Dr. Jeremy Weisz: 08:08
Sasha, early on, what was the original idea? Because like you started in 2019 and maybe talk about the landscape of AI then. I mean, it’s way different now, right? I mean, talking about AI then is, I don’t know, going to the moon or something. I mean, I don’t know what you compare it to, but what was the original idea? And then what was AI like at that point?
Sasha Orloff: 08:34
So we had no idea what the Commercializable version of AI was going to be, the concept of a large language model and commercial applications and an API just to access machine learning. I mean, AI wasn’t available at the time. But if you’re sitting here in Silicon Valley and you were part of Y Combinator, which in the startup world is kind of the, the premier incubator accelerator for, for early stage startups. And Sam Altman was my like group partner. And he was an angel investor in my last company. And when he left from CEO of Y Combinator to a small research lab that nobody had heard of at the time, except a couple sort of little local insiders. OpenAI. You knew something was going to be there because you wouldn’t give up the job of being CEO of Y Combinator to join a research lab if you didn’t think it was going to be your life’s most important work.
And he would say that. And so you had a sense that something big was going to happen. Now the foundations of AI and ML have some similarities. You need to have the data platform. You need to have clean, structured data. You need to have something where judgment and intelligence is going to play a big role. And accounting, I think, was such a natural fit. You have something that is really hard to do. You need a general ledger in order to do accounting. It’s the requirements, the backbone of accounting. You need the data, which was different from how QuickBooks had structured their business 70 years ago. You just get a repository of journal entries, not the data that turns into journal entries. And then you have the output, the general ledger that creates the financial statements. Effectively, what happened in the industry today is accounting is a set of rules to generate these 2 or 3 very specific reports, your profit and loss statement, your balance sheet and your cash flow statement.
And they’re very structured and governed in how they can be created and the rules in which they need to be created. But effectively, if you think about what was happening in almost every accounting instance in the entire world, you take all the data, you generate your journal entries, you put them into your general ledger to get your financial statements, and then you throw all the data away. Well, like that’s crazy. Like all that data is so valuable. And so we just said, well, what if we do all of that work? But we also do the hard work of building the data platform with the general ledger and the workflow agents. And we focus on accuracy. And this is very different from how the industry does, which is QuickBooks. Why do you care about QuickBooks? You care about QuickBooks because future compliance for accounting with the IRS is not that valuable. And so you just want to get it done as quickly as possible.
Except anybody that’s ever built anything meaningful knows you actually want accurate books, not QuickBooks. You want accurate books as fast as possible. And so this was our focus. Always high quality integrity, accurate accounting as fast as possible. And AI filled that missing gap of the long tail, meaning about 50% of accounting can be automated just with software and APIs. About 25% is nuanced to that company, but then can be automated once it’s set up. And then maybe that last 25% requires the judgment of an accountant. And so when we do all of this, you could put it all together in a single platform. You can actually get provably accurate books faster than anything that’s ever been before. So you actually get accuracy and quick where in the alternative you get quick with accuracy. That depends upon you getting a hold and trusting that person. Meaning now trust can be in the system. It doesn’t have to be entirely human judgment.
Dr. Jeremy Weisz: 12:03
I just looked it up real quick. Sasha Accurate Books. It says it is available on GoDaddy. I love that name actually. Like I never thought of it until you just said that. Like, yeah, it should be QuickBooks should be Accurate Books.
Sasha Orloff: 12:16
So let’s not air this. So I can get off of this and quickly go buy this.
Dr. Jeremy Weisz: 12:19
I’m sure it’s a, it says it’s a premium, you know, domain. So I’m sure like it’s, there’s, it’s not like $10.99, I’m sure. But, it does say accurate books right here, right at the top of, of Puzzle. So, you know, it’s crazy.
Sasha Orloff: 12:33
Also, you have to imagine it’s crazy to think in 2026 that our differentiation in one of the biggest markets that exist, that’s required by law. It’s also one of the few things where a business owner or a founder has personal liability for the accuracy that our differentiation is, we focus on accuracy, and that is our competitive advantage. That is our differentiation. It’s still mind blowing to me that the rest of the industry is skewing towards kind of close enough books. And so I think that when we think about people who are putting their hearts and souls into building a company and really want to build something that lasts, that you get bad insights when you have bad data and you have good data, you get good insights. And so our advantage in the AI world and should be in any world, though, is we focus a lot on data integrity to give you those insights faster and more confidently than anyone else.
Dr. Jeremy Weisz: 13:32
Talk about the evolution of the product a little bit because you started early on and how AI, you know, basically goes along with the evolution of Puzzle.
Sasha Orloff: 13:45
Yeah. Then, we really had to make a really hard decision twice. So we built the data platform. And the general ledger is always kind of the same. Like a general ledger you have a very set of structured rules in which assets, equal liabilities, shareholder equity and debits have to equal credits and checks and balances that are nuanced to accounting. Then you have the data platform, you sort of create a data ingestion and data model. Those things are the same regardless of whether you’re building it in a pre AI world or a post AI world. The two main differences are that we had to make two big decisions: we had to rebuild our core infrastructure twice the kind of workflow system.
The first was for LLMs. And what I mean by that is it’s not just having your data structured in a clean way, it has to be also labeled in a really clean way. And that required quite a bit of re-architecture. And these are anything from simple things like a bunch of meta labels on your chart of accounts. Your chart of accounts is like the list of categories that you use, and you have to just boost that with a bunch of extra metadata so that LLMs can read it the right way. That was a pretty big effort across thousands and thousands of different permutations in the accounting world. And then agents. And so we’re just at almost completion of rebuilding for an agent world. And what I mean by that is agents become a probabilistic sort of output.
And accounting has to be deterministic. So think about it as like the difference between a slot machine and a vending machine. A slot machine, same inputs, different outputs, a vending machine or an MRI machine, same inputs. You want to get the same output each time. And in order to do that, you really have to rebuild a lot of the core infrastructure to make it so that agents are doing accurate work consistently and repeatedly. And that were big board level decisions where we said, man, we really, if we really want to lean into the future, one of our advantages is we can be nimble and fast compared to maybe a 40 year old incumbent like QuickBooks or zero. But at the same time, it still requires a rebuild.
And somebody that started from scratch could rebuild it this way and somebody will. And so if we didn’t do it, then somebody else was going to do it. Now, it still would take them a long time to build the data platform and the general ledger. But if you could build the workflow and the tools from scratch, you would build it this way. And we couldn’t just say we’re like, we’re already one generation of technology ahead. We have to lean into constant building and rebuilding of tools. That is hard in an AI world, but I think agents are the thing that will stick in the business world for quite a long time. And so we said, this is just something we have to do.
Dr. Jeremy Weisz: 16:32
It’s a hard decision because it’s a lot of time and money and energy to do that. When I was looking at your site, it said AI close agents are new for accounting firms. What’s that?
Sasha Orloff: 16:44
So this is the realization of that value for accountants. And so we launched it first to accounting firms because they’re the ones who feel this pain the most. And what I mean by this is the simplest form is the best automation we have in accounting today is an if then statement. This is what we’re most familiar with. If Slack, then technology and software.
If Uber, then transportation. It’s like a very simple if statement. Well, a lot of accounting can be solved by that in basic categorization. But there are tens of thousands of other cases that need more complexity than an if then statement. And this is where agents come into play.
And so we have to get in the mindset of who knows how to run accounting. Accountants do. They’re not like trying to figure stuff out. They’re just trying to go do the work in a more efficient, more effective, more deterministic way. And this is where our agents really help them be able to do more than just an if then statement. They can do complex accruals and balance sheet work that typically is done in spreadsheets or done manually, because it’s more complex than an if then statement. So an example just to bring this to life is if you buy a computer, I’m guessing a lot of your listeners buy computers. Every time you buy a computer, you’re supposed to depreciate it. That means you have to categorize it as a fixed asset. You have to then create a schedule. Of course, there’s different types of formulas over different time periods.
You have to pick something: a start date or an in-service date, a salvage value. Do you expect to sell it at one point? At what time? And then you’re maintaining a spreadsheet every single month across all your computers. Well, that’s more than an if then statement. It requires a bunch of if statements and a bunch of complicated rules. This is something that every accountant knows how to describe, but it just is manual. It’s been manual for decades. It’s still manual today. Unless you use Puzzle, then you just say, here’s how I handle computers. Go look for computers and handle it this way. And that’s it. You’re done.
Dr. Jeremy Weisz: 18:50
Looking for markets, right? Who are the original customers? And then how did that change over time? Or maybe it didn’t.
Sasha Orloff: 19:01
No, it’s changed dramatically. So we always knew that what we wanted to do was enable the best accountants to become world class finance leaders. And so that was the path to get there. The problem is accountants know all of the edge cases and permutations, so it takes a while to win them over, to build the trust that they can actually do their job. Because guess what happens? Accounting is binary. If you don’t deliver 100% of the books to somebody, you get fired as an accountant. It can’t be like, it’s like a trip to the moon, right? You can’t like, I can’t sell you 85% of a trip to the moon and call it a trip to the moon and call it a trip to outer space. But like, I’m not on the moon if I’m not on the moon. And so accountants are like that.
Accounting is binary. So you have people to solve all the edge cases. So where do you start? Because you can’t just say, all right, I’m going to, you know, give me money. I’ll start having customers in 3 to 5 years. We need the data to train the system. So we started with a bunch of my friends. I’ve been doing this for 15 years, and I asked them just to give me the data and let us run the shadow books. And then we trained them, and then we started working with more startups, and then we started working with accounting firms. And now about 30% of our customer base are startups, and about 70% are small businesses from across the country, associations, services businesses, retail businesses, etc..
Dr. Jeremy Weisz: 20:18
You know, you said something interesting before we hit record, which is that the best product does not always win.
Sasha Orloff: 20:26
Yep.
Dr. Jeremy Weisz: 20:27
Why?
Sasha Orloff: 20:32
Well, I mean, I think we all know if the best product, sometimes the best product wins. But most of the time, like look at most of the software we use is Google Docs. The best software is Google Sheets. The best software is QuickBooks. The best software is Salesforce.
The best software is all of these softwares that we’re using the best that possibly could be? No. So I would say there are some times where the best software does win. At the time when I started my first company, Hipchat was the most successful communications like product. And then Slack came in and Slack’s product just worked. It didn’t have any more functionality than Hipchat, and they kept talking about the design. But the thing that worked is Hipchat would just go down once a week. It would just stop working. Well, it’s hard to rely on a communications protocol that goes down and Slack just worked. It didn’t really matter any other features, it was just you. Every time you typed in something, your colleague got it.
That’s really important. Zoom just worked like you would turn it on and it wouldn’t freeze and glitch and shut down. It just worked. And it had the low 60s. Sometimes the best product does work. I found most non consumer grade software. The best way to market works. And so why is that? Because you’re dependent upon capital to market and get your product into your hands. The best product still needs to get to the best customers. And those best customers need to sell themselves. So you know, you can have a referral motion. I’ve yet to see too many businesses completely take off just on a pure referral motion because they have the best product. And so you have the best sales motion and you have the best distribution channel and that becomes dominant. So the easy example, in the early days, we were one of Stripe’s biggest customers in the early days.
But then you land something like a Shopify and you just become a dominant player. You are Twilio and you land somebody like a Uber and you just become a dominant player. These partnerships in this go to market motion can create companies. The companies were good anyway, but it takes them from good to great. So sometimes it happens in a partnership model. Sometimes you hear about just the best sales motion wins. And so we hear, you know, recently about. Frank Slootman and the snowflake, right. Just came in with like a good product, a good product, but then they bring in a new sales leader that takes it to the next level. We’re seeing that right now with the new CRO. Taking that to the next level. Ramp and Rippling are notorious for like an incredible go to market and sales motion. And so you can take this product, combine it with a great product, and you combine it with a great go to market motion and you become nearly unstoppable.
Dr. Jeremy Weisz: 23:20
Talk about that for people who are good partners or should be partners.
Sasha Orloff: 23:26
I’m incredibly grateful that almost all of the modern tools that we use as startups on the finance tools all took a bet and invested in an accounting API pretty early on. Again, Mercury and Brex and Ramp and Gusto and Rippling. And we worked with them, even Stripe, to help design some more endpoints to make the concept of real time accounting even possible. It wasn’t possible without them to exist. And you can’t, I think you can’t overstate the plaid team from really sort of creating this long tail demand of banks and card providers. And so it’s we were riding on the tails of, of plaid and the momentum, we helped accelerate that with a lot of the more modern tools that enabled a lot of the stuff to happen, so it’s hard not to be incredibly appreciative of the dynamic founders building these legacy companies of the next generation, best in class tools and those those are great. But today, our main customer is the accounting firm because the startup or the small business and the accounting firm have the same goals.
They want to build enduring companies, right? Your accountant wants you to build an enduring company because they want a customer forever. You want them to continue to help. You have advice about where you’re doing well, where you’re not doing well, how you can make better decisions. You care about their success. And the thing that has held everybody back is bad software. And so when we can showcase to an accounting firm what the future of their business could look like, how they can increase their margins by making their customers happier, it is a clear win. And it took a couple years to get those first firms to kind of take a bet and see the vision. And now they’re leading the industry in terms of margin. They’re leading in terms of customer happiness. It’s a win for everybody because you’re already paying as a startup or a small business. You’re already paying an accountant. You just get a much better product now than ever before.
Dr. Jeremy Weisz: 25:30
Talk about that because sometimes, again, people get set in their ways. I know, you know, maybe early on there was this 40 year old firm. Do you want to talk about that?
Sasha Orloff: 25:41
Yeah. So I would say that the two places where we see the winds happen the most are one of our biggest and earliest bets is an incredible one of the best accounting firms in the country called Accountalent. They just had their 40th anniversary this week I believe of the founding. They’re based in Boston. They started as a tax firm and added a bookkeeping practice.
And they’re just incredible, like really high customer success. High NPS scores from their customers because they just get it. They care about the customer and they’ve been around and they’re so profitable that they’re not like trying to squeeze every dollar. They really care about their customers. And that’s what’s built up into one of the leading tax practices and bookkeeping firms for C Corp’s for like, more like startup type cultures. They’re 40 years old and they were like slowly leaning in, slowly leaning in. And, now they actually charge a premium if you use somebody else, because it’s just so much slower for them that it actually makes their product to their customer worse, less insights, less speed, less accuracy, more manual review.
So I think that is like one of the things that I’m most proud of for them for being really technology forward, even though they’re a 40 year old firm and this digital transformation of the age has just been incredible for them. Now, on the flip side of that, I see there’s also this kind of really growing momentum. In fact, Y Combinator published something saying the next generation of companies we want are services companies that start off AI native, meaning we don’t now believe AI is going to take care of everything without humans in the loop, but it is going to transform work. And if you can start a brand new firm in the accounting world, for example, which is one of the ones they called out, and you just think from an AI first principles, what do our customers want? How do they want to interact with us? How do we deliver a value prop to them?
That is a 24 over seven real time type of solution. The way an LLM and chatbots and modern tools like Puzzle can enable you to be free from needing to think about how do I create the best experience given the constraints of legacy software like QuickBooks? And how do I build around this to accommodate. And you just say, let’s build in an AI native world, what the future is going to list. And so we have a couple firms that are just starting off that we’re going to announce soon that are really starting. So one is starting from a chat first experience, one starting from a slack first experience. One is starting with a different type of delivery model and expectation of the user. And I think that these are going to be the leading players in the market. And, and by having these two bookends, we’ve proven that everybody can be inclusive. We’re not just enabling this new future.
We’re actually doing digital transformation for these decades old firms as well as these new firms. And who wins at the end? We do, we do as startups, we do as small businesses. We get a better product. We get a real time, insightful product the way that a CFO should deliver a month end product. So it’s, it’s actually a different type of experience than a summary of your financials from two months ago. Like that doesn’t help me make decisions today. I can’t compare right now. We’re on May 13th. I’m still weeks away from getting my financial statements. If I’m a QuickBooks customer or if I want to hire like a big team. So in like two more weeks, I’m going to see what April looks like compared to February as I’m heading into June. Like what does my cash flow look like today? Is everything working today? What should I be doing differently? Is my margin okay? I couldn’t do that when I’m looking months ago. That’s crazy.
Dr. Jeremy Weisz: 29:22
How do you convince someone you are skeptical about Notion? You had tools you’re used to and you used. How did you convince a 40 year old firm to try it out? Right. Because they’re just set in their ways.
Sasha Orloff: 29:37
We don’t win over everybody. There’s definitely firms who, you know, are like, we’ve been using QuickBooks for 30 years. We’re going to retire. Why would I learn a new technology? Like I’m just going to sell the business and I’ll let private equity kind of manage that in the future. Like I’m just going to run down the clock and go and, you know, I totally understand that point of view. I think two things are going to happen. And I think 2026 you’re starting to see customers starting to demand that people use AI. And you see this through searches. You see this through attendances at conferences, which are really on the rise in the fun world of accounting and AI. And so you’re starting to see some customers demand it and say, I can’t, I can’t just pay you to do the same thing every single month, like categorizing the same thing every single month. That’s crazy.
That’s not a good use of my dollars. It’s not a good use of your time. And we start to see firms hire the next generation and they’re like, why can’t we use modern tools? We can’t even test them and learn. And then when they test them, like it’s just such a better experience. Like you imagine an accountant, you didn’t study to become an accountant for six years in these tests to do the same thing again and again and again. You’re like one of the experts in financial health and you’re spending your whole time doing repetitive work. You want to use your brain more, you want to help the customers. That’s why you got into this business was to help your clients build better companies. And then sometimes it comes from the top. And we just see business owners saying, hey, I want to build an enduring company, just like I want my clients to build enduring companies.
And I see this wave happening and the implementation of AI from some of the legacy players, I believe is just taking the wrong approach. They’re moving towards a we believe we’re going to replace accountants with AI. And I think that gets into a dangerous game where they just let the system autonomously make changes without anybody’s knowledge. And in a probabilistic world, they’re always going to get stuff wrong. And so if that is the default, you, you know, it’s, you’re just, you’re going to piss everybody off. But there hasn’t been a choice before. Like the choice was QuickBooks or nothing. Now, for the first time ever as an accounting firm, you have a choice. You can choose QuickBooks or Puzzle, and eventually the market pressure is going to happen. Like you’re just going to start losing margin. You start losing customers because your competitors are giving a better value product at a better price.
Dr. Jeremy Weisz: 32:09
So basically, Sasha will give you an example of that 40 year old firm. And let’s say they have a lot of clients, but a client is using QuickBooks online. And, you know, this firm’s like, hey, it’s going to take us longer. It’s not going to be as good a product if you use QuickBooks online versus Puzzle, is that the conversation they’re having with some of the clients? Yeah, yeah.
Okay. That makes sense. I would love to talk. And we talked at length when I had the founder of Jotform about pricing and freemium, because it costs you real dollars to have a somewhat of a free account. Right. So I’d love to talk about how you, you know, think about pricing for Puzzle.
Sasha Orloff: 32:53
We’ve had quite a few iterations over time. We started off with a freemium model. And then we sort of came to the conclusion that it’s a new system of record. And so as a result, you do need to experience it because if you haven’t ever seen what good accounting software feels like, you like you just up to your imagination. And so what we did was we give people, we’re now in a reverse trial. So you have 14 days, you can try to play around with the product. We give you access to the full solution. And then you can choose which product is right for you.
Do you want just the basics? That’s kind of your starter plan. You’ve never done accounting before. You don’t have any accounting system. Or do you want core, which is just basically a clickable QuickBooks replacement? It looks like QuickBooks. You click around, you do a bunch of manual stuff, or do you want this complete solution where you get the best of automation and accuracy and checks to just keep you in the best possible place? And that’s our complete plan. And then there’s a scale plan for companies with tens of thousands or hundreds of thousands or millions of transactions. And so we let you kind of choose and pick your own plan.
I think as typical as with pricing strategies, the reverse trial helps with psychological approaches like reverse, like loss aversion, right? You get this like really incredibly automated, accurate books. That’s, and then we take that away and make you start clicking. You suddenly realize, well, maybe that, you know, incremental cost from $30 to $50 isn’t that much of a difference because I value my time more than that. Or you’re now doing hours of work in spreadsheets, or you pay an extra $100 from the 50 to the 150 teaser plan, and all of a sudden we can handle large volumes for you. And so I think about giving people the best possible experience of differentiation of what great looks like and then letting them choose. But by doing that, we’re sort of effectively showing them why we’re so much better off.
Dr. Jeremy Weisz: 34:59
And just I’m curious, what are the most popular features? I’m sure a lot of thought went into, okay, here’s the features on core. Here’s the features on complete. What are the most popular features on complete that you’re like, listen, this is, this is worth an extra amount of dollars to get the complete version.
Sasha Orloff: 35:20
Well, in accounting, it’s very easy to make a little mistake and that can throw off your books entirely. And so what AI is really good at is complicated automation and anomaly detection. And so I think that if you’re brand new, you’ve never done accounting. You don’t realize how complicated it is because it doesn’t sound complicated when you’re, when you’re, when you’re thinking about it, it’s like, oh, it’s just categorization. Actually, it’s much more than categorization, but that’s how you think about it because it’s something that feels very close to personal financial management tools like the mint dot coms of the world or, you know, some of the modern tools that help you think about your personal finances. Business accounting is much different. There’s different tax strategies. It’s more complicated. You need a balance sheet and a PNL and a cash flow statement.
And so it’s easy to make little simple mistakes that can throw off your books. And so the tools that are there are like just even the little ones, the wow moment is you press a button and it reviews your books to see if you’ve made any mistakes in accounting at the end. And it almost certainly finds between 5 and 20 things that you’re like, oh man, I forgot about that. Oh man, I missed that. Oh, I got that. The other part is reconciliations. And reconciliations is just a complicated accounting word that effectively means, do I have all the pieces of my puzzle? So imagine the world in which you’re sitting down with your grandma and you’re going to do a thousand piece puzzle. But you only have 998 pieces. And you realize that when you get to the end and you’re missing two pieces. Well, that sucks for everybody. You’ve spent weeks or months doing this stuff, so we have our own kind of AI counter. We count all the puzzle pieces for you, and we let you know before you get started, you have all your puzzle pieces before you start the Puzzle.
These are the little things that if you’re an accounting firm, you see this and know this value right away. If you’re a second time founder or business owner, you know and understand this. But sometimes your first time you don’t get it. That’s okay. That’s why we create these simple plans where you can do all this stuff manually. And then the first time you make a mistake or the first time you forget to do something and you click that review my books because we give you like a one time to try it. You go, oh my God, now maybe this extra $20 or $50 isn’t that big of a deal anymore because I just spent hours trying to debug or find this receipt or find this like a bank statement or find this reconciliation. And that’s okay. We don’t need you to understand that right away. We build in these easy plans. But, as soon as you realize how complicated it is, you think a complete plan is the best software deal on the market.
Dr. Jeremy Weisz: 38:10
Yeah. I mean, just from a value proposition, even if it saves a team member like 1 or 2 hours, I think it just pays for itself right there, right? Just like categorizing things that other people, you know, typically you have to go in and manually do these things. So there’s like a big time savings, which equates to real dollars for people in addition to the accuracy too, of course, I want to talk about the decision to raise money versus bootstrap.
Sasha Orloff: 38:40
There’s certainly different ways to grow a business. And, one of the things that was a reason why we’ve been stuck with this very old accounting software for generations isn’t because it’s impossible to build something better, but because it doesn’t fit within a general business model. We have not seen anybody ever build accounting software where they bootstrapped it. It just takes years to build. And so if it takes three years to build an MVP with venture funding, you could imagine it would take 5 to 10 years without. And the world changes pretty quickly. And so this was one of those industries that none of the incumbents have created, and none of the challengers have ever been successful without accelerated funding.
Now, sometimes it’s private equity and sometimes it’s venture. So. But this was when I decided that this was one of the three ideas I wanted to do. I needed to see if somebody would fund this. And. And it turns out that General Catalyst was creating a fund specific for repeat entrepreneurs who are taking on big, big industries in which capital was a precursor to why there weren’t challengers in the space. And so I think this is one of those opportunities where it’s kind of the right place, right time, right timing in the market to be able to fund this. I had a couple ideas that I were going to start without venture funding, but this was one that I felt would be a good fit.
Dr. Jeremy Weisz: 40:14
And it seems like some of the investors are great from a strategic standpoint. Also, like if you take a look at Fog Ventures, it seems like they would have a lot of connections. Can you talk about that for a second?
Sasha Orloff: 40:31
Yeah. So the world of venture is quite diverse. There’s sort of the, there’s the sort of the traditional big players like General Catalyst, one of the biggest and best venture funds of all time. There’s boutique firms who help support different industries or verticals, and they tend to specialize. So Fog Ventures is one of those. It is a syndicate of a couple thousand actual operators, people that are CFOs, CEOs, VP of finance, accountants in real companies. And they formed a network, just a peer network. And then they decided to start a venture fund. And so you go in and you pitch. It’s like a big master pitch. And then people decide they can write a 5000, 10,000, 20,000, $100,000 check into spaces. And, and it doesn’t guarantee funding, but it’s a slightly different model, but very similar to a venture model. They write a check, it goes on the cap table. You give them equity just like you would a venture fund.
Dr. Jeremy Weisz: 41:28
I’d love to hear some of the advice you’ve gotten right when money comes. Also with good advice and mentors, maybe a lesson advice from Sam Altman and any others that you think of as mentors for you along the journey to.
Sasha Orloff: 41:46
One of the things that I love about Silicon Valley is it gives entrepreneurs a chance to tackle a market where they don’t have any experience. I spent 20 years in payments and fraud and lending and risk, almost always in consumer and moving over to the B2B world where I’m selling AI software in a system of record to companies all over and to firms, the the go to market motions for a consumer business and a business B2B business are very different. I mean, just the most obvious example is you put an ad out, somebody buys or doesn’t buy in a B2B motion. Not everybody’s just like ready to switch over. System of record on the spot.
Like. And so we need to find them and convince them and product marketing and SDRs and sales and account management and success. It’s a very different motion.
So I feel very fortunate to have a whole bunch of investors who have spent time helping me be on my podcast and book reading podcasts and book reading, and like listening to people. What are some of the tactical approaches and problems and sequencing that we need to do? And I think people have been very generous. One of the other parts about Silicon Valley and being in San Francisco in this community is people are just very helpful in wanting people to be successful, and they’re very generous with their time. And so that has been pretty helpful. I was a big fan in the early days of product advisory councils. And we had one for founders in the early days, just to give them an incentive to give us feedback. And then the next iteration, as the product matured, was with accountants and accounting firms and then CFOs. And each of these give you a different perspective as you continue to grow and expand your market.
Dr. Jeremy Weisz: 43:35
Yeah. We were talking about customers and employees. We started off in the very beginning, just building trust and authenticity and maybe talking a little about your views on that. What do you do to do this again, especially with more AI and people aren’t sure what to trust, what’s fake, what’s not fake, what was created? What do you think about trust and authenticity?
Sasha Orloff: 44:04
I am a big fan of two things in this world. One is that the demos should go off script. If you’re just doing a demo in your own polished way, it’s hard to lose trust because we can all create fake demos that look really, really great. And the second one is letting people just play with the product and ask what their problems are and show them how they solve it. The other is helping enable customers to tell stories about how they use the product. And so a lot of, you know, there’s websites and there’s case studies that we create. And I think the world is moving more towards these micro-influencers, not people who do influencing as a core business, but are actual users of a product.
And you just help them tell the story of the good and the bad. If everything is amazing, you just know that’s not true. And if everything sucks, well, that isn’t something you want to share the news with with the rest of the world. But I think it’s okay to say, here’s what works. Well, here’s what we’re working on, here’s what we’re fixing. But letting people tell it in their own words, unscripted, but, you know, help promote. And so that’s two places where we see, we think the world is going if you can’t show somebody on a demo call how the product works for them, not how the product works for you in the best world, but what are the things that they want to see? And then show them how it works and let them play with it. And then two is less corporate speak and more letting customers talk about you in their words.
Dr. Jeremy Weisz: 45:38
Sasha, I know we have one more minute. I do want to just point people to Puzzle.io to learn more. And my last question is maybe one of your fan favorite episodes on your podcast. So we’ll pull it up real quick. People can check it out. Tech finance. Who are some of the fan favorite episodes here?
Sasha Orloff: 46:02
Well, I mean, if you’re an accountant, I have just about every founder that’s built a modern tool for accounting firms on there. But for your audience, I think the most recent one we did, if you scroll up to the top, Jeff Grimes from Perplexity. It’s hard not to think how cool what they’re doing is and how they’ve taken on Google and Bloomberg, like two of the biggest giants of all time. But I think that two of the most interesting and most listened to episodes was Casey Woo, who was running finance internally at WeWork during the magical heydays, and some of the. The stories there, even though we were a while ago, were fun. And Steve McLaughlin, who is a professional banker for. He helps people raise capital and some of his inside stories of how he’s raised hundreds of millions and billions of dollars for some of the best companies in the world is just a peek behind the scenes of the banking world that you don’t get to see or hear about too often. And those are pretty fun.
Dr. Jeremy Weisz: 47:02
Amazing. Everyone check it out Puzzle.io and more and we will see everyone next time. Sasha, thanks so much.
Sasha Orloff: 47:12
Thanks for having me.
