Long before the first AI images flooded the timelines, before ChatGPT became a household word, rooms full of graphics cards stood in data centres around the world — computing day and night, evaporating megawatts. Render farms for films. Supercomputers for weather models and climate research. And crypto-mining rigs, whose power consumption was criticised early and rightly. All of these are evolutionary stages of the same digital infrastructure. Then came language models, and suddenly every kilowatt-hour sounded like a confession — as though the appetite for power were fundamentally new and the question of its usefulness obsolete.
Physically, little is new — only that masses of people now fire off prompts instead of computing hashes. Whether AI workload is a sensible use of these machines and whether the energy calculation works out — there are two answers to that, and only one is currently being given.
Per rack unit
Look at power consumption per rack unit — per U, per 4.45 centimetres of server height in the cabinet — and this figure has been rising for decades. That is neither alarming nor exceptional; it is the trace of a straight line you can read in any data centre. What happens within the same rack unit has continually become dramatically more: more cores, faster memory, wider paths, more efficient conversion of incoming electricity into actually computed operations. Conversion losses — heat, line losses, inefficient voltage regulators — have been progressively minimised over the years. What is computed in a single rack unit today would have needed half a rack twenty years ago.
Whoever looks at a rack unit today compares it implicitly with the rack unit of five years ago, of ten, of twenty. The trend does not show: AI eats disproportionately. It shows: we keep packing more compute into the same sheet metal. That the sum ends up higher than before is the logical consequence — not proof of waste.
Power-hungry machines, then, are first of all nothing unusual. They are the steady state of an industry that has spent half a century learning to extract more from the same footprint.
Monsters we have always lived with
Open the door of a modern data centre and you see a monument of machines. High-performance routers that split and reassemble terabit streams. Ordinary servers, thousands lined up side by side. Complex, vast Kubernetes clusters that start and stop containers like heartbeats. Render farms that grind for months to produce a single film. Crypto miners turning algorithmically secure noise into money — an evolutionary stage in which undoubtedly much energy was wasted, but whose usefulness is not under debate here. Machines whose sole purpose is to reliably produce randomness, because simulations, cryptography and Monte Carlo methods need nothing else. And supercomputers on which meteorologists feed weather models and researchers hunt for active substances, recreate particle collisions, project climate developments.
All of it is power-hungry. All of it is large. All of it existed long before anyone had to spell the word inference volume. And for every one of these systems the same rule held silently: if it serves us, it is a good investment.
Utility decides, not the kilowatt-hour
This rule is not new — it is just currently unpopular. A weather model that provides early warning of storm surges is not measured by its electricity bill but by whether it saves lives. A supercomputer that helps find a pharmaceutical compound in months that would otherwise have taken years is not shut down because its annual consumption rivals that of a small country. A render farm that computes eight weeks for two hours of visual masterpiece is not delegitimised because Pixar had light cones simulated pixel by pixel.
In each of these cases the balance turns out positive because the yield justifies the effort. Of course there has been wasteful use — crypto mining was an example of that, and the criticism was justified. But whether cryptomining is useful is not a question we are putting up for debate here. It was merely an evolutionary stage of the digital computer and communications world. The only question under debate is whether it is legitimate to wire hundreds of thousands of cutting-edge computers into clusters so that masses of people can unleash their prompts upon them — and whether that is an energy calculation that works out.
My answer: yes. I believe we are on the right track with AI — and a damn good one at that.
The actual reckoning
Anyone who uses AI intensively has a fairly precise sense of what is actually saved there. Not in electricity — in human labour. A log analysis that used to eat half an afternoon takes thirty seconds. A first draft of documentation that would have swallowed an hour of preparatory work sits there in two minutes — raw, but usable. A code review that shortcuts tedious line-by-line comparison. An architecture proposal that would otherwise have consumed days of research. A translation that manages without a human copy editor, because the human only proofreads.
Nowhere on the electricity bill does this spared work appear. Nowhere is it accounted how much air travel, how many trips, printed pages, paper, heated offices, coffee machines and commutes are avoided because a task was taken over by an inference server that was running anyway. The debate books only the input — kilowatt-hour by kilowatt-hour — and blanks out the output completely. That is not a balance. That is a half-reckoning trimmed to come out badly.
A fair reckoning would carry the avoided effort along: the working hours freed for other uses, the tasks that would never have been attempted without AI, the democratisation of skills once reserved for specialists. Blank that out, and you have lost the debate before it began — not against AI, but against method.
The real problem: hypercentralisation
That settles the question of utility — but not the question of architecture. For here lies the core of the problem, and it has less to do with AI than with what large corporations want to make of it.
The trend runs towards data centres whose footprint rivals Manhattan. Entire landscapes are reserved for building complexes that do nothing but hold and process data — centralised to a degree that makes any thought of decentralisation seem ludicrous. Behind this stands a clear interest: whoever holds the data holds the power. Whoever runs inference controls access. Whoever centralises both creates dependency.
This drive toward hypercentralisation has hard-nosed economic reasons that have nothing to do with efficiency. First: whoever monopolises compute capacity creates scarcity on the end-user market. Prices rise, small actors are squeezed out, innovation migrates to where capital is concentrated — a situation that can fairly be called assholish. Second — and this is the subtler point: a provider offering inference centrally gets the prompt data of its users thrown in for free. And prompt data is the gold of the age. It is training material, optimisation basis, competitive advantage. Whoever runs local AI on their own hardware withdraws this data stream from the centre. That is precisely what is meant to be prevented.
It is no accident that local AI on-device or on owned hardware is either not offered at all by the major providers, or only in crippled form, behind paywalls and equipped with phone-home functions. Local inference without data leakage is a business-model risk. The user who runs their model at home is a lost data point. The providers’ tacit message reads: use our cloud, give us your prompts, and in return we give you something that works. It works — but at what price?
Prompt data and the voluntariness that is missing
Should we conclude that prompt data ought indeed to be used in some form for further training — then that should happen on a voluntary basis. Not as today, where opt-out is either impossible or practically impossible, because it is barely or not at all facilitated. Anyone who does not want their data used for training often has to dig deep into privacy policies, fill out forms that are nowhere prominently linked, or discover that there is simply no function for it. That is not opt-out. It is an opt-in where the default is set to “yes” and the exit leads through a labyrinth.
An honest solution would look different: active, informed consent. A dialog at setup, not in paragraph 47 of the terms of service. The ability to export, delete, exclude one’s prompt history from training — and that without friction, without holding queues, without the silent hope that most will give up anyway. Whoever wants data may ask for it. Whoever does not want to give it may say no — without disadvantage.
Instead, hunger for data is disguised as technical necessity. But it is not a necessity. It is a decision. And that decision is currently taken at the expense of those who have no lobby: the users.
All of these are problems of provision, not of the technology. Whoever turns the greed of providers into an argument against the machine shortens the debate once more. AI can be operated differently. And whoever operates it differently experiences something that possesses its own value beyond architectural critique.
Acceleration
What ultimately happens to users of AI can be summed up in one word: acceleration. Whoever learns to deploy AI correctly accelerates themselves. Not in the sense of hasty production, but in the sense of a shifted bottleneck boundary. Suddenly the limit is no longer available time, but the quality of the question. Ask well, get good answers — fast. Ask mediocre, get mediocre answers — fast. Bad questions still yield bad answers, only faster.
That is a shift some welcome and some fear. Both are understandable. Acceleration lifts those who think along, and it overwhelms those who do not. It reshapes markets, because small teams suddenly deliver output once reserved for entire departments. It reshapes education, because access to competent assistance is no longer bound to office hours or tutor rates. It also reshapes inequality — in both directions, and that is no contradiction but the reality of a technology that distributes tools but enforces no use.
Time, as every groundbreaking technology needs it
Caution is therefore in order — not as distrust of the machine, but as respect for what it does to us. Every groundbreaking technology must first be grasped and processed by us humans. The automobile reshaped the cityscape, and it took decades for safety, traffic management, road construction, insurance law and collective habits to catch up. With the internet we have actually still not managed that. For the overwhelming majority its construction remains a sealed book — they use it without understanding how packets are routed, DNS resolved, TLS exchanged. And that is fine, as long as enough people exist who do understand it and keep the infrastructure standing.
With AI we stand at the beginning of a similar cycle. It is young, immature, contradictory. It is misunderstood, overestimated, underestimated, sometimes simultaneously. That changes nothing about the fact that it has come to stay. The question is not whether — but how.
Let us not argue with fear
And here the spirits divide. There is a faction that conducts the debate with fear. Fear campaigns are convenient because they save the trouble of thinking. They need no differentiated argument, no figures, no counter-reckoning — only a bad feeling and a culprit. Such campaigns come, not by coincidence, from that political corner that elsewhere also prefers feelings over facts: pseudo- and would-be fascists who bend technological complexity into questions of national identity, because that is easier than explaining how a transformer model works.
We should not take part in that. Not out of naïveté — the risks are real, and denying them amounts to advertising. But out of an older conviction: we may believe in ourselves. In the capacity of humans to do something with powerful tools that does not merely maximise profit but also serves. Open science, accessible education, care for people needing support, translation for refugees, diagnostics in underserved regions — these are not dreams. They are already applications, today, this year. Ignore them in order to dismiss AI wholesale as a climate culprit, and you have blanked out half of reality.
A plea for decentralised operation
But fear has a legitimate side too, and it must be named, especially by someone who considers AI useful. The criticism of oversized data centres and of the nuclear reactors meant to feed them is not blanket anti-technology. It is directed at an architecture that nobody chose, but that affects everyone.
That is why I am making a plea for local use. For opening the market to a decentralised, private operation of AI. High-performance systems should be built regionally — municipally, perhaps somewhat larger on a communal basis, carried by cooperatives, municipalities, universities, associations. Not every inference cluster has to belong to a hyperscaler. Not every model has to live behind an API that siphons off data.
What is needed for that is not revolutionary — it is well known: reliable contracts for the use of private data. Clearly defined data sovereignty, no phone-home, no tapping, no training on user prompts without consent. Open-source models that run locally, on hardware you control yourself. An alternative AI world, built on transparency rather than convenience traps. People would accompany such a world with more understanding, because they can trust it. Trust does not arise from marketing promises but from comprehensibility.
Nobody has to run their model on a pocket-sized smartphone. But the option to do so — or to do it on one’s own server, in an association, in a municipal facility — that changes bargaining power. Whoever can choose negotiates differently. Whoever has only one option begs.
Confessions of an AI-video enthusiast
And since I am outing myself anyway: I love AI videos. r/aivideos is one of the few subreddits I regularly sink into. Much there is kitschy, much is amateurish, much is technically impressive and hollow in content — and some is simply brilliant. The speed at which these tools evolve is breathtaking. What looked like a curiosity two years ago is today a medium capable of carrying serious narratives.
I am certain: within the next few years I will have seen an AI film that impresses me absolutely. Not because of the technique — that impresses me already — but because of the narrative, the handwriting, the vision behind it. Perhaps it will come from someone who could never have filmed without these tools. Perhaps from someone who until now could only write and suddenly directs. Perhaps, who knows, eventually a film will emerge from my pen. I would wish for that. And I see no reason not to say so.
What this has to do with libcom.de
For a quarter-century I have built on open-source infrastructure — and for several years that has included operating AI stacks, honestly balanced, with measure and without illusions. The environmental reckoning belongs to that. But it belongs in a total reckoning that weighs input and output against each other, not in a polemic that counts only one side.
This is not only about one’s own inference server. It is about the question of what an IT landscape should look like that uses AI without surrendering sovereignty. Local models, privacy-compliant stacks, contractually secured data sovereignty — that is not a niche for idealists but a practical alternative that already works today.
If you wonder how AI can be deployed sensibly in your environment — useful and responsible — write to contact@libcom.de. We lay the reckoning open, honestly, without sales pressure, with a view to what lasts long-term.
The monsters in the rack are not new. What is new is who operates them now. And that, I believe, is good news — provided we mind who holds the plug.
And one more thing, in conclusion. To the people protesting against these oversized data centres, marching against the nuclear power plants that are to be bolted onto the AI giants — I extend my support. Not because I reject AI, but because, like them, I do not want a technology I consider worthwhile to end up in an architecture that nobody can control. Their protest is not anti-progress. It is pro-democratic. And it is necessary.
Note: This article offers general orientation on the AI environmental debate and replaces neither ecological nor energy-policy specialist advice. Comparisons are illustrative and reflect the state of discussion at the time of publication; all information is provided without warranty.