Wilfried Schmied

Ursula von der Leyen, Präsidentin der Europäischen Kommission, sagte: „Wir müssen in Erwägung ziehen, soziale Medien vorübergehend auszusetzen.“

1/2: Die Illusion der "billigen" Doppel-Infrastruktur – Eine Rechnung 📉🦊 Ich habe mir die 12 Studien zur "günstigen gesicherten Stromversorgung" im Detail auf der Werkbank angesehen. Fachlich brillante Detailarbeit, aber makroökonomisch beweisen die Papiere exakt das Gegenteil von billig. Hier ist die nackte, mathematische Realität aus den Daten: 1. Die Fixkosten-Explosion (Risiko & Baukosten): Ein wasserstofffähiges Backup-Kraftwerk (1 Gigawatt) kostet ca. 1 Milliarde Euro im Bau. Läuft dieses Kraftwerk klassisch 6.000 Stunden im Jahr, verteilen sich die Kosten. Die Studien (z. B. zu "Risk aversion") fordern aber den Einsatz nur für Dunkelflauten – sagen wir 300 Stunden. Die Fixkosten pro produzierter Kilowattstunde verzwanzigfachen sich! Die Studie bestätigt selbst: Geldgeber fordern für Anlagen, die kaum laufen, massive Risikoaufschläge. 2. Der thermodynamische Totalschaden (Ammoniak/Methan): Die Studie zur Techno-economic optimization der Methan- und Ammoniak-Synthese trifft auf harte Physik. Power-to-Gas-to-Power (Strom -> Wasserstoff/Methan -> Strom) hat einen Gesamtwirkungsgrad von bestenfalls 30 bis 40 Prozent. Das bedeutet: Ich muss 100 kWh teuren Windstrom erzeugen, um am Ende knapp 35 kWh Backup-Strom aus dem Kraftwerk zu bekommen. 65 Prozent der Energie werden bei der Umwandlung schlicht vernichtet. Das macht den Brennstoff astronomisch teuer. 3. Der Akku-Verschleiß: Die Studie zur Degradation von Brennstoffzellen und Batterien zeigt es: Speicher verschleißen. Ein Großakku kostet aktuell rund 600 bis 800 Euro pro kWh Speicherkapazität. Bei einer Lebensdauer von großzügigen 10.000 Zyklen kostet allein die Abnutzung des Speichers rund 8 bis 10 Cent pro entnommener kWh – und da ist der eigentliche Strom noch nicht einmal bezahlt! Fazit: Ich muss dreifach bauen (Erneuerbare + sündhaft teure Akkus für den Tageszyklus + ungenutzte Schattenkraftwerke für den Winter). Die Fixkosten verschwinden nicht, sie treiben den Preis ins Unermessliche. Genau diese Systemkosten zwingen unsere energieintensive Industrie zur Flucht ins Ausland!

A huge heat dome is sitting over France, with temperatures that might hit 40°C. It’s still spring. It’s still May. What was once considered “extreme” is increasingly becoming normal across Europe. Heatwaves are earlier, longer, & pushing ecosystems and health beyond limits.

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?