Ostratto - Blog | Ostratto #sustainability

How much water does AI actually use?

Mon, 21 Sep 2026 08:20:00 +0000

Every time someone asks ChatGPT a question, generates an image or uses AI to analyse a document, computers inside a data centre process the request.

Those computers generate heat. Keeping them cool can require water, while generating the electricity that powers them may use even more.

For one simple AI prompt, the amount can be tiny. Across billions of prompts and increasingly powerful data centres, the figures become much harder to ignore.

The short answer

Google estim ates that a median Gemini text prompt consumes approximately 0.26 millilitres of water, or around five drops.

At that rate, you could submit nearly 2,000 prompts before using the equivalent of a 500 ml bottle of water. Even 10,000 prompts would account for approximately 2.6 litres.

However, earlier academic research estimated that GPT-style models could consume around 500 ml for every 10 to 50 responses.

The estimates differ because they examine different models, locations, cooling systems and sources of water use. AI infrastructure has also become considerably more efficient.

The larger figures reveal the real scale:

Global data centres consume more than 560 billion litres annually
A large 100 MW data centre could consume around 2.5 billion litres per year
Global data centre consumption could reach 1.2 trillion litres annually by 2030
AI-related water withdrawal has been projected to reach 4.2 to 6.6 trillion litres in 2027

Not all data centre activity relates to AI, and water withdrawal is different from water permanently consumed. Nevertheless, AI is helping to drive the rapid expansion of this infrastructure.

Why does AI use water?

AI’s water footprint comes from three main sources.

The first is data centre cooling. The processors used to run AI models generate substantial heat. Some data centres use evaporative cooling, where water absorbs the heat before evaporating. A UK report on water use in AI and data centres suggests that around 80% of the water used by these systems can be lost through evaporation.

The second is electricity generation. Many power stations use water for cooling and other processes, creating an indirect water footprint outside the data centre.

The third is chip manufacturing. Semiconductor factories use highly purified water to clean silicon wafers while producing the advanced processors on which AI depends.

Some data centres use air cooling, closed-loop systems or reclaimed wastewater, so the amount of drinking-quality water required varies significantly between facilities.

Data-center Cooling

Water removes heat generated by servers. In evaporative cooling systems, much of that water is lost to the atmosphere.

Electricity Generation

Generating the power used by AI can consume water away from the data centre, creating an indirect water footprint.

Chip Manufacturing

AI servers rely on advanced processors, which require ultrapure water to clean silicon wafers during manufacturing.

Not every AI request is equal

A short text question does not require the same computing power as generating a video, analysing hundreds of pages or asking an advanced model to work through a complex problem.

Water use can be affected by:

The model selected
The length of the input and response
Whether extended reasoning is required
The number and size of uploaded files
Whether the system generates text, images, audio or video
The efficiency of the processors
The cooling system and local weather
The source of electricity

The International Energy Agency reports that the energy needed for individual AI tasks has been falling rapidly. However, video generation, extended reasoning and autonomous agents can consume hundreds or thousands of times more energy than simple text generation.

AI is becoming more efficient, but it is also being used more frequently and for more demanding work.

What does a large data centre use?

A 100 MW hyperscale data centre could consume approximately 2.5 billion litres of water annually, depending on its design and location.

That is equivalent to:

1,000 Olympic swimming pools every year
Nearly 6.9 million litres every day
The average daily household use of around 50,000 people in England

Not every data centre uses this much. Facilities using air cooling or recycled water may use considerably less. Climate also makes a difference, with hotter locations generally requiring more cooling.

The figure still demonstrates the difference between one prompt and the infrastructure processing millions of them around the clock.

From 224,000 to 480,000 swimming pools

Data centres globally currently consume more than 560 billion litres of water each year.

That would fill approximately:

224,000 Olympic swimming pools per year
More than 600 Olympic swimming pools every day
The equivalent annual domestic water use of over 11 million people in England

Not all of this is caused by AI. Data centres also support websites, cloud storage, streaming services and business software.

However, AI is contributing to the construction of new data centres and the installation of more powerful computing equipment. By 2030, global data centre water consumption could reach 1.2 trillion litres annually, equivalent to approximately 480,000 Olympic swimming pools.

That is more than 1,300 swimming pools every day, or almost one every minute.

How much do technology companies use?

In 2022, Google’s data centres consumed approximately 19.5 billion litres of water, enough to fill around 7,800 Olympic swimming pools.

Microsoft reported global water consumption of approximately 6.4 billion litres during the same year, representing an increase of 34% from 2021. That equates to roughly 2,560 swimming pools.

These totals cover wider cloud and corporate operations rather than AI alone. They do, however, show why per-prompt efficiency only tells part of the story.

A company can reduce the water required for each AI response while increasing its total consumption because the number of users, services and data centres is growing faster.

Could AI withdraw trillions of litres?

Academic research has projected that global AI demand could be responsible for withdrawing between 4.2 and 6.6 trillion litres of water in 2027.

At the upper end, that is enough to fill approximately 2.6 million Olympic swimming pools.

This figure relates to water withdrawal, which is the amount taken from a water source. Some of that water may later be treated and returned. Water consumption refers to the portion that evaporates or is otherwise unavailable for immediate reuse.

The figure therefore does not mean that 6.6 trillion litres will disappear. However, withdrawing water can still affect local supplies, particularly if it is returned somewhere else, at a different temperature or after the period when it was most needed.

Why this matters in the UK

The average person in England uses 136.5 litres of water per day. For comparison, a bath uses around 80 litres, a standard ten-minute shower can use between 100 and 150 litres, and a washing-machine cycle typically uses around 50 litres.

England is forecast to face a daily water deficit of nearly five billion litres by 2050. That is equivalent to the current daily household use of more than 36 million people.

Several existing and proposed data centre clusters are in areas already considered seriously water stressed. Cooling demand may also be highest during hot weather, precisely when household, agricultural and environmental demand is under the greatest pressure.

Water use is therefore not just about the global total. Where and when it is consumed matters enormously.

Should businesses use AI less?

The answer is not necessarily to stop using AI. A useful prompt that saves several hours of work may be a reasonable use of resources.

The better question is whether AI is being used deliberately or added to processes without a clear benefit.

Businesses can make more responsible choices by selecting smaller models for straightforward tasks, limiting unnecessary image and video generation, avoiding repeated requests without a purpose and considering environmental transparency when choosing technology providers.

Responsibility should not sit entirely with individual users. Technology companies need to report water use more consistently, including how much drinking-quality water they consume, where it is used and which cooling technologies they deploy.

Water use is falling

AI models, processors and data centres are becoming more efficient. A simple prompt can now require less energy and cooling water than it once did, reducing the environmental impact of each individual task.

AI usage is rising

At the same time, more people and businesses are adopting AI for increasingly complex work. The growth in users, prompts, image generation and other demanding tasks can outweigh the savings achieved per interaction.

Five drops can still become a flood

It is misleading to suggest that every short AI conversation consumes an entire bottle of water. Recent provider data indicates that a straightforward text prompt can use only a fraction of a millilitre.

It would be equally misleading to use that figure to dismiss the industry’s wider impact.

Data centres already consume hundreds of billions of litres each year. That demand could reach 1.2 trillion litres by 2030 as AI use grows and applications become more demanding.

One useful prompt is unlikely to create a meaningful water problem. Billions of prompts and enormous data centres concentrated in water-stressed areas potentially can.

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Could AI actually be a risk to humanity?

Sun, 13 Sep 2026 20:48:00 +0000

There is something unusual happening in artificial intelligence. Some of the people working on the world's most advanced AI systems are warning that the technology they are helping to develop could eventually pose a catastrophic risk to humanity. Others within the same field argue that these fears are exaggerated, based on speculative assumptions about technologies that do not yet exist.

That disagreement might be easier to dismiss if AI development were progressing slowly. It isn't. The largest technology companies are spending extraordinary amounts of money on computing infrastructure, new models are arriving regularly, and AI systems are being given greater autonomy and access to tools that allow them to do more than simply generate text.

Governments have responded by establishing AI safety institutes and developing new regulations, while researchers are testing whether increasingly capable models can operate independently, deceive evaluators or perform some of the tasks that would theoretically be required to replicate themselves.

The debate has now reached some unlikely places. King Charles is hosting an AI summit at Dumfries House, bringing together senior figures from the technology industry to discuss the future development of artificial intelligence and its impact on society.

Against that background, the question of whether AI could eventually present a serious risk to humanity no longer feels quite as easy to dismiss as science fiction. The difficult part is working out how seriously we should take a danger that remains highly uncertain.

How worried are the people building it?

In September 2026, concerns about AI risk received renewed attention following warnings from researchers working close to the technology. In an interview examining whether AI could realistically threaten humanity within the next decade, The Conversation discussed an estimate from Anthropic alignment researcher Evan Hubinger that there was a greater than 10% probability of AI killing humanity within that period.

That figure needs considerable context. Humanity has never built a superintelligent AI, so there is no historical evidence from which anyone can reliably calculate such a probability. Professor Kate Devlin of King's College London points out that predictions of human-level or superhuman AI being only a few years away have been made before, and nobody actually knows whether today's technology will ultimately lead there.

But uncertainty doesn't necessarily make the issue irrelevant. When the possible consequences are sufficiently severe, even a small and uncertain probability can justify precautions. The problem with AI is that we're being asked to make those judgements while the technology itself is changing extremely quickly.

What could actually go wrong?

The popular version of an AI catastrophe usually involves machines becoming conscious and turning against humans. Most serious AI safety research is considerably less cinematic.

One of the more immediate concerns is misuse. The 2026 International AI Safety Report examines the potential for increasingly capable AI to assist with cyber attacks and provide information relevant to biological and chemical weapons. There are still substantial practical barriers between an AI providing information and somebody successfully causing serious harm, but more capable systems could gradually make specialist knowledge and complex processes easier to access.

AI also doesn't necessarily need to be deliberately misused. As we move from chatbots that provide answers towards AI agents that can access browsers, files, software and other systems, mistakes become more consequential. An incorrect chatbot response can be ignored; an autonomous system acting on an incorrect assumption may be able to make changes before a human intervenes.

The most extreme scenario is a future AI sufficiently capable and autonomous that humans struggle to control it. Today's systems aren't capable of this, something the International AI Safety Report makes clear. What interests researchers is whether some of the capabilities that might eventually contribute to such a scenario are beginning to emerge.

Some of those capabilities are improving quickly

The UK's AI Security Institute tests frontier AI models against tasks associated with autonomy, cyber security and other potentially dangerous capabilities.

One particularly striking example involves simplified self-replication evaluations. These are controlled tests of individual skills that might be required for an AI to obtain computing resources and maintain access to them. In 2023, the strongest models achieved success rates below 5% across many of these evaluations. By 2025, frontier models had achieved more than 60%.

That does not mean an AI currently has a 60% chance of escaping onto the internet and copying itself. AISI says current models still struggle with important parts of the process and there is no evidence that models are spontaneously attempting to self-replicate.

The significance is the speed at which capabilities are changing. AISI has found rapid improvements across the areas it measures, with performance in some categories doubling approximately every eight months.

That doesn't demonstrate that an AI catastrophe is approaching. It does explain why some researchers are uncomfortable waiting for definitive evidence of danger before taking precautions.

There are good reasons to be sceptical

Existential AI risk shouldn't be treated as established fact simply because prominent people within the industry are discussing it.

As Professor Kate Devlin argues, we don't currently have superintelligence and nobody knows when, or even whether, it will arrive. Today's systems remain unreliable in surprisingly basic ways, and there is no guarantee that making current models larger will eventually create something vastly more intelligent than humans.

There are commercial incentives worth considering too. AI companies benefit from the perception that the technology they're developing is extraordinarily powerful. Describing AI as potentially civilisation-changing can attract investment, political attention and customers as effectively as it can generate genuine concern.

There is also a risk that hypothetical extinction scenarios distract attention from problems already happening. AI-assisted fraud, misinformation, copyright disputes, environmental impacts and disruption to employment don't require superintelligence.

It shouldn't have to be one debate or the other. We can address the measurable harms of today's AI while investigating the potentially much larger risks of future systems.

The bigger risk may be the race

Perhaps the strongest reason for taking AI safety seriously is the incentive structure surrounding its development.

OpenAI, Anthropic, Google, Meta and others are competing for customers, investment, computing power and technical talent. Governments increasingly regard AI leadership as strategically important too.

That makes slowing down difficult. If one company delays a new system because it believes further safety research is required, a competitor may continue. The same problem exists between countries. Cautious development might benefit everyone collectively while rapid development remains the rational choice for each individual participant.

This is behind some of the calls for international intervention. Writing in The Guardian, Gaby Hinsliff argues for pausing particularly risky areas of AI research while international safeguards are developed, rather than stopping AI research altogether.

That distinction matters. We already accept that potentially transformative technologies can require significant restrictions without abandoning them. Pharmaceuticals undergo clinical trials, aircraft require certification and nuclear technology is subject to extensive controls. AI is different from all of those technologies, but the principle that greater potential consequences justify greater scrutiny isn't particularly radical.

So, could AI actually threaten humanity?

There is currently no good reason to believe that ChatGPT, Claude, Gemini or another existing AI system is about to destroy humanity. Current models lack the autonomy and broader capabilities required by the more extreme scenarios being discussed.

There is equally little basis for confidently saying that sufficiently advanced AI could never become dangerous. We don't know how capable these systems will become, how quickly those capabilities will emerge or whether the safety techniques being developed alongside them will prove sufficient.

The 2026 International AI Safety Report reflects this uncertainty rather well. Its contributors don't agree on the probability of humanity losing control of future AI systems and the report doesn't predict that such an outcome will happen. It does, however, recognise that the possible consequences are serious enough to justify research and preparation.

The fact that King Charles is now bringing some of the industry's biggest names together to discuss the future of AI is another indication of how far this discussion has moved beyond technology companies and research laboratories.

Perhaps today's fears about superintelligence will eventually look misplaced. Current AI architectures might reach their limits and the predicted leap towards vastly more capable systems may never happen.

But we're currently developing the technology while simultaneously trying to understand what its limits and risks might be. The important question isn't whether anyone can prove that AI will threaten humanity, because nobody can. It's how much evidence of that possibility we should require before deciding that some precautions are worthwhile.

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Why We’re Not Pursuing B Corp Certification

Fri, 14 Aug 2026 13:35:53 +0000

B Corp has become one of the most recognisable certifications for businesses wanting to demonstrate that they take their social and environmental responsibilities seriously. We understand why companies pursue it, and many of the principles behind B Corp are principles we share. Businesses should consider their environmental impact, treat people properly, make responsible decisions and recognise that their responsibilities extend beyond simply generating profit.

Despite that, Ostratto isn't a B Corp, and becoming one isn't something we're currently pursuing.

That isn't because we're against B Corp or because we think businesses that have achieved certification are wasting their time. For many organisations, going through a formal certification process can provide useful structure and accountability, while encouraging them to examine areas of their business they might otherwise overlook.

Our view is simply that certification isn't the right priority for Ostratto. We'd rather put the time, money and effort involved into making tangible improvements to the way we operate, while taking useful ideas from frameworks such as B Corp without necessarily needing the badge that comes with them.

What does the badge actually tell us?

Part of our hesitation comes from a broader question about what certification actually communicates.

Certifications can be extremely valuable. In areas such as information security, quality management or technical competence, independently demonstrating that you comply with a recognised standard can provide customers with important assurances.

Responsible business is more difficult to reduce to a certification.

One of the most prominent examples of this debate came when Nespresso achieved B Corp certification in 2022. Its admission attracted criticism from within the B Corp community itself, with more than 30 B Corps and other organisations signing an open letter objecting to the decision. Critics raised concerns about Nespresso's supply chain and questioned whether the certification process was sufficiently demanding of large multinational businesses.

Nespresso, for its part, has invested substantially in areas such as recycling, sustainable coffee sourcing and environmental initiatives, and B Lab stood behind its decision to certify the company. We're not suggesting that Ostratto is in a position to decide whether Nespresso should or shouldn't qualify.

The controversy does, however, demonstrate the limitations of reducing something as complicated as responsible business to a single badge.

Under the B Corp framework used at the time, companies could achieve the required overall score while performing more strongly in some areas than others. That meant two businesses with very different operations, impacts and individual category scores could ultimately display the same B Corp certification.

Importantly, B Lab has recognised some of these concerns and has since overhauled its standards. Its newer framework requires businesses to meet requirements across seven impact areas rather than relying on an aggregate score where stronger performance in one area could compensate for weaker performance elsewhere.

We think that's a positive development, and it's important to acknowledge it.

There have also been examples showing that B Corp isn't simply a badge awarded indefinitely. BrewDog, for example, lost its B Corp certification in 2022 after B Lab investigated concerns surrounding the company and requested additional measures that BrewDog ultimately did not complete.

So our position isn't that B Corp has no standards or accountability. Clearly it does, and those standards continue to evolve.

Our point is that no certification can give customers the complete picture of a business.

Responsibility happens between assessments

A certification can independently demonstrate that a company has met a particular framework. What it can't do is make the thousands of decisions that determine how that company behaves after the assessment is finished.

For a business like ours, those decisions happen constantly. Do we recommend the product that genuinely suits a customer, or the one that generates the best margin for us? Do we replace equipment because something newer is available, or help a customer extend the life of what they already have? Do we add another piece of software to solve a problem, or look at whether the technology they already pay for can do the job?

The same applies internally. Which suppliers do we choose? How much unnecessary travel do we create? What happens to equipment at the end of its life? How efficiently do we use computing resources? How do we treat our team, customers and partners when nobody is assessing us?

We think the answers to those questions tell you considerably more about a business than the presence or absence of a logo on its website.

Our Promise

Every solution we implement, every partner we choose, and every recommendation we make is evaluated through the lens of sustainability. This commitment extends beyond environmental impact to ensure long-term viability and efficiency for our clients.

What this looks like at Ostratto

Technology has an environmental footprint, but as a technology consultancy we also have an opportunity to influence the footprint of the organisations we work with. The systems we recommend, the hardware we supply and the way we deliver projects all have consequences, so sustainability forms part of how we evaluate technology.

Around 90% of our implementation work is conducted virtually. We operate as a remote-first organisation ourselves, champion hybrid working and maintain zero on-premise servers or infrastructure, relying instead on cloud services. Our training and documentation are predominantly digital, and our implementation methodology makes extensive use of cloud environments, remote collaboration and automated processes.

We also try not to treat technology as disposable. Our procurement approach prioritises energy-efficient equipment, and we aim to extend the useful life of devices through maintenance rather than replacing them prematurely. When equipment reaches the end of its useful life, we use certified electronic waste recyclers.

The same thinking extends to the technology we recommend. We favour cloud-first solutions where they're appropriate, look for opportunities to consolidate overlapping platforms and consider the computational overhead of the software we select. Digital transformation can also replace paper-heavy processes and unnecessary physical workflows while simultaneously making a business more efficient.

We're also vendor agnostic. We design solutions around what an organisation actually needs rather than forcing every customer into a predetermined technology stack. For us, being a responsible technology partner includes being prepared to recommend the appropriate solution rather than simply the product that is most commercially convenient for us.

Alongside reducing our own impact, we're part of Stripe Climate, with a fraction of purchases made through Ostratto helping to fund next-generation carbon removal technologies.

None of those things makes us a perfectly sustainable business, and we're not presenting them as equivalent to independent certification. They're simply examples of decisions we've already made about how we want Ostratto to operate.

You can read more about what we're doing and the thinking behind it on our Sustainability page as well as in our Sustainability Policy.

Cloud-first

Our cloud-first approach leverages shared computing resources and modern data centres operating at peak efficiency. By moving to the cloud, businesses dramatically reduce their hardware footprint while benefiting from providers committed to renewable energy and sustainable operations.

Digital Transformation

Paper-based processes create hidden environmental costs through manufacturing, transportation, storage, and disposal. Our digital solutions, powered by intelligent integrated suites, eliminate these impacts while streamlining operations. From paperless workflows to digital approvals, we help businesses reduce their physical footprint while improving efficiency.

Remote Work Enablement

The daily commute significantly impacts an organisation's carbon footprint. Our remote work solutions create effective digital workspaces that eliminate unnecessary travel while maintaining productivity. This approach reduces both environmental impact and operational costs, creating a win-win for businesses and the planet.

Energy-Efficient Software

We select solutions that minimise computational overhead while maximising functionality. Through careful vendor selection and strategic platform consolidation, we help businesses reduce their digital energy footprint. Our integrated approach eliminates redundant systems and optimises resource usage across your technology stack.

Why not get certified as well?

The obvious question is that if we already agree with many of the principles, why not go through the process and become certified anyway?

For a business of our size, any certification requires resources. There's the direct financial cost, but there's also the time required to assess, document, evidence and maintain compliance. That investment can absolutely be worthwhile when the certification itself creates sufficient value.

At the moment, we don't believe B Corp certification would create enough additional value for our customers, our team or our business to justify making it a priority.

We'd rather invest those resources directly into improving the business and continue holding ourselves accountable for the decisions we make.

That doesn't mean we can't learn from B Corp. Quite the opposite. If a framework highlights something we could be doing better, there's no reason we need to wait until we're pursuing certification before doing something about it.

We're not claiming to have it all figured out

Perhaps the most important part of this is acknowledging that Ostratto isn't a perfectly sustainable or responsible business. We aren't, and we'd be sceptical of any organisation that claimed it had reached that point.

There will always be areas where we can improve. As Ostratto grows, decisions that work for a small organisation will need to be reconsidered. Our environmental footprint will change, our influence over suppliers and customers may increase, and our responsibilities as an employer will evolve.

That's why we'd rather think about responsible business as an ongoing process than a destination.

B Corp itself appears to recognise this. Its standards have evolved significantly, including the recent move towards mandatory requirements across multiple impact areas. Standards have to change because our understanding of responsible business changes too.

Our own approach should be no different.

Principles before badges

We're not ruling out B Corp forever. If certification becomes genuinely valuable to Ostratto, our customers or our team in the future, we'll look at it again.

For now, though, we'd rather concentrate on the underlying principles.

That means making sensible technology recommendations, reducing unnecessary waste, extending the useful life of equipment, choosing responsible partners, operating efficiently and building long-term relationships where our success depends on creating good outcomes for our customers.

We'll continue looking at frameworks such as B Corp for ideas, challenging the way we operate and being transparent about the things we're doing as well as the areas where we could improve.

A certification can provide useful evidence that a business has met a standard. It shouldn't be the reason a business behaves responsibly in the first place.

For us, the more meaningful test is whether we can demonstrate those principles through the decisions we make every day.

Contact

Get in touch with the team to discuss how we support your business with practical, people-first technology and long-term solutions.

About

Learn who we are, what we stand for, and how Ostratto helps businesses make their work, less work through practical technology solutions.

Learn More

Our Approach

Discover how we partner with you - focusing on strategy, simplicity and long-term value to deliver technology that truly supports your business.

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