The U.S. Justice Department charged Google software engineer Michele Spagnuolo with insider trading, alleging the employee made $1.2 million trading on Polymarket based on confidential business information.

Spagnuolo, who used the name “AlphaRaccoon” on Polymarket, has worked at Google for over 12 years, according to information on LinkedIn.

“As alleged, Spagnuolo violated the duties he owed to his employer and used Google’s confidential business information to make more than $1.2 million in trading profits on Polymarket,” Jay Clayton, the United States Attorney for the Southern District of New York, said in a press release. “Insider trading compromises the integrity of our markets, and the American people want this greed-driven conduct investigated and prosecuted.”

Prediction markets like Polymarket, Kalshi, and others allow users to bet on pretty much anything. Insider trading is not allowed on these platforms because it’s illegal, but some users still commit the offense. The Justice Department recently charged a U.S. Army soldier for allegedly using his insider knowledge of the U.S. military operation to capture Venezuelan president Nicolás Maduro to make $400,000 on Polymarket.

According to the complaint, Spagnuolo risked over $2.7 million on wagers related to Google’s 2025 Year in Search, a marketing campaign in which Google reveals the world’s most popular searches of the year. Spagnuolo allegedly accessed confidential, internal Google Search data about the most-searched celebrities to inform his bets.

“Polymarket worked closely with the U.S. Attorney’s Office for the Southern District of New York and the CFTC, and is the only prediction platform to date whose cooperation has led to insider trading charges in the United States,” a Polymarket spokesperson told TechCrunch. “Blockchain trading is transparent, traceable, and bad actors leave footprints. We are committed to maintaining accurate, fair, and transparent markets as well as enforcing our rules and working with our regulators and law enforcement.”

A Google spokesperson told TechCrunch the company is working with law enforcement on its investigation.

“The employee accessed our marketing material using a tool available to all employees, but using such confidential information to place bets is a serious breach of our policies,” Google said in an emailed statement, “We’ve placed the employee on leave and will take the appropriate action.”

How many Ps are in Google? According to Google, there are two.

There’s also is also “exactly 1 ‘r’ in the word ‘poop’,” Google’s AI Overview says, as well as two ‘d’s in the word journalism, yet spelled it: j-o-u-r-n-a-d-i-s-m. Google did at least identify that there is one P in the last name of the U.S. president, but spelled it as t-r-p-u-m.

You didn’t need to be a prophet to predict that Google’s AI-forward Search overhaul was going to go over poorly. We’ve done this before. The first time Google added AI Overviews to Search, the feature ended up citing satirical posts from The Onion and Reddit, advising people to eat rocks and put glue on their pizza.

This time around, as Google doubles down on its commitment to make generative AI the centerpiece of its 29-year-old flagship product, it’s not surprising to see it stumble.

“Counting within words has been a known challenge for LLMs, and we’re working to fix this particular issue,” Google told TechCrunch in an emailed statement.

These basic spelling errors may seem familiar. LLMs, the kind of artificial intelligence that powers chatbots and other text-generators, are not built to understand spelling. It’s been a running joke for years that whenever a company unveils a new AI model, you should ask it how many ‘r’s are in the word strawberry. These AI models — which can code an app in seconds, or solve problems that have stumped mathematicians for decades — are about as good as a kindergartener at spelling.

Google’s AI overview woes reach beyond silly spelling mistakes though. Google already patched an issue from last week in which searching the word “disregard” would yield what looked like a dictionary definition of the word, only the definition was shown as, “Understood. Let me know whenever you have a new prompt or question!” But these spelling errors have remained amusing because they’re so difficult to quash.

As researchers have previously explained when we’ve asked about these spelling conundrums, AI doesn’t perceive sentences as units of language made up of words and letters. Many LLMs are built on transformers models, which break down text into tokens, which can be full words, syllables, or letters, depending on the model. Instead of “reading” like a human would, the AI converts the text into numerical representations of itself, which are then contextualized to help the AI come up with a logical response.

“LLMs are based on this transformer architecture, which notably is not actually reading text. What happens when you input a prompt is that it’s translated into an encoding,” Matthew Guzdial, an AI researcher and assistant professor at the University of Alberta, told TechCrunch. “When it sees the word ‘the,’ it has this one encoding of what ‘the’ means, but it does not know about ‘T,’ ‘H,’ ‘E.’”

The token-based architecture that powers LLMs like Google’s AI overview is inherently limiting, and researchers haven’t been optimistic that they can solve the spelling problem.

“It’s kind of hard to get around the question of what exactly a ‘word’ should be for a language model, and even if we got human experts to agree on a perfect token vocabulary, models would probably still find it useful to ‘chunk’ things even further,” Sheridan Feucht, a PhD student studying large language model interpretability at Northeastern University, told TechCrunch. “My guess would be that there’s no such thing as a perfect tokenizer due to this kind of fuzziness.”

This isn’t necessarily an urgent problem on researchers’ minds, since the utility of LLMs doesn’t come in their capacity to spell. But these blatant failures help us remember that AI is not perfect, even if it may sometimes seem like an all-knowing power beyond our comprehension. We cannot blindly trust AI outputs without double-checking their accuracy.

Rivian has finally revealed that the first customers of the company’s new R2 SUV will get their vehicles on June 9.

The automaker has spent the last few months ramping up its efforts to release the R2, which is more affordable and aimed at a larger market than its current R1 lineup. The new SUV will initially be available in a trim that starts just under $60,000, though Rivian has announced plans to release a “standard” version that starts at $48,490 in 2027.

The company has teased an even more affordable version “starting around $45,000” late next year — a price tag Rivian has promoted since the R2 reveal in 2024.

Rivian has high expectations for the R2. Founder and CEO RJ Scaringe has said it is “maybe the most important thing we’ve launched to date.” The company is betting on an extremely fast ramp-up, with as many as 25,000 vehicles delivered by the end of this year. Ultimately, Rivian hopes the R2 and its hatchback sibling, the R3, will help the company turn a profit for the first time since its founding in 2009.