Schlangennester in der Stadt
Herr Buddenbohm über den Übertourismus in Hamburg. Leider ein Grund, warum die Stadt für mich nichts mehr ist – so schön und faszinierend sie auch sein mag.
Aber wie auch immer. Ich halte jedenfalls für die Chronik fest, dass Hamburg nun auch zu einer Stadt wird, in der man für alles anstehen muss. Für alles, was im weitesten Tourismus- und Social-Media-Travel-Kontext erwähnt und fotografiert, gefilmt und livegestreamt wird. Ich grüße an dieser Stelle freundlich in Richtung Venedig, Dubrovnik, Heidelberg etc.
Dann bin ich doch ehrlich froh um mein schmuddeliges Duisburg … oder wie sagte noch Horst Adler in „Unterwegs nach woanders“:
Ich muss nicht wo hinfahren, wo es schön ist. Schön bin ich selber!
(Zitiert aus dem Gedächtnis)
Post treibt Änderung an hunderten Standorten voran: „Deutlicher Rückschritt“
Schreibt Briefe, so lange es noch geht!
Erika, der Lenz ist da
Pistorius hin, Aufrüstung her. Ich komme einfach nicht in die Stimmung, um wieder in Polen einzumarschieren.
Rising Emissions, Depleting Water and Vanishing Land—UN Scientists: AI Is Threatening Natural Resources for Billions
REPORT IN BRIEF
AI’s environmental cost is being mismeasured. Most current assessments focus on carbon emissions from training. The report argues this misses a substantial part of the picture. Every kilowatt-hour of AI electricity also carries a water footprint, from cooling and generation, and a land footprint, from infrastructure and supply chains. These three footprints can move in opposite directions, so reducing one can magnify another.
Data centres are becoming country-scale consumers of electricity, water and land. Global data-centre electricity use, estimated at 448 TWh in 2025, could reach 945 TWh by 2030. The associated water footprint is projected at 9.3 trillion litres and the associated land footprint at over 14,500 square kilometres.
Inference, not training, drives most of AI’s energy use. Once a model is deployed, billions of daily user interactions consume an estimated 80 to 90 per cent of its total energy. ChatGPT alone is estimated to process around 2.5 billion prompts per day.
Per-query energy varies by orders of magnitude across tasks. A typical chat query uses around 200 times the energy of basic text classification. An AI image uses around 1,450 times. A single short AI video can match 200,000 spam classifications. Model choice and product defaults are footprint decisions.
Energy and water required to generate AI images and videos. The energy required to generate a typical AI image is enough to power a 10-watt LED bulb for 17 minutes, and the energy required for a high-complexity AI video is sufficient to run that same bulb for 42 hours. Similarly, the electricity-associated water footprint is about two tablespoons (29 mL) for a single image, but jumps to 4.1 liters for a complex video—almost equivalent to a two-day drinking water need for one person.
Efficiency improvements alone will not contain growth. The report cites the rebound effect, sometimes called the Jevons Paradox, to explain why per-query gains are typically absorbed by rising volumes. Caps on tokens, resolution and output length are needed alongside efficiency.
AI compute is geographically concentrated. Only 32 countries host AI-specialised data centres. Over 90 per cent of capacity is in two countries. More than 150 countries currently lack sovereign AI compute infrastructure.
The hardware lifecycle is the next frontier. AI infrastructure could generate up to 2.5 million tonnes of electronic waste each year by 2030. Critical minerals required for AI hardware are concentrated in regions with weaker environmental oversight, often in the Global South.
A six-principle governance framework. The report proposes a „responsible AI ecosystem“ built on transparency, efficiency by design, equity and environmental justice, lifecycle responsibility, global cooperation, and sustainable use, with specific responsibilities assigned across the AI ecosystem.
KEY POLICY MESSAGES
Carbon-only metrics are no longer sufficient for AI. Disclosure standards for AI’s environmental impact should require carbon, water and land footprints jointly, in standardised units, across both training and inference and across jurisdictions, so that regulators and investors can compare like with like.
Inference deserves the policy attention that training has received. Because operational use accounts for the majority of AI energy demand, governance should focus on product defaults, model selection and behavioural levers, not only on the largest training runs.
Siting decisions are environmental decisions. Where data centres are built, and from which grid they draw power, determines the carbon, water and land profile of the same workload. Permitting, environmental impact assessment and community consultation should reflect this reality.
Local capacity-planning needs to keep pace with global compute geography. The Irish, Mexican and Uruguayan cases described in the report show what happens when grid and water systems are asked to absorb workloads that serve users elsewhere. Transparent mitigation and benefit-sharing should accompany expansion.
Efficiency gains require demand-side guardrails. Without resource budgets, token-per-prompt limits, default low-resolution settings and similar guardrails, efficiency improvements will be absorbed by volume growth.
AI compute access is itself an equity issue. More than 150 countries currently lack sovereign AI compute. International institutions can help by supporting capacity-building, harmonising disclosure, and reducing incentives for cross-border burden-shifting.
The full value chain requires governance. Critical-mineral extraction at the upstream end and electronic waste at the downstream end are integral to AI’s footprint and currently fall on communities that capture little of the benefit.
Investors and financial institutions can move first. Treating carbon, water and land footprints as material risks in due diligence on AI infrastructure portfolios is described in the report as one of the fastest available levers.
AI within planetary limits is achievable. The report’s central argument is constructive. Capability and stewardship can grow together, but only with measurement, transparency, and shared responsibility across the ecosystem.
Internetzugang: Warum ihr (keine) Glasfaser braucht!
Eine gute Erklärung, warum Glasfaser die Zukunft ist. Besonders interessant, dass in Deutschland das Internet sowieso schon per Glasfaser in die Straße kommt, dann aber kompliziert, teuer und störanfällig auf Kupferkabel umgeschaufelt wird.
Jammer vs. Drohne – der Wettlauf in der Ukraine
Enno geht ein wenig auf die technischen Details ein. Mir war noch nicht bewusst, dass die Jammer ab einer gewissen Leistungsfähigkeit selber das Ziel von Angriffen werden.