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.McKinsey & Company & Company estimates that by 2030, data centers will need $7 trillion to scale AI… Two words: 👏 decentralize 👏 compute. We could build 100,000 hyperscale warehouses, or maybe we just launch a permissionless network at a fraction of the cost? Better yet, let users

"Analytics always expands to fill the available budget." Tristan Handy breaks this down with Jennifer Li and Matt Bornstein on the AI + a16z podcast: Save companies money on compute and they immediately find new workloads. This is Jevons Paradox and it explains the modern data

The global AI divide is widening. Over 60% of developed countries have a national AI strategy. In the least developed, it’s barely 10%. Yes, these countries have more urgent priorities. But that’s exactly the issue. Without access to compute, they’ll be locked out of the

There's a very palpable disconnect in how AI is perceived. In tech circles, it's hailed as the most important technology of the century. But in creative and cultural spaces? It's increasingly met with skepticism and backlash. 🧵

Excited to see where this goes Pluralis Research 🤝 We should chat!

Really excited about the 'AI-native productivity suite'! Congratulations Superhuman Grammarly

Making AI more energy efficient is critical, but efficiency isn't the endgame. As AI gets cheaper to run, it'll spread faster across everyday life (Jevons paradox). That’s why innovation has to focus not just on compute, but on meaningful, real-world use.

.Eric Schmidt says AI could one day consume 99% of the world’s electricity. I think he’s right. Compute needs two things: electricity and water. And we’re scaling both faster than we’re scaling generation. If demand keeps rising, short-term? Electricity prices go up. Long-term?

Apparently, owning a mint-green, toothy goblin is the new yacht! Celebs love it and Labubu just keeps grinning!