Anjney Midha's Plan to Radically Lower the Price of Compute

21:27And you know what? That's frankly often the most tedious part of the job anyway. And so I'm quite excited to see that progress because I'm terrible at spreadsheets. and I think if we could free up more of my time and hopefully other people's time to focus on the art of the spreadsheet, not the tedious part of it.

21:45Tracy Alloway:The entry and retrieval, yeah. Yeah, exactly. And in journalism, I think it's the same thing. There's so much craft that goes into the verification of a story before it goes out that's not legible to the world. I've had a chance to spend some time with some of the journalistic institutions of the barrier like Cade Metz or Brad Olson at the journal. And as you spend time with them, you realize, I mean, they're verifying every sentence that goes into each article. Fact-checking, absolutely. So fact-checking, that's an example where I think we should be leaning on these tools and we should expect more progress.

22:19And the parts then that will be more, to borrow your jagged frontier framing there, we will be in a regime of jagged frontier progress where wherever parts of workflows that are verifiable factually will essentially, you'll see progress there very predictably over the next few years. And consequently, wherever that progress, the workflows are not verifiable, is actually where humans are going to shine. And I think that's where parts of the economy, you're going to see extraordinary gains in the wages of humans who have creativity and craft that are not typically verifiable through traditional objective means.

22:59Does that make sense? Yeah, it does. And it dovetails with a lot of what we've been talking about on the show recently. Just going back to verifiable feedback. So, OK, the model spits out something and you can check whether it's right or wrong. Right. Is it important to understand how the model actually got to that answer? Because we have discussions with like big bank CEOs who are using more AI and their response to this question is always like, well, if we can put restrictions around the AI, if we make sure that it's like released into a sandbox before it's released into the wider world, we're all set from a regulatory perspective and regulators don't actually need to know what's in the black box model and how it's working.

23:40But like this seems a bit concerning to me. Yeah. No, I am quite strongly opinionated about this one, which is that technical literacy should be non-negotiable. It's the reason I spend so much time teaching this class at Stanford, putting it up online. And the idea of the frontier systems class is that end to end, And it's a full, simple, but first principles breakdown of how these AI systems are built from scratch, from land, power, shell, like the energy. Where do we get them? The data centers. Then how do we train the models? And the final project, the class with the kids was actually the one-person frontier lab, which is at the end, they're creating their own models and so on.

24:12Because the idea is that a person with the right tools today can scale themselves infinitely, but they need to know how to use the tools, what the limitations are, when to lean on them versus not. And I think this is a generalizable piece of technical literacy that all leaders should have. It's like saying, you know, I, in the 90s, I imagine if you knew, you could use the internet without really knowing how it worked. But, you know, on the margins when like the page doesn't like refresh or you're like this cookie thing is annoying me. Like over time, people who are more technically literate just realized sometimes you got to debug, you know, the browser.

24:50and those of us who've learned over time to do knowledge work are more adept at leaning on them versus not like just now when I was trying to get onto the internet I realized okay there's this you know wi-fi password whatever and then you don't end up relying on them in ways that they can't fulfill your need anyway and what's a little bit more dangerous with these systems is because we tend to anthropomorphize them yeah without the technical literacy that that I wish all leaders had about reasoning about how these systems were built, what you end up doing is projecting out in your mind what the capabilities are in ways that are inaccurate.

25:26You project out their impact in society that are not accurate. You project out their business models in a way that are not accurate. I mean, the very fact that when you started this conversation, I don't blame you for it. You're like, Anj, there's three models at the frontier.

25:38Tracy Alloway:Yeah. I'm like, well, which frontier and which three models? Because from where I'm sitting, there's like 17 different frontiers right now and there's four different players in each one and the businesses of all of them are kind of breathtaking. So I think that technical literacy should always for leaders be a basic requirement. And then if you're deploying these systems at Goldman Sachs, you won't oversimplify and get tripped up later when two years later, you realize half your employee base has been leaning on this like sandbox framing. When in reality, inside the sandbox, they were doing all kinds of, they were using the tools in ways that were prone to hallucination, prone to risks, prompt injection.

26:17They were leaning on it in ways that were not informed in the appropriate ways. Is this making sense? Yeah. At a minimum, they would not be using it in the optimal way. Correct. Or relying too much on it. You can't outsource your understanding to a model. You can outsource your thinking. You can outsource part of the tedious workflows, but you can't outsource your understanding. And if you keep thinking, if you say, if you create these simplistic frameworks of, oh, here's a sandbox and this is safe, you have to use that sandbox in the right way. Because if you say, well, now everything that happens in the sandbox is totally fine.

26:56If the model says, use the spreadsheet, the spreadsheet is good. It's deployed in our servers. But you didn't actually check the spreadsheet and what went into the spreadsheet. And did the model actually understand the particular structure of the business, the physics of the business that you're trying to model out, then you've outsourced your understanding to it. Does that make sense?

27:12Tracy Alloway:Absolutely. Let's talk about AMP. Yes. And because you're never going to get the frontier in anything unless you have access to compute. It seems pretty obvious. And there are various arrangements for acquiring compute. You have companies building their own data centers. You have smaller labs and maybe they use someone else's data centers or a neocloud, etc. What are you building at AMP such that at least as part of this story is trying to solve the compute bottleneck specifically? Yeah. It's very simple what we're doing at AMP. We're doing two things. We are trying to standardize the format for compute, which today is super fragmented.

27:53So in the history of infrastructure, if you look at whether it was the Industrial Revolution, the internet, streaming, there were usually formats of inputs that were quite heterogeneous. They were fragmented. And then to unlock productivity, you had to standardize a format. So in the case of electricity, until ACDC was standardized, megawatts would just sit in stranded pockets around the United States being unused. And then once we standardized the format to ACDC, then the question was, okay, great. Now we've turned all these stranded pockets of electricity into one sort of interoperable universal format.

28:35Now how do we distribute it to everybody who needs it? And we came up with this distribution layer in the United States called the grid. That's all we're doing. Yeah. You're building a grid for compute. Correct. We're trying to standardize the compute layer today. Different chip types, different manufacturers, different clouds. I mean, it's a complete mess. And if you're... Yeah, go ahead. Say more about how you plan to do this, because we've talked before about, you know, there are various people out there that want to create indices of compute, of futures potentially on compute. And the issue that always comes up is fungibility.

29:08Right, exactly. So we've got a couple of ways we solve the fungibility problem. This is a pretty thorny challenge. We solve it in two or three ways. The first is we have a system called the grid, which actually makes the compute fungible at a consumption layer. So under the hood, we have a bunch of different chip types. We support various different manufacturers. And there's a system that was built to do this already inside a little company called Google. And one of the technical leads on that project was called Borg internally at Google. He's my co-founder, Sebastian Lobo. He was my roommate at Stanford 14 years ago.

29:42He's my engineering co-founder. And we're building Borg for everybody else, which is essentially a translation layer that says no matter what the underlying chip type is, the machine learning researcher who's using the chip just has to worry about the workload. And we handle everything else underneath the hood. When you say system, is this hardware or software that's doing this? It's all software. Okay. Yeah. So we handle that translation layer in software. Huh. And it's a pretty gnarly challenge. But today, we're able to do that in ways that improve utilization sometimes from 50%, 60 % at labs that we have incubated on the grid to close to 95%, 96%.

30:17At Google, the utilization is roughly 99%. When Sebastian arrived at Google, it was about 62%. By the time he left, it was roughly at 99%. At Google, if utilization is at 96%, that's considered a major outage. Today, the average data center in the industry, in the ecosystem, in the independent ecosystem, is running at less than 70 % utilization. The Colossus 2, which is running in Memphis, Elon's 500 ,000 GB300s, was running at less than 60 % node utilization and less than 11 % MFU, model flop utilization, is how much of the chip is actually being used. So there's two kinds of utilization people care about in a data center.

30:54First is how many chips are being used. That's the highest. That's just the most naive measure. If that number is not at 90 plus percent, no excuses. So you have the chips. They should at least be doing something. And then within the chip, how much of the chip is being used? Within a workload. That number is usually much lower.

31:13Tracy Alloway:I'm very intrigued by this latter point about that even the chip itself may not be even used at full capacity. Yes. Because I see these numbers. Yeah. And you say like a lab has like, we have 200 chips, we've acquired 800 GPUs, et cetera. And when I see these headlines, I assumed that optimal utilization techniques must be so good that you can infer someone's capabilities simply by how many NVIDIA GPUs they've acquired. What you're saying is that there is actually quite a bit of heterogeneity about the techniques and approaches to getting the most juice out of a chip. Yes, you have to measure what matters.

31:56And what matters is output. Anytime I start a new lab, in the case of periodic labs, we started with Liam Fettis, who was the co-creator of ChatGPT, and Doge Chubuk, who led the physics teams at DeepMind. And when we sat down and we planned out the company's roadmap, the most important thing to us to measure was not the number of chips we had.

32:16Tracy Alloway:Yeah. It's the eval, what we call - So all this chip bragging, they're like, oh, we inquire, it's just a sort of - It's a lot of bravado. Yeah, all right, this is helpful. You don't measure the inputs. You should be measuring the outputs. No, I agree. I'm actually fascinated that there is a software solution to what I perceived in my head as a very physical constraint. How does this actually work? Feel free to get technical here. I want to understand the system. Yes. So let me give you the technological answer and the economic answer. The economic answer actually is a simple reason about. The way the compute business works today is primarily on the construct of the atomic unit of long-term leases.

32:56So I'm a researcher. I need some compute. I show up to a compute provider and say, hello, I would like some compute, please. And the compute provider says, no problem. Here's, you know, 500 AMD chips or NVIDIA chips that you can lease from me on various timescales. and you've got to pay for it 24 seven. It's like leasing an apartment. And whether you use it or not, that's your problem, but it's$2.50 per hour,$3 an hour. So instead you take a long-term lease and now the cloud provider, the compute provider said, great, I just booked revenue for the next two years that this guy rented. Now what happens with that compute, whether it's used or not, is the researcher's problem.

33:35They've outsourced that problem. As a result of this wastage that we're talking about, and I'm happy to go into why it's hard for individual teams to utilize most of the capacity. The primary reason is because research is spiky. It's hard to forecast. So you over-provision for your peak, not your base load. Because what happens, you're researching on these algorithms and the minute one is working, you go, guys, let's scale. We want to ship this thing. So let's throw as many chips at it. And then once we ship it, the needs go down. So between these spikes, there's just huge pockets of unused compute.

34:11As a result, the effective price per hour that you're paying is closer to$25 to$28, whereas the marketed rate that you think you're paying is$2.50. So that spread due to wasted is just insane. So from an economic perspective, that's the wastage. That's the deadweight loss. Okay. So now how do we, from a technological perspective, how do we utilize that opportunity? And literally all we do is from a software perspective, we take all of that unutilized compute, no matter what format it is, it might be NVIDIA, it might be AMD, we love AMD, might be some other chip, and we turn it into one fungible resource.

34:52And we standardize the format on something we call grid credits. So researchers don't even need to think about what chip type is under the hood. They're just paying what they need or what they use. And so from a fiduciary perspective, I'm on seven boards. As an investor, I get very excited when teams switch from this sort of long-term lease model where they're paying$25,$26 per GPU hour to now they're actually only paying the$2.50 that was marketed because everything they're not using gets reallocated to the grid and other research labs can use that resource.

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36:52Tracy Alloway:The problem you're mostly solving for is the training part, because there's training, right? Training and inference. Or is it both? Both. Yeah. So the beauty about having diverse types of compute on our grid is that once you make the resource fungible, you can do any workload. You just fill all the unutilized pockets with inference and then all the reservations with training. Got it. So can you explain, like, why is it that every lab also seems interested right now in customized silicon, including Microsoft announcing a chip that says, like, oh, our new MAI, I don't know, MAI, I don't know how it's pronounced.

37:32Tracy Alloway:Oh, MAI, I believe. We had Sacher in the class yesterday at Stanford, and he pronounced it as MAI. Okay, their new MAI model. Yeah. And he's like, oh, and we also have a new MAI 200 chip or something that's optimized with it. Right. Why is it that so many labs or companies that are in a lab, I guess, feel impelled to also design a chip that goes along with the model? And long term is what you're doing saying like this really is not necessary to have that sort of model chip alignment. Yeah. There's two technological reasons and two economic reasons. Okay. The first is from an economic perspective, about 80 cents of every dollar a lab spends today on their R &D close to a chip provider like NVIDIA.

38:14Okay. Right? And so as a result, your margins are just super, super rough. So from a unit economic perspective, you want more control over your margins. And therefore, when you look at your unit economics, you're going, wait a minute, for every dollar we make, there's this massive chunk that's going to somebody else.

38:29Tracy Alloway:So instead of spending 80 cents to NVIDIA, you spend 78 cents to TSMC and keep that two cents for yourself? Well, I think that the better our software gets, the more that margin should flow actually to the researcher. Because that's where the value will be captured. But wait, sorry, you were going to say, what's the technical reason why they're trying to do optimal model chip alignment? On the technical side, the primary reason is you want control over your supply chain. because today in a compute, well, we've been in a compute constrained world now for at least four or five years. But if you can't get the chips you need, you're not in control of your own supply chain.

39:07So you're dependent on compute allocations that the compute manufacturer thinks is optimal, right? By the way, that's how it works at the foundry level. Today, TSMC gets to decide which compute provider's business grows or not because they only have so much production capacity. And so the technological reason is you want supply chain independence. And so when you want economic independence, unit economic independence, anyone supply chain independence, you want as much control over your own chip.

39:33Tracy Alloway:But Microsoft doesn't have a fab. That's not what I'm saying. What I'm saying is in inference, for example, Satya would like more control over his unit economics. So he's making an inference chip. Right. Right. Because if you are dependent on a third party to give you the inference chip. Okay. And if you don't have an inference chip, you can't sell more product. You want more control. It's about having a predictable supply of chips for you rather than a predictable supply of chips. OK, got it. So there's a lot of discussion right now about more efficient model allocation. So this idea that you do not have to be using the latest model to ask what the weather is going to be tomorrow or something like that.

40:11And you also don't want to blow through your entire one-year token budget in the space of four months as Uber apparently did. So the spikes in usage that you're seeing that allow you to do the system and have grid credits, does some of that go away if people become smarter about which models they're actually using? Okay. So there's an embedded assumption I think I should tease apart in your question. Usage is different from the production of the model. So what's happening in terms of the pipeline is you use the grid to produce the model, and then the model produces tokens. If the end user is only using tokens, then as long as we have enough diversity in the end user base using models hosted on the grid, things actually even out.

40:59That cyclicality, in the same way electricity in America evens out, if you have enough scale. For the most part, yeah. Except when there's a heat wave. There's a heat wave, exactly. So some of that infrastructure we are having to reboot. But you can think about AMP in the broadest sense as a utility company. We're what's called an independent system operator of the grid. So we don't own our own data centers. We don't own our own labs. But we coordinate the capacity needs across different parties. And at sufficient scale, those usage patterns actually just get evened out. Does that make sense? Yeah.

41:32Tracy Alloway:Well, you are an investor in Open Router, I believe, which I think is an interesting company. Do you see – setting aside AMP for a second, do you think that there is at this point still within, say, corporate America, a certain lack of savviness about knowing which model to route to for the query and that there will be an improvement and learning within companies, within users, so that you don't have these incidents for like massive token consumption because perhaps everyone was using the wrong – The Cadillac model and the Ford model would have been just as fine for that purpose? Oh, yeah. We're absolutely in the medieval ages of this technology.

42:13I think what will happen is increasingly, based on my conversations with corporate American leaders and corporate leaders across the world, they don't really care about the models. They don't care about the underlying model, the technology. They just don't care. It's like too much complexity. We just want the work done. Yeah. Can you guys please figure out how to get the work done in the cheapest way, in the most efficient way, in the most secure and trusted way? And increasingly what you'll find is that which particular model is helping you out in a particular task will just be abstracted. You won't even think about that.

42:45It'll just be a companion. You're just going to talk to it. It'll be a companion provided by a brand you trust. And under the hood, they might be using 200 different models to orchestrate your task. And over time, that efficiency will get better and better and better and better. And that's why I just don't think there's only three frontier models that are going to win. It's going to be an ecosystem. This is, I know you don't want my take, Joe. No, I actually do. I want your take. This is my coffee pod theory of AI. I want your take, Tracy.

43:11Tracy Alloway:I want your take. It's all right. I love your take. I'll save it for the outro. Actually, on this note, we have seen some headlines recently. Obviously, there's the Uber one about token spending. And I think it was the COO said he wasn't sure if the ROI was there on Uber's AI usage. And we've seen, there was a good Vox article recently about a corporate reckoning with AI spend. Since you're going out and talking to CEOs, do you see any, like, has anything shifted in the past couple months or so in the way people are thinking about the return on this initial investment or the return on spending on tokens?

43:48Yes. I think it's a barbell distribution. So there's two types of CEOs, broadly speaking. The first is the CEOs who are using the tools themselves. And those folks are going, aha, I understand the jagged frontier. When they understand the jagged frontier we talked about, their strategies, their questions they ask me are completely different from the CEOs who are outsourcing their understanding. They're not trying the tools. They're mostly asking their kids like, hey, kiddo, this chat GPT thing, like, it's good, right? And your kid is like, yeah, it's pretty good, dad. And then -

44:24Tracy Alloway:My kids think it's really dumb, by the way. Yeah, so that's the other thing, right? So the kids are super smart and they're using the tools and they're like, it's good at this thing, but not at that. So they understand the jagged frontier part as well. Actually, you know what? They think I'm dumb for using it. They're like, dad, you're not doing anything smart. They don't think the models are dumb. They think it's dumb of me. Exactly. They might be going, the way you're using it is not optimal. So the C - No, what I'm saying is my kids are six and 10 and they have no idea about anything and they just think I'm dumb.

44:55Tracy Alloway:That just might be a generalizable That's really the only point I'm trying to make sure I see, okay, well you can send them over to me anytime I'm happy to be the fun uncle Yeah, that would be great, you can show them that actually this is fun to play with My wife and I are happy to host your kids anytime That's really what I'm trying to get at It's the summer, we have two nieces in London and we call it Camp Middashen My last name is Middashen, my wife's name is Shen and so you're welcome to send them to Camp Middashen anytime That's amazing But that's the bifurcation is leaders who are actually trying the tools out, they realize they're extraordinary at some things and not at others.

45:30And so depending on whether you get it or not, or you're actually getting your hands dirty or not, I find the questions are completely different.

45:37Tracy Alloway:So this has been an incredibly helpful conversation in terms of like understanding basically the problem of essentially tons of money is being spent. And your thesis is that it's massively suboptimally used up and down the stack. You mentioned this. OK, you get a credit, et cetera. Like, do you actually see that being financialized in a way? I mean, OK, I bought this capacity. I have a lot of unused time. I don't always have a research idea that is going to require a big model run test. I can resell that. Is that something that you see, something that genuinely resembles a financial market? I hope not.

46:16Because when you add speculation to production goods, it creates scarcity of a different kind. Because then you have financial traders and markets trying to trade the speculative value of the asset. And that's going to hurt a lot of our research teams in technology. On the other hand, I think that creates a need for innovation inside of the research teams. And so one of the core operating functions we have inside of our business is a forecasting capability where we have a team that's very similar to actually the kind of forecasting team you'd have inside of a hedge fund. We're constantly predicting demand and supply.

46:54And then we're actually procuring capacity in advance through call options on compute clusters. But our needs are similar to the kind of internal trading desk you'd have inside of a large steel company, right, where they need to lock up iron ore and so on for their production needs. So I'm a big fan of efficient markets. And I'm trying to actively invest in and help entrepreneurs out and teams out who are trying to drive more efficiency in the service of more productivity in science and engineering. I'm not that thrilled about the financialization of these products if it ultimately results in more speculation.

47:28Does that make sense? Yeah. I'm just curious, since you're tracking demand in that way, like if you were going to describe the slope of demand right now versus, say, like a year ago, is it steeper? Is it starting to plateau? Perpendicular. Oh, wow. Okay. If you look at the compute prices of long-term rentals over the last six months, between January and now, they're trading up 2x. So we started, for example, for 2026, we started securing our capacity in January at these long-term rates. We could resell that at a 2X markup if we wanted to.

48:05Tracy Alloway:Part of the reason that 2026 has become just totally AI has consumed everyone's mind, I think, is because people got very excited about Claude Code specifically. Yeah. But that was a breakthrough at the harness level, not the model level, right? Suddenly, like the really excited, like, wow, this is just so fun. It's just so easy. You have a computer inside your computer. That was a harness breakthrough. Do you see, like, when you think about investment among AI labs, do you see any shift in allocation away from pure scaling and improving the model towards sort of like tooling and harnesses as a way to get more juice out of the models?

48:47No, I'm sorry. I have to correct you there. It was not just a harness innovation. Those two things go hand in hand. It's a symphony of improvement between, it's a dialectic between the model capability and the harness. That harness was designed specifically for the capabilities that the new model was going to have. And so when you design these things, in the industry, we call this co-design. So you have the harness designed side by side with the researcher who's designing the next generation capabilities in the model. And you get a little bit of visibility in where the model is going to be good.

49:18Because as I described earlier, the pipeline is actually quite predictable. Pre-training, mid-training, continuous feedback loop. Once you have that visibility, you go, aha, we specifically want to improve the capabilities on this type of task. It's going to take us about three months to get there. Start designing the harness for that improvement. By the time they show up, then you can have the harness assume that the model will be able to do X, Y, Z on its own, whereas A, B, C, it's going to need third-party tools. So then the harness says, remember that three months ago, you were terrible at understanding a spreadsheet.

49:53Tracy Alloway:Yeah. So then we had to go use a third-party tool to use a spreadsheet. In the last three months, what we've done is added the ability to actually reason about a spreadsheet in the model. In the model, not the model. So now you don't need to use a third-party spreadsheet. And so then the harness gets updated to say, don't go out and use a third-party spreadsheet, which, by the way, collapses the time required to do that task by like sometimes a minute to two minutes. Now suddenly I've improved the user experience. And that's when things really sing. It's when both of those parts, the model and the harness, are co-designed to create a symphony.

50:27Does that make sense?

50:27Tracy Alloway:Yeah, absolutely. All right, Anjane Mida of AMP PBC, thank you so much for coming on OddLots. Really appreciate it. Thanks for having me. And everyone go out and check out the Stanford lecture series. It's on YouTube, right? It is. CS153.stanford.edu. Perfect. I have a big flight coming up, so I'll watch it then. You should download all the lectures. There's quite a few. Thank you so much, Anjan. That was fantastic. That was great.

51:02All right, Joe. That was a great discussion. Yeah. I should emphasize just how big a deal that lecture series actually is at Stanford. Like, students are beating down the door, basically, to get into that. And if it's free on YouTube, you should definitely check it out.

51:16Tracy Alloway:I just want to establish that if I had given you, A, the insinuation that I didn't want to hear your take, or B, the idea that I would have wanted to hear Anjane's take instead of yours, I want to hear your take. No, it's fine, Joe. I realize that most listeners are here for the guest takes. I get it. But I thought his point about the jagged frontier was an important one. And this idea that maybe the future, it's not going to be a winner-takes-all thing in terms of models. You're going to have a bunch of different models doing different things that might suit different companies. And also the idea that a lot of companies aren't going to care about which specific model they're using.

51:53They just want the cheapest one that basically gets the job done. In my mind, that sounds like more of a commodified market, rather than like, oh, people are going to pay up for, as you said, the Cadillac model.

52:12Tracy Alloway:So what I would say is by listening to Anjanae and Amp is that people will want a commodified service, but that under the hood, I mean, this just sounds like what she's really trying to solve. And it's very interesting. I, as a user or a company, buy a commodified service. But under the hood, the commodity has an incredible amount of variety of models through which it can route. Sure. Some of which will be the Cadillac. Some of it will be the Keurig coffee cup. Yeah, absolutely. But like my point is maybe in terms of valuation. Sure. Right. Like if everyone is assuming that the Cadillac is going to be like the one that everyone is going to get and the total available market, the TAM, infamously is like not just the world, but potentially the universe.

53:02Like that seems a stretch to me. Totally.

53:05Tracy Alloway:And just generally, I thought that was super interesting. And the idea, we've done a couple episodes recently specifically learning more about both chip level and box level optimizations, both how many chips you're using and how well you're using a chip. Definitely we have more to do on that. It still blows my mind that this is a problem that can be solved with software rather than like something physical. You just come up with a way to efficiently allocate the compute. Yeah. Because in my mind, like it's such a physical problem. And we've talked to, you know, previous AI market participants like Brandon McBee at CoreWeave.

53:42And they talk about like, oh, it's difficult to standardize because of the configurations of chips and things like that. But if you could solve it just through a software system, that's pretty crazy. I guess Google's already done it.

53:53Tracy Alloway:Yeah. All right. Shall we leave it there? Let's leave it there. This has been another episode of the All Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway. And I'm Jill Weisenthal. You can follow me at The Stalwart. Follow our guest Anjane Mida at Anjane Mida. Follow our producers, Carmen Rodriguez at Carmen Armid, Dashiell Bennett at Dashpot, Kale Brooks at Kale Brooks, and Kevin Lozano at Kevin Lloyd Lozano. And for more All Thoughts content, you should check out our daily newsletter. You can find that at Bloomberg.com forward slash OddLots. And you can chat about all of these topics 24-7 in our Discord, discord.gg slash OddLots.

54:28And if you enjoyed this conversation, then please leave a comment or like the video or better yet, subscribe.

54:34Tracy Alloway:Thanks for listening.

55:08We'll see you next time.

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