Speaker 1: Welcome to tech Stuff. I'm os Voloscian. There are many
Speaker 1: ways to think about AI. The one that recently struck
Speaker 1: me quite a lot comes from Demis Hassebus, founder of
Speaker 1: Google's Deep Mind, who said, if you stop to think
Speaker 1: about it, we've essentially found a way to make sand think.
Speaker 1: He was talking about silicon, the element that is melted
Speaker 1: and sliced into the chips that power AI, and today
Speaker 1: I want to talk about those chips. Our guest today
Speaker 1: is Steve Vassalo, a venture capitalist who was the first
Speaker 1: backer of Cerebras, a chip company that went public earlier
Speaker 1: this year in the largest ever IPO for a semiconductor company. Steve,
Speaker 1: Welcome to tech Stuff.
Speaker 2: Thanks Arles. It's great to be here, great.
Speaker 1: To see I'm curious what's your reaction to that, To
Speaker 1: that framing of demis making sand think.
Speaker 3: I mean, it's such a great reminder of what it
Speaker 3: is that we're doing and how fundamental it is, both
Speaker 3: from a physics perspective and all the hard work that
Speaker 3: is required to take a piece of sand and turn
Speaker 3: it into ultimately intelligence. And then there's also something that
Speaker 3: feels sort of I don't know, almost I would say
Speaker 3: sort of you know, just sort of scary and fun
Speaker 3: in a humanity sense, like, Wow, what are we building
Speaker 3: right now? What are we doing to be able to
Speaker 3: sort of turn this, you know, very basic material that
Speaker 3: surrounds us everywhere into something that is super intelligent.
Speaker 1: I want to ask you all about the story of
Speaker 1: Cerebrest and and also frankly, what wayfer scale means. But
Speaker 1: before we get there, can you just zoom out a
Speaker 1: little bit and give us the wider context of what's
Speaker 1: going on with chips right now? Because I think we
Speaker 1: will see the headlines. I mean, I'm going to read
Speaker 1: a few, but the New York Times recently had it's
Speaker 1: not just in Nvidia. The AI boom has ignited Asia's
Speaker 1: chip companies. Dull Street Journal had a couple of months
Speaker 1: ago inside Apple's push to build an all American Chip
Speaker 1: axios in July, Chip craze for the layman, what's going
Speaker 1: on here?
Speaker 2: Yeah? I love that question.
Speaker 3: So the way I think about it is really sort
Speaker 3: of starts with what I'll call workloads, And the workloads are,
Speaker 3: you know, what is the work of computing, And if
Speaker 3: you go back to the nineteen seventies and eighties, the
Speaker 3: workloads at that time were very serialized. They were kind
Speaker 3: of diverse if you think about the invention of things
Speaker 3: like the spreadsheet, or you know, as a kid, I
Speaker 3: wrote my college essays on word Star and an eighty
Speaker 3: eighty six IBM computer.
Speaker 1: Diverse in a sense that different computers for different applications,
Speaker 1: or diverse in what sense.
Speaker 3: Diverse in that you had one computing platform, which turned
Speaker 3: out to be Intel's X eighty six platform that turned
Speaker 3: out to be a great general purpose computing platform for
Speaker 3: a wide variety of applications. And then sometime in the
Speaker 3: kind of mid nineteen eighties, we began to see a
Speaker 3: new workload emerge. And that workload really came from two
Speaker 3: areas as one was from gaming and the rendering of
Speaker 3: pixels and a need for paralyzation of the compute. And
Speaker 3: then also in believe it or not, the CAD tools,
Speaker 3: so you know, building new products, whether its futures exactly.
Speaker 2: So computer aided.
Speaker 3: Design pushed the perimeter of what was possible on traditional
Speaker 3: computing CPU general purpose systems. So that was really the
Speaker 3: dawn of the GPU, and really that was Nvidia. And
Speaker 3: by the way, at the time of Nvidia's founding, there
Speaker 3: were like we're seeing today thirty other companies working on
Speaker 3: graphics processing units. And I say graphics processing units. We
Speaker 3: many people, you know, see the term GPU. They don't
Speaker 3: know or remember that, like graphics is the g It
Speaker 3: was purpose built for that workload. And then the next
Speaker 3: real shift was a few decades later, but was really
Speaker 3: a constraint a workload driven by mobile devices, and the
Speaker 3: constraints there are totally different, right. This is form factor,
Speaker 3: this is power consumption, battery life of course related to that,
Speaker 3: and so you've got a totally different workload. And then
Speaker 3: what we began to see this is now zooming forward
Speaker 3: to kind of the twenty fifteen timeframe, really starting in
Speaker 3: the twenty twelve timeframes, but the workloads began to really
Speaker 3: build in the mid twenty tens. And what you saw was,
Speaker 3: oh wait a minute, statistical inference over large data sets.
Speaker 3: Our portfolio companies were beginning to hire data science teams
Speaker 3: business models that weren't possible before you had sort of
Speaker 3: a real awareness of kind of the data underlying your This.
Speaker 1: Was the era when phrases like big data es out
Speaker 1: computing mark andresen saying software eats the world. Is this
Speaker 1: the area referring to sort.
Speaker 3: Of about five years later than software into the world,
Speaker 3: but kind of roughly contemporaneous with that. But really what
Speaker 3: you're seeing big data is absolutely right and in a
Speaker 3: sense that, wow, there are signals inside of our businesses
Speaker 3: that if we're more attuned to we could make better decisions.
Speaker 3: And what was interesting at that time, and this kind
Speaker 3: of ties back to the sort of the GPU wave
Speaker 3: was those kinds of workloads. AI workloads are very data
Speaker 3: io intensive, so that just means the data is moving
Speaker 3: through the system with with a lot of bandwidth, memory
Speaker 3: bandwidth and with high high frequency.
Speaker 1: Back and forth, back and forth. We had somebody who
Speaker 1: I'm sure you know, Nick McEwen from course on the
Speaker 1: show recently, who was at Cisco in the days of
Speaker 1: building the kind of you know dominat net Round.
Speaker 3: He was building networks, networking systems in that company was
Speaker 3: acquired by Cisco.
Speaker 2: Exactly.
Speaker 1: He was saying, today's data center, a single data center,
Speaker 1: has more back and forth connections than the whole Internet
Speaker 1: did in the nineties.
Speaker 2: Exactly, exactly. In fact, was fun.
Speaker 3: There was an announcement yesterday and videos new vera Rubin
Speaker 3: and you looked at I mean I sort of laughed
Speaker 3: when I looked at the photo of the launch because
Speaker 3: you're staring at this thing and it was just basically
Speaker 3: this medusa of basically cables and connectors behind all of that,
Speaker 3: you know, literally probably miles of AMFONYL cables.
Speaker 2: Was of course some graphics processing.
Speaker 3: Units, but what they're having to do is connect all
Speaker 3: that stuff back together. And so this ties to the
Speaker 3: point around wafer scale, which is, if you are going
Speaker 3: to build something starting from scratch, not using platforms that
Speaker 3: already exist, not relying on CPUs or GPUs or ARM architectures,
Speaker 3: and you were to say, what would you build that's
Speaker 3: really designed for these kinds of workloads?
Speaker 2: You build what we built at Cerebras.
Speaker 3: And that's the journey that we started in twenty fifteen,
Speaker 3: and really it's born of this notion of big, big
Speaker 3: workloads deserve custom purpose built silicon, and that has been
Speaker 3: really the story of every silicon wave over the last
Speaker 3: five decades.
Speaker 1: And talk about this concept of wafers scale because I can.
Speaker 1: I mean, if you look at the Cerebras chip, it's
Speaker 1: fifty times bigger than an Nvidia GPU. And as you
Speaker 1: mentioned with with Jensen holding up with verrub and it
Speaker 1: is this kind of like, you know, sort of different
Speaker 1: silicon chips connected together with cables and stuff, whereas your
Speaker 1: chip is is kind of a single huge piece of silicon.
Speaker 1: Why do they do that and why do you keep
Speaker 1: it as a single piece.
Speaker 3: The way to think about sort of silicon, MANUF is
Speaker 3: it's grown as you started this conversation with sand, you know,
Speaker 3: it's grown from sand into single crystalline columns, and then
Speaker 3: those columns are sliced very thinly that is then cut
Speaker 3: to basically turn it into a nine inch roughly nine
Speaker 3: inch square, and that nine inch square has trillions of
Speaker 3: transistors on it. And the way Nvidia or ARM or
Speaker 3: others build their chips is they might start with a
Speaker 3: twelve in wafer, but then they cut it. They cut
Speaker 3: that wafer into many smaller components that are, as you said,
Speaker 3: sort of fifty or fifty eight times smaller than ours,
Speaker 3: and then they cut it and then in many cases
Speaker 3: they're actually literally putting them back together. And you ask
Speaker 3: a question, which is why cut it? That's exactly the
Speaker 3: question that we asked ourselves, which is you shouldn't cut it.
Speaker 2: You should keep it all together.
Speaker 3: Because as soon as you as soon as you take
Speaker 3: communications from a chip off of the chip. So once
Speaker 3: once a bit needs to move from one chip to another,
Speaker 3: you basically have other components involved, and everything you add
Speaker 3: as you would imagine with any sytem them. As soon
Speaker 3: as you add another component between two pieces of silicon,
Speaker 3: you've slowed it down, You've created new constraints. And so
Speaker 3: the big idea here is don't do any of that.
Speaker 3: I'm sure you're familiar. I mean, Elon, you know he's
Speaker 3: working on other stuff in this domain, but you know
Speaker 3: he has this, this sort of rule of thumb. When
Speaker 3: you work at SpaceX or one of his companies, you
Speaker 3: hear this all the time, which is the best part
Speaker 3: is no part. And the point of that is you'd
Speaker 3: like to reduce all of your components down to their
Speaker 3: simplest sort of platonic ideal form.
Speaker 2: And that is exactly what.
Speaker 3: We decided to do with Cerebus, which is, instead of
Speaker 3: taking a twelve inch column of silicon, slicing it into
Speaker 3: hundreds of smaller pieces, then putting those on a motherboard
Speaker 3: and reconnecting them.
Speaker 2: With copper wires or copper traces.
Speaker 3: Let's keep it all on one piece so that the
Speaker 3: data can move as fast as possible throughout that single
Speaker 3: piece of silicon, and we'll get a thousand x improvement
Speaker 3: in performance. And that's from a memory bandwidth perspective. That's
Speaker 3: exactly what we delivered, and so's I were now call
Speaker 3: it roughly fifteen in some cases even more than that
Speaker 3: times faster than any other inference platform on the planet.
Speaker 1: What do you think the biggest misconception is about the
Speaker 1: quote unquote chip craze today.
Speaker 3: I would say the biggest misconception is how hard it
Speaker 3: is to build a new platform to yield those systems,
Speaker 3: and the yield it's basically for everyone you make, how
Speaker 3: many of those can you use? And that that is
Speaker 3: sort of one of those fundamental drivers in the semiconductor industry.
Speaker 3: But in the case of large semiconductor systems powering it
Speaker 3: so you know, most of these systems are now taking
Speaker 3: tens of kilowatts. Back when I was building startups a
Speaker 3: couple decades ago, you know, a rack was somewhere on
Speaker 3: the other of two to three kilowatts. We now have
Speaker 3: racks that are sixty kilowatts, and so you're trying to
Speaker 3: power this system now. Of course, once you're powering it.
Speaker 3: Guess what else you have to do. You have to
Speaker 3: cool it. You have to get that all the all
Speaker 3: the heat out of that system. And then of course,
Speaker 3: once if that's that's now working, now you're going to
Speaker 3: program this thing that has you know, two and a
Speaker 3: half trillion trans and then get you know, modern models
Speaker 3: to work on that and get them up and running
Speaker 3: in a matter of days or weeks instead of months
Speaker 3: or years. And so you're compounding many, many hard problems.
Speaker 3: And in most businesses, when you compound hard problems, what
Speaker 3: comes out the other end often doesn't work. And so,
Speaker 3: you know, the single greatest challenge really is can you
Speaker 3: actually make this thing at scale?
Speaker 4: Yeah?
Speaker 1: I think I think you said somewhere, you know, I
Speaker 1: sometimes joke that Cerebras was five startups in one. You've
Speaker 1: sold wafer scale, great nive power system, cool it, package it,
Speaker 1: deploy code onto it, integrated with an existing frameworks, get
Speaker 1: into data centers. Each one of those challenges was quite
Speaker 1: literally its own company. So I mean, let's go back
Speaker 1: to twenty fifteen. I mean, you had this insight, you know,
Speaker 1: every new wave of computing demands and new type of
Speaker 1: silicon right and We talked about the computing of the eighties,
Speaker 1: one type of chips and the GPUs for gaming, which
Speaker 1: ended up by sort of accident essentially powering the air revolution,
Speaker 1: the arm chips that power cell phones, and then these
Speaker 1: these new chips that cerebreast design for data centers. Essentially
Speaker 1: exactly who were you in twenty fifteen, and we've got
Speaker 1: the kind of we've got the global context, the industry context,
Speaker 1: what's the what was the Steve context? And what made you?
Speaker 1: I mean nowadays twenty twenty six. When we think about
Speaker 1: venture capital and investing, you know, we often think about
Speaker 1: like momentum based, like how do I get into anthropic
Speaker 1: before it goes public right? Versus like how do I
Speaker 1: make a ten year plus bet on something which which
Speaker 1: you know is not only requires all these macro things
Speaker 1: to break my way, but also has like just layer
Speaker 1: upon layer, up on layer of complexity. And you know,
Speaker 1: these these all these different elements we talked about in
Speaker 1: terms of cooling and packaging and integration. What what made
Speaker 1: you want to do this? And how much of your career,
Speaker 1: shall we say, or your capital, your financial capital, your
Speaker 1: relation capital did you stake on this?
Speaker 3: I love that, Yeah, so I mean the quick bounce
Speaker 3: on me is you know, I'm trained as a roboticist.
Speaker 3: I lived for the first ten years of my career
Speaker 3: at the intersection of electrical and mechanical systems. I worked
Speaker 3: at a company called Ideo, designing products.
Speaker 2: Some of those.
Speaker 3: Products brought those two worlds together as well as sort
Speaker 3: of the design world, sort of really answering the question
Speaker 3: of once you've solved those hard technical problems at inter
Speaker 3: section of electrical and mechanical systems, does anyone actually care?
Speaker 3: Do people love it and want it? So I spent
Speaker 3: really a decade building products. Came to Foundation actually as
Speaker 3: an entrepreneur in residence on the path to starting another company.
Speaker 3: And this is now in the spring of two thousand
Speaker 3: and seven. And so I'm a builder by nature, if
Speaker 3: I would say, sort of more of an accidental venture
Speaker 3: capitalist than one who sort of, you know, thought to
Speaker 3: do this from when I was a teenager. I didn't
Speaker 3: I literally didn't know the term venture capital until I
Speaker 3: moved to Silicon Valley back in the mid nineteen nineties.
Speaker 3: And the specific context and I love the question around
Speaker 3: sort of what was required to get an investment like this.
Speaker 2: Approved as it were, more than a decade ago.
Speaker 3: The answer to that question is is really sort of
Speaker 3: central to how we work at Foundation Capital, which is
Speaker 3: we go really deep in opportunity space, and we're not
Speaker 3: trying to be everything to everyone, and when we go deep,
Speaker 3: we try to kind of build what we call our
Speaker 3: points of view, a sense of what would need to
Speaker 3: be true for us to be excited about a new
Speaker 3: investment in this area. What are the attributes of the
Speaker 3: founders that would make them successful or not, What is
Speaker 3: the timing of the market opportunity, because of course many
Speaker 3: venture capital investments, you know, have great ideas, but are
Speaker 3: maybe a decade too early or in some cases multiple
Speaker 3: dedcades too early. And so for me, I had been
Speaker 3: studying this space, and I'd actually met Andrew Feldman and
Speaker 3: his co founder Gary Back no joke, in October of
Speaker 3: two thousand and seven, seven years before we really re
Speaker 3: engaged around the opportunity at Cerebras, and they were actually
Speaker 3: at that time just starting their prior company, a company
Speaker 3: called c Micro, which was kind of also in the
Speaker 3: kind of data center think sort of warehouse scale computing concept,
Speaker 3: and we didn't actually invest in that company. I hit
Speaker 3: it off with them and they were acquired by AMD
Speaker 3: maybe five years later, and a couple of years into that,
Speaker 3: I reached back out to Andrew and I could tell immediately,
Speaker 3: I mean, I had this intuition going into the conversation
Speaker 3: that he was not going to stay long for AMD.
Speaker 3: He had another startup in him. And so we basically,
Speaker 3: over the course of the next two years riffed on
Speaker 3: a whole bunch of ideas that at that time were
Speaker 3: kind of around that big data concept that you and
Speaker 3: I talked about earlier, and we were looking at a
Speaker 3: whole bunch of companies were using Andrew as a sounding
Speaker 3: board for the opportunity space. And then he and Gary
Speaker 3: and then Sean and then Michael and JP started to
Speaker 3: basically converge on this idea around Hey, wait a minute,
Speaker 3: there's this new workload, and this new workload we got
Speaker 3: to pay attention to, and this new workload while as
Speaker 3: you said, sort of NVIDIA is a bit of a
Speaker 3: happy accident that it happens to be better than CPU's,
Speaker 3: but it's not what you build. And so the you know,
Speaker 3: these five founders basically set out to go build something
Speaker 3: that was purpose built, and I had this prepared mind
Speaker 3: I'd been working with Andrew on kind of a range
Speaker 3: of concepts for two years. So when he finally said
Speaker 3: this is now kind of March timeframe of twenty sixteen,
Speaker 3: it's like, I think we're ready. I told him, I said, look,
Speaker 3: we want to be your first term sheet and so
Speaker 3: we It was no joke on April first, so April
Speaker 3: Fool's Day, I said, Andrew, you know, we presented it
Speaker 3: with an offer, and over the course of the next
Speaker 3: few weeks we basically converged on that initial financing, which
Speaker 3: we which we did in partnership with Eric at Benchmark
Speaker 3: and Pierre and Leora at Eclipse, And yeah, that was
Speaker 3: the beginning of a cerebras back in May of twenty sixteen.
Speaker 1: What was the closest moment between twenty sixteen and ringing
Speaker 1: the bet On in the New York Sulck Exchange to failure?
Speaker 3: Oh many, We had many, so many white knuckle moments.
Speaker 3: We didn't ship the first product for three and a
Speaker 3: half years from that May twenty sixteen. I'd say there
Speaker 3: were lots of challenges around getting that first semiconductor system
Speaker 3: to work. You know, we'd spent multiple millions building the
Speaker 3: first one and no joke. The very first system we
Speaker 3: plugged it in and it blew up. I mean we
Speaker 3: joke about like we call those thermal events because you
Speaker 3: never want to tell your landlord you've.
Speaker 2: Had a fire. But we had an event.
Speaker 3: We had a small thermal event around our first system.
Speaker 3: And what you're trusting in those cases is, wow, that
Speaker 3: was very expensive. That one system you could you could
Speaker 3: as certain cost you know, millions of dollars with all
Speaker 3: the R and D that was invested in it. And
Speaker 3: then you've got to just trust that we're going to
Speaker 3: come back, you know, the next day and and dig
Speaker 3: through what went wrong and figure it out.
Speaker 2: And and we did. And that team is just.
Speaker 3: Born of so much grit around sort of getting a
Speaker 3: system that again there were no there were no playbooks
Speaker 3: for this. TSMC had never done anything like what we
Speaker 3: were asking them to do. And then the other piece
Speaker 3: I would say that was a very scary moment was
Speaker 3: when we realized, you know, we were initially actually going
Speaker 3: to build an air cold system because every other system
Speaker 3: in the data center, you know, including all the GPU systems,
Speaker 3: were all air cooled, and we realized to make our
Speaker 3: system work, we would need to do we'd need to
Speaker 3: water cool it, and there was lots of questions around
Speaker 3: how that would work. APR kind of head of mechanical
Speaker 3: engineering and system design, had never done this.
Speaker 2: He kind of admended that to me later. And you know, keap.
Speaker 3: Dissipation at that scale is a really hard problem. And
Speaker 3: so we we built a lot of prototypes. We actually
Speaker 3: ended up having to bring in several consultants and I
Speaker 3: spent you know, probably you know it was not hundreds
Speaker 3: of hours, but you know up in that range working
Speaker 3: with the team on how do we actually solve this
Speaker 3: really really hard problems because you'd come out of the
Speaker 3: lab some days thinking, guys, we might just be trying
Speaker 3: to beat the laws of physics, which is never never
Speaker 3: a good idea.
Speaker 1: So you, in a sense you had all of these
Speaker 1: pod engineering problems and now engineering problems. Some of them
Speaker 1: are solved, but no doubt are new ones. But also
Speaker 1: of course you have you know, you have competition within Vidia.
Speaker 1: Is that is that essentially? I mean, how does the
Speaker 1: business look today? Is it? Is it a pitch battle
Speaker 1: of Nvidia?
Speaker 4: Like?
Speaker 1: Is that the central kind of business challenge?
Speaker 2: Yeah?
Speaker 3: I mean they are the eight hundred bound guerilla no question,
Speaker 3: and we are the insurgent and they've built you know,
Speaker 3: they've built an extraordinary category. When when Nvidia moved into
Speaker 3: the data center space, which was kind of in the
Speaker 3: mid twenty tens and it's now you know, it's more
Speaker 3: than half of their business, and so they are definitely
Speaker 3: you know there, they are the player that we most
Speaker 3: directly get compared with, which I'm fine with. And they
Speaker 3: have a different architecture and you know, we fundamentally believe
Speaker 3: we're doing we're doing things differently for again back to
Speaker 3: the sort of you know, initial premise here, because we
Speaker 3: think this workload deserves its own silicon.
Speaker 1: So so displacing them with clients, I guess is one
Speaker 1: challenge on the on the on the demand side, shall
Speaker 1: we say, what about on the supply side? I mean
Speaker 1: you mentioned that TSMC actually you know, fabricate these these
Speaker 1: chips for you guys, Are you like literally fighting of
Speaker 1: a factory flaw space in Taiwan with Nvidia to to
Speaker 1: to see which orders for chips they'll fulfill faster? I mean,
Speaker 1: how do you how do you navigate that relationship?
Speaker 3: Yeah, So TSMC has been an extraordinary partner to us,
Speaker 3: and they are to really everyone in the industry. And
Speaker 3: what I appreciate most about them, and I think what
Speaker 3: they are uniquely well suited to doing is working on
Speaker 3: new ideas even before the market is sort of clearly
Speaker 3: there for them. And they have been a great partner
Speaker 3: to us in that regard, because again, if you're looking
Speaker 3: at Cerebras back in twenty sixteen and your TSMC and
Speaker 3: you've got you know, customers like Video or Apple and
Speaker 3: many many others of scale that are you know, would
Speaker 3: dwarf anything that we could ask of them. For certainly
Speaker 3: several years, they had no business in working with us,
Speaker 3: and yet they know that and they've proven this out
Speaker 3: time and time again that by working with startups on
Speaker 3: hard problems, they get better. And so you really want
Speaker 3: you want partners like that. You want first customers that
Speaker 3: are like that, that help you make your systems better,
Speaker 3: that help you debug them, help you find problems that
Speaker 3: you wouldn't have and resolve those problems you you wouldn't
Speaker 3: have otherwise.
Speaker 2: And so they've been a great partner to us.
Speaker 3: But yeah, I think everyone in the world is asking
Speaker 3: more of TSMC. They're having to make you know, investments
Speaker 3: and you know, as is ASML and that the companies
Speaker 3: that make their machines are also in high demand. And
Speaker 3: I think the entire world is basically looking at this
Speaker 3: market opportunity in front of us and saying, well, what
Speaker 3: is the market for intelligence?
Speaker 2: It's unbounded, right like you you.
Speaker 3: Know, these are trillion dollar markets today that could be
Speaker 3: one hundred trillion dollar markets in the future. And so
Speaker 3: you know, working on for them, working on the fundamental
Speaker 3: technology underlying that wave that intelligence.
Speaker 2: They're in high demand, no question. But they've been great,
Speaker 2: great partners.
Speaker 4: To us.
Speaker 1: On the demands side. As I will send it. In
Speaker 1: twenty twenty five, between eighteen ninety percent of the revenues
Speaker 1: came from setting chips to to UAE based entities g
Speaker 1: foot two and the Muhammad Benzai a University of Artificial Intelligence.
Speaker 1: I guess, first question what do they use those chips for?
Speaker 1: And second question is how do you How are you
Speaker 1: diversifying the business and are you focus on winning market
Speaker 1: share from in video or just maintaining your position As
Speaker 1: you put it, the kind of the market for intelligence grows.
Speaker 3: Yeah, So I mean we got started really selling systems
Speaker 3: to great partners early in the life cycle of a company.
Speaker 3: We had partners with National Labs, we had pharmaceutical companies,
Speaker 3: we had energy companies working with US buying systems, and
Speaker 3: that was primarily in the training days of.
Speaker 2: Three versus History. In twenty twenty four, we launched our
Speaker 2: inference offering.
Speaker 3: We built it, we kind of had a board meeting,
Speaker 3: realized that as everyone was seeing, inference workloads were growing
Speaker 3: very very quickly, beginning to sort of really dwarf the
Speaker 3: training workloads, and so we ourselves realized that our architecture,
Speaker 3: our way for scale engine was going to be really
Speaker 3: appropriate for that. So we basically rotated towards an inference offering,
Speaker 3: and as part of that, we developed a partnership with
Speaker 3: G forty two, which is the entity you just described,
Speaker 3: and they have been purchasing our systems and working with
Speaker 3: our cloud infrastructure for their customers. So think of them
Speaker 3: as sort of the AWS for the Middle East, and
Speaker 3: they have been able to really work closely with their
Speaker 3: own customers as well as some of our customers. In fact,
Speaker 3: we have actually put a number of our customer workloads
Speaker 3: back onto those systems that we that we put in
Speaker 3: place now a couple of years ago.
Speaker 2: And then your question around diversification.
Speaker 3: So earlier this year we announced our deal with open ai,
Speaker 3: which is more than a twenty billion dollar multi year
Speaker 3: deal to build seven hundred and fifty megawatts of compute
Speaker 3: and we were after announcing that we were only six
Speaker 3: weeks to launching our first model with them. We love
Speaker 3: working with them. I mean it's so great to see
Speaker 3: too super high performance teams focused on, you know, making
Speaker 3: the fastest inference on the planet, which is which is
Speaker 3: what we launched and what we have continued to prove
Speaker 3: out with you know, lots more to come there. So
Speaker 3: open aye is probably the single largest sort of counterweight
Speaker 3: to the question of you know, kind of customer concentration.
Speaker 3: And then back in March of this year, we also
Speaker 3: announced our partnership with AWS. So I think what you'll
Speaker 3: see is over the coming quarters is you know, us
Speaker 3: scaling those deployments with those customers. And then there's many
Speaker 3: many more that are you know, kind of wanting to
Speaker 3: work with us. And I think back in the early
Speaker 3: two thousands, there are lots of companies building search products,
Speaker 3: and Google happened to have the best one. It was
Speaker 3: also one that was just ruthlessly focused on speed. In fact,
Speaker 3: you remember the early days of Google, they would tell
Speaker 3: you how many milliseconds it took to return the you know,
Speaker 3: to return the query. And they did that because a
Speaker 3: it was it was important to users. But if they
Speaker 3: if they weren't fast, you know, their users were going
Speaker 3: to go do other things. And so performance and inference
Speaker 3: is absolutely fundamental. And so we while while we might
Speaker 3: only be i'll call it sort of ten or fifteen
Speaker 3: percent of the market today in terms of who wants
Speaker 3: to have high performance, we think one hundred percent of
Speaker 3: the market towards most towards high performance over time.
Speaker 1: Obviously G forty two's connections with with with China cause
Speaker 1: some consternation in the US, and I believe that was
Speaker 1: part of the reason why this rebrass IPO was originally delayed.
Speaker 1: So I'm curious for your kind of take on on
Speaker 1: on the geopolitics of all this. And also, I mean,
Speaker 1: you are a you know, a sort of roboticist and
Speaker 1: an IDEO guy based in Silicon Valley with like a
Speaker 1: real passion of engineering, and all of a sudden you're
Speaker 1: you know, on the board and the lead investor in
Speaker 1: this in this company that's in the middle of you know,
Speaker 1: politics and geopolitics, how do you navigate that?
Speaker 3: Yeah, so we I mean we see these you know,
Speaker 3: these markets. Market again for intelligence is infinite, is unbounded,
Speaker 3: and it's coming from everywhere in the world.
Speaker 2: And and I.
Speaker 3: Would also assert that the you know, the geopolitics of
Speaker 3: the last four or five years have only made those
Speaker 3: international parties more to get access to alternative systems, alternative
Speaker 3: technologies than just the sort of you know, you know,
Speaker 3: kind of think of it as the traditional hyper scalers
Speaker 3: or Nvidia for that matter. I mean, we have prospective
Speaker 3: customers in Europe and they're desperately seeking any alternative to
Speaker 3: purchasing more in video gear. And so there's I think
Speaker 3: a really sort of a pull around the world for
Speaker 3: alternatives that are coming from parties who don't feel like
Speaker 3: they've they've got eighty.
Speaker 2: Or eighty five percent market share.
Speaker 1: Why because if fear that they're basically not going to
Speaker 1: get they're not going to be a big enough client
Speaker 1: to get preferential.
Speaker 2: Get the allocations they are.
Speaker 3: Yeah, there's you know, these are you know, you're you're
Speaker 3: you're seeing companies have to and and sovereign governments have
Speaker 3: to come to here in Silicon Valley and I mean
Speaker 3: Jensen's made more than a few trips to the Middle
Speaker 3: East over the last few years. But you know before
Speaker 3: that they were all coming here to kiss the ring
Speaker 3: and get allocation.
Speaker 1: Well knows you about the open a ideal. Obviously huge
Speaker 1: announcement a few guys, and then the market was sort
Speaker 1: of questioning, oh, is it going to be more expensive
Speaker 1: than they thought to actually service this deal which is
Speaker 1: not a not a problem, which is you know, confined
Speaker 1: to cerebras. I know, Google it getting beaten up for
Speaker 1: the kind of cost of deployment of that cloud computing.
Speaker 1: But how do you think about that?
Speaker 3: Yeah, So we it was interesting because when you know,
Speaker 3: we did the IPO roadshow, we shared all of that
Speaker 3: in conversations as well as in the S one, and
Speaker 3: you know, there was this question mostly in my sense
Speaker 3: was it was folks either not reading or perhaps misunderstanding,
Speaker 3: or just looking for ways to sort of throw bricks.
Speaker 2: At young companies.
Speaker 3: So we basically had a home run up and down
Speaker 3: the P and L beat really every metric. And the
Speaker 3: question that folks were raising was around gross margin, which
Speaker 3: was off by I think ten or fifteen percent, and
Speaker 3: that was entirely because we were renting back capacity over
Speaker 3: the next few quarters from G forty two, which is
Speaker 3: the thing we talked about earlier. So we had you know,
Speaker 3: they had deployed systems, we had customers who wanted more,
Speaker 3: and so we ultimately, I mean, we put those systems
Speaker 3: in place, and then we've developed an agreement with them
Speaker 3: which was, hey, can we rent back that capacity for
Speaker 3: these customers, which they were they were happy to do,
Speaker 3: and you know, we had to take a gross margin
Speaker 3: hit in the in the near term for that. So
Speaker 3: this was not a surprise for any of our institutional investors.
Speaker 3: But you know, folks are looking for, you know, whatever
Speaker 3: might be a crack in the story for your first
Speaker 3: earnings call.
Speaker 2: But yeah, this was this was a big nothing burger
Speaker 2: in my mind.
Speaker 1: And what about I mean, how do you look at
Speaker 1: the competitive dynamics beyond Nvidia obviously, Microsoft, Google, Amazon, Meta
Speaker 1: all now rushing into the space of chip design and
Speaker 1: and and you know for their specialized use cases in
Speaker 1: many case which I mentioned on how computeeing. How do
Speaker 1: you look at what they're doing, are you worried about it?
Speaker 1: And how do you maintain the edge?
Speaker 3: Yeah, so I think broadly the way you think about
Speaker 3: these things is you know, large markets like this, and
Speaker 3: as we said, these are the mother of all markets,
Speaker 3: you're never going to have them all to yourself. You know.
Speaker 3: If you're right about these things, they're going to attract competition.
Speaker 3: That's just basics and you know economics. And so what
Speaker 3: we observe is every one of the hyperscalers has already
Speaker 3: developed or is it you know, in development on their
Speaker 3: own silicon. I mean, obviously Google's had their TPUs in place.
Speaker 3: They're on their fourth or fifth generation now, the Trainium
Speaker 3: and Infernia platform at AWS, and again we're in partnership
Speaker 3: with AWS. Fact you know, we've we've shared some of
Speaker 3: the ways in which we're working together on a very
Speaker 3: novel disaggregated solution where we can kind of get the
Speaker 3: best of their hardware and the best of our hardware,
Speaker 3: which is where I think it will be really interesting
Speaker 3: things to sort of.
Speaker 2: See that play forward.
Speaker 3: Facebook of course, has been talking about their own platform
Speaker 3: to build their own silicon, which you know they've been
Speaker 3: doing in partnership with Broadcom for some time. And so
Speaker 3: you know, we see this as any large market is
Speaker 3: going to attract multiple players, and what I actually really
Speaker 3: appreciate about it is everyone's at least looking at their
Speaker 3: workloads and saying, what is it that we should build
Speaker 3: here that's well suited to us, And what we're building
Speaker 3: at THREEBRUS is very well suited these inference workloads. It's
Speaker 3: also well suited to training, and it's you know, it's
Speaker 3: something that took as I said, you know, it took
Speaker 3: us multiple years in three generations before it really felt
Speaker 3: like we had a system that was really singing. And
Speaker 3: most of the other insurgents, you know, are still telling
Speaker 3: you what's about to ship you know whatever in the
Speaker 3: fall or in Q one of.
Speaker 2: Twenty twenty seven.
Speaker 3: But we, you know, we have systems out in the wild,
Speaker 3: and we're scaling dramatically, and we're also working on our
Speaker 3: next generation systems, which are which are super exciting.
Speaker 2: So that's how I think about it.
Speaker 3: I think there's going to be many many more, could
Speaker 3: be more upstarts. You know this, this this market is massive,
Speaker 3: and so we should assume there's gonna be many, many winners.
Speaker 1: So you have this claim now, which which all vcs
Speaker 1: would love to be able to make. That you essentially
Speaker 1: soar around the corner, right, and so that must be
Speaker 1: a fun moment for you. And I'm curious about two things.
Speaker 1: One is, you know, ten years ago you saw this
Speaker 1: kind of very strong demand see all for you big
Speaker 1: data processing what became AI workloads and invested in and
Speaker 1: built Cerebrass entergy for thease opportunity. What are you seeing
Speaker 1: now ten years into the future in terms of demand
Speaker 1: signals that you're investing in, either through your fund or
Speaker 1: as part of Cerebress.
Speaker 3: Well, when I think about what today we're excited about,
Speaker 3: I mean what you see in our industry. I think
Speaker 3: in venture capital and tech more broadly, and you've seen
Speaker 3: this as much.
Speaker 2: As I have.
Speaker 3: Is every wave, every platform, new platform technology, whether you
Speaker 3: know we were talking about sort of the X eighty
Speaker 3: six platform or GPUs or mobile and now AI. There's
Speaker 3: a sort of quite roughly a decade of building out
Speaker 3: the infrastructure and then there's multiple decades of building out
Speaker 3: the application layer on top of it. And so there's
Speaker 3: no question and we're spending you know, we more than
Speaker 3: one hundred companies broadly, and that you describe as sort
Speaker 3: of AIRAI native, you know, across our portfolio where a
Speaker 3: thirty one year old venture firm we've been investing in
Speaker 3: AI really sort of the sort of fundamentals of it
Speaker 3: since two thousand and eight two thousand and nine time frame,
Speaker 3: and so we look at this next waves as involving
Speaker 3: a lot of innovation at the application layer.
Speaker 2: Now what's tricky about this, as I'm.
Speaker 3: Sure you're aware of as well, is well which parts
Speaker 3: of those application layer are going to get completely commoditized
Speaker 3: by perhaps even the fundamental foundation models that you're building
Speaker 3: on top of. And so we look for the sort
Speaker 3: of thin, sort of proprietary data loops, you know, ways
Speaker 3: in which there's a data flywheel where with every use
Speaker 3: of the product, you know, your insight around your customer
Speaker 3: and your value proposition gets deeper and richer. And we
Speaker 3: look for sort of really interesting ways in which things
Speaker 3: that were today delivered as a service, oftentimes literally as
Speaker 3: maybe it's a software service, maybe it's a human service,
Speaker 3: maybe it's labor, and we look for ways that that
Speaker 3: is going to be automated over time. And so we've
Speaker 3: made you know, a number of really interesting investments in
Speaker 3: this area. And then when it comes to sort of
Speaker 3: AI infrastructure, I mean we all will have to kind
Speaker 3: of like point to the you know, the the analogies
Speaker 3: to how are you know natural systems work, whether it's
Speaker 3: a human brain or not. But you know, these systems
Speaker 3: that we're building are far less sample efficient than the
Speaker 3: way natural systems work.
Speaker 4: You know.
Speaker 3: You know, if you've got a twenty whatt brain operating
Speaker 3: at two hundred hertz and it can do, you know,
Speaker 3: sample efficiency is much much higher than what is used
Speaker 3: to train a very intelligent next generation frontier model.
Speaker 1: Some of thefsionist means that you can learn from fewer examples.
Speaker 2: Yeah, exactly.
Speaker 3: Yeah, and so I think there's going to be some
Speaker 3: really interesting work in this area that then allows us
Speaker 3: to build models that are more efficient both from an.
Speaker 2: Energy perspective but from a data perspective.
Speaker 3: So yeah, I'm very excited about, you know, some of
Speaker 3: the work that's being done in this domain.
Speaker 1: Steve has outa thank you, thanks so much, as it's
Speaker 1: just so great to talk with you for tech stuff.
Speaker 1: I'm Osvoloshin. This episode was produced by Eliza Dennis. It
Speaker 1: was executive produced by me and Julian Nutta for Kaleidoscope
Speaker 1: and Katrian norvelve iHeart Podcasts. Jack Qinsley mixed this episode
Speaker 1: and Kyle Murdoch wrote Olph theme song,