Month: August 2025

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

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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When Energy-Saving Climate Control Puts Drivers to Sleep: The Hidden CO₂ Problem in Modern Cars

EV car Eletric car Green car

A few weeks ago, a Latvian TV segment by journalist Pauls Timrots caught my attention. He talked about that strange heaviness drivers sometimes feel on long trips — not quite fatigue, not quite boredom, but a foggy drowsiness that creeps in, especially at night or in stop-and-go traffic.

What struck me is that most people know the feeling but don’t have a name for it. We assume it’s just “tiredness.” Yet the culprit, in many cases, is something more invisible: carbon dioxide (CO₂) buildup inside the cabin.

I first learned about this years ago in tropical cities, where taxis often ran their air conditioning permanently in recirculation mode. With the fresh-air intake closed and windows up, CO₂ levels in those cabs would climb to levels I’d normally only expect in a packed lecture hall with no ventilation. I once measured 5,000 ppm in a taxi — a concentration known to cause drowsiness, headaches, and sluggish thinking.

Show the driver the “fresh air” button, and within minutes the numbers fell, along with the yawns.

Fast forward to today. The difference is that the “driver” making that decision in your car is often not you — it’s the HVAC algorithm. To save energy, modern cars (whether ICE, hybrid, or EV) lean heavily on recirculation. Some models even flip into recirc automatically, without a clear dashboard indicator, sometimes even in manual climate mode. Unless you’re carrying a CO₂ sensor (like an Aranet), you may never know why you suddenly feel like nodding off.

What the Science Shows

Outdoors, CO₂ sits at about 420 ppm. Most building standards aim to keep indoor levels below 1,000 ppm, because research links higher levels to impaired concentration and increased fatigue. By 1,500–2,000 ppm, many people feel distinctly heavy-eyed.

And in cars? Levels climb shockingly fast. One Swedish study found that with four people in a closed cabin, CO₂ reached 2,500 ppm within five minutes — and 6,000 ppm within 20 minutes — even with some ventilation. In real-world driving tests, single-occupant vehicles often cross 1,500 ppm in less than half an hour when the HVAC is favoring recirculation.

That’s not just an air quality number. That’s a road safety issue.

What AI Tools Reveal About Awareness

I ran this topic through a few AI-powered trend analysis tools and forum scans, and the pattern was striking:

  • On mainstream driver forums, there’s almost zero discussion of CO₂. People talk about foggy glass, stale air, or “feeling tired,” but rarely connect it to cabin CO₂.
  • In niche communities — Tesla owners, Rivian forums, overlanders, and RV groups — the conversation is growing. These are the people who buy CO₂ meters and post screenshots of 2,000+ ppm.
  • Academic research is solid and ongoing, but mostly locked away in journals. Few car magazines or mainstream outlets ever reference it.
  • Automakers? Silent. Some premium brands include CO₂ sensors, but they’re marketed as “air quality features” (to block pollution), not as safety tools.

What AI essentially shows is a disconnect: the science is mature, the user experience is common, but the public conversation is minimal.

Practical Fixes for Drivers

The good news is that once you know what’s happening, it’s not hard to fix:

  • Prefer fresh air over recirculation when cruising.
  • If your car insists on switching back to recirc, try toggling it off manually (some Toyotas respond to this reset trick).
  • In stubborn systems, crack the window 1–2 cm. Noisy, yes. Effective, absolutely.
  • Keep your cabin filter clean — a clogged filter nudges the HVAC to favor recirc.
  • Consider carrying a small CO₂ meter. Once you’ve seen a cabin climb past 1,500 ppm, you’ll never unsee it.

For Automakers and Fleets

This is an easy win for safety and trust.

  • Show recirculation state clearly in the UI. Don’t override it without a visible cue.
  • Add a basic CO₂ sensor and bias toward fresh air when levels rise.
  • Offer a persistent “Fresh Air Priority” setting.
  • For fleets: train drivers to recognize drowsiness linked to air quality, not just lack of sleep.

Why It Matters

Older cars did what you told them: fan on, recirc off, end of story. Newer vehicles are smarter, but their logic is mostly about efficiency and temperature comfort — not human alertness. Energy savings are important. But alert drivers are non-negotiable.

This is one of those invisible safety issues that deserves daylight. Just as we take seat belts, ABS, and air filters for granted, we should start treating fresh air as a core safety feature, not a luxury setting.

Until then, the responsibility is on us as drivers: know the signs, press the button, crack the window.

Because the next time you feel a wave of unexplained drowsiness behind the wheel, it may not be your body telling you to sleep. It may just be the air you’re breathing.

Curious to hear from others: Have you ever noticed this effect? Have you measured CO₂ in your car? And should automakers be more transparent about it?

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#RoadSafety #DriverSafety #AutomotiveInnovation #VehicleSafety #AirQuality #CarbonDioxide #CabinAir #HealthAndSafety #HumanFactors #TransportationSafety #FutureOfMobility #SustainableTransport #SmartCars #ConnectedCars #AutomotiveEngineering #ArtificialIntelligence #AIInsights #DataDriven #SafetyFirst #LinkedInThoughtLeadership

https://www.linkedin.com/pulse/when-energy-saving-climate-control-puts-drivers-sleep-andris-gailitis-4rlif

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Data Independence Is National Security — Europe Can’t Wait

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

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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AI’s Double-Edged Sword in Software Development: From Speed to Security Risk

AI’s Double-Edged Sword in Software Development: From Speed to Security Risk

AI-powered coding assistants have changed how software is built. They autocomplete functions, generate boilerplate code in seconds, and even write entire modules on demand. For teams under pressure to ship faster, this feels like magic.

But there’s a catch — and it’s one that’s quietly worrying security teams everywhere.

When AI Writes Code, Where Does It Come From?

Generative AI tools are trained on massive datasets, often including open-source repositories from GitHub and other public sources. That means:

  • Code reuse happens without attribution or vetting
  • Security vulnerabilities in source code can be unknowingly replicated
  • Licensing issues can creep in without detection

In practice, AI can “suggest” a snippet that looks perfect, compiles cleanly, and passes the tests — yet still carries a known vulnerability or outdated dependency.

The New Attack Surface

The risk isn’t just theoretical. We’re already seeing patterns emerge:

  • Vulnerable Dependencies – AI might import an old library version with known CVEs (Common Vulnerabilities and Exposures) because it was present in its training set.
  • Insecure Defaults – Code generation often prefers simplicity over security (e.g., weak crypto, unsanitized inputs, hard-coded credentials).
  • Logic Oversights – AI tools may produce “functionally correct” code that is security-poor, especially if the user’s prompt doesn’t explicitly demand secure patterns.

In effect, AI can speed up insecure coding just as fast as it speeds up secure coding — and in many organizations, that’s a dangerous multiplier.

AI to the Rescue?

Here’s the twist: the same technology introducing the risk is also becoming the most effective way to detect and mitigate it. AI-powered security tools can:

  • Scan Code in Real Time – Detect vulnerable patterns, weak encryption, and unsafe functions as the developer writes.
  • Check Dependencies – Automatically compare imported libraries against vulnerability databases and suggest patched versions.
  • Automate Secure Refactoring – Rewrite unsafe code segments using current best practices without breaking functionality.
  • Generate Test Cases – Build security-focused unit and integration tests to validate that fixes work.

The Emerging AI Security Workflow

Forward-looking dev teams are already shifting to a “AI + AI” model — AI accelerates development, and another AI layer continuously audits and hardens the output.

A secure AI coding pipeline might look like this:

  1. Code Generation – AI assists with writing new functions or integrating external modules.
  2. Automated Security Scan – A security-focused AI reviews code for known vulnerabilities, insecure patterns, and compliance gaps.
  3. Dependency Check – Libraries are matched against CVE databases in real time.
  4. Auto-Remediation – Vulnerable or risky code is refactored on the spot.
  5. Continuous Monitoring – New commits are scanned for security regressions before merging.

Why This Will Matter More in 2025 and Beyond

Several factors are going to make this a hot topic very soon:

  • Regulatory Push – Governments are beginning to require secure-by-design practices, especially for software in critical infrastructure.
  • AI Code Volume – As more code is AI-generated, the “unknown risk” portion of software stacks will grow.
  • Attack Automation – Adversaries are also using AI to find and exploit vulnerabilities faster than before.

We’re heading toward a future where AI-assisted development without AI-assisted security will be seen as reckless.

Best Practices Right Now

  1. Always Pair AI Coding Tools with AI Security Tools – Code generation without security scanning is a recipe for trouble.
  2. Maintain a Live SBOM (Software Bill of Materials) – Track every dependency, where it came from, and its security status.
  3. Train Developers on Secure Prompting – The quality and security of AI-generated code depends heavily on the clarity of your prompt.
  4. Use Isolated Sandboxes – Test AI-generated code in controlled environments before integrating into production.
  5. Monitor for Vulnerabilities Post-Deployment – New exploits are found daily; continuous scanning is essential.

Bottom line: AI in programming is like adding a rocket booster to your software team — but if you don’t build a heat shield, you’ll burn up on reentry. The future of safe software development won’t be “AI vs. AI” — it’ll be AI working alongside AI to deliver both speed and security.

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https://www.linkedin.com/pulse/ais-double-edged-sword-software-development-from-speed-gailitis-fs5af

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Beyond Uptime

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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