David Gurra

I think, though, you can only sort of serve so many masters,

And when it comes to domain-specific work, specifically in our situation, accounting, there's a need to build with that domain specificity in mind.

I think it comes down to a couple things.

First of all, you want domain-specific capabilities.

So for instance, we announced today the first example of a long-running agent completing an entire business tax return workbook.

That's something that you can't really do in any other AI tool that exists out there.

You need domain-specific accuracy, so we are able to guarantee to the firms that we work with that the AI will meet the requisite level of accuracy for it to be used in a real manner, and we can sort of guarantee that performance.

You need domain-specific user experience so that it's fluid and it works well for people who are experts in their field as they go about doing their work.

You can't just have a chatbot or, you know, the variety of other sort of basic user experiences that you need.

You need to build enterprise sort of great features, you know,

collaboration, audit trails, auditability, et cetera, things of this nature.

And finally, you need domain-specific deployment approaches.

The capacity of a lot of these AI tools to be extremely useful in workflows probably far exceeds the adoption today.

And there's going to be a great challenge over the next decade of figuring out how to get AI into all the places it needs to get into.

And so we think for all those reasons, being domain-specific is very important.

I think that's pretty evident to this day.

And so is that 100 million to ensure the adoption curve is where you need it to be?

Where does that money get deployed first and foremost?

Yeah, it's a combination of things.

I think for us, first and foremost, we're always focused on building the most capable and the most accurate AI for accounting.