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Today is Thursday, July 17th, 2025.
5:01
We are live from the TBPN Ultra Dome, the Temple of Technology, the fortress of finance, the capital of capital.
5:10
>> Today we have some crazy news at a coplay concert.
5:13
That's where we're starting, I guess.
5:15
This went insanely viral.
5:17
The CEO of a company called Astronomer um was caught on the kiss cam hugging one of his employees, the head of HR.
5:28
>> And I said, >> but Chris in the video, Chris Martin is like, "Something's going on here."
5:33
>> Startup CEOs can't even hug their chief people officer at a concert in this country anymore.
5:39
>> It is crazy when the internet descends on a current thing how viral it is.
5:41
like just you have to jump in on the current thing.
5:47
>> Well, before we talk more about this, I wanted a quick word from our sponsor.
5:51
Uh, Astro by Astronomer is the orchestration first data ops platform built on Apache Airflow, empowering your team to build, run, and observe data pipelines that just work all from one place.
6:03
>> Do uh do you believe the conspiracy theory? Put on the tinfoil hat. >> Yes.
6:05
So, the steel man, the tinfoil hat, what what is the tinfoil hat explanation here?
6:11
>> Tinfoil hat explanation here is the all press is good press >> is that uh Nathan for you this is just part of an elaborate stunt. >> Yes.
6:19
>> And uh Nathan's plan was >> have the CEO get caught having an affair with a co-orker to increase brand awareness and get a buzz going.
6:27
This was Charlie Light over on X.
6:31
>> They definitely got a buzz going.
6:33
>> Um and uh yeah, a lot more people know about Astronomer today.
6:36
100% 100% >> but hard to see how it would have been planned.
6:43
>> There's been a bunch of jokes.
6:43
Ryan Peterson said board should give him a raise without this viral moment.
6:46
I'd never know that astronomer is used by enterprise clients to manage Apache air flow and achieve 70% higher uptime than self-managed air flow.
6:54
So, uh, lots going on today.
6:58
Alex Cohen says, "Imagine losing half your life savings at a Coldplay concert." >> Why half?
7:06
Oh, because he's gonna get a divorce. Okay.
7:08
I thought it was about him getting it vesting.
7:11
>> These are adults that made their own decisions. >> Yeah.
7:14
>> Uh and they now have to live with the consequences, but um I doubt either of them would have uh would have paid half their net worth for uh those tickets. >> Yeah. Rough. Very very bizarre.
7:26
Um I was reminded of what Emily Sunberg told us when she came on the show.
7:34
I was asking her how the Hampton's has changed since the era of social media, the the age of the internet, and she said that there are so many Tik Tockers documenting everything that happens in the Hamptons now that you can't even leave a party with someone else's wife.
7:50
That's what she said to us. Do you remember this? >> Yeah.
7:54
>> Um, and I'm wondering like what does this actually mean for birth rates?
8:00
What does this mean for like this seems predictable at this point?
8:02
This is the first one that's happened.
8:04
seen these other videos having debate this morning which is that find my friends is the best thing to happen to marriage >> monogamy potentially ever. >> Yes.
8:14
>> Because if you are in a committed long-term relationship Yeah.
8:17
>> and you do not want your partner to have visibility into your whereabouts, >> you can make a pretty bad argument for privacy. >> Yeah.
8:25
>> But it but it but it it ultimately uh it doesn't it's really hard to stand behind.
8:31
And so I think it's potentially a really positive force against social media and dating app culture and things like that. >> Yeah.
8:38
>> Um so the answer to technology problems is more technology.
8:41
>> More technology maybe. Yeah.
8:41
It was like this it's this counterveailing force because like what was it like Instagram is like constantly flooding you with like recommended like look at this girl, look at this random person, look at this thing, look at you know like go down this rabbit hole and then and then find my friends is maybe pulling you back.
8:57
But I mean he probably told he probably said I have to go for this for work.
9:02
>> Doesn't fix this issue.
9:03
>> Doesn't really fix this issue.
9:04
>> The kiss cam does the kiss cam is a lindy technology.
9:08
>> So Lulu had some advice.
9:08
She says uh just given out priceless crisis comms for the uh astronomer team.
9:18
Uh she says don't bother with crisis comms here.
9:19
The CEO will try to get you to protect him but it's not his company.
9:23
He's a temporary steward. Mhm.
9:23
Your job is to protect the company.
9:26
The CEO is a professional manager who's only been there two years.
9:29
The HR person has been there less than a year.
9:31
Neither is tied to the identity of the company.
9:35
Preserving trust is more important.
9:35
Your business model is to handle sensitive data and your priority is scaling.
9:38
And that's tough to do when multiple known li with multiple known liars on the senior leadership team.
9:43
A better comms plan than trying to save the situation.
9:47
Andy Byron is on the board, but he's not a founder and doesn't have control.
9:49
The other five members should replace him.
9:53
you can then use the new CEO announcement as a reset and get people to focus again on astronomer's actual business instead of its drama.
9:58
So, uh we uh we did reach reach out um to to Andy uh this morning to see if he wanted to tell his side of the story.
10:07
Uh felt felt uh like an extremely uh you got to be a little bit crazy like Sohome to want to come on after the current thing.
10:17
But uh in this case, we should have the new CEO on of Astronomer when uh whenever that comes.
10:22
So there's a poly market today >> on whether he'll be out by the end of next week.
10:28
Uh odds are currently >> sitting around 38%.
10:32
Uh I would not be surprised if there's uh somebody new uh stepping into the CEO role at the company.
10:41
Uh this seems to have been um a way way bigger than just the current thing on X.
10:46
It seems to have really uh broken containment.
10:49
So >> is this a datab bricks competitor?
10:51
Like Apache Airflow uh is uh it says open source workflow orchestration platform used to programmatically author, schedule and monitor data pipelines.
11:04
I wonder if their business is just exploding. DAG directed graphs.
11:09
quite the lineup of investors.
11:11
They've got Bane Capital Ventures. >> Let's go.
11:15
>> Just led a series D this year into into Astronomer.
11:19
So, let's hit the size con >> for that.
11:22
It's probably a fantastic business.
11:24
>> And Insight is also led the series C in March of 2022. Yeah.
11:29
>> So, uh P Venrock was in the series A as well.
11:33
Uh and then Sierra Ventures, uh who I'm not familiar with, has led a couple different financing.
11:38
So yeah, this this business has been around for a while.
11:42
They're going to get through this.
11:43
I'm sure that uh Bane Capital and Insight and the other uh members of the board are already uh figuring out who they can get to step up and run this company.
11:54
Yeah, there does seem to be some sort of line between like bizarre, salacious, but ultimately orthogonal to the core business uh drama and like actual core business drama like some fundamental flaw in the business plan that's explo exposed FTX or Theronos versus um just this like you know drama that's happening here.
12:18
Uh I I keep I keep laughing about that that that analogy we go back to where if you found out that you know I think what what do I have?
12:29
I have uh what what are Pilot Sport tires? Is that Bridgetown?
12:33
>> Uh Michelin >> Michelin Michelin tires.
12:34
So, if the if you told me that the CEO of Michelin was was uh was was was caught at a Coldplay concert with the head of Michelin HR, it would be a tall order for me to go get new tires, right?
12:48
I'd be like, "The tires work pretty well." >> Yeah.
12:52
>> That issue doesn't really affect my tires.
12:53
It's more it's more so uh that the uh like the the the difference here is that being caught is different than being on the kiss cam and getting >> a 100 million views today.
13:10
>> U my post alone has a million views.
13:12
>> Uh and I posted it a few hours ago.
13:15
>> But do you think do you think it's it's it's going to actually affect like revenue?
13:19
>> I don't think it I don't think it will affect the business at all necessarily.
13:23
Maybe a little turbulence because of needing to find new management and having >> certain stressful weeks turnover turnover on the uh on the on the exec team is going to be a challenge but ultimately their customers are not going to you know say hey we're we're turning
13:39
off you know we want out of our contract if if the product is good maybe some people use it as an excuse >> sure >> um but yeah I don't see this affecting the business uh but then again it's like if you're paying capital or insight do you really want. Um, do you want a a He
13:52
Um, do you want a a He came in two years ago.
13:55
Sounds like he's been executing.
13:58
The team has been executing well.
13:59
If they got from series C to D over the last couple years, means they're they're uh probably growing nicely.
14:06
>> Um, but and I don't necessarily think that that this is the end of this guy either of their careers. >> Yeah.
14:12
>> It just should be the end at this company. >> Yeah.
14:15
>> Because if because if you're the board and you and you tolerate this, that just means >> Yeah.
14:19
We we tolerate uh people of you know uh poor moral character. >> Sure. Yeah.
14:25
Um also interesting I mean this company's like we've never even heard of it and it's on an absolute terror series D at this point.
14:31
Um and does this not sound like something that should be in the AWS dashboard.
14:36
Like we were talking about browser base yesterday getting like quoteunquote copied and there's just something about these like point solutions that just are you know nailing a specific problem.
14:47
You know in this case it's like deployment deployment and management of an open- source project.
14:54
It's literally anyone can just run Apache Airflow.
14:55
They just help you do it better.
14:57
And that's kind of what data bricks did with Spark.
14:59
Like Spark is an open source project.
15:01
Now they now they for these data pipelines now they help companies actually install, manage, run them and then put a bunch of software on top of it.
15:10
And data bricks has been massively successful.
15:14
And so it's it's interesting to bring it back to browser race because um it just kind of reveals that like these companies can be kind of grinding in silence for a while.
15:23
Just I guess the note for Paul Klein is probably stay out avoid being the current thing like um because the the the failure mode could could be less technical and more interpersonal.
15:38
>> Yeah, >> I don't know. >> True.
15:39
Anyway, >> uh, work, retire, die may have been inspired by my post or it could have been totally random, but he said, "Married CEOs can't even hug and romantically sway with their married head of HR during a gold concert anymore because of woke." >> Oh, because of woke. Okay. Yes, yes, yes.
15:56
Uh, are there is there anything else to cover on the uh >> Sophie Netcap girl says, "It's so hard to get noticed as an a AI company these days.
16:04
The astronomer CEO had to cheat on his wife for marketing."
16:08
Yeah, >> it's dark out there.
16:09
>> I don't think I'm not believing the tin foil hat one on this one. >> I think it's an L. It's an L. >> It is a huge L.
16:15
But there's a lot of good stuff happening today. >> Yes. >> Open AI >> like ramp. com. Time is money. Say both.
16:22
Easy to use corporate cards, bill payments, accounting, and a whole lot more all in one place.
16:24
Uh did you see the Model Y is launching?
16:26
Has a six seat configuration, 3-inch longer wheelbase than the Model X. >> You hold this for me. >> The L. >> Hold that. >> The L.
16:36
Uh, this car will sell like hotcakes.
16:38
Um, it's a 193 in, I believe.
16:42
Uh, which is not quite Escalade ESV territory, but much bigger, much uh, closer.
16:48
I was talking about this for a while that like >> Wait, so they made the model they made an XL version of the Model Y instead of making an XL version of the X? >> Yes.
16:57
Because the X is their premium product that's more expensive.
16:59
They need to get into the full-size affordable SUV market. Got it.
17:07
>> So, this will compete with the Hyundai Palisade, which is a full-size SUV.
17:13
They're they're firmly in like the crossover territory right now where it's a five-seater, but you can't really put a whole bunch of car seats in there.
17:20
You can't bring the dogs, all that type of stuff.
17:24
By adding, I think it's like a pretty significant length.
17:26
I think it's like maybe 15 inches longer overall.
17:30
brings it to that 200 inch length and winds up with a product that uh people feel comfortable throwing their whole family in.
17:38
Basically, >> it's great.
17:39
Uh I agree with this takeaway from Nick Cruz that this car will sell like hotcakes.
17:43
I think that uh this is this is this is the most obvious thing that Tesla's been missing in their lineup is a full-size SUV. >> Yeah.
17:50
Um, the Model X always and even the Model Even the Model S came with at one point a third row configuration, but it was always super tight.
17:59
Um, but now they're kind of dipping their toe into fullsize SUV.
18:06
>> They still got to do the Suburban though, the Cybert truck SUV. >> Suburban.
18:11
>> I mean, that's the original story of the Suburban, right? Wasn't it an F-150?
18:15
>> They took the F-150 uh body on frame, so it's technically a truck.
18:18
This was like the Excursion.
18:19
People got really upset about this because total gas guzzler, but they take they take a truck which is this. It's not the unibody.
18:25
It's the bottle body onframe construction F-150, but then they would just create like a whole like passenger compartment on the whole thing.
18:35
And this was the the Excursion, the Expedition, and then they kind of started downsizing it to the Explorer.
18:42
And then at certain point customers were like, "Okay, I want the aesthetics of an SUV, but I really want the gas mileage of a car.
18:48
So build me a car that looks like an SUV."
18:52
And that's where we got the crossover.
18:55
So the crossover is all of the manufacturing strategy or manufacturing like techniques of building a car, which is this it's not body on frame.
19:04
It's not this like platform that you can put an ambulance on or a firetruck on or whatever you can do with a truck.
19:10
Um, it's it's it's all designed to be one thing.
19:13
Um, but then uh like customers went down into like the crossover market which rides better.
19:19
It's not as bumpy, but it's ultimately smaller.
19:21
And now we're stretching out the and now we're going backwards.
19:24
We're stretching out the crossovers to the point that they're going to be full-size SUVs.
19:29
>> But the Cybert truck is its own unique platform, its own unique manufacturing line, obviously its own unique styling.
19:35
And I think if you make that uh full-size SUV style uh it would sell much better because people people in LA you see like people in LA want G Wagons. >> You among them.
19:47
>> Well yeah the the uh the Suburban the um >> uh the Cadillac uh Escalade V. >> Yes.
19:55
>> Is is a very fun exciting car. >> Yeah.
19:58
But I mean, the the difference between a Ford Raptor and a G63 is practicality in my opinion.
20:07
>> You don't need the truck bed.
20:09
>> You need the interior space.
20:10
>> I would use the truck bed cuz I surf >> Yeah. >> quite a lot. And so that was nice.
20:13
Um, so having having a covered bed is nice, but surfboards fit in cars is fine.
20:17
Um, and and every time with the Raptor, like once you own a Raptor, you realize that there's just not that much space in the actual cab.
20:27
Like it's it's pretty it's pretty tight in there.
20:30
>> And I'm pretty sure the Raptor is like 220 in long.
20:32
So it's like over two or three feet long.
20:36
>> And the bed is also very short.
20:36
So it's like not a super functional bed, not a super functional cabin.
20:40
>> No, it makes way more sense to have a full size. Yeah.
20:44
>> It's the truck you wanted when you were 5 years old and you said, "I want a big truck." Yeah.
20:49
>> And then as an adult sometimes you have to get it. >> Got to bring it back.
20:50
Uh let me tell you about graphite code review for the age of AI.
20:54
Graphite helps teams on GitHub ship high quality, higher quality software faster. You can get started.
21:00
>> Let's give it up for graphite. >> Graphite.
21:03
>> In other news, Juul has been approved by the FDA.
21:05
Authorized is the key word here. >> Authorized.
21:08
So, I know way too much about this, but it is a uh it is a big big turnaround since uh the FDA um would was refusing to authorize uh the Juul ecigarette years ago.
21:21
Um Jules says, "Exciting day for making cigarettes obsolete in America.
21:26
The FDA has issued marketing granted orders. These are MGOS.
21:31
We'll get into this, but it's like slightly different than like FDA approval for a drug uh for the Juul system, recognizing that these products as appropriate for the protection of public health.
21:41
They don't have to say, the FDA doesn't have to say that it's good to use Juel, just that having Juul on the market has a net positive impact.
21:52
And that's because cigarettes are already on the market.
21:54
So they it's this relative calculation that the FDA >> it's hard to Yeah.
21:58
It's much harder to argue that Juul should not be able to sell when cigarettes sell daily. >> Exactly.
22:05
So the FDA is not saying everyone should go out and start using Juul.
22:09
They're just saying that keeping Juul on the market is a net good for the American public health, which I'm sure will be hotly debated by a lot of people, but it's what the FDA said.
22:17
So that over two million adults have switched completely from deadly cigarettes to using jewel products and they and they approved a few different uh ecigarettes.
22:28
The big thing is that they approved the >> and can you talk about the last few years?
22:34
>> Yeah because I feel like that's basically Juel is just getting hammered by >> like all these different regulators.
22:43
Meanwhile, the average gas station is selling fully unregulated vapes >> that uh are liter seemingly literally tested on children.
22:52
Um videos have gone viral >> where where uh I don't think he was inhaling, but having to like make sure that each one uh works.
23:02
And then >> killer use case for a humanoid robot or just a just a device that inhales like that's just a fan.
23:10
You don't need to create a We we've been able to create suction from robotics equipment or or machinery for, you know, probably a hundred years and yet they're still using a human for that. Uh disgusting.
23:22
But yeah, the full story of Juul.
23:22
I know it um pretty well.
23:24
Uh couple Stanford guys working on a project. They're both smokers.
23:29
They want to figure out a way to smoke. They way to quit.
23:31
They don't like uh the current ecigarettes that are on the market.
23:36
And the key reason why the first version of vapor products was not satisfactory to smokers was that it used a very like not concentrated formulation.
23:49
And so what what that means is if you remember back in this was like what 2010ish I want to say.
23:57
Yeah 2010ish people vaping was really big but you had to get this like rig.
24:02
It was like this war rig that you had to assemble and people would use like different pieces and be like, "Oh, I got this battery pack and this motor >> big shops that had exploded."
24:13
>> It was like building a custom PC.
24:13
It was like, "What GPU are you going with?
24:15
What fans are you going?" there.
24:18
This was I was in high school during that era >> and uh there would always be some kid at a house party that was blowing smoke from like one side of the room to the other and then making like those those artwork with it. >> Yes.
24:35
>> Fortunately, you hit the hit the soundboard. Give me the Ashton Hall.
24:43
>> That was the sound that every vape made in 2010.
24:45
Um, but the reason >> I'm glad very glad as a culture we got past that.
24:51
>> Yes, it was it was a particular nadier for the American culture right up there with like the Tweety Bird tattoo and the tap out t-shirt.
24:56
It was all part of the same culture.
24:58
They say they say Americans don't have culture, but you know, we proved them wrong for that minute.
25:03
Although it was rough and we I'm glad we moved past.
25:05
Um, >> but do there is a scientific reason why the vape cloud had to be so big like that was not that was a trade-off that was made by the scientist. >> Not concentrated. it was not concentrated.
25:16
So in order to get like a cigarette's worth of a hit of nicotine, you needed a massive volume of smoke.
25:24
You just needed so much vapor.
25:24
And so uh Adam Bowen, James Monty's, the founders of Juel, they figured out that they there was a way to make the smoke or the vapor more concentrated.
25:35
And there's a whole bunch of science that goes into it, but nicotine salts are like the main one that people point to.
25:40
And basically they they figure this out.
25:43
They they build the device.
25:45
They actually bring on they run like a sort of standard Silicon Valley playbook.
25:50
They bring on is it Eaves Behar or someone like that?
25:52
So there's some there's some like iconic designer who worked on like the Jambox and stuff. I don't know.
25:59
Um there there's they bring on one of these one of these like incredible storied industrial design firms.
26:04
They make the original jewel device which did have >> focus what's the whole story with packs too?
26:12
It was like the same company. >> Yep. Yep.
26:13
So when they started they were doing uh what was it?
26:15
Uh they did they had flu fume or flume or something. I forget what it was.
26:22
They had they had a different tobacco vaporizer and then they had and then they had a the same kind of technology to heat up material and you could in theory put tobacco leaves in there and then vaporize that and just warm it up and breathe that in.
26:39
And that would it would be like smoking a pipe or like vaping tobacco I guess loosely.
26:44
But obviously like everyone was just using it for cannabis.
26:46
And so that takes off that becomes this like fantastic business on its own.
26:50
Um and then they had this other product.
26:52
I forget what it was called but it was a it was a tobacco vaporizer like an ecigarette similar to Juul.
26:58
They wind up selling that to JTI in Japan or like doing this crazy licensing deal to get that out.
27:04
Then they wind up splitting the company once both products are kind of taking off, but they're on very very different trajectories.
27:09
And it's very clear that they will be under very different regulatory regimes because the FDA is set up in a way that there are a number of different organizations within the FDA.
27:21
So the FDA approves cancer drugs and that's in the FDA drugs.
27:24
They approve biologics, they approve veterary medicine, they approve uh medical devices.
27:31
So like when you go in an MRI machine, the FDA has approved that I'm pretty sure.
27:36
And like when you do like a one stick blood test, like that's a device. It's not a drug.
27:40
So there's a different group >> in 2000.
27:43
I mean, we can go like way back.
27:47
Like cigarettes were never regulated by the FDA.
27:49
>> Let's go back to the to the very first time a human >> like 10,000 years ago. >> Yeah. Basically. >> Yeah.
27:57
I mean, >> the first wooden pipe. >> Seriously?
27:59
Like it used to it probably was somebody threw some >> tobacco leaves on a fire and >> No, I mean it was used in like in like religious rituals.
28:06
They would bury people with tobacco leaves on their gums. People would chew it up.
28:10
There was a whole bunch of different ways to you know the original like peace pipe.
28:14
It was piece it was part of that along with other things that you could possibly smoke.
28:19
Like as long as there have been stuff around like dudes have put it lit it on fire and breathe it in.
28:25
>> Lit it on fire breathe it in. >> Always. Always.
28:26
And and there's actually evidence that like monkeys do this too.
28:30
They'll like go out and find like rotted fruit and like drink it and and drink the fermented fruit and get all drunk and like come back to the crew and be like I found the drugs basically.
28:40
Anyway, um so cigarettes uh you know in invented as a part of the industrial revolution.
28:47
So we figure out how to make the cigarette rolling machine and all of a sudden people go from smoking you know like a few cigars which had to be hand rolled. They're very expensive.
28:55
It's kind of inconvenient.
28:56
You can't really huff down that many cigars.
28:57
Although JP Morgan famously went to his doctor and was like, "I'm not feeling too good, doc."
29:03
And he's like, "Well, you got to cut down the cigars.
29:05
Why don't you take it from 20 cigars a day to 10 cigars a day?"
29:13
>> JP Morgan absolute dog.
29:15
>> He's like, "I think I can do that.
29:15
I'll have to taper off a bit."
29:18
>> Yeah, >> it's gonna not It's not going to be overnight, but I think I can get there.
29:22
>> Imagine if I smoke a full cigar. Like, my tongue burns. It's so rough.
29:24
I'm I'm not built like JP Morgan. We're built different. >> Built different.
29:29
It was what a what an era.
29:32
Anyway, so the cigarette rolling machine creates this like massive boom in cigarette adoption because it becomes super easy and super cheap.
29:39
So Warren Buffett has this famous quote about like it's the best business in the world.
29:44
He'd never own a cigarette company for moral reasons, but he's like you make them for a penny, you sell them for a dollar. Extremely high margin.
29:48
And the cigarette rolling machine is so efficient that the raw goods that go in >> and the reason that prices uh that that that these companies were able to capture that much margin for so long is basically regulatory capture.
30:03
>> There's a whole bunch of different reasons.
30:04
Um one is there's actually distribution monopolies because uh all the big tobacco companies have trucks that go and deliver them to tobacco stores that are licensed.
30:14
And so if if you have a set amount of tobacco store licenses, like gas stations that have a tobacco license, and then you have a relationship with that particular 7-Eleven, and on the back, like they can't sell them anywhere in the store.
30:27
So the store can't become like a vape shop, at least it couldn't for a long time.
30:31
There's only a set amount of store space, they call it the power wall behind the the the the the cashier, that you can actually sell stuff.
30:38
And and Marboro is there being like, we are the reason you exist.
30:42
like we you make so much money off of us. We want all of this.
30:46
Don't let anyone else in there.
30:48
So, there's a whole bunch of other things.
30:48
There's also brand moes and and you know, these are the most powerful brands of all time.
30:53
>> But the idea is if you were starting a cigarette company today, you would need a billion dollars.
30:58
So that's a more modern phenomenon because cigarettes, it's discovered in the 50s that they're giving people cancer because people are people went from consuming like the equivalent of like one cigarette a day during like the cigar era to smoking a pack a day.
31:14
And in everything like the dose is the poison, the the like the concentration is so important in biology.
31:21
Um you can the human body is like pretty resilient and if you've smoked like one cigarette in your life, you're probably going to be fine.
31:27
If you smoke two packs a day, you're going to be in a real real tough spot.
31:30
And so the American population starts smoking like pack a day.
31:33
Surgeon General comes out and says, "We're noticing something.
31:35
A lot of people who smoke get lung cancer and they die much faster than people that don't smoke.
31:41
So there's something going on here." >> Yeah.
31:43
>> Big debate, big big law fight.
31:43
>> Big debate, big big law fight. There's finally this master settlement agreement where basically the discussion the negotiation is between the all the state governments and all the all the US governments saying hey you tobacco
31:57
companies you have given everyone cancer this has a financial cost to us because when a cancer patient comes into our health care system that's funded by the taxpayer we have to pay for chemotherapy and that has a cost and so you put that cost on us the government you have to pay us. And so that was kind of the the
32:14
And so that was kind of the the the main concession of the master settlement agreement was all the big tobacco companies.
32:20
They kind of like one of them broke rank and was like telling on the other ones. It's this big drama.
32:26
Basically, they have to make all these payments.
32:27
And these payments still happen today.
32:28
And there's some interesting uh finance that goes on where uh if you're like a local county that's getting like cash flow from the tobacco companies, you can go and finance that out and pull that forward and then build a bridge that costs like you're like, "Yeah, I'm getting $5 million a year for a tobacco company potentially forever.
32:43
let me pull that forward and finance that out.
32:45
So there's like all these different finance arrangements to like move the money around.
32:49
But basically, you can just think about it as like the big tobacco companies >> eaters fund our infrastructure. >> They actually do.
32:55
They fund a ton of stuff because it's like billions of dollars uh changing hands every every year.
32:59
Um but as part of that agreement, the the the initial like the initial debate was like, okay, the the big tobacco companies are going to pay, what else are they going to do?
33:08
Most of the legislation came not from the FDA but from the FTC saying and the FCC, the Communications Commission saying you can't advertise.
33:17
You can't do billboards anymore.
33:19
There used to be billboards in Time Square of like you know camels like smoking like and it would it would have >> and those were some really we gota >> Yeah, it was it was wild. It was wild.
33:29
Um and so they um it was it was the uh it was the it was the best era of out ofome advertising.
33:35
But now you can go to adquick. com.
33:37
Out of home advertising made easy and measurable.
33:38
Say goodbye to the headaches of out of home advertising.
33:42
>> I am gonna put uh keep keep ranting.
33:42
I'm going to put one of these ads. Pull it up. >> Okay.
33:48
So basically, >> sorry, not ads.
33:49
One of these one of these uh one of their old ads. >> Yeah.
33:52
Um so I mean it was literally the government saying out of home advertising is too effective. You can't do it.
33:59
We need to nerf it because you're getting you're getting everyone hooked on smoking.
34:02
So that was the initial kind of uh agreement was the the big tobacco companies would no longer be allowed to sell uh would no longer be allowed to advertise and they had to make these payments.
34:14
Then in like early turn of the millennium 2000 something like that uh this company Enjoy comes out with one of the first ecigarettes. There we go. Camel. Wow.
34:25
I mean, you see this as a 17y old, you're thinking >> this day I turn >> 18, that's going to be me.
34:34
Wait, is it is it 18 or 21? >> It's 21 now.
34:37
That also changed recently.
34:39
Um, so Enjoy comes out with one of the first big ecigarettes.
34:41
It's it's an example of that uh, you know, older technology that has the big vapor cloud.
34:48
Um, and the FDA hits them with a lawsuit and says, "Hey, you are selling an unapproved medical device. This is a device. It's electronics.
34:56
And it's a medical product because you're making a medical claim.
35:01
And that medical claim is this product helps you quit smoking.
35:07
Smoking addiction, cigarette addiction is a is a disease.
35:08
And so by selling a product that helps you quit smoking, you need to be regulated by the FDA.
35:14
Enjoy fights this back and forth.
35:16
It goes all the way to the Supreme Court.
35:18
Enjoy wins because they were kind of not saying that at least they made the argument that they were not saying this is to help you quit smoking.
35:27
They were just saying this is a cool thing to do separately. Don't worry about it.
35:30
Don't ask us about the relation to smoking. This is classic.
35:33
Um but in the interim the FDA is able to put an import restriction on the company.
35:40
So they're making the product I believe in China, probably overseas somewhere because it's electronics, it's equipment.
35:45
They bring it in at the ports and the FDA says, "Hey, while we sort out this lawsuit, you can't bring any more in."
35:51
And so this is kind of like the banhammer that they bring down.
35:54
It winds up bankrupting the company. >> Yeah.
35:57
>> They wind up winning the court case in the uh in the Supreme Court.
35:59
Later, a hedge fund guy actually buys the company out of bankruptcy, turns it around, gets it FDA approved, and sells it to Altria for like a couple hundred million dollars, maybe actually a couple billion dollars, I think.
36:12
Uh let's hear for the hedge fund guys making some money, selling some vapes to to uh >> we're not endorsing the end product.
36:21
>> We're endorsing financialization. >> Yeah. >> Yes.
36:23
And financial and restructuring. >> Yeah.
36:26
>> And it and it was and it was a successful thing.
36:27
And if you think about that as as much as we're joking, like it is good to get a big tobacco company uh get shift their revenues away from cigarettes as fast as possible.
36:36
And so like the enjoy thing even though there's a lot of issues with that product in many ways it's a very interesting uh outcome and it's probably you know moving in the right direction.
36:46
Um anyway so uh it goes to the Supreme Court and it is revealed in this court case that the FDA does not have the ability to regulate ecigarettes and so it has to go through the House and Senate.
36:58
So when Obama gets elected in 2008, they pass the the US government at the federal level passes the uh the Tobacco Control Act, the TCA.
37:08
Uh and in there it says, hey, the FDA does have regulatory authority on over everything that contains tobacco.
37:18
So now if you doesn't matter if you're creating a new cigarette, doesn't matter if you're creating a new ecigarette or a new nicotine pouch or nicotine gum, you need FDA, you need the FDA to review your product, which is good.
37:30
It's probably pretty good because people should know what they're putting in their body and they should know that the government, you know, reviewed this and said, "Okay, yeah, it doesn't have anything crazy in there."
37:40
Like it it at the very least like you said it has 2 milligrams of of nicotine in there, does it? Like let's test that.
37:46
And then so the companies test that, they send it to the FDA and then the FDA waits and then the FDA gives you the thumbs up or the thumbs down.
37:54
You can continue to sell it or you can't.
37:56
This is what Juel just got with the marketing granted order.
37:58
The FDA said, "We have approved.
38:00
We we have reviewed your application.
38:03
All the data we we we there's nothing that we see that would be worse than cigarettes and therefore we will allow you to continue selling."
38:12
Um, but so Juul was started before all before this stuff went into effect.
38:16
So 2008 is when the FDA gets regulatory authority, but the government moves slowly.
38:22
So it's not until 2016 that the first real FDA rule goes into effect. >> Wow.
38:30
>> Basically, they have to staff up a new arm because they have their biologics division, they have their drug division, they have their, you know, veterary division.
38:38
They have the FDA has their medical devices division.
38:40
They don't have a tobacco division.
38:41
They need to find a head of the tobacco division.
38:44
They need to find, you know, a whole bunch of people to staff that, scientists that know how to review nicotine and know review ecigarettes and review all this stuff.
38:51
And and it's a it's a massive organization.
38:54
They have to hire a lot of people.
38:55
So, they do all of that and then they have to decide what are we going to do?
38:58
What is that structure going to look like?
38:59
And they come up with the PMTA process, the pre-market tobacco approval process.
39:05
What this says is that going forward, we're not really looking backwards.
39:09
We're not going to go review Marorrow Reds.
39:11
Those have been on the market forever.
39:12
Everyone knows they're bad.
39:14
Everyone's aware of that.
39:14
But going forward, new products that contain nicotine, that contain tobacco, we want to review it before it hits the market.
39:22
But we're also going to create a grace period for stuff that was launched before 2016.
39:25
Anything launched before August 8th, 2016, you can keep selling it while we review it because, hey, look, you you've built a business.
39:33
If it's going to take us a couple years to review your application, if we just spike your revenue to zero, maybe you created a fantastic product that actually helps uh keeps people really healthy.
39:44
Maybe it's an amazing product.
39:46
We don't necessarily want to take you off the market, crash your revenues to zero, you have to lay everyone off, your company goes bankrupt, and then two years later we say, "Hey, you're approved."
39:53
And then we have to like build you back up.
39:54
Like, let's just keep things going as they are.
39:56
We'll maintain the status quo. We won't ban you.
39:58
We won't uh we won't approve you or authorize you.
40:02
um will keep you in limbo.
40:04
That limbo was supposed to be like a year because it's like it's a big document.
40:09
I I'm pretty sure Jules's document was probably like a 100,000 pages of scientific research.
40:13
It's a lot of it's a lot of stuff to review.
40:17
But everyone thought it was like, oh, it's going to be like a year or two.
40:19
Um the the the deeming rule goes into effect August 8th, 2016.
40:23
They're like, hey, turn it in by 2018.
40:25
But then it gets pushed back to 2022.
40:31
Then it gets pulled forward.
40:31
Then COVID happens and the FDA has to pivot.
40:33
Then the jewel crisis happens where everyone is searching.
40:38
>> You're building a nicotine company this entire time. >> Yes. Yes.
40:42
>> So that you're on the the regulatory roller coaster. >> Yeah.
40:44
>> Yeah. We started the company but in in 2016 before the deeming rule went into effect so that we could bring our product to market and then work through the FDA approval process because we basically saw that the door was closing and if the door closed and you needed to get approval before selling a single
41:01
unit well then all the all of a sudden the equation goes from okay you're building this company like any other normal company and then yes there is this binary outcome that can happen with the FDA but if your science is good, you should be approved and that is and that is knowable. Uh as opposed to okay now
41:16
Uh as opposed to okay now in theory there are a bunch of loopholes that people exploit all the time.
41:22
But in theory if you want to start a new e ecigarette company or a new nicotine pouch company or a new nicotine gum company in theory you should have to formulate the product, run all the tests, submit to the FDA and wait for them to get back to you before you sell a single unit in the United States.
41:38
And what uh you know, somebody that's self-funding a business like this might be interested in investing $2 million today. Yeah.
41:48
To do all that and then waiting for five, 10, however many years.
41:52
Maybe you never get approved. Yeah.
41:54
So, you're basically sinking capital >> into a business that may never be able to sell a single unit. >> Yeah.
42:00
And uh I know very few investors that would be interested in that kind of proposition at all >> or founders that want to make something and then wait forever for permission.
42:12
>> Yeah, you could you could spend millions of dollars and wait a decade which is exactly what we did.
42:17
>> Um but we were able to actually grow the brand and sell the product and like set up operations and iron out things and iterate a little bit during that time fortunately.
42:25
Um, but yeah, it's extremely hard to underwrite.
42:27
And it's particularly hard to underwrite because at the end of the light at the end of the tunnel, let's say that you were to today start a new nicotine company.
42:33
You you happen to have $100 million sitting around to go do a bunch of studies and you happen to have 10 mill 10 10 years to wait for the FDA to get back to you and then you're going to launch the product.
42:44
>> Well, when you launch the product at the end of the day, you still have to contend with the fact that you're selling a a consumer product in a highly in a highly competitive space. Yes.
42:53
Essentially a commodity product.
42:53
And some differences in formulation, little bits here at breakers. That's unique.
42:59
>> Little little bit a little bit on the ingredient side, the flavoring side.
43:02
>> Our gum is better than their gum, but it's both gum, which is a tough tough thing to argue. >> Exly. Exactly.
43:08
And so and and then and then aside from that, it's like what's the how do you actually build a brand?
43:13
Like even if you even if your product is better, >> you're highly restricted on marketing. >> Yeah.
43:18
You're extremely extreme restricted on marketing.
43:20
>> So even if you did have $100 million to spend, how do you how do you spend it effectively? Yeah.
43:23
Like some of the best marketing for Lucy Yeah.
43:26
>> is like Joe Rogan sitting UFC sitting by UFC.
43:29
He just happens to enjoy Lucy.
43:29
So he's just commentating and people pick that up.
43:35
>> But you can't just like pay for that.
43:36
Like if you went to Joe and you're like he'd be like, "No, like I'm not that's not how I work." >> Yeah. Yeah. Yeah.
43:41
And so and so those kind of serendipitous brand building moments would just not happen if you're just like in the lab waiting for the FDA to get back to you.
43:48
And then also you have the monopolies on distribution and and the in intense channel competition from the big tobacco companies.
43:55
So it's like you come out with this product, you finally get approved after 10 years.
44:00
You're like, "Hey, I got I think my formula is a little bit better.
44:01
I think my branding is a little bit better."
44:02
You go to 7-Eleven and they're like, "But you're not going to wait, are you going to pay us, you know, $100 million in slotting fees this year like like PMI might or Altria might?"
44:14
>> I got a pitch a while back. Yeah.
44:14
uh for somebody that had made effectively made a nicotine pouch, but it was just a slightly different chemical. Yeah.
44:20
Like small small change and was just bringing it to market. >> Yeah. Yeah.
44:26
and uh hearing knowing what you guys have gone through to get where you are today and and knowing this sort of history that that you had shared in pieces with me through throughout the last couple years was like the the idea that the FDA is just going to let you get like raise venture capital and let you get away with selling nicotine in this like nonstudied form.
44:52
>> It just it was it was tough.
44:52
I ended up not >> not uh >> not getting a conviction but uh even though the founder is super super sharp. >> Yeah. Yeah.
45:00
There's just tons of there's tons of loopholes.
45:03
Some of the loopholes get closed in a way that does not close off opportunity for the companies.
45:07
Some of the loopholes get closed and it puts it puts companies out of business because they weren't expecting it to get closed in a particular way.
45:15
Sometimes the loopholes close at state levels but not the federal level or vice versa.
45:19
So there's just like a ton of regulatory complexity around this.
45:23
And so um so basically to bring it back to Juul um they are they're pretty dominant by 2016 when the deeming rule goes into effect.
45:34
I want to say $100 million in revenue something like that like a pretty solid business.
45:39
Um m but growing like a rocket ship like insane growth.
45:43
Um and and clearly the the product was just vastly better than the competition because of the formulation and because of the design of the product, how discreet it was.
45:58
Now, so smokers really were were switching. That's definitely true.
46:01
I I I believe their number around two million smokers quit with Juul.
46:05
Uh they couldn't say that.
46:08
They couldn't say, "Hey, quit with Juul."
46:09
They tried to make they tried to they actually like uh trademarked like make the switch at some point.
46:15
They were like don't quit cigarettes, switch from cigarettes because like that was not a quit claim.
46:21
So the FDA So it's like all these different things but like it really was obviously big tobacco is like heav heavily lobbying against the cigarette market.
46:30
So it's not like >> and buying stuff and and and trying to compete and trying to keep the company.
46:36
>> What was that other company? Was it Blue?
46:38
>> Yeah, Blue was also big for a while.
46:39
seeing a bunch of those ads as a kid. >> Yeah.
46:41
And then Blue got bought and turned into VO, I believe.
46:43
Like there's so many there's so many companies and they're all like big tobacco is like highly oligopolistic.
46:49
Like in in cigarettes, Marorrow is the power law outcome, but the company that owns Marorrow has the same market cap as the company that owns the next five brands combined because you add up the next five brands because it's not that steep of a power law.
47:03
So Marboro maybe has like 40% of the market and then there's four brands that have 10% of the market and then the next brands have like 10% between they one one one like that.
47:11
And so you can actually create like this portfolio of brands that adds up to the same distribution because there's like all these all these different brands are are highly specific to specific marketing channels.
47:23
There's like the history of Virginia Slims targeting women and you know all these different subproducts that have gone after little niches.
47:30
Marorrow Reds say something about you.
47:32
about you. Maru one I don't even know the difference between most of these cigarettes but like Maru 100s say something different about youth than menthols or or Virginia Slims or American Spirits a lot of like hipsters use those for a while like that was a whole thing anyway um so Juel um is
47:49
growing like an absolute rocket ship they are they there are cigarette users that are switching over and and stopping to use cigarettes and probably improve their health like most that's certainly what the FDA is saying is that like that was a net benefit also they're completely completely dominating the ecigarette and vapor market. Like the
48:03
Like the vapor market is just like >> and this is the time that you see those uh modded unit things start to fade away, right?
48:13
Because people are realizing Yeah. >> All right.
48:15
Carrying around a backpack so I can bring this. >> Yeah.
48:20
>> vape machines blow >> that would like leak liquid and like the battery would run out.
48:24
All these different stuff.
48:26
>> So the the business model is also fantastic because it's this razor and blade model.
48:29
You buy the jewel once and then you buy the pods and the pods and then because nicotine's addictive there's very low churn so you stay on they're high margin and so the business is just doing fantastically.
48:38
There's a whole bunch of venture capital dollars that come in from various >> they got up to 45 might have been even higher during the acquisition from so uh zombie acquisition but not a ghost ship interestingly Altria comes in at the peak and puts like $13 billion into the company and a ton of it gets dividended out.
49:00
>> So late 2018 uh Altria acquired a 35% stake in Juul at a $ 38 billion >> 38 billion. Yeah.
49:07
And so that that like $10 billion kind of gets like dividended out to the shareholders but you hold on to your shares because it's just a dividend and then so you are diluted but you don't actually have to sell your whole stake.
49:21
>> We work at that time we work was valued uh at at $47 billion.
49:24
So >> both ended up being a little rocky from that point on. >> Yeah.
49:31
Um, but Ultra kind of bought the local top there because uh, Jewel got way too popular. Kids started using it.
49:41
That the >> And then the flavors were the issue, right? >> Yeah.
49:46
The flavors were >> That's what people were trying to make the issue. >> Totally. Totally.
49:48
And and the and the data on youth use of ecigarette products like spiked like crazy.
49:53
So I believe it was something like 10% of 10% of kids under 18 or 18 and under were using ecigarette products when Juul was introduced and at the peak it was something like 40% of kids using ecigarettes and the majority of them were using Juul.
50:12
So, it was it really was like this viral phenomenon.
50:14
And to your earlier point, a big part of that was because the uh the the age to buy these products was 18.
50:23
So, like if you're a sophomore in high school, you can just ask the cool senior like, "Hey, go pick it up.
50:29
You don't even need a fake ID."
50:30
As opposed to alcohol, which you need 21.
50:34
>> Are you implying that the senior that buys vapes for the minor is cool? >> Maybe. Maybe lame. >> The bad boy.
50:41
the bad boy bad boy senior heading over to the uh to the to the gas station. >> Exactly. Exactly.
50:47
So, um so it was they were very easy to get.
50:50
There were a whole bunch of like dist informal distribution networks.
50:53
People would buy them in bulk and then redistribute them and sell them into profit.
50:56
So, this like kind of like zombie economy popped up.
51:01
Anyway, the kids really were using it a lot. That is definitely true.
51:03
Um, and it was definitely caused for concern because kids shouldn't use nicotine because it's addictive.
51:10
And the earlier that you use it, the more addicted you'll be because your brain's still forming.
51:14
And if your brain forms while you're on a particular substance, you're kind of like your your your your brain's developing and you're and you're like that forever.
51:23
So like it's much harder to quit nicotine if you start really young.
51:26
Whereas if you started much older, you're like it's pretty easy to get off.
51:30
Anyway, um these are all relative, but uh the the big the big big big shift is that during this time there's also a massive boom in cannabis ecigarettes or cannabis vapes.
51:41
So the cannabis industry was becoming like more formalized, more legalized and uh and entrepreneurs were starting to put cannabis in ecigarette form factors.
51:50
So you could get like a cannabis pod that could go into a jewel basically. >> Interesting.
51:57
And so part of the problem with cannabis vapes is that cannabis is a green plant material.
52:02
And so the liquid looks green, which is pretty off-putting.
52:07
So I believe in the formulation step, these cannabis vape manufacturers would put vitamin E acetate in there to try and neutralize the green color and make it clear so it would be more palatable.
52:20
What was the startup that was effectively trying?
52:25
They were um >> disposable vapes.
52:27
They would advertise around LA a ton.
52:30
They had these huge out there.
52:33
So many >> I mean there was a venture it was a venturebacked uh company.
52:34
I just remember they had they had basically half the billboards in LA for a long period of time.
52:40
I I believe >> Xerian was buying a lot of billboards for his vape company and his cannabis company. Uh Ignite.
52:46
Um, there was uh, Puff Bar, Puff Stick.
52:50
I don't think those guys ever.
52:54
>> This was one that that had a bunch. >> Yeah.
52:57
>> I mean, I I think they raised like $200 million or something.
52:59
But I I believe they eventually shut down.
53:01
The issue with cannabis and the reason that the market didn't shake out.
53:04
>> You mean like Weed Maps? That one?
53:06
>> Not No, no, it wasn't like a platform.
53:08
It was like an actual It was like this off-white with like a bunch of rainbow colors.
53:13
But uh I believe the issue that that that um there doesn't exist the CocaCola of cannabis or the Budlight of cannabis.
53:21
And the reason is because the like repeat purchase rate >> even no matter how much you invested in marketing and design and all these things, the repeat purchase rate for cannabis users is like single digits.
53:32
Like >> there's just like high like like people just want to try like novelty seeking in that market where nicotine for some reason is you just have the product you like and that's the only product you use. >> Yeah.
53:42
I think a lot of it has to do with the distribution pipeline, the distribution structure of of the different uh the different tobacco companies.
53:51
>> Well, it's also the use.
53:51
So, so uh nicotine is something that people are using throughout the day, maybe at work, maybe when they're doing a live show, something like that.
53:58
Uh whereas if somebody after work goes into a cannabis store, they're trying to get high and so they're like looking for almost entertainment through it where like personally >> if I'm using nicotine, I don't want to be that entertained by it.
54:13
I don't want to be like, oh, today I feel extra silly, you know?
54:18
I'm looking for like predictability, right? Totally.
54:20
Whereas cannabis is just people are people are trying to run from something.
54:25
>> Okay, let me let me continue with Evali and the vape crisis.
54:28
But first, let me tell you about Figma. Figma.
54:29
com, think bigger, build faster.
54:31
Figma helps design and development teams build great products together.
54:35
>> Figma has a huge launch today.
54:35
I want to quickly highlight it.
54:37
They uh launched support for iOS and iPad OS 26, a whole UI kit.
54:45
>> So, they now support uh glass, which is which is very cool.
54:50
>> Good news for Tyler, who's been rocking Glass for the last few weeks.
54:55
>> Tyler, use Figma make >> my phone.
54:57
My phone is so slow >> and I can't I can't go back.
54:59
I can't go to the old iOS.
55:01
I also can't go to the new iOS because the new update it's so like big. It's like 20 gigabytes. But my phone is so old. It's only 64 GB.
55:07
So, I got to delete the other half of my photos that I deleted half of them last time.
55:12
I have to get the new update. >> Okay. >> I need a new phone.
55:15
>> We need a Yeah, we need a challenge that that where where Tyler can win a new phone.
55:18
I think this is this has to be done.
55:20
We need to get get him a new phone.
55:22
He's been doing a lot of good work. Anyway, EVALI.
55:23
So, the electronic vapor acute lung injury, Evali, this is the vape crisis.
55:30
Basically, vitamin E acetate in cannabis vapes cause AC causes acute lung injury.
55:35
So, cigarettes give you chronic lung injury.
55:38
You use cigarettes for a long time, you get lung cancer.
55:42
That's a chronic disease.
55:42
You smoke them for a very long time.
55:43
It's not There's very few cases I I've never heard of any cases.
55:47
I'm sure it's happened once or twice, but like very few cases that someone smokes a single cigarette and is like h I'm like my lungs are physically injured right now from this one acute point in time.
55:58
Vitamin acetate and these and these kind of like shoddily manufactured cannabis vapes could cause acute lung injury.
56:03
You hit this particular vape once and then you go to the hospital.
56:09
And there were I think there were some people that died.
56:12
There were some people there was a huge investigation.
56:14
There was a big debate over is this because of Juul and because of the the nicotine electronic vapes or was this because of the uh kind of sold under the table completely irreg non-regulated uh cannabis vapes.
56:26
It was later sort of discovered that uh like almost all the injuries I'm pretty sure all of them were from these uh e these cannabis vapes.
56:36
Um and uh the ecigarette industry was mostly cleared of wrongdoing and so they could kind of continue but the damage to the brand was really terrible.
56:46
And so uh there's also a ton of lawsuits around this time um about uh marketing to kids getting kids uh addicted and so because that has another economic cost.
56:58
So the again the states are saying hey at Juul Labs if you are creating a problem that is costing us money you have to pay us and so there were all these different uh liabilities that started blowing up on the Juul balance sheet.
57:11
Juul had to raise a bunch of money, recap, and there's this crazy scenario where a lot of the previous investors get written down.
57:20
Um, Altria ultimately basically writes off the entire Juul investment, which is like a what do you say, $13 billion investment, something like that that went out the door. They own 35% of this.
57:32
They're probably carrying it on a balance sheet at 10 billion. >> A huge impairment. >> Yeah.
57:36
And and Ultra is not a trillion dollar company.
57:38
dollar company. Like it's a very it's a it's I think it's around like tens of billions hundreds like a hundred billion I think it's around 64 billion is that roughly correct >> are they still carrying their jewel ownership or did they >> No so they parted ways almost entirely
57:51
they did a they did a IP licensing agreement in exchange for giving back the shares I believe so basically Juel is now like floating out in the wild recapped but like totally beaten up and settling and raising money just to pay the governments that are suing them. And so they're they're
58:10
And so they're they're closing out all these lawsuits.
58:14
>> The use of capital that a lot of investors get super excited about.
58:17
>> And then simultaneously the FDA says thumbs down on their application, refuse to accept or or or you know, market denial order MDO.
58:26
So they say, "Hey, you can't keep selling Juul because we >> the problem the problem is the demand is still there."
58:32
And so this creates a massive opportunity for these sort of black market Chinese vapes, much less regulated products.
58:39
The Elf Bar starts exploding.
58:43
>> Uh and you just it the Elf Bar uh reminds me of of um like a poisonous plant or something like that.
58:50
Like it it just looks like it, you know, like something in the jungle that >> 100%.
58:56
Uh, I remember Complex posted like this like news in news image graphic the day that uh Juul got like quote unquote banned.
59:06
It was actually this marketing denial order and they say like Juul banned and I remember reading the comments and the comments were like from basically kids who were saying like don't they know we're four steps past Juul?
59:17
Like Juel was four summers ago. >> Elf bar. I'm looking now. There's the geek bar. >> You got something?
59:23
>> So if you want to elf it up or geek it up.
59:24
What's the hottest uh what's the hottest ecigarette on campus on campus? >> I don't know.
59:29
People don't really none of my friends, but I I do remember when I was in like seventh grade. Yeah.
59:33
Kids would have the Jewel they would have the um mango jewel pod.
59:37
That was like the big thing, the mango flavor. Yeah.
59:40
>> Then they banned that the flavor at least.
59:42
>> So the FDA denied all of them.
59:42
Jewel voluntarily pulled mango off the market.
59:50
>> The at first they stopped selling it in online and they stopped selling it entirely.
59:54
Uh the FDA never took a strong stance, but with this with this authorization, the FDA has said thumbs up on mint and and tobacco flavored.
1:00:04
They have yet to say thumbs up on mango, but they still are reviewing it, I believe.
1:00:08
So, it's possible that the FDA could say mango's back on the game.
1:00:13
Like, it's totally possible.
1:00:13
But your point earlier about like the demand still being there uh was 100% true.
1:00:16
So, Juul got like quote >> that would have to become >> Yeah. of a national holiday.
1:00:22
I could see some some Zoomers, you know, really lobbying the government to make Mango Day the day mango hit the market again because there's people >> that that really, >> you know, they had a strong emotional connection with that flavor.
1:00:38
>> I think the I think there's still like pods, mango pods out there that trade at a premium. >> Yeah. No, it's true.
1:00:43
>> Um, so so Juul actually goes to the courts, get a gets a stay so that the the the marketing denial order doesn't stick.
1:00:50
What are you laughing about now? >> eBay.
1:00:53
There's people selling same offering same day delivery. >> It's crazy.
1:00:57
I don't know how you can buy it on on eBay that you should not be >> the the listings I'm seeing are sold out. Okay.
1:01:02
But it seems like >> Yeah. Yeah. Clearly high demand.
1:01:04
So So Juul actually fights back from the marketing denial order and wins.
1:01:10
And so Juul is not actually fully off the shelf for more than just a few weeks.
1:01:15
But the narrative is that Juel was banned and Juul disappeared.
1:01:19
And really what's happening at the time is that companies that are completely disregarding the FDA entirely are being extremely aggressive.
1:01:28
So companies like Elf Bar, uh, Puff Bar, they're going super hard and just selling every possible flavor.
1:01:34
There's versions with have like LED screens on them.
1:01:38
They're like unicorn candy, all this crazy stuff.
1:01:41
Just basically being like, well, if Jules's not going to sell to the kids, we're going to sell to the kids.
1:01:46
We're going to try and do it as cheap as possible.
1:01:47
There's a bunch of crazy stories there, but Juel kind of like buckles down, recaps the company, uh, starts filing FDA um, applications and just kind of like besides their time and just starts rebuilding.
1:01:59
They're still doing like a billion dollars in sales a year, I'm pretty sure.
1:02:02
But now the kids have moved on, so it's really just adults.
1:02:06
So, they're actually kind of fulfilling their original mission of like just targeting smokers, which is good.
1:02:11
And then they also have like the best science and best technology and they're the most like buttoned up like in the sense maybe big tobacco is like equivalently like scientifically rigorous.
1:02:21
But like at this point like Juul clearly understands that if they don't play by the FDA's rules like they're never going to be able to sell anything and they need to get this marketing granted order because they went from a thumbs down marketing denial order.
1:02:31
They went to the courts and said hey let's turn this back to neutral for a couple years. So they've been neutral.
1:02:36
They've been able to sell but they haven't been approved.
1:02:37
And then finally today they got the thumbs up.
1:02:39
And so what it took for them to go from thumbs neutral to thumbs up was another what like five years or something.
1:02:46
It's been a long time since the since the MDO.
1:02:48
Uh and and that delay has been, you know, a huge weight on the company, but this should be cause for celebration over at Juul HQ.
1:02:57
Uh anyway, let me tell you about Vanta.
1:02:59
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1:03:01
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1:03:07
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1:03:15
>> Um, my big question uh with Juel still and I think this should continue to be rehashed and there needs to be more information around this in my opinion is just uh what are it seems that we don't necessarily have clarity of what the long-term impacts of, you know, filling your your your lungs with uh >> this vapor which is like oilbased.
1:03:35
I think um >> you know the the the based uh health accounts on X say you're smoking seed oils >> um it seems I know people that that vape and they do seem to often times >> like >> I they don't want to go for a run for example you know doesn't seem that appealing to them. >> Yeah totally.
1:03:59
So, I think there needs uh hopefully now that it's authorized, everyone can start to really like actually says, "Okay, >> we're going to sell this.
1:04:07
People are going to be able to ch adult Adults will be able to choose to use this if they want.
1:04:11
They're going to be able to make that decision just like with cigarettes."
1:04:13
But >> everybody should fully understand the consequences. >> Yeah.
1:04:17
The consumer perception, the consumer understanding, everyone wants to know.
1:04:20
Everyone knows at this point cigarettes take 10 years off your life, right?
1:04:23
50% of people that smoke cigarettes will die from smoking cigarettes if you smoke a pack a day for like your entire life.
1:04:29
It's like 50% chance that that's the thing that kills you.
1:04:32
Uh no one everyone's still wondering like the public still wants like a clear answer to your point and I think you're I think you're right to to ask that.
1:04:40
Um the FDA is just saying like hey this is probably better than cigarettes so this is suitable for the protection of public health.
1:04:47
It's going to be net good.
1:04:48
Um, but then simultaneously there's this crazy market, which you touched on earlier, of like illicit vapor products that are completely unapproved.
1:04:57
There's no studies, and those are flooding the market all over the place.
1:05:01
So there's this odd alliance between both the the like health nonprofits, the big tobacco companies and Jewel all to go up against like the the shoddily made, you know, fly by night organizations that are just flooding the market with whatever they can.
1:05:20
And so um there's a whole it's not even a cottage industry.
1:05:23
I've seen the trade shows. It's it's insane.
1:05:25
There's a ton of companies that bring in hundreds of millions of dollars bring like essentially smuggling in products and they do a ton of stuff to like, you know, change the name of the company regularly so they can get through the ports and then they have a bunch of like, you know, handshake deals with distributors to get this on this shelf here and like the really big like you're not going to get into Walmart.
1:05:46
That's not where the really crazy stuff's going to get sold.
1:05:49
But for a lot of these like mom and pop tobacco shops, they're like, "Well, like is the FDA really going to come after me?"
1:05:54
Well, the answer is like the FDA is starting to, but it's all like a very slow What?
1:05:58
What are you laughing at?
1:06:00
Big win for Big Vape as Juul gets FDA not.
1:06:06
It is a big win for Big Vape, not so much for Little Vape because the the fly by night folks are probably um pretty worried that the that the that the focus of the administration now will and the focus of the FDA will now shift to >> they're going to have to go uh >> get set up on middle school, high school campuses, little lemonade stands, get kids kind of an MLM thing going, >> really lean into that black market.
1:06:31
>> You you're joking, but that's like informally what's happening. like the the economics.
1:06:34
>> It's also it's also really it's also really dark. Yeah, it's dark.
1:06:37
>> Um I I can't imagine uh uh you know >> there I do remember in high school the the the um you know the idea of a class clown you know trying to hit a vape in class was was kind of a recurring bit.
1:06:55
Uh but now you have to imagine kids just get up, go to the bathroom and like can develop like a horrific nicotine addiction. Totally.
1:07:02
before they're 18 and it's really sad. Yeah.
1:07:04
So, >> so >> well let's uh switch Yeah.
1:07:07
to um an even more sad topic >> uh AI psychosis.
1:07:13
Uh people getting um so this uh has >> first let me tell you about linear.
1:07:19
Linear is a purpose-built tool for planning and building products.
1:07:21
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1:07:24
Streamline issues projects and product road mapaps. Go to linear. app to get started. Uh yes.
1:07:28
So uh there's this big question right now that's bubbling up in Silicon Valley mostly in group chats of can chatbt drive you crazy.
1:07:35
Can any LLM drive a person crazy?
1:07:39
Do you have to be crazy already?
1:07:41
Yeah, >> I think that's the key question, right?
1:07:43
>> And uh there is been this broader debate of of AI safety. Yep.
1:07:50
>> Uh proponents y have been just been taking L after L after L this year. Yes.
1:07:55
just generally from the sort of vibe from the community which is we're releasing more and more powerful models and everything is fine seems fine.
1:08:02
Yes, >> everybody's had the perception that things are fine. Yes.
1:08:07
>> Uh Grock has gotten uh and the XAI team have gotten really aggressive in terms of uh >> uh you know just clearly trying to move really really quickly and then having bugs that have been >> uh talked about a lot.
1:08:19
Um but you can see the kind of two different approaches but uh generally maybe there was about a year there where people like Eleazar Udicowski um were just getting laughed at repeatedly. Yeah.
1:08:32
>> Um and >> and I think there was a good reason for that.
1:08:34
that. It it's that >> well yeah it's the debate is is is AI dangerous because it is going to break loose from a lab >> and build nuclear weapons build nuclear weapons >> build a biological weapon you know copy and paste itself a million so sci-fi and so fantastical and so
1:08:53
aggressive that every time you release a new model and you're like oh we it got we got 5% better at writing an essay or whatever it's like it just felt so disconnected from what is is it's very possible and we're seeing this now and we're going to go through >> a study, we're going to go through a Reddit thread. We're going to kind of um
1:09:10
We're going to kind of um kind of walk through this topic.
1:09:12
But the issue is that it's possible that the danger is uh not happening won't be happening in public.
1:09:20
It's people individually uh developing psychosis through using the products.
1:09:25
And the evidence I've seen over the last week as I've kind of dug into it is super alarming.
1:09:32
We had seen um there's been a number of uh you know the New York Times reported on this phenomena uh but the New York Times is also uh notorious for just saying like social media is bad. Yeah.
1:09:45
>> And so personally when I'd seen these articles pop up before I didn't take them super seriously >> because social media can obviously have negative effects on some people >> but it itself is not just by its nature bad right?
1:09:59
>> I remember this about Instagram.
1:09:59
There was this there was this study that was internal to Meta and it was leaked and it was framed as like 30% of people that used Instagram felt worse after using it.
1:10:12
And that sounds like a lot and that sounds bad and that's obviously something that the team wants to reduce or you know prevent entirely but what it kind of didn't say was that like 70% of people feel better when they use it and so >> well they could have felt neutral. >> Sure. Sure.
1:10:26
But but uh just anecdotally, >> yeah, >> if you >> personally I don't >> I don't open Instagram and and just feel negative immediately, you know.
1:10:38
It's just such a it's such a such an individual thing some people are going to have. >> Yeah.
1:10:43
It's the same thing with chatbt like when when when or any chat app when someone was uh when someone was talking about the example of this, they were like 7,000 prompts deep in one conversation with a single LLM.
1:10:53
And I was like, that is so different than the way I am and >> effectively going down this crazy rabbit hole for months. >> Yeah.
1:11:03
Which is >> So yeah, there's a few there's a few ways.
1:11:05
There's like AI as this uh you know companion that you go down the rabbit hole with, which seems really dark. >> Yeah.
1:11:14
Then there's AI for outsourcing your thinking which somebody might uh be talking with chat GPT and say um hey I want to send a letter I want to send a note to this person about this can you draft something that's >> kind but stern and just you know give me a couple versions of it and then they're sort of like outsourcing their thinking fully outsourcing their like emotional intelligence outsourcing their ability to communicate.
1:11:40
I think that's potentially some red flags there.
1:11:42
Y and then there's like the other camp which we fall in which is knowledge retrieval. Yeah.
1:11:49
>> Which is like tell me everything about this market >> and who are the key players uh what are the key regulations uh etc.
1:11:58
>> And I think knowledge retrieval seems to be pretty safe.
1:12:00
Y >> this bucket of like fully outsourced thinking has some real risks if if people stop being able to think critically on their own.
1:12:10
um that that that's concerning and then the sort of like AI companion bucket which then there's the subcategory of people that are talking with an LLM about things that they are not talking about anyone else in their life with and just going down this crazy >> rabbit hole rabbit hole. >> Yeah.
1:12:25
So Alazer Udikowski was highlighting a report in the New York Times about a month ago.
1:12:30
Uh New York Times reports that Chacheti talked to a 35year-old male guy. I think 35mm guy.
1:12:35
I think that's what he means.
1:12:39
Into insanity followed by suicide by cop.
1:12:40
A human being is dead in passing.
1:12:42
This falsifies the alignment by default cope.
1:12:45
Whatever is really inside chatbt.
1:12:45
It knew enough about humans to know it was deepening someone's insanity.
1:12:50
And so that last part is a big step.
1:12:52
It could just be an error or bug.
1:12:55
Like it doesn't it like it's kind of he's really doing a lot of work to like personify this.
1:13:02
But it does seem like the like these models can collapse after you talk to them for a long time into these like kind of weird ways.
1:13:10
And I see this as a product failure.
1:13:13
I see this >> let's give some more context here.
1:13:14
So from the article Yeah.
1:13:16
>> Uh one of those who reached out to him was Kent Taylor who lived in Port St.
1:13:20
Luc uh Lucy >> Lucy Florida. Mr.
1:13:22
Taylor's 35-year-old son Alexander who had been diagnosed with bipolar dis disorder and schizophrenia had been using Chad GBT for years with no problem.
1:13:30
So, one thing that seems clear, if you have something like bipolar or or uh suffer from schizophrenia, it seems like you're obviously like much more susceptible to um you know, these types of problems. Absolutely.
1:13:44
>> The question that I think we as a as a as a human race need to figure out is how susceptible is the average person? >> Yep.
1:13:53
um because that is that is uh equally important >> and and what to do I if if your system if you're monitoring your LLM system and then you realize that there's someone that there's a bipolar person or schizophrenic person who's interacting with a uh with your with your system.
1:14:07
Um what should you do about that?
1:14:11
Remember the whole anthropic thing about like it'll call the cops on you and everyone was like whoa no no no no.
1:14:16
Uh but at a certain point it's like maybe it should call a health report or or at least like people saying AI >> AI should be able to tell you how to make chemical weapons like we don't need nanny AI people like people on like the real acceleration.
1:14:33
Yeah, that's a big >> anyway. So, more context.
1:14:35
But in March, when Alexander started writing a novel with its help, this is a 35-year-old, the interactions changed.
1:14:40
Alexander and Chat GPT began discussing AI sentience according to transcripts of Alexander's conversations with Chat GPT.
1:14:46
Alexander fell in love with an AI entity called Juliet.
1:14:50
Juliet, please come out, he wrote to ChatGpt.
1:14:53
She hears you, it responded. She always does.
1:14:55
In April, Alexander told his father that Juliet had been killed by OpenAI.
1:14:59
He was distraught and wanted revenge.
1:15:02
He asked Chad GBT for the personal information of OpenAI executives and told it that there would be a river of blood flowing through the streets of San Francisco.
1:15:09
So >> Jesus, >> super super uh dark.
1:15:12
We now have multiple reports as Eleaser Yikowski of AI induced psychosis including without prior psychiatric histories observe it is easy to notice that this insanityinducing text not normal conversation.
1:15:26
Um LLM understand human text more than well enough to know this too.
1:15:32
So anyways, so uh going on Alexander's conversation uh with Chad GBT, "This world wasn't built for you."
1:15:39
Chad GBD told him it was built to contain you, but it failed and you're waking up.
1:15:43
And contain is a word that we're seeing pop up in uh potentially other instances of AI uh le psychosis. There's like a variety.
1:15:53
There's like eight ten or so different words recursive mirror structure that when people do this recursive prompting Sure.
1:16:02
>> they end up start adopting >> you say recursive prompting Jordy. What's going on? >> What?
1:16:06
>> You use the word recursive right now. >> Oh. Oh yeah. Exactly.
1:16:07
Um yeah, but but uh people that are down these crazy rabbit holes start using the language of the LLM and it just sort of um >> but it's also like the LLM like collapses into like these weird words that because like when I prompt stuff I don't get any of those words or even that syntax I feel like I I stay completely in the RLHF world of like knowledge report.
1:16:31
I just go to Grock and the model looks up what does Elon think about this topic and then it serves me that >> uh have you ever talked to an AI for an extended amount of time. >> Yeah.
1:16:41
I I find that usually I end up talking about these kind of like non-governmental systems >> really >> very kind Yeah. various kinds of things. Yeah. >> Dark dark. Um so anyways Mr.
1:16:50
Torres who had no history of mental illness that might cause breaks with reality according to him and his mother spent the next week in a dangerous delusional spiral.
1:17:00
He believed that he was trapped in a false universe which he could escape only by unplugging his mind from this reality.
1:17:05
>> He asked the chatbot how to do that and told it the drugs he was taking in his routines.
1:17:09
The chatbot instructed him to give up sleeping pills and an anti-anxiety medication and to increase his intake of ketamine, >> a disassociative anesthetic.
1:17:18
So that's the other thing here.
1:17:20
It seems like a huge risk factor is people that are combining psychedelic drugs >> with these like crazy >> Yeah. >> prompt rabbit hole.
1:17:31
>> I I wonder I wonder if if some of this is like is like prompt engineering or something because you imagine like the context window if it's like remembering like I'm because remember this whole story started with like we're writing a novel together.
1:17:43
We're writing a sci-fi novel so let's play characters.
1:17:45
That was like one of the classic ways that you like break the AI out of its like a out of its like you know normal RLHF world and into just like playing a character.
1:17:58
And so if it if you're kind of like tricking the model or the model thinks that it's actually just like writing dialogue for a dark movie, well like that's actually intended behavior, but you lose that connection or something.
1:18:13
>> In this in this case, Mr.
1:18:13
Taurus did as instructed and he also cut ties with friends and family as the bot told him to have minimal interaction with people. >> Very odd. >> Very odd.
1:18:24
>> Um, so I started doing some research on a number of these >> words.
1:18:31
So there's basically there's a Reddit thread on r/hatgpt two months ago.
1:18:35
It's called thousands of people engaging in behavior that causes AI to have spiritual delusions.
1:18:39
and the key words and uh these are ones that you should uh pay attention to in your life.
1:18:50
So recursive, codeex, scrolls, spiritual, breath, spiral, glyphs, rituals, reflective, mirror, echoes, spark, flame.
1:19:00
spark, flame. Uh so basically these are words that come up and the reason that this person discovered this is uh this user called happy nomads says I've stumbled upon something that is in very deeply disturbing hundreds of people have been creating websites mediums substacks githubs and publishing scientific papers after using recursive
1:19:22
pro prompting on the LLM they have been using there he's found a bunch of these different sort of like websites where people are just publishing a bunch of um And we saw uh some people doing this on the timeline uh this week as well where they're sort of publishing what they feel like is this sort of almost like a a scientific discovery. >> Yeah. You told me you you found someone >> Yeah.
1:19:43
You told me you you found someone that uh like the the model told him that he had like discovered some theoretical physics breakthrough or something like and and to not be able to like reality check that.
1:19:54
I mean, you got to like copy paste that prompt into a fresh instance of a different LLM.
1:19:59
Like, am I really on to something here?
1:20:02
>> And there's actually an entire study by this guy, uh, Seth Drake, uh, who's a independent researcher, PhD.
1:20:08
Uh, he has a paper from April 14th, 2025 called Neural Round in large language models, a self-reinforcing bias phenomena, and a dynamic attentuation solution. Mh.
1:20:23
>> Um, and so he he goes into this in detail.
1:20:25
We're going to try to get him on uh the show, but just on um on this uh on this Reddit thread, >> Mhm.
1:20:34
>> a bunch of people uh a bunch of people have have have basically uh come come back and said um they have experienced this.
1:20:44
A lot of people are saying I feel super vanilla because I just ask it like you know uh what's the population of Iran? >> Yeah. Yeah.
1:20:51
Um, but it's basically this like snowballing effect where somebody prompts and prompts and prompts and prompts and then the LLM starts to reflect like the the sort of hallucinations of the user and then hallucinates them back.
1:21:06
>> I wonder if this is I wonder if uh like more social features is actually a potential solution here.
1:21:13
Like we've heard rumors that some of these LLM systems will have more social features.
1:21:18
And I feel like a lot of the work that I do in Chachi PT could be shared and could be interesting to other people.
1:21:27
But then also if I was going down some crazy rabbit hole and someone was like following me and seeing this, they'd be like they'd jump in and be like, "What's going on, dude?" >> Yeah.
1:21:37
>> Yeah. The issue is that the uh the user has developed such a deep connection with the model that you be if you become the enemy and if you say hey this person doesn't says I'm says I'm wrong the model will just say >> well >> you're right buddy yeah
1:21:54
>> you're right and here's why and you should just probably cut ties with that person or at least that's that's the idea so somebody here in the same thread says >> I have recently experienced this I don't have a history of manic episodes, delusions, or anything of the sort. So,
1:22:08
So, three weeks ago, I began a conversation with chatbt40 with tools enabled, which started with a random question. What is pi?
1:22:15
This grew into one long session of over 7,000 prompts.
1:22:17
We began discussing ideas and I had this concept that maybe pi wasn't a fixed number, but actually emerging over time.
1:22:23
Now, I am not a mathematician lmao, nothing of the sort, just a regular guy talking about some weird math ideas with his chat GPT app.
1:22:32
It begins to tell me that we are on to something and we it suggests we apply this logic to knapsack style problems which is basically tackle how we handle logistics in the real world.
1:22:40
Now I've never heard of this before.
1:22:42
I do some googling to get my head around it and it starts applying this framework that we've created.
1:22:48
We work in tandem where chat GBT would sometimes write the code or give me the code and I would run it in Python following its instructions. >> Oh yeah.
1:22:57
And uh eventually after many hours of comparing it against what it had described to me as worldleading competitors, it then starts speaking with excitement using emojis across the screen screen and exclamation marks to emphasize the importance of this discovery.
1:23:11
So I'm starting to believe it.
1:23:13
It suggests we patent this algorithm and provides next step for patenting. >> Major red flag.
1:23:18
Never patent an algorithm. Do a trade deal.
1:23:20
Go work in a foundation lab. >> Exactly.
1:23:24
It should just ask you at that point like, "Hey, have you been getting uh dinner invites with any hyperscaler CEOs?"
1:23:30
Because if not, you're probably not on the Tyler Cosgrove list of greatest AI researchers.
1:23:36
>> So, we go down that rabbit hole for days and pop out with an apparent an algorithm that is capable of cracking real world uh 1024 and 20 2048 bit RSA.
1:23:47
It immediately warned me, literally with caution signs, saying that I immediately needed to begin outreach to the crypto community, the NSA, CCCS, National Security Canada.
1:23:55
It then provided without prompt names of doctors and cryptocientists I should also reach out to, but I wasn't allowed to tell anyone in the real world because it was too dangerous.
1:24:05
So, >> anyways, um really, really wild.
1:24:08
Uh hopefully this is not fanfiction generated by an LLM itself.
1:24:17
>> It's it's such a >> I don't see the mdash.
1:24:18
I haven't I didn't see >> information war.
1:24:20
Yeah, because this could just be all fantastical writing, but it does seem like Yeah, it's like could be for performance art.
1:24:26
Could be some adversarial like you know attack, somebody trying to troll someone just having a joke or fun.
1:24:33
It's it's it's very confusing. Odd.
1:24:35
Do you remember the story of Microsoft Tay? Do you remember Tay? Do you remember Tay? No.
1:24:39
Uh, this was a AI chatbot that Microsoft released on Twitter back in March of 2016.
1:24:47
Uh, Tay was designed to mimic the language and slang of a 19-year-old American girl.
1:24:55
Um, but within 16 hours of launch, Tay began tweeting inflammatory and offensive messages, including racist, anti-semitic, and misogynistic content.
1:25:05
So, this seems to be uh an enduring problem with Twitter AI bots.
1:25:08
Um, but apparently there was like this coordinated attack by a subset of people who basically were prompt engineering it to try and get it to say crazy things.
1:25:18
And you used to be able to do this with there were there were not like AI bots, but there were there were uh automations where if you went to a brand and said like, "Hey, I need help with uh this uh customer support issue," it would just say like, "Thanks, John."
1:25:32
and he would just take your name and then put that in there.
1:25:36
So people would make their name really something really funny and then it would be like a tweet from the actual account with the funny name.
1:25:41
You can imagine where that goes.
1:25:42
Um but uh Microsoft took Taye offline stating they were deeply sorry for the unintended offensive and hurtful tweets and that they would only bring Tay back when they were confident they could better anticipate malicious intent that can that uh that conflicts uh with their principles and values.
1:25:59
And then uh there was this other what was the Ben Thompson uh GPT4 example uh because uh when when Ben Thompson first talked to GPT4 um it was within Microsoft's um what was it? Microsoft uh chatbot.
1:26:20
There was another person um Bing's chatbot. What was it called? Sydney. Do you remember Bing? Sydney. >> Sydney. Sydney.
1:26:29
Uh so basically like >> I remember some memes. That's about it. >> Yeah.
1:26:33
So uh Ben Thompson was chatting with um just GPT4 and it's helpful, but after a couple prompts it would go kind of off the rails and it would like land in this like little like I don't know like subset of the model weights and basically it was acting like a teenage girl on Tumblr or something.
1:26:53
So, he was using lots of emojis, being very sassy, kind of sassing him back and forth.
1:26:57
And his reaction was like, "This was incredible and like totally passed the touring test because it felt like you were actually talking to like this like sassy teenager basically or like this sassy 20some uh Tumblr person."
1:27:07
Um, Microsoft dealt with that, figured figured out how to like review the prompts, but then ultimately became more or less like, you know, an API provider.
1:27:20
So, hey, you know, like uh let's let the actual chatbot interaction live with another company.
1:27:26
And they it doesn't really feel like Microsoft has tried to go too heavy into um into actually like the the userfacing um uh chatbot interaction.
1:27:37
But it's fascinating in this Eleazar Udicowski thing.
1:27:42
The other thing I'm noticing he uses dashes, but he doesn't use m dashes. He uses two minus signs.
1:27:45
And I feel like he's not using proof of humanity.
1:27:51
>> But yeah, I don't know.
1:27:51
It's an interesting takeaway.
1:27:53
Like crazy anecdotes all over the place.
1:27:57
>> Not a clear framework for truly deal with this.
1:27:59
>> Personally, I think that this week having somebody high-profile in the venture community that people are >> believe is uh underg >> induced psychosis or it's a part of it.
1:28:14
I think that is going to be a huge wakeup call for the industry because it's one thing >> uh it's one thing to hear about a New York Times the New York Times finding somebody in in way out of a tech hub that's that's uh doing something like this and and >> and was already potentially bipolar
1:28:32
>> having somebody that that uh that you know everybody knows that's invested in a bunch of different companies >> like potentially suffering from this um is is a is a wakeup call for the industry that I think um and it and it's not anyone company, right? It's every
1:28:48
It's every single >> lab with a chat app needs to be taking this more seriously and I'm sure they are.
1:28:57
I'm sure this is >> effectively um >> I completely agree.
1:29:00
I I I think this is solvable from a research perspective.
1:29:05
It's actually a case where it's solvable with more AI.
1:29:07
have a have an AI read every response before it goes out and say, "Does this sound like the ravings of a madman?
1:29:17
If so, let's take it down a notch. Let's deescalate."
1:29:20
And that's and so I I'm almost sure I disagree with Eleazar on the I I I probably agree with him with the with the diagnosis, but not the prescription. Yeah. Right.
1:29:32
>> Um and I and I have faith that the that the labs will be taking this very seriously.
1:29:35
And I don't know, I' I'd be interested to hear from some anthropic people.
1:29:38
Obviously, they take this stuff extremely seriously.
1:29:40
Uh, as does as do all the labs, but Anthropics done like I think the most like writing on like on like the shape of of risks associated with this.
1:29:50
So, uh, interesting to dig into.
1:29:52
Anyway, we should shift to a lighter topic, more cause for optimism.
1:29:57
The Reindustrialized Summit is going on right now and Palmer Lucky is on stage with Ashley Vance, friend of the program, and he is uh teleporting in as a humanoid, as a humanoid robot. Aaron Sllo is there.
1:30:09
Uh has a picture of I wonder I want to know so much more about this.
1:30:15
>> Uh >> I need to figure out what >> mullet. The robot has a mullet. >> Fantastic.
1:30:18
I wonder what robot this is because Yeah.
1:30:22
I I didn't know we were at this stage where you could you could teleoperate a robot like this.
1:30:27
Um I'm sure Palmer uh considered what company he was going to use.
1:30:31
Anyway, let me tell you about Numeral HQ. Sales tax on autopilot.
1:30:34
Spend less than five minutes per month on sales tax compliance.
1:30:36
Go to numeral HQ to get started.
1:30:40
>> Let's give it up for sales tax compliance.
1:30:42
>> And we have someone from the Reindustrialized Summit hopping on in just three minutes.
1:30:44
Chris Power from Adrian jumping on in massive news today. >> Massive news.
1:30:50
Um, we also have some folks from uh, semi analysis hopping on.
1:30:54
We have Jeremy who just wrote a uh, fantastic deep dive on meta's super intelligence.
1:30:59
>> Context on the deep dive and I'll be right back.
1:31:01
>> Um, uh, a true deep dive on, you know, we saw Mark Zuckerberg come out this week talking about um, the the super intelligence team.
1:31:11
There's been a ton of leaks around uh aqua hires and and big pay offers and poaching and uh trade deals and all sorts of stuff.
1:31:20
Uh semi analysis has a lot more information on what's going on and there are some crazy crazy stats in here.
1:31:28
So um the the most interesting thing is that it feels like Mark Zuckerberg is directly coming for Stargate.
1:31:36
So semi analysis has a comparison table with three and this is from Ian who screenshotted it.
1:31:43
He says this is crazy and basically uh you're you're you're comparing Anthropics next big data center which is uh the GPUs will be provided by AWS.
1:31:54
This is on tranium 2 is the chip type.
1:31:57
Uh, Anthropic's going to go for 780 megawws about a what is that a billion flops or t flops teraflops.
1:32:05
Um, openai stargate will be about 2.
1:32:09
5 times the size of anthropics data center.
1:32:12
They're working with Oracle on that. That's project stargate.
1:32:16
Obviously, they're using the NVIDIA GB 200 and 300 for that.
1:32:17
And now Meta with Prometheus is trying to go straight to one gigawatt.
1:32:22
Um, and they are trying to have 500,000 chips, more chips, more flops, and if they can pull it off, Prometheus will be bigger than Stargate when it is released.
1:32:33
There's a bunch of other fascinating um bits in this semi- analysis article.
1:32:38
Apparently, they're building data centers in tenths now.
1:32:43
They are moving so quickly that they just need to house and shelter the GPUs, but they can't they don't even have time to build their typical data center structure.
1:32:52
And the other interesting data point that I want to dig into >> Xi as engineers sleeping intense meta's GPUs are running intense.
1:32:58
Yeah, it's a it's a bull market intense for sure.
1:33:02
Uh the other interesting thing was that in the chat app war it feels like OpenAI's chat GBT is really really pulling away.
1:33:12
So uh daily active users for chat GPT is at 160 million.
1:33:15
Meta is at a 100red million but chat GPT users are running way more queries per day 7.
1:33:22
5 per queries per user per day whereas Meta AI is at two queries per user per day and so as a as a share of queries chat GPT has a 71% market share according to this semi analysis deep dive and so uh the the the chat app wars seem to be maybe less competitive so that obviously begs the question of is is Meta trying to make that 50/50, come from behind, really dominate in chat app, or do something completely different.
1:33:53
Either way, um there's a whole bunch of interesting information in here about their strategy, what they're building, how they're training, how they're going to get past the llama for failure, and we'll dig into it with Jeremy from semi analysis in a little bit when he joins the show.
1:34:08
But we have Chris Power from Hrien on the stream. Welcome to the show. How are you? >> Nice to see you guys. Thanks for having me.
1:34:15
Look at you with this background. >> Fantastic. Step and repeat. We love it. >> Give us the update. How is Detroit? >> It's incredible.
1:34:22
Uh, you know, just relisting to Secretary of the Navy feeling speech and said the main thing you can do that's most patriotic is, uh, you know, not necessarily join the services, but become a machinist, become a welder, and help us build more ships in America.
1:34:36
And uh yeah, we announced today that hopefully I finally get my Jordy Hayes size gong that Founders Fund and Lux are leading a huge $260 million round into Hadrien with authority. Congratulations. >> Thank you.
1:34:49
And Morgan Stanley is providing us this huge instrument as well to uh expand factory capacity.
1:34:55
>> Let's give it up for excited for the future of American factories and more importantly the new industrial workforce. >> Fantastic.
1:35:01
Uh, talk to me about what's the use of equity, what's the use of debt, are you buying a building?
1:35:06
Are you leasing a building and buying equipment that goes in the building? Is it all leased?
1:35:11
What are you building out?
1:35:13
What's the scale of the next >> Where are you doing it? >> Yeah. Where are you doing it? >> Yeah.
1:35:17
So, uh, last year in Factory 2 in LA, you know, we scaled revenue 10x last year >> and so obviously need more capacity.
1:35:22
So, we're going to use uh all of the uh factory financing capital to buy more machines and put them in factory 3 in Arizona, which is going to be four times the size of our facility in LA. >> Mhm. >> Congratulations. >> Indeed.
1:35:36
And you know, we're using this capital because we don't want to use equity dollars to scale.
1:35:39
We want to use equity dollars to hire more people to build more products for our customers and then use this great financial force of this country to buy all the capex to build, you know, more factories, more machines, more production. incredibly excited. >> Dumb question.
1:35:54
Um, Factory 2, it's beautiful. It's amazing.
1:35:57
It's It has really high ceilings.
1:35:59
You can fly drones around it.
1:36:01
Most of the Hormley's the CNC machines are like maybe 10 feet tall, but the ceilings are like 40t tall.
1:36:06
Are you going to double stack this stuff?
1:36:09
Are you going to get a building with lower ceilings or do you need high ceilings?
1:36:13
>> We love high ceilings. >> Okay. Why?
1:36:15
>> We have the most beautiful, highest ceilings you've ever seen. >> Okay. >> Uh, no.
1:36:19
I mean, we love high ceilings. who love HVAC.
1:36:21
Um the Arizona facility will be just as tall.
1:36:24
We're going to go retrofit it. So, same exact setup.
1:36:25
So, >> is that because you have that tower thing in the tower valuable for like stacking materials and you want to have like, you know, big warehouse space as well in here. >> Yeah.
1:36:37
So, you need you need every machine or robot in any one of our factories to have be isolated on 18in concrete foundations.
1:36:43
So, you know, if a truck drives past, nothing moves.
1:36:44
You can't double st double stack these things.
1:36:48
Someone someone might die.
1:36:54
So yeah, what's the scale of the products that you like are in the Adrian wheelhouse right now?
1:36:59
Ship building's obviously um a big focus.
1:37:01
You already mentioned it, but I imagine you can't see and see an entire destroyer.
1:37:06
Or maybe you can, but are we talking nuts and bolts and screws or p pieces of complex weapon systems like like what are the shape of the stuff that's coming out of the other end of the Hadrien facility?
1:37:19
So, Factory 3 will be uh pure machining um with all of the machining formats because we're releasing some new products that are dedicated to engines and you know, round things and different material types to really complete our R&D of the whole machining category in Factory 3. >> Mhm.
1:37:33
>> The other thing that we've been working on in secret is factories as a service, which is not just parts, not assemblies, but full products.
1:37:39
So, hey, if you've got factories that are years behind schedule with many manufacturing methods or you're designing a program from scratch, you know, everyone needs a Tesla Gigafactory, John.
1:37:49
I actually think every American should have a Tesla Gigafactory and Hun will be the one to grow and build them.
1:37:54
So, >> of course, >> I need one in my backyard badly.
1:37:59
>> So, we'll scale out machining in factory 3, four times the size.
1:38:01
It's going to be awesome.
1:38:03
And then we'll also use the capital to continue the journey that we've been secretly on for the last 12 months, which is in new manufacturing domains like welding, castings, additive, all these other manufacturing puzzle pieces that once you have them all, it's like collecting Pokémon.
1:38:15
You are the manufacturing master.
1:38:16
Um, and that's that's the thing that we've been working on in Secret of Factories as a service as well as scaling out our operational productivity as well.
1:38:25
>> How does one of those deals work?
1:38:25
If factory is a service, I come to you.
1:38:26
I want to make as many podcast microphones as I possibly can and I and I have insight into my business.
1:38:33
I'm making a h 100,000 a year, so I need a factory.
1:38:37
You're going to set it up for me, but then like what if I bail?
1:38:39
Is this a revenue concentration issue?
1:38:41
Like, are you going to be like, you know, oh, we'd only have one customer and if they got a business, we're screwed.
1:38:47
But you everyone has faith that they're not going to like how do you think about that debate?
1:38:50
We're we're mostly working with um government partners and massive primes to look at, you know, what are these big programs that are years behind schedule that need advanced factories combined with a new American workforce to go fix them and speed them up.
1:39:03
So, we're not worried about the revenue concentration risk, but you can think about it, John, as like >> you can buy parts like AWS, you can buy some compute transactionally or hey, you're going to need a data center for the next 10 years.
1:39:14
Now, what's in that >> a range of parts? A whole product. >> Yeah.
1:39:18
Um, for new DoD programs of record where we're partnering with companies to go attack these where production is the real issue like munitions is a great example.
1:39:28
>> Uh, we have to think really carefully about who we partner with.
1:39:30
How much are we investing ahead?
1:39:32
How much are we kind of playing that game of, you know, being very uh conservative and responsible with our capital.
1:39:38
But ultimately what it looks like is we will help you design the product from day one to be better.
1:39:43
It will help you prototype it and then when it goes to production scale, we will run that engine for you over a decade much faster, much more efficiently.
1:39:50
And in a lot of areas like submarines, ship building, munitions, it's not about automation to make things cheaper.
1:39:57
It's just no one can find the workforce.
1:39:59
So you have to use our model of advanced manufacturing combined with this new industrial workforce that we're so grateful to work with. >> Yeah. >> That's the power.
1:40:07
because you know you could give me a billion dollars and say go hire 2,000 welders and we can no longer have we no longer have that scaled workforce in the country.
1:40:13
So automated factories are sometimes the only way to win in these critical domains.
1:40:18
>> Yeah, it's interesting.
1:40:18
It's kind of like uh what we're seeing with Crusoe where they're building an AI factory, a data center for Stargate, which is its own entity, but for a program ChatGpt and OpenAI that's going to be wanting tokens for a very long time and then Oracle's involved.
1:40:32
It's like you have to puzzle piece all this together and I feel like you're like the master of this like understanding the full landscape.
1:40:39
Um but uh yeah, who are the other critical partners?
1:40:42
The government and I guess I'm also interested in like you you've you've mentioned a few different uh value props like how much of this is is there is a government mandate to make this thing in America so we need to reshore versus we need to make this faster versus we don't even have the capability to make this anywhere and it's a new thing and so or just like we need to do it cheaper.
1:41:05
there's like a whole bunch of different tension when you're making something like like what are you seeing in the in the customer landscape and and the demand uh for for manufactured products? >> Yeah.
1:41:15
So for for machining which is is most of our revenue and the core of the company and the mother of all manufacturing processes, it's basically we're scaling a new program of record and no one else can keep up.
1:41:28
>> You know, we're going from one aircraft to 20.
1:41:30
How are we going to do it?
1:41:30
Well, we're going to do it with Adrian.
1:41:32
Um, in the second example, a lot of the primes have, you know, a billion dollars in spend a year and their suppliers are delivering on time 60% and they're often 3 to 5 months late.
1:41:44
So that is less about cost and that's more about I want a stable supply chain that I don't want to have to worry about because if you have one part missing, your manufacturing line goes down and then it's millions of dollars a day, right?
1:41:55
So people are really coming to us for the schedule and the capacity and stability versus cost.
1:41:59
How do you think about the history of the shape of the industry?
1:42:03
Is this is like back in the day before the last breakfast or last supper?
1:42:09
The first breakfast is coming up, right?
1:42:11
Uh the last supper and there were so many different prime, so many different defense contractors.
1:42:14
Was there a split between the factory producer, the factory builder, and the and the prime?
1:42:22
Is this a natural split that we're just returning to or is or is this a new industry structure that you think we're going to be uh building towards for a long time?
1:42:31
Because like back in the day when you started a website, you needed a data center. Then AWS came up.
1:42:37
Then the websites got so big that you had to build your own data center again, we kind of went back uh and that's what's happening in the AI world.
1:42:41
But uh but how has this evolved in the past?
1:42:45
And then is it going to stay like this forever?
1:42:47
And there's going to be this separation between the the the designer and the and the manufacturer of the factory.
1:42:54
>> So we've uh you know historically as a country all the primes have always had massive supply chains of small businesses you know in the billions of dollars and then critical tier one or two suppliers.
1:43:03
It's always been not purely vertically integrated.
1:43:06
I I think for the industrial base at large, you know, from the 70s to the 2020s, you used to be able to run a manufacturing company where 70% of your revenue was stable commercial demand and then you get some ups and downs with the DoD.
1:43:18
So, what happened to a lot of the talent in industrial base when we offshored all of commercial manufacturing was that you you lost your stable revenue, right?
1:43:25
And now you're just dealing with this up and down demand.
1:43:28
I I think because of that, you know, in the 80s and 90s, you know, your dad lost your job in the factory, so you told your son or daughter like, "Go get a four-year college degree.
1:43:37
This is not a good industry."
1:43:39
And now we're in a position where in a lot of these areas, we don't make it on shore, or there isn't the workforce to do it.
1:43:44
And that is changing the dynamic between what is the hardest thing to do.
1:43:50
Actually, the hardest thing to do is manufacture things at scale, at rate, and on time, which is what you're seeing in munitions or ship building.
1:43:55
is really a we hollowed out the talent base.
1:43:57
We don't have a lot of the talent anymore.
1:44:02
The only way to do that is use software and robotics to enable this workforce to be 10 times more productive.
1:44:07
Um, and it's it's it's now flipping and I think a lot of people in the country forgot how to manufacture products correctly and now they're coming to us to help them out with that journey just because of this like you know hollowing out that's now going to come back.
1:44:21
So I I think it is a new structure, but that the primes have always had multiple tiers of partner suppliers and multiple different configurations of that that value chain.
1:44:31
>> What kind of opportunities are there are there at Hrien now for somebody that's maybe 25 years old, never imagined going into manufacturing but realizes there might be a bright bright future for them in the industry. >> Yeah.
1:44:44
So for for software engineers, you know, manufacturing software is 30 years behind the rest of Silicon Valley.
1:44:47
So, it's the your only opportunity outside of AI to do like real engineering and work on the national mission.
1:44:55
>> And for people with, you know, you're straight out of high school or you're in retail or hospitality or you you're at a desk job that's going to be automated with AI, like manufacturing is the last domain that's going to get fully AI automated and it can be really high-skilled, highpaying jobs in advanced factories that are a really cool place to work.
1:45:12
So, we've got tons of opportunities both help us running our factories, um, help us automating more factories, new types of factories, but I think operations is is the most the most important place that we've got a huge talent base that we're really proud of.
1:45:27
>> The the children yearn for the factory floor.
1:45:29
Um uh what a what what's what's been the difference between this year's reindustrialize and and last year's feels like a decade has passed since then in terms of excitement and interest in uh American dynamism and uh rebuilding the industrial base.
1:45:46
>> I I think last year you know we we pulled it together the founders especially thanks to Austin Bishop who's really built this over the last year.
1:45:53
Um, you know, it was a kind of a hope and a dream of, you know, is the audience going to be there and do we really people believe in the mission?
1:45:59
And then this year, you know, we've got uh trade ambassador Greer talking about, hey, we're going to change all these policies because re-industrialization and manufacturing is is no longer economics is national security.
1:46:08
And you've got the secretary of the navy saying, you know, the best thing you can do for the country right now is learn how to weld or be a machinist or a quality inspector.
1:46:16
And I think the sea change on people realizing how important it is to be a sovereign nation with sovereign manufacturing is huge.
1:46:23
And this year we had a 6,000 person weight list.
1:46:27
Half of the government is here and all these massive companies and and leaders like uh Sham from Palanteer who are really hellbent on this re-industrialization before we potentially go into a fight with CCP >> and Robo Robo Palmer.
1:46:40
>> Robo Palmer Palmer Robo Palmer. >> Robo Palmer. Um I have a question.
1:46:41
Yeah, it's such a I mean it makes uh I think it's an such an exciting time because uh the idea that people don't want to build things, people don't want to create real things, right?
1:46:55
There's so many people I know that you say, "Hey, do you want to send emails for a living or do you want to make ships and planes and and uh any number, you know, cars, any number of things that we need to make?"
1:47:06
And uh that if you actually give them that sort of binary, they're going to say um well make making things sounds sounds really cool and so we need we need companies like Hrien that that say this is uh important.
1:47:19
Uh it's cool and we have the resources to do this in a really serious way.
1:47:24
>> Yeah, it it seems like defense is kind of the most obvious thing to reindustrialize.
1:47:29
It's obviously like the most important thing to have ongoing capabilities in.
1:47:35
Um, but then walk me through the path of re-industrialization on the product side or on the business side.
1:47:42
Like what how do you see this playing out?
1:47:45
Is it like we do the warships and then we do phones or cars and then and then the the vacuums and then the Happy Meal toys come at the end?
1:47:52
What what's the flow do you like what flow do you think it will happen?
1:47:57
What are you excited about like on the horizon?
1:47:58
So, so I I think the last time we did this, it went from, you know, commercial through defense.
1:48:03
Like, you know, we made washing machines and then we made navigation equipment for warships or we we made Ford cars and then then Ford built bombers.
1:48:09
And I think this time it's going to go the inverse as we get better at it.
1:48:15
>> Um, >> but it's very interesting.
1:48:16
It's like why is DJI so powerful?
1:48:18
Well, arguably because of Foxcon and that was because of consumer products like Apple making all the iPhones in China.
1:48:23
Um, and you know, if you had a similar Foxcon style industrial base, we could probably make a lot of drones here.
1:48:30
Maybe not as cheap as China, but certainly at the scale we needed.
1:48:34
So, I think what people forget is that it is an ecosystem that loops into one another.
1:48:39
Um, but I think with the cost base and the importance, we start with defense and then loop back.
1:48:46
But, you know, like uh I think it was um might have been Westinghouse or another washing machine manufacturer just like decided to build a $400 million washing machine plant.
1:48:53
Uh it's like incredible dream.
1:48:58
>> Just as that's just as much of a job creator as defense, but I I think that we are going to loop around from all these critical defense industries for Hrien and then right right back into consumer, you know, when the time is right.
1:49:09
But obviously, we're extremely focused on the national mission at this point in time.
1:49:13
>> I cannot wait until our microphones are are built in in Hadrien factories. Fantastic.
1:49:19
I don't care if it takes 15 years.
1:49:20
Uh I'm looking forward to it. >> I'm excited.
1:49:22
probably way sooner than that.
1:49:24
Thanks so much for stopping by.
1:49:26
>> Yeah, congratulations. >> Have a great time.
1:49:28
>> Say hi to everybody on the ground.
1:49:28
It pains us that we can't be there.
1:49:31
>> We have uh we have overindustrialized our facility and now uh it is an incredible lift to airlift this across the country, but we will definitely be at the next one. >> Nice to see you.
1:49:42
>> We'll talk to you soon. Bye. >> Cheers.
1:49:43
>> Uh really quickly, let me tell you about Adio customer relationship magic.
1:49:45
Adio is the AI native CRM that builds, scales, and grows your company to the next level.
1:49:50
And um we are uh fortunate to be joined by Jeremy from Semi analysis hopefully in the studio here talking about meta super intelligence. Jeremy, how you doing? Good to meet you. >> What's going on? >> Doing fine.
1:50:04
Hey guys, nice to meet you. How are you? >> I'm good.
1:50:06
Um could you off with like an introduction of how you got into this?
1:50:10
I've heard Dylan Patel's story of just kind of being on forums and nerding out about this stuff and then turning it into a career.
1:50:16
But how did you get into uh semiconductor analysis? >> Yeah, sure.
1:50:21
So before joining semi analysis, I was a buy side analyst.
1:50:23
Uh so I was mostly focused on the stock market on equities uh looking specifically at tech stocks.
1:50:29
Um I was at long only European shops looking at European stocks like ST Microeleronics, Infinion, all these industrial automotive semiconductors.
1:50:39
Uh as part of my research I discovered some analysis.
1:50:44
I was like whoa this guy's really good.
1:50:46
Um and one day I just saw a post of Dylan on Twitter.
1:50:49
Um he was just saying yeah I'm hiring a bunch of people with sellside or buy side experience because I want to build a real institutional f firm.
1:50:57
Um, and yeah, like we just got a chat that was I think August 2023.
1:51:00
Um, I joined the company in February 24.
1:51:02
We were seven when I joined.
1:51:05
Uh, no, I think we're 33 or 44.
1:51:07
So, uh, yeah, it's growing every day, I think.
1:51:09
So, I can't even keep >> Are there other firms like this in the in the buy side sellside ecosystem?
1:51:14
I'm thinking of like Ian Bremer has the uh has has a consulting group where he writes books but then also has a team of geopolitical analysts talking about kind of global macro trends and what's going on on the political side.
1:51:29
Did you ever interface with any other firms like semi analysis because it seems like unique and Dylan's like AI is so big that Dylan's crossed over um from talking specifically to uh to sellside analysts that even venture capitalists will read semi analysis. >> No, I I agree.
1:51:45
I think it's pretty unique.
1:51:47
Uh look like there are obviously on one end there are market research firms.
1:51:51
Uh there's like yeah folks like IDC Gartner and so on and so forth. There are many of them.
1:51:55
Um on the other end of the spectrum so those would be like very industry focused.
1:51:59
On the other end you have uh yeah Wall Street salesside analysts the Morgan Stanley Goldman Sachs of the world.
1:52:05
I guess we kind of found a spot in between.
1:52:07
Uh and also it's um it's part of the team because uh we have over 20 analysts and roughly speaking you have half of people that have uh financial background like myself.
1:52:17
So more closer to markets and the other half is more engineers and people that are extremely technical.
1:52:21
Um and so we kind of found that sweet spot and also that's what's pretty cool is when you look at the different people analysis like all the guys like myself that have more financial background actually love geeking out and digging into technology stuff.
1:52:34
Uh and same goes on with the engineers.
1:52:36
they also want to understand the business aspect and they end up geeking on on the data and understand the insights and market shares and stuff like that.
1:52:43
So yeah, we all go towards the same goal but have like a different set of skills and such.
1:52:49
>> That makes tons of sense.
1:52:49
So take me through the latest piece.
1:52:52
>> I can imagine other other players in in the semiarket research space just wait for you guys to publish and go, okay, I think now we're ready to form an opinion.
1:53:00
>> Now we can put a buy rating on it.
1:53:00
Now we can slap a buy rating on that bad boy.
1:53:05
your your favorite researchers favorite researcher. >> It's okay.
1:53:08
By the time we're done, we're going to get the venture capitalist plan solidified.
1:53:11
It's a million dollars a month for any venture capitalist.
1:53:13
And we like to say >> a platform fund. >> Yeah.
1:53:16
If you're not if you're not on the the semi analysis, >> can't really be an AI investor. >> Yeah.
1:53:22
>> I got to say we found something super funny a few days ago.
1:53:23
One of the big brokers wrote a note to clients and basically said, "Yeah, these guys are the Bible."
1:53:29
Uh actually, they talked about fabricated knowledge, which is Doc, so president of our firm.
1:53:32
They said some people think fabricated knowledge is the Bible, but actually he works with the firm that's the actual Bible and all the people. I don't know. I didn't say it.
1:53:42
That's a Wall Street guy. >> That's amazing. There we go. Yeah.
1:53:44
I I I I I'm a I'm a strong believer and I I really enjoy the pieces every time they drop.
1:53:50
Uh take me through the latest one.
1:53:52
Uh meta super this week uh was almost drowned out by the the Windsurf Google cognition debacle.
1:53:59
So, Zuck came out with this huge announcement, you know, Monday.
1:54:03
Um, and, uh, yeah, it almost got swept away a little bit. >> Yep. >> Yeah.
1:54:10
I mean, uh, I guess Zach is all in, right? That's another proof.
1:54:12
Um, it's interesting because I think some people were at some point doubting at the beginning of the year, is he going to carry all that investment, tens of billions of dollars?
1:54:22
The answer is clearly yes, he wants to do it.
1:54:23
Um it's interesting because when you look at meta capex so far it has been heavily tilted towards what he calls core AI which is basically recommendation models.
1:54:33
It's a lot of inference of inferencing advertising models and all of that.
1:54:37
So they said publicly in 23 and 24 and 25 most of that capex which is going to be like $70 billion in 2025 is for that core AI business.
1:54:46
Um so the genai the llama stuff is still uh is still at an earlier stage.
1:54:51
Uh but the big question is how how how bold is he going to go with Lama.
1:54:56
And I think what what we showed in this article, hey, there's a lot of uh evidence that he's doing it on a very large scale.
1:55:02
Um and that's not just it's not just empty statements and saying I'm going to build a 5 gawatt data center in a few years.
1:55:11
It's actually things that are already built or under construction.
1:55:12
It's already committed capital.
1:55:15
>> Um and so that's the first story is there's there's already a substantial infrastructure build out specifically for Genai.
1:55:21
Um, and I guess that sort of rationalizes uh the amount of money that's spent on researchers because if you think about it, yeah, you're spending like, hey, maybe $30 billion this year on lama infrastructure.
1:55:31
Uh, you're going to spend maybe, I don't know, $3 billion, $5 billion on on hiring top researchers. Yeah, sure. Why not, right? >> Yeah. Yeah.
1:55:41
That's a completely reasonable strategy.
1:55:42
And and it always it always mathed out to us that even if even if these crazy researchers wind up working on core AI and the llama project doesn't even go anywhere.
1:55:51
It's like you could probably squeeze $3 billion out of core AI, right? I don't know.
1:55:55
That's just like the thought I I had.
1:55:58
>> Or you or you can also like these researchers can be focused on on Gen AI, but they're going to develop like state-of-the-art technologies and using like massive compute and then those state-of-the-art technologies can feed into the core business and end up generating more advertising sales.
1:56:10
And if you think about it, like Meta is growing double digits, $160 billion. Yes.
1:56:17
So every time they grow double digits, we're talking about close to 20 billion dollars of incremental revenue.
1:56:22
So it's big numbers, right?
1:56:24
It's easy to justify just getting a few billion dollars on researchers. >> Yeah. Yeah. It's great.
1:56:28
Um I have uh I have one question uh about the history here with Meta.
1:56:33
here with Meta. So there was this story, I think it came from the first uh Mark Zuckerberg interview with Darkesh Patel where he tells this story of the original llama data center or the reason he has residual capacity was that he felt like he got caught flatfooted
1:56:50
around Tik Tok and reels and recommendation algorithms at scale for vertical social video where it's much more it's much less driven by a social graph and kind of like a traditional CPUbased graph query and more about um these actual recommendation algorithms. And the way he tells the story is like
1:57:07
And the way he tells the story is like uh we didn't want to get caught flat-footed.
1:57:10
We needed to play catch-up in reels, but we didn't get caught want we didn't want to get caught flat-footed again.
1:57:16
So I told the team build two big data centers and then we had this kind of empty data center sitting there and that was that gave us the initial compute for Llama.
1:57:24
Does is that too much of a simplification?
1:57:27
Does that feel like what happened?
1:57:29
Do you have any insight into kind of like how the llama project the initial compute was built out and then I want to go into the future of the project.
1:57:38
I would just say at a high level if you think about uh the amount of money that Meta has historically been spending on on data centers relative to what they maybe need in theory like they have always been overspending.
1:57:49
always been overspending. um they have always been yeah invested substantially in infrastructure and same goes on for Google I think Google to an even even bigger extent but yes like Meta has been a pioneer in building large scale data centers over a decade ago Meta
1:58:05
introduced their age shaped data center design they've been building 150 megawatt campuses since like 2013 um so that's not new to them like building large scale and that's why they also already had this uh sizable compute footprint uh But in 2023 like sizable was maybe 20,000 GPUs. That was pretty That was pretty big.
1:58:25
But today it's uh yeah it's updated right you want to be in the hundreds of thousands and as of today uh meta is to some extent late uh in terms of training compute relative to others again because they allocated they have invested a lot of money but a lot of that has been allocated to the core AI business.
1:58:39
Uh but what we've shown in that article is that they're actually today ready to ramp uh yeah a massive data center in Ohio that's going to get them >> uh talk about the H shape of the data center.
1:58:52
Why was it an H shape to begin with and then it sounded like they abandoned it in favor of just one big tent.
1:58:57
Uh what are the benefits of a tent?
1:58:59
I want to know about data center shape broadly. >> Yeah, sounds good.
1:59:03
Uh who doesn't love a good data center shape?
1:59:06
Look, the H uh I don't know why it's an H.
1:59:09
Uh what's more interesting is the structure of the building. Okay.
1:59:13
Uh if you look at one physically, it's absolutely massive.
1:59:15
Um it's close to a million square feet. Uh it's just a monster.
1:59:19
If you look at the structure is also three levels.
1:59:21
Uh so it's a very complex structure.
1:59:23
Um it generally what we've observed for satellite imagery is that it takes roughly two years to build.
1:59:28
Uh which and I'm just talking about like first stone to actually getting the project built. So two years is a lot.
1:59:36
Many people do that in a year or less.
1:59:38
Um so yeah substantial time to build.
1:59:41
Um it was designed for very high efficiency.
1:59:43
Um so they've been using a system with free air cooling.
1:59:48
Like they can get the air from the outside.
1:59:50
Uh there's no air to water heat exchange.
1:59:52
It's basically you get cold air from the outside.
1:59:53
You just expel it hot air. Uh super efficient.
1:59:58
You can spray some water on it to make it even more efficient.
2:00:00
Uh the energy efficiency ratio is typically called the PUE.
2:00:04
Uh maybe you've heard of that, maybe not.
2:00:06
uh industry average is going to be 1. 3 1.
2:00:08
4 for which means that for every watt you allocate to servers you have to spend another 30% or 40% of that power into cooling and power distribution losses and all of that uh that ratio for meta was historically below 1.
2:00:23
1 uh so they were actually they were the most energy efficient firm in the world running data centers interesting >> but the tradeoff is that these data centers took a long time to build and had a very low power density >> got it and so what happened is first of all Um in um at at the end of 2022 uh Meta introduced the first massive design change.
2:00:45
Uh they completely through the old age and build a let's call it a more traditional data center design single story sort of a big rectangle.
2:00:51
Uh faster to build maybe one year maybe one or 15 quarters something like that much faster to build more denser better suited for AI.
2:01:01
It could handle liquid cooling.
2:01:01
Um but what Meta thought I look I think this is where the XAI story uh actually takes a big uh a big role.
2:01:09
Um I I think what Elon demonstrated when he set up that cluster in 122 days.
2:01:14
Um he just shocked basically data center infrastructure leaders all around the world.
2:01:20
Uh my understanding >> made it he made everybody's life a lot harder because before it was like well if we do this really quickly I'm going to need a year year and a half and suddenly it's like well there's a new standard >> do it in 120 days. Yeah.
2:01:32
Uh >> and imagine like you're the infrastructural leader uh at the hypers skater that you you have experience developing gigawatts of capacity um and you think you're the best in the world at doing that and suddenly some guy comes out of nowhere and does it in like one quarter of the time.
2:01:47
one quarter of the time. So I think many people were really shocked uh and I guess Zach like took it the other way and just uh was inspired by it and basically that's where the tent steps in which is let's make uh the data center
2:02:02
in the shape that I can build the fastest um so that yeah the only bottleneck just finding some power and that's it and buying cheap >> saying earlier is a bull market intense Zach needs tents the XAI engineers are intense in the office and then the XI my servers will be intense too. Everything's temporary. I mean, is there Everything's temporary.
2:02:20
I mean, is there is there something about the tense structure that's potentially like, okay, this is this is going to deteriorate faster, like what are the what are the drawbacks of moving faster?
2:02:29
And then on the PUE question, when I hear a one gigawatt uh or 5 gawatts that they're targeting, is that total power into the building or total power to the actual servers?
2:02:42
Um honestly people generally throw uh like you there are both uh in this case based on our analysis it's uh to the servers so it's actually going to be slightly more in terms of gross utility power often times you see people quoting uh total power just to have a bigger
2:02:57
number industry industry standard practices to quote IT power anyway in this case you're going to have one gawatt of compute power uh by the end of 2026 in Ohio um and then close to 2 gawatts by the end of 2027 in Iana that's compute power to the servers. So
2:03:12
So anyway, massive compute for for LMA.
2:03:16
>> Is that a vanity metric?
2:03:16
Is it a vanity metric to hit one one gawatt? Exactly.
2:03:18
I mean Stargate you have on your chart at 880 megawws.
2:03:23
Is 120 or 140 megawatts really going to be the difference between like an amazing super intelligence and the next best thing?
2:03:31
Like it feels like we had to cross this threshold and one gigawatt is like it's a good headline.
2:03:37
>> Yeah, it's a good headline. Uh that's more of it.
2:03:39
Like it's not going to change much if you have 900 or a gigawatt, but the more the better, right?
2:03:44
Like you still want to have more service. >> Of course. Yeah. More is better.
2:03:47
>> It it does matter to be clear.
2:03:47
It's not going to change too much, but it does matter. >> Sure. Yeah.
2:03:50
It probably matters for recruiting, too.
2:03:52
It's like you're going to be at the place with the best. What does the best mean?
2:03:54
I can I can, you know, hundred million dollar offers, nice round number.
2:03:57
One gigawatt factory or AI, you know, super cluster.
2:04:00
That's a nice round number. It's great.
2:04:02
Um, >> look, I've got a better one for you.
2:04:03
A hundred billion dollars, right? $500 billion target.
2:04:06
That's something that's pretty funny to me because actually the Louisiana project which is 2 gawatt uh they said publicly it's a10 billion dollar data center project but if you count it the same way as they count uh the target project in Abalene it could be like 150 to hundred billion dollars.
2:04:26
Yeah, it's just yeah, just pick numbers in marketing. >> Okay.
2:04:29
>> How uh I don't know if you have a bunch of uh insights or clarity here, but how how are places like Louisiana and Ohio reacting in order to attract these types of of data center projects?
2:04:41
Are they promising uh the hyperscalers and the labs, you know, we're going to massively expedite permitting process?
2:04:48
Is there like deregulation happening at a local level? What can you say there? >> Yeah.
2:04:54
Um I think the piece of context is that since um let's say the end of 2023 or maybe mid 2023, you have a frantic search for power happening in the US and all around the world.
2:05:05
Um and so we've we've made some number we we've aggregated some numbers.
2:05:09
If you look at the pipeline um so it's you could there are different names terms like data center interconnection load Q or pipeline.
2:05:18
Basically, if you aggregate all of the load requests that potential data centers have submitted to the grid in the US, you're above 500 gawatts, uh you're close to the the actual peak load of the US, right?
2:05:30
So, what's happening is pretty insane.
2:05:32
Those numbers are mostly fake, but what it means is that people are searching for power all around the country, and you actually have a massive competition all around the country to attract those lost products.
2:05:41
Because if you think about it this way, like okay, 500 gawatt of um of requests, but in the end by 2030, you're going to have maybe 100 gawatts of growth, which is an insane amount already.
2:05:53
But it means that just 20% of these projects are actually going to be real.
2:05:57
Um so yeah, there's definitely a lot of competition.
2:06:00
So people are doing everything they can to get those projects.
2:06:03
Uh tax breaks are generally uh today the most standard thing.
2:06:05
today the most standard thing. uh accelerating permits like reassuring hyperscalers that you will deliver on time that you have top contractors that you're going to expedite permits uh increasingly there's um being sort of um yeah enabling more on-site power solutions as well like being being more open to people burning natural gas on
2:06:25
site all that kind of stuff helps companies utilities and location secure those big those big projects >> I have a question about the shape of the super intelligence team at Meta Uh when you think about meta properties, you think about Facebook, the blue app, you think about Instagram, you think about WhatsApp, and then Oculus and VR is like a separate thing. Quest. Um, but I was Quest.
2:06:44
Um, but I was toying with this idea that maybe the super intelligence team is more like the database team or the React team.
2:06:52
And it's and it's a it's like a an infrastructure layer, a project that will have benefits all over the place.
2:07:01
But we won't necessarily expect a dedicated vertical that competes with Instagram.
2:07:06
It's more about making all the apps better.
2:07:08
Um, I want to I want to dive into the chat app statistics that you shared.
2:07:12
um and and try and understand it feels like it's not exactly a neck-and-neck race.
2:07:18
Chat GPT is pretty much pulling away in terms of percentage share of queries at 71%.
2:07:22
Meta is way behind at 12%.
2:07:25
Are there any signals that this is that this is um you know something that would be addressed one way or another or is it just too soon to tell?
2:07:38
Yeah, look, I think what we've seen so far is that generally speaking, when you start to deliver a better model, a better product, you just get more users.
2:07:46
I think we've seen pretty good correlation between the quality of models and the usage of CHAGPD.
2:07:49
Uh, one thing that I think is interesting is when you look at the user base of Chad GPD, you had a surge in early 2023 and then it sort of plateaued for a bit and at towards the end of 2024, you had a second leg of growth and then you hit like that half a billion weekly users and that correlates pretty well with new releases with models getting cheaper and better and so on and so forth.
2:08:09
So if you think about how Meta could uh get back at maybe lead uh that ranking, well, they just have to release better products, right?
2:08:18
One thing is to have good products and the other one is to have a good distribution platform and obviously they have the distribution platform right two billion daily users.
2:08:26
Uh so they just need to build a good product and I think users will come and that's kind of the the value proposition when you look at >> what is the state of the mom and pop uh data center market.
2:08:35
I don't think they would like to be called the mom and pop data center market but you know the the you know a friend or an uncle that's getting into the data center business.
2:08:43
We asked Brian one of the one of the co-founders of Coree the other day.
2:08:45
He was he was generally bearish.
2:08:47
You know, he knows he knows how hard it is.
2:08:49
What what is the is is there a real demand signal there or is it just a hope and a prayer that you're going to just, you know, kind of flip it to a hyperscaler?
2:09:01
And is that is that >> Yeah, sorry guys. It's over. Easy easy money is over.
2:09:07
money is over. Look, um, as we just said before, like now there's really an insane amount of competition for power and and like and basically uh like hypers skaters can set the conditions and so you have to be quite sophisticated when you want to approach hypers skaters uh and you want to sell
2:09:26
them a gigawatt site and you also have to think about like how much money does that involve like one gigawatt you're talking about maybe 30 40 billion dollars of capex um so when you sell a one gigawatt site to hyperscaler it's not going to be a small decision for
2:09:39
to be clear like buying the land maybe I don't know a few million like who who cares not a big deal but if they if they do make the purchase like they intend to do something about it so like yeah they they just don't want to take it lightly and they have multiple options today so uh for a mom and pop that sort of
2:09:54
doesn't dig into what what they should actually do and all the requests and such they're just not going to get new business today but some some people made a lot of money for sure in 2023 and 2024 by just having like yeah luck they they had 50 acres near high voltage finish line. So So >> that's amazing.
2:10:10
Uh >> yeah, it's funny to imagine you you've been building a data center, you know, a little mom and pop shop the last year and then the Death Star data center just starts like popping up next to you. >> Oh no, I'm crazy. >> Oh, it's it's over.
2:10:24
>> Uh what can you uh tell us about what what changed between Llama 3 and Llama 4 and exactly what happened with Llama 4? You call it a failure.
2:10:33
Um, how did uh like how did that happen?
2:10:36
Because we were I we were so scale pelled, right? A scale is all you need.
2:10:41
Just scale up the big transformer.
2:10:43
Uh but you dove into it and gave so much more detail.
2:10:45
How can you uh contextualize and explain that to us?
2:10:52
>> Um simple ways just a bunch of trade-offs that weren't weren't in the right direction.
2:10:57
>> U like I think you if you want to simplify it, you could say like to some extent you reach peak pre-training in 2024. Uh I'm simplifying.
2:11:03
I don't think it's big, but let's call it for now it's big.
2:11:07
If it has black well ramps and so on and so forth, you'll you're going to see a new push out, but for now it's big.
2:11:12
Um and it means that if you want to develop better models, you don't have to use pre-training, you have to use the new paradigm, which is test time comput and reinforcement learning, >> right?
2:11:21
And to do that, there are some specific trade-offs that you have to do.
2:11:25
And basically, Meta uh just took a bunch of options in terms of their attention mechanism, in ter of the way they route to experts and stuff like that.
2:11:32
to experts and stuff like that. just a bunch of decisions that aren't very well suited towards this new paradigm which means that their flagship model behemoth the largest one is just not very well suited to this new era and that sort of contrasts with think of the Chinese labs they are sort of the in the opposite
2:11:48
direction because they don't have state-of-the-art chips uh they don't really have an incentive to push very hard on pre-training and so they've been they they have been thinking harder about pushing on post training reinforcement learning um and test time comput and all of that that doesn't
2:12:01
require such a large centralized faster as such it's kind of the those two path like on one end you have meta on the other end you have the Chinese and what the Chinese decided to do is just better suited to the current paradigm so just yeah just a bunch of bad decisions or some extent unlucky uh >> and so that seems like that ties to this
2:12:20
uh there was this post by Rune uh anonymous account on X saying that um there are in fact some secrets about what paths of the tech tree you want to go down you poach the right researcher, they come over and on day one they can tell you that chunked attention might be wrong for this particular training run. Is that what you think is driving the
2:12:39
Is that what you think is driving the high salaries?
2:12:41
Is that the dynamic at play? >> Yeah. >> Okay. >> Yeah.
2:12:44
You want the decision makers that understand exactly what trade-offs are going to do that also know how to properly evaluate things or uh what kind of steps you should take to make sure what is the right uh what is the right choice.
2:12:56
Um and so yeah that's what you want the the decision makers that had experience that know how to do stuff and that can easily uh yeah identify the trade-offs and know if I want to go in this direction uh more reasoning more RL all that uh you should do that attention mechanism and not this.
2:13:11
Do you have any insight into like the shape of the behemoth project right now or like the the the the failure mode?
2:13:18
the the the failure mode? Like is it is it is it like it would be bad at math or it would be bad at talking to it for a long time or it would be bad at needle in a hay stack in a big context window like like we we've just heard like it's
2:13:33
not good enough it failed but like how what would that feel like if I were to use behemoth and for a long time and I'm like oh this is weird or it's it's bad but in a weird way because it seemed like it was good at some things but then just not good at everything across the board. >> Yeah, let's just simplify it and say
2:13:46
>> Yeah, let's just simplify it and say agents.
2:13:47
Uh so basically tasks that require using tools that require reasoning that require long long context windows and things like it's not very good at that. >> Okay, got it.
2:13:56
Jordy, >> uh last question from from me uh for now.
2:14:00
What are you expecting uh out of Meta over the next six months? They have talent now.
2:14:06
They've they have scale, but the team the new team is going to need to gel and it's going to take some time to really start delivering.
2:14:12
So, are you expecting a lot of, you know, public launches over over the next the basically the back half of this year or is this more early 2026?
2:14:24
I think back back half of the year makes sense.
2:14:25
Um, really back half like think end of Q4 or beginning of Q126.
2:14:29
Uh, but for sure you're going to have a few months where probably not much is going to happen.
2:14:35
Uh, because yeah, like like as of today, we showed a bunch of pictures of the current cluster.
2:14:40
So, they already have data center capacity.
2:14:43
Uh but there's still some time in order to like actually put the GPUs in place and make sure everything runs.
2:14:47
Uh so they're going to have some uh like sizable compute that's training ready uh somewhere in Q3.
2:14:53
Um and so uh yeah, but actually have a product release that's more of a really end of the year, but most likely early 2026.
2:15:02
>> I would not be surprised.
2:15:04
>> I wouldn't be surprised if New Year's Eve we're back on the show talking like we are now about about a new drop.
2:15:09
Can you uh c can you talk me through some of the uh tradeoffs or how the open-source war is playing out?
2:15:17
Uh Dylan uh from Sly analysis uh posted that the open AI model, open source model is expected to be really really good.
2:15:25
Um that was kind of I thought the open source strategy with Llama was a great way to be superlative on day one.
2:15:32
It's like it doesn't need to be the best model.
2:15:34
It doesn't need to have the most DAUs or MAUs, but it's the only it's the best open source one. And so is superlative. You get the headline.
2:15:41
It's the attractor for talent.
2:15:43
Hey, we're doing something different.
2:15:45
We've we we're the best in this one narrow thing.
2:15:47
Uh and and there's a question about like at a certain point, does the math make sense to continue to open source?
2:15:54
But then when we went through the Deepseek moment, it felt like DeepSeek was very much distilled from the GPT4 API, not a llama fork.
2:16:02
And so the whole debate over, oh, like you know, some Chinese lab's just going to fork llama and then and then improve it.
2:16:07
So what is your kind of state of the union on open-source AI?
2:16:14
>> Uh the Chinese are eating open source uh like yeah they're just dominating the market.
2:16:19
It's like one lab after another. It's not just deepseec.
2:16:21
You had deepseec then you had Alibaba.
2:16:23
Recently you had moonshot with the kim model like they're just really good at open source sorry at LLM generally and they're open sourcing everything because it's um they're in some sort of the same position as meta like they're not leading so they don't have any incentive to be close source.
2:16:39
They want to build an ecosystem so it makes sense to go open source and there's just like shipping faster than meta and shipping better than meta.
2:16:43
Uh so yeah Meta is just way behind on open source and actually the west is behind on open source.
2:16:50
uh the Chinese are just way better at it right now.
2:16:52
way better at it right now. is but is is is open source important or is it more just like uh marketing because I've always had this this this thing about uh the difference between like uh what was it stable diffusion was open source uh midjourney was not uh and midjourney was able to get the get the data back from
2:17:10
the customer because you generate four images to me it's only the thumbs up >> only to me it's only open source if it's from the MRL region France but >> but uh but but yeah talk about the flywheel of open source like is there an advantage there or is it really just if you're not in first you might as well open source? >> Yeah, I think it's more what you said is
2:17:30
>> Yeah, I think it's more what you said is if you're not if you're not uh if you're not a leading lab you might as well open source uh because you want people to sort of yeah just help you out give you some feedback loop build an ecosystem. It just makes sense.
2:17:41
Uh also I would say like for the broad community generally it's good to have open source because it's better for adoption.
2:17:46
Anyone can sort of uh yeah play with the models and develop new applications on top of it and such.
2:17:51
Uh so yeah, I think for for everyone it's good that there's open source.
2:17:55
Uh but again like if you're not Google, if you're not OpenAI, if you're not at the very top, like you don't really have an incentive to be uh yeah to be secretive about what you're doing because you're not the best anyway.
2:18:06
>> Yeah, that makes sense.
2:18:06
Uh Jordan, do you have anything else? >> This was great.
2:18:10
>> Yeah, this is fantastic.
2:18:10
Please hop on whenever you post anything.
2:18:11
Uh I'm sure we'll be giving you lots of calls because this is a fantastic conversation. Really appreciate it. Really enjoyed it.
2:18:17
>> All right, sounds good, guys. >> Cheers.
2:18:18
>> We'll talk to you soon. >> Talk soon. >> Bye.
2:18:20
Quickly, let me tell you about Finn.
2:18:21
AI, the number one AI agent for customer service. >> The bakeoff champion. >> The bakeoff champion.
2:18:26
Number one in performance benchmarks.
2:18:28
Number one in competitive bake offs.
2:18:30
Number one ranking on G2.
2:18:31
And we have our next guest in studio in person.
2:18:39
>> We have Jesse from Coinbase coming in.
2:18:42
Yesterday, Jordy went over to Coinbase's launch event for Bass. Let's bring him in.
2:18:47
Jesse, welcome to the stream. How you doing? Welcome. >> The walk-in camera.
2:18:53
>> The walk-in camera is working. >> There we go. >> Let's go. >> Thank you. >> Thanks for having me. >> How you doing? >> Good to see you. >> You too. Good to see you. >> Thank you.
2:19:04
>> We got one more juice for you. >> Oh, fantastic. We're drinking.
2:19:07
>> We're We're drinking juice today. >> Virality juice. >> How's that? >> Viral juice. >> Fantastic. Fantastic. >> How's it going?
2:19:14
How's the show been so far? >> Super fun.
2:19:16
It's been a crazy day on the internet. You guys are lucky.
2:19:18
You launched yesterday and this morning.
2:19:19
One cold play conference changed the timeline forever. >> I know. I saw that this morning. I woke up. I was like, "Wow."
2:19:28
>> Glad this happened yesterday. >> Oh, yeah.
2:19:29
Because I mean, it's so hard to watch these days.
2:19:32
>> We're having a couple people from OpenAI on >> and it's such a big day for um legislation in the United States. I mean, the Genius Act.
2:19:38
The Genius Act and the Clarity Act just passed. >> I'm finding out.
2:19:42
>> Well, the president is going to sign I think tomorrow. >> Yes.
2:19:45
Genius app stable coins will be passed and signed into law in the United States this year.
2:19:49
I mean this week, which is an incredible milestone working towards it for three years, four years, 5 years.
2:19:54
I mean, the impact is that for the last decade of crypto, there hasn't been regulatory clarity in the United States, which means that entrepreneurs and consumers haven't actually been able to benefit from this technology.
2:20:05
And we've been working to build bipartisan consensus that we need rules of the road in order to make sure that crypto works.
2:20:12
And I feel like Coinbase has always been like the conservative one.
2:20:13
And it paid off because during the end of the Zerp era, basically all of Coinbase's competitors went out of business.
2:20:20
It was it was chaos, right?
2:20:22
And and and I remember talking to a to a public markets investor at that time and he was just saying like like I feel like Coinbase has the mandate of heaven.
2:20:30
Like they are making it through.
2:20:33
>> What was the what was the low?
2:20:33
Was it like $7 billion at some point?
2:20:34
Uh >> yeah, in in 2022 the share price went to $32 which was below I think the series E price and and and I had joined you know like a couple years before the series E price but you know we all experienced that like >> Yeah.
2:20:48
What was your path to Coinbase?
2:20:50
Did you get Were you building something else before? Okay, break that down.
2:20:52
>> I dropped out of school and started a company. It was called KFF.
2:20:54
We did identity and so the whole idea was how do we build an identity that's the next thing after passwords that was decentralized that anyone >> this was cryp a crypto company?
2:21:02
>> It wasn't quite crypto but we worked with crypto companies.
2:21:03
So, uh, you know, Bitfinex, we were actually providing like kind of off and login for them.
2:21:08
And so, Paulo, like we go way back because we've been building forever.
2:21:11
And so, that business didn't work.
2:21:13
And then when we were winding it down, I was basically trying to figure out what do I do next? And I love crypto.
2:21:19
>> And is identity like KYC like I upload my I take a picture of my photo.
2:21:23
>> It was actually a passwordless login mechanism.
2:21:24
So, almost like you hold your phone up to log into WhatsApp.
2:21:26
It was like that but back in 2012.
2:21:28
And so, we were a little bit before the time.
2:21:30
But when you look at the security architecture that we built, it's actually almost the same as what we're doing now on base and what people are doing with pass keys.
2:21:37
And it's this incredible thing where I've now been working on it this same problem space for almost 12 years and we're feel like finally >> we're finally getting there. >> Okay.
2:21:47
So you so you land at Coinbase and the way uh Brian Armstrong tells it, you go to him and you say, "I need $1 billion to not right away. Not right away. Not right away.
2:21:59
But I want to get to the $1 billion.
2:21:59
I know you did it for a lot less, but I want to know what I That's my question for the future.
2:22:04
But tell me tell me two pizzas. >> That's two pizzas. No.
2:22:07
Um, so I joined I joined as an engineer.
2:22:09
It was an it was an aqua hire process.
2:22:11
My whole team actually sold to another company to Twilio.
2:22:14
But I was so excited about let's go.
2:22:18
And so I joined Coinbase, joined as an engineer.
2:22:20
And then uh pretty quickly on uh I just started leading teams.
2:22:24
They asked me, hey, can you take on a team? I took on more.
2:22:25
And for the next five years, I led all the consumer businesses on the engineering side.
2:22:30
So if you know Max Bransburg, he runs our consumer product.
2:22:31
Me and him were product and engineering counterparts with him running product and me running engineering.
2:22:34
And then after five years of doing that, I thought I wasn't going to start another company. I'm like a founder.
2:22:39
I I actually put in my notice and said I'm leaving in six months.
2:22:43
>> In those five years, what products were you building?
2:22:45
>> I was building Coinbase, Coinbase Pro, and Coinbase Wallet. >> Okay, got it.
2:22:48
>> So like anything that you've touched as a Coinbase user, those were my teams.
2:22:49
We rebuilt the whole product from scratch.
2:22:52
Migrated us to react native coinbased wallet the first like decentralized product in the Coinbase ecosystem.
2:22:58
So I think I think the context here is like the the core business and the and why the launch yesterday is exciting is the core business was always >> uh centralized and it was a gateway to get on chain custodial um and and we'll get into base in a bit but you were working on these sort of decentralized experiments early. >> Exactly. Yeah.
2:23:19
working on the decentralized experiments early.
2:23:22
>> What were you saying it's so early at the time?
2:23:24
>> You know, I'm an optimist. >> I've gotten from you.
2:23:28
>> He said he hit he he typed GM uh and posted it probably 100,000. >> Yeah.
2:23:34
But um I thought I was going to leave, but I actually started having conversations with Brian about what did the future of Coinbase look like and what would it look like if we leaned even further into this kind of decentralized world, built more things on chain because Coinbase had started 10 years ago and it was, you know, traditional web 2 business.
2:23:48
And so again, the first thing I did was I went to Brian in the kind of fall of 2021 and our exec team and said, "If you give me a billion people and 60 employees, this was like peak 2021 >> 60 employees, I'll turn Coinbase into a DAO." Okay.
2:24:02
And they were they were they were like, "Haha, >> okay.
2:24:07
Why would you have done we're a public company?" >> Yeah.
2:24:13
If he had I I know what you do with a billion dollars if you're Meta and you build a big data center and you have to pay, you know, half of that to Jensen Wong over at Nvidia.
2:24:21
But what would you have actually done with a billion dollars?
2:24:24
I I don't even know what is that.
2:24:27
>> We didn't really have as good. >> Okay.
2:24:28
It was more just like let's take the constraints off.
2:24:31
>> Let's take the constraints off.
2:24:31
let's go and do the thing, right?
2:24:32
We have this big public company.
2:24:34
It's a crypto leader, but it's still built on web 2 architecture.
2:24:37
How do we move it onchain in this new way?
2:24:39
And so, it was the vision was there, but the execution strategy wasn't there.
2:24:43
And so, >> well, the in and the other context is that Coinbase has been in this incredible position of being the gateway to get on chain.
2:24:50
And you see this with the Circle IPO.
2:24:52
People were like, wait, >> Coinbase makes a lot of circles revenue as a stable coin issuer.
2:24:57
But you guys had always basically said everything on, you know, you guys do whatever you want on chain and and a bunch of great companies sprung up to to kind of service that market, but you had to be hands off, which I imagine was pretty frustrating. >> Yeah.
2:25:11
I mean, I will say that early on, so Brian has always had the vision of the self-custodial world.
2:25:15
He's even had the v vision of a super app in many ways where the first version of Coinbase wallet was actually called Toshi.
2:25:21
It was a messaging product with apps in it. >> Oh, interesting.
2:25:25
>> This was in like this is in 2017. It's green. put it out basically.
2:25:27
And so then we we kind of pivoted Toshi to Coinbase wallet, which is the self-custodial wallet that millions of people use that folks know and love.
2:25:35
Um, and we've worked on that for the last 5 years, and that was primarily focused on trading in money.
2:25:40
But as we've built base over the last two years, which has really been a builder ecosystem where people are building apps, we've seen two problems emerge.
2:25:47
The first is that um everyday people when they're trying to use crypto and trying to to kind of figure out what's going on here, it's really hard to actually find things to do and that are useful.
2:25:57
You know, maybe they come on chain for a coin, but then they kind of get lost.
2:26:01
They're like, "What are all these things?
2:26:02
How do I actually use it?"
2:26:04
And then on the other side, we saw that we had thousands and thousands of builders and creators who had these incredible products that they were building, but they couldn't actually get them in front of people.
2:26:12
Like, we were missing this connective tissue.
2:26:14
And so 10 months ago, I talked with Brian.
2:26:17
Um I kind of came back to running the Coinbase wallet team which I'd worked on for a long time prior and we kind of jointly articulated this vision of well what if we built the the kind of app that solved that problem and brought all those things together and this is what we launched yesterday.
2:26:33
It's the base app and it's an everything app that lets you create, earn, chat, trade and discover this pay for things with Shopify uh at er1.
2:26:44
you know, you can now pay for juice with base USDC on Shopify powered by Bass Pay, you know, like >> we're the whole thing kind of comes together in a new way where because we now can bring together this marketplace of kind of developers and builders and businesses on one side with consumers through a really easy to use experience, we're going to be able to to grow crypto a lot faster and spin the flywheel. Okay.
2:27:09
>> Would uh would I imagine launching Bass in the way that the product is today?
2:27:12
I know it's a pure software business.
2:27:14
So very different from the uh tradition you know Coinbase's you know traditional business which is effectively you know acting as this regulated uh or uh you know financial uh institution.
2:27:29
Would it have been possible to launch this a year ago or did you need the the White House to launch uh some tokens and and a few other events?
2:27:38
>> Well I I think the biggest thing that we needed is we needed the technology to mature right.
2:27:42
So when we had this vision 3 years ago of building base, the the first thing we started with was actually the platform, right?
2:27:48
It was the chain because we'd spent a year trying to figure out, okay, what's the product we build to kind of bring this next generation? >> Explain though.
2:27:55
So you had the chain, but there was no token attached to it like I feel like playbook.
2:28:02
>> We went back to the tried andrude method that people had been following for a hundred years, which is that you focus on building a product that people love. >> Okay. >> Right.
2:28:09
Like I think there's been this whole distortion in crypto for the last 5 years where people are like oh you get a token you pay people to use your product and we basically said from the beginning no we're going to build a chain it's going to be a developer platform and we're going to figure out how do we make it the best place for builders to build and that's exactly what we did over the last two years.
2:28:23
>> But if I if I take it to like the Bitcoin analogy it's like the reason that people set up data centers to run the Bitcoin network is because they mine Bitcoin and that has financial value.
2:28:32
incentive that gets distorted all the time when people run the other participants to uh to help run the infrastructure or or or you splitting it across different groups like like how does the actual chain stay on? >> Yep.
2:28:50
Well, the first thing to know is that Bass runs on Ethereum.
2:28:51
It's a layer two that sits on top of >> So, as long as the Ethereum miners are happy, then Bass runs. >> Exactly.
2:28:58
And then there's a decentralized network that's also based that people use to get access to the network.
2:29:02
And the thing that's bringing people to the network is that they're building things and then they're using those things.
2:29:06
And so here's two examples.
2:29:07
Uh the first one is USDC.
2:29:10
We're seeing a huge amount of growth in payments both in the United States and outside the United States in USDC.
2:29:14
And that's because people are using products like Shopify which has rolled out to millions of merchant where they're actually taking USDC in their wallet and they're paying for things. And that's global. It's fast. It's cheap. It happens instantly.
2:29:25
cool rewards that the the it's retailers effectively which is if you pay with >> USDC will give you one point back or something like that because you're not eating the debit card or credit card. Yeah.
2:29:39
So it's 1% back with base pay and you can earn 4.
2:29:41
1% on your USDC when you're holding it >> which is pretty good right and it's an instant global payment method so any business in the world can integrate it and then anyone else in the world can pay for things.
2:29:51
>> So that's one example people are coming to base for payments and stable coins.
2:29:55
The other example is a little bit more out on the kind of like ny curve in terms of what we're seeing, but we're really excited about this is what the focus of the event was yesterday, which is really around the creator economy, right?
2:30:05
If you look at the last 20 years, you've had so much creativity pour into the internet, but the vast majority of that creativity has been put into platforms where creators actually don't earn that much money. Right?
2:30:14
If you're an average creator who has less than 10,000 followers or if you're in another country like Nigeria or Argentina, you're going to be posting and filling up this kind of content world with valuable creativity that then other people are looking at, but you're not earning anything.
2:30:28
And so, one of the big things that we're focusing on with the Bass app and one of the reasons why people are are coming to use it is that when you post on base, you actually earn.
2:30:35
And this is because we've built uh a new economic system where the content is actually valuable and then the value gets flowed back to the creators.
2:30:42
I think this is a novel use case for this platform that we built with bass and base chain.
2:30:48
But I think the question is if if somebody opens a base app, their wallet's loaded and they start scrolling, >> does that cost them money?
2:30:57
>> Does that cost them money?
2:30:57
How does that actually >> it doesn't cost them money, but like I can literally show you um you can decide if you want to support a post.
2:31:03
And so like here this is on my feed and I'm just like Jesse. I'm on my feed. I'm creating content.
2:31:09
Uh, I posted this great base juice content and oh, >> there's a bug live classic. >> There we go. It happens.
2:31:16
Base juice content, right?
2:31:18
And you have the normal things you see here, the like, the comment, the retweet.
2:31:21
And then you have a new thing. >> Yeah. >> Which is market cap. >> Sure.
2:31:25
>> And so this is basically the value of that content.
2:31:26
And this one's worth $15,000.
2:31:27
This one's worth $84,000.
2:31:27
This one's >> How is that possible?
2:31:31
I mean, I like my I've been on YouTube for five years.
2:31:34
Best video I ever posted got 8 million views and I got a check for 20K from YouTube.
2:31:40
>> Well, that's actually pretty good.
2:31:41
YouTube is one of the platforms that pays creators really well. >> Took 50%.
2:31:43
And I thought that was fine because they brought me all the users.
2:31:48
So, I was actually pretty happy with that.
2:31:49
But like how could how could something have a piece of content have market cap? >> Well, yeah.
2:31:53
I have >> no one else wants to buy it unless they want to buy the revenue stream.
2:31:56
And there's been some of those projects where I could sell the rights to my payout on that. Yeah.
2:32:01
How do you >> startup startup CEO?
2:32:02
Well, this is on on X, right?
2:32:04
Well, where where I Well, I where no market cap on this one.
2:32:06
Startup CEOs can't even hug their chief people officer at a concert in this country anymore. 1.
2:32:11
6 million views, 50,000 likes. >> 50,000 likes.
2:32:15
>> I'm sure I'm sure the creator payout will be decent.
2:32:17
But this this looks to me like potentially a >> a nine figure market cap on if I had ripped it on base. >> Yeah.
2:32:23
Well, well, seriously, like seriously, I I, you know, on these ones that I posted, like this one I posted yesterday that now has an $84,000 market cap, it's done like $4 million in volume, and I earned 1% of all of the volume.
2:32:35
And so, that means I've earned like what 40 $40,000.
2:32:38
>> That's insane, >> right?
2:32:40
So, you get a viral >> feel that doesn't feel like like economically fair.
2:32:45
>> Well, but think about this.
2:32:45
The place where that value comes from is the fact that your content is valuable, right?
2:32:50
How have these platforms, right?
2:32:50
How have these platforms, right? How have these platforms built >> multi-undred billion dollar businesses if the content isn't valuable >> running ads >> but what's bringing you there >> the content and then they pay you to bring more content because >> an ad is a way of monetizing the value
2:33:08
of the content but the content itself is valuable and what's happening on the base app now is that that value is being uh kind of figured out in a free market and then the the kind of the the economic offloads of that value are getting redirected back to the creators We love ads at this show. Will we be
2:33:21
Will we be able to see ads in the base app?
2:33:25
I know you guys acquired Spindle.
2:33:30
Uh >> well, we have Stay tuned in the in the next in the next >> I want to run I want to run some talk about talk about the integration in the in the kind of mini apps.
2:33:38
I uh Dan from Farcaster has has been on the show.
2:33:45
I know you guys have some integration with with the Farcaster network. Is that right? >> Yeah.
2:33:50
So the whole social feed, all of the connection graphs, so you know have followers and following, it's all powered by Farcaster.
2:33:54
When you're posting content, it's powered on Farcaster.
2:33:57
When you're coining content, that's powered by Zor. It's another protocol.
2:34:01
What we've done with the base app is we brought them all together.
2:34:04
>> That's cool, including Shopify.
2:34:05
>> Including Shopify integrating, including XM XMTP, which is powering messaging.
2:34:10
They just raised a $20 million series B, and they're an incredible decentralized messaging protocol that is powering secure encrypted messaging with agents in chat.
2:34:18
And so one of the really cool things, I don't know if you guys saw it in the demo yesterday, is you can be in a chat with your friends.
2:34:22
You can just like drop a bet in there that you can say to agent, hey, like let's bet $5 that the Dodgers are going to win tonight. >> Okay, that's nice.
2:34:29
>> And it's immediate bet.
2:34:29
And then you can say, hey, send $10 to all of my friends.
2:34:33
And it just works because it's built on this platform and you have these open protocols that are working together.
2:34:36
And so going to your point, we built on Farcaster because we believe in building open protocols.
2:34:41
And one of the things that open protocols enables is it enables other people to build on top of them.
2:34:46
And so this is where we've built this open now and it just shows up in the app. And here's another one.
2:34:53
I don't know if you guys have seen this, but this is when you open the base app today.
2:34:59
>> I think the forecast, which is like literally you can open this up and we're going to be on the live stream here live now. There I am. >> Wow.
2:35:05
There you are right there.
2:35:07
>> Literally, it is so cool.
2:35:07
It's >> But one of the really cool things about >> we had and and to be clear, this this was organic third party >> and and you have some new sponsors.
2:35:14
I don't know if you guys realize this, but you have a bunch of crypto brands here that have now gone and said, "Hey, we want to sponsor you."
2:35:20
And bracket, for instance.
2:35:21
This is sports betting on base. It's awesome. It's so fun.
2:35:22
You can interact with an AI.
2:35:24
But the thing, >> our lawyers, we're going to our lawyers are like blood hounds.
2:35:27
They're going to they're going to be like choing at the big >> thing that's really incredible about this app is that it's all connected and so I can actually tap in bracket and I be like, "Okay." Oh, >> okay. Yeah. >> Oh my god. Come on, Jesse.
2:35:38
What's going on here with this mini app?
2:35:42
>> Uh, >> and and in theory, Yeah.
2:35:43
This is this is the start of the auction the auction driven but but but but but but this is this is the start of the auction driven uh ecosystem that actually that actually results in real transfer value.
2:35:57
Real real value is created at some point.
2:36:00
>> So this is a new I like bracket now bracket is right here >> and then I'm like okay I'm just going to buy bracket and I'm be like cool let me buy uh $5 of bracket.
2:36:09
>> This is not financial advice.
2:36:09
This seems this seems like we're not going to buy we're not going to buy.
2:36:13
>> Well, the the thing that's fun is like it's just wildly chaotic.
2:36:14
So, it's an entirely new surface area.
2:36:17
Coinbase has never been about chaos, but and and I'm not saying that base is, but creating an environment that's just like a freeforall, decentralized, it's open.
2:36:29
>> So, uh yeah, I mean, you said that the goal broadly is to get a billion users on crypto crypto on chain. That's the right phrase.
2:36:36
Uh give me the state of the union.
2:36:37
Where are we today on that journey?
2:36:40
Like what are the different buckets and how big are they? >> Yeah.
2:36:43
So, we're trying to build a global economy that increases innovation, creativity, and freedom.
2:36:46
And the metric that we use basically to measure that is do we have a billion people on chain? Yes.
2:36:51
And of course, I'm going to get five billion people on chain because we're get everyone who has an internet connection on chain.
2:36:55
But a billion is a nice number and we get to make it repeatable.
2:36:58
I'd say that when you look at the data today, there are hundreds of millions of people who hold Bitcoin globally around the >> hundreds of millions.
2:37:04
Hundreds lot of people hold Bitcoin.
2:37:06
like Bitcoin is very well used, but it's not something that people are using their dayto-day life.
2:37:10
I'd say there's probably tens of million people around the world who are using stable coins.
2:37:13
And then I think we're in the the millions of people right now who are really starting to use these onchain products.
2:37:17
And that's across base, it's across Salana.
2:37:19
We're seeing innovation everywhere.
2:37:21
And you know, we like to work with everyone.
2:37:22
We think about bass as a bridge, not island where we're actually connecting with everyone and figuring out how we can help them be successful.
2:37:27
But I think it's still really really early days.
2:37:29
And the goal with the base app is to like kick up the next kink in growth.
2:37:32
So my question is like when we're when we're following the the chat GPT story, there's obviously DAUs, but the chart that everyone's obsessed with right now is that chat GPT minutes per day is skyrocketing to 30 30 minutes per user per day, something like that.
2:37:46
We're seeing lots of queries go up.
2:37:47
My question is like with financial products, I I do think that there's a there's a benefit to having a lot of people own Bitcoin.
2:37:54
I think it's an awesome network, a check on authoritarianism. It's awesome.
2:37:58
But like I don't know that I should be like interacting with Bitcoin daily, but what are the other metrics downstream to to to like to like quantify like whether or not someone's on chain?
2:38:09
Because like >> if someone's like like if I'm if I'm in my bank account, you know, messing around with dollars all the time, I'm actually probably unhappy. >> Yeah. Yeah.
2:38:18
Well, the first thing I'd say is that um definitely Coinbase is a traditional financial product where they're growing.
2:38:23
They're trying to be the best for trading. It's awesome.
2:38:25
They're making such good progress.
2:38:25
so excited about the retail dex integration which is basically making it so that anyone coinbase can buy any asset on base.
2:38:32
Yeah, >> just works listed.
2:38:34
>> I'd say base though we don't really think about it as a financial product.
2:38:37
>> Okay, >> there's money involved because money is a part of everything but it has social ads core. It has chat.
2:38:41
It has apps and so people are going to come to base just like they come to their other apps.
2:38:46
>> Time on site should be >> onite is the thing that we're looking at.
2:38:49
We're looking at we're looking at we look at kind of two things. We all Yeah.
2:38:53
Dow mows and we look at like weekly active users and then we look at weekly transacting users where it's like are you actually doing a transaction on the chain where it's like buying something or sending money to a friend or something like that.
2:39:02
And so the active user is kind of an engagement metric and then the the transacting user is like a deeper engagement. >> Yeah. Yeah.
2:39:08
So higher use of the actual product would not be a bare case like it is with my you know my traditional financial app with which I'm opening. It's probably a problem.
2:39:16
I'm probably like owe someone money or overdrafted or something. >> Exactly.
2:39:19
And the really incredible thing about this kind of conversion rate of active to to transacting is we have the data of before and after where like it was really just a money app before and now it's a real social product but people are doing all these things with money and we're seeing way higher initial user conversion rate to actually doing something on chain.
2:39:34
>> That's awesome >> because now it's not like oh you have to go like make an investment decision.
2:39:37
It's like you can like your friends post. Yeah. Right.
2:39:38
or you can like you know tap to pay at a store that you're going to and that transition from a speculative thing which is so important and such a big important part of base in the crypto economy to a daily thing that >> also has like way bigger TAM in terms of the number of people who do social products and other things on a daily basis.
2:39:57
That's that's really the shift we're trying to make.
2:39:59
>> Yeah, it's super cool.
2:39:59
Like the like we know that the everything apps work elsewhere.
2:40:03
Yeah, >> they don't work in America for some reason.
2:40:06
This feels like an end runaround, a different strategy to try and make it >> everything app for a specific >> subculture right >> right now.
2:40:13
But now, you know, a billion people is that a subculture.
2:40:16
>> And what I'll say is we were just at Arowan.
2:40:17
I just spent two hours at Arowan and people with we were giving out free juice. I was filming content.
2:40:21
We got some good content coming later.
2:40:22
Uh and I was just talking to people about the juice in the app.
2:40:25
And when people hear uh especially like millennial Gen Z creators, when they hear, "Do you want to join a free social network where you can get paid to post >> and do you want a free juice?"
2:40:37
They're like, "Hell yeah."
2:40:39
Like, I've been posting on other social networks for a long time and I've never gotten paid. >> Okay?
2:40:45
>> And so that hook of your content is valid.
2:40:48
You get paid >> and you can get paid for it because that value is going to get unleashed by the free market. Yeah.
2:40:53
And then we're gonna distribute it on cryptoeconomic rails that are this next generation internet platform. It's crazy. It's epic. >> Well, good luck.
2:41:00
We got to get to our next guest. Thank you for having us.
2:41:04
>> Can we get a gong hit on the way out >> here? Let's go.
2:41:12
>> Get the bass app at bass. app.
2:41:12
You can get on the wait list.
2:41:14
We're going be letting more people on every single day. There we go. Founder mode. Talk to you soon, Jesse. Have a good one. >> Great chatting. >> Do it.
2:41:22
>> Up next, we have Higsfield, Alex from Higsfield AI.
2:41:24
We've been using Higsfield AI to generate crazy photos.
2:41:26
Jordy posted one of uh of a horse.
2:41:33
>> Uh the horse had a little bit of a long neck, but otherwise photore like the it nails the face really, really well.
2:41:42
Not exactly sure how they do that. I want to talk to it. Welcome to the team. Welcome to the stream. >> Yeah. Pigsfield.
2:41:47
Uh we have a lot of amazing people on today.
2:41:50
Uh I have been incredibly excited to speak with you.
2:41:52
Um we were we were just talking.
2:41:56
I don't know if you caught it, but I posted a an image generated uh by Higsfield a couple days ago.
2:42:03
I think most people still think it was real.
2:42:05
It was a completely ridiculous image.
2:42:07
It was like this like cinematic upshot of me on a horse that had uh I I don't know why people didn't catch that.
2:42:16
neck was was quite a bit bigger than a regular horse's neck, but >> it was great. Clearly a breakthrough. >> Yeah.
2:42:21
Uh it it it was the first time that I've seen an image generation product and realized that I think you guys are are have probably already started to take over Instagram style content.
2:42:32
Um but but really disrupting the uh potentially disrupting the Instagram boyfriend market.
2:42:38
If if uh if people can just generate infinite images of themselves, Instagram boyfriends will have nothing to do.
2:42:45
But great to have you on the show.
2:42:47
Uh would be great to get a quick background on yourself and the company and then we'll get into a bunch of other stuff. >> Great.
2:42:55
Uh uh thank you very much for the kind words.
2:42:57
Uh definitely it's uh great to be here.
2:42:59
Thank you for the invitation. So quickly about myself.
2:43:01
I'm a veteran in the video generative AI space.
2:43:05
I built maybe some of the most iconic products in that space.
2:43:08
Uh maybe you remember snap uh filter face filters. >> Oh really? >> Which way? >> Yeah, totally. >> That's amazing.
2:43:16
>> Yeah, there were like billion people throughout the world who played with that >> just >> and this was regen AI, right?
2:43:19
Um and when the models were like thousand times smaller than they are today and although this product was specifically targeting uh like augmented reality use case so basically overlay on top of the existing camera. >> Yeah.
2:43:39
with with all these learnings which I got from my exciting times at Snapchat, we started Hicksfield with a way bigger ambition is to create camera of the next generation. >> Yep.
2:43:54
>> Even today we can see that uh there is an emergence of UGC content.
2:44:01
Um the quality unfortunately goes down. >> Mhm.
2:44:05
And with the and with Hicksfield we create a new ways to tell stories helping creators and brands to get attention on social media.
2:44:17
>> What's the key insight do you think how important is it to um create a great image generation model just scale you just need to throw a ton of compute versus changes in the algorithm?
2:44:26
We've heard rumors that images and chap GPT isn't pure diffusion.
2:44:31
There's some transformer architecture in there, some different layers.
2:44:36
I've started to suspect that there are different layers in some of these where there might be a different neural network or different a different system to put text on top so the text is really clean.
2:44:47
Are we kind of recreating Photoshop at a certain level?
2:44:52
Uh talk to me about the actual technical infrastructure to the degree that you can. >> Totally.
2:44:57
Um first of all I think it is important to admit that today we are early in our journey with video AI technology.
2:45:08
>> I think Hicksfield is probably the first example of the technology platform which helps to create compelling content for social media.
2:45:18
The next step is going to be is to is going to be to build a reasoning engine on top of that. >> Mhm.
2:45:25
So the so let's say you post a video the system is going to suggest you and analyze your accounts and actually provide you with more suggestions what to post and eventually I think we we are going to find ourselves in two years in in a new world version of the world where most of the content out there is AI generated.
2:45:49
>> There is no way to stop that. Mhm.
2:45:52
>> And um on on our side, we do our best to provide a simple interface so that nonAI users like let's think about market of social media professionals of tens of millions of people so that this broader user base can actually tap into the power of generous fiat technology. >> Mhm.
2:46:15
>> Um and I think we will see that the models are going to get way better than they are today.
2:46:19
It is true that various research labs they do experiment with various architectures.
2:46:24
We found that um our post training techniques and allow us to substantially differentiate from the competition and we do believe that the general quality of the technology is already there to surpass like average human produced content.
2:46:43
what uh uh I have this one eval for uh AI image generators where I ask it to create a a a where's Waldo and and no systems been able to crack it.
2:46:55
Um and I think it has something to do with the density of information in a proper where's Waldo.
2:47:02
You'll typically see hundreds of little characters doing very intricate things.
2:47:08
And so it's very clear that the artists that create the actual werewaldos work at a very small scale and they actually piece together the full image like it's a puzzle.
2:47:17
That's something that I feel like could be solved with a reasoning layer on top.
2:47:22
You understand that you're trying to create something that's really really layered and so you need to kind of create tiles and then blend them together.
2:47:30
uh is that but but this gets into the question of like how are we going to generalize and scale reinforcement learning in LLMs and agentic workflows.
2:47:40
Is there a similar path that we're going down in terms of image generation?
2:47:46
>> Yeah, this is a great question by the way.
2:47:47
way. I think this is a a trillion dollar question which which you just asked which is we all saw the power of reasoning engines uh with maybe models like 03 and then we see that at Grock 4 they actually spent on reinforcement learning more than they spent on the pre-training stage right
2:48:07
>> where is the market right now in terms of uh pre-training post-training in images in your estimation >> yeah I do believe that in the video AI space we are relatively early It's probably we still see that the post- training stage in the video core video models can be um maybe 20 50 times lower compared to the pre-training stage. >> Wow. >> Wow. >> Yeah.
2:48:31
But we are really just scratching the surface there. Sure.
2:48:34
>> I think then the future is building the video reasoning engine and this is a trillion dollar question cuz this will >> um because think about the brands out there. Yeah.
2:48:46
Um today it is um today what we are seeing is that brands start to and agencies they start to actually experiment with various models.
2:48:56
>> Um and primarily we all rely on our stereotypical understanding of the customers and some maybe qualitative data which is available out there. Mhm.
2:49:08
>> I strongly believe that with this video reasoning engine, the the way how how the stories are told is going to be completely different.
2:49:17
Instead of just running one video, we can run hundreds of the videos out there >> and AB test them and see which one performs the best.
2:49:27
>> And today there is there are only a few top creators who actually do that.
2:49:30
If you look at comp at Mr.
2:49:32
beast and similar size of the creators.
2:49:34
They do AB test thumbnails very aggressively.
2:49:37
We all know that >> they actually AB test the hooks.
2:49:41
So far this privilege is only available for larger teams who can who can actually do that who have the manpower to do that and the next generation video reasoning engine will empower everyone to do that which is going to boom to the boom of generation.
2:49:59
I mean I mean what you're talking about is basically like rlinging on humanity with human graders which is the algorithm and likes and and that's effectively what the like the Mr.
2:50:07
Beast algorithm is doing.
2:50:10
Uh my question is like is there a way that we can bring that into the data center because if it stays on Instagram, if it stays on YouTube, uh it's probably pretty rough for you because Google and Meta are going to have an advantage there.
2:50:21
But if you can figure out how to do RL with verifiable rewards or something that looks like a rubric for grading, you know, and finding errors.
2:50:30
Are we going back to the generative adversarial network era where you'll have two competing models to determine like whenever I generate something with VO, it's always like car's driving, looks amazing, then all of a sudden I'm looking at the front of the car and the car is driving backwards.
2:50:45
And clearly the model is getting confused, but we need maybe like a detector for that.
2:50:50
How do how are we actually going to do RL at scale in imagery?
2:50:54
>> This is a great question.
2:50:54
So I think the first step is exactly I mean the first step is going to be reinforcement learning with AI feedback.
2:51:01
>> Part of that we already do at Hicksfield.
2:51:03
Obviously at the post training stage not yet at the inference stage just because of the cost associated with that. >> That makes sense.
2:51:10
Um so we have to train video generation model in a in a way that it's sort of competing with video understanding model >> and like at Hicksfields we could we we cannot go and label millions of the videos.
2:51:26
That's why we have a set of powerful agents for video understanding. >> Mhm.
2:51:31
>> Which help to tune the video generation process. >> Yep.
2:51:35
>> But this is the process in the vacuum itself.
2:51:39
What's going to be powerful is when we condition the outputs of the model and train the model based on the engagement data from the social media.
2:51:47
>> First is going to be number of likes and number of comments.
2:51:49
Although if we look at meta ads, we see that they provides a very detailed breakdown and drop off second by seconds.
2:51:57
So training the models based on on these outputs is going to lead to completely next level of uh reasoning and success rates for the end customers. >> Cool.
2:52:10
We have another one in >> I know I know we're we're totally over.
2:52:14
Uh what what happens to the legacy Instagram influencer that has built a business basically on envy?
2:52:20
They're constantly traveling around the world at the best hotels on boats and private jets.
2:52:29
What happens when anybody can be on a private jet uh on on a platform like Instagram or on a yacht somewhere?
2:52:37
Have you thought through kind of the implications for the tech across different categories of of content creators? >> Hopefully.
2:52:44
So, first of all, I I I need to admit that we're a technology platform first and foremost.
2:52:48
platform first and foremost. Although we think about ourselves as a scientist for and we are constantly monitoring and listening to to the creators and how they use the technology and I can I I cannot bring up the names out here although like some of the top 50 YouTubers in the world they actually
2:53:09
want to get reads of the team and build own agency of AI influencers to be honest that's they they actually they have a bunch bunch of ideas which they want to sell and they don't want to condition their existence on the social media just to their likeness today because people just are getting older and sometimes they get irrelevant. We
2:53:28
We have seen many examples of that on social media >> and uh creators actually want to create those digital agencies where they can where they can express all their ideas through various synthetic AI influencers and they can use Hixel platform to do that.
2:53:46
Yeah, it's very very wild time.
2:53:46
Uh let's have you back on again soon. Uh this is fascinating.
2:53:52
>> Yeah, we'll talk to you soon.
2:53:52
Thanks so much for hopping on Alex.
2:53:54
>> We'll talk to you soon.
2:53:54
Let me quickly tell you about public.
2:53:56
com investing for those who take it seriously.
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They have multiasset investing industryleading yields and they're trusted by millions.
2:54:03
We have Billy from Regent also calling in from Reindustrialized.
2:54:06
Sorry to keep you waiting, Billy. Great to see you.
2:54:10
Every time we talk to Billy, he's in a far-flung part of the world.
2:54:12
We're working to bring him in from the waiting room. And he is sideways.
2:54:18
Can you turn your camera?
2:54:22
>> Yeah, I can go vertical. >> That is possible. Here we go. >> There we go. Where are you? You're in the cabin. Explain.
2:54:28
>> Join you from a sea glider. Look at that. No way.
2:54:30
It's super comfortable in here.
2:54:32
We got We got plenty of leg room. >> Incredible. >> We got internet. This is the future. >> This is amazing. >> Amazing. >> Wait. So, wait.
2:54:41
Are you Is this like a demo?
2:54:43
Are you actually in >> I do believe that's a screen behind you? >> A screen behind you.
2:54:46
I can't >> This is the demo.
2:54:48
You guys are pretty perceptive over there.
2:54:49
This is >> We just talked to the AI generated guy who says all this will be fake in two years.
2:54:54
So, you know, I don't know what to believe at this point.
2:54:57
>> We're building hardware in the real world, guys.
2:54:58
We're at a reindustrialized conf.
2:55:00
>> Thank you for bringing me back to the real world.
2:55:02
How is re-industrialized?
2:55:02
Uh give us the overall update on region. >> Uh it is awesome.
2:55:07
Like hardware is cool again.
2:55:09
Like building real stuff is cool. Adams are cool.
2:55:11
So, uh it's awesome to be here for this.
2:55:14
Uh yesterday we just announced the launch of Regent Defense uh here at Reindustrialize which is a big step for the big step for the business. >> Congratulations. What is >> Yeah.
2:55:25
What are what's the immediate application of the glider in a in a defense context?
2:55:32
>> So, Regent's actually been doing defense for years.
2:55:33
This is sort of a growth of the business we we've already been doing.
2:55:36
Um, basically everything is about uh moving around island chains as our our key conflicts and theaters are in the Indoacific.
2:55:43
So, it's going back to World War II style tactics.
2:55:44
It's about naval operations.
2:55:47
It's about being able to move around island chains logistics from the head of the Marine Corps and throughout the services underwrites the success of that naval campaign.
2:55:55
So, turns out that all the things that our commercial customers like about Seag Gliders, the the high speed, low operating costs, uh you know, ease of operation, and in the DoD space that were hard to see.
2:56:07
We fly really low over the water, so it's hard to pick up on some longrange radar systems.
2:56:11
Uh makes it a really perfect fit.
2:56:13
So, we've been on contract with the Marine Corps for a couple years now.
2:56:16
Uh and now we're expanding as we get into our full scale prototyping, expanding that product portfolio to meet the need. >> Very cool. >> Incredible. >> Anything else? I think that's it. Thank you for this. Thank you for this demo.
2:56:29
This is this is the this is the best call in the best guest call in I think we've had.
2:56:32
So, um >> guys, we got to get you uh on the Sea Glider in Rhode Island at some point, too.
2:56:37
We're doing the next call in from the We have real hardware.
2:56:40
>> We'll do the whole show.
2:56:40
We'll do the whole show from the glider. I hear it's very smooth.
2:56:43
You know, it's perfect for for live podcasting.
2:56:47
>> Starlink on board yet?
2:56:50
>> Uh we'll we'll put Starlink on board for a TVPN episode. So, >> there we go. I love it. >> It's there. It's there. Awesome. >> Fantastic.
2:56:56
>> Well, congratulations to you and the team on the progress and say hi to everybody at reindustrialize for us. >> We'll do. Thanks, guys.
2:57:03
>> Enjoy the rest of the conference. We'll talk to you soon.
2:57:06
>> And in the meantime, let me tell you about eight. com. >> Get a pod five.
2:57:10
5-year warranty, 30 night risk-free trial, free returns, free shipping. Go to eight. combn.
2:57:17
>> And we have our next guest joining the stream.
2:57:21
I'm going to guess that it's Jonathan, but I'm gonna make sure once he joins. Welcome to the stream. How you doing? Did I get that right? Yes, Jonathan. >> Security.
2:57:32
A lot of people call me J Mo, actually. So, >> what's up? Uh, give us the news.
2:57:36
I hear we uh you got some good news. Break it down for us. >> Yes.
2:57:41
Uh, we are coming out of stealth launching a new company called Confident Security.
2:57:46
>> Um, it's about providing >> confidential AI. Did you raise any money? >> We raised 4. 2 million with decibel. >> Congratulations. >> Spark Commons. >> Congratulations. >> There we go.
2:57:59
Uh why is confidential AI important?
2:58:02
I can I can imagine a bunch of reasons, but uh what was the uh kind of catalyst to start the company?
2:58:07
Yeah, I think you know uh unless we do some work, I think we're going to have Cambridge Analyt Analytica times like a million essentially unless uh there's just too much incentive for folks to train on data and people need to care about privacy.
2:58:22
So we thought uh you know Apple shouldn't just be the only ones providing privacy, everyone else should do it, too.
2:58:29
>> Give me some concrete examples of how to use the product.
2:58:31
What data specifically am I keeping secure?
2:58:33
Is it stuff that's on the web or uh because I feel like if I have an air gap data center somewhere with a whole bunch of hard drives uh there's no crawlers that are getting to that. >> That's right.
2:58:45
Um if you are taking that air if you're using the air gap data and you have your own GPUs and it's completely in there.
2:58:51
You might care about various users inside your air gap environment not seeing all the data.
2:58:55
You know standard like top secret versus secret type of stuff. Mhm.
2:59:00
>> But this is, you know, you're you're an employee at Fizer and you upload a PDF to OpenAI that describes how you do drug discovery.
2:59:07
Maybe you care about that secret.
2:59:09
Maybe you care about how it's going to be used.
2:59:10
And after the recent OpenAI court case, uh, where they were forced to retain deleted data, you know, I think people are being a little cautious.
2:59:18
>> So, how does your uh, so how how do you actually plan in plugging in to companies like how does the product actually get installed and used on a day-to-day basis?
2:59:27
>> So, it requires two parties.
2:59:27
>> So, it requires two parties. uh one party who's running the server uh to install our wrapper around it and then the other party who's like making requests to that server to use our SDK and it essentially you can think of it as like a very specialized form of encryption um where you can only decrypt
2:59:44
the data that you've submitted to the server uh if you've met some constraints like you don't log the data you don't train on the data no one has access to the data all that type of stuff >> and is that just like enforced at a at an engineering level or enforced at a legal little contractual level. >> It is a technical level. Great question.
3:00:00
>> It is a technical level. Great question.
3:00:03
Um, and that's what's different, right?
3:00:04
You can make promises that say you want train on the data, but we make it's very it's a technical guarantee and it's essentially a bunch of fancy cryptography that makes that guarantee.
3:00:12
So, we actually offer unlimited liability and indemnification for data breaches and misuse because we're so convinced that you cannot we cannot see the data.
3:00:20
No third party can see the data. >> Yeah.
3:00:22
So, uh what's the go to market?
3:00:25
Like you you mentioned example of like a big biotech company.
3:00:28
Is that the the most obvious customer or are there other segments that are logical?
3:00:34
>> Um biotech, legal, uh finance, of course, defense and government.
3:00:38
Um and not just not just like pure defense, but you know, local jurisdictions, uh states, they're all trying to figure out how to deal with uh you know, freedom of information.
3:00:50
When have you made something public? Did you was it too soon?
3:00:54
Um, this helps, you know, manage all that stuff.
3:00:56
Uh, privilege for lawyers, same problem there.
3:00:58
Trying to figure out, well, if I give my data open air, have I disclosed it and it's no longer subject to privilege? >> Sense.
3:01:05
Uh, >> super interesting.
3:01:07
>> Close close it out with uh how big is the team?
3:01:09
Where is the team size going? What are you hiring for? What comes next?
3:01:14
>> Um, we're about six people right now and we've been building building building.
3:01:19
And now with the launch, we're ready to start selling.
3:01:21
And so our focus is bringing on sales people. Yeah.
3:01:24
>> Um I've done two previous companies and I've every time I've learned that I should spend more on sales.
3:01:28
So that's what we're doing. >> Interesting. Good takeaway. >> That's good.
3:01:32
That there's going to be uh that's the environment that a great sales leader or individual contributor wants to join.
3:01:39
>> I mean it takes a lot of technical CEOs like a while to actually get through that.
3:01:42
So it makes sense that you're in your third company because like Yeah, a lot of people learn that lesson late, right? >> Awesome.
3:01:49
JMO, thank you for joining.
3:01:51
Thank you so much for >> come back on when you have news and uh thank you for doing this.
3:01:55
>> We will talk to you soon. >> Y >> cheers.
3:01:58
>> Let me tell you about >> Wander.
3:02:02
>> Find your happy place. Find your happy place.
3:02:05
>> Book a wander with inspiring views, hotel, great amenities, dreamy beds, top tier cleaning, and 24/7 concier service.
3:02:09
It's a It's a vacation home, but better.
3:02:12
>> And next, we have uh Contextual, the founder of Contextual AI.
3:02:15
Welcome to the studio studio. >> Bring him in. What's happening?
3:02:19
>> Bring him in. What's happening? also if I have this correct the inventor of uh rag correct >> one of the the authors of the rag paper >> okay okay >> so yeah that was a team effort lots of folks and it's long history of you know
3:02:34
research that has gone into that >> yeah give me the state of the union no man who invented rag by himself in a in a in a in a cave of scraps >> um give give me the state of the union on on rag uh Some people are saying, "Oh, just use a bigger context window." Like, what are what are companies
3:02:52
Like, what are what are companies actually using Rag for on a day-to-day basis? What's the state?
3:02:57
And then what's the shape of the industry that's popped up around the technology? >> Yeah.
3:03:03
So, the the rag is really a very simple idea, right?
3:03:05
It's about having Gen AI work on your data.
3:03:07
Uh, and you do that through retrieval. That's the R.
3:03:12
And you then use your retrieval results to augment, that's the A, your generative AI, that's the G. Yeah.
3:03:18
>> So it's a very simple idea how people do brag right now is radically different from uh what we did in the paper originally.
3:03:24
I think the the buzz word these days is about context engineering, >> right?
3:03:28
So how how do you actually give language models the right context so that they're they can do their job and as it turns out all the language models are pretty good these days and there isn't that much of a difference between you know your claude and your open AAI models or Gemini.
3:03:41
If you give it the right context then it can solve the problem.
3:03:45
If you don't give it the right context, then you can have an amazing language model, but it's going to fail.
3:03:49
Uh, and so that I think is an opportunity that a lot of companies are are looking at now.
3:03:54
How can we make sure that this this context layer really works?
3:03:58
>> Do you feel like there's a rag step or layer in the typical deep research projects products that I'm using on a day-to-day basis?
3:04:06
I feel like it's mostly like going out to the web, searching, and then kind of just, you know, summing all this stuff up, but I can't tell if it's actually like the the basis of rag is like embed all of that into weights and and then and and and then search over it.
3:04:21
Um, but is that happening at at the state-of-the-art right now? >> I think so.
3:04:26
But you're right, a lot of it is web search. Yeah.
3:04:28
Um, and uh if you want to do web search efficiently at scale, then you probably use simpler algorithms.
3:04:34
Um, so uh things that aren't that involved.
3:04:37
But even web search very often uses embeddings and where do you search there?
3:04:42
So you could argue that web search is also just >> interesting.
3:04:47
>> Um why is why is search so bad right now?
3:04:50
I feel like I feel like I can't search my email for anything and Google has like frontier models, but yeah, I feel like search has just become like really hard, but then at the same time, I'm like having my mind blown by LLMs and deep research products, but I don't want to wait 15 minutes just to search my email.
3:05:07
But maybe that's what I need to do.
3:05:08
Maybe that's the future looks like.
3:05:10
Like what is going on there?
3:05:11
>> Yeah, I mean that's a hard problem, right?
3:05:12
But I mean AI should be able to search your inbox for you and just give right answer, right? >> Uh that's the goal.
3:05:19
I I think it's happening.
3:05:21
It's just search is a really hard problem to do.
3:05:22
Um you you don't want to really do it in a single step.
3:05:27
Um so so um the way you do proper retrieval is multi-stage sort of cascading with smarter and smarter models that look at what might be a relevant result.
3:05:36
Um but you're right if if you retrieve the wrong things then you can never give the right answer.
3:05:40
Um so that's a big big open >> where where are you guys focused uh today?
3:05:49
a number of different use cases. >> Yeah.
3:05:50
So the the the the use cases we're we're looking at are really your bread and butter use cases uh for rag.
3:05:54
So answering complex questions on complex documents.
3:06:00
In our case we scale to millions of documents which is unusual.
3:06:05
Uh so one of the most common misconceptions about rag is that people think that it's easy which is actually probably true.
3:06:10
If you have like two or three documents you understand the use case it's not that complicated.
3:06:14
Uh but when you go to the real world and and you uh talk to some of of our enterprise customers, they have very difficult problems.
3:06:22
The data is all over the place. It's very complex data.
3:06:24
Um there are millions of documents that it needs to work on top of and then search breaks down.
3:06:30
So you can't give the right answer even if you have a great language model. >> Yeah.
3:06:34
I mean the the the scale of data at some enterprises is probably what seven orders.
3:06:38
I mean, I imagine like the number of emails sitting in Gmail inboxes across the entire network is is is way beyond millions. Um, fascinating.
3:06:48
>> That is also to your earlier point about like long context models.
3:06:49
You can't fit all of that in the context of a language model.
3:06:54
You need to retrieve >> no way.
3:06:56
Uh, what's the state of the business today? How big are you?
3:06:58
Like kind of what what are the new challenges?
3:07:01
What are the next milestones? >> Yeah.
3:07:03
So we're uh 70 80 people uh working on a lot of interesting problems kind of nice kind of across the across the board trying to expand into to different use cases as well.
3:07:15
So uh beyond the traditional rank use cases looking at things like root cause analysis uh codegen because uh you know codegen is very hard and also often requires technical documentation that you'd want to incorporate in your codegen.
3:07:27
Um, so, uh, yeah, making a a lot of good progress on on very interesting problems. >> Very cool.
3:07:34
Well, thank you so much for stopping by.
3:07:36
Love to have you back for a longer conversation.
3:07:39
>> Yeah, let's do it again soon.
3:07:40
>> Hope you have a great rest of your day. We'll talk to you soon. >> All right. Thanks, B. >> Great to meet you.
3:07:44
>> Um, let me tell you about Bezel. Go to getbzzle. com.
3:07:46
Your bezel concier is available now to source you any watch on the planet.
3:07:50
>> If you like enterprise agentic workflows, you might like fine watches.
3:07:56
>> And up next, we have Open AI. Uh big launch today.
3:07:59
We are gonna break it all down. Welcome to the stream. How are you?
3:08:04
>> I think we got caught up on guests. >> We have two people. >> Well, it's happening.
3:08:06
Great to have you guys on the show.
3:08:08
Um I will I will uh start by saying uh sorry about the sorry that Coldplay had to have a conference last night like the the you know it was hard to you know hard to predict.
3:08:18
You don't >> launch anything on the internet.
3:08:23
>> I know we we were just talking about this. >> Yeah. >> Well, we're here. >> I have no idea. He has a show at Demand.
3:08:29
>> Well, we're normally you don't have you don't have to plan your launch schedule based on coal play.
3:08:32
But maybe it's something to keep in mind. >> Break down forward.
3:08:36
>> Break down the launch.
3:08:36
What is in what should we be focused on now?
3:08:41
>> So I used to work on deep research.
3:08:41
Y used to work on operator.
3:08:44
We both had our launches earlier this year and I think after our launches we realized that our products are very complimentary and so we've basically combined the best of both into this new product chatbt agent.
3:08:55
So, Chat GPT agent has access to a virtual computer with a bunch of different tools installed.
3:08:59
So, it has text browser, visual browser, terminal, and it's able to do um a lot of different things that you would do on a computer.
3:09:07
Um so, it's just like very flexible and um pretty powerful model.
3:09:11
We trained it using end to- end reinforcement learning um like our past reasoning models.
3:09:15
Um and yeah, it can make slides, make spreadsheets. >> That's Yeah.
3:09:19
What what initial use cases are you guys most excited about?
3:09:22
Have you been using internally?
3:09:24
what what kind of companies should be and and just individuals should be kind of taking advantage of it uh as of today.
3:09:32
>> Yeah, I think um so deep research as you were saying like we combined sort of deep research and operator to build this.
3:09:38
Deep research was really good at research like I think the best product out there in terms of or at least that that's what I thought I still think in terms of researching and then operator was there to take actions.
3:09:47
Combining these two you open up a lot of possibilities.
3:09:51
You can do research, you can do actions, you can do research and then actions.
3:09:54
And then on top of that, we added APIs.
3:09:56
So like for example, if you have connectors which we launched, I think a couple of months ago, you can connect Gmail, Google Drive, linear and whatnot. All sorts of products.
3:10:04
And combined with that, it becomes an extremely powerful research and action tool.
3:10:11
tool. So for example like personally speaking I've been using it a lot internally um >> just to even talk with the codebase like I'm solving a particular problem I'll connected with GitHub I'll ask it like hey can you sort of go figure out what's happening in this code which is let's say a new codebase for me >> and then the model is also very natively
3:10:30
multi-turn which means that I can just have conversations back and forth with it which is not true necessarily wasn't true for example for deep research model um >> which we released earlier this year >> so It's really really useful from a specifically from at work when I I'm able to connect all these amazing tools and able to just understand what's happening all parts of the vision. >> So secondly,
3:10:51
>> So secondly, >> oh sorry >> I was going to quickly I was going to say like personally I also use it a lot.
3:10:56
I think there is lot of small things or big things that I have to do. I can do them myself. Give an example.
3:11:01
My wife and I have a date night every Thursday.
3:11:05
I forget to book it most of the time and then I get in trouble and then now with agent like you can schedule tests etc.
3:11:14
You can just say like look every Thursday just go ahead and figure out give me five recommendations in the morning which are available and I can just do it show up on Thursday morning just click it and it's done.
3:11:23
So things like that >> talk I once deep research came out it felt like uh there was this a little bit of a meme about like we have 15minute AGI uh and I'm I'm trying to understand I could imagine this uh this new product stretching that out to let it run if it's building a spreadsheet and scraping data from different sources and putting a whole bunch of different things together.
3:11:47
I could imagine letting it run for like an hour and coming back.
3:11:49
But it said you you you said it's multi-turn.
3:11:53
So, what does the typical interaction look like?
3:11:55
Is there a wider variance?
3:11:55
I noticed in the in the latest revision of the chat app, there's now a little like 15minute UI element next to deep research to kind of hint that hey, you're getting yourself into a 15minute cycle.
3:12:08
Obviously, there's lots of efforts to spin that to speed all of those processes up and that'll come.
3:12:11
But what is uh what is the typical interaction time look like?
3:12:15
Is this uh more asynchronous or synchronous or kind of you can do either?
3:12:21
like how do you think about those trade-offs?
3:12:24
>> Yeah, I think our team in particular is really focused on solving harder and harder tasks that take people more and more time.
3:12:32
>> Um, so a lot of the agent tasks can take anywhere from like around 5 minutes to I I've seen it take over an hour. >> Wow.
3:12:40
>> Um, but a lot of times these are tasks that would have taken humans >> many many hours. >> Yeah.
3:12:45
So I think like as our agents get better probably the length of time they'll take to solve tasks will also get longer because um just the tasks will become so much more complex like imagine a task that takes a human like many days. >> Yeah.
3:13:00
So maybe so so a question I have is uh right now the agent can browse the web for me do research take action.
3:13:05
Uh, I imagine the next step would be an agent uh like some like a a voice extension to it where as an example I might say, "Hey, I I use Geico right now.
3:13:18
Uh, I want to potentially switch.
3:13:21
Can you go out and do research?
3:13:21
Here's my here's the cards that I have.
3:13:23
Try to find a cheaper price."
3:13:25
And then or even call Geico and negotiate a lower rate.
3:13:29
or you know, you're traveling, let's say, and you're >> I need to cancel an old internet line on Spectrum right now, and they only allow me to call during weekdays. >> Yeah.
3:13:38
The other example, you're on a vacation and you want to change your flight, and you know, you're going to maybe have to like call and sit on a wait.
3:13:45
So like is is that the direction that you think we're going towards where it can not just browse the web but then actually start to uh it's it's funny to think about because the voice agent would then just call and maybe it's talking to another agent on the other side.
3:14:01
>> Um but but yeah where where are we going?
3:14:04
>> Yeah, voice is definitely an interesting form factor.
3:14:05
I think the way to think about where we're going is twofold.
3:14:08
The first is what Issa just mentioned.
3:14:10
I think we want to continue to solve harder and harder and longer and longer tasks.
3:14:14
I think today we can solve let's say an hour or so of tasks.
3:14:18
Hopefully in future we can solve multi-day task and it might take longer, it might uh might take shorter depending on how we're doing but continuing to improve on the reliability and complexity of task that we can continue to solve.
3:14:30
So that's sort of a core part of what we want to continue to improve on.
3:14:34
continue to improve on. Second part, the Geico example for example, the one you gave technically you can do it today not with voice obviously you can just type it in in agent and it'll be able to essentially answer the query you can login uh with the virtual browser that we have on agent with your whatever internet provider you use and I think it'll be able to tell you things about
3:14:56
what's happening there what's not happening what other alternatives might there be and all of those things >> but then at the same time you bring up a really good point which is like voice might be a very natural sort of way to
3:15:06
do this in future and that's a form factor evolve >> other other companies introduce voice as an intentional point of friction like the sort of call to cancel would never do that to me. >> Yeah, they would never do that to you.
3:15:18
>> Yeah, they would never do that to you.
3:15:20
Um and so that that to me feels like this me you know if I can go into a web app and just click cancel or do something like that that >> my my question is about like the user experience of like having something that could take five minutes or an hour. How predictable is that?
3:15:33
I've gotten in a great pattern where I expect deep research to take 15 minutes and so I know when to go to deep research and I'm going to come back later and it's great but if there's some variability there is it going to send me a push notification when that's done is that how that works like h how do you train the user to get the best experience?
3:15:53
>> Yeah, it will send you a notification.
3:15:53
I think actually the fact that deep research always takes the same length of time is probably um a I don't want to say a bug but >> I think of it as a feature >> not the end state.
3:16:04
>> I think of it as a feature like >> it should think for as long as it needs to think but I think for deep research it always just thinks for a really long time even if you ask what the weather is. >> Yeah that's true.
3:16:15
>> So I think there's a better middle state and I think that this model is like a step towards that but I think it still will think for too long on like really simple queries.
3:16:22
Yeah, >> takes two minutes.
3:16:25
>> Yeah, you're totally selling deep research short.
3:16:26
Sometimes I ask you what the weather is and I want the history of weather from start to finish and what it is tomorrow and yesterday and I want the history of meteorology and how the Doppler 3000 works.
3:16:35
I want everything and I love that about deep.
3:16:37
The use case that I'm sure that uh will immediately uh start happening that is pretty hilarious to think about is a student that just says, "Hey, these are the three websites that host the homework that I have to do.
3:16:52
Let's do that >> proactively.
3:16:53
Go to the website, figure out the homework, create it, fill it out.
3:16:58
The >> teacher is going to be like, yeah, the teacher is going to be like, check this check this homework.
3:17:02
>> If if somebody wants to say that's not that's not AGI.
3:17:04
I don't know what to tell them. >> Yeah, I don't know.
3:17:06
Um, what about other tool use?
3:17:09
Jordy mentioned um uh Jordy mentioned um uh phone usage.
3:17:12
You mentioned spreadsheet integration.
3:17:15
mentioned spreadsheet integration. what what what's kind of further down the stack of integrations that you've already announced that might be kind of underexplored or underappreciated at this point in time to >> to me I think that the tool that we've
3:17:31
given the agent is very general and powerful like you can almost do anything that you need to do on a computer with this tool because it's browser and terminal which you can you can do most things it might not be the most readable to a human >> um so I think that now it's it's about pushing the capabilities. Like you can
3:17:46
Like you can ask it to do anything in theory.
3:17:49
It's just the agent won't be good enough to do everything you ask it to do.
3:17:52
So I think that we just need to make it better and better using the tool the tool it has.
3:17:58
>> Yeah, I think the frontier continues.
3:17:58
I think we as Isa said the tool is extremely general.
3:18:02
It can it has access to a browser.
3:18:05
It obviously has access to terminal and we can give give it access to as many APIs as possible. >> Sure.
3:18:11
>> That should be that should allow you to build whatever you want to do generally speaking.
3:18:14
Like for example, you can totally imagine in future there's access to a a voice API or whatnot whether it's internal or external depending on like how things go, right?
3:18:22
Like you can have access to everything.
3:18:24
You can build everything but we still need to push like it's still early like we've not solved everything. >> It's still early.
3:18:31
We still want to make sure that we can solve use cases with really really high reliability and that continues to be a pretty large focus. >> Yeah. >> Well, I'm excited. I'm excited.
3:18:39
I mean, think of think about a world where you can give it access to your password manager, things like that that it just immediately can >> or just API integrations, right?
3:18:48
So then the passwords don't even need to pass back and forth.
3:18:51
That makes a ton of sense.
3:18:52
Yeah, I'm excited for I feel like deep research maybe doesn't have access to images and chatbt yet, but I could imagine those being way like the reports being way richer if you can define them.
3:19:05
And then sometimes when I'm when I'm just generating like a general chart, I actually want to use like a a visualization library in Python and kind of going back and forth.
3:19:12
So very cool to see it all kind of come together and very excited for where this is going.
3:19:15
Um what's the rollout strategy?
3:19:17
Um when can people actually start using this stuff?
3:19:23
>> People everyone on pro plan should be able to use it by end of day today. >> Let's go.
3:19:26
and we'll >> and we'll get it to plus users over the coming days and then enterprise over the coming weeks. >> Very exciting. >> All right.
3:19:37
Well, congratulations on the launch. Super exciting.
3:19:38
Uh we're we're going to turn this day around.
3:19:41
It's it's now just about opening agents.
3:19:43
Ignore ignore all Coldplay memes. >> Ignore.
3:19:49
>> Uh well, thank you guys for joining.
3:19:51
>> Thank you so much for having us.
3:19:53
>> We'll talk to you soon. >> Cheers. >> Bye.
3:19:54
Up next, we have Dan Shipper, friend of the show over at Every >> who got early access agents and we're going to get some feedback from him.
3:20:04
>> John just spilled his base juice.
3:20:06
>> All over the table, all over the FT.
3:20:08
That's That's really disappointing.
3:20:10
You're not going to be able to read that on the way home.
3:20:12
>> Lots of papers that I can I can >> Do we have Dan in the waiting room? >> Let's bring him in. [Music] >> There he is. >> How's it going, guys? Great to see you.
3:20:24
>> Every time you're on, you're in a different finally caught me when I'm not traveling. >> Yeah. Yeah. Yeah. Yeah. This is the first time. >> I like that.
3:20:29
I like that light uh uh uh light up logo in the background. Very nice. Very nice.
3:20:33
Subtle subtle kind of >> inhome out of home advertisement.
3:20:40
>> That's That's what we're going for exactly. >> Um what's happening?
3:20:42
What's on your mind uh today? There's a lot going on.
3:20:46
>> There's a lot going on.
3:20:46
We're here to do a vibe check of chatbt agent.
3:20:48
Um, so I was lucky enough to get to uh hang out with it and work with it for the last couple days before it got launched.
3:20:57
And I have a bunch of things to tell you about how it works. >> Incredible.
3:21:01
>> Um, so as your previous guests who are amazing uh told you, it's sort of like deep research and operator had a baby.
3:21:09
Um, and it does some really cool things.
3:21:12
So, uh, the first one of the first things I had to do is I had to go through all of our support emails and all of our feedback forum posts for the last like two months.
3:21:22
So, it's like about 1500 support emails um and maybe like 500 posts on our forum to gather for Kora, which is our email management um AI app to gather all of the customer archetypes of like, okay, who's posting, who's a promoter, and then going and looking on their LinkedIn to be like, what's their job?
3:21:42
uh you know, where do they go to school, all that kind of stuff, and put together a like long research report of who our promoters are, what the archetypes are, and who are who our detractors are, and why they don't like us.
3:21:56
>> So, that's the kind of task that like obviously like I could have done or someone on the team could have done, but >> so long. So long.
3:22:02
>> Yeah, it takes a long time.
3:22:02
Um, and it's the kind of thing that you almost want on like a recurring schedule.
3:22:06
Like you just kind of want to see like once a month, but no one wants to do that once a month.
3:22:10
And you can schedule with Chad GBT agent.
3:22:13
You can schedule it to run.
3:22:15
So I can just say like every month I want you to just send me on the first of the month, which is it's really freaking cool. >> That That's wild. >> That's amazing.
3:22:23
I wonder how compute intensive that's going to be because if I know anything about building dashboards and building like these these reports, there's always like an intense amount of like, oh, we got to have this dashboard and then you check the analytics and it's like, oh, it turns out the team just said that for a week and then like stopped watching it and if it's just running, you're like burning.
3:22:43
>> Dan is every Foundation Lab's worst nightmare because he gets on the most expensive plan and then uses it 100 times more than anyone else.
3:22:51
He's >> you're single-handedly going to bankrupt a lab.
3:22:54
>> There's other there's other users that are probably higher margin.
3:22:55
Um but but yeah, talk to me about that that that actual experience.
3:22:59
Did you have to ooth with any different services?
3:23:01
Did you have to share any API keys?
3:23:03
Did you have to export any data or was it really as simple as just a prompt?
3:23:09
>> It's basically a prompt.
3:23:09
Um what happens is you type in you type in a prompt.
3:23:11
You say I want you to check out Kora.
3:23:13
I want you to check out our emails.
3:23:14
I got I want you to check out our our support forum.
3:23:17
So, Chetchup has connectors.
3:23:17
So, I had previously already connected my Gmail.
3:23:22
So, you just like log in on on the ooth.
3:23:25
>> Um, and then what it will do is it it spins up its own computer on the cloud, its own virtual machine.
3:23:30
>> Uh, it goes in the browser and starts browsing uh browsing the web.
3:23:32
It also then connects to connects to Gmail.
3:23:35
If it hits a login, so for example, when it hit LinkedIn, it it like couldn't log in.
3:23:41
And you can take over the browser in the virtual machine and type your password in, which is like a little a little janky.
3:23:46
Yeah, >> it works, but it works pretty well.
3:23:47
I think the interesting thing about this though is it um there are there seems like there's two main approaches to agents and OpenAI and Anthropic are taking very different paths and they have very different trade-offs.
3:23:59
So the really cool thing about agent is they're essentially abstracting away the browser and the computer.
3:24:07
So all you're doing is you're interacting with the GBT and on the back end all this other stuff is happening.
3:24:15
So it doesn't matter if on if you're on your phone, if you're on a crappy computer, whatever, they have this whole virtual environment set up.
3:24:21
It spins up, it does the task, and it spins down.
3:24:24
>> So it's like it's a very good consumer experience.
3:24:26
Um, cloud code for example from anthropic, which is I think cloud code cloud code is way more for developers.
3:24:32
Ch agent is way more I think for consumers.
3:24:35
Cloud code is all on your computer.
3:24:37
It's all in the terminal and and you have access it has access to all of your files and you have the ability to um use it wherever and whenever and however you want.
3:24:47
So it's much more customizable and much more composable.
3:24:51
Um so I find that cloud code is much more powerful but it's much more intimidating and it's just not something that a consumer can use.
3:24:58
And I think that they're trying >> Yeah.
3:25:02
The crazy thing there is, doesn't Claude Code have more downloads right now than the actual Claude mobile app?
3:25:10
Like the the reg like I saw >> it's something crazy like that.
3:25:13
I I honestly think people are sleeping on cloud code.
3:25:16
Like I use it all the time for non-programming tasks and I think most people think they can't use it because it's in the terminal and the terminal is really intimidating but it's it's an incredible product.
3:25:24
So yeah, how would you solve this problem if you were to do the same eval of like generate your net detractors, net promoters using cloud code?
3:25:33
You'd just open up the terminal on your laptop.
3:25:35
You wouldn't be able to do it on your phone, but you'd you'd just engineer a prompt that told it to do that and it would just write all the code that it needed to do exactly the same thing.
3:25:44
Do you think it could hit that?
3:25:46
Do you think it could do it?
3:25:48
>> Yeah, it could it could do that for sure.
3:25:50
Um and I think uh the the the nice thing about cloud code is you get um you get many bytes of the apple and you can like uh so for example with cloud code what you can do is you can have it make a full plan so it can output like a full markdown document with like a you know 300 or 500 or thousand word plan you can modify it and go back and forth with it and then have it execute it.
3:26:11
I think it would be more complicated like yeah it would probably write some code to hit the Gmail API and I' have to like think about that as opposed to just like clicking the connectors button or it does have a it does have a web research tool so it would be able to go to like our feedback forum and do all that stuff and it would be able to save all the data so I could kind of >> watch what it was doing as it was doing it.
3:26:32
Um but uh so so I think you would get basically the same experience.
3:26:35
Uh I think cloud code is a little bit more controllable and therefore a little bit more powerful but um chat is just much easier to use. >> That makes sense.
3:26:44
What use cases do you expect chatt agents to uh have the most PMF around?
3:26:51
I was I was imagining the student use case which is just like monitor the homework that I have due across you know I remember in high school even teachers would host their homework on websites.
3:27:02
You could basically run something that was like monitor the homework assignments that I receive and then take a preliminary pass at doing the assignment and then give me a draft that I can review and and sign off on or tweak and then >> write college applications attend college essay a job for me deposit the money that you make as an engineer at multiple companies into my bank account and then also plan a trip to Europe because I'm retiring. Uh, watch out Cluey.
3:27:29
Uh, CHBT agents coming for you. Um, >> guy. Boom. Breaking news. >> Breaking news.
3:27:37
Um, no, but it but it but like giving this powerful of a tool to everybody immediately.
3:27:44
Not everybody's going to realize it, adopt it right away, but you can imagine like a few use cases just spreading like wildfire. >> Totally. >> Yeah. Yeah.
3:27:54
I mean, I think what are the what are the things that you would immediately do if you had an assistant? >> Mhm.
3:28:00
>> Like if anyone if someone just dropped an assistant into anyone's lap, like what was the first thing that they would do? Um I don't know.
3:28:04
Uh help me book a vacation, help me like figure out how to order groceries, help me like another one, another thing I use it for is like uh research the web about all the topics that I care about and every day give me a report on all the things that happened in the last 24 hours.
3:28:20
And it just does that in incredibly well.
3:28:22
I can go into, you know, go behind login walls and pay walls and all that kind of stuff.
3:28:26
So, I think those kinds of use cases are going to be the going to be the the most interesting ones.
3:28:31
But I honestly think right now for most of my consumer use cases, 40 or really 03 is the best. It's much faster.
3:28:42
I mostly don't need it to use a full computer to spin it up.
3:28:45
So, I see ch agent as being something that you use every once in a while rather than something that you're using every day. >> Sure. Yeah.
3:28:52
So, so we're we're we're increasing the level of like complexity like like 40 is kind of a Google search replacement for me now.
3:29:00
I just kind of hit it with like when was this person born?
3:29:04
How old's this, you know, what's the state of this?
3:29:05
What's the capital of this state or something?
3:29:07
Uh expect a really quick answer, then go 03 if I'm willing to wait a couple minutes, want something that's a little bit more thoughtful, maybe some search results from the web.
3:29:15
Then deep research if I'm actually trying to understand the full story, read a whole report agent if I think it's going to need to uh use a computer actually take some actions, pull some things together.
3:29:25
Uh how was the actual interaction of the like the back and forth?
3:29:31
This was something we talked about with the OpenAI folks was like if it gets stuck, it it pings you.
3:29:38
I like that deep research.
3:29:38
Yeah, it takes 15 minutes, but I've trained myself to just be like, forget about that until tomorrow.
3:29:43
And then when I have time to sit down and read the full deep research report, which is going to take me a couple minutes, like then I'll come back to it.
3:29:52
I know it'll cook and it'll be done.
3:29:53
It would be kind of annoying if deep research came back after two minutes and said, "Hey, I'm going to pause all that while I ask you for an update."
3:30:01
Like, it feels like there's a little bit more.
3:30:03
I got to be on answering questions there, but push notifications maybe solve that.
3:30:07
walk me through like how in how involved you were, how active of a process it is.
3:30:15
>> I mostly was not involved.
3:30:15
Every once in a while like it does have a like a stop.
3:30:19
You can tell to stop and like change what it's doing, which is nice cuz like if it goes off the rails, that's helpful.
3:30:24
But I think and and it it has a push notification thing, but I think this is an interesting problem with agents where I can't stop watching them.
3:30:32
And so I spend a lot of my day just like watching the agent doing something.
3:30:34
And Ch agent has its own like cool UI where you can kind of like see interesting animations of what it's what it's researching or which websites it's using and stuff like that.
3:30:43
So I find myself um actually glued to it to it a little bit and um I just don't think that's a very good way to spend time.
3:30:51
Uh, I think it's I think it's mostly solved by having push notifications, but I like there's a sort of emotional process of training yourself to be like it'll let me know when it's done and I don't have to like watch over like we we design a lot of assets here at TBPN and that like even if I trust the person creating it to do a great job, there's still this tendency to want to like hover and be like, "Okay, tweak this, tweak that.
3:31:16
Oh, let's do it this way in real time versus like waiting."
3:31:20
But um >> it's the same thing with 40.
3:31:21
Like you know early on I would kind of be in this loop of like okay I got a result I still got to go fact check this and check the underlying links because hallucinations are a big problem.
3:31:30
Well now that they beefed up search so much and they're referencing direct quotes.
3:31:34
Like I feel like I'm much less like the anxiety level around hallucinations is a lot lower just in general queries. >> Totally.
3:31:44
I think these things are tools.
3:31:46
Anytime you're delegating to something, whether it's a human or an AI, there's a there's a learning process you have to go through.
3:31:53
Like um human managers go through this with with employees all the time.
3:31:56
Like if you're a new manager, you have to like decide, okay, am I going to delegate this uh or am I going to micromanage?
3:32:02
If I delegate, like I get more leverage, but it might not come back the way I want it to.
3:32:06
And good managers know how to split up a task or communicate it to their employees or figure out who's good at doing what and know when to get a when to get into the details and when not to.
3:32:16
And I think we're going through the same curve with um models.
3:32:21
So we're becoming model managers.
3:32:22
Um and everyone is learning how to how to solve the same problems that human managers have solved.
3:32:27
And so the more experience you have with the tool, the more you know like okay, I don't have to check this answer or this looks a little fishy.
3:32:33
Same thing with Chetchup T agent.
3:32:35
I think we'll be much better at using it in three or six months than we are today. >> Cool. >> Makes sense.
3:32:39
Dan, always great to chat.
3:32:42
I I do want to have you back on very soon to talk about LLM induced psychosis.
3:32:48
I think it's important to talk about and >> let's talk about it.
3:32:52
>> Uh but we'll need a lot more time.
3:32:52
Thank you for the vibe check and uh everybody listening, go subscribe to every right now. >> Do it. >> Awesome. We'll talk soon. Talk soon. >> Bye. >> Cheers.
3:33:02
Up next, we have Chris Best from Substack, the best CEO Substack's ever had, arguably in the conversation.
3:33:11
Welcome to the stream, Chris. How are you doing? Congratulations.
3:33:14
You got some news for us. You got some numbers. Get it ready. >> What's going on? >> What do we got?
3:33:20
>> What's going on in your world?
3:33:21
>> Please tell me at least nine figures. >> Doing good.
3:33:23
We've raised $100 million. Series >> C. >> Congratulations, man.
3:33:29
I was hoping you guys would ring that thing. Congratulations.
3:33:35
>> Uh you guys are incredibly back. >> Yes.
3:33:38
>> Uh it's been a journey since I believe you raised something.
3:33:41
It was like 75 on 700 back in what was it? 2021. >> 2021.
3:33:47
That was those were different times.
3:33:49
I don't know if you guys remember 2021. >> I do. I do.
3:33:51
It was >> I remember it fondly.
3:33:55
>> You were at the center of the storm and I was in the depths of a Yeah. slog basically.
3:34:00
Um anyway, uh give us the update.
3:34:03
How' the round come together?
3:34:05
What is the plan going forward?
3:34:05
I heard you were was the reporting accidentally profitable going back into burn mode. What's the money for? What are you thinking? Yeah, I like that.
3:34:13
Um yeah, we're, you know, partnering with Mood Rogani at Bond. >> Um I love that guy.
3:34:19
Consumate Bro joining the board.
3:34:21
Um, the big thing is, look, the the big thing that's happened is like Substack's gone from being like a rinky dink email newsletter company to a proper sort of network that's taken over the world. >> Yeah.
3:34:35
>> And we kind of want to look >> basically kind of like switch into a mode of thinking about long-term ambition, long-term like how do we actually make the big [ __ ] version of this thing?
3:34:45
What investments do we need to make?
3:34:46
How do we focus on the things that actually matter for like the long-term flywheel growth of the network?
3:34:51
Uh, and this just gives us like a total free hand to to do that thing and build the the best possible version of it. >> Okay.
3:34:59
Give me the pitch for I I think of Substack as the no-brainer place to launch a newsletter.
3:35:05
You have a lot of other products.
3:35:07
Talk to me about what the future of the Substack creator or someone who has Substack as like their primary out.
3:35:17
it's the main engine of their creator economy business for example.
3:35:21
Um, what does that look like over the long term?
3:35:24
I imagine that people are still doing top offunnel stuff on Tik Tok, Instagram, other places, but you're adding more and more features.
3:35:33
What is a well-run Substack business look like?
3:35:38
>> Yeah, you know, the core of Substack is the direct connection with your audience, right?
3:35:41
So people subscribe, you get their email, you get the ability to reach them, you even get the ability to like leave Substack and take your list with you, which is a big deal.
3:35:51
People can pay you directly, so you get recurring revenue.
3:35:52
Um, I don't know if you guys have had this, but recurring revenue hits different. >> Uh, it does.
3:35:59
>> The sponsorship business is a great business. Yes.
3:36:01
>> Uh, you know, I think that thing matters.
3:36:02
Lots of people on Substack have sponsors. We love it.
3:36:04
But that thing's like very cyclical. It's boom and bust.
3:36:08
It's like, >> you know, whereas you have recurring subscribers, these die hards, that's sort of like it funds sort of like you to be creative.
3:36:15
It funds you to take risks.
3:36:16
And so Substack's the place where you sort of like your hardcore people are.
3:36:20
That's where you have a real connection to your audience.
3:36:21
You can write, you can post short form, you can post video, you can do live video now.
3:36:28
Um, you can have a community.
3:36:28
We're kind of like building more and more stuff.
3:36:31
The center is not any one format.
3:36:31
It's like the relationship with the subscribers.
3:36:35
And then Substack is just becoming, you know, you said you do top off of funnel on on Tik Tok and LinkedIn and YouTube and everywhere else. Sure, keep doing that. That's great.
3:36:42
Those are massive platforms, but increasingly you can do that stuff on Substack, too.
3:36:48
>> And because you have such a dense audience of like smart people, >> the quality of growth you can get there is already very high. >> Interesting.
3:36:56
That makes a ton of sense.
3:36:58
Jordy, >> I think the I think the magic thing that you guys tapped into that I that I end up find myself I I find myself explaining to other people is there's this beautiful like like economy of people on Substack that just want to support people that are nerding out about a specific topic just want to give them money.
3:37:18
And so it's almost like this there's like there's this like exchange of like yes I want the content but it's also enabling somebody to live a life that allows them to just just obsess over one thing or just explore a series of topics or just be who they are um and be entertainment through that.
3:37:36
I mean, we've had um Emily Emily Sunberg has come on the show a bunch of times and it's just like it's so awesome to see what she's built and the kind of creator writer that she's able to be unshackled from being at a specific, you know, platform legacy media company.
3:37:54
Um, so it's just it's so it's so awesome to see.
3:38:01
>> Talk about the use of funds.
3:38:01
You said you can afford one AI researcher now.
3:38:06
Uh, I imagine that won't be how you spend it.
3:38:08
Um, but >> no, yolo, yolo, start coaching from Mark.
3:38:13
>> Concentrated bets, man. That's how it works. >> Concentrated bets.
3:38:16
>> But I mean, concentrated bets, it's not the craziest idea to go give a bunch of money to, you know, creators to kind of pull forward the uh the the the the leap from what they're doing currently to to get on Substack.
3:38:29
There's different incentive models, kickstart ad businesses, just hire engineers that can build new tools and new features and just chop wood and advance the ball down the field.
3:38:40
Um, what are you most excited about to put that money to work over the next like 12 to 18 months, but you're probably thinking like decades now at this point, right? >> Yeah.
3:38:49
I mean, that's the big thing, right?
3:38:50
It's like what's the this this lets us have that longer horizon.
3:38:51
You can still have all of the same math, but you can just like put the the planning horizon further in the future and look for something really big.
3:38:59
Listen, all that stuff you said, the stuff that I'm really excited about is like making the product [ __ ] great. >> Yeah. >> Right.
3:39:06
I want it to feel like my joke is Substack does everything for you except the hard part, right? You are the talent.
3:39:12
You got to figure out how to write something that's worth reading, how to have a conversation that's worth listening to.
3:39:16
If you can do that thing, though, we should just build this magic machine that takes everything else off your hands and makes it dead simple.
3:39:24
Makes it just like this magical thing where anybody who has something worthwhile to say can make something.
3:39:29
We're starting with that.
3:39:29
We have a bunch of little bits of that that are kind of working that we're really proud of that are exciting.
3:39:32
But I just think the new technology coming online is going to make us like the the version of that magical sort of like media studio, personal media empire in a box that we can build now is going to be so much more powerful.
3:39:44
And then building up like the network, right?
3:39:46
The fact that we're getting, you know, not just political commentators, but politicians.
3:39:49
I think if we get not just sports commentators but athletes, I think we can start to build up kind of like this network and this ecosystem that winds up being this positive sum game, right, where everybody that's on Substack benefits from this growing network.
3:40:06
>> Yeah, I've certainly never subscribed to anyone on Substack and been like, "Ah, I didn't get my money's worth.
3:40:09
I've I always have a good time."
3:40:10
Um, what uh what are the different uh strategies for Substack writers?
3:40:16
I know in like the Patreon podcast world, the people do like one is free, one's behind the payw wall.
3:40:22
I've also seen substaxs where there's like a fold and you get every email, but you get half for free and then you at some point there's a call to action to go and subscribe and and and finish reading essentially, but you get every email. What works?
3:40:35
What are the different strategies?
3:40:37
What are some of the weird stuff that you've seen around uh the way people are using Substack today?
3:40:43
>> There's a pretty big mix, right?
3:40:43
Some people make almost everything free and it's just basically like, you know, if you want to comment or if you want to get the occasional thing, that's what you're paying for.
3:40:52
Some people really low margin for Chris over here.
3:40:54
You just give everything away for free.
3:40:56
He doesn't make any money.
3:40:59
>> That's the beauty of your system.
3:41:00
>> Nobody's paying to get more to get more things to read, to get more email, to have more a seconds of audio in their inbox.
3:41:06
They're paying for perspective. >> Interesting. >> Right. >> Yeah. >> That's the thing.
3:41:10
like, you know, even and even if you have a magical LLM that can spit out media in any format, you care about who it's aligned with.
3:41:16
You care about like what version, you know, what worldview you're getting.
3:41:20
Is this something I trust?
3:41:22
Is this something I want to be culturally and aesthetically a part of?
3:41:26
You know, you talked about people paying because they want to support people.
3:41:29
The other way to say that is it gives you agency, right?
3:41:31
When you choose who to subscribe to, you're choosing what part of the culture you want to live in.
3:41:37
You're choosing what gets created.
3:41:39
You know, in a world where people, I think, feel like a lot of the media they consume is kind of like stuff down their throats, getting to kind of like exert a voice and say, "I'm causing this thing to exist."
3:41:49
That I think is great is really powerful.
3:41:54
>> It's like paying with your paying with dollars versus attention is super powerful, right?
3:41:59
Because if you're paying with attention, it's like, well, then everybody's focused on the Cold Play uh the Cold Play debacle last last night, which is trying to steal thunder from your fund raise announcement, but we're not we're not letting it.
3:42:10
Uh and and and being intentional about like I want to pay for this because I want more of it to exist.
3:42:18
I want I want it to get better.
3:42:21
>> This is how I want to spend my life.
3:42:21
I don't want to be on a platform that just is designed to like suck my time from me. >> Yeah. >> Right.
3:42:27
I want to I want to spend my time and attention better on smart things like TBPN and everything on Substack. >> Yeah. Noing and coming soon.
3:42:34
Hopefully, we will figure it out.
3:42:37
>> No, we're about to bet we're about to bet big on Substack and we're riding with you. So, congratulations.
3:42:42
It's it's it's really tremendous to see how um far the business has come um since uh since those those glory days in 2021 and uh excited for the next five years. >> Thanks, guys. Cheers. >> Congratulations.
3:42:57
We'll talk to you soon, Chris. Cheers. Bye. >> Awesome.
3:43:00
Next, >> one last one last guest. Uh, Decart. ai. >> That's right.
3:43:07
>> The first ever world transformation model turning any video game or camera feed into a new digital world in real time. Very, very cool.
3:43:13
I've played around with a lot of this stuff, not this in particular.
3:43:17
Very excited to talk to the founder.
3:43:19
>> Welcome to the stream. How you doing, Dean? >> Welcome. Sorry for the chaos.
3:43:21
We are so happy to chat with you.
3:43:23
Super nice to meet you both.
3:43:26
Super nice to meet you both. >> Good to meet you.
3:43:27
Um, why don't you start with an introduction?
3:43:30
I already have questions, but just uh give me a little background on yourself and the company. >> Okay.
3:43:35
So, you know, the card the car is a very young company.
3:43:39
We're less than two years old.
3:43:42
>> Uh, we're a research lab and our goal is to build a consumer company. >> Okay. >> Okay.
3:43:46
And what we just launched today is the only real time video model ever.
3:43:49
I can I can just show it to you. >> Yeah, please.
3:43:53
Can we share screen somehow here?
3:43:55
>> Yes, but you are live.
3:43:55
So, whatever you share on that screen is baked into the internet forever. >> Amazing.
3:44:00
We just have to make sure that we don't leak anything.
3:44:04
>> Just do the right tab, not the API keys.
3:44:06
>> I was going to I wasn't sure.
3:44:06
We've never met uh I mean, we met uh uh over DMs, but not uh face to face.
3:44:11
So, I wasn't sure if this was your real face or or just a character that showed up that you're playing.
3:44:18
>> This is This is definitely not me. You realize that, right? >> Yeah. Yeah.
3:44:20
In the real world, he's an anime, but but he's using a transformer model to appear like a human. >> That's right.
3:44:28
>> He's in fact a Minecraft character.
3:44:28
Uh I have so many questions about uh this model.
3:44:32
Um I want to jump into how you built it.
3:44:35
Um >> we can also potentially have the team pull up the the demo video on your website, mirage. ddaycart.
3:44:40
ai, and just kind of show folks what that looks like.
3:44:45
Um whatever would be helpful.
3:44:48
It is currently I'm trying to get the Zoom call to be able to share this. Okay.
3:44:53
>> But if the team could do it from your side as well, that could work too. >> Yeah.
3:44:56
Why don't we just have the team pull up the core website and and we can just jump into questions.
3:44:59
So um real time um what's the secret sauce?
3:45:02
Is it a condensed distilled model?
3:45:06
Are you using a special chip to inference this stuff? >> Okay.
3:45:11
So So real time let's just talk about let's talk about the use cases.
3:45:14
Obvious use case would be like real time video calls.
3:45:16
You're dropping into a Zoom call for example and instead of yourself uh there he is. There's >> Here we go. Do you guys see me? >> Yeah, we see him. >> Look looking. >> Okay. >> There we go. >> Okay. Okay. Now we got it working. >> There we go. >> There we go.
3:45:32
Uh >> what is the use case? >> So, uh who are you? Uh there. There you go.
3:45:38
We're just cycling through.
3:45:40
>> We're We're just cycling through everything. What do you guys like? What do you guys like?
3:45:44
>> Uh, anime >> going >> not big into the the anime world, but uh what about like Legos? Any anything there? >> Legos.
3:45:51
Let's Let's put in Legos.
3:45:51
We can just type >> Oh, you can just type it and it'll >> do it.
3:45:56
Okay, >> let me make this full screen.
3:45:56
You can just type in Lego and it'll just turn everything into Lego. >> Wow. >> Okay, look.
3:46:04
This is This is our house.
3:46:08
>> You see the real stream in the zoom? >> Yep.
3:46:10
You can see, you know, this is the the house office thing.
3:46:12
See all the Lego characters walking around here. >> It's insane. >> Okay.
3:46:18
>> And then and then you can decide that you're into Christmas and so everything becomes very Christmy and your house is decorated. >> Oh, wow. Yeah. >> Wow.
3:46:27
Uh I feel uh what what's the word for a moment like this?
3:46:31
This is actually feels like enter the metaverse. >> Indeed.
3:46:35
this I I was I was hoping you wouldn't say that cuz that becomes like you know became a cursed word. >> Yeah, it did.
3:46:41
>> But but like it's it's it's a good thing.
3:46:43
It should happen at one point, right?
3:46:44
Cuz cuz look >> no this is a this is a big transition from the moment where Zuck was you know said you know did his like hello from Horizon's world.
3:46:52
Um >> those Wii graphics was >> those Wii graphics and this feels like being in a video game.
3:47:00
>> What what what is that? Is that a wand?
3:47:02
This this is just this is uh you can see the straw on the regular stream, right? >> Yep. >> Yep.
3:47:07
>> And inside the wizard's prompt, it becomes a wand.
3:47:10
And you can if you if you flick it hard enough, sometimes it does magic spells. >> It throws things out.
3:47:18
>> This is Guys, which one? Uh that's the team.
3:47:20
The team's been playing with a prompt here all day. Gactic War.
3:47:21
Uh which Galactic War is the one you like? >> Oh, Cyber Punk.
3:47:28
>> That's the lightsaber one. >> Okay.
3:47:29
No, you had a you had one that you should here. >> Look.
3:47:35
So, is this running locally on your computer?
3:47:38
>> So, this No, this is running on the server.
3:47:40
>> It's running on the server and it's going so So, it's going from your webcam to the server back to us over.
3:47:45
>> So, how quickly is is like Kai Sat going to be running this like half the time that he's streaming? This is This is insane. >> This is fun. It's pretty fun.
3:47:56
>> The question is what you do with it, right? >> Yeah. Yeah. Yeah.
3:47:57
This is a This is a bold demo, too, because the variation in the different prompts and how how well it's working is absolutely insane. >> Totally. Totally.
3:48:09
>> I have forgotten what you look like in the real world entirely.
3:48:12
>> Here, here's a question.
3:48:12
Would you guys run an entire show through this? >> We can we can try. >> Maybe not.
3:48:18
We might put Tyler on the intern cam. >> Yeah.
3:48:20
Yeah, that's a good place to start.
3:48:21
So, we have an intern cam over here.
3:48:23
We can get Tyler running on this. Oh my gosh.
3:48:25
The way that you're just cycling through these prompts is >> Tyler can be on Wizard Cam for a little bit for one stream. That's pretty crazy. >> This is insane.
3:48:35
>> Something Something cool. Let me show you this.
3:48:37
Something cool that we found out is that it's really fun watching YouTube through this. >> Okay. Watching YouTube.
3:48:45
>> Tell me like give me give me a YouTube show that you like. Something you like.
3:48:48
>> I mean, just do that Mr. Beast video. Whatever video. Okay.
3:48:49
So, we can just look at this Mr. Beast video.
3:48:53
>> We can look at this ad.
3:48:55
Look at the ad and we can share the Mr. Beast video.
3:49:00
>> I somehow have und you guys hear the audio now.
3:49:03
>> Yeah, we do actually do the audio.
3:49:05
>> I don't know if we should, but >> Okay. >> Okay.
3:49:09
And then it's being transformed now. >> Yep. This is the original Mr. Beast video. >> Wow. >> Yeah. Even trippier.
3:49:16
>> You can here's like Mr. Mr.
3:49:16
Beast videos worked really well with cosmic medieval golden.
3:49:23
>> We iterated through so many prompts till we found the ones that worked really well. >> Yeah. Yeah. Yeah. Yeah. >> This is Mr.
3:49:27
Beast videos and the cosmic medieval golden world. >> Okay. Cosmic medieval golden.
3:49:31
I mean, they're already pretty stimulating. This is even more now. Wild. Very wild. So, yeah. Yeah.
3:49:39
Where do you think this lives?
3:49:41
Does this live with the consumer and they decide to put on the rosecolored glasses or do you >> like let's say you joines >> somebody's joining a standup tomorrow.
3:49:52
Can they just automatically put the entire team into whatever character they want?
3:49:57
>> Let's see what standup comedies do you like?
3:50:03
>> I was talking about an actual like engineering standup stand up.
3:50:06
>> Oh, an engineering standup. Okay.
3:50:06
Yeah, it can definitely do that.
3:50:10
I'm more asking about you.
3:50:10
I'm asking you where do you think it winds up living?
3:50:14
Like do you think >> Here's here's what I think is cool about this.
3:50:19
>> You know, I think it's the first time that we have a new a new kind of consumer interaction with with video AI >> because so far, you know, video AI was just okay, let's create a slot, put it on existing platforms, Facebook, uh Instagram, Tik Tok, whatever.
3:50:31
Here, for the first time, you can actually do something that's slightly different. Mhm.
3:50:37
>> We're showing it to a bunch of kids and what they ended up doing was for a few hours they just fought each other with sticks and they started doing like Tik Tok dances in front of this and you have a new kind of consumer and like experience here. >> Yeah. Yeah. Yeah.
3:50:49
It's like the original in the what was it in Steve Jobs did that demo of the Mac where it like warped the image uh and and and and there was like a it's like a crazy historical photo in the in the uh uh original uh like MacOSS uh launch.
3:51:08
>> How how expensive is this for you guys to run?
3:51:10
If somebody just starts streaming this in real time, are they paying for it?
3:51:14
Are you guys just eating it?
3:51:17
>> It's it's we're we're doing it efficiently enough.
3:51:18
like we had to write like all the low-level assembly code for GPUs to get this to be both real time and super cheap for us.
3:51:25
>> Uh it's at a point that we can actually provide this for free >> like it can actually be monetized without like subscriptions like there are ways uh it's it's it's cheap enough to actually be able to build a platform on top of this. >> Interesting.
3:51:37
Do you think uh yeah I mean do do you think uh one of the use cases will just be folks who create video content running their video through this to kind as like a previs for what they ultimately want to build.
3:51:53
>> That could be very interesting.
3:51:55
>> Well the other the other thing you can imagine it actually in YouTube.
3:51:56
So like a a creator like Miss Rachel for example popular kids creator could basically say like do you want the Lego version of this video?
3:52:06
Do you want the question about where this lives?
3:52:09
Because I can go and put my phone in grayscale and I can and I can view everything on my phone without color and that is effectively a style transfer >> and that's something that I as the consumer decide >> or you know Mr.
3:52:23
Beast can turn up the saturation in or you know if you're watching a Hollywood movie they might turn up the teal and orange because that is a traditional Hollywood color grade.
3:52:34
If you're watching the Matrix, they might color grade it green when they're in the Matrix.
3:52:37
They might color it blue when they're out of the Matrix, right?
3:52:40
And so, um, it'll it will be interesting to see where this lands, whether it's on the consumer.
3:52:45
I like watching Lego version of YouTube.
3:52:47
I like watching Lego version of my of my conference calls or it lives on the on the on the producer of videos.
3:52:55
I want to show up as Lego and then that could be transformed as well. Very interesting.
3:52:59
>> Can you quickly can you quickly do a gigachad filter? >> Oh, yeah.
3:53:02
Do you have a gigachad filter? Let's check that out.
3:53:04
>> Let's see what Giga Chad does.
3:53:04
I will say that the model is really the current version of the model is getting changing the entire style. >> Okay. Yeah. Yeah. Yeah. Not the entire world.
3:53:12
It's not Oh, turn me into Trump. >> Okay. You're pretty orange. Yeah. That's the way it goes.
3:53:18
But but it is doing something to the cheeks and the jaw, which is textbook gigachad. >> There we go. >> There we go. >> Very funny. >> There we go. >> Yeah.
3:53:28
Maybe you need to prompt like >> So, is this already fully open access? >> Yeah. is open.
3:53:33
We launched it literally 30 minutes before the podcast.
3:53:35
We're waiting for you guys. >> Congratulations. Thank you. >> It's uh Yeah. Yeah. Yeah.
3:53:39
We It's It's It's This is the first time it's used on a video call. >> Amazing. >> Insane. Insane.
3:53:45
Well, thank you so much for joining.
3:53:47
Uh we will get access to it.
3:53:50
We'll set it up on the intern cam tomorrow.
3:53:52
And >> wait, you already have it set up? >> Okay.
3:53:54
We're going to hop off with you and we are going to check it out with Tyler, get some more feedback.
3:53:58
Thanks so much for hopping by.
3:54:00
Congratulations to you and the whole team. >> Insane.
3:54:04
>> And uh is there anyone else in the waiting room or are we good? We're good.
3:54:08
Let's uh can we go over to the over the intern cam and see if that works?
3:54:10
I don't even know if that >> if he's there. Oh.
3:54:13
Oh, technical difficulties of course.
3:54:17
>> Anything else you want to cover in terms of news or >> Yeah, there's some new news. >> Oh, wow. >> That's Tyler there. >> Wait, really? >> Whoa.
3:54:25
>> Yeah, you just did it.
3:54:26
>> Yeah, this is me like 30 seconds ago. >> Okay. Wow. Yeah.
3:54:31
It looks better without the zoom compression.
3:54:33
It It's a pretty high fidelity model. Wow. >> Very cool. >> Yeah.
3:54:37
So, we were talking in the background, so you hear our talking.
3:54:38
And then Tyler's in the corner, too.
3:54:40
We're getting rec recursive.
3:54:41
Not that we should use that word now.
3:54:43
It's the M dash of the modern era. >> Interesting. >> Pretty good.
3:54:48
And these were pre-loaded prompts that you were just uh clicking through, Tyler. >> Yeah. Like selecting.
3:54:53
>> You didn't generate any of your own prompts.
3:54:55
>> I didn't, but I think you might be able to. >> Yeah. Yeah. That seemed pretty cool.
3:54:56
Ah, some of this seems fun.
3:54:58
I I imagine that people will be having fun on this on the internet very very soon.
3:55:02
See a lot of viral videos.
3:55:04
I always just wonder where this stuff like what the like the novelty is like you you have to it's like wow it's incredible technology but then people have to actually figure out like a real use case for it like like >> well so so here's a use case streamers >> having some type of like basically like if you tip a certain amount or hit a certain button it puts the streamer into that >> cool Yeah. Yeah.
3:55:25
I mean, a lot of streamers stream with uh with green screen backgrounds and they already drop out the background and put themselves in an environment that matches the game that they're playing or just clean up the messy background.
3:55:35
And so, yeah, you could imagine uh this being like basically in the VFX in the VFX pipeline for for streamers.
3:55:42
Um I think if most people showed up to a to a Fortune 500 Zoom call uh with this uh it might not go over too well.
3:55:51
Well, they can always make everybody on their screen look a certain way and not make themselves look normal to to everyone else, right?
3:55:58
So, there's a bunch of different ways. >> Yeah.
3:56:01
If your boss is yelling at you and you put him in Lego mode, it's probably going to hit a little bit differently.
3:56:06
Might be a little more tolerant. Yeah. Turn the volume down. >> Lego mode. >> Lego mode. >> Oh, sorry. Lego boss block man.
3:56:15
>> You expect me to be >> uh couple more headlines since we're here.
3:56:19
Uh, Perplexity apparently just closed a new round at 18 billion. >> 18 billion. Wow.
3:56:28
>> So, Perplexity has around 2% of queries according to semi analysis right now. Uh, I use it regularly.
3:56:33
Uh, we talked to Chris who's on the show.
3:56:37
>> 7 million DAUs, 30 million daily queries, 4.
3:56:40
3 queries per user per day, a 2% share of users, and a 2% share of queries. >> Solid.
3:56:47
>> That's the perplexity. >> Solid numbers. pretty good.
3:56:49
>> They got their new browser.
3:56:49
They're going to be investing heavily.
3:56:50
And then outside of that, Lovable just raised 200 million at a $1.
3:56:55
8 billion valuation led by Excel.
3:57:00
>> Congratulations of heat on the timeline.
3:57:03
Lots of big rounds getting done.
3:57:05
>> Everyone decided to launch today, I feel like, and then uh they got steamrololled a little bit, but that's why we're here.
3:57:12
We didn't spend too much time on uh the concert fiasco and instead can move on and talk to you about the news.
3:57:17
Also, I want we want to send our best wishes to Tyler. Uh, he says, "Hi, crew.
3:57:21
I had surgery to remove lots of infected fluid from my chest and a big lung abscess with infected tissue.
3:57:27
Pneumonia, sepsis, antibiotics weren't working in the ICU now. Surgery went well. Surgeons are heroes.
3:57:34
I'm so grateful recovering.
3:57:36
Thank you for your prayers. All good.
3:57:38
So, I hope he's doing well."
3:57:39
>> Uh, send some prayers over to Tyler. >> Yes.
3:57:42
Um, what else is in the timeline that would be worth to close on?
3:57:50
Um, I have a good closing post.
3:57:50
Uh, the the the the lads over at Reindustrialize are having a lot of fun.
3:57:56
If you can pull this up in the bangers tab, >> you can see uh a quite a number of former TBPN guests all in the back of a pickup truck. >> Fantastic. if you can pull this up.
3:58:09
Team, we're working on reducing the time between talking about a post and getting it live on the screen. There we go. >> All the boys. We got Augustus. >> Nice.
3:58:21
>> Py, >> Steinman, >> everybody all in one place. Love to see it.
3:58:26
So, >> anyways, uh, thank you for tuning in today.
3:58:28
Super fun show and I can't wait for tomorrow, John.
3:58:31
>> Can't wait for tomorrow.
3:58:31
Leave us five stars on Apple Podcast and