Satya Nadella – How Microsoft thinks about AGI

Maybe after the industrial revolution, this is the biggest thing. But at the same time, I'm a little grounded in the fact that this is still early innings. If you're a model company, you may have a winner's curse. You may have done all the hard work, done unbelievable innovation, except it's kind of like one copy [music] away from that being commoditized.

We didn't want to just be a host for one company and have just a massive book of business with one customer. [music] That that's not a business. You can't build an infrastructure that's optimized for one model. If you did that, you're one tweak away.

Some like breakthrough that [music] happens and your entire network topology goes out of the window. Then that's a scary thing. Our business, which today is an enduser tools business, will become essentially an [music] infrastructure business in support of agents doing work. The thing that you have to think [music] through is not what you do in the next 5 years, but what do you do for the next 50.

>> Today we are interviewing Satya Nadella. We being me and Dylan Patel who is founder of semi analysis. Satya, welcome. >> Thank you.

It's great. Thanks for coming over at Atlanta. >> Yeah, thank you for giving us a tour of uh the new facility. It's been really cool to see.

>> Absolutely. >> Satya and Scott Guthrie, Microsoft's EVP [music] of cloud and AI, give us a tour of their brand new Fairwater 2 data center, the current most powerful in the world. >> We try to 10x the training [music] capacity every 18 to 24 months. And so this would be effectively a 10x increase 10x from what GPD5 was trained with.

And so to put in perspective, the number of optics, the network optics in this building is almost as much as all of Azure across all our data centers 2 and a half years ago. >> It's kind of what 5 million network connections. >> You've got all this bandwidth between different sites in a region and between the two regions. So is this like a big bet on scaling in the future that you anticipate in the future there's going to be some huge model that needs to require two whole different regions to train >> the goal is to be able to kind ...