The Exchanges

Q All right. So going from the infralayer of the cake, Or hardware layer of the cake to, uh, the software layer? Maybe let's start with foundation models. So sort of the same questions, like those are huge, um, kind of dollar at play. Is that, uh, something you're interested in?

A We're, so we study it a lot because it matters, but I, okay, so here's the thing, right? You look at like Lama three, eight billion, trained on 15 trillion tokens. The, I run it on my Mac book. It's awesome. Like latency super low. It works really well. It's basically on par with the Mistral eight by seven billion mixture of experts model. And I've stopped going to open AI because I can run on my machine. It's faster and it's integrated in my email client. And they're like, there are a bunch of advantages to it. And so, I, I think the small language models, particularly for B to B applications, if they continue to improve their performance like this, make a lot more sense. Uh, the, the, we ran this analysis, I mean, if you look at the pricing page for, like, OpenAI, and you look at the 4.5 turbo, and the cost per inference, and compare it to the next most recent model, there's a 160 X difference in pricing. And so what does that tell you? Well, a model that's six months out of date loses its pricing power rapidly, right? Commoditizes really fast. And then you have this open source dynamic with Lama where Meta and OpenAI are, are, are competing. So I think we'll see a lot of innovation there. I think we'll probably see a bifurcation of the model sizes where like the Lama three, eight billion parameter model on an MLU basis, which is the high school equivalency is doing pretty we…

AI assessment note: “we study it a lot because it matters”

Q now as, uh, with your theory ventures hat on, uh, which is really interesting to me because BI of all parts of the modern data stack is really To me, the one that sort of felt like the kind of like unloved child where, where you've seen less innovation. Uh, so what's the story about Omni and where do you think, I guess, uh, BI, modern BI should be going?

A Yeah. So, yeah, a sixteen billion dollar category. You're right. It's a super competitive, uh, hard to differentiate category. I think it's broadly misunderstood. Um, but the, uh, the way that we think about it is it has swung between a pendulum of control to Um, empowerment. So, uh, during the 2000 era, there were four centralized BI companies, MicroStrategy, Cognos, Business Objects, and Hyperion. And then Tableau came and unbundled the visualization layer. So it swung from really, really tightly controlled, centralized control to anybody can do whatever they want with their data. Then the cloud data warehouses came and Looker said, well, let's go back to centralization with the data modeling. And what Omni is trying to do is Narrow the, those swings and say, you can have the control and the modern data model And an individual marketing analyst who wants to define a particular kind of cost of customer acquisition can use an Excel-like UI to do that. If the metric is awesome, they can say, I want to promote this to the team, to the group, or to the entire company, and then that folds into the underlying data model. So the data team can define metrics, but also an individual person in the company can define metrics, and their approval flows to marry the two.

AI assessment note: “what Omni is trying to do is Narrow the, those swings and say”

Q learned, you know, for all the founders who may be listening, uh, right now that, you know, maybe part of the group of companies that, um, You know, you said should be acquired by Data Break of the Snowflake. Like, how does, how do, how do acquisitions happen? Is that something you can engineer? Is that something that you need to position yourself for? What have you seen and learned?

A Yeah, so the, the answer is, um, someone's putting their career on the line. Uh, so when someone buys a business, not an acquihire, but let's say like 50 to fifty million plus, someone in a company, in the acquiring company is saying, I am betting my career, effectively, in this company, that we should buy this company at this price. And that doesn't happen overnight, right? It doesn't happen in the course of like two or three months. It probably happens over the course of 12 to 18 months. It's a big, long enterprise sale. You can think about it that way. And There, it is a multi-party sale, where typically you have a GM or a head of product who decides, I really need this business as part of the product portfolio, and rather than building it, which would be much easier politically to do, to navigate that budget, I think we should buy. And I'm willing to make a case and go first to my manager, then to my VP, then to the board, To justify it, involve legal, corp dev, and many other teams, and manage what must be a very difficult cross-functional effort to get this through. And so somebody really has to care, there has to be a very strong personal relationship between the founder of a business and that buyer. Because that buyer is putting together a three to a five year plan to demonstrate some positive ROI on that acquisition. So they'll take the startup's plan and then discount…

AI assessment note: “someone in the acquiring company is saying, I am betting my career”

Q And in the same vein, uh, you know, Building data businesses, data infrastructure businesses, um, anything in terms of like the pace, the, you know, the do's, the don'ts, the positioning, like any kind of like pattern you've seen, uh, around successful data businesses or data infra businesses?

A So I think data is a much more ecosystem driven space than a lot of other spaces. If you're, uh, and what I mean by that is like, if you're a database, you need the upstream ETL vendors, you need the DBTs, uh, and the Zbicos in, in, in the center of the, and then you need the BI vendors. When Looker went to market, we initially partnered with Snowflake, and we were bringing Snowflake into deals, and that worked really well, and then Snowflake started to grow really fast, and Snowflake was bringing Looker into deals, and so, um, so why is this? Well, data is an ecosystem where there are lots of individual points, point products, but what a buyer wants is an end-to-end They, they want me to solve the problem. So that Looker or Snowflake combination was, I need a reporting stack. I need this report. And it needs to be this fast and be shipped on this day, and it needs to be in this format delivered to these people. Well, you need a database plus a BI. And so figuring out, like, what is that end to end stack that you're selling and becoming part of that, that stack is really critical. It really helps quite a lot. And the way that it starts is, It's typically AE to AE, so Looker AE plus a Snowflake AE get together, and they start selling two or three contracts, and then friends of that account executive say, hey, Matt, how'd you hit your number last quarter? And they say, well, I ha…

AI assessment note: “data is a much more ecosystem driven space than a lot of other spaces.”

Q Internally and also to share amongst companies or no?

A There will definitely be a data sharing use case. I think, uh, we're starting to see that happen more at the developer tools layer. Not so much at like the Snowflake, Snowflake Salesforce integration layer. That really hasn't happened yet. But we do think we do think that, um, security and data custody will be the one of the big drivers because like, so you have, I think by the end of 20, 25, something like 35 states in the US will have their own privacy regulation. And so California, um, you know, just the way we do with cars, we have different laws than everybody else. Uh, but then you have like Dubai has different laws and Germany has different laws and South Africa will have different laws. So if you're a big company and you've multinational Customers. The cost of compliance will become significant. Uh, and so at some point, and then there's also the honeypot effect, right? Where you have like Okta is storing all of these keys. And so hackers are really interested in breaking that open for obvious reasons. But imagine if all that information was stored in a decentralized way. Fine. You break into one key. You have access to that one key. It's actually much better to build it in a decentralized way for those reasons. So like wiring information. Um, the example that we give is, we talk about a lot is, imagine if you were to build Salesforce on a Web three stack today. So, uh,…

AI assessment note: “There will definitely be a data sharing use case.”

Q September, 22. Yeah, 18 months. 18 months. So how do the first 18 months feel?

A It's exhilarating. Uh, it's, I have a lot of, I mean, started a company when I was 17, it was a little bit of a toy business. Um, but starting it has been a real gift and a privilege. I think the, the first part about it that I really enjoy is setting out a strategy for very long term. So we have a 20 year plan inside of the firm. Uh, the second is, there's this like moment when, and I imagine it's like when the founder receives their first check, somebody believes in you. The first commitment into the fund, or the first hire, or the first 10 people that join who believe, that's really special. And I don't know if I appreciated that to the extent that I should have, being involved in the startup ecosystem. I think the other thing that I really love about, uh, the firm is, um, We're able to move really fast, and we have this, um, we just had an off-site on Monday. Everybody inside the firm is responsible for one experiment per year. We expect 70% of experiments to fail. So we have programs like this where we're just trying to innovate and push, and being able to architect those programs and execute them is exhilarating.

AI assessment note: “It's exhilarating.”

Q In terms of applications, you know, the, uh, now good old question, um, you know, co-pilot mode versus full execution mode, I use You, do you have any preference? How do you see things evolving?

A Yeah, we're not religious about it. I think it, coding, I think you'll, you'll have both, right? You'll be a software engineer, use Copilot, or any one of the others, and let's say you are, uh, reviewing, um, a pull request, it's very likely that you'll more likely use a Copilot than you will use a full agent. Whereas, like, if you look at Devon, and you want to create, uh, A stub of a, of a Shopify store, very likely that you'll end up using full automation there. So we think there's room for both. The, there's a difference in the productivity gains. So Microsoft and ServiceNow have said there's 50 to 75% increase in developer productivity. I don't know if they're measuring that through lines of code or some other composite metric. If the agent, so there, there's no data yet on how productive agents are, and it's probably use case specific. The crudest, the crude analogy that we have, which is Or the best that we found is for a mechanical robot on an assembly line, how many humans jobs does that robot replace? And the answers are about 2.5. So theoretically, if it's anywhere close, uh, can a copilot or, uh, sorry, an agent is probably four to five times more productivity boosting job producing than an agent, than a copilot. But I think it'll be really use case specific. Like I couldn't imagine an agent in legal domain or, uh, writing a, maybe writing an NDA would be okay, but …

AI assessment note: “we're not religious about it. I think it, coding, I think you'll, you'll have both”

Q Are you at all interested in the frothiest of all frothy areas, which is intersection of blockchain and AI?

A So we have looked a little bit there. I think the majority of the companies are GPU farms. I mean, distributed data centers for GPUs, renders in that category. And, um, I think they were just used on the new iPad pro demo yesterday. One of the games was using the render engine. I think the big question for those companies and just like the distributed GPU is latencies. Latencies really matter, particularly inference time, and so it seems like, though, it seems to us that most of those clouds were probably used for training, and then the question is, what is the relative arbitrage opportunity between a decentralized GPU network versus a centralized GPU network? Data movement, ingress and egress are significant, and then one thing that we learned yesterday was that, um, when you rent a GPU, it's, it's pretty, I mean, not one GPU, hundreds of GPUs in order to train or infer, It's really expensive, and so you want to move the data as quickly as possible in, so that those GPUs are saturated with the data, and then they can infer really fast. If you're spending 30% of your time charging, loading, infusing the data into the memory of the GPUs, it's not a very good use of your dollars. So I think it all has to do around latency.

AI assessment note: “So we have looked a little bit there.”

Q said generative AI, but also classical, uh, AI, which is sort of interesting, right, that there's, uh, been, uh, uh, that sort of trend in conversations and Twitter and whatever that, um, generative AI is going to replace everything, but, um, in reality, there's, uh, Absolutely a huge room for classical AI that works on tablet data and all the things. Is that, is that, is that what you think?

A I agree. I think there's, there's a role for both. Like the generative is, the way we think, I think about generative is, it's a really great knowledge compression engine, right? Like, uh, I can compress the internet into a model that's like three gigs, right? Let's just say for the, for Lama, Lama a billion. I can also use it as a class Classifier. Lots of startups use it. Email classification, for example, in order to identify phishing is very good at. Um, and then I can use it to create. Um, the hard part is, uh, it can be really noisy and unpredictable, and so you need, you'll need both. Time series prediction out of large language model.

AI assessment note: “I agree. I think there's, there's a role for both.”

Q And a little bit to that vertical kind of use case point and the earlier part about small, uh, specialized language model, like, is that, is that an area that you find interesting where, you know, you have, um, I don't know, uh, LLMs for bio something or LLM for, uh, you know, supply chain?

A Yeah, they will exist. I think you'll probably see, you'll have generic clouds that offer fun families that are not customized. We're starting to see some companies that are either vertically integrating the stack from the GPU and networking layer all the way through the application, particularly like images and video, the cost advantages are significant. There are yet other clouds that are specializing. Here's this collection of small language models that are focused on finance or legal use cases, again, to kind of bound. I think, um, I think the question there is, aside from the financial engineering, what's the sustainable competitive advantage? There's definitely a case to be made that There'll be a digital ocean like business where catering to different needs of the developers rather than like, um, a broad, super complex cloud makes sense. I just, I don't know. We have a hard time answering that sustainable competitive differentiation question at that layer.

AI assessment note: “We have a hard time answering that sustainable competitive differentiation question at that layer.”

Q Before we get into data and AI, that's actually partly, uh, what you do as well, right? Like, I mean, not that, uh, blockchain and crypto is necessarily cold, but it's, it's certainly an area where, uh, the heat has, has moved away from, but like you've continued to invest through the crypto winter.

A Yeah, yeah. So I think, I mean, so, 18 months ago, if you had a margin, like a great person leaving Facebook or Google, They were going to Web three, right? And this is all before the FTX disaster and the fed raising rates and the economic environment completely changing. And, uh, and then I would say like when we started the firm last year, you could really name your price. You could really approach most crypto businesses and say, we'd like to invest 10 and your, the post money didn't really matter. And, uh, And that was because a lot of builders left. Electric Capital puts out a report. They had 25,000 developers. I think that number's fallen. 15,000 in all of Web Three. Twenty-seven million software engineers in the world. So we're really talking about a fraction of a fraction of a fraction, yeah. And then, um, before the Bitcoin ETF, it was really quiet. And it's super cyclical. Just the way that the startup ecosystem is cyclical, I think crypto is that, but with greater, uh, amplitude changes. Uh, now it's come back. I mean, I think hot seeds are a 150. It looks an awful lot like AI. And then many of the token launches will raise equity rounds before they go public. And those will be in the several hundred million to billions again. So, uh, it's come back. It's come back in a really meaningful way. The number of players is much smaller. And I think one data point is that t…

AI assessment note: “It's come back. It's come back in a really meaningful way.”