Databricks

“I think it will be the nail in the coffin for almost every public software company. And what I mean is when Stripe and data, like, okay, there's OpenAI, there's Anthropic, we can put them in a different category, right? They clearly are on many levels. When Op…”

“But my guess is it peters out into some version of the venture firm just saying, we'll take off the board member on Fivetrans, whatever, right?”

“Our largest customers move petabytes of information daily, whereas like a data warehouse, as an example, like that might be, ah, a petabyte data warehouse is a huge data warehouse. Like that's for like a Snowflake or a Databricks, like that's a big data wareho…”

“There are gigantic tech partners like Snowflake and Databricks and Google and Amazon. They don't care about you when you're two people or 10 people or whatever.”

“I'm trying to, the big picture at the start was the basic point is, you know, cost per token is really not a useful metric. It's kind of cost per completed task was the first thing. How much does it cost to get things done?”

“Databricks is hiring as at 10,000 people is hiring as many entry-level folks as it ever has.”

“We have a really great dev info team. They built something called Isaac that's basically like a wrapper on cloud code and codex and let's you use them either on the web and like, Sandboxes or just on your dev machine or on your laptop or whatever.”

“We had a lot of engineers building, you know, their own Vibe coding setup. But then the other thing they all said is like, Hey, I built something that's amazing for me. But like no one else on the team can use it because I don't have a server to collaborate. A…”

“I think we had like five or six different agentic frameworks built by every different team. They do all do more or less the same thing.”

“One, so, I mean, one of the reasons to open source something is if you think it's a layer that will actually, there'll be some network effect. It'll benefit from many people collaborating on it.”

“Another way to think about it is like, imagine, you know we, our thing wasn't open. We had some kind of agent hosting thing, but it's not open. And then there is an open one. If you're, which one's gonna win in the long run? So like here, because there is this…”

“What people eventually run into, It's kind of a question of a unification and consolidation is, hey, do you really need to chop all of this into different pieces and work with so many different vendors and platforms in order to get like a very simple visualiza…”

“So we, on the analytics side, I think we launched Maybe 50 or sixteen million virtual machines a day across all three clouds, so which are one of the biggest compute orchestrators out there. Stuff for sure for CPU compute.”

“A lot of coding agents today have very basic things like you can tell me which tool patterns I'll allow or disallow or whatever. It's like yes or no, but that puts you in a very tough spot. So just as an example, like, should my agent be able to read, you know…”

“It's unlimited, but we do you know, we use our own product to like analyze the traces and stuff, and we have a team that's, you know, looking to optimize and to see if anyone's doing something weird.”

“HTAP wants to build a single engine for both. We think you can get 99% of what you need by unifying the storage and just have a single storage layer.”

“And as a matter of fact, once you transcode the data compresses better. So from those services writing to, for example, S three or other data lake, like object stores, you can actually write them faster because now they are now smaller. So there's no. Overhead…”

“I think the industry has a sense of, hey, maybe if you overfit to like one or two customers, it's going to be really bad for you. But I think the downside overfitting is much smaller than the upside itself. And if you sort of try to be too ambitious and boil t…”

“One of the challenges I think we probably see, and maybe many newer generation companies are seeing is, so tech companies are very, very different from non-tech companies or traditional enterprises. And if you optimize everything just for tech companies, you m…”

“Actually, every single database engine out there, especially on the analytics side, are kind of a decade old. Pretty much everything that had reasonable traction are about a decade old.”

“They use that to build a model. Like a machine learning model, not an L, a machine learning model. Machine learning model basically can very, very quickly tell us how any algorithm and how any implementation will perform for any specific type of queries with v…”

“It turned out that, you know, it's easier to go from that bod thing that's really good at the scale and ingesting and super low cost and create versions in it that have the speed and features of the, you know, super easy to use, like smaller data for business …”

“Even though we did launch open source model DBRX, and, you know, we went up to, like, sort of above the LAMA-R III scale, we decided that we really want to focus on, there'll be so many people releasing models, and instead of doing the general model where, lik…”

“Our team built this document sort of vision model that takes a page and gives you back a nice JSON with all the components. And it's very competitive. It's like probably like a hundred X cheaper than those frontier models and still better.”

“Five point something in billion re-accelerating at 50 or 60% growth.”

“You know, still how many of them actually properly adopted AI in the way of coding tools or like systems is pretty nascent.”

“And so now we have this other problem, which is happening where everybody's like, okay, all my employees are token maxing. My spend on tokens is going up, but I have no idea what I'm getting out for it.”

“The urgency of I need to kind of get my data state in order and governance around it and context has gone through the roof because people realize without that, they can't actually get AI to work in any meaningful way.”

“We just had a, that car manufacturer just loaded on an extra 70,000 users. They all are going and asking their own questions of it, right?”

“I think any industry that is highly valuable, that has a monopoly today, Will not have a monopoly, 12 to 24 months from now, because.”

“But now you can send LLMs in because the beauty of code is self descriptive. It goes in, it understands what everything is doing and why, and then it can also very easily convert it. And then the key is like, how do you write harnesses to do validation and rec…”

“I think the analytic world is getting reshaped already where you can see people like Snowflake or Glean or Databricks. All these people boast enterprise data lakes where you can bring the data and run LLMs against it and get you much more synthesized analytics…”

“Databricks come out today. No, no. We're going to do another round. We're going to do another round. One hundred and sixty five billion price up from one hundred and thirty four billion earlier this year.”

“Databricks can get capital at a higher revenue multiple because they're higher growth rate than Snowflake can and on hassle-free terms.”

“Like, 30% of our customers use Snowflake and Databricks both.”

“SpaceX, Anthropic, OpenAI, we'll add in Databricks, we'll have Arsan, one of the co-founders here in about half an hour after this. That's going to create more market value, more value, and more wealth than every other IPO since 2000.”

“At Databricks neon, which is their super base competitor, over 90% of the databases are built by agents, not by humans.”

“Snowflake today is worth what? Fifty five billion. So is somebody going to make the argument to me that Databricks is worth two and a half times snowflake? You are, nobody's making that argument to me.”

“And what I just mean by that is in Anthropik, they added eleven billion dollars of AR. And what is astonishing to me about this is that The SaaS and cloud revolution it created, we'll call it between five and 10 trillion dollars of value. And I would say argua…”

“You see, I saw this with Snowflake and Databricks, right? Snowflake would own everything, and then Databricks would get one small foothold somewhere, and suddenly now they're fighting over the same workloads, and someone's going to win that.”

“You look at like a Snowflake or a Databricks, that's their model, and it's because they're competing over workflows in the account.”

“People are skipping the SaaS tools entirely and like building on top of their data warehouse.”

“28 day month of February, where they did six billion in revenue, which was more than Databricks has done in their entire lifetime.”

“With that said, I think that applies to like the 10 best private companies in the world. Like Databricks can do those things. Stripe can do those things. There's a lot of companies that just do need to get public.”

“I do think if you're going to want to outperform you know, the index over a long period of time, you're going to need exposure to these companies.”

“You know, some of them will probably go public in the next 12 to 24 months, so that'll be kind of one impact of it”

“I was very lucky to be a seed investor in Databricks specifically”

“I mean, Databricks started in 2013, but it took three, four years, and a lot of going around in circles for them to get to the point where they had product market fit.”

“You look at the Stripe or the Databricks case, they're using those large funds to convince the companies to stay private longer, maybe forever that's just a very different world than the one that I grew up in. I think they turn around and the people that do t…”

“I think we were the only private investor that was Invested in Snowflake and Databricks when they were both private.”

“Snowflake and Databricks. Very low margin early. A lot of people passed on those early rounds because they're, ah, in SaaS, you have to have 80% gross margin.”

“I mean, I think, we said it in one of the shows earlier on, Thrive has done an amazing stock picking job of backing Stripe, of backing Databricks, of backing OpenAI, and of backing them at scale.”

“They have a new fund that has Databricks, Mercore, Revolut, Airwallex, Boom, Supersonic, Ramp, Aura, and Stripe, which is signed and pending, closed.”

“They bought Databricks at a 150 dollars per share. Now trading at 204 ramp at 90, now trading at 98. Airwallex 21 dollars. It's now trading at 18.8.”

“And so they will go public in the next nine to 12 months, I think.”

“And then they have the big AI native companies all as customers.”

“When I came there, we had about a twenty million dollar business, and it was, you know, seven or eight hundred million when I left, something like that. I think it's crossed well over a billion.”

“So actually Databricks is on the cap table as well.”

“Databricks just re-accelerated the last three quarters. They're growing over 60% at scale.”

“By the way, that might change in twenty-twenty-six, in big way, in a big way, because there are a host of a host of huge, unprecedented size IPOs in the works now. You know, companies like SpaceX, and Stripe, and Databricks, and, you know, and others. So there…”