SPEAKER_00: They go between 5% and 12% of the fund. You know, this is just at 5%. We would have bought more if we could have. The round wouldn't give us any more. So, you know, I'm hoping after this podcast, he decides to sell us some more shares. This round was super oversubscribed, and so we were scratching and cloning everything we get. We've offered to do some operations help, which I think, wow, we were able to get the allocation we got. But we were super impressed with these guys and with what they've done. And you'll, you know, you hear about the customers, the logos, the growth. I mean, I think you'll feel the same way.

SPEAKER_03: And this is a strategy that some savvy managers are pursuing now.

SPEAKER_07: Just, if you have that winner, trying to get as much into it as possible because of the power of law.

SPEAKER_06: I see Brian Singerman doing it. I saw one time Sequoia deal with WhatsApp. But I will say we invented it.

SPEAKER_03: You invented it, yeah. And we have even taken a page out of that book with, you know, our fund instructions. So, yeah, great artists copy. Thank you.

SPEAKER_12: What is it? Immature artists copy. Immature artists steal.

SPEAKER_15: This Week in Startups is brought to you by Command Bar. Seamlessly integrate an AI-powered guide into your software, making navigation intuitive and interactive. Visit commandbar.com slash twist to get a custom live demo. .tech domains. Don't miss our Jam with JCal contest. To apply and get more details, go to jamwithjcal.tech. Brought to you by .tech domains. And Lemon.io. Hire pre-vetted remote developers. Get 15% off your first four weeks of developer time at lemon.io slash twist.

SPEAKER_03: All right, everybody, welcome back to This Week in Startups. I'm your host, Jason Calacanis. And I'm really excited today because a friend of the pod, Antonio Gracias, is here. He is a tremendous investor, one of the greatest of this generation from Valor Capital. You know some of the amazing investments he's made over the years from SpaceX, Tesla, and now, actually, an investor in Athena, an investment we just did together. And, you know, we saw along the Newswire press release about this new investment you're doing in Weka. And so I thought, gosh, this seems like a very important company. And so I asked you and the CEO, Antonio, to join us. Laurent Zivbo is the CEO of Weka. It's pronounced Weka. And there's some background to the name, yeah, for people to remember, Laurent. Welcome to the program.

SPEAKER_21: Thanks for having me.

SPEAKER_22: So Weka is pronounced as any Greek unit. So you probably know how to pronounce mega, tera, exa. So you're just doing a Weka. Weka is 10 to the power of 30. And if exa is a thousand peta, which is probably the biggest number people really can interact with, Weka is a trillion exa. So it's a huge, huge, huge number. And, you know, Bill Gates at once said, we'd never end up getting more than 640 kilobytes in a computer. Why would anyone need that? I'm not going to predict whether we will or will not need a Weka, but hopefully we're all going to leave to the point that someone's going to need that.

SPEAKER_03: And so as we start here, Antonio, we're always interested when a capital allocator who likes to tackle hard problems comes into a company and writes a big check.

SPEAKER_27: So maybe you could tell us a little bit about what you discovered here with Weka and what the investment thesis is.

SPEAKER_29: Thanks, Jason. And thank you for having us. We're really grateful to be here. It's always enjoyable to see you and talk about our companies.

SPEAKER_00: So, you know, I think first and foremost, we're spending a lot of time in artist intelligence. As you know, we invest in DeepMind back in 2010 and I was on the board of Tesla during the B&I vision systems there. So we thought about that a lot and we have dozens of investments on artist intelligence. They're focused in two areas. One is infrastructure. The other is what we call verticalized applications. These are applications that have very good criteria data and then reinforcement that's very tight. Weka is in the first category, infrastructure. And what they're doing is making the data center much more productive. So the average GPU is utilized about 30% of the time. This is like a close regard and secret, but that's what we think. Weka makes that a lot better. And they do it in a very, very interesting way. I'm going to let Laurent tell you about the product. But I will tell you when I, a funny story with Laurent, when I, um, when I first met him and we actually went a second, the second time I met him, we went through the product. I asked him to kind of explain the math to me how this thing works, kind of amazing how it moves and makes the back end of the GPU, the memory behind the GPU much more productive. He went into the math and he started diving into the math deep in a whiteboard, like a, like a, uh, you know, kind of crazy professor kind of thing. And I'm watching this and I'm asking some questions and I realized that I actually remembered my math. I knew what he was talking about. So I was, I was very impressed, but he's a deep product expert. So that that's the, the first thing is the product is amazing. It fits inside of our investment thesis for the infrastructure in the data center. The second is that, um, you know, our strategy, as you know, is to write kind of a smaller check and, you know, it can be a bigger check, bigger, bigger check. We were a small check into WECA, um, followed them. They beat every number they ever gave us and then, and exceeded substantially. Product market fit was incredible. And then I got to know Leron because, you know, we get no companies and I was impressed with, with Leron and his team and how well they execute. I mean, they are really product market experts. They're product experts and they're built something before the market knew it needed it. And then the market came to them and the company just exploded. That's what we did.

SPEAKER_03: And this, uh, strategy to feel or bet, get to know the company and then place a second or a third bet. It's worked before. Yeah.

SPEAKER_34: Yeah. It's right.

SPEAKER_00: It's worked very well. I mean, you know, we, um, our portfolios, typically our biggest winners are largest investments, very different than like many, many managers to kind of spray out lots of positions. We like very concentrated positions. We'll write, you know, lots of small checks, but our, our big checks, and this is a hundred million dollar investment in our fund. That's a large position for us. They are typically our biggest winners.

SPEAKER_27: And those tend to be 10% of the fund, 5% of the fund size, 15, when you make a gigantic, you know.

SPEAKER_00: They go between five and five and 12% of the fund. You know, this is just at five and we could, we would have bought more if we could have. The round wouldn't give us any more. So, you know, I'm hoping after this podcast, he decides to sell us some more shares that this round was super oversubscribed. And so we were, you know, scratching and clawing everything we get. And, you know, we've offered to do some operations help for Weka, which I think why we were able to get the allocation we got, but we were super impressed with these guys and with what they've done and you'll, you know, you hear about the customers, the logos, the growth. I mean, I think you'll feel the same way.

SPEAKER_03: And this is a strategy that some savvy managers are pursuing now.

SPEAKER_07: Um, just if you, if you have that winner trying to get as much into it as possible, uh, because of the power of law.

SPEAKER_06: So, uh, but I will say we invented it, you invented, yeah, I see Brian Singerman doing it. And, uh, I saw one time Sequoia deal with WhatsApp.

SPEAKER_03: So I think collectively people are starting to become aware of this, uh, Antonio and you, I, I do give you some credit there for pioneering it a decade ago. And we've even taken a page out of that book with, you know, our fund construction. So yeah, great artists copy.

SPEAKER_12: Our, what is it? Great artists copy, some of the steel, something like that, uh, immature artists, copy, mature artists, steel is the strategy here. For us and for maybe some of our contemporaries. All right. So let's get into the product companies existed for a decade. Uh, I know you've been moving large data sets around. So explain to us, Lauren, what happened in the history of the company where you realized, Hey, managing large data sets, moving them around. And then all of a sudden these H one hundreds and AI sort of hit a tipping point. I think we'd all agree about two years ago. Maybe you could tell us a little bit about the strategy of the company here and getting that extra 70% out of every eight, 100 for people who don't know, people call it a chip. It's more like a platform, uh, made by Nvidia and Zuckerberg just announced they did their latest LLM on 16,000 of them. So to get an extra 70% from those 16,000 is they're not cheap, right? 30, $40,000 each, I believe, uh, or on average.

SPEAKER_50: So yeah, tell us about the product and how you got here.

SPEAKER_22: We've been doing data management. We've been doing storage for a long, long, long time. Work as our second company, the previous one called XAV storage and IBM acquired us. So anyone, you know, that's been buying high-end IBM storage in the last 15 or so years and years ago, previous company, we've learned a lot about the enterprise, about big customers and about why people buy storage while being at IBM. We left IBM at 11, at that point, we basically took an oath and said, Hey, never again. This market just doesn't make any sense. And it didn't make sense for two main reasons. One, the delivery mechanism for this product is flawed. While most of the engineers are software engineers. And when I'm saying most is like 99%, not 51%, the delivery mostly comes in some proprietary box, uh, it makes it hard on premises. It makes it impossible to run the product well on the public cloud. The second problem is that the market is fragmented like crazy. So it's a $150 billion market, but there are hundreds of products that actually sell. And when you're looking at from the large enterprises, the big corporations, they're using anything between 10 products. If they're really lucky to maybe 40 to 50 products across hundreds and thousands of silos. So it's hard for the customers. It's incredibly difficult for the vendors. So that's the reason we said we want it out. Then back in 2013, when we started three huge, huge, huge changes in what's possible happen. Containers happen. And they led to completely different thinking about software microservices that let you take a big, monolithic system, chop it down to tiny little pieces and run them on, on many, many, many machines. So microservices changed how you can think about infrastructure. NVMe happened, which allowed you to take flash connected directly to the CPU and control it very effectively in tiny little fragments. And then the third change, which was the most important one, is network finally caught up to the speed of the servers. So throughout history, the network may have been a hundred times lower. If you're looking at the eighties or nineties, maybe 10 times lower. If you look at the early two case in 2013, network has caught up. And by the way, by now, the network is twice as fast as the servers can create frames at 100 gig, but the network switches them at 800 gig. So this is incredible.

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SPEAKER_22: All of our competitors, the reason there are so many products out there, they are basically compensating for slow network by optimizing for locality. They're creating a storage array. I'm sure you've heard about the storage arrays before. As a storage array, you have a bunch of controllers. Each controller owns a portion of the namespace and you're picking whatever storage array you want to buy, but how the application basically spreads their data across the day, across whatever. And you want to break into controllers in a uniform way. The problem is once you've picked that, your solution now becomes a victim to the clients. Because if all the clients would approach a single controller, it's the bottleneck. So you may have a hundred controller system, you're getting 1%. On the cloud, it's even worse. Let's say a 1% now sits on an instance that has noisy neighbors, you're not even getting your 1%. What we have done between microservices and VME and fast networking, we've started as a clean sleigh and we have created a system. It took us years, by the way, about eight years to get it to really run well and then a few more ways to mature it. Where what we're doing, we're looking at what the server sent, the client sent, every split second, and we run the perfect load balancing of IO throughout all the NVMe devices and metadata throughout all of the CPUs. And we're showing that even on cloud with noisy neighbors, we're getting perfect linear scalability. So if you give us twice the big WACA, you're getting twice the results. And that's the reason, since what we're controlling is latency, what's keeping the GPU servers starved for data is high latency. Since we're controlling the one thing that actually matters, we're the only product that optimizes for the one thing that actually matters. We can get to the point that it doesn't matter how many GPUs you have, what is their access pattern. We can get them filled up to the nineties percenter.

SPEAKER_03: So this becomes an acute problem when you're trying to solve major problems in the world. Um, maybe self-driving or, uh, you know, running a language model.

SPEAKER_65: People really didn't face this problem all that often previously. Yeah.

SPEAKER_66: No, they have not. So now comes the big question of a product market fit and how do you take it to market?

SPEAKER_22: So we predicted customers would need bigger scale, would need bigger performance. We started this thing through simplicity saying, Hey, you don't need to buy these 50 different products to run all of your workloads. Cause we're just going to run them. Well, uh, had we had to go through that phase, it would have been a much longer journey.

SPEAKER_27: What are the biggest workloads today? If you were looking at like categories, I mentioned self-driving, I mentioned language models. Those seem like super obvious ones are, am I correct that those are in the top three or four?

SPEAKER_22: For sure. So we're now seeing the explosion of GPUs. You've mentioned that age one hundreds previously, there was the A's and the P's and the V's and the B, the B's are coming in, uh, in the next, and they're used a lot in AI. We are showing that we can get AI projects running on, if you're looking at the cloud, we have stability AI running us when they have switched to us on the AWS cloud. We're able to get their storage bill down from 5 million to 1 million. So five times cheaper. And as Antonio said, before they switched to us, their GPUs are running at 30%. The biggest problem was that they needed to get more job done out of the existing GPUs that they had. They couldn't get any more because there is a supply chain crunch after the switch to WECA, we got their utilization up to the high nineties. So over three times more output with five times cheaper, so 15 times better. So for AI, we're bringing big, big help. We have big car makers that they're using us exclusively for many years. And we got their time to epoch from couple of weeks to four hours.

SPEAKER_38: Explain time to epoch, uh, for people who haven't heard that term before.

SPEAKER_22: So time to epoch basically means, Hey, we're going through another round of training and you need to go through, Hey, I'm ingesting the new data or it's already there. You're running a bunch of ETL. ETL is not sexy enough. You cannot charge for it. So now it's called ML ops or LLM ops, but the ETL portion of it basically takes your tons of tiny files and prepping them for the next job. So you're reading and rewriting them in the good format. Now you're retraining one form of, of your data. So you're picking a subset of the data. You're sending it to tons of GPUs to retrain. So you need to copy that data into the GPUs. These, these operation takes a long, long time. And it's a really, it's really great that now you can parallelize these on so many GPUs while you're doing that, you actually need to save, to save, uh, checkpoints. So you're writing a lot, cause if it fails, you actually want to go back and you don't want to miss so much time. And at that point, at some point you're done with the next retrain you have now optimized for another portion and you would want to go and run regression tests and see, Hey, I've optimized the model to, to slightly better. If it's a car, let's say junctions with always stopped when it's raining. Again, there is a crowd that, uh, casts a shadow over, over the site. And now we want to see, Hey, with these crowds casting shadows, did we get anything else worse? So you, now you're running a huge amount of regression. You basically would drive your cars through all of the rest of your data. So you want to see that this has worked well. And finally, this is one, one, one circle and, and now you have a slightly better model and you start all over because it's crazy.

SPEAKER_03: Antonio to think like, we actually went full circle in the history of computing from there's way too much compute, way too much storage, tons of bandwidth, and there's no application for it.

David Friedberg: And now it's just YouTube is chugging along, Facebook is chugging along massive amounts of data, your iPhoto going to 4k, storing it all, sorting it all. And just, nobody seemed to have a use for us. And now here we are in talking with, uh, a moment in time when we have supply chain, you can't get enough, uh, GPU, CPU power. You can't get enough storage. You can't get enough bandwidth to move it around. And we're doing technology runs like it's pro systems in the seventies or something where you're time sharing and you have to wait to release your product and run another job and coming out with versions of it.

SPEAKER_12: I mean, is this going to last or do you think all of this incredible effort to make better chips, better storage, better operating systems and processes will result in us kind of catching up to the jobs as it were.

SPEAKER_00: It's funny when I was 12 years old, I went to computer camp at Michigan state university, and I had a hand in punch cards. That's how old I am and come back, come back overnight after the run. If you made a mistake on it, it just didn't run. Right. And you had to go back and like, you know, get in line. Yeah, exactly. Right. You read the code up by hand and didn't use the binary punch code thing. This is like air traffic, uh, or actually traffic control systems for the, for the GPUs. Right. So filling up the CPUs are always fully utilized GPUs. And I think the answer to your question is, I think this is always a problem because the technology is moving. Um, and the software side, the LLM side, that the model technology is moving faster than the compute. So absent a large breakthrough, like a quantum computer or something, which, you know, it's always like, it's always one year away or two years away, right? Yeah.

SPEAKER_79: It's perpetual. It's almost ready.

SPEAKER_00: Right. As long as you're on Silicon, I think you're always gonna have this problem because the models seem to be moving faster than the ability to create compute. Now it doesn't mean you won't go through some boom and bust cycles, you know, you and I were both far on the internet, right? So yeah, I was building infrastructure back then the connector business, right? So built over capacity, built, blew up. Then we were short capacity, went back up again, and you'll, you'll continue to have these kinds of cycles. But the artist intelligence, I think it's, it's kind of a super, super cycle in the sense that it is much bigger than the internet. And the, the need for train data is, is huge. It's global. And it's not going to stop with just at the very beginning of this. So I think it's a, you know, it's a long-term trend that will have a, it's a sine wave with a, with a very steep upward slope. Yeah.

SPEAKER_82: If we were to compare it to say, building out bandwidth, that was a 20 year story.

SPEAKER_27: And yeah, it, we had a boom, bust cycle as world common, all these places where they overbuilt the fiber and Google bought it for pennies on the dollar. People don't remember this, it was the early two thousands. Yeah. They went for it late so much fiber and that actually became, I think in some ways, the precursor for things like Netflix and YouTube and your photo library, not caring about bandwidth anymore. I mean, it used to be putting any viral video on the internet would result in a 10,000 or a hundred thousand dollar bandwidth upcharge from your provider. And then bandwidth suddenly became free and unlimited and the storage, that, that was another 20, 30 year story.

SPEAKER_86: So if we were to parallel to those, this could be a decade or two decade chase to get more GPA.

SPEAKER_00: Yeah. At least. And with, with the added difference that the, in that case, the end use, which is sort of like, you know, moving packets on the internet is kind of a fixed use. And it didn't change very much in this case, the end use is changing a lot. So the applications of our intelligence are going to exploding. Right. So right now we're at the very early stages. Like it's, it's an app on your phone. It's Grok and XAI, right? It's not, um, a robot yet with washing your dishes. Right. So imagine the amount of data required, just the things, just the things we can imagine, forget things we can't imagine, what's going to happen. But going from, um, you know, the, the Grok app on your phone to the robot washing your dishes is going to take a lot of compute. We don't have it today. It doesn't exist. Right. And it is going to be building. It is an enormous, um, lift and anything that makes the dentist more productive is going to be very valuable.

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SPEAKER_03: last three winners soon. And so take us through some of the logos, Lauren, that are doing the most interesting things. When you look at your lighthouse customers, people who are doing the biggest, most ambitious things that you can talk about, maybe you can give us a little highlight, either your big lighthouse customers or the type of customer, the type of job, if you want to

SPEAKER_22: abstract it a bit. Yeah. So I've already mentioned some huge AI project between deep learning, machine learning, the generative AIs. We, I mentioned stability, we have mid journey and a bunch of the other very, very exciting forefront of AI. But, you know, GPUs are really useful for other means as well. So if you've been to Las Vegas, you've seen the Sphere and you went to, to the U2 concert. No, U2 basically were the first show on the Sphere. When they got the residency, it was June. They had to start showing up and performing in September. Their initial plan was to use their own infrastructure that owned tons of GPUs, but no previous way of thinking about all flash arrays. They start that re-rendering their thing, it would have taken them six months. At that rate, obviously they didn't have the six months from June to September. They've switched to us, they've switched to WECA. From the moment they got it on the floor to the end of the first render, it took them under four weeks. So six times faster. But then what they've realized, hey, if you're taking a HD square thing that makes sense in a stadium, you're porting it to the Sphere, which this magnificent screen, it just doesn't look good. So the fact that we were six times faster, the fact that they got the first render and they still had two more months actually allowed their artists to come in and reiterate and change and reiterate until this magnificent experience happened. I don't know if you've been there, but at some point...

SPEAKER_97: I haven't been to it, but yeah, I've had some friends who went to see The Dead and the U2 stuff.

SPEAKER_22: Yeah. Yeah. So The Dead already, Dead and Call also used us and they have these experience where like the whole thing closes on you like a box or you're out there on the desert. And now that

SPEAKER_00: experience is like magnificent. It's so, I better say all the time, it's awe-inspiring. It's like being a medieval European and walking to a cathedral, right? That's how it feels. But I want to say this, it wouldn't exist without Weka. It wouldn't work. Yeah. And it was so important that Ron got invited to the opening night of YouTube because his technology enabled it to work. It wouldn't work otherwise. And, you know, the Dueling family made a bunch of bets here of technology that didn't exist before they built the Sphere. And one of them was this back-end GPU problem that Weka solved.

SPEAKER_12: Yeah. I mean, if you think about it, a 4K movie is probably 50 gigabit. If you were to download it,

SPEAKER_63: and I don't know if those are 8K or 4K, I'm assuming 4K is enough, but how many screens are there? Like if you were to actually make it into a television screen, like, is it 100,000 screens,

SPEAKER_22: 10,000 screens? It's some large number, yeah? Huge. I think they said like 150,000 screens. And every second of the show, it's over 400 gigabytes. And they did some really, really impressive stuff. If you go there, you look at the YouTube videos, you can see Bono and The Edge singing inside of bubbles. And if you're watching it, there is no lag. So there's not even a single frame of lag. So it all going through the, through the Weka, being GPU processed through the Weka, projected all in 120 frames where no not to spill light. Yeah. I was about to say, if you're doing,

SPEAKER_27: I was going to say 30 or 60 frames, but if you're doing 120 frames, it's obviously four times as much as that. Now you're talking about just an impossible task and they're doing some things in real time, like you're saying, they're stitching in the images of people, which is just unbelievable. And if you think about Pixar, which now has like, I think maybe half of the all time, uh, highest grossing films, their biggest problem when Steve jobs was starting that company, people don't know. This was just waiting for the render of toy story. And it would, you know, they would make story changes and they'd have to wait 10 days to have it output. And that was, you know, according to Ed Catmull, their, their biggest challenge was actually the rendering of it.

SPEAKER_106: I mean, what is it, what would it take a Pixar film to render today? A full length film, do you think,

SPEAKER_22: using your platform and everything? We're seeing a lot of these things go through and it's unlikely that you're watching something enticing late at night that, that didn't go at some, some stage through a Weka. Yeah. So a lot of the big ones are easiest. But just,

Chamath Palihapitiya: just benchmarking it to like, make these films now, to actually render a film. What do you think

SPEAKER_111: it takes now to, to render? So they, you know, a James Cameron, what was James Cameron one that he

SPEAKER_21: did that was super intense? So they had many, many dozens of thousands of CPUs. They're now switching

SPEAKER_22: to GPUs. Yeah. They have huge networks. So when you're looking at these impressive AI infrastructure, you know, AI is all the rage. It doesn't matter if you're running AI, if you're creating, uh, a new movie that the data center basically runs of the triangle of compute network and data. And now that everything looks so good because compute is four or five orders of magnet faster. The network is four or five orders of magnet faster. And all of these folks that were, when they're taking compute the network to the extremes, they also need to do it with the data. And this is where WECA comes in. Going back to what Antonia said earlier, hey, quantum computing or neural networks. I, I went to school in the nineties, back then neural networks and quantum computing both failed too far up. What has happened with GPUs is basically we won over Moore's law. So we couldn't create a single chip that has twice the transistors every 18 months. But with advances in networking, we can now create bigger and bigger and bigger computers that, that are disaggregated and, and scale. And it took us another 15 years, but this is what we're doing. So the world stops scaling up,

SPEAKER_115: we're scaling out. Which requires that bandwidth and storage and the, and the, and the sharing of

SPEAKER_27: the jobs. You're basically chunking all that data and then managing it. And then in between those GPUs, I mean, we're going to be seeing optics between them soon. Yeah. In some cases it's already out there. And so that's how large self-driving car data sets, or, you know, protein folding is getting moved around. Let's, uh, end on this, uh, Antonio, when you're looking at and just sort of zooming back, okay, you gotta manage the data loads. You got vertical applications here. There's another big piece. Um, people are starting to talk about energy and data centers. These data centers, uh, need to be at a different scale, Antonio, than what we traditionally thought of, and they need a different energy footprint. Maybe you could speak to what your team and you are looking at there in terms of opportunity for capital investment, opportunity for, you know, capital allocation, and just generally what these

Chamath Palihapitiya: footprints will look like because it's different. Yeah. Yeah. Look, we're, we're going to, um, you

SPEAKER_00: know, 100 megawatt and gate watt scale plants. And as you know, around the world, everyone's thinking about the problem at, you know, IJ's in the Middle East talking about this and our, our friends in the UAE and Abu Dhabi built a four gigawatt nuclear power plant to power data centers. Here in the U S you know, we are, um, we have a couple of companies and I won't name them all that are working on, um, renewable energy solutions for the data centers. And I think this is very interesting. You know, we have a power problem in the U S we were not going to have power to make this work, uh, in America. And so we have to work both on generation, creating more power and actually getting it to the, getting the grid stable. So we can both have power and the power to use and have it where we need it when we need it. Now, WECA is part in this is that if the data center gets a lot more productive, you need less data centers. Right. Right. So like three times more

SPEAKER_127: productive means two thirds, less hardware.

SPEAKER_00: Exactly. So basically the return on capital, we, we, we, you know, that's miles, but ROIC. Right. So the return capital data center goes up a lot if you are using, um, your GPU more and very simply put. So if you, if you invest in things like WECA and make the data data center more productive, you're also making the energy footprint much lower because you just need less data centers.

SPEAKER_27: How are people solving the problem today? Because, you know, creating energy and nuclear power plants, even in China, you're talking about five, six, seven years, maybe they could do it in three or four, but I, you know, it's seems like it's five, six, seven years. So we're not going to have nuclear power in time. So what's going to happen if you have to put your, you know, take out the crystal ball here, are we going to have, you know, data centers being moved to where nuclear reactors are

SPEAKER_07: putting them next to the existing ones and upgrading them. How is this going to get solved?

SPEAKER_131: I think there's two kinds of, there's two kinds of companies in the world. There are those that go

SPEAKER_00: slowly and, um, kind of do things straight by the rules and book, and they will take a long time to find power and get it right. And then those that move fast and figure it out. And we have a couple of companies in our portfolio that move fast and figure it out. Um, one of them is, um, is, is doing it in a very, I'd say creative and unique way. The other has been executing a strategy for some time of using, uh, stranded energy to build data centers. So we have a company called Caruso that's building a data center now, um, next to a trapped wind farm in Texas that was built because of subsidies. It can't all the power can get used and they're putting their data center kind of right next to it, use that power and then using battery backup as well. And I have backup behind that for, for co-gen. So, but there's, I think then I think this we're underestimating in America, how much energy we have around in hydro and wind that actually isn't being properly utilized. It's feeding old industrial assets can be repurposed, which we're seeing in the smartest players. And this is, I think,

SPEAKER_27: one of the great things about this country is you, you have such a fluid capital allocation strategy. Sometimes people feel overbuilt. We talked about fiber earlier. So somebody gets a subsidy over built solar wind, whatever hydro. And then that energy is going to find a customer eventually. And it's actually worth it to ship the GPUs to the energy, as opposed to building the energy where the GPUs or where you want the GPUs. Correct?

SPEAKER_123: Yeah. Yeah, that's correct. Because the network, you can get that work built. I think this is a period

SPEAKER_00: where right entrepreneurs will win because they'll free this problem out, right? This is a serious problem. And you're going to see a shakeout where the best, smartest, scrappiest, you know,

SPEAKER_123: most commercial people, if you're how to make it work and the other kind of slower moving, um,

SPEAKER_27: behemoths maybe don't. Yeah. All right. Listen, this has been an amazing, uh, dive into, uh, an awesome investment. Congratulations. Antonio and the team at Valor for finding, uh, another breakout company and, uh, Lauren, thanks for the hard work. Uh, this infrastructure is obviously going to solve some great problems from humanity beyond being entertained at the sphere at a dead show. You know, this is the kind of stuff that solves self-driving, uh, maybe drug discovery solves cancer, or maybe even tells us how the universe was created. So in advance of those amazing questions, uh, being answered, I thank you all for coming on the program. You're hiring, uh, Lauren. I know, uh, with all this money you raised, you're adding a hundred people or so. I read, how can people find out more and what top two or three positions do you need to fill

SPEAKER_135: acutely? Cause we've got a big audience here of people looking to work at great companies.

SPEAKER_139: So you can look, go to our jobs and on the WAC website. So we publish all of them. Some of them

SPEAKER_22: also go on, on our LinkedIn. We are hiring tons and tons positions in engineering. We have engineering in the Bay area. We have engineering in Tel Aviv with engineering in Bangalore, any, anything from infrastructure engineers to even kernel developers to, to front end to the cloud. So if you're a good software engineers, we're looking for you and the flip side, we're also hiring tons of very hungry go to market people all around sales, sales, engineering, marketing, uh, the field folks. So basically throughout

SPEAKER_35: the whole company. Okay. So if you, uh, can make it or you can sell it, WAC has got a seat for you.

SPEAKER_27: You know, I always tell people if you got a chance to get on a rocket ship, take the seat and figure it out later. We'll see you all next time on this week in startups. Bye bye. Right now, startups have

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SPEAKER_147: twist. All right, everybody. It's time for another jam session with Jcal. This is a very simple project

SPEAKER_55: that I came up with. This is my invention. No, it's not actually. You know whose invention it is? Travis from Uber used to do something called the jam sesh. And it was just like a couple of founders

SPEAKER_149: getting together. They hang out, you know, pop open a couple of cold ones and talk about business and jam out on ideas, man. It was some of the best times I ever had. And we're bringing it back to this

SPEAKER_147: week in startups. And we have got a partner on this program. The partner is dot tech domain names.

SPEAKER_149: And they came up with a simple idea. Hey, listen, if you got under 2 million in funding, and you got a dot tech domain name, which is a really cool domain name to have, you get to come on the program. If you've got a great idea and a great company. And so today,

SPEAKER_147: we're going to hear from Ramsey Schaefer, and he is the CEO and co founder of up trends AI, and they are up trends dash AI dot tech. Ramsey, welcome to the show. Thanks for having me, J. Cal. Excited to jam. Okay, let's jam out. Why don't you start just telling us for two minutes about your company, run us through it, show us the product, whatever you want to do. And then tell me, what's the what's the most challenging part of your business? Three, two, go.

SPEAKER_154: Sweet. Okay, I've got some slides. Love to just get your raw feedback on them. And then I've got

SPEAKER_147: some questions that we can get into. Okay, feedback on the deck. I get that a lot. Feedback on the deck. You're going to use this deck to raise money, or you just want to explain the product? To raise money. Yeah, intro call. Got it. Okay, good. So the audience for this is seed funds. I assume you're a seed state startup. So great. Three, two, go.

SPEAKER_157: All right. I'm Ramsey. I'm the founder of up trends. We help financial advisors stay ahead of the news. So this is Zach. He's an independent financial advisor. And a few times each day, he'll get an email from a client asking him something like, you know, John Deere is up 5% today. Why? Or I saw Nike fell 20% last week. What's going on? So I'll go to Google, I'll go to Twitter,

SPEAKER_158: he'll go to morningstar for some headlines, but more often than not, he's left scrambling to get

SPEAKER_159: back with a solid answer. Now imagine multiply this by dozens of clients, hundreds of stocks,

SPEAKER_157: thousands of daily news events. And you can see how this becomes, you know, a huge time consuming part of Zach's week. And frankly, it's holding him back from being a better advisor with more clients. So we're introducing up trends, the AI assistant automating the new cycle for investment advisors. Up trends monitors thousands of new sites, filings and financial data sources to detect, summarize and alert Zach about the trends and events affecting the stocks that matter to him and his clients. With up trends, he can easily see which stocks are trending in online chatter. You can click into those stocks and get an AI summary of recent market moving events, which he can then directly send to his clients. Things like John Deere's up 5% today, because they got an analyst upgrade from, you know, UBS. Most importantly, he can set instant, highly customizable alerts to be notified about the next big event, just choose the stocks he cares about pick the types of alerts he wants to receive from price changes to insider trading, set the frequency he wants to be notified. And we'll send him an AI summary via email about the chatter when it matters. So ultimately, what we what used to take him hours now takes him minutes. Up trends makes it 10 times easier to stay ahead of market moving events and find the answers he needs right away, without any doom scrolling or FOMO required of trends operates as a freemium monthly subscription, anyone can get started for free. And then we have premium plans for more customizable higher volume alerts, we have a $15 essentials package for DIY portfolio managers and a $50 pro package catered towards investment advisors like Zach. Now, there are 300,000 investment advisors in the US today, along with millions of DIY portfolio managers and retail investors. So for us to get from here to 10 million in ARR, we need to get to something like 16,000 advisors on our pro plan. And to get to 100 million in ARR, we need to get to 166,000 advisors. Last but not least, our team consists of myself as CEO and my co founder Sam as CTO. Let me say that again. Last but not least, our team consists of myself as CEO and my co founder Sam as CTO. Sam and I have 10 years experience as stock market investors. Together, we've written peer reviewed research on the relationship between new sentiment and stock market outcomes. I've previously been a financial analyst and Sam was employee number one of a 10 million ARR startup. We're rounded out by our PhD machine learning lead Joe, and our front end developer Hamza. So that is up trends AI. We're on a mission to save investment advisors from the news to help them build better relationships with more

SPEAKER_158: clients by staying ahead of the chatter when it matters. Thank you. Okay, so there are great job,

SPEAKER_149: by the way. Overall, the pitch is tight in that it explains to me what you do, who your customer is, and what the product is. So when you do these pitches, especially in a condensed format two or three minutes, you really have a very small number of boxes you need to check. This is not a 30 minute presentation. This is a three minute or less presentation, which is, you know, to be honest, all an investor needs to start a conversation. Okay, the customer of this product is a financial

SPEAKER_147: advisor. And there are registered investment advisors, there are wealth managers, there are financial advisors, there's a lot of different categories here. But it's for somebody who manages another person's portfolio. And it's a B2B to C type product. There's B2B, there's B2C, you're enabling a business to talk to a customer in the same way Shopify is, you would say Shopify allows somebody selling stuff on the internet to then reach customers. Fantastic. These businesses tend to be great because you're enabling of an existing business to do more business, to do business more

SPEAKER_149: efficiently, or to save money, one of those things. And so here you've identified a problem. Do I think you've identified a big problem? I'm not sure yet, your advisors will tell you, but we know that these advisors get paid a lot of money. Right? What is the average wealth manager make in the United States? What is the median wealth manager make? What is their compensation per year?

SPEAKER_158: I know each client for them is an average of $10,000 in the door.

SPEAKER_149: Okay, every year? Yep. Okay, so they have, but if they have 100 clients, that's a million dollars a year. And you know what, I see these guys and gals who are, and they come at me all the time, Silicon Valley guy, I'm a whale for them. But you know, they're going after also my mom and my dad, you know, there's somebody who helps them. And so you know, people have a retirement account,

SPEAKER_147: they got some, you know, 401k, and they got some equities, you know, to save the money, you know, and these guys make 1% of it 1% of $1,000, $10,000. Got it. Okay. So and there's lots

SPEAKER_181: of millionaires that's growing, because equities are growing. And America has a large number of

SPEAKER_149: millionaires. UAE has the most imported millionaires right now that in terms of where millionaires are flowing. So I do think it sounds crazy. If you want to raise money, one of the easiest places for you to raise money is to move to Abu Dhabi or Dubai, and put your company there because that's like kind of the new Hong Kong or New York. And you can get a golden visa and you can get them to invest 250k 500k out of the gate, boom, they do that for like almost any American, or European, or somebody from Singapore, Australian, that Indian that comes and puts their company there. So I'm just gonna put that as a little caveat there because money is moving to Abu Dhabi specifically, and then also Dubai, two amazing cities. Let's put that on the side here. I think

SPEAKER_55: the product looks okay. I think it needs a bit of a design refresh. It's a little bit too techy and not finance. I want to just talk to you about design for a second.

SPEAKER_157: I actually have a question around that. Yeah. Okay. Tell me your question. Sure. So speaking of design, I'm thinking a lot about our team right now. We're a team of four getting ready to fundraise. Yeah. And you've talked a lot in the past about the importance of your founding team, but thinking about what's next, my question is like those two to three next specific job functions, hires, where should we be focusing? Well, let's talk about the four you got. I'm hoping

SPEAKER_147: two of them are writing code. Four of us are writing code. God, I'm in love with your company already. You got four people writing code. How many of them are founders? How many of them are

SPEAKER_187: employees with two founders, two employees with stock? Perfect. That's great. So you got some

SPEAKER_147: redundancy there. Um, you can basically, there's going to be two more positions. You have to add at some point. One is going to be, um, somebody to do sales. Uh, and that person right now should be one of the founders. Why should you do founder led sales? Because you need to know you need to have customer zero customer one, two, three, and you got to be able to listen to them. So let's pause for a second here. Tell me about how many paying customers you have and how much you charge ballpark

SPEAKER_157: and how do you charge? Yeah. So right now we have a few hundred paying customers. Two, two paid plans,

SPEAKER_158: a $15 a month and a $50 a month. I will say originally we were more focused on the B2B retail investor. And after feedback, we've learned a lot about focusing on the pros. You made the cardinal sin.

SPEAKER_191: We did of all startups, but we've learned, you tried to run two different businesses concurrently,

SPEAKER_147: a B2C and a B2B, but you figured out that B2B is the one great. And then you've made the next cardinal sin, which is, uh, you are charging far too little. We just established that one customer equals $10,000. Do you believe you will keep, you will get your clients, these wealth advisors. Do you believe you can get them one extra customer a year? I think so. Yeah. Okay. Do you think you

SPEAKER_179: could save them from churning a customer every year? Easy. Yeah. Okay. What is the value of getting a new customer and not losing a customer to them? Yeah. I mean, that's my pitch right there. What is the value though? I'm asking you a specific question, a dollar amount.

SPEAKER_158: Well, if it's $10,000, 1% commission. Yep. Whatever that is. $10,000. And they would have

SPEAKER_179: lost one that cost them 10 and they would have gained one that's 10. So you've created $20,000 in

SPEAKER_147: value, which means the, uh, LTV. Do you know what that stands for? LTV lifetime value. Perfect. Okay.

SPEAKER_149: I'm just benchmarking where you're at in your startup journey. Your LTV is people will stick with this product for seven years. I'm going to guess maybe five on average. Let's pick five and be conservative. That means each customer you acquire is worth $100,000 to you. That means your CAC.

SPEAKER_147: What does CAC stand for? Customer acquisition costs. Perfect. Your CAC could be a thousand dollars and you would make it back very quickly. Um, so the value you're providing, if we blow ball it, you gain one, you don't lose one, you know, 20,000 a year, you're providing in five years, a hundred thousand of value. That means you really should be charging 10% of that number, which is $10,000, which is $2,000 a year. You're charging $50 a month, $50 a month, you know, is but $600 a year. So you probably for this product should be charging 500 a month, 400 a month, because you can really justify it. That first customer you save, you should get a hundred percent of it in my mind. Okay. You should get a hundred percent of it. So that would be a thousand or 800 a month. So I would just get rid of all this pricing and be taken seriously by your customers. A wealth manager is spending on lunch with their client, $600 or dinner. They're taking them to a Knicks game or a Mavs game. And they're sitting in the first four rows for $10,000. That's how they think.

SPEAKER_58: And you're coming to them asking for 600 bucks. It's like, they pay more for, you know, that's what they're paying for their Gmail account. Come on. Sure. Yeah. It's a signal.

SPEAKER_179: Let's raise the price on this, right? Signaling. Yes. So signaling is way off. Now what this will

SPEAKER_149: also do for you is it's going to have you capture the high end first and then go downstream. You want to capture the high end because the high end is going to have the best advice for you. So not only by raising your price, do you increase perceived value, you have more money to hire people and you have the ability to get the best chef's kiss best advice. So I want you laser focus, not on the number of customers. I want you to get, I would rather you have the 10, 10 of these, uh, wealth

SPEAKER_147: advisors who make $5 million a year or $2 million a year. And for you to have 200 of them that make $400,000 a year. You want to go for the really high end here and they're going to give you great advice. So I think you got to redesign the product at the UX a little better. And I think there's some virality here that you haven't thought of. And this is what a jam session is about. You identified

SPEAKER_149: like there's a problem. Oh, they send the customer customer says, oh my God, I own Tesla stock Uber stock. Uh, oh my God. Uber's up like four bucks right now. I just noticed before I got on air because, uh, the robo taxi is being delayed in some way, who knows what's going on. And now you got this like existential thing. I mean, this is like panic inducing for Tesla shareholders, Lyft shareholders, Uber shareholders. If I had exposure to that, I do, I would be like, oh my God, I'm not, uh, but anyway, putting on all side, be really interesting. If when you share the dashboard

SPEAKER_147: of the stocks with a customer, if the wealth manager got a ping, Jason just opened the website, Jason, just like a DocuSign. So look at the DocuSign. Hey, the customer opened the contract. The customer went to page two, where you send your deck to somebody in like whatever the slide deck thing that watches. Oh, they stayed on. They went back to slide five. You know, they spent two minutes on slide five and they just zip zip past six, seven, and eight. Why? Oh, six, seven, eight are irrelevant to them, but they really cared about the team slide, but they didn't care about the go-to market or vice versa. So you got so much you could do here in intelligence and people will pay for that big time. If I know that my customers are typing in the ticker symbol Uber, and then if they could write a question on that portal where they said, uh, you know, let's say, you know, I'm the, I'm the, I'm the, I'm the wealthy individual. You're the, the wealth advisor, Ramsey. And I'm on the website and I just say like, what, what is this about? Why is Uber up and Tesla down if Tesla is going to kill Uber, according to this story. And then you wrote back, okay, here's the Goldman Sachs report. That's a business insider story. Business insider is sensational. They want to get you to click. Goldman Sachs has an analyst who's been covering Uber. This is the analyst name. And I direct you to that story. Now the interface has the entire history of us going back and forth. And then additionally, this all has to be mobile at some point. So getting a mobile and a designer is critical. And then I think the next piece would be to have what's called an SDR or business development rep would be very good for you to have somebody trying to figure out who wealth managers are and a viral way to get them on the phone with you. Those would be my next two or three hires. If you could get the money in here, uh, you're doing a great job. You had a great idea. Um, and I understand you told me, uh, you were talking to some of your customers in a group chat somewhere like a discord or a signal or a WhatsApp. Yeah. Just I message.

SPEAKER_181: I mess it. So you got like, uh, you do you have one to one discussions or do you have like a

SPEAKER_158: product council yet? We've got a discord for like large group. And then I have like a small group of three or four advisors that I text weekly. Okay. Awesome. That's really what you want to do.

SPEAKER_149: Ramsey. I wish you massive success with this idea. It's supposed to be just 10 minutes, but your business is so great. You're at 16 minutes with me. Uh, you jammed with J Cal rate your jam with Jacob. How would you rate this in terms of helpful? Huh? Give me an honest number

SPEAKER_183: between one and 10. That was great. I would say 10. Yeah, for sure. Okay. There we go. I, I don't

SPEAKER_149: want to bias it in any way. Uh, but you, you know what you should meet our team. So you might be a good candidate to come to our accelerator. Um, because I think there's something here. And when I jam with somebody, I got to tell you, you're good to jam with. Cause you're quick. You give really good answers. You don't filibuster. And that's what people love when they're jamming. So that's a really good note for everybody listening to this week in startups. When you're interacting with an investor, who's a know-it-all investor, who's seen it all, who's invested in hundreds of companies, you got to be

SPEAKER_147: able to go back and forth quickly. Right. And you got to be able to have that real intellectual discussion. And you did good with that. Right. It's like kind of playing pickleball or ping pong or tennis. You want to have a good volley in just 15 minutes together. We had a great volley. I like you. I like the way you answer questions. I like the way you think you kind of did your thing. You made yourself likable. I understand your business. You're thoughtful. You seem like you're, uh, you got a chip on your shoulder and you want to be successful. I think lean into that a little bit,

SPEAKER_55: like a little bit of the drive. Don't be too mellow. Uh, and congratulations, Ramsey. And we'll see you all next time on Jam with JCAL. Thank you to our sponsor.tech.