The large language model itself has become a commodity — interchangeable like gas from any gas station, where price (or marginal quality) is the only real differentiator.

6:39success. Ali, follow up on that off These are great examples. Thank you. Maybe uh if you if you were to take it a layer up, what is common across these use cases or these organizations or these CIOs that's making these use cases work? Is there something that we can pattern match? Yeah, look, I I think the LLM is a commodity. People are not saying that, but it is a

6:59commodity. Like and you know, when I took took econ classes, commodity was when it's interchangeable. Like you can get gas from this gas station, you can get gas from that gas station, it doesn't matter. Just compare price. LLMs have become that way. Like it doesn't really matter. This one is better right now, next week that one is better. You can't even keep up anymore, right? What's happening? So, they're a

7:14commodity. So, it's not about that. It's really comes down to your company, what data does your company have that's special that your competitors don't have? Can you leverage that and can you build AI that really understands that data? Cuz that's not a commodity. There's not an AI out there that understands all your business processes in your company, your secret sauce, and your data. That's not a commodity. In fact, that's closer

7:38to the 95%. It really comes down to that. Or if you have a complicated process that just your company has, this is how you deliver your product and services in your company, and it's uh you know, it's that portion can be disrupted with AI somehow. If you can do that, now you can get ahead of your competition. But it comes back to what makes your company special. Unfortunately, a lot of

7:59companies are just building uh commodity stuff. Like you should not be building that cuz it's a thing that every company can do. It's not special to your company or to to your that's that's I think the problem in a lot of the industry. Uh another problem in the industry is that a lot of demo wear. It's really easy to make cool demos with an AI and you know, therefore we're seeing a

8:17lot of cool demos, but that's all they are. Yeah. Well, you know, something we say around at Altimeter quite a bit is your AI strategy starts with your data strategy. Yeah. So, you got to get the data house in order first. And you know, there's a lot of reasons for for for use cases that are, you know, we were trying, were not working. Maybe give us an example of the 95% of an AI bet that

8:36either of you had at Databricks, at Glean, that did not work out and why it didn't work out. It's actually an interesting thing you know, with engineering today is you build systems and and never never before have you been in this mode where you start with a great idea and doesn't seem like good idea anymore like within 2 weeks, you know, because we see a new development that happened. So, there are