SaaStr 863: The Enterprise AI Reality Check: From Dashboard Graveyards to 30-Day Migrations with Databricks' Co-Founder and SVP of Field Engineering

**Arsalan Tavakoli** (0:00)
The notion that I'm going to vibe code my own CRM, and I'm going to vibe code all my own applications, I think that it's just not a reality, right? Even if you could build it, maintaining it, evolving it, liability, I think what's going to happen is, which is an opportunity for many people here, 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-

**Jason Lemkin** (0:22)
Slow that down. Any business, technology or other technology that has a monopoly will lose its monopoly?

**Arsalan Tavakoli** (0:28)
I think that because the cost of building software has gone down, the ease of building it and figuring out, there will be more competitors in it.

**Jason Lemkin** (0:35)
All right, thanks everybody. Thanks for Arsalan for coming. I think this actually will be one of the, a perfectly timed discussion because we have had an intense journey here this year on building agents, deploying agents, what it means.
But not everyone's selling to what, what is Databricks, 50% of the Fortune 500s, 110% of the Fortune 500? It's a lot.

**Arsalan Tavakoli** (0:57)
Let's go 110%. I like that. You know what?

**Jason Lemkin** (1:00)
For real. So Databricks is, I mean, they do, even though they're not public yet, they do, it's 5.7 billion re-accelerating at 50 or 60% growth. Massive penetration in basically all leading enterprises, both pre-AI and AI. If you guys haven't followed the Databricks journey, an incredible lean-in to AI, tailwinds, incredible budget attached to it. And no one, I think, has better visibility into what enterprises are really doing versus what they say they want to do on Twitter.
And so I want to dig in on what's really happening with bigger companies, what's going to be happening, and how the world's changing. And just as a side, it's just kind of fun, and then we'll dig into it. I mean, the world, the last time we had any founder from Databricks coming, I certainly had never used a Databricks product. Today, ironically, you never know how the world changes. Every single action at this whole event is on Databricks in a sense, because it's on Neon Database and everything that Databricks brought. So every single thing we're doing, it shows how the world changes from the bottom, and we'll touch into that. It's a pretty cool story. But Arsalan, let's step back for a minute.
What about AI, everything that's happening on Twitter, especially maybe less LinkedIn, but on Twitter, what are folks exaggerating, getting a little bit wrong? What are you seeing for real from the front lines?

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**Jason Lemkin** (3:05)
What are folks exaggerating, getting a little bit wrong? What are you seeing for real from the front lines?

**Arsalan Tavakoli** (3:09)
Look, I think if you sit on X, it's kind of interesting. You'd get this viewpoint that everybody has figured out AI. Everybody's building their own LLM resource agent to do everything. I think the rest of the world is definitely a little bit different.
Still, how many of them actually properly adopted AI in the way of coding tools or systems is pretty nascent. But every one of them, we were talking about this backstage, is realizing every CEO says, if we are not adopting AI, we are behind.
And so now you have all of them telling their employees, if you're not using AI, we are behind. You've got to figure it out. We're going to measure your performance by it. Go use tokens. 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. So I think everybody realizes how important AI is. Outside of though, we're going to give them access to pick one of the models, and we're going to start using coding in our engineering team. It's still early days for many of them, and they're all looking for how do we redesign our processes, and how do we actually think about governance and data to make AI work well. So I mean, that's what we really see in many of the organizations we work with.