April 18, 2026 · 10:32 Uhr
Anthropic CEO Dario Amodei met with White House Chief of Staff Susie Wiles to discuss the new Claude model. In parallel, a PBS source reports that Claude is already being used by US government agencies. This marks a new level of entanglement between leading AI labs and the US state apparatus with direct geopolitical implications.
No longer just Anthropic, but also Google is actively hiring researchers in the field of 'machine consciousness'. This signals a strategic shift away from pure benchmark optimization toward fundamental questions of AI safety and ethics. In parallel, a solo DeepMind researcher claims in a much-discussed paper that AI can fundamentally never be conscious – further heating up the debate.
According to CNBC, the prevailing AI demand signal is massively exaggerated – actual paying demand falls significantly below communicated figures. Anthropic is considered the only company aligning its pricing with real demand and therefore structurally better positioned. This fundamentally questions the investment thesis behind the billion-dollar infrastructure spending of other providers.
According to reports, Anthropic is considering developing its own AI chips to reduce dependence on NVIDIA. Should this occur, Anthropic would follow the path of Google (TPUs) and Amazon (Trainium) and further diversify the chip market. For NVIDIA, it would be a warning signal, as the currently leading model provider is now exploring in-house developments.
The 2026 Stanford AI Index documents that the power capacity of AI datacenters has grown to the equivalent of energy supply for entire countries – figures receiving little public attention. In parallel, CBS News and other sources report growing societal resistance against building new datacenters in the US. The energy hunger of AI infrastructure is becoming a regulatory and geopolitical bottleneck factor.
The AI sector is in a phase of strategic consolidation: Anthropic is solidifying its leadership position with the Claude model while simultaneously deepening its connections to the US government, with Polymarket bets seeing Anthropic at 96% probability as the best model by end of April. The industry faces a dual dissonance problem – on one hand, communicated and real AI deployment demand diverge significantly, on the other, infrastructure investments (over 4,000 datacenters in the US) exceed what is energetically and regulatorily acceptable. The entanglement of leading AI labs with state institutions in the USA and UK is growing rapidly, while geopolitical questions around Chinese models and chip dependencies challenge the strategic autonomy of Western actors. An emerging medium-term escalation risk crystallizes around the energy and regulatory question: should capacity constraints or political pressure brake infrastructure expansion, an abrupt setback threatens the entire AI investment thesis.
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