TL;DR
SpaceX rented Colossus 1 to Anthropic after hitting latency and chip mismatch issues trying to use it for Grok. The newer facilities use uniform Blackwell chips.
Yen-Ling Kuo always wanted to understand how things worked. When she was growing up in Taiwan, reading the story of Michael Faraday in elementary school piqued her curiosity about the natural world. During that time, she was introduced to Logo, a computer program with a turtle cursor to help children learn basic coding through hands-on experimentation.
It was Kuo’s introduction to programming logic.
Yen-Ling Kuo
Employer
University of Virginia in Charlottesville
Title
Assistant professor of computer science
Member grade
Member
Alma maters
National Taiwan University; MIT
In high school she learned the capacity computers held. She could write programs that completed tasks independently, she realized.
“Once I discovered how powerful computers could be,” she says, “I knew I wanted to focus on using them to solve real-world problems.”
Kuo, an IEEE member, never lost her interest in the “how” behind processes and tools. Her curiosity, combined with a stint working at a Silicon Valley company, led her to focus on innovations that live at the intersection of cognitive and computer sciences.
Kuo, now an assistant professor of computer science at the University of Virginia in Charlottesville, last year received the IEEE Robotics and Automation Society’s inaugural Outstanding Women in Robotics and Automation Early Career Contribution Award. The award is part of the IEEE-RAS Women in Engineering’s Outstanding Women in Robotics and Automation (WiRA) Paper Awards, which promote excellence and recognize the impact that female researchers have on robotics and automation fields at different stages in their academic careers.
Kuo’s winning paper, “Diff-DAgger: Uncertainty Estimation with Diffusion Policy for Robotic Manipulation,” demonstrates a novel method to help robots better identify and estimate uncertainty when faced with scenarios on which they’ve not been trained. The method reduces the amount of human supervision, improves a robot’s rate of successful task completion, and opens up a path to introduce more complex models with bigger data demands into interactive robot learning.
She says her research will help people working in the robotics and automation fields more efficiently collect the data needed for effective model training.
Kuo earned bachelor’s and master’s degrees in computer science at the National Taiwan University, in Taipei, in 2009 and 2012. As she was nearing completion of her master’s degree, she did what many computer science graduates do: She pursued a summer internship at a tech company.
She spent the summer of 2011 at Google’s campus in Kirkland, Wash., working on the company’s comparison ads project.
When her internship ended, she joined the MIT Media Lab as a visiting student, working on the Open Mind Common Sense project with Henry Lieberman.
As she was considering pursuing a Ph.D., a call from Google changed her plans. The company offered her a full-time role as a software engineer.
“I viewed the job offer as a positive development,” she says. “I believe it can never hurt your future research career to get some real-world experience under your belt.”
She was hired in 2012 and helped build techniques that incorporate computer vision and natural language processing to improve the customer shopping search experience. She led the company’s Shop the Look initiative, a predecessor to Google’s current AI-powered shopping experience. The project connected social media content with search results, something the company had struggled to do in the past.
Kuo and her team were tasked with building a connection between the natural language people use to describe an item and an image that matches the searcher’s intent. It was at a time when the neural network—using deep learning models to power Google products—was gaining momentum at the company. Integrating neural network tools into her work was a requirement—which raised questions for Kuo.
“I was applying the neural network tools,” she says. “But I didn’t have 100 percent certainty about how they actually worked.”
She considered how she could become more knowledgeable about deep learning models. It was a full-circle moment. She decided that after nearly four years at Google, it was time to earn a Ph.D. in computer science. She returned to MIT in 2016.
Boris Katz, one of Kuo’s Ph.D. advisors, is a principal research scientist and the head of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)’s InfoLab. He also led the creation of the START Natural Language System, the world’s first Web-based question-answering system.
When the two met, Katz asked Kuo why she wanted to pursue a doctorate degree. She explained her interest in understanding how neural networks work and in using that knowledge to connect the physical world with human language.
He suggested she attend a summer course at MIT’s Center for Brains, Minds, and Machines, a research initiative that ran from 2013 through 2025. CBMM’s objective was to bring together computer scientists, cognitive scientists, and neuroscientists to understand how human intelligence works. The goal was to use the resulting insights to establish an engineering practice to build artificial intelligence systems.
For Kuo, it was a chance to better understand human intelligence and identify ways it could be replicated in machines.
“It was an opportunity for me to interact with other scientists and gain insight into how people learn, understand, and figure things out in the world,” she says. “I saw it as a very useful and inspiring way to incorporate those ideas into my own research work.”
During her Ph.D. studies, she was a research assistant at CSAIL. The experience helped shape her doctoral research, which focused on building AI systems that apply past learning to new situations. She developed machine learning models to support the efforts, including language understanding and social interactions.
She completed her Ph.D. in computer science in 2022 with a minor in cognitive science.
After graduation, she continued her work and collaboration at CSAIL, particularly on projects that involved the “theory of mind” concept.
Theory of mind isn’t new, having originated with primatologists studying chimpanzees in the late 1970s. The theory recognizes that others have their own thoughts, beliefs, and perspectives. It’s a skill that allows humans to infer someone’s mental state and predict their behavior without verbal communication.
“It’s like when college roommates are moving into their dorm. They may not talk too much, but they work together naturally to coordinate their activities and accomplish goals,” Kuo says. “They can infer and mentally interpret each other’s behaviors and signals to make decisions and complete tasks without words.”
She brought her theory of mind research to the University of Virginia when she joined as an assistant professor in 2023.
Kuo conducts her research in UVA Engineering’s multidisciplinary cyberphysical Link Lab. Her broad focus is on developing computational models that help robots interpret both direct data and silent signals, from language and movements to a person’s gaze. If successful, it could give robots the same sort of physical and theory of mind reasoning capabilities that power physical and social interactions among humans.
“There are no computational frameworks yet available that will translate this kind of understanding into a robot efficiently,” she says.
She adds that the process to get there begins with improving how robots learn to perform tasks.
Historically, one way robots learned was to mimic humans. A researcher would manually guide a robot through a task, like cutting an apple, and it would repeat the movements. The robot was successful until the environment changed, such as when its hand was in a different position or the apple was at a different angle. The robot was then faced with a situation for which it hadn’t been trained. Without any data available to help it correct course, the robot would start making small errors that eventually led to a full system crash.
This diagram describes how the robotic gripper’s visual perception and tactile sensing prevents a potato chip from breaking.Xuhui Kang, Yen-Ling Kuo, et al.
To solve the problem, researchers developed the dataset aggregation (DAgger) method. As a robot performed a task, a researcher was on standby to provide real-time corrections during unexpected scenarios. The correction data was continuously added to the robot’s model, teaching it how to recover from mistakes.
To reduce the human monitoring effort, robot-gated DAgger was created to enable bots to query humans when the machines became uncertain.
The most popular approach to make the query decision is to train multiple models to consider when determining a course of action. If the models all agree, the robot proceeds. If they don’t agree, the robot is likely to get stuck and ask for help.
Although the multiple model approach was widely adopted, it has limitations. Practically speaking, as models become more complex, it is hard or impossible to train multiple copies. A more fundamental issue is that disagreement among models doesn’t always imply uncertainty; it could just mean there are different ways to accomplish a task.
That is the gap Kuo’s research team closed with the novel Diff-DAgger research. The approach builds on diffusion policy, a technique that helps robots account for different ways a task can be performed.
The new method repurposes diffusion loss, the signal a robot uses to improve its model during training, as a real-time confidence check. During task execution, the robot computes the signal and compares it against values from its training data using a statistical test. The signal spikes when the robot faces an unfamiliar situation and is uncertain how to proceed. The signal stays silent when the robot’s current action is close to what it learned before.
The spike represents the robot’s ability to self-diagnose and predict an imminent failure. Human intervention is triggered only when the signal spikes. No spike means the robot can be left to complete its decision-making process on its own.
Kuo’s team achieved significant results: Failure prediction rates were improved by 39 percent. Task completion rates were increased by 20 percent, and tasks were completed nearly eight times faster.
Her research at UVA gained attention from the National Science Foundation, which honored her last year with a Career Award, the foundation’s flagship grant for early-career researchers. The five-year US $665,000 grant supports her research that builds computational models for human-robot interactions through theory of mind reasoning.
She also received the Toyota Research Institute’s Young Faculty Researcher Award to teach cars to reason about interactions on the road and with the driver.
As service robots and self-driving vehicles become more available, such works are likely to make interactions between humans and robots more intuitive and useful.
Kuo ultimately wants to build more robust robots that are able to integrate into a social space with humans by engaging with us through grounded interactions, she says.
Like many IEEE members, Kuo was introduced to the organization as a student. In 2018 she submitted her first paper, “Deep Sequential Models for Sampling-Based Planning,” to the IEEE/Robotics Society of Japan International Conference on Intelligent Robots and Systems while pursuing her Ph.D. at MIT. Her IEEE involvement grew alongside her professional career.
“It was a natural segue to transition from student to a full IEEE member,” she says. Today she is an active volunteer with the IEEE Robotics and Automation Society, a reviewer for submitted papers, and a presenter and panelist at conferences.
She says one of the best parts of attending conferences is having the opportunity to engage with students. She also enjoys participating as a panelist at luncheons, she says, because it gives her one-on-one time with student attendees. She can share her knowledge and offer insights as they prepare to embark on their career.
Her goal in the coming years, she says, is to broaden her involvement with IEEE initiatives and branch out to other technical committees. Sharing knowledge and learning from others is essential to anyone’s career growth, she says, and “IEEE offers a great opportunity for both.”
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Just in time for America’s 250th anniversary, Disney Imagineers tapped Apple Vision Pro to help give one of their most iconic flight rides a patriotic makeover.
Disney has shared a brand-new behind-the-scenes video as part of the Disney Unscripted series on YouTube. This time, the company shows off what it takes to revamp one of its existing attractions.
The attraction in question is “Soarin’, at EPCOT, which has been rebranded to “Soarin’ Across America” for the 250th anniversary of the United States of America. Rather than focusing on wonders around the world, or in California for another version of the ride, Soarin’ Across America takes riders on an airborne adventure across the United States.
The reimagining requires a lot of work. From capturing all new aerial footage to crafting an all-new musical score, the project requires filmmakers, musicians, and Imagineers to work together.
In the video, we learn that Disney’s audio media designers donned the Apple Vision Pro to create a digital workspace during the music and sound effects mixing phase.
“So, usually for a Soarin’ attraction, we need to build scaffolding, but that was a ‘no-can-do’ for this project because we were on such an accelerated schedule,” Megan Duncan, one of Disney’s Senior Sound Editors, says in the video.
By using the Apple Vision Pro, a virtual digital workspace easily replaced that scaffolding and extra equipment. Most of the workflow only required an Apple Vision Pro, a custom desk attached to the flight simulator seats, and a small selection of audio mixing equipment.
While the Apple Vision Pro hasn’t exactly been a consumer-facing hit, it’s continued to prove itself in professional work settings. Recently, it was learned that the Apple Vision Pro has been used for hundreds of cataract surgeries in New York in about half a year.
“Soarin’ Across America” has already opened in EPCOT, at Walt Disney World in Florida. It is expected to open on July 2 in Disneyland, in California.
SpaceX rented Colossus 1 to Anthropic after hitting latency and chip mismatch issues trying to use it for Grok. The newer facilities use uniform Blackwell chips.
SpaceX rented its Colossus 1 data centre to Anthropic not because it had surplus capacity, but because it could not make the facility work for its own AI models. Bloomberg reported on Friday that SpaceX encountered latency issues when trying to connect the Memphis site to two other data centre campuses located more than 10 miles away, compounded by aging network infrastructure.
The company had planned to train its most cutting-edge Grok models using a cluster of three facilities working together. Training large AI models requires ultra-fast connections between sites. If the links are older or lower bandwidth, they create delays that slow the entire cluster. SpaceX determined the facility would be more valuable generating revenue than sitting underutilised.
The hardware mismatch made things worse. Colossus 1 contains a mix of Nvidia chip generations, including Hopper and Blackwell systems alongside older accelerators. Colossus 2 and 3 were built more uniformly around Nvidia’s Blackwell chips. In a distributed training cluster, the workload is spread across machines that need to stay synchronised. Older chips create bottlenecks by forcing faster accelerators to wait. The cluster ends up performing closer to its slowest hardware, not its fastest.
The result is that Anthropic is now paying $1.25 billion per month to use a facility that SpaceX’s own engineers could not fully utilise. Combined with the $920 million monthly Google deal, SpaceX is collecting approximately $2.17 billion per month in compute revenue from infrastructure it originally built for itself.
The revelation complicates the narrative SpaceX presented during its IPO roadshow. Musk’s company repeatedly stressed that Colossus 1 was built in just 122 days, exceeding industry averages. Speed of construction was a selling point. Bloomberg’s reporting suggests speed came at a cost: the facility was not built uniformly enough to serve as part of a larger training cluster.
SpaceX CFO Bret Johnsen said the company has not given up on internal AI services, including Grok. Musk has described the Anthropic arrangement as a 180-day lease with a 90-day mutual cancellation right, preserving the option to reclaim the capacity. “If compute gets super tight I said we might need it back at some point,” he said.
But Grok’s trajectory makes reclaiming the compute less urgent. Downloads fell from 20 million in January to 8.3 million in April. Paid conversion is a fifth of ChatGPT’s. Federal adoption has stalled. The product that was supposed to justify the data centre investment is underperforming, while the rental income from Anthropic and Google is now a $26 billion annualised revenue line. SpaceX built a data centre for AI training and accidentally became an AI landlord instead.
Apple has finally brought Visual Intelligence to the Mac with macOS Golden Gate, and it is a boon when it works. Here’s how to get started.

I admit I have sometimes taken a photo of my Mac‘s screen and used Visual Intelligence on my iPhone to find out what I’m looking at. But as of macOS Golden Gate, I no longer need to do that because the Mac has Visual Intelligence built in.
Apple’s Sebastien Marineau-Mes, vice president of Intelligent System Experience Engineering, announced this during the WWDC 2026 keynote. But frustratingly, all he then said was that you could use it with “a dedicated keyboard shortcut.”
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China has opened its first dedicated photonic computing lab in Shanghai, a joint venture between Shanghai Jiao Tong University and startup Lightelligence. The facility signals Beijing’s bet on light-based chips as a strategic workaround to US semiconductor export controls that have restricted access to conventional AI hardware.
TL;DR
China has launched its first dedicated photonic computing laboratory in Shanghai, signalling that Beijing sees light-based chips as a strategic route around Washington’s tightening grip on conventional semiconductor exports. The Shanghai Key Laboratory of Integrated Photonic Computing Chips and Systems opened on 11 June at Shanghai Jiao Tong University, the state-backed Jiefang Daily reported.
The lab is a joint effort between the university and Shanghai-based Lightelligence, one of the country’s leading photonic computing startups. Lightelligence listed on the Hong Kong stock exchange in April, surging roughly 380% on its first day of trading, and claims to be the first company in the world to achieve large-scale deployment of hybrid optical-electronic computing, though that assertion has not been independently verified.
Conventional AI chips push data through silicon circuits using electrons. Photonic chips swap electrons for photons, particles of light that travel faster and generate far less heat.
The theoretical payoff is significant. Photonic processors promise higher bandwidth, lower latency, and a fraction of the energy consumption, qualities that matter as training frontier AI models pushes data-centre power demands toward their limits.
Zou Weiwen, the lab’s director and a photonics professor at Shanghai Jiao Tong University, said optical computing was “an important pathway for achieving breakthroughs in computing power.” The facility will focus on photonic chip architectures, silicon-photonics integration, optical components, and the algorithms needed to make them commercially viable.
The lab’s launch coincides with Beijing’s broader drive for technological self-reliance. Washington has restricted China’s access to advanced semiconductors since 2022 and has widened the rules repeatedly, forcing Chinese firms to hunt for alternatives.
That search has already shifted China’s AI chip strategy away from general-purpose GPUs and toward custom silicon. Photonics represents a more radical pivot, one that could let Chinese engineers sidestep lithography bottlenecks entirely by building on the country’s existing strengths in fibre optics and laser technology.
Chinese authorities have flagged photonics and photonic-electronic hybrid accelerator chips as strategic national priorities. Shanghai officials said they had mobilised coordinated funding across multiple science and technology programmes to back the effort.
Beijing is already pouring money into AI infrastructure through other channels. A reported $295 billion blueprint would build a nationwide network of data centres running largely on domestic chips by 2028.
Photonic computing, however, remains far from production-ready. Zou acknowledged that the field faces “fundamental scientific challenges,” citing the absence of a mature software and algorithm ecosystem capable of efficiently harnessing photonic hardware.
The gap between laboratory promise and commercial reality is wide. But with conventional chips increasingly hard to source and AI workloads growing exponentially, China is clearly willing to bet on the physics of light.
The 2026 World Cup is here, and if you’re still thinking about buying a new TV to watch the tournament in, we’d like it if you could take a beat and consider these five key features.
Big sports tournaments are usually when retailers bring out the big discounts, but before you snap up the cheapest deal you can find, we’ve laid out five features to give some thought to before you hit buy.
From size to HDR performance to motion processing, taking these five areas into consideration will help you in your search, and hopefully lead to you having the best AV experience to watch the tournament in.


Bigger is, genuinely, better. Unless you’re not able to fit a bigger screen in your living room, we’d always recommend that you go for a bigger size than you currently have.
The scale is the obvious benefit. Jumping from 55- to 65-inches reaps positives in terms of immersion. And of course, if you have multiple people around for a watch party, then having a bigger screen means you aren’t all cramming for space on the sofa and craning your necks to see what’s happening.
The last few years have seen a rise in the number of affordable, large-screen TVs. TCL’s 98-inch C7K is available for £1999, but for something considerably less expensive but still plenty big, Sharp’s 70GK4245K could be yours for less than £450.


George Lucas once said that sound is 50% of the experience. He was talking about films of course, but we’d say the same applies to anything, especially if we’re talking about sports.
Hearing the roar of the crow, feeling the intensity when something happens on (or off) the pitch, or the hush of the silence before a penalty is taken – sound matters and brings immersion to the experience of sports. So don’t buy a TV with tinny sound.
That’s easier said than done when even TVs that rack £3000 asking price have a sound that’s average. And a TV that has good sound might not have as good picture. As always, if you know (from reading our reviews, of course) that TV sound is on the weaker side, give it a boost with an external sound system.
We’d also avoid most of the built-in audio modes on TV, such as sports. Very rarely do they provide the kind of all-encompassing, immersive experiences they suggest they can.


While not every sports tournament is produced and broadcast in 4K, the last few football tournaments have been in available in HDR. For the 2026 World Cup, you can view the tournament in 4K HLG HDR on the BBC iPlayer.
More expensive TVs offer a better HDR experience because they can hit higher levels of brightness and produce a better colour experience. If you want to watch the World Cup in the best way possible, we’d suggest having a look at 4K TVs priced within the £1000 to £2000 price range for a better HDR experience. We have you covered with out best 4K TV list.


Leading on from the previous point is picture mode. Vivid (or Dynamic) is an option for some, but we find that too garish in terms of brightness and colours; and also brings in issues with the motion processing negatively affecting picture quality.
Film (or Movie) may offer the best, most accurate colours; but this mode is often for watching in the dark or when the curtains are drawn (considering some of the match times, this might be more useful).
The picture mode we’d suggest you watch the World Cup in, is Standard mode. Standard mode gives blues and greens a boost – helpful for bringing that rich green tone of the grass – and while it adds some processing to the mix, it’s less heavy on the picture than it would be with Vivid.
It’s also brighter than Film modes and will have more of impact if you’re watching during the day, but a lot of the matches at the World Cup will be on evening/night-time in the UK.


If you’re going to use Standard picture modes (or any mode other than Film/Cinema), your TV is going to automatically add some motion processing unless you dive into the settings and disable.
If you prefer motion processing for your sports, there are some TVs that do it better than others. Sony, Panasonic, LG are towards the top of the list; Samsung not far behind without tweaking the settings a little bit, with the likes of TCL and Hisense behind and a little less consistent.
Motion processing performance can vary depending on the price. Some cheaper TVs do away with it completely (Roku models tend not to have it), but sometimes it’s better to have an affordable TV that doesn’t do it, than one that does it poorly.
Congress failed to extend a key surveillance law on Thursday night, according to a report by Politico. This effectively means that Section 702 of the Foreign Intelligence Surveillance Act (FISA) will expire for the first time since 2008, as the House isn’t expected to vote again until June 23.
The House rejected a proposal that would’ve extended the law until July 2, on a 218-198 vote. The extension actually required a two-thirds majority, but didn’t even get a simple majority. Nearly 20 Republicans joined with Democrats to block the motion. A few hours later, Oregon Senator Ron Wyden blocked a couple of proposed extensions for the law in the Senate.
This law has been around nearly 20 years through multiple presidencies from both parties. So what’s the issue right now? There are some who don’t like it when the government engages in massive warrantless surveillance programs, sure, but that never stopped the law from being renewed before. Reporting indicates that Congress was close to a three-year extension, until President Trump announced he planned to install political ally Bill Pulte as director of national intelligence.
Democrats have raised concerns over Pulte’s appointment on the grounds that he has no intelligence experience and fears that he could use sensitive information gathered via Section 702 for political or personal purposes. Pulte regularly insinuated Fed board member Lisa Cook fired engaged in mortgage fraud, an allegation that has since been debunked; Cook was removed from her post by President Trump last August.
Trump has since nominated Jay Clayton, the top federal prosecutor in New York City, for the intelligence job. However, he has suggested that Pulte could take the job on an acting basis. “There needs to be a clear guarantee that Mr. Pulte will not serve as acting DNI,” Senator Mark Warner wrote in a statement.
As for Section 702, it lets the government conduct warrantless surveillance of foreign targets located outside of the United States. It also allows agencies like the NSA and the FBI to spy on Americans if the action is “reasonably likely” to collect information about foreign intelligence.
As one would expect, authorities have played fast and loose with that whole “reasonably likely” thing. Law enforcement agencies have been caught with their hands in the data cookie jar a lot since 2008. The surveillance-based FISA court found tens of thousands of improper database searches in 2017 and 2018 alone. A judge also ruled in 2019 that the FBI and NSA committed multiple violations of either the law or privacy-minded court orders when collecting data from phone and tech companies.
House Democrats are pushing for “meaningful reforms” of the law. “Section 702 is a critical foreign intelligence authority, but we cannot in good conscience vote for reauthorization without significant reforms to protect both national security and the constitutional privacy rights of Americans,” House Minority Leader Hakeem Jeffries and other leaders said in a joint statement.
Entrepreneur and former presidential candidate Andrew Yang has a theory about where the next wave of startup opportunity lies, and it starts with a question most founders aren’t asking: what if the business model was giving money back instead of extracting it?
Yang was inspired by Mark Cuban. Not by his wealth, or his celebrity, but by Cost Plus Drugs — Cuban’s startup that sells pharmaceuticals at cost. Yang made a list.
“Housing, education, food, fuel, transportation, media, and wireless,” Yang told TechCrunch on a recent episode of Equity. “The things we all spend money on.”
He picked wireless and last September launched Nobile Mobile, a new mobile virtual network operator that provides cell service for a fraction of what traditional carriers charge and gives customers money back if they use less data.
As AI threatens to compress wages and displace workers, Yang sees a business opportunity in bringing down the cost of living. Cost Plus Drugs, Noble Mobile, dumb phone makers like Light Phone, and even online grocery store Misfits Markets are early examples of an emerging business category where the startup’s value proposition is the margin it gives back to the customer.
“AI is going to suck up a lot of the value and the jobs, and then Americans are going to look up and say, ‘How do I meet basic needs?’” Yang said. He believes meeting people’s needs “less expensively” is “a very rich vein of opportunity.”
That instinct didn’t emerge from nowhere. Yang first launched himself into the public eye during his 2020 presidential campaign, during which he advocated for Universal Basic Income as a means of combating AI-related workforce displacement and wealth concentration. The campaign didn’t succeed but the thesis has only grown more relevant.
Yang is still an advocate for UBI, arguing that the value generated by AI companies needs to be redistributed into the hands of the average American. But whether the government will be the vehicle for that redistribution, or whether it will just use any collected wealth to “plug a hole and do something not terribly productive,” Yang is less certain.
“There is room for a direct connection between the money and the people,” he said.
That’s where the market comes in. Where policy fails, Yang argues, market incentives can step in. Noble Mobile is his attempt to prove the point. Since its launch last September, the company has grown to “thousands and thousands” of customers and is bringing in “millions in revenue.”
“We’re unit profitable per customer, but we just share the profits with our subscribers with the idea that it’ll make you happy, you’ll stay around, and maybe you’ll tell your friends and family,” Yang said.
The pitch is simple. Yang noted that the average monthly savings of $50, invested and compounded over 40 years, could amount to $24,000 — enough for a retirement down payment. And in this economy, who isn’t thinking about little ways they can upgrade their personal finance?
Whether investors will share that enthusiasm is another question entirely. Even if the opportunity is real, capital is concentrated heavily in AI right now, while consumer-facing businesses with thin margins and a social mission are a hard sell.
“I had at least one investor say to me around Noble Mobile, ‘Love you, Andrew, want to work with you — if you could just make this an AI company, we’ll invest,’” Yang said.
The tide might be changing, though, simply because even the most wealthy, extractive companies need an economy in which consumers have enough buying power to purchase their products.
“The value being concentrated in the hands of a handful of folks and firms is just bad for everybody,” he said. “There are some folks I know in Silicon Valley who are open to that for a variety of reasons…[like] they just don’t want to have to hire private security.”
Yang encouraged founders and investors to take on problems they’re passionate about and find a way to build a valuable enterprise on top of it.
“Think bigger and more broadly about trying to tackle problems and don’t subscribe so much to groupthink, because there are some valuable opportunities out there,” he said.
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The list of potential buyers for the Seattle Seahawks is starting to look like an NFL Pro Bowl roster of billionaires, venture capitalists and global business leaders.
Billionaire financier Todd Boehly is the latest high-profile name linked to the franchise, according to a report from Semafor, joining a field of prospective bidders that reportedly includes venture capitalist Vinod Khosla, steel executive Aditya Mittal and former Boston Celtics majority owner Wyc Grousbeck.
Boehly, the chairman and CEO of Eldridge Industries, is best known in sports circles for ownership stakes in the Los Angeles Dodgers, Los Angeles Lakers, Chelsea FC and the Los Angeles Sparks. Before launching Eldridge, he helped build the credit-investing business at Guggenheim Partners.
The Seahawks could fetch as much as $9 billion, a price tag that would eclipse the $6 billion sale of the Washington Commanders in 2023 and set a new record for an NFL franchise.
The Seahawks are being sold by the estate of late Microsoft co-founder Paul Allen, following instructions in his estate plan directing that his sports holdings ultimately be sold and the proceeds used for philanthropic purposes. In February, Allen’s estate formally listed the Seahawks for sale, shortly after the franchise captured its second Super Bowl title.
Among the other reported bidders is Khosla, the Sun Microsystems co-founder, founder of Khosla Ventures and an early backer of OpenAI, DoorDash and Stripe. Khosla — who also owns a small slice of the San Francisco 49ers — reportedly submitted a letter of intent as part of the bidding process.
Khosla spoke in Seattle last year, saying at the time: “I have found that the person who learns faster is way better at building businesses than the person who is a deep expert.” His firm has backed Seattle-area startups including Loti, Mudstack, Viome and Lexion, which was acquired by Docusign in 2024. It is also an investor in Seattle’s AI2 Incubator.
The Seahawks sale is shaping up as one of the largest ownership transfers in professional sports history, attracting investors from Wall Street, Silicon Valley, international industry and private equity.
For Seattle’s technology community, the process marks the beginning of a new era.
Since purchasing the team in 1997, Allen helped transform the Seahawks into one of the NFL’s premier franchises.
Formal bids are expected in the coming weeks, according to Semafor.
School’s (almost) out for summer.
When it comes time to throw open campus doors for the new school year in the fall, research tells us one out of every seven teachers won’t be returning — either because they moved schools or left the profession entirely.
But when the going gets tough, teachers don’t necessarily want to leave. Even when they’re burned out, they still love what they do.
So, the concerning data throughout the country tells a story about how stark the conditions of the teacher workforce are. In Wisconsin, for instance, teachers say they are exiting the profession at the highest rate in 25 years thanks to a range of issues, from poor leadership to safety concerns like students bringing guns to school.
Worse, shrinking student populations and rising costs have forced school districts like Portland Public Schools to make staff cuts in the face of astronomically high budget gaps. Early career teachers are thinking hard about whether they even want to continue in their chosen field.
That’s why we at EdSurge want to hear from educators who have recently left or plan to leave their jobs for another sector: What was the deciding factor? What could your school (or district or state-level leaders) have done differently to change your mind?
Your responses will help shape our coverage, and we may be in contact for an interview.
AI goes beyond digital interfaces like ChatGPT and Claude and it’s now showing up in physical productivity-boosting devices. One of the most useful examples I’ve found is the AI voice recorder. A device slightly larger than a credit card, an AI voice recorder captures, transcribes, and analyzes conversations in real time. It acts as your own personal automatic note taker.
As a freelance writer and entrepreneur who has countless interviews, webinars, client conversations, and Zoom meetings each week, I’ve personally tried two AI voice recorders: the Comulytic Note Pro and the iFlyTek AI Recorder S6. And there are several other similar devices out there at varying price points. Each device is smaller than an iPhone 17 Pro Max.
The Comulytic actually came with a magnetic case to fit onto my phone. That way, it’s within reach and ready to record the moment I answer a call. It sends its recordings and transcripts to a cloud storage system, which I can access via an app. The iFlyTek AI Recorder S6 is a little larger and reminds me of the digital voice recorder I used in college 20 years ago. It’s slim, rectangular, and a little smaller than my palm. Unlike the Comulytic, this one has a screen where I can see the transcription, AI-generated summaries, and other features. It can also record videos. The Comulytic and iFlyTek AI voice recorders have changed the way I do meetings and classes. Here’s how.
Science says that handwriting notes is better for your brain compared to typing, which is why I still enjoy note-taking the old-fashioned way. However, in my line of work, where I’m spending most of my day writing and typing and talking, doing things the old-fashioned way can be downright painful at times, regardless of whether you’ve got one of the best and most reliable mechanical keyboards. I get cramps in my hands and wrists, even with proper form and daily stretching. And if I’m trying to take notes while in a client meeting or online session, I’m not able to give one or the other my full attention. Even with pages of notes, I still feel like I missed part of the discussion.
Using AI voice recorders to do some of the lifting has made my work easier, physically and mentally. Instead of dashing to take notes and pick out all the important details, I can be present in the conversation. When my wrists and hands are aching after a day of writing, I’m more selective about the notes I take. But using an AI voice recorder, I can focus on what’s being said instead of how I’m capturing the information for later. It removes a lot of risk on my end because I know I will have all of the most salient pieces of information without having to pick and choose in the moment. Clients have to repeat themselves less, which saves us both time.
At the end of a meeting, I review my notes and figure out what needs to happen next, usually in the form of research, deliverables, or other tasks. My next steps are only as good as the notes I took, and again, if I miss a key detail or otherwise couldn’t fully immerse myself in our conversation, the rest of the process suffers.
Both of my AI voice recorders analyze the conversations and present me with action steps, summaries, and follow-up items. I know exactly what needs to happen next based on the meeting. And it’s in a digital form in the same place where I’m already doing my work. As a rule, I always end client meetings by repeating a summary and takeaways so that the client has the opportunity to clarify anything we spoke about or what our next steps are. I still do this, and AI picks it up and runs with it. I’ve discovered that taking notes doesn’t have to compete with active listening. Conversations feel more natural since I’m not constantly staring at my keyboard or notepad. I love that my new process removes a layer of mental clutter and allows me to contribute in a more meaningful way.
One of the most valuable features of an AI voice recorder for me is seeing what was said. This goes beyond basic call recording, which, truthfully, I loathe. I don’t want to listen to a 30-minute phone conversation to find one key piece of information. When I have a digital transcript, I can use CTRL + F to search for keywords and find exactly what I need in seconds. Transcripts are a major time-saver for me, and AI voice recorders create them without a separate tool.
With the Comulytic, transcription is free and happens in real time. The iFlyTek does transcription too, but has a limit of up to 300 minutes per month. Beyond that, I need a paid subscription. With both devices, I can go back and look at notes from past calls and have searchable documents. It seems like a small convenience, but transcripts have become incredibly valuable to me over the years. Details that seem insignificant during a meeting might be important days or weeks later when I’m in a different phase of a project. I don’t have to hunt through notebooks or the pile of sticky notes on my desk. All of my records are centralized in one place (well, two since I use two devices for different purposes). Plus, I save money by not having to upload recordings into separate software or transcription apps that convert audio to text.
When I’m not writing about tech or working with clients, I’m a travel freelance writer exploring mountain towns and hiking trails and unique attractions. And when I’m traveling, sometimes I still need to take client meetings. I used to lug around my laptop and pop into a coffee shop or cafe when I needed to take calls. The best note-taking apps were handy enough, but now I have an easy, one-touch way to record conversations without needing them. It takes notes on my behalf no matter where I am.
One of the biggest benefits is that these devices work offline. I don’t have to be connected to Wi-Fi because each device has internal storage, and when I regain internet access, the content will sync to my account automatically. Even when I’m not speaking with clients or stakeholders, this makes AI voice recorders useful for capturing my own thoughts. Some of my best ideas come when I’m walking in the park or hiking to see a waterfall. Sometimes I meet people unexpectedly and want to get their story on the spot. I have come to appreciate how much flexibility an AI voice recorder provides. I don’t have to plan my workflow around the availability of a laptop or internet connection.
As much as I appreciate AI voice recorders, I understand why some people are hesitant to embrace them. AI isn’t perfect, so there’s a risk of contextual misunderstanding. For example, AI might analyze your conversation and give an inaccurate summary or oddball to-do’s. Hopefully you’ll be able to spot this if you were immersed in the conversation, and it’s an easy fix. I haven’t experienced it (yet).
Some people are also put off by the idea of having AI listen to their conversations. Questions naturally arise about where the content is stored, who can access it, and whether the content is truly private. No one wants sensitive business information or client records on display, especially if it involves intellectual property. I believe transparency is essential. If I’m recording a meeting with AI, I let clients know. Most people appreciate being informed beforehand.
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