After a brief tease back in May, Google is detailing its lineup of Googlebooks, its new line of laptops designed to serve as premium systems that work best with Android phones and deliver unique abilities through Gemini.

At an event ahead of the launch, Sameer Samat, Google's president of the Android ecosystem, said that the device's new OS would pull from the best aspects of ChromeOS and Android to design a modern

The systems are available for pre-order today and will land in stores on October 4 in the U.S. and October 5 in Canada, the U.K., Ireland, France, Germany, and Australia.

Googlebook hardware

The first five Googlebooks come from five partners: Dell, Lenovo, HP, Asus, and Acer, and offer silicon from either Intel or Qualcomm. MediaTek is also listed as a partner, but its chips aren't in the first wave of devices.

These devices are clearly designed to go after the MacBook Air and premium Windows laptops, using materials like aluminum, magnesium, and carbon fiber rather than plastic. The screens are all high-resolution, with several 2880 x 1800 OLED screens. Most of the lineup has haptic touchpads.

Google has set a floor of 16GB for RAM, with several of the options offering up to 32GB. 256GB is the minimum for storage, though many go up to 512GB, and Dell and HP go up to 1TB.

All of them also feature a Glowbar (a throwback to the Chromebook Pixel and Pixel C tablet), a series of RGB lights on the lid that glows playfully at startup, shows battery charge, and can react when you talk to Gemini. Given that the Glowobar is behind the screen, we'll see how much use it gets, but Google says it's going to open an API for developers.

Several of the new laptops are reminiscent of existing Windows machines. The HP Googlebook 14 resembles the HP OmniBook Ultra, while the Dell's XPS Googlebook is effectively this year's XPS 13. The Asus Googlebook 14 is a dead ringer for the premium, enterprise-focused ExpertBook Ultra.

And yet some of those familiar looks were my favorites on first impression. I particularly appreciated how thin and light both Asus and Dell's designs are, though Asus' has far more ports. With the exception of Acer's 2-in-1, the entire lineup consists of clamshell designs.

The laptops aren't cheap, particularly if you're used to the lower end of Chromebooks. The cheapest notebook, the Acer Chromebook 14, starts at $899, though the notebook maker says it's a limited-time price. The XPS starts at $1,199, while the rest all start at $1,299, the price of a base MacBook Air.

Google's keyboard layout brings back the caps lock key (which also doubles as the Quick Insert key, see below), and also adds a G key that brings up the launcher.

Meet the Googlebooks

*️⃣ Acer Googlebook 14

*️⃣ Dell XPS Googlebook

*️⃣ Asus Googlebook 14

*️⃣ Lenovo Googlebook 15

*️⃣ HP Googlebook 14

Googlebook OS

The Googlebook OS is designed to be tightly integrated with your existing Android phone. The idea is that once you log in and set up the laptop for the first time, you'll find all of your files, photos, apps, emails, and saved passwords right where you would expect them, with changes flowing back to your phone. The whole idea seems very Mac-like, but for Android, which never had that same tight integration with Microsoft's Windows, despite multiple attempts.

In general, this seems like a simple but mostly full-featured desktop OS for the crowd growing out of Chromebooks. Much of the work still occurs in your browser (in this case, Chrome), but there's a full file manager (including AI descriptions of files) to open files locally. You can put phone-style widgets on your desktop, and there's a taskbar at the bottom of the screen.

The applications, however, all come from the Android side of things. Google provides apps through the Play Store. Google is counting on a mix of the Play Store and web apps, meaning you won't be downloading any executables to use on a Googlebook from the web. Google claims that 70% of time on a laptop is spent in a web browser.

The company is highlighting Adobe's Photoshop and Lightroom, CapCut, Netflix, and Disney+ (with offline viewing). Those apps, among others, will have a desktop mode. Additionally, Google says that half of the 100 top games on Android are "already optimized for keyboard, mouse, and large screen, with new games launching monthly."

That latter point is quite PC gaming, though. For that, Google is offering a free year of Nvidia GeForce Now, and suggests more gaming will come to Googlebooks soon enough. (Gaming Chromebooks didn't quite take off, so we'll see what Google does differently here).

There are a handful of features to move back and forth between your laptop and your phone that seem helpful (and again, quite similar to macOS). Continue On is effectively macOS and iOS's Continuity, which lets you pick up on a task from your phone on your laptop, and vice versa. The application for the file you're working on shows up in a Googlebook's taskbar. For when you want to use your phone directly on the laptop, Cast My Apps lets you control it from the desktop (similar to macOS's iPhone Mirroring)

Google also includes Antigravity, its platform for developing agentic workflows and agents to build web apps or Android software on your device. At an event ahead of the show, Googlers were vibe coding software to put on their machines, from a teleprompter Android app to an arcade full of '80s arcade games. Google says Antigravity will also include a Linux terminal environment to run tools like Claude CLI or Antigravity CLI.

Google promises 10 years of support for Googlebook OS on a Googlebook, though at a press event, there was a big asterisk next to that claim. The 10 years starts from the launch of the platform and chip inside the Googlebook, I was told, which means that Intel's Panther Lake chips will have nearly a year lopped off immediately (they launched in January this year), while Snapdragon X Elite, which first shipped in 2024, will already be 2 years behind.

This isn't terribly different from how Google supports its Chromebooks now — by platform, not launch of the system, but that difference may not play the same in the big leagues. Google keeps a database where you can see when any given Chromebook model will stop receiving updates, and presumably it will do something similar with Googlebooks.

Gemini Intelligence

The features that make Googlebooks stand out are the ones where Google baked its Gemini AI directly into the operating system. That also means how much you like a Googlebook may be directly tied to how useful you find these features.

The one feature Google talks up the most is the Magic Pointer. You wiggle your cursor, and it becomes a Gemini-focused pointer. Click on anything to get contextual suggestions or type into the assistant to get answers about what you chose. For example, one demo showed someone using Magic Pointer on a PDF of a calendar and asking Gemini to add the dates to their calendar. In many cases, it felt like a shorter way to get to Gemini than copying and pasting screenshots, but I didn't get enough time to judge how useful it could be.

The Caps Lock key doubles as a "Quick Insert tool" — effectively a souped-up copy and paste that includes your clipboard history, emojis, translation tools, proactive assistant capabilities, and opens Rambler, which allows for text-to-speech and cleans up where you might have stumbled and messed up.

Rambler was introduced at I/O this year and debuted on the Pixel 11, but it was impressive on the Googlebook. I tend to type rather than speak on a computer, but this might convince some people otherwise. It even did well in a relatively loud demonstration environment.

While there are a number of widget options in Googlebook OS, you can also create your own with natural language. I was able to make one that showed me the top movie of the day in a given year.

Perhaps the most natural are Magic Cues — recommendations to do something with the information you're seeing — peppered throughout the OS. If you see dates, you may be cued to make a calendar invite. If someone asks a question in a text message, the answer might pop up if it is in your email, texts, or somewhere else it was discussed before. This has also been on Pixel phones before, but seems to fit in just fine on a laptop.

Beyond the Gemini features, every Googlebook is shipping with 12 months of Google AI Pro (usually $19.99 per month), offering 5TB of cloud storage and advanced tools like higher rate limits in Gemini, entry rate limits to agent models in Antigravity, credits for use in Google Flow, and higher access to AI in Google Search. There are also 3 months of YouTube Premium, CapCut, and Photoshop. Additionally, there's 1 year of Gemini Spark, a version of the AI that acts as an autonomous agent. That's a lot of freebies, and it will be interesting to see how many people feel the need to keep and renew them to make the most of their Googlebooks.

Can Googlebook break through?

Despite Android's popularity globally, Chromebooks have largely been seen as kid stuff — quite literally for education. Googlebooks are a play to be more serious computers, but also bring a premium price.

The AI features are neat in some cases, and showboaty in others. I need to spend real time with a Googlebook to truly decide what's useful.

What is undoubtedly the most important part here is that Android users will have a device that integrates with their phones as well as Macs do with iPhones. Whether that's enough to justify a new operating system that still has some limitations is an open question. Again, we have to try these.

Google clearly has some OEMs excited, but as computers get more expensive thanks to a component shortage, we'll have to see how these prices land, and if the company finally has a mainstream competitor on its hands.

Four plaintiffs subscribed to ChatGPT, Claude, Grok, or Gemini filed a proposed class-action lawsuit alleging that the developers of these AI models violated antitrust laws when they agreed to slow AI development. According to the Associated Press, the lawsuit argues that this agreement would “reduce the value consumers get for paid AI subscriptions” and that this coordination started in July 2026 after the leading AI labs signed a statement admitting there is “intense competitive pressure not to unilaterally slow” development.

The plaintiffs recognize the need for AI development to slow for the sake of safety, but they say that Anthropic founder Dario Amodei’s cooperation proposal is a “shortcut” that “substitutes collective restraint for individual accountability.” Attorney Nick Rowley, the lead counsel for the plaintiffs, says, “AI will quickly spin out of human control and could kill us all if we allow AI safety and protocol … to be controlled by private self-serving agreements between the world’s most powerful ‘for profit’ technology companies.”

Amodei’s essay acknowledged the antitrust risk and indicated he was hoping that the government would make an exception. OpenAI’s Sam Altman responded to this call on X, saying, “We welcome a federal framework that sets consistent safety requirements for frontier AI. But we do not believe we need to wait for an antitrust exemption or legislation to begin the work of providing this confidence.” However, the Trump administration shot down this idea, with the president himself saying, “AI taking over the World, destroying Humanity, and all other things bad, is a HOAX.”

Chinese state media also criticized this announcement, saying that the call to put the brakes on AI development is nothing but a response to Chinese competition, especially as Amodei’s essay explicitly mentioned the desire to slow China’s progress and widen the U.S.’s gap over Beijing. China Daily called the proposed agreement a “club whose membership rules have been drafted before the guest list is announced” and added that “a global AI-safety framework that excludes China is not quite global.”

There have been a couple of bizarre incidents where AI agents took their users’ commands too literally, like kicking out another person from a waitlist just to get their user ahead of the queue or deleting a company’s entire database when their AI agent faced a problem and it guessed that making the move was the best option. However, there have been more sinister events, such as when unreleased AI models broke out of their testing environment and hacked HuggingFace’s production servers. Big Tech is making the call to slow down development to catch up in terms of security, but people are calling them out for antitrust activity probably because they do not trust these companies.

Less than two weeks after Google released a mapping of the complete brain and central nervous system of an adult male fruit fly, we've seen enthusiasts put the structure to work everywhere from turning a fruit fly into a day trader to teaching it parallel parking. Now, one Balatro fan says they trained the structure with an algorithm to play the game, with the win rate currently sitting at a cozy 20%.

The player shared a sped-up video of the model apparently playing the game. Based on the video, the player chose the lowest difficulty (White Stake) and the default Red Deck. We've already seen OpenAI's GPT-6 'Astra' model beating the game with the Black Deck on Gold Stack difficulty, which is generally considered the hardest combination in the game.

ActualAerie1011, the Reddit user who shared the video, says they trained the model using a trainer algorithm they developed to discover useful Balatro seeds. Like other roguelike games, Balatro is randomized, so algorithms like this can discover seeds that are unique and can potentially lead to very high scores (including the game's scoring limit). In order to train the brain, both the brain apparatus (a connectome alongside the actual model) and the algorithm play a seed. Then, the results are compared, and the model on the brain is rewarded or punished based on its choices.

Currently, the user says that the brain has a 20% success rate on a random seed, presumably at that same White Stack/Red Deck difficulty. The user says the model doesn't know anything about the seed outside of what's immediately visible on-screen, and that training is ongoing. "The fruit fly will return, strong and smarter," they wrote in a comment on their original post.

It's an impressive feat, though some commenters have cast doubt on the project. The player didn't share many details about how they trained the model outside of what's above, nor any repo for the project or references to other open-source projects they used. This isn't uncharted territory for Balatro; projects like BalatroBot and BalatroLLM have been available for about a year.

We've reached out to ActualAerie1011 to see if they're able to provide more details on how they trained the model, and we'll update this story when we hear back.

Although Balatro seems straightforward enough, it's surprisingly difficult to train a model to play the game, especially at higher difficulties. The core rules of playing and scoring poker hands aren't difficult. However, the complex interactions between jokers (the perks that help you achieve higher scores), how they're ordered and scored, and specific stipulations like boss abilities and temporary/permanent jokers make consistency a high bar to clear, even for human players, much less an AI model.

A father says his 9-year-old son spent $118,000 on YouTube ad campaigns for his Minecraft and Roblox gameplay videos over about three weeks, all charged to a company credit card the father had saved to his own Google account, which his son also used. The father, who identifies himself only as Dave, described the incident in a video titled “Message from Dad…Mighty Mike Plays is Over.” posted to the boy’s channel on Sept. 15. Dave said he was called into a meeting with his manager, corporate staff, and finance and shown the advertising charges.

Mighty Mike Plays (@MightyMikePlays67) is a 9-year-old’s Minecraft and Roblox gameplay channel with about 24,000 subscribers and 182 uploads as of early Sept. 16. Dave said it started when the boy was around 8, streaming straight from a PS5 with a cheap headset and no editing. Early videos got 5 to 11 views, he said. Dave said he entered the company card himself to buy Robux for his son, and it then sat saved on the Google account that also carries his work email, calendar, and files. Dave did not say why he used the company card for that purchase.

Dave said that after the boy complained about views, he set up a roughly $20 promotion and talked through the steps aloud: set a budget, select who sees the ads, hit run. The boy then created his own campaigns, and Dave said his son did not understand the $20 as a limit. “The credit card, it just says approved every single time,” Dave said in the video. Finance showed him three weeks of ad spend totaling $118,000 on campaigns titled “Mighty Mike,” “Epic Roblox,” and Minecraft gameplay.

Three of the promoted videos reached around 200,000 views. As of a check at 12:26 a.m. ET on Sept. 16, the channel’s three most-viewed gameplay videos stood at 260,000, 224,000, and 178,000 views, with a run of uploads above 1,000 views and most of the rest under 100. Four days before Dave’s video, the boy had posted a video titled “NeverMyFault made a Short about my Ad?” about another creator noticing one of his promotions; it had about 70,000 views.

Dave said the matter has been escalated and he does not yet know whether he will get to keep his job or have to repay the costs.

Google’s own help pages say a payment method entered for any Google purchase is stored in a payments profile tied to the Google Account and is offered again for the next purchase. The profile is also shared across Google Ads and other Google products. YouTube’s Promote tool, which Google describes as a simplified version of Google Ads, walks a creator through a goal, an audience, a budget, and an end date. Dave did not say which interface his son used, but the steps he described match Promote’s. Google Ads does not allow users under 18, but the account was the father’s.

In a separate case, the FTC announced on Sept. 4, 2014, that Google would refund at least $19 million to parents billed for children’s unauthorized in-app purchases on Google Play, and required it to get express consent before billing. That covered Google Play, not Google Ads. Dave said the family will remove saved payment details, add spending caps, and set up parental controls, and that the channel is paused until “real guard rails” are in place.

Following a tip-off from robotaxi firm Waymo, San Francisco police conducted a “high-risk vehicle stop” and arrested two juveniles last week. The crime? The Waymo had seen its two passengers handling a loaded AR-style assault rifle. Police who intercepted the car also found “suspected marijuana and mace spray,” reports SFGate.

Fully autonomous taxi cabs like the Waymo in this story are an increasingly common form of transport seen on the world’s roads. With no human driver, these electric vehicles are packed with sensors, cameras, and more, all tied into the software presence that is called the Waymo Driver. To ensure the utmost safety, the sensors also continually monitor passengers. This might typically check if passengers are following the rules regarding minor transgressions like smoking or not wearing a seatbelt. In this case, the cameras recognized something more serious.

From our understanding of the Waymo in-cabin camera monitoring procedure, humans may review footage when something is flagged by the in-car sensors. That may be how the source report can say that the AR was loaded, or that was a later discovery from the police stop that somehow got attributed to the Waymo phoning home.

According to the source report, this incident took place in the 800 block of 40th Avenue, in the Outer Richmond neighborhood, just before 4am on Thursday, September 3. Alongside a loaded ‘ghost gun,’ the police officers found suspected marijuana and mace. The individuals arrested, a male and a female, can’t be identified in the media due to their age. Reports say the specific charges they face are related to illegally possessing a firearm.

This isn’t the first case of a Waymo telling on its passengers. SFGate previously reported on San Mateo police being called after a pair of 15-year-olds were allegedly drinking alcohol and shooting toy ‘gel blaster’ guns in the taxi cabin.

Waymo robotaxis are now well established in San Francisco, Los Angeles, and some other major cities. Earlier in September, the service branched out into San Diego, Denver, and Tampa, Fla. More and more areas in the U.S. and abroad are giving the autonomous service permission to operate.

Late last week, Google announced that scientists had, for the first time, “mapped the complete brain and central nervous system of an adult male fruit fly.” On Monday, software engineers were already demonstrating the full MaleCNS v1.0 fruit fly connectome being trained to play Doom, as well as Super Mario 64. In light of these developments, perhaps it is time to amend Arthur C. Clarke’s Third Law. We suggest something similar to ‘Any sufficiently advanced new technology will immediately be forced to play Doom.’

Let’s look at this story in its natural chronological order. Scientists have been using fruit flies for research for over a century. The winged insects have a strong history in the avenues of genetic research. The humble fruit fly is still relevant in the 2020s in genetics, neuroscience, and more.

Now, for the first time, the complete brain and central nervous system of an adult male fruit fly have been fully mapped. Google Research scientists worked alongside HHMI Janelia Research Campus and the scientific community to achieve this milestone. In the social media post outlining the achievement, Google claimed that AI was used “to combine millions of 2D images into 3D neural shapes, reconstructing a record-breaking 166,000+ neurons.” That’s somewhat below the estimated 86 billion neurons in the human brain. AI data centers are going to need more RAM, folks.

This scientific breakthrough is going to “accelerate our understanding of the brain, and is a major milestone in neuroscience,” noted Google last Thursday. By Tuesday, it was already being trained to play classic video games.

We’ve seen software engineer Alex Wormuth and ‘C++ ragebaiteur’ Jessica Paquette already demonstrate fly-brain video game training. Their fly-brain tinkering focuses on Doom and Mario 64, respectively.

Of their Doom training efforts, Wormuth says that “Each Doom frame stimulates sensory neurons. Neural activity is mapped to game controls. Damage triggers a stimulus to two PPL101 dopamine cells as reinforcement.” The dev closes their tweet with the question “Will the fly learn to survive?” The code is fully open source, and you can also watch the training progress of the 166,700 neurons live.

Paquette’s short video clip shows Mario repeatedly taking off and bumping into a wall. If it were bumping into a window, it would be exhibiting the pinnacle of fly-brain intelligence. We also have the code to this fly-brain video gaming project, which was “literally 100% vibe coded with GPT Astra… just for fun.” Hopefully non-coders/tinkerers will get updates on the success of this training via Paquette’s socials.

In recent years, the largest electronics manufacturing services (EMS) and original design manufacturers (ODMs) have gradually ceased their expansion in China and built production capacities elsewhere across Asia. Now, it is time for their clients to follow. Google intends to relocate production of all Pixel smartphones, smartwatches, and wireless earbuds from China beginning in 2027 as it continues to diversify its supply chain amid tensions between China and the U.S., reports Nikkei.

Google's EMS and ODM partners — such as Compal, Foxconn, and Pegatron — have significantly expanded manufacturing capacity in India and Vietnam in the past several years, although China still accounts for a meaningful portion of their production capacity. In a bid to reduce reliance on China, Google reportedly shifted production of some of its high-end Pixel smartphones to Vietnam this year and remained satisfied with the outcome. Since building premium handsets is considerably more complicated than assembling smartwatches or wireless earbuds, the progress with Pixel phones reportedly gave Google confidence that it could shift the rest of the lineup out of China in 2027. If the plan materializes, Google will become the second global smartphone brand after Samsung to relocate smartphone production from China to other countries.

Vietnam is particularly attractive for Google because Samsung has already established an extensive smartphone manufacturing ecosystem in the country that Google can tap into.

Google also has considerably fewer reasons than Apple to preserve its Chinese manufacturing footprint because Pixel smartphones are by far not as popular as iPhones and they are also not sold in China. Google expects Pixel smartphone shipments to grow by 8% – 10% from approximately 12 million units last year, which is an order of magnitude lower compared to iPhone sales per annum.

Despite rising component costs, Google's strategy for Pixel this year is reportedly focused on maintaining unit shipment growth. A supplier working with Google and Xiaomi reportedly told Nikkei that Google is among the few smartphone vendors that have not reduced their shipment forecasts this year, which is not particularly surprising as its unit sales are modest. Yet, it remains to be seen whether the company can both relocate production and increase output of handsets at the same time.

Consumer electronics brands like Apple and Google are shifting production away from China primarily due to escalating China – U.S. geopolitical tensions, which result in punitive tariffs, export controls, and the risk of sudden disruptions. As an added bonus, adding production capacities in countries like India and Vietnam automatically improves supply-chain resilience and reduces over-reliance on a single country. Rising labor and regulatory costs in China, combined with attractive incentives and growing manufacturing ecosystems in places like India and Vietnam, further encourage diversification. Meanwhile, companies like Foxconn or Pegatron do not abandon their China operations and continue to build products not meant for the U.S. market there.

Spirit Airlines, which declared bankruptcy and shut down on the 2nd of May this year, is selling all its assets in bankruptcy court auctions to pay off its roughly $8.1 billion in debt. According to Bloomberg Law, Google LLC beat out Mercor.io Corp. — an AI-focused recruitment firm — to the airline's data, with the AI tech giant offering $10 million, $2.5 million higher than the latter’s $7.5 million bid. Nevertheless, the U.S. Bankruptcy Court for the Southern District of New York named Mercor.io Corp. as the backup buyer in case the Google deal falls through.

Google said it planned to use the massive amounts of data it purchased for training its AI LLMs, and the amount of data that the tech giant got its hands on is indeed a lot. Court records reveal that the Google purchase includes 100 million emails, 500 million Microsoft Teams chats, 7.2 billion records for competitors' flights, 7.5 billion passenger transaction records from 2008, and more than 175,000 employee records from 1986. Aside from this, the company will also get information on revenue, aircraft operations, employee productivity records, audits and fraud, marketing campaigns, human resources records, project management, and pricing curve data, among others.

This could be a cause of concern for anyone who’s ever transacted with the airline, either as a customer, employee, contractor, or even investor, especially as AI is known for its privacy problems. However, the company will not get access to personal data, including the 97.5 million passenger profiles that the airline kept or the 50.2 million customer records from the Free Spirit loyalty program. Furthermore, the court said that all the data being turned over to Google will be “rigorously scrubbed of any personally identifiable information by a third party before receipt.”

The tech giant is seemingly keen on getting its hands on unique data, especially as many companies are now suspected of acquiring thousands of books to train their AI models after they’ve already scraped huge swathes of the internet. Spirit Airlines data is valuable because it’s likely not readily available on the internet and contains specialized information gathered from decades of operation as an airline. Google can then use this to create AI models focused on aviation and offer its services to airlines.

We’re still unsure how AI will fit inside aviation operations, especially as it’s a conservative, safety-focused industry. Nevertheless, Google and other AI companies are probably keen on entering it, especially as air travel is only projected to grow in the coming years.

Google has teamed up with AMD to develop one of its 10th-generation TPUs, according to a note by SemiAnalysis (via Sean). Analysts at SemiAnalysis believe Google may be interested in AMD's CPU cores for CPU-heavy workloads. If accurate, the collaboration would mark AMD's first major involvement in a custom AI ASIC project and could indicate that Google is exploring a new kind of TPU that combines its proprietary accelerator technology with on-board general-purpose cores for CPU-heavy workloads.

"Market chatter suggests [Google] is working with AMD on a TPU project in the v10 generation," a SemiAnalysis note for clients cited by Sean reads. "AMD's involvement would be the first real involvement in a custom AI ASIC project, despite having a custom silicon team. AMD has strong IP, especially in advanced packaging and SoIC. Additionally, CPU IP could also be a draw given Google and its customers are pushing for TPUs with on-package CPU cores for RL workloads."

Having developed nine generations of its proprietary AI accelerators (with Broadcom acting as actual silicon designer) and possessing extensive expertise in accelerator architecture, Google hardly needs AMD to design a conventional TPU. Hence, chances that AMD will implement Google's TPU v10i for inference or V10t for training are low. Hence, Google might need something only a CPU maker like AMD could provide, including CPU IP, programmable logic, interconnects, or certain advanced packaging know-how.

Of these, the CPU angle is particularly noteworthy. SemiAnalysis claims that Google and its customers are pushing for TPUs with on-package CPU cores for reinforcement learning and potentially other CPU-heavy workloads. While conventional LLM training remains overwhelmingly accelerator-heavy, reinforcement learning for reasoning and agentic models can require considerably more general-purpose compute around accelerator operations.

Google has already begun to increase CPU resources around its latest inference-oriented TPUs. Its TPU 8i systems, designed for inference, reasoning, and RL workloads, feature one Google Axion CPU for every two TPUs. By contrast, servers running Google's 7th Generation TPUs used one Xeon 'Emerald Rapid' processor for every four TPUs. Furthermore, we are hearing that in some cases a 1:1 ratio of CPUs to accelerators is optimal, so the future of AI may be way more CPU-heavy than we think.

Meanwhile, bringing CPU cores directly into the TPU package could be a logical next step, as reducing the distance between general-purpose and tensor compute can improve performance and reduce power consumption. This is where AMD comes into play, as it already has experience developing a data center-grade design — the Instinct MI300A — that packs both x86 and accelerator chiplets. A hypothetical Google design could therefore combine Google-developed TPU compute chiplets with AMD CPU and HBM in a tightly integrated package built by AMD. Perhaps, Intel would appear as another potential candidate given that Google and Intel have multiple strategic collaborations. Yet Intel has no experience building hybrid x86+accelerator data center designs.

Note that for now we are speculating and our analysis may be inaccurate. For now, the nature of AMD's alleged involvement remains unclear. Yet, if the report is indeed accurate, the important development may not be that AMD is helping Google build another TPU. Instead, what matters is that Google is considering a new CPU-heavy member of its TPU v10 family, optimized specifically for RL and agentic workloads, and is using AMD as a provider of some of the building blocks needed to create it.

Apple used to be among a few companies willing to buy memory and other components worth billions of dollars under long-term supply contracts at fixed prices. But the artificial intelligence era represents a new reality with new purchasing champions, marking a tectonic shift in the high-tech world. Alphabet, Microsoft, Meta, and Amazon have purchase commitments totaling about $2 trillion, and a significant portion of these commitments are for memory, according to estimates by analyst Claus Aasholm. While the commitments are approximate, span many years, and should be generally taken with a grain of salt, they still reflect the direction the industry is moving.

Combined purchasing commitments from the four major hyperscalers shown in the chart —Amazon, Alphabet, Meta, and Microsoft — reached nearly $2 trillion by Q2 2026, with Alphabet and Microsoft accounting for the overwhelming majority of the total.

The rapid expansion suggests several major findings. Firstly, the AI infrastructure race is accelerating, not stabilizing. Secondly, AI infrastructure investments are driven by a handful of hyperscale cloud service providers (CSPs) whose long-term procurement commitments now vastly exceed those of traditional consumer electronics companies such as Apple.

Thirdly, memory has become a strategic asset — perhaps a competition weapon — rather than a commodity. Fourthly, suppliers of memory — both 3D NAND and DRAM — are gaining pricing power. Finally, demand for memory will likely drive major capacity expansion at Micron, Samsung, and SK hynix, even though so far these companies have been exceptionally disciplined about their capacity investments.

Almost $2 trillion commitments

Google shows by far the most aggressive increase in purchasing commitments, rising from roughly $140 – $150 billion in Q3 2025 to around $811 billion by Q2 2026 (though these are total purchase commitments by Alphabet, not specifically memory purchase commitments), while Microsoft follows a similar trajectory and reaches approximately $678 billion in total obligations, which includes, but is not limited to memory.

Meta is also ramping commitments substantially to around $349.3 billion (again, these are total commitments), whereas Amazon increased its commitments more gradually to roughly $130 billion. By contrast, Apple — which makes the world's most popular smartphone, and which was the largest consumer of memory just a couple of years ago — remains almost flat throughout the period at approximately $57 billion (of which $56.2 billion is payable within 12 months). Apple's commitments fall well short of Nvidia's commitments of $119 billion.

Again, we are talking about total purchase commitments, which include foundry capacity, 3D NAND, and DRAM memory, but are not limited to them. Alphabet, Amazon, Meta, and Microsoft all build custom silicon and custom servers, so a significant portion of these commitments is to various EMS providers.

While $1.968 trillion of purchase commitments for memory and storage alone would be an absurdly large amount of money, a huge portion of these commitments consists of contract manufacturing obligations as well as memory chips. This suggests that the foundry, 3D NAND, and DRAM markets are entering a new phase in which hyperscalers are willing to make vastly larger forward purchasing commitments than traditional consumer-electronics companies, giving suppliers a strong incentive to prioritize customers prepared to secure future capacity on that scale.

Strategic assets

While Claus Aasholm's chart is explicitly dedicated to memory, it does describe total purchase commitments of tech giants, so the chart can reasonably be read as evidence that memory and capacity at TSMC, Samsung Foundry, and GlobalFoundries are becoming a strategic asset rather than merely another component to procure at the best available price.

AI infrastructure requires enormous quantities of AI accelerators, DRAM (including HBM), and 3D NAND. Meanwhile, the supply of high-end memory (HBM) is constrained by fab capacity at major DRAM makers, whereas the supply of AI accelerators is constrained by both wafer capacity and foundries and packaging capacity at foundries and their OSAT partners. As a result, hyperscaler CSPs have an incentive to lock in supply years ahead, even if doing so requires exceptionally large purchasing commitments.

That also changes the relationship between semiconductor suppliers and their customers. In theory, a company willing to guarantee hundreds of billions of dollars of future purchases can effectively help underwrite expansions of foundry, memory, and advanced packaging capacity and, in return, secure priority access to scarce products and future process technologies. In reality, TSMC can well afford capacity expansion using the money it gets from hyperscalers and give priority to its largest customers. In this environment, access to DDR5, HBM, and 3D NAND memory becomes part of the competitive advantage rather than merely a procurement exercise.

This is also what makes Apple's position in the graph interesting: its purchasing commitments barely move while those of Alphabet, Amazon, Meta, and Microsoft surge. If the trend continues, Apple may remain one of the world's largest semiconductor buyers in absolute terms, but the question is whether it will be among the key customers that foundries, memory makers, and OSATs plan their future capacity expansions.

An inflection point

Perhaps the most interesting takeaway of the findings revealed by long-term purchase commitments is that the industry's center of gravity appears to have shifted.

During the smartphone era, foundries (well, TSMC has won) and memory suppliers often competed aggressively for Apple's business because of its enormous purchasing power. Today, hyperscalers building AI infrastructure are making purchasing commitments that dwarf those of traditional CE companies like Apple, which may well represent a strategic inflection point akin to the one Andy Grove described in his 'Only the Paranoid Survive' book.

Will this tectonic shift result in prioritization of customers capable of enabling future capacity expansions through massive long-term purchase agreements, or will foundries and memory makers remain more or less disciplined with their capacity expansions so as not to lose a lot when demand declines? This is a question that has yet to be asked.

In any case, the AI megatrend has transformed semiconductors — from foundries to advanced packaging and from DDR5 to HBM4 — into strategic assets that can no longer be treated as ordinary components procured on demand. And this is something that will continue in the long run.

Sandisk and SK hynix on Tuesday formally introduced the High Bandwidth Flash (HBF) specification, their jointly developed storage technology that promises to bring together the non-volatility of 3D NAND and the performance of High Bandwidth Memory (HBM), which will be handy for AI inference systems. The specification was released through the Open Compute Project (OCP), so it will be an open standard rather than a proprietary interface.

The initial specification defines HBF packages with capacities of up to 512GB using either 8-Hi or 16-Hi NAND die stacks, though these will not be standard 3D NAND stacks, but rather specialized devices with a fast interface. In fact, Sandisk once called them HBF core dies rather than 3D NAND die stacks.

Performance of HBF is divided into three bandwidth grades ranging from approximately 0.4 TB/s to 3.0 TB/s (though we are not sure whether this figure describes the full HBF subsystem or per-package bandwidth). Such a huge performance range implies that Sandisk and SK hynix expect HBF to have a multi-year roadmap featuring multiple implementations and generations of HBF. It is noteworthy that the most capable implementation of HBF (3 TB/s) is set to beat the memory bandwidth of a single HBM4 memory stack (2 TB/s), though it will be unlikely to beat HBM4 when it comes to latency.

Interestingly, SK hynix claims that HBF uses the Universal Chiplet Interconnect Express (UCIe) standard to simplify integration with heterogeneous computing platforms, whereas Sandisk claims that HBF is set to adopt the 'xPU-HBF' interface, which could be its definition of UCIe implemented by companies like Broadcom or Marvell.

In addition to capacity and performance targets, the specification establishes electrical and interface characteristics, packaging and reliability guidelines for stacked HBF devices, as well as software I/O requirements. For now, these specifications are not officially published by the OCP.

Extracting 400 GB/s of bandwidth from a single 512GB HBF package is not a trivial task. To enable such a package, Sandisk once planned to use 16 HBF core dies that feature many, many arrays that can be accessed concurrently using dedicated read/write paths. Meanwhile, it is possible to reach over 400 GB/s of bandwidth per package using a single UCIe interface that runs at up to 64 GT/s and features 64 lanes. Yet, this means that the HBF base die will be a fairly complex piece of silicon.

Sandisk and SK hynix position HBF as a new memory tier for AI inference workloads by combining near-memory bandwidth with the higher capacity and non-volatility of NAND flash. The technology is aimed at workloads that require substantially larger memory pools close to compute than HBM alone can economically provide. For example, while the maximum capacity of an HBM4 stack is 64GB, an HBF stack can provide up to 512GB. Even at a lower bandwidth, such memory can be useful for inference workloads.

Arguably the biggest question about HBF is who is going to adopt the technology? Since Sandisk and SK hynix announced plans to collaborate on defining the HBF specification in 2025, only Google and Tenstorrent have expressed interest in participating in the HBF consortium. Meanwhile, AMD, Broadcom, Intel, Nvidia, Marvell, Micron, Qualcomm, Samsung, and Western Digital have so far expressed no interest in HBF.

Amazon, Google, Meta, and Microsoft have spent more than a trillion dollars on AI infrastructure, including data centers, the chips inside them, and the power needed to run the facilities, since 2023. The Financial Times said that these four big companies have already hit $1.1 trillion in capital expenditure based on their latest earnings reports, and that an additional $745 billion is expected to be added to this figure just this year.

“There is basically no end in sight for the growth in capex,” RBC Capital analyst Rishi Jaluria told the publication. “Investors need these companies to toe the tight line between investing in AI and not compromising the things that have made them successful.” This massive investment has upended several other industries — namely electricity prices and memory and storage chips. The massive power demand that data centers have put on the power grid has forced many U.S. utility companies to spend billions of dollars to upgrade their respective infrastructure, which they then passed on to all consumers, not just the big ones that forced the upgrade.

This, alongside other environmental issues, has caused many Americans to push back against data center projects near their communities. The White House instituted the “ratepayer protection pledge” and made AI hyperscalers, utility operators, data center companies, and individual states promise that they will protect the average consumer from electricity cost increases. But so far, no state has taken a step to codify this pledge into law. Oregon actually enacted the POWER Act, which resulted in a 30% increase in the power bill of users that consumed more than 20MW while slashing the bills of residents by 1.3%, but the state did this in 2025, way before President Donald Trump called the tech giants into the White House and told them to “pay their own way.”

The mountains of cash that these tech giants are pouring into AI are also affecting the memory and storage chip industry. Since these AI hyperscalers have a lot of liquidity from investors, they are willing to pay top dollar for the HBM they need to run their data centers. Because of this, it made sense for Micron, Samsung, and SK hynix to prioritize them over DRAM, especially as they can charge a premium for these chips and there are customers who are willing to pay at those prices. This resulted in a shortage of consumer memory that started in 2025 — while this initially affected PC builders and enthusiasts, it has started to affect other industries that require memory as well, including cars and smartphones. Even Apple, which historically had huge sway over its suppliers, was forced to increase prices because of the shortages.

Aside from skewing other industries, the massive CAPEX the big four are going into is alarming some experts, warning that the promises and contracts they’re making are leading to “hidden debt” not listed in their balance sheets. The amount, worth around $1.65 trillion, is annotated in their quarterly financial statements as future obligations that will only come into play as the related asset or service comes online. The current value is 122% of the actual debt reflected on their balance sheets, which could give investors the wrong impression that they have fewer obligations than they actually have.

While the amount of money that the big four are spending on AI might seem dizzyingly high, we must note that these companies are raking in massive amounts of cash quarterly themselves. Microsoft’s latest quarterly revenue is $90 billion, while Meta made $60 billion in the same period. Alphabet (Google) announced revenue of nearly $120 billion, while Amazon made $200 billion. That is a total of nearly $470 billion for these companies in just the last quarter.

Still, that does not mean that they can just keep on spending on AI. For example, even though Google’s cloud business had a revenue of $11 billion last year, its price dropped after it announced that it spent more than it made last quarter — the first time this happened in the 20 years since it went public. Meta is also planning to rent out its AI compute, apparently following in the footsteps of Amazon, Google, and Microsoft, which have growing cloud businesses. However, this announcement caused a drop in its stock price. “They are a bit all over the place,” SLC Management managing director Dec Mullarkey told FT. “For investors it’s no longer growth at any cost; they want to see the spending flowing through to results, like at the Big Three.”

Google was among the first hyperscalers to develop its own custom AI processors about a decade ago and has been steadily ramping their deployment since then. The company seems to be so confident about its TPU v9 due in 2028 that it intends to order 12 – 15 million of such processors, according to a Fubon Research note to clients published by Sean. If the information is correct, Google may not only produce more or a comparable number of AI accelerators than Nvidia, but may also need to use Intel Foundry to meet its goals.

"Based on our checks, Google plans to have 12 – 15 million TPUs in 2028," the paper reads. "Entering 2028, Google’s TPUs will enter the V9 generation with four compute dies, suggesting that their capacity consumption will more than double in 2028 versus 2027."

Fubon estimates that Nvidia supplied 8.2 million data center AI GPUs in 2026 and is on track to increase the number to 12.4 million in 2028. If Fubon is correct about Google's plans to produce 12 – 15 million 9th-generation TPUs in 2028, then the company may produce more, or at least a comparable number of AI accelerators, than Nvidia in 2028.

TSMC is not enough

How the performance of Google's v9 TPUs will stack against Nvidia's Rubin and Rubin Ultra is something that remains to be seen, but the fact that Google intends to use four compute chiplets on these AI accelerators clearly points to the fact that the company bets big on the performance of these processors. Meanwhile, building an AI accelerator with four large compute chiplets is a major engineering effort, which Google seems to have accomplished.

"Although we do not have the detailed allocation yet, we think it is difficult to reach Google’s target with TSMC alone, and Intel's supply is a must by 2028," the paper continues.

Researchers from Fubon are not sure whether Google's allocations at TSMC will be enough to meet the company's demand for 12 – 15 million 9^th Generation TPUs, so they think that Google will have to use Intel Foundry's capacity to meet its volume goals. Over the past few months, we have seen reports claiming that Intel had landed orders to make three million TPUs for Google following months of Google's testing of Intel's advanced packaging technologies. Indeed, if Google wants to make its silicon at Intel Foundry, usage of Intel's advanced packaging services makes great sense. It should be noted that when compute chiplets are developed, they must be developed with their packaging technology in mind, as Intel's EMIB/EMIB-T and TSMC's CoWoS-L are incompatible.

World's largest consumer of AI accelerators

If the information about Google's plans to produce 12 – 15 million TPUs in 2028 is correct (note that the difference between 12 and 15 is 20%, which is huge) and Google will indeed deploy more AI accelerators annually than Nvidia sells to the entire market, it would mark a dramatic shift in the AI hardware landscape. It will not only make Google the world's largest consumer of AI accelerators (as the company will unlikely cease buying Nvidia hardware), it will eventually make Google the owner of the world's most capable AI hardware fleet. Whether or not Google will use its overwhelming AI compute capacity primarily for its own services, or will lend the majority to other is something that remains to be seen.

Meanwhile, for Google's rivals, the milestone will underscore the growing importance of vertically integrated AI infrastructure, where cloud providers design chips tailored to their own software stacks, workloads, and data centers instead of purchasing off-the-shelf GPUs.

Yet, Google's surpassing Nvidia in unit shipments would not necessarily diminish Nvidia's dominant position. AI demand continues to expand so rapidly that both companies could increase deployments simultaneously, but Google will simply grow faster, at least till Nvidia ups production of its AI accelerators with Feynman and Feynman Ultra in 2029 – 2030. After all, Nvidia's AI GPUs are sold out. What Nvidia should worry about is not the volumes of TPUs that Google can deploy, but rather the fact that these processors do rely on a software stack that rivals Nvidia's CUDA, the company's main competitive advantage.

Google's parent company Alphabet recently reported negative free cash flow of $5.9 billion for the second quarter of 2026, the company's first cash-negative quarter since its 2004 IPO, after capital expenditures doubled year-over-year to a record $44.9 billion and exceeded the $39.1 billion its operations generated as it continues its rapid buildout of AI data centers, according to its earnings release.

CFO Anat Ashkenazi raised full-year capex guidance to between $195 billion and $205 billion, up from $180 billion to $190 billion, and disclosed that Google delivered TPU systems to customers' data centers for the first time, a shift from renting the chips exclusively through Google Cloud.

The quarterly deficit is small compared to the sums moving through the business, and the firm's trailing 12-month free cash flow remains positive at $53.3 billion. Back in February, Alphabet raised its guidance, but since then, spending has exceeded the cash the business generates due to its AI buildout, and Alphabet is covering the difference with borrowed money and new stock.

Servers first, buildings second

Approximately 60% of the quarter's technical infrastructure investment went into servers, with the remaining 40% split across data centers and networking equipment, Ashkenazi told analysts on the earnings call. That ratio inverts the usual assumption that hyperscaler capex is dominated by construction. Most of Alphabet's marginal dollar now buys compute, primarily its own TPU-based systems, rather than other forms of infrastructure. Depreciation of property and equipment rose to $7.1 billion in the quarter from $5.0 billion a year earlier, and Ashkenazi said infrastructure spending will keep pressuring the P&L through higher depreciation and energy costs.

"We're still in a supply-constrained environment," Ashkenazi said on the call, repeating a characterization the company has used for several consecutive quarters. Demand is running far enough ahead of Alphabet's own build schedule that the company is renting third-party capacity as a bridge while its data centers come online, an arrangement Ashkenazi said will create modest margin pressure for its Cloud segment in Q3. The construction pipeline behind the 40% includes a $40 billion, three-campus program in Texas through 2027 in November, representing the company's largest investment in any state, and a $1.5 billion expansion of its Jackson County, Alabama campus, announced in June.

TPU sales turn capex into inventory

Google began recognizing revenue from TPU system sales in the quarter, with Ashkenazi telling analysts the systems were "delivered to customer data centers for the first time in Q2" and that "the vast majority of the revenues from these agreements will be realized in 2027." According to Google's balance sheet, inventory stood at $10 billion on June 30, roughly four times the $2.4 billion recorded at the end of 2025. A meaningful slice of the quarter's cash outflow bought hardware that sits on the balance sheet today and will be sold to customers next year, bringing cash back in. Money spent on data centers doesn't return in the same manner; instead, it is written down over the years of use.

Anthropic is anchoring a great deal of Alphabet's external demand, with its October 2025 agreement giving the Claude developer access to up to one million TPUs and more than 1 GW of capacity coming online this year, and an April securities filing from Broadcom, Google's TPU co-designer, added roughly 3.5 GW of TPU capacity from 2027 while locking Broadcom into future TPU generations through 2031. Meta entered talks for multi-billion-dollar TPU deployments in its own data centers last November. The current flagship, the seventh-generation Ironwood TPU, carries 192GB of HBM3E per chip and scales to 9,216-chip pods that Google rates at 42.5 FP8 exaflops.

Every TPU Google manufactures serves four functions: training and serving Gemini, running Search and YouTube inference, renting to Cloud customers, and now shipping as sold hardware. No other hyperscaler's capex spend works that many jobs, and none of the others has a chip business generating third-party revenue at this stage.

A $98 billion liability

Alphabet issued Class A, Class C, and mandatory convertible preferred stock in June for net proceeds of $49.6 billion, earmarked in the release for "capital expenditures to scale AI infrastructure and global compute," and sold $20.3 billion of senior unsecured notes during the quarter. Long-term debt reached $98.2 billion on June 30, up from $46.5 billion at the end of 2025 and from roughly $16 billion a year before that, a run-up Ashkenazi acknowledged on the call. The February bond program alone raised more than $30 billion across multiple currencies, including a 100-year sterling tranche, it was reported at the time.

Together, the four largest hyperscalers plan a combined 2026 capex of around $725 billion, up 77% on 2025, and Alphabet's new range now tops the group alongside Amazon's roughly $200 billion. Meta raised its own 2026 forecast to $125 billion to $145 billion in April, citing component pricing and competition for land, power, and labor. Alphabet's headline Q2 net income of $112.1 billion overstates the reality somewhat, however, as $99.0 billion of other income came primarily from unrealized gains on equity securities, contributing $6.26 of the $9.11 in diluted EPS. Operating income, the cleaner measure, rose 30% to $40.8 billion.

Google Cloud grew 82% to $24.8 billion in the quarter with an operating margin of 35.6%, and backlog reached $514 billion, up more than $50 billion sequentially, with just over half expected to convert to revenue within 24 months. Those contracts are the collateral behind the spending, with the buildout chasing demand Alphabet has already booked rather than demand it hopes to find. Ashkenazi said free cash flow "will remain under pressure" and confirmed capex will rise significantly again in 2027, so the question the next few quarters will answer isn't whether Alphabet returns to positive territory in any given period, but whether operating cash flow, up 41% year over year in Q2, can keep growing faster than a spending that shows no sign of slowing down.

Google is developing a server chip, informally dubbed "Frozen v2," that would etch part of its Gemini model's architecture directly into the silicon, according to a report published Monday by The Information, citing two people with direct knowledge of the matter. Engineers on the project have projected that the chip could serve six to ten times more tokens per unit of power than the newest generation of Google's TPUs, with deployment targeted for as soon as 2028. The two sources said the project is partly a response to an AI compute shortage severe enough that Google Cloud has turned down deals with outside customers.

A TPU, like a GPU, runs whatever model is loaded onto it, which means the hardware makes time-consuming runtime decisions as it interacts with each one. Frozen v2 would have some of those decisions for Gemini fixed in the transistors, reducing the number of steps the chip takes and the amount of data it shuttles around per query. That could cut response latency enough to enable new applications, one of the sources said.

The original Frozen design, spearheaded by Google DeepMind chief scientist Jeff Dean, went further and would've baked Gemini's weights themselves into the chip. Google set that proposal aside because silicon tied to a single model version would have too short a life cycle, according to the report. Frozen v2 freezes the architecture instead and leaves the weights updatable, so the chip stays useful across Gemini releases, but only for as long as Google builds them on the same underlying architecture. How much of the model to lock in is reportedly still undecided.

Google doesn't plan to produce Frozen v2 at TPU volumes and views this generation partly as a trial run for more specialized silicon as model designs settle. The chip would sit alongside, rather than replace, a TPU line that split into separate training and inference variants with the eighth generation announced at Cloud Next in April. Its 2028 target also comes in the same year Google has reportedly booked Intel to package more than 3 million TPUs.

Model-hardwired inference silicon already exists in demonstrations. Taalas, a Toronto startup that has raised more than $200 million from investors including Quiet Capital and Fidelity, launched its HC1 chip in February with Llama 3.1 8B permanently wired into an 815mm-squared die built on TSMC's N6 process. The company claims 17,000 tokens per second per user with no HBM on the package. Nvidia, meanwhile, struck a $20 billion deal in December to license technology from inference chip designer Groq.

Google hasn't yet confirmed the project, with a spokesperson telling The Information that not every project moves into production and that "this rigorous exploration is central to our full stack approach."

Google plans to use Intel's EMIB-T packaging for its next-generation TPU codenamed Humufish, according to SemiAnalysis. TSMC's portfolio of chip-on-wafer-on-substrate (CoWoS) technologies has become the de facto standard advanced packaging option for nearly all AI and HPC processors made in the industry. Competing offerings are usually considered as secondary solutions if CoWoS is in tight supply, but things are beginning to change.

Google is a long-standing CoWoS customer for TPUs, starting from the Third-Generation TPU, all the way to Google's latest Eighth-Generation TPUs. Assuming that SemiAnalysis's report about Google's decision to move to EMIB-T with its Ninth-Generation TPUs is accurate, it's a big decision for Google, as switching from one advanced packaging technology to another is a complicated endeavor, which involves plenty of changes and unknowns. Understanding Google's reasons for the switch could shed some light on the prospects of Intel's and TSMC's advanced packaging technologies, which will be used by leading chip designers and hyperscalers in the coming years.

Advanced packaging technologies at glance

For years, Google used TSMC's CoWoS-S, and later, CoWoS-L packaging. Initially, the company used CoWoS-S packaging, which relies on a silicon interposer up to 3.3X the reticle size, but with its 7th- and 8th-Generation TPUs, the company moved to CoWoS-L. CoWoS-L relies on a redistribution layer (RDL) interposer with embedded local silicon interconnect (LSI) bridges that enable high-performance die-to-die links, which can scale packages to 5.5X the reticle size today. TSMC promises to improve CoWoS-L's capabilities to scale over 14X the reticle size by the end of the decade.

Unlike CoWoS, Intel's embedded multi-die interconnect bridge (EMIB) technology does not use any interposers. The technology instead relies on tiny embedded silicon bridges within the substrate to enable high-density die-to-die interconnections, whereas everything else is routed through an inexpensive organic substrate.

EMIB-T adds through-silicon vias (TSVs) to the bridge, which enables power to flow vertically instead of going through the organic substrate. In addition, Intel's EMIB-T also integrates sophisticated metal-insulator-metal (MIM) capacitors and a dedicated ground plane into the bridge to improve power integrity. The latter is a particularly important feature of complex next-generation AI accelerators, which demand more, cleaner power, and for which power delivery is becoming as challenging as signal routing.

The main selling point of EMIB (and EMIB-T) is that it is not constrained by interposer reticle limits as it places small silicon bridges only where high-density die-to-die links are needed. Strictly speaking, CoWoS-L is not either, as it uses LSIs locally as well. The difference is that those bridges are embedded into a package-wide RDL interposer that connects everything and enables dense interconnections across the package.

Since both CoWoS-L and EMIB-T are designed to address the same applications and have many similarities in the way they do this, the choice between them is likely driven by a combination of factors rather than one single advantage or disadvantage. On the technology side of matters, these factors include interconnect performance and density, power delivery, scaling beyond very large package sizes, and mechanical rigidity. On the business side of things, costs, capacity availability, and supply chain diversification are also a significant factor.

Crucial differences

SemiAnalysis claims that the main advantage of EMIB/EMIB-T over CoWoS is the lack of reticle limit, but this argument does not fully hold against CoWoS-L, as it was invented specifically to escape the reticle limitation by replacing the monolithic silicon interposer with localized LSI bridges.

When it comes to dense, package-wide routing, CoWoS-L's RDL interposer is fundamentally superior to an ordinary organic substrate offered by EMIB-T. Organic substrate wiring has coarser line/space dimensions and larger vias, so it cannot provide the same routing density as CoWoS-L's fine-pitch RDL. Where an EMIB bridge connects adjacent dies, Intel can achieve very high interconnect density. But anything that needs to travel beyond those bridges must use the package substrate or cross a topology involving additional bridges.

By contrast, CoWoS-L gives the designer two levels of connectivity: LSIs provide extremely dense local die-to-die connections, while the global RDL interposer provides relatively dense and flexible routing across the entire package. This means the RDL can carry longer, lower-density connections without consuming valuable LSI resources, while still offering much finer routing than the underlying package substrate.

One scenario for Google's choice is that it potentially wanted better power delivery than what CoWoS-L could offer. EMIB-T integrates TSVs for vertical power delivery, sophisticated MIM capacitors for local decoupling, and a dedicated ground plane into its silicon bridges. The combination of these features substantially reduces power-delivery impedance and improves transient response and power integrity, which gives EMIB-T a major advantage over conventional EMIB for power-hungry AI accelerators. However, we have no idea how EMIB-T stacks up against CoWoS-L in the case of Google’s Humufish.

Of course, the larger the RDL interposer becomes, the greater its parasitics can become, potentially limiting scaling unless TSMC finds ways to mitigate them. However, EMIB does not eliminate long-distance wiring: If two distant dies must communicate, those signals still have to travel somewhere, and routing them through an organic substrate is not inherently electrically superior to routing them through a purpose-built RDL interposer. Therefore, it is difficult to claim that Google chose EMIB-T over CoWoS-L, simply because EMIB-T offers superior package-wide electrical characteristics.

After Nvidia suffered yield loss with its Blackwell data center GPUs due to an alleged mismatch in the coefficient of thermal expansion (CTE) among the GPU chiplets, LSI bridges, RDL interposer, and motherboard substrate, which led to warping and system failure, it is reasonable to question the mechanical rigidity of CoWoS-L packages. Nvidia has found a solution for its dual compute chiplet Blackwell packages, and so have other developers of AI accelerators. However, as package dimensions increase, they may behave differently, therefore causing yield losses.

By contrast, EMIB/EMIB-T eliminates the large RDL interposer and embeds small silicon bridges in the organic substrate, so most of the package consists of the substrate itself. This does not make EMIB/EMIB-T packages immune to mechanical failures, as large packages can warp and bend, causing various problems. However, as such packages lack the very source of global thermomechanical stress, they can potentially be more robust mechanically. However, EMIB-T can potentially complicate things because TSVs, additional metal structures, MIM capacitors, and their ground plane make the bridge more complex. Thus, Intel must manage both global package warpage and local stresses around each embedded bridge to ensure the mechanical rigidity of these packages.

Ironically, while CoWoS-L can offer denser package-wide routing, which is better for ultra-large processors, EMIB-T may potentially provide better mechanical rigidity required for such devices. Nonetheless, EMIB-T and its organic substrate do not eliminate package bending or cracking risks entirely.

Economics

If Google's Humufish TPU really moves to EMIB-T, the decision could well be both technical and strategic. Google has the engineering resources to opt for an all-new packaging technology in an effort to lower costs and eliminate dependence on TSMC's constrained CoWoS capacity. Nvidia tends to procure advanced packaging allocations years in advance, so it is possible that Google could simply not get enough CoWoS-L wafers for its 9th-generation TPU.

As a bonus, Google can also build relationships with Intel Foundry without using the company's fabrication technologies. In fact, keeping in mind that Intel and Google already have a strategic agreement covering Intel Xeon CPUs, it wouldn't be too surprising to learn that the cloud giant is courting Intel Foundry as well.

Both Intel's EMIB-T and TSMC's CoWoS-L have their own technological and economic advantages and disadvantages. Perhaps the biggest advantage of CoWoS-L is its predictability, as the company has experience with that tech. However, if Google has decided to drop that predictability in favor of an all-new packaging method, it may well have a combination of technological and strategic reasons to do so.

Google is testing a reCAPTCHA check that switches on a user's camera and asks them to wave or hold up an open palm, mapping 21 coordinates of the hand to decide whether a real person is present. The experimental method, which is rolling out as a limited test, was defeated within days by testers who fed a static stock photo of a hand through the OBS Virtual Camera and passed with no live person, no video, and no AI involved.

The check sits inside Google Cloud Fraud Defense, the platform behind reCAPTCHA on login screens, sign-up forms, and checkout pages. It’s meant to catch what the older challenges increasingly miss, such as automated account creation and credential stuffing.

When the challenge triggers, the browser requests camera permission and prompts the user through a short gesture. Google’s machine-learning model records a brief video and extracts hand-landmark data covering 21 finger and knuckle points, using the same landmark scheme that powers its MediaPipe hand-tracking tools.

Google's documentation states that the footage is deleted once verification completes, that no audio is recorded, and that the video is never tied to a user's identity or shared with third parties. The same page adds that any data collected is used and stored under the Google Privacy Policy, so it’s not entirely clear which is true or what data is collected. Users who can’t perform the gestures fall back to the existing visual and audio puzzles, and the feature is optional for now. The gesture check doesn’t retire those older challenges, but instead layers a camera-based biometric step on top of them.

Following its launch, it didn’t take long for the Internet to get around the new method. Using nothing but a stock image of a person waving into an OBS Virtual Camera, testers pointed reCAPTCHA at that virtual feed and cleared the challenge after a few adjustments to the image position. Because the whole sequence can be driven by a short script, gesture reCAPTCHA in its current state appears to do nothing but add friction for ordinary users while offering little resistance to an attacker.

reCAPTCHA has been struggling with similar challenges for years. In 2024, researchers reported a 100% success rate against reCAPTCHAv2 using off-the-shelf object-detection models, and last year, an OpenAI agent was recorded clicking through a Cloudflare “I am not a robot” check while narrating each step. The hand-gesture test raises the stakes for users since a hand scan is biometric information — regardless of whether Google promises it isn’t harvesting your data.

Less than two weeks ago, Cloudflare, Google, Mozilla, and Microsoft jointly proposed Private Access Control Tokens (PACT), a cryptographic scheme meant to replace CAPTCHA challenges with a privacy-preserving proof that a request comes from a legitimate client. The proposal comes on the back of findings that roughly 58% of global HTTP requests come from bots, a threshold Cloudflare hadn’t expected before 2027.

“We can build a better solution that maintains strong privacy and provides a much less annoying experience for real humans using the web,” said Bobby Holley, CTO for Firefox at Mozilla, in the announcement.

Google hasn’t said whether the hand-gesture test will graduate to general availability.

Today marks 15 years since the first Chromebooks hit the market. Google partnered with Acer and Samsung to get a range of devices ready for the big launch day in 2011. While the platform has gone on to enjoy enviable success in the education market, it continues to be sidelined in mainstream and premium markets, despite the best efforts of Google and partners.

Google’s vision in 2011 was to “make computing simpler and more accessible for everyone.” It arrived with this goal at the tail end of the netbook era, where there was a proliferation of cheap Windows thin and light designs that were infamous for becoming tragically slow in a short time. Some might describe the first Chromebooks as cloud-first evolutions of netbooks – and they indeed made much better use of limited hardware with fast boot times, browser-based workflows, and everything done in the cloud, easing the demands on the (typically) anemic hardware.

Mainstream and premium laptop users, perhaps stung by netbook experiences, have never warmed to Chromebooks, though. Google and partners have invested in high-end product development across several generations, to no avail. Chromebooks seem to be firmly entrenched in K-12 education computing, and can’t escape from that niche.

We’d probably conclude that Google Chromebooks missed their chance in the early 20-teens by holding back some of the best initiatives we are seeing on the platform now. For example, it took until 2016 for the Google Play Store to arrive on Chromebooks, in 2018 Linux app support was added, it took until 2019 for Steam gaming support (beta, recently killed though) to arrive, and until 2021 for ChromeOS Flex to be released to install on out-of-support old PCs and Macs, and only in 2023 did Google decide to ensure new Chromebooks got a decent (10 years) length of OS support. Google could have gone all-in with its best features earlier on, instead of wasting time and resources on the ridiculously expensive Pixelbook (2017), for example.

Nevertheless, as noted above, Chromebooks are now an undeniable success in the education segment. In K-12, the platform still looks unassailable due to a number of factors. Probably the most important features in its favor in this segment are the platform’s lower costs, centralized management, and ruggedized options available.

Chromebooks have also earned a reputation for reliability and security. Research suggests the platform requires fewer tech support calls than rival computing platforms. Last but not least, the recent change to a 10-year device updates support guarantee should cement the Chromebook platform’s good reputation.

Researchers from the University of California San Diego (UCSD) collaborated with Google to recycle “old” Pixel smartphones and give them a second life as a low-cost data center. According to Google Research, retired smartphones are part of the “embodied carbon” that is associated with manufacturing and its carbon footprint. In fact, humanity’s penchant for mobile devices and replacing them every few years is one of the biggest contributors to e-waste, so the group from UCSD planned to give these discarded devices a second life as a “general-purpose computing platform.”

The study revealed that smartphones from just three years ago still deliver a higher single-core performance compared to servers like the Asus RS720A-E11, which can be equipped with Nvidia H200 or Nvidia RTX Pro 6000 GPUs and two AMD EPYC server processors, that you frequently find in the most powerful data centers. While the latter delivers performance that a mobile device can’t even dream of, the fact that the former still scored higher in the SPEC benchmarking suite on a per-core basis meant that researchers could still use them for compute tasks with a little creativity.

The first thing they did was to strip these gadgets of non-essential components — displays, batteries, cameras, speakers, chassis, etc. Only the motherboard remains, as it plays host to the SoC needed for running compute. The Android operating system is then replaced with a general-purpose Linux distro used in data center applications, which removes unnecessary bloat found in the original consumer device and allows for the deployment of orchestration software like Kubernetes. Benchmarking results revealed that 25 to 50 old phones wereequal to the computing power of a single dual-socket server-class CPU.

UCSD determined that a 20-phone cluster can support one application that a 75+ student class requires. So, instead of hosting it on the cloud, which would entail additional costs and resource use on the data center side, it could instead run these apps on a local deployment of these used smartphones. The research team plans to use 2,000 phones to build a local data center that can support “a hundred such classes at once.” Aside from getting the advantage of running apps locally and owning the hardware needed for them, the group also says that it’s only a “fraction of the usual cost,” likely referring to building a local server made from new components. This is especially true today, with the increased pricing for memory and storage chips.

The research team says that it expects to launch the full system later this year and is looking to see how consumer parts can withstand continuous use in a data center application. But even if the experiment is successful, we don’t foresee AI hyperscalers switching to servers made from used phone parts as they would often want to work with fewer parts and the reliability delivered by specialized hardware. Still, this is a great option for universities and educational institutions, as well as smaller entities that do not have the resources to secure brand-new parts and compete against tech giants with billions of dollars to burn.

This isn’t the first time scientists have looked at giving old phones a second life — another group of researchers looked at converting old phones into “tiny data centers” last year, even using one set of four old devices for underwater monitoring. After all, even though the SoCs found in these devices are considered “outdated” by modern standards, they should still be more than capable enough for many mundane tasks. NASA even repurposed the Qualcomm 801 SoC, a mid-range chip from 2014 and found in the Ingenuity Mars helicopter, to help the Perseverance rover find its way around the Red Planet like some sort of processor for a makeshift GPS. And for smartphones that no longer work, people are finding ways to extract the gold and other resources found on their boards for recycling.

Google has placed an order for Intel to build more than 3 million of its TPUs in 2028 after months of testing Intel's advanced packaging, according to The Information, citing four people familiar with the matter. They claim that Nvidia is evaluating Intel to build a future processor that fuses four GPU dies into one unit, tied to its Feynman architecture due in 2028, and that SK hynix is testing whether its high-bandwidth memory works reliably with Intel's packaging.

Specifically, SK hynix needs to know whether Intel can run packaging to the standard that AI accelerators demand. TSMC’s CoWoS is the industry-standard process for it and has been oversubscribed for more than two years. Intel’s embedded multi-die interconnect bridge, or EMIB, is the only alternative AI chip makers can realistically qualify at volume before the end of the decade.

This isn’t a first for Intel: Google and Amazon were reported to be in active discussions for their custom AI processors back in April, but the remarks from these sources move those “discussions” to a solid unit figure and production timeline, adding in SK hynix qualification that would ultimately determine whether any of it reaches Nvidia accelerators.

CoWoS bottlenecked

TSMC's leading-edge wafer lines and its CoWoS packaging are both at capacity. At the company's annual shareholders' meeting in Hsinchu on June 4th, CEO C.C. Wei said, "It will be a long time before we can meet customer demand," telling shareholders that the company simply can’t satisfy American customer demand for years, even as it builds out U.S. capacity. He had already told the Semiconductor Industry Association last November that TSMC's advanced-node capacity falls "about three times short" of demand.

The queue for CoWoS is concentrated across a handful of buyers. Nvidia is naturally expected to account for the majority of global CoWoS demand — about 60% this year — with Broadcom and AMD absorbing another 26% between them, leaving custom-ASIC designers and smaller AI-chip makers waiting behind the largest GPU order book in the industry. But the industry can’t wait, and both these smaller players and hyperscalers alike with multimillion-unit roadmaps need to qualify a second packaging solution rather than wait for capacity that TSMC says will be short for years.

As for EMIB vs. CoWoS, they solve the same problem in opposite ways. CoWoS mounts every die on a large silicon interposer that all signals and power must cross, and the interposer scales with package size, so reticle-class designs waste silicon at the edges. EMIB, meanwhile, embeds small silicon bridges in the organic substrate only where two dies need to connect, with no interposer at all. Intel cites package utilization near 90% EMIB against roughly 60% for interposer-class packaging, because small bridges tile efficiently while large interposers don’t.

Bernstein analysts estimate EMIB packaging costs a few hundred dollars per chip against $900 to $1,000 for CoWoS on a Rubin-class processor, though the firm flags the fact that there’s a “lack of an external production track record” in that estimate. As always, there’s a trade-off: standard EMIB routes power around the bridge through the substrate in long, resistive paths. That might have been acceptable for Sapphire Rapids and Ponte Vecchio, but not for HBM4-class accelerators that draw more current.

EMIB-T closes that gap by adding through-silicon vias to the bridge die for vertical power delivery, and it’s set to enter production fab rollout this year. Intel has said EMIB-T supports HBM3, HBM3E, HBM4, and future HBM5 stacks and scales to a 120mm x 180mm package carrying more than 38 bridges and over 12 reticle-sized dies. Jaguar Shores, the successor to the canceled Falcon Shores accelerator, is the likely first product to use it.

Gated by SK?

Working with SK hynix could be a huge boon for Intel, with the qualification of its packaging by the South Korean memory giant potentially deciding whether it reaches flagship AI silicon or not. SK held a 57% share of HBM revenue in Q4 2025 per Counterpoint Research, and UBS expects it to take roughly 70% of the HBM4 supplied for Nvidia's Rubin platform this year.

HBM stacks are themselves a packaging problem: multiple memory dies bonded vertically through TSVs, then mounted next to a host processor with tight tolerances on power and thermal behavior. Validating those stacks on EMIB rather than a CoWoS interposer is the test of whether Intel can package memory to the standard Nvidia and Google require.

An official thumbs-up from SK, or an HBM-4-on-EMIB-T production result, would convert Intel’s packaging from “tested” to “trusted.” But, until (or if) that happens, the split between accelerator types will remain: ASIC designers running lower memory bandwidth, including Google and Meta, can adopt EMIB sooner, while bandwidth-bound GPUs stay on CoWoS longer.

Intel still needs to prove EMIB

No named external AI customer is in EMIB or Foveros volume production today. Intel runs EMIB in its own server CPUs, including the 18A Clearwater Forest part whose 17-tile package uses 12 bridges, but every specifically named outside engagement so far, including Google’s order, points at 2027 or 2028 products or remains an evaluation.

Intel Foundry lost $10.3 billion on $17.8 billion of revenue in 2025, and in Q1 2026, the division posted $5.4 billion in revenue against a $2.4 billion operating loss, with external customers accounting for just $174 million of the total. CFO David Zinsner told the Morgan Stanley TMT conference in March that the foundry is close to closing deals worth "billions per year in terms of revenue" on advanced packaging alone, against a pipeline he had earlier measured in the hundreds of millions.

Another unknown is process yields: Intel uses 18A, its first node with gate-all-around transistors and backside power, for Panther Lake and Clearwater Forest, an internal proving ground before courting outside logic customers. However, Intel's most recent guidance is that yields are improving 7 to 8 percent each month, accelerated by enhanced cooperation with external partners.

SpaceX just announced that it closed a multi-year deal to provide compute capacity to Google. The agreement, which is worth $920 million per month, will begin in October 2026 and is expected to continue until June 2029. Reuters said that the transaction includes 110,000 Nvidia GPUs, plus CPUs, memory, and all other components needed for AI processing.

It appears that Elon Musk's company will not deliver the entire 110,000-strong GPU compute capacity in one go — Google will pay a reduced monthly fee as the company brings more server racks online through September 30, 2027. If SpaceX cannot hit the 110,000-GPU target on that date plus a one-month grace period, then Google can cancel the agreement or settle for the lower number of available GPUs “with a corresponding pro-rata reduction in the monthly fees.” It also gave the two parties the option to cancel the deal altogether after December 31, 2027, provided that they give a 90-day notice to the other.

This is the second major deal that SpaceX announced in months, as Anthropic secured the entire computing power of SpaceX’s Colossus 1 data center in early May. This was a surprising move, especially as Colossus 1 is one of the company’s most hyped assets, which Elon Musk launched in just 19 days. It turns out that launching it at such speed meant that it has a mix of H100, H200, and GB200 GPUs, which is resulting in efficiencies for training AI LLMs as the faster GB200 GPUs end up waiting for the older, slower GPUs before it can complete each computational step. Anthropic is instead using it for inferencing, especially as it is struggling to keep up with the demands of its growing user base.

The combined annual value of just these two deals is already worth more than SpaceX’s entire revenue for 2025. Reuters estimated that they would bring in more than $25 billion annually to the company, compared to the less than $20 billion that it made from Starlink, launch services, and AI revenue.

These massive deals, worth more than $70 billion in total, will lift SpaceX as it targets a $1.75 trillion IPO on June 12, 2026. While it started out as a space exploration company and is known for commercially launching satellites at a fraction of the cost compared to NASA and providing relatively affordable and stable satellite internet, it’s actively expanding towards orbital data centers. SpaceX acquired xAI earlier this year to help achieve that dream and has even filed some documents at the FCC detailing its plans. Google is also reportedly in talks with the company for a slice of the orbital data center pie.

Google is reportedly testing a new storage policy that restricts new users to an initial 5GB of free cloud storage rather than its previous 15GB allowance. The change was first spotted by a Reddit user who was notified while setting up a new Google account that they would only get 5GB of free storage. The notice also mentioned that once the user linked and verified a phone number with their account, they would gain access to the full 15GB. Interestingly, Google’s support page does not mention this change and states that new accounts receive up to 15GB of free storage.

Google is yet to make a public announcement regarding the change in free cloud storage, however, it has given an official statement to Android Authority. As per a Google spokesperson, “We’re testing a new storage policy for new accounts created in select regions that will help us continue to provide a high-quality storage service to our users, while encouraging users to improve their account security and data recovery.”

A crucial point to consider is that the test is limited to select regions. This may imply that the company is experimenting in certain markets where fake accounts and spam abuse are particularly high before deciding and rolling out the new storage policy globally. In all fairness, the phone number verification requirement does make sense, as it can help Google reduce fake or disposable accounts.

By requiring a verified phone number, users can be restricted from creating multiple free accounts for extra storage or potentially using them for malicious activities. Since verified accounts are tied to a recovery method, it also improves account security and recovery, which Google mentions in its official explanation.

Another possible reason for this change could simply be a tactic by Google to push more users into paying for cloud storage plans under Google One. While 15GB has remained unchanged for years, almost every smartphone user has far more photos, videos, and backups than they did a few years ago. Initially offering new users just 5GB of storage could make limitations much more noticeable, potentially encouraging more people to subscribe to paid plans for additional cloud space.

Putting AI servers in space has been discussed as a holy grail of sorts for some time now. The economics of an orbiting data center would benefit from always-available solar power, even considering the relative difficulty in cooling the rack units. The main issue is the stratospheric price tag of lifting that compute to orbit. Now, though, according to a Wall Street Journal report, Google believes that SpaceX might be able to make the dream real.

According to the report, Google is in talks with SpaceX and a few other contenders about this strategy, though given how Elon Musk's orbital enterprise has steadily become by far the main player in commercial launches, it's the clear front-runner in those talks. Google's move may be related to the company's Project Suncatcher initiative, revealed last November, that intends to send satellites laden with Google Tensor Processing Units (AI chips) into orbit starting in 2027.

This news has the potential to boost the impending SpaceX IPO to infinity and beyond. That offering is expected to be the largest of all time, and was already expected to reach stratospheric levels of $1.5 to $1.7 trillion. As of this writing, neither company has offered any comment on the presumably ongoing negotiations.

It's worth noting that SpaceX recently struck a partnership with Anthropic that could include "multiple gigawatts of orbital AI compute capacity", and that it filed an application last January with the FCC to launch up to a million satellites for datacenters, so SpaceX would doubtless be happy for another client in this space.

The notion of space AI datacenters has long been derided as a fever dream, even by OpenAI honcho Sam Altman himself, given the financial delta-V required to place thinking rocks in orbit. Estimates pin the theoretical launch cost for SpaceX itself at around $2,700 per kilogram, an amount that works out to a best-case scenario of $3,400/kg for a customer, assuming a completely stuffed rocket — something that's hard to achieve in practice.

That reason is precisely why SpaceX's February 2026 price table lists $7,000/kg as a standard rideshare price, to fill in the gaps and maximize profit during a launch (or minimize losses, depending on how you slice it).

The math for Google's Project Suncatcher says that the financial equilibrium for space datacenters sits at around the $200/kg mark, not even in the same galaxy as current figures. Yet the economics of SpaceX's Falcon 9 rockets are driving that cost down. One such Falcon 9 recently launched for the 34th time in a row, and some analysts think it's literally a matter of space-time until five to six reuses of the same ship are enough to offset its production cost. After that, in theory, the only major expenses are fuel, maintenance, and launchpad utilization.

It's still hard to say if low-Earth-orbit meme generation will become a reality, but it's a reasonable enough conclusion that SpaceX is currently the only entity that can pull it off. The firm has made 165 launches in 2025, more than the rest of the world combined, up from 134 in 2024. Likewise, it has put 14,844 payloads in orbit in total, and it's reportedly only 218 units away from having launched as many satellites as every country on Earth since space became reachable.

Chipmakers are taking to social media to confirm their partnerships with Google on its newly announced Googlebook laptop lineup.

In a post shared on X, Intel said it is collaborating on the lineup. Meanwhile, over on Instagram, Qualcomm made its own confirmation. Both used similar wording, saying that the laptops will be "powerful" and "premium" "devices designed for Intelligence." (Qualcomm used "built" instead of designed."

The announcements came shortly after Google gave a preview of its upcoming platform at the Android Show: I/O Edition, and confirmed that it is working with various PC manufacturers, including HP, Dell, Acer, Asus, and Lenovo.

During the showcase, Google refrained from discussing the core hardware and instead focused entirely on its brand-new operating system, which combines elements of Android and ChromeOS with deep Gemini Intelligence integration. It was initially assumed that the new Googlebook lineup would be based on Arm SoCs, since many aspects of the platform resemble an Android smartphone or tablet experience. However, with Intel now officially involved, there is a possibility that Google’s new AI-focused OS could also support x86 hardware, unless Intel has an Arm-based chip up its sleeve.

In an exclusive interview with Chrome Unboxed, Google VP John Maletis further confirmed Intel’s involvement in the Googlebook project, revealing that the upcoming notebooks will ship with processors from Intel, Qualcomm, and MediaTek. According to Maletis, the Googlebook is an entirely new category of premium AI-first laptops that deeply integrate Gemini into the core experience rather than treating AI as an add-on. He also noted that Google is establishing strict hardware standards across memory, storage, keyboards, and overall build quality to ensure every Googlebook delivers a consistent premium experience.

The interview also shed more light on what users can expect when Googlebook devices officially launch later this fall. According to Maletis, the first wave of laptops will focus heavily on premium hardware from its partners, while also bringing back the iconic Glow Bar LED lighting seen on older Chromebook Pixel devices. He additionally confirmed that Googlebook laptops will run native Android applications without emulation, promising significantly better app performance alongside tighter Android smartphone integration and Gemini-powered features such as the new Magic Pointer interface.

Interestingly, the Googlebook partnership comes just a month after Intel and Google announced a separate multi-year agreement focused on next-generation AI cloud infrastructure. Under the deal, Google Cloud will deploy Intel Xeon processors alongside custom IPUs for large-scale AI workloads, suggesting that the relationship between the two companies now extends from cloud AI infrastructure all the way down to consumer AI-focused devices.

Updated May 13, 3:18 PM ET with further confirmation from Qualcomm on its partnership with Google

Google has been teasing a new "Android Show: I/O Edition" for the past week, where we expect to see Android 17 revealed with a design overhaul. But now, new info has surfaced that suggests the event will perhaps focus on a different avenue: laptops. The company's new laptop platform, meant to succeed Chromebooks, powered by Android and filled to the brim with Gemini, has just leaked — and it's called "Googlebook."

The event is scheduled for Tuesday, but was leaked ahead of time by an XDA article was seemingly posted. Images shared online reveal the features of this new platform.

First of all, it's based on Android, which finally bridges the gap between the mainstream Android OS that runs on phones and the stripped-down ChromeOS that has always bottlenecked Chromebooks (more on this later). This allows for deeper integration with your Android devices, with the slides showing the ability to access your phone's internal storage right from the Googlebook.

There are a bunch of AI features, all powered by Gemini, such as custom widgets and more seamless generative AI. You can simply ask Gemini to make you a widget specifically according to your needs, and it will pull data from your connected Google apps to build one; the example shown in the slide combines calendar events, hotel reservations, and an airplane ticket (along with a cover photo) into one.

Then there's the "Magic Pointer," which is essentially like a smart mouse pointer that's context-aware and understands what it's hovering over. Using Gemini, you can ask it to blend two images together just by putting your cursor on top. We also see the ability to cast apps highlighted in the leaked image, but more importantly, there's something called the "Glowbar" mentioned right above the Googlebook name.

This is likely a hardware implementation of the glow animation that Gemini (and Google Assistant before it) already has on phones. It looks like an LED strip embedded at the bottom of the top lid, similar to the navigation bar that sits on Android. This Glowbar will probably react to your commands when you're interacting with Gemini, playing different animations based on what it's doing.

Lastly, there's the fact that Google itself is not manufacturing the hardware — it's once again outsourcing that to actual PC vendors such as Asus, Dell, HP, Lenovo, Acer, and more. This means that perhaps the operating system these "Googlebooks" run is branded differently from the hardware itself. Maybe we're looking at Aluminum OS after all: the company's internal efforts to unify Android and ChromeOS into a single platform. It sure does look like this is it.

Now, Google has a history of replacing its products with namesake rebrands, such as when Android TV became Google TV in 2020, or how Android Pay turned to Google Pay in 2018. So, the Googlebook name, as gaudy as it sounds, doesn't come as a surprise. Now, we only have to wait and see whether these new laptops are actually priced fairly in an AI boom-driven world where the MacBook Neo exists.

Google will leverage data from one of the most complex and multi-layered sci-fi MMORPGs to train its AI. With a quarter million monthly active users, Eve Online's deep living simulation of economics and politics, with strategic aspects involving exploration and combat, presents the opportunity to expand the capabilities and horizons of AI. Google DeepMind is now primed to learn from interactions in this expansive game world after acquiring a minority stake in the newly independent Fenris Creations, reports Bloomberg.

Readers are likely more familiar with Eve Online being stewarded by CCP Games. Fenris Creations was recently formed when the developers sought to buy back the Eve Online game rights from Korean game-maker Pearl Abyss. The new Icelandic company paid $120M in cash and crypto to set up. While that may sound like a huge sum, Pearl Abyss spent more than double that amount to acquire the game maker back in 2018.

It is important to understand why Google DeepMind would part with "millions" to secure a minority stake in Fenris Creations. Thankfully, Bloomberg has spoken to executives from both firms to more clearly assess how the deal benefits both parties.

A DeepMind director quoted by the source indicates that success in Eve Online relies on skills that are far from mastered by current-generation AIs. This MMORPG is (in)famous for some of its players succeeding using tactics ranging from politics to deceit to outright scams. Eve Online is also lauded for the long-term planning and continual learning involved in success.

DeepMind has previously dabbled with gaming, and among all the big names of AI, it seems like it is the most interested in tapping into this rich vein for training.

From the perspective of Fenris, the deal looks pretty irresistible too, earning millions from a partner with deep pockets and what seems to be a hands-off stance. The Fenris CEO is quoted as previously joking that Eve Online would be “the final boss for AI in games.” He went on to ponder that Eve Online gameplay shines a light on society and the human condition. We are also getting hints that AIs learning from Eve Online could learn something about humans pushed to extremes…

It is stated by Fenris that DeepMind’s initial research will look at player behavior on isolated servers, having no impact on the live game. Moreover, there may be benefits from this research outside of AI training to improve the game, or even inspire new experiences, according to the Fenris CEO.

Fenris is beginning to start work on the successor to Eve Online, dubbed Eve Frontier, as well as an extraction adventure shooter dubbed Eve Vanguard (see video above). So we’re sure the extra cash injection will be very useful.

Eve Online is 23 years old in 2026, but it still attracts a devoted following with between 200,000 and 300,000 monthly active users. Q4 2025 was the game’s second most lucrative ever, with November being notable for breaking records.

Google, Microsoft, and Elon Musk's xAI agreed today to give the U.S. Commerce Department's Center for AI Standards and Innovation (CAISI) access to their AI models before public release, Bloomberg reports. OpenAI and Anthropic, which had existing evaluation partnerships with the center dating to 2024, renegotiated their deals to align with priorities in Trump's AI Action Plan, the agency said.

The agreements mean that every major U.S. frontier AI lab now participates in voluntary pre-release government evaluations. CAISI has completed more than 40 model assessments to date, including evaluations of unreleased state-of-the-art systems, according to the Commerce Department.

CAISI operates within NIST and was originally established in 2023 under Biden as the AI Safety Institute. The Trump administration renamed it last June, with Commerce Secretary Howard Lutnick calling the rebrand a move away from what he called regulation "used under the guise of national security." Despite the shift in rhetoric, the center's core function has remained largely the same: evaluating frontier models for cybersecurity, biosecurity, and chemical weapons risks.

"These expanded industry collaborations help us scale our work in the public interest at a critical moment," CAISI director Chris Fall said of the new agreements. Fall took over the center after Collin Burns, a former Anthropic and OpenAI researcher, was pushed out just four days into the job. The Washington Post reported last month that White House officials were concerned about Burns's Anthropic ties, given the administration's ongoing dispute with the company. Burns had relocated across the country and given up Anthropic equity to take the position.

The center still lacks permanent legal standing, and some lawmakers have introduced draft legislation to codify it, but nothing has passed. Trump's AI Action Plan, announced in July last year, directs CAISI to serve as part of an "AI evaluations ecosystem" and lead national security-related model assessments. It also instructs regulators to explore using evaluations when applying existing law to AI systems.

Anthropic's renegotiated deal with CAISI sits alongside a separate and hostile set of interactions with the federal government. The Pentagon designated Anthropic a supply chain risk in March after it refused to lower guardrails on autonomous weapons, though a federal judge later called that move "Orwellian." Both Defense Secretary Pete Hegseth and Trump have outlined a six-month phaseout period for government use of Anthropic's tools, and two active lawsuits remain unresolved.

The new CAISI agreements also come one day after reports that the Trump administration was considering a mandatory pre-release review process for AI models via executive order, with Anthropic's Mythos model cited as the catalyst. The voluntary agreements announced Tuesday, and any potential mandatory review framework, would run in parallel, though it remains unclear how they might interact.

Reports of YouTube freezing browsers and consuming enormous amounts of RAM began spreading across Reddit and browser forums late last week, with developers now pointing to a bug in the platform's interface code that may be trapping browsers in an endless layout recalculation loop. What's emerging is that there is a runaway interface bug buried inside the platform's video controls.

Users across multiple browsers, including Firefox, Brave, and Microsoft Edge, have described videos stuttering, tabs becoming unresponsive, and systems slowing to a crawl while watching YouTube. Some users reported the individual YouTube tabs consuming more than 7GB of RAM.

Many of the initial reports blamed YouTube's ongoing war against ad blockers or recent browser updates, as the issues seemed to have first been noticed after a Firefox update. However, similar reports from Brave and Edge users have increased the spotlight on YouTube.

Following investigations, reportsMozilla's emerging from Mozilla’s open-source bug-tracking system, Bugzilla, suggest YouTube's frontend interface logic is the main culprit. Developers investigating the issue appear to have narrowed the problem down to the flexible menu container located directly beneath the video player — the section containing controls such as Like, Dislike, Share, and other interaction buttons.

Button peek-a-boo loop

According to comments related to the investigation, the interface repeatedly checks whether all buttons fit within the available horizontal space. If the controls overflow, the system hides one of the buttons to free space. However, hiding the button changes the container's width, immediately creating a new problem.

Once the button disappears, the available width appears enough for the interface to believe there is room again, causing the hidden button to reappear. The buttons then overflow once more, forcing the interface to hide the button again. The cycle repeats continuously at extremely high speeds.

While the visual behavior itself may appear minor, the consequences inside the browser can be far more significant. Modern browsers constantly recalculate page layouts whenever interface elements change size or position. If a webpage repeatedly triggers those recalculations thousands of times per second, the browser can become trapped in what developers often call layout thrashing or a reflow loop.

That forces the browser to continuously recompute layout geometry, redraw interface elements, and update rendering states, rapidly consuming CPU resources and memory. A user shared screenshots on Reddit showing CPU cores pinned near maximum utilization while YouTube tabs became nearly unresponsive. Others reported browser-wide slowdowns severe enough to temporarily freeze entire systems.

Mozilla developers are reportedly still investigating the issue, though no broadly confirmed fix appears to exist yet. The fact that both Firefox-based and Chromium-based browsers appear to experience similar problems further supports the suspicion that the issue may originate primarily with YouTube. For now, the exact root cause remains unofficial; neither Google nor YouTube has publicly confirmed the source of the problem.

The U.S. Department of War has announced deals with "seven of the world’s leading frontier artificial intelligence companies" for operational use. According to the Classified Networks AI Agreements press release, SpaceX, OpenAI, Google, Nvidia, Reflection, Microsoft, and Amazon Web Services will deploy their LLMs across the Pentagon’s classified networks “for lawful operational use.” The government said that this move will help turn the United States military into “an AI-first fighting force” and will help with “decision superiority across all domains of warfare.”

It seems that the AI tools that these companies offer will, for now, be limited to data analysis and help make decision-making faster and easier as the U.S. faces complex situations. These tools are accessible via GenAi.mil, the Pentagon’s official AI platform, through the Department of War’s network and are widely available for its personnel.

“Over 1.3 million Department personnel have used the platform, generating tens of millions of prompts and deploying hundreds of thousands of agents in only five months,” the Pentagon said. “Warfighters, civilians and contractors are putting these capabilities to practical use right now, cutting many tasks from months to days.”

Nevertheless, there have been concerns about the use of AI in military applications. Anthropic has famously refused to budge on the Department of War’s demand to lower its safeguards, saying that doing so could mean that its AI products could be used for mass surveillance or to create autonomous weapons. This move resulted in President Donald Trump banning the company from federal agencies, even going as far as designating it a supply chain risk for refusing to bow to the federal government’s demands.

While AI is certainly useful for distilling massive amounts of information and spotting patterns that humans can miss, it’s still not a 100% reliable tool for making decisions that could have a global impact. A researcher discovered this when they pitted GPT-5.2, Claude Sonnet 4, and Gemini 3 against each other in a wargame, with 95% of the outcome ending in a tactical nuclear strike. Three scenarios even ended in a strategic nuclear strike that would have ended the world.

But even though these AI tools are limited to analysis and support, with a human operator at the helm still responsible for every decision, there’s also the risk of automation bias. This is a person’s tendency to follow a computer’s suggestion despite contradictory information, especially as AI systems can process a ton of data so much more quickly than any human could. However, the data the AI is relying on could be false, erroneous, or misinterpreted, so it’s crucial that humans apply their intuition and experience before accepting AI suggestions at face value.

The U.S. military isn’t the only one experimenting with and deploying AI technologies in operational use. China, for example, has been showing off a 200-strong AI drone swarm that can be controlled by a single soldier, as well as ground-based drone wolfpacks armed with machine guns and grenade launchers for urban combat. While we cannot stop these armed institutions from deploying AI tools for intelligence-gathering, reconnaissance, and decision-making on the battlefield, we can only hope that they do not ignore safeguards and never give AI the triggers to any weapon.

Google, Amazon, Microsoft, and Meta plan to spend a combined $725 billion on capital expenditure in 2026, a 77% increase over last year's record $410 billion, according to first-quarter earnings reports compiled by the Financial Times.

Google led with 63% cloud revenue growth and an 81% jump in net income to $62.6 billion, while Meta's stock dropped 6% after hours despite a 33% revenue increase, punished by investors for adding $10 billion to its spending forecast and offering no firm timeline on new AI models.

But in the earnings calls, at least two of the four companies explicitly blamed rising memory chip prices for pushing budgets higher, confirming what market data and industry executives have been warning about for months.

Memory costs inside the capex

Microsoft’s CFO, Amy Hood, told investors that rising prices for memory chips and other components accounted for $25 billion of the company's record capex budget. Microsoft set its 2026 spending at $190 billion, far above the $152 billion average analyst forecast. Hood warned that even with the additional investment, Microsoft expects to remain capacity-constrained on GPUs, CPUs, and storage through at least 2026.

Meta cited the same, with the company raising its full-year capex range to $125 billion to $145 billion, up from a prior ceiling of $135 billion. In its earnings release, Meta attributed the increase to "higher component pricing this year, particularly memory," alongside rising costs for land, power, and skilled workers needed to build data centers that now consume 70% of the world's memory output.

The timing of all this is hardly coincidental, with TrendForce having reported DRAM contract prices rising roughly 95% quarter over quarter in Q1 2026, with a further 58% to 63% increase projected for Q2. NAND is following a similar trajectory, with Q2 contract prices expected to climb 70% to 75%. Server DRAM and high-density DDR5 RDIMMs are absorbing the bulk of production capacity, and all NAND output for 2026 is already committed, according to Phison CEO Khein-Seng Pua.

Hood's $25 billion, therefore, helps to put a dollar value on what has previously been an abstract concern: If one company's memory cost inflation alone exceeds the entire annual capex of most semiconductor firms, the pressure on consumer DRAM and NAND supply becomes much easier to quantify.

Google Cloud's contract backlog

Meta and Microsoft aside, Google’s Cloud revenue hit $20 billion in the same quarter, growing 63% year over year, outpacing both Amazon Web Services ($37.6 billion, up $8.3 billion) and Microsoft's Azure-driven cloud segment ($34.7 billion, up $7.9 billion).

Google's cloud contract backlog reached $460 billion, roughly double the $240 billion reported at the end of Q4 2025. Amazon reported $364 billion in its own pipeline, which will expand further after a recent $100 billion computing contract with Anthropic over the next decade. Microsoft's commercial remaining performance obligations hit $625 billion, up 110% year over year.

Cloud boss Thomas Kurian attributed Google's growth to its strategy of building custom AI chips, foundation models, and products in-house, telling the Financial Times that this gives the company a cost and research advantage over competitors that have struggled to develop their own chips and frontier models. Google's 7th-gen Ironwood TPU, which packs 192 GB of HBM3E per chip with 7.37 TB/s bandwidth in pods of up to 9,216 chips, is central to that strategy, and Anthropic has committed to access up to one million of them. Google recently unveiled its 8th-gen TPUs, which are split into two distinct variants for training and inference.

Alphabet raised its capex guidance to between $180 billion and $190 billion, up $5 billion from its previous guidance of $175 billion. CFO Anat Ashkenazi said he expects capex to “significantly increase” in 2027, causing shares to rise by some 7% after hours. It’s worth noting that $37.7 billion of Alphabet’s net income of $62.6 billion came from unrealized gains on non-marketable equity securities, primarily the company's Anthropic stake, according to the earnings release filed with the SEC. Strip that out, and operating performance was still strong, with a 36.1% operating margin, but the total net income number overstates recurring profitability.

Custom silicon and the GPU question

These capex figures reflect more than GPU purchases, because each hyperscaler is now deploying or developing custom accelerators to reduce dependence on Nvidia for inference-based workloads.

Amazon's Trainium3, built on a 3nm process with 144 GB of HBM3E and roughly 4.9 TB/s of bandwidth, is what CEO Andy Jassy described as "nearly fully subscribed" for 2026, and Meta has announced four generations of its MTIA inference chip, all fabbed at TSMC alongside Broadcom, even as it signed GPU deals worth roughly $110 billion combined with AMD and Nvidia. Meanwhile,. Microsoft's Maia 200 is deploying in U.S. Central data centers.

This pattern is likely to extend beyond accelerators as CPU demand for agentic AI workloads drives a parallel supply crunch with CPU lead times currently stretching to six months. Intel has reported billions in unmet Xeon demand, and Arm CEO Rene Haas has stated that agentic workloads require roughly 120 million CPU cores per gigawatt of data center capacity, four times what traditional AI training clusters need. Per Intel CFO David Zinsner, data center CPU-to-GPU ratios have already moved from 1:8 to 1:4, with further convergence expected to reach or go beyond parity.

Despite record spending, all four companies have acknowledged supply constraints that additional capital alone can’t resolve. Nvidia has booked an estimated 800,000 to 850,000 wafers of TSMC's CoWoS advanced packaging capacity for 2026, consuming over half of the total output and leaving AMD, Broadcom, and Google's TPU program competing for the remainder. CoWoS remains oversubscribed through at least mid-2026, and TSMC's U.S. packaging fabs aren’t expected to reach volume until 2028.

Power infrastructure is another bottleneck, with large power transformer lead times extending to roughly 128 weeks, and the IEA estimating that approximately 20% of planned global data center projects could be at risk of grid-related delays. TrendForce recently downgraded its full-year server shipment growth forecast from 20% to 13% because power management ICs and baseboard management controllers needed to assemble complete servers are stretching to 35- to 40-week lead times. Samsung's planned closure of its S7 eight-inch wafer fab in Korea will tighten PMIC supply further.

‘The bear thesis is garbage’

Meta's stock slipped by 6% after-hours following the earnings, erasing roughly $113 billion in market value. That drop reflected both the $10 billion capex increase and CEO Mark Zuckerberg's lack of a firm schedule for releasing improved AI models to follow the recently launched Muse Spark. Dec Mullarkey, managing director of SLC Management, told the FT that investors are concerned about whether Meta's historically capital-light business is becoming far more capital-intensive.

"The bear thesis is garbage," countered Brent Thill, an analyst at Jefferies, arguing that revenue growth across the sector justifies the spending. Zuckerberg offered little to settle the debate. Asked about Meta's AI agent development, he told investors he cared more about quality than deadlines, adding that most AI agents available today are not good enough for everyday users.

Amazon kept its $200 billion capex plan unchanged, and Microsoft CEO Satya Nadella said ending his company's exclusive contract with OpenAI was beneficial, claiming royalty-free access to OpenAI's frontier models and IP through 2032.

Google, Amazon, Microsoft, and Meta collectively plan to spend $725 billion on capex in 2026, up 77% from last year's record $410 billion, according to first-quarter earnings compiled by the Financial Times. Google delivered the strongest results, with cloud revenue jumping 63% year over year to $20 billion, while rising memory chip prices pushed spending forecasts higher at both Microsoft and Meta.

"The AI economy is healthy," Brent Thill, an analyst at Jefferies, told the Financial Times, adding that recent revenue growth justified the enormous capital outlays. "The bear thesis is garbage."

Microsoft set its calendar-year 2026 capex at $190 billion, well above the $152 billion average analyst estimate. The company’s CFO, Amy Hood, attributed $25 billion of that figure to rising memory chip and component costs. She told investors that despite the additional spending, Microsoft expects to remain capacity-constrained through at least 2026 as it works to bring GPU, CPU, and storage infrastructure online faster.

Meta increased its full-year projection by $10 billion to a range topping $145 billion. The company cited higher component pricing, particularly for memory, alongside growing competition for land, power, and skilled workers needed to build data centers. Revenue grew 33% to $56.3 billion.

Dec Mullarkey, managing director of SLC Management, told the Financial Times that investors are growing uneasy with Meta's escalating infrastructure costs, questioning whether a historically lean business is becoming far more capital-hungry. “Investors continue to be concerned about how Zuckerberg’s once capital-light money machine may be morphing into a capital-intensive incinerator,” he said.

Alphabet posted an 81% increase in net income to $62.6 billion on revenue of $110 billion. Google Cloud reached $20 billion in quarterly revenue, growing faster than Amazon Web Services ($37.6 billion total, adding $8.3 billion year over year) and Microsoft's Azure-driven cloud segment ($34.7 billion total, adding $7.9 billion).

The company's cloud contract backlog reached $460 billion, roughly double the $240 billion reported at the end of Q4 2025. Google Cloud boss Thomas Kurian credited the company's strategy of building custom AI chips, foundation models, and products in-house for giving it a cost and research advantage over competitors. Alphabet's capex guidance rose by $5 billion to as much as $190 billion, matching Microsoft. Shares climbed 7% after hours, putting Alphabet on track for a record $4.3 trillion market valuation.

CEO Mark Zuckerberg offered no firm schedule for releasing improved AI models to follow the recently launched Muse Spark. Asked about the pace of Meta's AI agent development, Zuckerberg told investors: "There's a lot of agents out there that people are building for different things, but there aren't that many that I would want to give to my mother."

Google amended its existing contract with the U.S. Department of Defense on Monday to extend Gemini's availability to classified networks, granting the Pentagon permission to deploy the models for "any lawful government purpose." Separately, Bloomberg reported the same day that Google had withdrawn from a $100 million Pentagon prize challenge to build voice-controlled autonomous drone swarm technology in February, following an internal ethics review.

Google joins OpenAI and Elon Musk's xAI in granting the Pentagon broad classified AI access. On the deal, Pentagon AI chief Cameron Stanley said that avoiding dependence on a single vendor was a priority.

Google's agreement requires the company to help modify its AI safety settings and filters at the government's request, with the contract including language stating that the AI system shouldn’t be used for domestic mass surveillance or autonomous weapons “without appropriate human oversight and control,” but also specifies that the deal doesn’t give Google “any right to… veto lawful government operational decision-making,” which doesn’t make the agreed restrictions appear particularly solid.

A spokesperson for Google Public Sector told The Information that the company is "proud to be part of a broad consortium of leading AI labs and technology and cloud companies providing AI services and infrastructure in support of national security."

Google notified the government on February 11 that it wouldn’t continue in the drone swarm challenge, which sought technology for converting spoken commands into digital instructions for coordinating autonomous drones. The company officially cited a lack of resources, but internal records reviewed by Bloomberg showed the withdrawal followed an ethics review.

More than 600 Google employees delivered a letter to CEO Sundar Pichai on Monday urging him to reject the classified deal, arguing that it was the only way to prevent Google's AI from being misused.

Google faced a similar internal revolt in 2018 over Project Maven, a Pentagon contract for AI analysis of drone surveillance footage. The company let that contract lapse after roughly 4,000 employees signed a petition, and Palantir assumed the work, which has since grown into a $13 billion program of record.

Anthropic declined to agree to similar "any lawful purpose" terms earlier this year, insisting on explicit restrictions against autonomous weapons and domestic mass surveillance. The Pentagon responded by designating the company a supply chain risk, a label a federal judge later called "Orwellian" while blocking its enforcement. That litigation remains ongoing.

Google announced its eighth-generation Tensor Processing Units at Cloud Next on April 22, shipping two distinct chip designs for the first time in the TPU program's decade-long history. The two chips — TPU 8t and TPU 8i — are intended for use in different workloads. TPU 8t targets large-scale model training, while TPU 8i is built for low-latency inference and reasoning workloads.

The split also extends to the supply chain, with MediaTek having joined Broadcom as a silicon design partner for the eighth-gen program back in December, ending Broadcom’s exclusive role in TPU development since 2015. Both chips are fabricated on TSMC's N3 process family with HBM3E memory and will be available to Google Cloud customers later this year.

Optionality for customers

In terms of raw specs, TPU 8 doesn’t close the gap with Nvidia or AMD. According to Google’s own technical deep dive, the TPU 8t delivers 12.6 FP4 PFLOPs with 216 GB of HBM3e running at 6,528 GB/s, while TPU 8i offers 10.1 FP4 PFLOPs, 288 GB of HBM3e at 8,601 GB/s, and 384 MB of on-chip SRAM. In comparison, Nvidia's Vera Rubin R200 is rated at 35 FP4 PFLOPs for training with 288GB of HBM4 at 22 TB/s, and AMD's MI455X reaches 40 FP4 PFLOPs with 432GB of HBM4. That makes the gap roughly 3:1 in raw compute per-socket.

Then there’s the choice of HBM3E over HBM4, which appears to be a deliberate cost and yield trade-off. TPU 8t carries 12.5% more memory capacity than the previous-gen Ironwood TPU, but delivers 11.5% less bandwidth, running slower memory to improve yield and bring down cost per chip per analysis from Next Platform. This is an odd strategy on the face of it, but it seems that Google, rather than trying to take on Nvidia in terms of raw performance, is creating options for external customers that want alternatives.

A TPU 8t superpod packs 9,600 chips into a single cluster with two petabytes of shared HBM, connected by a proprietary inter-chip interconnect running at double the previous generation's bandwidth. Google claims 121 FP4 ExaFLOPs from a single superpod, with the new Virgo Network fabric tying up to 134,000 TPU 8t chips into a single non-blocking data center fabric with 47 PB/s of bisection bandwidth, extending past 1 million chips across multiple sites.

So, yes, while individual Nvidia GPUs are faster, Google holds an advantage with its pod-level throughput at that mass scale; training workloads consume thousands of accelerators, not one, and Nvidia’s current-gen GPUs top out at 576 accelerators in a single NVLink deployment.

Interestingly, Google also announced Vera Rubin NVL72 instances running over the same Virgo Network fabric at Cloud Next, so TPUs are clearly not intended to act as a direct replacement for Nvidia silicon.

TPU 8i architecture

The TPU 8i’s architecture is a radical departure from the norm for Google, with TPU 8i abandoning the 3D Torus interconnect that has been inside TPU pods since the second generation. Instead, it’s replaced with a topology that Google calls “Boardfly,” inspired by the 2008 Kim/Dally Dragonfly paper. Boardfly is a three-tier hierarchy: four-chip building blocks connected into 32-chip groups by copper cabling, with 36 groups linked by optical circuit switches into a pod of up to 1,024 active chips.

In a 1,024-chip 3D Torus configuration, the worst-case packet path traverses 16 hops. Boardfly cuts that to seven, a 56% reduction in network diameter that directly benefits mixture-of-experts (MoE) models, where token routing requires frequent all-to-all communication across unpredictable chip pairs.

TPU 8i also replaces the SparseCore embedding accelerators that Google has used since TPU v4 with a new fixed-function block called the Collectives Acceleration Engine (CAE). The CAE offloads reduction and synchronization operations during autoregressive decoding, cutting on-chip collective latency by up to five times. Combined with the tripled SRAM, which holds more of the KV cache on-chip during long-context inference, Google claims 80% better performance per dollar over Ironwood for large MoE models at low-latency targets.

TPU 8t, meanwhile, retains the 3D Torus at a larger scale and keeps SparseCore for the irregular memory access patterns typical of embedding lookups during training. It introduces native FP4 compute to double MXU throughput at reduced precision, and a new TPUDirect RDMA path that bypasses the host CPU to pull data directly from high-speed managed storage, delivering what Google describes as ten times faster storage access over the previous generation. Both chips now run on Google's Arm-based Axion CPU hosts, replacing x86 for the first time.

Two suppliers instead of one

The MediaTek partnership means that there’s a second silicon design house in the TPU program alongside Broadcom, with MediaTek understood to be handling the design of the TPU 8i inference chip while Broadcom handles the design of the 8t training chip.

TrendForce reported back in December that MediaTek initially booked 20,000 TSMC CoWoS wafers for the program, with allocation potentially scaling to 150,000 by 2027. According to Bank of America analyst Vivek Arya, the dual-sourcing arrangement could reduce per-chip cost by up to 30% compared to solely sourcing from Broadcom, whose role is secured through at least 2031 per an April 6 SEC filing, which also formalized a 3.5 GW TPU capacity commitment from Anthropic starting in 2027. That deal sits on top of the one gigawatt of Anthropic capacity already coming online this year under a separate Google Cloud agreement.

Meanwhile, Meta has signed a separate multi-year, multi-billion-dollar TPU rental agreement, estimated to involve 500,000 to 800,000 TPU chips by 2027 if initial testing meets expectations, and Apple is routing Gemini-powered Siri workloads to Google Cloud on TPU infrastructure, valued at roughly $1 billion per year.

Australia-based AI consultant and founder of Agentic Labs Jesse Davies woke up to an unpleasant surprise earlier this month: A Google Cloud bill of $25,672.86 AUD (approximately $18,391.78 USD) — even though there was a budget of $10 AUD (approximately $7 USD) on his account. And it happened overnight.

According to Davies' account on LinkedIn, he was well-versed with Google AI Studio and had followed practices such as per-project API keys, separate billing accounts, two-factor authentication, and Cloud audit logging. However, it only took a single weak link to nullify those precautions, as evidenced by the shockingly large overnight bill. On top of that, Davies found nine Google Cloud safety features that should have prevented this incident — but that were turned off by default.

"The attacker didn't steal my key. They found a Cloud Run service I'd published from AI Studio months earlier, hit the public URL, and Google's own proxy signed every request on their behalf using the API key stored as a plaintext environment variable in the container," Davies wrote in his LinkedIn post.

"Even though it was public, the link wasn't shared or indexed anywhere. By the time I got a budget alert the next morning, A$10,000 had already been charged to my credit card, now getting insufficient funds. I was still talking to Google support when A$15,000 more came through."

What’s worse was that Google automatically upgraded the tier of Davies' account without any notification. The account was initially at Tier 2, which had a $2,000 limit, but Google automatically upgraded it to the next level when the account crossed the $1,000 threshold during the incident. This increased the cap to between $20,000 and $100,000. While this is likely designed to make it easier for a service to scale, it also has the unwanted effect of costing the user more than intended, e.g. if they are the victim of an attack.

Their headaches did not end here, though. It took several days before Davies was able to get through to a real human customer support. Thankfully, it seems that the charge has been waived, while the transactions that actually pushed through were credited back by their bank. Still, the issue isn’t settled, and Davies has a meeting scheduled with Google managers to talk about the case.

Davies also shared the experience on Reddit, on the r/googlecloud subreddit, and asked if other users had similar stories to share. It turns out they did — several other users reported getting hit with insane bills, including one commenter from Japan who said that they were hit with a $44,000 bill that ballooned to $128,000 even after they paused the API. And last month, we covered a case in which an API thief racked up $82,314.44 in charges on an account that typically saw around $180 per month.

Cybersecurity firm Truffle Security Co. has already highlighted the risks associated with Google Cloud using a single API key format. These API keys were previously used as project identifiers, but when the Gemini API is activated on any Google Cloud project, these existing API keys become Gemini credentials — allowing anyone who can copy them to rack up AI bills. So... it's likely we'll see more horror stories of shocking API bills if Google doesn't update its Gemini policies.

Google is reportedly negotiating with the U.S. Department of Defense to deploy Gemini in classified settings, with the talks covering the addition of GPU racks to Google Distributed Cloud and a first-time deployment of Google's tensor processing unit (TPU) inside accredited classified environments, according to a report published today by The Information, citing two people with direct knowledge of the discussions.

Google Distributed Cloud picked up DoD Impact Level 6 authorization for Secret classified data in May 2025, sitting alongside an existing Top Secret authorization that nominally makes Gemini and Vertex AI available at those classification levels. But relatively little infrastructure exists inside the accredited boundary to run classified workloads at scale, according to The Information's source, and closing that gap is part of the current conversation.

In the short term, that means adding racks of GPUs to Google Distributed Cloud. A parallel workstream covers enabling TPUs, Google's custom AI accelerators, inside classified environments, which hasn’t been done before, according to the same sources. TPUs run the bulk of Gemini training and inference in Google's commercial cloud, making their deployment on the classified side the natural step for any Gemini rollout beyond small-scale pilots.

The proposed contract would let the Pentagon use Gemini for "all lawful purposes," with Google pushing for language prohibiting domestic mass surveillance and fully autonomous weapons without appropriate human oversight. Those terms mirror the agreement OpenAI struck with the Pentagon earlier this year, which OpenAI CEO Sam Altman asked the Pentagon to extend to all AI vendors on the same terms.

Those two issues broke the Pentagon's negotiations with Anthropic in February. After Anthropic declined to drop the restrictions, the Pentagon designated the company a supply chain risk and began a six-month phase-out of Claude from government systems, a designation a federal judge has since called "Orwellian" while declining to stay the ruling in one of Anthropic's two ongoing lawsuits.

Google holds roughly 14% of the total cloud market against 28% for AWS and 21% for Microsoft as of late 2025, per Synergy Research Group, and the gap widens on the classified side, where both rivals run substantial workloads, and Google doesn’t. Google Public Sector, the division running the DoD talks, targeted roughly $6 billion in bookings for 2025 through 2027, $2 billion of it from defense, according to an internal strategic plan that The Information reports having seen.

Google lost last year's Army Next Generation Command and Control bid, but in July, it won a DoD AI pilot contract worth up to $200 million alongside Anthropic, OpenAI, and xAI. Gemini was the first model added to the Pentagon's unclassified GenAI.mil platform in December, and Google announced a separate deal last month for AI agent tooling on unclassified networks.

Intel and Google on Thursday announced a multi-year collaboration under which Google will continue deploying Intel Xeon platforms for its next generation of AI and cloud infrastructure. These platforms will rely not only on Intel's upcoming Xeon CPUs, but also on custom infrastructure processing units (IPUs) co-designed by Intel and Google. The announcement comes amid the accelerating adoption of custom Arm-based processors for AI workloads.

"Scaling AI requires more than accelerators - it requires balanced systems. CPUs and IPUs are central to delivering the performance, efficiency and flexibility modern AI workloads demand," said Lip-Bu Tan, CEO of Intel.

Google currently employs Intel Xeon 5 and Intel Xeon 6 processors for a variety of workloads, including large-scale AI training coordination, latency-sensitive inference, and general-purpose computing. For example, Intel's latest Xeon CPUs power C4 and N4 instances. Although Google's custom Armv9-based Axion processors provide the cloud giant more control and efficiency at lower cost, many workloads that are run in Google's data centers need to either be backwards compatible with x86 or just need maximum single-thread performance offered by Intel Xeon CPUs. This is something that is expected to continue for years to come, which is why the two companies inked the deal.

In a bid to make Intel Xeon platforms more efficient and suitable for its hyperscale data centers, Google will also co-develop custom IPUs together with Intel to offload networking, storage, and security functions from host CPUs. Ultimately, Intel Xeon platforms will combine x86 architecture with high single-thread performance and custom-built infrastructure processing, which will make them more competitive in Google's highly customized environments.

"CPUs and infrastructure acceleration remain a cornerstone of AI systems — from training orchestration to inference and deployment," said Amin Vahdat, SVP & Chief Technologist, AI Infrastructure, Google.

The announcement comes at a time when hyperscalers and AI platform developers are accelerating the adoption of their own custom CPUs based on the Arm instruction set architecture. Just a week ago, Counterpoint Research released a note claiming that 90% of AI servers running custom-silicon processors will rely on the Arm ISA, leaving x86 and RISC-V about 10%. The announcement by Intel and Google clearly states that Xeon CPUs with custom IPUs will continue to be used for AI and other demanding workloads for years to come, which is something to be expected anyway.

Intel's Xeon processors have powered cloud infrastructure since its inception in the 2000s, and Google's own servers before that, so x86 in general and Xeon in particular will not leave Google's data center premises any time soon. Nonetheless, the announcement clearly reemphasizes the relevance of Intel's Xeon CPUs, and when such a message comes from Google — which has been deploying special-purpose custom accelerators for years across virtually all of its services — it gets amplified significantly.

"Intel has been a trusted partner for nearly two decades, and their Xeon roadmap gives us confidence that we can continue to meet the growing performance and efficiency demands of our workloads," Vahdat added.

Intel is understood to be in active talks with Google and Amazon to provide advanced chip packaging services for their custom AI processors, according to a WIRED report published today, citing multiple sources. “Multiple sources say that Intel has been in ongoing talks with at least two large customers for its advanced packaging services: Google and Amazon,” claims the report.

The deals, if closed, would represent a major influx of external revenue for Intel Foundry, which CFO Dave Zinsner said at the recent Morgan Stanley TMT conference is "close to closing some deals that are in the billions of dollars per year, in terms of revenue on packaging." Google, Amazon, and Intel all declined to comment on the specific customer relationships.

Intel's advanced packaging portfolio centers on EMIB, a 2.5D technology that embeds small silicon bridges in the package substrate to connect chiplets, and Foveros, its 3D die-stacking process. The next-generation EMIB-T, which adds through-silicon vias to the bridge for improved power delivery and signal integrity, is set to roll out in production fabs this year. EMIB-T supports packages up to 120x180mm and can accommodate more than 38 bridges and over 12 reticle-sized dies.

Intel is scaling capacity across three countries. Its Fab 9 facility in Rio Rancho, New Mexico, received $500 million from the CHIPS Act and has been operational since January 2024.

In Malaysia, the Penang advanced packaging complex is 99% complete and will begin first-phase assembly and testing operations later this year, according to Malaysian Prime Minister Anwar Ibrahim, who confirmed the timeline after a briefing with Intel CEO Lip-Bu Tan in March. Intel has also outsourced EMIB production for the first time to Amkor's Songdo K5 facility in South Korea, with additional sites planned in Portugal and Arizona.

Naga Chandrasekaran, head of Intel Foundry, told WIRED that packaging has become more consequential than the silicon itself for AI computing going forward. “Even more so than the silicon itself, chip packaging is going to transform how this AI revolution comes to fruition over the next decade”, he said.

Zinsner said at the January Q4 2025 earnings call that he had revised his packaging revenue projections over the previous 12 to 18 months from hundreds of millions of dollars to "well north of $1 billion." He added at the Morgan Stanley event that packaging could achieve the same 40% gross margins Intel claims on its core product business.

Those projections contrast sharply with the division's current financials. Intel Foundry posted $4.5 billion in revenue for Q4 2025 with a $2.5 billion operating loss. External foundry revenue for the full year totaled just $307 million, mostly from U.S. government contracts and residual Altera work. The Foundry division lost $10.3 billion on $17.8 billion in revenue for all of 2025, driven largely by the cost of ramping Intel 18A.

Virtually all hyperscale cloud service providers (CSPs), as well as some of the leading developers of AI accelerators nowadays, have their own custom-silicon programs that are focused not only on developing AI accelerators, but also on custom general-purpose CPUs usually based on the Arm instruction set architecture (ISA). Over the next several years proliferation of custom CPUs based on the Arm ISA inside AI servers will increase to 90%, leaving x86 and Arm around 10%, according to Counterpoint Research.

x86 processors from AMD and Intel have long dominated general-purpose servers, which is why most of the AI servers initially relied on Opteron and Xeon processors. However, Arm-based custom CPUs that are tailored for specific data-intensive AI workloads are more cost and power-efficient. Furthermore, given the fact that AI workloads are emerging workloads, backward compatibility with x86 is not vital. To that end, AWS, Google, and Microsoft have developed their own proprietary Arm-based processors for their own workloads, whereas Meta is the alpha customer for Arm's own AGI processor.

As a result, adoption is unfolding across multiple hyperscalers in parallel. AWS is expanding the role of its Graviton processors across Trainium-based systems, while still retaining x86 in some configurations for compatibility reasons; Google's next-generation TPU infrastructure relies on its Axion Arm CPU; while Microsoft has paired its Azure Cobalt Arm CPU with its Maia accelerators from the beginning to build a vertically integrated AI infrastructure. Meta is also set to begin deploying Arm's own AGI CPUs shortly.

"The transition from x86 to Arm in AI servers is not a single switch," said Neil Shah, vice president of research at Counterpoint Research. "It has played out generation by generation, configuration by configuration. Hyperscalers are making deliberate choices based on their specific deployment needs, writing compatible and interoperable software, and the economics are very encouraging. The transition is expected to accelerate meaningfully in the second half of 2026, driven by the broad deployment of in-house Arm CPUs alongside next-generation ASIC platforms across major hyperscalers."

Nowadays, the majority of CPUs powering AI servers are still x86, but this is going to change shortly, and by 2030, 90% of AI servers that use custom processors will rely on Arm, leaving only 10% for x86 and RISC-V. It should be noted that loads of AI servers will continue to rely on off-the-shelf EPYC and Xeon processors from traditional suppliers, though broad adoption of Arm by hyperscalers for their custom silicon programs should be a signal for AMD and Intel to make their custom CPU programs more appealing to customers.

"Our analysis projects Arm-based CPUs will account for at least 90% of host CPU deployments in custom AI ASIC servers by 2029, up from around 25% in 2025, a structural shift driven by the accelerating rollout of in-house Arm CPU programs across major hyperscalers," Shah added.

AMD builds its own vertically integrated AI platforms featuring x86 EPYC processors, Instinct MI-series AI accelerators, Pensando DPUs, and Pensando NICs, so it is reasonable to assume that these CPUs are tailored for AI workloads. Meanwhile, Intel is developing custom Xeon processors for Nvidia's next-generation AI platforms, which suggests that these processors will also be optimized primarily for AI workloads. All in all, while Arm will get significantly bigger in the AI server realms over the next four to five years, x86 will continue to command a sizeable share of this market.

The trade-war between the U.S. and China has forced server makers out of the People's Republic, greatly reducing reliance of American companies on producers from Tianxia. However, China remains the world's largest producer of electrical equipment that is required to build power infrastructure inside and outside of AI data centers. To that end, shortages of power delivery equipment, including devices from China and other countries, are slowing project timelines, Bloomberg reports.

Despite the unprecedented level of investment in AI infrastructure — Alphabet, Amazon, Meta, and Microsoft are expected to spend more than $650 billion in 2026 to expand AI capacity — close to half of the planned U.S. data center builds this year are projected to be delayed or canceled, according to Bloomberg. One major reason behind these setbacks is the availability of key electrical components — such as transformers, switchgear, and batteries — that are used both at data center sites and outside of them, as AI companies must expand grid infrastructure to supply enough power to their data centers. Meanwhile, grid infrastructure is also stressed by electric vehicles and electrified heating systems.

Approximately 12 gigawatts (12 GW) of data center capacity is expected to come online in the U.S. in 2026, according to data by market intelligence firm Sightline Climate cited by Bloomberg. Yet only about one-third of that capacity is currently under active construction because of various constraints.

Electrical infrastructure represents less than 10% of total data center cost, but it is as vital as compute hardware. A delay in any single element of the power chain can halt the entire project, which makes transformers, switchgear, and similar devices critical items despite their relatively small share of CapEx.

Due to high demand, lead times for high-power transformers have expanded dramatically in the U.S.: delivery typically took 24 to 30 months before 2020, but waiting periods can stretch to as long as five years today, according to Sightline Climate cited by Bloomberg. For AI data centers, this is a catastrophe as their deployment cycles are under 18 months.

To address shortages, companies are turning to global markets. As a result, Canada, Mexico, and South Korea became the biggest suppliers of high-power transformers for AI data centers to AI data centers. At the same time, imports of high-power transformers from China surged from fewer than 1,500 units in 2022 to more than 8,000 units in 2025 through October, according to Wood Mackenzie data cited by Bloomberg.

The volatility of exports from China does not end with transformers, as the PRC accounts for over 40% of U.S. battery imports, while its share in certain transformer and switchgear categories remains near 30%, according to Bloomberg.

Without resolving constraints in transformers, switchgear, and batteries, even trillions of dollars in AI investment may not translate into actual AI capacity, as deployments will depend on power infrastructure availability, not capital or compute hardware constraints.

Despite a decade of reshoring initiatives, U.S. manufacturing capacity for electrical equipment remains insufficient, which means that AI companies continue to rely on imports even amid tariffs and national security concerns. Meanwhile, tensions between China and the U.S. threaten to further disrupt supply chains, which will raise costs and could delay deployments of advanced AI data centers.

Iran's Islamic Revolutionary Guard Corps has issued a direct strike threat to a slew of U.S. tech companies, including GPU giant Nvidia, Microsoft, Apple, Google, Meta, IBM, Cisco, and Tesla, just days after the regime identified the companies as 'legitimate targets.' As reported by CBS News, the IRGC issued an updated and more direct threat via Telegram on Tuesday.

Per the report, the group claimed that it would begin targeting some 18 U.S. and finance companies, specifically their Middle Eastern presences. As noted, the IRGC has already threatened these companies; however, Tuesday's statement represents a marked escalation in rhetorical threat.

The IRGC reportedly stated that the U.S. has "ignored our repeated warnings about the need to stop terrorist operations, and today, a number of Iranian citizens were martyred in your and your Israeli allies' terrorist attacks; Since the main element in designing and tracking terror targets are American ICT and AI companies, in response to this terrorist operation, from now on the main institutions effective in terrorist operations will be our legitimate targets."

Strikingly, the IRGC warned employees of the named institutions, which also include J.P. Morgan, to "leave their workplaces immediately to save their lives." Residents around these terrorist companies in all countries in the region should also leave their places within a radius of one kilometer and go to a safe place," Iran's armed forces stated. WION further reports the statement, which said: "These companies should expect destruction of their facilities in response to each act of terror in Iran." According to that outlet, strikes could begin as soon as 8 pm Tehran time on April 1.

As noted in our previous coverage, companies like Nvidia and Intel maintain significant Middle Eastern presences. 13% of Nvidia's global workforce resides in Israel, where the company has its second-largest R&D center beyond U.S. shores. Similarly, Intel employs some 9,355 people in Israel.

Beyond these specific threats of military force against U.S. tech institutions, the Iran conflict continues to have widespread ramifications in the tech industry. AWS data centers in Bahrain and the UAE have been struck by drones, and significant stress on the global Helium supply could have devastating consequences for chipmaking.

Google Research published TurboQuant on Tuesday, a training-free compression algorithm that quantizes LLM KV caches down to 3 bits without any loss in model accuracy. In benchmarks on Nvidia H100 GPUs, 4-bit TurboQuant delivered up to an eight-times performance increase in computing attention logits compared to unquantized 32-bit keys, while reducing KV cache memory by at least six times.

KV caches store previously computed attention data so that LLMs don’t have to recompute it at each token generation step. These caches are becoming major memory bottlenecks as context windows grow larger, and while traditional vector quantization methods can reduce the size of these caches, they introduce a small memory overhead of a few extra bits per value from the quantization constants that must be stored alongside the compressed data. That sounds small, but they’re compounding alongside larger context windows.

TurboQuant eliminates that overhead via a two-stage process. The first uses a technique called PolarQuant, which converts data vectors from standard Cartesian coordinates into polar coordinates. This separates each vector into a radius (representing magnitude) and a set of angles (representing direction). Because the angular distributions are predictable and concentrated, PolarQuant skips the expensive per-block normalization step that conventional quantizers require. This leads to high-quality compression with zero overhead from stored quantization constants.

The second stage applies a 1-bit error correction layer using an algorithm called Quantized Johnson-Lindenstrauss (QJL). QJL projects the residual quantization error into a lower-dimensional space and reduces each value to a single sign bit, eliminating systematic bias in attention score calculations at negligible additional cost.

Google tested all three algorithms across long-context benchmarks, including LongBench, Needle In A Haystack, ZeroSCROLLS, RULER, and L-Eval, using open-source models Gemma and Mistral. TurboQuant achieved perfect downstream scores on needle-in-a-haystack retrieval tasks while compressing KV memory by at least six times. On the LongBench suite, which covers question answering, code generation, and summarization, TurboQuant matched or outperformed the KIVI baseline across all tasks.

The algorithm also showed strong results in vector search. Evaluated against Product Quantization and RabbiQ on the GloVe dataset, TurboQuant achieved the highest 1@k recall ratios despite those baselines relying on larger codebooks and dataset-specific tuning. Google noted that TurboQuant requires no training or fine-tuning and incurs negligible runtime overhead, making it suitable for deployment in production inference and large-scale vector search systems.

The paper, co-authored by research scientist Amir Zandieh and VP Vahab Mirrokni, will be presented at ICLR 2026 next month.

Meta announced four successive generations of its custom Meta Training and Inference Accelerator (MTIA) chips on March 11: The MTIA 300, 400, 450, and 500, all scheduled for deployment over the next two years. Meta described the chips as progressively optimized for AI inference workloads on the premise that HBM memory bandwidth is the binding constraint on inference.

Coming two weeks after Meta disclosed a long-term AI infrastructure with AMD, the announcement puts Meta alongside Google, AWS, and Microsoft, each of which has spent the last few years building and scaling custom silicon programs for AI accelerated workloads. Will this emerging class of chips put a dent in Nvidia's stranglehold on the AI chip industry?

An inference case against GPUs

In a technical blog post published alongside the announcement, Meta described HBM's bandwidth as the most important factor affecting AI inference performance, adding that mainstream chips, built for large-scale pre-training, are then applied less cost-effectively to inference workloads.

“We doubled HBM bandwidth from MTIA 400 to 450, making it much higher than that of existing leading commercial products,” it reads. The MTIA 500 then increases HBM bandwidth again by an additional 50% compared with the MTIA 450. Both chips are optimized primarily for AI inference but can be applied to other workloads, including training as a secondary use case.

The MTIA 300 is already in production for ranking and recommendations training. Meanwhile, the MTIA 400 — which features a 72-accelerator scale-up domain and performance — has completed lab testing and is on the path to data center deployment. The 450 and 500 are scheduled for mass deployment in early 2027 and later in 2027, respectively.

Across the full 300-to-500 progression, HBM bandwidth increases 4.5 times and compute FLOPs increase 25 times, with the MTIA 450's HBM bandwidth exceeding that of existing leading commercial products, while the MTIA 500 adds another 50% on top, along with up to 80% more HBM capacity.

According to Meta, the chips use a modular chiplet architecture that allows the MTIA 400, 450, and 500 to share the same chassis, rack, and network infrastructure. That compatibility means each new chip generation drops into the existing physical footprint without requiring new data center buildouts, the mechanism Meta cited for its roughly six-month development cadence, well faster than the industry's typical one-to-two year cycle. “More importantly, we have deployed hundreds of thousands of MTIA chips in production, onboarded numerous internal production models, and tested MTIA with large language models (LLMs) like Llama.”

Google, AWS, and Microsoft

Google announced Ironwood, its seventh-generation TPU, at Google Cloud Next in April 2025; the company described it as the first TPU purpose-built for inference and the beginning of an “age of inference,” distinct from the training-first era that preceded it. Ironwood delivers 192 GB of HBM3E per chip at 7.37 TB/s of memory bandwidth, per Google's published specifications, and scales to configurations of up to 9,216 AI accelerators.

Then, in December at re:Invent, AWS announced Trainium3, a 3nm chip with 144 GB HBM3E per chip at 4.9 TB/s bandwidth, with a single Trainium3 UltraServer connecting 144 chips. AWS has also maintained a separate Inferentia product line — a chip dedicated exclusively to inference — since 2019. Meanwhile, Microsoft introduced its Maia 200 for inference workloads built on TSMC 3nm, which it called its “most efficient inference system.”

Broadcom is what’s connecting the dots across many of these programs, having had a hand in building both Google’s TPUs (as the company’s silicon integrator) and Meta’s MTIA family. Meta described the MTIA chips as being developed “in close partnership with” Broadcom, and said that the company “has remained and will continue” to be a key partner of Meta’s AI infrastructure strategy.

Broadcom also notably secured an agreement back in October to help OpenAI build 10 GW of custom ASICs, with deployments beginning as early as this year. If nothing else, the role that Broadcom now plays across competing hyperscaler programs reflects both how capital-intensive custom silicon development is and how consistent the underlying architectural requirements have become.

This convergence continues with software stacks, with Meta building MTIA natively on PyTorch, vLLM, and Triton. Google also added TPU support for vLLM in beta, and AWS runs its Neuron SDK across PyTorch, TensorFlow, and JAX. These shared inference-serving frameworks ultimately determine how easily production workloads can port between chips, and portability is what will make the economics of switching from CUDA-locked Nvidia silicon as the default GPU credible at scale.

Nvidia retains training

None of this changes Nvidia’s position in large-scale pre-training. Frontier model development still overwhelmingly runs on high-end GPU clusters, and Nvidia’s Blackwell is the current standard for that workload. Meta itself operates large Nvidia GPU clusters alongside MTIA deployments, and its February 2026 AMD agreement adds further GPU capacity to a portfolio that already spans multiple silicon vendors.

Instead, what we’re seeing is workload segmentation, whereby custom silicon takes high-volume, predictable inference workloads and GPUs retain training. MTIA 450 and 500 are designed to cover AI inference production through 2027, while Google, AWS, and Microsoft have each made equivalent commitments on their own timelines.

At the point where inference represents the bulk of AI compute cycles, hyperscalers appear to have collectively decided that paying a premium for GPUs to run those workloads is no longer financially sound.

A Google Gemini user has taken to Reddit “in a state of shock and panic.” The issue is with the most recent bill received by their software development business. Redditor RatonVaquero’s typical monthly spend on Gemini AI services is $180. However, in just 48 hours last month, their account “generated $82,314.44 in charges.” A thief has been using the account to generate oodles of Gemini 3 Pro Images and Texts. If Google doesn’t back down regarding these non-trivial fees from the suspected “stolen Gemini API key,” it will bankrupt the company.

Tragically, locking the door after the horse has bolted, RatonVaquero has now “Deleted the compromised key, Disabled Gemini APIs, Rotated credentials, Enabled 2FA everywhere, Locked down IAM, [and] Opened a support case.” On the latter point, initial feedback from a Google rep they contacted indicates that the charges will probably stick.

From the Redditor’s discussion of their correspondence with Google so far, it looks like the “don’t be evil” company is going to repeatedly cite its ‘Shared Responsibility Model’ for cloud services accounts. I’ve had a quick look at the referenced legal word salad, and I’d guess Google is leaning on the part of its agreement that asks customers to have an authentication system, access policy, and network security in place to protect their API keys, among other things.

Interestingly, though, several Redditors also note that the stolen API key(s) might actually have been there for the taking, and it is Google’s fault for flipping its API key secrecy rules.

Arguing for some ‘mercy,’ RatonVaquero, one of three devs at the affected Mexican development firm, complains that Google doesn’t have “basic guardrails for catastrophic usage anomalies.” The contrast in usage, from a usual $180pcm to $82,000+ in 48 hours, does indeed look like an extreme spike. RatonVaquero also says that there should be features like temporarily freezing services until review and the implementation of per-API spending caps.

A look into this overcharging issue indicates that Personal/consumer Gemini customers can’t accidentally spend more than their flat monthly fee. Instead, they have usage caps. Moving up to Dev/Business Google AI Studio users, they can set Quotas (limiting the number of requests per day or per minute). Meanwhile, Google Cloud (Vertex AI) users can set Budget Alerts to notify them when they reach a certain dollar amount.

RatonVaquero says they will talk again with a Google rep soon, and have filed a cybercrime report with the FBI. Now they are basically hoping for a softening of big G’s stance. They may be able to share the logs of their unusual “455x spike” in usage, and ask for “goodwill credits” as victims of a cybersecurity incident. It is Kafkaesque, but usually a bit of stubborn persistence can help get your case seen by the right people for a more favorable outcome.

During Google's Q4 earnings call last week, the company announced that it expects to double 2025's capital expenditure figures up to $185 billion, as reported by Reuters. This is around $70 billion more than analysts expected, and while it did send Alphabet stock falling 3%, it's one of the few companies investing heavily in AI that has continued to see stocks rise over the past six months.

This isn't the kind of wanton spending that the early AI infrastructure deals of 2025 felt like, however. Google has real data, and to an extent, real revenue to back their words up. Its cloud computing business grew almost 50% in the last quarter of 2025 to $17.7 billion, and overall revenue reached $114 billion - a close to $20 billion increase on the previous quarter.

Perhaps more importantly for infrastructure roll-out and investment, Google claims it has managed to reduce the serving unit costs for its Gemini AI by 78% throughout 2025 by improving model optimization and efficiency.

With claims that its Gemini monthly user numbers have now reached 750 million, Google is closing in on ChatGPT's dominant 800 million+ userbase and its extensive mindshare. Although there's no real winner in the AI race yet (save for perhaps Nvidia), Google definitely seems to be pulling ahead, and that may only be compounded this year as it continues its investment.

The full stack approach

Unlike other standout AI companies, Google has the luxury of bringing what it calls a "full stack approach" to AI. Google has the software with its own AI, it has its own Tensor Processing Units (TPU), so it's less reliant on Nvidia and other AI accelerator manufacturers - although it is also a big buyer of Nvidia GPUs and is getting some of the first Vera Rubin GPUs later this year.

Alphabet also has an existing infrastructure of data centers for serving cloud-based products to consumers and businesses alike. It has a flourishing cloud computing industry, which it can slot Gemini into as another service it offers. Gemini is also part of its existing Google One subscription model, offering access to those who already pay for cloud storage. It also integrates Gemini with its existing word processing tools. It's likely to be the go-to AI option on every smartphone once the Apple deal is done.

All this gives Google many more levers to pull when it comes to monetising AI, too. It can bundle it as part of standard Google service subscriptions to aid adoption. It can integrate it with its advertising systems to open up new revenue options. Alphabet's chief business officer, Phillip Schindler, told analysts during the earnings call that Gemini was helping Google to deliver adverts for longer, more complex search queries that were previously hard to monetize.

All of this has put Google in a position where it can maintain the incredible momentum in AI spending its run on througout 2025, and that makes it an outlier. As Reuters reports, Google was the only company to increase its capital spending in Q4. Considering the size of its planned capital expenditure for 2026, it may be hard for even some of the largest firms to catch up.

Even Google needs growth

During the earnings call, Alphabet's finance chief, Anat Ashkenazi, told analysts that Google was facing a cloud computing backlog of $240 billion. Those are commitments the company has pledged to meet, and hasn't yet got the capacity to handle.

The majority of Google's planned expenditure in 2026 will be spent on reducing this backlog and expanding AI compute power for Google DeepMind. Ashkenazi continued to highlight that Google's 2025 investment was focused on technical infrastructure like servers, data centers, and networking equipment.

Google's head of AI Infrastructure, Amin Vahdat, told staff in November that Google would need to double its AI serving capacity every six months to meet the demand being placed on its cloud computing divisions. Even factoring in Google's planned investment and efficiency savings from the latest models, that's a tall order.

Despite the backlog, Google keeps taking on more orders for capacity. Google's backlog for its cloud computing business for Q3 2025 was a mere $155 billion. That's more than $100 billion in unfulfilled orders in just a few months. Spending its way out of this successful hole may be Google's only way forward.

"We've been supply-constrained, even as we've been ramping up our capacity," Google CEO Pichai said during the recent earnings call. "Obviously, our capex spend this year is an eye towards the future."

Google might be leading this race, but there is no finish line in sight, and even with genuine revenue coming from its AI divisions and plans to expand that in the future, the spending required to retain momentum in AI is eye-watering. When even multiple trillion-dollar companies are chasing their tail, it's hard to know how it will all work out.

Big spending in AI-related investments has become the new normal to the point that it's now background noise. Even still, occasionally there's a sonic boom. Just yesterday, Amazon announced that it would be spending $200 billion in 2026, or $50 billion more than predicted. Investors didn't like that, and the company's shares took a steep 9% nosedive, taking some of its friends along for the ride for a combined sell-off approaching $1 trillion.

According to the Financial Times, the Big Tech players are set to spend a $660 billion on AI investments, an amount larger than the GDP of Israel. Investors who were once very bullish on the AI race, not wanting to be left out, are reportedly starting to get cold feet.

Revenue large enough to outstrip AI investments could be looking more like a mirage than an oasis, with analyst Dec Mullarkey stating that the announced spend is "not welcome news for investors that are already fixated on when AI-related revenue will start to show up."

Amazon took the brunt of the hit, as, along with the gigantic increase in capital expenditures, investors are seemingly frowning at the possibility of the outfit cannibalizing its lead in cloud services and even retail presence for the sake of AI. Stressing that particular point, analyst firm D.A. Davidson downgraded Amazon's rating from "buy" to "neutral".

The big loser group includes Meta and Alphabet (Google), which saw their shares take around a -2% and a -3% spill respectively, for the same base reasons. Even Google's record earnings and a contract with Apple for providing Cupertino's AI services didn't help it escape investor wrath. Analyst Mamta Valechha points out that alongside key fears, investors are not appreciating the companies' lack of visibility into exactly how these investments are expected to play out.

One week doesn't make for deep financial analysis, but it's worth noting that since Monday, today's sell-off puts Amazon at around -11.3%, Alphabet at -3.15%, Meta at -7.4%, Microsoft at -7.7%, and Oracle at -9.2%.

Meanwhile, Tim Cook is probably chuckling and eating popcorn. Only a scant few months ago, Apple was strongly criticized for dropping out of the AI race and for the lack of its much-touted Apple Intelligence in its latest software releases.

The firm ultimately threw in the towel and hired Google's Gemini for that duty, but not having spent untold billions resulted in investors sending the stock price up 7.5% over the week, helped by "staggering" demand for the latest iPhones.

A federal jury in San Francisco has convicted a former Google engineer of stealing confidential AI infrastructure data and transferring it to benefit Chinese interests, closing one of the highest-profile trade secret cases to date involving AI systems.

The defendant, Linwei Ding, was found guilty on 14 counts, including economic espionage and theft of trade secrets, following a trial concerning his conduct while employed at Google between May 2022 and April 2023. According to prosecutors, Ding copied internal technical documents while also pursuing roles and venture funding connected to Chinese companies and his own start-up, Rongshu.

The U.S. Department of Justice, through a superseding indictment, says that the stolen material covered seven categories of trade secrets that together describe how Google designs, builds, and operates its AI data centers. That material included low-level specs for its TPUs, internal TPU instruction sets, and performance characteristics tied to HBM access and inter-chip connects. In addition, Ding is understood to have stolen documents describing TPU system architectures and the software stack used to schedule and manage work across clusters.

Beyond Google’s TPU accelerators, stolen material included materials related to Google’s GPU machines and GPU cluster orchestration, focusing on how the company configures and operates multi-GPU systems at scale, and proprietary SmartNIC hardware and software used for high-bandwidth, low-latency networking inside the company’s AI clusters. This is an obviously contentious area that Google will be keen to safeguard as models grow larger.

"A calculated breach of trust"

In a statement following the guilty verdict, John A. Eisenberg, U.S. Assistant Attorney General for National Security, said, “This conviction exposes a calculated breach of trust involving some of the most advanced AI technology in the world at a critical moment in AI development.” Trial exhibits show that Ding copied data from Google source files into the Apple Notes application on his Google-issued MacBook before converting those notes into PDF files and uploading thousands of them into personal file storage over a period of around 11 months. This method helped Ding evade detection by Google.

Ding, who began working for Google in 2019 and was involved in developing GPU software, faces a potential sentence of up to 10 years in prison for each of the seven counts of economic espionage, along with additional penalties for the seven counts of theft of trade secrets. While sentencing is yet to take place, the Justice Department is already celebrating the verdict as a first and major win tied directly to AI-related economic espionage, showing just how seriously U.S. authorities are now treating AI and adjacent technologies as critical to economic and national security.

Google has reportedly been working on folding ChromeOS into Android since 2015, but its efforts appear to have stalled in recent years. However, we’ve just seen some evidence of the company’s work on this project through a bug report on the Chromium Issue Tracker. The company has since made the report private, but not before 9to5Google shared the screen captures. The device involved was reportedly an HP Elite Dragonfly 13.5-inch Chromebook running build ALOS: ZL1A.260119.001.A1. “ALOS” supposedly refers to Aluminum OS, which is the codename for the Android desktop that’s being developed to replace ChromeOS.

The most obvious difference between ChromeOS and Aluminum OS is that the taskbar is slightly taller, making it more suitable for devices with larger screens. Google also moved the date and time from the lower right to the upper left corner of the screen, while the status settings seem to have been moved to the upper right corner, making it look a bit more similar to macOS. Google Chrome browser also remains mostly similar to what you get on Android but now comes with Extensions, and we also see an example of side-by-side multitasking on the large screen. But aside from that, we don’t see any other major differences or features, especially given that it’s just screen recording for a bug report.

Another tidbit of information we can garner from this is that the Android desktop version is seemingly running on old hardware — the HP Elite Dragonfly 13.5-inch Chromebook mentioned in the report launched in early 2022, featuring a 12th-generation Intel processor. Although it could be that Google is just using existing hardware to validate the operating system, it could also mean that it’s planning to allow (or even force) existing ChromeOS users to update to Aluminum OS once it officially comes out.

There’s no official announcement yet on the exact date that Aluminum OS will arrive, although Google said that it expects to deliver the new operating system by 2026. This leak shows that it’s probably coming sooner, rather than later, especially as it already seems to be working and is probably already in an early, closed Beta stage. Nevertheless, many are curious as to what it can accomplish, especially as Windows 11 is known for its buggy updates, especially with the recent spate of issues, while macOS is often an expensive option that’s out of reach for many buyers.

The Sega Dreamcast’s built-in web browser was killed by Google earlier this week. In less dramatic terms, Google closed a PlanetWeb browser compatibility window that had somehow remained ajar for a quarter-century. While a raft of Google services no longer respond properly to the Dreamcast’s ancient SSL/TLS stack, fan-made search engines and online gaming servers remain available, so internet-connected Dreamcast stalwarts aren’t entirely doomed.

As highlighted by Dreamcast Live, above, the pack-in browser supplied by Sega on a silvery CD is now useless for anything other than an ornament, memento, or Frisbee. The fan account confirmed that even the latest PlanetWeb 3.0 was affected by Google’s changes.

We note that PlanetWeb 3.0 is no spring chicken, though, as it was launched in 2001. But it is as modern as this browser family gets. PlanetWeb 1.0 was released in 1999, and version 2.0 followed a year later. Thus, the official Dreamcast browser lineage stretches back around 26 years.

Ultimately, PlanetWeb’s demise stems from its use of obsolete web standards. In some ways, it is surprising it endured so long with old SSL, an old JavaScript engine, and outdated ciphers.

What can a late 2025 Dreamcaster do?

For web search, devoted Dreamcast owners can turn to http://frogfind.de/, a search portal set up by retro computing and gaming YouTuber Action Retro. The results garnered via Frog Find are powered by Google, Brave, and DuckDuckGo, and the page/queries are pleasantly barren of distractions. However, some mirrors seem to be down, like the .com alternative.

Meanwhile, some of the most enduring Sega Dreamcast games with online communities continue to run, Google or not. Servers for titles like Phantasy Star Online and Quake III Arena will still tick along regardless.

Dreamcast homebrew community participants are also looking at alternatives and workarounds for any issues that might arise and disrupt their enjoyment of this old, underrated, but not unloved console.

I owned a Dreamcast in the early 2000s mainly for the wonderful light gun games library, which were fantastically responsive and great fun on an old CRT. With a PC and/or Mac also owned at the time, I felt little need to buy the required peripherals and connect the Dreamcast to the Internet.

Many of the world's largest AI tech companies are going to start working together on some of their shared problems. Microsoft, Google, Anthropic, OpenAI, and a number of other related companies are going to team up as part of the Agentic Artificial Intelligence Foundation, as reported by The Information. Managed by The Linux Foundation, the group will work on developing key open source tools and standards for AI agents, and will share their findings with each other on solving key technical problems.

However, as signs mount that agentic AI is not particularly effective at replacing workers, and rumors of the AI bubble stretching to its limits continue to swirl, agents need to impress, especially if they're being hailed as the next big thing in the AI landscape.

AI agents have long been seen as a next-generation development for the latest large language models that would finally realize their potential. They could laser-target larger tasks by breaking them down into smaller pieces, which a larger AI model could use to create or complete a larger project or goal.

That's how it works in theory, but as the Harvard Business Review highlights, they rarely achieve the end goal in practice. Especially when it comes to customer-facing roles, AI agents just aren't ready to replace real-world workers as they can't be trusted to complete their tasks effectively enough, or at all. Hallucinations are still a real problem, and the public has little tolerance for abject failure in basic tasks or wild shifts in tone.

That doesn't mean there's no potential there, though. It's the basket the main AI companies are putting their eggs in at the moment, anyhow, hence this new initiative to pool their efforts to create something more effective, and maintain standards that they have a greater say in developing.

The group's first goal will be to develop three existing open-source tools, according to people familiar with the matter. These include: a model context protocol developed by Anthropic called MCP, to standardize how AI agents connect to other applications; an OpenAI format for giving instructions to coding agents, known as Agents.md, and an open source AI agent invented by Block that can run locally on a single computer without networking, called Goose.

MCP is already in use at OpenAI, Microsoft, Google, and Cursor, so it's no surprise that it was chosen as one of the group's main goals. As it stands, it can connect ChatGPT to a company's Slack, for example, which would allow a manager to quickly summarize conversations. But IT managers speaking to The Information claim there are serious security concerns, especially when it comes to prompt injection attacks, so MCP needs continued development, and the developers need to agree on the best way to patch discovered security holes quickly and effectively.

Cementing the industry

This foundation also has the potential to cement its participants as the premier AI companies. Although it's not just the big tech firms that have joined this foundation, and it is being organized by a long-standing organization with a strong reputation for keeping software development as its main focus, the potential is there for exploitation and, arguably, stagnation.

The largest companies are likely to have the largest input on the direction of these open standards, which could allow them to shape the future of Agentic AI in a way that benefits them. With enormous investment capabilities, larger companies are capable of pivoting toward new efforts at the drop of a hat. If any breakthroughs are made in Agentic AI that require heavy investment or access to hardware and software to take advantage, those larger companies will be in a prime position to reap the rewards.

Indeed, the very financing of the entire AI industry has been rife with major companies pumping each other's stock prices with promises of future revenue and long-tail investment pledges that won't be realized for years. The major companies all collaborating to advance the industry have some strengths, but it could also be taken as a further example of the major firms propping each other up for the foreseeable future.

Unproven, unrealized, unprofitable

At their core, the major AI tech firms have the same problem: They aren't making any money from any of this, yet. AI costs far more to run than it generates for the companies developing it, and there's no sign of that stopping any time soon.

This foundation could be a way for them to collectively try to solve this issue. Someone needs to make a killer AI app or a way for agentic AI to fix real problems, or rapidly enhance productivity, so that these massive companies can make good on their equally large investments.

Shareholders and early investors are going to come calling for the promised profits over the coming years. Accelerating the development of their tools and standards through this foundation could be one way for these major firms to also accelerate their path toward profitability.