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4:19
Possible runners on the horizon. Stand by. You're watching TVBN.
4:35
Today's Tuesday, March 17th. It's St. Patrick's Day.
4:42
Uh we are live from the TVBN Ultradoom, the Temple of Technology.
4:45
>> The fortress of finance.
4:47
>> Let me tell you about ramp. com. Time is money. Save both.
4:48
Easy to use corporate cards, bill pay, accounting, and a whole lot more all in one place.
4:51
Uh let me also tell you about Shopify because that's why I have this green suit.
4:59
Shopify is a commerce platform that grows with your business.
5:00
Let's you sell in seconds online, in store, on mobile, on social, on marketplaces, and now with agents. >> Patrick's Day.
5:08
We are not drinking Guinness No.
5:11
currently, but we hope that you are. Yeah, enjoy it. Uh very exciting.
5:13
I haven't seen a lot of a lot of St.
5:17
Patrick's Day posts on the timeline. Uh what's going on? We got to step it up.
5:21
Uh I feel like I feel like we should have planned a planned, you know, much much more, but it seems like Tyler is in the St.
5:28
Patrick's Day mood uh and is wearing a fantastic St. Patrick's Day hat. I'm taking my hat.
5:34
How did you wind up here? That's a nice hat.
5:39
Where did that come from?
5:39
Did we just have that in the >> No, I had this.
5:41
Oh, you just have it here? >> You had it handy.
5:43
>> You just you just daily drive that? Yeah.
5:45
>> [laughter] >> Okay, yeah. It wasn't in your car?
5:47
Yeah, he wore it to the gym this morning. Okay. Oh, yeah. Cool. Yeah.
5:52
Apparently got to wear green.
5:53
Uh George George is going with a little bit of a darker green today, but uh we will still be having fun.
5:56
Uh let's let's pull up the linear lineup, show you who we have on the show today.
6:00
Carrie No Interest, the anonymous poster, is coming on the show at 11:45.
6:03
Then Chamath Palihapitiya from Palantir Technologies is coming for a massive book launch.
6:08
We're very excited about that.
6:09
And then we have a lightning round going through cybersecurity, ceramic AI.
6:14
Uh we have Gecko Robotics on the show.
6:14
I don't know how familiar you are with uh Gecko Robotics, but it's a it's a very very interesting company where they crawl up oil and gas infrastructure, like literally like geckos, and will like inspect everything.
6:24
Uh there's a lot more to the business.
6:27
>> So, like humans in gecko outfits crawling around? >> No, robots. Robots, okay.
6:32
>> been doing robots for a long time. Okay.
6:33
Uh well, Linear, of course, is the system for modern software development.
6:36
70% of enterprise workspaces on Linear are using agents.
6:39
So, uh in the news, The Wall Street Journal has an article about uh this is a scoop from Berber Jin.
6:44
We didn't have to break down the paywall because we get a copy of the Wall Street Journal every day.
6:50
Although, I don't know if this uh particular piece made it into the print edition today.
6:53
It might be in the print edition tomorrow.
6:54
But, Berber Jin uh has a scoop.
6:56
He says, "OpenAI's Fidji Simo told staff last week that the company could not afford to be distracted by side quests. Main quest only. Main quest only." What is the main quest?
7:09
Uh probably just scaling compute, but uh we'll get into that.
7:12
Uh so, uh the the and and and there's a whole bunch of takes back and forth.
7:18
Said the company execs are are actively looking at areas to deprioritize.
7:20
Uh of course, throughout last year, OpenAI launched a ton of different uh initiatives across consumer hardware.
7:28
And I think a lot of people are starting to say like, "Okay, like do you want to be fighting a battle on all these different fronts, or do you want to just really nail consumer, nail enterprise?"
7:37
And now, that is what they are signaling internally.
7:39
So, from the Wall Street Journal article.
7:41
Um OpenAI's top executives are finalizing plans for a major strategy shift to refocus the company around coding and business users, recognizing that a do-everything-all-at-once strategy has put them on the on the defensive.
7:52
Peter Chen, OpenAI's CEO of applications, previewed the changes to employees at an all-hands meeting, telling them that top leaders, including Sam Altman and Mark Chen, were actively looking at where which areas to deprioritize.
8:04
They expect to notify staff about the changes in the coming weeks.
8:07
We cannot miss this moment because we are distracted by side quests.
8:11
Side quests is the key term there.
8:13
We really have to nail productivity in general and particularly productivity on the business front.
8:16
Um I was listening to a podcast with the head of uh ChatGPT last night and uh there were a bunch of things. Yeah, Nick Turley.
8:25
Uh just talking about like it's such a weird product surface area because people come to it for all from all these different reasons.
8:32
So, how you prioritize things within there, like images in ChatGPT, clearly very important.
8:38
That was a separate app at one point.
8:39
It got rolled in to good success.
8:42
Nano Banana was also sort of like a Okay, everyone should download this particular app cuz you're going to get the best frontier image model for a while.
8:49
Uh Sora >> Yeah, we we had talked about this a while back and it seems to be confirmed now that that Sora's functionality will it just end up in in the main ChatGPT app. Yeah. Yeah.
9:01
Uh so, last year OpenAI announced a new an array of new products including the video generator Sora, uh a web browser called Atlas, a new hardware device, and e-commerce features for ChatGPT.
9:13
Uh some of those are like wildly different timelines for these things.
9:19
Um Yeah, and remember during this time that's when I started talking about viewing OpenAI's activities like they were a hyperscaler, right?
9:26
When Google launches a new product, yeah, not you don't have to assume that, hey, it's going to work every single time.
9:36
And in fact, it's the exact opposite.
9:36
A lot of these experiments don't end up going anywhere. It's fine.
9:39
And OpenAI uh like again, you have this like massively scaling, you know, core business.
9:46
You start to say like, "Hey, let's experiment in a bunch of these different areas.
9:51
Some of them are going to work, some aren't. It's okay."
9:53
Uh but then ultimately uh that that creates a scenario where you're sort of like opening up maybe too many fronts, right, to the competition, right?
10:04
You're competing with Meta in social, with Sora you're competing with with Google in some ways.
10:08
Uh you're competing with even like the Microsofts of the world in other ways.
10:13
And uh ultimately this just feels like, "Hey, let's like narrow the fronts."
10:17
And uh I think a lot of people expected something like this for uh the last uh few months.
10:25
OTB in the chat is surfacing a very interesting old take from Ben Thompson about OpenAI should not be in the API business.
10:31
And now, based on the way things look, it it it originally that was like, "Oh, wait, well, maybe Microsoft will be able to handle and scale the API demand."
10:39
But API demand has been so huge and it's allowed Anthropic to develop such a solid business there that it's undeniable that OpenAI should also be in the API business.
10:50
Uh especially when it Yeah, the the other side of that is like they clearly need to be in the harness business, too.
10:55
Which is why Codex is Yeah. Yeah. Yeah.
10:57
And so uh the hyper scalar like like your take of like think about OpenAI as the new as the new hyper scalar.
11:05
Uh I think that I think that takes aged extremely well, especially with recent interviews with Dylan Patel and Dor Cash.
11:11
Jensen just went on Stratechery.
11:14
There's a whole bunch of new data points that show how compute constrained we are, how uh how tricky it will be, how chips will be the the key bottleneck.
11:23
There was that interesting story that Tyler was recounting from uh Dylan Patel about how uh the TPU was developed by the TPU team.
11:32
DeepMind didn't realize how important it was going to be, how compute constrained they were going to be.
11:36
So, the TPU went and sold it to Anthropic.
11:38
And then DeepMind went back to them and was like, "Wait, wait, wait, wait, we want those chips.
11:40
Like, we need the chips."
11:42
Um, and so there's there's this like the hyperscaler phrase is is really, really important in that it designates not just a big consumer company or a big business.
11:53
It's particularly about uh, the ability to marshal compute at hyperscale, right?
12:00
Uh, and so I I was interested to look at like the history of side quests among Mag 7 companies, among trillion-dollar tech companies, because it's all over the place.
12:09
And so it's very hard to paint with a broad brush.
12:11
I think right now there's a huge uh, narrative around like OpenAI was doing way too many side quests.
12:18
They need to do zero side quests.
12:19
And the reality is probably like, you know, we've seen this with Riley Wallace joining the Labs team.
12:24
Like, there will be small projects.
12:26
There will be acquisitions.
12:26
There will be a continuum of bets.
12:28
Uh, there's just a level of refocusing that's going on right now. Yep.
12:33
Uh, and and reorganization and this has happened at many, many >> Yeah, and I think the bet with the OpenAI Labs team is you have a a really small group of people that are focused on creating or being quick to new product like approaches. >> Yeah.
12:46
And but that can be again like a two-pizza team and that type of experimentation is going to make more sense if the rest of the company needs to refocus on on enterprise and the core business.
12:58
Yeah, Tyler, is that is that data point you shared earlier like public about the size of that particular team?
13:04
I didn't want to mention it if it was not >> That's our team. Yeah.
13:07
I think it's people know it.
13:07
At least at at one point it was very small.
13:10
>> like six people originally.
13:10
Also, I was going to say, I think now that you have so many Neo Labs doing these kind of like weird like kind of moonshot research projects, I think it also, you know, adds to this thing where like maybe you don't actually need people internally doing the same things, right?
13:23
You can just acquire them if they work. Yep. Yeah.
13:24
Did you We we to get into that conspiracy theory later, but there's this idea of like uh, all the neo labs teaming up to marshal together.
13:32
We we we we we got to dig into that.
13:33
Anyway, history of side quests in tech.
13:36
Uh can we paint with a broad broad brush here?
13:38
Uh you know, spoiler alert, I think the answer is no, but there are some fascinating stories.
13:44
So, Google has a balloon internet play that's a serious company called Project Loon where they they inflate balloons that go into the stratosphere, I believe.
13:56
They go high up and then they deliver internet.
13:57
Sort of a competitor to Starlink.
13:58
Some promising stuff there.
14:01
They also have the fiber the fiber play, too.
14:03
Fiber play Oh, yeah, they just sold that. Yeah.
14:04
People are so upset about that because like dealing with your ISP is one of the roughest things ever.
14:10
I've I've had such a bad time with various ISPs.
14:17
Often times I would running a small business, running a startup, I would put it on my personal account and then I would have like six lines for like different people and then one of them wouldn't get paid and I'd get sent to collections. >> to me.
14:28
Yeah, that happened to you?
14:28
The only time I've ever had >> Yeah.
14:31
like some some kind of credit related issue Yeah.
14:33
was I I returned the the router >> and and then >> Yeah.
14:40
they they even though I still had an active account there was another another line with them.
14:45
>> They were like, you didn't return it. >> Yeah. We billed you. >> Yeah.
14:47
And somehow >> Wait, Nick, are we still paying internet at the Jonathan Club or did we fix that? I sent you this.
14:55
>> [laughter] >> We canceled the plan. >> You did?
14:58
>> But we haven't returned the equipment. Return the equipment. Okay.
15:01
We're definitely we're we're getting >> getting We're definitely getting fleeced, for sure.
15:04
But but but that was the that is the status quo and it was awesome when Google was just handling it with Google level service. >> sell ads against.
15:13
>> Hard to sell ads against, so they divested, I guess.
15:14
But that's not even close to the weirdest thing.
15:17
The weirdest Google side project that I found is they make a contact lens that will tell you how drunk you are. What?
15:24
I'm not kidding about this.
15:24
Google they have a project.
15:27
It It does more than that.
15:30
It's supposed to do biometrics and it can do a lot of different things.
15:33
But one of the things that it can measure it can measure glucose in your tears.
15:36
It can measure any sort of like biomarker that's in your tears, which apparently is like a rich source of data.
15:42
But one of them is blood alcohol level.
15:45
So you can put in this contact lens from Google and it will tell you how drunk >> Jarvis Am I drunk? >> Am I good? Am I good? Am I good to drive? You're absolutely not.
15:56
>> He's like you sir, you should take a Waymo tonight.
15:57
Would you like me to connect you?
15:59
See, this is all part of the plan.
16:01
They're going to stop people from drunk driving give them Waymos. It works.
16:04
Uh so that was a weird one.
16:04
Uh they also briefly owned Boston Dynamics, but most importantly they bought DeepMind, which was completely seen as like this wild card side project. How does that fit in?
16:13
You know, it's a bunch of researchers and then it became like the most critical thing.
16:16
Amazon has taken tons of shots at drone-related delivery and home security stuff.
16:21
They also own Twitch, but they never really linked the site to live shopping and closed that loop.
16:29
>> thought that that's weird?
16:30
>> doesn't do earnings calls on Twitch. >> calls on Twitch. She should.
16:32
And and a lot of the looks maxing live streamers have decamped to a different platform.
16:38
>> Let me get some uh what do you Ws?
16:38
Let me get some Ws in the chat for the Ws [laughter] for this quarter. for this capex guy.
16:45
Let me get some Ws in the chat for the capex guy.
16:46
Uh of course Apple tried to build a car, then pulled out.
16:50
Who knows where the Apple Vision Pro goes, although of course I hope it continues.
16:55
I'm hoping for another another Apple Vision Pro Apple Vision Air.
16:57
Just make it lighter, same screen. That's the trick.
17:01
Make it like a thousand bucks. I think it'll sell.
17:03
Um and they have a bunch of health moonshots.
17:05
They've been working on non-invasive glucose monitoring, which is there's some book about it called like the the white whale of biotech or something.
17:13
It's something that people have been working on for so long and they've never been able to crack it.
17:16
Uh they've worked on this.
17:16
The The best thing I think they have is that you can get a continuous glucose monitor.
17:21
Thank you for the W's in the chat, by the way.
17:23
There's a lot of W's in the chat.
17:24
Um you can get a continuous glucose monitor that is that is invasive, meaning it is pricking you.
17:30
It is measuring your blood and then calculating the amount of glucose in that.
17:33
That's helpful for continuous glucose monitoring.
17:35
There's a number of companies that do that in sort of a D2C realm levels.
17:39
There's a number of biotech companies that offer that as like a uh healthcare product.
17:44
Um and you can you can wirelessly connect that now to your Apple Watch, but they can't do it in the Apple Watch.
17:50
There's always been the question about like could you just shine a light through your skin and in and detect the blood the glucose in the blood.
17:58
Very very difficult to do.
17:58
Uh potentially always possible, everyone thought.
18:03
Oh, it doesn't break the laws of physics.
18:04
Uh lots of money spent with little to show for it, at least so far.
18:08
Uh Meta is probably the most, you know, egregious They do side quests.
18:10
They don't do side quests. They do full quests.
18:13
>> They they create side quests in in VR.
18:16
There's actually one of the popular games is all about doing side quests.
18:17
Um and uh yeah, I mean they bought Oculus, they bought a bunch of VR studios, they rolled everything up, they renamed the company, spent tens of billions of dollars on what is starting to feel like less of a side quest, but still is is so early, you know, the Meta Ray-Bans are like a success, but to the tune of millions of units, not you know, billions of units or anything like that or hundreds of millions of units.
18:38
Uh I think the iPhone has an install base over a billion now and uh the ramp from here to there on Meta Ray-Bans is going to be long.
18:47
Uh Tesla launched a premium tequila in a lightning bolt-shaped bottle.
18:51
So, you know, it comes from all different Mac 7 companies.
18:55
Uh that one's more of a of a stunt, but it truly like side quests come in all shapes and sizes.
18:59
Some are just good for morale, like it's just fun.
19:01
We do we we do side quests here all day long.
19:05
Uh TVPN simulator, that's a side quest.
19:07
We what what uh what was the other simulator?
19:08
Jeremy Gefon simulator, that was a side quest.
19:10
You know, they're just fun.
19:11
Some of them are good for marketing, good for attention, good for fun.
19:14
Uh some are complete dumpster fires where you just pour money in and it just it just sucks all resources and you get nothing out.
19:20
Uh and then some reshape businesses entirely.
19:22
DeepMind's a great example.
19:23
Um and the and there's certainly others in uh um in uh in in the Mag 7 that have really really changed the business.
19:31
Um >> [clears throat] >> And so >> said seems like the entire Boring Company is a side quest.
19:36
Yeah, it's technically a separate company, but Elon's uh yeah, king of king of side quest.
19:42
Although I think >> it just it feels like it feels like the company that gets the least amount of attention that's not like on the critical path for many of the other Totally. Totally. >> Yeah.
19:52
Still still a very cool idea.
19:52
I mean every time you're sitting in in traffic, it's so like tangible to know, oh, like if we just had more So we're in a urinal of half the minds, basically. >> Yes. Yes. Yes.
20:03
But very very very difficult.
20:06
And so I mean it's been what, over a decade and there's really like a very limited roll out of that technology.
20:12
So uh but it's still cooking.
20:14
I think the business is still is still going and they're working on it.
20:16
So um Some of OpenAI's teams come from very small teams with relatively tiny compute budgets.
20:22
Uh but they get a lot of attention sometimes because of the particular product category.
20:25
So uh Soar is a great example where small team, not huge resource investment, probably a lot of inference and training cost, but relative to Codex 5.
20:36
4 Soar I I I I I don't know.
20:39
I don't know actually know like how we're looking on the order magnitude there.
20:42
Um Yeah, I think I think a big Yeah, a big part of it is But super viral, right?
20:47
>> and what what percentage of the team of the overall team's energy is going towards a specific product, right? Yeah. Yeah.
20:54
Uh and then uh But so the the idea of experimenting quickly and then consolidating efforts even faster makes a ton of sense.
21:00
Nano Banana was a big deal for Gemini, bring image, video, and audio generation together in a single ChatGPT flow is clearly the next step.
21:07
And so, some of this is not It's probably not going to take the form of like, "Stop doing the side quest."
21:12
It's like, "Let's fold that into the main quest."
21:14
because clearly the interaction pattern of chat GPT is is where people want to go.
21:19
And so, once we've done the experiment, it works. Put it all together.
21:25
Um Uh Dylan Patel and Dor Cas said the TAM for GPT 5.
21:28
4 was north of a hundred billion dollars, uh which is crazy for a bunch of reasons.
21:33
I mean, it's just a huge market that was created in just a few years.
21:36
Uh the enterprise opportunity in general is crystal clear for anyone involved.
21:40
At the same time, you still do need to experiment to make sure you are early to create the next breakout product experience.
21:47
And so, with that backdrop, OpenAI labs and being more efficient with the shots on goal makes a lot of sense.
21:53
Uh I still think that the the overall narrative um around AI, just in AI broadly this year, will be about the main quest, which I see as like compute scaling.
22:03
Um And so, raise the money, do the deals, grow the capacity.
22:07
I was thinking back to Travis on the show saying, "If you're doing something and it's easy, it's not valuable."
22:12
The key is, "If money matters, which I think we say we would say it does, especially in certain categories."
22:18
He's probably thinking of ride sharing, where there was a capital war and AI compute, where there's also a capital war.
22:23
You need to be the best in the world at it. >> Yeah.
22:25
Yeah, and I think I think overall uh there's real competition now. It's intense, right?
22:30
You have You have OpenAI and Anthropic at the frontier pushing very, very hard.
22:36
Uh but, the the the other side of this with OpenAI is like all the Yes, there was a ton of stuff that was announced last year.
22:43
There was There was hardware, Sora, etc.
22:45
But, Sam was running around doing all these different mega deals for the compute side that now serve the main quest.
22:53
And so, the positioning is actually great. >> Yeah. Yeah.
22:56
Uh let me tell you about Labelbox.
22:58
RL environments, voice robotics, evals, and expert human data label boxes the data factory behind the world's leading AI teams.
23:04
And let me also tell you about Gemini 3. 1 Pro.
23:09
With a more capable baseline, it's great for super complex tasks like visualizing difficult concepts, synthesizing data into a single view, or bringing creative projects to life.
23:16
And speaking of Google, uh Google Capital bloke, the long-time Google bull, uh says so Anthropic and OpenAI are going to just give this consumer market to Google.
23:24
And I don't know how how I mean, yes, they're like like OpenAI reportedly planning to shift the strategy to refocus around business users.
23:34
And five coders, there's a lot of stuff going on there.
23:37
Um listening to some of the data around the retention curve of the various products, like I don't think that they're giving up on consumer at all.
23:45
That seems like sort of an an odd read.
23:47
Um it will be interesting to see the next iteration of Google's consumer surface area, cuz they have AI search overviews, AI mode, the Gemini app.
23:56
It's in Gmail, but there's clearly some like UI fighting going on.
24:02
I I just It's so funny when you're in Chrome and you have the ability to open one Ask Gemini panel in Chrome, and then you can open a second Ask Gemini panel in Gmail, the app.
24:15
And so, both at the web layer and the browser layer, you have two you have two chat boxes that look exactly the same and do the same thing.
24:24
This feels like a very much like a V1 of what they will do here.
24:27
So, um there's certainly an opportunity for Google to to to reintroduce the AI features, make them more uh tight more tightly aligned with um with what uh what people are actually doing.
24:41
And you have to imagine that a lot of the progress they're making on agent decoding can then come to uh the Gemini surface area in consumer overall.
24:52
Yeah, and and we can pull up this chart uh that Sam showed of Codex usage.
24:54
This is like the again, this is like what I think is informing like the battle with Anthropic as well as like this chart is going to inform the strategy shift, which is like, "Hey, we can run we can we can take revenue into the hundreds of billions of dollars with the current products that we have.
25:16
Let's focus on them and make the best possible products."
25:22
Dean Ball is using Codex.
25:22
Is that what he's saying?
25:24
Just hit a personal record for single coding agent session of a little under 10 hours. GPT-5.
25:30
4X high in the Codex app, unsurprisingly.
25:32
No flashy app, just really complicated economic research prompt.
25:37
At this point, most of my prompts do not stress these agents all that much.
25:41
To be clear, this is 10 hours of continuous work.
25:43
I have meaningfully exceeded 10 hours if we include periods when the agent was waiting for jobs to complete. So, what does this mean?
25:48
Is he talking about like firing off one prompt and coming back 10 hours later or just or just going back and forth cuz he says agent session.
25:56
I I think he's I think he's talking about firing off one 10-hour Yeah, what what do you I thought he meant he's in Codex like the you know, terminal and he's in there for 10 hours.
26:08
>> [laughter] >> Locked in. That's amazing. Oh, wow. Yeah.
26:10
Yeah, it it is very interesting to see I mean Doug O'Loughlin was talking about how how he moved a lot of his research.
26:17
He wasn't building software.
26:21
>> No, he's saying I just it's not a flashy app that he's building just a really complicated economic research prompt.
26:24
To be clear, this is 10 hours of continuous work.
26:29
I have meaningfully exceeded 10 So, yeah.
26:33
>> so I think what he's doing is he's is he's asking like, "Okay, pull together all the senses data about jobs.
26:36
Now go and pull together all the economic indicators.
26:42
Pull out pull together all the inflation >> somebody asked, "How much time was back How much was back and forth was going on versus the agent spending time on its own?"
26:49
He said zero back and forth.
26:49
So, this is 10 hours of the agent just like cooking.
26:54
Wow, [laughter] that's really crazy. What is this prompt?
26:57
It must have been pretty long, I imagine. That's crazy.
27:01
It fit within its current rate limits on the $200 OpenAI tier in the Codex app, where rate limits are currently higher than usual.
27:08
They outperform expectations.
27:10
The prompt was primarily about the political economy of central government transfers to Indian states {slash} union territories.
27:16
I think >> [laughter] >> I think honestly this matched my expectations of what 5.
27:22
4 can do when it's properly prompted.
27:24
What did it get stuck on?
27:26
It encountered a bunch of problems along the way, but doesn't appear to have gotten stuck for too long on any one thing.
27:31
It was just a steppy process, so it just kept working.
27:35
That is absolutely crazy.
27:35
Uh in other news, OpenAI is forming a joint venture with TPG Advent International, Bain Capital, and Brookfield Asset Management. This is great news.
27:47
>> Uh to form a joint venture that would distribute child enterprise products across the firm's portfolio companies and beyond.
27:52
The proposed deal has a pre-money >> AI is coming to Fogo de ChΓ£o.
27:56
Bain Capital owns Fogo de ChΓ£o, the Brazilian steakhouse.
27:57
Maybe now they can take Apple Pay. Oh, yeah. We got cooked on that.
28:01
That would be a good I don't know.
28:03
Maybe they deliberately don't.
28:05
I wonder I wonder if Apple Pay is expensive for them and then this is actually a cost consideration.
28:12
Um FGC MO said, "This news came out a little bit earlier than we planned.
28:16
We're excited to be building a deployment arm and we'll share more details soon.
28:19
Companies have a ton of urgency to deploy AI in their organizations and we're sprinting to meet that demand.
28:23
More than 1 million businesses run on OpenAI products.
28:25
Codex is now at 2 million weekly active users, up nearly 4x since the start of the year.
28:31
API usage jumped 20% during the week after GPT 5.
28:34
4 watch launched in Frontier, which launched last month to help enterprises build, deploy, and manage AI co-workers that can do real work has way more demand than we can handle.
28:42
That's why we launched Frontier Alliances, so we leverage our ecosystem of partners and scale and to scale and that is also why we're launching a dedicated deployment arm tasked with embedding forward deployed engineers deeply inside enterprises.
28:55
This project has been in the works with our investors and alliance partners since last December and we are grateful for them and their partnership.
29:03
We're still early, but the speed of adoption is a clear signal of where this is headed.
29:06
We are excited to not just be building these technologies, but also building many ways for companies to deploy them and get impact. Um Interesting.
29:17
Because there there was a big there was a big talk for a long time about like the the next gen private equity firm will will buy businesses and like deploy AI inside them.
29:26
And this feels like well, maybe that works at the mid-market private equity level, but you don't necessarily need a new AI native private equity firm because Bain Capital >> what I've always said like traditional private equity is not it's not going to just be like oh, we'll we'll figure out AI in like 2030.
29:44
It's not really on a road map right now.
29:46
They're working as hard as they can to implement it.
29:50
Question is like will will traditional private equity be able to uh basically like roll out and unlock the value of AI better than uh some of these like AI native more like venture oriented roll-ups. Okay, so 5. 4 mini announced today.
30:06
So different sizes of models don't count as side quest.
30:12
These are main quest, right?
30:14
These are main quest aligned.
30:14
But what is the pitch for a mini or a nano model?
30:19
Yeah, I mean so this is just like I I think someone ran some evals and the mini models are like equivalent to GPT-5 when that first came out.
30:25
But it's just way cheaper, right?
30:26
So if you're like cost sensitive, if you're running a ton a ton of queries, uh but they don't need to be like the max, you know, intelligence then you just use this.
30:35
So cheaper, but also faster? Yes, probably. Yes.
30:39
>> And potentially runs on older hardware?
30:43
Um I think that's unclear.
30:43
This is bullish for our vintage Neo Cloud. Yeah.
30:49
The oldest GPUs ever >> was Grace's idea yesterday.
30:52
>> Yeah, we're going to run this on 1080 TIs.
30:54
For my gaming experience at >> People love classic cars, why not classic GPUs?
31:01
>> Uh Yeah, I mean it's like [laughter] doesn't I like How small the model is doesn't actually like matter on what hardware >> Imagine Imagine running your vintage Neo Cloud with with a wood-fired [clears throat] hearth Yeah. powering it all. Yeah.
31:15
But uh I mean you have to imagine that What what What's after Blackwell?
31:17
What's the What one that announced today? Uh Vera Rubin? >> Rubin. Rubin. >> Rubin.
31:24
Like you have to imagine that that there will be models that only run on Rubin and need to be sort of re-architected to run on Hopper, I would imagine. Do you Uh sure.
31:32
I mean I think there there's like small performance boost that you can get.
31:36
>> might just be slower, right?
31:37
>> But but you can always You can run any model on like any hardware.
31:40
It's just like it could be like way way slower cuz you have Way way slower than >> The memory is not enough.
31:45
a 100s when you could just be on like like a smaller rack of Rubins. Interesting.
31:51
Uh well, that's exciting.
31:53
Um Uh Kalshi came out with the $1 billion uh >> Before we talk about this, let me tell you about Sentry.
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32:11
So, what did Kalshi launch?
32:13
Uh Kalshi launched the $1 billion uh perfect bracket challenge.
32:18
$1 billion for a perfect March Madness bracket.
32:20
So, you can win $1 billion for entering this competition.
32:25
I haven't read the fine print, but it seem it is positioned that way.
32:29
Vinnie says it would be hilarious if Citadel built out a team to attempt to financially impair their competitors [laughter] Sig through this.
32:36
So, Sig Parametrics Okay.
32:36
from Susquehanna >> Yeah, yeah, yeah.
32:40
is is basically like the financial backer for this promotion.
32:45
So, if Citadel can figure this out, they could leave their competitor with a $1 billion bill. Yes.
32:53
>> Obviously, trying to trying to nail the perfect bracket >> Yes.
32:56
is functionally impossible. So, hey yeah.
33:00
So, so I it's uh So, there are nine quintillion possible brackets.
33:07
So, the odds of a perfect bracket are one in nine quintillion.
33:09
And I believe that they are capping at three. >> odds. I like my odds. I like my odds.
33:16
>> [laughter] >> Uh and I believe that they are capping out entries at 10 million entries.
33:19
So, if you math that out and then you take some insurance out, like you should be able to, you know, hedge hedge hedge out any of the risk.
33:32
If you are good and you don't suffer from any skill issues, you can basically with strong basketball knowledge, if you have ball knowledge, apparently you can get the odds down to like one in 10 billion.
33:45
And at that, you know, not from nine quintillion to 10 billion, now we're talking.
33:50
>> you have nine quintillion, you can sort them by how likely they are.
33:51
Take the top 10 million, that's, you know, maybe there's some kind of power law thing here where we actually Yes.
33:55
you're going you're up against the house. >> You're getting close.
33:58
But I think I think if there's 10 million if there's 10 million like assume that there's 10 billion reasonable brackets.
34:05
Uh and and out of the nine quintillion, so you're you're discarding like all the craziest upsets to get to that 10 billion.
34:13
You already ranked it and narrowed it down by like what, six orders of magnitude or something.
34:16
And then you're submitting the top 10 million of those 10 billion, like your odds of winning are still one in a thousand.
34:28
You can make a thousand accounts. And that's it. No, no, no, no.
34:31
I think that they are capping the entire campaign to 10 the first 10 million entries.
34:35
So, you have to be on Cal sheet and you have >> My open claw already submitted.
34:41
Not not >> [laughter] >> 9 quintillion.
34:42
Uh uh the SEC prepares proposal to eliminate quarterly reporting.
34:49
If you liked earnings >> thinking like is this bad for us?
34:51
We we like earnings days, but it's not like our whole stick.
34:55
So, I think we'll be okay.
34:57
We will continue soldiering on. >> Good.
34:59
Alexander says finally less transparency around financial results.
35:04
Yeah, I don't I don't know.
35:07
I don't know if this is going to be enough to to make it uh easier to be a public company.
35:10
It's short certainly a burden, but the kind of shareholder lawsuit risk and all that other stuff feels like a much bigger burden.
35:18
Uh what this is you're just going to do is create more volatility, right?
35:20
If you're only getting updates, you know, twice a year, you can just see these like massive swings cuz it's like, "Hey, we haven't heard from this management team uh in in a in a formal capacity for 6 months."
35:35
The the business could have, you know, changed wildly, you know, growth rates fluctuating, etc.
35:42
Uh and so again, if you if you if you love volatility, uh you're probably going to love this.
35:48
So, it's good for the Vix.
35:51
That's bullish for Vix investors out there.
35:53
Um it there is a world where I mean the the typical like public versus private debate is that when you're private, you can think in multiple years depending on who your investors are, maybe think in decades even.
36:04
Whereas when you're in the public markets, you need to think in quarters.
36:08
And so, advancing that from quarterly to 6 months and you start putting management teams in, okay, what can we deliver that will show up in 6 months as opposed to what can we deliver that shows up in 3 months? That's twice as long.
36:21
It feels like you could wind up with better run companies doing more ambitious things. I don't know. There is a bull case.
36:30
>> the No, I mean the other side of it is you just get more complacency.
36:31
There's less [laughter] of that. There's less of that.
36:34
Uh you're you're not like on the daily the daily march, you know, you finish earnings and you're like, "Okay, 90 days.
36:40
We got to do this again."
36:40
Like there's no days off kind of thing.
36:42
So, are you are you an advocate for monthly reporting? Daily. Daily earnings calls. >> Potentially hourly.
36:50
>> Just put I mean, this is the this is the crypto folks.
36:52
They're like, "Put it on the blockchain.
36:53
I want to be able to see in real time the revenues of this asset stream and how things are moving around.
36:59
I want full transparency at all times as an investor." I don't know.
37:03
Uh I I think that there is a there's a potential for a good outcome here, but we will have to keep an eye on it and see what actually happens.
37:08
Uh this is just a proposal.
37:10
It's just is the proposal isn't even We are preparing for a proposal.
37:14
So, the proposal hasn't happened.
37:16
We're preparing for the proposal.
37:18
And then the proposal >> It's an advanced idea. >> talk.
37:21
>> It's an advanced idea >> Yeah.
37:24
for a talk for early talks around a proposal. Exactly.
37:27
>> Around a potential proposal.
37:28
>> You know, the FDA actually works that way.
37:29
ANPRMs, it's like advanced notice of proposed rule making.
37:33
So, they tell the industry, "Okay, we are thinking about making a rule. Good luck. >> Good luck.
37:41
And then everyone's like, "We're suing.
37:44
Don't change the rules because I have set up my entire business for this particular set of rules.
37:47
And if you change it, it's a completely over for me."
37:52
Cuz every business is like set up like this.
37:53
They're like narrowly thread whatever regulation is there.
37:55
Really quickly, let me tell you about vibe. com.
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Unlock seamless real-time experiences and new value with Cisco. So, >> Nvidia. Nvidia.
38:14
>> Announcing Nvidia DLSS 5 and AI-powered breakthrough in visual fidelity for games coming this fall.
38:20
>> Video games are going to get more realistic. Prepare.
38:22
Over the next couple decades, this is going to be This is It's entirely possible this happens.
38:28
Um >> [laughter] >> Continue.
38:29
Uh let's pull up the video.
38:31
>> funny because when I when I see the video I want to see it big.
38:33
When I see the video I my my thought was weren't video games already this realistic?
38:38
But I haven't played video games besides Tetris on the automatic. They Oh, yeah.
38:46
So, let's play GeForce RTX DLSS 5.
38:51
DLSS of course stands for deep learning super shading, something like that. DLSS. It uses deep learning.
38:54
It's used AI for a long time.
38:57
The whole pitch for DLSS super sampling has has always been go from a 720p render to 4K. Up res.
39:08
Just interpolate the pixels. This is different.
39:10
You can see that the It's not just becoming higher resolution. It is becoming gen AI.
39:15
Like there is a world where you render that at higher res.
39:18
The lighting is changing. The shading is changing.
39:21
The makeup, the structure of that person's face is changing, but it's still driven by the underly Why are you posting the bubbles? Cuz I'm ranting?
39:29
Um I want to see You're not happy. >> DLSS 5 do this. Oh, yeah.
39:36
Uh But But this is what we were talking about yesterday.
39:39
We were like, what will it take to get TBPN simulator photo real?
39:43
I think we should try and pipe it through DLSS 5.
39:47
Although who knows what this is going to run on because Nvidia's not shipping new graphics cards to gamers or something like that.
39:52
They're delaying the next uh the next gaming graphics card.
39:54
But uh I think that looks better. I don't know. People are upset.
39:59
There's a community note that has yet to be accepted, but uh the community notes are not happy.
40:04
Like one of the community notes is just AI slop.
40:07
And it's like, yeah, like that's in the name. It's deep learning.
40:09
Like what do you think the DL stands for?
40:11
And also, why are we on DLASS 5?
40:13
Oh, because this is like a decade old technology. Uh I don't know.
40:19
Uh People are people are very upset.
40:20
Well, one of the community knows AI technology SS5 has effects on the environment.
40:24
The majority of gamers did not ask for this. Yeah.
40:27
>> Is is that the are those the most photorealistic video games that we have today?
40:31
And then they're showing how they're even getting more realistic. >> no.
40:34
Yeah, because they don't look they don't look they look like games that are just like kind of like animated characters that aren't trying to be photoreal.
40:42
So, there's a range of games there that some of them have pretty outdated engines, pretty outdated outdated rendering pipelines.
40:51
Some of them are on the latest and greatest and look very high fidelity.
40:56
There's also a whole bunch of tradeoffs in game development around the scale of what you're building and how how many, you know, poker chips you want to put into great graphics.
41:06
If you're making some moody first-person shooter that's going to be dark and and there's going to be lots of, you know, like blood splattering and aliens and stuff like you could potentially make it ultra photoreal.
41:21
But if you're trying to build like something like Minecraft is like this massive world that's interacted with a ton of different people, like by narrowing down the scope, like the beauty of Minecraft was that the whole thing's voxel based, which is like these little blocks.
41:32
And so the so the computer can store like an entire world very efficiently because all it needs to say is just there's a cube there and it's green.
41:42
There's a cube there and it's black.
41:44
There's a cube there and it's gray. Uh what do you think?
41:46
>> I was going to say Minecraft is a good example of of what this can improve, right?
41:49
If you go to the next post. >> Oh, yes. Yes. Yes.
41:51
This is what Minecraft could look like.
41:52
This is what Minecraft could look like.
41:56
Uh there are uh there are drawbacks, obviously, because you're driving the the style transfer from the particular images from the shape.
42:05
You're not reconstructing the entire image.
42:07
You're just applying this like, you know, re-rendering on top of the underlying structure.
42:15
And so, you can get really weird outcomes like this.
42:17
Of course, you can you can re-render the full scene, but if you're doing that on a per-frame basis, >> That's powerful stuff.
42:25
basically what we're seeing with uh with DLSS is is the best AI can do in 10 milliseconds, I think, or something like that.
42:36
Because it's it needs to do this basically, if you're running a game at 60 FPS, it needs to be able to do it 60 times per second.
42:42
So, you can't be waiting around for images in ChatGPT or Nano Banana to cook for a minute on each frame. It has to be real-time.
42:51
>> But also on consumer hardware, right?
42:53
Cuz this is not this is not going to be like hosted in the cloud.
42:54
The whole point is that it can run in your gaming PC.
42:57
And and by that token, this feels this feels like it's not just 4 years behind.
43:03
This feels like it's like 1 or 2 years behind in terms of the actual quality of the output.
43:07
I'm I'm I'm actually pretty impressed.
43:09
If this was if this was going back to like DALL-E 2 quality, I would have expected that because DALL-E 2 was running on a fleet of A100s, H100s, something like that.
43:21
>> Sure, but I mean, the underlying tech is very different, right?
43:22
Because you have like a very clear ground truth, right?
43:25
Where you it's like you know what's supposed to be there.
43:28
You if there's this kind of upsampling.
43:30
>> Well, so I mean, the the data pipeline here, the actual ground truthing is a little bit harder for something like this.
43:36
They have they probably have to use Image Gen to generate the high-res versions to actually generate all that.
43:44
Because previously, DLSS was really easy to train because you would just go and run the game at 4K on great hardware, and then go and run the exact same you would render it twice.
43:54
Like from the same exact game footage.
43:56
So, you'd have someone play the game, and on one monitor it'd be 720p, on one it would be 4K, and then they would record both, and they would say, "These are exactly the same thing. Every frame is the same.
44:09
Learn how to take the 720p image and create the 4K image." >> Uh.
44:12
Now, they have to say, "Okay, well, we have a 4K image.
44:15
Let's run that through a GenAI pipeline to make it like photo real.
44:19
Do that a ton, probably on a fleet of GPUs that are not consumer hardware.
44:24
Then, you have your training data.
44:26
Then, you go run the DLSS pipeline and get this like style transfer algorithm that can run 60 FPS." Anyway. But, wait.
44:34
There've been like open-source image models that have done like really good upsampling for a while now. But, at this level?
44:38
Or Wait, are you talking about up-resing, or are you talking about like I would consider this like re-rendering using like something that feels like diffusion.
44:46
Like, this feels like because if you look at if you look at any of these examples, it's very clear that they're not just increasing the pixel count.
44:54
Like, the hair in that woman in in that woman's image like like the hair has a different shape after you turn DLSS on.
45:04
It's not just higher resolution. Yeah, that's true.
45:08
So, like there has to be something like like that they're training on.
45:11
But, and I think that there probably will be very small hallucinations still if you were like to zoom in, whereas that wasn't really true with like the the 720p to 4K pipeline.
45:22
Like, you would get like like whatever the cutout of that image was that you were up-resing would would remain the same, whereas this is actually like re-rendering it, basically.
45:29
Uh well, none of this will matter when we have a billion GPUs in space.
45:33
They'll First, let me tell you about Phantom Cash.
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45:50
>> Take us to space, Jensen.
45:50
Let's listen to Jensen talking about data centers in space. >> Courtesy of Nick.
45:57
I'll spend very little time on this as this time.
45:58
However, we're going to space.
46:00
We've already been out in the space.
46:02
Uh Thor is radiation approved and uh we're in satellites.
46:05
You do imaging from the from satellites.
46:07
In the future, we'll also build data centers in the in the in the in space.
46:12
Uh obviously very complicated to do so.
46:14
We have We're working with our partners on a new computer called Vera Rubin Space One and it's going to go out to space and start data centers out out in space.
46:23
Now, of course, in space, there's no conduction, there's no convection, there's just radiation.
46:31
And so, we have to figure out how to um uh cool these systems uh out in space.
46:36
But, we've got lots of great I'll spend very little time on this one.
46:40
>> engineers working on it.
46:42
Uh it was very funny last year Dylan Patel talk about space data centers and saying that like, "Yeah, even if you solve everything, like it's still hard to make chips."
46:50
>> [laughter] >> He's just like, "It's still comes back down to the chip bottleneck uh above above all else."
46:54
Yeah, I mean, this is why you see Elon doing the Terafab thing, right? >> Yeah. Yeah.
46:58
I I really want to know what his plan is for that because has he put in an order for a tool or is he going to do Tera ASML?
47:08
Like like cuz there's like the supply chain is what 10,000 companies or 10,000 >> historically, they've done they've gone super super early in the supply chain, right?
47:16
They've done like, you know, mining stuff and >> Yeah, yeah, yeah. Yeah. Um but, I don't know.
47:22
Tool making is uh special special industry.
47:25
Um well, speaking of Dylan Patel, SemiAnalysis was featured at GTC.
47:30
The inference king has been crowned.
47:34
Nvidia won a a massive belt and it looks like Jensen's holding it up.
47:39
Uh he is in fact standing in front of a LED wall or projector.
47:44
Um but, uh a beautiful thing to see the SemiAnalysis logo. >> WWE.
47:48
The entire world It's is WWE.
47:54
Nvidia extreme co-design revolutionized token cost the GB NVL 7 is the inference king with 50x higher performance per watt on inference X by SemiAnalysis. 35x lower cost. Very, very exciting.
48:11
And congrats to Jensen for becoming the inference king and winning the inference max award or inference X as it's now known.
48:19
Jensen is also confirming what we see in our GPU availability data.
48:22
There is an epic scramble for compute.
48:23
B200 basically unavailable.
48:28
Availability for GH200, H200, A100 also collapsing.
48:31
Low availability means high demand.
48:37
And people were people were kind of going back joking about like he he said like 1 trillion of demand or something over some period of time.
48:45
And And And people were like, "Oh, so he's guiding down."
48:50
>> [laughter] >> It's like I guess that's where we're at.
48:52
>> Yeah, it's hard when you're doing when you're doing cumulative Well, revenue.
48:56
Let's move over to a much smaller deal for Netflix and Warner Brothers and the final deal which went to Paramount, of course.
49:05
And David Zaslav is in the Wall Street Journal.
49:06
First, I will tell you about Vanta.
49:08
Automate compliance and security.
49:10
Vanta is the leading AI trust management [screaming] platform.
49:13
So, David Zaslav deal could deal pay could top $800 million after last-minute tax benefit.
49:19
So, Warner Brothers chief executive David Zaslav could collect more than $800 million in severance and other payments after rival Paramount acquires the company.
49:31
The sum of cash includes sum includes cash and payments for options and restricted stock holdings as well as a newly adopted tax reimbursement for Zaslav.
49:38
In a securities disclosure filed late Monday, the total doesn't include the more than 20 million he's likely to get for shares he owns outright.
49:45
So, he's been he's been investing as well.
49:49
About 504 million is due to Zaslav if the deal closes, the company said.
49:52
While 47 million would be triggered if he is fired or leaves under certain circumstances within a year of the close.
50:01
116 million in equity has already vested.
50:03
Some 335 million would only kick in if other payments trigger a 20% federal excise tax on golden parachute severance payments he receives.
50:11
The ultimate value of the payout is based on tax code rules that are expected to cause it to significantly decline with the passage of time.
50:19
The company said in a filing, "Without the tax payment, the total would be about 667 million."
50:26
Companies typically try to keep severance at or below three times salary and target annual bonus to avoid or minimize the tax.
50:32
And investors often criticize companies for reimbursing or grossing up executives for the tax.
50:36
The company said Zaslav wouldn't have to pay the excise tax if the deal closes in 2027, which would save the company costs of the tax gross up.
50:46
So there's there's been a lot of back and forth >> of people are are absolutely earned that.
50:52
>> Yeah, he created that much value.
50:52
Uh and the reason that I think it is warranted from a business standpoint is when we were sitting here last year talking with someone who won't be named in the in the me in the media space.
51:06
Somebody who has done deals not at this scale but in the in the billions of dollars.
51:11
He was sitting here telling us at this very table that he didn't think there would be any bidding process at all.
51:16
And it was just going to land with the Ellisons for a pretty predictable price.
51:22
And Zaslav did his job as CEO to get the best possible price for shareholders. And it's remarkable.
51:31
>> And that is why, you know, he was able to have outsize impact.
51:34
He's getting He's getting outsize compensation.
51:36
He increased the enterprise value through his deal making by tens of billions of dollars and he's getting a significant but I think warranted, uh payment.
51:47
And if you if you don't like this, then you probably have uh don't like the game of business very much, either.
51:58
Or you think uh Warner Brothers Discovery should have just used uh like OpenClaw on a Mac mini or something like that to negotiate the sale.
52:06
Put the whole company on eBay and let everyone bid. >> Just do an auction.
52:11
>> It's actually eBay's fees are pretty high.
52:13
I think I think eBay takes like a multiple percentage.
52:15
So, it might actually take a higher fee.
52:16
I think they would take a higher fee, actually.
52:17
It's like a hundred billion dollar deal. Don't they take like 3%?
52:20
So, by that, I mean, maybe we're saving money using a human in this case.
52:26
But, uh of course, um uh some of what he did, you know, between the dinners and the photos and all of these different negotiations and somehow continuing to get Paramount to make offers, but then say that it wasn't their best and final.
52:40
I don't know how you do that.
52:42
Uh he's clearly >> Multiple offers back to back that were not the best and final, but >> Quickly, let me tell you everyone about Lambda.
52:49
Lambda is the superintelligence cloud building AI supercomputers for training and inference that scale from 1 GPU to hundreds of thousands.
52:54
And, without further ado, we have Carried No Interest, the anonymous ex user, with There he is.
53:02
the most fantastic profile picture.
53:04
How are you, as always, No Interest?
53:09
Yeah, I'm doing pretty well.
53:09
I'm going to repeat Carried No Interest as many times as possible >> so that I don't say your real name, Carried.
53:17
>> to say your real name.
53:18
>> to have you here, Carried.
53:18
Thanks for taking the time to join the show.
53:20
Uh why don't you tell us what's been on your mind lately?
53:28
Well, it uh it feels like the chickens are coming home to roost in old private credit software land, and I'm going to take a little cheeky victory lap here and say >> have predicted this? Who could have known?
53:38
No one could have predicted this except you on December 12th of 2024.
53:45
You wrote a post called the bubble in software private equity private credit edition and you did a deep dive on a ton of lending data and discovered, you said, some hilarious stuff.
53:56
You said for a while there have been rumors forming that a bubble in private credit related to private equity I'm somewhat certain a mini bubble forming in software private equity private credit and then you get into it.
54:09
So what were you what were you seeing back then that prompted the post?
54:14
So what I was seeing, right?
54:14
And and it's wild that kind of a a nameless private markets investor with a PitchBook subscription, no intentional advertising for them just there, [laughter] was able to pull this data and knew it themselves, but anybody with a PitchBook sub could have done it.
54:28
What what prompted this is that I was simply being shown deals by investment bankers where the financing structure, I think, would make the average American kind of scratch their head, right?
54:40
Like if you had told the average person on the street that you could take a business with a hundred million dollars in revenue and no profit and lever it up to buy it with no profit at all.
54:50
And have to repay a good chunk of it in four years.
54:53
Your average American would go, "No way, that's not a thing that you're allowed to do, right?"
54:59
But depending on how good your relationship was with a certain subset of private credit lenders, that is certainly a thing that was allowed to do.
55:05
And so what prompted the post was that I kept being shown deals with with financing structures I I could not believe, right?
55:11
From very good sponsors historically who were excellent.
55:16
And then I thought to myself, "You know what?
55:17
I'm just going to pull this data from PitchBook and do this thing myself."
55:19
And what I found was this giant bar in the years 2028 and 2029 where a lot of my wonderful colleagues in software private equity would have to pay a good chunk of that debt back and it kind of terrified me because something else was happening at the same time that was somewhat scary, which was you could see on a There are a bunch of different investment banks who have published this.
55:39
Karl Square, a bunch of the other ones that are software boutiques, you could see on a graph that the multiple on ARR was coming down in private market transactions.
55:47
Let's not even talk about publics, which now become more relevant than ever.
55:51
And I kind of looked at that graph and the graph of all the debt coming due, and you know, my stomach churned a little bit, and then I wrote down >> And so And so the the sponsors, their idea was, "Hey, the debt's going to come due in 2028 or 2029, but we're going to roll it over, and we'll just kick kind of kick the can down the road."
56:09
But then now, with AI disruption and a lot of questions, people aren't going to want to lend in the same way to these businesses.
56:20
I mean, the irony is it all kicked off with interest rates back in like 2021 and 2022.
56:24
And you could see in the post, right, from Karl Square, in 2021, right, the the unprofitable growth software company, in Karl Square's own investment banking data, traded at 9. 1 times ARR.
56:38
In 2022, I'm so excited to hear what sound you use next. In 2022, it was 3. 4.
56:43
So, you went from nine times Yeah, there it is. That was very good. Very very immature. You went from 9. 1 times ARR down to 3. 4 times ARR.
56:54
And you can even Like, how much software M&A took place in 2020 and 2021 that was sponsor backed? It was a lot.
57:08
>> [laughter] >> Who is in control of this? I am sorry. I'll stop.
57:13
>> [laughter] >> You're really good at that, man.
57:14
You got You got a lot of talent.
57:18
>> [laughter] >> And so, it's kind of scary.
57:18
Now, we have, you know, 30% of sponsor backed transactions with software at one point, and everybody's been kind of kicking the can for a while, and now everyone is noticing. Okay.
57:30
Take us Take us back and explain like our When I When I think about these like software deals, take privates or buyouts of private companies, and there's debt involved, I go back to like the LBO boom, the Milken era, Barbarians at the Gate.
57:48
Obviously, those were big deals.
57:50
Is that the correct structure that I'm thinking of?
57:53
It's just LBO, but instead of like a cigarette company or a food company or some other industrial company, it's a software company now.
58:02
And And then after some of that history, I want to know like what is fundamentally different about these software deals because that LBO boom seemed to last for decades. Yes. Yeah.
58:15
And so, I think that it all comes down to the fact that they were borrowing against ARR and not EBITDA, right?
58:21
There's There's very little profit to borrow against with a high-growth, you know, enterprise software company. And so, what do you do?
58:28
You need to juice your IRR somehow, so you need to borrow.
58:30
So, you'll go to a bunch of private credit funds and you say, "Look, I've never lost money.
58:33
I'm talking about a very specific firm right now.
58:38
I've never lost money on a deal. You can't lose with me.
58:40
Just let me borrow against the ARR.
58:42
And before it comes due, you know me, I'm going to sell it.
58:46
And we're all going to buy, you know, another Gulfstream, and we're going to laugh about this, you know, in 4 years."
58:52
The problem was is that the liquidity just left, right?
58:54
Both in terms of the most important person [clears throat] who could buy it, which was a strategic acquirer, right?
58:59
When stock prices fell and interest rates went up, you're just not as acquisitive.
59:06
But now you lump in the fact that AI is creating all these existential questions for enterprise software companies, Yeah.
59:13
and you have the depressed public valuations, >> Yeah.
59:16
you know, and you have no profit, so your recovery rate with your lender might even be low, Sure. Sure.
59:22
>> You can't even You can't even take the keys of this business and milk it for dividends. >> Yeah. Yeah.
59:26
>> You have to do a massive restructuring.
59:28
That's a really scary proposition.
59:30
>> Did Did you Did you Did you see the the the AI disruption of enterprise software coming or did that hit you like a flash bang?
59:38
I mean, I did, but I was also an AI or data scientist, as you know, for for many years.
59:42
So, I thought it was somewhat inevitable.
59:49
>> [laughter] >> That was good. Throwing flash bangs.
59:55
Um yeah, no that's to almost said your name, Carried's credit.
59:57
This this time last year, you were talking about a specific product category and you were saying, [clears throat] you know, 10 years ago to build a product in this space, I would have needed to raise $30 million to build something competitive.
1:00:13
Now, I can do it with 300 grand and I'm going to go out I'm going to go after it.
1:00:18
You were working on some deal Yeah.
1:00:21
>> [clears throat] >> at the time.
1:00:21
And so, I think you did you know, I don't know about on on December 12th of 2024, but certainly at the very beginning of 2025, you were seeing that like hey, a lot of these companies that that previously would have taken, you know, tens to hundreds of millions of dollars to like rebuild and actually be competitive with Yeah.
1:00:39
you can now do with with much less capital.
1:00:44
Oh, 100% and I mean, I think that is kind of I think that and here's maybe even a spicier take that I swear I'm going to write a longer post on.
1:00:51
I think a lot of the stuff around, you know, a really good enterprise software company is cooked because somebody's going to buy it and code it.
1:00:59
I don't think that's going to happen, right?
1:01:00
I think that the scarier thing that's occurring now for private equity actually is actually that we're hearing rumors of customers that aren't signing three-year deals, right?
1:01:12
The entire basis of software PE being some of like one of the best private market categories was that you had enterprise customers with three-year contracts. >> Sure.
1:01:23
If every other enterprise customer says, "Actually, AI is just so amazing now, and we don't know where we're going to be and what it's going to look like, we're only doing one-year deals." Okay.
1:01:33
That has like much bigger implications than, you know, somebody vibe coded a Notion clone on Sunday. Sure. >> Right? That's a big deal.
1:01:39
And I think that the reason that that they're doing that, and this is the bigger threat, I think, to a lot of these companies, is the adjacency within the market map.
1:01:49
Like, if you look like Rippling, and you have a very talented like group of people like Rippling's Rippling's employees, they can attack so many parts of the HR platform space now that they simply couldn't 3 years ago. >> Sure.
1:02:02
And I think that's a lot scarier than the vibe coding to my colleagues in software private equity than, you know, somebody just whipping one of these up and going out with a bunch of cold emails.
1:02:12
It's the adjacency threat in the market map that's scary, cuz now who's Rippling going to go after? I don't know. >> yeah, yeah, yeah.
1:02:18
And and and so you have like previously, there was like it's not an AI like a HR company, it's like HR for hospital or HR for golf courses or some sort of narrow niche in a niche, and that all of a sudden has been pretty easily added to Rippling's TAM, for example. Yep. Okay.
1:02:38
>> And, you know, and another thing that I'm seeing just when I talk to people in the industry, I don't know that there's a scarier seat right now than at Thoma or Vista.
1:02:48
Because the liquidity is just evaporating.
1:02:50
The the appetite in the public markets gone.
1:02:52
The ability for these mega these mega technology companies to do splashy 15 times ARR acquisitions for $15 billion gone.
1:03:02
That's a really scary seat.
1:03:04
>> Yeah, all your, you know, that the hyperscalers want to put money into CapEx or like various like, you know, core AI bets.
1:03:10
The public markets are going to put a massive discount because they don't know what you're going to be doing in in 10 years or even 3 years.
1:03:17
There's so much uncertainty.
1:03:17
And who [snorts] wants to buy these companies at at least the the marks that that they've had over the last few years because of that same uncertainty.
1:03:26
So, it's like where where do these companies go?
1:03:29
They they they you know, the funds can continue to do continuation vehicles, but it's just like everyone is just kind of kicking the can down the road forever.
1:03:37
You know, it's a it's a scary time in the asset class.
1:03:41
I don't think that there's you know, I I think that there's going to be a massive kind of resurgence of super ops focused software funds that might even be far more AI focused than than we could imagine, right?
1:03:53
Where you have PE funds where 50% of management fees is going to AI professionals.
1:03:58
I could see that very near term.
1:04:00
Yeah, what's your read on uh partnerships between the labs and the private equity firms?
1:04:05
We've seen a bunch of those happening versus like starting a new private equity firm that is like {quote} AI native.
1:04:13
Oh, I mean I think that yeah, not to use the cliche, but like it has to be like founder mode from the top.
1:04:18
Like you can't be trusting Open AI to make your portfolios more money.
1:04:21
I I think that it needs to be very much from the top of the firm, really understanding what AI is capable of because I don't know how many times in history that it used to take 5 years to build a factory and now you can build it in 2 weeks. Mhm. Right?
1:04:37
If that's the scenario, you probably want an expert in factory building at the top of the fund, right? Or near the top.
1:04:43
Um and the rumors are that there's been a very slow adoption uh at some of these funds that were just used to serve.
1:04:50
And they were they were rolling in it, right? For 15 years.
1:04:52
And And when you say slow adoption and when you say slow adoption, slow adoption for AI?
1:04:59
Like actually pushing AI first initiatives across the portfolio companies? 100%.
1:05:03
Like [clears throat] not the deal teams, but the the actual operations teams. Yeah, makes sense.
1:05:08
>> so you know, you own you know, six different businesses that are doing 30 million low end of ARR, how involved is your deal team in I'm not even talking about AI adoption in the ops level.
1:05:17
I'm saying full blown AI adoption across both product and ops.
1:05:22
And I think that if you're not doing that, you're going to be in big trouble in like three or four years.
1:05:29
What's the what's the takeaway from Mark Leonard's, you know, kind of half retirement last year?
1:05:34
It felt like in many ways he he like perfect I don't know if he he didn't he didn't like his exit didn't top ticket, but he bailed right as there was going to be kind of like infinite questions around the sustainability of the the model that that got them to that point.
1:05:56
Obviously built a you know, fantastic collection of software companies. Well, here's the trick.
1:06:03
You know, Constellation never overpays.
1:06:05
So, like they didn't do what in my opinion firms like Vista and some others did, which was borrow and pay large ARR multiples for unprofitable businesses.
1:06:16
Like Constellation does not pay frequently more than six times even to offer a software company.
1:06:22
And price is like such a good insulation.
1:06:24
So, if [clears throat] you just assume assume like a certain gross retention and you pay a certain price, Constellation is is is likely safe.
1:06:31
I think that I think that they they face the same threat that others do in the adjacency of the market map, right?
1:06:38
But their risk is much lower simply because they never overpay, right?
1:06:44
So, How how are you seeing like diffusion amongst these like sort of deeper in the economy software companies where the small business that they sell to might not be tracking the latest frontier model development. Oh, 5. 4 nano launched today. I can cut my cost.
1:07:04
They're just like, yeah, my business is is you know, golf courses and I need to pay my employees, so I'm just on this particular platform and I don't even take calls from competitors because it's just so far down the stack.
1:07:18
It's only 1% of my cost here. I don't think about it."
1:07:23
Like, is there a world where because AI takes time to diffuse, there's a whole bunch of humans in the loop that some of these software companies are actually just stickier than we expect them to be even though on on principle [clears throat] they should be disrupted faster.
1:07:39
I 100% think that we are overestimating the the rate of churn, right?
1:07:42
I think that that is pure timeline nonsense.
1:07:45
As somebody who sees I was just going to write this like cuz I was actually getting frustrated.
1:07:52
I literally see these company financials, you know?
1:07:54
I've seen them many times.
1:07:58
I'm not seeing this alleged vibe code churn yet, right?
1:08:01
It's just not showing up.
1:08:04
Horse horse horse carriage manufacturer says, "Order order flow looks great. We have a big backlog. There's no issues." Sure.
1:08:13
Like and maybe at some point you're going to see, you know, every other, you know, middle market electronics distributor rolling their own ERP, but it's not happening right now.
1:08:22
And so I try to keep like a check on my colleagues in other industries, right? That are non-tech.
1:08:27
And I literally like once a month I just say, "Are you going to roll your own ERP this month?"
1:08:32
And the answer is always no, right?
1:08:34
And at some point that answer will start to shift.
1:08:36
And then one or two will do it.
1:08:38
It will go horribly Yeah, but but but but but but people people have, you know, VCs are still funding AI-native ERPs.
1:08:46
Like they're funding more ERPs, but I so so I don't think the question has ever been I I haven't seen anyone saying all you know, you know, these like core system of record businesses are totally cooked.
1:09:02
It's every other It's the next, you know, 50 pieces of software that might integrate into said ERP that there's a big question mark around.
1:09:13
So, this actually circles back to This actually circles back to what I was saying before, right? >> Mhm.
1:09:19
There's a reason I said like half of your half of your like deal team, if you're in PE, should have some level of AI expertise because of that exact situation.
1:09:29
That's actually what I'm talking about.
1:09:30
And and sorry sorry for to to clarify there, deal team you mean evaluating the transaction, setting the price versus the operations team that's actually going to go and work with management jump on the board. Great question.
1:09:44
Depending on the size of the fund, those could be the same group or they could be separate. Okay.
1:09:47
But no matter what, there should be a good amount of either AI first developers or just like AI first product people that are helping your portfolio companies compete as the VCs subsidize the cap of the next wave of quote-unquote AI first replacements, right?
1:10:05
>> Yeah, because my my my my my like default assumption would be, okay, yes, you need your deal team to be able to understand what motes are AI resistant, what aren't, like re-evaluate the the prices in the AI era, but then the real AI expertise of selecting products,
1:10:23
advancing software development workflows, figuring out the right structure of the team mix, like that's going to be much more on the operational side of the of the folks that you deploy into these companies, the management teams that you hire. Yes. Like an Yes.
1:10:35
Like an investment banker, no offense to my pals in investment banking, is not going to help your portcos be like, you know, AI first competitor proof.
1:10:44
That's not going to happen.
1:10:46
That's your biggest existential threat, right?
1:10:47
So, you know, I think you're going to see a re- reshuffling for the best firms that can see what is so clearly the future of ops around that to deal with all of these AI first, you know, YC and and in venture back competitors.
1:11:00
Um, I I think that's inevitable or you're you're going to be in trouble.
1:11:05
I think another side of it is is I think that you're going to see a lot of point solutions not trading hands at good multiples. Mhm. Right?
1:11:12
I think that you will see a big flight to pure mission critical software in PE, whereas I saw plenty of point solutions changing hands Sure. >> in 2023 2024.
1:11:23
Now, I I don't I don't see them trading and and I don't I think you can't underwrite that risk for your LPs. You simply can't.
1:11:33
If I can swap it out in 12 hours, I I don't think you can use your LP money on that in private equity.
1:11:39
>> How how existential do you think everything that we're talking about is for like the big platform software PE funds?
1:11:47
Like do you think do you think they can, you know, uh, eventually figure out how to exit some of these businesses?
1:11:54
They have some funds that that, you know, underperform or lose money, but then but then they're able to kind of reposition or do you think it's it's just it's over? I I don't know, man.
1:12:09
It it's not looking good.
1:12:11
Like I mean, you get the public markets are are saying are telling us what the appetite is for these late stage software companies without massive AI tailwinds.
1:12:21
And it's a very scary proposition.
1:12:23
I I I don't see a shift barring interest rates going down and everybody jumping back into the casino.
1:12:31
I don't I don't see it, but I've been wrong before. So.
1:12:33
Well, the good news is interest rates are going up.
1:12:37
The last couple of months.
1:12:37
>> [laughter] >> Perfect.
1:12:38
This is going to be great for everybody.
1:12:39
This is going to be wonderful.
1:12:40
Uh, but yeah, I think it's existential.
1:12:42
I think there's no doubt about it.
1:12:43
There's there's no seat I would want less than at one of the bigger software PE funds right now. Scary stuff.
1:12:48
And the rumor is that a lot of people are are looking looking for the doors. So.
1:12:53
What about what what about other stuff that's like software adjacent AI adjacent?
1:12:57
Like we've seen a lot of the the private credit funds do deals for, you know, large AI data centers.
1:13:03
And that feels like a complete balance against like I'm you know, people are people are worried about software PE debt, but then if that firm has a you know, some contract with meta for some big data center, like meta seems like they're going to be able to pay that bill.
1:13:22
Well, it's funny you say that.
1:13:22
I think it all comes down to recovery rate, right?
1:13:26
Like, you know, he he if if the recovery rates are poor enough, there will be nothing that that daddy Zuck can do to save them, right?
1:13:34
There's nothing he can do, right?
1:13:34
If >> Oh, he's paying his his his his data center bill on time. Well, yes.
1:13:40
But there there will be very little to hide from if the recovery rates are really low.
1:13:46
I I don't know what those are going to look like, right?
1:13:49
And and there's a whole new asset class coming, in my opinion, >> What's that?
1:13:54
of of software special situations that's imminent, right?
1:13:59
And you can think about a deal structure that that would look like.
1:14:00
And this is actually a really fun thought exercise, right?
1:14:03
So, you have a software PE fund that was over levered, right?
1:14:06
And they the private credit fund takes the keys.
1:14:11
And they're just trying to hope they're hoping to get anything back. Sure.
1:14:12
And you can see a special situation >> John Zeedo saying like I think I think you know, some of these deals you're going to be lucky if you get 30, 40 cents back.
1:14:23
>> And is that is that technically a bankruptcy or just like the the shareholders get wiped like like would you be declaring bankruptcy in that scenario?
1:14:31
Equity's taking a zero almost undoubtedly, right?
1:14:32
And depending on how the restructuring plans that pans out, it certainly could end up that way. >> Okay.
1:14:38
But you could see where a very like a Bending Spoons like entity might come in and say, you know, hey creditor, we're going to help you out.
1:14:47
We're going to we're going to run this thing.
1:14:48
We're going to shift it to the Constellation model.
1:14:52
You know, we're >> help you.
1:14:54
Yeah, help me help you, right?
1:14:54
And we're going to shift this to the Constellation model. And you know what?
1:14:58
We're not going to focus a lot on growth, and we're just going to focus on dividends.
1:15:02
And we're going to try and get you out, right?
1:15:04
Uh, that's something that that that a special situations investor might look at, right?
1:15:10
And I think that's a much more reliable way to derive IRR than we're going to turn around this thing that went flat and isn't profitable at all today.
1:15:17
And I think AI enables some of these special situations a little bit more, and that's kind of the whole Bending Spoons thesis on some level, right?
1:15:24
So, I think there will be a new wave of software special sits people that come in and try to do something with these over-levered software cos with the creditors in some form of agreement, right?
1:15:36
>> Okay, so so I mean, you you said that like software PE investors might be like heading for the door, but isn't it possible that they spin up a new fund that does special situations, like develop new expertise, like just reorient their strategy around, you know, curing logjams?
1:15:55
Yeah, I mean, well, you're already seeing people leaving Constellation to pursue this.
1:15:58
And I think that when LPs evaluate who's the better option, they're going to go with the guy from from Constellation than somebody from Teladoc or Mr.
1:16:09
>> thinking about I I was thinking about like internal to the >> it was a special situation when I paid 50x ARR for this.
1:16:15
>> [laughter] >> It was quite special.
1:16:18
Look, you just didn't see my vision for this thing, okay? Uh, what's your Sorry. Go ahead.
1:16:22
What's your take on uh on Zuck's move with Manas?
1:16:25
You were an early Manas ball. He got a deal.
1:16:30
When when I was when I was kind of when I saw that acquisition, I I was thinking, hey, uh, obviously a talented team that built a product that can get to real revenue.
1:16:44
But uh I think there were some questions of like, is Zuck just going to kind of wind down whatever they were doing and kind of refocus on it?
1:16:50
>> Uh was it a talent acquisition or product acquisition?
1:16:52
I was leaning more toward product acquisition.
1:16:53
They seem to continue to be Yeah.
1:16:55
investing a lot in it, uh which is kind of interesting because it's a product category that Meta doesn't They obviously serve a bunch of small businesses, but Manas is being positioned now as like an agent that can help you with your business or a school project.
1:17:11
And it just feels like, you know, obviously hyper hyper competitive.
1:17:16
Uh but what what's your read on on their strategy with Manas and and their odds to actually compete in productivity against OpenAI, Anthropic, you know, Microsoft, all these other players.
1:17:28
Zuck, if you're listening right now, you got a deal, man. That was good. That was a good deal. I love it.
1:17:32
I think that Zuck looked and he saw an absolutely magical product that he could have, and he bought it, right? It It's magic.
1:17:38
I have [clears throat] spent thousands and thousands and thousands of dollars on Manas, and it was worth every penny.
1:17:46
There are simply things that you can do with Manas that no other inference provider or product can do. Hm.
1:17:50
And I would reveal them, but they're they're alpha to >> by the way. Manas.
1:17:56
>> Manas, there's you're saying it's an American company now. Yeah.
1:17:58
And like there's there's like literally things you can do with it.
1:18:01
I'm sure he saw the same thing.
1:18:02
Like they figured out context, like LLM context in browser before anyone else, and ways that no one else has yet to figure out, right? >> That's cool.
1:18:13
And I think that I think that if Anthropic had figured it out, they would have pushed it.
1:18:16
I think if OpenAI could figure it out, they would have pushed it, and they simply haven't yet.
1:18:20
And he saw a piece of magic, and he said, "What do we got to pay to get the magic?"
1:18:24
And I think it was right, cuz it's generational.
1:18:26
Like I said, there's a lot I've spent a lot of money on Manas.
1:18:29
There's things Manas can do that no other inference provider can do.
1:18:31
Um and they benefit from open source.
1:18:34
So there's another cheeky thing that Zuck realized.
1:18:37
Are you Are you broadly an open source bull?
1:18:39
I feel like a lot of people are saying like all the money flows to the frontier.
1:18:42
Uh you know, everyone just wants to use the best model, the Opus 4/6, the GPT 5.
1:18:47
4, Codex Desktop can do a lot of these things.
1:18:53
Have you Have you actually tried the other harnesses and found like a real big delta?
1:19:00
Uh you know, I I am a Manas and Anthropic maxi.
1:19:04
So, those are my two favorites.
1:19:06
So, like I use Manas for certain tasks and Anthropic for others.
1:19:09
But, like I'm certain that what Zuck realized is that I don't have to care about inference and who's giving it to me cuz these guys figured out some really special stuff around how does an LLM process data in and around a web browser.
1:19:24
So, he realized, "Okay, this is kind of inference provider proof.
1:19:27
They figured out this piece of magic, this secret about LLMs using a browser.
1:19:32
I'm going to pay whatever price I need to get it."
1:19:36
And now, I've I've heard he's already integrating it with like ads manager.
1:19:41
Yeah, that that makes so much sense.
1:19:43
Manas, I want to grow my business.
1:19:43
All right, give me every dollar in your bank account right now. [laughter] What is this? >> Yeah.
1:19:52
No, credit to Zuck for kind of either knowingly or accidentally realizing that harnesses would be quite important. >> Yeah. Yeah.
1:20:01
And And like I said, there's a lot of stuff Manas can do that no other like AI related product can do for me.
1:20:07
And it's just It's just really special.
1:20:10
So, yeah, I think he got a steal.
1:20:13
You pay whatever price you got to pay.
1:20:14
Within like the re- like the history of M&A, right?
1:20:18
I think it's a good It's a great bet. Yeah.
1:20:19
And especially for their scale and and and the compute that they have, like there's a lot that they can deliver there that another buyer would not be able to.
1:20:26
Uh just to >> I actually think Manas is like one of the most underrated AI products today. >> Okay.
1:20:31
I mean, I I just downloaded it.
1:20:32
I'm going to try I'm going to try it out.
1:20:33
We we we had our intern Tyler download it and give it a try yesterday.
1:20:38
We'll get a review from him later on the show.
1:20:40
Well, I'll read out one somebody in the John in the YouTube chat says would be interesting to have Jeremy Gefon back on with Carried no interest do a sort of round table style back and forth on here.
1:20:51
I like Carried's analysis so much feels up Jeremy's alley.
1:20:56
>> be a cool >> I totally agree.
1:20:57
We should do that sometime. >> great.
1:20:59
>> I've heard of that guy.
1:20:59
I've heard of that guy before. Smart smart cookie.
1:21:03
You're both former TVP on guests. He's been on the show.
1:21:06
We'd love to have you back.
1:21:09
Let's let's throw a smoke grenade and get him out of here. Yeah, do it.
1:21:14
>> [screaming] >> We got It's great to see you Carried. See you later.
1:21:21
I'll get behind this production. We'll see you soon. It's been an honor.
1:21:25
And let me tell you about MongoDB.
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And let me also tell you about Cognition.
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1:21:43
Uh Midjourney's financials are crazy and underrated. This is from Amrit. Did not raise anything. Built things in house.
1:21:49
200 million ARR as of late 2023 confirmed 500 million dollars in ARR as of 2025 maybe. I don't know.
1:21:54
That sounds reasonable to me.
1:21:57
Yeah, that's like how was the how was the meta deal structured, right?
1:22:02
I assume that meta was just like we're going to give you a ton of money every year for some period of time.
1:22:07
So I'm sure that that that that factors in.
1:22:11
Yeah, yeah, they must be making a fortune. Okay, John.
1:22:13
I said yesterday I wanted to order beef from a ranch with a live stream. Yes. to allow me to be Yes. present >> Yes.
1:22:23
where where the cattle are being raised.
1:22:25
And this Indian startup lets you own a mango tree for $111 per season. That's cool.
1:22:32
Allows you to rent a mango tree and enjoy the entire harvest.
1:22:33
There are three types of trees you can rent.
1:22:38
Base, you can get a base tree. >> Base tree. Standard tree, max tree.
1:22:42
You can get 30 30 kg to 60 kg of mango.
1:22:46
>> That's a lot of mangoes. I love mangoes.
1:22:48
We're going through them now.
1:22:49
>> is operating in three states in India.
1:22:50
You select your favorite tree, pay the money.
1:22:52
You will get a dashboard with all the information about the tree you rented.
1:22:57
Connecting farmers with direct customers who love chemical-free fruits. This is awesome. Very, very cool. I love it.
1:23:05
Uh yeah, we should definitely get a mango mango tree.
1:23:07
>> you can you can you get us one mango tree on the max plan?
1:23:11
>> On the max the max mango max.
1:23:13
>> the mango max plan, please.
1:23:15
>> So so did you have Tyler, have you had a chance to fire off a a Manis prompt and see what it's cooking?
1:23:19
Yeah, I mean I I think um so far I mean I'm not just not creative enough, but it's felt very similar to like the co-work kind of thing, which is essentially just like a wrapper around Yeah.
1:23:30
cloud code where like I can interact with my desktop, interact with local files.
1:23:34
>> Um I I haven't tried like using computer use yet. I'll do that now.
1:23:38
>> I have like no local files anymore.
1:23:38
I like everything's either in the cloud or camera roll or like I just I get new computer I don't even like transfer anything over.
1:23:46
I just sign in to Gmail and like I'm good.
1:23:51
It's it's a very, very thin client these days.
1:23:53
Uh but uh yeah, if you do have a lot of files, I guess that makes sense.
1:23:56
Uh and and I still do like that that idea of like color grade every image off this off this uh off this thing.
1:24:01
And uh I mean just a just a ton of different research things that you can do.
1:24:05
Uh anyway, let me tell you about Railway.
1:24:08
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1:24:17
So, the AI boom has exploded the San Francisco housing market.
1:24:20
I believe we will have an expert coming on the show soon to discuss >> Dar is going to be joining next week to talk about this.
1:24:29
But the Wall Street Journal has a report today that we will read through some of it.
1:24:32
At a Pacific Heights open house in January, a line of people made their way up the steps of a two-bedroom, one-bath co-op.
1:24:40
There were 85 of them, steps not people.
1:24:43
Eight flights, no elevator.
1:24:46
The property received 14 offers and sold for over 1.
1:24:48
62 million, more than $400,000 over the asking price.
1:24:49
While much of the US housing market has been stuck in a rut slowly elevated by mortgage rates and slowed by elevated mortgage rates and home prices near record highs, pockets of San Francisco are rebounding in a big way.
1:25:01
The AI boom, a new mayor, and other changes in municipal leadership have helped bring the city back, reversing a years-long slump that was compounded by the ripple effects of the pandemic, crime, and persistent struggles with homelessness.
1:25:15
Rent citywide were up 14% year-over-year in February.
1:25:18
And this is what we dug into when we looked at Daniel Gross's AGI bets.
1:25:22
He was saying, "Is San Francisco the new Detroit?"
1:25:24
And it was unclear if he meant the current Is Is San Francisco going to be like the current Detroit or what Detroit was in its heyday as a boom town?
1:25:33
Well, it certainly feels like the latter.
1:25:35
Um an uptick in demand with the city's notorious lack of housing supply.
1:25:40
They got to build the cube.
1:25:42
They got to build some cubes.
1:25:45
They got to build some skyscrapers. Uh this is the way. The Munger Munger dorm.
1:25:49
Maybe he sent that out to general public. >> dorms.
1:25:52
They should build one of those.
1:25:54
They should tear down those those houses.
1:25:55
They call them like the painted ladies, you know, they should tear those down and build like the Munger dorm with no windows. That would be ideal.
1:26:02
>> [laughter] >> Maybe uh maybe BlackRock should start investing in San Francisco real estate.
1:26:06
I feel like they would have a good financial opportunity here if while everything's booming.
1:26:11
>> [laughter] >> Condo prices, which had been sluggish for years, grew 12% year-over-year as of February ahead of the spring peak, according to real estate brokerage Compass.
1:26:19
The median sale price was 1. 23 million.
1:26:21
Single-family homes prices are up 23% with the median price at 1. 96 million.
1:26:27
It's just sky >> By comparison, the year-over-year median increase for existing home sales nationwide is just 0. 3%.
1:26:36
Last month, 16 homes in San Francisco sold for 5 million or more, a 220% year-over-year bump.
1:26:41
It just skyrockets, said Kelsey Carson, 34 years old, an attorney who is expecting first child in June and has been house hunting with her husband since April.
1:26:50
You're more likely to get outbid by an all-cash offer.
1:26:56
Carlson was outside a packed open house for a three-bedroom, two-bath condo on uh Buchanan Street in Pacific Heights.
1:27:01
The area known for its breathtaking views and mix of mansions, Victorians, and pre-war apartment buildings has long been sought after.
1:27:07
Carlson and her husband have been outbid on four properties so far. Brutal.
1:27:13
Even a house in nearby Presidio Heights that needs hundreds of thousands of dollars of work.
1:27:16
With AI, everyone's coming in with these huge salaries, she says.
1:27:21
We just can't keep up the pace.
1:27:24
Um There is some good news.
1:27:24
There's a housing market that I think is potentially better than San Francisco, growing a lot slower.
1:27:31
It's only up 4% this year year-over-year as opposed to San Francisco, which is up 12% uh it's Alaska.
1:27:38
Um and I would highly recommend, if you made your money in an AI, uh pick up pick up a house in Anchorage.
1:27:44
Uh the average house price is up there, $400,000. Uh doing very well.
1:27:50
And you can throw a star in there.
1:27:53
>> San Francisco The locals will love that. Anchorage.
1:27:55
The locals will Yeah, John has a long history with uh I shouldn't even joke about it.
1:28:00
I'll need to lock my account again.
1:28:02
If you see me with a locked account, you know that I've I've uh kicked the hornets' nest.
1:28:07
>> [laughter] >> Um anyways, so we're we're going to have Rohan uh who is an SF realtor and uh has 100,000 followers on X just talking about investing and advising clients.
1:28:20
Let me tell you about Plaid.
1:28:20
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1:28:27
And uh let's see what Adam Neumann is up to because he's in Miami and was featured in an Instagram reel, a TikTok collab with none other than Caleb Simpson, Caleb W. Simpson. I knew it. What's up?
1:28:41
How much do you pay for rent in Miami? In Miami, I don't rent. I develop. Okay. He's a developer.
1:28:45
I come into the main lobby and I see a coffee shop.
1:28:47
What's going on over here, Adam?
1:28:52
You should do one of these in Alaska.
1:28:52
I don't know why he's doing it in Miami. He's getting after it. Yeah.
1:29:02
You can go as low as 2,000. Okay. What a view. Oh my gosh.
1:29:05
What does 2,000 get you in Alaska? To safety.
1:29:12
After it's safe and clean, now we can talk about community.
1:29:13
And if you can put those three things together, you start to have a great experience.
1:29:17
This company was I was telling you the show about it was called WeWork.
1:29:20
I think it looked like this. Still got bits. Still got bits. It smells so good. It smells so good. Yoga. Yoga.
1:29:34
That coffee shop was downstairs.
1:29:34
That's open and even the public can come into it.
1:29:38
This is just for the residents.
1:29:38
All these products are all Flow products done in the Flow kitchen.
1:29:45
And there's a whole spread out here. Everything is fresh.
1:29:46
And whatever one sell today, we go to the residents at the end of the day.
1:29:50
There are people working out in this gym all day long.
1:29:54
I've lived here as since I started, but I've lived in this building for about 4 years.
1:29:58
So he was here before we bought the building.
1:29:59
You got to see the building before and after.
1:30:00
When I moved here, it was a great building, but it's turned out way better now.
1:30:04
For the same price in Alaska, you can get a whole house. There you go.
1:30:10
>> But Okay, it's probably fly-in only or there's limited road access.
1:30:13
[laughter] Limited road access.
1:30:17
Limited road >> How many square feet?
1:30:18
Uh at least like a 2,000 square feet. Okay. Okay.
1:30:21
Yeah, I mean so so uh I think I think Adam will create a great apartment living experience. Yeah.
1:30:32
The question is how different is it from every other kind of apartment complex in Miami?
1:30:36
How much do you residents care about, you know, a cafe differentiator, workout classes?
1:30:41
A lot of a lot of apartments have gyms, they have, you know, spas, things like that.
1:30:48
So, I just think uh Yeah, ultimately I think Adam will be successful with this Mhm.
1:30:53
purely because he has high energy, he cares a lot about customer experience.
1:30:58
And then the question will like the question coming out of WeWork is like can he keep the costs in control because >> uh if you don't want to make it if you're if you don't care about making money, it's really easy to deliver, you know, the best like apartment living experience in a given area.
1:31:14
The question is like can he can he deliver services and keep the margin cuz I think ultimately this time around the business will be valued.
1:31:25
Maybe there's some brand equity with to the to the Flow apartment platform which is which is the brand that he's doing this around.
1:31:31
But ultimately, it will be valued based on earnings uh on given given enough time.
1:31:37
So, I think it'll just be a balancing act, but uh looks looks nice. Yeah.
1:31:42
Uh well, Florida man Florida man sold his house in just 5 days after letting ChatGPT handle the entire process instead of a real estate agent.
1:31:49
There's some community notes on this.
1:31:51
Uh he he he didn't go to ChatGPT and say sell my home.
1:31:56
He like you it's the same thing as the dog cancer story.
1:31:58
He used ChatGPT to conduct market research.
1:32:02
>> wants to eat move the goal post.
1:32:02
And yeah, but you can just sell your house. That's that's good news.
1:32:10
I I was listening to Ryan Serhant talk about the opposite scenario, which he said he was working on I think it was like a $50 million house sale and he and [clears throat] they'd spent he was like our agents had spent like months getting these two buyers very educated about the value.
1:32:27
They'd agreed to a price and then the seller went to ChatGPT and said like this should I sell or is the price too low?
1:32:36
And and ChatGPT said you're absolutely right, it is too low. You should pull out.
1:32:39
And then the buyer went like is this a good deal or is the price too high?
1:32:44
And ChatGPT said you're absolutely right, the price is too high.
1:32:47
And so they both walked away and Ryan Serhant was like what the >> [laughter] >> Like now I got to get these people to come back.
1:32:52
This is like a funny thing. Yeah, who who knows?
1:32:53
[snorts] >> I think they need to be in a shared chat window going back and forth arguing with the AI the same AI in the same chat window.
1:33:03
>> the group >> You can do that.
1:33:03
Yeah, that's group chats.
1:33:05
Yeah, that would be the the correct way because then they can fire back and forth.
1:33:07
Anyway, let me tell you about Figma.
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1:33:19
Reorg and the co-pilot division of Microsoft.
1:33:23
So, company scraps divide between consumer and business app teams.
1:33:28
AI chief Mustafa Suleyman will focus on AI models.
1:33:33
He's freed up apparently.
1:33:33
So, Microsoft is reorganizing the teams that work on the different versions of its flagship co-pilot AI product.
1:33:39
This is from the Wall Street Journal.
1:33:41
Altering a strategy that some employees said created a disjointed user experience and consumer confusion.
1:33:47
The software giant is unifying the teams that work on its Microsoft 365 co-pilot productivity offerings and the consumer version of co-pilot Uh according to a memo from Satya Nadella.
1:33:59
Jacob Andreu, who leads product and growth for Microsoft AI, A man who has sat right here. Yeah.
1:34:04
Uh we he he uh he brought back Clippy.
1:34:06
He's the man that brought back Clippy. Yeah.
1:34:10
Uh he will become the executive vice president of Copilot, of just Copilot itself. That's the brand.
1:34:16
And it's a great it's a great brand name.
1:34:18
Uh I'm a big fan of Copilot.
1:34:20
>> Yeah, these are prosumer tools. >> Prosumer tools.
1:34:24
>> they kind of they just they're going to be used in and out of people's work life. Yep.
1:34:27
think businesses everywhere are kind of realizing that. Yep.
1:34:30
Uh individual employees are bringing their own AI tools to work just because they're help them do a better job. >> Yeah.
1:34:37
Uh and >> happened with Apple.
1:34:38
I mean, Apple was like a consumer company and then all of a sudden everyone was using Macs at at work.
1:34:44
And I think that's also a little bit of what's happening with the the the Open AI story about like refocusing on business enterprise API.
1:34:50
It's like, no, like their Tibo's also saying he's going to put Codex in ChatGPT.
1:34:54
So, you're just going to be like, build me an app that does the thing that I want.
1:34:57
There's a lot of times when you go to ChatGPT and you ask something to, hey, go go uh transcribe this YouTube video.
1:35:04
And like it it within ChatGPT, it might not have that tool, but Codex could absolutely one-shot it.
1:35:11
And so, bring that ability, whether it's cloud-hosted or running on your computer, uh bring that over makes a ton of sense.
1:35:18
And Microsoft should probably follow a similar strategy.
1:35:20
So, uh exciting news there.
1:35:22
Let me tell you about the New York Stock Exchange.
1:35:24
Want to change the world?
1:35:25
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1:35:31
And we have our next guest, Sham Sankar from Palantir.
1:35:32
He is the chief technology officer.
1:35:34
In the recent waiting room. Let's bring him in. The TV got ultra loud. Sham, good to see you. Good to see you guys. How are you, Jordyn? Welcome back. It's been far too long.
1:35:43
It's been like almost a year.
1:35:43
You were one of our earliest guests.
1:35:44
A huge moment for the show.
1:35:46
Thank you so much for joining then, and thank you so much for joining today. How are you doing? >> I'm doing great.
1:35:51
I'm pumped to be back on St.
1:35:53
Patrick's Day, but more importantly on book >> [laughter] >> book launch day on on Mobilize.
1:35:58
>> Yeah, what's the Straus and Reed uh uh >> sent us a green package. >> we open this?
1:36:02
Can we open this on the You got to open it. Okay.
1:36:06
>> You sent us a green package because you knew it was it was going to be on St. Patrick's Day. Look at this thing. >> Look at this thing. So, it opens.
1:36:16
This is so This is a real piece of hardware. It's so big.
1:36:21
>> It's an actual US Army ammo box.
1:36:21
It it held 20 mm electrically primed munition.
1:36:28
And now it holds Mobilize. There you go. >> book. It's got some swag.
1:36:32
>> There's amazing stuff. You've outdone yourself. So cool.
1:36:35
>> Um I'm going to sit back.
1:36:35
You're going to you can move that.
1:36:37
>> might get in the way of the camera.
1:36:39
Anyway, [laughter] um so uh I mean, you've been working at Palantir for a long time.
1:36:43
Has it been almost two decades now?
1:36:47
It's it'll be 20 years this Friday. >> What? Oh, this Friday? Congratulations. Amazing.
1:36:51
So, uh when did you when did you feel like, "Okay, now's the time to write the book?" What was the thesis?
1:36:58
What was the uh what what was the the moment where you were like, "Okay, it makes sense to actually take all that experience and distill it down into something that can be instantiated in an actual book?"
1:37:10
Well, the book is the long-form version of the 18 theses.
1:37:11
So, I put the defense reformation out there in October of 2024.
1:37:15
And that was kind of the welling up of all these feelings I had working more or less in the bowels of the Department of War for 18 years, watching deterrence slowly erode, seeing that we had a problem.
1:37:27
Like, you you go back in time and you say we had the annexation of Crimea in '14.
1:37:30
We had the militarization of the Spratly Islands in '15.
1:37:33
Breakout capability for Iran to get the bomb in '17.
1:37:35
We've had a pogrom in Israel, the invasion of Ukraine, conflict with the Houthis. What's going on here?
1:37:42
Like, we're spending a trillion dollars a year. Where is our deterrence?
1:37:45
And uh that's that's one piece of it and you and you look at what's looming with China.
1:37:51
Then you look at the other side of this and you really look at history and you say like what what gave us deterrence in World War II and the Cold War? How did this stuff work?
1:37:57
You recognize how much the industrial base that won World War II and the Cold War is not the industrial base we have today.
1:38:02
You know the astonishing statistic is that only 6% of major weapon systems were built by defense specialist in '89 when the Berlin Wall still stood.
1:38:12
So 94% of spending went to companies that were what I call dual purpose.
1:38:17
Chrysler was the prime on the Minute Man Intercontinental Ballistic Missile.
1:38:21
So Chrysler made missiles and minivans. Ford made satellites.
1:38:24
General Mills the cereal company more made torpedoes.
1:38:26
We had an economy that was equally invested in freedom and prosperity.
1:38:31
But the corporate story is not enough.
1:38:32
So that's a precondition.
1:38:34
You could kind of say well what were these companies like?
1:38:37
You know because we think about it today as Northrop Grumman and Lockheed Martin but it was Glenn Martin, it was Jack Northrop, it was Leroy Grumman.
1:38:42
It's something that your audience would understand.
1:38:46
Foundationally, it was founders.
1:38:46
You know our entire industrial base was made up of founders. What's happened?
1:38:51
You know at the end of the Cold War we had this enormous financialization of defense.
1:38:57
These companies became run by the third, fourth generation.
1:39:00
Buybacks, dividends, financial engineering over real engineering.
1:39:02
And by the way that's not specific to defense, the same thing happened to Intel, the same thing happened to a lot of great American companies.
1:39:07
The rejuvenation of our economy comes from the heretics.
1:39:10
And then I started researching a lot about the the history of innovation and defense.
1:39:14
It's like nothing worked because of the system.
1:39:17
Everything that worked worked despite the system.
1:39:19
You look at Andrew Higgins, this Scots-Irishman in Louisiana who built the boat that won the war.
1:39:23
92% of all boats in World War II were Higgins boats.
1:39:28
But the Navy didn't let them compete.
1:39:30
Then when they finally let them compete and he won, they stole his designs and failed to copy it successfully.
1:39:34
In the end after all these things were like fine, we'll buy the freaking boat.
1:39:37
And the boys in Normandy landed on Higgins boats. That's great.
1:39:41
You know, and you you go story after story. Yeah.
1:39:43
Hyman Rickover, who built the nuclear navy, his first office was a women's restroom.
1:39:48
And I think part of documenting these stories of the heresy, because of course the navy wanted humiliate him into quitting.
1:39:53
And and then think about the chutzpah like Oppenheimer told him this is not going to work.
1:39:56
You're not going to be able to build a nuclear powered submarine.
1:39:59
And in the face of Oppie telling you this, you're like, "No, you're wrong. I'm going to do it." >> That's bold. >> No. Yeah. >> Bold. Bold.
1:40:06
Have you heard the story of Ball Corporation? They make Mason jars.
1:40:10
You know You know these Mason [clears throat] jars?
1:40:11
Also a defense contractor.
1:40:14
I I think they eventually sold it to BAE Systems in 2024.
1:40:16
But for a long time, the company that made the Mason jars that are in every like hipster millennial burger joint in America was also making like satellites and sensors and all sorts of stuff.
1:40:26
And so there's just endless stories about that of of reindustrialization.
1:40:29
I'm wondering like there's a huge boom in new startups that are saying, "We're going to build boats from scratch.
1:40:37
We're going to build missile systems from scratch.
1:40:38
We're going to build satellites from scratch."
1:40:41
But is there some underrated industrial capacity in America where we haven't actually gone to Chrysler and said, "What can you do these days?"
1:40:48
I know Chrysler might be a bad example cuz it's older company.
1:40:52
But is there still some is there still some latent industrial capacity where in the worst case scenario like America can actually adapt?
1:41:02
Well, that's that's the important part of the book is we're we're telling the story that it's not the facile version of the story where we flipped a switch in World War II and then >> bam Yeah.
1:41:09
automotive industry started making munitions.
1:41:11
It was actually a journey.
1:41:12
It took 18 months to retool and rebuild factories to produce war material. Yeah.
1:41:17
Uh and so you you really want to get moving early.
1:41:19
And now I think we have a lot of this latent capacity.
1:41:22
You think about GM produces a new Escalade every 90 seconds.
1:41:24
You know, right about now we need some SM-6s, SM-3s, and and Tomahawks rolling off the line every 90 seconds, too.
1:41:31
And so how do we take the kind of exquisite artisanal approach to a low number of munitions we built and start scaling that out.
1:41:39
And you're going to need a breadth of approaches.
1:41:40
You're going to need the new entrants building entirely new classes of things and you're going to need to make the exquisite things much more quickly.
1:41:47
What makes this stark is we have eight days of weapons on hand for a major fight with China.
1:41:53
Nobody thinks that's deterrence.
1:41:53
Nobody thinks that's enough, right? We need 800 days.
1:41:57
How do we really fire up the arsenal of freedom here and get serious about building?
1:42:01
And we're going to have to build those things in new ways.
1:42:02
And a lot of that skill exists in El Segundo.
1:42:06
It exists in the modern American manufacturing economy. Yeah.
1:42:08
Yeah, I I'm always reminded of this this like Palmer Lucky take about like the younger generation throughout the 2000s got obsessed with building consumer software ad platforms.
1:42:19
We love ads, but I take the point.
1:42:25
Is there something similar going on right now with AI because AI can be important for the military, but also you see there's only a few caterpillars or electricians and they're working on data centers.
1:42:37
If that doesn't become critical [snorts] to the defense and deterrence of the nation, it actually just winds up being more just juice for the economy, which is probably good, but at the same time it's it might be sucking capital, sucking human talent out of true industrialization efforts. Well, I I I think so.
1:42:56
Like I think with AI we have to remember that we have huge human agency. AI doesn't do X or Y.
1:43:02
Humans use AI to do X or Y.
1:43:02
Let's pick intelligently.
1:43:05
Let's pick things that are in the national interest that give the American people prosperity that actually propel civilization forward and aren't AI slop and you know, AI slop machines. Yeah.
1:43:15
Uh and I think the the promise in front of us is that AI is an opportunity to give the American worker superpowers.
1:43:21
How do you make the American worker 50 times more productive than any other worker anywhere in the world?
1:43:25
And that solves the math equation of like how do we reindustrialize economically?
1:43:28
This is actually Yeah, how do you how do you uh how do you rate the current re-industrialization process?
1:43:35
There's a lot of founders you mentioned in places like El Segundo that are working as hard as they possibly can. >> Mhm.
1:43:42
But but is are we 10% of the way there for what >> you want to see?
1:43:46
Are we 20% of the way there? Are we 5%?
1:43:49
Like where where do we stand right now given all the effort that is already started, but >> we're still, you know, early in this process.
1:43:58
Well, I'd say 3 years ago at the first re-industrialize, there was an aspirational aspect to it.
1:44:02
Now, I think we're closer to 5%. Like this is happening. Yeah.
1:44:05
We're in the early innings of it, but it's happening. Yeah.
1:44:08
Uh and I think people are starting, you know, one of the amazing things about the American spirit is people just roll up their sleeves and get busy trying things.
1:44:13
You know, and I'm I'm working with people on the factory floor every day who are using AI to change how they do production.
1:44:18
One of our submarine parts manufacturers actually added a third shift.
1:44:22
They were able to use AI to automate the planning process, which meant instead of having to have tools down while they did planning and quoting, they were actually able to get that done in 10 minutes.
1:44:32
They needed to hire a third shift because there was more work to do.
1:44:33
And these are the sort of narrative violations that aren't being reported.
1:44:38
And I think the underlying phenomenon is that we are listening So, these revolutions are always tools-driven revolutions, not concept revolutions.
1:44:46
Uh and it the the impact of the revolution is determined by the people who wield the technology, not the people who invented it.
1:44:52
Galileo did not invent the telescope.
1:44:54
He used the telescope to discover the planets and planetary motion.
1:44:59
The microscope, the power loom, the personal computer, thing after thing, it's the wielder of the technology that determined its impact on society.
1:45:04
Today, we are way over-indexed on listening to the inventors of AI.
1:45:09
They're very smart, but just like their creations, they have their own jagged intelligence.
1:45:12
And the future of AI is going to be written by the the American worker. Yeah.
1:45:16
Yeah, how how do you think uh AI interfaces with the re-industrialization effort?
1:45:22
There's there's also like, yes, use AI in the factory, but I imagine that retraining is a really underrated opportunity.
1:45:28
I've already heard just years ago, I was talking to Chris Power about from Hadrian about how he was able to hire someone and just get them forklift certified and teach them how to use these things, and reskilling has always been happening, but it feels like we're going through an acceleratory phase of reskilling, but what are you seeing on the reskilling side related to AI? Well, enormous things.
1:45:49
So, I mean, I think Chris has really led the way with Hadrian here.
1:45:52
He's going to have a huge factory opening on Friday.
1:45:53
Hopefully you guys get a chance to cover that factory floor.
1:45:56
Um the I had to Panasonic energy I work with them.
1:46:01
So, they're located in Sparks, Nevada inside the Gigafactory.
1:46:03
They make every cell for the Gigafactory for Tesla. Interesting.
1:46:08
>> The the population, your employee base there are prior casino workers.
1:46:10
This is high-end exquisite Japanese equipment.
1:46:14
It used to take 3 years of apprenticeship to learn how to be a battery technician for this equipment.
1:46:19
With AI, it now takes 3 months. Wow.
1:46:21
Yeah, so that's a very concrete example of the reskilling.
1:46:23
More profoundly I'd say, you know, that one of the things we cover in the book is the story of Colonel Kukur, the the father of Maven.
1:46:31
And it's one of it's the newest heretic. He's a contemporary.
1:46:33
He he's alive today and you know, obviously.
1:46:37
Um and what I think is is is really compelling about that story is like this is actually the most consequential AI system in the world today.
1:46:42
But because it exists in the Department of War, it's not something that broadly the valley interacts with or thinks about.
1:46:47
And I think one of the reasons it's so consequential is the stakes are existential.
1:46:51
People are not they don't have lame goals like how do I get 10% more efficient or, you know, reduce head count by X or Y.
1:46:57
It's really like how do I have complete dominance in OverMatch.
1:47:02
And as a as a result of this, the people who are building it are not just formally trained computer scientists over here, but it's become a platform that the vocational expert, the Intel warrant officer, the fires officer is able to really encode their knowledge, build agents that are their kind of team working with them to get things done.
1:47:19
And so, the efficiency, the speed, the scale of what's going on like really we're learning more from those users today than we are anywhere else. Yeah.
1:47:26
Uh how are you how are you thinking about um the role of the forward deployed engineer in the AI boom?
1:47:33
It feels like there's uh the capability overhang and incredible amount of uh you know genius intelligence from the machines and yet uh there's so many processes that are still manual.
1:47:45
I went to the doctor recently and I had to fill out a paper form.
1:47:49
And so in many ways like there's still a capability overhang just from like HTML web forms.
1:47:52
Uh and it feels like as amazing as the AI is getting and has we're seeing so much progress there.
1:48:00
Uh there's still something that needs to fall into place to actually get systems deployed. I think that's right.
1:48:06
There's a huge I mean I I in air quotes mockingly I'll call it the last mile problem.
1:48:09
You know everyone's the first 80% of the problem was building the genius technology.
1:48:13
The second 80% it turns out is actually how do you implement it for economic value?
1:48:19
And that's where we're and that's kind of our jam.
1:48:21
That's what we do for a living.
1:48:22
Uh and I think it's never been more fun to be a forward deployed engineer than right now because the speed at which you can take new product ideas that you're learning, systematize them, generalize them, it's crazy.
1:48:31
And I I know Ted's talked about this but like we have to reinvent forward deployed engineering as we go right now.
1:48:36
Things that we used to think would take four weeks take four hours.
1:48:39
And so the amount that you can get done, how you go to market with that, it's like let's just sit in a room with the customer that there's no sales meeting.
1:48:47
It's sit in the room, let's go build agents that are automating actual workflows today. By tomorrow you decide.
1:48:53
How do you think about uh advice for young people?
1:48:55
I imagine that you'd say you know come work with you but also it seems like there's some potential alpha in being a young person that goes to a Chrysler and says I'm going to be the AI czar.
1:49:07
I'm going to be the forward deployed engineer fully foiled deployed cuz I'm just going to work at this company.
1:49:12
Uh where where are the opportunities for young people in during this like tumultuous AI revolution?
1:49:18
So, I'll give you two two answers to that.
1:49:20
The first is what would I tell my children?
1:49:21
Like what should they learn right now? Yeah.
1:49:22
Uh and really what I would want to cultivate in them is agency, extreme agency.
1:49:26
Like I think all the other skills you'll be able to figure out as you go, but you know, do you really believe that your human effort can make a dent on the planet?
1:49:34
And and how do you experience that and live that?
1:49:36
Then, where would you spend time?
1:49:38
I I think Palantir is an amazing platform to have impact on the world.
1:49:41
The things that you do in the commercial sector impact the government and vice versa.
1:49:44
And you know, you have access to the problems. You're in it.
1:49:45
Um but second to that, it's like when you're thinking about being inside of a company, I think AI is going to be the antidote to the managerial revolution of the 20th century.
1:49:55
All this power that was sucked away from the frontline worker who actually knew what they were doing to an amorphous blob of middle managers.
1:50:01
And even actually they sucked power away from the senior leadership.
1:50:05
That's being reversed because all of the bureaucracy is getting cut.
1:50:07
The agency that someone has I was thinking about this cuz I in the military I'm seeing incredible AI application developers who are not formally trained computer scientists.
1:50:18
And I was like, what happened?
1:50:18
I've been doing this for 20 years.
1:50:19
This feels like a discontinuity.
1:50:21
Where do these people come from?
1:50:24
And I realized like, oh, they've always been there.
1:50:25
The the thing is like, what would this guy have done 10 years ago?
1:50:28
Make a PowerPoint, try to convince some program manager that his ideas were good only to be told they weren't?
1:50:32
Knowing full You know, he's too smart for that.
1:50:35
He's not going to waste his time.
1:50:35
Now he just goes away in a corner for 2 weeks and builds it.
1:50:38
And he's arguing about something that's empirical.
1:50:39
And the commander's like, [Β __Β ] this works. Let's go.
1:50:42
You know, and Yeah, I think that I think that's like the the most underappreciated part of this moment.
1:50:45
I mean, we we've been covering We covered a story yesterday of a guy in Australia who's gone on like a year journey to try to cure his dog's cancer.
1:50:54
And he had experience building uh in AI and ML, but didn't have any experience in, you know, biology or pharma or any of these things.
1:51:04
things. And he And just by leveraging the models, he was able to kind of figure out the right path to go down, figure out like even he took a recommendation from ChatGPT of like which professor to go to in their sort of local university system to get help with the problem that he was working on and he ultimately has been able to show like real results on the sort of experimental vaccine and you apply that to that type of that that sort of
1:51:32
uh nationwide realization that like you don't need to be an expert, you don't need to have gone to school for a certain thing, you don't need to be a software engineer to build software, you don't need to be an electrician to start
1:51:44
figuring out how this stuff works and I think that that that unlock across the entire world where just like bringing down the kind of like knowledge boundary around so many different tasks is going to dramatically transform huge parts of the economy. Yeah. Yeah. I couldn't agree more.
1:52:02
I mean, and talk about an example of agency.
1:52:03
Like he couldn't have started that unless he thought, "I could do this. It's going to work." Yeah.
1:52:08
Yeah, and like you said it wasn't it wasn't like it it didn't it wasn't like he just one-shotted anything and that's the point.
1:52:13
That's what people need to realize.
1:52:14
It's like if you just want AI to one-shot everything, that's like saying like I want results in life and I don't want to have to work.
1:52:20
And it's like anybody throughout all human history, if you want results and you're not willing to put in the work, you're going to have a bad time.
1:52:27
But like now, there's never been a better moment in history to want to build something, to do something and and have a better shot at actually achieving that or or learning how to do that than right now.
1:52:38
And so again, it's all it all comes down to agency.
1:52:40
I guess the question is like can you teach agency or is it is it is it innate, you know?
1:52:47
I find like if I'm talking with somebody about their career, sometimes it's like you know in your head like all the different moves that you should make in order to achieve the outcome and yet some people just think, "Okay, I'm just going to go back to like submitting, you know, resumes that never get answered because that's like the the straightforward path.
1:53:07
Uh so so anyways, we're going to find out if >> go back in. Remember?
1:53:12
You know, you wanted to eat his fake steak. Yeah.
1:53:14
And uh I think you Look, obviously there's a part of it that's innate, but there's a part of it that you can cultivate. Yeah, I agree.
1:53:21
Uh yeah, I was I was uh talking to my wife about my 5-year-old last night and was talking about AI and sort of like what he might do for a job and how it is nervous.
1:53:33
It's a It's nerve-wracking.
1:53:35
It's like, okay, what like what A- if I try to predict and set him up for success in some particular career, like how is that tractable at all?
1:53:42
And then I was reflecting on my own career and I was like, well, for like 10 years I sold things online and when I was born e-commerce literally didn't exist because I was born before amazon. com and before Webvan.
1:53:53
So, it was not fathomable to click a button >> a door-to-door protein salesman. >> Exactly.
1:54:00
And now I have a livestream, which was not a thing before the internet.
1:54:04
And so like every career opportunity that I've had has been adjacent to other things.
1:54:08
People sold things before, but or then they talked on, you know, TV before, but the actual shape of the career has been wildly different.
1:54:17
And so, I felt very relaxed at the end of this conversation, but it is nerve-wracking if you really do want to just think, okay, yes, like doctor, lawyer, merchant, chief forever and it will never change, but even the lawyer's role has changed a ton with the with the electronic revolution and the information revolution.
1:54:33
But, Yeah, it's a it's just a fascinating time.
1:54:37
Um where should we go next, Jordy?
1:54:37
Do you do you do you have >> Any other any other stories that stand out and you can give kind of like a trailer so that, you know, we want people to go and buy the book.
1:54:48
Uh so, just give us give us, you know, give us a another trailer, another story.
1:54:52
>> well, we can talk about Colonel Kukur a little bit more.
1:54:53
You think about So, you have this Marine Colonel who's just the I call them heroes and heretics, yeah, you know, because that that it is somehow their rebellion that that gets all these things started.
1:55:02
So, he had this seminal experience where he was on a helicopter waiting to land on Mount Sinjar to evacuate the Yazidi who were fleeing ISIS. Oh.
1:55:10
And uh there was a young uh uh marine who thought he saw a rocket-propelled grenade. Mhm.
1:55:16
And because of this misidentification, this human error, they waved off the landing. It was obviously unsafe.
1:55:22
And you have order of a thousand people who were raped, tortured, and enslaved from this small little decision.
1:55:28
And so, this is the sort of thing that he was like, "There there has to be a better way of doing this."
1:55:32
And that kind of set him up on this crusade to go figure out how to bring AI to the department.
1:55:36
What I think is interesting we document is how everyone tried to kill him in doing this.
1:55:42
I mean, up to the point, you know, the services were threatened by it.
1:55:43
Uh the bureaucracy was threatened by it.
1:55:46
People filed IG investigations.
1:55:46
People claimed he was housing Iranians in his basement.
1:55:50
Here you have this devout Mormon with four daughters living in a 1,400 square-foot home that has no basement.
1:55:56
They actually sent investigators out to go do this.
1:55:57
But just through that, never giving up.
1:55:59
And that's what you see consistently.
1:56:01
When you see Rick Over, you know, you see in his memoirs that the humiliation of the women's bathroom, all these slights, it's not that they didn't get to him.
1:56:09
You know, he he documents how much they did hurt.
1:56:11
But despite that, he would push through and get all these things done.
1:56:14
And perhaps one of the greatest heretics, Billy Mitchell, who's the father of the Air Force, he didn't even live to see the creation of the Air Force.
1:56:19
But he his little rogue act of rebellion was the Navy was trying to sink a ship at this they call it a sinkex, an exercise to do it.
1:56:25
And they were failing to sink this stationary ship.
1:56:29
And he's like, "You know what?
1:56:29
I'm just going to drop a bomb from a plane."
1:56:31
And there was no permission, there was no rules, and you know, you get this like feisty He sunk the ship.
1:56:34
Um but before then, people thought air power was about sending messages back and forth across the front line.
1:56:41
Nobody thought about actually using these as weapons of war. It's totally crazy.
1:56:47
Uh and so, I think he hearing these stories, what I what I really hope is both the heretics inside and outside the building are inspired.
1:56:52
Because your country needs your heresy right now.
1:56:54
And every one of these wars, it really comes down the Churchill and the tank.
1:56:58
You know, as the head of the Royal Navy, Winston Churchill funded and built a land ship.
1:57:05
He could only build ships, of course. That's the tank.
1:57:06
Because the the British Army was like, we got horses, we're good, dude. No, thanks.
1:57:11
You know, and and so you you start discovering these stories and you get emboldened to say like, I got to pursue what I think is right here.
1:57:16
And uh you go back to World War II, we built 154 different airframes, different types of aircraft.
1:57:23
I think 10 really mattered.
1:57:25
But just, you know, in in the sense of like the American free market system, like obviously you can't know.
1:57:30
You need a market for competition.
1:57:32
And that's that's part of what makes defense really hard.
1:57:33
It's a monopsony, there's a single buyer.
1:57:34
People have a penchant for control.
1:57:36
I like to equip that, you know, everyone's given up on communism, including Russia and China, except for Cuba and the DOD.
1:57:41
You know, we have this deeply centralized planning approach that we thought would provide for what the what the what we needed to win wars.
1:57:50
And it's just not the case. That's a good hot take.
1:57:51
Uh do do you think that the next batch of uh defense tech companies should go public earlier?
1:57:59
Like what advice do you have for the current crop of private uh defense tech companies It seems like there is appetite in the capital markets.
1:58:07
You Palantir's obviously done very well in the capital markets.
1:58:10
At the same time, uh the private markets seem to be able to find any amount of money in the couch cushions, especially if there's AI attached to the narrative.
1:58:17
Uh but how how how are you talking to leaders of private defense tech companies right now about the markets broadly?
1:58:27
My advice to them is all the same.
1:58:27
I think one of the hardest things about doing the defense tech thing is um you need to hold two contradictory ideas in your head.
1:58:34
One is like, you need to run towards the pain.
1:58:36
Like all right, you know, you need to run towards proving real results operationally.
1:58:42
But that's not your buyer.
1:58:42
Uh and so, you know, you could you could say there there ought to be a market-to-market moment right now.
1:58:47
Who's in the fight today in Iran? Sure.
1:58:49
Where have these companies started bending the curve?
1:58:52
What are the opportunities to prove this capability?
1:58:54
You know, I'm hearing about incredible things Shield's doing in in Ukraine right now.
1:58:57
Um that's really important.
1:59:00
You're not going to get paid for that.
1:59:01
But, that's the that's the validation you need.
1:59:02
And then, you have to figure out how to get programs of record, all those sorts of things.
1:59:06
But, if you just focus on the programs of record, if you just focus on treating the Defense Department as a buyer, you'll lose the magic.
1:59:11
You'll you'll lose your own heresy that leads to the innovation.
1:59:15
Yeah, there was a little bit of that in the space economy where we saw there was a there was pretty quickly a bifurcation between space companies that were doing a lot of interesting work and signing deals, and then other space companies that were like they got on the rocket and they went to space.
1:59:32
And I feel like that was like an important binary.
1:59:34
I don't know how I you know, the binary is kind of sort of coming down now as more companies get to space, but it felt like, you know, actual deployment where the rubber meets the road, it's always important in startups, but it it feels especially important in this scenario.
1:59:47
What about stories from history around copying what works? Does anything stand out?
1:59:52
And And the reason I ask is because one uh it was somewhat surprising seeing that we have American-made versions of the Shahed uh drone, and I feel like America has always prided itself on being inventors, you know, these sort of like zero to one uh zero to one projects, right?
2:00:14
You said a hundred something, you know, airplanes uh created or airframes created during World War II.
2:00:18
Uh and yet here it felt like the smart decision was like, "Hey, this is like a battle-tested form factor.
2:00:25
Like, we can just make the thing that is delivering results >> Mhm. uh on the battlefield."
2:00:30
But, are there any other kind of stories that stood out around America kind of swallowing its pride and saying like, "Hey, this thing has worked. >> Let's fast follow."
2:00:40
>> The best one that also speaks to the importance of founders and people, the primacy of people, is uh Operation Paperclip.
2:00:46
You know, as the as as the Nazis were losing, we started We actually had two competing programs.
2:00:51
We had Fiat and Paperclip.
2:00:52
Fiat's theory of the case was, "We don't need these people, these dirty Nazis.
2:00:57
We're going to go steal all the technical papers, and we're just going to be able to figure it out just by having all the papers."
2:01:03
That was an abominable failure. It did not work at all.
2:01:06
Instead, what worked was you go get the founders.
2:01:08
You get Wernher von Braun, you get the people who actually know because there's something more three-dimensional to the knowledge than what's on the just the 2D paper.
2:01:16
And I think that's a that's a huge swallowing of pride.
2:01:17
It's We had to really hold our nose to these Nazis and recognize that actually it delivered ICBMs, it delivered the space program, it delivered a fundamental offset against the Soviets.
2:01:27
But we did that other times as well.
2:01:27
I think probably the most favorite famous one is there was a North Korean defector.
2:01:34
I don't have the dates exactly right.
2:01:38
He flew out of North Korea on his MiG.
2:01:41
The MiGs were killing the Korean War.
2:01:43
>> [laughter] >> He flew out He flew out on his MiG.
2:01:44
You know, they they they tried to shoot him down, he escaped.
2:01:47
Fortunately, we didn't shoot him down, we figured out he was defecting.
2:01:50
So we reverse engineered the MiG and built our own.
2:01:53
I'm forgetting Was it the 86 F-86 or the Something like that.
2:01:58
And that actually helped us restore air dominance once again.
2:02:01
So, you know, it is the primacy of winning, whatever it takes to win.
2:02:05
It's You know, you you don't want to be like, "Hey, I didn't steal anything from from the adversary, but I died nobly." No.
2:02:10
Yeah, that doesn't make any sense.
2:02:12
Uh well, thank you so much for taking the time.
2:02:15
Congratulations on launching the book.
2:02:17
Yeah, I can't wait I can't wait to get into it.
2:02:19
We're going to have to We'll share our copy. We'll fight over it. But we maybe got two.
2:02:24
But yeah, congratulations on the launch Great to catch up. Goodbye.
2:02:30
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2:02:45
Uh the timeline is in turmoil.
2:02:45
Uh uh Doug O'Laughlin at Fabricated Knowledge is going back and forth with Bucco Capital bloke over Google.
2:02:51
So, Fabricated Knowledge says, "People keep thinking that their distribution, think Microsoft or CRM, is bigger than the technology AI.
2:03:01
But, the fact that people are buying Macs to mess around with Claude code and other coding agents, they're also good machines, tells me that this technology is much bigger than any distribution this time.
2:03:15
Like, whatever distribution is there, if you're willing to go to the Apple Store, buy a MacBook Pro or a Mac Mini, wire it up, install Open Claw on it, verify everything, like you're you're willing to jump over the default of the distribution."
2:03:30
>> Yeah, I think another another question here is how many small businesses in America don't pay for any AI products directly, and yet their employees are bringing the product to work, yeah, and it's just not appearing on the on the actual But, Google Capital bloke fires back.
2:03:45
He says, "These people are dweebs."
2:03:48
>> [laughter] >> It's like zero point It's like point zero zero zero zero zero zero zero one percent of the market that's buying Mac Minis, in his opinion.
2:03:53
Distribution does matter. It will always matter.
2:03:55
It doesn't mean the incumbents will win, but it certainly gives them an advantage.
2:04:00
Just look at the sentiment shift on Google in one year. That's a good point. Doug Doug fires back.
2:04:05
He says, "I am going to just put the distribution friction is the only thing that matters if we are going to be software brain.
2:04:12
I do not I do understand and appreciate it, but I do think this is a big thing, and making it all about distribution is such a technology loser way to look at it.
2:04:22
Lots of great tech companies with awesome with awesome distribution lost.
2:04:26
Like, IBM had CTO relationships with every company. Al Mao."
2:04:33
Bucco says, "Yep, I am not pounding the table for Salesforce, but they're not dead yet, the long game.
2:04:40
Also, IBM beat QQQ over the last 5 years by a lot. I had no idea.
2:04:43
Uh Let's give it up for International Business Machines.
2:04:48
>> And they keep going back and forth, but let's give it up for IBM, and let's give it up for our from Cyber Starts.
2:04:56
How do I pronounce his name?
2:04:59
Let's bring him in to the TBP Ultra Dome. Well, what's going on? Great to be here.
2:05:03
>> help me pronounce your name.
2:05:03
I don't want to get it wrong.
2:05:04
I really Hey John, hey Thank you so much for taking the time to join us. How you doing? I'm doing great.
2:05:10
I'm speaking for you with you from Miami.
2:05:13
We just finished uh first day of our annual conference, Cyber Sparks. >> Yeah.
2:05:20
Had a lot of fun, lots of terrific guests uh and and speakers.
2:05:26
We we just had Nikesh Arora from Palo Alto Networks, your good CEO of CrowdStrike. We love George.
2:05:34
And my partner from Sequoia Capital uh the founders of Wiz, a small company that you might have heard of, completed a tiny acquisition last week. Yeah.
2:05:49
Uh congratulations to to to to you on that one. Yeah. Great great outcome.
2:05:56
Uh yeah, what what is what what are people talking about?
2:05:59
What is what is top of mind for everybody at the event?
2:06:04
Well, the the whole idea of Cyber Sparks is to bring together the top 300 leaders in cybersecurity.
2:06:10
So, you've got here about a hundred executives, you know, CEOs and founders of the top cybersecurity companies in the world.
2:06:22
And uh the top practitioners, you know, chief information security officers of Fortune 500 companies.
2:06:31
And the whole idea is to work together and talk about what's next and how we can work together in order to deal with what's upcoming and there's lots of risk and you know, an expanding threat vector.
2:06:44
You you probably talk a lot about artificial intelligence and the new capabilities that it brings in your show, but with every technology wave, you know, think about internet, think about cloud, there are new risks introduced.
2:07:03
So, we kind of spend couple of days together to talk about, you know, the future of cybersecurity.
2:07:11
>> Do you think there's more more fear or excitement around the technology shift because there's obviously this new kind of new threat vector, but at the same time that creates opportunity for the industry, for product expansion, and so it's go time from a business standpoint to kind of meet meet the threat, but also I'm sure some people in the room are a little bit scared of what's coming.
2:07:37
I think there's mostly uncertainty about where this thing is going.
2:07:40
Uh you know, what we've seen in the past 12 months is accelerating and there's there are so many things that we simply don't know.
2:07:51
You know, I wish I could stand here and give you all the answers, but in the room we had a lot of conversation around the uncertainty on one hand and the pressing need to make some decisions about safeguards to make sure that you know, the Arnold Schwarzenegger Terminator movie, you know, doesn't become a mild a mild uh um story relative to the reality in in 10 years.
2:08:21
And I think that what we're are through right now, we call it or I call it technological doubling.
2:08:29
You know, if you think about technology um used by us as as a race, as human, you know, and you think about you know, what we have today as the the 100% and let's say the zero is the invention of writing like 4,000 years ago when we lived in caves and started to write in order to accumulate knowledge. So, we can build tools.
2:08:55
Then, the 50% is probably at point in time of the invention of the uh uh steam engine, which allow us to build machines that would perform tasks too difficult for men.
2:09:09
So, the last doubling took 170 years.
2:09:12
The next doubling would take place in the next 25 years.
2:09:20
That means that all of us, all the three of us and everybody who listen to or watch the show, are the first people that would witness technology doubling within their lifespan. Mhm. That never happened. That's acceleration. Yeah.
2:09:36
No living person have seen doubling of technology. And you know what?
2:09:41
When you just follow the curve, you know, the curve I just described to you, that means that in you know, 25 years will be at 200%, but in 100 years will be at 2,000%. This is just math.
2:09:56
It's it's not a prediction.
2:09:59
This is This is just following the math.
2:10:01
Meaning that people that would live 100 years from now would look at our technology in a very same perspective we look at the technology used by our ancestors living in a cave drawing something on the wall. That's exactly the same.
2:10:16
So, we are going through a a a radical change uh in technology that we have never experienced and that would require dramatically different type of thinking about, you know, safeguarding uh the uh new capabilities.
2:10:34
So, cuz this is enormous opportunity ahead of us, you know, AI would change everything about healthcare.
2:10:42
It would change everything about education.
2:10:44
So, there's an enormous opportunity condition under the condition that we can control it and that we are not losing control.
2:10:55
So, that was the core of the conversation. Yeah.
2:10:59
What what is uh What is the shape of the cybersecurity threat landscape right now?
2:11:05
It feels like there's entirely new capabilities when I think about like a fishing attack with a deep fake voice on the phone.
2:11:13
That's like you can I guess you could get a pretty good impersonator, but it's a it's it's sort of a new threat area.
2:11:19
But then there's also just like hammering a coding model to spam a whole bunch of like SQL queries or SQL injections, like all the old stuff but just multiplied.
2:11:28
Like where where are you seeing the biggest new threats emerge or what are people discussing on on that front?
2:11:36
It's all over the map just because of what you said cuz it it becomes very asymmetric, even more asymmetric than we used to see cuz the guys on the offense, you know, threat actors Yeah.
2:11:48
they have a huge advantage cuz they can simply take a a cloud and and just apply it for new attacks while the pace of adoption of AI tools and machines within for defenders within large organizations within the enterprise is significantly slower.
2:12:09
So, that makes, you know, that puts AI in the hands of the bad guys much faster than it puts it in the hands of the of the good guys. Yeah.
2:12:20
And if that's if that would be the case for long, then the bad guys would win.
2:12:27
And the bad guys today are really bad, you know, you look at state-sponsored attacks and you know, everything that's going on in the world and that's that's a real risk.
2:12:35
and that's that's a real risk. Is there a bit of like a white pill here in the sense that because there's a lag between open-source capabilities and proprietary systems, and then you also have the big frontier labs, DeepMind, OpenAI,
2:12:51
Anthropic, like they're definitely running agents over their user bases and their APIs to know, "Hey, this person just spent $5 million on our API and it's all cyberattack-related prompts, like let's maybe turn them off or figure out what's going on over there." They have a huge incentive to
2:13:09
They have a huge incentive to sort of uh you know, control their customer base so that their customer base is not using these tools maliciously.
2:13:18
The hackers sort of wind up on the lagging edge, not on the frontier, but all of the cybersecurity companies like Palo Alto Networks, like uh CrowdStrike, like the folks that you've had at your conference, um they maintain access to the frontier and so they're always fighting with a bigger weapon.
2:13:35
I- i- i- is that sort of the equilibrium we should expect here? That's a great question.
2:13:40
And and by the way, we did have today the two the two top uh cybersecurity experts at uh Anthropic, you know, the head of security and the head of product security, you know, sharing uh the road map and thoughts about about the upcoming capabilities of of Anthropic and and and other um you know, platforms.
2:14:05
I think the answer [clears throat] to that is our continuous um uh investment in innovation in the space.
2:14:11
We, you know, it's not just about Wiz that I mentioned or or Sierra, you know, those are large, you know, established startups.
2:14:23
But we we did we did have one company going out of stealth last week Onyx Security.
2:14:28
All they do is agent security.
2:14:32
We had today a major launch out of stealth for another company Surf AI. Yeah.
2:14:38
Which takes care of, you know, leverages AI to to complete tasks much faster for organizations.
2:14:47
So, there's definitely there'll be a battle between machines, machines that are used by threat actors and machines that are used by the good guys and we obviously our job in this world is to identify the best teams, the best talent, and help them build and utilize AI.
2:15:08
So, we live in a in a in a safer better world and and that's that's the plan.
2:15:14
What's the what's the general sentiment from guests around the actual competitive threat of the labs releasing various agents and security focused products?
2:15:25
Obviously they feel threatened when they announce new products cuz they tend to send the the stock price down or they have over the last month, but is there a real sense that this is a threat the labs could threaten their business models or is it more just um kind of frustration with with how the market perceives the threat?
2:15:50
We always overestimate the impact of disruption in the short term and we always underestimate it on the long term.
2:15:59
So, I I think that what we would see here is that in the next 2 years the Frontier guys, you know, they have different focus and different priorities.
2:16:10
So, their impact on cybersecurity companies would relatively be low.
2:16:15
But, in 5-7 years, I think that they they are they have a chance to to take over and and I think that we would see three cohorts or three groups of uh competitors.
2:16:31
Um you'd see the traditional cybersecurity platforms, you know, the Palo Alto, the Wiz, the the CrowdStrike of the world.
2:16:41
You'd see the cloud providers, you know, Google, Amazon, Microsoft.
2:16:44
And there'll be a third group, which is uh the AI platforms.
2:16:51
And you know, we shall see who would hold keys for cybersecurity.
2:16:55
And you know, that's you know, if you if you ask me the same question 12 months ago, I would not even mention the third the third group.
2:17:02
So, it's really hard when technology moves a deep space, it's really hard to make predictions.
2:17:11
And I'm not afraid of making a fool of myself.
2:17:15
I I do that almost every day.
2:17:19
But, but making predictions right now is is is guaranteed to make fool of yourself.
2:17:25
>> [laughter] >> I was just going to ask you for another prediction, but I guess I'll table it for next time.
2:17:31
Uh thank you so much for joining >> I I can share I can share you with you the predictions I made today in in a in closed room. >> Please.
2:17:38
The pre- you know, maybe I'll start with the predictions I made 12 months ago.
2:17:42
You know, 12 months ago, I told the room that my prediction and and keep in mind it was really pre- the big wave of AI.
2:17:50
I I told them that I I expect to see a million-dollar ARR company, a million dollars in revenues company with a single employee within 2 years. Yeah.
2:18:04
And I told them that I expect to see a hundred million dollar uh revenue company with less than a hundred employees within 2 years.
2:18:10
I was wrong twice cuz those two things materialized in less than 12 months, you know, we've seen we've seen Base 44 with uh one founder Yeah.
2:18:22
who reached uh three and a half million dollars ARR before acquired by Weex and we've seen Cursor reaching a hundred million dollar of ARR with less than 20 employees.
2:18:33
So, things are accelerating and and and and therefore the predictions I made today were uh that I anticipate that we would see Fortune 100 company uh with a security a cyber security group of less than 10 employees.
2:18:54
Keep in mind that today those companies employ thousands of employees.
2:18:59
So, I believe that we would see AI uh making huge impact on cyber security and would turn cyber security from a profession into function. Mhm.
2:19:15
That would be a major shift in the market. >> Yeah. Mhm. Makes sense. Cool.
2:19:19
Uh thank you so much for taking the time to come chat with us and we will talk to you soon.
2:19:24
>> see how these predictions play out. It's my pleasure.
2:19:27
We appreciate you making predictions all day long.
2:19:29
Risking risking risking it all too. Yeah.
2:19:32
>> We're we're in the random prediction off the cuff business and uh I completely agree with you.
2:19:36
It's it's uh it's extremely hard to make predictions that uh hold for any amount of time right now.
2:19:42
But it's an exciting time. I love it. It's entertaining. Thank you so much. Really enjoyed it. >> Yeah. >> Yeah.
2:19:47
Give our Give our best to everybody at the event. Great to hang. >> soon. Have a good one. Take care. Goodbye.
2:19:53
Let me tell you about 11 Labs.
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2:20:01
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2:20:07
Uh, someone in the chat shared this earlier, but it is uh, important to the Manas deal in the New York Times.
2:20:13
China ramps up scrutiny of a Meta AI deal.
2:20:15
The country appears to be cracking down on people linked to the acquisition of Manas, Wow.
2:20:21
a Singapore company with Chinese roots, Man.
2:20:23
as President Trump prepares to visit Beijing.
2:20:28
Chinese government is taking actions to penalize people linked to Meta's $2 billion acquisition of Manas uh, in an apparent effort to discourage Chinese AI executives from moving businesses offshore.
2:20:36
Sounds like they want to make an example of them.
2:20:40
Officials at China's National Development and Reform Commission, a high-level ministry that oversees economic planning including AI, called in Meta and Manas executives for meeting la- late last week to express concerns about the deal, which was announced in December, uh, said the people who declined to be named publicly.
2:20:57
The scope of the Chinese government actions remain unclear, but appears to include an effort to restrict Manas executives from departing China for Singapore.
2:21:06
Beijing has issued exit bans in the past for corporate executives who are under scrutiny.
2:21:11
"The transaction complied fully with applicable law," said Andy Stone, a Meta spokesman, said in a statement, "The outstanding team at Manas is now deeply integrated into Meta."
2:21:20
He added, "We anticipate an appropriate resolution to the inquiry."
2:21:25
Manas did not respond, but uh, they are owned by Meta and Meta did.
2:21:27
Um, so anyways, not not surprised to see China frustrated that one of their great AI teams just kind of bounced poached poached by the Zuck, but who hasn't who hasn't been poached?
2:21:45
Had Had If you haven't had somebody poached from Zuck, Yeah, had Zuck come to town, you're not doing something good. yeah. >> Work harder. Exactly.
2:21:51
Uh Chamath Palihapitiya chimed in on the timeline.
2:21:55
He said, "What if AI doesn't need to show an immediate ROI, but is instead is but instead is the plausible deniability companies use to riff 50% of the workforce they already knew did nothing?"
2:22:06
And Expa- T-Anon says, um "The real question here is why is an allegedly cutthroat hyper-capitalist economy with every large white-collar firm maintained by a workforce 3x the size it actually needs to run it run its operations?
2:22:22
Why would they stop now and not before?"
2:22:25
And that is a good question.
2:22:27
Like like private equity has been, you know, trying to find the right size for every company for a long time.
2:22:33
Um I I I I I I think there's like natural bloat that that happens.
2:22:35
Um We were talking about the the the question of like, "You are an AI company if your revenues are accelerating."
2:22:41
It would be very interesting to see what companies ramp up hiring this year.
2:22:47
Obviously, like the startups and grow high growth companies are.
2:22:49
But, of the, you know, older school economy, uh what management teams are guiding towards more human capital needs?
2:22:57
Uh that should tell you a lot about where that management team sees the the business and economy going in the AI era. Uh great story here.
2:23:09
Uh Formula 1 chief Bernie Ecclestone, the 80-year-old billionaire was badly beaten up in a brutal mugging outside of his Knightsbridge office last month.
2:23:16
I believe this is a >> What happened?
2:23:19
>> This is certainly a historical >> a old school >> piece.
2:23:22
But, undeterred, he allowed his bruised face, complete with an impressive black eye, to be used in an ad for an exclusive Hublot brand of Swiss watches last week with the slogan, "See what people will do for a Hublot." This is great.
2:23:38
That is [laughter] crazy. >> gnarly.
2:23:39
Bernie Ecclestone got mugged, and his reaction was, "How can I turn this unfortunate event into money?" That's remarkable.
2:23:45
Yeah, there's a there's a very there's a number of crazy Bernie Ecclestone stories.
2:23:49
The F1 acquired episode goes into a lot of Bernie Ecclestone's history and stories there.
2:23:59
It's it's a remarkable episode.
2:23:59
You should go take a listen to.
2:24:01
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2:24:03
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2:24:11
Uh Noah Smith sharing some unfortunate news.
2:24:15
We certainly hit the gong when the GDP numbers came out in Q4 of 2025.
2:24:22
Uh but the AI productivity boom story is gone, at least for now, according to Noah.
2:24:28
Instead, it's all just AI capex.
2:24:30
Data centers are the only thing keeping our economy afloat.
2:24:34
Uh of course, the other thing keeping our economy afloat is all the economic activity that is still there despite the lack of excessive growth. Yeah.
2:24:43
But uh certainly data centers are making the overall uh picture a little bit rosier. Makes sense.
2:24:53
Let me tell you about CrowdStrike.
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2:24:57
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2:24:59
Uh Tyler, have you had a chance to fire up Manas again?
2:25:01
Take it for another spin?
2:25:05
Uh I mean I I I had it organize my desktop. >> Okay. Was it effective? Yeah, it did a good job. I like it.
2:25:10
Cuz you look like a man with an organized desktop now.
2:25:14
>> [laughter] >> With a suit like that, >> Yes.
2:25:16
can't afford not to have an organized desktop.
2:25:19
>> What is a good benchmark these days?
2:25:19
We need We need a a new benchmark like our comedy bench, like our What What is it?
2:25:26
Uh uh the Shrimp fried rice. Shrimp fried rice bench.
2:25:30
We need a benchmark for a desktop.
2:25:32
Something with a very disorganized desktop that would be difficult.
2:25:36
I mean, I feel like that's not a good benchmark cuz that's like not arbitrary.
2:25:39
need to use that very often.
2:25:41
But it's something like interacting with actual applications like can you open premiere and edit a file that's a great computer use benchmark.
2:25:48
Yeah, when I was very far away from that when I was thinking about I was thinking like I would like to be able to pick a song and then have it go and find stock footage AI footage movie clips and cut together a vibe reel to the beat like I see on Instagram from just a prompt if I just have an idea of oh I like this song I would imagine that this song with this footage would go really well together.
2:26:14
That's like several hours of work.
2:26:18
I could make a lot more of those videos have a lot of fun with those meta Vibes is a little bit of that because you you pick a song and then you can generate one mid-journey image and do some light animation on top but it's not truly finding iconic footage from around the internet and that feels like something you could do in open claw.
2:26:35
You could do with a with a with a co-work product or or man ask because I mean you can just use like FFmpeg and you can cut videos down from the terminal they could do it without even using computer use time.
2:26:48
The models could do that.
2:26:50
Is that what you're saying?
2:26:50
Well, I'm saying like cloud code could do that right you can tell cloud code to edit a file and edit the video from this time and it should download FFmpeg do it but who knows how good it is yet. That okay.
2:27:02
That's our new benchmark music videos driven let me tell you about console.
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2:27:17
We have Anna Patterson from ceramic AI here what's going on? How are you doing Anna? Good to meet you. Great to meet you. Yeah, you too.
2:27:26
Thanks so much for joining.
2:27:26
Are you guys you are you guys having a good st. Patrick's Day green.
2:27:31
Yes, we're very green up.
2:27:34
Yes, we >> we we have these bright green suits from uh a show we did on Black Friday about Shopify and uh and uh I thought certainly I will not be using that until next Black Friday, but here I am.
2:27:48
>> All right, here we are. Uh well, happy St. Paddy's Day to you.
2:27:50
Uh since this is the first time on the show, please introduce yourself and the company. Hi, I'm Anna Patterson.
2:27:55
I'm uh founder of Ceramic AI.
2:27:58
I was a long-time Google engineer. I started in 2004.
2:28:05
I'm best known for building large search engines. That's amazing.
2:28:07
So, uh yeah, get you give me the pitch for Ceramic AI.
2:28:13
So, Ceramic brings the cost of search in line with the cost of inference.
2:28:16
As you know, inference costs have been going down and down and actually inference is faster and faster, but search is $5 to $15 per thousand searches.
2:28:29
But, inference is maybe 50 cents per thousand searches.
2:28:33
So, the kind of analogy I like to use is tacos and salsa, right?
2:28:37
Tacos is kind of the meat the meal and that's kind of what inference is.
2:28:42
It's the thing that's really delivering intelligence to your application.
2:28:48
Um but, salsa kind of makes it better, right?
2:28:51
But, search is now the dominant cost in in tacos and salsa.
2:28:55
You're adding search, but it's it's $5 per thousand queries.
2:29:01
So, it's really kind of time to bring um salsa in line with tacos.
2:29:09
So, Ceramic is 5 cents per thousand queries. Amazing.
2:29:13
>> Wait, so you're saying this historically it was like you were getting a taco and it cost you $5, but then they were like, "Well, if you want salsa, it's going to cost you an extra like, you know, $500." >> Yeah.
2:29:23
And you're like, "Well, I'm not not sure I really want the salsa." >> [laughter] >> Okay.
2:29:29
So, help me define help me understand what search means.
2:29:30
Because search can mean over the internet that's already sort of baked into LLM's, it can mean web active web search like in a proprietary database or just the open web.
2:29:43
How how are you thinking about the surface area of search?
2:29:48
Yeah, so we do have a 40 billion page web search. >> Mhm.
2:29:53
And that is the open web and then we've built proprietary systems as well. Mhm.
2:30:00
So one of the things that we're announcing is this idea of supervised generation that as the model is generating it's double checking what it's saying with search and it's also double checking with search what else should I say to make a comprehensive topic.
2:30:16
And so that way you can really enable new applications with 10x fewer hallucinations but also make the whole product affordable and fast.
2:30:27
Okay, so who who do you sell I sounds super valuable. Who do you sell this to?
2:30:33
Is this going to be uh maybe similar to kind of like the data labeling market where there's like five customers that like really matter and you want to get all of them or are there a bunch of other applications that you want to actually sell to vertical specific you know, AI applications that can uh bend you in in like kind of inline with their LLM products.
2:30:56
Yeah, I think there are kind of two strategies there.
2:31:01
Um you mentioned one of getting all the big players.
2:31:05
That definitely would be nice but we also have a self-serve every agentic workflow. We have one startup.
2:31:09
Their agentic workflows do 1,100 searches. Mhm.
2:31:16
So this our search engine responds in 50 milliseconds.
2:31:20
So it's both affordable and more real time than the other search engines.
2:31:25
So um, we see a number of agentic workflows happening, but to your idea of, um, a custom index or a custom application, we see that as well, because let's say you're a pharmaceutical company or some banks are very privacy-centric, they don't want their searches going out and they don't even want their searches to models going out.
2:31:48
So, they kind of host their own copy of a model and here they'd host their own copy of search as well, so that they get their own proprietary environment for all their agentic flows inside their enterprise. Mhm. Um, >> Makes sense.
2:32:03
Well, talk about just how the open web is changing in the age of AI.
2:32:07
I I've seen some crazy stats about how much more of the internet Google bot sees because everyone has been indexing and been very friendly to Google for a very long time.
2:32:21
Other publishers are getting more closed off.
2:32:24
How how easy is it to actually search the internet broadly these days?
2:32:29
So, the 40 billion pages that we have are available on the open web.
2:32:32
We do not, um, disobey or I should say we obey robots. text. >> Yeah.
2:32:42
So, we don't actually crawl news sources that, um, have blocked us, but we are in active talks to make deals to them Mhm.
2:32:50
and to have a proprietary API that costs more, but also, um, reflects back revenue to those proprietary sources. Yeah, cool.
2:33:02
Um, are are you uh are is there any value in having like the I've always been interested in the flip side of Google Search versus Google Alerts, where the search is happening internally and then and then the information's actually getting pushed to you.
2:33:17
That product is like probably like .
2:33:19
00001% as important to Google as Search, but it's always been interesting to me.
2:33:23
Is that interesting to you?
2:33:23
Is that relevant in the age of AI?
2:33:25
Does anything change about that ratio going forward?
2:33:30
Um well, one of the interesting things um is that inference a lot of times with these MOE models inference has a lot of spare uh compute cuz it's memory bound.
2:33:45
And so with that, it means that it could be thinking.
2:33:47
So as it's inferencing, it could be getting a stream of search results searching all all the time and actually bringing you sort of like alerts, only the most interesting information or information that it doesn't think that you already know by looking at your history.
2:34:06
So I think it's going to enable uh a lot of new um applications. Amazing. Uh wildcard question. Hit me.
2:34:14
How long until we see ads in Gemini?
2:34:18
There's been some reporting this week.
2:34:20
Obviously, Demis had come out and said, you know, why would we put ads in For the record, on this show, we are extremely pro We're extremely pro ad. We love ad. Exactly.
2:34:30
Uh and and I and I we both expect ads to be in Gemini.
2:34:34
I would say this year is my guess, but >> That's probably my guess as well, before the end of 26. Yeah. Cool.
2:34:45
That gives me a lot of hope.
2:34:47
>> We're going to be very excited >> [laughter] >> Faith Faith in humanity restored.
2:34:52
>> What's What's the story of the the pink guitar on the wall there?
2:34:56
Um well, if you want to do something really humbling, learn an instrument from your children. Oh, okay.
2:35:02
They >> [laughter] >> um they're absolutely brutal with telling you to practice everything you're doing wrong.
2:35:09
And uh and so I kept borrowing my daughter's uh electric guitar Mhm.
2:35:14
after she taught me acoustic guitar and so uh she decided to get me my own Oh, wow.
2:35:19
because um she saw her guitar like laying on my couch and she said, "A guitar should be hanging on the wall."
2:35:28
And I said, "I think I've been told to clean up my room by my child.
2:35:32
So, my work here is done." That's amazing. >> You're great.
2:35:36
Well, thank you so much for taking the time to come chat with us.
2:35:39
>> Yeah, great great to have you on. >> you.
2:35:40
Excited to follow Ceramic and uh we'll talk to you soon, Anna. We'll talk to you soon. >> Thank you. Goodbye. Thanks.
2:35:45
Speaking of ads, let me tell you about AppLovin.
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2:35:53
And without further ado, we have our final guest of the lightning round, Jake from [music] Gecko Robotics. Jake, how you doing? What's going on? Doing great. Doing great. >> for taking the time.
2:36:03
Uh we I don't believe we've met, but I I've heard about Gecko for a long time.
2:36:06
I think Trey uh introduced me to it when I was at Founders Fund.
2:36:09
Uh but since this is the first time on the show, I'd love a little bit backstory.
2:36:12
Like, how did you get into the industry?
2:36:14
How did you start the company?
2:36:16
And then we can kind of get up to speed on what's happening today. Yeah. Well, Trey's awesome.
2:36:19
Uh and I'm so glad he he uh originally um spoke so highly of us, I'm sure.
2:36:24
Um yeah, so I founded the company about 13 years ago out of a college dorm in western Pennsylvania, about an hour north of Pittsburgh, Pennsylvania.
2:36:32
And I was studying electrical engineering, really wanted to figure out uh how things like energy was created.
2:36:37
And so I went to a power plant in Franklin City, Pennsylvania.
2:36:40
Uh for those history nerds out there, it was like the that was where the first commercial oil rig was drilled.
2:36:45
And got to see a power plant how it was made and I dove in headfirst through this little manhole that I could barely fit through and and I got to see what a boiler was.
2:36:53
And so that was like a football field-size room that was completely covered wall-to-wall with these steel tubes.
2:36:59
And this whole job of this boiler was just to get really really hot.
2:37:02
And so uh you know, shot water through it, got really hot.
2:37:06
Anyway, this boiler kept on having failures.
2:37:07
30% of the year it was shut down because of catastrophic failure that was occurring because the pressure vessels would keep keep exploding.
2:37:12
And the only way to stop it from exploding was a guy on a rope 100 ft up in the air, you know, trying to figure out like where the next explosion was going to happen.
2:37:19
And that just wasn't working.
2:37:20
And that guy actually died that same year before doing the job of gathering data sets by hand in the in the real world.
2:37:26
world. And so, I was like, "My gosh, like where is where is the tech innovation like for these guys that are making making sure that our home stay heated and you know, just began to look more and more at this like how we understand the health of built the built
2:37:38
world and and also what kind of technology exists for these service of heroes that are you know, that are hidden behind the scenes if you will, whether they're a port engineer or they're a boiler they're boiler engineer and and folks that are just like helping us helping us do all this stuff. And so,
2:37:52
And so, Silicon Valley for the most part forgot about them and I decided to build a company that was specifically dedicated to helping these guys out. >> Yeah.
2:37:59
So, yeah, I'm imagining a big vertical tank sort of like what you might see at a brewery with but instead of filled with beer, it's water that's boiling.
2:38:09
Why not just fly a drone up and use video camera and and you know, just be close.
2:38:14
Why did you choose the what what decisions did you make technically and why did you make those decisions?
2:38:22
You basically if you're diagnosing the health of built structures, whether it's a boiler, or a pipeline, a bridge, you know, whatever it is inside of a ship, you got to actually get close to it just like you would for a sonogram.
2:38:29
So, you use jelly and then you use ultrasound to see inside of a belly for for the pregnancy example.
2:38:36
Same kind of idea, use ultrasonics as one way of gathering information and data sets.
2:38:40
In this case, you know, what you're seeing is some of the electromagnetics that we developed as sensors.
2:38:43
And the robots are just the vehicles by which we get sensors around places that are typically hard to reach and then also localizing and seeing like where you know, to track that year-to-year-to-year to understand how do you predict into the future.
2:38:53
I mean, this idea of creating the Minority Report you know, for the built world was kind of this idea of predicting you know, what death or catastrophe was going to happen for the built structures before it did.
2:39:02
And the precogs in this case are these robots, yeah, know, but but all the data is being collected and then like fed into the central source of truth, which is which is Cantilever and then we sell Cantilever, you know, as our way of predicting and preventing catastrophic failures for, you know, the built world.
2:39:16
Yeah, so what's the shape of the business?
2:39:18
So it feels incredibly dual use.
2:39:20
I mean, we just saw a video of the the robot crawling along what looked like an aircraft carrier or battleship.
2:39:26
But I imagine that the oil and gas industry, the industrials industry, like there's huge demand for this.
2:39:31
What's the mix of the business and what's what what what are the best practices for working with both the government and private businesses?
2:39:40
Yeah, we started in the energy sector in those power plants.
2:39:42
I actually bootstrapped for three years and then was down to 100 bucks to the name I know it was bad.
2:39:48
I was like homeless living with my best friend sleeping on the floors.
2:39:53
Really roughing it and you know, I was out in Pittsburgh, Pennsylvania.
2:39:54
You like there's no VCs out here.
2:39:56
And so then then before I got an acquisition offer and then you know, and then the folks over at YC in 2016 were just like you're going to build a huge slender company and like this is you're having incredible success. Come on and do that. That's all.
2:40:09
And so I was like I'm already poor.
2:40:10
I wanted this is an amazing business.
2:40:12
>> [laughter] >> So I already know what the worst feels like, you know, what I mean?
2:40:15
So it's like what could you what could be worse than this?
2:40:17
And so you know, just decided to do that.
2:40:19
Got one of the California and yeah, we're in this YC batch that's like really you know, just like black sheep of the batch, you know, working in energy and robotics space in Pittsburgh, first time founder.
2:40:28
So you know, what I mean?
2:40:30
It was just like it was kind of kind of wild and crazy.
2:40:31
Then we came out like one of the top companies.
2:40:33
But I mean, we started in the energy sector because that was what we were doing.
2:40:37
What was the actual first company?
2:40:39
Because like energy sector could be, you know, meeting with the CEO of Exxon or it could be like the local guy who just you know, wants to buy these as like a prosumer tool almost or something.
2:40:48
Like what was that actual deal like?
2:40:52
It was it was this group of power producers called IPPs.
2:40:54
So independent power producers.
2:40:56
So these guys were just like they actually don't have these like massive contracts utilities have that they can just like rest and just like pass all their losses down to me and you to pay more bills if our like if things just like blow up and don't work.
2:41:06
And so these these folks like actually have to be making sure that they make stuff.
2:41:09
And so in PA, there's a lot of them actually in the tri-state area.
2:41:13
And so we actually get a lot of access, you know, so the first three years I was bootstrapping, I was every single day at a power plant for the most part, like trying to to build the the robot like actually in the environment.
2:41:23
And that was a really core thesis and core principle for the company.
2:41:26
And we kind of still hold that today of build technology in the in the real world, not in the labs.
2:41:30
Actually one of the most prominent investors in Silicon Valley said, "Don't Don't leave build this in a lab, make it autonomous, and then launch."
2:41:36
I was like, "That's [Β __Β ] stupid.
2:41:38
That's a bad [Β __Β ] idea."
2:41:39
[laughter] And today he's like, you know, top three investor in the world, you know, saying like company's worth you know like you know 10 plus billion.
2:41:44
And I was like me and my co-founder >> shot it. Don't don't iterate.
2:41:46
Don't do don't don't >> Don't talk to your customers. Don't trial and error.
2:41:50
Just one shot it >> [clears throat] >> in the lab.
2:41:53
>> so So anyway, so power man, it was like we we I moved back to Pittsburgh because it was close to these customers.
2:41:57
And so that was just like the core the core ethos of the company was build tech, you know, for the people by it in the environment.
2:42:03
And so that's that led us to oil and gas the next.
2:42:06
We could apply the same kind of technology to diagnose the health of structures that make the oil and gas assets go.
2:42:11
And then it was mining and metal metal manufacturing.
2:42:15
And then I began to get into things like building and manufacturing for building ships or or or submarines.
2:42:23
Injecting the same kind of tech and robotics and software, you know, there because we were one of the we have the most data about the health of and the material science of the world.
2:42:31
And so applying it, you know, for actually building and welding.
2:42:33
And then then the navy side, you know, what we what we're doing there is helping to achieve readiness.
2:42:38
80% readiness was the Secretary Fallon's objective.
2:42:40
And you know, right now it's about, you know, two out of every five ships are are stuck in drydock somewhere.
2:42:46
And that's a global issue.
2:42:47
And so our technology allows for us to be able to make up that difference by getting as so much information, you know, in some cases two to you know, two, three, four months faster to be able to get these ships out of dry dock in time and and then begin to plan it for the future.
2:43:01
It's just like idea of you always had a living breathing understanding of the health of these these sorts of assets.
2:43:06
My goodness, like maybe you never have to shut down.
2:43:09
You know, that's that's the thing that I'm trying to build and you know, these You can you can see now like some pretty large energy companies like the ones that we work with are beginning to adopt this like you know, the data doesn't exist.
2:43:19
In order for the data to be able to drive AI models to actually be impactful, we actually have to go out and gather information and data sets.
2:43:26
And oh by the way, maybe one day, maybe not in the distant future, you can actually begin to augment these, you know, these very commoditized sectors and industries that are very capital intensive, you know, with robotics native AI native approaches and operating systems that make commoditized industries less and less commoditized.
2:43:43
And so, yeah, that's the kind of vision that we're trying to build, you know, at being the company that's, you know, that's very pragmatic in its results and, you know, aren't just promising in five or 10 years all this impact, but like actually delivering the thing today, you know.
2:43:53
Uh where are you uh are you I'm assuming the same kind of VC that told you to just one shot the product in the lab, make it autonomous, and then go to market would also ask you, are you doing anything in data centers?
2:44:06
Can you Can you get any Are there any tailwinds there?
2:44:09
I can imagine I can imagine one of your robots just crawling around a data center.
2:44:13
Uh but uh what what are you seeing on that front?
2:44:19
Yeah, well, it's it's a good question.
2:44:21
You know, what ended up happening was we ended up really putting a lot of effort and energy into customers where when things weren't working, it was extremely painful and expensive.
2:44:28
You know, so think of oil and gas when you're down for a day, it could be 30 million bucks you're losing or if you're down, you know, if your ship isn't patrolling, you know, the certain places in the Pacific or Atlantic, you know, that's really expensive and and harmful, too.
2:44:40
So, that that's where we focus, but what's happening now is like all this attention on AI infrastructure has actually put a lot of attention on how efficient can you run your your power plants in this case, a lot of natural gas.
2:44:51
And then, also, how reliable are those assets?
2:44:52
And also, the assets that were depreciated, not invested in for 10, 20, 30 years because banks wouldn't fund continuation of, um, you know, putting capital into a coal facilities or even natural gas facilities because of, you know, these these carbon these carbon, um, uh, strangleholds on these companies.
2:45:08
It ended up creating this really interesting opportunity for companies that are really good at understanding the health and the value of assets and can convert them into something, you know, really, uh, much more valuable, uh, something that we're taking a very close look at.
2:45:20
So, anyway, power and the ability to make a power plant run more efficiently and also more reliably, uh, is core competency of Gecko for a long time.
2:45:29
And so, we're putting, you know, a lot of efforts into that.
2:45:30
You'll have a big launch, you know, in the in the middle of, uh, for the 250th anniversary, uh, and, uh, coming out in July 4th on the power production side.
2:45:39
So, it's, you know, we've got something that's, uh, you know, if we pull off what our what our, you know, six or or or so, um, trials have have proved, we might be able to end the thermal fleet be able to increase by 15 to 20 gigawatts the amount of power in the US without even building a new a new power plant. >> Woah.
2:45:57
>> Um, it's it's this kind of efficiency, man, that's like is possible. >> [laughter] >> No. Yeah, it's all right.
2:46:06
>> [laughter] >> He said 30 to 50 gigawatts. I know. Let's hit the gong. Let's hit the gong. >> Let's freaking go.
2:46:11
[laughter] Where's the mallet? That's a big number. Hit it hard. Hit it hard. Oh my god.
2:46:16
I feel the vibrations over here.
2:46:18
[music] >> Yeah, we're feeling it.
2:46:20
I wish I wish you were here. >> All right.
2:46:22
For for a much dumber question, uh, how how do you actually stick a robot to the to a vertical surface?
2:46:28
I mean, Gecko, I imagine suckers, but are magnets involved?
2:46:32
Like, I imagine that there's some surfaces where a suction cup won't work because it's maybe it's like stuccoed or something.
2:46:38
Like, do you have multiple tools in your toolbox?
2:46:43
Like, talk me through that. Yeah, great question.
2:46:45
Well, I really was desiring to use nano fibers just like a gecko would use.
2:46:48
Unfortunately, we can't live up to the to the biology.
2:46:50
And so, we we actually use uh neodymium magnets.
2:46:54
>> get hundreds of get actual geckos on >> [laughter] >> A charity of geckos.
2:46:58
A charity of A biotech company, actually.
2:47:01
>> I actually that's a great idea.
2:47:01
That's a great So, you use So, you use uh rare earth magnets, is that right?
2:47:04
Yes, so we put them in a in a Hallbach array inside of wheels to optimize >> Okay.
2:47:09
>> pull force, so magnetic force into the Typically, like most of our infrastructure is >> Yeah.
2:47:13
is carbon steel, which is magnetic.
2:47:15
If it's not, if it's concrete or if it's um or if it's stainless, then we'll use the best adhesion is actually suction for non-ferrous materials. Like, you were right. >> Okay.
2:47:24
So, it's actually just like creating a really great um a really a really great um um >> vacuum, yeah.
2:47:28
And and uh and so, that can actually So, it's a little air pump that's sucking in air and and creating that like with >> Little Yeah, literally just a Dyson vacuum like, you know, head.
2:47:36
Um and and then you create like a a nice chamber, Yeah.
2:47:40
>> for uh you know, if you if you got to stick Unfortunately, it's like it's not great for dusty environments, but like, you know, you can >> So, so it's like it's less applicable, but also there's just less less cases.
2:47:49
Like, so for for the food and beverage space, a lot of stainless steel. Sure.
2:47:52
Um and so, there's there's not much dust there, either.
2:47:53
And so, you can actually apply force there. Okay, that makes sense.
2:47:56
But, it's the same kind of concept, too, which is like you don't want a crack.
2:47:59
You don't want a failure to occur.
2:48:00
And you don't want to damage the material you're inspecting with some crazy, you know, rock climbing shoe with spikes on it.
2:48:06
You're not going to be the ice climbing up the side of something that's going to Totally.
2:48:09
So, actually, funny a really a really like geeky cool like thing that we built Yeah.
2:48:14
for the Navy was you typically have like, you know, when you when when Maverick lands his like fighter jet onto the plane onto the onto the um the aircraft carrier, he's landing on this like um um this this coating, this non-skid is what it's called.
2:48:26
It's like really rough and grainy.
2:48:27
You have to actually remove all that stuff every like 3 years or so to be able to evaluate how healthy, you know, is the platform is the flight deck itself.
2:48:35
Cuz there might be a crack underneath that, and you got to take all that off. Got it. >> Totally.
2:48:39
And so, you just didn't even know like, you know, how healthy the structure is underneath it. Yeah.
2:48:42
>> So, we've developed technology that actually, you know, uses electromagnetics to excite the surface.
2:48:47
You can actually measure how healthy things are underneath the the non-skid. >> Sure.
2:48:52
You know, it's like things like this where you're like you just remove this like, Yeah.
2:48:54
you know, this this orthodox way of thinking about how do you figure out like what's wrong with the fix, how much budget to put to use, and where does the supply chain you know, how does the supply chain meet my need in terms of what what things I need to fix and get the ship, you know, back back patrolling and deterring conflict. >> Yeah.
2:49:09
It's like this kind of stuff that we we you know, I've just like built you know, such an expertise and now models around, you know, ensuring that we get all this information data set.
2:49:17
So, it really is like, you know, it's going from like a 3-4 month process to to now click a button and you know exactly like where to make all the repairs and and the next time you do it, you know, you can you spend a lot less time in in the in the in the dry dock and you should do the stuff more and more just like as it's as it's like you know, patrolling and on duty.
2:49:33
So, like robots, you know, specialized robots that can be like on these ships.
2:49:38
That's like that's the that's what we want to work towards.
2:49:41
In the in the basic example of like there's a boiler, Gecko robot climbs up, finds the crack, let's say, I don't know exactly what happens, but Corrosion corrosion cracking and >> Corrosion corrosion.
2:49:50
Are you thinking about an act two where you're actually doing repair as well?
2:49:54
Are you already doing repair or is that something where it's like, oh, that's a two that's a big technological challenge?
2:50:02
It's a great question and and it's something we're working on right now in the manufacturing side.
2:50:04
But, it I think the key was with the first idea was like what if you could own the health data of the world's most critical pieces of infrastructure? >> Yeah. That that'd be cool.
2:50:11
That seems like a pretty interesting, you know, thing to do.
2:50:14
And then, you know, then like you just begin to get into if you can understand the health of things, then you begin, you know, then like you can just pull in existing information time series data sets.
2:50:22
Like these are the kind of things we pull in now.
2:50:23
And so, you know, if you're a power if you're a power plant or you're a refinery and you want to you want to really capitalize on the fact that, you know, oil oil barrels per day is 100 bucks 100 bucks right now.
2:50:32
And so, you want to maybe increase production.
2:50:35
Well, you don't know if you can.
2:50:37
This is where like the consultant with software and AI companies really have a hard time is because you don't know if you push the asset, [clears throat] will it like break down and not work or explode.
2:50:45
So, that's like where the missing data set that never existed for that atoms to bits side of the robots allows for us to be able to make these operational changes now.
2:50:55
And which is a lot more valuable than actually fixing the problem right then and there.
2:50:58
So, we're going after the these big these big like value propositions.
2:51:00
And if you think about the ability to run functional and uh uh finite element analysis.
2:51:05
Like these like ANSYS type of like, you know, models.
2:51:09
Like these are the sorts of things that Gecko is working on because we have this unfair data advantage that I we've been collecting for 13 years on, you know, half a million assets now inside of Can Lever.
2:51:18
And And so, what we're getting to is >> success.
2:51:22
>> [laughter] >> So, what we're trying to get to, man, is like you're you're totally right.
2:51:24
And like where the where to go.
2:51:26
Like you want to find the problem, fix it right then and there using these tools.
2:51:29
And so, you want to build towards that, but you know, you want to be really smart on like the best techniques, the best kinds of approaches, and the ways to fix things, and solve the most valuable thing first, and you know, keep on solving more problems that your customers offer you. Awesome.
2:51:42
>> I think the future is really going to be it's going to be interesting because these like capital-intensive industries, you know, that can adopt technology like we're talking about here really really quickly are going to be so unfairly advantaged.
2:51:53
And this is like where we're we're like in those rooms creating the strategies, you know, for the top you know, top 10 top 15 like, you know, large oil and gas companies in the world and helping them with like how do we make and optimize this future because, you know, the future's going to really belong to folks that can figure out how to, you know, run this stuff unfairly. Yeah.
2:52:14
Well, the chat absolutely loves you. >> Yeah. So, come back soon.
2:52:16
This was really This was really fun.
2:52:18
Congrats Congrats to the whole team >> so much for taking the and I'm so glad you started this company 14 years ago.
2:52:25
>> [laughter] >> You're not coming on just pitching the pitching the concept, but you've actually done the the heavy lifting to figure this stuff out and and yeah the opportunity scale of it is just insane. Yeah, have a good day.
2:52:39
>> Thanks, appreciate it. Nice to see you brother. Thanks Jake. Thank you. Happy St. Patrick's Day. We'll talk to you soon.
2:52:44
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It's so funny because you for some people they would have to pay them three $3.
2:53:25
9 million to go to all 24 races cuz cuz it's not just if you're really doing this on a fixed schedule, it's like at least four days a week 20 24 times a year. >> Yeah.
2:53:37
And this feels like a full-time job.
2:53:40
>> Also There's some AI researchers out there that would just say like I can make >> Yeah.
2:53:44
So there's a wrinkle here.
2:53:44
They they're saying, hey, flat price for private jet to all the races.
2:53:49
And they're like, well, like even if you're based in Paris, like there's a race over there, or you know, you're based in Miami, there's a flight over there.
2:53:56
They haven't considered the fact that if I live off grid in Alaska, >> [laughter] >> it's going to cost them $10 million to fly the private jet to some remote landing strip that I've constructed outside of Nome, Alaska.
2:54:11
>> there is there like a SaaS company that could take advantage of this?
2:54:13
Basically being like we know we've got a bunch of buyers here.
2:54:17
We're going to take our top rep and we're going to have him jet-set around the world 24 strike missions.
2:54:24
Make it make it math out. >> Yes.
2:54:26
you're selling bigger deals, spend it in a conference room instead of doing demos the whole weekend. >> time. Demos and pads. >> hop back on the jet.
2:54:32
Somehow I think that if you price these flights individually, it would be cheaper. But it is very funny. Not by much.
2:54:42
You're talking about going It depends on the jet. >> Yeah.
2:54:45
I mean there's a lot of It's a lot of travel, a lot of miles, a lot of hours.
2:54:48
But $4 million is a lot of money.
2:54:51
>> [laughter] >> And I think it gets you a lot of flights.
2:54:52
>> this this 20 going to all 24 races if you're not a driver or working does not sound fun.
2:54:58
Also, they didn't They just said private jet travel between every race in the calendar.
2:55:03
They didn't tell you how many other people are on this private jet.
2:55:06
>> That's what I'm saying.
2:55:06
>> Imagine you hop on and then there's 17 stops while they pick up other people.
2:55:11
>> standing room only this this >> Yeah, it's kind of Southwest vibe. Yeah. >> Yeah, yeah, yeah. >> Anything can happen. Anything can happen.
2:55:16
But we'll let you decide whether or not you want to spend $3.
2:55:20
9 million to fly private to every F1 race on the calendar. If you do, let us know. Give us a review. Send us a message.
2:55:27
Subscribe to our newsletter at tbpm. com.
2:55:28
Then email us when we email you the newsletter and tell us how the private jet experience was, the ultimate experience.
2:55:34
Uh last but not least, the new Roadster is apparently going to be unveiled next month. Hopefully. Hopefully.
2:55:41
If Elon puts a hopefully in a tweet, I'm going to >> He said it will be It will be a banger next level. I'm very excited.
2:55:51
And Travis Kalanick said, "When I've run into people who are in the know I inquire. They tell me nothing.
2:55:55
But their eyebrows raise and their eyes widen in a way that can only mean something of sorcery and magic is coming.
2:56:00
Hopefully it's flying car.
2:56:04
And I hope that it it it is revealed in April.
2:56:06
He should He should unveil it April 1st.
2:56:08
Everyone would be so confused. It'd be very funny.
2:56:11
If not, I can wait till May. I can wait till May.
2:56:13
But I'm I'm very I'm very excited for the next Roadster.
2:56:19
Expectations are incredibly high.
2:56:19
There have been so many electric supercars.
2:56:21
A lot of depreciation, not a lot that have filled that like special territory.
2:56:25
Even the electric sports cars have not done well.
2:56:30
The Boxster, the >> sense is like it's not going to be like a true supercar.
2:56:33
It's going to be a Turbo S, the kind of thing that people are going to just daily and it's super fast. It's fun.
2:56:41
>> But it probably I mean I would expect that >> you keep like with low miles cuz you expect it to appreciate.
2:56:46
I I I would basically expect like a Rimac Nevera like it which is like a $2 million car but at like a $200,000 price point.
2:56:55
Everyone's like it goes zero to 60 in 1.
2:56:57
6 seconds or something like that.
2:56:57
Like the it's going to have some headline stat that everyone debates and is like oh well technically it wasn't this blah blah blah blah blah.
2:57:05
But it will be like shocking in its own way and I'm sure people will have a lot of fun with it.
2:57:10
So excited to track that story.
2:57:10
Well, thank you for tuning in to TBPN today.
2:57:15
We will see you tomorrow. >> Go have the best day.
2:57:16
Maybe you have a >> stars on Apple podcast and Spotify. >> an honor.
2:57:21
Can you throw the flash bang? Throwing flash bang. We'll see you tomorrow. >> Goodbye.