AI agents can compress equity research report production from the 2-hour industry standard down to 15 minutes by autonomously gathering and synthesizing earnings data.

4:07Um Royal Bank of Canada uh built agents with us that basically take as soon as an earnings report comes out, so equity research analyst, their job is to put together these, you know, reports that say like you know, this is a buy, this is you know, hold and so on. Um the agent goes, gets the earnings report, gets all the previous earnings reports, gets all the competitors' earnings

4:27reports, 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 2 hours. Of course, it's going to get commoditized and others are going to do that as well, but uh that's actually really important use case that we're seeing in finance. Um so, that's like finance example in

4:49finance, right? It's like and there's lots of examples like this. Sifting through hundreds of thousands of documents, SEC reports, so on. That's finance. Um let's switch gears. Let's go to to health care. Health care completely different. In health care, we have a you know, customer Merck uh that in the life science space created a model called Teddy. Teddy stands for transformer enabled drug discovery. And um this is a transformer model kind

5:15of 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. It's the beginnings, but this is going to actually help us do things that we couldn't do before. Let's

5:37pick retail also. So, I'm picking different. Health care is one. I gave you finance, right? The RBC one. Um let's go to retail, 7-Eleven. Agents that completely automate the marketing stack. I actually think the marketing stack is going to get disrupted pretty heavily. So, um these agents um can basically prepare, they can segment audience like this segment wants to hear this and it can prepare all the marketing material

6:00that's like directly targeting you guys and it can put the campaigns together and do that. 7-Eleven was doing this before as well, but you know, this and we're seeing this at Databricks as well. More and more is being done by agents and being automated so you can just do it faster and you can segment more fine-grained because before you had to create the content for the groups, that was a heavy content

6:19creation was something that was human manual labor. Now, you can actually do that much much more. You can have all your web materials completely customized for a target group. So, these are examples where it is working. There are also lots of examples where it's not working. Even with Databricks, we're not just the 5%, you know, we have some of that 95%, too, but some examples where it's where it's where we're seeing