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Possible hostile journalists on the horizon. Stand by. Founder inbound.
4:37
>> You're watching TVP and today is Thursday, March 19th, 2026.
4:39
We are live from the TVP and Ultra Dome, the Temple of Technology, the Fortress of Finance, the Capital of Capital.
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Let me tell you about ramp. com. Time is money. Save your money.
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4:57
>> That's a narrative violation.
5:02
>> Uh we're having some fun.
5:03
>> We're we're out of control.
5:04
>> We got a great show for you today, folks.
5:06
Carl Eschenbach is Eschenback. It's Sequoia. We love to see it.
5:09
We had the pleasure of chatting with Carl uh a couple months ago and I've always been a big fan of his, but we'll let him uh introduce himself.
5:18
Let's pull up the linear lineup.
5:19
A linear, of course, is the system for modern software development.
5:22
70% of enterprise workspaces on linear are using agents and you should be too.
5:25
Uh we also have Mark Cuban coming on the show.
5:27
And what a fantastic uh return to form for us because the first time we had him on the show, we discussed uh and uh we we we can talk about uh Cuban in a second, but of course we have our light lambda lightning round and Alex Konrad from Upstarts Media is joining as well.
5:46
Um anyway, last time we had Mark Cuban on the show, we were debating ads in LLMs.
5:50
And since then, we've gotten a bunch of data points about ads in LLMs, and I think that some of his takes have probably aged well, some of our takes have probably aged well, and it'll be a interesting time to re-evaluate what's actually happening.
6:04
There's been a lot more points >> know, John.
6:06
I said that ads would be fine.
6:09
>> Well, >> and now the world is ending. >> Yes.
6:14
>> Now we had Now we had ads in LLMs. >> ads, though.
6:16
It's not because of the ads.
6:19
Um, it is it is much more complicated than that.
6:22
But, uh, here's a white pill.
6:24
Samsung is investing 70 billion dollars to advance their fab capacity.
6:27
They're They're getting back in the AI chips game.
6:33
They've always been in the AI chips game.
6:34
Uh, so, brief history of Samsung.
6:38
You probably know them from the phones, from the TVs.
6:40
They, of course, are major player in HBM, high-bandwidth memory.
6:46
Uh, they are a massive company, over a quarter million employees.
6:48
They're close to touching a trillion dollars in USD market cap.
6:52
Uh, they pull in around 200 billion USD a year in revenue, maybe 250 billion this year in revenue. Really good. All that's USD.
7:00
When you look up Samsung, you get uh, South Korean won, but I like to think in USD cuz I'm an American.
7:09
Uh, and it's actually kind of complicated thinking in foreign currency.
7:12
Uh, they're the global leaders in memory and OLEDs displays as well.
7:16
So, a lot of the displays that you see in other electronics, even if it has a different brand name, it's still Samsung actually making that OLED display.
7:24
Uh, but they're second in smartphones to iPho- to the iPhone and Apple, and they're second in the semiconductor foundry business to TSMC.
7:32
Uh, semiconductors still make up 30 to 40% of their business, um, and they supply HBM to Nvidia for the H100 and Blackwell systems.
7:41
So, it's not like they're sitting out the AI bull market. They are doing great.
7:46
They are definitely participating.
7:50
Uh They're They're incredibly important in the AI buildout.
7:52
Um but if TSMC is bottlenecked and TSMC is sort of risk-off and they're not going to be, you know, guiding to like insane capex numbers while every American hyperscaler is, well, that creates an opportunity for Samsung.
8:05
And so, Samsung's stepping up and they're announcing that, "Hey, we're going to put another 70 billion to work on this particular business."
8:09
So, uh Tesla has been working with Samsung on the foundry side in AI for a while.
8:16
So, uh Samsung's never really been on the frontier with a direct competitor to the H100 or the Blackwell chip.
8:20
Uh that's been more of like AMD's game and AMD also fabs at TSMC.
8:26
So, there hasn't really been this like neck-and-neck battle between TSMC and Samsung.
8:30
Uh but it's like you can do AI inference on a Samsung chip and we know that because Tesla went to Samsung years ago and said, "We need a chip that can take in pictures from the road, decide where the lines are, and decide where the lines are."
8:47
They want their chips with the dip.
8:47
And now Samsung does too. That's how you know.
8:50
Uh and so, the the the FSD system is If you have a Tesla, you might be familiar with like HW3, hardware 3, uh that has been deployed into millions of cars uh and it was fabbed on Samsung's 14-nanometer process, which is a lagging node.
9:06
It We're not in the 3-nanometer or the crazy frontier stuff, but it's working and it's on the road.
9:12
And uh according to a US regulatory probe, there were 3.
9:14
2 million vehicles, Teslas, on the road in America with FSD systems that were uh basically all running Samsung chips inside.
9:26
And so, uh now, to be clear, Tesla, just like any uh foundation model lab company, they have training and then they They have inference.
9:34
They're a little bit different than many uh of the labs that you know and love uh because they do training in a data center using what's called the Dojo chip and that is fabbed at TSMC.
9:48
But so they train the system, they take all the data in from every Tesla camera, every road, all the information that they have, every time that there's a disengagement, that's feedback to the reinforcement learning system.
9:59
It says, "Hey, we were in FSD mode but then someone grabbed the wheel."
10:02
or someone >> stepped >> stepped on the brakes. You made a mistake.
10:06
Understand what happened to get you to that point where you made that mistake.
10:09
And so that all that data gets collected in the Tesla data center, runs on these Dojo Dojo chips, they do the training, and then they deploy the model onto the Samsung chips in the actual cars.
10:19
So if you're driving a Tesla, you have a Samsung chip in there that was trained and the model was trained on TSMC chips.
10:24
Uh and so uh the Dojo D1 is one example of their training chip.
10:28
That was fabbed at TSMC on on 7 nanometer.
10:30
Uh and it's completely separate from the in-car FSD chip.
10:33
So uh with the backdrop of Nvidia's massive GTC news cycle, they've done so much press around GTC and so many different launches, you know that Nvidia's just going to suck a lot of the air out of the semiconductor discussion this week. >> clean room.
10:48
>> Out of the clean room, yes, which is recycling all of the air every 3 seconds or something like that.
10:52
Uh so Samsung dropped this update. It was pretty quiet.
10:55
We were actually struggling to find it.
10:56
There was one Wall Street Journal article about it.
10:57
Um but it it has not SEO'd well.
11:00
They I I mean maybe they need to, you know, do some more podcasts or something.
11:03
But um but they did in fact I mean Jensen's doing a the whole fleet of of of, you know, shows and interviews and all sorts of stuff.
11:13
>> says companies should podcast harder. >> Yes, yes, yes.
11:16
Yes, the solution to everything is is more podcasting. Uh talk of my book here.
11:22
>> No, but I think this is like particularly important, especially this morning.
11:25
Uh the uh I guess the CCP put something out in the last 24 hours basically saying, "Hey, Taiwan is going to have an energy crisis due to the Yeah.
11:37
>> So we need to reunify peacefully.
11:41
But peaceful re- reunification, even if it was completely peaceful and all the Taiwanese people just say, "Hey, we want to be part of China."
11:47
And they all vote for it democratically.
11:49
Um that's going to be rough for the American chip buying industry.
11:51
If you're a buyer of of of of chips that are fabbed there.
11:55
And so having another chip on the board metaphorically uh to make physical chips is probably a good thing.
12:03
I you know, I was writing yesterday.
12:03
I'm I'm very excited about Samsung, very excited about Intel, very excited about all of the new fabs, the the gigafab, the ter- terrafab is the one that Elon's uh talking about. >> Launches in 5 days?
12:17
>> Oh, wait, 5 Did he actually say that? >> He said 7 days. >> 7 days? >> ago. >> Okay.
12:21
So wait, 5 days like the plan launches?
12:25
>> He just said terrafab launches in 7 days. >> Okay.
12:28
>> So I don't know what launches mean.
12:29
>> And and and Tyler, what's the lead time for an ASML lithography machine?
12:34
>> Yeah, I mean it's at least like, you know, 3 5 years.
12:36
Yeah, depending on it depends on what which like tools >> terrafab will be ready in 5 and and the ASML the ASML machine >> It's possible ASML, it's alien technology. >> It's possible.
12:47
>> Elon's going up and back to space all the time.
12:49
Maybe he got some of his own.
12:51
>> An extreme ultraviolet lithography machine on the moon, on the on the backside on the dark side of the moon.
12:56
They just had them stacked up there in crates potentially.
12:58
Um So yeah, uh Samsung's been doing well over the last 5 days.
13:02
Stock's up 11% during time when the Nasdaq is down 2.
13:04
2% and geopolitical tensions continue to rise.
13:08
Uh the compute bottleneck, we know it's important.
13:11
We've been discussing this uh constantly and it's going to be very constraining over the next few years.
13:16
So every in- in- increase in CapEx in the supply chain is a step in the right direction.
13:21
And so Samsung gets the first gong hit of the 70 >> Congratulations to everyone over at Samsung on making making a big bet.
13:34
Uh who else is making big bets?
13:36
Cursor is making big bets.
13:39
Before we talk about Cursor, let me tell you about Phantom Cash.
13:40
Fund your wallet without exchanges or middlemen and spend with the Phantom card.
13:45
Let me also tell you about Labelbox, RL environments, voice robotics, Evals, and expert human data.
13:50
Labelbox is the data factory behind the world's leading AI teams.
13:53
>> Cursor is out >> with Composer 2. >> Composer 2.
13:57
>> It is frontier level at coding, priced at uh 50 cents per million input tokens and two and a half dollars per million output tokens.
14:05
It's also uh they have a fast version.
14:07
They say we were able to significantly improve the model quality and cost to serve.
14:11
These quality improvements come from our first continued pre-training run providing a far stronger base to scale RL.
14:21
>> So >> It performed quite well on uh what is it? Cursor bench? >> Yes.
14:28
>> Which >> Just a funny bench, but >> Which is well, yeah, yeah.
14:30
TVPNA TVPN performs quite well on TVPN bench, too. >> Yes.
14:35
It's a little It's a little silly to design the bench and then publish the bench that your score on your own bench, but I mean, to be fair, like they're putting GPT 5.
14:44
4 high and medium above them.
14:47
So it's like they didn't It's not one of these graphs that's just like, "Oh, look, we made some arbitrary X and Y axes and like we're in the top right corner, of course, because the axes are like good and cool."
15:01
>> TVPN TVPN bench >> Yes.
15:03
>> Technology podcast publish at least three hours of content every week. >> Yes. Yes. >> Naturally >> Exactly.
15:10
>> Naturally we are the >> Right at the top. >> Right at the top.
15:13
>> Right at the >> And it's actually there's no one else on there. >> Yes.
15:15
>> But >> Um but yeah, I mean, this seems fair.
15:16
It is a little bit odd to read this because the cost uh the cost is on the x-axis and it's inverted.
15:24
So, the further you are to the right, the cheaper you are.
15:29
Which makes sense because people associate an x and y graph with you want to be in the top right quadrant and they certainly are.
15:35
And it does seem like in terms of this Pareto frontier, you want to be on the frontier.
15:40
You want to be pushing out across every single curve.
15:44
Maybe if you are interested in sparing no expense, you'll go with the GPT 5.
15:51
4 high or medium model and you can, you know, align cursor to to GPT.
15:59
I'm sort of I'm sort of surprised that Opus is not doing as well on cursor bench.
16:05
That that feels surprising based on like the general vibes around around Opus 4.
16:10
6 generally, but cursor has specific needs for specific customers and I don't know.
16:15
What else do you think is going on here?
16:19
>> Yeah, I mean, the cost is really big.
16:20
Like this is basically like 10x cheaper than Opus. >> Yeah.
16:24
>> Um so, I think also, you know, cursor has kind of been like not really a like dark horse.
16:29
Like everyone knows about it.
16:30
But in the coding race, it's like everyone's like, "Okay, there's Codex versus Claude Code." >> Yep.
16:34
>> But like, you know, if you imagine that, you know, Claude Code and Codex are kind of like these environments for getting a ton of like really good data for training coding models.
16:43
Like Cursor has had that for way, way longer than than OpenAI and Anthropic. >> Yeah.
16:48
>> So, you should imagine that like at least, you know, in the near term, like they actually have like really, really good data that they can, you know, train these these good models on.
16:54
And obviously, like this is a very specific model.
16:57
This They've said it like you're not going to write poems with this model.
17:00
It's this very like specific kind of almost like point solution model where it's it's just coding. >> to them, Tyler.
17:06
Write a poem with the model. Poem bench. >> Poem bench.
17:09
Um yeah, I I I would be interested to know like how many sacrifices were made because it's at a certain point like I I remember talking to uh an AI researcher actually semiconductor uh uh uh entrepreneur who was saying that like he actually thinks he actually does believe that importing like the Odyssey and like Homeric epics is key to humanoid robots learning to walk.
17:39
>> Yeah, well I think like if you look back at just like the general history of like machine learning AI like the the lesson is that like big general models always beat these small specific models.
17:46
But if you kind of zoom in on the time scale like you can still train uh you know GLM some open source model on a very specific task like accounting or something and you can like hill climb and you can actually make it better than the frontier models right now.
18:00
>> At that specific thing. And especially at cost. Especially at cost. >> yes. Yeah, very much so.
18:04
But like on the long term if you zoom out what actually wins here it it seems like it's basically always going to be the these big uh you know general models but >> And I wonder I wonder if that's true.
18:13
I mean we talk about this a lot where the big general model outperforms the smaller model but at at the limit like if you were to think about like a Python if statement just like flow control that is truly deterministic like yes if you if you piped the same question of like the if statement like is this number bigger than this number you pipe that into 5.
18:36
4 it's going to get it right all the time.
18:38
It's going to be very expensive compared to an if statement which takes like no no compute whatsoever right?
18:42
whatsoever right? But the if statement is 100% accurate like unless there's some bit flipped from cosmic radiation like it is deterministic and so if you're in a world where the small model that you've built the classifier that you've built
18:59
whatever machine learning pipeline or small model you built is actually functionally at 100% well then there's this upper bound that even like the bigger smarter model doesn't get you any benefit at all so you're just purely in cost control mode, I would imagine. But,
19:12
But, I don't >> Yeah, that's reasonable, but I I think >> This is not a great example because coding is more >> we're not at 100% saturation on just coding. >> Yeah.
19:22
>> It's just literally one time.
19:22
>> Yeah, I mean, look at the look at the chart.
19:23
Like, the best performing models are sitting at uh at 65%, 60 63 64%.
19:25
So, there's clearly more more room to saturate this particular bench.
19:33
I'm actually super interested to know about what goes into cursor bench at this point because um I feel like when I see every benchmark, it's like 100% now.
19:44
But, that that's just from uh you know, the old the old models, I suppose.
19:47
>> Legendary poster Sand Kalb says all s h i t s and giggles on that headline till Anthropic or OpenAI I decide to cut off their access to Cursor, referencing the Bloomberg article, "Cursor's taking on Anthropic and OpenAI with a new AI coding model."
20:04
>> Would would that matter?
20:04
Like, at this point, if they have if they have Composer 2 and it's a small model, but it's good at writing code and it performs well on Cursor bench and the Cursor users are satisfied with the Composer 2 model uh and they do Cursor does get their access cut off.
20:20
And when you install Cursor, you roll it out to your organization, you just get Composer 2. And you know what?
20:26
It's it's you know, maybe there's taste that would pull you towards the Cursor >> at this point right now, I don't think we have any visibility into like how much of Cursor's revenue right now is tied to using OpenAI or Anthropic models.
20:42
>> Well, I think like in some ways all their cost is, but is all their revenue?
20:47
It depends on the perception of the user base because the revenue might be, well, I pay for Cursor.
20:53
Like, I don't really care what they use under the hood.
20:56
There's a lot of people, this was Ben Thompson's argument for a long time was that there's for a lot of people, they just show up to ChatGPT and they wouldn't care if the model was powered by Gemini because they're just like, I just ask it a question and I get an answer.
21:07
And so, if you're a cursor user, it's possible that you're in the same boat where you don't really mind what happens but like under under the fold.
21:16
Um What else is going on here?
21:18
>> George says I'm hearing tons of complaints from cursor customers at enterprise companies that silently changed but almost all models cursor uses behind max mode devs who used to manage to spread out monthly credits over a month see all of it used up in one to two days. >> Oh, interesting.
21:33
>> Are furious and switch >> feel like there's a little bit of like an economic war here.
21:38
Um >> Yeah, and then this is what came up like you know, earlier this month around the lab sort of subsidizing >> Yeah.
21:45
>> Uh so >> know who's >> They're not they're not in an easy position, but they're such a talented team. >> Yeah.
21:52
Well, you know who's great at Pareto frontier pushing models? Gemini 3. 1 Pro.
21:58
It's here and it has a more capable baseline.
22:00
It's great for super complex tasks like visualizing difficult concepts, synthesizing data into a single view, or bringing creative projects to life.
22:06
And let me also tell you about Graphite which is owned by cursor, correct?
22:12
Uh code review for the age of AI.
22:13
Graphite helps teams on GitHub ship higher quality software faster.
22:17
Um >> Nikita says we're rolling out summaries for articles now.
22:20
Just tap the summarize button if you want to know if it's worth your time to read it. >> Yes.
22:25
>> And yeah, it's basically Grok.
22:25
Turn this into a regular tweet.
22:29
Uh I am excited about the listen button.
22:31
I've had this I, you know, on my commute.
22:33
There's so many moments where I'm like, I wish I could just have somebody read this article to me.
22:38
>> I actually wound up doing this with a number of Will Manidis uh long-form essays.
22:42
I would copy them, put them into uh 11 reader from 11 Labs, and have it read it to me in sort of a >> a silly voice. >> a silly voice. It was a good time.
22:52
>> Well, I was trying I was actually trying to use Grok in I was trying to use Grok in the X app >> Yeah.
22:57
>> to just take an article, paste it into Grok, and say, "Hey, can you read this to me?"
23:02
And it said, "Cannot find >> Yeah. >> the post."
23:05
>> It's >> like it it couldn't it couldn't kind of >> It is >> access to get the content.
23:08
>> of it is sort of crazy reflecting on the fact that there was so much software out there that you know, we we we we talked about DoorDash.
23:15
Like how can you completely reimagine the experience with a agents on the platform?
23:20
And there's so many different things that you can do to like sort of like re-architect what your product is if you've built a successful software company.
23:28
But then there's all these like little things where like yes, every product probably does need an LLM that can summarize text and expand it, and that's what Grok does.
23:37
And also everyone wants text-to-speech, and everyone wants speech-to-text.
23:41
And and everyone wants the okay, if there's an image, I want you to, you know, have a text version so it's searchable.
23:48
Like I've complained about if I see a funny meme on X, and then I want to go search for it later, impossible.
23:54
Like there's just no way you could ever do it because that is stored in Twitter's database or X's database as like image number 762542, and someone's comment was just like this changes everything.
24:08
And I'm never going to remember what they said.
24:09
But now you can run every image through a image model to understand what's actually going on, and then potentially surface that in search.
24:19
Now, that's going to be a long project to actually implement that, make it fast, make it cheap, make it affordable, make it like fit within the business model of X or whatever plat social platform is out there.
24:29
Um And I mean you can see Instagram like struggling through these things right now as as search becomes more important and as ranking becomes more important.
24:38
Meta's already seeing the lift from machine learning applied to ad ranking and whatnot.
24:42
Um But uh Uh th- th- this is uh th- this is a response to, you know, every article people would post, people would always say, "Grok summarize this."
24:50
And now there's just a button.
24:52
I wonder if this button will be gated by X premium because I recently learned that you can only ask Grok, like at Grok, is this true?
25:03
You can only do that if you're paying for X.
25:05
And sort of underrated how well X has seemingly I don't know how big the subscriber base is, but that was a crazy idea to have a paid social network.
25:19
Dalton Caldwell from formerly YC partner actually launched a competitor to Twitter back like maybe a decade ago that was paywalled only.
25:29
And and the whole pitch was like better content, more Substack model, no ads.
25:33
And he never really got it, you know, to perfect product market fit, but but it was an interesting idea and now now it's like >> I think it's because people are people are deeply addicted to X. >> Yeah.
25:49
>> It is very valuable to them to be on there to participate. >> Yeah.
25:54
>> And the paid functionality, the way that it was marketed and the way that it generally worked was like you were going to have a bad time on X.
26:00
Like if X was valuable to you and you didn't pay the $10 a month. >> Yeah.
26:05
>> You it was going to be like significantly less valuable to you.
26:08
Probably, you know, you might you might depending on what kind of business you're running or what you use X for, it might be the equivalent of like losing thousands of dollars a month of value or you could just pay the $10. So it was a good trade. >> Yeah.
26:20
>> But >> It was also just it was weird how the targeting never seemingly got dialed to the point where you could actually target the CEOs of companies who were on X.
26:30
Like I mean you see Travis Kalanick on X like replying to things.
26:34
It's like he's raising money, he's growing a business.
26:36
Like there's a lot of value in advertising to him because he's going to be picking a corporate card soon or he probably already has or he might be in that market.
26:46
He might be picking a payroll suite.
26:47
Like there's all these things where if you could deliver that to that audience, it would be incredibly valuable and the CPM should be like through the roof, but I think for privacy reasons and for variety of other reasons and sort of like like really monetizing that long tail has been very difficult across every platform.
27:03
So, they've just gone with scale and the products that have sold the most on social networks have been very broadly marketed and the and the the criticism that we saw from the Oscars was always like YouTube ads are generic.
27:15
It's just like for a pillow or like injury or like something that applies to every single person.
27:21
Um but there's always this like hyper-targeted opportunity there.
27:25
>> Yeah, the other the other thing is is uh the paid program with X has seemingly worked in that we know a lot of people that happily pay and have no plans to churn, but it would be a failure in the context of like meta scale, right?
27:39
I think the last reported number that I saw was something like they had like 1 to 1.
27:46
5 million paid subs at $10 a month. >> Wait. >> Oh, on X? >> Yeah.
27:52
So, you're talking about somewhere in the range of 100 to 200 million of like ARR. >> Yeah.
27:58
>> And if Mark if Zuck had launched a product like that, he would just wind it down, right?
28:02
Reels went from zero to 50 billion of run rate in like a handful of years, right?
28:07
That's what a that's what a home run looks like.
28:08
And so, I think it makes sense for X, but it certainly is not a home run from a, you know, consumer application standpoint and they still need the, you know, the overall business. >> Yeah.
28:19
Uh Olivia Moore had some extra context there around uh monetization of via ads versus uh versus subscriptions.
28:29
Um so, uh Neil Patel, who is the founder of NP Digital, a New York Times best-selling author, uh shared this is how ChatGPT ads are performing. >> guy, I think. >> Okay.
28:42
Um he said the data is from uh, is only from five businesses, but these were businesses also run Google and Meta ads.
28:49
Compared to Meta, uh, ChatGPT's rough quality uh, lead quality is 256% higher.
28:56
On the flip side, lead quality is 49% lower than Google.
28:58
I mean, that seems like a miracle to be in between two hyperscalers uh, on on like day one basically.
29:05
But on the bright side, due to ad costs, it's substantially cheaper from a CPA perspective uh, than Meta.
29:11
And this was sort of what we were talking to uh, the folks the good folks over at Ridge about was that uh, at least at least in the early days like you like being being early to a new ad platform that can you can potentially scale on can drive a bunch of new uh, conversions.
29:25
But Olivia Moore said, "A big story that most people are missing in the AI race for the consumer ChatGPT versus Claude is ads.
29:31
Right now, most consumer AI revenue is coming from power users who are willing to pay high subscription costs.
29:37
This currently skews positive for products like Claude, but this will not be the end state.
29:41
Google makes $460 per user per year in the United States more mostly on ads.
29:46
I didn't know that their ARPU was so high. Meta makes around 250.
29:50
Um, I mean, I guess those Google ads are really really valuable uh, and it's so intent-driven that it it it it makes sense.
29:57
Um, I would argue or she would argue uh, that ChatGPT's ad-based ARPUs will be even higher as they will ultimately have deeper more frequent user engagement.
30:07
Even at the $460 level, monetizing everyone in the US via ads is 152 billion in annual revenue.
30:12
By contrast, if you're able to monetize even 5% of the population at $200 a month subscription, which is a stretch, that's only 40 billion.
30:20
That's that that's actually a crazy difference cuz $200 a month subscription is is like super high.
30:26
Like, you know, you're talking uh, 20 times like Netflix or something else that's, you know, uh, premium and like really important.
30:34
>> $200 subscription at the time was crazy.
30:37
But even at that point, some of the people that were more kind of just like AI-pilled generally were like oh it's actually possible that someday you could spend $20,000 a month.
30:47
>> I was like give me the $20,000 monthly and it sort of came be a be a API but it was heavily subsidized.
30:52
Uh so she says I suspect this will be even more drastic outside of the United States where users are even less willing to pay or directly pay for subscriptions and the earliest data from a very small rollout shows chat GPT ads are already outperforming meta in in effectiveness.
31:06
Uh this just gets better uh it just gets better over time. So uh interesting.
31:10
The the question about Will Manidis and the article summaries, should he move to Substack? He's threatening Nikita.
31:18
He says I'm defecting to SBSTCK.
31:20
He won't even type it out.
31:22
He says they pay more and Nikita didn't reply.
31:26
Uh I think people would follow Will over there.
31:31
I think people would read his articles anywhere potentially.
31:34
But um if you're looking to advertise, why don't you head over to AppLovin?
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31:51
So >> Carl says it's time to return to the place where I know I can have the most impact.
31:58
I'm beyond excited to be rejoining Sequoia as a partner.
32:00
Here is what I shared with the team on how I am approaching my next chapter.
32:05
He wants to serve the ecosystem fire to win.
32:11
Let's see what this means.
32:13
Being a servant leader does not mean I have lost my edge.
32:15
In fact, the fire in my belly burns brighter than ever.
32:16
The difference now is that I'm not using that fire to light my own path.
32:20
I'm using it to light the spark in others so their fire burns brighter.
32:27
Leading from behind, I have no interest in the view from the front of the room.
32:29
I will leave that to our two great leaders, Pat and Alfred.
32:31
I want to lead from behind empowering each of you.
32:36
Ego-less impact, contagious energy.
32:39
Mentor and build great leaders, always ready to serve. Uh Carl's the man.
32:43
Uh we will have him on in just 30 minutes, so we can uh wait to cover more of that story then.
32:52
>> we got to go to his uh Allen & Co.
32:52
photo shoot, dual-wielding coffee, jump rope, and a faded Sequoia T-shirt.
32:58
Andrew Reed says, "Immediate, overwhelming response to the VC pushback debacle.
33:03
Welcome back to Sequoia, an all-time great partner.
33:07
Uh that's a great That's a great photo.
33:11
He's looking great there.
33:11
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33:13
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33:22
This is an interesting story.
33:22
This is an interesting story.
33:24
>> Apple is way behind in AI and still making a fortune from it. Let's see.
33:29
>> Begs the question, are they actually behind?
33:32
>> It's AI revenue is set to top 1 billion this year, reassuring investors wary of rivals' sky-high spending.
33:38
>> And keep in mind, this >> chart here showing gross revenue from gen AI apps, as well as Apple's commission. So >> Look at this.
33:46
The the the beginning of 2025 was really the boom of gen AI app growth.
33:53
400 million Is this Is this monthly App Store revenue? Wow. They're really cooking.
33:59
And then And then sort of a flat line.
34:02
>> Yeah, it's so so interesting that that it actually dropped. >> Yeah.
34:08
Well, we did read that article a few days ago about how Apple has been uh pushing back against some of the vibe coding apps.
34:15
And there's this question about, you know, where are the bounds?
34:19
Obviously, Apple's had pretty strict App Store rules around adult content and, you know, what else you can do, even just the app reconstituting itself, pushing changes, because they want to review every line of code that goes in the App Store.
34:34
If someone's pushing 10, 20, 30,000 lines of code a day, that's a lot of code for Apple to review.
34:40
It's going to slow things down.
34:43
So, that could be a little bit of what what we're seeing.
34:46
Maybe they've capped out on their ability to review all the vibe coded apps that are flooding the App Store.
34:50
But, let's go to the Wall Street Journal and dig into this >> Apple's on pace to surpass 1 billion in AI revenue this year, a tidy sum that demonstrates the company's AI advantage even as it struggles to deliver an AI strategy of its own.
35:00
Its Siri chatbot is still weak by modern AI standards.
35:02
What Apple does have that the other AI players don't is a dominant position making devices.
35:07
However, however fancy OpenAI, Google, Anthropic, and XAI make their chatbots, iPhones are still a primary way to deliver them to customers.
35:15
Uh that means they typically pay the App Store tax, roughly 30% of subscription fees in the first year and 15% a year thereafter. The rates vary.
35:23
GenAI apps paid Apple nearly $900 million in App Store fees in 2025.
35:31
With uh all, you know, almost a billion of revenue and very, very, very little capex.
35:37
3/4 of the revenue Apple rakes in from GenAI apps GenAI apps in its App Store come from ChatGPT.
35:44
Next at about 5% is XAI's Grok.
35:47
And that's There we go, Grok.
35:50
I mean, there's so many different like funnels.
35:51
They did the essay competition, they did the uh the video competition, and I mean, I've I've talked to people that are just still They're like, you know, like people that are in the Apple ecosystem, they're like in the Tesla ecosystem.
36:04
And so, they're like, "Yeah, I talked to Grok on my way to work." I I'm I'm not kidding.
36:11
>> Yeah, Grok in the iPhone App Store is at did 12 million last month. >> Yeah.
36:15
And I know I know like the the the true like AI heads will be like, "Grok's behind on this benchmark model or whatever."
36:21
And And Tyler, is that a correct characterization?
36:24
>> Yeah, Grok did more revenue Grok did more revenue last month than it than Claude in the iPhone App Store.
36:30
>> But but like I've I I've started having conversations with I'm I'm I'm using ChatGPT, but I I wanted to just I I wanted to get up to speed on on Taiwan and the the the just the like what was the the reason for the original Civil War and stuff.
36:48
And so I was just having a conversation back and forth.
36:49
And at no point was I like, oh, I really needs to be like, you know, GPT 5. 4 Pro.
36:55
It's like these are things that exist just like with one search to Wikipedia or one search to any it's probably baked into the weights of 3. 5.
37:06
But so so like if I'm just going to be like chatting with someone who's like reasonably smart, like I would say Grok is there and so what do you think?
37:15
>> you like you could be talking to someone who's really really smart.
37:18
>> No, like not if you're asking like basic basic knowledge retrieval questions that like they're like any model's going to one one shot and just be absolutely >> you're just describing stuff that you could just like actually Google.
37:28
>> Yes, but but I can't Google via voice in my car on the drive.
37:32
And for someone who's driving a Tesla and has a Grok integration right there, they're just like, sure. Like this is great. >> yeah, that's fair.
37:41
>> It's like not and I like the frontier use case is important.
37:43
Like that's where the action's happening.
37:45
That's what's driving the next order of magnitude of growth.
37:48
But like there are plenty of people who are like, Google's search overviews are amazing, you know?
37:52
And they're like, that's like that's my level and like that's good.
37:56
And they're like >> Yeah, but like I don't think those people have actually tried like GPT 5. 4 Pro. It's so good.
38:05
>> It is it it is good, but it's slow.
38:05
And truthfully like like you can fire off the exact same query to 5. 4 Pro and 5. 4 and 5. 4 5. 4 fast. Fast.
38:19
And and if the query is simple enough, the answer will be exactly the same.
38:23
Because if I ask if I ask 5. 4 5.
38:29
4 extended thinking, like what is the capital of California?
38:31
And it thinks for 10 minutes, and it just tells me same like Sacramento. >> See?
38:36
You >> I'm not >> That's why you need to think.
38:38
A lot of people a lot of people might think that >> I hallucinate a lot.
38:44
But >> People have said I have the mind of GPT-2. >> It's true. It's true.
38:50
But um but so so I think I think for I think for some use cases, you know, a smaller model, something that's a little faster, something that's not, you know, absolutely frontier is is fine. So, I don't know.
39:01
What what what what do you think about >> imagine that um >> Do you think there's something else going on? >> Pro Spark.
39:06
So, it's on, you know, Cerebras chips. >> Yes. Yes. Yes.
39:10
>> Would you hit that every single time?
39:11
>> Uh >> When would you not use it?
39:13
>> I would not use it if I was doing like a deep research report necessarily.
39:14
Uh it it because I'd want I would I just want extended reasoning for certain things.
39:19
>> No, no, but I'm saying like you could have that like >> Oh, oh, oh, so so so 5.
39:22
4 Pro Spark >> which is like super fast extended reasoning, but it's still super fast.
39:28
Good sound Cerebras chips, right?
39:30
>> Uh >> So, if you had that, you'd 5.
39:30
4 >> is no object, absolutely.
39:34
Like I, you know, I'm I'm happy to to pour out the glass of water for to to to get the best in and the best intelligence possible.
39:41
>> Like I I just think that even if you if you just care about speed, there's still better I think in my opinion, like right now there are better models than than >> Okay. Okay.
39:48
So, so so walk me through it.
39:51
Like 5% of App Store revenue seems really high. What's driving that?
39:56
>> Yeah, not everyone is like extremely tapped in to like the you know, the current model that came out 2 days ago.
40:01
You got to use it like >> agree with me?
40:03
I think you agree with me.
40:04
>> About >> About the fact that that like good enough intelligence is still like a good business to the tune of 5% of App Store revenue from GenAI apps.
40:12
>> Yeah, but I'm saying that like you, cuz Do know about the stuff.
40:15
You should like there's better things that you can do with it. >> not I I I told you.
40:18
I told you I'm talking to ChatGPT.
40:19
Look, don't don't shame me. I'm not the one.
40:21
But, I'm just saying like, I don't even think you should be shaming someone who's talking to an old model. >> them.
40:29
I think I'm saying that it could be better.
40:31
Like, you could have a much better experience. >> Okay, okay.
40:33
So, you want to evangelize the the frontier.
40:34
You want to evangelize the frontier.
40:36
But, I mean, I I'm just I'm just wondering like, if we did we need a new Turing test.
40:40
So, we need to have uh random people come in and they get to talk to 5. 4 or, you know, 4. 0 or something.
40:50
And can they tell the difference?
40:53
And which one will do they prefer?
40:55
>> They might just prefer 4. 0. >> They might prefer 4. 0.
40:56
This is the new This is the new uh New York Times writing test.
41:00
We should we should put one of these out and be, can you actually tell the difference?
41:03
This is interesting because I feel like a lot of people say they can, but they they they probably unless they're really really grinding and they're trying to do something that requires like a really long reasoning chain, it's totally possible that they're just like, yeah, like it's it's gave me the right answer. Like, it looks good. >> narrative violation.
41:24
>> Anyway, let's continue.
41:24
Apple's revenue from generative AI apps rose from about 35 million in January to a high of 100 million in August. >> Do nothing win.
41:37
>> Do nothing win created an app store over a decade ago and just keep reaping the rewards.
41:42
Uh They they sowed and now they're reaping.
41:46
Sales have fallen from their peak partly because ChatGPT downloads have declined, according to the data.
41:51
As proportion of Apple's total sales, $1 billion is small, yet genAI apps are genAI apps are a growth driver for Apple's services business, which investors have focused on in recent years because it has grown faster than device sales and boasts higher profit margins.
42:06
Apple's dominant share at the top of the smartphone market affords it another luxury, time to get its own AI strategy right.
42:12
So, they're making money while they figure everything else out.
42:16
Apple's AI plans plan runs counter to strategies of competitors that are spending hundreds of billions of dollars on chips and data centers to build frontier language models.
42:24
Apple is spending a fraction of that, aiming instead to use all of the personal information people store on their iPhones together with the chips that it designs itself to power an on-device AI strategy.
42:34
That strategy could prove a winner if, as some AI researchers have suggested, access to user data and strong privacy makes on-device AI the dominant way consumers access the technology.
42:48
Apple investors want to see progress from Apple's own AI strategy, such said Charles Reinhardt, chief investment officer of Johnson Asset Management, an Apple shareholder.
42:59
Quote, "If they can act as a toll road for providers of AI, then they'll probably end up looking good long-term for not having the big CapEx overhead."
43:09
Now, I have to imagine that Apple is not capturing any revenue from enterprises, developers, cloud code, codex, any of those developers.
43:19
They're probably not, even if they even if they are winding up using like a chat GPT subscription in codex, they're probably setting that new subscription up on desktop.
43:31
>> on on the on the actual >> Yeah, but it's a toll road on consumer, which is consumer sales.
43:36
All the more reason to get into ads, honestly, cuz does not tax those.
43:40
Um >> Yeah, and and and AI is exciting for Apple because they need they need a new product that they can just randomly bill you like $2. 99 >> Yeah.
43:50
>> anytime they need a cat like additional cash. >> $2. 99?
43:54
>> Like don't don't you get just random bills from Apple like here and there? >> $2. 99?
44:01
>> Yeah, like I feel like every time I check my email, it's like Apple has charged you a random amount for some for some subscription.
44:09
>> No, I do get emails from Apple, but it's always like 2 days after I bought I bought or rented a movie on Apple TV and it just says like you rented this movie and I'm like, yeah, I I know. I clicked the button. It's fine.
44:18
You don't need to email me.
44:21
>> Uh in other news, Rolls-Royce has scrapped plans to go all electric by 2030 as {quote} drivers prefer V12 engines. Would you look at that? >> Look at it.
44:33
>> I mean, and this is just a total shock. >> Yes. >> Total shock. >> Yeah. >> Total shock. >> Yeah.
44:38
>> Drivers totally had to experience, you know, being forced uh EVs forced upon them for the last few years to know that they they they preferred uh combustion engines after all.
44:50
Now, of course, I'm kidding.
44:51
I think a lot of people were just, you know, sort of saying this over and over and over.
44:56
Manufacturers were not listening.
44:58
>> Everyone said Elon has been saying the Roadster reveal will blow your mind.
45:00
If it has a V12, >> We've been We've been We've been talking about it. >> crazy.
45:08
If he drops a V12, that would be that would completely break the internet. >> Yeah.
45:14
>> It'd be It'd be incredible.
45:14
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Don't just build AI, own the data platform that powers >> Let's talk about this Tesla that you were following yesterday. >> Oh, yeah. Yes.
45:34
Did you drop this in the chat already? I sent it to you.
45:36
I know we we we shouldn't we shouldn't we shouldn't share the actual picture.
45:40
Um but I saw a Tesla that was a very funny mix of it had the anti-Elon club on it, but also an 1199 uh license plate and it was a plaid and it just like mixed every possible political ideology >> And and it had a vanity plate that was very sci-fi.
45:58
>> So, it was mixing like, I do want to go to Mars, but not with Elon?
46:04
>> And the had the license plate basically said beam me up. >> Beam me up.
46:07
>> So they want to go to Mars but not with Elon.
46:08
They support >> incredible amount of disposable income basically for the >> They enjoy high trim levels, but they they they do not agree with Elon's actions.
46:18
>> Well, maybe they work for a rival AI lab or something and so they they're extremely sci-fi people but they just don't like they just they they just feel like they're competing with XAI.
46:29
>> Uh California has uh now spent over a hundred million dollars on a new bridge to nowhere.
46:36
It is uh a wildlife bridge which uh I have driven by hundreds of times. I've been seeing it.
46:43
I've been experiencing the traffic that it causes.
46:45
I I'm not against the concept of a wildlife bridge.
46:51
In fact, I think it's fantastic.
46:54
>> It does feel like in a concrete jungle, this is beautiful. >> Totally.
46:58
>> This has a lot of opportunity to actually improve the visual aesthetics of this particular part of the state.
47:04
>> Caleb Hammer says, "Bro, this state cannot be real."
47:08
>> Isn't Isn't Caleb Hammer >> real.
47:10
>> Isn't Caleb Hammer uh he's like a finance >> Yeah, he's got like the the number one >> the one person you'd come to to be like, "Should I spend a hundred million dollars on a bridge?"
47:17
And he'd say like >> And it's actually it's actually quite a bit more than a hundred at this point.
47:22
Uh and the funny thing is like it's just kind of a bridge but it doesn't it's it's lacking the entrances to the bridge.
47:28
>> I feel like if they build like even just a little bit of wood to like like smooth it out so that it looks like there's at least the going to be start of a of a of a of a ramp to get on the bridge.
47:38
Like the bridge looks solid.
47:40
The actual center part looks solid.
47:42
It doesn't feel that hard to finish this bridge.
47:45
I'm I'm optimistic that this gets done in the next hundred years like top.
47:50
>> Apparently Colorado built a built a wildlife bridge for a a cost of fifteen million >> Oh, that's not bad.
47:57
>> uh functionally uh something very, very similar.
47:59
The The interesting thing is apparently the bridge is is uh in some part for cougars. >> Cool.
48:06
>> And the wild thing is like on one side of the bridge, you have a bunch of like residential homes. >> Mhm.
48:12
>> And on the other side, you have a bunch of cougars. And so >> Yeah.
48:16
>> they're now going to have the cougars are going to be able to go hang basically hang >> It's exciting. >> in all the backyards.
48:22
So, we'll see how this goes.
48:22
But, I'm I'm excited for this to be finished up. >> Mhm.
48:26
>> It's uh been as long as I have uh lived in Southern California, they've been working on this bridge. And uh it's about time.
48:34
>> Well, former partner of TVP and fall, a generative AI model hosting service that you know and love, uh is in talks to raise 300 million to 350 million dollars at an $8 billion valuation.
48:47
Annualized revenue has hit $400 million up from $200 million in October. That's the future of AI. >> Insane.
48:57
>> No, they've executed incredibly.
48:58
>> Really, really, really >> We're good friends.
49:00
>> insane >> So, congratulations to them. >> growth.
49:03
And uh look forward to having having them on after they move past the advanced talks phase. >> yes.
49:08
Um Uh what is Miles Brundage saying?
49:10
He says, "I'm a bit worried that Anthropic has an org-wide case of AI psychosis that makes them think Claude is good enough that they can ship random products features without breaking things, but they in fact do keep breaking things and they're not online enough to notice people complaining."
49:27
>> I don't Yeah, I don't know about the last part. They seem very online. >> Yeah.
49:30
Uh and I I I don't know too much about uh the the issues, but uh there there is like if they're truly competing in consumer, like there are like low-hanging fruit like uh text-to-speech on deep research reports is not a feature that exists yet.
49:46
Feels very obvious, but uh it's it's so interesting being this dynamic where, you know, you can ship things so fast and yet there's some obvious product improvements that are just sort of stuck in the queue because there's a lot to do and it's an exciting time.
50:02
Uh what else is Anthropic doing?
50:02
They're hiring for a policy manager who will be in charge of chemical weapons and high-yield explosives.
50:08
This reads like you're going to be building high-yield explosives, which sounds like an Anduril job posting, but uh it is in fact for a policy manager who will be hopefully stopping people from uh >> No, no, no.
50:21
I think I I I read this as somebody whose job it is to decide how Claude is used to create chemical weapons and high-yield explosives.
50:31
>> I think it's I don't know.
50:31
I I think it's probably like this person decides like where's the edge.
50:35
So, if you if you're asking like, "Okay, I have a firework and I want to make sure it doesn't go off.
50:43
Like, should I should I, you know, throw in the trash or put in the recycling or take it to a special place?"
50:48
Like, Claude should answer that, but if you go to it and you ask it like, "How do I build this this C4 or something like that?"
50:55
Like like there's all these like policy edges where if you're talking about Counter-Strike and you say like, "Let's plant the bomb."
51:00
It shouldn't flag that as, "Okay, you're actually trying to plant a bomb."
51:05
It's like, "No, you're asking about a video game.
51:08
We We know how to interpret that appropriately.
51:09
Uh but there needs to be like a human in the loop to decide like where that frontier is and where that particular frontier is." >> Anyway.
51:17
>> Orif says, "What terrifies me is if AI were to cure cancer and save 50 million Americans, imagine the backlash from hardworking scientists who wanted to cure cancer themselves."
51:27
>> They will be involved for sure.
51:29
>> Well, it's interesting that that was >> Yeah.
51:32
>> that was like at least one person's response to the Australia dog story where they were like, "Yeah, this we we've been able to do this for a while, but like don't do this." Mm.
51:40
Which was, you know, an interesting response to somebody who, you know, went on a multi-year journey to try to save their dog >> and seemingly is having some good outcomes. >> Very very odd.
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52:10
So, um >> Uh PG says anything made before 2028 is going to be valuable.
52:15
And he's quoting an OpenAI employee >> Hm.
52:19
>> who he says implicitly discloses their >> timetable.
52:24
Anything made before 2028 is going to be valuable.
52:26
That is such a vague Someone was hanging out with with Paul Graham and was like, "Let me vague post IRL." >> I require context. >> I I require context.
52:37
>> This This is going to be very valuable.
52:40
I'm not I wouldn't give this up.
52:42
>> We are working on something that will be incredibly valuable.
52:45
One One of our team members had a major breakthrough and it could be the blockbuster product of the year.
52:49
I'm very excited for this.
52:52
>> This actually is We were just messing around this morning with with an existing product that we had. >> Had a breakthrough.
52:58
>> We had a major breakthrough.
52:59
>> We I had nothing to do with it.
53:02
>> We And when I say that, I mean Ben had a breakthrough that I think will change one of America's pastimes forever. >> I think so. >> Actually.
53:11
>> It'll be like a before and after picture.
53:13
>> I'm actually so confident in this that I'm willing to vague post about it. >> For sure.
53:19
>> Get this Get this young man a patent. >> I'm on a patent. I have a patent. It's great.
53:22
Yeah, when you get a patent, you can also like frame it, get a little tombstone. It's very nice.
53:25
Regardless of what happens with the business, it's like a good moment in your business career to like have a patent.
53:30
>> Do you have a tombstone for your >> I don't. I need to order one.
53:32
It has been issued like my name's on it.
53:34
I'm like the you know seventh name on the list or whatever, but I'm technically on it.
53:40
Uh and so I should I should get my, you know, plaque or whatever.
53:41
I'm sure you can just buy them.
53:44
There's probably some of them out there. What did PG give?
53:46
He gave some more context.
53:47
He said, "This was after I mentioned the idea of buying rare old things as a hedge since the one thing AI won't be able to do is go back in time that we know of.
53:56
I don't >> I mean, that's a whole plot of Terminator.
54:01
>> not AGI Just say you're not AGI pilled.
54:04
Like just everyone's saying it's going to be like Terminator, it's going to be like Terminator.
54:06
Well, what happens in Terminator? They invent time travel.
54:07
And so you're going to be able to go back.
54:11
You're going to be able to go back in time.
54:12
>> Yeah, I feel like after we get AGI and we can like build incredible things like those will be really valuable, too. Right? >> Yes. Yes.
54:17
But yes, I mean, he is correct that like they're like like you know, images in ChatGPT and and and VO3 and Midjourney like don't decrease the value of the Mona Lisa.
54:29
Like that's just like obvious and everyone agrees on that. Except you.
54:33
You're like, I would actually like to go to the Midjourney Louvre.
54:37
>> Maybe that's why Banksy maybe Banksy sort of intentionally kind of kind of revealed himself.
54:43
>> I don't I would didn't follow that story.
54:44
Like how did that happen?
54:44
Because I feel like most people would just be like >> just caught him with a hopefully Scott is watching and can fill in, but I think he I think he was just kind of like caught in the act. >> Really?
54:57
>> Um but maybe maybe he's confident enough, hey, AGI is coming. >> Okay. >> AGI is here. >> Yeah.
55:03
>> My stuff's still going to be worth a lot even I don't want to be in the shadows anymore. >> Yeah.
55:08
>> Um >> That's very interesting.
55:09
Uh So anyway, A Palantir clearly does not believe in the Terminator thesis of the AI future where time travel is op is is is possible.
55:18
Imagine being in the Terminator future and creating the time machine and just being, "Okay, I'm going to go back in time and and and paint new paintings that then I can acquire over time and have new Mona Lisas."
55:32
You just It's just like, "No, we sent you back to save the human race."
55:33
Like, "But I got to hang out with Leonardo da Vinci.
55:39
I got I got to build my art collection.
55:41
He said, "I don't put too much weight on the specific year, but the shape of the idea is is interesting."
55:45
And I agree, it is an interesting thing to to noodle on.
55:51
Similarly, CrowdStrike, interesting idea.
55:52
Your business is AI, their business is securing it.
55:55
CrowdStrike secures AI and stops breaches.
55:56
Let me tell you about Turbo Puffers as well.
56:01
Serverless backend and full text search built from first principles and object storage, fast 10 x cheaper and extremely scalable.
56:07
>> Martin Shkreli >> What does he say?
56:09
>> He's coming on Monday for the great debate, the great peptide debate says good music is the last mile of AI. >> Mhm.
56:15
>> And Lil Wayne has some thoughts on AI music. Let's play this clip.
56:19
>> Let's play this 2-minute clip from Lil Wayne on a podcast. Let's see. Here we go.
56:28
>> How you handle AI in this in this business now? >> Challenge. Good challenge. >> Bro, this is wild. >> I love it. AI is a better thing.
56:33
I love that AI is what it is.
56:37
Cuz man, I love to be able to stand right next to whoever AI is, he, she, they, whatever or whatever AI is, stand right next to her and I'm still better.
56:49
I'm going to keep telling you what you do again? >> Yeah.
56:51
>> Go and go and run your list and I do this, I do that. I love it.
56:57
I love the challenge of it.
56:59
The first time I seen it, somebody was it was my my friends was a little worried.
57:02
They was like, man, bro, they got this AI stuff, are you good?
57:05
Just ask it to do give you a verse like Lil Wayne. And so I did it.
57:07
I said, "Let me have me a Let me have a verse like Lil Wayne."
57:11
And it gave me her best shot. >> Yeah.
57:14
>> I did it on a couple devices.
57:16
That you know, not only a phone, a computer, a even for a commercial.
57:18
I was shooting for the Alexa thing.
57:21
I want to hear and I have a thing called Proto and home they got his own little robot thing.
57:26
Asked her to give me one and they all I just suck.
57:32
So feel like we going to be okay. >> I with that. Who was that? Beanie Sigel?
57:36
I think he had to start using it cuz he like was losing his voice a little bit.
57:41
>> Yeah, another rapper to mog.
57:42
>> Basically, that's his take. >> That's so funny. >> That's great.
57:45
>> Well, uh how does it how are designers feeling about AI these days?
57:48
Samir says, "Bro, it's so over for designers.
57:51
Google Stitch is insane."
57:55
Uh Google launched a new uh generative AI design tool where you can sketch something out on a piece of paper, turns it right into an image.
58:02
Uh and there was a lot of back and forth over um you know, how uh how how how how this debate's playing out between Google and Figma.
58:12
Hadley Harris is 12 years later the VC who passed on Figma's seed because Google could kill them is finally feeling seen.
58:21
Uh lots of amazing work done, of course, in the interim. Um very very silly.
58:25
Uh will be interesting to see how Google pipes this into the other tools.
58:29
I was on Google's >> Apparently, apparently this is an uh engagement bait.
58:33
Other people are testing similar prompts and getting much much much worse results. >> Yeah, I don't know. Um I I I was on uh ai. google. com or just ai. google, not even ai. google.
58:48
com, looking at all the different Gemini features and uh it feels like the next the next challenge is is just integrating all these different things.
58:57
They have like so many great models, Nano V Nano V 3, Notebook LM, Gemini, Flow, uh AI mode.
59:02
There's There's so many and actually uh piping a workflow from one to another uh is is is certainly going to be like the next the next question.
59:15
And does this all live in the Google search box?
59:17
Is it in uh Google apps or something?
59:20
Um either way, they're they're certainly investing in AI across the organization.
59:26
And Ryan Peterson says, "Whoever named Gemini at Google really named it.
59:31
In the mythology, the immortal twin gives up his immortality to save the life of his mortal twin."
59:41
Uh it's just like Google giving up 100% of its free cash flow to make sure DeepMind survives.
59:48
Uh that is not actually the reason for the name Gemini.
59:50
But do you know the name for why they picked Gemini?
59:54
>> Cuz they had two internal teams that they brought together. >> Yeah, the twins. It's a good name.
1:00:00
Uh and it's really it they were suffering from a bit of a naming crisis for a while with Bard and Palm.
1:00:06
And they were definitely shipping a little bit of like not not necessarily shipping the org chart, but shipping the some of the internal naming schemes that were like abbreviations.
1:00:16
>> Even like a Nano Banana product image models used to just be like uh Gemini 3. 1 image. >> Yeah. Whatever. It didn't get to 3. 1, but There we go. There we go.
1:00:28
>> Like like Nano Banana was famously just like the internal name they used, right?
1:00:31
>> and sometimes these internal names can can really fly in consumer context.
1:00:37
Mostly I don't know what it is, but something about like Nano Banana like really sticks out. It's so funny.
1:00:42
There's it it it doesn't sound like an AI product.
1:00:44
Like when you have Siri and Alexa and then you have Rufus and Argus or something and Sparky and the wall from Walmart.
1:00:51
Like doing another human name can actually be a disadvantage because you just get lost in the clutter.
1:00:57
But if no one's really using the Nano Banana name, I know strawberry was used by OpenAI before, but no one had really like used that as a public name.
1:01:05
Um It certainly like helped them helped them break out.
1:01:08
But Gemini's been an interesting name. It's good it's good.
1:01:10
It like it balances both it sounds like a product, but it it it it sounds but it's just one word.
1:01:15
It sounds like a it doesn't it doesn't anthropomorphize too much, but a little little It is reference.
1:01:20
The there's a lot of deep knowledge there.
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1:01:42
So, without further ado, we have our first guests of the show, Pat Grady and Carl Eschenbach from Sequoia Capital.
1:01:51
>> What's going on, guys?
1:01:52
>> Carl, Pat, how are you guys doing?
1:01:55
>> We're doing great, man. We're doing great.
1:01:56
It's great to be with you guys.
1:01:58
>> Yes, thank you so much for hopping on the show on this day.
1:02:03
Walk us through the decision, how long have you guys been talking about potentially reunifying?
1:02:09
What what's the thinking? What's the role?
1:02:11
Sort of break it all down for us.
1:02:13
>> From the moment Carl left Sequoia. >> Yeah.
1:02:17
Come on back, come on back.
1:02:19
>> Yeah, no, uh Well, maybe I'll just start and then my my partner Pat here can jump in and I say my partner because I've been his partner for 10 years.
1:02:26
Like I joined almost 10 years ago.
1:02:28
Uh you know, at Sequoia, which was an incredible joint journey at that time to join.
1:02:35
Uh and then I decided to step away back into an operating role.
1:02:39
Um you know, after an 8-year total journey at Workday, 5 years on the board and then back into the operating role.
1:02:47
And I remember, quite honestly, when I left Sequoia uh 3 years, 3 months ago, I said to Pat and the team, you know, I'll be back. >> Mhm. I'll be back.
1:02:56
>> If you'll have me back, because I always uh had wished, I dreamed, and envisioned of coming back here, and I thought it would be a great place to end my career and provide the impact to the partnership, my partners, and companies that we work with.
1:03:12
And uh you know, when I stepped down at Workday, now 2 months ago, uh we start talking without exaggeration that day. >> Yeah.
1:03:22
>> Typical fashion, the news was out and it was within, no exaggeration, 5 minutes.
1:03:28
I started getting texts from Pat Grady. >> Yep.
1:03:31
>> Every single partner at Sequoia.
1:03:31
Not only did I get them, but my wife started getting them. >> Full circle.
1:03:40
>> "When is Carl coming back? We want you back."
1:03:42
And and I will tell you, it's it's a blessing to be back and here we are today. >> Okay.
1:03:49
Walk me through the bull case for becoming or, you know, going back into venture capital in 2026.
1:03:54
There, you know, it's maybe a little bit crazy narrative, but there's a narrative around like AI is eating everything, AGI is here, the big labs are dominating, they're going to eat everything, you know, Google is going to beat every company now, OpenAI is going to beat every company.
1:04:09
Sequoia has investments in the labs.
1:04:12
What's the opportunity that you see for new startup formation, new growth rounds?
1:04:18
Where do you want to see opportunity either in AI or in software or outside of AI?
1:04:23
Like, what is your thesis for why it's going to be a rewarding experience to be a venture capitalist now?
1:04:31
>> Yeah, well, it's a great question, but let me step back and go back two months ago first.
1:04:38
When I sat down at Workday, I did open the aperture up and look at everything.
1:04:44
I looked at everything from going back into an operational role at very large companies, big public companies.
1:04:48
I looked at operational roles in private companies.
1:04:54
And some of those private companies are the AI companies that our friends here at Sequoia at the time had invested in and being part of it.
1:05:02
And then I also started to think about, well, what would it be like to go back into venture, go back to Sequoia at a time of what I would describe as the most massive technology disruption we've ever seen in the history of mankind.
1:05:19
And to be part of it across many companies as opposed to a company is something that really excited me.
1:05:27
Joining someplace like Sequoia and I'm biased when I say this guy, the greatest venture capital firm on planet Earth ever.
1:05:35
Mission driven, the great the greatest partners in the world.
1:05:40
People that are smart as hell that even I at this age get to learn from every day.
1:05:46
And to be part of something so iconic in the midst of this massive tectonic shift and get to explore across so many different companies as opposed to a company is something that really excited me.
1:06:02
Just in my first two weeks while I haven't been fully you know, on board yet until today.
1:06:06
I've met with so many companies for Sequoia right, and got to experience the vibrancy of what's happening in the AI world.
1:06:15
And I just felt like Sequoia is a place that gives me the opportunity to stay engaged, partner with incredible people like my young man here next to me Pat Grady and his team.
1:06:30
And just be part of generational shift both as Sequoia inside the building and outside the building when it comes to building tectonic technology companies that will stand the test of time and I get to be part of that across all different dimensions of this crazy environment we're living in. >> Love it. >> It's amazing.
1:06:51
So, you talk about this massive technology shift which you know, we can all agree on is part of your thesis of coming back and and judging from the the letter that you sent to the team it felt like to me like some element of your approach is like hey, a lot of
1:07:07
stuff is changing in terms of technology and markets, but a lot of what you're trying to bring is kind of wisdom around maybe what isn't changing, which is human nature and leadership and how to work with people and how to get the most out of your teammates." Is that like
1:07:20
Is that like part of your mindset at all?
1:07:22
In just coming in and and helping both portfolio companies and the you know, the entire team at Sequoia just level up.
1:07:34
>> Yeah, no, clearly I think that's a big part of my personal mission going forward.
1:07:39
If if you read the letter that I I sat down and wrote one evening to Pat and Alfred in the partnership.
1:07:44
And by the way, I wrote that letter after looking at a lot of operating roles and talking to a lot of other venture capitalists and firms and I kept coming back to myself and saying, "Why not Sequoia? Why not Sequoia?"
1:07:58
That was always my grounding point when I was thinking about what to do next.
1:08:05
Fast forward, I wrote that letter because I have a mission in life.
1:08:11
A mission in life to give more than I get.
1:08:12
A mission in life to not focus on success, but focus on significance and impact to others, including my partners, the partnership and founders.
1:08:25
And now being in the industry for 38 years and having been here for six and spent 32 years in operating roles, I felt like I was uniquely positioned to bring wisdom, to bring advice, to bring coaching, to bring mentorship to younger founders, right?
1:08:40
To help them achieve their personal professional goals.
1:08:45
And the Sequoia platform and my incredible partners like Pat and Alfred and Andrew and the rest of the team here give me that opportunity to bring what I'm most interested in that is serving others to the table at a time that while, yes, technology is moving at a pace and rate that we've never experienced, there's also a need to help people understand how to build, scale, grow, lead, inspire, motivate others.
1:09:14
And that's something I'm super passionate about.
1:09:20
And my incredible partners here at Sequoia said, "Hey, this is a place for you to do all of that both inside and outside the building."
1:09:27
And And that's why I'm here.
1:09:27
And I couldn't be more excited about it, guys.
1:09:32
>> Nowadays, it feels like feels like and also in the data companies are growing faster than ever.
1:09:36
And you got, you know, what what have been maybe 10 years ago like 5 years of company building compressed down into 2 years.
1:09:42
When you're kind of mentoring CEOs or leadership teams, you know, in the portfolio, like what what advice are you giving them around that new dynamic, which is that, "Hey, you might raise three rounds in a year.
1:10:00
You might be adding head count at a at a much higher rate than you than you had to.
1:10:03
And meanwhile, like again, the technology is shifting at this insane exponential rate as well."
1:10:13
>> Yeah, so it's a great question.
1:10:13
And I'd And I'd have Pat chime in because they're seeing this, you know, obviously every single day.
1:10:18
Companies are raising a round and 1 2 3 weeks later they're raising another round.
1:10:22
And then they're trying to figure out what the the hell to do with that capital.
1:10:25
Um But let me go back and just share with you one of the principles that I've always focused on when it comes to business strategies.
1:10:37
And I always have talked about speed as being one of the best business strategies ever.
1:10:41
This is long before the current environment we're in.
1:10:45
And I say that because I use, if you will, a sports analogy.
1:10:47
If Pat is faster than me, is operating or executing faster than me, or running plays faster than me, it's hard to defend.
1:10:55
Speed is a business strategy and right now we're in the midst of everything happening very, very quickly.
1:11:04
So, speed becomes more important, but you can't be sloppy.
1:11:09
You can't do things in a wasteful way.
1:11:11
You can't just spend unlimited capital because at some point that capital will have to be replenished and they'll have to come to people like us and others.
1:11:18
So, I think there's a dimension of how to leverage speed as a winning business strategy, but also be smart about how to do it and where you're spending those dollars.
1:11:27
And I'll let Pat chime in on what they're seeing.
1:11:29
I saw it today on some of the calls that Pat was leading across the partnership.
1:11:35
>> Yeah, I I think that's well put.
1:11:35
Speed is, you know, one dimension of a vector, the other is direction.
1:11:40
I think the other thing that's interesting right now, it's not just how quickly things are happening, it's how dynamic the entire market is.
1:11:46
You know, our partner Constantine has this framework that there are some revolutions in computation and some revolutions in communication.
1:11:53
A revolution in computation is about the way information is processed.
1:11:56
A revolution in uh cation is about the way information is distributed.
1:12:00
Cloud, mobile, internet, all of those are revolutions in communication.
1:12:05
It's about the distribution of technology or of information.
1:12:09
This is a revolution in computation.
1:12:11
It's about the processing of of information.
1:12:13
The result of that is that the raw ingredients that you have available to build your product change every day or at least every week, possibly every month, but they're changing fast, right?
1:12:22
And so, you can be running like heck in a particular direction, if it turns out that that technology floor shifts under foot, all of a sudden you're going to change direction.
1:12:32
And so, I think one of the big sort of um things we have learned from the founders who are doing it best, the Daniel Nadlers and Winston Weinbergs of the world, is that talent density matters so, so, so much.
1:12:44
If you it's not about being bigger, it's about being better.
1:12:49
It's about having the densest possible talent pool so that when the world shifts, you're not caught flat-footed.
1:12:55
You go in the right direction. >> Mhm. Um >> Makes sense.
1:13:00
>> It feels like an amazing time to join Sequoia.
1:13:02
The technology industry is bigger than ever.
1:13:04
Venture capital is bigger than ever. We're in an AI boom.
1:13:08
But I'm wondering if you could turn back the clock for me and tell me what the vibe was like when you joined VMware because it feels like a very different time.
1:13:18
And And what was that like, you know? >> Was that? Yeah. >> 2002, right?
1:13:24
>> Yeah, wow, you're taking me way back.
1:13:26
Yeah, I joined uh VMware uh in 2002. >> Yeah.
1:13:33
>> And when I joined, I think there was a couple hundred people with probably 95% of them being engineers.
1:13:38
I think it was a deeply technical company by great great founders out of Stanford and Diane, you know, the founding CEO, great people.
1:13:46
And I remember joining, you know, this young little company, basically no revenue, and saying, "Hey, we're going to go change the world."
1:13:55
Like every startup says, "We're going to change the world."
1:13:56
And I remember Diane Green saying to me, "We're going to virtualize these little x86 computers and the entire world's going to run on top of them."
1:14:05
And I remember talking to Diane at the time.
1:14:06
I said, "Diane, you mean like mainframe partitioning, LPARs?
1:14:10
That's where we do but then there's no, we're going to do it on these little boxes."
1:14:16
And I'm like, well, who's going to help Who the hell's going to do that, right?
1:14:21
And then I went home and I thought about it. >> Yeah.
1:14:24
>> And then I had to go quite honestly to my wife and say, "All right, I just left working for a Bay Area company living in Pennsylvania commuting.
1:14:30
I committed I won't do that again."
1:14:32
And I thought about this more and I said to myself, "Wow, if Diane and this incredible technical team can do what they say they're going to do and turn that slide where into software that virtualizes these servers and allows you to run a multiple applications operating systems simultaneously.
1:14:48
I said to myself, if they can do that the next thing I said is I can figure out a distribution strategy and I can sell that silly. >> Yeah.
1:15:01
>> And the journey began there.
1:15:01
The first year or two no one was buying it.
1:15:04
It's like all startups go through that phase like, "Wow, you want me to take all of these 10 20 servers, physical servers, separated by physical computers and put them on one and if that server goes down then everything goes down?"
1:15:18
I'm like, "Yeah, that's what you should do. Yeah."
1:15:22
It didn't go real well at the beginning, but then we hit an inflection.
1:15:27
And then once we hit an inflection with this technology at the time that they brought to market was called vMotion, which would actually allow you to take a live running virtual machine and move it across physical servers without the application ever going down.
1:15:45
So now you had some redundancy and backup.
1:15:48
And when we started to show people that, first they're like, "Wait, this isn't really working."
1:15:51
So we had to unplug cables to show things were actually working.
1:15:56
And then it inflected and um quite frankly one of the greatest, you know, professional journeys of my life being there 14 years, a couple hundred people to 20,000. >> Yeah.
1:16:06
>> And and I was so blessed to be there because I got to do so many different roles in the company. I was never CEO.
1:16:13
Uh I was always uh I guess number two guy at the time uh across three great CEOs between Diane, Paul Maritz, and Pat Gelsinger and uh got to be a CFO for a while of a public company, got to help run product when our CTOs left.
1:16:28
It was just an incredible journey.
1:16:31
It gave me uh a view into a startup and it gave me a view of how to scale companies and it really helped me get a deep understanding of how to build global operations and build a mass scale at the enterprise level. >> Yeah.
1:16:46
>> And um >> Why did you What What made you push through that one to two years where you felt kind of silly even selling the product?
1:16:57
Just given that that there wasn't It didn't feel like you weren't feeling that pull from the market.
1:17:01
Eventually you got the breakthrough with vMotion, but what was the signal that made you keep just running down opportunities? >> question.
1:17:12
Um Very simply the engineering talent at the time, and if you look at now the industry is proliferated with incredible talent from VMware.
1:17:20
It's everywhere from CEOs to head of sales to head of engineer. It's everywhere.
1:17:25
Um what gave me the conviction and belief to keep pushing through is whatever that engineering team said the product would do, it would do.
1:17:39
And I kept saying, I remember at the time telling people, right?
1:17:41
We moved from forecasting numbers or revenue or bookings to forecasting literally our forecast calls was how many proof of concepts have we started because if someone tested it it worked. >> Okay.
1:17:59
>> It literally worked, and we were having >> So the market was like, we don't believe you.
1:18:03
And you just had to You just had to actually convince them to let you show them. >> Yeah, well said.
1:18:08
I had passion, I believed, I had conviction, and I witnessed the technology working as advertised.
1:18:13
A lot of times it doesn't in startups, and it takes a while to get there.
1:18:17
And I knew it was not if, I knew it was when the market would tip and come our way.
1:18:24
And then when it tipped and inflected it it took off very quickly, and then we started to see all these competitive pressures come in with open source technologies like KVM uh you know, OpenStack, Hyper-V by Microsoft, but we just stayed focused, we stayed convicted about the technology, and and we proved it time and time again to anyone who tested it.
1:18:48
It was an incredible journey, and I'm so grateful for that 14-15 years. >> Amazing.
1:18:54
Yes, it's such a such a wild ride.
1:18:54
I mean, you've at various points been working at organizations with 100 people up to 20,000.
1:19:02
Do you feel like you have a good calibration on when you're talking to a founder or an executive, just asking yourself the question, "Can you see this person thriving in an organization that's three orders of magnitude bigger?"
1:19:17
I don't even know if that's a relevant question anymore for a venture capitalist to be asking, but I'm I'm curious if you feel like there is a set of patterns that you've discovered or a set of skills that are on display from younger, more up-and-coming executives and founders that sets them up for success at massive scale.
1:19:38
>> Yeah, I think I I can identify patterns, and I have enough experience for the six years that I was here with Pat and the team learning from them about what to look for in a founder, and whether or not they can scale.
1:19:51
One of the things I absolutely look for is self-awareness in founders.
1:19:56
And are they honest about what they're good at?
1:19:58
Because a lot of these founders, quite frankly, are just incredibly intelligent human beings.
1:20:01
And sometimes they think they can do everything, when in fact they can do everything but not great, and they have something they're really good at.
1:20:10
And do they have self-awareness to say, "But I need to go get other people as part of the company to help me scale so I can focus on what I'm really good at?"
1:20:20
Um, do they have the ability to know when to turn things over to others is critically important, and I've seen that happen time and time again.
1:20:30
And and the other things, quite frankly, that I personally look for, I'm sure Pat I heard Pat talk about this this morning, there are attributes and characteristics of people that I look for that are way beyond the intelligence side of the equation.
1:20:44
You can't teach grit, you can't teach strive, you can't teach a great attitude, you can't teach determination, right?
1:20:51
All of those are things that are innate and part of people and you want to see that in these founders and when you find someone who has that passion, drive, desire, relentless ability to fight through challenges, issues, and opportunities and then they have the intellectual horsepower on the other side, when that comes together, that's a beautiful thing. >> That's your reminder.
1:21:13
The first time I I don't know Carl remembers this, first time I met Carl was 2010. >> Mhm.
1:21:18
>> And it was Doug Leone, Fred Luddy, who's the founder of ServiceNow, and I went to visit Carl to get some advice on scaling ServiceNow.
1:21:26
And uh I was probably in my I was in my late 20s, Carl was in his early 40s.
1:21:31
>> Still in his late 20s.
1:21:31
Look how young this guy is.
1:21:34
>> And uh >> He fathers the hell out of me, guys.
1:21:35
How good How young, how smart, helping run one of the most iconic, you know, partnerships in the world and here I sit turning 60 this year.
1:21:44
He pisses me off every time I see >> But so I I have like pages of copious handwritten notes from this meeting, which I'm sure exists somewhere around here, but the the one-liner that stuck in my brain, which is very consistent with what Carl was just saying, was attitude determines altitude and will determines skill.
1:22:03
And I always think about that and and we kind of morph that into the expression attitude is the ultimate input and I think when you see these founders, I'd also kind of use the Ray Dalio line, you know, pain plus reflection equals progress.
1:22:16
When you see these founders who are willing to take a risk and experience the associated pain and then reflect very honestly on what happened and what they can do better next time. Progress is inevitable.
1:22:28
And so I I'd almost say there's this like you have to have the right attitude to put yourself in that position, but if you're willing to take risk and experience a little bit of pain, and then if you're willing to be intellectually honest with yourself and self-aware and sort of clinically diagnose what you can do better next time, like those founders are just going to keep ranking. >> Yeah. Really well said. All the introspection.
1:22:49
>> Yeah, taking a side taking a side on the intros- introspection game.
1:22:55
>> I was going to try and tease something up and you really delivered.
1:22:57
Uh talk to talk to us about uh both of your agreements or disagreements maybe.
1:23:02
I'm sure you've been debating the future of enterprise software, the future of what's going on with the SaaS-pocalypse, public companies, like just the nature of business changing.
1:23:13
What is what remains true now that hasn't changed and won't change for decades versus what maybe has changed in the last few years and needs an update in terms of how people think about how businesses grow, how businesses flourish uh in when they're working in the technology industry.
1:23:34
>> Yeah, I'll let Pasture in then I'll I'll give you my perspective cuz I've answered this question about 17 million times in the last 3 and 1/2 years at Workday.
1:23:44
And I have a different perspective than probably most. >> Yes.
1:23:48
>> Um so, you know, it's funny, the first thing that comes to mind is a line that I learned from a man named Carl Eschenbach.
1:23:55
Who was who was a partner at Sequoia from 2016 to 2022.
1:23:56
And um the line is people do business with people.
1:24:02
And I think there's a there's an there's a foundation model maximalist point of view that the labs themselves are going to do every everything in every nook and cranny of the economy. >> Yeah.
1:24:14
>> And I just have a hard time imagining that version of the future coming to fruition because people do business with people. >> Yeah.
1:24:20
>> And I think that between a job to be done and the raw capabilities of a model, there's a lot that needs to happen.
1:24:27
Like shape it into the path of least resistance for you to travel down as a user to get to the right answer with the least amount of pain.
1:24:36
And there's probably a person in between who's going to do that work.
1:24:40
And as a customer, you want to do business with that person.
1:24:44
And so I I think people do business with people is going to remain true.
1:24:46
The shape that that takes in terms of what the businesses are is probably going to change.
1:24:51
I think in the world of software, you know, the first wave on the on-prem to cloud transition was this transitioning of systems of record, you know, the Workdays and Salesforces and ServiceNows of the world.
1:25:00
The second layer on top of that was a systems of engagement.
1:25:04
You know, those systems of record might own the core database, but then there are a bunch of different workflow applications that reside on top.
1:25:11
I think what we're going to see with this um the wave of AI software is this third layer on top of those, which some people call system of intelligence.
1:25:21
I don't want to call it that.
1:25:21
It's the layer that does the work, you know.
1:25:23
It's the It's the agents getting deployed that may or may not need those workflows beneath them, but certainly need access to everything that's sitting in that system of record.
1:25:31
I think that's what we're going to see and and as a result, I think those system of record companies are relatively safe.
1:25:36
They may not catch a lot of net new workloads because a lot of the net new workloads might go to the AI native companies, but I think they're overall pretty safe.
1:25:44
I think some of those workflow-based companies in the middle are in trouble because they're neither the system of record nor the agentic capability that's getting deployed.
1:25:53
And so they'll have to figure out how to become like that agent harness, so to speak, for whatever job to be done.
1:25:59
And then I think those AI native companies on top, the the basic thing they need to achieve is figure out the context of this organization, figure out the guardrails, come up with some sort of an eval framework, come up with some sort of a value function, basically wrap all the context around on capabilities of the of the foundation model to achieve the outcome that the business person wants.
1:26:19
And so I think there's a very important job to be done for those that new layer of companies.
1:26:24
And um and again, people want to do business with people.
1:26:26
Like there's a lot of value in you know, having somebody you trust take your hand and lead you into the AI future. >> Yeah. >> Yeah, I love it.
1:26:34
>> So So first of all, that's the first time I heard someone else ask this question to someone else other than me and >> And then you use your prior answer.
1:26:43
>> Actually, it's so funny.
1:26:43
I was sitting here thinking, I am going to respond, but you're going to hear something very similar from me.
1:26:49
I'll start in kind of reverse order.
1:26:52
I do think there is power to use Pat's exact analogy of a system of record, a system of action, a system of engagement, and I will use system of intelligence.
1:27:05
Because I think if you have the bottom three, and you can layer on an agentic or agent strategy, and you back that up with the data, the context of the data, and you own the business process workflow, you're in a unique unique position to have a great enterprise AI software company that stands the test of time.
1:27:31
I don't think we're in a world where does AI win or do the incumbents as companies win?
1:27:35
I think we're in a world of and.
1:27:41
And I think some people are the beneficiaries of the AI, like a Workday, like a Salesforce, and then others maybe have more headwind because they can be disintermediated because there's AI and you don't need access to all that data.
1:27:57
I personally believe all of the challenges to with software companies at scale and what's happening in the stock market are completely overblown.
1:28:04
I've been saying that repeatedly.
1:28:06
They're not going anywhere.
1:28:09
Incumbency is incredibly powerful in the enterprise.
1:28:14
Incumbency is even more powerful for a company like Workday who I was blessed to be there for over three years has a 98% gross retention rate of 11,000 customers 65 plus percent of them being fortune 500 companies. Not going anywhere.
1:28:31
And the other thing we can't forget is why I say and is because what matters in the enterprise is scale, security and compliance.
1:28:41
And some of these big SaaS companies have that.
1:28:43
That all being said, the pace and rate of change that can happen outside of the big incumbent and big SaaS companies who are innovating like crazy.
1:28:54
We're innovating at Workday like I've never seen have an opportunity to start completely fresh can start from scratch, get to leverage all the technologies and all the models that are out there and build agents and agentic solutions faster than anyone else.
1:29:11
And they're going to be able to go in the enterprise and provide value day one either on their own or on top of and through some of these SaaS companies.
1:29:19
So I think it's an and opportunity that's going to happen in the enterprise in the market as a whole.
1:29:24
There's going to be some winners, there's going to be some losers, but I think the current narrative out there of these SaaS companies being overblown being in trouble is completely overblown and obviously I'm biased.
1:29:37
I think Workday stands in a very unique position to completely you know, continue to crush the ERP market both on the HR and finance side and I couldn't be more bullish on the opportunity ahead.
1:29:50
But at the same time, I'm super excited watching these young talented people now that we get to invest in and how quick they can iterate iterate leveraging AI and just completely disrupt markets legacy markets that don't have all of the data, don't have the context, and don't have the workflows.
1:30:09
>> What advice would you give to a Fortune 500 CEO that maybe isn't an incumbent with that's thinking about buying versus building these agentic products?
1:30:21
>> I tell them to do both.
1:30:24
I think if you have all the data and you have the context of the data, and you can build a great engineering team, right, that comes with an AI background, build as much as you uh that you can.
1:30:35
At the same time, go do acqui-hires.
1:30:35
Go buy technology companies that are completely AI native from the beginning and bring them into your organization.
1:30:43
I won't say do one or the other, it's both.
1:30:48
You know, at Workday, in the last 6 months I was there, we bought four AI companies and they become part of the core fabric of the company that Aneel and Garrett and his leadership team there get to take advantage of.
1:30:58
At the same time, they bring in their talent on their own as they build out their organization.
1:31:03
So, I don't say think you say it's one or the other. You have to do both.
1:31:09
>> I think it's also one of the classic questions of what do you want your best people focused on?
1:31:12
You know, do you want your best people building the same sort of stuff you can get out of the box from somebody else who spends all day long thinking about that thing?
1:31:20
Or do you want your best people creating competitive advantage for your company?
1:31:24
Like, I think if you're a Fortune 500 company right now, kind of a no-brainer to go with Harvey for everything related to legal, kind of a no-brainer to go with Sierra for everything related to customer support.
1:31:34
You know, you should probably try something like Expo to work on pen testing.
1:31:37
Like, these are excellent companies with excellent people who spend all day obsessing over a particular problem.
1:31:42
Why take your best engineers and ask them to go do that?
1:31:46
Go go do something that's going to be unique to you. >> Okay, last question.
1:31:50
>> Pat, by the way, Pat makes a great point.
1:31:51
Having spent a lot of time with CEOs of Fortune 500s around the world, there's this whole narrative we're going to do it ourselves, we're going to build our own agents.
1:31:58
And they will do some of that.
1:32:00
But why, if you can go to a Harvey, right, get what they've already built and leverage it and very quickly get a return on that investment, so there's a time cost of value equation here for the enterprise. Go with that solution.
1:32:13
So, let them go focus on financials or insurance or retail or whatever is CPG, you know, let them focus on what their core business is.
1:32:21
Why build that technology if someone can do it much faster outside the company? >> Okay. Last question for Carl.
1:32:27
A few years ago you were spotted at the Allen & Company conference sporting two coffee cups.
1:32:33
You have an incredible amount of energy.
1:32:36
Are you a two coffee in the morning guy or were you bringing an extra coffee for a friend?
1:32:44
>> Well, it The answer, I hate to use the term again, but both. >> Okay.
1:32:48
>> bringing a coffee, I was bringing a coffee back to the room for my amazing wife of 35 years, Anna.
1:32:53
Uh, and and Yeah, and and and I used to drink probably eight to 10 to 12 cups of coffee A DAY. >> GO. >> WOW. >> THERE WE GO. >> That's amazing.
1:33:09
>> And that's all I have all day until I get to dinner when I eat dinner. It's my one meal a day. >> That's fantastic.
1:33:13
Wow, incredible amount of energy.
1:33:15
Well, it's clearly working.
1:33:17
Thank you so much for taking the time to come chat with us.
1:33:19
>> It's so good to see you guys both back together. >> Yes. Yes. >> It's >> you.
1:33:23
And I I just want to say, listen, I just want to thank Pat. Yeah. I want to thank Alfred.
1:33:27
I want to thank the entire partnership here at Sequoia for allowing me to join them and serve alongside them. >> Yeah.
1:33:34
>> Our our our partners, our customers, >> Yeah.
1:33:38
>> uh, our companies we're investing in, our founders.
1:33:41
It's a true honor to be back.
1:33:43
I'm super excited about the journey ahead.
1:33:44
And, uh, I think, you know, Sequoia is uniquely positioned to continue to be one of the most iconic uh, mission-oriented venture firms of all time and I'm proud to be back part of it.
1:33:56
>> Yeah, we're we're excited for you. Thank you so much. >> Incredible stuff.
1:33:59
>> We will talk to you soon. >> you guys.
1:34:01
>> Have a good rest of your day. Goodbye.
1:34:03
Let me tell you about >> 10 to 12 coffees a day. >> That's incredible.
1:34:07
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1:34:20
Uh and without further ado, we've been running long, but we will bring in Jim Cantrell from Phantom Space Corporation.
1:34:27
Sorry to keep you waiting, Jim.
1:34:27
Thank you so much for taking the time to join the show. How are you doing?
1:34:32
>> Uh please you have a fascinating background.
1:34:35
Give us a little bit of your background leading up to this company, and then I want to talk about how you're thinking about the business and also just the orbital economy more broadly.
1:34:46
>> Yeah, so I've been in the automotive and aerospace industries for more than 35 years, and uh I had a position everywhere from the front space agency to early SpaceX.
1:34:56
I was the guy that took Elon to Russia to buy Russian missiles, and when that didn't work, we started SpaceX.
1:35:03
And uh Well, after that, I I'm on my 12th space or automotive startup, and Phantom Space is the last one of them, but several of them have gone public >> Yeah.
1:35:14
>> since then, and you know, Phantom Space was sort of the ultimate >> Yeah.
1:35:18
>> of all these startups that that we're looking to solve the problem that I really needs to be done.
1:35:23
>> Yeah, that that story of going to Russia to try and buy the ICBM has been has been told and written about in books, but what what what does the current narrative get wrong?
1:35:30
What's your side of the story?
1:35:32
What were expectations like going into that meeting?
1:35:34
Uh did you was it seen as a long shot at the time, or did you think that it was likely to work?
1:35:46
>> Yeah, no, it was a complete long shot.
1:35:47
You know, when he when Elon called me, he had just left PayPal and >> had this idea of, you know, trying to inspire humanity to become multi-planetary as he still talks about. >> Yeah.
1:35:56
>> And uh he wanted to do what amounted to a stunt to to, you know, show that we could send creatures to Mars.
1:36:00
That turned into uh something that we put together, which was a growth chamber to land on Mars on a lander.
1:36:09
And we needed Russian rockets to buy it.
1:36:11
So, by the time we got done dealing with the Russians, they didn't want to sell to us.
1:36:15
They were just being Russians.
1:36:17
And uh Elon announced to all of us in a truly shocked way, we we we heard that he wanted to start the company SpaceX and build the rocket ourselves.
1:36:26
So, I will tell you that very few people gave us a snowball's chance in hell to make that happen.
1:36:30
Now, we we can see 25 years later where that ended up.
1:36:33
But, you know, everybody bet against Elon and the rest of us.
1:36:38
And uh you know, what the story's gotten wrong uh and what it gets right.
1:36:43
What it gets right was, you know, this determination of this guy who knew nothing about rockets who decided to learn everything he could.
1:36:49
And he he got a bunch of space cowboys around him.
1:36:54
And uh most of us were sort of revolutionaries in thought at least and wanted to uh stick it to the system and do something that everybody said we couldn't do.
1:37:01
What the story got wrong, uh sometimes people write and say I was never part of it.
1:37:05
I don't know why that got written in, but I was definitely an employee and had founder stock and the whole nine yards. So, here I am. >> There we go. Uh >> Amazing.
1:37:14
>> What's the modern version of the the long shot in space?
1:37:16
We've heard about colonies on the moon, colonies on Mars, space data centers.
1:37:20
What do you think is the most practical problem right now in space that people are maybe undercounting?
1:37:30
>> So, I think there's three tracks that it's going down.
1:37:31
One is is, you know, the military use of space, which we see ongoing today, right?
1:37:35
Wake up and read the news every morning. >> Yeah.
1:37:38
>> The second one is planetary settlement, which is what SpaceX is trying to accomplish.
1:37:43
And everything that Elon does, I believe, is aimed at that planetary settlement goal.
1:37:47
And then the third is kind of more recent, even though I've been thinking about it for almost a decade, is putting compute in space.
1:37:54
So now AI is the killer app that enables this, much like the internet came along as killer app that enabled Starlink.
1:38:01
So I think what we'll see is the next generation of AI in space, these so-called space data centers, but not in the way that the the the common narrative is going to portray it. >> Okay.
1:38:11
Uh how will it be different?
1:38:11
I I mean, Jensen Huang at GTC earlier this week was standing on stage saying that you know, he will be providing chips.
1:38:20
Elon has given some uh some outlines around what that might look like.
1:38:23
We've talked to Star Cloud, a startup that's planning to put data centers in space.
1:38:28
There's a whole bunch of other people that are approaching this problem, but how do you think people are getting it wrong?
1:38:33
And and and how do you think you fit in?
1:38:36
>> Yeah, so, you know, Nvidia, to just address that, is is nailed the silicon, right?
1:38:41
So so they're they're going to be the winners on that, I believe. >> so. >> Among others, right?
1:38:45
But they're going to be one of the primary winners.
1:38:47
And and thank you for doing that, Nvidia.
1:38:51
Uh number two is there's going to be a camp that I think is mostly hype that says we're going to put, you know, these these large language model hyperscalers into orbit.
1:39:00
If I'm generous, that's maybe 20 years out, right?
1:39:01
And and it's like flying a big factory on a on a huge rocket that doesn't exist there.
1:39:05
So everything that's going to happen in the future is going to be distributed data centers on a much smaller scale.
1:39:11
Everything's more expensive, right?
1:39:13
So we're at least 10 times, maybe 100 times more expensive to do something in space today.
1:39:20
Uh so so the the the the really killer app I see is to put AI inference in orbit close to where the data tsunami's being generated.
1:39:29
And it's today it's a tsunami, and tomorrow it's going to be a a mega tsunami.
1:39:33
And what the problem is is is being able to get that data back.
1:39:38
So there's there's this this funnel that restricts how much of that can get back.
1:39:42
So, AI is a natural way to reduce that data load and get it back to the Earth.
1:39:47
Maybe later we'll solve the the the issues with you know, with with with the Earth power and and heating, but you know, Phantom, you know, we've been at it for 10 years one form or another writing patterns, writing about it, speaking about it.
1:40:01
It's nothing new for us and we're putting together micro data centers to address exactly this along with with with data backhaul from the satellites to really exploit what we think is the next killer app there and create a space app store environment and an ecosystem for others to implement their their creativity on.
1:40:20
>> So, where do you think in the supply chain or the rest of the orbital economy there is enough maturity that you will never really need to build.
1:40:29
I imagine you're not going to build a new rocket, but are you planning to do connectivity through Starlink?
1:40:36
Like where will the partnerships happen and then where will your you know, your core value prop live within the supply chain?
1:40:44
>> Yeah, it's it's a great question because this is exactly where I think all the the differences between the approaches become evident.
1:40:51
So, it's my belief that in order to be successful in this you really have to vertically integrate much the way we did in the early days of SpaceX.
1:40:59
We saw that building the rocket and building your satellites and then implementing in their case Starlink and now you know, their version of XAI in orbit.
1:41:08
You really have to have that and you know, SpaceX is is going to dominate a lot in that.
1:41:14
Phantom Space we have exactly the same playbook.
1:41:16
So, those who don't have that vertical integration always going to be at the risk of what amounts to a very scarce launch supply even today.
1:41:25
You know, there there is a perception that there's more launch than we need and it's not true at all. It's very scarce.
1:41:31
You know, Phantom we're building something we call the Daytona which is quite a bit smaller than anything SpaceX builds.
1:41:36
So, we're more of the taxi, they're more of the, you know, the freight liner.
1:41:39
And so, so, you know, we find a real market for that.
1:41:44
People are buying our things and there aren't that many people that can actually build launch vehicles that work.
1:41:48
It's a really tough business and it takes 5 to 10 years.
1:41:50
So, so that's going to always be scarce.
1:41:53
The rest of it is really a matter of supply chain control.
1:41:56
So, so the, you know, the silicon, Nvidia, you know, all the rest of the satellite parts, suppliers, but, you know, you have have somebody to put it all together, operate it, and manage it.
1:42:05
So, we think of ourselves as building the railroad to space and then ultimately in a space app store on top of it so that so that people can build their own code. >> Yeah.
1:42:15
Talk about some of the tradeoffs of the launch vehicle decisions.
1:42:20
Are you thinking about reusability?
1:42:20
Is that less of a factor at this scale?
1:42:27
How frequently do you want to be launching?
1:42:28
Like, I imagine taxis, they go everywhere, they launch from everywhere.
1:42:32
Like, how else like play out the taxi analogy for mail and more?
1:42:36
>> Well, so so launch is like the critical cost of getting any data system into orbit, period.
1:42:42
And so, you know, it's it's incumbent on companies to control that cost.
1:42:47
And if you can build it internally, if you can gather the capital and the talent to do it, you're in better shape.
1:42:53
So, there's there's a trade on getting that cost down between building them super large like Starship and reusability and then mass production.
1:43:01
So, mass production like the car you drive probably cost $100 million and you maybe paid $100,000 for it. >> Yeah.
1:43:09
>> And there's a huge cost reduction through this your number.
1:43:11
So, at Phantom, we're going to apply both reusability and the mass manufacturing.
1:43:17
Because we're smaller, we can do that.
1:43:20
Whereas Starship, as an example, won't necessarily be mass manufactured but probably mass used.
1:43:24
So, reusability is a core thing more for the the logistics of these things.
1:43:31
And then the other side of it, here's the other choke point the business is launch sites.
1:43:34
We are really out of range capacity, launch range capacity in the United States, and we will be I think at that limit within 5 years.
1:43:44
And so, companies that control that range capacity are going to be in a position to control, you know, the railroads or the shipping lanes, as it were.
1:43:53
>> So, are you looking at the >> Who's working at Yeah, what what's the process to create more capacity?
1:43:59
So, uh it's very complicated, it's very bureaucratic, and it's very political.
1:44:03
So, we have uh five different launch ranges in the United States. Yeah, exactly. >> Music to my ears. >> Yeah, right.
1:44:12
So, most of them are old federal ranges that that we built during the Cold War.
1:44:16
And there's one in California, one in Florida.
1:44:17
We we know at least about the one in Florida very very nicely, Cape Canaveral.
1:44:22
And so, there are so many pads you can put on there.
1:44:24
Most of what we're using today is legacy from what what was built in the Cold War.
1:44:29
We're we're grandfathered into the you know, the the the the the the bureaucratic process that approved those pads.
1:44:34
So, any new ranges, you're going to run into huge opposition.
1:44:39
There've been people who tried to build new ranges on the coasts of this country.
1:44:43
And everybody not in my backyard comes out of their home to to oppose it, right?
1:44:47
And even in California, Vandenberg, you know, SpaceX recently got in a lawsuit.
1:44:53
We had about half their capacity, which was governed ultimately by the Coastal Commission in California. >> Yeah.
1:44:59
>> And uh you know, SpaceX had to sue, and they got a little bit more capacity.
1:45:01
But that that's what we're heading into.
1:45:03
So, that's why you see these launch ranges around the world coming into play.
1:45:07
And the problem for US companies is we're restricted from taking our launch vehicles to these foreign countries without government approval. >> Yeah.
1:45:16
Yeah, it's so tricky cuz I I can imagine living next to uh you know, a launch pad.
1:45:22
And and the first time the rocket goes up, you're like, "Wow, that was amazing."
1:45:25
And then if it's like, we're going to be launching those every 20 minutes.
1:45:29
Uh my windows are shaking a lot.
1:45:29
I actually would like >> Exactly.
1:45:33
>> And then the the novelty wears off pretty quickly, so >> Full disclosure, full disclosure.
1:45:38
>> Yeah, and so and so yes, obviously as a country we need to figure out where these go that are not disruptive and are scalable and are tied to the supply chain.
1:45:45
Maybe actually near a railroad, who knows? A physical railroad.
1:45:49
>> Give us uh give us any predictions around uh the moon economy. >> going to say moon. >> for the next >> Yes. >> decade.
1:45:57
>> Yeah, I have to say I'm surprised by the by the SpaceX pivot to the moon.
1:46:01
Um it's been something, you know, since I was first in this business that a lot of us saw as a logical pivot.
1:46:04
And uh there was it was almost like a religion between do we go to the moon first or do we go to Mars?
1:46:12
>> thought it was logical like 20 years ago, right?
1:46:15
And so you're surprised you're surprised at how late the pivots happened. >> Yeah.
1:46:20
>> Uh it doesn't really matter, honestly, in terms of their technical capability.
1:46:25
It's it's more incremental in terms of the development of the technology, which is probably why they did it.
1:46:29
I really don't know why they did it.
1:46:31
It could be a business decision.
1:46:32
There's certainly I think a more near-term economy there.
1:46:35
I think of Mars, you know, as as being so far away, eventually that economy will have to form on its own, kind of like this new world where we all sit formed as an independent economy from Europe 500 years ago it began forming.
1:46:48
And Mars someday will have its own manufacturing bases and you know, you you might have products labeled made on Mars, right?
1:46:56
That's a longer-term thing and uh that's really where Elon's mind always was, you know, from the early days that that I was with him, that it was all about Mars.
1:47:05
And to me the moon's just a stepping stone on on that way.
1:47:07
And I think there's a lot of people who see that as a you know, sort of a that effectively a theological choice.
1:47:15
Uh whereas really not, it's it's just a technical issue. >> Mhm.
1:47:19
>> Economic opportunities on the moon >> Yeah.
1:47:21
That you think are interesting.
1:47:24
>> We've heard about regular and the and maybe like a mass driver, but there's so many opportunities obviously besides tourism.
1:47:31
>> The most obvious one is helium 3.
1:47:31
So this is pushed out by the sun.
1:47:34
There's something like 18 kg of it in the United States in the strategic resource.
1:47:38
It's a byproduct in your nuclear weapons manufacturing.
1:47:42
Now, what are you going to use it for?
1:47:44
Well, you can use it for a couple of things.
1:47:46
Clean fusion energy is one of the few fuels that doesn't create radioactivity as a byproduct.
1:47:51
So that's obviously desirable, right?
1:47:53
The second part is for quantum computing which a lot of these have to be near zero.
1:48:00
So this is one of the few substances that you can cool to near zero temperature.
1:48:04
And the other is an absolute zero in temperature.
1:48:06
So there there's a huge demand on that.
1:48:08
Now, once you start mining it, does that, you know, that price collapse? Probably to some degree.
1:48:14
The second one that I see is this this mineral this rare earth mineral kinds of deposits and we honestly don't know enough about what's up there.
1:48:23
We have a pretty good idea from the Apollo missions, but there's probably a lot of rare earth deposits, my guess.
1:48:28
And I'm not a geologist, but it I would guess there's probably some rare earth minerals that you know, mining from the moon which would be more palatable than tearing up our beautiful earth would be as long as we can solve the transportation problem.
1:48:40
And it all comes back to the rocket, by the way.
1:48:43
>> Yeah, makes a lot of sense.
1:48:43
Uh well, thank you so much for taking the time.
1:48:46
>> Yeah, great to be here.
1:48:47
>> Congratulations and well, we'd love to >> talk to you soon.
1:48:50
>> Yeah, come back again soon.
1:48:52
>> Have a good rest of your day, Jim.
1:48:52
Let me tell you about Cisco.
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1:49:01
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1:49:10
So without further ado, let's bring in Tom Holm from GV. Tom, how are you doing? What's going on?
1:49:19
We could hear you for a second. Can you hear us? >> You're back. >> You're back.
1:49:24
Where are you calling in from? Can you hear us now? Yes, no? >> Check. >> Check. >> Can you hear this?
1:49:30
I'm going to tell you about Sentry.
1:49:31
Sentry shows developers what's broken and it helps them fix it fast.
1:49:36
That's why 150,000 organizations use it to keep their apps working. Could you hear that?
1:49:41
Cuz I could also I can also potentially tell you about the New York Stock Exchange.
1:49:46
Uh cuz if you want to raise the cap if you want to raise capital, Tom, you got to do it at the New York Stock Exchange.
1:49:54
I'm sure a lot of your companies are aiming for IPO.
1:49:56
Let's get them live on the New York Stock Exchange. >> Tom back.
1:50:00
>> We will we will have him trouble shooting.
1:50:02
>> There is some breaking news that we do got to talk about, which is that Jeff Bezos >> fired up than a personalized ad. Okay, breaking news.
1:50:09
>> Jeff Bezos in talks to raise a hundred billion for AI manufacturing fund.
1:50:14
Amazon founder has traveled to the Middle East, Singapore in fundraising effort linked to Project Prometheus. >> That is incredible. >> Very very exciting. >> news. Uh advanced talks. I don't care.
1:50:25
If it's just advanced talks, I'm hitting the advanced talks to Jeff Bezos.
1:50:31
>> He's meeting with some of the world's largest asset managers to raise funds for the project.
1:50:34
A few months ago he traveled to the Middle East to discuss the new fund with sovereign wealth representatives.
1:50:39
More recently he went to Singapore to raise funding for the effort as well.
1:50:44
Uh he's it's being described as a manufacturing transformation vehicle.
1:50:49
I >> Wait, he's going up against TK, right? >> Oh, maybe.
1:50:53
>> I mean TK's not as directly focused on manufacturing.
1:50:56
Like this is something I asked No no, he's saying manufacturing transporta- like it's a vehicle a fund for transforming manufacturing. >> Yeah yeah yeah yeah.
1:51:05
Yeah, it's like an investing vehicle.
1:51:08
>> to buy companies in major industrial sectors such as chip making, defense, aerospace.
1:51:12
Let's try again with Tom. Get out of here, Tyler. It's Tom time, Tom. >> Here we go. Hey, Tom. Can you hear us? >> Oh, no. Now we don't have audio.
1:51:22
We don't have audio still. >> Try it out. Nothing? Okay.
1:51:26
We We You can hear us, we can't hear you, but we can tell you about this post that we enjoyed from Lalarkin. Got my horse to water. Now for the easy part.
1:51:40
>> I'm going to continue The fund is aiming to buy major industrial sectors such as chip making, defense, and aerospace.
1:51:47
It would dwarf the size of the some of the world's largest buyout funds and rival SoftBank's $100 billion fund.
1:51:52
I got to wonder how much how much do you think how much do you think Jeff is pitching in himself?
1:51:57
I can see him, you know, anchoring.
1:52:00
He's like, "I'm good I'm good for 30."
1:52:02
You know, something in that range. >> Yeah. >> Uh >> Yeah, yeah.
1:52:05
He's He's got some funds.
1:52:07
>> But this is such a white pill. >> Yeah. Why?
1:52:10
>> I mean, the whole This is like, you know, we we need to manu- we need to manufacture, you know, basically we need to reindustrialize America.
1:52:16
We're not going to do it by just copying everything from the past.
1:52:20
There's some element of transformation that needs to happen as well as new efforts. >> Yeah.
1:52:26
>> And uh this is uh this is tremendous news. >> Yeah.
1:52:31
And I mean, there has been like a venture capital boom in reindustrialization, but most of the funds that we talked to that are in that category are 50 million, couple hundred million, certainly nothing at this scale.
1:52:44
And this has got to be uh incredible news for uh the the founders that we talked to that are part of the reindustrialization effort because they they they have a new potential investor.
1:52:55
Uh did you see that OpenAI has acquired Astral who will be joining the Codex team.
1:53:03
So, finally OpenAI has Astral Codex, which of course is a great play on Astral Codex 10, the the blog, which has some fantastic articles.
1:53:11
Let me tell you about Okta.
1:53:14
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1:53:18
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1:53:23
Will OpenAI increase the cost of Chat GPT?
1:53:26
This is a cal sheet market out there.
1:53:28
It's a 46% chance if either Chat GPT Pro or Plus have a price increase after January 2nd, 2026 and before January 1st, 2027.
1:53:38
So, in the year in the year 2026 >> the current pricing of $20 a month and $200 a month. >> What do you think?
1:53:45
I I feel like this isn't financial advice, but I feel like they're not going to move the price points because they're focused on so many other things.
1:53:54
>> only the only thing is I could imagine an an additional plan on top of it. >> Yeah.
1:54:00
>> But that would be an incremental plan >> Max Max is right there.
1:54:01
We got Pro, we got Plus, we got Pro.
1:54:04
Sign me up for the Max plan.
1:54:07
And I think they also have a light plan and and I I would imagine there's Go, yeah.
1:54:11
So, I would imagine that there's more tiers within there.
1:54:13
At the same time >> Give Tyler the goat >> Ripping the band-aid off of price adjustments is extremely painful and they're probably the earlier you do it the better. Like Netflix. Yeah, yeah.
1:54:25
Like Netflix has been increasing prices and when they're at $9.
1:54:27
99 a month or something, it's very normal.
1:54:30
And then when you go up, it start everyone is like, "Oh, they raised the price."
1:54:34
We just raised it every couple months. Anyway, we're good. Third time's the charm. How you doing? >> I'm very good. Can you hear me, guys? >> Fantastic. Introduce yourself. >> I'm Tom Wehmeyer.
1:54:46
I'm one of GV or Google Ventures' managing partners. I'm based in London.
1:54:50
I'm going to hope I'm going to bring as much energy as Karl. >> I love it. I love it.
1:54:55
>> It's a tough act to follow, but it's a good start. >> AI, good or bad? What's your strategy?
1:55:00
What are you investing in? What are you seeing?
1:55:03
>> As you can imagine, 80 or 90% of what we're doing is AI.
1:55:05
In truth, it's hard to imagine a credible founder that isn't leading with it at the moment.
1:55:09
And so, we're viewing everything.
1:55:11
I mean, in your email that you sent out today about Samsung, about some of the effects of the what's going on in the Middle East, we're even looking at that through the lens of AI and how it should affect our investing strategy. >> Sure.
1:55:26
And then, in terms of the portfolio of founders that you're talking to, uh how interesting is the sovereign AI efforts?
1:55:32
How interesting is just finding amazing entrepreneurs that are going to run through walls all over the globe, and they just happen to be there, so you're the first point of contact, you meet them early, versus maybe going to an American entrepreneur that has some traction, and you're going to help them, uh you know, if you join the board, you're going to help them go global. What are you thinking? >> Yeah, absolutely.
1:55:51
So, one of the things we're really proud of is that we're actually a sort of a global firm, primarily the US and Europe.
1:55:55
And so, we offer founders soft landings in Europe if they're American companies, and vice versa.
1:56:02
So, that's something that works well.
1:56:03
But in truth, we're finding we're meeting more and more technical founders, and Europe has an edge on that.
1:56:09
To give you an idea, I think 35% of the world's AI researchers or master's programs are actually in Europe. >> Yeah.
1:56:17
>> We've got four of the top technical universities globally in Oxford, Cambridge, Imperial, ETH Zurich.
1:56:21
And so, there is this incredible sort of talent building up.
1:56:27
So, historically, I think Europe's bottleneck was probably human capital and financial capital.
1:56:31
The financial capital is now global.
1:56:33
Your guys' show is global.
1:56:36
Now, the human capital is really growing, and we're seeing a real multiplier effect in two ways.
1:56:42
So, the first is the very best founders are starting companies over and over again.
1:56:46
So, we have investments in, for example, Snyk's founder, Guy Podjarny, who has now done Tessil.
1:56:51
Most of our European founders are actually repeat founders.
1:56:56
And the second thing is we have an equivalent of the sort of PayPal mafia happening.
1:56:59
So we were early investors in GoCardless.
1:57:02
We've now seen other companies coming through as a result of that like Monzo.
1:57:06
And I think I just keep seeing that multiplier effect. >> got it. Yeah.
1:57:11
Does does Europe broadly give give England enough credit for DeepMind because it got rolled into Google so quickly but I feel like like the UK punches way above its weight in terms of AI research with DeepMind and maybe that's under discussed?
1:57:31
>> Oh, I think it's under discussed.
1:57:31
And I think it's easy people will often post rationalize it and say how great it would have been if it had stayed independent.
1:57:38
But we've got to remember when DeepMind was really going in 2012, it was really hard for people to see.
1:57:45
The 2017 transformer paper had not been written for five, you know, years.
1:57:49
It's early days and so it's easy to look back and say it was obvious. It wasn't at the time.
1:57:53
But I think the person that single-handedly has done as much for tech in the UK as anyone else is Demis Hassabis, the founder.
1:58:01
He insisted on keeping a base here because he knew the technical talent was here.
1:58:05
And now we're seeing that kind of multiplier effect.
1:58:07
So there's great neo labs being funded right now.
1:58:12
You've got reinforcement learning companies like recursive super intelligence.
1:58:17
Like Tim RocktΓ€schel is based here.
1:58:19
Richard Sutton and Timo from the West Coast.
1:58:22
You've got Ineffable, Dave Silver's new company.
1:58:24
You've got world model and these are all coming out of GDM.
1:58:26
So you're getting this multiplier effect.
1:58:31
They deserve huge credit for that.
1:58:31
For everyone, sorry, after you. >> Yeah, yeah.
1:58:35
Yeah, when when when you look at a neo lab, it feels like there's a thesis where it's just okay, you have a brilliant technical mind, they're going to go explore.
1:58:42
Yeah, the price might be high but you're underwriting it as you know, a venture style bet.
1:58:47
There's a chance that something great comes.
1:58:49
But do you have or from conversations, do you have an idea of how the Neo labs might plug into the broader uh AI ecosystem, either through partnerships with big labs, or are are you talking to Neo labs that are saying we can leapfrog on certain vectors, or maybe we can launch in our own consumer product, that's happened before.
1:59:12
What what How do you think about where the Neo labs fit in post the research phase?
1:59:20
>> Yeah, one of the questions we've been asking ourselves is the current S-curve of technology that we're on, perhaps the third phase of large language models and diffusion models, like the existing companies are doing very well, and they're often chasing benchmarks.
1:59:31
You become what you measure, and so they're really driving fantastically into that.
1:59:38
The question is, what are the other S-curves of technology that could be explored?
1:59:41
And the two that I think are really interesting are world model companies at the moment.
1:59:44
So, Amie labs just got funded, our portfolio company, Odyssey, is going in that direction from diffusion, and then reinforcement learning, we think there's huge work can be be done.
1:59:55
So, if novel breakthroughs can be made on either of those, we think that they actually can be complementing to the existing companies, and they could work in addition.
2:00:04
So, very rarely, they may go full stack and create their own product.
2:00:10
Uh often, they will actually service their unique intelligence through API.
2:00:14
>> Yeah, that makes sense.
2:00:16
Uh predictions around the next breakout prosumer products from Europe.
2:00:20
We've seen the the lovable's, the granolas, anything that is like very on the radar in London, everybody's talking about, but hasn't necessarily broken containment and gone super global yet.
2:00:35
>> I mean, one that everyone's really excited about, but it's partly we just exited the business to uh Apple, is people keep asking me what Q is.
2:00:41
So, Q is a is a company, second biggest acquisition >> you guys it it the the net the it was in stealth until the acquisition, right? >> Correct.
2:00:53
So, I was on the board for the whole of that period.
2:00:55
We did the We led the seed and did the series A with our friends at Kleiner, Aleph, and Spark.
2:01:01
And that company's going to do something very special.
2:01:04
A few years ago, we had a thesis that actually voice is really interesting because you can communicate in voice about 150 words per minute.
2:01:12
It's high input information because of intonation, etc.
2:01:18
, versus typing, which might be 90 words a minute.
2:01:20
We invested in Neuralink, which is like the very invasive version of high throughput.
2:01:24
And then we started exploring what are private ways to communicate by voice. And Cue is that company.
2:01:29
So, I'm asked about that a lot at the moment.
2:01:32
The other one I'm excited about from a consumer perspective is our investment in Nothing.
2:01:38
>> Any any ideas or guesses is probably not your information to share, but like time you know, Apple is making, you know, huge push and effort right now.
2:01:48
They need to show the world that they still got it on the on the software side.
2:01:53
Is Cue something that, you know, Apple, uh, you know, iPhone users will get to experience in 2027? Is it the longer term?
2:02:02
Will they ever kind of like will they Do you think there'll be like a moment where they're like, "Wow, this is, you know, a huge >> I yeah.
2:02:08
I think it's going to be a wow moment, and I think it'll be in 2028. >> Okay.
2:02:13
>> And and, you know, our belief is actually that if you believe the software and the models actually kind of overshoot requirements, then a lot of the value will actually accrue to the physical devices because it'll be the distribution point.
2:02:26
So, we we, for example, invested in Nothing.
2:02:28
They just sent me their new product, which I'm going to unbox after this.
2:02:32
But they incredible smartphones.
2:02:34
We believe that actually the value accrues to the distribution.
2:02:39
And so, whoever owns those smartphones.
2:02:42
>> Yeah, Carl being in the Carl being in the position of the you know, they proven they can make beautiful products, but then also being able to be really flexible and quick around implementing AI all the way down to the hardware level is very cool position to be in. Yeah. >> Yeah, it's fun time.
2:02:58
Well, thank you so much for taking >> You brought the energy. You brought the energy.
2:03:02
>> And my apologies but I also brought the technical issues. So my apologies. Next time. >> It happens. Well, thank you so much.
2:03:09
>> great to meet you, Tom. Come back on to see you. >> that. Take care. Have a great day. Thanks.
2:03:13
>> Let me tell you about Figma.
2:03:13
No matter where your idea starts, Figma may cloud code, code acts, or a sketch, the Figma canvas is where ideas connect and products take shape.
2:03:21
Build in the right direction with Figma.
2:03:22
And without further ado, we do actually have >> Let's play Let's play if we have time before the Cubanator joins.
2:03:34
>> We're we're working on some >> We're working on it.
2:03:37
I don't think we have time.
2:03:38
I'll try to >> that screen and I think we're almost ready.
2:03:43
Uh the the the the one small news article here that we can talk about is that Meta has signed a 10-year lease for a 15,000 square foot townhouse on 690 697 Fifth Avenue to open Meta Lab New York, its first Manhattan flagship retail location.
2:04:00
They painted it completely blue, apparently.
2:04:01
The store will focus on hands-on demos of Meta AI glasses and VR headsets.
2:04:05
So, they're getting into the retail space.
2:04:08
Well, um let's figure out >> Still working on it.
2:04:13
I can tell you about Way Short Capital.
2:04:17
>> Okay, tell me >> They invested in SpaceX at apparently 200 and Okay, it's fake news. Brutal. >> That was quick. >> Brutal.
2:04:29
I was like this seems too good to be true.
2:04:32
PitchBook is uh Uh but we can tell you about Rivian robo-taxis. >> Oh, yes.
2:04:40
>> Uh, Uber and Rivian have announced a deal.
2:04:42
Uber's going to invest 1.
2:04:42
1 and a quarter billion in Rivian and deploy up to 50,000 R2 robo-taxis.
2:04:47
I'm so interested to see what Rivian can actually do on the robo-taxi side.
2:04:53
I I have friends that have owned Rivians have said the >> them.
2:04:59
>> the uh, autonomous driving is fantastic. >> Yep.
2:05:02
>> It just feels like this um, I I I will be very interested to see if more than, you know, a couple companies can really crack it quickly.
2:05:12
>> it's a young company, very agile, and uh, lots of opportunity.
2:05:14
Well, I believe we have Mark Cuban in the Restream waiting room.
2:05:18
Let's bring him in to the TVP and Ultra Lounge. Mark, can you hear us?
2:05:25
>> Yes, but you're too loud. Hold on 1 second. >> We're too loud? >> Too loud? >> Okay. >> Whisper.
2:05:30
>> no, cuz I'm wired cuz I just woke up. >> There we go. That's great.
2:05:32
Thank you so much for guys. >> Hey, how you doing? >> Hey, guys. >> Okay. >> I'll deal with it. >> Good to see you. How's your 2026 going?
2:05:40
We haven't talked this year yet. What's life like? >> I'm loving life.
2:05:44
I got no complaints whatsoever.
2:05:46
Yeah, I got some amazing Yeah, I'm loving life. >> That's great.
2:05:48
Uh, are you so you're not disappointed about the rollout of ads in LLMs thus far then?
2:05:56
Is that safe to >> Has it ruined your year?
2:05:58
>> Because I saw the first ad, it was for the Wall Street Journal, and it was just a little little bubble at the top.
2:06:02
Hey, you might want to check out the Journal.
2:06:05
Seemed innocent enough to me. But how are you feeling?
2:06:07
>> I have I haven't seen it at all, so it hasn't ruined my life at all. >> Okay.
2:06:11
>> What's your information diet?
2:06:11
Cuz it's hard not to log on the internet and not start black-pilling these days.
2:06:18
>> Yeah, no So, my first stop My first stop is a site called Memeorandum, >> Okay.
2:06:23
>> which kind of gives me an update on all the what's happening in the world.
2:06:25
My second stop is Drudge Report, cuz that gives me the hyperbole on everything that's happening in the world.
2:06:33
And then after that, all the different newsletters and emails that I get just that try to keep me up. >> Mhm.
2:06:40
How are you processing the flood of cold emails that appears to be thoughtful, but >> Is AI generated?
2:06:49
>> AI generated because you are notorious for your response rates and getting back to so many people that have reached out, but it but it feels like maybe an impossible task now.
2:07:00
>> Now, I do what everybody else does. I bought a Mac Mini. >> You did? >> You know. Yeah, for sure. And I'm still learning.
2:07:08
>> So, you're just like you hit me with AI, I'll hit you with AI back right away. >> Right back, right?
2:07:11
Because it's it's not even like the cold emails cuz that's pretty obvious.
2:07:15
You know, that it's pretty easy to see.
2:07:17
It's people subscribing me to and you know, the good news is Gmail has an unsubscribe button.
2:07:25
So, you just got to train it to hit the unsubscribe button and I just review it and all that So, it's still a work in progress, but at least I have a path.
2:07:33
>> Well, the the the issue with with us is that historically if you had a podcast and somebody wrote you an email and said, "Hey, I really appreciated this moment where you were talking about this one thing." >> Totally.
2:07:42
You're like, "Oh, they actually listened to the show."
2:07:43
>> tell like, "Hey, at least they they pressed play and at least they found a moment."
2:07:47
But now AI just does it instantly, so there's no way to clock whether whether somebody actually listened or not. >> Yeah.
2:07:55
>> And that's okay, right?
2:07:55
Because they're going to the response rates most likely will be so low.
2:07:59
We're in that trial and error phase where people are like, "We're going to try it, see what happens.
2:08:03
You know, maybe we'll get lucky."
2:08:05
And then they'll get bored and then they'll drop off. >> Yeah.
2:08:08
Uh is owning a Mac Mini a green flag for entrepreneurs these days?
2:08:11
Talk to me about what you're seeing in early-stage startups in this AI era.
2:08:17
Like where where where are the interesting builders?
2:08:19
What patterns are you seeing that are like, "Oh, I didn't think that this person would be going down the founder road, but they They now."
2:08:26
>> Yeah, agents for everything. >> Yeah.
2:08:28
>> You know, it's just cuz once you figure out how to do agents, then you can do them a little better than most other people, and then you can turn that into what would have been a SaaS business in the past is now, you know, we'll create your own marketing team and we'll, you know, do all these different things for you that you no longer have to do, and we'll charge you x number of dollars a month. >> Mhm. >> That's it.
2:08:47
And I'm seeing dozens of those, you know, typically one for every industry you can ever possibly imagine.
2:08:53
>> And are they growing revenue faster?
2:08:53
Are they growing profit faster? >> Neither.
2:08:58
They're just still trying to to get some just trying to get some traction at all, you know, um because if you're growing revenue quickly, you're probably not coming to me yet.
2:09:07
probably not coming to me yet. You know, you're because you know, the marginal cost to start is so low and it's so fast, and you're using the agent, so if they can get anybody to give them a credit card, you know, or sign up, or, you know, now
2:09:20
there's a little bit of a battle to use um USDC for payment as the payment rails, you know, to make it so that, you know, just give me your wallet, and that's, you know, it's going to end up being a scam, and a lot of people are going to get ripped off there. But uh um but I
2:09:31
But uh um but I think the real thing right now is agents for vertical verticals and trying to turn that into replace all your employees so you can start up or you can cut costs.
2:09:45
>> Do you think any of those agent-focused, sort of like niche, at least niche to start businesses would be a fit for Shark Tank? >> Yes and no. Yes, they should be.
2:09:54
No, people won't understand them, and and the other sharks wouldn't understand them. >> Sure.
2:10:00
>> I mean, effectively, anything you can do >> But you only need one shark to understand.
2:10:04
>> Yeah, there >> Yeah, right.
2:10:05
And I'm not on the show anymore, so >> It's time to It's time to go back. It's time to go back.
2:10:09
You got to be You got to be >> Uh yeah, what what what was the anatomy of I I I think we talk about what makes for a great company all day long.
2:10:17
We'll talk about that throughout the the show today.
2:10:20
Uh but what makes for a company that will put on a particularly captivating Shark Tank appearance?
2:10:29
>> Um you got to remember it's for TV.
2:10:29
You have to be entertaining.
2:10:31
And if it's not entertaining in some shape or form, it's just not going to work regardless of the quality of the business.
2:10:38
If you don't have a you know, charisma, you don't have a compelling pitch that's entertaining, it doesn't matter.
2:10:44
You could be selling dollar bills for 50 cents and it would fail. >> Interesting.
2:10:50
Uh how important is like the visual component?
2:10:52
There's a lot of physical products, but at a certain point it gets too big for the studio.
2:10:55
Uh how important is it like the physical product presentation?
2:11:01
>> Well, the good news is the producers will work with you on that and they make them practice over and over and over.
2:11:07
And of the hundreds, if not thousands of pitches I saw in 15 years, we only had one really just just choke, right?
2:11:15
Where they couldn't spit it out.
2:11:17
Um maybe two, which is amazing.
2:11:17
It's a testament to to Mindy and the producers there and how hard they prepare them. >> Yeah.
2:11:24
Uh how do you think uh how do you think Shark Tank and and shows like it uh will change in in the era of AI, video generation, you know, endless content?
2:11:37
Uh is is it is it stronger than ever because it's a known brand or is there some weakness there?
2:11:43
Like how do you see that playing out?
2:11:45
>> It all depends on platform.
2:11:45
It's like you guys, right?
2:11:46
You know, it really just depends on reach of the platform and quality of the product.
2:11:50
I think I think for Shark Tank, it's not going to have to change because of AI or technology simply because you're you're really communicating to a family audience and the message you're communicating isn't hey, here's a here's a bunch of businesses that are great.
2:12:07
The message you're communicating is that could be you on the carpet.
2:12:09
The American dream is alive and well.
2:12:11
And so that's really what makes Shark Tank successful, not the quality of the businesses.
2:12:18
>> Yeah, what about sports?
2:12:18
I've seen some some robots playing tennis.
2:12:20
They're going to be playing basketball soon.
2:12:22
I don't think I'll be watching robot basketball, but how do you think live events, sports, basketball will change over the next decade?
2:12:31
>> I mean, maybe for the referees, like you've seen in tennis, but that's it.
2:12:33
I think in reality, more people will want to go to in real life events than before because if you know, if you're just managing agents, looking at output, looking for exceptions, you're going to want some human touch, right?
2:12:49
You're going to want to be able to engage, and I think that's that's really, really important.
2:12:53
Um, and I think that's where sports will grow.
2:12:55
I mean, and you're starting to see that now with, you know, what happened with the Olympics, the World Baseball Classic, you know, became much more popular because people want that disengagement from all the the the stress that that's happening right now.
2:13:12
>> Yeah, at the same time, it feels like there's almost an opportunity for not to bring it back to targeted advertising, but but AI can tell me, "Okay, my favorite team's in town.
2:13:19
I should go to this particular game.
2:13:22
I should remind me at the right time."
2:13:25
instead of just signing up for the newsletter. >> AI.
2:13:27
>> Yeah, that's just good targeting.
2:13:30
>> Yeah, that's just targeting, right?
2:13:30
And I'm going to tell you what, in terms of I'm going to take it down a different path cuz I'm contrarian on this.
2:13:37
And that's with robotics. Mhm.
2:13:37
I think everybody's making this push for humanoid robots.
2:13:43
I think they might have a 5-year lifespan and then they'll fail miserably, maybe 10. >> Yeah.
2:13:48
>> Um, >> You mean the device, you mean the companies or the device or the individuals? >> robots. >> Or both? >> Both.
2:13:55
>> Right, because I think everybody defaults to, "Well, we live in a human world, and humanoids will take the place of humans for various functions, particularly in the home."
2:14:04
And I think there's just no chance.
2:14:06
I think, um, if you look at warehouses and what Amazon does, um, they're not humanoid robots carrying boxes.
2:14:14
They're robots that are designed to fit the environment.
2:14:17
And I think, you know, I've heard people say, well, a house is a house, you need a humanoid.
2:14:21
I think houses are going to be redesigned completely so that whatever the optimal robot is that allows it to simplify the house, that's where houses will go.
2:14:34
So, I'll give you an example.
2:14:36
If we had robots that look more like spiders that could, you know, but had hand, you know, the ability to carry and lift things, whatever, more like ants, I guess, maybe. Right?
2:14:50
But and you could create a house where the pantry and the refrigerator and the washing machines were hidden behind the garage, if you even have a garage.
2:15:01
And that way you could redesign it so all the living space was for people because you know that the robots aren't going to be full-form humanoids.
2:15:08
They're going to be whatever the optimal shape is and they're kind of co-designed.
2:15:12
You design the house to fit the robot and you design the robot to fit the house.
2:15:19
And I think that way you could go go along extensively on both.
2:15:23
>> You know, the humanoid uh founders will tell you, you know, how do you solve stairs, right?
2:15:28
Like it doesn't work for wheels.
2:15:31
But if the robots are really great, people you could put like a mini robot elevator, right?
2:15:37
Like if if it's on wheels >> What are the things? Yeah.
2:15:40
Like you did you see like the old house dumbwaiter dumbwaiters, right?
2:15:44
Where there's just the thing where you pull it you put it in there and you pull it up and it goes up to the next floor and somebody opens it up.
2:15:51
You're going to see, you know, a a mechanized equivalent to that, right?
2:15:57
Where the it's it recognizes the little, you know, ant robot that's coming up and it opens up a little door that's, you know, that leads to the size of whatever it is it needs to carry or whatever and then it goes up the dumbwaiter does its thing on the next floor the next floor the next floor and does what it needs to do.
2:16:15
I don't think you know stairs are an issue at all. >> Yeah.
2:16:19
Uh how do you think about uh uh just these types of ideas AI products generally getting rolled out and then hitting it bumping up against like human guardrails.
2:16:29
Like when I hear that I think like that sounds incredibly sci-fi and potentially possible from an engineering perspective but then you know you try and you know remodel your house and then you're stuck in permitting for 2 years and so that tends to slow the progress down a little bit.
2:16:46
Is that is that a real thing?
2:16:48
But that's all technology. >> Okay.
2:16:49
>> Yeah of course it is but that's all technology during the interim period right?
2:16:52
There's always a tran- transitional period where you go from the old to the new.
2:16:55
Like back in the day you know there wasn't enough electrical outlets for your PC.
2:17:00
There wasn't enough you know you had to go into the walls to to run all the ethernet cables and all that right?
2:17:07
You know and so the houses and offices weren't designed for that because it wasn't considered when they were built but they adapted.
2:17:15
You found ways to adapt and it'll be the same thing with homes it'll be same thing with offices. >> Yeah.
2:17:21
>> We'll find ways to adapt.
2:17:21
I think you know the biggest challenge going forward is going to be as we go from an LLM world to a world view a world view world of AI where you know we're taking in video and learning from the video and extracting rules from the video.
2:17:37
A lot of the things that we're going to do are going to be outside and are going to be going to have to consume in the interim at least either satellite bandwidth or 5G bandwidth and I don't think there's going to be enough bandwidth when you're working with video-based AI models. >> Interesting. Interesting.
2:17:57
You mentioned maybe a garage not existing in the future.
2:18:00
Is that a way to say that you're excited about self-driving cars?
2:18:03
Like, what are you what what what are you thinking is going to happen there?
2:18:09
>> You know, I I've played around.
2:18:09
I have a Tesla and I upgraded for a couple months and it terrified the out of me.
2:18:13
Not that I didn't trust it.
2:18:15
Oh my god, because like when you're going 25 miles an hour, it's no big deal.
2:18:19
You go on the highway, you're going 70 miles an hour and and there's a median right there in the middle of the highway. >> Yeah.
2:18:27
>> I was like shaking like I like I don't you know, Elon's cool for whatever, but you know, I ain't trusting him that much, right?
2:18:36
You know, and and I'm not >> You in Mad Max mode?
2:18:38
>> You know about Mad Max mode?
2:18:40
>> No, no, no, no, no, no.
2:18:41
Uh you know, but you can set a delimiter where it's like how much above the speed limit are you willing to go?
2:18:46
And if the speed limit is 65 or 70, you know, you've got to go the speed limit and it was scary as trying to go 70 miles an hour and I don't want to be there when somebody paints some adversarial You know how there's graffiti in the weirdest places?
2:19:00
Wait until there's adversarial graffiti. >> Oh, yeah.
2:19:06
>> Yeah, like somebody if somebody paint if they put a brick wall >> It's Road Runner.
2:19:09
They paint it to look like the road. Road Runner. >> Wile E.
2:19:12
Coyote >> No, don't be No, don't be No, that's like ridiculous RIGHT? >> WILE E.
2:19:17
COYOTE'S going to paint the the the tunnel and then you just slam into it.
2:19:21
>> There's somebody somewhere trying to figure out how to up self-driving mode, right?
2:19:27
And and just because it's just taking video input and you know, what it could be some pattern and all of a sudden you're seeing this pattern on medians or you know, overlaid on stop signs or whatever. >> Yeah.
2:19:42
>> You know, cuz you know, somebody's got to do that because it's just too easy not to. >> Yeah.
2:19:47
Hyperrealistic camo wrap gets confused, you know? >> Whatever, right?
2:19:53
>> You wrap your car in camo and if the camouflage is effective, you're going to confuse the AIs.
2:19:57
It It's a >> just might And there could be a predator, right? Alien versus predator.
2:20:03
Predator could show up, right?
2:20:03
And if Arnold isn't there to save us >> It's possible.
2:20:08
I mean, speaking of Arnold, are you It's It sounds like you're in a good mood.
2:20:13
You're optimistic about things.
2:20:13
Are Does the question of AI doom come into your mind?
2:20:17
These runaway robotics Are you worried about that at all? >> No. Not even the least bit.
2:20:22
For the For the reason I just mentioned, right?
2:20:24
In In order Like right now LLMs are basically bimodal with some video, right?
2:20:31
Where it's almost all text and pictures with some video.
2:20:34
You can't You can't model the world with that. You just can't.
2:20:39
AIs right now doesn't understand the consequences of its recommendations.
2:20:44
It has no idea what happens next.
2:20:46
A 2-year-old kid with a high chair and a sippy cup knows if it pushes the sippy cup over the off the high chair, mom's coming running, and the kid's going to start laughing at mom, right?
2:20:58
Large language models don't understand Every time Every time, right?
2:21:00
And there's no large language Ever, right? Ever.
2:21:07
And it's hysterical, you know, listen mom, right?
2:21:11
But But you get the point, right?
2:21:11
You the large language models we have today can't do that.
2:21:16
And so we have to evolve to models that can capture the world and physics and deal with the latency of capturing or not being having access to video that you can't see.
2:21:29
And so you have to try to model that.
2:21:31
And not only does that take up a lot of processing power, but it takes up a lot of bandwidth as I mentioned before.
2:21:37
And so the Terminators taking over, I just don't see how it's going to happen.
2:21:41
Now, you can have, you know, localized brains for for military applications and power get better and manual dexterity will get better, all that.
2:21:51
But that's that's not going to allow you to take over the world. >> Mhm. >> Right?
2:21:56
That's going to be application specific.
2:21:58
So, I'm not afraid of that at all, and I also think that, you know, we're talking about agentic applications, I I think particularly for small medium-sized businesses and some large businesses, they're not going to have that skill set.
2:22:11
It's not going to be natural for them to do that.
2:22:13
And I think kids coming out of school today that have taken some Python, don't have to be comp sci majors, but have done agentic AI.
2:22:19
Like when I go talk to schools, that's what I tell them.
2:22:23
You know, get into Claude, you know, teach yourself all the agentic stuff, and then go to small businesses because they're not going to understand how to do any of that at all. >> Yeah.
2:22:32
I mean, it does make sense.
2:22:32
Uh what what advice are you giving to friends, uh portfolio companies, etc.
2:22:38
around navigating as a business leader during uh a time where we have, you know, major global conflict.
2:22:46
I don't know what exactly you were working on in 2002, 2003, 2004, but there's so much I mean, right now uh everyone's hoping for a quick end to the conflict, but it's hard to lean on that. >> You know, it's funny.
2:23:02
>> You know, it's funny. In 2002, um when we attacked Iraq, um I created something called the Fallen Patriot Fund, um which, you know, it was just money available that I funded myself, money available for the families um of soldiers who didn't return, or soldiers
2:23:21
that were, you know, horrifically um injured or disfigured or whatever it may be, and we paid out millions of dollars, but the bigger point was the way the media world was back then, we kind of just trusted what was presented to us by the gatekeepers, right? The You could have an opinion
2:23:36
The You could have an opinion whether it was right or wrong, but hey, there were WMDs, right?
2:23:40
Weapons of mass discussion, the destruction, and we kind of trusted.
2:23:44
Now, there's so many information sources and social media, and we really only consume what the algorithms show us, you know, and each one of us has a different algorithm.
2:23:57
Like the three of us are out Algorithms are like fingerprints. No two are alike.
2:24:03
And because of that, everybody's got a different perspective on what's going on in Iran and what's happening around the world.
2:24:09
And to me, that's scary, right?
2:24:12
It's hard to know what's real and who to trust.
2:24:15
And now with AI video, you know, what's been created and it we really are in a cross-our-fingers and hope things work out for the best cuz I don't know that there's a way for anybody to to really participate in a decision or make a good decision.
2:24:35
Um >> Yeah, we're all trying to predict the future together, but based on different Well, based on wildly different kind of influences. >> Exactly.
2:24:46
You know, we don't have access to the information we truly would need in order to make a cogent decision or even have a decent opinion. I mean, we just don't.
2:24:55
And we spend more time trying to filter and determine what's real and what's not so that it's it's almost impossible to really do anything but just hope and pray. >> Mhm.
2:25:06
We have a couple questions from the chat.
2:25:08
The first one is about uh Cost Plus Drugs.
2:25:10
Can you give us an update there? How's it going? What's the thesis? >> Rush sheet is. Rush sheet is. Yeah. >> mode. >> Yeah. >> Fantastic.
2:25:25
But give us a sense of scale.
2:25:25
Give us a sense a reminder of the strategy. Reintroduce the company. >> Sure.
2:25:30
So, what You go to costplusdrugs.
2:25:31
com and you put in the name of the medication.
2:25:35
If it's one of the thousands we carry, then we actually show you our actual cost.
2:25:39
Then we show you that our markup is only 15% and we charge you $5 for shipping and handling and then the credit card fee.
2:25:47
And in doing so with only a 15% margin, we're unless it's like a $4 Walmart drug, we're almost always the cheapest option for anybody.
2:25:57
So if you're under insured, if you don't have insurance, you know, even to compare it against your copay or coinsurance, we may be cheaper than your copay.
2:26:05
And because of that, our business is just skyrocketed.
2:26:09
So that's part one of our business.
2:26:12
Part two to our business, for my company, for my my employees and their families, I went around and talked to the CEOs and CFOs of a lot of hospitals and found out where they were getting ripped off by the insurance companies.
2:26:25
You know, if you think about this and and I don't think many people do, if whatever your deductible is, if something happens to you and you can't afford it, even if you have great insurance, you might have a $1,500 or $2,500 deductible, which is big company good insurance, but if you don't have that money and 40% of people don't have $400 for an emergency, when you go to the hospital as an example, they literally end up loaning you the money.
2:26:54
And that as a result, we've turned hospitals and providers into subprime lenders. Think about that, right?
2:26:59
And then you have the denials and then they underpay, lay play, clawback.
2:27:03
So anyways, so I went to local hospital, Baylor Scott & White, who's a really forward-thinking hospital system, and I said, "Look, I understand where you're getting ripped off by the insurance carriers.
2:27:17
I'll pay you on time, I'll pay you what we committed, I won't claw back, I won't lay pay.
2:27:21
In exchange, I want two things.
2:27:24
I want a better price, I want it as a reference price of Medicare, 100 to 100% of Medicare, unless it's really complicated.
2:27:32
And more importantly, we created a site called costpluswellness.
2:27:36
com And we're going to post this contract on costpluswellness.
2:27:41
com so that any business, TV, you guys, TVPN, anybody, any size that wants to direct contract can reach out to Baylor Scott & White and get the same pricing that we get. >> Mhm.
2:27:54
>> And it's just blown up.
2:27:54
I mean, it's just incredible.
2:27:56
We have more than 9,000 providers.
2:27:58
And what we're trying to do is teach companies who control of their expenses.
2:28:03
You don't need to be dependent on the insurance company to to to come up with the right deal because they won't. They'll steal from you.
2:28:14
>> Did you ever I mean, you you it's such an interesting uh it's such an interesting company uh for you because I feel like when when when you have as big of a presence as you do, it could be a book or a course or a protein powder.
2:28:30
Did you look at anything else? >> Yeah.
2:28:32
>> Or have you burned out on that stuff? >> it. >> Protein powder. >> Protein powder. I'm in.
2:28:37
>> The Cubanator protein stack.
2:28:40
>> I'm in the No, this is obviously much more much more important.
2:28:44
>> Yeah, I just thought, you know, nobody looks at health care and says, you know, the economic side is great.
2:28:48
We're we're doing it the right way in this country. It's the exact opposite.
2:28:51
And so, if you're going to try to disrupt, go big or go home, right?
2:28:56
>> Another question from the chat.
2:28:56
Uh what's the most uh underrated business you've seen in your career?
2:29:00
I mean, I I think they're talking about something that like you uh it is the moment you saw it and you collected and you were like, okay, this is like a wildly mispriced asset or something that could really fly. >> Streaming. >> Streaming.
2:29:13
>> Yeah, we called it internet broadcasting.
2:29:14
I sat down with a buddy of mine in 1995 at a California Pizza Kitchen.
2:29:19
And he was like, how can we listen to Indiana basketball in Dallas, Texas? >> Yeah.
2:29:25
>> And this is right when the internet had just started, right?
2:29:26
It was brand new to everybody.
2:29:28
And I'm like, let me figure it out.
2:29:30
And so, we started a company called AudioNet and got the rights to you know hundreds of schools, hundreds of radio stations, TV stations, you know, back then the copyright laws were different.
2:29:40
Created our own um internet jukebox and unlimited number of internet radio stations and started streaming until we sold it to Yahoo.
2:29:47
That was the most obvious thing I'd ever seen in my career.
2:29:53
>> What was the domain name negotiation like?
2:29:55
Jordy's a big fan of of great domain names. >> Great question.
2:29:59
Great great great great question.
2:30:01
So when we started it was AudioNet and I just registered it. Nobody had it. >> Yeah.
2:30:07
>> But then we wanted to do video and AudioNet wasn't going to cut >> No, audionet. com. >> Okay. I like it, yeah.
2:30:13
>> Cuz we were just doing audio in 1995.
2:30:15
And then by '97 we we started to do video and AudioNet wasn't going to cut it. >> No.
2:30:20
>> And so I found broadcast.
2:30:20
com cuz we wanted to broadcast everything and anything and found the guy and paid him $8,000 and he was thrilled to get the $8,000. >> Wow. >> Wow. Yeah, this 1997 right?
2:30:32
>> did he ping you after that did he ping you after >> Yes, he did. Yes, he did.
2:30:36
But wait, it gets better.
2:30:38
But wait, there's more, right?
2:30:38
And so I'm like, oh this is nothing, right?
2:30:43
It's an automatic traffic generator.
2:30:44
And so I started going out there and just glomming up and just grabbing all kinds of URLs so that we could put content on them and then drive it back to broadcast. com.
2:30:56
So literally I own finalfour. com, I own baseball. com, I own sandwich. com.
2:31:05
You name it, I bought it.
2:31:05
I bought it for I would buy like just packages of URLs, right?
2:31:10
And just >> And this is because people were just going to their browser and being like sandwich. com >> Exactly.
2:31:19
>> So people would just type in sandwich. com. >> Exactly.
2:31:21
Everything was a a portal, right?
2:31:24
Everything was a front door.
2:31:24
And so I was like anything that generated traffic.
2:31:28
And I've done it since, like I own misterpresident. com, I own democracy. com.
2:31:36
All all kinds of >> He privatized democracy.
2:31:39
>> He took democracy private. >> I was worried. I was worried.
2:31:42
>> American thing I've ever I've ever heard. I love that. That's incredible.
2:31:46
Okay, the last question for the chat and we'll let you get back to your busy day.
2:31:50
I I want to flip it around.
2:31:50
What's a what's a business that you've seen in your career that you wanted to work so well, but for whatever reason, the business just didn't achieve what you had in mind. And why? >> Yeah.
2:32:03
Um >> And you don't need to be specific about this particular company.
2:32:07
I mean, like a a technology or maybe an anonymized company, something like that. >> Yeah.
2:32:12
Um God, I'd have to think about it.
2:32:14
You know, that what was not the motorized skateboards, they weren't called with the two wheels. >> Hoverboards. Hoverboards. Yeah, hoverboards.
2:32:22
>> So, I I wanted to >> a hoverboard future.
2:32:25
>> Everyone traveling on hoverboards to work. >> Yes.
2:32:27
So, a buddy of mine, his son was an engineer and I was like, okay, this kid could try to come up with some new ways to do hoverboards, make them safer, etc.
2:32:36
And so, we started a company that did hoverboards and there were so many more patents already in place than I ever imagined.
2:32:43
We couldn't get past them and that it failed miserably. >> Yeah. Yeah. Yeah.
2:32:47
That that was a very interesting boom, the hoverboard boom.
2:32:50
It sort of came out of Shenzhen fully formed because there was a massive supply chain and they were all over, but there was no one brand.
2:32:55
There were like a ton of different brands because really what was going on was there was one amazing supplier in China that had like 20 different companies that were reselling it all over.
2:33:06
>> And they were making a killing. Killing. >> Oh, yeah.
2:33:09
>> Yeah, what what uh is it still possible to create a widget and make like a hundred million dollars from it or does the or do the clones come?
2:33:17
Cuz I I I I know the guy who made like the the the fidget spinner. Like his claim to fame. >> Right. That's cool.
2:33:26
>> And but but he didn't >> It got knocked off like that. >> Yeah.
2:33:29
I mean it was it was the kind of thing that like was a hit product but >> on Amazon. >> Okay. >> All on Amazon. >> Oh. So it's >> Right.
2:33:35
So So I started talking to some Amazon resellers like mid-24 cuz I was just curious about some things.
2:33:40
I see some things on on X.
2:33:42
And um as it turns out, if you're an American seller, it may have changed.
2:33:47
So correct me if I'm wrong.
2:33:49
If you're an American seller, you can have one company that sells on Amazon. Right?
2:33:53
For for your But if you're Chinese, there's no limit. >> Yeah.
2:33:58
>> And you don't even have to have a nexus.
2:34:00
So if you're at that American company and you're making sales and making money, then you have to pay taxes and define your nexus and you know >> Oh.
2:34:08
So you're just screwed because you cuz your cost >> Yeah. >> Yeah.
2:34:13
>> Because so these Chinese companies to this day, as far as I know, these Chinese companies don't have to have a nexus, don't pay the taxes, even though they're supposed to. Right?
2:34:20
You can literally have a Chinese bank account and Amazon will send the money right to that Chinese bank account.
2:34:25
And I was proposing to these guys and talking to some legislators at the time that Chinese companies should have to post a bond before they can sell a product and post it on a website that whatever whatever.
2:34:38
gov so that, you know, the fidget spin guy spinner guy could come in and say, you know, we're having agent now that continually continuously checks to see if there's a knockoff of their product and then can challenge it.
2:34:49
And then at least there's that $10,000 or $25,000 bond that offsets the risk for that fidget spinner. >> Okay.
2:34:58
Hypothetically >> know one I know one widgets company that bought the next five most popular widget companies in the category that were knocking them off and they just continue to operate them. >> Yep.
2:35:10
>> But they have enough ranking on Amazon and they have the scale.
2:35:13
But yeah, it's it's >> It's just wrong.
2:35:15
It's just it's That yeah, because any but it whether it's China or Vietnam any country if you're outside the United States, you immediately have a cost advantage not the manufacturing but just from an IP from a and from an Amazon cost perspective.
2:35:32
Why in the world is it cheaper for a Chinese or Vietnamese company to sell on Amazon and to easily knock off than it is for an American company to sell the original product. That makes no sense. >> dumb.
2:35:44
>> And yeah, and and legislatively you could fix it in a heartbeat. You got to post a bond.
2:35:48
$25,000 bond depending on the size of the market maybe more and then give everybody 90 days to check it and all of a sudden the whole industry changes and American manufacturing skyrockets and the because that that cost of knockoff isn't just about the cost of losing sales.
2:36:06
It's the administrative the legal cost that there's just so many nuanced things that you have to spend money on.
2:36:15
>> We have a we have knock we have a knock we have knockoff issues and like we we spend thousands of dollars to have our lawyer like chase them down and send take down requests.
2:36:25
>> merch like just t-shirts and stuff.
2:36:25
It's >> Oh, yeah, merch is crazy and then IP too, right?
2:36:29
All the take the DMCA take down notices because they're just scraping and you know, reposting all that right?
2:36:36
It's easy to fix if you know, someone has the the guts to do it.
2:36:41
>> What is the anatomy of using your likeness once you've made an investment?
2:36:47
What what what does the the best relationship look like?
2:36:49
I imagine it's very open and transparent but I imagine that anyone who's been associated with you at all is trying to like slap your face next to their product and like clip it all over and maybe you haven't invested yet and you just said, oh, it looks nice and then they're like clip it.
2:37:05
He said it looks nice, you know.
2:37:08
>> Yeah, I mean it depends on the company, you know, usually I I I don't even care but two things.
2:37:11
One, you know what Synthesia is? Synthesia. io? >> Yeah. >> Yeah. >> I think so.
2:37:17
>> Yeah, they've been on the show. >> Yeah, yeah. >> Yeah, yeah. >> Okay, yeah, yeah. >> You're dog. You're dog. That's a unicorn.
2:37:32
>> And I gave them like a lot of money. >> Yeah.
2:37:34
>> I gave them a lot of money, and this was 10, 12 years ago, way ahead of the curve, yeah. >> There we go. >> Okay, so Synthesia.
2:37:49
>> Yes, and so Synthesia, so I'll push them to them, or like I'll just around like you saw with Sora.
2:37:54
They had So I just I put one picture of me out there, but I was playing with it cuz I want to learn all this stuff.
2:38:00
And they Sora had this thing where you can put conditions on how when it can be used.
2:38:10
Yeah, so I made a condition.
2:38:10
I made a condition so that at the end of every video that video that used my likeness, you had it showed the logo for Cost Plus Drugs. >> So smart. So smart.
2:38:21
>> And it's been used like hundreds of thousands of times, and I know we've seen a bump as a result. >> So smart.
2:38:25
John did the less commercial thing.
2:38:27
He said, "Portray me as a bodybuilder." >> It's funny.
2:38:33
>> It's a lot less commercial.
2:38:35
>> But of course, the Sora's kind of falling behind now, so they kind of I don't know if they're doing it.
2:38:39
>> it's going to It'll just get added into ChatGPT, and then you got a billion people just just pumping Cost Plus Drugs. >> Yeah.
2:38:46
Um >> And it's always it's always crazy to me to see it like I tell it, you know, don't, you know, cuz it has terms of service.
2:38:51
You can't show drug use and da da da.
2:38:53
And so there's pictures of me like doing lines of coke and and I got, you know, so it's kind of crazy, but >> ridiculous.
2:39:00
>> Um what when is the right time to for a company to apply to Shark Tank? >> Anytime. You just don't know.
2:39:06
I mean, they have open auditions um all the time.
2:39:10
So, if you go to um Shark Tank's website, it'll give you all the information there.
2:39:14
And you just got to go out there and have fun.
2:39:17
>> Go out there and have fun.
2:39:18
>> How are you processing the peptide boom?
2:39:21
Both FDA-approved >> Non-participant. Non-participant.
2:39:23
I'm not a believer in that at all.
2:39:26
Like every single LLM that I've put it into and asked for, you know, show me the trials and show me the um research >> You mean the non-FDA-approved, just just the the stuff coming off the boat? >> Right.
2:39:40
>> Or are you short Eli Lilly? >> No, no, no, no, no.
2:39:42
The insulin like the real Cuz when people talk peptides, you're talking supplement type stuff, right? Right.
2:39:49
>> But yeah, the Eli Lilly stuff No, no, not not Ozempic that's running Super Bowl ads and very heavily regulated. Yeah, that makes sense.
2:39:57
>> No, cuz that stuff's going to come down in price.
2:39:59
And now, you know, Lilly and Novo are smart now with their GLP-1s.
2:40:03
They're working around the PBMs and doing direct-to-patient, direct-to-company. And that was brilliant. That was really smart. >> Yeah. Yeah, that's very cool.
2:40:09
Well, thank you so much for taking the time, Jordyn. You have anything else? >> It's always fun. >> This is super fun. >> It's always fun. >> Thank you so much.
2:40:16
>> It's always fun, guys.
2:40:16
Enjoy the rest of your day. >> Appreciate it, guys. I'll see you soon. >> Thanks, guys.
2:40:20
>> Let me tell you about Shopify.
2:40:20
Shopify is the commerce platform that grows with your business and lets you sell in seconds online, in store, on mobile, on social, on marketplaces, and now with AI agents.
2:40:30
I think we got a future Shark Tank >> Uh >> Oh, yeah.
2:40:33
I I came up with the name. I came up with the name. I'm not going to say it.
2:40:37
I'm not going to say it, but the domain's available.
2:40:39
>> Okay, we're getting the domain. >> the domain.
2:40:40
You're going to Shark Tank.
2:40:43
You're going in the tank.
2:40:43
You're going in the tank, Ben.
2:40:46
>> I'm so excited for this. >> Not you, Tyler. >> Not you, Tyler.
2:40:48
>> Keep working on Keep working on our other our other launch.
2:40:51
>> Once uh yeah, what >> All right, we got to we got to hit the size gong.
2:40:55
>> Okay, what do we have to do?
2:40:55
>> Ro Khanna >> Ro Khanna, what do we have to do with Ro Khanna?
2:40:59
>> million of trading volume while in Congress.
2:41:03
He's fighting back against the elites by trading against them.
2:41:07
>> I was I was I was talking to Jordy about this this morning and I was like, is is like volume is hard cuz you can be very a very high volume trader, but I was reflecting on like there are years when I just don't really trade that much.
2:41:20
>> only volume is the buy.
2:41:22
>> The buy and then you just hold or something.
2:41:23
But >> But >> he's putting up big numbers.
2:41:25
But maybe he's running like maybe he has like 10K in like a high-frequency trading operation.
2:41:32
>> Yeah, long short strategy.
2:41:33
>> that that they're just eking out milliseconds of a tick.
2:41:36
>> Yeah, maybe he's running maybe he's got a stack of 40 open open clock.
2:41:41
>> Yeah, maybe he's been poaching from Jane Street and so he's putting up a lot of volume.
2:41:44
>> this is just insane volume from the anti-elite champion.
2:41:51
>> said, you guys want to dance?
2:41:53
I'm going to trade against you.
2:41:54
>> How do you even have time to consider 37,000 trades?
2:41:59
>> Like >> He's >> It I mean, how many >> that's >> How many minutes are in a year? In a year.
2:42:07
>> That is a lot of volume and a lot of trades.
2:42:09
>> 500,000 525,000 minutes in a year.
2:42:11
So, you can spend, you know, 20 minutes on each trade if you're working 24/7.
2:42:19
That is >> he's been in in >> I'm sure there's something else going on.
2:42:22
>> been in office for 9 years.
2:42:24
>> It's probably just like a like a investment manager.
2:42:27
Yeah, he says it's his wife's money prior to the marriage.
2:42:32
>> 11 trades a day since he went into office 9 years ago, roughly.
2:42:37
>> Yeah, and the and the stock ban legislation >> And that's no days off.
2:42:38
That's that's weekends, holidays. >> I don't know. Let everybody trade. Let what why not? What?
2:42:46
>> Yeah, sounds sounds like he's locked in.
2:42:50
>> Uh what else is going on?
2:42:52
>> Uh we're removing sanctions on Russian and and Iranian oil. >> Mhm.
2:42:59
>> Uh which is >> craziness. >> Craziness.
2:43:02
>> Didn't think that would happen.
2:43:02
Let's watch the public latest ad.
2:43:04
Is that Is that what we should pull up next or >> Pull Pull it up.
2:43:09
Actually, let's let's pull up this this video first of our president >> Tan.
2:43:14
>> But this is Garry Tan when he sees a YC applicant using G stack to ship 500,000 lines of code daily.
2:43:20
This is one of the best videos of all time.
2:43:23
>> Just pulling out a wad of cash. I'm ready to invest. >> Quickly counting it.
2:43:27
>> It is so funny how lines of code became like not important anymore. >> eyeballs.
2:43:32
>> And now it's new hot >> new eyeballs.
2:43:34
>> Oh, the lines of code are the new eyeballs. I hope not.
2:43:35
Uh I was thinking that we should do a challenge here in the studio where every member of the team has to race to generate 10,000 lines of code.
2:43:47
And whoever can do it fastest >> Okay, but how do you define a line of code?
2:43:53
Like I can just write a for loop that says >> I will run a for loop after the lines of code are generated, and I will pass every line of code individually through GPT 5.
2:44:02
4 Pro, and I will ask it one question, does this count as a line of code?
2:44:08
>> Okay, but can I have the same line of code 10,000 times?
2:44:11
>> You've invented the for loop.
2:44:13
Potentially, print print this.
2:44:13
Uh that would be the most efficient way to do it potentially.
2:44:18
>> Let's pull up this ad.
2:44:19
>> It might even be faster to just clone a repo, right?
2:44:21
Clone a 10,000 line repo.
2:44:24
>> Do you have to write it from scratch?
2:44:24
We need to write the the greatest >> time is >> Pull up the ad, sir.
2:44:30
>> What what yeah, what are we watching here?
2:44:31
>> Well, looking at your portfolio, you've got diverse equity exposure, broad market ETFs, some fixed income.
2:44:38
However, I am seeing a gap here.
2:44:41
>> College basketball, baby.
2:44:43
I'd recommend a three-leg parlay.
2:44:43
Maybe sprinkle in a few player props just to even things out.
2:44:48
I've got a strong read on an early upset.
2:44:53
>> If you're looking for a broker that's not also your bookie, we invite you to try Public. >> It's a good ad.
2:44:59
Really really solidifying Public's position as the very very consistent yeah, very very smart these days.
2:45:06
Taking shots at another >> I wonder if they're going to actually run that on TV for March Madness.
2:45:12
>> Would that be the right place to run it?
2:45:15
I feel like March Madness viewers kind of want a bookie.
2:45:16
Like they want to bet >> Well, March Madness is just like kind of everyone. >> Uh yes. Yeah, makes sense.
2:45:22
>> So anyway, we have our start to the Lambda lightning round.
2:45:25
Let's take a look at the beautiful cloud and let's tell you about Lambda.
2:45:30
Lambda is the superintelligence cloud building AI supercomputers for training and inference that scale from one GPU to hundreds of thousands.
2:45:40
And we'll bring in our next guest, John Kim from Paraform. How are you doing?
2:45:46
Thank you so much for taking the time to come chat with us.
2:45:47
Please introduce yourself and the company.
2:45:51
>> Hey guys, I'm one of the founders and CEO of Paraform.
2:45:52
We are a genetic hiring platform that makes hiring exceptional talent as easy as pressing a button. >> I love it.
2:46:00
>> So our first product We got We got your button in the mail. >> got your button.
2:46:04
We got a package from you guys. >> very well done.
2:46:06
>> Yeah, it was a great great execution.
2:46:09
The only issue is the chocolate completely exploded everywhere over the box.
2:46:14
So >> It made it way more memorable, honestly.
2:46:16
Maybe >> Yeah, yeah, yeah.
2:46:16
I will never forget >> alpha There's deep alpha there.
2:46:18
That's Anyway, >> Uh but but it No, it was awesome.
2:46:20
It's Thank you for the chocolate.
2:46:23
>> So who is the customer? Who's paying for this?
2:46:25
Is it Is it large corporations, small companies, startups, or do you have the applicant pay sometimes?
2:46:29
How how how does the business model work? >> Yeah, yeah.
2:46:33
So it's the companies paying.
2:46:35
We started out helping startups you know build their founding teams.
2:46:38
Now we have sort of SMB, mid-market, enterprise.
2:46:40
So you know, everyone from like a fast-growing startup all the way to like public companies like Palantir are our customers. Yeah. >> Wow.
2:46:48
And and you raised some money.
2:46:48
How did it come together? >> Yeah.
2:46:54
Yeah, we raised a $40 million round led by Scale Venture Partners.
2:46:58
Oh, I need to wait for the sound.
2:47:04
>> And yeah, and and and help me understand how you're going to deploy capital to accelerate at this particular moment in time because I imagine that actually building the platform has gotten easier, but engineers are still expensive.
2:47:19
You still have to do top of funnel work. There's SDRs to hire.
2:47:22
How are you seeing the shape of the business evolves over the next 12 to 18 months? >> Yeah, no, definitely.
2:47:28
I mean, might be just like a classic answer, but you know, we you know, obviously want to continue to do best-in-class growth.
2:47:34
We grew a ton 10x our revenue last year.
2:47:36
We want to continue to grow, you know, at a at a fast pace.
2:47:42
So, in order to do that, you know, you need to grow a team and, you know, all that stuff.
2:47:47
But and also accelerate our product roadmap, right?
2:47:49
I think in particular I'm personally spending a lot of time this year, you know, really just narrowing down, focusing on our product roadmap. So, yeah.
2:47:58
>> Uh what >> thing I will mention is we we sort of started in tech and helping tech companies hire, you know, EPD talent, sales, design, you name it.
2:48:08
Actually, we launched a new vertical.
2:48:10
So, we're helping law firms hire as well.
2:48:12
You know, our goal is to be a universal hiring platform, not just for tech companies.
2:48:16
So, obviously deploying capital going across industries as well. Yeah.
2:48:22
>> Talk to me about the legal hiring market.
2:48:25
Are you sourcing those people in a different place?
2:48:27
Like what's different about that that requires investment and changing the generalizing the platform? Uh >> Yeah. >> Yeah. Walk me through that.
2:48:37
>> Yeah, I mean, since we're a recruiter marketplace, Um, know, in order to scale to another industry, we basically need a sort of a new set of supply, uh, like legal recruiters, um, you know, actually a lot of, uh, lateral attorney and partner hiring at law firms are driven by, uh, recruiting agencies.
2:48:51
So, the like boutique, you know, sort of heavy hitters like who, uh, do all the recruiting.
2:48:57
So, yeah, I guess, uh, to expand we need a new set of supply.
2:48:58
Um, so, we're we're doing that. Yeah.
2:49:02
>> How are you thinking about, uh, just areas of growth in the US economy broadly?
2:49:07
If you stack rank, like there's a lot of energy around reindustrialization right now.
2:49:10
Uh, we were hearing that, uh, electricians might be the LeBron James of the next era.
2:49:17
Uh, but that feels like I don't I don't know that electricians are, uh, on the internet the same way that software engineers are where they might have public GitHub profiles, blogs, uh, LinkedIn profiles.
2:49:28
Uh, how are you thinking about solving those next verticals?
2:49:33
>> Yeah, no, I think like, um, definitely like defense and government seems to be a huge area of growth.
2:49:37
Um, you know, yeah, like you said, uh, manufacturing, like anything that's sort of like atoms, uh, not bits, I think it's like sort of also going to grow a ton.
2:49:47
Um, I actually think like travel, entertainment, like, uh, those industries, media, um, you know, uh, is going to do well as well.
2:49:52
Uh, so, yeah, we're looking at what sort of verticals to go after, like to your point, based on what what, you know, what we think are exciting.
2:49:59
Um, I also read the Anthropic report where they published sort of like what jobs are going to be like sort of replaced by AI, all that stuff. And >> Oh, yeah.
2:50:07
They got the spider chart and then Andrej Karpathy posted something similar.
2:50:12
>> Yeah, I mean, I I I think actually like, you know, my my point of view is that the economy is not like a zero-sum game.
2:50:17
Like it's actually like an abundance game.
2:50:19
So, I actually, you know, I'm not too worried about like AI like replacing people.
2:50:24
I mean, like sure, there's some like adjustment on like what how we need to upskill upskill ourselves and differentiate, but I think every technological revolution like humans figured out a way. Like I don't know.
2:50:34
>> Well, I mean, the that's the interesting thing about that chart is that what's missing from that chart of things jobs that will go away is like the new jobs that will be created.
2:50:41
Like live stream mirror business technology news was not a thing when I was a kid.
2:50:45
I there's zero chance that I ever could have put that down as like my future career will be live streamer. What? That wasn't a thing.
2:50:53
>> What in conversations with investors for this round, what were you like what kind of exits like were kind of referenced in the in the recruiting space?
2:51:04
I know there's like public >> Oh, yeah.
2:51:07
>> firms like you know these these companies scale to like massive massive revenue. >> had a company.
2:51:12
>> You know they're they're not No, I'm talking about like non-tech companies that are just like we do staffing and recruiting. >> Oh, yeah, yeah, yeah.
2:51:18
>> in different kind of verticals, but there's massive massive companies in the space.
2:51:22
Were you comping to those and saying like hey we there's we can be a billion-dollar business uh just based on serving a similar kind of sector. >> Yeah.
2:51:32
I think um not I'm I'm not too sure if that was like the focal point of all the conversations, but I think it's a great question nonetheless.
2:51:37
Like I think >> Well, yeah, and I just I just say that because there hasn't been like like every company has to hire a bunch of people, but there's not like the the >> the Facebook of it. >> the face recruiting.
2:51:48
There's nothing that like there's not like a perfect comp where Figma's like well look you have Adobe so we're just going to like some percentage of Adobe.
2:51:59
>> Yeah, I think the way I look at it is if you look at the total amount of like dollars spent on recruiting it broadly speaking like where does it go to?
2:52:05
And actually the biggest spend category is external recruiting.
2:52:12
So outsourcing recruiting, working with recruiting agencies, staffing agencies.
2:52:15
So like that's sort of the biggest spend, but actually if you look at traditional like BC dollars over the last 10-20 years it all went to like HR software or recruiting software and that category is actually not that big.
2:52:28
It's like maybe 10 billion.
2:52:28
I I'm not sure exactly, but it's not the biggest category.
2:52:32
Yet, 90% VC dollars went in there.
2:52:35
I think there's a little bit of a you know, I think mismatch there, but obviously the market we're going after is you know, like you know, labor market itself, right?
2:52:43
I think I've heard somewhere also like we're shifting from paying for like software to paying for work.
2:52:50
And instead of sort of building paying for tools, we're paying for outcomes.
2:52:54
And I think Paraform is like very much aligned with that trend.
2:52:58
So yeah, we're going after the biggest market in recruiting. Yeah. >> Amazing.
2:53:02
Well, thank you so much for taking the time to come chat with us.
2:53:05
Congratulations on the new round. Good luck. >> Thank you so much. Talk to you soon.
2:53:09
>> Great to see you, John. >> Have a good one. >> Cheers.
2:53:11
>> And we will continue our Lambda lightning round with Eugene from Edra, who's the co-founder and CEO.
2:53:18
He has some exciting news for us today.
2:53:22
Eugene, how are you doing? >> Good. How are you guys? Thank you for having me. >> Exiting stealth.
2:53:25
I love when companies exit stealth because they come on the show. >> the guy.
2:53:30
>> Welcome to the public eye.
2:53:30
Please introduce yourself and the company.
2:53:34
>> My name is Eugene Alpeus.
2:53:34
I'm the CEO and co-founder of Edra.
2:53:37
With Edra, really what we saw is that models are smart enough to do basically any work inside an enterprise, but the only problem is you have to tell them exactly what to do. >> Mhm.
2:53:47
>> And nobody has that, right?
2:53:47
Like you can't go to any company and tell them just tell me exactly >> what to do.
2:53:52
How am I supposed to tell you what to do? >> Yeah. >> Yeah.
2:53:56
>> Um so what we do is we build an authentic learning system that just hooks up to their existing systems of record, figures out what their people are already doing, writes it out for them so they can see it, and then we use that to actually automate and do the work. >> Makes sense.
2:54:12
So talk about what it means to like just hook up to their internal systems because I feel like there's 20 tools in every category and every company has 30 tools.
2:54:21
And you multiply that together, that's a lot of integrations.
2:54:25
Writing new integrations is easy, but is there a platform that you can sit on top of? >> For sure.
2:54:31
So, we have a couple of core systems that are really good for us.
2:54:32
So, we do ServiceNow, Jira, Outlook, of course. Can't forget Outlook.
2:54:39
Salesforce, Zendesk are some of the main ones that are kind of the first ones that we are working on top of. >> Yeah.
2:54:45
And then I'm sure if there's a client that's big enough, that could maybe prioritize an integration or something.
2:54:50
But how big are the companies that you're working with at this point? >> Yeah.
2:54:54
So, I mean, our our specialty is like the larger the company, the better it is.
2:54:57
The messier the process, the more of a challenge they have.
2:54:59
So, if you look at some of our customers that we went public with yesterday, including ASOS, Cushman & Wakefield, HubSpot, right? So, pretty pretty big. >> Yeah.
2:55:10
What was the process like for closing those deals?
2:55:11
Do you meet at conferences?
2:55:13
I imagine that it's not some sort of direct response ad. You were in stealth.
2:55:17
So, how do you get those deals done? >> Yeah.
2:55:20
Well, we've been around the block for about 10 years doing a lot of these things.
2:55:24
So, we had enough of a network.
2:55:26
And I think it's a just a very compelling pitch, right?
2:55:28
Like I'm not telling you, "Let me come in and it's going to take 3 to 6 months and we're going to figure out what we're doing."
2:55:35
I'm literally saying, "Hey, just give me one static cut of your data and in a week I will show you new things about your own company's operations you didn't know about, right?"
2:55:43
And if I can't come back with that one week with something new, don't hire me.
2:55:47
But so far it's been going pretty well.
2:55:50
>> Are you throwing frontier models at just every problem because you're in a high-growth phase, you want to have the best possible product, or are you already offloading certain jobs to lagging models, open-source models, cheaper models?
2:56:04
Just How much of the parade of frontier are you using these days? >> Yeah.
2:56:09
So, we need the smartest possible model to help us figure out what people are doing and what the process actually is.
2:56:15
But then, if you do that well enough, you don't need super sophisticated model for it.
2:56:20
So, we just need something that's good at instruction following and that tends to be fine. >> That makes sense.
2:56:26
Uh and how much did you raise? I want to hit the gong.
2:56:29
>> So, we raised $30 million. >> Woo!
2:56:37
Just Sequoia or did you let anyone else get a get a slice?
2:56:41
>> So, so Sequoia led our A, we had 8VC and they star who uh they led our seed already, so we we have continued support from them, too.
2:56:50
>> It's a murderers' row. Uh fantastic lineup.
2:56:53
Well, congratulations on the progress.
2:56:54
Congratulations on exiting stealth and thanks for taking the time to come chat with us today.
2:56:59
>> Yeah, great to talk to you soon. >> Thank you. >> Have a good one.
2:57:02
Let me tell you about Restream.
2:57:02
One live stream, 30-plus destinations.
2:57:04
If you want to multistream, go to restream.
2:57:06
com and we will continue our Lambda lightning round with Ari from Run Civil who's in the Restream waiting room. Let's bring Ari in. How are you doing? >> Howdy. Good to meet you. >> Good to meet you.
2:57:20
Uh please introduce yourself and the company. >> Yeah, happy to.
2:57:24
All right, so I'm a man on a mission where we're trying to automate hacker intuition.
2:57:27
Um I guess I can start with a brief intro to myself.
2:57:31
So, I was actually the first security hire at OpenAI back in 2019.
2:57:34
Um I was a grad student at Harvard doing my machine learning PhD and I saw GPT-2 come out and I was like, "Wow, um this would have been really useful back when I was a miscreant teenager doing insane operations on the internet."
2:57:47
Um so, uh I ended up bundling up a couple of demos of things that I would have made as a miscreant and I sent them to Sam Altman and and I sent them to Jack Clark who was the head of comms at the time.
2:57:57
Um and then kind of the rest is history.
2:57:59
They liked it enough that they invited me to come join and so I was there for 3 years.
2:58:02
Um I was a core researcher on GPT-3 and on the Codex model.
2:58:06
Um I also built our first monitoring system for when we started offering the API as a thing that customers were then using um to make sure the customers were following our terms of service.
2:58:16
Um and then I left the company in part because we just didn't have a good answer for when the bad guys have access to everything.
2:58:19
Um, black pill moment for me was uh when we were doing this anonymized review of model outputs, I saw somebody uh trying to lock a file system and that could be totally benign, you know, educational, like how does encryption work?
2:58:30
Or it could be somebody, you know, futzing around with malware and uh ransomware specifically.
2:58:35
And there's really no way of telling that particular intentionality.
2:58:37
And at the time, like our thought was, well, what if um what if we focus on the monitoring?
2:58:42
We just block people that are doing bad stuff.
2:58:43
But realistically, when you have something that says explosive as language models have become, uh you're not going to be able to like play whack-a-mole.
2:58:50
You kind of have to get um offensive with it.
2:58:51
So, uh I started this company.
2:58:54
Um, I have very blessed to work with my co-founder Vlad Anescu who uh built the red team at Meta.
2:58:59
Um, and then I have a team of really strong engineers that we pulled from some of the top security engineering teams um in the industry and we're focused on uh building something that will make the internet uh just broadly more safe and it's just really rewarding to see it pay off.
2:59:14
>> Are you seeing more uh more danger and risk from like large-scale state actors or like the script kiddies who are just trying to like wreak havoc or is it both?
2:59:23
Because I feel like some of the some of the so so like like some of the the problems with like the new AI security threats, like there's new capabilities, but there's also like a lot more cost than just like running some PHP script that like guesses WordPress passwords like back in the old days.
2:59:41
>> Yeah, I'd say it's actually kind of a combination of the two.
2:59:43
Like I said, like you have two types of threats and uh out of oftentimes it also depends on the type of uh organization that you are, too.
2:59:50
Um, so for our customers that are smaller startups, like they're not really seeing any of this kind of stuff.
2:59:55
But for our larger enterprises, uh we've been asked by a lot of them if they they basically want to replace us with their bug bounty.
3:00:00
Or they want to replace their bug bounty with us. >> Oh, yeah.
3:00:03
They want you to win all of the bug bounties. That's right.
3:00:06
>> Just don't make mistakes.
3:00:07
>> That's your That's your immediate TAM and I'm sure I'm sure much further, but uh yeah, talk to me about uh model distillation.
3:00:12
There's been a lot of news about uh uh it it's not as as serious of a threat.
3:00:17
It feels more like a business threat, but uh I've always been I've always been shocked by, you know, the stories about different open-source companies where it feels like they trained on American lab, and that seems like something that the lab should be able to detect. Is that hard?
3:00:32
Is that something you can help with?
3:00:35
>> Yeah, so that's not what we do, but it is something that I've dealt with previously.
3:00:39
Um and it is something that everybody does and everybody kind of knows about.
3:00:41
It's sort of a bit of a dirty secret.
3:00:41
Um but I'll also say that when you do distillation, the model that you get out of it is going to be like a net less good than the model that you're distilling off of.
3:00:49
Like that's just information theory 101.
3:00:50
So, it it's something that is somewhat of a business threat, but it's not as big of a business threat as, say, like stealing the actual model itself.
3:00:59
>> Yeah, just actually breaking into the system.
3:01:01
>> Can Can you give us your pitch to like a startup or a scale-up on the customer side and then all the way up to a lab?
3:01:09
Like how How do you How you sell the products right now?
3:01:11
Cuz I understand like the opportunity at a high level, you know, basically every all these companies are distributing intelligence that's hard to understand how it's going to be used.
3:01:20
A lot of people are going to use it for things that they shouldn't.
3:01:24
You want to stop that, but like what is the specific pitch in this moment in time?
3:01:29
>> Yeah, I'd say like for one thing, focusing on like security means a bunch of different things to different people.
3:01:34
And right now what we're saying is that smaller teams need different things from larger teams.
3:01:38
And the benefit in of the way that we built our product is that if you have more attack surface, it's just much more interesting for the type of things that we can find for you.
3:01:44
So, we've been moving up into enterprises, and we have a lot of strong response from enterprise teams that are large.
3:01:48
Uh they have a lot of old code bases that go back 40 years.
3:01:52
Uh there's a lot of cursed things in their environments.
3:01:55
Trying to get like additional coding tools in is is kind of tricky.
3:01:57
Um And actually I have kind of an interesting hot take for you if you'll take it. >> Please. Love it. >> Okay.
3:02:03
So, um I'll obviously there's a lot of movement in security in terms of like the markets Um especially when Anthropic dropped some of their news about some of the vulnerability discovery stuff.
3:02:14
So, I think a lot of people are concerned about whether or not like are are the language model labs just going to solve security?
3:02:21
And what I think is interesting is if code gets so much better in terms of security, the main question is like does that mean that hacking gets harder?
3:02:26
And my answer is no because I think it's speed that's what's going to kill us.
3:02:31
So, the space of these large possible attack vectors requires a lot more data than simply the code.
3:02:34
So, if you look at the code I like to kind of think about it as you're looking at the code of or looking at the bones of a dinosaur.
3:02:42
It's you're going to find a lot of interesting things about structure, but you're going to miss a ton about things like muscles and whether or not they have feathers and also like behavior and like broader things like that which are also very important for understanding the ecosystem.
3:02:52
And that's true of code as well and it's true of computers.
3:02:55
It's easier to find bugs with the code, you know, having the bones is very helpful for us even knowing that these dinosaurs exist, but you miss so much other stuff and that is where the real delta lies here.
3:03:06
Authentication for example is famously difficult to suss out.
3:03:07
Like there are not very many I don't think there are any good authentication scanners out there.
3:03:12
But the way in which we build our product, it's very good at finding off bugs and in fact one of our strengths that we've heard continuously is that like we're very good at finding these weird esoteric things that have existed in bug bounties for like the last 10 years and we we get like pretty nice payouts from that which is always fun. >> That's really cool.
3:03:30
Talk to me about your take on the forward deployed engineer pattern, model, trend, boom, whatever you want to call it.
3:03:39
Are you in favor of that?
3:03:39
Are you employing that at this time?
3:03:43
>> That's a good question.
3:03:43
I think that forward deploy is important if you're working with enterprise because a lot of them there's a a lot of a human factor.
3:03:50
Like at least in in startups what we've learned is that people just want to solve the problem.
3:03:53
Like the CTO comes to us and is like, "Hey, I have this deal blocked by SOC 2.
3:03:56
I really need to get a pen test." And we're like, "On it. Got you, fam."
3:03:59
And we we get them they're on their merry way.
3:04:01
But with these enterprise companies, it's a lot more of a political process. >> Yeah.
3:04:05
>> Um security in general is kind of it's it's partly the people um and it's also of course the software, too.
3:04:10
And what you can do with a forward deploy engineer you can provide more of that layer of trust.
3:04:14
You can communicate a lot more with the proper stakeholders so they don't feel like their job is going to be taken away.
3:04:19
There's a lot more um of the human factor that you're able to introduce when you have that.
3:04:22
And I think that's why it's so popular.
3:04:25
>> We do something somewhat similar >> there and we find it to be particularly helpful with bringing people on board and being able to serve them faster.
3:04:31
>> I'm looking at some of the customers here.
3:04:33
Cursor, Turbo, Puffer, Notion, Base 10, Thinking Machines. Uh congratulations.
3:04:37
Uh the business sounds great and makes a ton of sense and I obviously raising money.
3:04:41
Uh but I'm I'm curious if there's a almost like direct to consumer play at some point because everyone is going to be vibe coding.
3:04:51
Like we are a 10-person team.
3:04:51
We have like three systems and uh you know, we're sort of you know, security second maybe.
3:04:57
Maybe we're we're working on that. >> Perfect for you then. So tell me.
3:05:02
>> Yeah, I don't think so. >> You don't think so?
3:05:04
>> I don't so because people don't like paying for security. >> Okay.
3:05:07
But what but but isn't there another way that you can make it so cheap or bake it in or partner with a lab where, you know, I'm vibe coding something and I and I, you know, get run Snyk installed or it it it it's it's modeled into the system somehow?
3:05:20
>> 2 example's relevant because that's somebody that's like I have to do that because I'm blocked. It's hurting my revenue.
3:05:26
>> But we've heard so many things about somebody's using Open Claw.
3:05:27
They're vibe coding something.
3:05:29
They're running their business on it and increasingly it's turning into a system and at some point they need to think about security.
3:05:34
What's the >> of it think about it.
3:05:35
We we we we bought hundreds of thousands of dollars worth of like camera equipment before we bought cameras to secure, you know, >> Oh, yeah.
3:05:46
>> And even when we were doing that, we're like, uh like >> I think there's there's some truth to that.
3:05:51
But let's also think about like the economics of who buys these tools, too.
3:05:54
So, if you're paying like 20 bucks, 200 bucks for like a month long subscription, how much you going to pay for like additional security on top of that?
3:06:01
That's something that you're going to have to sell direct to that company and there's not a lot of companies that really offer code security related stuff.
3:06:07
So I think for companies that are making the bet in that space, they're focused a bit more on like the ecosystem which is we're we're focusing a bit more on like the the overall ecosystem within an enterprise that has a ton of ancient code that is going to require a lot more in order to fix and they also have just these enormous attack surfaces that need some help.
3:06:23
>> Yeah, yeah, that makes a lot of sense.
3:06:26
Well, how much did you actually raise?
3:06:27
Tell me about the fund raising round.
3:06:27
We want to ring the gong.
3:06:29
>> Oh yeah, so we raised 40 million which we love. >> Who came in?
3:06:37
>> So Coast led the round.
3:06:37
We had participation from S32, Conviction, Lachy and we also had a bunch of angels as well.
3:06:44
So Nikesh Arora who I know was on the show. >> Yeah. >> Friday.
3:06:49
>> Geoffrey Hinton and Goodfellow.
3:06:51
>> Wow, Ian Goodfellow too? >> It was pretty good. >> That's incredible. Congratulations. >> That's a lineup.
3:06:56
>> Thank you so much for taking the time to come break it down for us.
3:06:58
>> I can I can visualize the Coachella poster over the name.
3:07:01
>> Thank you for securing the American software ecosystem.
3:07:03
We appreciate that as well.
3:07:05
Get every bug bounty that is out there. You deserve it. >> Cheers. >> Goodbye.
3:07:12
>> And we have our last guest of the show.
3:07:15
We will leave the Lambda lightning round and bring Alex Conrad in to the TVP in the ultra dome from Upstart's Media. How are you doing Alex? Good to see you again. >> Back. Hey, I'm back.
3:07:26
It's it's great to be virtually in the ultra dome.
3:07:29
>> Yes, I love that poster behind you.
3:07:32
It's very >> That's a TV. >> Oh, is that a TV?
3:07:35
We're high tech here John. >> TV. Okay, that makes sense.
3:07:36
Anyway, um What's new since we last talked?
3:07:40
Tell me about the shape of Upstart's, how it's going, what type of beat you I don't know.
3:07:48
How have you defined your beat?
3:07:50
I think everyone knows your beat from before with the Midas list, of course, but what's changed, what's remained the same?
3:07:58
>> You know, it's been almost exactly a year since we launched and you guys had me on the show, which is awesome.
3:08:01
And as you know, year one startup, everything is crazy, but a lot of fun.
3:08:05
You know, we've we've launched a podcast.
3:08:07
We have started doing some feature stories.
3:08:10
We had one on William Hockey from Column that was a lot of fun a couple weeks ago.
3:08:14
And just and having a lot of fun experimenting.
3:08:16
You know, we we haven't been to the Ultra Dome in person, but that's year two stretch goal. Yeah. >> Yes.
3:08:21
What about what about lists?
3:08:21
I remember we talked about this and I was like, I know you can't do the Midas list, that's left behind, but I feel like there's a big gap in the tech media landscape around lists. Market maps do well.
3:08:36
People are split on them.
3:08:36
We had a lot of fun with with a Midas list of AI researchers.
3:08:41
Have you are listicles just cringe or are they just actually not that interesting to you or are they bad business?
3:08:47
Cuz I feel like there's a there's something there and you're the guy.
3:08:52
>> You know, I I'm sorry to say we don't have that list for you yet.
3:08:56
But maybe that'll be a thing this year.
3:08:58
We are trying to do really service-oriented coverage for founders.
3:09:02
I think, you know, the reality is founders are super busy, right?
3:09:04
And and so are builders at startups.
3:09:06
And so our podcast is one commute length.
3:09:07
It's it's a you know, if you're not listening to TVP and yet, you're driving to the office, you can you can tune in for Upstart each week, you know, 35 minutes.
3:09:15
And then similarly, we tried with our article this week a illustration where this woman, Natalie Fratto, actually drew how data centers connect to this new GPU startup so that people could visualize it.
3:09:29
And the hope is that it just helps people understand the info super fast. >> That's very cool. >> That's very cool.
3:09:34
>> Or I I I I read about this company, Giga, uh today that I don't even know if I should call it a startup.
3:09:42
They haven't raised any money, but they're in AI boom. It's just a business.
3:09:45
Are you seeing like these knock-on effects of the AI boom show up and are you getting pitches from those folks or do they see themselves as like outsiders and they're happy to remain outsiders or do they want to cross over into the tech ecosystem?
3:09:59
Like how do you think about the broader the broader ecosystem and knock-on effects of the AI boom?
3:10:06
>> Well, startup can mean anything these days, right?
3:10:08
Like I remember when we all started our career, a startup was venture-backed, it was maybe less than 7 years old.
3:10:12
It wasn't hiring people for a billion dollars, you know, it wasn't worth a trillion dollars pre-IPO. That's right.
3:10:20
And now a startup I think is more of an aspirational goal, you know, some days Upstart feels like a startup, some days it feels like a small business.
3:10:27
And I think similarly with these companies, my in my mind if they're trying to be really high growth, they're trying to move fast and if they're serving a tech-savvy audience, that's good enough to be a startup. >> Yeah, I love it.
3:10:39
What what What do you think about the the scoop economy, the big labs, there's so much drama, so many personalities?
3:10:45
There are some journalists who've gone out and carved out like, you know, they're just the scoop masters.
3:10:49
Is that something >> Scoop athletes. >> Scoop athletes.
3:10:54
Uh, is it I I I'm someone who's I don't think ever had a scoop.
3:10:56
I don't know if I just haven't felt the rush, is it addictive?
3:11:00
Like what are the pros and cons of getting into that side of the business?
3:11:05
>> There is a huge endorphin rush.
3:11:05
Like if if we're chasing endorphins and avoiding cortisol, I think like, you know, when you do publish that scoop, it can feel really good, you know, for a while our biggest story at Upstart was last summer we wrote about a startup that had left OpenAI and they had raised a ton of money to do an RL, you know, reinforcement learning company.
3:11:23
And when you looked at the spike in subscribers we got, like that felt really good in a way.
3:11:29
But you don't want to play that game all the time.
3:11:30
I think it it does end up being like chasing a rush that is not sustainable.
3:11:34
And so I think, you know, I will let Kitty Roof and those self-described scoop athletes chase it for the love of the game.
3:11:42
>> For me, it's only really relevant if if there's something concrete like a lesson or an insight for that wider ecosystem versus just the horse racing of hey, these guys from Open AI raised even more money than those last guys.
3:11:54
>> Uh in terms of the horse race, obviously you ran the Midas list for many many years.
3:11:59
Um Is there is there are there any venture capitalists who are who are underrated right now or or do you think that there's there's there's any like misconceptions in the venture capital community because I feel like the strategies have shifted
3:12:13
so much and we're seeing bifurcation between the small funds and the mega funds and there's folks who are venture capitalists but they're trading in public markets all day long or running private equity shops now or buying hospital networks. Like the strategies
3:12:25
Like the strategies have evolved so much.
3:12:27
Like what's what what other stories are interesting in the venture capital landscape broadly?
3:12:34
>> Well, first I think this year we're going to have to start a upstart spotted on the street VC thing cuz I I saw I spotted Keith Rabois at a restaurant earlier this week in New York and I gave him the eye, you know, and I said come over and we shook hands and you know, the poor founders who were with Keith were like who is this dude?
3:12:49
So, I do think we should do a segment of just where I spot VCs around New York City and San Francisco and awkwardly wave to them.
3:12:57
>> TMZ >> paparazzi Yeah. That's right.
3:13:01
Um that's the coverage we need, right?
3:13:02
We need the gossip coverage of VC again.
3:13:06
But on a serious note, I I mean I love the domain experts like the really nerdy guys who are not posting a lot on X, who are just really well regarded.
3:13:13
When I ask around like hey, who's really smart on GPUs?
3:13:17
And and so my advice to VCs usually is like have a thing you're known for.
3:13:19
If all you're known for is posting on Twitter {slash} X, that's probably not a defense you know, defensible strategy in the long run. >> Yeah.
3:13:28
Yeah, people want >> Uh what is what what advice do you give to VCs that might be due for their first Midas list appearance now that you have a bit of space and you're not involved in the process. Interesting.
3:13:42
>> I mean, at the end of the day venture is a results business.
3:13:43
I mean, you you guys have had guests on recently who talked about what is real and what is not.
3:13:47
And I think the numbers generally do speak for themselves.
3:13:51
You know, you get a big exit that is kind of the mic drop that I think Midas is a lagging indicator to notice.
3:13:57
Um, I think for VCs who feel like they have that portfolio that's not recognized yet, the first thing I would say is be top of mind for your founders.
3:14:06
You know, often journalists like me or or at the big shops like we'll talk to a founder and we'll be like, "Who are the couple of VCs who backed you who we should call to get to know your business better?"
3:14:14
If you're not one of those first two or three VCs that the founder mentions as a reference, that's that's not great.
3:14:20
So, I'd start there with like, "Are you top of mind for your biggest winners?" >> Yeah.
3:14:25
>> So, it's a mix mix of results plus the mic drop moment in the last 12 months plus kind of founder brand.
3:14:31
Is that a good way to think about it?
3:14:36
>> Well, the yeah, I mean, the Midas list is data only.
3:14:37
So, the the founder brand doesn't matter as much.
3:14:39
But I think if you're feeling like, "Hey, I I I want those flowers >> It's data Yeah, well, it's data it's data only, but it's not just like blended IRR across every investment, right?
3:14:50
>> like crazy internal politics at every VC firm of like, "Oh, yeah, that associate who was here for two years and uh it was the one who actually got that deal but then left."
3:14:58
Like, "That's my deal now."
3:15:01
>> Hey, I mean, you guys know venture is a tough game like that, you know, my wife just left VC to go into operating back at a startup Clay and you know, she she will have her deals that she sourced and she was involved in and will she be in the history in years, you know, we don't know.
3:15:15
And I think, you know, similarly, if you sourced the deals and you moved on to another firm or you went back into operating, like history will I think give you the credit in the long run.
3:15:23
But yeah, for Midas that can be tough cuz like five people at Sequoia claim each, you know, big deal.
3:15:29
>> Yeah, yeah, I I that there's one firm I don't know if it's Benchmark, but they have like a ledger that when the deal closes, they all agree on the allocation.
3:15:37
Okay, you brought it in and you're going to be on the board, you get 80% but I worked the deal with you, so I get 20% and we all sign and then we know who got the credit. >> System of record.
3:15:48
>> of record, ERP basically.
3:15:48
But I don't know if that that's employed at every every VC firm.
3:15:52
Has there ever been in your memory a situation where sort of like a VC stake was discovered in sort of an IPO prospectus or like an S1?
3:16:04
Because I imagine usually the VCs are taking plenty of victory laps throughout the process once things get close, but I'm I'm wondering if there's ever been like the quiet VC not on Twitter, not posting and then all of a sudden the IPO comes out and they're like, wait, they own 20% of this company? This is crazy.
3:16:23
>> Yeah, I mean a firm that was historically under the radar was Sutter Hill. >> Oh, yeah.
3:16:28
>> So Michael Speiser when Snowflake went public, he got tons of credit, deservedly so.
3:16:31
But they had been totally under the radar and so those more incubation type ones, those are really interesting. >> Yeah.
3:16:40
>> And I think I think otherwise the thing to know with S1 is it's usually like a big dog at the firm whose name is attached, but that doesn't mean they necessarily >> Yeah.
3:16:48
>> were the the person who did the deal.
3:16:51
What happened earlier in my career is people would be like, did you know I sourced that deal?
3:16:53
And then I left that firm and I hadn't been in the game long enough to like know any of this trivia and so that was terrifying for me.
3:17:00
Over time I start I start to know all the trivia like Airbnb was sourced by this person and then this person was on the board and then this person was on the board and I I don't wish that data on anybody's head. >> That's hilarious.
3:17:12
Um Do you view venture capital and the startup ecosystem is like a buyer's market or a seller's market?
3:17:18
Like like is it a good time to be in a startup versus it's good time to be a VC and where are we in the cycle?
3:17:30
>> What an easy question, right?
3:17:30
You're you're saving all the the easy ones for last.
3:17:33
I think, you know, where we continue to be in that have and have not market where I think you see crazy valuations for companies that have traction >> Yeah.
3:17:42
>> I hear from so many startups that are still like, "How do we meet these guys?
3:17:47
Like, how do we get anyone to pay attention to us?"
3:17:48
And it's it's humbling for me that, you know, even though I'm saying, "Hey, we want to cover startups that aren't getting that coverage."
3:17:53
Even then there are most that I just can't help or get to.
3:17:56
And so I think like I would encourage people to get away from the buzzwords.
3:18:00
Uh, you know, especially get away from Silicon Valley and there's still plenty of companies that are not getting funding. >> Yeah.
3:18:07
Will you ever write a book? >> About what? About you guys?
3:18:12
>> No, about your experience.
3:18:12
I mean, this is a common path, I feel like.
3:18:14
Uh >> Yeah, you find a company or team that >> And and and the scoop grows into a book.
3:18:23
Sometimes there's, you know, a composite profile of an industry or career. I don't know. It sounds like no.
3:18:28
It sounds like you've no appetite for book at all.
3:18:31
But is that >> Well, I think I just don't have the the bandwidth.
3:18:33
I mean, like you guys, I think I'm in the arena every day now putting points on the board and I think, you know, books seem like a beautiful stretch goal if Upstart really scales.
3:18:43
But, you know, I wanted to write books in the past maybe, but I think you need to really have the idea.
3:18:49
You can't just reverse engineer.
3:18:49
It's like saying I want to be a founder and not knowing what company >> Yeah, you have to have a book >> you want to write.
3:18:55
>> at the time and and then it just has to sort of >> to love the topic, right?
3:18:57
Like, how boring would it be to write the world's 10th book about Nvidia? Like, do we need that? I don't think so. >> I don't know. I don't know.
3:19:04
I >> Jordi's like, "You're going to do it."
3:19:09
>> I would I would I mean, I would I would I would read an entire book just about the GB200 potentially.
3:19:16
>> Or or the leather jacket or the leather jacket collection.
3:19:19
>> book just around the history of DLSS.
3:19:21
Just just deep learning super sampling.
3:19:24
>> Well, you know what I think you guys are speaking to that I do think about a lot?
3:19:27
Like information is so crazy right now.
3:19:29
You know, you guys have these amazing guests on every day. You're grinding.
3:19:34
And you know, so many people are out there putting out good information.
3:19:36
Is there room for that person who just kind of disappears for a long time on a crazy project and comes back with like a big fish? >> Yeah, yeah.
3:19:45
Yeah, no, we talked about this a while back with there's this YouTube channel that I love called the Corridor Crew.
3:19:50
And they talk about visual effects specifically very niche how would that ever be on T It just would never be on TV, but it's turned into a TV show multiple episodes every week.
3:20:03
Fantastically successful.
3:20:03
They built a whole business.
3:20:05
They have a studio and team.
3:20:06
And and their their dream was always, "Okay, we're good at visual effects.
3:20:11
We want to do a movie or be the VFX crew on a movie. We're capable."
3:20:16
But they kept building up their YouTube business.
3:20:18
And every time Hollywood would come to them, they would say, "Yeah, we'll give you, you know, we'll give you 300k."
3:20:23
And they're like, "But our business is making a million dollars now."
3:20:26
And they say, "Okay, it doesn't work."
3:20:28
And they come back and be like, "You know what? We're ready.
3:20:29
We got a million and a half dollars for you."
3:20:32
And they're like, "But our business is doing 3 million.
3:20:32
Like, we can't step away."
3:20:34
And so this this tug-of-war is always happening.
3:20:36
And I feel like the book is the same thing where, you know, can you really turn off the podcast?
3:20:41
Or can you turn off the reporting?
3:20:42
Or can you turn off the the all the all the >> Yeah, I mean, I I've just come back to this is like legacy media brands are the best place to go, you know, fishing because they're they will say, "Yes, you can we're going to pay you a salary and you're going to go work on the story and you might not be able to show any real results for a longer period of time."
3:21:04
Doesn't work that well for Substack model where you got to show value. >> Value every week.
3:21:09
>> Yeah, that's totally true.
3:21:09
I mean, every week I feel like I got to win win the week, you know?
3:21:12
I feel like I'm proving myself to my audience every single week and day.
3:21:16
I will say we are doing these quarterly profiles now.
3:21:21
Hockey was the first one with Column. >> It's great.
3:21:24
>> I think we have a really cool one cooking for the second quarter.
3:21:25
And the dream is that maybe like we put four of those in a little booklet. >> That'd be cool.
3:21:30
>> That you know can be on a coffee table and maybe we start to get into print and have fun that way.
3:21:34
But it's it's baby steps, you know. >> Yeah. Yeah.
3:21:36
Yeah, that makes a lot of sense.
3:21:37
Um do do you have sort of a a media critique take on like the future of investigative journalism, where that might exist, what the funding model for that is?
3:21:47
Because it feels harder than ever to have a journalist go and spend a year on something that may or may not work out.
3:21:55
>> Yeah, I think um the short answer is that I agree with Jordy in a lot of ways that you do need the sort of big shops that can still weather the storm for someone.
3:22:03
You know, at Forbes I would be hunting that big cover story and do it and then recharge my battery while other people were kind of putting up the singles and the doubles.
3:22:12
And now it's like you need the singles and doubles and then you look for the home run.
3:22:16
Um so I do think a team is important there.
3:22:19
But one one thing I would challenge people to think about is is there a way to fund a project for a year?
3:22:24
Like, you know, like whether it's Patreon or Substack or something like that where you do a year or even a multi-year subscription where you say we're going to give you money up front do the best craziest thing you can do in that time.
3:22:36
And you know, it can it can be as few as one thing. >> Yeah.
3:22:40
>> But then we'll be happy because I think the challenge as you said with Substack is Substack sends you the numbers, you can see them going up and down.
3:22:46
You never want to see them going down.
3:22:47
And so could we create that head space via some sort of crowdfunded model?
3:22:51
I mean, I would love to see the innovation. >> Yeah. Same stuff.
3:22:55
Well, thank you so much for taking >> Let's hit the gong for a year.
3:22:58
Is it actually the year of the the first year?
3:23:02
Did you hit the anniversary yet? >> We're a week away.
3:23:04
Can we hit the gong anyway?
3:23:11
>> Great to catch up, my friend.
3:23:11
Great to catch up and we will talk to you soon. >> to the team. >> Goodbye. >> Cheers.
3:23:16
>> All right, see you guys soon. >> Here's some advice. Don't buy AirPods.
3:23:21
You need the Sony WH-1000XM5 WHCH720N WF-1000XM5 CH520s. Get those. Just pick those up.
3:23:34
That's what Dylan >> I can't believe how mainstream this post is.
3:23:39
>> I didn't realize 150,000 likes. What is Sony doing?
3:23:42
They need to rename their products.
3:23:44
Just call it like the Sony, I don't know, headpods or something. Headphones.
3:23:49
They should I mean people say XM4s, which I think is the like the last three digits.
3:23:53
That's what they refer to them or the last three characters.
3:23:57
That's what they refer to the headphones as.
3:24:00
And so people just call them XM4s, but uh rough with the naming schemes.
3:24:02
Anyway, uh thank you for watching.
3:24:05
Leave us five stars in Apple Podcast and Spotify. >> an honor.
3:24:10
>> letter at the end and we will see you tomorrow. Goodbye. Boom.