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4:07
Today is Thursday, May 21st, 2026.
4:10
We are live from the TVBN ultra lounge, the temple of technology, the fortress of finance, the capital of capital. We have a massive show.
4:19
There's so much tech news.
4:22
People said the technology industry, they were out of news. They weren't.
4:27
There's plenty of tech news. >> to us.
4:29
All over the Wall Street Journal.
4:31
Uh A- AI companies are duking it out for prime placement in the journal.
4:34
SpaceX got top billing in the journal with the IPO filing.
4:40
SpaceX sets its IPO in motion.
4:43
The SEC filing starts move to raise tens of billions of dollars in record debut.
4:49
We talked about this a lot on the show.
4:50
Clock is officially ticking on SpaceX's huge stock offering.
4:53
What are you laughing about?
4:56
Ryan says I knew Jordy did this [laughter] two days in a row.
4:59
Did I not do two days in a row? Nope.
5:01
Haven't done two days in a row in months.
5:04
>> [laughter] >> We can roll the tape.
5:05
I I mean, after reading the uh that that that piece about us, I feel like some brand differentiation is actually to our benefit since some people can't tell us apart.
5:14
And so, I I I've become a fan of the the the the the split in >> of casual Friday. Yeah. Thursday.
5:21
I'm just a of a casual look over there, more buttoned-down, buttoned-up, button left and right over here. >> to be buttoned up.
5:30
>> Someone's got to do it.
5:30
Uh SpaceX on Wednesday revealed new details about how it's it's a about its financials and how chief executive officer Elon Musk will try to grow a sprawling enterprise dedicated to advancing cutting-edge technologies in space and back on Earth.
5:46
The company disclosed the information in an investor prospectus.
5:50
Publication of the document sets SpaceX on course to potentially raise 80 billion or more from a stock sale as soon as next month.
5:59
The rumored date is what? June? July? >> Was it June 12th? >> June 12th?
6:04
That's just 20 days away, basically.
6:07
Um They're going to beat out Saudi Aramco that raised 26 billion when it went public in 2019.
6:15
Musk has touted out of this world objectives for the company from deploying a huge number of artificial intelligence satellites in the future to colonizing Mars.
6:24
Texas-based SpaceX has distinct businesses ranging from rocket launch to satellite operations to a nascent AI unit that its rivals that is racing to catch up with rivals founded by Musk nearly a quarter century ago.
6:36
Yeah, it has been a long time.
6:39
SpaceX revolutionized the commercial space industry.
6:41
The company has grown from a startup with a handful of employees that almost went out of business to one of the world's most valuable private companies with over 22,000 workers as of March 31st.
6:49
It controls technologies that competitors and even nation-states haven't been able to fully match.
6:56
SpaceX reported its revenue last year at 18.
6:59
67 billion and Dan Primack had a post saying that the business was smaller than he expected.
7:06
He's going on CNBC today to talk about the IPO.
7:08
He's had a couple interesting takes.
7:11
People are going back and forth.
7:12
The overall the reception has been uh The S-1 is an extremely enjoyable read.
7:18
Kevin Kwock says it's the most enjoyable S-1 read in a long time.
7:20
Reads so easy like sci-fi or fiction.
7:22
And uh >> [laughter] >> The title is >> the perfect It's kind of the perfect post or just regular Because if you're pro-tech you like space, you're excited about space >> Yes.
7:35
That could be a positive. Yes.
7:35
But if you're if you're a bear Yeah.
7:39
>> say it reads almost like science fiction. Uh science fiction.
7:42
Of course, the best TAM slide ever.
7:44
Probably the best TAM slide ever.
7:47
Sawyer Merritt has the screenshot here. SpaceX in IPO filing.
7:52
We believe we have identified the largest actionable total addressable market in human history.
7:55
We estimate that our quantifiable TAM is 28.
7:58
5 trillion dollars consisting of 370 billion in space from space-enabled solutions, 1.
8:07
6 trillion in connectivity across 870 billion in Starlink broadband, and 740 billion in Starlink mobile, as well as additional opportunities in enterprise and government. 26.
8:18
5 trillion in AI across 2.
8:22
4 trillion in AI infrastructure, 760 billion in consumer subscriptions, 600 billion digital advertising. That's massive.
8:32
>> [laughter] >> Well, is that for X?
8:33
The the idea So so everything else is more believable.
8:38
Everything else is more believable to me than X getting meaningful digital advertising penetration.
8:44
Yeah, I guess I guess the time the time matters here because a lot of these markets aren't aren't aren't this big currently, I think. I I don't know.
8:55
But I guess over time, you know, if you think about the next 25 years, the next 100 years, I don't know if these are inflation adjusted, but there's lots of things that could happen.
9:03
For illustrative purposes of sizing our addressable market, SpaceX excluded China and Russia from global estimates.
9:10
I feel like you might want to put in China and Russia over the next couple decades. Who knows?
9:14
Maybe we become best buds with both company with both countries, you know? Anything can happen.
9:21
World peace might come, and that's going to expand TAM.
9:24
That's an economic incentive for world peace. I like to see it.
9:28
There were some beautiful photos that were shared in the start of the S-1.
9:32
Lots of pictures to start, and then it gets very text dense, but the photos were I I I liked them.
9:35
I thought that they were unique.
9:37
I hadn't seen them like that often and they felt like they were kept in the back pocket for a while and they I don't know, they just like reminded you of SpaceX.
9:45
It's like a beautiful thing.
9:46
Um, Dan Primack says, "Incredible that Goldman beat out Morgan Stanley for the SpaceX IPO left lead left given that Michael Grimes returned to Morgan Stanley in part for this deal.
9:57
Of course, Morgan Stanley is on the deal, but that is it is a big win for Goldman that uh DJ SpaceX is at the helm. Goldman Sachs and Co.
10:07
LLC lead left in the joint book running managers, but everyone's getting a piece of the SpaceX IPO at this size.
10:14
Uh, Will Bitski says, "Shout out to the Goldman analyst that was richly sacrificed to win this lead left IPO.
10:18
It must have been an incredible amount of work.
10:22
It's not just the biggest IPO of all time.
10:25
It's not just this incredibly complex structure with multiple businesses.
10:29
It's also you're reporting to Elon Musk.
10:31
Elon Musk is your client and he's going to ask for things He's going to ask you to do things probably more aggressively than anyone who's a CEO of a company that's going public.
10:42
So, uh, lots of lots of things.
10:42
Lots of winners from the SpaceX IPO.
10:45
Luke Nosek is a huge Fund, co-founded Founders Fund with Peter Thiel.
10:49
Uh, his next role will be leading this is from a long time ago.
10:54
He left to start Gigafund which was billed at the time as a new investment firm that initially will be focused on raising capital for Elon Musk's SpaceX.
11:03
A Founders Fund portfolio company where Nosek is a director.
11:05
And so, uh, David Cowan says, "Today I learned Luke Nosek left FF to fund to start a fund exclusively focused on investing in SpaceX."
11:13
There are a few of those that we're hearing about these days, of course.
11:16
Exclusive does not mean 100% of the capital went into SpaceX.
11:18
It just means that they were very, very focused on that.
11:22
Gigafund has a lot of different companies in the portfolio.
11:23
Cover, we've had a bunch of founders on the show who have raised money from Gigafund, but SpaceX is where Luke is a director, deeply involved, and has focused on participating in many, many rounds.
11:34
And so, conviction will do that to an MF, says Pocket Jacks Capital.
11:40
>> [laughter] >> Lots of big winners.
11:40
Uh, Frank asked Kodak for SpaceX fair value based on the S1.
11:45
Should be an interesting build-up to the IPO.
11:46
Uh, what was the result from from >> Sid Lanser on 1. 1 to 1 and 1/2. It's not bad. >> That's not bad. Bull case gets to 1. 7 to 1.
11:56
9 if investors assume Anthropic sticks, AI infrastructure margins are strong, Starship unlocks major new markets, and public market scarcity drives demand.
12:05
Uh, but 2 trillion means the market is effectively assigning something like 80 800 billion to 1 trillion to the AI orbital compute story on top of an already rich Starlink valuation.
12:16
Possible as an IPO mania print, but that's not what I'd call for fair.
12:19
Uh, so we'll see what happens.
12:21
I mean, uh, the big the big news was the partnership with uh with Anthropic where Anthropic is spending over a billion dollars a month, I think.
12:28
It's ramping $15 billion. a year.
12:30
And that's huge for SpaceX given that they did 18 and a half or something last year.
12:35
This is a huge jump up in uh in I mean, they have to be one of the biggest neo clouds like overnight with this.
12:42
Yeah, I was trying to find huge, huge uh I was trying to find some of our conversations from last year where we were uh you know, X sorry, XAI and Grok was was growing, but maybe not at the rate that uh not close to the rate that uh would require that much infrastructure.
13:01
>> Yeah, finding product market fit on the actual distribution side.
13:03
Obviously, we love X, but it's not the biggest platform.
13:08
>> Yeah, it wasn't it certainly, you know, shoot uh shoot for the stars. >> Yeah.
13:13
And uh if you miss, you have a pretty great neo cloud business, right?
13:19
Uh Anthropic has to pay way above traditional uh neo cloud pricing for this uh compute.
13:25
And so, it up being a great outcome uh for SpaceX. Yes.
13:32
Well, uh Antonio Gracias is another investor who's absolutely printing off the SpaceX IPO.
13:37
Uh Antonio Gracias at Valor uh has 30 entities invested in SpaceX, in case you were wondering what truly going long looks like.
13:44
Uh he added some corrections saying that uh peop- some people were assigning like, "Oh, he's the only investor."
13:51
And he says, "No, no, no.
13:53
I have a lot of LPs who do a whole bunch of different funds."
13:55
Like, this is not just his his his windfall, although of course he's going to be doing very, very well personally.
14:00
Um but uh so many different Valor Four Space Holdings LLC. Uh Valor M33 Sex.
14:05
Like, so many different investment vehicles because Elon has raised money so many times both for primary capital and for uh tender offers for employees who uh had worked at SpaceX for 15 years and needed to buy houses, needed some liquidity.
14:22
Uh Elon was very good about never doing down round, growing the valuation very, very gradually over decades to get to this point.
14:28
Uh and uh Peter Hague says, "Just reading the SpaceX SEC document, one thing that sticks out is the capital spend on AI is 3x that on space.
14:39
It's an AI company with some rockets."
14:44
Uh which is a wild, wild pivot at the la- at the it's the 11th hour, you know?
14:49
This has been uh a rocket company for for 20 years or 15 years, then an internet company with Starlink, but that was still so tied and so clear and so quick to get to uh like a logical link.
15:01
Like, you needed the launch capacity to build Starlink, and so uh you had this new capability, satellite internet, it was amazing, and and it went from idea to launching the satellites to consumers actually using it when they're traveling, camping, off the grid, uh real, and then showing up in in planes and all sorts of different applications.
15:22
It became very very relevant, very real, very quickly.
15:24
And and and and the the the Colossus XAI, that felt like a different company because it was.
15:31
It felt like a different initiative, but it has just become so so so big so quickly.
15:38
>> Yeah, and and looking back at the plays Elon and his investors have made around this over the last year, right?
15:44
There was that uh felt like somewhat of a coordinated effort uh at the uh was it beginning of this year or late last year when suddenly everyone started talking about space data centers very suddenly.
15:56
Remember Gavin Baker Yeah.
15:58
started coming out talking about it.
16:00
That's around the time when they sort of floated the uh I believe it was uh December of last year.
16:06
Uh started floating uh floating the idea of like what the potential valuation >> Yeah. >> would be.
16:11
Started building that AI narrative.
16:12
Started you know made a play for Cursor.
16:14
Uh you know partnered with Anthropic even though you know only a few months ago they were much more combative. Yeah. It's name-calling.
16:22
So yeah, he you know I think this is why Elon has been able to accumulate so much capital is like he is the pretty much the best in the world at like making just making plays. >> Yeah, making plays.
16:36
And doing whatever it takes. >> Yeah.
16:39
So the most recent play unrelated to the news that made the front page of the Wall Street Journal.
16:43
Anthropic revenue surges set to post first profit. Sales seen reaching 10.
16:47
9 billion in second quarter up 130% over previous quarter.
16:52
Truly in in the title of the in the in the actual URL of the Wall Street Journal article, they call it mind mind-blowing growth about to propel Anthropic to its first profit.
17:06
Absolutely fantastic execution.
17:09
So Tom Brown, co-founder of Anthropic says we're expanding our partnership with SpaceX and we'll be scaling up GB200 capacity on Colossus 2 throughout June.
17:18
Appreciate Elon Musk and the team helping us find find good homes for the Clauds. Is Claud plural?
17:23
I thought it was all one Claud and the purpose of Anthropic was to build Claud and Claud will eventually build Claud, but I guess you have multiple instance of the instances of Claud running on different servers on different GB 200s.
17:40
Ray Wang over at Semi Analysis sort of shares a little bit more.
17:45
Anthropic's Q2 revenue set to increase by over 200% will post an operating profit.
17:49
The the AI will never be profitable group is in absolute shambles right now.
17:56
There there have been a lot of folks who have been just doubting time and time again.
18:01
Will this ever make money?
18:01
Will this ever make sense?
18:03
And Dylan Patel sort of laid out on the Dwarp Kesh Patel show this idea that at a certain point the the leading the leading models might actually be able to raise money because they're they're raise prices because they're driving so much economic value.
18:20
Semi Analysis also put out a a table showing for particular workflows that would take them, you know, a thousand dollars of human time that they would have to hire more people for, they were able to use AI and actually get an equivalent result for a tenth of the cost or a hundredth of the cost or even, you know, a 30% saving sometimes.
18:39
Please sign up says can someone check on Gary February 23rd, 2026.
18:46
He said turns out Gen AI was a scam.
18:51
>> to check the date on this because this seems like something he would have written in like 2024 and I would have been like, yeah, okay, yeah, maybe the usages are a little limited.
18:59
Maybe where there is some sort of wall here, the data wall or, you know, maybe we won't be able to, you know, maybe we'll need new new paradigm, but to write this in 2026 when we're in like the fastest period of acceleration in terms of actual value from these models is pretty pretty remarkable.
19:16
I'm interested to see where he goes from this.
19:18
Is he going to double down?
19:20
Is he going to stick with this?
19:21
It has been a couple months since February.
19:24
>> I think there's I think I think the the entire crypto boom and NFTs in particular just broke a lot of people's brains.
19:33
Yeah, and VR, metaverse, too.
19:35
Metaverse >> thing that was like over promised. >> under delivered.
19:40
>> Met- metaverse, yeah, potentially even more. Yeah. Yeah.
19:43
Yeah, yeah, there was a lot of discussion about uh this will destroy Hollywood.
19:46
This will destroy movies.
19:48
Like everyone >> the metaverse, there was never there was never a moment where you could use a product and have a mind-blowing experience.
19:55
Well, without paying for it.
19:57
Like you had to buy >> I'm just saying [laughter] I'm just saying like period. >> No, no, no.
20:00
The Apple Vision Pro demo, like there was a day you called me and you were like, "Why is everyone losing their mind on the timeline over the Apple Vision Pro?
20:07
Do I need to buy one of these?"
20:08
And I was like, "Yeah, it's kind of like a preview.
20:10
It's not like perfectly there.
20:12
I like it, but it's still pretty good."
20:15
>> I don't think you're I don't think you're remembering correctly because there was no moment where I wanted to buy one. No, no, no.
20:20
You didn't want to, but you recognized that it was the current thing when uh the Apple Vision Pro launched.
20:26
For like that week, when everyone got them delivered and they tried them, there were a lot of people that were saying >> Pro psychosis. Vision Pro psychosis.
20:31
A lot of people had NFT psychosis, all sorts of psychosis.
20:35
Uh we'll see how the AI psychosis uh develops. It goes both ways. Yeah.
20:40
But anyways, comparing it to anyone can have >> Yes.
20:44
a pretty wild experience with AI >> Yes.
20:48
in you know, on on like a ton of different a ton of different services. >> Yeah.
20:55
You could never do that with the metaverse.
20:57
>> Lia- Lisan al Gaib is uh contrasting Gary Marcus's Substack post with uh what's happened in the AI industry.
21:05
Anthropic valuation up 173% since the start of the year, posting profits in Q2 according to the Wall Street Journal.
21:13
Open AI valuation up 67% since the start of the year and Open AI general purpose model solves long-standing and well-known Erdős problem. Is it Erdős or Erdős? I believe it's Erdős. >> Erdős Erdős problem.
21:25
The The two dots over the O get me on the pronunciation without a scaffold.
21:31
And so there was a lot of questions about what do you know how hard is it to solve these problems?
21:36
But fortunately we have Tyler Cosgrove who's going to take us through what actually happened with this solution to this math problem that people are very excited about.
21:46
Noam Brown said today we are sharing that a general purpose internal Open AI model achieved a breakthrough on one of the best-known combinatorial geometry problems.
21:56
Less than one year ago Frontier AI models were at IMO gold level performance.
22:01
I expect this pace of progress to continue and Sidharth Ramesh.
22:05
I don't know if he was joking about this bet but he says I have lost my $30,000 bet that AI would never solve the planar unit distance problem. >> that was a joke. That's a joke.
22:17
He jokes around a lot but uh um no but a lot of people were surprised and a lot of people were excited about this.
22:24
So take us through what actually happened.
22:26
>> Okay yeah so I can basically go through like a simple explanation of what the problem actually is.
22:28
So just for some context Paul Erdős kind of this legendary mathematician throughout 20th century proposes I I think the number is like a little over 1200 different like little problems.
22:39
These are the Erdős problems.
22:41
People talk a lot about these as like goals for AI to solve.
22:43
And you you've heard like over time there's been kind of like small iterative kind of solutions to a lot of these problems. >> Yeah.
22:51
Um Sort of like collaborative a mathematician working alongside the AI model or an easy one just getting There's like a main kind of uh, place where all of the solutions go.
23:00
So, sometimes people will will will find uh, like AI will like find a different paper that wasn't actually put on the website and then they they're like, "Oh, AI solved it." But, it's honestly true.
23:10
But, I this is kind of the the first time we've really seen kind of a big step change.
23:14
Like, this is actually a new solution.
23:15
This is using like, you know, kind of novel >> It doesn't exist in papers out there already.
23:20
>> So, so this was um, problem number 90.
23:23
So, I so I can kind of read Please.
23:23
uh, read the question and then I can explain what it means.
23:26
Um, so so it's does every set of n distinct points in the real plane contain at most n to the 1 plus O of 1 over log log n many pairs which are one apart.
23:37
Okay, so like what does that mean?
23:39
Ba- ba- basically, >> we have like the real plane, right? 2D.
23:44
And we have a bunch of points on it. Mhm.
23:45
Um, what is basically the the how many like pairs of those will be uh, basically one unit apart?
23:51
And what's like the max number?
23:54
Like, how do we basically organize those points such that we have the max number of them? Is that a grid? No.
23:58
So, so you would think that, but I I can basically explain why.
24:02
So, so let let's kind of like formalize this better.
24:04
So, we have uh, U of n, right?
24:06
And this is basically the largest number of unit distance pairs among n points in the plane.
24:10
Okay, so so basically, like it we're thinking about like how how do we solve this?
24:14
Naively, it's like, okay, what what if we just take all the points we have n points and we just put them in a line and unit distance apart, right?
24:22
So, so it looks something like this. Yep. Right?
24:24
So, for this example, we have four points, but it doesn't matter. It's just n. Mhm.
24:27
Um, so 1 2 3 4, how many pairs are there? There's three, right? Okay, yeah.
24:32
So, basically this scales with n minus 1, right?
24:33
So, you could have a billion Yep.
24:36
points and there's 9999, whatever. n minus 1.
24:37
Okay, so now if we put it in a grid, what happens, right?
24:42
So, if we have a square grid here, Okay.
24:43
there's a nine points here.
24:46
And then how many how many pairs are there? I believe there's 12.
24:48
And basically, as this number scales up, it's still linear. Mhm. So, it's 2n. Okay.
24:54
Basically, if you do a billion points, it's it's two billion pairs. >> pairs. >> Yeah. Okay.
24:59
>> So then, um basically >> that line.
25:01
The line represents the pair.
25:04
>> Because it's one unit distance, right?
25:05
Because diagonal doesn't count.
25:07
>> doesn't count because it's not one unit distance. Got it.
25:10
So so then, um okay, what's the next thing we can do?
25:13
Um the next kind of configuration is is what's called the the lattice construction. >> Okay.
25:18
Um and so if we can pull up a picture of it, it's this kind of crazy-looking grid >> Mhm.
25:23
that has all these uh super like intricate um you know, lines in between.
25:28
You can see it on the uh this is from the OpenAI blog. Mhm.
25:29
Um if we can pull it up here. Oh, I think I saw this.
25:34
>> Uh so so this is what it looks like. Okay.
25:36
>> So if you can like zoom in on on any of these points, you see that, you know, it it somehow it looks like a grid, right?
25:41
But it there's not uh just kind of pairs at the at the edges, right? There's like way more. Okay.
25:46
So this scales at n to the 1 + o 1 over log log n, right?
25:50
This is this basically that the best um kind of example that we know works. >> Yes, but not a proof.
26:00
So so so so we we know we can find this, but is this the upper bound? We don't know, right?
26:04
So this is basically the lower bound. >> Okay.
26:06
So so then the the question is like we have the lower bound, Yeah.
26:10
uh which is the this is the best one we found.
26:13
This is the most number of pairs. >> Mhm.
26:16
Um and then we we've theorized that that the high bound upper bound is uh scales with n to the 4/3. Mhm.
26:24
And then so Erdos, the original conjecture that he thought um that that it the upper bound is still going to be less than n to the 1 + o of 1.
26:33
So this means uh o of 1 it's like as it scales as n scales to infinity, right?
26:38
So o of 1 basically scales to zero.
26:40
It go to zero as n goes to infinity.
26:43
Um and then basically OpenAI figured out that this is not true and that um there there actually are some um uh There are some ends for which um this kind of max number of pairs is greater than the original Erdős conjecture. >> Mhm.
27:01
So so for infinitely many n, this is not for every single n, right?
27:04
So it's not like five points or whatever, but there are infinitely many Mhm.
27:07
ends for which this is true. >> Okay.
27:10
That it scales with um that it it it's greater than n plus n to the one plus some uh constant. Interesting.
27:20
>> so that was basically the big thing, right? This is like Yeah.
27:21
uh >> Huge if you're into math.
27:24
>> decades-old problem, right?
27:24
This is incredible thing.
27:26
Terence Tao is like, "Wow, this is incredible." >> Yeah, yeah.
27:29
Um but yeah, that's basically the overview of the problem. >> Okay.
27:32
Um but yeah, I think it's very exciting because this is uh not like a math model.
27:35
This is just an internal model, general like reasoning. >> yeah.
27:39
Um It it's >> say it's like generally intelligent.
27:43
Yes, I I think you could say that.
27:43
Um and then I think it's it's interesting because from like public perception, it seems like uh this didn't take like that many tokens.
27:55
This was not like millions of dollars of inference time. >> Sure.
27:58
It was like maybe something like uh hundreds to thousands of dollars >> Yeah. of inference compute.
28:02
That's very interesting cuz we were talking about like Waring's conjecture about like novel ideas coming from just like brute forcing different connections between things and uh this is more token efficient.
28:13
Yeah, this is not just taking some solution to a different Erdős problem and just like >> trans- spamming it on all 1,200 of the problems and one of them works. Okay.
28:22
This is like a kind of a a new novel idea.
28:24
Like maybe this solution is the way that they found this, it's like super complex.
28:28
You can read the proof, it's like 18 pages long. >> Wow.
28:30
I don't really know what it means, but but like the there's uh a lot of mathematicians are saying, "Okay, this is actually could be useful to a lot of other problems." >> Huh. That's cool.
28:37
This is like a new way to do things. Interesting.
28:39
>> think it's it's a very exciting.
28:39
This is like maybe similar to the you know, AlphaFold moment or something where >> now this is like a real kind of step change in in math capabilities.
28:47
Do P Are people going to run out of problems?
28:51
How many more problems do we have?
28:54
Uh I I don't know the exact number.
28:54
I I think So, there's 1,200 in total um of just Airdrop problems.
28:59
I I believe the number is around 500, 600 ones that have been solved.
29:05
Um But there there there I mean there there's so many of these like famous open math problems.
29:08
There's like the Millennium Prize, right?
29:09
Million dollars if you win. How much did OpenAI win?
29:11
If you get a million dollars for the Millennium Prize, how much do they get for cracking this puppy open?
29:18
>> every single Airdrop prize uh if you solve a problem, there is a prize. >> Yes.
29:22
Uh and so these >> angry at a million, so. Yeah.
29:23
So, these are going to be slightly less.
29:25
I think this one was around $500.
29:29
>> [laughter] >> Uh no, no, no.
29:30
I I believe the Airdrop problem scale, there's $25, $100, and $500.
29:33
So, this was kind of the big one, but non-dilutive financing.
29:35
Yeah, non-dilutive financing. Yeah, yeah.
29:37
So, so could be a revenue pathway for OpenAI.
29:40
I I have to imagine that the that the inference bill for this was over 500 bucks. I think so, yes.
29:44
So, uh the mathematician that just works with a coffee still in the university >> obviously like okay, what does this actually impact?
29:51
Does this you know, bring us to the next level of science?
29:54
May- maybe not, but but it does show that like, okay, these models are not just uh you know, the average of all the training data.
30:00
They can actually make novel ideas, novel approaches.
30:04
>> distribution, outside of the training data.
30:05
It's not just knowledge retrieval. >> Yeah.
30:07
I I think this is very much contrary to you know, kind of the the the Gary Marcus take, where these are just you know, uh parrots, these are just predicting next token.
30:14
These these aren't actually intelligent.
30:16
Um yeah, I think it's very exciting. >> Yeah. Cool.
30:18
Well, thanks for having me on.
30:20
>> you hit the gong for the researchers? Yes. Boom.
30:27
Thank you for breaking that down. As universities Yes.
30:32
>> classes become live podcast settings, podcasts are becoming universities.
30:38
>> Uh well, uh let's take everyone through our lineup today because we have Alex Tabarrok from George Mason University and uh co-host of the Marginal Revolution podcast with good friend of the show Tyler Cowen coming on Tabarrok-inator a minute.
30:50
Bill Clericow from Collective Capital coming on.
30:53
Alex Nordstrom Convective Convective Capital.
30:58
From Spotify, the co-CEO of Spotify is coming on.
30:59
Then we have Jordan Schneider back from China Talk.
31:03
Going to probably spend a few minutes talking about the underwhelming [laughter] summit in China and then move on to a bunch of other talks a bunch of other topics and then we have Christina Lee Storm from Secret Level joining at 1:00 p. m. Pacific.
31:14
So, the the other news uh rumors about OpenAI is being close closing in on the IPO fund filing and that pushed out Nvidia's results, which is normally something I would expect on the front page, but there was too much AI news.
31:31
Nvidia results skyrocket on rise of AI agents.
31:34
We talked a little bit about it yesterday, but the big news is that they're doing an $80 billion share back share buyback authorization and Take-Two from Collective Capital coming on.
31:45
Alex Nordstrom Convective He laid out pretty convincing case.
31:50
You can go listen to the interview.
31:52
It it aired yesterday on TVPN.
31:54
But, without further ado, we have Alex Tabarrok in the waiting room.
31:57
Let's bring him in to the TVPN Ultra Dome. Alex, how are you doing?
32:05
I think we don't have audio.
32:05
Can we check your microphone? Make sure it's working. We're good.
32:08
Okay, we are It was something on our end. How are you doing? I'm still doing great. >> Fantastic. >> Perfect. Fantastic. >> Perfect.
32:17
>> Um It's great to have you on the show. It's been far too long. I'm a huge fan.
32:21
I've read Marginal Revolution since I studied economics back in 2010 and huge fan of the Marginal Revolution podcast.
32:28
I very much enjoyed your debate with Tyler Cowen all about the cost disease or Baumol effect and I would I I think we should start there and then I I we can go all over the place in in AI and labor and and the the economy broadly, but do you want to start with an introduction on the Baumol effect maybe why it captured your interest and some of the work that you've done around it? Sure.
32:55
I mean probably a lot of people have seen this famous chart where you have a bunch of things going up in price and a bunch of things going down in price.
33:03
And the question is is why?
33:03
I mean the things are going up in price I think we all know is like, you know, health care, education, right?
33:10
And then things are going down in price often manufactured goods like automobiles quality adjusted or televisions, computers, things like that.
33:20
And and the question is like why?
33:22
Why do we see these big differences?
33:22
And what Baumol pointed to was that there's a problem with service industries, you know, like education.
33:35
You know, think about what I do, which is teaching students.
33:36
And you think about what Pythagoras did.
33:39
Like Pythagoras he's got, you know, 10 or 12 students around him and he you know, he puts a triangle, you know, in the in the sand and you know, draws us some draws some math there.
33:51
It's more or less what what I do, right?
33:54
I mean thousands of years later maybe I'm using chalk or maybe I'm using PowerPoint, but basically, you know, it's me and a few students and productivity really has not gone up at all in that in the education industry.
34:10
So, because of that, prices have to go up because you take an industry where productivity is flat and you might say, okay, well, prices are going to be flat. But no.
34:22
No, because all the other industries are improving in productivity and the education industry they have to attract labor from those industries which are getting better, right?
34:38
So, uh they still have to pay me as much as I would earn, you know, in another industry, uh but my productivity hasn't gone up, you know?
34:46
So, that means prices have to go up. Yeah.
34:49
So, that's the basic Baumol effect is that industries, especially services, where productivity is not going up, prices have to go up. Yeah.
34:58
Uh we have the we have the chart here.
35:00
Uh and I imagine that there are a few logical uh pushbacks. One is is regulation.
35:08
Everyone will say that uh the the number of licenses to practice medicine is restricted.
35:13
It's a taxi cab medallion system.
35:15
It's a regulatory capture situation.
35:17
Does that not play into this chart as much as people think?
35:21
Uh is that to be disregarded, or is overregulation still something worth contending with if you want to avoid uh runaway inflation in or or just disproportion inflation in important services that people have demand for, but uh are maybe paying through the nose for and not happy about? Right.
35:42
Yeah, look look, I'm a free market guy, you know, uh I'm anti-regulation, anti-bureaucracy, all that kind of stuff, but you have to understand with these trends, we're talking about increases in prices which have happened over a hundred years, For sure. right?
35:59
And, [laughter] you know, medical care, people were complaining about medical care going up in price in like 1920, 1930.
36:05
This is before, you know, uh Medicaid, Medicare, before a lot of government involvement.
36:13
Education has been going up in price.
36:16
So, I think it's deeper than just regulation.
36:19
And let me give you just one other example is think about uh car repair, okay?
36:24
Uh you know, or uh cobblers.
36:28
When was the last time you took your shoes to a cobbler? Yeah.
36:36
Uh you know, my mother will say, "Oh, this new generation, they just don't care about repairing things, you know?" Yeah.
36:42
And I say, "No, no, Mom, I love you, but uh look, it's that the cost of repair has just gone up so much compared to just the cost of buying a new pair of shoes."
36:53
>> Yeah, how would you like Yeah, how would you like to you know, spend $70 to fix your $60 pair of shoes? Exactly, exactly.
37:00
So, you know, I had a my car had some uh I I bashed it in in the parking lot, you know, and it was like not not a serious uh uh you you know, just a surface injury, but it was like a third the price of the car, the value of the car, just to repair the side, you know?
37:22
And that's pretty typical. Yeah.
37:24
So, uh I mean, a lot of people in in the AI world are saying that uh AI will do to services what technology has done previously to manufacture, to goods, to manufactured products.
37:37
Uh does that mean that the the Baumol effect goes away?
37:39
Is it more pronounced in the industries that are AI resistant, uh and you see some sort of runaway uh inflation in the things that can't be automated?
37:51
Because when I think about teaching a college course, I I do I do see the the sort of maybe linear scaling that you're discussing, but uh we have microphones now. We have amphitheaters.
38:03
You can teach more students.
38:03
There's online resources.
38:05
There's ways to delegate and manage teams of homework reviewers and and dis- like sort of disaggregate the works.
38:12
And so, you would imagine that technology would be a lever on services in some ways, but I'm wondering how you think it will change in the age of AI.
38:24
Yeah, so the big question is robots, right?
38:26
If you can replace labor with capital, Yeah.
38:29
then a lot of the Baumol effect goes away. Um which would be great.
38:36
I mean, that would be great.
38:40
Basically, you know, anything that improves productivity is good, but I think people also get a little bit too upset about the the Baumol effect because really why another way of putting it is why are services getting more expensive?
38:57
It's because manufacturing goods are getting cheaper. Mhm. Right? >> Yeah.
39:02
It's because what we have to pay for the services in terms of giving up other goods has gone up in price.
39:07
But still we're richer than ever before, which is why people keep buying more education and more health care. Right?
39:18
So, that's another reason why I don't buy entirely the regulation story Yeah.
39:24
because if it was through regulation and the price of health care was going up, people would buy less of it. Yeah.
39:31
But actually, they're buying more of it. Yeah.
39:33
So, it's the fact that, you know, our productivity is going up, we can afford more of it.
39:39
That's really why the price of medical care is going up.
39:43
It's because the price of computers and other things is going down. Yeah.
39:46
How how are you processing the the the latest and greatest in behavioral economics around maybe this concept of the vibe session, this idea that yes, we are richer than we have been in history, and yet I mean, right now consumer confidence is very low.
40:03
There seems to be a lot of dissatisfaction with the with the progress that the economy is making.
40:08
How much of that is grounded in in real economic data versus psychological factors?
40:17
I've I'm amazed at the amount of psychological factors.
40:19
It's something I've changed my mind on how big the psychological factors can be. Yeah.
40:23
Um I mean, if you just look around the world today, no other country has done as well from globalization as the United States.
40:31
I mean, it was it it was us which kept open the sea lanes >> Yeah.
40:38
and globalized the world to our benefit.
40:41
And we're the richest country in the history of the world, the richest at at any point in our history, and yet somehow we're upset about free trade and globalization.
40:54
And again, no other country has done as well at assimilating immigrants and doing well with immigrants than the United States.
41:03
And yet we're upset about immigrants. Yeah.
41:05
Uh so, I think it's very disappointing, and I hope we I hope we get over the bad vibes because the US has a lot to be proud of Yeah.
41:14
and uh a lot to be feel good about. Yeah.
41:17
Uh is is one of those psychological factors you know, the average American's perception of debt to GDP?
41:26
We seem to anchor debt and like nominal values of debt to GDP, which is a sort of an income stream, and we get very irritated or anxious when debt sort of touches GDP in relative values.
41:43
How are you thinking about the level of indebtedness that is appropriate for a modern economy to to sustain itself?
41:55
I'm not happy about the debt.
41:55
Um you know, we still don't know. The US has a big choice.
42:02
Do they want higher taxes, or do they want less spending?
42:06
And the US voter just keeps saying, "How about neither?"
42:12
>> [laughter] >> Sorry, this is not working, right?
42:15
You know, have your cake and eat it, too. No, you cannot do that.
42:20
So, the American public has just not decided which way it it it will Yeah.
42:27
Whether that is what is driving um the malaise, I don't know. I'm not I I'm not sure.
42:34
It's either may I don't know whether I don't know whether it's cell phones, I don't know whether it's Instagram, you know, um but it does seem that there is an anger a a grievance culture in the United States.
42:47
At first, you know, I thought it was just on the left, Mhm. right?
42:52
You know, when we had Everyone is complaining, "Oh, African Americans are treated so poorly, the women are treated so poorly, the poor are treated so poorly."
43:01
You know, and then with Trump, we've just changed our set of grievances, you know, and so now, oh, we're it's it's it's the the the uh the foreigners who are ripping us off, you know, crime is terrible, when of course it's not.
43:18
Uh you know, all of these things We've just changed our set of grievances uh without actually uh focusing on >> Is that Yeah, is that not How much does uh the fact that, you know, so many people are downwardly mobile.
43:33
I mean, I was I was born in the '90s and and I remember as a as a probably an early teenager hearing like, you know, statistically, this is the first generation where you're more likely to to do less well economically than than your parents did.
43:49
And I feel like part of the challenge with that is that America's culture is so progress-oriented, right?
43:57
Like the American dream is just centered around uh doing uh doing doing more, doing better, you know?
44:04
If your if your father was a was a cobbler, like you own a shoe store, that that kind of thing.
44:09
And And so, now when you have, you know, still this like massive wealth and and as you said, you know, the richest country in history, there's this constant comparison to to the past and I think frustration from that, which is making like, you know, huge swaths of the country frustrated.
44:31
And you want to you want to blame it you want to blame it on on, like you said, foreigners or or free trade, etc.
44:38
But but it's just like this sort of latent um frustration.
44:47
Yeah, there's definitely frustration. I agree with that.
44:49
Um compared to the past, we are doing better. Yeah.
44:55
There's no question of that.
44:55
Now, to be sure, there are some key areas, like housing, right?
45:01
Housing is much more expensive, you know, than it should be.
45:06
You know, that's a that's a zoning problem.
45:07
Um that's a choice people have been making, I think a bad choice.
45:13
I think we should ought to do something about it.
45:14
But even with housing, uh you know, houses are so much better today, they're larger, kids have their own rooms, you know, uh Air conditioning. Parking. >> Yeah, in 1960s, yeah.
45:28
Yeah, a large fraction of the housing stock in 1960 didn't have indoor toilet, didn't have indoor plumbing. Right?
45:37
That was still pretty common not to have indoor plumbing.
45:41
So, so I don't think it's compared I I think when if people are saying that we are worse off in the past, I think that's incorrect.
45:52
Maybe what's going on is that inequality has gone up somewhat, not as much as people often think, but it has gone up somewhat.
46:01
And of course, we have more access to uh seeing inequality.
46:06
So, you know, Instagram and stuff like that, which is sort of a fake >> [laughter] >> And the Lamborghini might be rented. No, I completely agree.
46:20
Yeah, the other the other uh the other factor is that the forms of entertainment that are constantly being chosen today, like doom scrolling, right?
46:30
When you maybe had the hippie generation, it was like, "Hey, we don't have jobs.
46:33
Let's go to uh Yosemite and just live there, right?"
46:37
And like what that what that would do to your sense of well-being versus like, "I don't have a job.
46:42
I'm going to sit on my phone all day long, or or, you know, last week there was >> Nobody really hitchhikes across America when they're in between gigs anymore. What happened? Yeah.
46:52
Yeah, but like just just I don't have anything going on.
46:55
I don't have opportunity.
46:57
I'm going to go outside and touch grass. Yeah.
46:58
going to have a wildly different impact on your psyche.
47:02
I know a super successful corporate lawyer today that like was in the middle of a career transition, spent a year like surfing in Southern in South America, and like that is unheard of by today's standards.
47:12
Like, that just doesn't happen.
47:14
Sorry, I did you have Yeah, no. Uh yeah.
47:16
Yeah, no. Uh yeah. Oh, yeah, I have also been thinking about like if there was a if there was an if there was an alien that showed up and had a quadrillion dollars, like if you would include that alien in the Gini coefficient, it sky rockets it, but
47:31
the existence of that alien doesn't affect your perception until you're made aware of the the diamond spaceship that that the alien comes to town with and starts flexing on you on Instagram, but the mere existence doesn't change your your economic well-being or anything real. Uh but it does change your
47:45
Uh but it does change your psychology once you see them flexing on you.
47:49
Um I'm I'm interested in the housing thing though.
47:51
I want to go back to Baumol effect.
47:55
Because housing seems to potentially fall in the middle.
47:57
Like there's manufactured goods that go into building a house.
48:01
There are also services that go into a house.
48:03
Like how do we apply the thinking of the Baumol effect and what you've learned to what would happen to housing?
48:11
Like is there a is there a second step?
48:12
Everyone when they talk about housing will say, "Let's fix the zoning rules.
48:17
Let's make it easier to build." But is that enough?
48:22
Yeah, I mean look with housing, it's not the construction of housing [clears throat] that has gotten so much more, you know, expensive.
48:30
It's almost almost 100% the land. Okay. The cost of the land. >> Yeah.
48:35
So I I mean you look I mean you just go to, you know, San Francisco, San Jose, which ought to be, right, some glorious metropolis, you know, of the future with, you know, sky-high buildings and people traveling around in, you know, uh fantastic uh Uber cars.
48:58
>> like a Chinese city where there's LED walls and yeah, we have that on Salesforce Tower, but outside of that there aren't these massive skyscrapers and it just it just hasn't happened.
49:08
>> it's a it's a land of strip malls. Yeah.
49:11
>> Silicon Valley is a land of strip malls. It really is.
49:13
And and strip malls are just as extremely extremely expensive.
49:18
They're just lying on Yeah.
49:18
uh you know, this very valuable land where people are not allowed to build. Yeah.
49:22
So I'm hopeful there's something like California Forever, you know, uh where they're trying to uh get permission to, you know, start a new city.
49:32
Um I'm I'm hopeful for things that of that nature. Yeah.
49:37
>> none of this this is all policy.
49:38
This is all of this is under our control. You know, in some sense.
49:43
This is not uh like we've we haven't been hit by, you know, or a tornado and we're all poor and have no housing.
49:50
It's because we've said no, you cannot build.
49:53
Yeah, I've I've I've been thinking about the the the the push into cities uh which which makes so much sense in the sense that the economic opportunity is in the cities and so we've seen uh you know, successful college graduates leave their hometowns, go to San Francisco, Los Angeles, New York, Miami, Austin, these these mega hubs that have been very expensive if they aren't building.
50:17
Uh but I was I was really optimistic that between remote work, self-driving cars, like faster highways, like we would get just an extension because I think as you look through history, uh the 45-minute commute is sort of a sweet spot where before the horse and carriage, people would live a couple miles cuz that's what they could walk in 45 minutes.
50:35
We got the horse and carriage.
50:37
Once we went to 60 miles an hour, it became, okay, you live, you know, 30 miles away from your from your uh workplace.
50:43
But I was hoping that we would get another leg up on that.
50:45
Maybe it's coming, but uh we certainly haven't seen it yet.
50:49
>> You been surprised by any of the the fallout or or or lack thereof of the COVID era, the shift to remote work, any any changes uh technology or otherwise in just the the housing and and labor markets?
51:04
Yeah, I I agree with you.
51:04
I've been a little bit surprised that um we haven't seen another city really uh take off.
51:08
I mean, we used to build new cities, right?
51:14
You know, uh like and you know, Chicago, you know, not that long ago uh was a city of 50,000 people. Yeah.
51:22
Um and Trump briefly talked about freedom cities for a while, which I thought was a a good idea. Yeah.
51:28
But it's sort of gone away. Okay.
51:31
Um it does tell you that there's something very strong about these so-called agglomeration, the technical term in economics, agglomeration effects, that uh like people just get more productive when they are near other productive people. Yeah.
51:48
And you really need this big push to try and get this into the new city.
51:56
I mean, Miami briefly, you know, seemed to be jumping ahead, but that that trend seemed to have gone away. Yeah.
52:02
Uh Las Vegas, you know, tried it with Zappos. Um but that Yeah.
52:07
you know, modestly only took off modestly.
52:10
I am somewhat surprised that uh we can't plant our flag and say the new city is going to be here and have a lot of companies all agree to move in at once. Yeah.
52:21
Yeah, I've heard it referred to as like the rainforest theory that why San Francisco is so resilient because you have so many different participants and the VCs can go to Miami, but they're just a phone call away while so many other key pieces of the economy are still chugging along and so San Francisco clearly made it through a trough and is on a major major upswing.
52:44
Jordi, what Uh what data are you most obsessed with following to try to understand the current moment?
52:49
We see we see AI in the GDP data primarily through CAPEX right now, but what kind of productivity data are you looking at?
53:02
You know, we've been very excited about, you know, what what what we've seen from Stripe.
53:05
They have this incorporation product and they're seeing companies, you know, more companies formed, growing revenue faster.
53:11
That's very exciting, but it's also like a certain type of person finds himself incorporating, you know, their business with with Stripe and is not perfectly a reflection of of the economy more broadly.
53:24
So, what are you looking at to try to understand the impact and and and try to ignore maybe the headlines from CEOs that say, "Oh, we we we we laid off this 20% of people because of AI."
53:42
because, you know, as we know, often times it's marketing.
53:47
Yeah, it's very interesting.
53:47
There's a lot of theories, you know, about is this going to be a job apocalypse or something like that? Not much data.
53:53
And of course all of the data we have so far is that AI is increasing the number of jobs, not decreasing the number of jobs.
54:02
What I'm most excited about and most interested in seeing is the effect of AI on medical care.
54:11
Um so, you know, we just saw yesterday that AI had you just had it on your on your show.
54:21
You know, had proved a new mathematical theorem or counter counter proof.
54:24
So, AI is making these inroads into the highest levels of mathematics.
54:33
If we could do that for drug discovery, you know, if we could have and I you know, a 5% reduction in cancer mortality, that would be worth trillions.
54:50
You know, that would be worth trillions.
54:52
So, the opportunities there for AI to make tremendous leaps in human welfare by improving medical care, health care, I think are really exciting and well within the realm of possibility.
55:10
You know, one new drug like solving an Erdős problem, that would be incredible.
55:16
And and what does that mean in the labor market?
55:18
I'm just I'm thinking back to like there was a time when there was sort of only one track for doctors.
55:27
He was just like a generic doctor.
55:28
Now there's much more specialization.
55:30
There's a dermatologist, a podiatrist, all sorts of different doctors.
55:34
Is this like there's a there's a fracturing and further employment creation from the administration distribution advisory around new treatments as it rolls out because you could imagine Okay, there's a new drug. That's great.
55:50
That's that that helps everyone live longer, but I'm unclear on how it interfaces with the labor market.
55:58
Yeah, I mean I think the the trend is division of increases in the division of labor.
56:06
It's talked about which Adam Smith talked about from the the pin factory. Yeah.
56:09
And when you apply that idea of the pin factory, you know, somebody shapes the pin, somebody puts When you apply that to the knowledge economy, then it's exactly as you said, you no longer have a physician.
56:26
You have a podiatrist and you have an optometrist and you have a ear nose ear nose throat specialist and so forth so forth.
56:33
And yeah, I think that will um continue and they will all be using, you know, AI for sure, right?
56:41
But yeah, they're going to get more more specialized and the tasks which physicians do will differ will change.
56:49
Um I but so far you know, I'm not terribly worried about the job market per se. Yeah.
56:57
Um you know, look, this is a problem of you know, people worried about people are worried about the oh, the AI is going to do all the jobs, right?
57:11
Like uh this means we're going to be fabulously wealthy.
57:15
And you know, even without any jobs, just being fabulously wealthy we'll [snorts] figure things out.
57:20
You know, this is the sort of problem you want to have, right?
57:24
Again, this is not like a tornado or hurricane or tsunami that destroys wealth.
57:29
This is a tsunami which creates wealth.
57:32
And yes, it's it could be a tsunami in the sense that it's going to be very dramatic, okay?
57:36
But it's going to be very dramatic like, you know, Santa Claus coming and leaving us goods, you know, under the under the Christmas tree.
57:45
So, that's drama that we can we can handle.
57:48
It won't be uh you know, it won't be without problem, okay?
57:54
But problems where the pie gets bigger are problems that we can solve.
57:59
You know, it's problems when the pie gets smaller when we are forced into a zero-sum society of one person versus another.
58:08
That's when society breaks down, not when the pie is getting bigger.
58:13
Like, we'll figure out ways to make sure everyone gets a decent slice if the pie is getting so much bigger.
58:20
We can solve the problem of dividing it up uh with everybody being happy.
58:25
I'm not I'm much less worried about that. Mhm.
58:27
How do you How do you think about value capture with this technology wave versus historical technologies?
58:35
You know, if you uh get really good at inventing engines, you can sell a lot of those engines and hopefully have a nice margin and maybe other people copy the engine and also make similar engines and and benefit from that.
58:49
But we're at a moment right now where frontier intelligence is like very widely available, right?
58:58
There's certain internal models that aren't with the public yet and if you have one of those Erdos you know, moments in medicine, it could just be an off-the-shelf model that helps make a breakthrough and you pay for the tokens or you pay your subscription, but then right now the labs are not really set up well to capture that value at all.
59:17
It could be, you know, some it could be a big company that that captures the value, it could be another startup, there's small businesses.
59:24
And so, how are you thinking about value capture versus like public benefit of this technology cycle and how it would differ to prior technologies, let's say like the internet and telecom and and all the way back to railroads and the steam engine, etc.
59:43
Yeah, it's a very interesting technology because this will sound odd, but it doesn't seem that hard.
59:50
>> [laughter] >> You know, the the fundamentals is, you know, it's linear algebra, it's very surprising.
59:56
I don't think anyone predicted uh uh this, but uh and it's true, of course, that the frontier models are ahead, you know, OpenAI and Anthropic, you know, have the best uh models, but they're like 6 months ahead, you know, of open-source uh models.
1:00:15
Um and for most of what you want the models to do, uh you don't even need the frontier models.
1:00:26
Um and, you know, what today is a frontier model, like tomorrow, you know, it'll be a much cheaper second-rate model, right?
1:00:34
Like like people are worried, oh, you know, some people have access to, you know, 5.
1:00:39
5 and other people are still working with 5.
1:00:45
4, but the big point is that even 5.
1:00:47
4, you know, the free model, is 100 times better than 3. 0, right?
1:00:58
Which was also incredible.
1:01:00
So, uh the models seem to be getting more powerful and cheaper at a faster rate than any other technology that I have ever seen.
1:01:11
So, I think the gains will be fairly widespread, if not at first, you know, then then soon soon afterwards.
1:01:21
I mean, you know, some people are obviously open AI and Anthropic, you know, the people who got in early and the and the programmers there, you know, they're going to be fabulously wealthy.
1:01:33
Uh no question about that, but the technology itself uh so much of it is open source, so much of it is really quite accessible.
1:01:45
There's no magic there as far as I can see, like it was just you had a few good ideas and then those ideas just turned out to be incredibly powerful.
1:01:58
So, I'm expecting to see really the technology being quite widespread. Yeah. Uh No.
1:02:02
I know I know we're we're out of time, but one last question.
1:02:07
How how do you think about the part of the reason I think we feel this insane acceleration with this technology shift, maybe, you know, a more more sudden than certainly mobile, but, you know, looking back to the internet and everyone in this moment wants to figure out or okay, are we in 98? Are we in 99?
1:02:26
Are we in early 2000 trying to figure out the moment that we're in?
1:02:31
And it feels like because we have the internet today, like there's this like compression in the technology cycle because ideas get distributed faster, products get distributed faster.
1:02:44
If there's a breakthrough, it's instantly everywhere.
1:02:45
There's instantly, you know, hundreds or or thousands of companies working on, you know, improving it, furthering it.
1:02:51
And so, um I find it hard to try to think about where where we are because it felt like in Q4 of last year we actually did have a correction.
1:03:01
Like we were joking we were joking like, great, the bubble popped because like there was over like maybe too much excitement around chatbots.
1:03:08
And there was somewhat of a correction and then we got agents that really worked and then it feels like in some ways we're in a new a cycle now and and um and so I'm uh I'm curious if you have any sort of Frameworks or thinking around that like compression in progress that we're getting because we're building on top of all these other technology Cycles for this new one.
1:03:33
Absolutely and of course the AI's themselves are starting to improve the AI's, right?
1:03:38
So some people worry that precisely because of that we'll get a sort of a a boom scenario under which one day everything is fine and the next day you have a god in the laboratory, right?
1:03:54
I'm not too worried about that but it's not it's not insane. It's not insane.
1:04:00
Yeah, so far my view is that I trust the technologists when they say that the technology is going to keep getting better quite rapidly.
1:04:20
Where I think the technologists are not quite right is that it's going to take much longer than they think for this to start affecting you know jobs and and the economy writ large and things of of that nature. That'll be slower.
1:04:40
So the economists generally are on the slow side in terms of it's going to take time to adapt to this technology.
1:04:48
I mean we saw like with electricity for example.
1:04:53
You know electricity was another you know incredible technology but it took time.
1:04:58
You know it took time to adapt to that even when the frontier you know was very far ahead but for that to work its way in the economy and for people to figure out how they're going to change their production structures. That took time.
1:05:13
And I think it will take time here as well.
1:05:18
Um, but as far as I can see the technology is going to keep getting better, which it does give one pause.
1:05:29
And I will say never in my life have I felt that the window of what is possible is as large as it is today, both on the possibilities uh for superintelligence, huge gains, the boomers, and also on the doomers side.
1:05:47
I I don't discount those entirely.
1:05:50
My view is more in the middle, but uh those two sides, they're not they're not insane.
1:06:00
Um, so we do have to do a lot of thinking. Yeah.
1:06:01
Well, thank you so much for taking the time to kind of chat with us.
1:06:05
We'd love to have you back on the show soon.
1:06:06
It was long overdue, uh but have a Thanks for the time, Alex. Rest of your week. Have a great weekend.
1:06:11
And hopefully we'll talk to you soon. >> Talk Goodbye.
1:06:16
Uh, GMU economists, always so much fun to talk to.
1:06:19
Uh, well, our next guest is Bill from Convective Capital here with a new fund. Get that gong ready.
1:06:25
Let's bring [music] in Bill from Convective Capital into the TV beyond the mainstream. Welcome to the show. How you doing? Doing great.
1:06:37
Thanks for having me, guys.
1:06:38
Kick us off with the news. What happened today?
1:06:41
Yeah, so uh we raised a new fund, $85 million. There it is. Congratulations.
1:06:46
It's uh thank you so much.
1:06:49
It's up 2X from our from our last fund, and we're focused on disaster resilience.
1:06:53
The the thesis of the fund is that the world's getting warmer, our infrastructure's getting older, and that's literally a recipe for disaster.
1:07:03
And so as as disasters rise and volatility rises in the world, there's got to be private markets that can respond and that can build solutions and services and technology to to stop that.
1:07:13
And so we back founders that are building those things.
1:07:14
Give us uh an overview of the fire Give us a fire market map.
1:07:20
What's exciting in fire top of mind right now?
1:07:24
There's a fire burning in uh LA {slash} I think Ventura County.
1:07:30
Uh and so my house has been very uh it's been very smoky around my house.
1:07:34
Uh and uh yeah, I want to know.
1:07:34
This This feels like a a particularly brutal industry because there's these like insane spikes of interest and then people just kind of forget about fire.
1:07:45
>> WatchDuty for-profit conversion? Yes. It's got to happen.
1:07:48
>> Yes, lead a Series A in WatchDuty, please. >> Lever it up.
1:07:53
Yeah, John WatchDuty is a good friend and we started you know, I started my company around the same time he started that and he will never take that for profit.
1:07:59
[laughter] He's a dyed-in-the-wool uh you know, mission first, you know.
1:08:04
>> But yeah, It's actually become the standard not just for people that care about their homes, but also you know, people in the fire service use it.
1:08:08
It's It's an amazing example of how technology can can address this problem.
1:08:14
So I guess to get to get to your question around market map, you know, we think about it a lot of like who is the end buyer because that's actually what's kept people out of this space historically.
1:08:23
You know, the you know, VCs typically don't get excited about companies that sell to utilities or insurance companies or government agencies.
1:08:30
Um and you know, our thesis is that that is in the middle of a really big change that you know, if you see like PG&E went bankrupt a couple years ago, the insurance companies have had to leave California and some of these really large markets and that's changing behavior.
1:08:43
You know, you lose 70 billion dollars of market capitalization and you like you respond and you do something about it and you act differently.
1:08:48
And so you know, we think a lot about the market in terms of who's going to buy these technologies.
1:08:53
We've had a lot of success investing in startups that sell to utilities like Overstory which you know, uses satellite imagery to help utilities trim trees around power lines.
1:09:04
Um we just invested in fund two in a company called Voltaire, which does autonomous drone power line inspections.
1:09:10
So, you know, utilities cause about 11% of fire ignitions, but about 60% of the acres burned.
1:09:14
And so, if you can actually just help reduce utility ignitions, that's like a huge leverage point. >> Interesting. >> Interesting. Interesting.
1:09:20
What what what's going on on uh disaster prevention in like more of the consumer prosumer space?
1:09:26
I uh I saw a house for sale over in Malibu and the it was uh by some actor and he had installed like fire shutters and like all this different equipment.
1:09:38
He was like living off grid and this was like his getaway.
1:09:41
And it feels like I I saw after the LA fires like the autonomous water sprinkler that would be bolted to the top of the house.
1:09:48
I think a lot of homeowners in Los Angeles at least were like, "Oh, I want one of those."
1:09:53
And they would have clicked the button to buy it if they'd seen an ad that day, but then a year goes by and nothing happens in the next fire season and you think, "Oh, maybe not."
1:09:59
But it does feel like there's some fertile ground in the consumer space, but is that more challenging than people might think it is in reality?
1:10:09
I think you're totally right.
1:10:09
Like consumer demand goes in spikes.
1:10:10
It's very seasonal volatile business.
1:10:12
You know, I just saw today Watch Duty is the number two downloaded app on the App Store today and you know, it probably was not the case a couple weeks ago.
1:10:18
So, you know, it's certainly that's the nature of the beast here.
1:10:22
Um in terms of home hardening and things you can do to protect your home, I think that also kind of goes in waves to your point, but to me the real unlock is going to be when insurance companies uh create incentives for people to actually install these things around their their home.
1:10:36
So, we're investors in a company called Stand.
1:10:37
Um they help model homes.
1:10:40
They use computational fluid dynamics to simulate wildfires moving through the property.
1:10:44
They come up with a list of recommendations for the homeowner of what they should do with shutters and windows and remove certain vegetation.
1:10:52
They remodel it and then they can actually provide discounts on insurance.
1:10:55
I think that's going to be the real unlock that drives, you know, at scale consumer behavior.
1:10:58
Yeah, it has to be a real-time like you're renewing your home insurance and they're like, if you do spend, you know, in California it could be like, spend $10,000 on this new system and if you do that, we'll give you a $10,000 reduction this year and, you know, further discounts in the future so that you end up, you know, um effectively, you know, saving real money. Mhm. Yeah, exactly.
1:11:22
I think historically insurance companies have not done that, but um that's the that's the key to protecting, you know, homes in this kind of new era and and it's it's got to happen, you know, the the California Fair Plan, which is like the state-backed insurer of last resort, uh just announced that they're going to raise rates 30% this year after the LA fires.
1:11:39
And so, you know, if we don't actually reduce the probability of homes burning, housing and insurance is just going to become unaffordable.
1:11:46
How are you thinking about uh selling to the government uh maybe in California or elsewhere?
1:11:51
Uh Anduril has this interesting story where they built a firefighting tank.
1:11:56
They were trying to sell it to California firefighters, but there was pushback of around job displacement, even though it was ideally a new capability that would actually have support staff and not really take anyone's job because no firefighter can sit in the middle of a blaze like this particular firefighting tank could, but it was still became a political issue and ultimately did not become a real product.
1:12:18
Uh is there movement there?
1:12:20
How are companies positioning themselves as additive when I think about drone uh review of of of power lines?
1:12:27
There's probably someone that went up there earlier. Cost savings is good.
1:12:31
Doing more with less is great, but also there's always that pushback around job displacement.
1:12:37
Yeah, I think there's kind of two issues at play here.
1:12:38
First is like, is there actually job displacement?
1:12:40
I think the reality is no.
1:12:41
I mean, it's just we are so under-resourced relative to the scale of these disasters.
1:12:46
They're happening three times more frequently with huge severity, you know, Cal Fire is the largest and best resource firefighting agency in the world bar none.
1:12:53
Like that's not going to change.
1:12:56
Uh and I think we're it's really about how do we get leverage out of those um investments.
1:13:00
I do think though you're pointing out a cultural issue though and that's something that we've really worked at trying to bridge.
1:13:05
Um you know, I think it can be really harmful if like a bunch of guys in Palo Alto sipping lattes like walk out to the fire line and try to tell people how to do their jobs.
1:13:13
jobs. You know, there's just this huge disconnect and so one of the things that we built is this conference called the Red Sky Summit where we actually get 600 fire chiefs and other emergency managers together every year in San Francisco and we kind of create like an off-the-record
1:13:26
venue for them to talk to people that are building stuff in technology and it kind of creates this great sharing back and forth where, you know, the you can show the value of this technology, you can have these two-way conversations, it sort of changes the tenor and that's been a real unlock. We've seen a lot of
1:13:39
We've seen a lot of buying behavior come out of that event.
1:13:43
Um I think things like Watch Duty that show how impactful technology can be, a firefighter see that and you know, I I think the tie in the cultural tide is really changing and so it's it's an exciting time to be building in this space. >> Yeah.
1:13:54
How how uh is there any relevance like we talked to some other sector-specific funds or thematic funds uh and the classic example is like in in CPG, they can be a harder business sometimes but there's a number of like clear acquirers for mid-scale companies like a Unilever or Coca-Cola will take out a lot of these uh a lot of these companies at a unicorn valuation and so it sort of changes the underwriting.
1:14:21
It's worked for a lot of funds in that category.
1:14:22
Is there a 800-lb gorilla in this category that's maybe not being disrupted but maybe can be a partner at some point in time or are are is the thinking like everything is uh IPO it needs to be a stand-alone business or bust or is anything different financially about these these businesses that you see?
1:14:43
Yeah, it's a good question.
1:14:43
I I don't look at wildfire as like a market in and of itself.
1:14:46
It's this sort of dynamic that touches these huge markets like energy, insurance, housing, real estate, forestry, you know, government emergency response.
1:14:57
And in each one of those categories, there are, you know, really large companies.
1:15:01
There are, you know, contractors in the utility space or providers in in all of, you know, all of those those markets.
1:15:06
So, I think there's certainly exit paths, but the economics here are just like immense.
1:15:10
You know, the Bloomberg just published a report disasters cost the US economy a trillion dollars a year.
1:15:16
That's like on par with what we pay for interest on the national debt or defense.
1:15:22
Um and so, it's like, you know, you there are really big public companies that will be built, you know, solving that because the that cost falls on these really large deep-pocketed institutions.
1:15:31
And, you know, we think that there's just there's very big businesses to be built. Yeah.
1:15:34
Uh is there opportunities for wearables uh for firefighters?
1:15:40
I was uh once the the the Sandy fire started here in LA, I was I was showing uh my son some videos on like wild just like wildland firefighters.
1:15:52
And I was shocked that a lot of them just like weren't wearing gear.
1:15:56
They were wearing they'd wear something like a piece of fabric over their face.
1:16:00
Seems like there's probably opportunities there and potentially a decent-size market.
1:16:06
Um Yeah, I mean, it's it's a travesty what we equip our firefighters with and send them out there.
1:16:11
I mean, it's like, you know, the the health impacts of wildland fire smoke are just terrible.
1:16:17
I mean, and so, and there's really not a practical way currently to help filter that air for these wildland firefighters.
1:16:23
So, they're out there, you know, with really with no masks, sleeping in smoke days at a time, carrying packs. It's 110, 120 degrees.
1:16:31
You know, they're working hard with chainsaws and axes.
1:16:32
I mean, it is it's a really dangerous, really grueling job.
1:16:37
And I think, you know, part of the original thesis for Convoy, was I was actually volunteering with my local fire department up in Mendocino County and you know I just was watching this and I'm like I can't believe we're doing this with you know trucks from the 1970s and you know axes and like that's the state of the art.
1:16:53
And so there's a huge opportunity there and I think you know it's the and you can look at you know wildland fire as a as a market and it's a certain size but it's also a path into all types of field service jobs and all types of worker safety and so we looked at a lot of companies there.
1:17:06
We haven't made any investments yet but that's that's category we're really interested in.
1:17:09
Uh we we we we jumped straight into discussion of the fund the strategy.
1:17:13
Can you take us back a little bit to what you were doing before why start this fund when you started it sort of the the prehistory of the fund? Yeah so I was a founder.
1:17:24
I started a company called WePay back in 2008.
1:17:26
It was an early fintech company and we built that up for about 12 years and sold it to JP Morgan. >> that headquartered? >> Overnight success. >> [laughter] >> Yeah. I wish. It was uh yeah.
1:17:40
It was uh it was headquartered in in Palo Alto. Palo Alto okay.
1:17:45
Yeah I I just remember running into it in Boston.
1:17:47
I think in like 2012 or something that was around the time.
1:17:50
I was probably seeing you guys around in Boston. >> in Boston. Okay that's right. That's right. That's right. That's right. Yeah okay got it.
1:17:57
Yeah we started in Boston we were funded by Y Combinator moved to the West Coast built that up for about 12 years and then we sold it to JP Morgan. >> Yeah.
1:18:04
I left JP Morgan and I think one of the things that really suited us well at WePay was we were early to like the financial technology market.
1:18:11
People called it like banking technology at the time and it a lot of the same skepticism that I hear around utilities and government we heard you know we're trying to raise money from Boston VCs in 2008.
1:18:20
And you know they were wrong.
1:18:24
I mean the fintech market was like the most exciting sector of technology over the next 10 years and you know so as I was leaving JP Morgan I wanted to work on something that was you know a market that was early and that we could be sort of one of a kind, that we could be a market leader in and that also had a important mission where we're helping people and doing good for the world and this sort of lined up all of those things.
1:18:46
My wife and I own a ranch up in Mendocino County a couple hours north of San Francisco and fire almost burned down on the property and you know all the the dots started to connect and and here I am. Yeah, that's amazing.
1:18:57
It's it's sort of like the VC version of like build something you use yourself like the YC ethos make something people want.
1:19:02
Well, thank you so much for coming on the show and breaking down for us.
1:19:06
Congratulations on the >> glad you're doing this. Yeah. Thank you. We need more. >> Thanks guys. Love what you're doing. >> of your day. We'll talk to you soon. Goodbye.
1:19:15
Up next we have the co-CEO of Spotify joining Alex Nordstrom. I'm a huge fan.
1:19:21
Calling in from Spotify Investor Day. Welcome to the show. How are you doing? What's going on? Thank you.
1:19:27
Hey Jordan and Jordy, it's good to be here. Great to have you. >> Fantastic.
1:19:31
First time on the show, why don't you kick us off with the news today?
1:19:35
What's new in Spotify's world?
1:19:40
We just had a fantastic investor day 2026.
1:19:44
We talked about what we've delivered in the past four years which put us in a really great position in terms of user growth and financial metrics.
1:19:51
We also laid out our four big ideas for for the future and then we also talked a little bit about how we can further monetize Spotify and grow grow all the sort of financial metrics and goals of the company.
1:20:04
I want to send my greetings from from from Gustav Söderström who was also on your show Yeah, yeah we talked to him at South by Southwest.
1:20:11
It was a great conversation.
1:20:14
We're very excited to have you here.
1:20:16
Um where should we start?
1:20:17
I I'm I'm I want to jump into >> big ideas.
1:20:20
Yeah, I mean there's so many different things.
1:20:25
Maybe we should start with the reserved ticket access because I saw that organically promoted to me and it seems so logical, and I'm fascinated by the rollout, the strategy, and also like sort of why did it take so long?
1:20:40
Has this been in the chamber for a long time? What has been the plan?
1:20:42
What has been the go-to-market with this particular product? Great question.
1:20:47
So, I've been with Spotify for I think it's now almost 16 years. >> Yeah.
1:20:51
And this is probably >> success.
1:20:52
one of the most one [laughter] one of the most lovely improvements to Spotify Premium that we've done I guess since the founding really.
1:21:02
So, you're right, you know, it's long overdue. It's a great one.
1:21:05
And the reason why it's so great is really that it solves a couple of different problems.
1:21:08
One is, you know, who hasn't been sitting there in front of a website trying to get tickets for a concert? Mhm. I mean, it's very hard.
1:21:15
And if you get it, it's going to cost you a lot, right?
1:21:16
And the second second thing is even if you get tickets, it may not be to the artist that you actually listen to and love. Yeah. Right? So, this solves that.
1:21:23
So, what we're launching is basically we're giving you we're holding tickets for you Yeah.
1:21:28
for some time when they're free to just go and and pay and collect with with with our partners.
1:21:34
So, it really solves the problem in a unique way because we're matching the tickets and the concerts and the participating artists and tours with the the users on our platform that are actually the true fans of this. Yeah.
1:21:45
We look at it from many standpoints like not just the number of streams that you're doing so you can't just grind yourself there. Yeah, I imagine.
1:21:51
catalog engagement and so on.
1:21:55
Yeah, so it's it's terrific.
1:21:56
This is a fantastic improvement in Spotify Premium. Yeah.
1:21:59
They may have a new problem which was which will be ticket scalpers setting up, you know, infinite the Spotify accounts.
1:22:06
>> subscription fee for years.
1:22:06
just to try to run algorithms.
1:22:09
No, but I I think it's absolutely brilliant.
1:22:11
Solves a problem, you know, even even from the artists like being in this position where you're trying to make your ticket prices affordable for your fans, but due to market dynamics no matter where you price it, they'll they'll go they'll go way up, and then the only beneficiary in that transaction is the is the person that's just buying the tickets to resell uh and and everyone else loses.
1:22:33
So, I think You're totally right.
1:22:35
I mean, there's like many things that we do at Spotify, we look at what organically happens on the platform and and what we do.
1:22:45
And then when we see that there is some sort of signal or trend that actually just come by way of organic behavior on the platform, then we double down on it.
1:22:52
So, we've done many events you know, with artists and groups and bands and so on over the years and you know, on a on a on a yearly basis we probably do 150 to 200 events.
1:23:05
And the the magic really happens when we look at who to invite to those events.
1:23:10
And if we can really strike the balance of, you know, getting enough people there that are really sort of on a very very high engagement level with that artist, not only again, not only just listening to the artist, but doing it maybe on a daily basis, listening having listened to the whole catalog and do it for, you know, at least an hour per day or something like that.
1:23:26
It really gives the artist also like a magic experience of facing, you know, your truest fans.
1:23:31
And this is something that uniquely we can do for for for artists. Yeah.
1:23:35
Uh AI is obviously at the top of everyone's mind.
1:23:39
The large taste model is the latest technology from Spotify in the AI world, but I feel like you have to have been embedding every song into some sort of machine learning learning model for probably over a decade.
1:23:53
Can you take me through a little bit of the history of machine learning or AI at Spotify and then what is actually changed?
1:23:59
Like where are you seeing a break in the graph?
1:24:03
Like where where are you seeing, okay, we're we're we're experiencing some sort of discontinuity in the progress we were making. Yeah. So, you're right.
1:24:13
It was actually just about a decade ago when when Daniel and Gustav and I started investing into into AI and that was called machine learning. Right?
1:24:21
And uh it was very basic the algos then.
1:24:25
Like it was almost like, "Okay, similar to what you do on Amazon, you know, John uh listened to this um is similar to Jordi who also listened to this other thing, therefore he should listen to that, too."
1:24:36
So, look-alike algorithm, if you will.
1:24:39
>> Yeah, collaborative filtering, probably based on tags, not actually looking at the waveform even. Exactly like that, yeah.
1:24:44
And so, now many, many years later, having continued to invest in that and just the the the the data, not just the data, but the learnings we have and the feedback loops we've had over the years, it's been compounding.
1:24:53
So, and now um you know, you're asking me what changed lately.
1:24:57
Well, obviously what changed is that, you know, we've got access to um you know, large language models like general intelligence that we can use to reason over the data that we have.
1:25:05
But what what it comes down to really is the unique data at scale here, right?
1:25:09
So, you know, we we log probably, you know, between I think it's three and four trillion uh events that happen per day on Spotify.
1:25:17
So, we really get billions of signals that are relevant to feed it uh to feed the algorithm with.
1:25:22
Uh and then the the second thing that happened is is obviously obviously because we started to sort of >> [clears throat] >> uh you know, understand English.
1:25:30
Algorithms started to understand English.
1:25:31
So, we we sort of started importing a lot of knowledge from outside from the outside world onto Spotify. Yeah.
1:25:36
Uh and and also the third >> has gotten so much better at detecting lyrics.
1:25:43
Like I used to have to go and Google or search uh a lyric and then go to Spotify.
1:25:48
And now I can just type it right in and I get it.
1:25:49
I'm like, "This is amazing experience."
1:25:50
Um I'm interested in uh your build versus buy versus fine-tune and open-source model. Uh what's working? What's interesting?
1:26:02
Music is, you know, obviously you're benefiting from transformer architectures, from all the all the open-source work, but I imagine that a lot of the tasks that you're doing aren't exactly, okay, just fine-tune uh the the a text-based open-source model.
1:26:17
You might need to go a layer deeper, you might need to partner with someone.
1:26:19
How have you been thinking about the way to deploy AI?
1:26:24
Yeah, we we certainly believe in general intelligence and you know, we've been riding that cost curve that's that's going to come down through collapse by a thousand X over the last four or five years or so.
1:26:33
So, we believe in in this industry commoditizing more and more.
1:26:37
So, we're going to buy, you know, the best reasoning capabilities that are out there and then we're going to use that on top of what we call our large taste model, which we are uniquely able to using proprietary data to actually build out.
1:26:49
Much thanks to the stuff that I was talking to.
1:26:52
The other side of the equation here that is important, I think, is also that we get a lot of feedback and data from artists as well via our Spotify for Artists application, which feeds us with data that's also proprietary and unique. Yeah. Which is great.
1:27:04
So, you you asked about building buy and so on.
1:27:07
I think it's a great question.
1:27:07
Uh, you know, if we see opportunities to actually build something to help, you know, bootstrap or add a capability, we will do it.
1:27:15
And I'll I'll draw two examples that are quite good.
1:27:16
Uh, I think three years ago we bought Semantica, uh, which really helped us create AI DJ. Okay.
1:27:24
Um, and you know, DJ X Yeah, it really accelerated the build out of AI DJ and now AI DJ is all over the world and we have it in different languages and people are using it at scale.
1:27:32
So, that was a that was a that was a great, I think, transaction that we made.
1:27:38
Another one that is also it's more recent, actually.
1:27:41
Uh, it was just before the end of last year we bought WhoSampled, which provided us not with so much a capability but a unique set of data. Mhm.
1:27:49
So, I I don't know if you've used Song DNA lately.
1:27:51
It's it's been killing it.
1:27:53
I think 50 to 60 million people are using it already.
1:27:55
You know, you can listen to a song and then you can check like which were the samples in that song. >> Oh, interesting.
1:28:01
And then you can also like follow the thread down to the original what the original.
1:28:05
Yeah, you can listen to the original track and then hear how they sort of flipped that into the new one.
1:28:10
And you can see who was in there, the studio technicians, the artists, the composers, and so on.
1:28:16
And a lot of this we could have done or it would have taken us much longer time without WhoSampled. Yeah.
1:28:20
Structurally, how how is AI changing the work that Spotify employees are doing?
1:28:25
Matthew Prince from Cloudflare was talking about this concept of you have builders and sellers, but just the raw measurement, that's less important as a discipline.
1:28:39
And so he's pushing more of his staff to build new things and then go and sell it.
1:28:44
Do you have a framework for thinking about how your employees should be using AI in the future? Yeah.
1:28:52
I mean, we you know, a couple of years ago I think we all you went through that the ChatGPT moment.
1:28:57
And just before the holidays of last year we had a similar moment around coding tools.
1:29:02
And we all sort of got enamored with it and started adopting it.
1:29:06
But at Spotify the engineering and development you know, teams have just you know, done a crazy good job adopting it.
1:29:14
So we now have I think close to 99% adoption across Spotify.
1:29:19
And and this goes, you know, beyond the engineering team, but also into the marketing team that are using it not just for design, but also for for better gaining insight into what type of compelling stories to to to to tell on top of the story.
1:29:30
So we do have very many different use cases.
1:29:32
And you know, people are crazy about just creating prototypes now all around Spotify.
1:29:35
Just test things in a way faster way.
1:29:38
So what you get basically is a lot of like productivity gains.
1:29:44
And I know for a fact one of my dear colleagues, the chief product architect of Spotify NGN, he was just at Anthropic and he was doing presentation for for their staff.
1:29:55
And I hear from Anthropic that we are actually one of the leading developers out there in adopting AI and using it to to gain productivity productivity improvements. Yeah.
1:30:03
productivity improvements. Yeah. Have you thought about the I don't if you there there is a trade-off you might be doing both but there's a world where you have some sort of engineering pod and there's an opportunity to have more members of that team pushing code
1:30:19
shifting to individual contributor roles but of course they're managing agents so it's also a managerial role versus embedding more technically minded folks across different organizations so you have more cross-functional teams a marketing team gets an engineer who can oversee agents. How are you thinking about
1:30:37
How are you thinking about just spreading different different AI tool use across different organizations that might not previously have been in a position to build a piece of software maybe they're in the market to buy a couple SaaS products for that whatever they're whatever their vertical is but now they have the opportunity to actually build.
1:30:57
What you're seeing actually happening organically at Spotify is that you know as you know adoption goes up it's typically you know you're by your own and you're you know looking at what's out there which MCPs to connect to and how that can help your your
1:31:10
[clears throat] work and then and then you're basically sort of copying some other people's skills around at the company and you just sort of experiencing how it is to use AI to do your work your particular field but what now is more happening is that we all of our Slack groups and in our
1:31:24
communities we see people sharing sharing you know use cases with each other and the next step really is that for instance someone a product developer and an engineer you know sits down with a marketer starts to understand hey okay we the three of us can actually build something of a
1:31:41
you know a holistic experience of not just new product and feature but also tell a compelling story about it you know in a in a much much more sort of amplified and leveraged way which I think is fantastic when that happens. We got to talk about fan-made covers and
1:31:54
We got to talk about fan-made covers and remixes the new deal with UMG super super excited about this personally and of course it's been really >> you want to cover?
1:32:05
You're going to do that you're finally going to do the Gunna cover you were planning on. Yes.
1:32:09
What's [laughter] your favorite What's your favorite track right now? Is it a Drake track?
1:32:12
>> Ooh, I I've been Uh no, I think I I think I was expecting Drake to kind of grow up by this point in his career and maybe evolve a bit more.
1:32:21
[laughter] I feel like I grew up maybe faster than Drake.
1:32:24
He's still obviously you know, an incredible incredible uh superstar. Yeah.
1:32:29
Uh but yeah, John was talking about Gunna.
1:32:30
I'm a big Gunna Gunna fan. >> Oh yeah. Yeah.
1:32:33
But uh so I'll be making some Gunna uh covers and remixes.
1:32:36
But yeah, to talk about this deal uh certainly controversial, but this feels like one of those things that uh people will have one reaction and then >> Yes.
1:32:48
What's the What's the What's the here What's the here What's the here What's the here Yep.
1:32:50
uh which I'm assuming will be you know, incredibly magical and and you know, we've had Mikey from from Suno on the show.
1:32:56
He's a friend friend of ours and uh you know, people people fundamentally they they they say one thing on the internet, but when they use the products, they love them.
1:33:07
And so I think it's I think it's super exciting.
1:33:09
So yeah, talk about this deal.
1:33:10
What what were the factors that were most important in getting this right so that you know, fans win, artists win uh and everyone involved wins. Yeah. Yeah.
1:33:19
Certainly I'm certainly very very pleased with it.
1:33:21
I mean, it's a landmark deal and it's a landmark deal from the vantage point of we're enabling something that is for the first time a legal product for users and fans to actually create these remixes and covers. Mhm.
1:33:34
Um and what's more is that it's really the first time um you know, in a controlled and licensed medium that artists get to partake in in in the AI economy really.
1:33:44
So that that is like the the core of it.
1:33:46
And then if you think a little bit about what is it that we're doing here?
1:33:50
Well, you know, people love to your point like people love remixes and covers um and they've always done that, but there hasn't been any scaled way to really sort of tap into this opportunity.
1:33:59
And what we're doing right now with with our product is that we're unlocking this this market.
1:34:06
Um, so where we think actually in the end it's going to be very additive to both the music industry and ourselves, including artists and songwriters. Um, so it's very cool.
1:34:14
We're launching it as a paid add-on, which means that, you know, if you have Spotify Premium, you're eligible to actually buy into this add-on.
1:34:22
And when you have the add-on, you can actually go ahead and create remixes and covers.
1:34:27
And what's cool with Spotify is that, you know, we've been we're very experienced when it comes to free and premium and, you know, how to convert people and lead people up the ladder.
1:34:36
And so what we're going to do is obviously give you a certain, you know, limited usage on premium so you can actually try it out, play with it, create habits around it, and then commit to buying add-on.
1:34:47
But the the important point here is that, you can think about it as, you know, creation is paid for and consumption is included.
1:34:54
So basically, you know, the output that you create around the gunner, yeah.
1:34:59
The gunner remix that you're going to create, you know, I'll be able to listen it, everyone will be able to listen it. >> Sure. Sure.
1:35:04
Yeah, is there So so I can imagine I can imagine a world, you know, maybe 6 months from now where the number one track globally on Spotify is a fan-made remix.
1:35:15
Like, you know, may may take more time, but it doesn't feel doesn't feel doesn't feel impossible.
1:35:25
Is there is do you think this could potentially create a new category of actual artists on Spotify?
1:35:31
Would the person that creates a remix ever be able to get, you know, some elements of rev share?
1:35:38
Or is this like entirely going to be more like, you know, more like a live DJ where, you know, you're putting you're mixing and matching and putting things together and and maybe you can build up your brand, but you're not monetized you're getting royalties for any any revenue share. >> Yeah.
1:35:55
Yeah, I can't speak to the specifics of it but but I can't tell you how I think about it.
1:35:59
You know, when I started at Spotify together with Gustav 16 years ago, we had roughly I think it was 2 million tracks in the catalog and I've lost count but I think it's in the order of 200 million tracks now, you know, 15 16 years later.
1:36:12
So, the catalog being big and growing is a good thing and the problem you're talking about really opportunity is for us to to do recommendations well.
1:36:21
That's always been at the heart of Spotify.
1:36:23
If you ask somebody why why do you love Spotify?
1:36:26
Well, most people say, hey, it's because they seem to know me.
1:36:30
So, we're able to sort of help you discover about way of personalization help you kind of find your tracks again that you like and so on.
1:36:38
So, really this is what you're talking to is is really a personalization problem, right?
1:36:42
So, when when this product creates more catalog, our job is to make sure that, you know, the best song gets gets to the best place.
1:36:50
Last question, talk about the app icon redesign.
1:36:51
I thought it was really cool. I loved it.
1:36:58
But I'm interested in the actual I mean I I I want to know how you process it internally but that a disco ball could get people but my assumption was that you know, it was the Spotify icon is on my home screen and I click it when I want to listen to music but it jumped out to me and I feel like that would show up in the metrics of jarring people awake.
1:37:22
But I'm interested to know like what actually happened.
1:37:26
Are you going to be changing the icon randomly every 5 years?
1:37:27
Is it got to be every 20 years?
1:37:30
Like what's the what is the learning from doing something sort of bold? Right.
1:37:37
So, about 10 days ago, we decided to change our the Spotify app icon or Spotify logo to one that is more of like a glittery disco ball version of the Spotify logo.
1:37:50
And and uh you know, to to your point, this just sparked a massive uh conversation around the internet on on basically every major social platform.
1:37:59
And um uh and and you know, what a lovely uh and wonderful uh piece of culture to have a conversation around the change of our our our logo icon. Right?
1:38:07
So, not only users, you know, have been sounding off, but also other big brands. Yeah.
1:38:14
Joining in I made their version.
1:38:14
Uh suggested that they would make a version like that.
1:38:18
Kit Kat Kit Kat joined in on the fun.
1:38:20
[laughter] Uh so, [clears throat] yeah.
1:38:21
So, when you have uh you know, hundreds of millions of people that passionate about culture and art and and and Spotify, this is what happens. Right?
1:38:30
So, uh this sparked like a massive conversation that I think is is is lively and fun.
1:38:34
And and to be honest with you, the reason why this is happening, you know, having reflected on it a few days later now, it's pretty intense when it happens because there are two sides, right?
1:38:43
And you know, that And then there's also all of this like uh byproduct, you know, the internet coined a new design term around this called disco morphism.
1:38:51
I don't know if you got that.
1:38:53
Yeah, it's kind of kind of funny. Yeah.
1:38:54
And people have been asking for an answer to flat design.
1:38:58
Lots of people have been lamenting how boring things have gotten.
1:38:59
You spiced it up and then people are like, "Ah, I didn't mean that."
1:39:03
Which is a very funny way to process it, but sorry, continue.
1:39:07
I mean I mean I just the the reason why this is happening after having sort of thought about it for a few days and discussed it internally really is because we are truly at the intersection of the humanities and technology. Sure.
1:39:17
And I think we're in a good place when it comes to that. We're at scale.
1:39:22
Uh you know, when when when hundreds of millions of people or potentially even billions are talking about you, um then you did something interesting. I love it. I love it.
1:39:29
Uh yeah, a really really well-executed stunt and uh got Yeah, just got everyone talking and and reminding.
1:39:36
And it's hard to break through with with something like a 20-year anniversary, with a milestone like but it's important to do something like that. So, congratulations. >> Yeah, thank you.
1:39:45
We did it mostly for ourselves in the beginning, but now it turns out that, you know, Music should be fun.
1:39:50
Building a company around music should be fun.
1:39:52
Like all of this, like it it's I I know people have their think pieces and their deep dives on contrast ratios and all sorts of things, but like at the end of the day, it's fun for a company like Spotify to do something fun with an app icon for a few days. I love it.
1:40:05
Also, I push back on those people.
1:40:05
I thought it I thought it looked exactly like a disco ball would look if you were in a dark room >> That's right.
1:40:13
>> listening to music and you woke up.
1:40:13
Uh so, I thought it was I thought it was pure to the to the disco ball brand. >> I agree. I agree. That's good. Thank you. Keep it up, man.
1:40:22
>> [laughter] >> Anyway, thank you so much for coming on the show. >> Alex. Thanks.
1:40:26
>> Congratulations on the progress.
1:40:27
>> Yeah, congrats to the team on all the new milestones.
1:40:29
>> We will talk to you soon.
1:40:29
Have a great rest of your day. Goodbye.
1:40:33
Uh and if you haven't been following, the stock is up 13% today.
1:40:34
Spotify is a $103 billion company. Not too shabby.
1:40:42
Our next guest, Jordan Schneider from China Talk, you know him, you love him.
1:40:46
He's back on the show for the third, fourth, fifth, sixth time.
1:40:48
Who knows how many times it's been.
1:40:50
We lost count, but it's been too long, and we're happy to have you here. How you doing? I'm good.
1:40:55
I got to be honest, was also pretty confused by the Spotify >> You were confused by it? I thought Well, yeah.
1:41:02
I mean, a big $100 billion company can't have fun in this country? Yeah. What's wrong?
1:41:06
I'm just saying, it took me longer than a flicker.
1:41:10
>> I thought I had like some weird iOS update, you know, Tahoe, whatever. Yeah. Oh, yeah. Sure. Sure. Sure.
1:41:15
I just think that like the fact that you processed it, even if it slowed you down, it put you in, you know, the fast thinking, slow thinking.
1:41:21
You were in slow thinking mode. That built the brand. >> enough.
1:41:25
You're not addicted to your phone enough.
1:41:26
Are they still Are they still rocking the Are they still rocking It's still a disco ball. I like it.
1:41:29
I think they should just keep this thing.
1:41:33
It stands out so much from every other app on my phone right now. I love it.
1:41:37
Anyway, how are you doing?
1:41:39
Give us Maybe let's start with the postmortem on last week.
1:41:43
How was the China summit?
1:41:43
And then we can just go into everything else. So boring, man.
1:41:47
I I knew I knew you were going to say that.
1:41:49
I was like, "We're going to talk about everything but China because it was so boring."
1:41:51
But And they let me down.
1:41:53
let me down. I mean, look, I think there is a clear um period of stalemate which we've gotten into where the US has some leverage over China, um which has been something that um really since like 2017, uh uh Trump won, Obama Trump won, Biden,
1:42:15
and then uh the start of Trump two thought that they could kind of keep turning the temperature up and squeezing on tariffs and export controls and investment restrictions, and all of a sudden China April 2025 punches back with rare earths. And all of a sudden um
1:42:28
And all of a sudden um uh they realize uh you know, they run the they run the experiment and it works.
1:42:36
That actually the PRC has real leverage over the US and is able to kind of constr- like meaningfully constrain what um American presidents want to do when it comes to um more course of actions on China's uh you know, technological and economic expansion.
1:42:52
So, um both sides have kind of realized that, and what we got is a you know, uh what was my headline?
1:43:00
Prestige on the cheap, um where uh the US decide where um you know, you have the whole cabinet uh flying over, you have every fancy CEO flying flying over, just being awed and wowed by um you know, their 36-hour vacation to Beijing. And >> Mhm.
1:43:22
Yeah, and look, there's an interesting debate to be had about whether the like giving them the face and giving them the atmospherics of that is actually like kind of in some level costless and maybe this like makes them feel less aggrieved, less likely to start World War III.
1:43:41
I mean, this is the argument with Putin, right?
1:43:43
It's just like we kept disrespecting him and then he decided no, like I'm going to show you just how tough I am.
1:43:50
So, I'm not kind of on face opposed to it, but you know, it was it was kind of just more of nothing.
1:43:58
>> Yeah, I mean, you mentioned every fancy American CEO was there, but were you surprised or were you expecting or did it break your expectations that no Sam, no Dario, no Demis, no Sundar, a sort of lack of AI representation.
1:44:15
Yes, you have Elon and yes, you have Jensen, very important figures in the AI American AI build out, but no true lab leaders from my, you know, rough estimate if I define it a certain way and it felt like okay, okay, maybe this discussion should be about AI and it's and it's there there they're not even bringing the people to let them get a word in edgewise, not even a single line of conversation will happen.
1:44:39
Was that as expected or how do you process that?
1:44:44
>> Yeah, I mean, it's interesting because like this is phrased this is framed as like a trade delegation, right?
1:44:51
And so almost all of those companies like meta excluded.
1:44:55
I mean, I guess they had a person there, but >> but Zuck wasn't there, yeah.
1:45:00
Yeah, but but that person's like a Trump one person that's kind of just like keeping with the vibes and I guess meta builds glasses builds glasses in China.
1:45:09
Um, Yeah, and they and they acquire AI agent companies, man.
1:45:11
It's they they they're a buyer of Chinese companies or formerly Chinese companies that have moved to Singapore.
1:45:18
Which they're now not allowed to do.
1:45:18
So, yeah, they had some lobbying to do, I guess.
1:45:21
But like the you know, the asks, I think are really inkoate.
1:45:27
Um, look, the US government, we just had news today that Trump, um, 86ed a, uh, executive order because he doesn't know what he wants to do about this yeah, sort of thing about AI safety.
1:45:40
And if like, you can't even figure out what you want from a domestic regulatory perspective, like are you really ready to like put all the cards on the table and have this big, you know, grand summit with the Chinese to make sure that bio weapons don't kill us all in 2029. I don't think so.
1:45:57
I mean, it'll happen at some point, but it's it's just a little premature, I think.
1:46:04
Maybe not from the technological perspective, but from a sort of policy perspective that, um, you don't expect much of a serious discussion between the two.
1:46:13
>> Yeah, are are you Uh, Sebastian Mallaby had an op-ed where he was sort of gesturing towards like the possibility of more collaboration on AI between America and China.
1:46:23
You're You have your hands in your face or your face in your hands, so I assume you are, uh, skeptical that that will take place anytime soon.
1:46:32
But all I mean, also the AI 2027 folks, very good at forecasting, have predicted a lot of what's happened.
1:46:38
Uh, say China wakes up in 2026.
1:46:42
Yeah, I mean, it's not impossible. Yeah.
1:46:44
I think the sort of, um, look, if the American political system has not yet woken up to, uh, an understanding that you need a different regulatory strategy in order to keep humanity safe, Mhm.
1:46:58
um, I think it is unrealistic to expect a strategic adversary who does who is, you know, 6, 12, 18 months behind where you are on the sort of technology development trajectory to come to the same realization.
1:47:15
Now, you know, it'll happen at some point.
1:47:18
I think, um, this stuff is really powerful and the the sort of logic that um America doesn't want uh you know, bio weapons to be developed by Al-Qaeda or ISIS or um Hezbollah just in the same way that um China wouldn't want it to be done by, you know, the Falun Gong or Tibetan separatists or uh you know, uh any any group that's they see as like a non-state actor that would that would want to kind of take down the party um applies, right?
1:47:54
Um but uh China I think is deeply the Chinese leadership is deeply hardware pilled.
1:47:59
I think they're deeply skeptical of these sorts of arguments on face cuz they're kind of sci-fi and um the the sort of journey the the analogy or the journey that um the US and Soviet Union went on over the course of the Cold War to start to have real kind of discussions about strategic stability and arms control happened after you had the Berlin crisis and after you had the Cuban missile crisis.
1:48:29
So like, look, Anthropic putting out a paper saying Claude Methuselah is really scary is one thing, but it is different at a fundamental level than like completely shaking a society and like having the entire world realize that you were on the verge of apocalypse.
1:48:43
So, I hope we don't need that um but I am I am Haven't had the ducking for a while.
1:48:51
>> that much that much will come of this in the What do you >> uh before it really accelerates. Yeah.
1:48:57
How much have you read into the theories that, you know, maybe China would would fund uh data center, you know, push back in the United States.
1:49:05
I don't know that they they would need to, but if they were, if they felt that was important enough, they would signal something about how they think about the technology.
1:49:16
I think this is cope by the >> [laughter] >> labs.
1:49:18
Um you Look like Yeah, pretty and the straight lines won the election for Trump.
1:49:24
Okay, I mean, you know, it it it is I think Ben Thompson had the right take on this a few days ago.
1:49:31
This can be solved with money.
1:49:35
Like if if you really um put it you know, put the choice to a town saying, "Okay, we will pay everyone within a 20-mi radius $10,000 a year."
1:49:49
Um People will start moving in.
1:49:49
People will start frantically trying to buy a house.
1:49:53
>> WANTED TO WANTED TO LIVE NEXT DOOR.
1:49:53
OR LIKE LOOK, make the data centers beautiful.
1:49:57
Build a park on top of them.
1:50:00
Like give me some playgrounds.
1:50:01
>> That is the one that I'm the most skeptical of in the American society.
1:50:02
I feel like they will be companies will be paying dividends and mailing checks far beyond far like for long before anything looks beautiful in this country.
1:50:14
We build massive like monolithic structures all the time.
1:50:16
It's a lot of cement over here. I don't [laughter] know.
1:50:21
Turn the turn the turn the GPU racks into free slots.
1:50:24
Every time I see one of these like beautiful industrial structures, it's always in the Nordics. It's never in America. Sometimes it's in China.
1:50:32
They build a train No, so so you have the the the data center.
1:50:35
You got all the all the different racks of GPUs and you just put a slot machine attached to each one that people can walk through and and you get like three or four I wonder we we I wonder what's the you know there's cell phone towers that look like trees and I don't think they look any better really, but I guess they do sort of blend in.
1:50:51
I wonder what the like economic mechanism was for going that direction because when I'm driving in LA, I see normal cell phone towers and I see tree like fake trees cell phone towers and there had to be a reason like it has extra cost to make it look like a tree.
1:51:06
Like is that just a choice?
1:51:08
Is this like a nonprofit that's funding this?
1:51:10
Like who's who's lobbying for this? Is there a law? I don't know. We got to dig in.
1:51:15
>> I drive by I see them.
1:51:15
Can we please get the TVPN segment?
1:51:18
>> Yeah, we need to dig into this.
1:51:21
>> the tree phone pole Come for the questions, not the answers.
1:51:26
>> [laughter] >> I'm just riffing about things I don't know and would like to know.
1:51:29
>> I drive by I'm and they get me.
1:51:29
I just start slapping and I'm like, "You got me big telecom. You got me.
1:51:34
I thought it was a tree."
1:51:36
From a distance from a distance. Yeah, every time.
1:51:39
Well, the bad ones are like when they're five times as tall as all the other trees around.
1:51:45
You're just like and like the branches only start like four-fifths of the way up.
1:51:50
It's just Yeah, yeah, yeah, yeah.
1:51:53
It's very it's very half-hearted in its attempt at disguising itself. Ridiculous.
1:51:59
Um Uh on on other China issues, did you see there are two sort of dueling predictions maybe maybe I'm making them dueling predictions.
1:52:09
Chamath says Taiwan not going to be a factor geopolitically in 18 months.
1:52:15
Dan Wang has talked about how technologists overrepresent the chip story in the Taiwan story and in fact the discussion over Taiwan goes back much further and is grounded in more of like capitalist versus communist decision-making and alliances.
1:52:30
Where do you stand on the on Taiwan today versus in two years?
1:52:36
I have to acknowledge Chamath's existence. I refused to do that.
1:52:38
Um uh I think that Taiwan is a place that has mattered for a while and will continue to matter for a very long time to come.
1:52:50
I mean first only on the chips thing, the idea like I don't know.
1:52:52
Ask Chamath analysis like the percentage of chips that are going to be manufactured in Taiwan 18 months from now is still going to be like 85% or 90%.
1:53:04
So yeah, it used to you know it was 100% for a window I am glad Intel isn't a flaming pile of crap anymore and it's like nice to see Samsung also starting to get their act together but if if really that's the only thing you care about no you will still have a you know global economic catastrophe if the lights go off in Taiwan in 2028.
1:53:32
Now why should you care about Taiwan besides the chips?
1:53:38
the chips? I mean it's a it's a democracy people have you know deserve to have self-determination we can set principles aside if we really have to do that but look I think from a from a sort of geo strategic perspective there's also this kind of concept of the first island chain I think it makes it
1:53:59
it it makes the the you know broader Pacific architecture that America has has has built over the course of the past 75 years in Asia a lot more tenuous if all of a sudden you know the the kind of anchor to both Southeast Asia as well as North Asia becomes you know a PL and base so yeah they're wrong. Yeah
1:54:24
Yeah >> [laughter] >> so so so many reasons that Taiwan will remain important into the distant future regardless of what happens with Intel and TSMC Arizona etc.
1:54:35
Agree with you on that what do you think the next catalyst might be in the rare earth story because we're starting to see rumblings about funding for American indigenous supply chains there eventually that becomes at least a stocking horse if not a poker chip on the table on the bargaining table but where are we on the rare earth stalemate that became an important bargaining chip somewhat recently as you put it in the intro.
1:55:07
I mean, it's a really interesting question.
1:55:09
I've I've read reports saying that even five years from now the leverage is going to still exist.
1:55:15
And I think there's a sort of broader question of like uh This is the second largest country in the world and largest economy in the world and it's going to stay that way for a really long time.
1:55:31
And the sort of ambition to fully decouple such that China does not have leverage over the US economy and can kind of squeeze it coercively seems to me to be a very tricky thing.
1:55:47
Now, you can spend lots of money to sort of make that less acute or sort of turn it down over time or or um you know, protect the particular things if you're worried about you know, individual like specialized inputs into fighter jets or you know, something that would be really catastrophic like insulin getting cut off or something.
1:56:07
But it is um you know, this is like a this is like a a generational challenge of shoring this up and B there's also an offensive side, right?
1:56:19
Of um you know, escalation dominance and deterring an adversary from from squeezing on stuff like this also requires you to be able to credibly send signals that you can take pain and cause pain in a way that would require the you know, the the other party to um uh to to back down and not necessarily use these tools.
1:56:43
So, we're you know, where where we're at this like awkward equilibrium.
1:56:46
I think there's a lot of like hard thinking that still needs to be done to like like really conceptualize like what's the right way way spend money and the right way to think about this relationship.
1:56:56
We actually just ran an essay contest on China Talk about economic security.
1:57:04
We're going to be I have at 4:00 p. m.
1:57:07
We're doing a >> [clears throat] >> mega pod with three of the winners. >> Amazing.
1:57:12
Maybe I'll have a better answer for you after that, but it's a hard one.
1:57:14
Does Xi day trade >> [laughter] >> Who's the best day trader in the global >> in China? Here's the thing.
1:57:24
I got a piece coming out about comparing American and Chinese corruption.
1:57:32
And the difference is that in China, they still feel like they have to hide it. Okay.
1:57:41
Well, it'll be an interesting piece.
1:57:43
Shifting [laughter] gears, shifting gears both literally and figuratively, cars.
1:57:46
Have you driven any Chinese vehicles?
1:57:49
Have you been outside in the BYD?
1:57:51
Are you planning doing car reviews on China Talk? I would love to see one.
1:57:57
Have you been in a Zeekr? I keep seeing these. They look amazing. Zeekrs?
1:57:58
I watch a lot of YouTubers I watch a lot of YouTube and Bilibili reviews. >> Okay.
1:58:05
I have been in a BYD in Norway of all places.
1:58:09
They had it like in the main like like park, they had a giant setup.
1:58:16
You know, it's really impressive.
1:58:16
I you should get some you should get some American car manufacturers on Youtub.
1:58:22
It's pretty embarrassing.
1:58:22
You know, one of the most interesting developments, I don't know if you guys have been tracking this, the Waymo using a Zeekr body. >> yeah.
1:58:32
And you I would have thought there were import restrictions because if if Zeekr is not here, BYD is not here, you would think that like the supply chain would be restricted, but they must have found a way around.
1:58:43
So, the trick is that it's their it's the chassis and the battery and literally nothing else.
1:58:50
So, they're saving money on that, but like the the rules right now are just on all of the sort of like electronics and connected anything.
1:58:59
So Waymo is filling up the entire car with stuff that is not >> it interesting. Okay.
1:59:06
Yeah, well that's probably like more reassuring that Xi Jinping won't be teleoperating.
1:59:10
You'll have a a Waymo employee beaming in if you see a traffic cone out of place.
1:59:17
>> What What do you How much attention do you pay to their like humanoid antics?
1:59:19
Like I feel like they're just leaking out videos of just this like all this silly stuff to kind of just get us a little bit comfortable with it, but if you actually go there on the ground it's like droid army, you know, they've got like 100,000 humanoids marching in in in unison and then they just put out the video of it like dancing to Michael Jackson like collapsing and getting dragged off stage.
1:59:44
You know, it's interesting because there isn't a market yet, right?
1:59:46
Like these are not These are toys and these are like things you sell to universities for research.
1:59:54
But at some point there will be.
1:59:58
And then there's going to be a really interesting challenge of like whether or not I think there's like some level of consensus now that the Western makers have like smarter software.
2:00:09
But if you can like you know, it's it's one thing to fast follow the models themselves, but like if you can only if you can apply them like at a 100x scale, right?
2:00:21
Then yeah, it could be really dramatic.
2:00:25
I mean, you know, I don't I'm not doing like the the episode, you know, Star Wars Episode 1 like droid army.
2:00:30
Like we'll we'll That might take a That might take a few more cycles, but just from a >> Can you imagine a Chinese humanoid company trying to distill Tesla Optimus after they have a fantastic data breakthrough and they're just having all the Optimus work in cubes like trying to farm the data out of it.
2:00:51
I just feel like it's such a it's such a Yeah, yeah, that's brilliant.
2:00:53
Because you have to physically distill the data.
2:00:57
So you buy you buy a million Tesla Optimus robots and lock them in a warehouse so that they can move boxes for you so then you can distill it into your own robots or something. This is a company.
2:01:07
This is a RL environment, right? I love it.
2:01:10
I should start I should start this. In China? >> [laughter] >> Oh, no.
2:01:15
I mean they're just like uh reselling >> US. Yeah, yeah, yeah.
2:01:18
You you need a hair dryer to remove the Optimus sticker and slap on some other sticker.
2:01:24
That's where the money's being made these days.
2:01:27
Uh, but you got to hide that corruption.
2:01:29
And and it's really great that we have a software advantage and they have a hardware advantage on this, right?
2:01:35
Like that's really bullish for us.
2:01:36
Yeah, cuz software is copying >> they would never be able to copy like software off of a hard drive and put it on their hardware.
2:01:44
Well, and that's the thing is like this is the difference with AI, right?
2:01:45
Is at least America has way more chips and way more compute as long as Taiwan still exists.
2:01:52
Uh, but that will not be the case for the like, you know, humanoid embodied AI takeoff is like the thing you need to actually use the fancy model is almost certainly going to be manufactured in China and manufactured at the scale that um, uh, could be like, you know, multiple orders of magnitude bigger than in the west.
2:02:15
Now, this may change like once this becomes economic, yeah, right?
2:02:20
Um, so America can I mean like you look at you look at space like like we have multiple companies that land rockets. Very capital intensive.
2:02:27
It's an industrial process.
2:02:29
You would assume that China would win that but they are but we have a lead in in master orbits still and so it's not impossible but uh, we do tend to let our leads languish like we have in the car industry uh seemingly.
2:02:45
>> be interesting cuz there'll be this lag where um China like because they already have all these humanoid companies that like have the ability to scale, will scale faster um once the kind of like economic utility of these things turns on. >> Yeah, definitely.
2:02:58
Uh if you if you believe in AI progress, do you have to believe in a Taiwan invasion?
2:03:04
Because assuming assuming, you know, we keep making progress or we have accelerating progress, doesn't that just increase the prize for Yeah, but it might increase the defensive capabilities. Sure.
2:03:20
>> Yeah, and I think there's there's like plenty of other things you can do to be obnoxious.
2:03:25
Um and there's so much there's Look, this is this is why Chamath is wrong about everything is because it's not all about technology.
2:03:32
Like there are other um risks and incentives at uh and like institutional dynamics and historical dynamics at play uh and there are a whole lot of downsides to starting something that you don't know how like you don't know how it's going to end, which uh is a lesson that uh she has now got to relearn from Putin over the past 5 years.
2:03:54
So, >> What uh last thing we touched on it briefly and then I I know [clears throat] we're over time, what happens with Manas? What's your prediction?
2:04:02
Are they going to Are they going to buy the company back from from Meta and just head home?
2:04:10
Is there any precedent for something like this?
2:04:13
I don't really understand.
2:04:13
I mean, like all of the employ like do they have to give the tech back?
2:04:17
They weren't really buying the tech, they were buying the It was like an acquihire.
2:04:23
Everyone's in Singapore except for the two founders.
2:04:25
So, like maybe the founders just get screwed out of all this money and uh you know, the employees continue on their merry way in Singapore.
2:04:33
I think it's uh uh it's a messy one and uh I don't know like maybe it's a loss. Yeah. Something.
2:04:42
I don't I hope Bill Gerley's Before you go, we have an answer to the question of telephone pole camouflage.
2:04:47
Tyler, do you want to do you want to give us a little update on this?
2:04:52
Can we can we do a Yeah, let's cut to Tyler.
2:04:54
>> yeah, telephone pole camouflage basically began after the 1996 Telecommunications Act.
2:05:01
So it basically like restricted the ability of local communities to regulate like the actual placement of the telephone pole.
2:05:07
>> can't say you can't put a telephone pole there.
2:05:09
You can't put a cell phone tower there, but they responded.
2:05:14
>> but but they basically made it so they made it so the local governments would say you have to camouflage it, right?
2:05:21
So so now we see there's all different forms of these.
2:05:24
There's there's trees, there's cacti, there's like church steeples. Yeah, yeah.
2:05:29
Yeah, they grew up in these.
2:05:29
A lot of chipmunks run up and down those those poles, too, right?
2:05:35
>> And camouflage can add over 100,000 to tower construction prices, which is still cheaper than losing a site.
2:05:40
Three towers can cost up to double a tower.
2:05:45
Hey, two can play this game. Two can play this game.
2:05:47
You have a soundboard, too? I got my buttons.
2:05:52
We have a guest with buttons. Let's see. Can he get it to work? Uh-oh. Oh no. Oh no. Another one.
2:05:58
That's a narrative violation.
2:06:01
>> [laughter] >> Will it work? Let's find out.
2:06:04
Uh your buttons aren't working. Anyway.
2:06:08
But bring him bring him next time. >> Bring him next time. It's been too long.
2:06:11
We'll talk to you soon, Jordan. Have a great day. >> YOU. HEY, THERE we go. Hey, well done.
2:06:19
Thank you so much for taking the time.
2:06:21
Have a great rest of your day. We'll talk to you soon.
2:06:23
We went over time, so we will bring in our next guest immediately.
2:06:27
We have Christina Lee Storm from Secret Level. Welcome to the show. Sorry for the delay. >> What's happening? Hey, how's it going?
2:06:34
We're having a little too much fun with our soundboard. I love it.
2:06:40
playing sound cues, all sorts of stuff.
2:06:42
But since it is your first time on the show, please introduce yourself a little bit.
2:06:46
Hi, I'm Christina Lee Storm.
2:06:46
I am head of studio over at Secret Level, which is an AI native studio.
2:06:51
And I also am co-founder over at Playbook PLBK.
2:06:57
I identify myself as a producer. Okay.
2:07:00
And film independent film producer. Yeah.
2:07:02
How are you thinking about the the blurry line of how AI works its way into productions these days?
2:07:08
You know, you have Toy Story with CGI, you also have you know, a little set extension and maybe even a little CGI creeps into a Nolan movie every once in a while.
2:07:19
But AI is very different.
2:07:19
People are thinking maybe it'll generate the whole movie, maybe it'll just sort of you know, make a green screen a little bit sharper.
2:07:26
How are you thinking about the opportunities and and the trends?
2:07:28
Yeah, I mean, it's interesting cuz we've had technology in the entertainment industry for a while.
2:07:35
I was a a consulting producer on a film called Jurassic Punk, which was at the about the birth of computer graphics.
2:07:41
It was about a gentleman who was from Canada, his name was Spaz Steve Williams and he actually created the first walk cycle. Uh All right.
2:07:52
T-Rex for Jurassic Park and he was a total rebel.
2:07:54
And in the in the documentary, we talk about how he just felt like you know, we could do this with computer graphics.
2:08:01
At the time, Jurassic Park was like, "Hey, we're going to do it animatronics.
2:08:06
We're going to do it old school. We'll do it on set."
2:08:09
And there was an an ability to actually now like, "Wait, hold on a second. We could create this."
2:08:15
And so when Spielberg and Kathleen Kennedy saw the first walk cycles he was playing on his computer screen, you know, just sort of he knew when they were going to walk by.
2:08:28
It would it changed everything and that was the birth of computer graphics.
2:08:29
So we know that technology has always been a part of that story and so I think you know, it is quite it a lot of people like to use the headlines like disruptive, but I think it's just an ongoing process of how things should sort of evolve and me being my background is traditional production producer.
2:08:52
I've been an independent producer for a while.
2:08:53
Um I think it's just it's part of the process and I think before I think a lot of producers would just offline, you know, any kind of technology or any specialty to different departments or groups and today I think anyone who's going to really survive and thrive into uh the new world order of this, you know, everything's sort of shifting and changing.
2:09:15
We have to actually lean in and there's this interesting like technology storytelling, you know, um convergence entertainment that is exciting and I think, you know, that's yeah.
2:09:30
>> on the topic of Jurassic Park, grade this grade this prediction.
2:09:34
Uh Jurassic Park massive success.
2:09:35
The franchise has grossed I think billions, maybe over a billion dollars.
2:09:39
It's it's it's a fantastic financial success.
2:09:41
Uh but interestingly, uh Jurassic Park of course is owned by Universal and uh but dinosaurs are not particular intellectual property. It's not like Superman.
2:09:54
The T-Rex is not something that a studio can own.
2:09:56
Oddly, no other studio has created like the Superman to Spider-Man.
2:10:02
There's not the Marvel universe to the DC universe.
2:10:04
Uh I would have expected a reaction series to Jurassic Park and that IP library at some point.
2:10:14
Never never really developed, but in the age of AI where it's increasingly easy to generate imagery of dinosaurs and you don't have any intellectual property concerns because you're not recreating a particular actor.
2:10:28
I predict that there will be a massive surge in dinosaur themed movies. What do you think? >> the Dino prize. The Google Dino prize. >> prize. Is there anything here?
2:10:37
Am I thinking about it correctly?
2:10:39
I think that you're on the right track in terms of like dinosaurs are not, you know, there's not a particular IP.
2:10:45
But here's the thing and this is sort of where it it does fall back to traditional methods like the storytelling is everything.
2:10:52
Like you could have there's a lot there's a lot of different ways, you know, that you can tell and share stories but it's really what is the specific you know, what are the specific things with the character, what's happening, what's the world and so yeah, someone could come up with a dinosaur type movie but what is it? Why why is that special?
2:11:14
Why is that undeniable for someone to say yes, I want to watch that and I want to and I want to engage in that.
2:11:21
So I think the concept of that is is good.
2:11:23
I think you know, at the end of the day it's still >> half-baked but >> [laughter] >> Well, that's the difference between really great like no matter what the technology is, you have to still be a really good storyteller. Yeah. Uh Please.
2:11:37
Article in the Wall Street Journal this morning about a new film called Hell Grind that's I guess premiering at Can.
2:11:44
Uh they're saying that it cost half a million dollars to make and around 400,000 of that was uh compute. Yeah.
2:11:48
Um I'm sure there were you know, there with all these things there ends up being costs that aren't kind of captured on on maybe a napkin.
2:11:58
Sort of a deep fake moment. Yes.
2:12:01
Um but uh but is this the beginning of of like do you expect hundreds of these over the next year or I I imagine you're you're hearing and seeing a lot that are in the works and and working on some yourself but Yeah.
2:12:17
>> Um So, I mean, I think there are, you know, can I I actually didn't go this year. I usually go every year.
2:12:24
Um I knew that there was going to be a lot of announcements about different projects and things and at Secret Level, you know, we actually will have an announcement next week.
2:12:31
I was like, "Should I come on the show today?"
2:12:36
Because next week there's going to be, you know, um so I At the uh I'm happy to come back, but at the end of the day, I think, you know, um it's it's Let's just be real. Let me just be real.
2:12:46
There's a lot of things that are being pioneered.
2:12:48
Like, what are the costs?
2:12:50
What what's happening here?
2:12:52
How do we align with even like guild-related things that are issues, you know?
2:12:57
How How can we It's not just like, "Let me just get this out."
2:12:59
You know, I think there's something even bigger um to to that degree, you know, uh I think people A lot of people could say, "Yeah, I can make a movie."
2:13:07
But I think it's really again, I'm going to go fall fall back on story.
2:13:10
I'm going to fall back on what is the process in which that's going to happen.
2:13:14
Like, at Secret Level, we do we have our own proprietary workflow pipeline that really really allows us to scale and I think those costs when those get flagged, it's like, "Yeah, what what's the total cost in it?"
2:13:26
Um there's a lot There are a lot of costs and so I don't think I think it's really I I think when people talk about like the the budget and they're just focused on the budget, I think they've missed the mark on Yeah.
2:13:38
The bigger Uh so, what what is your Maybe it's too early to say, but what do you think is going to be the winning formula?
2:13:47
Obviously, you're it's still an experimental time, but you said story matters a lot.
2:13:50
Obviously, technology matters a lot, the what models you're using, but where are you thinking of taking, you know, traditional filmmaking approaches versus like reinventing your approach?
2:14:04
Yeah, I think there's Okay, there's a couple things here. A great question, Brady.
2:14:07
I think that there will, you know, we had There was a time where like independent film was like on the rise. It It was awesome.
2:14:13
We saw really great filmmakers come out of that.
2:14:18
Like, I you know, going to Sundance and whatnot.
2:14:20
And I think that that's really important to feed and and bring about like creativity, really great story.
2:14:28
So, I I do believe that with the tools we have more of an opportunity for independents to sort of bring that to the forefront.
2:14:35
But, again, I you know, again, story It's about the story.
2:14:41
Like, is it going to be a really great story?
2:14:42
I think the other piece is is will we see this I Yeah, I I I I kind of share with this cuz I've been a former studio exec, surviving studio executive, and also a producer.
2:14:52
And I think that what's needed is, you know, is the chasm going to widen?
2:14:57
Like, how do we sort of bring that Do we want to bring the chasm closer together so it's not just traditional, you know, in um studio fair, or do we want to like really see these independent voices come out and we're we we can see those play out.
2:15:14
And I think as time tells in how people will refine their storytelling abilities, I think that's really important.
2:15:21
I think there's also, to be honest, there's a way in which um filmmakers who use AI tools are using it at their the best abilities.
2:15:30
Is Is it almost a slight um variation than maybe a traditional approach from let Let's just say like a studio, you know, pipeline type film.
2:15:43
I think um And that reminds me of like, you know, when I you know, when uh Lu- George Lucas came out, like, you know, he was breaking new ground in technology.
2:15:56
It was like, "Th- This doesn't exist.
2:15:57
So, how do we create something that doesn't exist?"
2:16:00
And I think we have to allow for that creativity to to to be to birth. >> Totally.
2:16:07
I want to take a question from the chat.
2:16:09
What is your favorite movie? Oh gosh. Woo! I have a lot.
2:16:15
>> Or or maybe something just with a great story that you think is really good. >> Inception? That's a great movie. Have you seen that? I have. Yeah, there we go.
2:16:23
Um I like to say and I I'm going to embarrass him, our founder Jason Zada from Secret Level.
2:16:30
I call him like the he's like the like the blossoming Chris Nolan. Ooh. Ooh.
2:16:37
And he's just really creative.
2:16:37
He the way he approaches story. Um Does he use a phone?
2:16:42
Christopher Nolan famously no phone. So no phone guy.
2:16:47
He did an interview with with he does no email either.
2:16:49
Printed out emails if somebody needs to email him.
2:16:51
No phone, also doesn't like maps because they reorient you.
2:16:55
They don't always face north and he doesn't consider that a map.
2:16:58
Very interesting lifestyle. >> Yeah. Anyway.
2:17:02
>> I would say Inception.
2:17:02
I would say like let's go really old school.
2:17:04
Um It's a Wonderful Life. >> Ooh, yeah.
2:17:07
Um and then if I get my like um like just real old school.
2:17:12
I I love like great character based When Harry Met Sally. I'm all over the place. Yeah. I'm all over the place.
2:17:19
I I like being that way so people can't say oh she likes sci-fi or she you know. >> Sure, sure, sure. Yeah. Godfather, Godfather.
2:17:26
I saw When Harry Met Sally in outdoor movie theater at one of the screenings you buy tickets to seeing an old movie outside with a bunch of people. Tons of fun.
2:17:33
Last question from the chat.
2:17:36
What is your single favorite piece of fully AI generated content? Mhm.
2:17:42
It could be like 30 seconds long, a minute long.
2:17:44
I can't really expect anything else.
2:17:49
I think for us it's like that Harry Harry Potter Balenciaga which funny enough was like a year a year a year and a half ago maybe at this point.
2:17:59
Um So if you haven't seen it, um yes, you know, there's a secret level the heist. >> Okay. Watch the heist.
2:18:07
When Jason shared his first 30 seconds, I was like, okay. Okay. We got something. We're cooking.
2:18:16
We're we're we're approaching some really interesting things. So, That's great.
2:18:19
>> you can see a lot of things at Secret Level. Yeah, I love it. Not CEO. Amazing.
2:18:24
Well, thank you so much for taking the time.
2:18:26
>> Really great to meet you.
2:18:26
Make sure to share your announcement next week with us and I will.
2:18:30
We'll cover it on the show.
2:18:32
Have a great rest of your day. Goodbye.
2:18:36
Up next, our last guest, Eric from Modal Labs with some big It's been a minute. It's been a minute.
2:18:42
>> I'm excited to catch up. How you been? Good. You been busy? What you been doing? Cooking. What happened?
2:18:49
We just announced a C round today, which is very exciting. How much? How much? How much? 4. 65 post value. I'm raising 355 million.
2:19:02
So, General Catalyst led it together with Redpoint. Fantastic.
2:19:08
>> What was the specific catalyst that led to this round?
2:19:13
There's been a lot of stuff that's been going on.
2:19:14
I I think one particular exciting moment in the last 12 months, or really last 6 months, has been we we now have a product called Soundboxes that's just growing insanely fast, almost 2x every month for the last 6 months.
2:19:27
So, Soundboxes, if you don't know, basically the idea is like you can take typically LM generated code and execute it in a safe environment.
2:19:34
So, powers a lot of reinforcement learning, powers a lot of um live coding apps, a lot of background agents.
2:19:41
But yeah, I mean, in general, we've seen tremendous growth also with inference.
2:19:45
Modal's always been kind of We started the company 5 years ago with the idea of building pretty general-purpose infrastructure.
2:19:49
We felt that a lot of infrastructure wasn't really built for AI.
2:19:55
And so, when you look at all these companies out there building sort of AI applications, they all need different things.
2:19:59
Some Some of them need sandboxes, inference, training, batch jobs, like all kinds of stuff.
2:20:03
And And our goal is to provide all of that.
2:20:05
And And really kind of build a new sort of almost like a new cloud in a way that that supports all these types of very cool applications. Very cool.
2:20:14
Uh I feel like it's been almost a year since you were on, which feel which is like, you know, 15 years uh 15 years It was back in October. October. Well, there there we go.
2:20:24
Feels like Feels like Feels like a year ago, even though it's maybe uh maybe less.
2:20:29
Um what Yeah, what have been the key kind of inflection point since then?
2:20:34
Sandboxes sounds like sounds like one, but I imagine a lot of this is uh the space is moving so quickly.
2:20:39
If you get a signal from two or three customers, "Hey, we have this problem.
2:20:46
Can you build a solution?"
2:20:46
And then you can just see tremendous growth in like a a very short period of time.
2:20:50
And so in some ways like you have an existing portfolio of products that are all, you know, growing, but are you trying to constantly be making, you know, new bets uh based on customer needs or is it Is that it maybe not even the the approach?
2:21:08
I I I think one of the benefits of building infrastructure is like you can sort of, you know, look at customers, of course, and and see what they're doing.
2:21:14
But you But I think with infrastructure you can also sort of argue a little bit more from like first principles, like what are the building blocks that people are going to need in terms of compute.
2:21:21
So, sandboxes was actually something we launched 3 years ago.
2:21:23
And it really started taking off like nuts in in in uh in the summer last year.
2:21:29
Uh and and and similarly I I think with with, you know, inference is sort of similar.
2:21:33
We launched it almost uh 4 years ago.
2:21:36
Uh we're working on some other really cool stuff around training and some other products.
2:21:40
Uh and and I think one of the benefits of infrastructure is you can have a little bit more sort of a first principle like, you know, let's build the right building blocks and then let our customers sort of figure out what to use them for.
2:21:50
How How do you think about uh planning?
2:21:54
Like planning planning demand. Add a zero to last year.
2:21:58
>> Add two add two zeros. Uh that's interesting.
2:22:01
No, but yeah, it just feels like the you know, the best this is the hardest like demand planning challenge that really any bus- set of businesses have ever faced in history. Uh >> Yeah. It's rough.
2:22:14
It like I mean, for us it's like we basically look at like the last couple of months, figure out, you know, we're growing 30 40% every month.
2:22:19
We just, you know, take, you know, take the power of three.
2:22:25
That's how much you're going to need in three months.
2:22:26
That's how many GPUs to go out and get those GPUs.
2:22:30
You can typically get GPUs with like about three years of sorry, three months in advance. Sure.
2:22:34
Uh but yeah, that's a big part of how we think about the business today.
2:22:37
It's like we need to get the GPUs now that we're going to need in three to six months, which is a lot. Yeah.
2:22:42
Yeah, in some ways it's like, you know, not not too dissimilar to the challenges that uh you know, a consumer consumer packaged goods founders facing you're growing super quickly, but if you want to stay on that growth curve, it means you need to be committing to things, you know, now and and uh committing capital.
2:22:57
Uh but but What What's exciting to you across >> have capital now. Yeah, yeah.
2:23:02
Well, what what what's exciting to you across the uh like semiconductor ecosystem?
2:23:06
There's a whole bunch of ASIC startups that have been on the show. Cerebrus IPO'd.
2:23:10
Uh every hyperscaler is working on uh different chips at this point.
2:23:16
Um what's most interesting, what's under discussed, what's on your road map or or or uh what have you already sort of uh sunk your teeth into?
2:23:27
Yeah, I mean, there's some like really cool alternative accelerators.
2:23:28
I'm quite bullish on on TPUs, AMD, Tranium, like all of these things.
2:23:34
We see zero demand from our customers for for any of those things, to be clear.
2:23:40
>> Because only the really big labs Anthropic can actually go and and figure out how to run it.
2:23:44
Is that is that >> Yeah, I I I think the cost today of rewriting this software to run on those stacks is just like very high. >> Sure.
2:23:51
So, while I remain like very bullish on the sort of, you know, 2 3 year horizon, you know, I do also want to like Yeah.
2:23:57
>> temper the expectation a little bit.
2:23:59
Like this is like, you know, >> fixed cost to rewriting for that chip stack, and if you're not doing billions a month in revenue, it's hard to amortize that cost. >> right.
2:24:07
It's just not worth it unless you're, you know, operating at a very large scale.
2:24:10
So, but but I think that that cost is going to go down over time.
2:24:13
We're going to have software that literally lets you take existing CUDA compatible stuff and run it on other alternative accelerators.
2:24:18
So, I I think it'll be good for everyone to have a little bit more competition in the space, but we also love Nvidia, and and that's what our customers want today. That's great.
2:24:25
Uh do you have a view or any opinions or predictions about uh the compute futures market that's been talked about?
2:24:32
Uh any I I've seen like these price charts of like B200s going up and down.
2:24:37
Everyone is trying to read the tea leaves, understand, you know, where we are in the various AI cycles uh based on it, but uh is that something that's relevant or or important at all?
2:24:47
Do you do you look at that data?
2:24:49
Do you have your own internal data set that's more relevant to you?
2:24:54
We we we look at it a lot.
2:24:54
I mean, also like I feel like we're very plugged into the market.
2:24:57
We talk to Neo Cloud all the time.
2:24:59
We get capacity all over the place.
2:25:00
So, we we have like a very good like pulse of the market.
2:25:02
I I think the market's going to remain quite tight. >> Yeah.
2:25:05
I I think fundamentally like also like that's big part of what we do is like we offer, you know, that as a product.
2:25:11
Like don't think about capacity, come to us instead.
2:25:12
And so, that becomes now our problem is like managing that capacity for thousands of companies at the same time.
2:25:18
Like we built a multi-tenant product that sort of aggregates all that demand.
2:25:21
So, if you need 1,000 GPUs, you can come to us and we'll give you to that give you those GPUs like often within minutes cuz we have like a very big pool we can sort of tap from. Yeah.
2:25:30
But but yeah, like I I you know, I think GPUs are going to be tight.
2:25:33
You look at the prices, they keep going up.
2:25:35
At some point, obviously, I think it's going to normalize like most markets do, but it might remain tight for the next year or two.
2:25:40
When I think about the big applications of a big pool of computer, I think training and inference of LLMs, coding models, agentic workflows, I think image, video, audio generation. Uh What's next?
2:25:55
What do you think the next big driver of demand is? Is it world models? Do those fit in?
2:26:00
Are those structurally different?
2:26:02
Is there something else that you're tracking where you're seeing customers come to you with sort of like a a different product, but it it fits in the same shape so you can work with them as a business partner?
2:26:12
Yeah, we have a very wide range of customers.
2:26:15
So, we have everything from Suno, which generates music using AI, Cognition is training coding models using a force reinforcement learning, Ramp uses us for background agents.
2:26:25
We have a lot of vibe coding platforms like Lovable.
2:26:27
We also have, you know, drug discovery companies like Chai using us to simulate, you know, molecular dynamics and you have weather forecasting companies, robotics.
2:26:38
So, so so we we we have, you know, very general, you know, we look at across like a lot of different verticals.
2:26:43
Um obviously like the the the big application in the last couple years has been LM inference and that keeps growing a lot, but we've also seen some really cool applications with the fusion models.
2:26:53
In terms of what I look forward to, like I I think that probably personally the coolest thing I kind of expect to happen in the next couple years is speech to speech.
2:27:02
Like imagine just like talking to a computer.
2:27:04
In order to to figure that out, we need to get the latency down, you know, for for for all the three components of speech to speech.
2:27:10
Speech, you know, doing the LM and the, you know, text to speech and stuff like that.
2:27:13
So, that's something very cool.
2:27:15
But also really cool about I I think it's incredibly cool if we can, I don't know, cure cancer or something like that. >> [laughter] >> Yeah.
2:27:21
Yeah, it's interesting because I'm with you on that on that timeline.
2:27:26
Speech to speech being, you know, somewhat impressive, but you're often not actually hitting a real reasoning model.
2:27:32
There's so much speed up, you know, we could be we probably need to be a 100 x faster, a 1,000 x faster on the response time to really have a breakout moment.
2:27:42
And then you watch Google I/O this week and you see with Omni a glimpse of Okay, it's more like a FaceTime call with the AI and it's generating a video in real time.
2:27:55
And you know how long it takes to crank on video models to get an 8-second clip.
2:27:59
It's minutes and minutes.
2:27:59
Uh and and and they still have errors and stuff and we're still not even at the the super the superhuman element.
2:28:06
So, lots to do, lots of servers to rack, lots of GPUs to set on fire, I'm sure.
2:28:11
Yeah, would at what And CPUs. Yeah.
2:28:16
Any any plans to actually, you know, go full stack, get your own, you know, land, power powered shells, etc.
2:28:23
Is that like not the highest and best use of your guys's, you know, talents?
2:28:30
We we think of our value as like adding a big software layer on top of the underlying computer.
2:28:34
And on the underlying compute layer.
2:28:36
So, we think of ourselves as almost like a cloud, like one layer up from the existing clouds.
2:28:41
The existing clouds are very good at running computers, you know, in the cloud and and offering that through an API.
2:28:46
We we would love to keep tapping that as much as we can.
2:28:50
If you can't get the capacity we need we might have to build it ourselves.
2:28:53
Ultimately, it's like, you know, it's a kind of a economics question or, you know, a practical question.
2:28:58
I I wouldn't rule out any sort of move.
2:29:00
Uh the idea of, you know, racking computers and plugging in cables and dealing with, you know, fires in data centers and whatnot.
2:29:06
Like, to to me that's not super appealing, but but at the end of the day we'll do it if we have to, absolutely. Yeah. Makes sense.
2:29:13
Well, congratulations on the progress.
2:29:16
>> whole team on an awesome milestone and and incredible progress.
2:29:18
It felt like a year, but it's only been since October.
2:29:22
>> It's only a couple months.
2:29:23
>> Yeah, it's like 10 AI years. I love it. Congratulations. We'll talk to you soon. >> you so much.
2:29:27
Have a great rest of your day.
2:29:30
Let's click through the timeline and see if there's anything that we missed before we jump off.
2:29:37
Uh DJ Cows has an idea here. Found a $10 bill.
2:29:41
It took 5 seconds to pull to pick it up.
2:29:44
That's $63 million in annualized ARR.
2:29:50
This is the thinking you need to be deploying if you're going to be raising money potentially. Uh no, don't do that.
2:29:57
Uh I didn't know that Messi the football player had a prime copycat called Moss. Moss.
2:30:03
And it's shutting down after 23 months in business.
2:30:06
I wonder if that has to do with the logistics of shipping internationally because he is an international icon, but setting up distribution and retail presence across many countries uh very quickly, it's not quite as easy as dropping it on Amazon and and flying off the shelves.
2:30:26
Uh a lot of those are sort of one country by country and I wonder if that has a piece of what happened.
2:30:32
So, this is shutting down fully, Moss Plus. Interesting.
2:30:37
Uh well, Mitchell Baldridge recommends setting up a Vanguard account.
2:30:41
Not Fidelity, not Schwab, Vanguard.
2:30:43
Smart advice you might think.
2:30:45
They do have the lowest fees.
2:30:47
If you're wanting to get up to speed on Vanguard, listen to the latest episode of the Acquired FM podcast. Uh but he says, "Wrong.
2:30:51
It's not because they have the lowest fees, it's because their interface is so awful you will never trade.
2:30:56
It's his It has made his clients millions."
2:31:00
Uh let's close it out with this video. What you got?
2:31:04
>> Playing Catan with a billionaire. Yes.
2:31:08
Uh I think I think you'll like this. >> this face?
2:31:10
Is this a face filter or a background replacement or both?
2:31:14
Something funny is going on here. >> Face filter. Okay, face filter. >> My turn?
2:31:16
I'm going to put down a data center.
2:31:21
>> [music] >> That's against the rules.
2:31:22
How else am I supposed to AI generate my Christmas card. Not popular. >> joking.
2:31:32
>> [music] >> Do you like it?
2:31:34
No, it's blocking all the land. Wait a minute.
2:31:35
Are you one of those paid protesters?
2:31:43
Next turn, I'm going to use seven water on my data center.
2:31:45
That's not how that works.
2:31:47
That's not a resource in the game.
2:31:49
>> How else am I supposed to power it?
2:31:52
The water [music] stuff is really It's so interesting that no one has moved to energy.
2:31:56
Like natural gas, there are natural gas turbines that would be opposed, and yet people are focused on >> Stop giving them ideas. >> I know.
2:32:04
I'm I'm I'm I'm doing their job for them, I suppose, but it's like [laughter] I I I don't know how I don't know why the water I think it's just because like water's delicious and electricity is is vague and abstract, and you don't think about it.
2:32:17
Like you can visualize a glass of water.
2:32:19
It's hard to visualize a battery in the same way.
2:32:23
But yeah, the uh what what what what is it?
2:32:25
It's like It's like dozens of LLM queries every day for a full year is equivalent to eating a single almond, something like that.
2:32:35
Uh but Matthew Ball is back at Xbox.
2:32:38
Congratulations on the move.
2:32:38
He announced it yesterday.
2:32:39
We're going to try and get him on the show cuz he's one of my favorite people, favorite authors.
2:32:43
If you haven't read his book or his his blog, he has fantastic mind for future of technology, and he I could not be more optimistic about the the future of Xbox with him on the team.
2:32:55
Take him says this hire is a literal game changer.
2:32:59
Matthew Ball knows gaming and what needs to be done.
2:33:01
This news makes me the most bullish I've been on Xbox in seven years. And I completely agree.
2:33:08
>> and to close it out, the White House is awarding 2 billion in grants. Oh, yeah.
2:33:12
1 billion of that goes to IBM to nine quantum computing companies and taking an equity stake for their grants.
2:33:21
>> Their grants and we're taking an equity stake. It's an investment. >> investment.
2:33:24
It's an investment and you American taxpayer will now own a basket of quantum computing companies.
2:33:32
Uh hopefully they'll do well.
2:33:34
>> is up >> That's not spaghetti computing.
2:33:34
It's spaghetti computing.
2:33:36
Spaghetti computing is up 30%.
2:33:39
>> Who came up with that?
2:33:41
That seems like a Trump like something he would say. >> I created that. You created that. Okay. >> Just now. I like spaghetti.
2:33:47
>> I'm sure I'm not the first person to think of it, but uh Anyways, folks. Thanks for watching.
2:33:52
Leave us five stars on Apple Podcasts and Spotify.
2:33:54
Sign up for our newsletter tbpn.
2:33:56
com and we will see you tomorrow, Friday. Goodbye. We'll smoke them out. >> Love you.
2:34:03
Have a wonderful evening. >> [screaming]