Dealing with shifting sands is par for the course for cybersecurity practitioners, but what's happening now is different. Indeed, it’s more of a sea change that upends conventional wisdom and business-as-usual approaches to safeguarding an organization. The impact of AI in cybersecurity changes not just the nature of the threats and attack modalities but also adds new challenges, such as dealing with massive spikes in volume.

These challenges are why many across the cybersecurity industry see value in building and propagating a pre-emptive cybersecurity architecture that deviates from a conventionally static and reactive approach. After all, getting ahead of any threat is better than letting it fester – responding only when the damage has taken place. Such an approach may seem like common sense, so why hasn't this notion become more widespread among practitioners?

What is pre-emptive security – and how does it differ from conventional cybersecurity models?

The traditional detect and respond cybersecurity framework has served businesses well for as long as they've tapped into cyberspace. But changes primarily driven by AI are exposing frailties in this model. For example, recent Crowdstrike research shows a plunge to just 29 minutes for the time it takes for an attacker to move from initial compromise to laterally breaking out to other systems. Garner, meanwhile, forecasts there will be a 300% spike in the number of documented vulnerabilities between 2025 and 2030. There's also the question of practitioner burnout, with conventional frameworks relying on overworked humans to investigate security alerts raised after, not before, an intrusion has been detected – and shut off a threat as a matter of urgency.

So what's the alternative? "In simple terms, pre-emptive security architecture means putting security controls directly in the path of an attack so that the attack gets blocked, diverted or contained before any serious damage has happened," explains Mudita Khurana, staff security engineer at Airbnb and cybersecurity expert with a decade of experience in application security, security tooling, vulnerability management and AI security.

"The idea is more architectural than a particular security product or control (thus the word architecture in the term itself)," she continues, adding: "It is a way of connecting security controls across different layers (like users, applications, systems, data, etc.) so that even if one layer is compromised, the blast radius remains limited because of other existing controls."

This is an environment in which attacks are far more likely to "fail on contact" or never really get a grip on systems, Steven Coppola, security engineer at BARR Advisory, tells ITPro. That is, instead of an architecture that relies on somebody noticing the attack while it's happening.

"I love analogies — for pre-emptive security architectures, I use a house," he says. "Detect and respond is akin to a good alarm and a fast call to the police department. A pre-emptive strategy is thinking ahead about where the doors will go, what they are made of, and whether the valuables need to be in the house at all."

What does pre-emptive security entail?

Framing pre-emptive security as a list of components or a single blueprint misses the mark, experts tell ITPro. Nor is this architecture a product that you can buy. Gartner's managing vice president Carl Manion distilled the trend into three elements, including 'deceive', 'disrupt', and 'deny' – and also warned product leaders that they may face losses in market share if they fail to embrace this security architecture in the services they provide to organizations.

His first thesis is to deny hackers entry with a preemptive shield, including advanced obfuscation technologies. Next, organizations should deceive bad actors using decoys, misdirection, and illusion to throw them off the scent. Finally, organizations should anticipate emerging threats and prepare to disrupt these rather than waiting for the attack to materialize and engaging in damage control after the fact.

"I believe that there are many other security controls that can support these three areas that Gartner has set out," adds Khurana.

"For example, zero trust access controls and separation between systems can limit the reach of an attack. Secure development checks can prevent vulnerable code from being deployed in the first place, and encryption can protect stored data so even if someone steals files, they can't read them without the encryption key."

She also noted that confidential computing – in which data is processed inside a separate hardware-protected area with only approved code running within it – can add another layer of defense.

Coppola adds: "Confidential computing closes the gap in the middle, which is data in use. The workload runs inside a hardware-protected enclave — essentially a walled-off section of the processor—so the data stays encrypted even while it is being processed. Even the cloud provider or someone with admin rights on the host cannot read it."

Even if the attacker reaches the underlying server or cloud infrastructure, there is no immediate access to the data, which should be protected. However, it must also be combined with proper access controls and sufficient application security.

How can businesses implement pre-emptive security?

Chris Bailey, head of innovation at systems integrator Jigsaw24, tells ITPro that any plans to implement pre-emptive security should be shaped around a clear idea of what your organization is protecting, how sensitive the data is, and the internal policies.

"Start by identifying the data and systems you’re trying to protect. From there, map out where the vulnerabilities lie, how people access the environment, and what the worst-case scenario would look like," he advises. "Once that is clear, it is important to establish clear policies around access, roles and devices. Ask who genuinely needs access to a particular system or data, what they need to be able to do, and whether that access can be restricted further."

At this stage, Khurana explains that it's important first to understand how an attacker may reach the services or data sources, a process similar to threat modeling, and then where the attacks can be denied, deceived, or disrupted. "This is where the team takes the threats and paths from the previous activity and understands where the controls can sit. If a path is found [to be] unnecessary, it should be removed. For legitimate paths, the principle of least privilege should be adopted."

After putting controls into place, organizations should test them under realistic conditions to make sure they work well, she adds. This may involve penetration testing – which Bailey adds is important because it gives businesses the chance to identify weaknesses before an attacker does – as well as continuous auditing and monitoring.

Organizations should also carry out attack exercises to see if the controls set in place actually stop the intended attack path, and that, importantly, things can be quickly rolled back if the attacks disrupt important business processes.

It's also worth considering escalating the approach. If pre-emptive security works for one service, teams may feel more confident in applying this to more critical services.

While implementing a new approach to safeguarding your organization, it's also important to remember not to simply ditch or replace old techniques. Coppola tells ITPro that pre-emptive controls can make alerts more meaningful – but don't replace them. Then, when it comes to measuring attack paths, he says: "The question at the end of the quarter should be: what can an attacker with stolen credentials reach now versus 90 days ago? If the answer isn’t shrinking, the program in place is not working, no matter how many products were deployed."

The Scottish government has agreed to tighten up the rules on new hyperscale data centers, but has stopped short of halting their development altogether.

MSPs voted down a motion brought forward by the Scottish Greens to introduce a complete moratorium, but said the government would only approve applications for new data centers with a capacity of over 50MW if they have undergone a full Environmental Impact Assessment (EIA).

The new rules come into force today.

"There are considerable benefits for all parties where the need for EIA is identified and planned for at the earliest possible stage, when there is greatest scope to integrate mitigation measures to minimize impacts from the outset in designing green data centers which support sustainable infrastructure growth," said chief planner Fiona Simpson in a letter announcing the decision.

"Embedding the consideration of environmental factors from the outset can also help minimize the risk of delay at later stages and can support a smoother passage of proposals through the planning process."

Meanwhile, until national planning guidance has been formalized – probably early next year – no decisions on planning for data center applications will be made.

The Scottish Greens had been calling for a full moratorium on new data centers, and MSP Patrick Harvie has expressed his disappointment at the decision.

"This debate was a chance for MSPs to reassure local communities and to put the national interest ahead of the interests of big business, but they have done the opposite," he said. "Instead they have chosen to muddy the waters and thrown up more questions than answers."

There are currently dozens of active planning applications for new data centers across the country, with decisions due for at least two by the end of this year. One, in Fife, is expected to be the second largest in the world.

The country's a desirable location, thanks to its leadership in renewable energy and the existence of former industrial sites with access to existing electricity infrastructure.

However, the Greens claimed that if just the first 24 of these active applications are approved, they would consume one-and-a-half times as much electricity as the whole of Scotland currently uses at peak times – between 4.5 GW and 6GW.

Meanwhile, current cooling techniques require large amounts of water, and people living near large data centers report high levels of noise.

But, said public finance minister Hannah Mary Goodlad, "We must balance the economic and employment interests in developing data centres with national energy, climate and community wealth building ambitions, which are vital to our future prosperity, as well as the potential impact on local communities."

The Green Party has similar ambitions to halt data center development across the whole of the UK. However, last week, Prime Minister Andy Burnham ruled out that idea, saying that data centers pulled in other investment and that AI Growth Zones were an important vehicle for economic growth.

Two years ago, the government classified data centers as critical national infrastructure, on the same footing as water, energy, and emergency services systems, and allowing some projects to be fast-tracked.

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Space-poor SMBs will love Qnap's TS-432XeU as it's one of the smallest rackmount NAS appliances around. Its standard-width 1U chassis measures a mere 292mm deep, so it'll slip easily into a small wall box or a standard two-post data cabinet

Stepping up as a lower-cost alternative to Qnap's TS-435XeU, it employs the same super-slim chassis but drops the two internal M.2 NVMe SSD slots and supplants the 2.2GHz quad-core Marvell CN913X CPU with a 1.7GHz quad-core Annapurna Alpine chip. Networking is slightly diminished too, as it sports the same dual 2.5GbE multi-Gig ports but halves the 10GbE SFP+ port count to one.

The appliance presents four hot-swap LFF/SFF bays at the front, and it's possible to expand capacity outside the box. The rear USB-A 3.2 Gen 1 ports can be used to add up to two of Qnap's tower or rack expansion units, and we suggest the TR-004U model as this 4-bay 1U rack shelf is the same depth as the appliance.

Qnap TS-432XeU review: Build quality

The TS-435XeU is well built with a solid steel chassis and sturdy metal drive carriers. The appliance includes 4GB of DDR4 SODIMM memory, which can be expanded to 16GB – half that of the TS-435XeU.

As you'd expect with such a small chassis, there isn't a lot of room inside, but the memory slot is located on the top of the motherboard and easily accessible. As with the TS-435XeU, there's only one slot, so any upgrades will require the base memory to be ditched.

Three cooling fans are arranged across the rear panel, with one drawing air over the CPU's heatsink via a small plastic shroud. During testing, we found the appliance to be whisper-quiet, making it a great choice for small offices that value their tranquility.

Qnap TS-432XeU review: Deployment and limits

For testing, we fitted four 4TB Seagate IronWolf NAS-specific HDDs and used Qnap's Qfinder utility to discover the appliance on our lab network. It provided quick access to the web browser setup wizard, where we left it to load the latest QTS 5.2 OS.

Note that Qnap's QuTS OS is off the menu, as this is only supported on Intel- and AMD-powered appliances and not ARM-based ones. QTS is a more nimble OS that doesn't have such a high demand for memory and is designed for entry-level NAS appliances.

QuTS provides superior data protection features such as ZFS copy-on-write, end-to-end checksums, and WORM (write once read many) policies for NAS shares. However, its data deduplication feature will only be enabled when you have at least 16GB of memory.

There are also software limitations, as Qnap's ARM-based appliances can't run high-profile apps such as the Virtualization Station and Hyper Data Protector. That said, we counted 79 apps available for this appliance, which include key data protection apps.

Qnap TS-432XeU review: QTS data protection

We used the Storage & Snapshots app to create a RAID5 array which also offers facilities for running manual and scheduled snapshots of NAS shares and iSCSI LUNs. QTS can manage up to 256 snapshots, with a limit of 64 per volume or LUN and Qnap's snapshot technology supports standard EXT4 file systems, so it doesn't require you to choose between this and BTRFS when creating volumes.

The licence-free Hybrid Backup Sync 3 (HBS 3) app is designed to protect your NAS data, helps you create a 3-2-1 backup strategy in only four steps, and uses Storage Spaces to define multiple backup destinations. These can be local repositories on the appliance, remote locations such another Qnap NAS and Rsync, FTP or CIFS/SMB servers, and cloud storage with Qnap supporting no less than forty providers, including its own myQNAPcloud service.

HBS 3 can run data integrity checks when backups have completed to ensure they can be restored, and file version management provides extra protection against ransomware. HBS 3 also offers one-way and two-way syncing services between NAS source folders and destinations such as remote Rsync, FTP and CIFS/SMB servers or any supported cloud provider.

QTS can boost performance for cloud storage as the HybridMount app uses standard file protocols to mount storage from providers such as Amazon S3, Dropbox, OneDrive and Google Drive as NAS shares and assign a local cache to them. You get perpetual licences for two providers included with extra ones costing £15 per year.

Alternatively, you can use the VJBOD Cloud app to present cloud storage as local block-based NAS or iSCSI volumes. One perpetual provider licence is included, with more also costing £15 per year.

Qnap TS-432XeU review: 2.5GbE and 10GbE performance

With the appliance hooked up over 2.5GbE to our Zyxel XMG1930-30HP multi-Gig switch, we mapped a share to a Dell Windows Server 2025 host on the 10GbE LAN. Performance is good with Iometer reporting raw sequential read and write speeds of 2.3Gbits/sec and 2.2Gbits/sec.

Real world speeds were in the same ball park, with 25GB file copies between the server and appliance averaging read and write speeds of 2.3Gbits/sec and 2.1Gbits/sec. Our backup copy test using a 22.4GB folder with 10,500 small files delivered 1.1Gbits/sec.

We moved to 10GbE by connecting the appliance using a DAC (direct access cable) to the switch's 10GbE SFP+ port. Running Iometer on the share again returned sequential read and write rates of 8.6Gbits/sec and 5.6Gbits/sec. Our real world large file copy delivered sustained read and write speeds of 3.8Gbits/sec and 2.9Gbits/sec while our backup test averaged 1.7Gbits/sec.

Over 2.5GbE, the TS-432XeU matches the TS-435XeU, but Iometer NAS read and write speeds over 10GbE are down by 4.6% and 22%, respectively. Even so, that's nothing to be sniffed at and will easily be enough for the target market of small businesses.

Qnap TS-432XeU review: Is it worth it?

The TS-432XeU is an affordable choice for small businesses with it listed on LambdaTek for £617 excluding VAT. The ARM CPU does have some limitations but Qnap's QTS OS delivers plenty of data protection apps, performance over 2.5GbE and 10GbE is quite respectable, and its compact dimensions make it a great choice where office space is at a premium.

Qnap TS-432XeU specifications

As AI initiatives scale, managed service providers are under pressure to help customers deploy new capabilities quickly while enhancing quality and controlling costs. But behind the scenes, engineers are running into a quiet bottleneck—power delivery at a reasonable cost.

Modern AI compute is increasingly limited by how quickly and efficiently engineers can deliver power to processors, accelerators and memory. For Managed Service Providers (MSPs), that makes power delivery more than an engineering issue. It becomes a strategic consideration that affects capacity planning, operating costs, cooling requirements and the ability to scale customer environments reliably. The challenge is not having enough watts—it’s getting those watts to the right place, with minimal latency, without excess heat, all while managing operational costs to keep revenue in check.

MSPs that overcome the AI power problem will be the ones that treat power not as an afterthought, but as a foundational pillar of performance, efficiency and scalability.

Why power density matters to MSP service delivery

A decade ago, a 200 W GPU was considered massive. Today, single AI accelerators regularly exceed 600 W, and full server boards can draw 5–10 kW.

That power needs to be delivered across a variety of distances, to multiple voltage domains, and respond in nanoseconds as workloads shift. Every bit of distance, impedance or delay creates loss, droop, and heat, hindering scalability and delivery service.

In short: as computing density grows, power density becomes the new productivity limiter.

This shift has also changed how engineers think about system-level design. Instead of optimizing only compute capability, architects must now consider the electrical and thermal realities that shape what is actually achievable.

High-performance silicon is only as good as the infrastructure that supports it, and many organizations are discovering that traditional power-delivery networks were never designed for bursty, AI-driven workloads. For customers and MSPs alike, these workloads demand not just more power, but more responsiveness and stability than previous generations ever required.

Leveraging power innovation to build customer trust

One emerging approach to this power delivery challenge is moving voltage regulation closer to—or even beneath—the silicon itself. Integrated voltage regulation (IVR) technologies are designed to deliver cleaner, faster, and more localized power that scales with chip demands.

For MSPs, these advances can support more predictable performance, better energy efficiency, and infrastructure designs that are easier to scale across customer deployments. Some of the positive outcomes associated with these advanced IVR solutions include:

• Higher current density compared with traditional discrete regulators, enabling denser AI infrastructure and serving modern AI workloads.
• Efficiency improvements by reducing long power-delivery paths and associated transmission losses.
• Low thermal resistance to dissipate power losses in a constrained environment, supporting more reliable performance and reduced cooling requirements.

These improvements are not incremental gains—they enable entire system architectures that were previously constrained by power delivery. This means a more profitable, effective, and efficient infrastructure that MSPs and customers can trust.

Building delivery-driven AI infrastructure

With IVRs and other localized power-delivery technologies, GPUs, CPUs, and custom AI accelerators can sustain higher operating frequencies under dynamic workloads by reducing impedance and minimizing voltage droop. This translates to:

• Data centers can deliver more compute per watt and reduce cooling overhead, improving the economics of managed AI services.
• Edge and distributed deployments can operate more efficiently, helping MSPs support AI use cases closer to customer environments.
• Compact, modular designs can simplify scaling and make it easier to standardize AI infrastructure across multiple customer deployments.

Instead of designing around power constraints, system architects can now design with power as an enabler of AI services.

Looking ahead

As more businesses and MSPs move towards AI to automate workflows and streamline services, power delivery for scaling AI must evolve too. The next wave of competitive advantage will not come only from smarter models or faster chips; it will also come from power architectures that help providers deliver performance, efficiency and control costs-- at scale.

For MSPs, the opportunity is clear: now is the time to reassess whether your AI infrastructure strategy can support the performance, efficiency and scalability your customers will expect next.

Providers that invest early in smarter power-delivery architectures will be better positioned to differentiate their services, protect margins and lead customers confidently into the next phase of AI adoption.

Google is set to expand its footprint in Finland as part of a €13 billion infrastructure investment in data centers focused on the towns of Hamina, Kajaani, Muhos and Vaala.

During the initial construction phase, which will run through 2027 and 2028, the expansion is expected to contribute €3.6 billion to Finland's GDP and support 37,000 jobs nationwide.

Once operational, Google said the facilities are projected to support 7,000 jobs annually, spanning on-site facility and technical roles, as well as roles in downstream businesses.

“This is Google’s largest single investment in Europe and a testament to Finland’s leadership in responsibly building AI infrastructure – demonstrating what is possible through strong partnerships to create long-term community value and world-class environmental sustainability,” the company said.

Running parallel to this, Google will also invest in energy infrastructure designed to support data center build-outs. T

his includes sa life extension power purchase agreement for the Loviisa nuclear power plant with Nordic energy provider Fortum, as well as upgrades to grid capacity and renewable energy.

Google said it will support two onshore wind projects, with development led by Valorem and Suomen Hyötytuuli. This will bring the total amount of onshore wind energy contracted by the firm to 629 MW.

Google builds on long-standing ties

Google already has a strong presence in Finland, having opened its Hamina data center 17 years ago. The location has since been transformed into a “digital data hub”, according to Google, and forms part of the company’s global network of 43 cloud regions.

Several organizations, including Nokia, Elisa, and the City of Hamina rely on the data center to support operations.

Google said the Hamina site and Finland more broadly serve as a “model for responsible data center development”.

Indeed, the Hamina data center’s liquid cooling system uses sea water sourced from the Bay of Finland. It also has an offsite heat recovery facility, which is used to warm nearby households and businesses.

Finland in the spotlight

Finland has rapidly grown to become one of Europe’s leading data center infrastructure hubs. Research from Savills in August found the Finnish data center market has grown by 57% since 2024, making it one of the fastest-growing regions both in Europe and globally.

Google isn’t the only tech giant capitalizing on the Finnish market boom. A host of companies including Microsoft and TikTok have made significant investment pledges.

Microsoft, for example, signed a preliminary agreement to acquire 190 hectares of land in Vaasa and Mustasaari in June this year for data center development. Microsoft is also developing its first data center region in Southern Finland.

A key factor behind Microsoft’s expansion lies in the region’s existing industrial capabilities, Microsoft said at the time. The land it acquired is located in the GigaVaasa industrial zone.

TikTok, meanwhile, announced plans last year to invest €1 billion in its Kouvola data center, which is expected to have a capacity of 150MW.

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Qualcomm has signed a major deal with Amazon, under which it will supply customized chips and networking hardware for Amazon data centers.

The 'multi-generation' collaboration covers customized silicon for Amazon's large-scale AI data centers, bringing together the hyperscaler’s AI infrastructure and Qualcomm's power-efficient processing, silicon design, and system-level integration.

The deal will also cover high-performance optical connectivity solutions specifically designed to support the growing scale and bandwidth demands of AI infrastructure, with speeds extending up to 1.6T and eventually beyond.

This work will be based on Qualcomm Technologies’ SerDes and optical DSP technologies.

"As AI demand accelerates, data center infrastructure will require advances in both computing and connectivity to deliver greater performance with more efficiency,” said Cristiano Amon, president and CEO of Qualcomm.

“Qualcomm is pleased to work with AWS on customized silicon and connectivity solutions, bringing decades of leadership in advanced processing and power-efficient compute, to deliver breakthrough performance and enable the next generation of AI infrastructure.”

As part of the deal, Qualcomm plans to expand its use of AWS AI infrastructure, including Amazon Bedrock, for its own electronic design automation (EDA) workloads, hoping to cut the time of chip design cycles.

The deal forms part of a broader announcement that will see Qualcomm issue warrants to acquire 25 million shares in Amazon for a total investment of $4 billion.

The issuance of some shares is contingent on Amazon purchasing up to $60 billion worth of Qualcomm products.

“This collaboration with Qualcomm Technologies builds on a strong foundation of partnership and reflects our shared commitment to pushing the boundaries of what’s possible,” said Prasad Kalyanaraman, vice president at AWS.

“By working together on customized silicon and advanced connectivity, we’re delivering more performant, efficient, and cost-effective infrastructure for our customers.”

Qualcomm eyes market gains

Qualcomm has hopes of challenging Nvidia's dominant position in the AI data center market. The latter currently holds between 80% and 90%, according to figures from Silicon Analysts, although the figure is predicted to fall to 75% this year. AMD is in second place.

Earlier this summer, Qualcomm updated its Dragonfly A1000 CPUs, Qualcomm High Bandwidth Compute (HBC), and its AI300 inference accelerator. All are engineered to maximize performance per watt and token throughput at lower total cost of ownership (TCO), the company said.

The company recently named Meta as its first customer, with production expected to begin in 2028 and to continue as Meta's server fleet expands. It has also, along with Nvidia and AMD, has signed a memorandum to supply the EU’s AI gigafactories.

Optical networking, meanwhile, is a major element of Amazon’s data center strategy, with the firm buying billions of dollars worth of hardware from fiber-optic cable manufacturer Corning earlier this year.

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A new direct-to-device satellite service for businesses has been launched by O2, with the network provider saying it will extend connectivity beyond the reach of its traditional network.

O2 Satellite for Business, as the service is called, is the first service of its kind in the UK, the company claimed. Jo Bertram, CEO of O2 Business, said the product aims to bolster support for organizations working in remote locations or with distributed workforces on the move.

“Businesses increasingly rely on always-on connectivity, but work doesn’t stop where mobile coverage ends,” she said.

“O2 Satellite extends connectivity beyond the reach of traditional mobile networks, helping organizations stay connected in more places through the smartphones they already use every day.”

According to O2, the service will automatically engage when customers are no longer connected to its mobile network and doesn’t require any additional satellite hardware or applications.

Services supported through the offering include messaging, maps, weather, and location sharing through apps such as WhatsApp, Messenger, Google Maps, and Apple Maps.

The service is available on Apple, Google, and Samsung smartphones and O2 said the new network will extend its landmass coverage from 89% to 95% of the UK.

Reliable connectivity

The move by O2 aims to bolster connectivity for the significant number of UK businesses operating in rural or remote areas.

One in five businesses in England are based in rural areas. O2 said reliable connectivity is often a challenge for these firms, which has a direct impact on productivity.

The technology that underpins O2 Satellite for Business has already been used in real-world conditions, the company said.

During wildfires in Derbyshire in late June, it provided 100 satellite-enabled phones to Derbyshire Fire and Rescue Service to support operations in areas without traditional mobile coverage.

“The phones connected directly to satellites, helping teams on the ground stay in contact with colleagues, share updates, and access important information across the affected area,” O2 said.

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In recent years, there have been tremendous technology developments and industry shifts. The rapid adoption of generative AI has been a significant disruptor, forcing both vendors and the channel to re-evaluate their offerings and take a more agile stance to expand market reach and boost operational efficiency.

New business opportunities are being driven by trends such as AI storage integration, cloud sovereignty, and the demand for proactive cybersecurity measures. However, given this rapid pace of change, with some partners still focused on legacy resale and non-proactive, break-fix models, are resellers really in a prime position for success?

The biggest trap channel partners could fall into is failing to recognise new opportunities and respond with agility. With AI-driven workflows, regulations, and shifting buyer expectations, those who stick to old approaches risk being sidelined and seeing their margins shrink.

Key industry and technology shifts

Major trends reshaping the channel landscape include the integration of AI with storage infrastructures, the rise of sovereign clouds for data compliance, and the development of advanced data protection platforms.

Another area we see skyrocketing is the edge-cloud-hybrid ecosystem. Organizations are seeking to operate across edge, on-premises, and public/hybrid cloud with seamless data mobility and unified management. With multi-cloud and hybrid architectures becoming the norm, especially for backup and disaster recovery (DR), scalability and cost control remain paramount issues for organizations.

Equally apparent is the need for consistent security and compliance measures across these environments, along with ensuring flexibility to prevent reliance on a single vendor. Data sovereignty, which requires that data be governed by the laws and regulations of the country where it is stored or processed, marks a shift away from generic cloud-first strategies towards more localized management, hybrid-cloud arrangements, and thorough sovereignty evaluations.

With organizations struggling to balance increasing data volumes against rising infrastructure costs, there is also growth in smart multi-tiered storage. This includes storage analytics and tools for real-time monitoring and automated management to enable visibility across different tiers. With the capability to assign "hot" or frequently accessed data to high-speed, premium storage and "cold" or archival data to more affordable options, organizations can avoid the costly mistake of over-provisioning with high-performance drives.

Meanwhile, legacy hardware sales and on-prem-only solutions will continue their sharp decline, falling short for partners who rely on them. The shift to cloud and as-a-service models, combined with economic pressures, has made this a low-margin area.

Channel opportunities for business growth

As global data volumes near 200 zettabytes and AI-native technologies become increasingly sophisticated, customers are demanding comprehensive services that extend beyond traditional project-based hardware or standard cybersecurity offerings.

Customers are seeking partners who can provide AI-ready, sovereign, and resilient data protection that works seamlessly across on-prem, edge, and cloud. With this in mind, key growth areas for the channel are:

AI-driven services and automation

Cyber-resilient data storage solutions and integrated, AI-powered security platforms are driving significant growth opportunities for channel partners. These companies can tap into new opportunities by providing services focused on AI integration that extend beyond just implementing AI tools. These offerings may include automating IT operations, AI monitoring, and intelligence in data protection.

The recent geopolitical shift created by the U.S. government’s unprecedented export-control directive - which restricts global access to the most powerful AI models developed by Anthropic (the prominent US-based AI research and safety company) - has created immediate channel opportunities. If Value-added Resellers (VARs) and Managed Service Providers (MSPs) pivot quickly, they can offer services such as sovereign AI consulting (providing data residency, technological autonomy, and jurisdictional control), regional cloud hosting with sovereign cloud space, as well as advisory services on cross-border regulatory compliance, such as offering risk mitigation audits.

Hybrid-managed offerings

Customers are increasingly adopting multi-tiered architectures that integrate edge computing, on-premises systems, and public cloud solutions. This shift calls for seamless data mobility, unified management, and real-time analytics to ensure efficiency. For channel partners, this trend opens new opportunities for designing, managing, and securing these distributed, complex environments.

The ubiquity of cloud has highlighted a challenge: the importance of operational visibility. Managing data across multiple environments without a unified dashboard has introduced unnecessary complexity and increased the risk of security blind spots. The lack of visibility across edge-cloud-hybrid ecosystems presents an opportunity for partners to act as trusted advisors while also delivering unified management and monitoring across fragmented infrastructures.

Cybersecurity and compliance-as-a-service

Cybersecurity continues to be the foremost growth driver for the channel. Providers are already broadening their offerings to counter the rising frequency of cyberattacks, introducing services such as managed detection and response (MDR), ransomware protection strategies, and compliance solutions. With the emergence of AI-driven threats, there is a growing demand for integrated cyber recovery solutions, presenting lucrative opportunities for partners capable of delivering robust managed security services.

Many opportunities will come from customers who are ready to adopt AI-powered infrastructures and hybrid governance models (the framework that enables managing operations across both physical offices and remote endpoints) in response to data sovereignty pressures. As organizations incorporate AI into their operations, they must address increased security vulnerabilities while ensuring their workloads and data adhere to relevant jurisdictional and privacy laws. Therefore, partners who can offer high-value consulting, data auditing, automation, and managed services, as well as architecting hybrid or sovereign cloud environments, will see the most growth.

Potential challenges

Obstacles to capitalizing on opportunities in the AI era include a lack of sufficient expertise (the talent gap) and longer sales cycles. The channel could struggle to find people who can manage hybrid ecosystems, navigate compliance, and handle the complexity of AI-driven environments. This is an area where vendors can really add value, especially in helping partners bridge that gap with hands-on training, workshops, and webinars.

Another hurdle is that most customers, especially outside the enterprise segment, will face tighter budgets and more cautious spending, which could slow adoption and delay new projects.

There could also be setbacks resulting from vendor complexity, too many tools, programs, and licensing models, which could further slow down the momentum if not simplified early in the year.

The path to success

To embrace these new opportunities, the channel must anticipate changes among customers, vendors, and markets. Buying technology is no longer what customers are looking for; they want solutions that deliver resilience, compliance, and sustainability. The channel must move from being generalists to specialists, guiding and supporting customers and accelerating the shift to service-led models in areas like AI and cybersecurity (such as delivering Backup-as-a-Service and cyber recovery).

Meanwhile, vendors must support this shift by making things easier: offering flexible licensing, providing specialised training and integration tools, and driving closer collaboration with partners. Vendors will also be expected to simplify hybrid and sovereign architectures, provide automation tools, and jointly develop go-to-market initiatives that help partners scale their operations.

The current industry transformation presents a huge opportunity for channel businesses to shift from just reselling to delivering high-value, integrated solutions. As the rise of AI-native and autonomous storage creates a demand for consulting and managed services, clients will most definitely need expert guidance to implement and manage these complex systems.

More HPE networking gear is going into Oracle data centers globally, in an expansion of the companies' existing collaboration.

The expanded deal includes a multi-year deployment of HPE Juniper Networking PTX and MX routing platforms, and QFX and EX switching platforms across Oracle data centers around the world, along with multi-year networking support services.

"Together, we are advancing a high-performance networking architecture that helps Oracle address the traffic management, power, space, and operational requirements of increasingly large AI environments,” said Rami Rahim, executive vice president, president and general manager, networking, HPE.

As things stand, HPE Juniper Networking routing and switching platforms are used to support key elements of Oracle Cloud Infrastructure’s (OCI) data center and edge networks. MX and PTX routers support portions of the edge network, while QFX switches power multiple layers of the regional data center fabric.

However, scale, performance, and availability are becoming increasingly important as OCI expands its AI superclusters. Large GPU clusters place significant demands on bandwidth, latency, congestion management, and fault recovery, HPE said.

Meanwhile, Oracle’s regional and edge networks need to scale to connect clusters with services, customers, and external networks worldwide.

The latest QFX switches will provide the high-density connectivity, dynamic load balancing, and advanced congestion management required for large RoCEv2 (RDMA over Converged Ethernet version 2) AI backend networks.

A common HPE Juniper Networking foundation across these domains helps simplify engineering and operations while enabling each environment to be optimized for its distinct requirements, Oracle said..

“Building AI infrastructure at Oracle’s scale requires an integrated ecosystem where every layer of the stack works together,” said Mahesh Thiagarajan, executive vice president, Oracle Cloud Infrastructure.

“HPE Juniper Networking has been an important part of our cloud architecture and now plays a key role in our AI clusters, regional data centers, and edge networks with the scale, performance, and reliability our customers expect.”

HPE & Oracle eye network visibility

To help OCI keep on top of performance and resiliency as its AI infrastructure scales, the two firms are also collaborating on intelligent telemetry, aimed at delivering deeper visibility into device and fabric behavior.

This should speed up the detection of queue buildup, packet loss, traffic imbalances, and component degradation, to help OCI resolve issues faster and minimize their impact on GPU utilization and network availability.

“Every microsecond matters to AI models and agents,” said Praveen Jain, senior vice president and general manager, data center and AI networking, HPE.

“Through high-performance networking and our work with Oracle on deeper fabric visibility, we are helping OCI detect bottlenecks faster, maintain network availability, and improve the efficiency of AI infrastructure.”

As part of the agreement, HPE has issued Oracle warrants to purchase shares of HPE common stock.

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IBM is opening up its most complex quantum processor to researchers, claiming it can solve quantum advantage-level problems ten times faster than its current best model and with no loss in accuracy.

With 120 programmable qubits and a new high-speed qubit reset architecture, Nighthawk r2 is capable of executing over 100,000 circuits per second – 25 times the circuit throughput of IBM’s existing Quantum Heron fleet.

It's already demonstrated accurate observable estimation on circuits containing more than 7,500 gates, according to the firm.

The increase in speed comes thanks to Nighthawk r2's ability to address a longstanding bottleneck in quantum computing: reducing the time needed for qubits to 'reset' between computations.

Previous generations of IBM Quantum processors, including Heron, have managed the reset through a technique powered by dynamic circuits and called conditional reset.

The processor measures the state of a qubit and, if it's found to be in the |1⟩ state and applies a bit flip gate – specifically a π-pulse – that flips the qubit back to the ground state.

However, this technique is limited by the accuracy of measurement, and can't reset qubits that have leaked outside the computational state, meaning a delay of hundreds of microseconds between circuit executions.

Nighthawk r2, instead, uses a dissipative reset gadget, in which each programmable qubit is linked through a high-dynamic-range tunable coupler to a cold environment that draws it back to its ground state on demand.

Activating that coupler pulls a qubit’s effective T1 – the measure of how long it holds its energy – from a median of about 200 microseconds down to roughly 25 nanoseconds, enabling significantly faster high-quality reset.

This active qubit reset leaves neighboring qubits undisturbed while reducing idle time between circuit runs to as little as a single microsecond.

IBM hails Nighthawk compute gains

In practice, IBM said these gains are most noticeable in the large-scale, repetitive workloads that are common in the research community.

"Nighthawk r2’s improvements in speed and quality are already enabling exciting demonstrations of its quantum computational capability," said the firm.

"For example, following recent demonstrations of quantum advantage through trusted quantum computation, researchers used Nighthawk r2 to perform the doped Clifford sampling advantage experiments originally developed in a collaboration between UChicago and IBM."

IBM also cited other advantages of the new processor, claiming it maintains the scale of its r1 predecessor while delivering targeted improvements in quality.

Because the new high-speed reset capability actively cools the qubit to its ground state, it cuts initialization error across the device by about 25 times, making for more accurate results.

At the same time, it keeps up with the gate fidelity of Heron, even while operating at much higher speeds, and performs individual qubit reset without degrading the performance of the qubit’s neighbors.

"Whether you’re scaling application research, exploring the power of dynamic circuits, or advancing the latest quantum error correction research, this new system will help you do more quantum computing, faster," promised IBM.

"Ultimately, the most important thing about a quantum computer isn’t the number of qubits or components; it’s the amount of useful computation the system can deliver."

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French infrastructure provider Bull has been selected to lead development of the Lumi-AI supercomputer in a €387.8 million ($449.7 million) deal.

The procurement contract from the EuroHPC Joint Undertaking (EuroHPC JU) will see the next-generation supercomputer hosted at CSC’s data center in Kajaani, Finland.

Under the terms of the deal, the new system will be hosted by the Lumi AI Factory consortium. The consortium launched in April 2025 with the aim of bolstering European HPC and AI infrastructure capabilities.

Anders Jensen, executive director of EuroHPC, said this marks the sixth AI Factory procurement contract since the launch of the scheme.

“Together, these systems will form a powerful European AI infrastructure ecosystem, enabling SMEs and startups to access world-class supercomputing resources, unlock the full potential of AI, and drive innovation, growth, and technological leadership across Europe,” Jensen commented.

Funding for the system will be shared equally between EuroHPC and the Lumi AI Factory consortium.

Under the hood of the Lumi-AI supercomputer

The Lumi-AI supercomputer will be based on the BullSequana XH3500 architecture and powered by AMD Instinct MI430X GPUs and 6th Gen AMD Epyc 256-core processors.

The BullSequana XH3500 platform is designed specifically for “advanced AI workloads”, the company said in a statement.

All told, EuroHPC said the system will deliver a “tenfold increase” in AI capacity and nearly double the high-performance computing (HPC) capability of the existing Lumi supercomputer, also hosted at the Kajaani site.

IBM will also contribute to development of the AI supercomputer, providing storage solutions based on IBM Storage Scale. Nokia, meanwhile, will lead on networking capabilities, leveraging Bull’s BXI interconnect technology for supercomputers and AI HPC workloads.

According to the Lumi consortium, the inclusion of additional compute and storage resources will play a key role in “maximizing” the overall performance and cross-functionality of the system.

“A key differentiator is its multi-tenant architecture, designed to meet the needs of diverse AI communities, combined with extensive API-based access to support modern development environments,” the company said.

Thomas Zacharia, AMD’s SVP for global public sector and strategic partnerships, said the inclusion of Instinct GPUs and Epyc processors marks an “important step” in the chipmaker’s existing relationship with EuroHPC and the Lumi AI Factory consortium..

The existing Lumi supercomputer already features AMD MI250x GPUs and AMD Epyc Milan CPUs.

“Powered by AMD Instinct MI430X GPUs and 6th Gen AMD EPYC CPUs, [Lumi-AI] will strengthen Europe’s sovereign AI capacity, accelerate scientific discovery and give researchers, startups and industry the scale and flexibility to shape Europe’s AI future,” Zacharia commented.

Bull’s first outing

Bull’s selection in the project marks the first major deal for the company after it was renationalized by the French government in March 2026. as part of a €404 million ($406.4 million)deal with Atos Group.

The motive behind the deal primarily centered around protecting French national and technological sovereignty.

At the time, Roland Lescure, French minister for the economy, said the acquisition marked a “decisive milestone for France’s technological sovereignty”.

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Anthropic has reportedly signed a $45 billion cloud computing deal with Nscale as the AI developer aims to scale compute resources.

Sources at the companies told CNBC the six-year deal will see Anthropic access 460MW of Nvidia's next-gen chips at the Monarch Compute Campus, starting at the end of next year.

The West Virginia site is expected to begin operations at the end of next, and will host Nvidia’s Vera Rubin chips.

Anthropic is yet to respond to a request for comment from ITPro. A spokesperson for Nscale said they could not confirm details from the reports.

Anthropic has signed similar deals recently aimed at scaling compute capacity, including a $5 billion deal with AMD. The company also struck a deal with SpaceX to cover up to 300MW of compute capacity. The AI developer also has an existing deal with Oracle.

London-based data center startup Nscale was spun out of an Australian bitcoin mining company in 2024, and was earlier this year valued at $14.6bn following a $2bn fundraising round. An IPO is widely expected to land imminently.

Anthropic isn’t alone in striking infrastructure deals, either. Major players including OpenAI have also invested incredible sums to build or rent compute capacity, helping to spark record results for cloud computing companies.

Last month, Google Cloud posted revenue growth of 82% — its best quarterly growth yet.

Monarch compute

In January, the American Intelligence and Power Corporation (AIPCorp) unveiled its plans to build the 2,250-acre Monarch Compute Campus in Mason County, West Virginia, dubbing it the first state-certified microgrid-based AI project.

The site boasts up to 2GW of power generated on campus by natural gas scaling to 8GW. The first phase is set to open at the end of 2027, and it is expected to become one of the largest AI infrastructure projects yet.

Two months after the unveiling, Nscale announced it had acquired AIPCorp, including the Monarch Compute Campus — and signed Microsoft as its first customer for 1.35GW of AI compute on Nvidia's next-gen Vera Rubin GPUs.

While the data center is drawing big-name AI customers, locals have complained about construction disruption causing damage to local streets and causing floods, with one report saying 60% of homeowners in nearby areas sold and moved rather than live near the campus.

Advocacy groups have raised concerns about water use, though Nscale has pledged there will be no impact on municipal water supply or residential users. As the campus is off-grid, local electricity prices or supply won't be impacted, but environmental groups have warned about the climate impact of the use of natural gas for generation.

A report from EY estimates the data centre will make up more than a third of Mason County's economy once operational.

Nscale has multiple sites planned — including Essex in the UK, and Texas — and one operational data center in Norway, with another set to open in the country imminently.

Beyond that, Nscale has several partner-run data centers in Europe and North America.

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Nvidia CEO Jensen Huang claims the AI industry is in the midst of a “golden age” after the company recorded huge revenue gains in its recent quarterly earnings report.

Revenue surged to $96.2 billion in the second quarter, marking a 106% increase compared to this time last year. The chipmaker said it expects revenue to surge to $108 billion by the end of the third quarter.

In a statement, Huang said the revenue gains reflect the growing impact of AI worldwide, with the technology having now reached an “inflection point”.

“It’s doing useful work. Its tokens are productive and profitable. Now, compute is revenue,” he said.

“And demand is accelerating. This time last year, one lab alone was driving the buildout; today, we have a golden age of new AI labs and startups, multiple frontier labs scaling in parallel, a thriving open model ecosystem and physical AI coming online — with strong momentum across the US and around the world.”

Huang noted that AI infrastructure build-outs have played a key role in recent revenue gains – an area in which the company also recorded significant growth.

Data center revenue across the second quarter increased to $89 billion, up 18% from the previous quarter and marking a 117% surge year-over-year. Notably, this earnings report marks the first since the Nvidia Vera Rubin rackscale platform announcement in May, which the company said is now “ramping into full production”.

Vera Rubin racks are now up and running at a host of major industry players, including Google Cloud, Microsoft, Oracle, CoreWeave, and Nebius.

Nvidia supply issues still a cause for concern

Forrester principal analyst Naveen Chhabra echoed Huang’s comments regarding AI infrastructure build-outs. However, for enterprise technology buyers, a combination of industry trends could dampen excitement - particularly supply constraints.

“The most important message is that AI infrastructure demand is still accelerating, but supply constraints, ecosystem lock-in, and ROI pressure are becoming the dominant strategic issues,” he said.

Chhabra said the Nvidia results suggest that the AI infrastructure market is “entering a new phase where enterprises must focus less on acquiring GPUs and more on extracting measurable business value from AI investments”.

Huang acknowledged that supply constraints are a lingering concern. The company expects revenue growth of 70% for its 2028 year, and while Huang said demand is “much greater” than that, it’s limited on the volume of products it can roll out.

Chhabra said supply issues show that multi-cloud and hybrid sourcing strategies have become “increasingly important to avoid dependence on a single provider” – especially for larger enterprises.

“Large AI training clusters and high-end inference deployments may continue to face availability constraints,” he said.

Heightened competition

While Nvidia has been center stage since the get-go in the generative AI race three years ago, enterprises are shifting their focus to alternatives in a bid to diversify infrastructure hardware.

A host of major industry players are also developing their own chips, in part to reduce reliance. In an earnings call, Huang brushed off concerns on this front, adding that he has “100% confidence that our technology will continue to be extraordinary for them”.

Alongside the earnings results, Nvidia revealed AWS plans to acquire another two million GPUs to bolster data center capacity. The announcement comes less than six months after the hyperscaler unveiled plans to deploy one million GPUs across data centers.

Chhabra noted that while enterprises are tweaking strategies, Nvidia’s broader ecosystem still holds significant weight and is a key differentiator for the chipmaker.

“Accelerators options are emerging. However, Nvidia's advantage is not simply silicon but the entire platform, software stack, and ecosystem,” he said.

“Nvidia supports "every model" and every deployment environment. The market is unlikely to become "Nvidia vs. one competitor." Instead, buyers will operate in a heterogeneous environment with Nvidia, hyperscaler chips, and specialized accelerators."

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SMBs seeking a powerful single-socket rack server that'll fit in the smallest of spaces will find Broadberry is offering a tempting proposition. Presented as an ultra-short-depth 1U rack system, the CyberServe EPYC EP1-102A packs a lot into its compact dimensions.

Targeting SMBs and workloads such as edge computing and web hosting, Broadberry offers it with a good choice of AMD EPYC 4004/5 CPUs, and it can handle up to 192GB of fast DDR5 memory. It has another trick up its sleeve as its single PCIe slot supports an Nvidia Quadro RTX A1000 or A400 GPU card for lightweight AI and rendering duties.

Remote server monitoring comes as standard, as the CyberServe sports an integrated AST2600 BMC (baseboard management controller) with a dedicated Gigabit port. You can also manage and monitor multiple servers with the Asus Control Center Express (ACCE) utility providing a centralised dashboard.

Broadberry CyberServe EPYC EP1-102A review: Build and design

The CyberServe showcases the Asus ExpertCenter Pro ER100A B6 1U rack platform. The chassis is solidly constructed, and removing the lid reveals a smartly designed and tidy interior affording easy access for upgrades and maintenance.

Broadberry supplied our system with a meaty 16-core 3GHz AMD EPYC 4545P topped off with a chunky passive heatsink. Flanked on one side by four DIMM slots, it came with one occupied by a 32GB DDR5 UDIMM module.

Cooling is handled efficiently by a central bank of three dual-rotor fans in front of the motherboard with a plastic air shroud directing air over the CPU's heatsink. We noted that the shroud is secured in place with four screws – many similar short-depth Supermicro rack servers we've seen leave their flimsy air shroud loose, with only the lid keeping it in place.

Another cooling fan sits to the left of the chassis and is angled in to push air over the PCIe expansion slot. All told, we found the server ran very quietly, making it a good choice for small office environments.

The rear panel presents dual 2.5GbE multi-Gig ports, and if you feel the need for more speed, Broadberry offers 10GbE, 25GbE and 100GbE PCIe adapters. You're spoilt for choice if you want to connect a local monitor, as the server offers VGA ports fore and aft, while the rear 10GbE 3.2 Gen 2 port supports DP Alt-Mode for piping output to an HDMI-enabled monitor.

The front panel also has a handy 2-character Q-code LED display. This provides a running status tally as the server boots up and warns if any errors are detected – the manual provides a full list of Q-codes and their meaning.

Broadberry CyberServe EPYC EP1-102A review: Storage features

Standard storage features are fairly simple, with the base system offering two hot-swap SFF bays at the front with the backplane cabled directly to the motherboard's embedded SATA ports. Drives are easily added by snapping them into the tool-free carriers, and it's worth noting that unlike the blue-chips, Broadberry allows you to install your own choice of storage devices without invalidating the warranty.

Further storage expansion is possible as the server has an extra internal tray behind the front air grill that accepts two fixed SFF devices. Power cables are already provided, and with spare SATA and SlimSAS ports on the motherboard right next to the tray, you can cable up SATA or U.2 NVMe drives.

The system was supplied with a 2TB Micron 3500 M.2 NVMe SSD installed in the single motherboard slot beneath the expansion bay. A nice touch is that the SSD is completely covered with a large aluminium heatsink, which also gets the benefit of the expansion bay cooling fan.

Broadberry offers a range of optional Broadcom SATA/SAS RAID adapters, but the AMD chipset does have embedded RAID capabilities, which come into play when you change the BIOS SATA settings from AHCI to RAID. Arrays can be created and managed using AMD's RAIDXpert2 utility, which is available for Windows Server 2022/2025. We dropped a couple of WD Red SATA SSDs in the front bays and had no problems using RAIDXpert2 to create a software-managed mirrored array.

Broadberry CyberServe EPYC EP1-102A review: Server management

The server's embedded AST2600 IPMI controller and dedicated Gigabit port offer a tidy web interface packed with plenty of information on system health and critical components. It isn't as feature-rich as Dell's iDRAC or HPE's iLO controllers, but its dashboard provides an overview of all the latest events, and you can pull up graphs of hardware sensor data, define thresholds for each one, and link them up with email alerts if any are breached.

Firmware upgrade tools are provided along with remote power controls, and the controller's built-in firewall provides options to create IP address and port-based protection rules. Unlike Dell and HPE, the IPMI controller includes full OS remote control and virtual media services as standard features and not as chargeable licence upgrades.

The ACCE software is available for Windows and Linux hosts and is used to manage multiple servers from a single console. A licence code is included on a card in the server's accessory box, and we loaded ACCE on a Windows Server 2025 host.

ACCE focuses primarily on managing Windows and Linux endpoints and offers a remote agent deployment tool. Systems with agents appear in the main dashboard page where you can view their hardware sensors and perform a variety of tasks such as running BIOS and driver updates and system inventory.

The management controller section scans for BMC devices, and after discovering the CyberServe's AST2600, it presented a list of tasks including pulling up hardware sensor details, updating the BMC's firmware, and loading event logs. As far as we can see, BMC monitoring doesn't need a licence, which is only required for each agent deployment.

Broadberry CyberServe EPYC EP1-102A review: Is it worth it?

With a price tag for the system on review of £3,656 excluding VAT, Broadberry's CyberServe EPYC EP1-102A is offering a very affordable alternative to the blue chips. It comes up a little short in the storage department, but it is available with a good choice of EPYC CPUs, has a big memory capacity, supports an optional GPU, and its short-depth chassis can go places most other servers can't.

Broadberry CyberServe EPYC EP1-102A specifications

Semiconductor revenue is set to almost double this year as the AI sector continues to reshape the market – and drive up prices for in-demand components.

New figures from Gartner project global semiconductor revenue growth to top 92% this year, reaching $1.6 trillion over last year's $809 trillion. In 2027, that’s expected to climb further to $1.9 trillion, the company said.

"The semiconductor industry is entering a fundamentally new phase of growth," said Ben Lee, Director Analyst at Gartner. "The pace of industry expansion is accelerating dramatically, driven by sustained investment in AI infrastructure and a stronger-than-anticipated memory pricing cycle."

That's good news for those manufacturing semiconductors, but the rise of AI – and the surge in demand that it's sparked for components – has led to price rises across a range of industries.

In the cloud market, a host of providers have issued price hike warnings. Meanwhile, consumer electronics are being hit hard by rising hardware costs. Apple recently warned that prices will rise this year, for example, while PC shipments have hit record lows.

Memory and beyond

Semiconductor gains are largely driven by memory revenue, with prices for the key component skyrocketing because of AI demand. Some predictions suggest that AI will consume a fifth of all DRAM capacity in 2026.

That shortage is sending prices skyrocketing and already impacting businesses' plans to upgrade tech.

This trend is expected to continue, according to Gartner. The firm predicted DRAM revenue would increase 246.6% this year, with NAND flash revenue up by 371.9% – and that’s despite additional capacity being made available.

Memory now makes up 54% of total semiconductor revenue, a huge leap from 27% last year. Revenue for memory will top $837bn this year and $1trn next year, Gartner predicts.

"AI infrastructure has fundamentally changed the dynamics of the memory market,” said Shrish Pant, Director Analyst at Gartner.

“While pricing expansion is accelerating growth in 2026, continued AI infrastructure deployments, higher memory content per AI server and sustained demand for high-bandwidth memory (HBM) will support memory revenue growth through 2027 and beyond."

While semiconductor revenue growth is dominated by memory price rises and demand, Gartner noted that the AI roll-out is benefiting all aspects of the industry, as AI data centers need CPUs, networking, power management, and more.

"AI is expanding the semiconductor opportunity across the entire infrastructure stack rather than benefiting a single device category," said Lee.

Gartner said semiconductor revenue growth excluding memory would grow from $589 billion in 2025 to $718 billion in 2026, an increase of 21.9% – and reach $864 billion in 2027.

AI data centers

Notably, the mass buildout of data centers is changing how semiconductors are used and where they end up, according to Gartner.

"As AI infrastructure scales, it is not only reshaping investment across the semiconductor ecosystem but also redefining the industry’s application mix," said Lee, saying Gartner sees a "a structural shift in where semiconductor value is created and how demand is evolving across the industry.”

Gartner predicted that the AI data center ecosystem would make up over a third (36.5%) of semiconductor revenue this year, climbing to more than half (53%) by 2030.

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AMD has boosted rack efficiency four-fold since 2024, and the company claims it’s on track to upping it by 20 times by the end of the decade.

The chipmaker revealed it recently exceeded its 2026 target of a three-fold improvement in rack-scale energy efficiency for AI training and inference.

Two 2030 AMD racks are now expected to deliver the same compute as roughly 570 racks did in 2024, enabling a 20x reduction in use-phase electricity and a 28x reduction in carbon intensity.

Alternatively, efficiency gains are expected to enable 20-times more compute, measured in floating point operations per second (FLOPs) per watt, using the same amount of energy.

AMD said energy improved energy efficiency delivers a range of environmental benefits, but also in helping to improve AI performance without increasing power, improving total cost of ownership, and helping customers scale faster.

“The next wave of AI efficiency will depend on tighter co-optimization across compute silicon, memory, interconnects, software and rack-scale system design," said Sam Naffziger, senior vice president and corporate fellow at AMD.

"Our estimated 4x improvement through 2026 reflects the strength of our approach and the progress AMD is making across the full system, putting us ahead of our projected pace toward the 2030 goal.”

The improvements in performance come down to compute capability, memory bandwidth, and interconnect bandwidth, according to AMD.

Advanced process technology and architectural improvements help increase floating-point compute performance per watt, advanced architectures and memory integration improve bandwidth, and high-speed interconnects and tighter integration increase the network bandwidths.

High-bandwidth memory, larger caches, and tighter integration between memory and compute can reduce unnecessary data movement, which uses energy. Meanwhile, high-speed scale-up interconnects help larger systems reduce bottlenecks and share data more efficiently.

AMD’s ROCm software driving gains

Elsewhere, AMD claimed its ROCm software stack and open standards help developers and organizations deploy AI workloads more efficiently on AMD platforms, with open ecosystems also supporting broader optimization across AI and high-performance computing (HPC) deployments.

"The projected rack-scale efficiency gains AMD has set forth can enable AI and HPC customers to achieve data center-level gains in power and cooling infrastructure, which can be limiting factors to increasing compute performance," said the firm.

"Better energy efficiency can help reduce operational electricity use, improve total cost of ownership, and support more sustainable AI and HPC growth."

AMD has also published its 2025-26 Corporate Responsibility Report, detailing its progress across product efficiency, operations, supply chain responsibility, and community impact.

It reduced operational greenhouse gas emissions by 30% from 2020 to 2025, sourced 58% of global AMD electricity from renewable sources and audited 99% of its manufacturing supplier factories for responsible business practices.

"We aim to cut operational greenhouse gas (GHG) emissions in half from 2020 to 2030 as we expand our use of renewable energy. In 2025, 100% of AMD manufacturing suppliers maintained public GHG reduction targets and 82% sourced renewable energy, said AMD chair and chief executive officer Lisa Su.

"The AI era is still in its early stages. Working closely with industry, government and academia, we will keep building the high-performance, energy-efficient compute foundation it requires."

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AI cloud firm Nebius is leasing high-density capacity at Vantage Data Centers' Newport campus, marking the first commercial capacity commitment in the South Wales AI Growth Zone.

Under the agreement, Nebius will lease Nvidia-powered capacity at Nebius's CWL1 facility to support AI training, inference, agentic AI and enterprise AI workloads, and serving enterprises, researchers, start-ups and public sector organizations.

“This is an important milestone for South Wales and for the UK’s AI infrastructure ambitions,” said David Howson, president, EMEA, Vantage Data Centers.

“Nebius is scaling AI cloud infrastructure in the UK at a time when demand for domestic compute capacity continues to accelerate. Vantage’s Newport campus provides the scale, connectivity and operational excellence needed to support that growth, and we’re proud to help establish South Wales as a leading destination for AI investment.”

One of Europe’s largest data center campuses, Nebius's CWL1 site opened in 2010. It runs on 100% certified renewable energy, with its latest facilities designed to minimize water consumption through a closed-loop cooling system that recirculates water rather than evaporating it.

The facility also uses air-cooled chillers and free cooling when ambient conditions permit.

The increased capacity forms part of Nebius’ broader UK expansion and commitment to scale domestic AI compute capacity.

In June, the company announced around £1.7 billion of committed capacity build-out across four UK sites, including the delivery of more than 1GW of AI-ready capacity across Newport, Bridgend, and Bro Tathan.

“The UK is one of the places where AI is being built, deployed and adopted by startups, enterprises and the public sector,” said Gary Tierney, general manager for EMEA at Nebius.

“Vantage’s Newport campus gives us a strong foundation to expand access to NVIDIA-powered AI infrastructure in the UK and support customers building the next generation of AI applications.”

AI Growth Zones take shape

The buildout is based on Nvidia's latest full-stack, end-to-end DSX AI factory reference design, and supports the UK government’s AI Opportunities Action Plan.

South Wales was designated as one of the UK's UK AI Growth Zones in November last year. At the time, the government said it could create more than 5,000 new jobs for local communities.

It's also home to the UK’s largest cluster of semiconductor businesses.

The AI Growth Zone stretches along the M4 corridor from Newport to Bridgend has the potential to harness over 1GW by early 2030s. 

“Today’s announcement marks an important milestone for the South Wales AI Growth Zone and demonstrates Wales’ growing reputation as a strategic location for the infrastructure that will power the AI economy,” said Adam Price, Welsh cabinet minister for enterprise, connectivity and energy.

"Nebius and Vantage’s investment will help unlock new economic opportunities, support highly skilled jobs and reinforce Wales’ position as a leading destination for advanced digital investment."

The move by Nebius comes hot on the heels of AI Growth Zone announcements elsewhere across the UK this week. The Lanarkshire growth zone in Scotland, for example, secured £300 million in investment to ramp up data center development, with Dell Technologies pledging its support for the site.

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Businesses of all sizes depend on a mix of technologies to access and move data.

Wired networks provide the critical data backbones and connections to data centers.

But more and more enterprise data is carried by alternative networks. Some traffic never even touches the conventional, corporate LAN but goes directly from a mobile user, an IoT device, or a branch office to a cloud provider or a SaaS application.

Cellular connections through 4G and now 5G routers, and satellite links are already common as backups to fixed lines, for events and emergencies, for hard-to-reach locations, and even for large sites, including warehouses, sports venues and transport hubs.

But alternative, wide-area network technology is gaining ground for routine use, rather than just edge or temporary use cases.

Businesses want to make more use of data-rich applications, from video and voice to AI, with high-speed, high-quality connections across their operations.

Then there is the growing interest in operational resilience. A single network connection is a bottleneck and a potential single point of failure.

Increasingly, this means looking at more than one network. And technologies previously viewed as only suitable for edge cases are now moving to the core of the enterprise network.

“The traffic patterns are becoming very bursty, asymmetric, and highly latency sensitive,” Hardik Ajmera, a vice president at Extreme Networks, told ITPro. It no longer makes sense, he says, to route all network traffic through the corporate office or data center.

But how do enterprises manage the inevitable complexity this brings?

Ever since the first groundbreaking microchip was created in the late 1950s, the race has been on to make these components smaller, more powerful, and faster.

In 2026, the quest for dominance in the AI sector is driving investment and innovation in the processor sector: AI may be software, but the power and speed of these chips will determine the success of the AI platforms.

AMD, Intel, and Nvidia are all leading producers of GPUs and CPUs for artificial intelligence, but coming up behind this trio is Amazon Web Services (AWS) and its Trainium series of chips. We explain what AWS Trainium is and how the technology industry is adopting these chips to deliver AI models at scale and speed.

What is AWS Trainium?

AWS Trainium is the name of a series of computer chips designed to power both the training and the running of artificial intelligence. The name ‘Trainium’ is simply a play on words: machine learning models need to be trained, while the ‘-ium’ part evokes chemical elements, such as titanium.

According to Amazon, AWS Trainium has been custom-built “to do one thing and one thing only: process massive amounts of data to help advance the development of generative AI”. In fact, Amazon describes AWS Trainium as “a powerhouse of brute computational force,” meaning every microscopic particle of the chip has been refined for the maximum amount of processing capability.

Amazon says a single AWS Trainium chip can complete trillions of calculations in just one second, and it’s this power the company hopes will see its chips at the core of AI infrastructure in the years ahead.

With all the major tech giants competing to place their specific chips at the heart of AI, it’s easy to see why this matters so much; a 2025 report from UN Trade and Development predicted the AI market would be worth $4.8 trillion by 2033.

When was AWS Trainium launched?

AWS Trainium was first announced in late 2020 with a roadmap to arrive in the first half of 2021. The AWS Trainium family is now up to Trainium3 (Trn3), unveiled at AWS’s re:Invent expo in December 2025. Compared with Trainium2, it doubles the compute performance and has 1.5x the memory capacity.

AWS Trainium chips are made by Annapurna Labs, an Israeli-based microelectronics company acquired by Amazon Web Services in 2015 for around $350 million.

Who uses AWS Trainium?

Direct Trainium customers are typically organizations that are involved in the training of large language models and other frontier AI.

Anthropic’s Claude, for example, has been trained – and runs – extensively on AWS Trainium chips. Additionally, Anthropic’s Project Rainier – one of the world’s largest AI compute clusters – is built on more than a million AWS Trainium2 chips.

Other AWS Trainium customers at present include Databricks, HCL, Hugging Face, PyTorch, and Ricoh.

What are the benefits of AWS Trainium?

One of the key selling points of Trainium is its reported efficiency.

Since the launch of AWS Trainium3, Amazon says customers have reduced their AI training costs by up to 50% as well as massively lowering inference latency, which is the time taken between sending a request to an AI model and receiving an answer. Energy efficiency has also been increased by 40%.

AWS Trainium3 UltraServers consist of 144 Trainium3 chips, and Amazon says these cut the time taken to train AI models from months to weeks, making previously impractical or too expensive projects now a reality.

How does AWS Trainium differ from AWS Graviton?

Both AWS Trainium and AWS Graviton are types of computer chips from Amazon Web Services. However, while AWS Trainium is used for the training and running of AI models, AWS Graviton handles the day-to-day cloud computing needs that keep the internet running.

It’s also the power behind the rollout of agentic AI systems by companies worldwide and is used to manage the workload of these agents to respond automatically to queries from users. AWS Graviton customers include Arm, Crowdtrike, HubSpot, Pinterest, and Snap.

What comes next for AWS Trainium?

Amazon is already working on AWS Trainium4, which it says will represent a “foundational leap” forward in processing power, speed, and efficiency for modern AI workloads.

This could include offering a 6x performance upgrade over AWS Trainium3. These chips are expected to arrive sometime in 2027 and will have unprecedented interoperability with Nvidia NVLink Fusion high-speed chip interconnect technology.

With data centers dramatically increasing pressure on the electricity network, the UK's National Grid has announced plans to improve power provision in North West London.

Five new data centers – equivalent to 1GW of demand – will be connected in Hertfordshire to the existing 400kV Elstree GIS substation, with a new 132kV Letchmore Heath GIS substation planned for next door.

Also in the pipeline are upgrades to Sundon substation in Bedfordshire, the reconductoring the 35km Sundon–Elstree overhead line route, and an extension to the existing Elstree gas insulated switchgear (GIS) substation.

Meanwhile, work on the route between Sundon and Elstree will see over 200km of overhead line upgraded, with 60km of new cable infrastructure also to be installed within the existing Elstree–St John's Wood tunnel.

Additional works include:

• The installation of a new 400kV cable circuit in the existing Elstree–St John’s Wood tunnel
• Upgrades to the Elstree–St John’s Wood tunnel and headhouses
• The reinforcement of the existing St John's Wood substation

“The North West London Upgrade is a nationally significant investment that will strengthen the electricity network, unlock new connections, support regional economic growth and help keep long term energy costs down for consumers," said Joe Senior, project director at National Grid.

“Working closely with our delivery partners and supply chain, we're bringing together substations, cables and tunnels, and overhead line reinforcement in a single program to deliver the infrastructure needed to power future homes, businesses and digital industries as electricity demand rises.”

In line with government guidance, communities near the new Letchmore Heath substation are set for £530,000 in funding for projects that improve the social, economic, and environmental wellbeing of the community.

National Grid eyes capacity gains

According to National Grid, these upgrades will increase network capacity and strengthen the resilience of electricity supplies across the region, helping to keep energy bills down.

It should also play a role in supporting economic growth across North West London as electricity demand rises – enabling new data center connections without jeopardizing power supply to homes and businesses.

As part of these efforts, National Grid is carrying out other ongoing work across the capital, including London Power Tunnels 2 (LPT2) and the North London Reinforcement (NLR) scheme.

Earlier this year, National Grid revealed that the queue for connections to the transmission network had grown by 460%, in large part because speculative applications were swamping the pipeline and delaying connections for strategically important projects.

The result, according to the National Grid, is that some waiting times have surged to up to 15 years.

Because of this, the government announced plans to prioritize AI data centers, AI Growth Zones, and industrial sites when it comes to new connections.

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Voice remains one of the most effective channels for customer relationships. For customers, having access to a human on the other end of the line, especially in times of confusion or crisis, is increasingly important in an era driven by automation and AI adoption.

This is exactly why so many Managed Service Providers (MSPs) are involved in running or supporting contact center infrastructure on behalf of their customers. However, despite the rewards, it naturally doesn’t come without some risks.

In April 2026, the UK government published the findings of its 2025/26 Cyber Security Breaches Survey, which highlighted some sobering statistics. According to the data, nearly half of all UK businesses (43%) experienced a cyber security breach or attack in the past year.

Phishing remained the dominant attack vector, cited by 38% of businesses, and rated as the most disruptive incident type by 69% of those affected. However, of those phishing incidents, a growing share is no longer delivered by email. Rather, they’re arriving by phone call, and thanks to AI, they are increasingly convincing.

The evolving threat

Voice phishing, or “vishing”, is not a new phenomenon. Bad actors and criminals have been spoofing voices and using fraudulent calls since the popularization of the telephone. What is new are the features that make voice phishing more convincing.

The proliferation of AI-generated voice deepfaking, caller ID spoofing tools, and lack of traceability have contributed to a new wave of fraudulent calls that appear more real than ever before.

Many organizations have spent significant resources securing their email environments, including spam filters, user training, and sandboxing. While this is a sensible investment, fraudsters have noticed and are adapting, which may explain why voice phishing has become an increasingly favored tactic for some bad actors.

Why voice is being targeted

The basic mechanics of voice phishing have always relied on social engineering. Impersonating a trusted person and creating a sense of urgency help to manipulate victims into taking a malicious action, whether that be authorizing a payment, sharing credentials, or granting undue access to a system. While traditionally a mismatched voice was a potential giveaway to fraud, AI deepfakes have made this process much more convincing.

Voice cloning tools that once required real technical knowledge can now be replicated by most people with a commercial PC. It’s now easier than ever to create a convincing replica of a known voice from a short audio sample, for example, from a CEO's recorded video message, a support call that was unknowingly captured, or a social media clip.

If a cloned voice calls a finance team member and asks them to process an urgent payment, citing relevant details such as a live deal, a regulatory deadline, or a supplier relationship, very few red flags are present for the human on the receiving end of the call.

Number spoofing compounds the issue. Fraudsters may be able to secure the capability to present any caller ID they choose, making a call appear to originate from a trusted source. The result of mixing emerging tech with good due diligence is a call that looks and sounds real.

MSPs can carry disproportionate risk

MSPs that support or run contact center infrastructure are in the middle of communications flows for multiple organizations and partners simultaneously. Critically, this also makes them the first port of call for customers when something technical goes wrong, which can offer an easy alibi for fraudsters looking to exploit them.

There are two distinct risks for MSPs: one operational, one reputational. The operational risk is straightforward: IT help desks control credentials, systems, and sensitive customer data, making them a target for malicious actors. The reputational risk, however, has a wider-reaching impact. If a customer is defrauded through infrastructure managed by an MSP, customers may find the MSP liable. Even if the MSP bears no technical responsibility, they are the supplier in the middle.

There is also a less visible third risk: the channel as a route of compromise. Fraudsters who have already gained access, whether through an earlier breach, open-source intelligence, or a compromised supplier, will probe the path that offers the least resistance. If an MSP's call handling has not been safeguarded with fraud in mind, that becomes the path of least resistance.

Practical steps to take right now

The tools to address this problem exist today, but gaps remain in general adoption and awareness. Firstly, MSPs should be implementing proper caller authentication where it is available. For example, in some markets, protocols exist to authenticate calls before they’re answered, such as the “STIR/SHAKEN” protocols used in the USA. These act as a digital "passport" for telephone calls, ensuring the number a call handler sees on their caller ID is legitimate and matches the actual person or business calling. These act as a strong defense against number spoofing.

Next, establish comprehensive verification workflows. Agents should never rely on voice recognition alone. Callbacks to verified numbers, challenge questions, and two-factor authentication must be embedded as standard security procedures. Call handlers must verify who is requesting sensitive information and challenge even those who appear familiar.

Additionally, staff should be trained to recognize the specific warning signs of voice phishing. Most security training currently focuses on email. Staff need scenario-based training covering AI-generated voice hallmarks, such as unnatural pauses and scripted rigidity, alongside classic social engineering tactics: creating a sense of urgency and requesting to bypass usual processes.

Finally, participate in industry data-sharing. No single provider sees the full picture. In the UK, mechanisms like the Home Office's Telecoms Fraud Sector Charter seek to improve cross-sector visibility of active campaigns. MSPs engaged in these networks get earlier warnings to ensure they’re not operating with only a partial view.

Voice is a channel worth championing

Voice is a communication channel with many strengths, and the human touch can be the differentiator between satisfied and dissatisfied customers. For so many reasons, it’s a critical channel that’s worth championing, and worth safeguarding.

Voice fraud is the channel's problem. But the channel is very well placed to address it.

Steps such as appropriate due diligence through the supply chain, monitoring for suspicious traffic patterns, and validating caller IDs used by customers are all effective. It’s also crucial that MSPs and contact centers take the appropriate steps now to ensure that criminals can’t get in and customers remain confident.

AMD is planning to offer up to £5 million in support for a new lab in Oxford carrying out research into frontier AI.

The company has signed a memorandum of understanding to provide compute resources for researchers at the British Open-ended Learning and Discovery Lab (BOLD) during the lab's first eighteen months in operation.

BOLD is led by the University of Oxford, and brings together researchers affiliated with the University of Oxford, University College London, and Imperial College London.

They, together with AMD researchers, plan to explore initiatives spanning compute access, software optimization, research collaboration and open-source development.

Work could include experiments to explore model training techniques beyond backpropagation, discovery using multi-agent systems and adaptive physical AI.

The lab will be built around three research aims - new learning algorithms, human-centered AI and embodied systems such as robots - and will translate breakthroughs into open source tools and encourage commercial spin-outs.

“Through our memorandum of understanding with the BOLD lab, AMD is exploring bringing together advanced computing, open software and technical expertise to help researchers pursue ambitious new frontiers in AI," said Thomas Zacharia, senior vice president, global public sector and strategic partnerships at AMD.

"By expanding access to compute and supporting open academic research, our aim would be to help BOLD researchers accelerate experimentation and contribute to the UK’s leadership in AI innovation and scientific discovery.”

AMD to provide underpinning infrastructure

AMD plans to provide support through infrastructure that will be maintained by the company itself, along with cloud service providers, public-sector partnerships and hardware contributions.

Phase 1 of the research program will focus on broad exploratory experiments, with later phases following up select research directions and validating the most promising ideas.

The company said it will work with BOLD researchers across the AI technology stack, exploring novel ways to optimize AI workloads using its open software ecosystem.

The ecosystem envisaged by the MOU would be built on permissively licensed open-source projects such as AMD ROCm software. AMD would help BOLD researchers publish research software, benchmarks, and evaluation tools.

"BOLD aims to advance a more open, efficient and human-centered path for AI through collaboration between academia and industry. Working with AMD, the University of Oxford would combine world-leading research with computing technology to test ideas, learn quickly and translate breakthroughs into applications," said associate professor Jakob Foerster, of the University of Oxford's Department of Engineering Science.

"This support would help BOLD develop approaches to learning, human-AI collaboration and embodied intelligence while expanding open access to computing resources that benefit science, healthcare, education, transportation and public services globally.”

UK pledges continue

The MoU marks the latest in a number of UK-related pledges made by AMD in recent months.

In June, for example, the chipmaker announced plans to invest £2 billion in the UK to drive AI innovation and research. This also saw AMD pledge to expand access to compute resources for enterprises across the country.

Along with University College London-led research lab Science of Fundamental AI Research (SOFAIR), BOLD is to receive a share of £60 million in government funding over the next six years.

This funding forms part of the £1.6 billion UKRI AI Strategy, which aims to strengthen the UK’s leadership in AI over the next four years.

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Most small and mid-sized businesses (SMBs) are grappling with networks that have evolved one tool at a time, as new vendors were added to solve individual challenges as they emerged.

The result is a patchwork of solutions, from wireless access points and firewalls to monitoring tools, bolted together with little strategic planning. Each one has its own interface, licensing model, and operating expenses.

This is costing SMBs time, money, and resources. Too often, the IT infrastructure and management tools businesses rely on today were designed for enterprise environments, making them unnecessarily complex for smaller organizations. Disjointed systems and multiple dashboards make it harder to identify issues, and if a problem arises, which support service should a stretched IT team contact first? Add in the licensing cost for supporting multiple tools from multiple vendors, and the burden quickly grows.

If SMBs are to escape from the network tool maze, they need to think about how all of their systems fit together and work with managed service partners to move toward a simpler, more effective cloud-based IT infrastructure.

From fragmented tools to unified management

No longer reserved for the deep pockets of enterprises, unified, cloud-managed platforms are transforming network operations for SMBs. By bringing together networking, security, and monitoring into one interface, IT teams get a single pane of glass view across their entire environment.

For businesses, this means clearer visibility across devices such as routers and switches, insights into performance and power consumption, and the ability to identify and resolve issues before they impact operations.

Many Managed Service Providers (MSPs) are recommending cloud-based systems because they recognize the operational and business benefits they deliver. With less hardware to oversee, time-consuming on-site maintenance visits are reduced, and the path from purchase to deployment is fast and straightforward.

A more strategic role for MSPs

For MSPs, the shift to the cloud also has advantages.

Remote management and automated software updates save time and costs. The advantages of this can be purely practical – looking after one unified platform will always be easier than overseeing several fragmented ones.

Moving clients to the cloud also allows MSPs to shift from reactive support when something goes wrong to proactive monitoring, optimization, and vulnerability management. They can deliver technical expertise, helping customers to scale seamlessly to meet market demands, and act as an outsourced IT department that transforms IT from being a burden to a strategic advantage.

Helping SMBs adopt cloud-managed platforms delivers another significant benefit - security. As compliance requirements and legal regulations change and cyber threats grow exponentially every year, the built-in cyber frameworks of cloud platforms become essential.

Compliance certifications, automated updates, and integrated monitoring provide a level of protection that was once available primarily to enterprise customers, and without the associated overhead.

Simplifying the path forward

If SMBs are to simplify their networks, they need to move away from tool sprawl and towards a unified platform with security built-in, not bolted on. The right solution will not only deliver improved visibility and simpler operations, but also the features businesses need without unnecessary complexity. This also makes implementation easier as the company grows.

MSPs can help identify the right solution for the customer, but vendors must demonstrate how their platforms work in real-world conditions.

And while even the best unified platform cannot do everything, integrating specialist tools should be seamless through open APIs and interoperability frameworks.

The future of networking is about using fewer tools to achieve more through simpler architecture, centralized control, and systems designed for how SMBs and their partners actually operate.

Secure access service edge (SASE) industry revenues are expected to surge to $24 billion by 2030 as enterprises eye rapid networking modernization.

SASE enables enterprises to integrate networking and security within a single unified framework. Simply put, it allows IT teams better control over access and identity.

Analysis from Dell’Oro Group projects the industry will grow at a 15% compound annual growth rate (CAGR) over the next four years, near-doubling compared to 2025 rates.

The consultancy noted this growth reflects “two complementary sources of strength”. In particular, SD-WAN and security service edge (SSE) modernization efforts are expected to accelerate over the next four years.

On the SD-WAN front, enterprises are ramping up security and lifecycle-led updates while managed service adoption is also growing. This is expected to drive revenues by up to 14% annually by 2030, Dell’Oro Group noted.

Meanwhile, SSE-related growth rests on two converging areas, with firms eyeing stronger digital experience management (DEM) and data security gains.

Mauricio Sanchez, senior director for enterprise security and networking at Dell’Oro Group, said a sharpened focus on these areas “expand(s) the SSE opportunity” by creating new monetization opportunities.

The consultancy expects revenues to exceed $13 billion in this domain by 2030.

“SD-WAN has regained strength because branch refreshes increasingly combine connectivity, security exposure, software subscriptions, and managed services in a common purchase decision,” Sanchez commented.

SASE in the spotlight

Several security providers have rolled out SASE products over the past few months.

In July, Zscaler announced a partnership with Schwarz Digits to launch a zero trust SASE service designed to combat AI-driven threats, bolster cyber resilience, and support data sovereignty efforts for European customers.

Zscaler’s announcement came hot on the heels of a similar move into SASE by Cloudflare. In June, the company unveiled the new Cloudflare One Design Partner designation for managed service providers.

This initiative will help organizations modernize legacy security environments and accelerate adoption of SASE architectures, Cloudflare said at the time.

Dell’Oro Group’s forecast aligns closely with previous research from Gartner. In 2025, the analyst firm projected a CAGR of 26% in SASE across five years, with industry revenue expected to reach $28.5 billion by 2028.

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Data center construction in Europe and the Middle East is forging ahead, with the region accounting for five of the ten fastest-scaling global data center markets.

According to new research from Savills, the US is – unsurprisingly – the biggest market in terms of capacity, at around 50GW of live IT power, and has grown by 19% since 2024.

China comes next in terms of existing capacity, with Japan, the UK, Germany, Ireland, and the Netherlands also boasting large and mature markets.

But while they show major absolute scale, they've experienced more modest recent growth thanks to increasing grid access constraints.

Some EMEA markets, meanwhile, are scaling at a rapid pace. The data center markets in Finland and Saudi Arabia have grown by 57% since 2024, for example.

Denmark (38%), France (28%), and Portugal (23%) are all recording significant growth.

Cities are getting in on the action

According to Savills, a host of European cities are capitalizing on the market boom and positioning themselves as go-to locations. Porto, Marseille and Malaga are offering comparatively low-cost power by European standards, Savills noted.

These cites also offer more permissive planning regimes and less competing demand than some of the region’s more established hubs.

Porto combines renewable power with strong connectivity, as well as more room to secure land and capacity.

This pattern extends across Europe, researchers said, with several AI projects being delivered further away from the major metros in locations where a faster route to significant power can be secured.

"For AI-driven data center growth, the Nordics, led by Finland, Denmark and Norway, offer one of the clearest delivery propositions globally, combining low-cost, clean power, cooler operating conditions and fewer physical constraints," said Rupert Duckworth, associate director of Savills' EMEA data center advisory.

"The region has increasingly become a focus for large-scale, AI-oriented development – with significant new campuses now progressing."

Asia markets are growing rapidly

According to Savills, it's Malaysia that's scaled at the highest rate over the last two years, recording growth rates of 132%. This is partly due to its proximity to data processing-hungry but land- and power-constrained Singapore.

Indonesia has grown by 66%, with India and Australia also having grown rapidly to hit a meaningful scale.

"The relative growth of countries such as Malaysia and Indonesia in just a couple of years has been extraordinary, with another tranche of countries not far behind," said Paul Tostevin, head of Savills World Research.

"However, these nations must now look to balance the issues which are sometimes constraining growth in the more mature markets: namely grid access, energy costs, land and water availability and environmental impact.”

Power constraints holding back growth

Power is the biggest factor holding back construction, Savills noted. In particular, grid access, where connection lead times can be far longer than build time.

As a result, developers are increasingly going where grid capacity is available, rather than simply following demand.

"Demand for data centres is not in doubt. But the harder question is where they can actually be built. Answering this will require close collaboration between operators, utilities, governments and local communities, particularly where AI workloads place new pressures on grids and resources," the researchers conclude.

"Climate risk compounds the challenge: higher temperatures, water stress and extreme weather will increasingly shape where capacity can be delivered and run reliably, and communities and governments will be asking whether development will exacerbate existing issues."

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One of our biggest gripes about network monitoring software products is their convoluted and confusing licensing schemes. Some charge by sensor, monitored element, volume, or network port; others do it by node or device, and there's also the extra cost of optional add-on modules.

The SolarWinds Observability suites cut through the confusion as you can choose from SaaS cloud-hosted or on-premises versions, and both are licensed purely by the node. We review the SolarWinds Observability Self-Hosted (SWOSH) version which offers three simple subscription plans.

With a monthly cost of around £6 per node, the SWOSH Essentials edition teams up SolarWinds Network Performance Monitor (NPM) module with the Log Analyzer, Server & Application Monitor (SAM), IP Address Manager (IPAM), VoIP & Network Quality Manager (VNQM) and User Device Tracker (UDT). The Advanced edition is well worth considering as for £10 per node/month, this adds the Network Configuration Manager (NCM), NetFlow Traffic Monitor (NTA), Virtualization Manager (VMAN) plus Server Configuration Manager (SCM) and also introduces the new ML-powered anomaly alerting service.

The Premier edition pushes the monthly node price to £13 and augments Advanced with storage and web performance monitoring and an incident response service. It also offers value-based licensing where you can monitor elements such as containers and on-premises virtual machines (VMs) without consuming a full node license.

SolarWinds Observability Self-Hosted review: Easy deployment

SWOSH offers a remarkable range of monitoring components, and all are neatly integrated into the Orion web console. This provides a single pane of glass, with the console's 'My Dashboard' tab presenting a drop-down menu for access to each module and its associated metrics, along with three other menus for alerts and activity, reports, and all settings.

We deployed SWOSH on a Windows Server 2025 Hyper-V VM in the lab with a single routine handling the entire process and taking 40 minutes to download and install all components. As we were evaluating SWOSH, we used the included SQL Server Express database, but for production environments, you'll need to source a full version of SQL Server 2016 or later.

Also, pay particular attention to the minimum hardware requirements. We started our VM with eight Xeon Scalable Gold vCPUs and 16GB of virtual memory but had to increase it to 32GB as we were receiving low memory alerts for the host system.

SolarWinds Observability Self-Hosted review: Voyage of discovery

The Discovery Central page offers wizard-assisted routines to discover cloud entities and network devices. We started with a scan of our lab network and ran through adding our network subnet and providing credentials for SNMP, Windows WMI, and VMware, after which SWOSH reported back in 10 minutes with a complete list of all identified systems.

Cloud monitoring services are provided for AWS, Microsoft Azure and Google Cloud Platform. We chose AWS and found the discovery process simple, as after providing our access and secret keys, SWOSH presented a dashboard of all our resources, including cloud instances and S3 buckets.

Discovery Central also provides a section for configuring the new AI agent. This uses the Platform Connect service to link up with the SolarWinds AIOps cloud service using a choice of three global data locations.

The agent presents a right-hand side menu where you can ask natural language questions about anything on your network or how to achieve something. It's currently in SW1 preview and focuses on the SolarWinds knowledge base documentation and the current state of your network. It impressed us nonetheless, as it came back with detailed answers to questions about, for example, the status of our VMware estate, the most critical nodes and patterns in alert summaries.

SolarWinds Observability Self-Hosted review: The Orion console

The home page presents a bold summary with at-a-glance views of your entire network. Widgets are included for areas such as node, group and application status, active alerts by severity, Top 10 views, hardware status plus anomaly-based alerts, and dashboards can be customised by creating new widgets for specific interests and dragging them to the desired location.

From NPM, you can pull up detailed summaries of all monitored nodes, each assigned a colour-coded status icon, see active alerts, a network Top 10 plus routing insights, and use NetPath to monitor external web locations and see hop-by-hop maps with latency and packet loss details for each step. NTA is another easy one to use as it automatically picked up the NetFlow feeds from our WatchGuard firewall appliance and provided detailed views of detected applications, the top conversations and busiest endpoints.

Accessed from the Home menu, the PerfStack Analysis tool can be used to identify the root cause of network problems by comparing data collected from multiple devices in one dashboard. You'll find the Quality of Experience (QoE) service here too, which uses sensors linked to switch mirror ports to identify, categorise and analyse traffic for over 1,500 applications.

The discovery process automatically identifies running apps, with the SAM module providing heaps of templates for monitoring server and app performance, although some are old enough to be in a museum. For MS365 monitoring, we were pleased to see SolarWinds has replaced its clunky PowerShell queries with API poller templates which are much easier to use.

SolarWinds Observability Self-Hosted review: Hardware health, VMAN, SCM and anomalies

If SWOSH detects a supported server during discovery, it'll assign a hardware health sensor resource that provides details on system temperatures, power consumption, and cooling fan status. It worked for our HPE ProLiant iLO-equipped servers as SolarWinds no longer needs HPE's System Insight Manager (SIM), but Dell fans must continue using the pensionable OpenManage Server Administrator (OMSA) as native support for iDRAC controllers probably won't be implemented until the 2027.2 release.

The Advanced version is ideal for businesses big on virtualisation as it includes the VMAN module. This augments NPM's basic availability checks with full host monitoring, reports on CPU, memory plus datastore usage, views of VM resource use and capacity planning reports.

SCM's hardware and software inventory were found to be accurate, and we used its baselines to keep track of server and application changes on our Windows hosts. The new anomaly alerting service allows you to create custom alerts that go beyond the static thresholds used by NPM. They're simple to create as you provide a name, assign one of five alert levels, apply it to a node, interface or VM, and add conditions such as average CPU load with a threshold of your choosing.

SolarWinds Observability Self-Hosted review: Is it worth it?

SolarWinds Observability Self-Hosted is good value as its cost per node is significantly lower than many competing products and made all the more affordable thanks to the sheer number of monitoring modules included. There are a lot of features to play with so it'll take some time to get it configured to your requirements but the suite is simple to install and easily managed from a single web console.

It's been a busy year for WatchGuard; along with a family of table-top Firebox appliances, it also launched a range of five rack-mount models. Not content to rest on its laurels, WatchGuard now delivers yet another new family of rack-mount appliances with a laser-sharp focus on high performance.

The range comprises three models, with the Firebox M4850 on review representing the entry point and targeting mid-sized businesses yearning for a major speed upgrade without being faced with complex enterprise level prices. All three appliances sport an Intel Granite Rapids-D HCC Xeon 6 SoC (system on chip), with the M4850 endowed with a 12-core model – the M5850 gets 20 cores while the top-dog M6850 receives 24 cores.

Add in 32GB of fast DDR5 memory, and you have some impressive performance claims. The M4850 boasts a high 66.5Gbps raw firewall throughput, 19.1Gbps for full UTM scans with antivirus (AV), the intrusion prevention service (IPS), and application controls all activated, and 8.8Gbps for full scan HTTPS content inspection.

The appliance offers a good range of high-speed network ports, and WatchGuard claims an industry first for a firewall platform. All three Fireboxes have an OCP 3.0 expansion slot with optional modules including a dual-port QSFP28 100GbE card.

WatchGuard Firebox M4850 review: Hardware features

The M4850 presents eight RJ45 Gigabit, four fibre Gigabit SFP, six fibre 10GbE SFP+, and a pair of 25GbE SFP28. The M5850 ups the 25GbE port count to four while the M6850 increases the 10GbE SFP+ port to ten and adds a pair of 100GbE QSFP28.

The rear OCP 3.0 port is the Gen5 x8 variety, and WatchGuard offers a good range of expansion options. Along with the dual 100GbE card, you can choose from quad 2.5GbE or 25GbE SFP28 modules.

Power is handled by dual 600W hot-plug PSUs while cooling is looked after by four (slightly noisy) hot-plug fan modules. An RJ45 port is provided for direct access to the appliance's CLI, with two USB ports available for system backups, restores and support diagnostics.

WatchGuard Firebox M4850 review: Subscription simplification

No need to worry about complex subscriptions and expensive optional add-ons, as WatchGuard offers only two subscriptions. All base Firebox models with a support contract come as standard with firewall, software-defined WAN (SD-WAN), and clientless VPN access portal services enabled.

Choose the Basic Security Suite (BSS) subscription, and you get gateway antivirus (GAV), web filtering, antispam, HTTPS inspection, IPS, application controls, network discovery, and WatchGuard's RED (reputation-enabled defence) cloud-based URL filtering. Moving up to the Total Security Suite (TSS) subscription enables every available security service, including the advanced persistent threat (APT) blocker with cloud sandboxing and ThreatSync XDR for collection, correlation and automated responses for threat events from all Fireboxes.

There's even more as TSS adds WatchGuard's DNSWatch service, which monitors client DNS requests and blocks them from accessing known malicious domains. The IntelligentAV anti-malware service augments the GAV with the Cylance AI-based engine scanning files such as Office documents, Windows portable executables, and PDFs.

You can manage the appliance in standalone mode, but both subscriptions include access to the WatchGuard Cloud portal with the latter extending log and report data retention to 1 year and 30 days respectively – features some other vendors charge extra for. TSS also enabled WatchGuard's Gold support, which provides 24/7 access to technicians, live calls for critical issues, a one-hour SLA for high-level issues, unlimited support cases and advanced hardware replacement.

WatchGuard Firebox M4850 review: Cloud management and Rai

Deploying the M4850 for cloud management is simple as we registered it with our WatchGuard support account and from the WatchGuard Cloud portal, allocated it to our site and chose the cloud management and monitoring option. A wizard helps with basic network configuration and all you need to do next is power the appliance up and leave it to connect to the cloud portal, after which it disables local management access.

The portal can remotely manage all compliant WatchGuard fireboxes, Wi-Fi access points, AuthPoint, EPDR, FireCloud and AI-powered ThreatSync + NDR services. A smart new feature is WatchGuard's Rai (Red AI), which correlates security feeds and telemetry from every cloud-managed product, regardless of their location, and presents a highly informative dashboard with AI-generated daily summaries of all activity.

Primarily aimed at MSPs, Rai is designed to cut through alert smokescreens and streamline problem resolution by automatically investigating and responding to security incidents. Rai impressed us during testing as we have three Fireboxes, EPDR and FireCloud running in the lab, and the dashboard told us everything we needed to know about their security posture, the number of incidents and all automated actions.

WatchGuard Firebox M4850 review: Security features

The M4850 makes the most of its port-fest as it defaults to mixed routing mode, allowing you to segment your network by configuring each port as separate interfaces, each with its own IP address and DHCP services, and designate them as external, trusted, optional, or custom. Other options are drop-in mode, which creates one logical network for all interfaces, and bridge mode if you want to place the appliance between an existing LAN and gateway.

The TSS subscription enables a wealth of security features, but the portal makes light work of configuration as all key services are presented in one page. From the content scanning section, you can enable both AV scanners, the APT blocker and spamBlocker for applying anti-spam policies to inbound SMTP, IMAP and POP3 traffic.

The network blocking section covers botnet detection, IPS, port and site blocks, detection of Tor (The Onion Router) exit points, and default packet handling. WatchGuard's WebBlocker and application control services are both accessed from the content filtering section, with the former offering 166 URL categories that can be allowed, blocked, or set to display a warning page to users.

App controls are equally impressive with nearly 1,300 predefined app and protocol signatures provided, and both services can be applied to a firewall rule by assigning a single action policy. VPN features are abundant with the M4850 supporting 5,000 branch offices and 10,000 mobile VPNs.

WatchGuard Firebox M4850 review: Is it worth it?

Yes. WatchGuard Online lists the Firebox M4850 with a 1-year TSS subscription for £22,610 excluding VAT, rising to £41,646 for a 3-year subscription. This compares very favourably with the competition in this space and WatchGuard's simplified licensing cuts through any subscription confusion.

The M4850 offers plenty of ports for your money with native 25GbE support and the OCP 3.0 expansion slot adding extra levels of flexibility and valuable future-proofing. The Intel Xeon 6 SoC delivers an impressive performance boost, security services are impeccable, and WatchGuard's Rai will appeal to busy MSPs.

WatchGuard Firebox M4850 specifications

At the heart of all of our technology in the 21^st century – from smartphones to computers to servers to manufacturing machines – lies the humble microchip.

These tiny components of integrated circuits could be described as the brain through which everything happens, whether that’s processing data or storing information.

Over the decades, the main goal when designing such chips has always been to make them smaller, more powerful, and faster than ever – and there are many different types created for all sorts of different devices and roles.

For example, logic chips such as central processing units (CUPs), graphics processing units (GPUs), and neural processing units (NPUs) are the key to making something work or happen. Separately, there are also memory chips that store data, for instance flash or random access memory (RAM).

All of these chip functions can also be combined to form systems-on-a-chip (SoCs), a single microchip featuring processing, graphics, and memory.

What on earth then is an ASIC?

The application-specific integrated circuit (ASIC) is a microchip that’s custom-designed and optimised to do a single designated task absolutely perfectly.

This enables it to achieve the sort of speed, power, and efficiency that isn’t possible when a microchip is designed to handle many tasks. Peter Groos, senior AE Group director, at Cadence Design Systems, explains: “They’re optimized for particular, predictable workloads, enabling higher performance and greater energy efficiency when that task is well-defined.”

Who makes ASICs?

ASICs are designed by companies such as Broadcom, Nvidia,Qualcomm, and many others and manufactured by foundries such as TSMC and Samsung.

“These companies collaborate across the value chain, with designers creating chip architectures and foundries fabricating them at advanced process nodes,” Groos adds.

What types of ASICs are in use right now?

The AWS Trainium family of chips from Amazon is an example of an ASIC. These have been specifically designed for training and running AI models and platforms.

Other examples of where an ASIC can be used are in:

• 5G network signal processing and packet routing in telecommunications
• Advanced driver assistance systems (ADAS) in motor vehicles
• Bitcoin Mining or for the creation of other cryptocurrencies
• Face detection or voice recognition in smartphones and other smart devices
• Hearing aids or medical imaging within the healthcare sector

What else is interesting about an ASIC?

ASICs are built on the idea of stripping away everything unnecessary to ensure they are fully focused on executing the single task at hand, however complex it is. But they can also be built to be SoCs, with everything in the one place that’s needed for the one job.

ASICs will often be found in modern data centers and industries where compact and energy-efficient computing is critical. According to BAE Systems, you will also find special radiation-hardened ASICs in nuclear power plants, spacecraft, and inside satellites in orbit.

Where do ASICs sit within AI?

“As AI systems scale, ASICs are becoming increasingly important for efficiency, particularly in data centres and edge deployments,” says Gross.

“The role of ASICs in the AI ecosystem is based on their ability to naturally complement the GPUs that are so critical to AI infrastructure.”

This allows ASICs to play a key role in optimising network traffic and workload distribution within systems, Gross further explains, adding: “ASICs are used because they deliver superior speed and power efficiency compared to general-purpose chips when handling predictable, repetitive workloads.

“In AI, this makes them particularly valuable for tasks where performance-per-watt and cost efficiency are critical, such as large-scale inference and data movement.”

What else is important about ASICs?

There are three different types of ASIC, each with its own clear role: full custom ASIC, semi-custom ASIC, and programmable ASIC.

As the names suggest, the first is built from the ground up, the second will use pre-designed parts, and the third can be programmed for a defined use once it is manufactured.

With such a tiny physical footprint, each ASIC has no wasted action or space onboard; everything serves a defined purpose.

Google's tensor processing units (TPUs) – a type of ASIC – are said by the company to push computing performance by up to 15-30x, while their energy efficiency can rise from 30-80x versus a general-purpose processor of the same level.

Why have ASICs become so important?

Amazon’s entry into ASICs with AWS Trainium shows how vital they are to the future of computing, technology, and AI. This is a battle for supremacy in a multi-billion-dollar race, and the potential efficiencies and cost-savings ASICs can go on to deliver are enticing other big players to join in. Google and Meta, for example, are also investing in their own ASICs.

ASICs’ role in processing data at the edge also makes them vital for smart cities, smart appliances, and autonomous vehicles.

What is the future for ASICs?

A lot of investment is being pumped into ASIC design and manufacture right now. For example, in April 2026 NVIDIA invested $2 billion in Marvell, a company that counts Amazon, Microsoft, Google, and Meta among its customers.

This comes as the entire semiconductor industry is expected to reach $1.6 trillion in revenue by 2030, up from $775 billion in 2024, according to McKinsey.

Broadcom is another benefiting from this surge in interest. Its Custom ASICs are provided to the likes of Google, Meta, OpenAI, and Apple, and Q2 FY2026 revenue for AI semiconductors soared 143% to $10.80 billion year over year. CEO Hock Tan projects AI revenue exceeding $100 billion in 2027.

Amazon, which makes the AWS Inferentia chip family (another custom-made ASIC), is also buoyant about the future of its chips business. Amazon CEO Andy Jassy said in late April 2026 that this part of the company saw 40% quarter-to-quarter growth in Q1 2026.

He also revealed that while its AWS Trainium4 chips won’t roll off the production line until 2027, much of its available inventory is now already reserved. “As best as we can tell, our custom silicon business is now one of the top three data center chip businesses in the world – and the speed at which we’ve gotten here is extraordinary,” he says.

Engineer and tech entrepreneur Marco Riedesser, however, sees another change coming over the horizon, pointing to first-generation hardwired chips from companies like Taalas. These chips put the AI model into the silicon.

“We are moving from a world where we run AI on computers to a world where the chip is the AI,” he explains.

“By removing the need to move data back and forth from memory, ‘hardwired AI’ can run medium-sized models at speeds 10x–50x faster than traditional GPUs.”

Riedesser describes how instead of loading a model like Llama 3 into a chip's memory, you instead “etch the model’s logic” into the metal of the chip itself.

He predicts that “while AWS and Nvidia are building chips to run software, companies like Taalas are fundamentally changing the game”.

Even if data center compute demand falls short of current projections, it still won't be easy for power suppliers to keep up, according to analysis from McKinsey.

With AI infrastructure projects continuing at a rapid pace, the consultancy anticipates that around 75% of power demand growth across the US in the next decade will come from data centers.

Based on current expansion rates, this will require the "equivalent of almost 30 GW of power per year”.

"Whether that growth materializes depends on whether demand for AI compute will last. Several indicators suggest that it will,” the consultancy noted.

In the short term, McKinsey a shortfall is likely in the US. But future demand is hard to predict, with parts of the AI ecosystem exhibiting bubble-like characteristics.

More than half of 2025's new unicorns are AI-focused, including several multibillion-dollar firms without commercialized products. Meanwhile, some AI-native players and infrastructure intermediaries are pursuing aggressive expansion, despite high leverage or negative cash flow.

Broader macroeconomic and geopolitical factors such as tighter capital markets, elevated power prices, or supply chain disruptions could also dampen demand and slow the pace of data center build-outs.

Concerns over compute capacity and infrastructure run counter to current trends in the industry, McKinsey noted, particularly in terms of concrete use-cases. Most organizations deploying AI are reporting good results, the consultancy noted, and there’s no sign of an imminent technological plateau when it comes to LLMs.

"Investments are increasingly tied to contracted capacity utilization and concentrated among scaled, investment-grade data center operators — factors that point to durable, committed growth rather than speculation," the firm said.

"Finally, demand is anchored by sophisticated, well-capitalized hyperscalers, whose capital spending has grown approximately 50 percent annually from 2022 to 2026 while maintaining strong margins and cash flow."

Infrastructure misalignment

McKinsey warned that at the current pace of growth, there's a misalignment between where stakeholders want to build data centers and where generation, energy demand, and grid infrastructure can quickly scale - raising the possibility of energy supply problems in parts of the US.

McKinsey's model suggests data center power demand could grow by around 27% annually between now and 2030, reaching 121 GW of data center IT demand.

However, it looks likely to grow more slowly after that, thanks to a potential saturation in demand for chips and slower AI adoption, as well as a shift from building new data centers to replacing today’s components with more energy-efficient, next-generation chips that add compute per unit of power.

McKinsey pointed out that the massive backlog in load interconnection pipeline requests isn't necessarily a sign of a demand bubble. Many applications are speculative and duplicative, with developers and hyperscalers submitting multiple requests to see which secures power first.

Meanwhile, early-stage developers are trying to secure interconnection rights before selling projects to well-capitalized parties that will do the building and install the IT hardware.

"Beyond 2030, our analyses point to a wide range of outcomes, but several structural dynamics are likely to hold," concluded the firm.

"In addition to the growing role of gas, we expect solar paired with battery storage to sustain strong momentum, given its cost competitiveness, modularity, and speed to deploy, though this is contingent to some degree on stable policy support, interconnection reform, and continued declining costs of storage."

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The European Commission is calling for tenders to establish up to seven AI gigafactories across the EU, as part of its efforts to secure European tech sovereignty.

The initiative will be supported by up to €10 billion in EU and national funding, making it the largest public-private partnership in the world for the development of trustworthy AI.

It's aimed at giving businesses of all sizes, academia, and public authorities access to infrastructure for training, inference, and fine-tuning advanced frontier AI models.

It's hoped that the facilities will also act as a catalyst for the European semiconductor ecosystem and stimulate domestic design of processors by providing a predictable, large-scale demand for high-end computing. It’s also hoped that, over time, this will evolve into the manufacture of European-designed AI processors in the EU.

The gigafactories, said the Commission, will combine advanced AI processors, software and cloud technology stacks, high-speed connectivity and energy-efficient data centers, dedicated secure cloud user access environments, and specialized secure AI-oriented support services.

The supporting infrastructure will be environmentally sustainable, particularly in terms of energy and water supply.

The plan was first revealed in February 2025 and an informal call for expression of interest drew 77 proposals, with potential sites in Germany, Italy, France, Poland, Czechia, Denmark, Finland, Greece, Portugal and Spain.

"The opening of the AI gigafactories call represents a milestone in our AI Continent ambitions," said Henna Virkkunen, EVP for tech sovereignty, security, and democracy at the European Commission.

"Access to the raw scale of computing power within AI gigafactories is a strategic necessity for Europe as AI development accelerates. I am pleased to see member states, industry and the Commission coming together to work on these facilities that are key to our technological sovereignty," she added.

The plan will involve two consecutive development phases and two lots, with participating member states matching EU funding.

The first lot will support up to four projects, each eligible for up to €100 million in EU funding in phase one and up to an additional €400 million per project in phase two. Successful projects will have to deploy at least as many of the most advanced AI processors as are currently installed in Europe's most powerful AI factory.

In phase two capacity must triple, with the second lot supporting up to three projects, each eligible for up to €200 million of EU funding in phase one and up to an additional €800 million per project in phase two.

To qualify, projects must feature up to twice as many of the most advanced AI processors as are currently installed in Europe's most powerful AI factory; in phase two, capacity must increase to at least four times that level.

The AI gigafactories can be set up on one site or across several locations in a single member state, but can also include cross-border developments through distributed AI computing facilities.

The call closes on 12 November 2026, and consortia or Special Purpose Vehicles bringing together companies, public entities, investors and other partners can apply.

Decisions are expected by early 2027, with construction starting later that year, and operations expected to start within 18 months.

Data center developers could be forced to pay an upfront fee when applying for grid connections, with the aim of wiping speculative projects from the queue.

With grid connections having long lead times and planning permission uncertain, developers have been making speculative applications. As a result, they account for a significant proportion of demand in the connections queue, currently amounting to around 73 GW.

This has been a problem for quite some time, and is only getting worse. Contracted demand offers increased from 41 GW to 125 GW between November 2024 and June 2025, driven largely by demand from data center projects, many of which may not actually go ahead. If that happens, energy regulator Ofgem said, other, more viable developments could be delayed and planning for future network investment efficiently made more difficult. 

Now, to try and combat the problem, Ofgem has opened a consultation into a proposal to require data center projects to pay between £237,500 and £712,500 per megawatt when they accept a connection offer. That’s equivalent to around 2.5% to 7.5% of average project costs.

The fee would be refunded when the project reaches energization but forfeited if it fails to progress according to the rules.

“Britain's electricity demand connections queue has more than tripled in size in less than a year, and consumers should not bear the risks created by speculative projects taking up space in the system," said Ofgem’s director for energy system design and development, Eleanor Warburton. 

"The connections system must work for consumers and for the projects that are ready to invest, build and connect. Where speculative projects take up space in the queue, they can delay other schemes and create uncertainty about future network needs."

To keep their place in the queue, Ofgem suggests, data center developers would have to demonstrate tangible progress through evidence such as financial capability, commercial maturity and procurement activity. They would need to have a credible end-user of the data center, and procure electrical equipment such as switchgear with long lead times.

"These reforms will help free up capacity for viable projects, improve confidence in network planning and support faster connections where they are needed most," said Warburton. 

“By helping projects that are ready to move forward and connect more quickly, these reforms can also support economic growth, bringing forward investment, enabling businesses to be built and expanded sooner, and ensuring the benefits of that investment are felt across the economy more quickly.” 

However, there's a delicate balancing act between prioritizing viable projects and putting a dampener on AI development, said Mark Pestridge, managing director of Telehouse Europe.

"Ofgem’s proposal is an important initiative, but it must be implemented in a way that maintains the UK’s attractiveness as a destination for AI and digital infrastructure investment. A refundable fee-based approach should not deter serious investors, but create a more transparent connections process that gives viable projects greater certainty," he said.

“However, reforming the queue will not resolve the underlying capacity challenge. A long-term solution will require sustained investment in the grid, alongside much closer collaboration between data centre operators, local councils, National Grid and network operators at the earliest stages of planning."

The consultation is open until 16 September.

Orange has announced a deal with global infrastructure investor Morrison to create a jointly-controlled sovereign data center company in France.

The proposed venture would build on Orange's existing French data center portfolio, aiming to bring total capacity up to 400 MW – nearly ten times its current level.

Orange would contribute five major data centers across four campuses in France – Chevilly-Larue, Aubervilliers, Chartres and Val-de-Reuil – to the joint venture, along with its operational expertise and commercial reach.

Morrison, meanwhile, would commit equity, with the platform supported by an investment program of €3 billion leveraging Orange's existing assets, Morrison's equity contribution, and debt.

"Europe's digital future will require significant new investment in trusted infrastructure capable of supporting the growth of cloud services, artificial intelligence and data-intensive applications," said William Smales, chief investment officer at Morrison.

"We believe France is exceptionally well positioned to play a leading role in Europe's digital economy, and this partnership represents an important step towards building the sovereign infrastructure needed to support future growth and competitiveness."

The companies said the platform will benefit from some of the most decarbonized electricity in Europe, and will aim for the highest standards of energy efficiency and environmental sustainability.

Orange Business will serve as the exclusive partner for the distribution of its colocation and hosting offers for large enterprises, SMBs, and public-sector entities.

Strategic planning

Beyond hosting and storage capacity, the sovereign platform will serve as a strategic foundation for Orange Business's cloud and AI solutions.

The firm's platforms and services will continue to be hosted in the planned joint venture's data centers, with Orange keeping operational control over the areas dedicated to its own operations.

This, said Orange, will give customers a level of security and availability for hosted systems and related data that meets the most stringent national and European standards.

“We are absolutely convinced that France will need sovereign, trusted digital infrastructure if it is to rise to the challenge of surging demand driven by cloud and artificial intelligence," said Christel Heydemann, CEO of Orange group.

"This contemplated joint venture with Morrison will allow us to unlock the value of our existing assets and strengthen our position in data centers. Together with a partner recognised for its expertise and investment capacity, we will accelerate the deployment of the capacity that the market needs."

Long-term goals

The two companies expect to have the deal signed by the end of this year, after a consultation with bodies representing employees and receipt of the required regulatory approvals. It's expected to close in the first quarter of next year.

While digital sovereignty is becoming increasingly important in Europe, things are set to move slowly over the next five years.

According to Forrester’s Global Sovereignty Forecast, European firms are only expected to make modest gains on this front. Nations such as Germany, France, and the UK rank among the most aggressive in their plans for sovereignty improvements.

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Gartner has revised its IT spending forecast upwards due to an expected surge in AI-related infrastructure investment.

Global IT spending will hit $6.37 trillion this year, up 14.2% from last year, according to the analyst firm.

This marks a steep increase on an original forecast to reflect "increased confidence in overall market expansion" — but only in areas directly benefiting from AI adoption.

Traditional segments are predicted to post positive growth, but they will be "comparatively modest".

According to Gartner's revised prediction, spending on data centers is set to increase by 62.5% to $822 billion this year, nearly catching up with global devices spending.

That follows 51.6% growth last year, the analyst firm noted. Infrastructure as a Service (IaaS) builds on 25% growth in 2025, with a predicted 29.3% bump to $287 billion this year.

"Data center systems and infrastructure as a service (IaaS) are the top growth segments, reflecting accelerating investment in AI infrastructure, cloud platforms, and intelligent applications," said John-David Lovelock, distinguished VP analyst at Gartner.

"Building the compute capacity required for AI is the largest infrastructure project ever attempted by humanity,” he added.

“Driven by the expansion of AI workloads and demand for high-performance computing, hyperscalers and enterprises are rapidly scaling next-generation data center capacity."

Big tech providers are spending record sums on infrastructure, with a particular focus on new data centers optimized for AI.

Last week, Google-owner Alphabet again increased its capital expenditure plans for the year, from $180bn to $190bn to a new prediction of $195bn to $205bn — that follows a $5bn increase in the last quarter.

Amazon Web Services (AWS), meanwhile, plans to spend $200bn in capex this year with Meta setting aside $145bn.

Such spending has sparked concerns from investors, despite such companies also posting massive revenue from the cloud as hyperscalers. c

Constrained supply

Growth isn't good news for everyone, however, as massive AI infrastructure projects mean constrained supply for other areas of tech — leading to rising costs.

While software, services, and communications are posting significantly slower growth than AI-adjacent sectors, they remain the biggest segments for the sector.

Software is projected to record growth of 15.5%, a touch higher than last year, while services climbs slightly to 5.3%, and communications services nudges up only slightly to 4.4% this year.

Device growth is flat at 9.8% — not a surprise given the rising cost of smartphones and laptops, driven by an AI-fueled memory shortage.

"Despite the strong growth in spending, this is not a rising tide lifts all boats market trend," said Lovelock. "Technology budgets are being strained by inflation, supply shortages, rising hardware and memory costs, AI funding initiatives and shifting priorities."

Put simply, AI might be driving increased investment in related infrastructure and software — but the infrastructure build-out is causing shortages that are making devices more expensive by reducing supply.

"IT spending growth is being fuelled by rising investments in AI infrastructure and software. Organizations are spending more on AI optimized servers, cloud services, and AI-ready software as they expand their AI efforts, while hardware markets continue to adjust to semiconductor and memory supply constraints," added Lovelock.

"The fastest growth is expected in AI-related infrastructure, cloud services, and software, reflecting the strong focus on building and scaling AI capabilities across the enterprise."

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AMD and AI infrastructure company Cerebras have unveiled a joint system integrating AI infrastructure from both companies.

The joint solution, which was announced at AMD Advancing AI 2026, features AMD Helios – the star product of the show – and the Cerebras Wafer-Scale Engine.

According to Cerebras and AMD, it's intended to solve the issue of inference workloads having variable requirements when it comes to latency, throughput, token capacity, cost, and scale.

For example, agents and copilots need faster response times, whereas high-volume workloads prioritize maximum code generation, the organizations said.

By unifying Helios and Wafer-Scale Engine, the system optimizes the two stages of workflow independently: Helios is tuned to process prompts and large context windows, providing ultra-high throughput.

Wafer-Scale Engine, meanwhile, accelerates memory and bandwidth-intensive token generation with ultra-low latency.

“The demand for ultra-fast inference is growing at an unprecedented pace,” said Cerebras CEO and co-founder Andrew Feldman. “Partnering with AMD gives us an incredible opportunity to bring that performance to even more customers.”

AMD CEO Lisa Su added: “AI inference is becoming one of the largest infrastructure opportunities in AI and its growing diversity requires a more flexible approach.”

“Together with Cerebras, we are extending that leadership into the most latency-sensitive applications and creating a powerful new platform for real-time agentic AI.”

The joint solution will be available in the second half of 2026, initially through Cerebras Cloud.

The announcement comes alongside a number of other Helios partner announcements, including new commitments from Microsoft and Anthropic to roll out the system to their infrastructure.

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AMD has launched its latest Instinct GPUs, the MI455X, in tandem with bringing its Helios AI architecture into production at AMD Advancing AI 2026.

Helios, which was announced at last year’s annual conference, is the company’s flagship hardware product, integrating Epyc CPUs, Instinct GPUs, Pensando networking, and Rocm software stack.

“Helios is really the system that scales to be an AI factory,” said Andrew Dieckman, CVP and GM of data center and GPU at AMD. “We set out to build the world’s highest-performing rack and we believe we have delivered the world’s highest performing rack.”

According to AMD, Helios has 15% more AI compute, 50% more memory, and 50% more scale-out bandwidth than Nvidia’s competitor offering, Vera Rubin NVL72.

At the heart of Helios – the “engine” in Dieckman’s words – is the new AMD Instinct MI455X CPU. The MI400 series features increased HBM4 capacity and bandwidth, expanded AI formats with higher throughput, and standard-based rack-scale networking.

In Helios, the MI455X acts as the accelerator, delivering 40 petaflops of peak FB4 compute, with 423GB of HBM core and a peak memory bandwidth of 23.3 terabytes per second of memory per GPU.

In addition to the new MI455X GPU, Helios also features 6th Gen AMD Epyc server CPUs with 96 high-frequency cores and 1.6TB/s of memory bandwidth, as well as AMD Pensando and UALoE networking fabric.

This can scale up to 260TB/s bandwidth and host 72 GPUs on one domain.

AMD showcases Helios partners

With Helios already in production, AMD was keen to show off some of its partners that are deploying – or pledged to deploy – the system. These include OpenAI, Meta, Oracle, Microsoft, Dell Technologies, HPE, Lenovo, Bull, Cisco, Tensorwave, and more.

“Helios is a big step forward for AMD because we’re moving to a full rack-scale architecture where we provide all the components in a really co-designed engine and co-engineered rack, so that [customers] can get the full benefit out of the complete AMD end-to-end solution,” said Dieckman.

“We continue to be very focused on driving improved economics for our customers – making sure that we deliver the most tokens per dollar within the market.”

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Microsoft has announced it will deploy AMD’s Helios rack-scale solution as it targets data processing and AI inference gains for Azure customers.

The collaboration will see three new AMD EPYC CPU-powered virtual machine (VM) offerings made available through Azure. These include:

• HDv2 VMs
• HCx2 VMs
• ND MI455X v7 VMs

The first of these, Azure HDv2, is designed for large-scale data processing and to “empower massive agentic workload adoption”.

Featuring 500 sixth-generation EPYC CPU cores, HDv2 VMs boast 4TB of RAM, 32TB of local NVMe storage, and 500Gbps Azure Boost networking capabilities.

Azure HCx2 VMs, meanwhile, are designed primarily for silicon design and technical computing tasks, such as scientific simulations and engineering analysis.

This series builds on the design of Azure HX virtual machines, which the company launched in partnership with AMD in 2023.

HXv2 VMs will include 176 AMD sixth-gen EPYC CPU cores. These offer “significantly increased” per-VM and per-core performance and will also include 800GB InfinBand for “large-scale MPI-based simulations”.

Elsewhere, the ND MI455X v7 specifically targets “production-scale” AI inference improvements for Azure customers.

“ND MI455X v7 is designed for the reasoning, search and agentic workloads behind modern AI services,” Microsoft said in a blog post.

“Powered by the AMD Helios rackscale solution, it expands Azure’s infrastructure options for large-scale inference and is designed to deliver strong performance and efficiency for demanding AI workloads.”

The move makes Microsoft the latest in a string of big tech customers flocking to the Helios system. AMD chief executive Lisa Su said the deal builds on long-standing ties between the duo.

“AMD and Microsoft have spent years building high-performance infrastructure together, and today we're extending that partnership across the full stack of AMD AI solutions on Azure,” said AMD CEO Lisa Su.

“Microsoft's new AMD deployments mark an important milestone as we deliver leadership compute solutions to Azure customers and scale the next generation of AI infrastructure together.”

Microsoft targets “scale and choice” for customers

Helios is AMD’s first rack-scale system designed specifically for frontier model training and large-scale AI inference. The system combines AMD Instinct MI455X GPUs, AMD EPYC “Venice” CPUs, Pensando networking, and ROCm software..

Microsoft CEO Satya Nadella said the collaboration marks a significant expansion of Azure infrastructure capabilities.

“Customers are looking for AI infrastructure that is optimized for a wide range of workloads, from training and inference to data preparation, search, and reinforcement learning," he said.

"Through our collaboration with AMD, we are expanding the Azure infrastructure portfolio with AMD Helios to give customers the performance, scale and choice they need to build and run the next generation of AI applications.”

Helios roll-out pending

AMD unveiled the Helios rack-scale solution at its 2025 Advancing AI conference. The solution offers an alternative to – and competes with – Nvidia’s Vera Rubin and Grace Blackwell systems.

At the time, Su described the launch of Helios as a “game changer” that’s “purpose built for the most demanding AI workloads”.

A host of major tech providers, including Meta, OpenAI, HPE, and Oracle have since confirmed they’ll deploy the solution in data centers.

The chipmaker will begin shipping Helios to customers in the second half of 2026.

Although exact timelines haven’t been revealed, the collaboration with Microsoft comes just days ahead of AMD’s 2026 Advancing AI conference, in which customers can likely expect real-world examples of the system in action.

ITPro will be live on the ground in San Francisco for AMD’s annual conference this week.

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AMD Advancing AI 2026 takes place this week at the Moscone Center in San Francisco, with the two-day event largely focusing on the second day, when we will hear from AMD’s CEO Lisa Su.

One notable thing, for me, is a sense that in many ways AMD and the wider chip market is in largely the same position as it was this time last year. Nvidia is still the talk of the town when it comes to AI infrastructure, with CEO Jensen Huang once again guesting at other hardware vendors’ flagship events.

AMD still has HPC pedigree (more on that below) but doesn’t have the cachet Nvidia does, while Intel trails further behind.

Despite this, there are still predictions to be made, so here are three things I expect to see at AMD Advancing AI 2026 and one I don’t.

Helios

AMD Helios, the company’s fully-integrated, rack-scale architecture solution, was unveiled at Advancing AI 2025 but we are yet to see any deployed (at least publicly).

This is despite HPE saying it would have a cluster available in its cloud in the second half of 2026 (for those counting, that’s now) and Oracle stating it will build its AI superclusters on Helios.

What better time to talk about Helios being out in the market than at Advancing AI?

My feeling is it will build on last year’s announcement by wheeling out some real-world examples of the hardware in use, whether as part of the HPE or Oracle partnerships or something else.

Altair MI450 chips

Annual company events in the tech industry are for product launches as much as anything else. Having already made several announcements about its gaming portfolio at Computex 2026 in May, we can probably discount major announcements around Ryzen or Radeon – although there may be a recap.

Instead, one of the flagship announcements could be the company’s highly anticipated next-gen GPU series the MI450 – code named Altair.

Oracle already announced in October 2025 that it would be putting MI450 into OCI once it’s available and rumour has it the company has already inked a deal to sell these chips to Anthropic. Speaking of which…

AI partnerships

AMD – perhaps more than other hardware companies – makes a lot of its relationship with AI businesses.

This includes the likes of Anthropic and OpenAI, with the latter’s CEO, Sam Altman, appearing on stage at Advancing AI last year, as well as Hugging Face, PyTorch, and Llama.

I also expect to hear more about what role AMD expects to play in the brave new world of agentic AI

Supercomputing and HPC

While we might hear about high performance computers (HPC) – for example Alice Recoque, which is supposed to start onboarding this year – there may be little conversation around supercomputing more generally.

This is because while an AMD machine has been ranked number one in the Top500 list of most powerful supercomputers consistently since 2022 and held the top two places since November 2024, it was overtaken by a Chinese supercomputer in the June 2026 rankings.

AMD Advancing AI 2026 takes place from 22-23 July, with CEO Lisa Su’s keynote happening on the morning of the second day. I will be on the ground reporting on all the news coming out of the event and providing the usual dose of analysis you have come to expect.

Follow here for all previous years’ coverage and everything from this year as it comes in.

Achieving true tech sovereignty could be a bridge too far for Europe, a senior Forrester analyst has told ITPro, as new research from the consultancy lays bare the scale of the challenge.

According to Forrester’s Global Sovereignty Forecast, the shift to bolster tech sovereignty will move “slowly over the next five years”, with many efforts to divest from US tech providers falling flat.

The report includes a ‘Tech Sovereignty Index’ used to measure a country’s ability to “develop, operate, and secure critical technologies independently” - and it paints a damning picture.

Between 2025 and 2030, European countries are only expected to make “modest” gains in building sovereignty. Germany’s score is expected to rise from 34% to just 36%, for example, while the UK from 30% to 32%.

This slow progress flies in the face of what has become a key recurring talking point in Europe over the last 18 months. Calls have been rising to cut reliance on US-based technology providers, largely due to a combination of rising geopolitical tensions and data privacy concerns.

Speaking to ITPro, Dario Maisto, principal analyst and lead author of the report, suggested the current focus on sovereignty is much like other tech industry trends: awash by hype.

“There is a lot of hype around sovereignty, because service providers see it as the money maker,” he told ITPro. “So why should an organization engage with me? Well, sovereignty.”

For many organizations, Maisto noted that the focus on sovereignty isn’t actually required from a compliance perspective, albeit in some limited capacities.

“I was watching a Red Hat presentation in the morning, and they said there are a lot of new regulations. No, there's not. There's none. There is no new regulation that mandates sovereignty in any way whatsoever,” he said.

“Nor is there any definition at any possible level, national, regional, international, for sovereignty. There are some requirements in terms of data residency, citizenship of people handling certain workloads, certain workloads being deployed on on-prem rather than public cloud … but there is no formal, overarching obligation towards sovereignty whatsoever.”

“Persistent dependencies” run deep in Europe

Maisto admitted that geopolitical tensions, particularly deteriorating transatlantic ties, have indeed prompted a rethink of how heavily European organizations rely on US providers.

The idea of a wholesale shift away from popular vendors would require a monumental effort, however. Combined, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud control 65% of the European cloud market.

“There is not much that we can do about it. Certain SaaS applications, for example, do not even work outside of the hyperscalers clouds,” he said. “So you have two options as a client: either take the degraded, not complete, less serviced, less updated on-prem version, which you can, or you get the most updated, the fanciest, the shiniest cloud version.”

“But then you are forced to deploy on a hyperscaler because the APIs don’t work outside of the hyperscaler cloud. Moving these applications outside of the hyperscaler cloud, it's not a lift and shift.

“It's not something that the client can do.”

Elsewhere, Europe simply doesn’t have the capabilities when it comes to macroeconomic considerations. When gauging sovereignty credentials, Forrester’s index takes into account a range of factors, including:

• Government spending
• Tech workforce availability
• Data center capacity and autonomy
• Chip production
• Rare earth elements processing
• AI model creation

Forrester noted that “persistent dependences” in areas such as energy infrastructure, data center infrastructure, and supply chains could hamper the region’s ability to operate independently of both US and Chinese technology ecosystems.

Back down to earth

Maisto’s argument is that the reality of achieving true tech sovereignty often fails to acknowledge the significant investment required to reach this point – along with the potential disruption and legwork needed to move away from US-based providers.

“I think seven out of 10 international SaaS solutions are out of the US,” he said. “So how feasible is it that organizations replace 20 years of investment in Salesforce, in Workday, in ServiceNow overnight?”

Put simply, it’s an idealized vision borne out of the reactionary, tumultuous geopolitics at play in the world right now.

“So many people see sovereignty as a green field,” he said. “For organizations that have been investing millions, if not billions, in cloud migrations, task transformation, it's not about greenfield. It's a retrofit.”

These questions are what prompted the creation of what Maisto described as the “minimum viable sovereignty model” used in the Tech Sovereignty Index.

“How do I do the least possible investments now to get the sovereignty that I actually need, what is it I actually need to be compliant? Maybe to mitigate a risk, and what is it I can actually achieve?”

Lifting and shifting anyway

Despite the difficulties highlighted by Maisto, efforts to bolster sovereignty have been gaining traction across Europe over the last year. In April, authorities in France announced plans to move away from Windows in favor of Linux-based operating systems for some 2.5 million government devices.

Speaking at the time, David Amiel, minister of public action and accounts, said the country aims to become “less reliant on American tools and regain control of our digital destiny”.

At an EU-wide level, similar efforts are being made. As ITPro reported in June, the European Commission unveiled the technological sovereignty package.

This aims to reduce reliance on US tech and shake up the union’s capabilities across several areas, spanning AI, cloud computing, open source, and semiconductor manufacturing.

On the latter of these focus areas, Maisto noted that this is a “move in the right direction because we need more options” in terms of hardware manufacturing.

At present, Europe accounts for less than 10% of global chip manufacturing, according to Forrester. Crucially, it lacks the “large designers that can compete with the likes of Nvidia or Qualcomm”.

All told, European companies design only 1% of global chips.

Maisto suggested this once again shows the scale of the challenge faced both by individual European nations and the region as a whole under EU leadership.

Looking ahead, he said the focus for many will be to “manage the dependencies rather than avoiding them”.

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Channel partners are increasingly adapting infrastructure strategies to help customers navigate hardware pricing volatility and component shortages, according to new research from Westcon-Comstor.

The distributor found that 87% of channel partners have already taken action to reduce the impact of supply constraints on customer projects, with project delays identified as the single biggest risk created by ongoing pricing pressure and hardware availability issues.

The findings are based on a survey of more than 100 members of Westcon-Comstor’s global Tech Xpert community, comprising technical specialists and decision-makers from resellers, systems integrators, and managed service providers across EMEA and APAC.

According to the research, partners are responding to these challenges by improving infrastructure planning and adopting managed services, while some are even switching technology vendors.

“Hardware pricing volatility and supply constraints are reshaping customer buying decisions and having a profound impact on the IT channel,” commented Callum McGregor, chief operating officer and chief financial officer at Westcon-Comstor.

“In response, partners are helping customers plan earlier, adapt architectures and stay flexible so they can keep projects moving despite uncertainty.”

Adapting mitigation strategies

In terms of mitigation strategies, improved infrastructure capacity planning was found to be the most common, cited by 60% of respondents.

Meanwhile, almost a third (29%) said they had already switched vendors or platforms in response to hardware availability constraints.

Meanwhile, 27% of partners said they were using storage tiering or caching to reduce reliance on memory-intensive workloads, while a quarter (25%) reported moving workloads or infrastructure components to managed services.

Evolving buyer behavior

The research also highlighted a shift in buying behavior, with 75% of respondents reporting some form of change.

More than half (52%) said either they or their customers had accelerated hardware purchases to reduce the risk of future shortages or price increases – although almost a quarter (23%) reported delays or cancellations as organizations weighed up growing uncertainty.

The distributor revealed that hardware availability is also influencing infrastructure decision-making, with 45% of partners stating that supply chain constraints had accelerated cloud adoption among customers and 7% reporting a shift back to on-premises infrastructure.

A balancing act for partners

According to McGregor, the ability to balance immediate performance with long-term requirements is becoming “increasingly critical” in order for partners to maintain success in the current climate.

“It also demonstrates the strategic role that channel partners, supported by distribution, can play in building resilience and helping customers navigate the pressures that have quickly become a systemic feature of the market,” he added.

Elsewhere, Westcon-Comstor’s research found that 39% of partners identified technical challenges – including performance instability, operational complexity, and reduced resilience – as key risks linked to hardware shortages.

Around one-quarter (24%) also said supply constraints were limiting their ability to scale infrastructure capacity.

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Pure Data Centres Group has announced plans to invest more than €7.5 Billion to build a sprawling data center campus in Finland.

The deal represents one of the largest ever inward investment projects in Finnish history, and will see an initial pledge of €1.5 billion to develop a 110MW AI campus in Seinäjoki.

Long-term, Pure DC said the site will eventually scale to a 500MW campus capable of hosting high-performance computing (HPC) and AI workloads.

Phase one of the project is already fully leased, the company revealed, with the substation for the first data hall constructed and live.

Gary Wojtaszek, Executive Chairman & Interim CEO of Pure DC, said the project marks the beginning of one of Europe’s largest-ever AI infrastructure projects and will help put Finland at the forefront of the region’s AI transition.

“Countries building AI infrastructure over the next decade will shape the global economy for the next fifty years. Finland has all the ingredients required to lead this transformation - world class engineering talent, abundant renewable energy and a culture of innovation that gave rise to Nokia”, Wojtaszek said.

“We are not building data centers in Seinäjoki. Together with our partners, including the Seinäjoki Municipality, we are helping create one of Europe’s most important AI ecosystems capable of supporting global tech leaders as well as the next generation of Finnish entrepreneurs and innovators.”

Pure DC will work closely with local partner, SDC Ventures, as part of the development, which is expected to provide over 3,000 jobs during the decade-long construction projects.

Inside the Seinäjoki data center campus

Designated SJK01, the Seinäjoki campus is a sprawling 370 acre estate that, upon completion, will support 550MW of IT capacity. Pure DC said the data center site is designed specifically for “mass-scale” AI and machine learning workloads.

The site also has access to 700MVA of renewable power, meaning it will operate with “exceptionally low” energy costs and a stable grid connection.

Notably, the development will use repeatable 40MW ‘AI-ready’ modules, which come equipped with built-in Direct Liquid Cooling (DLC) capabilities to support high-density workloads.

The data halls located at the site are also based on a modular design aimed at streamlining efficiency and predictability. Jaakko Kiiskilä, Mayor of Seinäjoki Municipality, said waste heat from the data center will be repurposed to support the district’s heating network.

“It’s fantastic to see a British-headquartered firm thrive on the world stage by delivering the UK's largest inward investment into Finland, cementing our position as a world leader in the AI space,” said Peter Kyle, secretary of state for the UK’s Department of Business & Trade.

“Through our Modern Industrial Strategy, we're ensuring the UK remains a leader in AI, setting ourselves apart globally while making sure our trillion-dollar tech sector continues to thrive for decades to come.”

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Intel has announced a €5 billion investment to expand its Leixlip campus in Ireland, with plans to scale capacity to deliver Intel Xeon 6 and next-gen Intel Xeon chips built on its Intel 3 node.

Using existing capacity across cleanroom space, the company said the investment will see it expand current production output and advance research and development activities.

As part of the move, the chipmaker will upgrade existing fabrication facilities and install new manufacturing equipment.

Key infrastructure enhancements, including an expansion of the automated track system, aim to integrate disparate campus modules into a single, high-velocity production environment.

“This €5 billion investment represents a definitive commitment to maximize capacity at our Leixlip campus and increase what we can deliver to Intel Foundry customers," said Naga Chandrasekaran, executive vice president, chief technology and operations officer and general manager of Intel Foundry.

“By investing in our existing fabs with state-of-the-art technology and installing cutting-edge tools, we are not just increasing output of critical products like Xeon 6 and next gen Intel Xeon processors built on Intel 3, we are ensuring that Ireland remains at the forefront of the world’s most advanced manufacturing ecosystems, while strengthening the region’s role in the global technology landscape.”

Work has already started, according to Intel, and the company expects it to deliver jobs for specialized tradespeople, including construction workers and equipment installers.

Full-time tech-focused jobs at the chipmaker will also be delivered as part of the expansion scheme.

“Intel’s latest multi-billion-euro investment in Leixlip is a powerful vote of confidence in Ireland, our skills base and our position at the heart of Europe’s most advanced manufacturing ecosystem," said An Taoiseach Micheál Martin.

"At a time of rapid technological change and global competition, this expansion strengthens Ireland’s role in securing resilient semiconductor supply chains and reinforces our ambition to remain a global leader in innovation, productivity and sustainable economic growth.”

Intel builds on deep Ireland ties

Intel has been active in Ireland since 1989, and has invested more than €30 billion in the Leixlip campus, which opened in 2023, in that time.

In 2024, the company sold 49% of the plant to US private equity firm Apollo, but bought it back earlier this year at a higher price. Last August, the US government took a 10% share in Intel at a point when it was valued at less than a fifth of its current market capitalization.

With the EU’s Chips Act aiming to reduce reliance on foreign supplies of chips, Intel said the investment will strengthen Europe’s semiconductor supply chain and support the EU's tech sovereignty ambitions.

“Intel is one of Ireland’s longest standing and most strategically important investors," said Michael Lohan, CEO of Ireland's Industrial Development Authority (IDA).

"This project demonstrates the value of Ireland’s skilled workforce, innovation ecosystem and stable business environment, while reinforcing Ireland's leadership in advanced semiconductor manufacturing, supporting both European competitiveness and resilient global supply chains.”

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Irish data centers consumed a quarter of the country's electricity last year, almost as much as every home in the country combined.

New figures from the Central Statistics Office (CSO) show that data center electricity consumption rose to 7,663 GWh in 2025 from 6,973 GWh in 2024 – a 10% rise – to hit 23% of the national total.

For comparison, 2025 consumption stood at 18% for urban households and 9% for rural households. Consumption by all users other than data centers, including residential and other business customers, went up by just 2% over the same period.

The figures represent the latest phase in what's been a continuous rise. The CSO's quarterly tracking from 2015 to 2025 shows that data center electricity consumption grew from 291 GWh in the first quarter of 2015 to 1,991 GWh in the fourth quarter of last year.

Total metered electricity consumption rose 34% during the period, the officials noted.

"Newly compiled quarterly figures spanning 2015 to 2025 highlight a substantial increase in metered electricity consumption by data centers," said Dr Grzegorz Głaczyński, statistician in the Climate and Energy Division.

"Data center consumption has grown every single year without exception, more than doubling between 2015 and 2019 from 1,240 GWh to 2,490 GWh, and tripling again between 2019 and 2025, reaching 7,663 GWh."

Ireland is a data center haven

Despite its size, Ireland currently has more than 80 data centers, mostly in the Greater Dublin Area.

In 2021, the country introduced an effective moratorium on the building of new data center connections to the electricity grid, which remained in force until late last year.

Developers were required to either generate their own electricity on site, or relocate to regions outside the Greater Dublin Area.

Even during the moratorium, data center electricity consumption continued to rise steadily, with the International Energy Agency (IEA) predicting two years ago that it would represent a third of the country's electricity consumption by this year.

Now, under a new Large Energy Users (LEU) Connection Policy finalized late last year, the Commission for the Regulation of Utilities (CRU) requires new data centers to meet at least 80% of their energy demand from renewable sources.

This requires large data center developers to provide 100% on-site, flexible power generation, with at least 80% of annual electricity to be sourced from new, unsubsidized renewable projects within their first six years.

At the same time, a new Large Energy User Action Plan (LEAP) was introduced to encourage investment in energy intensive sectors such as semiconductors, pharmaceuticals, precision engineering, and hyperscale data centers.

The plan calls for data centers to be sited in green energy parks and co-located with renewable energy sources, including offshore wind.

It also allows developments outside these locations, however, in some cases until beyond 2030, in line with existing planning arrangements. One such area is Dublin, which has raised concerns among opposition parties.

These plans were subject to a fierce cross-party pushback last year, as ITPro reported at the time, largely due to concerns about the impact on local communities and the environment.

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IBM has cut the ribbon on a series of new mainframe offerings as the tech giant eyes data center efficiency gains.

The new IBM z17 and IBM LinuxONE 5 series mark the first time the company is offering rackmount options alongside single frame systems.

According to Tom McPherson, general manager for IBM Z and LinuxONE, the move here aims to help customers consolidate data center footprints without sacrificing performance.

“The number of mission-critical workloads is rising at an incredible pace, forcing organizations to make tough decisions about performance, AI integration, and infrastructure footprint,” McPherson commented.

“With these new IBM Z and IBM LinuxONE systems, we’re making it easier to run workloads where they make the most sense, while opening the door for a wider range of organizations to benefit from these technologies for the first time.”

IBM eyes data center flexibility

The new IBM z17 and LinuxONE 5 configurations support up to 82 cores and 18TB of memory across two processor drawers, according to IBM. The company said this represents a 20% increase in core count alongside a 12% increase in memory capacity.

The IBM z17 single frame is a fully packaged solution in an IBM rack designed to operate as a “complete enclosed unit ready to deploy now”.

With the IBM z17 rackmount, customers will have the option to install IBM Z components within their own industry-standard racks.

The company noted that customers can also co-locate IBM equipment with third-party components to “achieve the best fit-for-purpose installation” based on their needs.

“Each system is designed to help organisations reclaim space, improve energy efficiency, and integrate seamlessly into existing environments,” the company said in a blog post.

LinuxONE upgrades target AI performance

Elsewhere, the new IBM LinuxONE series comes in two dedicated configurations: the Rockhopper 5 and LinuxONE 5 Express options.

The Rockhopper 5 series is a “scalable, multi-drawer” configuration designed specifically for high-density workloads, according to IBM.

This comes equipped with on-chip AI acceleration features, as well as confidential computing and post-quantum cryptography capabilities in both single frame and rackmount configurations.

Meanwhile, the LinuxONE 5 Express option offers the same capabilities, albeit in a compact configuration. IBM said these are designed for smaller workloads, with a particular focus placed on cost-efficiency and scalability.

The z17 single frame and rackmount configurations, IBM LinuxONE Rockhopper 5, and IBM LinuxONE 5 Express series will be generally available for customers from 12 August.

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The US National Institute of Standards and Technology (NIST) has announced plans for a $20 million center to drive quantum research, development, and manufacturing.

The Quantum Manufacturing Engineering Center (QMEC) is being set up with the help of non-profit R&D organization SRI International, with the aim of strengthening the manufacturing of scalable, high-performance quantum components and systems.

“NIST is a world leader in quantum science and technology based on decades of fundamental research that helped launch the US quantum industry,” said under secretary of commerce for standards and technology and NIST director Arvind Raman.

“This public-private partnership with SRI International will accelerate the development of America’s quantum industrial base — the foundation upon which the quantum revolution is being built.”

The QMEC will recruit companies and quantum experts to conduct research and development aimed at boosting US manufacturing and strengthening supply chains to enable quantum technology production at scale.

Initially, the center will focus on areas such as cryostats and lasers, with the specific goal of improving the manufacturability of quantum-enabling components within three years.

This will include developing scalable processes for quantum chips and integrated photonic circuits and establishing standards and quality control methods for quantum technologies.

The project also aims to build a US-based supply chain for materials and components required for quantum systems, build expertise through programs focused on quantum, and accelerate commercialization pathways from laboratory to production.

“Our approach is results-driven and timeline-focused,” said Lawrence Lee, SRI’s QMEC program director. “Each project we undertake will have clear goals, defined milestones, and measurable outcomes that are informed by collaborative discussions with quantum technology companies, value chain partners, and end users."

US eyes competitive edge in quantum

NIST and SRI have been working together since 2019, establishing the Quantum Economic Development Consortium (QED-C) in response to the National Quantum Initiative Act – work that led NIST to identify quantum manufacturing as a critical gap in national efforts to develop a robust commercial industry.

Earlier this month, the white House issued an executive order, Ushering in the Next Frontier of Quantum Innovation, aimed at accelerating the deployment and commercialization of quantum computing, sensing, and networking.

Among other things, the EO directed the Secretary of Commerce to develop a plan for advancing the commercial readiness of quantum sensing, quantum-sensor manufacturing technology, and quantum-network-enhanced timing.

This will include prioritizing research, development, testing, and evaluation of applications and hardware for quantum sensing and quantum networking.

The new manufacturing engineering center will be a key component in the government’s push on this front, according to NIST.

"Quantum science promises to generate new knowledge and technologies that will supercharge scientific research and unlock enormous economic potential,” said deputy secretary of commerce Paul Dabbar.

“The new Quantum Manufacturing Engineering Center will bring together top experts to ensure both continued US leadership in quantum technologies and that we are the epicenter of manufacturing quantum systems at scale to drive advances in sensing, communications, encryption, computing, biomedicine and other critical areas.”

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Three-quarters of utility firms are struggling to forecast energy demands due to rapid data center infrastructure expansion.

A new study from Capgemini Research Institute suggests that eight-in-ten utilities predict more extreme and volatile demand patterns. A key factor here is that consumption patterns from AI are less stable and more difficult to model.

The biggest problem is uncertainty, the study noted, with utilities increasingly forced to plan for demand that may never materialize.

Two-thirds of electricity executives refer to “phantom” data center load requests - with 19% of these never materializing.

This forecasting uncertainty creates real problems with capital allocation, Capgemini warned, with utilities having to decide not only how much capacity to invest in, but where and when to prioritize grid modernization investments to support future demand.

Hyperscalers are feeling the pinch

Things are just as bad for hyperscalers, which need to make major infrastructure decisions against a backdrop of uncertain demand forecasts, grid availability, and connection timelines.

The problem is made all the harder by the geographic concentration of data centers, which places significant strain on local grids.

More than half of electricity executives are finding that load concentration is a major obstacle to reliable service, while large clusters of high-density facilities are creating localized bottlenecks that affect system stability and investment planning.

“AI is transforming electricity systems far beyond demand growth. It is exposing structural constraints in grid capacity, planning, and power availability, while making demand more dynamic and harder to predict,” said Claire Gauthier, global head of energy & utilities at Capgemini.

“The challenge is no longer only how much power is needed, but whether it can be delivered reliably, where and when it is required. Utilities have a defining role to play as system orchestrators, leveraging AI-enabled insights to balance grid and customer-owned resources, accelerate deliverable capacity, and enable the next phase of data-center growth.”

What’s driving energy consumption?

The projected increase in electricity consumption is largely down to AI training and inferencing, according to Capgemini.

Training and inference processes currently account for around 25% of data center energy consumption, yet this is projected to reach 60% within the next three to five years.

The consultancy noted that this not only places significant strain on data center infrastructure, but also leaves other IT workloads on the sidelines as enterprises prioritize AI-related activities.

Data centers are increasingly shifting from backup-only approaches toward primary behind-the-meter (BTM) and near-site solutions. Nearly three-in-ten say they already deploy on-site power solutions and 39% plan to add on-site/BTM within the next couple of years.

Operating independently

Looking ahead, the majority (86%) of respondents see the ability to operate independently from the grid as a competitive advantage.

Around three-quarters of utilities and data center executives told researchers they were looking to try and establish a diversified energy mix to ensure reliability and long-term resilience.

Part of this lies in the fact that renewable energy alone cannot provide enough continuous power at scale for large data centers and AI workloads.

Battery energy storage systems (BESS) are emerging as a possible solution, with nuclear Small Modular Reactors (SMRs) expected to take time to deploy.

More than two-thirds of electricity and data center executives globally are looking to natural gas as a near‑term, transitional solution until renewable energy and storage technologies can scale.

"For both energy providers and data-center operators, the key challenge is no longer only scaling capacity, but doing so under uncertainty, speed constraints, and rising system complexity,” said Gauthier.

“Success will depend on the ability to align infrastructure investment, energy sourcing, and AI-enabled operations to manage both the scale and volatility of demand, while balancing reliability, cost, and sustainability.”

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While every CTO’s job varies, some stand out more than others, and being CTO of one of the USA’s national laboratories is one such case.

In eastern Tennessee, south of the city of Oak Ridge and close to the Clinch River, sits the aptly named Oak Ridge National Laboratory – or ORNL for brevity – and its CTO, Scott Atchley.

ORNL is one of two facilities – the other being Argonne National Laboratory in Illinois – that make up the Leadership Computing Facilities. These two locations, both of which are funded by the US Department of Energy (DoE), are home to some of the fastest and most powerful supercomputers not just in the US but in the world: Aurora at Argonne and, at ORNL, Frontier.

Atchley, a 16-year veteran of ORNL, tells ITPro that there have only been two weeks in his tenure as CTO that he’s regretted taking the job.

“That’s when we had deployed Frontier,” he says.

Full power failure?

Yes, even in one of the most advanced computing facilities in the world, it’s still entirely possible to have a relatively small fault cause chaos.

“[Frontier is] our current supercomputer, it's an HPE Cray EX system and one of the benchmarks – not the goal, but one of the benchmarks – for the system is that it would reach exascale, or 10 to the 18 flops,” says Atchley.

Supercomputers’ power is only announced twice a year – at ISC in Germany, which takes place in June, and SC Conference in the USA, which takes place in November. It’s from the announcements at these conferences that the Top500 list of most powerful supercomputers is drawn.

With the deadline for ISC 2022 fast approaching, Frontier was failing to perform.

“We were only about halfway [to 10¹⁸ flows], and we could not figure out why,” says Atchley. “We were having daily calls with HPE and AMD, and the deadline was coming.

“We were in a full panic, and this was the first time I thought, ‘my gosh, we might have bought the wrong machine’.”

Fortunately for Atchley and everyone else involved, he was wrong, and once the problem was identified, it was a “simple fix”.

“It was a one-line software change, and all of a sudden the performance shot up to right at just under an exaflop,” he says. “When they're new, [supercomputers] are very fragile. So, we rebuilt the machine during the day, and the next night we did the runs, and we finished it at about six in the morning.

“There were probably 50 people in line watching … and when the job looked like it was going to finish, all the chatter went quiet, nobody wanted to jinx it – we had been trying all night long, and it was really exciting when we saw the result that we actually exceeded an exaflop.”

Putting power to use

Top500 does have its limitations – only companies that run the LINPACK benchmark and submit the outcomes are measured, for example. There are also claims that it’s not particularly scientific, and Jack Dongarra, one of the creators of LINPACK, has himself admitted that its usefulness is limited.

“This benchmark reports the performance of this one application with its floating point operations and message passing. So there is one application and one number that represents the performance for the computer system. The weakness is that this is just one application and one number,” he told HPCWire in 2002.

Limitations and weaknesses don’t mean useless, however, and Atchley pushes back at the idea that Top500 is entirely meaningless.

“A lot of people will knock the Top500 … but we've actually had jobs on the system doing real science exceed an exaflop in performance, which is really cool, and so we're glad,” Atchley tells ITPro. “Our users love the machine, it's doing some incredible science.”

From aerospace to outer space – and back

Speaking of users, who is making use of Frontier? One case study Atchley gives is from the engineering firm GE. In partnership with French firm Safran, they have been developing a new type of jet engine.

“If you look at engines when commercial airliners came out in the 60s and early 70s, the engines were really narrow, really long,” says Atchley. “If you flew[today] and looked out the window, the engines now are really short, but really wide.

“Making them wider makes them more energy fuel efficient, but they're to the point now that if they make them any bigger, the gains they get in the efficiency they lose because of the drag on the nacelle.”

One option is to get rid of the nacelle – the housing around the engine – altogether. This isn’t a new idea, Atchley explains: “NASA has known about this for 50 years.”

The only issue is that using them on a commercial aircraft would deafen the passengers and crew, because they are – or would be – so loud.

It was only once Frontier came online with its exascale capabilities that GE and Safran were able to measure in high resolution the swirls of turbulence coming from the engine.

“Now they're tweaking the design to minimize those, and so that is the huge breakthrough, is understanding turbulence at a really fine scale. They can't do that in a wind tunnel. This engine is so big, there's no wind tunnel on earth that would fit it, so the only way they can do it is in a computer,” says Atchley.

As powerful as Frontier is, however, it can only model a 14-blade engine. In 2028, though, Frontier is expected to be superseded by the Discovery supercomputer, which may open up new opportunities for the likes of GE.

Once it’s online, ORNL will be home to three supercomputers: Frontier, Lux (which is expected to come online in 2026), and Discovery. At least for a time. Frontier will, ultimately, be decommissioned just as its predecessor, Summit, was.

“The challenge you get on these machines is even if the hardware doesn't run out or doesn't fail, the software, the vendors don't want to support something that's now 6,7,8 years old,” says Atchley.

“You think you want to have the latest software, you want to have the latest security patches, and so eventually you have to turn the machine off, but we had universities [and] industrial partners asking us, ‘can you run Summit longer?’ – we'd love to, but we can't.”

Sometimes there is a second life for these apparently beloved behemoths that isn’t just the recycling route.

“IBM was able to sell a portion of it to an oil and gas company, and so it is still doing work for them somewhere else.”

When the CrowdStrike update and associated outage adversely affected Windows devices in July 2024, many organizations were impacted. Just outside Houston, IT operations at the Spring Bank Independent School District (ISD) were among those affected.

Spring Branch ISD has over 31,000 children under its care across 46 schools, spanning 57 buildings and 6,000 staff members.

The organization faced significant challenges when the CrowdStrike outage hit, and it came at the worst possible time, given that summer school classes don’t run on Fridays.

“During the summer, school districts are primarily off on Friday, we work 4-10, so we’re closed on Fridays," Troy Neal, Spring Branch ISD’s executive director of cybersecurity and technology, told ITPro at Pure Accelerate 2026.

“[We] woke up Friday morning, turned on the news, and there’s this banner running across that says ‘CrowdStrike outage’ and I’m like ‘we’re a CrowdStrike customer, is this really affecting us?’

“Outages can be lots of things, so I looked at my phone, had some messages, and [I said] let’s start checking the systems, and [I] notice[d] the phone system was down.”

This posed a problem for the organization, given that these systems are hosted on-prem, Neal explains. Further investigation showed staff members couldn’t access some critical services.

At this point, the scramble to recover, or at the very least establish what was happening, was on.

Disaster recovery with Everpure

Spring Branch ISD works with a range of hardware vendors for storage, recovery, and backup purposes, Neal explains. Among these are Hitachi, Dell, Veeam, and Everpure, formerly Pure Storage.

High-capacity, high-performance storage is critical for the organization, which manages more than 60,000 endpoints spanning Chromebooks, iPads, and various Windows devices at schools across the district.

When Neal started at Spring Branch ISD some seven years ago, the organization was in the process of refreshing its storage capabilities due to the sheer volume of CCTV footage they stored.

“We have 4,400 cameras in our school district,” he explains. “We store about four to six petabytes of data on just CCTV, so we started doing our due diligence.”

This exploratory process saw Neal and colleagues scour the market for options, eventually narrowing the field down to Dell, Everpure, and Hitachi. Budgetary constraints were a concern, he notes, particularly given that Spring Branch ISD is publicly funded.

An initial offer saw Neal faced with a $7.1 million price tag for six petabytes across two locations over 10 years. He felt this was actually good value, but it was outside of his budget, which was just over half that amount.

Hitachi ultimately met Spring Branch’s requests, and the organization secured vital storage infrastructure to meet its increasingly demanding data needs. But it was here that the relationship with Everpure blossomed.

“We were really impressed with Pure at the time,” he says. “In the cyber space, 3-2-1 is the standard for backup strategies. I like 4-2-1, [as] you can never have enough backups.”

“I’m a layered defense kind of guy for everything we do, and so we had an opportunity with some extra bond funds to bring in another solution to be our secondary backup.”

That deal saw Spring Branch ISD adopt FlashBlade//S and FlashBlade//E solutions, which provide users with high-speed, scale-out backup storage. Notably, Neal says that when the CrowdStrike outage hit, these proved vital.

The road to recovery

After the initial furore of the CrowdStrike outage and the impact on Spring Branch ISD dissipated, Neal says he was in some way glad that it was a “CrowdStrike outage, not a Spring Branch ISD outage, and caused by an adversary.”

Focusing on the task at hand, Spring Branch found itself in a similar situation to many organizations elsewhere in the world: dealing with a botched patch.

“We’re in the office by 6.30 am that morning,” he says. “We pulled out our incident response plan, and [said] this is a live exercise.”

“Our first thing was their patch,” Neal adds. “That didn’t work too well.”

The initial workaround saw Neal’s team revert to a Veeam backup, which he says was “just a regular-sized virtual machine (VM) that we had”. This was expected to take around 45 minutes for a single VM.

Spring Branch ISD has over 200 VMs across its IT estate, meaning the recovery time could take days. With summer school students and staff expected back in on Monday morning, this could prove disastrous.

“We [said] this isn't going to work, let’s try Pure too.”

The resulting backup “took minutes,” Neal notes, adding “this is the performance we paid for and why we chose Pure.”

Leaning on Everpure backups, Neal says critical services were back up and running within around four hours. Thereafter, ancillary services were up by around noon.

“About six hours later, we were up and running, and then we did some cleanup work over the weekend. We also had to wait on some of our owners of some of these systems to test them, validate them, and then by the end of the weekend, we rank a whole other snapshot of every single VM,” he says.

“We had some good clean states going forward, and Monday came around, and summer school went on. Nobody knew the difference.”

Looking ahead, Neal says Spring Branch ISD plans to continue - and expand - on its relationship with Everpure. The organization is going back to a 3-2-1-style cluster, and recently brought in another FlashArray//C solution for additional backup.

Notably, the school district is also moving to EverGreen//One, the company’s storage as a service (STaaS) platform. This aims to unify on-prem and cloud storage resources, providing users with a single data storage platform based on a subscription. Neal notes that the flexibility and ease of use associated with this setup were particularly appealing.

“I’m going with a subscription-based model. So I don’t care what the hardware is, because that’s Pure’s problem to figure out and manage,” he jests.

Colt DCS has cut its greenhouse gas emissions across Scope 1, 2, and 3 categories by 27% since 2019 as part of its efforts to decarbonize the design, development, and operation of its data centers.

The firm’s 2025 Sustainability Report reveals it achieved 100% renewable electricity for Scope 2 emissions and a 90% renewable share of its total energy consumption across Scopes 1, 2 and 3.

"We are proud of the progress made in reducing absolute emissions in 2025 as we continue advancing towards our 2045 net zero target. Whole life carbon assessments are helping customers and partners identify key sources of value-chain emissions and understand the impact of design and procurement decisions," said Anthea van Scherpenzeel, the company's head of environmental sustainability.

"Through close collaboration with suppliers and industry partners, we are confident we can continue delivering projects at speed and scale while making steady progress towards our SBTi targets.”

Last year, the company expanded its Global Reference Design (GRD), developing a standardized blueprint it said is flexible enough to adapt to local requirements while keeping consistent core principles.

The GRD sees energy-efficient systems, lower embodied carbon materials and near-zero wastewater cooling technologies incorporated into all new data center designs.

According to Colt, the aim here is to maintain annualized Power Usage Effectiveness (PUE) below 1.2, along with waste diversion rates of 75% in construction and demolition and 90% in operations.

So far, the company has reached TRUE Zero Waste certification across three sites - London North, Tokyo Inzai 1-3, and Osaka Keihanna - and hit a global PUE of 1.41.

In terms of water use, the GRD incorporates chilled water systems using low-Global Warming Potential (GWP) refrigerants with near-zero water consumption, alongside rainwater harvesting for indoor use.

Meanwhile, closed-loop liquid cooling systems recirculate coolant, transferring heat without relying on evaporation.

Dina Nassar, sustainability analyst at Colt DCS, said efforts to improve sustainability capabilities come in direct response to AI-related pressure, which is creating “new efficiency challenges” for data center operators globally.

"Our revised GRD underpins the efficiency gains made in 2025, by ensuring lower embodied carbon materials and advanced cooling technologies are baked in at the design stage.”

Data center sustainability in the spotlight

The figures published by Colt come amidst rising concerns over data center efficiency and the environmental impact of infrastructure worldwide.

A recent UN report concluded that AI will be using the same amount of water as 1.3 billion people by 2030, along with 945 terawatt-hours of electricity, while CO2 emissions will rise to 399 million ​tons.

Similar research from PwC warned the data center boom is increasing electricity consumption faster than the supply of clean energy in some parts of the world, leading to higher power prices and increased reliance on fossil fuel generation.

In the US, the International Energy Agency (IEA) predicts that most of the increase in data center electricity demand through 2030 will be met by natural gas, holding back decarbonization efforts.

Earlier this month, the EU announced plans to develop minimum energy-efficiency standards for data centers, saying that there's an urgent need for them to become more energy efficient, reuse waste energy such as heat, and use more renewable energy sources, with a view to becoming carbon-neutral by 2030.

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Construction has started on the UK’s next national supercomputer, claimed to be 50 times more powerful than the country’s current model, ARCHER2.

Owned by UK Research and Innovation (UKRI) and hosted at the University of Edinburgh, the system is being built with the help of a £750 million investment from the UK government.

According to the university, the supercomputer will empower "world-changing" discoveries in fields such as aircraft engineering, extreme weather events, and drug discovery for cancer.

"This supercomputer has the potential to help UK researchers accelerate frontiers, unlock new paradigms, and develop solutions to challenges that were previously impossible," said Professor Liz Baggs, vice-principal for research and innovation and chair of food and environmental security at the University of Edinburgh.

"This includes creating the next generation of medicines, revealing untold stories about our planet, and, most excitingly, leading to discoveries that we can’t even imagine yet.”

The new machine is expected to be able to deliver at least a billion billion calculations per second, a marked increase compared to the 20 million billion delivered by ARCHER2.

This means the system will be able to carry out incredibly complex calculations in hours rather than days, and solve much larger problems than ever before.

Notably, the supercomputer will be more efficient than existing models, with surplus heat generated being used to warm university buildings. Developers also plan to assess whether it could also be used to warm local homes by warming water in disused mines.

To reduce the environmental impact, site demolition has been kept to a minimum, and there's a range of sustainability projects planned, including tree planting, protecting ancient trees, and conservation projects for local wildlife.

Construction work on the building, which is roughly the size of a supermarket, is being carried out by Robertson Construction Central East.

“You would never guess from this ordinary-looking building site just how vitally important it will be for the UK and how its contents could impact on all of our lives positively," said Professor Mark Parsons, director of the Edinburgh Parallel Computing Centre (EPCC) at the University of Edinburgh.

Building on ARCHER2’s success

ARCHER2 – also housed at the University of Edinburgh – played a big part in Covid-19 drug discovery, helped Rolls Royce to improve aircraft engine efficiency and sustainability, and enabled engineers to make wind farms more efficient.

The ageing supercomputer has generated £8 per £1 invested, enabling more than £4.2 billion in benefits for the UK economy, according to an independent report by London Economics.

The new supercomputer nearly didn’t happen, having been shelved by Labour when it came into power, and then reinstated a year later.

“The commencement of construction in Edinburgh marks a pivotal moment for the UK’s scientific infrastructure. We are providing the UK’s brightest minds with the 'industrial-scale' tools required to solve society’s most complex challenges, from decoding the next generation of life-saving medicines to engineering a net-zero future," said AI minister Kanishka Narayan.

"This £750 million investment isn't just about speed; it’s about economic growth, and ensuring the UK remains the premier destination for global innovation."

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IT, in its current form, relies entirely on networks. From office LANs to transatlantic fibre-optic cables, to WiFi and cellular connections, no business can really carry out its day-to-day tasks without using at least one network.

Yet, with all the attention focused on AI and its vociferous appetite for GPUs, CPUs, and even storage, the network risks being overlooked.

Without both investment and new ways of engineering the network, connectivity could derail progress in digital transformation, the user and customer experience, data science, and, of course, AI.

Recent research from vendor Cisco found that three in four enterprises expect to hit the limits of their campus and branch networking capacity within two years, and they are seeing network traffic double year on year. Already, some enterprises say they are holding back on scaling up AI investments because of network constraints.

Unlocking the problem

“You have to look at where bottlenecks are,” Paul Stringfellow, CTO at IT consultants Gardner Systems, told ITPro.

“This is less an enterprise network issue, but more a backend issue. So, looking at how you ensure high capacity [connections] between CPUs, GPUs, and back-end storage.”

As Stringfellow points out, there have been advances in high-speed connectivity, including faster Ethernet speeds and RoCE (RDMA over Converged Ethernet), which allows remote direct memory access over Ethernet technology.

These, though, along with Infiniband and 400Gbps and even 800Gbps Ethernet, are mostly targeting data centres and cloud providers.

For client devices, developments in WiFi should improve both connectivity for users and for IoT devices. Technologies such as WiFi 6 and WiFi 7 – and in the future, WiFi 8 - look to increase bandwidth and provide reliable connections, even as more users or devices connect to the WLAN.

“WiFi also needs to be included in this conversation, so many users now use WiFi as the primary office connection. The idea of cabled connections is foreign in a lot of places,” says Stringfellow.

The move to cloud and SaaS-based applications, as well as widespread home working, means that much of an enterprise’s data traffic has shifted to WiFi, with users never reaching for an Ethernet cable. Effective WiFi has become essential, with poor connections directly affecting productivity.

“Enterprise Wi-Fi has evolved significantly and now carries far more than simple user connectivity,” Phil Huang, business development and field application manager for the UK and Ireland at vendor D-Link, told ITPro.

“Modern Wi-Fi networks routinely support collaboration platforms, voice and video applications, IoT devices, and a wide range of cloud-based services,” he says. “In that sense, Wi-Fi has become a strategic access layer rather than just a convenience technology.”

But, he says, there are still significant differences between wired and WiFi networks. And improvements in WiFi connection speeds are pushing bottlenecks further into enterprises’ core networks. This, in turn, is driving investments in the wired LAN too.

“Newer Wi-Fi 6E and Wi-Fi 7 access points are capable of exceeding 1Gbps throughput, meaning a standard Gigabit uplink can quickly become the bottleneck,” says Huang.

As a result, businesses are moving to both 10Gbps connections and 2.5GbE. This is moving closer to “standard” 1Gbps connections in cost. “The premium over traditional Gigabit Ethernet has reduced dramatically, making 2.5GbE an easy investment for organizations refreshing their infrastructure,” he says.

Installing 2.5GbE, and even 10GbE, is simpler than fibre-optic or even higher-speed copper LANs. But it still works well for most office deployments, at distances under 100 metres.

Campus to cloud

The bigger challenges lie both in connectivity from businesses to data centers and the cloud, and within the data center itself.

Here, growing data volumes and the need to reduce latency are prompting CIOs to look at how their networks are designed, as well as the hardware they run on.

“As SaaS and AI adoption grows, hybrid work reshapes access patterns and digital security follows users beyond traditional perimeters,” Nauman Raja, a director analyst at Gartner, told ITPro.

“Enterprise networks are no longer built around the data centre. Instead, they now gravitate around the internet.”

AI too is playing its part: expensive GPUs are a waste of money if the network cannot keep up. Network architects need to meet the high-bandwidth demands of uploading data for model training, usually to the cloud, and the low-latency needs of inference.

Organizations also need to consider how they connect their existing enterprise applications to AI models. Conventional networks are rarely built with these demands in mind.

“I don't think we're having a bandwidth problem. We're mostly having a design or infrastructure problem,” Wim Remes, a technology consultant who has also run operations for a managed security service provider (MSSP), told ITPro.

“Compute and data are not always in the same place. Either we bring data to the compute or we bring the compute to the data. The paths we create on the network need to be the shortest.”

“The top-level issue, to me, is that we’ve ignored ‘the network’ for about a decade and a half,” he adds.

“Does a full-stack developer know the basics of networking? The clear answer is no. The stack in ‘full stack’ stops at the web server at best. At the same time, many a CIO also doesn't really care about the network anymore.”

The challenge is to bring together new and emerging hardware technologies, such as 400 or 800Gb Ethernet and RoCE, with a network architecture giving the performance data-heavy, latency-intolerant applications need.

“Bad network design, such as routing internet-bound traffic through centralised data centres for software as a service (SaaS) application, or for end-user collaboration, such as video conferencing, will result in poor user experience and inconsistent access for both remote and on-site users,” Gartner’s Raja warns.

“As demand fluctuates with remote working patterns, network architectures must allow dynamic bandwidth allocations,” Raja adds.

“In many cases, smart traffic management and automated policies can reserve sufficient capacity for business-critical applications even during peak usage.”

That, though, means thinking less about the network as hardware, but as an operational issue across the business. After all, no one wants colleagues, or worse still, customers, locked out of applications because someone “tokenmaxxing” is blocking the LAN.