This Excel AI Agent Built Me a $1M Financial Dashboard in 10 Minutes (LIVE Demo)

Join me as I chat with Nico Christie where he demos Shortcut, an AI-powered spreadsheet tool that functions like "Excel built for the future." Through several live demos, he shows how the platform can create financial models, update existing spreadsheets with new data, and build custom analysis tools using simple natural language prompts. The product aims to make Excel-based work significantly faster while maintaining transparency about data sources and calculations. Timestamps 00:00 - Intro 00:53 - Overview of Shortcut 02:08 - First Demo: How Shortcut works with Existing Excel files 04:44 - Different User Types Who Benefit From Shortcut 08:50 - How to Prompt Shortcut Effectively 11:20 - The benefits of Using Shortcut 13:42 - How Shortcut handles data verification and transparency 17:03 - Best Use Cases for Shortcut 19:03 - Obvious ideas and market opportunity 22:23 -Building a Utilization Model for Agencies 34:59 - Greg's Custom Utilization Rate Dashboard Demo 39:29 - Who should try Shortcut Checkout: https://www.tryshortcut.ai Key Points: • Shortcut is an AI-powered alternative to Excel that allows users to create and modify spreadsheets using natural language prompts • The tool can perform complex financial modeling tasks in minutes that would take hours in traditional Excel • Shortcut provides transparency by showing data sources and allowing users to trace where information comes from • The platform serves both Excel experts (making them faster) and non-experts (making complex spreadsheet tasks accessible. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ Boringmarketing - Vibe Marketing for Companies: boringmarketing.com The Vibe Marketer - Join the Community and Learn: thevibemarketer.com Startup Empire - a membership for builders who want to build cash-flowing businesses https://www.skool.com/startupempire/about FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND NICO ON SOCIAL X/Twitter: https://x.com/nicochristie Shortcut: https://www.tryshortcut.ai

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Greg Isenberg?

  I spent years inside Microsoft Excel, building models, forecasting revenue, cleaning messy data. I hated it. Every time I thought, there has to be a better way. Then, last week, I came across Shortcut. It's this AI app that plugs into Microsoft Excel, Google Sheets, and suddenly, the tedious stuff becomes magic. So, I invited the co-founder, Niko, onto the podcast. We built financial models in real time. We explored how Shortcut works under the hoods, and we talked about how to get the most out of this product. If spreadsheets are a part of your daily life, this episode will change how you work. Let's dive in.

• 0:53Greg Isenberg?

  So, I reached out to Niko, and I think the DM said something like, "This is one of the most impressive AI demos I had ever seen. Can I use the product?" And you said you'd come on to the podcast and share how people are using your startup, Shortcut. It's kind of like Microsoft Excel if it was built in the future. And I've never really been a huge Excel guy, because, to be honest, it's been overwhelming to me. The macros, like, I don't even know what a macro is.

• 1:29Speaker 01

  Right, right.

• 1:31Greg Isenberg?

  So, that's why I reached out to you. I just feel like there's an opportunity to, like, I know how valuable it is, but I just, I know how hard it is to use. Anyways, Niko, welcome to the pod. I want you to show me how, how I could use Shortcut to, to make more money and be more productive, and, and I was hoping you can do so.

• 1:56Speaker 01

  Yeah, thanks for having me. Super excited. I'm gonna just share my screen and kind of get into it. I think I've been doing live demos for some time now, and it's always just like the most possible fun way to do this. So, kind of like you said, it's like Excel if it was built in the future. Very intentionally, it's like exactly Excel. You can do whatever you would possibly want to do in Excel, and you would do it here. And we had to recreate a lot of it to allow this. The big difference is that it's also a superhuman agent that can do most of your work. So, like, the good way to think about it is not... As like a co-pilot for one or two steps, it's like it will do 90% of your entire job. And then you get to do other things as that happens. So I can give you some examples. But again, again, it's just like Excel.

• 2:40Speaker 01

  And it's not just for creating things from scratch. You can just open up existing Excel files in here and directly manipulate them. So for example, here's like a DCF file on Microsoft, which is a pretty nasty model to have to make. And then from here, you can do whatever you want. You can just ask it to be updated. The best way to really use it is to send it off and probably just come back in about 10 minutes. And one thing I want to show you, I guess, this would be like a technical demonstration of how hard it is. But like, I will say, you know, here is this huge DCF, which can take up someone like half a day or all day to build. Hey, take this. Please update it and use the exact template

• 3:26Speaker 01

  that I want to use Google now. Pull the 10 Ks from 2022 through 2024 and do forward projections through 2029. And it's going to do it. It's kind of crazy to watch. I think it's fun to watch the first couple of times. But again, you're going to want to just come back to when it's done. But Greg, the other thing I kind of want to address is like your question, which is like, how can people use this to make money and be productive? And I think the best way to do that or show you that is like to do exactly what I'm already doing right now or like I have to do today.

• 4:09Speaker 01

  So for example, this, and you'll see that I can come back to, and I have multiple shortcuts running at a time. This is like dummy data. I try to make it look just like our data without giving away sensitive information from our actual like revenues, expenses, and its sources and like its types. And what I need to do for work is like build a P and L, like pretty classic income statement. And specifically, I need to project it out two years as well while looking one year back. And it's going to be like for our data room. This, you know, I know you're not a big Excel guy, but this is one of the most like common financial models you'll have to make on Excel from Rookie.

• 4:44Greg Isenberg?

  I will say I'm not a big Excel guy as like a contributor to the Excel, but when people give me Excels, you know, I'm loving it. And I want, I want to be able to manipulate it, but I'm scared I'm going to break something.

• 4:55Speaker 01

  Yeah, that's fair. There's like two actual major use cases right now are types of users, archetypes. The ones that it's most sticky for are the people who are kind of Excel experts, but it takes hours of work and makes it like 10 minutes. But there's another class of people that like I'm learning more and more about, which is like, they're not super strong at Excel, but this thing makes them almost like Excel gurus pretty quickly. So we can even do an example together based on like what you want to do and like see how far you can take it and see how much better it makes you. So here's like what that example was for me, what I needed. And right before I do that, I actually will show you one thing.

• 5:32Greg Isenberg?

  Sam Altman, the co-founder of OpenAI, just said that it is the era of the idea guy, and he is not wrong. I think that right now is an incredible time to be building a startup. And if you listen to this podcast, chances are you think so too. Now, I think that you can look at trends to basically figure out what are the startup ideas you should be building. So that's exactly why I built idea browser.com. Every single day, you're going to get a free startup idea in your inbox and it's all backed by high quality data trends. How we do it, people always ask, we use AI agents to go and search, what are people looking for? And what are they screaming for in terms of products that you

• 6:16Greg Isenberg?

  should be building? And then we hand it on a, you know, silver platter for you to go check out. We do have a few paid plans that, you know, take it to the next level, give you more ideas, give you more AI agents and more, almost like a chat GPT for ideas with it. But you can start for free idea browser.com. And if you're listening to this, I highly recommend it.

• 6:39Speaker 01

  This is a kind of an interesting like user paradigm. One thing we've learned is like people specifically working on Excel aren't like extremely good at prompting. And they don't even expect that the AI really greatly understands its subject matter. But when you show clarifying questions, it can make them better. And it's kind of, it's like a first magic moment for people who use Excel a lot. So, you know, what are the growth assumptions I should use going forward? Conservative, moderate, aggressive. Let's just say like, You know, I want all scenarios. Let's make it hard.

• 7:10Greg Isenberg?

  That's really cool. I've never seen UI like this. You know, actually, I've seen like from time to time, a Claude or ChatGPT will be like, can you refine it? And I love when it does that, but I haven't seen it built like productized like this.

• 7:26Speaker 01

  It's become one of the magic moments, which I totally did not expect. But users are really not that great at prompting. And I think GPT does something similar. If you do deep research, or if any of you guys did use deep research to get them out. But they almost, they like necessitate that that clarification. But for us, we actually want to make it context aware so that the clarification is even better. And users have like, become much better prompting because of this.

• 7:49Speaker 01

  So I'll do all scenarios. Let's keep the same exact structure of the template and update the data with the same metrics and charts. Again, I think this is like the most similar to real finance work, for example, is like you have your templates and you just want to update them. You don't want to create things from scratch. It's definitely like, this is maybe even like the hardest kind of work. So it'll take that on. I'm going to go back to this example. I'll say, hey, I need to build a P and L for this last year of data and do a two-year projected out.

• 8:28Speaker 01

  This is for my data room, for VCs and bankers, make it very professional. It's always funny to see like how it interprets that. See how it goes. And what we can do also is like show you a third version of this and like something you want to try or that you think like would be valuable to you or valuable to your audience. And we can give that a shot as well.

• 8:51Greg Isenberg?

  Cool. I'm also like, as you're going through this, I'm, I'm, I'm just wondering, like, what is the, what is the best way to prompt shortcut? Like, is it long prompt, short prompt? You know, you've seen thousands or more of the prompts, like, what do you recommend to people?

• 9:08Speaker 01

  Yeah, it's a really good question. In general, I've always liked relatively vague prompts because it forces the frontier models to really get creative and then suss out clarifying questions from you. So specifically because of our clarifying questions, I'd like less for both prompts. I like less specific prompts and then that kind of encourages a little bit more creativity out of the model and then out of your clarifying questions. Cool. So we'll, you know, we'll fill this out as well. I'll say do this over many different sheets. Cool. And it will get going from there. And meanwhile, I'll check in on Microsoft. So what you'll see here is it actually looked for the Google's data and the 10Ks exist in like

• 9:49Speaker 01

  an SEC database that's like super hard to find and extract, but it found these 10Ks and extracted them. 10Ks are like 100 pages of PDF material for for public companies. It found all of these Google 10Ks and it's starting to extract the data. And you'll see it actually provided a task list here. So its current plan is to read and analyze all the current models, search for and download the 10K filings and extract these and then start to update the historical data and the drivers and the assumptions as well, knowing that like the P and L and the dashboard are more formula driven and will be updated automatically if you can change the source material.

• 10:27Greg Isenberg?

  That's crazy, man. That's actually like, that's crazy.

• 10:31Speaker 01

  Yeah, it's pretty crazy to see. You know, and actually, and we can talk a little bit into the technical details as far as you think, you know, that's interesting. But these 10Ks are so big and confusing and like horrible that you'll see that like we're running into context limits here. You see in the in the file on the right side. And it's actually agentially deciding, well, let me look at these like one part at a time or like one chunk at a time. So there's really no upper limit in terms of like how we can allow agents to go over material. I think historically, a lot of a lot of what we've been doing is rag in this industry. But as agents can learn to start to search for chunks of information selectively and then compact their context as necessary, that is, that is changing dramatically.

• 11:16Greg Isenberg?

  Yeah, I guess what this what's going through my mind right now is, you know, Excel has been around for how many years? 30 years?

• 11:24Speaker 01

  It's almost its 40th birthday.

• 11:26Greg Isenberg?

  40th birthday. Like Excel is a middle aged person.

• 11:32Speaker 01

  Yeah.

• 11:35Greg Isenberg?

  You know, probably a middle aged man, gray hair,

• 11:40Greg Isenberg?

  you know, khaki pants.

• 11:43Speaker 01

  Yep.

• 11:43Greg Isenberg?

  And, you know, and I'm looking at this is I'm like, okay, what, what, what does this unlock from a use case perspective?

• 11:54Speaker 01

  Yeah.

• 11:55Greg Isenberg?

  That, that Excel hasn't been able to do. And, and what are some unfair advantages that people who get onto shortcut early are going to be able to unlock? That's what's going through my brain right now.

• 12:06Speaker 01

  Yeah, yeah. So let me tell you a little bit about Microsoft and what we know about Excel. And then what these unfair advantages are. So Microsoft Excel specifically is like, I would argue, like I grew up using Excel. I started my career in finance, which is not a coincidence for why we ended up building this. In a lot of ways, I would say it's like the best design software maybe ever. Like it's 40 years of staying power, two billion users, the business, like the business world runs on it. But what it's, what's, what's that, what that has accumulated is like a lot of things that you have to satisfy for a lot of different enterprises. And for a lot of people who are still built, you know, 20 years ago for their tech stacks. So even Copilot, which is trying to do what this is doing right now,

• 12:52Speaker 01

  is forced into helping people use Excel better. It's not forced into like, how should Excel really work? Right? So we really had the chance to go from the ground up. And instead of doing a single step Copilot for the things that you only want to do in Excel, we have an end to end agent that can just do all of your work.

• 13:10Speaker 01

  And the kind of fun part about it is that like,

• 13:14Speaker 01

  you can import and export Excels directly. So no one would ever even know it's in shortcut. So in terms of what people are using it for, they have their standard things they do at work. And honestly, like there's a thousand companies already using this. And I actually think their bosses don't know. I think they have their four or five hours of things they do that have turned into 10 minutes. And ideally, good employees are doing more of them. But maybe a lot of them are just. Getting them done and enjoying their free time. And I can tell you like what those specific things are. But that's, I think, what the pattern becomes. So actually, by the way, here, one kind of really key, key thing is even if AI becomes perfect, which it's not, and I'm not sure it ever will be, it's like an indefinite hill climb, you will have to really, really trust its outputs in order to move forward.

• 13:57Speaker 01

  As in, like, if you don't know where things come from, they're almost useless. You have to review its work. So here you'll see that it looked, it found these 10Ks from Google. And it can actually cite every single part of the information down to like the exact figure, the exact page of the PDF it came from, the exact year the data came from. So that when things come into your Excel model, you can trace every single bit of it. It's almost like if you use Cursor, like code gen was very cool, but until you were able to see the diff, you couldn't quite trust it because like some arbitrary line in your code would break. So the one reason we're not building this directly in Excel, or not mainly focused on that, is that you can't really manipulate the UI. Like, right now, you're seeing that this agent is, it sees as an error in the calculations,

• 14:42Speaker 01

  it found it, and it's directly editing it. So we're changing the spreadsheet and the entire front end, like, in real time.

• 14:49Greg Isenberg?

  I'm happy you said that because it's going, it's probably going through everyone's mind, which is like, how can I trust this data? You know, the, the stakes are a lot lower with like, hey, write this blog post than, hey, build a financial model.

• 15:04Speaker 01

  Yeah. It's just that the finance world, until probably right now, wasn't ready for this. But this was the same things that we had to ask ourselves in software engineering. It was like, oh, well, I'll never use that LLM code. Remember when everyone was complaining about hallucinations in December of 22? And then we found out that if you can really observe it, if you can apply the diff, of course, humans are still responsible. And if my code doesn't build, like, I'm the one who gets in trouble. It's not Claude Code or Cursor. So what will inevitably happen, whether it's us or someone else who cracks it, is in finance, accounting, FPNA, real estate, wherever, where like, there's these giant people who use Excel, they'll progress to this role of supervisor, where they're 10 times faster. And if they're wrong, or if there is a bad number in there, of course, it's them that's

• 15:48Speaker 01

  on the line, but they would take that trade off because it's easier to supervise work much faster than it is to do the groundwork yourself.

• 15:55Greg Isenberg?

  I mean, ultimately, you're supervising work regardless, right? Like, either you're supervising human work, or you're supervising, you know, agentic work. And the bottom line is you, you know, I guess the question, it goes back to like, you know, are self-driving cars more safe than human, human cars?

• 16:19Speaker 01

  Yeah, it's an interesting thing to bring up. The reason that self-driving cars, well, I mean, even within certain cities where their accuracy is really good, hasn't haven't been adopted, is a little bit because of this accountability issue, where like, it seems like humans are actually willing to have more depth and chaos, as long as they can clearly point to whose fault that is. What I really believe to be true, though, is there's a certain accuracy threshold where if met, we will change that. A certain benefit, where if we really can, can experience this benefit, we're willing to think in a new way. But it's not just enough that it's better, it has to be better, it has to be faster, it has to be more observable and traceable. Because you're right, humans are already managing humans. And like, that's actually not that easy, as I'm sure you know, right?

• 17:01Greg Isenberg?

  And, you know, shortcut specifically, like, how, on a scale from like, 2005 self-driving car to 2025 self-driving car, like, where are we?

• 17:15Speaker 01

  Yeah, great question. Think of it in two dimensions. One is, or two, almost, let's do two kinds of use cases. There's a use case of like, build something from scratch. And let me show you, for example, this one, right? Bam. So this was the one I asked it to build something from scratch, or more or less. I said, here is, you know, data, build me, build me summaries, dashboards, right? And in this use case, like, it just did this in eight minutes, Greg. Like, that's, like, that would have taken me, you know, an hour or two, and I'm good at Excel. For use cases like this, we're already past this self-driving moment. We're past the Waymo moment. Now, and I'll continue to answer your question, but I'll show you why it looks like this,

• 17:58Speaker 01

  which is super cool. This is the observability we're talking about. You can see exactly what figures are hardcoded, which are formula driven and why, so that you can really review this better. But the real use case, like I was pointing to earlier, is you're going to update, not things from scratch, but you're going to update existing models. Now, for this use case, which I think is 90% of real Excel work, where like the billions of dollars are, I'd say we are at like the Tesla self-driving right now. As in like, if you're in the know, if you're willing to adopt frontier tech, it's very exciting and actually useful in your workflow. But humans are still by and large doing the manual driving themselves. I like to think of it as, we're probably heading for

• 18:41Speaker 01

  like an August 2024 moment, which is when Karpathy tweeted about cursor and it went like 20x. I think the question is, is are we like, is it August now, or is it May? But there's a very clear line that we can future predict towards that looks like we know what to solve and that it is solvable. So it's, it is, as you can imagine, super exciting for us.

• 19:02Greg Isenberg?

  And it's also very clear that, you know, a product like this should exist, right? Like using plain English to, you know, we've had vibe coding, we've had vibe marketing, we haven't had vibe Excel yet. But I think, you know, that this idea around, you know, taking your thoughts and, and building, you know, in this case, it's not code, but in, you know, it's kind of, it's kind of quasi-code in some ways, right?

• 19:33Speaker 01

  Yeah, no, of course. I wish I could show you the logs, because it's of course code.

• 19:38Greg Isenberg?

  Everything is code, right?

• 19:39Speaker 01

  Right. And everything will be code. And I think all code will be generated like dynamically. But the thing you're kind of getting to is what I've always thought was, you know, maybe one of my smarter ideas, but you know, it's not mine exclusively, is the best ideas are not definitionally contrarian. As in like, I didn't have to think of something that you didn't believe and then make it true. I just think the best ideas are really obvious in hindsight. And I think the best predictor of what that is, is if you can release something, do people say, I can't believe this didn't exist already? Right? So when people see this, I think that's the reason that the initial reception has been so strong. It's just, why hasn't this existed? Like, there has to be some explanation. And I'll tell you what it is.

• 20:19Speaker 01

  We're a research lab with 20 people that are all, you know, from MIT, Stanford, great researchers. But if it wasn't for my background in finance, we wouldn't have done this. And I just don't think that there are co-founders at, you know, the 10 or so frontier research companies in the world that care at all about finance. No, no researchers really spending material time in Excel at all. They are building coding agents because that's what they love. Right? So it took someone of a little bit of a different background to make it happen. And now I think the world knows how important it is because of the initial reception, and I'm sure everyone's sprinting towards it.

• 20:55Greg Isenberg?

  Yeah, I mean, that's why, that's why I reached out. Because I, I had this in my brain that I wanted this. And so it's, it's cool to see it working. So what's happening? What am I seeing on screen right now?

• 21:10Speaker 01

  Yeah, so this is actually a great one. This is the big hard task, right? Which is update the Microsoft, you know, DCF model using Google's new data. Just use the same exact template, don't change anything except for the data. And what you found is the agent has actually extracted all of this data, has updated the historical data, the drivers and the assumptions, is looking at the tax rate, and it's actually kind of finding errors as it goes. And it's like, that actually doesn't really quite check out. I think capex is too high, right? Or, here's an example. There seems to be an income calculation error. Let me correct the issue. I see the issue. It's that in the data sheet, it has the wrong references, and it's always finding its own mistakes and just chugging along. So you're seeing it actually do the kind of work that a human would have to do.

• 21:52Speaker 01

  And it's about, now you see, I see the issue, there's a dependency and it's causing a circular reference. One of the hardest problems in Excel, you have like these things that are codependent on each other. And it found the circular reference and the exact formula that it was referencing, which isn't a hard formula. Like, I don't know if you ever used like sums in Excel? But it found it. And it found it, and it fixed the error. So that error's gone. And now it found that there's a couple ref errors in this SGNA, and it's correcting those two. Yep. And now it's, that's correct, and there's still some remaining ones. And then Greg, what I'd also like to do is like, if there's anything you want to try, like, go push it. Go, let's go break this thing.

• 22:31Greg Isenberg?

  I mean, I'll tell you one thing that's been on my mind recently.

• 22:37Greg Isenberg?

  So we've got an agency and a design agency for AI companies called LCA. And one of the things that, you know, if you write it, if you run a tight agency,

• 22:53Speaker 01

  Yeah.

• 22:54Greg Isenberg?

  you, you need to have utilization rates that are, you need to have people basically on, on files.

• 23:02Speaker 01

  Yep.

• 23:03Greg Isenberg?

  They need to be utilized in order, and because the big mistake, you know, the big problem that a lot of agencies have is at the end of the year, they're making 5% EBITDA, 3% EBITDA, 7% EBITDA. Like, they're very hard businesses to run because, you know, people aren't utilized. So I wonder if there's a way to create some sort of like profitability analysis around,

• 23:29Greg Isenberg?

  you know, agency utilization rates based on like different teams and stuff like that. Like ultimately, my dream, Niko, is to look at a spreadsheet where it says like, this is how utilized this team is, this is how utilized that team is, this is how much revenue they're bringing in, this is how profitable they are, just like a bird's eye view of utilization and profitability.

• 23:54Speaker 01

  You can tell my spelling has deteriorated ever since using AI. So let's go ask, let's ask Shortcut for that.

• 24:02Greg Isenberg?

  Yeah.

• 24:02Speaker 01

  It'll ask clarifying questions and then we will see what we can do about it.

• 24:06Greg Isenberg?

  Okay, cool. I mean, I would, if this works, like.

• 24:12Speaker 01

  This is a good test. This is a good test. I had done something similar where I asked it like, hey, we're gonna do like some kind of launch soon. I'm looking for like a bunch of the most in distribution, best launch partners that I can like meet with, talk to. And I had it find that across every single platform. And then it get like their posting schedule, why it's a good fit, how much they expect it costs, even they got their people's emails. So it's kind of a similar kind of task like that. So you tell me, this is your dream task, right?

• 24:39Greg Isenberg?

  Yeah.

• 24:39Speaker 01

  Do you want this multiple sheets, custom?

• 24:42Greg Isenberg?

  Uh, single sheet.

• 24:44Speaker 01

  Single sheet?

• 24:46Greg Isenberg?

  Yeah.

• 24:46Speaker 01

  What time period should the utilization analyst cover?

• 24:48Greg Isenberg?

  Monthly.

• 24:50Speaker 01

  Let's do monthly. Key metrics?

• 24:52Greg Isenberg?

  Yeah, utilization, percentage, revenue. That's great.

• 24:56Speaker 01

  Cool.

• 24:57Greg Isenberg?

  All of them.

• 24:57Speaker 01

  Do you want a dashboard to visualize the data?

• 25:00Greg Isenberg?

  Yeah, why not?

• 25:01Speaker 01

  Make it hard?

• 25:02Greg Isenberg?

  Yeah, just make it hard. Let's see if we can break it.

• 25:05Speaker 01

  There's now, like, what kind of data? It's going to assume to make dummy data, I suppose. Do you want me to, like, get weird data from the web? Like, what do you want to do?

• 25:12Greg Isenberg?

  Is there a way to benchmark our utilization rates versus, you know, standard design agency utilization rates? Like, I want to know if we're ahead of the pack or behind. Maybe I'm making this way too complicated for this.

• 25:30Speaker 01

  No, no, no, let's do it, man. Can you benchmark existing and standard agencies? These are, which ones specifically? Design?

• 25:36Greg Isenberg?

  Design agencies.

• 25:37Speaker 01

  Yeah, right. Design agencies. And then for our data, do you want me to, like, say, like, use Greg Isenberg's company, like, to see what you can find?

• 25:45Greg Isenberg?

  Yeah, I mean, you can, you can go late checkout. The website's latecheckout.agency.

• 25:51Speaker 01

  Go to latecheckout.agency.

• 25:57Speaker 01

  That's my company, sure.

• 26:01Speaker 01

  Yeah, so, I mean, it's pretty vague. Let's see if we can, let's see if we can start marching towards your dream.

• 26:07Greg Isenberg?

  That's all I ask, because I'm, I feel like I bug the finance people on, like, on my team, and they, they don't want to hear from me. Like, add this column here and do this there. And I also think that a lot of people listening are, like, solo entrepreneurs, are small teams, startups, who don't have finance teams.

• 26:29Speaker 01

  Right, right, right.

• 26:30Greg Isenberg?

  Right? So is this, is this your finance team in a box type thing?

• 26:34Speaker 01

  Yeah, I mean, that's why I used that first prompt here, which was like, I have my expenses. Let me walk you through the, the actual answer here.

• 26:42Greg Isenberg?

  Yeah.

• 26:43Speaker 01

  I don't have a finance team. I'm one person of 20, and I'm the only business-oriented person, and I still spend most of my time coding. So I had the expenses here. I actually need to build a P and L. So I pulled this up, and in fact, actually, there's some errors here, which I'm not used to seeing, but let's go into why. Historical P and L projections. Oh, interesting. So it projected the revenue out for the fiscal years, you know, according to where these sources of revenue were in the expenses, and it made a dashboard here.

• 27:12Speaker 01

  So some bizarre formatting choices, quite honestly. But you'll see it's looking like it has revenue. It has, and these are all formula driven. So you can see exactly like where they come from. Some of the ones are going to be more assumptions. You see total revenue, gross profit, operating margin, and so on. Even charts it. And it took, you know, my data and built me, you know, a forward looking P and L. Right?

• 27:36Greg Isenberg?

  Crazy.

• 27:38Speaker 01

  I think we're probably, you know, for these net news, it's like having your own mini finance team because you, you don't want to do this from scratch, but you probably would want to just do your last tweaks at the end.

• 27:46Greg Isenberg?

  Yeah, exactly.

• 27:49Speaker 01

  So here, actually, we're going. You ready?

• 27:52Greg Isenberg?

  Yeah.

• 27:54Speaker 01

  Let's see, sheet one exists, it's going to create profitability analysis for late checkout agency. Well, interesting. By the way, Greg, I've never seen utilization like sheet like this. So tell me, tell me what you, tell me your thoughts as you're seeing it.

• 28:11Greg Isenberg?

  So, I mean, you know, going left to right. So I love, obviously, I love the, you know, breakdown of employee and level. Like that's, we level people and, you know, higher levels obviously get paid more and stuff like that. So I love that. Available hours by month. It's interesting to see it like that, but that's not exactly how I envisioned it. Like to me, I'm kind of like a percentage guy. Like, do you have, does Jane have 10% next month? Okay, let's go, you know, maybe let's not put her on a project because we don't want to be at 100%, you know? We want people to like.

• 28:51Speaker 01

  Right.

• 28:52Greg Isenberg?

  That's too much, right? But if someone has, if Jane has 70% availability, then she's kind of just sitting, sitting.

• 29:00Speaker 01

  Around, right?

• 29:01Greg Isenberg?

  Sitting around.

• 29:02Speaker 01

  Yeah, well, let's see. I think my, my guess is it will do some percentages as well. In fact, let's go through the task list because it's going to be pretty thorough. It has 13 tasks it wants to get done as a part of this.

• 29:14Greg Isenberg?

  Yeah.

• 29:14Speaker 01

  It's going to create an analysis sheet to calculate, yeah, look, to calculate utilization percentages by employee and team.

• 29:19Greg Isenberg?

  Okay.

• 29:21Speaker 01

  And revenue and costs as well. So let's, let's see how this does for you. Meanwhile, let's check on Microsoft. Boom. So this is the Microsoft file that it updated and changed to Google.

• 29:33Greg Isenberg?

  Yep.

• 29:34Speaker 01

  You can see which files, I mean, which form, which are hardcoded, which are formulas. Let's just go bit by bit.

• 29:40Speaker 01

  Right, so it made these new charts. This is for Google. It changed the name from Microsoft to Google. And it has new results, right? This is like a kind of a standard P and L. Change the view so it doesn't look like it's in the review state. P and L. So this formula didn't actually check out. We can actually just ask it to fix it. But I'll go back. Assumptions. These are like the growth rates for... These are the, actually, this is cool. These are the growth rates for the assumptions for Google Alphabet, which are like kind of hard to guess. Why would you assume that YouTube is going to grow at this rate? Cloud is going to grow at this rate, right? So if you turn this on, you can see that these are hardcoded and where they come from.

• 30:23Speaker 01

  Like, these assumptions are actually coming from the 10K. And if you actually click into it, like, why is this value this, right? This is something you can't do in Excel. You can open this up and say, hey, here's what the R and D is costing. And here is the part of the PDF that's telling you where this frickin' thing came from. It's not just completely made out of the blue, right? So anything hardcoded, you can find that for. And then we'll go back. Let's go drivers. So a couple of formula errors, which is why I'm saying I think editing existing templates is kind of like, sometimes your Tesla makes a turn and you're a little suspicious about it, you know? That's where we're trying to cross that line right now. So typically, what I would do is highlight this. You can select the range that you want to make specific edits to.

• 31:07Speaker 01

  Be like, hey, fix these formula errors, please.

• 31:13Greg Isenberg?

  That's it? Like, you don't give any more context to that? You just say, like, fix this?

• 31:20Speaker 01

  Yeah, let's see. So that's like a cool, I don't know, like, again, Command K would be like this feature in Cursor in which you would like highlight a range and like, you know, make a specific targeted edit. What it will do is it will strict the edit space, so it only changes those. But it's pretty bright about like, let me look everywhere for the necessary context to update this.

• 31:38Greg Isenberg?

  Okay, cool.

• 31:40Speaker 01

  And then let's go back to your task. All right, so, you know, I have to admit, I did not realize that building utilization matrix is as complicated as an LBO model. So let's see. Yeah, it's starting to add information to the sheet. Let's see. Boom, okay.

• 31:58Greg Isenberg?

  Okay, yes, this is exactly the type of vibe I had in my mind.

• 32:06Speaker 01

  Really?

• 32:07Greg Isenberg?

  Yeah, like with the utilization rate, how many available hours, because yeah, no, this is, this is, this is what I wanted. Something like that.

• 32:16Speaker 01

  Awesome. Yeah. And then what you would probably do like as a user is you would be like, this was good, but I actually wanted a little different. Let's go like change it, you know, and you might do it because you're, I mean, you're not as like a hardcore Excel user. You can do it yourself to some extent, if you think that's faster. But if you're like, actually, I need these values in red. I'd like, actually want to use my real data, so use this. You'll just go for that second version, you know?

• 32:37Greg Isenberg?

  I mean, I shouldn't say it's like perfect, like I, now I'm looking at, I'm like, okay, I would change this, but.

• 32:43Speaker 01

  Well, what would you change in specific? Yeah.

• 32:46Greg Isenberg?

  Well, I think like for me, you know, I would want, i- i- if someone's average utilization rate, let's say is above 70%, like make it red, like that's scary. Burnout mode. If, if someone's utilization rate is under 60%, make it green, maybe.

• 33:09Speaker 01

  You're such a, you're such a nice manager. You don't want your people working so hard, huh?

• 33:13Greg Isenberg?

  Well, I've owned agencies long enough to know that you, that's not the way to build a sustainable business.

• 33:20Speaker 01

  Yeah. Well, let's actually look at the, at the tasks here. Usually, so this is what you're talking about, Greg. In Excel, there's a thing called conditional formatting, which would be like, based on this condition, make certain thing look like this. Now, while we weren't, we didn't say this in the prompt, so it's, I'm not sure exactly what it will choose to do.

• 33:39Speaker 01

  But my guess is it will decide like, you know, certain numbers, if they're too high, will be in red, or too low, they will be in red.

• 33:44Greg Isenberg?

  Okay. What about automation? Like, how does, how do you, how can you automate?

• 33:52Greg Isenberg?

  Like, I don't want to go into this, and I'm not saying we're doing this today, but

• 33:56Speaker 01

  Right.

• 33:58Greg Isenberg?

  Like, I don't want to go in this every day and update how many hours. Like, is there a world where there's like, you can, you can program this to automate, similar to how like, the Gumloops and the Lindian AIs in the world have automated marketing?

• 34:12Speaker 01

  Yeah, yeah. It's a great question. We don't have, at this point, like, a super-strong integrations pipeline. But what you're asking for is kind of, like, the essential thing we're getting into now, which is, you will want to automate automatically extractions from QuickBooks. If you're in law, it's like, they want updates from Carta if they're in... Like, every industry has their thing, which is part of the challenge. If you're gonna do Excel, like, for a research lab, that's like, very niche and specific.

• 34:40Greg Isenberg?

  Yeah.

• 34:41Speaker 01

  But is product, it's actually almost too broad. Right? It's like, you mean 2 billion people, right? So, yeah, currently, what you will have to do is actually get your export and attach it, and then it will do that, but it won't auto-sync for you. So, you're, you're, you're kind of in charge of, at least, supplying the data for now. Currently now, we, we have this dashboard. It has utilization by departments, revenue and profit by department. It's actually looking for... So, this is kind of cool. There's like, a near-deep research-level quality of web, of web search here. So, you see, and looking, you know, based on the web research, there's utilization for certain benchmarks here, principals' averages, this, project managers' this. This looks to be... This could be a little out of distribution.

• 35:26Speaker 01

  This is for certain kinds of companies, let's see. And it has all of these sites that it's referencing. And so, adding industry benchmarks and strategic recommendations based on the analysis compared to your data. Yeah, so what you'll get at most, which actually makes Excel wonderful, is that you have formulas, at least. So, like, you can see that, like, this, like, you know, I'm good at Excel, but this would take me a while to write. You can at least see that it's taking an average of this stuff, right? So, like, there's some degree of, like, traceability, but it's not, like, cited, right? Like, it's not that... The job of observing in Excel is, is unfortunately, today, pretty brutal for that reason.

• 36:07Greg Isenberg?

  Totally. Also, like to you, there's a little bit of context to Excel, like when you see a formula, but to like a simpleton like me who doesn't know Excel that well, I'm kind of like, what is happening here?

• 36:20Speaker 01

  Yeah. So I don't think like, look at this formula. I, this is above my Excel mastery now too. Right. This is a, an average if certain conditions are met divided by an average like other conditions. Part of the really cool thing is you no longer will have to ever know this again. Like you will just be able to, as an Excel neophyte, just say like, listen, I just need this thing. Use Excel formulas because my boss is going to look at it eventually or whoever. But you will no longer have to speak in this language, just like I code in like Rust sometimes. And I really don't know Rust, but like I know enough of the patterns and I trust AI selectively enough to do it.

• 37:00Speaker 01

  And then I'll go over one more. So I just, it wrapped up. I'm curious. This is your dream, right?

• 37:05Greg Isenberg?

  My dream.

• 37:05Speaker 01

  It's a high bar, but what do we think? I'm going to, I won't show the review changes now.

• 37:10Greg Isenberg?

  Yeah.

• 37:11Speaker 01

  But we have, you know, you guide me. What do you want to look at?

• 37:15Greg Isenberg?

  I mean, I'm looking from top to bottom. So I love that. I mean, I love how like at the top, there's like the KPIs because like as a founder, I just want to know like, okay, what is happening here? Are we on target? Are we, you know, behind? I probably in the future would want to actually have like a, I guess it's called conditional or conditional standard or whatever. Like, okay, we're behind schedule here. We're behind target. Like, and then eventually if you had an integration, I would be like, you know, if behind target, then post to Slack.

• 37:50Speaker 01

  Right.

• 37:51Greg Isenberg?

  Saying, you know, to our finance, to our sales team that like, we need more leads to come in.

• 37:58Speaker 01

  Right.

• 37:59Greg Isenberg?

  For example.

• 37:59Speaker 01

  Yeah. Yeah, I mean, totally. And we'll flip it through. I go into the analysis. All right. So we have conditional formatting on your on your hypothetical employees here. It looks like it shows, you know, 70s in orange. So it's more it's more brutal of a manager than you are. It basically assumed if you are high, if you're high utilization, that's green. That's the best case. Below 78 painted as red here.

• 38:28Speaker 01

  Did department summaries. And then again, here was the original data.

• 38:33Speaker 01

  And sort of like the initial drivers.

• 38:35Greg Isenberg?

  I mean, dude, this is insane. I know you're probably numb to this, but this is insane.

• 38:39Speaker 01

  Yeah, I'm numb to it. Actually, to be honest, I'm like, a little bothered that the earlier live demo was like weaker than it usually is.

• 38:46Greg Isenberg?

  This is crazy. Like, we took an idea that has been in my head for a while. And we created that in a few minutes and it looks great and it's color coded and it it's simple and it's clean.

• 39:00Speaker 01

  Yeah, I appreciate it. What we can do for you also is like, check it. So you can just like, create a file and I'll call it Greg's dream.

• 39:10Speaker 01

  Create a share link. And now you can actually take this and you can not just see the file, but you can see like the entire history. You can just edit it from there on. And then the other thing you can do is like export it. So, you know, Greg. And now it's in my documents and like you would never know again that it was in shortcut.

• 39:30Greg Isenberg?

  Before we wrap up, I want to ask. You know, why, why should someone try shortcut? Should, you know, should they wait or should they try now? Like, you know, why, why should any founder or, you know, be, be, be using this product right now?

• 39:48Speaker 01

  Yeah, shortcut is for the billions of people who use Excel. Among them, there's like two types, the people who are really good at Excel and use it a lot. They should use it because it takes hours of work and makes it truly like I showed here, you know, 10, 15 minutes. Then there's the people who are more like yourself, who have to use Excel, but you're not Excel experts. It makes you an instant Excel expert. Right? You could now create this and you will speak to it in just plain English. And not only is it faster, but it's now also better than you at Excel.

• 40:19Greg Isenberg?

  And is it is alive? Like?

• 40:22Greg Isenberg?

  I mean, by the time this comes out, is it, will it be live?

• 40:24Speaker 01

  By the time this is out, it will be live.

• 40:26Greg Isenberg?

  Okay. Cool. Okay. And is it, from a pricing perspective, like, what does it cost?

• 40:34Speaker 01

  Yeah, right now, we're charging $40 a month for the Pro Plan and 200 for the Max Plan. Max Plan, actually, which I haven't shared, contains the analyst beta. So the analyst beta, you can actually just directly email it and say, hey, I need 10 different things at once, and it's 10x parallel. So now it's not just 10 times faster than your analyst, but you can have 10 of them at a time, which I think for enterprises has been like the big feedback is they just want to hire this thing already. So that's, so that's, that's where, that's where the pricing is a little different there.

• 41:09Greg Isenberg?

  Cool. Yeah, I think that's, it's probably where, I mean, that's, that's where a lot of AI startups are going, like, they have like kind of a more entry level, and then they have like a few hundred dollars a month analyst type of a product.

• 41:21Speaker 01

  Yeah. It's a fun time to be building an AI, but you have to be, you know, the, the ground truth changes a lot, and that will change the pricing, of course, that like this is very token hungry. You watched how many times it had to correct itself.

• 41:33Greg Isenberg?

  Yeah.

• 41:33Speaker 01

  But as these costs fall, so does our, our pricing strategy.

• 41:36Greg Isenberg?

  And, and just to summarize, like, if people want to get the most out of Shortcut, they want to be an instant Excel, super Excel person, like, what do you recommend to them to, to get the most out of the product?

• 41:51Speaker 01

  Immediately, just go to tryshortcut.ai. There's no other pages. It just loads it up and try a prompt for free. Find out how it would be valuable to you. And again, do the hardest thing and prove to yourself that like it can take just about anything if you're specific enough in your prompt.

• 42:07Greg Isenberg?

  Cool. Niko, thanks for showing it, showing it off. I appreciate you.

• 42:11Speaker 01

  Yeah, I know. My pleasure, Greg.

• 42:13Greg Isenberg?

  Later.

• 42:14Speaker 01

  See ya. Bye.