00:00Let's start off with the valuation. I know you'll probably say, oh, valuations don't matter, but this has been a pretty big run up for you to get from where you were just even just, I don't know, three or four years ago to where you are today at $134 billion based on that latest funding round.
00:14Yeah, you know, I mean, we've seen acceleration in the business and, you know, that's, you know, we work closely with investors and this is sort of, you know, this is not actually at the high end and it wasn't at the low end.
00:25We always try to make sure that the valuation is fair to investors. There's upside for them and also for the employees.
00:30You know, we are going to use some of this towards liquidity for those employees, but also to invest in the business.
00:35So, yeah, that's what that's where it netted out.
00:37Of course, Ali, everyone is asking you this question. I'm going to ask it, too.
00:41When I look at your valuation, I look at your revenue run rate. I look at the growth that you had.
00:46Why are you still a private company? Do you plan to go public soon?
00:49Yeah, look, we're seeing this acceleration in AI. We want to continue investing in these products.
00:54And, you know, I don't exactly know what's going on. If you look at the last few weeks, markets have been kind of wobbly.
00:59And actually, since we were on the circuit raising money, we talked to all these, you know, institutional investors, those that manage over, you know, trillions of dollars of AUM.
01:07And the number one question we're getting is like, hey, is there a bubble? What's going on with AI?
01:10So we'd love to just be able to invest in these products over the next many years. Right.
01:15We think AI is a secular trend. People are not going to like software engineers are not going to stop using AI for software engineering.
01:21All those pieces of software they have need the database. So that's why we have our lake-based database.
01:26All of them want to use agents. That's why we have agent bricks.
01:28So we want to just continue investing in these and, you know, just want to make sure that we don't end up in a situation where we go public.
01:34And then, you know, public markets are saying, you know, no, produce 30 percent EBITDA and, you know, stop investing in the future.
01:40Right. No, I hear what you're saying there. And I am going to ask you if it's a bubble.
01:44But I want to talk first a little bit more about a potential IPO.
01:49You're raising four billion dollars in this Series L round.
01:53I mean, how much more feasibly do you think that you could get in terms of private market funding?
01:57How long could you say in the private markets before you reach a point where, you know,
02:02you do actually need to tap those public markets to continue that funding?
02:05Yeah, look, first of all, we are free cash flow positive over the last 12 months.
02:11So, you know, we don't have to have access to huge amounts of capital.
02:14But secondly, there's a lot of capital on the private markets on the sidelines.
02:18So if we wanted, we could continue this way.
02:20However, we want to be a public company. We, you know, we do public company audits.
02:24We're ready. Our financials look the right way.
02:26I just want to make sure that we don't end up, you know, in a situation where, you know,
02:29we have to cut too much or whether we're like overvalued.
02:32I just want to continue making our customers successful with AI.
02:36Yeah, and I hear what you're saying.
02:37You did tell Bloomberg in September that if the plan was to really stay private long term,
02:42we wouldn't have focused on being cash flow break even.
02:45So that certainly says a lot.
02:46But when it comes to the idea of a bubble, we hear that term thrown around a lot.
02:51When you take a look at the AI landscape overall, both in the private and the public markets,
02:55I mean, are there any specific spots within the industry where you do see some of those bubble characteristics?
03:01Well, look, we try to focus on what we are doing.
03:03A lot of the industry is focused on data centers, energy, and, like, this kind of, you know, quest for superintelligence.
03:11We are focused on just, we call it boring AI for enterprises.
03:15Like, give you an example.
03:167-Eleven, we all know about them.
03:18They use our product agent, Bricks, and what they can do is they can automate a lot of their marketing stack.
03:24So, you know, previously, it would take a lot of effort to come up, which segment should you market, what material to,
03:29and you would have to create the material manually.
03:31Now the agents can actually just prepare that because they're very good, they're very intelligent at doing this particular thing.
03:36So that's, like, a big disruption.
03:38Or Merck, they have a model called TEDDY, which is transformer-enabled drug discovery, which is actually a game changer.
03:44It's a transformer AI model that actually understands the cell biology and helps with drug discovery.
03:51So we want to focus on these use cases that really have legs.
03:54And then, of course, there's a question of, you know, what happens with the data centers and so on, and I don't know.
03:59Are those use cases, Ali, persistent, meaning the business that you're getting from some of these companies,
04:05in theory, will that always be there, or is there just some sort of runway where you get to the end of it and they've had enough?
04:11No, I think we're just scratching the surface.
04:13Another one, this is, you know, maybe closer to your viewers, RBC, Royal Bank of Canada.
04:17They can automate, using good agents, the equity research analyst reports that come out.
04:24So, you know, within 15 minutes, which is, you know, in the industry it takes hours, they get the earnings calls from the different vendors.
04:31They can compare it to what's going on in the industry, and they can put together a report in the voice of the equity research analyst.
04:37These are the kind of use cases you couldn't do before.
04:39Now, I think there's huge room for these use cases to keep expanding, and we're just starting at the bottom.
04:45At Databricks, we're taking the simplest, most mundane tasks, the lowest amount that you're paying anyone to do, and trying to augment them.
04:53But as we move up the stack, I think it becomes more and more valuable.
04:56So we're just focused on these useful use cases.
04:59Of course, we're doing off the back of people who run data centers and AI models and research.
05:03That's where I think a lot of the cost is going.
05:05What we are doing is not that costly, as you can see.
05:08That's why we are free cash flow positive, and we don't need those amounts of capital that the foundation frontier model companies need.
05:14And Ali, I'm sorry, I have to go back to the IPO landscape, because some of the most interesting pieces of reporting in the past couple months
05:22is that SpaceX, OpenAI, some of these poster children for staying private forever, considering going public in 2026.
05:31That's, of course, the reporting there.
05:33And you think about the valuations of some of those companies.
05:35You obviously have a very hefty valuation over at Databricks.
05:39But if they do go public, how closely will you be watching that reception?
05:43And how will that inform your own decision, potentially, to IPO?
05:48Not too much.
05:49You know, I've said this to the team.
05:51We're just going to be focused on these AI use cases and making them successful for our customers,
05:54and we want to be able to invest in them.
05:56You know, it's secondary whether the timing of the market or what's the valuation of others or those kind of things.
06:02We're basically not trying to time this market.
06:04We're trying to win this market.
06:05We're trying to win this market.
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