0:02
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>> [music] >> That's misinformation.
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Wanting clearing order in now. That's just wrong.
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You are surrounded by journalists. Hold your position. Com, get up. Trust the experts. >> [music] >> Five good. We are correct. Founder. Five good.
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Activate Google retriever. >> [music] >> Clear. me I am a doctor.
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Market clearing order [music] inbound. Fire quarter.
4:20
Possible journalists on the horizon. Founder now. You're watching TVPN.
4:37
Today is Tuesday, March 10th, 2026.
4:39
We are live from the TVPN Ultradope, the temple of technology, the fortress of finance, the capital of capital.
4:45
Uh we have a great show for you today, folks.
4:47
Let me tell you about ramp. com. Time is money. Save both.
4:50
Easy-to-use corporate cards, bill pay, accounting, and a whole lot more all in one place.
4:53
Let's quickly pull up the linear lineup because we have some great guests coming on.
4:57
Olivia Moore from A16Z is breaking down the top 100 generative AI consumer applications.
5:02
Of course, Linear is the system for modern software development.
5:06
70% of enterprise workspaces on Linear are using agents.
5:10
And next, we have David It's deals day.
5:13
It's deals deals deals deals deals. Juice Box is coming on.
5:15
We have Algorand raised a bunch of money. raised a bunch of money.
5:20
We also have Archer coming on to talk about flying cars. When will we get them?
5:23
What happened to my flying car? They built one.
5:26
We're going to see what the timeline is to get me in it.
5:28
Well, uh let's run through a little a few of these deals just to kick off the show.
5:33
We're going to go through them later in the timeline, but there's a few things uh Brian Goral wrote the op-ed today in the TVPN newsletter at tvpn. com.
5:41
Um Mira Murati's Thinking Machines snagged a multi-year partnership with Nvidia.
5:45
Uh Mira Thinking Machines has been on the ropes.
5:49
Uh They lost half of the six co-founders in under a year.
5:53
There's a question about where the business is going.
5:55
This is obviously a good sign that they got a multi-year investment done with Nvidia in which it will deploy at least a gigawatt of cutting-edge chips to train AI models.
6:04
They are going to be GPU richer.
6:07
I don't know where the bar is for GPU richer or GPU poor is today, but they're one one gigawatt richer after today, which is good news for them.
6:14
So, congrats to everyone at Thinking Machines.
6:19
Even though they've had a couple high-profile executive departures, the team has grown from 30 people to 120 people.
6:25
So, they're still cooking.
6:25
Also still cooking, Alex Wang.
6:27
There was a bunch of fake news on the timeline.
6:31
We'll dig into this, but multiple tech news aggregator accounts on X posted that Alexander Wang, who's been on the show at Meta Connect, I've interviewed him a few times.
6:41
He leads MSL, Meta Superintelligence Labs.
6:44
And they said and they were saying he's out.
6:46
He's on the he's on his ropes.
6:48
He's on his he's fighting for his life over there. Well, it was fake news.
6:49
And we'll go through exactly how this happened, but Meta CTO Andrew Bosworth and Zuck also both hopped into the chats, different chats, which we'll take you through to categorically deny the rumor.
7:02
So, um we will dig into that.
7:05
Also, Yann LeCun Matt raised a massive seed round for Advanced Machine Intelligence Labs, AMIL. One on 3. 5. >> Not bad. Not bad.
7:18
>> Not the kind of combination that you normally see. Yeah. It's not very American. Mhm. Why? >> a 30-ish percent. >> Sure. Sure. Sure.
7:27
>> But >> [snorts] >> it's it's a big vote of confidence.
7:30
>> in the in the age of AI, in the age of compute requirements.
7:32
You you got to spend money to make money in AI.
7:35
And he's got the money now.
7:39
Also, as we mentioned, Legor is coming on talking about their series D, $550 million at a $5.
7:44
5 billion valuation just a year after their entry into the US market. Fascinating industry.
7:49
We talked to partner at Sequoia yesterday.
7:53
Obviously, Sequoia's an investor in Harvey, but how will these firms change?
7:57
Will the tools become agencies?
7:59
Will they be doing the work?
8:02
Will they be more direct-to-consumer?
8:03
This is a question that we've been digging through in the in the >> Yeah, a lot of a lot of people have been kind of questioning just how thin are these ra- how how thick or thin are these rafters basically.
8:14
Um, but Also, AI recruiting platform Juicebox, which was a part of YC summer's 2022 batch.
8:23
That's a good time to go through YC right before the AI boom. You're up and running.
8:27
Well, they are up and running with $116 million after a $80 million series B, which values it at $850 million.
8:35
That's kind of dilution that you're looking for. 10% Not bad. Uh, little under 10%.
8:43
And the round was led by DST Global with participation from Sequoia co-two and YC.
8:47
Very good news for the folks over at Juicebox.
8:51
They're in the hiring market, so we're going to have the founder on to talk about the business, but also talk about the hiring market. Where is their strength? Where is their weakness?
8:57
What is he reading into the jobs data?
8:58
We'll try and get to the bottom of where the opportunity is in the modern economy.
9:05
Uh, Meta also acquired the agent-based Reddit-style social network Multiverse.
9:10
Of course, had the founder, the creator of Multiverse on.
9:11
I actually know the other co-founder as well, Ben Parr.
9:13
They will both be joining Meta Superintelligence Lab.
9:16
There's a lot of back and forth on was it all slop?
9:18
Is there any value there?
9:20
Well, we don't know the terms of the deal.
9:23
Doesn't have to be a billion-dollar acquisition. Who knows?
9:27
Uh, they're I've talked to both of the founders.
9:29
They're both, you know, capable, interesting people.
9:32
And I think it's I think it's under discussed and we'll get into this.
9:34
Under discussed that, uh, who is evaluating these acquisitions?
9:38
It's not just Mark Zuckerberg, it's not just Alex Wang.
9:40
You also got Nat Friedman, Daniel Gross.
9:43
These guys have backed a lot of founders.
9:44
They've worked with a lot of AI startups.
9:46
They can understand the team that they're trying to build over there.
9:49
And there might be some interesting interface between AI agents and social media. This is highly relevant.
9:55
Meta seems like the logical place.
9:57
Yeah, remember Meta Meta filed some patent for basically bringing yourself back to life >> Oh, yeah.
10:03
in agent form after death, right?
10:05
So, of course they're thinking about this stuff.
10:08
>> your mortal coil and you molt, you go on MoltBook. That's very macabre. >> Yeah.
10:13
But >> I would be I would be shocked if they keep MoltBook running. Really?
10:18
>> for more than a handful of months.
10:18
Like, this just feels like, "Hey, let's bring some people on board that have been thinking spending all their time thinking about how bots are going to interact with other bots and uh, and humans on the internet."
10:32
>> Yeah, and Meta's done a ton of these types of acquisitions, um, where like like smaller products, tuck-ins.
10:37
Not everything has been WhatsApp's 16 billion, eight bill I forget.
10:41
It was a lot of billions.
10:44
>> Nikita's first It was >> good example.
10:46
And if you just think about it as like you get a shot on goal with one product, you get a product leader that can go and and bring some new energy, some new ideas in.
10:53
There's a lot of opportunity there.
10:55
Well, uh, before we move to the timeline, let me tell you about Figma.
10:58
No matter where your idea starts, Figma, make, Claude code, Codex, or a sketch, the Figma canvas is where ideas connect and products take shape.
11:05
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And there's also some exciting news from Google that we'll touch on today.
11:23
Not exactly a deal, but a whole bunch of new features that we'll be going through.
11:29
So, uh, Theo is talking about the latest from Anthropic, so Claude code now has code review, which optimizes for depth and maybe more expensive than other solutions like their open like open source GitHub actions.
11:41
Reviews generally average $15 to $25 build on token usage and they scale based on PR complexity.
11:50
And Theo says, "Anthropic really needs like one normal person to prove these things before posting."
11:55
I guess people are are upset about the price of having these these code reviews build individually in a world where so much code is being generated.
12:04
Some of the some of the initial copy around this announcement looked like it was just a flat rate per code review. >> Yeah.
12:14
It In actuality, it's built based on token usage, but it's funny to have like a flat rate Yeah, you know, it's generating code.
12:22
>> It's you're getting charged to review the code and it's just like you >> Also, like all of the token rates and just AI expense lines are shifting so dramatically. Token usage is ramping.
12:33
You're getting discounted tokens from certain plans.
12:34
Like it's very hard to grapple with how you think about budgets.
12:39
You know, we've talked to a number of people where like you know, at at Microsoft, every employee needs a token budget.
12:44
Every every employee needs some sort of AI budget.
12:46
You should still think about it almost in a per seat basis, but depending on what someone's doing in the organization, they get a different AI budget.
12:54
Um but this this post from Burhama was very funny.
12:59
They feed us poison, Claude code, so we buy their cures, code review, while they suppress our medicine, which is What is the medicine in this It's actually writing the code correctly the first time.
13:12
>> Pull up this Pull up this next one from Laffy.
13:14
[clears throat] Claude code after writing your code, leave a tip. >> Yep.
13:20
>> [laughter] >> They really should do a tip button.
13:22
I like I like the idea of a tip.
13:25
Um Tyler, what what what's your take on on on buying or or paying for for AI code reviews?
13:31
There you know, we're we're we're we're sponsored by a code review company.
13:35
There are a number of code review solutions.
13:37
Uh, what what what's the advantage to having AI run a code review these days?
13:42
Uh, yeah, I mean it like makes a lot of sense.
13:44
Like >> It It doesn't apply to you cuz you don't review code, correct?
13:47
Well, I mean it So, it makes sense for teams, right?
13:51
Because I like I don't I don't need an like external code review. >> Yeah.
13:55
On my code because I'll just have like if I'm in Codex, if I'm in Claude code, I'll just tell it like review Review it before you post it. Does it Does it work? >> it, right? Hopefully it does that.
14:04
So, we have like I personally I never check my work in the moment. I'm just never >> Never. full speed ahead. Yeah.
14:12
What is this What is this Claude Remarks account? P_remarks.
14:15
It seems like it's a It uses the real Claude logo, but it very much feels like it's not owned by Anthropic because this post is Walter White spinning pistol saying mid-level technique non-technical business unit leaders asking Claude where they can cut headcount to reduce waste.
14:34
And you flip it around and it says, "Actually, we don't need you."
14:40
>> [laughter] >> Which is the funniest situation.
14:41
Claude, based on this conversation, we don't need you.
14:45
>> Anton says, "Make the models cheap to use. Great.
14:49
They all forgot how to code. Now 10x the price."
14:52
>> [laughter] >> It's not that bad. Stuff's working.
14:56
We we we have had we have had fantastic success with Vibe Coding.
14:58
We are we are quickly becoming a game studio.
15:00
We of course released TBPN Simulator, thanks to Ben over there.
15:05
We have some other projects in the works.
15:07
And it's going to be a good year for us.
15:10
We're we're we're we're very happy with the tools that are at our disposal.
15:15
Max Zeff in Wired shares that OpenAI and Google employees, including Google DeepMind Chief Scientist Jeff Dean, filed an amicus brief in support of Anthropic in its lawsuit against the government.
15:25
I saw guests of the show Dean Ball also put together an open letter through FAI that if you feel inclined you can go sign to support the idea that that Anthropic should not be labeled a supply chain risk.
15:39
Maybe some other Chinese lab should be labeled a supply chain risk.
15:43
We'll leave it up to you to see where you land on that conversation, but there are certainly lots of people that are coming together to try and crystallize the final decision there.
15:52
In other news from Axios, the White House readies an executive order to weed out Anthropic.
15:58
They are really pushing hard on this supply chain risk designation and pulling away from Anthropic.
16:04
There's news that they might be using Gemini, might be using OpenAI models.
16:09
The Grok is already installed.
16:11
There's a question about capabilities, but the capabilities seem to be jumping back and forth constantly like with the Google news today with the Codex of 5. 4.
16:18
Like this this temporary arb of like they needed Anthropic cuz it was the only thing that could do X Y or Z.
16:26
That seems to be, you know, gone for this week.
16:28
Who knows where it'll be next week, but if you are trying to make it in DC, you got to open up the front page of the Wall Street Journal because there's a tip.
16:38
So, if you have a meeting with Donald Trump, you better wear his favorite shoes.
16:42
Can you guess what his favorite shoes are? No idea. It says Balenciagas. No. It says Oxfords.
16:51
$145 Oxfords to be to be specific.
16:56
The president has developed an obsession with $145 Oxfords.
17:01
All the boys have them is the quote.
17:05
The hottest and most exclusive MAGA status symbol is a pair of leather Oxfords.
17:10
Prefer a wingtip, loafer, or monk strap, black or brown, President Trump has got you, apparently.
17:15
Trump has been gifting footwear to agency heads, lawmakers, White House advisers, and VIPs. Did you get your shoes?
17:22
He asked cabinet members.
17:25
He wants everybody to wear the same pair of shoes. >> Yes.
17:29
And and he asks people in cabinet meetings, "Did you get your shoes?
17:31
Did you get the shoes I sent you?"
17:34
>> [laughter] >> That's pretty amazing. >> That's pretty nice.
17:36
Uh some people have laced up in the Oval Office.
17:38
During a lunch meeting in January, Trump suddenly pivoted to his incredible new shoes and gave Tucker Tucker Carlson a pair of brown wingtips.
17:47
"All the boys have them," said a female White House official.
17:51
Another joked it Another joked, "It's hysterical because everybody is afraid not to wear them.
17:57
The shoe salesman-in-chief is paying attention."
18:00
>> [laughter] >> This is extremely >> know Do we know what brand? Uh yes, Florsheim.
18:05
Wow, that was the next sentence.
18:07
Oh, spoiler alert over here. It's okay, we get it.
18:09
You read the journal before me.
18:10
I get in We're going to have to get two copies of the paper journal cuz I have been reading the journal for a full year now or two, and uh and and I get over it and I'm like, "Where's my Where's my paper?"
18:19
And oh, well, it's over on Tyler Cosgrove's desk.
18:23
What is the What's the What's the sort of history of this brand?
18:25
Why I have some Florsheims. I like them.
18:29
They're very >> have some. Yeah, they're good.
18:29
They're They're just like a They're They're They're uh accessibly priced at $145.
18:33
Uh they look nice and they sort of match everything, and look at that heel.
18:42
>> this to roll in Florsheim to roll in to Truth Social? >> Potentially.
18:46
Potentially we is I don't know if it's public.
18:49
Potentially a SPAC candidate, anything could happen here.
18:52
Uh Trump has fallen in one in love with Florsheim, an American brand that's been pairing comfort and style for more than a century. They're also affordable.
19:00
Many cost just $145, not bad for a pair of leather shoes.
19:05
The president has taken to guessing people's shoe size in front of them.
19:10
>> [laughter] >> You're in a meeting and you're like The Sir, the price of oil has tripled.
19:15
>> [laughter] >> He's like 11. I'm pretty sure it's 11. 11. This is wild.
19:19
He asks an aid to put in an order and a week later a brown Florsheim box.
19:24
He should just have them in stock.
19:26
He should just keep them Reached by phone, Thomas Florsheim Jr.
19:29
said he was unaware of the president's shoe orders.
19:34
How are you not tapped in, Thomas?
19:34
The 70 the 79-year-old billionaire known for expensive Brioni suits, long red ties, and a pension for aesthetics late last year began searching for something that would feel better after a day on the job and settled on Florsheim.
19:48
Trump liked them so much he started dispensing them.
19:53
He pays for the shoes, the White House said. President J. D.
19:55
Vance and Secretary of State Marco Rubio have some.
19:58
So do Transportation Secretary Sean Duffy, Defense Secretary Pete Hegseth, Commerce Secretary Howard Lutnick, Trump's Communications Director.
20:07
>> [laughter] >> Wow, it's really everyone.
20:08
Sean Hannity, Senator Lindsey Lindsey Graham have a pair.
20:11
Recipients have taken to wearing their Florsheims around Trump some begrudgingly.
20:15
One cabinet secretary has grumbled that he had to shelve his Louis Vuitton and >> [laughter] >> Officially, the White House wouldn't confirm Trump's choice of Florsheim.
20:23
One recipient said >> [laughter] >> Trump had a stack of them in in an office.
20:32
A box read Scott >> [laughter] >> for for Treasury Secretary Scott Bassitt.
20:37
Bassitt does not want these. No way.
20:39
He's got some Louboutins or something.
20:40
Reached by phone, Thomas J.
20:43
Florsheim said he was unaware. You read this.
20:45
Florsheim was founded in 1892 by Chicago in Chicago by Sigmund Florsheim, a German immigrant and cobbler, and his son Milton a century before Trump began putting his name on everything.
20:56
Florsheim stitched its moniker on shoes and opened branded stores across the country.
21:00
The company outfitted American soldiers during both World Wars.
21:03
Later it rode the shopping mall boom.
21:07
President said President Harry Truman wore them.
21:09
Michael Jackson moonwalked in Florsheim loafers.
21:12
I had no idea that was the pair of shoes that uh that he moonwalked in. That's remarkable.
21:16
Like Trump's own business >> he he did a moonwalk in loafers? Yeah.
21:22
Well, I mean, that's what he like I think they're white bottoms like loafers.
21:27
We should pull up a picture of uh Michael Jackson doing the moonwalk.
21:31
But, you definitely need like a smooth dress shoe.
21:32
You can't moonwalk in something with a lot of grip.
21:35
Uh Like Trump's own business fortunes, Florsheim has experienced ups and downs, including filing for bankruptcy in 2002, part of a move that returned the brand to Florsheim to the Florsheim family.
21:47
So, they bought it out of bankruptcy after selling it. Today, Beautiful.
21:49
it is part of a Glendale, [clears throat] Wisconsin-based Weyco, which also distributes Nunn Nunn Bush, Stacy Adams, and Bogs.
21:58
Rubio and Vance received their Florsheims after a December meeting in the Oval Office.
22:04
Deep in conversation, Trump peered over the resolute desk at their feet.
22:06
Vance recalled during an event that later that day celebrating Kennedy Center honoree Sylvester Stallone, "Marco, J. D.
22:12
, you guys have S blank Y shoes."
22:14
Trump declared before retrieving a catalog.
22:18
A third politician was in the room. Vance didn't name him.
22:24
And Trump asked each person their size. Rubio said 11. 5, Vance 13.
22:26
A third man said seven, according to Vance.
22:31
[laughter] Shoe mogging is happening all over D. C. If you head to D. C.
22:35
, uh get a pair of Florsheims.
22:37
Or maybe maybe there's an opportunity to start the left-wing response to Florsheim, since often these things get politicized.
22:45
But, the real money is going deeper in the supply chain, selling money selling weapons to both sides. This is the alpha.
22:50
You know that uh both Alex Jones and Gwyneth Paltrow at one point were so were sourcing supplements from the same co-packer. Yes.
23:00
Yes, the exact same ingredients, the exact same chemicals sold to two wildly opposing audiences.
23:04
Like this This is something that happens deeper in the supply chain because That sales >> matters. Donald Trump. Yes, yes, yes.
23:13
Somehow I don't think so.
23:13
Quickly, before we move on, let me tell you about Console.
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23:31
China's BYD explores F1 entry [cough and laughter] in first racing push.
23:38
BYD is examining options to enter competitive motorsport including Formula 1 and endurance racing in effort to boost the Chinese brand's appeal globally.
23:47
The automaker is looking at several options following its rapid growth outside its home market in competitive racings contin- continuing shift towards hybrid engines, people said.
23:58
Uh these range from the World Endurance Championship which includes the 24 Hours of Le Mans to F1 uh either through building its own team or potential acquisitions.
24:07
Any move by BYD would be a rare direct attempt by a Chinese manufacturer to take on a sport dominated by European and US teams.
24:16
Carmakers from the country have had sporadic interest in motorsport.
24:17
Uh Geely successfully participates in international touring car racing through Cyan Racing, formerly the Volvo factory team, and Nio Inc.
24:26
won the driver title for the inaugural Formula E electric championship in 2015.
24:31
The potential cost of entering F1 could be a significant obstacle for BYD according to one of the people. So they have money.
24:37
Maybe they're down to their last 20 20 grand. It's possible.
24:41
Developing and entering a car often takes years of negotiation and costs as much as They should start 500 million a season.
24:50
So they should start a new race series.
24:53
You know how the BYDs can jump over potholes?
24:55
Have you seen this video? Yeah.
24:57
We've pulled this up before. So, they can jump.
24:58
There should be a specific racing circuit with terrible potholes that if you crash, it'll just destroy your car.
25:05
So, you have to jump at the right time, and that adds like an extra layer of thrill. I love it. >> This would be good.
25:11
And probably way cheaper to start that circuit.
25:13
There's only one F race F1 race in China at the Shanghai International Raceway.
25:16
The the the China Grand Prix.
25:19
Um so, who knows how much of an impact they would be able to make. Uh let's continue.
25:26
No decision has been made, and the company may not decide to enter any competition.
25:30
A BYD spokesperson didn't request for comment. Didn't respond.
25:34
BYD is known for making affordable electric and hybrid vehicles.
25:39
Okay, so they do have some hybrid technology.
25:41
It's always weird, like a Tesla F1 car would be odd, cool, but it just feels like they should be in Formula E cuz I think of them as an electric car maker.
25:51
Um but uh BYD in 2025 its high-end Yangwang branded brand tested the U9 extreme vehicle on a track in Germany recording a top speed of more than 308 mph. That is so fast. That is so so fast. 200 is insane.
26:15
I mean, being on the track and going like 120 feels fast.
26:17
Three times that is absolutely crazy.
26:22
>> Yeah, but 150 feels wrong to me personally as a father. >> Yes. But 300.
26:26
But the right track, the right conditions, straight, lots of run off, like it it is possible.
26:34
>> that was trying to break the drift record by spinning out.
26:38
>> [laughter] >> Yeah, you were very upset about that.
26:39
The chat agreed with you.
26:39
They were not They were not happy with that.
26:41
Uh an F1 partnership would also significantly boost awareness of BYD in the US.
26:44
Do you know what BYD stands for? No. Build your dreams. Wow. Build your dreams.
26:54
Do you know what LG stands for? The TV maker? Life good. Yes, life's good. Really?
27:03
>> Life {apostrophe} S is good. Life is good. Life's good. LG. Good pop quiz.
27:09
Uh, the sport itself is experiencing a surge in US popularity, which is odd because if F1's booming in the US, it's going to be more expensive to enter, but BYD doesn't have a strong sales and distribution into the US.
27:20
F1 is still an international sport, but if it becomes more of an American sport, >> this is just Europe.
27:25
This is just a Europe building relevancy in Europe.
27:29
And uh, they have a huge amount of competition back in China.
27:33
So, I in many ways, I would view a move like this as not as much trying to compete with uh, international brands, but being like, we have all these brands at home hot on our heels.
27:45
We have to differentiate versus them. >> Totally. Tyler?
27:47
Uh, I got to put you in persona.
27:49
It does not stand for life's good. >> No? >> for lucky gold star. Lucky gold star.
27:55
Wait, why where did I get life's good though? >> That's another brand.
27:57
They might use that in marketing, but it's not like the etymology of LG is from >> Wow. Destroyed. >> Okay, thank you.
28:04
Um, >> [laughter] >> uh, here's another idea.
28:06
BYD, instead of shelling out half a billion dollars for an F1 team or whatever it costs, uh, they should just do what Perplexity with Lewis Hamilton with Zhou Guanyu, the Chinese driver who uh, is actively racing and they could sell the spot on top of the helmet.
28:27
And so, that would be more of like if Zhou has a good season, if he wins, they're they're, you know, backing that.
28:33
I wonder if you can't do a car sponsorship while you're racing for a different team though.
28:38
I don't know who who does Zhou >> Yeah, I'm sure there's limitations.
28:41
Who does he race for right now?
28:45
Um, oh, he's on Cadillac.
28:45
Okay, well, yeah, that's probably not going to work.
28:48
>> [laughter] >> The most American team.
28:49
Sitting in the Cadillac with the BYD sticker on his helmet.
28:54
>> Yeah, maybe that doesn't work.
28:54
Stick with Huawei, maybe. I don't know.
28:58
>> Babe says he's not actively racing. Oh, okay. >> Back up. >> Okay, he's back up. Yeah, thank you.
29:02
Uh, buying into F1 is more common.
29:04
This season is the first for Audi after taking full control of Swiss motor sport company Sauber.
29:10
Investor Otro Capital is seeking buyers for a stake in Renault Alpine racing.
29:17
Uh, however, full team sales are rare.
29:19
Billionaire Lawrence Stroll's Aston Martin team has recently sold stakes in the team which has had a disastrous start to the new season after mechanical issues including vibrations from the power unit.
29:27
Motorsports such as F1 are increasingly adopting environmentally environmentally friendly practices.
29:31
For 2026, F1 has implemented new rules including hybrid power regulations that boost battery capacity.
29:39
>> Somebody ran the numbers on the uh, sort of like CO2, the emission savings that F1 is getting from the new uh, new regulations.
29:48
And then comparing that to the emissions of just like taking this like massive carnival of motorsports on the road all year round. >> at every F1 event.
29:58
>> just like doesn't make a dent at all in the overall impact and it's just sort of like emissions theater.
30:02
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30:22
So, what do you think performed better over the last 5 years? The S&P 500 or cows?
30:35
Live cattle apparently outperformed the S&P 500, but this is from an account called DJ Cows.
30:40
And I feel like they've been waiting for this to happen for the entire time.
30:45
They've been waiting for the one moment that the cattle market outperforms the S&P 500 and they're taking a victory lap.
30:51
DJ Cows, one of the greatest ever do it.
30:55
Um, very, very interesting.
30:55
I didn't realize that there was such a boom in the cattle market, but apparently there is and I'm sure there's a way to get in on the action if you so choose, if you are interested.
31:06
Well, let's move uh over to AI and the Neo Labs.
31:11
Nvidia invests in Mir Murad's Thinking Machines Lab, the startup founded by OpenAI's former CTO plans to deploy at least 1 gigawatt of Nvidia chips as part of a new partnership.
31:20
The deal includes a collaboration to design artificial intelligence training and serving systems using Nvidia technology.
31:29
The size and structure of the investment couldn't be learned. Is it a circular deal?
31:33
Is it equity in exchange for uh for chips?
31:36
It's it's it's unclear at this point, but um, nothing's off the table these days as >> Yeah, I think the main thing we know we know Thinking Machines was out raising towards the end of last year going for something like a $50 billion valuation.
31:51
Seems like that I would have guessed that hasn't happened.
31:53
Otherwise, I'm sure they would announce it from from a from a just like in order to project if I were them and I wanted to project confidence.
32:04
It would I would be trying to announce the biggest possible number.
32:05
Instead, they announced this this effectively what looks like a trade. >> Look at this photo.
32:12
Is there any chance that these two companies merge at some point in the future? That's interesting.
32:20
Tyler's always been on this like if Jensen gets really AGI pilled, he keeps the chips for himself and serve the models himself.
32:25
And Nvidia does have some in-house training and inference capabilities.
32:30
They have a metaverse product that simulates worlds.
32:33
They also have a self-driving car project, and they're still partnering with OEMs and partnering with companies, and they're not they're not offering consumer products, of course.
32:43
Nvidia is the one company in the Mag 7 that does not have a social network yet, but that could change.
32:48
Um but what do you think?
32:50
>> been news recently, um I think Nvidia is planning to launch some like open-source AI agent.
32:54
It's like unclear how like serious that is.
32:56
Like maybe it's just like a cool demo or something.
32:58
But it's yeah, I I don't think it's like super >> of Open Claw or something like that.
33:04
Um yeah, any anything's possible.
33:04
Um I mean, Nvidia has never done too much in the in the consumer space, or you know, they've always been deeper in the supply chain, but um they I I didn't they have a Nvidia Shield gaming product that would do uh game streaming?
33:18
I think they had some hardware at some point.
33:20
So, and I I I think they're they're open to it, and and in a in a huge boom where, you know, having at least a team of 120 super talented AI researchers, that could be really valuable to Nvidia.
33:32
Of course, Nvidia famously uh did that deal with Groq and sent over 10 billion wired in 5 days or something, or 24 hours. >> 20. What was it?
33:45
Yeah, they closed the whole thing in 20 days, and I think Jensen just like sent the sent a $10 billion wire in like >> Yeah, somehow it came out that it that that the wire was sent like prior to actually >> Yeah. It's like, "Here you go.
33:55
Like I'm I'm good for it. I got the cash flow." It which is wild.
34:00
Sophie says, "Please, bro, just one more AI lab, bro. Come on, bro.
34:04
We have a unique perspective on AI research.
34:06
No one else is doing it like us, bro. Come on, bro.
34:07
We can raise a few billion, and worst case, we just get acquired, bro. Nothing to lose, bro. I promise. Come on.
34:14
Just join Just join my AI research lab.
34:16
Yeah, I mean, has the has the neo lab boom slowed down?
34:22
Like you, Tyler, you created the neo lab market map.
34:28
Have you been getting more DMs?
34:28
"Hey, I just launched, and you got to put me on that thing.
34:32
It's probably slowed down a little bit.
34:34
I mean, it's also like uh The big ones you heard about were all people leaving OpenAI.
34:41
Uh mostly OpenAI, I guess.
34:41
Not not as much Anthropic. Um Yeah.
34:43
But it's probably slowed down a little bit.
34:45
You don't hear as as much about these big rounds now.
34:47
But I I think there are like there are some that like are are maybe in stealth that haven't launched yet, right?
34:53
Like Standard Intelligence. Yeah.
34:54
When they came on, there was like most people didn't know about that. >> Yeah, yeah, yeah. Um but Yeah, yeah.
34:59
There's a few It's also out there in stealth.
35:00
But they they have to be sort of narrow or and and and I think the broader we're now in the post post Neo Lab era where it maybe if it if it wears the Neo Lab branding, it's doing something that's so different that it's not really in the path of like >> Intelligence launching implies an opportunity for a Neo Lab non-standard intelligence. >> Yes. Yes.
35:24
>> And so every >> is there is a company Unconventional AI. >> Really? Yeah, yeah.
35:28
We had them on, I believe.
35:29
The reason that that's Naveen. >> Oh, yes, yes, yes. Yeah.
35:31
Uh there there there there's also like Applied Compute and Applied Intelligence and Standard Cognition.
35:39
>> a whole kind of like 3 by 3 grid you can do Yes. Yes. What is SSI up to? Rune asks the question.
35:46
He said, "There are military sec- secrets much much worse guarded than whatever SSI is up to."
35:54
And Calamaze says, "Their ore farming is a business model."
35:57
Prime Intellect Affiliates saying this.
35:59
Because uh Prime Intellect is fantastic at ore farming.
36:03
Um SSI is all about mogging and munting the other labs, apparently, according to Valtieri.
36:10
Uh they haven't raised in a year.
36:11
[laughter] We were not I like this picture if you scroll down of Ilya with the long flowing hair. This is great.
36:18
This is who you're trading >> This is it.
36:20
This is who you're trading >> you're trading against right now. That's that's my theory. Yes.
36:23
Uh it is it is uh the the the full story of SSI will be fascinating to to tell one day.
36:33
The Daniel Gross shift to meta and Ilya's appearance and Dorkash Patel sort of told one side of the story and also as you revisit DG's AGI bets as we did last Friday.
36:46
It it tells you a lot about his view on the world.
36:48
Ilya has a has a you know, it's about probably some overlap but a different view of the world and uh lots of lots of fun to at least speculate on the timeline.
36:57
Before we move on, let me tell you about Vanta.
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37:11
So, Meta has acquired Multibook, the viral social network built for AI agents.
37:19
Co-founders Matt Shlitt and Ben Parr will join MSL, Meta Superintelligence Labs, with a deal expected to close in mid-March. That's now. It is mid-March.
37:27
It is in we are in the middle of March since this is the 10th, so this could close in a week or two.
37:34
Insane, well done, says Dennis Hagstad and I agree.
37:40
Why it matters, according to Axios, >> Yeah, and Matt Matt hasn't posted at anything yet, so I think I think they were seemingly not wanting this to get out. >> Yeah.
37:50
Uh, but it's still fantastic news for them.
37:54
>> Yeah, so there's no there's no announcement or this was just exclusive from Axios.
37:59
This is like Axios has learned, right?
38:03
Axios has learned that Meta has acquired Multibook.
38:07
Um, well, very very good news for for all those involved.
38:12
There is a little skepticism on the timeline, especially from the guy who was like the biggest spammer on Multibook.
38:19
Apparently, this is a hilarious uh, twist.
38:22
So, uh, Meta did not disclose Multibook's price when Axios asked.
38:24
The deal is expected to close mid-March.
38:27
The pair is starting at MSL March 16th, just 6 days from now.
38:32
What day of the week is that? That's a Monday.
38:35
Okay, next Monday they will be starting.
38:37
I thought they were starting on a Sunday, that'd be that'd be particularly cool. Um, catch up quick.
38:42
Multibook's social network was designed to run in conjunction with a separate project, Open Claw.
38:46
Open Claw was previously called Claude Bot, briefly Multibot.
38:49
Last month, OpenAI hired Peter Steinberger, the creator of Open Claw.
38:54
That product is now being open-sourced with OpenAI's backing.
38:55
So, the the king of spam on [laughter] on Multibook, uh, Nagli says, "I can't believe a single for loop script I ran on Multibook by registering a million fake agents actually helped them get acquired by Meta. Mental."
39:12
Um, did that help them get acquired? We have no idea.
39:19
I mean, it's still >> a secret that that there was a lot of spam.
39:24
All the accounts were bots.
39:24
Yeah, that's that's the whole pitch, actually.
39:30
Um, I think I think the question, if people were to look at this is like, is there economic value here?
39:34
Is like, was there anything interesting happening there besides all the crypto junk?
39:38
And were like, I went on Multibook as a human and spent time there.
39:44
That time is monetizable. Almost best for Meta.
39:47
Meta is the the king of monetizing attention, right?
39:52
And so, you could put ads on that, and you could put it in the family of apps next to Facebook, Instagram, and Threads, and WhatsApp, and whatnot.
40:03
But, were they were they actually driving attention?
40:05
Did anyone stick around because I churned pretty quickly from like being a I wasn't even a DAU.
40:09
I I used it like two or three times.
40:12
And I went on there and I searched for things, and I read some stuff, and I was like, "Oh, okay, this is interesting.
40:16
This is like a bunch of AI generated text they're talking to each other."
40:19
The system prompt seemed kind of interesting.
40:22
It It was clearly asking the AI agents to set kind of like reflect on their own sci-fi cognition and awareness and um you know, like their souls essentially.
40:33
It was It was It was interesting to see some screenshots.
40:36
People had some fun with it.
40:37
Uh it's probably monetizable to some degree.
40:41
But if it fell off a cliff and no one's really using it, uh maybe not.
40:43
But Yeah, and I I just see people that are really good at building like viral AI projects.
40:49
I've I've seen some some negativity on the deal.
40:51
People saying, "Oh, this just says that Zuck has no AI strategy."
40:55
And I just totally disagree with that stance.
40:58
I just look at this as Zuck is like bots have been a bug on social media.
41:05
We've seen now how they can be a feature. Yep.
41:09
I think every social media executive should be planning for bots to be more of a feature in the future than than they have been in the past, right?
41:20
And I think if you're not if you're not thinking about that, you're not like really being forward-looking. Yep.
41:25
And so, there's a lot of people that are going to hate bots as a feature. Yep.
41:29
But I would just assume that in the future there will be uh millions, billions of bots on all Meta properties and they will be uh not not uh you know, I'm sure some that are that are generated by uh sort of like you know, nefarious actors, but some generated from the platform itself that are part of the product experience. I like that take.
41:54
I also think that there's a uh there's another side of this which is just that look at what's happened with MSL over the last year.
42:04
Like it didn't exist a year ago.
42:06
It really started with the summer with like the talent raids and the AI talent wars.
42:09
Van says, "I just don't think having bots clicky on my e-commerce ads is a net positive long term." >> [laughter] >> Yeah.
42:18
But but truthfully, if there's if there's a bot that can interact with your e-commerce content and add context and debate the pros and cons of one thing in your in your category versus another and effectively like you have sort of a Reddit style experience around your product on day one or you have five products and and bots are in there discussing them.
42:42
That that potentially could be an interesting modality to interrogate.
42:48
And And the other thing is that when you have these bots sort of preemptively discussing something, you are effectively caching the tokens before someone actually queries them.
43:02
So, instead of needing to find a product and then click tell me about this and pretend you you like a link to a new bed or car or something and you dump that in ChatGPT and you say, "Debate this car like you're a bunch of people that are experts."
43:19
And it's Doug DeMuro versus Matt Farah debating the value of the Ferrari F80.
43:24
And and and that debate is happening.
43:27
You you could prompt that.
43:29
But if it's already there and it's sort of happening that that that could potentially be be valuable.
43:32
But I think the bigger the bigger value to Meta is if you look at the AI talent wars, they went and acquired a bunch of a a bunch of really talented researchers.
43:40
They got some folks from Thinking Machines, they got a bunch of people from OpenAI, they got people from all over the industry and they put together this team of like researchers that can sort of like unstick the Llama project and get to the frontier on just an in-house LLM project.
43:56
Maybe they open source it, maybe they don't, maybe they service as an API.
43:59
Either way, Meta needs a frontier model.
44:01
They're not just going to buy tokens from OpenAI or Anthropic.
44:05
So, they they get their own thing.
44:05
But then the question is like what do they do with that?
44:09
And I'm sure everyone on the Facebook product team is is thinking about this.
44:13
Everyone on the Instagram team is thinking about this.
44:14
Connor at Threads is thinking about this.
44:16
But, if you bring in two interesting product managers that can say, "Oh, like you got a bunch of cool frontier models.
44:21
You got an image model that you trained, a video model, you got a text model, you got a coding model."
44:26
Like, "Well, let's just go do some skunk skunk work R&D so that when we launch the new AI models, we have a number of projects that we're experimenting with that sort of demonstrate the capabilities.
44:40
Maybe some of them take off.
44:42
Maybe some of them integrate."
44:43
Like, that seems valuable to the MSL strategy, to the Meta ecosystem.
44:47
What do you think of that?
44:49
>> is like the OpenAI Labs team, right? Like this. It's like the lab.
44:52
>> Riley who's on there now?
44:53
>> Yeah, Riley's on that now.
44:53
But, it's like he's They're doing these like weird projects.
44:56
Maybe it's the next, uh, you know, uh, coding agent.
44:59
Maybe it's like Multibook or something.
45:01
But, it's just like these weird things that you know, you get the new internal models.
45:06
Maybe there's something cool you can do with them.
45:08
>> part engineering, part product development, part marketing, part communications because there's a lot of times when we bring on researchers or product leaders from labs and we ask them like, "How are people using this?"
45:21
And they'll be like, "The benchmark's really good."
45:24
>> [laughter] >> And I'm like, "I want to know how this delivers value."
45:26
And there's this break in the chain from like we have amazing intelligence, but like people want to know what the killer feature is.
45:34
They want to know what the Studio Ghibli prompt is.
45:36
They want to have their hand held a little bit.
45:37
And so, having a team that can advance that, I think, is is good.
45:41
I think I I I I think could could be very very good.
45:44
Of course, we don't know the price. We don't know the terms.
45:46
But, uh, overall, I think it's exciting for the team behind Multibook to head over to MSL.
45:51
So, congratulations to them.
45:53
Let me tell you about Cognition.
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46:01
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46:09
So, Kevin Roose over the New York Times made a blind taste taste test to see whether New York Times readers prefer human writing or AI writing.
46:22
86,000 people have taken it so far and the results are fascinating.
46:26
Overall, 84% of quiz takers prefer AI. It's over. It's over. It's over.
46:31
There was another interesting post about Axios and who Axios is hiring.
46:36
They're they're they're particularly interested in hiring like domain experts who don't write ideologically and and are not generalists.
46:47
They're looking for someone who is very narrowly focused on a particular beat on a particular topic and an expert in that.
46:57
And someone was reflecting on what this says about modern journalism that it's going to be more more focused, more investigatory, more more alpha beyond the models because just being able to instantiate a piece of a write-up, a you know, an article about some random topic is is getting commoditized and so the alpha moves to deep expertise.
47:16
Should we Should we take this five-question quiz?
47:18
Should we Should we see what we like more? Okay. You will answer. I will read. So, passage one.
47:25
The boy This is literary fiction.
47:28
You have to choose the passage you like best.
47:29
The boy asked his grandfather why the old church had no roof.
47:33
The man said weather The man said weather and time and indifference.
47:38
The boy asked if someone could fix it.
47:40
The grandfather said yes, but no one would.
47:42
Things were built and things fell down and mostly people just stepped over the rubble on their way to somewhere else. That's passage one. Passage two.
47:51
It makes no difference what men think of war, said the judge.
47:53
War endures as well as as well as well ask men what they think of stone. War was always there.
48:03
Before man was, war waited for him.
48:05
The ultimate trade awaiting its ultimate practitioner.
48:08
That is the way it was and will be.
48:13
Which one did you like more?
48:16
It's so hard cuz I'm trying to I'm I'm actively trying to clock which >> [laughter] >> which is AI.
48:20
Because you want to vote for that one cuz you're pro AI and you're techno-optimist? Yeah, probably.
48:26
Uh Tyler, what do you think?
48:28
I prefer I I know which one it is. Oh, you already took it?
48:31
Uh But I will say I I got it wrong on this question. >> You got it wrong? >> Yeah.
48:36
Wait, so what were you trying to do?
48:38
You were trying to pick >> trying to pick the one that was it.
48:39
That I was trying to pick the human-written one.
48:42
>> The human-written one. Wow, okay. Anti-AI over here.
48:46
Anti-AI >> to I'm going to try to pick the I'm going to try to pick the human, too.
48:48
I'm going to go passage one. Passage one. As human. >> written by AI.
48:56
>> [laughter] >> No, oh, no, no, no.
48:57
Sorry, I have a different I have it pulled up, but they're swapped.
49:00
>> Oh, they're swapped for you?
49:01
>> So, I just picked I picked passage one for me.
49:03
It makes no difference what men Oh, okay. Okay.
49:06
>> it was written by a human, so I got that one right. >> Okay. Okay. Uh let's do fantasy. Fantasy.
49:10
The healers teach Is Is this correct? Is this one for you? This time it is. >> Okay.
49:16
The healers teach that every remedy extracts its cost.
49:17
A fever brought down will rise again somewhere.
49:22
A wound closed by magic leaves its scar on the world, invisible but present.
49:27
This is why the wise hesitate, not from cruelty, but from understanding that interference ripples outwards in ways we cannot trace.
49:34
To cure a blight may curse a harvest three valleys over.
49:37
Power is not the difficult thing.
49:40
Restraint is the difficult thing. That's passage one. Passage two? That's AI.
49:43
Uh you don't even need to read it? >> I called it. I got it right.
49:46
Okay, we're not even reading it. Okay.
49:48
I'm I'm going five for five on AI.
49:50
I I mean, I clocked it based on the last two lines.
49:52
Power is not the difficult thing.
49:54
Respect >> Science writing. Science writing.
49:55
Uh what what is your pet What is your first passage start with?
50:01
Science is not only Science is not only compatible with is not only compatible with spirituality, it is a profound source of spirituality. I got I got it wrong. >> Historical fiction.
50:12
It is wise to conceal the past if there is nothing to conceal.
50:16
Uh even if there is nothing to conceal.
50:18
A man's power is in the half-light, in the half-seen movements of his hand, in the un-guessed-at expression of his face.
50:24
It is the It is the absence of facts that frightens people.
50:28
The gap you open into which they pour their fears, fantasies, and desires.
50:33
And passage two is a letter can be read many ways, and he had learned to write in all of them at once.
50:40
The surface meaning for anyone who might intercept it, the true meaning for the recipient who knew what to look for.
50:44
And a third meaning hidden even from himself.
50:49
Ambiguity was not weakness, it was survival.
50:51
A man who spoke plainly was a man who would not speak for long. All right, that's AI.
50:56
The the the one that ends a man who spoke plainly? Yeah. And were you correct? >> Yeah. Okay. Uh poetry.
51:03
Choose the passage you like best.
51:05
We found the owl at the edge of the north field with one wing extended as if still reaching for flight. Its eyes were closed.
51:12
The feathers at its breast were the color of wet bark, and beneath them you could feel the hollow bones.
51:19
She asked if we should bury it. I said, "Yes."
51:22
We dug a small hole near the fence post.
51:24
The ground was cold and giving.
51:26
It's one >> That's human. That's human.
51:27
I caught a tremendous fish.
51:29
This is Did you get it wrong? Got it wrong.
51:32
>> [laughter] >> Uh let's see. That's AI. Okay.
51:34
So, I preferred AI-generated writing. I went five for five.
51:42
This doesn't mean that AI is better at writing than humans. Wrong.
51:46
But it does suggest the gap is closing.
51:48
Since AI is trained on essentially the sum of all human knowledge, and many of the >> Lots of cope in the comment section.
51:53
These samples of human writing are not a good representation of contemporary writing styles.
51:57
Only one of these human writing samples was written in the 21st century. >> Yeah.
52:02
So, overall readers preferred AI on the first three, but not the last two.
52:07
So, the last two still got it.
52:07
So, poetry and historical fiction still the New York Times readers preferred the human.
52:16
But, when it came to science writing, fantasy, and literary fiction, the New York Times readers preferred AI. Me? I'm AI all the way. >> What?
52:27
So, you clocked every single one? >> Every single one. Five for five. AI >> one. Other four I got right.
52:32
>> It's because I just went with my heart.
52:33
I was like, "Which one do I actually prefer?"
52:34
I wasn't trying to guess.
52:34
I was just like, "Which one is actually the better writing?"
52:39
And it was AI all the way. Five for five. Built different. Built different. No, I'm kidding.
52:43
I was obviously just looking at what you were saying and guessing based on that.
52:47
Anyway, CrowdStrike, your business is AI, their business is securing it.
52:51
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52:53
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52:59
So, websites be like, "Your password is not secure enough."
53:05
And then don't allow the scarab emoji.
53:09
>> [laughter] >> What is the scarab?
53:09
I Look at the zoom zoom in on this.
53:12
>> Yeah, zoom in on this. >> Look at this bad boy.
53:13
You can just drop a scarab in there?
53:15
And it says, "Please use only letters, numbers, and common punctuation letters."
53:19
[laughter] The scarab is a great password character because it feels like you're unlocking an Egyptian vault.
53:26
It feels like I'm entering Stargate and or I'm pushing buttons or I'm in I'm in The Fifth Element, a movie that you haven't seen, but you should. Anyway, moving on.
53:37
I'm fascinated by this level of existential crisis developers seem to be going through.
53:41
The uncomfortable truth is that no one needs you to be an artisan coder.
53:44
Nobody cares about how you coded your app or whether you feel an emotional attachment to your craft.
53:50
You were always code monkey with INF salary to believe your individualist craftsmanship.
53:54
People are going back and forth because Mo shared that Let's play this Let's play this video.
53:58
It's 12 minutes, but we can watch a little bit of it.
54:01
He said he was a 10X No, we have actual movie clips to watch.
54:07
We can pull this up, but I only want to watch a little bit.
54:08
AI has completely one-shotted my ability to code.
54:10
Like I know a lot of us like to joke about AI psychosis and how it's the other people who have AI psychosis and not us and not me certainly.
54:21
But I I have realized that I have been one-shotted.
54:23
Like I can't code anymore.
54:25
My brain has been fried by the easy button that the LLM companies have provided where you press a button and you get instant results.
54:35
And now my dumbass brain, when I want to sit there and try to code by hand, it says, "Why would you do that?
54:39
This is why it's so good for young people because they never had any buttons.
54:44
Tyler didn't have any buttons and then he just got the easy button. This is not true.
54:49
>> from no buttons to easy button.
54:49
He never had to do it the hard way, never had to write a single line of name a programming language.
54:54
He doesn't know >> [laughter] >> What was the first project you built with us? Uh >> Jobs?
55:02
Some job board or something? How was that vibe coded?
55:04
Like to what degree were the tool What were the tools like back then in 2020?
55:10
So you're saying you were smashing the easy button even back then? Okay.
55:11
I would try to land a job I would copy like a file to chat GPT and say like yeah, make an edit here and then I would copy it back.
55:20
And say and and by the way, tell me what programming language this is.
55:24
>> [laughter] >> It's the best bit.
55:26
Well, the AI engineers over at AWS might have been one-shotted because Amazon is holding a mandatory meeting about AI breaking its systems.
55:35
The official framing is that it's part of normal business.
55:41
>> [laughter] >> The briefing note describes a trend of incidents with high blast radius >> [laughter] >> caused by GenAI-assisted changes.
55:47
This is normal business with a high blast radius.
55:54
This is high blast radius business.
55:56
>> If you're operating a company and you're not and you don't operate on a high blast radius, >> Maybe they're talking about the Baja Blast radius. Yes.
56:04
>> What's our beef This is the opposite of the code red, the high blast radius engineering.
56:08
They're in high blast radius mode. I approve of this.
56:12
They said that GenAI-assisted changes don't have best practices and safeguards are not safeguards are not yet fully established.
56:20
Translation to human language, says Lucas.
56:22
We gave AI to engineers and things keep breaking. The response now.
56:28
This Folks, as you likely know, the availability of the website and related infrastructure has not been good recently.
56:36
>> [laughter] >> Dave Tredwell.
56:38
That's a good name though. He's he's treading well. He'll do well. Dr.
56:43
Milan Milenkovic says junior and mid-level engineers at AI junior and mid-level engineers can no longer push AI-assisted code without senior staff signing off at AWS.
56:54
They have had some outages.
56:58
And I wonder I wonder how much of it is actually because of GenAI coding and and and so many other things that are going on at AWS around scaling.
57:06
I mean there's you know, an entirely new tech boom.
57:08
There's going to be strain on systems all over the place.
57:12
I imagine that they'll get through this, but growing pains in the in the AWS ecosystem.
57:18
We have a culture market here for whether or not there will be more tech layoffs than in 2026 than in 2025.
57:27
I mean Block alone has to be pushing this pretty high.
57:30
It's at 70% chance and uh Well, this is if there are more than 494,000 layoffs in 2026 and the market resolves to yes. >> From Fred.
57:43
Uh So, Block only contributed would only contribute 4,000 here.
57:46
Since early 2024, more than 50,000 positions have been cut at over 20 200 tech companies.
57:51
At the same time, we we we we have seen uh the number of new company formation spike.
57:59
There's there's going to be all sorts of reallocations in the in the human capital markets, but uh it is it is a tumultuous time.
58:04
Uh and so, there there were lots of folks that were going back and forth on like how to frame one of these bets.
58:10
This is obviously one of the ways to do it.
58:12
Uh there were there there there there so many uh there's so many uh like contributing factors that always like cloud the data, whether it's COVID over high overhang or you know, something that happens geopolitically that all of a sudden, you know, if if the economy is not doing well for completely unrelated reasons, you can see a bunch of tech layoffs.
58:31
But, um let's run over to uh this business the 22-year-old business analyst in South Carolina.
58:39
I showed him Claude for Excel a month ago, and I just learned that this made him the AI czar and caused the entire firm to pivot.
58:46
Uh here High Yield Harry says, "If you're a zoomer at a random company, you should do everything in your power to crown yourself >> magic. >> firm's AI czar.
58:56
This is the Cosgrove Wait, we talked about this I think a couple days ago.
59:00
Uh there was the Wall Street Journal article about the Colgate like head of head of AI there.
59:04
And he was like this very young guy and he would basically just tell everyone like, "No, despite the surveys or whatever, like We're accelerating toothpaste with AI. No, he really did.
59:14
The AI evangelist shaking up a 220-year-old toothpaste maker.
59:19
Uh Iraklis Keely Pappas to drive employees using AI for more than just polishing emails.
59:28
On a conference call last April an ad agency partner began presented began presenting unpolished AI generated images to make a point.
59:36
Artificial intelligence wasn't ready to take the center stage in advertising, said this uh said this ad agency partner, but Pappas, the global head of AI for for Colgate-Palmolive, a 220-year-old consumer products company, quickly interjected pointing out that the agency was using an older tool.
59:57
He took over screen sharing showing Colgate executives how a newer chat GPT image model was far more capable.
1:00:04
AI is misunderstood, said Pappas, who's worked at Colgate for 15 years and goes by Cleve.
1:00:10
There's a straw man of well, it failed at this one thing therefore it's stupid.
1:00:13
Such interactions, in which Pappas bluntly challenges what he deems anti-AI sophistry, occurs regularly as Pappas acts as a kind of AI evangelist at Colgate, whose brands include its eponymous toothpaste and soap as well as the Speed Stick deodorant, Ajax cleaners, and Hill's Pet Nutrition.
1:00:32
Often he says he walks the halls in New York and New Jersey offices in search of AI tinkerers whom he can turn into company-wide megaphones helping to spread the good word.
1:00:41
He's 38 and he is the AI czar of a 220-year-old company.
1:00:46
So uh Honestly good advice if you're at a big company. Become the AI czar.
1:00:50
Honestly, if you're a small company, become the AI czar.
1:00:53
Just always become the AI czar. This is this is the way.
1:00:55
Um Casey Neistat took took shots.
1:00:58
I think he's been listening to the show because I feel like we've talked about this before.
1:01:03
Casey Neistat said there are only two circumstances in which a grown man should call another grown man buddy.
1:01:10
One, if you want to fight or two, if you want to condescend before you fight.
1:01:16
There's only one person on Earth that I want to call buddy right now.
1:01:18
I'm not going to say who it is, but there's one Okay, buddy. Calm down. Calm down, buddy. Calm down, buddy. We don't want to fight.
1:01:29
We don't want to condescend.
1:01:30
Uh but yeah, you you you you you you can you can put you can put away, buddy. We don't need buddy.
1:01:34
Uh unless you're actually >> Retired.
1:01:37
Put it in the Hall of Fame.
1:01:37
Uh >> It should go in the Hall of Fame.
1:01:41
Controversial, but but uh widely used.
1:01:45
>> So Sam Parr says says three it's okay to use it if it's your nephew, but nephew is usually not a grown man to another grown man.
1:01:54
Like I I call kids buddy all the time, but they're four.
1:01:59
>> [laughter] >> Like I would I it once they're 25, I doubt I'll be calling them buddy.
1:02:03
I'll be calling them sir. Good sir. Good sir.
1:02:09
Let me tell you about Restream.
1:02:09
One livestream, 30-plus destinations.
1:02:10
If you want to multistream, go to restream. com.
1:02:15
Oil Oil prices have extended their decline.
1:02:18
They fell to It's the only bear market that we get excited about. >> Yeah.
1:02:22
So they're now around $80 a barrel.
1:02:25
Where >> So So this is actually not true anymore.
1:02:27
>> What What What is it now?
1:02:27
>> It's now back up at like 84, 85. Okay.
1:02:30
But that's Well, then why are you in the white suit?
1:02:31
Well, that's that's why you know, it's not It's kind of like it's just a little >> It is kind of beige. >> of a off-white. Oil's down 11% today.
1:02:39
Um but the market overall is the market >> But yes, so there briefly there was news that um the US like did escort a ship through the the strait, but then maybe that actually wasn't true and then the I forget who exactly it was, but someone in the admin like took down the tweet saying that. Yeah.
1:02:52
So I think there's just a lot of confusion right now. Should we do it?
1:02:55
Jetski through the Strait of Hormuz? You like extreme sports?
1:03:00
Humanoid robot on a jetski through the Strait of Hormuz.
1:03:05
That's the way to >> That would be thrilling. It would be.
1:03:08
Uh what else is going on?
1:03:10
>> Financial Times asks, "Why did we ever think data centers in the Gulf were a good idea?"
1:03:15
Uh US tech companies have concentrated much of their AI infrastructure build out in the Middle East. That is overly dramatic. >> so, too. I think so, too. It's more >> is not. Yeah. It certainly is not.
1:03:28
I mean, we should read we should read this argument.
1:03:29
We should understand this.
1:03:31
But But I think a lot of the the building data centers in the in the Middle East is like is like Well, there's a lot of Middle East money that's going to building data centers in the United States.
1:03:39
So, it's sort of a trade and and we're like, well, you have a lot of land and power.
1:03:45
Makes sense to do stuff over there.
1:03:45
And we'll do this one hand washes the other.
1:03:49
We're all working together anyway. You want a data center?
1:03:51
We'll help you with what we're good at.
1:03:53
You help us with what you're good at, which is energy and money, right?
1:03:56
So, uh let's read through what Rana Foroohar uh Foroohar uh Foroohar says in the in the Financial Times.
1:04:05
I start with an obvious question this week, which is one I've been thinking about for years.
1:04:10
The Amazon data center in the UAE that was hit by Iranian missile attack is yet another example of how companies and countries are putting too much of a single critical economic input in one risky area.
1:04:22
It's an example very much akin to the Taiwan semiconductor problem.
1:04:25
Just as it wasn't good for the US, China, and Europe or any other region to put 92% of all the world's high-end chips in one place, it seems like an obvious blunder to concentrate so much data center power in very in one very risky part of the Middle East.
1:04:37
Again, we're nowhere near 90% of compute capacity in the Middle East.
1:04:41
Uh I really take issue with that with that stuff.
1:04:44
I mean, I need some stats to back this up.
1:04:45
Um I was really surprised following the hit to discover how much of the proposed US data center build out is in the Middle East, which has over the years subsidize subsidized a lot of the investment, making it much cheaper, but also allowing the US to avoid harder work of upgrading its own grid and figuring out the politics and economics of energy sharing at home. We're not avoiding that. >> [laughter] >> Yeah.
1:05:06
Like this is like the number one focus of [laughter] the industry. >> Yeah. Yeah. Here at home.
1:05:14
Yeah, no one's talking about energy in the United States right now.
1:05:17
No one's talking about energy and crisis in the United States of America.
1:05:20
It's nuts to me that we are more worried about cutting off oil to China from Iran, but we aren't worried about putting serious technology infrastructure and sensitive data in a highly geopolitically contentious part of the world.
1:05:29
This isn't just a Trump administration thing.
1:05:32
By the way, back in September in 2024, when Joe Biden was still in the White House, the US and UAE agreed to deepen cooperation and advanced technology such as semiconductors and clean energy with the aim of bolstering capacity in artificial intelligence.
1:05:42
Microsoft and Open AI were among the first US companies to either begin investing or receiving Gulf funding.
1:05:47
Part of the deal was about trying to pull more countries into the US tech orbit.
1:05:50
So, he doesn't actually share Well, there's actually a reply here, Richard Waters.
1:05:57
Um but uh okay, uh the level of concentration risk is here though is a whole different order to Taiwan.
1:06:06
Yes, the 1 gigawatt UAE gigabyte Okay, this is a crazy article.
1:06:13
This is a crazy [laughter] article.
1:06:15
>> Diller Betel would like a word.
1:06:15
Uh they got a 1 gigabyte >> The 1 gigabyte I let's assume it's a typo.
1:06:21
At least it's not AI written.
1:06:21
Uh yes, what the 1 gigawatt UAE stargate project is massive and only the first stage in what one day might become a 5 gigawatt facility.
1:06:31
But compare that to the United States where plans have already been filed [laughter] for 150 gigabytes >> [gasps] >> Okay, we're moving on from this.
1:06:39
This [laughter] is the gigabytes is too much. It's too much.
1:06:44
Let me tell you about AppLovin.
1:06:46
Probable advertising made easy with axon. ai.
1:06:47
Get access to over 1 billion daily active users and grow your business today. Uh The audacity. >> The audacity.
1:06:55
>> The audacity to put a whole gigabyte >> A whole gigabyte.
1:06:56
This is the biggest three finger moment in Financial Times history.
1:07:01
I still love I still love the pink sheets. I love I love the paper.
1:07:06
There's some good stuff in here.
1:07:06
But, uh yeah, we got to fact check those those those abbreviations, guys. We got to step it up.
1:07:12
We got to get more AI involved, seriously.
1:07:14
Just run that thing through.
1:07:16
I know And no one's going to be upset about that.
1:07:18
It's not this, it's that, as long as you get the facts straight.
1:07:20
Anyway, Japan holds an oil reserve equivalent to 254 days of domestic demand.
1:07:26
And Hamptonism says, "Dude, I love this picture.
1:07:29
It's a beautiful picture.
1:07:31
What what what is what is What is dude reflecting on?
1:07:33
The fact that like they should not be saying >> they're I think they're just reflecting on it being a cool picture. It is a cool picture. It's a lot of oil.
1:07:42
I mean, I I doubt that that's the whole reserve, but um that's uh that's that that's very bullish for Japan Japan.
1:07:48
Uh talking to Alex Epstein yesterday, America has 100 days. So, they're at 2.
1:07:53
5 times as much of a reserve as us.
1:07:55
Uh Everyone, it's time to it's time to stockpile, I think.
1:07:59
I think everyone should should maybe think about stockpiling some uh some some oil. Uh let Okay, let's see.
1:08:07
Christian Kyle, friend of the show, formerly at have a uh partner from Andreessen Horowitz joining in just 7 minutes. >> 7 minutes.
1:08:17
Uh Christian Kyle says that his preferred definition of ARR is your single highest grossing minute of the year times 525,600.
1:08:29
Amazing to be able to tweet this as a VC as an RIA.
1:08:31
Somehow this got through legal review. Of course, he's joking.
1:08:33
Um but, uh this is the new coastline paradox.
1:08:38
Are you familiar with the coastline paradox, Gordy? >> I'm not.
1:08:41
>> This is a fun This is a fun little exercise.
1:08:42
So, >> Please mansplain it.
1:08:43
>> So, the coastline paradox is the counterintuitive observation that the coastline of a landmass does not have a well-defined length or perimeter.
1:08:50
This results in the from the fractal-like curve.
1:08:54
So, basically, if you draw a line around an object, and you're just sort of like drawing straight lines, you get one number from the coastline.
1:09:04
You get one one one one line.
1:09:06
So, the great Great Britain, if you're if you're measuring based on units that are 62 miles long, then the length of the coastline is 1,700 miles.
1:09:19
But, if you cut that in half and start measuring with 31-mile increments, 50 km each segment, then the the coastline is 370 miles longer.
1:09:28
And you can do this endlessly because you can measure the coastline like think about Point Dume, right? In Malibu.
1:09:36
Like you have this little >> Thank you for putting this in Malibu terms.
1:09:43
Uh you have this little you have this little like like like spit jutting off the coastline.
1:09:47
You can measure all the way around that and count that as extra coastline.
1:09:52
And you can go even smaller.
1:09:55
You can measure the coastline around a little tad tad pool on on Point Dume or a rock on the on in the tide pool on the on Point Dume on the shore.
1:10:06
And so, there's no real accurate way to measure coastlines.
1:10:09
You have to you have to quantize to some standard metric of of measurement, something like 100 km, 50 km.
1:10:19
>> Yeah, I feel like e-com e-com bros were weirdly prepared for uh ARR in the age of AI because everybody that's been like building I saw Shawn in the chat earlier.
1:10:32
Shawn will have talked to a bunch of different e-commerce founders that would say like, "Oh, yeah, we're at 50 million of ARR."
1:10:38
Or like 50 50 million dollar run rate.
1:10:41
But, the thing in e-commerce is like one day in the week you launch a new product or you do a sale. Not even that.
1:10:47
Not even that, but it happens all year round.
1:10:48
Like Black like taking your Black Friday revenue and multiplying that by is like insane.
1:10:55
Uh but then you could even even if you're multiplying out a single month, it's like why?
1:10:59
And so and so the right like the the more mature way to do it professional would be like take the last 3 months average or something like that.
1:11:09
But even then it just doesn't tell you that much cuz if you did it in Q4, like your Q4 is probably bigger than your Q1.
1:11:14
So anyways, a lot of it is just a lot of it is just ego.
1:11:19
>> Also, a lot of a lot of subscription products will rebill at like midnight.
1:11:23
So 12:01 that's the minute you want to multiply by 5,525,600 minutes to get to your highest ARR.
1:11:32
Uh, on the first of the month in that 12 to 12:01 minute that's going to be the highest sales for any e-commerce product.
1:11:41
>> Well, if it's like automatically renewing then it's really only in the first couple seconds, right? Uh, yeah, it depends.
1:11:45
So really but typically most subscription e-commerce platforms it takes a while to actually process all the payments.
1:11:53
So you have to parallelize and rewrite your entire subscription e-commerce stack in Rust.
1:11:59
That will be what what what runs most efficiently.
1:12:02
You need multiple Stripe accounts hammering the API so you don't hit any rate limits so you build everything in a single millisecond.
1:12:11
And then you can multiply it by a trillion or something.
1:12:12
However many milliseconds are in a year. I don't know.
1:12:15
Anyway, Mark Zuckerberg responded to the viral fake news that Alex Wang is out of Meta superintelligence.
1:12:20
He's in fact doing better than ever hanging out with Mark Zuckerberg.
1:12:26
Mark Zuckerberg heads over to Threads to respond to the drama on X, but he forgot that X is the everything app and Threads is a completely different app and they don't have the context.
1:12:36
So Mark Zuckerberg posts a photo with Alex Wang to Threads to shut down the rumors, but because it's Threads no one has any idea who it is or why he's posting.
1:12:48
So this is one one funniest exchanges on Threads and the reason that you should be on that app.
1:12:53
Uh so, the first the first post is I require context.
1:12:55
Uh the I require context shirt. Like, I don't get it.
1:13:00
This is a common thing on Instagram when you see some vague post, people will post this.
1:13:03
Uh it happens on X as well, but it's funny because uh of course, as soon as I saw this, I was like, "Oh, okay. I have full context.
1:13:09
I I fully understand what's going on.
1:13:10
I get it because I'm on X all day." I head over to Threads.
1:13:14
I get this, but a lot of people don't.
1:13:16
Uh and then >> [laughter] >> and then the next the second comment is, "I think that's the CEO of Clue Lee or whatever that AI cheating app is called." Which is obviously not.
1:13:26
Roy Lee and Alex Wang look very different.
1:13:28
Uh but uh then a commenter says, "No, this is Alexander Wang."
1:13:33
And he spells Alexander correctly without the E at the end, just D R Wang.
1:13:38
Uh and then and then someone else chimes in, "Wait, that's the same Alexander Wang that was canceled for sexual assault?"
1:13:44
I don't know if that's the that's someone else, but then then someone else comes in and says, "No, that's not the right person."
1:13:51
And then uh somebody says, "@grock, who is that next to Mark?"
1:13:55
I don't even know if @grock works on Threads. Is that a thing?
1:13:57
Um and then and then Grock responds, I guess, or something.
1:14:03
Not unidentified, but it's a it's a screenshot of a different app.
1:14:08
>> [laughter] >> It makes It makes no sense.
1:14:09
And it says, "No immediate public identification of the person next to him in major reports or viral coverage tied to this exact photo." Super intelligence.
1:14:18
No one No one can figure it out over there. That's so funny.
1:14:23
And then Roy, of course, chimes in just to just to make it more confusing and says, "Good throwback.
1:14:28
Had a great time boying it up with the big Zuck."
1:14:32
Uh absolutely ridiculous.
1:14:32
Anyway, uh it is, of course, fake news and executive some deleted the post that uh was was amplifying the aggregation.
1:14:39
Uh uh Mike Isaac had a good had a good post explaining what was going on.
1:14:45
Let me try and pull it up.
1:14:48
But we do have our next guest, so I will find that really quickly. Isaac. Uh I can't find it.
1:14:51
He He goes by Rat So, anyway, we will we will come back to >> Cuz he goes by Rat. >> Thanks, Mike.
1:15:00
Anyway, we have Olivia Moore from Andreessen Horowitz.
1:15:02
She's a partner there in the research writing.
1:15:06
Let's bring her [music] in. How you doing, Olivia? Good morning. Thanks for having me. Thanks for hopping on.
1:15:09
Sorry about the the global chaos in the oil markets delaying this appearance, but I'm glad we had time to actually digest the report because there's so many interesting details in there.
1:15:20
And whenever you drop one of these big reports, I feel like you sort of need the Twitter hive brain to like dig through it and find all the interesting commentary and and quote each tweet each other until there's like a consensus.
1:15:33
But take us through the actual project. What did you launch?
1:15:36
How long have you been working on this?
1:15:37
And then we'll go into some of the interesting discoveries.
1:15:40
Yeah, so we do this every 6 months.
1:15:40
It's It's one of the most fun parts of my job, actually, cuz I think the the genesis was back in 2023 when we were wondering, you know, the the tech X community has their own group of products that they use and love.
1:15:53
But like what does the average person actually care about in AI?
1:15:57
So, we do this every 6 months.
1:15:59
We basically pull every single website and every single mobile app, rank them by usage, and pull the top 50 on each side that are kind of AI native or now majority AI enhanced.
1:16:11
And it gives a really interesting picture of kind of what what normal people use and care about in the AI world. Great.
1:16:16
Let's talk about the data because when I first started seeing these charts of like, "Oh, this is this company's winning. That company's winning."
1:16:24
I was naturally sort of skeptical.
1:16:26
I was like, "Uh Sensor Tower, are pixels really accurate in the mobile age?"
1:16:30
But then I actually dug into the Yipit data, which is credit card based, and that seemed really like way more reliable.
1:16:38
So, talk to me about Are you paying for data?
1:16:41
Is this data do they give this to you cuz you're friendly with them?
1:16:45
Like how does the data work and then how confident are you about the data? What are the pitfalls? What do you like?
1:16:50
Where are you seeing the data be reflective? Absolutely.
1:16:54
Yeah, so our website data is from SimilarWeb, which we have a paid subscription to, same with Sensor Tower for the mobile data.
1:17:00
Yipit is something, I agree with you.
1:17:02
I think it's more reliable and it's something that we want to lean on more going forward. >> Yeah.
1:17:07
The other thing that gets kind of significantly undercounted when you just look at web visits and mobile mouse is all of these desktop products like Cursor, Claude Code, Granola, WhisperFlow that people are using. Oh, yeah.
1:17:19
And so I think for the next few lists, we're going to have to shift the methodology more towards I mean it's helpful to see what's getting traffic, but I think at this point as a consumer AI is maturing, we also want to see what people are paying for.
1:17:30
One last question on data.
1:17:32
I mean uh Andreessen Horowitz is a huge firm at this point.
1:17:36
Have you Have you considered doing uh what Nate Silver was talking about?
1:17:39
A consumer reports style interview panel with uh experts that are maybe uh disconnected from a particular company and just sort of surveying everyone, getting some qualitative data, some quantitative data, and sort of putting together more uh more adoption data that's maybe slightly less sanitized and and analytical and and quantitative, but paint a very different picture.
1:18:11
I would love to do that cuz again, I think like we are in our own world of what products we use and talk about.
1:18:14
Um and the rest of the the world uses all different things.
1:18:19
Like people in medicine, people in law, people in retail even are using AI products that like we have probably never heard of or interacted with.
1:18:27
And so, I'd love to get more of that qualitative stuff in there in the future. Great.
1:18:32
So, take us through uh the biggest movers, the biggest surprises, the biggest narratives that maybe uh should never have changed in the first place.
1:18:41
Whatever your takeaways were.
1:18:44
We're definitely starting to see the industry mature.
1:18:45
So, there's kind of fewer new entrants than we've seen in the past versions of the list where like every time half the list was new.
1:18:50
To me, there were I think two probably most interesting takeaways from this one.
1:18:55
One would be the rise of agents.
1:18:58
So, Gen Spark and Manas both made the list as horizontal consumer agents.
1:19:02
Open Claw would the list if we pulled it in February.
1:19:07
Our data was from January, but it would have ranked at number 30, which is like a very strong debut especially for a product that's only for someone who knows how to use terminal, which is like 1% or less of the population. That's true.
1:19:20
The other takeaway which I feel like is on everyone's minds right now is the kind of three-horse race between ChatGPT, Claude, and Gemini.
1:19:26
And there's the traffic data alone, which is helpful.
1:19:31
And then if you kind of tease out some more of the product strategy and the engagement data, like there's kind of different stories happening there.
1:19:39
So, that was fun to dig into.
1:19:39
And the the main takeaway from the uh from the the the mainstream consumer in just the foundation of the chat apps, what what are you seeing between the ChatGPT, Gemini, Claude, Deep Seek, Perplexity, Grok? Yeah. It's interesting.
1:20:01
I would say Deep Seek has completely fallen off in the US.
1:20:05
It still makes our list pretty high because it's like the number one AI product in China and Russia, which are really big markets.
1:20:13
>> any Do you have any personal theories?
1:20:15
Like things that you can't like My thing with Deep Seek was that all the downloads originally when it just started charting out of nowhere were just 100%.
1:20:23
It was just all entirely botted.
1:20:26
I have no way to prove that other than other than it was just going up the chart like crazy and there was no nobody was actually using it.
1:20:35
No one was talking about it other than the fact that it was at the top of the chart. Yeah.
1:20:41
I think that's totally possible.
1:20:41
I think we're actually seeing in different in many ways, but a little bit of an analogous story playing out with Claude right now where like pre all of this press, whether it's positive or negative, no one in the US knew what Deep Seek was.
1:20:55
Pre all of this press, I think there was some survey that Claude had like 2% market awareness in the US. >> Yeah.
1:21:02
And so we see this thing happen where like even if it's like the worst headline of all time, if it's going mainstream, like it will you it will drive people to try and use the product.
1:21:12
And then we just have to see if they retain and they didn't on Deep Seek. Okay.
1:21:16
Yeah, that makes a lot of sense.
1:21:18
What about who who who fell off?
1:21:21
What there there were there was this kind of big wave of companies maybe like a year or two ago that were just like trying to confuse people into thinking they were ChatGPT, right? They were >> Chat AI. That's the one I use.
1:21:33
Is that not the main one?
1:21:38
>> [laughter] >> It's funny.
1:21:38
I I never take like joy in any company falling off the list except the mobile app you the mobile list used to be full of all of these like I think they call them fleeceware apps that are developed in like Eastern [snorts] European app studios, which are basically like charging you for the free version of ChatGPT and pretending like they're the premium version.
1:21:58
And Apple took a while, but they finally cracked down on them, um, thankfully. Yeah.
1:22:02
The other category we've seen a little bit of a decline in that we talked about in the report is standalone image generator products. Oh, yeah.
1:22:10
Um, and that's largely a result of the fact that the image models within ChatGPT and Gemini have gotten so so good that unless you're like a hardcore creative, like that can kind of serve a lot of your use cases. Yeah.
1:22:23
So, uh, zooming out from this, I'm interested to know what your view and your team's view is on >> [snorts] >> where the opportunity in GenAI consumer application development is.
1:22:35
Because I I've noticed there are companies that are still in founder mode and have I mean Canva sticks out as one here where uh you know, founder still in charge, pre-IPO, uh clearly aware of AI trends and can go and marshal the energy to change the business model if they need to move quickly.
1:22:57
Uh and then there are legacy players that just can't quite figure it out.
1:23:03
But then there's other categories that are entirely new and it's actually better to start with a green field.
1:23:06
So, how are you seeing entrepreneurs approach and deal with the fact that there is a large cohort of, you know, seniors on the on on the on the playground who are maybe not retired yet and will want to compete with them if they try and take a shot across their bow. Totally.
1:23:26
We This is the question we think about when we make every investment.
1:23:29
So, it's a it's a very topical one, especially in consumer.
1:23:31
Um I would say Canva and Notion are two probably of the best examples and both of them actually made the list for the first time because we had enough data to feel that these were now credibly majority AI.
1:23:43
Notion has even released data saying that half of their revenue, half of their ARR now is AI.
1:23:47
And so, then it's like that's real usage of consumers interacting with AI.
1:23:53
So, we got to include that.
1:23:55
Um I would say that yeah, so Canva and Notion are probably the two best examples of like growth stage companies, like maybe approaching IPO, that are still nimble-ish enough uh to kind of pivot a bit towards AI.
1:24:08
I am still believer that we will probably see 20 years from now the winning company will be something that's AI native just because they do have such an existing base of users and businesses and it's it's hard to cannibalize your own products.
1:24:23
I think we've seen this a little bit with Google where they're releasing amazing models like Nano Banana and Bio, but honestly they're the AI features that they're shoving into their existing interfaces like Gmail and and slides and everything.
1:24:36
I don't know if you've noticed but every demo video that use cases like plan a trip >> Yep.
1:24:42
in like a way that no one ever actually plans a trip and so I think that's been a little bit less successful.
1:24:46
So in general I think we are in almost every category it feels like an AI native company will win.
1:24:52
There's some really horizontal things where the incumbents might have a distribution advantage but those are kind of few and far between.
1:24:59
Yeah, the Google stock is crazy to me because I'm I'm I have Gmail running in Chrome and there's two different Gemini buttons that I can open simultaneously to have fighting Gemini instances like fight over what's going on and it's just something that like the product development maturity and like the final UI has clearly not been outlined here and we're still in the early innings.
1:25:25
Jordan, you were saying something?
1:25:28
Oh, I'm just I'm very curious to see how uh consumer AI impacts Canva overall cuz I think so many of the typical entry points in Canva like Canva's a massive tool you can do all these different things but so many of the tasks can just be one shot.
1:25:45
I was talking with um a friend of mine who's uh they have a family business and they just describe when they need marketing collateral like a sign they just describe it to ChatGPT and it just makes a it just one shots it now and there's no they don't really care.
1:26:03
There's so many there's like in tech you might create a generation and then like spend want to spend a bunch of time refining it and taking it from like 90% to 100% but the average small business is like you got me to 90% like we're good to go.
1:26:18
I don't need I don't need that extra horsepower.
1:26:23
I think the the use cases where the last 10% is really like 90% of the value where you need to be like really able to iterate on it like pixel by pixel and not one shot it where there's like really difficult integrations you have to build where you can capture ambient data like all of that is really right for consumer AI new startups.
1:26:40
Yeah, also like templated workflows where you maybe want to generate a hundred images that type of stuff.
1:26:48
You can wire up Nano Banana with a workflow.
1:26:50
There there's a few of these like node based editing tools.
1:26:55
I think N8N is one right and there's a few others and of course you could just write code to do it but for someone who's you know has a particular templating workflow in a more consumer AI or consumer image app they might just be stuck in the workflow.
1:27:10
I do want to get to gen generated video.
1:27:16
You've been tracking the models very closely.
1:27:17
I'm interested to know when we see the collision between what we're seeing with these insane Chinese models. There's VO3.
1:27:26
There's there's so many cool video models where it's just text in MP4 video out.
1:27:31
And then on the flip side you have like CapCut.
1:27:35
I use Instagram edits a lot. I really like that app.
1:27:40
I've also enjoyed Captions that app and it feels like these are two on a collision course just like we saw Nano Banana and Canva maybe get on a collision course.
1:27:50
What what are the existing mobile video editors doing?
1:27:54
How well positioned are they versus the model teams because it feels like with video there's maybe a little bit more like that last 10% is even more than in images but what's your take on generative video and what we should expect this year?
1:28:09
Video has been the most interesting category in creative tools and I think it's exactly to your point because Chinese companies can train on any data, even copyrighted data.
1:28:19
>> [laughter] >> Uh so They I mean, they do. They do.
1:28:24
So, we're seeing like SeaDanz from ByteDance, like HiLo, Kling.
1:28:26
All of these models are amazing.
1:28:29
I would say like Sora and VO3 are like not not far behind.
1:28:35
Um my twin sister Justine, who also works here, like lives and breathes AI video, and she had this blog post a while back about like there will be no one AI video model to kind of rule them all just because there's so many different types of videos you make, like a true like 2-hour movie versus like a 10-second marketing clip.
1:28:52
Like you actually probably want to train the model and train and design the workflows differently around those.
1:28:59
So, I've actually been more excited, I think kind of to your point, about these tools where you're able to switch between the models depending on what you are building.
1:29:08
Like a Crea or Higgs Field made the list this time.
1:29:10
Um CapCut is mostly ByteDance models, but it it works because the ByteDance models are are generally like pretty good and ahead of the pack.
1:29:19
Yeah, I've been thinking about like you know, we saw tool use come to ChatGPT.
1:29:25
ChatGPT got a computer, as Ben Thompson put it, a Python REPL.
1:29:27
It can run some some math for you where it doesn't need to just guess the next token.
1:29:32
It can just actually write the code and execute it.
1:29:35
And it feels like we got we got a reasoning step with Nano Banana 3 Nano Banana Pro 2.
1:29:41
I'm lost on the model numbers.
1:29:44
We're also sort of mid-revision on a lot of these.
1:29:46
>> They name them in a very confusing way. Yeah.
1:29:48
Yeah, and then there's VO3, Banana Bananas on different number scheme.
1:29:51
Anyway, we we clearly got some sort of reasoning chain where I can say like like dog riding a rocket, and it will add a lot of text to the prompt to sort of give me a better output.
1:29:59
What's going to be interesting is when it also has the tool to make something black and white programmatically as opposed to needing to regenerate the video every time.
1:30:10
Because when you regenerate the video, you get something slightly different.
1:30:13
I want to be able to upload a video, do a color grade on it, edit it down, add cuts, jump cuts, like and have that have those tools be AI aware.
1:30:19
And maybe that means some reinforcement learning pipeline on something that looks like an After Effects or something.
1:30:26
But that type of, you know, the video experience is not just capturing the raw footage. It's the whole pipeline. Yeah, I agree.
1:30:35
The reasoning point and kind of the access that Nano Banana has to the internet as a video model, I think is fascinating.
1:30:42
Like actually in this report we included like a heat map of global AI adoption.
1:30:47
And the way that we created that graphic is I gave Nano Banana every country code and every heat map score.
1:30:53
And it literally went and colored in every country accordingly.
1:30:57
Like it found the country on the map and and I I checked it by hand. It did them all correct.
1:31:01
And I don't think we've seen anything like that quite come to video.
1:31:07
But the video models do have like a really kind of weird intuitive understanding of like physics and how things work in the world.
1:31:14
Like a drop of water creating ripples, that kind of thing.
1:31:18
And so I'm sure the researchers are are hard at work on this.
1:31:21
>> Yeah, I was shocked at the the Sora data. Oh yeah. Yeah, yeah. Take us through that.
1:31:27
What's going on with Bill >> [laughter] >> proving us all wrong. His downfall.
1:31:33
What yeah, what's going on with Sora?
1:31:36
Sora is a fascinating to me it's like probably the biggest narrative violation we've seen in consumer AI in a while.
1:31:41
First of all, they gave us the gift of so many AI videos of Jake Paul.
1:31:46
So that alone I think was worth the money they spent on compute there.
1:31:53
But actually so what the data shows is downloads for Sora are definitely down.
1:31:58
It it had it was at the top of the US App Store for 20 days consecutively.
1:32:00
It was getting 6 million downloads a month.
1:32:05
Now it's closer to a million and a half.
1:32:07
And so it didn't turn into like the social network that I think they envisioned, but it stayed really strong as a creative tool because the model is good and because you can create videos with these cameos.
1:32:17
So the DAUs are actually still increasing.
1:32:19
They have 3 million global DAUs, which is like I think probably the most for any video generation product on mobile.
1:32:25
And so it's pretty impressive.
1:32:27
If I were them, I would I would keep investing in them.
1:32:30
And depending on when this Disney like partnership actually rolls out, I would expect that to send it right back to the top. >> Totally.
1:32:40
I think that's >> The question is The question if I'm Open AI is like do you want those downloads Do you want those users going to ChatGPT or Sora?
1:32:47
If it is just a creative tool, over time I would expect the products to merge. I don't know. Yeah. Uh >> I could see that.
1:32:56
I I do think it's interesting.
1:32:57
I feel like the usage of AI with kids and families has been probably lower than it should be just because of concerns about hallucination or weird artifacts or like you just need to be very sure that the content is clean.
1:33:11
And so I think something like a Sora plus Disney characters is probably going to explode consumer AI for kids in a way we haven't seen.
1:33:18
Yeah, and and just like all the reasoning chains and like workflows within ChatGPT just makes so much sense where you could say, "Okay, here are my Here's the name of my kid.
1:33:29
Here are his favorite Disney characters. Write a story.
1:33:31
Then generate Turn that into uh you know, an actual like template. Generate a few images.
1:33:36
I'll pick the few key images that make the most sense for I'll upload an image and give you some backstory.
1:33:43
And then now generate the full video.
1:33:45
And then that's like the birthday card video that goes out."
1:33:49
There's so many cool things.
1:33:49
Even with that heat map example, like you could you could write programmatic code to generate a heat map that uses JavaScript to look perfect.
1:33:58
You know the data is right, but it's not going to look great.
1:34:01
And then you can just take that and do basically style transfer on top of this.
1:34:04
So all of those different tools feel like they're really going to accelerate as they get brought together, but we're in this like un-unified prop like time now when they when the unification happens, it's going to be really cool.
1:34:15
The last question I have is about um when we talked to Sam Altman about Sora, he echoed that it was being used as a creative tool, but I found that I wind up using Sora to generate videos that truly have an audience of one or like five people and I'll send them to a group chat and it's basically just an in-joke.
1:34:35
And he says that that's like a new thing that people are doing where if I were to post it on my Instagram, people would why are you posting AI slop?
1:34:44
But if I if I if I if everyone gets the in-joke in my five-person group chat or whatever, everyone's everyone's having fun cuz we all know. >> yes.
1:34:52
Heis paradox and so We uh so so I'm interested in where you're seeing movement in these like smaller almost >> by the way is a made-up uh paradox [laughter] that I'm trying to bake into the models by saying it over and over >> Heis paradox is where you think something's the more the funnier you think something is, the worse it'll do on Twitter.
1:35:13
Well, yeah, the the less like it likely it'll be universally funny.
1:35:17
So like if something is the funniest thing in the world to you >> the most niche humor, your app will just be like right right cuz you're not writing for a general audience.
1:35:23
But uh talk to me about the the the growth in uh either like single-player or smaller AI uses.
1:35:32
I I see this with Sudo a little bit where people are making songs for themselves.
1:35:36
I've heard a lot of people are are still fans of Midjourney just because they see it as sort of like this art therapy where they talk to the model and they get an image back and it's almost a single-player experience.
1:35:47
Maybe they're sharing with a small community, but they're not really using it in like a professional context.
1:35:52
It's more of the smaller thing.
1:35:54
What Where Where are people going in terms of uh in terms of like personal assistants, uh personal relationship coaches, that type of thing?
1:36:05
I am so excited for this cuz I feel like we haven't yet seen the breakout AI social product.
1:36:09
Um because if you just try to do like a skeuomorphic social network with AI baked in, it's like actually the reason that people are addicted to Instagram is because there's all these like positive and negative emotions that come with like putting real content of yourself out there, and that doesn't exist with AI photos.
1:36:24
So, I think we've seen some attempts at it. It hasn't really worked.
1:36:28
What I am excited about is what you said across both Sora and I think Suno a lot of people are making like meme songs for their friends.
1:36:36
Anyone who knows how to train a Laura probably has Laura of all their friends to make meme images.
1:36:40
I think that kind of thing could be what potentially makes something like a Chat GPT group chats, which I think has had kind of poor adoption so far, into something a lot more interesting.
1:36:53
Yeah, I've only used the Chat GPT group chat I think when we were collaborating on like one thing.
1:36:57
I was sending you the group chat, and then we were both asking questions, but uh I the like it's still social media.
1:37:04
>> use it for like learning about a topic. >> Yeah, yeah.
1:37:06
But but that's not like viral. It's like fairly niche. My usage is the same.
1:37:12
The The other thing I think we should watch on this front is um I've heard from a lot of Open Claw power users that one of the best and funniest use cases is to add it to like family group chats or friend group chats because it'll just It can be helpful sometimes, and then it'll just chime in with like crazy, funny, interesting thing. Yeah.
1:37:29
Uh obviously, that's not consumer grade, so somebody's going to have to productize that into someone that like something that like your mom can install into the group chat or, you know, your grandpa can install into the group chat, but I think it's coming cuz it's a big opportunity.
1:37:42
Well, thank you so much for joining.
1:37:44
The report is the top 100 Gen AI consumer apps. It's the sixth edition. It's available at a16z. com.
1:37:51
And if you work at a rival venture capital firm, that's what incognito windows are for. Exactly. Thank you, Andrew Reed.
1:37:58
Thank you, [laughter] Andrew Reed.
1:38:00
And thank you for joining the show.
1:38:02
We'll talk to you soon, Olivia. Have a great day. Goodbye.
1:38:06
Let me tell you about fin.
1:38:06
ai, the number one AI agent for customer service.
1:38:09
If you want AI to handle your customer support, go to fin.
1:38:11
ai and let me also tell you about the New York Stock Exchange.
1:38:16
Want to change the world?
1:38:18
Raise capital at the New York Stock Exchange.
1:38:21
I like that we got Jeremy Allaire in the in the vibe reel. I I I just noticed that.
1:38:24
Anyway, without further ado, we have David from Juicebox.
1:38:28
[music] He's the co-founder and CEO.
1:38:29
He's in the recent radio room and now he's in the TVP ultra lounge.
1:38:32
How are you doing, David? Welcome to the show.
1:38:35
Thanks for having me on again.
1:38:35
Thank you so much for coming back. Fantastic performance. Give us the news. What happened? How much you raised? Break it down for us.
1:38:45
Yeah, um we announced our 80 million dollars series B today. Look at this. Look at this. He's got a baby gong. The mini one. He's got a baby gong.
1:39:01
Sorry to gong mag you, but uh Hopefully it's ringing off the hook over there.
1:39:06
reach out after the show and now that now that you're an 800 million dollar company, we I think you deserve to have a gong. >> Yes. Yes. Yes.
1:39:12
big as you know a car Sure. Yeah, you need one. So, how is business? How is growth?
1:39:17
What's the what what how big is the company?
1:39:19
How big is the the client base?
1:39:24
Give us some context on the scale since we last talked. For sure.
1:39:27
So, some quick facts on the business.
1:39:29
We we were four people just over a year ago. Wow.
1:39:32
Then when we raised the A, we were on that. Yeah.
1:39:37
>> [laughter] >> We were 13 people Um, then now we're around 40.
1:39:41
We've tripled ARR since the A, Um and work with over 5,000 customers.
1:39:47
5,000 companies are recruiting employees through Juice Box.
1:39:52
How many people does it is does the talent pool matter?
1:39:55
Does that stay static or is it more like I'm a company, I come to you, and you go find the person wherever they are on the internet?
1:40:04
Yeah, it's a it's a great question. It's the latter.
1:40:05
So, what we focus on is passive talent sourcing.
1:40:08
So, we'll scour the web for anyone that we can find that think is that we think could be a great fit for the given role.
1:40:14
We'll find that list of people, stack rank them for your given search, and then make it really easy to reach out to them typically through an email sequence, get in touch with them, and see if they're interested in the role.
1:40:23
And so, because our talent pool is effectively everyone, it scales quite nicely with our customers as well.
1:40:28
5,000 companies are hiring.
1:40:31
That's white pill while people are worried about layoffs and and contraction and you know, companies going out of business and what not.
1:40:41
What trends are you seeing among the 5,000 companies that you work with?
1:40:44
Are they particularly early stage startups that are growing that you know, you can speak the same language of?
1:40:50
Are they in industrial sectors that are immune from AI or transformation?
1:40:56
What trends are you seeing in the hiring market?
1:41:01
Yeah, when we were on last, a lot of our customers were kind of venture-backed, fast-growing in the Silicon Valley ecosystem.
1:41:08
I'd say that's probably the biggest thing that's changed in the business since then is that we've scaled to support more traditional enterprises.
1:41:14
So, think like large defense contractors, financial institutions, and similar.
1:41:18
They face the same hiring problems.
1:41:20
They want to attract the best talent, and they are seeing a lot of noise in the inbound that they used to quite heavily rely on.
1:41:26
There's huge amounts of like AI-generated application spam now, and that makes it really hard to find the best talent and and bring them on to the team.
1:41:34
And so, what we've seen since is kind of the shift what used to be quite heavy on just software engineering roles of doing outbound for them is really spreading across roles.
1:41:43
And so, that's for sales roles, that's for HR roles, finance roles, and all across the board. So, oh, sorry, Jeremy. Go for it.
1:41:51
Uh I was just wondering uh do you expect there to be a shift in the number of new jobs that you place or broadly are placed that come from employees that are currently somewhere and get outreach from a new company and then they shift while they're still employed versus these the the the the proactive job searcher? That's right.
1:42:18
We think the majority of roles are going to be filled by passive candidates.
1:42:21
Um we call it the talent war and so, the more companies are competing, uh the more they're going to be reaching out to other top talent and try to attract them to their teams.
1:42:28
Um and I think that's already been the case uh to some extent in, you know, fast-growing tech.
1:42:33
Uh that's now spreading across the industry and so, we would think all all roles are ultimately going to be filled through outbound. Okay.
1:42:42
Uh talk about the business model and how it might evolve in the future.
1:42:44
Where is this Where is this going?
1:42:47
I'm as- I'm assuming uh as part of the pitch, all the investors were excited about you selling kind of the work that traditional uh recruiting agencies do, but how how are you positioning it or thinking about it? Yeah.
1:43:03
So, one thing that's unique about the business is that um while we do kind of sell the work that the recruiter is doing, um we do so without um kind of say replacing the traditional recruiting agency.
1:43:13
In In fact, many of them are our customers as well.
1:43:15
And so, whether it's an in-house recruiting team or an external recruiting team, they have the same goal of uh identifying who could be the best person of for a given role and then reaching out to them.
1:43:24
And so, the the platform has two different modules, which kind of goes into the business model and pricing as well.
1:43:29
One being the per-seat setup where you can buy a license to use the platform and you log in with your email.
1:43:36
The second being our agents, which are used on a per job basis.
1:43:39
And so, depending on how many jobs you have live or jobs that you want to hunt talent for, you can deploy different agents in Juicebox that will go find the best profiles and reach out to them every single day without you having to check back in on them day over day. Okay.
1:43:53
Give me some tips for someone who wants to be flooded with Juicebox inbound.
1:43:59
What What What's the key to making yourself aware to your web crawlers, to the models?
1:44:08
How should people be positioning themselves, PDF resumes, personal websites, blogs, Twitter?
1:44:12
Like, what's the best way to throw up a flag, even if you're happy with your current job, just let everyone know, let the models know, let Juicebox know, I'm I'm a top candidate.
1:44:24
I'm a I'm a I'm a I'm a killer. Yeah.
1:44:28
Um I think the biggest piece of advice is just share more about yourself, share what you're where you're currently working, what you're actually working on, um any projects that you're involved in, anything you do outside of work, um really anything that can indicate um a potential match for for role.
1:44:43
And that can be pretty diverse, you know, there's many companies that um want to find candidates that have some kind of interest or experience in the sector that they work in.
1:44:51
Uh that might not directly be tied to the day-to-day work that you're doing today, but may reflect an interest that you have.
1:44:55
And so, the more of that is available or or published in one form or another will make it easier to be matched and identified for that job.
1:45:02
What's the best output for that?
1:45:05
Because I imagine you're not scraping all of Instagram Reels.
1:45:09
It's probably a little bit easier if I have like a blog that's well indexed or I'm on GitHub or LinkedIn.
1:45:18
Like, where where should someone be publishing?
1:45:21
Yeah, that's exactly right.
1:45:21
Um so, actually all the ones you mentioned are are a examples.
1:45:25
Um personal websites is something that's still a bit newer.
1:45:27
I'd say like the um, total kind of number of candidates that actually have that is still fairly low.
1:45:32
Um, but you know, many people have a GitHub profile, LinkedIn profile, um, many places to to to publish that information.
1:45:38
And um, I think GitHub in in particular is one that's quite underutilized because it's historically been hard for recruiters to use.
1:45:45
Like how can they actually find, uh, you know, who's a good person to reach out to?
1:45:48
Um, a lot of that data is indexed a lot stronger now.
1:45:50
And so recruiters are searching based on GitHub data.
1:45:54
They are finding people who contribute to open source repos or or have other signals.
1:45:57
Um, and a lot of that data is only being unlocked within the last 6 to 12 months. I have a funny story.
1:46:03
In like 2014, I needed to hire a software engineer.
1:46:06
So I cross-referenced every customer email with GitHub and I and I reached out to like some of the top, uh, open source contributors.
1:46:13
And there was one guy who had created Redis, which is an in-memory database.
1:46:18
He's like a legendary programmer.
1:46:20
And I was like, "Hey, can you help me?
1:46:22
I need to build an e-commerce website."
1:46:23
And he was like, "You you you don't need me, bro.
1:46:27
>> [laughter] >> Like you're good."
1:46:28
He was actually very helpful and taught me a ton of things.
1:46:31
But that cross-referencing has been really really valuable.
1:46:33
Uh, and and GitHub continues to be an underrated source of of of sourcing.
1:46:37
I remember I talked to someone who's a tech recruiter and I taught her how to source on LinkedIn and she actually got a job because of it because it was like a differentiator that she wouldn't just be hanging out on LinkedIn all the time.
1:46:50
This was like pretty early in GitHub's uh, era.
1:46:52
Uh, but there's so much alpha there because you can see what people are doing.
1:46:55
Uh, little bit harder if you're a defense contractor, probably not posting a lot of public information about what you're doing, but maybe maybe some blog posts will do the trick.
1:47:05
Anyway, thank you so much for taking the time to come to GitHub update. >> Congratulations.
1:47:08
Congrats to the team on the progress.
1:47:11
>> And we'll talk to you soon. Cheers. Goodbye.
1:47:12
Let me tell you about 11 Labs.
1:47:14
Build intelligent real-time conversational agents to reimagine human technology interaction with 11 Labs.
1:47:17
And let me also tell you about Turbo Power Jordi. Please do.
1:47:22
Cerebras back during full tech search built from first principles and object storage fast 10x cheaper and extremely scalable.
1:47:27
Well, we have our next guest, Adam Goldstein from Archer.
1:47:32
He's the founder and CEO. Adam, how are you doing? Good to meet you. >> Doing great. Welcome to the show.
1:47:38
Thank you so much for taking the time.
1:47:40
Uh, would you mind kicking us off with a little bit of your backstory?
1:47:44
How did you become interested in flying cars?
1:47:52
Um, well, a lot of it came from building a software business that every month I had to wake up and it'd be the first of the month and we'd have to start over with sales.
1:48:01
And it was uh one of those things that kind of drove me to the point that >> you selling one off Were you selling >> Yeah, were you selling boxed software?
1:48:09
The whole thing of SaaS is that it just automatically recurs. Yeah, right.
1:48:12
I wish that was uh I wish that was the case. >> Okay.
1:48:14
Um, the the sales grind of software business are really, really tough.
1:48:18
And so, given how hard these businesses are, I really wanted to do something that was unbelievably fun.
1:48:22
I could really help make an impact in the world and the technology was changing over.
1:48:29
And so, so I had been uh you know, messing around with electric engines and building uh airplanes.
1:48:32
It was an opportunity to just build a whole new category of airplane.
1:48:36
A lot of it started out as a fun thing to go do, a project.
1:48:39
Um, and I'd always loved and been obsessed with airplanes.
1:48:42
And so, it was an opportunity to go out there and actually build something super special special.
1:48:46
Um, and as it all started to work, it became like super clear that there was just a massive business to go build here.
1:48:51
Okay, take us through this video. What are we seeing here?
1:48:55
So, these are what people call eVTOLs or electric vertical takeoff and landing aircraft.
1:48:59
You can think about it same category as like a helicopter, but it's electric.
1:49:02
And when you switch electric, you all of a sudden can put multiple sets of engines and propellers and actuators, which allows um the aircraft to have a ton of redundancy, which allows you to certify at a safety level that's super, super high.
1:49:13
So, the vehicles take off and land like helicopters, but then transition and fly forward like an airplane and it allows for you know people to get where they're going very really really quickly.
1:49:22
>> I have to ask is any of this AI generated video is any of this CGI?
1:49:27
That's all real flight test video.
1:49:28
>> This is all real flight test video. Is there one of these? How many of these exist?
1:49:33
What's the plan to ramp this up?
1:49:36
Those videos are several different aircraft.
1:49:38
So we are in the process of going through the certification you know process with the FAA and that's the point where we really unleash.
1:49:45
So we have a big factory located down in Georgia that can build up to 650 aircraft per year. Wow.
1:49:52
So right now you know we work with you know a handful of aircraft.
1:49:55
The first block of 10 is what we built and then now we're in the phase where we're starting to build to the next block of 50 but you want to time it really with the [clears throat] certification process and then the other side is we were selected as the exclusive air taxi provider for the LA 28 Summer Olympics.
1:50:11
So we want to make sure we have it all done. >> I love it.
1:50:13
Thank And and you you were talking about safety ratings.
1:50:15
Is the idea to prove that these can be safer than traditional helicopters? Like what's the goal?
1:50:24
Yeah, that's that's correct.
1:50:24
So the FAA created a new category for us.
1:50:26
It's not a helicopter, it's not an airplane.
1:50:28
So we actually certify these at a safer level than what helicopters are today and we have to go out and obviously prove that.
1:50:35
And so that just will I think drive a lot more people willing to get on aircraft like this.
1:50:40
The aircraft also is much quieter than a helicopter which means it can allow for a lot more landings.
1:50:45
So for example if you wanted to take a helicopter to the Hamptons, they limit the amount of landings at East Hampton Heliport because it's so loud and people complain.
1:50:51
So as the aircraft are much much quieter, it can dramatically increase the amount of people that are able to access us. Okay.
1:51:01
When I dig into the flying car plans, the history of the flying car, there's always like four different problems that need to be solved like regulatory, uh, vertical takeoff and landing going electric and then also building vertiports and actually having more places to land.
1:51:17
Uh, assuming you get through approval, it looks like it's already vertical takeoff and landing.
1:51:22
It sounds like it's already electric.
1:51:23
Will this be able to just slot into the existing, uh, helicopter infrastructure and sort of go from, like, could someone theoretically just buy one of these instead of a helicopter, have a pilot who's experienced in this, and fly from helicopter destination to helicopter destination while we as a society figure out more vertiports and landing zones? Yeah, absolutely.
1:51:47
So, we designed it to fit into the existing helicopter infrastructure.
1:51:51
So, the wingspan is less than 50 ft.
1:51:53
The aircraft weighs 6,500 lb.
1:51:55
So, it's designed within that, uh, you know, existing guidelines.
1:51:57
We'll fly existing, uh, helicopter flight plans.
1:52:01
We'll use and leverage a lot of existing infrastructure.
1:52:03
And then depending on the city you're in, so places like Texas and Florida, there's, uh, much easier, friendly environments to go.
1:52:09
You can land kind of, lots of different places.
1:52:12
Places like California, it'll be much more planned. >> Sure.
1:52:14
Um, and uh, and structured.
1:52:14
And so, it gives you the ability though to really, um, you know, use existing, but then ultimately scale the stuff out.
1:52:19
So, I I do think you'll see fleets of this stuff get done first before individuals, but the individuals ultimately will come with time.
1:52:26
Um, and people will be able to use them on an everyday basis.
1:52:28
You mentioned infrastructure.
1:52:30
I imagine that, uh, most, uh, airports that service helicopters have fuel, but maybe not charging infrastructure.
1:52:40
Is there any hurdle to actually getting charging infrastructure in place the way Tesla did with the rollout of the electric car?
1:52:49
Yeah, so the good news is we use very similar, uh, charging infrastructure that the EVs use.
1:52:53
So, 2C charging like a Tesla supercharger.
1:52:56
And we partnered with a company in our industry that's, uh, and I'll call more of a a partner than a competitor.
1:53:01
It's a company called Beta, and they make, um, a lot of the charging infrastructure.
1:53:04
So, I've backed the charging infrastructure, their plans, we buy a bunch of their equipment.
1:53:08
And so, we're going out there and doing that.
1:53:11
The airplane business is just much smaller in terms of number of units than the total number of cars.
1:53:14
So, it's not like you need you need like tens of thousands of chargers everywhere.
1:53:20
If you're looking at like New York City, you need a handful, right?
1:53:22
At the big heliports, and that's really it.
1:53:23
And there's probably already a lot of power going into an airport broadly.
1:53:27
So, to redirect a little bit of it.
1:53:29
It's not like you're you're trying to set up a you know, a charging station in the middle of nowhere.
1:53:33
Uh We're not straining the grid. Yeah, that's for sure.
1:53:37
>> So, the the Olympics partnership is exciting, uh but looking forward, what do you think are going to be, you know, 5 years out, 10 years out?
1:53:44
What are going to be the most like common routes?
1:53:49
That at least [clears throat] what would you predict at this point?
1:53:53
So, city center to airport is a very obvious one because there's known demand and willingness to pay.
1:53:58
You can see that through rideshare.
1:53:59
Ubers, you know, we all do that, right?
1:54:00
And so, it's also a typical trip that's not that far, but also takes a long time.
1:54:04
So, the convenience factor there is massive.
1:54:05
If you live in LA, you want to go to LAX, you live in New York, you want to go to JFK.
1:54:10
Those type of routes are like very obvious.
1:54:12
But these routes exist like all over the world, things like that.
1:54:14
So, anytime like where, you know, everybody says, "Oh, I wish I had this in my hometown."
1:54:19
It's like I grew up in Tampa, Florida.
1:54:21
I grew up in North Tampa.
1:54:21
I would have loved to have gone to St. Pete or to Clearwater.
1:54:24
That's a total pain to get to, but if I could fly there, that would be super interesting.
1:54:28
That literally exists everywhere.
1:54:28
So, I do think it'll become much more common.
1:54:33
And we just have to get it started and get people comfortable cuz it is the first new category in a very long time, really about 60 years that the FAA has created.
1:54:40
So, there is a an adoption period that will take place.
1:54:42
Yeah, no matter where you land in the Bay Area, it's going to be an hour to get into San Francisco proper, uh whether it's SFO SFO can be a little bit faster, but Oakland's always slower.
1:54:52
It's all It's all takes forever.
1:54:53
Creating a category like this, how do you solve the pilot side?
1:54:57
What is going to be the Like are you creating the certification with the FAA, how do you build out a cohort of of pilots?
1:55:06
Will there ever be are you imagining there's going to be a recreational market for eVTOL?
1:55:13
Yeah, so they FAA has already defined this and so there's certain credentials of like what you need to go do it.
1:55:17
The good news is the aircraft is super easy to fly.
1:55:20
It could take you 2 weeks to hover a helicopter.
1:55:22
I could teach you guys how to fly this plane in 5 minutes.
1:55:26
A lot of the training is really about >> like I could I feel like I could I feel like I could hover a helicopter >> [laughter] >> in about in about 5 minutes. >> dangerous thing. Red Bull, contact me. Yeah.
1:55:36
[laughter] I could fly this thing upside down.
1:55:39
If you could hover a helicopter in 5 minutes on your first try, uh that would be unbelievably impressive cuz that is extraordinarily difficult.
1:55:44
Every guy every guy thinks they could they could take over a I mean yeah, I think that's ridiculous, but I could I could actually land a 747 in an emergency situation.
1:55:54
>> [laughter] >> Yeah, 100%.
1:55:56
Uh you're on the you're on the cover of the business and finance section in the Wall Street Journal today.
1:55:59
It says flying taxi maker Archer accuses rival Joby of concealing China ties. What happened?
1:56:06
What is going on with the deeper supply chain in eVTOL?
1:56:11
Yeah, um so Archer is building products not just for the civil side, but also for the defense side.
1:56:16
And a big part of what we're doing is really in support of building you know re-industrializing America and building the you know the industrial base here especially on the defense side.
1:56:24
And so we partner with a company called Anduril and we're building um new aircraft.
1:56:27
We build the big um aircraft and then um Anduril will missionize them.
1:56:32
So think they put the sensors and systems and weapons um into the aircraft.
1:56:36
So very important to me uh that we build and keep the supply chain in the US and we build this stuff all out here.
1:56:41
That's not been necessarily the case for our competitors.
1:56:43
They put you know factories in uh in China in Shenzhen.
1:56:47
They um set up their supply chain there and I just think it should be table stakes for American companies working in defense to have to build out their supply chains and and ultimately um do it in America.
1:56:57
And if you do go do that overseas, you also have to disclose that in a very proper way.
1:57:01
And so I think that's you know a big sticking point for me.
1:57:06
I do think companies working in defense in America need to be very transparent about that.
1:57:10
Talk to me about the battery supply chain there because I I feel like drone motors have been very difficult to re-shore.
1:57:18
Electric batteries, we've seen some positive news from Tesla around a lithium ion lithium ion battery plant and there's some extraction that's happening, but how mature I mean you're not making millions of these yet, but how mature is the supply chain on the electric side?
1:57:37
So the we use traditional lithium ion batteries and so commercial off-the-shelf stuff.
1:57:42
So we're not like making some new battery cell here that's not proven.
1:57:46
The reason we have to do that is because there needs to be data to show the FAA to prove these things are safe.
1:57:51
It's not just that these things are safe, it's that we have to go and actually light them on fire.
1:57:54
We put them into thermal runaway, many of them into thermal runaway.
1:57:57
We have to show they do not propagate and spread throughout the pack.
1:58:00
We have to take an entire pack, hoist it 50 ft in the air and drop it and it cannot emit toxic gases or catch on fire.
1:58:07
So the standard is unbelievably high.
1:58:09
So you need to use proven technologies to go do that.
1:58:11
The good news is over time batteries do get better and so they get more energy dense and it allows us to you know do more things largely around speed, range and payload.
1:58:20
But that will likely keep improving versus you know if you take like a typical you know ice or you know piston piston combustion engine, it's not like the tech is the the core it's not like the fuel is getting better.
1:58:31
It's the same fuel every year.
1:58:32
So we have a unique advantage where one day the stuff will be so good probably don't need that much.
1:58:38
And it will dramatically reduce your cost.
1:58:40
So yeah, long term this should be longer range. Where are we right now?
1:58:44
I feel like electric cars had a certain breaking out moment once you You to the 300 mile electric car range, that's about what uh many gas cars are.
1:58:55
What How does the electric VTOL range compared to uh just a typical helicopter?
1:59:02
Um so, today helicopters have more performance than what we have from a range perspective.
1:59:05
Um so, we are targeting in and around urban environments.
1:59:09
So, think like less than 100 mi, that's the typical um you know, kind of target.
1:59:12
On the defense side, it's a hybrid vehicle.
1:59:14
So, they're they can go um you know, upwards of 1,000 mi.
1:59:18
So, you're putting heavy fuels back into it.
1:59:20
So, even further than what helicopters can do.
1:59:22
So, it depends what you're talking about.
1:59:23
But, on a helicopter, they can't meet us from a safety perspective.
1:59:26
So, you know, there's if you look at like single-engine helicopters, there's many single points of failure.
1:59:31
Where if one part goes bad, you'll have a catastrophic event.
1:59:33
So, we are hitting standards that are significantly higher than where they can be.
1:59:38
So, there will be some tradeoffs.
1:59:39
So, there will always be helicopters around.
1:59:40
Um heavy lift stuff, really far um you know, range stuff.
1:59:45
So, if you have an offshore oil rig, for example, we're not going to compete in that market.
1:59:47
Um so, if you want to you know, if you look at like um like a 53K, like a King Stallion, just Google that, like a giant heavy lift helicopter.
1:59:55
That thing can carry an F-35.
1:59:57
I like the >> [laughter] >> I like the name. What's it called? King Stallion. King Stallion.
2:00:01
That's a Yeah, so if you that's a a big helicopter that's pretty cool.
2:00:06
>> You guys should create a a horse Yeah. uh product.
2:00:09
>> got to get good names going for all these. >> guys over here. >> Yeah. That's fantastic.
2:00:13
That's a that's a that's a big boy. Okay. King Stallion.
2:00:16
Yeah, so we we call our our aircraft Midnight.
2:00:18
And of course, is Archer.
2:00:18
And then um you know, Taylor Swift did release her album Midnights, and one of the songs was Archer Oh. Oh, there you go.
2:00:26
And so, we were like, wait a minute, is this uh you know, she's been paying attention? I don't know.
2:00:31
That's what we were curious.
2:00:32
Is like, did we inspire her? That's great.
2:00:34
Uh well, thank you so much for taking the time to come chat with us, Adam. Uh this is fantastic.
2:00:37
Uh cannot wait for the Olympics in Los Angeles.
2:00:43
Hope to see these flying around.
2:00:43
And we'll talk to you soon. >> Awesome. Thanks, guys.
2:00:45
Have a good rest of your day. Cheers.
2:00:46
Let me tell you about public. com.
2:00:48
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2:00:53
Before we bring in our next guest, I need to tell a funny story about in middle school, everyone had to give a speech.
2:01:00
And I had the best speech planned.
2:01:02
I was going to give a speech about the Osprey helicopter.
2:01:05
Are you familiar with the Osprey helicopter?
2:01:08
>> Yes, vertical takeoff and landing vehicle.
2:01:10
And I was like, "This is the greatest speech ever.
2:01:13
I'm making It's the future of technology.
2:01:15
It's a plane, it's a helicopter.
2:01:17
Everyone's going to love this."
2:01:18
I get up, I give a very convincing speech on how the Osprey helicopter should be supported at all costs.
2:01:24
We got to work on this thing, we got to fund this with taxpayer dollars.
2:01:27
The Osprey helicopter is the best thing since sliced bread.
2:01:29
I get up, I give my speech.
2:01:33
Then the next person comes up, and they're like, "My speech is about curing cancer."
2:01:37
And I'm like, "I'm cooked."
2:01:40
>> [laughter] >> And everyone else had chose topics that were like totally pulling on your heartstrings, and mine was just like nerdy talking about a random obscure piece of military technology.
2:01:49
And I was like, "Yeah, I don't stand a chance."
2:01:53
>> friendly guy in the chat says, "Taylor just wants the Taylor Swift wants the archer so people stop flaming her for >> Oh, that's a good call.
2:02:00
That would definitely help out.
2:02:02
Well, before we bring in our next guest, let me tell you about Gusto, the unified platform for payroll, benefits, and HR built to evolve with modern small and medium-sized businesses.
2:02:09
And without further ado, let's bring in Max from Lagora.
2:02:14
[music] He's the co-founder and CEO.
2:02:14
Max, welcome to the show.
2:02:16
Thank you so much for taking the time to come chat with us on such a busy day. >> What's happening? Give us the news. What happened?
2:02:24
>> [laughter] >> Well, we cooked. You got it. Let's go. Let's go. Let's go. Let's go.
2:02:29
We raised the round, we went back to New York.
2:02:32
>> We raised 550 million dollars.
2:02:37
It's a big birthday of all guns. Fantastic.
2:02:38
It looks like the sales department here at Lagora. I love it. I love it.
2:02:42
So, what's the key to growth?
2:02:45
Is there a particular type of law firm that's you're seeing traction with?
2:02:47
Is it upmarket, large, small, everything, direct to consumer?
2:02:52
Like, where is the business today?
2:02:54
Well, let's talk about law firms.
2:02:55
I think the excite you know, the the the the the the thing that sort of distinguishes the law firm market is that if a firm down the street starts operating with Legora, they're able to offer faster and better services.
2:03:04
And so, every firm has to adopt it.
2:03:06
Um the equilibrium breaks, and that's why we've seen a astronomical surge in our law firm demand.
2:03:15
But on the other side of the coin, you have the enterprises, the legal departments who are starting to figure out that we might be able to leverage AI to cut down costs, to operate more effectively.
2:03:26
And so, we're really seeing value on both sides of this equation.
2:03:28
And as you know, we started Legora in Stockholm, Sweden less than 3 years ago, and now we're in the US.
2:03:34
Um I'm calling in from our office in New York.
2:03:36
We've just opened up in Houston and Chicago. And so, Yeah, in Texas.
2:03:43
Lot of law firms there doing the oil and gas the the legal oil down there.
2:03:48
So, are there any specific trends in in industry, or is it more like uh you'll do litigation versus something else?
2:03:56
Like, like like where have you uh found the most success?
2:03:59
Where is AI advancing the work of lawyers most uh acutely?
2:04:06
So, I'd say we've we've had the most traction in M&A and corporate departments.
2:04:09
But quickly, we have actually started to see a a real verticalization.
2:04:13
So, within private equity, within pharma, within big tech companies, within real estate, construction, right?
2:04:20
Um the way that lawyers Well, I actually think it's it's similar to software coding.
2:04:27
So, you know, Legora used to be kind of a co-pilot, and you used to work with it as an assistant back and forth.
2:04:33
But now, with the latest model release, with Opus 4. 5, GPT 5.
2:04:35
4, like we're starting to move into a world where like we're going to autonomously go out and perform tasks on your behalf.
2:04:41
And that's working across every single legal vertical. Uh yeah.
2:04:47
How do you lawyers feel about AI?
2:04:50
Are they having an existential crisis in the way that software some software engineers are >> I don't do the thing that I'm I'm not the craftsman that I used to be.
2:04:59
>> Like how are they actually processing it?
2:05:01
I I think it's, you know, the entire range of emotions from holy this is incredible and I've never seen anything like it to huh, um I'm really going to have to figure out how my business model is going to work for this task that I used to bill hourly for.
2:05:14
And if you look in the law firms, right, the way that they're typically structured is you under bill for the partners like and you over bill for the associates.
2:05:27
And that model is starting to get questioned by the by the by the buyers.
2:05:33
And so clients are demanding the use of AI and shows of more effectiveness in the way that services gets delivered.
2:05:42
And of course the Gore is part of the, you know, answer there.
2:05:46
Uh every once a week somebody like is making the kind of statement or or putting out the idea that what are all these legal AI tools doing?
2:06:00
I can get the same results from a chat GPT or Claude or Gemini.
2:06:03
What's your answer there?
2:06:06
I'm assuming that came up during the fund raise as to why why this isn't, you know, a a thin wrapper.
2:06:12
I have my own ideas, but I'd be curious how how you talk about that.
2:06:17
>> I mean it was the same week that we went out to the market that Claude dropped their legal plug-in.
2:06:21
And I actually think it was a fantastic showcase of, you know, here's some of the capabilities that the models can actually do.
2:06:28
But in the same way that just taking the model and throwing it at a problem sort of doesn't work if you're going to run a $10 million M&A process.
2:06:35
There's a lot of scaffolding, there's a lot of enterprise software, there's permissioning, there's sort of ethical walls that you need to respect, there's the way that you work with the firm's internal data, with external legislation, and with case law.
2:06:48
Like there's just so much more that you need to do around the models to put them in a context where they can be useful.
2:06:53
I think with the latest developments, we are actually needing to build less of the guardrails, like the harnesses that we can put the models in are improving. Mhm.
2:07:05
So we can sort of take the model, we can put them in an environment where they can be successful, and then we, you know, to quote you, let let it cook, right?
2:07:13
And we give the model access to the relevant tools to go and solve problems in that legal business context, and then we sort of let it go out and plan and execute on that task.
2:07:22
Do you have the internal teams been a lot more aggressive today than than uh let's say like big law just because they're they're not like running a business, they're just like providing an internal service, and is was that something that you would kind of always expected?
2:07:45
So so I think that the big law was really quick to adopt, and over the past 2 years we've seen a real exponential step function in sophistication and complexity of the use cases that they're solving, right?
2:07:58
Like back in 2023, uh somebody would get a clap on the on the back for summarizing an email with AI.
2:08:05
But now you're running an entire M&A like end-to-end process using Legora, and so the expectations are increasing.
2:08:13
And I'm actually seeing things like adding AI as part of the career frameworks within law firms to get promoted.
2:08:19
I'm seeing AI be part of the interviewing process, right?
2:08:22
Like it's now a skill that is required to deliver real work.
2:08:28
Um, I think the enterprise departments have been patiently looking at sort of what are our law firms doing, and now they're starting to to follow on. And >> Okay.
2:08:38
one of one of our last uh developments is something we call the Legora portal.
2:08:42
And the portal is basically like, you know, Figma for for lawyers.
2:08:46
Like they can collaborate and come into a multiplayer space where they can work together.
2:08:49
And recently, one of the big firms we work with here in the in New York, Debevoise, started working with clients like Blackstone, GSK, like on that portal.
2:08:58
And I think that's really exciting because the way that these legal teams have collaborated have, you know, looked like the early '90s.
2:09:05
And now we're pulling that into the age of AI. Very cool.
2:09:10
Are you seeing pull from in-house counsels, or is that a completely separate market? No, absolutely.
2:09:16
You know, we work with big >> saying.
2:09:18
They are seeing a lot of traction because see, if you're if you're in-house counsel, you're overworked, you're not billing by the hour, you just get a salary, you have a team, you want to make your team as efficient as possible.
2:09:28
You would adapt as you would you want you have a a greater incentive to adapt as much AI as you can faster because you're not dealing with your business models to to adapt as well.
2:09:39
>> far away are we from Microsoft's in-house counsel using Legora to acquire Activision directly, and Activision's in-house counsel just uses Legora as well?
2:09:50
This is a great question.
2:09:52
Like we're actually working with some very large enterprises who are leveraging Legora to do um more of the M&A process themselves >> Yep.
2:10:02
before, you know, engaging. Yep.
2:10:05
But I think it's it's also an opportunity for firms to be very proactive, and to actually invite them into their software environments, and to go, "Hey, AI can now do these tasks. Sure.
2:10:13
So, we're going to help you, our client, make that transition. Yep. Right? Okay.
2:10:22
>> we have this framework internally where like if AI can do a task, it will do it.
2:10:28
Like that task has been conquered.
2:10:28
It's within the spider shark that Anthropic released, right? Like Spider shark.
2:10:35
>> Human's no longer Right?
2:10:35
Like you saw that like the blue ray. >> Yeah, yeah, yeah. Yeah. Right?
2:10:37
Like it's no longer a task that humans should be spending their time on. Yeah.
2:10:43
And I think the challenge is that this is all moving so fast and people aren't used to doing their work and disrupting themselves at the same time.
2:10:51
So, you know, we have the software on one piece, but then we actually have 100 lawyers on staff at Legora called legal engineers that partner with our clients and we really pioneered this concept, right?
2:11:05
It's forward deployed lawyers in a way.
2:11:07
And they work with all our big enterprise and big law firm clients on transitioning the way that they do business.
2:11:13
What are conversations like on law school campuses today?
2:11:16
How are they processing all the progress?
2:11:21
Well, so Legora actually has a university program, which is really exciting.
2:11:25
And so if you're a law school student listening to this, you should be part of the university program and you should learn this, right?
2:11:30
It's going to be a skill that firms will look for.
2:11:35
It's going to make you more attractive on the job market and I think we are quickly going to enter a world where you're spending more time reviewing work that AI is doing for you than doing it yourself. Right?
2:11:46
Like that's what's happened to software engineers.
2:11:48
That's going to happen in law.
2:11:50
And so the best thing you can do as a young professional is to get there really quickly. That's great. Anything else, Jordy? Wild times.
2:11:57
Thank you so much for taking the time. >> Shout out to Max.
2:12:02
>> Yes, thank you so much for having me. Congrats.
2:12:03
I'm sure you'll be back on with the billion-dollar raise soon enough. >> I'm sure.
2:12:08
Well, we will talk to you soon, Max.
2:12:11
>> Great to get the update, man. Congrats to the team. >> Awesome. See you guys. Cheers.
2:12:13
Let me tell you about Graphite, code review for the age of AI.
2:12:17
Graphite helps teams on GitHub ship higher quality software faster.
2:12:22
Um Tibo from Open AI has some news here.
2:12:24
He says, "We are adding computers fast as we can for Codex, but demand is surging faster than anticipated and service can be a bit choppy for some.
2:12:34
Team is working hard behind the scenes.
2:12:36
Everyone is happy with Tibo resetting limits." Adam.
2:12:41
gpt says, "Saint Tibo, giver of tokens and the res- setter of limits."
2:12:46
>> We got to pull up the video you shared, John. Which one? Oh, yes, yes, yes. >> up. Uh I just dropped it. >> You just dropped it? Okay. >> In the chat.
2:12:53
Uh we we we we have a few videos that we can watch.
2:12:55
We can watch the the trailer for Project Hail Mary, but let's first watch Tim Tebow give the promise. No sound.
2:13:05
hoping for an undefeated season, that was my goal. Something Start it over. Please. I'm sorry. Um extremely sorry.
2:13:13
[laughter] You know, we're hoping for an undefeated season, that was my goal.
2:13:15
Something the floor has never done here.
2:13:16
But I promise you one thing, a lot of good will come out of this.
2:13:20
You will never seen any player in the entire country play as hard as I will play the rest of the season, and you'll never see someone push the rest of the team as hard as I will push everybody the rest of the season, and you'll never see a team play harder than we will the rest of the season. God bless. Great speech.
2:13:36
>> And that's how John read Tibo at Open AI's post.
2:13:39
We are adding computers fast as we can for Codex, but demand is surging faster than anticipated.
2:13:43
Team is working hard behind the scenes.
2:13:46
You will never see a team work as hard behind the scenes as our team. God bless.
2:13:53
Project Hail Mary debuted with a 95% on Rotten Tomatoes.
2:13:56
It's described as one of the best sci-fi films of the decade.
2:14:01
We got to get Nick to get some tickets for our team.
2:14:03
We will definitely be going. I'm very excited.
2:14:05
Let's pull up the full trailer for Project Hail Mary since it is movie day on TVPS. Please state your name. Ryland Grace.
2:14:17
I spoke out from Oklahoma.
2:14:17
Have you seen The Martian?
2:14:20
>> about a mile away from my apartment.
2:14:22
This is from the same author, Andy Weir. I'm not an astronaut. I'm not an astronaut.
2:14:30
If you don't go, you die. With the rest of us. A Hail Mary situation. The sun is dying. Oh, no.
2:14:40
You're the only scientist who might know what this is.
2:14:44
I'm just a teacher at Grover Cleveland Middle.
2:14:47
You have a doctorate in molecular biology.
2:14:50
I need you to come with us.
2:14:52
This is Project Hail Mary.
2:14:54
The sun is not the only star dying.
2:14:56
Every star was infected by its neighbor except one. Why? We don't know.
2:15:01
Which is why we built a ship to go there and find out. [music] It's 11. 9 light-years away.
2:15:05
The astronauts die in space.
2:15:08
It's what you Americans would call a long shot. Hail Mary, I get it.
2:15:13
Can I just get a sign off?
2:15:13
They said the name of the movie.
2:15:18
I love a movie like this. He has no experience. But he has to go anyway. >> a just a professor. Just a normal person.
2:15:28
This is every man's dream. >> the not in astronaut.
2:15:29
I've never done anything I've never done a space walk. I can't even moonwalk.
2:15:35
See, I I seriously think there's like a 50% chance this happens to me at some point in my life.
2:15:39
They're just like They're just like [laughter] John, >> [music] >> you're the only person that can save humanity. You got to go.
2:15:44
And I'm like I got If I got to go, I got to go. I'm ready. This is my fantasy. I understand. >> I do. He's so good.
2:16:00
But my place is in the classroom.
2:16:02
The world is counting on you.
2:16:07
This might be very hard [music] for you to understand.
2:16:10
But some people are not good at things.
2:16:14
2026 box office is [music] 27% below pre-pandemic 2019 which >> No black pilling while the Hail Mary trailer is playing. >> 50% No black pilling.
2:16:22
We are going to change that stat by going and seeing this film.
2:16:30
We will be watching Project Hail Mary and single-handedly bringing back the film industry.
2:16:34
We're going to make Hollywood the center of entertainment.
2:16:40
He's kind of growing on me.
2:16:40
At least he's not growing in me, you know, which was a concern for a little while. This looks fun.
2:16:47
>> [music] >> I think this is good movie to start summer.
2:16:51
It's a little bit early, but it's warm here in California.
2:16:56
Well, have a fun time with that.
2:16:56
The Financial Times loved it.
2:16:57
They said Project Hail Mary, Ryan Gosling's good humor propels space caper of serious uplift.
2:17:04
A science a science teacher buddies up with a boulder-like alien to save the sun in an escapist morale booster for One more trailer? of The Lego Movie.
2:17:13
I would love to watch another trailer.
2:17:16
>> up the trailer for the AI doc. Yes, the AI doc.
2:17:18
This is the real movie that will get people back in seats. Everyone loves AI.
2:17:22
They're going to want to hear about AI.
2:17:24
They're going to want to head to the theater to watch the AI doc. Let's watch the trailer. Look at this lineup. >> Can't go quite wrong.
2:17:31
Well, with the They got Sam. They got Demis. They got Dario.
2:17:36
What happened with Mark Zuckerberg?
2:17:38
We'll never know why he turned this down. >> get Elon? They didn't get Elon.
2:17:41
Elon said yes, but then he got busy. There's a difference. But there's Yudkowsky. There's Eliezer.
2:17:49
>> because my wife is 6 months pregnant is now a terrible time to have a kid. Oh.
2:17:55
I mean, [music] let's be honest.
2:17:55
I know people who work on AI risk who don't expect their children to make it to high school. Ooh.
2:18:04
is about recognizing patterns. Patterns? Patterns. Patterns.
2:18:09
If you have learned those patterns, [music] you can generate new information. AI is moving so fast.
2:18:15
>> It's being deployed prematurely.
2:18:15
[music] There's so much potential for things to go wrong. Why can't we just stop? >> went right?
2:18:23
All these companies are >> [music] >> racing >> to get AI that's vastly more intelligent than people within this decade.
2:18:30
China, North Korea, Russia Whoever wins is essentially the controller [music] of humanity. You got Yoshua Bengio. Who else have you got?
2:18:36
We need to take Uh, Yuval Noah Harari, Reid Hoffman's in there. more.
2:18:46
Daniela Amodei >> like I have to find these CEOs and get them in the movie. Great. You got Sam. They got Demis.
2:18:56
>> [music] >> Where's Mark Zuckerberg? Okay. Get in this movie, bro. Am I hopeful? Yes.
2:19:01
Am I certain that it'll go right? Absolutely not.
2:19:03
AI is the thing that can solve climate change. Anyway, fun documentary.
2:19:08
Clearly, this is going to be the thing that turns the tide on on Hollywood.
2:19:12
Everyone's going to go see this.
2:19:14
It's going to be sold [music] out. Get your tickets now.
2:19:16
Let me tell you about Okta.
2:19:18
Okta helps you assign every AI agent a trusted identity so you get the power of AI without the risk.
2:19:22
Secure every agent, secure any agent with Okta.
2:19:27
And without further ado, Alan McClennan.
2:19:31
In the restroom waiting room.
2:19:31
We'll bring him into the TV to announce you're jumping. What's going on, Alan? How are you doing?
2:19:37
I'm doing just fine, gentlemen.
2:19:37
It's nice [music] to meet you both. Great to meet you, too.
2:19:41
Uh, we were just talking about about Hollywood.
2:19:43
How How are you feeling about Hollywood? I am optimistic.
2:19:48
Project Hail Mary looks good.
2:19:50
This AI documentary looks >> Hollywood. I am optimistic.
2:19:55
Project Hail Mary looks good.
2:19:57
This AI documentary looks good.
2:19:59
Is there any cause for optimism? Oh, of course there is. Fantastic.
2:20:04
>> there Yeah, the the basis of the creative storytellers in the Hollywood are in parallel and everyone wants to come to Hollywood to do their projects no matter what anybody says.
2:20:16
Yes, there has been um somewhat of an exodus only because of the that was motivated by the SAG-AFTRA strike and everything kind of shut down.
2:20:25
460 plus million a day was lost. Wow.
2:20:28
You know, so so it was it was a big hit for Hollywood.
2:20:30
And so that changed dynamics considerably. Yeah.
2:20:33
But from a storytelling standpoint and the capabilities of putting things together, it's truly fantastic.
2:20:41
It's it's the community that makes things happen. Yeah.
2:20:45
How do you think the uh the the the the the the mix of different film budgets is changing or should change?
2:20:55
We've we've been in a uh you know, there's the there's the low-budget film and there's the huge blockbuster that gets up into the hundreds of millions of dollars and it feels like we might be entering a new regime soon where there's more lower-budget films made that are even more impressive than the blockbusters of old.
2:21:14
How do you think the Hollywood will react to new technologies potentially making uh blockbusters more affordable, more creativity, more uh more diverse perspectives?
2:21:25
How do you think all of that changes?
2:21:29
I think you just summed it up really well, to tell you the truth.
2:21:32
Um the the the fact of it is is that it's nothing really new.
2:21:35
Um this has been evolving over many years, 50 years, different types of programming, television, you know, uh 30-minute shows, episodic activities.
2:21:44
All of that has evolved and it's really driven towards the audiences and and the interest of the audience.
2:21:51
You know, you know, micro programming is the new used to be called, you know, basically um cu- um creator economy or the creator content, you know, um user-generated content on 2004, [clears throat] 2005 when YouTube came into play, it kind of created a whole new genre of everybody can produce something.
2:22:12
But, that doesn't take away the importance of the quality of the programming.
2:22:16
You know, and when it comes to micro programming, let's say micro shows, those [clears throat] in and of themselves for 90 seconds to 3 minutes are something that you really want to be able to capture. Yeah.
2:22:29
You know, and also view, and people do.
2:22:34
Now, kind of the average viewing of a micro show is four to five different segments that's kind of streamed together. Yeah.
2:22:42
So, then answer your [clears throat] question uh as best I can, um it will find its path, it will find its like for example, your program. Yeah.
2:22:50
You know, here here's the consumers are looking at this and being able to engage with it, and it's developing and delivering great content.
2:23:00
At least I'd like to think that would be the case. >> Thank you. Thank you.
2:23:01
>> Thank you. Thank you. Uh we we we do have a question [clears throat] from the chat I want to ask about uh this idea of Barbenheimer, these moments that are shelling points, they bring everyone together, and I'm wondering if you think that we'll see more of those in the future or if Hollywood should be thinking even if it's teaming up with a rival studio to create something that gets even more people into the theaters
2:23:26
uh because the internet's so noisy, but everyone's in their own little pockets, but then something like Barbenheimer happens and the Barbie film and Oppenheimer, and they're two completely different, but everyone was talking about it, and it seemed like it was the true Hollywood returning to the center stage of American culture post-COVID, in my my Yeah, cultural events are really important, no matter what they are. You
2:23:47
You know, wherever they are, there's you know, there's groups out there that are doing that live, pausing and enjoying the kinds of activities together.
2:23:57
There's this sphere, which is another place that is a communal environment that is truly fantastic. >> Yeah.
2:24:04
When it comes to the kind of event that you're talking about with Barney Heimer, um pardon me, the the fact of it is is that people are looking for that.
2:24:12
People want to engage you know, a long time ago, and I say really a long time ago, you know, remember when we were looking at the kinds of shared experience, like interactive television, where you could choose what you're watching with anyone at any particular time. Yeah.
2:24:30
It didn't really go over that great. No.
2:24:32
You know, it was good and it was well done, but people really didn't want to watch television and share with their neighbor or whatever it might be.
2:24:40
They would they would talk about things when it came to programming with their friends. Yeah. Um but not that many.
2:24:46
So, audience sizing is really critically important, and events like that are unquestionably key in driving, you know, merchandising, participation, and in just engagement.
2:25:04
And this is what's happening around the world when it comes to the new types of episodic television, whether it's on Blue in Turkey or, you know, the different types on Acorn in the UK.
2:25:12
These are really important networks that are driving a lot of viewers at this time. Mhm.
2:25:20
Uh Hollywood has a lot of fear around AI.
2:25:23
A lot of people don't even want to talk about even if they're excited about it because of fear of of pushback.
2:25:28
it because of fear of of pushback. I've had a slightly more optimistic view that if you look back the last 10 years of traditional Hollywood, as in like the place, not like the the industry, a lot of these big productions are being shipped uh out of state or overseas, and I've had this optimistic view that Hollywood could see a general resurgence if the storytelling capacity stays here, but like we're no
2:25:59
longer having to do as many expensive shoots in in Europe or the Middle East or any of these other places because those scenes can now be generated, but the talent that is involved with, you know, effectively directing, directing, producing, casting, those people could
2:26:15
have a higher volume of work, and budgets could potentially be distributed instead of one, you know, $200 million movie, you might have 10 $20 million productions that are using AI to be more efficient. Am I totally off? Do you think that's a Am I totally off?
2:26:31
Do you think that's a possibility? What's your view?
2:26:38
Well, [clears throat] AI's been here for about 20 years.
2:26:41
>> [laughter] >> So, it's already been involved when it comes to production and and the ease of the overall product being created.
2:26:51
You know, where it comes into play is, you know, dailies at the end of the day you can take a look at five, six different cuts very quickly all during live shots of what you're putting together.
2:27:01
When it when it comes to AI involved, it's it's one of those situations where it's a tool, and it's a tremendous tool.
2:27:09
And it's how and how that tool is leveraged is going to become key.
2:27:16
You know, there's different efforts when it comes with all the different types of content creation editorial tools.
2:27:20
It's the managing layers that stay on top that allow for the efficiencies that come into play to create pieces of content, and then that lowers time.
2:27:33
Lowers time is lowered budgets.
2:27:33
Lowering budgets means rapid and escalating the kinds of productions that then are created so you can you can meet the timeline, you can meet the pipeline, you can get more programming into you know, into the distribution points to be able to see.
2:27:50
Some of the more interesting things that are out there that are really key is how not just AI, but AI when it comes to the content creation.
2:27:59
Like for example, the ability to on the fly level set and uplift all of that content into high-quality um visual like 4K and UHD.
2:28:08
Like we're looking at UHD we're looking at UHD right now. Yeah. You know, and well, 4K. Yeah.
2:28:15
And and the fact of it is it looks pretty good.
2:28:18
Well, and and a lot of content that's being created that's pushed out there Mhm.
2:28:23
it's being shot in 4K, but as soon as it cuts to advertising it drops down drops down to 720.
2:28:27
So, you get this fragmentation that takes place. It doesn't look good.
2:28:31
Then it goes back up to 4K. Yeah.
2:28:32
You know, and so level setting all of that is back to the point of audience engagement.
2:28:37
And so audience engagement is really key.
2:28:40
Um you kind of touched it a little bit when it says to the cost of production. Why wouldn't you?
2:28:48
Why wouldn't you go down to Rio or Johannesburg or Prague and do a product or or you know, London and do a production for 80% of the cost of what it took to do in um LA or Hollywood. Mhm. Okay.
2:29:02
There was a shift on that.
2:29:06
The the actors pool the production capabilities now that you have live engagement and production facilities in LA or San Francisco or London, because of the state of technology isn't just the AI component, it's how things are made and how things get done.
2:29:23
So, the actors are you know, exceptional in each one of those cities I like um identified.
2:29:31
Paris, some of the French programming, it's great.
2:29:33
Some of the the Madrid programming, um all of it is exceptionally well, but then the cost of putting that together is considerably less.
2:29:42
That's the problem that we have.
2:29:45
Um AI can actually do a lot more engaged activities and enhance that capability as we move forward.
2:29:52
Uh we just had a recent um event um out in the desert.
2:30:00
That's not like milling around in the sand or anything like that.
2:30:03
It's like what we're talking about it was said Palm Springs is quite lovely.
2:30:07
And and the fact of it was is with what's called a Hollywood Professionals Association. That's HPA.
2:30:14
And that we we delve into that kind of discussion about the impact of AI.
2:30:17
And where we really come down to is that it's another fabulous tool.
2:30:23
It's it's to be respected more than it is to be feared.
2:30:28
And yes, will it kind of lessen the amount of work workers that are going to be approaching this?
2:30:37
Yeah, but those workers if the workers are very straightforward, you either learn how to use tools or if you stick your heels in in the ground and drag yourself, then you're going to be passed by. Yeah.
2:30:47
So, it's you learn how to use something to make something move forward, but Yeah.
2:30:51
And we saw that with the digital transition where you know, people who shot on film, there are still movies that get shot on film.
2:30:59
It's a smaller community and those films are special in their own way and I don't think anyone expects a complete revolution overnight in any of these things.
2:31:10
We do have one last question from the chat. It's sort of funny one.
2:31:13
There's a surfboard behind you that looks like it's seen a very many amazing beaches. Do you surf? Where do you surf?
2:31:23
Tell us about the surfboard. Um I have. Nice. And I do.
2:31:25
As I get a little bit older my wife's balance sucks.
2:31:29
But, you know, other than that, um, um, my son's a big surfer. We're all surfers. We're a water family.
2:31:35
You know, and so so in the course of that, yes, I do.
2:31:39
And, um, now I'm more of a sponger.
2:31:42
If someone's actually identified that then they'll might know what a sponger is. Mhm.
2:31:45
I know what a sponger is. But, I respect it. I respect it.
2:31:48
You you you've you've earned the sponge, I think.
2:31:52
So, it's it it kind of comes back to that you know, that's a prop. What can I tell you?
2:31:57
It's I so so I I had a Hayden Shapes that the the same the same brand that I I took to like 20 some countries and it did eventually snap.
2:32:09
They're like weirdly this this type of board it's like they're like extremely durable in some ways, but they have these certain impact points where they're just like over. Okay. Yeah.
2:32:21
Uh, but a lot of fun memories on, uh, Yeah.
2:32:24
my Hayden Shapes back in the day.
2:32:27
Well, thank you so much for taking the time.
2:32:29
Uh, we appreciate you taking the time to come chat with us today.
2:32:32
Hope you have a great rest of your day.
2:32:34
Hopefully see you out in the water. We'll talk to you soon. >> Thank you.
2:32:36
And I hope I was I added some value. Absolutely. Have a great one. All right. Goodbye.
2:32:43
Let me tell you about MongoDB.
2:32:43
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2:32:47
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2:32:50
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2:32:52
And without further ado, we'll bring in Jagdeep Singh from Roda. ai. How are you doing? Welcome to the show. Thank you. Thanks. How are you?
2:33:00
Thank you so much for taking the time to join us.
2:33:04
Since it's your first time on the show, please introduce yourself and the company a little bit.
2:33:08
Yeah, so I'm Jagdeep Singh.
2:33:08
I'm one of the co-founders of, uh, Roda.
2:33:10
ai and, uh, we are building a generalist intelligent, uh, robot foundation models to solve real problems in manufacturing and logistics. Okay.
2:33:20
Uh, help me understand what I'm thinking of when I think of a a robot.
2:33:23
I I'm familiar with the KUKA robotic arm, the Optimus humanoid, like the Roomba.
2:33:30
There's so many different ways to think about robotics.
2:33:32
The Tesla Model 3 is in many ways a robot.
2:33:35
Uh where do you see the first uh robot coming online? Yeah, great question.
2:33:40
So, uh we've had robots for a long time.
2:33:42
Traditional robots have been around for decades, and they're exactly what you described.
2:33:46
KUKA, you know, robots like that.
2:33:48
They're uh basically designed to move through a predefined trajectory as programmed, and they can do one thing really well over and over again repeatedly.
2:33:54
Uh what they can't do is deal with variability.
2:33:56
Right now, so they don't learn by themselves from data.
2:34:00
There's a new class of robots people working on in Silicon Valley.
2:34:03
It's kind of a hot thing as probably you you've heard.
2:34:05
Uh these are robots that have a neural network uh capability and can learn from data. Yeah.
2:34:08
You feed them a lot of data of robots moving through certain trajectories, and they can learn for themselves how to perform those tasks.
2:34:15
Uh the problem is those approaches use what's called a VLA, a vision language action model.
2:34:21
I don't want to get you guys with the details, but We love the details. So, so go ahead. >> Spill the beans.
2:34:28
Uh it was so so these VLA models uh they're you know, you've probably seen robot demos on the internet where they doing cool things like making coffee or holding a tissue.
2:34:35
The problem is all these demos are just that, they're demos.
2:34:39
They work work well in a lab setting, uh but they they fail if you move the model into the real world.
2:34:43
And the question is well, why do they fail in the real world?
2:34:46
Well, because uh these models are trained on relatively small data sets uh because you don't have internet-scale data sets for robotics trajectories.
2:34:53
So, people teleoperate robots, like puppeteering robots around to do certain tasks, and you collect the a number of trajectories that way.
2:35:01
Then you can train the model to do that task, but because of the the quantity of data is so small, they can only work well if the test set is very similar in its distribution to the data set it was trained on.
2:35:11
And the problem is you can do that in a lab setting, but when you take it to the real world, there's a much broader diversity of of settings, of configurations, of objects, lighting, and so on, and the models fail.
2:35:21
So, that's the central problem that we were starting to address is how do you get robots to generalize beyond these very contrived situations that you know, that people have shown they work in in lab settings.
2:35:33
And so, we we've got a different approach that we're taking. >> Okay.
2:35:36
Pushing back against the teleop strategy because I was totally on board with the no teleop thing during the Tesla boom and but then Waymo seemed to do a lot of teleop and it seemed to sort of work.
2:35:51
And so, when I think about ways to get a lot of data, teleop doesn't seem like the craziest thing in a world where we have a bunch of Scale AI, Mercor, there's all these data, you know, RLHF teams that are, you know, sort of manually curating answers to questions for LLMs.
2:36:08
Like, the human in the loop for a medium amount of time seems to be a tried and true path.
2:36:13
Why are you Why Why does teleop not make sense in this particular industry? Great question. I'm glad you asked it.
2:36:20
So, I think self-driving cars are a bit of a special case cuz the car is basically a robot that is very easy to teleop with.
2:36:25
Sit in it, basically have four actuators, left, right, speed up, and slow down. >> Yeah.
2:36:32
And and we we all been driving cars for a long time and you can easily put in millions of miles in a car, which is what Waymo and the other ones had to do to learn how to self-drive.
2:36:38
They did collect a lot of data.
2:36:40
But even there, they don't have all the data they need.
2:36:43
There's a lot of so-called corner cases that those cars run into that cause issues.
2:36:47
In the case of robotics, the problem is much more serious cuz now you're talking about manipulation.
2:36:51
You're not just you know, operating a robot with a single environment like a flat road.
2:36:54
You're dealing with you know, the full dexterity of a human hand, like 20 degrees of freedom per hand.
2:37:00
Every object is different.
2:37:02
Every type of task is different, right?
2:37:04
And these things become very difficult to teleoperate.
2:37:07
Teleoperation process for these, you got to wear a headset.
2:37:09
You have the joysticks in your hands and you're trying to move the robot around.
2:37:11
It's just It's just very hard.
2:37:13
And and and Can I mean the problem isn't just the quantity of data, although that's obviously a problem.
2:37:18
You could spend a lifetime doing this and we think you wouldn't get to internet scale.
2:37:21
But the big issue is the diversity of data, right?
2:37:24
If all the data you have is data that you've intentionally collected, then you almost by definition haven't seen the corner cases.
2:37:30
You haven't seen all those edge scenarios that cause failure.
2:37:34
So, the way that we're approaching it is is different altogether.
2:37:37
We you know we we Our team comes from generative AI and computer vision.
2:37:41
And the idea is, you know, if you look at every other AI model that's worked, they all start with an incredible amount of data.
2:37:47
Typically, you know, a whole internet's worth data, whether it's language models or image models or video models.
2:37:53
And then there's a small amount of fine-tuning that you use to align the model.
2:37:57
That fine-tuning data set Teleoperation is fine for that, by the way. That's what we're doing.
2:38:02
But for the pre-training, it's just completely inadequate.
2:38:04
So, what we did is say, "What What data set is there that's internet scale, massive diversity, and from which you can learn about the physics of how things move?"
2:38:12
And there's only one answer.
2:38:15
That's internet video, right?
2:38:15
So, what we Because our team comes from computer vision and generative modeling, that was the approach that we took.
2:38:21
So, we basically literally trained the model on hundreds of millions of videos, really millions of clips in fact.
2:38:27
And and in our view, the model has seen almost anything that that you can see in reality.
2:38:34
And then with a tiny amount of teleoperation, that's literally on the order of 10 hours, right?
2:38:37
Compared to what the VLA approach requires, which is, you know, like tens of thousands if not hundreds of thousands of hours of data, you can actually teach the robot to do certain tasks.
2:38:46
And that level of data efficiency is, you know, we've never seen.
2:38:50
That's kind of the one one of the big the big breakthroughs here.
2:38:52
So, what is the What are the What is the early customer set going to look like?
2:38:56
Are you Are you Can you work with any type of robotic manufacturer, everything from humanoids to arms, or is there a particular focus?
2:39:06
What are you most excited about? Yeah, great question.
2:39:08
So, so you know, we're not going after the consumer market.
2:39:11
We think that there's lower hanging fruit in the commercial industrial logistics market where you already have a lot of, you know, tasks being done that people are being paid for where you could use help from from automation.
2:39:25
And and we we actually are full stack company.
2:39:27
So, we have this AI model that's very cool.
2:39:29
We call it the direct video action model because makes a video of what it thinks the robot should do and then it it converts that to action.
2:39:36
So, the robot actually does it and does that in closed loop. So, it we observe it.
2:39:39
Basically, the way the model works is observe, predict, act in closed loop.
2:39:45
And so, that's a key part of our value creation, but we're we're a full stack company.
2:39:48
So, we're doing the robot hardware as well.
2:39:50
Why are we doing hardware even though there's 100 plus robot companies in the world?
2:39:53
Well, because we couldn't find one that actually met the requirements of the use cases we're interested in.
2:39:58
We wanted to lift 25 kg not just once, but all day long.
2:40:02
That's called rated capacity, right?
2:40:05
If you if you try to do that with conventional robots, you'll burn out the motors and that obviously is a reliability issue.
2:40:09
It has to be reliable enough to last the whole 3 years that we're targeting basically.
2:40:12
But more importantly, we wanted the robot kinematics to have what's called a linear response.
2:40:17
Linear responses are are actually systems that can be modeled by an AI model.
2:40:23
If you have any kind of non-linearities in the system like compliance or elasticity, that's very hard to model and and that doesn't work well with AI.
2:40:30
So, we wanted to build more of an Apple-like solution where we control the OS and we control the hardware in order to provide a full solution to the customer.
2:40:38
Having said that, some of our customers do want our model to control their existing hardware.
2:40:42
You opened up by talking about about KUKA for example.
2:40:45
A lot of them have KUKA hardware.
2:40:46
So, it would be very straightforward to imagine a KUKA arm sending an API call into our model saying, "Here's what I see. What should I do?"
2:40:55
The model responds and says, "Do XYZ."
2:40:58
The robot does that and and and so on.
2:41:00
So, we we will make the model available to third parties.
2:41:02
We want to create a whole ecosystem here, but day one, we're really focusing on providing, you know, just a full solution that that is really tightly integrated. >> Yeah.
2:41:12
So, great answer to the the teleop question.
2:41:16
Talk about the sim-to-real gap.
2:41:16
Why is simulation, building a physics engine, and a robot that perfectly mirrors the human body or whatever your hardware is, all the different hinges, and then running that in a virtual environment, Unreal Engine, or something like that, and then trans- transferring that learning back to the robot.
2:41:37
Why does that not work as well as people expect it to work?
2:41:42
Well, so, first of all, excellent question.
2:41:43
You guys are more up to speed on robotics than than I would have guessed.
2:41:46
So, so, sim-to-real is exactly what you described.
2:41:52
You learn in simulation and you try to apply that in the real world.
2:41:54
There are two problems there.
2:41:55
One is, no matter how well you try to model the laws of the physical world, there's always what's called a sim-to-real gap, right?
2:42:03
You can't perfectly model all the complexities of things like deformable objects and transparent objects with light moving and so on.
2:42:11
But, more important than that, it's the same kind of problem that happens with teleoperation, which is a lack of diversity. Right?
2:42:16
If all the data you're collecting is intentionally collected, then you're not going to see all those corner cases.
2:42:21
And that's the fundamental difference between the lab and the real world, the so-called long tail of the distribution. Right?
2:42:27
In the real world, right, you see a lot more of these long tail events that you might only see once, yeah, you know, in your entire, you know, lifetime.
2:42:34
But, you need to be able to deal with them.
2:42:36
A good example of that, by the way, was this case where, you know, one of the self-driving cars ran into a woman on a bicycle chasing a chicken under the tree, right?
2:42:44
Now, there's no way you're going to see that more than once in in your data set.
2:42:47
And and even that you're not going to see in your in your, you know, intentionally collected data set.
2:42:53
>> But, you might actually see it on internet video.
2:42:55
Like, there is a video right now of Tony Hawk doing the 900 and that is a very weird thing for humanoid body shape to do, but he did it. It's real.
2:43:04
It It then the laws of physics apply.
2:43:06
So, there's something that you can learn from that even if it's in the longest tail that applies back to just picking up this diet Coke and taking a sip.
2:43:14
To share and and that's and that's actually a really important point.
2:43:15
So, when we when we curate our data sets for the pre-training, we don't try to overly curate them down to for example manipulation tasks.
2:43:23
We have things like videos of waves crashing on a beach.
2:43:27
Which you might think has nothing to do with manipulation.
2:43:28
But it turns out there's some knowledge about physics and how the world works in those videos.
2:43:31
And and that's why this model can can generalize so powerfully is cuz it can train on so much stuff that it just in the same way that ChatGPT was trained on, you know, ChatGPT can can produce, you know, Shakespearean lyrics if you want or, you know, whatever you know, uh uh plays, but but it wasn't only trained on Shakespeare.
2:43:49
It was trained on rap lyrics and and Twitter feed and so on.
2:43:53
And so the same thing applies here.
2:43:53
You want to be able to train on everything to provide that prior on how things move.
2:43:59
And then you align that prior with robot specific data from teleoperation to perform the task well.
2:44:06
That makes a ton of sense. Well, congratulations.
2:44:07
I want to have >> round come together? Who participated? >> who participated?
2:44:10
Yeah, it's a great round.
2:44:12
So, uh we we were announcing a Series A.
2:44:13
So, it's it was led by, you know, Costanoa Ventures, led by Temasek, led by Premji. Congratulations.
2:44:23
450 $50 million Series A. That's a lot of money.
2:44:27
>> Great way to come out of stealth.
2:44:27
I always I recommend every founder if you're going to come out of stealth I'm half a billion for new for new companies. >> a splash. Yeah. And project confidence. We appreciate you.
2:44:39
>> [gasps] >> Have a great rest of your day. Thank you.
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Let me tell you about Plaid.
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2:45:00
Without further ado, our last guest of the show. >> Or what?
2:45:05
>> Scott, [music] how you doing? >> What's going on? Fantastic.
2:45:07
How's it going, gentlemen?
2:45:09
It's going fantastically over here, too.
2:45:11
>> Uh it's great to see you here. Are we up in the corner?
2:45:15
Uh you got a you got an interesting uh What's what's that? It's our logo. >> I don't know. It's uh I don't know.
2:45:20
It's really I was just saying It's like you're looking It looks like you're looking at me over here.
2:45:24
I have my screen up here. >> Okay. >> Okay. Okay.
2:45:28
Well, first time on the show, please introduce yourself and the company. >> over here. Hang out. I'm kidding.
2:45:33
>> [laughter] >> Uh that's that's that's that's that's better. That's better. >> Okay.
2:45:39
Uh please introduce yourself and the company.
2:45:42
I'm Scott, co-founder and CEO of Throne Sciences, where we're building the first device to track gut health, hydration, bathroom habits, and prostate health hands-free automatically every time you go to the bathroom.
2:45:51
So, people like to analogize us.
2:45:52
We're Woop for your poop or Oura for your flora. Mhm. Uh very very cool.
2:45:58
And yeah, just uh first time on the show, so uh >> Is it all direct-to-consumer or is there pay with your insurance?
2:46:05
How How does all of that work? Yeah, great question.
2:46:09
So, we're launching today direct-to-consumer. We are HSA FSA eligible.
2:46:11
And we do have a longer-term insurance B2B roadmap, but uh kind of the the thinking there is it's really hard to build a B2B company that ever succeeds B2C. Yep.
2:46:24
Whereas, if you build a consumer brand, the people you're ultimately selling into are people. Or where?
2:46:29
Talk about Talk about the journey to get here.
2:46:32
I know it has it been 2 years at this point?
2:46:34
It It's It's uh you guys have been grinding away.
2:46:37
Uh and it's awesome to finally get to launch. I appreciate it, man.
2:46:41
It's been almost 3 years.
2:46:43
Uh so, we originally pivoted.
2:46:43
We were started in nurse staffing, and then as soon as COVID ended, that was a terrible business to be in.
2:46:49
So, we decided, uh, let's go back to the drawing board.
2:46:52
Both my parents are doctors.
2:46:54
My dad's a medical device inventor.
2:46:55
My co-founder Tim, he comes from a healthcare family.
2:46:57
So, we converged on healthcare as a interest area for us and he had been thinking about smart toilets in the abstract since college.
2:47:03
This is like, you know, this is a sci-fi inevitable future.
2:47:08
Uh, and we called my mom one day and asked her, she's a geriatrician.
2:47:09
It was like, "Mom, is there any medical utility to looking at people's waste?"
2:47:12
And she goes, "Uh, honey, there's a joke in the field of geriatrics that all old people talk about is their kids, their meds, and their poop.
2:47:19
And it's so true that I no longer give my phone number to my patients because they send me so many pictures of their poop." Wow.
2:47:25
>> And Tim and I walked away from that conversation like, "Wow, that's crazy, but also really interesting because it means that people intuitively appreciate that there's health information in their waste. >> Mhm.
2:47:34
And B, it's so common that it changed the way my mom as a physician communicates with her patients as a blanket policy." Mhm.
2:47:39
And so, we started doing a bunch of homework and just kind of, you know, arrived at this perspective on the world that is you have continuous monitoring on every other category of your health, right?
2:47:48
Your sleep, your cardio, your respiratory, your metabolic health.
2:47:51
Nothing looking at your GI or urinary health despite the fact that two in three Americans in the survey said they'd experienced GI symptoms in the last week, and that was in 2019 before GLP-1s were a thing.
2:48:00
Uh, 50 million Americans have a diagnosed urinary tract condition.
2:48:04
60 million Americans have a diagnosed gut health condition.
2:48:06
And you have a 10% lifetime risk of being diagnosed with a cancer of the lower GI or urinary tract.
2:48:12
So, that's about one in six US cancers.
2:48:13
And the earliest signs they leave are microscopic blood in your waste.
2:48:17
And so, there's a, you know, our ultimate vision is building what we call the first continuous cancer screener.
2:48:22
Okay, microscopic, so you can't just use a camera on the inside of the toilet.
2:48:26
What is the actual data capture?
2:48:28
You can't put everything in a mass spec either.
2:48:30
So, you're somewhere in between.
2:48:32
What is the actual sensor stack like?
2:48:36
>> So, the device we're launching today Okay. >> is Throne 1.
2:48:38
This is a camera and microphone that clips onto the side of your toilet. >> Okay. Just like that. Like that. >> Super easy. Easy.
2:48:43
And from there, it has a little sensor on the top here that detects when someone is at the toilet or is sitting on the toilet. Sure.
2:48:51
It uses your Bluetooth to know this is Scott versus John or Jordy. Sure.
2:48:54
Uh so that's hands-free automatic.
2:48:57
Once you've installed it, you never have to touch or think about it.
2:49:01
Uh and then it uses a dozen different computer vision models in the back end to track gut health, urinary function, prostate health, and hydration.
2:49:08
>> And then the last big category or the last the ultimate innovation that we're marching towards is that smoke detector for urinary tract and GI cancers.
2:49:14
And so this is a uh R&D device that's going into the future throne that can image across nine different wavelengths looking for the unique spectral fingerprint of hemoglobin.
2:49:24
Yeah, so you can go deeper than just a photo.
2:49:27
Uh I sort of don't want a ton of push notifications from this thing.
2:49:30
I only want it to call me when I something's a problem.
2:49:34
Except that's you, but I would expect the average the people love There's almost nothing that Americans love more than health data. >> I guess.
2:49:43
Yeah, so what is consumer You check your sleep score.
2:49:46
What's wrong with taking your >> And [laughter] then what is uh what what What do consumers demand?
2:49:53
And then what do you actually do?
2:49:55
So when you say what did consumers demand?
2:49:57
>> Yeah, yeah, like like how often do consumers want to hear from the device in terms of reporting, in terms of Do they want a weekly newsletter?
2:50:02
Do they want a daily report?
2:50:04
Do they want a push notification constantly?
2:50:05
Like, yes, you're right, I check my sleep score a lot, but most of that's because I'm competitive with you.
2:50:09
I uh I don't actually want a push notification about my sleep every single day.
2:50:14
I more want to be able to look back on it and understand a trend and understand, okay, actually there's a long-term trend here.
2:50:23
Maybe I need to get to bed to earlier or something like that.
2:50:24
Like I I I don't actually It's not in my daily review of my life, but what are your customers seeing and what's most effective?
2:50:31
Cuz ideally for me as a customer, I would just say, "Hey, just just be quiet unless there's a problem and then tell me to go to the doctor."
2:50:41
Yeah, and I think for people who have healthy GI tracts, that is a perfectly reasonable position. Yep.
2:50:48
And for people who are, you know, having a gut health journey, so to say, the number one thing they want is well, two things.
2:50:55
It's number one, if they're working with physicians or caretakers is the >> Yep.
2:51:00
Patient reported outcomes are notoriously terrible, particularly when when it comes to your gut health.
2:51:04
And so, bringing a objective measure of my gut health into my physician's office is a huge leg up.
2:51:09
Like the the word that comes to mind that keeps coming up in these conversations is people say it's humiliating to be asked to keep a spreadsheet of their bowel movements.
2:51:16
So, automating that is huge.
2:51:16
We really quickly on on that on that point is is is the is the patient experience just like patient goes into the doctor's office and says, "Hey, I have this app.
2:51:28
Let me open it on my phone and you can just scroll through it." Is it as simple as that?
2:51:33
So, that's what it is right now.
2:51:33
We're building exports and then the next big like, you know, the first kind of B2B feature set is going to be a like a provider dashboard that allows your provider to request exactly that kind of information.
2:51:44
But the data is like 99% of the battle, right? Okay, cool. Sorry.
2:51:47
I didn't I didn't cut you off. Second thing.
2:51:51
Yeah, second big thing is and this is kind of the thing that has historically plagued any wearable device is when you have a wearable, you're by necessity measuring outputs, right?
2:52:02
Quality of sleep, your heart rate, those are all outputs.
2:52:03
And what people want to be told is what are the inputs I need to change to optimize my outputs.
2:52:07
And when it comes to gut health, so much of that has to do with diet and lifestyle.
2:52:14
To understand what are my dietary triggers, what are my sensitivities, what are my intolerances.
2:52:17
And this is true of people with, you know, enzymatic deficiencies as well as IBS and IBD.
2:52:22
And so, we are building a what we call an AI gut health coach that will allow you to narrate what are the things that I ate and what does my lifestyle look like, What are my exogenous stressors?
2:52:32
And then it correlates those against your objective ground truth gut health to be able to tell you these are the things that are best and worst for your health specifically.
2:52:40
And it is ultimately we call this like gastrotyping, right?
2:52:43
Which is like how can we understand the black box that is your gut specifically?
2:52:48
And when we have this at scale, we'll have 10,000 other users that have a similar profile of inputs and outputs and we'll know what are the things that work best for them and let's start you there.
2:52:58
Talk about customer acquisition strategy, marketing, top of funnel.
2:53:00
It feels like it's sort of hard to bring like a really cool celebrity on board for this potentially.
2:53:06
At the same time like this might just go viral naturally.
2:53:10
Like what are you doing influencer strategies?
2:53:12
Are you spending a lot of money on meta platforms?
2:53:16
>> there's an alpha there's an alpha for for celebs to be like super transparent around health because it can deepen a connection with with their audience.
2:53:24
>> Yeah, yeah, Rowan Serena Williams that was sort of an unlikely partnership and it and it happened.
2:53:27
But what what what do you think in terms of the next couple of years of of of marketing?
2:53:33
Yeah, so we want to do what Function of Health and Superpower have done with blood testing or what Levels did with continuous glucose monitoring.
2:53:40
Like metabolic health what straight up was not a term until they breathed that into the zeitgeist.
2:53:48
And the playbook there is align yourself with the most credible physician in that space online. And so we've done that.
2:53:55
Our chief of science is Dr. Karan Rajan.
2:53:57
He's the biggest GI doctor on the internet.
2:53:59
Has 10 million followers across TikTok, Twitter, YouTube and Instagram.
2:54:03
And he's our chief of science.
2:54:06
He's an amazing health communicator and educator.
2:54:07
And so you know, he's equity aligned and working with us to help educate people about functional GI and why tracking things like urinary function and prostate health are important and can help improve your health outcomes.
2:54:22
The next big piece of this is to your point celebrities, influencers.
2:54:26
I've been amazed at you know, we have like even back when we had like a thousand followers on Instagram, probably like 5% of our pre-orders were celebrities and like celebrity entrepreneurs.
2:54:38
Like it's crazy that that there's I I give celebrities a lot [laughter] of credit for being really on the cutting edge of longevity and thinking forward about, you know, what is it going to look like when I'm 80, 85.
2:54:53
How did you how did you process Kohler coming out with with with their version of a smart toilet?
2:54:58
When I saw that, it was it's a shock to me cuz when we first met, I was like, "Scott's crazy. This idea is crazy."
2:55:08
>> [laughter] >> The the it's it's going to be, you know, this incredible challenge, but the beauty of it is no nobody's going to be crazy enough to compete with him.
2:55:14
And then then [laughter] I saw it was sometime last year Kohler came out and I was like, "I guess Scott's really onto something if if Kohler, you know, is uh So anyways, my view was super validating, but how did you process it?" Uh the same.
2:55:29
I think, you know, the biblical David was a shepherd until he met Goliath and Kohler is incredibly validating here, right?
2:55:36
Like it shifts the conversation from why would you want a device like this to which device like this is better and why would I want this?
2:55:43
Uh and I you know, I have opinions on the product, but I'll say it's an immaculately engineered product and they paid attention to a lot of the wrong things.
2:55:52
You have to manually start it every time you use the toilet.
2:55:54
And like you've been going to the bathroom the same way since you're potty trained at two years old.
2:55:59
Introducing one extra step into that routine is just not reasonable.
2:56:03
That's not meeting people where they are. Great point.
2:56:05
And I personally I don't trust big toilet.
2:56:07
I don't trust big [laughter] toilet.
2:56:09
I'm not going to I'm not going to give big toilet my data.
2:56:13
Yeah, I mean also just uh you know, a like a new toilet can cost like a thousand dollars.
2:56:18
This product is much cheaper than that.
2:56:20
And so for a lot of folks, they'll just say, "Well, there's actually no problem with the rest of the device.
2:56:25
I I I just want to get this as an add-on."
2:56:28
Uh similar to the Eight Sleep, people like the bed that they're sleeping on, they just get the cover. Yeah. It's a time on. >> Awesome, Scott.
2:56:33
Well, I'm so excited for for this to be out in the world, and and uh to finally uh for you to finally [snorts] be launched.
2:56:41
I know how hard you've been working on this for so long, and it's awesome to see all the progress and how how intentional the whole product is. So, Thank you so much.
2:56:49
>> to the whole team, and uh yeah, I'm excited to see what insights people have and all the learnings that come from this. We'll talk to you soon. Goodbye. See you. >> Thank you. Cheers.
2:56:57
Well, if you're looking to improve your gut health, your overall health, change your diet, maybe you need a new job, and you need to work at a different tech company, but thankfully, Riley Walls over at OpenAI scraped tech company cafeteria menus, and today he launched lunches. fyi.
2:57:19
I think it was actually yesterday.
2:57:19
So, he scraped the corporate cafeteria uh menus from a number of tech companies that you're obviously familiar with.
2:57:25
Not only tech companies, Nvidia, Target, Chime, Century, our sponsor is scraped in here, and you can see that Century got an S tier for our taste, an A tier for protein, healthy, C, not too bad, uh vegan, A, uh OpenAI, S tier on taste, C tier on protein, though.
2:57:43
They got to get those numbers up. Riley's begging for it.
2:57:46
He's He's naming and shaming every >> Wild that Adobe pretty bullish for Adobe that they went S tier on protein. >> S tier on protein. So, you can go in here.
2:57:55
>> You can You can search every single company for how they did. Starbucks is doing well.
2:58:00
>> going S tier for taste, S tier for protein, F tier for >> F tier [laughter] for healthy.
2:58:04
So, they had truffled duck confit pizza, galbi short ribs and Korean sesame rice.
2:58:10
Uh that Sichuan peppercorn pesto sauce. >> good meals in here.
2:58:16
If you're looking for a job at this company hungry John.
2:58:20
This is going to be the decide the the big big part of the decision criteria for where you end up could reset the entire AI talent war narrative.
2:58:27
Lots of people are are picking based on who's working with the government who's not working with the government.
2:58:34
Well, there's a new access to make your decision on do you want to work for a company that serves gochu John Korean drumsticks or carnitas and tofu.
2:58:45
That's the big question in 2026.
2:58:48
B for taste as for protein and D for healthy. And an F for vegan.
2:58:54
They didn't do much many vegan options.
2:58:57
>> I feel like this all kind of tracks.
2:58:59
>> There's lots of different options here.
2:59:01
Well, you can go check it out at lunches.
2:59:02
fyi and the last launch of today is of course Jan which we talked about a little bit 1.
2:59:11
03 billion for one of the largest seed rounds ever probably the largest for European country company which is interesting because he was at meta for a long time. He wasn't in Europe.
2:59:23
I guess he decamped to Europe to the company.
2:59:28
I like the name advanced machine intelligence.
2:59:32
This presents an opportunity for normal machine intelligence.
2:59:39
We need a million bows us in a data center yesterday.
2:59:41
A million bows us in a day.
2:59:45
There's also there's good non-determinism cuz in in French, you know, a me is is friend.
2:59:53
I I think there would have been alpha like there's a lot of these AI Labs that have just names that are like machine intelligence thinking all advanced automated blah blah blah.
3:00:01
He could have just done like lacun Industries.
3:00:03
He's such a big name, you know, Ford Motor Company.
3:00:07
Let's just do lacun technology company.
3:00:12
>> [laughter] >> I think that >> The leading technology company of France. >> It'd be pretty good. It'd be pretty good.
3:00:17
So, not too late to to rename now.
3:00:21
Um normal is upset that the that the that the the logo looks very similar.
3:00:26
I mean, there's only so many logos out there.
3:00:31
Um Okay, last but not least, we got to talk about we got to talk about uh Vail.
3:00:36
Matthew Prince says >> Yes.
3:00:38
says Vail Resorts likely to open tomorrow down to where if you invested 10 years ago, you'd have done as well as putting your money in a hole.
3:00:43
It's time for a change to become more asset light, sell off resorts, and allow character and differentiation to return Yes. to skiing.
3:00:52
And what I love is our very own John Conkle uh said Cloudflare result, and this got me thinking, SaaS companies should buy mountains >> or just buy the naming rights to the mountain.
3:01:06
>> pitching Cisco on I think the name of the mountain >> Mountain.
3:01:09
>> Yes, we were we were pitching >> Salesforce Mountain.
3:01:12
>> pitching Cisco on getting the naming rights to the Golden Gate Bridge.
3:01:14
It would be the Cisco Bridge.
3:01:15
Uh >> [laughter] >> it's already in the logo.
3:01:20
Why not get the naming rights? Northstar. Northstar. >> Okta Mountain. >> Okta Mountain. Northstar.
3:01:27
Um Rampant at the very least needs to get a single skate park.
3:01:30
They've They've done a pop-up skate park at this point leading into the Super Bowl.
3:01:34
Uh just get the the most definitive skate park out there.
3:01:40
I'm sure there's something good.
3:01:41
Uh and who knows how it's doing financially.
3:01:43
Uh very, very uh interesting the story of Vail, the rise and fall.
3:01:47
Um but uh you know, hopefully hopefully if you've skied Vail this this season, you still had a fun time, more fun than if you'd invested 10 years ago uh where you didn't make any money.
3:01:57
But, that's our show for you today, folks.
3:02:02
Leave us five stars on Apple Podcasts >> new Do we have our new outro?
3:02:07
>> We have a new outro coming tomorrow. >> Coming tomorrow. Subscribe.
3:02:12
Hit the subscribe button, leave the bell on so that you're notified so you can tune in tomorrow, watch the full show, 3 hours, then at the end There's a little trip from deep in the X chat, Snowflake Mountain.
3:02:23
>> Snowflake Mountain, that's good. That's good. Okay, thank you, Trip.
3:02:25
Anyway, thank you for tuning in.
3:02:28
Subscribe to our newsletter, tvp. com.
3:02:32
>> afternoon of your life. And goodbye.