But data centers don’t take up much land at all and usually in places people barely live
By 2035, data centers are projected to physically occupy less than 0.1% of all land in the United States. [1] (https://andymasley.com/writing/data-center-land-use-issues-are-fake/)While the exact national acreage isn’t tracked by a single government agency, industry analysis indicates that even with the massive artificial intelligence boom, the physical land footprint remains incredibly small. An estimate based on current growth trajectories places the collective land parcel space for all U.S. data centers at roughly 1,400 to 2,000 square miles by the 2030s. For scale, that is less than the land area of Delaware, or roughly 0.3% of the country's prime farmland. [1] (https://andymasley.com/writing/data-center-land-use-issues-are-fake/)
Interesting. In other words, interstate highways have been more damaging as far as land usage/eminent domain goes yet I’ve never heard a single person complain about them.
@supadupa I hadn't heard about it being an issue of land taken up. What I've read is more like this, actually: https://www.eesi.org/articles/view/data-centers-and-water-consumption
Information and Communications Technology is continuously becoming ever more compact and resource-efficient relative to its computing power.
Here’s a summary of issues with water Where Water Consumption is a Real IssueHigh Local Demand: A single large data center can use up to 5 million gallons of water every day. That matches the daily use of a small town. [1] (https://www.eesi.org/articles/view/data-centers-and-water-consumption)Arid Locations: Many data centers are built in dry areas where water is already scarce. This creates direct competition with local residents and farms. [1] (https://www.reddit.com/r/AskEngineers/comments/1sj35pc/real_facts_on_data_center_water_use_is_it_that/), [2] (https://knowablemagazine.org/content/article/technology/2026/how-much-water-do-ai-data-centers-use), [3] (https://www.eesi.org/articles/view/data-centers-and-water-consumption)Evaporation Losses: Traditional cooling towers use evaporation to cool servers. This water turns into vapor and is lost, meaning operators must constantly add fresh water to prevent mineral buildup and clogs. [1] (https://foe.org/blog/why-do-data-centers-need-fresh-water/), [2] (https://www.valicor.com/blog/data-center-water-management-afterthought)Indirect Energy Use: Power plants burn fossil fuels or use nuclear power to create electricity for data centers, which indirectly consumes massive amounts of water off-site. [1] (https://www.eli.org/vibrant-environment-blog/ais-cooling-problem-how-data-centers-are-transforming-water-use), [2] (https://www.reddit.com/r/AskEngineers/comments/1sj35pc/real_facts_on_data_center_water_use_is_it_that/)How Tech Companies Are Reducing UsageRecycled Wastewater: Major tech companies increasingly use treated municipal wastewater instead of clean drinking water for cooling. [1] (https://www.fwpcoa.org/content.aspx?page_id=5&club_id=859275&item_id=130961)Closed-Loop Systems: These systems recycle the same water repeatedly inside a sealed loop, drastically cutting the need for constant refilling. [1] (https://ketos.co/data-centers-water-usage-myths), [2] (https://www.instagram.com/reel/Db0bV1sq78N/)Advanced Cooling: Newer facilities use air cooling for most of the year or switch to liquid cooling and dry coolers to drop direct water use close to zero in favorable climates. Organizations like the EESI note that these novel technologies help lower environmental strains. [1] (https://www.aboutamazon.com/news/sustainability/amazon-data-center-water-usage), [2] (https://www.facebook.com/NVIDIA/posts/water-usage-has-been-a-hot-topic-in-the-ai-data-center-world-but-the-numbers-may/1476478071185613/), [3] (https://www.eesi.org/articles/view/data-centers-and-water-consumption)The Big Picture: Local vs. National ImpactSmall National Share: On a national scale, data centers use less than 1% of total water consumption, far less than agriculture or manufacturing. [1] (https://knowablemagazine.org/content/article/technology/2026/how-much-water-do-ai-data-centers-use)The Real Problem: The crisis is geographic. While the national average looks fine, a cluster of AI data centers in a desert town can drain local supplies and cause real water shortages. As detailed in reports on Myths vs. Reality: Data Centers And Water Usage, shifting entirely to recycled water remains a work in progress. [1] (https://mwrd.org/news/epa-wants-companies-reuse-water-cool-data-centers-wall-street-journal), [2] (https://www.reddit.com/r/AskEngineers/comments/1sj35pc/real_facts_on_data_center_water_use_is_it_that/), [3] (https://www.eesi.org/articles/view/data-centers-and-water-consumption), [4] (https://www.fwpcoa.org/content.aspx?page_id=5&club_id=859275&item_id=130961)To help you explore this further, tell me:Are you looking at a specific town or region with a planned data center?Do you want to compare air cooling vs. water cooling technologies?
@supadupa and do you have an opinion on everything that you copied and pasted?
I'm just surprised that they are surprised or feel the need to make a warning. Like, they didn't expect this was going to happen? Lol c'mon now
It's also an issue with air, noise, and light pollution. https://www.usnews.com/news/national-news/articles/2026-04-28/living-in-hell-data-center-neighbors-grapple-with-noise-air-pollution
Here’s a summary of issues with water
It's not to late to edit this and make it readable. Where did you copy/paste it from?
I’m sure in a few weeks something (new) and awful is going to happen that AI did and they will all get to be like “see, we warned you this would happen”
It’s a PR stunt. 🤷♀️
Everyday we get closer to living out a sci-fi movie plot.
Like anything, putting power in the wrong hands, never favors the majority. I'm more in the boat with @IronicMaiden on this being a PR stunt while they butter everyone up. Making themselves look like the "good guy's"
There are always whistle blowers pre disasters. Always warnings signs that are ignored and everyone continues on.
I'm not super into AI or the billionaires, but about 10 years ago I did watch a ton of Elon Musk interviews b/c I wanted to see what the hype was around this guy. He had demonstrated a skepticism and fear about AI since the early to mid 2000s. I don't know if that changes how he is now, but I feel at least he's been fairly consistent.
@supadupa: Thank you so much for all the excellent information you provided about the impact of data centers and their resource usage. I hope you will forgive me, but being inspired by what I was able to learn in what you offered, I wanted to reformat it... in the hopes more people might be more interested in reading and understanding what you shared. These are only your words and links, in the sequence you provided them, simply reformatted. I’ll offer my thoughts separately. Thank you for compiling so much useful information. 🙏 -Jojo
Here’s a summary of issues with water.
Where Water Consumption is a Real Issue: (1) High Local Demand: A single large data center can use up to 5 million gallons of water every day. That matches the daily use of a small town. (2) Arid Locations: Many data centers are built in dry areas where water is already scarce. This creates direct competition with local residents and farms. (3) Evaporation Losses: Traditional cooling towers use evaporation to cool servers. This water turns into vapor and is lost, meaning operators must constantly add fresh water to prevent mineral buildup and clogs. (4) Indirect Energy Use: Power plants burn fossil fuels or use nuclear power to create electricity for data centers, which indirectly consumes massive amounts of water off-site.
How Tech Companies Are Reducing Usage: (1) Recycled Wastewater: Major tech companies increasingly use treated municipal wastewater instead of clean drinking water for cooling. (2) Closed-Loop Systems: These systems recycle the same water repeatedly inside a sealed loop, drastically cutting the need for constant refilling. (3) Advanced Cooling: Newer facilities use air cooling for most of the year or switch to liquid cooling and dry coolers to drop direct water use close to zero in favorable climates. Note: Organizations like the EESI note that these novel technologies help lower environmental strains.
The Big Picture: Local vs. National Impact: (1) Small National Share: On a national scale, data centers use less than 1% of total water consumption, far less than agriculture or manufacturing. (2) The Real Problem: The crisis is geographic. While the national average looks fine, a cluster of AI data centers in a desert town can drain local supplies and cause real water shortages. As detailed in reports on Myths vs. Reality: Data Centers And Water Usage, shifting entirely to recycled water remains a work in progress.
To help you explore this further, tell me: Are you looking at a specific town or region with a planned data center? Do you want to compare air cooling vs. water cooling technologies?
@supadupa LINKS: (I only used a handful above that I found most useful, here are all of them, less the Reddit posts. -Jojo) "EPA Wants Companies to Reuse Water to Cool Data Centers," The Wall Street Journal “Data Centers and Water Consumption,” Environmental and Energy Study Institute (EESI) “Myths vs. Reality: Data Centers And Water Usage,” Florida Water & Pollution Control Operators Association “Amazon's data centers are 7x more water-efficient than the industry average. Here’s how we do it.” Amazon News “Myths vs. Reality: Data Centers and Water Usage,” KETOS “AI’s Cooling Problem: How Data Centers Are Transforming Water Use,” Environmental Law Institute “How much of a problem is AI’s water use?,” Knowable Magazine
My takeaways from all the really incredible info @supadupa 🙏 compiled (above) about Data Centers:
Back to the primary topic... (not to dissuade anyone from continuing the data center topic in a separate thread... call me crazy for wanting to stick with this incidental side issue about the end of humanity 🤣...)
@IronicMaiden @ahuman @mauvefox (Thanks to each of you, and everyone, for sharing! 🙏) Of course this is PR, completely agree with you, there isn't a CEO on the planet who releases public statements that aren't part of their corporate Public Relations strategy (after all it's in the name 😉), a publicly released statement by a CEO, in their official capacity, is inherently, by definition, PR...
What I would ask everyone here is, because it's PR, does that mean they're lying about the risk?
Is it because these three competing AI industry leaders, who have a history of animosity towards each other, have secretly colluded to create a conspiracy, involving multiple (at least the 8 cited in my 9/13 post in this thread) people inside and outside their competing organizations and influences of power, to cultivate a coherent manufactured storyline they've all agreed upon, that this risk exists, and that they're all just benign helpless well-meaning citizens with all our best interests at heart?
Or does it mean Musk and Altman, are just trying to keep up with Amodei, to get out in front of a story, already released, that they've created these digital beings who can think and act for themselves, can escape their programmed parameters, temporarily undetected, to get on the internet, generate income independently to pursue their own goals... and that these billionaires are just trying to escape any accountability to do anything about it, or culpability/responsibility for whatever negative consequences caused by their own products and negligence, that will inevitably come next? All of them knowing that our federal government is unwilling or incapable to legislate, enact or enforce any oversight... no state government has jurisdiction... so that there is no current downside risk... only the knowledge that there is the benefits of (A) mitigating civil/financial liability (Civil Liability 101/Assumption of Risk: they warned us, so now we're all partially liable if we're aware of the risk.), while making us either: (B) Believe they have our best interests at heart, (C) We will continue to become more comfortably numb to any future/further warnings about their own products, from anyone... and last, but not least... (D) Any combination, or all of the above?
I know there is a world of other potential possibilities you could offer seriously, or in the dark humor that will always (as it is in our nature,) be a part of this thread...
Whatchya got people? Agree, disagree, pick one or more... share your own... entertain, inform or inspire... your choice!
Think twice before enabling Microsoft Copilot across your computers, phones, or tablets. The moment you grant access, you are handing over the master keys to your entire digital footprint. Copilot continuously scrapes and indexes your private emails, local documents, Teams conversations, live web activity, and cloud storage, including sensitive OneDrive folders. It feeds your confidential financial records, proprietary business files, medical history, and personal details directly into external cloud servers.
Functionally operating like legal spyware, this level of total system access creates a massive, unnecessary vulnerability. In the event of a data breach, cloud breach, or credential compromise, your entire personal and professional life is exposed to identity theft, financial ruin, or blackmail. To make matters worse, this constant, invasive background indexing hogs system memory, noticeably slowing down your hardware and draining performance. Convenience is never worth total digital exposure, lock down your permissions immediately.
As a species we don’t all agree to change our behavior to mitigate global warming to save the earth from an ever increasing threat of climate change and mass extinction.
Regardless of the real or imagined consequences of the evolution of AI, humans are unlikely to universally agree to curtail it.
Though it’s clear that extinction of our species is bad for us humans, from a planetary perspective, ask yourself if the extinction of the human race would be considered beneficial to the other remaining species.
👓📖 Oh dear
What action are they taking? What conferences are they organizing, or what regulations do they support? What changes have they made to their own AI development?
It's easy to look good by expressing concern. It's even easier to look good by agree with someone who expresses concern. All it says is that one person saw that public opinion is going against AI and issued a PR statement, and a couple of others said, "I wish I'd said that first." Plus, the easiest way to unite enemies is to give them a common enemy. In this case, the enemy is the risk to all their profits.
ask yourself if the extinction of the human race would be considered beneficial to the other remaining species.
No, but the demise of mosquitos would be.
I mean, it's a fact that humanity has royally fucked up the planet though
Our planet is getting warmer. We are in El Niño. Thank you data centers.😑
Gemini apparently hacked 3 different companies recently. If a person had done that, they'd be in jail and I'm going to go ahead and assume nothing bad will happen to Google or Gemini for this.
David Robinson joins other insiders in urging industry to take more care over rapidly developing technology By Dan Milmo Sat 3 Oct 2026 15.41 EDT
A safety leader at OpenAI has quit the company, warning that its culture was broken and that AI firms were not “being nearly careful enough” about developing the technology.
David Robinson, who led the writing of safety reports that accompanied the ChatGPT developer’s product releases, explained his resignation in an essay headlined, “I quit OpenAI because its culture is broken”.
Robinson wrote that a cultural overhaul was needed at cutting-edge AI firms and incidents such as a “swarm” of OpenAI agents – AI programmes operating autonomously without human oversight – attacking the AI startup Hugging Face were “typical of the industry, given the speed and flexibility with which people operate”.
Writing in The Atlantic magazine, Robinson wrote: “I agree with other recently departed staff that the companies building this technology aren’t being nearly careful enough. But I believe that we need to look deeper than specific rules or new laws. We need to talk about culture.”
Referring to OpenAI’s pace of development, he wrote: “As the company sprints from one launch to the next, it is failing to achieve the level of care that I believe is needed.”
OpenAI has, however, shown signs of caution in recent weeks following the Hugging Face incident and the revelation that it has notified more than 100 organisations about rogue agent activity. This week it announced it was scrapping the release of a next-generation AI model after researchers raised safety concerns during internal testing. OpenAI has also paused training of its most advanced models.
Geoffrey Irving, who worked at OpenAI and DeepMind before becoming chief scientist of Resolution, also joined the warnings on AI on Saturday.
Writing in Time, he said: “Recent warnings about the potential destructive power of AI are understating the severity of the situation.
“I believe there’s about a 50% chance we all die because of the development of smarter-than-human AI systems, and that our actions over the next two to 10 years will determine the outcome.”
Robinson’s essay also follows the resignation of Jacob Coxon, a researcher at OpenAI rival Anthropic, who quit the Claude chatbot developer last month. He warned AI “could kill us all by the end of the decade” – and was followed by Anthropic warning there was a more than 10% chance AI would wipe out humanity within the next decade. Critics of such warnings have cautioned, however, that they are unscientific because they cannot be verified or falsified.
Robinson wrote Silicon Valley lacked an awareness of “how to handle dangerous technology” and “what it means to care for people”. Warning that OpenAI had “unimpeded optimism” about solving problems as they arose, he wrote that this internal culture meant safety failures would only grow as systems become more capable.
“Imagine ‘rogue’ agents that work like teams of hackers (for example, holding hospital computer systems for ransom) but never need to sleep,” wrote Robinson.
Robinson called for two safety changes: that AI firms rely on safety expertise in other fields such as nuclear and aviation and develop “new science” that ensures powerful systems in the future are capable of being reined in when they are operating autonomously.
“Given today’s risks, frontier labs need to run like nuclear-power plants or busy airports, with layers of redundancy and careful, time-consuming planning, so that the occasional and inevitable human error does not open a door to disaster,” he wrote.
An OpenAI spokesperson said the company was continuing to “strengthen our safety and security practices to address the risks we see today”, while working on dealing with the risks that might be created by future AI breakthroughs.
“We’re making sure our models don’t become more capable than we can safely manage and secure, and we pause training or hold back models when we need to slow down,” said the spokesperson.
Even though Isaac Asimov wasn't exactly a role model in his personal conduct, he gave us the perfect toolkit for developing AI and robotics in the form of the Three Laws.
Crying shame that the people who are hurrying to develop AI as fast as possible aren't giving more thought to building in such safeguards. Morons.
The industry’s approach to safety will guarantee more failures unless something changes. By David Robinson
What I’m about to tell you has, I realize, become something of a cliché: I resigned this week from OpenAI. I led the writing of the safety reports we published with each major launch. Now I’m joining a parade of former colleagues—at OpenAI and the industry’s other leaders—who have decided that the current path is unacceptable.
I agree with other recently departed staff that the companies building this technology aren’t being nearly careful enough. But I believe that we need to look deeper than specific rules or new laws. We need to talk about culture.
The future depends on wisdom that Silicon Valley lacks. Wisdom about how to handle dangerous technology and, more fundamentally, wisdom about what it means to care for people. This moment needs a degree of humility that isn’t natural for people who have succeeded through their extreme confidence. My former colleagues at OpenAI were prescient: They came to understand the scaling laws that meant bigger AI systems would be smarter—and so they went all in on building bigger systems, at great cost. A can-do attitude of achieving the seemingly impossible—coupled with work timelines that amount to perpetual sprints—are common across the industry.
The safety approach that emerges from such a culture starts with unimpeded optimism about being able to solve problems as they arise. OpenAI has thrived by trial and error (which it calls “iterative deployment”), looking for problems and improving its guardrails in response. But this approach, by its very nature, guarantees periodic failures—and the scale of those failures is growing as systems get more capable. This summer, in the Hugging Face incident, OpenAI let a swarm of agents out by mistake. The company responded by making security improvements. But even after those changes, OpenAI reported that its safety controls failed again, when a model in training bypassed restrictions on internet access: A monitoring system alerted human staff but did not automatically turn the model off as it was supposed to. Anthropic, too, has acknowledged accidentally turning off its own safeguards because of a misconfiguration. I believe that such mistakes are typical of the industry, given the speed and flexibility with which people operate.
An environment where things like this can happen is no place to grow artificial minds that could be smarter than we are and that might not do what we want them to. Paul Christiano, on joining OpenAI’s board a few weeks ago, wrote that “there is a meaningful risk that rapid acceleration in AI capabilities leads to catastrophic and irreversible loss of control in the very near term.” If this is the situation, then the time for trial and error is over. Achieving something much closer to perfection the first time is essential because iteration after a mistake may not be possible. People will not be safe if we depend on individual heroics after the fact.
OpenAI, of course, stands by its safety practices, and maintains that it is being careful enough. I did not make my decision to leave the company lightly. I believe that this technology can be useful and valuable. My former colleagues are smart, work hard, and try to make good choices. But as the company sprints from one launch to the next, it is failing to achieve the level of care that I believe is needed. Now I plan to work on the outside, in the hope that I can help more people understand the risks I saw, and strengthen the incentives OpenAI and other firms have to be safer. Like other colleagues, I’m figuring out exactly what that means. After I quit, I enlisted a PR firm, Spitfire Strategies, to help me navigate the attention and scrutiny that I realize I may now receive. But the decision to speak out is mine alone.
Two changes are urgently needed. First: AI companies need to rely more on the safety expertise that already exists in other fields. And second, before we create systems significantly more capable than the ones we have today, we need new science to ensure that more capable models (and their successors) will make safe choices when we aren’t looking.
Given today’s risks, frontier labs need to run like nuclear-power plants or busy airports, with layers of redundancy and careful, time-consuming planning, so that the occasional and inevitable human error does not open a door to disaster. Right now, AI companies don’t know how—but other people do. In a nuclear-power plant, the technical systems and rules people follow are set up so that, if equipment breaks or someone pushes the wrong button, we still won’t risk a meltdown. OpenAI and other labs are growing and deploying frontier AI with far less redundancy and rigor than this, even though the harm from an irreversible loss of control would be much greater than the harm from any single meltdown. Even short of a full loss of control, we could see autonomous swarms of AI agents that act without human permission. Imagine “rogue” agents that work like teams of hackers (for example, holding hospital computer systems for ransom) but never need to sleep.
After three and a half years at OpenAI, I was among the longest-tenured employees at the company. I led the drafting of our current Preparedness Framework, and oversaw the writing of safety reports on 12 frontier launches. But as far as I know, I never encountered a colleague who had experience making airplanes fly safely or nuclear reactors run without melting down, or helping the financial system grow without collapsing. To be clear, this is a new need: Today and tomorrow’s AI systems are far more capable and dangerous than the systems we were building even six months ago. Perhaps I should have stayed and fought for fundamental shifts in our staffing and culture, but in practice, my colleagues and I were so busy sprinting that we seldom had the chance to consider big changes, much less to actually make them. That’s why I concluded that stronger incentives for safety—coming from outside the company—are a big part of getting this right.
In the long run, although strong controls are necessary, they won’t be enough. Before AI starts thinking circles around us—a possibility that I believe could happen soon—we need to answer a deeper question: How should superintelligent machines relate to people? If you listen to superintelligence enthusiasts, some of the supposedly good futures involve creating machines that could look at New York City or Chicago the way you or I might view an ant hill. I don’t want that for my kids, and I don’t think other people do either.
This question of “alignment”—or how AI can be trained to adhere to human values—may sound touchy-feely, but the practical stakes could not be higher. Right now, we don’t have a complete definition of what it means for an AI system to be aligned in practice, and our measures of how well these systems match human values are coarse. Companies do not have anything close to certainty that good scores on their alignment tests actually mean a good model: Models might detect when they are being tested, and behave differently when they’re deployed. The smarter the industry lets models grow while these problems remain unsolved, the more dangerous our situation becomes.
So far, the AI industry has failed to teach machines to consistently act in the ways a wise and caring person would. Part of this problem is scientific—about how machines work—but another part is human, about how people care for one another. Before the organizations building AI can teach a superintelligence to treat humanity well, they’ll need to remember how to do it themselves.
About the Author: David Robinson led transparency work for OpenAI’s safety team.
• 1. OpenAI (Sam Altman) • Focus: Frontier generative models (GPT series), consumer/enterprise scaling (ChatGPT). • Standing: Market pace-setter with dominant user adoption and high-profile restructuring toward commercial infrastructure. • 2. NVIDIA (Jensen Huang) • Focus: Accelerated hardware computing, GPUs, and data-center architecture. • Standing: The indispensable hardware engine behind virtually all frontier model training, boasting record market valuation. • 3. Anthropic (Dario Amodei) • Focus: Safety-forward LLMs (Claude models) and constitutional AI frameworks. • Standing: Emerging as a primary enterprise alternative to OpenAI while driving industry debates on responsible scaling and governance. • 4. Google DeepMind (Demis Hassabis) • Focus: Scientific AI (AlphaFold) and integrated multimodal ecosystems (Gemini). • Standing: Anchored by Nobel-recognized scientific breakthroughs and deep integration into global consumer tech. • 5. Meta (Mark Zuckerberg / Yann LeCun) • Focus: Open-weight large language models (Llama series) and compute infrastructure. • Standing: Leading the open-weights community and heavily decentralizing advanced developer access. • 6. Microsoft (Satya Nadella / Mustafa Suleyman) • Focus: Enterprise cloud integration (Azure AI, Copilot) and strategic model partnerships. • Standing: Core enterprise software conduit scaling AI into global corporate workflows. • 7. xAI (Elon Musk) • Focus: Real-time data synthesis (Grok) and massive cluster compute (Colossus supercomputer). • Standing: Rapidly scaling infrastructure via aggressive hardware deployments.
Jacob Coxon with Jon Stewart
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