Ken Griffin just asked the question everyone in AI is too scared to answer. Data center spending in the US this year alone is over $500 billion. Half a trillion dollars. To raise that kind of money, you have to make a promise. And the promise has to be big. "AI needs to be your savior almost. How else are you going to write $500 billion of checks in a single year?" He's not saying AI is fraud. He's saying the hype is structurally necessary. You can't fund a buildout at this scale without narrative that matches it. The real question is what AI actually delivers at the end. In some areas Griffin says it's going to be profound. Call centers. Software engineering productivity. Those are real, measurable, already happening. But in white collar work more broadly, he's more skeptical. A Harvard paper recently coined a term for it: AI Work Slop. Output that looks impressive on the surface. First few sentences read like genuine insight. Then you go deeper and it's all garbage. Griffin's colleague runs their commodities business. Got handed a report generated by an AI engine. First paragraph, genuinely good. The rest, useless. The model that can write a compelling opening can't yet think through the substance underneath it. This is the AI investing tension right now. The infrastructure spend is real. The hype is real. The productivity gains in specific verticals are real. But the blanket assumption that AI transforms every white collar job equally has not been proven yet.