Boy, it's been busy.
Let's just talk a little bit about the applications and just where you see the
wide open space at the moment for Databricks.

Yeah. Thank you for having me on.
There's so many applications across many different verticals. You know, lots of
lots of customers that are using us for their data processing, I should say,
starting with Rivian. For instance, the carmaker uses us to
optimize the batteries of their electric vehicles.

You know, Walgreens uses us to figure out how much they should stock up, their
inventory and their supply chain. It's super important for them to
be able to optimize their prices and lower their costs. And then Square, or
Block, they used to have this app where you
swipe your credit cards.

But to set that up in a store, it used
to be pretty complicated a few years ago.
Now it's super simple because you can use generative AI and just tell it, 'Hey,
I have these five products, this is the price,' and it's set to top up.
Just using, get everything I said before,

you were unifying, democratizing data.
Now it's about the implications of AI, but for that also, you need talent.
And I'm interested as to where the next layer of talent is coming from,
where you need to hone in on how you get them on board.

Yeah, I mean, there is a crazy war for A.I.
talent right now. I've never seen anything like it before.
I mean, the talent war was already crazy the last ten years for software
engineers.

Now, in AI, it's just completely insane.
So we are going to use a lot of the proceeds towards that to be competitive.
There's a lot of talent, you know, was hired by OpenAI and by Anthropic.
What happened is we've hit this thing

called the scaling wall.
Now, you know, "Oh, you think there is? It will do." Because some elements determine there isn't one. There *is* a wall.
I think it's pretty clear that most, even those big companies, many of them are now investing in other techniques, not

the classic scaling laws.
So that actually also has opened up the opportunity where people are now going, "There are more companies now that can participate in this next chapter after the, you know, scaling wall or scaling laws."
So that's also a very exciting time.

But you've got to pay up.
You've got to pay up. You pay up for US talent.
I'm going to make it personal, Ali, because you yourself are an immigrant.
Originally, Iran, moved over to Sweden, came over to the United States. And I'm interested as to how much you look

abroad, particularly in the context of the next administration.
Yeah, look, not because of the administration, but when there is this great, crazy talent war going on, you, of course, start looking at, you know, where can we go when it's not so intense, right?

Where the competition is not so intense.
So we look everywhere. You know, I think Eastern Europe is super interesting. Very smart people live there.
And, you know, it's just different. You know, you don't have that intensity of competition around the talent.

Europe and, of course, Asia, many locations. So we're looking at all of those. But still, you don't want to, you don't, you don't want to ignore Silicon Valley or the United States. It's still where you have the highest talent density.

It's absolutely necessary. So we're going to compete here as well. Is it about acqui-hiring? I mean, you've been acquisitive, and some of that in the MySpace Mosaic, and now, for example, some of that in the data

side, Tabula, for example. Are you going to make acquisitions because of the people, because of the products being built? Well, both. You know, there's three things you could get. You could also get revenue, right?

But there's a lot of startups, or startups with super talented people, amazing IP, but they quite didn't get to the revenue. So it's, you know, two birds with one stone. You get that talent, and you get the intellectual property. And in this race, you need that, right?

You need both of those things. So absolutely, we're going to actually double down on that even more now that we have this funding. It's a race, but it's also a race versus your competition at the moment to be getting those clients. Who are your key competitors?

Because I know on LinkedIn you've been throwing some shade at Snowflake, for example. We all think about Microsoft. Are they the two competitors for you? You know, which I don't like. I would say that was in previous years. You know, I would say a couple of years

ago we were really intensely focused on competing with them. Now, in this era, that's kind of shifted, and my perspective is, they're a super important partner. In fact, Microsoft, Amazon, and Google are investors in Databricks. There is some competition.

They have products that sometimes compete as well. But all of you know those companies, the majority of their revenue is in cloud storage, compute, networking, and you know, that might be 70% of their revenue. I'm speculating.

And we're a killer app. We drive so much storage, compute, and networking. In fact, we drive close to 30 million machines in the cloud every day that we launch. So we're super close partners, and they love us as customers, and vice versa.

But there's more competition happening as well, and probably that's going to expand over time. As you know, AI is becoming more and more important. It's one big game in front of us out there when it comes to technology. Ali, I'm interested in particular

when you're thinking about how you're raising money. $10 billion is a lot of capital front and center there. You've also got a who's who of other investors, with A16z, for example. What's interesting, you're also going to the debt markets, we understand.

How is that going? Because that seems to be trying to offset the tax implications to keep your employees happy. Yeah, I mean, that's a great point, I think. And let's take them one by one. Let's take the equities...

equity raise for us. It's super important to get partners that are long-term oriented. We want investors that are going to say, 'You know, we're going to be with you probably ten years, 20 years. This is a great price. We'll buy more later in the'

IPO. We'll buy even more and stick to your plan. We'll let you implement that long-term vision and not become too short-term oriented, you know, the 90-day quarterly sort of mindset. So that was the thing we optimized for, and we were pretty picky.

We had $19 billion of interest in this round, and we basically cut people back to half of the app, and we said no to most investors. And then, on the debt financing, that makes a lot of sense as well. If the company is growing in valuation so much, then actually you're better off

just getting a big loan. Even if interest rates are high and you're paying, let's say, just make that number a round number of 10% interest on that, you're better off taking that as a loan and paying it back in a few years

because you can pay that back in equity then. And presumably, the company has doubled in valuation, let's call it, in three years.

Databricks Hits $62B Valuation

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Discussion about Databricks' applications and opportunities.

Databricks is used by various companies like Rivian for battery optimization and Walgreens for supply chain management.

Square (Block) utilizes Databricks and generative AI to simplify setting up credit card processing in stores.

The conversation shifts to the importance of AI talent acquisition and the competitive landscape.

The intense competition for AI talent and strategies for acquiring it.

There's a significant 'war' for AI talent, even more intense than for software engineers.

Discussion about the 'scaling wall' in AI and how it impacts competition.

Exploring global talent pools, particularly in Eastern Europe and Asia, due to the intense competition in Silicon Valley.

Databricks' acquisition strategy and its focus on talent and intellectual property.

Acquisitions are driven by the need for talent, intellectual property (IP), and sometimes revenue.

Focus on acquiring startups with talented individuals and valuable IP, even if they haven't achieved significant revenue.

Discussion about Databricks' competitors and its relationship with major cloud providers.

While previously competitors, companies like Microsoft, Amazon, and Google are now considered partners and investors.

Databricks drives significant cloud usage, making them valuable partners for cloud providers.

Databricks' fundraising strategy, including equity and debt financing.

Prioritizing long-term oriented investors in equity rounds and being selective in choosing partners.

Utilizing debt financing (loans) strategically due to the company's rapid growth in valuation.