Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of the Odd Lots podcast.
Speaker 3: I'm Jill Wisenthal and I'm Tracy Alloway.
Speaker 2: Tracy, I have to say, unfortunately, I don't have AI psychosis.
Speaker 4: I'm certain of that debatable.
Speaker 2: I'm pretty sure. I'm pretty sure I don't have AI psychosis.
Speaker 2: I do have to say, unfortunately, like the amount of
Speaker 2: time now where it's like it feels like AI related
Speaker 2: questions and there's many of them are sort of like
Speaker 2: swallowing up the other thoughts that I have in my head,
Speaker 2: whether it's questions about which models best and why, and
Speaker 2: what are the economics of inference and how much training
Speaker 2: is pre training versus post training for each model.
Speaker 4: Like it's just sort of like this blog, there's a
Speaker 4: growing that's taking out more and more of my thoughts.
Speaker 3: What is your definition of AI psychosis? Because one would
Speaker 3: argue that maybe thinking about AI literally all the time
Speaker 3: would be a form of psychosis.
Speaker 2: Well, let's just say, like, I'm not the type who
Speaker 2: thinks that, Like, I don't like think that the AI
Speaker 2: is a friend, for one saying I'm not in love
Speaker 2: with the AI models. I don't think that in collaboration
Speaker 2: with chat GPT, that I'm stumbling on unified theory of
Speaker 2: physics and things like that.
Speaker 3: So like, but you do spend a lot of time
Speaker 3: in putting instructions, pressing the button, yes, what comes out, and.
Speaker 4: See what comes out.
Speaker 2: I'm just saying I think I'm aware that I'm talking
Speaker 2: to machine and that we're not establishing any great breakthroughs
Speaker 2: of which we are collaborators in partners and friends.
Speaker 3: Recognizing you have a problem is the first step towards
Speaker 3: healing Joe. Seriously, though, there's there's a good reason to
Speaker 3: think about AI more and more, which is a huge
Speaker 3: chunk of not just the market, but the real economy
Speaker 3: is now revolving around AI right totally.
Speaker 2: So anyway, again, within the AI conversation, there are a
Speaker 2: lot of subcategories. One of the subcategories happens to be
Speaker 2: another odd Lat's favorite topic, which is chips. Of course,
Speaker 2: chips are used in multiple different ways. The chips are
Speaker 2: used in different parts of the AI supply chain, different
Speaker 2: types of chips of different roles, and so we have
Speaker 2: to learn more.
Speaker 3: We have to learn more, and I have to say
Speaker 3: I'm particularly interested in the company we're about to speak
Speaker 3: to partly because the two things I know about them
Speaker 3: are number one, they just had a huge IPO yep, right,
Speaker 3: raising something like five point five billion dollars at kind
Speaker 3: of insane multiple. I can't even do a price to
Speaker 3: earnings multiple because they're not profitable yet, but I think
Speaker 3: just on a sales basis, it was like sixty seven
Speaker 3: times forward earnings, which is pretty juicy, pretty hot. And
Speaker 3: the second thing I know about the company is they
Speaker 3: make giant way first, which is just a fun image
Speaker 3: in your head.
Speaker 5: That's right.
Speaker 2: So if you were thinking it's like, okay, there is
Speaker 2: a hot entrance in this space. What is their differentiator? Well,
Speaker 2: one fact about them is their chips are just enormous.
Speaker 2: About the size of the dinner plate. One might think
Speaker 2: you're reading an onion article, but in fact it's real
Speaker 2: and apparently it actually has some real technical advantages.
Speaker 3: And it's different and so what everyone else is doing.
Speaker 3: So everyone else is, I guess, doing this sort of
Speaker 3: like modular networking thing where you get together a bunch
Speaker 3: of chips and you connect them together and that's how
Speaker 3: you get more compute, more memory, more power basically, But
Speaker 3: this company has done something different in the form of
Speaker 3: the giant wafers.
Speaker 2: The giant wafer, and if you figure that to get
Speaker 2: maximum performance, you sort of want to lessen the distance
Speaker 2: between things, then put it all.
Speaker 4: On one wafer.
Speaker 2: Anyway, we're gonna learn a lot more. I'm very sad
Speaker 2: about giant wafers.
Speaker 5: More.
Speaker 2: I'm very excited to say we do have the founder
Speaker 2: and CEO of Sarah Bras on the podcast, Andrew Feldman.
Speaker 4: Truly the perfect guest.
Speaker 2: So, Andrew, thank you so much for coming on the
Speaker 2: podcast on the week of your IPO.
Speaker 5: Well, thank you so much for having me. What a pleasure.
Speaker 2: Absolutely, Why don't you just start us.
Speaker 4: Off the big giant chip.
Speaker 2: They're apparently real, They're as big as a dinner plate.
Speaker 2: What is the technical reason why this actually makes sense
Speaker 2: as a superior form of architecture for at least some
Speaker 2: aspect of AI.
Speaker 6: I think larger chips process more information and less time, okay, and.
Speaker 5: That produces faster results.
Speaker 6: And everybody had gone to bigger chips and video had
Speaker 6: moved from four hundred square millimeters to eight hundred square
Speaker 6: millimeters over.
Speaker 5: The course of five or six years for.
Speaker 6: This exact reason, and in the compute industry wafer scale,
Speaker 6: which is building a chip for.
Speaker 2: Those, by the way, for those who are just listening,
Speaker 2: andrews Now holding up the chip, and yes, it looks
Speaker 2: it actually looks bigger than a dinner plate, to be honest.
Speaker 4: But that is a big that's a big chip.
Speaker 5: That's fifty think.
Speaker 6: It's fifty eight times larger than any other chip that
Speaker 6: had ever been Wow. And what it did was it
Speaker 6: allowed us to use a different type of memory, a
Speaker 6: type of memory that at the beginning, there are two
Speaker 6: types of memory. There's memory that can store a lot,
Speaker 6: but it's really slow, and there's memory that can't store
Speaker 6: very much per square millimeter, but it's blisteringly fast. And
Speaker 6: historically all graphics processing units use this memory that could
Speaker 6: store a lot but was really slow, and that's the
Speaker 6: reason they do inference so slowly. So if you're using
Speaker 6: Claude right now, or you're using anything but chat GPT,
Speaker 6: what you frequently feel is you'll enter your prompt and
Speaker 6: you'll wait for an answer, right, And that's because the
Speaker 6: memory is slow and they have to move a ton
Speaker 6: of information from memory to compute. Now, by going to
Speaker 6: wayfer scale use this fast memory. Now we couldn't make
Speaker 6: that memory store more information per square millimeter, but we
Speaker 6: could add square millimeters, and so by building this big chip,
Speaker 6: we were able to stuff it to the gills with
Speaker 6: this fast memory. And that's why we're fifteen times faster
Speaker 6: than the fastest GPU. That's why on some problems we're fifty,
Speaker 6: one hundred, even one thousand times faster than graphics processing units.
Speaker 3: Wait, can you explain how you actually managed to do this?
Speaker 3: Because I know there have been previous attempts to do
Speaker 3: wayfer scale, and I seem to remember there was even
Speaker 3: like an early attempt in the nineteen eighties or something
Speaker 3: to do it. How are you able to pull this off?
Speaker 6: Yeah, it was an ambitious undertaking, that's for sure. Every
Speaker 6: previous effort in the seventy five year history of our
Speaker 6: industry had failed, including Gene Amdall, who's sort of on
Speaker 6: the mount Rushmore of compute in our industry. He failed
Speaker 6: sort of spectacularly in the mid eighties at a company
Speaker 6: called Trilogy. Not only that, but after we succeeded, people
Speaker 6: who had visited us, who'd been in our labs tried
Speaker 6: to copy us, and they also failed. And so what
Speaker 6: we were able to do is solve a set of
Speaker 6: really fundamental problems, and those problems cut across a wide
Speaker 6: swath of technology. They cut across lithography, so we had
Speaker 6: to collaborate closely with TSMC, and they turned out to
Speaker 6: be a great partner. We had to make inventions in
Speaker 6: material and packaging. That's how you put a process, or
Speaker 6: how you put a piece of silicon on a motherboard
Speaker 6: deliver power and IO to it. We had to make
Speaker 6: inventions in power delivery. Right when you build a giant chip,
Speaker 6: you're going to deliver way more power to it than
Speaker 6: if you do a chip the size of a postage stamp.
Speaker 6: We had to invent ways to cool it. We had
Speaker 6: to write new types of.
Speaker 5: Software that ran on it.
Speaker 6: All of these had never been done before, and it
Speaker 6: was a decade long process. It took us five years
Speaker 6: and about five hundred million dollars to deliver the first one,
Speaker 6: and it's been an extraordinary run since. In December, we
Speaker 6: signed a deal with open Ai North to twenty billion dollars,
Speaker 6: one of the largest contracts ever signed in Silicon Valley,
Speaker 6: and then in March we signed a deal with with
Speaker 6: AWS where they would deploy our systems in their data
Speaker 6: centers in their AWS data centers, and so it's just
Speaker 6: been an extraordinary run. But it took a long time.
Speaker 6: It took extraordinary engineering, and there were certainly long periods
Speaker 6: of time when it wasn't clear we were going to
Speaker 6: make this work.
Speaker 2: Obviously you've hit this remarkable milestone you have in fact
Speaker 2: IPO and so forth, and right now market's valuing your
Speaker 2: company at sixty four billion dollars early days of the IPO.
Speaker 2: Just for the listener to understand, the chips are are
Speaker 2: a solely in inference as opposed to, you know, in training.
Speaker 2: When we think about AI, I think about, okay, there's training, training,
Speaker 2: the model, and then answer giving that's the inference.
Speaker 4: Are the tips for just for inference.
Speaker 5: So a couple things I think you framed it exactly right.
Speaker 6: Training is how we make AI, and inference is how
Speaker 6: we use AI. And so what happened was that in
Speaker 6: sort of twenty twenty five, in the first part of
Speaker 6: twenty twenty five, the models we made were smart enough
Speaker 6: to be useful, and there was an explosion of use.
Speaker 6: And we use AI by doing inference. So there was
Speaker 6: this sort of tidal wave of demand on inference, and
Speaker 6: that has continued in twenty twenty six, and we think
Speaker 6: it will continue for years and years to come. And
Speaker 6: so that's what had happened in twenty fifteen. When we
Speaker 6: began thinking about the company. We knew that AI was
Speaker 6: on the horizon and they would eat a huge amount
Speaker 6: of computer right, and we made sort of two fundamental bets.
Speaker 6: We bet that it would need dedicated silicon, and right,
Speaker 6: graphics had needed dedicated silicon, that's how you got.
Speaker 5: The graphics processing unit.
Speaker 6: Mobile compute had needed dedicated compute.
Speaker 5: That's where you got ARM processors.
Speaker 6: We made that bet, and we made a bet that
Speaker 6: modifying the GPU architecture wouldn't be right. You needed to
Speaker 6: start with a clean sheet of paper. And so what
Speaker 6: we started with was a new vision, and that vision
Speaker 6: could do training and it could do inference, and it
Speaker 6: was orders of magnitude faster at both. But right now
Speaker 6: what we're seeing is such an explosion in demand for
Speaker 6: inference that a lot of the business this minuted his inference,
Speaker 6: even though we're just as fast at the same amount
Speaker 6: faster than GPUs on training.
Speaker 4: That's interesting.
Speaker 2: Maybe we'll get more to the theoretical training market a
Speaker 2: little later.
Speaker 4: Just real quick on inference.
Speaker 2: Ben Thompson, who writes a newsletter about tech, He wrote
Speaker 2: a piece in which he distinguishes between answer inference and
Speaker 2: agentic So answer inferences like format by resume or whatever,
Speaker 2: or write me an essay on X or Y, or
Speaker 2: answer some questions, and then agentic inference is like, Okay,
Speaker 2: here's this thing that's going to go around. Do you
Speaker 2: distinguish and do services for you not producing visual answers?
Speaker 4: Do you distinguish between those two? Is that a real
Speaker 4: divide in your view? And can your chips do both?
Speaker 6: Our chips can do both. I think it is a divide, Okay.
Speaker 6: I think speed.
Speaker 5: Matters equally in both.
Speaker 4: Okay.
Speaker 6: I think if you are engaged with the AI, if
Speaker 6: you're writing code, which is agentic. If you're writing code
Speaker 6: or you're doing work, nobody wants to wait. I mean,
Speaker 6: we could just turn the question around and say, well,
Speaker 6: how big is the market for slow search zero? How
Speaker 6: big is the market for dial up internet zero?
Speaker 5: Why is that? Because nobody wants to wait? Right?
Speaker 6: So, if you're engaged with the AI, speed is of
Speaker 6: the essence. But if the AI is doing agentic work
Speaker 6: and your competitor gets three times five times, ten times
Speaker 6: as much work done in twenty minutes than you do,
Speaker 6: you're gonna get smoked. And so this notion somehow that
Speaker 6: been proposed that speed isn't very important in agentic flows
Speaker 6: is dead wrong. That speed is important in all aspects
Speaker 6: of productive work, and that your ability to get more
Speaker 6: done in less time is a fundamental advantage that accrues
Speaker 6: over time. Right If while your competitor is doing one
Speaker 6: unit of work, you can do three, and in the
Speaker 6: next time they do one unit of work, you do six. Sure, right,
Speaker 6: this adds up over time and you beat them in
Speaker 6: any line of work. And so speed, which is sort
Speaker 6: of our specialty, is important across the board.
Speaker 3: What do giant wafers and speed in general actually mean
Speaker 3: for I guess the economics of tokens, because one way
Speaker 3: I think about it, I have this sort of vision
Speaker 3: in my head, like, Okay, if I'm out shopping for toothpaste,
Speaker 3: I know I need toothpaste every once in a while,
Speaker 3: and I go into like a cvs A store, I
Speaker 3: get one thing of toothpaste, and then maybe a week
Speaker 3: later I get some more toothpaste. Or I could go
Speaker 3: to Costco and buy a giant thing of toothpaste and
Speaker 3: take it home, probably at a cheaper cost. And that's
Speaker 3: sort of how I think of the giant wafers. Maybe
Speaker 3: it's bad analogy, but what does speed actually mean for
Speaker 3: the cost of tokens?
Speaker 6: Well, I think there are a couple observations. I think
Speaker 6: people have chosen so far to price speed a little higher.
Speaker 6: For example, Anthropic offered a premium service in which they
Speaker 6: offered tokens twice as fast and charged six times as much,
Speaker 6: and they sold it out and they couldn't meet the demand. Now,
Speaker 6: just to give you an idea, we're fifteen times faster
Speaker 6: than there, twice as fast, and so people value speed
Speaker 6: because it allows them to do more work and they
Speaker 6: value their time. And when you can do more work
Speaker 6: in less time, you are making people more productive. That's
Speaker 6: why people have chosen to price them at a premium.
Speaker 5: They don't cost more to make.
Speaker 6: In fact, in the GPU architecture is an extremely good
Speaker 6: architecture and extremely efficient at building very slow tokens. And
Speaker 6: if you don't mind slow, the cost per token on
Speaker 6: a GPU is extremely low. But the GPU has a
Speaker 6: characteristic that as you try and go faster, the cost
Speaker 6: and the power used per token increase, sort of like
Speaker 6: as you go faster in your car, your miles per
Speaker 6: gallon decrease. Right, So what happens is as you try
Speaker 6: and get fast enough to be useful, fast enough to
Speaker 6: be interesting, fast enough to keep users intelligence focused on
Speaker 6: this product, they become extremely expensive and extremely power hungry.
Speaker 6: And so the question is is not just what people
Speaker 6: are paying for a token, what people are choosing to
Speaker 6: price them at, but what they actually cost to make.
Speaker 5: And GPS make very.
Speaker 6: Slow tokens very cheaply, and they're unbelievably expensive at fast tokens.
Speaker 6: We make fast tokens vastly less expensive than the GPU's
Speaker 6: and we use a tiny fraction of the power.
Speaker 2: Let's say we stipulate that this is not true and
Speaker 2: everyone wants the fastest and everyone's like, you know what,
Speaker 2: this is the solution that the Cerebras technology one big chip.
Speaker 2: This is really where it's at. How much of your
Speaker 2: market share for the inference market when you look out
Speaker 2: next year, the year after, et cetera, how much is
Speaker 2: your market share going to be dictated by your ability
Speaker 2: to get capacity at tsmc fabs. How much is that
Speaker 2: a gating mechanism for growth?
Speaker 5: You know, TSMC is a huge part of the supply chain. Yeah,
Speaker 5: but we have some real advantages.
Speaker 6: There are three areas right now that are limiting vendors
Speaker 6: in building AI computes.
Speaker 5: Number one is HBM memory.
Speaker 6: Is this memory we described earlier that can store a lot,
Speaker 6: but it's really slow. That's made by three companies approximately Samsung, Heienix,
Speaker 6: and Micron, and it's under unbelievable supply pressure. It's extremely
Speaker 6: difficult to get their very long lead times. It's unbelievably
Speaker 6: expensive right now, we don't use it. The second part
Speaker 6: that's limiting is a process inside of TSMC called cooths,
Speaker 6: and this is the process that in Nvidia and other
Speaker 6: GPUs use.
Speaker 5: We don't use it.
Speaker 6: The third thing is that at TSMC, the factory that
Speaker 6: is under most pressure is their three nanimeter factory.
Speaker 5: We don't use it. We use five nanometer.
Speaker 6: So we have managed to avoid some of the most
Speaker 6: binding supply constraints. Now, TSMC still has to give us
Speaker 6: a meaningful allocation, and they've been an extraordinary partner from
Speaker 6: the get go, and they are the greatest manufacturing company
Speaker 6: on earth by far. A fab is sort of a
Speaker 6: modern pyramid. It's an unbelievable thing. And I highly recommend
Speaker 6: you or any of your your listeners, if you get
Speaker 6: a chance to go to Taipei, go and see them.
Speaker 5: They are just extraordinary.
Speaker 3: Can you do faburs You can't, Actually, you can't do
Speaker 3: you can go and they have a museum of Innovation
Speaker 3: and it is an extraordinary thing.
Speaker 6: They are the sort of the national champion of Taiwan.
Speaker 6: But I think today TSMC is given a as many
Speaker 6: wafers as we've needed. Business today is constrained by data centers,
Speaker 6: and that's the grand irony, right You invent technology that
Speaker 6: has been unbuildable, never been invented for seventy five years
Speaker 6: in the history of compute. You write software that is extraordinary,
Speaker 6: You build a product that is vastly faster.
Speaker 5: Than the cumbent. And what are we all constrained by buildings?
Speaker 5: All right?
Speaker 6: The data centers right now are everybody's constrained in the
Speaker 6: entire industry powered buildings, So real estate.
Speaker 5: It is an amazing thing right now.
Speaker 6: And that is too sort of across the board, and
Speaker 6: that will not change for the next fifteen or eighteen
Speaker 6: months for sure.
Speaker 3: I mean, since we're talking physical constraints, I guess I
Speaker 3: should ask you. We did an episode about helium recently,
Speaker 3: a helium shortage given the situation in the Strait of
Speaker 3: Horror moves, and one of the things that helium is
Speaker 3: used for is lithography on semiconductor chips. Has that affected
Speaker 3: you at all or is that so thing that you're monitoring.
Speaker 6: We monitor, but there's not a lot we can do,
Speaker 6: and there's plenty of stuff to worry about that we
Speaker 6: can't affect. We obviously are in communication every day with TSMC.
Speaker 5: We're in communication with our entire.
Speaker 6: Supply chain every single day, and we stay abreast of
Speaker 6: the various issues. But it has had no impact on us,
Speaker 6: and we put that in the bucket of things that
Speaker 6: our manufacturing partners worry about also and that we can't help.
Speaker 2: You know, So, in addition to manufacturing these chips, you
Speaker 2: actually I didn't realize this. You have your own cloud
Speaker 2: we do, and or you have your own cloud services,
Speaker 2: which I have a bunch of questions about that. You
Speaker 2: have your own cloud services through which a user can
Speaker 2: actually get access to various open source models and so forth.
Speaker 2: It looks a little bit sort of visually, it looks
Speaker 2: a lot like the open router interface roughly the same environment,
Speaker 2: except like the open source What I'm something I'm curious
Speaker 2: about and maybe you could speak to this. You know,
Speaker 2: in traditional software open source. One nice thing about open
Speaker 2: sources you don't have to pay for it, so it's free.
Speaker 5: It's a little bit.
Speaker 2: Different when we're talking about there's no really such thing
Speaker 2: as like free AI software because even if it's like free,
Speaker 2: you still have to pay for the depreciation of the
Speaker 2: chips and you have to pay for the electricity to
Speaker 2: run them. So there's no real sarch things like free
Speaker 2: open source AI software. But what I am curious about
Speaker 2: in your experience as a cloud vendor, are the open
Speaker 2: sources models cheaper on a per unit of intelligence basis?
Speaker 2: If we had some way of saying levelized cost of intelligence,
Speaker 2: which I don't know if the industry has yet, Are
Speaker 2: open source models cheaper per IQ point whatever we want,
Speaker 2: however we want to measure intelligence.
Speaker 5: Yes, by a lot?
Speaker 6: Really yeah, I think in the closed source world you're
Speaker 6: paying a lot for that extra little bit of intelligence.
Speaker 6: Right the open source models. There are no open source
Speaker 6: models that are as good as.
Speaker 5: The closed source models.
Speaker 6: Okay, think of it as three four percent five percent
Speaker 6: different Okay, something in that range, and it could be
Speaker 6: a little more, could be a little less, but the
Speaker 6: cost to you using them. You can jump up right
Speaker 6: now and run KIMMI Kate two. It's a one trillion
Speaker 6: parameter model. It's an open source model on cerebras where
Speaker 6: ten or fifteen times faster than others. And what you're
Speaker 6: paying for is the cost of our power and some
Speaker 6: cost of the compute that took to calculate it. What
Speaker 6: you're not paying for was the cost to train it.
Speaker 6: And that's a battle that.
Speaker 5: Is underway in the market.
Speaker 6: You have open Ai with their coding software, you have
Speaker 6: Anthropic with their coding software. And you've got companies like
Speaker 6: Cursor and Cognition that are using open source. We power
Speaker 6: open Ai and we power Cognitive. You have a battle
Speaker 6: underway between closed source and open source, and I think
Speaker 6: that the winners of that battle is yet to be determined.
Speaker 6: What is clear is that the closed sources is strictly
Speaker 6: better by a little bit by how much varies, and
Speaker 6: it's more expensive.
Speaker 3: Yeah, I think we've talked about this before, but like
Speaker 3: I've heard of a lot of big companies in the
Speaker 3: US who have been like very quietly shifting from some
Speaker 3: of the closed source models to the open source models,
Speaker 3: like the Chinese ones like Kimmy Kimmy and Quinn. I'm
Speaker 3: sorry to press you on this point, but if you
Speaker 3: had to make a bet, like in twenty years, is
Speaker 3: the dominant aim AI model going to be a cheap
Speaker 3: open source thing or a more expensive, incrementally better closed
Speaker 3: source model.
Speaker 5: I don't think there's going to be one. Right, There's
Speaker 5: not one SaaS software. Right. There's some big dogs right.
Speaker 6: There, Salesforce, there's some other sort of giant players, and
Speaker 6: there are lots of other specialists. I can't think of
Speaker 6: many markets where we've sort of settled onto to one player.
Speaker 5: Right. If you look at the semiconductor market, you've got
Speaker 5: x eighty six, where you've got two major players.
Speaker 6: In AMD and Intel, and then you've got a whole
Speaker 6: adjacent market owned by ARM and the companies that build
Speaker 6: ARM parts, and then you've got customs silicon around that.
Speaker 6: I think that's the way you're going to have this.
Speaker 6: We're going to have you know, open aye is going
Speaker 6: to continue to do extraordinary things. They will be competitors
Speaker 6: to them, and they will be open source. I don't
Speaker 6: think I don't think any of those go away.
Speaker 3: Since we're on the topic of software, one of the
Speaker 3: things you often hear when talking about you know, new
Speaker 3: chip entrance going up against Nvidia, is this idea that well,
Speaker 3: you know, like in video chips, they're great and all,
Speaker 3: but the real mode of Invidio's business is Kuda, right,
Speaker 3: software stack that goes with it. What's your take on that, Like,
Speaker 3: is that a realistic concern for someone who's trying to
Speaker 3: go up against a company is big? And I guess
Speaker 3: as embedded in the software system as Nvidia currently is.
Speaker 6: In Nvidia is probably the greatest company in the first
Speaker 6: part of this century, right, you know, Jensen's one of
Speaker 6: the great CEOs of our era, along with hoc Ten
Speaker 6: at Broadcom and maybe Lisa at AMD.
Speaker 5: Just extraordinary.
Speaker 6: And Kuda was really important in the creating of the
Speaker 6: AI landscape, but it's not important now and it has
Speaker 6: no role whatsoever in inference.
Speaker 5: If you want to move from running.
Speaker 6: A model on GPUs today to running it on US,
Speaker 6: we can move it in ten keystrokes, just move point
Speaker 6: to our API. So that's the first part. The second
Speaker 6: part is that a year ago, every major frontier lab
Speaker 6: model had been built on a Kudah foundation, and today
Speaker 6: two of three haven't, so they lost seventy percent market share.
Speaker 6: They're three leading from tier models Gemini, Claude and GPT.
Speaker 6: Gemini built by Google on TPUs, trained on TPUs, served
Speaker 6: on TPUs.
Speaker 5: No Kuda.
Speaker 6: Anthropics models trained on Trainium, no Kuda served on TPUs,
Speaker 6: on Trainium and on GPUs, and Open Eyes GPT trained
Speaker 6: on GPUs in the Kuda environment. So two of the
Speaker 6: three leading models today used no Kuda. That's a hemorrhaging
Speaker 6: a share. And so I think what was true three
Speaker 6: or five years ago in which Kuda had a dominant
Speaker 6: position with central has shrunk significantly and not important at
Speaker 6: all in inference and shrinking in its role in training.
Speaker 2: You know, since we're talking about the economics, since we're
Speaker 2: talking about you know, the economics of inference and all
Speaker 2: this stuff, I've actually I would love to get your
Speaker 2: take one of the things that like, literally in the
Speaker 2: last couple of weeks, there's been this flurry of announcements
Speaker 2: of these attempts to financialize the market for compute and
Speaker 2: so it's like, oh, you're going to buy some capacity,
Speaker 2: the H one hundred benchmark et cetera. And people want
Speaker 2: maybe theoretically hedging it. I'm not entirely convinced. It still
Speaker 2: seems to me like I it's not like maybe. But
Speaker 2: on the other hand, like an inference provider can lock
Speaker 2: in a very long term relationship bilaterally with the data
Speaker 2: center and so forth, and no need for like these
Speaker 2: spot hedging markets. Do you think the market is going
Speaker 2: to evolve in such a way that there will be
Speaker 2: significant demand for financial instruments that allow inference providers to
Speaker 2: hedge their price exposure.
Speaker 5: I don't know. I'm not a financial engineer. It's the
Speaker 5: first thing, okay, But we can look a little bit
Speaker 5: at history.
Speaker 6: The guys at core Weave were enormously innovative in how
Speaker 6: to fund some of their massive deployments. They were some
Speaker 6: of the first to use a debt instrument that had
Speaker 6: a backstop with the GPU, and this enabled them to
Speaker 6: really leap out and sort of have first mover advantage
Speaker 6: in the neocloud space.
Speaker 5: And that was an innovation in financial.
Speaker 6: Engineering, and extremely creative others followed, and now there's a big,
Speaker 6: an active debt market and funding the building and the
Speaker 6: fit out of data centers. When you have a market
Speaker 6: that is that big and that active, you have people
Speaker 6: who want to make bets on either side, and I
Speaker 6: think over time those bets normalized and regularize, and you
Speaker 6: can wrap them up and you can make it easy
Speaker 6: to make the bet. When sort of CO two was
Speaker 6: one of the first to loan money against GPUs for
Speaker 6: core Weave, this was really innovative. And not only does
Speaker 6: core Weave get credit for the creating of the instrument,
Speaker 6: but so does the other side of the deal for
Speaker 6: doing it and making a successful innovative bet. And as
Speaker 6: sort of more and more people jumped in and these
Speaker 6: could be regularized, they could be more easily priced, and
Speaker 6: then once it's regularized and you have a market, then
Speaker 6: derivatives of that market are easy to make. Historically, and
Speaker 6: that's sort of the way I see this unfolding that
Speaker 6: as this market for data centers and compute matures, there'll
Speaker 6: be people making bets on either side, and financial instruments.
Speaker 5: Will be created to do it.
Speaker 6: Whether it's a good idea or not, I have no
Speaker 6: opinion at this time.
Speaker 3: Since we brought up finance, I was looking through the
Speaker 3: IPO filing and looking at some of the actual numbers
Speaker 3: in there, and I know you have the open AI
Speaker 3: deal now, but a huge chunk of your revenue comes
Speaker 3: from this company called G forty two in Abu Dhabi,
Speaker 3: and I think they're both like your biggest customer and
Speaker 3: also a major investor. What does G forty two actually
Speaker 3: do with all these chips?
Speaker 6: Sure last year they were a really important chunk of
Speaker 6: our business, a lot of it.
Speaker 5: They're a minority investor.
Speaker 6: They are the national champion, the national AI champion of
Speaker 6: the UAE, and they build a cloud that is used
Speaker 6: across the UAE's ecosystem.
Speaker 5: So it's used by leading universities there. It's used by leading.
Speaker 6: Companies there, companies like ADNOC, they're they're leading oil company.
Speaker 6: It's used by G forty two's nine operating companies.
Speaker 5: The deployments to date have been in the US.
Speaker 6: We have data centers that massive data centers that run
Speaker 6: equipment for G forty two here in Santa Clara, but
Speaker 6: also in Minneapolis and Dallas, stag this soon in Toronto,
Speaker 6: and so they're doing training and they're doing inference. The
Speaker 6: training they're doing, they have pioneered some of the leading
Speaker 6: English Arabic models. They've done genomic work. They are doing
Speaker 6: serving of models, and they're operating as a cloud, particularly
Speaker 6: for the UAE ecosystem, but also for global companies.
Speaker 2: Do you think that over time, corporate users and perhaps
Speaker 2: individual users, but corporate users will want inference served from
Speaker 2: a company that's separate from the model maker, such that
Speaker 2: they can be certain that they are not revealing and
Speaker 2: thus training the company that might replace them.
Speaker 4: I mean, look.
Speaker 2: Anthropic every couple of days and now to some new thing.
Speaker 2: Oh we have a new markdown file that could do
Speaker 2: this for tax or that could do this for whatever.
Speaker 4: And then a bunch of companies.
Speaker 2: File like our companies that you use AI increasingly going
Speaker 2: to want to want want to use data centers and
Speaker 2: inference providers that aren't the model themselves.
Speaker 6: Well, first, I think there is a type of professional,
Speaker 6: a type of job that is most directly under threat
Speaker 6: from AIK and they're almost always white collar, and they
Speaker 6: require you to have expertise over a body of knowledge. Right,
Speaker 6: That's what an accountant is, right, They have you have
Speaker 6: expertise over a body of knowledge of rulings, of previous
Speaker 6: examples of tax case law, et cetera. That's exactly what
Speaker 6: AI is good at right now, exactly so lawyer's accountants.
Speaker 6: There's sort of these professionals who have stood between sort
Speaker 6: of the ordinary person who doesn't know anything about IRS
Speaker 6: tax rules and the tax rules that is under threat,
Speaker 6: and that is something that it will be very easy
Speaker 6: for companies like open ai and Anthropic to chew through.
Speaker 6: There are other areas like say drug design, genetics, genomics,
Speaker 6: where companies like galaxosmith Klein have remarkable and.
Speaker 5: Unique data sets.
Speaker 6: This is true for one of our large customers, Mayo Clinic.
Speaker 6: It's true for Glaxosmithklin and other of our pharma customers.
Speaker 5: They have unique.
Speaker 6: Data and they will be able to find insight in
Speaker 6: that data, and they will be able to get value
Speaker 6: from that data, and they will certainly.
Speaker 5: Not want to share that data with the foundation.
Speaker 6: Model makers unless they are guaranteed that it will not
Speaker 6: sort of make the general model smarter. And these are
Speaker 6: companies that have spent twenty or thirty years spending tens
Speaker 6: of billions of dollars a year gathering data right patient
Speaker 6: care records or test results for drug design. They're going
Speaker 6: to mine the insight in this work and they're going
Speaker 6: to provide find extraordinary things and those are much more
Speaker 6: protected because the INSIGHT's in the data and they have
Speaker 6: the data.
Speaker 3: You know, you were talking about fabs in Taiwan earlier,
Speaker 3: and I'm now regretting not going on a fab tour
Speaker 3: when I was in Taipei, but it just didn't cross
Speaker 3: my mind at that time. Next time, Yeah, hopefully. There
Speaker 3: have been various efforts under the Chips Act and some
Speaker 3: other industrial policies to try to build more chip making
Speaker 3: capacity in the US. In your view, what's the big
Speaker 3: I guess impediment to actually do it? Yeah? A, is
Speaker 3: it happening? And then B why does it seem so
Speaker 3: difficult to actually make happen?
Speaker 5: Right?
Speaker 6: The first thing is difficult because it's a difficult problem
Speaker 6: to They're hard that they cost thirty or forty billion
Speaker 6: dollars and take five or six years to build. So
Speaker 6: that amount of money in that amount of time cuts
Speaker 6: across administrations, right, And that's a problem with the politics
Speaker 6: in the US is it's hard to make policy that's
Speaker 6: durable across administrations and across time.
Speaker 5: The first thing.
Speaker 6: The second thing is these are remarkably complicated buildings, and
Speaker 6: we have a sort of a hodgepodge, a sort of
Speaker 6: strange lattice work of local regional building codes.
Speaker 5: That a fab maker has to negotiate. Third is we're trying.
Speaker 6: TSMC has dedicated tens of billions of dollars to their
Speaker 6: fabs in Arizona and have committed hundreds of billions more.
Speaker 6: Samsung has dedicated tens of billions of dollars and committed
Speaker 6: hundreds of billions more to their fabs in Texas. But
Speaker 6: they take a long time, and we have to remain
Speaker 6: committed to building not just the fab, but the surrounding ecosystem,
Speaker 6: not just for three or five years, but for twenty
Speaker 6: years or twenty five years, because you want not just
Speaker 6: one fab, but you want a whole trajectory of fabs.
Speaker 6: You want them working at today's cutting edge, but tomorrow's
Speaker 6: and next years and in ten years cutting edge as well.
Speaker 6: And those are things that have proven really challenging in
Speaker 6: the US, and I think we needed their strategic assets,
Speaker 6: and I think we need to find ways to collaborate
Speaker 6: with those that have the expertise and to find ways
Speaker 6: to build policy that is durable over a length of
Speaker 6: time that can build a vibrant ecosystem in the fab
Speaker 6: and the associated elements.
Speaker 3: So the other big political economy theme I guess when
Speaker 3: it comes to semiconductors is this idea that they are
Speaker 3: in fact a strategically important technology, and so the US
Speaker 3: should place some limitations on their use abroad. And so
Speaker 3: we've seen things like export controls, export restrictions. You're an
Speaker 3: actual chip company, and so I'm very curious at an
Speaker 3: operating level what your experience of these kind of export
Speaker 3: controls has actually been, Like how much time does that
Speaker 3: take up for you? And then also given that one
Speaker 3: of your biggest customers is an international firm in Abu Dhabi, like,
Speaker 3: how important is the trajectory of those export controls to
Speaker 3: your future business?
Speaker 6: I think, you know, three or four years ago, I
Speaker 6: would have said not important at all. I think today
Speaker 6: they're really important. In the administration, I got to know
Speaker 6: the leadership and the Department of Commerce and in the
Speaker 6: Biss Division of Commerce, which oversees the licensing. I think
Speaker 6: this is an extraordinarily difficult job, and we saw really
Speaker 6: hard working, smart people doing a job.
Speaker 5: That is very, very difficult.
Speaker 6: I got to know the people in this administration and
Speaker 6: I found the same every single one of them is
Speaker 6: earning a tiny fraction of what they could earn in
Speaker 6: the private sector, and is doing this because they believe
Speaker 6: that this is an important mission. The problem is is
Speaker 6: that there are differing views about the right way to
Speaker 6: do this, and there are differing views on the right way.
Speaker 5: To achieve the goal, which is.
Speaker 6: To not give your most precious technology to your industrial enemy.
Speaker 6: And I think we can agree that today, in today's environment,
Speaker 6: China is an industrial enemy.
Speaker 5: Good well meaning people can.
Speaker 6: Disagree on whether the right strategy is to limit them
Speaker 6: from gaining access. Others argue, as those that Nvidia have argued,
Speaker 6: is that the right strategy is to give them access
Speaker 6: and to keep them working on our product, on us
Speaker 6: made on us sort of designed product. I come down
Speaker 6: on the other side of that argument. I understand they're
Speaker 6: good arguments in both directions. I think limiting the distribution
Speaker 6: the diffusion of our most precious technologies makes sense, and
Speaker 6: I think we have to do it thoughtfully and we
Speaker 6: have to recognize that means some markets will be foreclosed
Speaker 6: to us, and I'm okay with that.
Speaker 2: Just quickly, on the sort of like current business stuff
Speaker 2: you mentioned to deal with AWS, how does that work?
Speaker 2: Could customers right now like could customers of aws pay
Speaker 2: them to have infrint served specifically on one of your chips.
Speaker 5: Not yet, but soon, okay, they will be.
Speaker 6: It will be served in Bedrock, which is their AI
Speaker 6: as a service offering, and they will yes be able
Speaker 6: to go down the clickdown menu and get super fast inference,
Speaker 6: which will be delivered via a combination of what's called
Speaker 6: a disaggregated solution, which is using some tranium for some
Speaker 6: of the inference work and using the cerebras technology in
Speaker 6: our systems called the CS three for other parts of
Speaker 6: the work.
Speaker 2: And presumably someone who scrolled down and selects that they
Speaker 2: would pay some premium for that ultra fast in front.
Speaker 5: I think they will pay a premium.
Speaker 6: We will see this as entirely as Amazon wishes to
Speaker 6: price it to their product.
Speaker 2: See you iPod this week. It's May twenty six. This
Speaker 2: is not the first time that you've tried to or
Speaker 2: look towards going to the IPO market. Door headlines going
Speaker 2: back to twenty twenty four about wanting to try for
Speaker 2: the IPO market, and then there were headlines last year,
Speaker 2: especially because of the relationship with G forty two, about
Speaker 2: Syphius and some of the national security concerns, and maybe
Speaker 2: that was an issue with the IPO. And then but
Speaker 2: also last September, you got one of your looks like
Speaker 2: g Round g Round. One of the participants in the
Speaker 2: g Round investor was seventeen eighty nine Capital, which is
Speaker 2: of course the firm that's associated with Donald Trump Junior,
Speaker 2: which is a lot of things, and then the IPO happens.
Speaker 2: I'm a cynic, so I wonder if the participation, if
Speaker 2: Donald Trump Junior's investment in your company made it easier
Speaker 2: to get the green light from these national security concerns
Speaker 2: to do an IPO.
Speaker 5: I wish it were that easy. No, it had no
Speaker 5: role at all.
Speaker 6: We resolved all SIFIAUS issues in March of twenty twenty five.
Speaker 6: I believe that was before we took money from seventeen
Speaker 6: eighty nine. Okay, moreover, I wouldn't ask. That's not who
Speaker 6: I am and that's not the way we roll. So
Speaker 6: we took money because they are a thoughtful venture firm,
Speaker 6: and we don't believe that there's only one point.
Speaker 5: Of political view. There are lots of political views.
Speaker 6: They all have some merit, they'll have some weaknesses, and
Speaker 6: so we have right leaning political some investors, we have
Speaker 6: left leaning the fact that this firm had some right
Speaker 6: leaning in sort of investors. We were looking only at
Speaker 6: their ability to help us.
Speaker 5: Build an extraordinary company.
Speaker 6: And we have asked, and we will not at We
Speaker 6: have never asked, nor will we ever ask for political
Speaker 6: access or anything of the kind.
Speaker 3: What's it like to become a billionaire in a single day?
Speaker 3: This is something I assume will never happen to me,
Speaker 3: so I might as well ask you no.
Speaker 6: I think the honest truth is it was a big
Speaker 6: nothing for me. I had some wealth before and have
Speaker 6: some wealth after. Right, I think this is a very
Speaker 6: difficult way to make money, right, being a tech CEO,
Speaker 6: I think what you have to do is you have
Speaker 6: to love the work, you have to love the people,
Speaker 6: and you have to think every day about how to
Speaker 6: make your team rich. And far more important than sort
Speaker 6: of some change in my wealth was we made more
Speaker 6: than eight hundred millionaires. Nice, and that's something I'm proud
Speaker 6: of every minute of every day. And at my last
Speaker 6: company we made a hundred millionaires. And at this company,
Speaker 6: through our IPO, we made more than eight hundred And
Speaker 6: that's something that you wake up feeling good about yourself.
Speaker 5: Every single day.
Speaker 3: That was going to be my last question, but actually
Speaker 3: you just reminded me in that answer. You know the
Speaker 3: idea that getting here, I said, you became a billionaire
Speaker 3: in a day, but obviously this was the outcome of
Speaker 3: years and years and years of work. And if we
Speaker 3: think about technological hardware, one of the things most people
Speaker 3: associated with is really long lead times and really big
Speaker 3: research and development budgets. Now that you're a public company,
Speaker 3: how do you sort of balance that quarter to quarter
Speaker 3: financial performance pressure with the idea that you still need
Speaker 3: to be investing in capex, in new you know, new
Speaker 3: ways of designing chips, new improvements to the existing ones.
Speaker 6: Well, first, we think the opportunity for innovation, based on
Speaker 6: our way for scale engine, the best work is still
Speaker 6: ahead of us.
Speaker 5: Number one, we see an.
Speaker 6: Opportunity for extraordinary innovation in the years ahead to make leaps.
Speaker 6: Every bit is big and often bigger than what we
Speaker 6: made by building the largest chip on earth. When you
Speaker 6: love building hardware, the fact that it takes time is
Speaker 6: part of the deal, right that what we do can't
Speaker 6: be done in a week or a month or a year.
Speaker 6: And that's what you sign up for, and that's true.
Speaker 5: In every profession.
Speaker 6: You sign up for the good and the challenging, and
Speaker 6: you have to sort of make peace with that. If
Speaker 6: you're a person that wants to dive in and sort
Speaker 6: of begin iterating right away and fail quickly and code
Speaker 6: up something and look at it and throw it out
Speaker 6: in the market and see if it wins God speed,
Speaker 6: that's great, And that's not for me. You know, in
Speaker 6: our business, we measure twice before we cut once. And
Speaker 6: you have to put that in your soul, and you
Speaker 6: have to like it. You have to like that mistakes
Speaker 6: in our business are really expensive, and you have to
Speaker 6: like the fact that you breathe life.
Speaker 5: Into a chunk of silicon and you get it to do.
Speaker 6: Things that nobody else has ever been able to make
Speaker 6: a chunk of silicon do. And if that's for you,
Speaker 6: then this process that takes time and money, you love
Speaker 6: that too. And so I think I would love it
Speaker 6: less if you could do it in a week. And
Speaker 6: I think the people that I love to work with
Speaker 6: they feel the same way. And they like being engineers
Speaker 6: not because it's a path to money. They like being
Speaker 6: engineers because they like building things, and they like building
Speaker 6: hard things. And I like working with them for for
Speaker 6: exactly that reason.
Speaker 2: Yeah, you mentioned breathing life into a chunk of silicon.
Speaker 2: My dad, who's a physicist, always likes to point out
Speaker 2: how carbon and silicon are right next to each other
Speaker 2: on the periodic table. They are, and they're sort of like,
Speaker 2: here are the two things that we have closest to life,
Speaker 2: and they're literally touching each other.
Speaker 4: Maybe there's something deep in that.
Speaker 6: I think that's a really thoughtful thing, your father said,
Speaker 6: thank you, And I think that's really cool. And nobody
Speaker 6: pointed that out to me. The stared at periodic tables
Speaker 6: for a long time. But I think to the extent
Speaker 6: we can make artificial life, we need silicon.
Speaker 4: Yeah, and they're right next to each other.
Speaker 6: Right, carbon, carbon is the heart of all other life,
Speaker 6: and artificial life will be we've founded at least the
Speaker 6: elligent part will be foundered on silicon.
Speaker 2: Right below silicon is germanium.
Speaker 4: Maybe the next I don't.
Speaker 5: Know what what does that mean.
Speaker 2: Let's keep Yeah, let's keep an eye on germanium next. Andrew,
Speaker 2: thank you so much for coming on odd lots fascinating
Speaker 2: conversation right in the sweet spot of what we're interested.
Speaker 4: Really appreciate you taking your time.
Speaker 6: Hey, thank you guys for having me, and I really
Speaker 6: appreciate it.
Speaker 5: Look forward to seeing you against it.
Speaker 4: That was really fun.
Speaker 2: I'm super interested in this topic and it does feel
Speaker 2: to me like the economics of inference in particular and
Speaker 2: the market for ins ference inference capacity speed.
Speaker 4: Like it's still day one, you know what I'm saying.
Speaker 3: I just like looking at the giant.
Speaker 2: It's so cool. It's it really doesn't seem like an
Speaker 2: onion thing, doesn't it.
Speaker 4: It's like company solved in ference.
Speaker 3: With a giant building the biggest But.
Speaker 4: It is interesting.
Speaker 2: We did that episode of course with Ray Wang from
Speaker 2: semi Analysis and talking about the role like memory as
Speaker 2: being this really important part of the sort of cutting
Speaker 2: as chipsets, and it's interesting to think it's like, Okay, well,
Speaker 2: here is a bottleneck that doesn't run into that they
Speaker 2: don't have, and the idea that at least as he
Speaker 2: described it, they're not fighting to get the smallest animator
Speaker 2: chips and so maybe that gives them a little bit
Speaker 2: of breathing room ont capacity there too.
Speaker 3: Yeah, I mean, I do imagine there are some downsides
Speaker 3: to having giant chips, and you know, just as there
Speaker 3: are upsides that Andrew laid out the other thing I
Speaker 3: was wondering. I know he made the case for the
Speaker 3: reason speed is very important, but like I can also
Speaker 3: imagine a world where maybe it's not that important, you know,
Speaker 3: Like I think at some point, like the incremental speed
Speaker 3: factor just starts to become less important when weighed against
Speaker 3: like the incremental cost of generating it speed.
Speaker 2: I think it really this is like one of those
Speaker 2: things where it probably really depends what you're You're what
Speaker 2: you're using it for. Right, So it's like if you're like,
Speaker 2: you know what, I'm really curious why pterodactyls aren't actually dinosaurs?
Speaker 4: Can you explain it to me? Then it's like I
Speaker 4: don't care about that, Like that fraction of a second.
Speaker 3: I would wait five minutes for the chat bought to
Speaker 3: tell you you're wrong, Joe.
Speaker 2: You just you just don't really care that much. But
Speaker 2: if you're doing some sort of like agenta coding thing
Speaker 2: or whatever, et cetera, then like, yeah, that definitely adds up.
Speaker 2: And I will say, like, as you use it more
Speaker 2: like it's just like everything else the hit the treadmill
Speaker 2: of expectations. Here's some task that you can do in
Speaker 2: thirty seconds, which maybe several years ago would have taken
Speaker 2: you thirty minutes, and you get it patient in that
Speaker 2: thirty seconds, and you want it in ten seconds. And
Speaker 2: that's just like that competition to shave down seconds. I
Speaker 2: think it's always going to be there, so no one
Speaker 2: ever gets satisfied with this, is is my point. It
Speaker 2: always eventually becomes like it feels like waiting.
Speaker 3: But to me, this feels like this is the crux
Speaker 3: of the AI valuation argument, which is like how much
Speaker 3: of a premium are we going to place on a
Speaker 3: model that maybe a closed source model that is maybe
Speaker 3: slightly better than an open source model. How much premium
Speaker 3: are we going to place on compute that is slightly
Speaker 3: faster than this other type of compute or like other
Speaker 3: use of compute like that. To me, it's an unanswered question.
Speaker 3: And Andrew is pretty upfront about closed versus open source,
Speaker 3: but I think on the speed question too, like we're
Speaker 3: going to find.
Speaker 2: Out, We're going to find out, and you know, I
Speaker 2: think one of the things that is going to happen.
Speaker 2: And there have been all these stories about sort of
Speaker 2: like token shock, like how these companies are spending on tokens.
Speaker 2: My guess is one of the things that will happen
Speaker 2: at some point is there's going to be a lot
Speaker 2: more discussion about why are we using this ultra premium
Speaker 2: model when we could have done this, Like there is
Speaker 2: a lot of just like throw it at the AI,
Speaker 2: rack up.
Speaker 4: Those bills, et cetera.
Speaker 2: And at some point there's going to be this like, Okay,
Speaker 2: what really needs to be served fast? What really needs
Speaker 2: to be served on the most premium pro source models,
Speaker 2: and companies are probably going to get a lot more
Speaker 2: skilled at allocating from you know, different forms of inference
Speaker 2: depending on the need.
Speaker 3: Yeah, I think that's exactly it. And at that point,
Speaker 3: like we could well see some of the dynamics in
Speaker 3: the market start to change in terms of valuation. Shall
Speaker 3: we leave it there?
Speaker 4: Let's leave it there.
Speaker 3: This has been another episode of the Authoughts podcast. I'm
Speaker 3: Tracy Alloway. You can follow me at Tracy Alloway and.
Speaker 4: I'm Joe Wisenthal. You can follow me at the Stalwart.
Speaker 2: Follow our producers Carman Rodriguez at Carmen armand dash, Ol
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