AI researchers have recently been asking themselves a version of the question, “Is that really Zuck?”
Technology
Inside Mark Zuckerberg’s AI hiring spree
As first reported by Bloomberg, the Meta CEO has been personally asking top AI talent to join his new “superintelligence” AI lab and reboot Llama. His recruiting process typically goes like this: a cold outreach via email or WhatsApp that cites the recruit’s work history and requests a 15-minute chat. Dozens of researchers have gotten these kinds of messages at Google alone.
For those who do agree to hear his pitch (amazingly, not all of them do), Zuckerberg highlights the latitude they’ll have to make risky bets, the scale of Meta’s products, and the money he’s prepared to invest in the infrastructure to support them. He makes clear that this new team will be empowered and sit with him at Meta’s headquarters, where I’m told the desks have already been rearranged for the incoming team.
Most of the headlines so far have focused on the eye-popping compensation packages Zuckerberg is offering, some of which are well into the eight-figure range. As I’ve covered before, hiring the best AI researcher is like hiring a star basketball player: there are very few of them, and you have to pay up. Case in point: Zuckerberg basically just paid 14 Instagrams to hire away Scale AI CEO Alexandr Wang.
It’s easily the most expensive hire of all time, dwarfing the billions that Google spent to rehire Noam Shazeer and his core team from Character.AI (a deal Zuckerberg passed on). “Opportunities of this magnitude often come at a cost,” Wang wrote in his note to employees this week. “In this instance, that cost is my departure.”
Zuckerberg’s recruiting spree is already starting to rattle his competitors. The day before his offer deadline for some senior OpenAI employees, Sam Altman dropped an essay proclaiming that “before anything else, we are a superintelligence research company.” And after Zuckerberg tried to hire DeepMind CTO Koray Kavukcuoglu, he was given a larger SVP title and now reports directly to Google CEO Sundar Pichai.
I expect Wang to have the title of “chief AI officer” at Meta when the new lab is announced. Jack Rae, a principal researcher from DeepMind who has signed on, will lead pre-training. Meta certainly needs a reset. According to my sources, Llama has fallen so far behind that Meta’s product teams have recently discussed using AI models from other companies (although that is highly unlikely to happen). Meta’s internal coding tool for engineers, however, is already using Claude.
While Meta’s existing AI researchers have good reason to be looking over their shoulders, Zuckerberg’s $14.3 billion investment in Scale is making many longtime employees, or Scaliens, quite wealthy. They were popping champagne in the office this morning.
Then, Wang held his last all-hands meeting to say goodbye and cried. He didn’t mention what he would be doing at Meta. I expect his new team will be unveiled within the next few weeks after Zuckerberg gets a critical number of members to officially sign on.
Apple is accustomed to being on top of the tech industry, and for good reason: the company has enjoyed a nearly unrivaled run of dominance.
After spending time at Apple HQ this week for WWDC, I’m not sure that its leaders appreciate the meteorite that is heading their way. The hubris they display suggests they don’t understand how AI is fundamentally changing how people use and build software.
Heading into the keynote on Monday, everyone knew not to expect the revamped Siri that had been promised the previous year. Apple, to its credit, acknowledged that it dropped the ball there, and it sounds like a large language model rebuild of Siri is very much underway and coming in 2026.
The AI industry moves much faster than Apple’s release schedule, though. By the time Siri is perhaps good enough to keep pace, it will have to contend with the lock-in that OpenAI and others are building through their memory features. Apple and OpenAI are currently partners, but both companies want to ultimately control the interface for interacting with AI, which puts them on a collision course.
Apple’s decision to let developers use its own, on-device foundational models for free in their apps sounds strategically smart, but unfortunately, the models look far from leading. Apple ran its own benchmarks, which aren’t impressive, and has confirmed a measly context window of 4,096 tokens. It’s also saying that the models will be updated alongside its operating systems — a snail’s pace compared to how quickly AI companies move.
I’d be surprised if any serious developers use these Apple models, although I can see them being helpful to indie devs who are just getting started and don’t want to spend on the leading cloud models. I don’t think most people care about the privacy angle that Apple is claiming as a differentiator; they are already sharing their darkest secrets with ChatGPT and other assistants.
Some of the new Apple Intelligence features I demoed this week were impressive, such as live language translation for calls. Mostly, I came away with the impression that the company is heavily leaning on its ChatGPT partnership as a stopgap until Apple Intelligence and Siri are both where they need to be.
AI probably isn’t a near-term risk to Apple’s business. No one has shipped anything close to the contextually aware Siri that was demoed at last year’s WWDC. People will continue to buy Apple hardware for a long time, even after Sam Altman and Jony Ive announce their first AI device for ChatGPT next year. AR glasses aren’t going mainstream anytime soon either, although we can expect to see more eyewear from Meta, Google, and Snap over the coming year.
In aggregate, these AI-powered devices could begin to siphon away engagement from the iPhone, but I don’t see people fully replacing their smartphones for a long time. The bigger question after this week is whether Apple has what it takes to rise to the occasion and culturally reset itself for the AI era.
I would have loved to hear Tim Cook address this issue directly, but the only interview he did for WWDC was a cover story in Variety about the company’s new F1 movie.
- AI agents are coming. I recently caught up with Databricks CEO Ali Ghodsi ahead of his company’s annual developer conference this week in San Francisco. Given Databricks’ position, he has a unique, bird’s-eye view of where things are headed for AI. He doesn’t envision a near-term future where AI agents completely automate real-world tasks, but he does predict a wave of startups over the next year that will come close to completing actions in areas such as travel booking. He thinks humans will need (and want) to approve what an agent does before it goes off and completes a task. “We have most of the airplanes flying automated, and we still want pilots in there.”
- Buyouts are the new normal at Google. That much is clear after this week’s rollout of the “voluntary exit program” in core engineering, the Search organization, and some other divisions. In his internal memo, Search SVP Nick Fox was clear that management thinks buyouts have been successful in other parts of the company that have tried them. In a separate memo I saw, engineering exec Jen Fitzpatrick called the buyouts an “opportunity to create internal mobility and fresh growth opportunities.” Google appears to be attempting a cultural reset, which will be a challenging task for a company of its size. We’ll see if it can pull it off.
- Evan Spiegel wants help with AR glasses. I doubt that his announcement that consumer glasses are coming next year was solely aimed at AR developers. Telegraphing the plan and announcing that Snap has spent $3 billion on hardware to date feels more aimed at potential partners that want to make a bigger glasses play, such as Google. A strategic investment could help insulate Snap from the pain of the stock market. A full acquisition may not be off the table, either. When he was recently asked if he’d be open to a sale, Spiegel didn’t shut it down like he always has, but instead said he’d “consider anything” that helps the company “create the next computing platform.”
If you haven’t already, don’t forget to subscribe to The Verge, which includes unlimited access to Command Line and all of our reporting.
As always, I welcome your feedback, especially if you’re an AI researcher fielding a juicy job offer. You can respond here or ping me securely on Signal.
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.”
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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.
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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.
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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.
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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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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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