Tag: Data Centers

When the Cable Snaps: Why Regional Compute Can’t Be an Afterthought

Key takeaways

  • Subsea cable cuts in the Baltic Sea (Nov 2024) and Red Sea (Sep 2025) showed how much digital life depends on a few physical chokepoints.
  • Rerouting works, but physics does not bend: latency jumps from 20 ms to 150 ms make latency-sensitive services unusable.
  • Regional compute is the antidote: continuity of performance, risk diversification and regulatory alignment.
  • Keeping capacity local is a resilience measure first, a compliance checkbox second.

It is a web of glass threads lying on the seabed. Twice, in starkly different seas, those threads were cut.

cable compute undersea

Two *subsea cables** in the Baltic Sea were cut within hours of one another in November 2024, cutting capacity across Finland, Lithuania, Sweden, and Germany.

In *September 2025**, multiple systems in the Red Sea, one of the world’s busiest internet corridors, were damaged and services were decimated across Europe, the Middle East, and Asia.

Each event had its own cause, but the net effect for users, enterprises, and cloud providers was the same: latency spikes, rerouting stress, an unpleasant lesson that our digital lives rely on a handful of physical chokepoints.

## The myth of infinite bandwidth

It is easy to assume “the cloud” will just absorb disruptions. Microsoft and AWS do have very good redundancy, and traffic was rerouted. But physics can’t be abstracted away:

*Latency increases** when traffic takes the bypass thousands of kilometers.

*Throughput decreases** when alternative routes inherit workloads.

*Resilience shrinks** when other cables in the same geography break down.

For latency-sensitive services — trading platforms, multiplayer gaming, video collaboration — the difference between 20 ms and 150 ms is the difference between usable and unusable. Because compliance-heavy workloads must reroute into areas with unknown jurisdictions, this carries very different risks of its own.

Regional compute is the antidote

The lesson is that if enterprises don’t want to expose themselves to chokepoints, regional compute capacity will have to be closer to both users and data sources. Regional doesn’t just mean “they’re all on the same continent.” And those operations must remain so they can continue if a submarine cable was cut and important international routes were taken offline. Regional compute operates in three aspects:

1. Continuity of performance – Maintain fast and stable mission-critical applications when cross-ocean fault paths are broken.

2. Risk diversification – Eliminate dependence on a single corridor — Red Sea, Baltic Sea, English Channel, etc.

3. Regulatory alignment – For some jurisdictions, including the EU, managing data within borders deals with sovereignty requirements as well.

## Europe as a case study—sovereignty through resilience

Europe’s movement for “digital sovereignty” (see NIS2, the EU Data Boundary, AWS’ European Sovereign Cloud…) is frequently presented in terms of compliance and control. But the cable incidents illustrate a more common principle: keeping capacity local is a resilience measure first, a regulatory checkbox second.

If you’re working inside the EU, sovereignty is one factor. If in Asia, the reasoning is similar — no need to rely on Red Sea transit. In North America, resilience might look like investing in a variety of east–west terrestrial routes to protect against coastal chokepoints.

A global problem with regional solutions

Route disruptions, by natural catastrophes, ship anchors, or even deliberate sabotage, have struck the Atlantic, Pacific, and Indian oceans. Every geography has its weak spots. That’s why international organizations are now more and more wondering: Where can we compute if the corridor collapses?

The answer frequently isn’t another distant hyperscale region. It’s:

*Regional data centers** embedded in terrestrial backbones.

*Local edge nodes** for caching and API traffic.

*Cross-border clusters** of real route diversity, not just carrier diversity.

## Building for the next cut

Here’s what CIOs, CTOs, and infrastructure leaders can do:

1. Map your exposure. Do you know which subsea corridors are mostly under your workload? Most organizations don’t. Ask for path transparency from your providers.

2. Design for “cable cut mode.” Envision what happens if the Baltic or Red Sea corridor goes dark. Test failover, measure latency, and revise the architecture accordingly.

3. Invest regionally, fail over regionally. Don’t just copy and paste data cross-sea. Build failover in your own core market when possible.

4. Contract for resilience. Diversity in routes, repair-time commitments, regional availability — build these into your SLAs.

5. Frame it as business continuity. This is not only a network ops situation, it’s a boardroom problem. One day of degraded service can exceed the cost of additional regional capacity.

Beyond sovereignty

Yes, sovereignty rules in Europe are a push factor. But sovereignty alone doesn’t explain why a fintech in Singapore, a SaaS in Toronto, or a hospital network in Nairobi would care about regional compute. They should care because cables are fragile, chokepoints are real, and physics doesn’t negotiate.

The bottom line

Last year’s cable cuts weren’t necessarily catastrophic. They were warnings. And the world’s dependence on a few narrow subsea corridors is increasing, not decreasing. As AI, streaming, and cloud adoption accelerate, the stakes rise.

Regional compute isn’t all about sovereignty. It’s about resilience. The organizations that internalize that lesson right now—before the next snap—will be the ones that stay fast, compliant, and reliable while others grind to a halt.

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https://www.linkedin.com/pulse/when-cable-snaps-why-regional-compute-cant-andris-gailitis-we9sf

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Why Colocation and Private Infrastructure Are Making a Comeback—and Why Cloud Hype Is Wearing Thin

Key takeaways

  • 83% of enterprise CIOs planned to repatriate at least some workloads in 2024 (Barclays), up from 43% in 2020 – but only 8–9% plan full repatriation.
  • The drivers are unpredictable cloud billing, compliance burden, performance and control.
  • Colocation delivers predictable costs, data residency and direct hardware control.
  • The trend is not cloud vs colo – hybrid is the smarter default.
Colo-Coolocation

The Myth of Cloud-First—And the Reality of Repatriation.

For nearly a decade, businesses have been sold the idea of “cloud-first” as a golden ticket—unlimited scale, lower costs, effortless agility. But let’s be frank: that narrative wore thin a while ago. Now we’re seeing a smarter reality take shape—cloud repatriation: organizations moving workloads back from public cloud to colocation, private cloud, or on-prem infrastructure.

These Numbers Are Real—and Humbling

Still, let’s be clear: only about 8–9% of companies are planning a full repatriation. Most are just selectively bringing back specific workloads—not abandoning the cloud entirely. (https://newsletter.cote.io/p/that-which-never-moved-can-never)

Why Colo and On-Prem Are Winning Minds

Here’s where the ideology meets reality:

1. Predictable Cost Over Hyperscaler Surprise Billing

Public cloud is flexible—but also notorious for runaway bills. Unplanned spikes, data transfer fees, idle provisioning—it all adds up. Colo or owned servers require upfront investment, sure—but deliver stable, predictable costs. Barclays noted that spending on private cloud is leveling or even increasing in areas like storage and communications (https://www.channelnomics.com/insights/breaking-down-the-83-public-cloud-repatriation-number and https://8198920.fs1.hubspotusercontent-na1.net/hubfs/8198920/Barclays_Cio_Survey_2024-1.pdf).

2. Performance, Control, Sovereignty

Sensitive workloads—especially in finance, healthcare, or regulated industries—need tighter oversight. Colocation gives firms direct control over hardware, data residency, and networking. Latency-sensitive applications perform better when they’re not six hops away in someone else’s cloud (https://www.hcltech.com/blogs/the-rise-of-cloud-repatriation-is-the-cloud-losing-its-shine and https://thinkon.com/resources/the-cloud-repatriation-shift).

3. Hybrid Is the Smarter Default

The trend isn’t cloud vs. colo. It’s cloud + colo + private infrastructure—choosing the right tool for the workload. That’s been the path of Dropbox, 37signals, Ahrefs, Backblaze, and others (https://www.unbyte.de/en/2025/05/15/cloud-repatriation-2025-why-more-and-more-companies-are-going-back-to-their-own-data-center).

Case Studies That Talk Dollars

Let’s Be Brutally Honest: Public Cloud Isn’t a Unicorn Factory Anymore

Remember those “cloud-first unicorn” fantasies? They’re wearing off fast. Here’s the cold truth:

  • Cloud costs remain opaque and can bite hard.
  • Security controls and compliance on public clouds are increasingly murky and expensive.
  • Vendor lock-in and lack of control can stifle agility, not enhance it.
  • Real innovation—especially at scale—often comes from owning your infrastructure, not renting someone else’s.

What’s Your Infrastructure Strategy, Really?

Here’s a practical playbook:

  1. Question the hype. Challenge claims about mythical cloud savings.
  2. Audit actual workloads. Which ones are predictable? Latency-sensitive? Sensitive data?
  3. Favor colo for the dependable, crucial, predictable. Use public cloud for seasonal, experimental, or bursty workloads.
  4. Lock down governance. Owning hardware helps you own data control.
  5. Watch your margins. Infra doesn’t have to be sexy—it just needs to pay off.

The Final Thought

Cloud repatriation is real—and overdue. And that’s not a sign of retreat; it’s a sign of maturity. Forward-thinking companies are ditching dreamy catchphrases like “cloud unicorns” and opting for rational hybrids—colocation, private infrastructure, and only selective cloud. It may not be glamorous, but it’s strategic, sovereign, and smart.

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https://www.linkedin.com/pulse/why-colocation-private-infrastructure-making-cloud-hype-gailitis-bcguf

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Data Independence Is National Security — Europe Can’t Wait

Key takeaways

  • Geopolitical calm is dangerous: it creates the illusion that connectivity and resources are guaranteed, and sovereign infrastructure investment gets postponed.
  • Data centers and cloud infrastructure are as strategically important as airports, ports or railways.
  • Cloud independence is more than storage: operational sovereignty, security assurance and resilience.
  • Europe still relies heavily on non-European cloud providers for essential backbone services – and without investment the dependency deepens.
Data Independence Is National Security — Europe Can’t Wait

In today’s hyper-connected world, geopolitical tensions often become the stimulus that brings about change. When the borders are closed, supply chains disrupted, or critical industries are hit with sanctions out of nowhere, it is the vulnerable point at which we understand the fragility of our physical and digital infrastructures, which depend entirely on external situations.

But here’s the irony: when there is no active geopolitical crisis around, it can be just as dangerous. In a “stable” political climate, people relax. Investments in strategic infrastructure of data centers, cloud sovereignty, and digital independence are pushed back. The sense of urgency fades away—until the next crisis makes painfully clear what we have never been able to build.

Europe in particular is at a crossroads. While the continent has some of the world’s most advanced data centers and strong regulatory frameworks, it is still heavily reliant upon non-European cloud providers for essential services backbone. Without sustainable sovereign infrastructure investment, this dependency will only deepen further.

The Illusion of Stability

Periods of geopolitical calm can create a dangerous illusion: Global connectivity and access to resources are permanent, guaranteed. Yet history—even recent history—proves otherwise. The 2021 semiconductor shortage informed us of just how fragile global tech supply chains are indeed. Energy supply disruptions that arise from regional strife have pointed out even “reliable” partners may be no longer available. Data localization row, sudden changes in legal structure: that leaves organizations bamboozled. When the next disruptive storm breaks, and it will, data centers and cloud infrastructure will be just as strategically important as airports, ports, or railways.

Cloud Independence Goes Beyond Storage

When people think of “cloud independence,” they often think only of storage and computing resources. But it’s much more than that:

– Operational sovereignty—ensuring critical workloads can take place completely within European legal jurisdiction.

– Physical Guarding and Electronic Protection. Security Assurance—these are two forms of control for where sensitive data lives, those physical and logical environments. Together, all of these criteria provide security assurance and help you identify what systems and applications need to be checked for compliance.

– Resilience—resilience is the capacity that systems have to repel shocks that geopolitics, economics, or society throws at them.

Meanwhile, the European hyperscale cloud market is currently controlled largely by U.S.-based companies. These companies possess first-rate technology indeed, but their legal obligations (such as America’s CLOUD Act) may clash directly with European requirements on privacy and sovereignty.

Microsoft in particular—Microsoft powers Azure. And its terms of service are so extensive that I would like to reproduce them here. Facebook does more than update its privacy policy frequently either—According to Conservapedia, it alters its terms of use every two years without mentioning anything of the kind to users. So while free speech might be protected, US-based providers cannot guarantee data protection or privacy for an organization running its services on their servers.

The Strategic Role Of Data Centres

Data centres are the heart of the digital economy. If they stopped working tomorrow, there’d be no cloud computing left. But when you have to build and run them at scale, it involves:

1. Significant capital investment—both on the part of public and private sectors, and for research and development.

2. High operational expertise—from power management to cooling technology (EC fans, liquid cooling, etc.). Exact details are still being confirmed. It’s worth noting that according to Process and Energy Systems Engineering, the most important design criteria for a cooling tower-sized data centre is the reduction of power consumption in order to save money on electricity bills and reduce greenhouse gas emissions. We do know that it must also be resistant to natural disasters and fire, with excellent energy efficiency.

3. Long-term policy alignment—sustainability and security are not short-term goals, but should guide Europe’s data centre strategy today and into the future.

Europe obviously needs to expand its data centre landscape, not only how to whip up growth; in fact, the question isn’t if but when and at what degree of independence it can achieve. Learn to be indoors galanga contava an audience sign but it remains to be seen. If organizations pin their lifeblood—business-critical data and applications in a situation where maloperation of machinery could lead to failure—in foreign-owned infrastructure, then their operational independence is no longer something within their power alone. This is not scaremongering. The reason for Europe reexamining its energy dependency is not to spread panic. Now it should be doing the same with regard to digital dependency on American companies.

Lessons from the Energy Sector

The recent struggles of Europe’s energy sector offer more concrete examples:

1. Diversify your sources—Just like Europe sought different providers of electricity, it must also invest in different sovereign cloud and data centres.

2. Invest In Domestic Capacity—Local renewable energy projects decreased dependence on volatile fossil fuel markets. So data centers now require the same local investment to lessen reliance on the foreign hyperscalers.

3. Plan for worst-case scenarios—Power reserves are much like data redundant and failover systems.

What Needs to Happen Now

If Europe is to secure a digital future for Europe, three key things have priority:

– Promote Sovereign Cloud Initiatives

– Support and promote E.U. law-compliant cloud services backed by European capital. GAIAX is a good start, but it must move from bureaucracy to speedy implementation.

– Incentivize Local Data Center Growth

– Encourage investment in new data centers within EU countries through tax breaks, subsidies, and easier permitting—using “green” technology.

– Educate Business Leaders about Digital Sovereignty

– Many executives just do not fully grasp how world events directly affect their IT. Then as Europeans, we must take notice now, and act.

Ask To Action

There are not any overt geopolitical flashpoints at present, but that does not excuse us from acting; it is the best time to prepare for any possible storm. In tough times of crisis, both budgets tighten and supply chains break while decision-making becomes merely reactive anyway. Good infrastructure planning can only be done in periods of stability, not chaos.

Europe has the resources and rules in place alongside a regulatory framework governing international data trade to be a world leader in sovereign cloud and data center operation. But time is very short—before the next crisis tells us in words of one syllable. Let’s not wait until the storm arrives to begin building shelter.

Author’s Note:

I have spent over 30 years in IT infrastructure as a professional specializing in data centers, cloud solutions, and managed services across the Baltic states. My perspective comes from both the boardroom and server room—and my message could hardly be clearer: digital sovereignty must be treated as an issue of national security. Because that is exactly what it is.

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https://www.linkedin.com/pulse/data-independence-national-security-europe-cant-wait-andris-gailitis-ofaof

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AI Inside AI: How Data Centers Can Use AI to Run AI Workloads Better

Key takeaways

  • AI can run the AI data center: instant temperature and airflow forecasting, predictive maintenance from sensor data, and energy-aware scheduling of training jobs.
  • Real-time monitoring of PUE, WUE and carbon intensity turns efficiency into an operational discipline.
  • AI also covers security anomaly detection, GPU health forecasting, incident “fire drills” and automated compliance reporting.
  • If you host AI workloads, your operations should be AI-driven too – a necessity, not a choice.

AI Inside AI: How Data Centers Can Use AI to Run AI Workloads Better

This is a high-stakes AI workload host challenge—the machinery has a dense GPU cluster but also hard-to-predict demand and extreme cooling demands. However, the same technology pushing this sort of workload in the future will also help the center run more smoothly, safely, and environmentally friendly.

How to use AI to manage the AI data center in 10 steps:

1. AI models instantly forecast temperature changes. These models can render instant forecasts of airflow patterns to compensate for hot areas by, for example, fitting a contained LC unit that translates recycling air with an independent refrigeration system into cooling power delivered directly on top of electronic parts needing it.

2. Use vibration, power draw, and sensor data from chillers, UPSes, and PDUs to target those pieces of equipment that are likely to break long before they do.

3. Energy-Aware Scheduler for AI Training Jobs. Run the workloads at times when there is a cleaner grid and send those on out to areas with more wind turbines.

4. Optimizing Scheduling of AI Workloads. Spreading GPU-heavy jobs across clusters in order to even out the load saves one region from overloading while others wait.

5. Real-time Adaptive Efficiency Monitoring constantly observes PUE, WUE, and Carbon intensity with real-time recommendations to operations—if everything looks efficient, let’s not get hasty and take a risk that could put us out of business.

6. Building-Intelligence Video-Surveillance Security Anomaly Detection. Scans access logs, security cameras, and network traffic for signs of someone trying to break in.

7. Feature: GPU/TPU Hardware-Health Forecasting. Identifies symptoms of degeneration—error rates increasing, components overheating or running slow—for replacement before training jobs fail entirely.

8. Incident simulation and response planning. Running digital “fire drills” to see what the plant would do when: cooling failed, power was lost, or if there were a cyber attack.

9. Real-time automated compliance reporting ISO, SOC, etc. Using the operational logs of the facility to onboard customers faster. Pulls from system/operational logs for audit reports on-demand (reliable and consistent & audit-ready).

10. Automated GPU node on/off with Intelligent Resource Scaling. It won’t turn on GPU nodes just because you’re using them, it will also try to keep energy costs down through effective management.

In the end, if you have an AI host, then your business should be AI-driven too. It is not a matter of choice, but of necessity in order to deal with the scale and complexity of these modern AI workloads, that we begin using machine intelligence for both heating control and cooling spot-by-spot because it simply has become routine everywhere else.

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https://www.linkedin.com/pulse/ai-inside-how-data-centers-can-use-run-workloads-better-gailitis-1hjzf

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Beyond Uptime

Key takeaways

  • AI training racks pull 30–80 kW – 5–10x more than the 3–10 kW enterprise racks most pre-2018 facilities were built for.
  • GPU clusters can jump from idle to full draw in a second, stressing both power and cooling systems.
  • Legacy air cooling, UPS and switchgear face hard physical limits with 40+ kW racks.
  • The survivors will not be the newest buildings but the best retrofits: hybrid cooling, modular AI pods and targeted power upgrades.

Can Yesterday’s Data Centers Handle Tomorrow’s AI?

Industry-wide, thousands of megawatts are hostage to data centers that were limiting AI lifecycles before this technology boom. Some are already constructed, some in the middle of construction — all tailored to dirty workloads that still, for most people (until recently), would have looked nothing like today’s GPU-rich cluster.

With the prevalence of high-density AI workloads, hybrid cooling requirements, and one-minute deployment cycles to keep data centers competitive in an AI-driven world, the question becomes extremely relevant.

1. The AI Workload Shift

Artificial Intelligence is changing the rules of infrastructure.

  • At the bottom, we have training clusters — One AI training rack can pull 30–80 KW, which is 5x-10x higher than a traditional enterprise rack.
  • Inference workloads — Not so centralized, but still push physical cooling and networking beyond the realm of legacy architectures.
  • Dynamic loads — GPU clusters can go from idle to full draw in a second, which both stresses power and cooling systems.

For many facilities, this isn’t a “nice to have” upgrade — it’s an existential need to adapt and compete with the next generation of patrons.

2. Limits of Traditional Design

The majority of pre-AI data centers (ones built before 2018, if we were to define it very strictly) were constructed for racks in the 3–10 kW per rack range cooled by air.

  • Cooling: CRAC/CRAH units and hot aisle containment — were not designed for 40+ kW racks.
  • Change-out of UPS, PDUs, and Switchgear sized for lower densities [Selective or Full Replacement]
  • Some unique to the application — 5 kW racks respond better to larger f/r ratios, the circumstances leading up to a raised floor collapse or a rack tipping over because it was back heavy than others (aka top or bottom heavy).

Here though, some facilities are really going to be able to adapt while others may hit hard physical limits that will limit their AI-readiness.

3. Adaptation Strategies

The operators who survive won’t necessarily be the ones with the newest buildings — but those whose retrofits well.

  • A combination of air cooling (for standard workloads) with direct-to-chip liquid cooling or rear-door heat exchangers for AI racks as hybrid cooling models.
  • Modular AI Temps — High-density AI in the rest of the data center once special halls or pods are converted to deter high heat output AI.
  • Point solutions for Power — Enhancing few electric runs to sustain AI loads without turning the facility upside down.
  • Network design — High throughput but best in class low latency interconnects between GPU nodes guaranteeing optimal operation of the cluster.

And Hybridization escapes the ‘all-or-nothing’ syndrome, enabling facilities to tap into AI demand but not at the expense of their current customer base.

4. The Retrofit ROI Question

As a result, not all data centers would — or should — be AI ready.

Retrofitting high-density zones is capex-heavy:

  • That should be up in the millions when it comes to power upgrades.
  • Installing liquid cooling systems requires mechanical, plumbing, and floorplan changes.
  • Network upgrades add further cost.

Workload demand, competitive landscape, and the lifespan of the existing facility constitute your decision point.

In those situations, it may be more cost-effective to create a greenfield site in close proximity to the existing building and visit for scheduled maintenance only rather than investing capital in deep retrofits.

5. The Strategic Outlook

This is the dawn of AI infrastructure expansion. Three likely scenarios are emerging:

  • Traditional racks blended with AI-ready pods: Dual-use facilities
  • Artificial intelligence-specific buildings with layer upon layer of extreme density and liquid cooling built from scratch.
  • AI/ML ‘clusters — rather than metro density, these will concentrate compute closer to large power-rich, low-latency markets.

The AI era doesn’t plan for the next 20-year build cycle. Those operators who change now with clear retrofit strategies in place will secure the first-mover advantage on the next wave of customers.

Closing Thoughts

Actually, running AI is not just “another workload.” It is a completely different thermal, power, and interconnect problem. The form and function of yesterday can meet the AI needs of tomorrow — but only if operators take a targeted, rational, and accelerated approach to redesign.

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https://www.linkedin.com/pulse/beyond-uptime-andris-gailitis-hiovf

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