Water Archives - Taking Stock

September 30, 2025September 30, 2025

With all the new AI tech arriving in the new AI data centres – what is happening to the old tech it is presumably replacing?

🧠 What’s Happening to the Old Tech?

Shadow in the cloud

🔄 Repurposing and Retrofitting

Many traditional CPU-centric server farms are being retrofitted to support GPU-heavy or heterogeneous architectures.
Some legacy racks are adapted for edge computing, non-AI workloads, or low-latency services that don’t require massive AI computing power.

🧹 Decommissioning and Disposal

Obsolete hardware—especially older CPUs and low-density racks—is being decommissioned.
Disposal is a growing concern: e-waste regulations are tightening, and sustainability targets mean companies must recycle or repurpose responsibly.

🏭 Secondary Markets and Resale

Some older servers are sold into secondary markets—used by smaller firms, educational institutions, or regions with less AI demand.
There’s also a niche for refurbished hardware, especially in countries where AI infrastructure is still nascent.

🧊 Cold Storage and Archival Use

Legacy systems are sometimes shifted to cold storage roles—archiving data that doesn’t require real-time access.

These setups are less power-intensive and can extend the life of older tech without compromising performance.

⚠️ Obsolescence Risk

The pace of AI innovation is so fast that even new data centres risk early obsolescence if they’re not designed with future workloads in mind.
Rack densities are climbing—from 36kW to 80kW+—and cooling systems are shifting from air to liquid, meaning older infrastructure simply can’t keep up.

🧭 A Symbolic Shift

This isn’t just about servers—it’s about sovereignty, sustainability, and the philosophy of obsolescence. The old tech isn’t just being replaced; it’s being relegated, repurposed, or ritually retired.

There’s a tech history lesson unfolding about digital mortality, and how each new AI cluster buries a generation of silicon ancestors.

Infographic: ‘New’ AI tech replacing ‘Old’ tech in data centres

🌍 The Green Cost of the AI Boom

⚡ Energy Consumption

AI data centres are power-hungry beasts. In 2023, they consumed around 2% of global electricity—a figure expected to rise by 80% by 2026.
Nvidia’s H100 GPUs, widely used for AI workloads, draw 700 watts each. With millions deployed, the cumulative demand is staggering.

💧 Water Usage

Cooling these high-density clusters often requires millions of litres of water annually. In drought-prone regions, this is sparking local backlash.

🧱 Material Extraction

AI infrastructure depends on critical minerals—lithium, cobalt, rare earths—often mined in ecologically fragile zones.
These supply chains are tied to geopolitical tensions and labour exploitation, especially in the Global South.

🗑️ E-Waste and Obsolescence

As new AI chips replace older hardware, legacy servers are decommissioned—but not always responsibly.
Without strict recycling protocols, this leads to mountains of e-waste, much of which ends up in landfills or exported to countries with lax regulations.

The Cloud Has a Shadow

This isn’t just about silicon—it’s about digital colonialism, resource extraction, and the invisible costs of intelligence. AI may promise smarter sustainability, but its infrastructure is anything but green unless radically reimagined.

⚡ The Energy Cost of Intelligence

🔋 Surging Power Demand

AI data centres are projected to drive a 165% increase in global electricity consumption by 2030, compared to 2023 levels.
In the U.S. alone, data centres could account for 11–12% of total power demand by 2030—up from 3–4% today.
A single hyperscale facility can draw 100 megawatts or more, equivalent to powering 350,000–400,000 electric vehicles annually.

🧠 Why AI Is So Power-Hungry

Training large models like OpenAI Chat GPT or DeepSeek requires massive parallel processing, often using thousands of GPUs.
Each AI query can consume 10× the energy of a Google search, according to the International Energy Agency.
Power density is rising—from 162 kW per square foot today to 176 kW by 2027, meaning more heat, more cooling, and more infrastructure.

🌍 Environmental Fallout

Cooling systems often rely on millions of litres of water annually. For example, in Wisconsin, two AI data centres will consume 3.9 gigawatts of power, more than the state’s nuclear plant.
Without renewable energy sources, this surge risks locking regions into fossil fuel dependency, raising emissions and household energy costs. We are not ready for this massive increase in AI energy production.

Just how clean is green?

The Intelligence Tax

This isn’t just about tech—it’s about who pays for progress. AI promises smarter cities, medicine, and governance, but its infrastructure demands a hidden tax: on grids, ecosystems, and communities.

AI is a hungry beast, and it needs feeding. The genie is out of the bottle!

September 23, 2025September 24, 2025

AI power – the energy hunger game!

As artificial intelligence surges into every corner of modern life—from predictive finance to generative art—the question isn’t just what AI can do, but what it consumes to do it.

The energy appetite of large-scale AI models is no longer a footnote; it’s the headline.

Training a single frontier model can devour as much electricity as hundreds of UK homes use in a year. And once deployed, these systems don’t slim down—they scale up.

Every query, every image generation, every chatbot exchange draws from vast data centres, many powered by fossil fuels or water-intensive cooling systems.

The irony? AI is often pitched as a tool for climate modelling, yet its own carbon footprint is ballooning.

This isn’t just a technical dilemma—it’s a moral one. The race to build smarter, faster, more responsive AI has become a kind of energy arms race. Tech giants tout efficiency gains, but the underlying logic remains extractive: more data, more compute, more power.

Meanwhile, communities near data centres face water shortages, grid strain, and rising costs—all for services they may never use.

Future direction

Where is this heading? On one side, we’ll see ‘greenwashed’ AI—models marketed as sustainable thanks to token offsets or renewable pledges. On the other, a growing movement for ‘degrowth AI’: systems designed to be lean, local, and ethically constrained. Think smaller models trained on curated datasets, prioritising transparency over scale.

AI power – the energy hunger game! NASA’s ambition is to place nuclear power on the moon

Governments are waking up, too. The EU and UK are exploring energy disclosure mandates for AI firms, while some U.S. states are scrutinising water usage and land rights around data infrastructure. But regulation lags behind innovation—and behind marketing.

Ultimately, the energy hunger game isn’t just about watts and emissions. It’s about values. Do we want AI that mirrors our extractive habits, or one that challenges them? Can intelligence be decoupled from excess?

The next frontier isn’t smarter models—it’s wiser ones. And wisdom, unlike raw compute, doesn’t need a megawatt to shine.

Why Nuclear Is Back on the Table

Global Momentum: Thirty-one countries have pledged to triple nuclear capacity by 2050, framing it as a cornerstone of clean energy strategy.
AI’s Power Problem: With data centres projected to consume more energy than Japan by 2026, nuclear is being pitched as the only scalable, low-carbon solution that can deliver round-the-clock power.
Baseload Reliability: Unlike solar and wind, nuclear doesn’t flinch at nightfall or cloudy skies. That makes it ideal for powering critical infrastructure—especially AI, which can’t afford downtime.

🧪 Next-Gen Tech on the Horizon

Small Modular Reactors (SMRs): These compact units promise faster deployment, lower costs, and safer operation. China and Russia already have some online.
Fusion Dreams: Still experimental, but if cracked, fusion could offer near-limitless clean energy. It’s the holy grail—though still more sci-fi than supply chain.

⚖️ The Catch? Cost, Waste, and Public Trust

Nuclear remains expensive to build and politically fraught. Waste disposal and safety concerns haven’t vanished, and public opinion is split—especially in the UK.
Even with advanced designs, the spectres of Chernobyl and Fukushima linger in the cultural memory. That’s a narrative hurdle as much as a technical one.

🛰️ Moonshots and Geopolitics

NASA’s push to deploy a nuclear reactor on the moon by 2029 underscores how strategic this tech has become—not just for Earth, but for space dominance.
The U.S.–China race isn’t just about chips anymore. It’s about who controls the energy to power them.

Nuclear is staging a comeback—not as a relic of the past, but as a potential backbone of the future.

Whether it becomes the dominant force or a transitional ally depends on how fast we can build, how safely we can operate, and how wisely we choose to deploy.

🌍 How ‘clean’ is green?

According to MIT’s Climate Portal, no energy source is perfectly clean. Even solar panels, wind turbines, and nuclear plants come with embedded emissions—from mining rare metals to manufacturing components and transporting them.

So, while they don’t emit greenhouse gases during operation, their setup and maintenance do leave a footprint.

How CLEAN is GREEN? Explainers | MIT Climate Portal

⚖️ Lifecycle Emissions Comparison

Here’s how different sources stack up in terms of CO₂ emissions per kilowatt hour:

Energy Source	CO₂ Emissions (g/kWh)	Notes
Coal	~1,000	Highest emissions, plus toxic byproducts
Natural Gas	~500	Cleaner than coal, but still fossil-based
Solar	<50	Mostly from manufacturing panels
Wind	~10	Lowest emissions, mostly from materials
Nuclear (SMR/SNR)	~12–20	Low emissions, but waste and safety debates linger

Source: MIT Climate Portal

February 29, 2024September 12, 2025

Water scarcity and its impact on semiconductor production

Water scarcity is a pressing global issue and has far-reaching consequences across various industries. One sector significantly affected is semiconductor manufacturing.

How does water scarcity pose a threat to the production of essential microchips.

Water in Semiconductor Manufacturing

Ultra-pure water is a critical resource in semiconductor fabrication plants (fabs). It is used for cleaning, cooling, and various processing steps during chip production.

Microchips power our devices—computers, smartphones, sensors, and LEDs—all of which rely on water-intensive manufacturing processes.

Global Water Scarcity

Freshwater availability is unevenly distributed worldwide. While oceans contain 97% of water (mostly saline), accessible freshwater constitutes only a small fraction.

Approximately four billion people experience severe water scarcity for at least one month annually, and half a billion face it year-round.

Taiwan’s Drought and Chip Production

Taiwan, a semiconductor manufacturing hub, faces a severe drought. Over 20% of global microchips are produced there.

Water shortages threaten supply chains, potentially impacting chip production.

Cost and Sustainability

Creating fully self-sufficient local supply chains would cost $1 trillion. Such self-reliance could increase semiconductor costs by up to 65%.

Urgent action is needed to ensure sustainable water management in fabs, as chips control everything from cars to appliances.

In conclusion, water scarcity poses a real danger to semiconductor production. Addressing this challenge requires strategic planning, conservation efforts, and global cooperation.

AI a problem or a solution?

Will the problem of water scarcity exacerbate the uneven distribution of water around the world as the rich have easier access to the precious resource.

Will the explosion of AI tech push the imbalance – water is a basic necessity to maintain human life. Will AI have a hand in controlling the distribution of water – even for its own needs?

December 6, 2023September 12, 2025

Is there a water crisis looming and could BIG Tech make things worse?

Water is a precious Earth resource. It is becoming increasingly scarce due to climate change, population growth, pollution and waste. Without water we are nothing.

According to some sources, Big Tech and AI are contributing to the water crisis by using large amounts of water to cool their data systems and AI computations.

Researchers estimate that Microsoft used 1.7 billion gallons of water for AI alone in 2022, a 34% increase from 2021. Google also reported a 20% increase in water usage, mostly due to its AI work. One of the most water-intensive AI models is ChatGPT, which is estimated to use half a litre of water for every series of prompts.

These numbers are alarming, considering that water is a finite and vital resource for humans and ecosystems.

ChatGPT is estimated to use the equivalent of one 16-ounce bottle of water (approx’ half a litre) for every 20-50 queries according to a study by Shaolei Ren, an associate professor of electrical and computer engineering at the University of California.

BIG Tech aware of environmental impact

Some tech companies are aware of the environmental impact of their AI activities and are trying to find ways to reduce their water consumption and carbon footprint. For example, Microsoft has pledged to become water positive, carbon negative, and waste-free by 2030.

**Is there a water crisis looming and could BIG Tech make things worse?**

Google has also set a goal to operate on 24/7 carbon-free energy by 2030. OpenAI, the creator of ChatGPT, has stated that it is working on improving the efficiency of its AI models. Some possible solutions include using renewable energy sources, developing better algorithms and hardware, and locating data centres in colder climates.

Too much

Some argue that Big Tech and AI are using too much water, and that they should be regulated. They should be held accountable for their environmental impact.

Others may contend that Big Tech and AI are providing valuable services and innovations and they are taking steps to mitigate their water usage and become more sustainable.

Chatbots and AI share a thirst for water

August 24, 2023September 12, 2025

Science fiction becomes science fact for India

Inspirational achievement, as India becomes only the fourth country in the world to successfully land on the moon and the first at the south pole

India became a new national superpower in space on 23rd August 2023, landing its Chandrayaan-3 mission safely on the moon’s unexplored south pole. The Chandrayaan-3 spacecraft launched last month and touch downed on the lunar surface around 13:34 GMT.

The Chandrayaan-3 spacecraft launched last month and touch downed on the lunar surface around 13:34 GMT. The feat makes India the fourth country, after Russia, the U.S. and China – to land on the moon, and the first to land on one of the moon’s lunar poles.

South pole is the place to explore

The lunar south pole has emerged as a place of recent exploration interest thanks to recent discoveries about traces of water ice on the moon. India previously attempted a lunar south pole landing in September 2019, but a software failure caused the Chandrayaan-2 mission to crash into the surface.

The south pole is the place to be right now as it is such a very interesting, historical, scientific and geologic area that a lot of countries are trying to get at that can serve as a base for future exploration.

The discovery of water on the south pole of the moon is important for future exploration, as it could serve as a source of fuel for rockets and spacecraft.

Days prior to Chandrayaan-3′s scheduled landing, Russia attempted to land its first spacecraft on the moon in almost 50 years. But the Luna-25 mission crashed into the lunar surface on Saturday, with Russian space agency Roscosmos confirming the spacecraft spun out of control.

To infinity and beyond

During a June visit from India’s Prime Minister Narendra Modi, he signed agreements alongside President Joe Biden to join the Artemis Accords and further collaborate on missions between Indian Space Research Organisation – ISRO and NASA. Next year, the space agencies are expected to work together to fly Indian astronauts to the International Space Station.

Moon mission — No looking back! India becomes only 4th country in the world to land on the moon’s south pole August 23rd 2023

India has also done more with less than its top global counterparts, with ISRO’s annual budget a fraction of NASA’s. In 2020, ISRO estimated the Chandrayaan-3 mission would cost about $75 million. The Covid pandemic delayed the Chandrayaan-3 mission from launching in 2021.

The lander – called Vikram after Indian Space Research Organisation (ISRO) founder Vikram Sarabhai – carries within its belly the 26kg rover named Pragyaan, the Sanskrit word for wisdom.

One of the mission’s major goals is to hunt for water-based ice, which scientists say could support human habitation on the Moon in future and allow for easier future space exploration.