.
The debt behind the AI build-out
Artificial intelligence requires a physical infrastructure network of enormous scale. Data centres must be built, advanced processors purchased, power supplies secured and cooling systems installed before customers begin generating meaningful revenue from them.
Technology companies are increasingly supplementing their own cash with corporate bonds, syndicated loans, equipment leases, private credit and project-level financing.
JPMorgan describes the emerging funding model as a combination of conventional corporate borrowing and project finance tied to individual data-centre assets. In Australia, the Reserve Bank estimates that syndicated loans accounted for approximately three-quarters of data-centre financing during 2025 and 2026 to date.
The numbers are becoming large enough to affect broader credit markets. The Federal Reserve Bank of Dallas has cited Wall Street estimates suggesting AI-related investment-grade bond issuance could approach US$300 billion in 2026 alone.
That borrowing is not automatically problematic. Debt can be an efficient way to finance long-lived infrastructure when future revenue is sufficiently predictable. The concern is that AI technology, customer demand and computing economics are changing much faster than the repayment schedules attached to the assets.
A data centre financed today may carry debt for years. Investors must therefore make assumptions about future utilisation rates, electricity prices, chip performance, customer concentration and the amount businesses will ultimately pay for AI computing.
When interest rates rise, those assumptions become less forgiving.
Inflation changes the calculation
September’s US inflation data intensified the pressure. Consumer prices increased 3.7 per cent over the year, ahead of market expectations and up from 3.4 per cent in August.
The hotter reading reduced confidence that interest rates would fall quickly and pushed bond yields higher. That matters particularly for companies whose valuations depend on profits expected well into the future, as well as highly leveraged infrastructure operators that may need to refinance.
CoreWeave has emerged as a closely watched test case. The specialist cloud-computing company has accumulated substantial debt and lease obligations to build AI capacity, while relying on a concentrated group of large technology customers.
Its shares fell sharply during the sell-off, while the cost of insuring its debt against default climbed. CoreWeave has argued that its contracted backlog more than covers its obligations and that financial markets do not always understand its business model. Nevertheless, its securities have become a proxy for investor confidence in debt-funded AI infrastructure.
Oracle, another major investor in AI computing capacity, fell 8.8 per cent in Friday trading, while CoreWeave dropped 15.5 per cent.
The question is shifting from how much AI infrastructure can be built to how profitably it can be operated.
China adds another source of pressure
Interest rates were not the only catalyst. Reports of advances by Chinese memory-chip producer ChangXin Memory Technologies raised concerns that lower-cost Chinese products could increase competition across the global semiconductor industry.
Memory chips are essential to AI systems, but the industry is historically cyclical. Periods of tight supply and high pricing can encourage rapid investment, eventually producing oversupply and falling margins.
The prospect of a well-funded Chinese competitor expanding its technological capabilities unsettled investors in Samsung, SK Hynix and Micron. It also demonstrated that AI-related demand does not insulate chipmakers from conventional competitive pressures.
That combination—slower rate cuts, more expensive financing and potentially cheaper supply—challenged the assumption that every company exposed to AI would enjoy continually expanding margins.
A reset rather than the end of the boom
The sell-off does not establish that the AI boom is ending. Demand for computing power remains strong, the largest technology companies continue to generate formidable cash flows and many infrastructure projects are backed by long-term customer contracts.
What has changed is the market’s willingness to overlook the price and financing structure of that growth.
The most resilient companies are likely to be those able to fund investment from operating cash flow, secure long-duration customers and demonstrate that new capacity will earn returns comfortably above its financing costs.
More speculative operators may face greater scrutiny, particularly where debt is expensive, customers are concentrated or expected revenue depends on demand that has not yet materialised.
For Australian investors, the lesson extends beyond US technology shares. Local data-centre operators, infrastructure funds, electricity suppliers and property groups are increasingly exposed to the same investment cycle.
Artificial intelligence may remain one of the defining growth themes of the decade. But last week’s correction showed that enthusiasm for the technology does not eliminate financial discipline.
The next stage of the AI boom will be judged not only by computing power, model performance or demand for chips, but by something more traditional: whether the assets being financed can generate enough cash to repay the money borrowed to build them.
Fact box
Nasdaq Composite: Down 4.5 per cent for the week ended October 16, 2026
Philadelphia Semiconductor Index: Down approximately 11.4 per cent
Micron Technology: Down approximately 14.6 per cent
Samsung Electronics: Down approximately 18 per cent
SK Hynix: Down approximately 24 per cent
US September inflation: 3.7 per cent annually
Estimated 2026 AI-related investment-grade issuance: Approximately US$300 billion
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