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5:21
Um linear of course is the system for modern software development.
5:23
70% of enterprise workspace on linear are using agents.
5:27
So um there's been a debate uh truth bomb dropped by Emil Michael.
5:31
He says forgiving benchmark and others would be like letting the Wuhan Institute of Urology slide back into a good reputation because the new senior manager of pandemic causation has made more friends than his predecessor.
5:46
And so this got me thinking and I'm probably going to have to put on the steel helmet for this one because this is waiting into dangerous territory defending benchmark.
5:58
But my question is how close are we to actually being able to to uh forgive Benchmark? When is the right time? It's been a decade.
6:06
Uh obviously the the drama between Benchmark and Travis Kalanick was was awful.
6:12
I think everyone's against what happened.
6:14
Uh but the question is like is like what is a what is a venture firm? It is its partnership.
6:21
If the partnership turns over at some point like is it a new is it a new team?
6:26
Do you get a second shot?
6:28
Can you can you actually change uh change the reputation?
6:31
And so um believe me like I I get the benchmark criticism like Travis is truly a generational entrepreneur and was on such an amazing run, right?
6:42
So he was >> Jason Jason from Saster was reacting to our interview with Travis and uh his take which I totally agree with is >> it's hard to if Travis had stayed in the role it's hard to imagine Uber being worth less than something like a trillion dollars today. >> Yes.
7:00
So uh our friend of the show on McCabe over at in.
7:03
ai and Intercom uh said uh Whimo is superior to Uber in literally every way.
7:11
This was a year ago in March 25th actually to the day a year ago he said this in uh Whimo superior to Uber in literally every way that matters to consumers smoother safer more reliable no chatty weird rude rude drivers private quiet self-driving car services are going to dominate their human driver
7:26
incumbents and own says uh TBT when I think that's throwback two right throwback to when benchmark pushed Travis out of Uber and canned the self-driving division that he started literally 10 years ago and so uh this is this This is what's so so tricky about this is that, you know, Uber survived. It's 150 billion market cap. It's bigger
7:45
It's 150 billion market cap.
7:45
It's bigger than when Travis was ousted, but getting a 2x over a decade is not what I think people were expecting from Uber under Travis's leadership.
7:56
Uh it's and has fallen to just 5 billion.
7:59
like he won the capital war and Dar has done a great job managing the business but I feel like a lot of the success of Uber has been built on the foundation that Travis set up.
8:11
It wasn't a complete reinvention.
8:12
If anything, they just hone down the core business.
8:16
>> And the thing that the thing that is holding the business back right now, at least from a valuation standpoint, is this big question, right, around self-driving, >> how, you know, and and DAR has has answered this question, you know, thousands of times.
8:30
Right now the strategy is to invest in self-driving companies, partner with self-driving companies, but not the same as like having, you know, having >> developed their own internal IP and product starting a decade ago and seeing where that would have been >> by now >> is is just rough. >> Hard to think about. >> Yeah.
8:51
And so Uber is valued at 150 today, something like that.
8:54
Uh Whimo uh was valued in February of this year at 126 billion.
9:00
And so yes, Whimo's been working on self-driving longer, but you have to imagine that there's another 50 billion of market cap.
9:07
You have a serious >> what would Whimo be valued >> if Travis was the CEO, right?
9:11
You would get some type of Travis premium on it.
9:16
just >> just the market would be would would say totally totally totally totally you have this sort of one of one entrepreneur in the seat >> 100%.
9:25
Uh and and just to sort of recap uh where things stand I mean Shervin Pishavar been been on the show as well.
9:32
We've had like everyone from this saga in the TVPN orbit uh both Travis and Bill Gurley have been on the show.
9:40
Shervin's been on the show.
9:40
Emil Michael's been on the show.
9:42
We've uh we've we we've talked to a number of people that have been around this this story and it's a fascinating one.
9:47
It's one of the most interesting.
9:49
It was certainly formative in my career because I got to Silicon Valley and this was the first big story that played out really.
9:56
Uh so Shervin said, "In my opinion, Gurley single-handedly destroyed hundreds of billions in value.
9:59
Travis and Emil staying in charge of Uber would have led to a Teslasized win 500 billion plus for everyone including Benchmark's LPs.
10:08
He nuked decades of Benchmark's reputation with founders.
10:10
the market has spoken and no future Travis Quality founder would ever touch him or his former firm again, especially since three of the partners that approved of the ousting of of Travis are still at the firm.
10:21
And so my question is like how many partners need to be at the firm until we can call this a ship of Thesus.
10:29
So for those who uh are not up to speed on their Greek mythology, in Greek mythology, Thesus is the mythical king of the city of Athens.
10:36
He rescues the children of Athens from King Minos after slaying the Minotaur, which is his mythical beast.
10:43
Uh, and then he escapes onto a ship going to Delos.
10:46
Each year, the Athenians would commemorate this success by taking the the ship on a pilgrimage to Delos in to honor Apollo.
10:57
Over time, because they're sh they're sailing the ship every year, uh, various of its timbers rotted and were replaced.
11:04
A question was raised by ancient philosophers.
11:06
If no pieces of the original ship remained in the current ship, is it still the ship of Thesus?
11:10
If it was no longer the same, when had it ceased existing as the original ship?
11:19
So, some people might say 50/50.
11:19
Some people might say, "Yes, it is it is the the the same ship because replacing one board at a time, the ship is the concept."
11:28
And you can swap everything out 25 times, it's still the same ship.
11:33
Uh it there isn't like a it's a it's a paradox.
11:35
There is no like right answer.
11:37
It's a philosophical question, but it applies, I think, to benchmark because back when uh Kalanick resigned as Uber CEO on June 20th of 2020 uh 2017 uh after investor impressure after investor pressure that included Benchmark.
11:53
Uh on that exact date, Benchmark's equal GP roster was Bill Gurley, Eric Visra, Matt Kohler, Mitch Laski, Peter Fenton, Sarah Tavl.
12:00
Today, the partnership has changed dramatically.
12:03
The only two that remain are Peter and Eric.
12:05
And you have uh Chattton, Ev Randall, and Jack Alman uh who are new to the partnership post the Uber scandal.
12:14
And so it's not a full ship of thesis, but only onethird of the original 2017 partnership remains.
12:18
And my question for those who remain reluctant to forgive Benchmark is like what happens if Peter and Eric retire or leave at some point and the full ship of Thesius is complete? Like maybe you'll shift. >> Yeah, right now.
12:33
Right now 40% of the 40% of the partnership was there >> 33%.
12:37
Oh, I mean I guess two out of the five. Yeah.
12:40
So two two out of the five because they've added three but only onethird of the original partnership remains.
12:46
>> The question is did uh Chatan, Everett, and Jack come in and as part of the interview process say like you're absolutely right.
12:53
absolutely right. I mean to defend Evo two out of the three >> I E had just graduated from college like he was truly like not involved in the Uber scandal and yet uh you know people will visit upon him >> knowing Everett and Jack uh I'm sure I'm sure >> during the interview process they were like I I you know the storied firm >> I'm excited to join but we can never do we can never do anything like that again and so and so one one question that's
13:24
worth asking is like it's like is the firm that did this >> and ultimately >> it you know stained this this storied brand >> and has certainly suffered the consequences right they've put up in you know incredible returns >> uh since then but I'm sure they've
13:41
missed a lot of deals that would have made their returns even better because of that kind of narrative around the firm and so they have they I would say >> uh you know Emil Michael and others are upset that they're doing great deals at all. >> Yeah. >> Yeah.
13:56
>> But I think it is uh >> that a question I have is like are they if >> if they're in a situation again, right, in the same situation kind of situation, are they more what what decision are they more or less likely to make, right?
14:11
I would I would argue like they are probably less likely to >> I would think so >> go against the founder given given how this entire situation has played out. >> Yeah. Yeah.
14:19
The only the only steel man and this is this needs the full steel helmet because it's so hard to steel man the benchmark thing but the full steelman of the benchmark thing. It's really bad.
14:29
It's really bad to bench I'm sorry for everyone. I'm sorry.
14:32
Uh but it's basically that uh every partner at benchmark it's an equal partnership.
14:38
So every partner was going to make a clean $1 billion.
14:42
They were all going to be billionaires from this one deal.
14:45
And it was such a power law that that like there was no path to becoming a billionaire for you know from the other from the other investments most likely.
14:57
And so you see the endless 247 hit piece pile stack up and you got Mike Isaac, you know, bloodhounded on at the New York Times writing books.
15:08
They're turning into movies. Like it's getting rough. It's getting rough.
15:13
>> Mike Mike Isaac's at your door.
15:14
>> Yeah, Mike Isaac's at is at the door.
15:16
the barbarians are at the gate and you're like, I'm either a billionaire or I'm going back to a poultry 10 million and I can't I can't do that. I can't do that.
15:25
And so they freak out and they're like, we got to salvage this thing.
15:26
We got to just push it out in the public markets.
15:29
We got to get out of this name.
15:31
And so uh they basically just it's just too nerve-wracking.
15:33
And uh yeah, it's not a strong steel, man.
15:36
But I think I think that's a little bit more of what happened than than like taking a stand on like oh like this particular thing that happened was so egregious.
15:45
It was more just like okay like wow all my money like 99% of my net worth is in this asset and it's looking like it could be a zero because Lyft is coming from behind.
15:57
There's a whole bunch of VCs they're p piling into that.
15:58
The narrative is totally flipping.
16:00
there's a boycott Uber campaign like and everyone's like ah like what's going on? I gotta get out of this.
16:07
I I I I gotta salvage this.
16:09
And I think that was maybe more of the underpinning than like like I I I'm I'm taking some sort of like moral stand on a particular hit piece or something like that. Uh anyway, it's rough.
16:21
Uh but fortunately, you know, ship the process, maybe it happens. I don't know.
16:25
Would Delian accept that argument? Probably not. Would Emil probably not.
16:28
But uh they're not making it any easier with the Manis investment either because the like like there was a world where it was like okay yeah the Uber thing happened a decade ago the partnership is basically entirely new and they're focused on >> but it's but but >> it's not there yet. Yes.
16:45
>> It's just not there yet. >> It's not there yet.
16:46
But still there I think you can rewrite this in 5 years. >> Yeah.
16:52
>> Yeah. you yeah you made this point that this is it's too early to call this but >> directionally given that the firm is still is still putting up great returns they've gotten to a bunch of >> uh great companies over the last five
17:06
years >> uh I think we are on p on a path to the to the to the benchmark >> well yeah >> being the venture ship of thesis >> and uh VC Brag said Airbnb has no homes Uber has no cars and Benchmark has no partners of course That was an exaggeration. Uh but they did get down
17:22
Uh but they did get down to just three partners which is very very small for uh for a venture capital firm.
17:28
Uh some some have dozens of partners.
17:30
Um but different strategy and we will see where it goes.
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17:49
Uh, so Sora, rest in peace, Sora. The app is leaving. >> Ev is in the chat. >> There he is.
17:58
>> Class of 17 represent. >> Yes. Yes.
18:00
E was Ev was chugging beers while Uber was getting ousted.
18:02
Do not visit the the the sins of the father on the son. That's what I would say. Ev not guilty.
18:10
>> E was Ev was a boulder.
18:12
>> Yeah, he was hanging out.
18:12
He was having >> What happened senior year? What happened?
18:17
I I I think I think uh I think EB A E A E A E A E A E A E A E A E A E A ED deserves a fair shake and should not and should not have to bear the bear the cross from >> the question the real question do do E and Jack pull a benchmark and force out
18:33
>> the original partners >> they don't have the legal control to do so but neither did they during the Kalin >> at at this point Ev is probably already getting uh getting calls from Sequoia to come be the senior steward you know because He's he's he's on a meteoric ride. We're really all over the place today.
18:50
We're really all over the place today.
18:51
Uh anyway, Sora is now is it still in the app store?
18:56
I think it's like the announcement was that it will be leaving the app store.
19:00
>> Millions of people have made content on the app.
19:02
You have to leave it running for some amount of time.
19:05
>> There's a phase out, but the announcement happened and now it is is going out.
19:10
And there's I have a bunch of takes on this.
19:11
Um, obviously this is not the end of video creation for OpenAI.
19:18
This will be rolled into chatbt, I imagine.
19:20
Um, Tyler uh Hodgej put it well.
19:24
Bullish killing products quickly is hard. Almost no one can do it.
19:26
It's a good sign for OpenAI.
19:28
They're consolidating in in many ways.
19:29
It's like last week you heard about like the red the the code red was like a month or two ago and then it was like we're refocusing and then it's like here's step one of refocusing like a single app that we're going to push everything together.
19:45
Um and also I just I've I've enjoyed making some videos in Sora.
19:48
I've never enjoyed having to go to a separate app.
19:53
I want all of that to live in one place.
19:54
So that makes a lot of sense.
19:54
Uh let's see what Dax said.
19:56
It's lame to see all the people saying ha I called it.
19:59
I knew Sora wouldn't work. Yeah, duh.
20:02
Because everyone thought everyone thought that, including me who were working on it.
20:05
Uh uh they probably learned a lot trying to make it work anyway for every successful thing that exists.
20:10
A hundred efforts like this had to fail and those learnings are fed into making something that ultimately does work and provides you with a steady paycheck. Yes.
20:18
It's interesting because um this this quote of like people saying, "Ha, I called it. I knew Sora would work."
20:26
That is not how I interpreted the vibes around the Sora launch.
20:29
Like I went back and revisited the essay that I wrote on October 1st.
20:34
We had the slop versus farming debate.
20:34
I was really on a tear back then. Said slop is bad.
20:39
We the timeline don't want to be pigs at the trough.
20:41
We don't like it when tech leaders treat us like farm animals, but we love farming. Farming is lindy.
20:47
We the timeline want to return to a world where we are filling up troughs with slop on a daily basis, I guess.
20:51
So between Google Deep Mind, Meta Super Intelligence, and OpenAI, we now have three different variations on AI video products, each met with slightly different responses.
21:00
So the yeah, the interesting thing here is that like uh Google has been like charging ahead, launching it, it's in it's in real, it's in uh shorts.
21:07
Uh and that's just been like not a story at all.
21:10
What was interesting was that the the vibes around both Meta Vibes and Sora were like this is going to oneshot humanity.
21:20
They're like this is going to be too successful.
21:21
That was it was like >> it was it was like a entertainment doom loop. Exactly.
21:26
You you could imagine exactly >> it just getting so good at generating the next thing that you would want to see better than even a billion humans on Instagram >> Yes. >> could do.
21:37
And >> that's not what we've seen. >> Yes. >> So far.
21:42
>> And so like my big question was was like will this actually be sticky? Will people like this? And at what rate?
21:48
I mean, I read I read LLM generated text daily, but I also read a ton of not LLM generated text.
21:57
And my my ratio has grown exponentially.
21:59
Uh, but it hasn't gone to 100%. Nowhere near it.
22:02
Like probably 5% of the text that I read is LLM.
22:08
>> I mean, it we should actually revisit how we were processing it during launch when it was, you know, rocketing.
22:13
>> We should just throw on that three-hour stream and just watch that and react to how our jokes were that day.
22:16
But uh even at the time I remember saying very obvious that they built like a very cool creative tool. Yeah.
22:25
>> And uh they have the potential to seed a network with this.
22:29
There's all this you know novel content.
22:32
They had they had allowing creators and >> you know people like Sam to uh allow people to use their their IP. Yeah.
22:39
people to use their their IP. Yeah. Uh it was very very well executed launch but even from the beginning it was like okay obviously cool creative tool it's a totally different ball you know this like come for the tool stay for the network has been like an enduring >> strategy right Chris Dixon probably wrote that in like 2014
22:59
maybe or like a long time ago over 10 over 10 years ago >> uh but just because you build a tool that is attached to a network like that jump is just really really really really tough >> especially when there are three or four five serious networks that are at scale that can on day one support the format of the file that is produced from the model. So in a world where
23:23
So in a world where generative AI video came out not in a MP4 file or anov file.
23:30
It came out in some sort of format that could never be uploaded to Instagram reels.
23:36
then you have a chance to build a network and run away with it.
23:41
And this was the story of Instagram like like Instagram just had better support for images than Facebook did.
23:48
And then Vine had support for video literally before Instagram.
23:52
So Instagram it was like I have a video on my phone. It's cool. I want to share it. Yeah.
23:58
>> Sharing it to Instagram was not possible.
24:00
Now on day one you generate an AI video. You want to share it?
24:03
You can share it on Tik Tok. >> Yeah. It's just the incentive.
24:05
If you create if you created an amazing video on on >> Sora, >> what is the most logical thing to do?
24:10
If you're a creator and you want reach, post it to Instagram.
24:13
There's just naturally there's billions of people there there were millions of people on Sora and a lot of energy and momentum. >> Yeah.
24:21
Um, >> and it's not lost on me that the same day that Sorro was killed, uh, you have a viral breakout reality TV style series, uh, show putting up incredible numbers on TikTok for uh, Fruit, Love Island, I believe it's called.
24:37
It's an AI generated uh, twist on Love Island.
24:42
Uh, there's romantic intrigue and plot lines and stories and consistent characters and a lot of things that have come from a variety of AI models.
24:48
And we we we should talk to the person that's that's the entrepreneur behind that project because I would be interested to know what the stack is because I imagine it's not just, you know, going to a single Gen AI app.
25:00
I imagine that they have a whole pipeline of of like a workflow in place to actually generate that.
25:08
And so we're at this weird moment where you know Sora the app is going away, but we're also seeing more and more AI generated content slowly see success.
25:20
Whether that's the podcast that's at the top of the charts that's fully AI generated.
25:24
Uh there's this Love Island show.
25:26
There's a number of niches where they've found the right product market fit for AI generated content.
25:31
But it's not overnight we're living in infinite jest and we just can't look away.
25:37
It's like for specific things >> it makes a lot of sense and so it's working there.
25:43
>> Interesting to think about Google's strategy with with video.
25:45
Even even Google was like we cannot operate this for free at scale. >> 250 bucks a month.
25:53
I >> No, that was the that was the discounted rate.
25:56
>> Oh, I think I'm at 500 a month or something.
25:58
>> It was like an entry to start it was like $250 a month >> and it was brutally weight limited like >> Yeah.
26:04
Even even with we were we were laughing at this because >> three a day >> it I remember we'd be like at the gym in the morning you would fire off a couple prompts >> and and then you were like >> I hope I got it right. >> Yeah. Wouldn't get it right.
26:16
Then you were rate limited and you're like wait I'm rate limited >> on a on a $250 plan that's going to jump to 500.
26:23
You're probably paying Got to check the rate check our is probably paying 500 a month still. >> No, seriously.
26:30
>> No, seriously. and and that and that that's Google with >> all this cash flow insane data advantage with YouTube >> and and that let me tell you rate limits kill retention like nothing nothing is worse if you're in Instagram the endless scroll exists Tik Tok you can you can scroll endlessly you can use the imagine if Tik Tok was like after five minutes you have to close the app and come back
26:56
in 20 minutes like how successful do we think that would be it would be a disaster And that was the experience for both Sora and V3 where you would fire off a prompt and it would be like okay come back in a couple minutes going to
27:07
take me a while to cook and then and then you fire off five and it's like okay no more for today like and then and then you the next day happens and you forget about it and you and you go on something else. So clearly the compute
27:16
something else. So clearly the compute constraints are are immense and there's just so much more value value that can come from enterprise and can come from deep research and so many of the other models that are immediately economically valued like codegen like enterprise
27:32
workflows and it's maybe more boring and less viral uh and less controversial but it's it's where the compute needs to go and so I think you're going to see the chips be moved around inside of all the labs to like compute will find the most optimal output. Like the tokens of the
27:48
Like the tokens of the most value will always be the ones that it flow that the compute flows to.
27:53
And as a lot of people predicted like just endless random generations that aren't quite dialed yet, even the best video models like they're just not there.
28:04
They require a lot of work.
28:06
Uh is not the same as as where we are in terms of knowledge retrieval, where we are in terms of codegen.
28:11
It's just way more valuable.
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28:32
>> Market clearing order inbound. >> What did they say?
28:35
>> September people overestimate how much brain rod happens in a year and underestimate how much brain rod happens in a decade. So yes. >> Yes. still.
28:44
>> So, I mean, I'm using brain rot poratively there, but uh but I do think that like this this move does not really bend the curve of of just AI generated content, but I still think it's like a slow roll out.
28:56
Like it's it's fast in the sense that like we went from no slop on the timeline to lots and we went from like like actually zero.
29:03
There was like one one cool AI video Harry Potter Balenciaga was like entertaining to general people and now we get like five and then we get like next year we'll get like 20 and then eventually it'll be like hundreds and it'll be like oh yeah I'm actually into that.
29:19
Like people are into cartoons and people are into CGI movies and superhero movies and pe some people will be into it.
29:24
Some people will never like it.
29:26
Some people will always say I want a black and white film from the ' 40s.
29:29
That's what I want to watch.
29:32
uh and and and these rollouts, the diffusion of this stuff will happen.
29:35
Should we revisit uh one of >> Sweet Davidson says, >> "What what is this?"
29:41
>> Y'all are worried about the wrong open claw. >> This is a good post.
29:46
>> This is the open claw that uh that F.
29:49
Randall was worried about in 2017.
29:49
He was not He was not thinking about >> was Whiteclaw invented in 2017.
29:54
When did Whiteclaw get founded?
29:57
That's a great That's a great I feel like it it really took off. It had a fast takeoff. >> Yeah. 2016. >> 2016. Okay.
30:04
Very good chance he was an early adopter.
30:07
>> He might have been an early adopter of OpenClaw. It truly was.
30:09
Uh Open uh I'm calling it OpenClaw now.
30:15
Whiteclaw did have a fast takeoff for sure.
30:17
It went from 0 to 60 and it was just everywhere all of a sudden.
30:19
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I got a story for you, Jordy.
30:29
Today, as I was driving into Hollywood from my hometown of Pasadena, I was driving through Hollywood and I look over and there is a new Hollywood sign.
30:41
I'm not kidding about this.
30:41
The I I actually saw this. I took a picture.
30:43
I can maybe send it into the chat, but I can just show you because I'm driving and I just see this like >> Billy Bowman. Billy Bowman. I don't know.
30:55
I I I'll send it into the to the chat so they can pull it up. Uh let me see. Production. Here we go.
30:59
But anyway, uh it is a uh it is a remarkable story because Fiverr is running a one of the coolest out ofome campaigns like just from an out ofome inventory.
31:10
I didn't know you could do this.
31:12
Let's uh we can pull up either my picture or we can pull up >> let's pull up this video from KTLA 5.
31:19
>> KTLA 5 had a video breaking down what's going on.
31:22
Let's watch this and then we'll react to this.
31:23
And while the team pulls that up, I'm going to tell everyone about Labelbox, RL environments, voice robotics, eval, and expert human data.
31:31
Labelbox is the data factory behind the world's leading AI teams.
31:34
So, let's pull up the uh KTLA 5 report uh about uh AI coming for Hollywood, the mysterious sign along the 101.
31:46
underneath a logo for Fiverr and a search box that says, "Find the best AI directors."
31:52
It's a brash, bold statement.
31:53
AI >> bold state for Hollywood.
31:54
Fiverr has made a name for itself, connecting projects with freelancers.
31:58
Now, they're launching an AI video hub, which they say can make content at a fraction compared to traditional production.
32:04
This Billy Bowman guy is one of the directors that you can hire. He's based in Sweden.
32:10
He's made AI videos for Google, Universal Music Group, and others.
32:12
As you know, AI really hasn't taken over Hollywood yet, but it has certainly crept into commercials.
32:18
Brands like Google and Jeep rolling out AI on national campaigns.
32:22
Many are slowly or slowing rather to see the 30-foot sign which went up over the weekend.
32:28
I first noticed it stuck in traffic yesterday morning after someone was so entranced they rear ended somebody else. It's causing accidents. >> So interesting. AI director. Yeah. Yeah.
32:39
>> So, it's basically someone who puts the prompt into the machine and chooses >> is Fiverr going to pay the prompt in the box, buddy. >> Fender bender.
32:49
>> That's a great question. Yeah. >> Yeah.
32:50
Can they be held liable >> for such a distracting sign?
32:52
I >> thought you know flush with money.
32:59
Whether or not it is a bubble like you can debate.
33:02
>> That's interesting because Fiverr is not one of those companies.
33:07
KTLA 5 is not prepared for it not to be a bubble.
33:11
>> Uh so Fiverr's market cap now is $560 million.
33:15
Uh and that's down about 95% over the last 5 years.
33:20
>> Where are you seeing that? I'm seeing 350. >> Sorry. Yeah. 359. What did I say? >> 500. >> Oh, sorry.
33:27
So it's a $360 million company today.
33:29
Down 95% from uh from 5 years ago.
33:34
Uh it started selling off in 2021 uh sort of pre-Chad GPT.
33:37
So I think the AI narrative might be a little bit overblown there.
33:42
It did IPO um around this price.
33:46
It was a $700 million IPO I think uh maybe a billion dollars.
33:50
Uh went through a massive boom during COVID and then uh and then sold off.
33:55
Uh but of course the AI wave has not been kind to Fiverr because a lot of the tasks like you know generating >> AI is very very very good at $5 creative work $25.
34:06
Obviously the prices go well beyond $5 since uh since since the early days but in terms of the kind of projects that I always use Fiverr for AI just oneshots all of that >> and the nature of Fiverr is like you have to define your task in a prompt.
34:24
It's not it's not oh have like a long conversation, get drinks with somebody.
34:29
>> That was often that was often the the bottleneck. That was the bottleneck.
34:31
It was like, okay, I need to do this task.
34:34
I I need like 10 minutes to like properly define it all these things.
34:37
And it's honestly way more time than you spend prompting normally.
34:41
Because with prompting, you're just like, I'll just try it a few times.
34:45
>> Bunch of times, >> kind of iterate, >> hit my rate limits, and then fire back up. Yeah.
34:48
I mean, it was always a bottleneck when I remember as an entrepreneur, I found out about Fiverr and I was like, this is amazing.
34:52
I can get random stuff done for five bucks, but the time commitment, actually finding the right person, making sure the reviews are good, it it wound up being like hours of work, and if you have a consistent flow, you're better off just hiring a person.
35:04
So, the the So, they got kind of squeezed in the middle. >> Yeah.
35:08
The market is is not excited about Fiverr right now.
35:11
They're being valued basically at four times IDA. >> Okay.
35:16
>> Uh and uh so, yeah, >> this is an interesting pivot for them.
35:20
They're basically saying that uh you can come to us to hire someone who has all the tooling set up to actually sit there and and sort of you know nanny all the AI models because it is a hassle like you as you described with me in V3 I was sitting there like okay I fire off four
35:39
prompts then I go back like it's way better if you if you're on the API and you have Higsfield wired up and you have you know runway ML and you have access to the Chinese model seance you know, the right tool for the job and then you do fine-tune on someone's face. There's
35:52
There's a whole bunch of things that you can do to get better results, but it takes time and it's a hassle and it's more of a professional job.
36:01
It's not actually out of work.
36:03
>> Here's here's the main problem with the campaign. >> Yes.
36:06
>> Is that Billy Bowman >> is a real person, okay, >> with his own website, with his own Instagram.
36:12
you can just go and like the primary issue with with uh these labor market places like Fiverr is disintermediation.
36:22
>> If you uh if if a business hires somebody on Fiverr and has an amazing experience, eventually they're just going to go direct because they build up a lot of trust and it's very different than than a platform like Uber where you don't necessarily want the same driver every time because they're not around you and all these things.
36:41
And so the reason that that that the the Fiverrs and the Upworks of the world and and there's been a bunch of other like engineering focused marketplaces just have never reached like insane scale like Uber is because of the disintermediation and this campaign is effectively an ad for Billy Bowman who you could just go hire today.
37:03
>> Yeah, disintermediation has always been a problem on these platforms. Anyway, let's move on.
37:06
Uh let me but first let me tell you about Finn.
37:08
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37:20
Uh, speaking of stocks, uh, did you see that Bombardier, the manufacturer of private business jets, is down 10% over the past month.
37:28
A lot of people were wondering why this was happening.
37:33
I think we now know why, and it's probably because the shot across the bow from United Airlines.
37:38
So, what competes with a private jet?
37:41
Potentially United Airlines new product, which is an entire row of economy seats. We got to pull this up.
37:51
United Airlines says, "The entire row is all yours.
37:53
Welcome to the United Relax Row.
37:56
Three adjacent United Economy seats with adjustable leg rests that can be raised or lowered to create a cozy, lie flat space for stretching out.
38:06
You'll also get a mattress pad, blanket, and two pillows.
38:08
If you're traveling with kids, a plushy, too.
38:12
United Relax row will be available starting next year on more than 200 of the 787s and 777s, each with up to 12 of these brand new rows.
38:22
So, what do you think, Jordy?
38:22
Is this the way I was telling Tyler Cosgrove, who is out of the studio today, he's in uh Washington, DC, he's got to demo this.
38:29
He's got to get on one of these. I don't know.
38:31
Every time an airline announces something, it's always like 5 years until it actually is available.
38:35
I've been waiting for Starlink for a long time.
38:36
Took a long time for that to get rolled out from the PR release.
38:40
>> United has pretty good pace.
38:40
Haven't they been quick to >> Do you think Tyler could get on this tomorrow >> or today?
38:44
He's going to the airport today.
38:46
>> I think I think if Tyler's resourceful enough, he could just if he ended up in a row, two empty seats next to him, he could just figure out a way to detach >> the armrest blocking this. Yeah.
38:58
>> And just kind of kind of build your own. >> Yeah.
39:00
>> He might they might have to land the plane and arrest him. Yeah.
39:01
but potentially worth the risk.
39:05
>> He could also he could also potentially negotiate with whoever's sitting next to him say, "Hey, you go and spend the entire flight in the lavatory and in exchange I will vibe code you a sloppy app of which I don't understand what programming language is used.
39:20
>> I'll trade you an app for your seat.
39:21
>> I'll trade you an app for your seat."
39:22
And somebody might be like, "That's amazing.
39:24
I don't have anyone that can vibe code for me.
39:25
This is This is too good." No, I'm sorry.
39:27
Ryan Peterson says, "Now we just need to put stairs on the food drink cart so you can climb over the top of them to get to the bathroom instead of holding."
39:38
>> Yeah, this just this just feels like this just feels very very chaotic.
39:45
>> But this is this is wild. >> Good. >> I don't know.
39:48
Starlink, a relaxed row, a dream.
39:50
I I think that this could be a good option.
39:52
Uh, United built a product that everyone who has been who has ever been on a plane wanted, says John Collison.
40:00
>> John, you need to work on fixing Bombardier's stock price.
40:04
>> I don't think it's related.
40:04
Oh, apparently Air New Zealand launched this in 2010.
40:07
I wonder >> No, it is related because he he should he should help them build out their pipeline.
40:13
>> Well, he's he's just pumping his bags because he owns a uh a property in Ireland. How do you get there?
40:17
You got to fly on United.
40:19
So, you know, one hand washes the other on this.
40:23
He's he's he's talking his own book. No, just kidding.
40:26
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40:45
And >> Carter says, "If you say no to pretzels, the flight attendant should give you something called a coin of restraint while worth nothing now.
40:53
These coins will play a major role in the afterlife.
40:58
The coin of restraint is very, very good." I like that.
41:00
Uh well, Elon Musk is teasing something cooler than a minivan that might come because uh Elon said the Cybertruck rear bench has three seats of ISOIX attachments and is wide enough to fit three child seats or three adults.
41:16
So, there's been a big debate on uh the ISOIX attachments.
41:18
These are the little metal uh hooks that are installed in every car in the back seat for for child seats.
41:27
And so child seat requirements mean that you have to have a car that has these and it's been uh it's been called like the death of the five kid family or something like that or like the death of the big family because at a certain point in order to have another kid you have to upgrade your car and that has an expense associated with it.
41:46
associated with it. you need a three row SUV or you need a minivan or you need something bigger that's not as that's not as affordable as you know just a normal sedan that used to be able to just throw four kids across and maybe that was less safe >> regulation killed the birth rate
42:00
>> people people actually say this people say that uh that child seats have saved like like a million lives but then they've stopped 10 million from being born they they they talk about the relative exchange ratios I haven't really dug into it too much but um there's clearly demand for more spacious vehicles with more seating. The Model S
42:17
The Model S used to come with a third row, which I still don't understand how that was possible.
42:22
Um, the Model X came with a third row and there was uh and in China they actually sell a Model Y L which is a long wheelbase version of the Model Y.
42:32
I hope they bring that to the space.
42:33
>> People are saying make a minivan Elon Elon says something way cooler than a minivan is coming.
42:38
>> What do you think it is?
42:39
>> And people are speculating. Garcia here.
42:42
>> I think it might be a data center. uh test.
42:44
>> I think you might be like, "There's another data center coming." >> Chip Fab. >> Yeah, Chip Fab.
42:47
He's like, "You >> you're gonna be able I'm gonna and and and Texas is gonna change the child labor law so that >> instead of worrying about bringing your kids around to different places, no, they're in the fat.
42:58
They're in the clean room.
43:00
>> They're in the clean room.
43:01
>> They're fully suited up. >> Suited up.
43:03
>> They're making chips."
43:03
No, people are speculating. This is very cool.
43:05
Uh Genai has been amazing for car enthusiasts to create basically their own concept cars.
43:13
Yeah, these look very very cool.
43:15
>> I mean, it's funny because make a Tesla that look make a Tesla version of the Rivian.
43:20
>> Yeah, >> because it looks exactly like it.
43:21
But let's let's get into some of the speculation. So, >> okay.
43:24
What what are people saying?
43:26
>> Cia Banister says an RV.
43:28
>> Uh, that could be fun.
43:28
Uh, Arthur McWater says, "I can't wait for the next Roadster unveil."
43:33
Elon was teasing, it was on Joe Rogan, right?
43:37
This concept of maybe it'll fly.
43:39
And I think what Elon could be could be getting at is >> uh picture picture picture a roadster.
43:47
>> Not a great family car.
43:49
>> Hard to put kids in a in a sports car.
43:51
Some of them you technically can, but it's so uncomfortable and kind of chaotic. Very few people would.
43:57
>> And I think what we could see is the Roadster comes with five uh kind of like, you know, that Fury collaborative combat aircraft from Andre.
44:05
What if it comes with like up to five little mini roaders?
44:10
Roadsters that can that are just trained to autopilot behind the primary roadster.
44:15
So you can be in your sports car and then however many kids you have are in the mini drones following that fun.
44:23
>> And uh >> what about a what about a Chinook heavy lift uh helicopter with two massive rotors that can lift your roadster off the off the ground? Yes.
44:33
>> Technically a flying car then. >> Yes.
44:35
Anyway, let me tell you about phantom cash.
44:38
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44:40
Um, I have a question for you, Jordy.
44:44
Are you are you running the new AI model? It's on co-work.
44:46
It's literally on co-pilot.
44:49
You can probably find it on codeex, dude. It's on co-author.
44:52
It's a co-sign exclusive. It's on cocaine.
44:55
You can run it on cocaine.
44:57
You can literally go to cocaine and run it.
44:59
Uh, what a great copy pasta.
45:02
This is one of the funniest uh funniest formats.
45:04
Uh but yes, the war for co-pilot and co-work is heating up.
45:06
Uh we got to find a new term.
45:10
I think uh people have been really really fighting neck.
45:14
>> Well, I was telling uh Microsoft they should they just name it Coco. >> Coco.
45:19
>> Microsoft co-pilot coowork. It's just Coco. >> Coco would be good.
45:21
There's a few different there's a few different options.
45:24
Uh I do I think I prefer the nonthropomorphized AI names although they are a little bit colliding in the name space.
45:32
I have been a fan of uh of you know the the the codeexes and co-works and co-pilots.
45:41
Those feel more collaborative to me and they feel more like tools than the Bards and the and the series and the Alexexas and the Roffus and the Sparkies like that that's just a different uh vibe.
45:54
And I think that uh I if we're living in a world where people are going to form uh you know strong relationships with these tools uh introducing them truly as tools is probably >> Let's uh see what's going on with QVC.
46:08
>> Okay, >> let's pull up this video.
46:10
>> And we also have one of our guests joining early at 11.
46:14
>> No, he's he's he's going to be joining uh he'll be ready to join in in an hour. >> Are you sure? >> Yeah. >> Okay.
46:20
Uh, let's check the production team because I've already I've already texted. Okay, cool.
46:24
>> We'll continue for the next 15 minutes. >> Fantastic.
46:26
Well, take us through the next story. What do you want?
46:29
>> Uh, Q River says, "Que basically reinvented live streaming decades.
46:33
Invented live streaming decades ago. Ghost 247." They invented live TV. Uh, Ghost 247.
46:38
A good 80% of the show is just the host going on long personal digressions.
46:43
People watch it as background.
46:45
Heavy parasocial element. Hosts know the callers. 95% of repeat buyers.
46:50
It's 100% Twitch for grandmas. Let's pull up QVC.
46:55
>> And with the submarine, >> I wanted to tell you that I got that peasant blouse with the tassels. >> Yes.
47:01
>> And I used to wear the tassels on my pasties.
47:06
Do you know what pasties are? >> This is >> Okay. >> Ridiculous.
47:10
>> Do you know what what pasties are?
47:13
>> Let me I'm going to >> Okay.
47:13
Well, I have on to move on. >> We're moving on.
47:16
Let me tell you about 11 Labs.
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47:42
Uh SpaceX aims to file IPO as soon as this week.
47:49
Uh I know everyone is excited for the S1.
47:52
Uh particularly I think people will be focused on XAI. Yeah. What they have going on.
47:59
I think that uh I think >> are they going to have to break it out in the in the S1?
48:04
>> I would assume the first time we actually see the economics of inference, the economics of a foundation lab.
48:09
Even though yes, they are at a smaller scale.
48:13
hard to read too much into it because they've been investing so far ahead of. >> Yeah.
48:20
I I just think that like there is a world where we get broken out financials that you can dig through and you can understand based on Grock pricing which we see and topline revenue and cost.
48:33
We can actually see are they serving that model profitably and there will be you know a lot to dig into there.
48:40
Obviously the other labs have different strategies, different vertical integration points, different economics, different pricing regimes.
48:46
I mean the the the the true frontier, the the models that are are are dominating ARKGI uh which we will talk about in 15 minutes.
48:55
Um those command a premium, a price premium uh and and and there's a wild difference between charging 15 bucks per million tokens versus $2 per million tokens.
49:05
So uh it will be >> Yeah.
49:07
>> Yeah. If you and if remember it was I think it was in Q4 of last year if you looked on open router Gro was what had I think it was like Grock fast >> had a a ton of usage people are like okay why is this happening and part of it at least I believe was because they
49:24
were subsidizing it >> well >> um but okay so here here's what I think so so people were posting this as though it was fact uh but I think uh I think it's a very real possibility So, uh, I think Elon will will will try to aim for the company to actually go out on on April 20th, 420. >> Really? >> Really?
49:48
>> Um, and I think it is possible if he goes >> fast.
49:52
>> The the ticker, we'll see what the ticker ends up being, but I think some people would like knowing Elon's very millennial sense of humor, I think the ticker se is >> Oh, you think so?
50:05
plus the yeah April 20th IPO.
50:07
I I would assume that >> you think this is a real prediction. You're not trolling.
50:13
>> I'm not saying I'm not saying I would bet on it, but I think there's like a I think there's I think there's >> I I would put the the April 20th at at maybe like, you know, 30% and then the ticker maybe down at at 15%.
50:29
>> Calli has when will SpaceX officially announce an IPO?
50:31
before June 1st, which April 20th would be before June 1st.
50:36
>> I'm not talking about I'm talking about like list actual like listing day, >> like the day of the IPO.
50:41
>> Yeah, this is just announcing the IPO, >> which I don't even know if they >> they haven't even confirmed this.
50:44
This is now this is currently in in the scoop uh in the scoop thing.
50:48
So, they haven't even >> scoop you said >> they So, the >> Wait, John, did you say scoop?
50:54
>> Yes, this is a scoop from from >> What is What is this? >> From scoop doggy dog.
50:59
>> Can we go to the wide angle?
50:59
We got Katie roof scoop master.
51:03
>> We have a new award her the first TVN golden scoop.
51:07
>> The golden scoop award.
51:08
>> Do you want to show her the the >> I don't want to I don't want to pick it up yet. >> Why not?
51:12
>> Okay, we can we can show we we need to give the golden scoop award for the best scoop of the day to Katie Roof who moved markets with her scoop.
51:20
She of course is the deputy bureau chief of venture capital at the information and she's an absolute scoop athlete.
51:27
She's scoop doggy dog and and she moved markets. So, uh, SATS is up 7. 8%. BKSY is up 4. 8%. Uh, Lunr is up 4. 1%.
51:36
Every stock in the space and >> we're working on a new award award show, the Scoopies. >> The Scoopies. >> The TVPN Scoopies.
51:48
>> I think Katie Roof is a lock.
51:50
>> I mean, she's in she's she's a front runner for sure.
51:53
>> Putting on a generational run.
51:55
>> Front runner for sure. Yeah.
51:55
It's so it's so funny the the uh I mean incredible moment for the retail space investor community. >> Yeah.
52:04
>> Because they're just for some reason people people >> um anytime you get SpaceX repricing they're like well this >> this other random company that happens to be technically on the same market map definitely deserves to be worth 7% more. >> Yeah. I don't know.
52:20
I I I I I was as skeptical uh about the the rest of the lunar economy for a long time.
52:26
It felt like winner take all.
52:28
It felt like SpaceX was running away with it.
52:30
Um but there have been interesting dynamics where where companies that are buyers of SpaceX capacity, whether on the satellite internet side or on the launch capacity side, they don't want a monopoly to exist.
52:43
And so they're willing to throw money at competitors.
52:47
And Jeff Bezos has stuck around with Blue Origin.
52:50
Rocket Lab's done very well. There's been a bunch.
52:52
Uh people are saying we need to redesign the scoop.
52:56
I don't know what you're talking about.
52:58
>> I don't even know what you what you could be talking about.
53:00
>> It's an ice cream scoop.
53:00
It's an ice cream scoop that people get your mind out of the gutter. Head over to Shopify.
53:08
Shopify is the commerce platform that grows with your business and lets you sell in seconds online, in store, on mobile, on social, on marketplaces, and now with AI agents. >> Okay. Casey Hammer is worried. >> What is he saying? >> He's worried. >> Why is he worried? >> He's stressed. >> Why is he stressed?
53:23
>> What I worry about is a generation of talented, experienced engineers being too rich to work.
53:27
So, if you're worried about being demotivated by your incredible SpaceX liquidity, you have to do what I recommend, which is the Brewers Millions approach, where you have to spend all the money in 30 days without telling anyone why you're doing it.
53:41
This is from the 1980s, 1990s comedy, uh, called Brewster Millions, which I highly recommend you watch.
53:46
uh in it uh a man is is gifted an inheritance from a wealthy uh uncle or father figure, grandfather and uh and it is conditioned on the on the fact that he needs to spend something like $30 million in 30 days without telling anyone why and he can't acrue assets.
54:04
So he can't go and just buy cars.
54:06
He needs to throw parties and give money away and and and spend money wildly.
54:10
And it's uh it's to teach him that money does not bring happiness, of course.
54:14
And I think that's the solution to this.
54:16
Uh anyway, let me tell you about MongoDB.
54:18
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54:23
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54:26
>> Here's why I'm not worried.
54:27
>> Why are you not worried?
54:28
>> Because I think that these ultra talented, hardworking engineers. Yep.
54:34
>> Having some liquidity.
54:34
Let's say someone's been there for some number of years. Yep.
54:39
>> They have 5 million liquid.
54:39
They buy a nice house somewhere. >> Yep.
54:44
and then realize, hey, I've got a nice nest egg.
54:47
Yeah, I can keep working on crazy moonshot.
54:49
I don't have to go join, you know, the the the, you know, historical example would be like work on the hard thing or work on enterprise SAS.
55:01
>> And I think this just will give people um more confidence to work on the hard thing.
55:06
the thing that might have a 5% chance of success, but if it's successful, >> you know, hasn't a tremendous impact on on uh on on our country and things like that.
55:16
So, >> it might happen for some people, but in general, I I I do think that uh there is this liquidity wave coming.
55:21
Delian talked about on the show yesterday.
55:23
Uh at the same time like SpaceX has been doing tenders for over a decade and it's not like the early employees have never had a crumb of liquidity or secondary throughout their journey.
55:36
A lot of them have had opportunities to sell at least a portion of their stake and had been able to buy houses.
55:42
And there's always been a way to access some of that capital whether through a loan from a bank and you're obviously more creditw worthy if you own a bunch of SpaceX stock and it's going up.
55:53
Um, and uh, >> yeah, bigger a bigger issue for the kind of space economy overall is just the Blue Origin people who like wasn't didn't they have like way more?
56:04
>> Like they didn't they they didn't really know what their equity was worth.
56:06
They didn't it's not like there was regular tenders and so why work at the number two at least by many definition space company >> and then that's potentially ultimately bad for competition and it's bad for >> la you know launch pricing for all these other >> companies that are reliant on the SpaceX's and ultimately the Blue Origin.
56:29
So, >> anyways, uh >> crazy news out of China. >> Mhm.
56:35
>> Apparently, the co-founders of Manis uh are still in China and were called up, uh to uh talk with the government and are now blocked.
56:47
>> Have you seen The Dark Knight? >> Yes.
56:50
>> Has everyone seen The Dark Knight?
56:50
You You thinking what I'm thinking?
56:52
You thinking what I'm thinking?
56:54
We go there, wrap our arms around him, the plane comes, grabs the balloon, sucks him out of the back of the of the skyscraper.
57:03
It's one of the greatest scenes, and I think that's what we got to do cuz we need personal super intelligence.
57:08
>> This was surprising to me because I feel like we've been messaged to for a long time that they were in Singapore. >> Yes, that's true.
57:16
>> We shouldn't have been seeing this.
57:16
It's not a Chinese company. They're in Singapore.
57:19
The whole team's in Singapore.
57:22
>> And and you would It it takes some audacity to sell your leading Chinese, one of the leading Chinese AI companies during an AI a global AI race, >> this battle between great powers and to sell to a big American hyperscaler. >> Yep.
57:42
>> Maybe get out of the country before you do that because it's not at all s I mean >> this doesn't when when the manis acquisition happened >> Yeah.
57:50
seem very clear that you would be if you were China and you were competing in the AI race. Yeah.
57:54
Even though Manis is not like a lab, you're still like, >> okay, they're building a powerful harness. >> Yeah.
58:02
>> We probably don't want them going to serve >> and the harnesses are incredibly probably under >> more important now. Yeah.
58:08
Super important right now.
58:10
Everyone everyone, you know, obsesses over the models and the models are important, but the the harnesses have shown incredible promise for actual diffusion and making these models.
58:19
So Josh Wolf says, "I thought this wasn't a Chinese company."
58:21
And uh Delian goes for the jugular and says, "So much for all the arguments about Manis not being influenced by the CCPA, >> Bill."
58:32
>> Yeah, I I'm I'm mostly shocked just about how this is playing out.
58:34
You You would think that if you're the CEO of Manis and you get a call from Mark Zuckerberg and it even smells like a potential acquisition, you're like, "Yeah, I'd love to come see the headquarters.
58:46
Why don't I come and with my team, we'll just come and hang out in Menllo Park or Miami for a couple months while we hash out the deal?
58:54
And if the deal doesn't go through, we'll head back.
58:57
But if it does, we'll just stay and we won't go back because if it goes through, then there's going to be pressure and we're going to wind up in this situation.
59:04
This feels almost predictable. I don't I don't know.
59:06
It feels like there's something else going on here.
59:09
I'm excited for this uh this uh story to develop.
59:13
>> Authorities are reviewing the sale. >> Yeah.
59:15
and they're being asked not to leave.
59:17
Seems hard to reverse at this point. But again, not sure why.
59:20
I mean, when you look back at >> acquisitions that were blocked historically, who knows? >> Yeah.
59:30
>> Who knows how feasible it is to fully block the acquisition, but they can certainly block these individuals from, >> you know, materially benefiting from it in some way or contributing to Meta's efforts.
59:43
Well, here's some advice for the CEO of Manis.
59:44
When you get here to America, when you get that liquidity, that payday from Mark Zuckerberg, open an account on public. com.
59:51
Investing for those that take it seriously, stocks, options, bonds, crypto, treasuries, and more with great customer service. Just do it.
59:59
And then, you know, tell your story.
1:00:01
Start rereaming one live stream, 30 plus destinations.
1:00:03
You should be you should be multireaming. So, go to reream. com.
1:00:07
Tell your story live on the internet.
1:00:10
Gabriel says, uh, Meekmill has been going off. Yes. About AI.
1:00:15
AI is helping him organize his whole music career and other businesses in days and it's moving his business forward at a high rate.
1:00:23
Some tech Young Bull I met on LinkedIn gave me an incredible template which probably GStack. >> Yeah. Yeah.
1:00:31
>> Um, who else can help me with Claude?
1:00:34
Uh, Gabe says, "Drake, I don't do K2 Kimmy DeepSeek.
1:00:37
That's more for your kind.
1:00:39
My gal more like Demis theme park ting London deep mine.
1:00:43
Meek Mill I cla coded perks out of 18th and Burks.
1:00:47
I got 500 agent lawyers trying to free Lil Dirk. >> That's a good line. Bars. >> Drake singing.
1:00:55
You got me loco trying to be your shoulder. >> You didn't sing it. >> Sophie chimes in. >> Actually sing it. >> Two chains.
1:01:06
Open claw on my laptop trapping off these Mac minis. Shout out to YC.
1:01:10
Real G's want to stack with me. >> That's good. That is good.
1:01:14
I mean, yeah, it's not a it's not a bubble until until GStack makes it into makes it into a >> actual hit.
1:01:26
The funny thing here is there's some debate and and there's this general vibe like some people were latching on to this Trunk fan post about like you know are people making fun of me Meek Mill?
1:01:35
Do are people like underounting his ability to actually build something for real?
1:01:38
And the interesting thing is that I would definitely bet on Meek Mill to build a real valuable piece of software for his audience or his life or his business over over any of these tech people writing rap lyrics even with all the powerful LLMs.
1:01:56
Like the tools to actually build software are way better than the tools to write rap lyrics.
1:02:00
And and and it's interesting because you are seeing this dynamic where where the like like it I think a lot of people are latching on to the older paradigm of like oh like you know Jeremy Rener built an Instagram clone at one point and it was just the Jeremy Rener app and it was just his feed of his photos and people were like why does this exist?
1:02:20
You can just follow Jeremy Rener on Instagram.
1:02:23
It doesn't make sense that he would have like his own private Instagram.
1:02:24
Um and he probably spent a lot of money developing that piece of software.
1:02:28
Uh but and and I don't think it wound up working and it didn't scale and he wound up winding that project down.
1:02:34
up winding that project down. But uh if you think about like well what if this what if this cost of doing that is a h 100red bucks or 200 bucks or a thousand bucks like all of a sudden the the hurdle rate to clear something like that
1:02:47
actually does open up the creative aspects where I I wouldn't be surprised if there's a like maybe not like a breakout hypers scale incredible like generational company but just like in terms of like you like you can be a great musical artist and also produce
1:03:06
great clothing like you will now be able to produce software at the equivalent level like it is available and so if you are constrained by your ideas and if you're a creative artist you and you have a great idea you're no longer going to be in this world where you're like
1:03:20
well I need to put a couple million bucks down for a software engineering team and they're going to not really take me seriously and all of a sudden you get into this weird thing where like the the app that they launch is like sort of iffy and not that good. So, I So, I don't know.
1:03:33
I'm actually I'm actually coming away bullish on uh what Meek Mill winds up producing over the next few years.
1:03:40
>> And we are working on getting Meek on the show.
1:03:43
>> I really hope we can talk to him. >> I'll cover you. You're good. You're good. >> I'll cover Meta.
1:03:47
Do you want to jump for a second?
1:03:50
>> First, let me tell you about Crowd Strike. Your business is AI.
1:03:51
Their business is securing it.
1:03:52
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1:03:54
And I'm also gonna tell you about Vanta, automate compliance and security.
1:03:59
Vanta is the leading AI trust management platform. and I'm totally good.
1:04:02
So, I can take uh the next intro.
1:04:06
>> I have a personal thing I'm going to run to, but I will see you guys tomorrow. Yes. Love you. Cheers. >> Thanks, Jordy.
1:04:11
Uh up next, we have Mike from Ark Prize in the Reream rating room.
1:04:16
Let's bring him in to the TDP Ultra Dome.
1:04:17
I'm very excited to talk to Mike. How are you doing? >> Here we go. Hey. Hey. Good to see you again. >> Good to see you. Uh >> I'm so excited.
1:04:25
This is always the highlight of the show.
1:04:27
I love talking to you about everything but uh what are we talking about today?
1:04:31
Uh reintroduce uh ARC as an organization and then take us through the actual do you even call them benchmarks challenges what's the right term?
1:04:43
>> I think benchmarks is a fair word. >> Okay.
1:04:44
>> Um yeah couple almost three years ago now co-ounded the arc prize foundation me and France.
1:04:52
>> Um the archives foundation has a mission uh to be the northstar of AGI.
1:04:53
So uh sort of our sort of job we have two of them.
1:04:58
One is to help be a useful sense public sense finding tool for the public to understand how close how far are we towards AGI or not. Yeah.
1:05:07
>> Um and the second is to inspire progress towards AGI.
1:05:08
Um ARC is a a series of benchmarks that um help highlight what are some of the large remaining gaps between what's frontier AI is capable of and what humans are capable of.
1:05:17
And we sort of uh uh target that gap.
1:05:19
That is sort of uh our definition of ultimately AGI is you know we we produce an ongoing series of benchmarks um continually studying frontier progress and uh you know at some point we are not going to be able to do that job anymore.
1:05:31
We will run out of ideas you know we'll test frontier and say we can't find anything else any more gaps and I think that'll sort of be the moment when uh I think it becomes commonly accepted to say okay yeah we we've got AGI now and today we are announcing and launching uh the the newest best version of ARC arc AGI uh 3.
1:05:48
It's the latest in the series.
1:05:48
This is a really large uh format change from the first two.
1:05:52
Uh ARK 3 is designed to test agentic int intelligence and it is as I as far as I am aware and I've been sort of interviewing folks all over the AI scene in the last few weeks the only unsaturated general AI agent benchmark in the world.
1:06:06
Um the headline score is human score 100% and AI less than less than 1%. >> Okay.
1:06:12
uh unpack that like launch because I my conception of Arc AGI V3 is it's almost like a 2D game.
1:06:23
It's no longer the puzzles where I'm picking colors to match a pattern.
1:06:27
It's actual uh moving arrows on the keyboard.
1:06:30
I'm stepping on triggers.
1:06:33
I'm opening doors, switches, that type of thing.
1:06:35
And um I played it with you on the stream uh months ago. >> Yeah.
1:06:41
uh helped us launch our preview several months back. >> Okay.
1:06:44
So, that was the preview.
1:06:45
>> Today, we got the fin the full data set launching today.
1:06:48
>> So, so does that mean more I I'm going to call them games more more actual levels launching or is this that uh what you're launching is like you did the actual benchmark and got the four leading labs to devote the compute and actually open up their models to be able to interface with the system to get the scores.
1:07:07
>> Uh both things actually.
1:07:07
So today um on the benchmark side uh the public version of the benchmark is uh or I guess the the overall benchmark.
1:07:15
So over 100 games um nearly a thousand different levels across these game-like environments.
1:07:19
I think it's fair to call them games.
1:07:22
We've designed them to be fun and games are fun.
1:07:24
Um I think you could look at them you know from a research standpoint more more as environments though.
1:07:28
These environments are intended to test whether AI can effectively explore, discover its own goals, acquire strategy, develop plans, execute its plans.
1:07:39
>> One of the really unique things about ARK 3 compared to the one and two format is that it is interactive now.
1:07:43
Whereas you mentioned, you know, one and two look like these kind of static IQ puzzles that were on a page.
1:07:49
>> Uh three challenges both humans and AI to essentially figure out the uh figure out the goals of themsel.
1:07:55
um when you're dropped into one of these environments, your only goal explicitly is to win.
1:07:58
And so in order to figure out how to do that, you have to actually, you know, dedicate some uh some extra regulation to figuring out the rules, the mechanics, the strategy.
1:08:07
And um one important thing is you're sort of playing these environments.
1:08:10
This the strategy mechanic, they grow and they evolve and they change over time.
1:08:13
And this is one of the reasons I think AR3 will be a really useful tool for understanding agentic intelligence this year.
1:08:19
I think it'll be our first real test um or you know seeing early progress on these AI systems that are able to do on the-fly world modeling some degree of on the like on the continual learning.
1:08:30
These are both like critical capabilities um that we view as missing today that Arc 3 tests for. >> Okay.
1:08:35
Uh take me a little bit deeper on you you said there's a thousand games or a thousand levels.
1:08:43
>> A I think it's a a few overundred games. Okay.
1:08:46
Uh across those 100 environments nearly a thousand levels. across all of them.
1:08:51
>> Um, yeah, it's a much larger uh version of uh of the of the benchmark than than we've ever had previously.
1:08:55
And then like I mentioned before, the other major thing we're announcing today is Frontier scores.
1:09:00
Okay, so the benchmark is is launching.
1:09:01
We're also publishing as of today um the the the latest four models uh across all the four major labs.
1:09:06
And yeah, I think Soda is currently sitting at like 3% uh point4% >> Gemini 3. 1 Pro.
1:09:14
Uh maybe there's an extra hyphen it on there, but uh basically the Gemini uh Anthropic and OpenAI were all in the 0. 2.
1:09:24
3 something and then uh Grock was I think it's 0% uh walk me through uh the actual buildout of these hundred games.
1:09:35
Is this entirely human done?
1:09:38
Is there some sort of uh computer aided tooling to insert variation programmatically or is it important that they're all created by hand?
1:09:48
How do you think about the creation of >> I I wish we could use uh AI to help design games.
1:09:53
We'd be able to make the benchmark even big like better.
1:09:54
Uh the reality is um like humans are still the bottleneck on creativity and so every game has still been handcrafted and handdesigned by humans.
1:10:01
You could sort of imagine if you were embedding all these different levels on a big manifold, you know, in an embedding, you want them all as far apart as possible in that sort of space.
1:10:12
>> And today, still humans are kind of the limiting factor in terms of ensuring that, you know, every game is is different and as novel from from from each other as possible. >> Yeah.
1:10:20
>> Um there's a one there's a few interesting uh design changes actually from a benchmark standpoint compared to to one and two.
1:10:26
Maybe the the largest um is you know, ARC studies the frontier progress.
1:10:31
we have to design our future versions of benchmark uh to to adapt to to changing Frontier progress.
1:10:35
Um one of the design goals with one and two was we have what's called a private and a and a public test split where we have a public version of the benchmark and a private hold out version which is what we actually use to verify the performance of Frontier models.
1:10:50
>> So so so the the Frontier models get no freebies.
1:10:52
They don't get anything from the public set but they can try.
1:10:55
>> You can't memorize the public set, right?
1:10:56
>> Or or or or they can they can experiment on the front on the public set from like a prompting perspective maybe. >> Yeah.
1:11:03
The idea is the public set is intended to demonstrate the format. Okay.
1:11:06
>> Um and this was similar with ARK1 and two.
1:11:07
However, we held a design goal that the public sets and the private sets uh were were called um ID with each other.
1:11:13
Basically that they um are are supposed to be as close as possible to each other.
1:11:16
Um and it's just uh split along visibility.
1:11:19
Some are private and some are public. >> Sure.
1:11:21
with uh one of the the big advancements with AI reasoning is this actually like not a very useful way to run benchmarks.
1:11:26
Uh AI reasoning systems are so powerful now that they can actually generalize across ID test splits and this is what we saw with ARK1 and two.
1:11:35
So with three, one of the big design decisions is um we're actually releasing fewer games into the public demonstration set.
1:11:40
So there's only I think about 25 games that are in the public set.
1:11:44
We're actually explicitly not even calling it a training set anymore.
1:11:46
We're calling it a demonstration set just to show the format to humans, you know, be able to test your systems to make sure you can sort of create them, get a feel for them.
1:11:53
There's obviously fun marketing value in being able to play the games as humans, too, which we really love.
1:11:56
Uh, and on the private set, this is the set that's over 100 100 games.
1:12:00
Um, they're specifically different.
1:12:02
They're they're different.
1:12:04
We designed them with different characteristics, uh, different goals, different um, uh, intelligence capabilities required to beat them.
1:12:09
The difficulty, uh, the acceptance criteria is more extreme between human and AI performance.
1:12:14
all to hopefully produce the most useful like uh high signal uh benchmark towards whether we actually are getting real progress towards AGI with the foundation models.
1:12:25
>> So uh let me pitch you a strategy.
1:12:27
If I have access and I I you know I'm at I'm at Google or OpenAI or Enthropic and I I want to do well here.
1:12:34
Can I take the public set and uh create a log of all the steps and all the all the reasoning chains and all the key strokes that are required to pass those levels and then sort of like dump that into the context window before I go off into the unknown >> um and train your model that way.
1:12:58
basically >> maybe train my model, but also I'm I'm just wondering if if that's helpful for for uh setting up the context or or or like doing some sort of like pre compaction of the strategies that are learned.
1:13:12
Maybe not even training a custom model because I feel like that would maybe be like bench hacking.
1:13:15
I'm more thinking about just like uh okay we we went and we played all the public games uh to completion and we and we monitored them screen recorded them tried to extract as many learnings as possible into you know an MD file basically and then we and then we include that in the prompt that that that kicks us off that to sort of bootstrap the learning once we get into the unknown environment.
1:13:39
If we've done a good job on the benchmark, you should not be able to train a system on the public set and perform well on the private set.
1:13:45
Um, if we've done a good job, obviously every benchmark release, it's an it it's an experiment. Yeah. Right.
1:13:50
We make contact with reality.
1:13:50
We ship these systems benchmarks publicly.
1:13:54
We we try to analyze the performance, understand what they're good at and bad at and evolve, you know, future versions of the benchmark.
1:13:58
But intentionally, you know, and this actually is a um very closely related to another design decision that we're making with our uh scoring function going forward this year.
1:14:07
And this is again in response to like AI progress that we've seen.
1:14:09
You know, our our scoring methodology is basically AGI field at this point.
1:14:12
Um we uh going forward with V3 are using as I I kind of have um uh this idea of like um uh basically a philosophy of having uh essentially no harness.
1:14:28
>> Um we want to create a testing experience that's as similar as possible between the human and the AI test takers.
1:14:34
And when we have our human baseline, when we have a, you know, rented literally a testing center in San Francisco, had, you know, hundreds of humans play these games. Yeah.
1:14:39
Um, all they're given is uh you have sort of, you know, sensory input through your eyes and action motor output through your hands back into our testing interface.
1:14:48
And all of the intelligence happens between those two steps.
1:14:50
And so we try to emulate that as close as possible for our verification function where we have this sort of philosophy of having a very stateless client.
1:14:57
So that our scoring function basically tries not to introduce any kind of bias, any kind of help, any kind of maybe potential cheating strategy.
1:15:04
If you go read our prompt, it's extremely simple.
1:15:06
It's like, you know, you are playing a game, here's your actions, your your conversation will be carried forward to the next turn, and that's it.
1:15:12
Um, in order to again kind of produce this really clear signal towards when the there's real progress towards AI and the base intelligence layer, we're able to detect that. >> Okay.
1:15:21
So take me back through history a little bit because I'm surprised by why AI is struggling with this in particular because I remember it feels like almost a decade ago that OpenAI had a product I think it was called Jim where they were able to beat Mario and then they beat the Dota team Dota 5 and they were able to do things that I can't do.
1:15:48
I I certainly can't beat Lisa Doll in Go.
1:15:50
I certainly can't uh you know win Jeopardy or any of these things.
1:15:56
And yet AI systems were able to dominate those games.
1:16:00
You've created new games.
1:16:00
What's different about the games or the strategies by the AI labs where we're not matching up like we did in the past.
1:16:11
>> I think the biggest thing is the expectation of what constitutes real progress towards hi, right?
1:16:14
progress towards hi, right? when labs were using games in maybe the 2016 to 2018 2019 era when they're very popular um you know human researchers are studying the games trying to understand the failure modes of machine learning deep learning trying to build custom search like harnesses to uh and sort of
1:16:31
feedback mechanisms from the environments it's very very handcrafted it's loaded with what I'll call like human G right in the research process >> we are now at a point where we want to control for that actually we want to understand >> like we we want to control for as little human in these like systems as possible, right? We want to understand is can
1:16:48
We want to understand is can basically AI do what the human researchers were doing back in that era in order to beat those games that they had never been trained on or exposed to before. Interesting.
1:16:57
Um, so I do think it's kind of elegant that you know we are coming full circle where games are these very minimal representations of like actually important capabilities that humans possess around exploring and developing strategy and world modeling and being able to learn on the fly.
1:17:10
Um, they're really they're really elegant as far as an environment goes.
1:17:14
Um, but I think what's changed is our expectation of how much human crafting is needed in order to uh learn the games when they haven't been specifically trained on them is is is the big difference today, especially with AR 3. >> Okay.
1:17:27
Uh, remind me of some more history but more related to ARC.
1:17:29
I remember with one of the Arc AGI benchmark tests, uh, there was a version from of a model from OpenAI that was running on some sort of like extra high mode and I seem to remember like $2,000 per task being cited something 03 big launch. >> Okay.
1:17:48
>> Yeah, that was like a preview of 03 in December of 2024.
1:17:50
It's really the first, >> you know, there's a great chart on the Arc Prize homepage now where you can actually see this data point so clearly.
1:17:58
Um I think one of the really you know like I mentioned before one of our missions of the foundation is to try and be useful public sense finding tool. >> Sure.
1:18:05
>> And um I think you know when we first launched ARK one and uh two you know it was a very common critique.
1:18:09
It's understandable you know hey these things look like toys are they really economically useful?
1:18:13
Are they going to lead to any you know real progress?
1:18:14
Um and now in hindsight I actually think that's a pretty outdated view because we have pretty strong evidence that ARC held quite strong predictive power of noticing really important hit moments.
1:18:24
We only started seeing uh saturation on the V1 benchmark and remember V1 was like 5 years old.
1:18:29
We only started seeing any amount of progress from LMS on V1 once we got AI reasoning which was a really critical innovation I'd argue is is as important as the original transformer innovation.
1:18:38
Um and then a year later uh this was you know 4 months ago now uh with the November December 2025 class of models with GPT 5. 2 and OPUS 4.
1:18:47
5 we again started to see saturation on ARCV v2 and it precisely correlated with this like agentic coding capability that that emerged. Yeah.
1:18:56
>> Um and so I'm hope I'm optimistic that AR3 will again be a very useful sort of predictive tool to understand when you know basically AI agents are capable of operating in more open-ended environments. Yeah.
1:19:06
Um right now you know you need a lot of human handcrafting to get these intelligence systems to work in domains such as coding right with cloud codec and code codex and cloud code.
1:19:16
>> Um and that's we I I basically expect that like when you are doing very good on v3 uh which will mean by the way 100% score v3 means like AI can sort of beat all the games as efficiently as humans can on an action basis that will lead to economically useful systems where agents are able to operate in more open environments that they haven't been citically trained on. Mhm.
1:19:34
Uh I I I still remember from RKGI one uh you know you see these like 3x3 grids and the first time I ever tried it I tried on my phone and I think my phone was in some weird like landscape mode or something so it wasn't rendering correctly and I was like >> you didn't even get all the data points. >> Yeah.
1:19:51
No, so normally it's like you see the blocks and then you see the blocks to the left and the right and then I was like wow I'm like I'm cooked like like the fact that other people can do this.
1:20:03
Uh but of course once you load it on desktop it's very usable.
1:20:04
Uh I I want I want to continue down that path of the the the 03 extra high like what are you seeing from the labs that put forth models that did test on uh ARC AGI v3 in terms of just steering the models because we we we talk about GPT 5.
1:20:23
4 but that means a lot of different things these days.
1:20:28
Was this in the max reasoning?
1:20:31
Should I compare this to what I'm seeing in chatbt?
1:20:33
I'm getting more and more drop downs where I can go, oh, I can go pro and then I can go extended thinking mode.
1:20:41
>> Is it is it an offtheshelf model or are they able to sort of come to you and say, hey, we want to we want to actually marshall 10 times the amount of compute for this particular challenge.
1:20:51
>> On our verification leaderboard, we have a new testing policy.
1:20:53
It's actually something we did have with one and two introduced after 03 where we limit to $10,000 per verification run. >> Okay.
1:21:01
Um this is somewhat of a practical like consideration. Yeah.
1:21:03
Uh if we actually used like the most expensive highest you know million context window of the most expensive model I think testing of the full V3 private data set would be like $100,000 which is just kind of like silly right so we set a we set a reasonable limit like humans near nowhere near as much as sort of like dollars to sort of produce this same performance.
1:21:23
performance. I like that too because like that is the that is the like like getting AGI and it's like yes it can do anything but it costs $50 million per prompt to do one hour of human labor like that's not really economically
1:21:36
valuable and so bounding >> I think you want to know progress right and I think $10,000 is a reasonable amount of money where you will actually see some degree of progress and that will be a useful signal to start paying attention to it more. >> Yeah. Um it's and it's like just you >> Yeah.
1:21:46
Um it's and it's like just you know for practical reasons we just can't we're we're a strap nonprofit so you know we have to be sort of thoughtful on our on our sort of money on how we deploy things.
1:21:55
>> Yeah that that's that's where so I think the high reasoning mode is the most we used on uh for the official verification stuff that we've used today that I mean do you spend a lot of time thinking about your own AGI timelines?
1:22:04
Has your work at ARC shifted your timelines at all or do you feel like I've always been a 2035 guy I'm still a 2035 guy? Something like that.
1:22:15
like do you do you have an internal model of this or is that even useful these days?
1:22:20
>> I instead of um listening to my predictions uh you should probably follow our actions as our like best sign of a sort of review of of progress.
1:22:30
I think the reality is we have made tremendous progress with our reasoning over the last 12 months. >> Yeah.
1:22:37
>> Um ARC is uh operating to bring the next version of the benchmark.
1:22:41
We we've already started work on v4.
1:22:42
We actually have plans written down already for V5 as well.
1:22:46
Our intention is to bring these to market annually over the next two years.
1:22:50
Um and so that that's sort of our expectation of having the next version ready right now. >> Yeah.
1:22:56
>> Um now like will we actually launch them?
1:22:58
I think we'll have to just see where Frontier Progress is.
1:22:58
We want to we want the future benchmarks to be as useful as possible.
1:23:02
And so if there's like still a lot of utility and scientific value in the current version of the benchmarks, you know, we want to keep focus on those.
1:23:07
But to the extent that like the scientific value is starting to wayne, we want to have the next version ready that has sort of like identified, hey, are there other interesting remaining large gaps between what humans can do and AI can do in order to drive that gap to zero.
1:23:19
You know, again, we're a very we're a very AGI pill organization. We want to see progress.
1:23:24
We actually love seeing progress.
1:23:25
And part of our goal is to inspire as much progress as quickly as we can to to get to these AGI systems. Yeah.
1:23:31
>> Um so I'd say like uh yeah, that that's sort of the operating view.
1:23:33
Um will, you know, a common question would be like well is V5, you know, is V3 AGI?
1:23:36
well is V5, you know, is V3 AGI? V4 AI D5 AGI no the honest answer and this is something I've actually learned I had a different view of this maybe three years ago the honest answer is no single version of any benchmark is ever going to be a GI I think it is a uh the frontier of progress is a moving target and our job is to like understand that
1:23:56
the gap the remaining gap and and the definition of that gap is going to change as time goes forward in order to keep chunking up what are the largest pieces of that gap that we can find that are interesting you know that identify some missing important capability that humans are able do um and produce benchmarks that that showcase that gap. >> Last question, I'll let you go. Uh
1:24:11
>> Last question, I'll let you go.
1:24:11
Uh what's going on with the Pokemon bench that feels somewhat related uh similar tasks?
1:24:20
What are you learning from that?
1:24:22
Uh how are models becoming so good at that?
1:24:26
It it feels like they aren't specifically RL on Pokemon and yet they're learning, but also there's a massive amount of, you know, written text about what to do at every level in Pokemon.
1:24:36
Are they just learning that from the pre-training corpus?
1:24:37
What what what's your thesis on Pokemon?
1:24:40
>> It it certainly seems helpful.
1:24:40
Um if I use our experience in developing ARC as a tool to sense finder on this, um we have seen more understanding from the latest generation of uh AI reasoning systems over the last three months than we saw in the first six months when we were developing ARC v3.
1:25:02
Um I think you can kind of fork uh you can almost split the research problem of agents into two things.
1:25:10
You can split it into a problem that says can an AI agent effectively perceive some kind of environment state apply a strategy that's written down to produce actions and you know successfully execute a plan.
1:25:21
That's half the equation.
1:25:23
The other half the equation is can you have agents that are effectively able to develop what that plan is.
1:25:28
And to do that, you need to be able to on the fly build like a world model of your your task, acquire goals, create your strategy, create your plan.
1:25:35
We've seen a lot more progress on the uh perception through strategy to action problem than we've seen on the um the exploration problem, the strategy generation problem.
1:25:46
And I actually think this is one of the areas that that I would point interested Arc 3 researchers at because I think it's a lot more green field um and will unlock a lot more progress even on you know things like Pokemon bench uh where it's kind of coming down to like okay we know they can sort of I should say at execution um the exploration and planning step is still where there's a a large degree of bottlenecking still still happening today.
1:26:11
>> Well congratulations on the progress.
1:26:11
Uh where can people find it?
1:26:14
How can people participate? How can people help out? >> Yeah. Uh, go to arcprize. org.
1:26:18
Uh, you can play the games as humans.
1:26:20
Like I said, we've got almost 25 of them, I think, on the site that they're all they're all designed to be very fun.
1:26:25
We explicitly controlled for this actually when we were doing human baseline testing.
1:26:29
So, that actually should be fun. You can have fun.
1:26:30
And you can also get details there.
1:26:31
Enter ARP prize 2026, our new $2 million uh prize uh pool uh this year that's on AR 2 and AR 3. >> That's amazing.
1:26:40
Yeah, our uh our our teammate Tyler Cosgrove was climbing the human leaderboard for a while.
1:26:43
I imagine he's been knocked off, but we'll have to get him back on top.
1:26:47
Uh, thank you so much for taking the time to come chat with me. Uh, this was fantastic. We'll talk to you soon. >> Have a good one.
1:26:54
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1:27:06
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1:27:07
And without further ado, we have our next guest, Nathan from Airirst Street Capital coming in to the TV. Nathan, how you doing? >> Great. How are you doing, John?
1:27:16
>> Thank you so much for staying up late.
1:27:18
Uh, what time is it there? >> 7:25. >> Okay.
1:27:21
Not too bad, but past the workday.
1:27:24
Uh, uh, reintroduce yourself.
1:27:24
It is your second time on the show, but reintroduce yourself and give us the news. >> Yeah. Uh, I'm Nathan.
1:27:30
I, uh, started a venture capital firm called Air Street Capital in 2019 to invest in AI first companies.
1:27:36
And, uh, >> what year were you investing in AI?
1:27:41
Well, so I started the firm in 2019, but started investing in AI in 2013, which is probably around the time that like deep learning was still definitely cooking only in the lab and most people didn't really care too much outside of it. >> Yeah.
1:27:53
What were the median deal like?
1:27:56
What was the median deal like in 2013?
1:27:58
Was that like uh recommener systems?
1:28:01
Like we're going to bring Netflix recommendations to everyone like that type of thing? >> Yeah.
1:28:07
Well, it was uh e-commerce recommendation systems, adtech. >> Yeah.
1:28:11
>> Uh big data was the buzzword back then. >> Sure.
1:28:15
>> A little bit in finance. Yeah.
1:28:15
Like insurance underwriting, like loan prediction, credit detection, that stuff. >> Yeah. Fraud detection.
1:28:22
Just like a big uh I guess it was like a Were they doing deep learning yet or was it mostly just like >> Yeah, they were. Yeah.
1:28:30
I mean it was 2013 was the year that uh computers started to be able to recognize images better than uh than humans. >> Oh yeah.
1:28:38
>> That was uh you remember like Andre Carpathy was the infamous human benchmark on imageet in 2013 PhD. >> That's right.
1:28:45
>> So that was the year when uh when basically like Alex uh uh at uh University of Toronto like built AlexNet which >> was the first deep learning system running on Nvidia card. >> Yeah. Yeah.
1:28:56
What what a remarkable time.
1:28:58
So, uh, what has it has it been easy?
1:28:58
I mean, you just raised a new fund.
1:29:01
Has it been easier to pitch this to LPs?
1:29:03
What have been the challenges and, uh, and opportunities over the last few years?
1:29:10
>> Yeah, I mean, it's been a it's been a sea change like in, uh, 2018, you know, when I started, um, for Air Street, it was like I'm, you know, by myself.
1:29:17
So, solo GP, super contrarian, >> starting in Europe where risk aversion is extremely high.
1:29:23
uh trying to focus on AI, which most people didn't really care too much about, and then first-time fun.
1:29:30
Those are sort of like all the worst like buying selection criteria that one would have. Yeah. >> Yeah.
1:29:36
>> Um and then yeah, like I think, you know, this this is very much like a long-term journey like you know, I set out with I'm going to do these early stage investments, be high conviction, invest in biotech, defense, vertical software, dev infra uh you know, I stuck with what I said.
1:29:50
So you know investing in like Cynthia 11, Black Forest and and others.
1:29:55
>> Had like six exits to like Recursion and uh you know a company that went public and Amazon etc. >> Yeah.
1:30:01
>> And then um and then yeah like first fund was like $27 million.
1:30:03
Fund two was 121 about 3 years later again pre-Chat GPT.
1:30:11
>> Um and then uh and then this one's 232 million which at this point makes us the largest solo GP in Europe. >> That's amazing. So, us.
1:30:18
You said solo GP, but you said us. Who else is on the team?
1:30:23
>> I'm you know what, like I'm kind of guilty with the royal we thing.
1:30:24
Um, >> but uh but I I have uh two colleagues who run talent and operations and then a pretty sizable back office for like admin. >> Sure.
1:30:35
>> But everything that comes along with like you know building brand, planning founders, investing, fundraising, that's all me and the decisions are just me.
1:30:42
>> And then also the the is it annual report state of AI?
1:30:45
I mean it's uh Yeah, >> it's sort of a huge project.
1:30:48
Do you bring in collaborators on that?
1:30:53
>> Yeah, that started in 2018 to basically create like a kind of canonical open access document covering research, industry, politics, and talent.
1:31:02
>> Yeah, >> there are a number of contributors every year who are sort of like at the coal phase doing their PhD or like transitioning between roles in AI labs who help us kind of stay smart on things and and also folks who've been working on policy because it's become increasingly important as we see in the news almost every day. Yeah.
1:31:17
>> Um, and then the cool thing is like I get contributions from companies and labs and researchers every year and like I think this last year when we last talked there was like 50 people in the Google doc kind of like leaving comments being like hey I I tried this implementation this paper like I had this problem and then some other person's like that was my paper this is what I tried.
1:31:36
>> And it's like cool like community document basically where we can kind of get to the center of truth.
1:31:42
>> Uh not to call you like lucky on timing.
1:31:43
Uh it's very fortunate that you have the size of fund that you do for where we are in the market cycle, but uh how hard would it be to do what you're doing today with a $27 million fund because it feels like a 20 $27 million is like a seed round for like a you know startup with just an idea.
1:32:04
Sometimes that's like one one% of the of the seed round.
1:32:06
Uh but like is it can you even make plays with that size fund if that's what you were constrained to today?
1:32:15
I I think you have to decide like what I did which is either you want to be like a main player and lead rounds and express your conviction and be early etc.
1:32:23
or you play the like large portfolio model and then you have checks and a lot of opportunities and for me that job is like much more of like a network SDR style job and less of like I can make my own opinions like do the research be there early and then when I like something like really make the bet.
1:32:41
Yeah, >> I don't think you can do the former like be a lead investor in 27 million. No chance.
1:32:45
I think you can do the larger portfolio um you know chipping into a variety of rounds and still have like good performance, >> but it's just a different job that I don't particularly enjoy um and doesn't maximalize like my strengths and my interests as much. >> Yeah.
1:33:00
>> Um you know, so I I think at the end of the day like you got to pick what what flavor is good for you and then try as best as you can to bring the best product to the market given your circumstances.
1:33:09
And you I was fortunate with fund 3 to be able to really come with like a blank slate with, you know, long-term partners and say like this is what I think is going to be the most convincing model, you know, right up to $15 million in first checks and do a couple growth stage rounds up to 25 million. Wow.
1:33:24
>> But still, you know, high conviction 20 companies and and of course like I'm, you know, mostly based in Europe, but still invest in the US and spend decent time there as well. >> Yeah.
1:33:33
>> Yeah. I I mean I know you're you're you're in Europe but you're not you know an exclusive European investor by any means but uh I am interested in the thought exercise of like where is the AI opportunity uh internationally um if I were to just back of the envelope it I'm I'm you know I've seen some of the sovereign AI efforts it it sort of makes sense that like a certain government might want to buy from a
1:33:59
particular uh local lab but at the same time like you know Google has been very successful internationally and you know China got the the the Google of China but many other European countries didn't get the local Google competitor or the local Amazon competitor or the local Microsoft or Apple competitor just because like they were consumer products and consumers kind of flow wherever they want. Um but at the same time I can
1:34:20
Um but at the same time I can imagine if Google or OpenAI or Anthropic is going to build a data center they might want to go to a local Neocloud or there might be opportunities for you know the Harvey of some other country that has different laws and different rules and uh so how are you seeing the shape of like opportunity outside of America in AI?
1:34:42
I I think you covered it really well and I would generally agree with you on this uh you know Europe doesn't need its own Google per se and in fact historically like the government uh has tried there was Quero and
1:34:52
>> uh one or two other initiatives that you know had hundreds of millions of dollars pumped into them so that they could like capture European culture better than Google could and >> I think that's a bit bizarre when you're talking about a learning machine. Yeah. Yeah.
1:35:03
But um but so there's certainly some sectors where I think like the sovereignty like really matters and it's not just marketing speak.
1:35:08
So, you know, defense and security is clearly one where um you know, Europe woke up to this like two years ago and and and now even more so uh with the Middle Eastern war that's ongoing.
1:35:20
>> Uh you know, as an example, like I invested in a business called Delhi and Alliance Industries doing defense autonomous defense systems, right, in Greece.
1:35:27
And one of the number one things we got from people outside of Greece, particularly in the US, is like, why are you investing or building a company in a vacation resort that I go sailing in, you know? >> Yeah.
1:35:36
And then that narrative changes significantly once you start seeing shahit like drones hit you know UK bases in Cyprus and you realize like okay the border to southern Europe actually comes over the Mediterranean Greece.
1:35:47
over the Mediterranean Greece. Um I think the the other part uh which is which is interesting is like ambitions change and um and I think in Europe where traditionally there's been fewer role models that one can look to and say I'm going to be like that person and I
1:36:03
know the path >> um some companies grow and you know for example I think Lora originally started as Leia like started in Sweden right and it was the idea of >> hey we should do this locally in Sweden and um and now like clearly that company's ambitions are like no we can go ahead headto-head with Harvey. >> Yeah. Yeah. It does feel like in Europe >> Yeah. Yeah.
1:36:21
It does feel like in Europe like the the entrepreneurs that break out, they're not saying I'm building X for Europe.
1:36:27
It's like I'm building 11 Labs.
1:36:29
I'm going to go everywhere.
1:36:29
I'm building Spotify and I'm going to go everywhere.
1:36:32
And yes, I happen to be from some other country and I'm going to have a headquarters there, but get ready New York because we're going to have a headquarters there and we're going to have an engineering hub in SF and like we're just going to be an international company and have our roots there.
1:36:43
>> Uh and that seems great right path. >> Yeah.
1:36:45
So this is why I think if you're going to invest in Europe, it's really important to have this foothold and knowledge of the US market so that you can apply the same quality distribution >> that you see in the US over in Europe.
1:36:56
And either there are people who start from day one being that ambitious or there's others that are that grow and and kind of >> uh yeah just like fuel their batteries with ambition as they see and experience it, you know, like when you get success, you want more of it.
1:37:09
And so that's like the kind of recursive cycle that the continent's going through. >> Yeah.
1:37:13
I mean with all the progress from the big labs and they're at such incredible scale now like how are you processing the SAS apocalypse and just advice for founders of like what gets steamrolled versus what doesn't?
1:37:23
I mean there were some there was some founder we've had a couple founders on the show that have been like we're doing AI generated video social network and then it was like you're getting steamrolled and then it was like actually like you know Sora is not going to be in the app store anymore so like maybe that was a good bet. I don't know.
1:37:38
I'm not on that particular app but uh it feels harder and harder.
1:37:42
It used to be just like don't do an app that's just a prompt around the foundation model. Like that's done.
1:37:49
But now we're talking about oh is there pressure on CRM?
1:37:52
Is there pressure on databases?
1:37:54
Is there pressure like what will the labs do? It's it's unpredictable.
1:37:57
But how are you working through it?
1:38:00
>> I think you know one thing you could say is like what are the problem sets and areas that like the smartest AI people want to work on and like don't do that. >> Okay.
1:38:09
>> So like >> that's a good one.
1:38:09
like don't do coding and then like after it seems like AI researchers really like AI for science so like don't do that either. >> Totally. Totally. >> Yeah. Yeah.
1:38:19
We have a friend from the show that does like uh Yeah.
1:38:21
It's an AI company but it's for like small businesses like HVAC owners and helping them.
1:38:29
It's like yeah I don't think that's on the road map. >> That's really good.
1:38:33
Um, you know, jokes aside, I think I think uh it comes down to like this tacid knowledge and like where you can capture how people do a task and taste.
1:38:42
Like I see this a bit with our job like >> you know I'm using like I'm claing as well and like codeex maxing.
1:38:49
It's amazing how it can how you can imbue it with your taste and you're like at some point you realize okay we've built like a learning machine that basically is like a civ.
1:38:57
you can pour as much as you want into it and it'll still like learn stuff >> where before it was only one task at a time and like forget accumulating multiple tasks.
1:39:05
So at this point if you're really AGI pill like you have a call you pipe the feedback in you ask like hey how did I do and you get suggestions you do that on the next one you pipe it back in and then you're like make a skill file and then next time >> like here's a new opportunity and like dude if you're not doing that it's like game over. Yeah.
1:39:21
And so I really do think especially for our job like there is a point at which you're like you know solo GP with like 200 or$300 million with a bunch of AIS. >> Yeah.
1:39:31
>> So like >> I'll call you in 10 years and see if it works. >> I'm excited. I'm excited.
1:39:34
Uh well I want to hit the gong for the new fund raise. Congratulations.
1:39:43
>> Thank you for coming on the show.
1:39:43
Have a fantastic rest of your day and I will talk to you soon. Have a good one. Goodbye.
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1:40:06
And without further ado, our next guest is in the reream waiting room. We have Rohin Dar.
1:40:09
He is uh a real estate expert.
1:40:13
Thank you so much for joining the show.
1:40:14
How are you doing, Rohan? Good to meet you. >> Good. Happy to be here.
1:40:16
Uh since this is your first time on the show, would you mind kicking it off with an introduction on yourself and I'd love to know uh some of your background, how you got to where you are today?
1:40:26
>> Yeah, I um I'm a San Francisco real estate agent and I'm you know among the top highest producers by volume in the city and uh I came from a little bit of a traditional background. I did a YC startup.
1:40:37
I went to Stanford business school.
1:40:39
I did a bunch of startups and then >> got into real estate when kind of learning about Airbnb early on and built a small portfolio of that and uh and then learned the real estate craft from that.
1:40:52
>> Did you have to get a license at some point?
1:40:54
Did you join a big firm or did you just sort of strike that?
1:40:57
>> I mean I was my Twitter account started growing cuz I was like posting interesting houses that I was like >> just kind of curious about and then people were messaging me like, "Oh, I actually like bought that house you >> No way.
1:41:08
uh posted about and so I was like, "Dang, like someone's making a lot of money off of this and it's not me."
1:41:14
So then I like decided to get licensed.
1:41:17
And originally I was going to focus on like Airbnb markets and short-term rentals and vacation areas, but I live in San Francisco and I could see the prices were like after the pandemic just like crashing here >> while they were shooting up everywhere else in the country and I was like, "Oh, they're converging and that's that's like odd to me."
1:41:35
And so I was like, okay, well YC moved here.
1:41:37
Open AAI is just sort of like getting traction and the city's getting better.
1:41:42
I think everyone was sort of agreeing to that.
1:41:45
And then I was like, well, this is the opportunity and I'm getting licensed and like this is what I'm going to focus on.
1:41:50
And then uh and then I had a lot of traction with it.
1:41:52
So I just sort of got sucked in to be sort of a, you know, full service like, you know, Rowan in your corner real estate agent when you're trying to buy or sell a place. So >> I love it.
1:42:01
Uh when did the actual rebound start?
1:42:03
rebound start? where where was the trough after co >> um so the interesting thing about like co was 2020 2021 everyone was leaving San Francisco but the prices kept going up because interest rates were like practically zero and there were all
1:42:20
these liquidity events so prices were >> rising even though things were looking like a little bit desperate in the city >> and then when interest rates rose that sort of like tampered the liquidity in the market and then sort of at very end of 2022 things like abruptly dropped. So
1:42:34
So 2022, end of 2022, 2023, 2024 prices were way down.
1:42:42
Um, and then 2024 by the end of it there was like a little trickle and 2025 you sort of hit had come out of the bottom, but it was like a slow, >> you know, like you know, pretty stable market, but on the upward trajectory.
1:42:59
and then end of 2025 then it just sort of started booming like crazy in terms of pricing and then even from like you know March 2026 is like way up compared to like 2 months ago.
1:43:09
So, >> so yeah, what does it actually take to raise a family if you're working at a tech company in San Francisco?
1:43:15
Because I think a lot of people will move to suburbs, but walk me through, you know, if you're coaching someone that has a couple kids, they want schools, they want access to their employer, like how should they be thinking about what it takes to find a great place in San Francisco these days?
1:43:35
>> Um, you know, I don't even know if people think of it that way.
1:43:37
I think it's like >> San Francisco is just like such a scarce place in in all ways.
1:43:43
Uh it's like >> they there's not enough housing, there's not enough like restaurants, there's not enough this or that.
1:43:50
And it's sort of like >> if you want a place like in San Francisco, you're like so convicted on the idea of it of the city of like the tech industry, you're like, you know what, I'm just going to like do this and then I'll figure the rest of it out. Mhm.
1:44:03
>> Um, and so like I don't think people are like, "Oh, now I have to figure out like what school my kids are going to go to or like what my commute will be or this or that."
1:44:11
It's like if you sort of like dillydally around the edges, you sort of end up never really sort of being so committed and that kind of ends up being like what makes it hard to buy a place.
1:44:21
But like the people that like actually win these uh homes, whether they're at like, you know, any price point, it's it's like there's something mentally inside them that's like this is the place for me. Mhm.
1:44:31
And what does the the the like the down the fairway place in San Francisco look like these days?
1:44:37
Is everything over two million, three million?
1:44:41
Like where are we in terms of single family?
1:44:44
>> Kind of trying to find a place that is uh that would fit four people and might have parking and a second bathroom and two or three bedrooms.
1:44:53
um say like a year ago it was like around 2 million and then if you were slightly above that price there'd be like a big drop off in competition.
1:45:02
>> Uh and now it's like that level is sort of definitely moved up to like threeish million.
1:45:08
>> Um but there the bigger change too is that like there's like huge level of competitions at any price point now. >> Okay.
1:45:14
>> Um if it's like one that sort of checks the box for buyers.
1:45:17
>> So give me some advice if I'm trying to win one of those competitions. What do I have to do?
1:45:20
I have to show up with all cash.
1:45:21
just put the cash in the bag. What do I do?
1:45:25
>> I mean, all c I mean, in any given offer process, like there'll be a decent number of allcash buyers.
1:45:29
So, it's not like it's going to you can walk in and be like, "Oh, I'm going to >> win this cuz I'm all cash."
1:45:34
Like, assume like a third or you know, >> half might be over a certain price point.
1:45:40
Um, and like I think what you sort of have to realize is there's going to be a range of competition on any given house.
1:45:47
So, like some houses, >> uh, you know, this could be like a five or $6 million house, a pretty expensive house, and there might be like 15 offers. >> Whoa.
1:45:56
>> And like the seller's not like going to just sell it to you cuz you're a cool guy.
1:45:59
Like they're they the market will sort of dictate the price.
1:46:02
And unfortunately >> for buyers, you just have to say, you know, the highest price and the best terms to win. >> Okay.
1:46:09
>> Um, and so like what you're sort of trying to navigate is the level of competition any one house is going to have.
1:46:14
So like say it doesn't have as many bathrooms as you want, but you could figure out how to add one or say it's like offmarket and only a few people know about it, so there's less competition.
1:46:22
And so like >> your lever of getting a good deal on a house isn't like just like participating in a massive auction.
1:46:28
It's like trying to participate in a in an auction only you know about or a smaller sort of you know a different kind of property.
1:46:36
So, >> and you're probably expecting it to go a lot higher, I imagine, with IPOs.
1:46:42
The labs are getting bigger, there's more liquidity, there's new investment rounds happening. What's your forecast?
1:46:52
>> Um, well, obviously it's like uh, you know, hard to ch, you know, predict the future, but I wasn't like, oh, I want I'm a San Francisco real estate agent, you know, like I'm going to say like, you know, whatever.
1:47:03
or like I decided to get into the market cuz I thought like this was going to happen.
1:47:07
I was like, "Oh, >> like these scarce homes are going to become more valuable and people will wish they bought them and like I should focus on this."
1:47:14
And so like personally I'm convicted like >> the city is on the right trajectory now.
1:47:20
Uh so it's not like contrarian to buy a place here at the moment. Yeah.
1:47:24
>> Um and then there are like liquidity rounds and like that has made a big impact on the market and if the liquidity rounds get bigger like there's it's a fixed number of homes that the you know that money goes into.
1:47:34
So >> what do you think uh expansion looks like over the next few years?
1:47:39
Are we going to I know Mil Valley is booming. There's other suburbs.
1:47:43
Is Oakland going to happen?
1:47:45
There's this California Forever development that's happening that's further out.
1:47:48
like uh are you starting to broaden your horizons or do you want to stay focused and what do you think the real estate buyer will want to do in the near future?
1:47:56
So, I I solely focus on San Francisco like buyers and sellers.
1:48:01
Um >> cuz like I found that like >> in order to like every time especially on the buyer side to win, it's sort of like p pulling off this like mission impossible like heist uh where it's so elaborate.
1:48:13
You have to know like every detail about the market.
1:48:14
Um >> and so for me personally like I feel very strongly that I can really help people in San Francisco. That makes sense.
1:48:22
But then if you put me in Mil Valley, it's like, oh, I mean, I don't I mean, maybe it's good for me to help serve Mil Valley customers, but it's not good for them.
1:48:29
So, >> I think like, you know, if you're going to use a real estate agent, you should use one that like really knows a particular market really well.
1:48:35
So, for me, I want that to be San Francisco. >> Yeah.
1:48:38
How much of the San Francisco boom is attributable to the labs being based in San Francisco like specifically as opposed to the previous generation of of tech?
1:48:50
uh boom times happening in Menllo Park, Certino, sort of on the peninsula.
1:48:57
>> Yeah, I'd say it's about like 5050 like explaining what's going on.
1:49:00
Like on one hand, like what's really driving it is like the perception that the city is on the right track with like the mayor and like walking around and it just feels better and it feels fun and people are moving there and sort of like it sort of regained the zeitgeist of like a place you move to to invent the future. Mhm.
1:49:16
>> So, I think that's like half of it.
1:49:16
And then the other half of it is it like yeah, like we've had very successful companies in San Francisco, tech companies, but we never had the big one like Meta or Google or Apple.
1:49:25
And now like we have two, you know, that like just started that are uh within that range and like >> um it's somewhat unprecedented I think even though we've had like hundred billion dollar companies before and like massively successful but this is at a different level an order of magnitude and so that's just going to drive up uh attention and excitement and all sorts of things.
1:49:49
Uh what are the most underrated neighborhoods?
1:49:52
What's on the come up right now?
1:49:55
Um, you know, everyone sort of really wants like Noi Valley, Pack Heights, like I think if like if you're sort of like looking in the Noi area like Bernal, like the the m kind of the hills right next to it, Glenn Park, >> like you know, Sunnyside is like dramatically less expensive.
1:50:15
Um, like Pack Heights is now like getting to be like back to its sort of premier level pricing, but Russian Hill is like right next to it and like a little bit less like walkable and in this, you know, but like, you know, just as great, but now the prices are up there.
1:50:31
And then like Knob Hill is next to that and the prices are still a little bit down there.
1:50:34
So maybe I'd say Knob Hill and uh, you know, Bernal Heights.
1:50:40
>> Well, uh, where can people get in touch with you?
1:50:43
How do people reach out if they're looking to San Francisco?
1:50:47
>> You know, Google me, Rohandar, and then find my email or just reach out to me on Twitter and I'm pretty active there and you know, generally I just share what's going on in the market over there. So, >> fantastic.
1:50:56
Well, we appreciate you taking the time to come chat. Thanks so much. >> Awesome. Yeah.
1:51:00
>> Have a great rest of your day. We'll talk to you soon. >> Thank you. You too. >> Goodbye.
1:51:03
Let me tell you about Cognition.
1:51:04
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1:51:06
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1:51:10
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1:51:18
And without further ado, we have Eric Jorgensson, good friend of mine.
1:51:21
He is the author, the publisher of the book of Elon.
1:51:28
>> Thank you so much for sending these. It looks fantastic.
1:51:29
And I was particularly I want to get into the the book, but I I I think partnering up with Jack Butcher on this deeply underrated.
1:51:35
Uh he is uh an incredibly special illustrator, designer.
1:51:42
I don't even know what you call Jack Butcher, but obviously he was helpful in this, but uh thank you so much for taking the time to join the show. How are you doing?
1:51:49
>> Thank you for having me.
1:51:49
I'm I'm honored to be here.
1:51:51
Uh and extremely excited to put this thing out into the world.
1:51:56
>> Yeah, listen to that voice.
1:51:56
You got a voice for live streaming. Let's go.
1:51:57
The microphone's helping. You got a good setup. It's great.
1:52:03
>> Am I about to get discovered right now? >> I think so.
1:52:05
No, of course you've been online a million times. We Everyone knows you.
1:52:09
But uh uh maybe maybe take me back to uh a little bit of like how you got into publishing, your business overall, the the the Naval book, and then we can go into the Elon book.
1:52:23
>> Yeah, that's an amazing setup because then I get to shout out Jack Butcher again.
1:52:27
Um, basically like I was tweeting and blogging happily uh in like 2017 and I had been following Nal.
1:52:34
I learned so much from him over the years >> and I felt like he was putting out this timeless wisdom that was just dissolving into the the stream every day and it just broke my heart that it was going to >> get lost, get buried so quickly. >> Yeah. Yeah.
1:52:48
This is the thing with Twitter.
1:52:49
It's it's it's ephemeral which is amazing but there's no rediscoverability.
1:52:53
We actually put out this account once, Banger Archive, where we would just share screenshots of old tweets and they would go viral again.
1:53:01
But Twitter doesn't X isn't set up to resurface stuff like that like YouTube is, like Netflix is, and so the back catalog really goes stale, but you were able to obviously repurpose it. >> Yeah.
1:53:12
I I think um so many of the great books have this like long fat tale of timeless wisdom that we keep needing to revisit.
1:53:18
revisit. and Naval was was um you know obviously has done an incredible job distilling that wisdom and articulating it for sort of our era and I wanted to preserve that in a permanent format and that side project that I did on nights and weekends um and published you know
1:53:34
hoping to sell a few thousand copies has gone on to sell I think we're coming up on two million I've given away 5 million more digital versions and 40 languages >> crazy such a huge number I feel like it's hard to sell a thousand books and you've sold 2 million. >> That is a that is an unbelievable number
1:53:51
>> That is a that is an unbelievable number for those that don't have like inside context in the book industry.
1:53:54
Like I think the median outcome is like a few hundred and like 10,000 is top fraction of a percent.
1:54:01
>> Yeah, absolutely incredible.
1:54:01
Uh what was the strategy?
1:54:04
I mean Naval has a big audience but I don't see him pumping this book constantly on his Twitter feed or his X feed.
1:54:11
Like how did this book actually get in the hands of customers?
1:54:15
Is it like top of Amazon?
1:54:15
Is it on the New York Times bestseller list?
1:54:18
Like how do you even sell that many books?
1:54:22
>> I cannot explain it other than to say it has become like a word of mouth phenomenon.
1:54:25
phenomenon. I think it is just so many people tell me they buy it as gifts or they recommend it or they give it to you know I know teachers they give it to every class that comes through and it's just sort of um it's un who doesn't the subtitle of the book is >> a guide to wealth and happiness like big
1:54:42
TAM right like universal human desires and new people graduate into like trying to figure that out for themselves >> every day and uh I I think it is >> you know this really rich dense collection of >> Naval who's a really really gifted sort of uh distiller and articul some of the most important principles that >> make life make lives successful. >> What what principles from that book
1:55:05
>> What what principles from that book either stick out to you are timeless or maybe even underrated uh that you keep coming back to you?
1:55:14
>> I think uh I mean the on the wealth side like leverage um is is still an underrated one.
1:55:21
I mean you guys are living examples of this.
1:55:22
Uh authenticity is another one that like that word is thrown around so much that it becomes sort of a cliche.
1:55:29
Uh but the people that we tend to admire the most or that are doing the best are a really interesting combination of like excellent, authentic, and leveraged, right?
1:55:37
Like Jack Butcher is an incredible example.
1:55:40
You don't even really know how to describe him.
1:55:41
He's like he's an artist, but he's a contemporary artist in the digital era and he's a really gifted designer and he's kind of like inventing this category of worked art, but whatever he is is him and it's awesome and it's massively leveraged and you know, he's doing things that nobody else is is doing in the art game.
1:56:01
>> Do you think that being controversial is correlated with authenticity?
1:56:08
Because if you're not authentic, you can be this very polished one side, you know, me manyfaced thing.
1:56:13
You interact with one person in a certain way, another person in another way. You make everyone happy.
1:56:19
You're less controversial.
1:56:21
As soon as you start wearing your heart on your sleeve, being authentic, you're going to attract some people that don't like what you're showing them because you're showing them the true self. Is there anything there? >> Is a great question.
1:56:33
I bet there's two opposing archetypes.
1:56:36
I bet there is a type of person that is extremely inauthentic in how they >> court controversy for the benefit of, you know, the algorithm or just being elevated by being attacked.
1:56:46
And I bet there's another PE set of people that >> are authentic despite >> any headwinds uh or controversy that might come up.
1:56:56
And I think the only way to probably tell the difference is just zoom out and see who's been doing what for how long and in under what context. Mhm.
1:57:04
So, uh, how obvious was it that Elon was going to be the next subject?
1:57:10
I imagine that the playbook that you ran, the process that you ran with the Naval book could apply to a lot of entrepreneurs.
1:57:18
It's very it's a very interesting style where it's it it it's it's it's high leverage.
1:57:27
I I can only think I can only describe as high leverage because you're you're standing on the shoulders of giants which is like all of the work that they've produced all of the podcasts that they've done everything that they have written.
1:57:36
There's a lot of primary uh research there that doesn't necessarily require the same you know access and permission and you can do a lot of pre-work uh independent before you actually go in.
1:57:52
in. Whereas some other books it's like okay this author didn't even get the interview with the person that they're writing about like they sort of you know no one wanted this book to happen and that's a lot harder right um so so so I imagine that the list was pretty long how did you narrow it down uh how did
1:58:08
you land on Elon why this person why this time >> yeah this is an interesting it's an interesting type of book because as you point out like I'm not writing about someone I'm trying to get out of the way it's not about my opinion of them my northstar are with these books is to just be as for the book to be as useful as possible to the reader. I want the
1:58:26
I want the reader on every single page to be like, >> "Oh my god, this is a great use of time.
1:58:31
I got a highlight on every page.
1:58:31
I'm getting so much out of this.
1:58:33
I feel like I'm getting personally mentored by Elon Musk through a few hours of reading about his most valuable and timeless ideas." Right?
1:58:40
>> And I think for those of us in tech, Elon's been interesting for a very long time.
1:58:44
And over the last, you know, five, six years, uh, he's become more controversial.
1:58:50
But inside tech, he was before he was a household name, he was the most ambitious person in tech.
1:58:56
And nobody knew how that story was going to end, right?
1:58:59
He was running on a very thin tight rope for a really long time with both Tesla and SpaceX.
1:59:06
>> And more recently, people have started, I mean, Mark Andre and Brian Armstrong maybe most famously have started asking the question like, how does Elon do it? Like what is this?
1:59:15
Why is he an outlier among outliers?
1:59:17
And I wanted to answer that question.
1:59:20
And I think this book does that in more ways than I anticipated at the outset, right?
1:59:26
Like I there was some interesting stuff that everybody kind of knew would come in and there's like the greatest hits and then there's the back catalog and then how it all comes together is actually like what's really interesting. >> Yeah.
1:59:38
I remember in college someone called me and was like, "Oh, I heard about this this entrepreneur named Elon Musk and he runs two companies.
1:59:46
SpaceX and Tesla and I was like, "Of course I know about that.
1:59:51
I'd learned about it like three months earlier."
1:59:52
Um, but but it really was a very uh a very controversial thing to do.
1:59:58
The you know the the timeless wisdom is like focus focus focus.
2:00:02
How have you perceived Elon's ability to be like the exception that proves the rule versus a pattern that might actually be more replicable than people think if they just adopt a particular stance in how they leverage what they're capable of to build multiple companies simultaneously.
2:00:24
>> Yeah, there's a couple there was a period where Jack Dorsey was running two companies period where Steve Jobs was running two companies.
2:00:29
Uh, and there's plenty of companies that are collections of meaningfully different companies kind of in one under one name.
2:00:36
Um, I think it's kind of hard to extricate like what does run the company mean? >> Yeah.
2:00:45
>> Really on a day-to-day basis.
2:00:45
And who is around and who's running different functions like Elon running a company probably looks a lot different than Steve Jobs running a company or than, you know, Bill Gates running a company or Mark Benny off, right?
2:00:59
the the motions that he dives deeply into are very different kind of on a per leader basis. >> Yeah.
2:01:06
>> And there's an element of this that like NAL points this out.
2:01:10
I think it's super interesting.
2:01:11
He's like you are probably working harder on your company than NAL is or than Elon is working on any one of his companies just because he has this divided attention.
2:01:19
So let's just say he's working 80 hours a week but he's only working 30 hours a week on SpaceX. >> Yeah.
2:01:25
like how is he able to have orders of magnitude more impact uh in those 30 hours than you're having with your 70?
2:01:33
Well, it is sort of it is sort of interesting like maybe ironic that uh his main competitor over the last two decades has been Jeff Bezos at Blue Origin who's also running two companies and so like retired and >> yeah I mean now now maybe more focused you know he's retired but there was a
2:01:49
long time like a full decade where Jeff Bezos was running Amazon full-time and then you know Blue Origin was the halftime or side project and and Elon sort of didn't have a direct like full-time you by the book entrepreneur just building a direct competitor in that one space fully focused. So I don't know maybe
2:02:07
So I don't know maybe maybe that's luck maybe maybe things play out differently if if there was someone in that space but it's clearly it's clearly worked out.
2:02:14
Um yeah, >> we kind of end up conflating like Elon the person. Yeah.
2:02:19
Like Elon the core team around him and Elon the the symbol frankly.
2:02:24
Um and especially at this point in his >> career.
2:02:28
He's one of the most leveraged people alive, right?
2:02:29
So >> we are ascribing to like quote unquote Elon what is actually the effort of tens of thousands of engineers and you know on plenty of other employees and fans and supporters.
2:02:41
Um and there's there's beauty to that, right? like we are humans.
2:02:45
We rally around people um kind of better than we do with symbols.
2:02:48
Um but that becomes this rallying like this uh this rallying point for people to like organize around the values exemplified by this person.
2:02:59
And I think that's uh that's beautiful and magical and part of >> the formula.
2:03:06
Uh but it does tend to like if you conflate the conversation about the guy with the conversation about the symbol, you end up in this really weird >> Yeah.
2:03:13
kind of arguments with people, you're not even really talking about the same thing.
2:03:16
>> How how do you think about the the thinking in decades concept?
2:03:18
You know, it's something that everyone in Silicon Valley says, "Oh, you got to think in decades."
2:03:23
And then Elon comes out with something that's like 20, 30, 40 years away.
2:03:27
And everyone's like, "No, we didn't actually want thinking in decades.
2:03:30
I want to know something that's going to happen for sure in like five years tops."
2:03:35
Uh, and I'm thinking about this mass driver uh question and I'm wondering like now that you've you've written this book, you've you've studied Elon, like h is this a departure?
2:03:43
Is he thinking even farther in the future or has he always been thinking around this time horizon?
2:03:52
Like how similar is this crazy mass driver on the moon pitch compared to previous eras?
2:04:01
I think it's difficult to predict as are many things but the if his theory and his acting principle is that the future is arriving ever faster >> right and so things that at our previous growth rate or technology trajectory seemed like they were 30 years away are actually now maybe 10.
2:04:19
>> Uh and it's really difficult to adjust for that like recursion factor. >> Yeah.
2:04:25
>> Yeah, that makes sense. Yeah.
2:04:25
Um, well, where can people find the book?
2:04:30
>> Uh, anywhere you buy books. Amazon, Barnes &Oble. com. Yeah, >> Target.
2:04:34
Uh, it just came out like yesterday.
2:04:38
>> Are you going to do an audio book?
2:04:40
>> Yeah, the audio book's out. >> I didn't read it. >> Oh, you got to read. You got this. We'll be those pipes. Put them to work.
2:04:44
Well, uh, Eric Jorgensson, thank you for joining.
2:04:49
The the the book is The Book of Elon.
2:04:51
You can go find it everywhere. Books are sold.
2:04:53
And we will talk to you soon, Eric.
2:04:55
Thank you so much for to join the show.
2:04:57
Uh let me tell you about Figma agents. Meet the canvas.
2:04:59
Your AI agents can now create and modify your Figma files with design system context in beta starting today.
2:05:06
And our next guests are live here with us in the TBP Ultradome.
2:05:12
Thank you so much for taking the time to join us. How are you doing?
2:05:15
Please uh since this is your first time in the show, introduce yourself for everyone.
2:05:19
>> I am Jenny Jess, co-founder of Peak Six. >> Yes. And I'm Matt Holizer.
2:05:21
um Jenny's husband and also co-founder. >> Fantastic. Take us back in time.
2:05:26
I want to hear the founding story. >> Founding story. Start.
2:05:33
>> Um we both grew up on the option trading floor, Chicago and New York.
2:05:39
>> And we were at an sort of an infamous options trading firm called Okconor Associates.
2:05:45
>> You were both at the same firm.
2:05:46
>> We were both at the same different cities. Yeah.
2:05:48
So super lucky to be trained there.
2:05:50
when they did their merger acquisition UBS. >> Who was in Chicago? >> I was.
2:05:55
>> Did you ever go to Series? >> Oh, of course. Series is amazing. Yeah. Anyway, it's a bar.
2:06:01
>> Be kind of modern these days. >> Oh, yeah.
2:06:02
I worked at Citadel in college and that was the place where we'd go and hang out and they'd uh give you you'd order like a like a a rum and coke and it would just be a full glass. It was crazy. That's right.
2:06:14
>> A really good greasy sandwich to go with it. Yeah. Really good. Good fries. >> Okay. So, you're in Chicago? >> Yeah. So, we're in Chicago.
2:06:18
Um we decide we started working together. >> Yeah.
2:06:22
>> Um >> what were you trading at the time? >> Equity options.
2:06:25
Both of us were >> Equity Options. >> Yeah.
2:06:27
And then when they did the original Swiss Bank joint venture, we were part of the team that went to start the OTC. >> Okay. >> Derivative Desk.
2:06:34
So there was just three of us.
2:06:36
Um there's a gentleman from Goldman who came in.
2:06:37
And so we're like, "Oh, this is cool.
2:06:39
We're in our mid20s starting a business, you know, an entrepreneur."
2:06:42
And when things started going really well and then UBS came in and they were moving to the East Coast, >> we're like, well, we're not moving.
2:06:50
He had actually just come from New York. >> Yeah.
2:06:53
>> And my family's in the Midwest, so we're like, we're just we'll just do that thing again. Yeah.
2:06:58
>> So that thing was to partner with a bank. That was our plan A.
2:07:03
>> And >> do over over-the-c counter derivatives. >> Okay.
2:07:07
weeds 28 and a half years later.
2:07:10
We've still never done it, >> but we did invest really early in tech and education.
2:07:14
And so we c we created a proprietary options trading firm. Okay.
2:07:19
Which is >> 28 and a half years old that has never had a losing year. Wow.
2:07:23
>> And it allowed us to self-fund Yeah.
2:07:25
>> all the things we've done since then.
2:07:26
>> And what's the secret to such an incredible run with no losing years? Is that risk management?
2:07:31
Is that the particular uh strategy?
2:07:33
Like how how does that come together?
2:07:36
because I can't think of another investor who's ever done that.
2:07:41
>> It's unusual for sure. But >> it's a secret.
2:07:43
>> Well, I think it tends to be about like our approach.
2:07:45
Our approach was not to be smarter with our our algorithms.
2:07:48
It was smarter with our business model.
2:07:51
So, there are plenty of businesses that haven't had losing years in the last 29 years.
2:07:55
tend to be technology firms >> that are basically providing a service into the market which is the approach that we I mean we we >> we talked about ourselves as uh >> Wendy's or Walmart, Walgreens, right? We're merchandisers.
2:08:11
We carry inventory and then deliver it to customers.
2:08:13
We're not trying to take the the we're not disagreeing with customers.
2:08:16
We're we're providing a service. Mhm.
2:08:19
And then on the investment side, what is out further on the risk curve for you?
2:08:27
>> I mean, I think we've lost money in more ways than anyone you've ever had on this show.
2:08:32
>> Well, uh, collectively, >> what is the nature of a of a of an investment that doesn't pan out?
2:08:36
Is it just it's high risk and you know that going in or Yeah. >> Yeah.
2:08:42
Well, there's a wide range.
2:08:43
Obviously, in our trading business, it's really systematic.
2:08:45
Um, we have an amazing team.
2:08:47
and we educate kids right out of school into our model.
2:08:49
It's really rare that they would leave and be able to do the same thing because it is about the collective.
2:08:57
>> Um but interesting but we started a whole bunch of other businesses of course.
2:09:01
So >> Peak Six today based on what Eric was saying in your previous interview, it's a company of companies. Okay.
2:09:07
So we have started or bought and turned around 15ish companies at this point >> and primarily in the backend technology space in fintech definitely in insure techch which is relatively new to us and edutch.
2:09:22
So um those are the biggest risks we're taking and then fast forward as we built those businesses over the years we started uh an investing side of the business which is quite large >> because it's all AI stuff now >> and we started early enough so it grew really fast. We have some doozies.
2:09:37
I mean, I >> I don't want to dwell on it. >> Yeah.
2:09:41
No, we'll refer we're this is this is a call back to your previous uh >> person in 2008.
2:09:45
We had uh it was a good year for us.
2:09:49
Not because we were smart and short the market. >> Yeah.
2:09:52
>> Jenny was like, "Things are confusing. We should be in cash." And we were. So, I was lucky. >> Well, she was right.
2:09:58
Like the market goes crazy and then we have a lot of cash.
2:10:02
Everybody starts knocking on your door in the fall.
2:10:03
And she's like, "Well, why don't we do something good for humanity?
2:10:06
will do electric vehicles. >> Sure. >> So, we are early. >> Yeah.
2:10:10
>> So, we interview two different people at the time.
2:10:12
>> We're not investors at this point. We are traders.
2:10:14
So, it's two different things. We are operators.
2:10:16
I still don't know if we're in. We're just knuckleheads. >> Yeah. >> Okay. >> We get two people.
2:10:23
>> We're going to interview the two companies at the time. Yeah.
2:10:24
One is this PayPal guy who's trying to do it and the other is the guy who was the chief architect at BMW. >> Yeah.
2:10:33
The PayPal guy gets on the co on a call with us >> with you.
2:10:38
>> With me, >> just to be clear, the guy from this see where this is going.
2:10:44
>> Yes, you know where it's going.
2:10:45
>> I mean, if he's listen if he listens to this, he'll I don't know if he'll remember cuz I think he was stoned out of his mind. Okay.
2:10:50
>> It was the worst presentation I've ever heard.
2:10:54
>> Like this guy's never going to build a car. >> It wasn't about cars.
2:10:55
It was about It was about the the train.
2:10:58
>> He was talking about trains.
2:10:58
trains from like electromagnetic trains also be useful.
2:11:04
I was like, >> "What the hell are we talking about?"
2:11:07
>> This is supposed to be a business investment.
2:11:08
I want to hear the business pitch.
2:11:10
>> And the other person comes in buttoned up, right? German engineer. >> Yep. >> Amazing.
2:11:16
>> I've built these my entire career. >> 100%. We go with that person.
2:11:18
And then 9 months later, >> we find out that the one we invested in, when it rains, the cars catch on fire and explode. >> Crazy. >> Not a good one.
2:11:31
Yeah, that's not a good bad downside. Yeah.
2:11:34
>> Yeah, that makes a lot of sense.
2:11:34
I think a lot of investors have had to really come around to the the the other pattern of thinking.
2:11:40
of thinking. I know that's right some investors that uh I mean even on the other side I know I know an investor that passed on Tesla >> but not uh but not because Elon was thinking too futuristic uh he was thinking well all the cars are going to be self-driving and no one's going to
2:11:55
need a car anymore so if this guy's just building cars why should I invest in this and uh and of course the it was Tesla was going to be the one to do that but that was too hard to predict and it just gets very very hard when you think that far out so walk me through a deal that is in the wheelhouse house. Um,
2:12:08
Um, what's the structure?
2:12:11
Are you looking for a particular vintage, an age, a size of like, you know, meat on the bones of the company?
2:12:19
Um, and then what are you looking to do?
2:12:21
Because there's when when you come into a new company, you can be transforming the business with AI.
2:12:26
You can be uh focused on cost reduction, back office rationalization.
2:12:30
Like there's so many different techniques that you can use to drive value. >> Yeah.
2:12:34
I'll I'll start maybe I'll start with peak six trials.
2:12:36
So at its core, we're entrepreneurs, right? That's what we do.
2:12:40
So we're comfortable with that risk.
2:12:42
Ironically, it doesn't make us super comfortable doing venture because yeah, we're not doing it. Yeah.
2:12:45
So we have to find really special people.
2:12:49
We've been lucky in the universe of Peak Six to have some of those special people along the way.
2:12:52
We just started something called Peak Six Trials, which is an think about entrepreneurship and residents.
2:13:00
>> We have >> it's you know like previous accelerators >> except for that we have the capital. It's already there.
2:13:06
We have the resources, the tech resources for example, legal compliance, whatever it is already there.
2:13:12
>> And then we also have the customers. Yeah.
2:13:13
Because of Apex FinTech solutions.
2:13:16
So that's our back-end tech >> powers. Got it.
2:13:19
>> 40 million customers today.
2:13:19
So end consumers were B2B. >> Yeah.
2:13:24
>> So there's a unique opportunity for fintech and techch type of >> young entrepreneurs who want to do something.
2:13:30
That's how we want to make those bets.
2:13:32
>> Um that will go into our operating company.
2:13:35
sure >> scenario and then I know you want to talk about the investment side where we take what kind of what kind of investments we're looking for. >> Yeah, sure.
2:13:45
>> Or even just the story of Apex.
2:13:45
I'd love to know like how how that came into the portfolio, what the process was like.
2:13:51
That's I can tell it's already going to be a good story. >> Yeah.
2:13:55
Uh so there was a public company called Pensson. Yeah.
2:13:59
>> We owned and operated a brokerage called Options House at the time.
2:14:01
predates Wealthfront Betterment and Robin Hood, etc.
2:14:06
>> And that business custody, kept the assets right at at Pensson along with a million other customers, retail customers.
2:14:14
Um, >> so we're in 2012 at this point. >> Yes.
2:14:18
And we get a call from the CEO of the bank.
2:14:22
This is the bank that holds our money. Calls on a Friday.
2:14:24
He's like, "Hey, um, you guys have some money and could you lend us some money for a little bit?"
2:14:33
>> It would help us a lot. >> Sure.
2:14:35
>> I'm like, "Let me think about I got to talk to Jenny." >> Yeah.
2:14:39
>> She's like, "We got to That's not a good sign."
2:14:41
Just >> for your for your listeners or watch your viewers, if your bank calls you and asks to borrow more money, like that's trouble. >> Yeah.
2:14:50
>> So, s uh Monday morning, we get a call from the SEC There's fraud, announced fraud at the uh at the clearing firm. >> Wow.
2:15:00
>> And we want you to put $70 million into the business by Friday. >> Wow.
2:15:05
>> Or we're going to liquidate everybody.
2:15:07
>> And that's everybody in the market.
2:15:07
So, think pregeneration Robin Hood for context.
2:15:10
All those names out there, those middle tier names besides the big names. Yeah. Options.
2:15:14
Our firm was one of them. Sure.
2:15:17
>> They're all going to go. >> Yeah.
2:15:18
>> So, 13 days later, we bought it. >> Amazing. >> Yeah. Announced fraud and all. That's wild. Yeah. Yeah.
2:15:24
I've heard a number of these stories of like turning around a company when there is fraud.
2:15:28
I mean Strauss Zelnik sort of did this with Take Two is a fantastic turnaround of that business.
2:15:33
Uh it feels like an incredible cultural challenge to actually not just clean up the legal documents and make this SEC happy with whatever happened.
2:15:42
It's actually a cultural problem sometimes that led the company down that path. That's right.
2:15:48
>> Uh how are you thinking about cultural development generally?
2:15:50
I feel like when I when I dig into different funds, there's fascinatingly different approaches.
2:15:58
You know, the the the Ray Dalia is recording everything.
2:16:01
There's like so many different But you mentioned that like when you train a new grad, they come out with skills that are uniquely uh just uh synergistic with the rest of the firm.
2:16:14
And so, how do you think about uh the cultural values that you want to instill in the next generation?
2:16:20
generation? they are um they are critical >> really difficult in times of COVID and changing work from home and all those things and as we continue to build new companies right so >> um >> you know at our firm that's over 28
2:16:36
years old they are ingrained it is um >> a sense of urgency it's a work ethic like it's it's just high and even when the market is telling you you know we ought to be different or nicer or something like people are so engaged It's it's really fun to be in the markets. So, it makes that easy. So, it makes that easy.
2:16:55
>> Um it's fun to be part of an entrepreneurial culture. Yeah.
2:16:57
So, that makes that easy.
2:16:59
So, how do I >> And if you don't fit it actually they weed out pretty quickly.
2:17:03
We've had that benefit over the years.
2:17:05
But every time we take on a new company, uh it is it is a challenge for us to try and integrate or have them stand in their own culture, which is also fine with us, right?
2:17:16
So if we we sit at the top and we have CEOs of each of these businesses, they are dependent quite a bit on the the peak six core because it's it's facilitating the financial stuff.
2:17:28
It's facilitating the HR stuff.
2:17:31
>> And if they want to sort of ignore that and not join the club, >> it's it's it's a hard road because we've figured out such a rhythm.
2:17:41
When you get in the rhythm, it makes each the acceleration go so much faster.
2:17:45
The leverage we get with our people, with the culture is just really exponential. >> Yeah.
2:17:52
Some people refer to it as like you stress, the good type of stress.
2:17:55
Like it's a stressful scenario, but it gives you energy.
2:17:57
It doesn't actually drain you.
2:17:59
And I feel like if people get, you know, the runner's high.
2:18:02
So running is very stressful for some people, but for some people it's it's invigorating.
2:18:07
Uh, and I feel like if if you know being in the market at a tumultuous time gives you more energy during that day, uh, that's something where you'll probably thrive.
2:18:16
>> Yeah, I was going to say taking risk. Yeah. Right.
2:18:18
Starting as traders and becoming operators and then investors like at its core that that that trading that heart and soul of trading and taking risk every day, all day, getting used to it, that isn't in everybody's DNA.
2:18:33
>> Um, >> it is part of the reason why we like poker so much.
2:18:36
We're trying trying to teach a million girls and women to play poker solely to get these male-dominated areas for the women to feel more welcome.
2:18:43
But you have to be able to take that risk every day.
2:18:46
>> And that and the organization thrives on it. Yeah. Right. It's a little scary.
2:18:50
>> I know like 10 amazing female venture investors and they're all incredible poker players.
2:18:53
Never sit down with them.
2:18:56
They would absolutely smoke me.
2:18:57
>> Well, the funny thing is I didn't play all these years. Yeah.
2:18:59
When I started to play in 2019, it was a conversation we had.
2:19:04
It was about our daughter, etc.
2:19:04
But I realized I was like, "Wait, I've been playing poker my whole career." Sure. I just didn't know it. >> Yeah.
2:19:11
>> It's the closest thing I'd ever seen to options trading. Yeah.
2:19:14
>> So I was like, "Wait, is this what's missing?"
2:19:16
Because if we get who cares how >> people come into the puzzle, what the more differentiated their backgrounds are for us, right?
2:19:22
If you look at 1997 and >> he and I were partners.
2:19:27
That's what, by the way, we weren't married at the time.
2:19:31
We were together 10 years before we did. go making that decision.
2:19:33
That was a highly unusual decision to me. >> Chased me. >> I love it. I love it. >> You wish.
2:19:41
>> Uh, what are you looking for in a CEO?
2:19:43
If I want to come work for you and and work for one of your portfolio companies, what does it take to make it as a CEO?
2:19:50
>> I don't think there's any like there's no one thing.
2:19:52
There's no prescriptive formula.
2:19:55
The number one thing we've learned because we've look we've dealt with thousands of employees, CEOs, etc.
2:20:01
invested in I don't know hundreds not thousands of businesses. >> Yeah.
2:20:07
>> Self-awareness is probably the most important thing because what's going to it can get you in trouble if you think you're really smart like you >> you better be really smart.
2:20:15
What's p poker teaches you a lot of that.
2:20:17
That's a good call back there for you.
2:20:20
the uh >> the >> self-awareness like you control effort, you control attitude.
2:20:26
Those two things you really do control like so hard work, right?
2:20:30
You're going to be positive optimist >> but awareness like like hey we're the best. >> Yeah.
2:20:37
>> You know, by the way, these other people aren't that good.
2:20:39
Like you should think about it like constantly questioning where you're at and being humble. >> Yeah.
2:20:44
Is is is self-awareness around intelligence uh the main uh flaw for CEOs or or are there CEOs that are overconfident in their deal making ability and their emotional intelligence and their managerial ability and their ability to public speak and do there's so many different things.
2:21:02
The CEO is a bundle of traits.
2:21:04
I I I feel like intelligence is obviously super important and making good strategic decisions, executing, but there's so much else that goes into actually running a company.
2:21:15
>> I wouldn't say I would not say lack of confidence is not necessarily an an issue. Yeah.
2:21:19
Overconfidence is disaster. >> Interesting. Interesting.
2:21:24
>> You get yourself in a lot of trouble because you know for sure that this is going to happen.
2:21:27
And when it doesn't, >> you you're in a you're in a world of hurt that happens.
2:21:31
So, so what are the signs that you're looking for to sus out if someone is self-actualized in that way?
2:21:40
Aware of their their flaws, aware of their strengths, their weaknesses.
2:21:46
How are you interrogating that in an interview? >> We hate interviews. >> Okay. How do you recruit that? >> Um, it's hard. It's hard.
2:21:53
We've actually tried to build tech over the years.
2:21:56
We've done all different things to try and figure it out.
2:21:59
um we try and have without being inefficient as long of a process as we can >> um to see somebody.
2:22:04
So if we can >> okay >> get a student in December >> for two two weeks during their break and see them or we have a women's trading experience that's eight weeks in the summer.
2:22:16
Anytime we get >> an extension of time if we can I mean the CEOs is is the hardest.
2:22:20
We they often come from within for us. >> Sure.
2:22:25
>> Now some of our CEOs did not. Yeah.
2:22:25
Um but it is what our hit ratio I think on just cold interview making it right I think is really hard.
2:22:36
So then of course it's connections and recommendations and all those things because you don't know all of the different pieces of the puzzle.
2:22:45
>> I think people get snowed all the time. >> Yeah.
2:22:47
Do you have a a bright line between uh deal team, operating team like those who evaluate a great company to join the portfolio versus those who will be going and operating the businesses?
2:22:59
>> We have a very very small evaluating team. >> Okay.
2:23:02
>> Like this is the thing. >> Okay.
2:23:05
>> No, we have some really smart there's some lawyers and some analysts in there. Yeah.
2:23:08
>> But um at the end of the day, so we don't have outside money. >> Sure.
2:23:12
So it's it's ours and then our part our employees who have become partners over the years.
2:23:17
So that's who we're investing on behalf >> and um so but we are looking for any guidance.
2:23:23
We are just we know we're not the smartest, right?
2:23:26
That's what trading does for you.
2:23:27
It humbles you really quickly. So >> how do we connect?
2:23:30
How do we partner with great people on the outside and then with the best people internally to make a decision?
2:23:35
But we're also willing we're willing to take probably more risk on average I would say with some of these investments.
2:23:40
I mean, we're not, you know, on the energy side or on the power side or on that that that infrastructure side for us is new and but we started early and we try and get smart and try and be surrounded by >> I want to get to energy. That sounds fascinating.
2:23:53
I want to first ask about sourcing.
2:23:54
Are are you close with a lot of investment banks or are you cold calling people saying I want to buy the company like if you're into the deal team?
2:24:02
Uh where are the ideas coming from? Mhm. >> Yeah.
2:24:05
I'd say the good ones come from interpersonal >> relations. Sure.
2:24:11
>> Which is why like in the age of AI and everything's going to be automated, everything like this really matters.
2:24:16
Showing up in a studio, we're you'll hopefully send us, you know, you'll say, "Hey, I have an idea for you guys."
2:24:23
>> That's what And vice versa, huh? >> Yeah. Of course. Of course.
2:24:25
>> That I would say that's 99% of our >> 99% of it.
2:24:27
And but it's worked pretty well.
2:24:29
>> That's so interesting.
2:24:29
I mean, yeah, we we we we talked to investors across the category.
2:24:34
There's some that are doing tons of outbound.
2:24:36
They have a price for every company in their CRM.
2:24:38
They have an army of deal associates that are getting out there pounding the pavement.
2:24:43
There's other folks who Yeah, just wildly different strategies. It's fascinating.
2:24:48
>> Well, for being a very quiet firm for a very long time, >> um >> it didn't allow us to have what we now realize we probably should have been doing for a while is building those relationships.
2:24:59
But it's been quick coming out, like coming out um and building those relationships and figuring out how to make it work.
2:25:06
We're more mature doing it.
2:25:08
We know what we're looking for.
2:25:10
Um >> we made a ton of mistakes.
2:25:12
So like, >> you know, it's it's easy to start.
2:25:15
You know, they're not they're not perfect, but they rhyme with the past, right?
2:25:19
So, um we're we're good at we're good at saying no. Yeah, I would say. >> Yeah.
2:25:25
Uh let's talk about the energy side.
2:25:28
What's interesting there, it's a very broad category.
2:25:30
We talked to a founder yesterday who's refining uranium to go into nuclear power plants that won't come online for five years on the good side.
2:25:40
Then at the same time, we talked to uh you know Chase Lock Miller from Crusoe.
2:25:44
He's building data center putting up power plants today.
2:25:45
Uh there's so many other pieces of infrastructure, so many the supply chain is so complicated.
2:25:51
Where is the opportunity? >> You want to go ahead? Um, we like Crusoe.
2:25:58
We're investors in Cruso. Really? Oh, yeah. No way. Oh, yeah. That's amazing.
2:26:02
>> We've got a lot of investments. Okay. Okay.
2:26:03
And I like what is interesting in energy is >> it tends to be there.
2:26:08
There's some individuals or individual companies that are doing some stuff that's that we see as transformational.
2:26:13
So, everybody's very worried about energy. >> Yeah.
2:26:16
>> Whatever happens with the war, we don't have any thoughts on that. >> Yeah. Yeah.
2:26:20
>> But assuming that things are peaceful like, hey, is it going to be nuclear?
2:26:24
or is it going to be >> solar? >> Solar, etc.
2:26:27
Um, geothermal is really interesting.
2:26:30
So, we're big investors in a company called Fervo based in Utah.
2:26:34
It's um >> your your viewers should look it up.
2:26:37
It's uh it's transformational and it's a it's today.
2:26:40
>> So, it's built they're going to deliver >> I don't know 500 megs in >> Wow. That's a 2027. >> Wow.
2:26:50
>> That's enough for That's the average metac campus right now. >> Okay.
2:26:53
And remember, it's fracking.
2:26:55
traditional >> drilling, >> but not in traditional areas. Okay.
2:26:57
So, think more remote areas. >> Sure.
2:27:01
>> Are there side effects? It's unclear.
2:27:01
I don't think there are necessarily, but maybe there could be some seismic.
2:27:07
>> Um, >> but like that's super interesting.
2:27:08
And the people who are doing that, like by the way, there's plenty of heat down there.
2:27:13
It doesn't heat the planet.
2:27:14
There's a bunch of physics around that.
2:27:16
We're not physicists, but it won't make the planet hotter. >> Yeah. Because net zero.
2:27:20
>> Um, so that's a good one.
2:27:20
I think there's another company in Utah that we like a lot.
2:27:23
like a lot. we were talking to him earlier is Taurus energy >> and that is what effectively what you saw in cloud >> uh compute is cloud energy and so Taurus is a >> it's basically a it's a flywheel business like actually a flywheel
2:27:37
>> okay literal fly a literal flywheel except it weighs about 3500 lb power >> okay >> but it spins remember the issue with power is that it's >> everybody draws at the same time if you're snow basin in Utah and you draw on power to run your tram at the same
2:27:55
time that everybody's hey by the way uh open AI is going to run one of their >> sure sure >> uh you know one of their loops then you're going to end up drawing tons of power and that's expensive for the grid >> so what >> Nate has done and his team at at Taurus
2:28:09
is sol they're a balancer >> the load balancer >> it's the load balance and you realize there's actually quite a bit of power >> um how you actually balance the power is is the hard part and he's solved it and so they are operating uh operating ing today. Um, we talked to him earlier.
2:28:23
Um, we talked to him earlier.
2:28:26
He's he's in a bunch of different states and he's he's coming to a state near you. >> Yeah. I love it.
2:28:31
Uh, well, tell me more about Peak Six Trials.
2:28:34
Where can people get started?
2:28:36
How do people apply or or join? How does this work? >> So, peak6trials. com. Okay.
2:28:41
>> And we're looking for entrepreneurs who have ideas. Cool.
2:28:43
And everything else is sort of there.
2:28:45
You don't have to go and raise money.
2:28:46
You have to spend time doing that.
2:28:47
Think about the things that >> where your specialty is is your idea. Yeah. Right.
2:28:54
This is this is a place with AI like we can do a bunch of stuff around you. >> Yeah.
2:28:58
>> And interestingly like with Apex, right?
2:29:01
We have these 40 million customers.
2:29:01
They might want your product >> and if they don't want your product that's also really good news.
2:29:06
So we short circuit all these things that take to say like is this a good idea or not?
2:29:12
You don't have to worry about the capital.
2:29:13
You don't have to worry about paying your rent.
2:29:14
We actually pay you salary. No way. Yeah.
2:29:16
So, um, it's really nicely packaged for someone.
2:29:19
I wish we had it at the time.
2:29:21
It would have made me feel better. Yeah.
2:29:24
>> Um, maybe we were better off cuz >> we took so much risk. You never know.
2:29:28
But the balance here is we want the people who bring the ideas and we help support and build it ultimately to own the majority of thing, not for us to own the majority of things.
2:29:37
So, the the the the way we've structured the deals are really creative, I think, and different than the marketplace has seen so far.
2:29:45
than the marketplace has seen so far. So um finding those people right those young entrepreneurs or they may not be so young entrepreneurs they can be anywhere but it's really I mean I think it's super broad yeah >> what the fintech sort of space is like
2:29:59
everything's money every large >> CPG company who has a bunch of customers there's some money product that exists or could exist in that ecosystem so there's a lot of ideas I think that are out there we're going to pick >> 12 to 15 for the first year and we're going to see what we can do and see what we can pump through. >> That's great. Last question. What's the >> That's great. Last question. What's the best way? I'm terrible at poker.
2:30:21
What's the best way for me to learn and get better?
2:30:26
>> Well, we have amazing teachers around the country, which is kind of crazy.
2:30:29
We're like at 28 teachers.
2:30:29
We have taught at like 360 companies.
2:30:32
So, you can actually bring us to your company. Really?
2:30:35
The banks, the technology firms, the law firms.
2:30:38
>> I think they might like it.
2:30:38
I think these >> It's been wild how people have picked it up, right?
2:30:42
Cuz first of all, we're not playing for money.
2:30:43
actually teaching because these are people who know nothing >> but um >> you know 94% >> of poker players on the planet are men so yes it's really extreme so I mean after a couple hundred years of this game coming around it's probably time for women >> to be doing this um we're in 70 countries we're in ro rural villages in Kenya for example so >> we are super quick turnkey events best events that are on on the >> poker power. comour. com. I love it.
2:31:16
Well, thank you both for taking the time to come chat with us.
2:31:17
Uh we will close the show here on this camera.
2:31:23
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2:31:25
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2:31:28
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2:31:31
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