News
- LinkedIn will hold GPU investment and its compute and storage footprint flat rather than expanding its AI data centers
- The company says it roughly doubled the efficiency of its existing GPUs in six months through accumulated improvements to utilization, model distillation, and workload allocation, not a single breakthrough
- This makes it an exception to the rule under owner Microsoft's umbrella
LinkedIn has revealed it does not plan to spend aggressively on expanding its AI data centers over its next fiscal year - instead keeping GPU investment flat and holding its compute and storage footprint roughly where it is.
The stated reason is not restraint for its own sake: the company says it has found ways to get about twice as much out of its existing GPUs over the past six months and intends to spend that headroom on new features rather than new hardware.
Speaking to Wired, Erran Berger, LinkedIn's engineering CTO, framed the goal as keeping the compute footprint flat or close to it while still shipping more compute-hungry things into production - all while acknowledging this is an unusual position to take publicly right now.
Skipping the norm, both at the industry-level and the parent companyLinkedIn has been wholly owned by Microsoft since its December 2016 acquisition of the company for $26.2 billion.
Despite being part of the software giant, LinkedIn's approach seems vastly different from that of a company that recently saw its shares rally hard as it showcased AI spending finally resulting in measurable returns.
Microsoft also recently closed its own fiscal year, reporting $41 billion in capital expenditure in the most recent quarter alone. It added 31 data centers in that quarter and 88 across the year, and expects to spend more than $50 billion in the current quarter.
This draws an interesting parallel: while one part of Microsoft is deploying capital at a rate few companies in history have matched, a subsidiary with more than a billion users has decided to sit out the year, citing efficiency gains. This makes it a considerably more interesting approach than what would otherwise be a "company shows AI discipline" affair.
While parallels exist, it is prudent to point out that Microsoft's spending is overwhelmingly driven by Azure customer demand, and specifically by capacity it has contracted to supply to OpenAI, rather than by internal product workloads, while LinkedIn's compute is a rounding error against that.
Despite that, it offers an interesting alternative perspective when a large consumer platform can add generative AI features for a year without adding hardware; still, it runs counter to the prevailing assumption that AI features and capacity growth are inseparable.
What makes this possible for LinkedIn?The answer runs back to a decision most coverage treated as an embarrassment at the time.
LinkedIn announced in 2019 that it would migrate its infrastructure onto Azure under a project codenamed Blueshift. In 2022, it quietly shelved that plan. An internal memo at the time cited Azure's own demand pressures and said LinkedIn would focus on scaling its on-premises infrastructure instead; subsequent reporting established that the migration had also run into difficulties because LinkedIn's in-house tooling did not transfer cleanly to Azure.
LinkedIn instead committed to its own data centers in Oregon, Texas, and Virginia.
LinkedIn's CTO for infrastructure, Raghu Hiremagalur, now argues that owning the full stack is precisely what makes this year's plan feasible, because the company can instrument every layer and treat efficiency as a standing investment rather than a one-off cost-cutting exercise.
“I really want to double underscore that for a company of our scale, to say a full year we're going to do this with no incremental storage and compute is no small feat, but it's taken a ton of work to get there," Hiremagalur said.
Reading what, in 2022, was a retreat as a 2026 advantage is self-serving, but it is not obviously wrong. The efficiency work itself is described as an accumulation rather than a breakthrough: better GPU utilization and workload allocation, distilling larger models into smaller ones, and rethinking how work is divided across training, inference, storage, and systems design.
Hiremagalur has also said the cost of serving each query had been climbing steadily while stored data was doubling annually, which he characterized as unsustainable. That is the more revealing framing. The efficiency push reads less like a strategic choice about the AI market and more like a company that looked at its own cost curve and decided it had to bend.
It makes it worth pointing out that LinkedIn has not really solved anything; It is that a platform of this size has publicly said out loud that its compute constraint is deliberate, at a moment when the four largest US hyperscalers have committed to something in the region of $600 billion to $700 billion of capital expenditure for the calendar year between them.
Almost every incentive in the industry currently runs toward announcing capacity rather than efficiency, making it an interesting outlier in a field dominated by daily capex announcements.
The more useful question is whether the approach holds. If it does, the argument that AI product ambition requires proportional growth in hardware gets meaningfully weaker. If it does not, this will read as an efficiency drive that met the hardware demands of a real product roadmap but failed to do so at a time when AI spending is increasingly scrutinized, even as LinkedIn itself recently allowed users to mark what they feel is 'AI slop'.
Did you expect film and TV's Christine Baranski to be an AI overlord in an alternate universe in Stuart Fails to Save the Universe episode 2? No? Me neither.
I wouldn't at all be surprised if this week's episode 3 ups the ante even further. While details are largely being kept under wraps, we can expect Stuart (Kevin Sussman), Denise (Lauren Lapkus), Kripke (John Ross Bowie) and Bert (Brian Posehn) to a more magical realm, with a wizarding world of a comic book store to boot.
Consider me strapped in. So, when does Stuart Fails to Save the Universe episode 3 arrive on HBO Max?
What time can I watch Stuart Fails to Save the Universe episode 3 on HBO Max?For US viewers, Stuart Fails to Save the Universe episode 3 will drop on Thursday, August 6 at 6pm PT/ 9pm ET.
Internationally, you're looking out for these timings:
- US – 6pm PT / 9pm ET
- Canada – 6pm PT / 9pm ET
- UK – Friday, August 7 at 2am BST
- India – Friday, August 7 at 6:30am IST
- Singapore – Friday, August 7 at 9am SGT
- Australia – Friday, August 7 at 11am AEDT
- New Zealand – Friday, August 7 at 12pm NZDT
New episodes of Stuart Fails to Save the Universe will make landfall every Thursday in the US and on Fridays everywhere else. Here are the all-important dates you need to know about:
- Episode 1: out now
- Episode 2: out now
- Episode 3: August 6
- Episode 4: August 13
- Episode 5: August 20
- Episode 6: August 27
- Episode 7: September 3
- Episode 8: September 10
- Episode 9: September 17
- Episode 10: September 24
It’s hardly a surprise that Apple had to increase the prices of its iPads, Macs and other devices last month amid ongoing RAM and storage shortages, and I wouldn’t be surprised if the upcoming iPhones follow suit — but it’s not completely doom and gloom for Apple fans.
Apple’s wearables weren’t impacted by these price hikes, which is good news for anyone looking to upgrade to a watchOS 27-compatible device (as there will only be a select few left) or if you’re keen to get a swanky new watch to track your workouts.
The Apple Watch 11 was already going at a fantastic price of AU$429 during Prime Day last month, but surprise, surprise, it’s ever-so-slightly cheaper now, and it’s not even sale season! The Apple Watch Series 11 with the 42mm chassis and GPS-only option is now AU$2 cheaper, making this the lowest-ever price on Amazon Australia — that’s 37% off in case you were wondering.
This price beats the AU$429 we saw during Prime Day, officially making this 37% discount the best we’ve seen for the Apple Watch Series 11 in Australia. The 42mm is also the only size discounted for the GPS-only version, so you’ll have to get the LTE option if you want the larger 46mm model, which is also discounted to AU$677, or 25% off, but there’s very limited stock.View Deal
Our Apple Watch Series 11 review called it “the most capable and best-looking” mainline Apple Watch yet, thanks to its larger battery capacity and brighter, more power-efficient and more scratch-resistant Always-On Retina LTPO3 OLED display compared to its predecessor.
Apple rates both the 42mm and 46mm models’ battery life at 24 hours of typical use and up to 38 hours in Low Power Mode, with battery capacity up 9% and 11%, respectively. Our reviewer found the claim checks out, with tests getting a full day and a half of battery life with light use, but the Low Power Mode makes it go even longer.
Health tracking is more comprehensive with the Series 11, adding blood-pressure monitoring to the usual arrhythmic heart-rate alerts, ECG, wrist temperature, respiratory rate and cycle tracking. Hearing health and Sleep Score are other notable additions, but these are also available on the cheaper SE 3 and have been rolled out to older models too.
Admittedly, the Apple Watch Series 11 won’t be much of an upgrade over the Series 10 because of the very similar hardware (and watchOS 27 compatibility), but upgrading from the Series 8 or older models, or even the Watch SE and SE 2, will see a significant performance jump and a longer support period.
And at this price, you don’t even need to wait till Black Friday as we don’t see it getting much cheaper than this in a few months’ time.
Patagonia is one of the biggest names in outdoor clothing, and especially impressive when it comes to sustainability — great news if you want to protect those beautiful natural landscapes you're planning on hiking over or camping in this summer.
I'm a big fan of Patagonia's products, but they don't come especially cheap. So I was particularly pleased, when hunting for kit for my next adventure, to uncover a goldmine of Patagonia discounts at Cotswold Outdoor. There are deals on a wide range of kit for every weather eventuality — important if you're planning on spending any time in the UK, where you can never be quite sure if you'll be facing sweltering heat or a torrential downpour.
Below, I've rounded up my pick of the best discounts across the men's and women's ranges, including fleeces, waterproofs, base layers, tees and shorts, with a few accessories thrown in for good measure. Happy exploring!
Outdoor kit for summer adventures Patagonia Men's Boulder Fork Rain Jacket Patagonia Men's Nano-Air Light Hybrid Hoodie Jacket Patagonia Women's Classic Retro-X Fleece Jacket Patagonia Men's Baggies 5" Shorts Patagonia Women's Torrentshell 3l Jacket Patagonia Women's R1 Fleece Jacket Patagonia Women's Classic Retro-X Vest Patagonia Women's Retro Pile Marsupial Fleece Patagonia Women's Triolet Jacket Patagonia Unisex Terrebonne Cap Patagonia Women's Capilene Cool Daily Long Sleeve T-Shirt - Boardshort Logo Patagonia Women's Unity Fitz Easy Cut Responsibili T-Shirt Patagonia Women's Lightweight Synchilla Snap-T Fleece Patagonia Women's Granite Crest 3l Jacket Patagonia Black Hole Duffel Bag - 40l Patagonia Men's Retro Pile Fleece Jacket Patagonia Men's R1 Air Zip Neck Fleece Patagonia Men's Fun Hoggers Shorts Patagonia Men's Corduroy Volley Shorts Patagonia Women's Better Sweater Fleece Jacket- Advanced Full Fibre Broadband from EE offers download speeds of 2.3Gbps and 8Gbps
- Speeds are potentially 222 times faster than standard superfast fibre
- Initially available in Guildford and Woking, expanding as Openreach rolls out its XGS-PON glass fibre network
Following a successful trial in early 2026, EE has launched its new Advanced Full Fibre Broadband plans, advertising download speeds of 2.3Gbps and 8Gbps. This makes EE the first provider to sell access to the Openreach XGS-PON glass fibre network.
The plans are initially only available in the Guildford and Woking areas, two Surrey towns in the London commuter belt, where the glass fibre network is installed and active.
EE is targeting users who rely on video conferencing, remote working, and content upload, and with multiple Internet of Things devices, as well as online gamers and streaming consumers. Speeds are up to 222 times faster than the standard superfast fibre broadband plans.
Two plans for EE’s XGS-PON fibreCustomers can access two plans from EE. The 2.3Gbps Advanced Full Fibre Broadband plan starts at £54.99 per month, while the 8Gbps plan is available from £74.99 a month.
No upload speeds have been stated, nor are they listed on the sign-up page, but they can be expected to be similarly fast.
XGS-PON (10-Gigabit-capable Symmetric Passive Optical Network) is a high speed, high-bandwidth data network standard, currently available in various forms across Europe, North America, and other regions. In the UK, it is used in the nexfibre, CityFibre, and Openreach networks, with EE using the latter to provide these plans.
With streaming speeds supporting 4K and 8K video, these plans are likely to be popular where available, and support for up to 190 devices suggests the plans will include routers suited to IoT and smart home applications.
Will faster fibre improve British broadband?“Today marks a major milestone for EE as we become the first major UK provider to offer broadband speeds of up to 8Gbps on next-generation XGS-PON technology," noted Luciano Oliveira, Director of Product, Home and TV at EE.
"As homes become more connected and customers place greater demands on their broadband, we're investing in the technologies that will power the next generation of digital experiences.”
EE regularly wins recognition as the UK’s most popular network, with its business built on mobile (where its 4G, 5G and 5GSA speeds cover more than 90% of the UK) and an advertising campaign featuring Hollywood actor Kevin Bacon.
Unlike Virgin Media which owns its own fibre network, EE’s domestic and business broadband plans rely on third party infrastructure, mostly provided by Openreach, and due to the differences in connections across the country (and the legacy copper lines that remain in place in some locations), struggles to offer the same speeds for all customers.
This is a problem faced by all providers who sell access to Openreach lines, so EE’s immediate competitors will no doubt be watching with interest.
- UK’s Police National Legal Database (PNLD) breach leaks data of 100k+ criminal justice professionals
- Threat group ExfilSquad claimed responsibility, posting 1.9 GB of stolen records on the dark web and demanding ransom
- PNLD notified NCA and ICO, hired specialists, and confirmed passwords weren’t compromised but contact details exposed
The UK’s Police National Legal Database (PNLD) suffered a cyberattack recently, in which it allegedly lost sensitive data on more than 100,000 criminal justice professionals.
In a short press release, PNLD confirmed the breach, saying it happened over a weekend. The threat actors, which were not named in the announcement, were said to have taken names, organizations, and work email addresses belonging to police officers, staff, government partners, and customers.
The announcement also said the stolen information was already published on the dark web, adding that there is “no evidence to suggest that passwords or other security credentials have been compromised.” How the attackers worked their way in was not disclosed in the announcement.
ExfilSquad takes the blameFollowing the breach, PNLD hired cyber-security specialists, and notified the National Crime Agency, which started their investigation into the incident.
“All affected organizations were contacted in the days following the incident and provided with further information and guidance,” the announcement reads. “The Information Commissioner’s Office (ICO) has also been notified.”
At the same time, threat actors calling themselves ExfilSquad claimed responsibility for the attack, BleepingComputer reported. The group alleges it stole 135,000 contact records, sharing samples to support their claims. They also said they demanded a ransom in exchange for keeping the data safe.
In the dark web post, ExfilSquad said it obtained 1.9 GB of data, which includes information belonging to 114,000 PNLD subscribers and 21,000 Ask the Police users.
‘Ask the Police’ is a public-facing website where users can find answers to hundreds of commonly asked policing or legal questions.
ExfilSquad is a relatively new threat actor that's not known for any major attacks so far. Prior to the PNLD incident, it claimed the attack against Analog Devices, a US semiconductor company.
Artificial intelligence (AI) is reshaping the scale and complexity of data center infrastructure.
Traditional data center facilities were designed around relatively steady CPU workloads and predictable growth in power demand, allowing developers to secure energy supply alongside growing demand, cooling systems based on known and mature technology and infrastructure capacity with a reasonable degree of certainty.
AI workloads, however, demand far more power with greater energy density.
Electricity consumption from data centers has grown at 12% per year over the last five years and expected demand growth, particularly in AI training data centers, is set to drive a substantial increase in power demand.
Meeting this demand, while maintaining efficiency, is pushing developers toward gigawatt-scale data centers and with the timeframe for delivering these facilities rapidly compressing, the ability to deliver new infrastructure efficiently is increasingly critical.
In addition, as the scale of these developments grows, so does the complexity of delivering them. Grid interconnections can delay timelines by years, equipment supply chains are stretched, and projects must meet stringent reliability targets while navigating regulatory, environmental and community requirements that vary by region and country.
For owners and developers, the challenge is no longer simply constructing another data center building. The next generation of AI data centers requires a fully integrated approach across power, cooling, transmission, water, digital systems and long-term operations. Success depends on designing these facilities as resilient, flexible and energy-optimized industrial campuses.
Balancing site trade-offs to unlock faster deliverySite selection is one of the clearest expressions of this dynamic where teams are typically assessing a series of imperfect options, each with its own advantages and constraints. For example, one site may offer lower cost land but lack the existing infrastructure required to support large scale development, while another may provide access to grid power but at a significantly higher cost or with timelines that delay delivery.
In practice, few locations offer everything required, and selecting a site becomes an exercise in understanding what should be prioritized, what can be mitigated, and what must be accepted.
Factors like water availability, land constraints, fiber connectivity, permitting timelines and social license to operate are all deeply important to success. Developers must consider how to optimize within these constraints. Where grid power is unavailable or delayed, for example, off grid or hybrid energy solutions may be introduced.
While these approaches can accelerate delivery, they also bring different capital requirements, financing structures, and operational considerations that must be carefully weighed.
Combining power solutions can accelerate bringing capacity online more efficientlyAs AI workloads drive unprecedented levels of demand, power strategies also require a reassessment against expected scale timelines. Grid supply does offer lower long-term energy costs, stability and resilience advantages eventually but hinges on capacity constraints, and extended interconnection timelines.
In contrast, behind the meter generation, such as gas turbines or reciprocating engines, can be deployed more quickly and provide greater operational control. This, however, comes with higher upfront capital requirements, higher operational costs, fuel dependencies and more complex permitting considerations.
As speed-to-market is a key competitive driver, many large-scale developments are willing to pay a premium for off-grid or hybrid architectures, including battery storage and integration of renewables where accessible.
These systems are coordinated through microgrid controls, allowing operators to manage load variability, maintain resilience through islanding, and optimize overall system performance. The final configuration is shaped by how factors such as time to market, grid availability, resilience, and overall cost evolve.
Rethinking cooling can support high-density AI and optimize when energy is usedWith this increase in power demands comes a corresponding increase in heat generation. The physics and economics of air cooling are struggling to keep pace with the thermal loads generated by AI workloads, forcing a shift toward alternative solutions.
One solution is liquid cooling, which is gaining traction as a more effective way to manage higher heat loads. Transferring heat more efficiently, it enables facilities to operate at the densities required by AI infrastructure. However, it does also introduce new dependencies, particularly around liquid cooling solutions and the infrastructure required to support it.
At the same time, taking a broader view of cooling opens up new opportunities. Cooling systems can be integrated with wider power infrastructure, excess heat can be connected to industrial processes that can utilize it and waste heat from data centers can be repurposed for applications such as district heating, which is already quite common in the Nordics.
Approaching cooling in this way allows developers to design systems that make better use of energy and create additional value through heat reuse and integration with surrounding infrastructure.
Additionally, thermal energy storage gives AI data centers the ability to shift cooling demand away from peak periods by producing chilled water when electricity is cheaper or more available and using it later when loads are highest.
This creates valuable demand response capability, allowing the facility to reduce its grid draw during periods of system stress, lower demand charges and support utility programs without impacting data center operations. In combination with batteries and advanced controls, thermal storage can help stabilize both the data center and the surrounding grid.
Early efforts on permitting can identify the fastest development route and avoid delaysPermitting and regulatory considerations sit alongside these technical decisions, shaping what is possible and how quickly projects can move forward. Requirements vary by region, country and project type, but in all cases, they influence how projects must be designed from the outset.
For example, grid connected developments may be constrained by connection approvals and capacity limits, while sites incorporating on-site generation may require air quality or emissions permits that influence technology choices. Land use restrictions, environmental approvals and community considerations can further shape site layout, development timelines and even overall project viability.
Addressing these requirements early, and in parallel with technical and commercial decision making, is therefore as important as those other factors. When permitting is treated as part of the initial planning process, it allows projects to be structured in a way that is both deliverable and aligned with regulatory expectations from the beginning.
This, in turn, reinforces the need for a coordinated approach across the full range of stakeholders involved. Energy providers, technology companies, developers, regulators and local communities each play a role in shaping outcomes, and the interaction between them becomes a critical factor in how effectively projects can progress.
Having the right expertise in place to connect these elements enables developers to navigate this complexity more effectively, ensuring that decisions made early on are aligned across disciplines. This early alignment helps create a more integrated delivery pathway, reducing friction between project phases and supporting smoother progression from planning through to construction and execution.
Turning AI demand into operational capacity at the speed and scale the market requiresThe importance of this becomes clearer when looking at how these challenges play out in practice. In Texas, for example, early engineering work on a gigawatt scale AI training data center helped define the infrastructure strategy for one of the largest behind the meter energy systems supporting AI workloads.
The project includes 5 GW of gas generation capacity, up to 1.25 GW of solar PV, utility scale battery storage and a microgrid supporting 20 buildings totaling 10 million square feet, each designed for around 250 MW of power demand.
Projects of this scale reflect the sheer pace and ambition of AI demand, but ultimately, success comes down to how effectively that demand is translated into deliverable infrastructure. That means making early decisions that can withstand real world constraints, from power availability and permitting through to long term operational performance.
Bringing these elements together into a coherent strategy, and aligning the stakeholders needed to deliver it, is what will enable projects to move at the speed and scale the market now requires.
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