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6:44
Today is Monday, August 4th, 2025.
6:44
We are live from the TVPN Ultra Dome.
6:49
>> That's right, >> the temple of technology, the fortress of finance, the capital of capital.
6:52
We were recently featured in the New York Times podcast, the their headlines podcast, and they quoted that segment of the stream, and we snuck in an air horn.
7:04
So, we're bringing the air horn to Legacy Media.
7:08
>> Uh, it was it was very satisfying.
7:08
What a what a week last week. >> Absolutely insane.
7:13
>> We did the OddLots podcast. That's out now.
7:15
You can go listen to that.
7:15
Uh, Bloomberg OddLots with Tracy Aloway and Joe Weisenthal.
7:19
had a great chat with them about the AI talent wars and uh who who Mark Zuckerberg's white whale is in the uh Moby Dick analogy.
7:28
We kind of settled on Ilia Sutzk, but we're going to read the book.
7:32
We're going to have Joe Weisenthal on the show later and get to the bottom of the full metaphor of if we are living in Moby Dick world, who's who? Who's Ahab? Who's the whale?
7:41
who's there's a whole bunch of funny side characters that I was trying to map out to different AI people and uh I only got like halfway there but I want to do that anyway.
7:52
Uh big news from Microsoft. This was interesting.
7:56
So uh this is from the Wall Street Journal.
7:59
Microsoft is an AI darling but its core businesses are booming too.
8:03
Uh did a bunch of reading into this.
8:06
Also let me tell you about ramp time is money save both easy to use corporate cards bill payments accounting and a whole lot more all in one place. I love this one. >> You know what, John?
8:17
>> Switch your business to ramp. com. Love that.
8:19
Anyway, >> quick note, >> please. >> Microsoft. >> Yes. >> 4T. >> 4 trillion. They did it. Hit that gong, Jordy. >> Hit that gong. Jordy Hayes.
8:30
[Music] >> We found a video of somebody playing a gong like much much more professionally than we do where they like have different tools and they can bring out different songs.
8:40
They can really make it sing.
8:41
And so, you know, late night has the live band.
8:43
I think a live gong player might be in our future.
8:46
I think that >> if we ever if if it's ever discovered that TBPN goes bankrupt because of our incredibly high cost structure, >> it will be because we have the world's foremost gong athlete, gong player. Yes. >> Gong artist. >> Yes.
9:02
Truly, the greatest gong player of all time will be on staff.
9:06
>> But you know why the Microsoft News or hitting that number is meaningful? Why?
9:10
>> It's because everybody has always said >> Satcha is more of a more of a two to three guy.
9:19
>> Saying yeah, he's really good in that range.
9:20
I don't know if he's in the four trillion in the four club. >> Got it. Got it.
9:25
>> There's not a lot of guys in that club. There's one guy. >> Yeah, Jensen Wong. He's a founder. >> Founder mode. >> This is Yeah.
9:30
So, is Satcha in founder mode or is founder mode defeated?
9:34
Because if the manager mode can take the company to four trillion, you know, all bets are off.
9:38
put your company in uh manager mode.
9:40
As long as you can recruit Sacha Nadella to be your CEO, I think it's okay to step back.
9:45
>> Manager run companies can excel. >> Exactly. Yeah.
9:47
So, if you're like, I'm a little burned out as a founder.
9:51
>> I want to take a break. I'm going to step back.
9:52
I'm going to get a manager in run the normally very bearish, just give Sachi Nadella a call and you'll be okay.
9:58
You'll be okay in that case.
9:58
Anyway, this stream is made possible by Reream one live stream 30 plus destinations, multiream, and reach your audience wherever they are. Very exciting company.
10:07
We're happy to partner with them.
10:08
Anyway, so from the Wall Street Journal, uh, Microsoft's blockbuster earnings last week, they blew out earnings. It was very exciting.
10:13
They cemented its status as one of the biggest winners in the artificial intelligence boom. We knew this.
10:18
Satcha had carved out um just a massive amount of territory.
10:22
GitHub co-pilot, first real major breakthrough product that was monetizing on top of GT GPT 3. 5.
10:27
Great product, obviously led by Nat Freiedman when he was there.
10:33
Um, >> last we heard it was at >> around half a billion in ARR about two months ago.
10:38
So, it's probably in the billions now.
10:40
>> Yeah, probably bigger.
10:40
Uh, and then the crazy OpenAI deal, they got in very early.
10:44
They they have this massive revenue share. They have an ownership.
10:47
They get a copy of any software that OpenAI writes basically or acquires. That's kind of crazy.
10:52
And so, >> Satia did seemingly one of the best deals of all time for Microsoft >> potentially. Yeah, I think so.
10:59
It's hard to I mean it's an awkward situation now and it's and it's a really hard decision.
11:06
>> Sam is you know renegotiating the deal. >> Yep.
11:09
>> And Ben Thompson was noodling on this like should they take the money now or should they play it more like a venture style bet?
11:14
What is the role of the CEO of a $4 trillion public company where the shareholders have different expectations?
11:21
They're not seeing if you hold Microsoft stock you're not feeling like you're an LP in a venture fund. >> Yeah.
11:28
So, if if the CEO comes to you and says, "Hey, look, we're taking the cash flow now.
11:32
We're going to dividend some of this out, we're selling down the position, we're we're we're thinking strategically about this as opposed to just we want the highest multiple.
11:40
We want to ring the gong on the deal."
11:41
Um, that could be reasonable.
11:43
So, obviously, we're we're we're tracking where that goes.
11:47
But, um, they also have Clippy generational precursor to potentially all the AI agents, the original AI agent, Clippy potentially making >> the original super intelligence.
11:58
original super intelligence and >> that was the first time they had to move the goalpost right >> and this is why we're worried about getting paperclipipped because clippy will become too strong I mean with the power of open AI and Microsoft Azure anything's possible there uh so anyway outside the AI race Microsoft is minting
12:13
money from corporate customers spending on regular technology long a sweet spot for the company many companies are shifting from buying their own IT equipment to renting it from Microsoft through its cloud's computing service they are also renting more standard issue computing stuff, hard drives for data storage, for example, to support their AI efforts. And so, this is the
12:30
And so, this is the key uh stat from the earnings call that the that the Wall Street Journal is highlighting, and then we'll kind of dig into this number and what it means because there's a lot of different explanations for what could what could be going on, but Microsoft and the CFO and CEO didn't necessarily give all the context that we'd like to set this definitively, but I think there's some really good theories floating out there. Yep.
12:51
Uh the quote from the Wall Street Journal is a large chunk of the recent strong growth in Microsoft's cloud business called Azure stems from that.
13:00
More than half of Azure's 33% revenue jump in the company's March quarter came from nonAI services.
13:06
While the company didn't give a comparable breakdown, a comparable breakdown of the cloud unit's 39% growth in its June quarter.
13:13
It said that the core infrastructure business, Microsoft's lingo for its nonAI cloud business was the driver.
13:21
Jordy, >> uh, massive win for enterprise SAS.
13:24
Just good oldfashioned SAS there.
13:28
We had some we had some theories. We had some not SAS.
13:32
So, it's people migrating from on onrem to >> it's what's called infrastructure as a service.
13:38
So there's software as a service.
13:39
That's when you go and get teams or you go and get a subscription to Excel in the cloud or outlook in the cloud.
13:47
That's >> I was saying I was saying more more traditionally someone else >> building. Yes. Yes.
13:51
Using using it as as infrastructure as a service.
13:54
Then there's also platform as a service.
13:57
That's like Heroku on AWS where you go and you deploy an app.
14:00
You could think of maybe even like a replet as like a platform as a service almost where they're hosting you but they're not just providing you the raw infrastructure.
14:10
>> Azure's Azure's uh core infrastructure business is essentially infrastructure as a service is the term and that means oh you want some CPUs and some hard drives and some Ethernet cables and moving stuff around data transfer.
14:23
an interesting thesis offline earlier.
14:25
You comped to people in the internet era >> buying a computer.
14:30
They hear about the internet.
14:32
They're like, "Hey, I think this might be a thing.
14:33
I should get a computer."
14:34
And they just buy a computer.
14:36
And now you could see something, you know, where companies say, "Hey, this AI thing might be big.
14:41
We should get on the cloud." >> Yeah. Yeah.
14:45
You want to set yourself up for it.
14:47
for it. And I think if you have a whole bunch of data in some sort of on-prem, you know, you have a data center for all of your data in a bunch of hard drives and you have CPUs that can do the do different workloads and data workloads
14:59
and maybe you're using some SAS on top of that, but you realize that you're never going to be in a position to buy a 100,000 H100s and you're going to wind up being a leaser of that for some small fine >> other people's application layer products. Yeah. And so the integration Yeah.
15:17
And so the integration that comes from being in the Azure ecosystem that could be a driver. There's a few others.
15:22
Um, when I think about the the core the the AI Azure services, I think of that almost as, you know, it is SAS, like if they're if they're if you go to Azure and you say, I'd like to, you know, put my credit card down and I want to be able to use the GPT4 API and I also want to be able to use Llama 3 and I also want to be be able to use DeepSeek and I want to be able to call all these APIs within my within my product.
15:47
Um, I almost think of that as tokens as a service.
15:50
Like it is SAS, but it's something else.
15:53
And I think these token factories, I think this idea of how much revenue are you generating from your token generation business is really what we're talking about when we when we talk about Azure's, you know, core AI products um versus the infrastructure.
16:07
But there's a bunch of interesting wrinkles that could be going on within the classic, you know, core infrastructure business.
16:16
So this of course is uh virtual machines.
16:21
You just want a Linux box with a CPU to host a website.
16:24
That's something that you do on Azure storage networking.
16:28
Okay, I want to store all my data.
16:28
You could go to AWS and store it in S3.
16:30
You could go to um Google stored in BigQuery.
16:35
You could also go to Azure and fire up any sort of storage database or just you know raw uh raw hard drives.
16:40
Uh and then networking moving stuff around.
16:44
So this is the infrastructure as a service versus tokens as a service.
16:47
their higher higher level AI APIs.
16:47
Um but so GPUs are f so the question is like why is their infrastructure as a service growing faster than their tokens as a service product?
16:58
You would think that Microsoft is going to their enterprise clients and saying like you need to build AI.
17:04
You need to bring AI into your products and we have all the best APIs so just buy tokens from us.
17:12
And you would think that that would be the boom.
17:12
Well, couldn't the other factor here be that they are massively supply constrained on the GPU side and they have this, you know, multi- Yes.
17:22
>> tens of billions or hundreds of billions of dollars of backlog that they can't fulfill.
17:26
>> Meanwhile, they had a you, you know, more kind of like predictability on the the traditional data center cloud side that they were able to scale up to. Yep.
17:35
>> So, that feels like a potentially like a pretty big driver here. >> Yes, definitely. >> But still shocking.
17:40
And so and so as companies come out and they say, "Okay, we are we are scaling our our you know hardware and software foot our technology footprint broadly um going into this AI era.
17:53
We're excited about this stuff."
17:55
Well, we're also going to need more databases.
17:56
We're going to need to put more data in those databases.
17:59
We're going to need more CPU workloads.
18:01
We're going to need more of everything.
18:02
And Microsoft's like, "Yeah, of course we can definitely get you a whole bunch more hard drives and a whole bunch more CPUs.
18:07
We're not constrained on that at all."
18:09
And the capex is keeping up.
18:09
So they're able to service that.
18:11
Um there's also an interesting thing where tons of AI stuff can technically be happening inside the core infrastructure bucket.
18:22
You just don't necessarily know what what's in there.
18:24
So, um, some examples are like if you're, let's say you're a pure AI company or you're doing or you have a new AI workload, you could go to Azure and say, "Hey, I'm going to do a whole b I'm going to do my own AI thing, but I need a ton of storage for data because I'm going to be training on it.
18:41
I'm going to need a bunch of networking to move that data around when I do a training run."
18:44
So, that could be driving core infra up.
18:46
And then also um if a bank hypothetically spins up a huge cluster of H100 GPU virtual machines to fine-tune an open source model like MetaLama 3, this would show up as core infrastructure, not Azure AI services.
19:02
And so that's like textbook AI boom, but it's just happening in the in the wrong bucket.
19:07
And then I also saw a post that potentially uh chat GPT counts as Azure core infrastructure because they're not they're not serving chat GPT through the Azure AI API.
19:19
It's not this like snake eating its tail or a Boros.
19:23
It's like OpenAI just came a whole bunch of >> the headline itself ends up becoming pretty misleading. Exactly.
19:29
pretty misleading. Exactly. does and again this is this was probably you know a lot of people were reading into uh AWS's uh growth rate you know the reports that you know Andy or the comments Andy had given on uh AWS last week and the big thing that AWS is missing is having a
19:50
chat GPT building on top of >> AWS right there's no dominant consumer product uh at least at that scale uh chat GPT I think as of this morning, somebody was estimating getting to a billion uh weekly activives this year, which um again those types of products don't uh you know the the the power law is like extreme, right? So So >> yeah.
20:15
>> yeah. So if you I I mean I I believe Anthropic is pretty tightly hitched to Amazon and I think the next uh big cluster from Anthropic will be powered by Amazon for the most part and so they're getting there certainly on the if they're building all the all the infrastructure
20:30
>> but again that would show up on on >> token generation side like more of the the the >> no no so if anthropic goes to AWS and says we want you to build a huge data center to serve cloud code for us that's going on that's going on in infrastructure, not not actually APIs. But when you go to AWS and you're just
20:48
But when you go to AWS and you're just some random company and you say, uh, I need a database and I need some storage and I need, uh, a web server and I also need a bunch of tokens from whatever model you can serve me.
20:59
And they're like, we got Claude.
21:00
And then you're like, yeah, let's pull the claw tokens into my app.
21:03
>> It's a good model, sir.
21:04
>> That's token as a service. What?
21:05
>> It's a good model, sir.
21:06
>> It's a good model, sir. Exactly.
21:06
Um the one thing that stood out to me uh uh that has stood out to me across this year with Microsoft is they've done >> u more layoffs this year than >> the past three years before that combined. So 2022, 2023 and 2024. That's crazy.
21:23
And >> so this just shows the level that SATA is operating at is like the the company has been on a tear this year, you know, performing exceptionally well and he's still thinking about how do we get more and more fit?
21:34
Um and uh >> so >> so to be clear literally every piece of Microsoft business is growing and at a very a very solid clip.
21:43
So Microsoft 365 commercial cloud business which houses uh remotely accessed versions of Word, Excel, other productivity software that grew at 16% from a year earlier.
21:53
So that's I mean that's like it's not the most insane growth rate but that's still crazy because you think about like who doesn't have Excel that needs it?
22:02
like who are these people who are like, you know what, 2025 is the year that my company's getting on Excel, we're doing it.
22:11
>> Well, it's just crazy when you compare it to uh AWS growing at 19%.
22:12
Obviously, very different scales, but totally >> you would think uh you don't want to be in the same ballpark as uh >> and so that was the news of AWS if you missed it.
22:24
uh they beat earnings, they did very well, but they weren't growing as fast as the others uh the other cloud platforms, Google and and uh Microsoft.
22:34
And so this the Amazon stock traded down.
22:36
down. Um, and I mean the the narrative around AWS is different because Microsoft has open AAI, Microsoft research and has GitHub copilot and is like really moving things forward in the AI world and Satcha is seen as someone
22:51
who goes on the Doorcash podcast and talks about AI and it's clearly like really on top of it where and and obviously Google has Gemini and a million different products and and uh and strategies around rolling that out and staying on the frontier. I mean they
23:04
I mean they have a frontier lab internally and Amazon's just not there either on the partnership side or on the core um like training frontier lab side and so it's a little bit >> it's a little bit of both.
23:17
Uh anyway, let me tell you about Figma.
23:18
Think bigger, build faster.
23:20
Figma helps design and development teams build great products together.
23:23
We had a great week in New York celebrating Figma and the IPO.
23:28
Uh stocks been up, stocks been down.
23:30
uh wild ride, but we are still very happy to be partnered with Dylan Field and the Figma team.
23:34
And I'm super excited to see what happens next because >> truly it was Thursday was such such an incredible day just getting the uh if you didn't get a chance to listen, we talked with every the seed lead investor all the way through of course Andrew Reid who led the C. >> Yep.
23:52
Um, and then capped it off with Dylan and also got to speak with Lynn Martin, president of the New York Stock Exchange, as well as Chris, the uh CTO of Figma.
24:01
So, uh, really incredible day and, uh, it's just, um, very proud of the Figma team. >> Yeah, it was awesome.
24:08
Um, in one sense, investors might prefer to see AI businesses driving growth.
24:13
That after all is what has driven the company's valuation through the roof.
24:16
But tech companies stocks arguably hinge on too too much on AI to the extent that they can keep increasing other revenue streams.
24:23
They are on more solid financial ground.
24:25
Of course, none of that means anything if all of the growth is coming from OpenAI.
24:28
But at the same time, does anyone really think Chat GBT is uh is Yahoo anymore?
24:32
Like like you know they're they're generating what a billion dollars a month in revenue at this point. Everyone uses the app.
24:40
It's installed everywhere.
24:43
Like that that that token demand is not going anywhere.
24:47
that infrastructure demand is not going anywhere.
24:48
So >> on the other side, Amazon is down uh >> just over 9% since Thursday earnings.
24:54
Um and then this morning uh unrelated, they announced that they're shutting down Wondery, the podcast studio they acquired in late 2020.
25:06
And Amazon why did uh the market's way up. Wow. NASDAQ's up 1.
25:11
8% today after a brief selloff on Friday. >> Good news. bare market is over.
25:18
>> Yeah, we were so over, but we're already so back. It's fantastic.
25:21
>> Love when that happens.
25:22
>> Uh markets go up, markets do go down.
25:24
But the march of technological progress, the arrow points in but one direction. >> That's right.
25:30
>> And it's March is relentless.
25:30
Uh there is another silver lining for Microsoft.
25:35
NonAI sales can be substantially more lucrative than AI ones.
25:37
NonAI gross margins within Azure were around 73%. Wow.
25:43
Wow. that compares to that that compares to 30 to 40% gross margin for AI he estimated because of the huge cost of setting up AI infrastructure that makes sense you get more margin on just a bunch of CPUs and databases that you've
25:55
harnessed and built everything around also you know you still have that interesting dynamic where it seems like all of the cloud the hyperscalers like don't really compete on price because they're all pretty comparable but they
26:06
seem to all have really dynamic >> that's what it seems like >> uh I'm not exactly sure if there's something else that's more fundamental going on um >> and the Coca-Cola dynamic is like I don't know if we uh it's hard to tell
26:19
what conversations were off air or on air but but John last week uh forget when was describing how uh you would think that Coca-Cola or Pepsi would decide to get aggressive on price to try to gain market share >> and get people to switch. But ultimately
26:33
But ultimately that would just lead to a price war with both companies you know massively eroding their their uh you know uh >> margins >> margins and then uh >> all the RC cola coded then >> um every yeah every >> so if you have a fierce competitor consider entering uh >> an unspoken gentleman's maybe a gentleman's agreement.
26:58
It's not it's not even a gentleman's agreement.
27:00
even a gentleman's agreement. Not it is unspoken but it is a it is a nature it is a natural game theoretic Nash equilibrium like it is the natural state of things that both sides understand that to go to war would be mutually assured destruction and so they don't
27:17
even need to talk about it and so instead they both agree to keep prices where they are and instead compete on market compete on market really >> yeah I mean they don't form they don't reformulate that often they mostly compete on on on marketing Um, and that allows them to have this like continually compounding business. And
27:33
And that's why it's in the the Warren Buffett portfolio. Coca-Cola.
27:36
He's been in there for a long time.
27:39
And Pepsi's been doing work. >> And he was a DAU.
27:40
Of course, >> he was a DAU of Coke.
27:44
>> Still >> Diet Coke or Coca-Cola. I think Coca-Cola. >> No, Diet Coke.
27:48
>> Oh, he's a Diet Coke guy.
27:48
I feel like he was a Coca-Cola guy. >> Warren. >> Yeah. Look that up. I want to know.
27:54
Anyway, I'll keep reading from this Wall Street Journal report.
27:56
Luckily for uh Microsoft, demand for lucrative nonAI services appears to be reasonably strong.
28:02
Measures of broad IT spending. There we go. >> You know why?
28:06
>> Because of regular Coca-Cola. >> You know why?
28:08
It's because it has corn syrup in it.
28:09
It's literally corn grown from mother nature from the earth.
28:14
>> And then syrup, it's what you put on pancakes.
28:16
Like it's the most wholesome combination of of foods you could imagine. Maze.
28:23
This is something that's been grown in America for generations, for for centuries.
28:27
Corn is so popular in Nebraska, right? It's grown everywhere. It's cultural.
28:32
And the syrup that you put on pancakes, it it's the most American most wholesome ingredient.
28:37
It's not this like refined sugar, this crazy stuff from somewhere else. No, it's American. >> American corn syrup. >> There's nothing. It's Lindy corn syrup. >> Corn syrup. >> Yeah. >> Seed oil. Seed oil.
28:48
Seed oil haters are are in disbelief.
28:51
shambles that that we need to return to corn syrup.
28:53
None of this uh none of whatever's in this Coke Zero. >> Yeah.
29:00
Your grandpa was was drinking uh corn syrup.
29:04
>> Oh, and it's too good for you because you read a couple posts on axe. com.
29:09
Think you understand something better than corn? Delicious corn. Corn on the cob.
29:15
>> Something you have on a at a barbecue.
29:17
Oh, now it's too good for you.
29:17
Can't possibly have corn. What's next? No apple pie. What's next? No.
29:21
No rotisserie chicken on.
29:24
Somebody's gonna listen to the show for the first time today and and just go raging DMs for you promoting corn syrup consumption.
29:35
>> It's it's as American as apple pie >> and Warren Buffett knows best. He's doing great.
29:41
>> And of one study sort of a Brian Johnson. >> Yeah.
29:44
>> He's sort of the Brian Johnson.
29:45
>> He's the original Don't die. >> Yeah.
29:46
And he's been doing fantastically on that front. >> Yep. He's great.
29:49
Anyway, uh measures of broad IT spending were fairly muted at the start of the year as companies pondered the impact of Donald Trump's tariffs and concerns bubbled about the health of the global economy.
30:01
Attitudes appear to have improved somewhat in the second quarter.
30:06
Though a UBS survey of cloud computing customers in July showed a clear improvement in tone about spending.
30:12
Most were moving forward with e efforts to migrate computing work to the cloud.
30:17
They're like, "This internet thing is real. >> It's real.
30:21
>> We got to put the data in the cloud.
30:24
>> We held back as long as we could.
30:25
>> We could, but it's 2025. We have no more excuses.
30:29
The tariffs, it's come and gone. Now's the time.
30:33
>> We were resisting the 21st century, but we're a quarter of the way through. >> Put the data online. >> It's not going away.
30:39
>> So, let's let's use the computer in the online in the cloud.
30:42
>> Put the docs in the cloud." But put it in the cloud.
30:44
>> Just put the docs in the cloud.
30:47
Put the fries in the bag anyway.
30:47
And put your compliance process on Vanta.
30:52
Automate compliance, manage risk, improve trust.
30:53
Vanta's trust management platform takes the manual work out of your security and compliance process and replaces it with continuous automation.
30:58
Whether you're pursuing your first framework or managing a complex program, >> uh, in the longer term, there is little question that cloud computing is going to grow in ways that play to Microsoft strengths.
31:08
Its rivals, mainly Amazon.
31:08
com and Google, are growing quickly, too, but don't have all of Microsoft's broad corporate software offerings that enhance its cloud footprint, even outside of AI.
31:18
Amazon on Thursday said its cloud unit grew at 17 and a half% in the June quarter, disappointing investors and forcing CEO Andy Jasse to answer questions uh to answer uh questions about Azure's outperformance.
31:31
Why are you getting beaten?
31:31
You created this category.
31:33
You created this product.
31:35
Why is Satcha Nadella you are the cloud?
31:39
>> You are the the cowboy of the cloud and you're getting you're getting put out to pasture.
31:45
>> Recent quarterly earnings in Azure's favor were really just moments in time.
31:49
He said >> that chart, John, >> the uh Oh, yeah. Wow.
31:51
That That's worse than I thought.
31:54
I wasn't sure where we had that pulled.
31:56
I mean, >> it would have been hard to predict five years ago that we'd be sitting here with with Microsoft at 4 trillion and and uh >> what was Amazon at? I >> think 2. 2 trillion >> 2. 2 >> 2. 27. >> 2. 27. Okay. So, almost double.
32:17
>> Still magnificent, but >> still magnificent, but you got to keep fighting. Got to keep fighting.
32:20
Uh the company's stock fell eight% Friday and looks like it still sliding down even more.
32:27
>> The question for Microsoft's investors then is less about the its prospects than its valuation.
32:31
The company's stock is up nearly 40% since the beginning of April, pushing its forward price earnings multiple above 33.
32:37
That's a bit richer than Amazon and a large margin above Google, which is trading at a multiple of roughly 18 times.
32:43
That should be easier for in investors to digest because while Microsoft's AI growth is real, it is far from the only thing going right at the software giant because they got Excel, they got core infrastructure, they got AI APIs, they got tokens as a service, infrastructure as a service and software as a service, they got the royal flush.
33:04
It's going well over at Microsoft.
33:07
They won't need to call McKenzie, but maybe maybe Amazon. com will.
33:13
And that brings us to our next story, which is >> before we got to jump in quickly.
33:19
>> Graphite code review for the age of AI.
33:21
Graphite helps teams on GitHub shift higher quality software faster.
33:22
I'd love to do an ad re uh we got to hang with Mel.
33:27
>> Yes, we got to hang with him last Thursday, which was fun, which is great.
33:30
>> He came uh with a tux. >> She looks fantastic. >> Incredibly sharp. >> Yes, it was great.
33:36
>> Great sign of respect in our community.
33:38
Uh well, American Eagle Outfitters, Inc.
33:40
is up 23% today after >> uh Trump came out uh with a lot of uh a lot of excitement for the advertisement uh this morning.
33:52
I'll let people go read it, but he did describe it as the hottest ad out there.
33:56
Um and it certainly struck a chord last week.
33:58
Um but anyways, let's jump into this next story.
34:04
Really quickly before we go that the battle for the Mag7 is uh is heating up.
34:10
Microsoft is now in the four trillion club.
34:12
Let's go to the poly market for the largest company at the end of 2025.
34:17
Nvidia has a solid handle on it.
34:17
They as of December 31st they are expected to be the largest company in the world at 63%.
34:28
Microsoft is at 28% but climbing up from 22% earlier. Apple has been falling.
34:35
They were the largest company and now they are falling down to just 6% chance.
34:40
And of course Tesla's hanging out there at 3%.
34:41
Anything could happen with Tesla. One crazy move.
34:43
One naturally aspirated V12 Cyber Truck and boom.
34:49
>> It's all it would take.
34:50
>> It's a $5 trillion stock.
34:52
>> What about a V12 uh Optimus? >> Like a gas powered. I have a pitch for you. uh reshard mill of cars.
35:04
>> A reshard mill for the for the streets.
35:06
>> Rishard mill for the streets. >> A racing machine.
35:08
>> Basically, the pitch is that now it's routine.
35:12
I would say most people if they if they put their minds to it, they'll be making $100 million a year.
35:18
Seems like that's kind of the benchmark for like you did okay in life.
35:20
hund00 million dollars a year, even even a million-doll Ferrari is not really going to stand out.
35:28
That's three days of income, right?
35:31
So, we need we need a car brand to come out where the floor price is like $20 million. >> Yeah.
35:38
Bugatti doesn't quite get you.
35:41
>> And the thing with the Bugatti is that it's like this hyperar 2,000 horsepower. It's so extreme.
35:45
The whole point of the Rishard Mill is that you can wear it to the gym, but it's $250,000.
35:48
So, I'm looking for something that's like >> daily $20 million. Yeah.
35:53
$20 million sedan, $20 million minivan, $20 million, just crossover, just midsize SUV. $30 million.
36:04
>> This is what we're doing here.
36:04
This is what we're going to do.
36:06
Because a lot of these a lot of these AI researchers, they don't they don't want to pull up in a Bugatti. >> Yeah.
36:12
But they have the money to spend >> something a bit more >> understated that still says to the right audience, I spent tens of millions of dollars on my daily. >> Exactly. Exactly.
36:22
That's definitely what they're going for over at MSL.
36:24
If you pull up to Meta Super Intelligence Lab, like you don't want to flex, but you want people to know.
36:31
You know, >> we need we need satellite imagery of the of the MSL parking lot. >> Yeah.
36:38
>> That would be funny if it just looks like a car meet. >> Yeah.
36:42
just >> everybody has coner Ferrari >> F40 >> somehow I think it won't be that >> it won't be that at all but do you think that there is actually an opportunity to start a new car company that that it takes the Richard Mill approach and is 10 times what the luxury cars are.
36:59
So like maybe it's not 20 million but like you know a G Wagon is is a functional car in the $200,000 range. something at 200.
37:12
>> I think what the the thing with RM is they made something that was light and incredibly durable and that was effectively >> don't think you can make a light and durable car for $2 million.
37:22
Jordan, what are you talking about? >> World's lightest.
37:26
>> What are you talking about?
37:27
>> I mean that Yeah, that that'd be that'd be interesting.
37:28
It just drives like in a go-kart.
37:30
It's just totally insane.
37:30
Um but it feels like necessarily need to optimize for performance.
37:36
>> It it was in aesthetically in an entirely new category.
37:38
It was it had the perform it it new levels of performance. >> Yeah.
37:44
>> Within the category broadly from a from a durability standpoint.
37:46
Um so I think um I think with the RM it does stand out.
37:52
It looks completely different but it's still in this weird watch world where to just a completely average person.
37:58
It's not as bold as a Cyber Truck.
38:00
It doesn't it's a it's still an if you know you know type accessory, right?
38:05
And so that's where you need to target.
38:08
It needs to look kind of like is that a BMW or an Audi or a Model Y?
38:10
It's like it blends in a little bit.
38:17
>> I think I think one one reason not not to crush your dreams, one reason potentially many that this this is not uh as exciting as it sounds.
38:24
I mean I I I want to drive it to be clear, but I think that the the automotive regulations around you need to have a seat belt like this.
38:32
You need the seats need to work like this.
38:34
It needs to be able to handle this type of >> uh >> maybe maybe there's some sort of like you know how Steve Jobs famously bought a new 911 every single year >> or was every 90 days I thought >> every Yeah, I think you're right.
38:49
Every 3 months Steve Jobs would buy a new Porsche 911 so that he didn't ever need to have a license plate on it just for anonymity I suppose. >> Yeah.
39:01
So he would never be identifiable, >> which is potentially an incredible excuse for being just like, I like to drive brand new cars.
39:09
>> What What is What is breaking in three months on a 911?
39:13
>> The new car smell goes away by that point.
39:15
>> Maybe he just really love new car smell.
39:18
He's like, I could never do a new car smell air freshener.
39:19
Uh that's that simply won't have to buy new.
39:22
And then later in life, I believe he switched to a Mercedes and I think he got the SL63 or something equivalent to that.
39:29
So, like the the 911 equivalent of in Mercedes world.
39:35
>> Um, but yeah, maybe that's the hack.
39:35
So, so you you find something that would never be approved, but you do some crazy thing where you bring it in on show and display and you can only drive it a few thousand miles a year, but then you're swapping them out and so you're kind of doing like a subscription and so your real cost to own is in like the 5 million range.
39:56
Um but the beauty is that you're getting something that's just a complete regulatory end run around the rules so that you wind up with this very unique very different uh you know experience but only only made possible at the extra high end.
40:12
>> I think at that point helicopter >> maybe maybe helicopter is the move.
40:17
>> Uh let's get into the next story.
40:17
Uh the journal says AI is coming for the consultants inside McKenzie.
40:21
They're saying this >> McKenzie >> uh Mckenz McKenzie.
40:29
>> Somebody's got a case of the Mondays. >> Mackenzie Mackenzie.
40:34
>> They say this is existential.
40:36
I was talking I was talking with a buddy. Yeah.
40:38
Uh I was talking with a buddy who uh >> we were talking yesterday or not yesterday um >> uh last week >> and he said word for word adopting too much AI will hurt our bottom line. >> That's right.
40:54
>> And that totally makes sense, but it's not something that lawyers are >> actively saying out loud because you don't want to be the law firm that's >> anti- tech. Yep.
41:06
If you go out as a big if you're, you know, one of the one of the top hundred law firms and you're like, "Yeah, we we're excited about the potential of AI, but we won't be adop adopting it internally because uh it uh you know, naturally it's going to hurt our profits, which uh you can just translate to we're just not going to be able to bill our clients for the same number of hours."
41:27
And so that statement made me bullish on why am I blanking on the the name of this company?
41:32
We had this company on not Harvey the other one that is uh doing just like contract like NDAs and and >> a few other types of contracts but they're an actual law firm. >> Oh, interesting. Yeah.
41:45
Um >> kind of the Atrium Clear Spire model >> similar similar to that. >> Fascinating model.
41:50
I don't know if you remember this, but Justin Khan when he started Atrium had a very complex corporate structure where >> the company was half a law firm, an LLC, which is the traditional corporate structure for a law firm and employed lawyers and then there was a tech startup CC Corp that was bound together with a master service agreement or something like that.
42:14
So these two companies were combined to be a tech- enabled law firm and it was it was probably a good idea just a little bit too early.
42:22
But interestingly there was another attempt to do the exact same thing 10 years earlier called Clear Spire and that didn't go well either and it was started uh started by uh some all-star lawyers and some technologists and they came together and they were like let's build this AI uh enabled law firm.
42:38
Maybe now is the right time to build the AI native law AI native law firm, but I don't know that they need to be uh they need to be doing technology development.
42:49
I think that the I think that the the next world might be you have a power law winner in legal AI tooling.
42:57
Maybe it's Harvey, maybe it's someone else or a combination of firms.
43:01
And then you have a very very small ter small team of high EQ deals guys, lawyers who are phenomenal at the human element, thinking creatively, coming up with crazy structures, understanding.
43:17
Are you familiar with the battle of the forms?
43:19
This idea in in law firm in in like in the legal world >> familiar, but I can guess what what it what it looks like.
43:24
Basically, if you talk to most lawyers, they will tell you that you can express almost the exact same legally binding theories or like like rules in a contract or bullet points in a contract.
43:38
Everything you want in a contract can be expressed both in one page or 100 pages.
43:45
>> Y >> and so which one you choose to send to your counterparty is like an aesthetic choice.
43:50
It's like it's like for certain for certain deals, you want to send the 100page contract because you want to send the send the message that like we're taking this really seriously.
44:01
We're thinking of everything.
44:01
But for other people, you're more like we want to show that we're moving quickly. We want to use the safe.
44:06
We want to use and and structurally it can be the exact same deal.
44:10
And so a good lawyer will know when to when to send a lot, when to send a little, when to slow a deal down, when to speed things up.
44:16
And so you get a bunch of folks who are great at that type of legal thinking, the creative thinking, the human element, the the relationship building, the golden retriever mode.
44:27
Uh and then you and then you empower them with the best-in-class legal research and analysis tools to actually instantiate and do all the grunt work.
44:38
grunt work. And then separately you have a you have a company like Harvey that's building the the the the tooling that then sells to the team of you know humans and and that law firm will have a very different cost structure very different dynamic might even look like
44:54
an older school law firm where it's just a few partners and >> one thing I know >> lawyers are not going down without a fight they're like we're making 90% less per case we're going to initiate 10 times as many lawsuits That might be the equilibrium here. For For sure. For sure.
45:12
>> Um, well, if you're planning to manage your lawsuit or build a legal AI company, get on Linear.
45:17
Linear is a purpose-built tool for planning and building products.
45:20
Meet the system for modern software development.
45:22
Streamline issues, projects, and product road maps.
45:25
>> Good transition, not great.
45:25
I we're not recommending you use linear to man manage your lawsuit.
45:31
But if you are building product, yeah, John John's very in favor of using linear for everything, including workouts.
45:38
>> Haven't you seen those people that that that do art in Excel?
45:42
>> Then they zoom the grid way out.
45:42
They color each each cell is a pixel.
45:46
>> Linear really is pixel art.
45:48
>> I think it I think it should be uh anyway uh let's go back to Mackenzie.
45:51
Uh companies paid dearly for McKenzie's human expertise and for nearly a century they have had good reason. The elite firm.
45:59
The elite firm's armies of consultants have helped generations of CEOs navigate the thorniest of challenges, synthesizing complex information and mapping out what to do next.
46:10
Now, McKenzie is trying to steer through its own existential transformation.
46:14
Artificial intelligence can increasingly do the work done by the firm's highly paid consultants, often within minutes.
46:20
That reality is pushing the firm to rewire its business.
46:22
AI is now a topic of conversation at every meeting of McKenzie's boards.
46:26
Uh their board of directors, uh the firm's global managing partner has said this.
46:31
Um the Bob Sternfells, I wonder the nominative determinism there.
46:38
Do you think he's a very stern individual or do you think he's really friendly and fluffy? >> Get him on the show.
46:42
Just chop it up with him.
46:44
Just throw him tons and tons.
46:45
>> I don't need to have him on the show to know. >> No, he's stern.
46:48
>> He's just like, "You guys should be doing so much better.
46:50
You should be more serious."
46:51
Uh she immediately stern stern mogs us.
46:54
Uh the technology is changing the ways McKenzie works with clients, how it hires, and even what projects to take on.
47:01
And McKenzie is rapidly devel deploying thousands of AI agents.
47:03
Those bots now assist consultants in building PowerPoint decks, taking notes, and summing up interviews and research documents for clients.
47:11
What do you think about building PowerPoint decks using purely AI?
47:17
>> We were talking about this this morning.
47:19
I think that AI can do a tremendous job making you a generic deck template or just market analysis and turning that into page by page kind of analysis that you could generate slides against.
47:35
>> But I think uh just just grounding it in something more specific.
47:38
something more specific. I think if you asked if you were just trying to build a preede or seed or series A or series B really any type of fundraising materials that are or even like fund uh like a deck for a venture fund raise it's it's going to produce something that will will not have you looked at very seriously will put it will put together something that maybe like the most basic McKenzie associate would put together in terms of like
48:10
>> these are all the different types of slides you can have and you know here's data and and here's research that that support these things but um the best decks uh take you on a journey >> and uh I my framework for making decks
48:29
is you basically want in a fundraising context you want every single slide to have like a very short sentence that takes like walk that's walking somebody down this path that you're taking them on where they just agree agree agree agree agree. So they get to
48:45
So they get to the end >> and um if at any point in that deck they've read a headline and they're like I don't agree with that or that's dumb you're screwed. >> Yep.
48:56
>> But usually if you've you know thought through your confused or I'm bored confused. Yeah.
49:00
If you're bored or confused um doesn't work.
49:03
>> That's Ctier but B tier is decks uh decks just get looked at.
49:06
Um, it's not about like the supporting content.
49:09
It's about like the high level like narrative arc through the deck. Yeah.
49:14
>> And getting somebody to click through enough times to through enough pages to actually care about what you're doing.
49:20
And I think that um maybe there's some like really incredible prompting that you could do to try to come up with something like that, but I haven't been able to. >> Yeah.
49:28
I just think the models aren't there yet in terms of like putting disperate information together, coming up with novel insights, contrarian thinking, independent thinking, all of that stuff is where the models fall down.
49:38
But that's fine because they're fantastic at a bunch of other stuff.
49:40
And anything that they can RL on is fantastic.
49:44
And so I would think about it like, you know, we already did the the the Figma ad read, but we're going to wind up doing another one.
49:49
But, uh, I mean, they have a tool for building decks, and obviously you've built Dex and Figma for years.
49:55
And when I think about as that tool becomes more AI native, I don't necessarily want the it it to try and oneshot a prompt from build me a deck that convinces you that TBPN is a growing media company or something like that.
50:11
I want I want a prompt to be like go through and make sure all the text is centered and then it just clicks through every deck and does the grunt work of centering every piece of text.
50:22
Or there's so many times >> you put all the content in and you say generate other variations of this slide. >> Yeah. Yeah.
50:27
Or make sure all the black font is actually the correct color of black because I've updated that like the style guide and and then then pull in this data and then keep the data up to date.
50:37
Like all the grunt work that happens that should be the that that should be the domain of AI.
50:40
And for a long time that's been the domain of the low-level management consultant.
50:43
the person who is at a wedding and has to leave because their boss told them, "Hey, you gota update slide seven." Yeah.
50:52
Hey, yeah, there's a missed comma in this deck and I'm about to go present and it needs to be flawless.
50:56
Um, and so, um, >> uh, McKenzie is is continuing saying they're they've reduced the headcount from 45,000 people in 2023 to 40,000 through layoffs and attrition.
51:09
Uh, in part to correct from an aggressive pandemic hiring spree.
51:13
Uh it's also roughly rolled out roughly 12,000 AI agents which is a very like that's not apples to apples at all.
51:20
Like you cannot just you cannot >> it's also funny to be thinking about like the whole point of agents or that like anybody in a team can like spin them up and get value and shut them down >> and they're not necessarily perpetually running.
51:35
So just to to come out and be like >> yeah we have 12,000 agents on our team right now.
51:41
>> It's like how many >> we rolled out 12,000.
51:41
And it's like, okay, >> how many Google Chrome tabs do you have open?
51:45
Tell me that, too, because that's useful.
51:47
Like yesterday, I I used I used agent mode to do some like really deep research.
51:51
And then I wound up and then it like was kind of working.
51:53
It had this cool interaction where it asked me a question and I just wasn't online for that.
51:59
And it just it just typed for me continue and then it just kept going and so it wound up putting together a really really thorough result, but it took a long time.
52:05
But in the meantime, >> it was agent.
52:06
Are you sure it wasn't a ghost in the machine?
52:08
guest in the machine, but it was weird that it was like typing for me, but it was actually a great u UX because I I would have forgotten about it.
52:14
Uh but then I just went to 40 like in the in the interim and got like basically the same answer.
52:20
Uh because like I didn't ask that complex of a question and so I kind of like overestimate.
52:23
I was like I need the I need the nuclear bomb for this one.
52:27
This is the most brilliant question you could possibly ask and it was like something that like probably googleable.
52:32
Um >> I think the thing with management consulting yes is that it will the question >> this question has come up forever is is the is the value of hiring McKenzie actually the the uh the strategy that you get or the advice that you get or is it outsourcing >> like critical decision- making or backing up critical decision- making so that the management can say >> I don't want to do layoffs But Mackenzie told me I had to. >> Yeah. >> Yep.
53:03
It's a lot of cover and that >> or I didn't want to go into the cloud, but Mackenzie >> Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah.
53:09
It's a lot of defensibility.
53:09
It's a lot of like internal office politics it feels like.
53:12
But I do think that there is I I think that that narrative is a little bit overblown.
53:17
And I do think some of the top Mckenzie folks are actually great at business strategy.
53:20
Uh it's just that um when you think of McKenzie, you think of kind of the the midtier junior associate who's still just kind of learning.
53:29
And of course, you're not going to get that much out of that person.
53:32
So my question for this, so the the key quote in here is that uh Katie uh SMA SMA uh a senior partner that Sternfelds tapped to lead McKenzie's AI efforts earlier this year said, "Do I think that this is existential for our profession? Yes, I do.
53:52
I think it's an existential good for us."
53:55
But of course that read as uh they think it's an existential existential threat potentially if they don't get it right.
54:02
Um and so consulting is emerging as an early and high-profile test case for how dramatically an industry might must shift to stay relevant in the AI era.
54:10
Mackenzie like its rivals grew by hiring professionals from top universities throwing them at projects for clients and then billing companies based in part on the scope and duration of the products.
54:18
So my my analysis of this was that okay so clearly McKenzie is going to change.
54:24
The question is like in the medium-term like pre- super intelligence like how does it change?
54:29
And what's interesting is that like when you look at the hordes of like 80hour weeks from you know junior analysts on a McKenzie team like the typical like Harvard, Yale, Stanford, Ivy League grad doesn't exactly know what to do maybe goes into you know the big three Bane BCG McKenzie as just kind of a junior consultant and then after like two years it's like go to go get your MBA and then maybe come back or do something else.
54:50
maybe come back or do something else. um those 80hour weeks like what they are doing during that time there's a lot of hurry up and wait but there is a lot of actually hard work pulling data doing deep research doing stuff that can be done by AI but it also serves a as a as basically like an extended interview it
55:09
serves as a benchmark for is this person a grinder are they a creative thinker like they have to do the baseline they have to do they have to be very detail-oriented but then also it's a question of while you're there can you actually develop a relationship with a Fortune 500 CEO and that happens like I remember this crazy crazy story of uh a woman who was at this is a crazy story. So she was I think I've told you this
55:32
So she was I think I've told you this before.
55:34
So she was at I think McKenzie and she went to do some consulting in a Middle Eastern country maybe like what was it?
55:44
It was something in I don't want to be too offensive, but it's one of like the Boro countries.
55:49
I think it was like Kazakhstan.
55:50
Is that the one where he's from?
55:52
>> That's Bor that's where Bor >> That's where Borat from.
55:53
So So >> character the characters from Kazakhstan.
55:56
So she goes to Kazakhstan. >> Dr.
55:58
Bar Cohen, that's where he went. >> Yes. Yes. Yes.
56:00
So she goes to Kazakhstan and and Kazakhstan has a number of state-owned assets and one of the things they own, I believe, was a cigarette making facility.
56:11
So they built a factory for making for >> a heater factory national tobacco.
56:19
>> We need to secure heater production is critical to national security. >> Yeah.
56:23
No no I mean I mean basically like like when you are a big tobacco company in America everything is privatized except for like the roads here basically.
56:31
We're like the most privatized country.
56:32
But when you go to when you go to a foreign country and you're like let's set up you know we'd love to set up business in your country.
56:41
you have a bunch of hard workers who can work on this manufacturing project.
56:42
The government just says like okay we will be the customer like we will be the service provider.
56:48
We will be the counterparty to your to your request for certain services.
56:53
We will provide those and we will own this and that will be tax revenue for our government.
56:56
And so that's what had happened years ago.
56:58
Uh this country had built up this cigarette manufacturing business.
57:03
And then once you build up the business, the privatizing that can also be very lucrative because then you get all the cash up front.
57:11
And so if you want to if you want to raise a bunch of money for your government and then maybe build some roads, one thing that you can do is you can sell the assets that you've developed.
57:18
And so uh they brought in McKenzie to discuss how could they package this cigarette manufacturing building or factory or series of factories into an asset that could be sold to either private equity firm or to big tobacco.
57:32
I see a large IPO on the horizon >> basically.
57:36
I mean, this is what happened with like Saudi Ramco was going to go public.
57:38
>> Where do you where do you think uh where do you think Kazakhstan's uh nationalized cigarette uh infrastructure what do you think it would trade at if it went out on >> probably a pretty low multiple honestly >> good that very real meme stock potential there. >> Yeah. Yeah.
57:54
Can you imagine that be crazy?
57:54
Um anyway, so they bring in McKenzie and this woman's on the team and she does the whole analysis and says that like okay if you package it up this way you could get this multiple you could sell this and then that would bring in this amount of cash that you could go and use and you could reinvest in roads infrastructure.
58:10
You could build an airport data centers.
58:11
Yeah, you could build anything right. Uh it's just model. >> This is pre all that.
58:15
This is maybe like 2005 or 2010. Um so she does the deal.
58:23
It's massively successful.
58:23
comes back uh applies to Harvard Business School and she needs a letter of recommendation.
58:29
So she goes to like the prime minister of Kazakhstan and is like, "Hey, I helped you with that deal.
58:36
I helped you privatize your cigarette factory.
58:37
Uh can you write me a letter of recommendation?"
58:41
And I had the chance to read this letter of recommendation.
58:43
And it was one of the funniest things I've ever read because it presupposes >> endorsement.
58:50
>> It was a glowing endorsement.
58:50
It was like, "Wow, she was incredible.
58:52
She helped us with this deal.
58:55
that she get that she's getting in.
58:57
>> Uh, no, it doesn't assume that she's getting in.
58:58
Um, but it is worded in the craziest way possible.
59:01
It basically presupposes that it was unthinkable that a woman could do this and no way.
59:08
>> Oh, huge cultural bias because, you know, it's like developing nation and so this country, this prime minister is like, I was very surprised when she joined the team because I'd never worked with a woman before. That's >> 2005. >> Yeah. No.
59:22
And I didn't think that she would >> bore out the whole the >> I don't want to say it was Kazakhstan because it could have been any other country in that area.
59:29
But you can just think of it as generic developing >> Cohen was making jokes like that. >> Borax. Exactly.
59:35
And and so and so I saw I saw a real instantiation of like the joke that he was telling basically.
59:40
And so, uh, the the the the letter of recommendation, Harvard Business School, it starts with like with like, uh, it was mind-blowing that like a woman showed up to this meeting.
59:52
I didn't think that was possible and and I didn't think that she would be like actually contributing, but then she was really helpful and this like surprised me.
59:59
And so, like, you should definitely let her into Harvard Business School.
1:00:03
And then it ends on something that sounds like a vague threat.
1:00:06
He's like, "Great great great great uh great sorrow will befall Cambridge if you don't let her in."
1:00:11
And it's like, and it was clearly meant to just be like like you're you'd be you'd be making a mistake. Missed opportunity.
1:00:18
>> You'd be a missed opportunity, but it was worded in this very aggressive way that sounded like a threat. It was wild.
1:00:24
>> I mean, >> I guess uh anybody that's trying to get into HBS, get a a prime minister of a foreign government to write you to make a threat to the college.
1:00:33
And the funny thing is that is that you can do that.
1:00:38
>> So you could read you could read into it as like okay this this country is developing like they clearly have different cultural norms.
1:00:43
They're not the most like uh forward thinking in terms of women in the workplace.
1:00:47
Um, but instead of like instead of getting like dragged down in that, HBS clearly saw that like she basically went to a developing nation and like rised them like insanely and got a phenomenal deal done and made a ton of money for her firm and also for the country and it was just this like win-win take private.
1:01:07
Uh, and she got in and had a very successful career.
1:01:11
Uh, and uh, yeah, this is a funny story of of what can go right with Mackenzie.
1:01:15
But it brings me back to the question of, you know, there's a world where she would just be the like the PowerPoint person, and that's clearly not what she did with that opportunity.
1:01:26
She went there and actually interacted with the prime minister and the top top economic adviserss to this developing country and wound up driving a lot of value.
1:01:34
And I'm sure there were other folks on the team on the client side, but she developed such a relationship that she was able to get this like silly but glowing recommendation letter.
1:01:41
And so and so >> recommendation threat.
1:01:46
>> Recommendation threat. Yeah. Yeah. That's the real alpha.
1:01:48
If you if you're Don't write me a recommendation letter.
1:01:52
Threaten the person that I'm applying the job for.
1:01:54
>> I would write a recommendation threat >> for anyone on the team. Anyone on the team. Yeah. For sure. Uh >> great.
1:02:01
Your next training one run will fail unless you hire >> If you do not hire Tyler Cosg Grove, you will effectively curse your bloodline.
1:02:13
>> I would I would happily uh put pen to paper to say that.
1:02:15
Uh anyway, so uh the 80 hours a week, it's not just AI agent work.
1:02:22
Like you could not have an AI agent do what she that example like do what she did fly to a developing nation and actually sit in the room with someone who who was just on the cusp of learning what a take private was and the value that could be created and convince them that that this was a good idea.
1:02:39
And so uh I think that these these 80hour weeks they're gauntlets and they're used to identify top performers that can still lead the firm.
1:02:49
And the question is if you if you don't have people doing the hard work and you're not in and and like a lot of these things we we were talking to Danny Rhyr at Index.
1:02:57
We were like is venture is your model a a mentorship business?
1:03:03
And so even if you can like outsource to AI agents these low-level tasks, you still need to like build the next generation of talent in your firm and figure out who the top performers are.
1:03:17
Now, maybe you can collapse that to an eval.
1:03:19
Like the reason that she got hired at McKenzie, I'm sure, was because she went to Harvard undergrad or something and had like perfect SAT scores and like a bunch and like all of that might be able to be distilled such that you know if you just ask Goldman or McKenzie to just say take the top 10% of your class, don't hire the bottom 90% and then immediately put them on the executive track and they'll be your managing directors.
1:03:40
Like maybe that's maybe it's all predictable, but you do have a lot of churn where some of your top performers are going to leave and start companies.
1:03:47
Some of them are going to go to big tech.
1:03:48
Some of them are going to just burn out and want to be like teachers or something random.
1:03:54
They might want to change change trajectories entirely.
1:03:56
And so even your top performers aren't necessarily going to metriculate into the the the partnership leadership like leading the firm is a separate thing from just interesting.
1:04:05
It'll be interesting to see like if there's new archetypes of people that thrive in management consulting, right?
1:04:13
Because like the the right person that thrives in the first whatever it is, five years of just insane grunt work and just like toiling over documents and stuff like that.
1:04:22
um the person that thrives in that oftentimes clearly can like rise above it and and excel in >> client side firmwide kind of uh you know running the firm itself etc.
1:04:33
running the firm itself etc. But there's also probably other people that would never make it through the first five years but would just absolutely crush you know you know on on on uh uh for for the sort of the majority of the career >> and I think that does happen once in a while there are people that lateral from
1:04:51
entrepreneurship or tech into a big three consulting firm at the top so they're never the lowgrade analysts grinding they might have grinded somewhere else though like pretty much everyone grinds at some And I think that's kind of the the the part of the takeaway of founder mode is that the founder has has been grinding in the exact context. And so you talk about
1:05:12
And so you talk about like Dylan Field working on WebGL in the trenches of Figma and then you know that enables him to speak with a different level of of authority when he's managing a massive company that's now public.
1:05:24
And so there's there's there's a world where like the the grind is relevant to what you wind up doing.
1:05:34
But the big question is just like if like there is a risk like how do you how do you identify these killers early on in the interview process?
1:05:41
What does it mean for retention?
1:05:42
mean for retention? Uh, and then the other question is like today if you if you leave McKenzie to build a new consulting firm, you might be able to take some of your top clients with you like if they put you on the Coca-Cola account and you wound up being really
1:05:59
really impressive as an analyst and you spent a lot of time with the CFO and the CMO or whatever or COO or whatever you were working on and you built that relationship and and they see you as a critical consultant even though the managing director or the head was the one who closed the deal. They might be
1:06:15
They might be willing to take a shot on you if you leave and start your own firm, >> but then you still have to spin up all your own analysts to actually do all the work that you were doing before with your team.
1:06:25
And so that takes time to hire and train.
1:06:27
But in the future, if you leave, if you're if you're if you're really high performing and you leave and you can just be like, "Let me get a an account on ChatGBT Pro or whatever."
1:06:35
And then I effectively have 40 consultants on my team on day one. 12,000 >> 12,000. Exactly.
1:06:43
I would I would imagine that leaving to start your own firm and going like sort of sort of so low and just leveraging just the relationships, just the ability to just your innate ability uh could be like extremely high leverage much earlier in your career. Yeah.
1:07:00
And so because you don't have to do the the uh the thing where like you leave and you say I'm taking everybody with me, you know, you can just leave and be like, I'm taking the AI agents with me because they live in Azure and I'm just spinning up new ones. >> Yeah. Yeah.
1:07:14
The the real question will be uh is there IP in like real IP to develop around the agents or can you use chat GPT out of the box to say pretend like you're a McKenzie analyst and make me this you know 20page report.
1:07:27
I need sections on this this and this. Yep.
1:07:32
>> Use the entire internet as a data source as well as these materials from the client. Yep.
1:07:36
and uh give me this report and then uh yeah >> well here's an idea for a consulting firm uh leave McKenzie go to companies tell them to implement Finn.
1:07:47
ai AI, the number one AI agent for customer service, number one in performance benchmarks, number one in competitive bakeoffs, number one ranking on G2.
1:07:55
That's basically your hero slide.
1:07:55
You just show them the website and you say, "Pay me a million dollars and I'll help you imple implement it." >> Just kidding.
1:08:02
Of course, you can implement this yourself.
1:08:03
You could start a free trial. >> Mackenzie is private. >> Yes. >> But Accenture is not.
1:08:07
And they have traded down 35% in the past 6 months. Uh bookings are slowing.
1:08:14
They had a big government.
1:08:17
They had uh >> uh they were impacted by Doge moderately.
1:08:24
>> Um but McKenzie does have the benefit of being private and being able to you know make uh hopefully some some longer term decisions.
1:08:31
The scale of Accenture is insane.
1:08:33
How guess how many employees they have? >> 40,000. >> Close. >> Okay. How many do they have? >> 791,000. >> What?
1:08:43
I mean 100,000 they're they're 20 times bigger than McKenzie. Is that a real number?
1:08:52
>> There's more people that work at Accenture than the population of San Francisco. >> Oh my god.
1:08:59
>> Where do they hold their Christmas party? >> I don't Yeah.
1:09:05
>> I don't even know where a million people congregate. Like is that like Mecca?
1:09:09
They have to go to Mecca. Have you seen those?
1:09:11
a million people praying in one area.
1:09:14
I think that's where they have to go.
1:09:15
I've heard >> Coachella Coachella has a daily capacity of 125,000 people.
1:09:20
>> They can't even do Coachella. What? What? Where?
1:09:22
Where can a million people I mean I guess it was the million band march.
1:09:26
So they could they could march on Washington DC.
1:09:28
That's what they could do.
1:09:29
They could take over all of DC.
1:09:29
just this is their new they they just do rotations and they basically do like a daily walking ultramarathon where they just walk through and they just completely shut down towns and they all wear the company merch.
1:09:42
>> They probably never been in the same place.
1:09:44
They've probably never had a Christmas party with everyone.
1:09:47
>> They should use they should use reream for their all hands.
1:09:49
They need some real infrastructure there.
1:09:52
>> Um >> anyway, >> that is absolutely wild.
1:09:54
How many >> if you're selling something I have some consulting advice for you. This one's free. Get on numeralhq. com. Sales tax on autopilot.
1:10:01
Spend less than 5 minutes per month on sales tax compliance. Go to numeralhq. com.
1:10:07
>> The team just erupted in applause. >> Nal, we love it.
1:10:10
>> Uh, what else we got, John? >> We got Disney.
1:10:11
Uh, my question is, is AI a sustaining innovation for Disney or is it a disruptive innovation?
1:10:19
Where will Disney be in 10 years?
1:10:21
In the medium term, obviously, you're going to be able to generate a lot of AI slop.
1:10:24
You might be able to infringe on their a on their IP.
1:10:30
They might get paid by Google VO when you generate a Mickey Mouse AI slop edit.
1:10:37
They might get a little a couple pennies, but will it be good or bad for them?
1:10:41
>> Children's, you know, I could see them making a product that allows you to make a, you know, book or a story for your kid that actually uh, you know, and they get they get some type of revenue.
1:10:51
they should get revenue and I think that they will through the courts especially because as like big companies like you're not it's not this crazy oh there's like a bunch of kids doing random things like they never had to go after the the street artist on Venice Beach that would draw a picture of Mickey Mouse and sell it to you for 20 bucks.
1:11:13
They that was never material to their business.
1:11:16
They had to go after Napster.
1:11:17
They had to go after to Venice Beach.
1:11:19
I got them to draw me as Mickey Mouse. Really?
1:11:22
And then I performed a citizen's arrest because I respect IP. I'm an IP respectctor. >> Yes. Yes. Yes.
1:11:28
Citizens arrest is underrated. >> Citizen's arrest. >> Citizens arrest.
1:11:32
You're going to jail, buddy. >> This is for Bob. >> This is for Bob Iger. Yeah. So, big question.
1:11:38
This feels like a moment where you want to be in founder mode.
1:11:40
Bob Iger is one of the greatest CEOs of all time.
1:11:43
How will he navigate this?
1:11:43
The Wall Street Journal says, >> "I still not the founder.
1:11:47
The founder died in 1966.
1:11:49
Walt Disney >> claiming that he died still.
1:11:52
>> He might be able to turn it on.
1:11:52
Satcha Nadella certainly was able to do it.
1:11:55
He's he's navigating the AI uh the AI shift flawlessly.
1:11:59
We'll see what happens with Bob Iger and Disney.
1:12:02
Uh Wall Street Journal says, "Is it still Disney magic if it's AI?"
1:12:05
The stakes are especially high for the studio caught between how to use artificial intelligence in the film making process and how to protect its famed characters against it.
1:12:12
So, there's a little anecdote that we'll kick it off with.
1:12:17
When Disney began working on its new live-action version of its hit cartoon Moana, executives started to ponder whether they should clone its star Dwayne Johnson.
1:12:25
The actor was reprising his role in the movie as Maui uh a barrel chest. Have you seen Moana? No, you have not. Of course, we know this.
1:12:36
For certain days on set, Disney had a plan in place that wouldn't require Johnson to be there at all under the plan they devised.
1:12:43
Johnson's similarly buff cousin uh Tanoa Reed, who is 6'3" 250 pounds, would fill in his body double for a small number of shots.
1:12:52
Disney would work with AI company Metaphysic to create deep fakes of Johnson's face that would be layered on top of Reed's performance in the footage, a digital twin, essentially digital double that effectively allowed Johnson to be in two places at once.
1:13:05
Obviously, that's better.
1:13:07
>> Calls up his cousin, you want a job? I need you to be me.
1:13:11
>> Yeah, hit the gym, buddy.
1:13:11
Yeah, >> better be better be jacked. >> Get on a cycle.
1:13:15
>> Um, but yeah, I mean these these uh these movie schedules are famously tight three months in and out crazy schedules and then you move on to the next one.
1:13:25
There's that famous Henry Caval story where he filmed Superman, wrapped, moved on to another movie where he had to grow out a mustache.
1:13:31
He grew out a mustache and a beard and then they said, "Hey, we got to do some re-shoots.
1:13:35
You got to come back to Superman."
1:13:36
And he came back, but he couldn't shave his mustache and beard.
1:13:39
So they had to change it in in CGI and it looked terrible.
1:13:41
Probably not a problem now with with deep fakes.
1:13:45
That's actually a good use of AI and uh something that probably shouldn't be very controversial, but obviously everything in AI is controversial right now.
1:13:52
Um but we will continue with Disney.
1:13:54
They say what happened next was evidence that Hollywood's must discuss much discussed much feared AI revolution won't be an overnight robot takeover.
1:14:03
Johnson approved the plan, but the use of a new technology had Disney attorneys hammering out details over how it could be deployed, what security precautions would protect the data, and a host of other concerns.
1:14:12
They worried that the studio ultimately couldn't claim ownership over every uh that the studio couldn't ultimately claim ownership over every element in the film if AI generated parts were in there.
1:14:22
So, if there's AI training data from a DreamWorks film in there and they use the DreamWorks training data to make a Disney film, even if it looks like Dwayne the Rock Johnson, DreamWorks might come knocking and say, "Hey, give us a royalty."
1:14:37
I think that's the risk, but the lawyers are having fun.
1:14:39
Maybe full employment for lawyers over at Disney.
1:14:42
Clearly, uh Disney and Metaphysics spent 18 months negotiating on and off over the terms of the contract to work on the digital double, but none of the footage will be in the final film when it's released next summer. They went and shot it.
1:14:52
A deep fake Dwayne Johnson is just one part of a broader technological earthquake hitting Hollywood.
1:14:58
Studios are scrambling to figure out how simultaneously they can use AI in the film making process and how to protect themselves against it.
1:15:04
Is it sustaining or disruptive or both?
1:15:06
Can't be both, but we'll see.
1:15:08
Uh while executives see a future where the technology shaves tens of millions of dollars off a movie's budget, they are grappling with a present uh with a present present filled with legal uncertainty, fan backlash, and a weariness toward embracing tools that some in Silicon Valley view as their next century replacement.
1:15:26
And if you are trying to sell an AI tool into Hollywood, you got to get on Adio.
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1:15:38
So, the Academy of Motion Picture Arts and Sciences is surveying members on how they use the technology.
1:15:44
Studio chiefs are shutting down efforts to experiment for fear of angering show business unions on the eve of another contract negotiation.
1:15:52
And no studio stands to gain or lose more in the outcome than Disney, the home of Donald Duck, Bell, Buzz Lightyear, Stitch, and countless others, which has turnurned out some of the most valuable and protected creative works in over the past century.
1:16:04
So my take on this um two so two years ago I was hanging out with the founder of a very large generative AI image generation company um and he was telling me that by 2025 anyone with a laptop and an internet connection could generate a full Hollywood movie about anything they want with a single prompt.
1:16:26
And it was a hilarious conversation because uh we were on a Zoom call and his internet wasn't working.
1:16:31
And it was the classic example of like the technology is amazing but we got a lot of stuff to iron out.
1:16:36
Um so anyway extremely aggressive timeline but obviously things are going to change for Hollywood.
1:16:41
So my question is will Disney benefit?
1:16:43
It feels like a moment to be in founder mode but Walt Disney died in 1966.
1:16:46
So basically everyone believes that Meta will benefit from AI even if they miss the train on owning the next dominant consumer tech platform.
1:16:54
But if Disney got really AI pilled what would that look like?
1:16:58
I don't think they need to train their own foundation model just like they don't need to train their own.
1:17:03
They don't need to build their own cinema cameras.
1:17:04
They can just use IMAX when they the time calls for IMAX.
1:17:07
They can use Blender when the time calls for Blender.
1:17:11
They can use Houdini when the time calls for when the when when the the shot calls for some uh highle VFX.
1:17:18
Uh but they do need to rethink how they structure their business and negotiate with unions and underwrite content.
1:17:21
they might need to go more risk on.
1:17:23
Not just from a brand risk position, but taking more smaller bets.
1:17:27
We're in this weird barbell world where everything seems like it's either a hundred bucks and it's shot on an iPhone.
1:17:32
It's a viral like Tik Tok or it's a hundred million blockbuster with like $50 million of VFX.
1:17:39
Actually, that's kind of a low number.
1:17:40
It's usually like $300 million production with $150 million VFX and then no one sees it and it's a flop.
1:17:45
But then they they hit every once in a while and they're great when they're good.
1:17:48
But it's this weird like venture style betting at the high end, but there's nothing in the middle.
1:17:53
And maybe if that's like the death of the artthouse film, but I'm just wondering if in the Age of AI like maybe there's this interim step where Disney ladders down a little bit and gives like 10 filmmakers $10 million each and says, "Hey, you're still required to deliver >> a 90minute full film, but you're doing it for 10 mil and it's not quite Blair Witch level production, one notch up. You got to be creative. this really bullish.
1:18:20
Yeah, I'm really bullish on this idea that uh the you know historically like a TV show would film a pilot episode and they would use that to get uh the budget to shoot an entire season and you can imagine now uh you could you know even for film you can just make you know make the trailer ahead of time with AI.
1:18:41
The other advantage that I think Disney has that's very real just going in and and why they're just broadly seem to be positioned very well here is that I think that I think that broadly like content customization will probably take off because you could Disney can make a film now and then you could make millions of different variations of it that become interactive with like the underlying fan.
1:19:06
So like imagine >> you're watching Moana but like your kid is in the film is a character in the film and you can now do that at scale right and how much more would you pay as a parent to have something like that?
1:19:20
>> I have a funny story about this >> but last thing I'll say so um >> customization and just like democratizing like like basically making making being able to make variations of films y >> I think is going to be big.
1:19:32
I think uh just the the time it's the same way you it's it's so difficult to make a new luxury brand. >> Yep.
1:19:39
>> Uh it's so difficult to create it's easy to create IP.
1:19:42
It's hard extremely difficult and time inensive to create valuable IP.
1:19:47
And Disney's advantage is they have this sort of like 360 and that they can make a film and they can bring it to Disneyland.
1:19:54
They can bring it to a cruise.
1:19:56
They can create physical products around it.
1:19:58
products around it. And so they develop IP in a way that that new entrance are not you know they don't have the benefit of like having a Disneyland where they can make new experiences that that increase the value of that IP right so >> I think um >> so two things one if I were to go back
1:20:17
one of my favorite Disney properties is Star Wars a new hope the very first film if I went back and was like let's customize that for me I don't know that I would make any changes like do I really want a scene where Han Solo breaks the fourth wall and says like, "Hey, John, like I'm about to go, you know, save Luke at the Death Star." Like, that doesn't improve the product
1:20:36
Like, that doesn't improve the product for me.
1:20:38
I actually like that it's just the vision of George Lucas. So, I don't know.
1:20:44
>> But here's customization being better for me.
1:20:46
I don't know what I would change, >> but here's here's an example of how to make like a magical experience for a kid. Yeah.
1:20:52
>> Like imagine after a movie ends >> a kid could interact with a character and ask it questions in real time like about the story or have a conversation with them.
1:21:05
And that's what I'm talking about like bringing that IP to life. >> Totally.
1:21:07
Right now that exists and I was obsessed with this when I was a kid.
1:21:09
I would I would uh I would watch Star Wars and then I would read the the the books that in the expanded universe and I remember even having books that were just like encyclopedias of every single ship.
1:21:21
This uh you could read into this a little bit more, but uh we'll leave that where it is.
1:21:25
Um but I but I would learn every single every single ship.
1:21:28
This is what a star destroyer does and it would have all this backlog and stuff.
1:21:31
And so yes, I agree with you. That's very cool.
1:21:35
you finish the movie and then you can just interview Luke about how this how does the lightsaber actually work and he can talk about the kyber crystals. That would be very cool.
1:21:42
On the flip side, uh I had a very funny experience.
1:21:44
Uh I was watching a horror film in high school with a couple friends and I grew up in Pasadena and this horror film just happened to take place in Pasadena.
1:21:54
Like that's where they set the film because Pasadena, California is just like a place where you set films. It's just a real place.
1:22:03
So, if you've seen Kill Bill, which I know you haven't, uh, but Kill Bill, uh, by Quinton Tarantino, uh, there's a scene where, uh, where Uma Thurman just shows up and it says at the bottom, Pasadena, California, because like that's where the character went.
1:22:16
It exists in the real world.
1:22:18
But this horror film that took place in Pasadena was terrifying because I was watching it and I was like, this is happening here and it's night and it's dark outside and like I now am so much more immersed.
1:22:29
And so I was thinking back then that you could use like the IP address of the of a of a connected I think we were watching it on like a PS3 like a DVD player.
1:22:40
Like you could you could use the DVD player to dynamically change the location of this of the establishing shot.
1:22:47
So you're like this whole horror film is going to take take place inside of like one house where there's like a monster in the house and it's going to be like the usual like they're upstairs, they're downstairs, there's blood, there's you know someone's running and chasing.
1:22:58
Like it could be any town, USA, but they usually they usually establishes like this is happening in Amityville, this is happening in, you know, some random town uh Lake Placid, right?
1:23:08
But they could easily just dynamically change that with a few establishing shots to just show you, okay, it's happening in Malibu, right?
1:23:16
And now it's a lot scarier for you.
1:23:16
So, I think there's something interesting there, but again, it has to be the work of like an aur. It's Yeah.
1:23:22
It's not something you can copy and paste either because it works for some shows but then others like the place is so obviously >> the place that it would throw you off as a viewer. >> Exactly.
1:23:33
And so I do think that the that the fully AI generated fully custom content that will exist but it will exist on uh independent third party platforms.
1:23:41
It won't be the domain of Disney.
1:23:42
Disney. This will be something where if you in the future if you really want to see like you know AI generated stories about surfing in Malibu like there will be an endless stream of those and you will be able to go and and and
1:23:56
experience that particular content and you can already kind of experience that because there's probably some Instagram person who makes really great content about surfing in Malibu and if you follow them you get that vibe and that might be what you're into. Um, and you
1:24:08
Um, and you can kind of like and and it's handled just by the great democratization of create creativity. Yeah.
1:24:14
Uh, and we should talk to Samir about this later. He's coming on the show.
1:24:17
Uh, so we can talk about the future of of of uh content creation and whatnot.
1:24:21
But, uh, anyway, >> uh, let's talk about public.
1:24:24
com investing for those that take it seriously.
1:24:27
They got multiasset investing, industryleading yields, and they're trusted by millions. Go check it out.
1:24:32
Anyway, let's hit the timeline.
1:24:34
Let's go through the hottest posts on the internet.
1:24:37
the moment we've all been waiting for.
1:24:40
>> Last Friday after we wrapped the show, uh it was a timeline in turmoil over in DC.
1:24:47
Uh I believe the the head of the Bureau of Labor Statistics was fired.
1:24:52
This was after a negative job report.
1:24:52
It was very controversial.
1:24:55
People were not very happy about this because this is the data that we use to judge the health of the economy.
1:25:00
Insane, insane whiplash, too, because big tech, you know, Thursday printing, >> Figma, >> uh, massive uh, you know, biggest gain in a very long time.
1:25:11
Uh and then it turns out the uh uh real economy or at least the statistics coming out of the Bureau of Labor Statistics uh were not looking great and the prior two months have got revised.
1:25:26
>> Revised down >> and so we are still adding jobs but uh mostly in healthcare and >> over at Accenture. >> Yeah.
1:25:34
>> Although I think they're cutting as well. >> Yeah.
1:25:35
And so and so broadly the US economy feels strong but we are having some uh uh unemployment upticks and so that's always a uh you know a canary in the coal mine.
1:25:44
Uh but Ray Dalio chimes in with his >> also um I believe like tourism is is down pretty much across the board. >> Yeah.
1:25:55
And there's a lot of debates people immediately were were digging into what's driving the the job losses.
1:26:02
Is it is it does it have anything to do with Trump's immigration policy?
1:26:04
There were a lot of different a lot of different ideas as to why the why the job numbers are what they are and how how bad are they?
1:26:13
Obviously, you can see in the market the market tanked on Friday but then is up today.
1:26:17
And so there's you know there's not necessarily consensus now that we're like entering a recession or anything like that.
1:26:24
Um but it's certainly something to watch and something to dig into.
1:26:27
And obviously we want highquality data about everything from GDP to CPI to uh unemployment.
1:26:32
And so Ray Dalio is breaking down his argument for firing the head of the Bureau of Labor Statistics.
1:26:40
He says that's because its process for making estimates is obviously obsolete and errorprone and if there's no good plan in the works and there's no good plan in the works for fixing it.
1:26:51
The huge revisions in Friday's unemployment numbers are symptomatic of this, especially because the revisions brought the numbers toward private estimates that were in fact much better.
1:27:00
I assure you that this is something that I know a lot about because of how I use data to follow the economy and bet on where it's going.
1:27:10
Of course, if the way most people in the media are con are conveying President Trump's motivation for the firing is correct, such as the New York Times saying, quote, "When President Trump didn't like the weak jobs numbers that were released on Friday, he fired the person responsible for producing them."
1:27:26
That would be a big problem because leaders manipulating numbers that distort the truth to suit their political objectives is a classic sign of the loss of a functioning system with the rule of law and checks and balance.
1:27:40
And the loss of these things leads to the loss of confidence that underpins our whole economic and political system.
1:27:46
So it'd be good if President Trump made his thinking clear.
1:27:48
In any case, we do need big renovations renovations to the ways the government estimates what's going on in the economy to make them more, not less accurate.
1:27:56
And so this is interesting because Ray Dalio is not a perma bull on America by any stretch of the imagination.
1:28:04
Yeah, >> he literally wrote a book about the changing world order that was basically like America's cooked.
1:28:09
I mean to some extent he was like talking about the rise and fall of nations and is and he's been very bullish on China and and sees China as rising nation.
1:28:18
So he he he is not someone that is just constantly ringing the gong for America's you know unrelenting progress.
1:28:25
Um and so this is an interesting take from him.
1:28:28
Uh, and then and then I I mean I have personal experience with with how the how the government collects data.
1:28:34
I didn't work at the Bureau of Labor Statistics, but I I I worked uh I worked at the census department.
1:28:40
>> From the people that bought you the brought you the Department of Motor Vehicles comes labor statistics. >> Exactly.
1:28:45
And so I always in in in the year 2000, the economy was ripping and I believe the census was done by volunteers because it wasn't uh it was just kind of like oh let's let's get a poll of people.
1:28:58
Then in uh in 2010, the economy was in a recession after the great global financial crisis and it was seen as a stimulus program.
1:29:04
So people were making like 20 to $25 an hour, which is very good back then to go around and and survey people.
1:29:10
But the survey data is a little messy and I was always wondering >> this story on the show.
1:29:17
>> I don't think we have time today, but we we we should at some point.
1:29:19
Um, but but basically my my my takeaway was always was always like, couldn't we just ask Google?
1:29:27
Like I have a feeling that the big tech companies know not just exactly how many Americans are in each each jurisdiction, but they know everything about them, their IP addresses and their geo locations.
1:29:42
Like the big tech companies truly know so much.
1:29:44
And I know you're going to say like, well, not everyone uses Facebook.
1:29:46
And and that's true, but if you aggregated data across like most of the cell networks and most of the web services, like you could get a very accurate reading much more so than just randomly going doortodoor and seeing like, oh, this person opened the door and talked to me, which is like how the census currently works.
1:30:06
the census currently works. Um and so um it is interesting that that the unemployment numbers were the government numbers were less accurate than the private estimates which are just like because obviously every hedge fund wants the data and they want the data earlier than the government puts it out and some traders obviously trade on top of the
1:30:26
BLS statistics and those are important but um the private estimates are better um and so interesting to see where this goes how much modernization can be done how much and then there's always the of like privacy if you're like tracking everyone because it's like there's a lot of people out there who might say I don't want the I don't want the government to know if I have a job. >> Big data palunteer uh announcing
1:30:45
>> Big data palunteer uh announcing earnings uh I guess in I'm assuming in 30 minutes.
1:30:52
>> Another post here from Buco Bucko Capital Bloke. >> Yeah.
1:30:56
>> He says Trump we need to cut BLS.
1:30:56
Turns out the job market is weak. Trump no like that.
1:31:03
Uh, another post, uh, interesting data point here.
1:31:06
Derek Thompson says, "Spending on services, especially travel and tourism, has declined for three straight months for the first time since 2008."
1:31:18
>> Um, and uh, expanding when Trump uh, the the US economy is showing strains of weakness outside of the pandemic.
1:31:24
The employment rate for college graduates is over 25 is rising fast and is higher now than any year since 2014.
1:31:30
Job growth in the past three months is the lowest since 2010.
1:31:34
And spending on services has declined for three straight months for the first time since 2008.
1:31:38
How are companies, individuals, and the government supposed to deal with an economy slipping toward recession if we're if we're smashing the tools?
1:31:45
We need to know how fast we're falling uh in a tempest.
1:31:50
It's unwise to ransack the navigation system.
1:31:53
That is Derek Thompson's take. >> Uh what else we got? Um >> asleep. com. Get a Pod Five.
1:32:01
They have a 5-year warranty, 30 risk-free trial, free returns, free shipping. Continue, Jordy.
1:32:07
>> It was absolutely brutal having to cover the Figma IPO uh without sleeping on an eightstep night and RAMP and RAM series E2. These are big days. >> Yeah.
1:32:17
>> Um green shoots for us.
1:32:21
>> TBPN, of course, if you want to make the switch. Yes.
1:32:23
Uh Benji Taylor says, "People are finally starting to realize how few truly great designers there are and how disproportionately valuable they are."
1:32:31
Uh if you missed our interview with Dylan on Thursday, he said uh that design is becoming an enduring edge for companies.
1:32:40
We've seen this with companies like Linear, which came into a crowded category and is dominated by just building a truly thoughtful product.
1:32:49
Uh, and this is something >> people love to to like reduce design to to nothing. Oh, cursor windsurf.
1:32:55
These are just forks of VS Code.
1:32:57
And it's like, yes, but like if it was truly perfectly competitive, why is there a why is there a split in revenue at all? Why isn't it just 50/50?
1:33:08
Well, like there's probably some design and and and differentiation in the product that you don't realize until you're actually a DAOU of these.
1:33:17
And that is what's driving the adoption. >> Yeah.
1:33:22
But uh yeah, I have worked with uh hundreds of designers across my career and there's only a handful that when people come to me and say, >> you know, I I want to recruit a founding designer or something like that. >> Jacobe. >> Jacobe.
1:33:38
Jacobe in the comment section. Um >> yeah. >> Oh, he's there. >> No, no, no, no.
1:33:43
He's in the comment of Benji's post.
1:33:44
Anyways, uh >> well, if you're looking to design something, design a billboard, put it on adquick. com.
1:33:50
Out of home advertising, made easy and measurable.
1:33:52
Say goodbye to the headaches of out ofome advertising.
1:33:55
Only ad combines technology, out of home expertise and data to enable efficient, seamless ad buying across the globe.
1:34:00
>> We didn't make it to our billboards in New York, but I felt like I'd been there because so many people share that shared them.
1:34:06
>> Still love hanging fruit in billboards.
1:34:08
An anjane says, "Mistral, 11 Labs, Black Forest Labs, Lovable, Helsing, Granola, European AI scene is on fire.
1:34:15
Uh, so I just wanted to give some credit.
1:34:19
>> Top comment, not to mention lovable.
1:34:19
And then an says, "Reread the list."
1:34:22
Like it's literally the fourth one. H that's very funny.
1:34:27
>> Um, anyway, >> and uh, I know all these companies.
1:34:30
>> This is this is this is bait though.
1:34:32
This is bait for us >> because you you you you know I I I refuse to step foot on the old continent.
1:34:37
And so by posting something like this, this is just this is trying to get me out of golden retriever mode, out of being nice.
1:34:43
This is trying to bait a dunk from me because nothing makes me more upset than anything that's not America.
1:34:50
>> Well, one thing I would say I think you can be happy that the cap tables of these companies are majority US venture capital. >> Yes.
1:34:57
I'd like the cap tables to get 100% Americanowned and then I'd like the companies to relocate to America. >> What about Yeah. What about the teams?
1:35:06
>> Everyone and all the customers >> just move everyone to America.
1:35:10
>> Uh 11 Labs unicorns guarantee citizenship from my understanding >> uh is primarily based in New York.
1:35:20
>> The truth zone going on.
1:35:22
>> But uh I think >> an would know better considering Andre is an investor.
1:35:25
Uh, Rune says, "This is funny.
1:35:28
>> It's true what the misinformation is saying about us.
1:35:30
We indeed have >> Lilith >> Lilith, progenionator of human life, locked up under the Mission Bay headquarters.
1:35:40
>> Don't read the comments on this one.
1:35:41
>> We're skipping over the comments." >> Very funny.
1:35:43
But anyway, uh yeah, there was a GPT5 article that went out and uh this is Rune kind of putting it in the truth zone and saying like, "Hey, we're just grinding and we're building and uh you know, uh tell your story a different way basically."
1:35:57
>> Baze Lord says, "I propose we create the Terrence Tao Institute for Math and Science and fund it with tech money."
1:36:02
Uh Terrence Tao uh got his funding at UCLA cut >> not specifically but uh but but funding was cut broadly due to UCLA's reaction to >> Yeah.
1:36:18
He was sort of collateral damage. >> Yeah.
1:36:21
>> In in a in a u in a political fight between UCLA and the current administration.
1:36:25
And so I think someone should step up and keep funding him.
1:36:28
I think there's certainly value in having him do extremely undirected research.
1:36:32
He could land in a lab and do cool stuff.
1:36:39
>> You're saying that he should join Vlad's uh >> that would be very cool.
1:36:42
So Vlad Tennv uh founder of Robin Hood has a new uh foundation model company or AI lab focused on math and uh he's got the money to provide Terrence Tao with uh everything he needs and more.
1:36:55
And I think this would be a great use.
1:36:57
And uh if it doesn't happen soon, we're going to start negging every billionaire into doing it.
1:37:03
We're going to create uh the the shame on you list for billionaires who have not donated to Terren Da.
1:37:07
Um but Noah, I think it'll be taken care of.
1:37:11
>> Says we now have a big story about why this time is different.
1:37:15
Rapidly increasing debt funding one single sector.
1:37:19
an opaque corner of the financial system that has recently grown >> s systemically important lenders and meshed in the new sector.
1:37:26
Um, >> and I just want to say I really >> I feel like calling top signals was like a two weeks ago thing.
1:37:32
I think we're back at this point and I I don't know why he's calling top on everything right now.
1:37:38
>> I just hope that this time the music never stops.
1:37:40
Me too >> because I like playing musical chairs. Yeah.
1:37:45
>> And who doesn't like a little game of musical chairs?
1:37:47
But also, I mean, maybe this time there's enough chairs.
1:37:50
Everyone just takes a seat.
1:37:51
>> No one said if you're playing musical chairs, you you have to stop the music every day.
1:37:56
>> So, yeah, musical chairs, you could still land on a chair.
1:37:58
There's only one person uh gets gets eliminated.
1:38:00
So, just don't don't be that person.
1:38:03
Don't >> don't be that that guy, pal. >> Yeah.
1:38:07
Don't put yourself in the in the FTX bucket.
1:38:10
>> Do we have time to to go through this?
1:38:13
>> We uh we have 10 minutes. Okay.
1:38:13
Okay, but I think more >> Noah Smith on no opinion.
1:38:18
Will data centers crash the economy?
1:38:20
This time, let's think about a financial crisis before it happens.
1:38:23
Uh the US economic data for the last few months is looking decidedly meh.
1:38:27
The latest employment numbers were so bad that Trump actually fired the head of Bureau of Labor Statistics.
1:38:33
>> We don't know that that's why, but it's a reasonable take. >> Uh accusing her.
1:38:36
Yeah, his take would be, you know, she's been messing up the numbers a lot and we need a new approach, you know, kind of. >> Yeah.
1:38:44
but um accusing her of manipulating the numbers to make him look bad.
1:38:47
But there's one huge bright spot amid the gloom.
1:38:49
An incredible AI Dennis building. Boom. >> Let's go. >> Let's go. >> Look at the chart. Pull up the chart. Scroll down.
1:38:57
Capital expenditures quarterly.
1:39:00
We're so close to touching 100 billion across Meta, Google, Microsoft, and Amazon.
1:39:04
You add in you add in the independent capex that's going on and you're easily in 100 billion a quarter. You love to see it.
1:39:13
Um Little disappointing Q1, but Q2 we came back strong.
1:39:18
Yeah, no one's really talking about the the the sell off in Q1.
1:39:22
>> Noah says inference compute now represents most of the cost of running advanced AI models and increases and inference comput responsible for many of the ongoing performance gain.
1:39:28
So compute needs are probably only going to grow as AI keeps getting better.
1:39:32
Whoever provides this compute is going to make a huge amount of revenue.
1:39:36
Whether that means they'll make a lot of profit is another question, but let's table that for right now.
1:39:40
We reported earlier that Microsoft's margin on the uh on the uh like token and inferencing side was something like 30 to 40%.
1:39:51
>> I would just zoom out and look at it as like what's capex we're talking about 100 billion a quarter.
1:39:56
What's token related revenue?
1:39:59
So add open AI they're making three billion a quarter.
1:40:03
Anthropic making a billion a quarter.
1:40:05
You know, obviously there's, you know, core AI stuff happening at all the hyperscalers, but you add all that up, we're making like maybe 10 billion a quarter in revenue right now on AI stuff, and we're investing a hundred billion.
1:40:18
And so like we're not right side up on the trade generally, but there's no sign that that that G Chat GPT will ever make less than a billion dollars a month.
1:40:31
Like there's just no sign that that's not going to that that's going to fall off a cliff. >> Yeah.
1:40:36
I mean the other I mean same dynamic, right?
1:40:38
If you're building if you build a >> if you build a factory, do you expect to to to generate profit that >> month, that quarter, that year, the next year, potentially? No.
1:40:49
I mean this stuff depreciates like quickly because it doesn't stay on the frontier but like the base workload for just generating tokens for GPT40 like reasonable questions and just intelligent >> just basic stuff like that workload could stay on those H100s for a decade and you could make that money back.
1:41:08
Yeah, it is interesting that Apple, we covered after their earnings, Tim Cook called out that they're using third-party uh uh like lenders to finance a lot of their capex and they're choosing not to >> uh uh deploy uh too much capital themselves.
1:41:25
So, Noah says, "Roughly speaking, Apple is choosing the former while the big software companies Google, Meta, Microsoft, and Amazon are choosing the latter.
1:41:31
These spending numbers are pretty incredible.
1:41:33
We covered this chart already.
1:41:35
Uh for Microsoft and Meta, this capital expenditure is now more than a third of their total sales. >> Let's go. >> Let's go.
1:41:44
>> If you're looking to deploy a capital expenditure of your own, get on bezel. Get bezel. com.
1:41:48
Your bezel concierge is available now to source you any watch on the planet. Seriously, any watch.
1:41:52
It goes on the balance sheet. It's capex. It's personal capex. Do it.
1:41:58
>> Uh here's Chris Mims of the Wall Street Journal.
1:42:01
Mag seven tech firms have collectively spent a record 102 102 and a half billion on capex in their most recent quarters near nearly all from Meta, Alphabet, Microsoft and Amazon, Apple, Nvidia and Tesla together contributed a mere 6. 7 billion.
1:42:15
Investor and tech pundit Paul Kadrski says that as a percentage of gross domestic product, spending on AI infrastructure has already exceeded spending on telecom and internet infrastructure from the dotcom boom and it's still growing.
1:42:29
He also argues that one explanation for the US economy's ongoing strength despite tariffs is that spending on IT infrastructure is so big that it's acting as a sort of private sector stimulus program.
1:42:40
Capex spending for AI contributed more to growth in the US economy in the past two quarters than all of consumer spending, says Neil Duda, head of economic research at Renaissance Macro Research, citing uh data from the Bureau of Economic Analysis.
1:42:58
Uh here's the that chart from Kadroski who has been doing an excellent job following the story as it unfolds.
1:43:02
So we can pull this up right now.
1:43:06
infrastructure capex as a percentage of US GDP by era and you could see railroads we went pretty >> what's weird is that is that uh he's showing telecom capex in 2020 when that was not like the boom time for telecom
1:43:22
obviously 5G and fiber infrastructure are being built out but this doesn't surprise me we got to get this number up we got to get to 6% of GDP for sure >> 50% of 100% of >> cash talked about this >> yes yes if you play the scale is all you need thing. You wind up investing like
1:43:36
You wind up investing like 200% of GD global GDP uh eventually and it's just like the math doesn't >> ma is like I can I can come up with I'm good for it. I'm good for it.
1:43:46
>> I mean technically you could invest more than 100% of global GDP with enough debt.
1:43:52
Like you like you can or you could just save up for a decade like the humanity could save up and then just deploy it all at once.
1:43:59
Like like wealth is not the same as GDP.
1:44:02
is not the same as GDP. GDP is an income figure and wealth is a is a balance sheet figure and capex is a deployment from from the balance sheet essentially >> not in >> yeah I mean loosely you you you should you should benchmark GDP to to to to revenue or something like that more more
1:44:19
than just like like GDP like the US's GDP is not how much money the US has right now or can spend it's just how much money is flowing through the economy >> uh anyway >> um Noah says I think it's important to look at the telecom boom of the 1990s rather than the one in the 2010s because the former led to a gigantic crash. Uh the railroad boom led to a gigantic
1:44:40
Uh the railroad boom led to a gigantic crash too in 1873 before the investment peak on Kadroski's chart.
1:44:45
In both cases, companies built too much infrastructure outrunning growth and demand for that infrastructure and suffered a devastating bust as expectations reset and loans couldn't be paid back.
1:44:54
and loans couldn't be paid back. Y >> and Tyl doesn't Tyler Cowan also have a bunch of writing on on why these sort of booms and bubbles are actually can have a tremendously >> Tyler Cowan has written about it but I think you're thinking of Burn Hobart who
1:45:10
wrote the book Boom for Stripe Press fantastic book you should go check it out um but basically it's just that you get yes you do get these girrations these market corrections but uh the booms create new opportunity at lower prices because you have all this dark fiber. Who bought up that dark fiber?
1:45:26
Who bought up that dark fiber? Google.
1:45:28
And so we got Google post crash actually.
1:45:31
And we got Facebook post crash because the infrastructure was there and the railroads had been built.
1:45:36
And you could see the same thing happening where there's a whole bunch of inference capacity that's out there.
1:45:40
And then it's like ah yeah, we're really not using it all because we're kind of good with GPT4 class models and we don't need to inference at the 4.
1:45:49
5 level and those monster models because it's kind of not worth it.
1:45:53
>> I'm good with my Mackenzie analyst. with this one.
1:45:55
But then while we're kind of licking our wounds from the selloff, somebody goes and figures out how to actually use that crazy crazy higher amount of inference and and the capacity is there, which is very cool.
1:46:08
But anyway, if you're if you're looking to take a vacation and go visit a data center, you got to book a wander.
1:46:13
So you're sleeping in luxury while you're going out to the data center.
1:46:16
You can find your happy place.
1:46:17
You can book a wanderer with inspiring views, hotel grade amenities, dreamy beds, top tier cleaning, and 24/7 concier service.
1:46:22
It's a vacation home but better. Yes.
1:46:24
That are closest to the data center >> to the big data center.
1:46:26
If you want to do some data center tourism Noah says uh in the context of the telecom boom and the railroad boom in both cases the big capex spenders weren't wrong they were just early.
1:46:37
Eventually we ended up using all those railroads and all those telecom fibers and much more.
1:46:41
This has led a lot of people to speculate the big investment bubbles might be actually be beneficial to the economy since mania leave behind a surplus of cheap infrastructure that can be used to power future technological advances and new business models.
1:46:56
But for anyone who gets caught up in the crash, the future benefits to society are a are of a cold comfort.
1:47:02
So a lot of people are worrying that there's going to be a crash in the AI den data center industry and thus in big tech in general if AI industry revenue doesn't grow fast enough to keep up with the capex boom over the next few years.
1:47:15
A data center bust would mean that big tech shareholders would lose a lot of money like dot shareholders.
1:47:21
You're not losing if you never sell uh uh like shareholders in 2000.
1:47:26
It would also slow the economy directly because big tech companies would stop investing.
1:47:32
But the scariest possibility is that it would cause a financial crisis.
1:47:34
Uh financial crisis tend to involve bank debt.
1:47:39
When a financial bubble and crash is mostly a fall in the value of stocks and bonds, everyone takes losses and then just sort of walks away a bit poorer like in 2000.
1:47:46
Uh Jord, Schulerick, and Taylor survey the history of bubbles and crashes, and they find that debt, also called credit and leverage, is a key predictor of whether a bubble ends up hurting the real economy.
1:48:00
>> So I think we need to dig into this more.
1:48:01
We need to learn more about private credit, have some folks on who are financing this stuff, analysts who are covering >> the golden era of private.
1:48:09
>> It has been um and I think that there's a there's another three hours of of in you know discussion.
1:48:16
>> We had founders we had one of the founders of Coree on. Yeah.
1:48:18
We should ask him to to introduce us to a couple of the lenders that kind of financed that business and ended up printing. >> Yeah. Yeah.
1:48:26
Yeah, I mean I just off the top of my head, I've been talking to a bunch of folks about this and it's not quite the same because you can hold private credit for a much long longer time and there's a lot of this stuff is being funded off the balance sheets.
1:48:37
They're just cutting a dividend or or drawing down on a cash balance if you're a Mag 7 company.
1:48:41
So there are a lot of differences but uh definitely worth digging into.
1:48:45
So we will cover this uh more in the coming days.
1:48:48
But we have our first guest of the show, Dan from Armada. Welcome to the stream. How are you doing, Dan?
1:48:54
>> Hey guys, I'm doing great. How are you? >> I'm good. Welcome to the show.
1:48:57
>> Thanks so much for joining.
1:48:59
>> Can you kick us off with uh an introduction on Armada and how you're explaining the business these days? >> Yeah.
1:49:06
So, Armada is building the hyperscaler for the edge.
1:49:10
>> Look at a map of the world.
1:49:10
There's about 30% of the world where you have these big hypers scale data centers and I mean 70% of the the world doesn't have AI infrastructure today.
1:49:17
And so, we are filling the gaps in infrastructure building that hyperscaler for the edge. >> Got it.
1:49:23
How much of that is military versus, you know, we hear about the oil and gas industry.
1:49:28
They need a data center on site.
1:49:29
They're in some remote location versus just energy price arbitrage.
1:49:34
Let's go where the energy is cheap. Put a data center there.
1:49:36
Crusoe was doing that early on in a little bit less of a portable package, but how are you thinking about the the market and kind of the most the most tangible opportunities that you can explain the business through? >> Yeah, it's it's both.
1:49:49
So we're working with critical industries, a lot of energy companies, oil and gas, mining, >> um manufacturing, logistics, and then in the public sector, we're working with the DoD, uh as well as allies.
1:50:01
And then also doing a lot with emergency response, anything that requires sort of splitsecond decision- making, processing of large amounts of data in more remote locations.
1:50:13
That's a real focus for us.
1:50:16
And then what we've seen is that now that the kind of blocker for AI is moving from just the chips to energy and I think Anthropic just came out with a white paper last week where they're talking about we need to unlock 50
1:50:29
gigawatts of energy by 2028 for AI >> and so uh we actually just launched um a new product which is called Leviathan which is megawatt scale AI data centers that can rapidly uh colllocate with areas where there's stranded land energy. And you can have a a sort of a
1:50:46
And you can have a a sort of a plug-in a plug-and-play AI factory in weeks versus years with a traditional data center.
1:50:52
And that's important because we've got about six um gigawatts of stranded energy around the country.
1:50:59
China's moving really fast.
1:50:59
We need to unlock all that energy in order to win. >> Yeah.
1:51:04
Walk me through kind of the shape and scale of the products.
1:51:06
I've seen the containers.
1:51:10
>> I'm curious what what does stranded energy actually look like?
1:51:12
I have a bunch of guesses.
1:51:14
You know, you know, uh, infrastructure was overbuilt in a specific area.
1:51:20
>> Whales, if there's beached whales, you go, you extract the whale oil right there and then you can burn it and generate your power for your data center. No, >> just like that. Just like that. >> Just like that.
1:51:30
>> No, I mean there is a ton of uh stranded energy around the country and then also, you know, in allied uh parts of the world like the Middle East.
1:51:37
If you if you looked at like the top energy sources, what's top of the list? It's natural gas, right?
1:51:43
So, you have a lot of stranded natural gas >> in places like, you know, North Dakota, uh, Texas.
1:51:50
We're already working with developers that have a lot of that land and stranded energy.
1:51:55
And, um, you know, they're looking to utilize it for AI infrastructure.
1:52:00
And if you look at um, you know, the the AI action plan that just came out, I think part of what we're trying to do is say, okay, we have all this drained energy.
1:52:09
We're going to need hypers scale data centers, you know, sort of gigawatt scale data centers.
1:52:14
We need to build those really rapidly, but we're also going to need to build distributed uh infrastructure, distributed data centers that can be deployed very quickly to to use that stranded power.
1:52:23
So, uh, when I'm when I hear stranded natural gas, does that mean like some exploration company has gone and and done fracking experiments or research to understand that there's natural gas there, but maybe there's just no pipeline to extract it.
1:52:41
So, you can get it out of the ground, but it's very expensive to move somewhere else that's more valuable, or is it something else that's going on?
1:52:47
Like, how can natural gas be stranded? basically.
1:52:51
>> Yeah, that's definitely, you know, some of the scenarios that, you know, other scenarios are like there are major >> energy companies around the country that have, you know, sort of flare gas that's just being released today in the atmosphere.
1:53:04
>> And so that they're they're under pressure to do something with that gas.
1:53:08
And so what they're now doing is they're actually using turbines to convert it into electricity so that you can power infrastructure.
1:53:14
Some for their own internal use, but then also they're looking at this as, hey, I can create a new revenue stream.
1:53:19
And so it's a mix of different situations, but but those are common ones that we see. >> Makes sense.
1:53:25
Uh let's go back to the actual product, what it looks like.
1:53:27
I've seen shipping container renderings.
1:53:29
uh what are the different scales and products that you think are uh most important to push forward right now?
1:53:38
>> Yeah, so again we're we're building the hyperscaler for the edge and what's unique about us is we're doing it full stack.
1:53:42
So do >> the hardware, the software and the AI and the easiest mental model to have is like think about >> one of the current cloud providers.
1:53:53
Think about you know Azure AWS >> uh GCP what what are they?
1:53:56
They're they're you know really just uh full stack infrastructure companies.
1:54:00
They focused on building these large hypers scale data centers in you know areas that had a lot of data a major metro areas but again that's only 30% of the world.
1:54:12
So basically what we're doing is focusing on the remaining 70% but otherwise it's a similar type of approach.
1:54:18
We're building full stack.
1:54:18
Uh we proide everything to customers as a service in a way that's totally turnkey.
1:54:25
Um, and I'd say that the biggest difference is that we we see a world where it's going to be distributed compute.
1:54:30
So, we're building rapidly deployable mobile infrastructure that can be colllocated with not only energy but data because what we've found is that you've got, you know, some of these energy companies that have oil rigs that are generating 1 to 5 terabytes of data every day.
1:54:48
similar situations with mining companies, you know, similar situations on a a battle ship, right?
1:54:55
Where you might have just huge amounts of data being generated from drones, autonomous technology in the middle of the ocean.
1:55:01
Um, now you have connectivity via Starlink or Star Shield and other types of connectivity, but you also need the compute there locally to do any sort of real-time uh data processing at the edge.
1:55:13
Are you seeing more demand for kind of like tokens as a service managed models or just pure infrastructure bare metal let me do whatever I want on it?
1:55:25
It's all so again it kind of comes back to think about like the cloud provider like there's a reason why Amazon web services is called Amazon Web Services right it's it's the is the services that the people care about >> and so for us we also sell services or we call them solutions where it's like there's a specific >> uh thing that the customer is trying to
1:55:45
do whether it is you know rapidly standing up an AI factory uh in some location location where they have stranded energy or it's a scenario where they have lots of um unstructured data, let's say from drones, and they want to process all that data in real time versus having hours or sometimes, you know, days of delay so that they can do a specific thing. A good example is uh
1:56:06
A good example is uh we're working with the the state of Alaska, their department of transportation, and they're using our entire platform.
1:56:14
Um, they're managing all of their connected assets, meaning like Starlink terminals, uh, SkyO drones, SDWAN devices.
1:56:22
They used to have over a day of latency to process the unstructured data from the drones for response to avalanches in remote areas. Wow.
1:56:32
>> And, you know, we're helping them get to near real time to respond to those types of things.
1:56:36
And so, um, that's that's what we're seeing is there's different specific problems that these customers have at the edge.
1:56:43
And then we're unique in that we actually solve them full stack.
1:56:48
>> Jordy, >> I'm curious.
1:56:49
How many uh how many uh sort of like systems do you want to have?
1:56:55
Like what what are the sort of like more near-term goals in terms of deploying systems?
1:56:58
When I think of when I think of the word Armada, you know, obviously you think of like fleet, you know, a fleet that's stretching as far as the I can see.
1:57:09
like the Tesla power battery pack where I can figure like one and it's a couple grand or I can spend $192 million or something if I buy a ton of them, but it's really just like a lattice. Yeah. Super. >> Yeah.
1:57:20
And even like h how you're managing uh kind of like how to prioritizing different customers when I'm sure you get calls all day long and somebody's like, "Hey, I need a data center in an Antarctica."
1:57:33
Uh, and you're like, well, like we're more focused on, you know, the northern hemisphere, right? >> The IMO's next week. I need a data center. >> Yeah. Exactly.
1:57:41
No, we're we're exactly right.
1:57:44
We're focusing on the the largest sort of missional aligned entities in in the world.
1:57:49
And what I mean by that is we're working with some of the largest energy players um in the world.
1:57:53
We're working also with, you know, the DoD. It's public.
1:57:58
We're working with the US Navy.
1:57:59
Uh it's kind of nice that the the company's called Armada.
1:58:01
goes hand in hand with that.
1:58:04
>> But um the the the way that we approach it, we really focus on customers that we can see that they will have thousands of what we call connected assets.
1:58:12
So, you know, take take the Navy, have a a a gallion, we call our modular data centers gallion, sticking with the Armada theme, have one on every ship. Mhm.
1:58:24
>> Um you know have one in strategic locations around the world where we have bases or you maybe more of a mission scenario being able to support that.
1:58:31
Um similar situation if you think about like a really large uh energy company like we were in the news with a Ramco and Microsoft where we've already deployed our technology.
1:58:43
We're starting with one site, but they have rigs and refineries all over the world, onshore, offshore.
1:58:50
And so the idea is that this is distributed infrastructure for all of their operations.
1:58:54
And what's really interesting is if you open up our operating system, we call it, we call our platform the Armada Edge platform, AE, you see a fleet map and it shows all of your gallions as well as any other connected assets that you have all in a single place.
1:59:10
And so what we want to be is that single partner for the most important organizations in the world, you know, obviously aligned with the the US um for all of their operations.
1:59:22
>> Can you talk to me about the the history and development of Starlink that feels like a foundational technology that unlocks a lot of what you're doing? Uh how big is Starlink? How useful is it? Where is it going?
1:59:39
um where where do you expect the capability unlock points to be?
1:59:41
Should is there a framework where I should be thinking about Starlink in the same roll out as you know 3G, 4G, 5G uh something like that?
1:59:52
I imagine that we're we're still in you know the exponential uh rollout phase of that technology. >> Yeah, absolutely.
2:00:00
So Starlink only launched in public beta in November of 2020.
2:00:05
So it's a relatively new technology.
2:00:07
Um the first couple of years they were focused on consumer business and then they started selling to enterprise and then also working with the government uh with Star Shield.
2:00:15
So you're really only talking about a few years and they're now in well over 140 countries all over the world.
2:00:27
>> And what's been really interesting, we've been working with um Starlink from the beginning of Armada.
2:00:32
have been working with them since the inception of the company and we have a great partnership, but the technology is continuing to get better and better and better.
2:00:42
When we first started working with them, a lot of people were using it in like pockets.
2:00:45
Take like a really large energy company.
2:00:47
They might use it as as backup form of connectivity.
2:00:51
>> Y >> um but they wouldn't use it as a primary.
2:00:53
What's ended up happening is that everything that Starlink does is getting better really, really fast, including, you know, new generations of the birds, the satellites up in space, uh, which are getting better performance.
2:01:06
Um, there's, you know, new types of terminals.
2:01:08
There's even, uh, you know, a new kind of like priority lane, they call it multiu where you can get better performance, a little more expensive.
2:01:17
So what's happened is a lot of these um organizations they started to use it as backup and now they're actually starting to use it as a primary source of connectivity.
2:01:27
And what we expect is over the next 5 years it's going to cover the entire globe and you're going to have you know LEO connectivity that's as good as having fiber in a lot of these locations.
2:01:36
And so we're we're building for that world today and sort of completing the rest of the compute stack for for that world.
2:01:44
in in terms of fiber, I mean that seems like speed, but is is there a world where when you're you when you're in the data center space like you need the pipe to be extremely big and and we're we're hoping that you know the roll out of Starlink will enable bigger and bigger pipes.
2:02:02
Is that the correct like mental model? >> Yeah.
2:02:05
What's what's interesting is that a lot of times um people will not want to send all the data back.
2:02:11
So >> they they only want to send the meta metadata back.
2:02:15
So what's very kind of like synergistic between what Armada does and what Starlink does is >> we can work with them.
2:02:22
The the performance, you know, will continue to get better and better uh from a latency standpoint.
2:02:29
We will handle all of the, you know, local data processing and then we'll use Starlink to send the metadata back. Right. >> Yeah.
2:02:37
And um there's really interesting things also as we're continuing to evolve what we do.
2:02:42
So we started out with um inference that was the primary focus was like okay you take an AI model that's been trained in the cloud you run it at the edge on our infrastructure.
2:02:53
Now with this Leviathan launch that we recently did, we've kind of completed the picture for a AI factory that can be deployed in weeks um versus you know years with a traditional data center and it's still mobile.
2:03:04
So you can relocate it as needed.
2:03:08
>> And what is interesting there is that you can do very cool things with like federated learning.
2:03:12
People are going to want to take these models, sort of fine-tune them for different scenarios that and run them at the edge, but they're going to want to leverage the improvements in the model across all of their different sites.
2:03:24
And so the way that we're evolving, the way that Starlink's evolving, we think we're going to continue to unlock a lot of new high value use cases that way. >> Yeah.
2:03:34
Can you walk me through some of the some of the like enterprise like infrastructure that you bring to a customer that's not out of the box with Starlink?
2:03:45
Because I know that SpaceX is a fantastic consumer product, but there's a certain level when you get into the enterprise workloads that you need another partner in the stack to actually deliver capability.
2:03:58
Yeah, we're we're seeing that customers want to use multiple connected assets.
2:04:03
We call all these things connected assets and and it's you know LEO and geo satellite connectivity.
2:04:10
So they they sometimes will use Starlink but they'll use Starlink plus like a Hughes or a via SATA geo satellite connectivity.
2:04:16
>> They'll use SDWAN devices like Cradle Point and Peplink.
2:04:21
>> They'll use drones like we have partnership with Skyo on the drones. >> Oh wow.
2:04:25
And so what these customers want is they want somebody to help bring it all together to solve their problems at the edge.
2:04:33
And that's that's what we do.
2:04:36
Um and so a lot of times when we'll get involved um you know working on on opportunities with Starlink, they are already working with Starlink but then there's something else that they want to do in addition to that.
2:04:47
Uh and we help we help that.
2:04:50
And then the other thing there's um you know different things that customers want just in terms of um optimizing for different scenarios based on not just performance but for cost.
2:05:03
>> And so we help with a lot of those types of use cases. >> Yeah.
2:05:05
Um what what's the scale of the business these days?
2:05:09
>> So we've been growing really fast.
2:05:09
So we we've only been around for a little over two and a half years. >> Wow.
2:05:14
>> We spent the first year in stealth.
2:05:14
uh raised now well over $200 million from bunch of great investors like >> Congratulations.
2:05:25
>> Uh one of the the really uh exciting things that happened last year is that Microsoft ended up uh in making an investment in the company.
2:05:32
They invested in this round and then I met with Satcha Nadella. >> Yeah.
2:05:36
Yeah, >> he got excited about what we're doing and so uh he ended up talking about us on stage at Ignite, their big annual customer conference and we're going to market really globally now with uh Microsoft and and and SpaceX among other partners.
2:05:52
Um and we're working with some of the largest companies in the world.
2:05:55
I mentioned to Ramco, there's a bunch of other large oil and gas companies.
2:05:59
We're working with some of the largest mining companies.
2:06:03
Um, and then you know we're working with the Navy that's public and then with state.
2:06:07
So we're kind of just continuing to focus on ones where like we were talking about we can see fleets of connected assets like thousands and tens of thousands of connected assets being deployed all over the world and then you know scaling to become their single partner for the edge. >> Yeah.
2:06:22
Do you think uh the relationship with Microsoft looks more like in the future I go to Azure and I need to do a workload and leveraging Stranded Energy just gets me a better price or do you think it's more like a channel partner where a really big enterprise client goes to uh Microsoft and uh they can they can uh route to you as a specific solution for a very uh like you know I don't know like like off the off thebeaten track use case. >> Yeah.
2:06:58
I think what c we're already seeing is that customers want to optimize more and more workloads between the cloud and the edge and I I think that's going to continue like the cloud is always going to be there.
2:07:09
Um but more workloads are going to also be run at the edge and so the more those things work hand inand the better it is for the customer. >> Yeah.
2:07:19
And so how how it's working today is most of our customers are already working with Microsoft.
2:07:23
They have really large, you know, agreements. They have Azure credits.
2:07:27
They can actually buy all of our products under their agreements with Microsoft and using those credits. >> That's interesting.
2:07:35
>> And then Microsoft sellers also get some credits.
2:07:37
So it kind of all works together. Everybody >> Yeah.
2:07:40
>> Yeah. Um, but what's nice for the the the customer is there's different capabilities that they already are using from Microsoft that have never been available in these more remote sites and we're making them available uh in these mo more remote sites and that can range
2:07:54
from you know the latest AI model that's been trained in Azure that somebody wants to run locally at the edge y >> to you know things that are more basic like an example is in the gallions you can have what's called Azure stack which is a piece of hardware that Microsoft produces. Oh, interesting. Oh, interesting.
2:08:08
>> And a lot of applications that these companies like to use >> are designed to run and they already run on that, but that's never been available at the edge.
2:08:15
So, we had a partnership that we announced >> with Microsoft and also Hallebertton. Yeah.
2:08:21
>> Where there's a a series of applications for like production automation that >> these large energy companies are already using in their more connected sites. >> Yep.
2:08:31
>> We're just extending that to the more remote sites.
2:08:32
And I think that's >> so you don't have to completely replatform and go down to okay, I got to make sure it runs on Linux or it runs on CUDA.
2:08:39
Like you can assume that you have access to all the nicities of Azure but at the edge. >> That's right. Exactly. >> That's awesome. >> Congratulations.
2:08:47
Uh this is a fascinating business and I really appreciate you taking the time uh hopping on.
2:08:52
We love talking your travel schedule must be absolutely insane.
2:08:57
>> I'm on a plane last few days. >> Yeah.
2:08:59
Next time you're out at some Stranded Energy, call in from the field from your phone, presuming you have Starlink or or at least cell service, and we'd love to see what it looks like to see some some natural gas plant flaring for for the last time before it becomes tokens. >> Well, let's do it.
2:09:15
I'll I'll put my Starlink Mini in the backpack and fantastic. Make it happen. >> Amazing.
2:09:20
Thank you so much for hopping on team on all the progress.
2:09:22
>> We will talk to you soon. Have a good one. >> We'll talk soon. Thanks again.
2:09:24
Up next, we have Samir from the Colin and Samir YouTube channel.
2:09:28
Of course, second time on the show, we ran into him at the Coinbase base launch event.
2:09:31
Very briefly chatted in the stairwell.
2:09:35
We were moving past each other.
2:09:37
Very excited to welcome him to the stream. Samir, how you doing? >> Looking professional.
2:09:45
>> Looking extremely professional.
2:09:46
>> I don't know if we have your audio yet.
2:09:48
It might be on our team side. >> Let's check.
2:09:50
We we we are we are not getting audio, but um >> it doesn't matter because you look fantastic.
2:09:57
>> Yeah, the the the >> I'm clearly hitting the gym. >> The Yeah.
2:10:01
>> Ben, is this on our side or or >> he's muted?
2:10:04
>> Okay, I think you're muted.
2:10:04
Let's try and throw that on and we'll bring him in and uh and continue the the Glazenator 3000.
2:10:10
The white balance on that camera, phenomenal. The exposure to die for.
2:10:12
The lighting very soft, very neutral. Sorry. Let's see.
2:10:20
Uh can you hear us Samir?
2:10:22
>> Welcome to the stream. >> Nothing yet. Let's work on that.
2:10:24
Um we will bring >> I mean what what a what a what a moment you know even even creators at the absolute top of the top of their field the apex content creators still still learning still grinding. >> Yes.
2:10:39
Uh let's uh let's do one timeline post and then we will move back into Samir.
2:10:45
Uh this uh interesting data point to uh calculate the grind score of a company.
2:10:53
Have you heard of this metric before?
2:10:54
So uh FEGMA is in the top 15 companies in terms of grind score.
2:10:59
Grind score is the percentage of employees that have a positive business outlook divided by the work life balance.
2:11:06
divided by the work life balance. So you're working really really hard but you are extremely optimistic about the company and uh topping the charts are applied intuition who we talked to queser ununice we talked to him at Hillen Valley uh the boring company of course Elon company you're going to be
2:11:23
working very hard but if you believe that you're going to drill tunnels all over the world you're probably pretty optimistic >> I had a uh I had a a roommate when I first moved to LA that worked a buddy of mine that worked at the Boring Company and he I thought I was working hard, but he would would often uh leave before I was up and come back after I went to bed. >> It's it's an extreme extreme company.
2:11:44
>> It's it's an extreme extreme company.
2:11:47
I've heard of stories of investors who were trying to get allocation in the company and uh a prerequisite was basically that you go do a full month of full-time work to to, you know, advance the company.
2:11:58
It's like, oh, oh, you think you're going to value you think you're going to add value?
2:12:00
Yeah, come out here for a month. just be in the office.
2:12:04
>> I'd love to see RAMP up there and uh Vanta and ideally, you know, we can get all of our our sponsors into the top 15. >> Shield AI.
2:12:15
Uh yeah, it's a good crew.
2:12:15
Uh also, >> uh Goateier, I'm pronouncing it correctly finally six months into reading his posts on the stream.
2:12:23
Uh I just vibe coded this. It works flawlessly.
2:12:28
And it's a picture of a plane with an engine where the plane think this plane should exist. >> Would this fly? It might actually work. I don't know.
2:12:38
>> She got me now, >> Samir. Yeah, we got you. How you doing? >> Let's go.
2:12:42
>> We're just looking at a random image of a plane.
2:12:44
>> Oh, we got the I mean the the the other setup uh we got we'll have you on again very soon to get the the full experience.
2:12:51
But great great to see you. >> Great to see you. How are you guys?
2:12:55
>> Sorry I'm giving you this uh this nonremium experience, but >> I mean it's still better.
2:12:58
The nature of streaming baby% of our guests.
2:13:00
So >> yeah, this is fantastic.
2:13:02
Uh yeah, what's new in your world?
2:13:04
What's the latest and greatest?
2:13:06
Give us the little little update and then I want to go into uh just AI, creator economy, Disney, a bunch of stuff. We'll we'll try. >> Oh, amazing. Uh new in my world.
2:13:13
I mean, dude, there's there this has been the craziest year of my life.
2:13:17
I don't know if the viewers know, uh >> but both mine and Colin's homes burned down in January, which is a crazy experience.
2:13:25
And then 12 days later, I had a baby. >> Yeah.
2:13:28
Colin Colin and his wife just had a baby about three weeks ago.
2:13:30
So, >> well, congrats on the baby and sorry about the house.
2:13:34
Very, very tumultuous time for sure.
2:13:38
>> Actually, it was um a conversation with Reed Hoffman.
2:13:41
>> Okay, >> that was really interesting.
2:13:42
He uh on his way out, we were we we were chatting mostly about AI and and the world of creators in AI.
2:13:49
And then on the way out, he turned around and looked at at Colin and I because he knew about what had happened earlier this year.
2:13:55
and he just looked at us and he goes, "Never waste a good crisis."
2:13:58
And then walked out and I was like, "Whoa, that is uh that is some amazing advice."
2:14:04
So trying to uh what's new in my world is trying to understand that statement more and what that means to me.
2:14:11
>> Uh a bit of what that means is I' we've always wanted to do in-person events.
2:14:15
We're starting to do in-person events now.
2:14:16
We have a big one in New York on September 4th.
2:14:18
So um you know, on one side of the the spectrum is like content, AI, digital.
2:14:22
The other side of the spectrum is just like getting back out, being in front of people. Yeah.
2:14:28
>> Um being with the community.
2:14:30
>> I I I know you've done a lot of public speaking.
2:14:31
What is a good in-person event look like for you?
2:14:34
Because I can imagine like you could just you have such a dedicated fan base.
2:14:38
I remember running into you at uh one of those YouTube VidCons or something and you were getting swarmed.
2:14:43
I imagine you could just do Q&A for two hours and everyone would be satisfied.
2:14:46
At the same time, you could define like a very tight, you know, 45 minute presentation with slides.
2:14:52
take people through the history of the creator economy, where things are going.
2:14:55
You could also just do interviews and interview top tier people the entire time.
2:14:59
How do you think you want to play it?
2:15:02
>> So, uh we actually the first thing you do is like you ask so this is an application only event. Okay.
2:15:08
>> Um and in the application we just asked what what does this what does this event um have to do to be successful in your mind?
2:15:17
Like what does it mean to be successful?
2:15:19
Um, also like the two questions that Tim Ferrris gave us, one is what are you here to give?
2:15:24
Like what do you feel like you could give at this event?
2:15:27
And then what are you here to receive?
2:15:30
>> What what do you what like piece of advice?
2:15:31
So I would say like overwhelmingly right like there's there's 380 confirmed applications for this event represents over 300 million followers. >> Wow.
2:15:41
I would say 90% of people just said that being a creator is lonely and I want to meet other people in in the industry and like other like-minded individuals. Totally.
2:15:53
>> So I would say most events probably undervalue free time and space and prompted networking or prompted conversation and overvalue what happens on stage.
2:16:05
>> I'll tell you like not one application said, "Man, I can't wait for a panel." Yeah. Yeah. Yeah.
2:16:10
All of that is window dressing.
2:16:13
It's always window dressing just to like an excuse to get out there and then you're just like hanging out at the cocktail party the entire time.
2:16:17
But that's the best stuff.
2:16:18
What actually makes it good.
2:16:20
>> I feel like for me even like when I've been on panels like to to have a to have a mediocre panel is probably the easiest thing in the world. >> Oh yeah. Right. >> Yeah.
2:16:28
>> And so like to to make it good I think exceptionally challenging to make it worth your time.
2:16:32
Um so we're we're working hard to do that.
2:16:34
Uh >> but mostly like people want to hang out with each other.
2:16:38
like people are lonely, people people spend a lot of time at their computer.
2:16:41
Um, and like being in a curated group of like-minded individuals is fun.
2:16:45
So, I think >> Do you have a location or or or philosophy around what makes makes for a good spot?
2:16:52
>> Uh, I think New York City is like >> the greatest, right?
2:16:54
Like I just think coming to New York's really fun.
2:16:57
62% of our attendees are traveling in.
2:16:59
Uh, which is really interesting.
2:17:02
Um, and you know, we the venue is at the refinery Domino's sugar factory, which is like this big glass building, uh, that has like a views of the city.
2:17:14
So, I think what was important to me was like you walk in and you're already like, "This is cool.
2:17:20
>> I'm I'm excited to be here." Um, >> yeah. >> So, yeah. >> Yeah. Makes sense.
2:17:23
Um, we were debating this earlier.
2:17:26
Um there's an article in the Wall Street Journal about how Disney is uh wrestling with the way they use AI.
2:17:33
A lot of companies are going through uh disruption.
2:17:38
Maybe it's a sustaining innovation.
2:17:38
Like Microsoft's doing great, right?
2:17:40
And it's not disrupting their business.
2:17:42
They're just printing money all over the place.
2:17:46
Everything's growing from Excel all the way to their cloud business to their AI business.
2:17:50
Um but for Disney, it's a lot more complicated.
2:17:52
and they were giving this anecdote of The Rock and using a deep fake to essentially create a digital twin using his cousin.
2:18:01
Apparently, they had his cousin go to the shoot and they put a deep fake over his cousin.
2:18:04
Obviously, they pay his cousin, they pay Dwayne the Rock Johnson, but there's still some backlash from the fans potentially for using AI at all.
2:18:12
And I've just been noodling on this question of, you know, will Disney be disrupted by AI?
2:18:17
Will they just continue on unabated?
2:18:20
Will it be really good for them?
2:18:23
How like like So maybe we can just kind of noodle on it for a little bit and just kind of get your initial reactions and then we can kind of take it in a bunch of different directions. >> Okay.
2:18:32
So I think my my first POV on AI right now is that I think we don't talk about AI and prompting as entertainment enough >> and and what I mean by that is not like its ability to produce entertainment but the fact that it is entertaining to chat with an LLM.
2:18:49
with an LLM. And I think for me as a content creator um you know all of us are in the business of attention like you guys are in the business too like we're looking to uh create something meaningful enough to capture someone's attention and I think Sam Alman has
2:19:06
created something that is meaningful enough to capture a lot of attention and I think a lot of creators think about this as like oh man if if um AI can produce graphics in VFX then you know it's people are out of jobs and um you know that that is totally true like that is happening. Um
2:19:21
Um >> but at the same time us as creators we might be out of jobs if AI itself is entertaining. >> Yeah.
2:19:29
>> So I think on the uh you know no matter what we live in a in a in a landscape in a media environment where like creating stuff is really competitive. It's really hard.
2:19:39
Uh it's very it's gotten very expensive.
2:19:41
Like studios are going to use AI to to supplement production.
2:19:43
creators are going to use AI to I mean almost every creator probably uses it to supplement production in some way shape or form. >> Yeah, totally.
2:19:51
>> But I think if we think about it from the consumer side and what's entertaining about learning a new recipe or figuring something out on chat GPT or writing with chat GPT or asking questions with chat GPT um that that's where I I think about like time spent on the internet and is more time going to be spent over here.
2:20:10
>> Are are you guys familiar with showrunner?
2:20:11
It's it's coined as the Netflix of AI. I have heard of it.
2:20:13
I I'm not familiar beyond just seeing like one headline.
2:20:19
>> So, Amazon just backed it and I think it's an interesting one to look at.
2:20:22
It's essentially like they give you a show.
2:20:26
So, they have a show called Exit Valley.
2:20:28
It's kind of like this their version of Family Guy. >> Sure.
2:20:30
>> Um but you can uh choose your own adventure within the show and put yourself into the show as well.
2:20:36
So, you can kind of prompt what happens next.
2:20:41
And I think it's an interesting thing to look at of like making content playable, right?
2:20:46
Making it interactive because if we look at the most culturally relevant stuff from history as well as now, video games are wildly culturally relevant.
2:20:56
And those are stories that you can put yourself inside of, right?
2:20:59
That you can prompt what happens next inside of that story.
2:21:02
And you look at live streamers right now. Kaisen, I show speed.
2:21:05
As the viewer, you actually have impact over what happens in that story. >> Yep.
2:21:11
>> So, I've just, you know, rambled quite a bit about this stuff, but I think that we should really evaluate the like consumer and viewer interest in um having a say into what happens next and also having interaction with the internet.
2:21:26
>> Yeah, that interaction thing is so fascinating.
2:21:28
I I I haven't thought about it in that frame, but when I go to chat GBT and I get it to generate an image, >> it's definitely a little bit like hitting a slot machine. >> Totally.
2:21:39
>> Because I prompt it and I'm like, I have something in my mind and it's unblurring like it's in the like it's I'm in the dialup era and it's loading.
2:21:45
It's like, is this the right image?
2:21:48
>> The previous generation is you you you know talk with the designer and you give them an idea and they're like, okay, I'm going to I'll come back to you in a day.
2:21:57
You're waiting and you're waiting. You're waiting.
2:21:58
and they send you the files and you're like send you the fake totally dopamine hit but but compressing that down uh that makes like the dopamine. >> Yeah.
2:22:06
And you can see in the in the charts I think uh chatbt is up at 30 minutes a day for the daily active users and that's getting into Tik Tok numbers and that's getting into reals numbers and and so maybe it's not immediately disruptive but it's certainly taking time from somewhere.
2:22:24
And if you go back to Ted Sarandos at Netflix saying like our competition is Fortnite, well, you know, yeah, your competition is Catchy PT now.
2:22:32
If people are having fun on there, even if they're not watching, you know, movies yet on there, just the interaction, even if it's textbased, it's it's all in the same media, it's all in the same screen time. >> Yeah.
2:22:43
Casey Neistat came on, I think a week and a half ago, and was saying that he's just like feeling like it's like this title wave that's going to wash over everyone and nothing will be the same.
2:22:54
And it was almost I wouldn't say it was like super bearish or blackpilled, but it was just like get ready, it's coming, it's going to disrupt a lot of different verticals.
2:23:06
I'm curious what percentage of creators that you talk to are more excited about the potential of AI than they are scared.
2:23:14
>> The fear versus greed index. >> Yeah, exactly.
2:23:17
>> What they say in the public markets.
2:23:18
>> So, so that that's really interesting.
2:23:18
I think like the the aspiring creator, the young creator is uh fearful and angry and like we get a lot of anger when we talk about AI. >> Sure.
2:23:28
>> Um you know I think uh for me I don't I don't feel that way.
2:23:33
I think it's there's an inevitability to it that's like if you are if you do feel angry if you are a young creative and you're like this thing is making logos and people are using it to to do their branding instead of hiring designers and you feel angry about it.
2:23:46
It's like it's upsetting, but it's there's an inevitability to it.
2:23:50
Like, it's not going to stop.
2:23:52
It's it's this is the worst it'll ever be.
2:23:54
Um, so I think the uh from the the professional creator perspective, >> this is the best it'll ever be.
2:24:01
>> No, the worst it'll ever be.
2:24:02
>> This is the worst it'll ever be.
2:24:04
>> It's just getting better.
2:24:05
>> No, you're saying the AI is the best.
2:24:05
I was saying it's the best it's ever going to be for you if you're just angry that AI is doing worse. Oh, no.
2:24:10
Like >> the AI right now, what you can do with AI, this is this will seem comical to us in 5 years. >> Totally.
2:24:18
>> And so it's only getting better.
2:24:18
And I think um every professional creator uses it in some way, shape, or form.
2:24:22
I think one of one of the uh maybe the nerdiest things that I ask my friends and that I tell my friends is like if I have a favorite prompt or if I >> uh ask them what their prompts are.
2:24:33
I ask always if we write something I ask it to challenge based on being a different persona. >> Sure.
2:24:42
>> So, you know, I've done this as Casey before.
2:24:44
Hey, imagine you're Casey Neistat.
2:24:46
Give me feedback on this. >> Sure. Sure.
2:24:49
>> Imagine you're Johnny Harris.
2:24:49
Give me feedback on this intro.
2:24:52
>> So, um that's been a really like that's probably the simplest, most effective way, but all the way up to like we use it to mock up thumbnails now. >> Yeah.
2:24:59
I was going to ask you about the thumbnail because when when uh so so Mr.
2:25:04
Beast had his like AI thumbnail generator and then he got some some >> push back extreme backlash, but it seemed like it was from a >> a a bunch of uh bunching on this.
2:25:13
I my my point of view was that he should have said like I understand that this is disruptive to like that a small number of people that that do thumbnail art as a profession but I care more about democratizing access to great thumbnails by making anybody generate them and I'm going to keep this in the product.
2:25:37
point was that putting it out there was procreator anti- thumbnail artist and that was like the tradeoff that he should have taken.
2:25:46
>> So he in my view he he you know and I'm sure he has a bunch more context but in my view he was siding with the 10,000 people in the world that are that that make a living doing thumbnails >> instead of the millions >> instead of the millions of people that want to create >> I think actually the the core issue with the announcement was the face swap.
2:26:05
So what he did was he showed two thumbnails and he said it's it's it's very simple to put your face on this thumbnail.
2:26:12
Now look, that happens on YouTube all the time, right?
2:26:17
Like the reality is that when one creator makes something that's like really successful, another creator just goes, "Oh, cool.
2:26:25
That's almost like public domain, that idea, and I'll just take that and like reinterpret it for my own channel."
2:26:31
And sometimes people are so agre egregious to just put their own face on someone else's design.
2:26:35
We made a video about this called the copy and paste culture of YouTube because it is a big culture and I think what a lot of creators were feeling was that Jimmy was making that process very easy.
2:26:47
>> Thumbnail designers as a cottage industry of YouTube are some of like the most powerful people on the platform.
2:26:54
They literally are the gatekeepers between creators and like millions of views >> attention. Yeah. >> Yeah. Totally.
2:26:59
They are very very talented.
2:27:02
Even for us, like what happens for us is we just we're all visual people, so we mock it up with AI.
2:27:07
But I don't think anyone, you know, I think Dude Perfect did one thumbnail that was pure play from an AI generated thumbnail, which was a airplane with a bunch of basketballs coming out of it.
2:27:21
That was like an impossible photo to take or an impossible, >> you know, thing for them to mock up.
2:27:25
And the thing they got from AI, they're like, "This represents the video concept."
2:27:30
>> People were upset about that.
2:27:30
But I think mostly how creators operate is they go here's the concept.
2:27:35
How we operate is we go here's the concept.
2:27:39
Okay, here's the here's how it looks visually and now we hire a designer to bring that to life. >> And we still do that.
2:27:45
We I mean I think thumbnail designers are like wildly talented and they operate more as like strategists as well.
2:27:51
But it's it's a fascinating cottage industry that people looking into YouTube probably have no idea exists.
2:27:56
Do you think that there's a role in the future for like thumbnail conceptor?
2:28:02
Because I've made thumbnails and they like they live and die.
2:28:06
and they like they live and die. I mean, obviously the last step of the Photoshop work is extremely important, but you have this like 20 minute video where you talk about so many different things and there's so many different visual elements and then you're trying to distill it down into like the three
2:28:22
element rule or just like one thing and picking that one thing that like the plain I can imagine what that video is about right now just based on what you say, but that could have been botched so many times on the concept level and it doesn't matter if you had you know the
2:28:38
the best photographer in the world working with the best Photoshop artist and you know the entire AI team from all the different foundation model lab companies working on it like the wrong concept is just not going to break through because people are going to be like this is confusing like I don't want to watch this
2:28:53
>> you can't fix you can't fix a bad idea you just can't you can't but I think what makes a good idea is really hard to decipher today >> um because I I think on the internet we live in a world of two extremes One is if I'm going to click on something, it has to be something I've never seen before on one extreme, right? So, Mr. Beast, let's take that and go, So, Mr.
2:29:13
Beast, let's take that and go, uh, he just raced an F1 car and a cheetah.
2:29:19
I've literally never seen a cheetah race an F1 car.
2:29:23
>> So, fine, Jimmy, I'll click on that because I've never seen it before.
2:29:24
On the other side of this attention spectrum, I think it's show me something that impacts my day-to-day life um either with my friends and family or like is interactive.
2:29:34
So your guys show is a great example of something I think lives on that side which is like I get to turn this on and it impacts my life because I get to be a part of a water cooler conversation. Yeah.
2:29:43
I can turn to my friends and I have more things to talk about with my friends.
2:29:47
Love Island is massively culturally relevant.
2:29:49
I think that is something that you know even around my social circles people talk about and it they get to connect with it with their wife or with their girlfriend or with their buddies like so I think we live in in those two extremes.
2:30:03
Anything in the middle is really hard. Yeah, >> right.
2:30:05
Like educational stuff very hard right now.
2:30:09
>> Um I think viewership is going to go down in the middle.
2:30:10
It doesn't mean it's less impactful. It's still impactful.
2:30:14
But I I think >> it is interesting that the the educational content is it used to be if you wanted to quickly understand a complicated topic.
2:30:22
finding a short YouTube video on it was super effective because you're like, I don't have to Google and I don't have to find this summary and I don't have to piece together this Reddit thread and that blog post and this news article and I'm just going to watch this like three minute video and have a good understanding.
2:30:37
Now you can just prompt the model and say how does this work?
2:30:42
And then you can ask follow-up questions and you can ask follow-up questions and you can click in to see the source.
2:30:44
And I feel like that that that middle is just getting hollowed out.
2:30:48
And we're not that far from OpenAI just being like, do you want to generate a 10, you know, 10-minute video explaining this?
2:30:55
I'll turn this report into a video. Right.
2:30:59
And that's that kind of like >> I mean, you can already do it with podcasting.
2:31:01
Mustafa Solomon from Microsoft, uh, the the CEO of Microsoft AI. Yeah.
2:31:08
>> On our show prompted a podcast that was really compelling using Copilot.
2:31:10
And I I think though, have you guys ever used the like kind of FaceTime feature with Chat GPT?
2:31:18
>> Uh the it's the voice mode, right?
2:31:18
Yeah, >> it's voice mode video too. >> Oh, video now.
2:31:23
>> Oh, that so I I did that in the kitchen.
2:31:26
Um like three nights ago I >> uh made, you know, I saw some like everything on Instagram on my Instagram feed is like cottage cheese, right?
2:31:33
It's just like put cottage cheese in something.
2:31:37
So it was like these like cottage cheese protein brownies. Awesome.
2:31:40
And I was like, "All right, I'll make these."
2:31:42
And I uh in the middle of it, I like had a question.
2:31:44
And so I opened that up and I had like the video up and I was like, "All right, wait. I only have one banana. Like what do I do next?" >> Oh, interesting.
2:31:52
>> And it was just live prompting me.
2:31:52
And I think the obvious thing you recognize is that that won't be our phone soon, >> right?
2:32:00
Like maybe it'll be glasses, maybe it'll be contact lenses, maybe it'll be like contact lenses and an earpiece, >> but there will be like an omniresent nature.
2:32:08
And that's very dystopian.
2:32:08
But I think this all connects to that question of education.
2:32:13
Like educational content will be real time personalized in my ear and in my eyes. At least I think.
2:32:21
>> What did you think about the launch of Waves, the new uh uh glasses built initially for live streamers.
2:32:26
It felt like uh the most polarizing launch where if you were a live streamer that does IRL stuff, you were thinking, "This is amazing.
2:32:37
I I need I want to get this as fast as possible.
2:32:40
And if you were a uh a member of the public that values your privacy, you were, you know, saying like, please, you know, stop this before it launches.
2:32:54
>> Yeah, I think it's uh Okay, I think on the live streamer side, I mean, my perspective is POV is not that interesting.
2:33:01
Maybe I'm wrong, but I think what's interesting is like a filmer filming speed doing something.
2:33:08
>> Uh, you know, not necessarily POV.
2:33:08
The only guy who nails it is, you know, that guy in New York who like plays the uh the keys and like someone raps with him. >> Yep. Yep.
2:33:19
>> That's like the dopest kind of solo dude. I assume he's solo.
2:33:21
Maybe there's a cameraman with him.
2:33:22
But yeah, I mean it seems like it's just like one of many niches and there will be creators that are shoot wide angle, shoot drone footage of themselves doing things, shoot, you know, like like cinematic stuff.
2:33:35
It's just like one of the tools in the tool chest, but I mean people really like humans and they really like seeing speed or ka and they like that relationship. >> Totally. Yeah. Yeah.
2:33:45
You like feeling like like you don't feel like you're somewhere like you don't >> it you you don't >> like for example like the idea of an NBA player like wearing glasses like that might be cool for a second where they're dribbling down like shooting pulling up but then you probably want to just go back to like feeling like you're actually there in the stadium seeing it.
2:34:09
>> I would get bored quickly with that.
2:34:09
I think on the privacy side, like you know, you guys are in LA, right?
2:34:13
Like you see Whimos all the time.
2:34:15
I think I think there's a lot of concern about that too of like, okay, now we have more cars on the street that just have like tons of cameras on them.
2:34:22
Y >> and I think just generally there's, you know, there's a lot of concern about that, but it is like a natural evolution to us just unloading our lives and data on the internet.
2:34:33
Like people are concerned about that, but are they concerned about their chatbt search history or question prompt history?
2:34:39
Like um I think Scott Galloway said it like if you want real anarchy in the world just release everyone's Google search history.
2:34:46
Like that would end civilization.
2:34:48
And I think >> do you think it actually would or do you think everyone would just be like okay we're all equally weird like >> or it would be this like cool moment of just like the veils lifted like we all just search history.
2:34:58
How much creatine can I take for losing my life?
2:35:02
Um >> I don't know how this stuff's going to play out.
2:35:07
I just think that um >> yeah, >> you know, I I did Colin and I did get the opportunity to spend some time with with Sam Alman.
2:35:14
Uh and I think um you know, I I I I my curiosity with having a lot of the like like Mustafa on the show or Reed Hoffman on the show is to understand the incentives and the curiosity of the creators uh of of these programs and even with someone like Sam like it's actually hard for me to fully tap into.
2:35:36
I understand the desire to create and to innovate and to be number one and to um to push things into a place you could never uh imagine, but I think the implications of this are pretty different than anything else.
2:35:46
And I think the implications of advances in in in AI are going to change human society in a way that u like I think Casey's right like it's going to change things in in ways we can't imagine. >> Yeah.
2:35:59
>> Yeah. One one thing that's interesting when I think about creators that that I've watched uh over the last decade, you think about uh in music like Anthony Anthony Fantano like >> there will be an AI creator that just instantly reviews a new Drake album and it just compare, you know, and it and it
2:36:19
and it can come up with some analysis of the song and it'll tell you what this lyric meant and it'll say, you know, this sounds similar to a song or a unreleased track from this period and it'll give you this like context, but people will still want to hear what Anthony Fantino thinks about the album. >> I think we look at it in the terms of
2:36:37
>> I think we look at it in the terms of like efficiency and inefficiency.
2:36:40
There's certain times where we want efficient media.
2:36:43
>> So like let's say I'm going into a meeting and I'm like, wait, I didn't prep for this.
2:36:47
I need something really efficient.
2:36:48
Like when I was in high school, that was Spark Notes.
2:36:49
Do you remember SparkNotes?
2:36:50
You guys are too young for Spark Notes.
2:36:53
>> No, no, I use Spark Notes, of course.
2:36:54
Spark notes was like if I didn't do the required reading of class, which I rarely did, I would just >> not. >> Yeah, exactly.
2:37:00
I would go like, "All right, I got the Spark Notes."
2:37:02
So, I think there's like efficient media.
2:37:03
But then if you look at there's also people who just loved reading a book.
2:37:08
>> So, there's going to be both ends of the spectrum.
2:37:11
>> I wish I I wish I in college if you could generate a podcast on any topic.
2:37:13
I mean, that is be like, I'm going to go to the gym and when I finish this, I'm going to know like 90% of what I need to know about this book.
2:37:22
I'm gonna go on to the test.
2:37:24
I'm gonna write the essay.
2:37:25
>> I used to get the audio books for every book.
2:37:26
I'd buy the book for this for the co course and then I'd also buy the audio book because I was an auditory learner.
2:37:32
>> But we can't assume that just what humans want is efficiency all the time, right?
2:37:35
Because I don't want efficiency all the time.
2:37:36
Like >> you think about going to a concert, going to a concert is the least efficient way to hear the music from that artist.
2:37:42
You go, you have to park, especially in LA, it sucks.
2:37:44
You go, you have to figure out parking.
2:37:46
There's traffic on the way in.
2:37:48
You go in and like, why are you there?
2:37:50
You're there for a collective human experience.
2:37:53
You're there for the messiness of how John Mayer is gonna play.
2:37:55
I'm speaking about a John Mayer concert.
2:37:56
So you're you want to hear him play the guitar differently than he would.
2:37:59
If I just want to hear the song Daughters, I'm just going to go on Spotify and hear the song Daughters.
2:38:04
So I think I I think you just play with those two worlds of like we will always kind of crave efficiency and inefficiency.
2:38:10
There might be areas where we want it more.
2:38:12
So like brainstorming is a really interesting example.
2:38:16
Hey, I want 100 variations of this idea.
2:38:19
Presently, an LLM can do that better than five people in a room and faster than five people in a room.
2:38:24
If you want to see every variation of an idea, um, but you know, you want to just uh uh watch a movie and see if you can come up with ideas based on that movie.
2:38:35
Like it'd be hard for an LLM to match your taste in that way or Yeah.
2:38:37
I just think there's like efficiency and inefficiency and they're both going to play together.
2:38:43
>> We really got to book an hour for this.
2:38:45
We could just keep going forever. This is fantastic.
2:38:47
We do have another guest join.
2:38:48
No, let's do it in person next time. >> Yeah. Oh, yeah. You're in LA. Just come by, >> dude.
2:38:52
I would love to come see it.
2:38:52
Also, I mean, I totally botched my uh my good-looking setup.
2:38:57
So, uh it's high stakes with the live stuff, but we saw we we we saw the potential.
2:39:02
We saw a glimpse of the potential.
2:39:06
>> It was a human It was a humanizing moment for your a, you know.
2:39:10
>> But yeah, you should you should come in.
2:39:11
You should come one day and close out the show with us and then we can pick your brain about stuff and and you can help us get an idea.
2:39:15
John just said, "Pick your brain."
2:39:17
Well, I mean, I just asking Samir, >> I'm sure she gets that message a thousand times a day.
2:39:24
I'd love to pick your brain, Samir.
2:39:26
>> Tim Ferris Tim Ferrris gave me a hard and fast rule.
2:39:28
He said, "If anyone says pick your brain, don't go to the meeting."
2:39:31
Yeah, don't go to the meeting.
2:39:32
So, that's my hard and fast rule. I will not be there.
2:39:34
Um, if you have another suggestion, >> then I'll come in for that. >> We'll hang out. We'll hang out. We'll have lunch. We'll have to go. >> Awesome. >> All right, guys. Great to see you.
2:39:43
>> Thanks for having me on. See you guys. >> Cheers. >> Talk to you later. Bye.
2:39:45
Up next, we have Flo coming into the studio to the TVPN Ultra Dome >> Legend.
2:39:53
>> Welcome to the stream. Uh, we kept him waiting.
2:39:55
We'll try and move through these next guests quickly.
2:39:57
Been very excited to catch up with him. How are you doing? >> Boom. Welcome.
2:40:01
John will be back in a minute. How are you doing, Flo? >> Good, good, good. How's everyone doing? >> Great. Great.
2:40:08
Uh, you know, it's uh it's been a slow summer. Not much news in AI.
2:40:14
Everyone's just uh clearly taking a breather. Uh, of course not.
2:40:16
Um, I know you guys have been busy.
2:40:19
What's going on in your world?
2:40:22
>> Yeah, just announced 3. 0.
2:40:22
It's been an insane last couple of weeks.
2:40:24
Uh, been working on this update all year.
2:40:26
Uh, yeah, I mean, we're just making it way way way easier to create way more powerful AI agents. >> Incredible.
2:40:34
Uh, when did you decide to adopt uh the like versioning for Lindy?
2:40:39
because I it feels smart to like kind of anchor like the team and your customers around these like major updates to the to the product versus historically people might have a product launch but they're not necessarily like signaling like hey this is a massive advancement we're call it's an entirely new version >> yeah no we've done that from the beginning like 2.
2:41:00
beginning like 2.0 you know was like in November last year actually you know even smarter and that's what I want to do next time would have been to name them I want to like Lindy you know like how Apple does liked and stuff I want to give him cool names moving forward
2:41:14
>> yeah I'm trying to think uh we'll come up we'll come up with the right series of of names in the right category do something uh >> I'm thinking like sci-fi books like what would you think of like a fire up in the dip you know or like the metamorphosis of >> Lindy Lindy snow crash or Yeah, exactly. Exactly. Exactly. >> Um, that's great.
2:41:32
What uh how how are you guys using the new the new Lindy's uh internally?
2:41:38
>> Oh, we're using it all day.
2:41:38
Like I think one of the most insane things that it's automated for us is like it's replaced the QA engineer. >> Wow.
2:41:45
>> It just like so it's getting very meta.
2:41:48
Like we all using Lindy to QA Lindy.
2:41:48
And so every hour we have like a Lindy agent that wakes up that tries the entire like core flow like literally signs up with a new account, tries to pay, create a new uh agent, tests the new agents, you have an agent, creating an agent and testing it.
2:42:03
Uh and then if anything goes wrong along the way, uh pings the on call engineer.
2:42:07
Uh so we've been using that for weeks. It works beautifully. It's amazing.
2:42:11
>> How do you think about reducing the time uh I guess like time to value for somebody that's coming in to use Lindy? I imagine that's key.
2:42:19
Uh people are so used to like signing up for a SAS product knowing exactly what they're getting and having there be a lot more kind of controls around what the product does.
2:42:29
So it's not a sort of like build your own adventure.
2:42:31
Whereas I feel like with Lindy historically you you do like the the customer needs to really embrace like hey I have to figure out how to when you hire a new employee very few roles can somebody just come in and without any ramp up time just immediately start crushing it.
2:42:47
Um and and so I'm curious um what that looks like for you guys. >> Yeah. Can I show my screen? Does that work? >> It does. Everything's live.
2:42:56
So whatever you share will be >> okay.
2:43:00
whatever >> into the immutable record of the >> of the internet. Oh, that looks great. >> Well, yeah.
2:43:07
So, I mean, this is like an excellent question.
2:43:09
Like, that's the problem we've always had from day one is like it's almost too powerful.
2:43:12
You have to do everything yourself.
2:43:13
And that's why the vision from day one has been like it's just like an employee.
2:43:16
Like, it's very easy to set up.
2:43:17
You can just talk to it and tell it what you want to do.
2:43:21
And so, now the new app is just a text field.
2:43:23
It's like this thing and you tell it what you want to do.
2:43:24
It's like, hey, I want an agent that will monitor my support inbox.
2:43:27
Then you can like iterate with it like very interactively.
2:43:31
And we have a bunch of templates here for like sales, customer support, personal productivity, engineering right here like the QA assistant is one of them or you know like like a software engineer agent like human resources like a recruiting agent or like a company knowledge base.
2:43:44
So we give you a bunch of templates and then you can just create your agent in like a minute and a half.
2:43:49
>> Are you running into any problems uh just linking up private data sources?
2:43:52
is I mean I'm just thinking from like a consumer's perspective.
2:43:56
You have a bunch of uh you know news subscriptions you want to to log in to those news websites and kind of collate a digest for you.
2:44:08
That's something it would need a password for.
2:44:10
It would get run it would run into uh a number of uh pay walls.
2:44:12
Uh are you seeing a lot of luck with like jumping through login flows or are you getting hit with captures constantly that are intractable? >> Oh no.
2:44:25
I mean like so that is a big part of what we're announcing today is like autopilot.
2:44:29
So basically your agents now have their own computer >> and if you need to give it a login you can literally take control of the computer and login for them.
2:44:36
So let me just pause the screen share because I don't want to show anything crazy.
2:44:39
But >> does is that is that something that you do like once or because I like when I think of the benefit here I think of like a really longunning like cron job based uh >> um uh agent essentially something that feels more like a screen sharing still screen sharing.
2:44:58
Yeah, your list of passwords is up now. Just kidding. The API keys are leaked.
2:45:01
Uh and it's >> Oh wow, you did a billion in revenue yesterday.
2:45:09
Um, no, I mean, yeah, you love it only once.
2:45:11
So, it's like this is I I am screen sharing on purpose.
2:45:12
So, this is an agent that said this is actually a little bit of a dirty secret.
2:45:16
Like, I don't like LinkedIn.
2:45:17
I'm more of a Twitter guy.
2:45:18
And so, I post on Twitter.
2:45:20
I don't want to post on LinkedIn, but I have an agent that uh so it is a crown job.
2:45:24
Wakes up every day at 9:00 a. m. this morning.
2:45:26
And you can see it's logged in from my Twitter account.
2:45:28
Like, this is authenticated as me.
2:45:30
And so, it goes on my Twitter.
2:45:32
It's also authenticated as me on my LinkedIn.
2:45:33
And it basically looks at the posts on my Twitter and it's like, "Hey, this is what I think you should post on on LinkedIn." >> That's very cool. >> Yeah.
2:45:43
>> Yeah, that makes a ton of sense. >> That's awesome.
2:45:45
>> And it can obviously like hydrate things and add extra context and really like transform the post properly as opposed to just like a default >> flow post on on X.
2:45:52
Very excited to announce Lindy 3. 0.
2:45:54
It's our most powerful Lindy and then it converts it to the the Lindy then converts it to LinkedIn.
2:46:01
It's like what's >> what I learned about B2B SAS >> convert I today we're transforming the power of agents to unlock productivity for everyone in your organization.
2:46:13
>> No, the real the the real clickbait for LinkedIn would be like uh you know employee three weeks ago I hired a software engineer who was working at 25 different companies.
2:46:22
Here's what it taught me about building 3. 0 something like that.
2:46:27
>> Anyway, uh well congrats on the launch.
2:46:29
Anything else we should know?
2:46:31
Where can people sign up? >> Yeah, linda. ai. It's available today.
2:46:35
Like I don't know about y I find it frustrating when like there's a lot of like vapor wear and AI.
2:46:37
It's like this is coming one day.
2:46:39
It's like no no no this is available right now. It works. It's incredible.
2:46:43
>> Very >> What's the pricing model right now? >> Don't worry about it.
2:46:48
>> Consumption or subscription? >> It's consumption.
2:46:52
>> It starts at $50 a month and it's it's a tenth of the price of a human. >> Okay.
2:46:57
This is gonna be one of those things.
2:46:58
Am I going to hear horror stories about people that like forgot to, you know, turn off their Lindy and they ran up a $10,000 bill? >> Don't worry about it.
2:47:04
It's >> the cost of inference is declining rapidly.
2:47:08
It's all going to Everybody's going to make it.
2:47:10
We're all going to be fine. >> Yeah. Yeah.
2:47:11
We're going to distill the models.
2:47:12
These aren't >> I mean, look, I you know, this has happened before and we're very generous with credits. People reach out to us.
2:47:18
They're like, "Can I get a refund? This happened?"
2:47:19
And we're like, "Well, yeah, >> of course that." Yeah, that makes sense. >> Um, cool.
2:47:23
any other any other tools or or custom models or like RL environments that you're tracking or excited for progress to be made?
2:47:31
Uh you mentioned computer use, but what what else do you want uh the foundation labs to solve to kind of so that you're like a beneficiary riding the wave? >> Memory. >> Memory >> definitely.
2:47:44
Memory I think is is the big gap.
2:47:46
And I I think like there's a lot of attempts to solve this problem across the industry.
2:47:50
But I'm noticing this pattern where it's like people and companies like ours that are stuck and cannot change the weights of the models.
2:47:58
They think of all of those like convoluted schemes to like hack the model to do something it doesn't when really it belongs to to the model.
2:48:04
>> And when you talk to the researchers at the labs and you ask them like ah like we're working on memory this is what we're doing.
2:48:09
They tell you like don't don't do it like let us let us do this like we'll do it.
2:48:13
this belongs to the model and the model is going to do it a thousandx better than you do and I expect much more advanced memory schemes where it's like basically the model is going to be retrained >> to come out in like a year or something like six or 12 months.
2:48:28
>> What are you expecting from GPT5 >> just I'm expecting it to be smarter.
2:48:32
Um, look, we feel sparks of a GI daily at this point.
2:48:40
Like we had this moment last week like we basically accidentally built Lovable like because we gave a computer to Lindy like someone it was like 10 p. m.
2:48:47
at the office like Flo like hey if you ask me to to build a website she builds it and she deploys it it works and so like to me that was like a huge holy like like AGI Spark of AGI moment but like look it's still not as good as a human like sometimes it's just dumb.
2:49:01
It just makes dumb mistakes still today, you know.
2:49:03
And so I'm expecting GPT5 to be like a step change and and to be a big step toward GI, but I still people don't realize what's happening. And I get it.
2:49:12
People are tired of like all the AI hype and the noise.
2:49:15
And I tell them like if you're tired, man, like this is only the beginning.
2:49:18
Like it's going to get a lot more noisy out there.
2:49:20
Like it's it's gonna get pretty crazy. >> Yeah.
2:49:23
I mean, I I I'm just It should be exciting for everyone because Lindy uh today feels like the promise is is automate these tasks that are boring that a human does.
2:49:33
The first 10 times they do it, it's interesting.
2:49:35
The next 10,000 times they do it is very boring.
2:49:40
And so you just free people up to do these more like higher lever 0ero to one type activities that can then be automated and systemized over time. So um very very cool. Congrats on the launch.
2:49:50
I'm sure, you know, people are going to be, you know, making bets on Lindy 4. 0. Is Lindy 4.
2:49:55
0 going to be able to get a college degree for me, you know, all that good stuff. >> Congratulations.
2:50:03
>> Ironic is that is that these models are actually today able to get college degree so they can job as an intern, >> right?
2:50:09
From the top to the bottom. So, yeah. >> Yeah. >> Awesome. Well, congratulations. We'll talk to you soon. >> Great to see you. >> Thank you. >> Have a great day. Bye. You do.
2:50:16
Up next, we have Dave from Rune Technologies coming in to the studio. Get that gong ready.
2:50:24
Jordy Hayes and welcome to the stream. >> How you doing? >> Hey, how are you? What's up? >> I'm good. Welcome to the stream. >> Uh, >> thank you.
2:50:35
G >> give us a brief intro, give us an update, and tell us the news. >> Yeah. Yeah.
2:50:41
So, Dave Tuttle, co-founder, CEO of Rune Technologies.
2:50:42
uh founded the company last year with my co-founder Peter who I know was on a little while ago.
2:50:48
Uh we just raised our series A just announced it about two weeks ago.
2:50:50
Uh so excited to have uh closed that.
2:50:56
Didn't even give you the number. >> Sorry. >> Sorry.
2:50:59
Didn't even get to the number.
2:51:01
>> We didn't even get to the number.
2:51:01
But congratulations on the series A. Uh G give us the number.
2:51:06
Give us who led and then we'll go into more about what the company's building. >> Yes.
2:51:10
Yes, we closed $24 million led by human capital.
2:51:13
Um, all of our current investors from Andre and 72 and XYZ reinvested and then you know new investors human and uh, you know, Washington Harbor partners as well, new investor here and and uh, we won't announce it yet.
2:51:26
There's going to be another one uh, but we'll save that in a few weeks as a little surprise. >> Fantastic.
2:51:30
Uh, you're give us give us the brief history on uh, on logistics and supply chains in the US military.
2:51:36
Um what I mean what what were we using in World War II? What are we using today?
2:51:44
And then what does the future look like? >> Yeah.
2:51:46
And I mean in many ways the insane part is some of it hasn't changed, right?
2:51:50
Like we're tracking things in analog systems.
2:51:51
analog systems. you know the the means of a whiteboard has changed obviously from uh from scratch paper back in the day and you know sometimes it's even Excel documents but great like the basic nature of it is still an analog human centric tracking of things whether that's supply inventory levels vehicle maintenance levels vehicle locations and those sorts of things you know and
2:52:11
especially over the last you know I came out of uniform I'm still serving part-time even in the army national guard and when I think about like how do we track what we need and where we need it and when we need it that is still a humanentric analog process has remained generally unchanged at least in the last 20 years with the advent of you know computers at some level and connectivity at some level. >> So yeah um what's the path to actually
2:52:29
>> So yeah um what's the path to actually getting this this in the hands of the war fighter?
2:52:34
Obviously it's a newer company.
2:52:36
You've made a lot of progress but um it's never easy getting to program record.
2:52:40
It's never easy getting uh you know full deployment.
2:52:42
We've we've talked about the history of Palunteer.
2:52:46
Took a long time to get that out into the world. Uh what's the plan? What's the update? >> Yeah.
2:52:51
Yeah, I mean you said it.
2:52:51
I mean it is a long path.
2:52:52
We know that we all of us have come from this industry.
2:52:56
We've lived in the defense space for years at Rune, you know, from many other companies.
2:52:59
Um I'm excited to say right now like there's a team down at an army base as we speak deploying Turos at an exercise with an Army Corps.
2:53:06
Um so pretty excited about that.
2:53:08
That'll be our second deployment within the Army.
2:53:12
Um we haven't announced it yet, but we also have work within the Marine Corps as well. Yeah.
2:53:16
>> So we know we got to spin the flywheel with these types of things and core to our DNA is how do we get out to these exercises in real world scenarios with war fighters with our sustainer you know sustainment customers and do this and that you know build closely with the war fighters in many ways what what
2:53:30
Palanteer pioneered and what Andrew pioneered right how do we do this with them to then build momentum get those get ahead of the requirements do things very quickly and iteratively to then get to that goal of write an enterprise you know or production deployment in those types of situations. Um so you know I
2:53:43
types of situations. Um so you know I think you you guys well know that you know seed round is about hey do we have product market fit is the thing that we're building needed right we have proven that and what we thought was going to take 12 to 18 months it we did
2:53:55
it in 8 months with initial pilot contracts in the military and now series A is about how do we scale the team and how do we ruthlessly execute on the things we need to do right to meet the milestones we have to actually deploy this across the not just the joint force but even potentially our allies as well. How much uh you know we in the the
2:54:11
How much uh you know we in the the recent conflict in the Middle East, how much are you trying to learn and and how much are you guys paying attention to uh the challenges they're facing there versus hey we already know our systems
2:54:26
and processes are so broken that we just need to bring them and and and so um you know ancient uh I'm sure uh you know we just need to bring them into the present and then and then worry about kind of more specific deployment ments and and problems. >> Yeah. Well, I mean everything's a >> Yeah.
2:54:42
Well, I mean everything's a learning opportunity, right?
2:54:44
So whether we're talking about the Russia Ukraine war and how how the speed and the scale of sustainment that has to happen in in a peer or near peer adversary or we're even looking at the recent events in the Middle East, right?
2:54:54
There was a not insignificant consumption or expenditure of US surfaceto-air missiles even in the 12 days of conflict in in Sentcom, right?
2:55:04
So how do we think through knowing those expenditures in real time or near real time being able to even predict those now with those cons, you know, expenditures into future needs and being able to position things and reposition things, inventories, right?
2:55:15
In this case, munitions in the right place.
2:55:16
To me, those are all important lessons learned, right?
2:55:20
We have to be able to do it at the scale that is going to be required and at machine speed.
2:55:23
required and at machine speed. And then we have to take a real hard look at like what are the exquisite things that are very low density that we need to get like exquisite munitions and systems that are going to be required um you know in a conflict should a conflict come hopefully not but should it come
2:55:39
>> and I think you know Peter and I founded this company in many ways because I don't question that we're not going to have the most exquisite munitions the best weapon systems in the world what keeps me up at night is like can we get them in the place we need them at the
2:55:51
scale we need them at the right time right can we actually do that like that's something that I think is lacking and frankly has been overlooked by the technology you know companies of today right and how do we sustain the force and how do we do this with modern technology >> yeah yeah that's the entire conversation
2:56:06
um just uh yeah uh capacity more than capability so uh thank you for everything that you're doing and thanks for joining the stream >> to the team too tremendous progress love uh love to see you guys making 18 months of progress in eight months >> we love to see keep the future Yeah. >> Awesome. >> Awesome.
2:56:25
>> Uh, welcome back on the stream anytime.
2:56:27
We'll talk to you soon and have a great day. >> Talk soon. >> Thanks, John. See you later.
2:56:30
>> Up next, we have Will Ahmed from Whoop coming in the studio. Welcome to the stream. Second time on the show.
2:56:38
>> Um, we had him on around the last product release and we will invite him back to the stream. >> There he is. >> How you doing? Well, good to see you. >> What's up, guys? Good to see you. >> What's up? >> Great to see you.
2:56:50
>> Uh, where are you right now?
2:56:50
What's in the background? This is Whoop HQ.
2:56:53
I'm in my office and if you look right here, that's Fenway Park. >> Oh, that's right. >> We're in Boston. Yeah, >> Boston. Very nice.
2:57:00
Oh, we got some claps from the team in the background.
2:57:02
Uh, someone's happy about Boston.
2:57:04
Anyway, give us the update.
2:57:06
What's What's new in your world?
2:57:09
>> Well, we recently launched the Whoop 5. 0 and the Whoop MG.
2:57:12
So, two new hardwares, three membership tiers, bunch of new bands and accessories.
2:57:16
Uh, the market seems to be loving the product.
2:57:19
business has been uh has been really good. >> Great.
2:57:23
>> And uh yeah, we've launched a lot of new features.
2:57:26
We just came out with a with a health span feature that will tell you your Whoop age.
2:57:30
We worked with uh the Buck Institute, which is the leading institute in the country for longevity.
2:57:37
So, we found that, you know, the top nine biometrics that correlate with all cause mortality.
2:57:42
I'm sure you guys have very low whoop ages.
2:57:44
And uh and >> yeah, how do you think about when when just sorry to interrupt, but but a lot of anybody that uh anybody that does any type of like health testing, diagnostics, a lot of people are adding an age uh or or or this kind of like age score.
2:58:02
And it feels like there's incentive to for some players to just put the age as low as possible so that people share it.
2:58:09
That's obviously not great for the user.
2:58:11
They're like, "I'm 35, but this score says I'm 25, so I'm going to share it, you know, on on social media."
2:58:19
>> I took one of these biological age tests, and it said that I had the mind of a four-year-old.
2:58:24
>> The mind specifically, the body of an elderly man, but the mind of a >> It said I was sharp as a compliment. >> Okay.
2:58:34
But it is >> You have the mind people have said, "I have the mind of a golden retriever." >> Yes. Yes.
2:58:38
It said It says you're as smart as a four-year-old. Uh it was impressive.
2:58:41
Um but my question is yeah, it is kind of like a little bit of an optimistic flip because you could flip this around and just say uh your your life expectancy like the average life expectancy is is 75 and you're at 85 based on how you're aging.
2:58:55
So that's much darker that does like the death.
2:58:59
Yeah, I remember those apps and those are much darker and spookier and weird and uh and macob.
2:59:04
Um but this feels this feels uh much more optimistic, much more tractable.
2:59:09
something you can work towards.
2:59:10
But what are the pitfalls?
2:59:10
What do you what did you make sure were you without naming competitors like like what what did you want to avoid in the in the in how you designed your product? >> Yeah.
2:59:23
It felt like I it feels like some are just a random number generator. Yeah.
2:59:27
And you're doing it with the Buck Institute. >> Yeah.
2:59:30
>> Sounds sounds >> Yeah.
2:59:31
Look, we we worked with Dr.
2:59:31
Eric Verden, who's literally one of the best doctors when it comes to longevity and the research institute there, we found nine biometrics that most closely correlate with all cause mortality. >> Yeah.
2:59:46
>> Yeah. and uh and they were all things that that we measure and uh and so we're able to show in great detail how Whoop members stack up to other people their age uh in a bunch of different factors like sleep consistency, hours of sleep,
3:00:01
steps, uh you know, time spent in heart rate zones, your V2 max, your weight, uh a number of you know kind of obvious uh metrics that we can we can qualify and uh we also show all the research that we used in developing the feature and uh and look it's not everyone wins at soccer. This is not a participation
3:00:19
This is not a participation trophy type product.
3:00:21
Uh you know I think like people generally associate Whoop members as being pretty fit and healthy and I think it's like 55% of people on Whoop are younger.
3:00:33
>> I was about to ask >> that means like 45% you know are are seeing that they've got some work to do and uh and that's how we think it should be.
3:00:41
You know it's it's designed to be really transparent and honest. >> Yeah.
3:00:45
But that's a good balance because if it was like 99% of Whoop customers just happen to be, you know, half their biological age, you'd be raising some eyebrows, but it seems like you dialed it in appropriately.
3:00:54
Uh anyway, uh is that is that a health claim?
3:00:57
Do you need to get that approved by the FDA?
3:00:59
Are there other things that you that where the technology has progressed but maybe the the legislative framework or the the government regulation framework hasn't advanced as fast as you'd like and you feel like the consumer might be getting left behind because of some some uh some croft in DC.
3:01:20
>> It's a good question because we've had some challenges with the FDA recently.
3:01:21
I think it's worth just stating that the the role of the FDA is to regulate products that have a medical diagnosis. >> Sure.
3:01:31
>> And uh and the 21st century cures act makes it pretty clear that if a product is intended for wellness, it's not designed to be regulated. Mhm.
3:01:41
>> And the you know the structure of this exists for heart rate, sleep, so on and so forth where let's take heart rate for example that's existed heart rate monitoring has existed since the 80s with chest straps and other tools obviously Whoop does it today Apple watch so on and so forth and so you can show heart rate in the context of sleep or exercise or whatever and that is wellness and therefore it's not regulated.
3:02:08
If you want to tell someone that they have AIB, uh, that all of a sudden is a diagnosis and so that would need to be regulated by the FDA.
3:02:16
Now, just to say it, we also spent the last two and a half years getting regulated with the FDA doing clinical trials and so now we have a medically cleared product by the FDA to do ECG monitoring from the wrist and also to do a detection.
3:02:32
So that's just one example of the difference between wellness and medical.
3:02:36
You know, with sleep, you can say hours of sleep, you can do staging, that's wellness.
3:02:40
If you want to tell someone they have sleep apnea, that's medical.
3:02:44
>> And where the FDA is inconsistent on this is with blood pressure.
3:02:48
>> So, Whoop spent the last three years and tens of millions of dollars innovating to uh develop blood pressure insights.
3:02:57
And this is a really cutting edge feature that allows us to measure blood pressure from your wrist and give you a daily estimate which just to say it is a pretty big innovation in the wearable space.
3:03:07
And we brought this to market in in May and the FDA has come out saying that they think that it should be a regulated product.
3:03:16
>> Now again back to this distinction between wellness and medical.
3:03:19
We built the feature for wellness intended use.
3:03:24
So, it has a lot of disclaimers around it that it's not designed to diagnose hypertension.
3:03:27
It's not a medical device.
3:03:30
And it shows you how your blood pressure uh impacts sleep and stress, how it's impacted by nutrition.
3:03:35
Uh I had a woman in my office last week and she uh was seeing low readings on her blood pressure insights page.
3:03:43
Turned out she was dehydrated. She drinks more water.
3:03:47
It goes back to, you know, a more normal reading.
3:03:49
That's like a classic wellness use case.
3:03:52
So, we built this wellness feature and now we're we're in a debate with the FDA over it.
3:03:56
But we're fighting uh to keep this product in market because uh at the end of the day, it's it's the right thing to do. Consumers are loving it. It's innovative.
3:04:07
And by the way, it's the law because the 21st Century Cures Act says that you can't regulate wellness intended features.
3:04:17
What are what are some of the break points for uh amount of money to really uh have a good relationship with the FDA?
3:04:24
Like I imagine like a seedfunded startup has no chance of getting cleared as a medical device for you know $300,000 investment.
3:04:34
I imagine Apple should have no problem because they probably have an office in Washington DC.
3:04:41
But you know you're like a pretty big company.
3:04:43
I feel like you should be able to afford it.
3:04:45
But um like how how tricky is it to and how expensive is it to actually just go for the other designation with any of the the wellness features and just kind of shift them over into the medical feature and would there actually be a a product or consumer benefit if you did that?
3:05:08
>> Well, it's a good question.
3:05:08
There there are a lot of costs associated with being uh a medical device.
3:05:13
you know, you have to go through a clinical trial process.
3:05:17
You have to work with the FDA.
3:05:17
Depending on the functionality, you you might need to hire a lot of consultants to support that.
3:05:24
So, it's a it's a hard thing for uh smaller companies to do.
3:05:27
The other thing just to say it though is it's time consuming.
3:05:32
>> So, you know, it'll delay your time to market by two, three, four years depending on the type of FDA approval that you're seeking. >> Yeah.
3:05:41
And uh and then I think the real main question is what's the intended use?
3:05:46
Is the intended use around describing wellness or is the intended use around diagnosis?
3:05:53
>> And uh and so our intended use today is using blood pressure insights to help people understand their wellness.
3:05:58
And in the long run, of course, we can imagine having uh a more diagnostic product, but that's not what we've we've shipped yet today, nor is it what we're claiming to have. >> Yeah. Yeah.
3:06:10
And I imagine that even even if you have unlimited money, you still uh will be delayed by a couple years while the FDA reviews these claims.
3:06:18
And then the problem is is that you're shipping new hardware like every year.
3:06:24
So >> you have millions of people that are dehydrated and yeah blood pressure because of it and aren't even able to be aware. >> Very rough. Well, what's next? What is the plan?
3:06:33
Is there is there is there anything on the horizon that you want to get through or uh or move over or is it just kind of like sort this debate out and then move on?
3:06:47
>> Oh, we're going to keep working with the FDA.
3:06:48
I mean, like I said, we've been working with the FDA for years and and I have a lot of respect for the FDA.
3:06:52
I think they they have an important governing function in this country.
3:06:55
Uh I I just think they've missed the mark on this on this particular case.
3:07:00
And so we're going to fight for that and we're going to fight for health data for Americans.
3:07:06
Uh in terms of where Whoop is going right now, we just announced a bunch of new features that are coming out before the end of the year.
3:07:12
And one of which I'm very excited about is advanced labs.
3:07:16
So Whoop is going to be coming out with our own uh blood testing.
3:07:21
>> You're going to be able to upload all of your past blood tests.
3:07:24
doesn't matter if it was through a doctor or another product or what.
3:07:27
Um that's all going to be able to live inside Whoop.
3:07:30
It'll be grafted over time.
3:07:31
It'll become part of uh your Whoop data.
3:07:34
And then on a go forwards basis, you'll also be able to uh take blood tests.
3:07:39
And this is a real new capability for the company, but it's going to help bring more and more of this data under one umbrella, one roof, so to speak.
3:07:50
You know, we're trying >> all HIPPA compliant, I assume.
3:07:51
Is that what is that the compliance framework that matters here?
3:07:56
>> Yeah, you have to work with the right partners in order to enable it and obviously the testing is pretty sophisticated. Sure.
3:08:02
>> Uh we've got you know some great partners in the space but a lot of it's also explaining what the data means. >> Yep.
3:08:09
>> And I think that's where Whoop historically has been quite good is is you know presenting information and trying to coach on it. >> Yep.
3:08:17
What's powerful about the blood testing is we'll now be able to pair it with your 247 physiological data.
3:08:21
So, you know, maybe if you're not sleeping well, it has something to do with a biioarker or a blood test.
3:08:28
You know, maybe your exercise is improving improving something here.
3:08:32
You want to be able to have all this data under one umbrella so you can understand it. >> Makes sense.
3:08:38
Uh well, thank you so much for stopping by.
3:08:40
Good luck with everything at the FDA and good luck with the rest.
3:08:43
>> Have fun with your friends over there.
3:08:44
I'm sure you guys are spending a lot of time.
3:08:46
>> Enjoy the frequent flyer miles as you go to Washington DC regularly or maybe you take the train. Who knows?
3:08:50
Anyway, thank you so much for stopping by. We'll talk soon, dude. >> Thanks, progress. >> Have a good one. Bye.
3:08:57
>> Up next, we have a niche from NIT coming into the studio.
3:09:00
We are talking about them and I think we got some good news.
3:09:04
I think we might be hitting the gong.
3:09:06
>> I'll let you do the honors, Sean.
3:09:07
>> First gong hit for me for the >> day for the day.
3:09:10
I'm going to have to hit the gong for Palanteer.
3:09:11
But >> how are you doing?
3:09:13
uh would you mind kicking us off with a little bit of introduction on yourself and give us the latest news? >> Yeah, for sure.
3:09:18
Well, first of all, thanks for having me here.
3:09:20
My name is one of the co-founders and the CEO of NIT.
3:09:23
And uh yeah, we're excited. We announced our uh $16.
3:09:26
1 million series A on Thursday. >> Let's go. >> Congratulations. >> Back hand. >> You love it. Amazing.
3:09:35
Talk to us about the company.
3:09:37
How are you pitching it right now? Who are the customers? Break it down for us. >> Yeah, for sure.
3:09:41
So yeah, NIT, we have built a system of agents that is really building the simplest way for enterprise consumer researchers to go from, hey, I have a business question to here's a story I can share with my stakeholders.
3:09:55
Um, so yeah, what we're solving is this age-old problem of anytime you want to talk to your customers, especially these large enterprise brands, it's taking them four to six weeks, they're spending anywhere from tens of thousands to hundreds of thousands of dollars.
3:10:06
and we're helping them do that now in days instead of weeks and at a fraction of the cost.
3:10:12
Uh so working with the largest enterprise consumer brands today.
3:10:16
What is the is is the key differentiator just uh AI?
3:10:19
Are there other differentiations on the the go to market strategy or the the way you build the product?
3:10:26
like is there counterpositioning against the big you know 800 pound gorilla in this category that uh you know we we we know and uh might be at a basketball game but uh you know he's he's definitely not taken days off recently. >> Yeah.
3:10:41
So um yeah I mean great question there.
3:10:44
there. So basically at NIT you know what we've really kind what we're putting our hat or hanging our hat on right now is this idea of researcherdriven AI right so we were we're an AI native company we started the company you know a year or two ago when when uh AI was uh still not
3:10:59
not as prevalent in our industry as it is today but what we realized is that you know you can you can run this data through the AI but really what it's missing is the context that the researcher brings understanding how this shows up in your business how do you actually sell put into the organization. So throughout our plat platform, the way
3:11:15
So throughout our plat platform, the way that shows up is one, we always have the researcher at the center of how we run research.
3:11:21
So the researcher on our platform will go in and share this is you know more information about my business.
3:11:27
Here's the context of my business.
3:11:28
Here are my stakeholders and how they like seeing the data so that we can get the output as close to that stakeholder ready.
3:11:33
And then the second is we've invested pretty pretty heavily into an internal research team.
3:11:36
Um, so throughout the process, all of our customers are paired with a dedicated expert researcher.
3:11:42
And what we've seen is that that gets the AI to a better spot, a place where um, our research customers can actually then take this, it's a really good first draft.
3:11:51
They clean it up a little bit and then they can share with their stakeholders pretty quickly.
3:11:56
>> How are you closing all these logos?
3:11:58
You got Amazon, Mars, ESPN, T-Mobile, Paramount, NASCAR, MOET, Hennessy, Overtime, >> insane lineup, >> WNBA, crazy insane, >> crazy stacks for series A.
3:12:12
>> I'm sure I'm sure with some started off with some good old hustle, you know, I I flew out, met a bunch of our first customers that just kind of figured it out.
3:12:20
But really what we've invested, going back to this researcher-driven AI piece, is we've really invested in the human aspect of it.
3:12:25
So um we primarily meet most of our customers through events.
3:12:30
We show up uh we go to all the major events in our industry.
3:12:32
Um we we show up to their offices, take them out to you know take them out to dinner and and invest heavily in based meetings.
3:12:38
Um the other aspect is really around our >> What about NASCAR?
3:12:44
You you you did you ever crush a beer at at a NASCAR event for research purposes?
3:12:50
I feel like that that's a good good sign of respect in NASCAR.
3:12:54
The NASCAR team was kind enough to invite us out to Daytona when we kicked off our partnership.
3:12:58
So, yes, they they know how to have a good time for sure. >> Just watch.
3:13:01
They only drink champagne.
3:13:02
It's just the biggest narrative violation of all time. Uh it'd be great. Oh, what?
3:13:06
So, where I mean you're talking about the customer the human centric research where where do humans excel in this uh in in this problem set in this domain?
3:13:17
And then where does AI thrive?
3:13:19
like where do you not want to delegate a piece of the work to an AI and where do you want to spend zero minutes of human time focused on something? >> Yeah, absolutely.
3:13:30
So, you know, where we've seen humans really excel is is storytelling and our entire platform is built around how can we take all the time inensive kind of grunt work that goes into research.
3:13:40
So think, you know, setting up your first draft of the questionnaire, analyzing all this unstructured qualitative data, whether it's video data, open-end text data, and we've built really good AI that can get you that first draft, whether it's a questionnaire or a final report.
3:13:54
And really where the human researcher comes, both um our internal team as well as, you know, the external kind of partners that we work with is how do we take that those insights and tell a really powerful story here?
3:14:07
something that will actually drive action from our marketing team or product team or strategy team. >> Yeah.
3:14:13
How important is it to build a like liquid supply of people that are willing to answer surveys?
3:14:20
I you know when I think about Amazon, Mars, ESPN, T-Mobile, Paramount, NASCAR, uh Moa Hennessy, JBL, like a lot of those research projects are probably just I want to know how Americans feel about my product.
3:14:37
It's not this hyper specific talk to just my customers, my my 10 people.
3:14:41
I can probably call those people if I'm running that company.
3:14:43
But if I want to know how is my brand perceived >> or globally T-Mobile, T-Mobile also can do like like easily survey active customers, new cohorts, old cohorts, whereas like Moa can't necessarily.
3:14:58
>> And so how how important is it over time for you to be able to bring surveyable people to a platform? >> Yeah. Yeah.
3:15:06
Well, first of all, I would say we're we're very data agnostic, right?
3:15:10
So, we work with our partners where they layer us on top of their existing customer list.
3:15:13
Um, we work with some of the larger panel companies in our space.
3:15:17
But, as you know, making that stance that we're data agnostic, one thing that we've invested a lot of energy in and technology in is how do you make sure the quality of that data is really good.
3:15:26
So, a big problem in our industry today is uh data quality challenges.
3:15:32
There's fraud, there's bots, there's there's a lot of uh not so great stuff happening.
3:15:36
And so what we do is we've invested a lot in our AI tech to especially if you think about video for example, if you collect video responses, you can actually see the human on the other end.
3:15:45
We analyze across seven different plus parameters to make sure that that human is who they say they are and actually speaking on topic.
3:15:51
So the data quality is an area we've really stood out for our partners as well. >> Very cool. >> Anything else? >> No, this is great. >> I think we're good. Thanks so much.
3:16:01
Congratulations on the progress.
3:16:01
We will talk to you soon.
3:16:04
>> Have a great rest of your day. >> Cheers. >> Talk to you soon.
3:16:08
>> Let's rip some timeline.
3:16:08
I have been waiting for this.
3:16:11
You have >> some breaking news. >> Breaking news. >> Sawill Bloom. Five types of wealth.
3:16:14
Over 300,000 copies of that book sold. >> Incredible. >> Not surprised. Sell a million. >> Why?
3:16:23
Because we reviewed it on the show right as it went live.
3:16:26
And remember, interestingly, we tried to guess the five types of wealth.
3:16:33
>> Cars, watches, art, homes, >> horses. Yeah.
3:16:36
And we were wildly wrong.
3:16:39
Apparently, it's it's something about like living some sort of balanced life that's not that materialistic.
3:16:43
Money is only one of them. >> Yeah. Did we put it?
3:16:47
>> He had a little bit of a contrarian take. Yeah.
3:16:48
But, uh, it's done very well.
3:16:50
And congratulations to Sahill. Uh, what a run.
3:16:53
300,000 copies of that book sold.
3:16:56
uh absolutely massive number.
3:16:56
So, congratulations to him.
3:17:00
>> Well, uh Palunteer earnings came out after the close uh north of a billion dollars in revenue for the last quarter, up 48% year-over-year. Got beat uh by 7%. Uh going to be 1 uh 1. 08 billion next quarter.
3:17:19
Hit the >> gas uh estimate 11%.
3:17:20
Uh and then they raised their full year guide 6% from last quarter.
3:17:26
Uh and they had raised it already raised it 11% from the start of the year.
3:17:31
So they have 57% free cash flow margins. >> Congratulations.
3:17:38
>> Bad and 81% gross margins. >> 81% gross margin. So congratulations.
3:17:43
>> Doesn't look like a consulting company to me. >> It does not.
3:17:45
They're being >> jam jam uh at altimter broke it down.
3:17:50
Let's see how it's doing after hours.
3:17:54
>> Benjamin >> um jamming >> up another 3% after hours.
3:17:57
This is about to be I mean >> it's a big company.
3:18:01
It's going to have to be Mag 8. Throw them in there. >> Mag 8. Nice round. Nice nice nice number.
3:18:08
>> Chinese number of wealth, right? >> That's right. That's right, John. Of course.
3:18:11
Let's do some timeline because >> uh I >> I love it so much. I love timeline.
3:18:17
>> Uh Doomer says, >> "What you got?"
3:18:19
Uh, so Dave Asprey posted yesterday, huge news for gym bros.
3:18:23
A new study reveals that incline walking burns more fat than running.
3:18:28
>> We've been getting into this.
3:18:28
We've been doing this because of uh the bodybuilders when they're on cuts, they do incline walking and it's so much easier to get yourself into the mood of just walking at 3 miles an hour on a 10% incline.
3:18:42
>> Total bro science victory.
3:18:43
>> It's total bro science victory. This is hilarious.
3:18:45
Uh Dummer says, "Jogg cells absolutely molding and seething over hike Chads." >> It's remarkable. Yeah.
3:18:52
I mean, when you're when you do that incline walk, you throw throw on a podcast, throw on some TVP ad, listen to us, watch this, and start burning.
3:19:02
>> Mickey Freriedman says, "It's crazy that an LLM can win IMO gold before making a single funny joke.
3:19:07
The only thing >> it's not that >> they do the uh the that format where it's the be me jokes."
3:19:14
>> Yeah, they're pretty good at that.
3:19:14
But uh in general yeah uh RL uh is or funny jokes seem to be RL resistant uh whereas the IMO is uh uh easily defined by a benchmark >> and therefore hackable u but still you know still valuable and I I like this.
3:19:30
I I prefer that uh that it focuses on IMO gold because I can't do that but I can make funny jokes.
3:19:39
>> Tay Kim, author of the NVIDIA way says here's what I would do if I was CEO of Apple.
3:19:43
Quadruple the RAM in iPhones to 32 gigabytes and have max model at 64 gigabytes.
3:19:48
Memory is oxygen for local onde device AI.
3:19:51
More equals smarter and more powerful. Take the margin hit.
3:19:55
Memory isn't even that expensive.
3:19:55
Buy Mrol or Enthropic and invest 100 billion in AI compute annually. I don't agree.
3:20:04
How much does it cost to buy anthropic right now?
3:20:05
500 billion or something.
3:20:05
I mean the the bigger issue the bigger issue here is like one I just don't see France selling their national AI championally.
3:20:13
Why would Europe ever allow that?
3:20:16
That's the whole point of the company.
3:20:18
>> And then Enthropic is dominating in codegen and like very clearly could be seeming I I it seems like they could be a trillion dollar business someday just generating code for the world. Yeah.
3:20:30
>> Uh so I just don't see Apple um you know also Apple buying a you know whatever price they would have to be pay. >> Yep.
3:20:41
>> Uh and then and then like kind of I don't know keep the core codegen business running and then >> well regardless shout out to take him.
3:20:49
He's sending us some books of his book the NVIDIA way. >> But I agree.
3:20:52
He says Apple has the capital.
3:20:54
Apple now knows the AI computing shift is existential.
3:20:56
Whereas the bold action and urgency, they are the only big tech company not in the AI race. Completely agree.
3:21:02
Take the short-term pain to have long-term relevance.
3:21:05
>> Should definitely invest.
3:21:05
Might might tweak my recommendation, but uh love the thought experiment and very interesting.
3:21:11
And we should switch to the Mark German article power on.
3:21:12
Apple has a new answers team working on a stripped down alternative to chatbt for world knowledge as it looks to catch up in AI.
3:21:22
Um, so Apple has a new answers team developing a stripped down rival to chatbt.
3:21:28
This of course is like distill the model down, focus it, and you're going to have this there's this idea that you're going to have the really smart genius models, the big models training smaller models for specific things.
3:21:40
You're going to have one that just does code, one that just does, you know, Wikipedia, one that just does weather really well.
3:21:45
And so if you can just kind of route between those, you can be very efficient and you can probably run it on an iPhone.
3:21:50
So, um, >> equally important, Mark Zuckerberg says, "Without AI glasses, you'll be at a cognitive disadvantage."
3:21:57
Hopes revenge says, "Good. I'm used to it.
3:22:04
>> Buy my product or you >> one of the best posters >> in history." >> Revenge is so good.
3:22:09
>> Um, >> got to have him on the show.
3:22:11
>> Pavle Esperuh, former guest and friend of the show says, "People will congratulate me on raising money."
3:22:15
And it's like, "Bro, I am poor and stressed out.
3:22:20
It should be condolences.
3:22:22
>> Did you talk to Pavle in New York?
3:22:24
>> Uh, and Will says, "Tell that to your ramp equity."
3:22:26
Pavle says, "The Pekk store doesn't take ramp shares." Well, they should. >> They should. >> They should.
3:22:33
>> It's better than cash.
3:22:33
Better than a than a than a store of value. >> Exactly.
3:22:39
>> Uh Sam Alman says, "Entering the fast fashion era of SAS very soon gets uh 16K. Banger here."
3:22:45
Um I the thing that's interesting here is it uh my immediate thought was look at the performance of LVMH during the fast fashion era. >> True.
3:22:59
>> And it you know going off if you extrapolate this example it says that you know craft craftdriven uh luxury products will um you know will will endure.
3:23:10
Obviously fast fashion, fashion is much more about like signaling and trends and >> Yeah.
3:23:17
>> And uh SAS can be very utilitarian. Um >> yeah.
3:23:22
>> Were you going to were you going to hit a gong or something?
3:23:24
>> No, I was looking for the red flag.
3:23:24
We have the red flag somewhere.
3:23:27
But um but I think the fast fashion uh we have a red flag in the studio.
3:23:32
Ben, you want to come wave that on camera? >> I'll wave it. >> Wave the red flag.
3:23:37
>> Wave the red flag. Uh, but the reason I'm ra waving the red flag is be it's more like bait maybe maybe red flag is not the quite the right one but the fast fashion is there's a different there there's a different uh term here which is like disposable software and and I
3:23:55
like the formulation of like the meme generator era of SAS the meme generator era of SAS where you know everyone got everyone got uh the ability to make memes on their phone And uh and that's just a lot a lot less controversial because it's like democratizing uh democratizing um this is it's like the teespring of SAS. It's like I want to be able to
3:24:17
It's like I want to be able to print my logo on a t-shirt on at the click of a button.
3:24:22
I want the Shopify for SAS soon.
3:24:24
I want the the you know the the chat GPT for SAS.
3:24:27
The fast fashion feels like feels like very sloppy, but all the value and all the creativity being housed within Shien and Timu.
3:24:39
And I don't like that as much.
3:24:41
It's way more cooler when you see someone who's wearing like a small merch drop from one artist that they like and you're like, "Oh, that expresses something that's unique."
3:24:50
And it's only made possible by the industrial capacity that is so ramped up to the point that you don't just need to have the Nike shirt and everyone has the same shirt.
3:25:01
Everyone can express themselves differently.
3:25:02
Like when the screen printing era I remember like you used to just be able to take spray paint and just spray paint a logo on a shirt like uh that that is what I'm looking forward to.
3:25:11
We don't know how to do that anymore.
3:25:13
But yeah, I mean, you walk around Venice Beach, New York City, Time Square, you'll see like unique shirts that are very weird, cringe usually, but I like I like that idea of like democratizing the creation of SAS so that there can be oneoff websites, but that doesn't feel fast fashion to me.
3:25:30
I'm sure there will be fast fashion, but >> I think it's more there's we talked about this apps as memes. There's a lot of apps.
3:25:36
There's apps that should exist that shouldn't like you shouldn't spend time producing them because they don't have like an obvious business application. Yep.
3:25:45
>> There's also apps that should exist but shouldn't have 10 engineers working on them.
3:25:51
They should just have one person who loves building the product.
3:25:52
Uh my dad uh showed me an app yesterday >> that you just sat outside.
3:25:58
We were having lunch in my backyard and he put his phone down >> and it just was using the microphone to pick up which birds were no way >> which birds were in our yard and within like 5 minutes it identified like 10 different birds and was listing them out by name. Yep.
3:26:17
>> And he he was showing me the profile and it's he can see all the birds he's been around. It's like insane.
3:26:21
And so there's apps like that that I just think like probably a great small business that uh but >> and way cooler if you don't need to go and raise a bunch of money and then have this like cap table prefer pref stack like hanging over your head.
3:26:35
It's just like >> that might be at its best a $5 million a year business but you have a small team on it and it's >> and so the fixed cost of of developing the first version is $5 million in engineer salaries.
3:26:46
It's like you never really clear the prep stack.
3:26:48
Whereas if it's just an idea and you can just get it up and and then run it, it like can actually be a good business. So, uh I I like that. I like that world.
3:26:56
Anyway, we have a funny we have a funny take in the in the uh chat from AAR uh talking about Apple should buy Perplexity, which we've talked about before, and I think that one makes more sense than Mistral or Anthropic.
3:27:09
Much more getable at the current valuation, 18 billion versus what what's anthropic at 150 or something.
3:27:16
160 that seems and then they'd have to pay more obviously.
3:27:18
Uh so that but I don't know why he said Sony.
3:27:22
Sony is an interesting choice. I don't know. >> You mean Sonos? >> Sonos.
3:27:25
Sonos would be an interesting one for sure because then they could uh I mean they already >> they tried to do their speakers thing.
3:27:32
>> But Sony came example where Apple doesn't need to become a foundation model lab.
3:27:37
They just need to be able to build good products.
3:27:39
And I think Perplexity is consistently built pretty solid products.
3:27:45
I think at some point, you know, if you think like like iCloud is not hosted on AWS, like they did build their own data centers eventually, even though they're not a true hyperscaler.
3:27:54
Um, I could see them just being like we trained our own models so that we know the legal risks.
3:27:59
And I think that's a lot of what the hang-up is with these partnerships and stuff is like uh there's a lot of legal risk, a lot of brand risk, hallucination risk, all the stuff that the foundation model labs are grappling with, whether it's AI psychosis or uh you know intellectual property infringement or hallucinations or just bad recommendations, mediocre products, all the rough stuff.
3:28:20
It's great when that lives in the founder mode startup world.
3:28:24
Apple's not in that world right now.
3:28:26
And so it makes but but eventually you will be able to develop these foundation models in a way that that obiates all that risk. So >> totally.
3:28:34
Uh while we're at it, uh Mark in the chat says, "This has quickly become my favorite show." >> Oh, thanks, Mark.
3:28:40
Great to your last message quickly become my favorite message in the last uh I appreciate it, Mark.
3:28:47
Uh we have another post here from Matthew Zaitlin.
3:28:49
He says, "I only >> Did you see this one from Frank?" Sorry.
3:28:53
Todd Graves should lead product at perplexity.
3:28:55
That's the CEO of Raisin Canes, Chicken Fingers. That's insane.
3:29:01
>> Someone's Someone's clearly listening to to Founders Podcast 100%.
3:29:04
No, I should leave you Founders Podcast. Todd Graves fan. Hats off to you.
3:29:13
>> Apple should acquire Raisin Canes. >> That'd be amazing.
3:29:15
Put them right Put him right in the Apple store.
3:29:17
You come in for some chicken. You pick up a new iPad. Do you need the iPad? Maybe not.
3:29:22
But you enjoyed the iPad. You enjoyed the chicken. It's great.
3:29:23
Uh, Matthew Zitelland says, "I only trust reporters that are fully locked in."
3:29:28
And uh, this is from a piece on Joe Weisenthal in the New York Times.
3:29:35
>> In the New York Times, they did an amazing crushing yesterday. He says, "Mr.
3:29:39
Weisenthal, who said he used nicotine probably more than is optimal and was going through a full 15 pouch can in a day." >> Yeah.
3:29:48
You know what's better than than using a nicotine pouch?
3:29:50
developing a new nicotine patch with your co-host before you launch your podcast >> as a way to kind of test if we have, you know, on air. >> Exactly.
3:29:58
I I was I was reading this and you're like, it's like, how are we the more extreme version of this somehow?
3:30:04
It's like extremely odd and rare that we would be, but we actually did build a a nicotine pouch brand for Finance Bros called Excel.
3:30:11
And we and we launched this product before launching this podcast.
3:30:15
And that was kind of where we got this.
3:30:17
We we we realized that we were on to something because we sat down to record a promotional video that was supposed to be like 3 minutes long and we wound up yapping for over an hour.
3:30:24
>> Ben was like, "Did you guys prep for that?" Like, "What was that?"
3:30:28
>> Ben was like, "What was that?"
3:30:29
>> So, uh, we'll we'll end on this post.
3:30:32
Lena says, "Uh, August first Lena."
3:30:36
Yeah, we just go by her first name. Talk about her a lot.
3:30:39
>> She really is the biggest Lena in tech.
3:30:40
She says, "A great reminder that letting startups grow into independently successful businesses rather than be bought up by existing giants can generate enormous value.
3:30:47
A win for employees, investors, innovation, and the public and investment bankers."
3:30:53
>> uh talking about uh Figma. Yep.
3:30:56
>> And Sha Levy says, "Lena Han cuts off the right hand of a genius."
3:31:01
>> Lena Khan cuts off the right hand. You said Lena Hand.
3:31:04
>> Lena Hand cuts off >> Lena Khan.
3:31:06
Lena Khan cuts off the right hand of a genius pianist who nevertheless perseveres and produces a one-handed masterpiece for which she then takes credit. Yeah. Interesting. I don't know.
3:31:18
It's a It's a It's a hot take in tech relitigating this one.
3:31:23
Al Alex at Andre uh said something which was >> out well >> in a in a in a free society we let you know we should let people do things if they're not creating immediate harm right yeah >> and so uh deciding how other people are going to run their business is against uh >> is against >> I I still think you had the best the best post about the Lena news I'm wondering if I can pull it up but Where was it?
3:31:54
It was you replying to Lena Khan.
3:31:57
I'll I'll put it in the chat. Um, >> you found it. >> I found it.
3:32:00
It's in the timeline chat now.
3:32:02
But if we can pull up this image of Jordy Hayes replying to Lena Khan.
3:32:05
Lena Khan says, "A great reminder that letting startups grow into independently successful businesses rather than being bought up by existing giants can generate enormous value."
3:32:14
And Jordi Hayes shared a picture of a uh what is this?
3:32:19
A soldier who's fought a war.
3:32:19
And there's uh and there's a a dolphin and rainbows in the background.
3:32:27
>> He just lived through like he's clear, you know, he's in paradise, right?
3:32:31
There's tropical rainbows and >> and uh it's like, you know, he made it he made it through a war and he's smiling now, but clearly, you know, been through uh been through it.
3:32:42
So Lena really made the Figma team >> um she put put them in a dark place. earn it.
3:32:50
They earned it though and now it's a better story. >> I don't know.
3:32:54
Um, lots of nuance there.
3:32:57
>> Well, >> thank you for watching. Thank you for listening.
3:33:00
It is going to be a massive week in technology.
3:33:01
Uh, there's some some news dropping later in the week that we will be covering live.
3:33:05
We'll be up in San Francisco. >> I'm excited.
3:33:10
>> And uh, in the meantime, I can't wait for tomorrow. >> Yeah.
3:33:13
>> Leave us a fivestar review. Apple podcast.
3:33:16
If you're listening on Apple podcast, I reme I recommend switching to a more video native platform like Spotify or YouTube. [Music]