Lead times of nine to 12 or even 18 months. Costs rising by 35%, 45%, even 50% to 200%. More than halfway through 2026, the market for IT infrastructure that’s crucial for enterprise projects, including those involving artificial intelligence, is strapped.

Memory is at the root of the shortages. Memory prices “have risen by 50% to 200%, resulting in PC prices increasing by 35% to 45% and some server prices rising over 125%,” according to Jon Forest, VP analyst at Gartner. Network switches also need memory, albeit in lesser amounts than servers, so they are not immune, with prices and lead times likewise rising dramatically.

Industry experts agree that most of the issues stem from hyperscalers gobbling up memory capacity, which trickles down to servers, storage systems, and networking devices. But while the source of the problem may be new, supply chain disruptions are far from unprecedented.

As a result, industry insiders are not short on advice on how best to deal with the situation, with tips including making better use of what you have, considering options beyond your usual scope, and lots of planning with your vendors and internal finance teams.

State of the problem

Just how bad is the current supply chain problem? “It’s pretty bad,” says Matt Kimball, vice president and principal analyst with Moor Insights & Strategy. Companies accustomed to 30- to 45-day lead times for various infrastructure are now looking at 6, 12, or even 18 months.

“It’s real, and I’m hearing it from companies of all sizes, from the 1000-server to the 10,000-server shops,” Kimball says.

“Memory costs are expected to rise sharply well into 2027 and will reach up to 25% of network hardware expenses by the end of 2027,” according to an email Gartner’s Forest sent to Network World. The figure below shows the timeline Gartner expects for memory prices, and Forest notes that the same timing applies across networking, storage, and compute infrastructure. 

Gartner

“Enterprise network equipment pricing is projected to increase by over 20% in 2026. This upward trend is anticipated to continue with a further rise of 3% to 5% entering 2027, with no signs of price reduction until the end of 2027.”

But “reduction” will likely look more like “stabilization.”

“That’s something a lot of people don’t like to talk about. But let’s say prices went up 40%, they may come down five,” says Phillip Privett, senior vice president of vendor management with the global distributor and value-added reseller TD SYNNEX. “They’re not going to come down 40%.”

Perhaps worse, compared with past disruptions caused by issues such as fires in chip fabrication factories or the Covid pandemic, Kimball says this one is “durable” because its cause—the AI wave—is more long-lasting and just getting started.

“This AI inference wave we’re hitting is just beginning. It’s going to be longer and bigger than the training wave,” he says. “It’s impacting everything, from AI infrastructure to the traditional stuff that’s standing up your virtualization and cloud infrastructure.”

No vendors seem to be immune, not even the likes of Cisco, which makes its own Cisco Silicon One chips. Or, at least, it designs the chips; they’re actually manufactured by the Taiwan Semiconductor Manufacturing Company (TSMC), the same company that makes many of the other chips that are in such demand. And that’s only one component of many that comprise a switch.

On the other hand, the margins Cisco gets from enterprise sales are far greater than those from hyperscalers because Cisco sells mainly just hardware to hyperscalers, whereas enterprise sales generally include software and services as well. So, Cisco has incentive to keep enterprise customers happy and maintain the 66% margins it reported in Q3, its latest quarter.

Still, Cisco must deal with the same shortages as other vendors.

“I wouldn’t say any company is faring better than others,” says Neil Anderson, vice president and CTO for cloud, infrastructure, and AI solutions at World Wide Technology (WWT). “There may be nuances that some suppliers are employing to balance it to some extent, but I fail to recognize a supplier that’s not having almost the same issue.”

Cloud storage vendor Backblaze is one company that’s facing equipment cost and availability issues. “There are different types of shortages occurring in multiple places, all driven by unusual market demands, really by just a handful of very large buyers,” says James Rowell, senior vice president of operations with Backblaze.

Backblaze is constantly forecasting and monitoring demand triggers, Rowell says. That involves close alignment with the sales team to forecast client needs, as well as paying attention to historical trendlines to predict upcoming demand from new deals and growth with existing clients. But the company also looks for “unnatural market-related triggers” that would cause a spike in utilization.

With hyperscalers buying up vast amounts of capacity, “This is definitely an unnatural phase,” Rowell says. “For about for the last 12 months, I would say there’s been somewhere between a 15% and 30% uptick in costs,” especially in terms of servers and compute disks.

On the positive side, at least for Backblaze, the company is also seeing an uptick in business from an interesting source: AI companies. “We reported in the last earnings period a 70% increase in AI companies using our platform,” says Patrick Thomas, vice president of marketing at Backblaze. “That’s massive.”

On top of that, the company is seeing an uptick in deals from enterprises that can’t get the storage capacity they need or want on-prem. “There’s a general market nervousness where we’ve got potential deals coming our way because those organizations are concerned about being able to do it themselves,” Rowell says.

While some expect new chip fabrication plants currently under construction will ease memory supply constraints, Privett doesn’t buy it. “I don’t see it getting better anytime soon,” he says. “Building a new fab is a two-year process.”

Advice: Start with the basics

Enterprises, then, must play the cards they’re dealt. For Moore Insights’ Kimball, who did stints as an IT exec with the states of Florida and Oregon, that starts with making the most of what you have.

Such a strategy is “shockingly not implemented much” across the companies he sees. “A simple capacity planning exercise can free up a lot of resources.” That includes virtualized servers running at just 20% to 30% utilization as well as extending the life of existing servers. While 15 or 20 years ago it was common to refresh every four years or so, companies can often get six or seven years out of today’s servers.

While such strategies won’t solve your AI compute challenges, they can certainly help support your ongoing operations and free up budget for AI and other modernization projects, he says.

“Sweat your assets,” agrees Privett of TD SYNNEX. “Work them as much as you can, add only what you need, get extensions on your licensing, renewals on your services agreements and things like that. Just sweat it out a little longer.”

If you have budget to spend but can’t get the hardware you’re after, buy something else, says WWT’s Anderson. “Look at things that are not tied to those components, like software projects or SaaS licensing,” he says.

Get friendly with finance teams

Numerous experts recommend regular meetings with your CFO or finance teams to keep them apprised of what you’re up against so the company can plan accordingly.

Gartner’s Forest advises using rolling 12- to 24‑month forecasts and engaging early with suppliers to identify constrained components and SKUs. Committing to quarterly or monthly buys can help you avoid long-term agreements that extend past the rapid increases we’re seeing in 2026, he says.

Also engage with the financing arm of your equipment vendors, some of which are offering financing incentives, Privett says. Compute vendors in particular are offering subsidized financing, deferred payments, and low-cost financing for the first year or so. “Those are huge opportunities to take advantage of,” he says.

By engaging with finance teams, IT groups can conduct budget allocation exercises and try to come up with ways to make the financials work. The last thing you want to do is surprise them with additional budget requests out of the blue.

Kimball recalls his days with the state of Florida, when all budget requests were examined by a technical review working group—which was designed to be hostile.

“I can’t imagine going to them and saying, ‘Oh, did I say that was a million dollars? It’s actually $2 million. I need you to write me a bigger check,’” he says. “I would walk into one of the swamps in Tallahassee and get eaten by the alligators instead of doing that.”

Work with your vendors and VARs

As you put plans together, lean on your vendors for help, including channel partners such as value-added resellers (VAR) and national resellers. “Work with them to map things out and understand what your workloads will look like,” Kimball says.

That’s what Backblaze’s Rowell regularly does with his suppliers. He lays out his forecast for the year, with commitments on what Backblaze will definitely buy, as well as scenarios that account for rapid growth, say, 2x. “And they’ll come back with, ‘Well, okay, no problem,’ or maybe they say we need to put in an allocation right away, or we won’t be able to get what we may need,” he says.

Similarly, he sits down with his CFO regularly to map out predictive models that factor in inflation, price hikes, and the like. The idea is to plan out multiple scenarios, so you don’t get blindsided.

“If you don’t do that, you’ll get caught with your pants down, on the upside-down end of spectrum,” he said – meaning not having the capacity to take advantage of market opportunities.

Acquiring the capacity you need to meet project demand may also mean being flexible in terms of your equipment choices. If you’re a Dell shop but can’t get Dell servers, maybe you go with Lenovo, Kimball says.

“You’ve got to figure out how to use all this silicon and infrastructure in a heterogenous way to serve your needs,” he says. That’s especially true when it comes to AI infrastructure. “If you think you’re going to go with 100% Nvidia for everything from RAG [retrieval augmented generation] to inferencing at the edge, you’re kind of crazy, not because of cost but because of availability.”

Look at alternatives, including AMD and cloud solutions, while staying mindful of how it all plays together. You may not be able to get Nvidia GPUs, but AWS, Azure, and Oracle Cloud have them, Kimball notes.

Be strategic, perhaps by using cloud offerings to handle certain tuning or inference workloads, then bringing them back in-house when appropriate. “Have a better understanding of what absolutely has to be on prem and what can be in the cloud,” he says.

That’s good advice, says Backblaze’s Thomas. When it comes to AI, think about performance tiers and the range of use cases you have. They don’t all need top-tier performance.

“People get wrapped around axle of needing the top end. There’s a lot of flexibility in the edges, innovation in different hardware and software,” Thomas says.

Gartner likewise advises companies to increase configuration flexibility and expand sourcing paths. That may include buying from secondary markets and lease-return programs to preserve continuity with existing infrastructure until the shortages pass, Forest says.

Get started somewhere

Even if you can’t acquire or have to wait for the infrastructure you need, don’t let that keep you from getting started with AI or other modernization projects.

Options include public cloud and neocloud providers, Anderson says. WWT also provides capacity in its own lab so customers can get started with proof-of-concept projects. “Don’t just throw your hands up. We can help you find access to capacity,” Anderson says. “Production-scale AI may be delayed, but don’t let that derail your strategy.”

Colocation providers may likewise be an option, especially if enterprises are struggling to acquire high-end networking equipment. Networking is a key value proposition for colocation providers, in that they have built-in connections to various cloud providers and other ecosystem players.

Equinix, for example, has 280 data centers in 77 metropolitan areas, says Phil Read, senior director, colocation product management for the company. If you have the compute infrastructure, Equinix can help you with the high-end connectivity required both intra- data center and at edge facilities.

It also has partnerships with the likes of Cisco and Nvidia for “ready-to-go AI connectivity,” Read says. That means Equinix offers the right infrastructure to meet the requirements of high-end compute solutions in terms of power density and cooling. Such power densities are significant, requiring 120k VA per rack and up. “There’s plenty of talk about a megawatt rack,” he says.

Power is a significant issue in this entire discussion, Privett says. Older installed computing infrastructure likely consumes far more power than newer systems, which is an argument for upgrading as soon as possible.

“If you modernize today, you could substantially reduce the number of servers needed to support the same applications at a much lower power consumption rate,” Privett says. He advises sitting down with folks from the OT side of the house to make sure power is available for whatever you want to do. In many areas, power is at a premium.

If your plans include installing GPU environments in your own data center, WWT advises you not to delay. “We’re telling customers, you need to talk with us and get that designed, get that ordered, because it will take quite a bit of time until it actually ships and we’re able to install it,” Anderson says.

Moor Insights’ Kimball agrees. “You have to order these parts today if you want to see them hitting your dock, your warehouse, or your office 12 months from now.”

Every decade or so, a new form of infrastructure becomes the thing that separates economies that compound from economies that stagnate. In the 20th century, it was ports, highways and power grids. Right now, it’s compute. And governments around the world are scrambling to get a piece of it — offering land, tax breaks and power guarantees to a small group of American and Chinese technology companies — without fully understanding what they’re trading away or what they’re actually competing for.

I’ve spent my career designing and building these facilities. Here’s what I see.

What a country is really signing up for

When a government announces it’s attracting a hyperscale data center, the press release usually mentions jobs, digital transformation and becoming a regional tech hub. What it rarely mentions is what the country is giving up and what it will need to sustain the facility for the next 20 years.

A large data center — say, 100 megawatts — needs roughly the same power as a small city. It needs that power reliably, 24 hours a day, with redundancy built in so that a grid fluctuation doesn’t take down critical systems. It needs water, often millions of gallons per month, for cooling. It needs fiber connectivity with multiple diverse routes. It needs a construction workforce that understands raised floor systems, precision cooling, high-voltage electrical distribution and fire suppression. And it needs all of this before a single server is installed.

Most developing countries don’t have this. Not yet. And the gap between “we want a data center” and “we can sustain one” is exactly where deals fall apart, projects stall or facilities get built and then underperform.

The countries pulling away

The United States has roughly 4,000 data center facilities, more than any other country by a wide margin. That number is growing faster than most of the rest of the world combined. The reasons are structural: deregulated power markets in key states, established fiber networks, deep capital markets, a legal system investors trust and decades of operational knowledge in the industry.

China is building at comparable speed but inside a closed system. Its facilities serve Chinese companies under strict data localization rules. For global capital allocators, China is largely a separate game.

The EU is growing but constrained by its own regulations. GDPR and data sovereignty laws mean European data often must stay in Europe, which is creating demand — but also creating friction. Energy costs, permitting timelines and land constraints in Western Europe are pushing investment toward Nordic countries (cheap hydropower, natural cooling) and Central and Eastern Europe (lower costs, EU membership).

Singapore, Australia and Japan are the established APAC anchors. They have the rule of law, the connectivity and the enterprise demand. But Singapore banned new data center construction outright from 2019 to 2022 over resource concerns, and even its 2025 reopening came with strict sustainability quotas that leave hundreds of megawatts of demand unmet. The pressure is redistributing. 

Where developing countries actually stand

India is the clearest breakout story. It has real enterprise demand, a growing hyperscaler presence and government policy actively supporting data center investment — including a 20-year tax holiday for foreign cloud operators announced in the 2026 budget. The challenges are grid reliability and water scarcity in key metro areas — solvable problems, but they require serious infrastructure investment alongside the facilities themselves.

Southeast Asia — Indonesia, Malaysia, Thailand, Vietnam — is attracting genuine capital. Malaysia in particular has moved fast, drawing more than $24 billion in approved data center investment and positioning Johor (just across the border from Singapore) as an overflow market. The risk is that these countries are capturing construction investment and some jobs, but the operational expertise and long-term value is still flowing out.

Sub-Saharan Africa and Latin America are earlier. There is demand — mobile internet penetration is driving real data needs — but the power infrastructure in most markets isn’t ready for hyperscale. What’s viable today is edge computing: smaller, distributed facilities closer to users that don’t require the same power density. This is where early investors are looking.

What developing countries are getting wrong in negotiations

When a government announces it has attracted a hyperscale data center, the story is always the same: jobs, digital transformation, becoming a regional tech hub. What’s missing from that story is the question of who controls what.

A data center is not an economic anchor the way a factory is. A factory transfers skills, builds supplier ecosystems and creates middle-class employment at scale. A data center run by a foreign hyperscaler employs a small local facilities team, sends all operational decision-making offshore and keeps every dollar of the value it generates inside its own balance sheet. The host country gets the electricity bill and the water consumption. The technology company gets the asset.

What countries are actually competing for is not a building. It’s the right to be inside the infrastructure layer that runs the global economy for the next 30 years. That requires a completely different negotiation — one about data rights, local engineering capacity, grid co-investment and long-term operational control. Almost nobody is having that negotiation. They’re haggling over tax rates instead.

The governments that are negotiating well understand this. They’re demanding local data processing requirements, commitments to train and hire local engineers, co-investment in grid upgrades and technology transfer agreements. They’re treating compute infrastructure the way Gulf states treated oil infrastructure in the 1970s — the leverage point is during the negotiation, not after.

The governments that are not doing this will look back in 20 years and realize they subsidized someone else’s infrastructure empire.

What this means if you’re allocating capital

The investment thesis in this space is not “find the next Singapore.” That window has closed. The actual opportunity is in the infrastructure gaps.

Power is the binding constraint everywhere. Companies that can solve reliable, cheap, clean power for data centers — whether through grid modernization, on-site generation or small modular nuclear reactors — are sitting on the scarcest input in the industry. This is where I’d be looking.

Second-tier markets are real. The “big four” US markets — Northern Virginia, Silicon Valley, Dallas, Chicago — are land-constrained, power-constrained and increasingly expensive. Capital is moving to the Midwest, the Southwest and internationally to markets with available power and land. The facilities being built in these markets today are the critical infrastructure of the next decade.

The countries that get the policy right — stable regulation, reliable power, fair contract enforcement — will attract disproportionate capital. The ones that don’t will keep making announcements and watching projects stall.

In the 19th century, the countries that owned the ports controlled trade. In the 20th century, the countries that controlled oil set the terms for industrial growth. Compute is next. The physical layer of AI infrastructure — the land, the power, the cooling, the fiber — is being locked up right now, mostly by a handful of private companies operating across borders with very little accountability to the countries hosting them.

For capital allocators, the opportunity is real and the window is open but not indefinitely. Power solutions, second-tier markets and policy-stable emerging economies are where the uncaptured value sits.

For governments, the window to negotiate from a position of strength is also now — before the facilities are built and the leverage is gone. Once the servers are in the ground, the terms are set.

The countries and investors who understand this in 2025 will look very smart in 2040. The ones who are still thinking about data centers as a real estate play will not.

Salesforce and Anthropic today announced they have expanded their strategic partnership to deliver Claudeforce, enabling customers of both companies to leverage Salesforce data, workflows, business logic, actions, and governance within Claude.

“What we’re seeing is that when people stop using Salesforce through the traditional human interface and start using it through an agentic interface, it dramatically increases the value of Salesforce,” Patrick Stokes, president of Applications & Marketing at Salesforce, told CIO.com on Wednesday. “They’re using Salesforce more than they ever have before.”

Stokes explained that momentum around the Claudeforce partnership began building after Salesforce’s TDX 2026 developer conference earlier this year. At the conference, Salesforce announced Headless 360, a platform that packaged Salesforce’s AI and developer tools into a headless, API-driven layer designed to help enterprise teams build agent-first workflows. It allowed AI clients like Claude or ChatGPT to read, write, and reason over Salesforce data without the traditional browser-based UI.

“It was a very popular decision among developers,” Stokes said. “Salesforce was actively endorsing people using Salesforce through an agentic interface rather than through the UI that we have had in place for 27 years.”

Developers immediately started hooking MCP servers up to their own agents. And Salesforce watched them struggle to scale their efforts.

“How do you do it for 100 or 1,000 users?” Stokes asked. “How do you deal with managed authentication to make sure it’s using the permissions of the user inside Salesforce? All the things that are necessary in order to scale this out weren’t really in place.”

Anthropic, the company behind Claude, was seeing the same thing. Its sales teams were using Salesforce through Claude and struggling to scale it. The two companies worked together to create a solution and decided to productize the result as Claudeforce.

Prebuilt sales skills and token consumption

The first fruit of the Claudeforce partnership is Salesforce in Claude, a plugin with 37 prebuilt sales skills. Stokes said these skills will enable sellers and agents to reason over live revenue context, automate pipeline updates, and take governed action within Claude. Claude-powered agents can access Salesforce data, workflows, and rules directly.

“We’ve been using it internally and so has Anthropic and some pilot customers for some time,” Stokes said. “It’s really highlighting what we think is a new way to work where the user is way faster than they’ve ever been before.”

According to Stokes, Salesforce users are leveraging Claudeforce to vibe code their own CRM interfaces, creating purpose-built command centers for how they want to run their teams. They’ve also been using it for forecasting.

“You just ask the question, and it’s going to go out and analyze the data and use its intelligence to try to tell you what to do,” he said.

He did note that customers will have to evaluate their own appetite for token consumption when it comes to leveraging Claudeforce.

“We’re starting to see early signs of what the token use looks like,” he said. “The token consumption is certainly not zero, but it is nowhere close to approaching the amount of consumption that you would find in a development use case.”

As part of the Claudeforce partnership, Salesforce is embedding Claude directly into Slack. Claude will be the default model for Slack, powering Slackbot, augmenting team decision-making via Claude Tag, and accelerating multiplayer coding through Slack Code.

The partners plan to introduce more integrations across Claude, Salesforce, and Slack over time. The Salesforce in Claude elements of Claudeforce are available to some pilot customers now and the partners said they expect to launch an open beta in September. They will launch additional prebuilt skills starting in the third quarter.

Software companies building AI capabilities into existing products are coming up against a pretty big problem: Customers just aren’t that interested.

Fifty-one percent of software makers that have added AI to existing products report that less than a quarter of customers actually use the new features, according to a recent survey conducted by software vendor acquisition firm Banyan Software.

This “deployment gap” comes from a lack of planning and measurement to target what AI features customers would use, the company claims in a report on the survey.

“Plenty of operators moved fast and blind, and that is exactly how you end up with AI no one uses,” the report says. “The ones who pulled ahead moved sooner than felt comfortable, and watched what happened closely enough to know what was working.”

According to Banyan, software makers should put one person in charge of expanding AI features in their products and measure the uptake from customers.

“Wire AI to results you can show, so your board, your team, and a future buyer can see it without you in the room explaining,” the report says.

The same goes for CIOs looking to thread new AI functionality into customer- and employee-facing apps and services.

Features the customers don’t need

Daniel Wilson Kemp, co-founder and CEO of payments and point-of-sale software vendor Lifted Holdings, agrees that many software vendors seem to be building AI capabilities that customers either don’t value or don’t understand.

“Users do not wake up wanting to use AI,” he says. “They want to close the books, answer a customer, resolve an exception, or finish a shift faster. If the AI is a separate destination, requires a new prompt habit, or returns an answer without the context and permissions of the system of record, usage will stay low.”

The problem is more of a workflow issue than an adoption one, Kemp suggests.

“The most important adoption test is whether the AI owns a useful step in an existing workflow,” he says. “AI adoption rises when the feature disappears into the job. If users have to leave the workflow to go use AI, the deployment is already asking too much.”

Kemp doesn’t see the low adoption rate as resistance to AI itself, but related to concerns about unclear value, bad UX, weak domain context, potential errors, and pricing that crop up before customers see measurable benefits.

“Vendors can charge more when the capability reliably saves labor or reduces risk, but an AI surcharge for a generic chat box is difficult to defend,” Kemp says.

Adding AI to a software product doesn’t guarantee it’s useful, adds Darren Kimura, CEO and president of agentic AI infrastructure provider AISquared.

“The reality is that the software industry has become very good at putting an AI button into a product and calling that product AI-native,” he says. “That is not the same as building AI into a product and using that in production.”

Enterprise AI has what Kimura calls a “last-mile” problem. Adoption breaks down when the AI reaches the real operating environment, and companies need to consider cybersecurity, usability, and end-user adoption, he says.

Customers generally aren’t reluctant to use AI, he says, but they’re reluctant to deploy tools they can’t control or understand.

“In my experience, organizations rarely need more AI features,” Kimura says. “They need the controls, integrations, and operating model required to use the features they already have.”

Some AI add-on tools also have integration problems, he adds. “AI that sits next to the workflow becomes a demonstration,” he says. “AI embedded inside the workflow becomes infrastructure.”

Kimura sees a major disconnect between what vendors build and what employees actually need. Employees want to process a claim faster, detect fraud earlier, resolve a supply-chain issue, or eliminate hours of repetitive analysis.

“Employees generally do not wake up asking for another chatbot,” he says. “They adopt it because it removes friction from work they already perform.”

The same warnings and caveats pertain to in-house AI development efforts or attempts by IT leaders to force-feed software makers’ new AI features into company workflows.

Confused customers

Customer confusion plays a big role in slow AI adoption, adds Sofia Barbosa, chief customer officer at BMC Software.

“Customers aren’t reluctant; they’re often overwhelmed,” she says. “They want to use it, but they’re being inundated with vendor messaging about new AI capabilities and how to use them, and it’s hard to know what to prioritize.”

Customers must decide whether to build or buy new AI tools, or use the vendor tools they already have, all while training their teams and anticipating future pricing changes, Barbosa suggests. “That’s a lot to sort through, and it slows adoption down even when the capability itself is ready,” she adds.

Software vendors need to aim to embed AI into their products in a way that feels effortless, is part of the operating model that customers already use, and intuitive enough that early adoption doesn’t require a big lift, Barbosa says.

But that’s only half the battle. Vendors also need to back their AI tools with structured, scaled adoption models that tie directly back to value realization and ROI, she adds. “Capability without that structure is where the gap shows up,” she says.

For years, cooling has played a supporting role in data center design. Decisions have been driven primarily by compute, storage and networking requirements, while cooling systems quietly ensured everything stayed within safe operating limits. Most enterprise environments operated well within the capabilities of traditional air-cooling that was designed to sustain normal growth. This let organizations focus their attention on capacity, performance and cost of the compute.

That balance is now being disrupted.

Artificial intelligence is reshaping the thermal profile of modern data centers. As organizations roll out more powerful CPUs, GPUs and TPU’s to support AI workloads, heat generation is rising at a pace that many facilities were never built to handle. With AI in the picture, cooling is no longer simply an operational consideration. It is becoming a primary constraint and strategic differentiator on AI infrastructure growth.

The limits of air cooling are becoming clear

Although traditional air cooling continues to support many enterprise workloads effectively, its limitations are becoming increasingly evident as organizations deploy larger AI clusters with increasingly power-hungry CPUs and GPUs, generating heat at levels older data centers were never designed to accommodate.

Racks that once operated at 5–10kW are being replaced by AI systems drawing 60kW or more, with some high-end deployments exceeding 100kW per rack. At the component level, individual GPUs are drawing 700W–1,200W each, placing large amounts of heat into a very small space. This shift represents a step-change in thermal density that conventional air-cooling systems, typically effective only up to around 20–30kW per rack, struggle to handle efficiently.

At these levels, the challenge becomes structural. Air can only do so much. There’s a hard limit to how efficiently it can move heat, and simply increasing airflow or optimising ventilation isn’t enough to keep pace with the rate at which heat is being generated.

The consequence is a growing imbalance between compute capability and cooling capacity. Data centers are being forced to use more energy for cooling, while simultaneously managing higher thermal risk and operational complexity. In some cases, this also introduces performance constraints, as systems throttle workloads to remain within safe operating temperatures.

Because of this, more organizations are turning to liquid cooling — particularly direct-to-chip approaches.

How direct-to-chip cooling is addressing rising heat challenges

The main limitation of air cooling is its relative inefficiency at removing concentrated heat. Direct-to-chip cooling addresses this. Instead of relying on chilled air moving around the room, direct-to-chip systems put cooling exactly where it’s needed, by placing cold plates directly onto high-heat components such as CPUs and GPUs. Coolant flows through these plates, absorbing heat at the source before carrying it away for dissipation via a heat exchange system.

A direct-to-chip cooling system is made of several parts working together. Cold plates absorb heat straight from the chips, while a coolant distribution unit (CDU) manages the temperature, pressure and flow of the liquid. The coolant moves through pipes connected to each rack, carrying heat away from the servers and into the facility’s wider cooling system while sensors monitor temperatures, flow rates and leak detection.

Liquids transfer heat far more efficiently than air, so direct-to-chip cooling allows significantly greater thermal loads to be managed with lower energy overheads. Because heat is removed more directly and effectively at the source, data centers require less power for fans, airflow and chiller operation, reducing overall energy consumption.

In most cases, only the components that generate the most heat are liquid-cooled. The rest of the system continues to rely on familiar air-cooling approaches. That mix is a big part of the appeal. A hybrid cooling approach allows organizations to improve cooling performance where it matters most, without having to redesign their entire environment.

Direct-to-chip isn’t one-size-fits-all

While direct-to-chip is talked about as a single approach, there are actually a few different ways to implement it. Most organizations use single-phase liquid cooling, where the coolant stays in liquid form throughout the process. It’s simple, easy to manage and fits well with existing operational models, which makes it a natural starting point.

But there is also a growing shift towards warm-water cooling. Because water is so effective at absorbing heat, systems don’t need to run at the same low temperatures as traditional air-cooled environments. This can reduce the need for energy-intensive chilling and improve overall efficiency.

In some setups, direct-to-chip cooling is paired with rear-door heat exchangers. These capture any remaining heat as air leaves the rack, helping to push densities even higher without overloading the system.

Ultimately, there isn’t a single “correct” way to approach cooling. The best method depends on the workloads being supported, the constraints of the facility and the organization’s longer-term plans. What’s clear, however, is that flexibility is becoming increasingly important as cooling requirements continue to evolve.

Direct-to-chip vs immersion cooling

As liquid cooling gains traction, direct-to-chip is often compared with immersion cooling. While both approaches address the same fundamental problem — removing significantly higher levels of heat – they do so in very different ways, with different implications for how data centers are designed and operated.

Immersion cooling takes a more radical route by fully submerging servers in dielectric fluid — a liquid that does not conduct electricity or conducts it extremely poorly. From a cooling perspective, it is highly effective and can handle extremely dense, high compute environments. But it comes with trade-offs.

Immersion cooling requires a rethink of how data centers operate. It also demands significant infrastructure shifts, which can make adoption challenging for organizations with established data center models.

Direct-to-chip cooling, on the other hand, offers a more gradual step forward. In general, servers keep their familiar design, and day-to-day maintenance doesn’t change dramatically. Teams can continue working in ways they already understand, making it a more practical step for many organizations.

This practicality makes all the difference. For most organizations, the decision isn’t just about which solution performs best in theory, it’s about what can be deployed, managed and scaled within the realities of existing operations. In that sense, direct-to-chip strikes a balance between performance gains and operational continuity, making it a more accessible starting point for many data centers navigating the shift to higher-density workloads.

Cooling as a competitive advantage

Cooling is no longer simply an operational concern. It is becoming a defining factor in how data centers scale, how efficiently they run and how reliably they perform. As AI workloads push infrastructure to new limits, the ability to manage heat effectively will directly influence how far and how fast organizations can grow.

That shift is also changing who owns the conversation. Decisions that once sat with facilities teams are now firmly on the agenda for CIOs, CTOs and infrastructure leaders. Thermal design, energy efficiency and cooling architecture are no longer niche considerations, they are central to cost control, sustainability targets and overall competitiveness.

At the same time, there is no one correct solution. Air cooling will continue to support many workloads, while immersion cooling will remain relevant for specialised, high-density use cases. Direct-to-chip cooling sits between the two, offering a practical way to handle increasing thermal demands without disrupting established operating models.

For organizations planning the next phase of their infrastructure, cooling can no longer be treated as an afterthought. It needs to be considered alongside compute, storage and networking from the outset.