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4:37
>> Today is Monday, March 30th, 2026.
4:37
We are live from the TBPN Ultradome.
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>> The temple of technology, the fortress of finance, the capital of capital.
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Use corporate cards, bill pay accounting, and a whole lot more all in one place.
4:52
Let's pull up the linear lineup.
4:55
We got Tay Kim coming on to give us the Nvidia update.
4:58
He is of course the founder of Key Context, the Substack.
5:02
Uh Logan Barllet's coming on from Redpoint.
5:05
Uh been way too long since we had him on.
5:07
Been probably over a year at this point, maybe nearly a year.
5:11
Uh but he drops one of the greatest uh market updates, slide decks, analyses. Very, very good.
5:17
Tons of really interesting tidbits in there.
5:19
And then we have a fantastic lightning round for you today.
5:23
Linear, of course, is the system for modern software development.
5:26
70% of enterprise workspaces on Linear are using agents. So, >> lightning round.
5:30
We got Ben Broca from Pula. Sam, founder of Granola. >> Yeah.
5:36
>> On their one and a half billion dollar valuation. And then Brett Adcock.
5:40
>> What was your nickname for him again? >> Who? >> Brett Adcock.
5:43
You had some nickname for him, right? No. >> No, I didn't. Someone else.
5:48
>> Oh, you're on a first name basis, so you just call him Brett. >> Brett. >> Yeah. >> Or just B. >> Yeah. >> Hey, B. That makes sense. Hey, B. Uh, no.
5:54
This will be interesting.
5:54
He launched a uh a Neil Lab last week. >> Oh, yeah. That's right. That's right. models and hardware.
6:02
>> I hear the angel singing >> and then Andre from console joining.
6:08
>> Well, uh I've been addicted to social media lawsuits.
6:12
I cannot get enough of these lawsuits.
6:14
I keep reading about them, losing sleep.
6:18
>> You're potentially filing your own lawsuit against the lawyers. >> Yes.
6:22
>> That were coming after these social media. >> Yeah. Yeah.
6:25
So, there's actually a profile in the Wall Street Journal in the exchange uh this weekend, the lawyer who beat Meta and Google, and it goes into some of his uh addictive techniques that are are driving jurors crazy across the country.
6:38
Uh attorney Mark Laneir, he uses props. >> Come on. >> Come on.
6:44
>> What's more than props?
6:44
He use He also uses parables. Okay.
6:47
Parables, metaphors, >> axioms, all of the above.
6:51
Uh he moonlights as a preacher and it shows when he's taking on the world's most powerful companies.
6:59
The the 65-year-old came to court in downtown Los Angeles for closing arguments this month of one of the biggest trials of his career.
7:06
Armed with a parable of leavened bread that feels like something that is designed to make it hard to rip yourself away from. Exactly.
7:15
So, he knew he needed a simple way to show a jury that Meta's Instagram and Google's YouTube were designed to be addictive and were harmful to young people.
7:25
So, the veteran plaintiff's lawyer from >> We just say he looks fantastic for 65.
7:29
He >> does look fantastic.
7:30
Uh and and I and as much as you're joking, I do think he's doing important work and I do think uh there's a potentially really good outcome here that we'll that we'll go into.
7:39
Uh but we're still having some fun.
7:41
Uh, so the veter so the veteran plaintiff's lawyer from Texas showed them two grocery items, cupcakes and tortillas.
7:48
H >> social media, he told the the courtroom, was like the baking powder that makes a cake rise, exacerbating the struggles of already vulnerable teens.
8:02
>> We have an interactor, an amplifier, something that blows it up.
8:05
Linear said, "We have here social media that takes the vulnerable and goes after them in destructive ways. It's as easy as ABC."
8:16
So he's making the argument that social media is more like cupcakes than tortillas. Both contain flour.
8:21
Both are carb carbohydrate loaded, but one is bigger than the other or puffier, I suppose.
8:29
Uh the simple image delivered with Laneir's slight draw helped convince a majority of jurors.
8:34
On Wednesday, the ninth day of deliberation, the jury found that Meta and YouTube were negligent in a case that accused the companies of designing their apps to be addictive and harmful to teens.
8:44
And there's some interesting images both of him walking into the courthouse with a large box of papers.
8:52
Clearly very anti-tech movement there.
8:55
He's saying, "I reject technology.
8:58
This cannot be stored digitally.
8:58
I I'm I'm using paper, >> which I don't know, this seems a little bit risky because we've been addicted to the printed word in the past.
9:06
So much so that we we faced criticism from people that said, "Hey, printing is unnecessary.
9:14
You're >> not environmentally friendly, but we were forced to adjust.
9:20
>> Maybe he can flip over to to be our defense attorney when we are attacked."
9:24
Um, there is a there's a courtroom sketch showing linear questioning former TBPN guest Adam Miseri, the head of Meta's Instagram.
9:30
Uh, a jury ordered the company to pay $3 million each in compensatory damages and 3 million in punitive damages.
9:38
So, I think it's 6 million across both firms, but it's split compensatory and punitive damages.
9:42
And now, a now 20-year-old woman named Kaye who la whose last name was redacted in the case.
9:49
She had testified that social media use that started when she was a child dominated her life for years and contributed to mental health issues including anxiety, dis depression, and body dysmorphia.
9:58
Very, very sad situation.
10:00
Very unfortunate for her, of course.
10:01
Uh, in a statement, Meta said it disagrees with the verdict and plans to pursue an appeal.
10:06
Reducing something as complex is teen mental health to a single cause risk risks leaving the many broader issues teens face today unressed.
10:16
uh not mutually exclusive, but of course that is uh a reasonable position for Meta to take.
10:21
Google also put out a statement. What do you think?
10:25
>> They're like, "We we're not even a social media company. >> We're a VR company." >> No, no, no.
10:30
Google said misunderstands YouTube, which is respon which is a responsibly built streaming platform, not a social media site. >> That's true. Um >> got the wrong guy. >> Yeah.
10:40
I I I think of of YouTube very much as as in the same world as social media.
10:45
media. anyone can post but it is severely lacking in some of the greatest features of that social media uh sites like uh you if when you when you actually become a YouTuber you start putting out content that like there is sort of a I don't know like a group of
11:00
made men on YouTube like people that have that have ascended and they now have uh they they're now making content like professionally and they are in conversation with each other and they might be reacting to each other's uh content and of There are different communities. There's like the car
11:15
There's like the car YouTuber community and then there's the the you know the game show community and there's the business community and pretty quickly everyone sort of gets to know each other but there's no DM feature.
11:26
So even if I make a video >> which is a which is a good argument for it not being social media to be not being a social media. >> Yeah. Yeah.
11:34
So, like uh you know, we at this point have done the Colin and Samir show uh but we don't really have a way like we can go on to the Colin Samir YouTube channel and leave them a comment and they might see it if it's from the TVPN account, but we can't like just DM them and be surfaced to the top of the inbox.
11:52
People have always wanted an inbox on YouTube.
11:55
>> Yeah, that's a huge feature request.
11:57
It's insane because like it would be so cool to see to be able to see, okay, I got a DM from someone who has a 100,000 followers and I can click on their profile and see, oh, they're like, you know, in the same niche, like maybe we'd want to work together.
12:09
Uh, maybe we want to collab on a video or do something else.
12:12
Uh, because they're like an established YouTuber, uh, as opposed to everyone basically needs to flow over to Twitter or X and then DM there because the the DM functionality is much more mature on on it.
12:25
The other thing Google has >> in the in in this kind of position is that so much of the watch time on YouTube is happening on televisions, right? Something like 50%. >> Yep. Very different.
12:36
>> So they can make the argument that this is just modern >> television. Yeah.
12:39
So let's go through Lineer's career because the Wall Street Journal has some interesting backstory here.
12:45
Uh he says Laneir has built a career and fortune representing plaintiffs against corporate giants.
12:48
He won uh one of the first ma major wrongful death trials against pharma company Merc over claims that the prescription anti-inflammatory drug vio caused heart problems. He also won a $4.
13:02
69 billion verdict in 2020 in 2018 for women and their families who said oes asbestous tainted talcum powder caused ovarian cancer.
13:12
So I mean over his career it seems like he's done some very very good work uh and uh and has and has won some massive massive settlements against big companies with broadly damaging products.
13:25
So uh lot lot to uh lot to admire about his career here.
13:31
Uh the social media trial drew more scr sc scrutiny than he predicted before he joined the plaintiff's team last fall and was brought facetof face with meta chief executive Mark Zuckerberg.
13:39
Suddenly Lineer was at the episode.
13:41
>> I believe that Zuck is actually mewing in this picture.
13:46
>> If we can pull up this this image, >> it does appear to be something along those lines.
13:51
Suddenly, Laneir was at the epicenter of >> You agree, Tyler? Right.
13:54
You can tell his cortisol is not spiking here. >> That's true.
13:57
That definitely seems he seems calm, collected, but this is not his first time putting on a suit.
14:00
This is not the first time he's been in court.
14:03
Um, suddenly Lineer was at the broad epicenter of a broad public debate about social media and how people stay connected or are disconnected on platforms offering nearless uh nearly endless content curated by algorithms.
14:16
Quote, nothing compared to this, Lenir said, reflecting on the attention to the trial over oatmeal toast and a Coke Zero in downtown Los Angeles in a downtown Los Angeles hotel the morning after the victory.
14:27
Nothing even remotely close.
14:29
And I think that's accurate because even though those previous uh settlements were huge, they weren't major.
14:33
They didn't break through to the point where like I remember them vividly. Do you? No. No.
14:39
Vio, it does not ring a bell.
14:42
But this certainly will for a lot of people, especially in tech.
14:44
Social media companies have largely been shielded from being held liable for thirdparty content on their platforms by section 230 of the 1996 Communications Decency Act.
14:54
at trial lineer had to focus on the platform's features, not the content, to make a case.
14:59
And that's something that I want to talk about today and I wrote about in the newsletter.
15:02
Um, the trial was the first among the first among thousands of consolidated lawsuits filed by teenagers, school districts, and state attorneys against Meta, YouTube, Tik Tok, and Snap.
15:12
Uh, more are scheduled for this year.
15:15
Tik Tok and Snap settled the case.
15:17
Settle settled the first case.
15:19
Uh, a Christian who teaches Bible study classes to as many as 500 people at an evangelical church, Lenir turns a folksy co court courtroom demeanor honed over decades of trial work burst in Texas, now nationally.
15:31
Uh, he's known for showing jurors handdrawn road maps and illustrations on an overhead projector to guide them through his legal reasoning and evidence, including signposts and human figures that could have been sketched by a child.
15:46
to visualize microscopic ed asbestous fibers in talcum powder.
15:50
He bought he brought a bail of hay into a courtroom and dropped a needle into the blades into the blades the blades of grass. Oh, the blades of hay. Got it. Okay. Wow.
16:04
Uh very very interesting.
16:04
Uh he Yeah, he he he likes he likes uh props. That's it.
16:11
Uh when arguing for punitive damages against the tech company, Laneir held up a jar.
16:15
quite addictive >> of this is a good point.
16:18
He So he he held up a jar of 415 M&M's to show how a 1 billion fine would be a fraction of Alphabet's $415 billion in shareholder equity.
16:31
He needs a bigger jar because I think both every tech company is five times larger now.
16:35
Uh he says he tries to avoid being flashy himself.
16:39
He wears the same two unremarkable suits on rotation during a trial and then I go burn them. What?
16:43
He burns his suits after.
16:46
Is that a joke or he gives them away? I don't know. >> My work here is done.
16:51
>> I guess I guess it's I don't know. It's odd.
16:54
Uh Lenir graduated from college at 20 and is trained as a minister before going to law school at Texas Tech University.
17:01
Um hoping to make enough money to support his preaching.
17:04
He began gaining renown as a lawyer in an era when asbestous cases were swamping the US courts.
17:09
He won a jury verdict of about 115 million in 1998 for 21 steel workers who felt ill after using machinery that contained asbestous.
17:18
Linear and his wife Becky met in high school debate class.
17:20
They have five children and 12 grandchildren. Wow. Overnight success.
17:24
Uh they were known for years for their child-friendly Christmas parties at their estate of more than 35 acres near Houston, which has a model railroad that can seat 120 people.
17:35
Okay, this guy's got to win.
17:35
Oh, the I I have completely changed my position here.
17:41
>> We need a Mansion section article.
17:44
>> I think we have a direct line to him, by the way. We want him on the show.
17:47
>> Well, this >> maybe we should go do a show from the from the from the model train. >> Yes.
17:52
I'm I'm so ready to be convinced of his position.
17:54
I I I wrote a whole piece about how I disagree with with the result, but he's winning me over.
18:00
>> Disagree with his entire argument, but you're agreeing with his approach to life. >> Yes. 100% 100%.
18:03
I feel like we're kindered spirits. It's amazing.
18:07
So, the model railroad can seat 120 people. And guess what? He's got a menagerie. >> This is goals.
18:16
You need to be menagerie maxing in life. You need a menagerie. His contains lemurs. >> There we go. >> And llamas. >> There we go. >> Lemurs and llamas. >> This is incredible.
18:27
The family pulled the plug on the party which featured up to 9,000 guests and performers including Miley Cyrus, Johnny Cash, and Dolly Parton, he said because it was too hard on the lawn.
18:39
The guy cares about his grass too much. This is incredible. Inviting 9,000.
18:45
>> He's an environmentalist from the community.
18:47
I mean, that's like the entire I mean, Houston's a huge city, but that's like that is so so what a pillar of the community. This guy's a hero.
18:55
Laneir said the theme of his cases against mo major corporations is responsibility and integrity or lack of it.
19:03
Tech billionaires don't need his help.
19:05
Lineer said, but Kaye would not have anybody else.
19:07
Faith is much the same way.
19:08
God God's there to try to help people who need the help.
19:12
Two of Lenir's daughters who are lawyers were by his side during the trial.
19:15
He joined the social media case in New York.
19:18
>> By the way, you keep saying Lineer.
19:20
>> Is it Lenir or Lineer? >> Lir. >> Lineer. >> Lir. Lineer. >> Lir. I think it's linear.
19:26
Well, we'll figure it out.
19:26
Um, he has deep authentic.
19:29
I >> just don't want people to get confused with the system for modern. >> Yes, it's not lineer. It's Lenir, I think. Maybe it's Lanier. Maybe it's French. Uh, he's not a phony.
19:39
What he does is not a performance.
19:42
Even from you, even from Los Angeles, he posted short video selfies discussing Bible passages on YouTube.
19:49
So, he's a he's he's dog fooding the thing that he's suing.
19:53
Um, yeah, let's switch gears to your piece.
19:58
>> I will take you through my counterargument, but first I'll tell you about Shopify.
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20:19
So, uh, last week, uh, Brandon Guerrrell summarized the ruling this way.
20:24
He said, "In the case, the plaintiff's lawyer, Lark, Mark Laneir, argued that Meta and YouTube built digital casinos that use neurobiological techniques similar to those employed by slot machines."
20:36
Uh, the jury found that specific features of Meta and YouTube are designed to be addictive.
20:41
And I want you to really hone in on these features.
20:46
So, infinite scroll creates an environment where there are no natural stopping points.
20:53
Algorithmic recommendation feeds use highly users highly engaging content.
20:59
Feeds Algorithmic recommendations feeds users highly engaging content.
21:01
Autoplay removes users agency in choosing whether to watch the next video.
21:05
Notifications pull users back in by exploiting their need for validation.
21:11
IG beauty filters contribute to the plaintiff's body dysmorphia and features like the like button exploit users biological need for social approval.
21:19
Okay, so you got a bunch of features. You know this stuff.
21:23
You everyone uses social media.
21:23
We all know about this stuff.
21:25
Uh the question is like is are the features addictive or is the content addictive because social media platforms are of course protected from the content that is posted on Laneir's entire Lenir's entire argument is predicated on it being the features, right? >> Yes. the features. Okay.
21:41
>> And >> yeah, so the so uh you know, we talked to Eric Goldman from Santa Clara University of Law and he was saying that like yes, it's $6 million settlement right now, but this is this could be huge.
21:52
Uh the direct quote was whether we will even have social media in the future like this could be existential. >> Yeah.
21:58
And and there's thousands of other cases like this kind of percolating, right?
22:02
And so >> and it could turn into a class action.
22:04
He's gotten six billion before. He could get 50 billion. I don't know. He could get a lot.
22:08
And he's not like 6 million is he's not a 6 million guy. He's a six billion guy.
22:14
And so this is the precursor and it's going further and whether it's a ton of different cases or one big one like it's a big problem for the tech companies.
22:22
So uh I thought it was an odd coincidence that we sort of had what I called the the the the placeboc controlled trial for these exact features last week when Sora shut down.
22:35
So, uh, opening eyes nent social network sora shut down.
22:39
The reaction of the news was funny to watch because a lot of people were like, "Yeah, I told you it was always bad, but when it launched, it was exactly the opposite.
22:47
Everyone was like, "It's too good.
22:47
We won't be able to look away. It's simply too good."
22:51
Uh, and and Rune Rune summarized this pretty well, I I think yesterday, uh, or I think it was yesterday.
22:58
He said, "Sora was peak moral panic.
23:00
All of these breathless takes about making videos that are going to addict humanity and waste everyone's time."
23:05
Meanwhile, we made some funny videos that were less funny as time went on.
23:09
And AI slop is just one category among many on Instagram reels.
23:11
Don't worry so much about making videos that are going to blow up people's brains without making anything good uh without worry uh worry about making anything good at all.
23:20
The best Soras were up there with the best reels and the humor relied significantly on the voice of the creator. I completely agree.
23:26
Uh the the funny Soras that I >> Yeah.
23:30
Even the video we played last week of the cat on the porch. >> Yes.
23:33
That wasn't that wasn't >> oneshotted. Yeah.
23:34
The prompt was not was not make something that will retain users >> and it wouldn't have been funny if the person hadn't been escalating like the scene every new prompt and then stringing them together. >> Yeah.
23:46
Uh and so uh and and he and he closes by saying I know so many of you who are loudly concerned about this who won't update at all who will remain pessimistic about humans and their ability to use tools.
23:57
And I said uh so like what do you make of these two situations?
24:01
Uh it feels a little bit like a placebo control trial to me.
24:03
Of course, like there's a lot more nuance here.
24:07
This is like a a highle take, but uh Sora absolutely used all of the social media best practices or addictive and harmful neurobi biological techniques if you want to use the course language.
24:18
Sora app was basically the same as Tik Tok, Instagram reels, YouTube shorts, snap.
24:22
um in terms of UI and UX design. It had infinite scroll.
24:26
It had algorithmic recommendations. It had notifications.
24:28
It had a like button and it didn't have IG beauty filters, but like the whole thing is a filter because I could go in there and say, "Make me look like a bodybuilder."
24:36
And it did a good job and I looked great in the videos.
24:39
And so like it is it it really checks all of the same boxes >> to try to like match that every >> It gave me crippling body dysmorphia.
24:48
Obviously, I I I I dream for the for the day when I will look like my Sora avatar, my uh my what do they call it? Cameo. My cameo.
24:55
Um, no, but they they really did use all the normal tools and and that was for familiarity, but also because they're moving quickly and the key innovation was not the UI design or the fact that it's vertical or algorithmic feeds like we are in 2026.
25:12
We're not in 2014 when we're launching Vine.
25:15
Uh so the key in the key insight was purely AI generated content.
25:18
Uh and it and it didn't work like the features were not addictive because the people that downloaded Sora did not become addictive because the content was a little bit bit too sloppy. Right. >> Yeah.
25:32
Well well it was just one type of content and it turns out people like a broad selection and they like var variability.
25:38
They might want to see, >> you know, video of someone skiing and then some slop and then something their friend made and then some health content.
25:47
>> And it's really the collection of that.
25:49
The other thing I think that seems very obvious is if if it was the if it was the product itself and the features that were addicting, there would be so many social media there would be so many social media apps that were effectively thriving.
26:03
There would be a bunch of Instagram >> and this is where I get to the the cigarette comparison.
26:07
So, there's a bunch of comparisons to the cigarette industry and I think it's really worth revisiting like what is addictive about cigarettes because there are some people that say like it's an oral fixation like you just want to put like a stick in your mouth so you should like switch to carrots.
26:20
Like that is like maybe like 1% >> some could argue it's an addiction to looking cool. >> There you go.
26:26
Uh but but it is the nicotine. It is the nicotine.
26:29
And that's why you do have a long tale of like 50 different cigarette brands and a thousand different ecigarette brands and nicotine gum is addictive.
26:37
Nicotine pouches are addictive.
26:38
Nicotine pouches are addictive because they all contain the nicotine.
26:42
And if and if if the court is asking us to believe that the like button, the algorithmic feed that is addictive, then we should see addiction like results from any app that implements that because that is the case for all nicotine containing products.
26:59
They all addict people at I mean there are less addictive formats in general.
27:04
>> How many apps have you tried or test flights over the years that had any of these features that you used >> for 30 seconds? >> Exactly.
27:12
Because what actually keeps you coming back is the content which is created by the users and >> and so you're at you want Laneir to go after every single person that has ever posted anything on Instagram and jail them. Correct. >> No. No.
27:28
Uh, I think that some creators do create very compelling content. Some of that is No.
27:34
Some of that content is amazing.
27:37
Some of that content is great.
27:40
Uh, some of that content is bad.
27:40
There's a very, very wide range.
27:43
You can go to truly amazing educational content.
27:45
I'm thinking of like three blue one brown, this math channel that does visualizations of math concepts on YouTube. It's incredible.
27:53
Andre Carpathy's YouTube videos.
27:54
There's so many interesting uh educational history shows, podcasts, there's so much content that >> Tyler got Tyler got addicted to that video.
28:04
Are you destined to deal?
28:04
And we kept saying >> that's a great video.
28:08
>> He kept saying >> that's why you have a tie on today.
28:10
>> He he he called me on Friday night and said, >> "Why is this video >> 20 hours long?" >> Cuz he on loop.
28:19
>> Yeah, he was just looping. >> That makes sense. That makes sense.
28:20
Uh so yeah, but I mean but it is true like like I think the court is correct and and Laneir is correct that some people go on social media and make horrible content that depresses people that land on it.
28:32
And it goes without saying that social media companies do have an enormous responsibility to manage recommendation feeds responsibly and route people in tough situations to helpful resources.
28:41
So Google already does this very very well.
28:42
If you type in specific keywords that seem like you're in a mental health crisis, like it will not give you search results.
28:48
it will give you a phone number for someone to call and and they know when to route the right people to that.
28:54
And I and I do believe that all the tech platforms are thinking about this and implementing this.
28:58
Maybe they need to be more aggressive.
29:00
Uh I think that the big the big thing that most people can agree on is parental controls here.
29:04
Um and I think that that's like a much easier like middle ground here.
29:08
Uh and just in general, one other nice meet in the middle option is potentially just uh you know getting tech companies to give users and parents in particular, but users broadly more control over their experience.
29:20
So, it's possible to disable algorithmic feeds, endless scroll, uh the like button with browser plugins on mobile web, but it's a much worse experience because you have to load it on mobile web, which isn't the actual app, and and it uh and it's slower and it and there's a lot of things that are just kind of janky and don't load as well.
29:44
Um, but having those like in the settings to just say like I I know some creators on Instagram can turn off the like counter. Have you ever seen this?
29:53
So you can see someone post a post an image and it and it'll just have a like button there, but it doesn't have like 5,000 likes because the creators were getting like, you know, annoyed by, oh, this one.
30:04
>> Well, to be clear, that's because people didn't want to post because they were worried something wouldn't do well. >> Yeah.
30:09
and and the world would know that their content wasn't engaging or something like that, right?
30:15
So So that that was just an that that effectively is just an incentive.
30:18
I don't believe that that was done for the mental health of the creators.
30:22
No, >> that was done to encourage >> more people to post. >> I don't know. I don't know.
30:27
I mean I'm sure like there have like my mental health as a social media creator was at an all-time high before I understood the metrics because I was just like, "Oh, 300 views. I'm famous. This is amazing.
30:41
300 people sat down and watched my 10-minute video essay about a dying VR technology or something like that. It's like, I've done it. 300 people sat down.
30:50
It's like I'm a I'm a business school professor, basically, you know? Yeah.
30:54
But then eventually you you you get and you're like, wait, like the last video got 400,000 views.
30:58
Why does this one have 375,000 views? I'm a failure.
31:00
So, like there is a little bit of that, but I but I hear you. >> Yeah.
31:06
But the metrics are still available to the creators. Yeah. Yeah, >> you can.
31:09
The creator, >> but you can turn them off with a Chrome plugin. You actually can.
31:12
There's some creators that do this.
31:14
Um, but anyway, uh, uh, like surfacing those in apps, I think that will help users feel like they're in more control.
31:21
And realistically, I don't think it will be super damaging to any of the platforms because most people won't opt into that, but certain people will.
31:29
And in general, it'll it'll just like increase public perception broadly.
31:33
We've already seen this with a lot of the LLM companies where like you can go in, you can fine-tune and and add a custom prompt and kind of talk to it about what you like and don't like, change the personality.
31:42
I think people have been asking for that for a long time, surfacing it. It seems like a win-win.
31:48
Um, and so something something along those lines seems uh seems seems in the cards.
31:52
Uh, anyway, we can debate this, but first, let me tell you about Sentry.
31:56
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31:58
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32:01
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32:09
So, um, do you have any other push back on my take?
32:16
>> Tyler had some push back. Should we go to him? >> Let's go to Tyler. What do you think? I mean, yes.
32:20
So, I guess there was a few things.
32:22
I mean, one thing is that like >> not enough props, right?
32:23
Too many analogies, >> too many parables.
32:27
>> Like, I I think you can say that um like, okay, yeah, it's the content that's the problem.
32:30
Um but like the content is like kind of downstream of the features, right?
32:34
Because you didn't see like short form video, >> the medium is the message. >> Yes.
32:39
>> And so it it is possible for me to create a platform that incentivizes addictive content and that's like the retention curve.
32:46
So retention editing makes it more addictive.
32:48
You become addicted to the content, but it's because of the features. >> Yeah.
32:53
So I I think that's like broadly the steel man that you you can make for like Lineer's position. Yeah.
32:57
like Lineer's position. Yeah. And then I mean there's other stuff I I think on like just the nicotine analogy we were talking about this like okay so you have nicotine like broadly and then below nicotine you have like smoking which is like definitely very bad for you >> and then you have like uh you know
33:12
pouches or stuff like this which is like probably less bad like it's just nicotine there's no tobacco so like maybe this is like less bad and so maybe the equivalent is like >> uh you know the the cool snowboarding videos on Instagram y >> are like the you know the the cleaner like nicotine stuff and then the like >> still addictive but not harmful. >> Yes. And then there's like you're going >> Yes.
33:31
And then there's like you're going to try and do a double cork 1260 and and eat it get smoked into the ground. >> Yeah.
33:38
But but then on the other side you have like the like you know very graphic stuff on Instagram that like we don't want people to see and that's like the you know the the cigarettes that's like going to give you cancer whatever. for sure.
33:48
>> So I think there like >> um I mean I guess this is still in agreement with what you're saying but like >> well this is what nicotine if you're under 18 which was like there was an addictive component and then
33:59
there was a carcinogenic component and they needed to sort of separate those out and where we landed as a societ so society was like the addictive component is acceptable for the it's suitable for the protection of public health according to the FDA. Uh and so they are
34:11
Uh and so they are approving new products that are addictive but not carcinogenic.
34:14
And so you would imagine even in the most strict ruling where every new social media platform needs to be approved, you could potentially use all of those addictive features as long as the content was not carcinogenic with inside that app. >> Yeah.
34:30
>> And that would be like a new nicotine gum basically. >> Yeah.
34:32
Like basically I'm saying like right now if you're under 18 you can still like there's like parental controls and you if you can't be under 13 or whatever but like it's like very not it's like poorly in you know enforced like you can actually see a lot of the the bad stuff if you're under 18 on screen or whatever.
34:47
>> So like directionally like there you know you can be against the the the ruling of this. >> Yeah.
34:53
>> But like the the parental controls that people are like asked for are still like very much not there. >> Yeah. Yeah. No, that makes sense.
35:00
>> So I I I think I have a potential solution.
35:02
Let's pull up this image of a cigarette package in Europe. >> Oh yeah. What is this? >> So pull this up.
35:11
>> Let's pull up the hardest challenge.
35:13
>> While we pull it up, let me tell you about Octa.
35:14
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35:16
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35:20
And let me also tell you about Turbo Puffer. >> Turbo Search.
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35:30
Okay, so this is like typical cigarette packaging in Europe.
35:35
John, you probably wouldn't know this because you're very American and you're very loyal and you and you don't you you avoid overseas trips as much as possible.
35:42
So on any given cigarette pack in Europe, you're going to see like a really terrible image, this woman apparently is coughing up blood. >> Yes.
35:49
Uh, and so I think what what a potential solution that Meta could do is as soon as you open Instagram, Yeah.
35:56
>> it it makes an AI generated image based on the last picture of you that you posted on social media and it just makes you look terrible. >> Oh.
36:04
>> And it says like warning like social media will destroy you and then you can scroll past.
36:09
>> It could potentially show you a tech neck.
36:11
Are you familiar with tech tech neck? >> You go like this. >> Yes. Yes.
36:17
It's just a crazy image of you with Tech looking at your phone >> and then so you can scroll past it, but every time you open the app, it's a new image.
36:26
It's a new image of you looking the worst, wasting your life away.
36:30
>> Are the AI labs uh lobbying to get this?
36:33
>> All right, we can put this we can put this away.
36:34
>> Are the AI labs uh lobbying to get that removed because I think most of their timelines suggest that lung cancer will be cured by AI any day now.
36:40
So, uh potentially you could start smoking again.
36:44
Has anyone come out as prom smoking?
36:46
I don't think anthropics come out with their anti-anthropic, you know, has the joke that they make with journalists.
36:52
They kind of got caught on anti sunscreen.
36:55
>> If AI is going to cure liver cancer, >> it's game on. It's game on. It's game on.
37:01
You can drink as much as you want because that's because you get liver disease if you drink too much.
37:06
And so if AI, if I'm going to be able to vibe code an mRNA vaccine to cure my liver cancer, I'm going to be oozing for sure.
37:12
It's the only only rational thing to do. >> Yeah.
37:16
Well, this is also kind of like when companies like are saying like, "Oh, yeah.
37:20
Work life balance is super important." Yeah.
37:21
So then their competitors will, you know. Yes. Yeah.
37:25
People anthropic should tell people to open to start drinking a lot because AGI is going to cure liver. >> Yes. Yes. Yes. Yes. This is good. This is good. Okay.
37:32
>> Let's revisit the Jetson. >> Okay. Revisit the Jetsons.
37:33
I'm sure you've seen the Jetson.
37:36
>> Where's my flying car and three-hour workday?
37:38
So, I'm going to be learning about the Jetsons.
37:39
John is going to be revisiting.
37:42
>> The 1960s version of the future is way more fun than our reality, but when it comes to innovations, we're catching up. >> Interesting. >> Uh, let's see.
37:51
Nicole says, "I recently spent a weekend doing deep investigative research into future technologies.
37:55
I binged the Jetsons in my sweatpants.
37:57
For the uninitiated, the forgetful.
38:00
This space age family sitcom features George and Jane Jetson living the American dream in an apartment in the sky with their two children, dog Astro and robot made Rosie.
38:10
The show is set in 2062, a century ahead from its original 1962 air date.
38:16
It's full of fantastical inventions such as flying cars, dinner generating machines, and K9 treadmills complete with fire hydrants.
38:23
The upbeat vibe is markedly different from the apocalyptic, at times murderous sci-fi of today.
38:29
The 1960s were full of optimism of about what the 21st century would bring.
38:34
And some of it actually has come true.
38:35
While we've still got a few decades before the Jetson family is meant to arrive, I dug into some of the show's technological hallmarks and determined how close we already are.
38:43
Uh, video calling, she says. Absolutely.
38:46
In lie of a home phone, the Jetsons had a video phone.
38:50
shows creators couldn't fathom mobile devices, but they were spot on about video calling.
38:57
>> Now, to be clear, we we are still working on with one of our business associates like a video call that doesn't stop halfway through. >> Yeah.
39:05
>> Uh and just cancel, but >> So, the Jetson didn't predict the free tier of Zoom. >> Free tier of Zoom. >> Yeah.
39:12
The free tier of Zoom was not considered in in the in the Jetsons where you're couldn't fathom it.
39:16
you you're clearly going to go long on the meeting and Zoom's just like goodbye. >> It's over. >> It's over.
39:23
It kicks everyone out with no notice. >> Is that a new thing?
39:25
I feel like it used to do a countdown.
39:28
>> I think it did a countdown, too, but now it's just >> now they're just like, "We want to embarrass the host." >> Embarrassed.
39:37
>> The plus tier is going to blow.
39:38
>> So, in the Jetson, they could even create deep fakes to stand in for them on camera. That's cool. >> That's cool. I didn't realize that.
39:44
>> FaceTime's got on that.
39:44
You know, the other thing they haven't cracked with FaceTime is like if you FaceTime a group of people >> Yeah.
39:50
>> like most of the people won't even notification and don't know that it's happening.
39:54
>> So, we haven't cracked the the notification part of >> This is good. Read this next line.
39:58
When George secretly attended a robot football game, his similocum told Jane he had to work late.
40:05
He's like using a deep fake to lie to his wife. >> This is so 60s. >> Do not do this. >> Do not do this. This is dystopia. >> Flying cars.
40:14
It's not all optimism up over here.
40:17
>> Flying cars and travel tubes. Sort of.
40:19
There isn't much walking in orbit city.
40:21
A conveyor belt brings George from bed to the bathroom to get to and from his classroom.
40:24
Alroy jets through a series of air tubes called the school homing network.
40:30
>> When the wrong child shows up at the Jetson's home, Jensen sends uh Jane sends him back with the push of a button.
40:35
And they also use personal vehicles, though ones that typically fly.
40:39
George Aerero commutes and a glass dome saucer that folds into a briefcase man.
40:45
>> We're pretty far from there.
40:45
We do have helicopters, but they're very expensive.
40:48
I always fight people on the flying cars don't exist thing because like we do have helicopters and people some people get to use those, but they are not nearly cheap enough.
40:56
But we got to get them we got to get them way down.
40:59
>> Here in the actual future, we're still toing around on pavement pounding automobiles.
41:02
A version of flying cars, however, is very real. It's called an EV tall. Look at this.
41:07
Pivotal Blackfly is a solo piloted aircraft free to operate in unrestricted spare airspace.
41:12
An upgraded version called the Helix can be yours for 190k.
41:16
You don't even need a pilot's license.
41:19
That's like pretty close, but I mean I would still say like we are not near the flying car because they're just not like there are way less flying car rides than Whimos for example.
41:32
So we're just not right there.
41:34
Uh push button jobs almost.
41:34
George works as a digital index operator at Spacely Space Sprockets for approximately three hours a day, three days a week.
41:42
As a button pusher, he makes enough for to support a family of four, even though majority of his day is spent with his feet up on his desk.
41:52
>> Okay, they basically nailed this.
41:54
There's some people out there that are basically b button pushers right now. Vi vibe coding.
41:58
>> Yeah, >> TVD on on the revenue side. True.
41:59
But >> working three hours a day, three days a week.
42:04
You know, we we we work three hours a day, five days a week.
42:07
And uh maybe the future's three just Monday, Wednesday, Friday streams.
42:12
We can live the the Jetson's future. We work three hours.
42:19
>> That'd be devastating for us. >> Yeah.
42:20
Until then, we'll be working >> space colonization. Nope. >> Yeah.
42:24
They live above Earth >> with houses built on tall stilts. I like that.
42:28
uh to avoid the planet's environmental inconveniences.
42:31
The stilts can rise above any inclement weather and space itself isn't out of reach.
42:36
In a classic episode, Elroy goes to an asteroid on a school-filled trip. We're not quite there.
42:42
Musk had preached of populating Mars, but now his focus is turned closer to the moon.
42:46
Meanwhile, an interplanary space race between US, China, Russia, and UAE, and the European Space Agency is well underway. Robot Maids.
42:56
Not exactly, but we're getting much closer there.
43:00
>> It's funny that that Brett Adcock's coming on today. Yeah.
43:03
And he's working on the flying car. >> Yeah.
43:06
He's working on the robot made.
43:07
>> Working on the robot made.
43:09
>> He doesn't have a space thing yet.
43:10
>> He's now work his new lab is basically like a but you know, seems like a button pusher >> gadget gadget induced pain. Yes.
43:16
And now for the show's biggest oversight. No touchcreens.
43:20
There are lots of visual displays, but they're primarily operated by dials, levers, and other physical controls.
43:27
We got some levers back there in the studio.
43:28
Uh, while the show may not have anticipated touchcreens, it nailed a key side effect of constant use of gadgets.
43:35
Repetitive motion injuries.
43:37
Orbit City is full of buttons and overworked fingers are a running gag on the show.
43:41
Uh, Jane regularly does digit workouts and complains that her pointers are sore.
43:46
Here in 2026, office workers often suffer from texting thumb after scrolling through endless feeds and tech neck after craning down to look at mobile devices.
43:57
And don't get me started on my strained hand with carpal tunnel syndrome from all the clicking. 36 years in counting.
44:04
We may not be living as exceptional a future as the Jetsons, but we've still got three and a half decades to catch up.
44:10
By then, I will be twice as old as I am now.
44:13
I've already witnessed the dawn of highsp speeded internet, the iPhone, and generative AI, how many tech revolutions will we experience in another 36 years.
44:19
By the time we hit the show's 2062 deadline, maybe we will finally live in space or make our current planet more uh habitable and make a comfortable living on a 9h hour work week.
44:30
Tyler, what do you think?
44:32
Predict your timelines for 2062.
44:35
Will we get space colonization?
44:39
>> How do you define space colonization?
44:41
living not on the earth above the Carmen line for like that's your primary residence like more than half the year.
44:49
>> How many people do it?
44:49
Like just you can do that like anyone can do that. >> Yes.
44:53
Anyone with like a like if you can if you can afford like a apartment for a few thousand or like a house that's above $500,000 in America, you can you can choose to live in space.
45:06
So I would assume like population of millions.
45:11
>> I it probably depends on like the industry that like is chiefly, you know, benefited from people living there. >> Button pushing.
45:20
>> Uh we'll say, >> okay, >> big news.
45:22
Sean Frank is in the chat.
45:22
He says, "I'm here, guys."
45:24
H E A R which So he's saying I He's trying to signal that he's listening.
45:31
>> I am here >> to you Tyler and to you John coming in.
45:35
Uh, let's uh let's read him some ads since he's here.
45:37
Let's tell him about Reream.
45:38
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45:40
If you want to multiream, Sean, go to reream. com.
45:44
And you know what we got to tell him about?
45:46
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45:50
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45:56
>> Andrew Reed says, "The faster technology progresses, the harder it gets to print something in the office."
46:04
We have experienced this. It's very true.
46:05
Aaron Aaron from box says reads law.
46:08
I know you may have wanted a better law, but I don't make the rules.
46:14
>> Yeah, it's uh it's it's very very difficult.
46:16
Apple has just like never done the printer.
46:19
I think for environmental reasons.
46:20
I'm not exactly sure, but like there's never been like oh the gold standard the Tesla of printers just like the one you get and it does what it want.
46:29
It just does everything flawlessly and it's at that like you know 59s of reliability.
46:34
Uh we've had pretty good run with our printers but uh we're always in the market for new printers. So there's more.
46:44
We are we're looking for a new printer right now for a special project.
46:47
Uh anyway, let me tell you about Cognition.
46:49
They're the makers of Devon the AI software engineer.
46:50
Crush your backlog with your personal AI engineering team.
46:52
Um what I hate most about technology in hotel rooms.
46:55
Jordy, I want your take on tech in in uh in hotel rooms.
47:01
I want to know about your experience when you walk into a hotel room.
47:05
The Wall Street Journal says, "When you book a hotel room, you can count on some things like shampoo, a hot shower, some way to get a cup of coffee.
47:12
But a stress-free technology experience, no way.
47:14
Not even with basic technology you find just about anywhere like TV, Wi-Fi, and outlets for your devices.
47:20
Unless you carry a suitcase full of gadgets, cables, and adapters, you're risking every kind of tech frustration.
47:27
Did you know that Ben Thompson carries a special device that acts as a Wi-Fi repeater where when he travels?
47:33
So, when he goes to a hotel, he logs into Yeah, this is amazing.
47:38
He logs in to the the wi-i the the hotel Wi-Fi, I believe, or the or the or the plane Wi-Fi through that device and then all of his devices connect to that automatically.
47:48
And he brings he'll bring like a Fire Stick so he'll be able to watch TV shows and his laptop and his phone.
47:54
Everything automatically syncs to that device and it like reroutes it.
47:58
Thought that was a very interesting thing that uh he's clearly optimized a lot.
48:02
He's like a huge uh uh what is it? Uh like gearbag guy.
48:07
like he has all the wires like dialed as as as I would expect.
48:11
>> I usually forget to bring a charger.
48:16
>> Hotels are missing out on a fundamental truth.
48:17
In a world where so much of our work, travel, and relationship experience is shaped by technology.
48:21
The quality of a hotel's tech service is core to what it's like to stay there.
48:27
Give me a hotel room that lets all that tech fade into the background so that I can focus on my trip.
48:32
But no, here's what you usually get instead. TV muddle.
48:36
I suspect I've logged more hours troubleshooting hotel TVs than I have watching programs on hotel TVs.
48:40
Okay, there's a bit of a a retro charm in a TV that flips on and instantly tunes to a live network broadcast.
48:46
But in the streaming age, I'm just as likely to crave a little quality time with Netflix, Disney Plus, or Apple TV.
48:50
Many hotels have caught on to this reality by offering some sort of streaming option, but they approach this in so many different ways.
48:57
You never know what you're going to find or what tech you'll need to make it work.
49:01
Needy Wi-Fi is another one.
49:03
Most hotels I've visited recently seem to have figured out that charging extra for Wi-Fi makes about as much sense as charging extra for a better toilet.
49:10
Everyone needs to get online.
49:11
So, you might as well build it into the price of the hotel room.
49:12
So, now that we've taken that great leap forward, we why are we still forcing people to log into the network not just once per day, but over and over again, once per device each and every day or often several times a day. It isn't usual.
49:25
It isn't unusual for me to log into hotel Wi-Fi 20 or 30 times a day.
49:27
I think you're doing something wrong.
49:32
Honestly, honestly, I don't I don't This doesn't resonate with me at all. >> You're fine.
49:37
>> The only thing I want from a hotel >> Yeah.
49:40
>> is to be able to order room service >> without calling someone. >> Okay.
49:46
>> That's like the only thing.
49:46
And and hotels miss on that. Yeah. >> For the most part.
49:50
>> Like if you have a little iPad or you could even order on the TV app, >> that would be amazing.
49:55
And you get like a Domino's pizza tracker type thing. That's all I want.
49:59
That's all I want. I I feel like they kind of deliver on everything else and I don't watch >> I I was uh listening to I think it was George Hots was explaining how he ordered room service in a hotel he was staying at and he vibecoded an app that
50:14
interacted with the ordering service so that he didn't have to talk to them and it basically like read the entire menu and then like created like a voice agent to call or like or like reverse engineered the API of the ordering menu and he was able to order by command. hand line just like checking into hotel.
50:29
hand line just like checking into hotel. Time to build a CLI.
50:35
It's uh truly the future. I love it. But it is. It is.
50:37
And uh you know who else is vibe coding these days? Gary Tan.
50:39
Ben Hilac has a joke here.
50:42
He says the year is 2027.
50:44
Gary Tan has just crossed 1 billion lines of code per day.
50:47
Water to threeyear-old Californian towns were diverted in order to cool his locally ran LLMs.
50:54
Riots erupt and protesters demand answers to one single question. What is he building? >> We got to have GT on. >> Yeah, I can't wait. >> Let's get Gary on.
51:04
>> We got to get Gary on.
51:04
We got to know what uh what what Gary is building.
51:06
Uh people people are joking about this cuz what was the latest stat?
51:12
It was something like uh 80 >> 78,000 78 lines of code >> per day on I think on Gary's List.
51:19
>> On Gary's List, >> which is his his blog. >> It's a blog.
51:21
>> It's a blog. and he's built blogs before like he's uh he's built these he's built these sites but uh you know I guess like with all the testing suites and packages and mobile optimization I don't know uh I can't imagine the the the the volume of code that will be generated when he creates a mobile app for it it's going
51:40
to be it's going to be you know trillions of tokens going into that anyway uh let me tell you about Labelbox RL environments voice robotics evals and expert human data label box is the data factory behind the world's leading AI teams Sam says, "I remember when this was announced, but didn't fully appreciate the size. That's a hell of a
51:55
That's a hell of a cluster.
51:56
The Department of Energy will basically be a frontier AI company.
52:00
NVIDIA is collaborating with Oracle and the Department of Energy to build the US Department of Energy's largest AI supercomputer for scientific discovery.
52:08
The Solstice system will feature a record-breaking 100,000 black wells and support the DOE's mission of developing AI capabilities to drive technological leadership across US security, science, and energy applications.
52:21
Another system, Equinox, will include 10,000 Nvidia Blackwell GPUs expected to be available in 2026.
52:28
Both systems will be located at Argan and will be interconnected by NVIDIA networking and deliver a combined uh 2200 exoflops of AI performance.
52:41
>> We've talked about nationalization before.
52:42
We haven't talked about privatization.
52:46
We could potentially spin this out, take it public. There's an option here.
52:50
>> So, I I I was interested in this.
52:50
>> So, I I I was interested in this. I I looked this is going to be like somewhere around like a quarter of a gigawatt equivalent of 100,000 black >> half a half a meta campus I think I think meta is working on 500 500 megs >> yeah I I think I mean Hyperion like the
53:07
end state is like I think a gigawatt or more right >> more more but that's like the first big jump for them but the default metacampus I believe is around 500 megs um >> Cisco to acquire restaurant depot >> not our Cisco not >> not close uh to acquire Restaurant Depot for $29.1 1 billion.
53:27
>> Before we take you through this, let me tell you about the real Cisco.
53:29
Critical infrastructure for the AI era.
53:31
Unlock seamless realtime experiences and new value with Cisco.
53:35
There's only one Cisco in our hearts.
53:39
>> Uh this one's important. This is Cisco with an S. >> I I dislike Cisco. >> Why?
53:46
Because every time of I I find it a very frequent experience where there's a new restaurant coming to my area. I'm excited about it.
53:56
They invest >> million2 million dollars in building out this incredible space. Looks great.
54:02
>> And then you eat there for the first time and you can tell that they're just sourcing like Cisco. >> Interesting.
54:07
>> Uh I'm not going to say >> yeah, >> slop, but uh the the food quality is not great.
54:12
And then it and then it uh it's like why did you put all this energy into making a beautiful space and then you're just you know chefing up uh generic uh generic food.
54:20
Doesn't make any sense to me.
54:23
But um I believe the founder of Restaurant Depot.
54:25
Look >> I think I saw it somewhere >> in the Jetsons.
54:28
They had dinner generating machines.
54:30
Dinner generating machines.
54:32
Where how do you think that's going to happen?
54:34
>> Travis Palnick built this.
54:34
Isn't this cloud kitchens? >> Yeah. Uh, no, no, no.
54:37
Uh, the dinner generating machine or Cisco cuz Cisco >> dinner generating machine. >> Yeah.
54:43
Yeah, that's basically it.
54:43
But it's all part of of a pipeline.
54:47
>> The founder of Restaurant Depot who just sold for 29 billion. >> Uh, was born in 1932. >> 94. >> He's still kicking.
54:56
>> I think we should hit the gong for him. >> Let's do it.
54:58
>> Uh, >> congratulations on that.
54:59
>> Great to finally get a solid. >> It's never too late.
55:06
So, if you're 93, >> I can buy that sports car. >> Finally. Finally.
55:12
>> Yeah, that that is a true overnight success. Congratulations to him. Um excited.
55:16
I mean, you know, we got to get food to people. People are hungry.
55:19
There's some good things.
55:19
You know, maybe maybe they uh they stock some raw milk and you're on board then. You know, who knows?
55:27
>> Before we play the next video, let me tell you, every day is a fight between the advertisers and the viral videos.
55:35
Let me tell you about Railway.
55:35
Railway is the all-in-one intelligent cloud provider.
55:38
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55:44
And then we can play this video.
55:48
>> We're heading over to Japan.
55:49
>> Yes, this is the key sport that we will all be picking up in 2026.
55:52
This is going to be the hottest thing in San Francisco with the hills and the office chair.
55:58
>> Office chair racing league.
55:58
So look at the look at the speed and the technicality there. It's incredible.
56:06
>> This athlete says the corner once controlled me. Now I control it.
56:11
>> I think I got I think I got something.
56:14
>> You You have quite a bit of leverage. >> Yeah, with the legs. >> With the legs.
56:17
>> I think I got a build for office chair.
56:20
>> The six-year transformation is crazy.
56:20
I mean, this guy is incredibly quick. >> It lost me.
56:25
>> And we need to bring this to the US. >> Lost me. Let's go over here.
56:28
>> We need to bring this to the US.
56:31
Chair racer Mura going around the devil's hairpin. The >> devil's hair pin. >> All right.
56:38
So, uh, Tyler, what is happening on Axe in Japan? You break it down. >> Break it down. >> Uh, yeah.
56:44
I mean, I don't know all the internals, but it seems like like Nikita's been posting about this, but I I think, you know, they basically introduced like all of like uh Japan Twitter onto like normal Twitter.
56:55
>> Oh, because of translation. >> Yes.
56:56
But I mean, there's been translation for a while.
56:58
Um, but I don't know, like for some reason this weekend like half of my timeline was just like Japanese posts, >> uh, all about America, about how much they love barbecue, that, you know, they respect the cowboy aesthetics and all these things. >> Cool. I didn't know they were.
57:10
>> And we need to figure out We need to figure out how and why over 50% or something like that of Japan is is like a weekly active user of X, which is just crazy. They have great posts. >> Yeah. So, let's pull up. >> Wait, wait, wait.
57:23
This is a little bit of an update like narrative violation because that's a narrative violation.
57:26
I know >> that's a narrative violation >> because when Grock went viral, everyone was like, "Oh, it's good at anime. It's big in Japan."
57:37
And it was at the top of the Japanese app store.
57:38
But it appears that Japan's just using Twitter broadly.
57:44
Elon, >> they just like they they just like the app.
57:46
And that's like where they have conversations, which is very cool.
57:48
It's a narrative violation.
57:50
>> Let's go to >> Let's pull up this first post. >> This is hilarious. >> And it is pizza.
57:56
>> And this is the translation from Grock.
57:59
>> When I saw this quote pizza topped with a pizza in America, I thought there's no way we could beat these guys.
58:04
This is This is an amazing This is I've never I've never seen >> There's so many layers.
58:11
There's actually one, two, three, four layers of pizza. I'm going to make this.
58:17
I feel like this would be a smash hit in my house.
58:20
>> This is This is This is quite >> This is a peak performance. Peak performance.
58:24
We got to This may be for lunch today.
58:27
Let's get some peak >> this post which in Japan >> or I guess now everywhere got 93,000 likes is the the translation from Grock is I like this photo of American men and meat.
58:40
Someday I'd like to join in on this in person.
58:44
>> It's just some guys cooking a whole bunch of steaks. That's a lot of meat. Wow. That's a lot of food.
58:48
They're they're they're having a big barbecue in in Sassibo's dining establishment.
58:56
>> Someone else someone else says somebody's just I guess an American is posting their uh their grocery haul and someone says the amount is way too much. As expected. >> As expected.
59:10
We have a brand over here in America.
59:12
We do things this particular way. Hello Japan.
59:15
We love your fascination with our barbecue.
59:16
Here is me buying half a cow's worth of meat for our family.
59:20
We store it in a big freezer in our garage.
59:23
I actually have heard about this.
59:25
Buying in bulk obviously is more economical, but uh hilarious ratio by uh doctor something or other uh Dr. Nicholas.
59:32
The amount is way too much as expected.
59:35
What else is going on in Japan? Take me through.
59:36
Uh, in someone else says, "In Cassabo's dining establishment, it's common to spot US military personnel enjoying their meals with lively enthusiasm.
59:44
One day at a restaurant, I came across a group that reached an oddly intense level of excitement just upon seeing bacon. That's incredible. I love it.
59:57
Let me tell you about Lambda.
59:59
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1:00:05
And let me also tell you about 11 Labs.
1:00:08
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1:00:12
Uh in fundraising news, physical intelligence is in talks to raise $1 billion at 11 billion valuation.
1:00:21
>> I need to know why is Jeff Bezos here besides the fact that he looks fantastic in the tux.
1:00:26
>> Uh he might put in some money. Oh no, no.
1:00:29
The company has previously raised more than 1 billion in capital from investors including Jeff Bezos and Alphabet's independent growth fund capital G.
1:00:36
So you could have put uh Peter Teal because founders funds in you could have put uh Lightseed is a Danny Rack put or >> but uh seems to get the the viral attention.
1:00:53
So, uh, but very good news.
1:00:53
We actually interviewed, uh, both the co-founders of, uh, physical intelligence, both Locky and Carol, uh, this, uh, last year and they don't do a lot of media, so it's an interesting little segment.
1:01:05
We, we we spent maybe 20 minutes with them and, uh, you should go back and listen to it because, uh, it it's a very interesting uh, insight into the business that they're building, which I think a lot of people, you know, they're not a noisy firm.
1:01:18
They're not a noisy company that's like posting vibe reels and going and picking fights all the time.
1:01:25
So there isn't that much coverage of physical intelligence.
1:01:26
But like if you just look at the traction, look at the open source contributions, the data, the fundraising, like clearly something is happening there.
1:01:36
And so I think it's worth digging in and paying attention to if you're >> Last night Bill Aman hit the timeline. >> Whoa. I didn't know.
1:01:44
said, "Some of the highest quality businesses in the world are trading at extremely cheap prices.
1:01:50
Ignore the mainstream media.
1:01:50
One of the most one-sided wars in history that will end well for the US and the world, and we have potential for a large piece dividend.
1:01:57
One of the best times in a long time to buy quality. Ignore the bears."
1:02:01
And he says, "And Fanny May and Freddy are stupidly cheap. Asymmetry at its best.
1:02:06
They could be a 10x and it could happen soon."
1:02:08
And of course, Jerro Tickets comes in and says X.
1:02:10
com, the market manipulation app, uh, that Fanny May and Freddy Mack are up 42% and 37% as of this morning.
1:02:19
I think I think they've actually dipped back down a little bit.
1:02:23
Uh, but Justin says, "Posting your opinion on a public website is not market manipulation."
1:02:26
JT says, "Don't ruin the tweet."
1:02:30
>> Yeah, it's not it's not market uh it's not market manipulation.
1:02:32
Uh it doesn't seem like he has any inside information. I don't know.
1:02:37
Is he does he even have a position?
1:02:39
Isn't that disclosed in his filings? I'm not exactly sure.
1:02:41
Uh I would take every recommendation from a Twitter poster uh every piece of financial advice with a grain of salt, but uh this one certainly turned out to be uh some sort of pump going on.
1:02:53
And I I did dig in to this uh somebody asked Grock like, "Hey, break it down."
1:02:59
Like what is actually going on here with uh Fanny and Freddy?
1:03:04
They generate 25 billion in stable annual net income from guaranteed fees, low credit losses, outside crises.
1:03:12
Uh they're still in 2008 conservatorship and the stock trades for a total market cap of 10 billion.
1:03:18
So there's a world where you're sort of buying maybe I don't know exactly how aggregated this is, but maybe it's like 25 billion of cash flow at some point uh for 10 billion.
1:03:30
That feels like a very good deal.
1:03:32
get paid back in four months, five months.
1:03:34
Um, but of course there are a whole bunch of other uh a bunch of other um political >> and of course he does he does own uh the Fanny May and Freddy Mack are in his uh Persing Square portfolio.
1:03:46
Well, >> but uh again not not uh not illegal to share your opinion. >> Yeah.
1:03:53
Well, there are some uh not everything is up.
1:03:56
M Zakardi, Mike Zakardi, uh shares the current MAG 7 plus drawdowns from 52- week highs.
1:04:04
Nvidia is down 21%, Google's down 22%, Microsoft down 36%, Apple's down 14%, Amazon 23%, Meta 34%, Tesla 28%.
1:04:11
Uh and many others have drawn down significantly.
1:04:18
Fortunately, we have the perfect person to ask about what's going on with Nvidia because we have take him in the reream waiting room.
1:04:27
Before we bring him in, let me tell you about console because console builds AI agents that automates 70% of IT, HR, and finance support, giving employees instant access, instant resolution for access requests and password resets.
1:04:39
And let me also tell you about Vanta.
1:04:42
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1:04:47
And without further ado, let's bring Take Kim in to the TVP Ultra.
1:04:49
Take Kim, how are you doing?
1:04:54
>> Thank you so much >> for taking the time to come chat with us in >> congratulations on the launch of your business. >> Yes. >> Thank you.
1:05:02
I mean, it's been really gratifying that first day.
1:05:04
You never know who's going to show up. Totally.
1:05:06
I was like maybe 15 subscribers or 20 subscribers, but like hundreds of people showed up, tons of billionaires and tech founders.
1:05:15
It's insanely gratifying. >> Yeah, it's great. Uh, >> incredible.
1:05:20
>> So, uh, is it is it over for Nvidia?
1:05:23
They're down 21% we just read since the 52- week high. Is it doom and gloom? Is it over? >> No.
1:05:30
I mean, I think I was on last December and the stock has semis and chips that's gone up and now they're back down to where they were in December.
1:05:39
The chip sector's >> flat flat on the year. Nvidia is down 10%.
1:05:44
And it reminds me a lot about a year ago.
1:05:48
>> Do you guys remember >> everyone was freaking out about deepseek that super efficient models were going to destroy AI compute.
1:05:54
There will be a huge compute glut and then everyone freaked out about Trump's tariff wars in preparation day and this year seemed very similar to that almost. It's like groundhog day.
1:06:06
>> We have fears over AI capex.
1:06:06
People think that it might be the peak and then we have the Iraq war and uh one of these things is oil up here.
1:06:17
>> Iran easy to get them mixed up.
1:06:17
They happen so >> feels like the same same thing over. >> Yeah.
1:06:22
>> But um >> sorry to distract.
1:06:25
We wanted to throw we wanted to >> we wanted to show respect.
1:06:28
>> We wanted to show respect >> to a real podcaster.
1:06:33
>> I mean it's very similar to Iraq.
1:06:33
That's >> these are great.
1:06:36
Uh but in a $100 oil, this stuff is unsustainable and probably okay.
1:06:42
So so so because when when I when I like the deepseek analogy and I feel like the market half digested the agentic coding uh narrative and the catrini article, whether you thought it went too far, it was too hypothetical like clearly the markets did react and a lot of names sold off.
1:07:00
But in in a world where you believe that narrative, you would think that Nvidia would be going up.
1:07:06
But you're saying that there are other factors at play that are sort of uh tamping down the excitement in the market broadly.
1:07:12
>> I mean there there's no doubt.
1:07:12
Just like tariffs a year ago, Nvidia had 30% draw down when their business was actually flying actual fundamental of the business.
1:07:20
I think the same thing is happening here with >> the Iran war.
1:07:24
>> Um >> things will eventually subside.
1:07:25
oil can't be $100 for forever and Trump will probably backpedal in the next few weeks ahead of the Trump.
1:07:33
>> So, let's uh let's recap a few of the key stories around Nvidia.
1:07:36
We just came off of GTC and there's a lot going on uh at the company.
1:07:42
I mean, it's a huge company.
1:07:43
Uh maybe it'd be good to start with just uh next generation chips, changes to strategy, what people are actually buying.
1:07:51
Maybe that means uh Grace CPU standalone sales or the the development with the Grock partnership.
1:07:58
What's sticking out just on the actual AI product side to you that you're most excited about?
1:08:05
>> Well, inference demand is exploding driven by the AI agents, authentic coding assistants.
1:08:10
Um what I met with uh Ian Buck, I met with dozens of uh engineers at Meta, Google, Nvidia and all of them are seeing crazy uh inference demand and AI compute shortages.
1:08:23
So across the board, people are in crazy clamoring need for AI.
1:08:29
>> And and we're I mean we're Yeah.
1:08:29
You're seeing that from talking to engineering leaders at big tech companies, but we're also seeing it from Vibe coders who are just on X and Twitter and talking about how they're hitting rate limits and they're they're subsidizing.
1:08:40
They have multiple plans and they actually shift around from one model provider to another just to make sure that they're getting the tokens they need to build whatever they're building.
1:08:49
And you see the tweets like people are like um >> building uh bots to to pick up any kind of B200 GPU that can that they're waiting like weeks and months or whatever.
1:09:01
>> Like sneaker bots but for NeoCloud. >> That's crazy. >> Exactly. >> I can't believe that.
1:09:06
>> Um and the the the great thing is Jensen, you know, he's very precient.
1:09:08
He probably saw this demand months away.
1:09:11
He locked up all the the supply agreements for memory co-ass, you know, connectors ahead of time.
1:09:17
ahead of time. you saw this inference demand and uh to take advantage of this coding system uh boom which it's almost like a gold rush you see open AI pivoting toward it anthropic obviously is thriving on it uh billions of ARR every every few weeks yeah uh and uh
1:09:34
Jensen uh acquired Grock um acquired the assets of Grock and the people of Grock >> and this the combination of integrating Grock's uh technology together with Vera Rubin uh lets Nvidia has served this tremendous wave of compute demand economically and uh Ian Buck talked about it, Jensen talked about it. So
1:09:53
So Nvidia is positioned perfectly to thrive on this coding agent wave that we're seeing right now >> on on the Grock deal.
1:10:00
Uh Jensen did a fantastic interview with Ben Thompson and uh was sort of asked the same question two years in a row about A6, the threat of AS6, the idea that the GPU, the general like like general architectures can truly satisfy 100% of demand.
1:10:21
It feels like there's a shift in Nvidia's strategy there. Do you see that?
1:10:25
It feels like the right move, but do you do you see it as a shift in the philosophy of the company or the strategy or or is this just something that the gears have been turning for a long time and this is maybe just an unveiling of a strategy that makes a lot of sense and has made a lot of sense for a while.
1:10:43
>> I think what Jensen does, he sees where the market shifting and where the economic value is.
1:10:47
With Melanox, he did this in 2019.
1:10:49
this in 2019. He saw world shifting to >> um >> it's a networking chip but he saw the world shifting to like these 10,000 100,000 GPU clusters and melanops need for that in the same manner he saw uh AI agents and >> and the inference behind that taking off
1:11:08
and he said oh this grock thing will work perfectly rub it it doesn't replace everything and just talked about 25% of the >> inference demand would be uh grock would uh work on that But them working together where 75% of the inference is ver Rubin, 25% is a Grock low latency stuff. That's it's like the perfect
1:11:26
That's it's like the perfect combination to to take advantage of this.
1:11:30
>> Um and the other thing is like we're just in this great liftoff of AI innovation.
1:11:36
Yeah, >> we've talked about anthropic mythos, the blog blog post that leaked out.
1:11:39
So we're going to have this, you know, step up function.
1:11:43
They told fortune it's going to be a huge step up change.
1:11:45
Yeah, >> open AAI is coming out with their model soon.
1:11:48
And then when I went to GCC, the biggest takeaway I had was this uh session between Jeff Dean and Bill Dally, both chief scientists of Google and Nvidia. And it's it's online.
1:11:57
I highly recommend uh people watch it.
1:11:59
And he talked about Jeff Dean talked about um the context have context window innovations where they could focus on the 10,000 documents that that work well with your your request and query.
1:12:10
So we're going to have this context window innovation.
1:12:14
Both chief scientists talked about uh stacking memory right on top of the GPU or TPU and that's going to be a huge innovation uh in the coming months or years and uh so you have and then Jeff talked about uh synthetic data uh for audio and video there there's this huge runway that data is not over and then they're going to be able to take advantage of all all this data that uh people don't realize yet.
1:12:39
Um, so you have like all these vectors where AI models are just keep getting better and better. >> Yeah.
1:12:46
How are you processing the idea that Nvidia will be investing in an opensource frontier lab capability?
1:12:50
That feels like potentially competitive with some customers.
1:12:57
Nvidia's like never really been in that market before.
1:13:00
Uh, but at the same time, I've been the biggest uh, like supporter of open-source American AI models.
1:13:08
I loved when Meta was doing it. I want more of it.
1:13:13
I loved when OpenAI, open source, GPTOSS.
1:13:16
It feels really, really important, really great, but it does feel like a strategic shift.
1:13:20
How did you process that announcement?
1:13:24
>> It's it's not a hu I think it's like 25 billion over the next few years, which doesn't really compete with what OpenAI Anthropic doing.
1:13:30
Yeah, I guess these these smaller models are going to be helpful for people running these smaller use cases.
1:13:36
So, GPUs as long as they're utilized even locally or in the cloud, Nvidia benefits and saw the the the top people at Quen >> um left and we don't know where they left to.
1:13:48
Quen is an amazing model.
1:13:48
It's kind of like what deep is what people thought deep should be when works well locally.
1:13:55
Um it Gwen kind of subsides because all the people >> what's your theory on what's your theory on where they all went?
1:14:01
Another Chinese lab or >> I asked all the engineers when I was at GTC no one really knew but people people are trying to say Nvidia should actually hire them because the more capable open source model Nvidia doesn't care if you're using GP to run open source or not.
1:14:19
They just want, you know, more AI adoption across the world. >> Yeah.
1:14:22
And and Nvidia has more uh probably more levers to pull if it turn if it turns into a negotiation with China.
1:14:29
Like we're we're we're tracking like the Manis story with Meta.
1:14:31
And there isn't that much that Meta can give to China in exchange if there's like a hey like let look the other way on this particular deal like let this one flow through. We'll ch trade this.
1:14:43
Meta not really doing any business there.
1:14:46
really doing any business there. that Nvidia of course is going to be uh selling black wells at some point in the near future and there's probably some level of pricing you know it can be part of a larger discussion which makes a lot of sense >> and one thing that kind of went under
1:15:01
the radar Jensen literally said at GTC they got license approvals on both the US and China side we're going to see billions of dollars of H200 orders >> okay so yeah I mean it seems like it seems like there's a path on the demand side that's very very clear you've mapped it out a few times times. It's a It's a huge number.
1:15:17
Um, it's already massive revenues, just an incredible growth.
1:15:23
But, uh, what is what is the supply side looking like?
1:15:26
Because it feels like TSMC is not ramping capex nearly fast enough over the next few years.
1:15:32
And if we see another 10x increase in compute demand, uh, we could be really constrained on the leading edge bib uh, fab side.
1:15:39
Uh, so how do you think Nvidia is going to process that?
1:15:44
Well, Nvidia is in the driver's seat because Jensen goes there five, six times a year and his best friends at TSMC and speaks at their employee day.
1:15:52
So, they're going to get higher, they are getting a higher allocation to wafers and cos and all that stuff.
1:15:58
So, and they will benefit.
1:15:59
But I agree with you that industrywide like >> Google is dying to get more TPU wafer capacity. >> Sure.
1:16:07
>> Um, all the all the hyperscalers that have AS6 are trying to get more wafer capacity.
1:16:12
So there is going to be a AI comput shortage uh in the years to come uh just like you said. Yeah.
1:16:17
And Nvidia just benefits because you know they're the biggest dog in the house and they can uh uh prepay uh tens of billions of dollars to get the allocations they need. >> Yeah.
1:16:27
I mean maybe there's some offtake in AS6 that can potentially be fabbed somewhere else at some point.
1:16:32
I I don't I I know that a lot of the ASIC companies wind up fabbing at TSMC, but uh it feels like if you're already doing some sort of rearchitecture, maybe there's a way that you can get you can squeeze something a little bit out of uh you know a an Intel deal or something else.
1:16:50
I'm not exactly sure, but >> Samsung and Intel are the only Samsung and other fabs that can possibly do it. >> Yeah.
1:16:56
Uh >> that's the bookcase on Intel. >> Yeah. Yeah.
1:16:59
>> Yeah. Yeah. is that is that at some point the labs and and Google like like across TPU extra GPU capacity Nvidia the new AR like there's just so many buyers of lab capac capacity now that you could imagine everyone coming to the table potentially in Washington DC or Mara
1:17:20
Lago since the US government owns a slice now and everyone saying okay let's hold hands and jump across this and say that if the if the if the supply comes online, we will buy it at this price because we have really really solid use cases that that will justify the investment for us and for Intel. So that
1:17:37
So that would be a really really good case.
1:17:40
But again, even if the money is there, how long does it take to get to, you know, good production numbers?
1:17:46
>> I mean, I suspect like Apple and Nvidia are considering um either Intel or Samsung for um their lower-end stuff.
1:17:51
Uh yeah, whether it be like a mid-range iPhone or on Nvidia side definitely their consumer gaming GPUs, they they might go back to Samsung and maybe even Intel.
1:18:02
>> Yeah, I have one more, but go for it.
1:18:04
>> Uh I wanted to know how you're processing the ARM CPU announcement.
1:18:06
Uh it's an interesting dynamic because they're sort of frenemies with Nvidia now.
1:18:15
They're competing in many ways to break the x86 monopoly uh because they both are selling ARM CPUs, but then they're also competing uh and so I'm wondering how you think that plays out, what that means for Nvidia and just the rest of the semiconductor supply chain.
1:18:33
I >> I think ARM is uh this their CPU opportunity is a longer term, you know, for even they said 2030 2031.
1:18:38
Yeah, >> it's a longer term opportunity.
1:18:41
I don't really expect the major hyperscalers like Amazon to switch to ARM's you know uh product offering.
1:18:48
They have their own and same with the same with Nvidia.
1:18:50
They have their own ARM CPU that they're they're going to incorporate and sell.
1:18:56
So it's not that big of a um I don't think Amazon or Nvidia are really worried that ARM is going to take uh any big share.
1:19:03
It's probably going to be on the margin for companies that can't develop their own ARM CPU.
1:19:07
uh the more the uh mid-tier hyperscalers or enterprises that use these things.
1:19:12
But I I think the ARM thing is very important because it kind of confirms what the biggest underlying uh thing that that's not really consensus yet is this massive CPU shortage that we're seeing just over the last few months.
1:19:26
We have Dell, AMD, uh Intel CFO talked about they're talking about three to five year locked locked in supply contracts from hyperscalers.
1:19:35
So this this is a major trend uh that's going to go over the next few years and the reason why is AI agents need more CPUs.
1:19:44
The ARM CEO talked about four times more CPU corder cores versus last year's kind of a AI infrastructure model.
1:19:53
So we're going to see this massive uh demand for CPUs that people aren't really understanding yet because uh AI agents the whole thing requires orchestration tool calls database queries web searches and that's all handled by uh the CPU. >> Yeah.
1:20:13
>> Give me your bull and bear case for Terraab.
1:20:16
>> Terraab I'm not that optimistic.
1:20:21
I mean, it's so hard to >> give me the give me do do your absolute best to give me the bull case >> because TSMC is so short that you know Elon needs to find but even then like how they going to buy like semicap equipment from ASML and AAT like there there's just no capacity there.
1:20:41
So I'm I'm not optimistic on that.
1:20:46
>> And this is this is stuff that takes decades.
1:20:48
Um, chip fabs is almost like cooking and it's not like some you could just follow uh follow a manual.
1:20:54
It's like it's almost like cooking where it's takes a lot of uh trial and error accumulate over decades uh TSMC and even Intel.
1:21:06
So, it's not something you could just jump right in and do.
1:21:08
Yeah, it somewhat goes back to the Yeah, it somewhat goes back to the XAI debate about like do they need AI researchers or should everyone be an AI engineer?
1:21:20
Like are we in a research period or a you know the the Ilioskiver age of research versus the Elon Musk age of engineering?
1:21:27
Where are we in uh semiconductor production?
1:21:29
It feels very engineering like like an engineering process.
1:21:35
But what we've seen from ASML is that it and uh and TSMC is that it does feel like there's a little bit of research and art artistry to it and the cooking analogy.
1:21:45
cooking analogy. Yeah, I've been doing a lot of research in the space and it's a lot of trial and error and like almost like cooking a recipe >> and and and it also feels like in at least with XAI uh if all the researchers are in San Francisco, you can sort of just like walk across to the coffee shop, poach someone, but if if the best if the best uh you know uh semiconductor
1:22:08
engineers or or uh technicians are in Taiwan and they see it as a national uh you know urgency to you know bring you know stability to the country both economically and geopolitically then you have a very different calculation it's like oh yeah I could make five times as much if I left my home country to like be abandoned that's a very different
1:22:31
calculation and uh and everything that I've heard about the culture at TSMC is that uh the the folks who work there are extremely dedicated beyond the economics they are true missionaries uh not necessarily mercenaries and so it does feel like it's harder to do like a talent raid in in the leading edge fab world than even the AI world which is extremely competitive and there are
1:22:54
still tons of missionaries but fab >> I guess I guess another question I have is would you expect uh would you expect XAI/ SpaceX at any point to get to basically just open up a shop as like a neocloud because the thing that was like probably the one of the least compelling aspects of of the Terraab pitch was him just saying, "We need all of this compute. We need to do this because we
1:23:17
We need to do this because we we're going to be so chip constrained.
1:23:21
We're going to be so supply constrained."
1:23:22
But there was no explanation of >> where the demand was coming from, >> where the demand was going to come from.
1:23:27
Is it going to come from >> training Tesla models, Optimus or Grock or >> Yeah, it was it was just very unclear. >> It was a lot.
1:23:36
>> But there's even the question right now is should XAI be kind of renting GPUs? I don't know. I don't know.
1:23:43
renting out GPUs because the biggest win has been Colossus 2.
1:23:47
Yeah, Colossus 2, which was built very fast.
1:23:51
>> I I I think Elon's pitch with the SpaceX IPO and we'll see it in the coming months is the AI compute.
1:23:55
It's going to be so there's going to be so much demand over the next 5 10 years that you're going to have to uh use the SpaceX satellites that have GPUs in them um to to serve that.
1:24:08
And and maybe maybe I mean even though Tesla's been vertically integrated to the point of being a consumer product, SpaceX has not.
1:24:14
It's been a railroad and there is a world where you fab the chips, you put them on satellites on Starlinks in space and then you let other companies do whatever they want with those GPUs.
1:24:26
>> Think what Elon did with Starlink.
1:24:26
I mean that's a telecom infrastructure play and this will be an AI computing infrastructure. >> Yeah. Yeah. Yeah. >> Fits that model.
1:24:34
>> There's a world there.
1:24:34
Um >> I'm not going to bet against Elon. It might just take long. >> Yeah. Yeah.
1:24:38
Uh what about what's going on with helium?
1:24:40
What what are you tracking there?
1:24:41
There's chatter about uh helium shortages potentially.
1:24:46
>> Jensa has talked about this.
1:24:46
This is a risk, >> but >> there is probably like six months, six to nine months of inventory in the channel.
1:24:55
Bernstein has talked about it's not a risk in the short term.
1:24:57
So, so if this thing if this Iran >> stuff lasts >> in, >> you know, two, three, four, five months, then becomes a problem. Okay.
1:25:06
But if it, you know, gets solved or opens up with the toll or whatever uh final negotiation they come up with over the next few weeks, I don't think it's going to problem.
1:25:17
>> Yeah, I do think that like like most of these uh materials there are uh extra deposits.
1:25:22
They're just not economical to mine.
1:25:24
I don't think that all the helium exists in the Middle East.
1:25:27
That would be >> it's similar to the rare thing just like you said. >> Yeah.
1:25:30
where uh in a supply constraint scenario, it becomes more economical to mine American helium.
1:25:37
>> Let me put it this way.
1:25:37
If helium becomes issue, we're going to have bigger problems on our hands. Okay.
1:25:39
I mean, there's going to be world starvation. >> Let's hope not.
1:25:43
Let's hope that that'll be the least of our problems if helm becomes a problem.
1:25:48
>> Take me through uh depreciation gate.
1:25:51
How did you process that?
1:25:51
And where do we stand now with the fear that GPUs will depreciate precipitously and H100s will be worthless in six to 12 months?
1:26:01
>> It's it's totally not a problem right now.
1:26:03
Like Core Wee has talked about uh these things are lasting five to six years.
1:26:07
Uh and they're getting like almost 90 95% of the pricing.
1:26:09
So >> it could be potentially be a problem if the whole if this is a bubble.
1:26:15
I don't think it's a bubble. Yeah.
1:26:17
But if there's a bubble two, three years from now and there's a compute glut, then >> yeah, >> know the stock's gonna go down because there's a compute glut.
1:26:23
But as of now, it's the opposite.
1:26:26
Like you all the GPU rental prices, even for stuff that's six years old, is still being sold out and it >> the AI compute demand outpacing supply is so large that this is not an issue right now.
1:26:39
>> Do you have any theories on on where the next step change in token demand could come from?
1:26:44
come from? because right now we're seeing it in codegen and there's a lot of optimism around uh these types of workflows being applied to other forms of work but we were talking about this on Friday like even if AI can just
1:26:59
oneshot beautiful financial models it won't necessarily even make a real dent in token demand at least compared to to codegen because no company needs to just constantly be you know be generating ating uh models at the rate that let's say Gary Tan generates code. Um and and so I'm I'm like kind of been
1:27:19
Um and and so I'm I'm like kind of been trying to wrap my head around where where could these incremental um use cases.
1:27:26
>> I actually think codegen is still just early innings like >> Yeah.
1:27:30
And I I don't disagree with that.
1:27:32
>> 10 20 agents and they're kind of over uh overseeing them.
1:27:36
overseeing them. But then we have this other stuff where these models the mythos and open AI they're just going to get better where you could automate uh all these work uh process flows companies are going to use them for every single vertical customer service
1:27:51
research simulating chip design where they they can verify drug discovery where they verify uh drug molecules can do so so we're just getting started at this stuff so you can you're going to see vertical AI agents on every single category and I think a Logan's coming out. He wrote this great post on
1:28:07
He wrote this great post on >> X. Yeah.
1:28:09
>> Um that he says this the AI agent wave is is going to >> kind of uh attack this $6 trillion knowledge economy, right?
1:28:18
It's not just about programming anymore.
1:28:21
>> They're coming for us. >> Yes.
1:28:22
I don't think See, I'm actually >> They're they're attacking the key context economy and the TVPN economy.
1:28:32
No, I I think it's it's it's like a calculator or a spreadsheet.
1:28:34
You know, 30, 40, 50 years ago, we had like, you know, 50 accountants do doing the spreadsheet manually, right?
1:28:40
And now after a spreadsheet came, it didn't get rid of all of knowledge work.
1:28:44
It just uh enabled people to think at a higher level and get more done.
1:28:49
And yeah, I'm very optimistic about that.
1:28:51
I mean, one one one way that you 10x token demand on around a financial model without 10xing the number of financial models that you're building is having the agent go and collect 10 times as much data.
1:29:02
And so there's a lot of situations where uh I mean you you look at like hedge funds that want to understand uh the price of Walmart stock.
1:29:14
There are hedge funds that will task satellites to take pictures of Walmart parking lots, estimate the number of people on a day-by-day basis that are going into the Walmart to shop and then using that as a proxy to project revenue and then flow that through to cash flow and then flow that through to the DCF and the actual valuation of the company.
1:29:36
And if you think about all the different financial models and all the different businesses where you could go and say, "Well, for this company, I need to go to every single local, like I want to know the price of Squarespace.
1:29:47
Let me go to every single website that's powered by Squarespace and estimate the revenue that they're bringing in and their willingness to pay for their hosting service, something like that.
1:29:55
And all of a sudden, like it's just one spreadsheet at it's just one number at the end of the day, but it's like a thousand times more work went into it."
1:30:04
>> Let me give you this great example.
1:30:04
Um, every year I do this a um the same store sales for uh these fast casual companies.
1:30:13
So like Chipotle, Cabba, and I put out this tweet. It goes viral.
1:30:16
Uh a year ago when I do it, I would have to manually go to every IR website for these six fast casual restaurants. Yeah.
1:30:25
It would take me like an hour or two. Yeah.
1:30:27
>> Um I would try to use a chatbot, they would get it wrong. >> Sure.
1:30:30
>> I did it like a few weeks ago and all the chat bots got perfect.
1:30:32
So, it just saved me two, three hours of tedious manual labor.
1:30:36
So, that that's only going to get better and better. Like, >> yeah. >> Yeah.
1:30:40
And it's only going to take you one like like this year is the year that you you do it with multiple chat bots and you fact check it yourself and then forever it's going to be just one prompt and >> and it get it got it right.
1:30:52
A year ago, it wouldn't get it right.
1:30:54
But now, in one two minutes, I put give me the same store uh sales for these six restaurants.
1:31:01
Yeah, >> I put in Gemini, I put in Chachi PT and just to to make sure they're right and they're right.
1:31:06
So that all all the tedious labor, all the manual labor, all the data entry that you know all of us are used to.
1:31:13
Um that stuff is going away and we could think higher level.
1:31:15
So I could look at the same store sales and say, "Oh, the economy is at risk and whatever."
1:31:20
But all the grunt work, all the tedious work is going to be taken care of uh by these AI agents. >> I agree completely. I agree completely.
1:31:31
Uh >> we got a lot of a lot more sound effects since the last time you joined.
1:31:33
Uh last last uh last question for me.
1:31:35
What's your outlook on on meta?
1:31:38
It feels like the the broader market right now has zero faith in Meta to actually put >> all their AI investments to use.
1:31:51
>> I had I have this history with Meta is that you know every time it starts falling apart I say it looks cheap and then it goes down another 30%. But nothing has changed.
1:32:00
Like no one's going to replace better digital ad position.
1:32:02
I mean >> like I would even say in the AI world they're even better positioned because Google might lose digital ads share to AI chop chat bots their search position going future.
1:32:14
So like no one's going to replace Instagram, no one's going to replace Facebook.
1:32:18
Billions of people are still going to use those uh social media apps.
1:32:22
apps. And uh you know it's every six 6 months to 12 months everyone goes through this bare meta cycle but their pure competitive position really hasn't changed and you saw what's happened to Sora right like you know everyone's all excited about Sora and and that that got >> totally yeah and and there's just this
1:32:41
world where even even if like the AI spending is like a side quest it's like really they just pulled forward like three or four years of capex and they will use that for their other products it's probably even less like wasteful than reality lab spend which might take even longer to realize the cash flows from like they can recoup. Okay, we
1:33:00
Okay, we built this massive data center, we did this training run, we didn't get to the frontier, we're not getting a lot of like genai usage, but we can apply it to our ads platform and tools and reels recommendations and a million other things just in years 2028, 2029.
1:33:16
And yeah, we're a little bit ahead of schedule >> or ad engine monetization >> 100%.
1:33:24
Yeah, the gem model >> reality labs may he may have wasted 70 to 80 billion dollars.
1:33:29
He might waste a hundred billions of dollars on on these a frontier AI models or ad engine core business that money making engine has is not going to be affected by this. >> Yeah.
1:33:42
Well, thank you so much for taking the time to come hang out. Always a great time.
1:33:46
T uh go subscribe to key context on Substack.
1:33:49
Follow take him on social media. First adopters.
1:33:54
>> Join the many beaires that were the first adopters. >> Yes. Yes.
1:33:57
You'll be in good company and thank you so much. We'll talk to you soon. Have a great week.
1:34:02
>> Great to see you guys. Cheers.
1:34:04
>> Let me tell you about Figma agents. Meet the canvas.
1:34:06
Your AI agents can now create modify create and modify your Figma files with design system context in beta starting today.
1:34:12
And let me tell you about graphite code review for the age of AI.
1:34:16
Graphite helps teams on GitHub ship higher quality software faster. So >> uh Chimath. >> Yes.
1:34:22
Holly says the biggest threat to Instagram's moat is an incredible image model. >> Okay.
1:34:28
>> Ze Zephr says metabot.
1:34:32
>> Um >> an incredible image model. It should.
1:34:34
>> I mean, that's basically that's a that's a like you're basically saying, okay, if Sora was if the content on Sora was a hundred times better, >> would that be a real threat to Instagram?
1:34:47
>> And I still am not I'm still not convinced.
1:34:49
>> I feel like a lot of people have uh their network there.
1:34:51
They want to share with their friends. They have a graph there.
1:34:54
And even though the recommendation, like the content doesn't come through the graph anymore, having your friends on there to have the conversations and the comments, there's still a lot of left.
1:35:04
>> If they could make an AI agent of you that instantly reacts to every video I send you >> and feature >> killer feature. Killer feature.
1:35:12
There's some Yeah, between our DMs, there's a lot of stuff that you got to still react to.
1:35:18
Well, without further ado, we have Logan Bartlett from Redpoint, his managing partner there.
1:35:23
Welcome to the show, Logan. How you doing? >> Good, gentlemen. How are you? >> We are fantastic.
1:35:29
>> It's great to see you.
1:35:30
>> I like this camera setup. This looks fantastic.
1:35:33
>> You know, once upon a time, >> yeah, >> I was a, you know, I was a semi-professional podcaster before you guys stole all the thunder in the industry and forced us into oblivion.
1:35:42
>> Three cartoon avatars was >> back to market.
1:35:47
>> Just put the investments in the bag, bro. >> I know. I That's exactly right. Yeah.
1:35:52
the McDonald's bag of cash I have.
1:35:54
That's what I'm doing these days.
1:35:56
>> But you're also uh writing market analysis, which I uh always look forward to.
1:36:02
>> Yeah, this has been consistently some of like the the best content in the entire industrial complex and I've enjoyed it for many years.
1:36:10
It was it was extremely valuable during the interest rate crisis as well and and also the conversations that you were having on the podcast, but it felt like a really rational reset that wasn't a total black pill, wasn't a total white pill.
1:36:23
It was just actually like here's some data.
1:36:26
There are obviously some conclusions, but you can also make your own.
1:36:29
So, thank you for everything you do.
1:36:30
Take us through the biggest findings.
1:36:32
Take us through the process that led to this particular research report.
1:36:36
Yeah, it turns out there's a little bit of nuance that 75 slides give you more uh than 140 or 280 characters to kind of tease out in some ways.
1:36:45
I um so it started uh it started probably in January.
1:36:50
I have this uh process every year where I have a a panic attack that we have an annual meeting coming up and uh I I I got tricked in 2020 when I joined the firm.
1:37:03
They were like, "We're going to give you this this really illustrious honor that you get to do the market update.
1:37:06
We so trust you and what you have to say."
1:37:08
And I thought that, "Oh my gosh, this is amazing.
1:37:13
I'm being bestowed this honor of doing this market update deck."
1:37:17
Little did I know, no one else wanted to do it.
1:37:19
Uh, and so every every January I get a mild anxiety attack that I have this coming up.
1:37:25
this coming up. And uh over the past couple years there's been a bunch of different I mean that year it was co then it was kind of the zerp fallout zer era 2021 then zer fallout then 23 I think was sv 24 maybe I got like a little bit of a respbit then last year was the tariffs and so every year there's like something going on that forces uh us to
1:37:48
recalibrate but this year it became pretty clear it was going to be the software selloff and what was going on in the public markets and so um monitoring that I sort of started from a process of talking to a bunch of smart friends in the industry uh about what they're thinking about and trying to probe on questions that they wanted answered. And uh this generally involves
1:38:07
And uh this generally involves a lot of public investors because private investors in some ways are like fish in water uh where like you sort of just operate in the world around you.
1:38:19
And so if you're doing defense, you really just focus on defense.
1:38:20
If you're doing software, you just focus on you know that.
1:38:24
If you're doing whatever healthcare, you're focusing on that.
1:38:27
Public market investors I find are a little bit more zoomed out and they typically have an opportunity to play across different scale of businesses, different sectors, different um you know, types of companies, all that stuff.
1:38:38
And so I talked to a bunch of them and and software and like what the hell's going on was the big narrative.
1:38:45
And there felt like there was a major disconnect between what private folks were seeing going on and what uh public folks were thinking about.
1:38:51
And so trying to bridge that gap of how do we have this world where you know software companies are now trading at 4.
1:38:59
1 times NTM in the public markets but also getting priced at 2 3 400 times ARR in the private markets and so sort of setting out to bridge that gap was kind of the goal.
1:39:13
>> Is it a is it a gap or is it a gulf?
1:39:16
>> A golf uh I would say it's a optimism disconnect maybe.
1:39:19
Uh >> that's a good phrase.
1:39:20
I like that >> it is it is amazing.
1:39:22
>> it is it is amazing. You know, I think about the um I did a panel recently with a bunch of private equity investors and in hearing them talk, what I concluded and some of this is true I think for public investors as well is like >> what is the risk of going to zero and optimizing your process around like hey
1:39:42
we really can't have a zero X in the portfolio versus private market investors you're optimizing on like what are the chances you're missing out on a 30 50 100x and if you take those two lens It ends up with a very different place of like optimism versus cynicism, upside versus downside, you know, all the questions you ask. >> Yeah. So, let's uh let's start. >> Yeah.
1:40:02
So, let's uh let's start.
1:40:03
>> You'll appreciate this, Logan.
1:40:03
I had a a portfolio company at the end of last year that is a software as a service business.
1:40:10
And in one of their updates, they they made the announcement that workflows are now called agents in in the product.
1:40:19
And I was like, they were like, "This workflow stuff seems like people are not that excited about it now.
1:40:25
We're we're switching gears. These are now agents.
1:40:28
And if we just >> Do you know that Breaking Bad meme that like he says, "We had a good thing, you stupid son of a bitch."
1:40:33
I feel like that was that was like all SAS investors over the course of the last >> 100% when >> being like you you mfers who had to go mess up this like really good thing we had going on with this AI pixie dust >> with a nonprofit that didn't even raise a seed round until they were multi-billions.
1:40:50
It's like we couldn't even get in early. >> We had a good time. >> We had a good time.
1:40:54
Uh so uh let's start with the the public markets.
1:40:56
How much of how much of this is driven by the Catrini article?
1:41:00
How much of this is driven by actual data points where we're seeing uh I was I was just pulling like the top 50 SAS companies sort of pure place SAS companies and trying to ask answer the question like is revenue decelerating yet?
1:41:14
Are we seeing a kink in the graph like some change in the data?
1:41:18
And I didn't go nearly as deep as you go, but uh but how much of this is just like narrative and anxiety about a changing uh a changing curve to the financials versus uh actual data points where people are saying, "Okay, like we're not going to be growing as fast.
1:41:36
We're not going to be as profitable as before."
1:41:37
Something else that would change the valuation. >> Yeah.
1:41:40
I mean, I think broad buckets, there's two main things.
1:41:41
One is like the public market investors are fed up with stockbased comp.
1:41:44
And so like let's put that in a bucket.
1:41:46
And I I actually I do think venture investors and public market CEOs are um to blame for some of the softness and the cultures and like how bloated some of these businesses got.
1:41:59
But also you have to be practical.
1:42:01
There is a game on the field to play and like you could triage and say, "Hey, you know, we're really going to reduce the number of employees we have."
1:42:07
Uh, and you really have to be careful there because, you know, they could, your best people could just walk out the door if their friends are all getting fired and they could walk out the door and go work at Anthropic or Lora or uh, you know, one of the businesses that's growing at this crazy crazy rate and get >> stockbased comp.
1:42:26
And so I am sensitive to that, but that is a real part of it that like there's not true profits going on.
1:42:33
And so I think let's put that in a bucket though.
1:42:35
That that's like sort of a side.
1:42:36
The other thing, the far more interesting conversation to have is like are financials um uh deteriorating and the answer is really no right now.
1:42:45
It's more of this like long-term uh existential question of what terminal value of these businesses are worth.
1:42:52
And it used to be hey uh 85 to 90% of a business's value was tied up in the period beyond the DCF, right?
1:43:02
period beyond the DCF, right? the the terminal value of the you know the the long-term duration of it and that's really what people are asking questions on and to be honest I think what's really happened is the public investors are saying I can't tell the difference between Salesforce and Service Now and Snowflake and Crowd Strike and Guidewire
1:43:23
and Samsara and all these businesses and to be honest I don't even really want to go dig in and figure out all the little specifics here I'm just going to go put my my bankroll in Nvidia or Google or AMC or something else and I'll wait for this to sort itself out, wait for the market to do its thing and figure out what the buying opportunities actually are when it's a little more uh a little less uncertain. And so I think it's it's
1:43:45
And so I think it's it's that and people are asking like what is the long-term terminal value and saying I'll wait on the sidelines until other people really show the proof points that they're going to be able to survive this AI thing. >> Yeah.
1:43:58
Is there is there a world where we move into a regime where we're talking about not revenue multiples but like EBITDA multiples for these software companies?
1:44:07
companies? So I was looking at a company that was three billion market cap, 100 million of EBITDA, very stable the last five years and and one one one investor was making the case like oh AI winner and I was like I don't see that but also I don't see these customers turning I
1:44:24
just see them doing AI stuff on top of this particular company because they're more infrastructure layer more data storage that type of thing and so I was like I think you can count on a 100 million EBIDA and probably cash flow for 10 years, 20 years, but do you want to be paying 30 times that? Is that enough? Is that enough?
1:44:42
And I don't know if that's a rational framework.
1:44:45
>> Nobody nobody knows anything.
1:44:45
You have to apply the discount.
1:44:47
The other the slide that one of the slides I loved was was the slide on newspaper earnings. >> Oh, yeah. Slide 22.
1:44:56
>> You say newspaper earnings.
1:44:57
>> I mean, it is an interesting It's funny.
1:44:58
I I actually had that up on my uh screen here as well.
1:45:00
But yeah, newspaper earnings.
1:45:02
I mean, when these platform shifts happen, >> uh, you might not see it in the earnings or revenue initially at all.
1:45:07
And so, the newspaper example in the deck was that newspaper earnings were actually fairly stable for like the 5 years post internet while their value collapsed.
1:45:19
And so, everyone saw the writing on the wall of where this was headed, but it took a while for that to actually come through and show up in the uh in the actual financials themselves.
1:45:27
So John, I guess to your question on it, like I I I use revenue as a proxy.
1:45:32
Uh and maybe it's like too too flip of a nomenclature.
1:45:38
We really should be talking about like free cash flow with uh deductions for stockbased cop or whatever, but like all these things are growing at different rates and that's sort of been the historical lingua frana that that I've kind of used.
1:45:51
But to be clear, it makes it impossible to comp to the private markets because no one's generating any any so it's a useless comp.
1:45:59
But I'm just thinking like if I'm a public markets investor and I'm just and I'm choosing between Google, Apple, and then some small cap midcap software company, I probably want to have an IBA hat on or something like it to sort of understand my just my rate of return, which is going to be a lot less like, oh, all of a sudden they're growing at some unpredictable rate, so the DCF gets crazy and I'm paying some high rate. Yeah.
1:46:23
And and this might be a little uh uh simple for for some of your listeners and maybe helpful for others, but like at the end of the day, uh a business is valued at the the current value of all future free cash flows.
1:46:34
And so >> discounted back to today's dollars.
1:46:37
And so when when the reason software businesses have been so good is the the you you have annuity streams going out into the future and you're able to with some level of precision figure out what the discount back uh the the value as uh in the future.
1:46:54
And so that's a that was a great thing particularly when we had retention rates at you know 95 96 97% net retention rates at 1201 130 140 you could really do very little and you could discount back those dollars with pretty good certainty of figuring out what those are worth today.
1:47:09
It was almost bondlike and I think this equity made a bunch of money saying like actually you know this is this is better than a debt instrument.
1:47:17
this actually sits on top of the the debt in terms of your vendors are going to get paid before your debt providers will because the business needs to keep going.
1:47:25
Now, I think we're seeing a little bit of cracks in the armor and I think your analogy is a good one where it's actually not I worry less about the like the churn risk.
1:47:35
Are people really going to turn off of Salesforce or Workday or Service Now or whatever it is?
1:47:40
Like maybe, but I I worry less about that.
1:47:43
I worry more about the value abstraction that cap is captured on top of it.
1:47:49
And if if if the AI dollars, which we we found in one of the reports, AI dollars this year are are it's a bigger pie uh of net new dollar opportunities in AI than all of software combined by like 50% or something.
1:48:03
And so if you're not capturing the AI dollars, then your growth rate is going to go to near zero.
1:48:11
And if your growth rate goes to near zero, then >> it's worth something.
1:48:14
But it's not worth, you're right, like the >> think about it almost like a real estate investment.
1:48:20
It's like what's your cap rate?
1:48:22
You know, like if I'm giving you 100 bucks, am I getting five bucks this year, 10 bucks this year?
1:48:25
Because there's a lot of other options.
1:48:27
And then, yeah, the other thing historically with uh with software has been just low interest rates.
1:48:32
So, oh, oh, that cash flow is coming in 20 years. Fine.
1:48:34
Like, it's basically the same as today with zero interest rates.
1:48:38
But when you're at 6%, you know, you do discount it back and you get a lot lower number.
1:48:42
Um anyway, uh where where should we go next?
1:48:46
Uh I'm interested in uh >> did I'm I'm curious any of the public markets investors, you said a lot of them were just like I don't want to try to be the smartest person in the room and lean in and figure everything out.
1:48:56
It's safer to just like you know bet energy, bet semis, etc.
1:48:58
Was anyone like licking their chops being like this is the greatest buying opportunity?
1:49:04
the greatest buying opportunity? like actually had some wellthoughtout thesis around how it's like Toma Bravo some of their slides leaked from their LP summit uh and and they obviously are in the position where like they have no choice
1:49:18
but they can't be bearish now you know they have to like you know create the like 4D chess of how this is like a huge accelerant to to their uh to their businesses but >> yeah I think some of the public uh some of the public guys um they are uh very interested in trying to discern what's going on. And this is actually a really
1:49:37
And this is actually a really good buying opportunity if you believe people are going to figure out um the agentic opportunity or the AI opportunity because it's certainly not being priced in in a material way.
1:49:48
And the incumbent vendors are going to get every chance from their existing customers to get this right.
1:49:54
Uh, and so I think that's the if you were to paint the optimistic lens about, you know, Toma got dragged a little bit for some of their, you know, talking their own book, but I actually think some of the slides that people were were dunking on were it, it's true fundamentally that like, hey, your incumbent vendors are going to get shot one, two, three and getting it right.
1:50:16
getting it right. I think the problem and at least what we're seeing in the private markets is that the culture of building these AI companies is just so different than the culture of building what the historical software company
1:50:30
looked like and and you guys uh I think I think you know uh I was an investor in in RAMP and uh and and the stuff that they did uh like >> investors don't get enough credit let's keep it up >> it it is my crossar Logan in particular, he's he's never takes victory laps. That's the thing about it. We'll take it
1:50:50
That's the thing about it. We'll take it forward.
1:50:53
>> It's I um I'm unknown.
1:50:53
I was a silent investor for a long time.
1:50:56
And so it's I'm glad to come come out of the closet as a ramp investor here for you guys.
1:51:02
But uh you know, one of the things that >> Yeah.
1:51:05
One of the things they did culturally for a long time that uh that I thought was kind of crazy was they they shipped a lot of stuff and would just put it out in the market and see how uh people react to it.
1:51:17
And that was very different than the way that I learned you know the companies I invested in 2014 15 16 and how they built products was they had a very tight product roadmap.
1:51:29
They communicated with their customers, you know, they they had it over a three, six, 12 month period of time.
1:51:34
And they would only really release it when it was fully ready out of initially an alpha, then a beta, then they would take a GA with a handful of customers, then take a ramp guys sort of put that on its head where they would move really f fast, iterate, get it in front of customers, ship it at like 90% readiness, and then see how the market took to it.
1:51:53
And if they if it resonated, then they would continue to build around it.
1:51:57
And like that mindset is actually what I've seen with a lot of AI native companies now, which is like you're not totally sure what the model capabilities are going to be in 3 months time.
1:52:06
And so what you need to do is internalize what your customers are going to want like have enough of an appreciation for their job that you sort of know what workflows exist or like what existing pain points are.
1:52:21
And then when the model capabilities keep getting better and better, you need to internalize what that customer is going to want and what the capabilities of the models are or where they're headed and sort of let those two things intersect and then deliver that to the customer.
1:52:34
And so it's a very different way of like building product.
1:52:38
And that's one example and we have a slide in there of like all the different examples, but like it's sort of been flipped on its head.
1:52:44
And so I actually don't worry from a is it possible standpoint for the for the big public companies to do this.
1:52:50
I think it's totally possible and and I think some of them will figure it out, but the vast majority are going to have to totally change their culture that they built over the last 10, 15, 20 years.
1:53:01
And that's really painful and and I think that's where they're going to end up falling down more than anything else.
1:53:07
>> Yeah, this is fascinating.
1:53:07
I'm like sort of an earlyish adopter, I think.
1:53:09
And uh we I I recently wanted to know like how much have we spent on Apple products and I was able to get that answer in like ramps AI mode basically and I didn't need to like export any data but then I wanted to know how many how how many what I've spent with Apple over the last year on my personal financials and for that I had to vibe code something that exported all the data and did it manually.
1:53:34
And so the question of like you have a system of record there's going to be some new feature where where is that value going to be captured?
1:53:40
Are you going to capture that value or is another system going to come down and it's going to be a feature of a chatbot or a feature of another platform like this is entail as old as time.
1:53:50
>> Uh >> yeah it's abstracting the value on top of it which is uh which is interesting.
1:53:54
I mean, I guess if you guys think about like my direct visiting of websites has definitely gone down because I interface with Claude or or Chat GBT in a meaningful way.
1:54:04
And I think that same thing's going to play out within the enterprise as well.
1:54:08
And it's not just going to be retrieval of information.
1:54:12
It's going to be actually taking actions.
1:54:13
And so now I don't totally care.
1:54:16
I'm sure you didn't totally care if that information was coming from on ramp side, if it was by bill pay or credit card.
1:54:22
And ultimately once you vcoded that application, you didn't care if that information ended up coming from a credit card statement or an email receipt or whatever it was.
1:54:29
Like >> in fact, I wanted to I wanted to unify credit card and uh and like checks and and uh and like bank transactions as well and I wanted to put all of that in one bucket and that's something that's it's not a feature in my bank right now, but it will be if they move quickly, but it already was a year ago in ramp.
1:54:46
And so it just like the pace of play is like still on the order of years in a very interesting way and yeah definitely like encourage all the uh all of those companies have like opportunities but they have to go win them.
1:54:59
No one just like gets granted you know monopoly on the new uh on the new capabilities that emerge on top of their platform. So sorry.
1:55:07
uh in the in the deck you talk about, you know, you have some bub talk talking about are we in a bubble?
1:55:13
Uh and and with with every advancement with coding agents and things like that, it seems like there's plenty of demand.
1:55:22
There's plenty of demand for tokens right now.
1:55:23
People are willing to give real dollars >> for tokens and that's just going up and up and up.
1:55:28
And but I think there's a tendency right now at least for kind of the early stage private markets crew to say like AI is not a bubble.
1:55:36
So I should still be investing like tens of millions of dollars into all these different early stage companies and things like that.
1:55:43
And I've been like I've been kind of feeling the bubble in in private markets like just based on the number of companies coming out every single day that seem to all be doing kind of variations on like the you know the AI CMO, right?
1:55:57
And I'm like maybe maybe that ends up being a big category >> like a sort of niche vertical SAS player that's like AI will be like at a 50 cap or a seed. >> Yeah.
1:56:07
And I guess my my my point is like we can AI maybe isn't a bubble but that does not mean we're not experiencing like a massive bubble >> in the kind of venture world right now. >> Yeah.
1:56:20
>> Yeah. I mean it sort of goes to like where value is going to acrue and like if you we did a slide on percentage of GDP in there and if if if you were obviously investing in airlines like it was a transformative technology that didn't end up proving to be a material
1:56:36
investment opportunity and what actually presented opportunity was the second derivative considerations of like business travel or like you know lounges and airports or uh whatever B2B sales and all that stuff like there were second derivative things that were far more impactful. And you're right, like
1:56:50
And you're right, like it's possible, and this is what I I I've told our LPs that have asked is like I think we're operating in a world in which our mortality rate of companies we invest in is going to be higher than it's been in the past.
1:57:03
Uh it just like it is uh even at the stage we're investing in, I think um we're going to see a lot more businesses die.
1:57:09
I hope we will also invest in things with a lot more upside.
1:57:15
And so we'll end up with, you know, hopefully uh things that could be hundreds of billions of dollars, which used to be not in the realm of possibility.
1:57:24
And so I do think we're entering this like extreme period of uncertainty.
1:57:28
And the only thing I've really been able to come back to in all this is because you're right, these categories end up so crowded and they end up very dynamic in terms of like how the category evolves, what the product surface area ends up looking like, all that.
1:57:44
And so in some ways we're we're we're back to like investing in teams and investing in like the wedge or the general space that they're operating in and then hoping that those doors open or that that C parts and they're able to run through that in a meaningful way.
1:58:01
But it's it's very possible that the model providers end up soaking up a ton of the equity value.
1:58:06
And so just because there will be a CMO in the AI world that a company starts like it doesn't mean that any of these companies will be the one to capture that value.
1:58:17
Uh and actually it probably be very unlikely that it would and so that individual investment you might be very rational in in uh doing it or not doing it.
1:58:26
Uh and the opportunity will ultimately create a ton of value but it might not be a private early stage specialized company that's going to be the one to Yeah, the uh yeah, it's been interesting to to look at these businesses ramping revenue so quickly and still and and have like real customer love and pull from the market and still have that question in the back of your head of like does this eventually just get zeroed out, you know? Yeah.
1:58:53
And and for me, that's the one people >> Sorry, go ahead. >> Yeah.
1:58:57
For me, the my the only real comp I have because I kind of came I came kind of online in my career in 2018.
1:59:02
And so I got to see the you know Zerp era uh very closely.
1:59:08
very closely. But I I remember with OpenC, you know, and the NFT boom that was you I I remember the way that they ramped revenue even the thing that made, you know, I think a lot of otherwise, you know, great funds like pile money into it, what at what ended up being the top is there was like you could kind of just say like, okay, even if revenue drops by like 90% and this doesn't end
1:59:34
up being like this mainstream, you know, opportunity, there's still like a business here and and maybe you can just own the category and and but but then revenue ended up dropping like 99% or something like and and I think that's still so that that still stays in the back of my mind that that was more of a demand issue versus like new kind of competition from from an adjacent player. Uh but but still uh
1:59:57
Uh but but still uh >> are there any previous booms that you do like as comparison points?
2:00:02
If not do you like railroads? It's electricity. >> Yeah, electricity. What do you like?
2:00:11
>> Um, that's a good question.
2:00:11
I haven't actually uh I haven't actually thought of the right analog for what time we're I mean people, you know, the industrial revolution is the one that people come back to the most and uh that wasn't on our like GDP calculation chart.
2:00:25
our like GDP calculation chart. Uh but I do I think there's there's elements of like the shifting balance of of uh of uh worker dynamic and like where people are actually going to uh totally the leverage employ themselves in that way going forward and like wealth you know there's a lot of considerations on uh wealth capture and what percentage of
2:00:48
the population that's going to go to and there's definitely a lot of like populist rhetoric out there and so I think this is more of a uh I I think revolution than like a techn I mean it's both a revolution and a technological shift in some ways and so I think like the car or the airplane or the railroad or whatever like that didn't fundamentally shift the balance of an
2:01:11
entire workforce in some ways uh the way that I think this has the chance of doing and so that's the one I kind of come back to but it it's it's a good question I'll I'll think more about it >> how are you thinking about uh capability overhang diffusion the the the copability overhang >> this yeah the debate about like the models are good uh and they're getting
2:01:33
better really really quickly but there's just like you know teams in companies where they're like yeah I'm actually fine doing my spreadsheet job and you know I yeah I gota I've heard this direct quote I got to check that AI thing out >> I got to check that out and >> when did Jordy say that to you was that recent or was it >> uh I I think I you know it is interesting. I mean that's where you're
2:01:57
I mean that's where you're seeing a lot of this like FDE >> uh Palunteer era where bridging the last mile is really really hard and I think we assume that um that like if we build it they will come in some ways but it's obviously that's not the case.
2:02:13
AI to most people, I guarantee if you took whatever I 350 300 million Americans or something and you asked them like name an AI company, I would guess I'm making this up, but like 25% wouldn't actually be able to name an AI company.
2:02:28
And like 70% would say, oh, that's that chat GPT thing or something.
2:02:35
>> I talked with a guy I talked with a guy and said like, "What what a are you using any AI products?" And he was like, "Nope."
2:02:42
And then I was like, "What about chat GPT?"
2:02:44
And he's like, "I use that every day. I love it."
2:02:48
>> But he just doesn't think about it. It's just a website.
2:02:49
Like we've been to websites before, you know?
2:02:53
>> And I think that's an interesting thing.
2:02:54
You know, Brett Taylor talks about this from Sierra where there's so many capabilities that you're raising the waterline of and that they're needing to build inhouse themselves knowing ultimately the model providers are going to need to productize that are going to productize that and so they end up building things that they throw out six months later all the time. Interesting.
2:03:11
And I think I think that's kind of true on the go to market or like education side as well where like a lot of these customers if you're going into an industrial business or a healthcare business or an energy company or whatever it is like you're having to bridge the capability to competency uh and like bridge that gap to the individual person at the end of the day and so I do think this diffusion when everyone talks about like are we in a
2:03:36
bubble structurally at a big picture I don't I sort of reject that notion because of both the demand and when people talk about the power supply and all that, I actually think it's going to be it's going to take far longer to get
2:03:48
this out into society in a really meaningful way than people on the internet tend to think because the real world's a lot more complicated than I think we make it out to be when we're just, you know, living in our techno utopia. >> It's a good point. >> It's a good point.
2:04:01
>> If you can call X a techno, >> the techno nightmare except in Japan.
2:04:03
In Japan, it seems they love it apparently.
2:04:08
Uh I I did want to ask about buy versus build economics.
2:04:10
You talked about how you could just buy >> that's the one that people have been asking all about by the way today.
2:04:14
Uh it's Yeah, people like that one.
2:04:18
>> So So Logan makes a point.
2:04:18
You can buy Slack for a thousand employees for like a quarter million a year >> or you could build it in house.
2:04:24
You estimated around 2 million a year and then like other kind of random unexpected costs.
2:04:31
unexpected costs. I'm sure a lot of people I anybody that's pushing back on this just tell them like okay build me Slack build me >> it's it's a really funny thing where I I just think it's sort of the 8020 rule in some ways that people assume building a software product is like the getting to the proof of concept or like the
2:04:49
credible MVP in some ways >> look you can send messages you can create a group and then it's like oh >> I vibe coded this thing and it does all of what Slack needs to do but then there's not even to mention the network effect of Slack of you build the perfect clone and then it's like okay do any other companies use it? No. Okay, then No.
2:05:07
Okay, then we still need Slack. >> Yeah.
2:05:11
>> And so so like let's say I I think I mean people want to argue about the specific math on all this but like let's say that you're willing to do all the integrations and the SSO and the search and the file sharing and the you know whatever the admin controls and compliance and all that stuff. Let's say you do that. >> Yeah.
2:05:26
The emojis, the the GIF embeds, all all those things.
2:05:29
all all those things. Like let's say you do all that was that whatever that costs like what is the opportunity cost that you spent all this time doing that rather than like focusing on whatever it is your core business is and so actually like I we did the math here and it's 2 million versus 220k or whatever like let's say it's the same or let's say
2:05:50
it's cheaper like is is saving you know 40 grand let's say it's 180 versus 220 and >> it actually has to be significantly cheaper But but also I mean I I talked to a friend who runs a company and uh just about AI stuff and I was like oh yeah like you should probably you know be aware of this stuff but uh what percent of revenue is going towards like software broadly like what's your IT spending? He's like less than 1%. And so He's like less than 1%.
2:06:13
And so it's like yes like you could take something that costs $1,000 down to $200.
2:06:20
Like maybe you take that but not if it's a headache at all because 99% of the time you want to be with your actual customer suppliers because it's completely different business.
2:06:27
And so That was the point someone was arguing me about is like most companies actually, you know, aren't growing like uh like uh software or tech companies are and so these costs are really material to them.
2:06:38
And I'm like, you know what's material to them is like decreasing their workforce turnover from like 70% a year to like 60% a year and not having to pay incremental recruiter or staffer fees to get people on.
2:06:47
Like the difference of the the Slack budget and saving 40k I guarantee does not like resonate at all.
2:06:54
and Slack was a simple example because it resonates with people, but I think it's true across the board.
2:06:59
So, I was trying to think of like what a good bet would be with someone to try to like come up with.
2:07:03
It's a very hard thing to figure out of like what the right framework of of of thinking about this is cuz I love just codifying bets with people and being like, you know, okay, let's let's wager some money on what this is.
2:07:15
And I couldn't come up with a good one.
2:07:16
So, if you or if anyone listening can come up with a good bet on this, I would love to place whatever a significant sum of money on it. It's funny.
2:07:24
It's funny to think about the company building Slack in house and they're like throwing time.
2:07:27
They're getting 10 people on a call like, "Hey, like we need to meet and talk about some up.
2:07:32
We need to talk about like our road map for our internal Slack.
2:07:34
We need to kind of bat some ideas around about different tradeoffs that we're making."
2:07:40
>> Well, the deep irony here is that Slack was an internal tool for a game studio.
2:07:45
Like it was actually like the we need to build our own thing because we communicate so frequently.
2:07:49
communicate so frequently. And then it became >> and there's a historical analogy by the way of this that I I didn't include in the deck because I uh really like Drew Hston from Dropbox, but like they built their own data centers and like >> I don't know uh like >> if that was a good cost decision from them but like from a focus decision should they have just used AWS and you know uh or GCP or or one of the other I
2:08:13
don't know I don't know the answer to that and I didn't want to like put him on blast and actually get into the debate because I like him quite a bit but like >> I don't know if that's like the right decision for them and that they were
2:08:23
even like the furthest you know they're like a tech company that that was their business able to decrease cost and who knows what the opportunity cost of incremental products or mind share or whatever it was going and doing that. >> Yeah. No, that makes a ton of sense. >> Yeah.
2:08:34
No, that makes a ton of sense. >> Last question.
2:08:35
Uh uh did did uh the current AI suite make making your annual report significantly easier or was it still >> handcrafted? >> Handcrafted.
2:08:48
It's an artisal craft to this, but I will say it is interesting doing this deck every year.
2:08:52
It does serve as a uh a snapshot of like what the model capabilities are and like where uh how much progress it's been made.
2:09:01
And so I think if I go back like two years ago, uh that version of it, I could word smith like my talking points that I was actually talking, you know, when I get up there in front of the LPs and do it.
2:09:10
And last year it was actually a decent um I could ping pong some ideas here.
2:09:15
Uh, I would guess I don't know if there's se 68 slides or something.
2:09:18
I would guess 75% of them AI had some hand in either helping visually lay it out in some way, writing some of the text, maybe coming up with some of the analogies.
2:09:30
And that is such a step function change uh versus where it was 12 months ago.
2:09:35
And so it is helpful every year to like revisit, come back and like see what's actually possible because as you just go about your day, you sort of forget what 3 weeks ago was or eight weeks ago or 15 weeks ago.
2:09:48
But when I went through the process this year, I was like, "Wow, this is really uh much less painful."
2:09:53
And I think the the principal and associate on the team that worked with me on this uh were very much appreciative of where the model capabilities are going because I think if it made my life a little bit easier, it definitely made their life a lot easier. >> Totally.
2:10:05
Is is that alpha for upandcoming venture capitalists?
2:10:08
What what advice do you have for those who want to make a career out of venture capital?
2:10:14
Because it feels like coming in and surprising the entire partnership with a very deep analysis.
2:10:20
>> I I would just say have a non-traditional background. So maybe grow up.
2:10:24
>> Well, that's the thing PaloAlto area, go to Stanford. >> Stanford.
2:10:28
>> It's actually an interesting thing.
2:10:28
So, so, so if you guys uh have a minute, I can I can riff for a second on this, but like historically, so we've hired people out of investment banks largely speaking.
2:10:37
And so why do we do that?
2:10:38
Well, we hire people out of investment banks because it's an expressed interest in finance and technology. Okay, that's great.
2:10:43
Two is they have the model training of like what we need, you know, the cap tables and and you know, projections and all that stuff.
2:10:49
Three is there's like a high pain tolerance and like willingness to grind and do the extra thing.
2:10:55
And then four is like it's a referential network.
2:10:57
uh like we can call the same MD at Morgan Stanley or Goldman Sachs or Catalyst every year and be like, "Hey, how does this person calibrate to that person?"
2:11:04
And it gives us a qualified pool of people to to pick in.
2:11:09
The the thing that investment banking didn't have uh was you're you're very much like if you ended up in investment banking, you you followed a pretty straight path for the most part in your life.
2:11:19
And I say this as a former investment banker myself where like you you went to a high school, got good grades, got into a good college, you know, did interviews, got a good job, then at your investment banking job, you're staffed with like 90% of your day is pre-filled by someone else.
2:11:31
And so it's like, okay, well, if I work hard and I stay late and I do this pitchbook, you align the fonts the right way, I'll get a good bonus and then I'll get a good job.
2:11:40
Well, then we drop you in.
2:11:40
And increasingly now with the model capabilities, the financial modeling, uh, Claude can do it better than, you know, or as well as most of the people on our team.
2:11:51
And the the the the sort of remedial tasks are getting the water level keeps going up.
2:11:55
And so, uh, when we hire people in now, we've always had to train on the agency thing.
2:12:00
And it's a little bit of rewiring your brain where, hey, my day used to be 90% filled by this staffer, and now you're telling me just to go figure out what's a good company.
2:12:10
like where do I even start in that?
2:12:13
And so in some ways like investment banking is actually a bad pool of how it's wired and prepared people for this world now.
2:12:18
Historically it always was but we were willing to forego the agency because we got the modeling capabilities and the remedial tasks and we sort of took that as the basics and then we had to try to figure out if there was agency there.
2:12:32
Now increasingly like the models are getting so good that agency might be the only thing that matters and so like are you able to find differentiation?
2:12:42
>> We're actually working on an internal model for agency at CBPN we hit a huge unlock. we've cracked taste.
2:12:50
Now it's >> now it's and so so that's the thing that we now are trying to figure out like where do you find pockets of people who still want to do the job talentwise but or have the capability to do the job but also have agency.
2:13:03
And you might be finding people that are entrepreneurs.
2:13:08
You might find people that are project managers.
2:13:09
You might find people that have taken serendipitous paths in some ways.
2:13:12
And that actually might be a good sign and not a bad sign.
2:13:14
And so it's forcing us to think in a different way of like where we're hiring people from.
2:13:20
>> So what I'm hearing is that you're pulling up the ladder behind you. >> That's right. That's right.
2:13:23
That anyone any door I always say when people ask like hey how did you get to where you are in your career?
2:13:28
My my answer is always like well it's a specific question what I would do when I was in if I was in your seat cuz I can tell you that the doors I walked through but those doors aren't just shut. They're like shut. They're cemented over.
2:13:40
They've like been fortified.
2:13:40
You know you're not doing what I did.
2:13:42
I luck through this path.
2:13:48
>> Thank you for joining us.
2:13:48
Always a great time hanging out. We'll talk to you soon.
2:13:52
>> Just with with all the AI progress, try to ship one of these a week. >> You got it.
2:13:58
>> Have a great rest of your day. We'll talk to you soon.
2:14:00
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2:14:13
And without further ado, we have the founder and designer of Granola, Sam Stevenson.
2:14:19
Welcome to the show, Sam. How you doing?
2:14:25
>> What's going on, guys? I'm good. I'm good. How you doing? >> Congratulations. Massive news. Tell us what happened. Let's hit the gong.
2:14:29
Let's warm things up since we're in our Lambda Lightning round now.
2:14:33
I want to hear what happened.
2:14:36
So, we have raised a $125 million series C from uh Index Ventures. >> Congratulations.
2:14:44
>> I didn't hear it over the sound of the gong. You said Index Ventures. >> Index Ventures. Index Ventures.
2:14:48
Um and uh and with uh KP participating as well. >> Fantastic.
2:14:56
>> Uh what unlocked the round?
2:14:56
Is it just continued progress, new features, a little bit of everything?
2:15:01
Talk us through the progress over the last year.
2:15:06
Yeah, I think it's been uh I mean all the above.
2:15:07
Like we've been talking to these guys for for a while.
2:15:09
Um >> they've been fans of the product.
2:15:11
>> they've been fans of the product. I think as all of our investors have, they've all like used the product a bunch um before we've got to talking about investing and then um uh yeah like like growth has been good and continuing and like uh I think it's the combination of that and then like the I feel like the the environment is like
2:15:30
waking up like to the power of of um of having all of the context of what's happening in your meetings in in a company, you know, like uh I think >> like everybody adopting MCP is making it apparent that you can like if you have the right context about what's happening in the company, you can do that to power so much of what's happening in your company. >> Um, and I think we're just well
2:15:50
>> Um, and I think we're just well positioned as like that that context gatherer that a company can take advantage of. >> Yeah.
2:15:56
So, my my uh something I've been thinking about with this category is why do you think the why do you think the labs have not built a product in this space?
2:16:07
Sure, there's a number of reasons, but uh I'm sure they would generally love the context that you're gathering so that their agents could actually uh leverage it uh directly and uh and and so and so explain like why you guys have had kind of just a open uh open not not to say open but there's certainly competitors but like why have you guys been able to just market >> image? Yeah. Yeah.
2:16:35
I mean, yeah, I'm sure they I'm sure they are working on it.
2:16:38
Like, uh, OpenAI had a stab at it last year.
2:16:42
They had like a um launched like a longer running record mode thing, which I think was aimed at this, but like I think I think it's a few things like I think um we we're basically like people use for meetings, right?
2:16:54
which is like um it's a big term for a for a startup like us, but it's also like only a slice of people's life like in uh and if you're designing like a super open-ended general purpose chatbot like JBT, I think like they're probably questioning like does this make sense or should we be going for like always on recording of everything and and anything less is is like not good enough.
2:17:17
Um >> I think that's probably part of it.
2:17:19
The other the other thing is like um uh I mean we found building granola that that like you can build a granola clone like super easily, you know, right?
2:17:29
In like a weekend you could build a thing that transcribes your meetings and gets you a summary >> and like all the work is in um understanding like the all of the like social nuance of like who are these people in the meeting, why are they meeting, what's this meeting about, and like therefore like what notes do you want out of it?
2:17:47
What are the action items you should care about?
2:17:48
like um just just kind of all the work behind the scenes to like actually make this thing fit into your life in a way the way you'll use it and find the notes useful is uh is a bunch of work and yeah that's um you know we use the latest and greatest model so like you've still got to go and do that work on top of that at least today. >> Yeah.
2:18:08
Uh yeah makes total sense.
2:18:08
What what kind of progress are you guys make?
2:18:13
Like what is the what's the most kind of like sci-fi element of of the pitch for this last round?
2:18:20
Like are you imagining a future where the only thing that humans do is just kind of meet and talk about what should be done and then make a decision and then machines ultimately carry out all the work.
2:18:32
uh like like h how how far are you kind of like taking out the uh how far out are you kind of looking?
2:18:42
I mean I can see I can see that like yeah I can see that someone being true like I do think like um I mean we do it we do it internally in the company you know we'll we'll meet and talk about a thing and then and then go ask Granola to write a brief uh that we either go
2:18:58
then hand to a coding agent or um or we use it as the material to write a job description or a blog post or whatever like the conversations are like incredibly good input for a lot of the work that you end up needing to do at a company. Yeah. Yeah.
2:19:12
>> Um I think the like the more kind of like the most near-term but sci-fi things that that we we see is like um if you want like a pulse on what's happening with like any project or any group of people in the company um going and like looking and asking Granola what's what's happening with with this with this project um is like easy and incredibly like insightful.
2:19:36
Um, and I think that's that's essentially because like transcripts are just such a good up-to-date record of like what's happening in a company.
2:19:45
Um, so much more so than like a I don't know a notion doc or a Google doc that someone had to sit down and write like you know without making any effort you have kind of an up-to-date picture of what's happening in the company and um that's I think that's just going to useful to a lot of people in in so many ways.
2:20:02
>> What is uh what does diffusion look like inside a large company?
2:20:04
like how much training, education, messaging you have to do?
2:20:09
I'm sure you have playbooks for this, but how do you uh because once you like land I imagine the next step is expand.
2:20:16
What does that look like for Granola?
2:20:20
>> All word of mouth pretty much at the moment like for most companies like um we we focus so much on just making it like a good product for the individual.
2:20:29
um when we started that yeah like like still the majority of our growth is like patient zero finds it at a company and then tells their friends about it cuz they cuz they love it so much and and we just go on from there.
2:20:42
Um we I mean we're working on a bunch of team facing features that kind of let you harness the value of a whole group of people using it together.
2:20:51
And I mean the motivation behind a lot of that is that we kind of create a reason for you to just go wallto-wall uh across your company with Granola and kind of unlock the power of of sharing all that context of the transcripts together.
2:21:03
Um but for the most part it's still just word of mouth.
2:21:07
People people love it and they share share that with each other.
2:21:11
>> Talk about the end ofear wrapped campaign.
2:21:14
Uh I've heard fantastic reviews for it.
2:21:20
That was such a that was such a delight to to like explain it for those who who did not receive one or followed the story and then tell me about >> Yeah.
2:21:28
So so we did a like our spin on you know Spotify wrapped as as many companies do.
2:21:33
Um which for us was Granola Crunched.
2:21:35
Um the basic thesis was like you know Granola if if you've used Granola some some of our users have used Granola for like thousands of meetings over the last year.
2:21:44
And um and if you look in inside those meetings and across over a year, you can you can tell a lot about a person and what's going on and what's important in their lives.
2:21:54
Um so Granola Crunch was basically like every user could could go hit a button and generate a like Spotify rap style report about them about their year.
2:22:05
Um it was things like like uh who's your partner in crime?
2:22:11
What's your favorite catchphrase?
2:22:12
catchphrase? um what's like what are what are the some of the smartest things you said what are some of the dumbest things you said that you know that kind of thing and uh it was like you know mostly fun there there's some there's some things that could hit pretty hard
2:22:26
like like uh I remember mine was like kind I felt kind of uneasy sharing it with other people cuz it felt >> I've heard that from a number of people they've been like it was scarily accurate and I didn't want to share with the rest of my team >> you said nothing from my end thanks over 2,000 times. >> That's great. >> That's great.
2:22:46
>> Okay, so we we have this buddy who's who's, you know, built a massive company in a super regulated industry and like had been through ultimately had a had a huge exit, but has been >> uh been through kind of dis, you know, lawsuits and discovery along the way.
2:23:02
And so he's just like absolutely hates every product in this category.
2:23:05
He's like, I don't care how useful it is.
2:23:09
It's not worth, you know, someday having like, you know, basically line by line live kind of transcript of uh every meeting that you've had at a company.
2:23:19
What what are the I'm sure you guys are aware of of, you know, some of these more like sensitive >> uh use cases like what kind of fixes like what what are you doing at the product level for people that don't want every conversation? >> We sell saunas.
2:23:33
So, if you want to have a meeting that's off the record, you go into your company sauna and then nothing can record you.
2:23:40
There's no recording devices.
2:23:42
This is the This is Lindy.
2:23:42
You go to the bath house.
2:23:45
>> Maybe check check for any product rolls.
2:23:47
>> You got a wire on you. You take off. >> Anyway, Sam. >> Yeah. Yeah. Yeah.
2:23:50
I mean, I think like this is like a really tricky path for us to walk and like it's it's like so important for us to be, you know, doing what we think is right at every step of the way.
2:24:01
Like I think um I think we have to juggle like I think tools like this are kind of they're they will be somewhat inevitable in a in a work context.
2:24:10
I think I think like talking about the personal you know like always on recording type things is totally different but in a work context there's just so much value in having like you know like transcribes meetings and therefore being able to use that conversation data for stuff.
2:24:23
So I think that's kind of inevitable.
2:24:26
The question is like how do we how do we kind of make that okay for for people?
2:24:29
that okay for for people? um in the meantime and I think like some things we've observed are like uh for companies that use granola um they very like they basically just get comfortable with the idea that like we're going to make it a thing that like we use granola internally and um and that's the default like that should you should kind of just assume that's happening um and you know
2:24:52
you can like it's okay to opt out and say you don't want to you don't want to use it >> um >> but uh companies get comfortable with that very easily we Um then then like the the external question is like a question of following the laws in your state and and you know like granola >> we we make that clear to you but it's ultimately it's a tool and and you kind of it's up to you to follow the rules on it. But I but I meant more like even on
2:25:14
But I but I meant more like even on like data data retention, right?
2:25:16
So so like like if a company there there are plenty of meetings that are just >> not that important, but if that was pulled in discovery at some point down the road, it could be unnecessarily damaging.
2:25:32
Now, the bull case for this is that whoever's, you know, suing the company and like wants that discovery actually has more like they're they're more able to make a an effective case.
2:25:45
>> Um, >> yeah, >> but uh but but like yeah, I was asking more referral link for companies that I'm planning to sue.
2:25:55
We uh we had like we had this from companies in all all directions like uh >> some companies are like real like this is an opportunity for them to have things on record and so they >> Yeah. Yeah. >> them. Yeah.
2:26:06
>> But plenty go the other way, right?
2:26:07
Where they they want everything like off the record and deleted immediately.
2:26:09
Um >> we've ended up building a bunch of uh retention controls so you can do this either way.
2:26:15
Like some companies will >> set transcripts to self-destruct after 24 hours.
2:26:19
Um, so like you know there's no more record of those and you just get left with the notes.
2:26:23
Um, >> and uh, yeah, I think I'm I'm a big fan of that.
2:26:28
Like I feel like >> we there it's not in our interest to have like a like a real like on the record recordings of everything that's happened.
2:26:35
Like all granola needs is >> is the the kind of main notes of what was talked about. >> Yeah. >> Yeah. Yeah. >> Makes sense.
2:26:43
>> Like a healthy level of abstraction is like good for everybody there. I think. >> Last question.
2:26:46
Can you uh tell me about the brand because this could have been called like Panopticon.
2:26:50
It could have been black background, steel, silver.
2:26:55
You know, it it could have been very different branding wise, but it's granola. It's crunchy.
2:27:00
There's a you know, this this like green color that you've picked.
2:27:04
Like it's clearly intentional.
2:27:05
What are you What are you thinking with the brand?
2:27:10
Yeah, we basically like I think since the beginning when we first started studying how people take notes, I think one thing that was really apparent was like taking notes in meetings is a really personal thing.
2:27:20
And uh we like there was already a bunch of AI noteakers out there, but people hated them.
2:27:26
People nobody wanted to use it and no one felt comfortable kind of like writing their raw messy thoughts into them.
2:27:32
Um, and so we really just wanted Granola always to feel personal and like it's yours and that the more you use it, the more it feels like your space >> and uh all the all the branding is like downstream of that like the name, the the colors, the kind of messy like organic feeling textures and stuff like >> it's all meant to communicate that this is like your your thing.
2:27:51
This is not this is not your company's thing. This is your thing. >> Love it.
2:27:55
Well, thank you so much for coming on during a big day and breaking it down for us.
2:27:58
Uh, very exciting progress. Fantastic progress. >> Talk to you soon.
2:28:02
Have a good rest of your day.
2:28:04
>> We'll talk to you soon. Bye.
2:28:06
>> Let me tell you about Vibe.
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2:28:20
And without further ado, let's bring it bring in Ben from Pulsia. >> What's going on? >> How you doing, Ben? >> Hey, guys. How are you? Welcome to the show.
2:28:30
Introduce yourself since it's the first time on the show. Tell us what you do. >> Yeah, my name is Ben.
2:28:35
Uh I run a platform called Pulsia. >> Okay.
2:28:40
>> It's an AI that builds and runs companies autonomously. >> Yeah.
2:28:44
>> Uh you give it an idea and it's going to go about uh building the product, uh running the marketing, running ads, uh doing support. Yeah.
2:28:53
>> Uh and all of the things that uh a founder would do to start a company and grow it.
2:28:57
So, should I think about this as you've fine-tuned a bunch of agents, built MD files or workflows that then leverage other foundation models to deliver on those?
2:29:10
You have some playbooks in place like what what have you done that's uh that because I imagine you're not training the actual foundation model.
2:29:19
you're using different tools off the shelf and different integrations and then fine-tuning things.
2:29:24
But walk me through like the actual experience of using the product.
2:29:29
>> Of course, I mean the way to think about it is, you know, I've I I spent like a lot of the past 12 months uh spending 12 to 16 hours a day using AI, using, you know, cloud code, using codecs, um and and building my company companies with it.
2:29:45
And the idea is that like the the fundamental models are like super powerful and and I pretty much think that AGI is here at this point like the models are super intelligent and they are fluent at using any tools.
2:29:56
>> But I think the trick is knowing how to configure them >> uh to give them the right tools, the right orchestration, the right series of tools to get to an outcome. Right?
2:30:04
So for example, one of our agents on Pulsia can run meta ads campaigns. Sure.
2:30:09
But to do that there's a lot of steps uh that are needed, right?
2:30:13
It's like creating the creative um you know uh using maybe an AI an AI generation model. Yeah.
2:30:18
Um >> and different labs are good at different things.
2:30:24
You know nano banana is great and codeex is great and you know there's writing models and there's all sorts of different things.
2:30:30
So you're uh choosing those and rerouting those.
2:30:33
Um how do I think about it in terms of an actual payment flow?
2:30:38
payment flow? Is there a world where I give you a credit card or bank account and then you already have the integration set up so I don't need to go set up an AWS instance or I don't need to go set up a meta ads campaign and you can just kind of say hey we're running $100 test campaign we're going to
2:30:56
withdraw $100 we think we're going to bring back 200 okay we did now I need a thousand >> exactly I mean if you think about it like agents are essentially like AI humans that can act on the economy And and today uh obviously if an agent like if a thousand agents go on mid to create accounts like MA will say no you
2:31:17
need to verify your identity and all this stuff right and so there's a first layer of infrastructure to build that we've built at Pulsia which is >> how to make partnership with those platforms to to get to for them to understand that it's an agent working on behalf of a human for a certain task and
2:31:31
to sort of like have all that set up ready and and as you said like today we abstracted it quite a bit where like you know you pay a subscription and you It's sort of like one task every night uh of you know your agent doing work for you and then and then you you you can do various types of tasks. But in the
2:31:45
But in the future, as you said, you know, if you want to open a bakery in New York and you have this idea, there's going to be a lot of orchestration to like buy the real estate, to buy stuff, to hire staff, manage them, all the fulfillment.
2:31:59
And like an AI could totally do this, but you probably will have to say the Pulseia will tell you, you got to deposit 100k on an account because we're going to have to do a deposit.
2:32:08
We're going to have to pay the the realtor.
2:32:11
We're going to have to her staff.
2:32:11
And and that's something that like what Porsia is trying to do is really give access to all the best practices of being an entrepreneur to anyone who has an idea and wants to fund it and wants to try it >> and obviously it's going to be a much lower cost at what you what you can do today. >> Yeah.
2:32:30
So Pulsey has ramped revenue super quickly.
2:32:33
I feel like every time I see you guys the the the ARR's gone up, but what is actually what what are give us an example of like >> automated companies that are working on the platform like individual entrepreneurs that signed up? >> Yeah. What are they building? >> Yeah.
2:32:52
What what are they actually building? What are they selling? >> Yeah.
2:32:56
So there's like a, you know, an entrepreneur who's like building like a a service to create ads uh from a from a script, you know, autonomously using different APIs and reselling that to to people and like has a bunch of customers that are paying.
2:33:10
Uh you have another person who's building like AI receptionist for for businesses and so using like the agent SDK to like figure out uh how to respond correctly based on context.
2:33:21
You also have existing businesses who are using Porsia sort of like as a as an AI team that can build a landing page for them, create leads, uh sort of like lead uh lead capture and run ads to get customers for their offline business.
2:33:37
Um so there's a lot of different use cases.
2:33:40
It's actually very varied because obviously this platform's promise is so open-ended uh that you get and it's, you know, it's pretty affordable.
2:33:48
It's like $49 a month to try it for a month.
2:33:50
Um, and so you get a lot of people with a lot of ideas and >> yeah, it feels like the low code, no code, uh, like boom all over again where like there were low code, no code products at the hyperscalers and GCP and AWS, but there were still platforms that did a little bit more and became like low code, some no code.
2:34:09
And uh, and you're seeing the same like continuum of like how much do you want the platform to help you before you actually open up the terminal yourself?
2:34:19
Does the human matter a lot still? >> Yeah.
2:34:25
>> If I just go on there and I and I pretend to be like my 10-year-old self, >> am I still am I am I going to print or is it am I am I going to be cooked?
2:34:32
So, >> so I mean, first of all, like it's this platform is like to build real businesses.
2:34:40
So, it's not like a getrichqu scheme.
2:34:42
Um, it takes time to ramp up.
2:34:45
It takes time to build real businesses.
2:34:47
obviously uh trying to do a lot of things on the on the marketing side to automate more of like trying to get customers but also bringing on maybe people with OD influence uh that can bring on their their their their audience and sell them services.
2:35:00
But to answer your question about how much the human is needed uh I think that you you as long as like humans are the ones buying the goods and services uh you need another human on the other end who understand the subtleties of what people want.
2:35:16
want. these days right now what are the new trends what are the new things in a world where like there's going to be an abundance of new services and goods being sold all over the place because all those AI tools are augmenting people to build faster better um uh you need
2:35:33
humans for the taste so the way I I explain it is uh you got you got the 80% auto you know operational work day-to-day grind that can be fully automated by AI that's like engineering that's like support, that's like market research, that's like pricing. Um, and
2:35:49
Um, and that usually you would need to hire people for that.
2:35:53
And today with tools like Bossia, they can do most of the work.
2:35:57
However, the a 20% which is taste, which is branding, which is like marketing, you know, trying to market in certain ways, understanding how to position your product, maybe having an audience to sell to, however small it is.
2:36:11
You know, you have a thousand followers that are dedicated to what you do and they love you.
2:36:15
uh you don't need that much more to get like 10 20 30 paying customers and I start doing income.
2:36:21
>> Um and today they're selling merch and tomorrow they can sell yeah >> real services that may be more sophisticated.
2:36:27
Um >> so that's sort of the way I look at it.
2:36:30
Um there's a world in the future and where I'm going to introduce services where you can completely autonomously let the agent run wild.
2:36:37
Obviously, if you because you don't have to give it feedback like it will every day, every night wake up and do work and and I'm going to choose ways for you to let it run 10 times a day, right?
2:36:47
If you pay if you pay to compute, right?
2:36:50
>> Um and I'm sure that with work, it's just that like that becomes like you need to have a very tight feedback loop on like the user what the user feedback is.
2:36:58
So, it feeds back in u to what the service is and how to make it better.
2:37:04
And I think there's a world where like a human with a lot of capital can actually start building a lot of you know money printing businesses as the loop gets tighter and the platform gets smarter about what are the best practices.
2:37:15
Uh and I think this is where the world is going and ideally I want to give that opportunity to the 99% the people that like think that AI is judg that's that's pretty much it.
2:37:26
Um, and if we can give them the tools to to be economic actors in this new era, um, I think we will hold benefit and it will be a more a more just sort of like society.
2:37:38
>> Okay, last question we have to ask you about the name.
2:37:40
You rattled a lot of people out because it spells AI slop backwards. Is that intentional? Is that a joke? What's the name?
2:37:47
>> I mean, it started as a not as a joke.
2:37:50
It was like my lawyer asked me to uh come up with the the name for the ink when I started the company. >> Yeah.
2:37:56
And I was on my couch and I was like, "Oh, I could name it like, you know, Pulse. I slap in reverse."
2:38:00
That's That's a good name. Intental. So, it was intentional. >> It was intentional.
2:38:06
I mean, >> that's that's amazing. That's amazing.
2:38:07
I thought I thought it was like I thought it was by I thought it was >> But it was not intentional to I decided to use it as the product name cuz, you know, I started the company like in April and I I built the product in November.
2:38:20
Um, so and and I was like, that's kind of cool actually.
2:38:23
It's very and it will make people talk. So, and it did.
2:38:28
>> Well, thank you so much for coming on the show and breaking it down for us.
2:38:31
Have a great rest of your day.
2:38:32
>> Yeah, good to meet you.
2:38:33
>> And we'll talk to you soon. >> Bye. >> See you. Bye.
2:38:36
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2:38:50
>> I would love to I would love to sit in on that that pitch meeting.
2:38:52
the we're building an infinite money glitch.
2:38:56
>> It it seemed like that >> $49 a month. >> I mean, I don't know.
2:38:59
Like there there's a world there's, you know, Teespring was, you know, empowering entrepreneurs to sell a lot of t-shirts.
2:39:05
There's a lot of different things.
2:39:06
Uh depends on what what what you bring to the platform, I suppose.
2:39:10
Uh well, without further ado, we have Brett Acock in the Restream Radio.
2:39:14
Let's bring him into TV Ultra.
2:39:17
>> Brett, how are you doing, >> guys? Good to see you again.
2:39:20
>> Good to see you again. been far too long.
2:39:22
But since the last time we talked, you launched a new company.
2:39:24
So, break it down for us. >> Oh, hark. >> Yes. >> Yeah.
2:39:28
Let me uh >> I want to know about that. >> Okay.
2:39:30
Well, I mean, I guess the summary here is I've been working for the last three years on I think maybe one of the hardest AI problems in the world, getting AI to work on humanoid robots. >> Yeah.
2:39:39
separately, you know, separately I've been like watch I'm basically using and watching what's happening in the digital world like you know the the different language models and I to be honest I think they're just incredibly dumb.
2:39:51
>> Like I I they don't remember anything about me. It's not very personal.
2:39:55
>> They can't listen or talk to me really well. Can't see the world.
2:39:57
Can't use computers well.
2:39:59
I just I think this whole experience is just like >> I think it should feel very much like a sci-fi movie.
2:40:05
This should feel like Jarvis that can like really understand you very personalized use tools well.
2:40:10
So about seven months ago I started a new AI lab called HARK >> and we want to build really advanced personalized intelligence.
2:40:20
Uh in order to get there we think there's some fundamental gaps remaining in in the models.
2:40:25
So we basically have a we basically have like a large focus on trying to like basically build new multimodal models.
2:40:33
And the second thing is um you know we're interacting with AI today through like 20-year-old computers >> like my phone and like laptop these are all like decades old. >> Mhm.
2:40:46
>> And we feel very strongly that there's like a next generation of AI devices um that need to be like need to be built to kind of interface with AGI uh appropriately.
2:40:57
So we have a team dedicated not only to models here but also on the design side.
2:41:01
one of our key guys uh Abidor uh started about four months ago.
2:41:07
Previously led design for MacBook, MacBook Air, iPhone 13, 15, 16, 17.
2:41:14
Was keynote for iPhone 17 air about 5 months ago.
2:41:17
Uh so ABS is here with a killer team on the hardware side and we're designing next generation interfaces uh for the models that we're that we're working on here in >> Is it the interface that you think is the issue or do you need more compute locally?
2:41:34
>> I think there's like some big gaps in the model side.
2:41:35
I I think there's like twofold.
2:41:37
I think there's some large gaps remaining on the model development side that we want to try to close.
2:41:41
And I think secondly, there's like I think the just the interface of how we're using traditional computers right now to interface this AI is extremely broken.
2:41:50
>> Uh we think both need to be fixed to have like a really killer uh like uh like you know like super intelligent personal assistant.
2:41:58
We think you need to fix the hardware interface and we also think we need to fix the model side.
2:42:02
I mean there's just like simple things today that we need to be better like computer use agents today are just not very good.
2:42:09
like uh they're getting better every month, but there's still like a large gap in order to get there.
2:42:15
Speech to speech systems, which will be like a really natural UI into AGI, are just not not great.
2:42:21
>> They don't remember things I've I've told it.
2:42:23
They don't have access to my life.
2:42:24
They can't access my calendar.
2:42:26
They're not very like they're pretty high latency.
2:42:27
EQ and naturalness are not great.
2:42:30
So we're um kind of taking this holistic approach to this problem and saying we we have to work on the models and we have to like fix the interface uh issue here today.
2:42:40
Uh what is what is the hiring market right now for all this all this talent because uh you're basically going up against >> Apple OpenAI quad you got Demis like you're going up against you're you've already bit off bit off a lot obviously with with figure and I'm I we can move over and get the update there.
2:43:00
Uh, but I'm I'm just so curious when you're when you're recruiting talent for HARK, uh, I have to imagine any any of the any of the people that that you're hiring, if you want to hire them, they probably have the opportunity to work at these other companies.
2:43:15
So, what's what's working on that side?
2:43:20
>> I mean, I think the summary is like all the other companies are kind of boring.
2:43:23
They're all doing the same thing.
2:43:25
>> Um, they like they're like all copying each other.
2:43:27
We've like headed certain direction last three years.
2:43:28
I think that direction is like somewhat saturating.
2:43:32
Um to work on like vision understanding to working on like models that can go and interact with the world and get that interaction data we think is like these areas are especially important to push the boundaries and get the AGI uh this AGI feeling of like highly multimodal uh scenarios.
2:43:48
Um so we're finding like like from a from a hiring perspective are being extremely competitive.
2:43:53
We've brought on now over 50 people into the team.
2:43:58
About twothirds of that from the AI uh AI side from like top Frontier Labs.
2:44:02
Um I will say it's probably one of the most competitive areas I've I've hired for in general around compensation.
2:44:07
The space is just completely lit up.
2:44:09
Like I've never seen like it before.
2:44:11
You know, I've like hired people across all areas of robotics and AI and >> uh software and hardware.
2:44:16
Just like this is it's it's next level competitive.
2:44:18
I think we have a very small amount of people in the world that really understand how to build the right infra pre-training data mix like all of this is just like very tough.
2:44:29
So, and all the some of these spaces are just new like computer using agents that can really reason well in pixel space like this is just happening now.
2:44:37
So, there's not a lot of good precedent uh for out for how to go out and build these systems.
2:44:41
But >> why why not uh what was the decision-m process around doing this externally?
2:44:47
because I feel like a lot of the capabilities that you want to build with HARK, like I'm assuming you'll want to integrate into Figure if Figure is going to be a a a robot that can add value to my day-to-day life.
2:44:59
I I you know, where where is the where's the overlap and and and why why build it externally?
2:45:08
>> I'm a big fan of focus.
2:45:08
I feel like uh figure we have a singular focus which is like how do we solve for a general purpose humanoid robot?
2:45:13
How do we build like a human in a bodysuit that has like common sense reasoning?
2:45:17
A lot of the AI focus we have around figure is basically how do we predict physics around things like grab and touch and move through the world.
2:45:25
At Hark, we have like a different objective.
2:45:27
We want to launch like like next generation consumer electronics and we want to basically build extremely multimodal models that can almost like act like as like a Jarvis type interface to AI >> and the the the focus on those tracks are completely different and um with that said though I think there's like some some opportunity over time to closely collaborate.
2:45:47
The voice on the model on the robot today is using the HAR voice API.
2:45:52
M >> so if you talk to any of our robots here today it's using the heart of voice model that we designed here internally.
2:46:00
Um so I think there's like a a lot of room over time to collaborate the business together.
2:46:04
We're both uh we're like uh we're taking an entire data center of B200s in April here and uh figure literally has half the building and Hark has the other half the building.
2:46:15
uh obviously paying for things separately but we like literally between the two of us have an entire data center of like next generation black wells that we're using for training for AI models.
2:46:25
>> Uh I want your latest timelines on the chatt moment for uh robotics humanoid robotics.
2:46:32
We were talking to um Sean Magcguire about this.
2:46:35
He he he was putting in maybe like two to three years away, three to four years uh from just you know seeing them on the streets, seeing them in in restaurants, seeing them in the real world.
2:46:46
Uh maybe not economic impact because that could happen in in you know all sorts of different industrial uh settings but uh it will be a special moment I think when people uh wind up interacting with a humanoid robot.
2:47:01
What are you thinking these days?
2:47:02
You can come to figure right now and you can see robots running complete 247 shifts. >> Yeah.
2:47:10
>> Fully autonomously with neural nets all the way down the stack. >> That's amazing.
2:47:14
>> So like I think um this will be a big year for us to ship robots commercially to many different customers of ours. >> Yeah.
2:47:21
>> And then we're also working on trying to how do we integrate these into the home? >> Yeah.
2:47:25
>> How do we get robots that go and do like laundry and dishes and tidy the house?
2:47:28
like things that we just like I don't want to be doing.
2:47:29
Nobody really wants to do and use like robotics as a key tool for this.
2:47:33
Um I think we're I I think we're we're like having this moment now.
2:47:39
I think it's uh we're in it.
2:47:39
We're feeling it as like like right now like we're seeing these robots do uh long horizon autonomous work at Figure here and I think over like this year and next year it's going to be very the whole world's going to wake up to it.
2:47:51
Uh I think we saw a little bit of that at the White House last week with uh with Figure.
2:47:56
It was just like we saw like unprecedented demand like uh like uh it was um >> it was kind of crazy because we didn't show any like new capabilities.
2:48:03
So we're like uh internally we're like okay we're just going to be at the White House.
2:48:07
And it was a big it was a big um it was a big milestone like the first humanoid robot you know built in the US at the White House in history.
2:48:13
So it was like getting like you know getting getting the invite from the White House to come there and be able to be the first one to do it was just like huge.
2:48:20
Um, it was great like but like you know there was there was no new capabilities there but like the the whole world is just like waking up to the moment now of humanoids and it was very apparent from last week at all the the incredible reaction we got from like basically the entire world that uh this is we're still early here in the cycle. >> I love it.
2:48:38
Well, good luck and thank you for taking the time to come chat with us. >> One quick question.
2:48:41
Can humanoids reliably crack open a cold Diet Coke and serve it or is like I imagine the tab is like kind of a challenge. That's AGI for me.
2:48:52
>> I don't think they should have any problem doing this.
2:48:55
>> We can uh >> That's a demo.
2:48:56
That's a demo we'd love to see because honestly, John would would buy >> a humanoid just to just to come over with a cold one, crack it open.
2:49:03
He goes through a lot of these so we can get some real utility out of it.
2:49:08
>> All right, John, bring a six-pack over to figure and we'll we'll test them out in person. >> It's a deal. I'll talk to you soon. >> Awesome. >> Yeah. See you guys week. I'll talk to you soon.
2:49:17
>> Let me tell you about the New York Stock Exchange.
2:49:18
Want to change the world?
2:49:20
Raise capital at the New York Stock Exchange.
2:49:22
And let me also tell you about Crowdstrike. Your business is AI.
2:49:24
Their business is securing it.
2:49:26
Crowd Strike secures AI and stops breaches.
2:49:29
And without further ado, we have Andre from Console TVP Royalty in the TVP Ultra. Andre, how you doing? >> What's going on? >> Great to see you.
2:49:40
>> Great to see you, dude.
2:49:41
>> Thank you so much for taking the time to come shop with us.
2:49:43
>> You're always you're always with us.
2:49:45
>> You're always with us. >> Yeah.
2:49:46
I was going to say love special to have you here.
2:49:48
Um anyway uh uh why don't you just give us a general update on the business like where uh walk us through some features some customers and then I want to hear the latest and greatest. >> Cool. Absolutely.
2:50:00
Um thanks again guys for for having me on.
2:50:02
Um >> of course >> um yeah so I think as you guys know uh maybe the rest of the audience doesn't but uh we are we're console we build AI agents that automate service management or employee support.
2:50:14
Uh we do that directly in, you know, Slack or Teams.
2:50:18
Um and you know, things like onboarding, offboarding, PTO requests, access management.
2:50:22
Um you know, even telling the facilities team there's no more napkins left in the bathroom or something.
2:50:27
Um and so last week we just launched our kind of a big product called assistant.
2:50:33
Um and assistant helps you do tier 2 work.
2:50:36
Um so it's not just the tier one employee support stuff. Now it's an assist.
2:50:40
It's an agent that helps your IT, HR, legal, finance team automate the more complex tasks that often span, you know, multiple systems.
2:50:50
So, for example, say uh, you know, there's like there's an internet outage, you can actually now ask console to go and investigate uh across those different systems.
2:50:58
So on the back end, we're plugged into your Maro, your crowd strike, your octa, your entra, all these different systems and console knows how to go and pull data from different those different tools and kind of come back with a report for you.
2:51:12
Um you can also tell assistant to go and like fix things, right?
2:51:14
So you can say actually go and you know push this update to this user's laptop or something like that.
2:51:19
>> Um and then you can also have console build out itself now.
2:51:21
So with assistant you can tell it, hey, I want to connect into Koopa or Netswuite to to pull this information.
2:51:28
and console will go read the API docs and then build its own connector into that system and then write a workflow with it. >> Wow.
2:51:36
>> That's like that's basically like it's instead of doing a feature request just request the feature. >> Yeah. Yeah.
2:51:42
I was going to ask like it feels like years ago you would be spending like you know you'd be stack ranking all the different integration requests.
2:51:49
It would take like maybe a month or two to write each one by hand.
2:51:53
I imagine that that was accelerated when you first built the product but now it can be handled on the customer side which is crazy to me. >> Yeah.
2:52:03
So when we started consular you know we were like hey we're actually going to build this framework internally and then our engineers are going to use AI agents to like build out integration super fast using that framework.
2:52:13
And so we get things done in like 2 to 3 4 days.
2:52:17
Um and then I think you know a couple maybe two 3 months ago we were thinking to ourselves like can we actually just have console do that you know just iterate on itself um and that's that's where the idea came from um and and that's what it does.
2:52:30
So you just tell it you know the same way I would tell an engineer hey like we need to build this for this customer now the customer can just tell console directly you know I want to pull these you know I will pull this data I want to all these actions into these systems and >> uh you know it'll build itself out to do that.
2:52:44
that. How are >> you think that's going to be an entirely new basically part of every product where >> you'll still be able to request a feature but at some point it's like you're paying for the software you should be able to adapt it to your own >> needs but I haven't no one no one's come
2:53:02
on the show and like pitch that specifically as as a company in the application layer basically like we're going to give you the autonomy to adapt the product to your >> crazy new paradigm >> which is just crazy because like every every SAS like just >> every customer wants that. >> Yeah,
2:53:18
>> Yeah, >> they want it.
2:53:18
They want to be >> Oh, I want to wait for the support to get back or my account manager to talk to the engineers and and >> yeah um I think I'm sure it'll I'm sure it'll come you know come about in in other tools as well.
2:53:32
I would say the core unlock for us was actually building out a really robust again like kind of framework interface that you know we can plug into.
2:53:41
Um, I think once you have that, um, and you have, you know, the really important piece for us is this, you know, the context graph.
2:53:47
So, we have a context graph under the hood where we're ingesting data from these different systems and we actually like model out your organization.
2:53:54
And so, um, when you tell it, hey, like go and update, you know, John's laptop to this version or you ask it like, you know, what is John's version of his laptop? It gives you an answer.
2:54:04
You say, go and update it.
2:54:05
Um, you know, console already knows who John is.
2:54:07
You know, we know what laptop you have.
2:54:08
We know where to find it.
2:54:10
um if you were to build that from scratch like you know you might have too many lookups it would get kind of like you know a little lost in the sauce and so um that that context graph is is really important uh it's a really core part of what we've built here.
2:54:20
How are people thinking about >> You got to coin this by the way.
2:54:25
>> Yeah, there's there's >> this is an entirely you need you need a you need >> deployed something I don't know for yeah something is defin like agentically deployed engineering or something is >> I was saying you name it after yourself. >> Yeah method. >> Yeah. Yeah. The serb >> the serban. >> Yeah.
2:54:44
You got to get you got to get people posting like Figma needs to incorporate the serban method. >> Yes. Yes. This is it. Yeah, I think we got it.
2:54:52
We'll >> we'll work on it.
2:54:53
>> Wait, so so uh talk me through the the experience of of onboarding to console and how people are thinking about this in terms of like net new functionality.
2:55:04
So I'm basically increasing my AI uh my IT spend, but it's all justified because workers are happier.
2:55:09
we're getting more stuff done versus like ripping replacing an existing system or not going with an alternative solution or like at one point in like 2013 I was scaling a startup.
2:55:22
We had like 50 employees.
2:55:22
We had like a an outsourced IT partner that was like one day a week and they managed like a ticketing system. It was very manual.
2:55:31
Uh but there was basically like a consultant who was available every once in a while like a fractional IT person.
2:55:37
How how are how are uh companies actually like interfacing with console and like integrating?
2:55:44
>> Yeah, so I would say most IT and kind of service management teams roughly scale linearly with headcount growth.
2:55:51
Um so you have like one IT person for 100 people or maybe one to 150 maybe 1 to 200 um if you don't include ramp who has a very you know insane ratio.
2:56:01
Um but but you know most companies are in that range and so with console they're able to take that you know they go one to like 400 1 to 500.
2:56:10
Um so we act as ultimately like this force multiplier for for your team.
2:56:15
for for your team. So, you know, yes, there's there's spend going into console, but you're actually saving on the back end of that as you're, you know, we we work with companies like data bricks, you know, uh, cursor, Figma, Chime, uh, >> founder, >> you know, and these guys are growing incredibly fast and, you know, a lot of these guys actually have plans to keep their IT teams flat um, you know,
2:56:40
through this this hyperrowth phase that they're about to experience and, um, it's it's entirely because of console and so we we're seeing we're starting to see that now not just in IT but you know HR legal finance workflows as well um
2:56:52
where you know they're they're just doing this employee support where they're just answering questions that um you know answering questions or taking action into systems that they have kind of elevated permissions into. Now you
2:57:03
Now you can have an agent that just does that so they don't need to spend their time on that.
2:57:07
They can >> What's your approaching do you just uh are you the only IT person at console?
2:57:12
person at console? Like do you force yourself to to dog food the product to the extreme or you what's the >> we we take a bit of a crazy approach here where everyone has full admin access to console and everyone is encouraged to build their >> own smarter you want everybody using the
2:57:31
product >> at some point we probably need to pull it back u I think our head of security was complaining last week uh he's like okay we've got too many sales people in here um >> I can imagine >> um but like you know we've got uh We actually have like our I was actually just talking to to our office manager. Uh we're going to have console just do
2:57:47
Uh we're going to have console just do our dinner orders as we do dinner in the office every day.
2:57:52
Um and you know with with assistant now she can build out her own workflow.
2:57:56
She says hey I want to you know ping me every every week at 4 p. m. Ask give me the options.
2:58:00
I'll select it and she's like go and order it into I think she's using like ECater.
2:58:03
Um so not an integration we would have built out if if uh you know on our own but I think with assistant it can do that.
2:58:10
And so um you know our head of security actually he was just we he was presenting a use case to us the other week.
2:58:16
He rolled out CrowdStrike on our devices um and he did in like 40 minutes instead of I think what he said you know would have taken him like a day or two.
2:58:24
Uh and he was just he did that entirely through assistant.
2:58:27
He was just hey I want to you know plug into these laptops.
2:58:29
It went it understood hey you need to download these two binaries if you're going to deploy it on these versions of uh Mac OS.
2:58:35
Here's where you upload it.
2:58:35
Here's the script you write. Okay.
2:58:37
Do you want me to push it? Yes. And just deployed it.
2:58:40
Um, so I think there's there's kind of a lot there's more use cases than we can imagine and so I think we work closely with our customers where we're almost like just trying to show them the technology and then I think they tell us uh what they what they want to build. >> Yeah. Yeah.
2:58:53
I mean there's so many startups I'm sure a lot of a lot of founders in the audience have have felt this before where you're the CEO and you set up the you know all the IT systems and then actually offboarding as like the super admin is extremely difficult.
2:59:10
I actually I actually fully lost my Amazon account at a previous company because it was so deeply integrated into the company that they just couldn't figure out how to change the super admin.
2:59:20
I was like just take my just take my Amazon account.
2:59:22
And so I just don't have Audible anymore or like Amazon Prime.
2:59:26
I need to set up like a new account and basically just declare like Amazon bankruptcy because I was just the admin for like a decade and things just like built up and and I'd try and give people the other password.
2:59:35
Anyway, there's >> how are you how are how do you uh how do you try to um >> how have you been trying to model like the like overall opportunity for console because you're still early stage.
2:59:46
So at this point it's just like let's get as many great companies as we can on the product.
2:59:52
But then you know 5 10 years from now at later later rounds or stages you'll be kind of you'll probably be asked that question more seriously.
3:00:00
But how do you think about it?
3:00:02
how do you think about it? because you are at this moment like selling against what historically was like the kind of I don't know labor TAM to some degree where um but the other side of that the interesting thing is there's a lot of
3:00:15
companies that would like use console that never would have had a dedicated IT person and so it's not entire it's not just replacing people it's like bringing a kind of capability to a to a company but how are you thinking about the the overall category? >> Yeah, absolutely. So there's a there's a >> Yeah, absolutely.
3:00:31
So there's a there's a couple of things I think that are that are going on at once.
3:00:36
I think the first one is, you know, when we when we look at um it today, it's very much a reactive role.
3:00:47
It's very much a cost center because I think we've just it's kind of like escaped us a little bit, right?
3:00:50
In the 80s and 90s, it was actually an enablement center, right?
3:00:54
You were bringing in technology or deploying, you know, giving people computers, you're you know, deploying Wi-Fi and or not Wi-Fi, but internet.
3:01:00
uh you know giving an email and so on.
3:01:02
That's like a force multiplier.
3:01:04
Um >> we've done too much of that.
3:01:06
We have too many SAS apps.
3:01:07
There's all this sprawl and now all you're you kind of the team is just managing that, right?
3:01:11
They're just doing support.
3:01:13
Um, and so with console, the way we think about it is we're going to bring our teams back to kind of what it was like in the 90s where you can actually have this agent handling, you know, all of the the the ticket management for those those simple systems and um those those kind of basic requests.
3:01:31
Now you have assistant that can go and do more complex work across the the enterprise.
3:01:35
And the value there is now you can do 10 times as much as you were going to do in you know in that year.
3:01:42
And you know if you think of you talk to any uh company, uh no one will tell you, oh yeah, like our IT team is kind of always on top of it.
3:01:50
Our IT systems are, you know, perfectly set up.
3:01:54
There's always something kind of a little bit lagging behind.
3:01:55
And it's because they're just constantly drowning.
3:01:58
And so console allows them to to kind of focus on the more strategic and kind of be more outcome based.
3:02:02
Um and so I think when you think of it that way, um you know that the TAM is actually all of the work that you know all these companies would love to do.
3:02:12
uh and they just don't have the the employee headcount or or the cost really the the to to to go and spend on it.
3:02:17
Um and so that's that's one big piece.
3:02:20
The other one is we're actually just ripping out replacing you know tools like Service Now and um you know Jer Service Desk and Fresh Service and kind of replacing with a more AI native solution.
3:02:30
Um and so we we expect that to to kind of scale as as you uh on board more as as companies become more digital native right they they have more >> saying you're the SAS apocalypse.
3:02:43
You're flaming the you're flaming the fires of the SAS apocalypse. >> I didn't say that. >> I said that. Uh there you go. >> Very very cool.
3:02:52
Uh yeah, it's great great to get the update.
3:02:54
Um that uh yeah and and we're going to work on this coinage.
3:02:58
Go back with the team, brainstorm a little bit.
3:03:00
You tell us you tell us what and we'll start asking every company.
3:03:03
Are you guys using the Serban method?
3:03:06
>> Yeah, we'll figure it out. It's good. It's good. >> I love it.
3:03:09
>> I'll run it by my head of growth. We'll see what she says. Fantastic. >> Great to see you.
3:03:13
>> Have a great rest of your day. We'll talk to you soon. >> Thanks, guys. Great to see you. All right. Thanks a lot.
3:03:16
>> We'll talk to you soon.
3:03:18
>> Um, there's a bunch of news that we need to run through before we head out.
3:03:20
Uh, Artemis 2 is launching and Khi has it at 64% chance before April 2nd of this year. We're going to the moon.
3:03:30
Four people are going to the moon.
3:03:33
Everyday Astronaut says, "I'm honestly shocked at how the general public has no idea Artemis 2 is taking humans out to the moon and will be the furthest humans have ever flown.
3:03:43
Every non-space nerd I've talked to has no idea.
3:03:46
We got to get people stoked.
3:03:48
This is what I'm going to be writing about tomorrow.
3:03:49
I want to deep dive this.
3:03:50
I want >> Why is no one talking about our >> Why is no one talking about the moon?
3:03:55
We're going uh NASA is set to launch four astronauts around the moon.
3:03:58
the deepest human space flight since the final Apollo lunar landing on n in 1972.
3:04:06
And there's a bunch of goals.
3:04:06
So, uh you can go track that and uh and we will talk more about that tomorrow.
3:04:10
And of course, bring you a whole bunch of other news and interviews tomorrow.
3:04:14
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>> It's been a It's been an honor. >> It's been an honor.
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>> It's been an honor to to be here.
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>> It was a rough couple days.
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It was a rough couple days being away. >> Yeah. >> But we're back.
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>> We're incredibly back. >> I'm glad.
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>> It's going to be a great week and uh have a wonderful evening. >> Yeah.
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We will see you tomorrow. Goodbye. >> Throwing smoke. >> Throwing smoke. >> Okay. Goodbye everyone.
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>> See you tomorrow folks. >> Goodbye.
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We will be back when the smoke clears. >> Wonderful day. >> Goodbye.