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>> [music] >> You're watching TVN. Today is Friday.
4:39
It's the day before Valentine's Day 2026. >> That's right.
4:42
>> We're live from the TVPN Ultradome Temple of Technology.
4:45
>> We should have mentioned Valentine's Day. >> Yeah.
4:48
Earlier, >> two months ago, and then again one month ago.
4:51
>> I think most of the audience is prepped for sure.
4:53
But we have some we have some ideas, some recommendations if you're looking for advice this Valentine's Day.
4:59
Of course, live from the TV fan ultradum, the temple of technology, the fortress of finance, the capital of capital.
5:02
And here's an idea for Valentine's Day. Ramp. com.
5:05
This Valentine's Day, show her you care about your future together by putting all of your couple's spending on ramp because nothing says I will provide for our family like pulling out a ramp card.
5:21
[laughter] Knows you take you handle business. >> That's right.
5:25
>> Anyway, >> handling business.
5:26
Uh the big news, Anthropic has raised $30 billion at $380 billion post money valuation.
5:32
We've all seen the revenue chart 10x growth four years in a row.
5:37
100 million, a billion now 14 billion. Will they do a hundred? That's the question.
5:43
Will they be at a hundred billion revenue run rate by the end of the year?
5:48
They're growing on track to hit that, which is crazy and completely unprecedented.
5:53
Again, they're going after all of SAS.
5:55
They're going after all of software.
5:57
They're going after all of labor, all of white collar work.
6:00
>> All in your job specifically.
6:03
>> Yeah, it's not looking good for you. >> No, we're joking. >> Never doom. >> Never doom.
6:08
There's plenty of opportunity.
6:09
Uh there are plenty of good potential outcomes.
6:11
Daario has been on uh Dark Ces Patel today and he did something else with Ross Det.
6:18
And so there there's a number of places where you can go to hear his uh his latest takes on the good ending and what he's you know guiding towards.
6:26
Um be interesting to to to follow the the the question is you know what happens to the companies that are currently under pressure with the anthropic narrative.
6:38
They have to answer this question of you know is anthropic just going to steamroll you?
6:43
What is your real source of strength?
6:45
Yeah, not just anthropic but the labs >> uh every >> YC company that is building an AI native uh you know any company that is you know slapping AI native on their website. Yeah.
7:00
>> Uh everyone's going after the opportunity.
7:03
>> So we coined a phrase.
7:04
>> We we decided to coin something. Yes.
7:06
>> It's time to uh coin out.
7:08
>> And before we tell you the coinage, let me tell you about Cisco.
7:10
This Valentine's Day, give her the gift of enterprisegrade networking this Valentine's Day so your home Wi-Fi never drops during movie night because nothing kills the mood like buffering during a romcom. Get on Cisco. Head over to cisco. com.
7:27
Uh so yeah, we were we decided so so how do you what is a phrase that you can generally apply to businesses that uh can survive and then hopefully thrive? >> Yeah.
7:42
>> In uh during this moment in time intelligence is too cheap to meter. >> Yeah.
7:48
So, so the the the question, you know, earning cycle last couple weeks, uh, every CEO's gone on if basically like if you had to answer the question like >> what talk about the threat of AI, if you just had to answer the question, >> yeah, >> like basically the companies that were just the entire earnings call was just about generally about AI.
8:05
You know, if you're like a core weave or something like that, that's a little bit more straightforward.
8:10
But if you have to answer the question, do you have a durable moat?
8:13
Uh right now with uh AI progress, your stock is probably going to sell off uh but but uh you know e either way kind of however you answer it but um uh there's a second question which is like are you a true beneficiary?
8:27
So like do you have a durable moat and then are you a true beneficiary?
8:30
So we uh decided to coin the phrase uh unsloppable >> unsloppable.
8:36
So these are companies that we'll get into that have some type of moat in an era where it feels like more code could be written in the next 12 months >> than in all of human history.
8:47
Yeah, I was kind of running the numbers.
8:49
Uh it seems possible >> is specifically not okay.
8:52
You're a company that has just spent 10 years writing a bunch of lines of code and it would take a startup a lot of time and money and they would have to hire a lot of engineers and write a lot of code to create a copy of what you have. It's like no.
9:07
>> So like to rebuild Salesforce as a platform you would have to spend billions of historically you would have had to spend billions of dollars y >> hiring thousands of software engineers to you know piece by piece build out all of the functionality at Salesforce.
9:18
Of course, you could build vertical solutions and get some amount of traction, but in general, the idea was like there was some effectively uh just a a an engineering mode and that there was a lot of code that you'd have to write to effectively compete.
9:32
So, uh I talked about uh in the newsletter today um >> set the table first.
9:38
What's going on in the market?
9:40
>> Yeah, so software has undergone the largest nonrecessionary 12-month draw down in over 30 years.
9:45
It's minus 34% wiping out two trillion of market cap from the peak.
9:51
This is JP Morgan as of a couple days ago.
9:54
Uh AI threat sparks historic software stock crash.
9:56
Goldman Sachs warns of newspaper-like decline. >> I love the newspaper.
10:02
What's wrong with newspapers? >> Still got it. Still got it.
10:05
Uh, and then as of yesterday, over the prior eight trading sessions, more than 20% of the S&P 500 had a draw down of 7% or more in a single session, according to the compound. >> That's a lot.
10:18
>> Um, quickly before we continue, let me tell you about the New York Stock Exchange.
10:22
This Valentine's Day, I recommend [screaming] flying to Manhattan, take her to the floor of the New York Stock Exchange, IPO your company, and ring the opening bell together. Great.
10:32
Your love just went public.
10:34
[laughter] Good [snorts] one, John. Uh, so >> yeah. >> Yeah. The so, so continuing.
10:40
So, uh, I wrote everything was great when we were disrupting manual workflows, but as we enter the software singularity, we are having the uncomfortable experience of disrupting ourselves.
10:52
Assume the marginal cost of software development goes to zero.
10:55
If you are a software company where the where your moat was that a competitor would have to spend a billion dollars to hire a bunch of software engineers to write millions of lines of code to create a product and you have no other modes, it's going to be rough.
11:05
Thankfully, there are moes that are unaffected by coding agents and effectively zerocost software development.
11:10
Peter Teal PT outlined four key sources of monopoly power in 0 to1 back in 2014.
11:15
These are proprietary technology, network effects, economies of scale and brand uh or you can think of as trust.
11:23
Uh most of these still hold, but proprietary technology by itself is no longer sufficient as a mode.
11:29
>> In some cases, if you have a patent to a GLP-1 drug, that is a proprietary technology that will give you uh pricing power probably for as long as the patent holds.
11:41
holds. And there are patents on certain pieces of technology that even if they can be cloned or rederived from first principles with your million geniuses in the data center, uh the first person to patent it gets to reap that value and that's just the way our >> and the issue with software how many I I I a bunch of designer friends of mine
12:03
have like a design patent on a specific kind of workflow >> and it's cool to say that you have a patent but it's not Yeah, proprietary technology can just be okay, we have a big software system, but oftent times it's more like we have uh proprietary like a something that's regulated, something that's a cornered resource, something that's uh that's scarce and will remain scarce. But yes, if your
12:26
But yes, if your proprietary technology is just you're the only person with this particular Python script, that's uh that that's probably going away.
12:36
But network effects aren't.
12:37
And some of the economies of scale, some of the liquid liquidity on these platforms is going to be durable.
12:42
You can vibe code a uh I was talking to Dar Kosher at Uber about this.
12:46
You can vibe code a pickup app that you know looks like Uber, has a map, lets you click the button, accepts payment, but if there's no one on the other side of that network to actually come and pick you up, your your your Uber Uber clone is dead in the water now. >> Yeah.
13:05
Or if a customer if somebody does pick it up and the customer has a terrible experience, do you have the do you have the resources to actually make it right?
13:13
>> But Uber works because they spend a bunch of money getting to scale and cloning that scale is difficult.
13:17
Now the whole self-driving car thing is separate because you bring Yeah, we're working on that one that that'll happen.
13:27
But uh there is there there are a set of businesses that will have to contend with the the clankerification of the economy.
13:33
But that's that's >> so becoming unstoppable means two things.
13:38
First, your business actually has to drive its economic power from a moat that is unsloppable.
13:40
And second, you need to clearly communicate that to shareholders.
13:44
Right now, if the market thinks you're just a bunch of lines of code, you're cooked.
13:46
Uh tech companies we think of as unsloppable.
13:49
You have hardware, Nvidia, AMD, Intel, Cisco, Broadcom, SKH, Highex, uh Western Digital, data centers.
13:57
So Neoclouds, things like Cororeweave, Lambda, social networks, YouTube, Instagram, X, LinkedIn, even thinking Roblox, right?
14:07
Uh they're they can be not just, you know, they have the network and and they can be a beneficiary of AI because if it's easier to make games, a lot more people will make games, maybe you'll get uh more usage.
14:17
Marketplaces, Airbnb, Uber, Door Dash, IP holders, uh Disney, Netflix, Warner Brothers.
14:23
I think if you have a lot of IP right now and the cost to produce great content drops dramatically, you're going to benefit from that.
14:29
Uh, and then platforms, things like YouTube and Spotify as well.
14:33
Uh, I said it's been an incredibly rough couple of weeks for public market CEOs.
14:37
Really disheartening on the show.
14:37
CEOs have been putting up some great quarters and then, you know, they're trading down between seven and 20%. >> Yeah.
14:45
>> Um, >> there are two main question everyone wants to know.
14:48
even if they already sold your stock to buy atoms.
14:50
Uh, one, do you have a durable moat in the software singularity?
14:54
Two, are you a true beneficiary of AI?
14:55
Many CEOs are still still struggling to answer number one, because it doesn't really matter what you say.
15:01
Just having to answer the question equals a sell-off.
15:03
And two, this one can really only be answered in the numbers.
15:07
You aren't an immediate AI beneficiary if revenue is not accelerating.
15:10
Also, [snorts] separately, there are a bunch of crazy things happening uh in the broader economy right now.
15:16
I think Besson Bessant went on CNBC at 4:00 a. m.
15:17
It brought out the big dog.
15:21
>> Um I haven't been able to catch it yet. >> It was 7:00 a. m. Eastern, right? >> Yeah, 4:00 a. m. Pacific.
15:24
So very, very early for us.
15:28
>> Um it's possible to be unstoppable, but not an obvious beneficiary, but you'll still likely sell off as the market digests and interrogates the actual real world impacts of coding agents.
15:36
Some industries will be more resistant to change.
15:39
Other industries will be revealed to have a secret source of market power that was underappreciated in the before times.
15:44
in the before times. And what I was thinking was uh uh Neielen this company was you know in in one way Neielen ratings Neielson data uh a lot of consumer package goods companies use this I'm sure uh Matina is looking at
16:00
how is this Yerba Mate selling in this store and you go to Neielson you pay them and they give you data and it just feels like an interface to sales data but they have this whole network and you just have to pay for it and it's not really something that you can just spin up. I don't know. What do you think? I don't know. What do you think?
16:17
>> I mean, isn't that kind of like what what Simile is doing?
16:19
We had them on yesterday.
16:21
They're like trying maybe you can kind of do like polls or something about how market will that's more for Yeah. prediction.
16:28
The bigger one is like is like simile would actually want that data to update their models. >> Yeah.
16:35
like you want to know, okay, uh, what stores is are actually turning my product, which stores should I be doing promos in?
16:43
Which stores should I be doubling down on, running advertising in, or what chains are working, should I push more into?
16:50
Or or even just, hey, I need to go to one chain and say, I'm working like Target's working, so Walmart should carry me.
16:56
They're not really going to accept just a simulation of that data.
17:00
They're going to want to know that an independent rating agency sort of rated >> Gold Rock just bought unsloppable. com. >> Okay.
17:09
>> Quickly before we move on, let me tell you about Apploving this Valentine's Day. Use Apploven's axon.
17:13
ai to serve her hypertargeted ads for exactly the jewelry she wants.
17:20
That way, she'll be extra excited when she unwraps presents on the big day. >> Great call.
17:26
uh capping off uh the newsletter, I said a lot of the software market feels like the office equipment and imaging sector in the '90s.
17:33
So companies like Sharp, Canon, Panasonic, revenue was still up and to the right.
17:39
Uh but widespread adoption of the internet, emails, and PDFs was on the horizon.
17:44
Even today, you'll still find a fax machine in every doctor's office, and many of the giants of that era are still around.
17:50
But if you stayed in those names, you would have missed out on generational gains by simply being long PDF. >> Had to go long PDF.
17:59
>> Uh so yeah, a lot if you look at these companies, you know, Panasonic's still a massive company and they've obviously adapted over time.
18:06
>> Uh but uh but you know, it's a >> it's a shift from growth stock to value stock.
18:11
Investors are less willing to pay for earnings that might come 10 years out because they're worried about those or 20 years out.
18:17
Instead, they're asking uh what will my return on invested capital be this year?
18:22
What will the dividend be this year?
18:24
How much cash will you give me back if I invest for a one-year time horizon or um or or or shorter or longer?
18:31
Uh let me tell you about Turbo Puffer.
18:34
This holiday, Valentine's Day, here's an idea.
18:40
Turbopuffer store vector embeddings of every romantic moment you've shared in Turopuffer so you can do semantic search for that for that time in Paris and actually find it.
18:51
Turbopuffer is serverless vector and full text search.
18:56
It's built from first principles and object storage.
18:57
It's fast 10x cheaper and it's extremely scalable.
18:59
So no matter how many memories you're cramming in Turbo Puffer, you're good to go.
19:05
You're good to go this Valentine's Day. Just do it.
19:06
Uh uh anyway um it will be interesting.
19:10
I think that there will that there will be a reckoning around uh who is able to reveal a true moat and and help people help the market understand what their source of strength is whether it's the liquidity on their platform the network effect the IP if they have a real IP that's defensible.
19:29
Um, but just having a big bag of code right now is a little bit of a weight since you're seeing so many companies that are saying, "Well, I haven't our software engineers aren't even writing the code anymore.
19:39
Yes, we're advancing our products, but um, so many companies are are going all in."
19:44
are are going all in." It's also it's it's it's pretty wild how long it's taken for the public markets to react to this this kind of oneshotting concept or this zero marginal cost code you know coding concept where >> we were having these same conversations
19:59
in Q1 of last year being like what are the implications when you can just put in the prompt box build me xyz tool right and it took a while for the models to make progress but even a year ago >> it was pretty obvious that you would get to some point where you could one got >> a big platform. Of course, reliability
20:15
Of course, reliability still a concern, right?
20:18
Security is still a concern.
20:21
>> Uh there's a lot of businesses where >> um the the potential risks of using like a vibe coded product like far outweigh the the uh the cost of just paying for the product and having something that's reliable, trustworthy, battle tested. Yep.
20:36
And uh but uh >> yeah there I mean there's still a ton of questions about how quickly disruption happens, how quickly market structures changes change.
20:44
Some things go from monopolies to igopolies.
20:47
Some igopies are going to go to perfectly competitive.
20:51
Uh it's certainly a bull market for YC companies who can vibe code something that's as good as a public company SAS product and then go to those customers and say >> maybe not as good but as feature complete >> as feature complete or at least can compete a little bit faster and say hey I'll come in with an offer that's 10x cheaper and um and and and move you over and that's just going to create some pricing pressure.
21:16
The question is what's the rate limiting factor?
21:18
Is is diffusion a real factor?
21:20
Is adoption a real factor?
21:23
Or do you do do you need for deployed engineers to go help companies transform with with agentic coding?
21:29
Or do you or can you uh or will this happen inside companies and they'll be building their own platforms?
21:34
be building their own platforms? or will or will they want just a a cheaper product from a new third party that has a different business model that's maybe more consumptionbased and something where if it goes down on a Saturday they don't need to even fire off a prompt how long until these these vibecoded systems
21:52
are like self-healing in the way an enterprise platform is and has like a proper SLA what do you think Tyler >> um I just have a question like I'm curious what you guys think about this it's so it's like um if the market is just catching up now to to uh like coding models being very good and vi coding all this stuff and they're basically like a year late in one year. What do you think like is going to be
22:11
What do you think like is going to be the thing that they're like like do you think they'll still be late?
22:14
Is it going to be like okay actual like white color work is you actually can automate a lot of this stuff and only in in in a year that they're actually going to like catch up to this.
22:22
>> Yeah, it's a good question.
22:25
The next next thing I don't know >> Tyler if I knew the answer we'd be on Wall Street.
22:31
I think we'll I think we'll be talking about it over the next couple months.
22:34
We'll need to see like glimmers of demos.
22:36
Um I mean, >> yeah, the the one thing is is like coding never felt here's the thing.
22:41
So >> I have >> in in coding [clears throat] has been a white it's a white collar job >> but has always felt a lot less fake than most white collar jobs.
22:54
Like there's a lot of jobs like email jobs, laptop jobs >> where there's like six people on a call >> for an hour >> and like one person is doing like really doing the work and the rest of them are just saying like >> nothing from my end thanks, right?
23:10
And that's like their entire day.
23:12
that's like their entire day. Whereas coding like the the best engineers were actually just like grinding long putting in the hours just and so I think what's interesting like as as some of these like more broad knowledge work tasks get more easy to automate
23:33
do those people just like they're still going to be doing meetings like at some point these companies I mean to date the the AI I the AI job loss has just been primarily from companies I would say still processing the Twitter acquisition and saying >> hey we just we we need 50% fewer people here. >> Yeah. This this selloff is is much much >> Yeah.
23:56
This this selloff is is much much more related to business model competition pricing pressure than um than automation and job loss in my opinion.
24:08
It's much more that uh there will just be more com more competition in enterprise software markets and so you assume that margins will fall.
24:16
That that's my read on this.
24:19
Uh I do have another example but I will tell you about Gusto first.
24:22
The unified platform for payroll benefits and HR built to evolve with modern small and mediumsiz businesses.
24:28
So uh my answer is the the unclankerable uh companies.
24:34
So uh right now there are industries think about um uh think about mining like I have a piece of land there is gold in the dirt.
24:44
Uh there's another company that comes and their specialization is finding where the gold deposits are on my land.
24:53
There's a third company that shows up with tractors and people that dig the gold out.
24:58
Then there's a fourth company that takes the raw ore and refineses it into gold.
25:05
There's a fifth company that is a platform for for selling that gold onto the market. Right?
25:09
So you have you have like five different layers of the supply chain to get the gold into the market from the ground. Let's just use that.
25:15
It could be oil, could be any any mineral.
25:19
Um does does robotic labor too cheap to meter change the value of the land? Probably not.
25:26
But if you have a robotic uh digging machine that can show up and dig the dig the ore out of the ground, dig the gold out of the ground at a lower cost.
25:38
Well, the company that's been set up where their moat was they employed all the best miners and they had systems to know who's good, train them, make sure that they're doing it safely, train them on the tools, make sure that they have the right equipment to dig the ore reliably, work in shifts, all of that becomes attackable.
26:01
If you're like, "Well, all I have to do to start a company that competes in the gold mining business is place an order with a bunch of humanoid robots and go to the guy who has the land and say, "I want to dig the land and I will give you a little bit more than what the other team that's using a bunch of human labor and a bunch of unautomated systems."
26:22
So I would say that that's probably the next thing that the market would be processing and the ride hailing platforms dealing with the advent of the self-driving car is probably one of these like clankerification narratives but that will come to a whole host of industries.
26:42
The question is just on a five-year timeline, on a 10-year timeline, when will it be real?
26:47
And then when will the market price it accordingly?
26:51
Because uh a lot of the pressure that you're seeing in the market is not showing up in the financials.
26:58
Like the the companies are still growing.
27:00
They're still producing cash.
27:02
The business hasn't changed, but the perception of the future of the business has changed and the perception of the future of the market structure has changed.
27:09
And so that might be the next thing if we're just to play out AI broadly.
27:15
Anyway, Phantom Cash, fund your wallet without exchanges or middlemen and spend with the Phantom card.
27:19
Let's also pull up the linear lineup and take you through who's coming on the show today.
27:25
Linear is the system for modern software development.
27:27
70% of enterprise workspaces on linear are using agents. It's Friday.
27:31
We have a lighter show, but we got some great guests.
27:32
We got Martin Skrey coming on to talk about take a little victory lap about the quantum computing thing.
27:37
Connor Hayes is the head of threads.
27:38
We hung out with him at Meta Connect.
27:40
We're very excited to talk to him about the progress of that uh platform.
27:44
Then Alex is coming on from DDN and Brett Adcock from Figure is coming on to talk about humanoid robots.
27:52
They launched a new one today.
27:52
So >> finally on the show be an interesting one.
27:56
Uh Clvicular has also been in >> the news. Oh yes. What happened? >> Uh let's see here.
28:01
streamer Braden Peters to host boxing match build as test of physical dominance.
28:08
The Valentine's Day live stream pits two figures from the male self-improvement internet against each other.
28:14
Braden Peters, the live streamer known as Clavvicular, announced Thursday that he will host a boxing match on his kick.
28:20
com channel this Saturday evening, February 14th, at 7 p. m.
28:25
Eastern, directly opposite Valentine's Day festivities nationwide.
28:31
The bout will feature two personalities from the online male aesthetics community.
28:34
A figure known as ASU frat leader, an Arizona State University fraternity member who gained attention for his broad shouldered build, and a creator who goes by androgenic, a fitness influencer focused on hormone optimization and physical appearance.
28:50
Promotional materials build the event as a championship of skeletal frame superiority.
28:55
essentially a contest to determine which man is more physically imposing.
29:00
The announcement posted to X by the kick affiliated account Kick Champamp has drawn thousands of engagements and spawned a wave of commentary from users who noted the scheduling choice with amusement characterizing the event as a deliberate alternative to the holiday.
29:14
The matchup represents the latest example of niche internet subcultures.
29:19
In this case, communities organized around male physical self-improvement and body image optimization crossing over into live entertainment. Mr.
29:26
Peters, who has built his following around content related to physique and social dynamics, appears to be positioning himself as a promoter within the space.
29:35
No official venue has been announced.
29:37
The event is expected to stream exclusively on kick. com.
29:39
So, we'll be interested to see uh how how the news kind of reacts to the event >> over the weekend.
29:47
Uh certainly uh this story has gone mainstream.
29:51
>> You know, it's funny that uh so if you're if you haven't been following these uh we've we've been taking these like viral kick clip posts and turning them into uh professionally written articles uh just as a joke, but Clvicular actually has a profile in the New York Times and it's written like that.
30:09
And so I think I think our joke is like over because it's hit the mainstream.
30:13
Uh Joe Bernstein wrote uh something that sounds exactly like something that we were joking about.
30:18
Braden Peters, known as Clavvicular, has emerged as a beacon for a group of narcissistic status obsessed men.
30:22
He wants to take his fixation with looks maxing mainstream.
30:26
Uh it's a it's a wild piece. Clavvicular is a 6'2.
30:29
He weighs 180 pounds and has a 31inch waist.
30:32
His biochromial width, basically the span of the clavicle, uh, from which the 20-year-old streamer gets his name, is 19. 5 in.
30:40
He has a midface ratio, which is derived by dividing the distance from pupil to the mouth by the distance between the pupils of 1. 07.
30:48
His chin to filtrum ratio is 2. 6.
30:51
According to clvicular, these calculations make him handsome, just not as handsome as act as actor Matt Bulmer.
30:57
Uh, and then it goes on to explain the whole looks maxing phenomenon.
31:02
And uh, it was very funny watching this happen because uh, Clvicular streams so much that he live streamed the interview with Joe Bernstein.
31:12
But of course, normally when you do an interview with a mainstream media journalist who's writing a profile, uh, it's like under embargo and you don't know it's coming until it drops and like you don't even really talk about it and uh, [laughter] the chat is confused.
31:26
This is a very popular uh uh a very popular trend on social media these days and and the New York Times is breaking it down.
31:34
Um but anyway, there there's plenty more there.
31:38
Let me tell you about public.
31:38
com investing for those that take it seriously.
31:41
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31:45
[applause] >> Uh really really wild time on the internet.
31:52
>> Anyway, uh let's let's uh let's go back to the anthropic ground.
31:55
Uh Matt Slotnik says, "LOL at the jockeying behind the scenes to land on this wording.
32:00
Quote, "We have raised $30 billion in series G funding led by GIC and CO2 valuing anthropic at 380 billion post money."
32:11
The round was co-led by Dehaw Ventures, Dragon Founders Fund, iconic, and MGX.
32:17
Lots of folks getting in.
32:17
Um, a huge part of this raise is Claude Code, says Boris Churnney, who is the creator of Claude Code over at Anthropic.
32:24
Weekly active users doubled since January.
32:28
People who've never written a line of code are building with it.
32:30
Humbled to work on this every day with our team.
32:33
That is remarkable growth at this scale.
32:36
Doubling >> Kenneth uh uh having some [laughter] humble humble pie uh or maybe maybe showing just how early it is.
32:44
He says still less annual revenue than AirPods.
32:48
AirPods, last I checked, were a 20 billion uh revenue business. >> 22 billion in 2024.
32:55
That's a massive business.
32:58
But uh Anthropic will be there in what uh a week or two.
33:00
Uh they just broke 20 top 20 in the app store and now they're in the top 10. They're number seven.
33:07
The the consumer app Clawed by Anthropic is climbing in the charts.
33:10
Uh Chatbt is number one for free apps.
33:15
Google Gemini is number two and free cash is number three. Threads is number four.
33:20
And I wonder I wonder how much this is driven by momentum still.
33:23
But >> definitely driven by momentum, right? So like it does.
33:29
So >> there's so many people that have never >> there's so many people that have never uh tried Claude and and hadn't heard of it until recently.
33:37
And again, the uh we know they're putting a lot of paid spend behind the the anti- ads campaign. Sure. Right.
33:46
>> Um so that that's going to be a factor.
33:48
>> Uh Metacritic Capital uh was pretty funny.
33:52
Back in in March of uh 2024, he said, "I continue to be puzzled by Anthropic's $18 billion valuation."
34:01
>> And then followed up and said, "Uh market is so stupid sometimes. I have no words."
34:05
But of course the market the market was right on this one so far.
34:07
You can just see they were >> Are we sure that he was saying it was overvalued?
34:13
>> He could have been saying it's undervalued.
34:14
>> No, I think he was saying undervalued. Yeah. Yeah. Yeah. Yeah.
34:16
I I I think that's why he's taking the victory lap is because he was at the time a year ago he was like why is anthropic so like s has such a low valuation based on the market.
34:29
>> This is almost two years ago. >> Oh this is Yeah.
34:31
This is almost two years ago.
34:33
So, >> huh, >> I don't actually know.
34:36
>> I don't know which way Metacritic was going. Cell phone.
34:38
Ask Martin Scrowley who's coming on the show.
34:40
And >> oh, he he responded.
34:42
He said, "The puzzle meant I didn't understand why it was worth only 18 billion."
34:47
>> So, >> always vague posts so that you can take either direction.
34:49
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34:57
Uh, I'm glad this chart is now public because it is bananas. It is ridiculous.
35:03
It should not exist, says Bruno F, the founder of Magna Digital.
35:08
$5 billion in tokens managed. Interesting. Um, >> yeah.
35:13
What the crazy crazy >> Just another pod guy says Salesforce invests in anthropic colorized.
35:19
I think when Mark was on, he said they have about a point of anthropic >> going into this round, >> if I remember correctly. What is this?
35:30
Uh, >> it's a horse giving money to a car.
35:31
The car goes and buys a rocket launcher.
35:34
The car blows up the barn and the horse is sad.
35:41
>> And that does feel like an apt analogy.
35:44
>> It's very >> uh And anthropic has been all over legacy media. >> Yeah.
35:49
First, let me tell you about Crowd Strike. Your business is AI.
35:51
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35:52
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[music] Also, fantastic Valentine's Day gift.
36:00
Uh, this from this extraordinary piece in the New Yorker.
36:02
Last summer, while Mark Zuckerberg was conducting hiring raids on other labs, Schalto Douglas, the anthropic engineer and TVPN guest, told me, this journalist, Gideon Lewis Krauss, uh that a number of his colleagues, quote, could have taken a $50 million paycheck, but the vast majority of them hadn't even bothered to respond.
36:26
Well, conviction >> they are early at a $350 billion company and are clearly very optimistic but um it is funny to just >> Daniel money mogged >> uh Daniel says so wait Claude has seatbased pricing does this mean they're disrupting themselves too of course a lot of the concern has been around the seatbased model >> but it even feels like that is less >> of that is less of a factor than just the overall threat of zero marginal cost software. >> Yeah.
36:57
Why why does Claude have seatbased pricing?
37:00
It's it's it's essentially a consumptionbased product, but psychologically if I'm rolling out uh Claude to a company and I set up seats for a team, I know that there's individual rate limits so no one no one individual is going to like blow me up.
37:18
Basically, that's the idea.
37:18
Basically, that's the idea. But this this goes into like some people were posting like if you're getting a job you should ask what you're >> but in this case it's not this is claw this is not the API this not cla >> uh but you know when you fire up cloud code like you can integrate your claude
37:33
account and so like this essentially gives you credits to write code as well and so um the the uh uh yeah there's this new meme of like if you're going into a tech company like ask what your token budget will be like what's your inference budget Um, and so, uh, I mean, these can clearly skyrocket pretty quickly. There's debates over, you know,
37:53
There's debates over, you know, oh, should I let my employees use like the fast mode or the regular mode or pro?
37:59
Like, uh, is the work that they're doing really that valuable?
38:01
If they're spending thousands a month, if they're spending tens of thousands a month, I like at certain point I need to make sure that, uh, they're not being wasteful.
38:09
And so, I think the seatbased P plan still uh, achieves a little bit of that psychological security for managers.
38:16
And then there's also an interesting um there's probably a pretty biodal distribution in uh the value that or the actual cost associated with these plans.
38:27
I would imagine that uh there's a portion of of pro users that use 100% of their inference budget every month and they cap out and they're frustrated.
38:38
They might have a second plan or they might go down to a free plan or or or limit their usage.
38:41
And then there's a whole bunch of folks who just have a seat and never use it or they use basically like very little inference or they're just asking things that can be answered by a free uh by a free tier essentially but they just like let it ride.
38:54
>> Zack in the chat says we have 150 corporate cloud users purchased by seed 50% max out in week one because of Excel token >> token. Yeah, there you go. >> Good data point.
39:05
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39:11
>> Uh Dan Primac says, "Working on newsletter, it may be shorter to list the VC firms not in the new anthropic round. >> It's a party round."
39:17
Josh says, "Got grab at least one of the big labs logo.
40:10
The uh many of the firms have sniped all three at this point.
40:13
Uh Sequoia founders fund, CO2's in a bunch, Andre's in multiple I think.
40:19
Uh there's a variety of funds that have uh have have built stakes of various sizes in in all the different labs and uh >> yeah it seems like uh Josh Kushner is one of the one of the few that has remained deeply loyal. >> Yeah. Yeah.
40:37
There's there's >> uh and we do recognize we just had some technical difficulties but it seems like we're >> back on. We are so back.
40:45
>> Let me tell you about console.
40:45
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40:53
Uh, Slow Ventures is taking the other side of the allin on AI bet.
40:58
They said, "Congrats to everyone who figured out that found that foundation models are infrastructure plays, not startups.
41:05
Now, let's talk about what happens when the picks and shovels phase ends and we're back to building actual products."
41:13
And Will Manitis says, "Night nightmarish degrees of cope." >> Yeah.
41:18
Sam Sam was always super bearish on the labs.
41:22
>> He thought they would commit. Was that what it was?
41:24
So they wouldn't have pricing power >> effectively. Yeah.
41:26
Effectively he said, you know, open source models are going to get really good. He was right.
41:30
They have gotten really good. Yeah.
41:32
But I think maybe missed that the labs would turn into product companies and stop just being >> Yeah.
41:39
Uh, you know, >> it is it is sort of interesting like if you if you wound back the clock and you were like, "My job is just to invest well in tech startup booms from 2005 to 2025."
41:53
There's one world where you're like, "Okay, I'm going to go hunt for the Airbnb, the Stripe, the YC companies, the Coinbases, all the like application layer companies, the the Instagrams, uh the Twitter's, all of these different companies."
42:07
Um, but there's a different side where you're like, I'm going to buy like Broadcom, Cisco, Nvidia, AMD, and still do really well and maybe even better depending on when you got in and when you got out.
42:20
Um, but uh it's a yeah, it's a very like just because it's an infrastructure play, even if that's true, that doesn't mean that it's not a good investment for an an investor.
42:29
an an investor. there is a little bit of like purist vibes from like a venture capitalist should or or certain funds have strategies and so they say you know I'm gonna >> slow as a seed series A but but leans more early and so
42:46
>> by the time you're looking at some of these deals investing at >> uh you know 5 10 >> you know 20 30 40 billion >> yeah start to be rough >> yeah and there's a lot of uh there's a lot of funds who have expanded and will invest in anything. Like you're a mining
43:01
Like you're a mining company, great, let's do it.
43:03
You're, you know, uh you're buying Bitcoin on the balance sheet like, okay, let's do it.
43:10
Uh there were a lot of funds that expanded what it meant to just be an asset manager.
43:13
Uh and there were some funds that that stayed very focused and you know, we'll we'll see.
43:17
But uh interesting uh highlights from the Dario Amade interview on Dwaresh Patel.
43:22
Jacob Rintamaki, friend of the show, has a quote here.
43:26
All my lawyers never want me to say the word monopoly.
43:28
Dario Dario says, "I don't think that's true.
43:32
I mean, I feel like we're in an economics class."
43:36
Uh, Darcesh says, "Do you know the Tyler Cowan quote?
43:38
We never stop talking about economics."
43:40
And Dario says, "We never stop talking about economics."
43:44
So, no, I don't think this field's going to be a monopoly.
43:46
All my lawyers never want me to say the word monopoly, but I don't think this field's going to be a monopoly.
43:51
You do get industries in which there are a small number of players, not one but a small number of players.
43:57
And so that feels like the like where things are going with both the you know expressed viewpoints of the VC firms investing in multiple labs that there's a variety of strategies to deploy intelligence whether it's the best model and get deployment and traction whether it's on the on the infrastructure side.
44:18
Um, I do wonder how many more changes there will be in the horse race.
44:25
It feels like there's a new hot model every couple weeks and then someone fires back and then they go back and forth and back and forth and with all the uh with all the flow improvements uh do not seem to be they seem much more like economies of scale and uh and process power of being able to to train at ever larger uh ever larger scales, marshall ever larger chunks of capital and uh do whatever it takes to get to the frontier and Stay there.
45:35
Let me tell you about Vanta.
45:36
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45:36
Vanta is the leading AI trust management platform. Uh >> why' we play this?
45:44
>> Because the stream we're having issues again. >> Oh no.
45:48
Uh we're working to get it back up. Uh if you can hear us.
45:51
Markets now see a 30% probability of a Fed rate cut by April.
45:55
More than 80% of easing by June. >> Uh over on Kshi.
45:59
Uh we're still seeing the Fed decision in March March 93%.
46:06
>> Say maintains rate no cut.
46:06
So a cut would be a wild card at 7% 9% for any sort of cut.
46:13
So strong GDP growth, strong job numbers, uh you know stay the course would be the the logical thing.
46:21
But uh we will continue to follow it.
46:24
Let me tell you about railway.
46:24
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46:27
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46:30
Well, railway takes automatically takes care of scaling, monitoring, and security.
46:34
Um, let's play this timeless clip of George Hots and >> Nah, we don't believe in stealth. I'm a really open guy. You are pretty open.
46:43
I mean, I tell you everything I'm doing. Come on. Here's what I say. Here's what I say.
46:47
I'm going to tell you what I'm doing and you can try to compete, but I'll still crush you.
46:54
>> Nah, we don't believe in stuff.
46:54
I'm really >> It's so funny.
46:55
Also, I don't know why that person uh cut that to be [snorts] so widescreen.
46:59
It looks very cinematic, but I like the quote here.
47:01
You think the You think the the eggs You think the [laughter] You think the eggs I lay are valuable? I am the golden goose.
47:13
>> Meanwhile, >> I'm thinking thinking people will steal your ideas if you share them is a sign of low IQ.
47:18
And I agree, we are in the era of agency.
47:21
Uh and actually going and executing on the idea is the difficult thing.
47:27
You need to be charisma maxing.
47:30
>> There's still a lot of there's still a lot of secrets to every business. Yes.
47:34
And CEOs can you can you can uh CEOs can do a 100 hours of podcast and tell you a lot >> about what they're doing without telling you the the one or two things that are actually important and it's very easy for somebody to come in and try to fast follow >> and uh ultimately just like kind of get it entirely wrong even though it it looks like >> the right >> and I think there was a huge incentive.
47:57
I mean, going back to the SAS apocalypse, there was incentive for a long time for companies that where their moat was not software to say, "We're a software company.
48:04
We need to hire the best software engineers, look at our open source projects, focus on all the cool tech that we're building when really it was a marketplace or really it was a liquidity provider or really it was a network effect."
48:16
And there if you're a network effects business, it can be sort of boring and honestly anti-competitive to just be like, "Look, we can do nothing and win.
48:24
No one wants to say no one wants to hear a CEO say that.
48:28
But we're gonna find out who can do nothing and win because we'll see it show up in the margins over the next couple financial >> uh meanwhile over on LinkedIn. George Hotz is posting. Yeah.
48:40
>> And Reed says, "George Hots is the only thing keeping my LinkedIn feed good."
48:44
>> He says, "Hello, corporate participant.
48:47
You are building the machine that will eat you.
48:49
You think your fake money will keep you safe. It won't.
48:51
You think your social climbing friendships will keep you safe. They won't.
48:54
The only choice is to stop. Tell your friends. Tell your neighbors.
48:59
If you keep feeding this machine, it will eat you.
49:00
The proposed revolutions will not be enough.
49:02
A global scale nuclear conflict might.
49:04
But even then, I'm not sure.
49:06
The problem was never AI itself.
49:09
It's the collapse of trust in society.
49:11
Apps and phones have snuck between every crevice of people, and they are run by psychopaths.
49:15
The AI will be a further wedge, just another lever to manipulate you.
49:19
You will not be able to stand up to it and you will be discarded the second you don't serve it like layoffs.
49:25
You will die atomized and alone and you won't understand that you did this to yourself.
49:29
Uh brutal nice little white pill.
49:32
[laughter] >> Nice little Friday white pill.
49:35
>> He's such a white pillar.
49:35
Well, here's a white pill first. Figma.
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49:45
But here is the real white pill.
49:48
Uh, for just $33 million, you can have a private home on a remote resort in Utah.
49:54
Can you guess where it is? >> Park City. >> Nope.
50:00
It's at the Aman Luxury Resort.
50:03
It's in the Wall Street Journal.
50:03
The residence is the first to hit the market at Aman in in remote southern Utah.
50:10
Among Giri Resort, a crown jewel in the Aman hospitality company's portfolio.
50:15
>> They're doing residences.
50:16
>> They're doing residences. global.
50:18
They have a portfolio of global hotels and residences.
50:20
They're listing the first private home for $33 million.
50:24
Located just over the Utah border from the small town of Paige, Arizona.
50:26
The hotel currently features 34 guest suites starting at 5,000 per night and 10 tented pavilions at its Camp Sera, providing a temporary escape for travelers.
50:38
But the newly built house on 9 acres can be purchased outright.
50:44
Designed by Los Angeles-based firm Masa Studio, the roughly 12,000 foot residence has six bedrooms and comes fully furnished.
50:50
Uh, it's the first of 12 planned private homes, which will be about half a mile from the resort.
50:57
>> OTP says, "But does it have a bunky?
50:59
>> Does it have a bunker? Bunk bed?" No. Uh, >> oh, a bunker. Bunker. Bunker.
51:03
>> Oh, we're going to get into bunkers.
51:05
There's a whole piece in the journal about how to secure a mega mansion.
51:07
I know you've been asking. We have the answers.
51:12
Uh, until it's sold, the home is available to rent for $45,000 per night.
51:17
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51:19
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51:26
So, um, residents have been part residences have been part of the Among Giri vision since the resort opened in 2009.
51:33
Uh the decision to offer private private residences now was spurred by the success of the 2020 tented camp launch, rising demand globally for hotel branded residences, and a sense that the property was ready to take that step.
51:49
While future residences will share a cohesive aesthetic, each will be designed to respond to the unique contours of the specific site.
51:57
In Paige, the median sale price was $610,000 in August.
52:03
Uh there are currently around a half a dozen listings above 33 million though all clustered further north near ski resorts.
52:10
Aman giri buyer interest has been strong he said uh particularly among amman loyalists and North American clients additional residential plots priced between 5 million and 12.
52:20
5 million are under contract.
52:22
And so let's get into lambda.
52:25
Lambda is the super intelligence cloud, building AI supercomputers for training and inference that scale from one GPU to hundreds of thousands.
52:33
Um, the mega rich are turning their mansions into impenetrable fortresses, and we're going to tell you how to do it for yourself.
52:43
Anxiety over high-profile violence has the wealthy spending big on armed security, bunkers, a bunky, and even moes. They're building moes.
52:49
I haven't heard of an alligator in the moat or a shark in the moat, but people are in fact building moes.
52:56
>> That's un Being an alligator salesman I feel like is unsloppable.
52:59
I think it could be clankable.
53:02
>> Yes, but maybe >> still at the moment unsloppable.
53:05
>> But you got to build the you got to build a humanoid robot that can go in the water to wrestle the alligator and that might be well ask is it waterproof? Is it waterproof?
53:14
Can it go wrestle an alligator or not?
53:17
Because I don't want it just to do my dishes and do the laundry.
53:20
I want it wrestling alligators in my moat.
53:22
So, British music producer Alex Grant was living in an under construction mega mansion in Los Angeles one morning shortly after 9:00 a. m.
53:31
an intruder armed burst into the home.
53:33
He said uh Grant said he came in and we had a tussle.
53:39
He was formerly known as Alex Didd.
53:41
Grant managed to call his manager who phoned the police.
53:44
Soon officers and helicopters were on the scene.
53:46
He briefly considered abandoning the project after the 2017 break-in, but ultimately finished the 24,000t home, which has eight pools, a car elevator, and a nightclub. Wow.
53:56
Uh, but he doubled down on security features, installing a guard house, tall gates, and a security system with retina scanners that alert the homeowner to movement in the home.
54:06
Later, I found out he had these knives on him, Grant said, who recently listed the mansion and a neighboring house for 85 million after moving to New York.
54:12
Um, in an era of high-profile violence, including the suspected abduction of Savannah Guthri's mother from her Arizona home just over a week ago, the wealthy are investing heavily in their personal security, particularly when it comes to their homes.
54:26
Security measures once reserved for presidents and royalty, safe rooms, biometric access controls, laser powered perimeter defenses.
54:35
These are now mainstream items in luxury homes.
54:37
Executive protection teams and armed guards patrol gated enclaves and suburban estates, while tech startups are rolling out predictive threat detection systems built for the ultra wealthy.
54:47
The shift reflects a hardening view among the affluent.
54:51
Traditional policing and communal safety are no longer enough.
54:53
No security, so security is being privatized and customized.
54:57
The new emphasis is reflected in sales data.
54:59
Roughly 45% of luxury homes in 2025 included a reference to privacy or security, up from 38% the year earlier.
55:06
So breakins at the homes of celebrities and professional athletes have been putting the wealthy on edge.
55:12
A group of Chilean nationals was indicted last year for stealing items worth more than $2 million from sports stars, including Kansas City Chief players Travis Kelce and Patrick Mahomes.
55:23
Travis this had something to do with the visa process with Chile where you could very easily get a tourist visa.
55:31
>> So there was these like bas the allegedly there were teams that would uh be permanently based in the US >> and then they would be running kind of the operations they'd be in the kind of war room and then basically tourists would come >> for two weeks hit a bunch of houses and then bounce >> bounce. Wow.
55:47
Uh, and those were the only people that were actually exposed to or exposed meaning they were like carrying out the different ops.
55:55
>> Well, the Miami Dolphins player Tua Tago Viola Viola, I might be mispronouncing that.
56:01
Uh, said he hired personal security to monitor his house while he's on the road.
56:05
He says, "Let that be known. They're armed.
56:10
So, if you try to go inside my house, think twice."
56:12
Uh the homes like of celebrities like Brad Pitt and Nicole Kidman have also been broken into.
56:17
Miami real estate agent Danny Herzburg of Cold War Banker said he began noticing an increase in emphasis on security in 2020 when high-profile executives were migrating from New York to Miami during the early days of the COVID pandemic.
56:29
Uh private jet tracking websites have also been an issue.
56:35
They sent chills through the through the high networth community.
56:38
Uh corporations are taking note.
56:40
companies offering personal security benefits for CEOs increased by 10% according to Goldman Sachs.
56:44
Uh, one entrepreneur capitalizing on this growth is David Widerhorn, who got into real estate after selling a tech company in 2017. I wonder what he sold.
56:51
Uh, he recently built a heavily secured home in Scottsdale, Arizona.
56:56
and he uh in early December, Widerhorn walked through the 8,600 ft property, pointing out 32 casino AI powered facial and vehicle recognition cameras.
57:10
There's also a laser intrusion detection system around the perimeter.
57:14
um pausing at a steel double gate in front of the house.
57:17
He warned that the security system kicks in even before re visitors reach the front door which is f which is fashioned out of 3in solid 3-in thick solid steel and has 13 deadbolts.
57:30
He said even the landscape was designed as a deterrent >> cacti >> sour orange trees.
57:36
There are sour orange trees with 4inch spikes in concrete planters on the edge of the property.
57:44
And just beyond those trees separating the house and the street, >> a moat. >> Gators. >> A moat. >> Gators.
57:51
>> If you try and run through that bush, it will be a da a bad day for you.
57:53
He said, "Should anyone get past the trees, lasers will detect motion and the system will call the police inside the house.
58:02
Three earpiercing alarms will go off.
58:03
And this is an interesting thing.
58:03
the fireplace surround like around the fireplace uh in the great room it will change colors.
58:10
It's made out of crystalallo quartzite and it can change color so it'll turn red.
58:14
So you're sitting there and if there's anything detected on the property your fireplace turns red above the TV to show you that something's going on.
58:22
Very interesting that that's a visual cue.
58:24
Um, the home's most fortified feature lies behind a wood panled wall, a reinforced concrete safe room with a 2,000lb door and an air filtration system built to US Army Corps of Engineer standards.
58:37
Wider Horn declined to share specifics, but said it cost more than $10 million to build the house.
58:42
About $1 million was spent on bullet resistant smart glass, and the front [snorts] entry security features cost more than $1 million.
58:49
In Las Vegas, clients of luxury design firm Blue Herand are spending between a h 100,000 and $ 1.
58:56
5 million on security features, including safe rooms and bunkers.
58:58
Blue Heron is now working on new ways to incorporate architecture with security, such as exterior window shades that could be closed with the touch of a button to protect the home's occupants.
59:09
In Surfside, Florida, the developer of the Delore, a planned 37 unit ultra high-end condominium project designed by Zaha Zaha Hadid Architects and with units priced at up to 200 million has tapped a Washington DC based security firm to design the building security that >> $200 million condo. >> Yeah, that is crazy.
59:28
But I mean, I guess from a security perspective, if you're in some massive building, you're sort of like diffusing the cost.
59:34
There's more people that might notice something.
59:37
There's more security guard.
59:37
It's almost like a gated community in one building.
59:41
>> I'm just Yeah, layers of access.
59:41
I'm just purely thinking you're you're effectively looking at a $100 million a floor, right?
59:49
A couple floors, maybe maybe a few.
59:51
It's u it's up there for a condo. >> That is huge.
59:55
Uh the firm is working to integrate technology like biometric access, facial recognition, and iris scanning into the design of the project.
1:00:03
For instance, when a resident or visitor pulls into the building's parking garage, their car will be scanned for license plate recognition, but facial recognition may also identify the car's occupants and their level of approval to access the the building.
1:00:15
That in turn triggers the security system to allow the person to unlock only the doors and elevators that they are permitted to pass through.
1:00:24
Meanwhile, an AI powered security system will track movements captured on camera throughout the building, looking for anomalies.
1:00:29
Uh Herzburg said he recently had a client fly in a security consultant to evaluate a roughly $50 million house he had put on under contract.
1:00:38
The consultant looked into the viability of installing a complex camera and laser system that could sense any movement on the perimeter of the property, including the water.
1:00:46
So, lots of interesting stuff.
1:00:46
Uh let me tell you about Cognition.
1:00:50
They're the makers of Devon, the AI software engineer.
1:00:53
Crush your backlog with your personal AI engineering team.
1:00:55
Um, if you go further down, they talk about San Francisco tech entrepreneur Kevin Hart said he and his high netw worth peers in California are increasingly focused on security.
1:01:05
Kevin, of course, has a home security startup.
1:01:09
>> Hart said he co-founded his own security company, Sauron, in 2024 after being spooked by an attempted breakin at his home in San Francisco.
1:01:16
The person first rang the doorbell before making his way around the house, trying some of the doors and windows.
1:01:20
When he couldn't gain access, he went to Her's next door neighbor's home, where he tried to push through the front door.
1:01:26
He was arrested by police. That could have been us.
1:01:27
Her said the Sauron system, which has only been launched in beta across a few homes in the Bay Area, >> will differ from other security systems, and that it includes deterrent strategies, not only response.
1:01:39
For instance, if it senses an intruder, it could include a feature that automatically triggers sounds such as dogs barking or police sirens coming closer.
1:01:49
Just the sound of dogs barking feels like a great feature.
1:01:55
>> Just uh OTP in the chat was saying, "Do none of these people have gold uh uh German Shepherd?"
1:02:00
>> Yeah, German Shepherds.
1:02:00
Uh fun fact about German Shepherds, you can uh like a purebred dog might be like singledigit thousands, but there are companies out there that will train a German Shepherd for like the military basically and then also train them to be pets.
1:02:15
So, they're they have that level of training and then you can get up in like the 40 $50,000 range for dog, which is hilarious.
1:02:24
Dog as much as a car, but uh uh uh dogs are typically >> a lot of money, but it's a lot of dog. >> It's a lot of dog.
1:02:31
It's the GT3 RS of dogs, truth, truthfully.
1:02:34
Um but uh whenever you look at the list of like what what what's the most likely thing to eliminate uh you know, home home intrusion risk, like dogs are always at the top.
1:02:45
Quickly, let me tell you about another great Valentine's Day gift. MongoDB.
1:02:47
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1:02:51
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1:02:56
And without further ado, we have Martin Scarley in the reream waiting room.
1:02:59
Let's bring in Martin to the TV room.
1:03:01
Martin, good to see you again.
1:03:01
How are you doing, >> Technology Brothers? How are you? >> We're fantastic. How are you?
1:03:06
>> It's great to see you. >> Excellent.
1:03:07
>> Are you gearing up for the weekend? Are you excited?
1:03:10
>> Uh, caffeinated, ready to do more work? >> Fantastic. Locked in. Great lock.
1:03:15
>> What's your daily caffeine stack?
1:03:15
You know, we talked with Hubberman about this. You're the natural.
1:03:20
>> You micro do micro doser or do you like do 400 milligrams and then coast?
1:03:25
>> I do I do coffee several coffees and then just like keep taking drinking this all day long and it's uh >> is that like a 4 hour >> something like that? Okay. Five hour energy. Oh, wait.
1:03:34
How many hours are they how many hours are they doing these days? Four, five.
1:03:37
It's [laughter] a lot of energy. >> Five.
1:03:40
Anyway, uh what what are you seeing in the market?
1:03:43
Give us the update on just how you're processing the last week of chaos, whether you want to talk about software, quantum computing, what's going on, what's worth following. >> Yeah.
1:03:54
So, so I have this new potential product might productize this.
1:03:56
I've been tweeting it for now for free, >> but it's basically this something nobody's ever done before with VC investors, >> which is I'm using my network and some huristics, maybe even some AI >> to guess uh kind of what positions people took in rounds.
1:04:12
Obviously, for some cases, I know exactly what the cap table is. Yeah.
1:04:17
>> But in other cases, I don't.
1:04:17
So, I have this like list of >> of gains uh or or investors.
1:04:20
And it's very interesting.
1:04:23
So, you know, I started with like obviously the the joke one, which is uh FTX would be up 36 billion today. >> Sure.
1:04:30
>> Um, you know, putting in uh >> I what I guess was $300,000 in any sphere, which of course is cursor >> at a 4. 4 >> $4.
1:04:41
4, you know, million dollar pre pre- money.
1:04:44
Wait, is that really the free money?
1:04:46
>> That's still insane because in that era >> 10 >> getting getting into a great company at four.
1:04:53
Like if somebody was pitching you a company at four, it was almost bearish because like yeah, they didn't have the comp they didn't they didn't talk to anyone smart that was like hey you guys are really smart you can price it at 10.
1:05:05
I'm guess I'm guessing and have have like various uristics and obviously like I'd call somebody like you guys and say actually I think you want to talk to this guy or that number might have to go up a little bit etc.
1:05:13
But that's better than nothing.
1:05:15
And right now at Crunch Base and Pitchbook and stuff there's you just there's nothing.
1:05:19
And so it's a lot of fun.
1:05:21
And so that $300,000 investment >> they raised 400,000.
1:05:24
So my guess was was allated to 300.
1:05:27
I think I can look it's actually in the bankruptcy document so eventually we'll get the exact number but that's a $1.
1:05:32
2 two billion dollar position in today's money.
1:05:34
Obviously, the the bankruptcy estate lawyer is just like, "Oh, what the is this?" Any sphere? It sounds like zero.
1:05:39
Um, [laughter] >> yeah, it does. It does.
1:05:42
When you say Anyphere in in the context of FTX, it sounds like we're a blockchain company looking to do to build a live multiplayer game and your eyes start to glaze over a little bit.
1:05:55
>> Like, how many NFTs did any sphere drop? Not quite. >> Enthropic. They 32 billion now.
1:05:58
I'm sure you saw SPF had the >> Yeah, >> he posted a little thing about that.
1:06:05
Thrive is the big mystery player because nobody really sure how much money they they sunk into OpenAI, but they also did any sphere. >> Mhm. Yes.
1:06:14
>> Um, you know, so huge huge gains from Thrive.
1:06:17
They were in uh couple later rounds of scale and and uh and some other companies.
1:06:21
So big big numbers there.
1:06:23
there. Probably one of the more interesting ones is um is uh Reed Hoffman 50 50,000 $50 million first check in open AI with uh Klaw maybe 25 >> okay >> 25 or 50 and that you know worth many billions and then Yan Yan Talin the EA >> CEO of Altruism >> yeah Skype guy 100 million turns to 11
1:06:47
billion in anthropic first check with Reed Hoffman >> so 11 bill So, a lot of fun to look through these and see like, you know, you you can sort of calculate the returns and of course VC fund returns eventually they either go public uh or you can find them somewhere or like like oftentimes state pension funds and stuff do that. Anthropic obviously the big
1:07:06
do that. Anthropic obviously the big >> it is it is when as you break this down it's so funny that crunch base never like tried to roll out even something that was like gen generally accurate like it it is like very fascinating information and especially now where you know uh Dan Primac was joking like it's
1:07:25
easier to list like who's not in anthropic at this point from kind of the bigname funds and so that just makes this kind of information like more interesting because yeah it's cool that you're in a company and and almost anybody if they work hard enough can get some exposure to these names. You know,
1:07:39
You know, may maybe it's like via an SPV or an SPV and an SPV, >> but still this is the information that like is actually like super fascinating.
1:07:52
>> The other interesting one is Dustin Moskovitz who who's 25 million in anthropic as part of the effect of altruism mafia uh was able to make $4 billion which I think offsets his losses from starting Aana but I'm not sure. >> He didn't.
1:08:04
[laughter] [clears throat] That's ridiculous.
1:08:06
There's no losses from pounding.
1:08:08
>> Well, that's that that the come on >> the anthropic position would be worth 2x what Asana is. >> Yes. Yes. Which is crazy.
1:08:14
But he doesn't he's not sitting on losses.
1:08:16
Oh, you think he bought it at the time?
1:08:18
>> Well, he Well, we know he bought huge amounts of Asana with his with cash. So, >> okay. Okay.
1:08:22
So, maybe maybe I I doubt it, but you know your point.
1:08:28
>> He's doing he's doing well.
1:08:28
So, the lesson for folks is uh just get a get a small check in the next enthropic.
1:08:33
Get get a try to network on ineffective altruism.
1:08:39
I think that that seems to be the >> Yeah.
1:08:41
What was the alpha from EA? Like what? Yeah. Do you have a post?
1:08:46
>> There's a lot of smart people that have no other things to do.
1:08:47
So, their social setting is like replaced by this sort of like religion or anything like that.
1:08:52
And >> you know, this this cult and if you're in a a cult of really smart people, it's pro probably something good will come of it.
1:09:00
>> Do you think it's still a cult or do you think it's uh it's like B2B SAS now?
1:09:02
I think it's changed a lot.
1:09:05
It's like B2B SAS and and I think like the the new cult is >> cult of SAS. You're welcome. The water's warm. Come in. It's amazing.
1:09:11
We're automating workflows.
1:09:14
We're delivering enterprise value.
1:09:17
>> We're hiring consultants.
1:09:18
>> We will we will we will forget about all the earlier stuff.
1:09:21
We will welcome you into improving the economy, raising GDP.
1:09:25
This is what we stand for in this cult.
1:09:27
>> May maybe the new cult is the the AI agent, you know, website or whatever that, you know, or whatever's next.
1:09:31
that, you know, or whatever's next. uh in that world where you know the AI uh >> what's your what's your personal so so I wrote in the newsletter today like somewhat of a somewhat of a joke but a more serious topic become unsloppable the idea of of there are still real moes
1:09:49
that exist and the historical moat of just we had a bunch of smart people working on building this software for a long time so if you want to compete with us you have to also spend a lot of money and a lot of time hiring a bunch of software engineers that's going away yet
1:10:07
you're building what is a you know seatbased pricing tool and I expect you to do very well with it just because I think in the future individuals will want great access to data to make different decisions and maybe they're you know working with agents as well so like I can see that I
1:10:27
>> yeah the agents need the data [laughter] >> um but but like what what's your personal philosophy because you're clearly not if you were just caught up in the kind of like fear-based marketing of the labs, you might not be building, you know, seatbased SAS tool. >> Yeah. I mean, data's data is often >> Yeah.
1:10:46
I mean, data's data is often firewalled.
1:10:48
you know, there's there's uh you know, uh we we have a guy that just talks to every exchange in the world and you know, the amount of times he has to pull his hair out because you know, some exchange in Asia wants to meet yet again, >> you know, before before signing the deal and and you know, there's no selfch checkckout.
1:11:07
there's no agent, you know, it's you you have to the protocol is sit down meeting and every time you go to an enterprise SAS company and it says talk to sales, you know, it's it's sort of like what does AI do at that point.
1:11:18
like what does AI do at that point. So I feel like you know there's you know you also have this trend where you know why would you put huge amounts of data into the model the model should call out and you know compressing the world's information into some parameters and
1:11:31
weights it's just not a wise uh use of parameter space and I think everybody's been saying this in AI and and so the problem is okay shrink the whole internet but what happens when stuff leaves the internet you know there's a stock that Bloomberg doesn't have in its uh portfolio you guys weren't born yet I think but the It was called a web van. >> Oh, yeah. Oh, we know the >> Oh, yeah. Oh, we know the >> OG.
1:11:53
My family My family used Home Grosser, which got acquired by Web Van, and I think the Home Grosser founders probably got liquidity before Web Van crashed.
1:12:03
So, I think they wound up doing very well. They killed it. >> Check in with them. But yeah. >> Yeah.
1:12:08
They So, so Webban is not on Bloomberg, for example. Yeah.
1:12:11
>> Even though it's, you know, supposed to be this great tool.
1:12:12
And it's certainly not, you know, on on the web.
1:12:14
you know, lots of uh data gets like deleted from Google and there just isn't this like rich tabular data available.
1:12:21
So tabular data I think is going to actually thrive in the AI world because >> you know it's just not going to be in the models or if it is the models get get tired after a while.
1:12:31
It's not going to give you 3,649 you know SAS companies with this market cap.
1:12:37
It's going to say here's the top 200 and don't ask me about the next, you know, 30 3200 because that's just not what a what LM are really good at.
1:12:44
But the the Frontier technology has sold off very very hard in the last uh month or two and there's a lot of speculation in the quant community as to what's happening.
1:12:54
So there there's some funds that ran really well with with this uh frontier tech.
1:12:59
frontier tech. So I think that includes quantum computing but also includes nuclear uh drones you know space you know all the stuff that's sort of uh you know next generation things uh that aren't here yet and this was like the hottest sector last year and anybody who didn't have exposure to this
1:13:15
underperformed and there were some quant firms I think that that were very very very overexposed to this and then new year started and in the hedge fund and and uh quant world January 1 is like a brand new page like nothing nothing matters from last year everything you forget everything. And so this factor
1:13:29
And so this factor just, you know, flips in reverse.
1:13:32
And um part of the reason was was the the calendar, I think.
1:13:37
And now the quantum stocks look like like dog uh poo poo.
1:13:42
And uh they've gone down a lot and you know, nobody knows what to make of anything.
1:13:45
But in the private world, you know, numbers are still, you know, there's still big valuations, still lots of, you know, big up rounds uh so far.
1:13:53
So that disconnect will be really interesting as time goes on.
1:13:54
There's, you know, last time I talked about photonic computing.
1:13:57
There's new company, Teal Fellow, young guy.
1:14:02
>> You know, I'm telling you right now, these young guys think that, you know, I'm this old dog that can't learn new tricks.
1:14:07
I'm going to teach all of you young bucks, 22, 25.
1:14:09
You come into MySpace, I'm going to I'm going to show you.
1:14:14
I got the dog in me still. >> That's amazing.
1:14:17
>> But anyway, Olix is is what it's called.
1:14:19
and he's raised 220 or 250 to do photonic computing for for AI which you know I think is >> you know that's the second company or third company now that's come out and said we're we're going to do it and he's going to do it with SRAMM >> interestingly so he's got SRAMM on board
1:14:34
>> and you know so it's like Gro plus in essence okay >> and I think it's a really good idea but you know execution does >> does uh does matter >> what do you think about biological computing we talked to a fellow who built a neuron in the lab and It was way over my head. >> Feed some protein, sugar,
1:14:50
>> Feed some protein, sugar, >> sugar.
1:14:52
So, we like the protein part of the interview, but didn't get much further than that. >> Yeah.
1:14:57
I mean, look, that's that's how we do it.
1:14:59
So, I don't see >> why not, you know?
1:15:01
I think that uh it's a spiking neural network, right?
1:15:04
So, it's a little different from from the software neural network, but I don't see why you couldn't do it.
1:15:09
I think the reading the output is kind of difficult.
1:15:15
In photonics, you have to use like a almost like a camera, you know, uh you wouldn't use a camera, >> but you're using a camera-like sensor and >> you know that that sort of is your as your readout.
1:15:25
What's your readout here?
1:15:27
Well, probably in the body or the brain, we're using like calcium levels or like other things like that as well as synaptic firing.
1:15:33
Uh but if you want to have really good control of that, I I don't think we know yet how that works.
1:15:39
But of course, they've gotten the these brains in a vat to play pong >> and do other things like that.
1:15:43
>> and do other things like that. So I mean it's certainly possible and you know I I was thinking about this with my with my girl who uh is in the space about you know potentially do we buy pig farm and we buy pig farm uh pigs are really interesting they they're obviously the
1:16:00
pork part you know gets sold to to meat companies but what's interesting is different parts of the pig are are biological drugs so uh uh there's adrenicotropen hormone which is sold for a huge price and then the pig's Longs also make a surfactant that's sold for respiratory disease. And then finally
1:16:16
And then finally the brain we're going to keep and grow that in a separate, you know, separate container and we're going to rent it out to Sam Almond at the end.
1:16:25
>> Slop slop of the trial literal slop.
1:16:29
>> You will be literally feeding slop to pigs.
1:16:31
Yeah, play the pig noise 25 times.
1:16:34
um talk about uh the the what's happening in small caps in uh or sort of like the long tale of the market as a reaction to the AI boom.
1:16:43
I I I I texted a friend who >> he's laughing at and 20 years from now your your child will say my my father made his >> money pig farming >> is money in pigs. >> It's great. I love it.
1:16:56
Uh uh yeah, I I I texted I texted my friend saying like, you know, look, uh everyone is talking about a chip bottleneck.
1:17:03
There's this massive AI buildout going on.
1:17:05
Like, have you looked at TSMC?
1:17:07
And he was like, oh, like it's uh it's like too big to have like some breakout move.
1:17:12
Like, I'm not interested.
1:17:13
Like, call me when you're talking about uh you know, a $4 billion company that's like deeper in the supply chain.
1:17:19
I talked to one person that was like they found they were excited about Anderol.
1:17:23
found some tiny supplier to Anderol and they were like this is a proxy.
1:17:28
Um what what companies are actually interesting?
1:17:31
How do people think about those like longtail early smaller cap companies that are still like properly indexed to the correct narrative around AI?
1:17:41
>> Yeah, I think they're all they're all scams.
1:17:42
I mean it's it's an unfortunate, [laughter] you know, situation.
1:17:44
And this is why, you know, actually Joe Lansil, you know, I think I talked did I talk about this last time?
1:17:49
you know, he he gave a talk with the SEC commissioner and he basically said, "Why can I buy TBPN coin?"
1:17:55
No such thing, by the way, [laughter] um that triples >> um >> TB TBPN coin or whatever.
1:18:01
Um >> coin you make up on the spot.
1:18:04
I could I could put millions in it, lose all my money.
1:18:07
There's no investor protections, but if I try to buy and god forbid, you know, uh you know, you know, you guys blow the whistle and Matt Grim stops everyone from from buying it and so forth.
1:18:16
But uh the uh um hi Matt.
1:18:16
Um, but the uh in all seriousness, I I think that that's that's something we have to fix.
1:18:24
I mean, because you end up having people chasing kind of really lowquality companies.
1:18:28
There's companies that just change their name to AI and hope that somebody buys them.
1:18:31
Uh, same thing with with quantum and other things like that.
1:18:34
And I feel like >> two things should happen.
1:18:36
First, we should let people buy privates.
1:18:39
>> But but but two, more privates should go public.
1:18:42
And I think like demystifying and making that less scary.
1:18:44
Like if uh I was trying to convince Replet to go public uh because there's a drug company called Replemune >> and it would be the same ticker.
1:18:52
It's [laughter] like you can't go public without them.
1:18:56
So you have to buy Replemune. >> Okay.
1:18:59
>> The drug and you can go public.
1:18:59
And Elon had to buy United Steel uh because they had X for like a hundred years. Oh yeah.
1:19:06
>> And uh you know now X is available.
1:19:06
So he just waited for them to get bought out by somebody else. Perfect timing. But >> that's crazy.
1:19:13
>> In all seriousness, you know, going public's the best thing ever.
1:19:14
It's the freest, cheapest capital of all time.
1:19:16
We obviously have seen down rounds from privates >> in publics.
1:19:20
But, you know, that's mostly for like boring SAS.
1:19:22
Once you have AI, >> you know, you're going to have, you know, uh, a million times revenue.
1:19:28
Obviously, the difference between the two is hard to say, but >> I think I think if a company like Replet went public, like you'd be surprised at the valuation you could get.
1:19:35
I think you can >> there's enough demand out there that I think some of these guys should start going public.
1:19:40
going public. Did you have the same read on a lot of people that Michael Grimes going back to Morgan Stanley was incredibly bullish for late stage >> really big deal obviously of course incredibly bullish for latestage tech and the IPO window being
1:19:55
firmly open >> I think so I I think you're going to also see other people from DC you know um >> rotate back and you know it it was a really great thing for for these people to actually truly make a sacrifice because you know I I think there's that
1:20:11
much upside in TC and you know it's it's uh it's it's an amazing thing for them to come uh you know do something good for America and then um now you have you know people like you know folks like uh Anthropic and and Open AI where their capital needs are larger than the
1:20:28
private space to be frank and you know I think that the ability for them to raise you know a 100red billion or 200 billion or 300 billion it it the markets could realistically support that whereas I think the private markets you starting to stretch like you said. I
1:20:40
I mean that anthropic list of investors, the exclusive >> Yeah.
1:20:45
>> Uh syndicate was, you know, virtually a long list of every big fund. >> Yeah. Jordy, what else? >> What?
1:20:54
>> 11 Labs 11 billion numerology.
1:20:58
>> Do you use the product?
1:20:58
So we we so my my company our first six to nine months I think we spent trying to make a better 11 Labs and or or a compete. Yeah.
1:21:10
>> Truth truth be told we we couldn't make an equal so couldn't make a better one.
1:21:14
Uh but um >> it was sort of my my my fast lesson in software which is you know the only thing that matters is is sales.
1:21:19
You know product and uh second most important in engineering is like last.
1:21:24
engineering is like last. Uh but you know if you don't if you if you don't have distribution you don't try to sell the product >> uh it's not going to sell itself and and it's uh a sober lesson of those guys like very aggressive for the longest time if you did a one word or two-word sentence in 11 labs it wouldn't output
1:21:41
it at all there were hallucinations or all this stuff they just push you know they fixed all that stuff of course but you know they pushed really really hard >> on sales and and that sort of fixes everything and and I think that >> you know it's some of the best VCs I ever talked to said, you know, when are you going to launch your product? And I
1:21:57
And I said, ah, it's not ready.
1:21:59
And they said, just launch it. Just launch it. Just launch it.
1:22:01
And, you know, get off the uncomfortable like stage fright and just start selling and you'll get more feedback and so forth.
1:22:07
So, they I think they took that to heart really early on and just, you know, they didn't have better technology necessarily than other guys.
1:22:14
I think they just sort of, you know, realized, okay, who needs to buy this stuff?
1:22:17
Let's go build build an infrastructure around that. Just incredible success.
1:22:20
I mean, I I I tip my hat to them. >> Yeah.
1:22:25
uh how do you think about the moat that comes not from software engineering because generating code is cheap or soon to be free but uh training spend.
1:22:33
So if if I spend if I spend a hund00 million employing a bunch of great software engineers for four years and built some elaborate software system and you can just vibe code it for two orders of magnitude less cost in tokens.
1:22:52
You clearly have an advantage against me.
1:22:56
But if it's going to cost you $100 million to do the training run that I did for hund00 million, is that a durable moat? >> I doubt it.
1:23:04
You know, I think it's I think it's product and sales and brand and things like that.
1:23:08
I mean, it's trad business.
1:23:10
So, there's going to be a lot of people replicating products and then they fail and they're going to wonder why.
1:23:14
>> And, you know, it's it's the rest of the business.
1:23:16
You know, there's there's that's you're talking about 10 20% of your your organization.
1:23:19
I mean, you really have to get the rest of the organization excited about product and and I think you're going to find >> one interesting thing that'll happen probably is that >> folks from embedded entrenched industries like certain manufacturing and certain materials businesses, things like that, they're going to spin out themselves and say, "I'm going to solve the problem that's been plaguing my industry, but I'm not a programmer."
1:23:38
Uh, it's like that that uh I'm not a rapper, YouTube.
1:23:43
>> Uh, you know, and >> and you know, I'm starting a software company, but I'm not a programmer.
1:23:45
But I I know that our whole oil industry has had this huge well software problem.
1:23:50
I'm gonna build the well software.
1:23:51
And I think startups like that are actually gonna not only create tons of wealth for themselves, but they're going to actually help the economy.
1:23:56
And that's where just like the internet, you know, help GDP.
1:24:01
>> That sort of solution is where, you know, you're going to see GDP needle move.
1:24:04
And it's going to be it's a wonderful time to be like the nerdiest best guy in say equity research or something like that because you know you might have an inkling that like a rival might have an inkling that oh you know finance is going to be changed by AI.
1:24:18
I'm going to try to point my my apparatus at this and and figure it out.
1:24:22
But if you're the guy that's like I know everything about you know this type of little narrow thing >> you're going to really crush it because you know you you really know what the problems are.
1:24:30
So people coming out of industry, there's a rival of ours called Rogo.
1:24:34
Rogo is a uh AI company focused on finance, old Wall Street guys.
1:24:38
They have a much much better chance of succeeding because they're they did the job.
1:24:42
They they know what what to do.
1:24:44
And I think you're going to see so many people come out of the S&P 500 that just said, "Oh, I was working at Eaton or or Flu or like companies that are just like big, you know, py homes or whatever."
1:24:54
and they and all of a sudden, you know, they they're they're starting software companies that solve the key problems in that industry.
1:25:00
And >> so maybe more companies but fewer uh computer science background founders. >> Yeah, definitely.
1:25:10
I mean, so many of these problems could be solved, I think, without [laughter] knowing every single data structure and and things like that.
1:25:16
I mean, obviously, >> you know, there's there's going to be people it's going to be a barbell, right?
1:25:21
right? like there's going to be people who still need to know how to make an FPGA and program an FPGA and and you know when Elon said that you know he sort of set a lot of people on fire over the last few weeks when he said that you're going to see >> AI write assembler or even machine level
1:25:34
code uh you know compiled assembler and you know that's that's a pretty wacky you know idea and think about wacky ideas from Elon is they tend to be [laughter] right so it's definitely uh you know one of these things that you know is kind of mind-blowing that you
1:25:48
know if you think about AI safety um you know you know tell the tell the program uh you know give me a program that does SAS for oil wells cool here it is but by the way you know in the in the compiled assembly which you can't read because you don't you don't speak binary >> uh you know there's this thing that says
1:26:06
you know I I'm I'm taking 5% of the revenue and sending it in crypto to my [laughter] >> I like that that's your AI doom scenario just slight slight fraud >> flipping 5% off >> just clipping a griff to gri how do you how do you do you expect layoffs on Wall Street? >> Uh cuz because with with all of the
1:26:24
>> Uh cuz because with with all of the broad fear right now among white collar workers, I'm not seeing the layoffs that are explicitly, you know, hey, you were doing this thing for the company and now we're just running this agent to to do that and so we'll see you later.
1:26:47
We are seeing hey you were doing this thing now AI can help you do it a lot better so our expectations are going to rise we are going to expect you to do more and be more productive but you still have your job uh what what are you kind of hearing from uh people at different finance firms about how they're adopting AI and how they're how they're feeling about job security.
1:27:09
I think in general, you know, one of the things you learn in founder school after your fourth or fifth time is is that, you know, you're supposed to hate firing people and and you're supposed to learn to like it over time. Uh, nobody likes it.
1:27:21
You know, it's the worst thing ever.
1:27:22
And and the funny thing is like if you become more productive at work, the company doesn't say, "Oh, yeah, well, let's get rid of you and save whatever amount of money."
1:27:29
Because they're already making money with you employed.
1:27:33
So, the fact that you're becoming more productive means that, you know, uh, whatever the the margins were, they're probably improving.
1:27:38
Could they improve even further by getting rid of you? Maybe.
1:27:41
But I think there's this like slow atrophy maybe.
1:27:43
But I I think in general we as humans want to employ other humans and we we kind of want to be productive.
1:27:51
I mean uh nobody wants to needlessly employ people.
1:27:54
But I think that there is this idea of okay machine can do your job.
1:27:58
We we have this at my my office all the time.
1:28:00
And you know I say Chris you know I I wrote a program to do your job. Good news.
1:28:04
The you know you don't have to do it anymore.
1:28:07
But there's a new thing you have to do now.
1:28:09
And you know, our company just got twice as efficient. It's wonderful.
1:28:12
And if the day came where there's literally nothing for Chris to do, then you know, maybe you know that would be uh you know, that could be the day that made sense.
1:28:20
But >> but the thing is there's always there's always an incremental thing for a company to do. >> Yeah.
1:28:25
Almost no startup founder has ever thought, great, I >> did it.
1:28:31
I built I built the six products that that our customers really need and there's just no other way for me to expand the opportunity set. >> Time to kick back. >> Yeah.
1:28:40
>> I mean, yeah, those businesses die though. >> Yeah, they do.
1:28:43
>> You're either building >> Yeah.
1:28:45
>> I mean I mean you're going to I mean if that person has a couple more hours a day now it's great.
1:28:48
You go meet with, you know, uh some potential recruits.
1:28:50
Go meet with some potential customers.
1:28:52
I mean there's there's always something you can do and I think that's going to happen in finance.
1:28:56
Adoption of AI has been very slow.
1:28:59
Um, and it's probably going to stay that way.
1:29:01
Finance people are really stuck in their ways.
1:29:03
Uh, uh, which is a good thing and a bad thing if you're selling software to them.
1:29:06
Uh, once they get stuck in your way, [laughter] you're very you're very happy.
1:29:10
Uh, but you know, the you know, there's there tends to be a heavy dose of contrarianism in certain industries.
1:29:17
And I'd say, you know, across the S&P 500, there's this sort of like, ah, you know, technology, uh, we'll use it eventually.
1:29:23
And that eventually takes time.
1:29:25
And that's why the first people to adopt this stuff in great ways is not only because it works really well, but also because they're used to doing it is developers.
1:29:34
Developers love AI and they've embraced it, you know, very quickly.
1:29:36
Virtually all programmers now use AI.
1:29:37
There were a couple of holdouts even at our our company, but they eventually just gave up.
1:29:42
And I think you're going to see the same thing in other industries.
1:29:44
Binance is tough because like there's this mystical idea that the trader um is this like random, you know, far end of the bell curve like super talented person that just knows has this like weird zen kind of ability to tell what stocks are going to go up and down.
1:30:01
And then the other end of the barbell are the quants.
1:30:05
Uh and the quants sort of feel like AI is not not not good enough.
1:30:06
But many of them under the what I've heard is many many quants are are getting new ideas from AI and also implementing them with AI.
1:30:15
So I do think that >> yeah if you work at a hedge fund now you can just ask you know your [snorts] favorite LLM how should I hedge AI and just implement exactly that and you're guaranteed to to outperform. No, I'm kidding.
1:30:27
[laughter] Um >> it's a little bit scary for some quant funds because you you do have to wonder if if >> you know the thing you've been doing for 20 years that's your profit center is going to possibly be done by somebody else.
1:30:37
else. um you know that that is a little worrisome and then you know eventually and certainly there are firms I I can name them but you can just guess the big the big sort of institutions on the street that they're increasingly thinking about and and even in some
1:30:52
cases deploying transformers to do analysis and I don't see why you know the hard part about being Warren Buffett was discipline right is is saying no to so many things and if if you can put that in the prompt or put that in the you know in in whatever and the contacts
1:31:08
and you just say listen I really only want the best you know the highest quality companies the best returns say no to everything else and you know I I don't see how that's you know impossible just copy the Buffett you know strategy you might be better off a lot of the mistakes in investing come from
1:31:24
overdoing it in in things that you know in FOMO and things like that and resisting that FOMO and saying you know I'm just going to sort of buy buy these types of companies and do my thing I so I do think like investing as a lists are going to start change a little bit. >> Mhm. Lightning. I have I have four >> Mhm. Lightning.
1:31:38
I have I have four questions.
1:31:41
>> I've got one and then uh there's a lot of performative AI usage happening right now.
1:31:47
The people that are, you know, ordering a new Mac Mini on Door Dash, ordering 10 Mac minis on Door Dash.
1:31:51
We we were joking around.
1:31:53
Uh my girl just did this.
1:31:55
[laughter] >> Um but uh like who are you looking to for founder style roles?
1:32:01
for founder style roles? Like what do you think truly the most like like the best founders are doing like ideally like the most important working on the most important thing at the company which could be a recruit could be a customer could be getting a raise done
1:32:17
uh it could be going going on a long walk and and just thinking about the business but there's like heavy amount of delegation and ideally they're like delegating to people that are using a bunch of AI but like uh do you have any sort of like internal fear around am I using this stuff as efficiently as I should be myself. Uh are you looking to
1:32:35
Uh are you looking to anyone and saying like, "Okay, they're actually really tapped in because just buying a Mac Mini and setting it up and, you know, having it running and texting it, you know, here and there is not necessarily uh qualify you as like actually tapped in." >> Yeah.
1:32:53
I I wonder if there's a way to, you know, to to not annoyingly reach out to customers with AI.
1:33:01
And I think that, you know, we all get that email like I just hit block on all of them.
1:33:05
The hey, I noticed that you're doing this, so now I'm gonna, you know, I'm I'm, you know, uh, I'm a vendor that's offering you that.
1:33:12
And I think that's, you know, going to result in not too many sales, but I do think that these things have some yield and I wonder if there's a really good way that sort of in the back of my mind worries me.
1:33:23
And then I at this point I wonder if LLMs can do can replace recruiters, right?
1:33:28
right? where they say who's who is the best at you know time series tick programming or something like that and LM says John Smith here Dave Smith there you know they're also named Smith for some reason and Will Smith there and I think those you know at some point that
1:33:44
might happen of course some of that's just human knowledge where people whisper amongst each other that you know oh you know this is the best person at at this kind of investing but I think that um you know because we're all like blogging and like putting stuff out there. You know, we may also just be
1:33:58
You know, we may also just be able to s, you know, ask that person who's the best person.
1:34:01
So, I feel like there's definitely new ways, creative ways to use AI that that, you know, people are coming up with all the time that are really surprising and shocking and, you know, they're all like secret sauce, I think, for for most people.
1:34:14
But, you know, was our secret sauce for a while. >> Yeah.
1:34:17
On the recruiting front, uh I I met a guy a couple years ago who like only his entire recruiting business for years had been work Brazilian fintech engineers.
1:34:27
Like he had just done a decade and all he did was help companies hire the best Brazilian engineers that liked working on financial services companies and like he had carved out a great business.
1:34:39
>> And I do wonder I I don't know that identifying who the great ones are.
1:34:43
identifying who the great ones are. It's like maybe you did a certain number of years at at new bank and then you popped over here and then you popped back and like you can probably pick up some of that stuff but then what is the value of just like actually having like how
1:34:55
durable is the personal relationship with those people that you've placed over a long enough period of time and can you continue to uh basically extract rent because like they will respond to your text and not the like you know millionth you know AI text that that that is constantly kind of chasing them. Anyways, lightning lightning round.
1:35:11
Anyways, lightning lightning round.
1:35:13
John, >> I'll just do one.
1:35:13
Neolabs bullish, bearish. What do you think? >> I'd say bullish.
1:35:18
You know, this is a contrarian view.
1:35:20
I think because um I'm especially looking forward to JT, uh Jerry T-Rex, uh Neil Lab.
1:35:28
>> I think these are really smart people.
1:35:30
>> Are they product companies or are they research companies that will get acquired in?
1:35:34
>> I think they're going to all have a crisis and have to figure it out. >> Yeah.
1:35:37
So, here's here's the >> here's like the the be the the the the kind of bearish take on Neolabs.
1:35:42
The the from an investment standpoint, I get it because I I would say like really really elite smart team.
1:35:48
There's somewhat of a capped downside.
1:35:50
If you invest $50 million, you could probably get 50 of of worth of, you know, some one other lab that's actually working down the line out if it doesn't work out.
1:35:59
But these people were like working on something like, you know, uh, hey, these LLMs aren't really learning in real time.
1:36:09
They're just kind of like in a certain state and I'm going to leave this lab and go work on that problem.
1:36:13
Meanwhile, the lab is still working on that problem.
1:36:18
And we've also seen as different labs have different advancements, the other labs can like quickly just catch up, right?
1:36:23
So like one person has a breakthrough.
1:36:25
And so my question is like if a Neol raises a hund00 million and like actually has a breakthrough then they just have the problem of like are we actually going to be able to sell this better than the labs that will probably figure out how to do this than the big labs that'll figure out how to do this in the next maybe two months later but they have you know a million customers already.
1:36:45
So like that's the bare case for me is like even if you have this like breakthrough you don't have the like sales distribution. >> Yeah.
1:36:53
No, I mean the the bare case is what do these people know about business?
1:36:56
Um, you know, [laughter] they're they're starting a business, right?
1:37:00
So, it's it's kind of a scary thing, but I think that, >> you know, look at biotech and some other industries.
1:37:06
You know, this is pretty common.
1:37:07
And ultimately, >> I think they're they're doing the hard part.
1:37:11
You know, the easy part is you get a bunch of good-looking guys like you guys.
1:37:14
And, you know, you get you get them to start selling positioning product, but I think the hard part is yeah, how do you do continual learning?
1:37:21
How do you do a new form of AGI?
1:37:21
It's a little past most of our pay grades.
1:37:23
And I think, you know, there probably will be a crisis where like the real ones will be separated from the fake ones, but that's just human nature anyway.
1:37:32
Like there'll be some funding crunch and then somebody has to like emerge with that dog in them and say, "No, I'm going to raise another 200 million.
1:37:37
I'm going to come out with something tonight and we're going to do it."
1:37:41
and for that type of, you know, crazy, >> you know, person where and then there's going to be folks like I don't want to name a certain AI company that folded, but I'll throw in one that did, which uh was was uh the guys who made Hey Pie inflection.
1:37:55
>> Uh they sort of, you know, had that outcome that you're talking about.
1:37:57
And you know, but there'll be people like like that see that, you know, valley of death and say, "No, we have to finish this."
1:38:04
And I think that probably the one of the biggest things that people have to remember, but they don't because they don't care, is that the the investor's money is sacred.
1:38:11
And if you if you're just thinking about it as, oh, what's the worst that happens?
1:38:15
You know, I I wind down and Sequoia loses their money and this and that.
1:38:19
You know, I take that really seriously and everyone should and you know, I think that um the handful that do, you know, will see their their runway dwindling and saying, "Ah, we really got to do a product here and and and tough it out and figure it out."
1:38:31
And I think those will be the, you know, future, you know, future leaders.
1:38:37
>> Well, thank you so much for taking the time >> to hop on the stream.
1:38:39
Always a great time chatting with you. >> Always a pleasure. >> Have a great weekend. Good to see you. Enjoy your lock in. >> Enjoy the caffeine.
1:38:45
Enjoy your fifth 5 hour energy. >> Tell you about Plaid.
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1:38:55
We have Connor Hayes, the head of threads in the Mr. >> studio.
1:39:00
While he comes in, I'm going to tell you about Octa. Where's Octa? Octa.
1:39:05
Octa helps youident [music] assign every AI agent a trusted identity.
1:39:10
So you get the power of AI without the risk. Secure every agent. Secure any agent. >> The Vanguards.
1:39:14
The >> the Vanguards are out today. >> Yeah. Oh, they're out today. >> Well, they're out.
1:39:18
They've been out, but they're out here today.
1:39:21
>> They were [laughter] out like like months ago. Months ago.
1:39:23
>> I have to ask you guys before we start.
1:39:26
How are you recovering from Clav getting brutally put on by [laughter] the ASU frat leader? >> I just want to know.
1:39:34
>> It rocked men everywhere. >> Rocked my world. >> Are you okay?
1:39:38
>> I mean, I think the reason that that resonated is every everyone's experienced that, right? I get that. I get that every day. I get that every day.
1:39:45
>> John Kugan has never predictable.
1:39:49
>> I think it was predictable.
1:39:50
>> Yeah, I wasn't that surprised, >> but we did this morning. Sauna. >> True.
1:39:54
The whole team was in the sauna after our workout this morning and this guy must have been an ex bodybuilder.
1:39:59
[laughter] It was just ridiculous.
1:40:01
>> It was one of the widest backs I've ever seen from sea to shiny.
1:40:05
>> That's why you need the meta vanguards on so you can >> capture that.
1:40:08
That's a that's a feature that could be a hit is you is it basically like video model that reduces the if somebody's coming up to frame you, it just kind of reduces them down >> like haptic feedback that sends you out of the frame.
1:40:22
I mean, those are some frame mogs right there.
1:40:24
You call them frames, right?
1:40:26
They're they're fantastic. Uh, how is life?
1:40:28
What What is the day-to-day like for you?
1:40:33
>> The day-to-day changes a lot.
1:40:33
Um, we're doing, you know, >> threads.
1:40:36
I think a format like that only works if you are at the center of cultural relevance.
1:40:40
And so, that brings us into a lot of stuff that's going on in the world.
1:40:45
We were like all over Super Bowl last week.
1:40:48
>> I'm here this week cuz we're doing a bunch of stuff with NBA for the All-Star game.
1:40:51
Um, so that's been really I mean that's fun. It's work, but it's fun.
1:40:53
Um, and then the rest of the day is like how do we make the feed better?
1:40:59
>> What are we doing on you know content understanding?
1:41:00
Our models good enough to like you know do something like the deer algo feature that we just launched.
1:41:04
Um, >> talk about that first.
1:41:06
Uh, Super Bowl NBA like obviously we all know that social media content production is power law driven at this point.
1:41:13
There's a few creators that take it really seriously and they have expert teams.
1:41:17
Um, obviously threads is like a little lower barrier to entry than a polished two, you know, hourong YouTube video or something like that.
1:41:26
But, uh, are you shaking hands and kissing babies to get people on the platform?
1:41:31
Is that what >> I don't kiss any babies.
1:41:33
I have shaken a lot of hands actually though. Um, we Yeah.
1:41:37
So, we have a bunch of program.
1:41:38
It's actually one of the benefits of doing this at Meta is we have such an infrastructure of working with creators and partners.
1:41:43
And um >> I think we talked about this actually when I saw you guys in September, like >> the big learning for us was the people that rock at Instagram don't necessarily succeed on threads out of the box.
1:41:53
Um it's kind of a it's a very different format.
1:41:56
Um >> the cool thing about >> you got really good at making pictures and videos and now you basically need to be really good at captions that can stand on their own tech. >> It's wit. It's like insight. It's things like that.
1:42:08
But if if you're good at that, the to your point, John, the barrier is so low.
1:42:12
Like I I was on a flight here >> two days ago and I think I fired off like 15 posts on the flight, replying to people and whatever, and I'm not sending clips to a production team and having them edit it.
1:42:24
So that's the beauty, I think, of the format. >> Yeah.
1:42:26
>> Yeah. uh how is the AI spam revolution uh keeping you up at night or is there does meta have strong infrastructure there where you can kind of just like out of the box identify like >> yeah I mean we like I think as a company have made some good decisions in the
1:42:42
last decade of taking these things that are like >> basically infrastructure that you would need for any service you build ads financial services uh integrity detection and monitoring and we build central teams out that do that for the company and then build it in such a way that it can be applied to any app. >> Yeah. >> Yeah.
1:42:59
>> So, uh we just benefit from all the work that the central teams do.
1:43:01
We have some folks um inside threads as well.
1:43:05
>> But um maybe what you mean though is like the agent like agents coming into social spaces.
1:43:09
>> I mean I guess somewhat tangential question is just like when GBT3 dropped it was like okay like it can write text and then the surprise to me was deep research agentic coding.
1:43:21
I had sort of priced in like it's going to be able to write a couple sentences and yet I find myself when I'm on a short textbased platform not following AI accounts.
1:43:31
Like I'm more likely to go to a fully AI product when I want something that's more like a Wikipedia page in a deep research report, a utility.
1:43:41
But when I'm actually scrolling a feed of short news items and posts and commentary and hot takes, I it's not even that I'm like anti- AI and I would I would never follow someone who is using AI to post.
1:43:56
It's like no, like no one's actually solved that piece of the puzzle.
1:43:58
There's still some human element that's encoded in, >> you know, 16 words that are hilarious based on this moment and this experience and this audience.
1:44:10
And that's just stuck around a lot longer than I thought it would.
1:44:13
>> Well, that's like the I don't I don't know how much you guys talk about this on here, but I think like the word of 2026 in AI is going to be taste. Okay.
1:44:20
>> And that's what you're getting at.
1:44:20
It's like a model can produce output, >> but taste is the thing that differentiates good from great even on modeling, right?
1:44:29
You can have all the data in the world and inject it into a pre-training run, but actually the best labs are the ones that have people with taste that can hand select golden sets of like what is the best response for this thing or what's the best image aesthetic for this thing.
1:44:42
>> Um I think it's the same with like textbased posts like >> um it has to be real time.
1:44:46
It has to have a bunch of cultural understanding.
1:44:49
I do think models will get really good at that at some point.
1:44:52
>> Um my the thing that I'm most excited about though is like AI assistive in the creative process.
1:44:56
So, like if you're an NBA creator, >> half the stuff that you do is just clipping content and being like, "Did you see that Victor Wimbeyama dunk?"
1:45:05
>> Half of it is like weighing in on it and having an analysis that like comes from your point of view and feels native to you.
1:45:10
That first half if we can like automate for people and make super easy to do because they have a workflow that's like, "Watch all the NBA games, give me the content that I should be posting, and then I'll add on my little flavor on top of it."
1:45:21
Like, that would be amazing. >> And also factor.
1:45:24
>> Yeah, because timing timing with this stuff is so important.
1:45:25
I mean, we we this is obviously a big part of our business is like if you're getting to stories, you know, later than everyone else, it's just way less it's becomes goes from interesting to not interesting at all. >> Totally.
1:45:39
>> Uh and so I think for creators that want to build an account like that, uh any type of tool that allows them to be faster in that process is is >> it is crazy though like you I don't um you know, have you guys had uh Geo Rainbolt on here?
1:45:53
We I would love to have him on.
1:45:57
>> I'm worried he's gonna he's gonna he's gonna he's gonna dox us.
1:45:58
[laughter] >> We've given out so many little teasers with images behind the scenes.
1:46:04
>> He I think I'm I meet and love a lot of creators.
1:46:07
He is like the most impressive content creator I've ever seen. Like he's incredible.
1:46:11
But he I saw an interview with him recently and he's like, "Oh yeah, when I was like a teenager I had a Steph Curry fan page.
1:46:16
I think it's still up and it had like 50,000 followers."
1:46:20
And you meet all these kids that are like in their 20s.
1:46:24
They were raised in a a version of the world where Instagram was at the center of the universe.
1:46:28
And they create like fan accounts that get super huge.
1:46:30
And then it's like then what do you do with it?
1:46:33
I guess you get really good at geoging. [laughter] >> Yeah.
1:46:37
I mean I like we we we have uh some people on the team that are, you know, super early in their careers.
1:46:43
Maybe this is their first job.
1:46:43
Maybe this is their first job. And when we talk about and and a lot of the work is like selecting content, editing it uh you know distributing it on the right platform and it feels like like very much like manual labor like you're watching content uh and we've stressed
1:47:00
continuously that uh it's actually um it's very important training for doing almost anything because it's like developing taste, it's like developing consistency, speed, being organized, you know, being able to get like immediate feedback on the work that you're doing. Like the feedback loop is super tight.
1:47:16
Like the feedback loop is super tight.
1:47:19
And so we've consistently said like, "Hey, we don't expect you to be doing this in five years, but like for now, take it extremely seriously because if you can get really good at this one thing, you might be able to apply it in, you know, a bunch of other domains." >> It's kind of crazy.
1:47:32
It's like today's mail room basically.
1:47:34
Like >> No, it really is.
1:47:36
We went in this We went in the CIA mail [laughter] room >> when we were on we we were doing a tour of the building and we were like, "Hey, can we see it?"
1:47:42
And it it was it felt like exactly the mail room out of like the same mail room as like you know >> 30 years ago.
1:47:49
>> I was uh at their event last night for Allstar and they were the most excited I've ever seen agents about TVPN being on the CIA roster.
1:47:56
Like >> that's great >> ear to ear smiles when I brought you guys together. Congrats on that.
1:48:01
>> That Rainbolt story is funny.
1:48:01
The first uh social media account that I ever got to somewhat of scale was uh uh an Instagram for my dog that I got to like 20,000 followers. >> That's pretty good.
1:48:14
>> I I I'm going to get in trouble here because I used a bot to to automatically follow anyone who liked the page or leave a like on No.
1:48:23
Well, eventually the bot got shut down, but [laughter] it already like went up.
1:48:27
And >> what's the account? >> Yeah, you can ban it. I don't care.
1:48:29
I don't want to post any more photos of my dog. I was bored at the time.
1:48:33
But uh but it was an interesting thing of like how do you solve the cold start problem and I'm wondering about you know now there's a lot of platforms where I feel like there's an audition process.
1:48:42
You can go on to a completely blank account and if you bring a banger some heat like the algorithm will will audition you with like 500 random people and be like retention was really great.
1:48:53
Let's show this to more people.
1:48:55
Show this to more people.
1:48:56
Is that the way Threads is set up right now? >> We do a bit of that. Um yeah exactly.
1:49:00
It's like you you basically take any piece of content on the platform, you sample it to some people, >> and then you very quickly try to understand did this do well in this sample.
1:49:09
The smaller the sample though, the wider the error bars are.
1:49:11
So you have to keep auditioning. >> Yeah.
1:49:14
>> Um >> so it's like cycles of auditions.
1:49:16
>> Cycles of auditions and then um you know some people fail the audition, but [laughter] uh >> what happens to those posts?
1:49:22
>> What happens to those posts?
1:49:22
Well, they just languish like 500 views like saying like do they just they just don't end up getting served to many people or they have to get served later because >> No.
1:49:33
Well, because we also we have a really tight uh window of eligibility for recommendations like we want the app to feel very real time.
1:49:39
So something you posted 3 days ago won't be eligible to be recommended to someone who doesn't follow you in the app. >> Yeah.
1:49:47
>> So it all has to happen very very fast.
1:49:49
Um the bet on on threads, this is like a talk track that I give to every creator that's like, "What do I do?" It's reply to people. >> Yeah. >> The feed loves that.
1:49:57
The feed kind of loves reply guys.
1:49:59
And it's just not just replies though, like are you driving a conversation?
1:50:03
>> The best um actually he was at our event yesterday.
1:50:05
Draymond Green, number one example.
1:50:07
If you want to go look at someone's replies on threads, he I asked him if he searches his name and he's like, "No, man.
1:50:12
You just show me haters."
1:50:14
and [laughter] his feed is just people being like, "Daymond is the worst.
1:50:17
I I hate him on the Warriors."
1:50:19
And he'll be like, >> "I looked at your profile picture.
1:50:21
You should talk to your mom about how ugly you are."
1:50:24
[laughter] Oh my god, Draymond.
1:50:27
He gets a lot of joy out of it.
1:50:27
But that's his brand and his character.
1:50:28
And when he does that, it shows the world. I'm on threads.
1:50:32
I'm doing something that's true to me.
1:50:33
Um, I think the people who don't do as well are the ones who kind of just it's not organic.
1:50:37
It doesn't feel like them.
1:50:39
It doesn't have personality.
1:50:40
And replies are like a good way to get that out What what's what's Thread's relationship like with the rest of the app ecosystem?
1:50:46
Early on, you guys opened the floodgates, brought a bunch of people in.
1:50:52
I'm sure you looked at like retention, who's actually staying here, how do we get more people like this?
1:50:57
But like what does that relationship look like?
1:50:58
Are you like because I'll see like a meta a popup for like meta uh Ray-B bands, right? Right.
1:51:05
When I open the app and then maybe I scroll a few times and then there's like some threads content that's pushing me over. Yeah. Yeah. Instagram. Yeah.
1:51:10
I mean, we we uh we promote the app, the content from the app in Facebook and Instagram quite a bit.
1:51:18
>> I mean, I'm sure anybody watching this who uses Instagram has probably seen some of that.
1:51:21
So, that's that's the main point of integration that we have.
1:51:24
>> When we built threads, there was a bunch of like foundational decisions that we had to make in the beginning, which were like, you know, what app what app binary do we build on top of?
1:51:33
Like we actually just took Instagram and we're like because uh on day one of threads when it was like a very thin app, it was like 300 megs or something in the app store because we just had the IG codebase.
1:51:43
Um and we've like made it more efficient from then.
1:51:46
But it's like what namespace do you use?
1:51:47
Like we we mirror the Instagram namespace.
1:51:49
Um you can have a threads only account but you can only have a threads only account that isn't a name that's on Instagram, you know, like we maintain one.
1:51:58
>> So there's a lot of natural tie-ins to Instagram because of that.
1:52:00
we were backed by them effectively in the beginning.
1:52:04
>> Um, but now like a lot of our users come from that integration in the Facebook app.
1:52:09
>> Um, we we and you can sign up for threads from Facebook without an Instagram account.
1:52:13
Like we're trying to make it stand on its own independent of the IG history without like disrespecting the fact that that's like the best marketing channel you could ever ask for.
1:52:22
So we we try to balance that out. >> Talk about collabs.
1:52:24
I was scrolling this Instagram creator.
1:52:27
Uh, have you ever seen the the Let him Him Cook guy. Have you seen this guy? No.
1:52:31
He does this incredible thing whereame he'll bring the video in.
1:52:34
the video in. and it'll be like you can't cook an F1 driver and this song plays and he goes in and it transitions from like a tire to the road and it's like this amazing editor and I was scrolling and I just uh and one of them is just him uh doing the same like motion graphics effect this amazing edit and Adam Maseri sitting there with him
1:52:52
and it's very clear that he like collabed on this and they have the shared name space on them and I and I've seen there's a bunch of different ways but that feels like an interesting uh you know it's very popular in podcasting you have a big guest a bunch of their audience comes to your threads for someone who's trying to sort of network their way to broad account growth. >> Yep. Um we do a bunch of this. Uh I'll >> Yep.
1:53:14
Um we do a bunch of this.
1:53:14
Uh I'll give you a couple examples.
1:53:17
Like uh yesterday I actually put up it's like kind of mortifying uh video because I did a training session with Lethal Shooter, the the NBA shooting coach. >> Yeah.
1:53:26
>> I thought I was going to crush by [laughter] the way.
1:53:28
I was like, >> you know, you can edit the video.
1:53:29
You can edit out the misses.
1:53:33
I walked onto this basketball court.
1:53:33
I was like, I am going to be the greatest shooter of all time.
1:53:36
And it was so humbling and horrible.
1:53:38
But like we did it, we did that thing. He was amazing.
1:53:42
Um, but that's like, you know, I put some content up. He'll repost it.
1:53:44
He has a bunch of fans from Instagram that are on threads and like he's actually really good on threads.
1:53:50
He's um >> his mentality is very like, oh my god, there were so many oneliners.
1:53:55
He was just screaming at me the whole time.
1:53:56
But it's very much like you can only be great at a thing like shooting a basketball if you are centered as a human.
1:54:03
And he posts like motivational quotes like that on threads and stuff and people love it.
1:54:07
So that's one thing where it's like not only is he doing well on the platform, but I do something with him and show everybody there like this is big.
1:54:14
We also then have a bunch of like more homegrown talent where it's less like take someone who's huge on IG and bring them to threads.
1:54:19
There's a guy um >> there's always there's always been alpha and just getting being one of the first one of the first >> 10 million users but then taking it more seriously than any of the anyone else.
1:54:30
>> That's this guy Yo Rush on threads.
1:54:30
He's like they call him the mayor of NBA threads.
1:54:34
He was just like at home. He's an NBA fan.
1:54:35
Threads came out and he just started posting and people liked it and like he was with us yesterday at this thing we did in LA.
1:54:41
Him and his wife are here for the weekend.
1:54:43
They're coming to a bunch of events with us.
1:54:44
like, you know, we want people like that to I think it's really important if you have a content app to have homegrown talent, too.
1:54:51
You can't just be transitioning people from other places.
1:54:53
Like, you need to show everyone on the app that you could be successful here, too, if you just like do the right things and reach the right audience. >> Yeah.
1:55:00
Um, >> how what's your what's your philosophy around creator payouts?
1:55:04
How do you think creator payouts on on other platforms have worked well?
1:55:09
Clearly, they work well on YouTube.
1:55:11
Our our point of view is like making a great YouTube video takes an insane amount of work.
1:55:16
It's in the incentive of the YouTube platform to pay people because so they can quit, you know, quit their job or put more resources to it or buy gear, all these things where >> our are I I haven't felt like creator payouts have made X a better platform at all. Yeah.
1:55:34
uh because it incentivizes people to just like churn out kind of like lowquality content that might rage bait people into engaging but isn't actually making the platform better.
1:55:44
>> Yeah, I have I have pretty strong opinions on this and a bunch of priors.
1:55:48
I I agree with the way that you just position that.
1:55:50
Like >> the way that at least right now I'm thinking about this on threads is like I want to be in the business of directing traffic to the places where you make money in like a sustainable way. I don't know.
1:56:01
We've tried different versions of this at Meta.
1:56:03
X has obviously had their version.
1:56:05
I've never seen in a in an app like threads a sustainable creator creator payout product work well over time.
1:56:11
YouTube works well, you know, and then you have like the Substacks and Patreons of the world that are like more subscription based podcasts like getting subscribers and traffic to your podcast is a thing that you can monetize and you know run ads and have sponsors like you guys do.
1:56:24
And so we have been focused on that by you know we did this like pretty simple thing where >> uh we worked with Spotify to do like rich previews of podcasts.
1:56:31
Um, you can also pin the podcast, your podcast link on your profile.
1:56:35
Um, I would love to do stuff like then you can subscribe on Spotify from the feed and things like that. But the whole >> Yeah.
1:56:42
The re the reason that that philosophy like I think is smart is that's what we're seeing across the entire internet is >> because you're going to have different ways to make money.
1:56:50
Different creators will >> you can't just get paid for views because not every view is equal.
1:56:56
Otherwise, like the the kid running, you know, there's kids running meme accounts that are posting like funny, maybe controversial, edgy content on Instagram getting like a billion views a year, but it's it it actually has zero value.
1:57:10
>> It's like the videos of the kids that take the fake turds and put them in like Burger King bathrooms. Have you not seen this? Oh my god.
1:57:16
I'm like, what are we doing here, guys?
1:57:17
[laughter] >> Now you all all the viewers will have to look that up.
1:57:20
It's like the most horrible content. It's You're right.
1:57:23
It's the the incentive there is like how do I do something funny?
1:57:25
I actually like am very that whole like prank video space to me is just this like insane like I I guess you could have imagined it coming 10 years ago but whenever I see one I'm like how did we get here?
1:57:37
>> I feel like it blew up on YouTube like years ago.
1:57:39
I feel like there was this >> Yeah, prank videos were were the original >> I used to have a few prank videos, you know, my holster if I go to a friend's house and say, "Let's pull up YouTube. Let's go."
1:57:51
But but going going back to it, it's like yeah, if you can be a place that helps people have an audience and build a business, that is what every successful content creator has done.
1:58:01
They they're not just relying on views.
1:58:04
Even for us on X with creator payouts, you know, generating hundreds of millions of views in the last year.
1:58:08
Like the Xcreator payout is like such a rounding error that I wouldn't be mad if it went away. Right.
1:58:18
I think it's uh you end up it's funny because when you talk to people it's like there's people like you guys who hundreds of millions of views rounding error you wouldn't be mad if it goes away.
1:58:26
The people that tend to care the most about it are the ones who get paid out like $80 a year.
1:58:30
M >> um and I and I do I actually sympathize with that because it's like if this is a side hustle for you to your point before you you want to buy a new camera, you want better gear, like finding ways to get people enough money to sustain the thing that they're doing and give themselves more attempts to make it big or build a bigger audience, I think is great.
1:58:48
We just want to do that by pushing people to the places where where you're monetizing more efficiently. >> Sure. >> Yeah.
1:58:53
Are you seeing spawn con happen natively on the platform like on Instagram where uh I mean I see a ton of influencers who are like get ready with me and this outfit's brought to you by the gap or something and that's been a backbone for a whole variety.
1:59:06
I have a friend who's been working with figs for a long time and she'll talk about figs clothing and it works really well on Instagram.
1:59:14
Have you seen that flywheel start on threads?
1:59:18
>> It's interesting that you asked that.
1:59:18
I mean, we we have had some of these um >> I would say they're it's more like memes that everyone in the app participates in for a few days, but not like categories like that.
1:59:29
I mean, like get ready with me.
1:59:30
It's like Alex Earl was on Dancing with the Stars because she did get Ready with Me videos 5 years ago.
1:59:33
That's like >> I don't think we've seen equivalents on threads, but we had this um >> Do you guys know the like Sorry, I'm just bringing up memes on the show, but like hey, >> you started with a meme.
1:59:43
[laughter] >> Do you guys know the I hate gay Halloween thing?
1:59:47
that was like big on threads. >> Okay.
1:59:51
>> Wait, we we I I actually [laughter] do think I saw one thing.
1:59:54
>> It was just people being like, you know, there was actually one that I laughed at the other day.
1:59:56
It's like, "Hey, gay Halloween. What do you mean?
1:59:57
I'm like, you're going as the grass from the Bad Bunny halftime show or whatever."
2:00:01
You know, like there was like two >> all these like very obscure like niche references.
2:00:06
>> I think that's what Threads is good at is like the niche humor.
2:00:09
>> That is a great Halloween outfit.
2:00:09
We were during the Super Bowl, we I was just sitting there zooming in on on the grass because you could see there was like coordinators that would be like right up in the face of the grass just like yelling them like get to the right.
2:00:23
I thought they were going to do something, but then I found out it was because they had limitations on the number of carts you can roll out onto the field.
2:00:29
>> So they had to add people.
2:00:30
>> The way that they were able to do the set was to have humans walk on and off because there's like a restriction on the number of carts you put on the field. I like it. Nature finds a way. It's amazing.
2:00:40
>> How uh how big how big is the team?
2:00:40
How do you think about scaling the team?
2:00:45
What does it look like to go to to go to Zach and say I need I need a 500 more?
2:00:50
>> Yeah, I've never made that ask.
2:00:50
Uh we're we're relatively small compared to the other apps inside Meta like by orders of magnitude.
2:00:56
Um >> but uh we are growing this year.
2:00:57
Um we're investing in like two things.
2:00:59
One is like just relevance, making the content ecosystem better and stronger and the personalization of the feed.
2:01:04
Um the deer algo thing is like a part of that question for that.
2:01:10
>> And then um the other one is just like making sure that we can grow sustainably.
2:01:13
Like these promotions that we have in Facebook and Instagram are awesome.
2:01:16
I think that we will have them for a very long time.
2:01:18
Um but we also want to make sure that people are turning to threads without having to see a promotion.
2:01:24
Um there's a bunch of just like basic work to do well there that I think other companies have done really well over the years.
2:01:29
even just like SEO and getting yourself like if someone searches Super Bowl halftime show, I want threads content to come up on a search engine there.
2:01:36
And so th those are the two categories where we're growing, but it's still a pretty small team. >> Yeah.
2:01:41
>> Can uh I want to know more about Dear Algo and I want to share my experience.
2:01:46
You can tell me if this is just me being weird or if this is actually a trend.
2:01:49
Like there was a time when uh a social network would be all things to all people.
2:01:55
So if I liked sports and tech and cars, I would get all three of those sort of mashed together.
2:02:00
I could maybe go into certain communities.
2:02:01
Now I feel like I have different apps and different platforms.
2:02:06
Like for real-time tech news, I go to X.
2:02:08
But then if I'm watching a video essay or a car review, that's on YouTube.
2:02:12
My my Instagram is much more timely, much more funny, uh more reals.
2:02:18
And then I'll my podcast player is for like the conversations that aren't very visual.
2:02:23
So, I have all these different platforms and I'm wondering about like is there is there a future where someone's using threads for one interest of theirs and then Instagram for a different interest of theirs and there's kind of two separate communities and they're sort of intentionally steering it that way.
2:02:41
I think it's possible like like there is a kind of to my point before about what content works well on the app and not like >> I would say in a in a category as broad as sports. Yeah.
2:02:52
>> You probably always will have two types of content.
2:02:54
It's like show me the super cut of like Kenneth Walker in the Super Bowl and then show me uh you know Mina Kims talking about his free agency or something like that.
2:03:04
Threads is going to be really good at the the latter.
2:03:06
I think Instagram will be really good at the former.
2:03:08
Um, one of the ways that a lot of these apps think about how to get to the point that you just talked about, which was like which is like is like how can we get the user to tell us what they want to see without asking them.
2:03:20
So that's like what do you search for, what do you dwell on, what do you share with other people, what do you like all these signals and like our job is to figure out which signals are signal and which ones are noise.
2:03:30
Um, I actually think it's possible for an app like threads to be multiple things for people but probably not everything.
2:03:37
Like I don't I don't want threads to be a video app.
2:03:40
That wouldn't make sense.
2:03:40
We have like a lot of investment in >> short form video at Instagram and on Facebook.
2:03:45
Um >> the political infighting.
2:03:46
You've been like that's something we not really >> I don't need to do that.
2:03:49
Like I but I think that there's a space for the text format that's really big and and my biggest takeaway from the last few years of threads which when we first started it I think we very much saw growing the app as um we need to pull people from other services to grow.
2:04:05
I've been really pleasantly surprised at how we've grown the category.
2:04:07
There's a lot of people that use threads that never used X or similar platform in the past.
2:04:12
And so that's the thing that I've been really focused on is like why is that happening?
2:04:16
What are those people doing?
2:04:17
And a lot of it is like these niche interests.
2:04:19
Um dating threads is really big actually.
2:04:21
It's like people like singles go on threads and make a post and put it into the dating threads community and they're like, "Hey, I'm looking for love."
2:04:27
Um but then we also have book threads.
2:04:30
There's like a like crocheting community.
2:04:32
I tend to spend my time on sports and pop culture and stuff like that, but there are these very niche products so that you can find those people and kind of make your app about that.
2:04:43
>> So, how does Deer Algo work?
2:04:43
Is it plain text or buttons, UI?
2:04:46
How can someone actually customize?
2:04:50
>> We just like we just sort of built it off of what we So, there was this viral moment like a year ago where people were writing, "Dear Algo, show me more tech content or whatever."
2:04:56
Or, "Dear Algo, introduce me to people who are into these things."
2:04:59
Y >> and that I mean to say that it didn't work would maybe be incorrect but it's like the system wasn't architected for that to be like a strong signal totally.
2:05:08
>> Um of course if you write about a thing and you like a bunch of content about it maybe the algorithm will pick up you want to see more of that but it wasn't working with like high intent.
2:05:17
>> Um so now if you just go type dear Algo into a post on threads it like tags itself blue.
2:05:22
You can say, you know, the other day, actually, because I'm a Patriots fan, I was like, "Stop showing me NFL content."
2:05:27
And for three days, I got nothing [laughter] about the NFL and my feed. It was amazing.
2:05:31
Like, I don't even know if the Seahawks parade happened.
2:05:34
Like, it didn't cross my timeline.
2:05:37
>> Um, but then you could you can also say, "Show me more of something."
2:05:38
So, um, actually, I think I made one the other day that was like, "Show me more real >> grass people from the halftime show, not AI generated ones."
2:05:45
And that worked like the reason why we're able to do it is because content understanding and like topic trees have just gotten so much better with LLM.
2:05:53
Like 5 years ago, we might have had you as dog sports cars.
2:05:58
Now it's like this specific model of this car which is associated with this brand which is made in this country.
2:06:04
>> It's like a million parameters that no human can understand, but it's way better just like ad targeting. Love it.
2:06:09
>> You will get like a rejection if you if you say like show me more murder.
2:06:11
We will be like we can't do that.
2:06:13
Um, if you say, "Show me more of something that's like so niche that we don't have enough content," we'll tell you like, "Hey, there's not enough content for this."
2:06:20
And then we actually tell you in the feed when you see something that's because of the request that you made, it'll be like marked as such so you know what you're getting. Very cool. >> It's fun. You guys should try it. It's >> Yeah, I love it. I love it.
2:06:29
Yeah, I've been waiting for the plain text uh interface to the >> Jordy could make his first Threads post today maybe by trying it. I will do that.
2:06:37
I haven't done I haven't done one.
2:06:39
>> No, [laughter] >> I got to All right, I'm going to get there.
2:06:41
shame you on the on the live stream crossing >> I'll be on there Jordy Hayes on threads.
2:06:48
[laughter] Find me there.
2:06:50
>> Actually, I made my first ex post in three years today >> because you guys tagged me on there and I wanted to route people to >> There you go.
2:06:57
Always always be selling. Always be selling. >> Always be selling.
2:07:00
Well, the app the app looks absolutely beautiful. >> Thank you.
2:07:05
>> Number three in the app store. >> I love the polish.
2:07:07
>> Anywhere from two to three.
2:07:07
I want to get that number one.
2:07:09
Do you do you do you like wake up and check the app store charts?
2:07:13
>> No, that is not a thing I do.
2:07:13
I wake up and I look at like six dashboards.
2:07:17
>> Also, I mean, just to be clear, in the >> Well, it might help if you put a big monitor in the office that just has your app store position.
2:07:22
[laughter] >> Usually, when you put things up like that, it tends to like >> That's a great tip.
2:07:27
I'm sure my team will really enjoy that.
2:07:29
[laughter] >> Anyway, dude, it's great to hang.
2:07:33
>> Thank you guys for having me. Thank you. >> Yeah. Thanks so much.
2:07:36
>> We'll talk to you soon.
2:07:36
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2:07:58
And up next, >> next, Alex Bazari.
2:07:59
He's the co-founder and CEO of DDN.
2:08:02
He's in the re room waiting room and now he's in the TV pin. What's happening? How you doing, Alex? Good to meet you.
2:08:08
>> Hey, how are you guys doing? We're doing great.
2:08:10
I expected we expected you to suit Mongus. We certainly have. Outfit is fantastic. >> We certainly have.
2:08:15
Uh >> what's the background on the on the suit?
2:08:18
Have you always been to fashion? Is it particular?
2:08:20
>> I've always been into fashion.
2:08:20
I'm sure you guys appreciate it because you're definitely not like everybody else. So, >> yes. Yes.
2:08:28
>> Well, no, we'll we'll hit you up after the show for some tailor recommendations.
2:08:31
Uh but uh very very excited to meet. >> Yeah.
2:08:35
Well, first time on the show, please give us an introduction.
2:08:39
>> So, um, CEO co-founder of DDN.
2:08:39
DDN solves uh all the data problems associated with AI implementations >> for enterprises, sovereign, so nations, countries, large scale deployments.
2:08:54
>> Nvidia uses us internally for everything they do.
2:08:56
Elon large, Grock on 200,000 GPUs is powered by DDN.
2:09:01
Hundreds and hundreds of deployments like that.
2:09:03
hundreds of deployments like that. So that's what we do and solve the problems of AI and we help organizations monetize AI because it's great to invest but if you don't monetize what's the point >> what what's your background how did you
2:09:16
get into this business how long ago >> uh been in technology forever uh born in France uh came to the US in my early 20s went to school here loved it >> and then just did a bunch of technology companies uh this one uh my partner and I started about 20 some years ago. >> Wow. >> Wow.
2:09:36
>> At the time we were solving the problems of success. >> Yeah.
2:09:40
>> So high performance computing is basically government labs, academia trying to solve complex technology problems.
2:09:47
I mean those guys were our customers.
2:09:49
We ended up powering 60 out of the 100 fastest supercomputers in the world uh in every country basically three-letter agencies, >> Department of Defense, Department of Energy.
2:10:03
And then this little thing called AI started to happen.
2:10:06
And so Nvidia came to us and tapped us on the shoulder and they said, "Well, uh, we're trying to stand up a reference architecture."
2:10:13
That was >> eight years ago.
2:10:15
And they said, "We have all the pieces.
2:10:18
all the pieces. we don't have the data and so we became part of that architecture and video became our customer and you know here we are eight years later AI is booming as as you know as you see I mean it's expanding exploding in every aspect every industry and uh and that's been the journey and
2:10:37
the journey is super exciting >> walk us through uh obviously you're quite bullish on AI and implementing it across every possible industry But how did you personal personally kind of process the different evolutions and paradigms from you know the transformer architecture to uh all the different steps that we've had since then? Um
2:10:59
Um >> sure sure that's a great question.
2:11:01
I mean look uh when Nvidia came to us eight years ago uh I mean honestly I don't think anybody realized how quickly it was going to grow and evolve.
2:11:10
it was going to grow and evolve. Uh and so we walked away from that first meeting saying well we need to develop a radically different architecture and that architecture for AI to be successful needs to connect edge so edge
2:11:23
devices think of that as autonomous cars think of it as sensor data robots in factories uh that move things around so it has to connect the edge to the data center where the data is getting processed analyzed that's where a lot of the Nvidia infrastructure is being
2:11:41
deployed deployed and then multicloud and so the evolution really was um as Nvidia and other companies have been deploying faster and faster GPUs uh the resulting factor is that there's a scarcity in the number of GPUs available in the world uh scarcity in power there's not enough power in the world
2:12:01
and there's not enough data center footprint in the world so our technology has basically evolved uh to adapt to these limitations people are spending organizations are spending millions, tens of millions, hundreds of millions. We have customers who are spending tens
2:12:16
We have customers who are spending tens of billions in building out infrastructure.
2:12:19
Well, if that infrastructure is not productive and is not delivering value, then it's wasted and the ROI just doesn't work out.
2:12:25
So we've evolved our software stack call it the data plane >> to ensure that these infrastructures are running in the most effective way possible irrespective of what power shortages might be or data center footprint shortages might be or the number of GPUs that are available.
2:12:44
So so that's really been our evolution.
2:12:46
It's been you know lock in step uh a lot of it guided by Nvidia.
2:12:50
I mean our engineers and their engineers interact on a daily basis across all aspects of Nvidia's engineering.
2:12:57
Uh and the primary problem is how do you make it easier for enterprises to implement AI in their environment in a non-disruptive way. Mhm.
2:13:07
>> I mean, in essence, you're dealing with CIOS who are like, well, I don't want to have any glitches because if I have glitches, I'm going to get fired.
2:13:14
And line of business people who are saying, hey, I want to benefit from AI in developing better, more compelling, more competitive products and services.
2:13:22
So, you have this tension, which means you have to make it easy for them to deploy in their environment.
2:13:29
You have to make it risk-free.
2:13:30
Um, and so with Nvidia and others, we've developed these integrated solutions that are industry specific that can be deployed and make it easy for enterprises to bring in AI into into their environment and benefit from it.
2:13:46
So, so it's really that I think we're moving from an early adopter phase which is a handful of organizations are benefiting from AI you know the hyperscalers the you know chat GPTs of the world the gros of the world into one where the industrialization of AI is
2:14:03
underway and I think that's one of the most compelling things that is happening out there but but for that to take place >> easy easy >> uh the latest earning cycle I think everyone was shocked by some of the the capex numbers that were coming out from the hyperscalers. Was that surprising to
2:14:19
Was that surprising to you or >> not?
2:14:22
Not not really because again we're we're very very close to the center of the universe which is Jensen and uh and and if you look at it I mean the capex >> yeah Jensen Jensen was saying like in Q4 of last year he was throwing out numbers that implied that the hyperscalers would be raising their their capex projections massively.
2:14:46
So, it shouldn't have been that much of a surprise, but when Jensen was first saying it, he's obviously a salesman >> and uh like, you know, it it felt of course it feels a lot more real once once they're throwing out, you know.
2:14:58
>> Well, I mean, look, I mean, if you if you think about it, the hyperscalers have a software suite which they're monetizing across a very broad population, hundreds of millions of users, billions of users.
2:15:09
And so you look at that capex and you align it with how much they're charging and how sticky the offering is.
2:15:16
You just got to do it because if you don't one hyperscaler will emerge I think as a leader I mean just like the Google search engine there will be one leader and then there will be a number of others who will have market share but they won't be the leading market provider.
2:15:32
And I think everybody has come to the conclusion that you have to invest very very heavily because without massive infrastructure deployments you cannot train the model at at the level of complexity that is required at the real time elements that are required in order to deliver outcomes to organizations and consumers.
2:15:52
So so I think it's really that everybody is racing to be the market share leader in this newly created space.
2:15:58
I mean Google is doing it. >> Yeah. >> Uh OCI is doing it. Microsoft is doing it. Meta is doing it.
2:16:04
Yeah, >> there will be one leader.
2:16:08
>> Getting a little bit more specific.
2:16:08
Soft software engineers have done an excellent job adopting uh creating and adopting a bunch of AI tools.
2:16:15
uh what are maybe some underdised areas that you're seeing AI adopted and real uh usage growth that that the kind of broader tech community is less focused on because these are you know maybe companies or industries that aren't in you know typically at the center of of the conversation. >> Sure.
2:16:36
I mean look the the places where we see significant traction uh financial services because the ROI pencils out beautifully.
2:16:42
I mean it's a no-brainer.
2:16:45
uh the better your models are, the more complex you can run those models, the faster you can get outcomes, the more differentiation you create and so the better return to your shareholders.
2:16:53
So, >> and so that's like uh companies that are doing trading or >> so think hedge funds, high frequency traders, we have some very large customers in that space.
2:17:04
Uh those are very technical organizations.
2:17:07
very technical organizations. uh typically the people who are in these organizations have come from the world of high performance computing so they understand the benefits of it uh and so yeah that's that's one bucket which I think will continue to uh expand second
2:17:23
area is life sciences anything having to do with drug discovery bringing a new drug to market genomics the costs associated with bringing a new drug to market are staggering it's billions of dollars it's years and years of development and so in the end if you find yourself with a drug which is being rejected by the FDA. Well, you have a
2:17:39
Well, you have a problem.
2:17:41
So, AI uh gives them the ability to better triangulate what should an optimized drug be to cure a specific disease and how do you increase the likelihood of that drug getting accepted?
2:17:55
So the the cost will still be extreme because of you know animal studies, human trials, all the different steps, but you you we could enter a world where you have a higher success rate for drugs that are entering. >> Exactly.
2:18:09
I mean it's uh it's higher success rate.
2:18:11
It's better predictability.
2:18:12
Uh and it's also as the omniverse digital twin starts to happen.
2:18:18
I mean the ability to basically run what if scenarios that you don't have to do in the real world.
2:18:22
So, if you're bringing a new drug to market and you're saying, "Well, there's like eight different ways I could do this, but I'm not sure which one is going to be the best outcome."
2:18:32
You run simulation in the omniverse with synthetic data, and then the responses come back saying, "Well, if you combine parts of the first one and the third one and the fifth one, combine them together, likelihood of success will be higher, the drug will be better."
2:18:47
So I think increasingly we're seeing that the omniverse and synthetic data is coming into the mix.
2:18:52
Autonomous driving clearly because well if you have driverless cars on the road you have to collect data in real time.
2:18:59
Uh this is cameras audio this that the other you have to continuously reproise re reprocess the model at very large scale.
2:19:11
Uh so many car manufacturers are our customers in that space.
2:19:14
Uh manufacturing is starting to happen again uh factory floor automation u retail just how do you optimize inventory in various types of retail organizations uh and then the other really big ones is sovereign sovereign AI I think with what uh the Trump administration has done um they've created lots of concerns worldwide in terms of you know autonomy uh and risk and of the US is no longer there to just bankroll you.
2:19:45
Uh so you have to protect yourselves in some way.
2:19:48
So we're we're involved in lots of >> Have you spent have you have you spent much time in in France over the last few years?
2:19:54
There was obviously a little tiff between the the western uh the the the US tech community in Mcronone last week around he came out with an announcement that was kind of uh intentionally misinterpreted a little bit and then he was >> uh maybe by me.
2:20:09
Um but he uh uh was kind of you know putting out charts of you know [snorts] showing foreign investment and and uh uh we've you know heard from um that that a number of of labs have been excited about the energy availability in France and and look to uh capitalize on that but kind of just kept kind of kept u you know running into different blockers kind of from a regulatory standpoint or or a general speed standpoint.
2:20:34
what's your kind of um take on on France's progress around sovereign AI and just kind of catalyzing the industry locally?
2:20:44
>> So look, I mean one one of the one of the organizations in France that's very very active in the space is a company called Mistral.
2:20:50
>> Uh so Mistral is our customer.
2:20:50
Uh so we're deployed in their infrastructure.
2:20:56
>> I think look at the end of the day lots of super super smart people in Europe in general you know France, Germany, UK all of it.
2:21:02
uh but the scale of investments is just not at the same level as as the US and China.
2:21:07
I mean so today it continues to be a two- horse race.
2:21:09
I think it's US and China.
2:21:11
Uh and the Middle East is starting to deploy massive resources into it.
2:21:16
I mean uh uh Kingdom of Saudi Arabia I mean we're involved in those infrastructure buildouts.
2:21:24
So sovereign AI um the good thing there is that the cost of energy is very low.
2:21:29
uh land availability is very very significant and there's a desire to really step up uh what KSA is doing.
2:21:38
Likewise in UAE and well there are geopolitical tensions between the two but I think both have aspirations to set themselves up on the world stage as being the third player.
2:21:53
uh Europe I think will will will be there no question but Europe I mean all these countries have centuries of history and so getting things done quickly easily is not quite there whereas in the Middle East well you have one decision maker and if that decision maker says go it goes with infinite resources I mean right trillions of dollars under management in in both places so making a multiund $100 billion investment is nothing. >> Straight forward. >> Yeah.
2:22:27
>> Uh when you look at uh where DDN is spending money on software today, how do you think that'll change over the next 5 years?
2:22:36
We've been covering the SAS apocalypse.
2:22:38
Uh and it seems like a number of uh great companies today could be comparable to uh the sort of office equipment and imaging companies of like the '9s where they're they had the revenues were really high but then the internet and email and the PDF came along and suddenly people just needed less fax machines and and all that kind of thing. >> Sure.
2:23:01
I mean look I I think the market is overreacting in some ways as it sometimes does.
2:23:05
So you know somebody makes an announcement and then everybody freaks out. Oh my god. Oh my god.
2:23:09
This whole industry is going to get commoditized.
2:23:12
Everybody's going to go out of business.
2:23:14
Service now is going to go out of business. My god. My god.
2:23:15
Um I think you have the forwardthinking organizations in SAS uh who are adopting and integrating AI into their offering and I think those will do well provided that they do it at very high velocity.
2:23:31
And then you have the ones who will be more traditional in their thinking and and in the way they operate.
2:23:35
And I think those will go by the wayside. I mean just look at IBM.
2:23:39
IBM is a perfect example. Look at Intel.
2:23:41
I mean these are companies that had everything to to succeed.
2:23:46
I mean why is it that Nvidia is where they are and Intel is not well because the velocity of execution and the ability to adopt something that is happening that is completely different from what it was before was not quite there in the culture of the organization.
2:23:59
So, so I think the way to look at it is which organizations have leaders who are embracing the change, not fighting it, and who are going to integrate it into their portfolio and forge the right alliances.
2:24:09
I mean, you could say the same thing about gsis, Accenture, Deoid, all of these organizations.
2:24:15
I mean, Accenture has what 750,000 employees.
2:24:16
How many of those are going to be relevant in this AI enabled world?
2:24:24
Well, Accentra has to completely transform the way they operate and the value that they deliver.
2:24:32
Otherwise, uh it will just go like that.
2:24:34
So, you need really forward-looking visionary leadership that will force the change.
2:24:41
Um because if you stay in your comfort zone and you think, "Oh, I have a great business."
2:24:45
Like you said, the top line is steady, the bottom line is fine, you will get whacked.
2:24:49
That that's just the way it is.
2:24:54
I mean look the speed at which AI is is operating it's Jensen speed >> is pulling the whole industry at a velocity which very few can follow the speed at which he is turning the GPUs the integration of the software stack and the ecosystem into the GPU
2:25:15
enablement uh the open-source approach he's taking I don't know if you saw his CES keynote he's basically devel developing turnkey integrated software stacks and is open-sourcing them in order to accelerate adoption industry by industry. I mean, what he did with
2:25:32
I mean, what he did with Mercedes, it's okay, here's an open-source software stacks.
2:25:35
It's kind of the opposite of what Elon was doing at Tesla, which is a closed architecture.
2:25:41
It's like I'm opening it up because if I if I open it up, I will accelerate the adoption of AI >> by the automotive industry and they will buy more of my GPUs.
2:25:50
So I will put 5,000 engineers on this for many many years and then I will put it out there.
2:25:58
I mean it's a method really accelating adoption. >> Yeah.
2:26:04
when when you think about uh kind of forecasting and planning for your business, are you more scared of a uh like chip bottleneck or energy bottleneck when when we talk to different >> I would also like to put in research idea bottleneck and energy bottleneck.
2:26:20
There's sort of four categories that people are worried about progress halting on chip.
2:26:25
So >> and to date it's been obviously oscillating between chips, energy and >> energy. Yeah, >> I'd love to.
2:26:32
>> So, uh, so look, the the ability to process.
2:26:36
So, I mean, think of AI as you have models, you need to train the models, then you need to layer analytics on top of it.
2:26:44
>> And then the most important part which creates value is inference.
2:26:48
>> From that data, you need to get value.
2:26:50
So, you know, I always say we are to data what Nvidia is to compute.
2:26:52
And and in order to do AI successfully, you need to combine the two together.
2:26:56
So what that means is an infrastructure can only deliver benefits if it is cost-effective and and and in order to accelerate the adoption of AI you need to make it cost effective.
2:27:11
These shortages will continue I think for the next several years.
2:27:14
And so you have to say given the limitations that I have, how do I make these AI workloads more effective and have the ROI pencil out across industries?
2:27:25
I mean I was at Nvidia yesterday actually and and and we were talking about that how do we accelerate the adoption of AI by enterprises?
2:27:34
Well by packaging turnkey solution that optimize outcomes.
2:27:37
How do you make sure that these agentic AI organizations that are providing services and unfortunately these services are very consumptive of tokens which means many of these companies are now upside down.
2:27:49
They're losing money because what they're charging to their customers does not uh tie into what is costing them because they're relying on AI.
2:28:00
So I think the cost reduction and the compression in terms of tokens required to perform a certain task is really what it is.
2:28:08
So I think it's a software play uh the underlying infrastructure eventually it will happen.
2:28:14
I mean SSD shortages and shortages I mean we deploy our data plane on top of storage SSDs hard drives and so on.
2:28:23
Well >> over the last few months the cost of SSDs has tripled.
2:28:26
Yeah, >> so it's significant and it's not available on top of it.
2:28:30
Now we happen to have an architecture where we can tie into SSDs or hard drives and so on.
2:28:34
So our customers are able to do >> the same if not better with less money.
2:28:41
But I mean these are issues but but these are transient issues.
2:28:43
these are transient issues. I think eventually the problem that needs to be solved is how do you ensure that a task that is performed for a consumer or an enterprise is cost-effective for the organizations that are delivering that service and it's really that I mean that's what we're very focused on uh
2:29:02
that's what our partners are very focused on that's why many of our interactions with Nvidia revolve around this how do we make sure that we make it easier to deploy easier to integrate and lower the cost, lower the cost of power, lower the cost of building data centers, uh compress the velocity. I mean, look
2:29:20
I mean, look what Elon did with his data center and and we were involved every step of the way.
2:29:26
I mean, he built out the data center in four four and a half months, which was unheard of. >> Yeah.
2:29:31
you when you heard when when you and the team heard the initial timelines that they were planning around. Did you believe?
2:29:37
believe? I said it's completely I said it's completely mad because we had done probably more than a hundred large data center deployment and I had never seen it done in less than three years >> and um when the X team first came to us and said oh we're going to do it in four four and a half months I said that is
2:29:54
just ludicrous how is that even possible but see the way he did it instead of hiring people who are experts in building data centers and all of them would have said it's impossible mental block right if you've never seen it done in less in three years and somebody tells you four and a half months, he goes, "It's impossible. This is stupid." This is stupid."
2:30:11
So what he did is he hired very very smart people who were very good at connecting the dot outside of the box.
2:30:20
And he said, "Okay, I want this done in four and a half months.
2:30:22
Figure out how to do it." And and they did it.
2:30:23
I mean, we were there with them Christmas, New Year's, weekends, 24/7.
2:30:28
I mean, there were mattresses in the hallways.
2:30:31
I mean, everybody was sleeping there.
2:30:34
was just working to get it done and he got it done.
2:30:38
Uh now it was extremely painful but he got it done and so you go okay so the new benchmark now is not three years it can be done in four four and a half months.
2:30:47
have any other have you seen any other uh either Neolabs or or labs or hyperscalers be able to replicate that kind of timeline like once he set the bar?
2:30:57
Uh >> China >> they are very very good. I mean we are so behind. We are so behind. >> Mhm.
2:31:08
>> I mean they've developed models.
2:31:08
I mean I was looking at what they're doing in data centers.
2:31:12
U I mean the first one is the cost metric.
2:31:14
I mean in the US the cost metric is 10 to 15 grand per kilowatt to build the data center.
2:31:22
uh in China they're able to do it for between a third and a fifth of that. Why can't we do it?
2:31:27
Because they're looking at it in a very optimized manner.
2:31:32
What what Elon did, he did the first one in four months. Lots of issues.
2:31:37
Then he did another three and lessons learned from the first one, he applied to the next three.
2:31:42
By the fourth one, he's like, "Okay, I got this."
2:31:44
And then he did the next 32.
2:31:48
And and the Chinese are doing it the same way.
2:31:52
They're not looking at each one of these as a one-off.
2:31:54
They're saying we really have to focus on optimizing, optimizing, optimizing, and then we replicate.
2:31:59
And and that's something we need to do better in the US. For sure. For sure. For sure.
2:32:06
How do we lower the cost to build a data center and how do we compress the time to build a data center?
2:32:12
Uh and and China is way ahead of us right now. They're just way ahead. It's reality.
2:32:19
Well, hopefully more Colossus data centers coming online soon. >> I know. I know.
2:32:22
But but I mean it [clears throat] needs to be done.
2:32:24
I think the good thing is people are realizing that China is very good at certain things and and instead of saying, "Well, no, we're just going to ignore them."
2:32:34
They're saying, "Okay, h how do we learn?"
2:32:35
And I mean I had a meeting with one of our large customers from the Middle East and we're actually going through the design architectures from China looking at how they do it and we're like okay how do we apply that to doing it in the Middle East in a very modular manner and it's really remarkable.
2:32:52
I mean again we're not talking about 20 30% cost improvement or or timeline compression.
2:32:56
It's it's when when you say it's three to five times, the economics associated with that are huge. >> Dramatically massive. Massive.
2:33:07
>> Yeah, that makes a ton of sense. That's Yeah. Wow.
2:33:08
Uh thank you so much for coming on the show and really really enjoyed it. >> Appreciate it. >> Yeah. Great to meet you chat. We'll talk to you soon. >> Same here.
2:33:18
>> Have a good rest of your weekend.
2:33:20
>> Let me tell you about Label Box.
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2:33:37
Up next, we have Brett Adcock.
2:33:39
He's the founder and CEO figure and about 12 other companies.
2:33:43
Serial entrepreneur with a massive release today.
2:33:45
Brett, how are you doing? Welcome to the show. How are you doing? >> Yeah, thanks guys. Thanks for having me. >> Great.
2:33:54
Uh, I think most people will be familiar, but break us break it down.
2:33:57
Uh, where is Figure now and give us the news today. >> Uh, yeah.
2:34:02
So, we, um, well, several months ago, we unveiled Figure 3, our third generation uh, humanoid robot.
2:34:10
>> Uh, this morning, we actually gave a sneak peek at a future roadmap item we've been working on for about over three years. >> Okay.
2:34:16
>> Uh, which is our our newest generation hand. >> Yeah.
2:34:19
>> Um, I think we we we've been working on this project basically since the beginning.
2:34:23
Uh our generation one was like this tendon based hand that we designed in 2022. >> Sure.
2:34:29
>> Um had tons of problems with it and we basically been working on trying to reach human like how do we approach human parody in terms of uh >> hand dexterity sensors? >> Yeah.
2:34:39
Does does anything else about the robot really even matter if the hands aren't like human level capable?
2:34:47
Um I think like one thing we're realizing is like more and more if we want to like learn from humans we need to like look and do humanlike things.
2:34:55
>> Uh so even like from a from a visual perspective like having the right um kinematics of the hands so that we can do humanlike stuff.
2:35:03
>> Uh meaning like if a human folds like you know socks or towels a certain way like we need to really understand uh how to be able to do that on the robot. Mhm.
2:35:11
>> And so um if if we truly want to do like full general purpose work uh in a home across the whole world at billion unit levels, we have to start approaching like human like level dexterity.
2:35:23
>> There's somebody going for a stroll in the background. >> Yeah. >> What is that?
2:35:27
Is that just a walk cycle? Is that scripted?
2:35:29
Is is that did he decide did the robot independently decide to walk behind you right now? What's going on?
2:35:36
>> We have we literally have hundreds of robots here on our campus in California.
2:35:40
They're they're everywhere.
2:35:40
They're all over the place. >> Okay.
2:35:42
Why why jump straight to full dexterity humanoid form factor? Why not wheels? Why not pincher grabber? More incremental?
2:35:51
You know, we've seen Amazon acquire uh that uh that robotics company just to sort of move packages around.
2:35:59
There is a logical uh chain of events that you could do more incrementally, but you're going for the moonshot straight away.
2:36:05
It feels like what informed that decision?
2:36:10
I listen we have like a a very deep respect to trying to do what like human level work in the world without changing the world too much >> and if like we as humans built the whole world around our the way we look and feel like uh the way we like move around the world.
2:36:24
So we like you know we use tools uh doors like stairs like um like so like you know we've like built the world so that human body can interact with >> the ultimate form factor for this is a is a human >> uh you know like if you start like removing uh like the ability to like have legs or fingers or the different stuff you're just going to do less of what humans do in the world.
2:36:46
>> Uh so our view is that we want to go out and basically do everything a human can. >> Mhm.
2:36:50
>> Uh that that approach is basically a human form. >> Mhm. uh in the limit.
2:36:53
So we went after the hardest problem here which is like how do you design uh humanoid hardware?
2:36:59
How do we design neural networks now to work on that hardware?
2:37:01
It's a really difficult problem but it's like super tractable.
2:37:04
This is a problem that will be solved in our lifetime >> in the coming years and decade.
2:37:08
We will see like millions of humanoids uh out in the world doing all kinds of things.
2:37:14
What uh weird what >> uh what is the what's your bar for to get to the point where you're selling you're selling a robot that somebody can buy and put in their home and start doing tasks because it is there's there's a lot of you know I'm sure you're testing this stuff constantly and you're able to do think tasks like laundry or moving dishes from a sink uh cleaning dishes etc.
2:37:33
Uh and yet the bar is an individual just saying, "Well, I can just, you know, do this myself. It's quick." So that's one.
2:37:40
You have to overcome that.
2:37:42
It has to be so good, so consistent.
2:37:45
>> Um uh but like what what do you think is the bar?
2:37:47
There's obviously companies like 1x that are pushing hard to, you know, get robots into homes.
2:37:51
You guys are pushing >> operations pushing hard too.
2:37:54
Elon is obviously, you know, adapting his Fremont facility, right, to be able to, you know, make these at scale.
2:38:00
But I think everybody's sitting around being like, "Okay, once I can hit buy on one of these things, >> uh the the the the >> the amount of pressure that the first company that kind of comes out, if you don't countere, but the first American company to come out with a robot, the pressure to actually deliver real value when people are like, "Hey, I just spent 30 grand on this, 40 grand, 50 grand, the pressure is going to be immense." What is the bar for you?
2:38:27
Yeah, I would say like um the thing that we that really matters here uh in the world is getting to a spot where you have a humanoid robot that can go off and do like uh many minutes and then hours and days of work fully autonomously with neural networks. Like that's the bar. Mhm.
2:38:47
>> And if I think you if you if you look at like um who's doing that today, there's not a single group out there that can recreate the video we did 2 years ago. >> Mhm.
2:38:57
>> Which is basically we had a figure one just moving cure egg around with a couple hands for like a minute or two.
2:39:03
That was done with neural net.
2:39:03
We were just standing in place. It was uncut.
2:39:04
It was a few minutes long.
2:39:06
And I haven't seen a single company in the world able to do that today.
2:39:10
Uh, so we can like pretend that we're like teleyoperating robots and be super silly and act like that's going to work. It's not going to work.
2:39:17
We have to deploy neural nets at scale to robots that can be fully general purpose over a long period of time without any human intervention.
2:39:26
>> So for figure, I think we're just like by far and away the the best example of being able to do this today.
2:39:31
And we're still like >> we still have so much more to go in order to be able to put it into a home like for days and days and be like extremely useful in that respect.
2:39:39
So, uh, right now we're able to do like pockets of this work really well.
2:39:42
Like we're able to do like um clean up the home, do like we can fold laundry, we can do dishes like this stuff is being done with neural nets fully end to end.
2:39:53
Um, >> and a lot of times like doing it pretty high performance.
2:39:57
>> Uh, so my view is like we will only launch a product here at Figure into the home when we're really ready.
2:40:01
I think it's the I think the world will only accept a product into the home when it's really ready too.
2:40:05
Nobody's going to deal with like silly tele operating the room in the home. Things like this. >> Yeah. Yeah.
2:40:10
The other thing is like we've we've seen with uh we've seen with a number of of hardware like the humane pin, you had the rabbit R1 like people people might be willing to try like a digital product like a couple times even if a lot of people will try it if they have a bad experience they won't come back.
2:40:26
Some people might try it again got better >> whereas with hardware it really feels like if you launch a hardware product and it doesn't deliver real utilities you lose everyone like it basically kills the company.
2:40:36
>> So the bar is just so high.
2:40:36
So, >> so, so yeah, time timelines uh you guys have uh the benefit of being private even though you have a a valuation uh you know somewhere around the range of of Ford.
2:40:50
Uh you have time to to um figure this stuff out.
2:40:55
How like what are the timelines that you're setting internally?
2:40:58
What are you know what are you rallying the team around?
2:41:02
>> Yeah, we're we're we're working like kind of too too pass.
2:41:04
How do we ship robots in the industrial workforce as fast as possible?
2:41:08
>> That track is pedal to the metal like every single day.
2:41:12
>> We have many different customers there fully signed up, ready to go.
2:41:13
And we're excited to, you know, we had robots at BMW last year.
2:41:17
We have like more robots going into commercial customers this year.
2:41:20
The second is we want to solve a general purpose robot, like solve general robotics in a home. Mhm.
2:41:25
>> And um the best I one of our top goals is like be able to drop a robot of ours into an unseen home this year and do like full general purpose end to end work.
2:41:35
>> And um it's extremely tough. I think we can go do it.
2:41:37
Um but we're working like day and night to go get there.
2:41:41
>> Um and um yeah, it's a it's like it's it's basically how do we design it's it's the closest thing to like AGI for like like for physical world, right?
2:41:51
like how do we get something that can like re like have common sense in a home that you can talk with it can understand things uh maybe you can teach it something on the fly and can watch you uh and then ultimately be able to carry out those tasks at high performance all throughout the day.
2:42:05
So uh >> my my hope is we can make material progress on this this year >> like our goal is like working day and night to try to solve this um to the extent we can hit this goal of like being able to do full end toend work.
2:42:17
there's other like barriers of like privacy and safety and other things that are really hard that we're also parallelizing.
2:42:23
>> Um, but my hope is by the end of this year we're making considerable progress towards this uh being able to like show like um some some crazy insane things with these robots in you know in these type of environments.
2:42:35
>> Um but this is like this is a separate track to like the commercial side like we're already out like uh we've already been out being able to do this.
2:42:41
We're going to go out even larger this year in 2026.
2:42:43
They'll deploy robots at scale.
2:42:43
2026. They'll deploy robots at scale. is important for us to get like a real operational readiness like how do we make sure robot like how do we make sure we can run robots at scale really well here figured >> yeah how >> yeah and it's all I mean it's it's I'd say like much more straightforward to
2:42:58
have a robot in a setting where you have trained professionals probably wearing hard hats that can be kind of monitoring the robots from far away you're not dealing with the safety risk of like a robot falling on a dog or on a kid or any of the other challenges in the home uh when what what's your timeline to a robot humanoid being able to bench uh two plates. Is that a [snorts] is that
2:43:19
Is that a [snorts] is that an interesting uh is that an interesting problem to solve?
2:43:24
>> The only problem for us >> for us uh that we're very fascinated on when when that'll happen. >> Yeah. Is it bench or squat?
2:43:31
What do we want to do here?
2:43:34
>> I mean squat is probably the overall compound lift squat I feel like is pretty >> thousand pound club ideally, but we'll take just bench press if that's what you got.
2:43:42
We I mean we should if we can if we can bench press that we should be worth at least twice a quart, right? >> Okay. Yeah. [laughter] >> Yeah.
2:43:49
I mean just for that the tickets to the bodybuilding competition.
2:43:53
>> No, but I I feel like I feel like uh even as silly as that sounds, you know, >> is an interesting benchmark. Yeah.
2:44:00
>> I think that the >> I mean over in China they're doing robot Olympics.
2:44:02
They're they're doing marathons.
2:44:03
They're doing all sorts of stuff tactile. >> Yeah. Okay.
2:44:07
Can we be real for a minute on all this stuff? Yes.
2:44:09
>> Like let's just like let's Okay, let's be real serious.
2:44:10
the what really matters I think for us as humans is we look at the distribution of what humans do that's like useful >> and we try to do as much of that as possible. >> Yeah.
2:44:19
>> These things where we run like marathons or we do back flips or we do karate moves or we like try to deadlift 300 lb >> they're not in the main part or the fat part of the distribution. >> Yeah. >> They don't matter.
2:44:30
And if you really want to size for those and do those, you're going to build a really expensive and heavy and unsafe robot that's hard to manufacture. >> Yeah.
2:44:37
you're going to build like a superduty truck >> and like nobody like no people want the $10 20,000 humanoid that can do general purpose work, right? That's what we want.
2:44:46
So if you're trying to size a robot to do those kind of things, like silly things, I think of like gymnastics and other stuff. >> Yeah.
2:44:53
>> You're going to build a very specialized robot that is like that can do like a very small percentage of what normal humans do every single day. >> Yeah.
2:45:00
>> So our goal is to build a general purpose robot to do majority of what humans can do out there.
2:45:04
We want to do like laundry and dishes and be a companion.
2:45:08
I want to ship robots at scale and a billion level into the workforce to do logistics and healthcare and build buildings and you know build build data centers.
2:45:16
Like that's the stuff we want to do.
2:45:18
I don't need to do back flips to do any of that work.
2:45:19
I want other robots building other robots.
2:45:23
So I think like um I don't know.
2:45:23
I mean I I think of like you look at the silly stuff out there.
2:45:28
It's not only not important for the road map.
2:45:29
it makes the hardware extremely uh like heavy and hard and expensive uh and all that causes more problems.
2:45:38
>> So like none of that matters in our mind.
2:45:40
I figure we uh you know every once in a while we'll put a robot on a DJ stage with dead mouse and stuff for fun, but like we're we're definitely not trying to design a robot to be great at that. Yeah.
2:45:49
>> Uh we want to be great at like the the things I do every day and you guys probably do every day.
2:45:52
I mean, you guys are probably deadlifting 300 lb, but like uh at least for me, every day I'm trying to like, you know, just do normal like normal practical stuff that billions of people today are are doing that we can help offset.
2:46:05
>> How do you how do you think, you know, let's say we get the the iPhone moment for humanoids uh hopefully in the next few years and that it's a uh a a uh piece of hardware that has real utility that a lot of people are are buying.
2:46:19
How do you think the kind of form factor evolves?
2:46:23
Do do robots over time look, you know, do they do they follow the iPhone path and that they get like thinner, lighter, you know, that kind of thing?
2:46:32
Is there like what what are you what have you been learning so far that maybe people kind of misunderstand about the form factor long term?
2:46:40
>> Yeah, the long-term form factor is more and more approaching the average human in terms of like the range of motion, payloads, and speeds and like what you can do.
2:46:47
If you're too short or too tall, you're just like you're not in the right habitat for like interacting with the a human world that well.
2:46:52
Um, so in an extreme case, if three foot tall, it's like really hard to get most things out of cupboard or get into the get into the, you know, the sink or reach over a table.
2:47:01
These they actually become really practically hard to go do.
2:47:02
So it's going to be like an average human size overall.
2:47:06
I think we're in like the pre I I'll make an argument here that we're in the pre- iPhone stage.
2:47:10
We're in the flip phone stage for for humanoids.
2:47:12
And I think we we know this by anybody.
2:47:14
We've been building them like crazy.
2:47:16
We have our third generation out in three years.
2:47:18
We've walked three generations in three years.
2:47:20
Um I think what you'll see here is that we're trying to find this ideal product like product uh fit like longterm where that's headed and we're like learning every year where that's going.
2:47:31
Uh it certainly means being more humanlike in our mind so we can unlock more percentage of this distribution we just talked about earlier.
2:47:37
Um, I think I'll make I'll make a statement that's pretty uh pretty bold is that when you look at like figure 1 to figure two to figure three, we've had to like step up in performance and they're just better and better every year.
2:47:48
When we head to figure four here, it'll be the largest step up we've ever made by like by a long shot.
2:47:55
It'll be the first time that we feel that we probably hit like iPhone one >> level humanoid.
2:48:00
We're just like this is the right place to be in and then this will like this will you know this will go extremely far to a point where it saturates at some point in the future you know um maybe 10 more years.
2:48:10
Um but we feel like relatively early.
2:48:13
It's an extremely difficult piece of technology.
2:48:17
It's obviously early in this.
2:48:17
So it's got to be like earlier than phones, right?
2:48:20
We're not like uh we're not there yet.
2:48:22
So, but I think the the iPhone 1 moment will happen with figure 4 and it'll just be it's just an unbelievable machine.
2:48:29
And I uh I never would have uh suspected we'd be able to make that big of a move.
2:48:34
Like, you know, when figure 3 shipped, I'm like, this is it.
2:48:36
This is like the best it'll ever get.
2:48:37
And then, you know, the more we uh learned and ran it and the more we developed neural nets here with Helix, the more we really understood better about like what the hardware should like should look like and and be like, and the more we got folks in the room together with us and said, "How do we radically redesign the head, the the hands, the the kinematic systems, like all of it from scratch."
2:48:59
And I think um you're going to see something.
2:49:00
I mean, this stuff is just going to get crazier and crazier in capabilities.
2:49:05
uh what are your goals around uh consistency?
2:49:09
So for for example, a robot that uh can unload my dishwasher if one out of a hundred times it it like breaks a plate into you know 200 pieces.
2:49:18
Maybe that's not that big of a deal if it can like you know pick them all up easily and I'm not home and I don't see that, you know, you're exploding the the plate.
2:49:28
But, uh, what what level of like consistency do, uh, do you guys need to get to before you're you're you'd be at a point where, you know, you can sell one of these things?
2:49:40
>> I would say like we probably need something pretty high.
2:49:41
I think it would suck pretty bad if you're at my house and dropped like the number one mom coffee cup.
2:49:48
>> You're getting you're getting you're getting your ass booted, right?
2:49:49
Like uh [laughter] you know, like um I think you got to be especially around safety and stuff like this needs to be super high performance.
2:49:56
So we like we watch folks that are trying to like telly operate and ship early when the product doesn't even work and it's just it's just silly.
2:50:04
They're all going to die.
2:50:04
The you got to ship something really high quality and that that is just like a super hard thing to do.
2:50:08
So we we I figure we'll ship into the home when we're ready.
2:50:12
We're not ready right now.
2:50:12
We're trying to get like you know I'm here till midnight every night 7 days a week try to get more ready with my team.
2:50:18
>> Um I hope we get like we hit some place where we're getting really really close this year is what I really hope.
2:50:23
>> How are you pressing >> and but you're right. >> Yeah.
2:50:25
How are you pressing the the Whimo story of tell because I was completely on board with the Elon pitch for straight shot to FSD collect a bunch of data from the cars that are on the road train the big neural network and FSD.
2:50:36
I mean we talked to Alex Roy who drove without touching the steering wheel all the way from LA to New York like it clearly works.
2:50:45
At the same time a lot of people in San Francisco hop in Whimo and they're like that works too.
2:50:48
And so it was a bit of a narrative violation where a lot of people were saying like the Whimo teley op will never scale and it feels like it's scaling.
2:50:57
So how is it is there a world where both approaches work or or are they fundamentally like different industries?
2:51:06
>> I think what I'm seeing in the space here and it's been like a pretty big shock is like everybody in the humanoid space is just teleyoperating the robot with a human in the back and they're putting out video out not being very explicit.
2:51:16
It's very different than this.
2:51:18
would be like the most analogy for your Whimo Tesla of like your Whimo is being driven by some dude in Kentucky not with neural nets.
2:51:24
Whimo has neural networks.
2:51:27
The way they went about with the sensor suite to go do it is maybe harder to scale than cameras.
2:51:30
scale than cameras. the situation happening in humanoids is like there's a large percentage of the companies out there that are like have a dude in the back like that are like teleyoperating with the robot in real time and then like you know we're trying we we've done everything we've ever put out publicly
2:51:45
has always been with neural nets >> um on like you know stuff we've never teleoperated that you know in that in that case like those any of those videos um so it's just like the the self-driving stuff's not the greatest analogy um you're definitely not going to be able to human telly operate in people's homes and the latencies will be terrible. The data coming back will be
2:52:02
The data coming back will be terrible and train nets.
2:52:04
It's just not enough data.
2:52:06
>> So, there's just a bunch of problems with that story and um it's it's not going to work.
2:52:10
If it would work fast and we can get product market fit and get out earlier, we would do it.
2:52:13
Just like it's just dead end >> completely.
2:52:16
You really want to solve like for real neural nets, real autonomy from from the get-go. >> Mhm.
2:52:22
>> How big of a bottleneck is data?
2:52:22
What are you guys doing to solve it?
2:52:25
like is there hardware breakthroughs that you guys are looking to achieve or or do you feel like the obviously you have the new hand which sounds like it's it's a it's a step up but uh what what are kind of the key bottlenecks?
2:52:40
>> Uh we just unveiled Helix 2 about 3 weeks ago.
2:52:43
It's a robot that can basically do like fully end to end whole body work.
2:52:46
Uh we did it in like unloading the dishwasher and rerunning it.
2:52:50
>> Uh that was basically the whole the whole stack there was basically neural nets basically all the way down the stack.
2:52:55
Um the the only reason why it could do that now or like like say from go from there to do laundry is just a data problem like we need just more data uh to cover the distribution of those new tasks and then the robot can do it at this point.
2:53:10
So we we feel like the like the longest pole in the tent >> prior to like extremely high rate manufacturing is how do we acquire data at like a really high clip and so we're spending a lot of time on that.
2:53:21
uh that gets you to a point where like you know uh acquiring data through teleoperation is just not going to even be close.
2:53:27
Uh you have to embrace like learning from humans at scale that's kind of figures like you know core models as it comes to helix for neural nets and I I would say if we could snap our fingers and have enough of the right data today you would have a general purpose sci-fi future of robots in our office right now >> that we' be able to put anywhere like we have it.
2:53:48
It's just we are just extremely data constrained.
2:53:50
It's not as simple as just like going out and getting random data.
2:53:53
It's like it's got to be the right type of data to match the observations and action spaces of the models.
2:53:57
Well, um but we now know what that data is.
2:53:59
We are acquiring that data like crazy.
2:54:01
Uh here at Figure, we'll spend nine figures of capital on acquiring data like this in 2026.
2:54:08
So, uh it's a huge focus for us.
2:54:11
We think it's by far and away the biggest bottleneck to get to general robotics. >> Yeah.
2:54:15
Uh how do you think about China in the context of the race for humanoid robots?
2:54:20
Uh obviously there's competition from humanoid robot makers there, but there's also a bunch of great part suppliers at all levels of the supply chain that might be useful to build American humanoid robotics companies.
2:54:36
How does that puzzle play out?
2:54:39
I mean, listen, I think like as it relates to competition, I think what's extremely important is seeing robots that can do like humanlike work with neural networks that's useful.
2:54:45
We haven't seen any of that out of China today.
2:54:49
They they really don't have any >> good on hardware, but they're still behind on software.
2:54:54
>> Well, I would say like uh you probably don't have good enough hardware if you're not able to do the software really well.
2:54:59
>> Uh they don't have enough compute in a lot of cases to run like like things like Helix on board.
2:55:02
M >> uh Unitry for example has a very tiny computer where you can run like very small reinforcement learning controllers to do like openloop replay of stuff.
2:55:11
>> Uh they wouldn't be able to run Helix on a board of hardware like that.
2:55:12
They don't have any real humanlike hands of five fingers.
2:55:17
>> Uh so you're really missing a couple big parts of the story.
2:55:19
Beyond that, I think they've been um you know great in existing kind of industrial robotics and ex existing consumer electronics last like several decades.
2:55:28
I think those are playing a big part of the ecosystem supply chain for humanoids that are important in some cases.
2:55:34
Um, but you know, but listen, we we basically design almost everything internally here at Figure.
2:55:40
We don't we don't go buy we don't go buy designs from from China or elsewhere.
2:55:44
We do it all here internally.
2:55:46
We even manufacture the robots next door in our campus here.
2:55:48
We have a we have a figure three robot now coming off the line every 3 hours and I think we'll we'll be at every half an hour here uh in the coming, you know, few months.
2:55:57
Um, so we're like, you know, like things are coming out at a pretty high clip.
2:56:01
But I think today, like if you look at like who's doing the best humanlike work with neural nets over long time horizons, it's it's not China.
2:56:11
>> Um, and uh I think we can um I think it we're we feel at least a few years ahead of anything we're seeing out of there. >> What's compute like?
2:56:17
Uh you I mean you're mentioning you're working so intently on neural nets. You have to train those.
2:56:23
Is is it are you at a point where a training run is run on a massive cluster, it costs nine figures or something like that or is it more data collection at this point and then the actual training run is pretty tight?
2:56:36
>> Yeah, we spent like hundreds of millions of dollars on compute that is uh >> sound. >> There we go. >> There we go.
2:56:45
We spent Yeah, a bunch that went live already.
2:56:47
So, our next giant like step up is going in April, like 1 of April.
2:56:51
Yeah, >> that's for training helix models that we're doing here internally which are quite large.
2:56:56
>> Um and long runs and then separately we do all of our inference on board on two GPUs in the torso of the robot. >> Sure.
2:57:02
>> So we can run in like cases where we don't have a network.
2:57:05
>> We can run at much faster speeds >> and we can put all those models fully on board the system.
2:57:08
So all of our robots now are kind of they're running off brains that are all on robot.
2:57:14
>> Um so they don't need any outside network to be able to do work.
2:57:17
>> So talk about like input and output.
2:57:17
Is it is is the is the network sort of taking in voice input and trying to translate that into plain text actions that then get transformed into motor actions.
2:57:31
Uh what is like the reward function for a humanoid robot? >> Yeah.
2:57:36
We're taking in um we're basically taking in the like the like the the instructions through text or speech like what should I be doing? >> Yeah.
2:57:44
>> We're taking in vision from the cameras. >> Sure.
2:57:46
and the current state of the robot like where's where is it like you know what is what is the body doing what is the sensors look like and then we're basically processing that on board with Helix 2. >> Yeah.
2:57:55
>> Helix 2 is then outputting uh like basically trajectories of where the mo like what the motor should be doing. >> Sure.
2:58:02
>> Um basically figuring out like what do I put torque at in every single joint to produce uh to move my body in a certain way.
2:58:08
And I think um you know honestly one of the hardest problems we've had last two years is two years ago we were basically doing it's like you know coffee work and other type of stuff on tabletops and it was really it was unbelievable.
2:58:19
It was like the first time we're like man neural nets on humanoids work.
2:58:22
We spent the last two years trying to leave the tabletop. >> Mhm.
2:58:26
>> How do we walk around with neural nets fully end to end and it's extremely um it sounds kind of um sounds like a you know not maybe not the hardest problem in the world.
2:58:34
It's it was been like some of the hardest problem in the world for us >> of how do we get the whole body like 30 plus joints all running at like say 200 times a second and doing the right things with camera frames and a prompt coming in >> and that's what we did with Helix 2 unveil 3 weeks ago is we had all that done.
2:58:50
So it's the first time in three and a half years where we feel like we have the right technical stack to actually scale >> which we didn't have last like several years. >> Yeah.
2:58:59
>> We were running at BMW last year and we're like man this is going great.
2:59:02
We're learning a lot but it's not the tech stack I want to scale.
2:59:03
and we have that now with Helix 2.
2:59:04
So, we're really excited to hit the gas on uh we're going to hit the gas on this in 2026. >> Congratulations.
2:59:13
>> Yeah, it's great to get the update.
2:59:14
>> Yeah, this is awesome.
2:59:15
>> I know you said the the bench press is silly and things like that, but it's to us uh even even the bar even just repping out the bar.
2:59:24
I'd be pretty excited about a meme.
2:59:27
>> I also think it's a >> serious company.
2:59:29
>> It's a serious company, but even serious companies can have fun.
2:59:31
I'm also looking I'm looking forward to the moment that uh that you get a a figure robot surfing at Jaws too.
2:59:37
Maybe >> are they waterproof? Can they swim?
2:59:41
>> You guys, we got we we got a we got a gym here.
2:59:43
You guys you guys you guys swing by >> and let's get let's get the figure three and you guys in the gym.
2:59:47
Let's let's see let's see like let's see how you guys are all let's see how >> matching up.
2:59:51
Yeah, we'll figure out the match up. Thank you. >> Awesome.
2:59:55
>> Well, have a great rest of your day.
2:59:56
>> Yeah, great to meet you. We'll talk to you soon. Nice to meet you guys.
2:59:57
Let me tell you about graphite code review for the age of AI.
3:00:01
Graphite helps teams on GitHub ship higher quality software faster and I will also tell you about Shopify.
3:00:09
Shopify is the commerce platform that grows with your business and lets you sell in seconds online, in store, on mobile, on social, on marketplaces, and now with AI agents.
3:00:16
>> Uh there's a company that launched yesterday. >> Yes. What company? >> Open Cloth or Slack.
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I don't know what it's called, but they said they launched 3 hours ago and they just hit 1 million ARR just now.
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So, they made $350 in three hours, which is sick.
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>> This is 1 million of AR. >> We've done it. We've done it. Final four. >> They did it.
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>> I saw a fake post that was like, uh, Meta acquires Open Claw for a billion dollars or something.
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And I really had to fact check it because I was like, this seems so possible in this day and age.
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I think they they have that team with Manis. >> Yeah. Yeah.
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I think the Manis team is is in a good spot to sort of bring some of those functionality to bear.
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>> Ring apparently has terminated its partnership with Flock.
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Their Super Bowl ad did not go as planned. >> Huh.
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>> Uh the the doorbell company ran an ad during the Super Bowl that's outed a search party feature that uses AI to help locate lost pets.
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>> People uh >> Sounds amazing.
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quickly uh realize that maybe it could be tracking things other than pets.
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But >> anyways, they probably are the the Super Bowl >> loser.
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That was that was in the end the the worst blowback of any company that I that I've seen.
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Um >> this this Cersei post is so good.
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VCs love to be like, "Yeah, hedge fund guys may be smarter, but at least I make less money." [laughter] Good stuff.
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uh Goldman Sachs CEO David Solomon, we're going to see potentially some very very large IPOs, unprecedented in size this year.
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He's he's agreeing that it's about to rain. It's about to rain.
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Oh, one of the IPOs that could be going out is Coher.
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Aiden Gomez, we got to get him on the show.
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I'm such a big Aiden Gomez fan.
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Uh $240 million year set stage for IPO.
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This is um this is in TechCrunch.
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Um of course, Aiden Gomez, a Death Grips fan.
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So, you know, he's a good time.
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You know, he likes the death. >> Uh, sizeg moment.
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Airbnb, according to SAR and according to Chesy, has uh generated 19 billion in cash flow since going public.
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>> Uh, and not going to vibe code that.
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Not going to vibe code a house.
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You're not going to vibe code a basement that you can sleep on the couch on.
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I met my co-founders for my first company on Airbnb. >> I didn't know that. >> Yeah.
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Moved to Silicon Valley, get into YC, need to find somewhere to stay, was staying with like a friend who was sort of, you know, not doing a startup, so way different lifestyle.
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and uh and searched on a service that was actually uh the most vibecoded software.
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But in 2012, it was a mashup between uh Craigslist and Google Maps because Craigslist didn't have a Google Maps feature.
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So if you were looking for housing, you had to just guess where the places were. Insane.
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So it was called Padmapper.
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Someone took they scraped Craigslist and then put it on a Google map and so you could click and be like, "Oh, that's near me.
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That sounds like a good place."
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But Padmapper uh Craigslist had given Padmapper a a cease and desist.
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We don't want you scraping us because they were I mean every single marketplace created in Silicon Valley immediately scraping Craigslist. >> Totally. Totally.
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>> They've fought back aggressively.
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>> They fought back and they said, "Hey, we're going to get around to doing Google Maps on Craigslist."
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And so Padmapper, get out of here.
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So when I when my co-founder and I opened up Padmapper, the only data that was still flowing to Padmapper was Airbnb because Airbnb of course is a marketplace.
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Doesn't matter if if someone else is driving traffic. They love that. They were all over SEO.
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They wanted other people could flow in.
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So we didn't know this, but our future friends and co-founders uh had a large place uh in Sunnyvale that they had an extra room and they had thrown that on Airbnb that showed up on Padmapper. We go over take a tour.
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We're like, "This place is sick. It was a disaster.
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[laughter] All the toilets were broken."
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They were like, "It's like the social network. It's got a pool. It's got a jacuzzi.
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We're going to be hanging out.
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It's going to be the best summer ever."
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The pool and jacuzzi filled with algae.
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Like truly filled with algae.
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We spent the entire summer being like, "We're smart, guys. We can beat the algae.
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Let's go get one gallon of bleach. Pour it in."
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We're like, >> "We're going to need >> we're going to need >> gallons thousands of gallons.
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>> It's going to it's going to just we're going to be swimming in bleach." >> Yeah.
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No, it it was like there's nothing you could do.
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And then we were like there was like a filtration system, but that was super clogged.
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So, we were like empty out the filtration system, try and take it all apart.
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But like everyone who was like an expert was like, "Yeah, you just have to drain this and like declare like pool bankruptcy basically." >> Brutal.
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Any Well, New York Post says, "Have an AI girlfriend or boyfriend?
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Now there's a bar for you.
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There's a Hell's Kitchen establishment that has been redesigned for those who have AI partners so they can bring along their phone for romantic evening.
3:05:22
Uh very very dis dystopian her moment but not entirely unsurprising that that this bar is pivoting to AI like >> I mean it's a good day to launch right because 40 is is uh >> today deprecated today.
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Is it still is it still available or is or did they stop it at uh at at when the clock It's still uh on my chat.
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>> Well, it'll be interesting to see what the community does because I I did see some posts about people being like, I'm recreating 40.
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I'm fine-tuning some, you know, Chinese model.
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Kimmy could be potentially fine-tuned on 40 outputs and paid for and distributed.
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Like, there are other ways for those folks to get what they want essentially.
3:06:02
Uh well uh it is Valentine's Day weekend, but before we go, Tyler, we did have a recommendation for you this weekend.
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>> We uh you mentioned that you've been seeing a lovely lady, and we thought >> this was supposed to be abstract.
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>> We thought >> this was supposed to be a a recommendation for the audience. >> Yeah.
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Well, now it's there's a girl that that uh maybe can't believe you're doing this.
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Maybe Tyler likes and we were just saying go uh surprise tell her, "Hey, tomorrow just have a bag ready."
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>> This is so out of pocket. Continue. >> Have a bag ready.
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>> Uh we're going to go do an overnight trip. >> Yeah. >> Find a nice hotel. >> Nice hotel. >> Check in. >> Station.
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Basically, you're not getting on a flight.
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You're just going somewhere no local, but somewhere nice.
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>> And so, yeah, somewhere nice. Easy. Easy to set up.
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>> Uh check into the hotel. >> Yeah.
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>> Maybe get her kind of a a spa day. >> Yeah. She goes to the spa. You sit down.
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And she doesn't know this, but you actually booked her an eight hour spa, like a full day thing. You sit down. >> Time to lock in.
3:07:08
>> Time to lock in on some cheeky. >> I have cheeky pine. I have dores cashio. Yeah. Yeah. I got a lot of stuff. >> So, lock in.
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And then just start getting Guinness on room service. You're 21 now.
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>> Pint for go every time.
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Every time >> every time AI is mentioned, you take >> Yeah.
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Or every time John takes a sip, take a sip.
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>> Take a drink a whole beer. >> Yeah.
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And you basically are going to have >> Yeah. >> 25.
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>> She comes back 8 hours later from her 8 hour spa treatment and it's like, "What were you doing?"
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>> And you can just catch her up to speed on on everything you've watched.
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And I think I think they really [laughter] appreciate that.
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>> But you say you haven't you haven't listened to Dwar Cashelia.
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>> That one hits like a ton of bricks on Valentine's Day.
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Ask Ask your uh ask your spouses.
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Ask your girlfriends, your boyfriends.
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Have they listened to Ilia on Door Cash?
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Are their timelines up to date?
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If not, that's the best Valentine's Day gift you can get them.
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>> An up-to-date understanding of what's coming.
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>> Uh, but we hope you all have a wonderful weekend. We love you. >> Yes.
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>> Thank you for hanging out with us this week. >> Yeah.
3:08:07
>> And we will be back Tuesday. Monday is a holiday. It is. So, we're off. Yeah. >> Really? >> Yeah. >> Yeah. Market's closed. >> I did not know that. >> Yeah.
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I'm learning this for the first time.
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>> I'm learning this for the first time. >> Yes.
3:08:21
Uh we we we experimented with with with streaming on holidays and it was there was there was not a lot of news.
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So we'll be back Tuesday uh 11 a. m. Pacific. We'll see you then. >> Nice work, brothers.
3:08:34
I'll see you on the next one.