Helped an old guy unload his boxes from Home Depot and he hit me with this

Landlords are using AI-generated images to make properties look better than they are in reality A report found examples of rooms being bigger and buildings appearing different to what they are in person

A new set of rules. A new level of fear. Five Nights at Freddy’s 2 only in theaters December 5.

what would u do chat??

Satya just told you the entire AI trade thesis is wrong and nobody is repricing anything. Microsoft has racks of H100s collecting dust because they literally cannot plug them in. Not "won't," cannot. The power infrastructure does not exist. Which means every analyst model that's been pricing these companies on chip purchases and GPU count is fundamentally broken. You're valuing the wrong constraint. The bottleneck already moved and the market is still trading like it's 2023. This rewrites the entire capex equation. When $MSFT buys $50B of Nvidia GPUs, the Street celebrates it as "AI investment" and bids up both stocks. But if half those chips sit unpowered for 18 months, the ROI timeline collapses. Every quarter a GPU sits in a dark rack is a quarter it's not generating revenue while simultaneously depreciating in performance relative to whatever Nvidia ships next. You're paying data center construction costs and chip depreciation with zero offset. The players who actually win this are whoever locked in power purchase agreements 3-4 years ago when nobody was thinking about hundreds of megawatts for inference clusters. The hyperscalers who moved early on utility partnerships or built their own generation capacity have structural leverage that cannot be replicated on any reasonable timeframe. You can order 100,000 GPUs and get delivery in 6 months. You cannot order 500 megawatts and get it online in 6 months. That takes years of permitting, construction, grid connection, and regulatory approval. Satya's point about not wanting to overbuy one GPU generation is the second critical insight everyone is missing. Nvidia's release cycle compressed from 2+ years to basically annual. Which means a GPU purchased today has maybe 12-18 months of performance leadership before it's outdated. If you can't deploy it immediately, you're buying an asset that's already depreciating against future products before it earns anything. The gap between purchase and deployment is now expensive in a way it wasn't when Moore's Law was slower. The refresh cycle compression also means whoever can deploy fastest captures disproportionate value. If you can energize new capacity in 6 months vs 24 months, you get 18 extra months of premium inference pricing before competitors catch up. Speed to deployment is now a direct multiplier on chip purchase ROI, which means the vertically integrated players with their own power and real estate can move faster than anyone relying on third party data centers or utility hookups. What makes this really interesting is it changes the competitive moat structure completely. The old moat was model quality and algorithm improvements. The new moat is physical infrastructure and energy access. You can train a better model in 6 months. You cannot build a powered data center in 6 months. This is the kind of constraint that persists for years and creates durable separation between winners and losers.

Deadlock Keychains anyone? #deadlock #deadlockfanart