Speaker 1: Bloomberg Audio Studios, podcasts, radio news.
Speaker 2: Cerebres reported quarterly earnings for the first time since going
Speaker 2: public last month. Its sales outlook beat Wall Street estimates
Speaker 2: but still disappointed investors hoping to see the company carve
Speaker 2: out a bigger slice of the aident center market. Right now,
Speaker 2: shares down seventeen point five percent, its biggest drop in
Speaker 2: its quite short history as a public company. What's behind that?
Speaker 2: CEO Andrew Feldman's with us and welcome back to Bloomberg Tech. Andrew,
Speaker 2: you know, there was a time where we would talk
Speaker 2: about the merits of top to tail server ownership, how
Speaker 2: owning all of the content, and now we're going to
Speaker 2: talk about margin contraction and we're going to talk about
Speaker 2: the stock being down seventeen percent. That to me is
Speaker 2: kind of the mismatch that the outlook on the sales
Speaker 2: side beat Wall Street estimates. I think a lot of
Speaker 2: people are trying to understand the sequential margin decline and
Speaker 2: for me, this is about ramping output for two big customers.
Speaker 3: Is that true? Yeah?
Speaker 1: I think what we did is we put forward a
Speaker 1: plan in the start of twenty six, we shared it
Speaker 1: with investors as we went public.
Speaker 3: And we're ahead of plan.
Speaker 1: You know, we delivered record revenues of one hundred and
Speaker 1: ninety one million of ninety two percent year over year,
Speaker 1: and for our cloud business, you know, it was up
Speaker 1: one hundred and sixty seven percent year over year. We
Speaker 1: beat margin consensus substantially, and then we guided for full
Speaker 1: year the growth margins would be ten percent better than plan.
Speaker 1: We also shared the in Q two and Q three
Speaker 1: we would go back to some of our customers and
Speaker 1: we would rent back gear that we'd sold them to
Speaker 1: try and keep up with demand, and then that would
Speaker 1: have a margin impact on the order ten or fifteen points.
Speaker 1: We did that to keep our customers close, to be
Speaker 1: sure we could keep up with their extraordinary demand and
Speaker 1: for our product for fast inference, and so that was
Speaker 1: the story.
Speaker 3: On every metric we put out, we're ahead of plan.
Speaker 2: Have the proceeds from the IPO actually allowed you to
Speaker 2: move more quickly in ramping up capacity?
Speaker 1: Yeah, I think capacity is the largest constraint right now
Speaker 1: for everyone. Data centers are and we've significantly increased our
Speaker 1: our ability and our pipeline for data centers which is
Speaker 1: now very large. You know, we announced a data center
Speaker 1: partnership with Bell, Canada for one hundred and twenty megawatts
Speaker 1: that will be delivered in twenty twenty seven. We are
Speaker 1: pursuing data centers across the US, in Canada, in Europe,
Speaker 1: in the Middle East.
Speaker 3: There are.
Speaker 1: The The vast resources that we have now at our
Speaker 1: disposal give us tremendous advantage in the pursuit of this
Speaker 1: The limiting factor data centers.
Speaker 2: What you're talking about, like matter of factly, is buildings
Speaker 2: not necessarily the compute, right, It's not what you guys
Speaker 2: are offering. How difficult right now is it is to
Speaker 2: get moving in America or other markets, to get planning approval,
Speaker 2: get the concrete, get the labor, get the thing built.
Speaker 1: Now that that's the irony of this market, that the
Speaker 1: AI market is moving at blistering speed, and we are
Speaker 1: being constrained by data centers which move with the speed
Speaker 1: of real estate. And so that is a problem that
Speaker 1: is being confronted by everybody in the category.
Speaker 3: By the hyperscalers, by the neo.
Speaker 1: Clouds, by the new generation clouds. Everybody is confronting this
Speaker 1: similar problem.
Speaker 2: Andrew Cerebris does not rely on traditional off chip HBM.
Speaker 2: Would you just explain that the basics of the technology,
Speaker 2: but how insulated are you from the memory bottleneck that
Speaker 2: others are experiencing.
Speaker 3: Yeah, that's a really good point.
Speaker 1: Because of our innovative architecture, because of our wayfer scale approach,
Speaker 1: we don't use HBM. HBM is a type of DRAM
Speaker 1: and it's made by three companies, one of whom is
Speaker 1: reporting shortly right, that's Micron, Heenex and Samsung. There's a
Speaker 1: global shortage, it's extremely expensive, lead times are long, and
Speaker 1: we don't use it, so we have a tremendous advantage there.
Speaker 1: The other constraints in the supply chain for many are cooths,
Speaker 1: which is a process inside of TSMC.
Speaker 3: Again we don't use it.
Speaker 1: And the third is a capacity at the three nanometer node.
Speaker 1: That's space in TSMC's factory that makes three and nanometer chips.
Speaker 1: Again we don't use it. We're at the five nanometer node.
Speaker 1: So our architecture has allowed us to deliver the fastest
Speaker 1: inference in the world by an order of magnitude while
Speaker 1: avoiding the main supply supply chain constraints faced by others
Speaker 1: in the field.
Speaker 2: Can you say, hand on heart, not just winning business,
Speaker 2: but have you actually been able to go to a
Speaker 2: customer and say we can get this compute online faster
Speaker 2: than others for those reasons you just outlined and then
Speaker 2: actually gone and done it.
Speaker 3: Oh for sure.
Speaker 1: I mean in any case I signed, we signed. For example,
Speaker 1: we signed our contract with open Ai on December twenty
Speaker 1: fourth and had them in for production on February first.
Speaker 1: That's unheard of.
Speaker 3: Stay, that's quick.
Speaker 2: I went really quickly, Andrew this morning. Open Ai is
Speaker 2: out with Kalapino intelligence processor. But everyone is compute constrained, right,
Speaker 2: How do you interpret the Frontier Labs going to custom
Speaker 2: a six alongside their other compute options.
Speaker 1: And one of the things we've been saying all along
Speaker 1: is that this market is enormous and is going to
Speaker 1: be met with a heterogeneous collection of architectures for hardware.
Speaker 1: This market will not be consolidated around GPUs. There will
Speaker 1: be a six, there will be a six from Hyperscalers,
Speaker 1: there will be a six from from Labs, and then
Speaker 1: there'll be companies like Cerebras with pioneering architectures who all
Speaker 1: of us will will will take big bites of this
Speaker 1: enormous market. I think one of the things that's difficult
Speaker 1: to get your head around is just how big the
Speaker 1: compute market is right now that One of the things
Speaker 1: AI does is it makes tractable for compute much of
Speaker 1: the world around us, and that really wasn't the case
Speaker 1: prior to AI.
Speaker 2: Andrew's if the world is simple and it's money that
Speaker 2: greases the wheels to get your industry going, can we
Speaker 2: see you come back to capital markets in some form
Speaker 2: just so you can move quicker.
Speaker 1: We have more than nine billion on the balance sheet,
Speaker 1: I think, so we're really pleased with our position there.
Speaker 1: But of course we are always scanning both the capital markets,
Speaker 1: both equity and debt for ways to accelerate our growth.
Speaker 2: Finally, how are you judging success yourself? What is the
Speaker 2: milestone that you'd pitch to the market to keep closest attention.
Speaker 1: To Well, I think I think if my mother's proud
Speaker 1: of me, I think that's the biggest thing you can
Speaker 1: you can ask for. I think for markets, I think
Speaker 1: when you lay out a plan that is aggressive and
Speaker 1: you crush it, Uh, that's how you feel good about
Speaker 1: both your ability to execute in your ability to predict
Speaker 1: your own execution. And so you know, we announced yesterday
Speaker 1: that we would beat our full year We gave full
Speaker 1: year guidance that was ten gross margin points above consensus.
Speaker 1: You ought to be proud of that, and we got
Speaker 1: to continue to execute and continue to set extraordinarily high
Speaker 1: bars and then continue to.
Speaker 3: Beat them.
Speaker 2: Cerebrasy Andrew Feldman, thank you very much. Indeed,