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Artificial Intelligence (AI) is being adopted across industries at remarkable speed. From finance to healthcare, AI is driving new services and unlocking new business models – fundamentally changing the way we all live, learn and work. But with progress comes challenges, and in the case of AI, fast adoption means that the infrastructure supporting it is under increasing pressure.

Data centers, once optimized for traditional enterprise workloads, are being pushed to accommodate entirely new operating patterns. The rise in high-performance computing means more power, more heat, and more volatility. Established systems are struggling to keep up.

AI workloads don’t just demand scale. They require IT infrastructure that can react to dynamic, unpredictable demand. And as organizations expand their use of AI, the supporting environment must evolve too.

Rack density is climbing quickly

One of the clearest shifts data center operators are experiencing is in rack density. Standard deployments have typically operated at around 10 kilowatts to 15 kilowatts per rack. But AI hardware - especially clusters of Graphics Processing Units (GPUs) - consumes much more power and generates far more heat.

In many AI deployments, racks now draw 40 kilowatts or more. Some experimental training environments exceed 100 kilowatts. This isn’t just about energy consumption. It’s a challenge for every part of the power chain, from uninterruptible power supply (UPS) systems to power distribution units (PDUs), to the facility’s own switchgear.

Older data centers may not be able to support these loads without major upgrades. For those expanding into AI, the layout, redundancy, and zoning of rack space needs careful planning to avoid creating thermal or electrical bottlenecks.

Cooling is reaching its limits

Conventional air cooling was never designed for today’s thermal loads. Even with hot aisle containment and optimized airflow, facilities are finding it hard to remove heat fast enough in high-density zones.

This is why liquid cooling is gaining ground. Direct-to-chip cooling systems, already common in high-performance cloud computing environments, are being adapted for data centers supporting AI and where densities exceed 50kW/rack. Immersion cooling is also being explored where space is tight or where energy efficiency is a priority where densities exceed 150kW/rack.

Installing liquid cooling involves significant changes - from plumbing and pumping systems to maintenance protocols and leak prevention. It’s a major shift, but one that is becoming necessary as traditional cooling approaches run out of headroom.

Load volatility is forcing a rethink

AI workloads behave differently from legacy compute. Training cycles can move from idle to peak and back in a matter of seconds. And inference jobs often run continuously, putting steady pressure on electrical and cooling infrastructure.

That variability puts systems under stress; power systems need to be fast and responsive and cooling systems must avoid overshooting or lagging behind, sensors and controls need to act in real time, not based on average load assumptions.

This is driving investment in smarter infrastructure. Software-based power management, predictive analytics, and environmental telemetry are no longer add-ons. They are becoming essential for resilience and efficiency.

Commissioning is getting more involved

Designing infrastructure for AI is one thing. Proving that it works under pressure is something else.

Commissioning teams are having to simulate conditions that didn’t exist just a few years ago. That includes sudden spikes in compute load, failure scenarios under high thermal pressure, and mixed environments where air and liquid cooling run side by side.

This means that simulation tools are being used earlier in the design process, with digital twins helping to test airflow and thermal modelling before equipment is installed. On-site commissioning now includes more functional testing, and more collaboration between electrical, mechanical and IT teams.

Power constraints are slowing progress

In some parts of the UK and Europe, getting access to the grid has become a significant barrier. Long connection times and limited capacity are delaying new builds and expansion projects.

This real and growing challenge is leading some operators to turn to on-site energy generation, energy storage systems, and modular buildouts that can grow in stages. Others are prioritizing regions with better access to power - even if they aren’t the original target location.

Cooling strategies are also directly affected. Liquid cooling systems require consistent energy supply to maintain stable operation. Any power disruption can quickly become a cooling issue, especially when workloads can’t be paused. And, in high-density environments, even brief interruptions to power can have thermal consequences within seconds - leaving no room for infrastructure to catch up after the fact.

Heat reuse is being taken seriously

AI workloads generate a lot of heat- and more than ever, operators are exploring ways to efficiently reuse waste heat.

In the past, heat recovery was often seen as too complex or not cost-effective. But with higher temperatures and more concentrated thermal output from liquid cooling systems, the picture is changing.

Some new facilities are being designed with heat export capabilities. Others are considering connections to local district heating systems. Where planning authorities are involved, expectations around environmental performance are rising, and heat reuse can be a strong point in a project’s favor.

Infrastructure is becoming more adaptive

AI is creating new expectations for data centre infrastructure. It needs to be fast, scalable and adaptable. Standardization helps, but flexibility is becoming more important - particularly as AI workloads evolve and spread from central hubs to the edge.

The next generation of data centers will need to manage high loads with minimal waste. They will need to recover energy where possible, stay efficient under pressure, and respond in real time to shifting demand.

This isn’t just about capacity. It’s about designing flexible systems that stay effective as conditions change.

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This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

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• NCA arrests four people suspected of participating in recent attacks
• M&S, Co-op and Harrods all hit by cyberattacks
• Arrested group are accused of organized crime, money laundering, and more

The UK National Crime Agency (NCA) has arrested four people suspected of taking part in recent cyberattacks against M&S, Co-op, and Harrods.

An NCA press release said the police apprehended two 19-year-old males, one 17-year-old, and a 20-year-old female in the West Midlands and London, all of whom had their electronic equipment seized.

They are suspected of Computer Misuse Act offenses, as well as blackmail, money laundering, and participating in the activities of an organized crime group.

DragonForce

"Since these attacks took place, specialist NCA cybercrime investigators have been working at pace and the investigation remains one of the Agency's highest priorities,” commented Deputy Director Paul Foster, head of the NCA's National Cyber Crime Unit. "Today's arrests are a significant step in that investigation but our work continues, alongside partners in the UK and overseas, to ensure those responsible are identified and brought to justice.”

In late April and early May 2025, three major UK retailers, M&S, Co-op, and Harrods, all suffered major cyberattacks.

The hit on Marks and Spencer affected stores nationwide, and resulted in “small changes” to store operations in order to protect customers “and the business." The retailer confirmed Click and Collect services were impacted by technical issues as a result, and some stores were unable to process contactless payments.

Co-op suffered differently, as threat actors (who named themselves “DragonForce”) walked away with a hefty archive of sensitive company data, including customer information.

"This data includes Co-op Group members' personal data such as names and contact details, and did not include members' passwords, bank or credit card details, transactions or information relating to any members' or customers' products or services with the Co-op Group," the company said at the time.

"Cyber attacks can be hugely disruptive for businesses and I'd like to thank M&S, Co-op and Harrods for their support to our investigations. Hopefully this signals to future victims the importance of seeking support and engaging with law enforcement as part of the reporting process. The NCA and policing are here to help."

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• Perplexity’s new Comet browser promises an AI assistant that travels the web for you
• Comet joins other AI browsers aiming to beat Chrome and other popular platforms.
• Though AI companies are betting on their browser's enticing users, their mainstream appeal is uncertain.

Perplexity has officially made the move to web browsers, embedding its AI tools into Comet, its new Chromium browser.

It’s available now, initially only to some subscribers of Perplexity’s $200-a-month Max plan. At first glance, Comet is like most browsers, but Comet has a unique sidebar. You can highlight a word, sentence, or image, and Comet will discuss it with you. You can get a summary of an article, write an RSVP, or organize the itinerary of your next vacation. There's also a privacy benefit. Perplexity says all of the AI processing stays local and that it won’t train its AI using your site visits.

It makes sense. Browsers are central to modern work and life. If AI models can latch onto our browsing flow, then we will be using them all the time. But will it stick?

The browser space has seen a lot of failed Chrome clones, from Yahoo Browser to Internet Explorer 6. Chrome and Safari claim over 90% market share globally. The subscription price alone might be the biggest deterrent. Perplexity’s Comet is $200 a month. Compared to the price of 'free,' it would take a lot more than an occasional paper summary to make people pay up.

And while Comet shows how it might be useful as a way for Perplexity to meld its AI with a web browser, it's hardly alone in pursuing the idea of augmenting web browsers with AI. OpenAI is building a ChatGPT-native Operator browser. Currently, The Browser Company, Opera's Aria, Microsoft's Edge with Copilot, and others are all providing similar services. And Google is continuously adding AI to Chrome, offering quick overviews, summaries, and image explanations.

AI browsing future

Perplexity, OpenAI, and any other contender face the same challenge of getting people to switch. And while it's possible that Perplexity breaks through to a new group of users with the idea of an AI browser, they will then have to face off against each other as well as the AI-enhanced versions of Chrome, Safari, and the others on the rise from Brave to Firefox, each with their own pitch for a better AI, more privacy, or another appealing feature.

Or, maybe the concerns about technical issues and user privacy will keep the AI browser a niche product with limited appeal for hardcore users, a bit like Linux. AI might be the future of browsing, a brief fad, or something in between.

The question of what makes it worth the effort will need to be answered. If Comet and others can streamline the online experience and save us a lot of time, they'll be popping up everywhere, but for now, they’re novelties priced at a premium. We'll have to see if the AI-enhanced browser can find the users it needs to last.

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After the category defining moment of ChatGPT’s launch, the subsequent rush to AI tools has proved intense. On both sides of implementation and innovation, companies have dashed towards AI as a solution, many without knowing the problem. In certain cases, it’s felt half-baked – more than 50% of organizations have an AI leader, but nine in ten (88%) indicate their AI leader does not have the title of ‘Chief AI Officer’.

From this initial excitement phase, progressing to maturity in AI deployment becomes key, yet just 1% believe they are at maturity. This is the critical task – recalibration towards AI maturity could contribute a staggering $15.7 trillion to the global economy by 2030. At the AI Action Summit this year, Sundar Pichai stated AI is “the most profound shift of our lifetimes”. This is even “bigger than the shift to personal computing, or to mobile”. But in thinking about these patterns of change, it begs the million dollar question – what’s next after AI?

New appetite

Spurred on by the success of AI-related innovation, there is an appetite in the tech sector for the next big thing. We’re hungry for more. And the next ‘tech feast’ we can expect is likely to be quantum computing. In terms of upgrading processing power, quantum computing could be the next game changer, making a - pun intended - quantum leap for science and technology.

Faster processing is the key to unlocking huge efficiency value in the tech sector; McKinsey predicts quantum technology could create value worth trillions of dollars within the next decade. And, recent advances in AI could bring forward the timeline to operationalizing the technology at scale.

The quantum rush

Quantum computing uses principles from quantum mechanics, specifically superposition and entanglement, to process information in fundamentally different ways from traditional computers. A regular computer stores information in bits, which can be either a 0 or a 1 at any time. A qubit, by contrast, can be in a state of 0, 1, or any combination of both at once, thanks to superposition. This allows quantum computers to evaluate many possible solutions at the same time.

Entanglement creates a connection between qubits, where the state of one directly affects the state of another, even when separated. This makes quantum systems highly efficient at solving problems involving many interdependent variables.

A business laptop might be able to simulate, say, a small molecule, by brute-force calculation, taking hours or days if the problem is complex. A quantum computer with enough stable qubits could perform the same task in seconds by exploring all configurations in parallel. It's not just faster; it’s a fundamentally more scalable approach to certain classes of very difficult problems.

To provide some real-world perspective, Google developed Willow, announced at the tail end of 2024, a quantum chip, which completed a benchmark task in under five minutes that would take a top supercomputer over 10 septillion years – far longer than the entire age of the universe.

Quantum leaps for business

In the long term, quantum computing could significantly impact businesses by unlocking new levels of computational power that enable faster and more efficient problem-solving. As quantum technology matures, businesses may benefit from breakthroughs in areas like optimization. It’s like switching from walking through a maze, to having a bird’s eye view of all the paths.

Spiros Michalakis, at the Caltech Institute for Quantum Information and Matter, stated that "even if 1% of what we expect comes to be, it will exponentially transform your business", anticipating we will see its impact within the next five years. Across a variety of sectors, quantum computing can vastly accelerate the automation of strategy, in supply chain management, resource allocation, data encryption, enhanced cybersecurity, and processing complex simulations, such as in product development or drug discovery.

While quantum computing may not directly replace current systems for most - small - businesses in the short term, those that adopt or integrate quantum solutions early on could gain a competitive edge in innovation, cost efficiency, and scalability that puts them lightyears ahead of competitors.

However, the pace of this transformation will depend on the accessibility and affordability of quantum technologies, which may initially be more available through cloud-based quantum computing services rather than requiring direct ownership of quantum hardware, which will likely remain expensive for considerable time.

How do we prepare?

The lessons we learn here and now from AI strategy can teach us how to expect and deploy new, esoteric technologies. It’s about finding those fault lines or cracks within your business – what isn’t working, and what is your tech stack limited in solving – and then resourcing around that. It’s important for leaders to stick their heads above the parapet and see what advanced solutions are out there in play, before they become mainstream and the early adopter advantage is lost.

The democratization of AI is teaching us that there is widespread accessibility for developers and deployers around emerging technologies. After DeepSeek hit the market it signaled a change. IBM underscored this – “it won’t just be the very elite that have access to incredible compute who will be able to build the next series of models”.

What has happened with AI goes a long way to show that these incredibly high-tier computing resources are not just top-shelf and exclusive. The more abundant and integrated they become, the more urgent it is for businesses of all sizes to look at how tech can help them. This is the thinking we need to apply to quantum computing.

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This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

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• Qantas suffered a cyberattack in early June 2025
• A thorough investigation has now placed the number of affected individuals at 5.7 million
• Passwords and payment data is safe, but crooks took names, addresses, and other PII

Qantas has confirmed sensitive information on 5.7 million customers was exfiltrated in the recent cyberattack it suffered.

Australia’s largest airline said it recently spotted an intrusion after a threat actor targeted a call center, and accessed a third-party customer servicing platform. Initially claiming that six million people were affected, Qantas has now came forward with more precise figures.

In a press release published on the company’s website, it said the attackers took four million customer names, email addresses, and Qantas Frequent Flyer details. For the remaining 1.7 million, they also stole postal addresses, dates of birth, phone numbers, gender, and meal preferences.

Scattered Spider

Credit card details, personal financial information, passport details, as well as passwords, PINs, and other login details, were not compromised, since the data wasn’t even held by the company, Qantas confirmed.

It said that it had started notifying affected customers of the breach, and urged them to remain vigilant and independently verify the identity of unsolicited callers.

The company did not say who the threat actors were, or if they tried to deploy any ransomware.

However the incident shares many similarities with other attacks recently made by the group known as Scattered Spider, a financially motivated hacking group known for targeting large US companies using social engineering and SIM-swapping techniques.

This group has not yet claimed responsibility for this attack - but in recent weeks, multiple reports have emerged of airlines being hit by cyberattacks, with Hawaiian Airlines confirmed suffering an attack and both WestJet and GlobalX suffering the same fate recently too. The FBI even released an advisory, warning US companies about Scattered Spider activities.

At press time, there was no evidence that the stolen data was released to the wild. Still, Qantas said it continues “actively monitoring” the web, with the help of specialist cybersecurity experts.

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