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An 8-Year Old Can Build An App. What Does That Mean For The Future of Building?

Colorado Tech People · 2026-05-12 · 29 min

0:00--:--

Key moments - from our scoring

Substance score

56 / 100

Five dimensions, 20 points each

Insight Density12 / 20
Originality11 / 20
Guest Caliber13 / 20
Specificity & Evidence10 / 20
Conversational Craft10 / 20

Wisary is a tool designed to help builders - from eight-year-olds to non-technical founders - clarify what they actually want to build before coding begins. Rather than jumping directly into AI code generation tools like Claude Code, Lovable, or Gemini, Wisary uses AI to ask probing questions that force users to think through requirements, user flows, edge cases, and feature scope. This approach reduces AI hallucinations by ensuring comprehensive context upfront. Ala Stolpnik explains how his own transition to an AI-first development model - replacing his human engineering team with Claude Code while maintaining strategic direction and code review - has accelerated work 20x for his small startup. The episode explores the paradox of AI democratizing product building: technically, an eight-year-old can now define and launch an MVP, but success still requires disciplined thinking about problems and customers. Wisary has pivoted from a Confluence plugin for professional product managers to a standalone platform serving any builder. Stolpnik also contrasts the startup environment in Colorado - with its focus on community, mentoring through programs like Exponential Impact, and lifestyle balance - against Silicon Valley's intensity, and warns that large enterprises will struggle to adopt AI efficiently because execution speed isn't their constraint; meetings and organizational friction are.

Key takeaways

  • →AI has commoditized the implementation layer of product building, but defining what to build and finding customers remain the core bottlenecks for startups.
  • →Wisary's value is forcing users to articulate complete product requirements before passing specs to AI code generators, eliminating the context gaps that cause AI hallucinations.
  • →A single person using AI tools can now do the work of a 20-person team, meaning startups no longer need massive headcount to scale, only two complementary people per critical role plus AI.
  • →Large enterprises will remain slow at AI adoption because their bottleneck is meetings and organizational coordination, not coding speed - even a 50x boost in code generation doesn't help if engineers spend 90% of their time in syncs.
  • →The role of expert team members is shifting from hands-on execution to coaching and training AI models, ensuring feedback is fed back into AI capabilities so the expert isn't needed for repetitive work.

Guests

Ala Stolpnik

Topics in this episode

GeminiChatGPTClaude CodeAI hallucinationsLovableAI code generationMCPs (Model Context Protocol)RAG (Retrieval Augmented Generation)WisaryProduct requirements gathering

Questions this episode answers

How does Wisary prevent AI hallucinations when building products?

Wisary uses AI to ask comprehensive questions that force users to provide complete context about what they're building - who the users are, what they'll see, what edge cases matter - before any code generation occurs. By ensuring all necessary information is articulated upfront, there's less space for AI to fill gaps with incorrect assumptions.

What's the difference between using Wisary and going straight to Claude Code or ChatGPT?

Claude Code and ChatGPT excel at writing code once requirements are clear, but users often don't know what they don't know. Wisary acts as a requirements-gathering layer that uses AI to ask the right questions so users think through their product thoroughly; only after that output is fed into Claude Code or similar tools for actual implementation.

Can an eight-year-old really build a usable app with Wisary and Claude Code?

Yes - Ala's eight-year-old used Wisary to define a walkie-talkie style messaging app for his iPad, clarifying features like what friends would see when called and how messages display. Once the product requirements were clear, those specs went into Claude Code and a real MVP was built.

Did Ala Stolpnik replace his entire engineering team with AI?

Yes, a few months ago after Claude Code's latest release, he transitioned from managing human engineers to working directly with Claude Code, achieving 20x faster implementation. He still performs the same role - giving direction, setting requirements, and reviewing outputs - but now with AI instead of humans.

Why are large companies struggling more with AI adoption than startups?

Large enterprises have different constraints: engineers spend only 10% of their time coding and the rest in meetings, syncs, and collaboration. Even a 50x boost in coding speed doesn't help if the real bottleneck is organizational friction and meeting overhead, which AI doesn't accelerate.

What our scoring noted

Our reviewer’s read on each dimension, with quotes from the episode.

Insight Density

12 / 20

The episode contains several substantive ideas about AI-driven development, product definition, and startup scaling, particularly the insight that smaller teams with AI can replace larger ones and that the real bottleneck shifts to ideas and distribution. However, much of the value is repetitive explanation of how Wisary works, and large sections drift into Colorado-specific tangents unrelated to B2B operators' core concerns. The density of novel, non-obvious claims per minute is moderate but diluted by filler.

you still need to know what you're doing. So you still need to know what do you want to build? What problem you want to solve, who you're solving it for
the bottleneck there is not implementation anymore, is not execution, is the right idea and the distribution

Originality

11 / 20

The core argument - that AI shifts the bottleneck from execution to problem definition and that smaller teams can scale further - is relatively fresh in the context of mid-2024, but not contrarian or deeply counterintuitive. The positioning of Wisary as a requirement-clarification tool via guided questions is sensible but not novel. The guest reiterates widely-circulating frameworks about AI adoption challenges in large enterprises and the advantage of startups. No first-principles thinking or sharp pushback on assumptions.

just because it's so easy to go to AI with everything and just let it do stuff. I think it requires discipline to actually slow down
large companies, they will remain slow

Guest Caliber

13 / 20

Ala Stolpnik is a founder with ~3 years building Wisary and direct experience migrating her own engineering team to AI-first development. She has concrete operating experience as a CTO and engineering manager. However, she is not a widely-recognized operator at massive scale (Series B+ with material revenue/users at time of recording), and the guest caliber is solid but not exceptional - appropriate for a regional podcast but not a marquee operator.

I am your host, Monisha Saldanha an executive with 15 years of experience in product management
as a result of completing my own development to AI first

Specificity & Evidence

10 / 20

The episode lacks concrete metrics, named customer examples, revenue figures, user counts, or detailed case studies. The son's app-building story is anecdotal but vague (no description of the final app, adoption, or learnings). The claim of '20x acceleration' is stated without definition or evidence. Most other assertions about startup trends, large company dysfunction, and AI capabilities are unsupported by data or named examples.

accelerate work 20x, which is pretty crazy for a small startup
my eight year old son, he decides he wants to build an app for his iPad to talk to his friends

Conversational Craft

10 / 20

Monisha asks reasonable opening questions but rarely follows up with genuine pushback, specifics, or productive tension. She accepts claims without probing (e.g., the 20x acceleration claim, the assertion that large companies 'will remain slow'). The Colorado section dominates the latter half with softball questions about scenery and accelerators, derailing substantive discussion. No moment where the host challenges the guest's assumptions or tests the boundaries of their thinking.

So do you find that Wisary helps eliminate hallucinations from AI?
And you mentioned earlier that you made a transition from a human team to an AI team. What was that transition and when did it take place and why?

Conversation analysis

Computed from the transcript - who did the talking, and the words that came up most.

Most-used words

product31monisha28saldanha28stolpnik27build26building23wisary23colorado17started12different12today11startups11code11interesting11realized10customers10

Full transcript

29 min

Transcribed and scored by The B2B Podcast Index.

Monisha Saldanha: Welcome to Colorado Tech People, the podcast where we talk with the founders and innovators building companies that shape how we live, work and connect. I am your host, Monisha Saldanha an executive with 15 years of experience in product management. Today's episode explores how AI is impacting the launching and scaling of startups. And I have with me here today, our guest, Ala Stolpnik.

Ala Stolpnik: stuff. Monisha Saldanha: the CEO and founder of Wisary We're going to talk about how software development has changed even two years ago to today, how you can do more with less people and what the future brings for our children. So very excited to dive in. Ala, thank you so much for joining me today.

Ala Stolpnik: Thank you so much. Super fun to be here. Monisha Saldanha: Great. So first question for you, what was the problem that Wisary was created to solve and what is the solution?

Ala Stolpnik: The journey started about three ago and back then, ChatGPT just came ⁓ and ⁓ I realized that the way build is going to change and started to think about how AI can us with ⁓ our existing processes, with the ways we use build software. ⁓ So basically what Wisary is doing, it's product managers think more thoroughly about what they want to build and why, and to make sure that they create better requirements and it not just using AI to kind of accelerate everything they're doing.

as a result, when we know what exactly we want to build, engineers can go and build it. That three years ago. A lot changed in the AI world. And we will talk about that a bit today about how that old world that I started building ⁓ has been changing and what's the future software development in general.

Monisha Saldanha: Yeah, well would be great to hear a little bit about that now. Can you tell us about the changes that you've seen? Ala Stolpnik: So, so there, there are two things. There's one that what we're seeing in the industry, all the vibe coding, a kind of hype that now suddenly everybody becomes a builder.

So suddenly you don't need to be a professional product manager or professional engineer to start something. You still may need experts to turn it into reality, but you can, you can start a company much, much, much easier. So that's just like from observing the industry and talking to my customers. ⁓ But at the same time, I building a product and I building an AI product.

And just a few months ago, I fully transformed from ⁓ human team to AI And just experiencing that, got me to realize where actual future of software development is, where the future of a startup is what's what becomes easier. What does not become easier. So basically anybody can start a company now, but, you still need to know what you're doing. So you still need to know what do you want to build?

What problem you want to solve, who you're solving it for. And then, at what point do you bring engineering expertise? what point, Claude Code can't just do everything for you and you actually need somebody human behind the scenes. So as I was transforming my own development to AI first I also saw startups around me.

Basically, starting company today is very, very different from what it used to even a year ago. And even though I started building Wisary, for professional product managers and larger organizations that are writing the requirement documents, they have their processes. Now I'm realizing that you don't need to be a product manager to be a builder anymore. You have a bunch of product builders out there and all they need is clear thinking clear understanding what they actually want to build.

So that's where Wisary is heading is to become a tool for any builder. And the funny story, just two days ago, my eight year old son, he decides he wants to build an app for his iPad to talk to his friends. And I'm like, well, let's decide what this app is going to do. So basically he was able with the help of Wisary to articulate what he wants to build.

And the moment we iterated and he really clarified what this app should be doing and memes that should be sending to his friend and all fun voices going to make. I could just take that, throw it into Claude Code and have it built. So eight year old define and build ⁓ a an MVP, but build something real, now anybody And the only thing that some people still need a little bit of help with is actually clarifying like what is this thing that they actually want to build. Monisha Saldanha: Yeah, fantastic.

you clarify how helps? Because a of people are using Cloud Code directly to create products. But you mentioned with what you were doing with your son, he first used Wisary ⁓ and then you took output and you put it into Cloud Code. So can you talk a little bit about that?

Ala Stolpnik: Yeah. So when we all go to kind of all the AI tools like, ChaiGPT, Claude, Gemini, we need to know what we don't know. We need to know what questions to ask and how to ask them. My son was a perfect example for that.

He has never defined the product. He has never built a product. So he didn't know what he didn't know. But the way Wisary works, ⁓ it uses AI ask the questions.

It ⁓ doesn't try to anything. It tries to ⁓ get that information from the person in front of it. So basically AI is not only used to write stuff and definitely not to blindly the accelerated stuff. It's used to ask the questions that force us to stop and think.

For in that kind of a walkie talkie for Roblox app, son had to think about what is his friend going to see when he calls him or what he's to see what his friends sends him a message. ⁓ So these questions, Wisary is asking the help of AI to help the person in front of it actually pause and think and make sure whenever AI Claude or Lovable or whatever, whenever the AI is going to build something that that something is going to be what you actually wanted to build and not what it's kind of just decided ⁓ on that day.

Monisha Saldanha: So do you find that Wisary helps eliminate hallucinations from AI? Ala Stolpnik: ⁓ Absolutely. What is hallucinations? Hallucinations, it's context that we didn't provide and AI filled for us.

So what Wisary is doing is it's making sure that we provide all the context that we need. And if we didn't say something upfront, it's going to ask us about it. And the moment I provide the context, then there's obviously much less hallucinations because there's like much less space for ⁓ AI to and kind of go wild and do whatever. Monisha Saldanha: And you mentioned earlier that you made a transition from a human team to an AI team.

What was that transition and when did it take place and why? Ala Stolpnik: a few months ago, basically with the last release of Claude Code, the moment I tried it, I realized what I was doing before as the CTO of my company, ⁓ I supervising and I was giving direction to the engineers to go and build the thing. So I was giving them the requirements. was giving them the technical direction, and then I was reviewing their work and helping them go in the right direction.

So now I'm doing exactly the same thing with AI. So my job didn't change. I'm still doing exactly the same thing. What did change is the speed of implementation.

Also the quality of implementation because we're humans. We can't notice everything. Like obviously, if I do review the code very, very thoroughly, I'll always miss something. And having AI double checking me.

So basically what I realized is by replacing people with AI, unfortunately, very, sad, but I was able to accelerate work 20x, which is pretty crazy for a small startup. Monisha Saldanha: So you're no longer working directly with engineers, you're working directly with AI. Ala Stolpnik: At the moment I am. Something really, really interesting.

I didn't only have engineers, also had designers, kind of other, other experts. And now I'm realizing that I need to pull them in for a different period of my product, but in very, very different role. So if you think about what was the role of experts before AI. We're doing hands on work.

Products people were writing requirements, engineers were writing code, finance people were doing ⁓ projections. And then AI came. With AI, we stopped doing hands on work. We started doing more management work.

So we give the goals, we set the direction, we review the outputs. So that's how working with AI ⁓ was up now. But something that I'm realizing now is like really, really fascinating for me personally ⁓ think the role of experts becomes training AI to do their job. So if now I'm bringing in a designer, two ago, I would ask her to go and design whatever mock-ups or whatever screens for my app.

A year ago, I would ask her to use AI and guide AI to do the right thing. Today, I want her to my product, give me feedback, and I'll be able to feed this feedback into AI skills so the next time the feedback, today's feedback, she wouldn't need to be pulled in. AI can figure it out. She will be pulled in when the next level of complexity is going to be needed.

So what's really interesting is suddenly experts become the mentors and the coaches and not just not ICs and not the managers. Monisha Saldanha: That's fascinating. And where you see this heading? If you to predict the future with AI, what do you think is going to happen a year from now?

Ala Stolpnik: Yeah, think the answer is the AI itself, models will keep getting better, hopefully also cheaper. So that trend will keep going. But I think what's really interesting in the startup industry and in the software industry is the adoption side. So small startups are adapting AI.

The same way I transitioned to AI first, not only on development, also on operations and everything else. I see a bunch of startups, early stage startups doing that and being extremely, extremely efficient. So they're moving really, really fast. And the bottleneck there is not implementation anymore, is not execution, is the right idea and the distribution.

Actually finding the customers. That's one side of the spectrum. It is like super fun, evolving super fast. The other side is this all huge SaaS companies.

They're not adopting AI efficiently. And even if you have some individuals who are able to optimize work, that's not significant for these huge companies. Even if you think about software development, a engineer in a corporate maybe spends 10 % of their time on actual coding. ⁓ So even if you the coding by 20X, 50X, this is not significant enough.

If they spend all their day in meetings and syncs and collaboration, and you didn't accelerate that, that is not helping. And these are the parts that are hardest to solve and hardest to accelerate. So I think large companies, they will remain slow and ⁓ eventually they may ⁓ find new business ⁓ or ⁓ out. ⁓ What's really interesting is what's going to happen to this early startups today as they grow.

I think the really, really fascinating thing is you don't need that many people to ⁓ your business anymore. If one person can do a job of 20, let's say to have a stable team, you probably have two people in each role that compliment each other. and use AI to do everything else. ⁓ So suddenly don't need to scale the companies to that many people anymore.

You just can have much more companies doing much more different stuff now. Monisha Saldanha: What an exciting future. You mentioned earlier that there are some that are easier with AI and some things that are more difficult. Can you elaborate upon that?

Ala Stolpnik: Yes, I think anything that is repeatable, anything that you can pattern match and can automate is much, easier. Writing code, writing tests, reviewing code, running tests, documentation, double checking that code matches the requirements. All these things you can do much, much more efficiently. and much faster AI.

What I think becomes harder ⁓ to remember to pause and think. Just because it's so easy to go to AI with everything and just let it do stuff. I think it requires discipline to actually slow down for the tasks that are ⁓ really And that's exactly the idea behind Wisary ⁓ is to force us pause and think. Are you sure you want to build this product?

Do you really need that edge case or can you maybe push it out of scope or maybe forgot something else? So kind of to force us to stop and think and not just take whatever AI generated is for granted. I think that becomes harder. And even for people who are aware of it and like trying to be responsible, it's just too tempting to outsource our thinking to AI.

And we need to find a way not to. Monisha Saldanha: ⁓ talk a little bit about building a company in Colorado. How would you characterize environment for startups in Colorado? What makes it easy?

What makes it difficult? ⁓ And how is it building startup outside of traditional tech hubs like Silicon Valley? Ala Stolpnik: Yeah, think it's super interesting. So I moved here from San Francisco.

So kind of a very, very different environment. So have there, have everything, have like a variety of companies, you have talent, you have funding, you have much more ⁓ But the thing that surprised me most when I moved to Colorado is when I asked somebody, what do you do? They don't start telling me about the cool startup they work for. They tell me that they ski and run and bike.

That's the main difference that people here, don't ⁓ only live for ⁓ their jobs. They care about community. They care the people around them. They care about their ⁓ free time.

And, I found that kind of that's sense startup. community, it's obviously much smaller, but feels much more intimate and much more fun. And three later, I feel like that I know more people here and I'm much closer to much more people here in the startup community than I was ⁓ in Francisco after eight years. Monisha Saldanha: How do you find the environment from a support of startups perspective, infrastructure, ⁓ mentoring, accelerators, all that?

How do you see that in Colorado? Ala Stolpnik: I think there's a lot of investment in ⁓ in startups, in women starting ⁓ companies. So I was, for example, part of an Exponential Impact accelerator in Colorado Springs that was amazing. ⁓ I think, again, it's all about community.

It's all about building the right connections ⁓ making the right introductions. So I think there's definitely a lot of awareness and investment of startup ecosystem. It's just really fun to build a startup here just because there is this support and community and bunch of other people trying to do the same thing. Monisha Saldanha: That's wonderful to hear.

what made you decide to leave Silicon Valley and settle in Colorado? Ala Stolpnik: So there like two decisions. One was to leave Silicon Valley, which I made long time ago, but I didn't know where to go. And then I visited Colorado and I discovered the mountains.

The moment I discovered Boulder, like I realized, okay, now I know where to move. It was a really easy decision at the time, even though I had to quit my job and move with the family, but yeah, I think it was like the best decision. Monisha Saldanha: You're living in Boulder now and you did the Exponential Impact in Colorado Springs. Did you relocate to Colorado Springs to do the accelerator?

Ala Stolpnik: The drive is not that bad, kind of once a week, once every two weeks, we had in-person meetings. Part of it was virtual. So it wasn't an issue at all. So yeah, but got to get to know the Colorado Springs community as well, which is, it's really interesting how diverse Colorado is in some ways.

Boulder is different from Denver, is different from Colorado Springs, and each one of these places has its own character and vibe. So even that experience was really, really interesting. Monisha Saldanha: And what type of support did you get from the accelerator? Ala Stolpnik: The XI specifically, it was all about networking connections.

I made really good friends. I got exposed other kinds of businesses. That's another difference between Silicon Valley and I guess the rest of the world. You have different companies here.

You have people doing different things. Whether this is ⁓ hardware, government something that totally forgot that existed when I lived in small bubble in San Francisco. Monisha Saldanha: Great. And you mentioned you changed your ideal customer profile from expert product managers to is it everyone now because anyone can build or who would you say is your ideal customer persona now?

Ala Stolpnik: So it's it is still including ⁓ product managers in larger organizations, but now it's also available for any builder. As I said, from eight year old ⁓ that wants to his first app even product people who are their startups and want to scope out their MVP ⁓ and all the way to non-technical founders that have amazing ideas, but they don't know where to start. What I've seen happening before a lot is business people with great ideas would go to engineers, ask them to build MVP.

Half a year later, they get something half broken and they don't know what to do about it. And it's not what they even imagined. So, so what I'm helping them with is to understand very, very well what is this thing they imagine. And the moment they can understand it, they can communicate that to the other side.

And the other side could be AI for prototyping, or it could be like actually engineers that are going to build it. Monisha Saldanha: And in building Wisary, have you been surprised by anything in terms of how people are using the product? Ala Stolpnik: I think building an AI product kind of in this time is super, super interesting because what I was witnessing is including my own experience is how people AI and how that perception changes along the way. So we just started and Chat just came out, like the first reaction I heard from people like, ⁓ it's writes with a style that is recognizable as AI.

And I, I'm proud of my job. I don't want people to know that I'm using AI. So that was probably two, three years ago. Now, you're not using AI, people look at you what's wrong with you.

So I think, ⁓ to see people perception about is changing also forced me to change how the looks and behaves. ⁓ So we change, ⁓ we basically. read it kind of the entire user experience, twice, of based on kind of how people ⁓ it to interact with AI. Starting from one one attempt prompt and getting something to much more interactive process.

⁓ And now we're talking about MCPs we're talking about connection between different tools. ⁓ Seeing how people expect to interact with AI and building the product that supports that was super, super interesting. Monisha Saldanha: Can you talk to us a bit about your tech stack? So what are the tools that you're using to build your product and how are they interacting with each other?

Ala Stolpnik: The infrastructure of the tech stack is basically the same as it used to be before. Standard full stack, a web app. The tooling is a, Wisary on the product side to help me define the requirements for the next feature. And then Claude Code on the development side.

So that's anything that engineer is doing basically is happening in Claude from writing the technical plans to implementation, to review, to debugging, testing, whether it's unit tests or regression tests. So all that is happening in Claude. And then the of that is basically test plan, the documents, what has been built. And that's kind of a great connection between the engineers and documentation of what they built and the product that we started with.

⁓ And now you actually see where they disagree. Monisha Saldanha: Is there anything that you've learned along this journey that you would love to tell your former self who was just starting off building Wisary? Is there anything that you know now that you wish you'd known when you started? Ala Stolpnik: Yeah, I when I just started, it was like the early days of AI and I think like similar to many others, like I was panicking.

Like things are changing like every day. Like there's a new model, there's new capability. And initially I was trying to stay on top of everything and be the cutting edge of everything until I was able build kind of a mental model of, okay, like this is where the models are. ⁓ Yes, they're getting better.

I don't have to always use the most recent one. I can stick to certain technology for a while ⁓ then switch when I need to. ⁓ I think just, yeah, stop panicking if you're feeling that like I'm behind all the time. Because it's also kind of when I talk to a bunch of people ⁓ out everybody feels that they are behind with AI and it doesn't matter how far along they are.

⁓ Like feels they're behind. And I think it just doesn't make sense to every new change. The moment you have the mental model of, what's possible today? What will likely be possible tomorrow?

It's much easier to do what's right at this moment. Monisha Saldanha: And did you make any pivots beyond, you know, widening the scope of your ICP? Are there any other pivots that you made in building Wisary? Ala Stolpnik: Thank you.

Yes, it's funny because when I just started, was actually building a tool for junior managers like myself. And initially I was thinking like, okay, like I was engineering manager, what was hard for me? Was a task breakdown and technical plans. I actually started to build a tool for that until I realized that nothing matters if the are not good.

And kind of that's, that was the first premise to realize like who I'm solving the problem for and who, will be the user of the tool. And then also as our understanding of AI evolved, ⁓ the tool itself evolved. initially we all discovered that ChatGPT can write stuff. ⁓ So initially the product writing stuff.

And then we realized that hallucinates and if it doesn't have the right context, ⁓ it just creates garbage and not really helping. And then it's like, okay, let's, let's bring rag. Let's bring the context. And then I realized that in software development world, we never document our knowledge and all the knowledge is just in somebody's head.

So ⁓ matter how well the technology is, if it's not available in digital format, AI is not going to do ⁓ with it. ⁓ that's where it ⁓ pivoted to the direction of using AI to ask the questions ⁓ and actually get this from our heads. ⁓ Since then, that's the core thing core value of ⁓ Wisary is these questions and pulling the information out of human heads so that AI can do something meaningful with it. And then the most recent pivot was ⁓ our interface was as a Confluence app.

We're integrating as add-on into Confluence because initially as I said, I was building solutions for professional product managers in larger organizations who are often Confluence. But as I started to see all these new builders coming along that want to hear anything about Atlassian or Confluence or old antique SaaS but they do need So we pivoted to a standalone platform. now anybody can just sign up with their email and start using immediately. So yeah, so there's like a interesting evolution and it's, only has been like three years, but so much has happened in like the AI world, whether this is technology or the landscape of how companies operate and who is doing different stuff.

So it's a fun journey. Monisha Saldanha: Yeah, really exciting journey. there any mistakes that you made that you'd like to share? Ala Stolpnik: So I think one the mistakes that ⁓ probably ⁓ early founders are making is trying to raise money when they're not ready for it or when the market is not right for it.

I think I wasted a lot of energy on trying to raise money until I realized that the industry not there. The requirements, especially now in kind of the age of air that everybody can build, the requirements for traction are much, higher. And the other hand, we don't need that much cash anymore. So when I realized that I can keep building my company very lean, very fast without external capital, it's freed up ⁓ so much of my and so much of my energy to focus on actual thing of building the company.

as opposed to to get funding. So I think if looking back, I wish would have realized that earlier and I didn't that much energy on trying to raise money when it wasn't right time. Monisha Saldanha: That's a really great lesson. Don't try to raise money until you're actually ready for it and need it.

You may not need it anymore in this new world. Thinking ahead to the future, what is your vision for Wisary? What impact will it have on people's daily lives? Ala Stolpnik: I think anybody who wants to build a product will be able to build a product.

⁓ it starts with just identifying the idea ⁓ and the prototype and even like deciding that I want to test out this idea. So, my hope is every product builder out there, Wisary will be there to do tool for that phase. Now let's say they ⁓ vibe coded 10 different ideas they decided this is the one that is going to work. Now need to go and translate that into actual products.

They will need real engineers. They will need real designers. They will need real experts. So here, Wisary will help them define what is the gap between what they have today and what they actually need to ship it to real users and real customers.

Once they did that, and hopefully it succeeded, now they either bring in a professional product manager or by this time they became a professional product manager. And here they start to iterate. They start to more functionality and more capabilities to their existing product. here again, Wisary's with them to help them evolve their product, moving forward.

So, the idea is to allow anybody to start a company and to grow with ⁓ as they their company. ⁓ Monisha Saldanha: it your intention to stay in Colorado and keep building Wisary here? Ala Stolpnik: Amazing. Yeah, absolutely.

Snowboarding on the weekends or climbing on the weekends depends on the weather and yeah, and building Wisary the rest of the time. Monisha Saldanha: Nothing. Well, fantastic. And last question for you.

What is one book you would recommend every builder read and why? Ala Stolpnik: The Mums test, that was one of the first books I got recommendation for when I was just starting about how ask the questions with minimum bias and how to try to get the truth from answers. In way, that's also ⁓ what is trying to do is the questions and ⁓ try to get to the root of problem or the solution. But yeah, I think mom's test, was something that forced me to rethink how I interview the customers, how I validate my idea and decide what's going to work and what's not going to Monisha Saldanha: And how often are you talking to customers?

Ala Stolpnik: Um, all the time. I think what's interesting is the scope of who the customers or potential customers are keeps growing. So the more and more people are building a random person I run into and just randomly chat, chat with maybe one day a builder and a customer. So for some customers, I talk intentionally with some customers they become customers their progresses.

⁓ Monisha Saldanha: Yeah, so the mom's test is really helpful for you then. Great. Well, Ala, thank you so much for your time. This has been such a wonderful conversation.

I've learned a lot. I think our listeners have probably learned a lot about the changes that AI is bringing in of building and launching startups, particularly in Colorado. Ala Stolpnik: Thank you so much. Monisha Saldanha: And I'd like to thank our listeners for listening to this episode of Colorado Tech People.

If you enjoyed our conversation, consider sharing this episode with someone who loves building products. Be sure to subscribe so you don't miss future conversations with founders and leaders shaping Colorado's tech ecosystem. Until next time, keep building, keep connecting, and keep creating experiences that bring people together.

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