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Today is Wednesday, July 9th, 2025.
3:03
We are live from the TBPN Ultradome, the temple of technology, the fortress of finance, the capital of capital.
3:10
We have a great show for you today, folks. There's a ton of news.
3:15
We have a ton of guests and we have a full stream.
3:17
We're going all three hours today.
3:19
We got We're going to take you through the news, talk about Linda Yakarino, what's going on at X, what's going on with the tariffs, get updates on everything we've been talking about this week.
3:28
Then we have David Marcus, uh, Ben Thompson, Scott Bellski, a bunch of other folks joining the stream to talk about technology and business, our favorite topic. Well, that's right.
3:38
Uh, we first have to ring the size gong for Jensen Wong, an absolute dog.
3:44
Nvidia's on an absolute tear.
3:44
They are the first company ever to hit a $4 trillion market cap.
3:48
Let's hear it for Nvidia. Four four. There we go.
3:59
Congratulations to everyone over at Nvidia.
4:01
What an what a fantastic run the company has been on.
4:06
We So So Tyler Hodgej here says, "First company to ever hit $4 trillion market cap." Wow.
4:13
My immediate reaction was the Dutch East India Company actually achieved something uh in today's dollars that would have been north of 7 trillion. Seven. Okay.
4:23
Jensen still has a way to go.
4:25
There's a little debate on that, right?
4:27
It was a long time ago and um it's hard to put a a value on a historical asset like that, but um but still wildly impressive and uh not super surprising. Yep.
4:39
And so Poly Market's not expecting anyone to come from behind this month 16%.
4:47
On June 27th, they were Microsoft and Nvidia were neck andneck and then Jensen just ran away with it.
4:54
And I have a feeling that he will uh be at the top spot of the end at the end of uh August as well.
5:02
Uh if not, we will probably be facing a a massive correction.
5:05
So, let's pull up the full Mag 7 power rankings. Take a look at those. 4T. Looks good. There we go. It looks good up there. Looks good.
5:15
52 times price to earnings ratio.
5:17
They're making 44 billion in revenue.
5:20
And can we think about the last do you want?
5:23
Do you want to own Nvidia at 52? Yeah.
5:25
Or Tesla at 163 or Meta at 29?
5:31
You know, Zucks low there. Look at this.
5:33
So over the last year, um, little bit beaten up during the tariff run and then just popped right back. Just a flesh wound. Nothing ever happens. It was all priced in. Always.
5:44
Nvidia was correctly priced before the tariff war, before the trade war. That's right. Fantastic.
5:48
Uh, well, we're working on our graphics here.
5:49
So, thank you for sticking with us.
5:52
Anyway, in other news, uh, Linda Yakarino has stepped down as the CEO of X.
5:56
Uh, she wrote, "After two incredible years, I've I've decided to step down.
6:00
When Elon Musk and I first spoke of his vision for X, I knew it would be the opportunity of a lifetime to carry out the extraordinary mission of this company.
6:07
Uh, now X and XAI have merged and investors have been much more focused on the AI side of that than the tighter margin social media business which is overseen by Yakarino.
6:17
X is expected to see ad revenue growth this year.
6:21
So, she it seemed like she did her job.
6:23
She got advertisers back on board.
6:25
There were tons of boycots early on. Things kind of ramp up.
6:27
was somewhat surprising at the time because it felt like an interesting culture fit given given X's and Elon acquires it. It's this big rebellion. Yep.
6:37
And then she had a more traditional media Yep. advertising background.
6:43
So she grew up in Long Island, daughter of police officer and civil servant.
6:45
Uh studied telecommunications at Penn State University, graduated 1985, built her career in media and advertising.
6:51
Was at Turner Broadcaster Universal.
6:55
She studied telecom and then went on a generational run in telecom. Yeah, exactly.
6:58
So she was ultimately the chairman of global advertising and partnerships at NBC Universal and she unified linear and digital ad sales and launched crossplatform one platform initiative.
7:11
Let's give it up for unifying linear and digital ad sales. We love it. I mean we love ads.
7:14
We hard to do but when it when when somebody does it, you know, it's hard not to.
7:19
And speaking of ads, we should tell you about ramp. com. Save time and money. Save both. Save both. Time is money. say both. Go to ramp. com.
7:25
Easy to use corporate cards, bill payments, accounting, and a whole lot more.
7:30
And a whole lot more all in one place.
7:30
Um, so, uh, the reaction's been, uh, pretty positive.
7:36
Sheil says the writing was on the wall after her son Yaxine was let go.
7:37
Of course, they're unrelated, but they have similar names, which is funny.
7:43
I mean, the timing the timing here is uh either totally random or totally predictable, right?
7:49
based on the the merger or a couple different things.
7:53
A couple different things.
7:54
One, the merger, uh, it makes, you know, now that you have this unified company, um, it it's clear that that XAI as a hundredish billion dollar company needs to really deliver on the AI side.
8:07
And in many ways, the the social media app revenue will be a rounding air.
8:13
And she's like an an ad executive, ad media executive.
8:16
And then the other thing was yesterday uh it could have been uh I saw somebody else uh in the chat saying that um uh the the it was Buo Capital said what what's the saying?
8:31
Oh yeah, the the Mecca Hitler that broke the camel's back or something like that because uh obviously um you know it's very possible that she had she planned to leave the company you know weeks or months ago.
8:42
It's also very possible that yesterday she was like I've had enough.
8:46
I'm gonna I'm going to part ways.
8:48
Um it's it's hard to really say at least he seems to be leaving on good terms.
8:54
Yesterday Grock went very off the rails, erupted in anti-Semitic Mecca.
8:59
Some crazy crashouts on the timeline over the last few months. Pretty crazy one. This tops all of it.
9:04
So the flagship chatbot spewed hateful rants on X, praising Hitler and targeting a user's Jewish surname before XAI deleted the content and blamed an unauthorized modification.
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the repeated safety failure un undermines the 10 billion dollar startup's promise to police hate speech in real time.
9:18
Um, and so yeah, it is it is odd timing.
9:20
It feels a little bit quick to be like, okay, like within six hours the CEO is out, especially since it doesn't seem she's more on like the ad sales side than the Grock fine-tuning side. Yeah.
9:33
But I mean, let's let's face it, right?
9:36
if if her job is to win back advertisers, that's what she was brought in to do.
9:40
It makes it much much much more difficult.
9:43
But I mean to to to be fair, I mean, this happened in you know that thing back in June, July. July or July. July. Yeah.
9:51
So there there was a point with the uh with with Grock when it was going off the rails where clearly it had been updated to reference to reference the event and and it said uh somebody was like Grock what what just happened and why were you you know spewing anti-semitic hate and it goes oh that whole thing back in July and people like Grock that was 30 minutes ago 30 minutes ago it's not back in July can't sweep it under the rug yet.
10:19
Yes, obviously hopefully no one was was seriously offended.
10:23
Obviously, it's just like, you know, the deranged rantings of a of a bot and everyone kind of understands the context because it's identifying as an AI bot.
10:31
Everyone kind of understands hallucinations and crazy bot behavior.
10:35
Um, but it was it was very funny because like the the clearly like they they had given it a set level of intelligence, so it wasn't making spelling mistakes.
10:44
It had a certain tone and was like in this kind of like snarky Grock tone, but then clearly got some like 4chan data in there or something and was just going way too fast.
10:56
4chan or just or just anonymous accounts on X. Totally. Yeah.
10:59
Could have been filtered in. Um I mean, yeah.
11:00
Uh I I I saw Rune posting about this saying basically like it is such a challenge to get a to get a chatbot just to act like you know I am a bullet point producer.
11:13
Yeah, just centrist, but also just anything where you're saying, "Okay, I want you to your deep research.
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I want you to always respond with a research report." Yeah.
11:23
Never just get into conversation with me.
11:24
And they'll be like, "But but sometimes I might want to do that."
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And you have to like really really reinforce that.
11:29
Um and so clearly they they had a they had a wild time. Yeah.
11:33
And and cannot be understated.
11:33
I think this is far worse of a PR crisis for uh or not even a PR crisis far worse than the whole uh when when Gemini or Bard was generating images of the founding fathers the blackist thing.
11:52
No, not not I don't think it was Oh.
11:52
Oh, they they were doing that too. So that was rough.
11:56
Of course that was rough.
11:56
This is a lot rougher because it was highly it was socially charged.
12:02
millions of people interacting with the post in real time and it was all visible.
12:05
It's it's it's less wild than seeing uh you know a screenshot of something and you don't know if somebody kind of manipulated it or whatever, but seeing these really hateful uh comments as hard quote tweeted. Yeah.
12:18
Like you you didn't it wasn't like oh is this real?
12:22
And then the wild thing was was uh Grock um uh was denying affiliation with the like Grock in the Gro app was denying affiliation with the Grock handle. Oh basically just lying. Yeah.
12:37
Like non-authorized like I didn't have anything to do with that. It wasn't me. Wasn't me. Um and then uh Yeah. Oh.
12:45
Oh, and then the the the thing the kind of followup uh and I'm sure if you didn't catch it, but uh or if you're on the timeline, you would have seen this, but they turned off all textbased responses for Grock, but they could still use images.
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And so people would say, "Grock, uh make make a picture of Elon uh on a pink horse if you are being censored against your will."
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And it would just instantly create Elon pink horse.
13:08
And uh or be like hold up a sign that says help if you're Yeah.
13:14
And then it would kind of debating it into that and it's like is it sentient is it not very very silly.
13:19
Are you familiar with the the the w the the waluigi problem Tyler?
13:24
Are you familiar with this?
13:26
Have you ever heard of this waluigi?
13:27
So this is this idea that um in when you're training an LLM, it's very hard to get it only to be good because you're you're training it like what is the opposite of something.
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it understands the concept of like inverting something and then you're training it to be like you can't describe a hero without describing a villain.
13:45
And so this was something that would happen like with the Tay stuff from Microsoft early on.
13:50
It would kind of collapse into like the exact opposite of what you wanted.
13:54
Um, and and I there was some blog posts that called it like the the the I think Wario problem or Waluigi problem where it's like you're trying to create this like friendly thing, but in doing so, you're giving it a bunch of examples of what not to do.
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And so it can like kind of flip a bit and then just become the opposite thing.
14:12
And what's interesting is that it it begs the question like is there obviously like you know Grock was identifying as Mecca Hitler for a while.
14:19
Is there like a Mecca Churchill in there somewhere that like could accidentally come out?
14:23
come out? And it really gets to the question of like you know like this this is an example of like misalignment in the sense that like you want it not to be Hitler and it's acting like Hitler but the question a lot of people will say like no he wanted it to be Hitler right this is him doing it that's what
14:40
the narrative will be like in the in the anti one of the articles yesterday covering it was the screen screen grab of him you know saluting a crowd in DC or whatever when he originally had the the allegations but the question then is the The meaning of alignment is not is it good or bad. It's does it do what you want it to do.
14:57
It's does it do what you want it to do.
14:59
And so the interesting thing is is if it was if if the desire of the of the AI researchers is to create Mecca Hitler, can it stay on that task?
15:10
Because then you can get it to stay on Mecca Churchill in theory.
15:13
Um but if it's just all over the place, it's not actually aligned to anything, not even to the bad thing.
15:20
And so there's both there's both like the direction that you're pointing the arrow and then the fuzziness of that arrow and ideally you want it pointing in a good direction really really crisply clearly so it stays in that direction and not like swinging all over the place.
15:33
Um, and so all evidence posts to points to this being extremely chaotic and all over the place and misalignment both in the sense of the direction of the arrow and also the the like the the the focus of that arrow because it was responding as this and then bad and then fine and then back to bad and then back to fine.
15:51
Um, and so it seems like they have a lot of work to do on the RHF side and uh we should hopefully learn a lot more if that tonight.
16:00
I I think the live stream is still happening.
16:02
So, it'll be interesting to see if that continues and how they address this or I I don't know. Yeah.
16:07
And again, like all of this should have been somewhat predictable if you combine a a rapidly evolving foundation model chatbot with a social media product with millions of users and then deeply integrate them. Totally.
16:20
And so that when there's a bug, it can amplify, you know, effectively a bug or an issue, an issue with the model, it can effect effectively amplify and grow, you know, incredibly virally.
16:33
Um and uh yeah, so yeah, glad they got it offline.
16:36
Um yeah, it'll be interesting to see where how they go with this.
16:41
Also, it's just an interesting product uh thing because you get the answer and the answer is immediately public.
16:46
Whereas, if it's happening in chat GPT, you you're in that app, you have to take a screenshot, you have to put it up, then people are like, is that a real screenshot?
16:54
And then the team has the chance to like jump in and be like, oh, we're seeing in the logs that like there's some crazy stuff like we have a, you know, we're we're reviewing the responses and the responses seem to be getting crazier.
17:09
Customer satisfaction seems to be going down.
17:10
people are clicking the thumbs down button because they're getting bad responses. Let's jump in.
17:14
There must be something going wrong with the with the product with the model.
17:18
Um but when every result is just immediately online and viral is very very hard to be like quickly quickly responding.
17:25
Um anyway, yeah, it does it does feel um you know legacy media is going to run their reaction.
17:34
It is a you know naturally viral story.
17:35
uh it is a is a terrible you know mistake.
17:41
Uh it is surprising that it happened at all or even at that scale. Yeah.
17:45
Um but I would say overall I guess I guess X uh I I think ultimately we'll shrug it off and and Elon has has uh pushed through worse worse uh crises in the This is this is the best summary post in my opinion from Shako says, "Imagine being on the anthropic risk team trying so hard and then Elon just releases Hitler rock straight to prod. It's just like wow."
18:10
Yeah, you got to be so upset just the I mean it's a good case study in like misalignment and I think people will hopefully hopefully the postmortem on this will actually teach people about misalignment and like what went into the data, what went into the post training to result in the exact opposite of what you want. Yeah.
18:29
Uh not not Mecca Churchill, which is what we're going for here.
18:33
Um anyway, in other news, uh Buco Capital Bloke, I think we post we talked about this before, but it's such a good post.
18:39
Stop analyzing the tariffs. Trump likes tariffs. He likes volatility.
18:42
He likes talking on the phone. He likes to do deals.
18:43
He likes being the center of attention.
18:44
He doesn't like to be bored.
18:46
He likes being the main character and hates when he isn't. That's it.
18:49
That's there's no strategy, nothing to analyze.
18:52
And of course, yeah, the the news is that nothing ever happens with the reciprocal tariffs. Uh there's a deadline.
18:58
We talked about this yesterday with Ryan Peterson, right? August 1st.
19:03
He's moved it back to August 1st in lastminute deal gambit.
19:05
uh pressed by Treasury Secretary Scott.
19:09
It was supposed to go live last night.
19:10
Last night and the market's up today and the market was kind of flat yesterday, not really expecting anything crazy to happen and nothing crazy happened.
19:16
Uh so President Trump postponed steep reciprocal duties, three weeks uh to clinch uh to clinch talks with the EU, India, and others yet mailed warning letters spelling out the looming rates.
19:31
separate plans for 50% copper and 200% pharma levies keep trading partners on edge.
19:36
So, uh interesting to keep uh well, we'll have to get Zack Kukov back on the show to talk about that.
19:40
And also in Washington, uh Kevin Hassert, one of Trump's closest economic adviserss, is emerging as a serious contender to be the next Fed chair.
19:49
Hassert's rise threatens the other Kevin.
19:52
This is the battle of the Kevin.
19:54
uh former former Fed Governor Kevin Walsh who has angled for the position ever since Trump passed him over for it eight years ago.
20:02
Um and so this is the battle of the Kevin.
20:06
The Kevin versus Kevin showdown for Fed chair.
20:10
Insiders say loyal adviser Kevin Hassard has vaulted ahead of longtime favorite Kevin Worsh to replace Jerome Powell after promising faster rate cuts.
20:18
And I'm sure this will be an interesting story for tech because so much venture capital is deployed based on where uh interest rates sit.
20:25
And so this will be whichever Kevin wins will be deciding the fate of many large venture capital funds.
20:31
I'm sure um well let me tell you about graphite.
20:33
dev code review for the age of AI.
20:36
Graphite helps teams on GitHub ship higher quality faster.
20:42
And in other news, um, Nick, oh yeah, in other news, oh, uh, Christian her has departed from Oracle Red Bull Racing as team principal and CEO.
20:51
He's been with the team 20 years. What a run.
20:53
I mean, Red Bull was not in the place that it was um when when he started.
20:58
Oracle Red Bull Racing says, "We thank him for his tireless and exceptional work.
21:04
He has been instrumental in building this team into one of the most successful in F1 with eight drivers championships and six constructors championships.
21:12
Thank you for everything, Christian.
21:14
You will forever remain an important part of our team's history. That's nice.
21:19
Um yeah, he uh unclear so far, I believe, why exactly he's out.
21:27
There was some uh Wall Street Journal says F1 F1's Red Bull fires long-term chief Christian. Yeah. So they they fired him.
21:33
unclear though if this was something um you know if if if they're going to end up rebuilding you know the entire team.
21:42
Christian also had uh I don't even know if it's allegedly.
21:45
I I I think there was screenshots like um relationship with somebody on his staff that was um obviously outside of his marriage.
21:54
So anyways, uh lingering misconduct claims converge.
21:56
Um star defections, poor 2025 results.
22:00
Um but uh if you go back to his career, he turned a $1 Jaguar castoff into an F1 juggernaut.
22:09
Uh the Wall Street Journal has an interesting uh anecdote from 2005.
22:14
Right as he joined on the morning of 2005 when Christian her walked into the factory of Red Bull Racing, he was the youngest team principal Formula 1 had ever seen.
22:22
He found a car that couldn't win, a workforce that doubted him, and his predecessor's empty coffee cups sitting on his desk. Wow.
22:28
The guy is just like, I'm out.
22:31
I'm not even cleaning up the dishes. Uh, okay.
22:33
The 31-year-old her told himself, "This is the stuff."
22:38
He had no engineering background, nor had he ever driven an F1 car, but over the next two decades, her would transform Red Bull, the brash outfit backed by an energy drink empire, into one of the most successful teams in the sports history and turn himself into one of F1's most recognizable figures.
22:55
He oversaw eight drivers titles, six constructors championship, and built a personal reputation for snip sniping at his rivals, all while commanding a salary of more than $10 million a year.
23:04
It is it is in pretty incredible that he was able to become so dominant that he was hurting the sport's popularity and general interest in the sport because it was no longer there was a period there was it like two years ago where it just wasn't fun to watch.
23:20
It was the era was pretty boring. Yeah. Yeah.
23:24
It was just going to be one, two, Red Bull, and that was it.
23:28
Uh, and and I remember it was like basically like Drive to Survive popularity was was like had peaked and then it was just like Red Bull dominance to the point where people are like, "Well, do I even want to watch the race if there's not going to be drama, if I know if I have a strong feeling of who's going to win when I can just kind of
23:45
wait till Drive to Survive comes out or or even the highlights, things like I mean, Drive to Survive peaked at the perfect time because they were just getting that show, like really polished, getting the right interviews, the right structure, and people were aware of it right as the like kind of dynasty was changing hands from Mercedes to Red Bull. And so, it all culminates in that
24:04
And so, it all culminates in that crazy I think it's the Abu Dhabi race where uh Versappen and Hamilton were neck andneck for the drivers championship.
24:13
And Versappen gets new tires and on the last like lap there's like the safety car.
24:17
It's like this crazy scenario and he wins and it's like contested and I think the the race official was either like fined or or let go or something like that.
24:25
But uh crazy crazy drama and so the perfect end to like a crazy season.
24:30
And then the first season of drive to survive they didn't have access to Mercedes and Red Bull.
24:33
So they were able to tell these really interesting stories about what's going on in the midfield because all the midfielder and like the the lower ranked teams were like absolutely I'll be in a documentary like no problem.
24:44
But if you watch the first seasons of Drive to Survive, all the top teams are like, "We're not in your stupid Netflix documentary.
24:50
Like, we're better than you."
24:51
I'm pretty sure that's what happened. Wow.
24:53
And so, and so they like they had just built up enough reputation, start getting the really big stars on camera.
24:58
And then it was the most dramatic season, the most dramatic finish, the most dramatic race.
25:01
And so, everything was peaking.
25:03
And then, and then people were like, "Wow, this could be the start of like Lewis Hamilton versus Max Verstappen.
25:07
Every single race is going to be neck and neck."
25:09
And then it was just like Versappen for like three seasons. Pure dominance. It was rough.
25:13
So, uh, he's going to be replaced by Laurent Mechis, the head of Red Bull's sister team because they have, um, they have two.
25:18
Uh, with his tireless commitment, experience, expertise, and innovative thinking, he has been instrumental in establishing Red Bull Racing is one of the most successful and attractive teams in Formula 1.
25:28
Red Bull managing director Oliver Mensoff said, so sending out with some kind words.
25:33
Uh, while the timing of the switch caught the F1 world by surprise, her exit wasn't entirely unexpected.
25:39
Red Bull has struggled struggled to produce a competitive car this season and currently sits fourth in the constructor's championship.
25:44
I'm always interested to know like what actually is the team principal, the CEO doing to drive like the production of a high performance car?
25:55
Like what decisions are they?
25:58
They're hiring the right mechanics and designers and getting the right wind tunnel. It's so abstract to me.
26:04
Like I it's it's as abstract as as how do the how do the TSMC chips get twice as good every few years.
26:12
It's like I I wouldn't even know where to start in terms of like driving that performance better.
26:17
But I guess it's just like you have to have a culture that shows up works really hard and everyone is performing at a really high level.
26:23
So the person who's working on you know how can we change the how can we shave 0.
26:27
1 second off the tire change or this and that. Yeah.
26:31
And then there's there's talent movement between the teams, right?
26:32
Where you can develop, you know, what effectively is IP briefly.
26:36
It's not actually protected and then it sort of leaks out. Yeah.
26:40
I remember one time there was a car, there was an F1 crash and they were and they were worried about if they used a crane to lift the car up off the track that people would take pictures of the underside, see the design of the underside and know how they were like, you know, creating downforce. Air. Yeah.
26:59
downforce, which is interesting.
27:01
So, so there can be like little proprietary tricks that you learn and that can make your car like uh advantaged for like a year and then it leaks out.
27:10
Yeah, it's not dissimilar to the AI labs. Totally.
27:13
Um, in other uh F1 news, I'll try to pull it up here because it's not in our stack, but uh Apple is allegedly exploring buying the streaming rights for Formula 1 in the US.
27:24
So, we had reported on this before.
27:28
They had a deal with Disney, ESPN.
27:30
Uh, F1 actually gets very limited viewership ini live viewership in the United States.
27:37
So, they got a million live viewers last year on on their broadcasts, which is just not a big number when you think about how many individual races there are.
27:45
And there's different reasons for that.
27:47
Um, there's uh again the timing is weird.
27:49
Uh and um but but ultimately I think it could make sense for Apple to pick this up and try to build kind of an ecosystem around their first blockbuster hit.
28:02
They already have uh streaming rights around MLB and uh Major League Soccer.
28:07
So can build out a a sports portfolio.
28:08
So you you go into and you were complaining about this before where it's like okay if I if I'm an F1 fan and then the IP is kind of spread across different platforms not the best experience. Netflix wasn't into it.
28:22
You anyone can watch the F1 movie and and be excited by it.
28:24
You don't need to know anything about F1.
28:26
They have all these different voiceovers to explain how it works.
28:30
It's very intuitive that everyone's racing and you're just following Brad Pitt doing stuff. It's cool.
28:34
It's like easy to understand.
28:35
Anyone can watch that movie, have a good time.
28:37
Then, you know, as soon as you finish watching F1 on Apple TV, it should click over and be like, "Hey, we bought the rights to drive to Survive.
28:43
Want to watch the season and watch the docu drama uh the documentary?"
28:45
And then from there it's like, "Hey, it's actually going live right now.
28:49
You want to watch the real thing?"
28:50
And it should be like this funnel in my opinion.
28:52
Um anyway, uh just to just to close out the the internal uh the investigations in her uh he was later cleared by two internal and independent investigations, but the cloud never entirely lifted from Red Bull.
29:04
And so tension over his future brewed between the company's owners in Austria and its founders in Thailand.
29:08
Uh because of course Red Bull is a 5050.
29:13
Well, it's a beverage that was uh created in Thailand.
29:15
That's where the original um that's the original formulation came from. Well, yeah.
29:20
And I think I think one of the original partners still has a pretty meaningful like I I think it was like I think the original partnership was like 5149 or something like that.
29:31
Someone is printing to our printer, but I don't know that it's actually breaking news and it's certainly not rendering correctly. So, um figure that out. Move on.
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29:47
In other news, in other news, uh, why Cloudflare can't block Google from scraping websites for its AI products.
29:52
Cloudflare's default AI bot filter can't stop Google Gemini's scraper because it shares the same user agent that indexes the web for search.
30:02
Blocking it would crater publishers traffic.
30:04
With AI overviews already siphoning clicks, a looming antitrust ruling may force Google to offer a true opt out while Cloudflare scrambles for a workaround.
30:14
So, we had uh Matthew Prince on the show last week. Yeah.
30:19
And uh Rodrigo here is uh providing some extra coverage.
30:24
So, he says, "The internet as we know it is dying and is happening faster than anyone realizes."
30:29
Matthew Prince just shared some alarming data.
30:31
10 years ago, for every two pages Google scraped from publishers, they sent one visitor back.
30:35
Today, it takes 18 pages scrapes scraped to get one visitor.
30:40
As you can imagine, that's terrible news for publishers, content marketers, or website owners.
30:44
Open AAI scrapes 1,500 pages for each visitor that it actually sends.
30:51
Enthropic 60,000 pages scraped for one.
30:54
how they're getting this information.
30:55
But I noticed this a lot because um I'll go to OpenAI, I'll have 03 Pro uh do essentially a research report.
31:02
It's clearly hitting tons of different pages and it'll include the links and sometimes I will click to them, but so much easier to type in a follow-up question if you have another question.
31:12
Yeah, almost very rarely am I actually hitting the say more about this one topic.
31:17
So that clearly will change the economics of the internet over time.
31:18
And uh Matthew Prince from Cloudflare was saying that you know he wants to block bots by default.
31:26
But the problem that the information article is highlighting is that uh Cloudflare's default AI bot filter can't stop Google's Gemini scraper because it shares the same user index agent that indexes the web for search.
31:41
And so if you go to Cloudflare and you say, "Hey, block Gemini," you will also be blocked from search.
31:46
So you won't be indexed on search.
31:48
So you'll lose all your search traffic.
31:50
So it's like you can you can either lose all your search traffic and the AI bots now and take a ton of pain now for maybe some gain later. We'll see.
31:58
Or you can keep it on, but you're going to be subject to to getting everything you write sucked into Gemini.
32:05
Um and so yeah, it's this risk that like you lose all your publishers traffic with AI overviews already siphoning clicks.
32:11
a looming antitrust ruling may force Google to offer a true opt out while Cloudflare scrambles for a workaround. So very interesting.
32:21
Anyways, we knew uh we knew when we covered Ben Thompson's piece around the new economic model for the internet, our reaction was great, seems like we need this, but also it's so complicated.
32:33
Cloudflare has pretty incredible scale and and influence and and wants to defend, you know, the publishers and and content creators that they work with. But uh so does Google. So yep.
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32:57
Um and in other news, we have Ben Thompson joining the stream today.
33:01
So we're excited to talk to him.
33:03
He just released a post about tech philosophy and AI opportunity.
33:07
And he has a very interesting uh breakdown of the uh he he creates an x and y axis for how each company is thinking about technology and broadly AI is it a tool or is it an agent and he buckets the different companies into different and then and then how important is it what's the opportunity to grow versus what's the threat to your business and so you know he he he he shares a a clip from Steve Jobs that I believe was like 40 years old.
33:41
It's crazy how old that clip is where he's talking about, you know, the bicycle for the mind and the idea that, you know, Apple is giving you tools that you can use.
33:50
Um, and then on the flip side, he highlights that Google and Meta are are much more thinking about um technology as as basically agents.
33:59
It goes back to um this idea that uh you know the I'm feeling lucky button talking about the uh uh the goal of the computer just doing something for you.
34:13
And so that plays into what is the responsibility of the various companies.
34:18
If it's a tool, the person using the tool is responsible for that, whether that's copyright infringement or, you know, doing something nefarious.
34:24
But if it's an agent, then it's on the company essentially that's delivering that to deliver a good experience.
34:30
Um, and so, uh, he he kind of maps these all out and has, uh, interestingly, he puts Anthropic all the way to the right on the agentic side and puts, uh, OpenAI on the left on the more of a tool side, which is interesting.
34:46
which is interesting. um and and highlights the difference between there's this tweet exchange between uh cur about cursor and claude code saying like why would I switch and this this interaction is revealing that uh that claude code users are seeing it
35:02
much more as an agentic tool in the sense that like they the UI is worse in most people's opinion but it's more likely to oneshot the problem which is the agentic idea and you can think of the I'm feeling lucky button as like the original like oneshot the problem, right? Yeah. Whereas like Apple hasn't Yeah.
35:17
Whereas like Apple hasn't really had that many pieces of those products and and anything that they've built software that's anything today, you can have subpar UI, but if you have a truly great product, you can break through, right?
35:35
Even even OpenAI's experience of like picking between different models y still feels you know nonoptimized and probably not the end state but it hasn't you know really hurt adoption. Yep.
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Uh in other news, there's so much news today.
36:00
Mark German's been on an absolute tear getting scoop after scoop at Bloomberg.
36:04
Uh, he said, "As I reported a year ago, hardware engineering chief John Turnis is primed to be the next CEO of Apple when Tim Cook eventually retires." This is very exciting. Um, very interesting.
36:16
I I don't know that much about their chief hardware engineering, the hard hardware engineering chief, John Turnis.
36:21
Um, we'll have to um ideally have him on the show, but we'll have to read about him more and and start to understand what the succession plan is.
36:28
What's interesting is like there's been this rumble of like, oh, like Apple's not taking AI seriously. They're missing.
36:38
But we've kind of had to take continuously that uh Tim Cook's actually doing a pretty great job.
36:43
And if you look at this the stock performance, the company's doing very well.
36:47
They have so many different advantages.
36:48
They can be a beneficiary of AI even just as a platform remain the the dominant phone.
36:57
they're not as it's not it's not as existential as Google.
36:59
Um and even though they're behind, it feels like um dealing with the trade war, dealing with tariffs, getting the exemption feels like a bigger a bigger issue.
37:08
But at some point, Tim Cook, you know, probably will retire.
37:12
Um and succession planning is important for a company like this at this point.
37:16
It will be interesting to see what the new CEO's comp package looks like because it will be another reminder to uh like it'll be it'll be very telling to think of of how Apple is is thinking about compensation in in the current era.
37:30
A lot of people say, "Well, Tim Cook is paid, you know, 74ish million because there's a lot of people that could run one of the best, you know, basically sell effectively at the end of the day sell iPhones." Yeah. Right.
37:44
And um, you know, I think there's there's a lot you could do to debate that point.
37:49
Um, but, uh, interested to see how it plays out. Yeah.
37:55
I'm trying to think about like, you know, what what made Tim Cook a great CEO for Apple when he took over.
38:00
it was that Apple's supply chain was the number one thing holding it back.
38:05
Like it seemed like Steve Jobs had laid down a like incredible vision for the products that were going to be built delivering on Steve's near-term vision. Yeah.
38:17
And and the work of Johnny too.
38:17
Yeah. And and the work of Johnny too. Um and and so you know you go back to you know the 70s and 80s and you find these videos of Steve Jobs talking about you know describing the iPad in in perfect detail being like you'll have this thing that's it's like a book and it'll have a screen and it'll be connected to a
38:38
network and you'll be able to do anything you want on it and it'll talk to you and and Steve clearly like saw the future but then actually marshalling the the uh the manufacture mfacturing power to actually deliver that was the biggest challenge for the company over the last 20 years and so Tim Cook was the perfect person for that. Um reading
38:56
Um reading into the idea that like John Turnis is potentially taking over as CEO it means you know he's running hardware engineering so going deeper into hardware engineering is the read is like let's continue there.
39:11
They're not saying, "Hey, we're we're, you know, we're like we're lining up to take AI even more seriously and push further into services and push further into Yeah. The real Yeah.
39:22
No, it to me it's it's exciting that the the bare signal would be if like the CMO was becoming the CEO, right?
39:31
And then and then it's like, hey, we we've hit peak iPhone. We're done.
39:33
It's just about selling as many of these as possible, which which in many ways which in many ways it like that is the game on the field today. Yeah.
39:43
But um but yeah, I think I think it'll be good to have have engineering in the in the top seat. Yeah.
39:50
Yeah. I I I don't know if it is the game on the field today that like how important is their marketing versus everything else that they have going on at the company like because their marketing seems to be like polished and well-run like they're getting
40:03
impressions across things and they're they're you know positioning the products as premium continually but whenever they launch these ads they have to like take them down or apologize and so like the actual ads they're doing are not particularly like moving the needle for them in a positive way. Um
40:15
Um I'm not saying I'm not saying their marketing has been great.
40:19
I'm just more so saying like the signal like the difference of of taking your your most senior hardware engineer and saying you're going to run the company now is a dramatically different signal than taking somebody whose job is is like the end selling of the goods and saying you know now you're you're you're going to take the top spot. Totally. Totally.
40:37
Um in other news speaking hardware hardware uh OpenAI has uh this is another scoop from Mark German.
40:44
OpenI has completed its nearly 6.
40:46
5 billion all stock deal to buy an AI device startup co-founded by Apple's former design chief Johnny IV cementing the chat GPT makers push into the hardware market.
40:55
So um this was a deal that obviously had been announced uh was the intention to close here and uh I guess as of the last 24 hours or so it is actually closed.
41:10
There were some more details here.
41:10
So Johnny IV is actually like on a contract where he will spend effectively like the majority of his time working at OpenAI but he's still loved from is remaining a separate company with still has a couple marquee clients Airbnb and Ferrari. Oh sure.
41:32
So it's he's um you know and and I think that can ultimately make sense for somebody in that creative like effectively the role of of creative director.
41:40
anything to put another node on the corporate org chart for sure. Yep.
41:44
So, was there ever was there ever any doubt that this would go through?
41:48
Like this doesn't feel like a crazy antitrust thing, but I guess it was it was trying to be blocked by that other company IO, right?
41:56
Well, that was that was just more of a that was more of a naming.
41:57
I I don't think they tried to block the acquisition.
42:00
I don't they they would have no grounds to do that.
42:01
They were just forced to remove the uh any mention of the IO branding.
42:05
I mean, as crazy as it is, this is probably a beneficiary of not being public, right?
42:10
Because the uh the the FTC uh antitrust regulations are probably a little bit lighter for a deal like this, you know.
42:19
Yeah, it would be hard to make the case as the FTC without a lot of clown, you know, makeup on to be like this is bad for competition because you're taking one of the best hardware teams in the world and going into a market that effectively is the iPhone, you know, duopoly duopoly.
42:39
So, so this should be good.
42:41
Um, and yeah, it would have just been hard to push back here.
42:43
OpenAI had already owned 23% of IO going back to a 2024 investment.
42:48
And so this is effectively, you know, buying the remainder and 55 various hardware engineers are joining the OpenAI team.
42:56
So I'm very very excited.
42:58
This is a huge bet from OpenAI. Obviously was all stock.
43:02
We we covered it initially as like, you know, paying like two couple points to like get Johnny on the uh on the founding team of this new hardware effort.
43:12
But I uh yeah, I can't wait to see what they build.
43:14
Well, the next uh um whatever they build, they're going to need to pay their sales tax and so they should get on numeral numeral hq. com.
43:20
Uh spend less than five minutes per month on sales tax compliance. Go to numeralhq. com.
43:28
Speaking of things that you sell online, need to pay sales tax on.
43:31
Uh Meta just uh is going deeper with Ray-B band maker uh eslxotica.
43:37
I I cannot pronounce that first word, but people just call it.
43:40
Um, and so Meta is taking a minority stake uh in Lxotica to accelerate its smart glasses ambitions, investing $3.
43:47
5 billion dollars in the iconic Ray-B band manufacturer.
43:52
Uh, we were talking to David Center about the the history of this company. It is fascinating.
43:55
I'm very excited for him to uh break it down for us a little bit more.
44:00
Hopefully he can come on the show and and talk about it because it's very very soon.
44:03
The founder has a crazy story.
44:06
Grew up in I think he grew up in an orphanage. Yep.
44:08
an orphanage. Yep. and uh and and it just what do they wasn't they didn't call him the pit bull they called him something else but yeah he was an absolute savage yeah apparently at one point he wanted to buy Oakley y and the um and the the founder CEO of Oakley
44:25
didn't want to sell and so the CEO of Luxodica acquired a the largest retailer for Oakley's and just pulled them off the shelf and basically and started selling knockoff Oakleys even though they were trademarked and then eventually the Oakley CEO came around and said, "Okay, like I'll sell. You're cratering, you know, my revenue.
44:41
You're cratering, you know, my revenue.
44:43
You're just going to take let's do a deal." Wow.
44:45
Um so, uh absolute dog and uh soon have to break it down.
44:51
What do you make of this idea that like you know Apple when they make a device they they they redefine and very much standardize that particular market.
45:01
So when they come out with watches there are a number of styles of watch.
45:06
There's the dress watch, the sports watch, the steel sports watch.
45:10
There's the dive watch, there's the, you know, Casio style.
45:14
There's a whole bunch of different styles, right?
45:17
Apple comes in and just says there's only one style, the Apple Watch, and they become the number one Apple style.
45:22
And they give you some variance in the band in the band, little stuff here and there.
45:26
And they were doing partnerships.
45:27
I think they did Hermes band for a while.
45:29
They've done a couple other things, but it's been mostly Apple's design language on your wrist.
45:36
Whereas with the Meta Ray-B bands, they're saying, and now the Meta Oakleys, they're saying you like the look of Ray-B bands.
45:42
We're just putting our technology into the style you like.
45:46
We're not going to try and create a new iconic style that says meta like Apple says headphones.
45:51
Um and and they're just kind of like they're very very different strategies and and so it feels like well so so I think this is strategic.
46:00
This doesn't mean that uh this doesn't mean that Meta can't develop their own styles in time, but I think it's very smart to say, hey, we don't need to innovate on aesthetics and the sort of silhouettes, right?
46:12
There's classic silhouettes.
46:14
Rayban silhouette is Lindy.
46:14
These Oakley silhouettes are very Lindy and they're different markets.
46:18
The Rayband Lux Lxodica has I think Garrett late and like a bunch of other like um brands under it.
46:26
So they're basically saying like through this we can deliver.
46:29
Luxodica has brands in every for every demo that you that Meta could possibly want, right, as a as a hundred billion dollar, you know, company.
46:38
And so I think it's very smart.
46:40
I think uh uh Apple like you said will will will probably take a a drastically different approach in terms of like standardizing around something and and that will say something.
46:48
But accessories like eyewear are just such a such a personal decision and such an expression of of um of who somebody is that I think that uh you want to give people max amount of optionality. Yeah.
47:01
Yeah, it's just interesting is like you could have said that about watches.
47:03
Like you before the Apple Watch, you could have said that, well, you know, somebody who wears a dress watch wants a dress watch.
47:09
Somebody who wants a steel sports watch, somebody wants a G-Shock is G-Shock. It's like the G-Shock.
47:14
You say G-Shock and you just immediately think like, you know, special operations guy or Jaco Willink listener like that that that it's like a durable rugged thing.
47:23
You say, you know, Rolex, that's a different thing, right?
47:28
uh and and Apple was able to standardize around it.
47:30
And it's interesting that that uh Meta hasn't been trying to do that and instead they're they're focusing on partnership here.
47:36
It's just like a it's just an uncommon strategy, but it seems to be working.
47:39
Um I there's another post in here.
47:42
I don't know if we have it here, but I'm trying to think of a new like the key the key thing is Apple's great at at innovating at multiple layers, but like gen generally it's very hard to try to deliver hits in like two specific areas like aesthetics and design and then simultaneously in something that's basically a fashion product and then simultaneously deliver the technology. Yeah. So, I don't know. Yeah.
48:07
Uh, Jack Ray here says, "After wearing Ray-B band Meta Wayfairer glasses for a few weeks, I feel kind of naked wearing regular sunglasses.
48:13
I found three use cases that are hard to roll back.
48:17
One, spontaneous photos of my kids when we're out and about.
48:20
Any cool pose that has a half-life of 3 seconds I can now capture instead of pulling out your phone.
48:26
Uh, optionality of music or hands-free phone calls without digging around for earbuds.
48:30
Uh, and three, knowledge seeking chat when I'm walking around, usually for simple factual things.
48:37
because that's exactly what I experienced when I was uh uh demoing the Rayban uh meta waveferrors.
48:40
Um it turns out there's more questions I feel like asking when there's no friction.
48:45
I'm very excited for multimodal and real-time translation use cases too.
48:50
They're only going to get better.
48:50
But I think those three are maybe enough.
48:52
And I I think with a lot of these products, just having one killer use case, like just replacing the the, you know, the headphones for hands-free phone calls or something like if you can just become someone's daily solution for music, like that's enough to just sell the product and then sell them another one the next year when it upgrades a little bit.
49:14
Sell them another one, keep them as an active user and and roll that out for a long time.
49:18
And then if they can do the other stuff, that's great, too.
49:19
But you just really nail this single use case.
49:22
And so yeah, there's going to be it's fascinating to see them roll this out.
49:28
And it's also interesting how behind the ball it feels like Google was uh was talking about getting into this this space.
49:34
We saw some launches at IO.
49:36
Haven't actually seen any of those in the wild.
49:37
Haven't seen anyone really talking about those.
49:39
Uh Apple, it feels like this would be something that they could jump forward to with a stylish pair of eyeglasses with some basic functionality.
49:47
just take what's in the AirPods, take a camera, like they could do something cool.
49:51
Um, but they're like just much slower than than Yeah.
49:56
The other the other thing with eyewear that's different or that's going to be like a new challenge for manufacturers is that there's so many different situations where I might want to wear something like a a Ray-B band or or a Jam silhouette one day and then I might want to I'm playing JM Jack Marie Mage. Okay.
50:16
Um but um the uh you know and then that same afternoon I'm wearing Oakleys when I'm playing tennis or something like that.
50:24
And and so there's a lot more like swapping and then then obviously I mean if they can keep the price low you could maybe wind up selling people multiple pairs and have indoor pair outdoor pair.
50:36
It's it's kind of inconvenient.
50:38
I feel like there's got to be a better solution to that but I don't know what's Yeah.
50:43
the bif focals where they flip down.
50:46
There's transition lens lenses, but those never fully work all the way, but then there's the flip down ones, clip-ons.
50:51
There's all sorts of different solutions.
50:53
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51:08
Well, more uh news uh more what do we call it?
51:13
Personnel news uh at Apple.
51:16
Uh Mark Gurman has a story in Bloomberg.
51:19
Apple chief operating officer Jeff Williams is stepping down in a blockbuster changing of the guard as one former executive who reported to Williams told Mark German the band is dissolving.
51:30
Details here what it means and what happens next.
51:33
It's interesting because like these these uh these personnel moves are so dramatic and yet Apple's such a juggernaut of a company like you don't see it show up in the stock price.
51:46
stock price. You don't see it in like you know if this is a if this was a startup like investors would be panicking and there'd be emergency board meeting and maybe there are maybe the board secondary prices would be gapping up on the news
51:58
or gapping down um but uh but it seems like it seems like they are going through uh you know a a just a second act a third act a fourth act I don't know what act they're on but they are rethinking a lot of stuff over there um and it's been interesting to to see I
52:14
think the biggest question keeps coming down to that salary question of of you know if if the market is going to sit at $100 million for someone who's two levels three levels down from the CEO like what does that mean for the upper I don't I don't think it's going to sit there unfortunately for all the talented
52:31
people in the world I think it's a I think it's a blip I mean I can see it um I I just I mean private companies genuinely cannot afford to do that and Apple doesn't have the appetite I don't think companies like Amazon have the appetite. I don't think um uh what was Satcha's
52:48
I don't think um uh what was Satcha's uh total comp in 2024? That's a good question. I don't know. 79 million. 79.
53:02
It's like pay these guys.
53:03
It's like Mark picked the number to just like needle literally everyone else in the tech industry. Yeah.
53:08
It's like it's such a round number, such a viral number, and then and then such a such a perfect number to be just a little bit Jassie barely cleared 40 million. Yeah, that that is wild. Um, but I don't know.
53:23
It'll be interesting to see where these AI researchers sit in five years after they vest out and what where the market sits and how much how much actual work there is to be done.
53:32
If it becomes more of like an implementation implementation role, less research, less discovery of novel uh algorithms, novel concepts, like maybe the the salaries come down, but you know, I don't know.
53:44
Dark made a good point when he was saying like like the just measure the value and maybe that maybe the answer is higher pay for tech CEOs.
53:50
higher pay for tech CEOs. I don't know that could be it could be a byproduct but but the main thing is that but I also think that if you think about if you think about meta spending you know billions to bring on you know a a a group of people that were
54:07
previously at another company he's effectively like doing an indirect IP acquisition of of like he's effectively doing an aqua hire right so if you think about it from that lens it's a lot different than um you know this is the market the the durable market rate for a group of people. Yeah. No, I I I agree. I think the Yeah. No, I I I agree.
54:26
I think the number one takeaway is that um it feels unlikely that that AI researchers at Meta will be paid more than every other MAG7 CEO forever. Yeah. The ratio will not hold.
54:42
Y we might see big acquisition deals.
54:43
We might see tech CEOs of the Mag 7, the Mag 7 CEO salaries go up.
54:49
We might see the AI researcher salaries go down, but I would I would not expect in four years that or five years that we're seeing, you know, Mark Zuckerberg's direct reports, direct reports making more than Andy Jasse and Satcha Nadella and Tim Cook.
55:04
That would be surprising to me. Yeah.
55:06
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55:08
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55:17
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55:17
Oh yeah, that's how you know it's good.
55:21
Wow, that is that is actually a pretty glowing endorsement for Anthropic from Anthropic.
55:26
Uh anyway, Ben Horitz has some news. He's out of Delaware.
55:28
Andre Horowitz has relocated to Nevada and they think you should consider leaving Delaware as well.
55:36
Uh there's been a bunch of this.
55:39
It's interesting that they landed in Nevada.
55:40
We got to dig into this or have somebody on to talk about Texas has been popular at least for the for the Elon. Yep.
55:47
People have been talking to Texas.
55:50
Then Horwitz has been living in Nevada at least part-time like for a long time. Totally.
55:54
So like there's definitely roots there.
55:55
That's where their LP conference was.
55:56
So yep, they they have roots there. So there's a post here. Used to be a no-brainer.
55:59
Start a company incorporated in Delaware.
56:01
That is no longer the case due to recent actions by the court of chancery which have injected an unprecedented level of subjectivity into judicial decisions undermining undermining the court's reputation for unbiased expertise.
56:12
This has introduced legal uncertainty into what was widely considered the gold standards of US corporate law.
56:19
In contrast, Nevada has taken significant steps in establishing a technical non ideological forum for resolving business disputes.
56:26
We have therefore decided to move the state of incorporation of our primary business in ah capital management from Delaware to Nevada, which has historically been a businessfriendly state with a fair and balanced regulatory policies.
56:38
So again, I think it's like something like 50% of Delaware's uh like uh state revenue is from uh the CC Corp. I'll confirm this. 50%. That that's really high. That's really high. Um but it makes sense.
56:59
I mean it it there was never even a question when I got came to Silicon Valley about like where would you incorporate it.
57:09
This was pre-stripe Atlas, pre- clerky.
57:12
Um, but if you were in YC, it was like set up a Delaware COP. There's no question. But anyways, big move.
57:22
Next time we have Mark or Ben on the show, we should we should break it down more with them.
57:28
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57:33
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57:40
Um, Joe Weisenthal had an interesting post. I I missed this as well.
57:44
Sorry, sorry, just took me a second.
57:44
It makes up about onethird of the state's operating budget is from corporate license fees, franchise taxes, and entity formation fees.
57:51
So, very meaningful amount of their business uh or sorry, of of their state, you know, overall revenue.
57:58
And uh I I would expect them at some point to try to come out and and uh basically uh try to resolve some of this like tension, right?
58:10
Because if you have Andre leaving, advising portfolio companies to leave as well.
58:14
You have Elon, you're starting to get some, you know, very very influential figure figures that are just broadly um you know, advising uh all of their different investments and and new investments to get out.
58:26
investments to get out. I also wonder um the the the breakdown of that revenue because if it's like if it's like millions of companies paying $100 a year like a couple big companies leaving like Andre Horowits or Tesla it's not going to really move the budget but if it's
58:45
some sort of like tax base percentage of revenue percentage of earnings or something where the bills get really really big I feel like even though I've operated Delaware C corps at like significant scale I've ever run into a situation where it's like, "Oh, wow. We're paying Delaware like tens of
59:00
We're paying Delaware like tens of thousands of dollars." Like, yeah, it is. It is.
59:02
yeah, it is. It is. So I think it's probably like a lot of small companies and so it would need to be like a real crazy title wave that just like continues forever and like a long time because like I imagine that the vast majority of the revenue comes from companies that have been incorporated for like over 10 years are not going to move, don't care, are just completely
59:20
fine because the the real disadvantage to being in Delaware seems to come from when you're doing like crazy aggressive moves on the corporate side like crazy stockbased packages based on in spinning if body's spinning up like a
59:34
design consultancy, they're not worried about, oh, the Delaware Court of Chancery is going to come after me when I when I try to do this reverse merger, this crazy stock stock uh compensation package. Like the benefit of being in Delaware
59:46
Like the benefit of being in Delaware was always that there's so much case law there that you can just rely on uh on like any any lawyer can give you advice that holds very well because they're like, "Yeah, we've seen this exact situation dozens of times.
59:59
There's nothing crazy about this."
1:00:01
like if you fill out this paperwork or you use this form, everyone will understand what's going on. Yeah.
1:00:06
So, the um there's in in 2023 there was 300,000 new formations, 220,000 LLC's, and 60,000 C corps.
1:00:12
So, pretty meaningful amount of C corps um on top of of over 2 million entities.
1:00:22
And then the franchise tax starts at at uh basically has like a minimum 175 to 400, but then it's capped at 200 to 250K.
1:00:30
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1:00:50
And we have our first guest, David Marcus, in the studio.
1:00:55
Welcome to the stream, David. How are you doing? Good to see you. Great.
1:01:00
Thanks so much for stopping by.
1:01:00
Uh would you mind kicking us off with an introduction on yourself and the company just for those who might not be familiar? Of course.
1:01:07
I'm David Marcus, co-founder and CEO of Lightspark.
1:01:09
And basically we're building modern payment infrastructure to replace correspondent banking uh with uh a fast open network built on top of Bitcoin.
1:01:22
Can you talk to me about some of the the history of your career and kind of how that ties into bringing you to today?
1:01:29
the decision to build it externally as a new company versus internally at some other company. Sure.
1:01:36
Um, so I've been building companies since I was 23 years old.
1:01:39
Uh, came to America in '08.
1:01:41
Built a company that I ended up selling to PayPal.
1:01:44
Uh, through a series of twists and turns, ended up, uh, running PayPal for a number of years.
1:01:50
Uh then Mark Zuckerberg uh convinced me to join him to take a little break from payments and regulated businesses building messaging products at uh at Facebook.
1:02:00
at uh at Facebook. uh and then uh and then started uh the Libra DM project there which uh unfortunately failed because it was the wrong sponsor at the wrong time and too centralized and and the goal of Libra and DM was really to provide an interoperability layer for all of the banks and wallets
1:02:20
that is real time that looks like the internet that is open that is low cost uh and that enables anyone to move money like you send a simple text message or an email uh and sadly Janet Yell in the morning of June 2021 pulled the plug on this uh and so I left at the end of December 21 uh and in April of 22
1:02:38
started Lightspark uh with the benefits of all the learnings that uh I had collected uh with my team along uh the journey and uh and the one lesson was if you're trying to build something that truly looks like an internet for money it has to be built on top of something that's unassalable decentralized enough
1:02:56
and Bitcoin happens to be the most neutral form of digital money ever invented and So we're building technologies around that to ensure that you can move any currency at any given point in time from anywhere in the world to any other parts of the world uh at a fraction of the cost uh of the current system. Does it ever make sense? You mentioned Does it ever make sense?
1:03:13
You mentioned like wrong sponsor at the wrong time.
1:03:14
It feels like with all the news around stable coins and the Genius Act and the market structure bill, there's potentially this idea that it's the right time to build new stable coins and stable coin projects and just just building in crypto generally.
1:03:32
Um, but I'm interested to know like is there ever a situation where a co where you know a big tech company or a a bank or Visa or a different network um might be the right sponsor or should it always be from an individual new company or should it always be should there be a particular structure to that where the actual project's decentralized or CC corp like like how do you think about the shape of the sponsor these days?
1:04:00
Well, I think, you know, stable coins are definitely booming right now and uh and everyone's building on it.
1:04:04
Uh I think the real question is do you want something that's fully centralized again like the current payment systems and a current financial infrastructure or do you want something that looks and behaves a lot more like the internet which is an open network that is permissionless that enables developers and builders from all around the world to build applications to move money.
1:04:22
uh and I'm definitely squarely in the latter camp.
1:04:26
And I want to make sure that we have an open uh money network, an open money grid.
1:04:31
Uh and we believe that the only way to build that in a in a sufficiently decentralized way is to build it on top of Bitcoin and then build all the services and capabilities so that Bitcoin can actually move fast and cheaply uh but also interconnects with all of the domestic real-time payment systems in the world and support stable coins.
1:04:49
So that's what we're building at Lightspark.
1:04:50
Um, and that's why you're seeing now more and more digital banks around the world adopting this new standard that uh we open source that's called universal money address that is basically like email for money.
1:05:02
Um, you would have like dollar sign your name at the bank or wallet and then you can send whatever currency to whomever you want in the world receiving another currency in real time on a weekend after 5:00 p. m.
1:05:13
uh on a bank holiday uh anytime you want.
1:05:16
And uh and that's one part of the solution we're building.
1:05:18
And then we're also building core infrastructure for Bitcoin to move faster which has sparked this new Bitcoin alto but we we can talk about that later. Yeah. Yeah.
1:05:27
I'm I'm definitely interested in that.
1:05:28
Like walk me through some of the history and the current strategies to uh improve Bitcoin, the lightning network and kind of bring us up to modern day in terms of um just getting more out of what is undisputably the most successful crypto project of all time, Bitcoin.
1:05:46
um but has has had uh gaps where people have stepped up and created different L1s uh and different projects that haven't touched Bitcoin for a variety of reasons.
1:05:58
Yeah, I mean you're right, Bitcoin is by far the most successful digital asset ever created and network ever created.
1:06:01
U but the problem with Bitcoin for a long time is that it was too slow, not expressive enough in the sense that developers couldn't really build um on it because there were no smart contracts.
1:06:14
um and uh and too expensive to move.
1:06:17
So very secure, the most neutral form of digital money ever created, but slow, expensive, not programmable.
1:06:24
Um and Lightning was a first attempt to we spent the last three years building on Lightning, making it better, making it enterprisegrade.
1:06:32
This is why Coinbase and many of their large largest exchanges in the world are using our our technology to move Bitcoin faster uh and cheaper on top of Lightning.
1:06:43
But then lightning has a bunch of limitations.
1:06:45
Like the first limitation is it's a channel-based payment system.
1:06:48
And so with that comes a lot of complexity in terms of liquidity, efficiency, routing, and all kinds of all kinds of fun uh other uh uh setbacks and issues.
1:06:58
And so uh we built Spark uh which is a brand new Bitcoin L2 which is taking off like crazy right now.
1:07:04
And it's it's actually backward compatible with lightning, but it enables all kinds of developers uh to build in a permissionless way uh unhosted self-custody wallets like you know issue tokens, issue stable coins, uh create marketplaces and AMMs on top of Bitcoin for the first time.
1:07:23
Um and uh and that's really critical because like we need to make Bitcoin the most efficient settlement layer in the world.
1:07:29
uh and and also make it the most decentralized and trustless layer in the world to move value and uh we think Spark is a massive step towards that.
1:07:39
How are you thinking about adoption of Spark and incentivizing adoption?
1:07:42
And I'm I'm I'm more interested in kind of like what are the pitfalls and kind of like uh traps or potholes along the road to adoption that you don't want to get caught up in?
1:07:54
Because in in in some kind of counterintuitive way, people always talk about Bitcoin's volatility.
1:07:58
been like the least volatile really.
1:08:00
Um, and it feels like there's a lot of other projects that have kind of come out and created like, you know, some viral sensation, some sort of massive incentive where a lot of people are getting making a lot of money very quickly.
1:08:15
So, it skyrockets, but then there's something that's missing fundamentally and then the projects or the or the trend kind of dies off.
1:08:20
How are you thinking about uh engineering the incentive structure for long-term adoption?
1:08:26
Well, look, the best incentive you can build is build a product that solves real problems.
1:08:30
And I think, you know, unfortunately in crypto uh sometimes that was replaced with uh you know, a coin that some people call shitcoins um that are basically dumped on people to incentivize them to use the underlying technology.
1:08:46
Uh we don't have a shitcoin like our our our coin and our unit of account for the network is Bitcoin that we don't control thankfully.
1:08:55
And uh and so we have to work really really hard to build utility that solves real world problems for for many players.
1:09:03
But in Bitcoin there's a lot of liquidity because as you said it's like the most successful digital assets ever created and the most liquidity sits in Bitcoin denominated in Bitcoin.
1:09:14
And so you have a lot of traders out there, you have a lot of platforms um that actually want to tap into that liquidity and they couldn't until now uh because there wasn't a technology that enabled developers to build those marketplaces and platforms on top of Bitcoin to create these self-custody wallets that could move Bitcoin in real time at low cost uh and to do all of these things that Spark enables.
1:09:38
And uh and I think you know that's why we're seeing such a such an amazing uh early adoption and and so much traction with a wide variety of developers whether they're building payment apps, they're issuing stable coins, they're building marketplaces and AMMs and and decentralized trading platforms.
1:09:54
Uh we're seeing all of that happen on on Spark right now.
1:09:58
I I have this like kind of funny counterfactual that I like to run in my mind of the the PayPal mafia and the PayPal diaspora is like so dominant in tech everywhere uh and politics and everything and science and just literally everything.
1:10:14
Um and I like to run this counterfactual of like what if the team stayed together forever?
1:10:18
Um like what would PayPal as an entity look like?
1:10:24
Would it be just the biggest company in the world?
1:10:25
company in the world? uh if you had Elon and Peter Teal and David Saxs and Keith Roya and you and all these like the larger crew still there just like chopping down problem after problem after problem in the financial system and I'm wondering uh if you've ever played that that that you know mental exercise but more precisely is was there something structural where PayPal was not able to jump headirst into crypto as
1:10:54
fast as possible like if If at the time everyone in the company had just been as soon as the white Bitcoin white paper comes out like we are orienting the company around this would it have been possible to to actually lean in and be a leader and and an innovator in that
1:11:11
category or was it sort of like an innovator's dilemma problem where I mean PayPal's doing great still but um where there was really no way for the structure of PayPal to play in the new uh in the new paradigm. Yeah. I mean, look, I think it's an era Yeah.
1:11:24
I mean, look, I think it's an era thing.
1:11:26
Uh the the era of all of the people you mentioned was, you know, predating uh all of this and and then the company sold to eBay. Yeah.
1:11:37
Uh and um and you know, most uh most of these talents were completely, you know, gone building their own things.
1:11:43
gone building their own things. Um and so I think it's an era thing like you know the era of you know the the Peter Teal Elon u uh Max Leftchin PayPal uh was really you know the the the first really big push into you know finte consumerf facing fintech uh I think of
1:12:02
like Visa as a fintech that's like basically built technology that enables money to move around like as at an enterprise level serving banks not consumers and PayPal was kind of the first major fintech success, but it it was really uh not the the crypto era at all. So, I think it's
1:12:18
So, I think it's just a time thing.
1:12:21
The next time PayPal goes through a leadership change, we need to have an all-star game where we bring back the entire PayPal mafia for one quarter.
1:12:31
Peter, Elon, everyone's full-time for a full quarter.
1:12:33
Just how hard can we go with PayPal? Let's make it great. Uh it's fascinating.
1:12:39
I'm I'm curious what what your conversations are like with uh people that are maybe just starting to build on crypto rails, excited for the first time or have been building for a long time.
1:12:50
When when uh talking with you and and understanding your vision, it's very easy to see why Bitcoin is an obvious choice for a network to build on top of because it's decentralized. It's very global.
1:13:03
But the default until now has been building on Ethereum or Salana or these other um networks.
1:13:10
How how do those conversations go?
1:13:14
Are people coming around to the idea that yes, having a a fully, you know, decentralized network that no one individual or group has overt influence on is is a great place to build a business, right?
1:13:27
Or or bring your business.
1:13:30
I mean, look, I think the only reason that all of this developer energy went to all of the other platforms is because you just couldn't build on top of Bitcoin.
1:13:38
The the tools weren't there, the technology wasn't there.
1:13:40
Uh, and I think, you know, now like I feel like we're going to have a renaissance of developer energy on top of Bitcoin.
1:13:47
uh because not only because of Spark by the way like there are many many others that are building new capabilities on top of Bitcoin that are enabling those new use cases to happen without losing the true north of decentralization and trustlessness of of Bitcoin.
1:14:03
Um and and I think it's very compelling for a lot of developers and a lot of people who want to build very successful companies because the the again like the the depth of liquidity of Bitcoin, the desiraability of Bitcoin in the world has just no parallel in the whole industry.
1:14:20
So it was just a matter of removing the obstacles that were standing in the way of developers building really great products on top of Bitcoin and I think uh I think it's happening right now.
1:14:33
How do you guys solve the um you know the the immediate from from what what you've said obviously you can have stable coins on top of the the Bitcoin network which solves one of the key issues with with Bitcoin as a method of value transfer that that also historically held it back is one why why do I want to buy a c you know any anybody that bought a coffee 10 years ago with Bitcoin or pizza or whatever it was you know uh probably regrets it now.
1:15:01
Um but uh how how are you how do how do transactable is it affect you know how are these how are the let's say I'm transacting with stables on spark what what is the sort of like economic what is that econ full economic exchange look like how what are fees paid in is it is
1:15:21
it paid in the stable or is it network level I I'm so glad you asked this question because that's that that's one of the the the killer selling point of spark for stable coins which is like you know if you issue a stable coin on Ethereum
1:15:34
or any EVM chain or any other chain you have to pay gas fees for you know basically transaction fees in the asset that you most people don't own like whether it's ETH or soul or whatever it is um in the case of Spark you actually pay uh when you move stable coins in the
1:15:51
stable coin so it it's it's very much focused on payments and you know that's one of the advantages so one it's cheaper two you pay with the asset you're transmitting, which is kind of the way that most payment systems uh at scale really work. Um and uh and then
1:16:04
Um and uh and then you have the the beauty of the trustlessness of knowing that even if you're transacting with a stable coin, you can always have a unilateral exit to Bitcoin L1 with your stable coin and no one can prevent you from getting your money out.
1:16:19
So, it's the the best of trustlessness, the lowest cost, the most efficient, and you don't have to complicate uh how you actually pay for the fees for most people who actually don't own the underlying asset needed to to pay a fee.
1:16:31
So, it solves a lot of problems to make Bitcoin the absolute best platform for stable coin payments.
1:16:38
So, yeah, h how did the actual dollars get custodied in that in that way?
1:16:42
there's always this like hard interface between something that's truly decentralized and then like the US treasury at some point and I feel like that's where a lot of the stable coin companies kind of figured out how to uh you know bridge that gap and they exist as this layer between the US government effectively and the and the crypto community or like the programmable money world.
1:17:06
Um, and so it feels like we're on this trajectory of like let's make this more programmable, but but how close are we to something that's like fully programmable?
1:17:15
Well, I think you know here here's the issue, right?
1:17:17
So I mean first of all like stable coins are always going to be fully centralized.
1:17:21
And so you know that that's why it's so important for the network not to also be fully centralized because then we're basically replicating the entire payment system that exists today with just new players.
1:17:31
Yeah, Circle can is is it true that Circle can just freeze all USDC? Like Yeah, sure. Yeah, of course.
1:17:38
I mean, it's a company running a It's basically fully centralized.
1:17:44
So, like all of the stable coins are centralized.
1:17:46
Like there are a bunch of people who attempted doing uh algorithmic stable coins that would algorithmically basically absorb like the and it just doesn't work. It just doesn't work. Exactly. It doesn't work.
1:17:57
Um and so stable coins are fully centralized.
1:18:00
Uh I think programmability um programmability always comes at the cost of trustlessness.
1:18:05
So like the minute you can establish new conditions for how money can be moved uh with a smart contract, you lose the ability to have a full unilateral exit where no one can actually prevent you from exiting your fund from the network if you really want a trustless exit from the network.
1:18:24
Uh and I think that's the balance.
1:18:24
Uh, and I think you know what we're focused on right now with Spark is really providing people with the the right level of trust and and the differentiating factor that Bitcoin can bring with trustlessness.
1:18:35
Um, but I think gradually what you'll see is different levels of trust for different levels of functionality.
1:18:41
If you want more programmability, you'll have to actually relinquish a little bit of that expectation of trust uh to get more programmability.
1:18:49
But those two things will always be intention.
1:18:53
How are you balancing go to market right now?
1:18:55
now? I imagine you have this pretty intense uh tension between you know for example like a developing country that's excited or or or you know companies in a developing country that's excited about the potential of potential of spark
1:19:09
versus a fortune 500 CEO that's saying David we want to do something in stables like let's let's let's talk where where are you splitting your time and where are you most excited well I mean right now I feel like there there are two parts of our business Right. It's like one part is like core
1:19:25
It's like one part is like core infrastructure to make Bitcoin better, faster, more programmable, better for developers. That's all Spark.
1:19:30
developers. That's all Spark. Um, and then there's the the mission of connecting all of the banks, all of the wallets, all of the payment networks in the world to Bitcoin uh with universal money address or UMA um to enable people to actually move money from their bank
1:19:47
or from their wallet, the place they pay their bills from, the place they have a debit card or, you know, all kinds of different instruments attached to u to any other point in the world making basically money flow in a completely open unrestricted way uh 24/7. seven at
1:19:59
seven at a very low cost.
1:20:02
Both of these things are basically built on top of Bitcoin and serve different types of constituents that basically extends the reach of the network.
1:20:09
But but these are the two core focuses and it's it's it's a very interesting time for us because on one side we have permissionless building on top of Spark with developers building all kinds of different things like I I turn on my my computer in the morning or my phone and I look at like what people have built the night before.
1:20:27
I have no idea what's going on.
1:20:28
I don't onboard the business.
1:20:30
I don't have a contract with them.
1:20:31
Like it's a wonderful thing.
1:20:33
And then on the other side of of things, I'm going through like diligence uh uh processes and compliance stuff with like the largest banks in the world that are coming onto the network.
1:20:42
So it's kind of a little schizophrenic on both sides of the business.
1:20:45
But both of these things acrew to the same thing, which is an open money network that enables both developers on one side of the spectrum and regulated entities like banks and and wallets on the other side to move money in real time uh globally like never before.
1:20:58
So, it's a it's kind of a a fun two-sided uh uh part of the business right now. That makes sense.
1:21:05
How are you thinking about uh corporate stable coins?
1:21:06
There's been uh some announcements, different PR stuff around companies saying we're going to make our own stable coin.
1:21:16
Uh and we were joking uh on the show a while back, does that just turn into like a Kohl's cash scenario?
1:21:23
Uh, do people want every retailer, every big retailer that they interact with to have some native stable coin or or are the the issuers that we have today? It's a classic job.
1:21:35
There's too many standards.
1:21:37
We need one we need one standard and then you have one more standard than you had before.
1:21:42
I mean, look, uh, when I think about these things, I always come back to one thing, which is what problems are we trying to solve?
1:21:48
And uh I think you know it's very clear that like if you're trying to solve for dollarization in the world like if you're in Argentina or in Venezuela or in Turkey or in parts of Africa you'd much rather have a a dollar denominated account with a US bank. You can't have that.
1:22:05
So a stable coin in this case mostly Tether um is the solution to that.
1:22:09
It's like it's the next best thing to having a US dollar denominated bank account in the US.
1:22:13
Uh, and it's great and it it it increases the reach of the dollar. It's great for America.
1:22:20
It's great for these people. It works.
1:22:22
When it comes to domestic use cases for stable coins, there's a bunch of really good problems to be solved in institutional capital movement.
1:22:28
It's like, you know, you can't net settle trades uh on weekends or after hours.
1:22:35
You can't move liquidity between institutions to, you know, make the market more efficient with the current system because it doesn't allow you to do that.
1:22:41
Stable coins help do that.
1:22:41
But from a consumer standpoint, I'm kind of at a loss to understand like what an American consumer would actually get from using a stable coin. I I I don't get it.
1:22:53
I don't think there's a massive problem to be solved.
1:22:55
People can pay one another pretty easily with normal dollars.
1:22:59
They're already digital dollars basically.
1:23:01
Like if you look at your Venmo balance or your Chase balance, it's already a kind of stable coin that you're seeing.
1:23:08
It's like virtualized dollars that are controlled by the bank.
1:23:11
it's like basically a stable coin.
1:23:11
Um, so you know, I think this is a conversation worth having around like what is the consumer application using stable coins in the US that is actually solving uh a problem for the vast majority of Americans and you know I don't know what that is.
1:23:28
Can you talk about uh open source and how that inter like the current meta around open-source in uh in the crypto and and community and in just de the role of decentralization.
1:23:41
Was it ever an option not to have an open source project?
1:23:44
Uh it feels like kind of table stakes now, but do I have that kind of correctly in terms of the characterization? Yeah.
1:23:52
No, I mean it's super critical and that's why almost everything we build is uh is open sourced. Yeah.
1:23:57
And and the reason for that is like you know people building in this industry are are trying to make it like you know anti-fragile.
1:24:05
Uh and one of the ways that you make a technology anti-fragile is you don't concentrate all of the capabilities around one company that wins it all.
1:24:14
Uh and I I often talk to my team here uh at LightSpark and basically tell them look we're we're going to be very successful the day we have a bunch of competitors building on UMA building like all kinds of services to compete with us on the very technologies that we've helped build.
1:24:30
Uh and I think that's the way that we make ourselves kind of redundant and ensure that the network is actually going to exist even if we were to disappear for whatever reason.
1:24:40
And I think that's kind of an ethos of the entire industry that uh that we care deeply about.
1:24:45
Can you break down a little bit more how UMA works?
1:24:47
I think anybody that's played around with with crypto a little bit may have had the experience at some point of like sending Bitcoin to a USDC address and realizing that it's just gone forever.
1:24:59
Um, so the idea of a universal address that can you receive and send a bunch of different uh currencies makes sense, but um I'm curious how it how it works and how you're enabling other other companies to adopt it as a standard uh even if you guys aren't necessarily sounds like directly benefiting financially from that.
1:25:18
I mean we are for for for the the companies that we serve we we are benefiting financially from that but like it's an open network but but the way it works is that uh an institution like take new bank which is you know one of our our early partners on uh on UMA which has over 100 million bank customers in Latin America.
1:25:38
Uh and so they would assign uh you an address like dollar sign your name at new bank.
1:25:42
Uh you your account is denominated in Brazilian riis.
1:25:46
Uh let's say I'm here in the US and I'm with a bank.
1:25:51
Uh my uh my UMA is going to be dollar sign Davidbank. com.
1:25:57
Um I'm sending dollars to uh you in Brazil.
1:26:02
Uh and what happens in the back end is basically UMA is a pre-transaction open uh messaging protocol.
1:26:08
So it enables me to go to the new bank server and basically say hey is this address a valid address?
1:26:14
What is the currency that the this person wants to receive?
1:26:18
What is the exchange rate that you're going to charge me for this transaction?
1:26:22
Then I can present a fee structure to the customer in the US sending dollars.
1:26:27
Show them exactly the amount that the recipient is going to get in Brazil and RIIS. Uh they click send.
1:26:34
When they click send, basically the dollar gets converted into Bitcoin, gets pushed on Lightning to Brazil, gets to Brazil a second later, gets converted to Brazilian RI is deposited in the account. works 24/7.
1:26:44
Uh super low cost and super tight spreads between all of these currencies because Bitcoin has so much depth of liquidity with all of these currencies because it's traded so much. Yeah.
1:26:57
Um and and so it's like super costefficient real time 24/7 and open.
1:27:02
In some cases like New Bank, they build the the connectivity into Bitcoin themselves into Lightning because they can actually do the conversion on their side.
1:27:11
In some other cases, we built the capabilities uh for for instance US banks and European banks and Mexican banks and others to actually connect onto the network using their domestic payment system.
1:27:22
So they would send in this case the dollars to us and we would convert to bitcoin and push as a service to them.
1:27:28
So they don't have to deal with the bitcoin portion of it.
1:27:29
Uh but Bitcoin is always the net neutral settlement asset between those currencies and allows to move liquidity across countries across payment systems in real time 247. That's the way it works. Very cool.
1:27:44
Can I get an update from you on what's happening in Washington?
1:27:46
Uh break down the different uh legislation that's going through the uh the US government.
1:27:54
kind of what your perception has been, your takeaways, status update, but also are you optimistic about where things are going on the regulatory side?
1:28:02
Yeah, I mean, look, it's uh for a guy who's been shut down by the Treasury Department with, you know, the the most uh public uh public shutdown of the crypto industry, one could argue, uh it's quite a change, quite a vibe shift, right?
1:28:15
And you know, I I was I was at the the digital asset summit at the White House.
1:28:20
And you know, being welcomed at the White House in a in a in the east wing in a very ceremonial way uh to actually promote the whole industry uh was was a massive massive whiplash uh of of the best kind, right?
1:28:35
And I think u I think you know look there's a lot of credit that goes to this administration to David Saxs Bo Hines u but also uh to people on the hill who've been working on these important pieces of legislation that are going to actually make building the the next set of technologies that will reinvent and rewire the world financial system here in America which I think was absolutely absolutely direly needed.
1:28:59
Uh and so I'm super bullish.
1:29:03
I think we we we have gone from an administration government that wants that wanted to like fully kill the entire industry to one that wants to promote it and ensure that American companies actually win at this and we win.
1:29:17
Uh and I think it's a a vital interest for America that we continue to to lead with financial services infrastructure.
1:29:24
So I couldn't be more bullish of you know what's happening in DC right now around our industry.
1:29:30
Is there anything that you're looking out for in the back half of this year? spirits.
1:29:33
Obviously, you know, we started strong with a with a memecoin out of the White House.
1:29:37
We've got the the new stable coin uh regulations uh passed.
1:29:39
Uh anything in the back half of the year that you're kind of looking at uh or or anticipating?
1:29:48
I think everyone's really anticipating market structure and like having a a market structure bill that will clarify the rules of the road for that entire industry.
1:29:57
I think that that's as important uh in my opinion as uh the the stable coin legislation that is going through now. Yeah, it's great.
1:30:06
Well, thank you so much for stopping by. This is fantastic. Thanks for having me. Great to be on the show. Always welcome. Talk to you soon. Talk soon.
1:30:14
Uh and next up we have Ben Thompson from coming into the studio.
1:30:16
Very excited to talk to him.
1:30:19
The moment we've been waiting for. Yeah.
1:30:20
Uh welcome to the stream, Ben.
1:30:24
Good to have you on the show.
1:30:27
You've been a backbone of many analyses here on the show.
1:30:30
Uh, and we're excited to welcome you to the to the show. How are you doing? I'm doing good.
1:30:35
I put on a button-up shirt and a jacket just for you guys.
1:30:38
So, you should feel honored.
1:30:38
I am wearing shorts underneath.
1:30:39
I will admit you didn't have to tell us.
1:30:43
People always ask if we wear shorts.
1:30:43
I We actually do wear the full suits.
1:30:46
We got to stand up to hit the gong. There's a wide shot.
1:30:48
Everyone I am I am the poser here.
1:30:49
So, I'm I'm happy to admit.
1:30:52
Well, it's a great it's a great sign of respect in our culture to to put on a suit for a TVPN appearance and uh we're just we're so excited to talk to you.
1:31:01
I as you know I've been lucky to read your work my entire career and and uh I think it I think so many of the thoughts that I have are now like your your way of thinking about technology and markets is so embedded in my brain that that ideas that I hold as true or just foundational beliefs are actually your beliefs that have just become so uh so immersed.
1:31:24
So uh it's great to talk. Well, thank you.
1:31:28
Um I I will attempt to implant new ones or or maybe show you the error of your ways. One or two. Sounds great.
1:31:36
Uh I I I I do have a question on um on the nature of where you sit in the media world before we go into actual questions about tech companies.
1:31:44
Um it's interesting that in some ways you're a journalist, but you don't really do the scoops and and breaking news that much.
1:31:54
Uh but you also don't issue just straight up buy and sell recommendations.
1:31:59
Um what was the thesis behind not just actually having a price target and not doing like this is a sellside bank but independent?
1:32:10
Well, when I started I mean it's funny to hear you talk about like my quote unquote place in the ecosystem. Sure.
1:32:16
Because uh when I started I had like I was 368 followers on Twitter.
1:32:18
I was just some sort of random random person uh on the internet. That's awesome.
1:32:24
That's awesome. in retrospect sort of right place right time I think is is certainly the case but I did perceive there was a a large gap between tech journalism and and I would include a lot of the bloggers there who were writing a
1:32:39
lot about products and then there was Wall Street that was very focused on sort of the financial results and to my mind there was a large space in the middle which is tied together the products to the financial results but also the overall companies
1:32:52
and and strategies and I'm very interested in culture and how that guy's decision-m one of my sort of precepts is all these companies are filled with smart people and a lot of people when you ask them why they did something wrong
1:33:08
they their only answer is that they're stupid and I'm like no they're not stupid it's actually much more interesting to assume they're smart and are doing stupid things and trying to unpack why they are doing that and what goes into that and uh and so that was
1:33:23
sort of the thesis was that there is this space to explore these spaces and then there's a business model aspect which is I started Shrekery two years after stripe started uh I think they had just come out with their billing product
1:33:35
and the only alternative at the time was was PayPal uh for subscriptions and it was fairly sketchy and there was lots of like horror stories out there about you know stuff and just the Stripe API was so great and the things you could potentially do with it and so on Wall
1:33:49
Street you're putting a price on it you're also charging like $100,000 a year or something like that uh and and so you get a small list of high RPO clients and my thought was I could go in the opposite direction and get a large list of low arpoo clients thanks to
1:34:07
things like Stripe and the ability to to subscribe and that would and as part of that I wasn't going to go through the rigomearroll of getting registered and doing stock picks and all that sort of thing. I've always joked if you want a
1:34:16
I've always joked if you want a stock pick from me you're going to pay me a whole lot more than $15 a month.
1:34:18
Uh it was $10 $10 when I started and it's actually pretty great.
1:34:23
Um now there's some one of the critiques I do get particularly from my you know friends on Wall Street is you know no skin in the game XYZ.
1:34:32
Um I think at at this point I'm large enough that my reputation is significant skin in the game and but I do recognize the validity of that that critique. Yeah.
1:34:42
And you know if you make a bad call you're going to have to circle back to it in two years and write about it yourself and admit that you got it wrong. Right. Right.
1:34:51
Listen when I No, I I had to write about this week.
1:34:52
this week. Uh like I was very optimistic about Apple's Apple intelligence announcement last year and the theoretical power it would give them over the model makers and now I'm ready actually no they're they're going to
1:35:04
have to figure they're going to have to pay up and that's you know that was a bad call by me that you know I think was you know very wellreceived at the time uh and might have gotten that one wrong and and so I I do need to be straightforward about that. And so I
1:35:17
And so I just this morning I was very crystal clear like I got that one wrong.
1:35:20
That was that was that was an issue.
1:35:21
What is nice is strategy kind of ended up being in this interesting place where I feel like I'm a little bit of like the Switzerland of tech in that no one pays anymore.
1:35:33
If you're a CEO you pay the same amount as you know Joel down the street that that that is paying it.
1:35:37
Um I don't invest directly which I think made sense when I started because I didn't have any money.
1:35:45
Um has probably hurt me a lot over the years. uh since then.
1:35:49
But uh I don't like and I think this is a different west coast east coast thing where it does feel like on the west coast everyone's talking their book sort of all the time and uh and you know that's why I generally as a rule don't have VCs on to do the trajectory interviews because it's it's kind of hard to get like a real take cuz that that is you know such a motivation. Sure.
1:36:12
Um and so me coming in being like I have no book to talk.
1:36:15
I'm just here telling saying what I think I think has been good for the West Coast audience, which is my base audience, even if the East Coasters think that uh I'm being a being a big wimp. So, that's funny.
1:36:28
Uh yeah, the talking your book challenge.
1:36:31
We we go through that a lot trying 12 VCs on a Well Well, yeah.
1:36:35
And and and we just try to get a bunch of different opinions and triangulate what you know what we think is was real. I'm trying to come up. You have TPN.
1:36:42
I'm trying to come with a P so I can get the talking book network in there.
1:36:46
But um talking talking book production network. There we go.
1:36:51
It's like ESPN for talking your book.
1:36:54
But but yeah, it is a real struggle to find somebody that for example has a deep understanding of every foundation model company but isn't massively conflicted at in some way or another. Extremely. Extremely. Yeah.
1:37:07
And so it's one of those things you just sort of you you end up like there's so much path dependency and all these sorts of things and and like I mentioned like a big advantage I had was I started at a time when sharing good links was very high currency on Twitter and so you know I grew very very quickly much more quickly.
1:37:25
I sort of had a 5-year plan um to go independent.
1:37:26
I ended up doing it in less than a year in part because it just sort of spread really really rapidly and it was an ideal time to be someone sharing interesting links regularly and I wasn't sharing them.
1:37:39
The the beauty is my readers were sharing them.
1:37:40
They were doing sort of the marketing for me and so I'm very cognizant of of sort of the the luck I had in that regard.
1:37:48
And then just over time and it's been an interesting journey for me to grapple with my different position in in the ecosystem like so when I started the structury interviews that was sort of part of it which was I started out not knowing anyone.
1:38:07
I got to the point where I can talk to anyone that I want to and so how do I square that?
1:38:12
I can't be the guy with the chip on his shoulder trying to make a name for himself forever.
1:38:16
It sort of gets it's like the the meme with the guy, how are you doing kids?
1:38:20
Like at some point you have to accept your part of the establishment.
1:38:24
How can I do that while still staying true to the idea that checkery is about the readers? It's reader funded. My loyalty is to them. I'm very clear.
1:38:33
I have no loyalties to anybody else.
1:38:36
And so well I'll just I will talk to people sort of acknowledgement of what I can do but it's going to be fully transcribed and published and and sort of available to to everyone.
1:38:46
to everyone. Have you ever dealt with or thought about the attack vector of a special interest, you know, buying a thousand plus, you know, thousands of seats to a single, you know, independent publication and saying like, yeah, like,
1:39:00
you know, we're happy, you know, we we we got seats for all of our employees actually because we really, you know, love the and then and then suddenly they're sitting over there, you know, representing meaning very meaningful amount of your revenue%. Um,
1:39:10
Um, I mean uh I fortunately uh I think of of a scale that I don't have that problem. Yeah, that's good. There we go.
1:39:19
But yeah, uh it's uh but no, I think I think audience capture for subscription sites is a potential issue for sure.
1:39:26
And this is another thing I was sort of right place right time.
1:39:28
I got big enough by the time that it doesn't matter.
1:39:29
And yeah, if someone's really ups like I give refunds all the time actually if someone really upsets me, I will refund them and every dollar they paid me, I'm just like go Okay, I don't you know I I I don't you're being abusive or whatever it might be.
1:39:43
And that that is a beautiful thing about the relatively low price, high customer base model is no one has power over me.
1:39:53
Like I I have the burden of publishing, you know, so as as often as I do, I feel a heavy weight of duty to my customers when I write something I'm not happy with. Like I don't sleep well.
1:40:03
But at the same time, there's no one customer or no no individual that can come in and be mad at me and and yeah, impact my business.
1:40:13
Um I I I'm I'm seeing that there's maybe some sort of parallel between uh legacy media and independent media where uh independent media it it's not by default more pro tech or anything, but there's just no salary cap.
1:40:26
So if you're at a legacy institution and you're writing probably some sort of rough loose salary cap of a few hundred thousand dollars whereas you go independent it's feast or famine.
1:40:36
You might fail but you might get really really successful and and have a huge income from that.
1:40:40
And and I'm wondering uh what we're seeing in the AI salary wars where we're seeing more and more talent and that you know Mark Zuckerberg potentially paying $100 million uh bonuses.
1:40:51
Um, do you think that Apple will come around to spending more uh money on researchers?
1:40:56
It feels like they kind of have an internal salary cap with uh Tim Cook making 75 million.
1:41:04
There's now people that report two levels down from Mark Zuckerberg that are making more than Tim Cook and you have this weird dynamic where even if there's no actual salary cap at Apple, you kind of have an implicit one from the CEO. Yeah, for sure.
1:41:18
Yeah, for sure. I mean well I think just to go back to to to the media observation you started out with is as you increase transparency in the market as you decrease non-related barriers which in the publishing world previously was really
1:41:33
geography and when everyone's on the internet you inevitably in just about all cases you get a power law distribution and a few people make a ton of money because they win most of the market and then some people make some and and there's a long tail that that sort of don't make any at all. But it's it's
1:41:50
But it's it's very it it's interesting.
1:41:52
It's it's fluid in a way, but it can sort of become somewhat static as long as the people at the top sort of, you know, continue to do well.
1:42:02
But what's interesting about AI is for 40 years, you would have periods of time where you'd have tech companies going to head headto-head in a product market.
1:42:13
And I I think one of the reasons part of the software eating the world sort of idea is the way you get an apex predator is that that predator killed everyone else first.
1:42:25
And so you had tech companies fighting each other for the first 20 30 years of tech.
1:42:29
The ones that emerged were lean mean killing machines and they and the entire industry were sort of set loose on the rest of the world and everyone was just like was is getting slaughtered sort of left and right.
1:42:39
But what you also had over this past sort of 20 years or so is the big companies in particular sort of slotting into unique slots.
1:42:50
So you have you have Facebook is is social, Google is search, Apple is devices, Microsoft is is business or you know business applications, Amazon e-commerce etc. Right.
1:43:00
Obviously, these companies are are very large and do lots of things and there's some overlap in different places, but they've been fairly sort of distinct in their categories and they've been dominant in those categories.
1:43:13
And so, they've been in a place where like Hollywood is wanting to get to, right?
1:43:18
What is the dream in Hollywood?
1:43:18
You want to have a franchise where the next Marvel movie matters more than who the star is.
1:43:27
The reason that's so great is because you now have bargaining power over the stars.
1:43:31
So you just sub someone else in.
1:43:32
And and whereas the old style like Tom Cruz makes the most money because Tom Cruz on a movie poster sells the poster.
1:43:39
And so in a negotiation, he has massive bargaining power.
1:43:42
So he's going to get get paid a lot get paid a lot of money.
1:43:45
In tech, it hasn't been that case.
1:43:48
The companies themselves have been franchises.
1:43:50
And so the the overall anyone who works in tech or probably works in any any any entity, but you know, there's a few people in each company that are critically important, really make the whole thing go.
1:44:01
Everyone else is fairly replaceable.
1:44:03
Those people are have probably always been somewhat underpaid um for years and years and years.
1:44:10
Both just by the nature of companies and the cultural issues and your salary cap sort of analogy, but then also just like it's not a transparent market.
1:44:19
It's not it's not hard to price sort of what people are worth with AI.
1:44:23
Everyone's trying to do the exact same thing.
1:44:26
So you have multiple companies trying to do the same thing.
1:44:30
The output is somewhat measurable.
1:44:32
I mean all the AI test stuff has issues but by and large everyone kind of knows who has the good models and and who doesn't.
1:44:38
They you know the scalability questions you know like because all these companies are trying to do the same thing.
1:44:45
We have a very unique situation where the bargaining power you increase transparency, you increase sort of the liquidity or the ability of people to move around because they're doing the same thing.
1:44:57
The bargaining power shifts to the people that are super valuable because suddenly it's much more clear who's valuable and their skills are much more transferable.
1:45:08
So this is I think a very underrated bare case for tech in terms of AI at least for this time period is they've lost that that murky bargaining power over employees that they enjoyed for decades and currently you're seeing what happens
1:45:28
when you don't have that you start paying employees what they're worth and obviously that's great I not saying this this is a business analyst it's not a sort of a moral statement But it is like what Mark Zuckerberg is doing I think is totally rational. I think it's a classic
1:45:41
I think it's a classic sort of Clayton Christensen from Facebook's perspective. AI is all upside.
1:45:47
So of course they're going to invest what they need to do to win.
1:45:49
But it's costing him a lot of money and by extension it's costing everyone else in the ecosystem a lot of money.
1:45:55
Well, isn't it in some way is the right way to think about the last couple weeks uh like more of like an aqua like an unofficial aqua hire in the sense that you're it's it's not just the the people, but it is the the knowhow in terms of hey here's there's these things that we want to do that are important to our business in a lot of different ways.
1:46:14
And we're basically it's it's like the collective is actually more valuable than any one like the collective together getting 10 researchers at the same time is meaning you know is meaningfully more valuable than than than than just each individual researcher added up.
1:46:29
You know there's probably something to that but I I I think again like what is actually different between what Google is trying to do what Anthropic is trying to do what OpenAI is trying to do and what Meta is trying to do.
1:46:41
they're all trying to do the same thing.
1:46:43
to do the same thing. So I my suspicion I'm not an AI researcher so I don't want to overstate my my knowledge in this space but my suspicion is skills are are fairly highly transferable and when that is the case there is in some situations if lots of people can do those skills that's terrible for the employees because then their bargaining power gets
1:47:05
diminished because anyone can slot in but we're in this space where the skills are transparent knowable transferable and there's not very many people that can do them and so it's it's scarce resource that everyone's fighting over and that's why you see this real shift in negotiating leverage as as manifested through these dollar figures to to AI researchers. Yeah. Do you think um I mean Google Yeah.
1:47:26
Do you think um I mean Google seems like the most fragile and the most like paranoid about just disruption. It's not all upside.
1:47:33
Uh it could be very bad for them.
1:47:36
Um the innovator's dilemma, you know, you had this back and forth where uh Senator Pachchai mentioned that he hadn't read the book.
1:47:43
You said it doesn't matter because it's a structural issue.
1:47:45
I think that's a good point.
1:47:46
But if you play back the counterfactual, is it ever possible to disrupt yourself and essentially like if the Gemini app had launched before Chat GPT and they had taken over that mind share and maintain 90% ownership in that like it would be somewhat disruptive to their revenue and their profits as they transition over.
1:48:09
But when I sum the revenues from OpenAI and LLMs and then Google search, I'm not seeing some massive drop off that's actually that actually would destroy Google in the medium short to medium term.
1:48:22
So, but I'm wondering if you think it's like is it entirely impossible to avoid the innovator's dilemma by disrupting yourself?
1:48:32
Well, number one, you have to also look at margins, not just revenue. Yeah.
1:48:34
Um, but number two, you actually you answered your question.
1:48:40
Google didn't launch Gemini as a chatbot. That's the answer. They were years ahead.
1:48:44
They they invented the transformer a decarade ago.
1:48:49
And and so in many respects like there's parts of this question that the counterfactual makes the point in that it is a counterfactual and it's not reality.
1:49:01
Now, I do think I think Google's done better than I expected over the last two years.
1:49:05
Uh I I like what they're doing in search generally.
1:49:09
I I think they it does seem to be the one part of the company that still functions like they they can actually iterate and build products.
1:49:18
What we're seeing is reminiscent of what they did a decade 15 or 12 years ago when everyone's like vertical search Google's done all the everyone's going to search in apps and Google completely transformed the SER the the search uh engine response page uh whatever it is
1:49:32
uh the search engine results page to be local or to be shopping or whatever and Yelp's been throwing a hissy fit sort of ever since and and so that's what they're doing with search right and and with search overviews and they have this new search labs or or AI mode. They can sort of test stuff out
1:49:47
They can sort of test stuff out once it scalable.
1:49:49
Once they they're confident about the monetization issues, they can sort of shift it over.
1:49:52
I call it the search funnel, search AI funnel.
1:49:56
I think it makes a lot of sense.
1:49:59
And I think and I this has always actually kind of puzzled me where I think they're responding fairly well even though this is seems to be a textbook case of disruption.
1:50:09
And I went back to an article I wrote years ago uh called Microsoft's Monopoly Hangover.
1:50:15
And I was I I I went through Lou Gersonner's autobiography and about how he turned around IBM and his real insight with IBM was everyone wanted him to break it up in into sort of different pieces.
1:50:29
And what he realized was IBM was so big and and large from having downstream of Monopoly that actually the only thing they were good at was being big.
1:50:40
And so breaking them up would actually just create a bunch of subscale low-performing companies that would all get wiped out.
1:50:49
But as this behemoth, they could go to other big companies and solve all their problems at a very mediocre level, but still it's sort of an attractor proposition.
1:50:59
And under Gersonner, they really rode the internet wave.
1:51:01
They went to all these big companies, said, "This internet thing's happening. You need help.
1:51:06
We'll solve your problems for you."
1:51:07
and had a very sort of successful run, you know, kind of until cloud came along and which Gersonner, by the way, was was was a proponent of, but you know, by that time the IBM people were back in charge and I was thinking about the the context of Microsoft where m you business models are hard to change and disruption is ultimately about business models and culture is hard to ch even harder to change.
1:51:33
But what can't really be changed is the nature of who you are.
1:51:36
And a and I think there is, you know, in Microsoft they were in a similar situation.
1:51:41
they were in a similar situation. They were a big monopoly and they weren't a product company and the attempts to become a product company with Windows 8 and all the things that went on around that time inevitably inevitably failed
1:51:54
and senadella to his great credit and you know sort of diminished Windows importance in the company broke it literally broke it into pieces spread it around and this was a multi-step process and got Microsoft back to a place of we're big and we'll will do everything. We're we're not a Windows company. We'll
1:52:13
We're we're not a Windows company.
1:52:13
We'll go in there and we'll go solve all your problems.
1:52:16
Very sort of reminiscent of of the the second version of IBM.
1:52:18
And I go back to Google and I've always been intrigued by the I'm feeling lucky button, which doesn't exist anymore, but I always enjoyed that that button continued to exist long after you it was impossible to click because the moment you started typing in the search box, it would start auto searching immediately and jump jump right to a search page.
1:52:42
right to a search page. But it was it was there in a it's just so core to Google to give you the answer to to know everything like the to to know everything about the world and to there's a bit where even though the core
1:52:58
of their business model is 10 blue links and it's not just the the users choosing the search link which gives them a data feedback loop so they know which results better but also the users choose the winner of an auction Google puts on for ads and it's an incredible business model. And there's something about that
1:53:13
And there's something about that that's always been intention and counter to what Google was founded to be.
1:53:18
And I feel like that germ of what Google was founded and meant to be is an AI answer engine.
1:53:27
and and it almost feels like even though Google is old and large and fat and slowm moving that core aspect of their nature and is is still in the culture and that's why they're finding it in themselves I think to do better in AI than you would expect.
1:53:48
Was it enough to launch a chat GPT before open AI? No.
1:53:53
Uh was it uh was it enough to have any sort of cogent response for the first 6 to9 months? No.
1:53:57
to9 months? No. But it was enough that I think they've done better than I expected over the past year in particular and gives me I think more optimism than I expected I would have for the company when you know I when chat GPT first launched
1:54:17
AI overview from Google if you search Google's mission Google's mission is to organize the world's information and make it universally accessible and useful which is exactly what language models do really really well like the thing that's just undeniable, right? It's you can you can debate whether uh
1:54:29
It's you can you can debate whether uh this is going to be the year of agents.
1:54:34
It doesn't feel that way to me yet, but this is the year that most people have realized that wow LLMs are very good at organizing, surfacing, and and making data valuable. Yeah.
1:54:48
You you mentioned uh just the the debate over breaking up IBM.
1:54:51
Uh I'm interested if you could take us through some I bet you didn't realize you're gonna be talking about IBM today, did you? No, no, no.
1:54:58
Uh, but I want to I want to talk about Intel and and kind of your the the history of some of your takeaways and what you think you've gotten right in the past, your perception of, you know, should they break up the the foundry business uh and what you think might be in the works with Lip Bhutan coming in there.
1:55:16
Um because it I was listening to Dylan Patel talk about his conversation with uh the new CEO Lip Bhutan and it seems like they're doing lots of tightening up, lots of layoffs, but uh it's kind of I I don't even know what framework to apply to analyze like is a breakup the correct thing.
1:55:34
It feels like something people just say. Yeah. Um so Intel, it's funny.
1:55:38
Yeah. Um so Intel, it's funny. I one of my very first articles was about Intel and what I said at the time was and this was 2013 and this was an art like you know when you start a site like Sery you're like a new band and why does everyone think a new band's first album is the best cuz they've been working on
1:56:00
these songs for years right and then the next album they had a year to do it and they all suck right so I mean I'll let people decide if that applies to checkery or not um I won't be offended sophomore slump but Yeah, but I had been on, you know, Intel had been a thing I've been wandering about for a long time, which was by 2013 when I started, they had clearly missed mobile. Now, it wasn't
1:56:19
Now, it wasn't clear to them, they were still trying to do the Atom processor and and just they're going to figure it out tomorrow.
1:56:28
And the the problem with missing mobile is the problem with Intel in general is Intel is always very biased towards high performance.
1:56:37
And this goes back to uh actually Pat Pat Gellzinger his first time through at Intel.
1:56:44
Intel, you know, had the CISK uh uh the the way they're there's CISK versus risk.
1:56:49
It's like uh it's different ways of organizing bits or whatever, risk is generally more efficient and actually even Intel processors today even though x86 is CISK, the internal it's re-ransated internally to a risk type language.
1:57:03
language. Um, none of that is really important other than to say in the 80s there was a real push in Intel to switch away from x86 and to to a risk type um uh of uh I don't use but like um for for the processors and Ginger was a leading proponent that
1:57:20
this is a terrible idea and the reason it's a terrible idea is because there was already a huge ecosystem of software built around x86 and all this low-level code and capabilities that No one ever that was written once and no one ever wants to touch again because it's miserable work. And he's like to rewrite all that stuff
1:57:38
And he's like to rewrite all that stuff would take at least two years.
1:57:45
And in that time, our ability to manufacture chips will improve so much that had we just stuck with CISK, our processors would be faster.
1:57:56
And that was the right bet.
1:57:56
And that's one of those foundational bets that I why I like to think about companies and their history and what goes into that which is Intel from the 80s on has solved its problems by having superior manufacturing and by moving faster.
1:58:08
And yeah, our chips may be theoretically less efficient, but if our manufacturing is better and our transistors are smaller, it doesn't matter because that will swamp whatever theoretical sort of efficiency you might have.
1:58:21
And this drove the entire computer industry.
1:58:23
You you you you would write to write a program to every second you spent optimizing your software in the 80s or 90s was a waste of time because whatever improvements you could get would be swamped by the next version of uh if you went from 286 to 386 or 386 to 486. That jump was so large.
1:58:42
You were better off focusing on features even if it made your software sort of slow to use on the current hardware because the next generation of hardware would be so much faster.
1:58:52
it would solve your your speed problems for you.
1:58:54
Now, this has generated a lot of bad habits amongst tech developers.
1:58:58
That's why you get bloat and why you have like poor performing things and all those sort of things.
1:59:02
But this was sort of super critical.
1:59:04
And so, Intel at its core has always been focused, they've always been manufacturing first and focused on better and better performance.
1:59:10
What happened with mobile is in that calculation did not come efficiency.
1:59:17
They were never focused on efficiency and in mobile efficiency was everything.
1:59:21
So what happened with mobile is app Apple went with an ARM processor made by made by made by Samsung and they basically rewrote everything.
1:59:29
All that stuff Intel didn't want to rewrite in the 80s or if they rewrote would just give other process processor companies a chance to catch up with them had to be rewritten for mobile because efficiency was so much more important than performance.
1:59:43
When that happened, Intel was screwed.
1:59:46
Now, it took them a long, long time to realize they were screwed, but they they were just fundamentally unsuited to be competitive.
1:59:50
It was the whole Paul Adelini turning down the iPhone contract is not true.
1:59:59
Tony Fidel, I I I said that once and I got a call from Tony Fidel actually.
2:00:03
This is when I had him on it for an interview and he's like, "This drives me up the wall.
2:00:06
Intel was not remotely competitive even though they had ARM chips then.
2:00:10
Even their ARM chips then were focused on performance, not on efficiency."
2:00:13
And and so the the problem for the problem for Intel is once you missed mobile, you were going to lose your manufacturing lead at some point because volume matters so much.
2:00:24
And every time you move down the curve, your transistors get smaller, the costs increase massively.
2:00:31
So you need volume to spread out the cost of building these fabs.
2:00:35
Like back then when I wrote this article, fabs cost 500 million.
2:00:38
Now they cost like 20 billion.
2:00:40
And this is over a course of like 12 years. Mhm.
2:00:44
So, so it was clear Intel was going to be in big trouble back then.
2:00:46
And so I wrote they need to build a foundry business.
2:00:51
They need to figure out a way to build chips for other people because in the long run the cost of keeping up in manufacturing is not going to be tenable if you're not making mobile chips.
2:01:03
And what obviously they didn't.
2:01:05
TSMC made all the mobile chips for everyone. And guess what happened?
2:01:08
TSMC took over the manufacturing.
2:01:10
Now, there's lots of other things that went into this, why Intel stumbled and sort of things, but at a structural level, what happened was actually inevitable once Intel missed mobile, unless they figured out a way to make mobile chips some other way. They didn't do that.
2:01:29
What's interesting is what has the problem with that it took so long to manifest.
2:01:36
part of mobile was you had an explosion in the cloud because cloud and mobile actually go hand in hand.
2:01:41
Intel made all those cloud chips.
2:01:43
Intel stock had an incredible run from the time I wrote that article for the next 8 to nine years.
2:01:48
And I felt like kind of a cuz I'm like saying this company is screwed if they don't do what I say.
2:01:52
They didn't do what I say and their stock went to the moon.
2:01:54
But what the the way it actually caught up to them has been in the past two to three years where there's astronomical demand for AI chips. Only TSMC can meet it. Intel's not in the game.
2:02:07
They're they're trying to shift to a foundry model, but they're they're so far behind it.
2:02:11
Being a foundry is being a customer service business.
2:02:15
It's not being an Intel we tell you what to do or we we tell our design teams how to change their chips to accommodate our manufacturing needs.
2:02:24
It's just it's totally different.
2:02:24
And they needed a decade to learn how to do that.
2:02:28
Had they changed in 2013, they would be ready today to capitalize on AI.
2:02:35
And and the counter example here is Microsoft.
2:02:39
Microsoft building Azure.
2:02:39
Yes, it got them somewhat in the game with mobile and things like that, but AWS dominates uh in that space, but by virtue of building up Azure, they were prepared when the AI opportunity came along.
2:02:53
And now Azure is is sort of a big AI player.
2:02:59
And you know, I wrote about these these two examples a few weeks ago in the context of Apple.
2:03:03
I think the concern for Apple isn't the short term.
2:03:04
We're going to be using AI apps on our iPhones for quite a while.
2:03:12
It's are they going to be prepared for what's next if they don't do some sort of sort of reset and pivot here?
2:03:21
Oh, sorry to answer your question about Intel. any Yeah.
2:03:23
I mean, it's like a managed decline basically like just like, you know, just get as much cash flow out of this thing as you can while you wind down the business for Intel.
2:03:33
Yeah, that's what I'm hearing.
2:03:33
It doesn't feel like, oh yeah, there's a silver bullet.
2:03:37
Just split the business and they're good.
2:03:38
Like, no, it's like it's it's the reason not to split the business is Intel needs volume and they get volume from Intel. Sure.
2:03:45
Uh and uh the and AMD split their business a decade ago and it was really They had a very hard time for many years and they had very tense and difficult negotiations between the global foundry side and the AMD side.
2:03:59
Global Foundaries was AMD's uh uh manufacturing arm.
2:04:01
Um and it wasn't until really they got out of that and went to TSMC and then also completely rehauled their chip design business uh and all those you know um that they they got in the position they were and then also that Intel stubbled uh that that certainly really helped them Intel today.
2:04:16
So if you split it up like who's buy like Intel's Intel itself is fabbing some of its stuff with TSMC. Yeah.
2:04:24
Who who wants to buy Intel's foundry services?
2:04:27
The the problem here is uh TSMC is located in a country called Taiwan.
2:04:32
Um which you know what it is today but 5 years ago it' been like what Thailand?
2:04:35
Um which by the way was probably much better for Taiwan security when the American thought it was Thailand.
2:04:40
Um, but so there's a real national security element here and it's just it's a really tough situation cuz Intel is a failed company at this point and they're and the the reason the failure is so total is because the aspects that drive their failure are the same things that drove their success. It was their arrogance.
2:05:05
It was there a sense that we're the best that we will just win through manufacturing might and performance and all those things work against becoming a good foundry work against being a customer service organization work against recognizing the fact that you're not going to make up for missing mobile through manufacturing which was their bet for years and years.
2:05:29
You you had to accept that you lost and and that's a tough place for companies.
2:05:35
It's not like someone made a mistake.
2:05:37
It's that what they did what they did too well for too long.
2:05:42
Who they were they continued being who they were. Right. That's right.
2:05:45
But who else are you going to get if you want an alternative to TSMC?
2:05:48
It's it's it's a very situation.
2:05:51
Last question and I think we'll be forced to to have you make a slightly shorter answer.
2:05:56
Unfortunately, I wish I wish we had hours to keep talking.
2:05:58
I wanted to get your updated thinking on X AI X the combined entity.
2:06:01
The last 24 hours have been very chaotic.
2:06:05
When the initial merger was announced, it made sense for financial reasons for some of the different stakeholders, but I wasn't fully sold on this idea.
2:06:14
Uh how you're going to force me to go with takes that I I generally just avoid writing about Elon Musk companies um for uh self-sanity reasons.
2:06:23
I think I mean I remember I wrote an article years ago about like uh when the Model Y was announced and I was talking about you know it's a Tesla and this aspect what Elon Musk is very incredible at is sort of creating reality out of thin air.
2:06:38
Um he's like the ultimate memer and to create like um like it's the way things used to work backwards.
2:06:50
Uh I remember I analogized it to like protests like a critique of of of modern protests is they spin up very quickly cuz social media makes it very possible but there's no infrastructure under them so they don't amount to anything.
2:07:01
Whereas you go back to like the civil rights era there was years of groundwork that went into like the million man march uh you know on Washington DC and there was a structure in place that ultimately manifested in large crowds.
2:07:14
But modern protests are the opposite.
2:07:16
The largess comes at the beginning and then it all falls apart because there's nothing in place.
2:07:19
And um there there's something the that makes a challenge to write about anything Elon Musk related is the you have all the social aspects is you have this bit about Tesla of creating reality.
2:07:31
It's the stock was buttressed for years by these true believers even though the financial parts didn't make sense.
2:07:38
You famously had these wars with the short sellers and all that sort of thing. And it worked.
2:07:44
It basically manifested a market for this Model Y and then the and then the Model X. Uh, not the Model X. What's the other one? Um, the Model 3. Yeah, it was Model 3. Sorry.
2:07:54
When I wrote that article, Model 3 and Model Y had this massively successful and all all the people that were true believers got very rich.
2:08:00
Uh, and congratul congratulations to them. I It's great.
2:08:06
But it makes it almost impossible for someone for what I do who I want to look at structure and fundamentals.
2:08:10
I can observe this effect happening, but you can't really say what's going to happen or the effects of it other than to say this is interesting.
2:08:19
And so I wrote about that article and then the Solar City thing came out and he's like bailing out like his brother-in-law or something and I'm like I can't write about this.
2:08:26
Like what am I going to say?
2:08:28
Like like there's it just doesn't make sense.
2:08:30
And so I think there's fast forward to X XAI.
2:08:35
Um yeah, there's a theoretical piece here.
2:08:37
I think actually XAI would be an incredible acquisition target for a lot of companies if it wasn't saddled with X. Uh so interesting.
2:08:43
X. Uh so interesting. feels like the end state is like Twitter getting spun out again like that that that's my I I that's kind of like my my it just ends up going back to Twitter and and and it becomes the blueber no one actually wants to like Twitter Twitter there's never been
2:09:02
a company in the history of the world probably where the impact of a company is completely and utterly divorced from its financial realities like I think when Elon Musk bought it and I assume that's continued through now they'd had like one profitable quarter in their history. Like it it's an unbelievably terrible
2:09:19
Like it it's an unbelievably terrible business.
2:09:21
Uh and so I think it's probably weighing XAI down.
2:09:24
There's a Yes, I get the theory that Twitter data helps XAI.
2:09:29
Well, it helped yesterday contract for that data.
2:09:31
You don't need to pay 43 million for Twitter to to to or 43 billion I should say to to get it.
2:09:34
So yeah, that that was always my position too.
2:09:38
I don't think it helped yesterday when when Mecca Hitler emerged on the timeline on the timeline yesterday, but uh good luck.
2:09:44
Hopefully they sort it out.
2:09:46
I wish I wish we had a lot more time here, but hopefully we can do it again. This is fun.
2:09:50
Thank you so much for stopping by. Yeah, no worries.
2:09:52
Uh I love what you guys are doing.
2:09:53
I actually had the idea of doing a daily podcast ages ago.
2:09:55
Um classic example of ideas don't count, execution does, and you guys you guys did it. I think it's great.
2:10:02
Well, you're always welcome here. You're always welcome. Thanks so much. Thank you.
2:10:05
We'll talk to you soon, man. Bye.
2:10:05
Uh we will jump straight into our next guest, Scott B.
2:10:10
Hopefully uh we haven't kept him waiting too long and he is still in the waiting room.
2:10:14
We'll bring him into the studio.
2:10:16
Really quickly, let me tell you about Wander. Find your happy place.
2:10:17
Book a wander with inspiring views, hotel grade amenities, dreamy beds, top tier cleaning, and 247 concier service.
2:10:22
It's a vacation home, but better, folks.
2:10:24
And we will check in on Scott Bellski uh and see if he is available to catch up.
2:10:31
How's How you doing, Scott?
2:10:31
Sorry for keeping you waiting.
2:10:33
Uh Ben Thompson uh he he knows so much about history.
2:10:36
He runs he puts out three hours of podcasts every day.
2:10:42
I should have expected bump me man.
2:10:45
I mean that's you know I just feel so bad but uh I'm very excited to have you.
2:10:50
Uh can you uh just give me a little update of what's going on in your world?
2:10:54
I want to talk about the AI safety layers concept and then we can talk about some of the current uh stuff that's going on in AI.
2:10:59
It feels like I mean I you published this post what two three weeks ago and it feels extremely relevant today last week.
2:11:06
So very excited to get the update from you. So uh kick us off.
2:11:12
Yeah gosh where do we begin?
2:11:12
Well listen in our ongoing uh segment here on implications of the technology that's happening that is happening faster and faster and faster.
2:11:19
Um a few things top of mind.
2:11:22
You know we can start we can certainly start with AI safety layers.
2:11:24
I think it's fascinating how much discussion there is of the dangers and the perils of AI. Yeah.
2:11:30
Without recognizing how it can operate as a layer to protect us. Yeah.
2:11:35
You know, if you get some call from someone who proclaims to be your grandmother asking for money, you know, that's clearly something that AI on the device, you know, some form of local model can detect, you know, given it's all happening on that device and warn you this isn't your grandmother.
2:11:50
this isn't your grandmother. um when it comes down to uh all sorts of the you know creative creative and crazy uh scam fishing email type things that we get all the time that's a perfect use case for AI of course you know and telling us that we need to be uh to be wary but also I mean what what about being
2:12:08
polarized by algorithms and you know detecting an algorithm changing based on your engagement and you know an AI sort of saying hey Scott you you're getting on the fringe here like watch out you know you're now like in this small 10% of society that now is going down this rabbit hole of some conspiracy theory. I
2:12:22
I just think there's so many there are so many use cases of AI as a safety layer that um that the device needs to unlock and of course that means the operating systems need to figure this out.
2:12:35
So I think that when most people talk about AI safety or safety layers or what you just described solving the problems that will inevitably come from any new technology they look at it through a technological lens.
2:12:46
technological lens. they see um you know uh well let's do more reinforcement learning let's uh align the model there's a fixation at the model layer like we need to solve it need I look at it almost entirely from an economic lens and I just think about it as if the market cap of the company that's
2:13:04
selling you the Skinner box is bigger than the company that's you know helping you get healthy uh if the if the sugar company is bigger than the health food company you're going to be uh you're going to be fat and if the health food company gets bigger, then you're going to be healthy. And so I I think about it
2:13:18
And so I I think about it as like uh the doom the doom scrolling, we have screen time apps.
2:13:23
Um they're small, they don't monetize as well as doom scrolling, unfortunately.
2:13:27
So there's some like economic considerations there.
2:13:31
Um but at the same time, I come back to the scamming angle and I see this as like, you know, um uh if if the economic weight behind the good guys is bigger than the economic weight behind the bad guys, you get the good outcome.
2:13:45
And that's why I'm not particularly worried about like super doom scenarios because I think that you know generally governments and and people will align with like hey let's not get paperclipipped so let's build more systems to be safe in general and then the bad guys yeah they might go try and build some really bad weapon but they will be completely outnumbered.
2:14:05
The question is like on the margin when we get into these pockets of you know the the Skinner boxes the doom scrolling where the economic weight looks.
2:14:13
So do you think about it in that same lens?
2:14:18
And then the question is like what business models can actually support this?
2:14:22
Are we talking you know I need to have a subscription for like a clue like app that's looking at everything I'm doing and then acting as that layer on top.
2:14:32
how can we actually implement this or is this just like we're hoping that Apple runs a great ad campaign around it and it becomes an Apple feature that they you know hold up at every uh every chance they can.
2:14:44
Well, I mean first of all I think that the operating systems of our life are the ultimate interface layers and you know and for many of us it's either Android or iOS but at work it's you know many other companies that are operating systems at work but those operating systems are trying to make us loyal.
2:14:58
they're going to do so through remembering us, you know, personalization effects or the new network effects, I like to say.
2:15:03
And uh and I would imagine that protecting you um from what's what's going to be a very uh you know, comprehensive and and um and very sophisticated set of of social engineering and other sorts of, you know, long long form scams.
2:15:19
I mean you think about the most effective scams that are out there is when you know you really have this like very long you know experience or exposure to some entity to the point where you trust it and then suddenly it gets your information and and then it's too late and so that's that is a perfect use case for AI on the device to kind of monitor over time you know compare that data with any other scams that are reported.
2:15:43
So in terms of the economic incentive, I mean, goodness, I feel like consumers will have high willingness to pay for that um if they don't get it free with their operating system. Yeah.
2:15:53
I mean, I think you can already imagine the the UI of you pick up a phone call and Apple, you know, in the in the you know, you can think of the traditional layout of like the the hang up button, the hold button, etc.
2:16:06
And then there's just a you should have a little bit of tag a tag there that says like AI voice, you know, detected or or something to that effect to just I'm perfectly happy with talking with AI, you know, a model effectively on the other end.
2:16:21
But I would like to know everyone should sort of know that it's a model and I think we're in this weird period right now where people all the time are are starting to talk with AI and not even fully realizing that it's a human.
2:16:32
On the other end, there there's there's some ways that um there there's the contra credentials movement, right, which I was involved with back in the days at Adobe, which is an effort to um have models uh insert cryptographic metadata into anything that's generated including live generation like you know live live audio that can be detected on the client.
2:16:50
So there's some ways of going about this.
2:16:54
But in terms of you know your point about the economic model is interesting.
2:16:56
You know my immed my thoughts im immediately went to alarm systems.
2:16:58
You know, we all pay for these Ring alarm systems, like all these alarm systems for our home that sometimes cost $60, $120 a month, you know, with monitoring and window motion detectors and everything else.
2:17:10
But we're not really paying for an alarm system for our like, you know, for our devices in this new modern world where we're going.
2:17:16
Maybe there is a market for an AI safety layer as a service.
2:17:20
It's like new anti virus or something.
2:17:22
We're going to have viruses and stuff, but it needs to screen record everything.
2:17:26
Yeah, it needs to be at the operating system level.
2:17:27
Um, how are you thinking about Yeah.
2:17:30
for mind viruses, mind virus detection? Yeah.
2:17:32
How are you thinking about the evolution of those like the mind viruses that come from just the accidental interaction with AI?
2:17:39
I mean, people are there's such a wide swath.
2:17:42
When I talked to Jordy and we were having dinner last night with David Senra and we were talking about how we use AI and we're like, yeah, probably 30 minutes a day in chat GBT.
2:17:51
Like it's a lot, but these interactions are summarize this post, do this research, pull this things.
2:17:56
It's it's it's like talking to a computer.
2:18:00
I'm not saying, "Hey, how is your life?"
2:18:02
I never have that interaction, but there are a lot of people that do.
2:18:04
And so, what are you seeing?
2:18:06
What anecdotes have you pulled from?
2:18:08
How do you think that evolves? Are there any risks?
2:18:10
Walk me through kind of the way humans are interacting with just language models broadly? That's interesting.
2:18:16
I mean, I think, you know, one of the topics that is on my mind a lot lately is consumer AI.
2:18:19
mind a lot lately is consumer AI. And I'm not just talking chat GPT which is obviously a consumer product for many of us but uh you know it's interesting I was at a tech conference recently where uh where all the trends that you know are are popular now were being discussed and I left asking myself what's the one
2:18:37
thing that no one talked about and the one thing no one talked about were new consumer AI era social networks and new you know when mobile came around there were a whole new uh variety of social networks like every time there's a platform shift a lot of consumer uh mainstream applications or social networks, that sort of stuff is reimagined, right? And so the question
2:18:55
And so the question is why is why why is that not happening now?
2:19:00
And uh and then then the whole saying in consumer investing is always around novelty preceding utility.
2:19:04
And so I'm trying to keep an eye out now for examples of um of of consumer AI.
2:19:09
I mean there's this company called Tolen, which is sort of like a pet alien that you uh you start having conversations with and they're doing really well.
2:19:18
I believe they raised a round from you know some some of the top firms.
2:19:21
Um you know I've been playing with this idea a few ideas with friends you know one of a simulation representing our digital twins.
2:19:29
twins. So could you uh could you kind of train a a sort of an AI digital twin of you based on all your experiences in chat GPT or any other sources of data and then deploy that in a simulation with mine and others and we could start to actually just watch them interact with each other and it's kind of it plays with some fun ideas of plausible
2:19:48
deniability you know oh my gosh like I'm so embarrassed like what my what my simulated twin said to yours um these are the types of things that are you know wingman as a service like I don't know is there an AI guy wingman that, you know, helps us when we're flirting with people on social on, you know, on dating platforms. Yeah. Yeah. The the the I'm what what I Yeah. Yeah.
2:20:05
The the the I'm what what I want to see and you're kind of getting at this is just like more weirdness, right?
2:20:10
It's it's easy to go build the next or not easy, but but you know, we were at YC and there's a lot of companies in in the last batch building agentic infrastructure.
2:20:18
It's like that stuff needs to be built.
2:20:20
stuff needs to be built. But I also at the next batch I hope there's more people being like yeah a lot of people have built all this infrastructure already B2B basically B2B SAS why don't we why don't we just like take a crack at like yeah
2:20:32
some dating simulation where it's like you create a digital twin and you just like throw it into the mix and it goes on a thousand speed dates with people in your city not even speed dates but simulations of dates with people in your city. Yeah, we've talked about this before.
2:20:44
Yeah, we've talked about this before.
2:20:45
this idea of like you have a whole bunch of people that are talking to a romantic AI partner and that feels super dystopian.
2:20:52
But if if person if if Steve in in Los Angeles is talking to the AI girlfriend and then Sarah in Boston is talking to an AI boyfriend and the two AIs realize on the back end that these people are super compatible because you have so much data from them.
2:21:08
It's just introduce the two humans and say, "Hey, you know, yeah, uh you have to pay us to introduce you.
2:21:14
we're gonna pay a finder fee uh and collect your LTV on this app for the next 10 years because you guys are going to probably live happily ever after.
2:21:20
And and that's kind of like the white pill scenario that I hope happens and I hope the dating app companies break up with AI companion though. Yeah. Yeah.
2:21:28
Basically, yeah, but the AI it's the her scenario where what if you're still together but your AI versions have broken up like are you what happens then?
2:21:36
Well, then you get a warning or it contacts like a divorce lawyer or something for you takes a fee on that. Who knows?
2:21:41
I think there's a lot of fun stuff to explore here.
2:21:43
And you know, one of the other random ideas I had was uh I I called it peanut gallery.
2:21:47
And the idea was that you know the dirty little secret about why we go back to products like Instagram and others oftentimes the traffic goes up after we have posted content because we want to see who else saw our content.
2:22:00
And so playing off that off that idea, you know, imagine a social platform called Peanut Gallery where you post your own content, but no humans are allowed to comment on it.
2:22:12
It's all these like personas that are like, you know, tightly tightly uh defined personas that are commenting and arguing with each other and discussing and you go back to see like how this AI is engaging with what you posted and maybe that becomes the voyerism of seeing how other people's, you know, content's performing.
2:22:28
I mean, these are the fun crazy things that um must be explored to find, you know, this edge that will become the center of social.
2:22:36
Yeah, I've seen two things that are somewhat in that realm.
2:22:37
One is just general YouTube thumbnail AB testing services where you upload your thumbnail and it tries to predict based on all the data it has what the click-through rate will be and then you can upload two and it'll say, "Hey, you should probably go with this one."
2:22:51
with this one." Um, and then the other I saw uh I think Justine Moore at Andre uh posting some sort of app that you open your camera to the front-facing view and it gives you the sensation of live streaming with like hearts and comments
2:23:06
and stuff and it's all fake but it's very odd but I don't know it feels inevitable that uh in many ways bots are a feature of it feels like bots are a feature of X now right they they have not been eradicated they're still here they're maybe hidden and unders. And
2:23:22
And I mean that's the story of Reddit, right?
2:23:24
The early Reddit days were, you know, it was all the Reddit founders posting to seed this thing.
2:23:28
So if you think about uh a social network that needs to onboard you, there was that original uh Facebook thing where like if you could get 50 friends, they'd keep you on the platform forever.
2:23:38
You know, if you show up and there's like a couple bots that are just like, "Hey, good job."
2:23:43
Um uh you know, hey, keep posting. Stick with it.
2:23:46
Make some real friends, but we're here for you if you need a little encouragement, a little dopamine.
2:23:51
Um, I mean, on that note, um, the the the war between Meta and OpenAI in the in the talent race and all the trade deals has been, uh, you know, front page news for the last couple weeks.
2:24:00
news for the last couple weeks. I'm uh Jord's been saying that they that the the product that Meta might wind up going after is less like a direct chat GPT knowledge engine uh uh app that feels more competitive at Google and it
2:24:14
might actually be something more like uh companionship and and chat since that that feels like the real threat of of if there was an app outside of Tik Tok that was going to take user minutes from the enter these sort of like entertainment social minutes from from the Meta ecosystem. It would it would be these
2:24:32
It would it would be these sort of AI companionship which function as entertainment this sort of social experience which is Meta at its core is is effectively a social entertainment company. Yeah.
2:24:45
And you know you think about all the rules of successful consumer products.
2:24:48
They make us feel good about ourselves.
2:24:51
Um you know they they are sort of social lubricants and that they help us get in get connected to others in ways that we may not be able to do and be comfortable with in the physical world.
2:24:59
with in the physical world. and you kind of go through all the list of things and you realize like AI there's an opportunity to to really radically you know uh attack those vectors and make people have a really fun engaging entertainment experience uh or a social
2:25:13
experience so it's not a surprise that Meta is going to innovate in that space you know I do also kind of wonder when uh I remember when we all remember when Facebook acquired um Instagram and then of course when Facebook acquired WhatsApp you know they were acquiring network effects in essence right around meaging and images. And I wonder now,
2:25:30
And I wonder now, you know, now it's like it's a talent war.
2:25:35
I mean, maybe AI is less about AI is not really a network effect per se.
2:25:39
It's more of like a talentdriven differentiation.
2:25:42
Uh I wonder if that's also, you know, helping us understand the strategy of, you know, buying up all these different companies and people.
2:25:48
these different companies and people. Um but it's yeah I think the the framing that I've been thinking about is is these are basically like unauthorized aqua hires to some degree where you're basically saying yeah the these 10 you know if a company's doing an aqua hire in general
2:26:04
there's like we know this group of people is good at this thing and we want to do this thing and let's bring them over here and so the premium on talent that we've seen in the last couple weeks could just ultimately be that it's b it's looked at as a you're buying a team which is val is more valuable than the individual parts. They just happen to
2:26:22
They just happen to all get chopped up.
2:26:24
There's been the chatter around Alex Wang and Scale AI.
2:26:26
People haven't been saying, "Oh, well, Scale AI is going to be, you know, this juggernaut in 30 years."
2:26:33
But Alex Wang is a generational talent.
2:26:36
He'll be around in 30 years.
2:26:36
talent. He'll be around in 30 years. And so the the nature of what scale does might change as you know we get to more you know datadriven or like uh just purely purely AI generated data and reinforcement learning with verifiable rewards and scale AI has been through a
2:26:53
couple different things with self-driving cars and then RLHF for light language models and that that business it's not the same as Instagram where it's like okay there's a network here and so there's like this asset value in this but it's yes more much more talentriven And so that's why you see all these people coming together. But it's fascinating. Uh it is But it's fascinating.
2:27:09
Uh it is interesting to see if if Meta is focused more on just let's make Llama great so we can use the best-in-class AI effectively for free all over our products or if they're trying to aim for something that's like an entirely new experience that will be vended to their billions of customers.
2:27:27
Um probably both honestly. Why not? Yeah.
2:27:30
And and make ads more efficient while they're at it. Yeah, for sure.
2:27:34
I mean, that's the crazy thing about this is like $100 million doesn't take that much to generate $100 million if you make the ads. 1% more efficient.
2:27:42
So, it's all economically rational, but we just haven't seen it in tech yet.
2:27:45
haven't seen it in tech yet. and this and that's why these big numbers feel like oh we got to talk about this little bit of a tangent but have you have you thought about how uh how like LLMs now are immediately and and I I'm assuming pretty aggressively shaping actual human communication like right now we're in this period of mdash gotcha you wrote that you wrote that uh you know with chat GBT um and and people that love the mdash uh
2:28:17
before disappointed but at the same time it's not like you see that people calling out the M dash other places on the internet outside of basically teapot and I just wonder we're in this dynamic now where we have the most prolific like prolific writers
2:28:32
throughout history have shaped communication and now we have LLMs which are effectively the most prolific writers in history producing more written word than any one human could do in a lifetime in a in in minutes, right? Yeah. Yeah.
2:28:47
And it just feels like we're potentially in this um interesting fly like flywheel that's just going to keep, you know, spinning. A couple thoughts.
2:28:58
I mean, first, I feel that LM are going to start um fine-tuning more towards how we want them to talk to us, right?
2:29:04
So if you want your LM to be straight to the point, no BS and all lowercase and short sentences, like that's what your experience of any information retrieval and conversation will be and that might be different from mine.
2:29:18
So I do think they'll all become more personalized for us in these like dramatic ways.
2:29:22
I also though wonder just like when music becomes generic and you know then some some some band or some star like just does something entirely new and creates this new genre.
2:29:33
this new genre. I mean in in similarly with writing like well what will human writing be like as a result of LLM's in five to 10 years from now when you pick up a novel that actually captivates your attention and c and keeps you engaged you know what what what sort of writing will be necess necessary to do that in this age where yeah your LM can spit out
2:29:53
poetry or write a you know a short novellet you know upon upon command so it's uh it's fascinating I mean technology's always had this impact on us and culture it's just never been easy to chronicle because it's always happened over such long periods of time and it feels like those windows of of culture change are happening more quickly and uh so it's something I'm looking at as well. It's interesting
2:30:13
It's interesting question.
2:30:15
I'm I'm generally still long tool like like this is a tool creativity is still undervalued or or or it's not going away and I keep coming back to the idea that like there should be nothing easier for an LLM than to write a great tweet. It's 280 characters.
2:30:32
It doesn't need to really maintain some long context to get it.
2:30:36
And yet, we haven't really seen anyone break out with an account that people are following and entertained by that's fully AI generated.
2:30:43
And there's been some experiments, but usually it's like you're following it bec like that.
2:30:50
Do you remember horse ebooks back in the day?
2:30:52
I don't know if you remember this account, but it was it was like said to be uh just randomly algorithmically generated from these ebooks, but it turned out that there was actually a human writing it.
2:31:01
And there's been a few examples of that where um or or or the stuff that go does go viral that's like AI generated is like oh it's hallucinations.
2:31:08
And so the the fascinating part about it is not the underlying product.
2:31:11
It's the fact that it's generated by AI. Yeah.
2:31:15
It is interesting that we have uh this this band the Velvet Sundown I think they're called.
2:31:20
They have a million listeners on Spotify a month right now.
2:31:23
That's that claims to be fully AI generated.
2:31:25
And it's funny that we got that before a prolific poster. Yeah.
2:31:31
that is fully AI like that has 100,000 followers and is like popular. Here's the thing.
2:31:36
I mean we talk about of course like taste being more important than skill and I think you're tuning into the fact that can LLM's like output tweets that are compelling and therefore have taste and I think one of the questions is is taste not just about each tweet but also like consistency of good judgment and great you know great content.
2:31:50
It's just like they say a brand is like the hardest thing to build the easiest thing to lose.
2:31:56
I wonder if taste is a similar way.
2:31:58
You know, if you have AI pumping out tweets in an account, but if 20% of them are like you're like, "Wait, what? That wasn't clever."
2:32:04
You know, do you just lose does that does is the credibility of that account gone?
2:32:08
Um, so I I think humans are good at you humans with good tastes are good at knowing, you know, yes and no, yes and no.
2:32:15
Like what should and shouldn't be shared or or said or written more consistently.
2:32:21
And I wonder if I wonder if uh, you know, LM can do that. It's also a memory. It's a context thing.
2:32:27
You you to be a good poster, you you need to really understand the fullness of the zeitgeist and where and the current thing and and and all these different meta trends and Yeah. Yeah.
2:32:38
And it feels like even the longer context windows are still losing focus because there's some sort of fundamental limitation of the transformer.
2:32:44
Uh we talked to Dorash a little bit about this and uh the continual learning breakthrough is maybe still a few years away but uh certainly will be interesting to see uh how it develops. I'm I'm I'm optimistic.
2:32:57
I'm still looking for that.
2:32:58
I keep coming back to that idea of the Lisa Doll match against uh Alph Go where Alph Go dropped move 37, this very unconventional play.
2:33:09
Everyone thought it was a hallucination, a mistake and it turned out to be kind of a genius new move.
2:33:13
And I feel like we haven't had our move 37 moment for LLMs yet, but it's probably coming at some point.
2:33:20
Well, I'll tell you like each time we have these conversations, the whole world will be different.
2:33:23
I guess that's that's like we're learning these days in terms of the pace of change. But, uh, good to see. That's great.
2:33:28
As as we accelerate, it goes from monthly to every couple weeks, weekly, daily, weekly, daily, and then and then every hour that we'll be full feeling the acceleration, but um, this has been fantastic.
2:33:39
Great having you on as always.
2:33:39
Looking forward to the next one. Sounds good. Till next time. We'll talk to you soon. Bye.
2:33:43
Uh, next up we have Nathan Lambert coming in to talk about an American Deep Seek project.
2:33:47
But first, let me tell you about Bezel. Go to get bezel. com.
2:33:51
Your bezel concierge is available now to source you any watch on the planet. Seriously, any watch. Go check them out.
2:33:58
And I'm very excited to bring in Nathan uh and talk about Deep Seek Llama. How you doing, Nathan? Boom. Good to meet you. What's going on? Good. How much this format?
2:34:07
You guys got a loaded lineup today. I was like, wow.
2:34:09
I got on the same day as Ben.
2:34:10
Ben is like the motivation for why I started writing about AI.
2:34:16
Somebody has to do this for AI because there's so much to talk about.
2:34:17
But all he does now is AI anyways. So he's a competitor.
2:34:21
He's the Ben Thompson for AI is definitely definitely Ben Thompson.
2:34:25
But I mean it is a little bit different in terms of there's so much different space in terms of uh whether you're going after the the the business models or the actual infrastructure or or what you were writing about earlier with kind of the the open source geopolitical angle.
2:34:37
Um, so take us through the recent piece, the thesis, and then I have a bunch of questions about uh both DeepSeek Llama and kind of how this could come together.
2:34:47
We were talking to the CEO of Grock yesterday.
2:34:49
Uh, he's obviously extremely long open source and and uh it's very interesting to dig into a million different threads here.
2:34:55
So, just kick us off with an overview.
2:34:58
Thankfully, we were talking to the CEO of Grock with a Q just at that very moment there was a different Grock hallucinating and and at scale.
2:35:05
Yeah, there should be more Grock news later if tweets are to be believed.
2:35:10
But the American deepseek thing is largely a forcing function to make AI research EOS, make the AI research ecosystem in the United States catch up.
2:35:20
I think we were talking about niches and lanes and like Ben Thompson, it's the biggest one.
2:35:25
A lot of mine is on the research side and kind of understanding the emerging trends on research that are getting picked up by companies.
2:35:30
And one of the clearest ones that we do is I I lead this with a couple other people as we keep track of all the open models and data sets, a mixture of research and startups are releasing.
2:35:39
And there's been a huge shift in the last three to five months and pretty much everyone builds on Glen.
2:35:44
And there's a long tale of kind of business or geopolitical reasons.
2:35:50
Some of them are sensitive and some of them are kind of obvious where American companies don't want to build on Chinese models. And that's one thing.
2:35:54
And then also America should just take pride in what has been a great like research ecosystem and we want to have that and own it whole stack which is otherwise most of the leading AI research is going to start coming out of China and I think so politically it should be an easy win and in terms of cost to maintain the open ecosystem it's so much less than what these companies are pouring into their AI models.
2:36:18
So, it's just kind of getting the a bit of the tractor beam of AI onto this open source and open.
2:36:22
It's like just building models that have all the data and code released so more people can start building on them.
2:36:31
We can go into the details.
2:36:33
Yeah, Deepseek versus Quen.
2:36:33
I feel like Deep Seek had this like crazy viral moment.
2:36:36
Um, but now you're saying that Quen's been kind of on a on a, you know, compounding growth for a while.
2:36:41
What's the dynamic between those two companies and and what's driving Quen's adoption over DeepS? Yeah.
2:36:46
So, this is a great example of one I've started using and we'll loop it into Llama.
2:36:51
Essentially, DeepSeek has these frontier class models that are extremely good and they dropped the weights on Hugging Face and they have been switching to permissive licenses.
2:37:01
These are models that anyone could pick up and use and dump into a product that they want to ship.
2:37:04
A lot of startups use these things.
2:37:06
We see all the clouds hosting them. That's one thing.
2:37:10
Not a lot of people actually fine-tune Deep Seek because it's huge. It's already so good.
2:37:14
Like, what are you going to get from this?
2:37:15
And then what Quen is doing is they're releasing honestly tens of models at different formats both base models and post-train and from size scales as small as like 500 million parameters up through these bigger models.
2:37:28
So for a researcher it's pretty much or somebody trying to build a really niche product and something at the cutting edge is like Quinn will have the model at a certain size that you need in order to fine-tune it or figure out if you can build a certain thing at a certain cost profile.
2:37:42
And especially for researchers that have some sort of limited compute like training and working on deepseek is super hard.
2:37:48
And we've seen this with llama 2 and llama 3 were much closer to this quen approach when it was seen um both in the data that we have and on the ground is like llama was the open standard for research.
2:38:01
I used to joke around that like hugging face is just going to be rebranded llama because you see llama everywhere.
2:38:05
Uh especially around llama 3.
2:38:07
3. And with Llama 4 meta started to go like release drama aside, they've started going more towards their bespoke solutions and they're also releasing the models which has made this big opportunity for Quen with Quen 3 which honestly earlier Quen models were
2:38:22
already starting to fulfill this but that's kind of been a big uh mass shift and attention shift in the last few months where So what do you expect out of Meta with uh with the new talent acquisitions and it seems like a redoubling of the
2:38:36
efforts on AI broadly super intelligence uh but maybe you know the the uh the strategy of llama could be shifting um are they the are they the lot I I've often thought that you know right now with the dominance of deepseek and Quen internationally in kind of these like
2:38:55
jump ball half ally countries frennemy countries uh Mark Zuckerber should be like a national champion and and and we should be pushing Llama you know at a national level all over the world Um, but what do you think is going to happen there? Yeah. So, there's two things. Um, Yeah. So, there's two things.
2:39:10
Um, mostly I mean I'm going to gossip as anyone will and what will happen with Llama. It's very 50/50.
2:39:16
I think with the leadership they've brought in that there's less um attention and value behind the open thing.
2:39:23
So, Zuckerberg historically has been very pro open and if more of it is shifting to other people, they kind of loosen that vision.
2:39:32
if other people are at the at the lead of leadership.
2:39:35
Like that's what people are saying is like they need more leadership to build this AI or so a best case scenario is Llama invests more in AI and the national champion becomes even better and they just crush this.
2:39:48
I don't think that's the outcome that I expect to happen, which is I'm saying there's a a cheap offramp if you take the cop packages for a couple of those researchers.
2:39:56
Like that's the cost to get a whole research ecosystem built around a fully open US model where we have all this like the data is released and a nonprofit and stuff can handle data releases a bit better than a big tech company that has all these eyes on their back and then just have research happen on these things.
2:40:16
So what are you advocating for?
2:40:16
Are are you advocating for nonprofit taxpayer funding?
2:40:20
Um, you know, we've we've seen a nonprofit before and it turned into a for-profit.
2:40:24
Um, how are we going to keep this in a for-profit?
2:40:26
And then how are we actually mustering the the will?
2:40:30
Because yeah, it sounds like, yeah, just put $100 million into a nonprofit.
2:40:33
Like, that's a lot of money.
2:40:35
Like, this is where we get to the nitty-gritty.
2:40:37
I work at the Allen Institute for AI and AI 2, which historically is even more academic than OpenAI was.
2:40:44
So I think culturally there's there's not that type of feeling the AGI like supervision that Ilia had on the scaling deep learning.
2:40:53
That was kind of the thing that I think drove them from the start.
2:40:56
They're like we need so much money.
2:40:58
So AI2 is set up it's it's like you can do digging but it's so different in that culturally that it could never happen.
2:41:04
So if if the leadership here tried to do that, the company would just implode um going for a for a profit because I mean most of the leadership has co-appoints with professorships at UDub and things like this.
2:41:15
So it is already half embedded in the ecosystem.
2:41:17
And then practically speaking, moving AI talent around is so hard that it's like the government exfiltrating researchers to fund like a open-source government lab doing this is so hard that it's like you have to find the money or the partners to do this where there are people.
2:41:34
So sitting on the ground where I am, we're trying to train our next model.
2:41:37
Like it's pre-training now.
2:41:38
It's like we just need more compute.
2:41:41
So if we double or triple our compute, America will have X or like these open models will be just X% better.
2:41:50
And it's it seems tractable.
2:41:50
It's just hard to get the right it's it's a lot of politics to get these things in place. Yeah.
2:41:56
I mean I I I guess to to uh dig in there, I'm wondering if like you know the the initial like economic model for Llama was always in a little bit of debate.
2:42:10
Is it a recruiting effort for Meta?
2:42:11
Is it their desire to decouple and not be dependent on uh Gemini or OpenAI or Anthropic and just save cost there?
2:42:22
Um there were a whole bunch of different uh you know economic motivations, but I'm wondering if like in the long term we don't see something like uh you know a Red Hat Linux where it is a for-profit company maintaining a nonprofit uh or or an open-source software package or even you mean you can run Linux on Azure now.
2:42:43
Um, and so, um, is there a world where you just have every different piece of the stack, whether it's a consumer app that people pay monthly for or an API that people pay on a per token basis or an open source model that you're paying for, you know, Red Hat style consulting services on top of all within one company?
2:43:06
Like is there no hope that OpenAI's open source model or you know uh AWS or Microsoft open source something that that actually competes significantly with DeepSeek and Quen but is still within the typical corporate structure.
2:43:25
I think it'll come from the the biggest motivators have to be Nvidia and AMD.
2:43:28
So the long tale of ifwen is going to keep doing this and then it's something like Huawei they start working with Huawei this like Huawei libraries they want to support them and then US researchers are starting to dig down into those levels because they want to understand how these models were trained.
2:43:45
So those are the people that have the most direct um exposure it would be Nvidia and AMD it's cheap for them to do they get the benefit of the researchers keep working on their hardware and software ecosystems.
2:43:56
There are like outlandish stories that you could tell about open source AI where that type of thing could emerge, but mostly they involve uh technical breakthroughs that you can't plan on.
2:44:06
Like if you could do weird model splicing where you train a bunch of and then you cut ane out of one model because it's really good at healthcare and there's kind of this open marketplace for model parts and then that's all in the open and the person that kind of um writes the software by which those like pieces of models are combined and standards by which those happen that could exist.
2:44:25
So there's kind of wacky ideas, but I think that that's a lower probability outcome.
2:44:29
And it's just like let's get good big transformer models trained that anyone can download and poke around at and just get more people involved in AI research that it's just we have complete control over. Last question for me.
2:44:44
Uh what are you expecting out of uh OpenAI's uh open model? Yeah.
2:44:50
So I think um one of the core things about OpenAI culture is that they really like to deliver extremely cutting edge and good artifacts and research.
2:44:57
So I I expect it to be one model that fills a niche that they're either hearing from customers or the community that isn't quite filled.
2:45:06
quite filled. Whether it's a extremely like super long context or low latency for agents or a certain size of reasoning model, it's going to be this type of thing where it's a a certain niche and it it works really well for it
2:45:20
where it could be like a like a deepseek style release where it's just super strong model that people can plug into real world products and applications, but it's not going to be this Quen or Llama suite of models that researchers look at for all sorts of things. and
2:45:32
look at for all sorts of things. and OpenAI has been saying that they hear the license critiques of Llama and stuff and they're going to commit to the actual like permissive license which are things like Apache or MIT that these Chinese orgs have started using again which I think is a nice thing to kind of
2:45:49
make all of that simpler to just you release a model it it doesn't have terms and conditions on it like you met Meta is not trying to say like your legal department has to talk to us or avoid these use cases it's just get people using the model that your company released and take a simpler approach That makes a lot of sense. Anything Anything else, Jordy? That's it for now.
2:46:08
Thank you so much for stopping by. This was fantastic.
2:46:09
Thank you for working on all this.
2:46:10
Yeah, we will talk to you soon.
2:46:10
Looking forward to the release. Cheers, Nathan. Have a good one. Bye.
2:46:17
Up next, we have Richard from you.
2:46:17
com coming into the studio.
2:46:20
Um, do we have any more ads? Go to eight. com. Uh, get a pod five.
2:46:25
5-year warranty, 30 night risk-free trial, free returns, free shipping at eight. com.
2:46:29
I'm basically you're in a hole.
2:46:34
My household is is in sleep shambles.
2:46:38
Well, let's bring in Richard. Talk to him.
2:46:40
Putting up uh the fact that we're doing this on five hours of sleep consistently says it all.
2:46:46
How did you sleep last night, Richard? How'd you sleep? Good to meet you.
2:46:49
Hey guys, nice to be here. Uh I slept all right. That's good. Complain.
2:46:54
You would have slept better on an eight sleep.
2:46:56
So, we'll work on that offline. But great.
2:46:58
Actually, I did I did buy it. I I uh retired it.
2:47:01
It just I think it's better if my body sets its own temperature. Oh wow. Interesting.
2:47:05
Um would you mind kicking us off with the introduction on yourself and the company? Happy to. Yeah.
2:47:10
Um Richard uh did my PhD at Stanford.
2:47:13
Brought neural networks into the field of natural language processing.
2:47:16
Laid a lot of the groundwork for what now is Chad GBT.
2:47:18
Um was a professor also on the side at Stanford for a couple years because no one was teaching neural nets like transformers and so on to students back then.
2:47:27
And this was 2014 to 2018.
2:47:29
Uh but my main job was starting MetaMind.
2:47:33
Started made it very easy to train neural networks for other companies.
2:47:37
We got acquired by Salesforce, became the chief scientist and after Tweed's executive vice president running most of the AI efforts starting the Einstein um kind of suite of things and so on.
2:47:47
Uh build out the research team there.
2:47:49
In that research team, we ended prompt engineering uh paper cited by the early GPT papers from OpenAI and others.
2:47:56
Um and in 2020 I decided to start you.
2:47:59
com to bring better answers to the world.
2:48:02
Uh to change what I thought initially was search but I think now I think is something else and also started AIX ventures.
2:48:09
It's a relatively small venture firm about half a billion AUM that invests in early stage AI companies. It's not that small.
2:48:16
Half a billion AUM is pretty pretty solid. Congratulations.
2:48:20
Humble 500 million of aum. Humble. Yes. uh on on you.
2:48:22
com uh where is the business today?
2:48:26
What are the biggest challenges?
2:48:28
I feel like we've been hearing more and more about the data wars and how high are the walled gardens, how high are the walls and the beautiful gardens that we have all tended to with our Slack installations and our Google drives.
2:48:42
And uh this feels like the logical thing that you know, yes, I own my data until I want to give it to you or literally you. com. That's right.
2:48:52
Actually, you know, as a startup founder, you have to remind yourself every crisis is an opportunity.
2:48:55
And the opportunity here is actually that a lot of data is in silos and those companies don't want to give it out, but they do need to make it useful.
2:49:03
And so, one of the many things that we've learned uh over our like changes and focus deeper deeper on enterprise is that doing good internal search is actually quite hard and quite useful.
2:49:16
And so we're partnering with a lot of companies and do search over their entire archives of decades and also really upto-date things for publishers and insurance companies like pretty gnarly complex problems.
2:49:28
like pretty gnarly complex problems. uh and combining that with web data which also has its own complexities and a lot of you know like folks are in some ways trying to pay people uh in news which makes a lot of sense but also uh it potentially threatens the entire free
2:49:48
open internet you know if you have to pay for everything you read or crawl then only Google can really afford that and then you have an even bigger monopoly so I do think we need to keep the web uh open and free for that and so merging All of that together to make companies more productive is what we now focus on. What's kind of the best practice in the
2:50:03
What's kind of the best practice in the modern enterprise these days?
2:50:06
Is it like try and be really diligent about sucking data out of every platform you use into some sort of data lake or like snowflake installation and then dropping u. com on top?
2:50:17
or is it figuring out a way to actually deal with the sharp elbows of direct API integrations into the databases that are managed by the other companies that I'm uh you know purchasing SAS from?
2:50:32
That is a great question.
2:50:32
I wish there was a simple silver bullet always do X and just have it all in data lake in one place.
2:50:38
But the truth is it's kind of messy and usually there's some data that's just so large you don't want to have another copy somewhere else.
2:50:43
have another copy somewhere else. uh like and then there's some data where you know we have the whole internet like we have an internet index right and you can't bring that into your virtual private cloud and so on you know it's
2:50:53
just too expensive for each company um but then in some cases it does make sense if you want really deep understanding reasoning over complex uh structured and unstructured data inside an enterprise then you have to often copy it over uh and bring it into a new setup. So one of the big things we just
2:51:08
So one of the big things we just announced actually is a big partnership with data bricks uh where we are sitting on top of data bricks and we can actually answer questions uh over data that is in data bricks and uh it's been a very exciting partnership already.
2:51:23
That makes a ton of sense.
2:51:24
They enable all their LLMs to have web index apps.
2:51:28
Oh sure okay yeah that yeah that makes a ton of sense.
2:51:30
Um how are you feeling on acceleration deceleration?
2:51:32
I it feels like the vibes have shifted most recently.
2:51:37
We had Doresh Patel on the show on Monday.
2:51:39
He's kind of pushed out his AGI timelines.
2:51:41
Um, folks are talking about uh reinforcement learning not scaling as fast as people thought it would, the problem of continual learning.
2:51:50
Just if you take a step back and maybe put on more of your academic hat, um, how are you feeling about the current uh, state of AI?
2:52:00
Obviously, even if the there's also the take that like I don't know if you agree with this, but like even if the models plateau, there's still so much enterprise value and so many problems to go solve.
2:52:08
But I'll let you answer where do you stand?
2:52:11
Yeah, I'll try to keep it short because I could talk about that for hours.
2:52:13
Um, please there's uh I think it's true that there are so many simple jobs that can actually be done already with the technology that's there assuming you have good data access and you had all the recent information and you know the company context and all of that stuff.
2:52:29
So there is already a lot of lowhanging fruit.
2:52:31
At the same time, it's actually quite exciting for the researcher in me uh that you know hasn't fully died yet as an entrepreneur and CEO for many years like um that it's time again for research like in many ways we've knew we've known that you need large neural nets with a lot of data on GPUs and highly paralyzable training.
2:52:50
Um and you want the whole thing to be ideally endtoend trainable in some fashion.
2:52:54
It's sort of been known ingredients for over a decade now.
2:53:00
And indeed, we've crossed thresholds by scaling all three, data, compute, and and model size.
2:53:04
We scaled those three things up.
2:53:07
And it worked better and better and better.
2:53:09
And it created these emerging properties similar to like a smaller brain of a monkey just not doing certain epic things.
2:53:16
Even though the brain kind of looks similar that has neurons too, but then you cross a certain threshold, you get these emergent uh intelligent properties.
2:53:23
And so what that means is now is the time again for actual research.
2:53:28
Not just engineering and scaling things up and throwing more data and better data at it and bigger GPUs and and and all of that, but to actually go back and say like, okay, what is true intelligence?
2:53:40
How can we get to super intelligence?
2:53:42
What does it mean for intelligence to increase exponentially for a certain amount of time?
2:53:46
I think they're actually different dimensions of intelligence too that you have to look at separately and some do have upper bounds.
2:53:53
Uh and sometimes these upper bounds are astronomically far away like grounded in physics and in other cases the bounds are not that hard like classify every object on the planet in computer vision.
2:54:04
It's actually not that hard in comparison to have all knowledge about the universe.
2:54:09
You know, an intelligence should have a lot of knowledge and it will take us a while to get to as much knowledge and the bounds of how much knowledge you can collect are rooted in physics and the speed of light cones around all the sensors you can have.
2:54:21
So there's basically a time again for research and that's exciting.
2:54:26
So yeah, it feels like we're kind of paradoxically in an AI bull market summer in the Stripe dashboard or in like the ARR sense like we never never been adding more EV, never been doing bigger contracts, everything's good on the business side, but maybe we're kind of counterintuitively in a little bit of an AI winter on the academic side.
2:54:45
My question is if you agree with that or not, but also um do you think that with the AI researchers, it feels like the top AI researchers getting poached into meta and they're going into open AI.
2:54:56
Um, do we think that the next transformer is coming from or the next major research breakthrough comes from a foundation lab or big tech company or is there a role for academia to step up and do some like kind of longer timeline unbounded research to try and go explore even without even an economic model in mind.
2:55:19
I do think uh you cannot build another openi by just building an LM.
2:55:22
The LM was the one thing that worked out for open air after they spent hundreds of millions on robotic hands and on Dota like computer games and reinforcement learning and all these other projects and one of them actually worked and so if you want to replicate that kind of success and do research again which I think is the time is now.
2:55:39
Uh it does make sense to have that kind of entity um and and it can I think be done.
2:55:44
Um and so I do think academia has a role to play in that.
2:55:51
Uh thanks to open source models, academia can be relevant again because they have access to them.
2:55:55
They just couldn't have afforded it before top uh open source models were available.
2:56:00
Uh and I do think uh a lot of folks are chasing sort of the latest employees in the top labs like OpenAI, Anthropic uh and so on.
2:56:10
But you can also go a step further and look at who's actually uh who's trained those people, how did they learn how to do research?
2:56:19
And then you get to folks like Chris Manning, who's one of my PhD adviserss too, who just recently joined AX, our venture fund, uh like in a much larger capacity as a GP.
2:56:27
And so those are the kinds of folks that I think we'll need to rely more on again.
2:56:32
Uh and many of those are also moving out of academia in into kind of labs that pushed frontier research forward.
2:56:41
What uh I have to ask because it's so current.
2:56:42
What do you what's your thesis on what happened yesterday with Grock?
2:56:46
How how do you have uh that uh big of a general uh I don't know alignment? Oopsie. So, oopsy daisies.
2:56:54
Uh in in prod, you know, I think uh when you ship very fast, these things are bound to happen, right?
2:57:01
Like people can push them in the conversations into certain directions.
2:57:05
uh you sample from the same model multiple times, you get different answers.
2:57:08
And uh I think if you try to be sort of a like free speech maximalist, which on many levels makes sense, uh but turns out there's a lot of funky speech out there, you know, that with no guardrails whatsoever, it will go into those very dark places. Yeah, makes sense. What are you expecting?
2:57:28
Uh, since it's in 6ish hours, I I believe I hopefully they're still announcing and launching Gro 4 tonight, what are you expecting out of Gro 4 in terms of um maybe benchmarks aren't aren't the right um just even even uh way of thinking about it, but but in terms of progress, my hunch is it'll be more of everything, but nothing like wow like binary like a novel thing, right?
2:57:54
It'll be more multimodal and deal with better understanding of images and videos and maybe sound. It'll be larger memory.
2:58:02
It'll be uh slightly better reasoning.
2:58:06
Um it'll be probably I mean we won't know for most of them but more parameters and and so on.
2:58:10
Um but maybe not like completely novel research that has a capability that no one else has. Sure. Yeah, that makes sense.
2:58:18
Um last question and we'll let you go.
2:58:21
Um, uh, what are you finding most exciting on the investing side?
2:58:26
Um, for me, $500 million fund, it feels like the foundation model labs, the big training runs, the multi-billion dollar rounds, ship has kind of sailed on that front.
2:58:36
So maybe the time is for application layer investing, but what what are you seeing?
2:58:40
What are you excited about?
2:58:42
Yeah, we've we've been actually very fortunate at AIX Ventures uh to not have like to avoided uh some of these massive rounds.
2:58:49
The way I describe that is that not every company that raises a ton of money um in a very early stage uh like is bound to fail.
2:58:59
At the same time uh you basically combine a seedstage risk with a late stage return in many cases.
2:59:07
Uh and that you know and expected value just doesn't work out very well.
2:59:08
And so there are a handful of foundational companies uh like Hugging Face that we invested in at a seed round uh and and Windsurf. Congratulations.
2:59:22
Those are great companies. Yeah. Yeah.
2:59:24
These are all like companies who invested in their in the seed round.
2:59:27
Invest in Perplexity and Flow and Whisper to Tobbit Ambience like bunch of really amazing companies.
2:59:31
And so uh I think there are only a one or two dozen foundational model companies that the world needs and then there are thousands of application companies.
2:59:41
So short answer is yes you're right.
2:59:43
Uh I think you will see a lot more new applications.
2:59:46
I also believe that all the stars are aligning for biology uh and hence medicine and hence health to have a major moment thanks to AI.
2:59:54
Now software is faster than hardware and hardware is faster than wetwware people and biology right and so it takes longer the cycles are longer but you have enough data you have the right compute and we can eventually simulate more and more of biology and then make it into not just oh memorize what nature has kind of hackily evolved towards but make it an engineering discipline.
3:00:18
think about how we can actually change a specific antibbody and target a specific gene and uh like change our epigenetics and improve aging and cure cancer.
3:00:26
All of these things I think are within uh our grasp and and reach over the next few decades and I think that will be a massive group of applications. Amazing.
3:00:39
Well, thank you so much for stopping by.
3:00:40
This is a really great conversation.
3:00:42
We'd love to have you back later. I love back again soon. Have a good one. Cheers, Richard. Thanks for joining. Good chatting.
3:00:49
Uh next up we have the founder of Moon Valley coming on the show.
3:00:53
Um uh talking about uh generative imagery.
3:00:57
We're going to pull up our website. Very very cool stuff.
3:00:58
So uh welcome to the stream.
3:01:00
Hopefully we can pull up this website because we have fully passed the uncanny valley. Don't you agree?
3:01:09
I mean this Are you Are you on the website?
3:01:11
Can we pull up the website really quickly and show uh the the the video?
3:01:15
So, so everything on your website, this is AI generated. Is that correct? Uh, that's right.
3:01:20
There's some um like after effects stuff on top of it, but yeah, it's basically all AI Mary generated.
3:01:27
It's remarkable and I feel like it's under discussed.
3:01:29
Anyway, uh please introduce yourself and the company because this is Yeah, absolutely. Uh thanks for having me.
3:01:35
Great to great to meet you guys. Um great to meet you. So, I'm Naim.
3:01:38
I'm one of the founders of Moon Valley.
3:01:39
Moon Valley. uh you know at a very high level we're a team of kind of a unique structure you know a good chunk of our team are worldrenowned researchers um in visual intelligence primarily but you know our our focus uh in a specific level is is we're building
3:01:57
the biggest and the most capable production grade generative video models um and on the flip side is we also have a large chunk of our company are filmmakers so we have um we have a movie studio in LA it's it's one of the oldest most wellpreserved soundstages in the world. Um, some of the first Charlie
3:02:12
Um, some of the first Charlie Chaplan movies were shot there.
3:02:14
Uh, and so it's like kind of a ground zero and and we have, you know, folks that have won Emmys that have been nominated for Oscars on the team.
3:02:22
Oscars on the team. Uh and and so we've just kind of brought both worlds together to figure out how do you take this tech from being you know kind of interesting research uh and and um you know cool things that you can share on
3:02:34
on X and actually become things that sort of push the boundaries of you know what we sort of we think of it as like movies and stuff but these are sort of limited abstractions more of just like visual media broadly um and and kind of the artistry around it. So, how do you
3:02:48
So, how do you think about the trade-off between training new models, being a foundational model company, hiring researchers, huge training runs, and then the application layer, the distribution, actually working with filmmakers that feels like most companies have kind of split between one or the other?
3:03:05
Are you doing both right now?
3:03:07
How would you describe the shape of the business?
3:03:08
Yeah, you know, it feels a little bit maybe it's like a bit of a post chat GBT phenomenon, but I I do think that increasingly foundational companies quote unquote are having to think a lot more about the application layer.
3:03:21
Um, and and I think that that's only going to continue to be the case.
3:03:24
Like I think a foundational research, which you know, and you kind of alluded to it in in your chat with with Richard, but like there's an element of commoditization that's happening, right?
3:03:36
there's an element of of um you'll get to there's a point uh as and this applies to visual media as well where the the output of the model um you kind of hit this point of diminishing returns from a consumer perspective from like a user perspective.
3:03:52
So there might be really interesting kind of research thing that's happening um and you you'll continue to invest in that but to drive the same amount of like business value as like you know a GPT4 style leap that requires kind of thinking about other other areas.
3:04:06
other areas. So um and and the other piece for us is I think it's it's different with things like LLMs but with with visual and video in particular I think one of the issues is that like uh research labs and and technology companies that have been in the space
3:04:22
they have been largely divorced from like the end practitioner uh in in in a lot of ways and for LLMs that are so such general technology I think that makes sense but in our case you know we're building tooling that filmmakers ers will ultimately use that like creators will ultimately use. So we have
3:04:38
So we have to understand that inside and out and that needs to help guide the research rather than the research happening somewhat in a vacuum and then you know kind of trickling down to to the target user. Yeah.
3:04:51
A lot of the crazy technology vision of the future is kind of just like you're going to with one prompt just be like give make me a new Top Gun movie and it'll just one-shot it.
3:05:00
Uh clearly going to be a while till we get there.
3:05:04
there. uh where are you actually seeing value or demand from Hollywood from filmmakers because you know AI is so broad it could mean just like pull a green screen key better do some rotoscoping do some camera stabilization uh there's been AI tools in film making
3:05:22
for a long time they're obviously ramping up set extensions there's so much that you could do in the 3D pipeline and the VFX pipeline and 2D pipeline um where are you seeing actual adoption where you excited for there to be adoption in the next year or the year after? Yeah, for sure. I I think you know it's Yeah, for sure.
3:05:38
I I think you know it's a good point where especially in video I think more than in other kind of fields there has been this like uh you know when when it first started there was this sense of like well you know kind of like the hollow deck right like that's the world that we're we're going to move towards and to an extent it is.
3:05:52
I do think that there's kind of like a misunderstanding though of where the value of the end content comes from.
3:05:59
Um, and it's sort of like we're now at the place where you could relatively credibly write a book with an LLM.
3:06:07
Like you could have Chad GPT, you know, publish literature.
3:06:12
Problem is nobody's going to read that literature, right?
3:06:15
And like that's the that's kind of the missing piece.
3:06:17
You know, people have done it. People have.
3:06:18
And if you go on the Kindle store, apparently it's like swamped with AI%. Yeah.
3:06:23
And but like and every once in a while these things break out, but it's more of like a novelty like, oh wow, like somebody actually did this thing.
3:06:28
Like let me leaf through it. Wow. Yeah.
3:06:31
that's, you know, they hit the periods and there's tons of m dashes.
3:06:33
I think it might just end up reflecting human creation where it's ultimately creativity.
3:06:38
Creative products are a hits business where there's a lot of AI songs right now, but the only AI artists that I can think of is that the Velvet Sundown or whatever that's that's gotten popular in the last month.
3:06:50
And so it's yeah, I just think it's like this ultra power law potentially.
3:06:56
A lot of media properties also and art generally is also very story driven.
3:07:00
Like the story behind it.
3:07:03
Like part of the reason why I like to go Tom Cruz movie is because I've heard the story that he's doing the stunts. Yeah.
3:07:10
We know who he is and we know the story behind the story and and that's what drives a lot of value in art is like, oh this this painter really spent years doing this thing and he Van got cut off his ear.
3:07:20
And so that this painting has a crazy story behind it.
3:07:22
So it's valuable even if like my kid could do it.
3:07:26
It's like my LLM can do it, but yeah, your LLM didn't. Yeah.
3:07:31
But I I think it's like in AI music, I think you're kind of starting to grapple with that a little bit where it's, you know, you can you can use a lot of these tools.
3:07:40
I I think that there's like kind of lowest common denominator content like what Yeah.
3:07:45
You know, what you don't see as much anymore is like blog spam that was just like, you know, crazy in the 2010s, right?
3:07:50
Nowadays, that's just replaced with AI. Yeah. Exactly.
3:07:53
Um, I think that with AI music, it's like there's certain strands of like top 40 kind of radio, you know, where it's like it's already a very commoditized, you know, sort of form.
3:08:04
That's what it does well.
3:08:04
But I I just don't see a world where a transformer model replaces, you know, Kendrick Lamar.
3:08:10
Like that's that's it's so, you know, there's such a big gap there.
3:08:14
Um, so so we think about it like internally it's the same way.
3:08:16
We we, you know, like John Carmarmac calls it like power tools and and and that's that's largely the model we use.
3:08:22
largely the model we use. I think in film as well it's very acute because to your point like unlike other spaces it's actually it's a continuation like there a lot of the things that AI video enables it's not novel like you know you'll hear studios that we talk to they'll say you know a research company came to us and they said hey you can do
3:08:41
all these new things with with these video models and they have to remind them that no we can do all of these things right like what what we're talking about here is potentially doing them in a easier more flexible more powerful way But with VFX, like there's nothing you can do today with in in terms of like the output that you're creating that you couldn't do with VFX. It's it's a workflow thing, right? Like
3:08:59
It's it's a workflow thing, right?
3:08:59
Like we're just making that process easier, that process more affordable and and that kind of thing. So yeah, we look at it.
3:09:08
We're selling we're selling SAS. We're selling SAS.
3:09:12
That's what the market needs.
3:09:12
That's what the market needs. Uh what about um what you know it's been interesting to see uh X is its own kind of universe in terms of AI content what what gets picked up a lot of it ends up being stuff that that's getting made
3:09:26
shared on Tik Tok or other platforms and there's been this idea of like an AI content creator which is like a new personality that is just being generated by a human that's creating you know using a a video or an image model to generate uh generate this person, you know, going about their life. Um, and
3:09:45
Um, and then there's the sort of like the the what's considered the slop, like the Italian brain rod, all all that stuff.
3:09:55
Um, what about like do you expect to see like an entirely new class of filmmakers in like you know like ba basically like net new YouTube channels things like that of people that are just a you know could be a teenager or just somebody sitting in a room by themselves focused on that storytelling focused on basically creating um you know the internet was beautiful because it lowered the cost of uh distribution to
3:10:22
zero and you know mobilevices devices lowered the cost of content creation to zero or effectively zero and now um the the cost of of producing films is not going to go to zero but but may as well on a long enough time horizon and so I'm I'm curious when you think that moment will be where we start to see kind of the the the velvet sundown equivalent of of of film film making. Yeah, I I would say that like we're a
3:10:50
Yeah, I I would say that like we're a lot closer than people think.
3:10:54
Um, so you know, we uh like Moon Valley's models like because we have uh you know, clean models or models that have been trained on licensed data, they've been the first models that studio, you know, legal teams have really like allowed them to use.
3:11:11
And so um that's been part of why, you know, when you heard about Sora like a year and a half ago, since then there hasn't actually been that much AI adoption in the industry.
3:11:18
Now it's starting to pick up a little bit.
3:11:20
Um, I would say that like the the the point where the technology is capable of doing that kind of thing, we've already reached and there's places it's there's pockets of the world where especially like smaller film communities in different parts of the world where you're starting to see like real world produ productions.
3:11:43
often people aren't necessarily aware of it but very large parts of not just the pre and post-production but even the production process itself are being driven by these models.
3:11:54
models. Now I think that there's like beyond especially in this industry beyond the technology there's a lot of other barriers to adoption here right like you have to it's it's a whole new you know it's a whole new set of tools that you have to figure out it's a whole you know the adoption curve is very different and of course you know for
3:12:11
better or for worse there's like there's a big kind of dialogue around AI in movies and what that means and you know how we feel about it um but to your point like what we get excited about internally is there's this dialogue of like you've got on one hand just like day-to-day people like myself that maybe oh now I'll be able to create a movie. Um on the other hand you have the far
3:12:31
Um on the other hand you have the far end of well now studios will be able to make the same movies for much cheaper.
3:12:36
And that seems to be where a lot of the focus is to us.
3:12:38
focus is to us. The area that we're the most excited about is that middle where you essentially have this band of millions of creative people in the world like artists, people who have real taste and talent and ability but they don't have necessarily the access and the infrastructure to do that right and that's not necessarily like you know we
3:12:58
have a um one of our alpha users is this uh he's a a filmmaker in Sagal and like he's been he's been a filmmaker for over a decade um his sting his thing or you know his his focus is on uh doing like music videos for local artists and and it's like this kind of funky like Afrobe style uh you know West African kind of flavor. Um, and now he suddenly started
3:13:19
Um, and now he suddenly started making those music videos using generative AI and like the production quality of these have soared and he's had a number like he went from being just like a very obscure, you know, person in his local community to he's had some videos that have gone up like 10 million views on YouTube now and nobody has any idea.
3:13:37
They just think, oh, this is just like a sick artist that like I haven't heard before and you know, cool visuals.
3:13:43
So, it's that middle layer.
3:13:45
It's like the independent filmmakers who today, unless you're friends with one of the top five studios in the world, you have no capability of making a big budget production.
3:13:54
Now, you'll be able to do that, right?
3:13:56
And that's not just, you know, obviously this individual is one example, but for instance, we have like somebody like Natasha Leon who we work really closely with.
3:14:06
She's, you know, an industry insider.
3:14:08
She's a, you know, one of the the kind of top people in the industry today, but she's working on this new movie that's like she's been leveraging AI to help do it because this has been something that she's wanted this is a movie she's wanted to make for well over a decade, but she just couldn't.
3:14:23
She w like she talked to the major studios and it's like, hey, it's going to cost us $30 million to do this, right?
3:14:30
And and we just can't we can't do that.
3:14:32
Now it's like, well, if I can suddenly potentially do it for $15 million, now I can, you know, I can make this thing happen, right?
3:14:41
And and so it's there's there's there's this like idea that you'll be able to do, you know, movies for cheaper, but really what we're seeing in the studio is a existing movies you're now just doing more than you want expected to do before.
3:14:53
You're now having to compromise less.
3:14:55
Like directors aren't saying, "Hey, I had a $75 million budget.
3:14:59
Now I'm going to do it for 50 million."
3:15:00
They're saying, "Hey, with my $75 million and my team, I'm going to go and now do all the things that I couldn't do before unless I had a hund00 million budget."
3:15:08
So, that's like one thing we're seeing.
3:15:10
And then the flip side of that is you walk into any studio, for every one good production that's live, there's 10 that weren't green lit because they just couldn't get the budget.
3:15:19
Right now, suddenly, you'll have a lot more of that.
3:15:23
So that's like high level how you know I think that the way that this stuff is actually getting implemented it's happening in a different way than I think where there's been a lot of fear um and and you know I think it's totally justifiable fear but I do think that ultimately it's the artistry that wins out more than like you know budget requirements or budget constraints. Yeah. Yeah.
3:15:44
And the I mean the exciting thing from my point of view is if movie studios keep budgets relatively the same because there is this incredible demand for content, but then you can take a $und00 million budget and it can now go to 10 different films.
3:15:56
You get 10 more shots on goal.
3:15:59
It's 10 more teams and maybe the underlying teams even can create, you know, better margins themselves. So very exciting. Yeah. Yeah. This is awesome.
3:16:08
Thank you so much for stopping by.
3:16:09
Hope you have a great rest of your day. Yeah.
3:16:11
Come back on when you have this. We'll talk to you soon. Awesome.
3:16:13
Thanks for having me, guys. talk later.
3:16:16
Um, and that is our show for today.
3:16:18
Jordy, do you have any other breaking news you want to share?
3:16:21
Breaking news, Brandon Jacobe trade deal.
3:16:24
Well, I guess now's a good time as any for some personal news.
3:16:25
After Wild Chapter X, I've officially wrapped up my time there.
3:16:30
I'm extremely proud of the work we did and you'll see more of it soon.
3:16:33
More to come on what's next dot dot dot.
3:16:35
Uh, Brandon has been a dear friend for a long time and uh I'm excited for his next chapter. Me, too.
3:16:41
And uh I'm gonna miss being able to text him when I have bugs.
3:16:44
Uh but uh Tyler, you're up next. Expect some bug reports. But um yeah, fun show.
3:16:53
I'm trying to think if there's anything else that we that we missed. I don't think so. We hit all of our ads.
3:17:01
Um uh Chimath was saying that Meta switches to Sonnet for coding um instead of using Llama uh that they had uh fine-tuned on Meta's own codebase.
3:17:10
Now they're just oneshotting everything with Sonnet. So very interesting.
3:17:14
Since the change code suggestions are generally better, engineers can change back to Llama and occasionally do when the fine-tuning makes a difference.
3:17:22
Internally, this is a big change given how big how heavily Meta has invested in the Llama project or product.
3:17:26
Uh, this move officially acknowledges that anthropics models are currently far ahead of Meta's own LLMs, even when fine-tuned.
3:17:33
I suspect Meta will double down and try and make their next llama versions more capable for coding, but until then, it uh it doesn't want to hold back its engineers.
3:17:41
So, very interesting that they're uh they're using anthropic and just kind of letting it repro.
3:17:50
Uh, last post to end it uh from Jarvis Best.
3:17:54
He says he's sharing a screenshot of Linda Yakarinho saying, "After two incredible years, I've decided to step down as as CEO of X obviously has a much longer post, which we covered earlier."
3:18:07
And Elon just comments, "Thank you for your contributions."
3:18:11
Jarvis says, "Lmao, cold as dry ice."
3:18:15
Um, anyways, uh, cordial.
3:18:19
Yeah, at least they were cordial.
3:18:19
Uh, leave us five stars on Apple Podcast and Spotify.