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Today is Wednesday, September 23rd, [applause] 2026.
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We are live from the TVPN Ultradom, the Temple of Technology, the fortress of finance, the capital of capital. Tell you about ramp. com. Time is money. Save both.
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Easy use corporate cards, bill pay, accounting, and a whole lot more. All in one place. >> What happened, John? We the the mallet broke.
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>> No, you broke the mallet. >> We broke the mallet.
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I mean, does that have anything to do with the number of days until Christmas? Are we at 90?
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Are we at a round number?
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So, we got to hit the gong. What are we? >> I can't read that. Is it 90? >> 92 days. >> Okay. Well, yeah.
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If it's 92 days till Christmas, you got to celebrate. Of course. >> That's right. Get ready.
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>> TBPN road to Christmas continues. 92 more days. Get ready.
0:48
Um, Nvidia CEO Jensen Wong went toe-to-toe with Ezra Klein on the New York Times podcast, the Ezra Klein show.
0:57
Uh, >> that's actually a crazy environment to go in. What >> as Jensen?
1:02
I I just think he's brave.
1:04
Ezra is a real journalist.
1:04
[laughter] >> It's like he's brave.
1:09
He engaged with a real journalist.
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No, it's like it's like, you know, you have to imagine he was a bit traumatized after Dwar. >> Okay. Yeah.
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>> Who who isn't, you know, I don't think of as as a journalist by any means, but he's he's uh obviously has very strong beliefs >> and and very Yeah.
1:29
And and and gets to like the root question for a particular audience.
1:34
Now, Doresh, although he was going super hard on the the open source question, on selling chips to China, on sort of AI safety and and roll out and like how the model how the AI race should go.
1:47
Uh, you know, uh, Doresh is not the person to push push Jensen on like politics specifically, right?
1:52
Like that's going to happen somewhere else.
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But yeah, it is it is a hot seat, but I think he did well.
1:57
Two hours almost hanging out with Ezra Klein.
1:59
Uh there's some clips and I have some reactions and it's sort of interesting to dig into the mind of Jensen because he's simultaneously the biggest force in AI, the central bank of artificial intelligence according to the economist.
2:12
He's the backs stop of all backs stops.
2:14
backs stop of all backs stops. He's backstopping everything from you know the biggest data centers in the world lending investment grade designation to the debt and the credit lines uh to like sort of acting as a soft landing for startups that get acquired at the application layer anywhere in the stack
2:30
uh he's been an acquirer that's sort of underwriting um if you're a VC and you and you did a deal at a billion dollars um >> and you're kind of in trouble on it >> there's a chance that you could get out because Jensen is willing to do a 10 billion$15 billion acquisition whereas Like in the prior era, Apple had the money, wouldn't do it. Google really
2:47
Google really didn't do that many deals that huge.
2:51
Facebook would do one big one every 5 10 years, but Jensen's been quick on it.
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And then he's also been investing directly in basically every lab and basically every AI project.
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So, uh, he's been an incredibly important force and yet he's now starting to stand alone in his sort of pdoom equals zero take, which was, uh, very much the consensus in the in the technology community.
3:17
>> Would like a word, >> I think. H, true, true.
3:17
[laughter] Yeah, I think it still is the consensus in the business community, in the finance community.
3:22
Basically, anyone at the application layer is PDM0.
3:24
Anyone deeper in the stack on the semiconductor side or the or the the energy side, the buildout side, the neocloud side, they're all pretty much PDM0ero.
3:34
But the labs and Jensen's been playing there, he's he's at that level.
3:37
He's he's as important as a voice as as Daario, Sam, Elon.
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And so, uh, to hear him not jump on the bandwagon when Elon, Sam, and Daario all agreed on pacing the frontier, he's saying, "No, we don't really need to pace the frontier."
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Um, it's it's interesting to hear him uh say this.
3:56
There's there's a couple clips.
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Let's play one, and then I I have sort of an allegory for where I think his position is. But let's play this.
4:02
>> So, what I've been hearing from the labs, what they've been saying publicly is that they are facing a hard problem. >> Yeah.
4:09
partially an engineering problem, partially an alignment problem, partially an operational excellence problem in Darham's framing.
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And what they are worried about is that in competition with each other, in national competition with China, that they are being pushed to move too fast, that they all feel they're in a collective action dilemma.
4:28
Now, I watch you on the All-In podcast stage.
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Donald Trump, President Trump gave you a call there. >> Oh, no.
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This is not planned, but we know who it is. >> Oh, no. >> [laughter] >> Mr. President. >> Oh yes, sir.
4:45
>> And you and and the president and the other members of the stage were very resistant to the idea any kind of regulation or collective action was needed.
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>> And uh they're just playing right into the hands of a lot of people that don't want to see it happen.
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And that could be political people. It could also be China.
5:04
And we're not going to let that happen. It's a It's a hoax. And >> you're right.
5:10
>> You come out as pro-regulation.
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It's illegal to slow down.
5:14
>> That's a form of regulation.
5:16
>> Like I I think I think AI regulation is is incredibly important.
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We need accelerationist regulation.
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[laughter] >> Really frustrate everyone.
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Uh >> yeah, it's interesting.
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Didn't he al didn't Jensen also do Jolene Kent on CBS just recently? I saw a clip from that.
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But very different clips going out.
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The one from Ezra Klein is talking about maybe we need to shut the labs down.
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A very uh you know in the weeds hot take about the current thing and the AI debate.
5:42
I think the clip that I saw from CBS was talking about his leather jackets.
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Like I I I imagine that that that interview will be more substantive when it when it gets clipped properly, but it's funny that that's the one that made it out um uh made it out initially.
5:57
Uh let's play the other clip from the the Midas project.
5:59
They said uh uh here's the clip of Jensen actually talking about uh about property slowing down.
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Why is there >> release the product?
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>> That's the simple answer.
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Um if you're if you're going to build a car, a self-driving car and and let's say it's a robo taxi and there's a really difficult condition. >> Mhm.
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>> And it just as an engineer, we just have no idea how to solve this problem because these cars are not programmed. They're trained.
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And so we have no idea how to train these cars and we have no idea how to align them to the safety standards uh that are expected on the road.
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And so what's the answer? Don't ship it.
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>> These products were unreleased. >> What's that?
6:42
>> These products were unreleased.
6:43
>> Ah so now it's comes back to engineering problem again.
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And so one is one you have to recite it.
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Second you have to you know think about what's the what you could have done what's the solution for it.
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and then in the future you just you know improve your process so that you could you could avoid this from happening again.
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I am fairly certain I am fairly certain they will say yes they need they know how to solve this problem and if if that's the case then that's the problem it's as simple as engineering and and now the alternative the alternative is that um if they say that if they say the alternative which is there is no way to contain our experiments there's just no when we test our AI models, uh, it will get out and it will damage the world.
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Then I think the answer is we have to shut the labs down. >> Yeah.
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I mean, that that is kind of what happened with self-driving cars, you know, like they do before they ship them, they do test them on roads, and they don't test them on open roads.
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They test them on closed courses before they move to open roads.
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they sort of have their own sandboxes, their own their own environments to test these in.
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Uh I do wonder if there's a uh uh sort of a legislation or liability gap between the liability incurred by a self-driving car company who causes property damage from a car running into another car uh autonomously and a uh an AI agent that hacks and defaces or causes some economic damage.
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I don't think there will be.
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I think the courts I I if there was true economic harm like one model accidentally took down a payment system for an e-commerce website, I think it would be pretty easy to sue that company and say, "You caused me to lose this much revenue. You owe me."
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And the courts would say, "Sure."
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And even if the lab argued, hey, we didn't tell it to take down your your e-commerce system or your your payment rails.
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Uh the judge and the courts would say, "Doesn't matter."
8:52
uh even if you expressed a duty of care like you still have to pay in this in the scenario but it is possible that there's a gap there and that's where regulation could fit in. Um, it's interesting.
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He spent the first like 20 minutes sort of steelmanning the the the jobs question and talking about jobs because I think that's I think that's throwing a lot of people off.
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Like we we sort of moved past the job apocalypse, SAS apocalypse narrative which was predicted from somewhat of the same community into actual apocalypse.
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And people are like, whoa, like you were wrong about the SAS apocalypse.
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like Salesforce is still doing fine, like Slack still exists.
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Um, and you were wrong about the job apocalypse.
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Like the unemployment rate is like 3% for American white collar workers.
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You were predicting like 50% or 30% uh something like 10% overall.
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Uh, and even even in the Philippines, I mean, I remember seeing uh I think it was Tristan Harris on Modern Wisdom, like he he's now sounding the alarm bells about existential risk, but he was saying something like like the Philippines would see like, you know, 90% of their economy go away.
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Uh, because they're they're heavily dependent on call centers.
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Call centers are actually only like three and a half% of the Philippines job market.
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uh they do in fact make things and have agriculture and all sorts of other uh all all sorts of other economic endeavors going on in the country.
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Um but even in the even in the Philippines like the unemployment rate is uh is right now in the Philippines it's 4. 9% in June of 2026.
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Now, it went up to 6% in August, but it still feels like the AI effect is pretty minimal, and most people were predicting that uh like the offshore call centers would be affected first.
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And that one seems like like maybe we're there, but it's just taking so much longer that everyone feels very vindicated in saying like, hey, let's watch that play out first before we move on to the the X-Risk discourse potentially. Okay.
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I think that's a lot of what >> Well, and now with with Muse floating the idea of having uh human in the loop on personal agents, you can imagine those people would be former like >> Oh, they could just move over. Yeah. Yeah. Potentially.
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>> Um >> um but yeah, I mean it's all the same uh you know in the limit in the exponential add five orders of magnitude maybe things look very different.
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Uh but Jensen just rejects that.
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I mean he's an is he an AI is a normal technology guy?
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I saw I saw Joe Weisenthal posting about this.
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He said that he had to he had to differentiate between Well, I I got to pull it up because it's funny. Weisenthal.
11:30
Um Joe Weisenthal said he had to dis he had to uh disagregate. Where is it?
11:36
He posts a lot so I got to dig it up.
11:39
Um, normal technology man, you post a lot.
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>> He said, "The splintering around AI discourse is really a sight to behold.
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was in a conversation yesterday with some folks during which it became necessary to distinguish the people who see AI as normal technology from the AI as normal technology people because there is a group of people that have that have rallied around a particular
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thesis which is AI as a normal tech AI as normal technology and that's different than people that are just like casually into that idea because it's actually like a different ecosystem anyway um it does feel like Jensen is uh is AI is normal technology. I mean, he's
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I mean, he's certainly seen plenty of technology revolutions come and go.
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And uh he's been in this industry for what 30 odd years.
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Um and uh I I I think of the jobs on the jobs question.
12:37
We were talking to Joe Wthal about this when he was in the studio.
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Uh like where is the where is the economic impact of the internet?
12:45
Like why can't you see, you know, a kink in the graph of really any economic data when like the internet takes off?
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It's not like productivity went way up.
12:53
there's nothing really to grab on to.
12:56
You actually go back to 1970 if you want to see like the real the real trends shift.
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And there are other, you know, trade and globalization moves that have had bigger economic impacts on the internet, which is crazy to think because so much wealth was created, so many companies were created.
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Uh, and it did change the world, like the day-to-day experience, but didn't actually show up that much in the economic data.
13:16
And that's what we're seeing now where jobs are sort of changing, tasks are changing, but uh we're not seeing uh dramatically different uh economic statistics like the 10 years at all-time highs or not all-time highs, but 19-year highs, you said uh five over 5%.
13:30
And yet everyone is sort of a consensus agrees that that's because of the war.
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Are are you rooting for people that are buying the bonds and getting higher yields now? >> I guess. >> Okay.
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[laughter] >> I don't know.
13:43
Sometimes big a record-breaking number.
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It's just exciting to you.
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Just general just real golden retriever mind.
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>> You understand the implications and and you >> you don't even think about the implications potentially.
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You're just cheering for a bigger number. Yeah.
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It's like now it begins with a five. Great.
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I like it's a higher number.
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>> Uh but there's an interesting uh there's an interesting allegory uh around the effect that technology has on the labor market at least historically.
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Now things might change but uh Steven Kovi highlighted this in first things first his book on time management.
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Uh he said, "I attended a seminar once where the instructor was lecturing on time.
14:16
At one point, he said, "Okay, it's time for a quiz."
14:19
He reached under the table and pulled out a wide-mouthed gallon jar.
14:21
He sat on the table next to a platter with some fist-sized rocks.
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On how uh how many rock how many of these rocks do you think I can get in the jar? He asked.
14:31
After everyone made their guesses, he said, "Okay, let's find out."
14:33
He set one rock in the jar, then another, then another.
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Uh I don't remember how many he got in, but he got the jar full.
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Then he asked, "Is that jar full?"
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Everyone looked at the rocks and said, "Yes." Then he said, "Ah."
14:43
He reached under the table and pulled out a bucket of gravel.
14:47
He dumped some gravel in and shook the jar, and the gravel went in all the little spaces left by the big rocks.
14:53
Then he grinned and said once more, "Is the jar full?"
14:55
By this time, people were in on him.
14:57
They said, "Probably not.
14:59
There's something else coming."
14:59
"Uh, good," he said, and he reached under the table and brought out a bucket of sand.
15:04
He started dumping the sand in, and it went in all the little spaces left by the rocks in the gravel.
15:08
Once more, he said, "Is the jar full?" No, everyone roars. Uh, he said, "Good."
15:11
And he grabbed a picture of water and began to pour and the water went in between the rocks and the sand and filled up.
15:17
Uh, and and somebody said, "Well, there are gaps and if you really work at it, you can always fit more into your life."
15:23
He said, "No, that's not the point. The point is this.
15:25
If you hadn't put these big rocks in first, would you have ever gotten any of them in?"
15:29
And so, the effect that Jensen's describing is that like at one point in human history, there were maybe only two jobs, hunting and gathering, something along those lines.
15:38
Then we invent agriculture.
15:38
the uh industrialization.
15:40
At one point, everyone's farming.
15:42
Now, very few people are farming, but we still have more food than ever.
15:47
>> There might have been a third job singing.
15:49
I could see Tyler while everyone else was hunting and gathering just kind of jester [laughter] backing and just singing for all the hunters and all the gatherers, kind of like providing ambient entertainment. Yeah.
16:01
But yeah, anyways, >> but it does feel like that that was the effect that the internet had.
16:05
Like it didn't it didn't it didn't dramatically reshape [clears throat] the labor force, but like obviously like lawyers use the internet to communicate.
16:14
And now like you you see the AI agents thing.
16:16
It's like every every little document will go through an AI pass.
16:20
Every little interaction in the economy gets this like small effect, at least right now.
16:26
And and that's what Jensen's like living in.
16:28
He's like, I have a real business to run in the real world today.
16:30
So, he says he's worried about but or he said he says he thinks about the future, but really he's clearly very much living in the present.
16:39
>> It's it's interesting to think about how much uh like technologydriven efficiency gains just get effectively wasted.
16:47
>> So, I would imagine today it's faster to clo to to like close a venture round postterm sheet than it was in like the '9s, right?
16:57
You would think so with the with the internet and and AI and all these different things, but I bet you >> that it's not as maybe >> as fast as efficient as you might think if we can now generate docs on the fly and you can easily go back and forth on red lines. Yeah. >> Right.
17:13
Like >> maybe it went from 6 weeks to four weeks, >> but theoretically it could have gone from six weeks down to five days. you know. Yeah.
17:24
>> Um and I think you're just seeing that even with AI now, people can do their jobs faster.
17:30
>> And you've seen some management teams tell their employees, "Hey, I know you're getting a lot more efficiency.
17:36
That doesn't mean you should just do the same.
17:38
I want you to do more work in the same amount of time.
17:40
Don't do the same amount of work with with less time, but you're just sort of like wasting a bunch of time." >> Yeah.
17:47
Seems like no one's really saving time.
17:48
Everyone's just doing more stuff.
17:50
Um there is an interesting like like [clears throat] correlary of that which is potentially these technologies they don't necessarily change the growth curve but they are responsible for the growth like if you don't have the internet speeding up commerce from a
18:06
week to get an item to two days that is actually the source of the 2% growth and without the technology you have no growth and so there there is there is another side of an argument of course there's like population growth and a whole bunch of other things that are affecting economic growth broadly. But
18:20
But there is a there is a world where yes, if you if you're compressing the timeline on everything, you're building the house faster, you're deciding to buy the house faster, you're exchanging everything faster as things move quicker.
18:33
Even if you're not doing entirely like net new jobs, just the fact that you're doing them faster, you do more of them, and that's what actually creates the economic growth. I don't know. We'll figure it out.
18:41
We'll get to the bottom of it tomorrow.
18:43
Uh let me tell you about the New York Stock Exchange.
18:46
Want to change the world?
18:47
raise capital at [cheering] the New York Stock Exchange.
18:51
>> Uh the the and and yeah, it's always a good time to share the Financial Times black pill, white pill, uh chart of which way things go.
18:59
Either either GDP goes to zero, infinity, or it stays the same.
19:06
It's like that Mitch Hedber joke.
19:09
I used to be in a metal band.
19:09
People either loved us, they hate us, or they thought we were just okay. Stupid joke.
19:16
[laughter] Do you know Mitch Hedber?
19:17
Oh, he's a great like oneliner comedian. He's he's fantastic. RIP.
19:21
Uh anyway, um let's move on to some other reactions.
19:24
Uh I'll tell you about Console.
19:27
Console builds AI agents that automate 70% IT, HR, and finance support, giving employees instant resolution for access requests and password resets. Uh Claude Opus 5.
19:33
5 drew every frame in this animation in JavaScript.
19:39
They went all in on LLMs, on the big model, on the great model, and now it can do basically video generation, but do it in JavaScript, do it in Blender, do it in Python, do it in SVG.
19:51
Uh, and you can just hill climb on SVG, and people are sharing a bunch of cool demos and good for launch.
19:55
People are sick of benchmarks.
19:59
People want to see visual stuff, entertain me, make a song. Uh, and uh, yeah.
20:05
>> Yeah, really, really cool style.
20:05
Yeah, >> I'm already sort of mourning it just because this is probably going to be everywhere on the internet like by next week.
20:14
>> But for now, a good format uh I would, you know, if you see maybe a co-orker doom scrolling, send them a custom animation like this saying, "Hey, stop doom scrolling." >> Yeah. >> And do some work.
20:26
Uh you could maybe send this to the uh I think the the president of Syria. >> Oh, yeah.
20:31
He was [laughter] caught caught caught using Instagram reels at the UN.
20:39
>> So >> uh naughty naughty as they say. >> Um very very cool. >> Yeah.
20:45
Mike Bird says, "Total cultural victory.
20:47
Man has yet to create an ideology that can compete with short form video-based social media scrolling.
20:53
[laughter] Ahmed al-Shara spotted scrolling and sending Instagram reels during UN General Assembly with the translator headphone on. He's scrolling.
21:01
Hey, you know, you got to keep those you got to keep those group chats going.
21:07
You got to keep [laughter] you got to keep the content flowing.
21:10
You don't want to be falling off.
21:12
He might be sending him to other world leaders.
21:13
He might be recontextualizing what's happening at the UN with a funny reel.
21:18
like he might the person might be talking about some peace plan and he might be sharing some hilarious Instagram reel that makes fun of that to let his friends know that he's not buying it. Something like that.
21:31
>> He also could be just his feed could be so dialed into just local political content.
21:37
He could be mainlining public opinion and trying to understand what's important to voters heading into the next election. Yeah. You know. >> Yeah. Yeah.
21:45
>> Yeah. Yeah. Uh there there's one more there's one more Claude post I want I want to show the GIF animated in Python and rendered in Blender and at this point you know uh I posted that AI video of you and people were asking like what was the workflow and I was like it's you
22:00
just ask it to do exactly what you want it to do and you don't you you actually don't even need to know the word blender like you can just >> you can even misspell every word >> and it will still >> No, you just open up the voice mode and I just talk and I'm like you can you can even do multiple things now. So, I was
22:16
So, I was like I have this uh children's story that I made up for my 5-year-old and uh and I was like in one shot I was like uh take this story, turn it into uh a series of stories, then a series of children's books, go find a place that can print the children's books, illustrate it, turn it into short form video, and also make a video game. And it did it all.
22:40
It was just like spawning sub agents to do it.
22:41
And you can just do it all in one prompt.
22:43
And I didn't need to be like, well, I want you to use JavaScript for this and Blender for that.
22:49
You can just ask for what you want and basically get it from all the models right now.
22:52
Uh, you have an interesting thesis.
22:54
You're you're black pillar on the on the application layer now.
22:56
You think the models are getting so good that people are just going to use the AI tools.
23:03
Does this change anything for you?
23:05
Because I see this and like I still feel like these are these are great.
23:10
There's probably a couple revisions.
23:13
When I do the prompts, I'm like I I still sort of filter and review.
23:15
I'll usually get like 20 outputs, pick the best one.
23:19
I actually talked to an AI video uh founder yesterday who's doing uh like AI movie production and whatnot.
23:26
And uh it was absolutely printing using all the all the latest models.
23:29
The business is doing fantastically.
23:32
He's hiring four video editors like a week or a day or something like he's hiring lots of people because there still is a lot of it's not even prompt engineering.
23:41
It's more like processing the output, curatorial work, understanding what is the right thing to fit together.
23:47
And I think that that mainly comes into picture when you're looking at something that's a bigger project.
23:52
You want to go from a one minute thing that might have some consistency, but when you go to two hours, the consistency becomes much more important.
24:01
the the style, the pacing, matching everything matters more. I don't know.
24:05
Um I I I I think we might be gearing up for another application layer versus versus model layer debate.
24:10
We we saw this with the Harvey discourse earlier this uh I think we discussed that yesterday actually.
24:17
People were sort of blackpilling because their margins went negative, but um but then all the models got cheaper since that uh released and so you think better and cheaper back up. Yeah.
24:27
But of course uh if the models can do it at at a base level there is a world where every company just has you know a relationship with a foundation lab.
24:37
So I don't know we'll see anyway people are feeling the AGI uh the serious adult uh ML enjoyer at Anthropic.
24:46
He said I'm feeling the AGI.
24:48
I gotta follow this guy back. Uh I'm feeling the AGI.
24:50
He made this cool video with Opus 55.
24:52
Then Shalto comes in from the top rope. We don't have AGI. The job's not finished. I love the debate.
24:58
Even internally, people are saying they're maybe loosening up comms over there. I think it's cool.
25:02
I like seeing I like seeing different takes.
25:06
Uh, of course, these companies are not monoliths.
25:08
It's nice to actually get a glimpse into everyone's different perceptions.
25:12
Everyone has different definitions for AGI, and I think it's cool to actually toy with them.
25:15
Early in the show, I coined like definition of AGI, which was just purely economics.
25:22
just when AI revenues equal nonAI revenues, you have AGI.
25:25
So like when the AI economy is as big as the human economy, then that's AGI.
25:31
And of course, that's completely arbitrary.
25:34
Who knows if that's a valuable metric.
25:36
Certainly interesting, but I can definitively say we're not there because AI is contributing like a quarter percent to GDP and uh and total total lab revenues are in like the hundreds of billions while we're doing like tens of trillions in the in the global economy or in the US economy even.
25:53
So, uh interesting stuff, fun. Go play around with it.
25:55
Let us know what you build.
25:57
Uh I like that they put the horse riding the astronaut on the moon.
26:01
Uh, and I think there's probably some Blender under the hood, but it was cool that the model was able to do the post-processing and the and adding grain and texture that made it look very special.
26:11
Like it popped out on the timeline, at least to me, because of that. Um, very cool.
26:15
I also had an interesting uh test like you really can climb SVG.
26:20
At one point, I just took an image that was AI generated image of a pelican riding a bicycle, which is of course because it's AI generated a genai image like looks amazing.
26:29
And then I just told Astra like, "Turn this into an SVG pixel by pixel."
26:33
And I was able to send it to you.
26:36
It was like a 40 megabyte SVG, but it looks exactly like a pelican riding a bicycle.
26:40
And so it calls into question like what what what even matters like because you can you can use a generative AI tool to then create a Blender model to then create an After Effects file to then bake it down to a PNG or change it like every format changes into every other format.
26:56
Uh use the best tool for the job.
26:58
the best tool for the job. uh we're we're definitely in the regime of like how much did it cost to actually get that thing done as opposed to some like artificial syntax benchmark around like a line of code costs this much like no one cares no one cares what tools are being used under the hood uh there's
27:14
definitely a world where you go to a model you ask for a thing and if it needs to use Slack it uses Slack if it needs to use Photoshop it uses Photoshop if it needs to write its own thing in Python it does that it it uses the right tool for the job and it intelligently chooses things just like a real person on your team would, which is exciting. Uh, what else? Uh, what else? >> EV News.
27:33
>> EV News >> from from Dave over on X. He flagged this first.
27:36
>> We're let me tell you about Codeex.
27:38
Codex is a powerful workspace [applause] for getting work done with AI agents.
27:40
Whether you're writing code, analyzing data, creating content, or automating business workflows, Codex helps you move projects forward from start to finish.
27:48
>> We are one week out from the Roadster reveal. >> Yeah.
27:52
>> And I'm Did you listen to the end of the show when we b when you bounced?
27:55
>> We went deep on the Roadster. We got a scoop.
27:57
We got a scoop from our guest who was next to a Tesla employee on a plane and was like pulling facts about him just to go leak it on TVPN. Yeah. Crazy.
28:08
No, no, I mean I I don't know how real this was because the guy could have been messing with them. I don't know.
28:10
It's it's all rumor mill stuff, but the guy was basically like, "Yeah, I was next to a Tesla employee and he was saying that uh the jets are not to make it fly.
28:18
It's actually for downforce.
28:20
It's going to suck it to the ground and it's going to go 0 to 60 in 1 second." Very cool prediction.
28:26
very different from what we were saying. So, who had the lowest? >> Yeah.
28:29
The only thing is it doesn't it doesn't track >> had the lowest number.
28:31
So, you're closer to win if that's what happened.
28:33
>> Well, it doesn't track with what Elon was saying on Rogan, which is that ground fly.
28:39
>> And that's what I was getting at.
28:39
It was like if you can have a fan or a jet that sucks you to the ground for one purpose, you can probably reverse those and boost up at least for a little bit for a little jump. But we'll see. We're counting it down.
28:51
Well, in other EV news, >> the road to Christmas, the road to Roadster, but we're still keeping an eye on it.
28:56
>> Saudi Arabia is aiming to disrupt the auto industry with these radical EVs. >> They're wedges.
29:02
>> They have a brand called CER, Exobot.
29:06
>> It's coming as a sedan and an SUV.
29:09
>> It looks like sort of a futuristic like Lamborghini option.
29:14
>> Yeah, there's a little bit of Huracan wedge in there.
29:15
Little Cybert truck in there.
29:18
uh is that >> this is Saudi Arabia's first true production car brand.
29:22
So making these there's such a hard trade-off between the wedge looks great but it's a terrible use of space.
29:30
Like you're like the the optimal use of space is like the new Whimo vehicle which is basically just a box or like a bus.
29:38
>> You care you just care about utility now.
29:41
>> I'm just saying it's a trade-off.
29:41
like the more wedgie you get, the less utilitarian the the vehicle's space is.
29:49
Uh, of course you need to have >> This one looks like somebody broke up with the new generation of Prius and now they've leveled up again.
29:55
That was the that was the original like read on the Prius is like, "Who hurt you?"
29:59
You know, cuz the the new Prius is looking >> pretty pretty stanced, you know? It's a level up.
30:08
>> But this is a level up. >> Is it the Prime?
30:09
Prius Prime looks pretty good.
30:11
Yeah, they've done a good job.
30:13
>> Anyways, uh 850 horsepower tri motor electric powertrain.
30:16
Uh >> this thing looks insane. >> It does look insane.
30:19
It it I wonder where they'll hit in terms of price.
30:23
That'll be that'll be interesting to see. Um I mean it's cool.
30:25
Saudi Arabia obviously known for, you know, pretty outrageous car culture in many places.
30:32
>> What if I could what if I what if they could make this the performance of the luch >> at only 60% of the cost? >> Yeah.
30:40
still hundreds of thousands of dollars. Uh hopefully not that.
30:43
Um but uh we'll be interesting.
30:46
>> Yeah, I have no idea where they would price this because it could be sort of like a a national treasure uh a point of pride.
30:53
It could come in at a very high price.
30:55
At the same time, uh EV is a tough sell when you're in the when you're in the six figures.
31:00
Uh should we watch the trailer?
31:02
The official trailer for you can see everything.
31:05
We watched the preview clip.
31:07
Now we get the full trailer.
31:09
And I want you to pay attention to the quotes because the reviewers were floored by this movie.
31:14
I've never seen quotes like this on a movie. >> I know. >> It's crazy.
31:19
>> Uh people really really enjoyed this. >> Triple glaze. >> Yeah. Seriously.
31:22
While they pull that up, let me tell you about public. com.
31:25
Investing for those who take it seriously.
31:26
They got [applause] stocks, options, bonds, crypto, treasuries, and more with great customer service.
31:29
I >> feel you did nothing wrong. >> I did not.
31:36
>> So where did things get lost?
31:36
like how are you where you are right now?
31:42
>> I I wish I knew, you know? I I don't know.
31:46
I don't I don't feel like I understand that.
31:53
So, I know you guys invited me to to stay here.
31:58
Does any part of you worry about me being here 24/7? >> I don't think so. >> Okay, so pause.
32:06
>> Well, what if I see 247.
32:10
He's actually, I think, living with Elizabeth Holmes. >> Yeah.
32:15
>> And it's crazy because only Nathan Fielder would even think to throw out an idea of, hey, would you mind if I moved into your house before you go to prison >> and documented the entire process?
32:31
because you would think that there would be no circumstances where where that would uh be a good idea.
32:39
>> I think it's just pure upside.
32:39
There's there's very little downside to to doing this.
32:44
I mean, you're going to prison.
32:46
Like worst case, you look worse, but you're in prison either way.
32:49
Like, yeah, the only thing you the only way you can go is up and actually uh endear some people to you, which is the attempt. We'll see how it lands. Let's keep watching. that makes you look bad.
33:08
>> I think this is the moment in time.
33:11
>> No other film I've ever experienced >> this story before I'm gone.
33:17
>> I fell in love in that first [music] conversation >> before or after that she revealed she was the founder [music] of Theronos. >> Probably before.
33:25
>> I mean, weren't you worried she was uh sort of scamming you? >> 100%.
33:29
But [music] what did she have to gain from me?
33:32
>> Well, isn't your family rich? >> Yeah.
33:34
I [music] think that there's like a lot of different levels of wealth. >> Come jump in with me. >> You asking me to go? >> Yeah.
33:48
>> You've never What is this for?
33:48
[music] >> Why are they doing a 3D scan?
33:52
>> That's just something Nathan would do.
33:54
Do [music] you mind if we scan of your body?
33:56
I saw that I was like, "This has nothing to do with Theronos or going to prison."
34:06
>> He master can prepare you. >> No, you don't.
34:18
>> I may think about this movie for the rest of my life. That is a crazy quote. be okay with >> it.
34:27
Feels like there's going to be some twist or something something. Very interesting.
34:31
But I'm I'm h >> No, I think the I think the whole thing is going to make the viewer feel like they're in a really bad acid trip. >> Mhm.
34:39
>> Like all these scenes. >> Yeah.
34:41
>> The pauses, the silence, the way the lighting, the the sort of sense of impending like doom, right?
34:46
Because she's going to she's about to go to prison. >> Yeah.
34:50
uh the the the the kids, you know, being present in these scenes.
34:54
Like the whole thing is just like incredibly >> I dark and weird. I don't know.
34:59
I feel like it has to break expectations in some way because the average viewer is going to sit down and believe that she is guilty and that she lied and is like a sociopath because like that's sort of her brand, right?
35:13
is that like she never admitted any guilt and was guilty in the John Kerry retelling of the story and obviously the court findings and so the for it to be for it to be like that I feel like there has to be some twist or something. I don't know.
35:30
I'm I I would be shocked if it's just like yep like she defrauded investors and didn't really build a good blood testing device and now she's in jail. Like the end.
35:41
Like would that be would that be >> I don't think it would get those quotes.
35:46
>> I don't think it would get those quotes.
35:47
So I feel like there's going to be something crazy that happens some like complete twist like maybe she's innocent or something or or maybe she's like I don't know guilty of something else.
35:55
There's >> the question is how does the uh uh who's who's the guy that that she would call Tiger? >> Oh yeah.
36:04
Uh R uh I actually don't know.
36:07
Um uh yeah like because there there has during the during the court case there was a question about his responsibility um should should he bear more of the responsibility and so uh but he doesn't seem to be in the trailer so I don't know um because one one weird like twist
36:26
would be if you came away being like that guy's more responsible than I thought you know but anyway let me tell you about Figma agents meet the canvas your AI agents can now create and modify your Figma files with design system context. Uh we have the president and
36:40
Uh we have the president and CEO of Qualcomm Cristiano in the waiting room with Bren TVP Ultradome.
36:45
Cristiano, how are you doing? >> Very good. How about yourself?
36:50
>> We're doing fantastically.
36:50
Where are you calling in from >> Maui, Hawaii?
36:54
It's uh our Snapchat summit. This is day two.
36:58
>> How How long have you been doing Hawaii? Why Hawaii?
37:02
>> Uh it's probably more than 10 years.
37:02
I think it's uh uh we have a lot of uh you know global press that come here from all of our countries, a lot of partners.
37:12
Nobody complains about coming to Hawaii. >> Yeah, I can imagine.
37:14
Yeah, it's a good place to be.
37:16
Uh what are the highlights?
37:18
What are the messages that you're trying to drive home uh today and and with this conference?
37:24
Look, at the Snapdragon Summit, we always announce our latest uh you know, Snapdragon flagship uh processor for smartphones, but this one's it's special because uh for the past couple years, we have been seeing we have a clarity of vision.
37:38
We have seen how a smartphones going to change to an AI smartphone and I think that's actually started to happen right now. >> Yes.
37:47
What uh how does your business actually change in an era of uh folks wanting to do more ondevice inference and AI on something like an Android phone, one of your big partners?
37:59
Um how how do you think you need to adapt?
38:01
Is there something you need to change or has this been years in the works? You think you're ready?
38:07
>> No, years in the work.
38:07
Look, we if you we were kind of a long time ago.
38:11
Um it it feels like a long time ago when we're kind of showing you know multi-billion parameter model running on devices.
38:18
Look there's this whole conversation about AI running on the cloud and on the edge and people often ask the question why the edge why the cloud and I think that's probably the wrong question.
38:30
probably the wrong question. M >> it's almost I like to go back and and say okay everybody get their phone out of their pockets get your 200 300 apps that you have and let's go have a conversation about what part of your app runs on the device or runs on the cloud and if you and I know even though we
38:48
build incredible processors if you put on airplane mode you you don't use your phone so at the end of the day AI is no different than that it's going to be running on the device and on the cloud it's going to be all transparent to you >> yeah uh how how are Are you viewing what the next couple years will look like in automotive? It feels like the demands
39:04
It feels like the demands for AI on device at the edge are even more important potentially, but maybe it's equally important.
39:11
How are you seeing the automotive business develop?
39:15
>> Well, automotive has been uh ahead of that like we we've been very fortunate.
39:20
Automotive is a new business for Qualcomm and and we now uh serve all the uh car companies in the world.
39:25
And uh what has been interesting about that is AI on the edge actually is being very big in automotive because of assisted driving and autonomous driving.
39:36
That's kind of AI running on a processor in the car.
39:41
But what's happening with the car right now is when you're behind the wheel and you don't have any legacy of uh OSS and apps, a gentic experience is very natural when you're behind the wheel.
39:53
wheel. We also see now the fusion of the systems in the car that was designed for navigation like the cameras for example being also being used uh for see what I see and have an agent experiences like uh you know people are in their car and they say well the agent this restaurant
40:12
on the right what's the Yelp review and do they have availability for lunch right now those are kind of uh some of the experience we're seeing and I that's accelerating for us >> got it Um, how are you uh processing the narrative around like the CPU crunch? We
40:26
We saw the the agentic era boom and you know there were the whole websites that were going down because so much code was getting pushed.
40:37
It feels like agents like the the the idea that everything would be done at the model layer on a GPU is not the way things are playing out.
40:45
CPUs are incredibly important especially in the data center.
40:47
I'm wondering if are we going to experience like another CPU crunch as we enter the era of like personal agents because we saw the effect that the uh enterprise agents and software coding agents had on the CPU demand.
41:02
What does the next couple months even look like for CPU demand in the data center?
41:09
Okay, let me separate that conversation into I think one one topic is >> there's so much more demand for computes than availability right now and across the board.
41:19
Uh so I think what we've seen this right now and and by the way as a semiconductor company I'll tell you the supply chain it's uh operating at 100% capacity everything everything is short uh because there's so much more demand than the availability of compute.
41:35
than the availability of compute. Now the second part is the way we see it and we're just entering the data center but we kind of see that also on devices of the edge you the data center is going to have to evolve and it's already on its
41:52
way to do that into a hoggenous compute you have different engines to do different things CPUs are going to be very important for orchestrator and agents and CPU demand will continue to rise and that's also Drew on the other side of data center phones as as those
42:10
agents get deployed on devices they have a lot of CPU demand but you're going to have different engines for different things like for example for inference we see there's engine now for prefield engine for decode decode attention and so forth >> how are you thinking about uh the
42:29
changing landscape of consumer devices I mean meta connect is today I'm sure that they're going to announce some new consumer devices But uh we talk to founders all day long that are building just like really cool small robotics projects, wearables, devices there. There's rings and and wristbands and
42:46
There's rings and and wristbands and ankle bands and anything you can wear, put on your body. It's happening.
42:52
And I'm wondering if you're seeing that actually start to move up your to-do list as like you got to engage with that smaller community even though it might be nent.
43:03
Um, and are you are you are you actually trying to engage with like the smaller device community uh on the consumer side at a conference like this?
43:17
Look the and I'm going to say this in all humility uh but the majority of those new classes of devices are actually used in Archae and there is now there's a there's a multitude of uh of those products now and there's big companies and small companies and and the reason is because uh when you think about um uh agents and agents are not bound by oss or applications and and and you have mult multimodal like uh think about glasses for example.
43:48
I actually a big believer that glasses is going to see an inflection point.
43:52
The glass is a prime real estate.
43:55
Close to your eyes to your mouth to your ears your head turns your camera see it >> and then those things like see what I see, read what I read, hear what I hear are going to come up.
44:06
But we've seen all sort of form factors.
44:08
We've seen earbuds with cameras, jewelry, pins, buttons, and uh and this is kind of the new personal AI category, and I think that would be a very big category.
44:20
>> How are you grappling with uh uh the open-source ecosystem versus closed source uh tooling to enable the next generation of devices to uh integrate?
44:29
I mean, obviously, you've already won a huge portion of them over, but uh you want to keep that forever, right?
44:39
Oh, thank you for asking this question.
44:41
I think it's maybe a great opportunity for me to to make a plug about what we're doing with modular.
44:45
So, I don't know if you heard about what we're doing with modular. >> Tell them.
44:50
>> Look, uh we made an acquisition of a company and that's a great team.
44:52
It's uh the modular team.
44:55
I think the founder is Chris Latner.
44:57
I think uh he's probably a legend within the computer science world.
45:01
He was the inventor of the Apple Swift programming language.
45:03
He was the inventor of LVM >> and he built a stack which is like CUDA uh but is designed to work on any hardware doesn't matter uh you know CPU accelerators and will run on on Nvidia on AMD on on Qualcomm or on any hardware whatsoever.
45:25
So we bought that company and we're making that open source.
45:28
That's what we're doing because we actually believe that there the industry will benefit from an open-source uh stack that scale from the data center across different hardware in the edge and it doesn't matter.
45:40
I'm going to celebrate that stack in each and every one of my competitors because we probably need an open stack to to drive innovation and AI otherwise it's just one company doing most of the innovation.
45:54
Uh thank you that that's a very helpful explanation.
45:58
Um uh we were just earlier in the show talking about Jensen Wong sitting down with Ezra Klene engaging with some of the very uh deep questions about AI jobs and existential risk.
46:11
Uh is this something is this conversation that we're seeing bubble up in the public sphere actually making its way to the seauite of your customers that you engage with or are you sort of in a mode of put one foot in front of the other and uh let those conversations take place in other platforms?
46:30
Have you been engaging with all these debates around slowdowns and open-source AI versus closed source AI?
46:37
It feels like every week there's a new big hot topic and meaty almost sci-fi scenario to engage with.
46:45
But what has your been appro What has your approach been as the CEO of an important company in the space?
46:52
>> Well, I wish we have like at least like half an hour to have this conversation.
46:56
This is a very big topic. It's a very big topic.
46:58
But look, in one minute that we have, I'm going to try to maybe give you an answer.
47:03
Um, >> solve the whole problem. One minute. >> In one minute.
47:07
>> In one minute. [laughter] I think there's a lot of different conversations and look and it kind of changes for example in the United States you see a lot of conversation about safety of the models I I look I think people talk about safety in general and
47:25
but there are there are very specific things I think cyber uh security is actually a big one and I think it's really important it's important to have products that are done responsibly I think that nobody's going Nobody's going to argue against that. Hey, do I want me
47:38
Hey, do I want me to build a product that is going to go crazy?
47:42
No, no, I don't want you to do that.
47:44
I think that's kind of a logical thing.
47:46
And cyber security is actually a a serious issue.
47:50
You don't there's a big surface area.
47:52
But if you go to places like China, the conversation is very different.
47:56
It's about putting AI in every card in every phone, every PC, every industrial, and it's kind of very different. >> Yeah. Yeah, it is. Yeah.
48:03
We were talking about that how uh there's just such a wide gap in the discourse between uh the impact putting AI in all these little places uh and then you have like the bigger questions.
48:13
But uh we'll get to that next time.
48:15
We'd love to have you back on the show.
48:17
We can spend a full hour solving >> 30 minutes we can get to the bottom. >> Yeah. Yeah. Then we'll solve it. It will be solved.
48:23
But congratulations for calling in from your >> Thanks so much for taking a couple minutes today to come chat with us.
48:26
Have a great rest of your day. We'll talk to you soon. >> Cheers. >> You too.
48:30
Great talking to you guys. Thank you. >> Goodbye.
48:33
Let me tell you about MongoDB.
48:35
What's the only thing faster than the AI market, your business on MongoDB?
48:37
Don't just build AI, own the data platform that powers it.
48:42
Coming back on the show, we got Talia Goldberg, partner at Bessemer Venture Partners.
48:47
We very much enjoyed her last appearance. >> What's going on? >> How you doing?
48:54
>> Hey, great to see you guys.
48:56
>> You had a hot take last time that went very viral and proved true.
49:01
Everyone thought, everyone was like, "No, it's about to collapse."
49:03
And you were like, "No, I think like it's okay if there's some, you know, margin compression in the short term.
49:08
Uh, things will iron out, models will get cheaper."
49:11
And everyone was like, "She's not taking finance seriously."
49:14
And here you are vindicated. >> Vindicated. So, congratulations. Victory lap package.
49:20
>> Your margin is my opportunity. >> There we go. There we go.
49:22
Uh, how are you processing the current moment? What's exciting to you?
49:27
Uh, is the is the lab trade over?
49:29
Is the more opportunity above the fold at the application layer, below the fold at the at the semiconductor neocloud buildout level?
49:38
Like what's exciting to you personally? >> I mean, yes and yes.
49:41
Um I'll say um a few things I've been thinking about lately.
49:46
Um one is physical AI and one is this new concept that a portfolio company of mine fall >> Oh.
49:56
>> coined called token market fit.
49:56
Um, >> and I'll talk about like I love this idea of token market bit.
50:01
I wish I had invented it but they did.
50:03
Um, and the idea of token market bit is like simply put like what are the areas in the categories where we've found um the ability to productively use the average end user like $10,000 a month of tokens.
50:19
And if you actually look at where a lot of the spend has gone, it's really like there are basically only three categories I can think of right now that have like real token market fit.
50:25
One is in coding like you know enormous spend in coding.
50:30
The second is in um is is in video and in media um uh and and in your line of work where you can very productively spend you know huge amounts of money creating great content.
50:40
Um um and then maybe the third is in high frequency trading.
50:44
But if you look at like on average you know legal customer support sales like we actually haven't really hit like product market fit in a real way in a lot of those areas.
50:54
We will and I think the bottlenecks are moving away from things like code and video.
50:59
>> Is it possible that you can have product market fit without massive costs?
51:05
>> Because like like >> I would argue that that you have like token market fit in something like a legal but it's just not you're not going to see massive spend in the way due to the nature of the work. Right.
51:18
So it's like possible that you can have I I'm just this is maybe a question.
51:22
is like can you have token market fit with just like relatively modest per person spend at a company?
51:30
>> Is this sort of the argument that like like we need a lot more software but maybe we don't need that many more legal briefs or something like the market size is small.
51:37
is small. Is that >> Yeah, there's there's in some ways almost like infinite soft like a product is never finished but in legal work like you do a deal and then it's sort of like done and there's not you know I don't know my question yeah my question is
51:51
like I believe there's I believe there's going to be some categories where >> like AI is incredible it transforms like the the task or the job but you just don't end up with that much it's just very efficient >> and you don't end up with like you know exceptional spend Right. You're saying
52:06
You're saying like $10,000 a month per company in a category. >> It's possible.
52:12
I mean, I think um like I guess yeah, there's difference between product market fit and token market fit.
52:17
But legal is like a really interesting example.
52:18
Best been a longtime investor in Lora, which is like a a great example of this.
52:23
And I think they're still very early actually in this transition towards what's possible and what spend even for their customers is possible.
52:29
So I think legal is like yet to be unlocked in a lot of ways.
52:34
And um uh in a lot of use cases, Lora is still co-pilot.
52:37
It's not really autopilot.
52:40
It's not like truly doing the work of of lawyers and and you don't see like large law firms like you know massively changing their their team compositions yet.
52:49
I think that is still to come.
52:52
Um and that's on the come and as we can give and figure out how to productively leverage um models to give more work to to agents like we will be spending more on agents and on models in in in fields like legal too. >> Yeah. Yeah.
53:07
I mean we saw that transition uh maybe last year earlier this year with like the software engineer who says like I don't read the code anymore and that can sometimes be a little bit risky but in a lot of places >> the lawyer who's like I don't even read the contract.
53:22
That would be token market fit.
53:23
Like if I heard a, you know, a star lawyer say, "Yeah, I don't even read the the the filing anymore, uh, the document," I'd be like, "Okay, yeah, super intelligence is here. It's working." Like I don't >> Yeah.
53:36
Well, the the difference is like software, you can ship a feature and it can be it can be broken 1% of the time.
53:42
Where in legal if you have a 1%, you know, major error rate in contracts like it's a lot of money on the line. >> Yeah.
53:50
Maybe you have to be a little bit higher up. Yeah. Um >> Yeah. >> Yeah.
53:54
Look, there are these categories that like I guess the point is like they're still to come and like they're still growing and so like what are those next areas with token market fit from an investment perspective are interesting because while you're right there will be some that are maybe a lot more efficient.
54:06
A lot of the dollars are in the token flow. >> Yeah.
54:09
How how important do you think it is to be the entry point to a per to a certain uh token flow uh to be an aggregator in the strategy parlay?
54:17
Uh I was just thinking about fall and Higsfield.
54:23
They they you know access to incredible models.
54:25
Uh they there's a few other companies in the category but uh a lot of people the workflow is like go to their prefer a for their preferred AI agent then send an API key over and they've already put some credits in an account and then they're using another tool to sort of access that inference.
54:45
And there's a world where that billing relationship lives within cloud code or lives within codeex and they're taking sort of an app store cut. Is that a nightmare?
54:55
Is that a death now for the inference provider or could that actually be uh the future way of these companies working together in a positive way that actually grows the market and the distribution so much that it offsets whatever whatever rent the model labs and the and like the the front the the front doors are taking. >> Yeah.
55:16
>> Yeah. Yeah, I look I think it it's probably not like black and white in every single category and so it's somewhat different like even in you know in the case of fall as an example like they are the front door and so you know that you can even access a model like you know in the past models like nano banana and Google's models even through fall so you don't have to go to Google so like they have they have their cake
55:35
and eat it too but we do see this playing out and I think like instinct and muse and what's happening with meta or um Amazon and Shopify are other really interesting analogies of this aggregation disagregation theory and at at a high level like I I I [clears throat] am a believer like there will be places in for both and like both is exist Amazon exists and Shopify exists and like the the tension between the two is real. >> Yeah. >> Yeah.
56:01
>> Personal agent predictions.
56:02
>> How are you thinking about the category?
56:05
Um, well, we're very small seed investors and instincts, so we're, you know, uh, very bullish on on their uh on their I don't know if I should be offended by that sound or >> No, no, no. That's >> No, no, no.
56:16
It was a small check, but now it's But because you went in the seat, it's big now.
56:21
>> I wish we were bigger.
56:21
I wish we were bigger investors.
56:22
But um um uh like it is such a magical experience.
56:27
I'm obsessed with these products.
56:28
obsessed with these products. Like >> other than when chat GPT launched like this is the second oh my god moment that I've had and like my parents are having this moment and my sister's having this moment and they don't even use technology and so it's kind of crazy
56:42
like >> it is much broader than what we saw with the cloud codeex boom where it was this magical experience for people in tech and for people who knew how to open up a terminal and then were familiar with code and had these tasks that sort of fit neatly in that world. This is
56:57
fit neatly in that world. This is something that you can give to a family member who uh doesn't have a GitHub account and they'll have fun and they'll and they'll get some value out of it and be and and sort of see the progress which they might not have updated on in
57:10
three years or something but >> and so yeah, everyone is going to need to own this and there's going to be a major war and every large lab, every large company is going to be out there trying to figure out how do I create like similarly such a delightful experience and how do you bring this to enterprise? I think it's just like the
57:23
I think it's just like the the interaction modes, the delightful proactivity, the simplicity of the interaction is something that could absolutely be ported over to the enterprise use cases as well.
57:34
And so I think um uh this is not a winner take all market.
57:40
There will be multiple um agents and and different angles on it.
57:47
Um and I think this is about to be arguably the most important next category in AI.
57:51
>> It's another knife fight, too, which is fun.
57:53
Uh yeah, give us the update on Bessemer raise some more funds, but what's the structure of the fund?
57:57
What's the structure of the of the >> strategy evolving? All that good stuff.
58:04
>> Yeah, look, so we have uh exciting news that we just raised 5. 75 billion.
58:06
Uh one point, >> that sounds good.
58:15
[applause] >> That's how we feel. We're we're so excited. Um, we have $1.
58:20
75 billion of that is dedicated to early stage companies, which lets us write really meaningful investments in companies at their earliest days when conviction matters a lot when it's not, you know, very obvious.
58:36
>> $800 million seed rounds. Let's do it.
58:39
>> Get it done in two deals.
58:42
>> [clears throat] >> probably not, but >> and look like 70% of our investments have started early, often way before there's even revenue or or sometimes even a company name.
58:51
>> Um, and we're going to continue to do that.
58:53
And then $4 billion for growth, which lets us keep backing companies as they're at their inflection points.
58:58
Um, and not just participate um or let others kind of invest in them.
59:03
We're going to be leading these rounds.
59:04
Um, returns are concentrating, as you all know, in fewer larger winners.
59:07
And so the right move for us is to be a meaningful investor and have meaningful positions in the companies that really really matter.
59:16
And we don't want to spread it thin and be peanut buttering across our growth dollars across a bunch of companies.
59:22
We want to be super disciplined but disciplined not by taking small checks in in growth companies by making big checks being highly selective and really committing fully to um a smaller subset of companies. So that's the strategy. >> That's exciting. >> Amazing. >> Very exciting times.
59:36
Well, thank you so much for coming on the show.
59:38
It is so funny to rewind like 10 years and and if somebody from from the future came and they're like Talia like you're gonna have a $5.
59:45
75 billion fund and you would be like so we're the biggest venture investor in the world right [laughter] it's like well you know this is now like if you want to be like a real fund you got to have >> companies are staying private so much longer I mean the yeah trillion dollar IPOs are you know unthinkable years ago and now there's of them it's Wild time >> and >> lot lot to come.
1:00:11
Well, thank you guys for having me. >> Great to see you. Great update.
1:00:14
>> We'll talk to you soon. Cheers. Have a good one. >> Goodbye.
1:00:16
Let me tell you about Cisco.
1:00:18
Critical infrastructure for the AI era.
1:00:21
Unlock [applause] seamless realtime experiences and new value with Cisco.
1:00:22
We have our next guest in person. Welcome to the show. How you guys doing?
1:00:30
Sorry, let me get this out of here. Welcome. Welcome.
1:00:33
Introduce yourselves for those. Hi.
1:00:37
introducing great name as well.
1:00:37
Uh, introduce yourselves for the audience.
1:00:42
Tell us about the company. >> Yeah. >> Yeah.
1:00:44
I'm uh my name is Lewis Anteneelli. >> I'm John Antelli.
1:00:47
Um, we're brothers if you haven't couldn't guess.
1:00:49
Um, >> but yeah, we're so we started real six years ago.
1:00:54
It's a score stat tracking app.
1:00:56
It's very social, engaging, >> um, and it caters towards those fantasy players, betterers, just avid fans >> that want to know what's happening in sports.
1:01:05
Is it the ultimate second screen experience?
1:01:07
Is that the trend or is this actually like a third screen now?
1:01:09
How do you How do you think about this?
1:01:13
>> Yeah, I think it's the ultimate second screen and then I mean it's the penultimate first screen.
1:01:16
So like when you're not watching the game, it's the fastest way to know what's happening in sports. >> Okay.
1:01:22
>> Um we started like when we started live data was kind of >> playby-play data was kind of this concept that was kind of shoved on a fifth tab on ESPN.
1:01:29
You'd have to like tab over to the playbyplay.
1:01:31
you'd have to look at like really tiny Excel looking rows of data to try to find those small numbers and see, you know, what's happening.
1:01:39
So, we wanted to bring engagement, you know, comments, reactions, a bunch of community around the data.
1:01:45
And then the latency too at the time with gambling companies like low latency data is super important, right? Sure.
1:01:51
>> So, we brought that more from a media perspective to, you know, show people, you know, bring people that knowledge in real time about what's happening. >> Yeah.
1:01:58
What what is the data source?
1:01:58
are are are there like sort of open APIs and and companies that are comfortable sharing the raw data at some point like someone has to write down what happened in the stadium I imagine or >> it's pretty commoditized like Genius Sports Sport Radar we scrape a lot of the data as well. Okay.
1:02:17
>> So it's pretty like >> the fact that Steph Curry hit a 3 isn't owned by the leagues or anyone.
1:02:21
So like we've kind of taken a spin on that's our content >> public ball knowledge. Exactly.
1:02:26
And then uh but but so so the magic uh like the secret sauce is really uh transforming that into a feed that is uh intelligible not an Excel sheet more like a Twitter time >> and contextual too like I think we add like we started to add historical milestones.
1:02:42
This is a player's 500th career three or like a player's 12th straight point in a row.
1:02:47
A lot of the times when you're on these products like you won't know or even when you're watching or you just tune into a game like you won't know how well a player is performing or what they've did in the first or second quarter and this kind of brings them that like it adds like what an announcer might tell you. >> Yeah. >> Okay.
1:03:02
So you guys have one one and a half million monthly activives.
1:03:03
half million monthly activives. Uh, and it sounds like the social product is actually like working and real, >> which is notable because >> I feel like so many people think about this idea of like, I'm going to create a data product and there's going to be a
1:03:18
social layer and then >> for every hundred pitches like that or people that take a shot like yet maybe less than one are going to actually build something where there's like tons of highly engaged users just because I think about where these sports communities pop up. It's on Instagram,
1:03:32
It's on Instagram, on on X, on Reddit, on all these existing social platforms.
1:03:37
So, how was that like how did you actually make that happen?
1:03:44
>> Yeah, I think so from my perspective, and John can touch on his, but um we basically on Twitter for example, you'll have a million people who are all separately leaving the same comment about some big play.
1:03:54
We take that play as the piece of content and then underneath that you'll have the million people.
1:03:59
So, we've kind of inverted it y >> a little bit.
1:04:01
So instead of like one to many, it's like kind of Yeah.
1:04:03
just flipped and then >> and it's more ephemeral.
1:04:06
So people feel like instead of posting on Twitter, like sometimes when I see somebody in tech will post like >> it's over like and you're like, "Oh, this must be about like no, it's actually about the game that just >> Yeah. Yeah.
1:04:20
And so like I didn't realize that you like AI discourse and also basketball."
1:04:23
basketball." That was the issue is super fragmented across like tons of different I mean there's only a few people that control the conversation like meme pages and fan pages for every sport team and player and we kind of built for those fan pages and me pages
1:04:36
>> people used to use sort of hashtags on X to sort of organize around a particular game or something but that has gone so far out of fashion the algorithm sort of replaced a lot of that but you lost in that place the ability to actually tell the app today I only want focus on the Lakers game like Exactly. And so even if
1:04:54
And so even if something player >> Yeah. Yeah. Exactly.
1:04:58
>> And you'll see X has added like a live chat now at the game level.
1:05:00
So we break down our chats to box scores.
1:05:02
So instead of just everyone in one massive chat, we are like, "Oh, I'm talking about Seur's box score or his most recent three or his, you know, block shot."
1:05:11
So like you can dive into these very like deeper places.
1:05:16
>> Um a cool stat too is like 30% of our monthly users leave comments which is crazy.
1:05:20
It's like an order of mag couple orders of magnets.
1:05:23
>> Yeah, normally it's like 99% lurkers. >> Just work. Yeah.
1:05:25
So, we bring that like really cool global community feel to to everything. Yeah.
1:05:30
>> Uh did you did you get your start on Vine? >> I started on Vine. Yeah.
1:05:34
>> What were you making on Vine?
1:05:35
>> So, I was taking highlights and putting songs to them.
1:05:37
I was like one of 30 people that were like the OBJ catch that went viral. I put a song to it.
1:05:40
It went viral on when I posted it. >> That's awesome.
1:05:44
>> Um and then I grew a page.
1:05:44
It was close to 100,000 followers in 6 months. >> Okay.
1:05:48
So no no no frontf facing no personality like original faceless content. Wow. >> Yeah. Exactly.
1:05:53
I was and I still know like decade later a lot of the same and this is partly why real why we were able to grow is through these meme fan pages.
1:06:03
Every page on Instagram close to a million followers. Yeah.
1:06:05
>> And I would just curate content. >> Yeah.
1:06:07
>> What I we noticed we're like why do millions of people go to Instagram and follow hundreds of basketball pages, NFL pages, >> super fragmented um and sensationalized.
1:06:16
We I would pull like the top three stats in a game and like make a graphic out of it.
1:06:20
And so a lot of what we've done is we've codified that content.
1:06:23
>> So people share real it's the most sharable score app. >> Sure.
1:06:26
>> So it feels like I mean you see it all over.
1:06:28
That's how we've been growing is people are using as a means to like slander appraised players.
1:06:33
>> Slander [laughter] >> like in the first quarter you can see like oh >> TA went like 0 for 11 or whatever and then people will screenshot that and >> you know use as a as a fueling of like narratives. >> Yeah. Yeah.
1:06:44
>> Yeah. Yeah. What's been the mix of uh of top offunnel awareness and I guess how is how has it changed amongst I can imagine using uh you know paid partnerships with creators we want you to you we want you to promote us uh versus in-house clippers or even like distributed clippers like uh like
1:07:03
like what's been what's worked in the past what's working now uh how has it changed >> I like we just started to seed like we started with just the NBA I think but the way that we started to grow is like we added now we have 18 leagues >> um but we seed this content among like >> there's creators that talk about the game >> sure
1:07:21
>> and they might use like a score in the background or whatever we encourage them or pay them >> to use the scoreboard a real or whatever >> and our first thousand users were all my friends that run these meme fan pages >> and there's like thousands of these meme fan page they have some have like hundred thousands to millions of
1:07:37
followers the NBA sentels or NBA centrals of the world legion hoops like we would take their >> I I would just encourage them to use it or naturally weave real into the conversation >> because they might just I mean they're already posting like oh LeBron had a sick game >> might as well add that like >> war whatever. >> How do you make money?
1:07:55
>> How do you make money?
1:07:57
>> It's a great question. >> Yeah.
1:07:58
So we started with a kind of virtual bucks model like Roblox or Fortnite >> um that users could you know earn and then also collect different moments.
1:08:05
So, you know, instead of collecting like a player will collect his home run or his three or his touchdown and build your fandom kind of based on how many you've how many cards you've collected and >> you have to be like in the app when a play happens in order to collect it.
1:08:20
>> Yeah, there's like real-time raffles and then there's also just like general pack.
1:08:23
So, it's cool cuz like as soon as every touchdown right now in the NFL happens, you can kind of get like a digital representation of it.
1:08:27
It's a statbased representation like a this is a 30 yard touchdown and a really cool thing we do is we rate everything between zero and 10 or it can actually go above 10 so we don't become like a [laughter] guy the dunk contest 14.
1:08:45
[laughter] >> So show his Yeah.
1:08:45
So show's like 10 RBI that like legendary 10 RBI 6 for six 50/50 game was like yeah 15 or something.
1:08:53
>> [laughter] >> So Ashton Genty regularly was sitting in his college days at like 12.
1:08:58
>> So it's sort of logarithmic like 15 is 10 times better than 14.
1:09:01
>> It's like the dunk contest is the worst.
1:09:03
Back when it was like everything was a 10. It was stupid. So we didn't want that.
1:09:06
>> Um but people actually now reference our scores and it's really good for like rookie ladders for MVP.
1:09:09
Like usually it reflects really well without us needing to like subjectively say like this is just a it's a cool. >> Do players like this?
1:09:19
>> A lot of them players a lot of them use it. Yeah. Cuz it's just fast.
1:09:21
I think that's like the difference.
1:09:23
>> They're opening up during the game.
1:09:23
How many time like Trey Jones?
1:09:27
They open up at half >> every single game. >> That's crazy.
1:09:31
>> Um, don't tell like Microsoft Surface or whatever cuz I think they're supposed to use that.
1:09:34
[laughter] >> It's all good though.
1:09:38
>> They're like, "Damn, damn. I've gone 0 for 15." >> Wow.
1:09:42
>> I really thought I was on a really thought I was on a hot streak. >> That's so funny.
1:09:47
It's the opposite approach of like the score takes care of itself.
1:09:49
It's literally just like obsessing over the [laughter] score.
1:09:53
>> I think another thing we do different too is a lot of like the ESPN's Bleacher Reports, >> their score apps would never send real-time notifications only like halftime end of end of game moments.
1:10:04
>> So we'll inform you like this is like >> Tatum's 12th straight point in a row or like this touchdown just happened.
1:10:08
We'll take you straight to that moment.
1:10:10
>> And I think that we send like 50 million notifications a day. Yeah. >> On average.
1:10:15
>> Is that sort of like predictive like you can based on the AI model or Okay.
1:10:17
So you have a rating and then if something's happening, you can be like, "Oh, you should really tune into this game.
1:10:23
Something cool is happening."
1:10:24
>> We'll tell you when it happens.
1:10:24
We we do some like cycle watches are the coolest one.
1:10:28
So like when a player's >> about to hit a cycle in baseball and they have like three of the four things.
1:10:33
We won't tell you when they need a triple cuz that's very unlikely.
1:10:34
So we'll only bring you in when they need a single, double, or homer. >> Okay.
1:10:38
>> So we'll bring people together for kind of more of that like expected things to build that hype.
1:10:42
But most of the time, >> most people there's so much going on in sports.
1:10:46
sports. I just want to know kind of what happens and just be >> is real replacing like sports radio in some ways >> because I guess when I was a kid I would I would be like you know cycling through the radio and I would hear like a baseball game on and as a kid like who
1:11:02
kind of grew up on the internet I'm like who's who's like tuning in to like just the ra like who's watching sports you know who listening to radio but it's just sort of that you're kind of like passively following a game or or you know a team that you care about. This
1:11:16
This feels >> and now people can stream the video on their phone.
1:11:20
Like I feel like that's probably more dominant. >> It is fast.
1:11:22
I I like to think of it as like written radio and Twitch.
1:11:23
Like it's faster to read than it is to watch than it is to even listen.
1:11:29
>> Especially when there's like 15 games on at once.
1:11:30
Like you're not streaming 15 games on like an NBA Wednesday, right?
1:11:34
So like this is like kind of the red zone of every league.
1:11:37
>> Um is kind of like how we >> What's the What's the smallest league you support currently? Um, >> you like curling?
1:11:45
>> We just added That's It's a good question because like every league technically can be considered big.
1:11:48
I mean, we added we we're starting to add like golf, tennis, some of the more longer tails that we haven't had.
1:11:54
F1's going to be coming on the product as well.
1:11:58
Um, but we cover >> we have tons of like soccer world carits. You've seen that one.
1:12:05
>> Ping pong would be >> We're considering putting a community vote for like some obscure hot dog eating.
1:12:11
>> Hot dog eating would be fun.
1:12:11
Joey People ask for everything.
1:12:13
And we have this like pretty strong Australian fan base.
1:12:17
So like I get every at like 3:00 a. m.
1:12:20
every day it's like add the AFL. Add >> Oh yeah. Okay. >> The Down Under. Okay. >> Add the AFL.
1:12:26
>> It's like our third biggest city is >> How cyclical is the business?
1:12:27
>> How cyclical is the business? is the su I mean I imagine the Super Bowl is your Super Bowl but uh NBA finals has been >> um but but in general is there like a pretty stable demand for this across >> it's definitely cycl I mean like summer
1:12:42
when it's just baseball it's just like natural cycles um >> but >> world cup was big for us as we continue to add more things >> are you thinking about Olympics >> we're talking about like Olympic basketball the tough thing is like it's four months out of the year >> sure >> so like put engineering time into it. >> But I think now that I mean our
1:13:00
>> But I think now that I mean our >> team hasn't really grown from like a I mean our engineers are outputting >> because of like AI and everything more.
1:13:08
>> We used to be outputting updates every 3 to four months now it's every like two to three weeks. >> That's great.
1:13:11
>> And so like we're just continuing to add more insights, make the data more contextual.
1:13:15
I think that's how we continue to stay like ahead is like how do we like we added nextgen stats to NFL.
1:13:21
You can see like telemetry data >> and then we're also see you can see like personnel like personnel coverages and like >> things you wouldn't like while while you're watching like you wouldn't be able to conceptualize.
1:13:31
We're just trying to bake it in into a very digestible way cuz then that makes it more sharable.
1:13:37
You'll tell your friend more like it's >> all that helps is that helps like the growth flow.
1:13:42
>> Can go really deep on live still like live for us is like everything that's most of our usage is live.
1:13:45
So just keep being just go as deep as we can.
1:13:47
What's the house philosophy on prediction markets?
1:13:52
Partner, roll your own, stay away from.
1:13:55
How do you puzzle it out?
1:13:57
>> Yeah, we have an engagement feature we launched kind of with the virtual bucks.
1:14:00
Kind of more of a kind of fun play thing um to build your profile and like compete with others on that dimension.
1:14:07
>> Um we've considered kind of the the prediction markets.
1:14:09
We >> Yeah, a lot of our I mean our recent >> I mean we in the last year and a half two years a lot of our usage has come from fantasy players and betterers.
1:14:17
Um, and people just like sweating their bats basically like around >> sections are like >> I need another 12ks or 10ks or whatever.
1:14:26
Um, or like another touchdown.
1:14:26
Um, and so we were like, how do we play into like more of a real real money gaming spa the real money gaming space?
1:14:32
Um, >> and we've talked across the board from the cowies to market and all them.
1:14:40
>> Um, we we ended up landing on FanDuel as like a partner for us.
1:14:42
Um, so that's another revenue stream. Okay.
1:14:44
I was like, how do we >> bring odds to the product?
1:14:47
I mean, we were talking about it's like we have so many betters and we can't even serve them with live odds. >> Another data source. >> Yeah.
1:14:55
The data layer of the live odds, too.
1:14:57
Like it adds a dimension that people kind of expect now.
1:14:58
You see it on every broadcast now.
1:15:00
It has live odds and what like what Vegas thinks, what the people think.
1:15:04
So, I think there's like a lot of cool things we can do with like real-time charts about how the lines are moving for everything.
1:15:09
People can discuss every market.
1:15:11
they can kind of just like have it where they expect it with especially with our we've signed up millions and millions of the of the gamblers in the last couple years.
1:15:18
So >> a lot and I mean use for the people that are interested in it.
1:15:22
I mean we we've just been missing that as like a source of data and it's a good conversational piece too >> and you can turn it off like obviously there's people that don't want to see that so they can just toggle it off.
1:15:32
>> Do you think AI is allowing people more time to just watch sports [laughter] >> more?
1:15:37
I think the thing that's missing though which I think we separate ourselves even across Instagram and Twitter and Tik Tok is they don't have deeper community.
1:15:44
Like I can spend six hours a day on NBA Twitter and get nothing from my fandom. Like I don't get badges.
1:15:50
I don't get like anything related to like I'm you know viewing these games we build up your profile and I think that's something we want to continue to like triple down on is that community side because a lot of these I mean you look at like there's hundreds of millions of Taylor Swift fans. Yeah. Yeah.
1:16:04
Well, there's no like I still think there's these communal things that could be built for like tons of different niches. >> Yeah.
1:16:10
A lot of it's offline like you have the tour merch or the signed, you know, album or the jersey that's signed, but on the digital world.
1:16:17
Uh there was I mean there was some movement towards this with like NFTTS, but it sort of died off.
1:16:24
>> Think about it with Belly and even like the letter boxes of the world like same pockets. >> Yeah.
1:16:29
Clout people like >> they want that like community profile that they're building >> focused thing.
1:16:32
Yeah, >> I think sports is pretty safe though and AI because it's like the last kind of human thing that people value.
1:16:37
I mean that sounds dystopian.
1:16:40
[laughter] >> I don't know.
1:16:42
It's like art >> more than family >> more than your job.
1:16:47
>> But it's like if you think about it it's like >> the only live thing >> and that's why you see these ticket prices are going. >> Yeah.
1:16:51
Ticket prices are high and of course there's a bunch of venture capitalists that are trading AI more than anything too. They're humans. Yeah. >> Yeah.
1:17:00
>> Um h uh community building.
1:17:00
Uh, how do you deal with moderation?
1:17:03
Do you have an in-house team, an AI system, hybrid?
1:17:05
I imagine that like culling the most disruptive folks and or >> there's probably a dance there.
1:17:13
How do you >> It's very It's a huge dance because our demographic is like that 18 to 30.
1:17:16
So, you definitely want to keep some of that edgginess like whitewash everything.
1:17:22
>> Go and talk some trash. >> Exactly. Yeah. And not Yeah.
1:17:25
>> Would you say liel or slander?
1:17:25
There needs to be an appropriate level of slander. >> But we use Yeah.
1:17:30
We've been using AI for moderation from from day one and just building that up over time.
1:17:33
So, we've it's always going to be a dance and a balance, but we we're pretty confident now.
1:17:39
We actually scan like every single reply in real time. >> Okay.
1:17:42
>> Um just to make sure we catch like the really really bad >> you use like a really cheap commodity open source model for that.
1:17:47
Are you actually passing that through AR or is it just like lookup table if bad word like >> Yeah.
1:17:54
Well, we we use the lookup table which catches like 80 80 85% and then we have like a classifier. >> Okay.
1:17:59
that's super fast that catches another 10% and then anything that falls through there will hit like any of the latest >> and then you can and then you can escalate.
1:18:07
>> So we have a couple layers there. Yeah.
1:18:09
And escalate if we need to generally it's what's worked super well and I think it's something that's missing on a lot of these kind of live chats.
1:18:14
There's a lot some of these >> live chat streams [laughter] just like you just have to close the chat.
1:18:19
So like we don't >> we don't want that.
1:18:22
So um yeah it's a dance but I think it's uh it's important.
1:18:26
What's stopping you guys from having one and a half billion monthly activives?
1:18:30
Right now you have one and a half million.
1:18:32
Yeah, [clears throat] >> it's a great question.
1:18:34
I think the I think we just need more sports, more content, >> more sports, [laughter] start creating more >> just more data within the product live events in general.
1:18:44
live events in general. Yeah, most I mean if we want to go for like the billion users say we could have every like live show the bachelor from the bachelor to like you take data >> I mean you can talk about what you said >> yeah the original idea came from so my now wife and I we built like a fantasy
1:19:04
bachelor app where we would sit and type in everything that was happening every kiss every hug and we had [laughter] like we built it it was just us two we built like we had like >> funniest >> yeah we had like 50,000 walking in for the Bachelor >> 50 50,000 weekly users for every kiss in every house. >> So I I watched
1:19:22
>> So I I watched >> we watched four years.
1:19:24
So you're sitting there like R them up them up four years and we had to be on live because if we weren't on live people would just be like where where's the data? Where's the data?
1:19:32
But the most used screen of the app was the live feed with people commenting and watching as their second screen.
1:19:37
And of course like John at the time was building his like incredible uh social media properties and I knew what he could do and like It was just like a perfect mix with playbyplay and sports and like this live feed concept.
1:19:47
So that's kind of >> and him not having to type in the data every time. >> Yeah.
1:19:52
Not having type it in even though the some of these third party providers aren't >> but you look at like politics there's tons of like even like mention markets and stuff >> especially around like the elections.
1:20:03
It's like would be cool in theory.
1:20:03
We don't want to go down that path before we dominate.
1:20:06
I think sports are still like a 10x with what we have.
1:20:08
People do ask for elections like a lot and it's kind of does it is has similar feel to like a prediction market with like the >> Yeah, everyone's watched like the election map populate and go red and blue over the night and and that's sort of like a similar visualization for sure.
1:20:24
>> We are a daily use product though.
1:20:24
I love talking about like monthly activives, but we peaked at like 1. 1 million daily.
1:20:31
>> Sometimes our peak Yeah.
1:20:31
Our DM is like 70% at its peak like during >> moments you would think people would be watching. >> Sure.
1:20:38
But they're almost like people want to also see because a lot of the people watch alone like in their >> like [clears throat] just at home or whatever.
1:20:44
They want that community or that second screen.
1:20:47
>> Um like during the BAM auto bio game we had like 200,000 concurrent people like >> tracking his like every point. >> Yeah.
1:20:54
>> Towards his 83 points. >> Yeah.
1:20:56
>> So we see like during big moments like that or even just like the NBA finals or the Super Bowl, we have like very concurrent usage like high concurrent usage.
1:21:04
Um, yeah, >> just like figuring out how to >> make that more engaging and then also more fun with your friends, I think, is a big growth unlock for us.
1:21:12
>> And all the all the betting and and prediction market companies must be so pissed off that they don't have this product because it's like actually like all they care about is is, you know, user acquisition and figuring out how to get engagement and deep usage.
1:21:24
And you guys feel like you built something that um >> built something that's going to be very hard to actually replicate.
1:21:31
Um but uh would be would be very valuable to them. >> Yeah.
1:21:37
>> Uh are there like walk me through the how a subculture emerges?
1:21:41
I'm thinking about like on Twitter it's just one big global chat room but then there's like teapot like that part of Twitter like these like the the the SF tech insider community has has created its own little sub community.
1:21:55
Are there groups of people that break out and build like a discord for their or or do you have the functionality to create like a group of people that are all commenting on similar games and then they you know become friends on the app and can [clears throat] interact more communally outside of a particular event.
1:22:17
>> Yeah, we have like sub we have groups in the app so people can like create groups and it's kind of nested under any piece of content.
1:22:22
You can kind of talk in that group.
1:22:23
Um, I think most of the sub communities though are still global, but they're around like kind of those meme moments.
1:22:29
So like when Brandon Pudzmsky was trying to hit 30 points, for example, like everyone would every game was just this community and then they would grow for people discovering. Yeah.
1:22:39
Like we need the 30 and he would get like 28 and everyone just be devastated.
1:22:42
So like tracking those type of moments when they pop up is really big.
1:22:44
Like the meatball sub thing with the interceptions right now in NFL like there's like random stuff.
1:22:50
It almost it feels like you're just making up stuff that [laughter] >> I don't know any throw one in that doesn't exist and we'd be like damn that's >> but they happen all the time.
1:22:59
It's like crazy and >> but we don't create them.
1:23:02
It's kind of like they're already a known like people are already talking about like yeah like um like the Brandon Pazki 30 point or like it comes from NBA Twitter almost.
1:23:10
I think we can do a >> like at we haven't really been at this this scale before.
1:23:16
So, I think we can start curing those like subgroups or encouraging them beyond just like teams or players or whatever. >> Yeah.
1:23:24
>> Um, >> yeah, >> it's awesome.
1:23:24
Well, thanks for coming by.
1:23:26
Thanks for breaking it down.
1:23:29
>> Have a great rest of your day. We'll talk to you soon.
1:23:31
Let me tell you about Railway.
1:23:33
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1:23:41
Our next guest is coming in just a minute.
1:23:47
We have Max Levchin from a firm coming back on the show.
1:23:49
Always fun to talk to Max about his role as CEO, how he's adopting AI, what he's seeing in the consumer markets and debt markets and all sorts of things.
1:24:01
Um, while we are waiting for him, let's talk about another electric vehicle, another international electric vehicle, the the Xilei EV that can charge under five minutes.
1:24:13
Is this is this fast enough?
1:24:17
How long does it take to get gas? 2 minutes? Four minutes? 5 minutes?
1:24:18
This feels like maybe a tipping point where people will uh, you know, be more likely to adopt this.
1:24:26
Um, it does make me wonder about the Roadster.
1:24:28
Is that going to be a new will they roll out a new charging technology that allows them to charge faster?
1:24:34
Um, they've been sort of Tesla has not moved fast into the uh like sort of wireless charging.
1:24:40
The actual Porsche, the Cayenne EV does wireless charging.
1:24:43
You just put a mat down on your on your garage floor, drive over it, and then it charges.
1:24:48
That's that seems like a no-brainer.
1:24:50
I'm surprised that Tesla hasn't done that, especially since they were working on that crazy robotic snake arm. Yeah.
1:24:53
Uh, and >> well, I'm hoping the Roadster will >> just send the battery out the side rocket with rocket engines. >> Okay.
1:25:03
>> Uh, but but we'll see.
1:25:03
But yeah, it's an interesting trade right now.
1:25:06
You know, it's charging at at these charging stations can cost uh, you know, in the tens of dollars.
1:25:14
It takes quite a bit longer than gas, obviously.
1:25:16
So, dropping that down, I think, is going to be pretty key.
1:25:22
>> Well, Nikita Beer is Oh, do we have RXS? It's red.
1:25:24
So, >> it was green for a second. >> Let's wait.
1:25:27
Nikita Beer has a has a Oh, I I guess we do have Max. Is that correct? >> Okay, great.
1:25:32
Let's bring in Max Linen, the founder and CEO of a firm.
1:25:34
Welcome back to the show, Max. How you doing? >> It's been too long. >> It has been too long.
1:25:41
uh uh let's start with uh the latest and greatest in in your world in a firm and then as always I have so many I have so many questions about how you're running the company what you're seeing what's working manageriarially on the new
1:25:55
technology on adoption side but first what's the biggest news in a firm world >> today's news we are available in the UK on Amazon that's a massive announcement >> huge not everyone can get an Amazon deal done these days. Congratulations. Congratulations.
1:26:13
[laughter] Do you have to go straight to the top?
1:26:16
Are you negotiating with Jasse?
1:26:16
What what >> Amazon does feel like a company that might build this themselves?
1:26:22
Like what is your pitch to a big company with a lot of engineers and they have AI tooling?
1:26:28
Uh what do you bring to the table when you're partnering with another company at that scale? It's a huge company.
1:26:34
But it doesn't hurt that we've been partners for quite some time in the US and and so we're we're definitely no strangers to working with the Amazon engineering team. They're excellent.
1:26:45
They're very capable of building things. >> We're a specialist.
1:26:47
We know what we're doing in things like underwriting.
1:26:49
We have extraordinary diverse capital markets program that allows us to fund the loans that we we do for for them and for all our other merchants.
1:26:57
And so I think [clears throat] every company that's not a financial service specialist at some point or another flirts with the idea of like, hey, maybe we should do this ourselves.
1:27:07
And if they're not serious about it, they sometimes stick with it.
1:27:10
If they're very serious about it and they're of a certain scale, they usually say, "Wait a second, we should partner with the very best."
1:27:17
And, you know, I'm obviously biased, but I think we've demonstrated that we're we're pretty great.
1:27:21
Uh, so this is a a great continuation of the relationship we we've built with them over the years here.
1:27:26
And uh UK is certainly a super important market for us.
1:27:32
We're very excited to be there.
1:27:33
Also, you we came there a little while ago with Shopify, but been meaning to expand >> the relationship and are excited to be live with Costco and now with Amazon and many others.
1:27:44
>> Having already worked with Amazon for so many years, I imagine that uh the hurdle to rolling this out is not technical.
1:27:50
It's not the actual integration.
1:27:50
They're they probably have great team.
1:27:52
You have a great team in place.
1:27:53
uh where are you seeing uh technical challenges emerge?
1:27:59
Where are you seeing uh acceleration in your ability to deliver a better product?
1:28:03
Is it on the underwriting side?
1:28:06
Is AI helping there or is it on productization, conversion, all the downstream customer service?
1:28:10
Like there's so much in the business.
1:28:12
What's really moving the needle for you?
1:28:15
>> It's like you write our press releases.
1:28:17
So [laughter] I I I'll answer a bunch of there's actually a lot of really cool stuff in in the question you just posed.
1:28:22
in the question you just posed. uh the the thing that I was referring to especially so we just announced we launched an entire new family of underwriting models and this has been a long long time coming so we [clears throat] we are a biopil specialist financial services especially
1:28:37
we've been underwriting building underwriting models for 15 years we have >> pabytes of data that we train on so we we we've been a MLI specialist for a very long time but up until recently we primarily stuck to treebased models they're deterministic they're easier to audit they're easier to explain the regulators we have to do every year. And
1:28:53
And so all of that has been kind of the the the stronghold of a firm.
1:28:59
And about 3ish years ago, we said this attention idea that you see in LMS in the transform architecture is really compelling because it just opens up new ways of capturing complex patterns in a way that humans actually cannot.
1:29:15
And fundamentally improving underwriting models for things like underwriting is expressing patterns you see in behaviors over and over again in a way that can be reused across multiple humans.
1:29:27
And so we started an internal research project into using attentionbased modeling to understand behaviors to surface these patterns all in the service of underwriting people better figuring out a little bit more about them.
1:29:40
So about a year ago, we had something we thought was really compelling and we've been testing it quite obsessively.
1:29:46
We're finally live as of a few days ago with a full suite of these attention-driven models that outperform our own gradient boosted treebased models.
1:29:57
And the way I mean just to give you a sense of just how compelling this this this breakthrough is.
1:30:04
So every quarter we launch a minor addition of the model.
1:30:08
Every year or so we launch a brand new approach to the the core model all using these treebased architectures.
1:30:14
We measure the improvement and that's what we report to ourselves and you know our shareholders on the improvement for this new we call it arc.
1:30:22
It's the new the code name for the architecture.
1:30:24
The arcbased model outperformed the next planned improvement by a factor of two.
1:30:32
>> I don't remember the last time I've seen a [laughter] factor of two implementation improvement.
1:30:35
So it's just very hard to overstate how how compelling this is.
1:30:38
And so this is I'm very very proud of the team and this was a very very large scale project that was just unbelievably successful. >> That's awesome.
1:30:46
>> Uh talk about what you've learned about the the the timelines it takes for ex basically like the the difference in execution between two companies to become obvious to the market.
1:30:59
become obvious to the market. And when I say that right now there's a bunch of new like AI companies for example let's say two vertical AI companies they both have 500 million in funding right now it seems like like you know may maybe there's twoish years uh where where where it's sort of unclear like just how
1:31:21
much better is one company versus the other but over time you know that like one will will surface to the top and then I would say we've also you can basically see that in every category where there's like there's a category like prediction markets hits last year there was like two heavily funded companies and then you saw like
1:31:36
difference in execution and they sort of like bifurcated over time but I'm just wondering from your view how you how you work with your team like when I talking to you you just get this sense that like competing with you would be like living hell [laughter] um and and it's because of the just like the the experience level and then the
1:31:55
approach to all these different layers of the stack and the understanding of the category and your business and like it just feels like you know a firm is just pulling away very very strongly from other players in the market whereas it looked like it was a pretty even race in many ways like you know 5 years ago. >> Thank you first of all that's a I mean I
1:32:16
>> Thank you first of all that's a I mean I I happen to agree but I'm obviously biased.
1:32:20
Um, I do agree that these things take a while to play out and who knows which inning we're in and sort of how many more sort of ups and downs we're going to see in kind of the superficial judgment, you know, aka the stock price, >> but like private markets seem to like muddy this a lot.
1:32:36
It's like almost like companies have to get companies have to get public and then have to to actually >> I'm not even sure public markets make it that much better.
1:32:47
that much better. public markets force you to be very transparent about a quarterly checkin like you know one of the things that I think we did really well as a public company if I do say so myself is we put a timeline of getting
1:33:00
profitable out on the map and said we're going to get there and we did it quite far out so 24 months before we were profitable we said we're going to be profitable 24 months from now and we just printed quarter after quarter after
1:33:11
quarter showing like look we are that much closer and then on the dot actually a couple of couple of months prior said yep profitable Now here it is and I think it was a big credibility thing that private companies don't get to have
1:33:24
because the only people who know their internal metrics intimately are the venture capitalists and even if they publish metrics they wouldn't be held to the sort of a standard GAP accounting SEC regulated way of communicating. So
1:33:34
So in that sense public companies have it probably a little bit easier but you also get slapped around if you miss on a metric or the market thinks that you you messed up one of your metrics.
1:33:44
But I think [clears throat] the way you know who's pulling away kind of early if if if I were you know putting on my occasional investor hat I think you know I I'm feitting your compliment to me back to myself but I I kind of happen to agree with it.
1:34:02
I think you can tell operator to operator, people who are quoteunquote, for lack of a better term, serious people, you can tell like people who know their metrics, people that understand the entirety of their stack that don't just say, well, you know, I have a great team and so my AI engineers told me to say these words and those are the words I'm going to repeat now obsessively.
1:34:23
Like >> maybe the shortest answer is like companies run by engineers.
1:34:30
We we are skilled in not BSing.
1:34:30
And so if that that may be like a good one eight predictor like how likely are they to to do what they say they will like if the person running it has an engineering degree in whatever engineering >> probably going to be pretty truthful. >> Evergreen evergreen.
1:34:46
Uh I had one more one more follow-up question.
1:34:48
I I'm very curious to get your point of view.
1:34:50
Um, we've heard a million pitches on this show about how agents are going to need to pay agents and we need all of this new financial infrastructure and I've been consistently incredibly bearish on that just because we have a bunch of really robust financial infrastructure that's regulated.
1:35:08
We even have companies that like, you know, think about Stripe for an example.
1:35:11
They've built for developers at the core from the very beginning, which means that they're inherently well set up to work with agents.
1:35:18
agents. And using an example, we see new personal agents like Instinct and Muse and there's a bunch of others coming and there's no point where I'm sure somebody's pitched like a firm for agents, you know, or some some silly
1:35:33
pitch like that, but at at with with all these new, you know, sort of applications, the agent will just go and tell the user, do you want to pay cash or do you want to use a firm and like a firm and and they'll just get to select like they would as a consumer. And so
1:35:47
And so there's no new financial infrastructure needed.
1:35:50
And I feel like Agentic Payments, like net new Agentic Payments might be an entire mirage and we may have gotten a bunch of like, you know, posts online and blog posts and all this stuff and then really nothing new happens. But what do you think?
1:36:07
>> Um, I'm going to make a a bold claim. >> There we go.
1:36:11
>> A firm for agents will [laughter] >> want to put it out there. I know it's risky. I know what I'm saying.
1:36:20
>> Um, no, I I I I happen to agree with you.
1:36:23
I think there are definitely many really cool exciting developments in Agentic.
1:36:29
I am trying out all the same agents myself.
1:36:31
Some of them are surprisingly good.
1:36:34
Some of them are still lumbering through the same problems you see with some of the earlier attempts.
1:36:39
But it's very clear that we will get we will all have agents doing our chores for us.
1:36:45
I happen to believe that quite a lot of shopping isn't actually a chore.
1:36:50
In fact, it's a form of entertainment.
1:36:52
And so human in the loop will not just be a requirement.
1:36:56
It will be a loss to humanity if we are not allowed or if we're not participating in some of the shopping choices, which includes, by the way, the way you pay.
1:37:04
But some of these things will go to the agent, the underlying plumbing.
1:37:08
And by that I mean everything from deciding the best way to pay all the way down to figuring out the smartest choice of a plan, most rewarding transaction, best 0% loan, etc.
1:37:23
I think that's going to primarily accrete to people who know what they're doing or specialists in the space.
1:37:28
And that's why we have to continuously work on improving underwriting.
1:37:31
We want to be more inclusive as in say yes to more people while maintaining the same level of credit performance.
1:37:38
And so all of that is still like the work we have to do and we have to do it faster and we have to pull away from the competition as aggressively as we can.
1:37:44
But I don't think there's an opportunity to dislodge a firm by showing up and saying we are just like a firm but smaller, less profitable with less credibility in the market and the capital markets in particular, but we are agentic. We are agentic too.
1:38:03
We're pretty pretty agentic ourselves. >> Yeah, I know. I love it. That's a great take.
1:38:08
um how how have you been approaching leveraging open source models in various sort of like employee use cases and workflows?
1:38:17
I think it's been you guys are so such an you know incredible engineering culture.
1:38:22
I'm sure a lot of your team has been using open models in a bunch of different ways.
1:38:26
Yet at the same time, if you were focused maybe five months ago about, you know, building your own harness or or using these harnesses and open source models and then the cost of the frontiers drops like so dramatically to the point where you now have like frontierish models that are cheaper than open source in some cases, maybe that wasn't the best use of time.
1:38:47
So like how are you thinking about allocating time to getting the most out of open models where it makes sense versus trying to avoid just wasting time when the cost of intelligence will continue to fall.
1:39:01
>> So we actually did something pretty smart if I do say so myself pretty early on.
1:39:06
So I'd sort of predicted that we're going to go through these moments.
1:39:08
were like, "Oh my god, the best harness, the best model, the best the combination of RS model, user interface is going to change."
1:39:19
And there's so much money, there's so much innovation.
1:39:22
There's so many really brilliant people who are working all day, every day in making AI useful specifically for software engineers.
1:39:30
It is foolish to commit to a configuration today.
1:39:33
you know, someone else is looking at it and saying, "Wait a second, that is the best way of writing software, except I have a better idea."
1:39:40
And writing software just became the best it's ever been by the hands of the company I'm about to compete with.
1:39:46
So like the whole like this self recursive self-improvement that everybody's sort of either excited or terrified about.
1:39:51
It hasn't come to the models yet, but it's certainly come to the development industry.
1:39:54
Like we are living through recursive self-improvement of software engineering for humans and agents together.
1:39:59
And so sometime around January of this year, we split off a team of about 12 people and basically said, "Your job is to make our development experience the absolute best for the current state-of-the-art in a way that is easy to take advantage of now, but switch out to the next best thing later with a thoughtful continuous matter.
1:40:22
So we don't want to have this disruptive moment where everybody stop.
1:40:26
We're all gonna switch to product X. Oh, wait a second. product wise available.
1:40:31
So we've had this team and it's it's run really really well by a bunch of very very smart engineers who love their craft and know what they're doing as practitioners but also great thinkers when it comes to developer experience.
1:40:45
They have been keeping us at the almost the cutting edge of both the commercial frontier models as well as open weight models, harnesses etc.
1:40:52
where we organize the entire process through their hands and whatever it is they offer to the entire company is usually within a hot second of whatever is considered cutting edge.
1:41:06
But it's thoughtful enough where if you yesterday you were on harness A and today we really believe harness B is better, they will make the transition really simple.
1:41:15
So just having a dedicated team that gives us the best possible developer experience without having to do a handbreak turn every 3 months has been unbelievably good investment.
1:41:25
Like when when we locked off the steam and said we're going to have this big group of people whose only job is to make us more productive at the meta level, I think some people were kind of doubting the validity of the idea.
1:41:34
Yeah, it's interesting because like the alternative is you know similarly sized companies you have hundreds of people that are experimenting in real time and be like well I think I think I found the best way to do it and the other person's like well I'm using this thing and then it's just whiplash.
1:41:48
>> So I'll give you real stats uh on this one since I'm I'm a fan of numbers.
1:41:50
one since I'm I'm a fan of numbers. So we were in the experiment away mode until we had this developer experience team um developer productivity team and we were probably I think the percentage of code written by machines and humans together versus prior to this team's arrival in
1:42:10
increased by a factor of 10 when we organized the team and said look here are the prescriptive approach we're going to take and there's always a menu like you can use cursor you can also use cloth there we support all sorts of different harnesses and models, but we have a menu versus go figure out what works for you best. The tyranny of
1:42:26
The tyranny of choice is a terrible thing.
1:42:29
And telling a software engineer, go explore over the weekend your favorite way of writing code with an agent.
1:42:33
It's not going to be a weekend project.
1:42:35
It's going to be a six months long project.
1:42:36
So lpping that off into a separate area where you have a rigorous approach and then we constantly produce here's the best way according to this team and here's some of the choices you have in there has been really really useful.
1:42:46
We know it's doing well for us.
1:42:49
doing well for us. are so we measure productivity long before AI in PRs pull requests per engineer per unit time the cost per PR fully loaded everything from salaries all the way down to AWS costs has come down 30% since we created this developer productivity team and so not
1:43:09
only are we increasing the amount of code we're writing because we're able to leverage all the agents the true cost per pull request is coming down quite steadily and has been for a while and so >> very very excited about what's to come there, but I love the fact that we have this really well constrained approach. >> Makes a lot of sense. Uh one uh one word >> Makes a lot of sense.
1:43:26
Uh one uh one word answer for the next one.
1:43:29
Uh since you like numbers, what's your P do? [laughter] >> No answer. >> No answer.
1:43:38
All right, we'll get to it next time.
1:43:40
Um >> thanks so much for coming on the show. Great day. We'll talk to you soon. Goodbye.
1:43:46
>> Let me tell you about Crowd Strike. Your business is AI.
1:43:47
their business is securing it.
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1:43:51
We got Sam Ross from Numemer, the co-founder and CEO in the way.
1:43:56
>> Sorry for keeping you [music] waiting. Sam, how you doing? Welcome to the show. Doing well.
1:43:59
>> We got a fresh gong knowledge.
1:43:59
Jordy already broke one, I think, because he was celebrating that it's only 92 days till Christmas.
1:44:05
But we got some bigger news than that.
1:44:07
A number that's bigger than 92.
1:44:10
>> Mostor the most boring AI company in the world. Which which No, no, no. They ran that as an ad.
1:44:16
Oh yeah, that's >> that's our campaign and and you guys trigger a bunch of you guys trigger a bunch of people. >> You triggered me.
1:44:23
I don't find it boring at all.
1:44:26
>> Uh anyways, let's talk about the round.
1:44:28
Let's talk about >> How much did you raise?
1:44:30
>> We raised $100 million >> with Insight Partners.
1:44:39
You got Salesforce Ventures.
1:44:42
>> You're being careful if you uh shake hands with Mark Benny off in the wrong place.
1:44:46
You don't want to get frame mogged.
1:44:47
That's a big risk partnering up.
1:44:49
>> I know he's I know he's already modded you guys.
1:44:51
So, I [laughter] way >> Yeah, we were Jordy more than you, I guess. Yeah. >> Well, both of us. >> Absolutely brutal.
1:44:58
>> What unlocked the round?
1:44:58
What was the most exciting thing that Insight latched on to?
1:45:02
Is it just topline growth?
1:45:02
Are the margins better than what we're seeing in other companies?
1:45:06
Like what was the thing that they were like, "Okay, let let's back up the truck." >> Yes.
1:45:12
Well, we saw sales tax, RSI.
1:45:12
Um but beyond that um I think you know ultimately this this business is you know sales tax is not going anywhere.
1:45:23
In fact it's growing in California starting in January all businesses selling software have to collect and remit tax on software.
1:45:31
So there's just a growing trend where as the world as AI takes a bigger share of the economy uh there's going to be more and more of the tax dollars are going to be taxing things like AI.
1:45:40
like AI. M >> um on top of that I think you look at the world of accounting and really our space is like tax advisory >> and AI has not penetrated that space as much as areas like law and so there's you know there's some really large potential there to build u really large
1:45:57
business and so I think you know we we've been building this business for about 3 years we've had um you know phenomenal growth and I think just uh yeah with a great engineering team and great customers uh that's what gets the the investors excited. >> Here we go. What what are the most >> Here we go.
1:46:10
What what are the most valuable growth channels for you?
1:46:12
Are you able to sell through tax accounting firms and then the accountants tell their clients, hey, you should be using numeral >> or do you go direct to the CEO of the biggest software companies in the world and say you got to use numeral? What are you thinking?
1:46:30
>> Yeah, look, we do all the above.
1:46:30
uh more and more fir partnerships with firms is is getting important for us especially as you move up market and these complex businesses really trust their adviserss.
1:46:41
Um I've always been someone who has you know before this I was running e-commerce businesses so I I love the world of growth and so you know a lot of direct sales as well um all all the things you'd imagine that most uh SAS companies are doing a a lot of marketing. >> Oh yeah. Uh sorry >> go for it.
1:47:00
I was just wondering about hund00 million dollars. Uh are you staffing up?
1:47:04
Are you hiring a lot of salespeople?
1:47:06
Uh are you uh just going to run even bigger ad campaigns?
1:47:10
Like how are you seeing deploying this capital?
1:47:15
>> Yeah, I think for this money is primarily focused on R&D.
1:47:17
primarily focused on R&D. So again there's a lot to be building in the space around you know building things in the tax advisory space for firms for companies directly um and I think the space is heating up you see companies like sponsor ramp
1:47:35
building in the space as well um and so it's a it's a you know I don't I don't see them as competitive like this is a big space with a lot of different subverticals we're really focused right now on like the indirect tax space so things like sales tax VAT we file in 80 plus countries. Um, and so there's a lot
1:47:50
Um, and so there's a lot of low-level grunt work that gets done by armies of tax, you know, um, tax workers throughout the globe.
1:48:00
And that's what we get excited about going and making their lives easier so people can be more strategic.
1:48:06
>> Last question, and it's a choose your own adventure.
1:48:09
You can answer either of these questions.
1:48:11
You don't have to answer both. One, what's your P doom?
1:48:15
Two, what's the biggest fish you've ever caught?
1:48:19
PDoom is that that's that's based on time.
1:48:21
Um, well, we already we already solved we we already solved RSI uh sales tax RSI.
1:48:26
So, we're fine and we're fine.
1:48:30
I'm living in the future to tell the tale.
1:48:32
So, PDoom zero, a vote of confidence. I love to see it.
1:48:35
Well, congrats on the new round.
1:48:37
Thanks so much for hopping on the show.
1:48:39
>> We're going to close out the show with you.
1:48:40
We We did have a hard stop.
1:48:40
Sorry for the little uh running late on the schedule, but I wanted to close out the show with you.
1:48:46
Well, then I got to tell him about Shopify.
1:48:47
Shopify is the commerce platform that grows with your business and [applause] lets you sell in seconds online, in store, on mobile, on social, on market places, and now with >> AI.
1:48:55
Sam, I wanted to uh throw a flashbang with you as as an early partner of the show and friends.
1:49:01
So, I'm going to throw it and then we'll sign off and congrats to the whole >> numeral team on on an awesome milestone. >> Yes. >> Thank you.
1:49:09
I wanted to also give a shout out to our customer, Lucy. >> Oh, yeah. That's right. Mr. I don't have it here. Aurora water filter.
1:49:18
It's another customer of ours.
1:49:20
So, you know, >> the Shopify numeral ecosystem is cooking. It's cooking.
1:49:24
Everyone's working together. Let's hit it.
1:49:27
>> Well, thank you for tuning in to TBNN. We'll see you tomorrow. Bye.