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Founder You're watching TVPN.
4:32
Today is Monday, July 20th, 2026.
4:35
Let's go to the fans zone.
4:38
We are live from [applause] TV Ultra.
4:40
The template technology, the fortress of finance, the capital of capital.
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Let me tell you about ramp. com. Time is money. Save both.
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He's to use corporate cards, bill pay, accounting, and a whole lot more all in one place.
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We had a fantastic talk with Eric, the co-CEO of RAMP on Thursday.
4:54
But we are excited to be back.
4:56
It's Monday and there's a lot of news.
4:58
The big stuff, uh, Kimmy K3 dropped.
5:00
But first, we should recap the weekend. How was your weekend?
5:05
>> The elephant in the room that I'm wearing a suit.
5:08
>> Yes, the elephant in the Did somebody [laughter] say that?
5:09
The elephant in the room. >> Jord suit. He's feeling good. He's refreshed. >> Yeah, refreshed.
5:14
I uh Yeah, was in was in the weekend >> to the old country. >> Yes. Yes, exactly. Uh, really fun time. Glad to be back.
5:24
Uh very very uh refreshed, >> very fun.
5:28
I was at a Caruso property this weekend and I got to say being there, being at the the Myiramar Rosewood, uh I I can never vote for him for public office ever again. >> Why?
5:39
Because you want him to develop more properties like that.
5:42
>> I want him laser focused on resorts and malls.
5:45
>> It doesn't really hold up because the whole the whole point was that he could there was some possibility that he could make greater Los Angeles something like his incredible got to get a rosewood in every city in America and uh maybe >> or take private of the of the city of Los Angeles, turn it all into a Caruso >> potentially.
6:04
But uh it was it was a good weekend. Glad to be back.
6:06
Uh the timeline was burning up over the weekend over uh the AI cold war that's breaking out between the US frontier labs and Chinese open source.
6:15
Uh there's been a whole bunch of new Chinese open source models that have dropped with some uh promising benchmarks.
6:22
Uh the latest one that everyone is up in arms about is uh Moonshots Kimmy K3.
6:27
Uh it was unveiled last Thursday.
6:31
Now they aren't actually Yeah, >> I kept opening X and thinking looking scrolling a few times thinking, "Oh, I'm I'm happy for you." Or sorry that happened. I put my phone away.
6:43
I feel like I missed a lot.
6:47
>> Yeah, I mean it's it's gated behind of uh behind an app right now.
6:50
Um the waits are not actually open just yet.
6:53
That should happen Monday if things go to plan.
6:57
But of course there's a lot going back and forth uh with the administration.
7:01
People are uh you know uh weighing every possible consequence of this.
7:06
Um it's a you know the classic it's over. We're back.
7:09
Is this good for semiconductors?
7:11
Is this bad for semiconductors? Is this Jebans paradox?
7:15
Is it the counter example to Jebans paradox?
7:17
Um, it's all very interesting debates and everyone's uh coming out with their own opinion.
7:21
Dean Ball was going back and forth with uh two people uh David Saxs and also uh Emil Michael were duking it out on the timeline, fighting it out back and forth.
7:32
Um but you know, decent takes on both sides. Lots to chew through.
7:36
Um the headlines for Kimmy are impressive.
7:39
Um but not wildly surprising based on the current trend lines and model progress.
7:44
trend lines and model progress. Um but it does seem to invalidate uh the thesis that open source would start falling behind which was something that was start there was that chart from the government I believe that showed uh closed model progress was you know
7:59
accelerating on this particular exponential and uh open models were falling behind uh this seems like a proper catchup something that was predicted from you know many lab leaders that you know the six-month gap three-month gap ninemonth gap it will remain just that gap um for the foreseeable future. But there
8:16
But there are of course a bunch of other nuances here.
8:20
Um in a in the most above board scenario, sort of here's what happened.
8:26
Like the moonshot team, it's just a bunch of highly talented AI researchers who marshaled enough compute to release to release a great model.
8:31
Uh open sourcing Kimmy is a great way to grab attention, attract talent, drive demand for hosted API services uh that are more turnkey eventually.
8:39
There's a very reasonable business model behind open- sourcing.
8:43
Uh Red Hat Linux of course is an open-source company uh owned by IBM.
8:49
Now I think the acquisition price was in the $30 billion range.
8:51
And at any point you could just say I'm just doing Red Hat on my own, but a lot of companies pay for uh you know consulting hosted and all sorts of different uh services around the open-source piece of software.
9:05
Uh and so uh that's sort of like the the the the bull case like you know everything's going well.
9:10
The more skeptical folks on the timeline are pointing to uh you know ideas such as uh maybe the compute was smuggled in to China against chip controls.
9:18
Maybe the data was exfiltrated from frontier lab APIs routed through rapper companies.
9:25
Maybe this is a distillation attack.
9:25
Um the open- source strategy is a deliberate attempt to destroy American companies.
9:30
It's geopolitical warfare. It's the AI cold war.
9:32
Um, and there might even be spies inside companies stealing IP directly.
9:36
And so that's sort of like the bad version.
9:39
Uh, the reality is probably a mix of both, but we'll know more over time.
9:43
And we'll hear more statements from the labs and from all sorts of different uh, participants to understand there.
9:49
People were going back and forth on, you know, if you ask Kimmy its name, will it just say it's Claude or GBT?
9:54
And, uh, there were some examples of people doing that.
9:57
Seemed like it was photoshopped. Seemed like that.
10:00
Also seems like the easiest thing to fix possibly.
10:02
It's just put it in the put in the prompt, put it in the fine tuning.
10:06
Never can, you know, find and replace. Seems pretty easy.
10:10
>> They were showing it in the reasoning tracing. >> Yeah.
10:12
But, you know, I I I I don't know.
10:14
All these things are hard to hard to assess over time.
10:15
It takes time to actually work through what's real, what's fake, what's a hallucination.
10:19
You know, they all they all make mistakes from time to time.
10:23
But, um, there are bigger questions about AI safety in a world of powerful open- source AI.
10:27
Uh, I haven't seen a ton of takes from this crowd, but you have to imagine that they aren't happy about it.
10:33
Uh, maybe the uh the stop AI protests will head over to Beijing next and beat down the the doors of uh Moonshot directly.
10:41
Um, but it is an odd thing because even if you're not worried about the fast takeoff, you know, gray goo paperclip, uh, from a cyber security perspective, like things are going to get more complex.
10:56
There's reasonable solutions to all of this, but uh it is a more powerful tool in the hackers or the act hackers tool chest.
11:03
Uh fortunately, defenders have had roughly six months of frontier exclusivity with GPT cyber and mythos for a while and so there's been a lot of preparation.
11:12
We've talked to Nicash at PaloAlto Networks and George at CrowdStrike about this like they have been aware that this is coming for a while and so a lot of systems have been patched.
11:22
patched. Uh but at the same time you can just imagine now anyone who's a hacker can spin up a very very intelligent model and start sending a bunch of spam emails or spam text messages that stop seeming so AI and they're not as clockable because it's just another level of intelligence and it can do uh
11:43
reasoning traces and research and and so you know that the the the the spammer or the fishing attack can actually know what insurance you have and and pose as your insurance agent and actually know a little bit about your insurance agent instead of just sort of sending you a generic spam text. You're things are
11:58
You're things are going to get more customized, more personalizable.
12:02
Of course, there will be more advanced defense layers, but the war is continuing.
12:07
Um, there there really is no debating that open source is just cool, like open source AI.
12:12
It just feels punk rock to me.
12:15
It feels like Napster, like as a kid, the idea of just like liberating all the information culturally aligned to the internet itself.
12:24
[clears throat] >> Yeah, it's just a cool thing even though you do have to grapple with all the different problems that come from it downstream.
12:30
Uh like the there's something just deeply satisfying about the idea of uh of like a solar powered server rack serving you up unstoppable intelligence which is another good name for a neolab.
12:43
Um I don't know if there is one already but uh you know a country of geniuses.
12:47
In some ways to me it feels like >> aligned to America's ideals. >> Oh yeah.
12:54
It's like >> like it feels like like very empowering.
12:59
>> It's total second amendment. Totally second. Exactly.
13:00
It's like I should be able to go off-rid and have uh you know not a country of geniuses in a data center controlled by a corporation, but just like my own genius who's riding with me no matter what in my basement, you know.
13:13
And it's like and and you know even even if you're not thinking like nefarious it's like this it's this idea that you know uh I like to have a copy of a movie in case like the grid goes down.
13:23
You know the the more like you know apocalyptic thinking.
13:25
This is a very general thing that I think a lot of people align to especially if they're at all cryptonative.
13:31
It's like why do you want Bitcoin?
13:33
Well it's unstoppable money.
13:35
The government can't take it from you in certain scenarios.
13:37
Of course like there's a bunch of ways they can of course like anything else.
13:40
Um, but uh there there is just something very very attractive about having just a machine with all of human knowledge on it.
13:48
It's like a it's like the you know every house used to have the the full encyclopedia bratannica and you can just look up anything and now you have the LLM that can solve you know any math problem for you.
14:01
Um but uh you know the US government is going back and forth on this issue uh how to handle the situation and I imagine we'll see you know developments this week.
14:10
Uh the Kimmy K3 weights drop next Monday and so this is the week where if the government wants to do something one way or another um they have the opportunity to do that.
14:22
There was some back and forth on like would there be a a direct ban, a hard ban, a soft ban, maybe just some like messaging around hey like please be a patriot, don't use this foreign product, you know, there's all sorts of different ways like the original like buy made in America, buy American.
14:39
That's just marketing, but it does have some effect.
14:44
It's not enough to make GoPro a, you know,10 billion dollar company, but it is enough to, you know, cause a little bit of motion in the in the in the economy.
14:54
Um, >> one thing that was notable over the weekend, uh, Kimmy on X said that Kimmy K3 has received far more love than we expected and our GPUs are feeling it over the past 48 hours.
15:04
Demand has pushed close to the limits of our current capacity to protect the experience of existing subscribers.
15:12
We're temporarily pausing new subscriptions and prioritizing compute for current members.
15:17
>> We're adding capacity as fast as we can and we will reopen new subscription spots in batches.
15:21
So again uh Kimmy and the world more broadly are quite uh supply constrained um and uh yeah just showing or compute constrained and showing that uh the other dimension of this whole geopolitical uh battle is around the hardware side. So >> yeah. Yeah.
15:42
Um, I mean, in general, I think my my takeaway was, and Tyler, I'd be interested to hear how you process this, but, uh, like I I just don't see this as a black pill, even if it's like a deepseek moment, the markets sort of bounce around for a little bit.
15:55
Uh, it seems like there's still a lot of demand for frontier intelligence, a lot of demand for near frontier intelligence, a lot of demand for really cheap older models that have been baked down to basically intelligence too cheap to meter.
16:10
And we are going to be in this process of go and find an actual use for something and then bake it down to be really really cheap so that it can be offered by >> Yeah.
16:21
And in so so many moments over the last two years, >> we've there's been a company that's come out, maybe they've raised some money, you maybe forget about them for like 6 months, >> and then they pop up and it turns out even though they're the number 10 company in their category, they're still growing faster than uh almost any company uh that existed like six years ago. >> Yeah.
16:45
And a lot of it goes down to deployment, too.
16:46
deployment, too. There's like certain companies if they're in consumer like they just have a really good go-to market motion like they're just able to get customers to subscribe or use their product or if they're in enterprise like they just have a great like forward
16:59
deployed motion to actually do the change necessary as opposed to just like here's an API law firm like they could have done that and yet you have legal AI companies that have done a great job of coming in and actually creating a product that works within the confines of the the broader organization. Well,
17:14
Well, what what are your Kimmy K3 uh reactions, Tyler?
17:19
>> Uh yeah, I mean I think it was definitely like a a surprise, an update, like this is extremely good model.
17:23
The idea that open source is falling behind seems like very much.
17:28
>> We sort of got a preview with GLM 5.
17:28
2 like people were already saying like oh GLM 5.
17:31
2 is sort of catching up.
17:31
So >> yes, but even even that that was closer to you know maybe a year maybe a little less than than a year behind.
17:38
This seems like much closer. >> Okay.
17:41
Um >> but I I think I I broadly agree like >> you know we have okay there's this great open source model and now we have okay the jacobian you know conjectures like this this kind of storied math problem.
17:52
>> Yeahven and so like >> yeah there's just going to be a lot more use like everywhere.
17:55
I I expect you know these kinds of models to to >> be used everywhere.
18:00
It seems like a lot of the hype is around like specifically the parameters, the size of the model, the way it was trained.
18:04
the way it was trained. It'll be and then also it did very well in the front-end arena which was interesting because that's I mean it jumped ahead of everything there and that feels like a particular like it's it'll be interesting to see because you see more and more taste going into these models
18:19
like OpenAI is really big into the math stuff and uh Anthropic was really big on code early and there's all these different like you can just tell that like oh this lab cares about chess now and now the model's good at chess or the model got better at writing because like
18:34
they they took the time to spend a month sprinting on that and it'll be interesting to see like where did they sprint if anywhere where didn't they sprint and it's sort of lagging uh and then what harness it works best with how how how important is the harness relative to everything else but uh we'll
18:51
be will be interesting to to continue following um there's a whole bunch of great reactions semi analysis uh it breaks down the architecture uh where should we start here let me tell you about Cisco first so critical infrastructure for the AI era unlocks seamless less real-time experiences and new value with Cisco. Um
19:06
Um there's a there's a long post by >> Kimmy achieved another uh pretty meaningful benchmark. >> What's that?
19:17
>> Uh oh yes on the Belgian Grand Prix.
19:20
>> Is there anything these Chinese models can't do? Says Alex Conrad. >> Pretty good.
19:25
>> Kimmy Antonelli of course of the of F1.
19:27
>> Uh anyways, over to semi analysis.
19:27
What do they have to say?
19:30
>> Where where are they here?
19:30
uh semi- analysis said similar to the panic over DeepSseek R1 some uneducated people think Kimmy K3's use of linear attention KDA is bad for Nvidia HBM DRAMM and networking because it has relatively lower KV cache requirements the opposite is true and they'll explain Kimmy K3 is actually quite positive for Nvidia as large model inference is where the NVL72 shines because K3 has more than 2. 8 8 trillion parameters.
19:57
It requires a large scaleup domain to store its weights.
20:01
Secondly, although Kimmy delta attention has up to 10x lower networking requirements for KB cache transfers, its large weights require even more network bandwidth to implement an optimization called wide EP which spreads weights across different GPUs.
20:14
across different GPUs. It is interesting that uh I mean it feels like if like is China getting TPUs those have to be uh chip export restricted and probably more difficult to get into the country because there's less resellers and in order to set up a TPU based data center
20:31
you probably have to work very closely with Google whereas Nvidia GPUs are sort of going to like all sorts of small Neoclouds and there's a lot of places where seven steps down the chain it can wind up in a suitcase with a with a haird dryer removing the label or something like that. >> Well, it's not even that. You can
20:46
>> Well, it's not even that.
20:46
You can imagine like if you were a Chinese lab and you wanted to get access to compute, >> there would be a number of ways to like set up US entities that then basically get compute through variety of different sources here in the US. Yeah.
21:03
>> And you're you're uh just getting it remotely. Yeah.
21:06
Uh CK Capital says, "The hottest model in the world just started turning away paying customers because there isn't enough commute compute to serve them.
21:14
This is the Neocloud thesis in one uh post."
21:16
Um and uh the the Neoclouds were sort of getting beat up was it last week or the week before?
21:22
And uh and it seems like they should be back.
21:27
I don't exactly know what uh what's core we >> Yeah, iron is up uh 21% today.
21:30
W [laughter] I mean core we've only >> they've been they've been sort of a a lagard >> and down over the last 5 days in the last month.
21:41
Um but yeah uh if you're if you're flexible and ready to serve this the day it comes online that's probably profitable very very quickly even at you know lower than expected margins.
21:52
Um, uh, Nicholas Bamante at Microsoft, uh, who is working on AI for knowledge workers, uh, says, "Guys, the world is so computed.
22:03
I honestly don't understand how anyone thinks otherwise.
22:05
Almost nobody is seriously using AI today, and we're already hitting capacity.
22:11
Everything is a bottleneck.
22:11
Land for data centers, permits, electricity, grid connections, chips, construction, cooling.
22:16
Demand is growing far faster than supply and expanding supply is insanely expensive in this game.
22:22
The best positioned companies are the ones with massive net income from nonAI products.
22:28
They can fund hundreds of billions in capex.
22:30
He's given a little bit of a bullcase for Microsoft.
22:34
Hundreds of billions of >> mo over at Versel says >> tested it. >> What's that? >> Oh, yeah. That's good to hear. >> Yeah.
22:42
Kimmy K3 is top tier at cyber security.
22:45
>> Uh that will uh that will be interesting as the as basically uh the weights are released.
22:52
do we see an uptick in uh attacks?
22:56
>> And it's also sort of a a reputation of like the it's just distilled because the models that you would be distilling from are pretty severely nerfed on cyber security at least in theory.
23:06
And so it's like you would need to distill on the non uh the non- nerfed model which is probably way harder because those are much more restricted.
23:16
And so uh it it it does feel like there's a uh uh there's a little bit of a reputation of the idea that these are pure purely distilled.
23:25
>> Uh soul is a leap ahead in cyber capability at a significantly higher cost but quite remarkable still fable refuses everything.
23:31
We couldn't get it to complete the run at all.
23:33
What is interesting is that soul in comparison was much more open to helping with defensive cyber hardening.
23:38
Uh TLDDR frontier open weight cyber capability is here and um yeah they have uh their own product for uh defensive cyber security over at Purcell. Um this was good.
23:52
Hi Harry says rip the bear case July 16th to July 19th perfectly coinciding with my uh trip uh which which is which is nice.
24:07
>> Well let me tell you about console.
24:08
Console builds AI agents that automate 70% of IT, HR, and finance support, giving employees instant resolution for access requests and password resets.
24:15
Um, Dean Ball was going back and forth.
24:18
Uh, oh, some deleted posts now.
24:21
Um, but, uh, his his observations were getting taken to task going back and forth.
24:28
He says it's a very good model.
24:29
I don't think its performance can be explained away by distillation or anything like that in agentic coding sessions.
24:34
It seems pretty much on par with the best public models of Q1 2026 in my fairly limited use.
24:38
It also seems very token hungry.
24:41
There was some interesting back and forth around the pricing because it might not actually be that cheap at least through Kimmy's API or Moonshot's API.
24:50
But >> yeah, I think the the input per million was like $3, output was 15, which is like uh cheaper than Frontier models, but like it's it's certainly not at like% >> cheaper or 30% cheaper. >> Yeah.
25:03
And if if the if it's like more token hungry, >> yeah, token efficiency is not very good.
25:08
That's actually going to be much.
25:09
>> But you have to imagine that if it's open weights, then then companies bring it in, they start quantizing it and distilling it and tightening it up and optimizing it and then there's probably a whole stack of like NeoClouds that will be competing for well, you know, we have this cheap energy over here or we have some older chips that it can run on or do whatever they can to to bring down the price. out.
25:27
Uh Dean says, "I am personally surprised the Chinese state continues to allow the open sourcing of models this good given potential risks."
25:36
Now, it seems like the that is very much like from the top they are pro- opensource right now.
25:41
Um Dean says, "To be clear, I myself might be fine with models presenting this level of marginal risk being openweight, but I'm surprised that China is fine with it.
25:50
I suspect the reason is that they uh they are is 75% explained by strategic blindness, lack of AGI pledness.
25:57
The CCP is very lean lacuni in its view of AI.
26:00
Uh the other 25% or so is their lack of compute for customer inference making China's openweight strategy an unintended product of US export controls. That's interesting.
26:13
Uh and the normal Chinese strategy of aggressive exports for the companies as opposed to the government.
26:19
The decision to open source is partially ideological and partially because they are behind and they know that very few people would pay for subfrontier models from China.
26:26
Yeah, you have to imagine that it like the idea of sending I mean people are having discussion over like should you send your corporate data to a to an American frontier lab.
26:35
Imagine that that same level of should we upload our entire codebase to the moonshot API because we've gone all in on on Chinese closed source which seems like that's a very very hard sell.
26:50
So there is a world where there's like no money to be made in America. I don't know.
26:54
Maybe there's some American businesses that would just be like yeah I'm cool with it.
26:57
Like take my take everything take all my IP.
26:59
Like I I'm I'm I'm uh uh what's the phrase? Uh, like anti-fragile.
27:06
[laughter] I'm anti-fragile.
27:08
I can't be like you can't clone me.
27:10
So, and I have no IP, so just upload it all.
27:15
>> I mean, if you're a local plumbing company.
27:17
>> Yeah, maybe >> probably in a good spot. >> Maybe. Maybe.
27:20
Uh, he This is where he gets uh pretty This is a pretty hot take.
27:24
Dean says, "Oop weight models are inherently decelerationist."
27:26
And Tyler and I were debating this before the show.
27:30
Uh, he said, "I'm continually surprised to see the so-called accelerationists."
27:35
so excited about openweight models.
27:35
I suspect the reason is that they know openweight models are effectively ungovernable. That's correct.
27:40
Uh and they simply like the overall cloak of ungovernability openweight models uh create over the whole of AI. It's not a bad strategy.
27:51
It reminds me of James Scott's recounting of the hill people in the art of not being governed.
27:55
Still, in the end, openweight models deter further AI capex.
28:00
AI capex. And this is the debate that we were having was like how capex intensive are these frontier training runs because moonshots raised I think a billion dollars recently uh and and it just feels like even them even the latest labs like there's a lot of cost a lot of researchers and they do a lot of experiments but the but the core run
28:22
might not we might not be talking about 50 billion yet maybe in the future like you you need to acrue towards that and maybe people wouldn't fund it if they're like ah you're only going to be able to monetize this for for like three months because you're going to get distilled so fast or you're going to get copied so fast. That is tricky. Like it is nicer That is tricky.
28:37
Like it is nicer when you have a monopoly or igopoly for some period of time to to actually earn profits off of what you invested against.
28:46
>> Well, if anyone listening to this is over at Kimmy or Moonshot, >> come on.
28:52
>> No, don't come on the show.
28:52
Just uh feel free to let us know how how you made it. >> Yeah, maybe.
28:58
Feel free to to leak the secret sauce >> on the show. Come on the show. >> Yeah. Yeah.
29:04
If you're so if you're so open, why not?
29:07
>> Uh so growing Daniel was was calling this dumping.
29:09
He said what China is doing in AI is called dumping.
29:11
They do it in literally every industry they enter.
29:14
The goal is to kill all local competition by subsidizing their own industry so they can produce at a loss.
29:19
Then once all competitors are dead, they can charge profitable prices and control the market.
29:23
In steel and automobiles, this is just as just bad.
29:25
in AI it's potentially fatable fatal to our country.
29:32
Um it's interesting it's like I the there's a deeper AI supply chain across data centers and fabs and chips and energy that you could potentially have a lot of GDP growth and a lot of industrial AI capacity on an openweight model.
29:51
Um, so it's not like you're losing everything, but it does feel like a little bit of a slippery slope where you're like, "Sure, like we'll if you let China win the the the model layer, then you also are like, sure, they set up a fab like let's start buying the chips for them.
30:04
Sure, like just put the data centers there. Put everything there."
30:07
And then all of a sudden you've lost like everything potentially and you're just in that consumer mindset and then you're back to well h how hard is it to make a t-shirt in this country? Is that a problem?
30:17
Uh, not if we're friendly and we're trading and we have something to offer, but if we don't have anything to offer, then we can't buy anything and then we become a poor country. I don't know.
30:26
Uh, it's a it's a tricky back and forth.
30:29
People like cheap stuff, like they like cheap TVs and they don't like pollution associated with TV factories, so you send them abroad and then eventually you're like, wait, what do we make in this country?
30:38
Uh, anyway, we have Tyler Cowan joining at noon.
30:40
We can break down all the economics of this and other topics in just a minute.
30:43
Uh, let me tell you about the New York Stock Exchange.
30:47
Want to change the world?
30:49
Raise capital at the New York Stock Exchange. Just do it.
30:51
Uh Growing Daniel says, "Unfortunately, dumping uh unfortunately dumping is the very common argument for rent seeking domestic firms who want protection.
31:01
So even if you're in just a normal competitive industry, uh if you're an electric vehicle manufacturer, you're going to claim dumping even if it's just fair competition from BYD.
31:10
uh even if there's no government subsidies, they're just making great products and they want to send them here.
31:16
Um Dean's position here seems to be a Jones Act of sorts and this has famously not saved our ship building industry and I suspect it won't save our AI labs.
31:25
Stopping open weights is virtually impossible.
31:27
So our only other option is governments taking stakes in labs and subsidizing our own industry.
31:33
Uh, if you scream and yell about either Jones acting, Jones acting AI or subsidizing GPUs, then you should also explain how we will escape the dumping trap China is trying to push on us to destroy our labs and leave themselves with the only functioning AI industry in the world. That is a take.
31:50
Uh, Dean Ball says, "I know I've said this a bunch, but I wasn't proposing a Jones Act for AI as a good thing.
31:54
I was saying that if USG feels pressure to do something, the likeliest thing they'll land on would be soft law discouragement of Chinese model use.
32:03
China does the same to our tech. Um, yeah, that is true.
32:08
There's a whole bunch of I mean, even with the Nvidia chips, for a while there was rumors that uh Beijing was recommending that uh tech companies in China not buy them even once they were like approved.
32:18
And so uh soft power of the government happens uh all over the place. Uh uh LeBron chimed in.
32:23
Augustine LeBron that is uh former guest of Dwaresh Patel. I really like uh him.
32:29
Uh he says there is no predatory dumping trap to escape.
32:35
Keep thinking it through.
32:37
One, China subsidizes openweight models below their cost, taking big losses in the process.
32:41
Two, say they put Anthropic and Open AI out of business.
32:45
Three, they start charging monopolist prices to recoup losses.
32:47
So, uh, now there's room for new entrance and perhaps government support the monopolist prices era will be short-lived.
32:56
All these complaints about unfair competition are BS.
32:58
If you don't want to be surfs to a more competitive rentier class, then go compete.
33:03
The more I think about the China dumping openweight models at a loss to hurt the US idea, the stupider it sounds.
33:08
Now, you can lose the industrial capacity over a generation.
33:13
Like if you if you truly like lose all your AI researchers and they go off and do biology research or they they just go do something else.
33:22
And this is what happened.
33:22
Like we just don't have that many nuclear engineers in America anymore because nuclear engineering was seen as like a dead end.
33:29
And so they all went into computer science and now we're really strong in computer science and that's great.
33:32
But if you if you lose all of those people eventually you can't just like flip the switch and recompete.
33:37
But I I take his point that it is difficult to uh to to like you do create a market incentive.
33:45
It's like wait there are Chinese companies that are charging us 99% margins on intelligence like any venture capitalists want to fund something a neolab perhaps and people will just be like absolutely I would love to compete with China like people do this all the time.
33:58
Um >> yeah we're doing this in in manufacturing.
34:01
>> No yeah yeah we're literally doing it right now. It is slow. >> Yeah.
34:04
The automotive example is interesting.
34:05
Um in uh over the last few days I saw a ton of BYD cars. >> Oh, really? >> Uh just all over.
34:10
Uh and it's interesting because uh they were I saw I I would say like less than I would have expected based on what a lot of people say on X, for example.
34:22
They're like, I was in Europe and there's >> Chinese cars everywhere. I saw I saw plenty.
34:27
But what's really hurt uh the European auto manufacturers is not that the Chinese companies are selling into Europe.
34:35
It's that Chinese consumers are just buying Chinese cars, right?
34:39
And so you look at a car like the uh like the Porsche Cayenne, right? That was a big seller.
34:47
>> Huge seller in China probably still they still decent amount but uh people are very frequently uh choosing local options and they have a lot of national pride in China around buying uh local at this point.
35:01
>> Yeah, I like this final post from Augustine LeBron.
35:03
Uh he says the SFAI scene needs fewer philosopher kings and more CEOs laser focused on serving the best possible tokens at the best possible price.
35:15
Your margin is my opportunity.
35:17
Less Marcus Aurelius, more Ron Vakris, Costco CEO started his career as a forklift driver.
35:23
Can you imagine if in a decade we have a uh we have a a Frontier Lab CEO who's like, "Yeah, I started digging ditches to build data centers."
35:34
Like, I was racking GPUs when they were fighting it out over who was on the frontier and now I got the cheapest models in the business.
35:41
I got the cheapest intelligence.
35:45
>> I got the best value in the industry.
35:45
I mean like for for like 99% of businesses, they just want tokens that are good at the right price.
35:53
Like um but maybe maybe if Costco is the model, there will be a an LVMH of AI.
35:58
Something that really says your your your company takes luxury tokens seriously.
36:04
It's like I'm I'm happy to pay 99% margins.
36:07
I don't even think >> these aren't just tokens.
36:10
Yes, they're a statement. >> This is a statement.
36:12
At this company we we use premium AI lux luxury AI.
36:15
Uh Jakan is sharing uh some news.
36:19
The USI's tougher curbs on Chinese AI.
36:22
Now there are like logical curbs, right?
36:25
Like distillation attacks and IP theft should be investigated.
36:31
And I don't know what resources there are to fight that but I I mean I like the FBI busts people all the time.
36:39
Uh there was a there was a spy at Tesla a couple years ago who was like just straight up like saving the files, sending them to China, you know, like that happens and they bust those people.
36:51
Um and so there should be like an effort there and I'm sure there already is one.
36:55
Um but uh Jukan says, "Damn, this is exactly what Dean Ball said, isn't it?"
37:00
And here's the news from Axios.
37:00
The big picture, the administration wouldn't need to impose an outright ban to get US companies to drop those Chinese platforms.
37:08
quote, "What's actually happening is slower and more durable."
37:13
Uh, one source familiar with the government decisions said, citing procurement rules, entityless threats and public pressure campaigns aimed at US companies using Chinese models.
37:23
Instead of a ban, another source familiar with government discussions described a push to highlight potential backdoors and lack of security with Chinese models.
37:30
It feels like it should be pretty doable to hunt down back doors and deal with security risks of Chinese models.
37:38
um especially if they're being run in American data centers uh and the governance issue that brings uh it's an offensive approach where the administration encourages a more innovative US open- source ecosystem the source added.
37:50
So, uh, this will be playing out over >> says token atilier. >> Mhm. >> Token atilier. I like that.
37:59
>> We'll take your token atilier over to codeex.
38:02
It's a powerful workspace for getting work done with AI agents.
38:06
Whether you're writing code, analyzing data, creating content, or automating business workflows, codeex helps you move projects forward from start to finish.
38:15
>> The Odyssey is up to 264 million.
38:18
biggest non-an animated opening of the year until Spider-Man in two weeks according to Lucas Shaw over at Bloomberg.
38:25
Um, >> for reference, $375 million budget.
38:31
Production was $250 million.
38:31
They recouped that uh immediately.
38:33
$125 million in global marketing apparently.
38:39
Um, and looks like a big financial win, but very much, you know, priced in like uh, you know, the base case here.
38:45
Uh the film will make a little bit over a billion dollars.
38:49
Uh the cash flow to Universal and Comcast, who are uh the studio behind this, uh you're looking at $250 million in pre-tax lifetime profit.
39:01
That's 2% of Comcast's market value.
39:01
So, it's not like this crazy thing for the corporate entity, but it is exciting for the film and it's exciting for Christopher Nolan, who's in the journal today in the business and finance section uh because Nolan's The Odyssey shows directors are franchises.
39:19
Now, they have an interesting statistic in here that uh 53% of Odyssey attendees said that the director was their number one reason for attending.
39:31
Not the actor, not the fact that they love the book and they were waiting for the IP to get adapted.
39:38
Not that it's a, you know, oh, they like Sword and Sandals, they were looking or that it was IMAX, they wanted a Nolan film.
39:44
And that's, I mean, everyone knows this intuitively from talking to everyone.
39:48
Everyone's like, I want to see the next Nolan film.
39:49
But uh it's just interesting that that there has been this huge shift towards uh franchises built around particular intellectual property verticals, Marvel, Star Wars, Disney stuff, Pixar and whatnot.
40:04
But uh now people are saying I'm I'm just going to ride with this director and whatever he does and he can take me to the past, he can take me to the future.
40:13
Wherever he goes, I'm riding with Noah.
40:15
I had a funny runin with Christopher Nolan.
40:18
I was uh walking on the beach and uh Christopher Nolan was walking in front of me.
40:30
>> Yeah, [laughter] >> I'm not hitting any sound effects right now to be clear.
40:34
Um and uh he's walking with his dog.
40:37
I don't know who he is or what he looks like.
40:39
Someone else who I'm with says that's Christopher Nolan. I said, "Who?"
40:47
Uh, and then and then uh it came to me.
40:50
Uh but then this dog ran up behind us. He was walking his dog.
40:52
Another dog joins and is like really messing with his dog.
40:56
And so I had to be like I just had to say, "Hey man, it's not my dog because it seemed it seemed like my dog was offramed.
41:04
>> I was being framed for having a poorly trained dog that was harassing his dog."
41:09
>> Um but he he was pretty unbothered.
41:12
Um, let's head over to this picture from Goier.
41:15
He had the best seat in the house.
41:19
>> This is Is this a real photo or do you think this is like take a photo and then put it in AI and say like make it even more distorted because this is what it looks like to be truly in the front row of an IMAX uh showing.
41:30
Uh, people really were really crazy about like the IMAX looked way better.
41:36
I think some folks on the team saw the film once, twice, three times. Uh, who saw it in IMAX?
41:40
Okay, we got some IMAX chat back yet. Mike's seen it. >> Give us the review. You liked it.
41:46
What What How many thumbs up out of 10?
41:48
What What do you got for me?
41:52
>> Tyler, you can you can chime in, too. What do you think? >> It was fantastic. >> It was fantastic.
41:57
>> Were you taking Were you taking notes the whole time?
41:58
Like, did you have a a notebook? >> Uh, no. Mentally? Mentally? No. But it it it's great.
42:05
It's an amazing accomplishment.
42:07
You're like, "Shoot the next movie in IMAX, [laughter] >> dude."
42:10
I mean, it's one of the best looking films I think ever.
42:12
I mean, the quality is just like unmatched.
42:16
>> You did 70 mm >> 70 mil IMAX.
42:18
It's It's It's incredible. It's incredible.
42:22
>> How many times do you think How many times do you think you'll see it in theaters?
42:26
>> Well, I mean, I'm already scheduled for four total. >> Four total?
42:29
[laughter] >> That's crazy. >> We'll see. We'll see how that go.
42:31
I was sluming it in normal theaters.
42:33
I I don't want to spoil it in case you haven't read it yet. >> Okay. Yeah. Thank you. You know. >> Yeah. >> Just Yeah.
42:38
But I I think uh my >> I'm really excited to find out if he gets home. >> Yeah. Yeah. We'll see. >> Can't wait.
42:43
>> Uh my my take I posted it was missing some some high jinks and some gas.
42:46
There there are a few scenes missing from the actual poem that I I would have liked to see.
42:50
I think it was >> Wait, are there high jinks in the poem that you didn't that didn't make it in?
42:54
>> Yes, that's what I'm saying.
42:56
>> It was It was low on high jinks.
42:57
>> We should do an episode on IMAX. >> Oh, yeah.
42:59
We got to film this show in IMAX. That' be awesome.
43:01
Um, how would you rank it in the Nolan Cannon? Top five, top two. What are we doing?
43:10
>> He's thinking >> I I thought it was I don't know. Production team.
43:14
It's not top three for sure. >> Not top three.
43:16
What are you putting above it?
43:18
>> He's not going to beat the Dark Knight. >> Yeah, Dark Knight. Dark Knight's better. >> Inception. Tenant.
43:22
Obviously, >> Mike's coming back.
43:25
>> I love [laughter] Tenant.
43:26
>> I think it says a lot about the filmmaker.
43:27
If if this film doesn't make his top three for some people that says a lot about or top five just with like his filmography. >> Top five right now. Top five Nolan films go.
43:37
>> Inception, Dark Knight, Interstellar, >> Oppenheimer.
43:41
Odyssey's got to be up there. >> Okay.
43:44
Odyssey is in the in the top five. >> There you go.
43:46
Well, hopefully uh Jordy can take a look at it.
43:50
I think we >> said we'll wait till I can chime in [laughter] >> to solidify that top five.
43:54
Let me tell you about public investing for those who take it seriously.
43:57
They got stocks, options, bonds, crypto, treasuries, and more with great customer service.
44:01
Uh yeah, the uh the Wall Street Journal has this article, the no uh the Odyssey proves Hollywood top directors or franchises now.
44:10
Uh every Hollywood studio wants its own Christopher Nolan.
44:15
And I'm interested to know like how locked in is he with Comcast particularly like Universal?
44:20
like it it feels like it's this huge asset, but it's wildly different than, you know, when you're Disney and you just own Marvel and there's absolutely no key man risk.
44:32
Um, but it's clearly worked out very well.
44:34
Uh, The Odyssey was written and directed by Christopher Nolan.
44:36
It opened to an estimated 600 uh 264 million worldwide domestically.
44:41
It's 124 million debut was the third biggest for any film this year. So, it's not that huge.
44:51
It didn't beat Toy Story 5, didn't beat Super Mario Galaxy, but it does feel like it'll have a longer run of people seeing it multiple times, paying more for IMAX, and it is just a breakout for an R-rated historical movie.
45:01
That's a niche that typically doesn't put up massive numbers, uh, like a Toy Story, for example.
45:08
Um, the opening was higher than many 2026 releases based on current, more current intellectual properties.
45:14
It smoked Star Wars, The Mandalorian, and Grou.
45:16
smoked Supergirl and it also beat Scream 7.
45:19
Nolan's brand power and commercial track record got Comcast Universal Pictures to say yes to a roughly $250 million R-rated adaptation of an ancient Greek poem shot across Europe with largely practical uh visual effects, including a 35- foot tall Trojan horse.
45:39
According to data data from Renrak, uh 53% of the Odyssey attendees said the director was the number one reason for attending.
45:47
It's not often you see that, said Universal president of domestic distribution.
45:54
Chris Nolan has earned the audience's trust and respect.
45:56
And that's completely true.
45:59
Tenant had a very, very weak box office opening.
46:01
Oppenheimer did quite well. Dunkirk did well.
46:04
I recently rewatched Dunkirk. That's a great one.
46:07
That needs to be up there in the top 20. They're all good.
46:10
Uh, in the late 2000s and 2010, franchises dominated the movie industry.
46:14
Studios hired directors who could pump out sequels, spin-offs, and remakes as efficiently as possible.
46:19
Nolan was the most successful and prolific among a tiny number of filmmakers who still called their own shots along with the hugely successful Dark Knight trilogy.
46:27
He made his original hit movies uh like Inception and Interstellar.
46:32
But Marvel, Fast and Furious, and Transformers have all stumbled at the box office recently as audiences have grown tired of them.
46:38
Gen Z, in particular, appears skeptical of the industry's reliance on aging brands and technologydriven filmm.
46:48
They're more drawn to stories told by an authentic, recognizable filmmaker with a point of view, says the Wall Street Journal.
46:56
Like Curry Barker, director of surprise horror blockbuster Obsession.
47:00
But that film was one of the several new to the big screen director-driven hits this year, including Project Hail Mary and Back Rooms.
47:08
In response, studios that used to obsess over franchise management are devoting more energy to signing the most promising filmmakers.
47:15
So, you can imagine a 10 movie deal.
47:17
I think Curry Barker already has a multiple movie deal just on the ba back of back rooms, even though it's his first film because it was so successful.
47:25
Studios are lost as far as knowing what works and what doesn't, said Byron Lord, uh, CEO of CIA.
47:31
Uh, things have generally generationally changed and individual singular voices are taking advantage.
47:40
Warner Brothers gave Black Panther and Creed director Ryan Cougler an unusually generous deal to the rights to make his own original horror movie Sinners, which became a hit last year.
47:48
Weapons director Zack Kger is helming an adaptation of the video game Resident Evil for Sony Pictures this fall and was part of a recent multi-million dollar deal Warner made for a new horror property.
48:00
Barri director Greta Gerwig used her clout to convince Netflix to make her adaptation of The Chronicles of Narnia. Interesting. They're rebooting that.
48:09
Uh Resident Evil has had a rough go.
48:12
They've they've done a number of movies but never a really major breakout beyond the core fan base.
48:16
Uh, was there was there a a movie that paired we we were so close to getting another Barbenheimer with the Odyssey.
48:23
Was there another movie that people could go see at the same time for the double feature? >> They got to do that.
48:29
>> No, they kind of >> it was the Citizen one, Tyler. No, >> just kidding. >> I don't know. It would have been good.
48:35
It was It It made It made the whole Oppenheimer weekend so much more enjoyable having like the contrast there.
48:41
Uh Nolan should have almost uh almost >> Did you do the Barberheimer same day?
48:43
I don't think I did it same day. >> Wow. >> Same weekend though. >> Laring over here. >> But it was fun. It was a good movie.
48:54
>> Uh Netflix disclosed it has used generative AI in about 300 different productions this year already.
49:01
>> People are going to love this.
49:02
>> They wrote across the production life cycle from concept and previsualization through post and delivery.
49:08
>> Genai utilization by our creative partners is scaling quickly.
49:10
In 2026, Genaii workflows have been used in roughly 300 of our titles with the largest concentration of work in post-prouction.
49:19
We are increasingly leveraging these tools to deliver higher quality output more quickly and at a lower cost than traditional methods. >> Yeah.
49:26
One of the clearest examples came from the American Experiment, a docu series about the American Revolution.
49:33
Co-CEO Ted Sarando said the series included 17 minutes of AI enhanced footage that explained the scale of the project and that expanded the scale of the project and would not have been financially feasible using traditional production methods.
49:47
The company insists that AI is not being introduced to replace writers, directors, actors or other creative professionals.
49:54
Instead, Netflix is positioning the technology as the next evolution of filmm software, allowing productions to create shots and sequences that would previously have been impossible or overly expensive. Uh, very cool. I don't know.
50:07
I think if used tastefully, it can be good.
50:10
I saw uh the the uh the designer behind Quarter, those quarterly uh earnings posters, put together a number of Odyssey film posters.
50:23
posters. and uh immediately got community noted because he his his post was like I made I went and saw the movie and then I made these posters uh and a bunch of people liked them and uh and immediately got community noted he used
50:35
midjourney and and you know he's just >> all right here's a question is this AI >> Honda Canada posted an ad for the Odyssey starring your son your son's bff you your daughter backseat snacks and your dog >> it's pretty good >> it's a good ad might be could be AI. >> Could just be Photoshop? I mean, they
50:57
>> Could just be Photoshop?
50:57
I mean, they just have a picture of a >> Well, no, no.
50:59
I I was like genuinely curious.
51:01
Is this a Did Honda actually post this?
51:03
Like, you just never know at this point. >> Oh, yeah. Yeah. I think they did.
51:05
I I think because it's Honda Canada, it's a more regional account.
51:11
They can be a little bit more wheeling. >> They did. They did.
51:15
>> I don't believe that it's real.
51:15
Plus, Trunk Fan, he knows when to flag things with real or or AI or fake.
51:18
They say the cast of characters that have been in the backseat of your family's Odyssey could make a movie of their own. >> Yeah, >> man.
51:28
The last drive in in your Honda Odyssey was a motion picture.
51:31
[laughter] >> The Honda Odyssey.
51:37
>> Uh this is uh crazy and disgusting and disappointing.
51:42
>> Uh apparently trial lawyers are lobbying against self-driving cars because they're too safe.
51:47
They need people to be killed and injured so that they have material for lawsuits.
51:51
Um it says so with thousands of lives annually in the balance who is against autonomous vehicles trial trial lawyers.
51:59
Remarkably the trial lawyers saw the writing on the wall very early and have been lobby lobbying against AVs for nearly a decade.
52:08
The American Association for Justice, the trial lawyers lobby, has been a prominent opponent of AV legislation.
52:18
Um, uh, it's crazy that on my drive to work, at least 80% of all billboards that I see are for uh, personal lawyers. >> Big business. >> Yeah.
52:32
The Morgan and Morgan founder is a billionaire off of it.
52:34
I mean, he scaled that business.
52:36
And uh I saw some viral clip of a guy who just does lead genen and makes 30 million a year. >> Yeah.
52:42
>> Um and doesn't actually he's needs to be a lawyer to put up the ads, but doesn't fight the cases, just finds the client finds the plaintiffs, vets them, and then passes along to another lawyer who actually uh will fight the case, get the settlement.
52:56
Uh and so there's a little bit of back and forth about this.
52:57
It might have just been that they're lobbying to hold the automaker liable so there's still someone to sue and that might be a little less of uh a little less egregious.
53:08
But um it is uh if if if that's what delays the roll out of self-driving cars, that would truly be uh disappointing.
53:17
Nick Carter says, "My jaw dropped reading this."
53:19
Who is the most opposed to self-driving cars?
53:23
Ambulance chasing lawyers who sue over car accidents.
53:25
They are explicitly on the side of preventable death. Not not not good.
53:30
Oh, that's really there's some really dark potential secondary effects.
53:37
Um it it fully rage baited Alex Tabarok who wrote about it at Marginal Revolution and we can talk about um we can talk about it with Tyler Cowan in just a few minutes when he joins.
53:47
Um there were uh people going back and forth over Brian Chesy. Was his account hacked? Was it not?
53:53
Uh there was a AI generated quote slopp thread about crypto or something like that.
54:03
>> I think it was about uh yeah real world assets.
54:05
>> Yeah, real world assets >> which could be something that he gets excited about.
54:09
It does feel like a little bit off topic right now in a world that is so AI dominated no one really wants to >> Yeah.
54:16
He's in the mid like the the rumor >> Yeah.
54:19
>> Uh there's been reporting that he's in the midst of raising for a Neol design. Well, yeah.
54:24
What why would he be posting about that?
54:27
>> And so he he told Fortune that uh his account was hacked and a lot of people weren't buying it.
54:33
Um but uh Max Sparrow uh the the king of slop detection, the slop janitor over at Pangram says, uh I keep seeing people skeptical that Brian Chesky's AI slop crypto thread was not written by him, but to me it seems like the most likely story.
54:50
My guess is that the hackers were trying to warm up his account before dropping a pump and dump coin, but the actual scam post got flagged before it ever went live.
54:58
And so that's that's sort of an interesting twist because people were saying like, well, he might have just decided to post it because he thought it was good content or whatever, could get some some virality.
55:08
And the fact that it didn't have an address or wasn't immediately like the obvious scam, that was evidence that it was in fact uh just him slopping it up on the timeline.
55:18
But uh this this warm-up thesis uh does seem more likely to me. >> Yeah.
55:24
And if you had access to uh Chesy's account, you probably would think like, okay, do the warm-up post and then do something related to like real estate onchain that is monetized. >> Yeah. You got to bridge them.
55:38
You can't just immediately be like, I'm giving everyone Bitcoin.
55:41
Like send me your address.
55:43
Like send me one Bitcoin. I'll send you back two. Like like that.
55:47
people that probably don't even fall for those scams anymore. Hopefully. I don't know.
55:51
Um, [clears throat] people were going back and forth on this.
55:53
It was It was a fun time on the internet.
55:55
Um, what else is going on?
55:58
The Jacobian conjecture was solved.
56:00
Um, another math problem. False.
56:04
>> Look, if if if this is surprising to you, >> if you needed this explained, >> if you needed it explained >> Yeah.
56:11
>> or you're surprised that it fell, you're telling on yourself. >> Yeah. Yeah.
56:15
You are telling it to yourself.
56:16
Uh I like this from Bonito Torrellini.
56:19
They just created a million Jacobian conjectures.
56:21
You have no idea what's coming.
56:24
Uh but no, it's a it's a very exciting another math problem.
56:26
Also an interesting full circle moment because uh the the uh the mathematician who solved the uh the Jacobian conjecture had been working on it I believe for like a decade.
56:39
So he's a he's an AI researcher at Anthropic now.
56:43
uh and he you know was able to use uh fable to uh you know uh prove this math problem that he had been uh struggling with for a very long time.
56:52
Was it him or was it his advisor?
56:54
Like he had worked on this for a while or been exposed to it at least.
56:58
>> Yeah, I think loose like same area of math also. He didn't prove it.
56:59
He a counter example counter example. Yeah. Okay.
57:04
Well, for everyone who's like, "Oh, do I really need these data centers? Like I don't like AI."
57:08
Like this should swing them.
57:10
like put this on a billboard.
57:13
Like I I I I got to send this to Sager and Jetty and and let him know like, "Hey, this is this is there's populism is going to swing around on this now."
57:21
I think >> was this conjecture on Sagerbet?
57:25
[laughter] >> I don't think he was.
57:26
We got to get it on there.
57:27
>> Oh, he's kind of asleep at the wheel if he's if he wasn't, you know, trying to make a >> Now he's in charge of this thing.
57:33
>> No, I mean, you know, on Sagerbat, he should have had he should have had a market for this. >> For sure.
57:39
Um, what else is going on in the timeline right now? Uh, five cups of coffee.
57:45
You got to be drinking five cups of coffee. This is the new meta.
57:47
This is what, uh, everyone recommends.
57:50
The Wall Street Journal has an article here.
57:52
Got to pull [snorts] it up. Uh, where is it? WSJ. Five cups of coffee.
57:57
How many cups of coffee do you do per day?
58:03
I do one cup of coffee, but then I do two euromatics and three diet cokes, which I think is around five cups of coffee in terms of caffeine.
58:11
>> How much how much caffeine is in a cup of coffee?
58:13
>> I do a commenter on the way to the gym.
58:16
>> Oh, you drink caffeine before the gym? That's smart. >> Oh, yeah. I have to. I have to. >> Oh, that's good.
58:22
>> Commenter on the way to the gym. >> Okay.
58:24
>> Regular coffee during breakfast. And then two of these. >> Okay.
58:28
And then you're done for the day.
58:30
>> Then I'm done for the day. >> Okay. Okay.
58:31
Uh, five cups of coffee a day is fine for most adults.
58:34
The heart association says.
58:36
Uh, Tyler, did you get me?
58:38
What's What's in a cup of coffee? >> Uh, around 95. >> 95.
58:42
So, so putting up 500 milligrams a day.
58:45
>> So, that's like two Celsius.
58:45
Two, maybe one and a half depending on which how strong it is.
58:49
They're between 200 and 300, right? Celsius.
58:52
>> I think they're up there. >> 200 is the max.
58:54
I think >> No, I think 200 is the min. >> No. >> Yes.
58:59
>> I thought I thought I thought 200. >> Yeah.
59:03
So, beverage companies start to get >> performance edition and it goes up to 300 cuz they have a bigger can >> Celsius max >> and they have Celsius heat which is 300 >> heat. >> Oh, yeah. >> Is it spicy? >> Who's to say? >> Spice up your mind.
59:16
Uh anyway, research has been mixed but association says evidence of coffeey's safety and even benefits has been growing over time.
59:24
This from the Wall Street Journal.
59:24
This is important for everyone listening.
59:26
Uh there's good news for coffee drinkers.
59:30
Good news for coffee drinkers.
59:30
Up to five cups of the brew a day are safe for more for most adults and may even have benefits for heart health. That is crazy to hear.
59:40
It always seemed like it would be a fouian bargain at the very least like you know worth it in the short term but at some point it's going to come back to bite you but not in this case.
59:50
>> I mean um like famously Voltater would drink like 40 cups of cups of coffee a day. >> No. Voltater. >> Yeah. Yeah.
59:57
>> Maybe we even know this stuff.
59:57
That's >> I have like >> of course. >> Yeah. >> Yeah.
1:00:01
>> So, I mean, >> you did pretty well.
1:00:03
I would say >> if you learned that Voltater drank 40 cups of coffee a day from Tyler right now, you're telling on yourself.
1:00:09
[laughter] >> You're definitely telling on yourself.
1:00:11
Well, let me tell you about Railway.
1:00:13
Railway is the all-in-one intelligent cloud provider.
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Well, Railway automatically takes care of scaling, monitoring, and security.
1:00:21
And without further ado, our first guest of the show is in the waiting room.
1:00:26
We have Tyler Cowan, the American economist to talk to us about all things.
1:00:30
How are you doing, Tyler? Good to see you again. >> Hello. How are you? I don't drink coffee.
1:00:34
Anyway, I have to ask every guest. Are you going to start?
1:00:39
>> I've only had coffee twice in my life.
1:00:42
Both times in rural Ethiopia in a coffee ceremony and it was amazing, but I thought this is the peak. I can't beat this.
1:00:49
>> Is that because you'd become too powerful or do you see it as a Fouian bargain?
1:00:53
I see people getting addicted to it and if they can't get their morning coffee, they have headaches.
1:00:58
They don't want to be there.
1:01:00
>> Yeah, it it does become a supply chain though.
1:01:02
>> How often are you somewhere where you you can't possibly get a coffee in the morning? >> I don't know.
1:01:09
It can't, but you have to go out and get it or go to some effort or you're in a hotel room and you make a crummy coffee there and then you don't really enjoy it.
1:01:17
It's just simpler not to have that addiction.
1:01:20
I think >> what's very interesting is that uh coffee uh caffeine pills are incredibly economically efficient.
1:01:27
It's like one cent for the equivalent of a cup of coffee if you buy them in bulk and yet absolutely zero adoption.
1:01:34
People love a cup of coffee, even if it's a couple bucks.
1:01:38
It's like a simple pleasure. It's a joy.
1:01:40
People just can't stop paying for them.
1:01:42
>> Do you do anything in the morning?
1:01:42
Any type of ritual that you feel like you have to do to be yourself? >> He's like Celsius.
1:01:48
[laughter] No, >> I get high from mineral water, but if I don't have it, I'm fine. I'm not craving it. >> I'm addicted. Okay. Okay.
1:01:55
Well, uh let's talk about artificial intelligence.
1:01:58
Uh how are you thinking about the latest debate around Kimmy K3 open-source AI?
1:02:07
Where should we begin the discussion?
1:02:07
Uh and then I'm sure we can narrow down a bunch of different ways.
1:02:13
>> Open source and is coming and is here whether we like it or not.
1:02:16
Of course, a lot of it will be from China.
1:02:19
We should allow American competitors to proceed on an even footing.
1:02:23
But basically, the attempt to outlaw it or ban it or use sanctions against it is going to fail miserably.
1:02:30
Like what about multinationals?
1:02:31
What about companies whose affiliates use open source?
1:02:33
What about all the major US companies that use open- source Chinese products right now?
1:02:40
Uh I hope I plead that the Trump administration gives up on this crusade. >> Sure. >> It cannot work. >> Okay.
1:02:47
So, I want to push back on on it cannot be banned because I at first blush I agree with you like it is software.
1:02:54
It is something that can just be downloaded.
1:02:55
You can download the weights and just have it.
1:02:57
Anything that that that's that's replicable infinitely uh should just proliferate across the world very quickly.
1:03:04
Um but the fact that you need an NVL72, you need a whole server rack, you need equip equipment and infrastructure that acts as a point of leverage.
1:03:15
I'm not saying that we should ban it.
1:03:17
I'm just saying if you put me in charge of banning it, I think I would have an easier time banning open source AI than banning music distribution, for example.
1:03:28
>> What will you do when it's on device?
1:03:30
How long is that from now? Right. True.
1:03:31
Why go down the path at all? >> Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. That's a good point. >> Yeah.
1:03:35
What what uh what lessons are there from the automotive industry for this moment?
1:03:41
this moment? um just just given you know the the use of of tariffs and how that those have worked and haven't worked and uh we were talking you know earlier about how Europe's approach the problem you see BYDs all over Europe now
1:03:59
>> uh but at the same time many of the manufacturers have been hurt most by just losing the Chinese market right or not losing it but having declining sales there because of local alternatives I think if Europe starts a major trade war with China, Europe will lose. This
1:04:15
This is a sad reflection on Europe and it's a sad reflection on Europe that they can't just say, "Oh, we'll make fewer cars.
1:04:24
We'll do five or six other great things that were readily positioned to shift into.
1:04:28
They're not really there.
1:04:28
Uh but simply putting up big tariffs isn't going to help them get there.
1:04:34
It actually will postpone it.
1:04:35
And those tariffs over time end up applying to the inputs for your other sectors.
1:04:39
and it makes them less competitive.
1:04:41
So Europe's really in a pickle.
1:04:43
There's no good way out, but I don't favor the tariffs either.
1:04:47
H >> how do you think about uh the open-source models um in in terms of like the broader AI stack?
1:04:55
Like are are is it important that America have some position in energy, data centers, uh chip manufacturing, networking?
1:05:07
There's this whole stack and I've been surprised that we've been building as many data centers as we have in the United States given all the push back.
1:05:16
That feels like something that could have gone to at least other western nations uh energy-rich countries, but that's been fairly centralized.
1:05:23
Um but there's there's clearly it feels like there's a potential slippery slope for oh well if China's handling the open source models then maybe they should have the data centers.
1:05:33
Maybe they should also do the power generation over there. It's dirty.
1:05:36
We don't like having data centers here. Put them over there.
1:05:38
And then at some point it feels like you could lose leverage.
1:05:41
And but I don't know how risky that actually is.
1:05:44
>> I live right next to all the data centers in Northern Virginia. It's great.
1:05:47
They pay for half of the taxes of their county.
1:05:50
The energy, the imbey, all that. I'm completely for it. The chips. Bring it on.
1:05:56
>> But at the end of the day, people are afraid that the American models can be yanked from them. >> Sure.
1:06:01
>> That's partly our fault. Yeah.
1:06:01
And if there's open source as something in the background, uh people are more willing to lock into our systems to a partial extent.
1:06:09
So the two are compliments in fact.
1:06:12
And again, there's all these major American companies.
1:06:14
I don't typically advertise it, but they use a lot of Chinese open- source right now. Yeah.
1:06:20
>> You just going to pull that out of their guts? >> Yeah. Yeah.
1:06:22
>> Yeah. Yeah. I mean both enterprises actually deploying these models for cost savings and also I mean we saw with thinking machines they they used the Chinese open source model to bootstrap a step in the process of building their latest product and so it's a whole
1:06:36
supply chain um where do you think the data center push back comes from if you are there and have a positive experience it's polling very poorly what is it all uh is it all fear around job displacement is it anything particularly on the ground like Where where did this come from? I
1:06:53
I >> think right now people are just negative about many many things.
1:06:57
They're negative about their country.
1:06:59
They're negative about uh social institutions except the military.
1:07:05
They're very low levels of trust.
1:07:07
>> They were told during COVID times during the great financial crisis everything's okay when it was not.
1:07:13
>> So you can't completely blame them.
1:07:13
But look, as you know, this country has 50 states, and if Mark Zuckerberg needs to pick up the phone and call the governor of Louisiana, thank goodness that can work here.
1:07:25
>> And I think we're going to do it.
1:07:27
>> What is it about this?
1:07:27
We're in this like counterintuitive vibe session moment.
1:07:31
Kyler Scandlin sort of uh written about it, coined the phrase this idea that when uh when you pull people on how are your neighbors doing economically or financially, people say they're doing fine but I'm doing terribly or the economy will grow, the job market will be stable, but people will report the consumer confidence is very low.
1:07:50
Uh I'm wondering if you've reflected on that more generally and then I do have a follow-up question, but uh what is the state of the Vibe session? Is it just continual? Is it permanent? Is it getting worse?
1:08:03
>> I call it negative emotional contagion.
1:08:05
And you see it in many other eras of world history. It feeds on itself.
1:08:07
I'm not sure we know what starts it.
1:08:10
Maybe it is some negative events, but once it gets going, it's hard to reverse.
1:08:16
>> Sometimes you need actually a war or some kind of catastrophe. I hope we do not.
1:08:20
But if you look at wealth accumulation, the job market, stock prices, real wages, many other indices, they're doing like okay to fine.
1:08:28
They're just not doing terribly. >> Yeah.
1:08:32
No, no, I completely agree. And so I had this idea.
1:08:34
I don't know if you'll agree, but uh maybe the fact that we're uh in this vibe session and the vibes are so bad and there's so much negative emotion around the economy that if we actually were to go through a brief recession or a market correction, uh we might be a more resilient country sort of ironically because of that because everyone would look around and be like, "Well, I already thought it was bad.
1:09:01
It got a little worse, but I'm ready to reinvest.
1:09:05
I'm maybe this is what I've been waiting for.
1:09:07
Finally, I'm justified in thinking things are bad.
1:09:10
And so, I can get out of this and do whatever is necessary because I am I'm I'm there's no longer a cognitive dissonance between the what the market is doing and how I feel.
1:09:24
>> One hopes, yes, it could be a bullish signal.
1:09:26
But then again, if something terrible does happen, everyone says, "I told you so."
1:09:30
And maybe we get stuck in the mire. >> Yeah.
1:09:34
>> About a year ago, uh, you had a blog post, if I remember correctly, one of many, of course, that that was something to the effect of like AI is real and it's amazing, but the economy is still going to just sort of generally continue growing as it's grown, maybe a little bit faster hopefully.
1:09:52
>> Uh, are you seeing anything that could update your worldview on that?
1:09:55
Because so far I feel like you've been pretty vindicated. deserve a victory lap.
1:10:02
>> Well, I'm seeing a lot of confirmation.
1:10:04
There's so many people they will say to me or say in chat groups or on Twitter, my goodness, if we had Fable 5, I would have thought the world would be totally different. And it's not. >> Yeah. >> And that's my view.
1:10:14
But look, you go back to 1995 through 1998, we got half a percentage point of extra growth out of those advances.
1:10:21
They to me seem less impressive than AI.
1:10:24
So, I think over time with some slowness, we'll get that and maybe a bit more, which is great. >> Interesting.
1:10:32
Uh, have you seen The Odyssey?
1:10:35
>> I have seen The Odyssey.
1:10:37
>> Can you give us your review?
1:10:39
>> Well, look, compared to other movies, it's fantastic and it's dreamy and it's impressive, but mostly I was disappointed.
1:10:46
I thought the dialogue was lame.
1:10:49
The casting, I don't mind the cross-ethnic casting at all.
1:10:51
I just didn't think they were the right people.
1:10:55
>> Matt Damon is not Odysius.
1:10:55
He's just not impressive enough physically.
1:10:58
The woman who played Calypso just seemed like a suburban housewife.
1:11:03
I love Zenaia, but she didn't have any real role.
1:11:08
>> So, they screwed up many things. It didn't quite cohhere.
1:11:10
Uh, I'm very glad I saw it. Everyone should see it.
1:11:13
It's worth the three hours, but relative to expectations, I suppose a disappointment.
1:11:19
Do you think it's Do you think classicists are satisfied with it?
1:11:24
Do you think it's good for the field?
1:11:27
>> They're delighted to have any attention at all and the different translations of the Odyssey are now on the bestseller lists.
1:11:33
So, what's their alternative, right?
1:11:36
They shouldn't complain too loudly.
1:11:39
>> Uh, do you think technology is making soccer worse?
1:11:46
I don't watch soccer, but I think technology in some ways has made basketball worse.
1:11:51
And I wouldn't be surprised if the same were true of soccer that it homogenizes things and everyone chases after the same targets, the same players, teams have less individual style.
1:12:02
Uh, but to soccer, I cannot speak. >> Fair.
1:12:07
>> Um, what about AI maniacs?
1:12:07
take me through what defines an AI maniac, why it's important, and I have some uh some push back and follow-up questions.
1:12:18
>> I spoke to two teenagers this morning. One was 13, one is 17. Abu Dhabi, Finland. They're AI maniacs.
1:12:24
They spend their spare time mastering AI.
1:12:27
They know way more than their so-called teachers.
1:12:33
They're starting companies are building models.
1:12:35
They realize this is the future.
1:12:38
You're going to have a large number of companies with quite a a very small number of employees but pretty high revenue.
1:12:44
And I think AI maniacs will compete in medicine, law, business, consulting, banking, wherever it is we let them compete.
1:12:51
So this to me is the future.
1:12:53
Like if you're asking, will the UK recover? I would rephrase it.
1:12:56
Is the UK going to encourage its AI maniacs?
1:13:01
>> How can they encourage these AI maniacs?
1:13:03
It feels like AI naturally lowers the barrier to entrepreneurship.
1:13:06
And so, uh, there's not much encouragement that's necessary.
1:13:11
I mean, as long as the models are available and affordable for young people, they will do things with them.
1:13:18
>> Young people should be allowed to rise to positions of authority, they should be respected.
1:13:22
They shouldn't be looked down upon.
1:13:24
They should have the chance to buy computer, get grants. It's not automatic.
1:13:29
>> So, you look at the United States, we do great on all these metrics.
1:13:32
Estonia is a country that does pretty well on these metrics.
1:13:36
A lot of parts of the world are too small C conservative and we'll see where different countries are going to fall on this spectrum, but they're not all going to do it automatically, just like not all countries have made great rock and roll. Sure, >> though.
1:13:49
The technology is pretty straightforward. >> Yeah. Uh yeah.
1:13:52
Estonia is famous for um uh major government investing in in high-speed bandwidth. Is that correct?
1:14:01
Is that the is that the rough story of Estonia? >> Yes.
1:14:04
>> Um and but I'm interested in what that would look like for AI in America or the UK.
1:14:11
Like it feels like many of the models or application layer companies, they have sort of a free tier that gets someone potentially from zero to close to AI maniac and then at some point they can go and find a customer to help pay for their compute bills.
1:14:28
But is there more the government should be doing?
1:14:32
>> But as you know with venture capital, there's a valley of death.
1:14:35
>> I don't know that the government should be making grants to 16 year olds, but someone should. I'm doing it.
1:14:39
Other people should as well.
1:14:42
>> So this is really the province of youth.
1:14:45
There's nowhere you can go to learn this stuff other than say some of the major AI centers and companies.
1:14:49
So you've got to teach yourself.
1:14:51
And is there the support in your environment for that? We will see.
1:14:57
give uh give us an update on how you're thinking about grants.
1:15:00
Are you going younger, broader, smaller, different partners?
1:15:05
I mean uh there's been some folks who have been able to do compute grants and then they sort of cap they sort of the dollars go further because of the implicit margin >> with AI maniacs.
1:15:15
I'm going younger and weirder.
1:15:18
And I've also noticed Europe is doing pretty well on the AI maniacs metric.
1:15:23
We'll see how it develops as the costs rise. >> Yeah.
1:15:28
>> Uh but I've been pleasantly surprised and I'm now more optimistic about Europe than I was 6 months ago. >> Mhm.
1:15:34
How does that uh like where did that come from?
1:15:38
That feels like a come from behind story for Europe.
1:15:39
I feel like the for the longest time the the brightest technologists in Europe have gone to America to start their companies.
1:15:48
The the Collison's famous example.
1:15:50
There's many others uh with the exception of like the Swedes and Spotify I suppose but in general it feels like many people have come.
1:15:56
Are they staying more frequently? Like what's happening?
1:16:01
>> Well, these kids may yet go to America.
1:16:03
They're too young to be going now.
1:16:05
>> Uh maybe it's that, you know, chat bots didn't interest the Europeans that much.
1:16:10
They seem to like compete with culture. >> Yeah.
1:16:13
>> Uh agents are a chance to do something.
1:16:15
Now American kids have had opportunities to do things for a long time.
1:16:17
Yeah, >> but for the European kids, this is something new.
1:16:22
>> So I think at the margin, you'll see more extra activity there.
1:16:26
>> And a lot of new companies from quite young people coming out of all of Europe, including Eastern Europe.
1:16:31
>> Do you think young people are better at avoiding the fake work trap that happens with some AI users?
1:16:37
with some AI users? you know the person that just builds an automated workflow to you know check summarize every meeting and then they never look at the notes and it's just endless token consumption in service of like creating the ultimate second brain that they never actually refer to and uh it's it's
1:16:56
ultimately a lot of make work but it feels very productive sort of the toolshaped shaped objects formulation >> I think the young people are more playful with it they take more chances in the aggregate they might even do worse But in terms of coming up with the new things that really matter, I think they're going to win. >> Uh what do you think about the the risk
1:17:14
>> Uh what do you think about the the risk of of the oneshotting from AI?
1:17:17
People that uh go so deep in it they lose they lose their grip on reality.
1:17:25
>> It happens with everything.
1:17:25
You know, when I was a kid, people oneshot it on baseball. Can you imagine that? Or like drugs. >> Yeah.
1:17:34
>> AI is one of the better things to be oneshotted from, I would say.
1:17:35
No, I I was laughing about I know some guys who have been basically oneshotted by golf and they and all day every day they're like reading about golf, studying golf, trying to improve their golf game and it's the same thing as AI. Yeah.
1:17:50
>> What uh if a billionaire came to you and said, "Tyler, it seems that my type is very unpopular right now.
1:17:58
[laughter] What should we do to to try to uh shift public opinion?"
1:18:05
Uh right now there's a I think a $80 million campaign being spent to uh uh block the the the billionaire tax here in California, but it seems like there are other things more broadly that that billionaires could do to uh shift public perception. But what do you think?
1:18:26
>> I would say don't apologize and keep on going.
1:18:29
I hope the billionaire tax fails.
1:18:31
It will wreck California if it passes.
1:18:34
It's already harmed California.
1:18:36
>> Even a lot of the Democratic Party politicians don't support it.
1:18:38
Uh I think again there's just this negative sentiment about many many things including billionaires.
1:18:45
You might say some of them deserve it.
1:18:47
Fine like with all humans.
1:18:49
But at the end of the day I'm reminded of the old saying from Jonathan Swift and he said censure is the tax a man pays to the public for the privilege of being eminent. Unquote.
1:19:03
If you succeed, people will hate you. >> Okay.
1:19:07
>> You saw this, you know, was it Malcolm Gladwell, Steve Levit, Paul Krugman, whoever did well, got hated. >> Oh, yeah. That's right.
1:19:15
>> At the same time, would uh building some massive, you know, parks and or you know, any any type of of public work. >> Yes. I I >> be be worthwhile.
1:19:28
The interesting question is, is there is there some sort of like >> greater leverage from having like the library that you walk by versus like 1% lower levels of malaria that's like this abstract thing that you don't see?
1:19:43
Like there's something about building like a monument as a wealthy person as opposed to having an abstract diffuse impact on humanity.
1:19:53
Even if the reduction >> maybe the modern version of a park is just like owning an NFL team.
1:20:00
>> Uh it doesn't really feel the same because >> Obama just built a monument. >> True.
1:20:05
>> Does anyone love him for it?
1:20:07
>> Now I'm all for today's billionaires doing more to support art, music, libraries, whatever we all might come up with, but I don't think it's going to make them loved. >> Yeah. Yeah. Maybe not.
1:20:17
Uh what about religion and AI?
1:20:20
There were Anthony Lewendowski was working on an AI religion way before it was cool.
1:20:24
I think 2017 he was thinking about this.
1:20:27
Um where do it feels like we're maybe behind the curve on a true AI based religion.
1:20:35
It feels like based on the capabilities of the models you could have something happening.
1:20:42
There's little pockets of people that are obsessed with certain models but uh I haven't seen anything formally being built out.
1:20:48
What where do you think that goes?
1:20:50
I think it'll be more like sects than new religions.
1:20:53
So you could write your own version of Hindu sacred books or your own version of Shiite Islam with your own, you know, slate of who the true imams are or should be.
1:21:04
>> And we're just going to see proliferation like for a while in history, we had a proliferation of different versions of the LDS church, >> different kinds of Mormons.
1:21:13
A lot some are still around. >> Yeah.
1:21:15
And using an agent, you'll be able to whip up your own religion, I don't know, in less than a day with rituals, sacred books, practices.
1:21:22
Most won't catch on, but a few will.
1:21:24
They'll have a few hundred, a few thousand adherence.
1:21:29
That may sound trivial, but if you know half of 1% of the population does this, and some of these get some number of adherence, over time, the whole religious landscape looks quite different.
1:21:40
I think it will be more diverse and feel weirder and be less centralized in religious authorities.
1:21:48
>> Uh where do you think the Amish will be in 200 years?
1:21:51
I saw someone try and use one of these AI models to predict out project out the human population in and in 20 uh 2200 and uh the prediction came back as like America will be 34% Amish.
1:22:05
And I think it was just extrapolating, you know, linear trends of their birth rates versus everyone else.
1:22:12
But, uh, it feels like the idea of the Amish lifestyle is actually becoming invogue in almost popular culture with, uh, don't use screens as much, try and put uh, limit your Instagram usage, you need to log off and touch grass.
1:22:28
All of these memes are sort of uh, you know, the the potential gateway drug to the Amish lifestyle.
1:22:38
>> I visited the Amish many times.
1:22:38
I admire what they've done, but it's possible because the surrounding society cross-subsidizes them.
1:22:47
>> Oh, >> so you can't have a third of America being Amish.
1:22:50
And I think they keep people in the fold because it is pretty small and tightly knit.
1:22:54
and were there say three or four million Amish which we're very far from the rate of leaving would be much higher.
1:23:01
So I think it's bounded on the upside but I'm glad they're there.
1:23:05
They've done a great job.
1:23:07
Their communities are quite interesting and pretty prosperous. They have a lot of kids. Good for them.
1:23:13
>> Uh do you agree with Jeremy Gon that people are constantly looking for a priestly class and in recent years it's moved from the billionaire class to the online poster?
1:23:26
I don't know, maybe online posters peaked five or 10 years ago >> and now there's a lot more video and just the AI itself competes against the online posters. >> Sure.
1:23:37
>> Uh I liked his dialogue on that topic, but I don't exactly agree with what he said. >> Sure. Sure.
1:23:42
Uh I I'm interested to know what level of entrepreneurship you think is is reachable.
1:23:52
Can can every young person become an entrepreneur?
1:23:55
Or are there some people who are more interested in just finding a stable job and then they need to figure out how to navigate changing jobs in the future?
1:24:07
>> Most people cannot become entrepreneurs. They're not ambitious.
1:24:10
They should not be ambitious.
1:24:12
You could argue the numbers, but it's a small percentage and other people want to work for them.
1:24:19
>> So people like working in large institutions.
1:24:21
large institutions pay better, it's lower risk.
1:24:23
I don't think that feature of the world's going to change.
1:24:28
>> So advice for someone who's in an organization, they're seeing things change because of artificial intelligence.
1:24:38
Maybe they're not set up to be an AI maniac, go out and start a company, but should they be trying to find a role that is messier in their organization? >> Absolutely.
1:24:48
follow AI, learn how to work with the leading models, it's a big time investment because everything changes so quickly as you know. >> Yeah.
1:24:56
>> But if you don't, you're left behind.
1:24:58
And for all this talk of the leisure dividend from AI, right now we're still in a setting where most people have to work some amount harder. That will change.
1:25:06
But it's not about to change. Time to work. Get to work. >> You do.
1:25:11
Doesn't it exhaust you how many new and different things happen in the world of AI and you've got to stay on top of them. >> Yeah. Yeah. It is a lot. AI leisure dividend. >> Uh yeah, a lot.
1:25:20
But at the same time, I don't know.
1:25:24
Learning learning about AI I see as different as actually learning AI.
1:25:29
Like there are a lot of there are a lot of facts around the there are there are six AI tigers in China and like I just need to memorize what those six companies are, their histories if I want to understand. >> Yeah.
1:25:41
can follow it like a sport and not learn really anything at all about how what what the models are.
1:25:48
>> That's different than using the models which the models >> are so good.
1:25:52
>> are so good. it it feels like it's it's less of a learning curve and more of just the valition to just go and do it because even when someone says oh well uh I haven't tried the the latest coding models uh and I'm like oh well they can do a lot more like at the very basic like if you wanted to ask chatpt for one
1:26:10
image you can now ask codeex for a hundred images and it can just go and do it in a for loop uh and it can write a little code for you and do whatever it needs to to get you your answer that's more robust and hydrated uh But also you can just ask chatpt how do I do that and it will tell you how to do it. And so
1:26:26
And so the learning curve this is a Joe Weisenthal opinion but he he says that like there there is no learning curve because the AI is good at teaching you how to use it but there is something there where you talk to people all the time that are like ah I haven't tried AI yet. I got to check that out.
1:26:45
It can tell you what to do, but if you're always chasing it, waiting for it to tell you what to do, you're not on top of it, it's not really the way to excel in the area.
1:26:53
So, I think you do have to stay on top of both like the baseball team side of it and the how do you do this side of it. Again, it's hard.
1:27:04
>> How big can gambling get? >> It's already too big.
1:27:09
I don't know how we pull it back in. Um, it's a problem.
1:27:15
Now, people must enjoy it, the people who don't wreck their lives with it, or it wouldn't be that addictive, but it is fairly addictive for many people.
1:27:23
>> And I don't see how we turn the clock back.
1:27:25
>> The thing that's telling is even people that lose a lot of money gambling, I've noticed that they don't hate their like bookie or the app that they use to lose money. >> Yeah.
1:27:39
they seem to have like gen, you know, maybe maybe it's like a toxic relationship, right? Where where >> No, no.
1:27:45
A lot of times people will hate like the player that missed the field goal and they will be cyberbullying the particular athlete that lost them the money and they won't be >> not their account manager at DraftKings or whatever. >> Yeah.
1:27:58
I mean, isn't >> But how would what like you know you if you have a big red button that the ban, you know, sports betting or or gambling, do you do you hit the button?
1:28:07
Like what what would be your practical solution for reigning it in?
1:28:13
>> I wouldn't hit the button.
1:28:13
Banning it would not rain it in. We know that already.
1:28:18
Uh you hope norms change.
1:28:18
It's a bad thing to be one-shotted by.
1:28:21
That said, I'd like to see a costbenefit analysis.
1:28:26
all the people who enjoy it and add them up and see how that compares to the cost.
1:28:30
No one wants to do that for obvious reasons. >> Uh just say no.
1:28:36
>> Yeah, but it's but it but but right now >> don't do it. Be honest.
1:28:41
>> Yeah, I guess I guess I I was watching uh the the latest uh UFC event and almost every single ad is a gambling ad.
1:28:52
Certainly reigning in advertising for gambling would probably >> we have a playbook from the tobacco uh >> yeah and I think it's in like advertising you can restrict you can add taxes on a state and local level as well as federal taxes and you can sort of try and internalize the negative externality. >> Yeah.
1:29:11
There was that law that said you could only write off like 90% of your losses which which for you know which is really bad if you're you know a sports book or >> the problem is is that I I don't think the the impact of gambling the negative impa impact has been fully quantif uh quantified in sort of a sound bite yet like with cigarettes it's like cigarettes kill millions of people every year.
1:29:34
50% of smokers who smoke a pack a day will die from cigarettes or a cigarette related to illness.
1:29:39
There isn't that for the gambling industry.
1:29:43
It's just like a lot of people lose money, but you technically lose money when you go see the Odyssey.
1:29:46
You just get the enjoyment of a three-hour film.
1:29:49
You lost money and people get the entertainment of gambling.
1:29:51
And so that like that's the same trade.
1:29:53
It doesn't hit as hard as like you will die if you smoke cigarettes.
1:29:57
I think the campaign against smoking worked because people like me hated it when someone next to them would light up or on a plane or in the movie theater.
1:30:07
>> If someone if my next door neighbor on their phone >> really >> on the airplane, I don't care. >> Yeah. >> No, you used to Yeah.
1:30:16
>> If you're on old commercial planes, they would have a cigarette uh an ashtray in the armrest.
1:30:23
>> I suffered under that for years. >> Yeah.
1:30:25
You you try to not sit next to the smoking section. >> Yeah.
1:30:30
>> But again, if there's like a a no gambling section on the airplane, it's ridiculous. >> Yeah.
1:30:34
>> The guy next to me could be gambling on his phone. >> Yeah. You wouldn't even know. >> Who Yeah, exactly. Let it be. >> Yeah. Yeah.
1:30:41
>> That is shocking to me.
1:30:42
>> I like [laughter] that you just learned that.
1:30:43
When did uh when did smoking on planes end? 80s.
1:30:45
I feel like it was about maybe like early 90s at the very very latest.
1:30:52
Uh >> yeah, I recall celebrating, but I don't know the year. >> Celebrating. Yeah.
1:30:56
Um anyway, uh what uh what what have you been reading into the jobs numbers that have been coming out like the overall unemployment rate seems to be holding steady?
1:31:07
It seems like it's buoyed by healthcare. Yes.
1:31:14
>> Smoking was banned in 1988 on domestic flights lasting two hours or less.
1:31:17
They were saying you can go two hours without a smoke. >> Okay.
1:31:22
[laughter] Yeah, >> we know you can.
1:31:23
And then eventually they expanded it >> to all domestic flights in 1990. >> Yeah. 1990. There you go. Uh jobs, data, hiring.
1:31:31
It feels like there's sort of stagnation everywhere except healthcare, which is sort of the messy job thesis playing out.
1:31:38
But what else is going on in the hiring market that you've noticed from the last couple releases?
1:31:44
>> Well, the unemployment rate for 18 to 24 year olds, it's back to where it was before we had good large language models.
1:31:50
So AI is not destroying that market.
1:31:53
It seems we're not going to have a recession anytime soon. That's good news.
1:31:56
Labor market has slowed down quite a bit, but that's to be expected.
1:31:59
And some of the reports sound less impressive because we have fewer immigrants.
1:32:05
>> But overall, I've been pretty happy.
1:32:05
The economy hasn't crashed. It's kept on going. Great. >> Yeah. No, that makes sense.
1:32:10
Uh then what are you attributing the like the vibes around like young people entering the workforce?
1:32:18
Everyone says it's so bad.
1:32:18
Is that just the really cushy tech jobs are sort of a little bit belt tightening there?
1:32:25
They're not hiring as much and so there aren't as many of these like really nice laptop jobs or what do you think's going on there?
1:32:33
>> I think there are big sectoral shifts going on and the new jobs being created people who think of themselves as educated and high class may not want or may not even be good at and they complain and they're vocal and they have the ear of the media. It is a problem. I wouldn't deny that.
1:32:49
But in the aggregate, a lot of other people are doing better. Yeah.
1:32:53
>> And the AI maniacs are about to take a huge leap upwards.
1:32:55
Y >> and there'll be this massive reallocation of status. Yeah.
1:33:00
>> It'll be very hard for our politics to handle that. >> Yeah.
1:33:02
I saw Oh, that's interesting. Uh Wow. Wow.
1:33:06
Um uh yeah, I I saw some stats around like uh uh new grad computer scientists from Stanford uh not getting as many job placements and uh Tyler on our team was saying like that's a skill issue.
1:33:20
If you're if you're a Stanford graduate with a computer science degree, like you can get a job or you can start a company, you can do something, but you might need to think a little bit differently because the track might be slightly different.
1:33:31
Um but I don't know if you agree with that or not. >> I agree with that. >> Yeah.
1:33:35
Um, and it also just feels like one of my latest uh like thesis is uh that a lot of these CEOs who are doing like AI based layoffs, the optics are just terrible.
1:33:49
And if they actually have a good business, they should be going on the offensive.
1:33:52
We we we love that Matthew Prince hired I think like a thousand interns or something like that.
1:33:58
And it feels like young people who understand AI coming into an organization that has some sort of moat, some sort of durability, some business, like the leverage is just going up.
1:34:07
So you should be hiring more instead of doing layoffs if you actually understand what's at stake and how big your business can be.
1:34:15
But if you just went through a hiring glut, maybe you do need to sort of retool.
1:34:20
>> The AI companies themselves are hiring plenty as you know, right? >> Yeah. Yeah. Totally.
1:34:25
Uh can you recall a point throughout history where the majority of people uh uh uh their let's say their their sense of like self-worth and identity was not tied to their employment or their job >> because that's a whole separate issue but obviously that's a major qualifier. >> Sure.
1:34:50
uh medieval >> medieval times. I think we lost him.
1:34:57
>> His internet went back [laughter] >> to 1988 >> growing food, but in a very different way from how things work now. >> Yeah.
1:35:06
Uh well, thank you so much for taking the time to come chat with us.
1:35:10
It's always a pleasure to go all around the world topics.
1:35:13
>> Uh have a fantastic week and a fantastic summer.
1:35:16
>> Catch you all next time.
1:35:16
And like I said, work harder. >> Yes.
1:35:19
Next time you're in California, three of us, a cup of Joe, we will try to we will try to create an experience.
1:35:28
>> Third cup of coffee in history.
1:35:30
>> That would be >> do a 4 hour showc. >> Let's see. We'll peer.
1:35:34
I don't think it'll work.
1:35:36
Anyway, thank you so much, Tyler. We'll talk to you soon. Bye. >> Bye.
1:35:40
>> Have a good rest of your day.
1:35:40
Let me tell you about CrowdStrike. Your business is AI.
1:35:43
Their business is securing it.
1:35:45
Crowd Strike secures AI and stops breaches.
1:35:48
Um, we have a picture here of the smoking section on a plane if we want to pull up.
1:35:54
But we also have our next guest. Um, we have Danny Young.
1:35:58
>> That is absolutely insane.
1:36:00
>> This is like blowing Jordy's mind.
1:36:02
Biggest update to your world model.
1:36:02
Uh, let's bring in Danny.
1:36:04
He's the co-founding partner and CEO. >> Danny, how you doing?
1:36:10
>> We got questions for you.
1:36:11
>> Thanks for having me on.
1:36:11
We before we even get to introductions, how many cups of coffee a day do you drink?
1:36:17
>> Only drink I am eight. >> What? Oh wow. Oh yes, of course. There we go.
1:36:22
>> But uh do you guys ha have caffeine in the product?
1:36:25
>> There's no caffeine in the product. Zero caffeine.
1:36:27
So So you're just two for two. Zero life. No caffeine.
1:36:32
>> Tyler Cowan doesn't do coffee either.
1:36:34
Maybe maybe there's a secret second tier.
1:36:36
There was a new study in the Wall Street Journal that said up to five cups of coffee a day is okay.
1:36:38
people are celebrating if they've already been fully addicted.
1:36:42
But uh anyway, enough of that.
1:36:45
Introduce yourself and the company, please. >> Sure.
1:36:48
My name is uh Danny Young, CEO of Krenetics and IM8.
1:36:50
Um you know, we started a brand IMA about 19 months ago, and now we're the fastest growing supplement brand ever recorded in the industry.
1:36:58
Um of course, you may know David Beckham.
1:37:00
Uh you know, no introduction, co-founded IMA with him 19 months ago.
1:37:05
has been a crazy journey ever since.
1:37:07
We hit 100 million AR within 11 months.
1:37:10
We hit 200 million ARR within 18 months. >> There we go.
1:37:15
>> What stopped you from hitting 100 within, let's say, 6 months?
1:37:19
>> That's hilarious, but actually a good question.
1:37:21
[laughter] >> Wait, wait. Yeah.
1:37:22
I mean, what was the constraint? Was it supply chain? Was it distribution?
1:37:26
>> Not enough products or figuring out >> Yeah. >> advertising?
1:37:30
>> No, it's just um you have to scale, right?
1:37:32
And then you can't scale so fast.
1:37:34
Even in 11 months, you know, we're the fastest ever, right?
1:37:36
Typically at 100 million for supplement brands.
1:37:38
On average, it takes roughly 3 to 5 years to hit 100 million.
1:37:43
So for us to being able to do in 11 months um is yeah quite amazing.
1:37:50
>> Do you do you remember what you were modeling for like where where you wanted to be after the first let's say 12 months? >> Yes.
1:37:59
And the model was very different than reality, right?
1:38:01
So I think our first model fullear revenue was approximately 30 million USD and that was already quite high for a new brand out of the gate >> in this very competitive category. Right.
1:38:13
So our first full year revenue we ended up doing 60 million. >> Oh wow.
1:38:17
[snorts] >> Um this year we'll well we'll do well over 200 million um in our second year of full operations. >> Wow. Uh okay.
1:38:25
So what like David Beckham I think of him as a very like global star.
1:38:32
Did you is part of the growth did you guys go global early?
1:38:35
you guys go global early? What did that strategy look like uh compared to let's say a traditional like US e-com uh traditional DCZ comp uh company in the US you probably wait to go global two three four five years out right for
1:38:51
us day one global day one meant 31 countries shipping um so we did that from day one last year and then so US is our number one market Canada number two UK number three Australia number four Singapore number five uh now we ship to 43 countries we're delivering $200 100,000 servings daily. We've now
1:39:07
We've now delivered 52 million servings in the last 19 months. >> Wow.
1:39:13
Uh talk about the latest round.
1:39:13
You raised a billion dollars, but uh what does that look like? Who participated? All that stuff. >> Sure.
1:39:24
So, uh not many people know IMA is actually part of a public listed company, Prenetics, uh listed on the NASDAQ under the stock taker PRE.
1:39:30
And then so with that again the brand so new we've gotten a1 billion dollar growth commitment. >> Congratulations.
1:39:46
>> Uh you said thank you >> growth commitment from >> general catalyst.
1:39:52
>> And is this their new is this their their program that helps uh basically finance uh customer acquisition? >> Correct.
1:39:59
>> Correct. So it's from their CVF fund and then so they typically work only with multi-billion dollar companies right so they did this with Lemonade again Lemonade was public company as well in 23 market cap was1 billion now today
1:40:11
they're five billion they did this with Grammarly they gave them billion dollars to scale up as well so this only works if you have great retention cohort data because what they're doing is they're underwriting the risk of predictable revenue in the future. So what they're
1:40:26
So what they're doing is that for example for every dollar that we're spending on marketing they're giving us 70% or 70 cents and based upon our gross profits then we pay that back over you know the next you know two three months and they charge a percentage interest rate roughly 3.
1:40:40
5% if that payback period is under three months. >> Yep.
1:40:45
I'm on uh the board of a of a company that is uh in that program >> as welling.
1:40:51
Uh, and I was we had a board meeting last uh Thursday.
1:40:53
I was in Europe, so it was like 2:00 a. m.
1:40:55
, but we we spent uh spent quite a lot of time going over all that data in the program. So, very cool.
1:41:02
What percentage of uh IM8 does Prenetics own? >> 100%. >> Crazy.
1:41:12
>> So, you guys are at you guys are going to do 200 million this year.
1:41:13
you're or you're projecting 200 million sitting at a market cap of of uh 354 million. >> So, >> yeah.
1:41:24
And we have roughly about 140 million in cash. Yeah.
1:41:26
Even [laughter] without the general catalyst. >> Yeah.
1:41:31
And then so I think there's a big [clears throat] disconnect between private markets and public markets.
1:41:34
And to be fair, yeah, we had a lot of legacy in the past.
1:41:38
We had diagnostics business.
1:41:40
We grew very fast during COVID. >> Yeah.
1:41:43
>> Yeah. during co we're doing 40,000 PCR tests you know postco I had the opportunity to meet with David and then we got along really well we're like hey what if we did something together what would that look like and then we recruited >> yeah some of the world's best doctors scientists and professors on board and yeah and so it's been crazy >> very cool well congratulations and thank
1:42:03
you so much for coming on the show to break it down >> yeah great to meet you >> your day >> really really impressive and come back on soon >> yeah we'll talk to you soon >> appreciate it >> let me tell you about Shopify Shopify is the commerce platform that grows with your business and lets you sell in seconds online, in store, on mobile, on social, on marketplaces, and now with AI agents. We have our next guest already
1:42:21
We have our next guest already in the waiting room.
1:42:23
You got love from it's Andre Horowitz now.
1:42:26
[music] Connor Love, welcome to the show.
1:42:29
Congratulations on the new gig. >> How you doing? >> Thanks, guys. Appreciate it.
1:42:33
>> First question, how many cups of coffee a day do you drink? >> Oh gosh. Uh, four.
1:42:36
Oh, you're you got room to run because there's a new report.
1:42:42
>> I'm a zero Celsius guy, too. Zero Red Bull.
1:42:44
Back when I was in the military, I would pound Red Bull and then, you know, the stomach gets all messed up, so I got to declanse. So, I'm on a journey. I'll come back to it.
1:42:52
>> Well, the American Heart Association says you're well within the limits. You're healthy. Congratulations. You're doing it right.
1:42:58
Uh, so funny like I had seen a study showing that you could basically coffee max as of like two years ago.
1:43:08
I've been on that I've been basically on that on that program. >> Okay. Well, >> for a while.
1:43:13
>> Conor, you were early.
1:43:14
>> The other thing I got to just say since we're on the topic of caffeine, I found out Austria has uh like uh glass.
1:43:17
You can get Red Bull in a glass bottle just at gas stations. >> Oh, yeah. It's from there, right?
1:43:26
That's where I almost went to the headquarters when I was there.
1:43:29
>> I was like, I got to import I got to import this stuff. >> Maybe. Maybe.
1:43:33
>> Anyways, we're not here to talk about glass [laughter] bottle Red Bull.
1:43:35
We're here to talk about you >> caffeinating the capital markets with new American Dynamism investments.
1:43:41
Talk to me about the journey.
1:43:44
Talk to me about what you're excited about to be doing there.
1:43:48
>> I mean, I I mean, look, um I've known uh the folks at AD for a while.
1:43:51
uh you know we all know four years ago they didn't just uh you know point to a category and think it was cool.
1:43:58
I mean they they helped create the category u both branding it but also you know backing some of the you know earliest most interesting companies in this ecosystem.
1:44:06
Uh so it's been a relationship that I've had for a long time and when I got approached with the opportunity to join the team and help lead the team here.
1:44:11
Um in a lot of ways it's one of these things where you know our job as VCs is to sit behind entrepreneurs and put them in the best position to succeed.
1:44:19
And this platform offers you, you know, a really good place to do that.
1:44:22
And, you know, it's it's kind of one of these things where, uh, it's a good fit for me. Let's just say that. >> Yeah, I think so.
1:44:28
Uh, so take us through the structure of the team now because, uh, you're you're saying you're help leading it.
1:44:33
It seems like you're in a GP role.
1:44:35
Uh, does this mean Katherine's running?
1:44:37
Is she Is she on the way out? >> No. No.
1:44:39
Cath Katherine's part of the team. She's kicking it.
1:44:40
She's she's doing amazing things.
1:44:42
I think the beauty is um when you know like we all know when you grow the fund then you know there's more opportunities to invest it means you need to kind of you know push it to the next you next version and so you know we got $1.
1:44:53
2 2 billion dollars of fresh capital to deploy into a category and kind of supercharging away.
1:44:58
So you know it's kind of us four GPS uh you know operating as a team and you know this is the beauty of venture is there's individuals that have good backgrounds and good beliefs and good good views of the world but you know I
1:45:09
think in the end like venture should be a team sport and yeah you got to put one individual ahead of the other you know every now and then but look I mean when you look at the platform and you see kind of what's been built so far I mean
1:45:19
I'm excited to to help take it to the next step in the future >> and the structure of the fund I mean I imagine you're still going to be focused on like the earlier stage you obviously have enough capital to lead follow on rounds but sweet spot series A B >> I'm interested in like the health of that category because it feels like there are many category defining companies and all like when I look at a market map I'm like are we good on AB
1:45:45
and then I find like the whole next turn of like oh we didn't think about this whole section of the economy >> yeah all right I'll give you two perspectives here the first is uh in any new category that is going through a
1:45:56
tectonic shift like this category it is uh you are 99% wrong about the size of the category when you start investing like just go back and look at anything I mean the nature is this category is going to be far bigger than we all think it is
1:46:08
>> with that being said I do think you're right there have been a let's call it a you know neo primes in the defense space or kind of neo manufacturers whatever you want to call them elsewhere that have clearly pushed themselves into hey
1:46:18
we are a real player we've raised real capital we have real customers the question is you know how many people are going to be at the table and you know I'll answer that question two ways which is not all of them number one. And the
1:46:25
And the second one is, as I said before, it's going to be a bigger category than we all think.
1:46:31
So, what I love doing, and maybe this is a little bit controversial here, but it's like my favorite type of investing is really at that inflection stage where you're potentially writing a growth check or a growth size-like check, but the company is still early in terms of the product market fit.
1:46:44
And in my view, especially in AD where these companies take a lot of capital to grow, that's how you return funds.
1:46:51
I mean, that that is that is the exciting place to invest in my opinion.
1:46:54
Yeah, Jordy, I want to give you a second if you have something or I can go. >> Yeah.
1:46:58
Update on I want I want the update on the deals you did while at Lightseed that you're most proud of because I'm assuming you're going to, you know, first day on the job, you got the [laughter] new email, you you hit them up.
1:47:10
How how would you like uh another 100 mil? >> Yeah. Yeah.
1:47:13
[laughter] I mean uh I mean, look, this is another thing about venture is when when these when these companies raise a lot of capital, there's a lot of the same people around the table over multiple stages.
1:47:20
So, you know, I've had the privilege of investing in a lot of businesses.
1:47:25
Everything from Castellian to Andreal to K2 Space to AMA, uh, a lot of other things.
1:47:31
And the beauty is Andre has actually been an investor in a lot of those businesses already.
1:47:34
So, it's a relationship that I get to kind of carry over now from a different side of the seat.
1:47:39
But, I think, um, I think you're right.
1:47:41
I mean, um, look, there's going to be, uh, there's going to be many new entrance.
1:47:44
I I am very happy with those investments that I've made.
1:47:47
have a great relationship with with with Ravi Matrey and the rest of the folks out at Lightseed.
1:47:51
I mean, they're going to keep doing amazing things and investing in this category.
1:47:54
But yeah, I just think it's still early.
1:47:56
I mean, again, like nine nine times out of 10, you are wrong about the size of this market.
1:48:00
Uh the space market's going to be massive.
1:48:04
Defense market is definitely not spoken for yet.
1:48:05
The industrial base and manufacturing, we haven't even really talked about and touched.
1:48:09
Like, we're talking trillions of dollars of industries and we, you know, look at great companies like Hrien or AMCA and we think, "Oh, the market's been solved." No.
1:48:16
like it's there there's there's a ton of room left left to run in my opinion.
1:48:21
>> Uh on the on the financing side, uh we just talked to the CEO of IM8 which uh is doing a new sort of billion dollar financing with uh GC I think through their customer value fund.
1:48:33
Is there anything like that emerging in in defense?
1:48:39
I'm I'm assuming a lot of your port codes are using venture debt or maybe getting financing against some of these bigger contracts, but what are you seeing?
1:48:49
>> Yeah, I mean I I think first of all, if you look at what the federal government's doing, the federal government is stepping up in a real way.
1:48:54
I mean, there's been a long office called the office strategic capital that has SBA, small business administration money that can put towards these companies and their growth.
1:49:01
There's also a new unit formed called the EDU, the I believe it's the economic development unit within the Department of War.
1:49:07
The idea is that if you can look at these industries that either have a supply chain uh a problem or some mis misimbalance with China, there's not free debt but close to free debt of capital that you can receive from the federal government.
1:49:20
And in the end, it's like when there's such a you know misimbalance for you know in in this case like the Chinese supply chain versus ours, it is only in America's best interest in order to do this and in the end there's equity capital sitting behind it.
1:49:32
What I will say though is like uh debt is not an answer to your problems when you're a company.
1:49:36
problems when you're a company. I mean you need to obviously create real value and have real kind of you know and in my view some type of technology uplift of what you're doing but in the end I do think that you know if you look at official capital like I said if you even look at what private equity is starting to do in this ecosystem they're entering an ecosystem and saying hey I don't need
1:49:55
to control a company I don't need to you know flip the entire management team it's just a different you know type of risk asset and they need to enter at the right time but again I think the capital markets are super rich here the world you know I I won't break any anyone's mind here, but the future of returning venture funds is investing in AI and it's investing in American dynamism. And
1:50:13
And you know, again, the beautiful thing in Andre, we have both of those things.
1:50:16
And now I get to, you know, do it in in a different way. >> Yeah.
1:50:20
Aren't they going to collide really soon, if not already?
1:50:22
I'm just thinking about like >> there's a bunch of great defense companies.
1:50:27
A lot of them already been started.
1:50:29
The defense budget is growing, but a few percent a year run rate.
1:50:31
Uh and then you have like AI capex which is like tripling every year and so uh a lot of the same motions of like we have a machine that makes a product that does a thing.
1:50:44
It feels American dynamism e but then it plays in the AI boom.
1:50:47
Is that going to be a bigger trend?
1:50:50
>> I mean I mean again I think uh I think you're right there they are colliding far quicker than you think they were they will collide.
1:50:55
I think to some extent though that is a good thing.
1:50:57
I mean again if we if we look at what's happening and how AI is transforming other software industries it's going to look different in hardware for sure.
1:51:03
I mean there are different barriers that you need to overcome with you know h you know again let's talk about putting a a fully autonomous robot with a weapon in the hands of the military.
1:51:13
There are layers of kind of integration and policy and all these other things that need to happen.
1:51:18
So it's not going to happen overnight but I will say it will happen.
1:51:22
um on on the competition piece which is a slightly different you know answer to your question is um again I I I think you're right there's a small percent of the budget that has gone historically to these neoprimes if you look at what this administration has done that those numbers are changing on an order of
1:51:38
magnitude and then if we also want to take a step back and look at Europe look at Asia look at other places I mean literally markets are being created overnight so I do not think the problem is hey is there budget or is there ability to capture I actually think the problem is and this is what not enough
1:51:52
people are talking about is will these companies actually deliver on the you know massive solutions that there are you need to build I mean I'm serious like you need to we need to have 40 Tesla factories we need to build thousands of these things I mean it's not just defense but it's like the folks at base power and you know kind of
1:52:09
everyone in the manufacturing ecosystem will we have companies that can produce not thousands of things but tens of thousands or hundreds like that is the question and if that doesn't happen it doesn't matter how big your market is it doesn't matter how much AI I pixie dust you throw on top of it. If you can't
1:52:22
If you can't build real hard stuff, you're not going to have lasting value over time in my opinion. >> Yeah.
1:52:28
>> I saw a video last week of a of a drone uh that was designed so that the entire body of the drone spins and it makes the whole structure almost invisible.
1:52:38
It was very uh very scary looking.
1:52:40
and and the the person posting it was saying that drones are currently like in their sort of World War I era of evolution of warfare.
1:52:53
Is that uh how does that track with what you're saying?
1:52:56
>> I I first of all, I love this comparison.
1:52:58
I always use the World War I comparison when talking about what's going to happen in space, but I actually think that the drone comparison is better.
1:53:05
And don't quote me exactly on these numbers, but if you go look at the amount of manned airplanes that we in the west had in World War I, uh it is roughly the amount of satellites that we as a nation had up in space, you know, call it, you know, three or four years ago.
1:53:19
And now if you flash forward and see the order of magnitude amount of more manned airplanes we have, I mean, you literally have like, you know, it's it's a hockey stick like growth.
1:53:26
So to your point, you know, we have what, you know, thousands of tens of thousands of drones that exist in the DoD today.
1:53:32
I mean, we're we're literally buying millions of them and yes, they're a tradable so like they will, you know, kind of decline [clears throat] and go away, but I just think people have not wrapped their head around the sheer number of these system systems.
1:53:43
And I think a drone specifically to your point is like it's always going to be this cat-and- mouse game.
1:53:49
So just because you've built, you know, a million of these things that are treatable and can do these thing, the the enemy is going to adapt and then all of a sudden you're going to have to build a new version that's EW resistant, that can go further, that can do.
1:53:59
So, it's not a one problem, you know, one time you just solve the problem and you buy a million of them.
1:54:05
I mean, this is decades long that's going to be happening from our from our federal government. >> Very cool.
1:54:09
Well, congratulations on the new gig.
1:54:11
Look forward to talking again soon, chopping it up. >> Great.
1:54:16
Looking great in the suit. >> Look fantastic.
1:54:18
>> Looking great in the suit.
1:54:18
>> Well, I always like to wear a suit [clears throat] just for you guys.
1:54:19
And hey, anything that gets me on the pod to uh to to talk with Delian more, I'm I'm always about that.
1:54:25
I'm just trying to elevate my media game just so you know Delhi and I can can riff with each other some more.
1:54:30
So that's what I'm here for. >> Fully elevated.
1:54:33
>> Great to see you Connor and congrats on the move. >> Congratulations. We'll talk to you soon. >> Cheers. >> Goodbye.
1:54:37
Let me tell you about MongoDB.
1:54:40
What's the only thing faster than the AI market, your business on MongoDB, don't just build AI, own the data platform that powers it.
1:54:45
Before we bring in our next guest, we got to talk about the Aston Martin SUV.
1:54:49
[snorts] Is this really a collab with Call of Duty?
1:54:53
You got to pull up this image.
1:54:56
You uh you think this thing is a little ridiculous?
1:54:59
You think this isn't uh >> Oh, I I love the way >> I see this is like the new Model Y.
1:55:03
I think like this will be the car that's on like you're just seeing so many of them.
1:55:09
>> And this is what you've been asking for because it is a V12. >> It's a V12.
1:55:11
[laughter] >> So, first of all, let's go with what's amazing. >> Yes. >> The name. >> What is it called? The Dreadnot. That's a good name.
1:55:20
Uh, and so I >> Yeah, so here's the thing. It's digital only. >> What do you mean?
1:55:28
>> A digitally a digital only military spec vehicle designed exclusively for the new Call of Duty Modern Warfare.
1:55:35
>> So they will not be making this physical.
1:55:38
There's no there's no V12 if it's not if it's digital.
1:55:40
That doesn't make any sense.
1:55:41
I thought this was a real car.
1:55:42
They're not even making this.
1:55:43
>> A lot of like it it spread so quickly around that I think a lot of people thought it was real.
1:55:47
I think they should have made at least a few of these oneoff.
1:55:51
I don't know about the Call of Duty partnership in general.
1:55:54
I just feel like I feel like cheapens >> the brand and I feel like Aston is trying to appeal to a younger audience, but having a madeup car, I don't know if it really does that.
1:56:06
>> This is Yeah, this is a very weird partnership.
1:56:08
Uh, no, you imagine you you you this is what you have to tow your Valkyrie to the track like that.
1:56:12
Like you could sell one of these to every Valkyrie owner in theory because it's so so unique.
1:56:18
I >> It reminded me of the the Rambo Lambo a little bit.
1:56:22
Sort of a modern take on that. >> Yeah.
1:56:25
>> Uh so make the car Aston. Just do it. >> Just do it. >> Just do it.
1:56:31
>> And we will just bring in our next guest, Khalil from Natural, the CEO and co-founder. How you doing? Welcome to the show. >> Thanks for having me.
1:56:40
>> Would you pick up follow that?
1:56:40
Would you buy that if that was a real car?
1:56:42
Would that would uh Are you in San Francisco?
1:56:47
>> I am in San Francisco. >> Yeah. So, it's a norainer.
1:56:50
>> It feels like an LA car though. >> Yeah.
1:56:52
There was like the wrapped cyber truck era for startups.
1:56:54
I like to see a wrapped dreadnot. >> It would work. It would work.
1:56:58
Anyway, uh enough talking. >> The new BMW car. >> No. What's that?
1:57:03
>> The predecessor to the XM. >> Predecessor.
1:57:06
>> That might be more >> or successor. >> Successor. >> Successor. Successor.
1:57:10
>> The XM did not sell well.
1:57:10
Well, that was a widely panned that that's their luxury performance SUV, right?
1:57:16
>> Oh, this is you're talking about the new X5. >> Oh, >> yeah.
1:57:20
It's like a G Wagon lookalike. >> Uh-oh.
1:57:23
>> Oh, I don't think that is actually I don't think that's actually released.
1:57:24
I think it maybe they'll tease it at Car Week in a couple weeks, but I don't think I think every every >> render so far has been just like >> Oh, just fan. Okay.
1:57:35
Well, we'll keep track of it.
1:57:38
Anyway, enough about cars. Great to see you, dude. How you doing?
1:57:42
>> Thanks for joining us on a big day.
1:57:43
Introduce yourself and tell us about the company. >> Sure. Uh, I'm Khalil. I'm the CEO of Natural.
1:57:48
Natural builds payments infrastructure for agents.
1:57:52
>> We're announcing today that we raised a $30 million series A led by Kirsten Runner. Thank you. There we go.
1:58:00
Um, payment rails for agents.
1:58:03
Uh, what does that mean today?
1:58:07
And then I want to talk about where this is going in the future.
1:58:12
>> Today that means that agents can store balances.
1:58:14
They can pay and request other agents or businesses or consumers.
1:58:17
They can do transfers from external or internal accounts.
1:58:21
Shortly they'll be able to collect card information over the phone, make payments.
1:58:24
Uh it's a bunch of different payment primitives.
1:58:30
>> And so those use cases rank them from uh by traction.
1:58:33
So, like what what agent payment use cases have the most traction today?
1:58:40
Because this feels like one of those things that uh people have been talking about for a long time.
1:58:45
It's a great venture bet because if if you know if this category pans out the way that a lot of people have been theorizing, there's going to be some great big companies built.
1:58:55
But I haven't used I haven't used any of these.
1:58:59
Um like I haven't had an agent pay for anything on my behalf quite yet outside of you know using traditional checkout flows.
1:59:09
Maybe I'm a boomer lagger.
1:59:11
>> I sent like 100k to Claude.
1:59:11
Uh but it turned out this guy named Claude.
1:59:14
I just sent it to him on WhatsApp and uh I don't know if I'm getting that back. So need this.
1:59:20
>> You got you can try your first aentic payment today.
1:59:22
If you sign up uh and request our agent, we'll send you back between $5 and $10,000. >> Whoa.
1:59:27
[laughter] Okay, that's I love that. >> Variable.
1:59:31
>> That also sounds like a scam.
1:59:31
Like, send me five and I'll send you 10. This is like a classic.
1:59:36
>> Wait, are are people not already using agents to create millions of accounts to [laughter] >> uh but but uh I I do think there's uh there's a very interesting opportunity.
1:59:48
wondering how you can like walk me through this particular problem that I've seen.
1:59:52
So, uh there's a really interesting AI YouTuber, his name's B Ben Awad, and he's been doing this series of like I tried to use a Frontier model to like make me money and he went to Fable and he did GPT soul and he gave it like a week and he was like just do whatever and and it and it does a bunch of stuff and it's very it's very impressive.
2:00:10
He makes like 5 cents, but it's like, you know, a demo, but everything that he's doing is like the reaching for the high shelf.
2:00:18
So, he's always it's like the genius model that can solve the IMO gold medal goes to him and is like, it's time to buy a domain for $10.
2:00:27
And he'll be like, "Okay, I did that."
2:00:30
And then and then it'll like go off and write the beautiful code.
2:00:31
And then it'll come back and be like, "I need you to go set up an API key here."
2:00:37
And so what I'm interested in is is there a way that I, you know, soon I will be able to safely upload some sort of credit card payment balance and then give that to an agent and say, "Look, I want you to go deploy a website on this technology on this service, use this API, go to 11 Labs, grab their voice agent, and go and actually set up the accounts, do the payments, and then just here's your budget.
2:01:02
Don't go over, but go crazy and do whatever >> you can do."
2:01:06
most of that today with natural and the parts of it that you can't should be out at the end of Q3.
2:01:12
I'd say the way we think about the world is agents execute a majority of global payment volume over the next decade.
2:01:19
>> That that world is monopolistic or dualistic in nature because payments are highly network effects driven business >> and that that world is determined in the next like 12 months. >> Um high stakes. >> So >> good luck.
2:01:31
[laughter] You're like, >> I think I think whoever is like the default developer choice in 2027 is likely to be the default developer choice in 2028 and 2029 and 2035.
2:01:40
And so if it doesn't feel like a land grab market, it very much is about to be.
2:01:48
>> Uh so a company called Stripe uh is uh >> couple Irishmen, >> couple Irishmen.
2:01:56
uh they they're certainly not asleep at the wheel uh with AI by nature of being like very developer friendly with their products overall.
2:02:05
I think they ended up being like >> accidentally really well positioned >> for AI because agents can really easily understand their products.
2:02:14
I'm wondering have you figured out anything where like they are at some type of structural disadvantage because of their existing business model or things that they can and can't do that creates an opportunity for natural or is the bet that the market is just going to grow so quickly there will be a lot of land to grab over these next call it 12 to 36 months.
2:02:39
>> Uh I'm a big fan of Stripe.
2:02:39
I think that Agentic Payments is actually much larger than Stripe.
2:02:43
Like when you think about how agents are going to interact with the world, the way I frame the opportunity is like we have the chance to build what looks like a bank, a PSP, and a network all at the same time.
2:02:53
So it's like JP Morgan and Stripe and Visa is the TAM, not just Stripe. Uh, >> yeah.
2:03:02
So, right now, if I go to if I go to Codex and I'm like, go build me an app and deploy it and pay for a bunch of things, it might pull Stripe off the shelf as payment infrastructure, but I don't really have a Stripe balance that's sitting there necessarily that it can deploy from.
2:03:16
I could give it my credit card, I guess, but that's a little bit different than having a wallet.
2:03:22
>> You you might have a Stripe balance.
2:03:22
>> You you might have a Stripe balance. Uh but I'd say you know the magical experience about with AI is like you think of something you say can you do this and the answer is yes and then you think bigger and you go can you do this and the answer is yes >> and I think the same will be true of agentic payments which is like
2:03:39
>> uh there shouldn't be a limit in terms of number of financial flows your agent can accomplish and that is much easier to do when you're building ground up and net new stack with all the primitives an agent might need under one unified architecture versus trying to rearchitect an existing payment system to do everything that you need to do. >> Well, congratulations. 193 days old when
2:03:59
>> Well, congratulations.
2:03:59
193 days old when this round was done.
2:04:03
What a what an overnight success. Truly. >> That's right.
2:04:08
And we're going to we're going to we're going to have you back on hopefully this year, but certainly July 20th next year to talk about your [laughter] prediction of a of a fast take off in aic payments. Yes.
2:04:19
Uh look forward to following along and and congrats to the whole team on on uh the new capital, all the progress. >> Thanks for coming on.
2:04:28
>> We'll talk to you soon. >> Appreciate it. Have a good one. >> Goodbye.
2:04:31
>> Let me tell you about Figma. Agents meet the canvas.
2:04:33
Your AI agents can now create and modify your Figma files with design system context.
2:04:37
And up next, we have the CEO and co-founder of Kchi, Turk Mansour, coming back for the third time with a bunch of exciting updates. Tick, how you doing?
2:04:48
Hey guys, how's it going? >> Great to see you.
2:04:52
>> Taking a little a little nap there.
2:04:52
You had a long long weekend.
2:04:54
[laughter] >> Well, he's launching GPU futures, right? >> Okay. >> Yeah.
2:05:01
Before Before we get into that, let's let's talk about uh let's talk about the World Cup. >> Oh, yeah.
2:05:06
>> Because uh every time I like to I I like to check the the iPhone app store >> rankings uh just to get a pulse on what are what are consumers >> doing.
2:05:17
And uh Cal, she's consistently been at or very near near the top over the last month.
2:05:22
But um break down what you guys feel like you did well. Yeah. >> Didn't do well.
2:05:26
Um you kind of >> how you kind of approach the whole event in general. >> Yeah.
2:05:33
I mean there's like a lot a lot to unpack there, but the >> I mean a few things. Yeah.
2:05:38
We've been pretty consistently top of the app store for throughout the month. >> Yeah.
2:05:42
Um but the other interesting things and some of the sort of leading indicators um the you know yesterday I was looking some of the data around that but in terms of number of impressions and trends on search uh we ended up being I think we're basically still confirming data but it looks like we were number one.
2:05:57
It looks like we essentially generated more impressions um as a brand than essentially all the other consumer brands that were going pretty hard uh at the World Cup.
2:06:05
And I'm talking about Coca-Cola and Adidas and and some of the others.
2:06:10
>> Um and um and yesterday in terms of search volume, it looks like we spiked above uh uh uh you know Chad GPT and Instagram and some of obviously some of the biggest consumer brands in the planet.
2:06:20
And it really goes back I mean obviously there's a chunk of people that are trading but the majority of our users are actually looking at it as a mechanism to figure out what's happening. Yeah.
2:06:28
>> Uh in uh uh as a mechanism to engage with the sort of underlying event.
2:06:32
And so in these peak moments whether it's an election or like a Fed decision obviously the World Cup final uh you see a massive spike uh in the prediction markets.
2:06:41
In terms of strategy I mean I think it's multiprong.
2:06:42
We just go very hard maximally hard all you know pretty much max out all types of channels uh very small lean team and you know idea to sort of execution within 48 hours just be super adapt adaptable.
2:06:53
So there was no strategy going into it.
2:06:55
just like all dynamic as things were coming and as things were happening.
2:07:00
>> Uh really quickly, the video got a little blurry. Can you like refocus?
2:07:04
>> Yeah, let me >> just give and uh >> I will give everyone an update that the the the Jensen jacket sold for $960,000, 20 times the estimate.
2:07:17
But you had it clocked higher.
2:07:17
You you you you predicted the million dollar sale like roughly roughly roughly a million because I think a previous one had sold anyway.
2:07:24
Uh may maybe uh maybe a future market on call she's back.
2:07:27
Were you the buyer of the Jensen jacket? >> I was not.
2:07:33
>> Um anyways, let's let's talk about GPU futures. >> GPU forward curve.
2:07:36
The market implied forward curve.
2:07:39
What is the actual product?
2:07:41
What's the launch been like?
2:07:43
Can you actually read into the data yet or is it still uh rolling out?
2:07:49
>> Uh we can definitely read into the data yet and you can find it on Kashi.
2:07:51
Um I mean you know the maybe just some baseline a forward curve is something that's very common in traditional commodity markets or rates market or any really traditional financial markets.
2:07:59
Uh and you know it's it's actually pretty simple.
2:08:03
It's a curve of what uh the uh underlying whether it's uh oil or metal or interest rates or um compute will transact at at a uh at various points in time in the future.
2:08:15
And it's a very useful indicator because obviously as you as some of the let's say grain farmers or or or um older refiners are are figuring out their yearly planning they basically you know look at the future price is going to be to basically manage their risk make you know investment decisions and allocations of resources.
2:08:33
Um and so it's inevitable that compute as it becomes a core part of our economy um uh will require a forward curve and then soon after a futures and derivatives market on top of it.
2:08:44
Um and uh we want to lead with that and we because we feel that prediction markets are very uniquely suited to get that answer to basically help build the forward curve and what we've done is essentially listed a number of prediction markets that go all the way to the future.
2:08:56
So every week for the next four weeks and then every month thereafter to create forward curves um on a few uh uh uh um kind of compute like on a few GPUs.
2:09:06
So, >> um, yeah, where are you looking for like, uh, to sort of verify pricing data because, you know, you're going to see wild differences between, let's say, like a Neo cloud that has, you know, incremental capacity versus a deal between, you know, a meta and a big AI lab. >> Yeah.
2:09:29
So, it's it's a very interesting question, right?
2:09:32
Because um, the answer is not clean yet.
2:09:34
And this is I think there's a bit of a this iterative process that usually goes on in the early days of a of building a dive market or which effectively standardizes the underlying um uh commodity.
2:09:43
So right now we're using an index from um this uh company called OR and they've done a great job at a aggregating transaction prices from a massive number of nodes um and and we're seeing how it goes.
2:09:56
So, we're going to build a number of uh uh uh forward curves, see which ones consumers basically trust and abide by over time and and then, you know, as we as that sort of consolidates, we're going to basically go harder on the one that that emerges victorious.
2:10:09
Um there's kind of something a bit self-fulfilling with these things.
2:10:14
So, the reason why we look at WTI or Brent is because we started looking at those.
2:10:18
It's not that they were intrinsically the right >> uh uh kind of measure of where oil is at, but as people started looking at it, it [clears throat] became the thing.
2:10:27
because we could be trading derivatives based on the price of gasoline, but we've chosen Brent just because of that that's the market that got traction. Interesting.
2:10:36
And so and so this product is this something that you know you expect the vast majority of volume to be these sort of uh institutions and or like Neo uh sorry Neoclouds themselves or like who are all the different types of of players in this
2:10:54
market and then um maybe tie that back to like traditional commodities like I I don't even know in in oil and gas I can imagine a few different types of players that would be uh wanting to hedge uh hedge their exposure. But but within uh
2:11:06
But but within uh the overall market for compute, who are you thinking about um all the different potential um uh types of people that are going to want uh to be able to sort of hedge or or trade these markets?
2:11:23
>> Yeah, I mean I think that it really is anyone who is basically a natural long or natural short >> on compute.
2:11:28
So anyone who naturally benefits from compute prices going up and anyone who naturally benefits from compute prices going down.
2:11:34
So a producer versus a consumer which is usually how you know a producer of commodity versus consumer of the commodity.
2:11:40
>> Um and for both of these cases what we're starting to see is you know for the first time in the last year compute is not in this sort of uh uh uh continuously decreasing trend.
2:11:49
It is actually perked back up.
2:11:50
And then we're start going to see the sort of natural volatility and cyclicality to compute which is like there's going to be times where demand is going to outpace supply and vice versa.
2:12:00
And that's kind of a healthy dynamic to build a derivative market on top.
2:12:03
And so anyone who's a natural long will over time want to take a short position and vice versa >> to manage the risk. Right?
2:12:08
>> to manage the risk. Right? So if you have essentially certain amount of budgeting for how much compute you're going to basically um how much you're going to spend on compute in the next year which is now becoming an increasingly bigger part of uh you know
2:12:19
the the line items and you know public and private companies uh financials um it becomes natural for you to basically want to smooth out some of that exposure by hedging it um on the forward curve and over time on the futures curve which we can sort of overlay on top of the forward. Do you have an idea of the of
2:12:33
Do you have an idea of the of the TAM like uh for this market? Like how big?
2:12:38
Because obviously you're going to build liquid liquidity over time.
2:12:39
But when I think of like the natural longs, I'm like yes.
2:12:43
So hyperscalers with a trillion dollars of capbacks are going to be taking out a short position on the order of a hundred billion dollars or something.
2:12:54
Um like how how do you think about sizing the market over time?
2:12:57
Obviously, it's not going to materialize instantaneously, but it could be very big.
2:13:03
You know, it's interesting.
2:13:03
So, so I I was, you know, I wrote about this uh last week, but you know, the rule of thumb is every time you have an underlying market where people are transacting the spot, which is buying and selling the thing. >> Yeah.
2:13:15
>> When you overlay a derivative market on top of it, historically, if that market has any degree of success, it ends up being at least 10 to 15 times the underlying market. >> Okay. >> Right.
2:13:26
>> That's insane if that happens here.
2:13:29
>> I mean, this is the opportunity, right?
2:13:30
I mean it looks like you know we're using around a trillion dollars of spend uh by now on compute right and by 2030 that number is going to 10x. >> Yeah.
2:13:39
>> Um and so you're talking I mean yes it is an incredibly large >> market trillion in derivatives potentially just stabilizing the market and hedging various things and insuring different projects and whatnot >> because you know the way that these markets evolve is obviously you get the exact insurance uh use case the hedging. Yeah.
2:13:57
But that tends to be usually like 5% of the entirety of uh the liquidity in the market.
2:14:02
The rest is speculation and people arbiting and doing a lot of other things.
2:14:06
>> And so those markets tend to be very very large. Yeah.
2:14:08
>> And I think the the compute >> as a commodity is going to probably be the largest commodity >> uh on the planet.
2:14:14
And so the derivative market for it will probably be the largest derivative market on the planet outpacing treasury futures and a bunch of other things. >> Yeah.
2:14:20
What what does this mean for the for the customer base?
2:14:22
It's it's interesting because when I think of Khi, I think of uh the first era, the act one sort of political markets.
2:14:29
Uh the people that were actually participating in that market were some there were some sophisticated Wall Street uh investors, but it wasn't like the daily sports better.
2:14:40
It was a more like like uh you know um somewhat sophisticated but sort of like mid-tier investor trader.
2:14:47
Then you get to sports betting and that's very broad, very general, just like average Joe watching sports.
2:14:53
Of course, there are larger firms that are participating.
2:14:57
But then this market feels like something that would be the domain of almost entirely sophisticated hedge fund investors.
2:15:03
Is that where you see this going?
2:15:06
Do you think this will be a broader product or it will maybe open you up to a new class of investor or trader?
2:15:14
>> Yeah, I mean the way I've thought about our business and it's been one of the things that's been interesting.
2:15:17
So >> uh we have a core set of users I think of the forecasters super like people that like stats and analytics and predicting things >> and that's a skill and people get better at it over time and those are a big chunk of our value.
2:15:29
Those are the same people that are doing it on um predicting elections, predicting who's going to win an Oscar, predicting the inflation next uh month and predicting sports and over time these are the same people that are predicting the compute prices for us. >> Sure.
2:15:42
>> Sure. uh uh for curve and that's why it becomes so accurate and you know we've put out a lot of uh data on why uh the calibration like the accuracy of these markets and these people is actually the best in class there's nothing better than it now that enables a lot of other use cases in different areas right in sports it could be people that are passionate about the sports and want to
2:15:58
engage with it in politics sim similar or people that like campaigns that want to follow it and in compute once we have that sort of layer of pricing liquidity we can essentially enable you know hedging from this you know supercalers hedging from consumers of compute um uh uh you punting and speculating from people that basically want to arbitrage compete with other markets. Um, but we
2:16:14
Um, but we see I mean it's it's a marketplace at the end of the day.
2:16:19
You need a vibrant set of participants for it to get liquid.
2:16:22
You know, if you just bring hedges and you don't have anyone on the other side, it doesn't quite work.
2:16:26
>> Uh, that you know and so so in some ways kind of we build on the success of some of the other products or or every other product to to build some of the new products that we've we've been doing and that's why I think we're going to be very successful in the forward curve and that's why we already have a forward forward curve.
2:16:40
If you look at capture today, >> I'm looking at the forward curve.
2:16:42
I pulled up the one for the Nvidia H200. I sent it to the team. They can pull it up.
2:16:46
The hourly price on July 24th is up there, the week of 30.
2:16:49
And I see this very flat forward curve.
2:16:52
And I'm wondering uh just because I I feel like there's a huge benefit for people in AI just to be able to look at this and get even if it's low volume now, like get an idea of like what is the market thinking about GPU pricing over time? It's pretty flat.
2:17:08
So is my read on that that the market is basically saying >> GPUs will continue to be about as useful as they are today for the next year >> yeah it's a market and you know these markets move it with obviously new information etc.
2:17:24
There's obviously what it looks like, but there's also how it moves based on new information, which is what where I think markets really shine.
2:17:31
It's like, okay, how do I really price this when someone launches a new GPU and a new model? >> Sure.
2:17:37
>> What is going to be the impact on the prior models? So, that type of thing.
2:17:40
But right now, it looks like it seems that the innovation in the space is going to be sort of on pace.
2:17:45
>> Y >> with the consumption, which I think we're seeing some some stabilization. >> Yeah. Yeah.
2:17:49
Yeah, and we see that a lot where there's a new model that comes out, but a lot of businesses keep their agentic workloads running on the older GPUs with the older models because they have found an economically valuable use case and if it takes an hour to do a bunch of inference, they get more than $5.
2:18:06
So, they're happy to pay that.
2:18:06
Um, >> very, very interesting. Um, yeah.
2:18:09
I mean, also like people have been reacting to this like second deepseek moment, Kimmy, and what that means for for uh for chips and GPUs, and it feels like at least in this curve, like it's pretty uh neutralized.
2:18:22
I'm interested in uh in the the risk associated with this market.
2:18:29
There's been these like viral stories about uh certain like small markets where someone gets an edge, they do some sort of manipulation.
2:18:36
this one feels more resistant to that.
2:18:39
How are you thinking about just uh avoiding market manipulation broadly right now?
2:18:47
How are things going on that front from either a regulatory or an internal policy perspective?
2:18:51
And then uh is this a step forward into a market that's even harder to have market manipulation happen?
2:19:02
>> Yeah, I mean the man manipulation risk exists in all uh commodity markets really any markets.
2:19:06
I mean, it's always been the case.
2:19:08
Now, what prediction markets have shown themselves to be is pretty resistant to manipulation.
2:19:12
>> And we put out actually a research piece a week ago about someone who tried to manipulate the price of Spencer Pratt winning >> in uh in the California election and they put $2 million to move the price up and and you know that price move lasted 9 seconds only. >> They got destroyed. Yes.
2:19:27
Because I I I I've thought of if you're a long shot if you're a longshot political candidate, uh there was at least a thesis.
2:19:32
I think you just debunked it, but thesis that you should put your a bunch of your money on. Yes.
2:19:38
Then you everyone's like, wait, this person just spiked.
2:19:43
>> You create the percent >> and then you go and do the podcast circuit and you're like, I'm not that much of a long shot.
2:19:48
Look, I'm at 10% on Kshi because and it's like I put in $10,000 to move the market.
2:19:52
Um but you but you say that someone just got wiped for that. >> Yeah. Yeah.
2:19:57
I mean the the because you now have created a massive arbitrage opportunity for people that are pricing the actual thing.
2:20:02
I mean that's why markets are beautiful is that they give an incentive to always correct the price of the true thing. >> Sure.
2:20:07
>> And so it went back in and then you know so if that person had taken the $2 million and you know put it to commission some polls are going to be biased or >> spent it on ads they would be much more effective.
2:20:15
It'd be actually put to much better use.
2:20:18
>> Um and in many ways actually people do that in polls.
2:20:20
You know polls come out left and right and they're all biased.
2:20:23
And so I always tell people like look at prediction markets in conjunction with everything else, right?
2:20:26
And right now in compute a lot of what we have is people just saying things.
2:20:29
People are often self-interested saying things.
2:20:31
And so it's good that you have a market-based approach where you know that the incentive in the market is is is very clear.
2:20:39
It's people make money if they're right and they lose money if they're wrong which is I think an elegant way to put it.
2:20:43
Uh there was a lot of drama around onion futures back in the 50s that resulted in the Onion Futures Act which banned all futures uh for for onions.
2:20:54
It's still in effect today.
2:20:54
Do you know if there's something about is there something is is there something intrinsic to onions that make it a market that's easier to manipulate or is it maybe time to let uh to let markets flourish around onions again?
2:21:12
It just tells you sometimes you know policy makers sort of overreact or underreact and and and at the time you know because you know two people basically cornered the onion market and at the time people were like well the problem seems to be the vegetable the onions not people >> and and you know there's nothing endemic
2:21:27
to onions I mean you know you could manipulate any of these markets there's nothing special about onions uh we should have you know features on onions and and I hope [laughter] that one day we get we bring those back uh but um you know these things tend to be you always have to take a delicate approach to
2:21:43
regulated regulating things right otherwise you either undersshoot or overshoot and you know the job is not not easy as we see in many industries AI crypto prediction markets self-driving and all of that and you know um and this is part of why cash has been so proactive in you know self-regulating
2:21:57
and being very vocal about you know banning insider trading how should we do it how it should be done because we want to we want us to land we want to help the industry land in the right place when it comes to regulation >> well congratulations on >> very cool we will be we will be tracking the GPU markets. Uh, it's cool. It's Uh, it's cool.
2:22:11
It's it's it's going to be cool to have another place where yeah, as these as as news drops, like you know, Kimmy, >> it's going to be deeply underwhelming because you go on X, you get crazy hot takes. You get it's over. We're back. It's going parabolic. We're in the AGI future.
2:22:29
Everyone's going to be a trillionaire.
2:22:31
Uh, and then you're going to look at this and be like, ah, like, you know, mild productivity and uh, continued performance in the GPU curve.
2:22:38
It's gonna be like much less exciting than watching a hot take play. >> No, but it's good. >> It is good. Yes, of course. That's the whole point.
2:22:45
I mean, that's the point of it's like deolarizing the narrative around politics and all these different I mean around comput.
2:22:50
It's like things tend to be a little less, >> you know, uh, you know, aggressive and extreme and and you know, I mean, the extreme makes for a good tweet. >> Yeah. Not not a good trade. >> Yeah. It's a counter force.
2:23:02
It's a counterforce to algorithms because like the algorithm >> feeds on the most uh the most viral take of the moment like it's over.
2:23:11
We're so back and yet and yet people don't >> bet their money on the most viral narrative like sometimes just betting on >> Yeah.
2:23:22
So many times, especially in politics, where you'll just see some minor news breaks or some new political attack ad comes out and everyone who's a fan will be it's oh, they're running away with it.
2:23:33
It's they're trying to convince you and you check the market and you're like, "Okay, 1% bump today. That's exciting. Good for them. >> Congrats."
2:23:41
>> I mean, when skin in the game, right?
2:23:43
Like, you know, this whole markets don't like when there's skin in the game, people get much more careful all of a sudden. >> Yeah.
2:23:48
>> And that's kind of the beauty of prediction.
2:23:50
>> How is tracking the midterms going?
2:23:50
Is is is is politics still like a driver of business?
2:23:55
I know the I know the sports market became so large, but is there still uh a very healthy business there?
2:24:03
>> It's always about what's top of people's minds.
2:24:05
So, you have to look at it on a per day basis.
2:24:06
Figure out like, okay, what are the things are top of mind?
2:24:08
The things that you would be covering and talking about more frequently than other things.
2:24:12
And those would be where the volume will aggregate in prediction market.
2:24:14
So, we're starting to see the ramp up for the midterms.
2:24:15
Obviously, come October, November, that will be a very very big uh set of markets for us. Sure.
2:24:21
>> Are you uh you're in the same exact spot that you were the last time you called into the show, but I'm sure the team is like three or 4x.
2:24:27
Are you guys running out of space in that office? What's going on?
2:24:32
>> We're actually moving pretty soon, but yeah, I don't really move much from this spot.
2:24:37
>> Well, we appreciate you hopping on the show. >> Awesome.
2:24:39
Yeah, great to get the update. Good to see you.
2:24:41
Have a great rest of your day. We'll talk to you soon.
2:24:44
>> Up next, we have Tony Xiao from Sunday Robotics.
2:24:47
He's back on the show with an amazing update >> with some remarkable >> benchmark 99 zero shot success folding laundry across 785 autonomous attempts.
2:25:02
They're calling it laundry super intelligence. How's it going?
2:25:06
[laughter] >> Is it solved? >> Good.
2:25:09
>> Is it sol is laundry solved? >> Yeah, we call it LLM.
2:25:11
Oh yeah, >> which means large laundry models. >> Perfect. Perfect. move the goalposts.
2:25:18
>> Yeah, we got to move [laughter] them. Uh, no.
2:25:19
Uh, g give us the broader update like what what is act two and uh what like is this a discrete training run?
2:25:25
I mean where are you in actually commercializing this? How much is in the lab?
2:25:31
Like give us the broader update and then I'm sure there's a bunch of ways we can go deeper.
2:25:35
>> Yeah, I think this is actually a really packed update with many things uh that we want to share.
2:25:39
Uh I think the first thing is uh this is the first time we can train a policy a model to be both generalizable and reliable.
2:25:46
What it means is that by generalizable is that what you see is what you get.
2:25:50
The performance you're seeing will be the same if we deploy the robot into your home because these has been tested in places that unseen that the model generalizes into.
2:26:00
Uh and what we mean by reliable is that like after filling like close to a thousand garments uh the success rate is like 99. 1%.
2:26:09
uh which is very very reliable and is the most reliable one uh to date and it's handling like very diverse uh amount of garments.
2:26:15
Um so this is kind of the more the most literal part of the update but I think there are also many like new ideas uh that we introduced.
2:26:25
Uh, one of them is that it turns out that when we scale up pre-training, you can do oneshot learning with these models.
2:26:31
And what we mean by that is that you can teach the robot to fold your shirt in a new way with one demonstration >> and the model can extrapolate that to an unseen shirt on an unseen bed.
2:26:42
It actually like understands it.
2:26:44
So this is something that is like really really surprising that just emerges from like scaling up uh the training uh the pre-training uh both the data and the compute.
2:26:53
Uh the other thing that we are like really deep into is to introduce the idea of a self.
2:26:59
So I think this is there's been like a huge amount of confusion around like what is 99% this 99% that.
2:27:06
But 99% can mean very different things when the scope and the adaptation budget is different.
2:27:12
Let's say if you're doing 99% folding one shirt in one room.
2:27:17
This is very different from being able to fold any shirt in any new places it deployed into.
2:27:23
into. So what we're trying to distinguish here is that the performance let's say success rate only matters if you uh very well like very nicely defined the scope which is like what is the long tail of things you want to
2:27:39
handle and the adaptation budget which is another way to say like are am I allowed to train on my test set am I allowed to train on the deployment situation uh that I'm going to do uh and only after these boundaries are established does a success rate makes sense. And one example of that is like
2:27:54
And one example of that is like if you think about self-driving doing 99% in a closed course versus on highway versus on city streets, they're like very very different challenges.
2:28:04
And so I think we as a field in robotics are at a point that we want to clarify these to like better be able to track the progress in the field.
2:28:14
>> What does prompt engineering look like as this gets deployed?
2:28:17
Like I can imagine, you know, you go to chatbt and you say like write me a blog post and it'll be like very mid and then you give it an example of how you write and it gets a lot better.
2:28:28
Uh and and there's a lot of you know style transfer examples.
2:28:33
You feed in an image to midjourney and say I'm looking for generally like this but use a make it a dog.
2:28:37
And I'm wondering if there's a pattern where at least in the in the interim in the midterm you in the medium term uh you will say okay uh you know best practice is when uh memo comes to my house give it a couple examples of how you like your socks folded uh you know show it the long socks and then also show it the short socks to give it a couple reps and then you'll be good.
2:29:02
Is there sort of even though obviously you're doing a lot to make the product just work out of the box, is there going to be some sort of like lastm minute ad hoc, you know, on the-fly fine-tuning that's essentially happening in the home? >> Well, yeah.
2:29:16
A good example is like folding all the clothes is one challenge and then putting them in the right places is another. Sure.
2:29:21
Which uh yeah, everyone's had the experience of being like, okay, well, not not everyone, but like [laughter] I'm not home.
2:29:31
I'm not home when my when my laundry when my laundry gets folded and sometimes it gets put somewhere.
2:29:36
I don't know where it is.
2:29:38
I want it to be put >> in the right place at the right time. >> Yeah, of course. >> Yeah.
2:29:43
I think what you guys are highlighting is really the the need of personalization for homes and that's actually a hallmark of intelligence, right?
2:29:50
Is that if you're so smart, you should be able to learn from one single example which is what we saw or like from the engineering side.
2:29:55
Yeah, >> I think this is something not something we're going to roll out uh this year, but maybe like either super late this year or like early next year of like how can we allow users to customize the behavior of the robot.
2:30:08
Let's say I just want my laundry to be done this way or I want it to be placed into the cabinet in that way.
2:30:14
I want my home to be organized in this way of like uh specific way of laying out uh the living room.
2:30:19
And can you do that just by one video, one photo or like one demonstration?
2:30:25
This is actually what this research update uh the one example oneshot learning is about that it turns out as we scale pre-training that capability just emerges.
2:30:34
And of course there are work that needs to be done there to make it a actual shippable feature.
2:30:38
Uh but it's like really really promising to see that it's even possible.
2:30:43
>> How do you think if you're really successful if there's a a fast take off of of this product category broadly?
2:30:49
hopefully you with you uh at the forefront.
2:30:52
How do you think it will change the design of American homes?
2:30:56
Like I can imagine uh some people have laundry rooms on the first floor, bedrooms on the second floor.
2:31:02
You don't have the ability to climb stairs.
2:31:04
I could imagine people saying, "Hey, maybe I'll remodel and put a a laundry room upstairs."
2:31:09
But have you thought about the knock-on effects of like if somebody really leans in, you know, they're getting the electric charger for their car, they're modifying their home for the best experience in the future. How will things change?
2:31:26
>> Yeah, I think there are a couple of uh things that are really interesting here that you mentioned.
2:31:29
One is that like I think we're definitely in a period of time that the capability will take off in the sense that like when we work on this laundry task, we didn't make any assumptions about the laundry.
2:31:41
The recipe itself is extremely general.
2:31:45
>> So there's really no fundamental reasons why we cannot work on a thousand of these skills in parallel and which means that you know the capability is only exponentially growing scale as if we can scale data and compute accordingly.
2:31:57
Uh and I think as you said like the the derivation of this is that if the robot can do so many things in your home a lot of them are like maybe across different floors and across uh different places.
2:32:10
Is there ways to modify the environment?
2:32:12
Is there ways to modify the robot?
2:32:12
And I think this is also where our technology comes in which is the way we design a data collection device is agnostic to the hardware that the same data set the same model can be used to control memo as it is right now but maybe it can control a le version of memo in the future. >> Sure.
2:32:32
>> So we're actually super open-minded about the form factors but within the ecosystem that we're going to build which is going to be beautiful high quality hardware. >> Got it.
2:32:40
Uh, you said there's a thousand tasks you could think of.
2:32:43
I can think of like four things that a robot could do in the house.
2:32:46
It's like laundry, cooking, dishes, cleaning generally, maybe like gardening, but like there's got to be a power locking the doors at night.
2:32:57
Yeah, I guess there are some more.
2:32:58
But how how long is your task list?
2:33:00
Obviously, uh, uh, laundry really stands out as like something that very few people enjoy.
2:33:06
It's very obvious and it seems very unbounded.
2:33:09
So if you can solve that, you can imagine it also being like I would trust that robot right now to fl throw a lock.
2:33:14
That seems easier than than folding a shirt.
2:33:17
But uh how how long is the list of of tasks that you want to that you want to knock down over the next couple years?
2:33:27
>> I think there are almost like two parts of this question.
2:33:28
One part is that what is a minimum number of tasks that a robot needs to do to justify his own existence that people love having in their homes?
2:33:36
And as you said, I actually think the list is pretty short because the annoying chores are just that many, right?
2:33:42
They're pretty repetitive.
2:33:42
You just need to learn it and you know, you just keep doing that over and over again, like folding shirts or like loading dishwashers.
2:33:50
>> Uh, but I think I think that's one part of the equation.
2:33:52
But the other part, which is what we noticed, is that I think we're also having this longer term goal of can we get to the general intelligence?
2:34:01
Can we solve the quote unquote physical AGI? Yeah.
2:34:03
And what that entails is a way longer list of tasks.
2:34:08
And what we are seeing just like uh I think home and this objective uh actually aligns quite a lot.
2:34:14
We call like research market fit that the research that goes into the product also advances uh on a general intelligence side.
2:34:23
>> Um so so overall I think the there are like obviously infinite amount of manipulation skills even for one task you can do it in many different ways.
2:34:28
Um and I think we'll first cross the boundary of making a product exist but we'll keep going towards the northstar of being able to solve any task with like very very few demonstrations. >> Mhm. Very cool.
2:34:46
>> I can believe the scenario where a winning robotics company starts off by just doing something cute like folding laundry and uh and then you know 10 years later it's doing everything. >> Yeah.
2:34:58
No, it's a good place to start. I love it.
2:35:00
Uh well, congratulations.
2:35:02
>> What's your what's your what's your uh your cooking timelines.
2:35:08
[clears throat] >> I think cooking is such a fantastic task.
2:35:11
Like imagine everyone can have Gordon Ramsay level of cooking in their homes.
2:35:15
That would be like crazy.
2:35:15
Like people pay like so much money for it.
2:35:19
But at the same time, I think laundry is also one of the tasks that are like difficult, right?
2:35:23
Like if you like accidentally mess up, you need to clean up after yourself, which is okay.
2:35:27
Uh so I think we think about laundry as this almost like um like a final boss.
2:35:31
Uh that's the upside is so high.
2:35:34
But >> you think laundry is harder than cooking the opposite because oh sorry you can't shatter. Yeah.
2:35:40
Cooking feels like the final boss because you can break glasses. There's heat involved.
2:35:43
There's like come back into your kitchen.
2:35:48
It looks like >> whereas like if I have a shirt and the robot messes up and and gets the shirt all bundled up. It's just a messy shirt.
2:35:55
It was already a messy shirt.
2:35:56
>> Not if it's chrome hearts. >> Yeah. I guess.
2:35:58
But if it but but if you come back and it's like, "Okay, you actually like broke a glass of wine and you like you broke a glass of a bottle of olive oil on the floor." >> Yeah.
2:36:08
There it's a little bit riskier.
2:36:09
So, but it seems like it will transfer pretty well at least in >> I think you should go lock up all the great chefs and and lock up their IP and say I'm going to give you a little bit of money now to have the right to >> uh like offer the Gordon Ramsay as a service uh in you know 5 10 years. >> Maybe that's it. Maybe that's it.
2:36:29
>> Um thanks for the update.
2:36:31
>> Thank you so much for coming. >> Great stuff.
2:36:33
>> Have a great rest of your day. We'll talk to you Tony. Congrats. >> Have a good one.
2:36:38
Uh, did you hear that the founder of Deep Seek turned it down?
2:36:41
He had the opportunity to go to DJI and he turned it down. >> He was so younger. >> He was so younger.
2:36:48
Like then came >> they wanted him to they wanted him to go for a decade.
2:36:53
>> That would [laughter] that would define everything by what he refused.
2:36:56
In his college years, DJI founder Wang Tao asked Leong to join as co-founder.
2:37:01
He was going to be the co-founder of Wow.
2:37:07
Leang declined the invitation to pursue artificial intelligence methodologies in financial markets.
2:37:13
He said, "I got to I got to go to Wall Street.
2:37:17
>> I got to make markets efficient." >> Yeah.
2:37:18
Wango was >> bullish for Wang to to >> identify great talent. Yeah.
2:37:22
You imagine that the rest of the team is go too uh building flying devices in a small Shenzhen workspace.
2:37:27
At the time, the venture would become DJI, a drone company now valued in the tens of billions. Leong said no. He turned it down.
2:37:34
While pursuing his graduate studies at uh the university, Ylong was convinced that artificial intelligence would change the world.
2:37:41
A belief dismissed by many in 2008. That's a long time ago.
2:37:45
The certainty was not performative.
2:37:47
It was the kind that makes a man pass on a lottery ticket because he has already decided where he is going.
2:37:53
You know, people have been debating the uh the the the turn it down guy.
2:37:58
He's been viral many many times.
2:38:01
Uh, and the general consensus of the joke is that it's funny because of course he was not offered a $5 million scholarship to drum.
2:38:07
That's something that's impossible. That could never happen. I dug into it. It is possible.
2:38:13
You can be offered a $5 million scholarship. It's very difficult.
2:38:15
There have to be like 10 things that go right for you to actually be offered a $5 million scholarship.
2:38:23
But it is possible and I and I have it for you today.
2:38:25
So, the mathematically correct version of the $5 million scholarship. This is the low end.
2:38:31
Again, he he did start with 15 million, but uh without uh without pretending.
2:38:36
Uh so, uh the key is that it needs to be a privately endowed 10-year fellowship.
2:38:43
It actually has to be a decade, but a decade is possible if you are pursuing a 10-year dentistry track where you become a medical doctor as well as as doing many different fellowships cranioacial reconstructive surgery, clinical oral and max uh maxillo facial surgery residency.
2:39:08
Um so this could actually happen in Jacksonville. It's possible.
2:39:15
>> Jacksonville University, they have a master of science in dentistry. >> Yes.
2:39:18
So, you do your bachelor's, then you do your masters, then you do a DDS, DMD program.
2:39:23
So, you're getting a medical doctor.
2:39:25
It can actually take 10 years to become a dentist.
2:39:27
But then you are of course also there on a drumming scholarship.
2:39:32
So, it's a requirement that you drum, but they are paying for your school. >> Yeah.
2:39:36
You're not on the drummer tracks.
2:39:38
you're just drumming >> to to grind for >> and this happens all the time where someone gets an athletic scholarship but then they study history and the history degree is a normal price.
2:39:47
The dentistry degree or the series of degrees that would be required.
2:39:51
Uh but there's more because that alone is not is not 5 million uh for 10 years of dental school.
2:39:57
You also need an incredible amount of dependence.
2:39:59
If you had if you had seven children who are all minors, two adult relatives who require supervised care, and maybe an elderly parent who requires daily assistance, legally a scholarship can cover your dependence. That's right.
2:40:15
And so that's adding another, you know, couple hundred,000 a year um in dependent care.
2:40:22
Then the last thing that really adds up uh is housing and food.
2:40:26
Uh with all those dependents, you could be looking at a budget of $85,000 annually.
2:40:30
So almost a million dollars over the 10-year program uh for something that's, you know, you need you need a nine-bedroom rental if you have 10 dependents at this point. That's right. Uh transportation.
2:40:45
It also helps if you uh require wheelchair accessible uh accommodations because that can drive up the cost of everything if you require a a a wheelchair accessible dental studio for everything.
2:40:59
Uh and then lastly, study abroad can be included in uh in scholarships.
2:41:05
So, if this if this 10-year dental scholarship that requires you to play drums at a big at a big college in Jacksonville also allows you to do recon cranioacial reconstruction in Switzerland, then implant dentistry in Sweden.
2:41:20
These are all the most expensive places where you can go abroad.
2:41:22
Robotic oral surgery in in Japan.
2:41:25
So, you're constantly touring the globe doing the most expensive study abroad programs.
2:41:29
That could add another $600,000 to your decade in school.
2:41:34
Cooper's calling it the perfect storm.
2:41:36
>> All the [laughter] perfect storm.
2:41:36
All for a total of $5 million.
2:41:38
Uh >> so I have confirmed that that Jacksonville University has its advanced special education program in orthodontics and dental facial orthopedics which started in 2003.
2:41:48
So the timing does line up.
2:41:51
This program would have sort of been hitting its stride. Yes. As Big Boogie Yes.
2:41:58
>> would have been you know his drumming talent was emerging.
2:42:01
They knew they wanted to sign him.
2:42:03
And Jacksonville University also has uh the notorious Ripcurren marching band plus a pep band, >> wind ensemble, jazz ensemble, orchestra, and they have some smaller chamber groups.
2:42:15
So, it's possible they it was this perfect storm, right, where they wanted him to not only go on >> go on an orthodontics global tour, but also participate in all the different musical groups on campus >> to get to that >> 5 million, which he still >> in the end, even with that perfect storm, he still may have decided to turn it down >> because he has gone on quite a run in the music industry.
2:42:41
John, if you look at his songs, >> he's doing well.
2:42:44
>> He's putting up he's putting up some big numbers. >> Absolutely. Yeah.
2:42:47
So, the key with a scholarship is that in order for it to be a donation, so legally a scholarship, you can't just pay someone to go play in the band.
2:42:57
It needs to be a scholarship.
2:43:00
So, it has to be for taxdeductible expenses, which is why the numbers so big because it's of course sounds like he's thinking of a a name, image, and likeness deal that college athletes are able to get now.
2:43:10
But that's a separate thing.
2:43:12
But a private individual, private donor.
2:43:15
>> But here's the other thing. Here's the other thing.
2:43:17
Part of this whole thing, they could have been saying, "Hey, we think that NIL is actually going to become big in in sort of uh in we we think it's going to be a thing in college. >> Okay.
2:43:29
>> It's going to start in sports, but it'll it'll sort of expand into musical pursuits." Yes.
2:43:35
>> So could that's where he could have had the 10 or 15 or five, right? Which is like base case.
2:43:39
This is a $5 million deal, but >> if NIL comes to band, we will extend you.
2:43:47
We will be the first check in the door. >> Yeah.
2:43:49
We'll help facilitate up to, you know, $5 million extension. So, >> yes.
2:43:54
So, a private donor could commit $5 million to cover 10 years of dental education, family care, disability accommodation, international training, housing, and taxable living support on the condition that the recipient remained active as a university percussionist. That would be legal. It could happen.
2:44:12
And maybe it just did to Big Boogie. But he turned it down.
2:44:16
>> Jack in the chat says, "Dare you say he turned it up?" He really did. >> He did.
2:44:20
>> The line is he turned it down. >> Yeah.
2:44:22
But in some ways, you know, going on this run, >> you know, the beautiful thing, the beautiful thing would be him to go viral, make a ton of money in the music industry, and then go and endow a $5 million scholarship at a big college in Jacksonville for the next generation.
2:44:40
The Turn It Down scholarship.
2:44:40
If he should do it, >> my problem is like, why is he not taking advantage of this?
2:44:47
Like he should he should have turned it down. Yeah. for sure >> it would be.
2:44:54
>> He's only released one track that I can see this year, which is uh 2 minutes and 56 seconds.
2:45:02
We'll have to listen after the show.
2:45:02
I don't think it will be show appropriate, >> but >> that is our show today, folks.
2:45:09
>> Thank you for tuning in.
2:45:10
>> It's an honor to be back here in the Ultradome.
2:45:12
>> It's fantastic >> with you all.
2:45:13
We'll see you tomorrow at 11:00 a. m. Pacific.
2:45:15
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2:45:17
Sign up for our newsletter at tbpi. com.
2:45:21
We will see you tomorrow. Goodbye. [music]