00:40:12 · Ying Hua
[00:40:12] S0: Yeah. That's a great question. I'll give you a few kind of bulleted thoughts on some of the things you mentioned. Number one, I don't think the functionality or the advantage of Excel can be replicated by dashboards or Python completely. I think a lot of it can, like if you're doing time series you should do that in python and dashboard, should not do in excel. But if you're doing a model where it's like interactive, you can change your one assumption, you want to see how everything else flows, that should stay in excel. Like there's no better platform to do it it in Excel. So that's number one. Do you think Excel is necessary? Number two, is, I think the current methodology that most vendor out there is doing, which is let me treat Excel as a coding tool and let me just write code to write excel has natural limitations. And there are few limitations some come from the LLM side, some come from Excel side. LLM side is very easy. So let's say I want a model to remember, to update a model, right, well, Excel to... Agent to update a model. Then what agent has to do is go read a document, it has to remember all of the numbers, and then it has to remember where to put that number. Right, so there is like a remembering this, putting over here. Here becomes tricky. Language model next token slash next war prediction. When you have a stream of random numbers together, very likely it's going to miss something because it's no there's no pattern. How do you how do you do next number? I don't know if you ever run to it. So when we build, so sometimes when we do a even like a simple or like a temporary file that have a long string of random numbers words and sometimes like LLM would just miss that or mess up a letter a number and also nothing works. But that's the same issue. You're doing numbers to remember numbers to put into Excel. I think that's an issue. Then there are issues coming from limitation of Excel. So in my dream world, I think the process should be when earnings release hit someone should update all of my models. I should be focusing on reading, doing other things, whatever. But I often have companies 50 like five to 10 companies reporting at the same time. What when I have an Excel add in, what am I supposed to do? I'm supposed to like let AI take control of my computer and open 10 Excels and try to update it?
00:42:39 · Ying Hua, Brett Caughran
Like I don't think that's very practical, right? So our vision is there should be an Excel exist. One, it should be a better Excel, an AI native Excel that exists, that solves the first problem. Second thing is there should be an AI native Excel that exists that exists in the cloud, so they can metal update stuff behind, like your move away from a computer can auto update. So think about when company reports earnings, it can auto update. When company is doing call, it should be able to update your forward number and tell you, hey, at the end of call, Brett, your SM needs to move up 5% based on the consensus estimate. So it should be doing all of that in the cloud. Now you should be able to download it in Excel to play around with things. But I think that is the ultimate form. [00:43:28] S1: The last that gave me sort of the idea of, like, almost a Google Sheets. Right? Where it's like it could be it could be updated in the cloud. [00:43:36] S0: Yeah. Shared. [00:43:38] S1: Yeah. Shared. How does that architecture emerge in the Microsoft Excel ecosystem?
00:43:45 · Ying Hua
[00:43:45] S0: I'm not sure. I don't think our opinion is consensus. I think we looked at everyone. Everyone more or less defaulted to let's build out topic excel because I think one is already available. I'm pretty sure every client would be like we need you to be a plugin, we need you to eat excel. Like every client talking about it but I do think that you're going to naturally run into a limitation. It's kind of like again AI, I do think that you should you need to rethink about every single tool. What used to work for human now needs to work for agent? How do you make it easier for agent to do it? So the way we did it, have an AI native excel spreadsheet. Agent doesn't have to remember anything. Agent just have to remember the formula. It constructs one single formula for every single thing. Every excel power user knows like the whole point of Excel is you never actually manually do it a lot. You just do it for one column or one row, you drag it down. That's a whole purpose. You don't have that advantage in using code to write Excel. You don't have that, you lose that power of trackability. That's that. There's another piece I think, I don't know, we started to hear rumblings a little bit, but I don't know how big or whatever we'll catch. I think Excel, add ins have more security risk. Excel ultimately is a programming language. And so you are letting AI write formulas in your Excel. Some can be visible. You can write non visible formulas to you and it can do whatever it wants. So I think it's a little bit tricky. So we basically have taken a view that we let our hardware and into excel sheets sit in the cloud. We aim for auto updating without interfering with your normal workflow. And then we just alert you when things are done. So that's kind of the approach we've taken.
00:45:34 · Brett Caughran
[00:45:34] S1: Yeah. So maybe, Ying, just sort of frame, if you would, sort of the moment in time? Everyone wants to know what's everyone else doing? Sort of like the Spider Man meme, pointing at everyone. What do you think the super users are doing with these tools right now? What does the of right end of the bell curve look like today? How has that changed? And sort of force you to pull out your crystal ball and happy to hear any predictions on where you think this goes in the next three, six, nine months.
00:46:10 · Ying Hua
[00:46:10] S0: Yeah. I think I'll give you the progression we've seen, right? I think the very early AI users, the power usage is I think of the glorified search and summary tool, right? What a managed business, how things change is basically search and summarization or search and synthesis that's basically that. I think we're starting to do more complex things. So think about multi step, kind of like, it used to be like, you know what did management say but that could be just one section in a preview. Think we started to see people stack that. I think that that's what agent has made available or made it easy to do. We definitely have some power users who are using our platform in a very interesting way. I was at another, I talking to a client of ours and he was like for some companies I don't even look at my Excel anymore. I just have implied not even in our native Excel spreadsheet just in the chat basis. They use all the data he needs, all the commentary he needs, your predicted number, compare consensus, that's it. He was like I don't need everything else. So that was very interesting. And then the other the power use we have seen is automation. So it's not enough to be like, oh I can do a six part earnings preview, right? Like, yes you can do it, but what are you going do? Type in do a preview for this ticker, do a preview that ticker, do it 50 times if you have companies. Like that just doesn't make sense. So, what we started to see people using us is basically how do you all those schedule things that I always have to run so that I don't have to do anymore. I do it once. I set up this as my framework for earnings preview. Now go run it three weeks before any company in my coverage report's earnings automatically. I just come in and just see what the result is, right? You can even do like, this is I have a thesis about this company. Every day I'm going to go fetch all the transcript that's relevant, all the news that's relevant and tell me what are data points that support my thesis, what are data not supported my thesis. Every day give me a word and let me know if there's a meaningful change. Right so that, so I think we're moving towards that.
00:48:23 · Ying Hua
My view on kind of the next leg is truly instead of a chatbot, instead of a platform, you truly try to mimic a having a intern, having a junior person, having another extra set of hand that is always there, that, learns from your feedback. You know say don't ever do this again, will not actually not do It learns your process, it learns your process and it makes customizable. And it's just always there, it's no longer a single chat, it's a one continuous chat. So that's kind of what we our latest feature ProtoJ, that's kind of that. I think that's the direction everyone should go is it's basically like a human. You talk to you, come in, say hey you got five things I need to get done it's doing in the background. And then maybe you have a quick question like why is snow up so much? You can pull up the guidance whatever and it answers you. And then the pre scheduled one like for example we have something, hey alert me when any naming my coverage changed revenue guidance. And today there's a Bernstein conference, a bunch of companies actually give new guidance. It flags me. It's proactive, right? So I think that is the future of what investment work look like. So I think one person with an AI can do a lot more coverage, wind their coverage, can do deeper analysis, can focus on things that actually matter, actually offer generating. So I think that's the future that we expect.