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OpenAI signs massive AI compute deal with Amazon

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On Monday, OpenAI announced it has signed a seven-year, $38 billion deal to buy cloud services from Amazon Web Services to power products like ChatGPT and Sora. It’s the company’s first big computing deal after a fundamental restructuring last week that gave OpenAI more operational and financial freedom from Microsoft.

The agreement gives OpenAI access to hundreds of thousands of Nvidia graphics processors to train and run its AI models. “Scaling frontier AI requires massive, reliable compute,” OpenAI CEO Sam Altman said in a statement. “Our partnership with AWS strengthens the broad compute ecosystem that will power this next era and bring advanced AI to everyone.”

OpenAI will reportedly use Amazon Web Services immediately, with all planned capacity set to come online by the end of 2026 and room to expand further in 2027 and beyond. Amazon plans to roll out hundreds of thousands of chips, including Nvidia’s GB200 and GB300 AI accelerators, in data clusters built to power ChatGPT’s responses, generate AI videos, and train OpenAI’s next wave of models.

Wall Street apparently liked the deal, because Amazon shares hit an all-time high on Monday morning. Meanwhile, shares for long-time OpenAI investor and partner Microsoft briefly dipped following the announcement.

Massive AI compute requirements

It’s no secret that running generative AI models for hundreds of millions of people currently requires a lot of computing power. Amid chip shortages over the past few years, finding sources of that computing muscle has been tricky. OpenAI is reportedly working on its own GPU hardware to help alleviate the strain.

But for now, the company needs to find new sources of Nvidia chips, which accelerate AI computations. Altman has previously said that the company plans to spend $1.4 trillion to develop 30 gigawatts of computing resources, an amount that is enough to roughly power 25 million US homes, according to Reuters.

Altman has also said that eventually, he would like OpenAI to add 1 gigawatt of compute every week. That ambitious plan is complicated by the fact that one gigawatt of power is roughly equivalent to the output of one typical nuclear power plant, and Reuters reports that each gigawatt of compute build-out currently comes with a capital cost of over $40 billion.

These aspirational numbers are far beyond what long-time cloud partner Microsoft can provide, so OpenAI has been seeking further independence from its wealthy corporate benefactor. OpenAI’s restructuring last week moved the company further from its nonprofit roots and removed Microsoft’s right of first refusal to supply compute services in the new arrangement.

Even before last week’s restructuring deal with Microsoft, OpenAI had been forced to look elsewhere for computing power: The firm made a deal with Google in June to supply it with cloud services, and the company struck a deal in September with Oracle to buy $300 billion in computing power for about five years. But it’s worth noting that Microsoft’s compute power is still essential for the firm: Last week, OpenAI agreed to purchase $250 billion of Microsoft’s Azure services over time.

While these types of multi-billion-dollar deals seem to excite investors in the stock market, not everything is hunky dory in the world of AI at the moment. OpenAI’s annualized revenue run rate is expected to reach about $20 billion by year’s end, Reuters notes, and losses in the company are also mounting. Surging valuations of AI companies, oddly circular investments, massive spending commitments (which total more than $1 trillion for OpenAI), and the potential that generative AI might not be as useful as promised have prompted ongoing speculation among both critics and proponents alike that the AI boom is turning into a massive bubble.

Meanwhile, Reuters has reported that OpenAI is laying the groundwork for an initial public offering that could value the company at up to $1 trillion. Whether that prospective $1 trillion valuation makes sense for a company burning through cash faster than it can make it back is another matter entirely.

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Trump on why he pardoned Binance CEO: “Are you ready? I don’t know who he is.”

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President Trump says he still doesn’t know who Binance founder and former CEO Changpeng Zhao is, despite having pardoned Zhao last month.

CBS correspondent Norah O’Donnell asked Trump about the pardon in a 60 Minutes interview that aired yesterday, noting that Zhao pleaded guilty to violating anti-money laundering laws. “The government at the time said that C.Z. had caused ‘significant harm to US national security,’ essentially by allowing terrorist groups like Hamas to move millions of dollars around. Why did you pardon him?” O’Donnell asked.

“Okay, are you ready? I don’t know who he is. I know he got a four-month sentence or something like that. And I heard it was a Biden witch hunt,” answered Trump, who has criticized his predecessor for signing pardons with an autopen.

Zhao was charged with failing to maintain an adequate anti-money laundering program as required by the Bank Secrecy Act and pleaded guilty. He was sentenced to four months in prison in April 2024, and released in September 2024. The US government’s sentencing request asked for three years in prison.

Trump family wheeling and dealing

Trump pardoned Zhao on October 21 in a move that is likely to help Binance fully return to the US market. (Since 2019, Binance has operated a separate exchange for US customers.) Months before the pardon, the Trump family reportedly held talks with Binance about taking a financial stake in the crypto exchange’s US arm.

Binance facilitated a $2 billion purchase of the USD1 stablecoin offered by the Trump-backed World Liberty Financial and built the technology behind USD1, a Wall Street Journal report last week said. Trump sons Eric and Donald Jr. have played a leading role in making lucrative crypto deals for the Trump family business.

“My sons are involved in crypto much more than I—me,” Trump said on 60 Minutes. “I—I know very little about it, other than one thing. It’s a huge industry. And if we’re not gonna be the head of it, China, Japan, or someplace else is. So I am behind it 100 percent.”

Did Trump ever meet Zhao? Did he form his own opinion about Zhao’s conviction, or was he merely “told about it”? Trump doesn’t seem to know:

This man was treated really badly by the Biden administration. And he was given a jail term. He’s highly respected. He’s a very successful guy. They sent him to jail and they really set him up. That’s my opinion. I was told about it.

I said, “Eh, it may look bad if I do it. I have to do the right thing.” I don’t know the man at all. I don’t think I ever met him. Maybe I did. Or, you know, somebody shook my hand or something. But I don’t think I ever met him. I have no idea who he is. I was told that he was a victim, just like I was and just like many other people, of a vicious, horrible group of people in the Biden administration.

Trump: “A lot people say that he wasn’t guilty”

Pointing out that Trump’s pardon of Zhao came after Binance helped facilitate a $2 billion purchase of World Liberty’s stablecoin, O’Donnell asked Trump to address the appearance of a pay-to-play deal.

“Well, here’s the thing, I know nothing about it because I’m too busy doing the other… I can only tell you this. My sons are into it. I’m glad they are, because it’s probably a great industry, crypto. I think it’s good… I know nothing about the guy, other than I hear he was a victim of weaponization by government. When you say the government, you’re talking about the Biden government. It’s a corrupt government. Biden was the most corrupt president and he was the worst president we’ve ever had.”

Even though Zhao pleaded guilty, Trump said shortly after the pardon that he was told Zhao “wasn’t guilty of anything.” The statement came when CNN correspondent Kaitlin Collins asked Trump at a press conference why he pardoned Zhao and whether it had anything to do with the Trump family’s crypto business.

Trump answered, “I don’t know, he was recommended by a lot of people… are you talking about the crypto person? A lot people say that he wasn’t guilty of anything. He served four months in jail and they say that he was not guilty of anything.”

Trump told Collins, “you don’t [know] much about crypto, you know nothing about nothing, you fake news… I don’t know him, I don’t believe I’ve ever met him, but I’ve been told… he had a lot of support and they said that what he did is not even a crime, it wasn’t a crime, that he was persecuted by the Biden administration. And so I gave him a pardon at the request of a lot of very good people.”

Zhao is no longer CEO of Binance but maintains a controlling stake in the company and has an estimated net worth of $52.6 billion. After being pardoned, Zhao said in an X post that Binance “will do everything we can to help make America the Capital of Crypto and advance web3 worldwide.”

Planning its return to the US, Binance “is considering a range of options including consolidating Binance.US into its global operation or having its global exchange enter the US market,” Bloomberg reported.

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fxer
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> Trump, who has criticized his predecessor for signing pardons with an autopen.

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Real humans don’t stream Drake songs 23 hours a day, rapper suing Spotify says

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Spotify profits off fake Drake streams that rob other artists of perhaps hundreds of millions in revenue shares, a lawsuit filed Sunday alleged—hoping to force Spotify to reimburse every artist impacted.

The lawsuit was filed by an American rapper known as RBX, who may be best known for cameos on two of the 1990s’ biggest hip-hop records, Dr. Dre’s The Chronic and Snoop Dogg’s Doggystyle.

The problem goes beyond Drake, RBX’s lawsuit alleged. It claims Spotify ignores “billions of fraudulent streams” each month, selfishly benefiting from bot networks that artificially inflate user numbers to help Spotify attract significantly higher ad revenue.

Drake’s account is a prime example of the kinds of fake streams Spotify is inclined to overlook, RBX alleged, since Drake is “the most streamed artist of all time on the platform,” in September becoming “the first artist to nominally achieve 120 billion total streams.” Watching Drake hit this milestone, the platform chose to ignore a “substantial” amount of inauthentic activity that contributed to about 37 billion streams between January 2022 and September 2025, the lawsuit alleged.

This activity, RBX alleged, “appeared to be the work of a sprawling network of Bot Accounts” that Spotify reasonably should have detected.

Apparently, RBX noticed that while most artists see an “initial spike” in streams when a song or album is released, followed by a predictable drop-off as more time passes, the listening patterns of Drake’s fans weren’t as predictable. After releases, some of Drake’s music would see “significant and irregular uptick months” over not just ensuing months, but years, allegedly “with no reasonable explanations for those upticks other than streaming fraud.”

Most suspiciously, individual accounts would sometimes listen to Drake “exclusively” for “23 hours a day”—which seems like the sort of “staggering and irregular” streaming that Spotify should flag, the lawsuit alleged.

It’s unclear how RBX’s legal team conducted this analysis. At this stage, they’ve told the court that claims are based on “information and belief” that discovery will reveal “there is voluminous information” to back up the rapper’s arguments.

Fake Drake streams may have robbed artists of millions

Spotify artists are supposed to get paid based on valid streams that represent their rightful portion of revenue pools. If RBX’s claims are true, based on the allegedly fake boosting of Drake’s streams alone, losses to all other artists in the revenue pool are “estimated to be in the hundreds of millions of dollars,” the complaint said. Actual damages, including punitive damages, are to be determined at trial, the lawsuit noted, and are likely much higher.

“Drake’s music streams are but one notable example of the rampant streaming fraud that Spotify has allowed to occur, across myriad artists, through negligence and/or willful blindness,” the lawsuit alleged.

If granted, the class would cover more than 100,000 rights holders who collected royalties from music hosted on the platform from “January 1, 2018, through the present.” That class could be expanded, the lawsuit noted, depending on how discovery goes. Since Spotify allegedly “concealed” the fake streams, there can be no time limitations for how far the claims could go back, the lawsuit argued. Attorney Mark Pifko of Baron & Budd, who is representing RBX, suggested in a statement provided to Ars that even one bad actor on Spotify cheats countless artists out of rightful earnings.

“Given the way Spotify pays royalty holders, allocating a limited pool of money based on each song’s proportional share of streams for a particular period, if someone cheats the system, fraudulently inflating their streams, it takes from everyone else,” Pifko said. “Not everyone who makes a living in the music business is a household name like Taylor Swift—there are thousands of songwriters, performers, and producers who earn revenue from music streaming who you’ve never heard of. These people are the backbone of the music business and this case is about them.”

Spotify did not immediately respond to Ars’ request for comment. However, a spokesperson told Rolling Stone that while the platform cannot comment on pending litigation, Spotify denies allegations that it profits from fake streams.

“Spotify in no way benefits from the industry-wide challenge of artificial streaming,” Spotify’s spokesperson said. “We heavily invest in always-improving, best-in-class systems to combat it and safeguard artist payouts with strong protections like removing fake streams, withholding royalties, and charging penalties.”

Fake fans appear to move hundreds of miles between plays

Spotify has publicly discussed ramping up efforts to detect and penalize streaming fraud. But RBX alleged that instead, Spotify “deliberately” “deploys insufficient measures to address fraudulent streaming,” allowing fraud to run “rampant.”

The platform appears least capable at handling so-called “Bot Vendors” that “typically design Bots to mimic human behavior and resemble real social media or streaming accounts in order to avoid detection,” the lawsuit alleged.

These vendors rely on virtual private networks (VPNs) to obscure locations of streams, but “with reasonable diligence,” Spotify could better detect them, RBX alleged—especially when streams are coming “from areas that lack the population to support a high volume of streams.”

For example, RBX again points to Drake’s streams. During a four-day period in 2024, “at least 250,000 streams of Drake’s song ‘No Face’ originated in Turkey but were falsely geomapped through the coordinated use of VPNs to the United Kingdom,” the lawsuit alleged, based on “information and belief.”

Additionally, “a large percentage of the accounts streaming Drake’s music were geographically concentrated around areas whose populations could not support the volume of streams emanating therefrom. In some cases, massive amounts of music streams, more than a hundred million streams, originated in areas with zero residential addresses,” the lawsuit alleged.

Just looking at how Drake’s fans move should raise a red flag, RBX alleged:

“Geohash data shows that nearly 10 percent of Drake’s streams come from users whose location data showed that they traveled a minimum of 15,000 kilometers in a month, moved unreasonable locations between songs (consecutive plays separated by mere seconds but spanning thousands of kilometers), including more than 500 kilometers between songs (roughly the distance from New York City to Pittsburgh).”

Spotify could cut off a lot of this activity, RBX alleged, by ending its practice of allowing free ad-supported accounts to sign up without a credit card. But supposedly it doesn’t, because “Spotify has an incentive for turning a blind eye to the blatant streaming fraud occurring on its service,” the lawsuit said.

Spotify has admitted fake streams impact revenue

RBX’s lawsuit pointed out that Spotify has told investors that, despite its best efforts, artificial streams “may contribute, from time to time, to an overstatement” in the number of reported monthly average users—a stat that helps drive ad revenue.

Spotify also somewhat tacitly acknowledges fears that the platform may be financially motivated to overlook when big artists pay for fake streams. In an FAQ, Spotify confirmed that “artificial streaming is something we take seriously at every level,” promising to withhold royalties, correct public streaming numbers, and take other steps, like possibly even removing tracks, no matter how big the artist is. Artists’ labels and distributors can also get hit with penalties if fake streams are detected, Spotify said. Spotify has defended its prevention methods as better than its rivals’ efforts.

“Our systems are working: In a case from last year, one bad actor was indicted for stealing $10 million from streaming services, only $60,000 of which came from Spotify, proving how effective we are at limiting the impact of artificial streaming on our platform,” Spotify’s spokesperson told Rolling Stone.

However, RBX alleged that Spotify is actually “one of the easiest platforms to defraud using Bots due to its negligent, lax, and/or non-existent—Bot-related security measures.” And supposedly that’s by design, since “the higher the volume of individual streams, the more Spotify could charge for ads,” RBX alleged.

“By properly detecting and/or removing fraudulent streams from its service, Spotify would lose significant advertising revenue,” the theory goes, with RBX directly accusing Spotify of concealing “both the enormity of this problem, and its detrimental financial impact to legitimate Rights Holders.”

For RBX to succeed, it will likely matter what evidence was used to analyze Drake’s streaming numbers. Last month, a lawsuit that Drake filed was dismissed, ultimately failing to convince a judge that Kendrick Lamar’s record label artificially inflated Spotify streams of “Not Like Us.” Drake’s failure to show any evidence beyond some online comments and reports (which suggested that the label was at least aware that Lamar’s manager supposedly paid a bot network to “jumpstart” the song’s streams) was deemed insufficient to keep the case alive.

Industry group slowly preparing to fight streaming fraud

A loss could smear Spotify’s public image after the platform joined an industry coalition formed in 2023 to fight streaming fraud, the Music Fights Fraud Alliance (MFFA). This coalition is often cited as a major step that Spotify and the rest of the industry are taking; however, the group’s website does not indicate the progress made in the years since.

As of this writing, the website showed that task forces were formed, as well as a partnership with a nonprofit called the National Cyber-Forensics and Training Alliance, with a goal to “work closely together to identify and disrupt streaming fraud.” The partnership was also supposed to produce “intelligence reports and other actionable information in support of fraud prevention and mitigation.”

Ars reached out to MFFA to see if there are any updates to share on the group’s work over the past two years. MFFA’s executive director, Michael Lewan, told Ars that “admittedly MFFA is still relatively nascent and growing,” “not even formally incorporated until” he joined in February of this year.

“We have accomplished a lot, and are going to continue to grow as the industry is taking fraud seriously,” Lewan said.

Lewan can’t “shed too many details on our initiatives,” he said, suggesting that MFFA is “a bit different from other trade orgs that are much more public facing.” However, several initiatives have been launched, he confirmed, which will help “improve coordination and communication amongst member companies”—which include streamers like Spotify and Amazon, as well as distributors like CD Baby and social platforms like SoundCloud and Meta apps—“to identify and disrupt suspicious activity, including sharing of data.”

“We also have efforts to raise awareness on what fraud looks like and how to mitigate against fraudulent activity,” Lewan said. “And we’re in continuous communication with other partners (in and outside the industry) on data standards, artist education, enforcement and deterrence.”

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Panthers RB Rico Dowdle penalized for 'Key & Peele'-inspired TD celebration

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New England's Stefon Diggs did a similar celebration earlier Sunday and was not penalized.
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Sam Altman wants a refund for his $50,000 Tesla Roadster deposit

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2017 feels like another era these days, but if you cast your mind back that far, you might remember Tesla CEO Elon Musk’s vaporware Roadster 2.0. Full of nonsensical-sounding features that impressed people who know a little bit about rockets but nothing about cars, the $200,000 electric car promised to have a suction fan and “cold gas thrusters,” plus 620 miles (1,000 km) of range and a whole load of other stuff that’s never happening.

Plenty of other electric automakers have introduced electric hypercars in the eight years since Musk declared the second Roadster a thing, with no sign of it being any closer to reality, if the latest job postings are accurate. And it seems that over time, a lot of the people who gave the company a hefty deposit—some say interest-free loan—have become tired of waiting and want their money back.

And that’s not quite so easy, it turns out. Musk’s current Silicon Valley rival is the latest to discover this. According to Sam Altman’s social media account, he placed an order for a Roadster on July 11, 2018, with a deposit of $45,000 ($58,206 in today’s money). But after emailing Tesla for a refund, he discovered the email address associated with preorders had been deleted.

A screenshot of Sam Altman's X posts about cancelling his car Credit: Twitter

Perhaps Altman forgot to ask ChatGPT how best to go about getting his money. If he had, he might have stumbled across the experience of streamer Marques Brownlee, who eventually had to pick up a telephone and call someone to get most of his $50,000 back. Or perhaps some of the threads at Reddit or the Tesla forums, where other people who fell for the cold gas thruster-equipped two-seater with Lucid-busting range and F1-beating acceleration have gathered to share stories of how best to make Tesla return their money.

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Neural network finds an enzyme that can break down polyurethane

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You’ll often hear plastic pollution referred to as a problem. But the reality is that it’s multiple problems. Depending on the properties we need, we form plastics out of different polymers, each of which is held together by a distinct type of chemical bond. So the method we use to break down one type of polymer may be incompatible with the chemistry of another.

That problem is why, even though we’ve had success finding enzymes that break down common plastics like polyesters and PET, they’re only partial solutions to plastic waste. However, researchers aren’t sitting back and basking in the triumph of partial solutions, and they’ve now got very sophisticated protein design tools to help them out.

That’s the story behind a completely new enzyme that researchers developed to break down polyurethane, the polymer commonly used to make foam cushioning, among other things. The new enzyme is compatible with an industrial-style recycling process that breaks the polymer down into its basic building blocks, which can be used to form fresh polyurethane.

Breaking down polyurethane

Image of a set of chemical bonds. From left to right there is an X, then a single bond to an oxygen, then a single bond to an oxygen that's double-bonded to carbon, then a single bond to a nitrogen, then a single bond to another X. The basics of the chemical bonds that link polyurethanes. The rest of the polymer is represented by X’s here.

The new paper that describes the development of this enzyme lays out the scale of the problem: In 2024, we made 22 million metric tons of polyurethane. The urethane bond that defines these involves a nitrogen bonded to a carbon that in turn is bonded to two oxygens, one of which links into the rest of the polymer. The rest of the polymer, linked by these bonds, can be fairly complex and often contains ringed structures related to benzene.

Digesting polyurethanes is challenging. Individual polymer chains are often extensively cross-linked, and the bulky structures can make it difficult for enzymes to get at the bonds they can digest. A chemical called diethylene glycol can partially break these molecules down, but only at elevated temperatures. And it leaves behind a complicated mess of chemicals that can’t be fed back into any useful reactions. Instead, it’s typically incinerated as hazardous waste.

To find something that could work better, the research team focused on finding an enzyme that could be integrated into the process with diethylene glycol. To begin, they tested all the enzymes reported in the literature as capable of breaking down polyurethanes. After testing all 15 of them, only three had decent activity against the polymer they were testing with, and they largely failed to break the polymer down to its constituent starting materials.

So, the researchers focused on the enzyme that had the highest activity, searching for related proteins in public databases, and using the AlphaFold database of predicted structures to identify more distantly related proteins that folded up into a similar structure. On their own, none of these worked especially well either. But they turned out to be useful because they could be used to train an AI to look for sequences that could fold up into a similar structure.

A new enzyme

The tool the team started working with is called Pythia-Pocket, which is a neural network that specializes in determining whether any given amino acid in a protein is likely to contact whatever chemicals that structure can bind, along with any other functional features. That was combined with plain old Pythia (also a neural network), which predicts whether any given protein is likely to form a stable structure.

The researchers reasoned that a good candidate for breaking down polyurethane would have a number of features. It would look, structurally, like the enzyme they had already been working with. It would also face a trade-off between having a structure that was ordered enough to form a similar binding pocket that would have enzymatic activity, but not so rigid that it couldn’t flexibly fit around different types of polyurethanes. To strike this balance, the team used a message-passing interface that updated amino acid positions with each pass and balanced optimizing the structure and binding pocket. They called the resulting software GRASE, for graph neural network-based recommendation of active and stable enzymes.

The results were pretty spectacular. Of the 24 most highly rated proteins the software evaluated, 21 of them showed some catalytic activity, and eight did better than the best enzyme we had known about previously. The best of these designs had 30 times the activity of that enzyme.

Things got even better when the researchers mixed in the diethylene glycol and heated the mixture up to 50° C. Under those conditions, the newly designed enzyme was over 450 times as active as the best-performing natural enzyme. It took 12 hours, but it could break down 98 percent of the polyurethane in the reaction mixture. And the enzyme was stable enough that it could be given a fresh mixture of polyurethane two additional times before its enzymatic activity started to wear out.

Shifting from lab tests to kilogram-scale digestion showed the same thing: 95 percent or more of the material was broken down into the starting materials the polyurethane was made from.

The researchers highlight the fact that their tools go beyond simply focusing on the structure formed by the protein, but incorporate information about its function, such as its stability and the amino acids that are likely to interact with the material it’s digesting. And they suggest that these approaches may tell us more about how to get functional proteins by focusing on forming a similar 3D structure.

Science, 2025. DOI: 10.1126/science.adw4487 (About DOIs).

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