AI Enterprise - Databricks & Glean | BG2 Guest Interview

**Ali Ghodsi** (0:00)
I think we have AGI. I think we have Artificial General Intelligence. We really have it.

**Arvind Jain** (0:04)
You hear these 95% of projects fail, but that's actually what you want.

**Ali Ghodsi** (0:09)
I think the LLM is a commodity. People are not saying that, but it is a commodity. We can get gas from this gas station, we can get gas from that gas station, it doesn't matter, just compare price.

**Apoorv Agrawal** (0:17)
Is AI in a bubble?

**Ali Ghodsi** (0:19)
There is an AI bubble. Okay, so then Glean is also in the bubble, everybody's in the bubble. No, I would say there is a bubble. I would say those three camps.
There is a super intelligence quest camp. I would be very worried there. There's a second, the researchers doing the, that's definitely not in a bubble. They're like the-

**Apoorv Agrawal** (0:36)
They're sober.

**Ali Ghodsi** (0:37)
Yeah, they're super sober and nobody cares about them. And they're probably the ones that arrived, unfortunately. And then there's a third camp, which is us trying to make this valuable. We're not in a bubble in a sense that we're not spending huge amounts of capital on what we are doing. We're just trying to get actual economic value inside of these organizations.

**Apoorv Agrawal** (0:59)
Two legendary builders, Ali, Arvind. I'm so thrilled to get into this with you, because both of you have seen every super cycle I've lived through, Internet, mobile, cloud, data and AI, not just through the super cycles, but also through the hype, the trough of disillusionment. And this time it's different. Today we're going to chop it up on the state of AI.
You know, let's start with a 20,000-feet view. Take stock of where we are. AI, we've seen consumer AI, billions of users, Chad GPT said the guns went off three years ago, cloud perplexity, Chad GPT, people use it in the room. On the SMB and developer side, you've got hundreds of millions of users, with Cursor and Codex and Cloud Code and so on. Enterprise, on the other hand, there's a lot of divide. It's hard to see a lot of fog of war. On one side, you've got models that are earning math benchmarks and science benchmarks and engineering benchmarks. But on the other side, you've got the MIT report that's saying 95% of AI deployments don't work. What's the reality? Bridge the gap for us, lay it out as you see it, view from the top.

**Arvind Jain** (2:13)
So I think first of all, I think we should know that people use AI in their personal and work lives both. So there's not so much of a divide. Everybody in your company is probably using Chad GBD and Cloud and other tools on a daily basis.
The thing that I feel is happening in enterprises, you hear these 95% of projects fail, but that's actually what you want. When you are actually experimenting with new technology, if all of your projects are failing, that means you're just not trying enough at the moment. When I read the study, it was not a surprise for me. We're going to see similar stats next year too, because we want everybody in the industry to be really eager and experiment and figure out how to get benefits from this technology.

**Apoorv Agrawal** (3:08)
This would make you guys by default the 5% of AI that is working, which is 1 in 20 Maybe you go to the 5%.
What is the use case that is working? And not just working like it's like saving me time, but like it's working and it's transforming my company. Something that you can take to the bank, to the CFO, while the CFO will notice it, but the legal won't shut it down.

**Ali Ghodsi** (3:31)
All right, I mean, look, we're seeing a lot of use cases that are working. It's just that, you know, you just have to, it's not just you can just unleash the agents and it just works. It's an engineering art. Like if you're going to have a company that's going to be really differentiated, like my company or your company or anyone's company and you want to beat the competition, you can't just quickly put something together and think that your competition is not going to do the same thing. So that's going to be something that needs evaluations. It needs something that you're going to productionize. It's going to take effort. You need a great team around it. But we're seeing a lot of them. Like I'll give you some examples. Royal Bank of Canada built agents with us that basically take, as soon as an earnings report comes out. So equity research analysts, their job is to put together these reports that say, like, you know, this is a buy, this is a hold and so on. The agent goes, gets the earnings report, gets all the previous earnings reports, gets all the competitors' earnings reports, gets everything that's going on in the market, does the full analysis, the news, everything, puts it all together and it can get the equity report out in 15 minutes from the earnings call. Industry standard is two hours. Of course, it's going to get commoditized and others are going to do that as well, but that's actually a really important use case that we're seeing in finance. So that's like finance, example in finance, right? And there's lots of examples like this, sifting through hundreds of thousands of documents, SEC reports, so on. That's finance. Let's switch gears. Let's go to healthcare. Healthcare is completely different. In healthcare, we have a customer, Merck, that in the life science space created a model called TEDI. TEDI stands for Transformer Enabled Drug Discovery. And this is a transformer model, kind of just like large language models that can predict the next word, but it instead can figure out which genome is missing if you remove a genome. So it really understands the gene regulatory network and can really start telling you what's happening with gene expression and so on. So this is really important for drug discovery.