On the Ground at UBS’ Private AI, Software and Internet Conference | Menlo Park

Welcome, everyone, to a special edition of The Information's TI TV. My name is Akash Basricha. It is Wednesday, July 15th, and we are here on the ground in Menlo Park at UBS's Private AI Software and Internet Conference, where we're going to be talking to a number of people here about the current state of the software sector. We're going to talk about the current state of AI research. We're going to talk about the future of robotics and what that has to hold. We're going to have some great conversations for you. Before we get started, I want to turn it over to Rocket Drew, who is in our San Francisco Bureau. He has a quick news update for you. Rocket, over to you. Thanks, Akash. The Informations Asia Bureau published exclusive reporting that strong revenue growth at DeepSeek is empowering the company to raise its second funding round of about $7.4 billion and plot a potential public offering in Shanghai next year. Their annualized revenue reached between $400 million and $500 million recently. We're going to have more from our Asia Bureau on that story tomorrow. Another piece of news, Stripe and PE firm Advent International have submitted an offer to buy PayPal for more than $53 billion, according to reporting from Reuters. The outlet says PayPal has not responded to the offer and that Stripe and Advent want to advance discussions in the coming weeks. We'll keep our eye on that story. Meanwhile, Cursor, the code-in startup set to be acquired by SpaceX for $60 billion later this year, has been mapping out a major transformation. Let's bring in our colleague Grace Kay for more on this story. All right, welcome back on the show, Grace. Hi, Rocket. Great to be here. So you heard about a company-wide meeting earlier this year. What was it that Cursor CEO Michael Truel told his staff at that meeting? Yeah, so Michael outlined kind of a new strategy for the company. You know, what has famously been a coding startup is now pivoting towards general AI models. He put some deadlines out there. They want to be one of the top AI companies by the end of the year. They want to have the most compute by 2027 and be pushing the boundaries of AI by then. He also addressed some concerns from staff around the SpaceX acquisition. So it was a pretty big meeting for the company. Does this shed some light on why SpaceX AI was interested in acquiring Cursor in the first place? I mean, there's always been a little bit of a mystery around that to me. Yeah, I think like initially going into it, my thought was, you know, obviously they want the coding data just because they've been struggling so much, you know, with their Grok coding model. But one thing that Michael noted in one of the meetings shortly after, you know, this partnership was announced, he talked about how SpaceX was really excited because of Cursor's brand and, you know, some of the enterprise relationships they have. A lot of Fortune 500 companies are, you know, their customers. They also have a really large go-to-market, you know, sales, marketing team, much larger than XAI's. It's funny, like, XAI didn't have a great brand going into this. It's hard to imagine they would look at any company and think that brand has a worse brand than ours. But I guess Cursor does have, like, a great brand. So it makes sense. How are staff feeling about the acquisition right now at Cursor? Yeah, I think feelings are mixed. I think there are some people who are maybe excited to partner with SpaceX. You know, they have the IPO. You know, it's a big company right now. But there are also a lot of people who are concerned about the acquisition. I talked to some people who, you know, they looked at Elon Musk's acquisition of Twitter, you know, when he cut three quarters of the company. And, you know, there's obviously concerns about cuts, I think, with any acquisition. But with Elon Musk, he's kind of known for that. So that's definitely a concern. Yeah, he's kind of hard to work for at best. And then when he acquires your company, it can be a little ruthless, I guess. How is Cursor changing ahead of the acquisition right now? Yeah, so Cursor is, you know, trying to pivot to become more of a generalized AI company. They're working on this Claude co-work competitor, which is internally being called Sand, and, you know, that they've rolled out and are testing. They're also looking at some chatbots. They're looking at different ways they can push beyond coding. Michael internally has kind of characterized this as something that customers have been pushing for. But it is interesting that like some of the goals he outlined in these meetings with staff are very similar to goals that Elon Musk has outlined at XAI. So it's kind of similar how like right now they're in parallel, but like they're aligned on a lot of things. Sand is kind of a funny codename for your upcoming really exciting new product. Sort of like naming it like dirt, but I guess it's kind of cool. It's like people joke about AI chips being just like sand that we melted down and taught how to think. So maybe there's something kind of appealing about that. Yeah, I think the codenames are always interesting because, like, you know, there was Garlic at, you know, one of the companies. There was Avocado at Meta. Yeah, it's like a technique. Yeah. So what do antitrust rules require for a company like this that's pending an acquisition? They're in this awkward spot where they want to do all this work together. They're probably making plans, but the deal hasn't totally gone through yet. Yeah, it's really interesting. So ordinarily, they wouldn't really be able to work together until the deal went through. But because they have this separate partnership that includes working on models and other AI products together, it's very complicated. I think right now they're trying to keep separate, but there's also a lot of ways that they're working together very closely because of that other partnership. Is Elon taking a big role in the acquisition himself? So initially I'd heard at XAI, you know, Elon Musk was meeting with Michael. He was meeting with Amon, you know, the co-founders of Cursor. And, you know, Michael has been seen at XAI's office. But on the Cursor side, you know, they're not seeing Elon Musk as much. He hasn't really met with rank and file or anything like that. He has met with some higher ups at Cursor. So it seems like right now, like Elon isn't as involved, you know, and it probably won't be until the deal goes through. Okay, do we know yet how cursor hopes to fit into the bigger picture at XAI? I mean, if it's making all of the models itself, it's making these products, does that leave any room for Grok? Yeah, I think that's something we'll kind of like wait to be seen. Right now, it seems like cursor is really building up their sales team and trying to sell themselves on being the enterprise. They've added 60% to their GTM team since April. So that seems to be something they're really bulking up on. I also think, you know, they're going to be a huge part of the coding push, obviously, with their data that they're coming in with and their expertise that way. For sure. Well, it makes sense that Cursor would maintain a strong focus on coding, even as its purview expands to include other kinds of models and products. So thanks for coming on, Grace, and breaking that down for us. Thanks. The information published exclusive reporting that more startups are turning to newer cloud providers to secure the NVIDIA GPUs they need during the ongoing AI compute crunch. Our Amazon reporter, Catherine Perloff, wrote about why startups are choosing these newer providers over AWS. Catherine joins me now with the details. Welcome on the show, Catherine. Hi, Rocket. Hey. So you opened up your piece talking about an open source AI developer, RC, which had committed $8 million to AWS, but couldn't get the NVIDIA chips they needed. How common is this issue for AI startups right now? I think, you know, it is a, it's not an uncommon problem throughout the industry. And it's, you know, not just with AWS. The, you know, NVIDIA chips are hard to come by. and there are capacity issues everywhere, even at some of the NeoClouds. But the startups I talked to found that it was just really hard to get the NVIDIA chips they needed at AWS. Sometimes that they were just too expensive or they were only available in sort of bigger chunks or bigger commitments than they needed and they wanted a kind of more flexible arrangement. And there are some other reasons that companies might find, you know, a neocloud or another type of cloud startup more suitable for their needs. But yeah, in the hunt for capacity, you know, AWS doesn't always have half it. I see. So it's not always that these startups are getting turned away at the door and being told there's literally no capacity. Sometimes you say, well, we have capacity, but if you want it, you're going to have to accept these terms that maybe aren't what you're looking for. Yeah, I think it's like, you know, it's like, sometimes it's like, okay, do we, is there capacity? And it's like, no, but you look tomorrow, maybe there's something, but it's too expensive. Or it's like, if you want exactly what you need, you have to buy it for like a year and you don't have the money for that. So it's kind of a combination of things. You know, AWS says like they don't have a minimum. And if you want to buy one chip, you can buy one chip or, you know, rent, rent, buy compute for one chip. You can. So I think, you know, like I think the options exist, but, you know, in practice, they're not always what a startup might be looking for. Yeah. You wrote that Nebius, one of these neoclouds, is getting 75% of its business from startups who have already maxed out at the big cloud firms. Is that right? So what are the pros and cons of switching to a neocloud from a big provider like AWS? I think, you know, yeah, it's interesting. So then that kind of like contact, so 75%, you know, they've already tried to use, uh, or Microsoft, AWS, Google, and they can't get any more capacity. So they go to NavUS. I think, you know, the, the pro of, um, a Neoclod or sort of like these infrastructure providers that I don't know if you'd quite call them Neoclods, like these together and RunPod, they're also sort of in the conversation. They sort of offer more like inference as a service, but people are still running workloads on those companies instead of AWS. The advantage is, you know, sometimes it's easier to find capacity there and sometimes they can be a lot more flexible in what they can offer or they're more likely to sort of do a deal with the startup on the fly. Another advantage I've heard is that And on that flexibility, I talked to one startup that kind of specializes in post-training. And they said, you know, a lot of the current cloud architecture doesn't really work for us. But we were able to kind of like work with RunPod and together to kind of create an environment that was better for us. So I think there's just like a bit more customizability and flexibility. You know, having said that, you know, the NeoClouds are not a panacea and the NVIDIA chips are hard to come by. So, you know, sometimes the neoclouds can be more expensive. They're not always cheaper. Sometimes they have capacity issues. So, you know, but I think it's more just sort of flexibility and, you know, at times like a cheaper price, which can also just come from being able to rent less up front. Yeah, still that flexibility is probably really appealing to a lot of startups, even if there wasn't this dramatic compute crunch where they were getting turned away from larger providers. But you asked AWS, of course, what do they think about this? And they gave you this statement that, like, frankly, to me, Red is a little bit defensive. But they said that, you know, a few anecdotes does not make a trend. So they pushed back on the idea that there was a trend happening here. What did you think of that? Is it a trend in your opinion? You know, I think that, you know, it's undeniable that AWS is doing well. Like they just, they accelerated their growth to 28%, four percentage points from the prior quarter. And, you know, sometimes like some of the rise of some of these upstarts might benefit AWS because like some of the sort of infrastructure as a service provider, some other kind of cloud startups like Vercel and Render run on AWS. So you know it a complicated story And a lot of top startups do use AWS obviously OpenAI Anthropic if you want to call them startups You know they do So I think it a complicated picture But I also think it's undeniable that a lot of these neoclouds and, you know, infrastructure providers are growing. We report on their, you know, funding rounds and revenue. And, you know, some of my colleagues do. I feel like AI infrastructure might be one of the hottest areas of venture investing right now. So, you know, if these companies are growing, if there's a thesis behind them, you know, something has to be propelling that growth. So, and, you know, I talk to a lot of startups and that, you know, they're seeing these as, you know, viable alternatives for at least, you know, some of their workloads. Great. Well, it seems like the compute crunch is here at least for a while longer. So I think we're going to continue to have to learn about these dynamics. So thanks for explaining the trade-offs to us about sticking with AWS or going to a neocloud. It's very helpful. Thanks again, Catherine. Thank you. All right, Akash, back over to you. I'm here with Daniele Magadzani, Chief AI Officer at UBS. Daniele, thank you so much for having us. Really appreciate it. Thanks for having me. So before you were at UBS, you were a professor of AI. And so I want to get into some of the technical topics of AI with you. My first question is, what are the technical challenges that AI researchers have yet to solve right now? Yeah, it's a good question. There are a number. I would say what is critical for the actual implementation of this AI in critical processes is the ability to trust this model. Therefore, being able to assess the reliability and accuracy of this model is very important. And from a technical point of view, there is a lot of research going on on evals, which you can think about them as test cases that, given different questions, assess the answers. However, given that this AI, as we know, is probabilistic, it's a type of statistical assessment. You know, it's a hard problem, but it's doable, and there is a lot of research going on. The other thing is that you can actually mathematically prove that the agents are doing what they are supposed to do. It's not easy, it's hard, but it's doable. And I believe that that research in that direction will be very, very valuable and very important. So let's get into both of these challenges then, one by one. So evals, I mean, this is the effectiveness of the models themselves. We have these benchmarks. There's a question around which benchmark is the best, how to even benchmark the models against each other. What are the challenges with that? I mean, here at the Information we've written about, for example, when the models know that they're being evaluated, they can pretend to be better than they are, I guess. I've never done an eval before. What are the other flavor of challenges with respect to evals and models that are coming up? Look, I would say one challenge is that when it comes to business, use of AI, the knowledge to evaluate the models is actually in the human experience, human knowledge. Subjective. Correct. And based on, again, the expertise and the experience that humans doing the job, you know, during the year. So the challenge is how do you translate the human knowledge and human expertise into a machine-readable format so that they can actually use to assess the model? That's definitely one challenge. And as you said, you can fool the model in a different way than you fool humans. So it's hard, but it's very important. And for this, I guess you really need AI researchers working very closely with the domain expert in the business. And then the effectiveness of the agents, the mathematical proof that they're doing what you hope for them to do. I mean, is that not just another flavor of eval? I mean, what are the questions that are coming up with respect to agent effectiveness? I guess one difference is that in evades, you assess one LLM into a specific task, whereby in the mathematical proof, you really want to assess the behavior of an agent across multiple tasks that this agent is tasked to. Again, it's a hard problem. It's not easy, but it's doable. So more and more research. And just so I understand this, I mean, is this the idea that chance alone wouldn't have been able to produce that result? Is that what you mean by mathematical proof? Correct. So it's the difference between a statistical assessment versus a proof of correctness. And by the way, verifying the correctness of AI systems is old topic in academia and in research before the LLMs. Therefore, I guess one opportunity is to leverage what has been done before and see how, if and how, can be adapted to deal with this new generative AI. I want to ask you about safety. Is safety keeping pace with the innovation in AI models right now? I guess the focus on safety remains extremely high. Also for UBS, for example, trust is paramount, is the key element of how we work. So also when it comes to AI, we want to make sure that we can trust the AI we use and therefore our clients can trust the way we use AI. Of course, as you mentioned, the pace is very rapid evolving, so we need to catch up. But also that's why we want to go faster, we want to accelerate, but without compromising in safety. So that's where there is always a gap between what you have outside versus what we can deploy in production in a bank. Right. That's because we care about safety. And so here at UBS, I mean, you guys are a bank, financial services, financial institutions. This is one of the most regulated industries that there is. Are you using the frontier models? Are you using open source models? Do you have to wait a year to work with these models? And what's your process? So first of all, we really want to diversify. So we use frontier models as well as small language models, which are good for a growing number of tasks. My view is that you do not need a frontier model for a non-frontier problem. Therefore, one clear strategy we have at UBS is to make sure that our colleagues use the right model for the right questions or for the right tasks they want to solve. And is this generating recommendations for clients? Is this coding your own tools internally? What do you end up using these models for? Well, a couple of use cases I'm happy to share is that when for sure we have a number of AI generated insights to better serve our clients, our focus is always how we can use also AI to better serve our clients. It's still a business around trust, but AI can help people to provide better insights. Of course, we use AI when it comes to developer productivity. We are using it a lot. And when it comes to research, the way we consume information, we produce information is really enhanced by the AI. The way we see AI is really how to empower people to work in an even smarter way to better serve our clients. That's our focus. And you mentioned small language models. Yeah. Is that the dominant way for how you optimize for costs of AI as well? Yeah, look, it's important from a cost perspective as well as for a sustainability point of view. You really don't want to use the latest model for very, very simple questions. So you want to leverage the appropriate model for the question you have and to do so, the strategy is to have a proper model garden where you host a number of models and then to have an AI that can help route the right question to the right model. We don't expect everyone to select the models, but we want to do them for them in a very optimized way. I wonder, as you've led the process of getting UBS internally to adopt AI more and more, I mean, what's been a cheat code for you to adoption? You know, we hear a lot about the cultural changes that have to happen in terms of using AI, teaching people how to use them, building trust. How do you actually do that? I mean, is this running webinars? It's a good question. Is this people holding your hand? Do you have forward deployed engineers on the ground sitting next to associates saying, hey, maybe you should do it this way, that way? What do you do? First of all, it's a good question. And I always say it's people first challenge and opportunity. As important and powerful the technology is, it's really the willingness of our colleagues, ourselves to willing to learn how to use this technology. I believe a few things I noticed in terms of what is that slow down adoption is, first of all, maybe people tried models a year ago, weren't so good, but now models got better over time, so they really need to go back. The other thing people need to, we all need to invest time to learn how to use this AI. A pattern I've seen constantly is that as soon as anyone finds a very productive way of using AI, they cannot stop using it. So one initiative we started this week actually is what we call AI Power Hour, whereby we really want every UBS employee to be able to invest one hour a day a week to learn about AI by doing. And we have a number of resources, including agents that can teach you how to use AI. So the idea of investing time to learn about AI is something which is very good for the company as well as for each individual in the company because they learn. But it's really about the willingness of people to want it to learn. It's non-negotiable anymore, I guess. Right. Let me ask you one last question again, back to your background as a professor. Recursive self-improvement. Does it scare you? Does it excite you? Where do you land on it? Look, it's both, I would say, exciting and scary. I do believe that given the great focus around guardrails, controls, validation, the exciting part wins over the scary part. But I totally agree that remaining careful and cautious, also aware of the limitation and the risk is absolutely critical. Great. Well, Daniele, I want to thank you for joining us. Thank you very much. That is Daniele Magazzini, the Chief AI Officer at UBS here on TI TV. I'm here with Carl Kirstead, head of AI and software research at UBS. Carl, thank you so much for having us. I really appreciate it. Thank you for coming. So, Carl, I want to start with the software landscape, broadly speaking. I was looking at a basket of about 80 software companies that I track regularly this morning. That index is down about 26% in the last year. It's up 26% in the last three months, though. Yeah. And so with that in mind, give us the overview here. Where is the software sector at right now? Are we still amidst the SaaS-pocalypse? What's the story? We are. I would say investor sentiment on at least the SaaS or application software stocks remains very depressed. The stocks have had a bit of a rebound, I'd say 20-ish percent off the bottoms in late April. But I think that's primarily a function of a fade in the semis trade and a broader portfolio rotation into defensive cheaper stocks. I don't think it's because investors are picking up evidence of a fundamental improvement in the application software space. So at least the view of our team is that given that the environment for application software still remains tough, procurement officers are trying to limit their spend. I worry a little bit that the upcoming results across the SaaS space might be a little bit soft and that rally could fade. Why is that? Well, I think it's partly a function of AI, to be honest with you, where even in the IBM results that were pre-announced this morning, Right. We're seeing pretty strong evidence that rising AI spend inside Fortune 500 enterprises is beginning to crowd out spend. It's crowding out traditional software spend. It's crowding out IT services spend. You can see that in the results of Accenture recently in the Indian firms. And as per the IBM pre-announcement this morning, it's beginning to crowd out non-AI hardware spend. So this is a fairly dominant theme. So I think that's probably the main one. And then I think the secondary one is that a lot of the traditional SaaS firms have fairly mature end markets now Now there are pockets of the software space that are doing relatively well So, as you know, Snowflake, Datadog, those names. Anything backend seems, you know, we've been talking about this on the show, this idea that if you can build the backend yourself and create your own application layer using AI, That seems to be the formula that everyone is going for. Or put another way, the bottom of the tech stack. Right. Infrastructure, data, cybersecurity is doing fairly well. The layer on top, the application layer has struggled. And fundamentally, that's been the call of our team for the last year to summarize long infradata security, cautious apps. So what are investors looking for then? Is it just growth rates going up? Is that the number one priority? Yeah, at least when I talk to large, long-only investors, hedge funds, and I ask them the question you just asked me, the consistent answer is we need to see a bending of the growth curve. So that's what we're all waiting for. I want to ask you about a couple specific names. Microsoft, the stock is down about 20% in the past year. We obviously had the layoffs last week in the Xbox unit. What are investors looking for there with that story? Yeah, this is an interesting stock. We're probably seeing the widest divergence between the long semis trade and the hyperscalers. Both are exposed to AI, but one is at all-time highs. And as you point out, Microsoft, Amazon, Google, Oracle, Corweave have generally lagged. So that's interesting to me. So I've generally got a constructive call on the hyperscaler level. Now, Microsoft is going to report soon, so it's a good question. What do we need to see to bend that narrative and to close the gap between the hyperscaler stocks and the semi-stocks? I think you probably need to see more reserved CapEx estimate revisions. I think what's embedded in Microsoft shares is a concern that when the company reports and gives color on CapEx, that it'll massively exceed street estimates. Even more. Even more than they've provided so far. So that's one concern. I think the other issue that needs to get resolved is that there's this background fear that OpenAI and Anthropic are going after the knowledge work software space. And the giant in the knowledge work software space is Microsoft. So how can Microsoft remain competitive in the Office co-pilot franchise? That's a TBD. Those are probably two of the most critical questions. It does feel like the perception around Microsoft, I mean, you know, I wonder what it takes to bring that back. Because like you said, OpenAI Anthropic, there's a perception that they're targeting the same customer base. You've got Copilot in the mix here. There are questions around how quickly it's being adopted. They're fiddling with new pricing models as well. You've got the gaming business. Do you think the gaming business gets spun off? I mean, what do you think here? I don't think it does, but I don't think it matters for the stock, to be honest. I think the street interpretation of the recent layoffs in the gaming space was constructive, good headcount management to maintain earnings. That's the Wall Street perception. But yeah, this question of Microsoft's competitiveness with OpenAI in particular is a fascinating one. Obviously, Microsoft owns 25% to 30% of OpenAI. Yeah. They're, along with Oracle, the largest provider of compute for OpenAI. And yet it seems fairly evident that over the next five years, they're going to become increasingly competitive in the productivity software space. So I think the street needs to believe that Microsoft can win that battle. I want to go back to the delta that you were talking about here between the chip stocks and the hyperscaler stocks. So if I understood you correctly, you said the chip stocks are getting a lot of love right now. Hyperscalers are not. What's the core reason for that delta right now, do you think? Well, I think the semi-stocks are believed to be easy longs right now. There's a lot of upward estimate revisions. There's cost inflation, you're aware, with DRAM and memory that's creating significant revenue upside. And yet on the hyperscaler stocks, generally speaking, free cash flow estimates are going down as CapEx goes up and there's heightened competition. So I think that probably explains a lot of it. So our call is that we might, over the course of the next several months, several quarters, see that gap start to close a little bit. Did you read Seth Nadella's post on X? What was your reaction to it? Yeah, so there's been two of Satch's posts. Both are similar. I think what Microsoft is getting at is to make AI successful, you need not only access to world-class frontier models, but you need that broader, call it frontier ecosystem. You need the data, all the security, and all the governance. So I think Satch is correct to point out that that's what enterprises need to be successful in AI, and Microsoft can bring that to the table. I think the confusing part for some investors is this notion of sort of creating a little, it sounds like a little bit of conflict is brewing between Microsoft and the Frontier Labs. Right. Well, it's not just Microsoft. I mean, like. Alex Karp of Palantir. Yeah. Salesforce. I mean, Mark Benioff, he didn't call them out, but he had that post about zero data retention. Yeah. And so it does feel like there is this movement right now. It's everyone against the AI labs, or at least that's the way these CEOs are trying to position the argument. Do you think that the AI labs have a trust issue right now? I don't think they have as big a trust issue as perhaps some of these software CEO companies are suggesting. I'll admit, as part of our research process, we are talking to enterprise IT executives all the time. I can probably count on a couple of fingers the number of times a Fortune 500 enterprise IT executive has expressed a worry that the frontier models are basically going to ingest their corporate IT and diminish their value. This is what Satya said, is that you can't get usefulness out of it unless you give it. I don't hear that as much as the blog would suggest. Interesting. So I think bigger picture, what might be happening is over the next five years, we have significant overlap developing. The frontier labs are probably facing a more competitive model market as a lot of companies essentially down tier to cheaper, smaller models. And their reaction is going to be to vertically integrate up into the software space where a lot of these software companies have their home. So we have a significant overlap between the frontier labs and incumbent software firms coming in the next several years. So it's not shocking to me that you get some measure of conflict beginning to brew. So if they're not talking as much as Satya points out about giving up control over their data, what are the concerns that they're talking about? Are they concerned about cost of the models, I presume? A lot of it is cost. And that's why we're seeing this notion of token optimization, which is becoming a dominant subject in tech circles. And I would say the concern is that enterprises that have leaned in aggressively year to date around AI are finding their compute token costs far exceeding what they budgeted for at the beginning of the year. and they're starting to throttle it back. And the concern that tech investors have is as they do so, how can that be good for any of the tech ecosystem that is dependent upon open AI and Anthropic? In other words, if you pull back on your token consumption and you potentially down tier to cheaper models, that might not be good for the frontier labs. And if it's not good for the frontier labs, it's negative for most of the tech trade. That's what the street is absorbing right now. So in other words, what I'm hearing from you is, and maybe this is a bit backwards, but if Satya is saying use open source as a way of controlling your IP, your data. Or maybe even Microsoft's own models. Right, Microsoft's own models. I mean, the idea here is that don't use the Frontier models as much. By the way, people can't really afford to use it as much as they might hope. But that in turn could end up hurting the hyperscalers because of the fact that the frontier models, if they can't afford all the compute, then that comes back to bite them, no? It could. That's one concern. I would argue that we preface this conversation with the notion that the hyperscaler stocks are feeling a little bit heavy. Yeah. This might, in fact, be one reason. In other words, if there's a down-tiering away from the premium frontier models like Anthropic Cloud Opus 4.8 to open-source Chinese models, for instance, that's not great for the frontier labs, and therefore that's not great for Microsoft, Oracle, and the whole infrastructure layer. But there's an offset. And that is the hopeful idea of Javon's paradox, which is that if you make AI cheaper, which occurs when you down tier to a cheaper model or when OpenAI and Anthropic launch their next models based on next generation NVIDIA chips, and they are much more token efficient, that you're lowering the cost of AI. And by doing so, enterprises like UVS will lean in even more to AI. And if they do that, that's good for the hyperscalers. Right, right. Let's go through a couple of other names quickly. So Oracle. Look, last couple of months, there have been a lot of concerns about the data center delays. Is that still a driving story for the stock? What are you seeing? I'd say that part of the story is faded. I think you're aware Oracle has come out pretty aggressively pushing back on the notion that there are delays. And they spent quite a bit of time on the last earnings call going through each of their five major data center builds and laying out exactly when they are expected to go live in an effort to push back against that. I think the bigger issue is that The street has gone through waves, I think you're aware, in the last couple of years, where sometimes more capex is good because it's a positive demand signal. Sometimes more capex is bad because it depresses free cash flow and there's concerns around overbuilding. We are back into one of those more capex is bad periods. So to put it a different way, I think the street's back to being worried about what the return on all this AI capex is. And I think that's weighing on Oracle shares in addition to Microsoft. Right. I want to ask you about the Salesforce acquisition of FIN that we just saw a couple of weeks ago. What other pockets of AI companies do you expect to see acquisitions in? Where else will the big software companies that you cover, where else will they look to buy things rather than build? I think they probably should buy more. I think if I'm an incumbent software firm and I'm facing a generational technology shift in AI, I'm going to move faster by acquiring more. So I'm actually applauding these efforts to acquire where the puck is going. Are you expecting more application layer acquisitions or more of the underlying AI infrastructure acquisitions? I mean, what are the hot pockets here that you're watching? I'd say it'll be far more at the apps layer. Why? I think just because more incumbent software firms are application software companies that are facing the prospect of decelerating growth. Right. And they need to react. And these are horizontal product acquisitions that they can then point to their customers. Either horizontal or, in some cases, vertical AI-native companies. And we frankly have a lot of them on stage in the next two days. Let me ask you one more question about the stickiness that enterprise software companies like to point to. The idea that, hey, the SaaSpocalypse is not a real concern because of the fact that we have tens of thousands of customers. It's very difficult for us to rip out a system entirely and swap to something else. How is that argument holding up in the market right now It not I would say the So people are willing to tear out softwares that they have I say the perception that got priced in application software stocks January through April is that that historical argument of durability stickiness is flawed. It's flawed because for the first time in 20 years, customers now can harness these ever-improving AI models to custom build alternatives to incumbent software. Now, software investors are not extrapolating too much with that argument, thinking that a firm like UBS anytime soon is going to harness Anthropic Claude to replicate our SAP system that owns the bank. No investor, serious investor, really thinks that's going to happen. So in that sense, yes, there's stickiness. But I think the more realistic bear case that's been priced into these stocks is that a lot of the ancillary applications, the upsells that drive growth, that might get replicated by custom built AI software. Well, and that's what I'm trying to sort of figure out is that the perception is there, is that the reality of what's happening on the ground. Sounds like it's the perception is as strong as ever, but the reality is it's not happening just yet. Correct. I'd say both observations are accurate. The threat that it may happen is absolutely getting priced into the stocks today. Is it happening today? No. But we do talk to Fortune 5 and enterprises about what the world might look like in two to three years' time. And I can assure you, because I've had many of these conversations, they absolutely are articulating a view that given the performance improvements in these AI models and the ability to custom build alternatives, that they would like their spending with software company XYZ to be down 30% over the next three years. I do hear this. And so let's bring it full circle now, back to the multiples, back to where the stocks are trading at right now. A year from now, are software companies, are they trading higher? Is there a little more of a recovery? Do you expect them to stay the same? What do you see? I'm a little bit more in the multiples staying the same cap. Okay. So at least in my... So grow the top line and figure the rest out. Yeah, at least in my large cap application software coverage universe, I don't have a single buy. So I'm in the camp that it's going to be a rocky ride for the next 12 months. And as a team, we have a much stronger bias long at the infrastructure data security levels for the reasons you and I talked about earlier. Right. Well, Carl, I want to thank you so much for having us. That is Carl Kirstead from UBS here on TIT. I'm here with Dustin Peterson, CFO of Locus Robotics. Dustin, welcome to TITV. It's great to have you here. Thank you for having me. Locus Robotics. Tell us what the company does. Yep. So we're an autonomous mobile robotics and software company focused on automating fulfillment and distribution warehouses. So effectively making people more efficient by using robots to do a lot of the movement around a warehouse so people can pick more efficiently and effectively. And we just launched a new solution that also does the picking as well. So it's all about making these operations more efficient. So this is, is it just a platform? Does it have arms, claws? What does it look like? Yep. So it's an autonomous vehicle that can drive around the warehouse completely, you know, free of any grids or tracks in the floor that just goes to where it needs to. So think of a robot on wheels that can drive around. We have a certain version that in which the human actually touches the tablet and does the picking. And a different version, which actually has an arm on it, so can extract it to pick the item and put it in a destination bin and do the picking for itself. And you sell to companies operating warehouses? Yep. For film distribution centers, about half of our business is third-party logistics companies. So think of large companies that are focused on doing these operations for a living. And then about a third of our business is retail and e-commerce companies. And the remainder is healthcare and industrial companies. Basically, all these companies have warehouses that have things on the shelves in bins that need to be moved out of the bins to the shipping area or from the loading dock onto the shelf. How many robots do you have in warehouses today? Yep. So we've got north of 15,000 robots in warehouses today across over 350 facilities. And so how does pricing work? How much does a robot cost? Yep. So we have a subscription model, so we're a little unique for the industry. That's not common, right? That's pretty... Yeah, that's a little bit novel. We've been doing it since day one. And what that allows customers to do is it allows for them to be operational expenses versus CapEx and for them to have flexibility. So we do three-year deals in which customers subscribe to the robots, but they can also add additional bots for peak seasons. They sign a separate order form, and they can effectively scale up their operations almost overnight to handle more volume and then reduce it back down. So we effectively manage the fleet, and the customer gets what they need in terms of technology capacity. to match the demand going through the warehouse and get a good return on their investment. So how does a subscription work then in terms of, I mean, you know, refreshing the fleet? How often are you adding new robots to a subscription? You know, I don't know what the lifespan of a robot is, but how does that work? Yep, so the robots actually have very little mechanical stress and strain. And what a customer gets when they subscribe to the robot is they get updates periodically with new software so they can handle new use cases. They can navigate more tightly and continue to improve over time. And effectively, they keep the same robots for as long as they need them, and they can add at any given point in time. And at renewal, they can always reduce. If their volumes are lower, they can reduce the number of robots. And that's fine because we can take those back, refurbishment, and send them out to a different facility. Right. I want to talk to you about some of the broader topics in robotics right now. On the show this week, we've been talking about the software component to robotics, some of the capital constraints right now? Where are you seeing a bottleneck in terms of your own development and the development for the industry as a whole? Yep, so in terms of our own development, we just launched our picking bot. So we've been doing a lot of work to gather real world data in terms of picking. So think of being able to grab an arm, being able to grab things out of a bin and move them to a destination bin. So that's an area where we're learning. We've obviously, over the years, we've gotten a lot of experience in terms of navigation, as well as fleet orchestration. So how do the robots work as a team? all the time, every time, as well as navigate the facility really, really intuitively and smoothly. So that's an area where we've built up a lot of learnings because there's a lot of edge cases out there. What about training? In terms of training, so we're using both the data that we collect from our customer sites, but we're also using off-the-shelf, we'll call it models and data. And that's an area from an industry standpoint. I think there's a lot of synthetic data. There are a lot of models. But when it comes to physical AI, when it has real world applications, real world data is really, really needed to get those edge cases. If you think of companies like Tesla or Waymo, obviously there's huge implications to not solving for all the edge cases. In warehouse robotics, it's a little lower stakes from a safety perspective, but still very important relative to think of your average LLM where you can deal with a hallucination from time to time. In robotics, it just has to work and it has to work all the time. And you have to also keep worker safety in mind. But let's go back to, I mean, capital constraints here. I mean, on one hand, there's the question of, is it too expensive to get the data you need? On the other hand, it's, does the data even exist? Yep. Which of those two is a bigger problem for the sector right now? I think for the sector, it's probably, does the data exist, honestly? Because, I mean, some of this stuff is, I mean, you can simulate it. You can, world models, I think you can find ways to go into the world and videotape stuff, right? Yep, you can certainly do that. And there's a lot of companies that are doing that. But I think because there's been a lot of investment in the broader, we'll call it robotics ecosystem, especially over the last one to two years, there's lots of capital. But at the end of the day, you need real live robots doing things to collect that real world data. So I would say the constraint is more in the existence. It's like a chicken and the egg situation. It's like you need a robot to get the data, but you can't make the robot in the first place. And you need robots deployed in a real-world environment versus maybe in the back of a warehouse or in a training. That helps to some extent, but you really need it out in the wild in order to get real good data. What about the foundation models here in robotics? I mean, we live in this world here where in AI, I mean, we've got these big labs that have become, you know, there's a couple big players, okay? And they have the models. Everyone wants to get their hands on them. Is that same story playing out in robotics? Are there a few big robotics, physical AI model companies that you suspect will become the giants that everyone will need to buy from? Yeah, potentially. There's a couple of companies that are out there that have raised a lot of capital that are very much in the news. And we've got a little bit of it. Which name should we be paying attention to? Physical intelligence, skilled AI are a couple that are out there that are buzzy. I mean, I don't have any unique insight into one versus the other. But they've got an approach where we'll be the robotics brains for every type of hardware over time. That may be the case. And then you've got other companies that have a little more nuanced approach to solving very discrete customer problems in a very finite way, such that the business case works, the customer sees a lot of value, and kind of building from the bottoms up in that way. And that's been our approach. So do you anticipate that robotics will stay on this trend of there being a couple big model companies and then the rest? or do you think it ends up more fragmented in the long run? Yeah, I think robotics, it's a little bit harder than just pure software LLMs because, as I mentioned before, there's the integration with the hardware. So every hardware, all different components of hardware are a little bit different. And then the second aspect to it is it has to be safe and it has to work all the time in an integrated fashion. So, you know, ideally you'd have some sort of, we'll call it, robotics brain that can control everything, but you have to be able to do it 99.99% of the time. It's got to be safe. It can't get hung up and it's got to solve the customer problem for it to really have value. So there's a long road ahead relative to, we'll call it Claude or OpenAI where you can deal with some, I'm going to call them hallucinations, but some errors and exceptions and people are just, you can deal with that. Whereas in the real world, it's a little bit, you can't quite deal with the same exception. So fragmented or? I think it'll be fragmented, but at some point in time, the question is, what's the time scale? There will be giants, but I think that's, we're talking tens of years out, probably not single digit number of years. Right. Let me ask you a couple of quick questions before I let you go. The chips story right now, I mean, the memory chip shortage we've been talking about, is that affecting your business? It is affecting a little bit. We have on-site servers, so we've seen the lead times and the cost of servers go up because they have memory in them. And so we've certainly seen that a little bit, but not to the extent of probably folks like Apple and some of the other folks in the news. Okay, and very quickly, I mean, open source versus closed source models in robotics. Yep. Is the same debate playing out? Do you prefer one versus the other? Yeah, right now we don't really have probably an opinion on that, but I think that that will be a debate that plays out more and more as the industry matures. I think this is, robotics in general has a massive opportunity, but it's much more in the early days relative to, we'll call it, the AI models that people use in their day-to-day life. So we'll see how that plays out. Don't really have a strong opinion one way or another at this point in time, but I guess we'll see and talk to you in a couple of years. Great. Well, Dustin, I want to thank you for coming on. That is Dustin Peterson from Locust Robotics here on TIAT. That does it for today's show. I want to thank you all for joining us, and I want to thank UBS for having us here on site at the event today. We're going to be back to our regularly scheduled programming tomorrow on Thursday at 10 a.m. Pacific, 1 p.m. Eastern. I will be coming to you from our San Francisco Bureau. Thank you so much for tuning in. We'll see you tomorrow. Bye-bye for now.