A new Amazon data center is set to become the single largest source of environmental pollution in the U.S., according to a New York Times report on August 8. The tech giant — which already operates the most data centers worldwide — is reportedly building a natural gas power plant in Pecos County, Texas, to power its new data center at the same site. According to project permits, the plant would generate up to 7.65 gigawatts of power using 35 natural gas turbines. The new plant — which burns natural gas to generate electricity — has been authorized to release 33 million tons of CO₂ annually, more planet-warming gases than any other power plant in the country, says the NY Times report.

Conversely, Amazon had pledged to achieve net-zero carbon emissions across all its global business operations by 2040. The company made the promise in 2019 when it initiated and co-founded The Climate Pledge — a voluntary coalition of over 700 companies and partners — aimed at addressing its own corporate footprint and rallying global supply chains. However, the company has seen its emissions rise each year for the past several years, something analysts attribute to a spike in data centers to support the AI boom. The company is currently building several additional AI data centers globally, including the one that the new gas plant would power in Texas.

Amazon has acknowledged the impact of its intense AI ambitions on its earlier climate pledges but insisted the company remained committed to them, despite obvious struggles. “The world looks different now than when we co-founded the climate pledge,” said Margaret Callahan, an Amazon spokeswoman. Still, “our commitment hasn’t changed,” she said, echoing similar statements by Microsoft, which is struggling to fulfill its 2030 sustainability promise amid carbon-heavy AI expansion, but insists it remains committed to it.

As detailed in our data center power roadmap, an increasing number of hyperscalers and data center operators are switching from grid connections to dedicated on-site power generation. The “behind-the-meter” moves are being triggered mainly by the long timelines required to set up new power infrastructure to connect new power-hungry data centers to the grid, but also by growing anti-data center sentiments over irreversible hikes in the electricity bills of nearby communities.

While many companies are also pursuing nuclear and renewable energy options — examples being Microsoft’s Three Mile Island nuclear deal and Meta’s agreement to secure up to 1GW of orbital solar energy capacity — gas appears to be the leading preference due to its scalability and speed of availability. The Trump administration has thrown its weight behind such projects for data centers, promoting oil, natural gas, and coal over renewable energy sources.

The community of Gilroy, California, about 30 miles southwest of San Jose, was surprised when an Amazon data center started construction in their town last month. But as many projects like these have been suffering from opposition and delays across the country, the Wall Street Journal reports that this specific site avoided scrutiny by adhering to local zoning rules set 45 years ago.

Amazon’s Gilroy project sits on a 56-acre piece of farmland between a Walmart Supercenter and the Gilroy Premium Outlets. The company and the city didn’t exactly keep the project secret when it started its application in 2020. In fact, there were disagreements between the tech giant and the city government during the process. The publication noted that some Amazon officials were frustrated by slow permitting progress, which was delayed by at least one year. At the same time, the city pushed back on some of the company’s requests, especially if they were no longer up for discussion.

Roger Wehner, Amazon Web Services vice president of economic development, told WSJ that the site had gone through a long approval process, which required public notices and public-comment periods. However, when Gilroy resident Rosa Rodriguez wanted to state her concern about the damage a data center can do to a region suffering from drought, she was told that the comment period had already lapsed in 2024, long before data centers became a hot topic across the country.

Gilroy City Mayor Greg Bozzo said that other industrial projects that had a similar scale to the Amazon data center have previously been approved without resistance.

“It didn’t bother them that a food-distribution center came to Gilroy without their engagement, but now it bothers them that a data center is coming to Gilroy without them having the possibility of engagement,” Bozzo said.

Still, he conceded the people are now more aware about data center developments and added, “We…have an opportunity to change the way that we do business in Gilroy. We’re adapting to the times.”

The tech giant also said that it’s engaging more with the community, with Amazon officials claiming that they met residents directly and even hosted an open house where people can ask questions and leave comments. It also addressed fears that the development would cause a water shortage, which other data centers have already caused, saying the company is constructing a wastewater recovery system to supply its water needs and will even build a pipeline to bring wastewater from the source to its plant.

Bozzo also highlighted the benefits that Amazon will bring to the city, especially increased tax revenue, construction jobs, and a million-dollar donation towards a new fire truck. He also pointed out that the tech giant has been giving grants to non-profit organizations in the city, as well as sponsoring the annual Gilroy Garlic Festival.

But at this point, it’s not the Amazon data center the people of Gilroy are having an issue with. Instead, it’s the fact that a major project that has the potential to have a major impact on the community has been approved without the knowledge of many residents.

Amazon Web Services is cracking down on internal use of EC2 instances among its engineers. In May, the company reportedly met with engineers and told them to reduce CPU waste to ensure AWS has enough CPU capacity to meet customer demand, The Information reports. The message comes as demand for CPUs in the data center has hit a fever pitch, with the traditional eight-to-one or four-to-one ratio of GPUs to CPUs moving closer to parity.

EC2 instances make up a large chunk of the modern internet, and they're used in private deployments, as well. Traditionally, AWS engineers have been able to spin up their own instances for development, leveraging the relatively low CPU utilization required for web infrastructure to use more virtual machines. Now, engineers say they're waiting days to get access when they previously could get access within hours. One engineer told The Information that they've never had to wait this long for an instance, even after several years of working at Amazon.

Amazon deploys several different types of CPUs in EC2 instances, including AMD and Intel options and its relatively new Graviton5 chip. Graviton5 is Amazon's most powerful CPU to date, and it uses an Arm-based architecture along the lines of Nvidia's Vera CPU and Arm's own AGI.

The increased demand for CPU comes on the back of AI agents, a new paradigm in productivity that even companies as large as Amazon are struggling to reckon with. Last month, for instance, a coding agent blew through $1.8 million in token costs at Amazon, surpassing a development budget by 860%.

Much of the AI infrastructure currently in place is designed around inference, a workload that's accelerated by GPUs. With the four-to-one ratio, the CPU served as a way to keep the GPUs fed, and nothing more. However, agentic workloads are much more complex. They often involve tool calls that run on the CPU, as well as more complex orchestration of inference on GPUs. This is what has brought CPUs center stage in the agentic era. Intel, AMD, and others have echoed what we've heard from memory and storage companies over the past several months: the demand is so high for CPUs that most companies will take whatever they can get.

The major players are capitalizing on that demand. AMD just recently unveiled its portfolio of Zen 6 'Venice' CPUs for the data center, marking the first time AMD has launched a new architecture in the data center before the client market in decades. Nvidia has also pivoted its messaging away from accelerators and toward its new Vera CPU, vying to stake its claim in an expanding market of agentic AI infrastructure.

Although Amazon is wrestling with CPU capacity across its internal engineers and external customers, The Information reports that shortages are largely a problem for spot instances. A consultant told the outlet that contracted capacity hasn't experienced any shortages.

Following publication, an Amazon Web Services spokesperson reached out with the following statement:

"Demand for AWS services, including EC2, is incredibly strong and growing. Even with this heavy demand, we continue to satisfy the overwhelming majority of compute needs for both our internal and external customers. We work closely with internal teams to meet their compute needs while ensuring they use EC2 resources as efficiently as possible, such as reclaiming idle instances, right-sizing, and scaling up and down as needs change – just as we’ve always done. These efficiencies help manage capacity for internal and external customers alike, and any suggestions that these long-standing efforts reflect new capacity constraints is simply wrong.

This premise is sensationalized. Frugality is in our DNA since Day 1. We have always encouraged our teams to operate efficiently. We also share best practices for how customers can optimize resources to external customers. This isn’t a new directive, and encouraging efficient use of resources isn’t unique to Amazon."

We pressed Amazon on if engineers had been given deadlines to reduce their compute usage, as The Information originally reported. An AWS spokesperson responded: "This narrative on EC2 is inaccurate. As part of our normal business operations, and backed by our leadership principle of frugality, we are always driving efficiency across our resources to ensure teams are optimizing capacity."

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.”

Amazon has several internal reports that show how AI is causing the company to overspend on various projects. The Financial Times reports that the cost overruns reached $1.8 million, and that is just for one project. These mistakes used to be “trivially cheap,” but AI models made them “catastrophically expensive,” especially as token spending drastically increased with the deployment of AI agents.

The biggest blunder, so far, is the $1.8-million bill that came from a failed Claude Sonnet AI deployment, which was supposed to match author details with listings on Amazon, representing an 860% increase over the allocated budget that was only detected some five months after the issue started happening. Other problems that surfaced include a $541,000 additional cost that came from a project building, ironically, a financial auditing tool, and a $134,000 extra expense for a system designed to reduce delivery times in the company’s logistics network.

“As with any new technology, we’re experimenting, learning and improving how we use it, including how we drive cost efficiencies,” Amazon said in an internal presentation, according to FT. “Cherry-picking small, isolated examples where teams are learning from one another and portraying them as business as usual doesn’t reflect how teams across Amazon are using AI.” And even though overspending more than a million dollars on failed AI projects might seem excessive for the average person, the tech giant’s latest quarterly revenue sits at more than $181 billion, meaning these excess AI expenses don’t even account for 0.1% of what it makes in a month.

This is not the first time that AI-related issues have cropped up in Amazon’s workflow. Earlier this year, AWS reported several outages that were driven by AI coding bot blunders, but the company fixed this by limiting the access of AI agents instead of giving them the same permissions as the senior engineers that they’re tied to. It also used to have an internal leaderboard that showed which employees used AI the most, but has since dropped it as spiraling AI costs made them think twice about the policy.

Many tech companies have been pushing their people to use AI, supposedly to increase productivity through tokenmaxxing. However, the Uber CTO said that there is no link between this policy and shipping successful products. And as agents took over and AI providers switched from subscription to per-token models, costs have become so great that companies are using up their annual budgets in a matter of weeks. While this might not be an immediate issue for tech giants like Amazon and Microsoft, it is unsustainable for most other companies out there.

Five U.S. tech giants heavily invested in AI and its related infrastructure reportedly have an estimated $1.65 trillion in hidden debt, with the figures annotated in their quarterly financial statements instead of being listed in their balance sheets. According to Nikkei Asia, this is higher than the $1.35 trillion officially listed, meaning investors could be caught unaware once the hidden figures come to light.

The publication says that Meta has a high off-balance-sheet-to-recorded-debt ratio, with the company owing $420 billion in unlisted debts compared to the $140 billion written on the balance sheet. Oracle purportedly also has a massive $273.3 billion of hidden debt, which is a 2,900% jump from the hidden debt it had from 2022.

This may sound strange, but it’s actually an accepted accounting practice. The “hidden debt” stems from long-term contracts that have been signed but have not come into force yet, which, Nikkei says, is mostly related to the billions of dollars promised to data center operators. The AI race has got many hyperscalers signing contracts and agreements with data center operators, saying that they will pay for the compute they generate once their project goes online.

While any institution promising to pay any amount of money for services or goods delivered is obliged to list them as a liability, the fact that these data centers haven’t started operations means that these agreements are off-the-books at the moment. But when these projects come online, the contracts that the tech giants have signed will come into force, and they’ll have to pay for the compute that these sites will deliver, no matter if there is demand or not.

Nevertheless, these tech companies aren’t just pouring money into future contracts just for the sake of it. Alphabet, Amazon, and Microsoft reportedly have a cloud service backlog worth $1.45 trillion, meaning these are services yet to be rendered and paid. Amazon Web Services CEO Matt Garman also told the publication that the investments that the company is getting into are “not speculative.”

While this may seem like a good way to secure capacity — sign customer contracts that guarantee demand and then enter into long-term agreements with data centers to get the compute needed to deliver the services- it opens these tech giants to massive amounts of risk. That’s because if the demand fails to materialize, then they’d be left paying for excess compute without having any customers to sell them to. What’s more alarming is that Nikkei says that these investment expenditures are exceeding their earnings, meaning these big tech companies are increasingly relying on corporate bonds and new shares to fund them.

Even though demand for AI compute is increasing, it’s still a relatively new and unproven technology, with many experts saying that it should benefit more people to avoid a bubble. The cost of using AI for nearly everything, called “tokenmaxxing,” has also caught some companies by surprise, with agentic AI eating up annual AI budgets in a matter of weeks. Because of this, some companies are reducing their use of AI or are switching to more affordable models from China. This uncertainty, paired with the way tech companies “hide” these liabilities, is quite concerning, as they would appear to have less long-term obligations than they actually do.

This isn’t the first time that an industry giant has used similar accounting techniques. The publication cited Enron’s 2001 collapse, which was due to the company hiding its troubled assets through special purpose entities and marking unrealized gains from trading contracts into its current income statements. While the tech giants are not hiding underperforming assets off their balance sheets and committing fraud, they’re still using a similar mechanism to list their upcoming obligations. Although these are technically not debt, they still behave like one, and the way they’re reported is what’s concerning some experts.

An extremely unfortunate PC builder is the latest to fall victim to Amazon's prolific returns scams, after the Nvidia RTX 5070 they received from the website turned out to be a DVD re-writer, a mousepad, and the busted logic board from an early 2000's Kenwood AV receiver.

The victim was gifted a brand new MSI Ventus 2X variant of the RTX 5070 from Amazon by a friend at a cost of 605 euros ($700). A few days later, when the package arrived, it weighed the same as the real thing, but it certainly did not have a graphics card inside. Instead, the customer received what appears to be a broken logic board from an old Kenwood AV receiver, a DVD rewriter, and what seems to be a mousepad.

"This was shipped and sold by Amazon," u/luutherr told Tom's Hardware. They also provided us with proof of purchase and confirmed the item was a gift from a friend, making the scam all the more galling. That means someone in the middle is the bad actor. In reality, what most likely happened was a scam-swap, where someone previously bought the item, emptied its real box contents, and filled it back up with junk that weighs the same. They then filed for a return, probably citing personal preference as the reason.

Since the box still carried the same weight and the product wasn't returned because of a defect, it ended up back on sale after passing some basic checks. Someone else buys it, and Amazon or the seller unknowingly sends that person the fake item, completing the circle. Believe it or not, this whole process actually used to be worse before Amazon ended its commingling program last year, which sellers exploited to avoid being traced.

In the end, the same system that delivered OP the wrong product should come in handy for delivering justice. A simple return request should get them their money back, but it will certainly erode the customer's trust in the platform. We would say this is a reminder to always stay vigilant when buying PC hardware with prices that seem lower than usual. But since this was bought from the official MSI store and sold directly by Amazon, it's simply a consequence of deceitful human nature.

Amazon employees Darius Irani, Patrick Schloesser, and Liesl Wigand, who are members of Amazon Employees for Climate Justice (AECJ), say that they’re currently being investigated by their employer for breaking company policy after they testified in favor of the Seattle data center moratorium. According to Bloomberg, the three were called to separate Zoom meetings with an HR staff member after the city passed its one-year AI data center ban, where they were told that the company was investigating concerns about their public comments. Staff say they were intimidated during calls, are being monitored at work, and may face possible termination.

The company said that even though the three were free to discuss their working environment, they’re not allowed to speak as its representatives. “As we looked more closely at how these employees represented themselves, and how their comments were received by others, it became clear that they may have been speaking in their capacity as Amazonians and not as private citizens,” Amazon spokesperson Margaret Callahan told the publication. “We believe it’s important to apply our policies consistently so, just as we would with anyone else, we’re investigating whether there was a violation of our policies and may or may not take action based on what we find.”

Legal representatives of the three personnel said in a letter to the Seattle Office for Civil Rights (SOCR) that the investigations could lead to discipline, with one of them even saying that they could possibly be terminated. The lawyers argued that they were investigated by the company because of what they said when the Seattle City Council heard public comments on the measure, and said that Irani, Schloesser, and Wigand did not use company time when they made their comments, nor did they make any mention of their employer or shared propriety information.

The SOCR is a local government agency that enforces laws against employment discrimination, among other things, and the three affected employees are asking the agency to look into the situation. They alleged that they were intimidated during the Zoom meetings and complained about being monitored while they were at work. This also isn’t the first time that AECJ-affiliated workers faced an issue with Amazon. The company reportedly fired two people in 2020 who were part of the group’s leadership for putting a spotlight on Amazon warehouse workers’ safety (or lack thereof) during the pandemic.

Amazon says it is just investigating the situation and hasn’t made a decision yet. However, news like this will not do any good for the company and industry in general, especially as they gather bad press because of their AI data center projects. While the company has been trying to put its good side forward, like comparing its water consumption with other hyperscalers to highlight its high water efficiency, it still cannot shake off the perception that it could cause higher electricity costs, reduced water pressure, and noise pollution, among others.

Data center water use is one of the hot topics right now in the U.S. and is one of the primary reasons why such projects are being blocked across the nation. However, Amazon claimed that it’s improving its water efficiency and that it uses the least amount of water on a per kWh basis among the AI tech giants, including Microsoft, Google, and Meta. Aside from that, it also said that despite using 2.5 billion gallons of water across the world in its data centers, it said that that amount is still a fraction of what other industries in the U.S. are using annually.

Amazon pointed at EPA data from 2017 that said Americans use 9 billion gallons of water daily just for landscape irrigation. This amounts to nearly 3.3 trillion gallons of water every year just for watering plants and gardens and doesn’t even include the irrigation needed for food production. This makes its 2.5-billion-gallon water consumption a literal drop in the bucket, amounting to just 0.075% of water used for keeping gardens green and thriving.

These gardens, however, have a direct benefit in that they could potentially keep ambient temperatures lower (versus an all-cement environment) and add to the aesthetics of the immediate area — the only question is how many people these developments benefit. Moreover, many new data centers in the U.S. are reportedly being built in areas suffering from water issues. While the overall data center water consumption might seem small if you look at the larger picture, we still cannot turn a blind eye to its potential effects on the local community.

For example, a Meta data center has allegedly caused a Georgia town’s deep-well water source to muddy — a sign that the level of the local water table is hitting low levels, allowing mud to be siphoned by the residents’ water pumps. Another site reportedly sucked 29 million gallons of water in 15 months, which caused low water pressures for residents. Reports like these make it harder for other data center projects to secure permits from local governments to start construction, especially as communities fear that these issues could happen in their area, too.

Amazon boasts water efficiency improvements

As the pushback against data centers’ egregious resource consumption has become front-and-center in many permitting fights, Amazon is showing that it’s taking steps to reduce its water consumption and claims that it’s on track to being “water positive” by 2030. It achieved this by implementing innovations in its data centers to reduce water use without reducing performance.

The company says that it mostly uses air cooling for its data centers, which uses up a lot less electricity compared to water cooling. But when ambient temperatures rise, it switches to Direct Evaporative Cooling, with the company spraying water on an absorbent medium, which the company describes as “a sophisticated, giant sponge,” and then runs the hot air through it to reduce temperatures by five to 10 degrees. It says that this is more power efficient than using chillers, reducing power use by about 20% to 25% during the hottest time of the day when power usage by other users is at its highest, too.

It also said that it raised the temperature thresholds in its data centers. While this meant that its servers ran hotter compared to previous years, it didn’t impact on the longevity of their hardware and they were still able to deliver the same amount of computing power as before. Through trial and error, the company discovered that it could keep running its servers using air cooling only, with its water-cooling systems only kicking in once the ambient temperature reaches 85 degrees F (or more than 29 degrees C). This resulted in 50% reduction in water use, says Amazon water specialist Beau Schilz.

Efforts like this are essential for data center operations to become sustainable. After all, despite the pushback against new data center projects, we cannot forever put a moratorium on them as AI’s demand for compute is increasing. But until AI hyperscalers can earn back the trust of the people and prove that they can build infrastructure without increasing costs for everyone else and hoarding the resources that communities need for their survival, then they can only expect opposition to continue.

Amazon has unveiled a new data center networking architecture that it claims delivers up to 33% higher throughput while reducing network power consumption by 40%, marking what the company describes as the first large-scale deployment of a flat network based on random graph theory, Wired reports. The company revealed that it had been quietly deploying the design in its data centers since last year, confirming that it has already become the default data center network for most AWS workloads.

The architecture, called Resilient Network Graphs (RNG), replaces the hierarchical networking structures that have dominated cloud data centers for decades with a flatter, quasi-random architecture designed to move data more efficiently between servers. Amazon says the design uses 69% fewer networking devices than traditional architectures and can reduce infrastructure costs by up to 45%, potentially translating into billions of dollars in savings across its global cloud footprint.

The company first deployed RNG in a Dublin data center in 2024 before expanding the architecture into facilities in Germany and Spain. AWS says the design is now being rolled out across most newly built data centers and will form the foundation of future deployments.

Data center and AI capability conversations typically focus on processing and storage. However, networking is one of the most important aspects of capability. Every request sent from your device to a cloud application, database, AI model, or storage service depends on data moving rapidly across thousands, or even hundreds of thousands, of servers. As facilities continue to grow, ensuring information reaches the right destination quickly and efficiently has become an increasingly difficult engineering challenge.

For decades, most large-scale data centers have relied on a hierarchical networking structure known as a "fat-tree" topology. In this design, data travels up and down layers of switches and routers arranged in a tree-like hierarchy. While the approach is proven and reliable, it can create bottlenecks and strain available bandwidth. Traffic can become concentrated at specific points in the hierarchy, even when capacity is unused elsewhere in the network. The approach also requires a large number of expensive networking devices.

Researchers have long theorized that a flatter network based on random graph principles could solve many of those issues. Instead of forcing traffic through predefined layers, routers are connected in a highly distributed mesh-like arrangement that creates numerous possible paths between endpoints. In theory, this will improve resilience, increase utilization of available bandwidth, and reduce the likelihood of congestion.

The challenge was making such a network practical. Although random graph networks have been subject to academic research for more than a decade, major obstacles have prevented their successful deployment at hyperscale. Routing traffic efficiently through such a network is considerably more complicated than routing traffic through a predictable hierarchy, and physically connecting millions of fiber-optic links without creating an operational nightmare presents an entirely different challenge.

AWS says it overcame those obstacles with two key innovations: software and custom hardware. The first is a custom routing protocol called Spraypoint, which distributes traffic across large numbers of available paths rather than relying primarily on the shortest path. The second is a passive optical device called ShuffleBox, designed to organize and standardize the immense amount of cabling required to build the network at scale.

According to Amazon, the resulting architecture not only moves data roughly one-third faster than conventional designs but also requires significantly fewer switches and routers. The company says those reductions lower both capital expenditures and operating costs while simultaneously reducing power consumption.

AI models are growing larger and more capable, and so are their user bases. To sustain this growth, hyperscalers have to continue optimizing the entire technology stack. Faster chips remain crucial, but so are advances in cooling, power delivery, storage systems, optical interconnects, and networking. AWS believes RNG is one such advantage. The company rightly considers the achievement of deploying random graph theory at scale, “a breakthrough that will deliver greater reliability and performance for AWS customers, save billions of dollars in hardware, and lower CO2 emissions.”

Amazon is the latest hyperscaler where employees have been caught inflating AI token consumption to hit internal usage targets, following similar behavior documented at Meta and Microsoft last month, the Financial Times reports.

The company set targets requiring more than 80% of its developers to use AI tools each week and tracked consumption on internal leaderboards. Some employees told FT they had been using MeshClaw, an in-house agent platform that can initiate code deployments, triage emails, and interact with Slack to maximize their token numbers. Amazon said usage statistics would not factor into performance evaluations, but multiple employees said they believed managers were monitoring the data. One said there was "so much pressure to use these tools," another described how tracking created "perverse incentives."

The practice — dubbed "tokenmaxxing" — has become widespread enough to generate its own vocabulary and leaderboards, but beyond workplace culture, if a meaningful share of AI consumption is performative, how reliable are the demand figures that hundreds of billions in AI infrastructure procurement are being allocated against?

Combined 2026 capex from Amazon, Microsoft, Alphabet, and Meta is tracking between $650 billion and $700 billion, with some Wall Street projections exceeding $1 trillion for 2027, and every hyperscaler has told investors that inference capacity is being absorbed as fast as it can be deployed. Internal developer consumption is obviously part of that absorption, and it sits alongside paying external customers in the usage data that informs the likes of capacity planning, GPU orders, HBM procurement, and power infrastructure.

Tokenmaxxing doesn’t mean the demand is fabricated — enterprise AI adoption is broadening, and inference workloads are scaling into production — but there’s a distinction between adoption and consumption intensity. The former is a durable driver of demand, whereas the latter is gameable, and it’s currently being amplified by the incentive structures that these companies built. The water is further muddied by reports that AI is more expensive than actual workers.

Meta's internal leaderboard lasted days after public exposure, and Amazon recently restricted visibility of team-wide usage statistics. And when measurement shifts, the consumption intensity they incentivized will shift with them.

Nvidia CEO Jensen Huang has highlighted per-engineer token consumption as a key metric, stating he’d be "deeply alarmed" if a $500,000-a-year engineer was not consuming at least $250,000 in tokens. Nvidia's inference growth obviously depends on that consumption being a productive workload that persists and compounds because every inflated token is real GPU time.

Angie Jones, formerly VP of engineering for AI tools at Block, told LeadDev she expected the industry to pivot toward measuring efficient token usage rather than celebrating volume. In a cycle where GPU orders and power commitments are being placed years in advance, the quality of the demand projections behind them matters. The hyperscalers are building for a world where every knowledge worker consumes hundreds of thousands of dollars in annual compute. Whether that consumption proves productive or performative will determine how much of this year's $700 billion generates durable returns.

Amazon’s data centers in Bahrain and the UAE have been hit multiple times by drone and missile strikes from Iran since the U.S. started bombing the country in February 2026. This left the company’s ME-CENTRAL-1 and ME-SOUTH-1 disrupted, with the AWS Health Dashboard indicating that it will take months before they can go back online.

“The Middle East (UAE) Region (ME-CENTRAL-1) has suffered damage as a result of the conflict in the Middle East and is currently unable to reliably support customer applications,” Amazon said in its latest update. “While some workloads continue to function normally, we strongly recommend customers migrate all accessible resources to other Regions and restore inaccessible resources from remote backups as soon as possible. Relevant billing operations are currently suspended while we restore normal operations in this AWS Region. This process is expected to take several months.”

These sites were first hit in early March, a few days after the U.S. started its bombing campaign on Iran. A few weeks after that, AWS Bahrain was hit once more, this time by drone strikes, further disrupting operations. The last reported strike happened in early April, putting several zones into a “hard down” status. Although both sites remain partially operational, it seems that Amazon has refrained from billing customers who used these servers and continues to encourage them to move to other regions and avoid further disruption due to the conflict.

Even if the missile and drone strikes haven’t completely leveled the two data centers, Amazon is still on the hook for months of repairs. Aside from these direct hits, the shockwave and post-explosion fires they caused have compounded the damage to the sensitive equipment used in these sites. The company also reported that fire suppression systems have caused flooding and water damage to various equipment, while cooling systems have suffered from mechanical failures.

There’s currently an uneasy truce in the area, as the two parties are at the negotiating table trying to end the war. However, hostilities could resume at any time, especially as the U.S. and Iran cannot agree on several points, making it risky for Amazon to start repairs right now. But even then, the ongoing chip shortages could make it harder for the company to get the components it needs to get these services up and running again, unless they have backup servers in their inventory ready to be deployed if peace in the region has been achieved.

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."

Meta signed a multibillion-dollar, multi-year deal with Amazon Web Services last week to deploy tens of millions of Graviton5 CPU cores across AWS data centers, making Meta one of the five largest Graviton customers worldwide. The deal focuses explicitly on CPU-intensive agentic AI workloads, not GPU training, with Amazon CEO Andy Jassy saying in a post accompanying the announcement that agentic AI is “becoming almost as big a CPU story as a GPU story.”

Meta already has GPU and accelerator contracts worth hundreds of billions across Nvidia, AMD, Broadcom, Google, CoreWeave, and Nebius, and it went to AWS specifically for general-purpose CPUs. Santosh Janardhan, Meta's head of infrastructure, said in the joint announcement that "diversifying our compute sources is a strategic imperative," and that Graviton allows the company to "run the CPU-intensive workloads behind agentic AI with the performance and efficiency we need at our scale."

Graviton5, which AWS unveiled at re: Invent in December, packs 192 Arm Neoverse V3 cores on a 3nm process with roughly 180 MB of L3 cache, a fivefold increase over Graviton4. AWS claims a 25% performance lift over its predecessor and 33% lower inter-core latency. AWS vice president Nafea Bshara confirmed that the contract runs for at least three years and that the majority of capacity will be deployed in the U.S.

The CPU-to-GPU ratio

The meteoric rise of agentic AI is driving notable shifts in CPU-to-GPU ratios. While training LLMs relies on large deployments of GPUs, agentic inference is fundamentally different, involving processes like branching control flow, tool invocation, sandbox execution, validation loops, and orchestration across many concurrent sub-agents. All that work falls on CPUs.

In its recent earnings call, Intel’s CFO David Zinsner said that the ratios of CPUs to GPUs in data centers have already moved from 1:8 to 1:4, adding that as workloads continue migrating towards inference and agentic AI, ratios could converge to 1:1 or even tilt further in favor of CPUs. “As you think about the growth rate now going forward, it’s [CPU demand] going to become a significant part of the AI [total addressable market],” Zinsner said.

Arm has also quantified the rising demand for agentic AI in terms of core counts. At the company’s Arm Everywhere event in March, Arm launched its first in-house silicon product, the 136-core AGI CPU, with Meta as lead partner and customer. Arm CEO Rene Haas told the audience that a typical AI data center today requires around 30 million CPU cores per gigawatt of capacity. With agentic workloads, however, that figure rises to roughly 120 million cores per gigawatt, a fourfold increase driven by agents that run continuously, spawn sub-agents, and generate queries at more than 15 times the rate of human chatbot users.

Meanwhile, AMD CEO Lisa Su said at the Morgan Stanley TMT Conference in March that "we're seeing a significant CPU demand, frankly, as a result of the inference demand picking up." She added that "the CPU portion of the business has actually far exceeded my expectations in terms of demand."

Supply constraints and rising lead times

The surge in CPU demand is running into a supply chain that planned for a GPU-dominated world, leading to server CPU lead times stretching to roughly six months, up from about two weeks before the agentic demand spike.

Intel acknowledged on its Q1 earnings call that unmet Xeon demand "starts with a B," referring to billions of dollars in lost revenue, with CEO Lip-Bu Tan saying that “In recent months, we have seen clear signs that the CPU is reinserting itself as the indispensable foundation of the AI era." Revenue would have been higher had Intel been able to produce more chips, the company said: Q1 data center and AI revenue came in at $5.05 billion, up 22% year-over-year.

Server CPU prices have climbed 10% to 20% since March, with analysts expecting a further 8% to 10% increase in the second half of the year. Intel raised prices in both February and March, with a third increase reportedly planned for May, bringing the cumulative hike to roughly 30% above 2025 levels. AMD’s Lisa Su told the Morgan Stanley audience that AMD's own customers described the demand as something that "was perhaps… under-forecasted," adding: "We are in the process of catching up."

The bottleneck extends well beyond CPUs themselves, however, with TrendForce downgrading its full-year server shipment growth forecast from 20% to 13%, per reporting from The Register, because power management ICs and baseboard management controllers needed to assemble complete servers are stretching to 35- to 40-week lead times.

Foundries are prioritizing higher-margin AI-specific chips, squeezing capacity for the mature-node components that general-purpose servers require. Samsung's planned closure of its S7 eight-inch wafer fab in Korea will tighten PMIC supply further. Even with all the GPUs and HBM in the world, you can’t ship a rack without the host CPUs, PMICs, and BMCs.

Compute diversification

In response to this, Meta is seemingly attempting to spread its CPU procurement across every available source. In addition to the Graviton5 deal, Meta co-developed the Arm AGI CPU announced in March and plans to deploy it alongside its Broadcom-built MTIA inference accelerators, and the company has struck a $100 billion deal with AMD that includes EPYC server CPUs and Instinct GPUs. Nvidia also announced that Meta will deploy standalone Grace CPUs in production, with Vera to follow. Intel and Google separately announced a multi-year Xeon collaboration in early April, further demonstrating how x86 supply is being locked up through long-term agreements across the industry.

Nvidia's decision to launch its 88-core Vera CPU as a standalone product, separate from its GPU systems, reflects the same dynamic, with Jensen Huang saying that he expects Vera to become a multibillion-dollar business at GTC in March. This, in addition to Arm breaking 35 years of pure IP-licensing precedent to ship finished silicon, and Intel redirecting wafer capacity to Xeon, shows that all the major players are either manufacturing or securing long-term supply of CPUs for agentic workloads.

In terms of infrastructure spending, CreditSights projects that the top five hyperscalers will spend roughly $750 billion on capex in 2026, up around 67% year-over-year. Amazon alone has guided to $200 billion, and Meta has set a range of $115 to $135 billion. Most of that is naturally destined for AI, with every gigawatt of agentic capacity requiring four times the CPU cores of traditional AI training clusters.

Meta's Graviton deal is a sign, by the company spending more aggressively on AI infrastructure than almost anyone else, that its own supply can’t deliver enough general-purpose compute to keep pace.

Amazon’s Eero is the latest router manufacturer to gain “Conditional Approval” from the Federal Communications Commission (FCC) to sell its routers in the United States. Netgear was the first to announce that it received the FCC’s blessing last week to bypass the router ban. Router manufacturers have been working around the clock to submit documentation to the FCC and provide assurances regarding the origin of components used in their products and where they're assembled to comply with an increased focus on national security.

In March, the FCC deemed that manufacturers selling routers produced in a foreign country be added to the "Covered List," as they "pose an unacceptable risk to the national security of the United States or the security and safety of United States persons.” Moving from the Covered List to Conditional Approval ensures that manufacturers of the best Wi-Fi routers can continue to sell in the United States for a set period.

"As part of this effort, router manufacturers were asked to submit information allowing the government to assess safety risks associated with their devices," the Eero team wrote in a blog post. "We’re pleased to share that the U.S. government has recognized eero as a trusted and secure provider of routers... We remain committed to delivering innovative, reliable products our customers can depend on."

For customers inquiring what this means for the Eero products that they currently own or wish to purchase in the near future, the Eero team continues, "Your experience remains the same. You can continue to use and buy eero products with the certainty that they meet rigorous standards."

According to FCC documentation, the Conditional Approval applies to Eero, Eero Pro, Eero Max, Eero PoE, Eero Outdoor, and Eero Signal. In other words, every product in the Eero/Leo family can be imported and sold without any further interference.

For the Eero products, the Conditional Approval is guaranteed from April 22, 2026, through October 31, 2027, covering the 18-month FCC exemption period. It also covers all previous and current Eero/Amazon Leo products, as well as certifications for new, unreleased products.

Although Eero did not go into any specific details on what documentation it submitted to the FCC, at least one requirement was to provide a “detailed, time-bound plan to establish or expand manufacturing in the United States" and a “description of committed and planned capital expenditures, financing, or other investments dedicated to U.S.-based manufacturing and assembly over the next 1-5 years, including expected timelines and milestones.” Given the substantial capital investment required to build factories in the United States and the need to support a higher-paid American workforce, this part of the plan submitted to the FCC will be the most interesting detail to learn as these discussions proceed.

While Netgear and Eero can breathe a sigh of relief (at least for the next 18 months), TP-Link is still waiting for its Conditional Approval. The company submitted its proposal earlier this week as it attempts to convince the U.S. government that it has divested itself of previously concerning Chinese ownership ties. According to its own internal statistics, TP-Link controls 20% of the U.S. consumer retail router market.

Amazon’s Eero family has grown to include a diverse set of mesh routers and even a wireless outdoor satellite. Now, Amazon is adding a new member: the Eero Signal. It’s a 4G LTE device that provides backup internet for a compatible Eero mesh network. Like all Eero devices, the Signal is easy to set up. Ot is governed completely by the Eero smartphone app, and it does its job with minimal fuss.

At just $99, the Eero Signal is a relatively affordable failsafe if you live in an area with frequent internet outages.

Design of the Eero Signal

The best way to describe the Eero Signal is that it looks like a shrunken Eero Pro 7/Eero Max 7 satellite. It features the same overall shape, with a single status LED at the top of the front fascia, and Eero branding at the center.

The Eero Signal is made from the same materials as the larger Eero satellites. That means the front features shiny white plastic, while the back is white plastic with a slightly textured surface. The materials have a high-quality feel.

The Eero Signal includes a fixed “Y” shaped power cable. The cable exits from the back of the unit and forms one branch of the Y. The second brach ends in a USB-C connector that plugs into the back of an Eero satellite. The “trunk” of the Y ends in a USB-C receptacle, which accepts the USB-C connector from the wall plug. Once everything is connected, a single USB-C wall adapter powers both the Eero satellite and the Signal.

The device is relatively small, measuring 3.09 x 5.76 x 2.80 inches and weighing 0.65 pounds.

Eero Signal Specifications

Setting up and using the Eero app with the Eero Signal

Once the physical connections have been made, the Signal automatically appears in the Eero app. When you enter the Eero app, it will present you with two options to enable the Eero Signal cellular backup service:

• Eero Plus: You get 10GB of backup data per year for $99/year. Six months of service is included for free for new Eero Plus subscribers who opt for an annual plan.
• Eero Plus 100: You get 100GB of backup data per month. During the first year, you pay just $99/year for this tier, but during the second year, the price doubles to $199.99 year.

If you currently subscribe ot Eero Plus monthly, you are not eligible for cellular data backup service with an Eero Signal. If you live in an area that experiences frequent outages, the base Eero Plus plan, which provides 10GB of data per year, seems like a poor value. $199.99/year for 100GB of data per month seems like a far better value and would provide greater peace of mind.

Our Eero Signal review unit was provided with Eero Plus 100 service, giving us access to 100GB of backup data per month.

From the Eero app homepage, there are only two giveaways that an Eero Signal is connected to your mesh system. The first clue is that the main Eero gateway (in this case, an Eero Pro 7) shows green bars indicating the Eero Signal strength. The second clue is under Security & Privacy, where you’ll notice a green light next to Internet Backup.

If you click the Internet menu from the Eero app home page, it will display available connections. In our case, it confirmed my wired internet connection (T-Mobile Fiber) and that the cellular backup was ready. Clicking the Eero Internet Backup option opens a submenu that lets you turn the feature on or off.

Eero Signal Performance

The Eero Signal serves as an internet backup to your current home broadband connection, e.g., cable or fiber. And before the gears start turning in your head about using the Eero Signal with the Eero Plus 100 plan as your primary internet service, I've got some bad news. The Eero Signal cannot be used as your sole source of internet -- it requires that you have an existing ISP delivering service to your Eero mesh router. The Eero Signal serves only as a backup if your primary ISP goes offline.

I tested the Eero Signal’s primary function as a failover if your primary ISP goes offline. To do this, I removed the cable connecting the Eero Pro 7 to my fiber modem. It took about 20 seconds for the lights on the Eero Pro 7 and Eero Signal to change from white to blue, indicating that the latter was now working as an internet backup. I tested internet connectivity across multiple devices connected to the Eero mesh network, and all remained connected without issue.

However, you must remember that the Eero Signal is only a 4G LTE device, not 5G, unlike some popular home cellular internet plans from companies like AT&T, T-Mobile, and Verizon. With that in mind, don’t expect to see speeds that approach what those services can provide when operating in internet backup mode.

I saw SpeedTest download speeds of around 600 Mbps and upload speeds of 60 Mbps when I used T-Mobile 5G Home Internet as my ISP. With my current fiber connection, I achieve symmetrical speeds of around 2 Gbps. However, the Eero Signal could only muster a fraction of that, given its 4G LTE bandwidth limitations.

For comparison, I saw up to around 80 Mbps for downloads and 30 Mbps for uploads using the Eero Signal (the device connected to AT&T’s cellular network). Those are not breakneck speeds by any means, but remember that the Eero Signal is only meant as a backup internet service. So if you need to conduct a video call for work, download some essential files for a work project, or even watch some TV in the background, the Eero Signal should have no problem handling your workload.

Another thing to consider is cellular reception. I live outside of a major metropolitan area (Raleigh, NC), so I have solid uptime for my fiber connection and a strong cellular signal. For people who live in areas where their wired ISP might be unreliable, there’s a strong chance that cellular connectivity will be spotty as well. So while a device like the Eero Signal could work in that situation, your performance may vary greatly.

Bottom Line

The Eero Signal offers a lifeline to people living in areas with frequent internet outages. Of course, it is only compatible with Eero mesh routers, so you’ll already need to be bought into that ecosystem to take advantage of the functionality.

The device is easy to set up, doesn’t require user intervention to activate if your primary ISP goes down, and the upfront hardware cost is just $99. The $99 Eero Plus plan only gives you 10GB of backup data to use for an entire year, so that option doesn’t really seem cost-effective for the target audience of the Eero Signal. However, the $200/year Eero Plus 100 plan gives you 100GB of backup data per month, which seems like a much better option.

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.

Iranian strikes on AWS data centers in Bahrain and Dubai have disrupted services that the company declared multiple zones in the region to have “hard down” status, meaning the affected areas are completely unavailable. According to Big Technology, AWS issued an internal memo stating that operations in the two data centers have been disrupted and that it's working to migrate affected clients' workloads to other regions. The Islamic Revolutionary Guard Corps (IRGC) has conducted strikes against AWS sites in the Middle East since the start of the war in early March.

“These two regions continue to be impaired, and services should not expect to be operating with normal levels of redundancy and resiliency,” the internal memo said, according to Big Technology. “We are actively working to free and reserve as much capacity as possible in the region for customers, and services should be scaled to the minimal footprint required to support customer migration.”

The AWS sites in the Middle East each have three compute zones, with both data centers reporting “hard down” and “impaired but functioning” zones. More importantly, the company said in its internal communications, “We do not have a timeline for when DXB and BAH will return to normal operations.”

Amazon isn’t the only tech company that the ongoing conflict between the United States and Iran has directly hit. The Middle Eastern country has threatened to strike Nvidia, Microsoft, and others as early as the second week of March, after the alleged targeting of a Tehran bank that killed several employees. It has reiterated the threat at the start of April and even struck an Oracle data center later that week.

However, while damage to data centers in the Middle East is concerning for the region, the global tech industry has bigger concerns. The regional war has disrupted the flow of oil and its derivatives, especially those that go through the Strait of Hormuz. These include aluminum, helium, and LNG — all of which are crucial in the semiconductor supply chain. And even if the war ends today, the damage to infrastructure could mean it takes months or even years for supplies to return to pre-war levels.

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 (IRGC) said on Thursday that it had hit a data center linked to Oracle in Dubai as part of its war against the U.S. and U.S. technology companies in the region, reports The Times of India. The government of Dubai was quick to deny the report, according to Gulf News. An Amazon facility in Bahrain was also targeted, according to NDTV, which cites the Tasnim news agency.

Several days ago, the IRGC named Oracle among a group of American corporations it accuses of enabling U.S. and Israeli military activity, alongside Apple, Boeing, Cisco, Google, HP, IBM, Meta, and Microsoft. Oracle has cloud and artificial intelligence contracts with the U.S. Department of Defense, and its chairman, Larry Ellison, has longstanding ties with Israel, which were among the factors cited in the accusations. In addition, the IRGC targeted American aluminum and steel industries in Bahrain and the UAE, as well as Rafael arms factories in Israel.

While the officials in the United Arab Emirates have not confirmed any successful hit on infrastructure in Dubai. Bahrain's Ministry of Interior confirmed that an Iranian strike has set 'a facility of a company' on fire. That company is said to be Batelco, the country's largest telecommunications company that hosts infrastructure for Amazon Web Services.

While hitting traditional targets — such as arms factories, industrial plants, and military facilities — causes a lot of costly damage, hitting AI data centers equipped with hardware that is worth billions causes dramatically more financial damage. For example, an Nvidia NVL72 GB300 system can cost as much as $6 million, so a data center hosting 50,000 Blackwell processors houses hardware worth $4.16 billion. This estimate excludes networking, storage, racks, power delivery, cooling, building shell, and deployment, so even if supporting infrastructure adds 50% on top of that (which is a conservative estimate), we are talking about a data center that costs $6.24 billion. A hit on such an object causes more damage than a hit on a traditional target. Furthermore, it potentially causes severe damage to American companies.

Of course, for now, we do not know for sure whether any AI data centers belonging to AWS or Oracle were hit by Iran's IRGC, though the risks for American companies that have AI infrastructure in the Middle East are exceptionally high.

Although authorities in the Middle East claim successful interceptions of Iranian strikes, an investigation by Bellingcat published on Thursday suggests that not all attacks are intercepted, official damage reports may not fully reflect actual impacts, so some incidents are minimized, mischaracterized, or even not publicly acknowledged.

Should you splash out on those upgraded audio cables to take your music listening pleasure up a notch? Probably not - affordable Amazon Basics RCA cables are more than good enough - confirms audio TechTuber Amir from Audio Science Review. This won’t be a surprise to regular readers. However, it is interesting to see Amir’s $7 vs $4,000 cable analysis using scientific audio equipment – as well as drawing from his personal music listening experience.

RCA has been around since 1919

Amir compared the above stereo audio RCA cables. These are mostly used to link Hi-Fi components. Some PC sound cards and breakout boxes will feature them, as will some home video devices. The RCA standard dates back to 1919, so it is over a century old, and audio enthusiasts connecting older analog gear like certain amplifiers, turntables, and legacy components will make most use of these cables. Due to this, RCA will probably be supported for another few decades in its strongest current niches, like Hi-Fi.

Physical comparison

Back to the $7 vs $4,250 RCA comparison and what we have pitted against each other here are the Amazon Basics 2 RCA cables, 4ft in length, that are actually on sale currently at $6.76 (and up to 15ft in length for $8.96). In the other corner, there are the Kimber Kable KS 1036 Select Series Analog Interconnect (Pair), which we can see at Safe and Sound (US) in 1m (3.3ft) length for $4,100.

Amazon promises “crystal clear audio [and] reliable performance [with its] gold plated connectors.” However, the Kimber product aims for premium with its “Black Pearl solid silver conductors drawn in diamond coated dies and insulated with virgin FEP dielectric under the most exacting tolerances which offer the purest transfers of the most demanding highest resolution signals.” It should hardly be a fair fight.

Our host, Amir, noted that the Kimber product gets off to a bad start, with a totally unnecessary flight (Pelican) case. Indeed, it looks very cheap compared with the cable’s prestige target market.

Probably worse for the expensive product is that its locking cable design throws up as many negatives as positives. Basically, locking isn’t a particularly attractive feature here, and could precipitate accidental damage with someone unfamiliar with (or who forgets) the locking mechanism. Making this worse is the observation that inside the connector are “flimsy plastic tabs,” which is an inferior design feature vs traditional RCA cables.

Audio signal analysis

Amir then shifted to scientific audio analysis using Audio Precision analyzer hardware. Is this where the $4,000 cables would at last show their superiority?

Using a constant 4 KHz sine wave transmitted through the cable, a tiny bit of distortion was observed with both cables. There was actually a bit more mains power noise seen with the Kimber cable, but “practically speaking, the two are identical,” Amir said, pondering over this first set of metrics.

Next, we saw the cables exhibit identical response across a very wide frequency range. There was no phase difference seen in the tests. Same with the square wave rise response graph. Even a hugely magnified graph from the Audio Precision analyzer shows that these two cables performed identically.

Last but not least, Amir moved on to comparing jitter spectrum profiles in a bandwidth stress test. It was shown that the Amazon Basics cable shows a slight (picoseconds) increase in jitter. However, our host noted that the cheaper cable was noticeably longer than its boutique rival. And it would cost a lot extra to get one to match the Amazon alternative’s length.

Prestige RCA cables simply unnecessary

The results weren’t unexpected, but from around 13 minutes into the video, Amir explains why no one should be surprised that audiophile RCA cables aren’t worthwhile.

Competent cables like the Amazon model and better have “the lowest noise, lowest distortion, and widest bandwidth of anything in your audio system,” says the audio TechTuber. It isn’t a weak or sensitive part of the signal chain, prone to issues, and audio signals don’t push the ‘capacity’ of RCA cables, so there is little point in investing above a competent standard product like the Amazon Basics.

“Everything else in your system is the bottleneck,” Samir underlines, as far as quality is concerned. He likens the Hi-Fi audio signal chain to two cities connected with a 100-lane freeway (the RCA cables) – traffic problems are in the city, not on the freeway, he says.

To conclude, Amir admits to perceiving differences between cables when enjoying music, but puts the human experience down to feelings, mood, focus, and so on. When actually being clinical about A/B testing – like blind testing, etc - such differences evaporate, suggests the experience of this audio TechTuber.

OpenAI keeps cutting deals left and right, one of the latest ones being a massive partnership with Amazon that ought to see a total of $188 billion circle between both companies. Sam Altman may have cut that one a little too close, according to Microsoft, which is reportedly considering releasing the lawyers over an API exclusivity clause.

Sources from the Financial Times (FT) say the key item in this discussion is OpenAI's Frontier multi-agent platform targeted at large enterprises. Broadly speaking, Frontier offers to make it easy for large enterprises to effectively use AI by wiring up multiple agents ("workers") with shared memory and business content.

Microsoft is apparently taking umbrage with the situation, despite its position in the partnership having been repeatedly revised. Redmond was originally OpenAI's sole cloud services provider, but eventually changed to having the right of first refusal over said services, and was further weakened in October 2025.

The PR about that latest agreement states that "API products developed with third parties will be exclusive to Azure. Non-API products may be served on any cloud provider." Under that logic, OpenAI has the freedom to develop and implement new products, but if they offer them as APIs, they have to go through Azure.

Redmond believes that OpenAI's offering access to Frontier via Amazon Web Services (AWS)'s Bedrock platform would be in breach of the agreement. Getting even more technical, the dispute may well come down to the definition of a "stateless" versus "stateful" when applied to AI models.

Even though it appears to remember your information, a standard chatbot is actually stateless — adding a new question requires the bot to re-process the entire conversation again. A storage and orchestration layer to facilitate something like Frontier is arguably a "stateful" implementation, more specifically a "Stateful Runtime Environment."

According to FT's sources, Microsoft thinks that running Frontier on AWS instead of Azure would breach either the spirit or the letter of the contract. This is illustrated by a report that Amazon is pointedly instructing its staff to never say that SRE "enables access" or "calls on" ChatGPT as a backend, instead preferring vaguer terms like "powered by," "enabled by," or "integrates with."

The whole stateless/stateful discussion has reportedly been a hot topic among lawyers from both camps, though FT states that a Microsoft employee referring to the situation isn't mincing words, saying that "we know our contract," and that "we will sue them if they breach it. If Amazon and OpenAI want to take a bet on the creativity of their contractual lawyers, I would back us, not them."

Predictably, OpenAI's position is the opposite, as the firm seemingly believes the Amazon deal is compatible with the Microsoft agreement. FT further reports that Amazon and OpenAI are building an unspecified system meant to work around the contract.

Nevertheless, FT points out that this latest development may cast a pall over OpenAI's upcoming IPO by placing doubts in prospective stockholders' hearts. That would be a nightmare scenario for the company, as the magnitude of its ongoing investments means that the flow of money cannot stop.

If you've been anywhere near a PC in the last 20 years, especially if you've used one for gaming, then SteelSeries is a brand name that you no doubt will have come across. They've been making peripherals for a long time, with their mice, keyboards, and especially headphones being a very popular choice for PC gamers. With Amazon's Spring Deals Days sales event in full swing, we've spotted this money-off deal on SteelSeries' flagship gaming headset, the recently released Arctis Nove Elite, now priced at £559.49. A saving of £40.50 from the original £599.99 launch price. This headset was only released within the last five months, so it's nice to see a discount so early. However, this headset still commands a high price, as it flexes its audiophile-grade Hi-Res audio muscles. If you care not for the quality of your soundscapes, then avoid, as you can get a much cheaper headset for your needs.

• Grab this deal at Amazon

So what's so special about this version of the Arctis Nova? Well, the Arctis Nova Elite has Hi-Res audio certification. This Hi-Res audio gives you ultra-fidelity over a wireless connection for 96kHz/24bit audio. SteelSeries states this as a world first.

The headset looks very pretty, especially in the sage and gold colour scheme, which also happens to be the model that is discounted. Carbon fiber drivers power the sounds with brass surrounds for a "pistonic" motion. The headset also features powerful active noise cancelling (ANC), which can be a blessing for gaming in noisier environments. Plus, the retractable microphone uses AI to remove background noise, along with an additional discreet onboard mic to help keep comms clear during the heat of battle, or while chatting with friends over Discord.

The Arctis Nova Elite comes with the same premium features that you would find on the Nova Pro, including seamless multi-source mixing with OmniPlay to connect and mix up to four sources at once, handy for switching between various audio sources on the fly.

As with other SteelSeries headsets, the Arctis Nova Elite uses a ski-band design for the headband to make the headset feel lighter on your head and not add pressure and discomfort after hours of use. And speaking of use-time, the Nova Elite uses an infinite power system with two batteries for no downtime, always have a battery charging, so you can swap in a freshly charged battery before the headset dies on you.

The flagship SteelSeries Arctis Nova Elite is an audiophile-grade gaming headset for those with a significant disposable income. It's certainly not a cheap or budget headsest and is in the price range of some serious audiophile competition.

If you're looking for more savings, check out our Best PC Hardware deals for a range of products, or dive deeper into our specialized SSD and Storage Deals, Hard Drive Deals, Gaming Monitor Deals, Graphics Card Deals, Gaming Chair, Best Wi-Fi Routers, Best Motherboard, or CPU Deals pages.

A standout deal spotted in the current Amazon Spring Day Deals sales event is this Acer gaming laptop. The Acer Nitro V 16 AI gaming laptop is just £1,199.99, shaving £300 off the £1499.99 list price. Saving any money is always a good thing, and when you can save a tidy £300, it's even better. Although there's an AI moniker slapped on the product, don't be put off. This is a gaming laptop through and through. From the gamified aesthetics to the choice of hardware, inside the sleek black chassis. Don't be tied to your desktop, and play your games anywhere, thanks to this powerful little gaming machine.

• Grab this deal at Amazon

The Acer Nitro V 16 AI is a powerful gaming laptop equipped with an Nvidia RTX 5070 laptop GPU, accompanied by the AMD Ryzen AI 9 365 processor, able to bring games to life with crisp graphics and fast framerates. If you need a little more juice to hit the 180Hz refresh rates of the WUXGA (1920 x 1200) display, this current-gen Nvidia GPU supports DLSS 4, which enables multi-frame generation. In games that support this feature, you'll be able to boost your frame rates by the GPU inserting AI-generated frames, ensuring smoother gameplay, but there may be some trade-offs in texture and picture definition.

With 32GB of RAM, an RTX 5070, and 1TB SSD, the Acer Nitro V 16 AI is more than equipped to handle most gaming tasks. This is one of the best value-for-money gaming laptops right now, going by pure specifications. Thanks to the AI crunch causing surging memory and storage prices, the cost of gaming laptops is only going to rise in the near future.

If you're looking for more savings, check out our Best PC Hardware deals for a range of products, or dive deeper into our specialized SSD and Storage Deals, Hard Drive Deals, Gaming Monitor Deals, Graphics Card Deals, Gaming Chair, Best Wi-Fi Routers, Best Motherboard, or CPU Deals pages.

Amazon allegedly called its engineers to a meeting to discuss several recent incidents, with the briefing note saying that these had “high blast radius” and were related to “Gen-AI assisted changes.” According to the Financial Times, one of the contributing factors listed in the meeting notes was the use of generative AI tools “for which best practices and safeguards are not yet fully established.”

There has been a spate of problems in Amazon’s operations recently, including a six-hour disruption on its main retail website, wherein customers were unable to see details and complete transactions, which the company said is attributed to erroneous code deployment. We’ve also seen reports that Amazon’s AI assistant could be easily jailbroken to answer questions unrelated to shopping, as well as reports of AI coding bot-driven outages with AWS, the company’s cloud service.

“Folks, as you likely know, the availability of the site and related infrastructure has not been good recently,” Amazon Senior Vice President Dave Treadwell allegedly said in an email, according to the publication. He also said that the meeting will take a “deep dive into some of the issues that got us here as well as some short immediate term initiatives,” and that AI-assisted changes must now be approved by senior engineers before deployment. This meeting is reportedly usually optional, but it seems that Treadwell asked the staff to be in attendance this time.

Amazon hasn’t officially confirmed the cause of all its woes, but the details shared in this meeting seemingly point to the use of AI. “TWiST is our regular weekly operations meeting with a specific group of retail technology leaders and teams where we review operational performance across our store," an Amazon spokesperson told Tom's Hardware. "As part of normal business, the meeting will include a review of the availability of our website and app as we focus on continual improvement.” It also isn’t the first big tech company to take things seriously after many firms took the “move fast and break things” motto literally when it came to generative AI. Microsoft said in late January 2026 that it’s working to fix many of Windows 11's flaws and restore its reputation. This came nine months after CEO Satya Nadella said that AI writes up to 30% of the company’s code, with some projects completely coded by AI.

While generative AI does have its uses, especially in specialized fields like medical research, it still needs observation, and we still cannot rely on its output 100% of the time. Unfortunately, many are overselling the capabilities of this tool, and many CEOs aren’t getting the promised benefits of higher revenues and reduced costs.

If you’re looking for a new laptop, you’ll need to double-check the storage on those listings. A worrying new trend, spotted in recent weeks, shows that third-party sellers of popular laptop brands are marketing their laptops with terabytes of storage, despite only including a measly 128GB of physical storage capacity, with the rest made up of Microsoft OneDrive cloud storage. It's enough for it to be called a "borderline scam" by one angry commentator.

This has been rumbling on for some time, with complaints on Reddit and social media from bemused consumers about the practice. It’s fair to say that marketing a laptop with over eight times the actual storage it has isn’t ideal, but a further fly in the ointment is that the capacity being offered isn’t even permanent.

The problem appears most prevalent on Amazon. A quick search of the retailing behemoth shows several laptops on sale from major brands, including HP and Lenovo, with listings that prominently advertise over 1TB of storage, with the majority made up of Microsoft’s cloud storage offering. This HP laptop, for instance, shows a 'limited-time deal' price of $499.99 for a laptop that supposedly offers 1.1TB of storage, but 1TB of that is from OneDrive cloud storage.

It appears, too, that the practice was even more egregious a few weeks ago, before it gained public attention. A Reddit post in the r/LinusTechTips subreddit showed HP laptops being sold with misleading product headlines. In one of those examples, a HP laptop on sale at Amazon describes 1.2TB of storage, with a mention of its 128GB of physical storage relegated to the second line of the product’s description. That report also confirmed earlier examples of the same issue at Newegg, although those appear to have been removed, at least for now.

At Amazon, however, the issue continues. Multiple examples of laptops with similar headlines, albeit slightly more honest combinations like “1.1TB Storage (1TB OneDrive + 128GB SSD),” remain on sale, including this HP laptop for $499.99. All are being sold by third-party sellers who, in many cases, have established feedback dating back at least a year, with hundreds or thousands of reviews. That laptop, as with many of the other examples I’ve seen, shows only a one-year subscription to Microsoft 365 (which includes OneDrive storage) advertised – that “1.1TB” will start to cost you extra after 12 months.

Practices like this from sellers are certainly not popular. A post on X by Max Weinbach, showcasing another HP laptop on sale, gained nearly 1 million views at the time of publication, with hundreds of reposts and comments, with one user giving it the "borderline scam" description, although there's nothing to suggest that anything illegal is actually taking place here.

It might not be illegal, but it does leave a bad taste in the mouth. Even if you’re reasonably tech savvy, you’ll be expected to look out for the storage ruse from now on. Those who aren’t, who couldn’t tell a OneDrive from an SSD, will be left with a potentially inferior product as a result. While it appears that some of the less descriptive headlines have now changed, there's no indication that the practice is stopping at all.

Let this be a PSA for anyone looking to buy a laptop in the coming weeks and months: don’t trust everything you see, always do your due diligence, and if you’re buying from a third-party seller, make sure to double and triple-check those specs. If it looks too good to be true, well, it might just be.

It's the season for deals, and tomorrow brings Amazon's Spring Deals Days (10th - 16th of March, 2026) sales event to the UK. There are already savings to be had on a host of tech items ahead of the sales' official start. We're focusing solely on the UK in this roundup, scouring Amazon and all the main UK retailers like Currys, Argos, Scan, Overclockers, and more, for the best prices and discounts on a range of tech and tech-related products.

With the big push in AI affecting the prices on a host of our favourite hardware components, such as RAM, GPUs, and SSDs, now is more important than ever to search out the best deals and save some precious money.

We use the experience we've gained through our reviews, extensive benchmarks, and comprehensive historical price analysis to look for the best deals, and these are the biggest and best we could find.

Quick Links

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Two years ago, Amazon announced Rufus, its AI-powered shopping assistant built right into the Amazon app and website. The goal was to let customers not just search for items, but also allow them to talk with an expert who can recommend products and deals naturally. Under the hood, Rufus uses multiple LLMs, and some people have realized it's quite easy to trick the chatbot into forgetting its purpose.

The tweet above shows the author prompting Rufus for a complex modeling question to figure out how to map sensory data into digital data for robotics. It's entirely detached from any shopping query, which is exactly why it's so funny to see Rufus answer it so swiftly. The formula provided is correct, too. There's a chance that terms like "tactile sensors" were flagged as product inquiries by Rufus.

When we tried it ourselves, we were able to get it to talk about architectural differences between x86 and ARM on the first try. Ironically, after asking whether it thinks the AI bubble will burst this year, Rufus started answering but cut off abruptly. Our other efforts were in vain, and it almost felt like the AI was learning to keep the guardrails up more securely in real time as we poked it more and more.

There is conflicting information online as to what exactly Rufus is using underneath — it could be Amazon's in-house frontier model 'Nova,' while the majority says it's Anthropic's Claude, but some argue that it's not smart enough to be running Claude. One Reddit post points towards Rufus being based on Claude Haiku and not Claude Sonnet, saying it's extremely hard to break and not worth the effort to try to "jailbreak."

Regardless of whatever model it's using or switching between, the ease with which its guardrails erode is both fascinating and funny. You could certainly try to continue your work on Rufus if the free tier of Claude has rate-limited you for the day. It also goes to show that integrating AI into every aspect of the internet is perhaps not the best idea because it's just another point in the chain that can potentially break. And not everyone will try harmless prompts to pass the time.

Drone strikes damaged three Amazon Web Services data centers in the UAE and Bahrain on Sunday, March 1, knocking two of the ME-CENTRAL-1 region's three availability zones offline and triggering outages across EC2, S3, DynamoDB, Lambda, RDS, and other core services, thereby marking the first confirmed military attack on a hyperscale cloud provider, according to Uptime Institute.

AWS confirmed on its health dashboard that two facilities in the UAE were "directly struck" and that a third site in Bahrain sustained damage from a nearby explosion. The strikes caused structural damage, disrupted power delivery, and, in some cases, triggered fire suppression systems that produced additional water damage, according to the AWS Health Dashboard. Amazon told customers it expects recovery to be prolonged "given the nature of the physical damage involved".

Each AWS region is built around multiple availability zones, which are physically separated data centers, each with independent power, cooling, and networking, designed so that the loss of one zone does not take a region offline. ME-CENTRAL-1 has three availability zones; the strikes took out two of them (mec1-az2 and mec1-az3), leaving the region significantly impaired. The Bahrain region (ME-SOUTH-1) lost one zone (mes1-az2) to a localized power issue. AWS's redundancy model is designed to survive the failure of a single zone, but not a coordinated attack across multiple sites within the same region.

These outages then cascaded into consumer-facing services across the Gulf. Ride-sharing and delivery platform Careem, payments firms Hubpay and Alaan, data management company Snowflake, and several major UAE banks — including Emirates NBD, First Abu Dhabi Bank, and Abu Dhabi Commercial Bank — all reported disruptions. AWS advised customers to activate disaster recovery plans and migrate workloads away from the affected Middle East regions.

Iran's Islamic Revolutionary Guard Corps stated it targeted the Bahrain facility specifically because AWS hosts U.S. military workloads there; AWS declined to comment on that claim. Sean Gorman, Air Force contractor and CEO of Zephr.xyz, told DefenseScoop on Tuesday that classified government workloads at Impact Level 4 and 5 are held in U.S.-only facilities, but acknowledged that “contractor and non-operational data… may have been impacted,” at the struck sites.

The attacks followed joint U.S.-Israeli strikes on Iran over the last week. AWS urged customers with workloads in the region to migrate to unaffected regions while repairs continue.

Amazon’s data center business in the Middle East has been adversely impacted amid the 2026 Iran Conflict. The AWS Health Dashboard notes there are ongoing issues with multiple services at its data centers in the region. Specifically, the status page reports that service disruptions/impacts are ongoing at AWS Middle East (UAE) Region (ME-CENTRAL-1), and the AWS Middle East (Bahrain) Region (ME-SOUTH-1) data centers.

“In the UAE, two of our facilities were directly struck, while in Bahrain, a drone strike in close proximity to one of our facilities caused physical impacts to our infrastructure,” explains the AWS Health Dashboard. And these incidents have precipitated dozens of ‘disrupted,’ ‘degraded,’ and ‘impacted’ services.

As per the quote above, the UAE data center was impacted most severely by the drones. From broader reporting of the conflict, we assume these drone strikes are part of Iran’s response to U.S. Operation Epic Fury and Israeli Operation Roaring Lion strikes on Iranian targets over the weekend. Both the UAE and Bahrain data centers were hit by drones in the early hours of March 1. Whether Iran purposely targeted AWS facilities, we cannot say for certain.

ME-CENTRAL-1 in the UAE

This morning’s update regarding AWS Middle East (UAE) Region (ME-CENTRAL-1) assures customers that “teams continue to make progress on recovery efforts across multiple workstreams.”

While engineers are working to safely restore the full gamut of AWS services, the firm says that it “strongly recommend[s] that customers with workloads running in the Middle East take action now to migrate those workloads to alternate AWS Regions.” It would be wise to enact disaster recovery plans, recover from remote backups stored in other Regions, and update applications to direct traffic away from the UAE, for now, too.

ME-SOUTH-1 in Bahrain

In Bahrain, the most recent update says that engineers “continue to work toward restoring power in the affected Availability Zone (mes1-az2) in the ME-SOUTH-1 Region.” The facility still has no firm time/date for when it expects to restore power and full connectivity, with significant work still required, says AWS.

As with ME-CENTRAL-1, above, AWS is recommending users migrate or replicate their ME-SOUTH-1 Region data to another AWS Region.

These are some of the first ‘tech’ impacts we have seen precipitated by the 2026 Iran Conflict. They surely won’t be the last, with shipping, the costs of raw materials, and energy resources already rapidly inflating due to emerging geopolitical risks and pressures.

Amazon and OpenAI have announced a sweeping multi-year strategic partnership, with Amazon committing $50 billion in investment, AWS securing exclusive third-party distribution rights for OpenAI's enterprise agent platform Frontier, and OpenAI agreeing to consume approximately 2 gigawatts of Amazon's custom Trainium compute capacity, according to a press release.

The investment is part of a $110 billion funding round that values OpenAI at $730 billion pre-money, with Nvidia and SoftBank each contributing $30 billion. The companies will also co-develop custom AI models for Amazon's own products, including Alexa, and jointly build a new stateful agent runtime on Amazon Bedrock.

Split between Trainium 3 and 4

Amazon's $50 billion commitment is structured in two parts: $15 billion upfront, with the remaining $35 billion contingent on conditions that, according to sources cited by The Information, may require OpenAI to complete an IPO or reach an as-yet-undefined "AGI milestone." The deal also expands OpenAI's prior $38 billion AWS compute agreement, struck in November 2025, by an additional $100 billion over eight years.

Meanwhile, the 2 gigawatts of Trainium compute will span both the current Trainium 3 generation and the upcoming Trainium 4, which is expected to ship next year. Amazon launched Trainium3 — a 3nm chip delivering four times the performance of its predecessor at 40% better energy efficiency — at its re:Invent conference in December 2025.

AWS has stated that customers can achieve cost savings of 30 to 40% running training and inference workloads on Trainium compared to equivalent Nvidia GPU configurations. Each Trainium3 UltraServer holds 144 chips, and up to 1 million of them can be linked in a single cluster.

Trainium4, meanwhile, is being designed with support for Nvidia's NVLink Fusion interconnect, which allows Trainium4-based systems to interoperate with Nvidia GPUs within the same server rack. Nvidia's CUDA software stack remains the de facto standard, which nearly all large AI workloads are built on, and migrating away from it means rewriting significant portions of a codebase.

Anthropic, in which Amazon has invested at least $8 billion, already trains its Claude models on Trainium at scale — Project Rainier, Amazon's largest dedicated AI data center, houses more than 500,000 Trainium2 chips running Anthropic workloads exclusively. But Anthropic is financially entangled with Amazon. OpenAI is not, which makes its decision to commit 2 gigawatts to Trainium a notably independent validation of the platform.

Let’s not forget that OpenAI also has a separate deal with Broadcom to develop its own custom ASICs, uses Nvidia GPUs through both Azure and AWS, and has committed to AMD chips — so, again, its willingness to stake 2 gigawatts on Trainium is a weighty decision.

Stateful Runtime Environment

Beyond compute commitments, Amazon and OpenAI have announced that they’re co-developing a so-called “Stateful Runtime Environment” (SRE) built on Amazon Bedrock and expected to launch within the next few months.

Most AI agents run on Retrieval-Augmented Generation (RAG) architectures that effectively use a model as an advanced search engine over a set of embedded documents. The issue with this architecture is that the agents can’t retain memory between sessions or carry context across different software tools, and they reset with every new interaction.

SRE, Amazon says, keeps context across calls, retains memory of prior work, integrates with AWS data sources, including S3 storage and IAM identity controls, and allows agents to operate persistently across ongoing projects rather than treating each call as isolated.

Frontier, OpenAI's enterprise agent platform for building and deploying coordinated AI agent teams across business systems, will be distributed exclusively through AWS as its third-party cloud provider, and the SRE on Bedrock is where that infrastructure will sit.

Microsoft-OpenAI partnership ‘strong and central’

Several initial reports following the announcement have framed the deal as AWS displacing Microsoft's position with OpenAI, but that’s not accurate. Azure, per Microsoft, “maintains its exclusive license and access to intellectual property across OpenAI models and products,” with Azure remaining the exclusive cloud provider for OpenAI's stateless API calls.

Microsoft also retains the option to participate in the current funding round, with both companies issuing joint statements affirming the partnership remains "strong and central." In terms of the Amazon-OpenAI deal, AWS gains the enterprise agent deployment side, while stateless API traffic stays on Azure.

AWS holds approximately 30% of the global cloud market heading into this announcement, compared to Azure's roughly 20% and Google Cloud's 13%. Despite that market position, Amazon had been widely characterized as trailing in the generative AI race relative to Microsoft's early OpenAI integration and Google's push with Gemini.

Exclusive distribution rights for Frontier, combined with Trainium's cost positioning, fit Amazon's consistent approach to cloud competition, whereby it prioritizes infrastructure scale and cost efficiency over all else. Amazon is now financially backing both of the leading independent frontier AI labs simultaneously — OpenAI and Anthropic — which positions it as infrastructure for the industry, regardless of which organization's models prove most durable commercially.

This comes with significant financial exposure for Amazon, which is spending approximately $200 billion in capital expenditure in 2026, the majority directed at data centers and AI infrastructure. Its stock had fallen about 8% on the year as investors weighed the return timeline on those outlays. Andy Jassy told CNBC today that he expected OpenAI to be "one of the very big winners" over the long term, but added that Amazon “still has a very strong relationship with Anthropic.” OpenAI CEO Sam Altman said the company now has more than 900 million weekly active users and more than 50 million consumer subscribers, and described an IPO as its "most likely path" given ongoing capital demands back in October.

The FTC issued subpoenas to Amazon, Microsoft, Google, OpenAI, and Anthropic in early 2024 to examine AI partnerships, with particular attention to exclusivity arrangements, and AWS's exclusive rights to distribute Frontier will no doubt give regulators a concrete point of focus. It’s still too early for a legal challenge, but it’s understood that the FTC’s investigation is ongoing, and these new terms could lead to a bite.

However, the $35 billion contingent tranche means a meaningful share of Amazon's headline commitment depends on a trigger — an IPO or AGI breakthrough — that comes with no known or guaranteed timeline. Until one of those conditions is met, the investment stands at $15 billion.

Amazon Web Services (AWS) has reportedly suffered from a couple of outages due to misbehaving AI agents. According to the Financial Times, the most recent interruption happened in December last year, when its Koiro AI coding tool decided to erase the environment it was working on, resulting in a 13-hour disruption.

“We’ve already seen at least two production outages,” one senior AWS employee told the publication. “The engineers let the AI resolve an issue without intervention. The outages were small but entirely foreseeable.”

On the other hand, the company reported that the incidents were relatively minor, with the December disruption only affecting a single service in parts of mainland China, and the other one having no effect on customer-facing services. Amazon said to the Financial Times that the involvement of AI tools in these incidents was just a coincidence, and that the same issue would have occurred even with other developer tools or through manual action. “In both instances, this was user error, not AI error,” the company said.

AWS employees report that the company’s AI tools are treated as part and parcel of the person using them; therefore, they receive the same permissions. But because the engineers involved in the two incidents did not require secondary approval, their AI agents just went ahead with their changes that broke the systems. It’s because of this that the company treated the errors as a user access control issue and does not consider it a problem with its AI tool. Nevertheless, the company assured that it has taken steps to avoid this issue in the future and mitigate the risks of an AI agent going rogue and taking down systems with its actions.

Amazon isn’t the only big tech company deploying AI tools in its workflows. Microsoft CEO Satya Nadella said that nearly 30% of its code is written by artificial intelligence, while over 30,000 Nvidia engineers use a specialized version of Cursor AI. Nvidia CEO Jensen Huang himself allegedly asked managers not using AI “Are you insane?”

All this use of AI tools meant that new entry-level coding jobs are drying up, with studies revealing a 13% drop in openings over the past three years. This has raised fears that artificial intelligence will decimate white-collar jobs, with industry leaders, CEOs, and educational institutions warning of the impending catastrophe if society does not prepare for it.

Every week, there's a new scam switcheroo story where someone buys one thing and receives something else entirely — often nothing more than just rocks or pasta. We return to Reddit once again with yet another entry in this long-running list. This time, it's a CPU bought from Amazon Warehouse under the pretense of being an open-box item in "like new" condition. What it actually was was a completely different CPU.

As u/Leviathon6425 unceremoniously describes above, they ordered a top-of-the-line Ryzen 9 9900X3D from Bezos' retail behemoth, scoring a nice discount by buying it used. When the parcel arrived, the Redditor noticed something off: the CPU was shining through the cutout in the middle of the box. It read "Copyright 2019 AMD" for a chip that wasn't released until 2025.

That alone would set off any alarms, but the processor's shape started to look suspicious, too. Despite not being clearly visible, one can still make out the squared-off perimeter of the processor inside, which belongs to the AM4 family. AM5 chips have a distinct look, with a notched frame serving as the IHS, visible even from inside the box, as seen in the picture below.

The OP immediately took to the r/pcmasterrace subreddit to confirm their suspicion, and, sure enough, the comments were quick to point out the reality. Judging by the serial numbers printed on the chip, it was a Ryzen 9 3900X — a $499 flagship from its time, but worth only around $150 today. In contrast, the Ryzen 9 9900X3D, which was actually supposed to arrive, is currently priced at $529.99 on Newegg and Amazon.

Incidents like these have become commonplace with Amazon orders due to the retailer's generous returns policy. People often buy the real thing, replace the box's contents with something much less valuable, and return it. Amazon then puts the item under its Warehouse label and sells it to you at a low price, without knowing what's really inside. The second buyer then suffers the scam switcheroo.

There's really no solution to this problem other than not buying from Amazon's open-box market, a sentiment echoed in the comment section. The company doesn't lose enough money issuing double refunds to justify taking any action. Brick-and-mortar stores like Micro Center can serve as a solid antidote to these worries, while dedicated retailers such as Newegg are still around for decent deals.

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.

Amazon Web Services is encountering grid connection wait times of up to seven years for new European data center projects, a delay that exceeds the usual finance and physical construction timelines by quite some margin.

This is according to Amazon’s head of energy markets and regulation for AWS EMEA, Pamela McDougall, who, in an interview with Reuters, said that timelines for getting a grid connection have become one of the biggest deciding factors in Amazon’s data center investments. "And we're finding more and more across Europe that certainty of the delivery date has continued to be delayed," she said during the interview.

Grid queues into the 2030s

McDougall says that AWS typically develops a data center in roughly two years, with connection queues in the U.S. averaging one to three years, as per the International Energy Agency, but these can sometimes stretch to seven years. Europe faces similar timelines, with long grid connection queues hampered by seven-year waits that will potentially stretch into the early 2030s for projects just now breaking ground.

In Italy and Spain, even projects with land and permits in place are unable to secure firm delivery dates for grid connections, effectively freezing new capacity irrespective of demand, though it’s understood that Amazon continues to expand its data center across Europe, with ongoing expansion efforts in countries including France and Germany

Data center power demand has risen sharply over the past three years, driven by the likes of AI infrastructure and the higher rack densities it requires. Facilities that were once designed around 6 to 12 kilowatts per rack are now being planned around loads several times higher, with AI clusters introducing large, concentrated demand blocks that must be provisioned upfront by grids that were designed to accommodate predictable industrial and commercial loads.

The International Energy Agency has warned that procurement timelines for core grid components now routinely extend beyond two years for cables and up to four years for large power transformers. Even when regulatory approval moves quickly, actual, physical delivery might not follow until years later. This disconnect between construction timelines and grid expansion timelines has left transmission operators unable to respond at the pace hyperscalers like Amazon need to bring their new facilities online in a timely fashion.

Meanwhile, Europe’s data center electricity consumption continues to grow exponentially. Estimates by the European Commission place EU data center demand at roughly 96 TWh in 2024, with projections rising to 168 TWh by 2030. That growth is heavily front-loaded in AI-capable facilities, which concentrate power demand in ways traditional grid planning models don’t handle well.

Speculative capacity bottlenecks

The most severe bottlenecks can be found in nations like Italy, where grid operators have received tens of gigawatts’ worth of connection requests tied to speculative or early-stage data center projects. Similar patterns have been observed in Spain and parts of Northern Europe, with capacity being reserved several years in advance and without any sort of policing or enforcement against companies that sit on secured capacity without then proceeding to define or achieve construction milestones.

This capacity squatting creates a situation where grid capacity exists on paper but is, for all intents and purposes, unavailable. Projects that are ready to build are forced to wait behind projects that may never materialize, turning access to the grid into a race of who can get to it first, versus whose projects are actually ready to get moving.

In the United Kingdom, efforts are underway to reform this system, with energy regulator Ofgem moving away from strict first-come-first-served allocation toward a “first-ready” model that prioritizes projects with land, financing, and permits in place. It’s understood that similar reforms are being discussed in Italy and Spain, but bureaucracy is slow, and existing queues will take years to unwind.

No short-term solution

All of this is influencing where hyperscalers are willing to invest. Reuters, citing analysis from energy think tank Ember, has warned that poor grid planning could push Europe’s data centers away from established hubs like Frankfurt, London, Amsterdam, and Paris, where waiting times are the longest.

Italy, meanwhile, is an interesting case study in both opportunity and risk. It offers available capacity on paper, but in practice, it has been hampered by surges in connection requests that have overwhelmed planning processes, leaving even well-funded projects uncertain about timelines. We’re also seeing similar dynamics emerging in Southern and Eastern Europe as hyperscalers explore secondary markets like Greece (Microsoft) and Poland (Google) to escape congestion in traditional hubs.

Amazon is responding by exerting its influence in energy policy discussions and by backing initiatives aimed at accelerating grid investment. The company, along with Google and Meta, is a founding member of the Green Industrial Grids Association (GIGA), which is lobbying for faster permitting and coordinated transmission planning. In parallel, large cloud providers are exploring ways to reduce grid dependence, including on-site generation and investments in capacity such as nuclear.

Ultimately, grid access is dictating where and when new capacity gets built in Europe. Projects that can secure a firm connection date move forward, while others stall regardless of demand. In markets with long and poorly enforced queues, this has inevitably turned grid capacity into a scarce, front-loaded resource.

Efforts to reform this and accelerate grid investment are underway, but they’re competing with years of accumulated backlog and long lead times for equipment. Until those frictions ease, European data center expansions will continue to be throttled by power delivery.

Update 02/11/2025 9:37am PT: Article and headline amended to clarify grid delays.

Following a gloomy bunch of GPU shipping stories on Reddit, we return with an extraordinarily positive tale that highlights the same system that often fails customers. One lucky customer bought an Asus ROG Astral RTX 5080 White OC on Amazon and received the GPU despite cancelling their order. Amazon then told them to keep the card and the refunded money, netting the user a free GPU worth $1,850.

The Redditor, u/spaceman293, posted in the r/ASUSROG subreddit, mentioning how they'd cancelled the Amazon order because Micro Center had the same GPU in stock. However, a few days later the RTX 5080 ended up showing up on their doorstep anyways. Once our diligent citizen informed Amazon of their mishap, the multi-trillion-dollar corporation generously told u/spaceman293 to just keep it.

This win serves a stark contrast to the usual accounts we see on Reddit — most recently, someone who ordered an RTX 5080 received a relabeled RTX 5060 Ti instead. Funny enough, that case and this one are both results of the same system. Amazon is a big company with operations so sprawling that it's often not worth it for the retailer to go after smaller incidents — and sometimes, those end up favoring the customer.

Delays in shipping go hand-in-hand with major vendors, so it's not strange to see a cancelled order still get delivered. By the time u/spaceman293 pulled out, there's a chance that the unit was already in the pipeline, ready to travel to its now forfeit destination. In these trying times, small victories like these are just as important to highlight as the upsetting ones.

Despite Elon Musk's announcement on Monday that xAI's Colossus 2 data center had reached a 1 GW scale, the supercomputer is not even close to that, a satellite image published by Epoch AI researchers reveals.

Based on 550,000 Nvidia Blackwell AI accelerators, xAI's Colossus 2 is advertised as the industry's first AI facility that consumes one gigawatt of power for AI inference and training. But for now, the data center codenamed 'Macrohard' purportedly only has 350 MW of cooling capacity — not nearly enough to cool down 550,000 Blackwell GPUs at full power, even in winter. As a result, Musk's Jan. 19 announcement may have been premature, to put it mildly. Epoch AI expects the supercomputer to reach 1 GW by May.

Interestingly, when Grok, xAI's AI bot, was asked about Colossus 2, it confirmed that the launch of the supercomputer may be phased. Furthermore, it recalled media reports claiming that xAI may be using unpermitted gas turbines for extra power.

At the pace that Colossus 2 is being equipped with cooling systems right now, the new supercomputer will become a gigawatt-class machine sometime in May, according to the research. Meanwhile, the machine was once predicted to use a million GPUs, and then Musk said that it could scale to 1.5 GW or even 2 GW when the time comes. When this could happen is not known because xAI needs to get enough AI servers, procure enough power, and then get cooling systems.

Even though xAI's Colossus 2 will hit its 1 GW milestone later than expected, it is still projected to be ahead of rivals from Amazon and OpenAI, according to a graph by Epoch AI. The company will have more resources for AI training, AI inference, and agentic AI workloads than its rival for some time.

Based on the graph's reference lines, the power consumption of the whole city of San Diego averages ~800 MW, Amsterdam consumes around ~1.6 GW, and the power consumption of Los Angeles is about ~2.4 GW, which puts modern frontier AI data centers in the same class as major cities. When fully equipped and ramped, xAI Colossus 2 at roughly 1.3 GW – 1.4 GW, would consume about 1.7× San Diego's power, slightly less than Amsterdam, and around 60% of Los Angeles.

A new entrant has emerged in the long-running series of GPU scam cases documented on Reddit — this time, it's an RTX 5080 being covertly swapped for an RTX 5060 Ti. An unfortunate customer ordered an RTX 5080 from Amazon, only to be left bewildered by a different GPU inside the box that showed no signs of being a 5080, despite being labeled as such.

The Redditor added images of the product he received on Imgur; those, along with the post's caption, show that this supposed Asus Prime RTX 5080 has an 8-pin PCIe connector. Nvidia's Blackwell cards above the 5060 Ti class don't ship with that style of power plug anymore, opting for the incendiary 16-pin connector instead, so this is a dead giveaway we're not looking at a 5080.

Despite that, someone clearly tried to hide the obvious fallacy by putting genuine RTX 5080 stickers on this card, which you can tell by the slightly crooked application. What we're actually looking at is an Asus Prime RTX 5060 Ti 16 GB. The design for that and the Prime 5060 (non-Ti) is nearly identical, but subtle differences are noticeable. Anyhow, this RTX 5060 Ti was dressed in 5080 clothing to pull off the scam, which seems to have worked.

The leading theory in the comments points toward a deceptive swap on the customer's end. Not this customer, but someone else who ordered both an Asus Prime RTX 5060 Ti and 5080, filed for a return on the 5080 but sent the 5060 Ti in the box instead. They guess Amazon didn't bother checking the package contents — even if it did, the mere presence of a GPU would've likely been enough to accept it — and resold the 5080. Our very own Matt Safford scored an i5 CPU for $10 thanks to another such scam last year.

The unlucky buyer on Reddit then became the new recipient of this GPU. The story tracks and, beyond speculating that someone else in the shipping chain was responsible, lines up with previous "comingling" incidents. Another RTX 5080 was swapped for a literal brick, and before that, people have received pasta instead of silicon, too. Usually, scammers don't go to the length to swap stickers between two legitimate SKUs, so at least this one put some effort in.

At this point, filing for a return/refund on this GPU should get the Redditor their money back, or maybe they can work out an exchange with Amazon's customer service. In times like these, where AI has snatched production lines and GPU prices are on the rise, securing a good deal and watching it slip away can feel extra upsetting, so we hope the victim (and the scammer) gets their due.

Amazon has struck a deal to buy up the first new U.S.-mined copper in more than a decade to fuel its AI data centers in the country, WSJ reports. The agreement, inked with Rio Tinto, will provide some of the copper using the mining company's innovative new Nuton Technology, which drastically reduces the mine-to-market chain. It follows multiple reports in 2025 that copper markets are feeling the pinch due to aggressive AI expansion, with industry watchers warning that only 70% of 2035 demand could be met.

As WSJ notes, the copper will be sourced from Gunnison Copper's Johnson Camp mine in Arizona, a mine which has been restarted as a testbed for Rio Tinto's new method for opening up low-grade deposits. This Nuton Technology was deployed at an industrial scale at the Johnson Camp copper mine last month, according to Rio Tinto. "The process produces 99.99% pure copper cathode at the mine gate and removes the need for traditional concentrators, smelters and refineries, significantly shortening the mine-to-market supply chain," the company explains. The extraction method purportedly uses substantially less water and produces lower carbon emissions compared to conventional methods.

According to WSJ, the new deal "will satisfy only a sliver of Amazon’s needs." That's because each of Amazon's mammoth data centers require tens of thousands of tons of copper. The Arizona Nuton output is expected to be 14,000 metric tons in four years, according to the report, not even enough for one such facility. Rio Tinto notes that a further 16,000 tonnes will come from a conventional run-of-mine leaching pad. This will push the deal's output to nearly 30,000 tonnes, but ensures that less than half will be delivered using the more environmentally friendly method.

"Amazon’s Climate Pledge goal to reach net zero carbon by 2040 requires us to innovate across every part of our operations, including how we source the materials that power our infrastructure," its Chief Sustainability Officer, Kara Hurst, said. "This collaboration with Nuton Technology represents exactly the kind of breakthrough we need—a fundamentally different approach to copper production that helps reduce carbon emissions and water use. As we continue to invest in next-generation carbon-free energy technology and expand our data centre operations, securing access to lower-carbon materials produced close to home strengthens both our supply chain resilience and our ability to decarbonize at scale.”

SK hynix is expanding its U.S. presence with a new office in the Seattle metropolitan area, placing the world’s leading HBM supplier within minutes of Nvidia, Amazon, and Microsoft.

According to industry sources cited by DigiTimes, the South Korean company has leased approximately 5,500 square feet at City Center Bellevue, just east of Seattle. While modest in size, the location and timing make the expansion far more consequential than a routine regional office opening.

SK hynix is sitting squarely in the middle of the ongoing AI hardware boom, supplying the majority of the HBM used in Nvidia’s data center accelerators and increasingly serving hyperscale customers building their own AI silicon. Establishing a physical foothold in the Pacific Northwest puts the company closer to the customers driving its fastest-growing and most important business.

Moving closer to the center of AI development

SK hynix has spent the past two years transforming itself from a cyclical commodity DRAM vendor into a leading supplier for AI infrastructure. That's visible in High Bandwidth Memory (HBM), where SK hynix was first to mass-produce HBM3 and has remained ahead in yield and volume as customers transitioned from HBM2E. In August 2025, the company overtook Samsung in global DRAM revenue for the first time, a change largely attributed to HBM shipments for Nvidia’s H100 and H200 AI accelerators.

Seattle and its surrounding suburbs have become one of the densest concentrations of the AI industry ecosystem outside of Silicon Valley. While Nvidia maintains a significant engineering presence in the region, Amazon’s AWS Skills Center is based nearby, and Microsoft’s Azure silicon and systems groups are spread across Redmond and Bellevue.

HBM is not a plug-and-play component. Memory stacks are co-designed with GPUs and AI accelerators, and performance, power, and reliability targets are refined through repeated cycles of joint validation. Physical proximity will allow faster iteration when issues arise, whether related to signal integrity, thermals, or packaging tolerances.

Amazon’s recent launch of its Trainium3 AI accelerator, which integrates 144GB of HBM3E, shows how quickly hyperscalers are increasing their reliance on stacked memory. Microsoft and Google are following similar paths with custom accelerators. Each of those programs depends on close coordination between the silicon designer and the memory supplier; a Seattle-area office gives SK hynix a seat at that table.

Broader U.S. localization

The Bellevue office also fits into a wider U.S. expansion that extends beyond customer support. In 2024, SK hynix announced plans for a $4 billion advanced packaging facility in Indiana, its first manufacturing investment in the United States. That site is intended to handle advanced HBM packaging and testing, with production targeted for 2028. While the Indiana project focuses on manufacturing, it appears the Seattle office will be oriented toward R&D, applications engineering, and customer engagement.

Taken together, these moves suggest SK hynix is deliberately localizing more of its AI-related operations in the U.S., where the bulk of demand is being generated. Advanced packaging has become as important as wafer fabrication for AI accelerators, and the ability to package memory near customers reduces both logistical complexity and geopolitical risk. It also aligns with U.S. industrial policy aimed at securing domestic supply chains for critical technologies.

There has been periodic speculation that SK hynix could eventually build a full DRAM fab in North America, though the company has not committed publicly to such a plan. Even without a fab, expanding engineering and packaging capabilities in the U.S. strengthens SK hynix’s position with American customers at a time when memory supply is extremely tight, thereby making long-term capacity planning a competitive differentiator.

The race to HBM4

SK hynix’s Seattle expansion also reflects intensifying competition in the HBM market. Samsung remains a formidable rival with deep manufacturing resources and its own U.S. investments, including advanced packaging capacity tied to its Texas operations. Micron, the sole U.S.-based DRAM manufacturer, is pursuing high-end server and automotive markets and sampling its own HBM4 designs, though its near-term capacity expansion is more constrained.

Meanwhile, SK hynix has already completed development of HBM4 and is understood to have delivered samples to Nvidia. Early engagement is particularly important with HBM4 because the transition involves higher stack counts, tighter power budgets, and more complex thermal challenges. Winning those designs early could easily lock in multi-year supply relationships.

Ultimately, with its Seattle expansion, SK hynix is telling Samsung and Micron that it intends to be embedded in the AI ecosystems of its largest customers rather than a distant component supplier. Being physically close to Nvidia and Amazon’s engineering teams during the transition to HBM4 will greatly improve the company’s odds of maintaining its lead, while also supporting adjacent efforts such as joint work with Nvidia on AI-optimized SSDs.

As prices for DDR5 RAM get ever higher and availability becomes ever scarcer, it's unfortunately no surprise that the number of corresponding scams increases. The latest story comes from an unlucky European shopper who got DDR4 sticks in DDR5 clothing.

The story in question comes from the shores of Reddit. User and enthusiast Leading-Growth-3861 (Growth) already owns a prebuilt PC, but he was rolling his own build for the first time. He chose a good time, as supplies for components are dwindling rapidly.

Upon opening the RAM package and inspecting the DIMMs, he noticed the heatspreader on the sticks seemed a bit loose. Even still, he went to plug them in... and couldn't, because the notch on the PCB wouldn't line up no matter what he did. That's when he realized what had happened: someone had removed the DDR5 sticks beneath the heatspreaders and inserted DDR4 sticks to make the scam appear convincing.

Where it gets interesting is that, according to Growth, the RAM was not marked as second-hand or open-box, merely "Sold and Dispatched by Amazon," signaling that it was meant to be a fresh, new item. In turn, this seems to indicate that the replacement theft occurred somewhere along Amazon's supply chain, as Growth made no remarks about the main box the item(s) arrived in.

His tale thankfully has a reasonably happy ending, as Growth got a refund from Amazon (UK, presumably, given his mention of pounds). The e-tailer even went so far as to refund him more than he paid, since he originally paid "about £100 less than what it's currently priced."

This latest story is but one in a long line of scams involving DDR5 ever since the proverbial hyperinflation started. Not too long ago, a Spanish buyer got DDR2 modules with weights posing as DDR5, while another Reddit user ordered laptop memory and got his package stolen. It's a sad state of affairs that leads us to post this advice, but if you're ordering PC components, ensure you video the opening of the package so that you're protected against any potential surprises like this one.

Amazon Chief Security Officer Stephen Schmidt said the company has blocked more than 1,800 suspected North Korean applicants from joining the tech giant. Schmidt states in his LinkedIn post that this is the number of profiles his team has intercepted since April 2024, with attempts increasing by 27% quarterly this year alone. Amazon uses AI to screen profiles and check for anomalies, which are then verified by human operators. “Our AI model analyzes connections to nearly 200 high-risk institutions, anomalies across applications, and geographic inconsistencies,” the Amazon CSO wrote. “We verify identities through background checks, credential verification, and structured interviews.”

Despite being a tightly controlled country with most of its citizens having no access to the internet, the Democratic People’s Republic of Korea (DPRK) has a highly skilled IT workforce in its employment. However, instead of serving local needs, the North Korean regime uses its skills to gain funding, especially as the country is still an international pariah with heavy economic sanctions meted against it. This has become easier in recent years as the popularity of remote work has made it easier for anyone to pose as a United States citizen and apply for a job — in fact, CrowdStrike has investigated over 320 incidents last year, with many of them using AI to create fake profiles.

As more companies become aware of this technique, Pyongyang is changing tactics and now using stolen identities to apply for jobs. They would often target dormant LinkedIn accounts of actual IT workers and software engineers and use them for their application. They also work with U.S.-based persons to set up laptop farms, so that the hardware that companies send over, especially those with tracking systems, will remain in the U.S. while North Korean workers remotely access the computer. In fact, several people have already been convicted of this, and over 240 companies have been victimized by this scam. Notably, Amazon recently busted one North Korean contract worker after spotting abnormally high keystroke lag.

Although it might seem obvious that tech companies are the ones that are most at risk of being targeted by the DPRK, The Register said that North Korean actors are increasingly getting interviews in other industries like finance, healthcare, and public administration, among others. One way hiring managers can protect against scams like these is to verify details, such as applicants claiming to have degrees from schools or universities that do not actually offer them, or enrollment dates that do not align with academic schedules. Aside from these, you can also check for subtle details, such as applicants using formatting not typically used in the U.S., such as +1 in phone numbers.

Update 12/21/25 6:20am PT: In a statement of clarification to Tom's Hardware, an Amazon representative clarified, "The DPRK actor was hired as a contract system developer," and not as a sysadmin, as previously thought.

Original story follows.

A North Korean imposter was uncovered, working as a sysadmin at Amazon U.S., after their keystroke input lag raised suspicions with security specialists at the online retail giant. Normally, a U.S.-based remote worker’s computer would send keystroke data within tens of milliseconds. This suspicious individual’s keyboard lag was “more than 110 milliseconds,” reports Bloomberg.

Amazon is commendably proactive in its pursuit of impostors, according to the source report. The news site talked with Amazon’s Chief Security Officer, Stephen Schmidt, about this fascinating new case of North Koreans trying to infiltrate U.S. organizations to raise hard currency for the Democratic People’s Republic of Korea (DPRK), and sometimes indulge in espionage and/or sabotage.

Schmidt says that Amazon has foiled more than 1,800 DPRK infiltration attempts since April 2024. Moreover, the rate of attempts continues apace, with Amazon reckoning it is seeing a 27% QoQ uplift in North Koreans trying to get into the Amazon corporation.

You have to look for them to find them

Amazon’s success can be almost entirely credited to the fact that it is actively looking for DPRK impostors, warns its Chief Security Officer. “If we hadn’t been looking for the DPRK workers,” Schmidt said, “we would not have found them.”

With this company policy explained, a blip on the Amazon security radar was caused earlier this year when a new sysadmin’s Amazon laptop monitor alerted security personnel about unusual behavior.

Amazon security experts took a closer look at the flagged ‘U.S. remote worker’ and determined that their remote laptop was being remotely controlled – causing the extra keystroke input lag. Schmidt emphasizes that good-quality security software was key to this investigation.

It turns out that the DPRK had access to this Amazon laptop located in Arizona. A woman found to be facilitating this fraud on behalf of North Korean imposter workers was sentenced to several years in prison earlier this year.

As well as red flag computer network symptoms, the fumbling use of American idioms and English-language articles continues to be a giveaway when conversing with such impostors.

Tip of the iceberg

The problem of North Koreans infiltrating U.S. corporations for profit, mischief, and more is undoubtedly a serious one. We’ve covered sizable FBI seizures of equipment recently, perhaps showing just the tip of the iceberg. More successful infiltrations by the DPRK, as well as hostile nations like Iran, Russia, and China, are likely to be ongoing.

This month, Amazon Web Services introduced the Graviton5, its fifth-generation custom general-purpose server processor, designed to compete against industry-standard CPUs from AMD and Intel in AWS's data centers. The new processor extends AWS's in-house Arm-based CPU program with a CPU that packs up to 192 cores and 180 MB of L3 cache, and is designed to compete with higher-end AMD EPYC and Intel Xeon processors, potentially replacing some of them in AWS data centers.

At a glance

The AWS Graviton5 processor is fabricated using a 3nm-class process, likely by TSMC. The processor integrates 192 Neoverse V3 cores alongside an assumed 180 MB L3 cache. AWS says that the new CPU will deliver 25% higher performance compared to its predecessor, which appears to be conservative, as the Graviton5 offers a twofold increase in the number of cores. The chip uses the Armv9.2 ISA that brings several microarchitecture enhancements and a fivefold increase in L3 cache size.

The new processor is now available in Amazon EC2 M9g instances in preview, while compute-optimized C9g and memory-focused R9g variants are scheduled for launch in 2026. The current EC2 M9g instances are up to 30% faster for databases, up to 35% faster for web applications, and up to 35% faster for machine learning workloads compared to M8g, according to AWS.

Diving deeper: 192 Neoverse V3 cores

Amazon Web Services is intentionally opaque about the exact specifications and internal design of its Graviton5 CPU. Nonetheless, it offers comparisons with the previous-generation Graviton4 chip, which allows us to decode some details and delve into them with a little more depth.

AWS and Arm officially confirm that Graviton5 integrates 192 Neoverse V3 cores per package, fabricated using a 3nm-class process, making it the densest CPU in the Graviton lineup and the densest Armv9.2 processor available to date. The internal layout of the processor has been redesigned to reduce communication overhead, and AWS claims up to 33% lower inter-core latency, which is particularly noteworthy given the twofold increase in core count.

When we discuss Neoverse V3, we cannot help but think about the Arm-developed compute subsystems (CSS). While Arm confirmed that we are dealing with Neoverse V3, neither Amazon nor Arm has confirmed that Graviton5 uses Arm-developed CSS. That means we're likely dealing with a unique design in Graviton 5.

In performance comparisons between the Neoverse V3 core and its predecessor, Arm claims a 9%-16% uplift over Neoverse V2 across general cloud workloads and up to 84% in AI data analytics. This is one of the reasons why AWS is so conservative about performance upticks, both for Graviton5 as well as compute-intensive M9g instances in general. Another reason for AWS's conservative performance estimate is that it does not sell leading-edge performance like AMD or Nvidia, but rather predictable performance per dollar and scalability in the cloud. Nonetheless, with a 192-core processor, AWS puts itself into the highest league among CPU developers.

L3 cache replaces system-level cache

One interesting thing to note about Graviton5 is that it comes with L3 cache, not system-level cache like Graviton4. While L3 and SLC in data center CPUs have a lot in common, they are not the same thing. Traditionally, L3 cache is a last-level cache located inside each compute tile or core cluster in a data center CPU. L3 primarily serves CPU workloads by reducing DRAM access; it is optimized for low latency and directly participates in the core's coherence protocol. Therefore, L3 is tightly coupled to the cores and is physically close to them.

By contrast, SLC sits outside the core clusters on the SoC fabric and is shared by all CPU cores, various other accelerators, I/O devices, NICs, and DMA engines. It tends to be much larger (often 100–300+ MB) and optimized for throughput rather than latency, as it acts as a global buffer that reduces pressure on DRAM and provides coherent access for heterogeneous compute blocks. SLC can improve scaling for very high core counts and enables unified memory semantics across CPUs, GPUs, and on-die accelerators, a role traditional L3 caches cannot fulfill on their own.

Amazon has not publicly explained the design decision, but based on Graviton4’s architecture and what we know about Graviton5, the reason is almost certainly architectural scalability. The move from SLC in Graviton4 to a large 180 MB L3 in Graviton5 is not cosmetic; it reflects fundamental changes in how a 192-core processor moves data, manages latency, and maintains coherence.

Graviton4's architecture — 96 Neoverse V2 cores, a CMN-700 mesh, 12 DDR5-5600 channels — operates efficiently with a centralized or semi-centralized SLC. But doubling the core count to 192 dramatically increases mesh traffic, hop distances, and contention on any unified cache structure. At this scale, a monolithic SLC could almost certainly become a latency bottleneck and would not support AWS’s claim of up to 33% lower inter-core communication latency. A distributed L3 sliced across the die allows hot data to remain physically close to compute clusters, reducing average access latency and improving overall coherence behavior.

The fivefold cache expansion AWS advertises reinforces this architectural necessity. Scaling Graviton4's 36 MB SLC by that factor yields 180 MB, and AWS's additional statement —2.6X more cache per core, at double the core count — implies ~187 MB total, which aligns with a large, multi-slice L3 rather than a single SLC block, which would create routing complexity.

Finally, L3-based designs offer stronger multi-tenant performance predictability, which is crucial for AWS. Under cloud workloads, shared caches experience heavy cross-tenant interference and variable latency, so when designing cache subsystems, developers must take into account AWS's use case. To sum things up, the shift to a distributed L3 was a necessary architectural evolution for Graviton5.

New memory subsystem, I/O, and security features

Just as AWS didn't disclose many details about other design aspects of Graviton5, it also didn't disclose much about the memory subsystem of the CPU. It goes without saying that Graviton5's memory subsystem is more powerful than that of Graviton4, as it supports higher memory speeds, which likely means that it at least retains a 12-channel memory subsystem of the Graviton4, but with higher data transfer rates (i.e., higher than DDR5-5600).

A 12-channel DDR5 design operating at 6400 MT/s would provide around 614 GB/s of aggregate bandwidth, which translates to approximately 3.2 GB/s per core, which is actually lower than 5.6 GB/s per core in the case of Graviton4. However, the larger L3 cache could compensate for this decrease in memory bandwidth. Then again, we do not know the exact number of memory channels supported by Graviton5.

Input/output throughput is similarly increased, according to AWS: network bandwidth is up by 15% on average across instance sizes, with as much as double the throughput for the largest configurations. Storage bandwidth through Amazon EBS rises by around 20% on average, according to AWS. These gains are designed to improve performance not only for compute-heavy applications, but also for distributed systems that depend on fast storage and networking.

On the security side, Graviton5 is built on the AWS Nitro System, with sixth-generation Nitro Cards that handle virtualization, networking, and storage. AWS has also introduced a new component called the Nitro Isolation Engine, which the company describes as a formally verified isolation layer. Instead of relying solely on conventional security validation, the Isolation Engine uses mathematical proofs to demonstrate that workloads are separated from each other and from AWS operators. The architecture enforces a zero-operator-access model, and AWS plans to allow customers to review the implementation and the formal proofs behind it to ensure maximum security. Such security measures could be a part of the company's effort to attract clients who have traditionally used on-prem servers.

Wrapping it up

AWS's new Graviton5 processor offers a 192-core, 3nm Arm-based CPU with around 180 MB of L3 cache. This positions the cloud giant as a competitor to the high-end AMD EPYC and Intel Xeon solutions for data centers. The CPU integrates Neoverse V3 cores and delivers an advertised 25% performance uplift, which is conservative given the twofold increase in core count, large microarchitectural improvements in the Armv9.2 ISA, and a fivefold increase in cache capacity. Also, AWS confirms 33% lower inter-core latency due to a redesigned internal layout but has not disclosed whether it uses Arm's CSS, suggesting that Graviton5 may be a unique Annapurna Labs design built around Neoverse V3 cores.

A key architectural shift is replacing Graviton4's SLC with a large distributed L3 to enable better coherence scaling across 192 cores and predictable latency. The processor also gains a faster memory subsystem (likely retaining 12 channels at higher DDR5 speeds), improved network and storage bandwidth, and the new Nitro Isolation Engine, which uses formal verification to guarantee tenant isolation and enforce zero-operator access.

At present, Graviton5 powers new EC2 M9g instances — up to 30% – 35% faster for databases, web services, and machine learning — and compute-optimized C9g and memory-optimized R9g variants will follow in 2026.

Amazon Web Services this week introduced its next-generation Trainium3 accelerator for AI training and inference. As AWS puts it, the new processor is twice as fast as its predecessor and is four times more efficient. This makes it one of the best solutions for AI training and inference in terms of cost. In absolute numbers, Trainium3 offers up to 2,517 MXFP8 TFLOPS, which is nearly two times lower compared to Nvidia's Blackwell Ultra. However, AWS's Trn3 UltraServer packs 144 Trainium3 chips per rack, and offers 0.36 ExaFLOPS of FP8 performance, therefore matching the performance of Nvidia's NVL72 GB300. This is a very big deal, as very few companies can challenge Nvidia's rack-scale AI systems.

AWS Trainium3

The AWS Trainium3 is a dual-chiplet AI accelerator that is equipped with 144 GB of HBM3E memory using four stacks, which provides peak memory bandwidth of up to 4.9 TB/s. Each compute chiplet, allegedly made by TSMC using its 3nm-class fabrication process, contains four NeuronCore-v4 cores (which feature an extended ISA compared to predecessors) and connects two HBM3E memory stacks. The two chiplets are connected using a proprietary high-bandwidth interface and share 128 independent hardware data-movement engines (which are key for the Trainium architecture), collective communication cores that coordinate traffic between chips, and four NeuronLink-v4 interfaces for scale-out connectivity.

A NeuronCore-v4 integrates four execution blocks: a tensor engine, a vector engine, a scalar engine, a GPSIMD block, and 32 MB of local SRAM that is explicitly managed by the compiler instead of being cache-controlled. From a software development standpoint, the core is built around a software-defined dataflow model in which data is staged into SRAM by DMA engines, processed by the execution units, and then written back as near-memory accumulation enables DMA to perform read-add-write operations in a single transaction. The SRAM is not coherent across cores and is used for tiling, staging, and accumulation rather than general caching.

• The Tensor Engine is a systolic-style matrix processor for GEMM, convolution, transpose, and dot-product operations and supports MXFP4, MXFP8, FP16, BF16, TF32, and FP32 inputs with BF16 or FP32 outputs. Per core, it delivers 315 TFLOPS in MXFP8/MXFP4, 79 TFLOPS in BF16/FP16/TF32, and 20 TFLOPS in FP32, and it implements structured sparsity acceleration using M:N patterns (such as 4:16, 4:12, 4:8, 2:8, 2:4, 1:4, and 1:2), allowing the same 315 TFLOPS peak on supported sparse workloads.
  
• The Vector Engine for vector transforms provides about 1.2 TFLOPS FP32, hardware conversion into MXFP formats, and a fast exponent unit with four times the throughput of the scalar exponent path for attention workloads. The unit supports various data-types, including FP8, FP16, BF16, TF32, FP32, INT8, INT16, and INT32.
  
• The Scalar Engine also provides about 1.2 TFLOPS FP32 for control logic and small operations across FP8-to-FP32 and integer data types.

Perhaps the most interesting component of NeuronCore-v4 is the GPSIMD block, which integrates eight fully-programmable 512-bit vector processors that can execute general-purpose code written in C/C++ while accessing local SRAM. GPSIMD is integrated into NeuronCore because not everything in real AI models maps cleanly to a tensor engine. Modern AI workloads contain a lot of code for unusual data layouts, post-processing logic, indexing, and model-specific math. These are hard or inefficient to express as matrix operations, and running them on the host CPU would introduce latency and costly data transfers. GPSIMD solves this by providing real general-purpose programmable vector units inside the core, so such logic runs directly next to the tensors at full speed and using the same local SRAM.

In short, NeuronCore-v4 operates as a tightly coupled dataflow engine in which tensor math, vector transforms, scalar control, and custom code all share a local 32MB scratchpad and are orchestrated by the Neuron compiler rather than by a warp scheduler used on Nvidia hardware.

Performance-wise, Trainium3 outperforms its direct predecessor in FP8 compute (well, MXFP8) by almost two times and hits 2.517 PFLOPS per package (clearly ahead of Nvidia's H100/H200, but behind Blackwell B200/B300) and adds MXFP4 support. However, Trainium3's BF16, TF32, and FP32 performance remains on par with Trainium2, which clearly shows that AWS is betting on MXFP8 for training and inference going forward. To that end, it does not develop its BF16 (which is widely used for training nowadays) and FP32 capabilities, as it seems to feel comfortable with the performance it has, given that these formats are now used primarily for gradient accumulation, master weights, optimizer states, loss scaling, and some precision-sensitive operations.

One interesting capability that Trainium3 has that is worth mentioning is the Logical NeuronCore Configuration (LNC) feature, which lets the Neuron compiler fuse four physical cores into a wider automatically synchronized logical core with combined compute, SRAM, and HBM, which could be useful for very wide layers or big sequence lengths that are common with very large AI models.

AWS's Trn3 UltraServers: Almost beating Nvidia's GB300 NVL72

Much of Nvidia's success in the recent quarters was driven by its rack-scale NVL72 solutions featuring 72 of its Blackwell GPUs. Supporting a massive scale-up world size and an all-to-all topology, which is especially important for Mixture-of-Experts (MoE) and autoregressive inference. This gives Nvidia a massive advantage over AMD and developers of custom accelerators, such as AWS. To enable this capability, Nvidia had to develop NVLink switches, sophisticated network cards, and DPUs, a massive silicon endeavor. However, it looks like AWS's Trn3 UltraServers will give Nvidia's GB300 NVL72 a run for its money.

Trn3 UltraServers, powered by Trainium3 AI accelerators, will be offered in two sizes: one configuration packs 64 accelerators and presumably an Intel Xeon CPU, while the larger variant brings together 144 accelerators and an Arm-based Graviton in a single rack-scale solution. In the larger system, the 144 Trainium3 accelerators are distributed across 36 physical servers with one Graviton CPU and four Trainium3 chips installed in each machine. In many ways, such an arrangement resembles Nvidia's NVL72 approach, which uses Nvidia's CPU, GPU, and connectivity silicon, highlighting AWS' direction of building vertically integrated AI platforms.

Within a server, Trainium3 accelerators are linked through a first NeuronSwitch-v1 layer using NeuronLink-v4 (at 2 GiB/s per device, though it is unclear whether we are talking about a single direction bandwidth, or aggregated bidirectional bandwidth), and communication between different servers is routed through two additional NeuronSwitch-v1 fabric layers, again carried over NeuronLink-v4. Unfortunately, AWS does not publish aggregate NeuronSwitch-v1 bandwidth across the domain.

From a performance standpoint, the larger configuration with 144 Trainium3 delivers 362.5 MXFP8/MXFP4 PetaFLOPS (dense) performance, which (on par with GB300 NVL72), 96.624 PFLOPS of BF16/FP16/TF32 throughput, and 26.352 PFLOPS in FP32. The system is also equipped with 21 TB of HBM3E memory, featuring an aggregate memory bandwidth of 705.6 TB/s, leaving Nvidia's GB300 NVL72 behind in this metric.

In general, Trn3 Gen2 UltraServer appears very competitive against Nvidia's GB300 NVL72 in terms of FP8 performance. FP8 is about to get more popular for training, so betting on this format makes a lot of sense. Of course, Nvidia has an ace up its sleeve in the form of NVFP4, which is positioned both for inference and training, and armed with this format, the company's Blackwell-based machines are unbeatable. The same applies to BF16, which got faster compared to Trainium2, but not enough to beat Nvidia's Blackwell.

Overall, while the AWS Trn3 Gen2 UltraServer with 144 Trainium3 accelerator looks quite competitive when it comes to FP8 compared to Nvidia's Blackwell-based NVL72 machines, Nvidia's solution is more universal in general.

AWS Neuron going the way of CUDA

In addition to rolling out new AI hardware, AWS announced a broad expansion of its AWS Neuron software stack at its annual re:Invent conference this week. AWS positions this release as a shift toward openness and developer accessibility, so the update promises to make Trainium platforms easier to adopt, let standard machine learning frameworks run directly on Trainium hardware, give users deeper control over performance, and even expose low-level optimization paths for experts.

A major addition is native PyTorch integration through an open-source backend named TorchNeuron. Using PyTorch's PrivateUse1 mechanism, Trainium now appears as a native device type, which enables existing PyTorch code to execute without modification. TorchNeuron also supports interactive eager execution, torch.compile, and distributed features such as FSDP and DTensor, and it works with popular ecosystems including TorchTitan and Hugging Face Transformers. Access to this feature is currently restricted to select users as part of the private preview program.

AWS also introduced an updated Neuron Kernel Interface (NKI) that gives developers direct control over hardware behavior, including instruction-level programming, explicit memory management, and fine-grained scheduling, exposing Trainium's instruction set to kernel developers. In addition, the company has released the NKI Compiler as open source under Apache 2.0. The programming interface is available publicly, while the compiler itself remains in limited preview.

AWS also released its Neuron Explorer, a debugging and tuning toolkit that lets software developers and performance engineers improve how their models run on Trainium. This is done by tracing execution from high-level framework calls, all the way down to individual accelerator instructions, while offering layered profiling, source-level visibility, integration with development environments, and AI-guided suggestions for performance tuning.

Finally, AWS introduced its Neuron Dynamic Resource Allocation (DRA) to integrate Trainium directly into Kubernetes without the need for custom schedulers. Neuron DRA relies on the native Kubernetes scheduler and adds hardware-topology awareness to enable complete UltraServers to be allocated as a single resource and then flexibly assign hardware for each workload. Neuron DRA supports Amazon EKS, SageMaker HyperPod, and UltraServer deployments, and is provided as open-source software with container images published in the AWS ECR public registry.

Both Neuron Explorer and Neuron DRA are designed to simplify cluster management and give users fine-grained control over how Trainium resources are assigned and used. In a nutshell, AWS is moving closer to making its Trainium-based platforms much more ubiquitous than they are today, in an effort to make them more competitive against CUDA-based offerings from Nvidia.

In a nutshell

This week, Amazon Web Services released its 3^rd Generation Trainium accelerator for AI training and inference, as well as accompanying Trn3 UltraServers rack-scale solutions. For the first time, Trn3 Gen2 UltraServers rack-scale machines will rely solely on AWS in-house hardware, including CPU, AI accelerators, switching hardware, and connectivity fabrics, signalling that the company has adopted Nvidia's vertical integration hardware strategy.

AWS claims that its Trainium3 processor offers roughly 2X higher performance and 4X better energy efficiency than Trainium2 as each accelerator delivers up to 2.517 PFLOPS (MXFP8) — beating Nvidia's H100, but trailing B200 — and is accompanied by 144 GB of HBM3E with 4.9 TB/s of bandwidth. Meanwhile, Trn3 Gen2 UltraServers scale to 144 accelerators for about 0.36 ExaFLOPS FP8 performance, which brings it on par with Nvidia's GB300 NVL72 rack-scale solution. Nonetheless, Nvidia's hardware still looks more universal than AWS's.

To catch up with Nvidia, Amazon also announced major updates to its Neuron software stack to make Trainium-based platforms easier to use, allow standard machine-learning frameworks to run natively on the hardware, give developers greater control over performance, and open access to low-level tuning for experts.

Amazon has unveiled Leo Ultra, a high-throughput phased-array terminal for its upcoming satellite internet service, now rebranded from Project Kuiper to Amazon Leo. The device is part of an enterprise-grade hardware lineup that will support initial customer deployments beginning in 2025, with broader availability scheduled for 2026.

The Ultra terminal is Amazon’s largest and fastest offering to date, designed for permanent installation at fixed sites. It measures roughly 20 by 30 inches with a chassis depth of 1.9 inches and is intended for pole-mounted outdoor use. Internally, the device incorporates Amazon silicon and a full-duplex phased-array system capable of simultaneous uplink and downlink, supporting download speeds of up to 1 Gbps and upload speeds of up to 400 Mbps.

Leo Ultra was one of three customer terminals shown as part of the new network push. Alongside it, Amazon previewed a mid-sized Leo Pro terminal designed for portable or vehicle-mounted use, and a compact 7-inch square Leo Nano unit rated for up to 100 Mbps. All three run on custom silicon developed by Amazon’s device teams and share the same core waveform and protocol stack.

The new hardware arrives as Amazon begins rolling out its constellation. As of November 2025, the company has launched more than 150 low-Earth orbit satellites under Project Kuiper, with multiple mass deployment launches completed since April. The company plans to launch several thousand satellites in total to meet its global coverage targets, and CEO Jeff Bezos has stated ambitions to begin deploying space-based data centers in the next 10 to 20 years.

Amazon is positioning the network as a hybrid connectivity layer that ties directly into AWS infrastructure. The Leo architecture includes support for “Direct to AWS” (D2A) links that route satellite traffic straight into cloud workloads without passing through the public internet. The company has not yet disclosed pricing details for the Leo terminals and service plans, but it is thought that the company intends to begin commercial service with select enterprise partners, followed by general availability next year.

Amazon’s entry into the satellite internet market arrives as rivals, including SpaceX’s Starlink, continue to expand their own constellations and service offerings. With gigabit-class performance and integrated cloud hooks, Leo Ultra represents Amazon’s first full hardware and network package aimed at the high-end segment of the market.

RAM pricing is quickly developing into an existential problem for high-end PC gaming new-builds and upgrades. If you didn’t already have an uneasy feeling about a 64GB RAM kit costing more than an entire PS5, Epic Games CEO Tim Sweeney’s sage observations might begin to stir your gizzard.

Sweeney’s social media musings, embedded above, came in reaction to a fellow Twitterer’s observation. In brief, the Fortnite figurehead was commenting on a post expressing shock-horror that a Crucial Pro DDR5-6000 kit they bought from Amazon for $260 in mid-October, jumped to $498 little more than a month later. This is despite being in the midst of the Black Friday ‘deals’ extravaganza, which seems to mean nothing in relation to this vital PC system component.

On the massive inflation observed by the Amazon shopper in the U.S., Sweeney remarked, “RAM price increases will be a real problem for high-end gaming for several years.” That’s a worryingly far-reaching prediction. Many users might be able to fend off new build or upgrade plans for a few months, but if we are talking several years, plans for next-gen systems could be adversely affected, or have to be shelved long-term.

Don't blame the resellers (yet)

Sweeney explained to his social media followers that “Factories are diverting leading-edge DRAM capacity to meet AI needs, where data centers are bidding far higher than consumer device makers.” That’s simple economics, where DRAM makers shift production to more profitable lines. It is hard to blame them for making hay while the sun shines, with the boom/bust cycles the industry is prone to. However, the Epic boss’s statement avoids pointing fingers at consumer-facing companies like Amazon or Crucial for engaging in any pricing shenanigans.

We have also recently highlighted the perils of buying PC RAM kits, even as we approach the peak of Black Friday. Great deals on great RAM kits like the G.Skill Trident Z5 Neo RGB Series 64GB (2 x 32GB) 288-Pin PC RAM DDR5 6000 kit on Newegg at $599 ($40 off), are definitely not that great if we step back and look at prices for just a few weeks prior. This same G.Skill Trident Z5 RAM kit was sold for as low as $220 as recently as September 20. That shift will give plenty of folks non-buyer's remorse.

Of course, the AI server building frenzy isn’t just affecting DRAM markets. In recent months, it has also been sucking bargains from consumer SSD-land, and effects are even now being felt on storage solutions like HDDs and high-capacity microSD cards. Where will it end? Some financial soothsayers have gotten more vocal about an AI bubble popping in recent weeks. However, Sweeney doesn’t seem to feel like an AI bubble is anywhere near popping pressure, going by his “several years” prediction.

Amazon seems to be on an investment spree lately. Shortly after penning down a deal to help build the U.S.'s first modular nuclear power reactor, the company is expanding the reach of its worldwide network, in the literal sense. The company has pulled the curtain back on Fastnet, a new intercontinental cable wiring Maryland, U.S., to County Cork, Ireland. The new fiber cable has a capacity of at least 320 Tb/s, or, as Amazon puts it, enough to transfer 12.5 million HD films per second.

The cable should be operational in 2028, though its main task appears to be to act as a backup and load-balancing route. The extra capacity will be welcome for services that rely on AWS wares like CloudFront, Global Accelerator, and presumably, good ol' S3. Future plans include adding another 10 Availability Zones and three more AWS Regions to its growing portfolio of connectivity and data centers.

Amazon remarks that its real-time traffic monitoring system as "complete visibility into every link" in its global network and implements "millions of daily optimizations" so that traffic can always follow the best possible path. Right until its automated DNS orchestration brings everything down anyway, at least. Additionally, Amazon notes that Fastnet was "designed with two strategic landing points that deliver critical route diversity away from traditional cable corridors", probably meaning not wired to the UK or France as most other cross-Atlantic cables.

Quite interestingly, the shore-side sections of the cable are literally armored with two layers of protective steel wire, a wise decision in this day and age. It was not too long ago that some cables were cut in the Red Sea under questionable circumstances. Undersea cables are particularly critical and vulnerable infrastructure, as evidenced by the recent cut and Taiwan's recent sea patrols against telecom shenanigans. Geopolitics, unfortunately, play a part in connection locations, seeing as Vodafone recently chose to wire Europe and Asia with a route that bypasses Russia.

Amazon is creating Community Benefit Funds for Maryland's Eastern Shore and County Cork. The company says it'll work directly with stakeholders to fund local initiatives, including "sustainability and environmental programs, health and well-being services, Science Technology Engineering and Math (STEM) education, future workforce development, economic development and leadership training, inclusion and diversity initiatives, and programs addressing homelessness and hunger."