One more time with a little more energy. All right, more energy. Hello and welcome to this week's episode of Inside Blackstone, where we bring together data and insights from across our portfolio to give you a fuller picture of what's happening on the ground in the real economy. I'm Christine Anderson, Global Head of Corporate Affairs, and here's the lineup. Our Monday Morning Meeting guest is my friend Gilles Dellaert, who leads our Credit & Insurance business. Then it's over to the Economic Weather Report with Winfield Sickles, and next my wide-ranging conversation with Eric Schmidt, CEO & Chair of Relativity Space, and former CEO of Google. And finally, a debrief with Jas Khaira, the Head of our AI investing platform, N1.
Gilles, thank you so much for joining us.
You and I spent a lot of time together at the beginning of this year when private credit was just in the news constantly driving the headlines. It seems to have quieted down quite a bit. What are you seeing?
Yeah, the headlines were loud and they were everywhere at the start of the year. I think things have definitely quieted down. You're seeing less of it. I think it's in large part because the performance has held up a lot better than the kind of doom predictions that were being made at the time.
Another dynamic playing out is AI. What are you seeing?
Well, we're living through this unprecedented build-out across the globe. And with that come really large financing opportunities. The projects are substantial and they touch every part of the supply chain, if you will, all the way from the power that fuels the data centers to the equipment that goes in, the chips, the cooling, the services required. And so we're active across all parts of that as a lender because a lot of financing dollars are required.
Why chips and energy specifically?
I would say power and compute right now are where things are most constrained and where things are most constrained, that's what we tend to run towards. Because that's where the supply-demand imbalance is largest. That's where it creates opportunities for us to lean in and provide private solutions to borrowers to help fuel the CapEx build-out and the growth that will come.
And I heard you say yesterday, it's not just an AI story, there's something broader going on.
Yeah, I think it's broader. I think the global economy is growing. With that come needs from companies to finance that growth and the CapEx that is associated with it, as well as the strategic M&A that might be associated with it. And we're active there with industrial companies, with companies that are in the aviation industry, the companies in the telecom industry. So, it's a much broader playbook. It's global and it's sizable. And all of it has one thing in common, which is we're backed by real assets that offer some form of inflation protection, which we really like in this economic environment.
Well, exciting time in your business. Thanks for joining us.
Really appreciate it, and now over to Winfield and the Economic Weather Report.
Thanks, Christine. It's Tuesday, September 29th, and there is a lot going on with major central bank meetings, the US-China Summit, and, as everybody that was stuck in traffic knows, the United Nations General Assembly last week. As I mentioned, when I was on air with Christine last week, the Fed recently delivered its first rate hike since 2023 with a unanimous vote. In his press conference, Chairman Warsh cited robust economic growth, competition for capital, and geopolitics as the driver of long-term rates. Treasury yields continued to move higher last week on concerns of prolonged Middle East conflict with the associated commodity inflation, as well as strong economic data and continued AI infrastructure-related borrowing. The five-year US Treasury Yield moved above 5% for the first time in nearly 20 years, and then globally, government bonds reached an average yield of 4%, which was a post-global financial crisis high.
Despite this volatility in treasuries, AI continues to underpin markets as adoption broadens. Meta shares jumped roughly 13% after launching its new AI agent, Muse, which already has millions of downloads and really highlights how AI is starting to move from the infrastructure build-out stage to real world usage. We're seeing that shift firsthand across multiple parts of the Blackstone ecosystem. Anthropic spend across our portfolio companies, borrowers, and GP Stakes PortCos grew approximately 27 times over the past 12 months. This dynamic is creating winners across the AI value chain, with AMD recently becoming the fourth US chipmaker to surpass a $1 trillion market cap. Expanding use cases, this rising utilization and productivity gains continue to accelerate AI adoption and really reinforce this powerful investment cycle, as well as the deployment opportunity for so many of our businesses and funds. That's it for this week's Economic Weather Report. The week ahead brings jobs and inflation data and much more. Until next week, good luck out there. Now over to Christine's conversation with Eric Schmidt.
Eric Schmidt has had a front row seat to nearly every major technology shift of the last three decades. You know him from running Google and since then he's become one of the leading voices shaping the conversation around artificial intelligence. He is CEO of Relativity Space, giving him yet another vantage point on how breakthrough technologies move from idea to reality. Eric, welcome to Inside Blackstone.
Oh, thank you. Thank you very much. Well, of course, I love the founder and the firm and everything you guys are doing, so.
You're a technologist, you're a computer scientist. You give these incredible technical talks. I think your superpower is that you're able to break down complex ideas and make them more accessible. And that's ultimately what we're trying to do here.
Well, thank you. It's amazing the breadth of things that you've done in your career, that you are still doing.
You're not dead yet by any means. And I guess that's my question is, you're 71. When most people achieve the kind of success you have, or not even close, they spend the rest of their life sort of talking about it and what they accomplished. You keep reinventing yourself.
I can't seem to stop. My own view is as you get older, you should take on more risk because you have less to lose, right? And so, you might as well try the things that you thought were impossible. And if you fail, well, it's okay. You know, you tried. And as long as you're trying in service of the ethics and the world that you care about. I care about democracy and I care about freedom, that's what I wanna work on. At this point in my own life, I'm interested in impact. Most of the problems in the world could be solved in Eric's little brain with an improvement or an adjustment in technology. If you look at sort of improvement in our lives, improvement in our health and so forth, it's all been related to technological progress. Starting with the invention of fire, which was extremely useful, right, way back when. But also dangerous, right? Of course, and everything is dual use and everything has to be managed. But just look at the dollar value of labor over 1,000 years, or 500 years, or 100 years, right? Human output, human hours in terms of productivity, are vastly improved. And because we've somehow collectively decided to have fewer children, not a good thing. I have four.
Congratulations. Doing my part.
Have as many as possible. More children are good and more grandchildren are good and so forth. We're going to have to automate. And so human wealth, human health, human wellbeing, one of the major determinants will be over the next 100 years, can we continue to accelerate progress in this? I profoundly believe we can. And I profoundly believe that long after I'm gone, humans will be much better.
So, you tend to be way more optimistic on the benefits of AI than most. What gives you that confidence?
Would you like most human diseases to be solved in the next 15 years?
I would. I would. Sign me up, especially given my age.
Would you like solutions to climate change?
I would love that. Absolutely.
Maybe we could have safer products. Maybe we could have better educational solutions. Maybe we could change the educational system so that kids are not bored, but rather they're engaged. The opportunities are significant. In the last month, 10 major math problems were solved, including one called Navier-Stokes, which is historically important, by computers. Now you sit there and go, like, why do I care? Well, math is the foundation of that acceleration.
For those listening to this podcast that are not technologists, what was the significance of that?
Navier-Stokes is an equation that involves fluid flows. It's one of the unsolvable math problems, until now. The specific conjecture had never been proven. And so, they use approximations for Navier-Stokes. I indeed funded some physics work in this area… which I never understood, but it was really smart people. And the key thing is that these fluid flows model, for example, lift of an airplane, how do air conditioners work, there are many, many examples. So, being able to solve the problem in a particular way indicates better algorithmic answers. We can get a better and more accurate answer. Why do you care? The airplane will fly faster. The airplane will use less gas. Right? I can go on. The rocket will go faster. The fuel will be less. You'll get to Mars quicker. You know, what have you. All of these fluid flow things matter in a way that we sort of take for granted.
Let's go back to the internet, because I think it's so interesting. You've been around the rise of the internet and other platform, you know, shifts, the rise at enterprise software, search, cloud computing, mobile, all of these major technological innovations. Why does this moment feel different to you?
It's not different, it's just bigger. The internet felt exactly the same as this. I started with mainframes, so that's how long I've been in this field. I've doing tech for 55 years, and when the PC revolution came along, everyone said, oh my god, this is this huge wave. It created Microsoft and Apple and so on and so on. This was 40 years ago, 45 years ago. So, these things take a while, and I think what, another example. Let's look at self-driving cars. Did you arrive at work today in a self-driving car?
I suspect not, nor did I. And so, what do we think about that?
When do you think we will?
Well, it's beginning. Now self-driving cars…
When do you think New York City?
New York City will be one of the last ones. But it'll come. It'll come. But it's coming in the different cities, including obviously Waymo and other competitors. But why do I mention self-driving cars? They were largely, they were designed in the 90s. The first real test of self-driving cars was 2004. So that's 22 years and going. So, the diffusion rate is different from the invention rate. So, the faster the diffusion rate, the sort of broad adoption, the quicker the societal change. Why does it occur in what I do first? It's because we're already connected, right? It's essentially a softer problem. It's just connectivity. The marginal cost of connectivity, given we're so connected, is very low. The incremental cost of connecting you and me is essentially zero. Boom, it just happens. It's straightforward economics. Whereas getting self-driving cars is a great deal of capital. It's hard, and so forth. People are working on it. Why 20 years versus zero years, and that's the answer. You see the same, by the way, in AI. All of the gains right now are occurring in what is called, essentially, scale-free. And scale-free means that you can just keep doing it and get smarter. So, in math, I'm not a mathematician, but as best I can understand, they can just invent stuff, and they just keep inventing things, and they keep inventing things, and they're so clever. Well, now a computer can take all of those ideas, and if you just give it enough juice, it can do the same thing. Because it doesn't need data, it's self-contained.
Unlocking all these solutions.
Unlocking these solutions. In software, it turns out that once you start writing code and you start learning how well the code works, you can make it better. And then you can make it better and better. And there's what is called a recursive self-improvement loop, where it's getting smarter on its own. But this progress that I'm describing is happening faster than I have ever seen, right? What is true is each wave is bigger, but it's also faster. And we are not ready as a society.
Eric, you gave me like 10 things to unpack there, but let's try to do this. So recursive self-improvement, right? This is the thing that had the Anthropic researchers, two of them, making comments, one leaving making comments, the other sort of validating that there are serious concerns about the speed with which AI is learning. Right?
So, by the way, every month there is a group of people who quit over this issue.
Over the same topic, exactly.
Silicon Valley, and in particular San Francisco, they're absolutely convinced that this is going to happen so quickly that it will threaten humanity in one way or the other. I personally do not agree with this.
