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5:00
>> Today is Thursday, September 4th, 2025. We are live from AIPCON.
5:02
It's volunteers conference.
5:06
It's the what do we call it? the office of ontology. >> That's right.
5:10
>> The tent of tactical strategies.
5:10
Many people have been saying this.
5:12
Uh we have a great show for you today, folks. We're interviewing Dr.
5:16
Karp in just a few minutes.
5:18
Uh we're interviewing a ton of folks from Palunteer.
5:20
Ton of customers from Palunteer.
5:21
Uh some founders, some folks who work at companies that use Palunteer.
5:26
Should be an interesting day.
5:28
But first, there is massive news because the browser company of New York has been acquired by Atlassian.
5:34
>> This morning, yeah, I was headed to the airport.
5:36
Y >> I got a push notification from the browser company Substack and I opened it >> and I saw that they were getting acquired from their own announcement and I opened X and nothing had been shared.
5:47
>> That's actually >> I kept I kept scrolling >> randomly substack.
5:51
>> I mean they have actually a cool thing.
5:52
It's their username is open. substack.
5:52
So So the URL is just open substack.
5:58
>> Okay, which is interesting.
5:59
>> Um so I opened it up and I'm like oh browser company's getting acquired for 600 million.
6:03
Yeah, >> posted it a few minutes later.
6:04
I think people kind of woke up to it. They announced it.
6:08
So, sorry to frontun them, but Josh Miller shares, "The browser company just signed a merger agreement to be acquired.
6:13
We will remain independent. Our focus is DIA.
6:15
I've written and rewritten this post more times than I'd like to admit.
6:19
But what I keep coming back to is simple.
6:21
The work continues and we're grateful for this moment.
6:24
The work continues because when I stop by the coffee shop near our office, nobody is using DIA yet." Uh, very humble.
6:31
Our internet computer vision hasn't been realized.
6:33
DIA has hasn't yet changed how you work on a Tuesday morning.
6:37
This deal is about giving us the resources, distribution, and monetization muscle to get there.
6:40
At the same time, it feels disingenuous not to pause and briefly celebrate this milestone.
6:46
It reflects our team's craft craftsmanship and relentlessness, the support of our coaches, board members, and advisers, and the incredible effort from our deal team.
6:54
Most of all, we're grateful for what this means for DIA.
6:56
It means we can hire faster, ship faster, and bring DIA to more people.
7:00
We can now invest in cross-platform support and secure syncing, train custom AI models designed specifically for DIA.
7:09
>> Uh we could uh we could see the uh the company from down under getting into the foundation model game.
7:13
I guess >> the weird thing about this is that Atlassian already has they have a rovo I think it's called or something like that.
7:21
Like they they they they haven't been asleep at the wheel in terms of AI.
7:24
They definitely have been adding AI features.
7:26
>> You were reading from the last earnings call, right? >> Yeah.
7:28
I mean the last earnings call Atlassian Atlassian is just a fantastic company.
7:32
Five billion in revenue, 82% margins, 1.
7:34
5 billion in free cash flow, 1.
7:36
4 billion in free cash flow.
7:36
I'm so glad we brought the soundboard. We're back.
7:41
>> Uh and so and it just doesn't strike me as the like their last the last few acquisitions that they've done like Loom uh just makes so much sense in the context of the rest of the uh product suite that they have.
7:52
You know, they have Trello, they have uh Hip Camp, which never really beat Slack.
7:56
which never really beat Slack. tickets or they have Jira which named after the >> named after the poster the poster >> Jira tickets um >> and so all of that kind of makes sense is like a bundle you sell into one in the enterprise and then once people are tracking issues with Jira you sell them
8:11
on okay let's do your project tracking let's do your looms let's do a whole bunch of other things and then the DIA browser >> sure it could be a useful beneficiary for like if you're in an enterprise context maybe you want to track some stuff but it's At last makes a lot of Atlassian makes a lot of tools that live in your browser. >> Yeah. So, >> Yeah.
8:32
So, >> but they all run really fine in the browser.
8:33
So, I I think people are puzzled by this generally >> and and and I think the timeline is generally like you saw the Will Depw post uh like there are definitely people that are against this >> and are saying that like >> well the vibe the vibes had turned on the browser company massively over the last call it 6 to 12 months >> purely because of the valuation relative to the monetization and the and like the the the progress of the business. million years.
8:57
I >> think they had they had incredible incredible marketing, incredible sort of like messaging, >> coms.
9:03
>> The videos are incredible.
9:03
Like I I watched their announcement video and like the little details of the lens flares and they created taste. >> It's very tasteful. Um but I mean we demoed. >> So it's cool.
9:14
I mean what what I like to see is one it it's a real acquisition.
9:18
They like cleared the prep, you know, massively.
9:21
So the team uh the whole team's getting paid.
9:22
There was some there was some uncertainty about how much they'd raised, but it was uh somewhere between like 50 million or 75 million and 125 million.
9:29
Like it was definitely not 300 million. Yeah.
9:33
>> And and they and they and at 620 in cash like everyone's getting paid out, which is great.
9:39
>> So, um so yeah, I think uh >> and put another way, it's only six months of Atlassian's free cash flow. >> Yeah.
9:45
>> Yeah. Yeah, >> which is like it feels like a lot, but at the same time it's like okay like half a year of free cash to take a big bet on consumer in an interesting way in a market that >> I am curious to see how they
10:00
>> focus in the product on consumers versus enterprise like is an enterprise software >> conglomerate right so you would imagine >> that they would take the product in that direction >> um and I do think there's a lot space to play in there, right? It's like bringing
10:16
It's like bringing AI into the browser where Yeah.
10:20
>> people do all of their work. >> Yeah.
10:21
What's the steelman for this actually benefiting the Atlassian enterprise suite?
10:25
Something like >> So, here's So, there there's a post here from Verdane.
10:30
He said, "Ala, Atlassian bought Vibes, not a browser.
10:32
Never ask the best art collectors how they made their money or why they bought the art.
10:36
At a $610 million purchase rhymes with that, the Atlassian problem.
10:41
They invented bottoms up SAS.
10:43
Anyone could sign up for Jira. No procurement needed.
10:45
They were the cool tool of 2010.
10:46
But success forced them up market.
10:49
Enterprise features, enterprise pricing, enterprise vibes.
10:53
Today, when founders start companies, they choose Slack, not Hip Chat, linear, not Jira. Notion, not Confluence.
10:57
Uh, #team has near zero inroads with the next generation.
11:03
Their Microsoft circa 2014.
11:06
Rich but irrelevant to anyone building something new.
11:08
Um, why the browser company and Loom?
11:11
These aren't product acquisition.
11:12
their guest list acquisitions.
11:14
Every founder using Arc, every startup using Loom, that's At Lassian buying access to users they lost and might never get back.
11:21
It's building a gallery in Brooklyn so you could get invited to the right dinners in Manhattan.
11:27
>> I just I I understand the Loom acquisition so much more because Loom is an enterprise tool. It's used by startups.
11:33
It's used in a business context.
11:34
Sure, it's probably used by some consumers, but >> it just feels like the the price feels it feels extremely steep given >> like Loom had product market fit. Yeah.
11:45
>> It's just that it wasn't necessarily going to turn into this massive platform and compound, >> but it's growing like crazy actually from within Atlassian.
11:52
They called that out on the earnings and so like I think that the >> but it felt like a standalone it felt like a standalone product not a platform that fit nicely into at last.
12:00
Completely agree with that.
12:02
Whereas paying 610 million for a company that um that people use but not a lot of people, >> it's a million DAUs apparently, something like that. >> I don't know.
12:12
I think I I thought that number was total.
12:15
>> Maybe some I thought that was like total signups. >> Yeah, but it's small. It's small.
12:19
>> Um yeah, >> and nobody think people would adopt Loom and start embedding it in their work life in a way that they would be upset if they no longer had access to it.
12:29
I I'm not sure that DIA is quite at that level yet.
12:34
>> So, one bullc case I can think is something like this where you bring in this team that clearly has taste, great design, and they kind of give the rest of the Atlassian product suite like a fresh coat of paint and and they kind of revitalize the box.
12:46
>> The message the messaging here is that they're going to continue operate independently and scaling the DIA team. >> Yeah.
12:51
>> Yeah. that could just be something that they do for a little bit and then eventually they get interested in hey let's bring the team over and work on Jira and work on a v2 of of uh you know loom or something like that like that that's a possibility and then the other
13:04
the other kind of maybe bull case which I'm a lot less clear on is is there a world where if you have everyone in your organization using an enterprise AI powered browser even if they're not on the full Atlassian stack let's say they use two products and Then they're instead of using Hip Chat, they're using Slack. Can you scrape more easily the
13:23
Can you scrape more easily the data out of the other enterprise products and centralize them somehow?
13:31
Because I bet you if you're a company that's using Jira and Slack, those two companies don't get along because it's Salesforce versus Atlassian.
13:39
But maybe if I if I'm if I'm like >> they're kind of forced to get along to some degree, >> but the the integration is probably really rough.
13:46
You've heard about the data walls and the data the the data wars.
13:50
And so if you say, "Hey, instead of trying to, you know, set up some API and and scraping out your Slack data and dumping it into your Jira instance every day, instead of that, have everyone on your team use this enterprise browser and no matter what tool they use, the data is >> centralized."
14:10
So let's go over to Mike Cannon Brooks, uh, the founder of Atlassian.
14:14
He says, "Couldn't be more psyched to welcome Josh and Hirs and the entire browser company team to Atlassian.
14:20
With DIA browser, we're going to collectively redesign the browser to help knowledge workers kick butt in the AI era.
14:26
It's a mission, a joint mission, a huge mission, and one I couldn't be more excited about joining with this team to get cracking on. Let's go."
14:33
Um, so yeah, this just tells me, I mean, the the most important line here, collectively redesign the browser to help knowledge workers in the AI era. Yeah.
14:44
Uh the last option is that it's just uh it just buys them time to kind of take some more shots on consumer AI, which is clearly a growing category.
14:50
And there's and and Atlassian can underwrite like crazy opportunity more than VCs can. Anyway, we have Dr. Karp. Welcome to the stream. How are you doing? >> Great to meet you. I'm John.
15:06
>> Uh we're going to have you hold this microphone. >> Great. >> Where's the camera?
15:08
The camera's right there.
15:10
You can just see it wherever you want.
15:11
Um what is the big announcement from today?
15:13
is are are you uh uh are you trying to tell more of a story around uh enterprise with this?
15:21
>> Um you know, we're kind of not we're I I think we're just it's more like >> we're crushing it. >> Yeah.
15:31
>> Uh uh everyone tells us to be super modest about 93% growth in the US and >> 94 rule of four of 40.
15:37
They they may be redefining the rule to like make sure the other people don't like have to live in shame.
15:46
>> I keep seeing these articles like in the Wall Street Journal.
15:48
It's like rule of 40 isn't real. It isn't real.
15:49
Yeah, it isn't real cuz we're like >> crushing everyone.
15:54
>> You were forced to be humble for a really long time.
15:56
>> I was forced Well, people were showering me with humble nuggets all day.
15:59
It didn't It didn't really exactly work, but you know, I I do think you have to judge humility by the delta between performance and ego.
16:07
And I would say somewhat ill modestly I'm the most humble I've ever been.
16:11
And uh uh and uh and uh and and and now and I just I think it's like so what we try to accomplish with uh the we've been doing these kind of conferences forever basically because everything we've done at Palunteers like >> completely uh it it's anothetical or at least orthogonal to what you would how you would build a business.
16:35
you guys looks look at a lot of businesses.
16:36
You would never build a software downstream from value creation.
16:40
It's all basically how do I make the client feel like they're getting laid when they're getting That's the whole way you build a software business.
16:47
In our business, we began in the beginning.
16:48
I used to tell people, you know, this is a we're a mutually >> uh servicing business.
16:53
Both sides should like be happy.
16:55
And uh and the way we built the business was it basically underlying metric I always thought was you know the logic of software should be we charge you something downstream of of of value creation that sum is a percentage of the value you create.
17:09
It's better for both sides because it's uh it's it's it's significantly less than the value you create.
17:15
It's good for us because there's a multiple on the value.
17:18
The flaw in the logic was always that FDE model would basically uh mean that you'd get a one multiple.
17:23
So we were structurally misaligned with everyone in finance, everyone >> not at the founders fund but basically everybody else because of that.
17:30
Now what we've proven withtology uh FDE structures where FD are actually technical and internal orchestration which is largely artistic basically was now we got very lucky because without large language models this would not be hypercharged.
17:45
So it still didn't exactly make sense but lo and behold we have large language models it hypercharges everything.
17:50
So downstream value creation is an enormous amount of money. Yeah.
17:54
And because of our unit economics now which are you know and some people believe are the best in the world we actually get fairly valued.
18:01
valued. What are we doing actually downstairs is we're saying America's central advantage is the plasticity of how we approach the pragmatism right so businesses have to move from businesses where it made sense to have parasitic software products that are like basically helping you set it's like one
18:20
of these things it's like you believe you're learning to sell they're selling you on something that is you can't get rid of you then run to Wall Street and say our clients all we have 50,000 clients that all hate us they're like great that's a software business cuz the hating means they can't get rid of you. >> But a but a platform business means that
18:33
>> But a but a platform business means that you're creating more value than you capture.
18:37
>> Well, the way we do the way we sell is like and this is why it's just all is like all these things are hugely contrary.
18:43
We our revenue is going up, our sales is going down.
18:44
The number of people we plan to have in the future is less than now.
18:48
We are very focused on you know everybody's like high volume.
18:53
Uh the volume makes up for you know the fact that revenue decreases per client.
18:56
We're not focused on that at all.
18:56
We believe we're going to make more from people in the future than in the past, sizably more because it's like why should we not capture part of the value that we helped create?
19:04
Actually, it doesn't have to be the majority.
19:06
In fact, it's usually the minority of the value you create.
19:08
We also believe that if from more kind of like kind of architectural implementation technical perspective, the value is in high fidelity data captured in in an ontology with FDES and where there's a an enhancing factor with LLMs and that that's going to be very very hard to replicate.
19:27
Um but um but but again all of this is kind of very non-traditional.
19:36
And so what we're really doing in these conferences is saying the same thing we say on the outside.
19:39
Don't believe anything we're saying.
19:42
>> Talk to other people have done it.
19:45
>> We're not we don't chaperoon the people here.
19:47
So there like you can talk about things you like, things you don't like. People are on stage.
19:50
But learn how to build the business of the future.
19:53
What is the business of the future look like?
19:56
Actually the interesting thing is workers become more valuable.
19:58
Like actually trained workers become more valuable.
20:01
This is exactly the opposite of what people are saying but it's true.
20:05
the person at the top is actually crazy valuable.
20:09
People with technical expertise are crazy valuable and everything else is going to be done in foundry ontology and something like an FDA.
20:17
So like the orchestration of the business is completely different. >> Yeah.
20:21
Where are Fortune 500 companies getting screwed by these AI pilots?
20:23
We saw this stat like 95% of AI trials in the enterprise aren't converting. Like what's going on?
20:30
What does it look like when somebody sells someone?
20:32
Well, I mean there's a technical reason these are LLMs are probabilistic. They're not precise.
20:39
>> The the value of LM is when it's essentially in an ontology wrapper because to to to actually create value, you have to be able to take the output, serialize it and deserialize it in the context of the business.
20:51
So the logic, actions and security of the business and its tribal knowledge and what it's trying to accomplish.
20:56
LLMs are vertically crucial, but the but but the error bound is very very very narrow.
21:04
And the way you actually do LLMs in the real world, not in theory, not is like is that you essentially put them in a concatenated chain where each single thing has to be done as a street unit because otherwise the underlying math is 95 times 100 separate chains.
21:17
It's like totally unreliable.
21:19
And if you do it any other way, you're getting a steak dinner >> and you're that steak dinner is super tasty. It's not going to work.
21:26
And even worse than the steak dinner, honestly, is that you're being taught how to do something incorrectly.
21:33
It's like, it's like, okay, I'm gonna learn how to learn from a wokester. >> Yep. >> Great. Great.
21:39
The damage that woke is doing mostly on the left, but occasionally on the right.
21:42
The real damage they're doing is they're teaching you how not to learn.
21:47
>> Like, and if you just pick your favorite person, right, left, center, who's just selling complete garbage.
21:52
It's all conspiracy the whole thing. Yeah.
21:53
It's like uh it's like it's like there's no such thing as building.
21:58
There's no such thing as agency.
21:59
You can get away with FPS.
22:01
Well, if you want to like Palanteer's lifted, one of the things I'm proudest about in the world is we've lifted people from their mom's garage to their own house. Millions of people.
22:09
You want to stay in that garage, you listen to those people.
22:13
And it's the same thing happens in enterprise.
22:14
They're selling you something where you think you're getting laid and you're getting And that once you're like that, it's very hard to undo it. And like Yeah.
22:24
You know that the crazy thing about my life is I'm like this wacky dyslexic.
22:27
It's actually much harder to be dyslexic, but it's also much harder to get because you don't believe you you don't but you don't believe in any of this BS.
22:33
It's like >> Well, so speaking speaking of sales, there was a the CEO founder CEO of a of a CRM company that was making some comments yesterday.
22:41
Did you did you catch >> I look Palanteer, we structurally mind our own business and I love that everyone minds our business.
22:47
But I would say the what I we constantly have people on TV.
22:52
It always sounds like, you know, the guy in high school who's like, "But I'm so nice. Why don't I get laid?"
22:57
It's like, it's literally like, it's the same. I'm so nice. I'm so nice.
23:02
I create all the value and I I'm so nice.
23:04
I'm begging to get laid and no one.
23:06
It's like, I have such a big this, I have such a big that.
23:09
And we're like, yeah, we're not trying, dude. We're here, you know?
23:13
And yeah, >> I don't think about you at all.
23:14
>> I don't think about you at all. And well I it it it's like >> we are very focused on value creation and we ask to be modestly compensated for that value and you know if you disagree like you don't like us as a client or you love us as a client but
23:29
you think it's like great we're doing our thing you know in Palunteer right now in the US is the market account that counts we don't have the people we don't have the time we orchestrating completely perfectly at Palanteer which of course we don't do as we're like an artist colony, right? We don't have a
23:44
We don't have a time to like actually focus on like what we need to like extending certain components of ontology we have to do um extending Maven for the sake of the west um building things in classified environments.
23:59
uh extending things with high value.
24:02
It's like, yeah, we're focused on that and we don't have the time.
24:06
Like when you're growing 93% off of a very serious base with a de facto dimminimous Yeah. Yeah. It's the 93.
24:12
And that's not even our best number. It's 94% rule of 40.
24:19
It's like, and then people then people are like, "Oh, yeah. Yeah.
24:24
Well, but we have all the skills.
24:24
We have all the motion but but like somehow our ocean isn't working.
24:28
It's so big but it's not. It's like yeah great.
24:30
You have problems to you have time to focus on us.
24:33
We got things to focus on here that are crucial >> and you guys are re it feels like you're reacting to uh the changing world and actual like customer needs whereas other players are reacting to >> let me give you let me give you a more kind of slightly philosophical economic thing.
24:48
the what the large language model does it models do in combination with ontology and FTEES and and knowing what you're doing is it creates period optimality over time.
24:57
We're not there exactly but every single tech company in the world is going to be paid based on value creation.
25:04
Maybe that's not completely true today.
25:06
It will be true tomorrow.
25:08
tomorrow. So when any company is saying something you really have to ask given that the the aspiration of LLMs are transparency and uh and competence it broadly defined they've actually the big cultural shift on enterprises people
25:24
running enterprises believe that this thing should work I should know the cost of the components in my business to the second I should know how to rebuild things if there's a macroeconomic I should be able to put the bomb on your head and not on his head. Okay. So, uh Okay.
25:36
So, uh that basically means every conversation in the future is going to be I you create X value, I'm going to pay you Y.
25:46
And the central problem a lot of the larger kind of less a agile scotic companies have is it's like they can't you it's very hard to move from I get paid because you can't get rid of me to I get paid because you could get rid of me but you don't want to because you're creating so much value.
26:03
But that's where the future's going.
26:05
And like people talk about like you know how are we going to you know get do 10x in revenue blah blah blah with the same or less people.
26:11
It's like yes but the whole market's going to have to move to value creation and we're in the business of that and try to do it you know it's not >> yeah do you think long term that the gross margins of software companies will change materially because of like LLM inference costs like token factory costs that type of thing.
26:30
>> Well you mean like enterprise software companies?
26:31
If I if I look at like the Fortune 500 right now there's like a set number of gross margin that's out there.
26:37
Uh should we expect like gross margin compression based on >> well I basically >> uh what well first of all I think I let me just give you the trends.
26:46
I think first of all skilled workers are going to become more valuable.
26:49
You're going to be paying them more.
26:50
They're going to be happier.
26:51
Uh it's exact um downstream politically it's very hard to argue for anything but high-end immigration.
26:56
So like why do you need more people?
26:58
like we got to make the people we have here work. Yeah.
27:02
So like politically it's like >> like you know I'm an unhappy Democrat but running around saying oh crime isn't an issue when everyone knows crime is an issue is like it's like suicidal BS and no one believes it.
27:13
And now that wokeism is luckily mostly at least in that way you know not as punishing we can all just admit the obvious.
27:19
So like transparency is going to be like so the people are like workers are going to become more expensive the overhead's going to become less truly basically artistshaped people are going to be incredibly valuable and they're going to demand to be very highly paid.
27:32
So but the aggregate cost structure will come down but more importantly the products you build are going to be much closer to what the market wants in real time.
27:40
And then again just an obvious thing >> this is happening like we have 10x growth in America compared to Europe.
27:47
Same people same products same everything.
27:49
So it's like and then I the other thing I the point that's a little less obvious that I think people ignore is time is not time.
27:53
We always assume a minute of time is a minute of time.
27:56
It's not like it's like from the time you want to do something to the time it happens.
28:01
If that's 10% of the time you've just gra you just got a 10x.
28:03
So it's like you know it's like pounder is not these kind of atrophied companies.
28:09
They really every it takes them three years 5 years to get a year.
28:12
It takes us a week to get a year.
28:14
a week to get a year. So it's like you know it's like that that's actually what what explains the numbers in a weird way is yes but what if five years represents 40 years what if I'm saying in the next five years it's not we're actually it's like the whole problem with the DCF model actually that experts love is a
28:30
they don't understand product and then b they kind of extend the DCF if they like you so it's like oh I like the person the DCF is super like give them an extra decade of steak dinners but but the real problem that they somehow don't understand in the DCF mount is a year is not a year for pounder like a year is like we don't do holidays. I'm working
28:46
I'm working all the time.
28:47
I'm orchestrating honestly I I I sometimes hate the enemies of Palunteer, but god do they get me to go back to orchestration because I'm like I'm gonna these people and like like you know and and the basic way I'm going to do it is go, you know, going back to like dyslexic, you know, like organization orchestration of we're going to have the best products, the best people.
29:04
I'm going to recruit those people.
29:06
I'm going to make sure they're the most valuable and I'm going to put them in enterprises that value us.
29:09
And if you don't value us, go go work the go work with the people that hate us. Try them out. >> Yeah.
29:15
Do you have a advice for young people?
29:16
I mean, you said like artists like people, not literally artist.
29:20
>> You said you said the company's like an artist's colony.
29:22
What did you just become an artist if you're young?
29:25
>> Well, you people underestimate like their artistry because like from a young age >> you get huge benefits for conforming.
29:33
And you can say, well, I don't I mean the central advantage of being dyslexic, we can't conform.
29:36
So that was that ends up being a huge because you just can't.
29:39
So you're going to have to So your basic thing you have to emerge do not conform.
29:42
And by the way, the people who are telling you simplistic that means, you know, like meritocracy isn't going to matter.
29:49
You're not going to judge all these conspiracies, it's you can't do wealth accum accumulation if you're in this country. Yeah.
29:54
Like in America that I think actually a lot of these things are true in other country.
29:59
But in this country, they're teaching you how not to learn, how to be complacent, how to give up your agency, how to fail, and how to blame it on anyone else.
30:06
And if you're So you have to say like all that. Yeah. Reject that.
30:10
That's kind of and then you have to really really look at people and judge them by their fruits.
30:14
The best way to learn is to look at somebody and say okay well you know it's like you know you work with somebody like the co-founding team at Palunteer.
30:21
So you have Peter, Joe, Stefan, Nathan like part of what made us so good is it's like okay you can measure yourself.
30:28
It's like you know >> when I started at Palunteer I actually just because I just wanted to be left alone.
30:34
I was like yeah I'm going to make some money.
30:36
I'm going to move to Berlin.
30:37
I'm going to live a debaucherous life. That was my goal.
30:39
Like I'm moving to Berlin.
30:41
I I thought I needed 250k.
30:41
I was like at 250k is a minimum.
30:44
A million dollars a maximum. I'm moving to Berlin.
30:47
I'm going to like debauchery forever. >> Burger and Yeah. Uh yeah.
30:49
Well, I had to like Yeah.
30:51
So it's uh and then set up a remote office.
30:55
But like you then measure yourself and it's like okay well I'm highly differentiated on measure on on managing complicated people who have to believe their opinion is their opinion but still have to build a product that actually delivers value.
31:10
>> That's my differentiation.
31:10
And so like you you you surround yourself and then remember you have to remember the >> persuasion pers being persuasive and being right are not correlated.
31:19
being right are not correlated. So you have to really look at people who are historically right rebuttably give them the rebuttable presumption that they are right and work back to discover if they're right or wrong not just and like and all these things and like for
31:34
example on the pounder thing is a great lesson >> go listen to our critics whatever critic you love we're a conspiracy theory so like you could take the leftwing version which is like Palanteer is stripping you of your civil liberties which some people on the right believe Palanteer is a Jewish conspiracy run by a a a mut somehow. Okay, whatever. You know, it's Okay, whatever.
31:52
You know, it's like, okay, well, go actually, how does the product work?
31:56
Does the product protect data? How does it protect it?
31:58
Is it better than any other company in the world at doing this?
32:02
How do you build a company?
32:04
Do you think it's just like an allocation based on a conspiracy? Why did we work? >> Yeah.
32:08
Just pick your conspiracy and that's the strategy. >> Yeah.
32:10
And then and then but then unpack it and learn for yourself like did this work? How did this work? How did they do it?
32:16
Assume that at every single decision, if it was a decision anyone else would have made, you would not have worked because that's a commodity.
32:22
Commodities aren't valuable.
32:22
And then apply that to your life.
32:24
What part of this do you understand?
32:26
Like, you know, what part do you not understand?
32:28
What part do you understand better than them?
32:31
What part could you do better than them?
32:32
And the weird thing about LLM ontology foundry is this actually will work for anyone watching this podcast. Yeah.
32:41
If you're watching this podcast and you enjoy this, you've already passed the test.
32:47
I don't care whether you're a welder, a plumber, a carpenter, an astrophysicist, or a somebody who'd like to build a business or just want to get rich or you want to get enough money and move somewhere and do what I want to.
33:00
It's not the right place anymore.
33:00
But any case, uh but but you've already passed that test.
33:03
Now go out and pass the test for life. >> Yeah.
33:06
Uh you said Germany is not the right place anymore.
33:08
Like what is your current mental model for the state of the world order?
33:11
Like is is is America in decline?
33:14
Are we do we need to bring things back?
33:16
Like who are the power players?
33:17
How >> America is power payer number one right now.
33:20
And like all this media BS.
33:20
It's like you know you got to compare America to and you can't compare America to some thing you're pretending in your head could be America. Compare it to Europe. >> Yeah.
33:32
>> Compare I don't know what you want to compare it to China.
33:33
Like you want to have no rights, you know?
33:34
I mean again I'm actually not anti-Chinese culture but CCP, you know.
33:38
It's like compare it to Europe like no tech industry. >> Yeah.
33:43
>> Everyone rich was born rich basically or with almost no exceptions.
33:45
The most important Germanic company I hope someone from Germany's listening to this uh compt aloto is petal unish like the only German company since SAP that's real like and they won't listen to us.
34:01
Like just think about that.
34:01
You have Peter Teal, like the most important venture person maybe that's ever lived, co-founder of Palunteer, and you have meas like somewhat, you know, basic partially dramatics, did my PhD in German, and you have no tech industry.
34:16
Wouldn't you have us on speed dial? >> Yeah. >> Yeah.
34:18
>> I mean, like on speed dial, like you don't have to listen to what we're saying.
34:22
>> You don't have to agree with what we're saying. Who are you talking to? Who are you talking to?
34:25
You're talking to your like, I don't know, >> expert that came here and studied us. Trust the experts. >> Trust the experts. It's like so it's Yeah.
34:34
It's like energy like we're >> Do you think they will?
34:37
Do you think that there's there's optimism around the idea of >> still pick up the phone call? Right. >> Oh, no. No.
34:41
I just No, I pick I mean I pick up it's crazy who calls me.
34:43
It's like it's honestly like I I I can't talk out of school calls me.
34:47
You'd be surprised the number people come and I begin every call with >> don't listen to me. Very few people have.
34:53
>> I'm going to give you the freak shoe answer.
34:54
You probably want to ignore it. This is what I think.
34:56
And they're like huh okay. Yeah. Yeah. Okay.
34:57
Some call back, some don't.
34:59
But um yeah, of course I would I mean I have a lot of I mean like honestly we have a huge retail crazy thing about Germany is a huge retail investor base.
35:06
They don't admit it in public, but in private they're like, "Keep going. Keep going." But uh but uh yeah.
35:12
No, I'm just saying the point I'm saying is uh you know, it's like uh Oh, so then it's like energy, technical talent, understanding how to manage the technical talent. That's an art.
35:24
Like we have the right venture people, the right entrepreneurs, the right spirit.
35:27
We have generations of people who are entrepreneurial here.
35:31
It's like >> kind of tall poppy syndrome.
35:34
>> Well, it's funny you mentioned that. That's a like Yeah.
35:35
That's a like Yeah. like you we we're very well this is the thing we have to fight for this >> because that no tall pop what that basically means in every people may not realize this but in any every other culture I know of and like and I lived abroad in Germany Europe incredible
35:49
cultures but if you you your head sticks above the line it gets cut off y >> there's one culture where that doesn't happen is here the only thing is we have to fight for that because the thing that unifies the woke left and the woke right is they don't like the consequences of meritocracy they want to work back to the inputs. So, and that that like just
36:06
So, and that that like just will screw society.
36:07
It's like you've got to be able to allow people to succeed wherever they go.
36:12
Now, I I was kind of still progressive even though I believe it.
36:15
I super would like the inputs to be fair, but the outputs, those are the outputs, my friends of freedom. >> Okay. Last question.
36:20
We got to get you out of here.
36:22
Um I walked by your office.
36:25
There were some kettle bells.
36:25
What What are the kettle bells for? >> Oh, okay.
36:28
Well, this is slightly long.
36:30
I'll give you a short version.
36:30
So, to be a cross-country skier, you've got to train year round.
36:34
So you need substantial V2 max and actually uh you need to be strong per unit of weight.
36:39
So as an example, I do um uh three days a week of um kind of above and below lactate threshold uh running but mostly pretty far and then once a week kind of at >> and then I do uh two days of strength, one day of like um endurance strength. Mhm.
36:59
>> And currently the thing I'm actually really proud of is I I just started doing hang from a bar as a dead hang like 4 months ago and I I hit 4 minutes and 36 minutes.
37:08
>> 4 minutes and 36 seconds.
37:08
What's the goal for the end of the year? What do we do?
37:12
>> Well, actually my goal for the Yeah, you got hit. This isn't just money.
37:14
This is a No, I mean my goal for the year uh was uh for actually the next 12 months was um was 4 minutes. Okay.
37:26
>> But then there's the number You got those numbers out. >> Yeah. Yeah.
37:28
Well, no, but the number two, the second best uh um mountain climber in Norway, I don't know if we know his name.
37:35
Uh but he I have a picture.
37:36
He did 4 minutes and 22 seconds. There you go. >> What can I do? This is fantastic.
37:45
>> Thank you for having us. Bye. >> Appreciate your work.
37:47
>> We'll talk to you soon.
37:47
Have a great rest of your day. Congrats. >> Yeah, you too. Congrats to you guys. >> Thank you. Thank you.
37:51
Um we will bring in our next guest in um just a few minutes.
37:56
We have >> Can you imagine Can you imagine the the Fortune 500 CEOs that just want a meeting with with Dr.
38:01
Karp just to get energized? >> Oh yeah. Yeah.
38:05
>> Like they don't they're like I'll pay for the steak dinner even though you're selling to me. I'll pay for the steak. You bring the energy. >> Yeah.
38:11
Who pays for the steak dinner? Um fantastic.
38:14
Well um I believe we have our next guest um pretty much ready.
38:16
Ben Harvetine uh from Palunteer for deployed engineer that has been at Palanteer for nearly years.
38:23
Um, what what was >> so many good quotes in there.
38:26
I don't take holidays off.
38:28
>> I don't take holidays off. Oh, yeah.
38:28
The team is getting ready to post.
38:30
Uh, anyway, um, >> I'm excited for this one.
38:36
>> Ben, >> Ben, welcome to the show. >> Good to have you. >> Good to have you.
38:39
>> Uh, we are going to have you hold this microphone as much as you can.
38:41
Um, but why don't you, uh, kick us off with an introduction on yourself and kind of, I'd love to know how you found your way to Palanteer.
38:47
That'd be super interesting.
38:49
>> Yeah, it's, uh, kind of an odd path.
38:49
Um, I studied mechanical engineering and architecture in college.
38:53
So not what you would think for a software company.
38:57
>> Uh worked for Annheiser Bush. >> Oh, no way.
38:59
>> Beer company for a year.
38:59
That was a great sort of transition from college.
39:04
>> What were you doing at uh Annheiser Bush?
39:05
>> Yeah, it was a it was a management training program kind of rotation based. So yeah.
39:10
>> Um after that ran a hardware startup for a bit. Okay.
39:13
>> Went to another hardware startup.
39:13
Um but I had some buddies from college who had worked here.
39:16
And >> thing about Palunteer seemed like everybody had um just kind of like more autonomy and authority than >> Yeah. I saw anywhere else. >> Yeah. >> Yeah. Amazing.
39:24
Uh so what do you want to show us today?
39:26
Can you uh give us a little tour of what's going on?
39:30
>> I've got a little >> brought a robot. Yeah. One robot slide in.
39:34
>> Bringing a robot is a great sign of respect in our culture. So thank you.
39:38
>> Well, you know, you can imagine uh you know, when we have uh you know, events like this, there are a lot of demos, it's pretty screenheavy with software stuff.
39:45
Um, and we've seen a lot of, I'd say, like increasing demand for our edge offerings, hardware offerings, really trying to push the technology further and further down to the shop floor and into the field.
39:57
>> And so I wanted to put together something, you know, just a little kind of toy demo that made that a little bit more tangible for people who are here. >> Yep.
40:03
Um so uh walk me from my understanding to how we get to the edge, how we get to robotics because uh my famous like the case study that comes to my mind for uh Palunteer in terms of like making things in the physical world is like I think the Airbus example.
40:17
So I and and and whenever somebody says, "Oh, what what does Palanteer do?"
40:22
I'm like, "Okay, imagine a plane.
40:24
There's a bunch of different parts.
40:26
You got to have a certain amount of seat belts.
40:27
You got to have a certain amount of engines.
40:28
You got to have a certain amount of fuel lines.
40:30
You got to have a certain amount of chairs.
40:31
And all those come from different places and they all have different lead times and strengths and they need different safety requirements.
40:38
Did they get checked off?
40:38
And so you put all of that instead of just in a loose database, you put it in a database, but then you have Palunteer that's actually tying everything together.
40:44
So you know if there's a lead time on engines, you need to order more seat belts in three weeks instead of two weeks.
40:49
And that's kind of how I explain Palunteer in terms of like make a big thing that's complex. Is that roughly right?
40:55
And then how do you walk from that to like we need Palunteer to somehow interface with like a robotic arm? >> Yep. Yeah.
41:02
I mean that's roughly right.
41:03
Like the way I think about it, it's like anywhere you go people have data scattered all over the place.
41:06
So the first step is can we get that all into one place? >> Got it.
41:10
>> Then can we model that data so it's as easy to work with it as it is to talk about the concepts that represents, right?
41:16
Just like make it kind of >> so so there's this big meme in Silicon Valley and defense tech right now that like there's a whole host of manufacturing guys. They're all aging out.
41:24
they're 65 and everything that they know about how to make a widget, whether it's a chair or a rocket motor, it's in their head.
41:31
They haven't written it down.
41:33
Maybe it's some some loose notebooks.
41:34
And so, this is kind of a way to jump and start getting more data online, right?
41:38
We're actually not throwing out the data. We're capturing it. >> Correct. Yeah.
41:42
And really, like the whole point of any of these data exercises is you just want to put the right data in front of the right person at the right time to make the right decision. Yep.
41:48
>> And then just be able to close the loop and learn from it.
41:49
Um, and so if you're looking across a supply chain, that's how you do it.
41:53
If you go down to a factory floor, the process is there. That's how you do it.
41:56
And so when it comes to this robot, we're basically just like pushing that edge further.
41:58
So instead of um you know, popping up an alert on a screen that tells somebody to go do something, >> what if you could actually just tell the robot to go do it. >> Okay.
42:08
>> Um so again, sort of a simple like toy example here, but the basic idea is that, you know, this is a little work cell that we made with a a robot arm and a camera. >> 3D printed, right?
42:17
>> Yeah, it's it's all Yeah, it's all 3D printed.
42:19
>> Even the arms are Oh, wow. Okay.
42:19
Yeah, I didn't realize that. >> Cool.
42:24
>> Um, and so, you know, it's it's kind of set up to be a dumb terminal that kind of works and looks like, you know, the robot arms you'd see on a factory floor.
42:31
Y, you can give it moves to take, maybe you can ask it for a picture, but past that, it's not doing any heavy computation on board.
42:37
>> Um, but then you can push uh, you know, that data to an edge hub that can run embedded models, um, can run embedded ontology.
42:44
So you can actually take that that kind of model of the world in terms of objects, relationships, um actions and models and you can push that down to the edge and even if you have um say like a like a network sparse environment where you don't have that real-time uplink to the cloud, you can continue to run off of that ontology.
43:03
>> Yeah, we were looking at uh semi analysis.
43:05
They put the the five levels of robotics.
43:07
I forget exactly how many levels there were, but they were trying to map the self-driving car analogy to physical robotics.
43:13
And I believe like level zero or level one, like the most basic was you have a pre-programmed robotic arm that's doing the exact same move.
43:20
It's taking the windshield and putting on the F-150.
43:22
And it's this huge arm and you can't go near it because it's there's no cameras on it whatsoever.
43:26
And if you step in that work cell, it will kill you if you don't if you're not careful.
43:30
Um, and this seems like uh a step towards like level two where we're able to actually understand what different products mean.
43:38
If there's, oh, this type of product shows up, there's going to be more likely that there's a defect or you need to adjust what the robot is doing.
43:45
How can you actually get that data into something that's actionable? >> Yeah. Yeah.
43:49
And even in like this simple demo, we've got, you know, it'll trigger alerts on, you know, it tries to execute a move and you end up with like a block like jammed up here.
43:55
It'll say, "Hey, you got a jammed hopper.
43:57
You need to declare that sort of stuff." Okay. >> Um, interesting.
44:00
Um, uh, where does this play in like the stack of other software?
44:07
I know when we talked to what was it DRA, uh, our buddy Phil, he was saying that like he's working with automotive companies, but then they also have a lot of there's a lot of like lower level control software on machine lines.
44:20
Some of that's from German companies that I think we just talked about with Dr. Karp.
44:23
Um but uh like where do you see Palunteer playing in the stack?
44:28
Uh you have a bunch of data the database you put Palunteer on top but then at a certain point there might be uh some robotics company that makes the robot and then they also might have some control software with kind of a messy API or something like that.
44:40
>> Yeah, I think we can be pretty agnostic about how far up or down the stack we go.
44:44
So we've got I'll pull this box.
44:46
>> Yeah, please hold this.
44:49
>> This is uh this is the node that goes on the edge, right?
44:51
So this is >> so this is an example of an edge node that um one of our partners Edgecale makes.
44:56
So this is that box that you can stick in the closet factory >> network to those existing machines that you have on the floor if you just need a turnkey solution. >> Yep.
45:04
And then I think at the other end of the extreme that's where we've got something like this where >> this really at the end of the day is an ontology defined piece of hardware in that the machine itself its entire configuration the state machine is running everything about it is defined in the ontology lives in the ontology and it's like really just like a bespoke piece of hardware >> running that ontology native software. >> It's a monument.
45:25
So yeah, you you you know, if you've got like more nason operations, more green field operations, you think about some of the companies we work with in um defense tech, it's like >> they can go all the way down the stack if they want to. Sure.
45:38
>> For some of the, you know, the larger, more established customers that we're working with, you know, the plug-andplay solution. >> Yeah.
45:43
What's the sweet spot for the specs on an edge scale like uh edge node?
45:48
Like something on the edge like do you need to be running like a large language model?
45:52
That feels like something that you could do on >> you could I'd say it depends on the application like we we've done we've done some um some like examples of that even like previous AIP cons.
46:01
It's like do we need the uh like the local app served up with a chatbot for the line operator who can just be like what's going on >> and it just talks to you. >> Yep.
46:10
There's and it's not just purely deterministic. Okay.
46:12
If if the block is blocked then send the error message.
46:18
instead it's it's actually interpreting a bunch of data in a kind of non-deterministic way.
46:23
>> So I'd say it's like you know I think like anything it really depends on the application and the users because again there are a lot of guys that are working on these lines guys and girls where they don't need another screen in their life and so it's really finding like what's the right way to interface with those operators to ultimately just drive the better decision making.
46:38
uh how much is uh like how much is the what is the role of the FDE in in this kind of new era new territory because it feels like >> Yeah.
46:47
Are you graduated from being an FD yet or is it once an FD always an FD?
46:52
>> I I think it's once an FD, always an FD.
46:54
I I try to keep my hands on keyboard as often as I can still.
46:56
Um you know, still flying out to whoever axle factories in rural Kentucky or whatever. >> Awesome.
47:03
>> Um yeah, I think the closer you can stay to that stuff the better.
47:05
I think really like the role of the FD is like just like it always has been.
47:09
Go on site with the customer. Yep.
47:12
>> Don't just understand but internalize their problems, their challenges, you know, and uh solve >> go create some value.
47:17
Uh well, thank you so much for hopping on the stream. We appreciate that.
47:21
>> Congratulations on everything.
47:23
>> Thanks for bringing your baby. >> Yeah.
47:24
Yeah, you can definitely take this out here.
47:26
I will grab this and we will have our next uh guest Danny Lucas uh from Palunteer coming in. He also has a demo.
47:34
Um, do you guys know if the demo is uh is gonna need the HDMI cable? Is that right? Okay.
47:40
So, uh, we will bring in Danny uh whenever we get a chance. Yeah.
47:44
Let's let's bring in our next guest. >> Here he is. What's going on? >> Welcome to the show. >> How are you? Great to have you.
47:55
>> Did you do a live demo? Always. That is bold.
47:59
Doing a demo is on a live stream. This is live.
48:01
So literally anything you share on your screen potentially will go out to the internet forever to be baked into the future super intelligence >> the future. >> Yeah.
48:10
Baked into the training models of the future into the pre-training data. So be very careful. Don't leak anything.
48:16
But uh but but introduce yourself.
48:16
Tell us what you're going to show us. >> Yeah. Absolutely. >> Uh microphone. >> Oh yeah. >> My bad.
48:22
>> Uh what's going on guys? Um my name is Danny. >> Yeah. >> Let's see here.
48:26
Uh I'm an engineer at Palunteer.
48:27
I've been a Palunteer for about 12 years.
48:30
>> In terms of like my role It's hard to describe.
48:33
>> Like I'm sure everyone at Palunteer said that.
48:35
Uh I guess like if I had a role or a title, I I uh do a lot of our business in the Midwest at this point.
48:40
So >> first six years at Palunteer, I was on the government side.
48:44
I did work with Department of Justice. Yeah.
48:48
>> US Special Operations, CIA, National Counterterrorism Center. >> Sure.
48:52
>> After my wife and I had our first kids, she was like, "Hey, could you not go to weird places in the world anymore?"
48:55
And I was like, "Totally reasonable." Yep. >> Reasonable request.
48:58
We we moved back to the Midwest and I switched over to the commercial side and that's kind of like what I do now is like grow our business in the Midwest. >> Yeah.
49:06
What what's like a what's a uh like just line drive uh solution that you like just total wheelhouse uh solution for uh you know I imagine like a large enterprise customer in the Midwest. >> Yeah.
49:20
Uh what I focus on a lot is manufacturing in the Midwest.
49:22
So you can like there's huge manufacturers in the Midwest whether that's like >> Johnson Controls or Eaton or >> um Molson Kors uh Cummins Engine.
49:34
>> So it's a widgets factory. >> Yeah.
49:36
>> They're making widgets. They're buying parts.
49:37
They're assembling them and you have to understand the flow rate.
49:41
Where's the where's the rate limiting factor?
49:43
How can we increase flow?
49:43
factor? How can we increase flow? This is where I think we have the most differentiation from a product perspective because it's like >> like I can actually affect the physical world and then I can measure how I affect it and then I can learn and improve how I affect the physical world the next time right whether that's like >> hey I'm in supply chain and I'm
50:01
>> short on inventory like how do I solve that problem in the most effective and optimized way versus like I'm trying to manufacture something and like how do I make sure my machines are running I have the right labor I'm trying to do the right thing and so like the the real magic behind all this too is like these yes they start off as like singular use cases that are like pretty great like
50:22
straight shot but then like when you start to connect these workflows together and it's like oh the machine's down like and I have this material like what do I do and how do I go do it >> uh what do you want us to show us today I can kind of hold this for you if you want >> we getting good sound on this okay cool yeah walk us through it >> what I was going to demo is I think like
50:43
one of the interesting things and I'm I'm sure you've like talked to a lot of different palunteerans today is like we are never going to purport to be like a strategy consulting type of thing when we engage with customers like we're never going to purport to be like oh like a hey we're experts in x y or z and the great thing about that right is like we're true to like who we are. Or the
51:03
we're true to like who we are. Or the bad thing about that right is like companies will identify and the organizations that we ident like that we work with will identify like hey I know this is a problem right but like there's a huge amount of time between like hey there's a problem and then let's go like implement a solution >> and the dependencies on actually getting to that faster are like uh I have the
51:25
internal SME that can actually like understand the problem and come up with the right solution and do the feasibility and all that great stuff or I go work with like strategy consulting, I pay millions and millions of dollars
51:38
to get a deck that tells me like, hey, this is the solution that we think you should employ with the right like ROI in this approach and we've done this feasibility study and we think that you should go do that. And so like we find
51:48
And so like we find that as a huge impediment to like our own growth, right?
51:52
Like why should I wait months? >> Yeah.
51:54
You don't want them to go spend millions of dollars with some random group to then recommend >> a palenteer product. That's 100% right.
52:03
And so like what we've been exploring more is just like well why can't I use AI to do that?
52:07
Like why can't I like give a fairly haphazard business like a a description of business problem and use agents essentially to like structure that into a better business problem description to do the necessary research about like what are the potential solutions of of of things that I could and should deploy to go solve this problem.
52:30
Can I generate ideas with all the requisites of how I actually employ those ideas and actually generate a proposal where then I also have like agents as critiques on that proposal to be like is this technologically feasible?
52:43
Is this like financially feasible?
52:45
All the things that you would expect like strategy consultants to do for you like I should just be able to do that in a day and come up with a proposal.
52:52
But then like I don't know if you guys have talked to anyone about AI FTE, but then like I should just then be able to use the output of like this to then go build it. >> Yeah.
53:05
>> Yeah. Like I should just be able to say like cool here's the solution I need to go build input into AI FTE build it right and go from like you know what would have taken six or nine months until we ever get engaged to like well I
53:21
think I this is a problem like let's just go do it like in the next week right >> okay >> does that make sense yeah it makes sense um I I have some follow-up questions but maybe maybe jump into the demo first >> cool >> I think like like my my immediate I
53:36
guess question maybe it's relevant uh is like how do how do you ensure kind of quality right because like you didn't say this but like someone else in another context might call this like vibe coding right sort of like generating like a deep research report
53:52
on like a problem and a potential solution and then like you know sort of prompting your way to an implementation totally and uh today uh you know just like code quality and product quality ends up popping up. But I'm sure that
54:04
But I'm sure that you're already think about that.
54:07
>> Like my take on this is like when you start doing anything with AI or large language models like it there has to be a human in the loop, right?
54:16
a human in the loop, right? not only to make sure that quality is coming out of the other side but also to ensure feedback loops are occurring and right and then and then you can take that context and start getting closer and closer to a Jesus take the wheel moment where like um where like you actually
54:32
have built trust because like part of this is not actually like I think a technology problem it's like a people and process problem where like people actually build trust in it and also you get all the tribal knowledge that's not in any system um actually incorporate in some knowledge context that you can start to build off of over time. But I
54:50
But I think that's like that's the that's the trick is like humans always have to be in the loop, right, that to begin, but then like you build trust until you actually do the >> Jesus take the wheel moment. >> Yeah.
55:02
So yeah, with this demo, what is the uh is it designed as like an internal tool or something that you would actually?
55:08
A lot of our customers are starting to use this to start to shorten the the the cycle time of going from like initial problem identification to implementation.
55:18
So like >> and is that for is that for customers that are already using Palunteer? >> Yeah.
55:23
Um so like we we've started using this primarily with like a lot of existing customers, right?
55:28
But then the cool thing about it is I don't know if you guys have heard where like >> all of the things I'm going to show you are kind of like native components of the platform.
55:35
But then we've developed this capability where we can say like hey this is actually a really repeatable workflow.
55:41
What if we package this up and then just it's way easier to deploy where we can just like deploy there deploy there deploy anywhere basically. >> Uh cool. Yeah.
55:50
So walk us through >> pull it up and maybe bring it a little bit closer so you can see it.
55:55
>> You share your whole screen. >> Oh yeah. Yeah. Go ahead. >> Ready? Yeah. >> Okay, >> let's do it.
56:01
>> No saying text messages or anything like that. >> All right, cool.
56:05
Um, I used to work in the aviation space a lot and I fly in and out of Newark.
56:09
Um, which like if you guys do that, you know that's a real pain in the ass. >> Yeah.
56:14
>> So, let's let's start there.
56:14
Let's just say like um >> redesign.
56:18
>> Hey, I'm a um >> Oh, yeah, for sure. Go ahead.
56:25
So like the problem the problem that I'll type in basically is like hey I'm an aviation expert like um we're seeing significant delays around like Newark airport because there's not enough runways and the runways are too short.
56:38
Uh like what should I do to optimize my flow? Okay.
56:41
>> Basically to to solve this problem. >> Sure.
56:43
>> So like uh you now you guys get to see me type which is always fun. >> Yeah. This is interesting.
56:48
Um I yeah a ton of questions about >> I've always wanted to redesign the LAX uh like uh like streets like uh the flow of traffic.
57:02
>> Yeah, that is a wild choice by LAX just constant constant traffic.
57:06
Uh wasn't too bad uh this morning fortunately but we did have a funny incident with a member of our team who uh first day >> John arrives got through security. >> Oh yeah.
57:18
and almost managed to miss his flight because he was getting a breakfast >> by a former guest and friendly.
57:24
>> I would call I would called and texted and said >> you know this is no time to take shots at the dyslexic.
57:30
He had missed he had he had made a mistake and and confused uh gate nine for uh for gate six right >> and and there is no gate six at this particular terminal headed to a different terminal. Thank you for covering. So everyone Yeah.
57:45
So it doesn't have to be.
57:45
So right now I just I typed in I like pretty rough problem statement. I'm an aviation expert.
57:50
I want to solve problems around EWR airport. Yep.
57:54
>> Uh there are too few runways and the runways are too short.
57:55
How do I optimize traffic flow around it to minimize disruptions?
57:59
So that's kind of like the first point.
58:01
And what's happening here is like the first set of agents is basically taking that as a problem description and actually like putting more structure around it.
58:07
more structure around it. So it's not like my um you know my like missper effectively working right and so you on the left side of the screen you can actually see some of the logic of like what happened the train of thought here of like hey >> here's the problem statement I can see
58:26
the system prompt like what the task prompt is what the LLM like responded to when they saw this to them actually then creating and structuring this problem which is like hey the core objective is I want to optimize air traffic flow around uh Newark Liberty International Airport to minimize disruptions, de delays, and efficiencies. It puts out
58:43
It puts out like key requirements. Yep.
58:47
>> Like prioritize aviation safety standards.
58:50
It gives out restraint uh constraints.
58:53
>> Nathan Fielder would be happy to hear that you're >> Yeah. Yeah. Right.
58:55
Um it gives out constraints like limited number of existing runways, restrict uh simultaneous operations, etc. , etc.
59:03
>> So like this looks pretty good to me like as the initial problem description.
59:07
um way better than like the garbblegook like two sentence thing that I did.
59:09
So now I want to like start to get into the phase of >> um like actually starting to do research on this to say like what are potential tools, what are potential approaches to actually solve this problem. >> Yep.
59:22
>> And so what's happening right now is like now we're going into kicking off into more of like an agent. >> Yeah.
59:28
Just branching a bunch of agents to go do deep research. >> So yeah, exactly. Yeah.
59:32
>> So like now on this screen I can see that same like core objection uh objective function over on the left >> what it's working towards. >> Yep.
59:38
And then I can start to see as it's running on the left like research topics as it's doing research popup and modeling.
59:45
This is all built in like native foundry tooling. >> Sure.
59:49
>> Um >> how how inferenceheavy is this?
59:49
Because it feels like it's going to town right now. >> Yeah. Yeah.
59:53
I'll I'll show you I'll show you kind of like the under of how we're actually doing the research. >> Yeah.
59:58
It is a unique uh it is a unique like like I don't know like problem set because it's like going to town is something we worry about when we're talking about like oh yeah you have a billion consumers and $10 really adds up. Yeah.
1:00:12
>> But if it's like >> a problem as important as this.
1:00:15
>> If you're talking about if you're talking about you know optimizing an airport I think I can I think I can deal with a $100 inference bill.
1:00:20
You know I'm gonna be okay with that >> for sure.
1:00:23
Um, so the other thing that I think is interesting here is that like I think agent is like a very >> there are a lot of definitions for what an agent is.
1:00:31
I think at this point in time like one definition is like >> uh and this was like kind of our first approach was like hey let's let's build a set of logic that an LLM actually orchestrates different parts of that logic between and it can use tools like >> you know deterministic tools or it can write back uh or it can access and query things to ultimately do some type of automation.
1:00:56
>> I think the other definition of like what an agent right now is like >> more of a chat interface.
1:00:59
Um and then in that regard, right, like I want to be able to give that um chat interface like access to tools. Yep. Right.
1:01:08
And so in this case, like what I've given um the agent access to is a bunch of different tools.
1:01:15
First, like I can see the model that I'm using behind the screen here.
1:01:19
And like for our from our perspective, like we think the models are mostly like commoditized at this point.
1:01:23
There might be certain models that are better at different things.
1:01:26
And you actually probably want to use these things interchangeably and actually have an evaluation framework that based on the task that you're asking it to do will like select the right model for that particular task.
1:01:37
>> Uh but in this case right I'm using gro 4 and then like for the tools in particular like I've given it access to like conduct research.
1:01:44
So I've given it some ways in which it can actually reach out and use different either internal or proprietary information uh of the organization that we're working with or reach out and use something like perplexity to do like more AI based search.
1:01:58
>> I've given it the ability to like generate like create code blocks if it's like coming up with an ROI and it needs to do napkin math like I want to say like I want you to allow you to actually like generate the code but also then run the code to see like what what the result is.
1:02:13
result is. And then I mean it seems like all of this is all of this is kind of like frontier level but available broadly but the palunteer that you actually have like data that isn't just available on the web and so like if I'm actually an airport and I actually have specific data about
1:02:30
>> you have the thing stands out to me is like if you're a large enterprise >> you want to work with with >> foundry and and have that ability to be model agnostic and like where does the leverage flow in that situation when Foundry can just sort of decide on the fly what what form of intelligence do I want to use for this problem set. >> Very cool. >> Very cool.
1:02:51
>> So I can see like kind of like the train of thought on the right like what it's doing.
1:02:54
Um and so it's going to go it's already using the um research um kind of tool and you can already see the research topics starting to like pop up here.
1:03:05
>> So like this is an example of an application right that like a user would use.
1:03:09
They would they know nothing about Foundry, right?
1:03:10
they're logging in to an application.
1:03:12
Their job is to like go do this thing, right?
1:03:15
>> But then behind the scenes, you have a lot of different options for how you're setting up this logic.
1:03:18
I don't know how much you guys have seen Foundry, but this is an example of what we call AIP logic.
1:03:24
>> I could write all of this orchestration in code if I wanted to.
1:03:26
I'm fairly lazy, so I use the lower code tool, which is AIP logic.
1:03:32
>> And so here, I can just like set up a bunch of different orchestrations for how I want a function to run.
1:03:36
In this case, I'm I'm putting in inputs for what I want the query to be, which is around like that problem statement we talked about.
1:03:42
And I'm setting up functions for how it can like reach out to different types of sources.
1:03:46
So like the first one is like if I had internal kind of like proprietary information on schematics of a runway or planes or what types of runways planes can land on, things like that.
1:03:58
Like that's all information that then I can make available to the LLM to go do a combination of like semantic and keyword search against it to find the right information to go do research against.
1:04:09
>> But then like as a backfall then I'm just like also giving it access to go and query perplexity, right?
1:04:13
And go say like hey go find what's out what else is out on the internet to actually go do this research about this particular problem, right?
1:04:20
And then bring that back.
1:04:22
And then the last part of this is like an action then to like go capture all that information and store it back into the ontology layer in Foundry. >> Awesome.
1:04:32
>> So this is kind of like what it's doing live is like >> um >> it's still working.
1:04:36
>> It's working like and it's and it's writing as we like as it's doing research, right?
1:04:40
So it's like what is the current runway configuration, operational capacities and key limitations at EWR including details on runway lengths numbers and how they impact uh aircraft operations. >> Sure.
1:04:51
And so then it actually gives me like this is this is pretty good information.
1:04:55
It will site the sources where it's coming from and everything like that, right? >> Yeah.
1:04:59
>> What are effective non-infrastructure strategies for optimizing airport throughput, >> right?
1:05:03
Uh and so in this case, right, it's actually saying like, hey, there's this performance-based navigation as a cornerstone, right? >> Yeah.
1:05:10
I remember hearing that if you if you have the plane board from the back to the front, it'll load way faster, but no one wants to do that because the >> it's a it's a business model thing. >> Yeah.
1:05:20
because people pay to be at the front of the plane and they want to get on the plane first.
1:05:23
But if uh there was another proposal that was like uh load all the passengers that have window seats, then all the passengers that have middle seats, and then all the passengers that have aisle seats, and they all kind of just flow in.
1:05:33
Um no one's quite figured that out, but yeah, I mean, I can imagine that it could come up with a bunch of different proposals for uh you know, similar just kind of like rethinking of this the flow of traffic.
1:05:45
>> I think we're getting short on time here.
1:05:47
One question one me let me like zoom forward.
1:05:50
I'll show you kind of like an end product here which is like let's go I already ran this today.
1:05:53
I was like hanging out with the American Airlines guys cuz like we were making fun of EWR >> which is not their hub.
1:06:02
>> which is not their hub. Um but yeah, this is like an idea that it generates and then like I get a summary of what that idea is and then it automatically develops critique agents >> that are like looking and evaluating on different type of like uh different criteria right which is like hey can I what's the risk assessment and
1:06:22
mitigation evaluation what's the economic feasibility of actually doing this >> like what is the safety and regulatory compliance evaluation and then it's going to run like those evaluations using that agent as a like a task criteria to actually then say like I can see the the guidance that we gave the
1:06:39
agent right and its task and then it has to go evaluate >> to see if it makes sense from that perspective y right and it even like generates its own models and its own code to say like hey is this feasible from like can I do basically nap uh like napkin math and say like can I come up with like how I could calculate this and
1:06:59
actually go and like run and see how close Does this output do you think to what a larger >> it's like pretty I think it's like pretty aligned right because like they're not they in in normal times like these strategy consulting firms aren't getting access to all the data and so they're like being like okay come up
1:07:17
with the idea do the research generate the idea for a little bit >> then like I need to do some napkin math on like how I would think about actually like critiquing this idea and then ultimately like I need to come up with a proposal right and here's like the end proposal for what I think you should go do same framework where I have agents
1:07:36
then writing portions of that proposal and then uh from there right it's just like copy paste that proposal in the AI FTE and like start building right >> last uh last quick question are you feeling the reindustrialization yet are you seeing new entrance into the Midwest building things or is it more >> legacy players just trying to trying to
1:07:58
increase >> I think it's legacy a lot of what I work with are companies like Eaton which are like hundred-y old companies or like Johnson Controls 100 euro companies um that are saying like >> how do I actually use this as an advantage to do to do better right like and and that's like where I think is interesting is that like maybe five
1:08:19
years ago this was really hard like people were like yeah I don't trust it or I don't believe in it I think now what's interesting is they're like I trust it let's go like it's just >> you can give them you can sit down and give them a demo >> that's right that's Well, thank you so much for coming on. Thanks so much for
1:08:32
Thanks so much for joining.
1:08:34
>> Thanks for having me, guys.
1:08:35
>> Brave to do a live demo next guts. >> Great.
1:08:37
Hey, great work listener. So, >> thank you. >> Love it.
1:08:42
>> Have a great rest of the conf. >> You're the man.
1:08:45
>> And we will bring in our next guest, >> Jonathan Web from the nuclear >> man himself. Welcome.
1:08:53
>> Sorry to keep you waiting. >> Good to meet you. I'm John.
1:08:54
Today is a great name to have a company that starts with the I don't know if you saw the browser company.
1:09:02
>> The free press sold for $200 million.
1:09:05
The browser company sold for uh $620 million.
1:09:08
Everyone is all in on companies that start with the today. >> There we go.
1:09:12
>> But uh give us the intro on the nuclear company.
1:09:15
What's the plan and where are you in that plan? >> Uh what's the plan?
1:09:20
So to my understanding, we're the only company in the Western world focused on the deployment of new nuclear. What does that mean?
1:09:29
Um I assume some of your communities probably followed the nuclear industry a little bit.
1:09:33
I mean there's no AI without power.
1:09:35
I just talked in that talk earlier about you know China is about to pass the US as the largest nuclear power in the world. >> Yeah.
1:09:44
>> Um our thesis is the reactor is not the problem.
1:09:47
There's a lot of legacy reactors that are operating in the US.
1:09:51
There's some of the best performing reactors on planet Earth.
1:09:53
Uh there's a lot of startups, dozens, designing new reactors that are all going to be great reactors.
1:10:00
Uh the problem is being able to deploy those reactors on time on budget.
1:10:04
We have the safest operating nuclear fleet, the highest performing operating nuclear fleet.
1:10:10
>> You're talking about the Navy or >> I'm talking about the US.
1:10:11
We have about a 100 operating plants.
1:10:13
I mean today 20% of the power in the US comes from nuclear.
1:10:19
>> That's nuclear that was built in the 60s and 70s.
1:10:21
We've built two reactors in 30 years. So what are we?
1:10:23
We're the deployment arm.
1:10:25
And why what does that mean?
1:10:26
So think of if you're American Airlines or Delta, >> you don't call GE or Rolls-Royce.
1:10:30
You you don't you don't just call to buy a jet engine. Yep.
1:10:35
You call Boeing or Airbus.
1:10:37
what if I handed you a jet engine or a Ferrari engine or a Bugatti engine, no matter how great that engine is, you're going to be like, "What are we doing?"
1:10:44
>> So, we want to be the full solution to deliver that power plant uh to either a hyperscaler, to a utility, to a foreign government, uh or potentially to operate those on our own.
1:10:53
Uh and the good thing is we're not competing with any of those reactor companies in the market. We're a partner them.
1:10:59
partner them. So once they go from R&D to you know manufacturing to design to implementation uh there's a big difference between white lab coats designing projects in an R&D lab to living in a construction site where you know I've done much of our team's done I mean I built 8 million square feet of
1:11:17
stuff at the last thing you know got a team of builders that worked for Elon building gigafactories built the last nuclear power plants here we want to be that team that when you're ready to go deploy your reactor you know we can partner with you get that reactor in the field and get it up and operating. >> Your partners on the reactor side, how
1:11:32
>> Your partners on the reactor side, how much of what they're doing is just remembering how we used to build reactors as a country versus doing net new innovation.
1:11:41
>> So there's really only two incumbents in the US and that's Westinghouse and GE and you know obviously we're talking to them and then there's a lot >> and they built Vodal the most recent uh nuclear power plants to come online that were successful but over budget and over time correct.
1:11:56
Oh man, it was Yeah, I hired everybody off that team.
1:11:58
So, Georgia uh Vogle three and four first of nuclear. >> Yeah. What we wanted? >> No, no, no.
1:12:05
We wanted to hire like if people look at that and go abject failure. I go, no, no, no.
1:12:09
These are lessons learned.
1:12:11
This is like what in the what went wrong, >> guys? It's nuts, man.
1:12:14
Like, it took 10,000 people at the peak of construction uh on that construction site.
1:12:22
Um >> guys, go to a rock concert.
1:12:24
Look at 10,000 people and think they're showing up to work every day. >> Yeah.
1:12:29
You don't want an amphitheater just to meet your team.
1:12:32
>> 10,000 people managing the project with paper. >> No way.
1:12:35
Not the last decade of construction.
1:12:38
>> We're not talking 40 years ago.
1:12:38
I'm talking in the last This thing finished last year with wheelbarls and wagons of paper.
1:12:45
So you're looking at 10 to 20% efficiency for the people working.
1:12:48
And you know the audience and the larger viewership might go, "Ah, lazy Americans." No, I'm not buying it.
1:12:55
Yeah, we are not giving our teams and people the the advantages to win the American spirit and fight alone.
1:13:01
God, I'm believing it as much as anyone. It's not enough.
1:13:03
We got to bring technology, tools, capability.
1:13:06
That's where we're partnering with Palunteer.
1:13:08
So, I'm taking hundreds of thousands of pages of documents, which is what it takes to build one of these power plants, putting it into a data lake, segmenting that data out.
1:13:16
So, if certain parties want to secure their data, they can.
1:13:18
Then having LLMs and AI on top of that, giving predictive analytics.
1:13:22
So, when the supply chain's delayed the night before, a construction man or woman's waking up in an RV in a trailer at 3:00 a. m.
1:13:28
, okay, I'm going to be redirected at 3:15, I go there. At 3:45, I go there.
1:13:35
Giving our frontline teams all the tools, technology, and information. We can do it.
1:13:39
We're not splitting an atom. We're not going to Mars.
1:13:41
We're just building the most dominant AI enabled platform on planet Earth.
1:13:45
And we're going to slash that 10,000 down to 5,000.
1:13:49
We're going to go to seven years instead of 12 years.
1:13:50
China's building these 1 gigawatt reactors for five billion in five years.
1:13:55
There's no reason we can't do it in five or four years.
1:13:58
I'm not going to name the number.
1:13:58
My team will get really upset with me on the price side.
1:14:01
But um >> there's no reason these two reactors took 12 years and 36.
1:14:06
Let's talk about timelines in the industry broadly because there's some recent I guess I don't I don't know if I can't remember if it was an EO or just a broad directive from the White House saying like we want new nuclear breaking ground in the US in the next 12 months.
1:14:20
Is that is that >> brother? It could be us.
1:14:23
So, we are imminently close to a recovery project that I'm not supposed to talk about, so I'm not going to name the state.
1:14:30
And uh but it's a $20 billion recovery project.
1:14:35
>> Bring bringing old capacity back online. >> Yeah. Yeah.
1:14:37
So, uh $9 billion walk away.
1:14:41
They spent $9 billion on this nuclear 2 gawatt nuclear power plant. Didn't finish it. Walked away.
1:14:48
>> So, we are getting brought in.
1:14:48
We're we're imminently close.
1:14:51
If we win that, you all should definitely come.
1:14:53
this tiny little team that's 2 years old that partnered with Palunteer to go recover this animal and finish it.
1:14:58
Uh would love to have you all. >> Yeah. Yeah.
1:15:03
When when you think about what they spent, what is the value that's just sitting there on the dirt?
1:15:09
Certainly not 9 billion, but are you picking up a couple billion billion in legal fees, 100k?
1:15:15
>> No, it's it's a lot infrastructure.
1:15:18
Hopefully they pou some concrete that's still there.
1:15:19
It looks like I mean if you walk on it, we're on uh I'm not allowed to say where we're at, right? Yeah. Oh god, I almost did. Um so we're in America. >> We're in America.
1:15:30
>> Play that American sound effect. We are in America.
1:15:32
We're not afraid to say it. We're in America.
1:15:35
>> But uh the when you walk this site and you look at it, it looks like, you know, aliens landed and just left because it's in rural America where this big infrastructure.
1:15:45
So there's a lot of value there.
1:15:47
there's been some value that's, you know, not not quite where it should be.
1:15:51
Uh, but we're going to go we're going to get that thing hopefully later this year, early next year, and be under construction.
1:15:56
>> Uh, we had an author Dan Wang on the show maybe last week.
1:15:58
He wrote a book called Breakneck and he and he >> uh and he contra compares and contrasts China to the United States and he calls China the engineering empire driven by an engineering mindset.
1:16:09
The solution to everything in China is just more engineering.
1:16:12
Uh, build a train to nowhere, build a bridge, just build housing, build everything.
1:16:15
housing, build everything. build bill build and in the United States he calls us this the lawyerly society and and we are too everyone in politics is lawyerly or lawyer lineage and so one of the problems that I've heard in nuclear is that oftent times you go to build something you think okay I got a plan it's compliant with all the laws and
1:16:34
then the laws change and all of a sudden you're back to square one you got to rip out all the pipes because they said no copper now you got to use lead pipes again or whatever um how much of that do you think is is real or how much do you think because That feels like something that you can speed up by analyzing all the legal code constantly and with the regulatory filing speeding that up. But
1:16:52
But some of it also has to happen on the other side, right?
1:16:55
Like like we it's not just enough for you to be using AI to to submit documents fast. You need review fast.
1:17:02
So what's going to happen on the other side?
1:17:04
>> Oh god, I have so many comments on this just rant.
1:17:06
>> So uh how long do we have?
1:17:06
Seriously, five minutes some.
1:17:08
Uh so yeah, I mean this is the hot button issue for me.
1:17:11
We have the safest operating nuclear fleet in the world and the highest operating capacity.
1:17:17
This industry, don't get me wrong, the legal BS, yes, we we all agree, but the victim mentality of the industry, the victim mentality of of of entrepreneurs in San Francisco acting like high school kids, blaming the regulator.
1:17:32
Brother, it ain't that hard.
1:17:34
We hired the number two at at the NRC, Laura Dudes. She's on our team.
1:17:37
We're walking into the NRC going, "What do you need?"
1:17:41
We're going to be fully transparent.
1:17:42
We're going to be fully compliant.
1:17:44
They should be incredibly critical.
1:17:46
It's nuclear for God's sakes.
1:17:49
If there is one and and here's the other one big misnomer >> and it's working, right? The fleet safe.
1:17:54
>> We have had in decades 100 operating nuclear power plants.
1:17:59
Not one person in this country has died from radiation fallout. 0. 0. That is perfection.
1:18:03
Uh, so the the the private sector needs to stop being a victim and just start doing what we're doing and and and figure out how to partner with the regulator. We're seeing no problem.
1:18:16
So the other kids that want to cry on Twitter, go for it.
1:18:19
You want to sue the regulator, go for it.
1:18:21
Uh we're just going to go on and partner with them and and figure out how to how to build uh bigger, faster, lower cost, safer, higher quality than ever before.
1:18:29
And I will say what we're doing with Palunteer.
1:18:31
Well, here's the good news to the to the people designing reactors and you're ready to go deploy them.
1:18:37
What you're doing and what I'm doing have nothing in common. I have a team again.
1:18:41
We lit me and my wife were living in an RV, got got engaged on the last construction site.
1:18:45
I've got guys that were building Vogle 3 and four, had heart attacks on the construction site, had people living at the gigafactories.
1:18:53
That is a totally different world.
1:18:53
let us take your drawings, your great R&D, drag it into reality.
1:18:58
Uh, and we're going to build that trust with the regulator with you.
1:19:01
Uh, but I do think we got to go pencils down, swords down on blaming the regulator.
1:19:06
Now, the the legal, you know, that's a whole verse engineer thing.
1:19:10
That's a whole another topic we could we could take on.
1:19:11
Uh, but we need the regulator to challenge us to be safe and we just as as as an industry have to figure out how to comply and get the job done. >> Yeah. >> What >> great rant.
1:19:22
I would love to see you and Karp rant together. >> Yeah. Yeah.
1:19:25
What uh what did Palunteer show you that made you go with them?
1:19:28
Was there was there a key case study that >> So we studied So we are a 2-year-old company that's about to be the f the only company in the US with commercial nuclear under our watch.
1:19:40
>> I'm like what did we do right? What are others doing?
1:19:42
We're just building a team to go build and and kind of reactor tech an agnostic.
1:19:47
>> Is the other is the other stuff managed by the government? Is that what you mean?
1:19:50
like or is it just older companies that that that manage?
1:19:53
>> There's no one that's actually focused on building.
1:19:55
Everyone's designing new reactors.
1:19:56
I just want to go build stuff.
1:19:58
So, I could build a Westinghouse, a GE reactor, you know, any one of the new advanced reactors. We just want to build.
1:20:04
So, then the last year what we did is we looked at everything.
1:20:06
I hired somebody over here a lot smarter than me. Was it Tesla? Was it Microsoft?
1:20:12
>> Um looked at all the different AI platforms. What can we do? We knew what we wanted. Nuclear OS.
1:20:15
we wanted. Nuclear OS. So nuclear OS is the you know again all all aspects of data related to the project into a data lake predictive analytics our frontline teams >> no one's even close man yeah >> this is it I'm not trying to be like a sales job I would like to get like a commission >> I was going to guess that there's not
1:20:33
another great alternative that it would have been nice to at least look at a couple options and decide >> well here's the good thing I mean it's just the most secure platform the way the way it it is configured you know we're going to go build the most dominant AI enabled nuclear platform and we're doing it with Palunteer. So it took us
1:20:49
So it took us about a year of study.
1:20:51
It took us a couple months of planning and now we're just racing right now to go kind of build those solutions and it's it's working. >> Yeah.
1:20:59
What's the uh what's the structure of the financial milestones for you?
1:21:03
Because I imagine that a lot of this doesn't look just like fund everything with venture capital.
1:21:07
There's probably some project finance, right?
1:21:08
And then there's actually a customer who might be not you that's paying paying you just to advance the >> construction.
1:21:15
So for our business model so topco you know the nuclear company you're investing your VC dollars into technology and team which this town knows that big you know buckets of capital project capital.
1:21:26
I hired a big boy CFO that's raised 10 billion in his life.
1:21:30
He was CFO with JB at Redwood.
1:21:33
learn how like the NeoClouds will go and build new data centers, but then there's there's project finance debt equity on the project.
1:21:38
You know, we're the ones getting it to completion.
1:21:40
We could get an equity earn out in the project.
1:21:41
We could get a fee during construction.
1:21:43
And then there's multiple either we could build own transfer to a large utility.
1:21:50
We could build own operate for a hyperscaler.
1:21:52
We could build own transfer to a foreign government.
1:21:54
Uh or we could we could operate it ourselves.
1:21:56
So, you know, our there's a few ways we get there, but uh the debt and equity is going on the project. not through us now.
1:22:03
I mean, our valuation's not to a point to where I could put 20 billion on our balance sheet. >> Yeah.
1:22:08
>> Uh but I don't know, maybe maybe in a couple years. Let's talk. Let's see how this goes. Fantastic.
1:22:12
>> Um so, you know, we're, you know, again, I very just bullish on on Palunteer.
1:22:15
And I don't know whoever listened to that talk earlier, it's I mean, the binary outcome is it's us versus China.
1:22:21
And to all the tech bros and the badass CEOs and the badass five Fortune 500 tech executive, here's what I would say.
1:22:27
We got to leave our ego at the door.
1:22:29
China is kicking our ass.
1:22:31
That I hope was not recorded. >> Everything recorded. We're live.
1:22:37
>> So the look, it is look, the reality is it's not even a competition.
1:22:42
>> We're losing so bad and we've got to work together.
1:22:44
So I would say to the community watching, you know, push me, be hard on me, critical on me, that's fine.
1:22:50
But let's figure out how to challenge each other and work together because it's a binary outcome right now. It's us versus China. It's not even close.
1:22:57
close. They're winning at so many categories and we've got to figure out how to work together and that's what I think Palanteer and a unique framework they're bringing uh not only the technology but the mentality of how do we work together and win and you know now it's all going to be about
1:23:12
performance on that construction site on time on budget high safety >> and I love your position in the in the nuclear kind of market broadly and that if somebody can build great reactors you can help them actually become a real business based on it and not have to worry about every single point in the stack. >> We got a partner, man. That's the thing,
1:23:29
>> We got a partner, man. That's the thing, right?
1:23:31
This is where China is going into the Middle East fully vertically integrated going NBS. We will do it all. One shop stop.
1:23:38
They don't want to work with three constructors and uh somebody selling a reactor. No.
1:23:44
So, like how do we partner together? Go as a coalition.
1:23:49
We're going to deliver power globally.
1:23:50
We're going to deliver power in the in here in the US.
1:23:52
But I do think figuring out how we, you know, bring down this ego of like there's so many silos and we need to challenge each other.
1:24:00
But that's what I would say to you all because there's a lot more people on this listen to you than listen to me.
1:24:03
Um, how do we bring our tech community together, our big CEOs who are important and great, >> but if you compare them to China, we're not winning.
1:24:13
So it's like how do we do that and go win collectively? >> Fantastic.
1:24:16
Well, I think we have our next guest here.
1:24:17
We're going to take a look at some rocket motor.
1:24:19
So thank you for Thank you so much for having us. Thanks for joining us.
1:24:22
Thank you for uh doing this work.
1:24:25
>> Have a good rest of your day.
1:24:25
Up next, we have Nancy Cable from Ursa Major. Uh we will bring her in.
1:24:30
And do you want us to try and bring that in here? What are you thinking?
1:24:36
>> I'm happy to bring it in. >> Bring in the engine. >> Bring in the engine. >> Bring in the engine. >> It's engine, right? >> Okay. It's device.
1:24:44
>> We got an engine coming.
1:24:46
>> It's It's shocking that it was clear through security.
1:24:48
We we we when we do these remote shows, we sometimes have to bring uh very very suspicious looking Wi-Fi hotspots.
1:24:55
Uh Ben and the boys brought a Wi-Fi hotspot through the Actually, I think I had to walk it into the capital through a very odd place >> here.
1:25:05
Maybe pick up the microphone and we'll throw it on the table. >> Set it gently.
1:25:09
>> Yep, we can throw it on the table.
1:25:10
>> I think we'll be okay.
1:25:10
Yeah, just set your own gently down. >> Okay. >> Incredible. This is a wild demo.
1:25:15
We've >> rocket first rocket engine to meet you. I'm John. John, I'm Nancy. Pleasure.
1:25:23
We're gonna have you hold this as much as you can.
1:25:24
Um >> we've had uh we've had people brought bring uh fish to the show.
1:25:28
Sushi uh that was uh extracted or the fish was killed with a robot. Shink. That was a fun.
1:25:37
>> Somebody promised us a SpaceX engine, too. >> Yeah. Oh, yeah.
1:25:39
We got to follow up on that.
1:25:41
Um but this is this is the best demo we've gotten so far. >> SpaceX. Fantastic.
1:25:44
This is a good day for us.
1:25:47
>> So, so explain to us what is this and what's your business and introduce yourself. >> Yeah, absolutely. So, I'm Nancy Cable.
1:25:50
I am the director of operations for Ursa Major >> and we are an aerospace and defense company.
1:25:56
Uh, so we are deploying um primarily right now hypersonic rocket technology, which is what this is.
1:26:02
Uh, this is our Hadley engine, so a 5,000lb thrust class.
1:26:06
Uh, proven hypersonic flight capability.
1:26:09
So, this thing right here has uh flown Mach 5. Okay.
1:26:11
um really critical in the defense space right now.
1:26:16
We must field technology and we must do it faster and that's what Hadley and some of our nextg products are enabling.
1:26:24
>> Now the correct me if I'm wrong the the value of the hypersonic missile is that it has the maneuverability of a cruise missile with like the speed of an ICBM and it's not and so is maneuverability a piece of this is this like a >> uh maneuverability is a piece of this for our customers.
1:26:36
So a lot of interceptor technology is what uh current applications and for our nextg products um the maneuverability and the storeability of the fuels are also front of mind. >> Yeah.
1:26:49
And and uh help me understand where Ursa Major fits in the overall stack of like the primes and the different supply chain like uh are you developing whole weapons systems that sell directly to the DoD?
1:27:00
Are you partnering with other companies that we might be familiar with?
1:27:02
Uh where does Earth Major fit in? >> Yeah, absolutely.
1:27:05
So we're we're doing uh we aim to be disruptive.
1:27:08
Uh and disruptive means that we want to break the mold of what some of the primes in the government have traditionally done which is these like years or even decades long deployment cycles of development and qualification.
1:27:19
Um and to do that we do want to push the industry.
1:27:24
So that does mean not necessarily fielding the weapon system ourselves, although that is on the horizon, but putting ourselves in the position where we're partnering with the government, partnering with the primes and forcing them to push the envelope on how fast we can get these products into the spaces that they need to be.
1:27:39
>> So, so right now, uh, huge focus on just manufacturing excellence, cost, speed, reliability. >> Absolutely. Yeah.
1:27:46
And that is uh most of my role is on the manufacturing side and making sure that I can take this excellent technology that our rocket scientists have developed and scale it so it's available to market.
1:27:56
Right right now we're on you know looking at the order of tens to hundreds of units a year.
1:28:02
That needs to be tens of thousands of units a year and that's really where the Palunteer partnership comes in. >> Yeah.
1:28:08
How how does Palanteer fit? >> Yeah, absolutely.
1:28:10
>> Yeah, absolutely. Um you might think that engineers are great at data flow but if we were to look at this rocket engine here uh different engineers designed the turbo machinery and the injector and the chamber and all of them came up with a unique way to process their data a unique test system uh you know a different network drive a
1:28:30
different place to store the information and >> different network drive >> that is I wasn't expecting that >> well and that's well and I think this >> and when I think about you know we have a small company here of maybe 10 people and We probably do have like six different like Google drives and uh and different folders for different data. It's natural. It's just a natural It's natural.
1:28:46
It's just a natural everyone um in every industry rocket propulsion included ends up feeling like man I'm 15 years behind.
1:28:53
How could anyone possibly store something on a C drive?
1:28:57
But when you're focused on getting the hardware to work, you're not necessarily focused on the efficiency.
1:29:04
And so putting the data efficiencies front and center.
1:29:06
Even before Palanteer, our aim was right data, right people, right time, right decisions. Um, I loved what Dr.
1:29:13
Garp was saying about people happiness.
1:29:15
People are not happy when they feel behind.
1:29:17
They are happy when they feel ahead, when they can make realtime decisions.
1:29:20
And leveraging Palunteer out onto the shop floor and into the back end of our data structures means that we can get the information to people so they can be real time and then even predictive about how we're doing manufacturing. >> Yeah.
1:29:33
So, uh, how does how does someone at Ursa Major actually interact with Palunteer?
1:29:40
Is it on an iPad, on a phone, on a computer while they're working on test bench? Like in every phase? >> Yeah, great question.
1:29:46
So, we've been with Palunteer about 3 months now.
1:29:50
>> And right now the daily interactions are mostly with our engineering and programmatic teams.
1:29:54
We've built some inventory modules.
1:29:56
We've built in, you know, looking at our engineering uh line of balance, our change management systems.
1:30:02
Um but like we were hearing from our our nucle you know from nuclear the people on the floor doing the work are actually the most important people in the factory.
1:30:11
If my technicians can't build an engine we cannot deliver to our customers.
1:30:16
So that is the next endeavor that we are a few weeks into with amazing results so far is to actually make Palunteer a manufacturing execution system.
1:30:24
Make it the shop floor portal.
1:30:27
one data source, one source of truth, one program from raw material, ordering >> ordering all of the parts, producing all of the parts internal through fielded data on at our customers. >> Yeah.
1:30:39
Is is is you almost call it like an ERP almost. >> Yeah.
1:30:43
So, we actually we have an ERP, right?
1:30:44
This is what everyone does.
1:30:46
Everyone has they have an ERP for enter resource planning. Yep.
1:30:48
Accounting accounting function, all of your work orders.
1:30:51
orders. a PLM a product life cycle management and then an MEES is the traditional thing a manufacturing execution system and we have said why not use Palunteer it's already integrated I don't want one more monolithic software connect it with the ERP actually pull some of the functions out of the ERP's better I remember
1:31:09
hearing a story I don't know how true it is but something about like SpaceX built like a ton of custom software for everything they needed to do and then eventually I think the team like spun out and and and built a business around that uh yeah >> yeah well SpaceX uh actually so they they have a product and it's kind of the gold standard. Everyone who's worked at
1:31:22
Everyone who's worked at space is like I want that one.
1:31:25
>> It's like I want that one and that really is the you know the magic of that software is everything in one place which is what ontology brings.
1:31:34
Everything we need in one place. >> Very cool.
1:31:36
Um >> what's it so what's it going to take to go from making tens or hundreds of these to tens of thousands?
1:31:44
>> Uh the physical process matters of course right we are a hardware company.
1:31:47
You look at the complexity of this and you can understand why we're not going to be forward with a robotic automation line.
1:31:53
Um so making sure we have the right tools, the right fixtures, the right machines, uh you know >> 3D printing um is critical to what we do here. Yes.
1:32:05
>> Uh 80% of the rocket, all of these metallic components are metal 3D printed.
1:32:10
>> Uh yeah, developing some of our own unique alloys.
1:32:12
So scaling the machines is probably the longest lead time for us.
1:32:16
and then setting up the correct tools, fixtures, um as you can imagine test and infrastructure is really big >> but not having the data around that in silos.
1:32:25
So when we need to build hundreds of these uh I need to know where every piece part is at every moment so that we can make the best real-time decisions possible for quality for the customers.
1:32:37
Um, so the the physical infrastructure is really what we're most familiar with and now Palanteer's helping us with that digital infrastructure side of things.
1:32:46
Um, I've been in manufacturing my whole career. Yeah.
1:32:48
>> Uh, 80% of the line down scenarios I've ever had where we stop building product.
1:32:52
You want to guess what they're from?
1:32:55
>> Lacking inventory or >> it's lacking inventory.
1:32:57
It is not having a component.
1:32:59
And so we think about like, yeah, a rocket engine is really physically complex.
1:33:02
That's not actually the hard part.
1:33:07
to build a 1200 compet I don't know I don't know if this is uh hubris but I feel like you could put this together John >> well that's kind of that's the point a manufacturing but it's just like so so actually putting the pieces together is the easy part but it's like making the parts and making sure you have them at the right time >> is the real challenge so it's like doing a puzzle over like you know, 20 days type of thing. >> Yeah.
1:33:35
I mean, we joke it's like, right, Lego, Legos for adults, but you can see it really just is a collection of fittings.
1:33:40
Um, fittings and fasteners.
1:33:40
I And that's kind of the point.
1:33:43
How can we have a system that makes it so easy and so obvious how we manufacture these that I could pull the two of you in and say, "Build a rocket engine."
1:33:51
And you could do it with confidence.
1:33:53
That's >> got young kids.
1:33:54
I think they would enjoy putting one of these together. >> Yeah. Yeah.
1:33:57
A couple years ago, I sat next to somebody on a plane who was uh selling it was pipe bending, pipe fitting, whatever this is. >> Tube bending. >> Tube bending. Yeah.
1:34:05
He said, "I'm in I'm in my my business is tube bending." And I was like, "What?" And he was like, "Yeah."
1:34:10
He was going to SpaceX specifically to sell tube bending machines to them.
1:34:13
I didn't realize it was a whole industry, but he made his money to be there in person to make sure that they don't run out. >> Absolutely.
1:34:20
Because it's a rate limiting factor.
1:34:21
If the tube isn't bent, you can't make the rocket.
1:34:24
>> If the tube isn't bent, you can't make the rocket.
1:34:25
And tubes actually carry some risk.
1:34:27
They're some of the thinnest walled components on the rocket, right?
1:34:31
This this has a lot of mass to it.
1:34:33
>> Tubes are often can be where failures happen.
1:34:36
So in an ecosystem, right, we need to test them, but also where did this tube come from? What day was it bent?
1:34:42
What was the lot of stock material?
1:34:43
What revision was I on in my CAD model?
1:34:45
You know, what testing did this engine undergo?
1:34:47
All of that currently I could find in our systems. >> Interesting.
1:34:52
>> And it would take me hours.
1:34:52
Yeah, >> but >> but if it's all in one place, >> if it's all in one place and we have a consolidated tool, it's that traceability.
1:34:59
>> That's incredibly cool. >> Yeah. >> Fantastic. Anything else?
1:35:01
>> Thank you so much for bringing your baby uh on the show.
1:35:03
>> This is a great sign of respect. >> Yeah. >> Yeah, absolutely.
1:35:05
I mean, what's cooler than carrying around a hypersonic rocket engine, right?
1:35:08
Everyone loves it but the TSA.
1:35:10
They don't >> That's a rough one.
1:35:12
Rough one to travel with. >> Yeah.
1:35:15
>> Anyway, thank you so much for coming on. Thanks for coming on. Great meeting. Thank you.
1:35:19
Um, >> uh, yeah, >> we have our next guest ready. Or should I talk?
1:35:23
>> We have a couple minutes.
1:35:23
Why don't you tell us about some ads?
1:35:24
Do you have some ads you could run?
1:35:26
I'd love to hear some ads.
1:35:29
>> You want to talk about ramp. com? >> Ramp.
1:35:36
Let's go through some I did want to, while you pull that up, I did want to uh talk about Matt Hang, the Paradigm, and the Stripe team introducing a new payments first blockchain uh, called Tempo.
1:35:48
Matt says, "As stable coins go mainstream, there's a need for optimized infrastructure.
1:35:52
Tempo is purpose-built for stable coins and real world payments born from Stripe's experience in global payments and paradigms expertise in crypto to ensure Tempo serves a broad array of needs.
1:36:04
We're excited to be working with an incredible group of initial design partners including Anthropic, Coupang, Deutsch, Deutsche Bank, Door Dash, Lead Bank, Mercury, New Bank, OpenAI, Revolute, Shopify, Standard Charter, Visa, and more.
1:36:18
Tempo's payment first design includes predictable low fees payments gas and any stable coin uh payments first UX opt-in privacy scale 100,000 transactions per second and EVM compatible built on wreath uh tempo
1:36:35
eases the path to bring real world flows on chain such as global payouts payins and payroll embedded financial products uh and accounts fast and cheap remittances tokenized deposits for 247 settlement microtransactions, agentic payments, and more. Matt says, "We're
1:36:50
Matt says, "We're building tempo with principles of decentralization and neutrality.
1:36:54
That includes stable coin neutrality.
1:36:56
Anyone can issue a stable coin.
1:36:58
We might be able to have a TBPN coin. That sounds exciting." And any >> Oh, yeah. Yeah. Yeah.
1:37:03
Uh, that was clearly a joke.
1:37:05
No, but I was talking about a a a USDTp.
1:37:10
That's just a one for one stable coin that that uh that we issue to >> It does not move. It does not move. You can't make it move. It won't budge.
1:37:18
Uh independent and diverse validator set with a road map toward a permissionless model.
1:37:24
So apparently they're already in a private test net.
1:37:27
And uh anyways, two two power players, Paradigm and uh and Stripe coming together.
1:37:35
It sounds like they're they're positioning I guess Matt is running Tempo, but they're positioning this as uh they're both investors in Tempo.
1:37:42
So I think they really do want to take a >> decentralized approach.
1:37:47
>> So not so this is not downstream of like the stripe acquisitions directly. Privy and bridge. >> No.
1:37:52
So I have a post here from Zach Abrams uh founder of Bridge. Okay.
1:37:54
Uh he says Bridge was one of the first companies to use blockchains to solve core payments problems.
1:38:00
During our journey we've seen how even the most performant blockchains struggle with basic financial services use cases. A few examples.
1:38:08
uh a payroll transaction consistently failing when uh when Trump launched. That's interesting.
1:38:13
So when the Trump coin launched, apparently people that were running payroll like you know couldn't get >> bridge with stable coins. >> No, no, no.
1:38:20
He's not talking about he's not talking about bridge specifically, but he's saying like if you were trying to pay employees at the time that Trumpcoin launched >> paying employees in Trumpcoin. >> No, no, no. Not not in Trumpcoin.
1:38:29
like that that day I think it was like a Saturday or was a Friday I forget exactly but when it launched if you tried to pay there was so much activity on chain at that moment but like good luck you know paying like a a freelancer or something.
1:38:44
>> So yeah the example would be like I'm trying to pay a freelancer in stable coins like onchain because like obviously like your default payroll providers are just using like >> you know web two rails or whatever and and that wasn't brought down by the Trump launch. Right. Okay.
1:38:58
um aid dispersements taking days due to low transactions per second and projects to later cancel due to six figure upfront gas costs.
1:39:04
Um Tempo is a new L1 built specifically for payments and so um anyways uh quite the team they've put together here.
1:39:14
>> Yeah, we got to get some of the folks on the on the show and and have them break it down because um I'm very interested in why not Salana? Why not uh Circle?
1:39:24
You know, like it feels like there's a Why not another L?
1:39:27
Like why not an L2 built on? >> Exactly.
1:39:31
But this is something uh unique and they must have put a lot of time and effort into it.
1:39:35
So congrats to them on the launch but we will you know want to know more.
1:39:38
Uh anyway I believe we have our next guest. Welcome to the show. >> I'm John Ryan. >> Pleasure.
1:39:49
You hold this microphone.
1:39:49
Uh why don't you kick us off with an introduction on yourself and what brought you here today? >> Perfect. I'm Ryan as Dorian.
1:39:55
I'm the chief marketing and strategy officer for Lumen. Okay.
1:39:58
And we're here at AIPCON talking about all the great things we're doing together to modernize telecom.
1:40:06
>> Uh Lumen's a >> Let's give it up for modernizing telecom. >> Yeah, exactly. >> Finally, finally.
1:40:11
>> It's it's fun because it's decades of complex operational.
1:40:15
I mean, Palanteer is helping us modernize into this new world that you need for AI ready multicloud world that >> is what everyone's here talking about. >> Yeah.
1:40:27
How do you define uh h break down more of what you do in telecom specifically? >> Yeah.
1:40:32
So, Lumen is you know for for decades we have basically been connecting the world. Okay.
1:40:37
>> It starts with connection and then >> in the last uh in the last bit of time yeah >> the world has needed new ways of connecting. Yeah.
1:40:45
>> We're bringing that infrastructure. We're bringing control.
1:40:47
If you think about the way it was before, it was like fiber in the ground. >> All fiber, right?
1:40:52
Everything that's running across fiber, >> those super fast connections you need, >> one port, one connection was the way of the was the way of the world. >> We're changing that.
1:41:01
We're getting it cloud ready, cloud enabled, remote controlled, all of those things that give you that redundancy, latency, all the things that power AI.
1:41:09
Yeah, >> that's what Lumen is doing and we're connecting the world. >> Okay.
1:41:14
Uh who's the customer right now?
1:41:17
We have lots of customers.
1:41:17
We have lots of customers. So it start so we're really focused on the enterprises the enterprises that are building these capabilities data center operators hyperscalers of course and so we've announced some of the work we've
1:41:30
done on the backbone the infrastructure backbone of the AI economy but what we're really doing is enabling businesses new things new new technologies that they want to give them a technological advantage >> we're disrupting this industry to help them disrupt their industry. >> Yeah. Yeah. Yeah. Uh so I mean obviously >> Yeah. Yeah. Yeah.
1:41:47
Uh so I mean obviously there's like an immense amount of money flowing into data centers.
1:41:50
Is a lot of that actually going into like new bandwidth requirements between data centers like the basic narrative is like yeah they might spend a billion dollars training something but it's all happening within one data center.
1:42:02
>> Well so the the thing you hear about a lot and you guys have talked about a lot as well is compute storage cooling all those things that are needed. Yeah.
1:42:09
The missing link is connectivity.
1:42:11
And realistically, it's something that has really emerged as of recent to say there are new types of connectivity, new nextgen fiber. >> Yeah.
1:42:22
>> That has way more capacity >> Sure.
1:42:25
>> than the world has ever needed before. >> Sure.
1:42:27
>> We're we're growing leaps and bounds over by 2028, we'll have about 66 million uh route miles of fiber.
1:42:33
And that is growing, you know, 3 to 5x what we've had before. Okay.
1:42:38
And that is the capacity the world needs. >> Yeah.
1:42:42
So there's uh some >> and is that capacity being used inefficiently today or or is or or is demand still way out stripping supply?
1:42:51
>> The demand is completely maxing out.
1:42:54
It's why we are putting these investments in the ground and we're not only the hyperscalers I'd say the tip of the spear.
1:43:02
>> They're consuming a lot of this.
1:43:02
They're looking for a lot of this data center to data center connectivity, but it's really enterprises everywhere that are now saying, you know what, we also need that type of bandwidth.
1:43:13
And some will take it dedicated, some will take it shared, but the need is completely outpacing what the needs of the last couple decades have been. >> Yeah.
1:43:23
Try and make that more concrete for me.
1:43:25
Uh because I feel like most people's interaction with AI is uh I send the most condensed packets possible across the internet.
1:43:33
Just a couple lines of text. Yeah.
1:43:36
>> And then a bunch of GPUs light on fire at the AWS data center, Azure if I'm using uh GBT.
1:43:41
And then uh it sends back text. This is not rich video. This is not VR.
1:43:47
I I buy I im immediately like intuit intuitively understand like if we're in the metaverse world and we're streaming 4K stereoscopic that's super bandwidth heavy.
1:43:56
How is AI bandwidth heavy?
1:43:58
So, it's actually great listening to the customers that have been here at AIPCON because you hear American Airlines, you hear BP, you hear some of these customers that are talking about their infrastructure, all of the scheduling, the inferencing, the the planning that is happening in real time and adjusting.
1:44:15
That is not just people typing in their prompts into the text.
1:44:21
It is systems talking to systems.
1:44:21
And this is where the data explosion has come from.
1:44:26
It's all happening in the background. >> Okay. Yeah. Yeah. Yeah.
1:44:29
So, so even though I fire off one query to GPT5, it if it's doing deeper research, it might be pinging 75 different websites and that's driving up total internet use. >> Yes.
1:44:39
And the systems >> spanning out >> are also creating their own queries. Yes. Like >> Yeah.
1:44:44
We saw that with the demo from Palunteer like you know he he typed one line of text like help optimize this airport. That's right. Okay. Yeah.
1:44:53
And so this is where the disruption in telecoms.
1:44:56
And if you really think about what has changed in telecom over the last 25 years, the answer is not much.
1:45:04
>> When you can take one port and you can put lots of services on that port and put the control in the customer's hands.
1:45:12
>> You've changed the way people inter it's cloudifying telecom.
1:45:14
And in this new world of what is happening with cloud like cloud 2.
1:45:19
0 I know that is the necessary bandwidth control and uh precision yeah >> that you need in connectivity.
1:45:29
>> What does cloudifying telecom mean?
1:45:29
Does that mean like more like multi-tenant on the actual fiber lines like instead of a hyperscaler owning one route then they're they're bidding it out and spot rates or something.
1:45:41
rates or something. Yeah, multi multi-tenant is a good way to think about some of the services on top of you know in the past you've literally if you think even back to old telephone switches you've had you know the the one wire to one wire it's been one port to one service you add a service you add a
1:45:56
port it's a truck roll it's a person coming out >> cloudifying it is bringing all of that technology to the users giving them that interface that portal where they can say I need these services I need them in these locations I need this speed I need the bandwidth turned up it's network as a service. >> Yeah. So higher level of abstraction. >> Yeah.
1:46:12
So higher level of abstraction. Yes.
1:46:14
And uh yeah more like almost like a virtual machine on top of the the the telecom infrastructure.
1:46:19
So can we be be provisioned like on an ad hoc basis. >> Yeah.
1:46:23
And and one of the biggest changes I think in the economics this AI economy is also if you think about a network subscription if you will of the past >> you sign up you get a certain amount of bandwidth.
1:46:35
But if you look at the companies of today, if you look at the sports industry, manufacturing industry, healthcare industry, they have these spikes that are massive.
1:46:43
And so we're providing that network as a service where it turns up, turns down.
1:46:49
And then customers are paying for what they it's a consumption model.
1:46:53
And again, that's part of this cloudifying model which has not hit telecom till what we're looking to transform.
1:47:00
So yeah, help me understand the the new shape of the uh telecom industry in your business.
1:47:06
Like I imagine that there's some genius scientist that comes up with a faster fiber optic cable that is manufactured somewhere.
1:47:14
Then someone purchases that, they buy some land, they bury it in the ground, maybe they get some rights, and then at a certain point someone's uh you know leasing or essentially charging a toll along that toll road.
1:47:23
Uh do you sit all are we completely vertically integrated?
1:47:27
So we we sit vertically integrated but I think what >> you do R&D on on new fiber optic technology.
1:47:34
>> We work with a number of partners on that and then we're also thinking about the AI optimizations on on that fiber.
1:47:41
So if you think about intelligent routing if you think about redundancy if you think about all those things where you could have something as simple as a fiber cut in the ground. Sure.
1:47:49
Maybe it's on purpose maybe it's not on purpose but >> you're aware of that and then you need to dispatch someone to go fix it.
1:47:55
you can't have any interruption to the services you're running.
1:47:58
So we have to have that redundancy.
1:48:00
Y >> on top of that our customers and enterprises everywhere I think they started mostly building with one cloud.
1:48:08
>> Now if you think about this multicloud world where they're hitting Azure, GDC, AWS, they're hitting all of them at the same time with the same applications in different regions across the US.
1:48:19
They have to seamlessly let those systems talk to each other.
1:48:23
>> And they don't want a direct connection to each of them. That's where we started.
1:48:28
>> But now they want to be able to live in this fabric where their systems can talk to all of these in all the regions, get all of the data and process faster because that's part of the disruption they want.
1:48:41
>> Uh last question for me.
1:48:41
Um uh how how does Palunteer fit into that? >> Yeah.
1:48:45
So if you think of the operational complexity of the decades of past. >> Yeah.
1:48:50
>> Uh you know you've built all these networks.
1:48:52
We we talked about fiber in the ground.
1:48:54
>> Think about the systems over those decades that have been built up. >> Yep.
1:48:59
>> One of the things Palanteer is helping us with is this managing this operational >> complexity.
1:49:03
You sort of see an abstraction of this in LA when there's the fire and like the the boxes with the telephone lines just explode. Yeah.
1:49:10
>> You're like why didn't they build a box that doesn't explode?
1:49:11
And so you imagine that, okay, that's where that's how the power lines work.
1:49:18
>> The fiber optic lines.
1:49:18
Yeah, they're newer, >> but there's probably still some stuff that might go wrong if it was installed 30 years ago.
1:49:24
>> Yeah, you got to identify that early.
1:49:26
>> There's that and there's the software layer that is running all of those.
1:49:30
>> Got to make sure that that's up to date, not crashing.
1:49:32
>> And Palanteer is helping us optimize those, helping us bring them together.
1:49:37
And and what we are building for customers >> is then a system that they don't have to think about the optimization they need in their network.
1:49:47
We're going to help automate that.
1:49:49
We're going to help bring AI to that network.
1:49:51
And that's part of this partnership.
1:49:53
And it's also frankly the most exciting part about disrupting telco.
1:49:59
>> It's not an industry that too many have talked about disrupting for a while. It's ripe for it. It's needed.
1:50:03
and this AI multicloud era, Lumen's here for it. >> That's very exciting. Anything else, Jordy? >> Love it.
1:50:11
>> Uh, we're running late, so thank you so much for having me.
1:50:14
All right, >> I'll grab this. Thank you.
1:50:15
Uh, we have our next guest coming into the studio. Uh, Drew Cukor.
1:50:21
I think we actually have multiple.
1:50:23
We might need to pull up an extra chair. >> Um, we have lads. We have lads coming in.
1:50:29
Uh, if if we want to bring everyone in, we can.
1:50:31
We'll we can pass the mic around.
1:50:33
Whatever, whatever you guys want to do. Um, we have multiple. >> Just >> Oh, okay. Hey. >> Oh, hey. >> Just me. >> Oh, how you doing? >> Sorry. What's up? >> I'm John. Welcome. >> What's up? Great to meet you. >> How you doing? >> Good. Good. >> Uh, how's the day? Could you kick us off? Grab the mic.
1:50:47
Kick us off with an introduction for those who don't know. >> Okay. Uh, I'm Dave. Uh, Dave Glazer.
1:50:53
Been a Palunteer for 12 years. Uh, and I'm a CFO. >> PreIPO. >> Pre-IPO. Yeah. Like basically. >> Or DPO, right? >> DO. Yeah.
1:51:01
uh since like when our our prior CFO retired who's actually on the show recently talked to him.
1:51:08
>> He retired in 2017 and since then I've been leading the finance team. >> Yeah.
1:51:12
Uh so my big question for you uh gross margins for the Fortune 500 in the AI era.
1:51:18
Are we going to see a structural shift?
1:51:19
You know the the the the inference bills are skyrocketing.
1:51:24
Inference per token is is dropping.
1:51:24
But then Jevans paradox and we're doing more token inference than ever before.
1:51:30
reasoning models are kind of staying expensive and we saw in the journal earlier this week maybe last week uh software company called notion said that they saw their gross margins drop from 90 to 80% not bad still but uh there is does seem to be some sort of impact and I'm wondering how you think it might play out for the really big companies
1:51:51
>> yeah look I I think so one of the things that we've been sort of saying is like LMS are commodity commodity cognition right and so like essentially it's like It's getting they're getting better and better y right uh ELO score is better and better tokens are getting cheaper right uh and as Alex said I don't know
1:52:07
if you watch keto but like you know he's talking about okay like what like how do you actually derive value from that raw output of an LM and so it's like I think it's like the raw output it is getting cheaper we're still like very early days on these models and you're seeing them just sort of like up and to the right in
1:52:20
ELO score and so these like things combined I think are going to make it cheaper and cheaper over time >> and I think >> we'll see sort of on on gross margin I think you look at some of the other things like hyperscaler costs, right, from a lot of these places that I think like people's gross margins have survived, right? They're more efficient. They're more efficient.
1:52:36
They're all this and so I think like we will see, but like I think that is it's going to be much more about like how are you deriving value from them like well the cost is going to be so overwhelming but they're super like >> totally >> it's like focus on the value and I I do think over time it's like people are going to be able to manage those costs. >> Yeah. Yeah.
1:52:51
It feels like it feels like higher costs potentially but so much more value and it's pretty easy to tell. Yeah.
1:52:58
I'm spending a lot on inferencing a certain LLM API, but obviously I'm delivering more value and so I'm charging.
1:53:05
>> Also, you have to think about the position that >> Palanteer sits in.
1:53:07
We got a product demo earlier.
1:53:09
Hivemind was leveraging like a bunch of different models and like that position of having leverage and being like we are the product, we have the data, we have the customer relationship, and we can vend in whatever intelligence sources we need in order to accomplish the task.
1:53:23
Like that's a better position than being if you're a GBT rapper and your product is really 40 and you're just kind of like reselling that, right? >> Yeah. Yeah. Uh yeah. Sorry. >> Yeah. Yeah.
1:53:34
Like and I do think it's like Yeah.
1:53:36
Like I think it's going to be all about the value rather than like well the the value is there but the cost is so prohibitive. >> Yeah.
1:53:42
H how are you pos how are you thinking about positioning uh Paler story in commercial in the United States over the next couple years?
1:53:48
Like what is the right framework?
1:53:50
People have always had the wrong mindset. Uh it's consulting shop.
1:53:53
What are they even doing? Blah blah blah.
1:53:55
Like what is the right frame of mind to be in?
1:53:58
>> Look, I I I think the right frame of mind is like we're delivering a tremendous amount of value. Yeah.
1:54:03
>> Uh to these customer with these customers, right?
1:54:04
It's like like and and they're needed to in this, right?
1:54:06
And it's like you deliver that value uh and we're like just at the beginning.
1:54:11
So you look at like our our US commercial business like grew over 90% last quarter.
1:54:15
It's still relatively small, right?
1:54:17
And it's like we have there's so much runway there.
1:54:19
Uh right like we it's like just that that business has like sub 400 customers. Yeah. Right.
1:54:23
Like that is when you when you look sort of across a lot of other companies it's like that's you know so it's like we're doing all this with such a like a small customer base and obviously it's rapidly growing but you know it's like it just shows the amount of runway that's ahead. >> Yeah.
1:54:36
Do you do you think that uh people should be thinking about the commercial business as like a a bundle like a a competitor to a bundle of products that already exist or something that's entirely net new or displacing an entirely different uh class of spend in the enterprise?
1:54:56
Like how can how should people even wrap their mind around that?
1:54:59
>> Some version of all the above, right?
1:54:59
So it's like when you think about you know you're not like headtohead who are we competing with, right?
1:55:04
And then everyone's like but I don't get it.
1:55:05
It's like is it a combination, right?
1:55:06
It's like we're not really we're competing against like the Frankenstein monster that almost every large corporation has and then you're also competing like particularly in government but it also applies in you know particularly large corporations is like customuilt software.
1:55:20
So it's like those two you're competing against that and over time >> you're obviously going to sort of eat into a lot of into a lot of the spend but it's like only because of the value that's being delivered and then it's like you don't maybe need some of these point products. >> Yeah. Yeah.
1:55:33
It it it feels like the it's like it's like transformation new net new technology that would not get built in the enterprise otherwise. >> Correct.
1:55:42
And then once you've built that once you built that like compounding data asset then perhaps you don't need >> some of the other products. Yeah. That makes sense.
1:55:47
>> How is your framework or philosophy approaching the finance function at Palunteer changed?
1:55:51
Because I I feel like there's like very distinct eras where you know the >> it changes them every day.
1:55:56
Does it does it do you do you feel like you have to to update it every day?
1:55:59
Like because because in some ways like when you talk when we talked with Karp earlier, it's like yeah, he's bringing that same energy and like philosophy uh uh it feels like it's it's somewhat consistent uh even though you know numbers go up and down and and all that good stuff. >> Yeah.
1:56:16
Look, well look, I I challenge any CFO working for Karp to have hair, right?
1:56:20
right? Uh so look um I think you got to step back and say like okay like how do we approach finance right and it's like >> this is a company like and you know people have said it a lot like we don't have a playbook right and obviously there's a way that's run the the company's been built over the last you know 20 20ish years like I've been lucky
1:56:38
enough to be here for 12 of them but like you know and and and because of that it's like we're very unique right and what that means is like we are constantly changing what we're doing right and so like a lot of things you know you talk about for deployment engineers in the early days oh that's consulting that's obviously helped us build the product that we have today, right? And so
1:56:52
And so >> but you weren't what you weren't optimizing on in those days was financial statements that Wall Street would want, right?
1:56:59
Because it's and and then it's like but because of what we built today, not because >> or because of what we built, we have financial statements Wall Street loves, >> but it wasn't built for that purpose, right?
1:57:10
And which is crazy valuable, right?
1:57:13
Because it means we're we're so differentiated and we're doing things the way that like >> we want to do them, >> right?
1:57:19
and and and built the company was built that way.
1:57:21
Um, so >> can you tell me the story of how the COVID era changed Palunteer's financials?
1:57:28
I remember seeing that T& fell off a cliff and it never really came back and that was at the time I was talking to people who were looking at the company.
1:57:37
Uh, they were pretty excited about what that meant and it felt like it was almost like a structural shift for the company.
1:57:43
But is that a reasonable story to tell? Is that apocryphal?
1:57:46
Look, it's it's it's it's part of the story, right?
1:57:49
And and so I think like what what happened with co it was we could no longer like you you just couldn't be as much at a customer site, right?
1:57:58
And so then it's like well we got to extend the product further, right?
1:58:01
And like and this is a story that keeps happening in Palunteer.
1:58:02
It's like well you know we only have um >> you know uh around 4,000 people, right?
1:58:09
And or you or you look at sort of our our headcount growth like if you go back two years it's up 12% from two years ago revenue is up 88%.
1:58:15
It's like, well, how do you do that?
1:58:17
It's like, well, the product's got to be better. Yep. Right.
1:58:19
And and you have to have products like AFTTE, like all these all these things that are constantly evolving and like that is a story of Palunteer.
1:58:24
It's like you're trying to do something.
1:58:26
You're either resource constraint or somehow constrained.
1:58:29
It's like what do you do to meet that and almost always is productled.
1:58:33
>> Yeah, that makes a ton of sense.
1:58:33
Uh I know you're have a busy day, so we'll let you go. >> Awesome.
1:58:38
>> Thanks so much for helping. >> Thanks for joining.
1:58:40
>> We'll talk to you soon.
1:58:40
Uh we will bring in our next guests in a minute.
1:58:42
Jordan, do you have any breaking news here from Skooks?
1:58:46
Skook says, "Alex Karp trying his best to get TBPN banned from YouTube."
1:58:50
I will say, uh, I think it was like the the least familyfriendly 10 minutes segment of the hundreds of hours that we put out. It was some of the best. >> Some of the best. >> Some of the best. >> It was a lot of fun.
1:59:02
I'm glad that Skooks enjoyed the stream.
1:59:03
Uh, and uh, and thank you for YouTube for keeping us up. >> Keeping us up.
1:59:08
We might >> keep going strong.
1:59:10
Thank you to Reream for keeping the stream live. Thank you.
1:59:14
Uh couldn't do it without him.
1:59:14
Uh we will bring in our next guest guests.
1:59:17
We are ready to keep rocking and rolling here in >> at two chairs. Two chairs coming in. >> Come on in. Come on in. Pull over. >> How you doing?
1:59:30
>> We got we got an indie car driver for you. >> Oh, fantastic. >> Performance engineer. >> Very cool.
1:59:34
>> And then a finance guy. >> Fantastic.
1:59:35
I mean, you have to sell me now. I'm in. How you doing? Good to meet you. I'm John. >> Hey. >> Pleasure. I'm Johnny. Nice to meet you. How you doing?
1:59:46
>> Lads, we got the lads. Take a seat. Take a seat. >> Take a seat.
1:59:50
>> Do you guys want to share? >> Yeah, we'll share.
1:59:53
>> We'll share my >> Great.
1:59:55
So, yeah, why don't you to uh kick us off with the introductions? Let us know who you are.
1:59:59
I'm sorry you got stuck with a rough chair.
2:00:01
I couldn't figure out how to get the chair to up properly. >> Don't even try. It's not going to work. I already tried it.
2:00:08
Uh anyway, introduce yourselves.
2:00:09
Uh, so I'm Zach Porter, uh, a senior simulation engineer with Andrea Global on the Indie Car program. >> Cool.
2:00:14
>> And I'm Kyle Kirkwood, driver of the number 27 Honda for Andrea Global. >> Fantastic. >> Yeah. And I'm Drew from TWWG. >> Fantastic.
2:00:21
Um, h how do how do all of you fit together?
2:00:25
>> We're all under the TWWG umbrella.
2:00:27
Basically, a bunch of different businesses within that.
2:00:28
Drew can probably speak to it a little better than I can. >> Yeah. I mean, it's a family.
2:00:31
It's a great holding company.
2:00:32
We have tons of businesses from insurance to >> uh asset management, investment, banking and you know sports, media, entertainment, western lifestyle. >> Yeah.
2:00:45
>> And of course the crown jewel of just about everything is the awesomeness of motorsports. >> Yeah.
2:00:51
>> And the Andredy team and Indie Car.
2:00:52
>> How long have you been involved with Andredy?
2:00:54
>> Uh it's my my fourth season at Andred. >> Fourth season. >> Yeah. Alth [Music] time here. I think it's my fourth. No, it's my third.
2:01:03
It's my third season with them, but I've also I've been a part of the family for longer than that.
2:01:06
I was with them in Indie Lights and then I joined back with them in in Indie Car.
2:01:11
So, >> really five seasons, actually, if you combined it all. >> Yeah.
2:01:16
>> And, you know, I'm I get to be this suit guy, so I sit and watch this, but I've been here a year. >> Oh, fantastic. Yeah.
2:01:22
>> Uh, and yeah, and walk me through the flow of like why you're here specifically at AIPCON, why are you uh working with Palunteer? Uh, >> yeah. Yeah.
2:01:29
So in indicar we have we we have a ton of data in a ton of different siloed places and >> it sits you know from stuff that we control like our car setup database and stuff and but it also sits in like databases from Indie Car that we don't control.
2:01:41
We have to consume all these things and they're all connected.
2:01:43
They all represent performance.
2:01:44
They all represent the pieces of the car and how they go around the track and and how we get faster and how we're relatively performing against the competitors.
2:01:50
And so >> we we came to Palunteer and worked down this path to to try and connect all these disparate data sets into one place where where our engineers can make better decisions faster, sooner um because in the end you know from practice one to practice two or practice two qualifying whatever it is that there's this limited amount of time that we have to make a decision.
2:02:08
The practice is coming whether you're ready or not.
2:02:09
Y >> so the more informed we can be the better decision we can make in theory the faster we can iterate and be more competitive.
2:02:15
So yeah, it feels like the maybe we're just in the era of like, you know, small micro optimizations just add up to greatness.
2:02:24
Uh are there any stories from your career or just uh racing in general that stand out to you where someone just discovered some secret that just gave them a mass advantage?
2:02:36
advantage? I'm thinking of uh in sailing there was this maybe it's a fake story I don't know but this idea that there was uh in in the what's the big sailing cup that Allison races in America's cupg >> yeah yeah it's all it's all catamarans now and and the and the story goes that
2:02:53
they were all racing monoholes and someone looked in the in the rule book and said there's nothing that says you can't bring a catamaran and then in one day somebody brought a catamaran and just beat everyone and it was just one of the most fantastic stories. Have
2:03:04
Have there been any eras uh that you've studied where someone's just figured out something that just rewrote the whole >> I mean it would never be like this again but you had the the the fan car in F1, right? >> Tell me about this. Yeah. Yeah. Yeah.
2:03:17
Tell tell me the full story.
2:03:18
>> Uh >> I don't know the full story. >> I don't know.
2:03:22
We're in an era of motorport now that things are super tightly regulated.
2:03:24
It's really hard to find these big gains.
2:03:26
But but what he's referencing back in the day there there was an era where where aerodynamics were kind of king and they the guys did a similar thing.
2:03:32
They looked at the rule book and said, "Hey, there's nothing that says we can't power the air inside the car on our own."
2:03:36
So, they built a car that had big fans at the back of it and skirts that ran down the side and the car literally suck >> sucked its way down.
2:03:42
So, just so much extra down force.
2:03:45
>> I don't remember exactly how long it existed, but it wasn't very long.
2:03:48
>> I'm sure it got banned. >> It's amazing.
2:03:50
>> But it was fundamentally dominant and there's there's been a lot of those kind of things now and and over time, but now we're kind of in this era of of fighting for these hundreds of second, these little micro moments.
2:03:59
That's where being able to drill down through big data is so powerful for >> us we'll be like we do a live show right so speed and timing is important and sometimes we're like oh this document isn't here we don't have this link and things like that you guys are racing around a track where every millisecond matters and so if you're jumping between different data sets and and uh systems of record I can imagine that's that's uh can be a disaster. >> Yeah.
2:04:27
And it's not just while while Kyle's on track.
2:04:29
Yes, he's he's doing all of that, but then as soon as he's back, it's it's between sessions as well.
2:04:34
It's it's the the clock's always ticking.
2:04:36
We're competing on the track and off the track. >> Yeah.
2:04:38
I mean, we just have such little time to go through so much data and to be able to piece it all together and understand a full picture.
2:04:45
You have to do a lot of different things, which our engineers are very good at, but it's time consuming.
2:04:49
So, if there's a way to actually consolidate it, simplify it, and make things more efficient, then it's going to allow our engineers to make better decisions down the road, which is optimizing performance on the racetrack. >> Okay.
2:05:00
Talk about the tension between the three of you.
2:05:02
I imagine that you only care about speed.
2:05:04
You care about speed and manufacturing. Can we make it?
2:05:06
And you care about speed, manufacturing capability, and cost maybe. >> Cost. So, what Yeah.
2:05:13
What What are the trade-offs?
2:05:15
Obviously, everyone cares about speed and winning, but uh but there are layers to the trade-offs because you can't you can't just always turn every dial to 11, right?
2:05:23
>> Well, I mean, look, you know, I spent 30 years in the Marines. >> Yeah.
2:05:26
>> And um you know, we got tired of fighting wars on PowerPoint. >> Mhm.
2:05:31
>> And you know, for business, we're getting tired of like making decisions off of rudimentary and incomplete systems that provide only partial solutions and it just takes forever to get data together. >> Yeah.
2:05:43
And so, you know, from a business perspective, we have to look at it and basically say, look, we want to transition to something better.
2:05:50
>> And the cost of that is not just material like dollars. It's also change. It's changing mindset.
2:05:56
And as you can see from Andredy, like they're all into this.
2:06:00
Like this team is ready to make that transformation, but it'll still come at a cost, right?
2:06:04
There's people who are stuck in their ways.
2:06:05
Look, I like to do things this way.
2:06:07
I'm not used to that much data coming at me.
2:06:08
I can't make decisions that fast.
2:06:10
like this is transformational and really fundamentally it's people, money, it's organizational and obviously when you got a great team like it's just going to go like a hot knife through butter.
2:06:20
It's going to be amazing. >> That's great. >> Yeah.
2:06:23
Where uh walk me through some of the benefits and and and try and give me some anecdotes about where gains have come from throughout your career.
2:06:31
Yeah, I mean like for for us, we we take in so much time series data on the car specifically that's the representation of what Kyle's doing on the track, right?
2:06:41
And what the car is doing and all of that and being able to connect that data to his feedback and ensure also that that data is is clean and it is correct.
2:06:50
You know, it's it's not like a a car that's just rolling down the road and it's >> hanging around and putting some sensor data out like he's fgging the thing around the racetrack and occasionally touching walls and other cars and it's >> more than touching.
2:07:01
It's really difficult sometimes to keep to make sure every system is working perfectly, right?
2:07:06
It's it's a never- ending battle of trying to do that.
2:07:07
of trying to do that. And so, you know, we're we're working really hard with some ML models and some stuff to pick out sensor anomalies and flag them automatically so that our our systems engineers don't miss them and they can
2:07:17
go drill down and figure out why that sensors failed or where and what their knockon effects are and and in the end just get that part replaced immediately so that the next outing, the next time we're on track, we know the data is going to be as good as it could be. That's that's been the the the earliest
2:07:26
That's that's been the the the earliest easiest wins for us is is kind of in that space. >> Yeah. Yeah.
2:07:31
Is there a uh a how do you think about budget budgetary constraints?
2:07:36
Is that something that's just set internally?
2:07:37
like how do you work through?
2:07:39
work through? I'm happy that I don't have to worry about >> you don't have to worry about >> but but I mean even zooming out for those who might not be familiar like like uh I mean we saw some we saw some drama earlier this week about salary caps and and different ways to get
2:07:53
around things like how do you think about setting the budget for the team and then actually executing against that because that's got to be the last the last phase against uh how do you actually deliver something that you can deliver on race day every single day with reliability and not need to cut the cost later. Let me let's talk like this
2:08:08
Let me let's talk like this is innovation. Yeah. Okay.
2:08:10
So, we got to be careful here. Yeah. Right.
2:08:12
So, if you come in I mean obviously there's dollar budgets, right?
2:08:16
Because it's not unconstrained. >> Yeah.
2:08:19
>> But at the end of the day, like what we want to do is we're talking about a fully connected business here. >> Sure.
2:08:24
>> So, they've got an HR shop, they've got a tech team, they've got engineering, they've got a ton of groups that all need to be brought together.
2:08:31
>> So, apart from just the car and the magnificence of what we're doing, you've got to bring it all together.
2:08:35
And so we need room in space to be able to build out a complete connected business >> because frankly every signal across the business is value >> and by squeezing and optimizing and making things run more efficiently we end up with a better sport. >> Yeah.
2:08:52
>> And like I think at this point we're in that journey >> and so costs are going to be you know not giant but constrained and we're going to deliver and we're going to watch and see as this evolves until we land somewhere where we can finally say this is it.
2:09:04
this is the benchmark and this is what we should manage off of. >> Yeah.
2:09:07
For us, for us, we're gonna ask for every tool we possibly can to make to make the car better.
2:09:11
He's gonna he's expecting us to do that to do that job and and in turn, we turn around to the commercial side of our business and and look at them and say, "Hey, it's it's your guys job to go out and find that sponsorship, find those things because if we don't use this tool, our competitors will." Yep.
2:09:25
>> And and you know, we're in the business of winning and and if we're not going to try to do that, then why are we here?
2:09:30
>> Uh take us through the the the next few months on the calendar, the rest of the year, the next year.
2:09:33
So, we literally just ended the last race of the season like three days ago, four days ago.
2:09:36
Um, so we officially start our offseason and and this is where we sort of take some of our use cases and our ideas that we've sort of halfbaked and triled some stuff and look at it and and productionize it and and in in the end try and get all of these >> at least the first initial use cases ready to go for St. Pete 2026.
2:09:53
That's that's kind of the target and there's ton of prep from here to there. >> Yeah.
2:09:58
And I'd say in the offseason racing is so expensive that you you're limited on how much testing you can actually do on a racetrack, right?
2:10:05
So it's very important that all the data that we collect and we utilize is is actually making a difference and we're actually able to progress with with with the data that we have.
2:10:16
So um that's where the engineers come in, right?
2:10:20
We've got a a massive group of engineers that um take a lot of pride in their work and and they have five, six months from now until till the start of the next season that they dig in through maybe one or two tests that we get, maybe some wind tunnel stuff, maybe some various other things, shaker rigs we call it.
2:10:35
Um but we can't really get on track that much because of because of how expensive it is.
2:10:39
So a lot of what we do is in the sim world and it is very data driven. >> Yeah. What Yeah.
2:10:44
What does the rest of your offseason look like?
2:10:45
Are you training and running?
2:10:47
I saw the F1 movie and Brad Pittz running around.
2:10:49
Are you are you running guy or both?
2:10:54
>> Uh, you know, it training is important, right? Uh, yeah.
2:10:57
I mean, you you have to be as a racing driver, you got to be like a certain weight, certain size.
2:11:01
You have to be um >> you got to have good endurance, but you also need to have some strength to be able to wheel the car around. Right. Right.
2:11:09
We don't have power steering.
2:11:10
You're hitting the brake pedal as hard as you possibly can, and we're pulling up to four or 5gs for an hour and 40 to 2 hours at a time.
2:11:15
So it it can get very physical very fast. >> Power steering. >> No power steering. No.
2:11:19
And the car and the car makes over 5,000 6,000 lbs of downforce.
2:11:24
So um imagine driving your road car that weighs 8,000 lbs or something like that um around without power steering.
2:11:31
>> Flash that on the screen when there when you got the driver view so that you guys get a little credit because I think people assume it's like turning the wheel of you know a Tesla or whatever. >> Yeah.
2:11:40
No, it's uh it's much tougher than people tend to realize.
2:11:42
I that's specific to Indie car racing though.
2:11:46
Indie car racing, we don't have power steering.
2:11:47
F1 does a lot of sports cars that you see, they do have power steering, but Indie car itself, they they do it for the sport.
2:11:53
Um, and they've kept it that way for many years.
2:11:56
So, um, it's a little bit old style, but at the same time, it's good because it really >> translates it from the boys >> a little bit, right? Yeah.
2:12:02
It's like it creates a sport out of it, right?
2:12:04
It's a little bit more physical.
2:12:06
People don't look at it as much as like, oh, you're just driving a car around some roads, right?
2:12:10
Pushing pedals, turning wheels.
2:12:12
No, there's actually physical side to it.
2:12:13
So, um the offseason is a lot of training, >> uh preparation.
2:12:17
We do a lot of sim work and and uh driver in the loop simulators and um yeah, it just being ready for for the next race that comes up. It's hard.
2:12:25
It's hard though because you don't have G-forces.
2:12:26
You can't you can't simulate G-forces for a driver.
2:12:28
So, um having that involved is is um is something that you get acquired to as the season progresses if I'm being honest. >> Yeah.
2:12:37
>> Uh what's your daily >> I'm sorry.
2:12:39
>> What's your daily driver when you're not on the track? >> My daily driver.
2:12:41
So that is one is that is one of the great things about being a racing driver is you don't have to own a car.
2:12:47
>> Oh, you don't own a car.
2:12:49
>> You uh so I I race for a loner or something. Is that okay?
2:12:52
So I race for Honda, right? An indie car.
2:12:54
And I have a >> S2000 word with under glow.
2:12:57
You have glow on the S2000. No.
2:13:02
>> I have a Acura MDX since they're they're sister companies, right?
2:13:05
Um and then I also >> they're not sending you an NSX.
2:13:09
>> They don't make the NSX anymore.
2:13:09
They still got them laying around. Give them a call.
2:13:13
We'll we'll we'll talk to them.
2:13:15
We'll say we need we need it ripping around at NSX.
2:13:17
>> And then I also race sports cars for for Lexus as well.
2:13:20
And >> LFA every day, obviously.
2:13:23
>> They also don't make LFA anymore.
2:13:23
So, >> yeah, just a million $2 car I can just go rip and depreciate real quick.
2:13:32
>> I have an IS-500 at home.
2:13:32
So, that's the other car. >> That's great. Fantastic.
2:13:35
Uh, well, thank you guys for coming on. This is fantastic.
2:13:38
Anything else worth sharing before you get out of here? Okay.
2:13:40
Enjoy the rest of the conference.
2:13:42
Thank you so much for wrapping up. >> Thank you.
2:13:44
>> We will talk to you soon. >> Cheers, guys. >> Have a good one. >> Thanks. >> Goodbye.
2:13:48
Um, Jordy, any other breaking news going on?
2:13:51
We have our next guest coming into the studio in just a minute.
2:13:57
I believe we have >> Who do we have?
2:14:00
>> We have someone else coming on. >> Okay. Okay, cool. Yeah. Yeah. Yeah.
2:14:04
We're we're we're good whenever. Uh, we kind of ran late.
2:14:06
Now we're now we're running a couple minutes early. We will keep it going.
2:14:12
>> Palunteer CEO Alex Oh, I got these again.
2:14:14
Palanteer CEO Alex Karp thinks the value of skilled workers is spiking even as big tech companies, possibly his own, may shrink. Our revenue is going up.
2:14:22
Our salesforce is going down.
2:14:22
He said on TVPN, "The number of people we plan to have in the future is less than now." >> Very cool. Um, >> scoop. >> We scoop. We're scoop maxing.
2:14:31
We're newsmaxing, everybody. >> We're newsmaxing. Uh, what else?
2:14:38
>> I think we're ready for our next guest if you want to timeline. Looking good. Lots of posts. Have fun.
2:14:41
Um, welcome to the stream.
2:14:44
If you're ready, we're good.
2:14:46
We can we're we're happy to have you. How you doing, John? Nice to meet you.
2:14:49
>> Thank you so much for taking the time. >> Yeah, welcome. Thank you.
2:14:52
>> Any relation to Brandon Jacobe with >> I don't think so.
2:14:55
>> I think you guys differently.
2:14:55
We have a we have a buddy who works uh he's a designer and we like to we like to poke fun of him because he is uh we call him Jacobe.
2:15:03
>> Uh and and whenever we have a design problem, we always call him The last name sticks with that one. >> Yeah.
2:15:07
Anyway, um please introduce yourself for the stream. Who are you? What do you do? >> Happy to.
2:15:11
Sorry, I'm out of breath. That's the last name. >> You're good. You're good.
2:15:13
>> Um so, Matobi, I'm the uh head of data science and analytics at Racetrack. >> Okay.
2:15:18
>> Um Southeast based fuel and convenience retailer. Yeah.
2:15:21
>> Um and shout out to my wife for letting me come up here because we're technically on vacation this week. >> I heard this. This is crazy. The grind never stops.
2:15:28
We got the memo about lockin season.
2:15:31
>> Well, it's you gentlemen.
2:15:31
I couldn't pass up the chance. We really appreciate it. Okay.
2:15:35
So, uh great to have you. >> Yeah.
2:15:36
So, so break down the business a little bit more.
2:15:39
Give me a sense of the scale.
2:15:40
Uh what the day-to-day is like, customers, you know, obviously we have a general idea, but give us more. >> Yeah. Yeah. Happy to share.
2:15:46
So, um roughly 700 retail locations >> across our family of brands of uh Racetrack. Yep. >> Raceway. Yep. >> And Golf.
2:15:54
A lot of people don't realize that we we own Golf. Golf. >> Yep. Yep. Cool.
2:15:57
um 10,000 employees um associates in our stores and people at our our um store support center in Atlanta.
2:16:04
>> Um a lot of people don't know either.
2:16:06
We're top five largest privately held company in the state of Georgia and we are top 15 in the United States. >> Thank you. We have Minecraft.
2:16:15
>> Um, so walk me through a little bit of the history of the company because I imagine that what we're going to talk about in terms of like, you know, software artificial intelligence is, you know, a revision to the way it was done years ago, right?
2:16:26
So, so yeah, walk me through a little bit of the history. Get me up to speed. >> Oh, wow.
2:16:31
Well, I can't speak to all of it.
2:16:33
Um, I've been there about two years.
2:16:35
>> Um, but what I can say is that >> we've done a really great job of focusing on transformation, specifically data enabled transformation.
2:16:41
Um actually I just wrapped up a conversation about this downstairs but um if you ask me one of the purest um use cases for transformation is converting from gutbased and tribal knowledge based decision-m to datadriven y >> and therefore after that analytics and AI based transformation.
2:17:00
So um >> you know we we've really focused heavily even before my time on making the best decisions we can with data.
2:17:08
>> Y >> and so our partnership with Palunteer has really allowed us to to take that to the next level. Right.
2:17:11
the proverbial next level.
2:17:13
Um, promised myself I would avoid buzzwords in this conversation, but it may not happen naturally.
2:17:17
Um, >> but but but yeah, it's um it's been a a conscious and concerted effort um by our leadership top to bottom to to really make that happen.
2:17:26
And it's not it's not easy at times, right?
2:17:27
You're you're asking people to step out of what they've done in the past and to trust data and math um that may or may not be right if we're just being candid. Yeah.
2:17:39
>> Um, and so we've we've really grown and focused and and developed on on uh building that muscle with the organization top to bottom.
2:17:44
It's been a it's been a really really interesting and uh impactful two years with with our team this thus far.
2:17:52
>> Walk me through some of the concrete ways that you can use data to make a decision at racetrack.
2:17:57
I remember there's this funny story.
2:18:00
It might be might be apocryphal, but uh I heard that uh I always do this where I tell some story that might be entirely >> hallucinates.
2:18:10
You're an LLM, but taking over.
2:18:13
So So the story goes is that is that McDonald's needed to figure out how to place a bunch of restaurants.
2:18:17
I'm sure that this is something somewhat related to what you have to do.
2:18:20
you decide where the restaurants go and they did a ton of analysis and they figured out this street corner was the best and that street corner was the best and they spent millions of dollars in consulting and they put them all there and then Burger King came along and said, "Yeah, just put one next to to McDonald's."
2:18:33
And uh there's some be there's some beauty there.
2:18:37
There's some there's some hilarity there.
2:18:38
Uh but uh but you can imagine that that's the type of very tractable problem.
2:18:42
Where should I put a put a thing?
2:18:44
Uh, also like store layout, planagrams, uh, figuring out what goes on promotion when, pricing, dynamic pricing.
2:18:51
Uh, there's a whole bunch of things that I could imagine you do, but like walk me through what you did >> or even at the individual at the individual store level where it's like, hey, we're out of this product. >> Yeah.
2:19:00
What are the what are the problems?
2:19:02
What's the most recent like case study you did? >> Yeah. Yeah. Great question.
2:19:05
Look at you talking about planagrams.
2:19:07
>> Um, so so yeah, we we like to say that we're always focused on on the the customer, right?
2:19:12
At the end of the day, it's our customers and it's our associates that make this massive business continue to run and thrive.
2:19:19
>> Um, and so you're hitting on inventory.
2:19:21
That's that's a really important use case.
2:19:22
Um, but even more important than that is making sure that we have the right levels of people at our stores to meet that customer demand.
2:19:28
There's nothing worse than when you go up to a gas station to fill up your your gas tank and there's a yellow bag on the handle or or I would actually argue it's it's even more painful when you you put it into your to and then it's slow or Yeah.
2:19:43
or so there's there's that and there's also the inside experience, right?
2:19:47
We um we take pride in our um in our our food offering.
2:19:49
So, totally fresh pizza um fresh sandwiches, breakfast sandwiches.
2:19:55
>> Um and that takes people, that takes time, and that takes hours.
2:19:57
and making sure that we we have the right level of people in the store, right number of hours and and the right skill sets as well.
2:20:05
It's not just an you can't just throw hours at these problems.
2:20:07
Um you need to understand the skill set um to meet that demand and meet those expectations of the customer because at the end of the day um it really is that customer that that makes us continue to thrive.
2:20:17
And you know, we got this pin on.
2:20:19
We're celebrating 95 years.
2:20:19
Um we've been here a long time and we expect to be here a lot longer.
2:20:24
>> 95 years ago, software didn't exist. it truly did not exist.
2:20:28
And now you're sitting here implementing AI and and the largest enterprise software platform possible.
2:20:34
Um uh switching gears, a little bit of a hot take.
2:20:37
Uh have you been surprised by the developments in just how the electric car has rolled out?
2:20:46
Like there was a moment when everyone was like do not get in the gas station business at all.
2:20:51
It's going to be all electric.
2:20:53
All these companies are cooked.
2:20:55
Um, and then we saw the consumer kind of pull back from that and want a different experience and maybe they have a daily that's a, you know, Tesla and it's great, but then they also still are in the gas world in some ways.
2:21:03
Um, have you has has has there been optimism inside the company for the future?
2:21:11
>> Well, we we are certainly investing in the future.
2:21:13
Um, we >> Yeah, I was going to say people that are charging EVs, they want to they still want to get fresh pizza, right? >> They do. Exactly. >> Yeah.
2:21:21
the um and we we're actually taking a unique approach where we're we're developing that infrastructure and and those um those customer venues um on our own.
2:21:29
So we've chosen to to really understand the customer and do it in a way that that meets their expectations because um we can't predict what the future is going to going to hold 100%.
2:21:39
>> Also a different experience right now because you might be stopping for 20 minutes instead of two minutes or five minutes.
2:21:44
>> That's a great point too.
2:21:44
So you have a more captive audience for a longer period of time and um a lot of pride and all. Exactly. >> Throw something else.
2:21:51
>> Come get Come get some racetrack swag in in the gas station. >> Yeah.
2:21:54
Or anything of fresh pizza or what have you.
2:21:57
But sit down for a minute.
2:21:58
>> We're certainly not turning a blind eye to what lays ahead.
2:22:00
Um you know, we have certain strategies and things that we're talking about um to to make sure that we stay ahead.
2:22:05
But >> it's a unique opportunity now to actually take that seriously.
2:22:09
actually take that seriously. you've seen where this market stabilizes and there's also just the standardization around NACS now like the actual charging port is standardizing so that probably makes the infrastructure cost a lot lot a lot less or a lot less risky I guess for you um yeah very very exciting um
2:22:24
what so walk me through the actual like scale of the palunteer implementation are you early days are you trying to roll this out to all the employees you said 10,000 wasn't it something like that uh do you want everyone to interface with this or is this more of like a managerial tool that will be used to like make decisions about how to run the business. >> Yeah, that's a great question. Um, I
2:22:41
>> Yeah, that's a great question.
2:22:41
Um, I think right now we've really focused in on use cases that um are driven at the managerial level or or the head uh kind of the store support center level.
2:22:51
>> Um, but that's certainly not to say that there aren't implications at our stores.
2:22:56
Um, because there certainly are.
2:22:56
Um, and I think as we as we progress and as we deploy more and more use cases, um, I I very easily could see getting the technology in our frontline associates hands as as a real value ad and frankly a differentiator. >> Yeah.
2:23:10
Have you uh have you had any problems with uh different enterprise software companies not playing nicely together?
2:23:18
You don't have to name names, but uh we've just been tracking this story that there's now some AI companies that come out and say, "Hey, we want to take your, you know, your Google Docs and get it to talk to your Slack and Slack is owned by Salesforce, so they don't want to talk to each other."
2:23:28
Uh and and and I'm wondering in the retail context if like a POSOS system and an inventory management system like there might be some similar sharp elbows or is it all pretty copacetic?
2:23:39
Yeah, I think it's uh fairly copathetic, but mostly because of of our IT team and the really great work that they've done from a data architecture standpoint and consolidating everything centrally and and really removing the need for kind of >> call it peer-to-peer communication of those of those platforms.
2:23:53
But >> because everything goes into data lake exactly and and you know again I I I think that that team really deserves a shout out too.
2:24:01
So while while our team is in the business um the IT and and the data team has really been an enabler for us uh we have a wealth of information and data that we can make some of these really complex decisions with.
2:24:14
>> Um and without it we would be severely hamstrung and would be working on challenges like >> pulling out of POS systems or what have you.
2:24:22
And so we we've kind of we're past that level and we have a really strong data lake and infrastructure and architecture to to support all of the the nerdy math that my team loves to do. >> Yeah. >> Awesome. >> Yeah.
2:24:33
What uh what what what else are you trying to uh identify going forward is I mean I imagine that like the base case is just like I want to know what stores are overperforming underperforming but then ideally you want to be able to predict which stores are going to start underperforming and intervene beforehand.
2:24:48
Is that roughly the >> Yeah, roughly.
2:24:50
I think it depends on the use cases and and again not to throw buzzwords out there again but we break down analytics into four main types.
2:24:57
>> There is the descriptive so the old school reporting and dashboarding Tableau PowerBI >> u the diagnostic which explains the descriptive >> um and then my team really steps in uh on the predictive and the prescriptive front.
2:25:10
So, um, you know, think about, uh, predictive maintenance or, hey, this this fuel pump is predicted to go down in the next two or 3 weeks.
2:25:19
That that predictive and prescriptive approach >> allows us to pivot again transformally away from being reactive >> um to being proactive with things that really impact our customer.
2:25:31
So, we like to really focus on, hey, where are the customer pain points?
2:25:35
How can we um, peel that onion?
2:25:37
how can we how can we solve some of those so they have a better experience and that that drives a lot of it too.
2:25:42
So so yeah it it um there's a world of use cases out there and we're really just scratching the surface. >> Very cool.
2:25:49
>> One last question for me.
2:25:49
Are there bad actors in the gas station business that intentionally pump the gas slow to drive people into the convenience store? >> Oh my gosh.
2:26:02
>> I would that flies in the face of everything that we think.
2:26:06
>> Well just because just the um so there there's we like to joke a lot about um you know on my team and maybe others share this sentiment or don't but um is it worse if a if a pump isn't working >> or or is it actually worse if a pump is slow >> and I actually think my experience are the most painful um when I go up to a
2:26:27
pump and it just it's slowly ticking >> at least when when you see a bag you see the yellow handle just don't even >> don't go there yeah don't go there and I don't think >> I just remember maybe maybe it was cuz when I was a kid and I was broke and I'd put like $20 on pump five and it just felt like it'd go fast. And now as a as
2:26:41
And now as a as an adult I'm I can I just get but but I'm getting like five times the amount of gas, right?
2:26:47
I'm just like you weren't going to race tracks cuz we predict when that's down for racrack do anything slowly.
2:26:54
Speed is in the name of this company >> for 93 years. 95 years 95 years. >> I can't wait for 100.
2:27:01
You'll have to come back on.
2:27:02
That would last you a hundred years of racetrack data analysis. Break it down.
2:27:06
We'll do a 100 hour stream year by year.
2:27:08
I mean, it must be fascinating.
2:27:13
>> Name every data point.
2:27:14
>> I mean, just pulling like the revenue over a 93 year ramp.
2:27:16
Like, that's got to be fascinating. >> That' be interesting. >> Fascinating. Anyway, thank you.
2:27:21
>> Thank you so much for coming on and interrupting your vacation. >> This is great. We'll talk. >> Enjoy the conference.
2:27:27
>> Have a great rest of your day. Enjoy the conference.
2:27:29
>> And that's uh our last guest for the day. Right.
2:27:31
our last guest for the day.
2:27:34
>> Started out with a bang.
2:27:35
>> We should run out we should run uh well run through a thank you to all the sponsors that make this possible. We told you about ramp. com. Time is money. Save both.
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Uh we are of course powered by Reream one live stream 30 plus destinations.
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Manage risk, prove trust continuously. We also got graphite. dev supporting us.
2:28:03
Code review for the age of AI. Poly market of course.
2:28:04
Uh some big news out of Poly Market.
2:28:08
There was a major trade deal.
2:28:09
We'll talk about that tomorrow. >> Okay. Okay.
2:28:12
>> Um uh Julius, what analysis do you want to run?
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You can chat with your data and get expert level insights in seconds.
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Linear, of course, is a purpose-built tool for planning and building products. >> Big day for linear.
2:28:36
Big day for big uh if if uh >> getting lots of shout outs at the Atlassian.
2:28:43
>> If Atlassian is paying 620, 610 for the browser company, they should get ready to pay 6 trillion for linear. >> I think so.
2:28:51
Uh we are we have we are of course supported by numeral numeralhq.
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com >> sales tax on autopilot. Uh Finn.
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AI the number one AI agent for customer service.
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Uh we of course sleep on eight sleeps. You can go to eight. com.
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>> Uh we are also supported by public.
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com investing for those that take it seriously.
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Uh, and we always tell you about adquick.
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com advertising made easy and measurable. >> Adquever.
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>> And if you noticed, Dr.
2:29:26
Karp was wearing a fantastic PC Philippe Aquinaut orange strap.
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Uh, and if you want one for yourself, you can go to bezel get bezel. com.
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Your bezel concierge is available now to source you any watch on the planet. Seriously, any watch.
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I'm sure that they would love to find you uh a >> orange band aquat.
2:29:50
>> Uh Business Insider has a scoop here that says Palanteer CEO Alex Karp says top tech talent is about to get crazy valuable.
2:29:57
Alex Karp, CEO of Palunteer set on quote unquote TV.
2:30:02
>> Why do they put us in quotes?
2:30:04
>> This is the dividing line. They put us in quotes.
2:30:06
>> This is the dividing line. Close the laptop.
2:30:09
>> Okay, so business insider. Oh wow.
2:30:12
>> The website Business Insider, >> wow, >> says that top tech.
2:30:17
>> I think I think we got to I think we just got to put like the just one of the words in quotes.
2:30:21
It can't be quote business insider.
2:30:26
>> Be a business insider. That is the way we talk.
2:30:30
>> I got to look in I actually have to look into this company because um >> I love business and I love I love >> insider trade.
2:30:37
Insider >> insiders in business. >> Isn't that the lore? Isn't that the lore?
2:30:43
Henry Bogget, the guy who started business insider >> loved insider.
2:30:47
>> I thinking I think he lost his I think he lost his license. I'm not kidding. I'm not kidding. >> Okay, look this up.
2:30:54
>> Business insider insider history.
2:30:59
>> And more breaking news.
2:30:59
Justin Bieber is uh launching Swag 2 tonight. The new album. >> What does that mean?
2:31:04
and Meek Meek Mill posted two hours ago.
2:31:07
Meek Mill becomes a AI founder.
2:31:11
>> So according to Wikipedia, according to Wikipedia, uh Henry Blogget was charged with civil securities fraud by the US SEC settled the charges uh with payment of 4 million.
2:31:22
Uh he was permanently borrowed from the securities industry by the SEC and the NYC.
2:31:26
The charges uh arose during the dotcom boom at Meil Lynch um where which included issuing materially misleading re research reports on internet companies and making exaggerated or or unwarranted claims about them to customers and uh and then in 2007 four years later he co-founded Business Insider which is a fantastic punt. Like it's it's so funny. It's so funny.
2:31:53
He's the he's he was in the business of insider trading and he said why did I com combine >> they didn't say insider trading they said civil securities fraud it doesn't sound >> great but uh you know after your run Jeff Bezos purchased a stake in Business Insider and he he had a great run 2007 to 2023.
2:32:14
Uh >> anyway, >> there's so many great quotes from the the carb segment.
2:32:18
This one I would say, he says, I would say modestly, I'm the most humble I've ever been.
2:32:24
You would never build a software company downstream from value creation.
2:32:28
It's all how do I make the client feel like they're getting laid while they're getting effed. So good.
2:32:36
Uh the founder Adam who introduced AI key, a small device that lets AI control your entire phone.
2:32:43
Just plug it in and ask it to complete a task.
2:32:47
He's saying all of this all of this and still no TVPN invite.
2:32:50
Uh we should we should probably have him on.
2:32:52
A lot of people a lot of people were uh said no thanks because uh I guess he previously worked in military intelligence and and uh people didn't feel inclined to plug a hardware device into their into their phone.
2:33:05
But but we're we're in the capital of military intelligence right now.
2:33:10
>> It looks like uh he sold out uh the the initial batch.
2:33:13
So >> let's have him on uh put the timeline in turmoil.
2:33:17
Anyone who puts the timeline in turmoil is welcome on the show.
2:33:20
>> I'll give him a follow right now and we will >> make it happen.
2:33:26
>> We're we're a lot of people are having fun with the stream.
2:33:27
This is a great reaction.
2:33:29
Uh anyway, uh that's our show.
2:33:32
We got to get out of the United States and back to the United States. >> We do.
2:33:36
Uh, last thing, this just because it is breaking and it's funny.
2:33:39
OpenAI plans to launch an AI powered hiring platform by mid 2026, putting the outfit in close competition with LinkedIn >> with with LinkedIn.
2:33:49
>> The company also wants to start certifying people for AI fluency.
2:33:54
>> Uh, that >> you are you AI fluent? >> This seems bad.
2:33:58
Yeah, this seems like more of a Meror competitor than LinkedIn maybe.
2:34:04
I don't know like um I yeah, we we need to dig in more to that.
2:34:07
But the but the other odd thing is that wouldn't Microsoft get a copy of whatever they build.
2:34:12
So wouldn't wouldn't Microsoft get access like if they build a new >> I mean that's the deal.
2:34:17
That's the nature of the deal is that they get they get the rights to AI OpenAI's IP.
2:34:20
So if they build something that's valuable but if they build a network then that's a separate thing, right?
2:34:27
because the the the IP doesn't matter as much like like the the weights to GBD5 are not as valuable as asatform as the chat GBT app.
2:34:36
So yeah, maybe may maybe there's something there. I don't know.
2:34:39
People have been complaining about LinkedIn for a long time.
2:34:41
So maybe maybe there's What is this?
2:34:43
>> Donald Boat says that he has art for the Ultra Dome. >> Oh yeah. Yeah.
2:34:47
I was I was talking to him about that. I'm very excited. Great. >> He made something.
2:34:50
So >> Well, I wish we could keep streaming, but we got to get back to uh we got to go. Okay, let's go. >> All right, folks. Anyway, thank you today. We love you.
2:34:59
Back to a regular show tomorrow. Have a great afternoon. >> Bye.