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This guy is literally gonna hover over us. This pilot hates podcasting. What a prick.
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>> The besties are broadcasting from the USA House at the World Economic Forum.
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Our episode is sponsored by the New York Stock Exchange. Are you looking to change the world and raise capital? Do
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it at the NYSC. The NYSE is a modern marketplace and a massive platform built for scale and long-term impact. So, if
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you're building for the future, the NYSC is where it happens. >> I'm Jason Calakanis. This is the All-In
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interview show. Uh, last minute we got added to the roster here at the World Economic Forum, and we had time to do a
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halfozen interviews and, uh, Brian was here, and uh, this is your Brian Armstrong from Coinbase, of course, and
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friend of the pod. This is probably your fourth or fifth appearance on the pod. You come to Davos because this actually
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isn't for you about networking. This is about serious regulations on a global
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basis. Yeah. >> Well, that's been the focus of this attendance at Davos is we are trying to
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get market structure legislation done for crypto. But actually there I mean there is a lot of networking that
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happens here. We've done a lot of commercial meetings. You know five of the top 20 global banks are now using
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Coinbase to build their crypto infrastructure into their products. uh we meet with heads of leaders of
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different countries and talk to them about economic freedom and how crypto can update their financial system. So
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there's all kinds of good meetings. >> So uh you had embedded in there these
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partnerships with banks. Uh is that like a white label type thing so they can sell crypto to their customers? Is it
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disclosed which banks are doing that and how that works? >> Um a couple of them are public. Uh we've
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talked about integration with JP Morgan, PNC Bank. Um, you know, there's a couple
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others that are not public yet, but five of the top 20 gibs are now using Coinbase for that. And then we're also
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powering integrations with like Black Rockck and, you know, they they've said
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they want to tokenize every single one of their funds. And so, a lot of these financial institutions are coming on
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chain, which is great. >> And this is quite I mean, I was thinking on the way over here how you've really
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struggled to work with regulators over the last decade. I remember under the Biden administration, the 46th
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administration, you went to DC and were like, "I'm here. I would love to talk to
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you." And they were like, "Yeah, we don't want to talk to you."
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>> Now, some people might have varying uh feelings about Donald J. Trump, our our
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47th president. But one thing he has nailed is interfacing with the business community and taking regulation and
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creating a legal path for crypto specifically very seriously. What's the how's the last year? How have the how
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have things changed for you in the last year? >> Yeah. Well, I know you like to call
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balls and strikes and I think just looking at it objectively, you know, the Biden administration really tried to
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unlawfully kill this industry in America from my point of view. And Donald J. Trump, you got to give him credit. I
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mean, he campaigned on this idea of making the United States the crypto capital of the world. He's kept his
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promises. He's leaned in and tried to get clear rules and regulations passed
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so that uh American companies can thrive, American consumers can earn more money on their money. Um and he
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understands also it's an important political issue. There's a huge base of
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there's like 52 million Americans who've used crypto now, >> right?
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>> And they want to see clear rules. They want to see this, you know, get better
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financial services in the United States. It's also, frankly, a global competitiveness issue, right? I mean,
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China just announced that they're going to pay interest on their central bank
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digital currency. Some of the largest stable coin issuers are still offshore. He wants to repatriate that capital and
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bring it into the US. >> This is the crazy thing we went through. I was never a fan calling balls and
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strikes of people doing things that weren't buttoned up. >> Mhm. >> But I was even less of a fan of the
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prior administration just not meeting and saying, "Hey, this is uniquely different. let's figure out a way to
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give you a path to do it properly. And so, >> you know, in our industry, sometimes you
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have to reinterpret rules. Airbnb uh Uber the biggest success of my investment career like they bent rules
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too. Crypto bent some rules. Um some cases people broke them uh and uh they paid the price. But here we are now the
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rule set is being refined. The most important one I think for you is stable coins and your competition with the
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bank. You have banks as partners, but you're also a competitor to them. Yeah.
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>> Well, I I'd say it's mostly collaborative. I'd say the of the bank
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CEOs that I've met with here, I most of them are actually very into crypto. They're they're starting to integrate
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it. You know, I met with one of the top 10 global banks in the world yesterday and the CEO told me, "Crypto is is my
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number one priority. We view that this is existential. We're all in. We're
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going to put all >> Why is it existential for them? What do you think?" they're seeing it's like
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it's like when the internet came around um you know and you had Amazon competing
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with Barnes & Noble or you had blogs competing with not New York Times like in print, right? Yes.
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>> And so anytime there's always change happening in the world and you can think
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of it as an opportunity or you can think of it as a threat and bury your head in
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the sand and pretend it's not happening. But the reality is that uh crypto is
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massive like something like 500 million people have used it globally. You know Bitcoin was the best performing asset
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class of the last decade. um the largest financial institutions of the world are
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now integrating this. And so at this point, I don't I think it's foolish to
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pretend that this isn't happening. And we also, by the way, have the Genius Act, the stablecoin bill is now passed
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into law. So we're not going to undo that. That is that is law of the land. Like Congress just put that into law.
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>> And it's very important because um what uh David Saxs uh my my bestie um
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I think led there was these have to be audited. These have to be above board. We can't have a run on stable coins,
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which let's face it, people anticipated Tether would have at some point. There
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were lots of fines they got. There was these attestations people didn't know if
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they even had the resources they had. And now it's pretty clear you have to keep your assets in treasuries. Correct.
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That's correct. Under the Genius Act that got passed into law last year, US regulated stable coins have to have 100%
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of the assets stored in short-term US treasuries. So I something like 30 days. 30-day treasuries are the max I believe.
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So um that's pretty much the safest thing you can get. You know, you're basically trusting the United States
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government is not going to fail in 30 days, which I think is a pretty safe bet. >> I'm going to go safe bet.
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>> Yeah. And um you know, I've been making this point as well that um you know,
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banks do something called fractional reserve lending. They actually don't store all your money there. They're
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lending it out. That's why they have such high regulatory overhead because there can be a run on the bank and it
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gives them a very unique business model. Um they can basically lend it out. You know the the old joke is like you lend
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it out at 6% um you pay three and you're on the you know the tea time by 3:00 or
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whatever. But um that business model is not available to you unless you have a business a bank license. But in a stable
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coin world with 100% reserves, you don't need a bank license for that and and you
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can give people >> because it's safer, >> right? And we saw this Silicon Valley
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Bank essentially >> had mistimed their allocations with um >> uh treasuries, I guess. And what
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happened? They had a run on the bank. Literally, I was in a board meeting and in the board meeting on I think it was
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like a Thursday and the run happened Thursday afternoon. >> I get a text like get your money out of
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Silicon Valley Bank. I'm in the board meeting. We're having it's on the docket
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like the third thing is to talk about Silicon Valley Bank and we're have 100%
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of our money in there. Two of the board members are like we can't just take all
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the money out of Silicon Valley. They've been incredible partners for 30 years. I
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said how about we take out half so we can make payroll. I insisted. >> Yeah.
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>> Literally that night, boom. Uh and so the key issue now is your business
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model. You you need to have revenue and the revenue from these stable coins is paying some interest and the people who
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are putting their money in there being able to make some interest on their hard-earned capital. Yeah, that's the
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sticking point for you. >> Yeah. And it's not interest, it's a rewards program. This was carefully
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negotiated in the Genius Act. >> And yes, that's that's our view. I mean,
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look, in my opinion, it's actually >> What's the difference there? What what
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the reward program, we should think of it like American Express points. >> Yeah. I mean, there's lots credit card
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reward programs, but the difference legally is that rewards can't be based solely on the balance you're holding.
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The customer has to do some sort of other activity like payments or trading or they have a subscription to Coinbase
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1. So, when customers do that, we actually pass along about 100% of the economics to them uh for holding those
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stable coins with us. And that's a big driver of growth. Now, you know, there's
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always been um this balance between do people want to put their money in money markets or do they want to put it as
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bank deposits. Um I think that that is not this crypto is not really new in that dimension. Like it's just another
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flavor of this happening. And um there's been a lot of hand ringing about, you
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know, this is going to destroy all the lending market. And like I don't think
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that's true. Like money markets are already trillions of dollars and there's
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high depos high yield checking accounts. But these banks haven't had to deal with
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a disruptive competitor, a technology competitor who's really good at what they're doing. So, they're a little bit
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nervous about their franchise. Yeah. >> Is my interpretation. Am I correct?
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>> Some of again, some of them are nervous, some of them are leaning into it as an
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opportunity. I think the latter we want everyone to win here. I think that's
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what um you know, I don't speak for the president, but like my interpretation of
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his comments is that he wants all American businesses to win. There there is a win-win outcome here. Um, but if
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someone, you know, is going to try to undermine his legislation that just got passed in Genius, he'd probably
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>> Is that what's happening now? The banks are trying to retrade the deal.
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>> I, you know, I don't want to like I mean, be careful here. I There's the
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bank trade groups which I believe um are trying to undo the Genius Act. It just got passed into law 4 months ago. And
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for us, that's a red line. I've talked to many others in the industry that for
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them that's a red line. I think we have to accept that that's law and that's
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going to continue to exist. But that doesn't mean banks and crypto companies
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can both win in this new world. >> Yeah. So this is just a classic tale of
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uh incumbents. >> Yeah. Incumbents and and new folks and you want to partner with them. You want
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to enable it? You have a good partnership with Jeremy Lair, old friend of mine at Circle. Are they like the
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default stable coin in Coinbase or how do you think about the relationship with them? How should we think about the
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relationship with them? Yes, we've got a strong relationship with Circle and USDC
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is the largest regulated stable coin because it is compliant under Genius in the US. They're compliant under Mika in
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Europe, etc., etc. Um, you know, there's another one that you're familiar with
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which is still, >> but I think it's in the process of being >> they're trying to clean it up is my
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understanding. >> Yeah. >> So that they can participate. The likely scenario is there'll be two tethers, a
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United States one and then that complies and then there'll be the Wild West one
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outside the US. Is that what you've heard as well? >> Yeah. >> Yeah.
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>> Yeah. And I should mention um we don't have like an exclusive with Circle or
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anything like that. We we actually list other stable coins on our platform. >> Do you list Tether?
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>> We support it in certain ways. Um you know, especially people who want to
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convert Tether, but we we support it. We also support PayPal stable coin. We're
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open to listing others, too. So, we we don't have an exclusive on USDC. >> Yeah. But you're not endorsing it. And
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do you let people trade into Tether or just let them trade out of Tether? Like, how does it work mechanically?
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>> I think it's different in different countries. I want to make sure I get it
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exactly right. But in countries where we're allowed to do it, I mean, we support Tether,
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>> right? >> It's it's nuanced. >> Are you concerned about or have you been
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historically concerned about Tether and like their little bit of a loosey goosey
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approach to regulations and trading? I'm giving it that descriptor, not you. But
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I I would think having it on your platform with regulators pretty focused on it over the last 5 or 10 years and
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this, you know, belief that this thing could all come apart and create a run that to you is just not worth the risk
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to to be too close to it in case it does flip over. Yeah. >> Yeah. We we've definitely gotten
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questions on it. And look, I I want to be careful here. I actually like the Tether guys. I think they've done a lot
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of good things in the world. There's people who really are they're struggling
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with local currencies that are have like 70 100% inflation year-over-year. And so
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there was high demand for the dollar. They got great distribution in a lot of the emerging markets. I actually think
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they've done a lot of good for the world. Um but yeah, it's not currently
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compliant under the Genius Act in the US. And so it doesn't follow those same
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100% reserves and short-term US treasuries is my understanding. So people have to make their own
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determination on that. And I think other countries are following suit in terms of
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cleaning this up. Is there in crypto now a way to give a rating that is sort of objective for consumers to say like this
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one has this grade? >> Mhm. >> And follows these regulations. Hey, this
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one is follows this level. It's a lower grade and this one doesn't follow and
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it's you know it's a memecoin. Whatever. This is the wild west. Like no crying in
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the casino coins. like what what is your responsibility as a platform or opportunity as a platform to like inform
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the people who are participating? >> Yeah. So what we try to do is have minimum listing standards. Um so if we
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believe that there's a cyber security risk to it um the developer could you know rug everyone or if if it's really
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it's illegal from a compliance point of view. You know there's a few different
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areas we look at. So if it meets the minimum bar, we will list it and then we let customers decide it. I don't feel
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like it's our job to be recommending investments, you know, like in the traditional financial world, there's
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like these AAA rated bonds and you know, and I it always felt a little bit like those organizations that do the ratings
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are kind of always be politicized and like >> go see the big short. >> Yeah, exactly. So I don't it's I think
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of it a little bit like um the app stores, right? I mean you or the or let's say Amazon. I mean, you want to
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have the everything exchange. You want to have the everything store. You know, everything that's legal should be in the
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store, but um maybe there's customer reviews we we could add at some point. We actually tried that for a little bit.
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Like if you see a two out of five star thing on Amazon, you can still buy it, but it's kind of at least you're
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informed. We we tried making user ratings at one point. It didn't go that well cuz people were basically voting
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with their whatever they like, you know. >> Sure. They're talking their book.
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>> Yeah. Talking their book. So, um, anyway, we we right now we're in the
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regime of just disclosures and minimum listing standards. >> Yeah. Which crypto projects do you find
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the most fascinating right now? The Bit Tensor one, some of these ones that are popping up that are actually providing
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technological solutions to problems, distributed computing, etc. You I find those fascinating.
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>> They are. I mean, people are trying to tokenize data centers and like oil
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reserves. I think I think the biggest trends happening in crypto right now is number one it's the everything exchange.
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So it's not just crypto you can trade um you're getting equities you know are
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increasingly getting closer to being able to trade uh onchain you know um prediction markets are
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>> you have a partner for that right? >> Yeah we're working with KI currently
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>> is that exclusive or are you willing to put anybody up on the thing? >> It's not exclusive so we're looking at
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others as well I know you guys work with Poly Market or whatever on the show. I mean, I'm a I was one of the original
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angel investors in uh Robin Hood and I think they're doing Kelsey, too. It seems like people are plugging different
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ones in. Poly Market's our favorite. Yeah. >> Yeah. We So, we're talking to Poly
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Market, but you know, we can also list our own um prediction markets. >> Oh, yeah. So, you could fire up your
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own. >> Yeah. So, anyway, we're along I think that Yeah, the biggest trends are all
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assets are coming on chain for trading. Um prediction markets are growing like crazy and stable coin payments are
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growing like crazy. Those are the probably the three biggest trends in crypto right now. When do you think
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stable coins tip into the area of businesses two-part question businesses using it
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for payments? You know, maybe >> uh to reduce friction. You know, some designer does some work for Coinbase and
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makes sure a new logo and you want to send them $25,000. It goes through a stable coin. When does that start to
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happen? And then for consumers, when do people at a poker game start settling up
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with, you know, through their Coinbase account with a stable coin? Yeah. So the biggest growth area over the last year
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has been B2B crossber payments and crossboarder. >> Yeah. Cross border especially because
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there's a lot of these companies you know they might be buying goods from Asia or Europe trying to sell it in
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their shop in Brazil or whatever it is and they have to wait 7 days and there's
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high FX fees and all this kind of stuff to move >> crazy the fees. >> Yeah. Uh so that's been growing really
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nicely. You know we we launched a product called Coinbase Business which is serving lots of small and mediumsized
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companies who want to do crossber payments and invoicing and tax and accounting and all that. How do you find
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those customers? You I mean that that's like a real unique group of people or do
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they just find you? >> Currently they're beating a path to our door. We we actually have a huge backlog
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we need to people waiting to onboard. So we need to staff up that team. >> Um we also launched something called
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Coinbase developer platform uh which provides it's kind of like AWS if you want. So that's like you can white label
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anything like with the banks but lots of other businesses are using that for wallets, trading, payments, staking,
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financing, all kinds of things. You mentioned uh tokenization. I was talking with Vlad. He did a little experiment.
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Hey, why don't I tokenize some OpenAI shares? Sam wasn't Sam Wman wasn't too
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thrilled with that. How do you think about that opportunity? I'm a private market investor. I would love to be able
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to take my early position in Robin Hood or my early position in Uber as it was going up and put it into a market and
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let people trade it. that would be very interesting uh to for VCs for you know angel investors to be
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able to move that around. How do you think about it? >> Well, I think it has to be done with the
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permission of the companies cuz you know if you're if you're a private company
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you don't want your employees to be able to get liquid after one year. you're
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trying, you know, that's why you have vesting. That's why it's it's a
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retention mechanism, right? Let's all build this together and like maybe when
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we go public because there are stories of founders who like took a little secondary too early, then the company
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didn't didn't work out and it's, you know, bad off. >> So, I think what's going to happen in in
00:18:11
crypto is that um in the private markets like first of all, we should make onchain capital formation way easier for
00:18:18
private companies. So, if you want to go um this is this is what we're chatting
00:18:22
with the SEC about and others is like, you know, can you go register a security? Um right now you'd only be
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able to raise money from credit investors in the US. I know you you and I agree on this like we'd like to expand
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how you can become an accredited investor. >> And there is a bill right now that's
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working its way and it would basically mean the SEC and and they've already been charged with this, but they they I
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don't know if you've uh studied the SEC at all. They tend to take their time and
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then not do what they've been told to do. Uh, and that was one of the things
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they were supposed to do is create an accreditation test. >> Uh, well, this I say this SEC is
00:18:54
actually moving very quickly. But this one is Yes. >> Yeah. Um, but yeah, I think that would
00:18:58
be a more fair way because otherwise it's kind of like a regressive tax like
00:19:02
only rich people can get richer on private investments. But um, anyway, I think onchain capital formation is going
00:19:07
to be massive for private companies. I think eventually like you'll actually
00:19:11
just be able to go public totally onchain too. And um yeah, these markets are just going to get
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>> would lower the cost massively, reduce the friction and increase the democratization of wealth creation.
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>> Yeah. >> If you think about when you were a private company. >> Mhm.
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>> Like you had all this pent-up demand, people trying to buy the shares like
00:19:31
crazy. They were all doing backdoor kind of shady stuff, popping up SPVS. I don't
00:19:35
know if you've been following the SPV market now, but there's like it's turned
00:19:39
into a boiler room. It's no longer Chris Saka >> representing Twitter and you know doing
00:19:44
an orderly thing or >> Elon doing every six months an orderly thing where SpaceX keeps control of it.
00:19:50
Now people are going out raising money from dentists and civilians high net worth individuals to buy SpaceX or
00:19:56
Andrea or whatever it is and then they go try to find the shares and they charge them 10% loadin fee no carry.
00:20:02
>> Mhm. >> I mean think about how crazy that is. >> Yeah. there's such high demand for some
00:20:06
of these large private companies and >> you know it's kind of a good example of
00:20:10
like the unintended consequences of higher regulation sometimes. >> Yeah. >> Like um you know Sarbain Oxley and all
00:20:16
that kind of stuff really cut down the number of how how long companies stayed private before they went public and then
00:20:21
yeah I mean Uber and Airbnb and a lot of these things like they all the money was
00:20:25
made by private investor or credit investors like you know yourself and then when they went finally went public
00:20:29
it kind of went sideways. >> Oh yeah. Airbnb, Uber, they all had like a 5-year uh indigestion period, I would
00:20:35
say, you know, and it it and some people, I think Instacart wound up going from 30 billion down to 10 billion when
00:20:41
they went public and it was like, okay, we got to dig out of a hole the last series of investors. And that is the
00:20:45
unintended consequence of this because you don't have anybody setting a proper
00:20:49
valuation for the company in some reasonable way. What about funds? You know, I get approached by a lot of
00:20:55
people offshore, etc., hey, take your next seed fund. You're going to do a $50
00:20:58
million fund. to put it on the chain. And then, hey, if you were one of my LPs and you were like, "Oh, I need
00:21:04
liquidity. I could just sell it to somebody else or if I was an LP in a Sequoia fund and I wanted to sell you
00:21:09
the interest, you could buy it for me and we could just >> take our wallets out and zip zip."
00:21:13
>> Yeah, I think that's that's absolutely going to happen. I mean, Coinbase
00:21:16
launched a product actually called Coinbase tokenize. So, we're helping any fund or real estate project or anybody
00:21:21
who wants to tokenize their products and it just democratizes access. It increases demand. It gets rid of a lot
00:21:27
of the back back office fees, gets rid of the settlement risk because it can be settled instantly on chain and there's
00:21:32
some very innovative company. I mean like Black Rockck, Apollo, like these the top funds in the world are putting
00:21:37
they they've come out publicly and said they want to tokenize every single one
00:21:39
of their products. It's it's absolutely happening. >> How do they keep control of it? Because
00:21:44
you have this more liquid back to consequences or this would be downstream effects, second order effects, third
00:21:51
order effects. What are the third second and third order effects that would happen if a venture fund or a REIT were
00:21:57
onchain? >> Have you thought that through? >> Have you thought it through? Yeah.
00:22:01
>> Yeah. I mean there so there's different types of funds. Um some are going to be
00:22:04
only available to institutions accredited. Some would be open to retail and so um for the retail side I mean you
00:22:10
could actually get I don't know like tens of millions of people around the world in five minutes to all put in in
00:22:16
the average price might be $100 or $1,000, right? So it starts to really democratize access. I mean, we actually
00:22:21
just published this um this report recently and people have heard about the unbanked, but there's actually 4 billion
00:22:28
adults who are unbrokered as well, which means they don't have any ability to
00:22:30
invest in these highquality assets. So, it's like this is the engine of wealth
00:22:34
creation for capitalists, you know, like you and I. A lot of people are just stuck. The only way they can earn is
00:22:39
from their labor, right? and they they want to probably put like even if they're they have $100 or $1,000, they
00:22:46
might want to put 10% of that into the S&P 500 or Coinbase stock or whatever,
00:22:50
Nvidia, whatever. And they can't do that because >> but they can use prize picks. They could
00:22:54
use some other thing. No, and I like prize picks. I use prize picks to bet on Nick's parlays. Uh but they they wind up
00:23:01
putting it somewhere else and why not being able to, you know, if they want to bet on the Knicks, that's fine. But or
00:23:06
go to Vegas, that's fine, too, and play in a poker tournament. Yeah, maybe they
00:23:08
hear about this company LinkedIn because they work in the HR department. Yeah. And everybody in the HR department's
00:23:14
over the moon about it. That rank and file 75K person working in HR, they understand what the next big product
00:23:23
will be. They'll know Indeed or LinkedIn's going to work and they can make a life-changing bet with just
00:23:28
$1,000. They make it a,000x return. >> Yeah. Yeah. I mean, there's a financial
00:23:32
literacy component to this as well. And I think actually the AI agents are now getting really good. that we've
00:23:36
integrated one into Coinbase app. It can kind of teach people about dollar cost averaging and you know tax loss
00:23:41
harvesting. So there's a the financial educa literacy is is a part of it but
00:23:45
then yeah let's make high quality investments available to them and democra it's just like lifting people
00:23:52
out of poverty. It's great. I mean you're thinking about the whole globe
00:23:55
but just thinking in the United States a lot of what people are upset about and the topic we've been talking about a lot
00:24:00
um is the rise of socialism in >> um New York specifically California. I'm
00:24:06
not sure if you're still a resident. I won't put you on the spot here, but
00:24:09
>> considering options >> considering I mean >> as we all are.
00:24:12
>> I two years ago I moved to three years ago I moved to Austin. I was like I'm
00:24:16
done. I just the social issues and you know I saw the writing on the wall. I mean I actually think there's a chance
00:24:21
>> that the that California goes bankrupt and I said that on the podcast like this
00:24:24
feels like it's trending towards insolveny. I never thought they would get to the wealth tax or just seizing
00:24:29
people's assets. >> Yeah. >> What's your take on all that? Some I was reading something this
00:24:34
morning which said that actually just the people who have already left which is probably like
00:24:39
>> my my estimates would probably be 20% of the billionaires have already left in
00:24:42
California >> that it's already created a negative 10 billion tax hole even if even with the
00:24:47
amount that they hope to raise from the people who stay. >> Yeah. >> So it's one of the biggest cell phones
00:24:52
I've ever seen. Um it's a disaster and I uh you know I'm torn actually because
00:24:58
part there's always kind of this question of voice or exit right like voice do you try to fix it from within
00:25:03
exit do you leave like you did >> and I think um >> the incentives are strange because on
00:25:08
the one hand like I love California um but on the other hand it's been kind of
00:25:12
um it's like an abusive relationship you know it just keeps coming back with
00:25:15
another thing and another thing >> and in some ways like if you do make the
00:25:18
decision to leave um you don't have really much incentive to try to fix it at that point you actually want to get a
00:25:23
lot of the the builders and the top talent out of California at that point and just all resettle in a new place
00:25:29
that is welcoming to us and businesses. >> Yeah. I don't think people understand
00:25:34
how easy it is for somebody who's in a certain strata to is already operating
00:25:40
globally. I'm on planes and on four different continents every year, you know, like I it doesn't matter where I
00:25:47
am. It matters my wife and my kids are happy and they love the place we live and we love Austin. And then the thing
00:25:52
I've seen you the probably one of the hardest thing you had to deal with with
00:25:56
your employees and your team at Coinbase is the price of their housing. >> Like how many times did you try to
00:26:01
recruit somebody to come to California >> and it's like a family of, you know,
00:26:05
four and they need private school, they need a house and you're like, "Oh my
00:26:08
god, what is their nut going to be here?" >> Yeah. My their nut. I love that. There's
00:26:14
a great South Park episode on that. But um yeah, I mean I you're right. That's a
00:26:18
major barrier whenever we make an offer to somebody um in California, you know, or for New Limit the Biotech, like
00:26:24
that's always like, well, my cost of living is going to double like you need
00:26:26
to pay me more. So, it's getting expensing through to you, the business owner.
00:26:31
>> Yeah, it does. I mean, San Francisco had this um kind of like revenue based tax
00:26:36
that was very punitive on um financial services companies. Stripe Stripe moved out when that happened.
00:26:42
>> I think Mark Benoff regrets supporting that one. >> Yeah, I think so.
00:26:45
>> Yeah. But in fairness, he did want to finance the homeless industrial complex,
00:26:51
which has been completely ineffective in reducing the number of homeless individuals because
00:26:56
>> they're not homeless. They're addicted to drugs. >> A home doesn't help that problem.
00:27:01
>> Exactly. I mean, I I'm I'm preaching to the choir here, but yeah, I think people
00:27:05
would have a lot more tolerance to pay higher taxes if they felt like it was working. But the history of the last 10
00:27:11
years in California is that the budget has gone up dramatically and the services have gotten worse. It's like
00:27:16
it's actually creating the wrong incentives. Like the more ser the more money we spend on homelessness, the more
00:27:22
homeless people are. So, you know, and then the waste and fraud. Oh my gosh. Like you guys Nick Shirley and all that.
00:27:28
I think >> what do you what I mean what could we even guess is the level of abuse in
00:27:33
California? It's going to it's going to make Minnesota look >> like peanuts. Such a big economy with so
00:27:40
many NOS's and so many homeless organizations taking down hundreds of millions of dollars in San Francisco
00:27:46
alone. >> Yeah. >> Um so tell everybody about the the side hustle your your other company. Yeah.
00:27:53
That you >> Oh, the biotech. >> Yeah. >> Yeah. Well, when Coinbase went public in
00:27:58
um 2021, you know, I got some liquidity from that and I just I thought it through and I was like, "All right, I
00:28:04
want to be CEO of continue to be CEO of Coinbase. Being a public company CEO is a really cool thing. I just feel like
00:28:08
we're at the beginning of our journey." >> Um but I also felt like I wanted to
00:28:11
start to use some of that capital to go after these like big bets, right? I was kind of a little inspired by Elon
00:28:16
actually. I think, you know, he did the thing with um PayPal and X and um and then, you know, he went into these like
00:28:23
the world of atoms, not bits, right? It's actually like in software is more forgiving. Startups are all hard, but
00:28:28
software is a little more forgiving because you have higher margins. The world of atoms is much less forgiving.
00:28:33
So anyway, I was lucky enough to meet um some really amazing co-founders um that
00:28:38
came together with this idea in the longevity space and it's called, you know, there's the fundamental science
00:28:42
behind it is called epigenetic reprogramming. It's sort of you can reprogram your cells to restore function
00:28:47
they had when they were younger. There was some really cool research being done. I hosted a couple dinners. Anyway,
00:28:51
I decided to uh fund these guys and a bunch of other people have invested now and uh I'm a board member. I've been
00:28:57
helping them in in small >> and the name of it is >> New Limit. Yes,
00:29:00
>> New Limits. >> So, they've been incredible progress. It's
00:29:03
>> when will they have a product? This feels like a 20-year investment, not a
00:29:07
two or five. >> Yeah. Well, biotech does move more slowly, but um it's moved faster than I
00:29:12
would have thought. I thought this was going to be like five years of just basic research. M um but it turned out
00:29:17
actually within the first uh 2 or 3 years they were able to successfully demonstrate reprogramming of human cells
00:29:23
to restore function they had when they were younger and the first drug candidate is probably going to go into
00:29:27
clinical trials next year. So >> amazing. >> Yeah, >> that's super rewarding. Five minutes.
00:29:32
Yeah. Okay, great. Um so coming out of Davos, uh what's your take on the state of the world? Everybody
00:29:43
when they get here seems to uh it seems like the ESG DEI kumbaya stuff >> has switched in the last year or two to
00:29:53
you know brass tax deal making whether it's between countries um and businesses like this is turning
00:29:59
into a business conference that used to be this is what everybody's telling me
00:30:03
on the streets in in the you know in the houses >> um it's it's about business now and on
00:30:09
the margins there's a patina of you know globalization versus nationalism. What's
00:30:14
your take on the state of the world in 2026 talking to regulators, talking to, you know, people who work in government?
00:30:22
>> Mhm. >> I think you're right. I mean, I've only been at Davos once before, but it did
00:30:27
feel more um, you know, how do we make a global government? How do we do lots of
00:30:31
ESG and DEI? And that's really not what anyone's talking about now. I think
00:30:36
partially it's because of Larry Fing coming in, the new leader defect, you know, more or less. And um I also think
00:30:42
it's because of Donald Trump. I mean, >> yeah, he shook it up. >> Yeah. Like the numbers that the United
00:30:46
States is putting up in terms of GDP growth and low inflation and just that business environment. It's like, hey,
00:30:52
how do we all win? That's how you create prosperity for everyone in society. Um I
00:30:55
do think it actually benefits everyone like you know even the poorest people in society are they do the best in high
00:31:01
economic freedom countries that are anyway. >> Growth solves a lot of problems.
00:31:04
>> It does. It does. I mean, and the growth is objectively calling balls and strikes
00:31:10
spectacular. We have not seen this level of GDP since we pumped a bunch of money
00:31:14
and printed a bunch of dollars during co like >> 5.6 GDP is pretty spectacular. Let's
00:31:19
hope it keeps up. Unemployment very reasonable 4.6 lowest of our lifetime. I think 4.3 was the lowest it hit.
00:31:26
Inflation, yeah, closer to three than two, but you know, the actual average has been like 2.8 2.9. So the two is the
00:31:32
target. >> Yeah. So we're right around the average. not hitting the target yet, but I think
00:31:36
we'll get there. So, yeah, it seems like >> growth does not come from government
00:31:40
spending, right? That's like the key that this is this Keynesian economic argument I think is basically wrong.
00:31:45
Like growth comes from uh having like deregulation, having lowcost energy, allow the private markets to bill, let
00:31:53
them have clear rules about what's allowed and not and then create a level playing field. Everyone competes, the
00:31:58
consumer benefits, the b the companies benefit, all the employees, the shareholders. Capitalism is like the
00:32:03
biggest win-win. You know that someone had a great rant about that recently. >> Yeah.
00:32:06
>> And so we're seeing the private companies in the United States really
00:32:10
cook. It's great, >> right? And if you're cooking, uh, you create more jobs, hopefully pay more
00:32:15
taxes, all that just starts the cycle in the right direction. How do you I'll end
00:32:19
on AI. Big debate on the pod. You haven't been on with the four of us in a while, but when somebody gets sick,
00:32:24
we'll definitely rotate you in. Everybody loves when you are on like the the quartet. You're like a fan favorite,
00:32:29
by the way. Um, but I'm curious what you think about AI and job displacement. Sax
00:32:34
and I have been debating this. When is it going to be here? Is it here? Young people can't find jobs, but we're still
00:32:39
a pretty low unemployment rate overall, but then Elon's position and Bernie Sanders position is in sync. Hey,
00:32:46
listen. It's going to be a lot of job displacement. So, how do you think about
00:32:50
it? Obviously, Amazon also is the one I'm watching because the idea that somebody's going to drive packages or
00:32:55
pack packages in the age of Optimus and Robo Taxi and Whimo sounds crazy. >> So, those jobs are going away. How do
00:33:02
you think about job displacement? And what are you seeing with the most AI first employees in Coinbase?
00:33:09
>> Yeah. Well, just zooming out for a second, I think like crypto and AI are
00:33:12
the two most important technology trends happening in the world. And what's cool,
00:33:15
most people don't realize actually they're going to come together because
00:33:18
AI agents need to get work done and they have to do payments >> and the whole traditional financial
00:33:22
system is built around kind of knowing your a human behind every product with your you upload your
00:33:27
>> Oh, know your customer. Yeah. >> Yeah. So AI agents I think are going to
00:33:30
use stable coins and wallet crypto wallets. >> Know your agent. >> Well, I don't even know if you need to
00:33:35
know the just but yeah. Anyway, that's one of the important trends that we're
00:33:38
trying to help happen in terms of job displacement. Um I don't know. I I maybe
00:33:43
this is a bit of a techno optimist take um but I actually think you know if you go back and look at like the 19 early
00:33:48
1900s I think it was like 80% of the US population was in was working in agriculture
00:33:54
>> and so that's like hard manual labor out in the fields and when agriculture got
00:33:59
automated you know now it's like 3% or something of the workforce working >> that happened over 30 years. Yeah.
00:34:04
>> Yeah. And so I think they would look at what you and I do for a living like
00:34:09
we're just having a cool conversation in Davos talking. They'd be like that's not
00:34:12
a real job. Like real job is like manual labor in the fields, right? >> Uh you just you're on vacation all the
00:34:17
time. But we we think of it as a job and you know people who are like typing on a
00:34:20
keyboard all but you get to sit in an air conditioned office or whatever. >> It's stressful. That's for sure.
00:34:24
>> It can be stressful but that's just I'd rather be doing that than be doing
00:34:28
backbreaking labor in this in the sun digging a ditch or something. Right. So I think that job displacement is like
00:34:34
not a bad thing actually. Um if it means that people can do new kinds of work and
00:34:39
new kinds of job. Now is there going to be a transition period? Yes. I I basically I think if the AI uh plays out
00:34:45
as we all think it will with robots and a lot of there will be job displacement but it means that we'll be in a world of
00:34:52
more abundance and people are going to have jobs that's there like they're
00:34:55
streaming on video games on YouTube or whatever. Like I don't know what it's
00:34:58
going to be but >> or they'll think about the future. they'll think about like great philosoph
00:35:02
you know philosophical works might be written uh because we don't have to burden ourselves with like the tedium of
00:35:09
packing boxes. So I I'm basically an optimist on it. I think it'll be good.
00:35:12
>> I'm pretty optimist about it as well. I just having watched the robo taxi
00:35:18
self-driving thing >> and just watching the velocity that it's getting better and having watched the
00:35:23
Uber story up close for 12 years. I'm like yeah that's going to happen in six.
00:35:28
Mhm. >> I think it's just like it's going to just >> Yeah. It feels to me and the thing I'm
00:35:33
starting to see in the field is you have in Wuhan there and and Beijing, they're
00:35:38
having protests and they're saying, "Well, we're just going to give out a
00:35:40
certain number of self-driving license. We're going to contain it." >> And then you have Boston and a couple
00:35:45
and California now. They're saying, "Hey, listen. We're only going to allow
00:35:48
a certain number of robotoxies." Or Boston's like, "We're not going to let
00:35:50
you have them here. We're going to protect these jobs." So, this is going
00:35:53
to become, I think, one of these uh very class, you know, debates we're going to
00:36:00
have over the coming years. But what about employees in the company? You have the same number of employees as you had
00:36:04
a couple years ago. You overhired for a bit maybe or hiring for growth. >> Mhm.
00:36:09
>> Now, how do you think about hiring versus hiring and training young people
00:36:13
versus just automating stuff or just having those AI? You must have some people on the staff who are using claude
00:36:19
co-work or something and they're just like 10x knowledge workers. Not developers are obvious, but yeah, talk
00:36:26
to me about knowledge workers and what you're seeing with your most AI first
00:36:28
employees. >> Yeah. So, one of the big pushes we made in the last year was um we got our own
00:36:35
internal hosted AI model that was connected to all of our data sources, right? So, it's like every Slack
00:36:40
message, every Google doc, every Salesforce data confluence, you know. So now um this is all linked up in one like
00:36:48
the data is all aggregated and you can ask these agents. So every team's legal
00:36:52
is sorry every team is using it um legal finance everything just >> it's like the oracle of Coinbase.
00:36:57
>> Yeah. And I've started to ask it really it's not just like prompting it hey can
00:37:01
you write this kind of memo for me or something. It's like I'm asking these AI
00:37:05
agents now um as CEO like what should I be aware of um in the company that I might not be aware of? and it'll tell
00:37:11
me, "Did you know that like there's actually disagreement on this team about
00:37:14
the strategy?" And I was like, "Actually, I didn't know that cuz it can
00:37:17
read every Slack message in every every Google doc." And then, you know, I've
00:37:21
been prompting it like I actually Toby on my board, he he he said this is like it's call he's calling it reverse from
00:37:27
Shopify. >> Yeah. Yeah. He he said this is like reverse prompting. So, instead of
00:37:30
telling the AI agent what you want to do, you ask it what you should be thinking more about,
00:37:35
>> right? And >> it's a mentor. >> Yeah. It's like a coach.
00:37:38
>> Yeah. Like, what could make me a better CEO? And it's like, well, I notic I
00:37:41
looked at all how you spent your time in the last quarter. Here's how you said
00:37:45
that you wanted to spend it, but you actually spent like 32% of your time on this instead of 20. Um, and it'll I've
00:37:51
asked it other questions like, you know, what's the thing that I changed my mind
00:37:54
on the most over the last year? Things like that. So, it's been it's now
00:37:58
becoming like it'll it'll like prompt you with information you should be
00:38:02
thinking about instead of the other way around. >> I recently did this and I I don't know
00:38:06
if you play with Claude Co yet. Uh, have you played with it? this week play with
00:38:11
there's claude opus 4.5 or something. >> Yeah. But there's co-work which is kind
00:38:15
of like >> you describe what you want to do as a knowledge worker and it starts to build
00:38:20
it. So instead of doing vibe coding and saying hey I want to write code for this
00:38:23
you just describe like what do you want the end application and the result to be
00:38:26
and then it kind of like a wizard kind of takes you through it. It's pretty scary cuz I connected my notion to it,
00:38:31
my Slack to it >> and my Google Docs and it did the same type of thing. I was like, "Tell me
00:38:35
about myself." And it was like, "Whoa, you need to spend more time with your
00:38:38
founders that are winning as opposed to more time with your internal team." It
00:38:41
was >> really interesting to analyze your uh your teams. And I think that's going to
00:38:46
be like the future of this. All right. Listen, Brian, you got a lot more meetings to do. Uh thanks for coming on
00:38:51
the program. >> I'm thrilled because my guest started building AI chips seven six seven years.
00:38:58
Six, seven years before Chat GPT was launched. Andrew Felman is of course the CEO of Cerebra Systems and they are
00:39:06
building wafer scale engine WSE. Yep. >> That's the category of chips you're
00:39:12
working on and they're for inference. >> For inference or for training both
00:39:16
>> or for training or both. And you have one with you? I do. >> So here is a way for scale engine.
00:39:21
Remember usually chips are the size of a postage stamp. >> Yeah. >> And so this is say 56 times larger than
00:39:28
a B200. >> Wow. And it's a 4 trillion transistor part. And for AI work, big chips process
00:39:35
more information and they deliver results in less time. So you can faster results for your your query.
00:39:42
>> And what does that cost for the typical system today? And how does it compete
00:39:46
with like the H100 200s? >> Well, the first one cost us half a billion to make.
00:39:50
>> Yes. >> First one I've heard is the most expensive. >> Turns out the first one's the kicker,
00:39:54
>> right? >> Yeah. >> These come in a system. >> All right. And we can deliver the system
00:39:59
on premise. Yeah. Or you can use it in our cloud. >> On premise they're about a million
00:40:04
million and a half depending on how you have it configured. And on in the cloud you can rent it by the token. So by the
00:40:11
million tokens it'll vary by different models from 50 cents a million tokens to
00:40:16
several dollars per million tokens or or you can rent it by the month or the year. How did you know
00:40:23
seven years before Chat GPT was launched? Or did you that the AI revolution would be this fast, furious,
00:40:31
you know, unstoppable? I mean, it and has what's happened in the last two years even surprised you?
00:40:37
>> For sure. I I think Yeah, I think anybody except maybe Sam and Ilia. >> Yeah.
00:40:42
>> Uh who really saw it? M >> you know we talked to them in 2015 and
00:40:46
and what they were saying is now it was unbelievable how how right they've been
00:40:51
but I I think what we saw was the rise of a new computational problem called AI and it would put new and different
00:40:59
pressure on a processor and we saw this on the horizon and we said what would happen if this got giant
00:41:07
>> and we had no idea how big it would get or how fast it would come but as a
00:41:11
computer architect you try and think about could I build a machine that's way
00:41:15
faster at this new thing >> and will there be enough of it to build a business around? And so we saw AI on
00:41:21
the horizon. We said to ourselves, could we build a a processor that would be unique in its performance? Could we
00:41:29
build something not one or two or three or five times faster, but 20 or 50 times
00:41:34
faster? And we came to believe we could. We chose an approach that that solved a
00:41:39
problem that had been open in the comput industry for 75 years. Nobody had ever built a chip this big. Um, many smart
00:41:46
guys had failed. Uh, and we delivered it and it's blisteringly fast. >> What were the first applications? You
00:41:53
know, Nvidia got to perfect their compute >> and really their company off of the
00:41:59
backs of video game >> uh players, then Bitcoin and crypto. It was almost like there were just a number
00:42:08
of way points before AI emerged. You didn't have those. >> We didn't have that. And you know, if
00:42:14
you look at Nvidia's stock price from 2004 to 2010, it was flat. >> Yeah.
00:42:19
>> Right. And um they were trying to find a new market, right? That they had a lot
00:42:23
of the graphics market and that market was sort of flat. They they found love with gamers. Um they tried to go into
00:42:29
the supercomput world. Um we were focused entirely on AI. And so at first we we found love with with the national
00:42:39
labs, with the military, uh with some pharma. Um >> what were the applications they were
00:42:46
using? >> They were training various types of models. >> Got it. And this is before large
00:42:51
language models. >> This is before language models existed. >> So they were doing models for sequencing
00:42:58
you know proteins. >> Sequencing models. They they were doing different forms of vision models. they
00:43:05
were doing uh work at the at the edges of high performance computing and AI. >> What is now um taking the most compute?
00:43:16
We see a lot of applications now images, video production, the training of the models, um and really deep learning,
00:43:26
deep thinking, I guess, where it's firing off many many threaded jobs. Which one of those is the most compute,
00:43:33
the most limited? Right now, >> deep research is deep research uses an enormous amount of compute.
00:43:40
>> Explain to the audience what happens when they do one of those deep research
00:43:44
queries. As an example, I've been playing with the latest Claude and they have um a co-working Yep.
00:43:51
>> co-pilot type application and I made a prompt every time we have a guest on the
00:43:55
podcast >> and I had it do, you know, maybe 15 or 20 steps every podcast you've been on,
00:44:02
every news item, a timeline, >> right? >> And it was unbelievable when it makes
00:44:08
this document. It's better than anything a human has ever made for me. And I've
00:44:11
been doing interviews for 20 years. lots of pretty smart assistants whose job that exact thing was.
00:44:16
>> And when I tell you they would ask for 48 hours to do a dossier that was 20% of
00:44:22
what this does in under 10 minutes, I I'm I'm not even joking. And and last
00:44:27
year I told them like you can use it to kind of get ideas and get some links, but you know, keep doing it the old way.
00:44:33
So it kind of cut their time from 16 hours to 8. Now it's 16 hours to I don't
00:44:38
need them. >> Literally don't need them to do this work. Right. Now imagine if you could
00:44:42
get it in 10 seconds. >> Yeah. >> Right. That's what we do. >> Yeah.
00:44:45
>> That's it. Exactly. And so what what happens remember we we make AI with
00:44:49
training and we use AI with inference. All right. And that's the simplest way.
00:44:54
The reason inference is going through the roof is because everybody's using AI. Yes.
00:44:58
>> All right. A task that you kicked off, right? It starts a bunch of little
00:45:02
threads and each of those asks queries and each of those queries deliver results that are the input to other
00:45:08
queries. So you've got a cascade of queries that is going on. >> It's wild.
00:45:14
>> And each of those requires more compute. And so you've got, you know, 20
00:45:20
different queries being kicked off. Each query asks 20 queries. Each one of those
00:45:24
requires 10 or 15 or 20 seconds to get done in traditional comput. So you you have this giant waterfall of time and
00:45:32
answers. And we built this part so you can get all those answers back in 4 seconds in 10 seconds. And when does
00:45:39
that happen? You know, right now it seems like when I do these kind of deep research,
00:45:43
>> it's, you know, grab a cup of coffee time, >> right? >> 5 minutes,
00:45:46
>> right? >> Not 15, but it seems like about 5 minutes is what it averages. When you're
00:45:51
doing an image or a 5-second video, it seems like it's 90 seconds or so. >> When does that come down to, you know,
00:45:58
the experience we had with dialup going to, >> you know, fiber? >> That that's the the perfect analogy,
00:46:04
right? When when the internet was slow, Netflix delivered DVDs and envelopes. I know you remember this, right? Oh, I do.
00:46:11
When Netflix got fast, when the internet got fast, Netflix didn't get better at
00:46:15
delivering DVDs. Netflix became a movie studio. >> It enabled them to be something
00:46:20
different. It wasn't a change in degree. It was a fundamental change in kind. And
00:46:24
what speed does for AI is the same. So, we have customers uh like Cognition who use us to power their coding engine.
00:46:34
All right. And if you read the tweets and you read people's comments, they're
00:46:39
they're odd. There is zero latency between their requests and their answers. So they can stay in the flow as
00:46:45
they write code. All right. And so this is the idea. The idea is you shouldn't
00:46:49
have to wait at all. And uh Claude is not a anthropic is not a customer, but we recently announced OpenAI,
00:46:57
>> right? They were original investors. They were >> and now they've just put in a major
00:47:01
purchase order. they have and this is really exciting and part of it I think was because what we could do is we could
00:47:07
deliver extraordinary speed so that the user experience changed >> and as we know having watched Google
00:47:13
Larry and Sergey Marissa the team over there came to a conclusion when we shave off milliseconds it's the number one way
00:47:20
we get usage to go up >> that's exactly right published that paper years ago that said even
00:47:24
milliseconds even even amounts of time that the individual user doesn't recognize
00:47:31
noticeable That's exactly what is it GM? What is the psychological just notible
00:47:35
perception? I believe your mom would know. She's a behavioral >> She would know.
00:47:40
>> Just noticeable. It's 15% of whatever the number is. So like if you could cut
00:47:44
15% off the time people they use it more, they leave less. Yes. You know, there's a uh Paul Graham had a had a
00:47:52
great tweet. He said, "I I' I'd use Google half as much if Chat GPT weren't
00:47:56
so slow." >> And if you think about that, that's what happens, right? while you're waiting for
00:48:02
Claude or you're waiting for Chad GPT, you you get a coffee or you poke around
00:48:07
somewhere else and you've lost the customer. That's the cost of being slow
00:48:12
is the customer has gone somewhere else >> or you do what I do which is I have a
00:48:15
nice wide Dell monitor. >> I have three browser windows open. I pay for all three services. I have them all
00:48:22
Gemini, I got Claude, I got chat. I pay for all of them. I'm paying probably
00:48:26
close to $600 700 personally a month. So, I'm spending 10,000 a year just for
00:48:31
me, >> right? >> And I just take the same query and I go bing bing bing bing and I start them all
00:48:37
>> or start them all. And >> I'm probably burning like 10 trees. I
00:48:41
mean, there's it's probably probably being a little greedy. >> It's it's not 10 trees, but um
00:48:47
>> it uh I I think that's a really interesting way to manage how slow it
00:48:51
is, >> right? >> And so we exist to fix that problem. And what what we partnered with OpenAI to do
00:48:57
is is to to deliver blisteringly fast speed across the world's most popular models.
00:49:02
>> What's the scope of the deal? Uh >> what we announced was 750 megaww
00:49:08
>> is what was announced. >> When did we switch from talking about the number of chips, the number of units
00:49:16
being sold to the amount of power being sold? It's a little bit confusing for
00:49:20
folks and it started probably about last summer. So actually what happened was that the change has been coming a lot
00:49:25
longer. We used to talk about data centers in terms of square footage. I got 100,000 square foot data center,
00:49:30
>> right? And now nobody cares how many square feet they you have. They care
00:49:33
about how much power you have. So the limiting constraint on data centers is always their power footprint. And right
00:49:39
now for large deployments, the limiting constraint is how much power can be delivered. So by talking about how many
00:49:46
p how much power is delivered, you're talking in in the unit of the limiting
00:49:50
constraint. >> Mhm. And so the limiting constraint is power. We're trying to find this huge
00:49:56
amount of power uh for OpenAI. It'll be delivered over several years. >> And you're responsible for the power as
00:50:02
well or is that like a joint effort? >> So uh it's a cloud uh deal. So
00:50:07
>> Oh, so they're utilizing your cloud. So you've got to do all the work.
00:50:10
>> We are we are building the the cloud infrastructure for it. >> Got it. Where are you going to where are
00:50:14
you building your data centers? What's the best location here in 2026 to be placing these things? Is it overn gas?
00:50:20
Is it near hydro? What's the state of the art now? >> So the the the cheapest power in the in
00:50:25
the world is is hydro. Yeah. >> Without question. After that is natural gas.
00:50:30
>> And so where places have natural gas, you you have an abundance of relatively
00:50:34
lowcost power, >> which is Texas. >> West Texas, Wyoming, um outside the US,
00:50:39
uh in the Caribbean, in Gana, you have a huge amount of natural gas. You do in uh
00:50:44
you have geothermal, which is its own thing in in the Nordics. Um, but natural gas is a is a very inexpensive way,
00:50:52
particularly if it's coming from uh as a byproduct from from petroleum mining
00:50:57
where where what you have is it used to be what's called flare off gas. They
00:51:01
used to just throw it away. They used to just burn it at the top. Bitcoin miners
00:51:04
found the Bitcoin miners found that, right? And so, we'll take that. >> That's right. Like, whoa, don't make
00:51:09
that fire make >> Yeah. >> So, basically, you look for the existing flare off. And then tell me about hydro
00:51:14
because it does seem to me that people knew that for a long time they were moving data centers there. Heat still an
00:51:21
issue with your chips and others. >> We're water cooled and and so water is
00:51:25
an extremely efficient coolant. >> So, you know, we we we knew early on that we'd be going to water. We were
00:51:31
some of the first production AI systems to use water. The TPU early on moved to water as well. Before that, there'd been
00:51:37
some water cooling, mostly in the the Department of Energy supercomput labs that they'd used some water.
00:51:43
>> Does it matter because I remember early on when people were talking about water
00:51:46
to cool 10 years ago, the source of the water and how cold that water is coming in or just water's cool enough?
00:51:53
>> No. >> And you're fine. >> It depends on on your particular design,
00:51:58
>> but you'd like cooler water. >> Sure. >> Is better. >> So, Alaska and Canada feel pretty good
00:52:03
about that. or or you bring chillers or or you cool the water, >> right? You can often you can take
00:52:09
general groundwater or other forms of other locations for >> there's a huge misperception today that
00:52:15
this water is not recycled and that AI is using all this water when it's it's
00:52:20
not even comparable to golf courses. Let's say >> first golf courses are are extremely
00:52:25
water inefficient. Most of our data centers use uh a closed loop, >> right? So we we're we're passing the
00:52:33
water by the back of our chip. They pull the heat off. They warm the water. The warm water goes down and through a
00:52:40
closed loose system is chilled and pumped back. And so you're not using >> and the water is not damaged.
00:52:47
>> The water's not damaged. It's just >> not like some chemicals or anything that
00:52:50
gets put into them. >> No, not at all. >> There's a lot of misperceptions about AI
00:52:54
right now. There's a bit of a it seems like it's almost like there's
00:52:59
some dark PR forces at work trying to make the data center build out seem worse than it is. And then there's also
00:53:08
I think maybe some valid concerns around jobs. When you look at each one of those
00:53:12
issues, what do you think is, you know, the ones that are most frustrating as an
00:53:17
AI executive building data centers? >> It's a really good point. I think some
00:53:19
of the hyperscalers sort of they they made a bad call in the way they they went into some of these rural
00:53:26
communities. Right. So what are you looking for? You're looking for a place that land is cheap that has an abundance
00:53:30
of power and they went to these communities and they didn't do a good job talking to people.
00:53:36
>> Right. >> Right. And >> tech people didn't do a good job talking
00:53:39
to humans. Right. What a surprise. And they went into these communities and they cut deals with the power company.
00:53:45
And the power company was looking to build new infrastructure to support them. Yeah.
00:53:50
>> And traditionally the regulated power industry would then advertise that cost
00:53:55
over 20 or 30 years. So they ended up increasing >> the local people's power rates, right?
00:54:02
And so the people got upset and that very reasonable, very reasonable. If instead
00:54:06
>> you'd have gone in and said, uh, look, great. We we we're going to be good
00:54:11
citizens. We're going to be big taxpayers here. Let's build more schools. We we can build a school for
00:54:16
you. It's it's a rounding error in the cost of this facility. We're going to
00:54:21
make a bunch of construction jobs and we're going to be good citizens. Yes. >> Um they would have had a very different
00:54:27
approach. >> And not only that, they kind of were a little heavy-handed early on where they
00:54:31
said, "We're going to play three communities off each other and who's
00:54:35
going to give us the biggest tax discount?" Right. >> That was another cell phone mistake.
00:54:39
Yeah. >> Now, what I just saw is that Microsoft just put out, >> we talked about it this week on the
00:54:43
show. >> I thought it was a very thoughtful and reasonable sort of approach for a
00:54:48
company that's sort of a national champion, right? That that they're going
00:54:51
to be good citizens. They want to make sure that that your rates. >> They basically said just to catch the
00:54:55
audience up, we guarantee you our usage of energy will not increase the uh >> cost of your utilities.
00:55:01
>> The cost of your utilities. >> That's fair. I mean, >> I mean, very reasonable. And I think the
00:55:06
next step we were brainstorming on the pod. There are people putting solar on roofs and uh there's base power. Um
00:55:13
Michael Dell's son doing a really interesting project. >> Yeah. Where they just put batteries on
00:55:17
the side of your house. They don't have to be super intricate and they load those batteries up when there's extra
00:55:23
power and it's cheap and they deploy it when you know the duck curve or whatever
00:55:27
the demand hits. So if you think Microsoft and you talk about rounding errors, well, if you give everybody a
00:55:31
battery at home to store some energy when it's cheap and there it's and then
00:55:36
that can be used to flow back into the data centers. I think we're could live
00:55:39
in a world where you say, "Hey, we're going to put a data center here and
00:55:42
everybody's energy is free." >> I think well, there are a couple things.
00:55:45
First is we chose as a nation not to invest in our grid for 40 or 50 years, right? And so our our our grid is behind
00:55:52
and vastly in need of improvement, right? our our grid is is decrepit compared to other advanced nations and
00:56:00
in particular compared to what what China has done. >> I think the ability to store
00:56:06
power at your home and to use it when power is the the most expensive is an obviously a reasonable thing to do,
00:56:15
>> right? Obviously the reasonable thing to do >> and it takes load off the the grid as
00:56:20
well. And people like the idea of being a little resilient, right? >> They do. If you do lose your power,
00:56:24
which in California, I think they turn it off about on the peninsula, was it about a half dozen times a year for you?
00:56:31
>> Only when it's really hot or really cold. >> Either either one.
00:56:35
>> Yeah. >> And they'll leave it off for 2 days cuz it's wind and it's just a complete
00:56:40
disaster. Oh, and by the way, I don't know if you knew this, there were subsidies given for nuclear where the
00:56:46
people living around nuclear power plants in France and where they just said in exchange for living near a
00:56:52
nuclear power plant, which some people might have concerns about. Uh maybe they're reasonable, maybe they're
00:56:57
unreasonable. Put that aside, we're going to just give you free energy for life. Very interesting. What is your
00:57:03
thought on small modular and nuclear? It's just it's too far out. Yeah. For
00:57:08
you to be concerned with right now. I think it's both the obviously the right
00:57:12
thing to do and probably not the source of data centers for the next three or four years.
00:57:16
>> Yeah, >> both. Right. That that obviously we need to be working on that. Obviously, it's
00:57:20
an extremely efficient form of power creation. Um nuclear has uh over 20 or 30 years is vastly more efficient than
00:57:30
any other power we know how to how to create. Um it has a disadvantage. It upfront it's a little more expensive,
00:57:36
right? So the fees are are are upfront and then you get the benefit over years. But clearly we should be working on
00:57:42
this. >> And if you had the ability to do one, you would do it. >> Oh yeah, for sure. And you're seeing
00:57:47
some of that in the more aggressive nations. The UAE >> uh is building uh modular uh nuclear
00:57:53
data center based data centers. Um putting huge amounts of power on the grid with nuclear. And what a great
00:58:00
idea. >> I went to see Elon a couple weeks ago on a Sunday afternoon. and we're talking he
00:58:05
really thinks um that putting data centers and chips in space, cooling's pretty easy in space, solar is much more
00:58:13
effective. What do you think, and he's been talking about this publicly, so I'm
00:58:16
not speaking out of school, but what do you think about data centers in space? Have you started researching it?
00:58:21
>> We have. I think first betting against Elon's ideas is is probably >> longterm
00:58:26
>> not a good betting strategy. However, he getting the timing right is something
00:58:31
that that has been uh less uh a less >> he's never wrong. He's frequently late.
00:58:37
>> That's right. I always I look I I think that's the both the blessing and the
00:58:42
curse of visionary. >> Yeah. >> Is you see things other people uh can't
00:58:46
see and in your mind they're just some technical hurdles to overcome. >> You took a couple years to build that.
00:58:51
>> It took took a couple years. >> Were you on time? >> Um >> we we were we were plus or minus a year.
00:58:56
>> Okay. in delivery of something that nobody had ever done. >> Um, >> it's hard to predict.
00:59:01
>> It's it's it's really hard. I I think the idea of using uh space for to grab
00:59:08
solar power is obviously a smart idea. >> Yeah. >> Right. You're you're you're you're miles
00:59:14
closer to the sun. You have much uh thinner atmosphere blocking the rays, so you can gather up the power. I think
00:59:22
there's a lot of technical work to be done. Yes, it's cold there, but you're
00:59:26
also in a vacuum. So, the actual cooling isn't an easy problem. It's a solvable
00:59:32
problem. I think the communication among satellites >> is a real real issue. And figuring out
00:59:40
which technology you want to do to get the data back to Earth, right? Remember, uh when you got your uh your cable TV,
00:59:49
uh when you tried to get internet from those satellites, it was really glitchy. Yeah,
00:59:53
>> there were these big delays. Now, those were higher orbit satellites. The ones
00:59:57
he's thinking about are much lower orbit. They'd have lower latency, but
01:00:00
there's some real work to be done. >> I I think it's in the 8 to 10 year
01:00:04
category, not in the 3 to five. >> Yeah, I I think it maybe split the difference, but yeah, it's and it it's
01:00:10
all of these are worth pursuing if you believe that we're not going to overbuild. So,
01:00:17
knowing what you know and watching this build out, is it possible that we're
01:00:22
overbuilding right now and we'll need a digestion period or do you think, you
01:00:28
know, based on what we're seeing there's just going to be the next workload, next
01:00:32
workload, next workload. >> I think we are still really early in in the demand for AI compute. And I I think
01:00:39
if you think about, you know, what portion of enterprises have really adopted AI in a meaningful way that has
01:00:49
changed their workflow, it's tiny. I think even the most frequent users at the consumer level are going six, eight
01:00:56
times a day. What what when what happens when they go to 100 times a day? What happen when all their devices are going
01:01:01
for them? What what happens when everybody in GNA, right? when every engineer is using it as a as a coding
01:01:09
co-pilot, right? We're going to see enormous amounts of demand for inference. The models are getting
01:01:14
better, more people are using, they're using more often, and the the amount of
01:01:19
compute taken with each usage is increasing. So, I think we're just at the beginning. How do you portion off
01:01:26
the effort when you're making systems right now in terms of energy efficiency,
01:01:31
the raw horsepower, and then the transport layer? these seem to be the three most important parts of what
01:01:38
you're doing. Um, correct me if I'm wrong. And and then how how do you allocate with engineers and your overall
01:01:45
team tackling those three major issues? >> One way to think about it that that I
01:01:50
don't hear often enough is the way you make a computer is you think about three
01:01:54
things, right? How fast you can do a calculation. >> Where you can store the result.
01:01:59
>> Mhm. >> All right. Memory. and how fast you can get the result to somebody who wants to
01:02:05
use it. >> Transport, >> not transport. These are the three things that make a computer. And if you
01:02:10
do really fast calculations, but your storage is is slow. >> All right, bottleneck.
01:02:16
>> You're bottlenecked. If you can do fast calculations, you can store it, but your
01:02:20
IO is slow, then you can't get it to the user. >> You are constantly thinking about as a
01:02:25
computer architect the balance >> of these three dimensions, right? And so
01:02:30
you make a a jump in your in the the performance of calculation. You got to think about storage.
01:02:36
All right. Then you got to think I mean it is a constant. >> Are you thinking about those three
01:02:40
simultaneously or are there teams grinding out each one of those individual verticals? How do you
01:02:45
architecturally build a group of engineers to do that? So you usually your most senior architects, your your
01:02:53
CTO and your your technical leads are thinking about that as the the the basis of a design,
01:03:02
>> right? I mean it it it doesn't matter how fast the car can go if it can't turn
01:03:06
you, right? It's not a good car except maybe for drag racing, right? And so the
01:03:11
the designers are constantly thinking about um where should we use power in the design? What can we make the memory
01:03:18
faster? Can can we can we add memory? What is the cost of adding memory versus making it faster? The GPU, for example,
01:03:25
has a lot of capacity of memory, but it's really slow. All right? And that's a huge bottleneck
01:03:31
in inference. It's why they can't be fast. It's why they just spent 2020 20
01:03:35
billion dollars buying Grock is because they didn't have an answer for fast inference. Fast inference needs fast
01:03:42
access to memory and the GPU doesn't have it. So, these are things we're
01:03:47
constantly thinking of >> and we're having a massive memory shortage right now because of this. How
01:03:52
does that get resolved? Is that like just a short-term bottleneck or is that going to be a long-term problem?
01:03:57
>> I think what it's a a crazy problem. I think everybody knew that the demand
01:04:02
would increase. Um, and this is true among the the major memory makers. Um people get a little scared and what
01:04:09
happens is they place a full year's worth of demand and they get the wrong answer back which is we don't we don't
01:04:16
exactly know h when you can have it. So then their response is all right we'll
01:04:20
give you 18 months of demand. So suddenly everybody went from giving six months of demand to 18 months of demand.
01:04:26
>> Okay. >> All right. Everybody all the supply chain is confused. All right. We were
01:04:31
making the exact same amount of memory we are now as we were four months ago. All right. And what's happened is the
01:04:37
signal into the the makers has exploded and it will take us about 18 months to digest. The prices will stay high. Um
01:04:45
this is a known phenomenon in the memory market. This happens every six or eight
01:04:49
years. Um what's different right now is the the GPUs are using a huge amount of
01:04:54
HBM which is a flavor of DRAM and they're chewing through that and that's
01:04:59
maybe leaving a little less for other devices consumers. far along are the Chinese in catching up to your company,
01:05:05
Nvidia, Grock, and how do you think about the geopolitics of the AI race? Like if they is there a scenario where
01:05:16
they win and we lose, we win, they lose, or is that overblown in your mind? >> I think the geopolitics are a real
01:05:23
issue. >> Okay. >> We are in we are well ahead in chipm. >> Okay. within a few square miles of Santa
01:05:31
Clara. Uh you you had Intel, you had uh AMD, you had Nvidia, you have our team, you have ARM, you have one of ARM's
01:05:40
great teams. You you have maybe talent, you have six of the world's great 10
01:05:44
chip teams. >> Um I think the the way you get good at building high-speed chips is to build
01:05:50
high-speed chips. >> And when that that's really how you do it and >> you play the game, you get better at the
01:05:55
game. >> That's right. Turns out you get better at the game. and that that's been a a
01:05:58
weakness in the Chinese chipm ecosystem. Now they're running hard and and they
01:06:04
know they're behind on that. I think on the other side, I think they have pushed
01:06:08
ahead in the open model category. >> Yes, the open source. >> The open source model is an area where
01:06:14
they've pushed ahead. I think >> because they're a top- down economy,
01:06:18
they were able to make decisions like we're going to bring a huge amount of power onto our grid. We're going to
01:06:22
modernize our grid. So they were able to bring on huge amounts of power and that's something that we're behind on. I
01:06:29
think it's unpleasant to to think of them as as adversaries and we got to figure that out together. Um I think the
01:06:36
world is a better place where we're not adversaries but right now we are >> and I I think certainly in an in an
01:06:42
industrial context we're we're adversaries. >> There's the industrial context and then
01:06:47
there's as we discussed what impact does this actually have? What's downstream of
01:06:52
us winning? And it's every developer being a 100x developer and then every knowledge worker being a 100x knowledge
01:07:00
worker and every biotech innovation. >> Systems that are recursive, right? That
01:07:06
build on themselves at rapid rates >> have a huge winner take all feel, right?
01:07:11
Right. By getting ahead, you get further ahead. Your iteration speed accelerates
01:07:16
and even small differences at the beginning are magnified very quickly. That's why this race is so important.
01:07:24
>> So, here we are. We're at Davos. It's a lot of politicians. My friend David Sax, co-host here on the
01:07:31
pod. He's our AIS are. And Trump, whether you voted for him or not, is very focused on this issue. Biden, their
01:07:40
team wasn't courting Silicon Valley. In fact, they kind of looked at us as the
01:07:44
problem, demonized to a certain extent. How how do you think objectively, you know, independent of how you might
01:07:52
feel about ICE agents in our cities or Greenland, etc. How do you think the Trump administration is doing on their
01:08:00
AI policy and the support they're giving the AI industry? >> I think even a lot of fronts are doing
01:08:04
really well. I I I think >> unpack it. >> I think we we had made a mistake in the
01:08:08
previous administration keeping our chips from our allies. Let's keep China separate for a second,
01:08:13
but uh the UAE is clearly an ally. absolutely >> as an ally, right? Modern Arab nation,
01:08:19
been a source of peace, uh made peace with Israel early on, huge Western influence. Um and we kept chips from
01:08:27
them, >> right? We like KSA, we like the Kingdom of Saudi Arabia to move in the same
01:08:32
direction, kept chips from them, >> right? We then made no sense. We we then
01:08:36
made a hierarchy >> that that made the Danes feel second rate, right? We said, "You are a number
01:08:41
two friend." Um bad idea. um we should be empowering our allies, right? So that's the first thing and and I don't
01:08:50
think the previous administration did a good job. They didn't understand that at
01:08:53
all and Trump did a good job of that. Not only do we want those institutions, those nations and their institutions
01:09:00
using our technology, we want them investing in the US and we under the previous administration, we had a
01:09:07
cifhious organization in Treasury that was was was difficult to work with and that's all of those much improved and
01:09:15
>> they were unclear. They were not communicative. >> They they were impossible to deal with
01:09:19
>> impossible >> impossible to deal with. This is super important because
01:09:25
as David has said many times on this program, hey, we want to be the standard, >> right?
01:09:31
>> And then all of that energy goes back into our standard now >> into our ecosystem in a development on
01:09:36
top of us into the recursive system we just described. >> Exactly. And if you look at Huawei,
01:09:41
right, >> and what they did with 5G, their networking up against Cisco and and you
01:09:44
know, our national champions, they they ran the table in a lot of countries. >> They clobbered us in Africa. They
01:09:50
clobbered us in the developing world. They they absolutely ran the table. >> Right. And now those places have
01:09:56
spyware. >> That's exactly right. >> And it's a real issue. >> It's a real issue.
01:10:01
>> It's a real issue. >> So I I think those were all areas where
01:10:06
this this administration did absolutely the right thing. >> Energy. >> Uh energy. Another area. Um, I I think
01:10:13
one of the things that kills a company like us is trying to grow extremely quickly is having to deal with different
01:10:19
regulations in each of 14 localities we're trying to put data centers. >> Right.
01:10:24
>> Right. That that is brutal. Right. What what you don't want when you're trying
01:10:28
to grow really quickly is to have 17 lawyers, each of whom is trying to figure out the the local, right? And so
01:10:35
his effort to try and say, look, let's get some reasonable laws across the board. that's obviously smart and if we
01:10:42
could get some money to to to improve the grid across the nation that also be really helpful. All of those are
01:10:48
extremely positive. The work he's done with the Department of Energy, >> Chris, right?
01:10:52
>> Right. Under uh I think it's called uh is it the Genesis program? I think it's
01:10:57
sort of the equivalent of a Manhattan project for AI. Of course, we need this. Of course, we need to be thinking among
01:11:03
our researchers, not how we can get a little bit faster, 10 or 20, but what can we use AI to to increase the rate of
01:11:09
research by 5x, by 10x, and how can we get the the things that impede government out of the way?
01:11:15
>> Yeah. >> Right. So, those are good. China's a really sticky problem. Um, and I I'm not
01:11:21
sure I agree with the the current push to to allow the selling of of H100s there, but it's reasonable to disagree
01:11:27
with me there. I I think it it is not it is not clear-cut like some of the other
01:11:32
ones at all. It's a hard problem, and I I think there going to be lots of different views there.
01:11:36
>> Yeah. And I don't know if you've been watching the news, but Canada just made
01:11:39
a strong alliance with China, announced, I think yesterday or today when we're
01:11:43
taping this. And this is where, you know, maybe the Trump administration can improve is, you know, we we do need to
01:11:49
maintain this alliance with our neighbors so that they don't they feel like they can trust us. This is what
01:11:56
I've heard, you know, spending time in Japan where the Japanese feel like maybe
01:12:01
we are not the most reliable partner. Canada feels we're not the most reliable
01:12:05
partner because of the tariff issues, military issues and maybe just the constant changing of uh you know
01:12:13
consistency is really important in policym and and they have to know >> and I I think for a country like Canada
01:12:19
that has a huge amount of raw material exports right they have wheat they have lumber they have a huge amount of stuff
01:12:26
that that either we imported they got to take elsewhere we we have to be aware of
01:12:30
of sort of the real politic of the situation they have to sell their raw material, right, which is a huge part of
01:12:36
their export. They have to sell it somewhere and China is a big buyer, right? And and so it's we have to go in
01:12:43
understanding that there are nations which are proud and if you're constantly
01:12:49
attacking them and saying things to the populace there, well then it gives the leaders the ability to say, well, hey,
01:12:56
China is courting us and they're going to invest and so why don't we build some
01:13:00
ports with them? >> Yeah, ports. You should do a whole show if you haven't already on the rise of of
01:13:06
Chinese ownership of major ports and shipping. It is crazy when you look I mean basically they own the world's
01:13:12
large shipping routes. >> The belt and road strategy >> and it's really
01:13:17
>> and then if you get out of sort of I don't know I'm not here in in in
01:13:22
Switzerland very often but if you go into many parts of the third world you begin to see uh by was it BYD cars?
01:13:28
>> Yeah. They're gonna be shipping them to Canada now, >> all over the rest of the world. We We
01:13:33
don't see it, but it's unbelievable. >> I was just in Mexico City with the wife
01:13:38
for a couple of days. >> Isn't Mexico City fun? >> I It's my first time there. I had a
01:13:42
delightful time. The food is spectacular. I love it. Really fun. Really fun, trendy, great place.
01:13:50
>> Yeah. Good vibes. And every guard of BYD. >> Yeah. >> And we got to think about that. And
01:13:56
they're talk about national champions. There's no way that the government isn't
01:14:00
subsidizing those by 30, 40, 50%. And I think what their goal is to put the Germans, you know, the English car
01:14:08
manufacturers, they've been at it for a while, but the Germans are still making
01:14:11
pretty great cars. And if these BYDs, which they're starting to get footholds
01:14:15
in Europe, the same thing will happen. Like who's going to buy a 40, 50, $60,000 Beamer, Volvo, Audi when you can
01:14:22
buy a 20, 30, $40,000 BYD? Yeah, they're they're nice cars. >> They're price dumping though, and that's
01:14:30
what tariffs are meant to protect against. >> They're they're subsidizing at the top
01:14:34
of the at the finished product, >> right? >> And that benefits the whole supply
01:14:39
chain, right? That's what they're trying to do. They think about it as the
01:14:43
battery maker, the transmission maker, all are benefiting while they subsidize at the very top.
01:14:49
>> All right, let's end on employment. Let's put the crystal ball out there.
01:14:53
Microsoft, Uber, Coinbase, Meta, Google. Four years ago, five years ago, more employees than they have now or they're
01:15:01
flat. >> Yep. >> Young people unemployment starting to hit 10, 20% among some college age
01:15:07
demographics. >> Yep. >> You know, David Sax and I have this debate all the time. Is it AI? Is it
01:15:13
entitled kids who don't have a work ethic? Is it the overfunding and the digestion or indigestion of tech
01:15:19
companies that hired two years out? It's pretty clear to me watching startups who
01:15:23
are the most resourceful, they're doing so much with AI. They are AI first. They're building agents.
01:15:29
They're doing everything with AI. >> There's no world in which we're not
01:15:32
going to have AI displacement. >> Job displacement. >> That's not why it's displaced now, but
01:15:38
100% it's coming. >> Okay. So, when you look at it, you're in the camp of it's coming, but it's not an
01:15:44
issue today. Define when it's coming. what why it's not an issue today is when
01:15:48
I look at the uh the people who have been let go um in middle management in particular
01:15:59
>> okay be candid where you're on all in you >> no I mean this is middle management what
01:16:03
I think has happened is this is the delayed impact of good SAS tools >> ah that what's happened is your ability
01:16:13
to extend your reach as a leader and as manager to stay a breast of what's happening. Your scope is much much
01:16:20
bigger. And so the role of middle management, which was frequently to move information.
01:16:25
>> Yeah. >> To manage small teams and move information, >> keep people on track.
01:16:29
>> That's right. That job h has shrunk in value. I don't think it's yet AI.
01:16:36
>> I think halfway there. >> That's right. I think AI is coming,
01:16:39
>> but I don't think that's what this is. And what happened was there was this
01:16:43
ballooning of these jobs and you know Mark Zuckerberg and and and Satcha they look one day say competition is coming
01:16:52
it is much more intense. What are these waves of people doing? >> They're slowing us down let's be honest.
01:16:57
>> That's right. And so they they're flattening their organizations as well.
01:17:01
So it's not just they're moving people out but they're changing the the shape
01:17:05
of the organization which is why I don't think it's AI yet. >> Yeah. I think what we're going to see
01:17:09
down the road is whole categories that that are vastly more efficient and therefore need less people.
01:17:16
>> It's uh pretty clear and it's almost rest in peace Scott Adams, creator of
01:17:22
Dilbert, but >> huge fan. >> Yeah, he just passed away this week. I >> I saw that. Huge fan.
01:17:26
>> What a giant. >> What a giant of of ridiculing uh corporate America. That exact
01:17:33
layer is gone. >> Corporate America. It's actually great that Scott got to see it.
01:17:38
>> Yes. >> Happen. Yeah. >> Towards the tail end. And uh we didn't
01:17:41
get to mention it on a previous episode, but rest in peace, Scott Adams. I think
01:17:44
it's a good place for us to uh end there, Andrew. I know you got a lot to do here. Enjoy your time at Davos.
01:17:49
>> Thank you. >> Yeah. If you see any of the Germans, ask them why they turned off their nukes.
01:17:53
>> All right. >> Yeah, that's right there to joke to them. How's Greta Thurber? How is your
01:17:58
secretary of energy doing, Greta Thurberg? Like, what are you doing? They turned off three of their six nuclear
01:18:04
reactors. I know. So they they decided instead to import natural gas from Russia.
01:18:08
>> Where did they get it from? >> Right. From Russia. >> Oh, from Russia.
01:18:10
>> Yeah. Bad. Depended on Russia. Really not smart. >> Yeah. Not smart.
01:18:14
>> And you know what? All because of Davos. I blame the WF and Davos. They literally
01:18:18
got so caught up in virtue signaling about the environment. They never just looked from first principles at how safe
01:18:24
nuclear is compared to burning fossil fuels. >> Nuclear is is is safe and we can make it
01:18:30
safer. We got to put the time and effort in. I was just in Japan last week. >> They're putting in new nuclear reactors
01:18:36
and they just got over a Fukushima and they realized, "Oh, we made some mistakes putting it below sea level.
01:18:41
>> We're not going to make those mistakes again. Nuclear is obviously the way to
01:18:44
go." >> Let's get better at it. >> Hey, let's get better at it. That was
01:18:46
awesome, dude. >> Thanks for all the time and for a great discussion. You rocked it. All right,
01:18:51
everybody. Welcome back. We're grifting. I mean, interviewing the top CEOs here
01:18:56
at Davos, the World Economic Forum. Uh this is our first time here and we're
01:19:00
having a great time. Tons of CEOs. We've had hundreds of interview requests.
01:19:05
We're going to try to do about a half dozen of them. A friend of the pod, Jake
01:19:09
Lucerarian, is here. Uh you've been on the pod before, both this week in Startups and Allin. Uh you of course the
01:19:15
CEO and co-founder of Gecko Robotics. You've been at it for almost a decade now. Yeah.
01:19:20
>> You build robots, as people who have seen the pod before know, inspect. These
01:19:25
are purpose-built robots that will inspect ships, bridges, whatever it happens to be. And you started this long
01:19:31
before Chad GPT and this recent AI revolution. I'm curious, these robots which are very purpose-built, you know,
01:19:37
I think very straightforward. >> Have you started to put AI into them yet? Because I I was just curious
01:19:44
thinking about your previous presentations. It was pretty straightforward, right? Like we know the
01:19:48
bridge, inspect the bridge, but >> now can it do things and start thinking
01:19:52
on its own and maybe be more adaptable because of AI? >> Yeah, good question. Well, I changed my
01:19:56
title now. It's now chief uh grifting officer. >> Oh, chief drifting officer.
01:19:59
>> Thank you. Especially when I'm in Davos. This is my title. >> You've been here a couple of times.
01:20:03
>> Yeah, exactly. >> Did you catch the tail end of the DEI? And >> I came I came right at the like the
01:20:10
heart of it. Yeah. And so it was it was you had to learn a different language actually.
01:20:14
>> Really? Yes. Did they check your fluency in ESGdei buzzwords? >> They did. They did. Well,
01:20:18
>> no, but everything was super precious and now I guess since Trump is here,
01:20:22
>> it's kind of brass tax like doing business negotiating and like less of
01:20:26
this performative stuff. >> It's a lot actually performance tonight. Um, but there's a lot there's a lot more
01:20:31
focus on okay, let's get down to the to the brass tax. We hear a lot of CEOs
01:20:36
talking about AI and but actually a lot of the conversations I'm having in the
01:20:39
Congress already is just about okay, like where's the ROI from all the AI? business.
01:20:44
>> It's actually business. It's actually trying to get to the to the first
01:20:47
principles to the roots of okay, how do you actually get artificial intelligence
01:20:51
to to deliver on the promise and funny enough a lot of it comes down to this this really interesting uh gap that's
01:20:57
exists in AI which is like all the information and data set that you need to actually turn all this into actual uh
01:21:03
return on investment and productivity gains especially for these like large infrastructure large asset owners uh
01:21:09
like the energy or mining or manufacturing companies of the world. So that's a big focus and that's what
01:21:14
>> it really seems to be turning into a business conference. I was astounded by
01:21:18
the amount of inbound I had that was just pure business capitalism building products and services to make life
01:21:25
better. Also the world has changed a lot since you started the firm. >> Um we've got a new sort of military 2.0
01:21:34
know thing happening and I think a lot of your customer base moved from just maintenance of bridges and tunnels and
01:21:40
infrastructures to military. Tell us about that. >> Yeah, that's exactly. Yeah, that's
01:21:43
right. We we do about 30% of our business is defense. So, we we work or department of war, I guess I should say.
01:21:48
So a lot of it's focused on how do you actually use technology to you know fight against the speeds of development
01:21:55
of of countries like China for example in terms of manufacturing speeds and a big part of that is actually
01:22:00
understanding the quality and the and the welds um the putting together the actual welds put pieces together. This
01:22:05
is actually a huge bottleneck for the US. We have you know these manufacturing and forges that are 100 years old doing
01:22:10
things 100 year old um you know today like they did you know back then. So, the technology that we're using to
01:22:14
deploy to help manufacture um certain, you know, components of uh uh a submarine or or be able to help expedite
01:22:22
how fast a destroyer is in turn around to get out and patrolling borders and deterring conflict. Um these are sort of
01:22:27
the things that our robots are using to help to speed up decision-m process and make sure you're accurate. Um and so in
01:22:33
some cases that Admiral Houston has talked about 90% improvements to speed up manufacturing using the technology
01:22:38
that Gecko builds and you know, you're seeing companies like Ander now um you
01:22:41
know, working with us. Palmer lucky. >> Palmer from lucky. Yep. Of course. And
01:22:44
>> that's his helicopter up there. He might drop a bomb any minute. >> Yeah. I think he's doing a speech pretty
01:22:49
soon. Um and so um so it's just amazing to see the adoption. But on the energy
01:22:53
side, right? Like that's been the biggest the biggest growth areas for our company. It's just been these large
01:22:59
energy companies and power companies. They're trying to figure out, okay, all
01:23:02
of these um all these hyperscalers are trying to figure out how to get infrastructure. They're focusing on
01:23:07
capex a lot, right? Well, what if we started to play a game where, you know, we have access to these problems to
01:23:12
these really um, you know, these GDP driver companies. What if we actually took an AI native or in our case what
01:23:18
we've seen a lot of companies be is like how to be robot native first >> um to support the AI initiatives um by
01:23:23
injecting and taking a very aggressive approach and how to implement and and put robotics to use to help build up the
01:23:29
the data infrastructure to then layer on AI models. And so that's at the heart of
01:23:33
what Gecko does. That's why I started this company 13 years ago with this premise of data matters as it relates to
01:23:38
being able to uh to have all the games. >> For people who don't know, the robots
01:23:42
have sensors in them, different arrays that can inspect metal, whatever the fabrication is, and go right to the
01:23:49
seams of a submarine and make sure this is all been done perfectly >> and measure it perfectly.
01:23:55
>> That's right. We build the robots and the sensors that go around and look at
01:23:58
diagnosing the health of the built world. So that means just like understanding and getting the largest
01:24:02
inventory and database of information about the health of build structures, bridge, dams, submarine, whatever it is.
01:24:07
Um now in along that journey, you're able to figure out that if you centralize all that information and data
01:24:11
and then layer on top of it operational data which exists, you know, for the most part is a decent like
01:24:15
infrastructure of sensor data at these companies. Well, wow, you get to make some pretty interesting decisions
01:24:20
because you can figure out how to extend the useful life of an asset. And if I push an asset harder, can I produce
01:24:25
more? like wow my focus is how do I help to create cleaner as well as um more barrels per day and at lower costs. I
01:24:32
use the word cleaner. >> So if you have a refinery or you have a nuclear power plant, you inspect it.
01:24:37
>> Robots should be dedicated to figuring out how to solve for the business
01:24:41
problem. The b what is the fundamental business problem that the customer is trying to to solve for? If it's making a
01:24:46
barrel or making a kilowatt or getting a ship out of dry dock faster, that is our
01:24:49
initiative and our goal as a company to build robotic solutions towards that. Now, I haven't built we haven't gotten
01:24:54
into building the humanoids of playing the humanoid game. And that's actually
01:24:57
when I was on your podcast, actually the summit, I talked about we're going to be
01:25:00
the biggest um purchasers of the Optimus robot is because the real question is how do you actually employ robots? How
01:25:05
do you get robots to return ROI? Because, you know, folding laundry and cleaning dishes is not a high ROI use
01:25:11
case. It's going to be the $20 an hour. >> You're not going to pay 40,000, you
01:25:15
know, for or 20,000 whatever it is. But, you know, US has to be the best in the world at figuring out how to use robots
01:25:21
to make um unfair advantages, you know, with these companies, whether it's oil
01:25:25
and gas, whether it's >> power need somebody between Tesla or Figure or Boston Dynamics, those robots
01:25:32
are going to be sold. there's going to be need to be an application layer and
01:25:36
operational excellence in the field will be that that's exactly what we are with
01:25:40
a nervous system to pull all this information from robots together and then you can build and use um AI models
01:25:45
on top of that to use the information data to then also begin to take actions back into the real world. So you
01:25:50
actually see it find not just right now you find problems or monitor situations and confirm things are being built
01:25:57
properly that there's no potential problems with this nuclear power plant or this ship but down the road do you
01:26:04
see yourself actually taking actions to build andor repair? Yeah that's exactly
01:26:10
the road map for us but first you have to figure out like what what is the state of the built world? What is the
01:26:14
state of the health? Um what sorts of actions should I take when it comes to repair? What sorts of automated welding
01:26:19
solutions, for example, are the right ones and which ones could use information about the how well that weld
01:26:24
was done is a feedback you to create a foundation model for welding, being the best in the world at welding. And so the
01:26:29
archite >> we're going to Yeah, we're going to be the the company that builds robots um to
01:26:35
both identify and then solve for the most important and highest ROI problems for the customers, whether they're
01:26:40
manufacturing new assets or they're or they're trying to operate and maintain
01:26:43
existing ones. Funny enough, like I've been talking a lot about like how do I
01:26:46
reduce hazardous work hours for humans? How do I extend the use life of assets for infrastructure? How do I, you know,
01:26:51
increase the capacity and production uh and uh and prevent, you know, catch failures from assets. Funny enough,
01:26:56
these are all very like easy to underwrite problems and it's something that you don't hear roboticists or AI
01:27:01
founders talk a lot about, but like that's my bread and butter. That's the
01:27:04
world I live in and I wear the steel toe boots to be able to understand the problems. You're going to need humans in
01:27:08
the loop for some time to come and there are there's going to be plenty of work
01:27:12
for welders, but there might also be incremental jobs created because >> yeah,
01:27:18
>> and you'll have one welder maybe supervising 10 of these robots. Is that
01:27:21
what you think is going to happen? >> That's what's going to happen. You want
01:27:23
to be able to get the experience and the subject under expertise to make sure the
01:27:26
robot is actually understanding like what what are the sorts of ramifications if I do this action versus that action.
01:27:31
be able to, you know, you you want also be able to what we don't, you know, need
01:27:35
to understand too is there going to be a lot of tele operations, you know, with mobile robots in particular. And so
01:27:39
you're going to have humans in the loop just they might not be in the field as
01:27:42
much. They might be more, you know, in a in an AC, you know, uh, building, you know, being able to operate and and
01:27:47
build build information and data to train the foundation model. If you think how risky some of these jobs are, uh it
01:27:52
might be nice to not have a human risking their life to maintain, you know, this part of the bridge,
01:27:58
>> you know, as brave and amazing it is they're doing that work. And we
01:28:02
obviously appreciate appreciate that over the centuries. >> Yeah. >> It might be nice to actually take the
01:28:07
human out of the deep sea welding and out of the bridge climbing business. >> That's exactly right. Well, I mean, the
01:28:12
story of Gecko has been a story of building robots to help to reduce the barrier to entry of these like jobs that
01:28:17
sometimes take 10,000 hours to be great at and actually making it something, you
01:28:21
know, you can attain within a few months of being able to, you know, use the technology in these fields. And so, you
01:28:26
know, whether it's manufacturing different parts and inspecting the quality of those parts or it's actually
01:28:30
gathering information, data set, understanding what kind of decisions to make. My goodness, like you have a
01:28:34
shortage of welders, you have a shortage of inspectors, you have a shortage of all these trades and you have to be able
01:28:38
to augment to take a Home Depot employee in a couple months, make them, you know,
01:28:41
able to create a, you know, make 100,000, $150,000, you know, running your robot, doing it safely. And so, you
01:28:46
know, that's an exciting, bright future. I think the the key unlock for us, you
01:28:50
know, in the robotics community is just like you got to get your robotics into the field. Uh, you got to um you got to
01:28:55
fail fast and and also rapidly prototype really quickly. And then manufacturing them is is the big is the big issue. And
01:29:00
so you as we focus on these sorts of problems because you know in this journey in over the next 5 years you
01:29:06
know to be that company that's the best in the world of taking robots and making
01:29:09
ROI from them you know we uh that's that's what we're focused on in terms of
01:29:13
setting ourselves up to be the world dominant there. >> And you uh wrote an editorial on the way
01:29:16
in here and dropped it. What was your take in the editorial? >> It was basically on the concept that
01:29:20
we're talking about here which is like you know I'm a I'm a I'm a I live in
01:29:23
Pittsburgh. You know at some point Pittsburgh had like more millionaires in 1930 than New York. It made 70% of the
01:29:29
world's steel. Um, and it's, you know, the economy changed a bunch and and that
01:29:32
doesn't happen anymore in in Pittsburgh and and so but but like the insights and
01:29:37
the the companies there like they're still the backbone of our of our economy
01:29:40
and and I was just inspired by, you know, in that time frame, the industrial revolution, how steel, you know, was
01:29:46
invented and then manufactured and then distributed to help create all the infrastructure that we rely on and, you
01:29:51
know, that that that fuels the energy or fuels the manufacturing sectors. you know, it was the it was the
01:29:56
infrastructure that you needed to be able to have all these big gains um that came from the industrial revolution.
01:30:00
Same thing is is what I was talking about in the editorial about uh is what we're doing with robotics collecting
01:30:06
information and data sets um to help support um and create uh the avenue uh create the infrastructure for AI models
01:30:12
to actually be able to you know return the kinds of returns that we're all betting on. So, you know, you it's just
01:30:18
important people to understand like that the the robotics is the is the you know,
01:30:21
it's almost the it's almost the the foundation to be able to get the massive
01:30:25
returns in the sectors that were that are mostly all here. Great. Yeah. Being able to eliminate some Dilbert level
01:30:32
middle managers who aren't adding value >> with, you know, some automation. Okay,
01:30:37
fine. Uh but we really need to get out there in the real world to get that serious ROI, whether it's a robo taxi or
01:30:43
self. And and I think that the risk you have like with like I think this the forum is like it's like you're saying
01:30:48
it's changing right there's like a there's a ecosystem in a bubble when you
01:30:51
live in a certain place talking a certain way in Silicon Valley we we only exist in the world of of the internet.
01:30:56
We we don't build the kind of technologies that started in Silicon Valley. And so this world of energy of
01:31:01
metal manufacturing of of mining all these like sectors defense like they're
01:31:05
they're just not and when I was starting the company they were like taboo to talk
01:31:08
about. So you just don't think about the kind of you know applications and things
01:31:12
you >> in some ways we ran out of things to solve for. I mean like what's the next
01:31:16
when I would be pitched 10 years ago on SAS software it was like okay great and then it was okay this is the 50th SAS
01:31:23
software company in this vertical okay this is the 15th in this vertical we're
01:31:26
kind of running out of >> what's crazy to me >> those verticals to go after.
01:31:30
>> You're exactly right and this is why like you think of like an incredible
01:31:32
invention like a humanoid robot and the first demo that me and you saw right was
01:31:36
a folding laundry. Oh my goodness. like the the like that that was the the thing
01:31:40
that you know I do when I go home. So that's what a robot should do. There's
01:31:43
so many other important like really important applications to use this technology for and I live in Pittsburgh,
01:31:48
right? So I get to see a completely different world with a completely different little bubble. Um, and then
01:31:52
you know going obviously and talking to to CEOs of energy companies all day like
01:31:55
it changes your world and so like it really is an advantage and I encourage a bunch of the startups that you're
01:32:01
talking to you know to to focus on like where the eyes are not where the conversations are not go out of the
01:32:05
ecosystem and find the really important problem to solve. If you think about it,
01:32:09
we had Boston Dynamics doing backflips with these robots, you know, a decade ago, but they they didn't have an LLM
01:32:16
behind them or a vision model or a world model yet. >> Yeah. And now when they have it, you'll
01:32:24
be able to, I think, tell it, "Hey, I want to lay some bricks >> and it'll just go out to the web and
01:32:29
find all the brick laying YouTube videos and the history of brick laying and every manual on brick laying and
01:32:37
every skew of every device ever used for brick laying and it's going to know
01:32:43
>> how to do it >> without ever having to be trained or is that the the you know, I'm just giving a
01:32:49
very silly example, but >> when would be able to just tell Optimus, "We need you to lay some bricks." And it
01:32:54
goes, "I know kung fu." Boom. It just does it. >> Yeah, that's right. I think that the
01:32:59
>> How soon? >> How soon? Uh, it's I don't think that's going to be as far out. I think it's
01:33:03
like I I I take maybe take like more like the three-year kind of time frame for those kind of things. I think you
01:33:08
can kind of see this these like um you know, these big bets like SoftBank um and Nvidia I think just put a billion
01:33:13
dollars into um Skilled AI, which is creating the brain for robots. Um actually a $14 billion valuation. um the
01:33:20
founder of which is in Pittsburgh by the way, Deepo. Um but I think the I mean the big the big problem I think with
01:33:25
like that um extrapolation is in the world of the world that I live in every day whether you know the energy the the
01:33:32
the the defense etc etc. Um we don't have those videos. There is not a corpus
01:33:37
of information in data set and so I'm focused on that. I'm focused on >> how do you get that data? You put GoPro
01:33:43
cameras and sensors on people's arms like I saw >> totally training. Is that how the
01:33:49
training will be done or where you're like modeling and actually watching a human do it and then having the robot
01:33:56
analyze it or >> Yeah, it's about uh we we think about like you know there's not many customers
01:34:01
that are going to pay for that cuz the ROI is just like not not clear, not there like no no big like energy or you
01:34:07
know manufacturing company's going to be like yeah let's do that and I'll pay you
01:34:09
like 10 million bucks a year to do that. And so like we're collecting it by solving for you know important problems
01:34:15
on critical infrastructure and assets of which like you know we're we're walking
01:34:18
around these like Manhattan size refineries all the time. And so there's information data sets that were like
01:34:22
building >> and the refinery inspection is done by a human today. Yeah. >> They take a bunch of pictures. They they
01:34:27
use a bunch of sensors and now the robot >> 100t up in the air on a on a rope
01:34:31
collecting data by hand. It's, you know, so if you use a robot that has a bunch
01:34:35
more sensors to fuse together, it begins to create a world that doesn't exist on
01:34:38
the internet, which gives Gecko a very big advantage. >> That's a that's a lot of world building
01:34:43
you're doing. >> That's exactly right. >> Continued success and we'll see you next
01:34:47
time on the AllIn interview program. Bye-bye. >> Good job. >> Nice. >> Awesome.
01:34:51
>> That was fun. >> Oh man, cold out here, huh? I'm going all in.