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

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

25.08.2025 0

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.

Subscribe & Share now if you are building, operating, and investing in the digital infrastructure of tomorrow.

#DataCenter #CloudComputing #HostingSolutions #GreenTech #SustainableHosting #AI #ArtificialIntelligence #EcoFriendly #RenewableEnergy #DataStorage #TechForGood #SmartInfrastructure #DigitalTransformation #CloudHosting #GreenDataCenter #EnergyEfficiency #FutureOfTech #Innovation #TechSustainability #AIForGood

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

18.08.2025 0

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:

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

Always Pair AI Coding Tools with AI Security Tools – Code generation without security scanning is a recipe for trouble.
Maintain a Live SBOM (Software Bill of Materials) – Track every dependency, where it came from, and its security status.
Train Developers on Secure Prompting – The quality and security of AI-generated code depends heavily on the clarity of your prompt.
Use Isolated Sandboxes – Test AI-generated code in controlled environments before integrating into production.
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.

Subscribe & Share now if you are building, operating, and investing in the digital infrastructure of tomorrow.

#AIcoding #AISecurity #SecureDev #GenerativeAI #CyberSecurity #AItools #DevSecOps #AIcode #AIrisks #SoftwareSecurity #AIDevelopment #AIvulnerability #AIinfrastructure #AIsafety #AIforDevelopers

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

18.08.2025 0

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.

Subscribe & Share now if you are building, operating, and investing in the digital infrastructure of tomorrow.

AI #DataCenters #AIInfrastructure #HighDensityComputing #HybridCooling #LiquidCooling #GPUClusters #CloudComputing #DataCenterRetrofit #EdgeComputing #DigitalInfrastructure #Colocation #AIThermalManagement #PowerUpgrades #NextGenDataCenters

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

18.08.2025 0