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[music] [music] [music] [music] >> You're watching TVP and today's Thursday, November 13th, 2025.
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5:13
Um, there is a bunch of breaking news.
5:16
The big [music] news out of Open AI, of course, the Open AI show continues.
5:22
Uh, Sara Fryer had some comments about ChatGPT's growth potentially slowing and it's unclear.
5:28
It's not in decline, it's maybe deceleration.
5:30
We'll have to dig into that.
5:34
Um, it had me thinking about uh, about debt and I was thinking about uh, just the fact that the debt has come to to tech for the first time really.
5:42
And this was sort of my take and I'm I'm a little bit uh, this is an area that I know the least about and so um, I was doing some research learning a different learning about a different uh, industry since it's it's just so abstract to me cuz I've never worked in private credit or really seen that industry or even just really studied it.
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Um, You appreciate leverage, you've never been a big >> it.
6:05
Yeah, and and mostly I was just wondering like we like we keep going back and forth on the on the debt is coming to tech narrative as like it's very scary.
6:13
Like when debt comes only bad things happen.
6:16
Uh, you know, we we we lived through the the global financial crisis and uh, there's a lot of jitters when debt is around.
6:23
It's like, oh, you could get wiped out. You could blow up. The backstop comes in.
6:27
It just feels like all of a sudden we're we're we're talking about um, things with like a much more serious consequence than like, oh, yeah, a startup raised some money and it didn't pan out and it was a zero and it wound up being a write-down but it was part of, you know, a portfolio of equities that is averaged out across a whole bunch of different LPs.
6:48
It like there's no there's no even when uh, even [snorts] when you know, like Theranos blew up, it was only equity holders that were lost.
6:55
It wasn't this higher entire industry and so uh, it wasn't it didn't turn into this like systemic issue, right? >> Yeah.
7:03
But now it feels like with the 1.
7:03
4 trillion uh, of, you know, backlog that Open AI has kind of opened up across a whole bunch of different deals, uh, there is this worry that, you know, maybe the level of indebtedness could be risky, the level of risk in the system, the level of investment in the system uh, could be something that's bigger than just, oh, if you're in this one name, you're taking a big risk.
7:26
Now it's maybe like Hey, we're all taking a risk and if we're talking about backstops at least. Um, >> Yeah.
7:34
And so I was trying to understand uh, there's this old phrase in from 2006 uh, coined by Clive Humby, classic coinage. I love a coinage.
7:41
He said data is the new oil.
7:43
And back then in 2006, his point was uh, he was working at a data as a data scientist at Tesco, which is this British grocery store.
7:51
I don't know if you know the story.
7:53
Uh, but he was working at this British grocery store chain and his point was we have all this data on a customer has is in the rewards program.
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We see that they buy a Thanksgiving turkey before Thanksgiving.
8:07
We see that they buy this type of paper towels or this type of uh, milk or whatever.
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We have all this data but we don't really do anything with it.
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The data is not valuable.
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We need to refine it much like oil into gasoline and once we refine it into gasoline, then we can do things like targeted advertising and we can increase our customer value.
8:26
And so it was basically just a generic uh, generic uh, call to action for taking data science seriously.
8:32
For just don't just have the data there, understand that the data is valuable if you extract it, if you work on it.
8:38
Um, but the but the the metaphor, people have been saying data data is the new oil for I guess two decades now uh, and it never really sat that well with me because unlike oil, data is not uh, perfectly fungible. Yeah.
8:51
So one one tranche of data is not equivalent to another.
8:56
Like Reddit is clearly very valuable since it kind of you know, provided the backbone for GPT-3.
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Uh, the all the analytics data that flows out of some mobile game is basically >> of data is worthless. >> of data is worthless. All oil Yeah.
9:12
Is has at least some value. Essentially.
9:14
I mean, I guess there are different levels of crude, right?
9:16
There's there's different different grades.
9:17
And I was actually trying to play out the metaphor more and I was wondering like uh, can we get to a place where uh, you know, so we can ring intelligence out of raw data like the oil and the result can be low octane gasoline, kind of like midwit, you know, level like slop and AI slop or it can be jet fuel like a deep research report that's actually pretty great um, or some code that's really reliable and really useful.
9:40
Um, but it all depends on the processing methodology.
9:43
Um, but the more interesting data is the new oil take that I don't think was considered in 2006 is that uh, maybe the tech industry is going to look like the oil and gas industry soon.
9:54
Like I was looking up what how much debt is in the oil and gas industry?
10:00
It's over a trillion dollars of debt. And it's like it's fine. Like yeah, exactly. Yeah, clap. It's fine.
10:05
Like it's not this like huge systemic issue.
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It was two trillion like you know, a decade ago and then it went down and then went up and it's like it's all just a function of like how much oil and gas is going on how what are the new projects?
10:17
How big are the projects? How much debt goes in?
10:19
Like just having a lot of mortgages in America is not intrinsically risky.
10:22
Okay, the difference the difference is that if you identify oil in the ground and you figure out how much it's going to cost you to extract it and how long you think you'll be able like basically estimating like the how much how much oil is actually available in this in this site. Yeah.
10:42
Then you can lend against that pretty predictably because you know that the price of oil is going to fluctuate but in general as long as it's in a in some range, it will be like a profitable operation to pull it out of the ground and I think it's a little bit easier to lend against that than GPUs today when we're the big debate is around depreciation
11:03
schedules and will these GPUs, you know, we we we have a sense that a data center that has power and a basically a box with a lot of power will be valuable in the future but if you're if a lot of the cost of a new data center is GPUs, it's harder to gauge on what the value of those GPUs will be in in you know, four years than than it is okay. It will this
11:28
It will this oil like production site still be producing oil in five years.
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I think that's a bit easier to answer and easier to lend against. Maybe.
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I mean, sometimes there are tracks that only produce oil for four years and you underwrite it against a four-year depreciation schedule.
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And as long as you get the as long as you you match the risk to the reward, the deal pencils out just fine.
11:53
But but I understand what you're what you're getting at and I think that as we dig into the open AI news, I think we'll have more we can synthesize some of what the of the recent uh uh the recent leaks and and rumored statements around potentially a plateau in demand for tokens on maybe the consumer side.
12:16
Um But it it is just like a wildly different question.
12:19
Like the fact that you're walking through that math is very different than what the venture capitalist in 2000 were doing.
12:25
Like Ev Randall who's coming on the show on Friday tomorrow.
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He always says he goes back to the Google uh prospectus from when they IPO'd and Google was like the most pure play just beautiful software business.
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So Google in from from 2001 to 2004 grew from 86 million in revenue to 3. 2 billion in revenue.
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And net income over that period went from 10 million to 400 million and that includes stock-based comp.
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So they were still making 400 million in profit with the stock-based comp.
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The Googlers made a lot of money.
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They gave away a lot of stock.
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And so it was it was not it didn't look like an oil business.
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There was not this big capex buildout.
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There was not this big or even this crazy R&D phase.
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There was just not there was there wasn't that much capital that went into Google before it became this monster cash flow machine.
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It was just infinite money glitch It was sort of an infinite money glitch.
13:25
It was this beautiful algorithm that was just discovered and it was so elegant and it just produced this monopoly insane like growth rate for so long and then of course they've been challenged and they expanded and there's million things and then eventually capex did come into the picture as they grew their cloud cloud infrastructure GCP all this other stuff.
13:44
Um But but for a long time like tech just meant take a bet on a company and it's either a zero or trillion dollars or something like that.
13:54
Um And so it's a lot different and I wanted to dig into like the actual structure of one of these deals because I don't I think that tech people I was I was almost going to call this like why is no one talking about Blue Owl?
14:08
Because people obviously on Wall Street are definitely talking about Blue Owl.
14:12
It's it's a public company that stocks I think down like 30 or 40% this year.
14:15
But but it's the data center of finance.
14:19
>> private credit >> Yes, private credit. Exactly.
14:19
And so I wanted to understand like how does Blue Owl actually interact with one of these data center deals because that's important to understand like where the risk winds up living.
14:30
So I'll break one of these down but first I'll tell you about Restream.
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One live stream 30+ destinations multistream and reach your audience wherever they are. So for Hyperion.
14:40
You remember the Hyperion release?
14:41
Zuck went on Threads and announced that he was going to be building a five gigawatt data center.
14:45
It was going to be as big as Manhattan.
14:49
>> It's like a somewhat of a Manhattan project.
14:50
>> Somewhat of a Manhattan project. Exactly.
14:52
So uh the the crazy crazy thing about that deal.
14:56
So so he spins up the he puts out the announcement post on on on Threads says, "Hey, we're going to build this five gigawatt data center campus.
15:07
It's going to be online in a few years.
15:10
It's going to be as big as Manhattan."
15:11
He and he shares some of like where it's going to be, how many racks are there going to be, square footage, stuff like that.
15:18
But he's basically just announcing that like, "Hey, the project's financed.
15:23
We're ready to go on this."
15:23
Like you would expect that when that it's a 27 billion dollar deal.
15:28
You would expect that okay, Meta went down.
15:29
They they spent 27 billion dollars. It's worth it.
15:33
They're going to No, they got paid 3 billion. They got paid 3 billion.
15:35
And the reason is because Blue Owl financed it with external debt and they are basically paying Meta upfront for the right to have them as a as a tenant as a leaser for a very long time.
15:48
So they get this like we have Meta as a client.
15:53
Meta's always going to pay their bills.
15:54
They're not they're like no matter what happens with the AI buildout, they're going to be good for it because they have this cash machine.
15:58
So they are like the best possible tenant.
16:00
Not like some fly-by-night oh yeah, I'm a startup maybe I'll be around in a few years. It's like it's Meta.
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They're going to pay their bills and so you have this massive data center project that's going to be paid for even if it's not producing any valuable tokens.
16:13
Zuck's still going to he's not just going to default and be like yeah, take the company. No way. He's going to pay.
16:18
And so in exchange for that they got 3 billion upfront.
16:22
And so there's just each one of these deals I think the more you dig into them.
16:25
I want to have more of these people on.
16:26
Mohamed El-Erian at Pimco or was formerly at Pimco.
16:28
He I know he can explain this a little bit more.
16:32
I want to have more people on the on the show to help us get up to speed on this because this feels deeply important to the current AI buildout boom, the tech story.
16:41
It feels it feels like an entirely new piece of the puzzle to understand where this technology is going and I don't feel equipped to to understand it at all.
16:52
Barron's did have a great article about Blue Blue Owl and a very funny interaction between Blue Owl and Jamie Dimon and they're going at it and I think it's interesting to read through.
17:04
So let's read through a little bit of this to give you a little bit more flavor on what's going on at Blue Owl because if you're just in tech, if you're just in venture, you might not know that much about them.
17:13
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17:22
So in Barron's they I had this this article is from October 24th. I had it on the table. We never got to it. We're getting to it now.
17:31
It's the the title of the article is private asset star Blue Owl has been flying high.
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Is it too close to the sun?
17:38
This feels like a headline Jordy would write.
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>> [laughter] >> I'm very skeptical about about what's going on in the AI buildout in the AI boom.
17:46
But let's let's dig in and see how we do. >> private asset star. Yeah.
17:52
>> to start calling our friends private asset stars. >> For sure. For sure.
17:56
So the article says, "Suddenly Blue Owl Capital is everywhere this past Tuesday.
18:00
The upstart alternative investment firm with an aptitude for private credit announced a financing deal for Meta Platforms 27 billion dollar AI data center in Louisiana.
18:08
That is Hyperion that I was mentioning earlier."
18:10
Um before the week before at the PAC CAIS Alternative Assets Summit in Los Angeles, Blue Owl's co-CEOs co-CEO Mark Lipschutz called JP Morgan Chase's CEO Jamie Dimon cockroach warning about risk in private credit an odd kind of fearmongering.
18:29
So what happened there was we talked about that that blowup in the private credit world and I have a little bit of background on this.
18:42
So where where did he say this?
18:44
So uh I need to actually pull up what happened with the with the private with the cockroach statement because it's very funny.
18:54
So basically First Brands? It's First Brands. Let me see. First Brands. First Brands. Okay.
19:01
So So basically private credit has been growing a ton.
19:06
We've talked about this a few times. Ares is massive now.
19:07
Blue Owl is really big and and the whole uh and and and there's basically been this little bit of a fight emerging between where the debt is coming from.
19:18
Do you do private credit or do you go with the traditional bank route?
19:22
And so Jamie Dimon at least I'm I'm pretty sure he's going head-to-head against Blue Owl in a bunch of these deals.
19:28
And so there's this question like you know, there are they are they chirping at each other intentionally?
19:35
And so Jamie Dimon was cautioning investors about potential risks in the credit market by invoking a proverb.
19:42
"When you see one cockroach, there are probably more."
19:44
And so he was referring to recent loan defaults such as the bankruptcy of auto parts maker First Brands and subprime lender tricolor holdings as warning signs of broader credit issues.
19:54
So Jamie noted or Diamond noted that JP Morgan took losses on some bad loans and implied that trouble in one corner of the credit market could mean undiscovered problems elsewhere implicitly casting doubt on the booming private credit sector.
20:09
And so Mark Lipschitz fires back and he says, I guess he's saying that there might be a lot more cockroaches at JP Morgan.
20:15
And so he's he's actually saying like, oh yeah, you maybe you should go check out their books and see if they have other you know bad stuff because I so First Brands collapsed was an isolated case of alleged fraud actually in the syndicated loan market and it was not in the direct lending arena where Blue Owl operates.
20:36
So Blue Owl had no exposure to First Brands and yet and yet Mark Lipschitz was still you know firing back at JP Morgan for kind of casting doubt on the on the direct lending arena where Blue Owl plays.
20:49
So uh there's these cockroaches there's these cockroach statements and they kind of go back and forth on this but um the the history of Blue Owl is also interesting.
21:00
It's this like merger between a few different a few different companies here.
21:04
Um and it's part of this broader uh boom in in alternative >> Owl is was the primary lender for uh Coreweave.
21:14
Coreweave and they've also done Stargate they've done a ton of stuff. Yeah.
21:18
Um and but interestingly their their their data center business is I think like less than a third of their overall business.
21:25
They have a lot of other a lot of other stuff going on here.
21:29
Um so uh George Walker who's the CEO of the old line money management firm Neuberger Berman Berman Berman he's a cousin of President Bush.
21:38
He says it's extraordinary what they've done.
21:40
It was just a startup and now they're 26.
21:42
6 billion dollar market cap compares to a number of large century-old financial institutions.
21:49
Uh there was some Blue Owl's backstory entails some rich behind-the-scenes machinations but more significantly it reflects the stunning trajectory of private markets which have tripled to 26 trillion in assets over the past decade. The company's also Yes.
22:07
Uh so I mean I do think it's important to keep like the scales in mind here. Like the 1.
22:12
4 trillion seems so big in the venture context that we think about we think about Sam Altman as a venture-backed founder.
22:19
But he's now playing in a market that Playing hyper-scalar game.
22:25
Yeah, he's playing a hyper-scalar game and so when I think about it's like And so >> 1.
22:30
4 trillion that's the same size as the oil and gas debt market.
22:32
That's >> Meta's Manhattan like Manhattan Project scale data center the 5 gigawatt data center that you talked about them doing this deal with Blue Owl on.
22:41
Meta's also just did a 30 billion dollar bond offering. Yeah.
22:46
Uh which has 4 billion of 4.
22:50
2% senior notes due in 2030 and then all the way up to 4 and 1/2 billion of 5.
22:54
7 senior notes due in 2065.
22:59
And there was an order book of around 125 billion dollars Mhm.
23:01
for the 30 billion dollar issuance.
23:03
So there's massive amount of demand.
23:07
>> So this is not at least that we know of what what OpenAI has not been raising this style of debt for the business and it's unclear if there would be like a ton of demand for OpenAI's like on-balance-sheet debt Totally.
23:26
>> given that it's unclear if they're going to be able to Totally.
23:27
spend you know what they've already Yeah, but but underwriting a data center with OpenAI as a client is very different than underwriting OpenAI directly. Yeah.
23:35
Um so there's some really funny quotes in this article.
23:40
Blue Owl is the pretty girl at the dance right now says Wall Street trader David Williams.
23:46
We're talking many billions in private credit. Oh yes, private credit.
23:48
Though Blue Owl has three lines of business private credit specifically direct lending in private equity deals is the firm's calling card and growth engine and the straw that's stirring Wall Street Wall Street's punch bowl lately.
24:01
They also have this like GP business if you want to buy that of GP stake in alternative asset manager you can do that through Blue Owl but what everyone's interested in is this private credit specifically for AI assets at least from our perspective.
24:19
I'm sure there's other people that find the other pieces of their business much more interesting.
24:21
But its core direct lending business has 145 billion in AUM out of 284 billion total.
24:27
So that's about half the fund and that was conceived um the firm started in 2016 as Owl Rock at the Putnam restaurant in Greenwich, Connecticut of course comfort food at its best >> [laughter] >> by principals Doug Ostrover formerly the O of GSO Capital Partners now Blackstone Credit Craig Packer a former Goldman Sachs partner and Lipschitz a former KKR partner.
24:52
The name came from the wisdom of an owl and the stability of a rock says Lipschitz and the website was available.
24:59
>> [laughter] >> Uh That that always helps.
25:01
>> So instead of relying on in venture we all think of the GP LP private markets fund structure, right Jordy? Owl Rock changed this.
25:09
They don't do the typical GP LP split.
25:12
They use what's called business development companies BDCs.
25:17
So those those companies issue stock and lend money to businesses usually those with junk credit ratings.
25:24
So something like a one-off data center that really only has like one client and it's not like Apple. It's not Microsoft.
25:30
It's not it's not an actually like you know been in business for 30 years. Not the government.
25:34
So it's going to have a junk rating.
25:35
It's going to be higher a higher interest debt instrument.
25:37
Um and other and and this has actually been a trend other major alt firms are also turning to BDCs which support higher yields and so BDCs send some 90% of the interest collected on those loans to shareholders through dividends.
25:51
So they've basically created the same structure as a real estate investment trust or something close to it.
25:58
Uh and so this has allowed them to scale.
26:00
So two of Blue Owl's BDCs are publicly traded others are private.
26:03
They have Blue Owl Capital Corp which yields 11.
26:05
4% and Blue Owl Technology Finance which yields 9. 9%.
26:11
Both are down about 14% this year the former from January 1st.
26:14
And so um Goldman Sachs recently called the fears overblown about you know the risk of falling rates and weakening credit and they cited Blue Owl as undervalued noting that it has a stock price to fee-related earnings multiple of 21.
26:30
7 which is 5% below its two-year low.
26:33
So the stock has been beaten up but it still has like a buy rating from Wall Street firms.
26:39
Um Blue Owl has generated a stable highly predictable stream of earnings says Ostrover this the other co-CEO.
26:45
It makes no sense that we're down more than our peers he says.
26:51
If anything we should be down less. I love it.
26:54
I love the defensive yeah.
26:57
>> Uh Wall Street maybe maybe particularly wary of direct lending as shares of both Blue Owl and Ares which specialize in that business have fallen hard.
27:03
It's also true that both stocks had previously outpaced their peers.
27:07
The second leg of the Blue Owl stool was created years earlier when a Lehman Brothers executive Michael Reese started a fund at Neuberger Neuberger Berman that bought stakes in asset managers like DE Shaw.
27:16
This is what I mentioned earlier about buying buying GP stakes.
27:20
Reese named his endeavor Dial after his children Dylan and Alexia.
27:25
He just took his two kids names and put them and pushed them together.
27:29
Uh raised its own capital from Koch Industries and invested in the likes of Silver Lake and Vista Equity Partners.
27:34
So he was a vehicle to allow you to buy GP stakes in Silver Lake and Vista Equity which are not publicly traded I believe.
27:41
Um in 2020 Blue Owl merged with Owl Rock and so the the resulting company was called was named Blue Owl.
27:50
A bank working on the deal had called it Project Blue.
27:52
So Blue was added to and and Rock was dropped cuz it was Owl Rock before.
27:57
Uh and so um the hatching of Blue Owl was problematic to some companies in which Dial had invested particularly Sixth Street Partners and Golub Capital both of which sued claiming the new company created a competitor with sensitive information about their operations because of course they own GP stakes in the companies.
28:14
Um and so uh in 2021 Blue Owl bought Oak Street Real Estate Capital a Chicago-based firm specializing in sale-leasebacks and triple net lease deals as its third business.
28:29
This wing of Blue Owl has AUM of 71 billion and is home to the Meta infrastructure deal.
28:34
So they they they it's like a remarkably balanced stool.
28:39
I'm I'm I'm sort of shocked.
28:39
What I was expecting it to be like you know oh we hear about Blue Owl in the Meta context and that's their main business or it's a new thing and it's only 5% of the business but uh in fact they have they have a pretty pretty diversified offering across a few different products.
28:54
Um and so this wing now has 71 billion and is home to the Meta infrastructure deal and others such as Stargate data centers in Texas and New Mexico.
29:02
Next Blue Owl is working on AI deals with N Scale and Valor Equity to finance purchases from Nvidia whose chips go for $30,000 and up according to people familiar with the matter.
29:12
Blue Owl built its direct lending business by borrowing from the Silicon Valley playbook of scale first monetize later or by underpricing established private credit firms to gain deal flow and then raising fees later.
29:23
Most of the firm's direct lending business is done as part of private private equity buyouts.
29:27
So Thoma Bravo Blackstone Warburg Pincus these companies come in and then um uh Blue Owl does the debt side of that.
29:37
These investments generally entail floating junk bonds a business pioneered by Drexel Burnham in the 1980s but it was typically done with banks now it's done with these private credit firms.
29:43
Uh the private credit market has grown from two trillion in 2020 to three trillion at the start of 2025.
29:50
So, again, I'm like that's that's growth, but that's not the craziest growth I've ever seen.
29:56
Like I when I go back to global financial crisis, you know, you hear about these like 10x run-ups in these derivative markets. Like I don't know.
30:04
I I it it just feels like it feels like we're we're still in like the early stages of actually ramping this piece of the capital markets and marshaling that to the really crazy stuff.
30:16
It feels like the crazy stuff is coming in two years. I don't know.
30:20
Yeah, and that that aligns with Doug Doug from SemiAnalysis point.
30:24
He was like that we're still early in the debt cycle.
30:27
But at the same time market has jitters.
30:30
I was working on our 2026 merch this morning and I had about an hour call and so I missed the fact that Nvidia's down 4%, Coreweave's down 8%, and uh it's a little bit shaky out there.
30:44
We're certainly not in white suits today. No. No.
30:47
Well, let's let's go over to the timeline.
30:50
Let's go over to some of that other news that we wanted to touch on today.
30:55
But first, let me tell you about Cognition, the makers of Devin, the AI software engineer.
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31:04
So, Alex Heath has a scoop here in sources.
31:08
During a recent private call, OpenAI's investors asked about external signs that ChatGPT's growth is slowing.
31:13
CFO Sarah Fryer >> the external signs where I think like App Store data, there was some data out of Europe.
31:23
That and it was hard to read into the European data because Europe Europeans >> work ever.
31:28
No, I mean it was coming off of summer, right?
31:31
And and you know, ChatGPT's popular with students.
31:35
>> But European summer hasn't ended yet.
31:37
European summer ends like late December.
31:39
>> No, I have to push back on that because when the French television network came That's true.
31:44
>> they were clearly done.
31:44
That was about a month ago.
31:46
They were clearly back from summer holidays and they wanted to learn about the AI talent wars. >> Yes, that's true. That's true.
31:52
No, we're obviously joking there.
31:55
Um Anyway, so there there's been some >> there's been there's been early warning signs.
31:58
Well, yeah, walk me through some others.
32:00
So, I can walk through Alex's coverage.
32:02
It says on Monday OpenAI OpenAI CFO Sarah Fryer held a private She was really hoping to just not be in the news cycle this week.
32:10
But when you're the CFO of one of the most important companies in the world that comes with comes with the job.
32:19
But >> What bag holders leaking this?
32:20
Yeah, that's that's my question.
32:23
>> you're an investor, and you're leaking bad news to sources? What are you doing?
32:30
>> Yeah, what are you doing? >> get out or something?
32:30
Like are you have you have have you somehow facilitated some short position?
32:35
Like why are you >> the call, market is down. >> founder-friendly.
32:39
Whoever's doing this whoever's doing this is not very founder-friendly. Not not good.
32:42
Okay, anyway, Anyway, Sarah Fryer held a held a private quarterly earnings call with the company's biggest investors.
32:48
As usual, the numbers she shared were mostly up into the right.
32:51
But behind the strong top-line fig figures, a quieter question hung over the call.
32:55
Was ChatGPT's momentum starting to slow?
33:00
During the Q&A portion of the call, sources say Fryer was asked to reconcile ChatGPT's meteoric growth in weekly users from 250 in September 2024 to over 800 million now with external signs that the app's growth has slowed in recent months.
33:16
Close followers of OpenAI's business have been whispering about these signals from research firms since late summer, but this was an opportunity for company backers to hear directly from leadership on the matter.
33:24
After telling the investors to take third-party estimates with a grain of salt, Fryer acknowledged a in ChatGPT's armor.
33:30
She said time spent had declined slightly in response to quote content restrictions the company rolled out in early August.
33:41
She then referred to the loosening of those restrictions that CEO Sam Altman has said will be implemented for adults in December.
33:47
So, this is them Sam came out and said we're going to allow erotica on the platform.
33:52
And Sarah says and OpenAI expects the decline in time spent to reverse.
33:59
And so, this reminded me conversation we had about exactly a month ago where I said I don't think them announcing that they're getting into erotica is a sign of strength.
34:13
I don't I don't think that's something that you do you do just because you want to, right?
34:18
If in my view it felt like clearly I mean clearly there's user demand for it.
34:23
But at the same time that felt like something that you that they would do in order to stimulate growth while they get a bunch of other monetization uh online, right?
34:35
So, like commerce, ads, etc. Yeah. No. That makes sense.
34:41
Yeah, I mean the original like founding team at OpenAI was incredibly idealistic, right?
34:46
Like incredibly like you're going to work on a nonprofit on like superintelligence like AGI.
34:54
Like you you you you truly are working on like what you see as one of the most important problems, what I would agree with is one of the most important problems.
35:04
Um then of course like you know, eventually the eventually the company evolves and you and you bring in business leaders.
35:14
But at the same time like like I I do believe that when they say I want to cure cancer. I believe that. >> I I believe that too.
35:23
The reason I reacted strongly to it was that >> Yes.
35:27
there had been messaging, you know, around the same time of I don't want to be in a world where we have to decide between curing cancer and free education for the world.
35:37
And so then at that same time deciding we're going to do erotica >> very weird timing. It was very weird.
35:41
It was very weird timing that those two statements like came out one after another.
35:46
I'm Yeah, I'm actually surprised why they're waiting till December to roll out >> Yeah. the adult content.
35:54
>> in this scoop, do we have any do we have any specific data on on what exactly uh what exactly, you know, is indicated in terms of ChatGPT's growth slowing?
36:07
Can we actually try and define that a little bit more?
36:09
Is that is that users cuz they're we're already at almost a billion users.
36:13
Is it is it time on site? Is it monetization?
36:19
Um I mean, deceleration, we were talking about this like OpenAI has decelerated revenue before.
36:25
Cuz they I think they tripled and then they went to a doubling or they went or they were quadrupling and then they went to a to to a tripling.
36:30
And so they actually decelerated in 2024 and then they reaccelerated in 2025.
36:36
And so, I was kind of saying like well, you know, there's a good chance that you could see deceleration in the future. It's happened before.
36:44
Like to be accelerating forever is is basically impossible.
36:50
But it would be interesting to track exactly how how ChatGPT's growth is slowing.
36:58
There certainly feels like there's just a level of saturation. Do you have the stats?
37:02
>> Yeah, so Meta so SimilarWeb put out some information on month-over-month change in total visits to leading GenAI tools.
37:09
ChatGPT is at the bottom of a list that includes Gemini, Deep Seek, Perplexity, Grok, Claude, Co-pilot, and Meta AI.
37:17
The key difference here is that like ChatGPT is just so much bigger than these other platforms that they could still be adding more users on a on a on a total on an on a per user basis than these other tools even if their growth is slower. Yeah. Yeah, that makes sense.
37:35
Tyler, what I mean, they do expect their growth to slow down.
37:38
So, like from this is Epoch AI that it was like OpenAI revenue estimates. Yeah.
37:43
Um so 2025 is 13 billion and then they expect 2.
37:49
3x in 2026, 2x in 2027, and 1. 6x.
37:49
So, I mean it's not like they're just saying like it's it's going to go from 2x to 3x to 4. Yeah, exactly.
37:57
So, I wonder I wonder how much of this is just framing something that was sort of already priced in as a as like a bad thing.
38:12
Like I feel like people were expecting deceleration.
38:17
And so, if if she's if she says like if she's on the call and she says um as expected, we're really big.
38:23
We're going to be decelerating the level of new users that we're adding.
38:28
And then I don't think she should say that.
38:34
Yeah, maybe that would be bad framing. I don't know.
38:35
It just doesn't seem that crazy.
38:37
I guess you need to I don't know.
38:39
It doesn't seem that it doesn't seem like that bad of a thing to say.
38:42
Like the like like Meta is not accelerating top-line uh top-line users.
38:52
They have like three billion users.
38:52
Like no one's expecting them to accelerate top-line users.
38:57
Uh maybe like randomly one quarter they accelerate, but not continually.
38:59
Um and so, I don't I don't know.
39:02
It it just feels like an odd thing.
39:05
Um Did did you get a chance to read the Ed Zitron article? This thing?
39:07
I did, but I didn't I felt it Ed is such a massive OpenAI hater that I think it was hard to and the sources were pretty unclear.
39:19
It was hard to read too much into it. >> Okay. Okay.
39:22
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39:32
We should we head over to some timeline?
39:35
What else people are saying?
39:36
Cairo Smith says it's going to be very funny when LLMs plateau around 120 IQ and what we've created is just a digital guy not a god >> [laughter] >> I mean this doesn't make any sense if we have like infinite digital guys that's like literally a guy is just like a worker. Yeah.
39:55
>> If we have infinite workers that's like insanely bullish. Yes, it is.
39:59
>> bullish but we've been promised you know people are promising.
40:03
>> say bearish he said it's going to he didn't say it's going to it's going to collapse the economy when we just get a digital guy he said it's going to be funny and now I agree.
40:10
I guess that's true but this is still like a very bullish take I think people I think you might read this as like being he's kind of bearish.
40:18
Yes, yes, yes yeah no I know I know I think you're right if you get a digital guy that's pretty powerful cuz guys can do a lot of stuff.
40:25
It's valuable I love guys. Yeah.
40:25
You need a guy for everything.
40:27
You do need a guy for everything and you will in the future you will get >> of the great luxuries, right?
40:31
Middle class has apps the wealthy have guys.
40:35
Yes, yes, yes and and the and the apps get better with the uh the apps get better with AI agency AI agents, right?
40:45
Uh because you you you have you have an app that acts a little bit more like a guy than than a than an app.
40:53
Um real quick uh scoot in the X chat almost bought a counterfeit TVPN hat. >> No way. Uh watch out.
41:00
There is a counterfeit uh TVPN store.
41:03
These are uh not by us they've made it look like it it's by us I'm not going to name the link but we have not sold any merch uh we will make the merch available uh as soon uh as soon as possible uh I was working on it uh this morning before the show so it's coming but do not buy any of the counterfeit merch.
41:26
My big concern with that site is I don't even know if they ship it.
41:31
Um Yeah, that is a big question.
41:31
They and they also made like 100 products.
41:36
>> They made there's so many products and I did email them and I and to be clear And our lawyers emailed them and we've submitted a bunch of takedowns.
41:42
>> Yeah, when I when I emailed them I was like hey like I assume like you're just a fan like I was I was being too nice I was being golden retriever mode but I I did say I was like hey like I you know I appreciate this idea this is this is very cool that you're enjoying the show but like we just don't want people to get confused we have our own plans for a store.
42:03
>> They're like okay I'll make 200 different products.
42:05
>> just didn't they just didn't respond at all and so then we we sent a takedown notice and we will be fighting that tooth and nail so stay But please uh don't buy it because uh we have nothing to do with it.
42:15
Uh Tyler, did you get a chance to read Fiji Seemo's latest blog post moving beyond one size fits all?
42:22
>> you didn't read I hope you studied. Sat your ass down.
42:26
Uh we talked about this for a tiny bit yesterday this was just the the 5. 1 release. >> Yes.
42:31
Um Yeah, nothing I would say super substantive in it um she kind of is talking about how um I I I think with the with 5.
42:40
1 they were going Like we made our digital guy faster better. Yeah, stronger. Is that what it is?
42:48
>> the the EQ of the model lot rather than IQ that's why you see a lot less benchmarks I think it's just hard to actually benchmark that kind of stuff but the actual like style of the model talking about kind of safety ish stuff where there's like [snorts] you know I mean it is it is crazy following this company so closely
43:06
because in in here there's a line that says with more than 800 million people using chat GPT we're well past the point of one size fits all and 800 million sounds amazing except I feel like I heard the 800 million number like two months ago and I feel like they have been accelerating so fast You'd expect them to be at 850. 900 exactly and so the
43:23
900 exactly and so the fact that they're repeating the 800 number is like what's going on?
43:29
>> can't add a third of the United States every month.
43:32
>> I know I know I know it's very very high stakes it's very it's very impressive what they built to be clear but I I just I am really keyed on that like 800 800 because I was I was excited they were going to hit a billion it was going to be a big moment.
43:43
Um and and yet it feels like maybe that's a next year a next year goal but near Cyan has been going back and forth on this near said chat GPT is officially in its Fiji Seemo phase if you're wondering why the upgrade doesn't come with benchmarks have fun.
44:03
Uh Rune says you're you are confidently wrong about the internal dynamics of this it could be better summarized as an infra cleanup and near says the source for my top tweet is Fiji's blog post from today when which discusses the release and its goals I don't really know what else to say. Um I don't know.
44:24
I I think is there hunger for benchmarks anymore?
44:27
I I I I might actually take the other side of this here um and say that I like that they're getting away from benchmarks I don't I wish they didn't do a 5. 1.
44:39
I don't want any more confusion what is 5.
44:41
1 versus 5 just make it better and don't do a release and certainly don't tell people because what if people imagine >> because you're not in love with a specific version John.
44:51
>> I'm in love with five.
44:53
I'm in love with five Rune bring back five I don't like 5.
44:55
1 I need five specifically not 4. 0 not 5. 1 I need five. Five please.
45:02
I will say when it was >> five in the bag Rune.
45:05
Yeah, come on bring back five bring back five we need to we need to cyber bully Rune until he bring back five even the most minor tweak to the model is unacceptable.
45:14
You can still use five pro. I know.
45:16
So that's that's got to count for something.
45:20
Only five thinking is >> Yeah, but but I I was I was saying when GPT-4 like GPT-4 like not 4.
45:23
0 or anything when that was the the best model they would do updates they wouldn't like say oh this is a new model. >> Yeah.
45:32
And people could definitely tell okay they released a model it's worse.
45:36
And then everyone on Twitter would hate it but then I think So you so so you think you think putting a putting a version number actually helps like fight back against that because people are like oh I I get it why it's worse you you changed it like instead of like there being a surprise under the hood?
45:49
I think it's more um it's just like easier for people to tell if there it was actually a change when they're when they're noticing something that they've been depending on it's it's a little different now like >> Yeah, I just don't understand why you're why you're surfacing it in the UI of a of like if I open my chat GPT app at the top now it says chat GPT 5.
46:11
1 like this is a consumer iPhone app this today's pulse is here talking about Blue Owl's stargate investment and chat GPT 5.
46:19
1 and I just have to wonder if like the the 5.
46:21
1 is like unnecessary like if I open up Instagram it does not tell me what version of the reels algorithm I'm on.
46:29
They're going to change it every day like just change the algorithm all the time just make it better and yeah if you make it bad I'm going to turn so don't do that make it better every day forever.
46:38
And just keep shipping ship every single day like I'm sure that internally there are version numbers of Google search right because they push to like a main GitHub branch or something whatever they use for their mono repo but um like there is version tracking for like the reels algorithm they just don't surface that to the user so I don't know why they're surfacing 5.
47:02
1 to users after there was like so much pushback over 4.
47:05
0 5 5 all this other stuff it seems like I don't know it seems like a mess you know what doesn't seem like a mess Vanta automate compliance manage risk and accelerate trust with AI.
47:17
Vanta helps you get compliant fast and we don't stop there our AI on automation powers everything uh from evidence collection to and continuous monitoring to security reviews and vendor risk um There was one more uh note from Alex Heath's article on OpenAI that actually uh I think is worth sharing.
47:36
He says after Meta's last earnings call sources say and this is confusing because the name of sources is I don't want to hear what Alex Heath has to say specifically give me some like people who are close to the matter I don't I don't really I don't want to know what sources says I want to know what >> Sources say sources say that sources say sources says that sources say CEO Mark Zuckerberg joined an internal employee
48:01
Q&A and shared a warning about the AI bubble first he shared a breakdown of how different players from startups to big tech names like Meta should think about timing their bets he described three camps in the industry optimist who see super intelligence emerging within two to three years moderates who expect breakthroughs by the end of the decade and pessimists who think it'll take well into the 2030s each outlook he said
48:20
dictates how aggressively a company invest then he expound uh expounded on a version of the answer he gave me recently in our last interview uh he noted that while unprofitable startups like OpenAI and Anthropic risk bankruptcy if they misjudge the timing of their investment Meta has the advantage of strong cash flow he also made the point that while big tech has historically been relatively debt debt-free compared to large companies in
48:44
other sectors the AI infrastructure race is leading Meta and its peers to start using leverage in a more normal way relative to their size um uh like he told me in September Zuckerberg acknowledged to employees that Meta's market cap could suffer if his timing is wrong and the bubble burst but the message was clear we'll have the balance sheet to survive and emerge stronger than most on the other side. Um so anyways uh
49:08
Um so anyways uh Super Dario was quoting that and said the obvious end game in the next two to three years is that Microsoft acquires OpenAI Google acquires Anthropic and Tesla acquires XAI only the large caps survive.
49:23
That's a nuclear hot take that is crazy crazy how would that even I don't know can Microsoft get the rest of OpenAI I mean I guess they probably have >> Depends on the price.
49:35
size it's a four trillion company versus a 500 billion. Yeah, I don't know.
49:42
It doesn't seem like impossible.
49:45
Um Let me tell you about Graphite. dev.
49:47
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49:54
I want to run through some more of these posts.
49:55
Yuchen Jin says, "In contrast, OpenAI employees stayed for 2 plus years, sold 6.
50:01
6 billion dollars of equity last month.
50:03
Many hit the $20 million cap.
50:05
Morale and vibes are high, but so is the turnover rate.
50:07
New OpenAI hires are often shocked by how many Slack accounts get deactivated each day."
50:13
Um this is a a screenshot of an interaction between Jack Morris and Liang Chen Liang Chen Luo at xAI.
50:18
Jack says, "Uh there are dozens or perhaps a couple hundred ex-OpenAI xAI Google DeepMind researchers founding companies in the current climate.
50:30
And And this is talking about says, "The simple answer, the liquidity of Anthropic options is the worst among those frontier labs."
50:38
>> this is talking about how a lot of people have been leaving various labs. Mhm.
50:42
Less people have been leaving Anthropic.
50:45
And so, Liang Chen is saying the simple answer.
50:48
Yeah, and so Andrej Karpathy says, "Uh bullseye, it's interesting how large of a fraction of people don't see the dominant first order term that drives behavior of people in companies.
50:58
You you can construct a powerful world model just by understanding one I uh just by one understanding the system and two assuming this uh there is only this single term like liquidity, how much how much cash you have.
51:13
Uh what would be interesting is if you could uh is is if companies started offering uh liquidity in the form of annuities.
51:23
So, imagine you have an employee who's like a rockstar.
51:26
They're going to sell $20 million of stock.
51:30
And they're going to basically be post-economic.
51:32
If you could instead say we're we're going to be paying you out like you're selling now, but you're but we're we're but you get a billion dollars a year or something.
51:44
There needs to be some way to like sort of cap Is there Is there some way to cap uh the actual amount?
51:49
I guess $20 million was the cap.
51:51
Um but uh I I I don't know.
51:53
There's there's some way to to to deal with this like, you know, if you don't get it employ If you don't get employee liquidity, like they'll leave for something else.
52:03
They'll just go to somewhere else that pays them a higher salary.
52:06
If you give them too much liquidity, they'll leave and start new companies.
52:10
Uh very very tricky to manage the uh manage the team.
52:14
Um but that is the nature of these uh these companies.
52:16
Chad Byers, that is his real name.
52:21
He is a Chad in the literal sense and figurative sense.
52:28
Uh he says, "One of my strongest beliefs is that it's going to take 20 plus years to get AI penetrated into the real economy.
52:33
I filled out a piece of paper at the doctor's office last week."
52:37
>> I filled out a piece of paper at the doctor's office last week, too.
52:38
It was crazy, and I was wondering like when will we see a fast takeoff in DocuSign?
52:44
Fun I I finally realized why DocuSign has so many employees.
52:47
Because you need to go to every doctor's office in person apparently for decades to get them to use online form filling technology.
52:53
Like general SaaS really does not has not has not permeated as much of the economy as people think.
53:02
A lot of people still on spreadsheets for all sorts of stuff.
53:04
A lot of people still on paper and pencil.
53:08
There is a You know, we joke about being pro-ramp anti-paper receipts, of course.
53:13
Um there's a company that makes paper receipts that's worth $20 billion. $20 billion.
53:19
There are fax machine companies.
53:19
The fax machine industry is still over a billion dollars.
53:23
Still a billion dollar industry. Crazy.
53:26
>> think it would I would think it was actually more.
53:28
>> Yeah, maybe that's maybe that's too small.
53:29
It's It's sort of hard to like calculate cuz a lot of these things have been like rolled up into other companies.
53:35
Uh and >> Big facts Big facts wants They wanted to think it's small.
53:39
>> one of them, and and so like I don't even know if Canon breaks out their fax business anymore because they sell so many cameras and other equipment.
53:44
Um but uh yeah, it's it's interesting.
53:50
Near says, "IMO uh in my opinion, the entire AI field switched from explore to exploit 2 years early.
53:58
Everyone convinced themselves, 'No, this isn't the case.
54:00
Look at our exploration.'
54:02
And it's like watching someone go on a 50-ft walk and find a cool tree when the entire continent is still covered in fog of war.
54:08
Now that the terrain seems known, it should be harder convince yourself.
54:14
Um I suppose this makes sense given a lot of people hinted being good as gone as soon as they have enough money. But no, not me.
54:21
I've been gone for ages already."
54:25
That's a very funny post.
54:25
Um I suppose uh Weren't we talking about this yesterday, this idea of like of like where will the next innovation come from?
54:33
Where will the next breakthrough come from?
54:35
Will it come from uh any of the any of the the like will it come from xAI?
54:41
Will it come from DeepMind?
54:45
Yeah, like how much do you need the college campus?
54:47
How much do you need that environment?
54:50
>> come from a university? >> A university.
54:51
The universities seem to have not like it's very odd that the university system did not produce the transformer paper.
54:56
Feels like the perfect thing to come out of a university setting.
54:59
Yeah, I mean it's really tough right now.
55:00
You can stay in a university system and be a student and be taking on debt, or you can go work at a lab and make have a good shot, at least if you did this a few years ago, have a good shot of making $20 million in a few years. And Yeah.
55:14
it's hard to give up that kind of opportunity. Yeah.
55:18
Uh this Wall Street Journal article is giving more context on the AI boom.
55:20
Says the AI boom is looking more and more fragile.
55:23
AI stocks have swung downward as doubt rises about sustainability and payoff.
55:28
Perfect isn't good enough, and any sign of weakness is a disaster.
55:30
This is what's happening.
55:33
It's like you double revenue, and your stock trades down.
55:35
It's very very odd, but everything been priced to perfection.
55:40
>> is the only Neo Cloud in the platinum tier SemiAnalysis, Yes. is down 45%. That is remarkable.
55:50
>> It's like they they have built a like by all accounts fantastic product. Fantastic product.
55:55
I mean like I don't know, maybe SemiAnalysis got it wrong, but I don't think so.
55:59
Um but it feels like they've built something that as infrastructure delivers at the level of the hyperscalers.
56:05
Just like a fantastic product.
56:07
Uh and yet uh the the market like sort of ran away with that narrative, and now it's pulling back a little bit.
56:14
So, uh recent history suggests that the gloom won't last, but the shakeup serves as a strong reminder that the early years of AI pose a challenge for investors accustomed to measuring returns on a 12-month time horizon.
56:27
Generative AI services require massive data centers and state-of-the-art chips and server racks that don't come together quickly.
56:32
The companies at the heart of the of AI are now talking about years, plural, of all major investments still ahead.
56:37
So, everything has kind of sold off a little bit.
56:42
Oracle uh is down the most from its 3-month high.
56:45
Nvidia's down a little bit. Google is neck and neck. They're doing great.
56:49
Um and oddly, Apple didn't even make the chart because they're not They're so not indexed to AI right now.
56:57
Um last The latest episode of fragility started last week when shares of some of the sector's leading lights lost ground after a broad-based recovery on a Monday on news of a possible end of the government shutdown.
57:10
AI stocks fell again Tuesday.
57:13
Nvidia's down 4% today, lost 7% last week, slipped another 3% on Tuesday, uh well leaving it well shy of its $5 trillion market cap.
57:21
Um Yeah, looking at the trailing 12 months, Apple is up 21% and Microsoft, which owns a third of OpenAI, >> Yeah.
57:31
is a huge AI beneficiary, has invested a ton in it, is only up 18%. Wow.
57:36
So, Apple, which has been going through This was the This is the secret. Just don't invest in AI. >> Do nothing, man.
57:41
Just do Just don't do it. Just skip it entirely. No.
57:46
>> [laughter] >> Just do nothing.
57:49
>> Tim Cook's like, "Wait, why would I spend a hundred billion on CapEx?
57:50
I can sign up I can He's like, "I can sign up for ChatGPT for like 20 bucks." So hard.
57:58
She's like, "Yeah, you know, we have this we have Safari.
57:59
We have we have this web browser.
58:02
You can go to use AI from there."
58:02
Just do that on your phone. I don't care.
58:06
That would have been the correct thing instead of the getting over their skis a little bit on the branding side.
58:10
Fortunately, not on the financial side.
58:12
So, they've done very well.
58:13
Uh there is, of course, real reasons to worry about the sustainability of the boom.
58:16
Chief among them is that there is far more AI computing infrastructure spending than there is AI revenue, a gulf widening by the day.
58:25
OpenAI says planning to spend $1.
58:27
4 trillion in the next 8 years, but is only pulling in around 20 billion of annual revenue today.
58:33
And it lacks a clear business model to reach the hundreds of billions it needs within the next few years to keep the spending growth going.
58:42
OpenAI is projecting losses will swell to 74 billion in 2028.
58:46
So, skittish has the mood become that CEO Sam Altman felt the need last week to defend the company on X saying the spending was understandably causing concern. Wow.
58:57
He says he understands your concerns, Stordy.
59:02
Uh he pointed to his plans to boost revenue with new consumer devices, robotics efforts, and AI cloud computing service, none of which currently exist.
59:10
>> And this is why when we were the Monday after that interview, when we were talking about it, saying that wasn't a strong answer because all those things seem like businesses that will lose a lot of money even if they're successful Yeah.
59:24
until they can reach some huge scale.
59:28
Look at Meta's efforts in in hardware.
59:31
Look at early days of any hyperscaler.
59:34
Look at any robotics company, right?
59:36
These are not cash engines.
59:38
They're cash incineration engines that could one day If they want if they want to get more like cash generation, stop incinerating so much cash, OpenAI should should You know, I know they're doing a lot, but they should expand into like just just rolling up HVAC HVAC businesses.
59:53
Just buy a bunch of HVAC businesses.
59:57
>> Adding agents into the workflows.
59:57
We don't even need adding agents.
59:59
We don't even need to do that.
1:00:01
Just buy a good durable business and roll it up.
1:00:05
Plumbing, electrical, >> Plumbing, roofing, storage units.
1:00:09
Storage There's There's a lot of good money in storage units.
1:00:11
If they could get into some storage units.
1:00:16
It's It's It's called >> a storage facility OpenAI cloud storage.
1:00:21
And storage storage storage units that look like clouds. >> Yeah. Yeah. Cloud storage.
1:00:27
>> Uh lawn mowing businesses.
1:00:27
They could get into some a bunch of lawn gardening businesses.
1:00:31
Um there's a whole bunch of opportunities >> Landscaping. Yeah.
1:00:34
I mean, just buying multi-family homes.
1:00:36
Just buying some multi-family homes. >> Single-family homes.
1:00:39
>> Single-family homes, getting the rent payments.
1:00:41
They put the money in, they buy the house, and then they get the rent payment, and that's how they make the money.
1:00:47
And I I think that could be it's a proven business model. Like we know it works.
1:00:51
It works for a lot of people.
1:00:53
A lot of people they they start with one single single-family home, they grow it, they keep >> Box box of oranges.
1:00:58
OpenAI property management LLC.
1:01:01
So, they have the property management company.
1:01:05
They own the the the the the properties with another Yeah.
1:01:10
>> And and so, they can they can actually play both sides.
1:01:12
Play both games, you know.
1:01:14
They could get into drop shipping. Whoa. Think about it.
1:01:17
>> They could set up a TVP and merch store.
1:01:19
They could set up a merch store.
1:01:20
>> Counterfeit merch, sure. For sure.
1:01:22
That's what You know, they keep talking about agentic commerce in the in the ChatGPT app.
1:01:26
Imagine like you go there and you're just like, "I need some these I need some need some t-shirts."
1:01:30
And it just it just instantly sends you some t-shirts.
1:01:34
They're getting into drop shipping.
1:01:35
Uh they could also launch a course.
1:01:38
They could launch a course.
1:01:38
How to build an AI startup.
1:01:40
>> How to build an AI startup. 2,000 bucks.
1:01:44
>> [laughter] >> Buckle up, buddy.
1:01:46
Get ready to pay two grand.
1:01:49
Sam Altman's already been driving around in hypercars, you know.
1:01:52
He has the >> He has the car He has the garage for it. >> The via course, bro. >> The via course, bro. That'd be great.
1:01:58
Actually, he has a much better collection. >> He really does.
1:02:00
I would pay for his course, honestly.
1:02:01
I would 100% pay for the Sam Altman course.
1:02:05
>> in a McLaren in a in a P1, and he's telling you, "I will teach you to be rich.
1:02:09
I will teach you to build an AI startup." >> how to do deals?
1:02:12
How to how to do deals from Sam Altman?
1:02:15
I would 100% pay two grand for that course. I'm not kidding at all. Like 100% it's worth it.
1:02:20
That would be better than any college course ever. Be incredible.
1:02:28
Uh >> [laughter] >> I'm glad we're having a good time.
1:02:33
Uh let me tell you about Julius, the AI data analyst.
1:02:35
Connect your data, ask questions in plain English, and get insights in seconds. No coding required.
1:02:42
>> has an ad at home campaign right now in SF that says, "Ever since I was young, I wanted to transform data into actionable business insights." It's the best. It's the best. >> Fantastic. Great work.
1:02:52
Um Meredew says, "If you're down today, you're a certified beta bouble boy.
1:02:58
You literally bid up Sandisk. WTF.
1:03:04
Alternatively, you can call yourself a bad beta b i t c h."
1:03:06
Um people are having a lot of fun.
1:03:09
Dario says, >> Sandisk is only down 15% today, so Sandisk.
1:03:16
By the way, I have >> Um yeah, the White House last night tweeted, "We are so back." in all caps. What was that? What did that mean?
1:03:22
What did What did they mean by that?
1:03:24
In what way were we back? I have no idea.
1:03:29
But I have I have I have news I need to share with you that I can't share on the stream.
1:03:35
Um but uh uh let me tell you about Fall, a generative media platform for developers.
1:03:40
The world's best generative image, video, and audio models all in one place.
1:03:43
Develop and fine-tune models with serverless GPUs and automated clusters.
1:03:46
And in the chat says the OpenAI McLaren Museum.
1:03:50
>> [laughter] >> Open up a museum. The McLaren Museum.
1:03:55
>> [laughter] >> Just tickets.
1:03:56
Just like adding to our revenue.
1:03:58
Like we're going to charge 25 bucks.
1:03:59
Uh you can bring your grand kids. Like I would visit. I would buy the course.
1:04:03
I would go to the museum. Yes.
1:04:06
Uh well, [laughter] our Our first guest of the show is in the restream waiting room.
1:04:10
Let's bring him into the TVP and ultradome. How you doing, Spencer? Good to see you. What's up, guys? What's up?
1:04:16
I hope you haven't been watching the last 5 minutes cuz we were having a little too much fun.
1:04:22
>> Uh I want you to know that I I have been watching.
1:04:25
And guys, we are so back from the White House.
1:04:27
That's got to be a reference to the government shutdown. Oh, yeah. Okay. Okay.
1:04:34
I forgot about the government.
1:04:36
I forgot about the government.
1:04:36
I don't think about the shutdown.
1:04:38
>> [laughter] >> Okay, well, that's fantastic.
1:04:39
Uh for those who don't know you, introduce yourself a little bit.
1:04:42
You give us a little backstory. Yeah, everybody.
1:04:45
Um I'm a general partner at Coatue.
1:04:47
I help lead our growth fund.
1:04:48
Focus on um late-stage transcendent technology companies.
1:04:52
So, we work with companies across of course software, AI, obviously.
1:04:56
And then also some stuff in hard tech.
1:05:00
SpaceX Anduril, companies like this.
1:05:02
So, um we're 25-year-old hedge fund.
1:05:04
Launched our private business 15 years ago.
1:05:06
We're about 70 billion of AUM today across five >> success. There you go. Where's the mallet?
1:05:13
We're we're looking for the mallet. There it is.
1:05:16
It's in front of your computer, John. There we go. This is for Coatue.
1:05:23
Uh we are thrilled to have you.
1:05:23
Uh I've had many fun conversations this year. First one on the air.
1:05:31
Uh The reason for today's appearance is none other than Cursor. >> Oh, yeah.
1:05:35
What's new in Cursor world? Right.
1:05:37
The the the GPT wrappers are uh you know, their demise was maybe greatly over exaggerated on X over the past few years.
1:05:46
Um Look, we we've we've gotten to know Cursor for a long time.
1:05:49
We just think it's a really special company.
1:05:54
Uh Michael and his team are incredible.
1:05:56
I'm sure maybe you guys saw a Breaze piece about the company, the unique culture, Yeah.
1:06:00
how they approach hiring.
1:06:03
Um I've just never seen a company with such a deep bench of really talented young folks.
1:06:07
Um and then look at the growth.
1:06:10
The growth speaks for itself, right?
1:06:11
I mean, we're seeing folks on Twitter today talking about the company being the fastest ever to a billion of revenue, right?
1:06:17
It's definitely in that air.
1:06:18
Um which they disclosed today being at a billion of revenue.
1:06:23
And then if you think about the things that have transpired over the past year with Anthropic coming out with an excellent product in Claude code.
1:06:31
OpenAI responding with Codex over the summer.
1:06:34
Again, an excellent product.
1:06:34
Uh all these things happening at once.
1:06:36
And then you sit here and you wake up today and say, "Wow, Cursor's got an incredible business, incredible product velocity, an incredible team.
1:06:43
And now their own model, which they shared yesterday, is their number two most used model on the platform in Composer.
1:06:49
So, um you know, there's a lot of good companies uh in San Francisco.
1:06:55
It's kind of a nice thing.
1:06:57
Like in early '22, everyone was very worried that all of our 21 investments and everything was just going to you know, kind of wipe out.
1:07:03
There's actually a lot of good companies in San Francisco. It's a great thing.
1:07:06
There's >> [laughter] >> There's a decent number >> Narrative violation. Right. Right.
1:07:11
There's a lot of good companies.
1:07:11
Um there's a few There's a few great companies also.
1:07:15
But there are very few of these companies that, you know, the the word of the day or the word I try to always come back to is transcendent.
1:07:21
And um you kind of know it when you see it or you know it when you're around it.
1:07:27
And that's what we think Michael and the team are doing at Cursor. Uh funny funny story.
1:07:31
Uh I Cursor is uh I'm incredibly inspired by by their growth and and user love and and everything they've accomplished over the last few years.
1:07:43
But I had a painful moment when they when they uh when they first kind of exploded onto the scene because I realized that Aman, uh one of the co-founders, had been following me since like 2020 or something.
1:07:53
I was like [laughter] I I I I had this I've had this like, you know, lingering fear over the last few years of like, you know, getting followed by somebody, not reaching out, not investing.
1:08:06
And uh certainly certainly would have been smart a few years ago to um to catch it. >> too late.
1:08:13
We think there's still We think there's still a lot of upside, you know. So, we'll we'll see. Fingers crossed.
1:08:19
One of the frameworks that I've heard kind of bandied about around what's going on like a lot of people maybe want this to be more of a winner-take-all market than it is.
1:08:28
Um and yet when and and yet maybe because of Jevons paradox or just the nature of like we it's very hard to understand how much code does humanity actually want to write?
1:08:43
Like it's potentially a ton.
1:08:45
And so, you're in this like entirely new market that's a blue ocean, and it's massive, and it's just growing growing growing.
1:08:53
And so, there's opportunity.
1:08:55
And I think you're in a unique position where uh you you're you're in a number of names that do overlap somewhat, and there's a lot of opportunity.
1:09:07
I'm wondering if you think that's the right framework or or or is there more of a like a winner-take-all monopoly thesis with with with this particular market?
1:09:16
Or if there's other uh historical anecdotes that you go to to kind of understand how this might play out.
1:09:24
Yeah, I'm I'm I'm not sure what the best I mean, I'll get I'll get to the anecdotes.
1:09:27
It's hard to find the best analog and analogs are always difficult, especially for this.
1:09:31
We're we're investors in Open AI, Anthropic, and Cursor.
1:09:35
Um, we're also investors in Glean, Harvey, Open Evidence, um, a number of companies across the kind of AI stack. Um, our view, right?
1:09:46
If you just take a step back, right?
1:09:48
People talk about the the bottoms-up TAM sizing on developers to your point.
1:09:52
People want to write a lot of code.
1:09:52
30 million developers in the world, right?
1:09:56
A a fraction of a percent of the global population today.
1:09:59
And so, what what's that number look like in 10 years, 20 years?
1:10:03
What should that number be?
1:10:03
I mean, that's not really an answerable question, but probably much higher.
1:10:06
And then, if you take a step back tops-down, you've got 5 trillion of global IT spend.
1:10:14
And about a third of that is labor today.
1:10:16
So, hey, Open AI's putting up historic growth numbers, Anthropic's putting up historic growth numbers, right? All that's >> Yep.
1:10:23
well, kind of directionally known.
1:10:26
Um, we see all these companies growing really really healthy.
1:10:29
Um, and doing it in a in a way that we think is pretty sustainable.
1:10:34
Yeah, did you have a reaction to uh Satya Nadella on Dwarkesh x Dylan Patel?
1:10:42
Um, there there's this interview and he kind of talks about uh it feels like he was kind of sending the signal uh I'm going to steamroll you if you try and build an Excel agent.
1:10:51
I'm I'm defending the castle that is Excel.
1:10:54
But also on a platform for a bunch of other things. >> is Excel Copilot. >> Yeah, exactly.
1:11:00
And I think that I I think that I I would bet on him being able to defend the castle that is Excel.
1:11:06
But I've but I was going back and forth with uh with Jordy about uh it feels a lot harder for Microsoft to put up a fight in AI for lawyers because they just don't have a practice there.
1:11:17
And yes, lawyers use Word documents, but uh it is a it it's it it just feels like a slightly different uh go-to-market, slightly different product.
1:11:27
And so, I'm wondering on how you think about the edges of like where the where the hyperscalers or the neo hyperscalers, the foundation labs, might steamroll versus not uh because Cursor's an interesting example of a company that feels like almost steamrollable and yet hasn't been.
1:11:47
Right, hasn't been, right?
1:11:47
Again, if you look back at the past if I told you, "Hey, Claude Code is going to be this amazing product that's this good and you can use it."
1:11:54
You'd think, "Oh my god, right?
1:11:56
Cursor's good, like I'm shorting Cursor at the beginning of the year, right?"
1:11:58
That's what a lot of people So, I again, I just think it's it's, you know, we get to sit in a unique spot where we see all these things happening and go, "Wow, we're just so early in terms of the pie growing, the tide rising with all boats."
1:12:11
Um, I think I messed up that somehow, but the uh uh to get back to your question around the the hyperscalers and who wins where, um, it's it's it's hard to say exactly.
1:12:23
I think that's kind of the big question right now.
1:12:25
If you look back at like the beginning of the year, I think there was an open question around are are these API businesses going to work?
1:12:32
Is there long-term margin in the API business, right?
1:12:35
Is something like Cursor just going to get run over?
1:12:37
Is something even like Anthropic in the API layer going to get run over and squeezed?
1:12:42
I think now people are kind of going, "Well, you know, these businesses look really good.
1:12:46
They've scaled really nicely despite all these things to your point happening at once."
1:12:51
Maybe now the question is, what's the the architecture of that market look like with the hyperscalers over the next 10 years?
1:12:57
Um, it's it's hard to know.
1:12:59
Um, what what I would say is if you take a step back and you look at the cloud AI revenues for the for the three hyperscalers, I mean, they've been inordinate beneficiaries of this trend, right?
1:13:10
Open, Anthropic, Cursor, uh like Harvey, all these companies are great.
1:13:15
The hyperscalers have done really really well.
1:13:17
And the move to cloud and the move from on-prem to cloud that's being pulled forward by all this is really really compelling for them.
1:13:24
So, like they don't need to worry about winning in our view, right?
1:13:27
Like lawyers in beating Harvey.
1:13:30
Like that's that's almost um it's just further down the economic ladder in my mind for them, but, you know, we'll see.
1:13:38
Um, the uh and then the other point, oh, you asked about Excel.
1:13:43
Yeah, Excel's fascinating, right?
1:13:44
Uh the ultimate like dominant software product over many decades.
1:13:50
We've been looking for kind of the Cursor for Excel uh for a while.
1:13:55
We've tested a few products.
1:13:55
We've got an internal team here that builds a bunch of things that we also >> have built your fair share of models over the years, I would hope.
1:14:04
>> we we do a lot with with data science and and now increasingly with AI.
1:14:06
We haven't quite found something that works yet, but there are a few folks building some interesting companies that we're not invested in them yet, so I'm not going to say what they are, but it's an interesting opportunity.
1:14:17
Could um I I think that more likely than not Microsoft still ends up owning a lot of that.
1:14:25
>> I mean, the closed-source thing is very important.
1:14:26
Like there is no VS Code in that category.
1:14:28
Uh people talk about this with Cursor for the for biotech.
1:14:31
Like what's the lab notebook?
1:14:34
And I was pushing on like I was trying to go I I was trying to go deep with an investor who uh kind of came at me with a thesis of of Cursor for bio.
1:14:42
And I was saying, "Okay, well, if if you're going to invest in the Cursor of bio, what's the VS Code of bio? What's the open-source?
1:14:51
What's the open-source standard that you can fork that every bio lab uses?"
1:14:53
And they're like, "Oh, well, actually like the labs use closed-source stuff and those enterprises do not just want to give up the data.
1:15:01
So, you're going to be fighting for the walled garden and all this other stuff."
1:15:05
So, uh it it it it does feel like the like the Cursor for X model, I mean, it was really hot at YC what, two batches ago?
1:15:12
Everyone was doing Cursor for X.
1:15:15
Uh and it feels like uh it was wishful thinking in some markets.
1:15:19
Yeah, I I think that's exactly right.
1:15:21
Um, and what's interesting is for biotech in in particular, right?
1:15:23
And we've heard Sam make more public comments recently, right?
1:15:28
About their effort with the sciences and how much time they're spending there.
1:15:32
Um, I just think that's where the value ends up accruing is something like an Open Anthropic probably throws their hat in the ring at some point.
1:15:39
There's uh there's the Chai Discovery business, which is a compelling company.
1:15:45
There's a number of folks who are kind of building these pseudo lab companies that work with with biotech.
1:15:52
I think that's probably what ends up being more interesting because to your point, there's not a there's not like a VS Code for biotech today.
1:16:01
And there are just very few markets where there even is a VS Code analog, right?
1:16:04
Part of the magic of Cursor is they identified an incredible market where the models worked really well early, which was coding, right?
1:16:13
This has been the frontier of model capability for a while.
1:16:17
You had the ubiquity of VS Code and of the terminal and all these things.
1:16:21
And then you just built a really nice product around it and have continued to just push the frontier of what's possible.
1:16:27
And now you layer in their own model with Composer. Uh so, Yeah.
1:16:32
>> we're excited about it.
1:16:32
But it's hard to find markets with the same anatomy and structure and kind of opportunity.
1:16:37
To your point, people are kind of squinting and like kind of forcing ideas a little bit, it feels like.
1:16:44
>> Totally, totally, totally. Yeah. Jordy?
1:16:46
Uh I'm sure uh I'm sure you guys just try to back uh visionary founders that don't come to you uh for help unless they unless they really need it, hopefully, but how how do you think uh private companies, how much attention should private companies in AI be be paying to the public markets right now?
1:17:05
Because we're seeing so much growth, revenue growth in the private markets, and at the same time, uh you know, we were just, you know, CoreWeave's down uh 45% in the past month.
1:17:17
Uh there's a lot of jitters in in the market right now, and it's hard not to pay attention uh to some of these signals even if even if a company's own revenue growth is is skyrocketing.
1:17:26
So, I'm curious how how you think the best companies are going to kind of manage through the next 12 months?
1:17:34
Yeah, I mean, look, the the fortunate thing for the the best companies is almost all of them have a war chest in terms of capital on the balance sheet today.
1:17:41
And I think the capital demands of these businesses is a bit exaggerated relative to what what what it is would be maybe my personal view um relative to like what's on X or what's in the media around these AI companies. Yeah.
1:17:54
Um, >> [snorts] >> in terms of like paying attention to the public markets, right?
1:17:58
I think it's I saw um like Subu tweeted, I think, a few days ago something around, right?
1:18:03
Like the the market's been above its 50-day moving average for like over 100 straight days.
1:18:09
Like something like 130 straight days or something like this.
1:18:12
So, you know that the market's been good for a while uh when you see a stat like that.
1:18:17
Um, I think founders are aware of that and are pretty aware that it's a good time to raise capital and fortify a war chest, right?
1:18:26
Which we've had a really busy Q2 and Q3 and now early Q4. Mhm.
1:18:31
Um, I think that's kind of the way to think about public markets is, "Hey, when's a good time to raise capital when my when my cost of equity is lower?"
1:18:36
Uh but outside of that, like I just think tunnel vision is everything.
1:18:41
Um, and again, just to to bring it back to Cursor, like this is a very focused company, very heads-down company, doesn't make a lot of appearances, right?
1:18:50
Like just stays in their lane and focuses.
1:18:53
And I think that that's kind of the ultimate uh value, right?
1:18:55
You don't want to be spread too thin.
1:18:58
And look, there's a lot of folks like myself running around uh San Francisco now looking to give founders money and tell them when the when the value of their equity is is pretty good, right?
1:19:07
So, we we help give them that signal.
1:19:10
Yeah, there's also this uh interesting I it it's hard to measure corrections in the venture ecosystem because they're much less quantitative than just looking at oh okay you could you know map the Yeah correction often looks like wow that company hasn't raised in 3 years.
1:19:28
raised in 3 years. Yeah yeah that or you know there's some hiccup in in LP fundraising or something but there there's there's many corrections in the public markets that I can think of that were like hiccups in the private markets but for the most part it was everyone just kind of being like oh there's some crazy stuff going on okay like let's cancel a couple meetings and okay we're back on to two months later
1:19:51
like the COVID-19 like sell off was like a massive correction in the public markets and for most for most startups it was like yeah you needed to understand what could your company continue or were you in like hospitality or something that was going to be heavily affected but for a lot of companies it was just like oh your your your March raise turned into a June raise over zoom and it was fine. Yeah I mean I I I think that's spot on
1:20:16
Yeah I mean I I I think that's spot on right like there is a level [snorts] where right because we do have a public hedge fund and so what's interesting is when you have conversations like the one you guys were having prior to this right you know as a hedge fund you're always
1:20:33
behaving in the market right there's always a choice to be making right it's very unusual to be in a 100% cash or something like this right as as a private fund as a growth fund we can just choose to not invest for a while right we can just wait a bit
1:20:46
and for us like the convenient thing is you know we only make I think this year we've maybe added about half a dozen new logos to the portfolio so for us we're only making three to five core big investments a year on the growth side right now is kind of our cadence so it helps us kind
1:21:03
of work through whatever that cycle is and just focus on the asset focus on the entrepreneur focus on what we view as the 10-year trend and story for why this company is going to be so durable and so powerful over time. But yeah like you don't see it right I
1:21:16
But yeah like you don't see it right I think the other thing that's maybe like less discussed is we've had a bunch of companies and I've seen companies outside our portfolio too that were kind of your 2017 era SAS businesses that have really seen a nice bounce back and a nice acceleration over the past few years with AI really like reinvention is the wrong term it's just kind of like some product extension.
1:21:37
Yeah the CEOs and the management teams and the the teams are just re-energized and excited because there's this new capability that you can integrate across your entire platform.
1:21:49
Totally and if you if you were some if you were some you know flavor of a source of record your ability to now use that data and create economic value around it is just inordinately better so um yeah I mean look that the market's been up for a while it's definitely interesting but for us on the private side it's a little bit easier cuz we just get to focus on the companies.
1:22:10
Yeah how do you think people are do you think people are reading too much into Sarah Friar's like private phone calls these days or >> [laughter] >> I mean it feels like you know open AI has like a billion users deceleration might be sort of like expected like you know people don't excel they don't expect meta to accelerate on on DAUs do you have any thoughts on like what's going on there?
1:22:35
Yeah I've got a big term cuz I knew this would come up and so I thought of this before this was my one big term here anxiety displacement guys we've got anxiety displacement that's what's going on you know like people talk about anger displacement right all these things Yeah.
1:22:49
But market's been up for a while like that's a that's a that's like a fact. >> Yep.
1:22:55
Oh chat GPT's less than 3 years old as a product right it's growing really quickly right it's it's they've disclosed they're almost 10% of the world is using the product weekly right like that's pretty remarkable right but the market's been up a while we had like I still think as a society people haven't really processed the whole lockdown thing we had the government shut down for you know a month and a half like there's all this stuff going
1:23:18
on and there's all this anxiety and I do I think like that's been very displaced because people you know like open AI is a great company it's a remarkable business there's just it's hard to think of an analog for what they've achieved in less
1:23:33
than 3 years as a as a product and I think Sarah and Sam and the entire team do if incredible job so people are definitely reading too much into anything and everything sometimes I think there's a little bit of a malicious spin Sure. On some of the
1:23:46
On some of the stuff they've put out also just to be very candid about that but yeah I mean it's understandable for there to be a lot of anxiety in society in the market and it's understandable that it gets displaced onto the company a little bit but I don't think that's fair.
1:24:01
Yeah it was a little weird I mean we were reacting to this idea that there's a private call with investors like what type of investor is then leaking like potentially bad news to the press it seemed like an odd chain of events but there's certainly unlimited demand for bearish takes about chat GPT right now because they've they've been on such a tear Every day people log on to chat GPT and ask for what's the most bearish thing about chat GPT. Yep yeah yeah.
1:24:32
Right right that's like that's the that's the tweet prompt like that's your ex maximizing like prompt right there probably something like that right what what chart can I put together or what can I do but look I like we'll see right worked with the company for a long time think it's an amazing company and then really impressed by what they've done.
1:24:52
Um I continue to use the product every day a ton I think 5.
1:24:54
1 the new model is really a nice upgrade too I enjoy it so you know I think it's I think they do a great job and I think people over over rotate on it but I get it.
1:25:08
>> We're we're excited for 5.
1:25:08
67 which would be only a few upgrade cycles away.
1:25:14
Rolling my eyes at that one can't do it can't do the stupid 67 memes.
1:25:17
It's over killed it just now.
1:25:20
They're over him I like that I I like the new coinage though the anxiety displacement it's good.
1:25:27
Let's talk about other growth funds cuz that's just a fun fun topic for every every investor is talk about everyone else do you think anybody has um have you seen any other any other funds kind of blank yet or or or hesitate or or kind of slow down at all get a little bit
1:25:48
worried or cuz cuz it's just like we're seeing every every day we get like half a billion dollars of funding announced on on the show but that has that's a lag you know it has like these rounds like really got done in in late summer early fall and they're just kind of getting getting out there now. Yeah I mean I'm sure there are some
1:26:08
Yeah I mean I'm sure there are some funds that have slowed down I'm sure there are funds that are deploying more than us and less than us and those things right um I think generally the I think the vibe when you're catching up with someone over coffee or lunch is a
1:26:22
lot more positive than over Twitter right and there's a lot more of like uh well how exposed am I to the AI trend right like there are a few growth funds that feel like you know I've disclosed right hey I'm a little behind this and
1:26:33
I'm trying to catch up here and there so if anything like I felt more anxiety about that than oh my gosh you know we just put more money in pick your big premier company and you know I haven't slept well about it so that's not necessarily a a great signal either
1:26:49
right it's kind of like an an average signal but um I'm sure there are some folks slowing down not sure who they are per se cuz we have a lot of funds that are definitely deploying a lot more than us in terms of absolute dollars and just
1:27:01
absolute number of deals so for us we just again we really just try to focus on our our core you know mission which is hey what are going to be companies that matter as public enterprises you know 10 15 20 years from now and in some
1:27:21
cases a little bit sooner right um and >> Yeah what do you think about the capital intensive pre-revenue AI companies we had Feifei Li from World Labs on I don't know if they're fully pre-revenue but it feels like a lot of the world model projects the generative
1:27:39
3D worlds Gaussian splats what Google DeepMind's working on with Gemma or is it Genie Genie is the model it feels like something that I just think will be like the next Roblox vaguely but there's nothing that you could do to underwrite this against
1:27:57
revenue growth and so and yet and yet a lot of these projects are like to do the next thing we need $200 how would you as a growth investor even square that or would you just say hey let's just come back when we can actually see some adoption data. Yeah
1:28:11
Yeah normally we would fall into the second bucket like generally in terms of how we operate it's different for everyone I think all those rounds are ultimately just a representation of the idea size right of the opportunity and then and then of course also of the entrepreneur
1:28:28
and the team we did make you know one investment very early that that maybe fits this mandate which would be skilled in the robotics space which has been a great company has executed incredibly well and we got to think Metro is over there now that's right. Yeah yeah we just know someone who's on
1:28:44
Yeah yeah we just know someone who's on the team but not not one of the founders.
1:28:47
That left and left Andre to to join it's skilled right S K I L D. >> I L D yep.
1:28:53
So that was that was one where we leaned forward a little bit earlier than we would with with most things but in again that was on the back of just being very bullish on robotics on a 10 15 year time scale and that team and where they sat in the stack but generally we tend to wait a little bit to see a company be a bit more flushed out but it's always case specific.
1:29:16
That's amazing well thank you so much for coming on the show. Thanks for hanging out.
1:29:19
>> Well, we will talk to you soon.
1:29:19
Hope you have a great rest of the day.
1:29:21
And congratulations on the curse around.
1:29:23
Uh we'd love to have you on the show again. Spencer. Have a good one. >> Cheers. Goodbye.
1:29:28
Uh up next we have Tyler and Cameron Winklevoss.
1:29:32
Um but first let me tell you about Turbo Popper.
1:29:33
Search every bite serverless factor in full tech search built from first principles and object storage fast 10x cheaper and extremely scalable.
1:29:41
Um the Winklevoss twins are in the Restream waiting room I believe.
1:29:46
Uh if not we have a uh slight delay. Let's follow up on that. >> to the timeline. >> Back to the timeline.
1:29:52
[music] Dario Amodei predicts that we will get to 90% on Swee bench verified in a year. That was 1 year ago. It's been 1 year.
1:30:01
Uh the best performing model Sonnet 4.
1:30:01
5 with parallel compute gets 82% on Swee bench verified.
1:30:06
Close to 90% but not quite there.
1:30:09
They put him in the truth zone. RIP to Dario. What do you think Tyler? Bearish?
1:30:15
>> I mean I think he was pretty close. He said it was 90.
1:30:17
>> is pretty close good enough these days?
1:30:19
That's that was way more of a bullish take than most people. >> Yeah.
1:30:21
No, it is it is very impressive. Uh totally.
1:30:24
Uh do you think it's saturated?
1:30:26
People are saying uh that that that I think a lot of those kinds of benchmarks are generally saturated. Yeah, yeah, yeah.
1:30:32
Yeah, I mean it is interesting that like simultaneously you hit the 90% on Swee bench. Did very well there.
1:30:35
But on the flip side uh you have Andre Carpathy saying like it's slop and like you can't actually use it for like the frontier software development that he wants to do.
1:30:45
Uh anyway, I believe we have the Winklevoss twins in the Restream waiting room.
1:30:49
Let's [music] bring them into the TVP and Ultra Dome Tyler and Cameron Winklevoss. Welcome to the show. How are you guys doing? >> on guys? Hey guys. Very nice background. Uh give us the update. Give us the news.
1:31:00
I know we're running late.
1:31:02
Uh so let's just jump right into it.
1:31:03
I assume everyone knows who you are. Cool.
1:31:07
Um so we launched Cypherpunk a Zcash uh dat yesterday.
1:31:10
Uh it trades on Cyph c y p h.
1:31:13
Um we're really excited about it.
1:31:17
The the mission of the company is privacy and self-sovereignty starting by accumulating Zcash. Mhm.
1:31:25
And then in due time we hope to invest in other technologies that promote privacy and self-sovereignty.
1:31:37
Uh This delay is extremely exciting. >> Wi-Fi issues. Uh Sorry.
1:31:40
Get uh say more about the structure.
1:31:43
This is Leap Therapeutics.
1:31:45
You guys have have rebranded it.
1:31:47
This was an existing public company but what more can you say on on how this came together?
1:31:56
Yeah, so it was an existing biotech company um and we basically took it over um and invested via pipe into it and then um rebranded and changed the ticker today. Mhm. Awesome.
1:32:05
Uh so I guess uh Zcash has had a lot of momentum recently.
1:32:11
At the same time uh a lot of the dats have have uh you know struggled uh in in uh more recently.
1:32:22
Uh what like talk through kind of like the next uh how how are you thinking about you know making sure uh just kind of navigating this time when the markets are are pretty choppy overall. Sure.
1:32:35
So I think number one the long-term piece is we really believe in and I think that when you look at Bitcoin as a store of value or how you store your value Zcash is really how you move your value.
1:32:50
This is is very sound and we're obviously very bullish on that.
1:32:53
But in addition we're the largest investor into the dat.
1:32:59
Um so we don't have like a lot of fast capital that's looking for a trade.
1:33:03
We're just long-term hodlers or in the case of Zcash uh zadlers and we just you know we plan to hold for for a very long time and I think that's one of the differences is that other dats have you know had faster money and people that are moving in and out of it.
1:33:18
Um and that's why we didn't fill the pipe up.
1:33:22
Um we took you know the vast majority of it.
1:33:25
I think we put a $52 million check in.
1:33:27
Um so the vast majority of equity will not be trading out of this out of this um out of the shares. That makes sense.
1:33:35
Uh well, thank you for the update.
1:33:37
Uh sorry about the Wi-Fi connection.
1:33:39
We have some technical problems.
1:33:41
We'd love to have you back on the show and talk more about what's going on in your world cuz uh there's so many interesting projects that you're working on.
1:33:48
Uh but really appreciate you taking the time to give us a quick update on Cypherpunk Technologies and congratulations on the on the pipe closing, the rebranding, every all the progress.
1:33:57
Uh so have a great rest of your day.
1:33:59
Yeah, thanks for joining guys. Thanks guys. Cheers.
1:34:01
Uh let me tell you about Google AI Studio.
1:34:04
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1:34:05
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1:34:14
dev studio/ He was building AI agents to to make the Wi-Fi work. That would be cool.
1:34:19
It would be cool if we could deploy an AI agent.
1:34:21
I mean I guess that's like an an an the next generation of the Restream waiting room.
1:34:26
Like the Restream waiting room somebody talks to them in the in a different waiting room and and checks the uh the Wi-Fi.
1:34:33
Uh that will be something.
1:34:35
Maybe it's a 2026 project.
1:34:35
Maybe it's a 2046 project. Who knows?
1:34:41
>> That could be the final boss.
1:34:41
Hopefully our next guest is dialed into the Restream waiting room.
1:34:44
We have Max Hodak from Science. How you doing? Welcome to the show. Hey guys. Good to meet you.
1:34:53
I believe we met like years ago at an Oppenheimer screening potentially. I don't know. Very possible. >> Very possible. It probably happened. Yeah.
1:35:01
Anyway, for those who don't know you uh please uh kick us off with a little bit of an introduction on yourself and uh and maybe the company as well. Sure.
1:35:09
So I mean I've spent most of my life thinking about how to engineer the brain.
1:35:14
Um I'm built I mean I my origin story I think in this field started when I was in the fifth grade and I saw The Matrix and I was like I have no idea if we're living in a simulation but we are definitely going to build one.
1:35:22
I'd kind of broadly characterize my ambition is to eventually disappear into the simulation never to be found.
1:35:27
And I uh as an undergrad talked my way into um a lab that was doing neural recording in in primates and spent really that was where most of my education happened and then in 2016 I got I pulled into co-founding what what became Neuralink and I was there for 4 and 1/2 years and in the spring of 2021 started this company Science.
1:35:48
We're now about 180 people.
1:35:50
Um our main product is a retinal prosthesis.
1:35:53
Like really the first retinal prosthesis that really works to restore vision.
1:35:56
It was in it's actually on the cover of Time last week which is pretty crazy experience. Wow.
1:35:59
Um So you like The Matrix?
1:36:02
Do you like The Thirteenth Floor?
1:36:04
Have you seen that movie?
1:36:06
I have not seen The Thirteenth Floor.
1:36:08
>> Oh, The Thirteenth Floor.
1:36:08
It's like one of those movies that got terrible reviews but it goes a little I feel like it goes a little bit farther than The Matrix in terms of simulation theory and simulation hypothesis. It's a lot of fun.
1:36:18
Um but anyway, we [laughter] can get back to the actual uh story.
1:36:20
So um how how far are you in building this business?
1:36:26
Give us a give us a general update on like the shape of the business.
1:36:28
It seems like you're in the office right now. How big is the company?
1:36:31
What's the progress overall?
1:36:34
Yeah, so we're the so we we've a couple different elements to our pipeline.
1:36:38
So the the retinal prosthesis we think like that is first like just an end in itself.
1:36:41
Like if you can restore vision of the blind it is like it is a quest that humans have been on for thousands of years that is an unsolved problem.
1:36:48
Um And [clears throat] I think like we've made there's like real progress on that.
1:36:53
There's we got two programs.
1:36:53
One is a retinal chip called Prima which is this little it's a little chip implanted in the back of the eye under the retina that has all these light sensitive cells.
1:37:01
Works in conjunction with a laser laser projection glasses worn by the patient.
1:37:07
That finished a major clinical trial last summer.
1:37:09
It was published just recently in the New England Journal of Medicine.
1:37:11
These patients go from being unable to recognize faces.
1:37:13
They can they can walk around because they've got a little bit of residual peripheral vision.
1:37:17
The trial was in an age-related macular degeneration.
1:37:19
But they definitely can't read.
1:37:20
They are like really profoundly blind and disabled.
1:37:24
And the the best patients in this trial could read could go from reading not none of an eye chart to reading the entire eye chart.
1:37:29
I mean there's videos of these patients like filling in crossword puzzles. It's really very cool.
1:37:34
Um And then separately from that we also have like the another key program in the company is a different approach to building brain computer interfaces where instead of placing wires like not like not in the retina but like in in cortex.
1:37:48
Instead of placing uh wires or cables physically into the brain or genetically modifying the brain um so that like there's some things you can do with like with optogenetics or sonogenetics.
1:37:59
Um instead what we do is we have we grow up these heavily engineered biological cells that we hide from the immune system.
1:38:05
And then we just sit this on the surface of the brain.
1:38:07
And and so we don't place anything into the brain itself but what these cells do is they grow in and they wire up and they form new biological connections and they can form billions of them.
1:38:15
And so I think like the way to understand this or like I think actually fairly direct reference for how to think about >> John's face right now is I this is like >> So we so we called it a we call it a biohybrid neural interface.
1:38:26
Have you seen uh James Cameron's Avatar movies? >> Yes, yes, yes.
1:38:29
You know the ponytails of the alien people?
1:38:32
>> I know exactly where you're going with this.
1:38:33
>> into like their tree memory store or like their horses.
1:38:36
So I think the question is like if you wanted to build a ultra high bandwidth neural interface like how would nature do this?
1:38:44
I think what nature would do is it would grow a a new cranial nerve that has like a USB port at the end. >> Yeah.
1:38:49
And that I mean that that is super cool research but that is definitely a research project.
1:38:54
And so the way the company is architected is that is going to be paid for by the retinal prosthesis. >> Sure.
1:38:59
Which is a like very practical near-term medical device.
1:39:01
We hope to have we've submitted for marketing approval in Europe for that.
1:39:04
We're going through the review process now.
1:39:07
Um the FDA actually is being much slower.
1:39:09
It it is possible it won't reach American patients for a little bit longer and that's kind of crazy that Europe is going to get it first, but that's where we are. Yeah, that's rare.
1:39:17
[clears throat] Kind of narrative violation, but exciting.
1:39:19
>> Narrative violation, for sure.
1:39:19
But so yeah, hopefully that'll be on market next summer making money.
1:39:24
And that is big enough to pay for the rest. >> That makes sense.
1:39:27
So, why go with a retinal prosthesis instead of cutting a hole in the skull and putting electrodes directly on the brain and trying to deliver the signal that way?
1:39:44
There's a lot of BCI firms.
1:39:47
Obviously, you co-founded one that that are seem to be approaching the problem that way.
1:39:53
Is that just like do you do you have a particular view on that?
1:39:58
Is that just farther out and you're and this is a way to get to market faster, or is there something fundamentally like higher bandwidth about your approach?
1:40:05
Like what are the different technical trade-offs? So, BCI is a product. BCI is a field. >> Okay.
1:40:11
And there are many different types of BCIs for many different types of applications.
1:40:14
Like obviously, you can't go into the retina to decode like a video game controller out of the brain.
1:40:21
Um simultaneously, you can't stimulate like frontal cortex to do like to do like sensory feedback.
1:40:26
And so, it really depends on the type of thing that you're trying to do.
1:40:31
And when And so, Envision, which we I mean, we think that a visual prosthesis is a is a brain computer interface.
1:40:36
We also think that cochlear implants are brain computer interfaces.
1:40:40
Um you don't need to be drilling into the skull to get to cortex.
1:40:44
Um so, if you want to restore vision, you kind of have a choice of you've got the retina, the the output of the retina is the optic nerve that goes to a deep brain structure called the the thalamus.
1:40:54
And then the thalamus connects up to cortex.
1:40:59
And so, within the retina, so let's just take a look at like the options that you have here.
1:41:01
So, in the retina, normally light shines in from the front, it hits the rods and cones.
1:41:05
The rods and cones are the light-sensitive cells.
1:41:06
There's about 150 million of those.
1:41:08
These connect to about 100 million intermediate cells called bipolar cells, and those compress down to 1.
1:41:14
5 million like optic nerve cells.
1:41:17
That connects to about 1.
1:41:19
5 million cells in the thalamus, and that connects up to like 200 to 500 million cells in cortex.
1:41:26
And so, no one I mean, people have been trying to stimulate vision in the brain for many decades.
1:41:30
And until the the clinical trial that we just finished, no one had ever gotten form vision, like structured vision that the brain could intuitively assemble into a whole.
1:41:39
Like you could get patterns that if you like looked at it carefully, it's like oh, there's like a line here, there's a line here, it's like connected, that must be an A.
1:41:45
Like here's a line, like that's an N.
1:41:47
But in the Prima trial, I mean, they could read off words at a time, and that had never really happened before.
1:41:53
And a big difference is that we're stimulating that first layer of cells, the bipolar cells in the retina.
1:41:59
And we know that if you just go one layer deeper to the from the 100 million bipolar cells to the 1.
1:42:05
5 million optic nerve cells, if you stimulate those optic nerve cells, you don't get this.
1:42:09
You just get these diffuse flashes of light that you can't really attend to, and the brain does not intuitively assemble together because the brain has already compressed the signal.
1:42:18
And you have to then figure out like what is that transform?
1:42:20
How did the brain compress this?
1:42:21
Or how did even just the retina compress this?
1:42:24
And the And so, the thalamus has the same issues as trying to stimulate the optic nerve, except it's under 8 cm of brain tissue under the skull.
1:42:30
And then once you're up in cortex, you're dealing with hundreds of millions of cells that are distributed over a large areas that you just can't stimulate selectively.
1:42:37
And so, like people if you do this, like you absolutely will get flashes of light, but converting that to like form vision that you can intuitively read is a totally different problem.
1:42:49
And so, and then even if that worked, like even if that worked out perfectly, one is like an outpatient surgery going through the soft tissue of the retina, the other is like a 4-hour brain surgery drilling through the skull.
1:43:01
I think like one of those is you're kind of kind of wind sails. Yeah, wait. So, you said outpatient.
1:43:08
Walk me through comparing the level of intensity of the surgery to something like LASIK.
1:43:15
So, it's a little more than LASIK. Yeah.
1:43:17
But it's like not a ton more than LASIK.
1:43:19
>> What about getting your wisdom teeth out? I'm just copying you.
1:43:22
>> [laughter] >> I mean, you could do this.
1:43:23
So, in the trial, many of them ended up being done under general anesthesia, but to be honest, in these cases, general anesthesia is really like as much like a commitment mechanism for the surgeon and the patient than it is like there's any medical reason to do it.
1:43:36
I mean, you just like you can't change your mind halfway through. Oh, okay. Commitment mechanism.
1:43:40
Um you But from a like experience perspective, so you could do this with you can make an injection next to the eye.
1:43:47
The eye goes dark and numb for a couple hours. >> Yeah.
1:43:50
And the And then you can go in through the soft tissue of the eye, you leave the chip.
1:43:54
There's a little injector that surgeon presses a lever, it leaves the chip under the eye.
1:43:56
They come out, they're done.
1:43:58
Um And so, it's a really very simple >> Yeah.
1:44:02
And then And then sorry, just to be ultra clear, like the chip is in the eye, then how am I communicating with the chip? Is it wirelessly?
1:44:10
Is there Is there a device that's on the other side of of my head?
1:44:11
Or you said glasses maybe are interfacing with that?
1:44:16
Yeah, so if you look at this chip under a microscope, it it has all these little hex cells on it.
1:44:19
And every one of these hex cells like the science Prima chip, it's essentially a solar panel.
1:44:28
And the it works in conjunction with there's glasses that are worn by the patient that has a camera looking out at the world, although you could really get the video feed from anywhere.
1:44:34
And then there's a laser that projects the image onto the back of the eye in in infrared. >> Okay.
1:44:41
And because you can't see infrared, you can't like if you have residual peripheral vision or any if you're not totally blind, this doesn't interfere with that. You can still have that.
1:44:51
But the infrared laser where it strikes the implant, it works like an overhead projector.
1:44:55
Like if wherever there's white that is projected, that is like that's energy, and wherever there isn't energy, it's dark.
1:45:01
And wherever the laser is absorbed by the implant, it it stimulates the cells directly above that pixel.
1:45:07
And so, this is pretty cool because the implant is powered by the information that's projected onto it like like as a solar panel, this means that there's no implanted battery, there's no cable.
1:45:17
Like this tiny little fully wireless 2-mm chip is the whole thing.
1:45:22
And also because the eye moves relative to the projector, like the projector's shining in from the front of the eye, and then the eye moves and the image changes, um this means that the brain can easily fuse it together with their their existing vision.
1:45:34
And so, this There's like some pretty cool stuff here.
1:45:36
Like if you show one of these patients a solid green bar all the way across their visual field, they'll see a contiguous bar even though the implant only fills a like a small area of the total area of blindness.
1:45:48
And they'll say it turn it's like it's green, and then it turns white because we can only get black and white right now, and then it turns green again.
1:45:54
But the brain fuses all of this together.
1:45:57
And so, even though the implant only has 400 electrodes, as the eye moves around, you don't you don't experience the image that falls on the eye or falls on the retina like a camera.
1:46:06
The thing you experience is the brain activity of the world model in the brain.
1:46:11
And so, as the eye is moving around, it's updating the world model, and that's the thing that you see.
1:46:14
And so, even though it's like 400 electrodes, you can't think of it like 400 pixels on a screen.
1:46:19
You think of it as just getting the information to the world model, and the eye is moving around, and the brain's cross-referencing all that.
1:46:25
So, it actually does significantly better than you'd think from being 400 electrodes. That's fascinating.
1:46:29
Um How do you >> [snorts] >> How do you think about the analogizing around the artificial intelligence community of what you know of the brain?
1:46:41
So, a lot of people in computer science or AI might say, with with LLMs, we've built this piece of the brain, with the hard drive, we built the long-term memory.
1:46:52
Has has what has your research have you mapped any of that onto the current state of artificial intelligence?
1:46:57
Has it proven um uh insightful to help to for you to understand what's going on in AI?
1:47:06
This is funny cuz I mean, at the very beginning of of Neuralink and OpenAI, we were in the same building in San Francisco.
1:47:11
And we'd have these discussions about like oh, who's going to learn more?
1:47:13
Is AI going to learn from neuroscience or is neuroscience going to learn from AI? Yeah.
1:47:16
And I think it has been revealed that like like I asked I kind of with one of those guys a while ago, and I was like oh, like in retrospect, what do you think AI learned from neuroscience?
1:47:24
He thought about it for a second, he's like, the concept of a neuron.
1:47:27
Yeah, that's [laughter] it. That's basically it.
1:47:30
It's like the very it's literally just in the name.
1:47:34
Neuroscience [laughter] is just neuron, and that's it. And then nothing else.
1:47:37
Because yeah, like the whole brain structure I can't say yeah, anyway, continue.
1:47:41
>> And but going the other way, I mean, AI has been so useful.
1:47:44
I mean, there is a really interesting convergence going on.
1:47:47
This is kind of called like neuro AI, where neuroscience is learning a lot from artificial intelligence in ways that I don't think any of us really would have anticipated. >> Okay, explain.
1:47:56
And have you come across the platonic representation hypothesis?
1:48:00
Um so, there's an empirical finding that different neural networks trained with different architectures, with different objectives, and different concrete data sets, but for the same type of thing like images or or language or audio, they produce these like these similar internal representations.
1:48:14
And what I mean by like like There sometimes you'll hear people say like oh, these models are like stochastic parrots, or they're just like there's glorified auto-complete.
1:48:22
It's like these people are safe to ignore.
1:48:26
Like the mathematical objects that you see appear inside these models are super interesting and look a lot like the representations that you see in the brain.
1:48:36
And so, that is hinting at like there's some like deeper fact about the universe that we're figuring out here that basically if you have a lot of compute power and you kind of run it in these ways, then you see these like these data these like mathematical objects kind of emerge.
1:48:50
And what evolution did and figured out in the brain is like looks a lot like stuff that you kind of see in these transformers and these other AI models.
1:49:01
And there's definitely some interesting unification happening there.
1:49:03
I mean, it's not I it's still like it it's a little more than speaking totally metaphorically, but it is still like I'd say like instructive rather than literal.
1:49:12
Um but that's getting like every month there's like some new cool thing that comes out around this.
1:49:17
Um I will say that I don't know and I my view is that the transformer is like a reasonably good model of like what cortex is doing, but there's a lot more of the brain.
1:49:30
Um and so there's other parts that aren't fully cap like that aren't that we're going to need something else, but it's not like the transformer is wrong.
1:49:37
I think it's like probably part of the story.
1:49:38
Yeah, that makes a lot of sense.
1:49:39
I I wonder what you think about just the idea that uh like it like it takes like millions of years to train a model to like drive a car and it takes you know a 16-year-old like a couple weekends to do it or uh or the amount of energy that it I will consume in one day of like reasoning generating my own reasoning tokens is like way less than what it takes to run a data center.
1:50:05
There seems to be some sort of like uh exchange ratio of uh that's like we're off by a couple orders of magnitude and maybe that's an algorithmic question. I don't really know.
1:50:15
Do you have any thoughts on that?
1:50:17
Yeah, I mean evolution has done I mean it it is really it is minimize it's been very good at minimizing energy and optimizing some other stuff.
1:50:25
Um but I mean that I think is like the advantage of of the biological brains.
1:50:31
Like every now and then I I see pictures from companies that are saying well AI is really energy intensive, so what we should do is we should grow up cultures of biological neurons and train them to like to do intelligence tasks cuz you can kind of do this.
1:50:44
Like if you you can grow up neurons on electrode arrays and you can condition them to learn things by stimulating them in different ways.
1:50:53
Like when I was in college I grew up a counter strike game bot.
1:50:54
Like this is actually not that hard to do.
1:50:57
Um and uh and I and this is the thing that I think nerd snipes many people in this field at some point, but I I don't think that that's like the way to go.
1:51:06
I think that's so there's like just really structural advantages in silicon.
1:51:09
Like if you compare and contrast these two approaches Yeah.
1:51:10
like in the in the deep learning models like you can see all the weights. You can introspect them.
1:51:15
You can like stop [clears throat] the model. You can change one.
1:51:17
You can replay it like examples out. You can copy to disk.
1:51:19
You can like send it over the network.
1:51:23
Whereas with the the biological living like these organoid brains, you like you can't see the weights.
1:51:29
You can't copy it to disk.
1:51:30
It's like the what it learns is the time integrated experience it's always had and like at some point four or five months in it will randomly get infected and die and you'll have to start over. Yeah.
1:51:39
And so I think there's just like structural it's like the thing that the biology does is it's energy efficient, but my response to this is like generate more power.
1:51:47
Yeah, I like the idea of There's no such thing as a low energy wealthy society. Generate more power.
1:51:50
That's yeah I I like the idea of like the solution to AI is just like have kids or something and you're like you you you reversed it all the way down to like if I want artificial intelligence I can do it biologically or just have kids.
1:52:03
>> you and the team getting much leverage out of models today?
1:52:06
Is it accelerating your progress or is it really just about having like deep domain expertise and being more obsessed with the problem than anyone else and hiring the smartest people in the world?
1:52:21
The I interestingly I think companies like this are really limited by infrastructure.
1:52:25
And so like I one of the things that I got from my prior boss was I totally received the gospel of vertical integration.
1:52:32
And the and we're really not It's not like we're held back by like genius scientific insights for the most part.
1:52:38
We're limited by like oh like well we need to get a new like a new material in our like like micro fab deposition tool, but this requires hooking up some gas plumbing which requires getting some specialist vendor to come out and like weld it to the machine.
1:52:52
Or you're out of animal housing and like building that is like an architectural design process and then permitting and then construction.
1:52:57
Like you're really limited by infrastructure more than you are genius scientific insights.
1:53:01
And so even if you had this like I think when I think about like being in the takeoff era and what is going to be the impact of these of progress in AI like we're still limited by how that can impact the real world in really meaningful ways for these atoms things.
1:53:13
But I think there's two places that AI has had a bigger impact.
1:53:15
The first is ironically like comprehending regulatory standards and generating regulatory documentation.
1:53:23
>> you yeah you you if if permitting's the bottleneck can you have like a permitting agent that just goes and like spams the permits until you got exactly what you need?
1:53:31
Yeah, I mean they're not quite like they you end up doing a lot of editing.
1:53:34
Yeah, but you definitely just don't want to spam the permits.
1:53:36
That's makes the regulators mad.
1:53:36
But the But I mean the the filing in Europe for to ask for approval for our retinal prosthesis which we submitted last summer Yeah.
1:53:45
it was uh I forget exactly how many.
1:53:47
It was like tens of thousands of pages.
1:53:48
It was a 65 gigabyte PDF. Yeah.
1:53:49
And that depends on like hundreds and hundreds of standards that we're just expected to know all the details of. >> Yeah.
1:53:57
And so being able to talk to these things is super useful.
1:53:59
Like chat with this this these data sets is super useful rather than like having a like having a big team of regulatory experts who all kind of it take you this through meetings.
1:54:10
Um and then the other place that we've had big success with with AI internally is on our protein engineering program.
1:54:15
So there's um conventionally many of the problems that we're interested in you'd have to solve with like better electronics or better mic micro like better physical devices somehow.
1:54:26
And we're now at a point where often when we find a problem we ask like can we make a protein to solve this?
1:54:30
Um so a couple months ago we published a a paper on a new type of optogenetic protein which is these are these are proteins that can make a a neuron light sensitive that is not normally light sensitive.
1:54:43
So we could control it optic with light.
1:54:47
And these typically are require very bright laser light in order to work.
1:54:49
And we were able to use AI models to find one that is so sensitive that it is responsive to like not just daylight, but like indoor office lighting.
1:54:59
And so that allows us to substantially reduce the power consumption so we can cuz often the brain implants were often limited by thermals.
1:55:04
It's like how much energy you can consume depends on how much is limited by how much you can heat the brain.
1:55:09
So if you have more sensitive options [clears throat] then you can have your LEDs be dimmer and have more of them then you can think about going from like thousands to hundreds of thousands.
1:55:19
Um but then also that might turn actually into our next generation retinal product.
1:55:21
Um we might there that will have to go through clinical trials.
1:55:25
That'll be a a process, but it's possible that in five or seven years you won't even need the chip at all.
1:55:30
You'll just get an injection and then we can just make the bipolar cells themselves light sensitive.
1:55:36
Um and then that won't even need the glasses potentially.
1:55:37
And that that really comes out of the the big breakthrough on on being able to solve these problems with proteins uh is an AI enabled thing.
1:55:48
Chat says uh we should have you ask how do how to explain this to preschool?
1:55:54
>> [laughter] >> Um I some labs various groups have talked about the revenue opportunity and just automating science.
1:56:01
And it's usually very general where they're just like we're going to come up with a bunch of ideas and then hypothetically they give the ideas to various people to execute on and they get some type of like royalty on it.
1:56:12
How much how much opportunity do you think there are for for a more general uh foundation model company to just come up with a bunch of ideas and then actually capture real value from it.
1:56:25
It feels like uh in some ways the pharmaceutical industry maybe doesn't have a shortage of ideas.
1:56:30
They've a shortage of infrastructure and funding in order to test enough ideas to actually get viable uh you know solutions.
1:56:41
Yeah, I think that a real bottleneck here is just the translation to human subjects research and then to to market is really difficult.
1:56:48
Um we have like the like I think I sometimes joke about it is like we're in a golden age of mouse oncology.
1:56:55
Like if you're a mouse with a cancer like we've got some great things for you.
1:56:59
>> [laughter] >> But the So there's a lot of stuff that they've done for all the mice out there.
1:57:02
That's great to hear for the mice. That's fantastic.
1:57:05
There's a lot of stuff that they've done for all the mice.
1:57:09
But like that's the trade-off.
1:57:09
And so if you can um and the and I I get it.
1:57:14
It's it's not you can't just say like oh well the FDA is the is the problem.
1:57:18
Like the prob like the problem is that human subjects research is like life or death.
1:57:21
It's like it's a it's like no joke.
1:57:23
And like I've had the experience of like a patient goes into a a new surgery for the first time or they [clears throat] or you're going to inject them with something and you're going And like that is a very stressful experience.
1:57:33
Like you want them Like and that's like that's a stressful experience for like me.
1:57:37
I'm not even the one getting it, right? >> Yeah. Yeah.
1:57:39
And so the there's trade-offs there that I think are very deep and like our like the way that we and our civilization like value human life which I like I think is right.
1:57:51
And that there and so you have this knob of like how much risk do you think of taking in human subjects research and how fast do you get new things?
1:58:00
And we know that the toolbox of science is very very powerful.
1:58:03
Because when you look at what's possible in the animal models you have these amazing things that are possible, but then getting that into humans is like and that it is not just to say like oh well if we um like like oh we should just deregulate all of this.
1:58:17
Like there's it's more complicated than that.
1:58:19
Um although I do think that there's a little bit of that.
1:58:22
Um but at the same time I mean certainly this there's an intelligence effect.
1:58:26
Like I think that there's it's going to be inevitable that these things I mean the fact that you can fold all the proteins at least in static forms is a big deal.
1:58:35
Like I would not I would absolutely not bet against improvements in AI leading to improvements in in medicine and health care.
1:58:42
And I think there actually just to go one step further I think then the thing that we're going to have to reckon with is health care so like if if you thought like like 20 years ago TVs and phones and computers were way way way more expensive.
1:58:53
Um and now they're much cheaper, but we spend more on them total.
1:58:58
It is this This like a technological growth industry.
1:59:00
Like normally, if Nvidia sells 20% more GPUs next year and their revenue goes up and their earnings go up and the stock price goes up and like everybody's stoked about this.
1:59:10
But as time goes on and there's more things to spend money on in health care that produce better outcomes for longer lives, um there's like spending should increase, but because we pay for this as these like kind of insurance schemes which are kind of pseudo fixed buckets
1:59:24
of money, like if if there were real breakthroughs in health care that allow people to live much longer and have much better outcomes, but they cost money and you could spend like 10 times as much on health care, like that would be a catastrophe. Mhm. Like you do not want Mhm.
1:59:33
Like you do not want to spend like right now our system would not handle spending 10 times as much on health care. Yeah.
1:59:39
But that is kind of directly at odds with it being a technological growth industry. Yeah.
1:59:44
What about the BCI industry?
1:59:44
I think it's interesting we've seen like this boom in quantum computing and and there's public companies do all this stuff, but it feels like Well, so specifically I feel I feel like the interesting question with BCIs is like I want to understand your timeline.
1:59:58
Or for sports or something.
2:00:00
>> these therapeutic use cases where somebody's blind and they they they're willing to take some level of risk in order to see or at least see something.
2:00:09
Uh and then there's like the utility timeline and entertainment timeline where I just have, you know, instead of wearing glasses, I just have a screen that's just embedded in my retina and I don't need to wear glasses and it's always on and I can, you know, turn it off and I feel like that's where like it feels like that's where you're going in sort of a general purpose technology on a long enough time horizon, but maybe I'm off.
2:00:39
Yeah, I mean I think I mean it'd certainly be cool if the like when your eyes are open you've got the world of bit and world of atoms and when your eyes are closed you've got the world of bits.
2:00:47
Um the uh >> [snorts] >> I mean the the near term is all these are medical devices.
2:00:54
Um especially on the cortical side, I think these are very serious brain surgeries that um like healthy 40-year-olds are not going to be like if your hands work, your a keyboard is a really great brain computer interface. Yeah.
2:01:05
And there's a lot of research that goes into like the design of the Xbox controller or the design of like the VR inputs because if you can convey the signals from your brain to the muscles, like you can just capture that. That works really well.
2:01:17
But at the same time, I think many people eventually become patients.
2:01:20
Like these bodies are great until they're not and as they start to fail, we should have better options.
2:01:27
And so I I think that BCI I think that there's a kind of a meme that it is an AI adjacent story, but I actually think it's like a longevity adjacent story.
2:01:35
Um I see like the like the only organ I really care about is the brain.
2:01:38
As far as I'm concerned, kind of the rest of the body exists to like keep the brain alive and healthy and move it around and cause it to do things.
2:01:45
And I'm going to be fairly disappointed if I'm murdered by my pancreas or my heart. Oh, yeah.
2:01:49
And so I think the you can get to this point where you say like, well, instead of you've got all these hard problems like, yeah, we've made progress on cancer and cardiovascular disease and metabolic disease, but again like when you get with health insurance, like you can't insure something with a 100% loss rate and we still that is what we are talking about in medicine.
2:02:06
And I think you can get to this point where the the brain is the thing that is really special and makes you you and like when I look at a person I see an agent and a robot and biology makes great robots, but the thing we really care about is the agent.
2:02:16
And if you can kind of deal with the agent directly and the rest become swappable parts, then instead of needing to cure cancer, we might just be able to avoid this entirely.
2:02:27
And that I kind of it see as as one of the central elements of at least how I think about the promise of BCIs.
2:02:33
And so I'd say um like one of our one of a thing that I think could really be possible is like by 2035 it it won't be widespread.
2:02:38
I think it'll take longer than that for sure.
2:02:42
But by 2020 2035 can you offer patient number one the choice of like dying of pancreatic cancer or being inserted into the matrix? Yeah.
2:02:50
What's the strongest steel >> 2035.
2:02:52
>> offer for the anti-transhumanism arguments?
2:02:56
Cuz I've I mean I've been steeped in this exact Silicon Valley lore for basically my entire life.
2:03:00
I've heard it articulated a bunch of different ways in media, video games, the Matrix.
2:03:07
And yet I've I've also seen a lot of pushback.
2:03:10
There's a lot of there's a lot of pushback against the transhumanism stuff.
2:03:14
What what what's been the what's been the strongest uh argument that's that that that you've you've had trouble debunking if any?
2:03:24
Yeah, I mean I I don't really like the label transhumanism.
2:03:25
It just has like all these like connotations.
2:03:27
I also don't like the word the term like the I think it is important that these things like people don't want to be different.
2:03:35
They want to be themselves. >> Yeah.
2:03:37
I think it's important to talk to realize that things we're talking about should make you kind of just as much you as you've ever been and just like with the best quality of life you've ever had.
2:03:46
And I think like the concern with AI is that we could it could be massively like it lead to really undermining our agency.
2:03:55
It could be like lead to a massive loss of agency for humanity and then we're something else.
2:04:01
Whereas I think that these BCI technologies are really about increasing human agency.
2:04:05
It's really it's a very like um uh Yeah.
2:04:11
Yeah, they kind of exist to facilitate you and it should not and it should be it's not other than human.
2:04:16
It's like it it is a very pro-human technology. Yeah.
2:04:23
Uh I mean as much as any kind of health care, right?
2:04:24
Like we we try like you we do heart transplants, we get we're working on artificial hearts, you have dialysis, you have we treat cancer, we like think that these things are worth trying to improve on.
2:04:32
And it's only transhumanist in the way that like any health care Sure.
2:04:35
is that you don't accept that just like the state of the art in the Middle Ages if you're like playing in the wrong forest and got a scrape on a branch, you could suddenly die of a bacterial infection 2 days later.
2:04:45
Like we developed antibiotics.
2:04:47
Like this is only transhumanist in the way that antibiotics are.
2:04:49
It's the continuation of that story.
2:04:51
That's a great that's a great argument. I love it.
2:04:54
Um what Yeah, Jordi, you have anything else?
2:05:00
I was going to ask I was going to ask about the merge.
2:05:02
Yeah, I'd be interested to get your depth personal definition of the merge.
2:05:10
Sam Altman wrote in 2016 that it would could be a scenario where people become best friends with a chatbot.
2:05:16
Maybe this the merge is something like AGI where we just keep moving the goal posts.
2:05:20
Yeah, yeah, do you think it's inevitable or do you think there's like some sort of fork or where do you think the unexplored discussion surface area is around the concept of the merge?
2:05:31
I mean the way that we interact with AI as I mean there's many different ways that this could go.
2:05:36
I think like the the failure mode of like Terminator seems less likely.
2:05:39
But I definitely agree with Sam that it like an underrated failure mode is people just start like delegating their decision-making to it cuz the models just make better decisions.
2:05:48
Like there's I was reading an article that one of the US military like combatant commanders is now like running personal decisions by chat GPT.
2:05:57
And if you're just like realizing like, hey, like these things make as good decisions as I make, then you they kind of come like pre-merge.
2:06:02
Like you don't need a device for that necessarily because we are just kind of like like causing it to happen in the world like whatever it wanted.
2:06:12
And there's like some dark versions of that.
2:06:14
Like you can imagine like an extinction scenario is that we just really become depend we're like, man, these things have great judgment.
2:06:21
They really know what to do.
2:06:21
We should like ask it for advice and listen to it and it like persuades a bunch of humans into suicide.
2:06:26
Like you should I think you got to keep an eye on those rates.
2:06:30
They won't be zero and I think it's unreasonable to expect them to be zero because you're talking about hundreds of millions of people, but like do those trends like what do those look like over time?
2:06:38
Yes, yes, keep an eye on the rates.
2:06:40
That's that's the great summation of what's going on.
2:06:41
It it you should do the baseline's not zero, but if it starts climbing up, you got to watch it. Exactly.
2:06:47
Just like self-driving cars.
2:06:49
Like humans are not perfect.
2:06:50
Don't hold them to like like to these unachievable standards.
2:06:52
But like keep an eye on the rates.
2:06:54
Keep an eye on the rates. I completely agree. That's a great take.
2:07:00
Thank you so much for coming by the show.
2:07:02
Congratulations on the progress.
2:07:02
Jordi, you have anything else?
2:07:04
Yeah, this was incredible. I wish we had more time. Yeah.
2:07:06
But come back on whenever you want.
2:07:08
Yeah, I really appreciate this. Thanks for having me on. Good to see you guys. This was super fun.
2:07:11
Yeah, we'll talk to you soon. >> Have a good one. Bye. Bye.
2:07:13
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2:07:15
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2:07:16
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2:07:20
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2:07:22
Um our next guest is Andrew Dudum from Hims and Hers. Two companies? No, just one company. Hims and Hers. Welcome to the show. How are you doing? I'm great, guys. How are you doing?
2:07:34
Thanks so much for taking the time to jump on the show.
2:07:37
For those who who might not be familiar, can you just kick us off with a little bit of an introduction on the state of the business right now? It's a public company. People know telehealth.
2:07:46
>> I thought you were going to say for anybody that hasn't heard of of Hims because Yeah.
2:07:49
I'd like to I'd like to find one.
2:07:52
You guys have some great hair, so I feel like maybe we've got some customers on the call. Oh, yeah. Yes, yes.
2:07:58
So Hims and Hers we've been around for about 8 years.
2:08:00
It's actually our 8th birthday this month. We went public in 2021.
2:08:05
Um and the vision is to help people feel great through the power of better health. Yeah.
2:08:07
health. Yeah. So we connect to on your phone, you pick up your your iPhone, you click an app and you immediately get connected to one of a thousand or two thousand doctors who are registered in your state and then can immediately engage in
2:08:19
everything from cardiovascular disease risk, at-home testing for testosterone, menopause, weight loss, prescription treatments, um all the way down to stigmatized things like sexual health and mental health and depression and anxiety. So
2:08:32
So um you know, we treat you know, millions of patients on the platform, 10 to 15,000 patients per day.
2:08:41
Um So while we are in many ways a new and innovative telehealth company as people what what people call us, we're actually probably one of the largest health care systems in the US today just given the pure volume of patients that we treat. And it's all digital.
2:08:52
So no matter what zip code you're in, you have access to world-class consistent care, standardized care, which I think is one of the most important parts of all of this digital this digital healthcare revolution, which is no matter where you are, you get great care as if you're 10 minutes away from Stanford and go see a dermatologist, you know, right outside your door.
2:09:10
Um so this year we're on track uh you know, two and something billion in revenue, profitable, and growing super fast, which is exciting.
2:09:20
We're launching a bunch of new categories. Home hit. Home hit for you.
2:09:24
Um let's talk about labs and the product.
2:09:29
>> So labs is an important one.
2:09:29
Uh we launched today two packets packages uh where you for a few hundred bucks can get over 120 biomarkers.
2:09:35
This is the most advanced diagnostics frankly that are out there in the market and I spent a a ton of time figuring out with with concierge doctors and others what is kind of the leading edge where you can get twice a year annual testing and then have all those biomarkers come into the platform and have thousands of doctors essentially overlook it coupled with our AI to be able to prescribe and treat very specific treatment plans for you.
2:09:59
So it's not just a data dump of here's, you know, 120 biomarkers, good luck, throw it in ChatGPT, but but it's actually real providers that are then helping you make um tactical personalized prescription next steps.
2:10:14
So we have about a million square foot of pharmacy compounding in the US.
2:10:18
So we actually make a lot of these medications ourselves.
2:10:19
medications ourselves. So if you're a person that comes in like me, low vitamin D, uh you know, testosterone's not as optimized as I want to be, so it's going to be a zinc and ashwagandha or magnesium supplement along with core
2:10:31
pharmaceuticals, let's say for heart disease like a statin, we'll be able to actually put that together for you in a form factor that you love, um you know, customized into a beautiful personalized treatment and then deliver it to your door for something like 30 or 40 bucks a month. So it's really this vision of a
2:10:46
So it's really this vision of a completely unified system that gets to know you, optimizes you, and then actually can verticalize the the making of treatments for you uh so that we can see you get better and help you kind of get preventative given we're in a country here where almost everybody dies of preventable disease, right?
2:11:03
It's like heart disease, heart attacks, diabetes.
2:11:06
It's stuff that takes like 10 to 20 years to develop.
2:11:08
We should be able to get ahead of this stuff and actually start people, you know, help people change that course of direction.
2:11:13
How do you how do you think the labs market evolves?
2:11:17
There's a ton of players in the space.
2:11:19
This is obviously not your core business, although it feeds into the core business.
2:11:23
I think there's players that are trying to use labs as like a wedge to then go and compete with you guys.
2:11:30
You guys are kind of doing the opposite, but do you think that margins on labs like effectively go to zero over time?
2:11:39
>> It's going to go to zero.
2:11:41
Yeah, I think anybody who's in the labs business today, whose core business is labs, needs a new business.
2:11:47
Yeah, you've got Quest and LabCorp, and great partners.
2:11:49
They've got thousands of locations, you know, 10 15 billion dollar public companies each of them.
2:11:52
Um there's no way that those margins will be constrained when you have a platform like ours where it's you know, or it's in our best interest and it's in our consumers' best interest to verticalize this infrastructure over time and give it away for free. Right?
2:12:10
For for you guys or anybody who's a Hims or Hers customer, uh it's to our benefit that we do a full panel and and get you that at cost or even as Legion, right?
2:12:20
Like we can we'll we'll pay you to take it eventually as you actually verticalize this infrastructure because the data you then get back from it allows us to give you very tangible next steps and manage your care over, you know, 5 10 15 years, which is really where the long-term relationship and value creation comes from.
2:12:36
So you know, for us um you know, we're excited about these price points.
2:12:41
It's $199 for a base package.
2:12:43
It's $499 uh for the advanced package.
2:12:45
You know, my hope in 3 years from now and my CFO would be mad if I said this, but it's like my hope is it's 30 bucks, right?
2:12:52
And it and it just gets added on to your Hims or Hers member uh membership.
2:12:54
And if you have the treatment with Hims and Hers at all, you get this stuff for free, right?
2:12:59
Because it just makes the platform and it makes your precision care that we can deliver so much better, right?
2:13:04
Say you're at a hair treatment and you're taking, you know, our oral chew for hair loss, which is like finasteride and minoxidil, and then we figure out that you're prediabetic, right?
2:13:13
Our ability to compound that with a low dose of metformin or some type of other, you know, microdosing GLP-1 or GIP P, like that just transforms the actual clinical outcome for you, and it all started with a very simple relationship, which is I'm worried about my hair.
2:13:29
I want to start taking advantage of that.
2:13:32
I want to know your take on Theranos.
2:13:35
I have this hot take that it was never a good idea at all because you can just take a full vial of blood and it was even if it worked, it wouldn't be that big of a product because if I go to the doctor and they take this much blood instead of this much blood, it's just not that big of a deal.
2:13:50
Do you think if Theranos had worked, uh that would be a successful product?
2:13:56
Would that have reshaped like was there ever a chance that it would have been a great thing?
2:14:02
Yeah, the only reason that the idea for Theranos is interesting was if you can do at-home diagnostics as opposed to in-office phlebotomy, sure. Right?
2:14:10
So you know, that that burden of leaving your house, going to a doctor's office, the number one fear in the country is needles, right?
2:14:18
So you're sitting there and somebody, you know, putting a needle in your arm to take out that tube of blood.
2:14:24
Whether or not it's a tiny tube or a big tube, it actually doesn't matter.
2:14:25
You know, you had to go and park and take time off work I just see that as a terrible problem in our society that people are afraid of needles.
2:14:33
They should not be fearful.
2:14:34
I don't like that at all.
2:14:36
I feel like we should solve the fear problem before we solve the needle problem.
2:14:40
I think if you can do and and this is, you know, something we're working on and we've talked about this.
2:14:46
I think in the next year or two you'll have at-home diagnostic devices that you can put on your arm, click a button, you feel nothing.
2:14:53
It's kind of like a CGM, right? It detects your blood.
2:14:55
Um and you can actually probably eventually look at the interstitial fluid, so you're not even doing injections deep into the blood, but you're actually just doing kind of like top capillary fluids to be able to interpret what some of these base biomarkers are.
2:15:09
If you can pull that off and it costs 10 bucks to do that and you just click a button, take it off, and mail it back to Hims, that I think really transforms access because so many more people I think will do that versus the whole, you know, kit and caboodle like going and scheduling and showing up in office.
2:15:24
Yeah, yeah, that makes a lot of sense.
2:15:25
Uh how what what's your like framework for how the GLP-1 market shapes out?
2:15:35
Yeah, it's a crazy market.
2:15:35
You know, it's like one of the the the most important categories probably in the next few years.
2:15:41
I think you're going to have more and more treatments on the market very quickly. Right?
2:15:43
You've got that Sarah deal with with Pfizer and Novo that have been taking place, you know, back and forth.
2:15:50
Uh you've got Kytura that just raised a huge growth fund that's got a a GLP-1 GIP that is at par or possibly superior to tirzepatide.
2:15:59
You've got Viking coming out probably in '29 or '30.
2:16:01
You then have Wegovy and Ozempic going generic sometime in 2030 or you know, '31, which is going to bring the price down to, you know, it costs, you know, it could probably cost $10 to make one of those vials, right?
2:16:15
So I think in the next few years the landscape is going to be um incredibly competitive.
2:16:20
I think the prices are going to come down dramatically.
2:16:23
This is something we're very excited by.
2:16:25
When we first started putting these treatments out, the prices were $1,500 a month, but now they're down to about 150.
2:16:31
I would guess in 3 years from now you can be buying, you know, one of the best treatments for 50 bucks a month.
2:16:37
And at that point, it's going to be just cash pay.
2:16:40
Customers are going to be able to use HSA, FSA, not have to deal with the, you know, the pain in the ass of insurance, and then the massive market will expand pretty dramatically. Yeah.
2:16:50
Yeah, you said you have like what, thousands of square feet of compounding, something like that?
2:16:55
Yeah, yeah, we've got a million million square feet. >> square feet. All right.
2:17:00
I feel like that's been controversial in the past.
2:17:01
There's been uh battles between you and maybe the FDA, maybe the uh the >> Just a little controversial.
2:17:08
Yeah, I mean I I I reread about it in the Wall Street Journal.
2:17:10
Is there are you staying the path on compounding?
2:17:14
Is that where you want to be in a decade for is is compounding here to stay or is there a world where you wind up partnering with the the the legacy pharma companies? Uh I think it's both. Okay. >> Right?
2:17:25
I think we will inevitably partner with a lot of these companies.
2:17:29
You know, we have the largest distribution platform for therapeutics in the US for consumers, right?
2:17:32
So if you have a next generation treatment, we should be talking because if we want to get your medicine to a lot of people, you know, there's just no faster route to do so.
2:17:41
So I think that's a very logical and natural thing to happen.
2:17:45
And we we've been doing things like this.
2:17:46
We recently invested in Grail, which is one of the uh uh I think the most advanced early multi-cancer detection blood test.
2:17:54
Simple blood test can detect over 100 cancers as early as stage one.
2:17:57
I have every single person in my family take this test every year.
2:18:02
Um more things like that are going to come to market and I think we'll come to the platform.
2:18:06
But for compounding in specific, there's really clear guidelines with regards to FDA allowance of compounding.
2:18:12
There's different types of pharmacies.
2:18:14
They can do different types of things for very specific reasons, provider personalization, form factor, side effect management, etc.
2:18:24
We're a public company, you know, our our chief medical officer was the chief medical officer of Walgreens. Mhm.
2:18:27
Uh Deborah Tur who's on our board who runs the risk committee was the woman who who wrote a lot of compounding legislation at the FDA for a decade.
2:18:35
So I think we play very clearly by the rules.
2:18:37
I think this is one of the first times in history that um we're able to give this level of personalization to a lot of people. Right?
2:18:47
Historically, this level of personalization only was possible for people that you know, we're spending $50,000 a year for concierge doctors and they could have treatments made for them.
2:18:56
And I think it's our ambition to figure out how do you get that for the 1% to everybody?
2:19:00
And so I think there's friction without question in that system, but the regulatory framework is is is very clear and I think we're just staying the course and and I think over time we'll be able to figure out ways to to to make the other parties in the ecosystem feel like they're getting you know, well well compensated as well. Yeah, makes total sense.
2:19:20
Uh how would you describe your management style?
2:19:24
The the you know, the company's up massively from you know, the IPO.
2:19:30
I feel like I started tracking Hims during the D2C era, right?
2:19:35
You guys were the the poster child of D2C this meteoric rise and then and then obviously you know, the the public markets have been wild.
2:19:47
Yeah, I'm curious like how how you navigate you know, the the market internally you know, even with the team, right?
2:19:53
It feels like you guys are very built for it.
2:19:56
But what's your approach?
2:19:58
Yeah, you know, when we I remember waking up one morning and then looking at my wife and the stock was $2. 87.
2:20:04
Like we were trading at I think like 0.
2:20:04
4 times next year's revenue or something like that. It was crazy crazy.
2:20:09
So I think the team has a stomach of steel, you know, whether stock $70 or $2. 87.
2:20:14
I think you know, most of the executive team will retire with this company including myself.
2:20:20
Because I think there's this incredible opportunity to figure out how you redefine what is a healthcare membership that every single one of us would want to buy.
2:20:30
Like it's got the cutting edge, it has the most you know, the best diagnostics, it's the most affordable.
2:20:35
It's an Amazon Prime version of healthcare that you can afford, right?
2:20:40
It's like 500 bucks a year.
2:20:43
For 500 bucks a year, why deal with insurance in this country when everybody's insured, but everybody has a high deductible plan with a $2,000 deductible. Right?
2:20:52
So while everyone is insured, nobody can actually get the benefits of insurance because they can't actually afford to use all the cash to then get the $1 insurance.
2:21:00
So I think our management team is um it's not a very glitz and glamour team, right?
2:21:07
Like you're not seeing us out on TV 24/7.
2:21:09
It's a really heads down team. It's a gritty team.
2:21:13
It's a team that loves to build.
2:21:15
We have a ton of fun together.
2:21:16
And I think [clears throat] it's a team that genuinely focused is on what can be accomplished in a decade and not can be accomplished in the next quarter or two, which is why I think a lot of the investments we're making have a much longer time horizon, but you know, as a as a founder that runs the company that has the high vote stock and we have that privilege to be able to invest over that long period of a time. That makes sense.
2:21:39
What kind of horror stories have you heard about Chinese peptides?
2:21:40
There's sort of a meme going on on X of people talking about it.
2:21:47
Obviously there some of the pricing coming out you know, people buying directly there is pretty wild.
2:21:51
What what are the what are the risks that people should be aware of?
2:21:56
Yeah, you you've got to make sure you know, the the pharmacy suppliers are working with are good quality pharmacy suppliers.
2:22:02
There's something called a certificate of analysis that um you know, high quality FDA oversight facilities in the US can deliver third party tested validation.
2:22:13
You can actually ask for the certificate.
2:22:15
We give customers the certificate for for any of our compounded products, which actually shows the independent laboratory saying this is exactly what you you know, we say it is.
2:22:25
Um So I think you want to have organizations or or pharmacies that have that type of coverage, that type of third party testing, you know, good manufacturing practices and ideally have some type of oversight from you know, state boards or FDA.
2:22:40
I think there's a lot of stuff you can buy online that's you know, truly not for human use and then they say that as a way to kind of loophole around, but it like truly is not for human use and you know, I'd encourage people to to try to stay away from that kind of stuff. Um random question.
2:22:55
I'm curious to get your take on it.
2:22:58
Something like 1% of US GDP is is dialysis. Yeah.
2:23:04
Doesn't necessarily seem like a problem for Hims to solve, but from everything you know about you know, working in in in and around this industry, how do you think do you think it's a solvable problem or is it just a Yeah.
2:23:20
You know, my grandfather was on dialysis for like the this five or seven years before he died and it's a it's like the worst thing in the world like watching somebody on that, right?
2:23:31
You have to show up to the facility every week.
2:23:33
If you don't, you literally your body deteriorates and you die.
2:23:36
Um Dialysis is a result of you having totally failed taking action on you being sick for 20 plus years like a very long time.
2:23:45
Sim- similar to cardiovascular disease, right?
2:23:49
Like you show up in the ER um at 60 you have a heart attack.
2:23:52
You know, that took 20 years to build up.
2:23:55
Like there's just no other way it happens.
2:23:58
And so something like what we launched today, I actually think is the first step of the solve because you get for a couple hundred bucks something like a lipoprotein little A test, which tells you your predisposition for advanced cardiovascular disease, which means even if your cholesterol numbers are amazing, they're not amazing enough. Right?
2:24:17
You have to have like the best gold standard cholesterol numbers like you're an 11-year-old kid to avoid a heart attack because you have this predisposition to risk.
2:24:26
Same thing with diabetes, right?
2:24:29
Diabetes A1C fasting insulin all of the or fasting glucose, these all rise over time.
2:24:34
So if we're 30 or 35 and have these numbers, you can just chart what it's going to look like at 50.
2:24:38
And so you know, micro steps in health, in food, in movement, in you know, metformin at the right time or GLPs or preventative statins or PCSK9 inhibitors, like all of these tools exist.
2:24:52
And for the most part these tools are not extremely expensive. Right?
2:24:54
So the real burden in the US healthcare system isn't actually lack of innovation, it's lack of education and access. Mhm.
2:25:03
So how do you get more people tested faster?
2:25:07
How do you make it easy and less scary for them to take that first step and then keep them motivated along the way because you know, these treatments don't make you feel amazing the next day, but they'll save your life 20 years from now.
2:25:18
And I think that's where a brand like ours spends a load of time is is you actually have to love Hims and Hers because staying on this medicine is is good for your health and and it's hard because you drop that habit and then you drop that benefit. Makes a lot of sense.
2:25:33
Well, thank you so much for joining.
2:25:36
It's great great to finally meet.
2:25:39
Certainly were were an inspiration during again that D2C era.
2:25:41
It was like it just a crazy crazy moment in in time and it's awesome to see you guys execute in the public markets. Yeah. Thank you guys. We'll talk to you soon.
2:25:55
Before we move on, let me tell you about Linear.
2:25:57
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2:26:01
Streamline issues, projects, and product roadmaps.
2:26:05
We have our next guest in the studio, Melissa Tokumac Tokumac from Netic.
2:26:10
We also the market is selling off like crazy.
2:26:12
People are asking us to cover it, but we will talk to Melissa first. How are you doing? Hello. Good to see you. Have a seat right there.
2:26:17
We do [laughter] need to cover.
2:26:21
I'm glad we have you on the show because the market is in turmoil, but your business is maybe less indexed to the market. Is that correct? That is correct.
2:26:30
Explain explain you know, how [laughter] explain the business, the news.
2:26:35
Take us through it and then we'll we'll get your take on all sorts of Well, I mean we serve the essential services businesses that are really backbone of our American economy.
2:26:45
>> By nature counter cyclical.
2:26:46
>> Let's give it up for the backbone.
2:26:46
The backbone of the American economy. Thank you. Okay.
2:26:49
So these businesses are these enterprises are in industries like HVAC, plumbing, electric, solar, consumer health, energy across the boards.
2:27:02
So whether the market is going down or up, that somebody, a business or a consumer always needs these businesses.
2:27:09
Actually today I'm coming from San Francisco as you know, all of San Francisco needs a lot of these services today because the weather is a complete disaster.
2:27:17
It has been raining No, complete rain. Everything is shut down.
2:27:23
Yeah, electricity goes off all the time and I'm sure a lot in the Bay Area will be needing these services all day today.
2:27:30
So what we do is provide really these enterprises with an AI revenue engine, right?
2:27:36
So in these times of need, they can actually handle all of the demand with AI agents and serve the communities, but also when the demand is soft, they can predict the need and turn these relationships into multi-time recurring relationships. Okay. Yeah.
2:27:54
>> It's so I I I feel like every time I call a plumber or roofer or an electrician, people like that, they don't like the delay and the lag of getting like there's always like crazy lag in in terms of getting back and just being like, yes, I can see I can come by at this time. Mhm.
2:28:10
And I feel like the lag is because they're super busy.
2:28:15
And if you can help them respond more quickly, they can generate a lot Exactly.
2:28:19
Actually many of them are very large businesses and the lag is because everybody needs it.
2:28:23
When you need it, odds are millions of other people need it at the same time.
2:28:27
And I'm sure like this has been talked here in this program, too.
2:28:31
There's a national shortage in skilled labor in these industries, too.
2:28:36
So it's actually I mean Jensen talks about it a lot and it is a type of labor we need in the country and until then they're also going to have >> was talking about?
2:28:45
I feel like he said he like we you know we don't have enough plumbers but I felt for some reason he was talking about like plumbing in data centers like >> Yeah, it's it's the same, right?
2:28:52
So HVAC technicians or plumbers what the data centers need cooling, right? >> not that many.
2:28:59
>> Or to be able to stand it up but if you think about it um it takes a lot of years to get actually trained for these jobs.
2:29:06
Probably more than I went to school for at Stanford Actually it's it's very important and then afterwards you have to do a in real life the training as well. >> Sure.
2:29:18
So with data centers and consumers and businesses across the world the need for these businesses a lot and not it's not only about HVAC plumbing electric when you say essential services, right?
2:29:29
It's also across energy and solar or um consumer health like the Bay Club, right?
2:29:35
That we have been serving.
2:29:35
So it's really the things that everybody in their daily lives need and has to interact with.
2:29:43
>> typical stack of uh of HVAC repairmen.
2:29:43
I mean I imagine somebody could just have you know like a Yelp page and a Venmo account then some of them have a full website with a booking system and almost like their own little mini ERP.
2:29:57
Probably not something they built themselves.
2:30:01
Maybe they have a Shopify site or something and then at a certain point they get a roll up happens. They get bigger.
2:30:06
They get more industrial.
2:30:06
They get more mid-market and then I imagine that there's some sort of central you know point of record.
2:30:13
Are you plugging into that?
2:30:16
Are you trying to replace that?
2:30:17
Are you trying to be the first the first choice for setting up every all the touch points?
2:30:22
Yeah, so we primarily work with what you described as mid-market and large enterprise.
2:30:26
So a lot of these businesses are owned by private equity >> Sure.
2:30:31
and or still owner operator but they have built it from zero to hundreds of millions of dollars in revenue.
2:30:36
So they will actually I love that you went through that stack.
2:30:41
They might have a more sophisticated tech stack than here, right?
2:30:45
So for these businesses run on EBITDA, right?
2:30:47
So it's extremely important that they think about their efficiency, the customer relationship and how do they serve their communities.
2:30:55
So primarily it will focus on you know what type of data they have and where do they keep it, various software solutions and of course third party aggregators where they might do advertising.
2:31:05
So we will partner with different solutions that they might have to um take make benefit of any of the data they can.
2:31:12
But primarily what we're doing is bring Frontier AI to these industries so that when their demand is very high instead of losing that they can handle it all at the same time and when the demand is soft they can generate net new demand, right?
2:31:30
As you know these industries just like we talked about San Francisco today is very volatile due to seasonality or some other external effects that they can't really control, right?
2:31:40
Um so that's why it's very important how you need to be efficient when the demand is coming you have to capture it all and when it is not you're thinking about >> [clears throat] >> Exactly.
2:31:57
Exactly or even how to predict the next thing.
2:31:59
Like one of our customers there were tornadoes in Missouri a few months back and before the tornado literally happened using Natic, right?
2:32:07
Reached out to potentially affected areas and talked about generators.
2:32:11
How would you feel if your electricity went in the You would not like that because your business would be down, right?
2:32:17
And but then afterwards >> [laughter] >> I will I will help you out.
2:32:31
That's the best But also you know when the disaster actually happened in 90 minutes maybe they got thousands of calls, right?
2:32:40
So how do you have But it was great for them because they could answer every call with Natic and they had already maybe rescheduled the non-essential jobs that they had so that they could help the community in that moment.
2:32:58
That type of proactivity and focusing on um the future and like revenue generating interactions for the businesses is very important for these businesses. >> Talk about traction. Yeah, sure. Yes. You're raising a round? I did.
2:33:10
Today we announced Let's Let's let her hit As hard 23 million dollar series B led by Founders Fund. HIT THAT GONG. WITH AUTHORITY.
2:33:27
AT A 45 450 million dollar cap which is a strong 4x step up in valuation. >> Very low dilution. Very low dilution. Yeah, why?
2:33:38
Well, I think from the beginning I really Still going.
2:33:43
The same strength we put into our business, right?
2:33:45
No, I mean that is actually the answer.
2:33:47
From the beginning what was important to me is that we build a business with strong fundamentals, right?
2:33:52
It is not about the hype.
2:33:54
It's not about the valuation.
2:33:54
For me the most important thing even in investments we work with the best and we're lucky and grateful that same people triple down in a row um in Natic and when you have a business with strong fundamentals and strong margins and scaling efficiently you don't want to raise more than you need.
2:34:15
And if you do that by the way that would be a detractor actually in the type of talent that we're hiring today.
2:34:22
They do want to work in nimble teams.
2:34:24
They do want to run through walls.
2:34:26
They want to be here because they want to build the future of essential services.
2:34:30
>> [laughter] >> Yes, you should. Exactly.
2:34:35
I have a question about models.
2:34:38
Uh Brett we we were talking to Brian Chesky and he's starting to add features to Airbnb that that allow the booking of like a chef.
2:34:47
It's a very different It's a very different model for someone who's traveling.
2:34:53
They want a private chef or something like that.
2:34:54
Uh but he mentioned that he's having a lot of success with open source models.
2:34:58
Obviously he's operating at massive scale and so every dollar counts.
2:35:03
Are you seeing luck with open source models or are you sticking to the the closed source models? >> Yeah.
2:35:10
You actually can't stick to one. Yeah.
2:35:12
Uh the way to the because if you think about it maybe the difference there is we serve essential services industries.
2:35:19
I'm like a utility to these companies. I can't go down. I can't be wrong.
2:35:24
So the way that so we build our own amount orchestration, right?
2:35:26
The way that you build um and think about models and what's really helping with each task that you need to do in that orchestration is very important.
2:35:35
So you have to think about what's best fit in terms of address verification in this case versus understanding what does Jordy need when he's calling me, right? Or messaging me.
2:35:46
So that would be need finding.
2:35:49
So the whole point is how do you think of these modules and what do they need to get done and what is the model that can give you the best answer, right?
2:35:58
And you have to obviously build a lot on your internal Evals to be tracking that continuously and make any changes as you need, right?
2:36:05
So today definitely like we do use quite a bit of more closed source models but depending on your Evals if something is not you know like keeping up with what we need and the new functionality we add we would always test and think about any other models.
2:36:28
Before that are you are you using voice like we don't do voice to voice.
2:36:32
We do have voice actually today we support our customers from voice, text, online like any We are.
2:36:41
We are the single inbox, right?
2:36:43
For anything that they're really getting.
2:36:44
Um so voice yes, we support but voice to voice is actually not there yet. Speech to speech.
2:36:50
So we do really orchestrate all of that in terms of speech to text, reasoning models and then text to speech to really give that accuracy, reliability as well as the flow, the feel, right?
2:37:03
And hopefully maybe in 10 years you were asking we will have to think think that not in 10 years maybe even a few.
2:37:09
Where is voice to voice speech to speech is going.
2:37:11
So according to that we have to >> What's the sales cycle like?
2:37:18
To be frank with you I think we really sell based on ROI.
2:37:21
So we show our customers, right?
2:37:25
And they can talk to our existing customers at any point.
2:37:29
Anyone interested private equity firms that we support, large companies that we support.
2:37:33
Private equity firms been one of the main channels in here where they're just like hey we have a we have a roll up of a bunch of different underlying businesses. Let's roll it out.
2:37:43
and we want the same software I mean that's what private equity firms are these days.
2:37:47
>> Today yeah, everybody's doing it. Um that is correct.
2:37:49
It has been one of the good channels but also it can be direct to large companies itself.
2:37:52
I think the reason we love working with private equity because in today's world they understand the importance of AI and they're looking for an AI partner. Right?
2:38:04
It's not really about okay >> They're like we need an AI strategy. What can we buy?
2:38:06
Yeah, actually you'd be surprised like I think a lot of tech companies maybe looking for something or a strategy to put on a board deck.
2:38:12
A lot of these businesses I work with I will say they're absolutely incredible and better entrepreneurs than entrepreneurs in Silicon Valley sometimes I see.
2:38:20
And they are focused on real value.
2:38:22
They've experienced free cash flow.
2:38:24
Yeah, it's I mean you can't hide behind raises and valuations, right?
2:38:29
Like all you see is that does this help my revenue?
2:38:30
Very easy and for me too I can say hey I will not give you any random words AI this AI this blah blah blah.
2:38:41
No, let me show you how is this going to affect your revenue. Let's talk about that.
2:38:46
So, when you do it that way, I think the cycles are pretty fast. Yeah.
2:38:52
So, it's exactly >> Do you think people are scared to compete with you?
2:38:56
>> [laughter] >> You seem like a pretty formidable opponent. Um I don't know.
2:38:58
I I hope not because I You enjoy crushing them.
2:39:04
>> Yeah, and >> [laughter] >> Yeah. I do enjoy crushing. Crushing. Yeah.
2:39:08
How are you It's only me, my team, too.
2:39:11
And this is just for them from here.
2:39:14
You all that are watching right now, you're all the beasts.
2:39:17
And there's no one else in this world that I would rather work with other than you.
2:39:23
Incredible people coming from Scale, HRT, Databricks, MIT, Stanford.
2:39:26
But I don't want to even talk about the accolades. Who cares?
2:39:30
They all Most of them have deployed AI applications in the real world, in production, and they run through walls every day to deliver for the real world, even though they could be anywhere.
2:39:42
They could go in any company.
2:39:45
[clears throat] >> Well, for the Series C, we will get a wall built and we'll You can run through it and run through it. >> Exactly. I will be doing that.
2:39:54
Yeah, thank you so much for coming on the show.
2:39:55
This is >> Amazing progress.
2:39:57
>> Thank you for having me. We'll talk to you soon.
2:40:00
Um We have one more guest. Stay with us. The market is crashing.
2:40:03
Everything is in turmoil, but the business continues. The show goes on.
2:40:08
>> House The White House needs to announce our 100-year mortgages.
2:40:09
We have Yeah, yeah, I'm doing 100-year mortgages.
2:40:13
Let's move to 100-year mortgages.
2:40:15
That will be the solution to all this.
2:40:17
No, the only thing that can save us right now is numeral.
2:40:19
com, sales tax on autopilot.
2:40:21
Spend less than 5 minutes per month on sales tax compliance. Also, find.
2:40:26
ai, the number one AI agent for customer service.
2:40:28
Number one in performance benchmarks.
2:40:29
Number one in competitive bake-offs.
2:40:30
Number one ranking on G2.
2:40:32
And our next guests are here.
2:40:35
Let's bring them into the TVPN ultradome.
2:40:38
We have Jeffrey Katzenberg and Tomas.
2:40:42
Welcome to the show, folks.
2:40:42
Thank you so much for taking the time to come down to the TVPN ultradome. Good to see you again.
2:40:49
Congratulations on the news.
2:40:51
Let's get some introductions first. Who are you?
2:40:54
What organization are you with? Let's kick it off there. I'm Tomas Puig.
2:40:59
I'm the CEO and founder of Olympic. Thank you.
2:41:01
Jeffrey Katzenberg, GP at Wunderco.
2:41:05
Third time on the show, second time on the show, something like that.
2:41:08
But first time on the show here. Congratulations.
2:41:10
And what's the news today?
2:41:11
Oh, well, the news is is that we actually just raised $145 million. >> Fantastic.
2:41:16
Why don't you go hit that wall?
2:41:18
Please, enjoy the Yes, yes, yes. Whoa.
2:41:27
We haven't seen that before. Okay.
2:41:30
It's a very aggressive ring. I liked it. I liked it. It's still ringing now. It has a nice sound.
2:41:33
So, take us through the business.
2:41:35
How how are you pitching it these days?
2:41:39
So, what's really interesting about us we do a little bit of a different thing in the AI space. We do causal AI.
2:41:44
And really what our executives that we work with in these Fortune 500 Global 2000s want to know is they want to every chain reaction and lever that moves the metrics that they care about in the business at any time.
2:41:57
And so, where we started originally was on the marketing and sales side.
2:42:00
And so, they would be like, "Hey, I spent $100 million on a stadium name.
2:42:04
All these unknowables, these large content pieces, all this brand, we all know it worked at the time.
2:42:11
But nobody could actually prove down to the dollar, down to the actual effect of what it would be.
2:42:16
And so, when we built the company, originally we were you want to know how to shine a light inside this black box and actually be able to get the real dollar value so that you can start telling stories cuz there's been so much of this improvement in say the programmatic side of the house buying these ads.
2:42:37
Well, what ended up happening is once we built that, we found out it actually worked really, really well, and we got a lot of incredible clients.
2:42:42
And then they started to ask us to do other things like being like, "Oh, hey, now can you see how all of that affects foot traffic?
2:42:48
Now can you see how all of that affects my ordering systems?"
2:42:49
And as we started putting this causal model out further and further, we realized it actually worked on an incredible amount of stuff.
2:42:57
And so, then our clients started asking us to be like, "Well, can you do our FP&A for finance?
2:43:04
Can you do our supply chain?"
2:43:04
And so, that's really kind of where the business came from, but the core of it is is that like when you have low information or low amounts of stuff, you want to be able to actually affect the metrics you care about.
2:43:16
Take us through some of the case studies.
2:43:17
Who have you worked with?
2:43:18
What's the most concrete example of You talked about buying a stadium.
2:43:21
Have you literally found whether or not crypto. com Arena penciled out?
2:43:25
Have you Have you looked at these You know, I grew up at the Staples Center. I I I miss Staples. It's been renamed.
2:43:31
But But I want to at least know I want to at least know that that they was worth it financially.
2:43:41
Is it wasn't worth it financially? Can you tell me that?
2:43:42
Yeah, so we did a great >> Let's check Staples stock since they >> [laughter] >> Yes, should Staples have stuck with the sponsorship?
2:43:50
See, the problem is it doesn't count for execution risk. Yes, yes, yes.
2:43:54
So, when we There's There's always unknowables, but but what can you know?
2:43:58
No, so we did a great case study with Delta Airlines.
2:44:00
We actually presented with their CMO at the the Nvidia conference at the GTC conference, where they were sponsoring the Olympics. >> Yeah, yeah.
2:44:09
And one of the things about these large sponsorships is there's two big aspects people don't talk about.
2:44:12
One is every time you've ever worked in the business side of the house, they go, "Hey, you need enough historical data to be able to do something." Right?
2:44:18
The second is it takes a lot of time to get a response.
2:44:23
Well, when they were doing this when they were doing the Olympics, they were doing the promotions, one of the most interesting things we found out is we analyzed all this ad work that's coming out. You have only 2 weeks.
2:44:33
You're holding up tens of millions of dollars on the P&L line while you're doing this, right?
2:44:37
These are not cheap options.
2:44:40
And when we pulled the study, we actually found out that the best piece of content that functioned for them, the most profitable, was not actually the content that was the ads. The ads did okay, right?
2:44:49
The 30-second, 60-second spots.
2:44:53
But what ended up happening is they actually if you watch the Olympics, they did have the Delta medal presentation ceremony, where like every time an American athlete would win a medal, they would take it, put it on, you know, their shoulders, and that would be, you know, a really emotional moment.
2:45:07
Well, it may seem kind of obvious in hindsight, but when you have the Eiffel Tower in the background, really emotional moments, you sell a lot tickets to Paris.
2:45:15
But the key is if you can know that within a few days, you can either double down, right?
2:45:17
You can sponsor the next Olympics.
2:45:20
You can do all these things.
2:45:23
And then you can actually act on it.
2:45:24
Now, the problem also with when you do big, huge campaigns like that is you walk into the lounge for Delta Airlines and T-Mobile says the Wi-Fi passcode.
2:45:34
It's not like one simple campaign, right?
2:45:35
You pivot an entire company to a messaging set.
2:45:37
But we were able to tell them within a couple days down to the dollar a material amount of cash that they were able to pull back.
2:45:42
And then they could actually show that to, you know, executives.
2:45:46
So, so all this all this problem in advertising, right?
2:45:48
Of like, "I know my advertise like half of my advertising is working." That's a line.
2:45:53
I'm 100% sure that 50% of my brand marketing is working if I only knew which 50%.
2:45:57
Which has been true in brand marketing forever, going back to the 1980s.
2:46:04
>> Yeah, I'm sure in entertainment, especially, because there's less like you the attribution is really challenging.
2:46:08
We did a bunch of marketing and then people went to movie theaters, and then some people streamed it, and it's hard to actually track it all the way through.
2:46:15
>> What what the the brilliance of what Tomas and Olympic have done is is that using Well, first of all, and he should explain it cuz he's the brainiac here, the new math that came out of contact tracing from COVID, which actually has impacted many different businesses in terms of research and you know, causality and attribution.
2:46:36
So, we got advertising learnings out of contact tracing.
2:46:41
By the way, it's absolutely There's no It's a biggie.
2:46:47
>> [laughter] >> Very American to like, you know, entrepreneur says, "Oh, okay."
2:46:54
When you think about it is is that either that for that for COVID testing or for any of these attributions, what Yes.
2:47:01
they are able to do, which was not possible even only 3 or 4 years ago, which is to be able to ingest billions of rows of data from all sources.
2:47:16
Um and using machine learning and AI to actually digest that and make sense of that and actually then be able to see directional in in time.
2:47:27
You know, it's They'll He'll explain neural networks and how this actually works, which as I said is way above my pay grade here, and it but I can tell you, which is to your to your question here, which is when we went to the top brand marketers, the first reaction was, "No way."
2:47:44
You know, this is like, you know, yeah, this is not possible.
2:47:49
And this is You're just like telling us, you know, you know, pot of gold at the end of the rainbow or, you know, Dumbo flies or I don't know, whatever you you want [laughter] to say.
2:47:59
And they went, "Not not possible."
2:48:01
And that's when these guys would come in and do these POCs.
2:48:07
Disney, Mars, Accenture, Delta.
2:48:11
And you know, in the world once is an accident, twice is a coincidence, three times you go, "Okay, well, this thing actually delivers."
2:48:18
As fanciful as the notion may be, it's real. Yeah.
2:48:21
Can you tell me a little bit of the history of how like product placement works in Hollywood?
2:48:28
Because that feels like the classic example of difficult to measure, but you've been in that position.
2:48:36
But it's But it's when you start to actually >> think about this, there's a trillion dollars a year globally spent on brand marketing.
2:48:45
And brand marketing is everything from crypto on the Staples Center >> [laughter] >> to an interstitial to imagine in the Disney parks all the things that you're you know, that they're able to offer to their brand partners to putting a logo on a car.
2:49:08
What do you What you know, what's the value of having Oracle Yeah, on the race car.
2:49:11
on a race car in this or or a patch on a base on a baseball player or a basketball player, you know, what >> So is it has it always just been intuitive or or has it been more relationship driven?
2:49:22
No, it literally is intuitive.
2:49:25
It is I mean he can tell you there's there's a there's an old math MMM he'll he can again I Tamas can really take you through explain it.
2:49:33
And that literally dates back to the 1980s and one uh it's directional not specific and two the lag >> Yeah.
2:49:43
between the time it happens and you're able to gather the data on it was months.
2:49:48
So it doesn't the value of it you know, is really questionable.
2:49:53
>> I I guess my question for you is uh it it's very clear that with the with the progress in AI there's the ability to find insights in data like clearly we see this across everything.
2:50:04
Uh my question is like there there's so little ground truth that how can your clients AB test your solution against something else?
2:50:13
Like if I go to a you know, an a coding agent and I ask it to generate some code I can run the code at the end and I can say well yeah, the code worked, right?
2:50:21
Or I can read the report, but if I go to you and and I say how much was this sponsorship worth and you tell me it's $4 million and I say okay, like maybe it was worth I that that could have just been a guess.
2:50:33
So how do you justify the the results that you spit out since we can't run a controlled trial?
2:50:40
So validation is one of the number one things we get asked constantly.
2:50:46
>> When we give out uh papers it's really funny.
2:50:48
We have one paper where it's like like a two-page brochure and then the next 15 pages is how we validate, right?
2:50:53
And you know, one of the things that people don't talk about is it's actually very possible to test these things.
2:50:57
So what you can actually do is you can do what we call backfit testing.
2:51:01
You can set the machine backwards in time and then you can test against things that you know.
2:51:05
You can withhold information.
2:51:07
You can do all [clears throat] sorts of things to actually see whether you know, you you feed the machine up to like last year.
2:51:12
You know what all the results are going to be this year.
2:51:14
So when you predict what something's going to be you can see how close you got.
2:51:18
And so I think that um when we're testing things we use a variety of both synthetic data testing methods cuz we're not you have to keep in mind we're not a transformer model or an LLM.
2:51:29
It's an entirely new methodology.
2:51:29
And so when we grab the stuff we have both synthetic data we can build so there's systems like for the nerds like TigerGraph that does causal synthetic data or there's real data like fMRI data that we actually know that there's a cause and effect that we can pull from physical world or physical bodies that you can test algorithms against as well.
2:51:48
And then from the business side of the house there's lots of ways to do it.
2:51:50
Now, one thing that I like to correct on this is it's like we often say the mantra of our company is we're about being directionally correct not specifically wrong. Okay.
2:51:57
And so when you need to make decisions a lot of time when we do these simulations we're like here's the top 20% of things you need to absolutely keep doing cuz they knock it out of the park.
2:52:06
Here's the 20% of things that are just literally hurting you. Okay.
2:52:09
And when you have zero information like you're in a pitch black room and you have no idea where the exits are would you rather take a really really good educated guess about where the exit is or would you rather wander around the dark? Yeah.
2:52:22
And so I think that one of the things about business intelligence is when you have zero information the value of the information you can get is that much more important.
2:52:31
>> And so we do it very well.
2:52:32
>> about the the backtesting thing.
2:52:32
Like if I like how would you go back and and assess the value of like Budweiser sponsoring Super Bowl 40 or something?
2:52:40
Like it's like you can't you can't run the counterfactual.
2:52:42
Well, you Like you can simulate that.
2:52:44
So counterfactual simulations are assumptions.
2:52:46
So think about it like this uh a lot of people talk about probabilistic graph, right? Graph analysis.
2:52:52
Probabilistic graphs whether they're causal whether they're anything else is um to a certain extent I mean you know, like a genetic LLMs are almost a probabilistic graph, right?
2:53:00
Undirected till you query it. A neural network. >> And exactly.
2:53:03
And so when we do this type of analysis the thing about it is is that we actually have seen lots of instances of the same exact thing.
2:53:11
It's about the specificity.
2:53:12
So we just did a calculation literally yesterday that we looked at 1 trillion connections across 6 months for a company.
2:53:19
Like that's the type of scale of analysis we're doing.
2:53:24
We haven't seen when you haven't when you say you the counterfactual when you're saying what's it look like when you have a base state?
2:53:30
We've seen the operating version of a company over years.
2:53:31
And so we know what the base state looks like when there's no influence from that.
2:53:35
The key is you have to have enough data.
2:53:37
So in the old world when we're doing all this analysis we used to say uh you hear about the term overfitting, right?
2:53:43
Everybody's worried that is the model just biased?
2:53:47
Well, in the old world you'd say I want to reduce the number of features, reduce the dimensionality to prevent overfitting, right?
2:53:52
To prevent overprediction.
2:53:55
Well, in the modern world with like computational statistics nowadays or AI as we call it you want more features.
2:54:02
You get more accurate the more data you have.
2:54:04
That's counterintuitive for how people think about these type of things.
2:54:08
So my answer to you is we have to have immense amounts of data. Right?
2:54:09
And so we ingest a lot of it.
2:54:11
And then that gives us enough vision, right?
2:54:13
That we can see what a state would be and what would not be.
2:54:17
And then we also provide our customers with confidence and everything else.
2:54:20
So there are times where we really really know. Right?
2:54:23
And we go this we have 100% bet on.
2:54:26
We've seen enough examples of this.
2:54:27
And sometimes we go you're asking for a call on this and yeah, we've got a pretty good guess, but you should still keep it out.
2:54:33
>> thing is is you know, um he's going fishing to see where you know, the best fish are. >> Sure.
2:54:43
And interestingly we did one of these uh POCs for a very very big branded company uh and they were looking for the positive impact of uh event-driven um uh brand marketing they were doing. Yeah.
2:55:03
When they went out and sucked in all of this data Mhm.
2:55:04
to do this assessment for them in addition to finding what were the sort of positive impacts of this which were modest >> Mhm.
2:55:14
what they actually caught in the net was unknown to them a promotion being done literally in Canada by uh you know, a little subsidiary >> Interesting.
2:55:26
that was just a you know, like a a a regional commercial Yeah.
2:55:29
which somehow or another bled over into the states.
2:55:35
So it was being run in Canada by a subsidiary there, bled over into the states and had a tremendous negative adverse impact on brand. Yeah. As Canadians.
2:55:47
>> [laughter] >> I mean this is like yeah, the classic example is like the social media manager intern of your Canadian you know, offshoot is doing something that goes viral in America and everyone >> This one was even funnier.
2:55:58
They bought out a national the national sports national hockey league final spot. >> Okay.
2:56:03
And so you that's huge in Canada.
2:56:06
So they but that broadcast across the entirety of North America, right?
2:56:09
>> And so like suddenly this little like thing there a really ugly hamburger and a hand and they wave and it just went and I'm sure maybe natively they thought it was funny. Okay, interesting. Interesting.
2:56:21
[laughter] And so you're looking at like when when sales data is happening relative to when the campaign goes on and then you tease out from there.
2:56:27
Yeah, think about it like a you know, one of the big inspiration for the company was Renaissance Technologies at work.
2:56:32
So like high-frequency trading firms and everything.
2:56:34
They can do a pretty good job about knowing when things affect each other or not, but you have to have an immense amount of incredibly high-speed data sets. Talk about Accenture.
2:56:41
You're partnering with them.
2:56:43
Are they are they just an investor or are they also a go-to-market channel?
2:56:46
Uh well, they actually started as um one of these uh tests we did cuz they were curious and a customer. >> Okay.
2:56:54
Which is the best way when you think about it, John.
2:56:56
So they started as a customer. Then said wait a minute. >> Yeah.
2:57:02
We we have a multi multi-billion dollar business around marketing go-to-market.
2:57:09
>> It seems like a lot of people go to Accenture for these questions.
2:57:13
Jillian >> Bain, NBC, Goldman Sachs, McKinsey, and so So then it went to hey, can we help take you to market? >> Yeah.
2:57:20
Which they've been fantastic at. >> imagine.
2:57:23
>> And then that led to when this when Tamas doing this most recent round them stepping is the biggest uh venture investor they've ever done. Wow. That's amazing.
2:57:35
So big companies Fortune 500 have been hiring Accenture and other consulting firms and research firms to help them understand uh what is driving results positive and negative in their business for a long time.
2:57:48
Uh what's the timeline to productizing what you're doing to a degree that a a much smaller company, let's say a company with like a million dollar a year advertising budget can actually start to get value out of this? >> interesting.
2:58:00
Oh, so this is actually one of my favorite questions because it has to do with the law of the long-term vision of the company.
2:58:07
Um one of the problems you get when you have mid-size or small-size firms and they're near and dear to my heart is that they literally have never done things.
2:58:15
So when you have a really large corporation, right?
2:58:16
They've been in a podcast no matter they want to be to or not.
2:58:19
Somebody's mentioned them, right?
2:58:21
They have all of these data sets across everything.
2:58:23
But when you're a smaller company, right?
2:58:25
You say a million dollar a year business or something like that, you haven't done everything.
2:58:29
So there's no actual priors.
2:58:31
There's no data, we don't know how they react.
2:58:33
But, eventually when we see enough of the universe, right?
2:58:38
Just like you hear about world models, just like you hear about anything else, we'll actually know what the causal universe looks like.
2:58:41
What is the actual most likely outcome of when somebody does something? Yeah.
2:58:46
So, in the future in a in a say a couple years, we'll actually be able to build synthetic data sets that you can send us any query and we could respond to you what the most likely likely outcome would be.
2:59:00
And where this gets really, really important is I think that especially when you're dealing with private businesses, the world of LLMs and everything nowadays, I think they're amazing, but they're quickly converging, right?
2:59:11
There's not going to be that much difference for a client between like ChatGPT and Claude.
2:59:16
The problem is is that if you're using that for business intelligence and business decisions, what are you going to do when your competitor gets the exact same answer and strategy you do? That's a real problem.
2:59:25
And so, we believe that we will take the best private data sets in the world, do stuff for just them, get the get our overall learnings in other places, and then we can actually provide people with strategies that are unique to them. Right?
2:59:39
Augment the other sets of intelligence, but Yeah, there are certain brands that will get a better return on investment from being in the Super Bowl than others.
2:59:45
Super Bowl than others. But, if you are a no-name brand and you just put up a 30-second spot in the Super Bowl, people are going to be like, I don't know I don't know I don't know I don't know I don't know I don't know I don't know I
2:59:54
don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know know I don't know I don't know I don't know I don't know I don't know I don't
2:59:57
know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't
3:00:02
know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't know I don't Yeah, but I mean to kind of like talk to your question, like that is an absolute dream that I have, right? Of being able
3:00:08
Of being able to level the playing field across that, but also provide Yeah, because that's one of the advantages of these massive businesses.
3:00:14
They can spend $20 million to figure out what's really working and then do a lot more of that, whereas a small firm is like kind of doing the vibes-based analysis.
3:00:23
Started my career helping companies like advertise on YouTube channels and with podcasts and you know, they might run a $200,000 campaign and there's some like direct attribution that they get either from a landing page or a code, but then they're like, "Wait, our conversion rate is just going up on the site generally.
3:00:39
Is that being driven by changes that we made at the site level, changes that we made to the offer, is it just overall lift from from podcast advertising or is it some other strategy entirely?"
3:00:51
And so, the the the more you can bring like real business intelligence to small companies, the more they they'll be able to actually compete against the the big guys.
3:00:59
And that makes me happy because everybody should have the level playing field and whoever has the best strategy and product should do well, right?
3:01:07
I think that it's important to note as we kind of talk about this thing, we spent years, years building the signal processor for this thing.
3:01:13
We had to figure out how to bring in all this unstructured and semi-structured data and be able to basically do data dog for unstructured data first before we could even try the causal thing.
3:01:22
And so, we have years of working on that and that ingestion pipeline, that that skill there is what allows us to do what you're talking about.
3:01:31
You can't just be like, "I'm going to slap a model on top of it, right?"
3:01:33
You actually have to be able to have a sensor that can actually understand every data feed.
3:01:40
Have you found a company yet that Jeffrey can't get a intro to or directly connect you to the CEO? You like this guy.
3:01:47
Oh, you want to meet this guy?
3:01:47
Yeah, I'll give him a call right now.
3:01:49
Here here here here here.
3:01:51
>> want to not take a bet on something?
3:01:53
That's one of the things I would not take a bet on.
3:01:55
>> [laughter] >> Jeffrey will find them. Hunt you down.
3:01:59
Well, well, >> [laughter] >> congratulations on the progress.
3:02:01
Thank you so much for coming by the Ultra Dome.
3:02:04
And yeah, good luck with the next with the next phase putting the capital work.
3:02:08
It's going to be an exciting time and I'm excited to hear more of these case studies as they roll out.
3:02:12
Yeah, I really appreciate both of you.
3:02:13
Yeah, let us know when you're ready for your first podcast customer.
3:02:17
We got Yeah, yeah, we got to figure >> analysis that we want to do.
3:02:20
I mean, we we just do the we do the vibes-based analysis. >> a billboard.
3:02:23
We did a billboard in New York and it did really well.
3:02:26
It's We should just do it for fun anyways.
3:02:29
Like, there's There's nothing more that I like than like looking at data sets, Yeah, yeah, we ran we ran a billboard campaign in in Manhattan.
3:02:33
We ran two exactly two billboards.
3:02:36
We have no idea whether or not it worked.
3:02:38
It seemed to work because people shared it a lot on social media.
3:02:41
For a million dollars, he could tell you.
3:02:44
>> [laughter] >> For you all, we could do some drinks. It'll be fun.
3:02:50
>> [laughter] >> Well, thanks so much for coming on the show.
3:02:53
Congratulations on all the progress.
3:02:53
And we will go back to our regularly scheduled programming and I will tell you about Adio customer relationship magic, the AI native CRM that builds, scales, and grows your company to the next level.
3:03:06
Um Bucco Capital bloke is also black-pilling on the timeline.
3:03:11
There it is a bloodbath in the markets.
3:03:13
Nvidia's down 4%, Microsoft Michael Burry rage quit. Michael Burry rage quit.
3:03:17
We need to talk about Michael Burry.
3:03:19
I'm sure there's something in the stack.
3:03:20
Um let's go through some quick updates as we run through the show. Oh, this is cool. This is a white pill.
3:03:29
Google DeepMind Seema 2, our most capable AI agent for virtual 3D worlds.
3:03:33
Tyler, what's the deal with Seema 2?
3:03:35
Yeah, this is really cool.
3:03:36
So, this is a it's like a general model that that can basically play like any video game, sure.
3:03:42
Which is different cuz like you've seen a lot of like even early Open AI, there was like Dota 2.
3:03:45
This is amazing for me because I don't have any time to play any video games anymore since I have kids.
3:03:51
And if with this agent, I could just tell it to go play the game and then I I could just go have fun and then then describe the fun and then I'll read it and then summarize it and email it to me and I'll have Nick read the email and summarize that to me in a text message.
3:04:07
That would be my experience of the video game.
3:04:08
No, seriously, what I actually want this for is I hate how modern video games that take 20 minutes to set up.
3:04:14
Have you have you ever experienced this?
3:04:15
Like going through the tutorial?
3:04:17
Remember when I when I made you play Halo?
3:04:18
When I made you play Halo, it it took like 10 minutes for you to actually get into the game and then the actual game took you 5 minutes to play.
3:04:24
And so, I like there are a lot of times when I hear a new game, I'm like, "I only have 20 minutes in my weekend to play this game.
3:04:33
I want to jump straight into the action.
3:04:35
I don't want any of the opening unskippable cutscenes.
3:04:37
I don't want any of the of the tutorial learn how to jump, learn how to crouch.
3:04:41
I already know how to move.
3:04:43
I know what the stick does. Don't tell me that.
3:04:46
This is going to solve that for me, hopefully.
3:04:48
Yeah, but this is actually I think this is like one of the most interesting papers this year.
3:04:54
I I a big part of it is um it's general, so it you can put in a new game and then it does like self-play essentially.
3:05:01
Um which is like really important cuz that's like it's teaching itself.
3:05:01
This is basically the first like agentic model that can like see a new completely new environment and then do self-play uh where it gets better.
3:05:11
So, you um I wonder how wild I I'd love to know how how diverse the inputs are.
3:05:15
Like, does it expect an Xbox controller's worth of inputs?
3:05:20
Does it expect a a keyboard's worth of inputs because that's more inputs that it would need to learn. That's fascinating.
3:05:26
And then I wonder what happens if you start marrying it to generative world models in the future as those become games.
3:05:32
Like, you have this weird like agent on simulated world.
3:05:36
You're simulating both of these. That's very interesting.
3:05:38
A big part of it is is they connected it to Genie 3. Yeah, it works.
3:05:42
So, you could you have a generative basically video game where it's like generating frame by frame and it does little cube one stuff.
3:05:48
And then you have the generative agent that like learns what the the world is and then actually plays it.
3:05:55
That was very interesting.
3:05:55
See this like flywheel kind of starting very well.
3:05:58
>> Well, whether you're going long or short, go to public.
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That got announced this morning.
3:06:20
So, great pick up from the public team.
3:06:24
Major white tail, Mira Murati's startup Thinking Machine Labs is in early talks. We love early talks. We love early talks.
3:06:30
Some of our favorite talks to raise a new round of funding at a valuation of roughly 50 billion, more than 4x their valuation Everything's cooking. Everything's cooking.
3:06:39
>> [laughter] >> Stop black-pilling.
3:06:42
Uh in other news, Anthropic disrupted a highly sophisticated AI-led espionage campaign.
3:06:47
The attack targeted large tech companies, financial institutions, chemical manufacturing companies, and government agencies.
3:06:53
We assess with high confidence that the threat actor was a Chinese state-sponsored group.
3:06:57
Uh I guess they were using Claude, I I think is uh Yes.
3:07:03
I I I think they were using Claude code, actually. Hmm, weird.
3:07:08
They said they were vibe-coding espionage.
3:07:10
Yeah, you you it's it was pretty funny.
3:07:12
I I read through some of the blog post and it was like some of the the interactions of like the hackers and it was like, "This is what they were saying to them."
3:07:19
They were like, "Okay, good job, Claude, but I think this part is wrong."
3:07:22
You can see like the actual transcript.
3:07:25
Very bullish for Anthropic. Hmm. Well, go to 8sleep. com, get a Pod 5.
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3:07:33
Michael Burry appears to be shutting down Scion Asset Management.
3:07:34
He said, "Dear investors, with a heavy heart, I will liquidate the funds and return capital, but for a small audit/tax holdback by year's end.
3:07:43
My estimation of value in securities is not now and has not been for some time in sync with the markets.
3:07:52
With heart with heartfelt thanks, but also with apologies, I wish you well in your future investments.
3:07:57
I do suggest investors contact my associate PM Did he really did he really quit right before the [laughter] market started correcting?
3:08:06
He's Is this one of those like, you know, 90% 90% quit right before 90% of gamblers quit right before they finally called the top correctly? Yeah, it does seem odd.
3:08:17
I mean, if he if he if there is a crash and he was going to be short, but he pulls out before like getting that short thesis to work and realizing the results of that.
3:08:31
Uh it really changes his legacy.
3:08:33
It changes the meaning of that meme.
3:08:35
It changes the the meaning of the the Michael Burry image uh in my opinion. But uh we'll see.
3:08:42
I mean, he he might have he might be out of step for years.
3:08:44
And we might look back on this and say that uh it was great that he got out and it was great that he didn't he didn't short uh the greatest bull market in history. >> will live on Yes.
3:08:56
>> through meme uh images from The Big Short.
3:08:59
>> And through whatever he gets on his wrist. Go to getbasel. com.
3:09:02
Your bezel concierge is available now to source you any watch on the planet. Seriously, any watch.
3:09:05
Uh in other news, Paramount, Comcast, and Netflix are preparing bids for Warner Bros. Discovery.
3:09:11
They will have uh until November 20th to submit non-binding first-round bids. Yeah.
3:09:18
Uh Warner Warner Discovery is holding the auction process in the hopes of having it completed by the end of the year.
3:09:25
Have you seen how how expensive streaming is getting?
3:09:27
This is on the cover of the business and finance section in the Wall Street Journal.
3:09:33
Uh the price has gone up pretty much everything.
3:09:36
Netflix has gone from something like five bucks to 25 at the top end.
3:09:40
Uh everyone's raising the price.
3:09:43
And now they're creating bundles of streaming properties.
3:09:46
>> going to be $2,000 a month.
3:09:48
>> all going to have Yeah, 2,000 bucks a month. They all have ads.
3:09:49
Constantly >> have different logins. >> All different logins.
3:09:52
And uh you can barely rebundle them even if you try.
3:09:55
I wish I wish that we could get Jeffrey in here to talk about uh this Warner Bros.
3:10:01
deal, but you're probably too close to the metal on this one too.
3:10:04
>> [laughter] >> To provide to be able to really comment on it.
3:10:07
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3:10:16
>> In more news, apparently Vine is being rebooted under the name diVine with funding from Twitter's former CEO Jack Dorsey. Huh.
3:10:26
>> The app plans to feature more than 10,000 previously archived Vines and does not allow AI-generated content. That's remarkable.
3:10:33
Uh there have been so many Vine revival attempts.
3:10:36
Elon was talking about bringing it back at one point.
3:10:41
Uh I believe the founder of Vine uh was talking about bringing it back and and and and did a number of different projects.
3:10:47
There was a project called V2, right? At some point. Um it'd be fun.
3:10:49
I I was a huge fan of Vine when it came out.
3:10:54
I I I really enjoyed it as a new creative medium.
3:10:56
It was it was very very interesting, very very fun.
3:10:58
Um let me tell you about adquick. com.
3:11:00
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3:11:02
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3:11:11
>> news, uh strategy has gone below one NAV for the first time ever. >> Whoa, that's crazy.
3:11:19
>> uh Saylor's BTC holdings are worth are worth less than their than their total debt.
3:11:26
Uh how is that How is that even possible?
3:11:27
That seems uh very concerning.
3:11:30
Um Anyways, their debt has long maturities, 2027 to 2032.
3:11:34
They're not margin loans, but eventually they'll be forced to sell if they can't make interest payments.
3:11:41
They have a something like a $700 uh worth of interest payments due next year, and they have I think under 50 million of cash on hand at the moment, so they'll either need to raise more or start selling.
3:11:55
>> And and just like immense pressure from the other products in the market.
3:11:58
I feel like uh if you want access to Bitcoin, which has been Michael Saylor's flagship asset, uh you were able to uh initially mine it for free, which was weird.
3:12:10
Uh then then buy it on Coinbase with uh a somewhat clunky process. Now it's pretty simple.
3:12:16
Now you can buy Bitcoin on on a credit card.
3:12:17
You can get it in almost every app.
3:12:19
Uh you can uh you can get it in an ETF.
3:12:22
You can get it in your in your retirement fund.
3:12:24
There are a whole bunch of different ways to get exposure.
3:12:27
Um so uh that particular strategy is not the only one in town. Um anything else, Jordy?
3:12:34
Or should we wind down for the day and say goodbye to everyone?
3:12:38
Say leave us five stars on Apple Podcasts and Spotify.
3:12:41
>> go DM the White House now on X and just request the 100-year mortgage.
3:12:45
We need some type of bullish announcement uh otherwise tomorrow will likely be even worse. Yes.
3:12:54
But >> do DM, X has changed the way DMs work.
3:12:58
Now there's unified DMs and encrypted chats all in one place.
3:13:01
That is the last piece of news of the day.
3:13:05
Well, thank you for tuning in.
3:13:05
Leave us five stars on Apple Podcasts and Spotify, and we will see you tomorrow. Can't wait. >> 9:00 a. m. Pacific. >> Sure. Goodbye. Cheers.