At this point, it’s becoming easier to say which AI startups Mark Zuckerberg hasn’t looked at acquiring.
Technology
Meta held talks to buy Thinking Machines, Perplexity, and Safe Superintelligence
In addition to Ilya Sutskever’s Safe Superintelligence (SSI), sources tell me the Meta CEO recently discussed buying ex-OpenAI CTO Mira Murati’s Thinking Machines Lab and Perplexity, the AI-native Google rival. None of these talks progressed to the formal offer stage for various reasons, including disagreements over deal prices and strategy, but together they illustrate how aggressively Zuckerberg has been canvassing the industry to reboot his AI efforts.
Now, details about the team Zuckerberg is assembling are starting to come into view: SSI co-founder and CEO Daniel Gross, along with ex-Github CEO Nat Friedman, are poised to co-lead the Meta AI assistant. Both men will report to Alexandr Wang, the former Scale CEO Zuckerberg just paid over $14 billion to quickly hire. Wang told his Scale team goodbye last Friday and was in the Meta office on Monday. This week, he has been meeting with top Meta leaders (more on that below) and continuing to recruit for the new AI team Zuckerberg has tasked him with building. I expect the team to be unveiled as soon as next week.
Rather than join Meta, Sutskever, Murati, and Perplexity CEO Aravind Srinivas have all gone on to raise more money at higher valuations. Sutskever, a titan of the AI research community who co-founded OpenAI, recently raised a couple of billion dollars for SSI. Both Meta and Google are investors in his company, I’m told. Murati also just raised a couple of billion dollars. Neither she nor Sutskever is close to releasing a product. Srinivas, meanwhile, is in the process of raising around $500 million for Perplexity.
Spokespeople for all the companies involved either declined to comment or didn’t respond in time for publication. The Information and CNBC first reported Zuckerberg’s talks with Safe Superintelligence, while Bloomberg first reported the Perplexity talks.
While Zuckerberg’s recruiting drive is motivated by the urgency he feels to fix Meta’s AI strategy, the situation also highlights the fierce competition for top AI talent these days. In my conversations this week, those on the inside of the industry aren’t surprised by Zuckerberg making nine-figure — or even, yes, 10-figure — compensation offers for the best AI talent. There are certain senior people at OpenAI, for example, who are already compensated in that ballpark, thanks to the company’s meteoric increase in valuation over the last few years.
Speaking of OpenAI, it’s clear that CEO Sam Altman is at least a bit rattled by Zuckerberg’s hiring spree. His decision to appear on his brother’s podcast this week and say that “none of our best people” are leaving for Meta was probably meant to convey a position of strength, but in reality, it looks like he is throwing his former colleagues under the bus. I was confused by Altman’s suggestion that Meta paying a lot upfront for talent won’t “set up a great culture.” After all, didn’t OpenAI just pay $6.5 billion to hire Jony Ive and his small hardware team?
“We think that glasses are the best form factor for AI”
When I joined a Zoom call with Alex Himel, Meta’s VP of wearables, this week, he had just gotten off a call with Zuckerberg’s new AI chief, Alexandr Wang.
“There’s an increasing number of Alexes that I talk to on a regular basis,” Himel joked as we started our conversation about Meta’s new glasses release with Oakley. “I was just in my first meeting with him. There were like three people in a room with the camera real far away, and I was like, ‘Who is talking right now?’ And then I was like, ‘Oh, hey, it’s Alex.’”
The following Q&A has been edited for length and clarity:
How did your meeting with Alex just now go?
The meeting was about how to make AI as awesome as it can be for glasses. Obviously, there are some unique use cases in the glasses that aren’t stuff you do on a phone. The thing we’re trying to figure out is how to balance it all, because AI can be everything to everyone or it could be amazing for more specific use cases.
We’re trying to figure out how to strike the right balance because there’s a ton of stuff in the underlying Llama models and that whole pipeline that we don’t care about on glasses. Then there’s stuff we really, really care about, like egocentric view and trying to feed video into the models to help with some of the really aspirational use cases that we wouldn’t build otherwise.
You are referring to this new lineup with Oakley as “AI glasses.” Is that the new branding for this category? They are AI glasses, not smart glasses?
We refer to the category as AI glasses. You saw Orion. You used it for longer than anyone else in the demo, which I commend you for. We used to think that’s what you needed to hit scale for this new category. You needed the big field of view and display to overlay virtual content. Our opinion of that has definitely changed. We think we can hit scale faster, and AI is the reason we think that’s possible.
Right now, the top two use cases for the glasses are audio — phone calls, music, podcasts — and taking photos and videos. We look at participation rates of our active users, and those have been one and two since launch. Audio is one. A very close second is photos and videos.
AI has been number three from the start. As we’ve been launching more markets — we’re now in 18 — and we’ve been adding more features, AI is creeping up. Our biggest investment by a mile on the software side is AI functionality, because we think that glasses are the best form factor for AI. They are something you’re already wearing all the time. They can see what you see. They can hear what you hear. They’re super accessible.
Is your goal to have AI supersede audio and photo to be the most used feature for glasses, or is that not how you think about it?
From a math standpoint, at best, you could tie. We do want AI to be something that’s increasingly used by more people more frequently. We think there’s definitely room for the audio to get better. There’s definitely room for image quality to get better. The AI stuff has much more headroom.
How much of the AI is onboard the glasses versus the cloud? I imagine you have lots of physical constraints with this kind of device.
We’ve now got one billion-parameter models that can run on the frame. So, increasingly, there’s stuff there. Then we have stuff running on the phone.
If you were watching WWDC, Apple made a couple of announcements that we haven’t had a chance to test yet, but we’re excited about. One is the Wi-Fi Aware APIs. We should be able to transfer photos and videos without having people tap that annoying dialogue box every time. That’d be great. The second one was processor background access, which should allow us to do image processing when you transfer the media over. Syncing would work just like it does on Android.
Do you think the market for these new Oakley glasses will be as big as the Ray-Bans? Or is it more niche because they are more outdoors and athlete-focused?
We work with EssilorLuxottica, which is a great partner. Ray-Ban is their largest brand. Within that, the most popular style is Wayfair. When we launched the original Ray-Ban Meta glasses, we went with the most popular style for the most popular brand.
Their second biggest brand is Oakley. A lot of people wear them. The Holbrook is really popular. The HSTN, which is what we’re launching, is a really popular analog frame. We increasingly see people using the Ray-Ban Meta glasses for active use cases. This is our first step into the performance category. There’s more to come.
What’s your reaction to Google’s announcements at I/O for their XR glasses platform and eyewear partnerships?
We’ve been working with EssilorLuxottica for like five years now. That’s a long time for a partnership. It takes a while to get really in sync. I feel very good about the state of our partnership. We’re able to work quickly. The Oakley Meta glasses are the fastest program we’ve had by quite a bit. It took less than nine months.
I thought the demos they [Google] did were pretty good. I thought some of those were pretty compelling. They didn’t announce a product, so I can’t react specifically to what they’re doing. It’s flattering that people see the traction we’re getting and want to jump in as well.
On the AR glasses front, what have you been learning from Orion now that you’ve been showing it to the outside world?
We’ve been going full speed on that. We’ve actually hit some pretty good internal milestones for the next version of it, which is the one we plan to sell. The biggest learning from using them is that we feel increasingly good about the input and interaction model with eye tracking and the neural band. I wore mine during March Madness in the office. I was literally watching the games. Picture yourself sitting at a table with a virtual TV just above people’s heads. It was amazing.
- TikTok gets to keep operating illegally. As expected, President Trump extended his enforcement deadline for the law that has banned a China-owned TikTok in the US. It’s essential to understand what is really happening here: Trump is instructing his Attorney General not to enforce earth-shattering fines on Apple, Google, and every other American company that helps operate TikTok. The idea that he wouldn’t use this immense leverage to extract whatever he wants from these companies is naive, and this whole process makes a mockery of everyone involved, not to mention the US legal system.
- Amazon will hire fewer people because of AI. When you make an employee memo a press release, you’re trying to tell the whole world what’s coming. In this case, Amazon CEO Andy Jassy wants to make clear that he’s going to fully embrace AI to cut costs. Roughly 30 percent of Amazon’s code is already written by AI, and I’m sure Jassy is looking at human-intensive areas, such as sales and customer service, to further automate.
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As always, I welcome your feedback, especially if you’ve also turned down Zuck. You can respond here or ping me securely on Signal.
Technology
Microsoft’s carbon emissions went up 25 percent last year
Microsoft may once again be struggling to keep up with its own climate goals, according to its 2026 sustainability report. As reported by GeekWire, the report states that Microsoft’s carbon emissions increased 25 percent in 2025, totalling 34 million metric tons “without select interventions.” Microsoft says this was “driven primarily by the expansion of our datacenter infrastructure,” as well as the company’s decision last February to stop purchasing “non-additional, unbundled renewable energy certificates.”
Several years ago, Microsoft set itself a goal to be carbon negative by 2030, meaning it will need to remove more carbon emissions than it produces. This isn’t the first time Microsoft has faced setbacks toward accomplishing that goal, as its 2024 sustainability report showed a similar rise in climate pollution. This year’s report admits that, “While AI infrastructure is driving demand for energy, water, land, and materials, sustainability solutions are not scaling fast enough to meet demand.”
Technology
Google turns old phones into cloud servers
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That old phone sitting in your drawer may have more life left in it than you think. You may look at it and see a dead battery, an outdated camera or a screen that no longer feels worth using. Google and researchers at the University of California San Diego see something else: a tiny computer that may still have useful processing power.
Their idea is called phone cluster computing. Instead of treating retired smartphones as electronic waste, researchers remove the motherboard and redeploy it as part of a low-carbon computing system.
Google says UC San Diego plans to launch a data center built from 2,000 Pixel smartphones in fall 2026. The goal is to provide low-cost cloud computing for students and researchers while reducing the need for newly manufactured server hardware.
That means the next chapter for an old phone may not be a junk drawer. It may be a server rack.
YOU COULD GET PAID FROM GOOGLE’S ANDROID DATA LAWSUIT
Researchers plan to launch a 2,000-phone data center at UC San Diego in fall 2026 to support students and research workloads. (Kurt “CyberGuy” Knutsson)
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What is phone cluster computing?
Phone cluster computing takes retired smartphones and turns their core hardware into a computing platform. The process starts by stripping each phone down to the motherboard. That board holds the processor, memory and storage. The display, battery, cameras, chassis and other phone-specific parts are removed.
That step is important because a full phone does not belong in a data center. Batteries can create safety issues. Screens and cameras waste space. The motherboard is the part that still offers computing value.
Once the board is removed, researchers load a general-purpose Linux system onto it. Android already runs on Linux at its core, but Android is built for mobile apps and personal devices. A data center needs something more flexible for cloud workloads. After that, the phone boards can be grouped into clusters. Many small boards then work together like a collection of tiny servers.
Why Google wants old Pixel phones for cloud computing
The AI boom has created a huge appetite for computing power. Data centers need more chips, more electricity and more cooling. At the same time, billions of phones fall out of use around the world.
This Google-backed project takes that conversation in a different direction by asking whether some useful computing can come from hardware we already made.
The project focuses on embodied carbon. That means the emissions created before a device ever turns on. Mining, manufacturing and shipping all add to that carbon footprint.
If a phone motherboard already exists, reusing it can avoid some of the environmental cost tied to manufacturing new hardware. Google says the motherboard accounts for about half of a phone’s embodied carbon, which makes it the most valuable part to recover.
How retired smartphones become low-carbon servers
You cannot plug a pile of old phones into a rack and call it a data center. The process requires careful teardown, new software and a way to manage many boards at once. Google says the project uses containerized applications managed by Kubernetes. That helps coordinate the work across many devices.
The phones are organized into self-managing clusters of about 25 to 50 boards. Each board works as a small Linux machine. Together, they can handle tasks that would otherwise run on traditional cloud servers. That does not make one phone equal to one server. A server has many more processor cores, more memory and data center-grade hardware. A phone board has fewer resources and tighter limits. Still, some jobs do not need a giant machine. They need enough compute to run efficiently without wasting resources.
GOOGLE ENGINEER STOLE AI SECRETS FOR CHINA, SENATE HEARS IN EXPLOSIVE TESTIMONY
Google and UC San Diego are testing a cloud computing system built from retired Pixel phone motherboards, giving old smartphones a possible second life. (Google)
Can old phone processors handle cloud workloads?
The technical case is stronger than you may expect. Google says the single-threaded performance of modern smartphone performance cores can match or beat the per-core performance of some modern multicore servers. In one comparison, a 2023 Pixel Fold was tested against an ASUS RS720A-E11 server using SPEC benchmarks. The Pixel Fold’s performance cores beat the baseline data center server core on many of the tests. That sounds impressive, but there is an important catch.
A smartphone board has a smaller memory limit and fewer cores. It also lacks the management tools and hardware durability that servers are built around. So the project needs the right workloads.
UC San Diego is starting with educational and research computing. That makes sense because many classroom tasks can run on small cloud instances. Google says early experiments showed that a 20-phone cluster could support peak submission rates for a class of more than 75 students. The grading latency also came in below the default AWS backend used in the comparison.
Why UC San Diego is testing a 2,000 Pixel phone data center
UC San Diego plans to use the 2,000-phone cluster to support computer science classes and research workloads. Google says the deployment could support about 100 classes at once. It also describes the system as providing about 50 server-equivalents worth of compute at a fraction of the usual cost.
For a university, that could be a major advantage. Cloud computing costs can rise quickly, especially when many students submit assignments at the same time. If a reused phone cluster can handle some of that load, schools may save money while reducing demand for newly manufactured servers.
This also gives researchers a chance to test phone-based computing at scale. A small lab demo can look promising. A 2,000-board deployment will show much more about reliability, maintenance and day-to-day performance.
Phone cluster computing still has big limits
Phone cluster computing sounds promising, but it still has a lot to prove. Your smartphone was made for daily use in your hand, not nonstop work inside a data center. Data center servers are built to run for years with steady cooling, fast repairs and constant monitoring. Phone motherboards come from devices made for pockets, backpacks and kitchen counters. That alone raises some big questions.
The boards could fail faster than expected. Cooling may also become a challenge once thousands of tiny processors run side by side. Then there is the labor problem, because someone has to safely remove batteries, screens and other parts before the boards can be reused. Cost will be the deciding factor. If teardown, maintenance and replacement work get too expensive, this idea may stay in the research lab.
Phone clusters also will not replace the massive GPU systems that power advanced AI training. They make more sense for smaller cloud jobs, classroom tools and research tasks that fit within smartphone hardware limits. That still leaves plenty of useful work. After all, not every cloud task needs the newest chip.
Why old smartphones could help cut e-waste
The world’s e-waste problem is growing fast. The Global E-waste Monitor projects that electronic waste could climb to 82 million tonnes by 2030, while formal collection and recycling rates are expected to fall to 20%. Old phones are a big part of that problem because many never make it to a proper recycling program. They sit in drawers, land in closets or get tossed out with valuable parts still inside. Even when a phone no longer feels useful to you, its processor, memory and storage may still have work left to do.
CyberGuy has covered related second-life ideas before, including old smartphones being turned into tiny data centers and repurposed EV batteries helping power AI data centers. The common theme is hard to ignore. Some of the hardware already in circulation may still have useful work left to do.
FIVE DATA BROKER OPT-OUT MYTHS THAT LEAVE RETIREES EXPOSED
Google says reusing smartphone motherboards could cut hardware waste and reduce the carbon cost of building new data center servers. (Yawar Nazir/Getty Images)
How to safely recycle or reuse your old phone
This research does not mean you should toss your old phone into a random donation bin tomorrow. Before you recycle, donate, trade in or sell an old phone, you need to protect your data. Back up anything you want to keep. Then sign out of your accounts and securely wipe the device.
CyberGuy has a helpful guide on how to securely get rid of your old cell phone. Privacy comes first whenever you part with a device.
You can also consider trade-in programs, certified refurbishers or reputable electronics recycling programs. If the phone still works, buying refurbished can also keep devices in use longer. CyberGuy has covered what to know before buying refurbished electronics, which is helpful if you want to save money without taking a gamble. The key is to avoid letting old devices sit forgotten forever. A phone in a drawer helps no one.
What this means to you
That old phone in your drawer may not be as useless as it looks. Even if the battery is tired or the camera feels outdated, the processor inside may still have real value.
Now, you probably will not be mailing your old phone to a Google data center anytime soon. Still, this project points to a bigger shift in how we think about retired tech. Instead of sending every old device straight to recycling or letting it collect dust, companies, schools and researchers may find smarter ways to reuse the parts that still work.
There is also a money lesson here. If your current phone still runs well, you may not need to rush into an upgrade just because a newer model comes out. A battery replacement, trade-in or refurbished option could save you money while keeping perfectly good hardware in use longer. To me, that is the real takeaway. The phone you forgot about could possibly still have a job to do.
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Kurt’s key takeaways
Google and UC San Diego are testing how to turn retired Pixel phone motherboards into a low-carbon cloud computing platform. The project could give old smartphones a second life while reducing the need for newly manufactured servers. That is important as AI data centers keep demanding more computing power and more electricity. The first major test is expected in fall 2026 with a 2,000-phone data center at UC San Diego. If it works, the cluster could support students and researchers at a lower cost than traditional cloud infrastructure. However, this idea still has to prove it can handle the grind of daily use. Reliability, cooling, teardown labor and maintenance will determine whether phone cluster computing can grow beyond just research. To me, the most relatable part is sitting in your junk drawer. That old phone may seem useless, but its processor could still be powerful enough to help run cloud jobs. Maybe the future of computing starts with hardware we already forgot we owned.
Would you feel good knowing your old phone could help power cloud computing? Let us know by writing to us at CyberGuy.com.
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Technology
Google’s Nest Thermostat has hit its best price of the year
If you’re looking for a relatively affordable way to cut down on cooling costs, Google’s Nest Thermostat can help. It’s packed with smart controls and energy-saving features, and right now it’s on sale in white for $79 ($50 off), which is its best price of the year, at Amazon.
The smart thermostat is quick to install and makes it easy to adjust your home’s temperature whether you’re relaxing in bed or on your way home thanks to the Google Home app. You can also create schedules and control it with your voice using Google Assistant, Alexa, or another Matter-compatible voice assistant.
Once it’s set up, the Nest Thermostat can automatically turn the temperature down when you’re away to help reduce unnecessary energy use, while Google’s Savings Finder feature suggests additional ways to save over time. It also monitors your HVAC system and can alert you if something doesn’t seem right, making it easier to stay on top of maintenance before small issues become bigger, more expensive ones. If you’re eligible, Nest Renew can also automatically shift some of your heating and cooling to times when electricity is cleaner or cheaper.
That said, this is Google’s entry-level model from 2020, so you do miss out on some of the premium features found on the latest Nest Learning Thermostat. Unlike the flagship version, it won’t learn your schedule automatically over time, for example, and lacks support for Nest Temperature Sensors that let you prioritize the temperature in a specific room. Even so, if all you want is an easy way to adjust your home’s temperature remotely and potentially lower your energy bills, the Nest Thermostat is still a solid investment at this price.
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