Raise. Raise. Paris, Paris, Paris. Paris is beautiful, but it's hot as...
Paris in the middle of the summer, middle of the heat. People are ignoring it. They're showing up.
We have Casser of Applied Intuition. Casser, how are you doing?
I'm doing fantastic. Thanks for having me again. Good to see you.
So, you're coming off of what some people are saying a historic commencement speech. You didn't get cancelled for talking about AI. So, what happened there?
Yeah, it was at my undergrad. I think, I mean, to give credit where credit's due, I think it's, you know, I went to this place called the General Motors Institute, Kettering University. Now, I talk about it in the commencement speech. And let's say the audience, it's not like East Coast, West Coast elite schools where I think like the GMI, you know, mostly guys. The GMI guys are like more, you know, Midwest pragmatic. So, I didn't expect booze, thankfully, and I'm, you know, from there and hometown, hometown person. So, but yeah, I mean, I gave I think what is my authentic opinion, which is I think you can have a lot of anxiety about this. You're a new grad. I mean, if you talk to new grads, we hire obviously lots of them. That's the prime fears like is my career done? Is everything... and I just don't believe that. I like fundamentally I don't believe like jobs are all going away and I just... it doesn't... there's something in my... and I could be... this might age really poorly.
By the way, it's also not true already. It has been reported that jobs have only increased.
Yeah, and if you look at like when I was graduating, there was this whole big looming technological reckoning and that was the internet. And you know, all these things were being changed at the time and there was a whole view of like what's going to happen to the businesses. I'm still in, you know, 25 years later. And obviously, we still drive and we still, you know, we still need construction equipment and we still... So, I think there's something similar in the future like these things are not going away. They're going to change because technology changes things. So, I talked about that and I think it like landed well. I think the risky bet in the commencement speech was it wasn't like everything will be, you know, lollipops and rainbows. It's like, hey, that's life. Like your 20s are actually a pretty tough time in your life. You got to kind of figure out how to get in the right, you know, groove and you have to do this in this like very volatile environment that we have. Yeah.
Well, that's a soft way to put it. I think Jensen Huang once said at Stanford, I wish you all pain and suffering. So...
Yeah, exactly. It's part of that. And I think like there's like the other extreme which is like imagine you don't have to work or you don't... that's very unfulfilling too. And so if you are going to work, then you should work on things that are really important because you only get to live once. And so I think like I'm a believer like you know doing the laundromat, like you know the classic immigrant like laundromat or doing those jobs my parents are... those jobs are just as hard. And every time people ask me like as a founder like oh must be tough like Applied Intuition or whatever, it's like yeah but you know what's harder? Like working at like Taco Bell, like that's a hard job. And so I think like for new grads you have to have that perspective too which is like there's going to be difficulty even if there wasn't an AI boom. There's always difficulty, that's the part of you know of growing up and finding your way and stuff like that. So I managed to wrap that in like a more cohesive way in 10 inspiring bows.
Exactly. Yeah. Yeah. Yeah.
Okay. So, to bring it to current day, we're at the Raise Summit in Paris. AI is buzzing all around us. It's like a big expo. What are the trends that you're seeing in the room and around what's going on right now in AI?
Obviously, the broad stuff that we don't need to talk about are things about the IPOs that are happening and kind of just how some companies are, you know, sucking up lots of revenue. Super positive. That proves that there's something there. It's not hollow. I think the below the surface which is like the rumblings is what's that reconciliation. And I think reconciliation in enterprise, the world that we are, we're an enterprise company, is like budgets and cost consciousness is becoming like a more core theme. Whereas let's say if we were talking 12 months ago there was way more of give me everything and I'll take five of it. And now it's like what are you giving me and how much am I going to pay? And I think that reconciliation will have downstream impacts on everybody not only the big companies going public but also the small startups that are trying to find their little niche group. So I think that's a big talking point. Talk about in the physical AI world where we're at. The way to think about physical AI versus let's say like the large language model universes, the diffusion is slower because you're dealing with safety. You're dealing with physical machines in the real world and so it's way more linear so that you don't have these like big spikes and crashes. There aren't like new entrants that come in and it's just because it's safety oriented. So that's a bit more of what you know when we last talked or in the last year it's kind of continuing the same trajectory.
So when we last talked you had a lot of big announcements that came out. Since then you've had even more. So what's going on at Applied Intuition?
Yeah, I mean one part of it is we are like a sizable company. So we have a thousand plus engineers doing a lot. So every month we're hopefully producing a lot of good new products that our customers are consuming. But like at a high level in all of our major areas, you know, our mission is to get intelligence onto a billion machines. And so when you kind of deconstruct that like how do you do that? The obvious ones that people always think about us are cars and trucks because that's consumers. They understand that. But similarly we've had announcements in defense and where we are, we're working with you know ship makers to put intelligence onto actual ships, Huntington Ingalls in that specific example. Or we're working with H. Heidelberg Materials to put intelligence in quarries and in ports and in mines. So we continue on that. So each of the... we had a bunch of announcements. I won't you know bore the audience with them. But you can go through all those verticals and we're just continuing to push intelligence into each of those verticals. And our macro hypothesis which is you can take a model and put it on many machines and it can perform really well, I think continues to hold. Why we believe that's really important is it's very expensive to train and deploy these models and these are models and we're not like repurposing other folks. So collecting data, training, pre-training, we're talking big numbers in the way everybody talks big numbers, but what's different about us is we want to also reconcile that with customer revenue. So we're always trying to balance that, which is, you know, different about us than other companies. There's a version where you just like, hey, we're going to spend a lot of money on this and we'll figure out how to make money in the future. We try to create some balance there.
Isn't it right you barely touched any of your funding?
Still? Yeah, we're still in that phase, which is great, which we're super happy about. I'm less... it's less about like the ego of saying that. I think it's more about we have a lot of resources to put towards any opportunity that we think is worth deploying a billion dollars towards or deploying multiple billion dollars for. And so, yeah, and I think we have some big announcements coming in the next... like the biggest announcements in the company's history. Yeah. So I can't say... in my notes they said whatever you do don't talk about X, Y and Z because internally it's something we've been working on for a long time. Couple of things we're working on product side and the customer side. But you know that's the way I always think about our company. We're a thousand engineers. We have some resources. Our mission is to put intelligence on a billion machines. So how do we do that in the most effective way possible within the context of everything happening in the summit and everything happening in the competitive world, everything happening with our customers. Yeah. But I think one thing that is worth saying is like I think everybody who makes physical machines whether it's defense companies or construction companies or automotive companies, they're not like... their head isn't in the sand about AI. They recognize they need to put intelligence on these machines. And so that really works well for us. Yeah.
I don't know how you're going to go bigger than the last Physical Intelligence Day. You had Marc Andreessen there. You had so many big announcements. So we're going to have to sneak into this one somehow.
Yeah. Yeah. Exactly. Yeah. I think I mean I'm believing that I'm pitching my own book here, which is like I think the physical world and AI going into that is... I think when we look back, I think that's going to be the bigger story. As we look back 25 years from now. I think we'll look back. It's kind of like if you look at the early days of the internet, there were companies that really focused on getting websites up, static websites up. And it's like when you look back over the last 25, 30 years of the internet, you really think about Amazon, you really think about Apple. So I think like those broader themes of like phones getting in everybody's pockets and delivery becoming ubiquitous, those macro themes I think will be the bigger ones rather than like a specific model launching and then the government saying no and like that all is going to be forgotten. So I think and that's where my mind is always at.
On that note, I have to ask you what is your hottest take right now?
I think... oh man, the hottest take... I think the cost structure that we're currently seeing from whether it's from how much we spend on model training and development to employees to... we're at that phase of the bubble where it's like you know, like all the opulence in the companies is what people talk about rather than the products. And so I think there's going to be a little bit of a pullback. Like maybe that's not a hot take, but I think it's coming. And a lot of times when people talk about this concept of a boom and bust, the view is the bust always takes longer than you think it is, but I think the cycles move a lot faster. So if there was really a bust in '21, it's like, oh well, you typically have a longer time horizon where you have a buildup and maybe that pullback happens. It's not necessarily a hot take, but it's like I think about it. I think about like... so I'm just... you and I were talking about books. I read, you know, a lot and I just finished this book called The Rise and Fall of LTCM. The book title is When Genius Failed, but that's really... LTCM was this hedge fund in the late '90s that was a bunch of these, you know, quant geniuses and they kind of do what's happening now with compute, which is like they just use leverage and they found themselves in a really bad position and suddenly this untouchable firm, so lauded in New York, in Wall Street, collapses and collapses very, very dramatically. And like maybe because I read right before I go to sleep, like it's in the back of my mind, it's like could there be an LTCM situation in our business where you have something that's so elite and hopeful and then suddenly like some things don't work and it stumbles and it has real repercussions. In the LTCM case that was because the broader global markets in Russia and in Asia started behaving in ways that nobody thought would happen and it compounded. So like is there something that happens more peripherally in the AI business and then it has a domino effect into our businesses? Like that's what I kind of think about a little bit. So but I'm like, you know, I'm in the... you know, Only the Paranoid Survive. So like I think if you ask me this question at any point in the last 10 years I'd always be like where is the hidden risk? Like that's like one thing I'd really like to implore founders to do is like you always want to be thinking about hidden risk in your company, in your leadership team, in your technical strategy, in the market at large and how you play in it. So...
I was going to ask you about the books.
So what else are you reading right now?
Like literally at this moment and I always have to keep reading new stuff because if I get an interview I can't be recycling books. The non-technical books I'm reading which are interesting are Society of Captives, which is about the American prison system and then it's written in the 1950s, really good book. They also... I recommend to founders to read stuff which is outside of our industry to give you like some, you know, like reflection on like how does this kind of apply here. I'm reading The Courage to Be Disliked, which I think people think is hilarious because nobody thinks that I don't mind being disliked. So it's like that's a great book. I'm reading... I'm also reading something else and I just... it skips my mind. Oh, Genghis Khan.
I just finished Jack Weatherford's Genghis Khan book as well. Also fantastic. Making the Modern World, also fantastic.
What were your biggest lessons from Genghis Khan?
That's just a nice like popcorn book. A better book that I just finished and I would really recommend especially if you are an enterprise watching this, like you're on the consuming side of AI, and it's a book written by John DeLorean, who is supposed to be the next CEO of General Motors in the '70s or next president of GM, and he leaves... he created the DeLorean, the car that's in Back to the Future. So he wrote this like bombastic book called On a Clear Day You Can See General Motors. But then after he wrote it, he was like, I... this can't be published. Like it's too crazy and I'm starting a car company and I can't alienate it. But his co-author basically publishes it anyways through lawsuits and everything. It's... but it's a phenomenal book because at the time General Motors was like at the top of... we just folks walking through here... at the top of the business world.
And it's like right now, like saying like if somebody wrote a bombastic book about SpaceX or Anthropic or something and saying like it's all screwed up, 300 pages of why it's not good and the crazy — it's written in the late '70s when GM is doing so well, but the punch line is like those things actually came to be true. The reason GM actually struggled, it would be another 20 years, were very clear in that book. So if you're an enterprise — the reason I was just with the leadership at one of the big OEMs in Germany, the board and the CEO, all the most senior people, and I told them, you should read this even though it's from the '70s, because I think large organizations have the same almost endemic problems, AI or not AI, it's like how leads work together. But anyways, long way of saying, on a clear day you can see General Motors is a good book.
Sounds like we need a bit of reflection, some personal audits going on there.
So, as we close out, what are you most looking forward to? You know, it could be in any timeline. Some people say 12 months is too long. Maybe it's the next weekend. I don't know. What are you most looking forward to?
I mean, it's like to a hammer, everything's a nail. Like, I think self-driving cars is really everything. And you know, when I go around Paris, you see data collection vehicles and you're like, it's even coming here, like you know, which is like not sunny bail. And so now, like every city I go to, typically with a trained eye, you can see like, oh, that's a data collection — those are sensors that are not for self, they're to collect, you know, data to train models. And so I think that's all I see all the time. Yeah.
That's fantastic. Well, Kir, thank you so much.
Yeah, thanks for having me.
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We're here with Dylan Patel of SemiAnalysis out here in Paris. Dylan, how are you?
I'm doing well. Yeah, it's — I mean, Paris is beautiful, but it's hot as hell. Yeah, I don't — no AC. Paris is meant to be enjoyed in spring and fall, not in summer.
It's not an optimal place for data centers, right?
Um, I think it's mostly a regulatory thing, but yeah. Yeah, Paris is, you know, they have all this spare power. One of the funny things is like they were like, 'Oh, okay, let's build a bunch of data centers.' But then Spain, Italy, Belgium, the Netherlands, and Germany all freaked out because it's like, 'Oh, wait, all this spare power is actually like keeping our grid alive. If you stop selling us the spare power and you build data centers, our grids are screwed.' And so there's been a lot of pushback from the other EU countries like, don't build data centers in France because we need the nuclear power. It's very interesting.
So you are a conference surfer. You love conferences. I just listened to your interview with Sequoia on this. You built SemiAnalysis off of riding the wave of conferences. So I'm very curious. What are the biggest trends that are going on at RAISE this year?
Um, I would say a lot of people here are just trying to figure out what the hell's going on. And I think there's like a wide array of people. Like there's a lot of people who are super deep in infra and like, you know, they're making deals here, but then there's a lot of people who are just like, I spent too much money on my tokens. What do I do? Right? I spent too many tokens. So there's a wide range of sort of people here. I think it's fun. It's fun that way because then you get like such a wide perspective of people and you can walk up to someone and they're like a G and they know a lot of stuff and like a great connection. Then you walk up to someone and you're like, 'Wow, I need to get out of this conversation as soon as possible.' Like Molly, you know, like...
Oh god. Okay. Well, anyways, let's cut to the chase. What is your hottest take right now?
Um, I think a lot of people are trying to build all these optimized solutions for data centers and they're just looking at like a backward-looking view, right? Like what happened at the lab six months ago and what does it look like there or what does it look like now? Okay, I'm going to build my three-year infrastructure with that in mind, instead of like flexibility and general purpose, you know, capabilities. And then when they actually end up building that infra, it's going to be a lot of it may be useless or not useless, but like less optimal than something that is more general purpose or more flexible. It feels like a lot of people are trying to optimize on the current rather than like think about where their workload is heading and then optimizing. And that's going to lead to a lot of wasted infra spend. I think most people don't know what they're doing. They're just buying the Nvidia stuff, but a lot of people are also just like they think they know what they're doing and then they're buying and building and optimizing and customizing and then ultimately they're going to waste a bunch of money. So it'll be fun to see where things head out and how they plan out and then what people do with all this infra that maybe isn't super useful with where models are in two years.
Okay, we're going to run through some quick topics. Okay, I want your take on all of them. Let's start with co-design. How do you feel?
Yeah, I mean, co-design is the important thing, right? But when you try to do software-hardware co-design without knowing where the software is headed, where the models are headed, then you end up in a pit, right? That's sort of the issue. But if you don't do software-hardware co-design, you're also going to end up with like inference that is not optimized. So it's tough.
Open source first, closed.
Um, the theme here is that all like there's multiple Chinese model labs who are telling all the inference guys, hey, yeah, our next model is not going to be open source. We're going to license it to you. So open is dying quickly, unfortunately.
How do you think the costs of memory should trickle down? Should they go to the customer or should players be taking that price on themselves?
I'm a very big fan of trickle-down economics out of pocket. So trickle-down, I think they're going to trickle down a lot. I think you've already seen server pricing go up. Like B200s, B300s that you're buying now versus last year, the price is significantly higher. We've seen next-generation hardware receive price increases before they've even started production. Like here was the quoted price and now the quoted price is higher. So it's definitely going to get passed down. And we see that with cost of tokens, you know, not falling as fast as they were previously because the cost of memory is flowing through.
How do you feel about token maxing?
I love token maxing. I think anyone who doesn't token max is going to get left behind. I think all this token budgeting stuff is loser mentality. If you're token budgeting hardcore, then your people are not going to learn the new workflows and then they're going to get, you know, they're not going to reshape your company and make it more efficient and drive a lot more work with fewer people. So ultimately token budgeting is a fallacy. Now ROI is important, but ultimately the only way to get people to change their behavior is you measure them and you force them to do things differently. And token budgeting is like pulling back on them. So I think, you know, there's got to be some rationality. Don't just waste money on tokens for the laws. Don't give bonuses to people based on how many tokens they use or comp. But at the same time, like don't just clamp down on people wholesale.
Is it true you fired someone for using Haiku?
I did not fire — I got mad at someone because their model was set to Haiku and they didn't even realize it. They were like, 'Claude Code's mid, blah, blah, blah.' And they spent — they still were trying to use a lot of tokens. They spent like $1,000 on Haiku. And I was like, 'What the hell? What are you doing?' And they're like, 'Oh, my bad. I didn't even realize they changed to Opus or Fable now.' And they're so much more productive because they're not just like asking the model to do the same thing over and over and over again.
Okay. Lastly, this is a pressing question. What is your favorite chip right now?
Um, I've been a real big fan of the Takis Blue Heat. It's quite spicy, limey. It's good. It's good. Right. I think, you know, the other thing that I have a theory on and I don't know if this is true or not, but chips in Europe have less seasoning than chips in America and Mexico. And like there is this like entire thing right where like developing countries are less — cultured countries are less flavoring their food. And the European idea is like, oh, you know, our ingredients are high quality. And so I think this extends to chips as well. It's just horrible. You need more flavoring.
More MSG, please.
Amazing. Thank you so much, Dylan.
All right. See you. Thank you.
We have Mark here from Nebius. Mark, how are you doing?
Fantastic. It's been a great time here at RAISE.
How are the vibes at RAISE?
You know, the energy is real high. You can see it around us right now. Ton of attention, ton of participation. Really impressed at how they pulled a global audience to Paris in the middle of the summer, in the middle of the heat. People are ignoring it. They're showing up and it's been a fantastic experience.
What are the biggest trends that you're seeing here?
It's incredible the discussion shifting from what AI-native is building to when our enterprise is adopting. So it's really incredible to actually have the discussion where we're seeing the bridges being built to see enterprise adoption take place. Lots of discussion about, you know, obviously the capex and power and stock prices, which I think at the end of the day is a bit of a distraction because this is a marathon that we're running and actually being able to drive that enterprise adoption, that's the critical next stage for the entire industry.
Well, I can understand you guys have a lot of fans over there at Nebius and a lot of fans of the stock too. So I want to ask you, what is your hottest take today?
Hottest take today. And boy, that's such a wonderfully generous offer. Hottest take today is the incredible opportunity that's in front of us. The staying focused on the long term to not only serve today's immediate needs, but how are we going to help to transform industries and how are we going to help to make enterprises successful with AI?
What is the biggest problem you're seeing with the buildout of data centers?
The biggest problem with the buildout of data centers is something that Nebius is not experiencing, which is that a lot of people are focused on single individual projects. Our organization has built a portfolio of opportunities today. We have 20 data centers. We're multiplying that into the future. So the biggest challenge that many of our competitors are facing is being single-threaded, which is strange when you're in a multi-threaded parallel processing industry and getting stuck with a single project. The challenges are that a lot of things are happening locally with different community challenges, regulatory challenges, power challenges, which everybody faces. Fortunately at Nebius we're actually pursuing a broad diversified portfolio approach. The beautiful thing about the data centers being created today is they have some of the most advanced technology data centers have ever had.
So whether it's from new memory to optics, how are you guys thinking about that?
All the time looking at the interesting innovation that's taking place. The fact that we can actually build from scratch, from dirt up, and be able to look at each layer of the entire data center stack and be able to consider new methods, new technology, new providers puts us in a unique position to be able to take advantage of all those advances. So we're always looking at the new and innovative things that are taking place to be able to improve the quality, the capabilities, the reliability of the solutions that we're delivering.
Amazing. Okay. As we close out, what are you most looking forward to in the next 12 months?
The next 12 months in AI is an eternity. I mean, I have been at Nebius for 12 months, actually 13 months. And if you look back 13 months ago, it was a completely different company. So the next 13 months is talking about the tens of billions that are being generated in Nebius revenue and the extraordinary traction we're gaining with not only the phenomenal AI-natives that we're working with but also making our way to scale enterprise and big brand adoption and being able to expand our solution set from the model training that people are doing today into the inferencing that we started to offer and into agentic workloads.
Fantastic. Well, thank you so much, Mark.
Pleasure. I really enjoyed the conversation.
We're here with Arvind of Glean. Arvind, how are you doing?
How's RAISE treating you?
This is a fantastic conference. I didn't expect, you know, what I saw here.
What is the major trend that you're seeing that's going on here today? Have you been able to actually see anything?
Well, I've not been able to attend most of the talks because there's so many good people at this event. So just like, you know, getting to meet them all has taken all of my time. But some trends are clear. Number one, like...
You know, all the new AI companies are here. And the big trend, in my opinion, is open source and how that is fundamentally changing the full AI stack, you know, and bringing sort of rise to a whole bunch of new companies, you know, that have new opportunities with open source.
So what's going on at Glean? What's hot right now?
Well, Glean has actually — we're finding ourselves in the midst of very, very good timing. So when you think about AI making it work in the enterprise, the two big things are context — like how do you bring, you know, like all these agents that you want to automate the work that humans do, like they need that context, that data, that information that humans use to do the same work — and that's actually like something that we are really, really good at. So being the leader in context graphs is actually helping create a massive demand for Glean. And then the second big trend in the industry is people keep complaining about like they can't measure the return on investment. Where is the business value coming from? And so Glean comes in handy on that front, at least from a bottom line perspective, because we do really, really good in terms of helping a customer reduce their token usage in two different ways. One, we can pick the right model since we work with all the closed domain and open source models. We can pick the right model for the right task, which is cheaper for them but still gets the work done. And second, with our context graph, we can actually — when a model is trying to do some complex work, it doesn't have to spend like all this time just trying to assemble the raw materials to do that work. You know, with Glean, they get that context in one shot. So we actually make most of your AI workloads much faster and very cheaper.
What is the biggest challenge that you have been hitting recently?
Challenge, I think from a business perspective, it is finally businesses are running into this, you know, that well, we're going to actually measure our spend on AI. And so that is creating a little bit of friction, you know, now with all these customers having so many different tools, so many different choices, you know, to bet on, it's getting confusing for them to pick, you know, who's the right partner to, you know, to actually start their AI transformation journey on. So that's been always like, you know, the challenge in AI, like, you know, because, you know, like suddenly when AI became hot, every software company in the world became an AI company and it's confusing the buyers. So, you know, clarifying that doubt in them, like helping them understand the landscape, what companies do what work and why is Glean relevant, like, you know, in this crowded mix of technology providers, you know, that's always been like, you know, the difficult task for us.
All right. Now I have to ask you a difficult question. What is your hottest take right now?
Well, I think open-source models are going to dominate AI inferencing. You know, you'll see in the next two years, we'll shift from where there's almost no open source to where it's going to be almost all open source.
Amazing. Okay, Arvind, thank you so much.
We're here with New York Stock Exchange star of the show. How are you doing?
Well, I'm better now that I'm with you. I mean, but this is kind of incredible, actually. It's my first time in Paris. Um, I'm Laura, by the way. Hi. Um, I run social for the New York Stock Exchange. So, I like to think I have the best job in the world. You might have the best job in the world, but luckily being at the New York Stock Exchange means I get to work with you a lot. So, it's kind of a win-win. Um, but yeah, this is kind of incredible. First time in Paris and I get — first time in Paris.
Yeah, I get the whole halaloo. I get it. It's kind of — Paris is amazing.
Yeah, it's pretty amazing. Also, it's hard to beat New York in my eyes. Uh, tried and true. So, I understand the lore and the love of the Lou. How about that? Wo. When I was on my way over here, I was like, you know what? I could commute straight up. If the flight wasn't that bad,
I came from LA. I just slept the whole time. I could probably just commute here.
Pop, skip, and a jump. Yeah. And we have the match tonight and I'm very excited. It's France, Morocco. So,
there might be riots. We're going to see. And you will see Molly and I on the pitch. No, I'm kidding. We are not going.
No, that's in the United States, right?
All right, Laura. So, you have been around Raise. You've been seeing all of the companies come in and through and around the New York Stock Exchange booth. You've done media yourself. What is something we should all be paying attention to? What is the trend of the day? The hottest thing happening here at Raise?
I would say folks are going to be saying AI all day. Agentic, cloud, quantum, all of our favorite words. I believe the thing that's making these companies stand out is their access and visibility. It's their partnerships. A lot of those partnerships are happening here. We're seeing it in real time. Watching real C-suite founders, entrepreneurs actually meeting in person to close deals. It's actually kind of crazy to see it with my own eyes. You only hear about it, I think, in, you know, closed doors and golf courses and karaoke bars. But I firmly believe that it is about the story you're telling, how well you're telling it, and where you're going to go from there, how it's going to set you apart. And that has a lot to do with what we're doing, right? Like, there's a reason why NYSE and Ward are the main media sponsors of Raise. Hello. There's a reason NYSE is one of your sponsors. Hello.
And also Raise rang the bell. Come on. Come on. The biggest financial stage, like the epicenter of global finance, there is a storytelling aspect to it that sets people apart. And if you can stand out and if you can, you know, cut through the noise — I think there's a lot of noise, honestly — and I think if you have a clear message and you have a clear goal with really good people, that's what helps with being in person and stuff, is you meet the actual real good people. Talk about vibe coding, it's all about vibe, you know, vibe meeting, vibe media. Yeah, our new podcast. Molly and Laura, amazing place to end it. Laura, thank you so much.
Thank you so much. See you at the New York Stock Exchange.
We're here with Apoorv from Altimeter. Apoorv, how are you doing?
Things are definitely upgraded since last year.
Big, big time, big guests, big speakers, big companies.
Great. Like really good setup. Lots of great founders. Really excited to meet a lot of the CIOs. I got to know the CIO of Goldman last night, CIO of Procter & Gamble, and where they are on their AI journey has been the biggest learning for me.
I've been waiting to ask you this question for quite some time because you've gone viral a lot recently with your Stanford lectures. So, Apoorv, what is your hottest take right now?
Well, today of all days, there's four seasons in AI. They're called OpenAI, Anthropic, SpaceX, and Google. So, if you're a CIO, if you're a CEO, if you're about to make a multi-million, billion-dollar decision of which lab to build your intelligence on, plan for the climate, not for a weather. What does that mean? That's multimodel routing. That's eval. That's thinking about post-trained custom models. Yeah, that's out of stake. It's a four seasons of the year. So, what's the climate?
The climate is evergreen, right? So, you've got to plan for — if you're about to spend $100 million, you don't want to get stuck in one of the seasons. Sometimes the summer's long, sometimes it's a heat wave. But I think you want an evergreen season. And I think that evergreen season, the climate looks like you'll have a portion of your workloads going through open source, as Jesse from Decagon wrote very eloquently. A big portion of their volume goes through open source, about 90% now. And then the frontier stuff, you know, discovering new use cases, will always go through the most intelligent models. Coding might stay there for a long time. And so planning for that multimodel world, planning to not get stuck in one regime, is the climate.
