Mario specializes in the intersection of technology and healthcare, focusing on the FDA's oversight of artificial intelligence, Medicare's reimbursement for health technologies, the integration of AI in clinical practices, mental health chatbots, and consumer wearable devices. Additionally, he co-writes the STAT Health Tech newsletter, which is distributed for free twice a week. You can contact Mario through Signal at mariojoze.13.

The Food and Drug Administration is currently working on a strategy to regulate medical devices that incorporate generative artificial intelligence. 

According to Rick Abramson, the director of the FDA's Digital Health Center of Excellence, the agency will publish documents detailing its policies on this emerging technology. 

In a conversation with STAT, Abramson stated, "Our aim is to establish formal policy guidance. The ecosystem is seeking clarity, and we intend to provide that. In addition to general guidance on generative AI, the ecosystem can also anticipate specialized advice on specific topics of interest or complexity within this realm."

FDA digital health chief pledges forthcoming regulatory guidance on generative AI.

A modern FDA headquarters bathed in warm morning light, a poised digital health leader in business attire walking up the steps with a tablet in hand, above the building a translucent blueprint of AI circuitry and policy text swirling into a clear pathway, hopeful atmosphere.

FDA digital health leader promises generative AI regulatory guidance is coming - STAT

- FDA is planning regulations for medical devices utilizing generative artificial intelligence technology.  
- Agency aims to release formal policy guidance on generative AI in health care.  
- Guidance will include both broad and specialized regulations for complex AI topics.  

Mario covers technology in health care, including FDA regulation of artificial intelligence; how Medicare pays for health tech; the use of AI in clinical care; mental health chatbots; and consumer wearables. He’s also the co-author of the free, twice weekly STAT Health Tech newsletter . You can reach Mario on Signal at mariojoze.13.

The Food and Drug Administration says it is developing a plan for how to regulate medical devices that use generative artificial intelligence.

The FDA will eventually release documents spelling out agency policies regarding the technology, Rick Abramson, director of the agency’s Digital Health Center of Excellence, told STAT.

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“Our goal is formal policy guidance,” he said in an interview. “The ecosystem is expecting clarity. We seek to provide that clarity, and I’ll also throw in that I think the ecosystem can expect not only broad guidance on the overall topic of generative AI, but also some more narrowly constructed specialty guidance on particular generative AI topics of special interest or special complexity.”

FDA digital health leader promises generative AI regulatory guidance is coming

In a recent interview broadcast on Sunday evening, President Donald Trump expressed strong support for the expansion of artificial intelligence data centers across the United States, asserting that communities resisting their establishment are making an ill-informed choice. This assertion comes amid escalating public dissent and pushback from governors across the political spectrum. 

During his discussion with former aide Michael Cohen, Trump stated that the U.S. has a significant advantage over China in the AI sector. He emphasized his support for AI companies to independently develop their own power facilities, claiming these data centers are not drawing from the already strained power grid. “Communities that refuse to welcome a data center are making a mistake,” he stated, noting that many other communities are eager to host them. He elaborated that these centers would create jobs and yield considerable tax revenue for local governments, asserting that the "smart ones" recognize the benefits and are advocating for their development within their areas. Last week, Trump articulated his support for data centers while hosting leaders from the cryptocurrency industry at the White House, indicating that he would be thrilled to see one established in his own locality, whether in a mayoral or gubernatorial capacity.

In contrast, Texas Governor Gregg Abbott voiced significant concerns regarding the AI industry’s impact, stating on ABC News that companies in the sector had caused their own backlash by failing to coordinate with state or local officials. Abbott’s critique marks one of the strongest rejections of data centers from a GOP leader, a sentiment echoed by various Democratic governors. Recently, he declared a temporary halt on new data center approvals until there can be a thorough audit and verification of their implications on the state’s power grid. Similarly, New York has imposed a moratorium on large new data center constructions as it seeks to develop new environmental regulations and assess their effects.

As Trump champions these data centers, concerns are mounting that growing public opposition could pose challenges for Republicans in upcoming midterm elections. An internal memo from the National Republican Senatorial Committee highlighted this potential risk, noting that Democratic candidate Sherrod Brown has made his opposition to data centers a focal point of his campaign against incumbent Republican Senator John Husted in Ohio. The memo cautioned that data centers could be a significant liability for Husted, stressing that if he loses, the blame linked to data centers could resonate nationwide.

Polling data further underscores the contentious nature of data centers. A Gallup survey released earlier this year noted that 71% of Americans expressed some level of opposition to having data centers in their communities, surpassing the 53% who oppose nuclear energy facilities. The resistance is particularly high among Democrats, with 75% opposing such constructions, but it also includes a substantial number of Republicans (63%) and Independents (74%) who share the same sentiment.

Trump Claims Those Against AI Data Centers Are Making an 'Error'

Donald Trump stands at a podium draped with a U.S. flag, bathed in warm sunset light, confidently pointing toward a vast AI data-center complex behind him—its rows of server racks glowing softly through glass walls—while a small group of protesters at the scene hold “Stop AI Data Centers” signs, their faces in shadow. Natural lighting, cinematic news-thumbnail style.

Trump Defends AI Data Centers—Says Opposing Them Is A ‘Mistake’ And ‘Smart Ones’ Want Them - Forbes

- Trump defended AI data centers, claiming they benefit communities with jobs and tax revenue.  
- Texas Governor Abbott criticized AI companies for lack of coordination, supporting a data center moratorium.  
- A Gallup survey revealed 71% of Americans oppose data centers in their communities.  

Topline President Donald Trump defended the ongoing buildout of AI data centers across the country and said the communities opposing them were making a mistake, as he continued to embrace the controversial buildout of AI infrastructure despite growing public backlash and opposition from governors from both parties. US President Donald Trump speaks as he meets with lifeguard Ryder Williams in the Oval Office of the White House in Washington, DC. AFP via Getty Images

Key Facts

In his interview with former fixer Michael Cohen, which aired in full on Sunday evening, Trump claimed the U.S. was “leading China in AI by a lot.” The president said he was letting the AI companies “build their own power plants” and claimed these data centers were not taking “power from the grid,” which he described as old and “tired.” Trump said, “Frankly, communities that don’t take a data center are making a mistake,” and claimed that there’s “plenty of communities” that want them. He argued that data centers will lead to more jobs and “tremendous tax revenue coming into the towns and cities,” and said that the “smart ones” are calling for them to be built in their communities. Last week, while hosting several crypto industry executives at the White House, the president said he would “absolutely want” a data center in his area if he were given the chance, whether as a mayor of a town or the governor of a state.

What has the Texas Gov. Said About Data Centers?

The president’s comments in support of data centers were aired shortly after Texas’ Republican Gov. Gregg Abbott appeared on ABC News on Sunday morning and said the AI industry, “basically dug their own grave for the problem that’s been caused for them, and that’s why they got the backlash they deserve.” Abbott blasted the companies, saying they did not coordinate with state or local governments. Abbott’s comments are the most serious pushback that data centers and AI companies have faced from a prominent GOP leader—and it echoes similar stances taken by several Democratic Governors. Earlier this month, Abbott announced a moratorium on the approval for new data centers until authorities can “comprehensive verification and audit of all data centers,” being added to the state’s grid. Last month, New York also instituted a moratorium on large new data center buildouts until it develops new environmental regulations and examines their impact on the power grid, among other things.

Tangent

Trump’s remarks about data centers come amid concerns that the growing public opposition to them could negatively impact the Republicans in the midterms. Last week, the National Republican Senatorial Committee sent out an internal memo warning that Democrat Sherrod Brown had made his opposition to data centers the “centerpiece” of his campaign against incumbent Republican Sen. John Husted in Ohio. The memo warned: “More than any other thing in this race, data centers are the anchor hanging around Husted’s neck. If he loses and data centers get the blame, politicians across the country will take notice.”

What does polling data about data centers show?

A Gallup survey published earlier this year found that 71% of Americans are somewhat or strongly opposed to data centers in their communities, a higher share than the 53% who oppose nuclear energy plants. Opposition to data centers is strongest among Democrats, with 75% somewhat or strongly against such build-outs. A majority of Republicans (63%) and Independents (74%) also oppose data center construction.

further reading

People Would Rather Have Nuclear Power Plants In Their Area Than AI Data Centers (Forbes)

Trump Says Those Opposing AI Data Centers Are Making A ‘Mistake’

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Dr. Dre Reveals Why He’s ‘Embracing’ AI In Making Music - Yahoo

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Alibaba's stock experienced a significant decline of up to 10% in Hong Kong on Monday, following the announcement of an extensive share placement worth 80 billion Hong Kong dollars (approximately $10.20 billion) targeted at non-U.S. investors.

The tech powerhouse outlined its intention to allocate all proceeds from this move toward bolstering its comprehensive AI capabilities, which includes strengthening and expanding its AI infrastructure.

The company will issue 710 million new shares priced at HK$112.70 each, a reduction compared to the stock's previous closing price of HK$123 on Friday. As a result, shares were trading down 8.4% at HK$112.70.

This share placement, set to finalize on Wednesday, comes shortly after Alibaba revealed a staggering 75% drop in profits for the quarter ending in June, largely attributed to substantial investments in AI technology. Capital expenditures surged by 75% to reach 67.7 billion yuan during this period.

Alibaba's shares fall sharply following the announcement of a $10.2 billion share placement to support its AI initiatives.

A dimly lit trading floor bathed in deep shadows, walls lined with screens flashing red Alibaba stock tickers plunging; overhead, a massive illuminated Alibaba logo hangs like a looming giant. A jagged neon‐red arrow slashes downward across one screen, while shredded share certificates drift through the air like ash. In the foreground, a lone trader’s silhouette clutches his head, under a single warm spotlight that heightens the tense, dark atmosphere.

Alibaba plunges after announcing $10.2 billion share placement to fund AI push - CNBC

- Alibaba's shares fell 10% after announcing an 80 billion HKD share placement.  
- The funds will be used to improve Alibaba's full-stack AI capabilities and infrastructure.  
- The company's profit dropped 75% last quarter due to high AI-related expenditures.

Night view of Alibaba's headquarters building located by the Huangpu River in Shanghai, China on Nov. 16, 2025.

Alibaba shares plunged as much as 10% in Hong Kong on Monday after the Chinese tech giant priced an 80 billion Hong Kong dollar ($10.20 billion) placement of newly issued shares to non-U.S. investors.

The company said it plans to use all of the net proceeds to invest in its full-stack AI capabilities, including expanding and enhancing its AI infrastructure.

Alibaba will issue 710 million new shares at HK$112.70 apiece, compared with the stock's Friday closing price of HK$123. Shares were last trading 8.4% lower at HK$112.7.

The share placement, expected to close on Wednesday, comes just days after Alibaba reported a 75% drop in profit for the June-quarter as heavy AI spending weighed on its results. Capital expenditure jumped 75% to 67.7 billion yuan.

Alibaba plunges after announcing $10.2 billion share placement to fund AI push

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Former Texas Governor Rick Perry claims there is a prevalence of 'misinformation' regarding AI data centers.

A dimly lit data-center hall with rows of server racks receding into shadow. Ghostly red digital fragments—“hoax,” “fake,” “misinfo”—swirl like smoke overhead. In the foreground, former Texas Gov. Rick Perry stands at a simple wooden lectern, arms raised defensively under a warm spotlight. A small Texas flag drapes the lectern.

‘Misinformation’ about AI data centers is widespread, former Texas Gov Rick Perry says - Fox News

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‘Misinformation’ about AI data centers is widespread, former Texas Gov Rick Perry says

Dr. Dre, the influential rapper and acclaimed seven-time Grammy-winning producer known for classics like Tupac’s “California Love” and Eminem’s “The Real Slim Shady,” is an advocate for leveraging artificial intelligence in music creation.

In a recent conversation with the New York Times, Dr. Dre, along with Jimmy Iovine, the co-founder of Interscope Records, explored the rapidly growing role of AI in music production. Dr. Dre dismissed concerns about AI as a threat, suggesting that apprehension mainly stems from those who struggle with creativity. “I don’t view it as a looming threat. The only individuals who see it as such are those who find it difficult to innovate,” he shared. He drew a parallel to past technological advancements, stating, “It’s reminiscent of the resistance people had against drum machines or synthesizers when they first emerged.”

The popularity of AI-driven music creation tools has surged over the past year. In an effort to address the mounting criticism surrounding AI-generated tracks that claim authenticity, both Spotify and Apple Music are set to categorize AI “artists” on their platforms in the coming weeks. This initiative represents a dual narrative: the growing acceptance of AI as a legitimate tool in song production and the concern over misleading consumers. Additionally, Spotify is collaborating with Universal Music Group to develop a feature that enables users to create AI-generated covers, with plans to monetarily capitalize on this new functionality.

Many, including Dr. Dre, view AI as a beneficial technological resource that can streamline music production processes by reducing time and expenses traditionally shouldered by producers or human collaborators. However, there are significant concerns regarding the potential ramifications of widespread AI integration, including job automation, potential copyright infringements, and the risk of producing uninspired, formulaic music.

Iovine expressed optimism about the technology, stating he doesn’t see any negative aspects. “There will definitely be some subpar music, but that’s a reality we already face,” he said. “When talented creatives harness AI in the studio, they’re likely to produce superior records.”

Dr. Dre noted that he employs AI as a means to experiment with his creative output. He also mentioned that many industry peers share his views but may hesitate to openly acknowledge their use of AI. Iovine referred to them as “closet AI producers,” highlighting Timbaland as another major figure embracing AI in production.

The discussion surrounding AI extends beyond the music industry, affecting various creative fields, including writing and visual arts. The surge in generative AI technology is prompting similar debates among writers, filmmakers, and visual artists. Some, like directors Martin Scorsese and Steven Soderbergh, along with actress Reese Witherspoon, have shown support for integrating AI in filmmaking, while others remain resistant. For instance, Guillermo Del Toro expressed a strong aversion to using generative AI, stating he would prefer death over such practices. Seth Rogen also criticized the mindset of using AI for scriptwriting, arguing that genuine writing requires a personal connection to the process. “If your first instinct is to rely on AI rather than engage in the craft, perhaps writing is not for you,” Rogen stated in May.

Dr. Dre Claims He Utilizes AI for Song Production

A sunlit, high-end recording studio where Dr. Dre stands at a glossy mixing console, warm natural light streaming through a large window. On the central screen a sleek AI-driven waveform pulses and morphs into the letters “AI.” Vinyl records and studio monitors surround him as he nods thoughtfully, embodying the fusion of his legendary craft and cutting-edge technology.

Dr. Dre Says He Uses AI to Produce Songs - Gizmodo

- Dr. Dre supports AI in music, viewing it as a beneficial production tool.  
- Spotify and Apple Music will soon label AI-generated artists on their platforms.  
- Debate continues over AI's impact on creativity and job automation in various industries.  

Dr. Dre, the pioneering rapper and seven-time Grammy-winning producer behind hit songs like Tupac’s “California Love” and Eminem’s “The Real Slim Shady,” is very much pro using artificial intelligence tools to make music.

In an interview with the New York Times, Dr. Dre and Interscope Records co-founder Jimmy Iovine spoke about artificial intelligence’s recent foray into music production, with the producer characterizing those who oppose AI song generation tools as “afraid of learning new things.”

“I don’t see it as a threat. I think the only people that see it as a threat are the people who have trouble creating,” Dr. Dre said. “I had a discussion with a few people a few days ago. They were against AI, and Iâm like, ‘OK, you sound like the person that would have been against the drum machine when it came out.’ Or synthesizers, right?”

AI song generation tools have experienced a surge in popularity in the last year. In the coming weeks, both Spotify and Apple Music will begin labeling AI “artists” on their platforms as such, a move that simultaneously highlights the increasing criticism for AI-generated music that presents itself as authentic, and the growing acceptance of these tools as a legitimate way of creating songs. Spotify is even partnering with Universal Music Group to build a tool that will let users create AI-generated song covers, even going so far as to bank on Spotify users to pay to use the upcoming feature when it launches.

There are many, like Dr. Dre, who view these tools as just another piece of technology to aid the greater production process, cutting down barriers like time and cost by automating some of the work that the producer themselves or perhaps a collaborator in the form of a real, paid human would have otherwise done. Others are worried about wider implications of the wholesale adoption of this technology, including the automation of jobs, the murky (and some argue illegal) use of copyrighted material, and the end products that sound like soulless, repetitive AI slop.

Iovine, the music executive and Dr. Dre’s partner in Beats by Dre, for his part, says he doesn’t “see the downside at all.”

“There will be some crappy music. There’s crappy music now,” Iovine told the NYT. “In the studio, when gifted people have AI, they’re going to make better records.”

Dr. Dre said he uses AI in producing “as a tool to see what it would do with what I just did.”

The award-winning producer also said he is definitely not alone among his peers in his stance on AI either: many producers use AI but fear to admit it. Iovine calls them “closet AI producers,” and says Timbaland, the four-time Grammy-winning singer-producer behind a long list of hits like “Promiscuous” with Nelly Furtado, is also one.

AI’s role in the creative fields is a topic of controversy that extends far beyond just music. The generative AI boom is resulting in AI tools that also automate the work processes of writers and visual artists like videographers, photographers, models and actors. Those industries are also facing a similar split, with some creatives embracing the technology in moderation while others raise concerns about any form of acceptance.

In the film industry, for example, directors such as Martin Scorsese and Steven Soderbergh, and actors like Reese Witherspoon have all come out in support of adopting AI into the filmmaking process. Others have not been as keen. Director Guillermo Del Toro said earlier this year that he would rather die than use generative AI, and Seth Rogen said that if you use AI to write scripts, then maybe you just should not be a writer.

“The idea of a tool that makes me write less is not appealing to me, because I like writing,” Rogen told Brut in May. “And if your instinct is to use AI and not go through that process, you shouldn’t be a writer. Because you’re not writing. Go do something else.”

Dr. Dre Says He Uses AI to Produce Songs

As the creative landscape grapples with the role of artificial intelligence, Dr. Dre is enthusiastically integrating this technology into his music production processes.

The 8-time Grammy-winning artist recently revealed his use of AI in crafting music, emphasizing that he perceives it not as a danger, but rather as "a new tool for creativity." In an interview with The New York Times, he stated, “I don’t see it as a threat. I think the only people that view it as such are those who struggle with creation.”

Dr. Dre elaborated on his perspective, drawing parallels between current attitudes towards AI and the resistance seen during past technological advancements. “I had a conversation with a few individuals who opposed AI, and I said, ‘You sound like someone who would have been against the drum machine when it first emerged. Or synthesizers. It’s simply a new creative tool. Some may be daunted by the prospect of learning something new, but I’m eager to embrace it. I can’t wait to see the future it will help create.’”

When probed further, the former N.W.A member acknowledged, “Yes, I’m using it, primarily as an instrument to explore what it can contribute to my existing work.”

Jimmy Iovine, his long-time collaborator, noted the existence of “a lot of closet AI producers out there,” a sentiment Dr. Dre agreed with, remarking, “That’s an apt way to describe it. Many are utilizing it; they just prefer not to disclose it.”

Dr. Dre’s remarks are particularly relevant as the music industry faces growing scrutiny over AI use. Recently, Suno has faced legal challenges for incorporating copyrighted material within its generative AI music model, while the technology has also begun identifying pieces of AI-generated music.

Dr. Dre Employs AI for Music Production, Views It as No "Threat"

A sunlit professional recording studio: Dr. Dre sits at a sleek mixing console under warm natural light pouring through large windows. Beside him, a laptop screen glows with an “AI Assist” waveform interface. Vintage microphones stand ready, gold and platinum records line the walls, capturing the harmony of human genius and AI-powered music production.

Dr. Dre Admits He Uses AI To Produce, Doesn’t See It “As A Threat”: “I’m Embracing It” - Deadline

- Dr. Dre embraces AI as a new creative tool in music production.  
- He believes resistance to AI stems from fear of learning and change.  
- The music industry faces scrutiny over AI, including legal issues with copyrighted material.  

As creative industries remain divided on AI, Dr. Dre is full steam ahead on implementing the technology in his own work.

The 8x Grammy winner recently admitted to using artificial intelligence in his music producing, explaining the he doesn’t see the technology “as a threat,” but as “a new tool for creativity.”

“I don’t see it as a threat,” he told The New York Times. “I think the only people that see it as a threat are the people who have trouble creating.”

Dr. Dre added, “I had a discussion with a few people a few days ago. They were against AI and I’m like, ‘OK, you sound like the person that would have been against the drum machine when it came out.’ Or synthesizers, right? It’s a new tool for creativity. Some people are afraid of learning new things. I’m embracing it. I can’t wait to see what’s going to happen with this.”

Watch on Deadline

When asked, the NWA alum confessed, “Yes, I’m using it. Again, as a tool to see what it would do with what I just did.”

Longtime friend and business partner Jimmy Iovine claimed there’s “a lot of closet AI producers out there,” to which Dr. Dre agreed. “That’s a good way to put it. They’re using it, they just don’t want to admit it,” said Dre.

Dr. Dre’s comments come amid increased scrutiny around the use of AI in the music industry, with Suno coming under legal fire for featuring copyrighted material in its genAI music model. Meanwhile, AI has begun flagging AI-generated music.

Dr. Dre Uses AI To Produce, Doesn't See It As "Threat"

Cara, a specialized social network designed for artists, has experienced yet another scraping incident, marking its third attack in just ten days. Founded by Jingna Zhang, a well-known fashion and fine art photographer, Cara was established to provide a platform for artists who want to retain ownership and control over their work, explicitly prohibiting the use of their content to train generative AI models. This latest incident saw copyrighted images and related metadata from Cara appearing on the open-source data repository Academic Torrents on August 22. Prior to this, similar datasets were illegally shared on Reddit (though they have since been removed) and by Hugging Face, a platform dedicated to AI development.

In a heartfelt message on X, Zhang expressed her frustration: “We are facing a targeted attack. Scrapers know that we operate on a volunteer basis with limited resources to fight back. They consider their actions legal, acting as if they are beyond reproach.” She emphasized the importance of consent for artists wishing to share their work and condemned the notion that consent can be disregarded. Zhang added that she wouldn't yield to pressure to restrict Cara’s access to members only, advocating instead for community support to initiate a legal defense through GoFundMe.

Since its launch in late 2022, Cara has garnered over 1.5 million users, including both professional and amateur artists who view AI as a potential threat to their careers and an affront to the essence of art. While sharing and promoting their work is essential for gaining clients and collectors, these artists are determined to prevent their creations from being used to train AI systems that could ultimately seek to replace them. The initial emergence of generative image models like Midjourney, Stable Diffusion, and Dall-E highlighted the risks, as these models were trained on vast collections of images, many protected by copyright, which were scraped from the internet using automated bots. Recognizing this peril, Zhang launched Cara to create a safe space for artists to share their work without any implied consent to its use in AI training. Although no online platform can guarantee complete security, Cara offers some legal and technological protections along with a clear ethical commitment to artist rights. 

Zhang maintains that even small efforts can create significant impacts. “In a climate where it feels like nothing is being done, Cara is striving to effect change,” she said in a phone conversation. “While our approach may not resolve every issue or revolutionize the internet's framework, our efforts still stand to make a meaningful difference.”

However, scrapers frequently challenge artists' claims to ownership and control over their work. They argue that the advancement of AI justifies the extraction of any data, irrespective of consent. This rationale was evident in the actions of the original scraper, known as “MandarinDrawnPoppy994,” who posted a collection of nearly 12 million images from Cara on the sub-Reddit r/DefendingAIArt, asserting that scraping is essential for developing effective AI models. After engaging with the original poster, Zhang successfully convinced him to remove the dataset, leading to an unexpected collaboration on creating an open-source tool designed to help artists identify if their work has been scraped.

Unfortunately, the problems did not end with this victory. Shortly after, a different entity scraped Cara's data again. This time, the images were uploaded to Hugging Face by a user named “Ioannis/Captive Dreamer.” In response to reported copyright infringements, Hugging Face's Trust and Safety team stated, “We have thoroughly reviewed these reports. Since we do not host copies of the artworks, but instead link to the originals on Cara, there is nothing we can disable under our takedown policy.” The team clarified that their decision does not reflect ownership determinations but rather their responsibilities regarding content stored on their servers. Hugging Face has not provided additional comments regarding the ongoing situation.

Artists Created a Platform to Avoid AI, but Scrapers Are Still Targeting It

A dark, moody scene of a digital fortress built from oversized wooden easels and framed paintings glowing with warm, golden natural light. Inside, a lone artist silhouette works at a canvas, brush poised, while outside sinister mechanical spiders—metallic limbs etched with streaming code and glowing red eyes—creep toward the fortress walls, ready to scrape its art.

Artists Built A Site To Escape AI. Scrapers Are Coming For It Anyway - Forbes

- Cara, a social network for artists, experienced its third scraping attack in ten days.  
- Founder Jingna Zhang calls the attacks targeted and detrimental to artist consent and rights.  
- Scrapers justify actions by claiming AI progress benefits outweigh artists' copyright concerns.  

User page from Cara, a social network for artists to share their work where AI usage and AI data scraping is explicitly forbidden. Courtesy of Cara

Cara, an image sharing social network built specifically for artists who do not consent to have their work used to train generative AI models, suffered a third scraping attack in the past ten days, with copyrighted images and metadata taken from the site without permission appearing on the open source data network Academic Torrents on August 22. This follows previous scrapes of the dataset posted on Reddit (since taken down by the original poster) and the AI model clearinghouse Hugging Face.

“We have been scraped for a third time. This is now a targeted attack meant to cause artists pain," wrote Cara founder Jingna Zhang in a thread on X Sunday morning. “Scrapers know we’re a volunteer project with no financial means to respond. So they say it’s legal, believing themselves to be untouchable.”

“I believe artists who want to share their work should get to do so without violating the principle of consent. I won’t be bullied into making Cara members-only because some people think ‘consent isn’t always valid.’ You don’t blame victims after harming them.”

She concludes with a plea for community support to raise funds for a legal defense via a GoFundMe.

Zhang, a fashion and fine art photographer who was named to the Forbes 30 Under 30 list in 2018 and whose work has appeared in Vogue, Elle, Harper’s Bazaar and Time, founded Cara, a public benefit corporation, in late 2022. The network shares some of the same functionality as popular commercial applications like Meta’s Instagram, but has explicit terms of service and features to protect the rights of artists from having their work incorporated into AI training sets without compensation, control or consent.

Artists Seek Refuge from AI Since it launched, the service has attracted over 1.5 million users, including many professional and hobbyist artists who see AI as a threat to their livelihoods and, often, as a basic affront to the ideals of art. As working artists, they need to share and promote their artwork to get work from clients and collectors, but they do not want their work used to train systems designed to replace them. When the first wave of generative image LLMs like Midjourney, Stable Diffusion and Dall-E appeared, they were trained used a huge archive of images, including many under copyright, scraped from the Internet by automated bots. Artists realized that participating in public platforms, even their own personal websites, exposed their work to these systems. Zhang built and launched Cara on a shoestring as a place where artists could share work in an environment where no consent could plausibly be implied. While no site can be perfectly secured in today’s online world, it provides a modicum of legal and technological countermeasures and a clear, human-centric ethical stance. Jingna Zhang, photographer and founder of the art site Cara Courtesy of Jingna Zhang “In a world where it feel like nobody is actually doing anything, Cara is trying to make a difference,” said Zhang in a phone interview. “It might not be perfect, it might not solve all the problems or change how the internet is designed, but we are trying, and that makes a huge difference.”

Scrapers defend their actions Artists’ claims to own and control their own work online are disputed by individuals, groups and commercial entities who believe that advancing the progress of AI entitles them to any and all data they can obtain, regardless of consent. This was apparently the motivation of the original scraper, who posted an archive of nearly 12 million images from Cara to the sub-Reddit r/DefendingAIArt under the handle “MandarinDrawnPoppy994.” “Scraping is necessary to develop good models. It’s like building a highway – some houses must be demolished, but in the end everyone benefits,” the poster wrote in a thread titled “[AMA] I scraped all of Cara.” Zhang says she and others reached out to the original scraper and eventually prevailed on him to take down the post. She adds, “not only did the first scraper delete the dataset, but he has turned around now to offer help, and we’re now co-creating an open source tool separate from Cara that will help people check if they've been scraped in new datasets in the future.” Unfortunately, that was not the end of the problem. Several days later, the site was scraped again by a different actor. This time the data was posted on Hugging Face, a hub of resources for AI developers rooted in the open source community, by a poster under the name “Ioannis/Captive Dreamer.” After some people reported the post to Hugging Face Trust and Safety, the team responded that “we have reviewed these [copyright reports] carefully. Because no copies of the artworks are hosted here, and because the URLs [in the dataset] point to the copies the artists published on Cara, there is nothing hosted on Hugging Face that we can disable through our notice and takedown process. This is not a judgement about who owns the works (the artists do); it is about what is stored on our servers. Further copyright reports on the same basis will not change this outcome.” Hugging Face did not respond to a request for further comment for this story.

Artists Built A Site To Escape AI. Scrapers Are Coming For It Anyway

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Regarding GPS: China's progress in AI to match the US

Two monumental AI “brains” sculpted from interlocking circuitry hover above a subtle world map, facing each other in a spacious, sunlit tech hall. The left brain is draped with China’s red flag and golden star; the right bears the U.S. stars-and-stripes. Delicate glowing lines crisscross the map beneath them, tracing data routes that bind both powers in a high-stakes race of innovation.

On GPS: How China has kept up with the US on AI - CNN

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On GPS: How China has kept up with the US on AI

Experts are currently sounding alarms about a potential "debt bomb" crisis, specifically highlighting concerns around major data center developers like Meta, Oracle, xAI, and CoreWeave. These companies are raising vast sums of capital to build expansive facilities, yet they're not reflecting these long-term debt obligations in their balance sheets.

Is this a cause for alarm? I would argue it isn’t, based on past experiences.

Here's how the process typically unfolds: Meta, for instance, aims to establish a data center to support its escalating demands in AI and cloud services. To achieve this, it creates a separate entity that is not included in its financial reports, which will manage the construction of the data center. This entity secures funds from various sources, including investors and financial institutions, with significant backing from Meta itself. Once the data center is completed, Meta is granted exclusive access to it. This arrangement allows Meta to benefit from the data center without directly showing most of the incurred debt as a liability on its financial statements.

Concerns have arisen due to the staggering amounts of money flowing into these entities. A report by the Financial Times in December 2025 indicated that tech companies had successfully funneled over $120 billion in AI data center expenses off their balance sheets through special-purpose vehicles and related financing methods. Goldman Sachs projects that hyperscale companies will invest $5.3 trillion into AI and data centers by 2030, anticipating a growing reliance on private markets for funding this expansion.

Skeptics argue that these tech firms might be obfuscating the long-term ramifications of this debt, drawing comparisons to Enron, the energy company that famously collapsed in 2001, leading to massive losses for investors and triggering a market crisis.

Critics should indeed analyze these companies' finances closely. Examine the details, debate issues around financial consolidation, and scrutinize the methodologies used. However, it’s misleading to liken them to Enron. While Enron's downfall was catastrophic, it's improbable that any similar large-scale fraud is occurring within these tech firms today.

Reflecting on my career's early days in accounting during the mid-1980s, my primary client was Centocor, a publicly traded biotechnology company pioneering monoclonal antibody treatments for various conditions. To finance its operations, Centocor utilized off-balance-sheet financing by establishing limited partnerships in which it held minority stakes. These entities raised funds through debt and investments from limited partners, which Centocor then used for exclusive drug development rights.

At that time, substantial investments were made into these partnerships, a common practice within the biotech sector. While some drug candidates did not succeed in clinical trials, this did not spark a stock market turmoil. Just like with today’s data centers, the risks associated with those biotech ventures were diversified. During the period before the internet and rigorous SEC regulation, these financing vehicles were not widely understood by the public. Fortunately, accounting practices have improved, though the fundamental concept remains unchanged—Centocor’s balance sheet did not reflect any debt from these partnerships. Ultimately, this funding mechanism became less favorable as traditional methods of raising capital became more accessible.

The off-balance-sheet financing techniques used by contemporary tech giants involve distinct risks compared to those faced by biotech firms in my early career. Today’s companies are subject to stringent disclosure requirements, and the level of public scrutiny has intensified. Investors have become more educated, and the risks they face today are notably different and potentially less severe.

In the 90s, companies like Centocor, Genentech, Amgen, and Biogen were amassing millions through partnerships aimed at developing products that often faced high failure rates during clinical trials. In contrast, today’s investors are financing tangible assets—land, buildings, electrical systems, and advanced computing equipment. While a data center might not achieve its financial forecasts, it can still retain value as a physical asset, unlike a drug that fails clinical testing. As Jeff Bezos described AI assets, they could be seen as part of an “industrial bubble,” which leaves behind infrastructures like railways, fiber-optic networks, and factories—along with data centers.

Interestingly, even amidst concerns of oversupply, the landscape remains robust; North American data center capacity grew by 36% last year, yet vacancy rates dropped to an unprecedented 1.4%. Recent findings from CBRE's North America Data Center Trends H2 2025 report reveal that demand is currently outstripping supply in nearly every key market. The need for data centers is legitimate and pressing; AI technologies are not vanishing. In fact, Microsoft suggests that only 17.8% of the global working-age population is currently utilizing generative AI—indicating that we may be at the early stages of widespread adoption.

Certain investments will inevitably falter. Some lenders might incur losses, and certain data centers may not hold their initial value. However, these financing models exist precisely to mitigate substantial capital requirements and distribute risk among investors. The financial obligations are disclosed, the underlying assets are tangible, and the demand for computing power remains robust. Consequently, I remain unconcerned about the off-balance-sheet debts of major tech companies. What I observe is a sophisticated approach to financial engineering rather than an impending debt catastrophe.

Is there an imminent AI 'debt bomb' crisis? No, it's not Enron 2.0.

A bright morning scene outside a sleek glass high-rise bearing a large Enron logo on its facade. In the foreground, an oversized steel bomb stamped “AI Debt” lies on its side with its fuse cleanly snipped and wisps of smoke rising. Sunlight catches faint, confetti-like sparks drifting in the air, symbolizing a crisis that’s been peacefully defused.

Is there a pending AI ‘debt bomb’ crisis? No. This isn’t Enron 2.0 | Gene Marks - The Guardian

- Experts warn of a potential "debt bomb" due to off-balance-sheet financing by tech firms.  
- Tech companies are raising billions using special-purpose vehicles for datacenter construction.  
- Strong demand for datacenters persists, suggesting risks are manageable and not fraudulent.  

Some experts are warning of a looming “debt bomb” crisis because big datacenter builders such as Meta, Oracle, xAI and CoreWeave are not only raising billions to construct these facilities but are also not recognizing these long-term debt obligations on their balance sheets.

Should we be concerned? No. I know because I’ve seen this movie before.

It works like this: Meta wants to build a datacenter to accommodate its growing AI and cloud computing needs. It forms a separate entity that’s not consolidated in its financials, which builds the datacenter. To fund this, the entity raises money from investors, banks and other financial firms (including some from Meta) that own the majority of the entity. There is a contract by which Meta has exclusive and full use of the datacenter once built. This way Meta gets its datacenter but most of the debt incurred to build it is not shown as a liability on its books.

The worry comes from the enormous sums of money being plowed into these entities. The Financial Times reported in December 2025 that tech companies had shifted more than $120bn of AI datacenter spending off their balance sheets through special-purpose vehicles and similar financing structures. Goldman Sachs estimates that hyperscalers could spend $5.3tn on AI and datacenters through 2030 and expects private markets to play an increasingly important role in financing that buildout.

Skeptics are worried that these tech guys are pulling the wool over the public’s eyes and not properly disclosing the long-term impact of all this debt. They point to Enron, the energy firm that spectacularly failed in 2001, causing tens of billions of dollars of losses to its shareholders and contributing to a historic market meltdown.

Fine. Scrutinize them. Read the footnotes. Argue about consolidation. But don’t call them Enron. Enron caused the Enron crash. It’s highly unlikely that fraud at that level is being perpetuated now by these companies.

When I first started out in accounting back in the mid-1980s, my biggest client was a publicly held biotechnology firm called Centocor, which, at the time, was developing drugs using a technology based on monoclonal-antibody treatment for various purposes including sepsis. To raise money, Centocor also did off-balance-sheet financing. The company formed limited partnerships in which it held a minority interest but the entity issued debt and took in investments from limited partners in order to fund the development of various drugs, which Centocor then had the exclusive right to use.

In the 1980s and early 1990s, hundreds of millions of dollars were plowed into these partnerships. This was common practice in the biotechnology during that time.

Did some of the drugs fail in clinical testing? Yes. But did this cause a stock market panic? No. Like with today’s datacenters, the risks were spread. During that time – before the Internet, SEC disclosures and 24/7 media scrutiny – the public little knew of these vehicles. The good news is that the accounting has evolved, although the economic idea has stayed the same – no debt was shown on Centocor’s balance sheet. Ultimately, this financing vehicle became more expensive and less popular because it was easier and more affordable to raise money other ways.

The off-balance-sheet financing strategies today that are being used by big tech firms have different risks than what the biotech companies were doing when I was younger. Today, the disclosures required by companies doing this are significant. The scrutiny is intense. The investing public is smarter. Today’s risks are different and arguably more recoverable.

In the 90s Centocor, Genentech, Amgen, Biogen and other companies like them were raising millions in partnerships that were developing products with a high probability of failure during clinical testing.

Today’s investors are financing land, buildings, electrical infrastructure and computing equipment. A datacenter can disappoint financially, but it doesn’t disappear because a clinical trial fails. It’s why Jeff Bezos calls AI an “industrial bubble”. Industrial bubbles leave things behind. Railways. Fiber-optic cable. Factories. And this time, datacenters.

If this is a glut, it’s a strange one: developers increased North American capacity by 36% last year and vacancy still fell to a record 1.4%. According to CBRE’s North America Data Center Trends H2 2025 report, demand is outpacing supply in nearly every major market. Unlike the drug companies hoping for success, there’s a legitimate market need today for datacenters. AI isn’t going away. Microsoft estimates that only 17.8% of the world’s working-age population currently uses generative AI. If that’s anywhere near correct, we’re still much closer to the beginning of adoption than the end.

Some of these investments will fail. Some lenders will lose money. Some datacenters will be worth less than their owners paid for them. But that’s exactly why these financing structures exist: to spread enormous capital requirements and risk among investors willing to take it. The obligations are disclosed, the assets are real and demand for computing capacity remains strong. That’s why I’m not losing sleep over big tech’s off-balance-sheet debt. I see financial engineering, yes. I don’t see a debt bomb.

Is there a pending AI ‘debt bomb’ crisis? No. This isn’t Enron 2.0

Inside a data center, rows of servers generate the computational capabilities necessary to train and operate artificial intelligence models. AI computing is poised to become one of the priciest assets in business history, with significant financing targets hinging on its potential.

When you inquire with business leaders about AI computing, they often gesture towards a warehouse filled with chips, offering a vague description tied to a $500 billion financing initiative. 

To clarify, AI compute encompasses the entire stack of technology that is essential for training and utilizing AI models. This includes the chips along with memory, networking, power, cooling, and the software that makes everything functional. It's important to highlight the "AI" in AI compute, as "compute" generally refers to a broader category of technological resources. For instance, Amazon markets compute as a general service that includes servers and serverless code. The distinction is crucial: it illustrates how individuals may sound knowledgeable while still misunderstanding what they are actually purchasing.

Traditionally, "compute" served as a verb related to calculations—a process performed by machines rather than a line item in financial management. When you purchased a computer, the notion of computing was intrinsic to its operation.

However, with the rise of cloud services, computing has evolved into a commodity that can be bought hourly, similar to electricity. This expanded definition now includes a variety of devices—from smartphones to weather prediction systems—all contributing to an everyday understanding of what compute entails.

The introduction of artificial intelligence has further refined this concept. Training AI models demands substantial resources, leading to a situation where one company dominates the supply of data center GPUs globally. Consequently, “compute” now frequently refers specifically to the hardware associated with AI unless clarified otherwise. While traditional computing remains relevant—billed based on usage, usually metered hourly—the conversation has shifted primarily towards this newer interpretation.

Moreover, compute has transformed into a significant asset that companies buy, finance, depreciate, and account for on their balance sheets. Recently, Nvidia and six leading financial institutions signed agreements to mobilize over $500 billion in capital for AI compute, with the completion of these agreements pending execution.

This important change highlights a key factor influencing financial viability, which is often overlooked.

To understand what companies are actually purchasing, one should consider the essential components involved—chips, power, and CUDA software. A GPU, for instance, is not particularly effective by itself; it requires speedy memory, robust networking, processors to supply data, and storage solutions. Collectively, these create an "AI factory"—a specialized data center centered around high-value hardware. Additionally, software, particularly CUDA, plays a critical role, as engineers must interact with it to utilize the chips effectively. This dependency reflects a switching cost: firms are not just acquiring silicon but also committing to a specific set of tools their engineers are trained to use. Elements of a data center do not all depreciate at the same rate; buildings may last decades, while chips are a variable investment. This phenomenon exemplifies how compute functions as an asset, with many discussions concerning AI development branching from this fundamental understanding.

Importantly, compute serves two distinct purposes—training and inference—with each stage operating on different timelines. Training involves building and refining the model, characterized by intensive and finite resource usage. Conversely, inference refers to the ongoing process where a trained model generates outputs continuously. Each individual output may be minimal, but when multiplied across vast user bases, the demand for processing power can become significant. Consequently, older chips that no longer excel at training tasks often find a second life in inference applications, where performance requirements may be less stringent.

The pricing and sales of AI compute typically follow three models, each addressing the question: who assumes the risk if the asset’s value declines? Companies can either purchase the hardware, becoming responsible for its depreciation, or they can rent by the hour, paying a premium while shifting that risk to the provider. Alternatively, firms can bypass hardware acquisition altogether by accessing AI capabilities through APIs, avoiding ownership and depreciation concerns. While costs fluctuate based on usage patterns, organizations often mistakenly invest heavily in hardware, only to utilize a fraction of its potential, allowing their assets to diminish in value. Industry averages suggest that substantial ownership benefits are realized only at around 70% consistent usage, while renting tends to be more economical below 30%. Evaluating how well companies utilize their systems becomes crucial, as discrepancies often exist between perceived and actual capacity usage.

This financial landscape has piqued Wall Street's interest in financing a staggering $500 billion for AI compute. The logic is simple: data centers equipped with GPUs generate reliable cash flow, which can be leveraged for loans. However, challenges arise concerning collateral. Unlike established assets, such as leased planes or toll roads, the market for compute has only recently emerged, lacking robust benchmarks for secondary sales. The absence of clear valuation makes lenders hesitant, as the lifecycle of computing hardware can be unpredictable. As one expert noted, “Technology obsolescence is sudden, not gradual,” highlighting the risk involved. Despite this, older chips can remain functional and profitable, as evidenced by companies like CoreWeave signing contracts for several years of older architecture.

Ultimately, Nvidia's involvement adds another layer of complexity. While the company announced the financing initiative, Jensen Huang pointed out that Nvidia might provide a residual-value support mechanism for a maximum of 25% of an opportunity, determined on a case-by-case basis. This dilutes any assumption about the level of risk the company is willing to absorb, revealing a cautious approach towards potential shortfalls in asset valuation.

Understanding AI Compute: The $500 Billion Investment in Outdated Technology

Interior of a sunlit industrial hall lined with dusty, legacy server racks. In the center, a polished golden balance scale: on one pan rests a weathered, timeworn circuit board; on the other, a neat pile of cash bundles tagged “$500 B”. Beams of warm natural light stream through high windows, illuminating drifting dust motes and a faded banner overhead reading “AI Compute,” capturing the high-stakes bet on aging hardware.

What Is AI Compute? The $500 Billion Bet On Aging Hardware - Forbes

- AI compute encompasses the entire tech stack needed to train and run AI models.  
- Companies finance AI compute to manage risks of hardware depreciation and capacity usage.  
- Nvidia may only support 25% of the value of the AI compute projects.  

Rows of servers inside a data center provide the computing power used to train and run artificial intelligence models. getty

AI compute is set to become the most expensive noun in business history. Here is what the thing actually is, what you are really paying for, and why the company that makes the chips will only backstop part of what they turn out to be worth.

Ask any executive what AI compute is and you will usually get a wave at a warehouse of chips. That loose definition now has a $500 billion financing target riding on it.

So, plainly. AI compute is the whole technology stack that trains and runs AI models: the chips, plus the memory, networking, power, cooling and software that make them usable.

Note the “AI” in front. Compute on its own means something much wider. Amazon still sells it as a general-purpose category covering servers, containers and serverless code. The gap between the two isn’t academic. It’s how someone says the word confidently and still doesn’t know what they’re buying.

The chips are the part that gets pictured. They aren’t the part that decides whether the money makes sense. In everyday business use, compute has gone from a verb to a very expensive noun in about twenty years.

It used to be a verb. To compute meant to calculate. Computing was something a machine did rather than a line item you managed. You bought a computer, and computing was what happened inside it.

Then the cloud turned it into something you order. Once Amazon began selling processing capacity by the hour in 2006, compute became a unit you buy, like electricity. It still meant any processing power at all: your phone has compute, a weather model has compute, the payroll system has compute. Ordinary, and unambiguous.

Then AI narrowed it. AI training models got enormous, one company came to supply more than 90% of the world’s data center GPUs , and in a lot of business conversation “compute” now arrives meaning that specific hardware unless someone says otherwise. The older sense hasn’t gone anywhere; your cloud bill still meters ordinary compute by the hour. But the two now travel under one word. Same word. Two jobs. Plenty of people using it have only met one.

Then it became an asset. Not just metered capacity on someone else’s bill, but something firms buy outright, finance, depreciate and carry on a balance sheet. In August, Nvidia and six of the largest firms on Wall Street signed memorandums to mobilize more than $500 billion of outside capital around it. The agreements remain subject to execution.

That last shift gets explained least, and it’s the one that decides whether the money works.

What You’re Actually Buying: Chips, Power And CUDA Take the list above and put weights on it. A GPU alone does very little: it needs fast memory beside it, networking to act as one machine with thousands of others, processors feeding it data, and storage underneath. Then the building, the power and the cooling. Fill a site with that and you have what Nvidia calls an AI factory: a data center built for one purpose, wrapped around a very expensive hardware core. There’s also software, and it’s the part buyers underestimate. There’s a software layer too, called CUDA. Your engineers never touch the chips directly. Their code goes through CUDA to get there. That’s not a technical detail, it’s a switching cost. You aren’t only buying silicon, you’re buying into the toolchain your engineers already know and your code already assumes. None of it ages at the same speed. The building stands for decades. The cooling runs for years. The chips are the question mark, and semiconductors and their related components run to roughly two-thirds of what an AI data center costs. It’s why counting transformers turned out to be a useful way to read this buildout. A long-lived shell wrapped around a fast-moving core is the most important fact about compute as an asset, and nearly every argument about the AI buildout is downstream of it.

Training And Inference Are Two Different Clocks Compute does two jobs with completely different shapes. Training is building the model, and any single run is bounded: enormous, expensive, and it ends. Inference is the trained model producing answers, and for a live service it doesn't stop. Each answer is small, but multiply it across millions of users all day and it becomes a meter that keeps turning. That explains where old chips go. Training wants peak performance. Inference will settle for less, and batch work for less still. So a chip that has aged out of the first job is often perfectly employable in the second.

How AI Compute Is Priced And Sold There are three common ways to pay, and underneath the pricing each one answers the same question: who absorbs the risk that this machine is worth less next year than it is today? Buy it. You own the asset and you own the problem. Rent it by the hour. You pay a premium and hand the aging problem to somebody else. Skip the hardware and pay per token through an API, a software connection that lets you buy model output without owning machines. You own none of the depreciation. Whether it costs more depends on volume: cheap when demand is spiky, expensive when it's constant. That trade rarely makes it into the pitch. The mistake I've watched companies make, more than once, is to buy their own hardware for the satisfaction of owning their AI. Then they run it at a fraction of what it could do, while it quietly loses value on the shelf. The public numbers point the same way, though they aren’t all measuring the same thing. One 2026 model says owning only pulls ahead above roughly 70% sustained use, and renting wins below 30%. Where your own line sits depends on what you paid and how long you assume the hardware lasts. Against that, Cast AI’s telemetry across tens of thousands of enterprise clusters found average GPU use at 5% of what had been provisioned, measured across a full day. Then ask companies to estimate their own, and 53% put it between 51% and 70%. Where instruments are attached, they read in single digits. Where people are asked, they answer in the fifties. That distance is the part worth worrying about.

Why Wall Street Wants To Finance $500 Billion Of AI Compute The pitch is clean and it isn't stupid. A data center full of GPUs throws off cash, and cash under contract can be lent against. I wrote about the mechanics when the platform was announced. The problem is the collateral. A toll road still collects tolls in year forty. A leased plane trades in a market with decades of sales behind it, so lenders know what a used one fetches. Compute has neither. A secondary market only opened in July, and it posts estimates rather than completed sales. There's no public benchmark for what a whole cluster fetches when the seller has no choice. Bernie Margulies sells insurance against this hardware losing value, so he has every reason to talk the market up. Here’s what he told the equipment-finance trade press: “The disagreement is whether the right number is 10% or 60%.” Fifty points of spread on the same asset. That's the difference between a sound loan and a hole. Computing hardware has been here before. When the mainframe market turned, Margulies writes, it "repriced immediately," and lessors who had booked aggressive residuals "went bankrupt within months." His summary is six words: "Technology obsolescence is sudden, not gradual." The counter-argument is real. Old chips keep working and keep earning. CoreWeave told investors in August it had signed a contract for A100 capacity, a 2020 architecture, running into 2029. Silicon migrates down the ladder rather than dying.

How Much Of The Risk Is Nvidia Taking? The most revealing number isn't the $500 billion. Announcing the platforms, Jensen Huang wrote that "in some cases, NVIDIA may provide a residual-value support mechanism for up to 25% of an opportunity, assessed carefully on a project-by-project basis." That sentence isn't in the press release. TechCrunch and Bloomberg picked it up separately. Read the qualifiers. May. Up to. Of an opportunity. Assessed one project at a time. The company that designs the chips, and decides when they're replaced, has capped how much of that question it will answer for you.

FDA digital health chief pledges forthcoming regulatory guidance on generative AI.

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