Leo Aschenbrenner's Situational Awareness Blows Up | Moonshot AI Raises $3.5B at $35B Transcript, AI Summary & Key Points

The long term average intelligence is going to be free and the average intelligence will get smarter. Land, permits, energy. See, this is the thing that is going to get priced for the next 3 to 5 years. And hold up, Nikash Aurora joins us in the studio. Oh yeah, baby. PaloAlto Network CEO, $280 billion company for this incredible session today. And we discuss Leo Ashen Brennan's situational awareness imploding.

Sad face. Air Table being acquired by Bending Spoons for $1.285 billion. Sad face again. and they were worth $1 billion before. Anthropics model breaching three companies. Oh god, when does the security problems end? That and so much more in this incredible conversation. Absolutely right. On trend absolutely wrong on portfolio construction. It's almost like it was inevitable.

If you bought in in April, May or June, you've been wiped. We did a hedge fund, but appears it wasn't hedged, right? And we lost all our money in a week. >> I think it's a bit of a gold rush moment. I think every consumer app will get rewritten in the next 5 to 10 years. >> In the face of insatiable demand, all things are possible. A palunteer can do it.

They came back from 15% growth four years ago. Why can't you do it, kids? Work harder team. It is so good to be back. And we have the one and only Nikash joining us. Nicash, thank you so much for agreeing to join Rory and me and Jason. >> I am uh a little apprehensive watching Jason and Rory there uh in their full glory. So let's see how this sort of plays out for >> we we have Professor O'Driscoll in the corner.

Uh but we're going to start on the news of the day and the news of the day is Air Table one of the big names from the last decade has been bought by Rory Europeanbased >> the company was doing $485 million growing 20% yearonear ultimately at a 1.285 285 billion um acquisition price. >> It's not the outcome that everyone quite wanted or expected, but it's what we have today.

>> Rory, why don't I hand over to you first? I'm sure you've got some perspective. >> Well, you know, we're all going to do the air table side of the analysis, but just worth pointing out the bending spoon side. Um I'm buying stuff at 2.8 while I'm trading in the market at naugh to 10 times revenues. They're going to do this all day, every day. And I think as we said a few weeks ago, one of the big advantages is they're in the game with capital and a traded currency to hoover up a whole bunch of this stuff.

So on their side of the table, totally get it. You know, obviously on the air table side, you know, the lot of comments on is this giving up? Is this a reflection of where SAS is? If we weren't anchoring off the 11 billion, it would be a great price. If you said someone set up a company 10 years ago, grew it to 450 million in revenues and sold for 2 billion plus or minus, they'd be like that's an amazing outcome, right?

But of course, we all anchor off the 11 billion 2021 price and it feels like a lowball. But I think it's it's still a great value creation achievement and you know, we have a talented entrepreneur on the show with us and we need to start with that. It's a great outcome, >> you know. Can I ask Nicash one particular question on it Harry if it's okay cuz I was going to ask you I have a lot of interesting thoughts on this deal whether Air Table matters in the age of agents and AI but >> I mean Nicash is also one of the

best dealmakers out there the shocker to me with air table wasn't the price because I think it's low market the shocker to me is no one else stepped up no PE firm no Tommo bravo no Vista it's 20% at 50 mil 500 million with some AI dust going on it um there do you think there was even another offer. That's the I just assume someone would outbid them.

>> And you're asking me that because I'm like >> you're the deal you're dealmaker par excellence. We just happen to have the king dealmaker on the show. >> Yeah. Look, I you know I I met Jav a few times. He's a great guy. He's done built a great business. I think uh I don't know to Rory's point there's a bit of founder fatigue here. I think uh he's been through a lot of sort of ups and downs in terms of sort of internally and in the market but it's got a good product.

I think the broader question which Jason hits on right is you know what is going on in the SAS marketplace. You know you see dislo is this a pricing dislocation or is the fundamental change in the long-term growth rate that people expect out of SAS. If it's a fundamental change in long-term growth rate people assess, then the multiples are right now, you know, and that's I think where the market is grappling with this.

I think the people you mentioned, uh, Jason, the the PE guys, they might have a full roster of stuff they'd like to sell to Bending Spoons as opposed they'd like to buy against Bending Spoons. So, I think they they be caught in the sort of demand and supply problem right now. They, you know, they have a lot of inventory. >> It just worries me. I let you we just I I because you see more deals than we do uh on the on the acquirer side, right?

I mean, we see it on the on the target side. It's just >> when you look at long-term growth businesses. >> Yeah. Say say that again. Sorry. >> So, we stick to long-term unprofitable currently unprofitable long-term growth businesses. But yeah, >> I get it. And that might be the answer. I just with I mean when Francisco Partners raised 22 billion to do deals sort of like this, right?

uh you know Tommo Bravo was like we're looking for AI infused B2B companies you know we could pick at Air Table but it it it did do that it did infuse AI workflows and others and it and I don't know that it's growing but it did that right 20% at 500 million in AI workflows isn't nothing I just would have for all the founders out there looking to be picked up this one would have seemed to me to be just above the fold like the they should have leaned in on this one not the one at growing 8% and shrinking because it was

destroyed by AI and it's cash flow positive and it has plenty of cash. So all the boxes you check right are sort of there as an attractive target and yet no one no one outbid them. >> Well look bending spoons did buy them so clearly somebody saw value there. Not everybody seen it but Jason I want to go back to something you said around AI infusion. I'm a little wary about this AI infusion stuff and this is what I talk to my team every day from an operator perspective.

I say to them you know are we Mercedes? We're trying to sprinkle a little bit of AI in our car and say I have a little bit of AI. Are we Tesla? Are we making sure that our car will drive, you know, the next 10 exits by itself and might have to grab the steering wheel once in a while building a Whimo? And the question back to you is did Air Table do a bit of a Mercedes action, little Tesla action, or is a little bit of a Whimo action?

Because my biggest fear is a bunch of people out there in the garages getting funded by Harry and Rory and they're going to build wayos of the future and we'll be we'll be busy putting lipstick on the pig. >> It's a tough It's a tough one, right? But >> yeah, it is. And I because I didn't feel Jason that this was the kind of category that PE would sweep sweep up because Yeah.

kind of merging the two sets of comments. one is yeah they definitely you you're trying to add some AI pixie dust but you're fundamentally your your core productivity app and you've been the proatizer Jason that you know oh my god look at all you can do with lovable if I wanted to build my own CRM and I wanted to be personalizable 10 years ago I might have used air table because it's way more configurable than Salforce but today if I'm the nerd that wants to build my own CRM I might just go to lovable replet or claude

code and just bang it out from scratch so if you want to do that, I'd call you a person with no low imagination or there's so many other Google things to build. The last thing you want to build is a user. >> I agree and I Yeah, and I am a person with low imagination. I cop that. But I agree. But but the point is this what it means if you own a horizontal productivity app that's mainly individual user.

I just think that's one of the tougher categories for PE to get their head around. Ironically, given the Evernote purchase, this is right in the bending spoon sweet spot. If you think about it, this is, you know, just like Evernote, it's like the people who have stuck with this product are going to stick with it. They're going to apply their formula.

Maybe what I'm saying slowly as I process is capitalism works and it ended up in the arms of the best owner of that product, which is the people who can take it and turn it into a cash flow machine. I bet you two years from now it's doing 600, not 900, but I bet you it's 300 million of free cash flow. >> Yeah. Or more, right? Just double the price. and um your data is locked for for two years, right?

>> You know, to your point, I don't think PE has the stomach or the willingness to do that, but I don't think it's what they do real well, right? >> But yeah, I think I think their stomachs might be full. I think it's unfair to say they're >> That's exactly right. If as I often say to people when they show me a turnaround deal in my business, right, I said, "Look, if I wanted a shitty turnaround deal, all I have to do is look at my portfolio.

I'll have four of them already. I don't need a fifth problem, right? I make problems on my own accidentally. I don't need to go actively, proactively say, "Let me get more of this shit." You're exactly right. Anyone in PE has done five software restructurings in the last 12 months, they need a six like a hole in the head. >> Nash's points, I mean, both his points are obviously great that the the one on the the the Whimo versus the whatever.

We can come back to the founder fatigue one's a tough one today because another way to look at Air Table is, man, so early to no code, right? such a clever product back in the day. Like there were two products that I wasn't even smart enough to understand why they were cool in the day. There was air table which turned a database into a spreadsheet. So I understood it right and then there was notion which to turned a database into a document that I didn't even real and they were both so clever preai and they both took

off in different ways but we don't really you know superbase is doing a million Postgress databases a week on its own. We don't need we it didn't it it it we don't need that no code database today. And as a founder, you know, after all these what founded in 2013, it's tough to pick yourself and he already picked himself off the floor, right? Already did the layoffs, already got profitable, already rebooted, already went founder mode again.

I'm all in. And you're looking at yourself and you're like, can I do it another 13 years? And uh it's a tough one today when you've already done the uh the uh whatever. I'm sorry, Nash. The what's what's below the Whimo? the T the well when you've already kind of kind of checked the box and done it and you're like god damn it I got us to 20% growth just it's tough to not tap out we're human beings it's tough to not tap out >> I mean one of the things that's interesting here and I've said this in the context of venture

in general is your comment Jason is like basically the technology trends moved on from the thing they built and you know one of the things is you know one of the weird things about venture with the holding private for longer thing right is now the holding period of venture is longer than the technology platform chain cycle. So if you join halfway true, right, this is coming at you and you know in the old Yeah.

20 years ago which I like this would long since have been public. It would be trading as common stock and it would just get hoovered up like on that basis. It's like a lot of these late stage rounds long since would have been public in another world. I think the biggest fear right now is something that was started 10 years ago. Is it past the point of yes rebuilding and are you better off building from scratch than trying to tinker with something that was built 10 years ago?

I think that's where the challenge is. >> That's interesting. I mean, you've done some recent acquisitions. Like it's interesting. It used to be my mental model was apps last longer because end users are pretty, you know, get kind of stuck in place and they keep the forever. and the infrastructure market move quickly but we've definitely seen some of the apps companies get stranded right and that whereas the infrastructure companies that have been able to evolve to link in you're absolutely but to link into the AI

demand have been able to actually kind of go from strength to strength I mean look at data dog you know we were investors in Jrog privately held you guys are killing it but you're co-attaching to the AI trend and the poor little apps companies there's just nothing to co-attach to to give you lift >> I think that's I think it's a moment in time. I think one of the things which we all know we don't talk about it too much is AI still has a lot of false positives.

There are too many edge cases that it can't solve. You still need grinders to solve the edge cases. The Whimo doesn't drive on the street without tons and tons of people being paid for labeling and tens of billions of dollars to find every tree and mark it. So it's the equivalent of sort of the the Whimo mark the tree. That's a tree idiot. that stuff needs to happen for a lot of enterprise for AI to be effective.

So I think until that that process sort of we go through that process we sort of leverage AI put all the hoops around it from machine learning perspective there's life for infrastructure businesses and the choice we have in the next five six years is can we build all that plumbing all that guard railing with machine learning and chain the core engine to some version of AI at the right price we survive if you don't then you know maybe bending spoons it is >> bending spoons it is on bending spoons it is those kids Okay,

leave us on. >> Now, final question before we move on is just like does this put a marker in the ground in terms of enterprise value for companies like this? If you're a notion that raised 10 billion last time, how do you feel looking at this? If you're a Monday.com again, two products with similar motions, it was a shocker to see it, right? Especially the way everybody presented it, right?

Uh enterprise value and all this. But Rory's right. It's it's market low. It's market low. And um >> we'll find out tomorrow. >> Yeah, >> true. >> I think one of the things is going to happen either like the Leo >> so far. >> What's the hedge fund guy? Sorry that we we might not even talk. We already forgot about him. >> Leo. >> Okay, I already forgot about him.

Okay, so I think one of two things is going to happen. We're going to forget about Air Table tomorrow cuz other stuff's going to happen. >> Yeah, we are. or or or what I think might happen is this is the one where people capitulate both founders and investors where they say look folks have already had markdown since 2021 but but they're not consistent.

this deal in in a many ways was everyone capitulated. every the the late stage got 1x the founders made 150 a lot less than they thought but certainly enough to survive even today in in San Francisco with rents up right everyone said they capitulated to the markets and I think we're also all in board meetings where we're seeing the opposite 30% growth at nine figures where we're we're going all in guys right we're 20% growth but we may see a quiet wave of airtabling it it's time guys it's like how we did it it's time

it's time to compete he's a great founder But like that time has moved on and it's time to capitulate and um that that's a question right there. It may create more conversations or it may be forgotten about in 3 hours. Not sure which. But it was a jaw-dropper for a brief moment in time like losing losing most of your hedge fund during your wedding. But uh we move on.

>> Well, I mean we we'll talk about that. That's a brilliant um transition. Leo Ashenbrer, uh, famed wonder kid who wrote the situational awareness piece, which was an incredible memo that then he parlayed into a $225 million vehicle that at one point had $45 billion of assets. Um, really rode the wave, so to speak. He did it with Forex leverage. Um, and in the past week, it kind of came crashing down and then Ken Griffin and Citadel bought his public book for a reported $16 billion.

Ken has made out like a bandit, reportedly making about $3 billion on the back of it in a very short amount of time. Um, how did we think about this? He was the wonder kid of the AI wave. >> I mean, I think he was absolutely right on the trend. I mean, absolutely right on the trend and remains to date right on the trend. In other words, the data just last week about capex absolutely supports his memo.

So conceptually right on the trend and then absolutely wrong on portfolio construction. If you accumulate a portfolio of high volatility stocks with forex leverage, the math makes it clear your probability getting wiped out once is just very high. This is as simple as that. Absolutely right on trend, absolutely wrong on portfolio construction. It's almost like it was inevitable.

>> I'm really sorry. How do your investors let you get to that place? because you made him 10x last year and you probably don't question anything and he did amazing you know and which of us really let's ask ourselves honestly when someone makes you a 10x do you sit there is your first response yeah but what can go wrong I was like oo can I put in more money you know that's what happened and and my wife said when I was talking about she said I don't want to see any shaden father she was like you there's a lot of shaden

father laughing the p I feel sorry for him it was a tough call to have to go through that just to put it out there on a human level. I mean, he was clearly wrong on the bet. But that was a brutal week. >> Will he be okay? It says that he's still managing both a private and a public, but he's got his anthropic position. And then other people are like, "Oh, no, no.

Lawsuits are coming and it's not going to be okay. Is he going to be okay? >> I think he'll be fine. I I promise you he's not going to be caught at the same place again." So, you know, good news, he's he's learned a lesson and he's going to live he's going to survive to sort of live. >> I'd ask. >> I mean, Harry, it's your job to mentor some of these younger kids like like Leo.

So, I think maybe you you could step in. What? He's 26 or something like that. >> He was 25, dude. >> 25. Yeah. So, I think you've got it's time for you to to become the elder statesman in the industry and start mentoring him on leverage, when to leave her up to Forex, when not to. Uh, I mean, in all seriousness, I mean, >> I need someone smart. I've read it.

>> Grouping plants and and, you know, Yes. >> expanding homes with, you know, multiple people living in the same house. Don't don't bother him. >> I assume his LPs or his investors knew this was a highly levered fund, right? Um, am I wrong, Rory? I mean, if they know it's Forex levered, then they know there is black swan issues when there's short squeezes and others.

And I don't think that his investors should cry if they knew how how it was playing. Um I mean I my limited experience as an LP in funds with leverage not quite this much is you you know it's not free. >> I have a feeling I I have a feeling is LPs didn't lose any money. If you are up 440% and you go down billion you're back to where you started. So I think it's fine.

>> The interesting thing about that not quite because actually this is where I think it could get a little hard. It all depends on timing, right? Because the hedge fun things are weird. If you came in early, you made a ton of money and then you lost twothirds of what you made and you still made money, right? Brutal comedy. If you came in in the last 6 months, you might have been wiped 80%.

Right? Cuz hedge funds unlike unlike venture funds, people come in at different times at different bases. So I think the real and I think I thought even perhaps Jane Street had put in some money recently but a bunch of people had put in money and if you bought in in April, May or June, you've been wiped and then to your comment on those pe I mean the litigate I mean I think fundamentally yes he will be fine and there's a long Larry Frink who you know founded and runs Black Rockck had a blow up early in his career.

There's lots of people who had blowups early in his career. Nash has worked for one of the most aggressive risk-taking human beings on the planet that's softbank. He's seen ups and he's seen down. So you can survive >> what >> what's Forex is for babies. Genuine comment here. So So you can survive and 10 years later be well successful in France. I think the the crux of the near-end question will be those investors who came in late who let's be really direct here will be pissed.

you put money in a hedge fund in April and you lose 90 cents on the dollar in July, you're going to read the docks real carefully and if there's any disclosures that weren't made or if you've done something beyond the remit of the fund, you will have liability and all this will happen. I in the end it'll all this is America everyone will sue everyone and it'll all be fine.

But there will be some dynamics going on now cuz if you can you imagine going back to your investment committee and saying you we did a quarter we did a hedge fund but it appears it wasn't hedged right and we lost all our money in a week but if you're Collison's on the other hand you came in on day one you still made out great >> well that's a relief I was worried the Collisons would be short of cash so that's good to good to know that at least their whole Rory I don't have any cash in Silicon Valley well they'll be

fine >> they need money to buy PayPal So >> they do and they need money to buy what was it? Open router. I mean they're doing such a lot. I mean actually we're just talking about it's been interesting to see them do all this corporate development while still private. Just super interesting in terms of you know a lot of this stuff would be marginally perhaps easier with a public staff but >> I had a CEO of Open Rder on the show on on Friday Rory.

So there we go. >> Uh I'm excited for this next topic cuz with Nick Cash I think we've got the most pressing person. Anthropics models breach three companies too. This is obviously on the back of the open A and the hugging face tobacco. >> Really? Anthropic breaching three models. Also, is this just like the most epic beginning of like a bull run in security?

Like first and foremost, this is all flex, right? They all want to tell you how good their models are, how powerful they are. So it's kind of bizarre because normally if you end up breaching somebody's infrastructure it's not a good thing but we're all saying look look at these models they're so powerful. So fine granted they're all very powerful. I think the first things our friends at anthropic and open I should have done which I've told them just point your models at your own sandbox to make sure sandbox doesn't

have any zero day vulnerabilities and make sure your sandbox is your first capture of the flag flag exercise. But they decided to give a target to say go to the wild, persist, take as long as you want it or go capture a flag. So fine, you know, we have these models which have these capabilities. I think the the challenge we have from a cyber security perspective is we're filing vulnerabilities which would take us days, months to find.

Uh the average time to patch a vulnerability or zero day vulnerability found in the wild is 55 days. Just think about that. These things are finding vulnerabilities in split seconds and then turning around and building an attack on the back of that. So I think the the fundamental speed at which cyber attacks will happen and need to be defended changes.

Now this is good for us. It's kind of like you know the sound of revenue but I think from a more fundamental perspective I was thinking this capability is going to show up in 6 months. I think I said that with you Harry and it showed up in four months and I think in 2 3 months from now open source have distilled all these capabilities. will find open source models out there which you can fine-tune if you're an attacker to actually do this from a task basis.

So it's going to change the game. >> How are your enterprise customers reacting? Because this feels to me like the mother of all of I mean security sells on fear and this is terrifying. So what what are you seeing in the enterprise customer base when this is knowable? >> Sure. Uh, look, the good news is the the flex that Anthropic did with Mythos has every CEO talking about Mythos.

I spent eight years trying to get CEOs to talk about cyber security. Couldn't get him to do it. And Daario did it in one fell swoop. So, this is goodness. He saw everybody like all hot and heavy about mythos and the capabilities of anthropic and how these models are going to go attack your infrastructure. I've never had so many CEOs call their CO and say, "Are we ready?

what's going to happen to us? Well, the answer is you're not because what being ready means is that I have no vulnerabilities either in my code, any vendor that I've got deployed in my infrastructure, any open source I'm using. That is fundamentally not true. Now, we we found 14,000 vulnerabilities in open source in the last 14 weeks testing open source packets.

So, it's bunch of stuff that's been used out there. So, a every company has vulnerabilities. They got to figure out a way to patch them. B, these models will figure out misconfigurations. If you've left the door open, if you've got a device configured wrong, if you got a piece of software configured wrong, and there's tons of that out there. Not every IT person building infrastructure or configured infrastructure is genius.

There's misconfigurations. All these things need to go away. So, at the base, as the face of it, a lot of organizations are going to have to go fix a bunch of these vulnerabilities and misconfigurations. On the flip side, even if you fixed most of these, the bad guy just got to be right once. So, he's going to find one, she's going to find one. to get into infrastructure.

The question is what is your time to detect and respond in that circumstance? The average time to detect and respond is 4 days. How are you going to get it down to a minute? So, it's not a fear problem. It's a capability problem. It's an infrastructure readiness problem. And now it's come to bear. See, it's time to pay your taxes. >> Yeah. I mean, that's the last sentence.

I mean, you're right. We can argue fear versus thing, but what you're basically saying is the security infrastructure that you had a year ago is wholly unfit for purpose in the next year and you, Mr. Enterprise buyer, are going to be buying a whole load more stuff or you're going to be the weakest link when these capabilities are everywhere. >> Yeah, you said it so well, Rory.

>> I mean, I'm It just feels so good. I don't mean to be >> I think you should be on the podcast talking about how people need to buy more cyber security. I'm just going to buy the stocks. But >> now Nash, can we get him some swag? I mean, Jesus, he's a basic give give Nash a hard question. You >> I want to ask Nikesh as my I want to ask him as my cyber security therapist.

I had two fable issues and they're internal security, but I'd love to get your and you can make fun of me for this. Like I I've got I pretend to have a thick skin. I don't, but I I I I love criticism. >> I already figured I don't have a thick skin the first 30 seconds of this conversation. >> Okay, good. So, I'm building an app for the Saster community.

It's called Saster Connect to help with recruiting. That details don't really matter, but it's it's the biggest thing I've built, right, myself in this era. And um and I'm among other things, I've got a Google doc. It's called Jason's gems. It's my ideas on how to improve that. It's just ideas. They're just scratches, okay? No one's seen them. It's not ready.

Just a side dock I keep. So, the other day I I went into Claude and I just turned on the Google Drive connector since it's one of the three primary connectors. This is not esoteric. This is not third party. And I never knew. It went in, scanned all my docs, found Jason's gems, found the ideas. Fable then went and changed my code and my algorithm without telling me.

Never got a notice. Never was told. Never was in a change log. Never was anywhere. I only found out later when the agent flashed conflict with Jason's gems when I was trying to fix something else. I mean, I'm not saying it's terrifying, but how can organizations deal with this fact when an LLM will go out and change your core code, your core corporate OS without even telling you what if you have a thousand employees doing this?

And this is goo this is just oh and and the the the hack the part I didn't tell you is the way it did it is it mcped in. So I had I had Google Drive to Claw to Fable to MCP and so it was able to do with whatever it wanted probably thinking it should implement Jason's gems but it shouldn't have and it never asked me and it and it never told me it did it.

Is this scary? Is it not scary? Is this Orwellian? What what happened? >> The wild west. >> It's wonderful. It's the wild west. And part of the challenge is the good news is that or the bad news I know which you want to look at it. I think the small business entrepreneurs, people playing with their own stuff are doing this without any regard for security.

>> Yep. >> Same reason you're on Tik Tok, >> right? So people are doing that with no regard for security. They want to experiment with open cloud. They want to connect to all their stuff. They have no idea if all that data is being used for training. They have no idea what credentials are going to get used, what permissions these agents have. And that's happening all over the place.

On the enterprise side, there is some cohesion around it. I think the the most obvious ones are people saying you can't use this. Now that only encourages people to use it more. So, but there is this stream of thought saying if I don't allow you to use it, I have time to go figure out how you're going to use it. The challenge you have, Jason, is and I I think this is burden.

You know, when we when the the Wright brothers built a plane, they didn't invent TSA. That was not the first thought that crossed their mind. No, TSA came a lot after. So you don't think about security when you start playing with new things and cool technology. And that's what's happening. You're seeing people play with open claw. You see people play with agents.

You're seeing people play with all this stuff with LLMs. Everything's happening. LLMs are training on data if you're not careful because that's the value of giving. And what is the what is the adage that if the product is free, you're the product. Well, guess what? So we are the product of all these post-training data that has been collected by every model out there on our consumption which is not regulated ring fence the enterprise use case.

That's why enterprises are paying a lot of money for all the free stuff that consumers are getting. So we are the product is learning on all your behavior. Is the security architecture a year from now is it more the same better faster or is there some new new things that you just have to do utterly differently to protect? I mean, is it just same problem, just higher velocity, or is it oh we never even thought about that before?

>> Yes, both of the above. Okay, both of the above. >> Look, I mean, fundamentally, cyber security is kind of a very straightforward thing. If it's a known bat, I'll stop it at the door, right? You show up with guns blazing, I know who who you are, and I'm got security at the perimeter. I'll stop you. >> Yeah, it's a known bat, I'll stop you at the door.

The problem is no cyber attack happens because I stopped a known bad. Every cyber attack happens because >> you didn't know. >> You didn't know it was bad until it got into your infrastructure. The question becomes, if you know it's a known bad, you stop it at the perimeter. If it gets through, how quickly can you find it and stop it before it creates harm or damage.

So from a cyber perspective, you want to be in the perimeter business. You want to be on as many perimeter endpoints in the world as you can because that becomes a sustaining business. The more parameters I'm on, the longer my tenure for my business is. So I'm on endpoints, I'm on, you know, devices, I'm on servers, I'm on firewalls. I'm protecting the perimeter from multiple infrastructure set of components in the world.

That's good. That's kind of good. Now the question is how quickly I find known bads will change using AI, right? There's a concept of data classification. You had to write static rules. Guess what? NM can sus it out much faster from a content perspective. you know, we we track every malicious website in the world. Can AI tell me it's a malicious website much faster?

Yes, it can. So, the the sort of the ingredients of my parameter security will change using AI. The act of stopping things in line will still be needed. So, when people tell me, "Oh, open AI is going to eat my lunch or MTO is going to eat my lunch." Guess what? They're in no perimeter security scenario, which means I still need to block the bad guy.

They need to be the ingredient in my product. they're not going to take me out of business because people have all kinds of infrastructure on the perimeter. The other part is if you want to sus out all the bad stuff in your infrastructure and find out the bad actor. Guess what? Imagine collecting all your enterprise data and running an LM bride on it >> and say find me all the abnormalities.

Find me behavior that you've never seen before. >> Now I'm ingesting 19 pabytes of data a day. Think about it. 19 pabytes of data a day of enterprise data to look in it for anomalous behavior. I have machine learning techniques. I have static techniques. I have rules that I look at it. Guess what? I'm going to throw some LLMs in there just for fun to see what they find.

Now, if I can find the unknown bad actor in your infrastructure much faster using LLMs, I can detect it and block it. Right now, we run it one minute. >> Okay. >> Machine learning. This is a good thing. The only problem is I only have 1,200 customers who've bought and deployed it. I need to get the rest of the world to go buy it and deploy it. So that's the second half of the problem.

The third part is there is stuff which is new which does not have any security guardrails that have been built agents. The world is talking about agents. We can have a whole episode 90 minutes on about what are agents, what is really an agent, how do you give agency and how do you control an agent? People tell me they have identified stuff but then I ask them does it actually have agency?

Like what does that mean? I'm like, a whimo has agency. They can drive you into the wall without human intervention. This is a bad problem. But most people haven't actually given agency to their agents. So they're running glorified workflows which are deemingly agentifying things. But when you start giving agency to things, when pieces of code can decide what happens next, we're going to have a whole different conversation around how do you secure those agents?

How do you build kill switches? How do you intercept them in line? How do you stop them from doing bad things? >> Yes. like Jason's agent, which is a bad thing, and took Jason's gems, and now the whole world will find out what Jason's gems are. >> Totally agreed. >> It's a It's crazy. >> We said open, we said China. Uh whether we have comment on it, Moonshot closes 3.5 billion at a 35 billion valuation.

And if it's free, you're the product. Moonshot's free. >> I think one of the comment on the if it's free, you're the product is is totally true, especially in the consumer side. The interesting thing here is I'm not sure how it's true, put it another way, unless you're one of the really interesting things about these openw weight models is the impact they're having and the ability to be a drag on price for the US, you know, closed source frontier model companies.

It's not as and in the abs if you're running the inference as well, then I get the model the get the business model. The model is free and the inference is how you make your money. It's not as clear to me long-term if it's possible to continue on a sustaining basis offer openweight models without monetizing in some way. And we we'll see if what it is when when people like reflection and thinking machine start when these models start to happen in the US.

It will be interesting to see you know what the business model is of which of which an openw weight model is a part right it's definitely not hey download it have a go and you can do whatever you want wherever you want right I mean look open source has evolved the business model of support so it'll be just interesting to see you know what version of if it's free or the product emerges for these companies in the medium term >> I'm going to give I'm going to give Harry a sound bite Good.

Average intelligence is going to be free in the long term and the average intelligence will keep getting better. >> Nice. Exceptional intelligence will be paid for. >> Can you give me some tone with that, Nicash? That was all monitor. I want like drama. Come on. You got to deliver the sound. >> I was watching somebody speak. I was watching somebody speak the other day and they said if you whisper loudly into the mic, people lean over and pay more attention.

So, I'll say it again. I saying in the long term average intelligence is going to be free and the average intelligence will get smarter. Oh, >> but you but do you think we'll rely less on frontier intelligence? We won't need we won't need it. >> Oh, no. We'll need exceptional intelligence. We need exceptional intelligence to discover the the cure for cancer.

We need exceptional intelligence to send rockets to the moon. We need exceptional intelligence to build a space data center. Those are exceptional intelligence tasks. They are still going to require exceptionally intelligent people or exceptionally intelligent models and people will pay for it because the outcome is so spectacular. I don't think you need to pay $6 a million tokens to answer a call saying how can I help you?

I'm so sorry your network connection is not working. >> Agreed. Yes, customer support will not be using frontier models >> maybe. But but I my limited listen I of course you're right over the long term right in the short term customer support is requiring more and more tokens to do more and more sophisticated resolution >> and guess what those are the primary candidates that all these open models are going after with open weight fine-tuning saying I don't need elucination I need I think that the part where we're where

sort of this is what I mean the capability and intent gap is I think there's a lot of work that needs to happen to go from building a frontier model or any model and taking that and making useful the enterp price context. The amount of effort that goes the problem we have is like and I sorry to go back to the Whimo example because it's kind of I think it's the most obvious one out there.

It took I I drove in the first sort of Google self-driving car. I don't know what I was thinking in 2009 when I used to work there. It was a Lexus with a bunch of cameras. It drove me from San Francisco to San Martin on the highway and my hands were not on the wheel and then I took the they told me at 11 p.m. to take the the wheel in my hands. as I was driving a quarter wall and I did and I sort of was more relaxed about saying, "Oh, maybe it's just going to figure it out when I make a wrong turn because it was so

smart drove me." I was like, "No, dude. This does not drive when it turns off." So that was 2009. It's taken 14 years after that to get one with all the edge cases trained from machine learning perspective for us to rely on that as being the agency that we've given the agency that to that replacement. So I don't believe we're going to give 100% agency to use cases for some time.

And for us to be able to do that, the amount of data collection and and context we're going to create is going to be humongous. Basically, you have to literally take every edge case in customer support, get into your AI brain of your organization so that you can start relying on AI instead of the human. So you're getting 80% right now. You're getting 80% of customer support solved.

All the edge cases are waiting to be solved with AI. Then there is the for a given app how much of the value is purely in the model versus all the other thing and you're right for something like customer support we you know you're probably paying 10 or 15% of the revenue you're getting for intelligence and the rest of it is all the other it takes to make that intelligence actionable in the context of answering tickets.

And one of the things we look at is just super interesting on the app level is the tokens as a percentage of total revenue and it varies from you know the sales forces the we were in intercoms stuff like that where it's you know sub plus or minus 10 15%. Obviously in coding and things like that it's 70 80% which means it's just raw intelligence and a mild harness and those are just very different.

>> I think over the next 3 four years we won't be paying for intelligence we'll be paying for compute through our nose. I mean, speaking of paying for compute through our nose, we often get chastised for being too public markets focused or too anthropic and open AI focused. Valor Atomics triples to $6 billion price as Sequoia bets on nuclear for AI.

Uh, it's a three-year-old small modular reactor company. Um, raised at 2 billion, now Sequoa leading around at six. Um, and specifically, there's an Nvidia partnership to power AI data centers, which caused a lot of excitement for the company. Harry, I met somebody who's got, you know, I was talking to him and he's in the the business where they take, you know, chicken feces and turn that into methane and and produce gas.

And I thought it's like, oh, it's a cute project he's got running somewhere in the middle of the country. And then he told me that he has money, billions of dollars, and he's selling the energy to hyperscalers. Anybody who can produce any energy source it doesn't matter where you are is right now trading in multiple because land permits energy compute this is the this is the thing that is going to get priced for the next 3 to 5 years and I think it's almost like the question will become between anthropic and openi who

has more access to more compute in the next 3 to 5 years and that's what people going to buy it's very hard to find compute right now you can also take all the free Chinese models you want where are you going to run them Jason, >> no. No, for sure. I I just to to Nikesha's point, I I actually I used to be a little bit in advanced energy storage in my first startup and things like chicken manure didn't used to make sense.

The these models actually used to work. >> Literally chicken >> So did cow. So did cows. I even looked at some of these things, but the mar the margins were so low. The IR was so low, but the business work. Now AI is a there's such a demand for comput everything works including all types of nuclear like valor, right? Including chicken manor. laugh, but like I remember talking to a manure farmer doing this back in the day and he's like, "Well, the best the best we can commit to is 8% annual return if everything goes

well and it's just, you know, it's hard to get 20 VC excited for for for for those returns, but maybe it's 80 today." >> We're going to be talking about that on this show. >> Me neither. You brought it up, but uh but everything >> and nothing in energy storage worked before AI, right? There's the battery startup, right? What's the one that just raised at 12 billion, too?

>> Yeah. None of these things worked without AI, right? Neither did RAM. Like none of these products really were that great. But now they're the greatest products in the Give me my RAMs. I can't even get my Mac Studio with more than 64 gigabytes. Give me my everything's working right. I >> I'm going to ground us in a little facts here. Just on the three, right?

Just cuz I think they're all super I mean Valor and B 10 base power super interesting but very different. I mean you're right. The Valor is purely the we need more power for compute bet. You're right. And you know they did plug into an Nvidia chip and they basically showed that you can get criticality and generate power but no I think everyone knew that I mean just to say I think the the regulatory journey for all these things is still a hole just to be clear in terms of when you can actually plug it in on an ongoing

basis right and it's interesting the only there's a bunch of these private and then a bunch of these public and I think is it new scale is the one that's doing the existing technology that's well understood I think light water not the nuclear but it's like this is the way we built them so are when it's pretty much the same and it's the furthest along in the regulatory path and then all these guys including Valor and Oak are doing new different things and the big question will be after you get the initial demonstration

that it works what's the regulatory path I mean it just I'm you have to be wildly supportive because there's no way to get cheap electricity without doing nuclear but so from a public policy perspective go team but just from a don't spend the electricity yet um you got to plow your way to the bureaucracy. And at some level, you want people to be mildly cautious before you permit these things.

Probably less cautious, dare I say it, than we've been for the last 30 years where I think we've stifled immig um innovation and possibly a little more cautious than you might be right now to get it right. So there is there is an approval journey ahead, but I'm just it's awesome we're doing it. >> The question we're we're sort of debating is where's the money going to go?

How much are we going to pay for intelligence? And once we pay for intelligence and we talked about compute, I'm pretty sure Harvey will Harry will want us to talk about all the capex that's going to happen out there. At some point in time, somebody's got to pay for all this compute and that that money has to come from some version of some people paying for AI and that's the only thing that's going to be that's going to allow the valors and the chicken manure world to actually be worth something to.

Yes. In the end, in the end, someone's got to buy a trillion dollars worth of tokens in corporate America. and >> over Europe. >> Well, if we buy a trillion, they'll buy half a trillion a decade later. I hate to be cold, but as a former European, I can say that, you know, a current European, whatever. Cynical deed on Europe you'll ever meet. >> Yes, we have two Europeans here.

unbelievable. Unbelievable. Uh is this not just another layer of companies which is dependent on Rory to your point open AI and Anthropic continuing to go on their charge and hit their number? Like we've never had an ecosystem that will be so dislocated if open AAI and Anthropic do not hit their 2027 numbers. I I don't think so. Whether open AI or anthropic hit their 2027 numbers or not is orthogonal to the fact that there is infinite demand for AI at this moment and that infinite demand needs to be satisfied by

compute. Now whether it's OpenAI that builds the data centers or buys the data centers or pays for them or somebody else pays for them there is demand in the market. Look, if you think about what's going on, I still posit 70% of the compute demand for AI is being consumed by consumers who are getting a free ride. So maybe they'll go to reallocation.

Maybe we're going to have to give more compute to enterprises over time as they become better monetization capabilities or you'll find that eventually the promise of consumer monetization is going to start showing up. We all talk about why can an agent book my airline ticket and buy make me a restaurant reservation and these are simple use cases. I don't need to go you know solve cancer to get that stuff to work.

That stuff's going to work. When that stuff works there's going to be monetization opportunities consumer side. So I believe that you know at first principles there will be tremendous amounts of compute that will be needed to satisfy the AI use cases both on consumer and enterprise. which player ends up monetizing them becomes a question for the markets to decide and that's a timing question no different than Leo's question that's a question of who builds the capability and the the the the services you know Google was

not the first search engine >> okay I'm arguing against and you know just at one level obviously if you zoom out enough you're right but if you zoom back down and I'm we notice in these discussions I'm always >> I see where you live Rory >> yeah I I dude I'm just you you you you're running your $280 billion company. I'm just trying to turn 20 million into a bill 20 million investment into 100 million and call it a day.

I'm I'm a small guy. But the genuine comment is kind of mixing infinite demand for intelligence and let's assume it's just the enterprise now because I think you are right the the consumer side is super interesting especially for open AI but let's leave it to aside because it's just we can only do one thing at a time. I think the question where it does matter is right now the assumption is 70 80% of that demand gets channeled through entropic and open AI in other words cuz there's 70% of the Google compute backlog

there's 70% of the Amazon backlog so in the short term most the the market is assuming that open AI entropic buy the compute buy all the stuff that's further down the stack buy the chips and resell that resell that intelligence on a frontier model basis to US enterprises and if it doesn't happen that way it's going to take a there's going to be a pretty big dislocation.

Yes, it's perfectly possible that there is a public market dislocation because the table the players at the tables might change and that's great. That's called a buying opportunity because that doesn't take you with the infinite demand. It's extremely possible that perhaps this wonderful company called Moonshot, which we talked about 3 seconds ago, could be the model of choice and that somebody's going to take that compute which is not going to be used by Frontier LM and put Moonshot on it and sell it to enterprises at

10 cents a dollar on the 10 cents a dollar tokens. >> Yeah, Moonshot is happy. Nvidia is happy. Enterprise is happy. Open AAI very very sad. You're right. That's that's the dislocation. But the question becomes you know what where are the mark which ones of these are the markets going to support right is the market going to give you infinite capital to be able to build the compute because they believe you're the anointed winner or does the market believe that you're running it differently and it wants you to run

differently. So I don't think the demand goes away. I think in all these conversations one variable goes away when we run into this sort of technology shift you know infinite bull market. We take execution out of the picture. >> Y >> doesn't matter. Every chicken manure company and every nuclear reactor company who says the words in PowerPoint is going to get funded by everyone because they assume flaws flawless execution.

And you look around and then poor Jason is looking at SAS companies and saying, "Holy some of them not exe not executing as well as the others." So eventually execution matters and that's going to decide the winners and losers in the market not the shift of you know which intelligence is the best. I mean the best example of that would be two years ago OpenAI was first and Anthropic was second and now Anthropic is first and Open AAI.

So >> and Google was written off >> and Google was written off. >> There was a show point Gemini was non-existent. Google was written off. Now suddenly Google has the compute the cloud sales and Gemini. Yeah. >> But still not the amazing open frontier model. Still not the coding agent. There's still not >> customer support agent is going to be extremely unhappy because he didn't get a chance to answer it using the best model.

Just kidding. >> Well, to Nesha's point, Harry kicked this off by saying, you know, well, have we ever had an ecosystem so dependent, right, on the success of open anthropic, I mean, it is, but maybe to Nesh's point, you know, so much has changed since we started the show, right? When we started the show, it actually seemed like everyone would benefit because average intelligence or whatever term Nesh would use would permeate software and that would be good enough.

That has now the front that we this is the revenge of the frontier, right? We may not care in a year what model we like we need frontier models. We need the best, but we may not care who wins. We may not care who wins this battle. We may this may all blow over and it all may be about compute and we may not whoever wins wins. Whoever wins will plug in.

Jason, I think the models will get better and better and the distinction between models may not be enough for you to decide to rip one out because I think the part which we will be build, we are starting to build and we will be building for the next 3 to 5 years is context. So think about it for a second. Like you know I run a simple firewall company or a simple complicated firewall company.

You can stick any model you want. The model doesn't know why my customer's infrastructure is down. It does not know because my model doesn't know what product my customer is using. My model does not know what operating system it's using. My model does not know what the configuration of the customer is. My problem model does not know why this happened the last five times as a customer.

All that knowledge, all that learning is being captured by me in effectively vector DBs and in context learning systems. And that's what my team is doing. I have more people collecting context than I've ever had. It's kind of like the Whimo thing. I got people planted saying this is a tree. This is why it goes down. So as I build that organizational instead of context, then I can stick any model I want on it.

And the model distinction will not matter because the context will become as important or perhaps more important. And you're clearly 100% tracking Satia with the kind of Microsoft comments recently on you know age companies and it makes him enterprises need to build their own value build their own context rather than do it in front of your model right and that's >> well I think it's it's yes you know he's saying something different I understand what he's saying uh that's a different comment mine is different comment I

think there's three parts to it there is there's the model which is the raw intelligence let's just call it that >> there is the context needed to answer your queries or needed to answer your problems. And then there's the context needed to train >> that ecosystem. I'm talking about the context needed to train the ecosystem which means I've got every customer case that ever happened at Palo Alto getting transcribed.

So my model has knows what is a good answer was a bad answer. Right. >> And what is your model? What core model will you start to build all this context on do you think or have you decided? I remember calling Thomas Kurin at Google when the whole thing just started, you know, the shiny object called LLMs and I said, "Hey, do I need to build a cyber model?"

He's like, "Dude, over time, what's going to happen is the models are going to get smarter and smarter and small models will not be as smart as the big models." And he was right. You know, the small models are more intelligent than the big models. Now at some point in time if your average intelligence becomes smart which is what I said then the distinction between little more intelligent less intelligent is less important than knowing the domain and the context.

>> Got it? >> So I think we're coming to a world where in the next 5 years domain becomes equally important with the model intelligence. And I think Satia is saying something different. And Sant is saying you can't parse every problem into multiple models without carrying the context to the model to give it enough context to get the answer. So he's giving an architectural point.

>> Yeah. >> Because he's saying put all the context in a harness which is sitting beside the model which I provide and then use whichever model you want and commoditize it. Every model company is saying no, I'm going to only make my model smarter context because otherwise I get commoditized. So I think that's a bit of a way commoder commoditization battle that's going to happen between models and models plus context >> but nic just on that do you um but you also said something not in conflict to it but relate so you've

got all your intelligence in your vector database or whatever it is all your context right um and then and then you can pick and choose your LLM on top of it but as you said the LLMs don't perform the same you know even even opus 5 and fable and and unlet at PaloAlto Networks you have a team that can manage that right those those changes We're learning as we go along.

So, >> so you're learning. What about the average enterprise that doesn't have as strong a team as you? How can you really switch out these LLMs? Even even if all the contacts in your vector database and have confidence that the results will be the same. >> Bending spoons. Bending spoons. >> None of the above. >> But I mean it's it's a it's it's a Darvinian moment.

>> Yes, I got it. >> Moment do not suggest that everybody survives. I I understand now. I understand. In other words, what you're really saying is if we don't figure this out, we will be working for the Italians, so we're going to figure it out. Got it. >> I love the way I went to private markets to to get the private market discussion. And the straightaway discussion is, well, it depends on what Open AI and Anthropic are willing to pay for it.

And it goes back to that. And it's just funny how everything just rotates back to compute and what the big buyers are willing to pay. And and you're right, Eric, because you know, I pushed on why we always talking about just the same two companies, but we internalized that no matter what you talk about, you end up back talking about them because they're to they're giant sucking sound on demand that's just pulling everyone along all the way up and down the chain, right?

Which is why I think you're correct. If that demand signal turns out to be attenuated or dips or even is true in the long term but blinks for a year or two, it'll be a weird time in tech. >> And that's why we're all focused on the poster child for the trend. But I think the trend is bigger than the poster children. >> Yeah. AI and intelligence is bigger than open AI and anthropic is what you're saying.

Yeah. >> Yes. You are right and enterprises are going to want to consume it a lot but the structure the change if it turns out to be 70 80% beneficiaries other than open AI and entropic there will be a pretty significant dislocation up and down right I mean I think >> was a $200 billion company at one point in time and two years later I joined Google was a $14 billion company >> good call you're a good stock picker >> we have four we have former Mr.

Google, we touched on Microsoft and um you know uh Google being told to you know what was it like you have to dance like two or three years ago whenever it was we obviously had um all of them coming out saying capex we're going to keep spending and maybe it's going up. Um how did we analyze the results and the reaction from them? Obviously cloud was an acceleration from both uh Microsoft and Amazon.

Um really incredible numbers. Um, how did we analyze this? Roy, do you want to set context in any way? You often like to set context in a way that >> I mean, it's not that hard. I mean, you had four people report that would be relevant here. You had Amazon, Google, Microsoft, and then Meta, right? And the big picture is the people who have a business selling cloud inference all had an amazing quarter.

I mean, Google Cloud, the smallest, grew 82%. Um, AWS grew 37% at scale. It's always hard to know at Microsoft because they bundle a bunch in but they grew 20 30%. So the big picture comment is people sold a ton of inference right and because of that people said I'm going to buy a lot more compute because it appears that I can turn compute into money and you know the CEO of AWS in particular made a very declarative the ROI here was amazing and the market was really happy all you know in particular Amazon and Microsoft

got marked up pretty significantly and then by contrast Meta also said I'm going to spend a lot of money but it wasn't as obvious how they're going to make money so that stock went down $20 Probably the most surprising thing going right back to the layer thing is god those are really strong numbers. I mean all these people are selling a ton of compute.

I mean seeing I mean these are $400 billion runway rate businesses plus or minus in total and they added 30% which means a hundred billion more a year of revenue across these four companies in compute. It's just the scale the scale of the things you can lose sight of. That was that was for me the big aha. Will it persist? Who the hell knows? We can talk about that again.

But the facts on the ground, the new information in Q2 was bullish. That was my take. >> Well, look, I think we already hit that. To to me, just maybe it's perpendicular, so I don't want to take off off track, but to me, the the Palunteer, which just happened, was more interesting, right? I mean, growing almost 100%, right? And bookings up 153% backlog.

>> Um uh I mean, you know, you you can sell this AI. >> Yeah. What Jason? What should we take from that? Like, hey, enterprises need help with it. Palunteer is the best. >> I think what we should do is is send it to our portfolio companies and tell them to work harder because there's no excuses. I mean, if Palunteer can do it, they came back from 15% growth four years ago.

Why can't you do it, kids? Work harder. Work work harder. I mean, I don't know what the message is. I mean certainly certainly to to Nikesha's point I'd love to hear Nesh Nikesha's thoughts if you can if you can package and capture intelligence right the demand is is inexhaustible at Palanteer right um and it and you can also capture somewhat model agnostic intelligence but the demand here for for intelligence for compute at the I mean you know at some level palunteer is a very sophisticated harness on top of massive

amounts of of data right and maybe vectorized databases to nash's point I might be wrong or oversimplify find it and but they've they've captured that to a a magical element in the age of AI. People need to solve these problems with data. They need answers and Palunteer Palunteer gives I think less than a thousand customers, right? 1,049 customers. They're giving 88 88 8 billion worth of answers growing 100%.

These thousand customers will pay almost anything to get these questions answered with AI. They'll pay almost anything. >> We're in a capex cycle. You know there's a trillion dollars of capex that has been committed for the next one year across all these people broadly speaking and the market is saying great I see these large pe large companies it has the ability to fund this trillion dollars of capex and there are signs that they're getting compensated for some part of the capex that's out there now whether that's

because of higher price being commanded people demanding deployment of AI and deployment of cloud this is good news you know that capex dislocation is not happening today. Now it could happen tomorrow if some of these people who are committing to capital are not able to show up with the capital. But for now, you know, we have one more run at the roulette table.

So that's what that's what's happening. We're being told that this market is going to support capex until it can't. I think it's a bit of a gold rush moment. I think every consumer app will get rewritten in the next 5 to 10 years. You know, why would I not have my agent talk to my Door Dash app or Uber app? Why do I have to go to every one of them and click seven times and have it have no context or learning?

If you talk about con learning and agents, so everything is up for grabs. Every consumer app that was ever put on the iPhone has to be redone. Every enterprise app in SAS you just debated has to come back with I have an opinion. So the the demand, the construction, the work that's needed is humongous. Let's take that for granted. That that's going to happen.

This market is proving that. I think until the market can keep funding it and you know the timing works. I think the biggest only problem we have right now is a timing problem. Would the revenues show up fast enough to keep funding the capex cycle or is there going to be a dislocation in in capex versus outcomes? Now the telecom industry is very used to this because they used to spend billions of dollars building 3G, 4G, 5G and then they'd see the rewards would come later.

So they went through a capex cycle and that's pretty established in the market. Looks like we're going through this compressed version where capex and revenue have to show up pretty close to each other because the numbers are just way too big to be funded by speculators for long periods of time. So I think that's kind of what we're seeing and that's why this bring back the whole open anthropic debate.

It doesn't matter if they show up with the money or not. Somebody will show up because there's enough demand. I think the next dislocation could happen is in the supply of compute. You can bring all the capex to bear but I think to your point the valors may not get their their regulatory uh set of approvals. Europe may not allow data centers. You may find 30 states with you know picket fences which say no data centers in my state.

So there's a supply problem that happens in comput side which could have a knock-on impact on all our infrastructure buddies in the semiconductor space saying holy doesn't look like all the stuff they're building is going to go out as fast as we thought it was going to go out. So I think that's kind of that's kind of where we are at the the market mechanics level.

I don't think there's a demand problem. I don't think there's a jobs problem. I don't think there's a appetite or intent problem in terms of all us all of us wanting to rewrite this stuff. And I think to Jason's point, you know, why not? Palunteers at the party, they also are saying I can package intelligence make sense of it for you. You don't have the capability.

You don't have the resources. Let me make sure you don't become extinct in this wave of technology. I'm going to go back, you know, like in 1997,9989 when we saw the last big pivotal technology called the internet. A lot of the characteristics were similar except you just didn't need a trillion dollars a year to keep building the internet. I think the interesting thing as I play all the comments you've made is the odd thing is if if if if the most likely failure mode is not ultimate demand and I agree with you it isn't

but just an enterprise ability to digest that speed then to some extent and I think Gavin Baker made this point to some extent if enterprise can't digest fast enough then to some extent if this if the spend slows down because they can't get it online quick enough it may be timed perfectly with the enterprise ability to digest right and if and if or the better digesttors will win and the poor >> digesttor will heartburn.

>> No, that that that is an interesting point is that that's that all that data that says the companies that are digesting AI quickly are growing faster than the companies are not. So you if and I think that's not true in every but my guess is to your point if for example you're playing in finance and your competitor is using advanced LLMs and you're not for trading or whatever at some point you will be you will be bending spooned to use your point.

Yeah, >> those may be those may be Leo. They may not have been a bending spoon. >> Yeah. Though the other thing is Nicash, in your next analyst call, can you do like a an ode to us where when you get a shithe question, you just just say bending spoons, just drop the mic. >> I think you'll find Harry that when you're worth $280 billion or whatever enormous market cap this man has, you're not paid to joke on the earnings call, Harry.

You're paid to look down the line and deliver the product. And that's how you keep your job. Sorry if you hadn't figured I ain't here to bring IQ to the conversation. Okay. >> Yeah. >> Do Nash, do you think more established enterprises can process this rate of change infinitely? Do you think they've changed permanently? Um because it's so like what I found with a lot of vendors now is for example the last year they've made one-year commitments where before it might be three or five or seven, right?

And that's and they're like, well, the world's going to change so much. I want to see what agents and what AI product. That's totally rational today. But most enterprises traditionally, you know, you can't rebuild your whole stack every 8 to 12 months. It's it's destructive on the org. Um, but your point is that that's a skill to win today. Do do you think that's changed?

You think we'll revert to the mean where we can only process change every 5 years after we get over a hump? What what are you seeing? I I think the enterprises ability to absorb this or digest this or perhaps leverage this to their advantage depends on their ability to create training data as fast as they can and I think not enough people are focused on training data.

This is not a problem can solve for me. This is not a problem that fireworks can solve for me. This is a problem I have to solve. I have to parse through for and sorry to go back to the same thing. I get 400,000 customer cases a year. I know when they come in, I don't have enough context. Some human beings solve it. I don't know how they solve them.

I don't know what logic they applied, but they solve them. I need to find get into the brains of those people who solve them and abstract all that knowledge and codify it so that I can write my own playbooks and rules as to how to solve the problem the next time it shows up. So, I've told my team every new phone call, every new case is a learning opportunity.

is not just to solve it, you have to learn. So we have to go into this learning mode as enterprises just the way you should never let your VP of finance just decide. You should say every time the V finance reaches a conclusion, you have to surface it to the human called Jason and say no dear VP of finance book it cuz we book every transaction. So you have to you have to give the organizational knowledge to some learning system that you have to build.

And I think that still is going to take 3 to 5 years for every enterprise, every use case. And I think that's kind of what we're not paying attention to. And I think the same thing applies to SAS companies. They all have to go rebuild their stacks. But not just the stack. The stack rebuild is the easy part. Now, can I string along, you know, I'm pretty sure Fireworks will take my money and fine-tune an open weight model for me if I want and keep training my use cases to a point.

But beyond that, how do I get from, you know, 70% accuracy to 99% accuracy? That's the problem. The problem is I don't know which 30% is inaccurate. So everything's useless. It's funny your point on learning. I was literally just trying to make sure I got the quote right. But there's the Darwin quote that said, "It's not the strongest of the species that survives or even the most intelligent, but the one that's quickest to learn."

And I think you are right about that. doing what it takes to digest it quicker will be the key management skill in the next five or 10 years. I think what Palanteer is selling and you're right maybe it's not a full I think what the reason they're doing so well is they're able to say dude we know this is the biggest problem as the CEO I at least have some kind of answer here let me help give me 10 million bucks it'll be great >> look I think I think the don't let's not underestimate what Palanteer might be doing um

there is a capability that AI has already demonstrated where it can it can troll large corpuses of data >> summarize it look for anomalous behavior, look for trends, capture them, reason around them, and reach conclusions. Now, the good news is if you're doing any kind of offensive work, any kind of if you're looking for amazing insights, it could troll through pabytes of data and produce 20 amazing insights and you can go judge them and say, "Well, 15 of them are okay and five are amazing."

But the five that are amazing will change my ROI and give me 300 basis points on my top line and improve my margin by 100 basis points. Hallelujah. You know, you just paid for everything. That's you don't have to put a learning system into place of nothing. is just taking enterprise data and doing a lot of that stuff and I think you know places like you know oil discovery or nation state analysis or a whole bunch of stuff where lots of people are required to go to this and write code doesn't need to happen anymore >>

totally agreed >> we have final one we have new CEO scale AI um as an option they hit billion and a half in era we mentioned the importance of data there obviously scale AI being one of the biggest providers of data we have Mailchimp revenue declines for eight straight quarters. me. That's That's not a nice headline, is it? Uh, Rory sells drone deployed to Procore for $900 million.

Go Rory. 13-year journey. Amazing outcome. Um, we have Visa cutting 2,600 jobs. Nikesh, you said it's not a jobs problem. Well, CEO of Visa says it's efficiency and shaping the way work gets done. So 2,600 people >> gone there. Uh what not raising at $20 billion. Can I can I ask Rory about drone deploy because it ties to the beginning of the conversation with deals and Nash, right?

>> So that deal what's interesting. So so drone deploy was bought by Procore, right? Great classic software founder founded by >> Tui to to do um software for real estate. Dominated it. Had a great run, right? Growth slowed, right? Uh, most importantly, net new customer count sort of stopped growing. Growth slowed to like 17. So, they make a big bet.

They, and I'm not an expert on drone deploy. Obviously, Rory is, but they buy a next generation platform, right, to use drones to accelerate this construction industry. And, and structurally, what's interesting, and I find these deals are always really stressful. Okay, so Procore market cap is beaten down. It's got to come up with 900 million, a lot of it debt.

Pay 112x while it's trading at four. I find in the old days, I'm not saying that happened here. These deals are stressful, man. They are. It's It's not It's not Palo Alto Network spending 0.01% of its market cap on some smart kids. This is bet the farm at a much higher revenue multiple kind doesn't have to work, but man, this is the big the big bet, right?

And it wasn't cheap. I mean, you'll say it's cheap because you're on the board, right? But Procore is going to think this is expensive to pay 12x when it's trading at 4x, right? And we started this on deals with Nash and we started this on whether 3x to 4x for air table was a lot. Well, Procore is one of the ones basically trading there too. So, was this deal like super stressful?

Did did you lose hair? Were people shouting and throwing things through the window? >> I'm not going to speak for the acquirer cuz I'm not in that side of the room, but genuine one of the least stressful deals I've ever done because honestly, I would have been happy to continue. This was not a founder tired. I mean, I think actually some of the interesting lessons, there's about two or three interesting lessons here.

First of all, when you have capital discipline and you know, modest fundraisers, you you're set up for success, not failure. You know, none of you know we always raised below the price we sold at. We didn't raise a ton of money. We were profitable. We're just you it was a fine little company growing nicely. And then the second thing is I think really important the trend was our friend, not our enemy.

I think some of these very basic SAS companies, you look back and go, there's been a platform shift and you're on the wrong side of it. When you're software that's enabling drones and robots, you're actually on the side of the future. And in fact, one of the lessons I learned having invested 10 years ago is in the physical world, AI takes a lot longer to happen.

I mean, when it happens, it's amazing, but it's clearly, you know, I look back 10 years ago, I thought drones would have exploded 5 years ago. They're really starting to explode now, as are robots. So, it took a long time. So in fact we were on the upswing of this feels really good. We're happy to hold and then obviously we got an offer that made us do different.

I don't want to comment on specifics of the offer but I think one of the ahas here is building companies is hard and you know by being disciplined by putting ourselves in position the founding team did an amazing job three founders all together all still while actively involved. So no it was genuinely not a stressful thing at all. It's like at the right price you'll do this deal at another price you won't.

And for what it's worth from a distance, I think it's a interesting super interesting for the other side too. I think actually market expansion is what you need to do in some of these spaces. You need to say and you know probably Nash has done these kind of big strategic where you just say I need you know my thing is this big I need to add the next thing my customer wants.

And I think at some level the customer wants not only to be told the accounting of his business project but also the physical progress of his building project. And that's what things like physical inspection do. >> At what percent of market cap does a deal become a BFD, a big deal, uh a core strategic? This needs to work. >> Um look, every deal needs to work.

We're not buying companies because we have money to spare or my shareholders think we should you know we should rely on waste and not I think the hit rate requirement in us is more than a VC I think in the last 8 years we've bought north of 40 companies and I want to say 75% have worked 25% haven't our largest deal was a $28 billion deal which probably is currently valued at north of $50 billion that's one that one's got to That one's kind of career-defining move.

If you take a company at $28 billion when your market cap is 200 and you spend 14% of your market cap or 16% of market cap and buy something, it better work. Um, now when you make that work, then you can you have the market gives you credit for making deals work. I said that in my earnings call and they got all freaked out. And then I'm just saying you have to make the big ones work.

If you don't make the big ones work, then you lose the license to to run your business. And that big one was Cyber Arc, right? >> Yes. >> Yeah, that was a great got it. >> Rumor has it that agents are going to be important. If agents are important, they're going to need identities. They need to be treated like privileged identity. So that's our thesis.

Sort of simple. >> Like all the best deals. One of my partners always said, if you can't express it in a sentence, it's probably a bad deal. And if you can, it's probably a good one. Got it. >> As have as you've seen with my examples, you don't know what these agents are going to do. Man, >> in your case, you're just going to restrict agent behavior, Jason.

>> But they're so good. But man, they're so good. But it's so powerful, >> Jason. To the point when actually it kind of ties back when said, I just was reading some stuff last night that really does accord someone made the point, you know, if you can't what they're doing, you have to be very clear on who they are as an identity and where they're allowed go.

if you've got this, you know, as I said, this this kind of AI employee and you're not quite sure what they do, you've just got to bound the systems they can access very tightly. So, I actually think I I totally get your point that the the ability to >> the only danger is if you take it to the extreme that's called automated workflows, that's deterministic outcomes.

If it's deterministic outcomes, we already had that technology for the last 20 years. So, the question is at what point in time do you let an agent think >> do something? Yes, that's that's the big debate. >> But the flip side is it does a really good job. And listen, we have a we have a we don't have the perfect security profile to your point, right?

But even with what we have, which is probably one agent has about a thousand rules to your point, right? The rest probably have five, right? Or zero, right? But even with the 0 to 5, 99% of the time today, right, uh it's pretty since since January, since the models upgraded, pretty darn good in the last couple months, like really good, right? So it's a trade-off.

This takes one destructive example to sort of make it all unwind. You know, if you give it access to your bank account, let's see what it does. Your VP of finance allowed to write checks. I might want to have a conversation. >> Speaking of people being wrong, I'm going to say I was totally wrong on something. Scale AI. The fact that they've continued that business, I would have taught the acquisition, left them a husk.

But I think it proves one of those rules that you kind of know but you forget which is when you're in a great market and you have a product that can meet that need. Even losing your top people it's all fine. You know they were selling data data products to an insatiable demand for data and I give them huge credit. They kept the thing going. >> Hey Win ser sold to cognition right >> and exactly >> all these stub deals are working >> but they sold really quickly.

Both sides sold quickly and then value. This is even more impressive cuz they were kind of and I even called it a husk a year ago. They were left like a husk. But I was wrong. They built a business out of that. So, you know, all credit to them. And you know, um, >> Grock could be the next one, too. >> What? >> Grock could be the next one, too. >> Yeah, you're right.

The remaining Grock. You're right. Yeah, they're doing they're offering hosted inference with their technology. Yeah. No, I mean, >> in the face of insatiable demand, all things are possible. That's the aha. >> That's a good quote, Rory. Now, Nicash, do you see why I go home early from dinners? Because I need to be fresh for podcasting. You see, this is hard.

This is hard work. You builders building enterprise value in your public companies. This is where the real grind is. >> He's sitting there going, he's doing this, eyes closed, and he'll go back to making his $280 billion market cap company work later. >> It's got to be built one deal at a time, my friend. This is totally enterprise is 1% inspiration, 99% perspiration.

Totally. >> No, I do. I do not. >> It's what I tell my agents every day, guys. >> Agents don't sweat. >> Get to work. Stop it. Get to work, boys. Perspiration. >> Nick Cash, it's been fantastic, dude. Thank you so much for having it. >> Thank you, guys. Fun. >> Yeah, I really appreciate the time.