Why Our 2024 Financials Don't Tell the Full Story | StartupScience.io Founder Q&A

One of the challenges with community funding is that the financials that you publish have a big lag between the current reality. And so I think a lot of the investors looking at investing in Startup Science on WeFunder, they just see our financials from 2024. So I wanted to ask you some questions, Greg, to give people some more context. So talk me through the overall history of the company and What money was raised in the past and what that achieved, and then where are we at now? Because if someone looks, they're like, hey, they're burning a lot of money. They've got some revenue, some traction, but I think people are missing some context just because they're looking at 2024 financials and we're already in Q2 of 2026.

I think to add some flavor to this, so we've been at this for 5 years. And we've raised $5.8 million over that period of 5 years. The majority of that 5 years was before AI coding came out and it was sufficient to build something like what we've built. So to build a multi-tenant, meaning multiple different, each individual instance of the software is its own platform connected through a core, is a massive project when you're talking about 5 different, it's like building 5 platforms, you know, one for each. So it is a huge lift. Recently, I should say, as soon as you started seeing AI come out, we were testing AI to speed up this process. And we, it finally, when Claude Code came out and Anti-Gravity from Google and a lot of other platforms came out, we were able to really speed things up. So that's why it took so long because we're building a monstrous, a huge platform that has multi-tenant capabilities that's built on top of an octagon-shaped infrastructure with APIs for scalability. This also protects you from AI, right? It's a moat. So when you're looking at, oh, somebody can build AI, well, Building something like what we built with AI is not currently possible. So we invested a lot of money building infrastructure in the beginning so that we could hold a massive company, a billion-dollar, millions of founders, hundreds or tens of thousands of different advisors and accelerators, incubators, et cetera. So for the timing question, This is a very, very large multi-tenant octagon-shaped infrastructure built off of APIs. So it's unlimitedly scalable. You can do pretty much anything with it. That takes a long time. Now, you can't start selling software until you have the software. All you can do is give a minimal viable product to customers. Which is what I teach in the Startup Lifecycle book. So we followed the book exactly. We did 5 years of research, then we did 5 years with a customer base that has never experienced anything like this. There's nothing like startup science out there, so there is no blueprint. There is no copying anything. You're forging the way that's never been done before. So while we're sitting there plowing the way, We put the product out there in front of accelerators and let the accelerators and the incubators and the hackathons and the chambers of commerce and all the different forms of entrepreneur support organizations use the software and then give us feedback. That process takes a really long time. We could have just built and done it a lot faster without doing that, but then we wouldn't end up with the product that they actually want. So the latency in income is actually not— we finished the product about 90% and we started selling the product about right about when we started doing the community funding. So 45 days, 60 days, and we went to what, about $150,000 in ARR and 88,000, 89,000 startups and I mean, you know, in like 2 months. So judge us based on that 2 months, not based on the 5 years, because the 2 months is the result, the impact of the 5 years of work.

Well, and I think something that'll help people, first of all, I want to clarify before Startup Science began, you did 5 years of research, right? So this is almost 10 years into this big project of addressing these problems you found in the startup ecosystem.

Yeah, it's a huge problem, right? I mean, this is not a— we're not tackling, you know, a better mousetrap here. We're tackling the entire startup economy. It's a— this is a giant project. It takes time.

Yeah, it's— yeah, so that's a big point is like we're almost 10 years in with lessons learned. And so even from when Startup Science began to now, I'm sure there were some assumptions that you made from day one that some were true and some you had to adapt. So tell me about that. Like during those— I feel like in the last year, the software has really become what customers are loving, and that's why the traction just spiked. What assumptions were right and wrong that you made from day one?

It's interesting because the vision has always been the same. The vision hasn't changed, which means that we did a good job on the research. What has changed is the experiences that we're getting with customers. Like when we started, we built a platform thinking we were gonna fill it up with all the courses, all the education and everything that the founders needed. And then that would, would be something that the accelerators would want to use out of the box. But it turned out that they wanted to put their own courses and classes in the software, not have something that's prebuilt. So that required us to go back and make some changes to the software to allow them to customize it. When we did that though, we learned that really what they wanted is to have the entire platform customizable. So we did that. We went back and customized the entire platform so that they can put their own data or use our data or a combination anywhere. So these are things that any company learns as they're going. The difference between us and them is that we have 100, 200-something accelerators out there using our software, giving us input, and we're trying to keep up with that input with a small group of engineers to keep everything tight. I mean, you can look at the numbers, but we're already in 60 days over 20%, maybe even 25% to profitability. So a quarter of the way to profitability in 60 days with a burn rate as high as we have. is actually really not bad.

And that's what I think stands out is you see a pattern over time of learning and adapting and getting closer to that, you know, what that final product will become. And that's what really stood out to me when I joined the Startup Science team is that there's been a lot of change, a lot of adaptation, but that's enabled because you have a strong group of users that have been in the platform for a while, for years, giving feedback.

Yeah, like UCSD, University of California San Diego, has been using it for 5 years. It started out in one of their schools and now it's all over the campus. And UCSD is right under MIT in pushing out startups. They have a huge startup program. So the fact that they use it and they have been for 5 years, And helping us along the way is a pretty big sign right there, you know, just right in that pocket. Startup Grind, which is the largest community of founders in the world, 6 million startups, they use one of our platforms. So it's really, you know, you can look at it and you can look at another company and say, oh, this company's ramping, but Is that a big company or is that going to be a small company? Companies that grow really fast usually are smaller companies, and if they're growing really fast off of something built from AI or something like that, somebody else can do the same thing. The fact that this has taken 5 years means that there's a moat around us. It means we have protection from competitors because anybody else that tries to do what we do, and nobody has ever tried, we're the first, is going to run into the same things we ran into. So it'll take them years to do as well. So if you're an investor and you're investing into Startup Science, you're getting a really stable, big deal investment. This is not a small type of investment. My whole objective here was to make it so that Startup Science, which is the infrastructure layer for the startup economy, is owned by the same economy that it supports. That's the whole reason why I did this.

Yeah, it's a lot of work, right? Like, it's a huge effort to raise from the public to— and to me, it seems like a reflection of our values and what that mission is rather than necessarily optimizing for the lowest effort, highest impact. You know what I mean? Like, it— Yeah.

I mean, it's easier to go raise money from investors, honestly, But it's really important to me that we share the concept of democratizing the startup economy with other people and not just on the side of what we're doing for those founders out there and those accelerators and those mentors and those providers and investors, but also for the broad range of what we call retail investors, which are those investors that invest in Stocks or community funding. So it, the idea is to really be true to our word and show it instead of doing what everybody else does. I'm democratizing, blah, blah, blah. You're not doing anything. We are actually democratizing because we put our company on the line.

Yeah, it's incredible. And I think one of the best things an investor can ask is like, What if I'm right? Like when you make an investment, if this turns into what they're saying it will be, what is the actual outcome? And I think startup science has a very strong answer when this becomes what we're aiming for. Yeah.

And remember this, I've been doing this my whole life for like 35 years and I haven't failed. So I mean, the reason why I haven't failed is because I do the research and because I move slow. into things so that I don't fail. I'm very cautious because I've been doing this my whole life and I've seen it and studied it, right? So I'm going through the process, de-risking the company as we go.

Yeah, it's fantastic. Any, is there anything else that you'd want to share, Greg, to, I guess, fence-sitters, maybe people who Have looked at investing, they're considering it, but haven't made the final decision to put money in?

My family, my friends, pretty much everybody I know, including the Navy SEALs that I interviewed, the fighter pilots that I interviewed during the process of understanding founders and what to do when founders are in difficult situations, they're all invested. You know, I'm invested almost $2 million. So if you're sitting on the fence, you're sitting there alone and maybe it's time to jump off. You know, we are closing this thing out at the end of the month and now's the time.

Thank you, Greg. Thank you for the discussion today.