Google is experiencing significant challenges on the stock market, poised for its most significant decline in a year, as concerns surrounding artificial intelligence escalate and the company recently lost two notable researchers to competitors.

On Monday morning, shares of Google’s parent company, Alphabet, dropped by 7%, trailing behind both the Nasdaq and other major tech companies.

The worry regarding talent loss intensified last week when Noam Shazeer, Google's vice president of engineering and one of the leaders behind the Gemini AI models, announced his move to the rival firm OpenAI. This exit occurred less than two years after Shazeer rejoined Google.

In August 2024, Google had welcomed Shazeer back along with fellow researcher Daniel De Freitas to its DeepMind AI division, following a collaboration with Character.AI, which the two had co-founded after their initial departure from Google in 2021.

Shazeer's exit follows the recent launch of new AI innovations by Google, including the Gemini 3.5 Flash model and the Gemini Spark AI agent, showcased at the company's annual I/O developer conference.

Alphabet braces for its worst day in a year due to AI worries following notable departures.

A dim, tension-filled corporate lobby at night, lit only by a deep red glow from a huge Alphabet logo mounted on the far wall. Beneath it, a digital ticker displays a plunging stock chart with a bold red arrow. In the foreground, three suited figures—executives—walk away in silhouette through revolving doors, their backs to us. Heavy shadows and a black-and-red palette underscore the mood of sudden crisis.

Alphabet paces for worst day in a year on AI concerns after high-profile exits - CNBC

- Google shares fell 7%, marking the company's worst stock day in a year.  
- High-profile researcher Noam Shazeer is leaving for rival OpenAI, raising concerns.  
- Google recently launched new AI products amid increasing competition and talent departures.  

Google is on pace for its worst day on the stock market in a year, as artificial intelligence concerns have mounted and two high-profile researchers departed for rivals in recent days.

Shares of parent company Alphabet slid 7% on Monday morning, underperforming both the Nasdaq and the rest of their megacap peers.

The brain drain concerns began last week when Google's vice president of engineering and a co-lead of its Gemini AI models, Noam Shazeer, announced Wednesday that he was leaving the company to join rival OpenAI. Shazeer's departure came less than two years after he returned to Google.

In August 2024, Google brought back Shazeer and fellow researcher Daniel De Freitas to its DeepMind AI unit as part of a partnership with startup Character.AI, which the pair founded after leaving Google in 2021.

The departure came weeks after Google unveiled new AI products, including its Gemini 3.5 Flash model and Gemini Spark AI agent, at its annual I/O developer conference.

Alphabet paces for worst day in a year on AI concerns after high-profile exits

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Rep Chip Roy emphasizes the need for an important discussion in Washington regarding AI.

A wood-paneled congressional hearing room bathed in warm natural light, Representative Chip Roy standing at a podium before a single American flag, shadowed senators seated behind him as a large screen glows with the letters “AI,” their serious expressions hinting at a pivotal debate.

There needs to be a serious conversation in Washington about AI: Rep Chip Roy - Fox Business

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There needs to be a serious conversation in Washington about AI: Rep Chip Roy

During the World Robot Conference held in Beijing on August 21, 2026, both local attendees and international visitors gathered to explore the latest advancements in robotics.

At the conference, the founder of Unitree highlighted a significant hurdle facing humanoid robots: the acquisition of human-like skills. While artificial intelligence plays a role, the search for a definitive solution continues.

Greetings from Beijing, I'm Evelyn, and I’m here to share insights from the local business landscape as part of The China Connection.

QJ Robots, an innovative startup focused on enabling robots to grasp human skills, has received support from Temasek, as revealed by founder and CEO Haichuan Gao. Their AI framework is designed to enhance robots' ability to anticipate their environment and execute tasks with greater efficiency.

QJ Robots reports impressive earnings of over 100 million yuan (about $15 million) through the application of its technology across more than 100,000 robots, primarily utilized by companies in mainland China. This substantial user base allows the startup to gather extensive data each month, according to Chief Technology Officer Tianren Zhang. However, he emphasized that the current data pool is insufficient for optimal model training.

Both Zhang and Gao earned their PhDs in automation from Tsinghua University, where their three-year-old venture originated within the school's brain computing research center. Their work aligns with the tech sector's increasing emphasis on creating world models, which seek to better replicate the dynamics of the physical environment, as opposed to the predominantly textual focus of large language models like ChatGPT.

To foster AI integration in industry, Beijing is actively encouraging major corporations to enhance their technological capabilities, an opportunity that startups are keen to exploit.

Another Tsinghua graduate, Chao Yu, established the robotics firm Lumos two years ago, with partnerships that include Mitsubishi Electric. Lumos has also initiated a center aimed at amassing training data. Earlier this year, Yu recognized that factories and other enterprises needed a system to translate data and AI directives into actionable tasks. As a result, Lumos launched the NexCore platform last week, designed to interface with various types of robots, not limited to Lumos products.

The competition to create superior autonomous robotics solutions is heating up. Just a year ago, Ace Robotics was founded, with Wang Xiaogang, co-founder of SenseTime, taking on the role of chairman. Wang stressed that a successful humanoid robot must integrate hardware, models, and real-world data scenarios. He cautioned against relying on online videos for training, as they may introduce unrealistic visual effects into the model.

Ace Robotics' offerings include wearable sensors capable of accurately capturing human activity data. By the end of next year, Wang anticipates that the industry will amass enough data to trigger a pivotal moment, similar to the transformative launch of ChatGPT in late 2022 that reshaped business applications of AI.

However, it's uncertain whether the market can afford to wait until then. Following comments from Unitree's founder Wang Xingxing that tempered commercialization expectations for humanoid robots, the company's shares experienced a drop. Meanwhile, visitors at the conference expressed their skepticism about the technology, with many finding the performance of robots—such as those responsible for folding clothes or selling water—slow and awkward. One individual waiting to purchase a beverage remarked in Mandarin that patience was only extended to the robots, asserting, "A human would definitely be scolded if it took this long."

CNBC's The China Connection newsletter: AI firms are hurrying to assist robots in acquiring human skills.

A bright, sunlit tech workshop with warm natural light streaming through tall industrial windows. In the foreground, a humanoid robot sits at a wooden workbench, flipping through a manual titled “Human Skills,” while a human instructor guides its hand holding a paintbrush. Behind them, other robots practice handshakes and cooking with utensils on worktables. On one wall, a large CNBC peacock logo glows on a digital screen, with a small Chinese flag pinned beside it.

CNBC's The China Connection newsletter: Robots need help learning human skills. These AI companies are rushing to meet the demand - CNBC

- Unitree's founder discussed humanoid robots' challenges in learning human skills at the conference.  
- QJ Robots has generated over $15 million by enhancing robots’ predictive tasks with AI.  
- Industry competition is intensifying, with startups focused on improving data-driven robotic functionalities.  

The public and foreign visitors alike visit the World Robot Conference in Beijing on Aug. 21, 2026.

Unitree's founder reminded everyone at the World Robot Conference last week that the biggest challenge for humanoids is learning human skills. AI helps, but is there a secret sauce?

Hi, this is Evelyn, writing to you from Beijing. Welcome to the latest edition of The China Connection — a snapshot of what I'm seeing and hearing from local businesses.

QJ Robots, a startup that's helping robots learn human skills, has been backed by Temasek, founder and CEO Haichuan Gao told me.

The company's AI model focuses on helping robots predict the world around them and perform tasks more efficiently.

QJ Robots claims it's already made more than 100 million yuan ($15 million) from deploying the model to more than 100,000 robots, mostly by companies in mainland China.

That gives the startup a large base from which it collects data every month, Chief Technology Officer Tianren Zhang said. But it's not enough — he said more kinds of data are needed to train the model effectively.

Zhang and CEO Gao both have PhDs in automation from Tsinghua University, where their three-year-old company got its start in the school's brain computing research center.

It's all part of the tech industry's growing focus on world models, which aim to imitate the physical world more closely than ChatGPT-type "large language models" that focus on text generation.

One of Beijing's approaches is to encourage industrial giants to integrate and develop AI capabilities. It's a manufacturing ecosystem edge that local startups are tapping into.

Another Tsinghua alumnus, Chao Yu, launched industrial-focused robotics company Lumos two years ago. Its backers and partners include Mitsubishi Electric.

Lumos has launched its own center for collecting training data. But Yu said that earlier this year, he realized that for factories and other businesses to use more robots effectively, they needed a platform to translate data and AI instruction into task completion. The startup last week officially launched its NexCore system that claims can work with a range of robots, not just those from Lumos.

But the race to build the best autonomous robotics solution is intensifying.

Just one year ago, Ace Robotics launched with SenseTime Co-Founder Wang Xiaogang as the startup's chairman.

An effective humanoid robot solution, he said, must combine hardware, models and data with use-case scenarios. Real-world data is critical, Wang said, as using online videos for training can introduce unrealistic special effects into the model.

Ace Robotics' products include wearable sensors for precisely capturing data from human activity.

By the end of next year, Wang predicts the industry will have gathered enough data for a breakthrough moment, similar to how ChatGPT's launch in late 2022 transformed what businesses could do with AI.

It's less clear whether markets will be willing to wait that long. Unitree's shares tumbled after founder Wang Xingxing sought to lower expectations for humanoid commercialization, while the public visiting conference exhibition booths were often bluntly unimpressed.

Dancing robots aside, machines folding clothes or selling water moved slowly and stiffly. One person waiting to buy a bottle of water noted in Mandarin Chinese that people were only patient because it was a robot.

"A human would definitely be scolded if it took this long."

CNBC's The China Connection newsletter: Robots need help learning human skills. These AI companies are rushing to meet the demand

Close to 200 activists connected with President Donald Trump's "Make America Healthy Again" initiative are encouraging him to rethink his initiative aimed at constructing artificial intelligence data centers across the country.

There has been a noticeable bipartisan backlash against these resource-intensive facilities due to their detrimental effects on the environment and public health. Meanwhile, Trump and his supporters have been characterized as taking a villainous stance, insisting that the establishment of new data centers is unavoidable and dismissively suggesting that critics should be ignored.

It's particularly noteworthy that this opposition includes members of the MAHA movement, which has typically endorsed various policies that could jeopardize public health and potentially lead to increased illness among Americans. Clearly, the issue of AI data centers might be where they draw a line.

In a letter sent on Friday, these activists—operating under the banner "United We Eat"—urged Trump to retract an executive order that places coal power at the forefront of energy sources for these data centers.

"The executive order from April 2025 links the rising need for electricity stemming from AI data processing centers to the enhanced support of coal power," the letter stated. "It also tasked federal agencies with pinpointing areas where coal-based infrastructure could sustain AI data centers. This connection warrants immediate reevaluation."

The activists expressed their desire to voice their concerns to maintain the integrity of their stance:

MAHA activists call on Trump to change his stance on AI data centers.

A moody nighttime protest scene: adult activists clutch “Stop AI Data Hubs” placards outside a sprawling, dimly lit data center whose windows glow ominously in warm reds and ambers. In the distance atop the building’s roof, a lone silhouette of Trump stands at a podium. A single US flag billows above them under stormy clouds, casting dramatic shadows across the crowd.

Opinion | MAHA activists urge Trump to reverse course on AI data centers - MS NOW

- Nearly 200 Trump-aligned activists are urging a rethink of AI data centers nationwide.  
- Bipartisan backlash highlights environmental harms and public health impacts of these centers.  
- Activists call for reconsideration of coal power use in data center operations.  

Nearly 200 activists aligned with President Donald Trump’s “Make America Healthy Again” movement are urging him to reconsider his push to build artificial intelligence data centers nationwide.

There’s been growing bipartisan backlash to these resource-sapping centers over their environmental harms and negative impacts on public health. Meanwhile, the president and his allies have basically sounded like comic book villains with their rhetoric about the supposed inevitability of new data centers and the need to “roll over” critics.

So it’s telling that the opposition includes activists in a movement like MAHA that, on many fronts, has advocated for a host of policies that undercut public health and stand to make Americans sicker. Perhaps AI data centers are their red line.

In a letter dated Friday, the activists — under the banner “United We Eat” — pleaded with Trump to reverse course on an executive order that prioritizes the use of coal power to help fuel data centers.

“The April 2025 executive order on coal connected rising electricity demand from artificial intelligence data processing centers to expanded support for coal power,” the activists wrote. “It also directed federal agencies to identify regions where coal-powered infrastructure could support AI data centers. That connection deserves urgent reconsideration.”

The activists said they’re speaking out to avoid the appearance of hypocrisy:

On AI data centers, MAHA activists urge Trump to reverse course

This narrative is derived from a discussion with Chris Domanski, a 41-year-old business management and development director at Renau, a manufacturing plant located in California. Domanski resides in Thousand Oaks with his family. The following content has been edited for brevity and clarity.

According to Domanski, ChatGPT has significantly eased his workload by acting as a study companion for his children.

The motivation to utilize AI was ignited by the progress reports from his daughters, aged 8 and 6, which indicated that one was having difficulty with math while the other faced challenges in reading. While their performances weren't poor, they were not meeting the district's expectations.

Recognizing the need to support their educational growth, Domanski felt uncertain about his ability to effectively teach specific concepts, such as TouchMath, a unique math curriculum he was unfamiliar with.

In an innovative approach, he began taking photos of their assignments and using AI to clarify concepts. By leveraging ChatGPT's voice mode, he found that it could engage his daughters with quizzes, tailor questions to their needs, revisit misunderstood topics, and explain ideas using multiple methods—all without showing signs of frustration.

Initially, Domanski questioned whether the time spent setting up the AI assistance would be worthwhile. However, just five minutes into the process, his daughters were discussing "KPop Demon Hunters," prompting him to make some adjustments to their focus.

Through a mix of experimentation, the AI system became a valuable asset for their educational journey, improving their grades while saving time. Although he is uncertain how it will adapt to increasingly complex schoolwork, the current arrangement seems effective.

Domanski capitalizes on AI to ensure his daughters fully comprehend their study material.

With both girls preparing for weekly math and spelling exams, he routinely informs ChatGPT of their test requirements. By uploading images of their study sheets and instructing the AI to maintain focus amidst the inevitable distractions from his daughters, he crafts a structured learning environment.

He prompts ChatGPT with clear expectations like: "You are a tutor supporting grade schoolers with math and spelling studies. You will receive study guides and will continue to work with the students until they can confidently and correctly answer all questions."

To keep the lessons on target, he provides additional guidance, instructing ChatGPT to express interest in other topics but redirect the discussion back to the studies at hand. This ensures that if his daughters can quickly solve equations, such as determining 9+3, they’ll confidently record their answers during tests.

Chris Domanski works closely with both his children and ChatGPT to optimize their learning process.

Domanski remains present during these sessions to monitor both his daughters' responses and ChatGPT’s input, allowing him to promptly intervene if he notices any misconceptions.

AI has proven to be a resourceful tool in maintaining their attention, adjusting to their immediate thoughts, and repeating concepts multiple times without any feelings of frustration for the children. This adaptability has considerably reduced educational friction in their home.

Since introducing this approach, his daughters’ progress reports have improved from "slightly below satisfactory" to "above satisfactory" levels. While both have shown academic improvement, the real benefit lies in creating a more engaging and less confrontational study experience. Domanski feels more equipped to observe their challenges instead of solely administering questions from worksheets.

Additionally, he incorporates AI to digitize their school handouts into a more manageable calendar system.

Every Monday, Domanski reviews their take-home folders filled with school correspondence, scanning them to extract important dates, deadlines, and necessary supplies. ChatGPT processes this information and organizes it into actionable calendar entries, dramatically simplifying what would typically be a cumbersome task.

His prompt to the AI is straightforward: "You will receive images of take-home folders. Identify key dates and action items that require follow-up, then add relevant tasks to my calendar." ChatGPT efficiently exports these events as calendar files that Domanski can integrate into Outlook.

So far, ChatGPT has functioned reliably, eliminating the stress of last-minute project discoveries. The family now avoids late-night scrambles to prepare for assignments due the next day.

Lastly, the Domanski family enjoys the creativity that AI brings to their lives.

In their artistic endeavors, they often turn to ChatGPT for generating custom images for drawings. Initially asking for character mashups like Disney's Ariel and Elsa, they encountered copyright issues. However, after requesting more generic, open-source alternatives, they found a compromise everyone could enjoy.

Now, several times a week, both children create art featuring their preferred character combinations.

Ultimately, AI does not just create additional free time but rather enhances the quality of their existing time together. They can focus more on projects, artwork, games, or simply enjoying each other's company, minimizing distractions and the need to decipher instructions from scratch.

I created a voice-activated AI tutor for my kids to assist with spelling and math, freeing up time for more fun together.

A cozy living room bathed in warm morning sunlight: an adult sits on a soft couch holding a tablet showing colorful spelling letters and simple math equations, with gentle voice waveforms rising from the screen. Two small empty children’s chairs and backpacks sit nearby on a rug strewn with crayons and worksheets beside an open board game, hinting at playtime off-screen. The adult smiles, turning toward unseen laughter—natural light, warm tones, story of tech freeing up family fun.

I made my kids a voice-mode AI tutor to help them with spelling and math. It saves me time to have more fun with them. - Business Insider

- Chris Domanski uses ChatGPT to assist his daughters with studying math and reading.  
- AI helps manage school-related paperwork, organizing important dates and tasks efficiently.  
- Generative art prompts from ChatGPT encourage creativity, allowing for fun family drawing sessions.  

This as-told-to essay is based on a conversation with Chris Domanski, 41, the director of business management and development at Renau, a manufacturing facility in California. He and his family live in Thousand Oaks, California. This interview has been edited for length and clarity.

More than anything, ChatGPT has saved me time and drama by being a study partner for my kids.

What spurred this were the progress reports that my daughters, ages 8 and 6, got from school last year. I found out that one struggles with math, and one struggles with reading. Not that they're doing poorly, but maybe below where the district thought that they should be.

I knew we could try to spend more time on these areas. But at the end of the day, I thought I might not be the best person to explain a concept like TouchMath, a multi-sensory math curriculum, which I'd never heard of until I had to help the girls with homework.

Domanski's daughters, ages 8 and 6, use AI for homework help and customized drawing prompts. Chris Domanski

I decided to take a picture of their homework and have AI explain some concepts to them. Using voice mode, ChatGPT now quizzes them, varies the questions, revisits what they missed, and explains the same concept several different ways without becoming impatient.

Related video How AI takes a toll on the freelancers who train, test, and refine it

At first, I wasn't sure if the time it took to set this up was worth it. Five minutes in, they were talking to ChatGPT about "KPop Demon Hunters." I needed to revise it a little.

Through trial and error, it became something that really saved time and I think improved their grades. I'm not sure how it's going to look as things get more and more complicated with their schoolwork, but at least for now, it works well.

I programmed an AI tutor to make sure they truly understand the material

Domanski uploads his daughters' homework and has ChatGPT create custom quizzes to test their knowledge. Chris Domanski/ChatGPT

Both my daughters have weekly math and spelling tests at school.

In general, I tell ChatGPT what kind of test my daughters are preparing for, take a picture of the study sheet, and instruct it to stay focused — despite the inevitable interruptions by my daughters.

I prompt ChatGPT by saying something like: "You're a tutor helping grade schoolers study for math and spelling tests. You'll be given images of the study guides, and you'll work with the students until they can confidently and accurately answer all questions."

I also add some guardrails: "You will not be distracted if other topics come up. Express interest in those topics, make them relevant to what you're discussing, but keep it on the task at hand."

Many of the instructions are for ChatGPT to politely and firmly make sure my daughters are comfortable and confident in the material, not engaging just for the sake of engaging. If they're able to quickly say what 9+3 is, they're able to write that on a test.

An example of Domanski's instructions to ChatGPT. Christ Domanski/ChatGPT

I also make sure I'm present to hear both what they're saying and what ChatGPT is saying. If I hear that something is very clearly wrong, I can jump in and steer things back to the task at hand.

Overall, AI has been pretty efficient at maintaining their attention, adapting to how they are thinking in that moment, and repeating something for the sixth time without making it feel like punishment. It's surprisingly useful at absorbing some of that friction.

Since last year, their progress reports went from being a little "below satisfactory" to being "above satisfactory." Both of their grades have improved, but the more valuable result may be that studying has become more interactive and less adversarial. I am still involved, but I can spend more time observing where they are struggling instead of simply reading questions from a worksheet.

I also use AI to digitize their school handouts into a calendar

Domanski uses ChatGPT to scan paper handouts and transfer the info to his Outlook calendar. Chris Domanski/ChatGPT

AI also helps me manage the constant stream of information that comes with having two grade-school children.

Every Monday, I open their take-home folders and take pictures of school flyers, calendars, permission slips, and classroom notices, then use ChatGPT to extract dates, deadlines, supplies, and action items. It turns paperwork that would otherwise require another round of mental sorting into something much easier to track.

The prompt I use is: "You'll be given images of take-home folders. Please review this for key dates and things that require action or follow-up on behalf of anyone. Add relevant tasks to my calendar." ChatGPT then exports the scanned events as .ics files that I then import into a school calendar in Outlook.

At this point, ChatGPT hasn't really dropped the ball. It is really nice not to discover at 8 p.m. that there is a project due the next morning — and then spend the rest of the evening scrambling to figure out what we need.

AI frees me up to have more fun with my kids

Domanski also uses AI to generate custom images for his daughters to draw. Chris Domanski/ChatGPT

We also do quite a bit of generative art, asking ChatGPT to provide an image for my daughters to draw on their own.

Originally, we'd ask for something like Disney's Ariel and Elsa dancing underwater in a sandcastle. ChatGPT declined the request, citing copyright restrictions. I asked it to provide an open-source alternative, such as a generic red-headed mermaid and blonde ice princess. Everyone was happy.

Now, a few times a week, my daughters can draw or paint whatever character crossover they want.

AI does not so much create new free time as help us get much more out of the time we already have. We spend less time looking for things, figuring out instructions, or trying to understand a new concept from scratch, and more time actually doing projects, making art, playing games, or just having fun together.

I made my kids a voice-mode AI tutor to help them with spelling and math. It saves me time to have more fun with them.

AI systems developed using data from the Ukrainian battlefield are set to play a crucial role in safeguarding UK defense facilities, rail networks, and energy infrastructure from potential threats posed by protesters and foreign adversaries. This initiative comes from an agreement between the UK and Ukraine.

The scheme allows private firms to tap into the extensive data sourced from Ukraine’s Avengers AI lab, marking a pioneering collaboration in the UK's approach to defense technology. A trial will commence at a UK defense location, focusing on creating a model capable of identifying both protests and attacks from hostile entities. This involves utilizing AI-enhanced sensors embedded in fiber-optic cables to monitor various movements.

If the technology proves effective, the government anticipates its future application in airports, prisons, railways, and energy sectors.

The recent decision to proscribe the activist collective Palestine Action came in light of their July 2022 breach at RAF Brize Norton, which was a worrying lapse in security. The group managed to vandalize two military aircraft and leave without apprehension. Palestine Action has defended its actions by claiming it aimed to disrupt Britain’s involvement in actions they view as genocidal and criminal in the Middle East.

Private companies looking to engage with this data will need approval from the Ministry of Defence (MoD) and will utilize a secure platform for access, which has raised concerns among privacy advocates. Initial pilot programs have received backing from three British AI firms: Sintela, Mind Foundry, and Skyral. Sintela, based in Bristol, has previously entered a $35 million contract with the Trump administration focusing on surveillance at the US-Mexico border.

This collaboration not only allows for the incorporation of operational drone datasets and mission footage into British security frameworks but also includes insights into Russian sabotage tactics against vital Ukrainian systems. The comprehensive military data accumulated over the past four years offers a more nuanced view than previous models, which largely relied on publicly available information.

The UK government envisions employing AI models to swiftly assess threats to critical infrastructure, including railways and military bases, by using advanced fiber-optic cable systems capable of detecting distinct patterns of movement around sensitive areas. Sources from the MoD clarified that this monitoring includes identifying potential protests, not solely actions from foreign states.

Prime Minister Andy Burnham emphasized the opportunity for British firms to meld their AI innovations with battlefield insights from Ukraine, thereby enhancing national security while ensuring the protection of vital infrastructures across the nation.

Future initiatives could explore developing new AI chips for drones, robotics, and automated weapon systems aimed at bolstering defense capabilities.

Steven Murdoch, a professor of computer science at University College London, noted that while the underlying technology for movement detection via fiber-optic sensors is established, it's uncertain how the Ukrainian data will enhance its effectiveness. He acknowledged, however, the potential for discovering valuable insights related to emerging threats that may soon challenge Western Europe.

"What's valuable now could be significant for what other nations will confront down the line," he remarked, citing drones as a relevant example.

Defense Secretary Wes Streeting highlighted Ukraine's role in advancing defense innovation in its fight against Russian aggression. He asserted that leveraging the UK's scientific and technological prowess alongside Ukraine’s battlefield experience would bolster defense and security for both countries.

UK plans to utilize battlefield data from Ukraine to enhance AI training for safeguarding sensitive locations.

Dimly lit command center with flickering screens displaying Ukraine battlefield footage and live drone shots, streams of glowing code mapping a fortified site’s blueprint on a central terminal, a Union Jack draped on the wall, military gear casting long shadows in a tense, protective atmosphere

UK to use Ukraine battlefield data to train AI to protect sensitive sites - The Guardian

- UK and Ukraine will use AI models trained on battlefield data to enhance security systems.  
- Private companies can access Ukraine's AI lab data to develop new surveillance technologies.  
- Fiber-optic sensors will detect movements around sensitive sites to prevent potential attacks.  

AI models trained on Ukrainian battlefield data will be used to stop protesters and foreign states targeting UK defence sites, railways and energy plants under a deal struck between London and Kyiv.

Private companies will also be given access to the vast trove of data from Ukraine’s Avengers AI lab to help build new systems, the first agreement of its kind in the UK.

The scheme will be piloted at a UK defence site, building a model to identify protesters or a hostile-state attack. It uses AI-optimised sensors in buried fibre-optic cables to detect different kinds of movement.

The government has suggested the technology, if proven workable, could be used at airports, prisons, railways and energy infrastructure.

The controversial proscription of the activist group Palestine Action was sparked after the group breached RAF Brize Norton on scooters last June, a highly embarrassing security lapse as a result of which the group was able to deface two military aircraft with spray paint before escaping the site without being detained.

Palestine Action said at the time that it had targeted RAF Voyager aircraft used for transport and refuelling, and that “activists have interrupted Britain’s direct participation in the commission of genocide and war crimes across the Middle East”.

Private companies will be able to access the data once approved by the MoD on a secure platform, though the data sharing is likely to raise alarm among privacy campaigners.

Pilot projects have already been approved with three British AI companies – Sintela, Mind Foundry and Skyral. Sintela, a Bristol-based company that specialises in fibre-optic sensors, recently struck a $35m (£26m) deal with the Trump administration to provide surveillance on the US-Mexico border.

The partnership with the Ukrainian AI lab means new UK security systems can be trained on operational drone datasets, strike mission footage and flight profiles, likely to include Russian sabotage attempts against critical Ukrainian infrastructure.

The cache of four years’ worth of military data contains a far deeper picture than models outside Ukraine have previously been trained on, which had been mostly restricted to open-source data.

The government eventually hopes to use the AI models to predict quickly when infrastructure such as railways, the power grid or military bases might be coming under threat or stress from hostile actors.

That would primarily come from the laying of fibre-optic cables that are able to identify unique movements of people or vehicles around sensitive sites. Ministry of Defence sources said that would specifically include when a site might be targeted by protesters – not just hostile states.

The prime minister, Andy Burnham, said it would be an opportunity for British companies to combine their AI expertise with Ukraine’s battlefield data, which would “accelerate the development of capabilities that not only strengthen our national security but better protect our critical infrastructure and growth in every postcode here at home”.

Other potential projects could include new AI chips to power drones, robotics and automated weapons systems.

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Steven Murdoch, a professor of computer science at University College London, said technologies to detect movement from sensors in fibre-optic cables had been around for decades and that it was unclear if data from Ukraine would refine their capabilities.

“I haven’t heard anything that is very convincing, but there is a plausible scenario where they could see something that could be predicted easily and isn’t in any publicly known dataset,” he said.

Ukraine, however, has a lot of other data that may be valuable, especially related to new threats that countries in western Europe might face in the short to medium term.

“What’s valuable is something we’re seeing today which other countries will be worrying about in the future,” Murdoch said. “Anything to do with drones, for example.”

The defence secretary, Wes Streeting, said Ukraine was “pushing the frontier of defence innovation as they bravely resist Putin’s illegal invasion”.

“By combining the UK’s world-leading scientists, engineers and tech companies with Ukraine’s unique battlefield experience and data, we will strengthen both of our nations’ defence and security,” he said.

UK to use Ukraine battlefield data to train AI to protect sensitive sites

Amidst the bustling environment of the New York Stock Exchange, a Goldman Sachs partner has expressed concern about the implications of artificial intelligence (AI) proliferating on Wall Street. Chris Churchman, who oversees the bank's prominent AI initiative known as Marquee, cautioned that over-reliance on these advanced systems may diminish critical thinking skills among emerging financial professionals.

In a recent episode of Goldman's "Exchanges" podcast, shared exclusively with CNBC, Churchman highlighted the potential pitfalls of AI integration. He likened the situation to how technological advancements have impacted personal navigation and memory, warning that essential analytical skills could be lost if algorithms take over cognitive tasks.

"Reasoning remains crucial," Churchman emphasized. "The ability to conceptualize and articulate arguments is vital, yet we are increasingly delegating this reasoning."

The ongoing drive to incorporate AI in trading and banking practices presents a double-edged sword for the finance sector. While it enhances current profitability, there is a looming risk of undermining the very talent needed for future growth. The automation of routine tasks, which traditionally enabled new bankers and traders to develop their decision-making skills, threatens to erode the foundational workplace culture that shapes junior employees into experienced professionals.

The trend may even lead to a reduction in the demand for junior bankers. In a report from last year, CNBC noted that Wall Street firms were exploring AI solutions to decrease the number of junior staff in relation to their senior counterparts.

Churchman stressed the necessity for banks to strike a balance between leveraging AI technologies and maintaining the apprenticeship model that has historically defined the industry. Drawing from his experience running currency trading at UBS before joining Goldman in 2021, he stated, "Learning comes from hands-on experience, much of which is based on tacit knowledge that has never been documented."

It is critical for Goldman to safeguard this innate knowledge possessed by its most skilled professionals and ensure that it is passed down to future generations, he added.

For example, junior traders typically gain invaluable insights by responding to client pricing inquiries under the guidance of seasoned risk managers. Churchman acknowledged that while this process could be automated, it raises concerns about the competence of senior traders who may not fully grasp the complexities involved.

He advocated for systems that empower employees to make significant decisions in unpredictable, high-stakes scenarios rather than relegating them to passive roles. Despite being one of the leading investment banks globally, Goldman Sachs has yet to finalize how it will navigate the ongoing transition brought about by AI advancements, Churchman remarked, noting his role as co-chair of the firm's Global Banking and Markets AI working group.

Goldman Sachs partner cautions against the significant risk of allowing AI to take over bankers' critical thinking abilities.

A moody, shadow-filled trading room with a single warm spotlight on a suited banker slumped at his desk, head in hands. Behind him looms a giant metallic AI cog with crawling wires that seem to reach for his brain. On the dark glass wall above towers a large, embossed Goldman Sachs logo in muted gold, casting ominous shadows across the floor.

Goldman Sachs partner warns of 'huge danger' in letting AI replace bankers' reasoning skills - CNBC

- Goldman Sachs partner warns AI could hinder young financiers' reasoning abilities on Wall Street.  
- AI's adoption in finance might reduce junior banker roles and erode essential skills.  
- Firms must balance AI use with preserving the apprenticeship culture for future talent.  

A screen displays the the company logo for Goldman Sachs on the floor at the New York Stock Exchange (NYSE) in New York City, U.S., May 7, 2025.

A Goldman Sachs partner leading one of the bank's flagship artificial intelligence projects warned that AI's spread across Wall Street risks hobbling the thinking capabilities of the next generation of financiers.

"There's a huge danger here that in the era of AI, we outsource our reasoning to these models, and we have cognitive atrophy that stops us being able to reason from first principles ourselves," said Chris Churchman, who leads Goldman's digital platform for institutional clients called Marquee.

The comments came during the latest episode of the firm's "Exchanges" podcast, according to a transcript provided exclusively to CNBC.

Just as people lost navigation and memorization skills with modern inventions, bankers risk losing analytical abilities if algorithms handle all the heavy lifting, Churchman said.

"Reasoning is still important," he said. "You still need to reason about [problems] and structure it into an argument, and now we're delegating reasoning."

Wall Street's push to enmesh AI into all of its trading and banking processes could be a kind of devil's bargain: It will make the industry more profitable today while potentially eroding the talent it needs for tomorrow. With AI taking over more of the routine work that has traditionally taught young bankers and traders how to think and make decisions, firms risk sacrificing the culture that turns junior employees into seasoned Wall Street talent.

It could even reduce the need for junior bankers in the first place. Last year, CNBC reported that Wall Street firms were examining ways of using AI to lower the ratio of junior bankers to senior employees.

Banks need to find a balance between using AI and preserving Wall Street's apprenticeship culture, said Churchman, who ran currency trading at UBS before joining Goldman in 2021.

"You learn by doing, and a lot of knowledge is tacit, it was never written down," he said.

Goldman needs "to make sure we don't lose that tacit and intuitive knowledge that some of our best people have today [and] to ensure the next generation have it too," Churchman said.

For instance, junior traders learn by fielding client pricing requests under supervision of experienced risk takers, Churchman said.

"We can absolutely automate that," he said, "but then do we get the senior traders that fully understand?"

Systems must be designed so that employees still call the shots in high-stakes, high-uncertainty decisions rather than becoming passive operators, Churchman said.

Even Goldman, one of the world's top investment banks, hasn't yet "figured out" how it will manage the transition the company has begun, said Churchman, who is also co-chair of the firm's Global Banking and Markets AI working group.

Goldman Sachs partner warns of 'huge danger' in letting AI replace bankers' reasoning skills

Microsoft has introduced a comprehensive framework for AI governance that shifts the focus from static policy documentation to dynamic enforcement mechanisms, continuous monitoring, and verifiable audit trails as organizations integrate AI applications and agents into their operations. This framework encompasses nine key governance areas and four primary functions: policy, control, visibility, and proof, emphasizing the importance of ensuring that governance requirements are actively applied and can be monitored in real time as AI systems are operationalized.

This architecture is designed as a feedback loop for governance. Initially, policies set the groundwork by defining requirements and classifying risks. These are translated into actionable runtime rules through controls, while observability mechanisms track system performance. Continuous evaluations assess both quality and safety, and audit processes convert this operational data into concrete evidence for compliance and retrospective incident analysis.

Manasa T. Ramalinga, a Cloud Solution Architect at Microsoft, elaborated on the inspiration behind this architecture in a LinkedIn post. She emphasized that organizations transitioning AI workloads to production are rethinking their core frameworks to create safer systems, rather than relegating governance to an afterthought.

Microsoft underscores the principle that effective scaling depends on the ability to govern successfully. The governance framework identifies nine distinct domains: policy, data governance, model governance, observability, evaluations, security, identity and access management, audit and compliance, and agent governance. This ensures that runtime controls can encompass all interactions involving users, agents, models, tools, APIs, MCP servers, and enterprise systems.

The governance architecture merges Microsoft Foundry with essential services like Microsoft Purview, Microsoft Entra ID, Defender, and Azure API Management. The AI Gateway within Foundry establishes a secure runtime boundary for tasks including authentication, token constraints, quotas, and policy enforcement. Additionally, Microsoft’s documentation details how this gateway manages MCP tools with centralized authentication, rate limiting, IP restrictions, and audit logging, all without altering MCP servers or agent software.

Evaluations are essential at both the pre-deployment stage and during ongoing operations. Microsoft Foundry provides support for assessing AI applications and agents against various datasets through built-in and customizable evaluators, allowing teams to ensure quality and safety before launch and monitor behavior afterward.

Anthony Bartolo, a Principal Cloud Advocate at Microsoft, highlighted this operational distinction in a LinkedIn post, claiming, "Your AI policy is not governance until production can prove it." He outlined the governance loop as starting from policies that define rules, followed by runtime controls that enforce them, observability that monitors behavior, evaluations that assess quality and safety, and audits that convert telemetry into evidence.

This governance model extends beyond a Microsoft-centric control plan. It integrates aspects of the NIST AI Risk Management Framework and Generative AI Profile, which offer a vendor-neutral strategy for managing AI risks throughout their lifecycle, including governance, measurement, evaluation, and risk reduction. Microsoft’s architecture effectively aligns these governance needs with specific platform controls and operational telemetry.

In addition to the general governance framework, agent governance introduces protocols governing agent identity, access, activity, and workflow checkpoints. Microsoft’s open-source Agent Governance Toolkit enhances the security of autonomous agents by providing capabilities for policy enforcement and establishing interception points. The architecture also includes an Agent Control Specification, which acts as a framework for implementing checkpoints across agent inputs, model calls, tool executions, and outputs, requiring human approval for more critical actions when necessary.

Microsoft Advances AI Governance from Policy-Making to Real-Time Enforcement

A bright, high-tech operations room flooded with warm natural light: on the left, transparent glass panels display static policy documents sketched in white outlines; on the right, live dashboards stream real-time enforcement logs in green and orange. In the center a large Microsoft logo glows subtly on a sleek monitor above a glass control console. A lone professional silhouette stands behind the console, hands resting on the glass as the policy panels visually morph into active runtime alerts, capturing the shift from policy to enforcement.

Microsoft Moves AI Governance From Policy to Runtime Enforcement - infoq.com

- Microsoft introduces an AI governance architecture that emphasizes runtime enforcement and continuous evaluation.  
- The framework consists of nine governance domains and focuses on operational compliance during AI deployment.  
- Microsoft's architecture includes tools for agent governance and aligns with NIST AI Risk Management Framework.  

Microsoft has outlined an AI governance architecture that moves governance from documented policies toward runtime enforcement, continuous evaluation, observability, and audit evidence as organizations deploy AI applications and agents in production. The framework spans nine governance domains and four functions: policy, control, visibility, and proof, addressing the need to verify that governance requirements are enforced and observable during AI system operation.

Microsoft AI governance architecture (Source: Microsoft Blog Post)

The architecture treats governance as a continuous operational loop. Policies establish requirements and risk classifications, controls translate them into access and runtime rules, observability captures system behavior, and evaluations test quality and safety. Audit processes then turn operational telemetry into evidence for compliance and incident investigation.

Manasa T. Ramalinga, Cloud Solution Architect at Microsoft, described the motivation in a LinkedIn post accompanying the architecture. She added that organizations moving AI workloads into production are re-architecting their foundational structures to build safer systems, rather than treating governance as an afterthought.

Organizations cannot scale what you cannot control

Microsoft identifies nine governance domains: policy, data governance, model governance, observability, evaluations, security, identity and access, audit and compliance, and agent governance. Runtime controls can span interactions among users, agents, models, tools, APIs, MCP servers, and enterprise systems.

The architecture combines Microsoft Foundry with services including Microsoft Purview, Microsoft Entra ID, Defender, and Azure API Management. Foundry's AI Gateway provides a runtime boundary for authentication, token limits, quotas, and policy enforcement. Microsoft also documents using the gateway to govern MCP tools, providing centralized authentication, rate limiting, IP restrictions, and audit logging without modifying MCP servers or agent code. Microsoft Foundry AI Gateway documentation, Microsoft guidance for governing MCP tools

Evaluations are positioned both before deployment and in production. Microsoft Foundry supports evaluation of AI applications and agents against datasets using built-in and custom evaluators, allowing teams to assess quality and safety before release and monitor production behavior afterward.

Anthony Bartolo, Principal Cloud Advocate at Microsoft, described the operational distinction in a LinkedIn post

Your AI policy is not governance until production can prove it. He outlined the loop as policy defining rules, runtime controls enforcing them, observability capturing behavior, evaluations testing quality and safety, and audit converting telemetry into evidence.

AI governance lifecycle from policy and risk classification (Source: Microsoft Blog Post)

The approach is broader than a Microsoft-specific control plane. The NIST AI Risk Management Framework and Generative AI Profile provide a vendor-neutral framework for managing AI risks across the lifecycle, including governance, measurement, evaluation, and risk mitigation. Microsoft's architecture maps those governance concerns into concrete platform controls and operational telemetry.

Agent governance adds controls around agent identity, access, activity, and workflow checkpoints. Microsoft's open-source Agent Governance Toolkit provides runtime security capabilities for autonomous agents, including policy enforcement and interception points. The architecture also describes the Agent Control Specification as a mechanism for checkpoints across agent inputs, model calls, tool execution, and outputs, with higher-impact actions able to require human approval

Microsoft Moves AI Governance From Policy to Runtime Enforcement

In Taipei, prosecutors have initiated charges against nine individuals, including former staff members from Nvidia and Super Micro Computer, for their roles in facilitating the shipment of sophisticated artificial intelligence servers to China. This move comes as Taiwan intensifies its efforts to prevent advanced technology from reaching the mainland.

Among those indicted, eight face serious allegations, including breach of trust and forgery, while one person is accused of illegally diverting funds from a firm involved in the case.

In an effort to impede China's access to cutting-edge Nvidia chips, Washington has implemented strict export controls. As a result, some Chinese companies have turned to unauthorized channels to acquire these vital components. Taiwan, known for its production of high-tech computing hardware, is now under increased scrutiny to implement stringent measures against such practices.

According to prosecutors in Keelung, two defendants falsely represented the intended use of 130 AI servers purchased from Super Micro, which were equipped with advanced Nvidia chips. They claimed the equipment would remain in Taiwan, assuring authorities that the servers would not be sold or exported to entities facing U.S. sanctions and that all transactions would adhere to American export control laws.

Furthermore, the indictments reveal that an employee from Super Micro disclosed confidential internal vetting procedures and enlisted the help of an Nvidia staff member to secure server allocations.

Taiwan Accuses Nine Individuals of Smuggling AI Servers to China

A dimly lit port warehouse at night, rows of half-open wooden crates stamped “AI Servers,” a Taiwan flag draped on a concrete pillar, uniformed officers escorting nine shadowy figures in handcuffs, flickering overhead lamps casting deep, tense shadows across the scene.

Taiwan Charges Nine in Connection With Smuggling of AI Servers to China - WSJ

- Nine individuals, including former Nvidia and Super Micro employees, were indicted in Taiwan.  
- Charges include breach of trust and forgery related to AI server shipments to China.  
- U.S. export controls lead to Chinese firms seeking Nvidia chips through illicit channels.  

Chinese companies have sought access to Nvidia chips through backdoor routes. Vernon Yuen / Nexpher Images / ZUMA Press

TAIPEI—Taiwan prosecutors charged nine people, including former employees of Nvidia and Super Micro Computer, with aiding the shipment of advanced artificial-intelligence servers to China, as Taiwan tries to limit technology reaching the mainland.

The indictment charges eight of the individuals with crimes including breach of trust and forgery. One individual was charged in connection with allegedly siphoning funds from a company involved in the case.

Washington is trying to block China from getting access to the latest Nvidia chips and has imposed export controls. That has pushed some Chinese companies to seek the chips through backdoor routes. Taiwan, which manufactures advanced computing components, is under pressure to find ways to clamp down.

Two of those charged made false claims to secure approval for the purchase of 130 AI servers from Super Micro equipped with high-end Nvidia chips, including by saying the hardware would remain at a facility in Taiwan, prosecutors in the port city of Keelung said Monday.

Defendants said the servers wouldn’t be sold or exported to U.S.-sanctioned entities and that the hardware would comply with U.S. export-control regulations, prosecutors said.

According to the indictments, a Super Micro employee leaked the U.S. company’s internal vetting procedures and asked an Nvidia employee to help secure server quotas.

Taiwan Charges Nine in Connection With Smuggling of AI Servers to China

Luis DeSantiago, a substitute teacher based in Los Banos, California, found himself entangled in a distressing situation that began with a few texts. On March 12, while working his part-time job at a coffee shop, his phone buzzed with messages from a fellow barista who had seen a concerning AI-generated image making the rounds on social media. A relative of hers was inquiring if the depicted individual was, in fact, DeSantiago.

Initially, he wasn’t overly anxious. He presumed that such inappropriate images would breach TikTok's community guidelines and would be taken down promptly. However, when he finally viewed the image, he experienced a shock akin to "a punch to the gut." The picture bore a striking resemblance to a mirror selfie he had taken in college; yet, in the manipulated version, he was depicted wearing lingerie rather than his work attire, and the pose was suggestive instead of the casual stance he initially held.

This incident marked the beginning of a challenging saga that spanned across two schools and involved various students and faculty. DeSantiago's experience is not an isolated case; other educators have also reported similar violations, where students have allegedly created sexualized AI photos and videos of them. These occurrences have left lasting emotional impacts on the educators involved, forcing some to reconsider their professional attitudes and their relationships with students.

As DeSantiago began to investigate the origins of the image, he unearthed that one of the accounts sharing it belonged to a student from a local middle school where he often substituted. He had suspicions about two additional students, based on their interactions with the account and their social circles. With a scheduled job at the same middle school the following day, he decided to escalate the matter to the assistant principal, who promised to address it.

Later that night, DeSantiago searched for his name on TikTok and found another student had created a false account impersonating him, using a slightly misspelled version of his name and the same photo as their profile picture.

In the following weeks, he engaged with school officials, the parents of those involved, and even law enforcement. Concerned about the broader implications of such images being distributed, he communicated his worries about possible private sharing of the content among students. One student who confessed to creating the image received a minor disciplinary action of in-school suspension. However, DeSantiago learned that the photo had also been shared on Snapchat, and it wasn’t long before it continued to circulate among students, along with disrespectful comments directed at him on TikTok.

Feeling unsafe at the middle school, DeSantiago expressed his reluctance to accept assignments there until the issue was adequately resolved. He took a substitute position at the nearby high school only to find that the harassment followed him; during one class, an AI-generated image of him was AirDropped, though it was quickly revoked. 

The principal assured him that a thorough investigation was underway and offered him support from the school counselor. Nevertheless, DeSantiago has decided against returning to the district this year, emphasizing his current discomfort.

The rise of nonconsensual explicit images, especially those fabricated through AI, can potentially expose violators to charges of cyberstalking under federal law, specifically the Take It Down Act. This act classifies the unauthorized sharing of both authentic and AI-generated intimate images as a crime.

Most teachers interviewed did not report the incidents to the platforms hosting the harmful content. Snap Inc. spokesperson Monique Bellamy highlighted the company's stance against harassment and sexual harassment in their community guidelines, stating that they have processes to handle such issues while continually developing new tools to enhance user safety. TikTok did not respond prior to publication.

They Committed Themselves to Education. Then the Deepfakes Began

A dimly lit, nearly empty classroom at dusk, the last sliver of sunlight cutting through blinds and catching floating chalk dust. An older teacher in a tweed jacket stands at the lectern, shoulders hunched, chalk in hand. Surrounding him are cracked, ghostly screens emerging from the darkness—each flickering with a warped, glitching version of his own face. Shadows stretch across the wooden desks, and on the chalkboard behind him the word “TRUST” is half-erased in trembling strokes.

They Dedicated Their Lives to Teaching. Then the Deepfakes Started - WIRED

- Luis DeSantiago, a substitute teacher, became a victim of AI-generated sexualized images.  
- He discovered students circulated the image, which caused ongoing distress and fear.  
- The incident raised concerns about student behavior and lack of safety in schools.  

For Luis DeSantiago, a substitute teacher in Los Banos, California, it started with a few text messages. On March 12, as he was working his other part-time job at a coffee shop, his phone lit up. A fellow barista said there was an AI photo circulating on social media. Her cousin wanted to know if it was DeSantiago.

At first, DeSantiago wasn’t worried—he figured that inappropriate photos violated TikTok’s community guidelines and were usually quickly removed.

“When I finally saw the image, that’s when it felt like a punch to the gut,” DeSantiago tells WIRED. Parts of it resembled a mirror selfie that he remembered taking at a job in college. But instead of his work uniform, he was wearing lingerie. And instead of standing normally, as he had been in the original photo, he was posing suggestively.

The texts kicked off a monthslong saga that crossed two schools and involved students and administrators. And DeSantiago is far from the only educator who’s been victimized this way.

WIRED spoke with three other teachers who allege that their students made sexualized photos and videos of them, likely using AI. They say the incidents have been profoundly distressing and disappointing—and in some cases, it’s led them to reassess how they view their work and their relationships with their students.

“This Could Include Other Students”

After he received the texts, DeSantiago began to piece together who might have been behind the image. One account that shared it used their real name in their TikTok profile—it was a student DeSantiago knew from the local middle school, where he regularly filled in. DeSantiago suspected the involvement of two other students based on people who followed and interacted with that account, and his knowledge of student friend groups. He was scheduled to work at that very middle school the next day.

DeSantiago reported the incident to the assistant principal, who assured him that the school would "take care of it.” Later that evening, he decided to look for the image by searching his name on TikTok. He discovered that a different student had created an account impersonating him—it used his name, slightly mispelled, and had the same photo as their profile picture.

Over the next few weeks, DeSantiago says, he went back and forth with school administrators, the parents of the students, and even the police.

“If these students aren't afraid of generating and posting these images of me, I am concerned of what they may be circulating privately through text messages and group chats,” DeSantiago wrote to the principal. “Keep in mind this could include other students as well.”

One student, who admitted to creating the image, was disciplined with in-school suspension. DeSantiago was later told that the image was also shared on Snapchat. But DeSantiago says the image continued to proliferate, and other students began posting it. He says he also received several crude, sexual comments on his TikTok account.

DeSantiago told administrators he didn’t want to take assignments at the middle school “until this matter is fully addressed.” The district did not respond to WIRED’s request for comment.

He took a substitute assignment at the local high school, but the problems followed him. During one class, he received an AirDropped photo of the same AI-generated image before it was quickly revoked. He could tell it was circulating among the class.

The principal told him the school was launching an “immediate investigation” and offered to meet with DeSantiago or make the school counselor available, but DeSantiago says he probably won’t return to the district this year.

He tells WIRED that, at least right now, he doesn’t feel safe doing so.

“It Makes You Question Everything”

When fake nude images of nonconsenting adults are disseminated, it could constitute cyberstalking and violate the federal Take It Down Act, which makes it a crime to publish nonconsensual “intimate visual depictions of individuals, both authentic and computer-generated,” on the internet.

In most of the accounts shared with WIRED, the teachers didn't directly report the content to the platforms on which it appeared. Snap spokesperson Monique Bellamy shared the company's policies on harassment and sexual content, which prohibit sharing AI-generated or manipulated content to humiliate or sexually harass others. She told WIRED that the company has "established processes" for reporting such content, and that it continues to "develop features and tools designed to support the safety, privacy, and well-being of our community." TikTok did not provide comment before publication.

They Dedicated Their Lives to Teaching. Then the Deepfakes Started

The landscape of professional sports ownership is evolving rapidly, with franchises like the Los Angeles Lakers, Super Bowl champions Seattle Seahawks, and a significant portion of the New York Yankees experiencing a surge in valuations. This trend is largely driven by a growing number of billionaires eager to invest in sports teams, resulting in increased demand against a finite supply of available franchises, thus pushing prices to unprecedented levels.

While the influx of wealth is a key factor, experts point to an unexpected influence: artificial intelligence. Many view sports as a robust investment that remains resilient against technological disruptions that might affect other sectors. Sal Galatioto, a prominent investment banker specializing in sports franchise sales, states, “I’m more confident that in 100 years, the Yankees will still be around compared to a company like IBM.”

Having facilitated team sales and investments for three decades, Galatioto reports unprecedented activity in the market. He emphasizes that investors have historically viewed team ownership not primarily as a cash-generating venture but as a long-term asset and a status symbol akin to fine art. Now, teams are being perceived as a protective measure against technological change.

Victor Matheson, an economics professor specializing in sports business, suggests that while there are various businesses that might withstand or benefit from AI, none possess the allure of sports teams. “No one dreams of leading an electric utility, but many aspire to own or manage a team like the Yankees,” he remarks.

Evidence of this sports investment boom is evident, with former Disney CEO Bob Iger and venture capitalist Josh Kushner recently agreeing to acquire a controlling interest in the Lakers, valuing the franchise at a staggering $12.5 billion. Similarly, Fenway Sports Group sold a 40% stake in Liverpool FC to a group that includes Amazon founder Jeff Bezos.

Additionally, Major League Baseball just sanctioned the $3.9 billion sale of the San Diego Padres to a private equity billionaire and his wife, surpassing the $2.4 billion that hedge fund manager Steve Cohen paid for the New York Mets in 2020. The NFL is moving closer to approving a remarkable $9.6 billion sale for the Seattle Seahawks, while the NBA's Minnesota Timberwolves and WNBA's Lynx were sold for $4.5 billion last week. Meanwhile, the Yankees secured a $2.6 billion investment from Apollo Global Management for a minority stake in the team.

Irwin Kirshner, who heads the sports law group at Herrick Feinstein, observes that the increasing involvement of private equity in sports ownership is indicative of the market's appeal, with valuations consistently climbing. 

The rise in media rights and the growing acceptance of sports betting are fueling this expansion. In a time when streaming services like Amazon, Apple, and Netflix are influencing media rights fees, live sports still attract advertisers, making them a valuable asset. Galatioto notes the unpredictable nature of content distribution technology but maintains that owning the content itself continues to be paramount.

The legalization of sports betting in 2018 has further intensified interest, as wagering on games encourages audience engagement. Matheson estimates that gambling could inject up to a billion dollars annually into teams and leagues, thus attracting new sponsorship opportunities. Bettors are also more inclined to watch games that might not typically attract their interest, contributing to increased attendance and viewership.

Moreover, sports teams remain a rare commodity, with limited availability impacting their market. While some franchises may only become available due to the owner's death, others, like the NFL’s New York Giants, have been in long-standing family ownership but now consider selling minority stakes. 

Selling minority shares in teams has become a more prevalent strategy, with American investors increasingly acquiring European soccer clubs and foreign buyers showing interest in North American teams. Many investors believe that even if they initially overpay or encounter short-term losses, the eventual sale of the team will yield substantial returns.

“You can afford to overpay as long as you believe that future buyers will be willing to pay even more,” Matheson concludes.

The AI factor driving billionaires to seek ownership of sports teams.

A lone, sharply dressed billionaire stands center field in a vast, empty stadium bathed in warm, late-afternoon sunlight. Subtle, golden-glow AI strategy lines and circuit-style patterns drift above the turf, tracing play formations in mid-air. Corporate luxury boxes with soft gold accents line the stands, hinting at high-stakes investment. The image tells of sports and cutting-edge AI converging into the new billionaire obsession.

The AI-related reason every billionaire suddenly wants to own a sports team - CNN

- Valuations of sports teams are skyrocketing due to increasing billionaire buyers and limited supply.  
- Private equity interest is driving up team valuations, reflecting strong investment potential.  
- Growth in media rights and sports betting is enhancing viewership and revenue opportunities.

The Los Angeles Lakers, the Super Bowl champion Seattle Seahawks, a chunk of the New York Yankees. Teams are turning over at a rapid pace, driving valuations to stratospheric heights.

One reason for the sales boom you might expect: There are an ever-increasing number of billionaires with the resources to buy teams. More demand, plus a limited supply of teams, equals higher prices.

But experts also offered a surprising reason: artificial intelligence. Sports is believed to be a relatively AI-proof investment. Teams are not likely to be upended by the game-changing technology the way other investments might be.

“I’m willing to bet odds are greater that in 100 years that the Yankees will be here compared to IBM being here,” said Sal Galatioto, a leading investment banker in the field of selling sports teams.

Galatioto has been negotiating deals to buy teams, or a stake in teams, for 30 years. He said he’s never been busier.

“People have never bought teams for cash returns,” he said. “You bought it for long-term appreciation, and ego gratification and scarcity value. You bought it like fine art. Now it’s a hedge against technology disruption.”

There are a number of other businesses that may be AI-proof or even benefit from its growth, such as electric utilities, said Victor Matheson, an economics professor at the College of the Holy Cross and an expert in sports business. But he says sports teams are much “sexier.”

“No one has ever dreamed of being CEO of (electric company) National Grid,” Matheson said. “But everyone dreamed of being the owner or manager of the Yankees”

The gold rush is tangible

Just this month, former Disney CEO Bob Iger and venture capitalist Josh Kushner agreed to buy a controlling interest in the Lakers, in a deal valuing the team at a record $12.5 billion. Fenway Sports Group, which also owns the Boston Red Sox, reportedly sold 40% of Premier League club Liverpool to a consortium that includes Amazon founder Jeff Bezos.

Venture capitalist Joshua Kushner, left, and former Disney CEO Bob Iger, right, plan to purchase the Lakers. Jamie McCarthy/Mike Coppola/Getty Images

And last week, Major League Baseball approved the $3.9 billion sale of the San Diego Padres to a private equity billionaire and his wife. That beats the $2.4 billion hedge fund manager Steve Cohen paid for the New York Mets in 2020. Meanwhile, the National Football League moved closer to approving a record $9.6 billion sale for the Seattle Seahawks.

On Friday, the NBA’s Minnesota Timberwolves and WNBA’s Lynx were sold in a deal valued at $4.5 billion.

And baseball’s most valuable team, the Yankees, agreed this month to a $2.6 billion injection from Apollo Global Management, a private equity firm, for a minority stake in the team.

Private equity’s growing interest in sports teams is another sign of the buying boom, said Irwin Kirshner, the head of the sports law group at the law firm Herrick Feinstein.

“Every year (valuations) seems to go up more, and so I think private equity started to recognize the value of this opportunity,” Kirshner said.

Media rights, sports betting help drive growth

The value of live sports broadcasts has never been higher. In the age of on-demand viewership, sports broadcasts are one of the only things people will watch with advertisements. The entry of streaming services such as Amazon, Apple and Netflix have only increased the rights fees.

“Who knows what new technology will come out to distribute the games,” said Galatioto. “If you own the content, it doesn’t matter how it’s distributed.”

Sports betting, legalized in a 2018 Supreme Court decision, has growing mainstream appeal, supercharging viewership. When people have money riding on an event, they’re more likely to watch two teams they otherwise would have little interest in.

“You might have as much as a billion dollars a year in total gambling money being handed over to the teams and leagues,” said Matheson, including a new pool of sponsors. “And there’s the thought that people who are gambling more are more likely to tune in, which means more butts in the seats, as well as more eyeballs on the TVs.”

Lastly, sports teams are a limited commodity — there are only so many available for sale.

Some, like the Padres, only go on the block when their owner passes away. But there are also teams like the NFL’s New York Giants, who have been under the control of the Mara family for more than a century, that are willing to sell off a portion of their team.

Selling minority stakes in sports teams is nothing new, but the practice has become more common. And the makeup of the buyers is also becoming broader: American investors are swallowing up European soccer clubs, while North American sports clubs are getting purchased by foreign investors.

Many of these owners are counting on one fact: Even if they overpay or lose money in the short term, eventually selling the team will result in a big payday.

“You can afford to overpay as long as you believe that there will be people in the future willing to overpay by at least as much or more,” said Matheson.

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