How Capital is Powering the AI Infrastructure Buildout with Magnetar Capital Managing Director Neil Tiwari

15:26Neil Tiwari:Yeah. Crazy. Help me address like this criticism around circular financing.

15:33Sarah Guo:Yeah, I know. It's obviously a topic du jour. And I think, you know, the way we see it and frame it really has to do with the demand signals and who are the eventual buyers and how is this being used? And so, at least from our perspective, we continue to see insatiable demand. And if you go back to, you know, the previous kind of big tech build out back in the early 2000s, there was obviously a lot of fiber that was being built and you had dark fiber, you know, and overbuild happening. And I think what you see here is, you know, you don't see any dark GPUs. No, I've been looking. Exactly. Any GPUs used.

16:14Neil Tiwari:Yeah.

16:14Sarah Guo:And then number two, you're starting to see actual economic value. So I think last year, Enterprise AI had about$37 billion of total TAM, and it's continuing to grow like crazy. And at least personally, and I'm sure you see this too, but I use these tools all the time. Continuously. And I find it incredibly valuable, right? The actual tokenomics of positive ROI is actually here now, I think, from our perspective. And so the circularity comment, I think, applies when you're building speculative computing capacity or if you're purely doing vendor financing and you're trying to do some type of unique, some type of revrec type item related to that.

17:00Sarah Guo:And that's not what we see. What we see is financing to support to build out the demand against use cases that are very positive in their ROI. And so like our perspective is that that's, you know, not a real, real concern that we have. And it really has to do with who are the ultimate buyers here. The ultimate buyers have been at scale, the hyperscalers, they're deploying this at scale. And the economics are positive when you look at a unit economic basis in terms of deploying intelligence. And I think we're at a moment in time where you're really starting to see that.

17:34Neil Tiwari:In my own experience, I have been a heavy AI user for several years, but reasoning advances the ability to scale up inference, especially around code, means I'm up against my max limit all the time in a way that was not true initially. How does the inference workloads actually growing? I mean, it's a good demand signal that there is value, but how does that change your business?

17:59Sarah Guo:Yeah, so I think one thing that's interesting that we're seeing is obviously there's been the shift from training to inference over the last few years. That split continues to grow on the inference side as usable and ROI positive applications get developed. I think the two things I see on the inference side now is inference is a lot more complex than I think initially thought. And what I mean by that is it's not as simple as you train a model and then it's easy to inference it. In certain cases, you can do that on similar infrastructure, but there are issues around latency, fungibility of that, and really optimizing the cost of your compute on the inference side.

18:44Sarah Guo:How do you manage, you know, peaks of inference demand? And obviously it's not linear. Like training, your GPUs are on all the time, you know, 100 % of the time. And so with inference, you have a lot more variability. And so there's a lot more nuances in optimizing inference. I think the second thing that's observed that I've seen is inference is definitely a memory problem, a memory throughput problem. On the inference side, you have these kind of phases called pre-fill and decode, right? And how you optimize that across a fleet of GPUs is actually a unique technical problem. And then the third is what I would say is distribution.

19:24Sarah Guo:A lot of times training infrastructure is quite centralized. What you're seeing with inference is in many use cases, as this becomes more ubiquitous, you're going to have more and more decentralized inference clusters. And actually, one of my favorite companies is one of your companies, Base10, which is really optimizing distributed inference at scale. And I think one thing that's interesting when you look at companies like that and other inference clouds is how do you optimize the compute and build out these clusters that could actually look very different than a training cluster? Where a training cluster might be 50, 100, 150 megawatts in one kind of four walls.

20:06Sarah Guo:I think you're starting to see distributed inference, which could be, you know, four or five megawatts and five separate data centers and stitching them together in different areas. Right. And that looks very different from a kind of power perspective, how you, you know, the software matters a lot more when you're doing like distributed inference. And then in terms of your question, how it impacts us, I think one of the things that we've been focused on is where we started this conversation with you on financing compute. That was really obviously it started with mostly training. A lot of those hyperscalers are now doing a lot of inference on that same infrastructure.

20:46Sarah Guo:But these are investment grade counterparties. You know, it's easy to it's easier to lend money to build out these clusters to those customers. I think now that you have this new crop of inference clouds and application layer companies that are needing tons of inference, I think the key question that we're really focused on is how can we finance the next build, which is distributed inference? And maybe the last one or two takeaways would be one thing I'm seeing is for every application layer company out there, the highest line item from COGS is compute. And then the inference companies and inference clouds out there, most of them are purchasing up compute from either other clouds or unused capacity.

21:33Sarah Guo:And when you look at margins for that, you've got layered margins. And so there's a push to kind of own your own infrastructure to really drive and increase profit margins. But also it's the ability to kind of have control of your own destiny. And I think a lot of folks are starting to, the application layer companies and inference clouds are grappling with how can we build and own and operate our own infrastructure. And that's something I'm really looking into.

21:58Neil Tiwari:I am too. And I think one of the things that is going to make a big difference in this ecosystem is like, can the inference clouds like Base 10, can they deliver reliability that you would expect from a cloud, like a traditional cloud? Because the like distributed data center operations that they consume today do not offer that reliability.

22:25Sarah Guo:Right. And the other thing that's interesting is, you know, this is additional reporting from last week. If you're familiar with Silicon Data, they put together a lot of data on spot pricing and price per token performance. This is Carmen Leigh's company. And one thing that I think was really interesting in an article she published last week had to do with how two pieces of compute that look identical on paper have wildly different performances. Everything from reliability to cost to speed. And I think as you distribute, you know, have distributed inference, how do you mash together very different types of compute and try to optimize reliability, I think is super interesting.

23:07Sarah Guo:And that gets to kind of one thing I find really interesting that NVIDIA is doing is this concept of AI factories and building AI factories behind corporates and AI companies. And maybe the way I unpack that is you've got kind of more large monolithic cloud players, the hyperscalers and the nino clouds that are building large scale cloud environments. And a lot of where I think NVIDIA and others see this going is, yes, those are going to be important components and those are going to be huge markets. But corporates, Fortune 500, AI companies that use a ton of compute will want dedicated AI factories associated with workloads that they run and that they have control over.

23:53Sarah Guo:And so I think you're starting to see the early indications of how do you finance and build out? almost think of like literally AI factories that sit on-prem with a company that can operate their workloads.

24:06Neil Tiwari:You're talking about my Mac mini farm. Exactly. No, but all joking aside, I think one thing that is another supporting factor for use of all of the compute we have is and can create over the coming years is power is clearly the limiting factor. It's easier to get more power in smaller units. I think that as inference demand is growing, anyone who has usable compute for inference is going to find a lot of partners for offtake.

24:41Sarah Guo:Exactly.

24:41Neil Tiwari:Okay, let's look at the future a little bit while we have 10 minutes. Let's talk about the macro. People talk about energy. They talk about natural gas, the grid, the slowness of nuclear. Like, what do you think about over the next six or 12 months?

25:01Sarah Guo:Over the last year, I've been spending a ton of time in the power and energy markets and looking at interesting solutions that can help scale power for the gap that we see. I think a few observations that we've seen. The first is we do have a power problem, but I think it's a bit more nuanced than a lot of the reporting out there where.

25:21Neil Tiwari:It's just we can't generate. We can't generate.

25:23Sarah Guo:Yeah, I think there's actually quite a bit of stranded power across the grid, across the country. And what I mean by that is a lot of the utilities are built in a way where they're focused on peak power. Right. So they've got natural gas peakers and they're focused on providing peak power for those moments where demand is kind of off the charts. And that's obviously only for a few days out of the year. So there's lots of generating assets out there. The question is, they're a bit stranded, right? And so there's kind of, I look at the power problem as being kind of multiple fold. The first one is, how can you take the power we have on the grid and actually make it usable?

26:00Sarah Guo:And a lot of that has to do with flexibility and storage. And so we've been spending a lot of time looking at an energy, in the energy storage business and distribution. How can you store unused capacity, peak demand shave capacity, store it and then distribute it when it's needed? We made an investment in a company called Taurus, I think I mentioned to you, which is building like this distributed utility layer, almost like this mesh infrastructure to store excess capacity or store capacity from a variety of sources and then distribute it at the time when it's needed. And so I think that's kind of a critical layer that needs to be built.

26:38Sarah Guo:And then longer term, there is a generation problem. But I think in the shorter term, it's really it's more on the distribution and storage. And then the other piece I would say is, you know, the true bottleneck, at least in the short term, the next six to 12 months is it's incredibly I don't use the word simplistic, but it's things like structural steel. It's finding electricians that can.

27:02Neil Tiwari:Sorry, you can't get enough.

27:04Sarah Guo:You can't get enough steel. You can't. This is not something I was aware of. You can't get steel. You can't find enough electricians to build out the power infrastructure, substations, transformers, air chillers. These are very specific power infrastructure needed to just get to a point where you can start to build a powered shell on a piece of land. And so the bottlenecks in the short term really are people, equipment. And then the other interesting thing is that on the generation side, what you're seeing is regulatory, obviously, is a big challenge. And so there's a combination of bring your own capacity.

27:43Sarah Guo:There's a lot of that that's interesting right now. And so a site that can potentially grow to 50, 100 megawatts might start with only 10 megawatts of grid interconnect. but can you add solar, nat gas, turbines, put these various bring your own capacity kind of pieces of technology together to make that site usable? And so I think a lot of what's being looked at and a lot of what I'm looking at right now is really on the bring your own capacity, at least in the short term.

28:09Neil Tiwari:Yeah, I think if people don't know the origin story of Crusoe and Flurgas, like it's actually really interesting as an example of, you know, there is actually lots of energy. You know, some energy out there and you can make much more of it consumable. Yeah, exactly. Couple topics to hit before we lose you. New players. How do you think about the Sovereigns and what they're doing in their build outs?

28:35Sarah Guo:Yeah, I think they seem to be able to fund themselves.

28:38Neil Tiwari:Exactly. Yeah.

28:39Sarah Guo:You know, you saw the news from India last week. Obviously, a lot of the news in the Mideast, Southeast Asia. I think, you know, we're continuing to see that Sovereigns view, compute, and AI, you know, as and even we do here in the United States as a matter of national security. And obviously, the funding of those clusters is very different than funding like a private cluster. And so you've got, you know, government capital that can be used for that. So I think there's two things that, you know, I find interesting in that space. I think one is, who are the partners that are going to build those that capacity?

29:17Sarah Guo:and what are the cybersecurity kind of implications and environments for that. And so those are the two nuances, I think, with sovereigns is they need to find players that can rapidly scale compute in their countries. And oftentimes they don't necessarily have these players that know how to build and scale GPU compute. And I think that's a great place for the United States to lean in and help build sovereign ecosystems around the world. And then there's a matter of cybersecurity and how do you make it into a truly safe ecosystem for those sovereigns. And so I think there's a lot of work to do still on the cyber side, especially as you look at scaling sovereign AI.

29:54Neil Tiwari:What is your thinking on physical AI? It's another, you know, if it works, CapEx intensive build.

30:00Sarah Guo:Absolutely. And, you know, maybe I'll just take a second to say one of the things that we observed from 2010 to like, you know, the early 2020s was we were in a very capital asset light mode of build. Like SaaS was, you never heard Magnetar in SaaS, right? Because it was just purely asset life. Compute and everything we saw starting in 2021 is asset heavy. That's where you started hearing a lot more about us. And I think physical AI is actually an extension of that. And so what you're seeing is part of the reason I think, and I think we all have scars from the 2010s of hardware companies that did not make a lot of money for us.

30:35Sarah Guo:Part of the scars was it was so difficult to scale hardware companies, you know, because the software was so difficult to build. You needed to spend so much money building the hardware, the software was an afterthought. What you're seeing now is now that you have more general purpose software via AI, it can make the hardware easier to scale because you have, you know, software that can be, you know, can interact with more hardware. And so I think the natural kind of extension of what we see is kind of what happened in the compute markets where you really needed flexible capital, where it wasn't just equity, it was debt and, you know, a variety of project finance to really scale CapEx, you're going to see that same kind of need in physical AI.

31:19Sarah Guo:And it simply has to do with capital intensity, right? You know, on the compute side for like CoreWeave, as an example, they needed billions of capital to scale, you know, that cloud. And I think whether it's a robotics company or whether it's a, you know, a manufacturing-focused company, drones, defense, all of these areas are incredibly capital-intensive. And then now that you add AI into them, I think it can help them scale faster, quite frankly, and capital intensity is still there. And so there's a moment in time now where you have to really look at optimizing balance sheets for physical AI to really grow and scale.

31:56Neil Tiwari:I think to your point of how the early AI compute contracts were structured, I went from learning to be an investor in an era and an environment where robotics was a great way to lose a lot of money for a long period of time. You remember that too. Now I sit on the board of two robotics companies so let's hope it's not true anymore. But I'd say like it's just a question of capability to me. Like you know whether it's in the home or industrial settings where like it is simply not a good human job or we don't have the labor. You are going to have, I think the products will support investment-grade buyers who are going to have contracts that say, like, we want it, and you can raise debt against it.

32:43Sarah Guo:Exactly. Right.

32:44Neil Tiwari:And so I think, actually, that feels of a very similar shape. Last question for you, because it is so timely. What do you make of the general capital rotation out of software, the end of software? And it's all infrastructure labs and AI natives, I guess. Yeah.

33:00Sarah Guo:It's interesting to see that every day there's another industry that kind of tanks, whether it's, you know, you saw the wealth advisors tank for a few days, you saw the consulting companies, you saw real estate, payments, real estate, right? I mean, I think what you're seeing at least is, at least in my view, what I saw was towards the tail end of 2025 and into 2026, like there was, at least in my view, a big step up in performance of usable AI. And I think what Anthropic was doing really, and Claude, and like, we use it all, you know, obviously we use all the models, but, you know, there was a definite step up in performance in making AI usable.

33:37Sarah Guo:And seeing that it can, you know, truly disrupt these, you know, non-AI native industries. I think the reaction and rotation out of each of these names is a bit much because when you, I think there's two factors I look at. One is when you look at valuations, as an example, I think from a free cash flow perspective, SaaS companies are valued at the lowest they've been in years. And there's a huge margin difference between what those rev multiples are today and what they've been in the past. And so free cash flow margins have steadily increased significantly for SaaS as a whole over the last four or five years.

34:14Sarah Guo:And revenue multiples have stayed the same or gone down. And so to me, that's a bit of an exaggeration because it really has to do with individual names versus sectors. And I think that's kind of at least my take is like in all of these sectors, there are individual names that will learn how to maximize their value using AI. And there's those that won't. But what's happening right now is there's a hammer being hit across all names and not specific individual names that might not be using it as well. And then the second point, at least, you know, my view is there are a number of applications that, you know, on paper sound really interesting.

34:51Sarah Guo:Like, oh, yeah, I could just rebuild Slack or I could rebuild Salesforce or could rebuild X, Y and Z. I think, you know, the it's not just the product. It's the way that's integrated across multiple services and systems across the enterprise. That is a lot more difficult to replicate than I think some of the public markets are kind of reacting to.

35:11Neil Tiwari:And I do think there's a fundamental question, in addition to what you said, which I agree with, of like, does anybody want to rebuild it and own it? And, you know, there are, to your point of like within the software sector in particular, there are companies where they're structurally more protected and there are companies that are at more risk. And I think it's as simple as like you got to go select.

35:36Sarah Guo:Yeah, exactly.

35:37Neil Tiwari:This has been so fun. Thanks so much, Neil. Yeah, I really appreciate it. Congratulations on all the innovation and on building out all the compute.

35:43Sarah Guo:Awesome. Thank you. Good to be here.

35:46Neil Tiwari:Find us on Twitter at NoPriorsPod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.