Think AI Podcast - Speed Is No Longer a Moat | Ep. 16 with Alex Galert (BRNZ/DUDE.AI)

Episode Date: August 4, 2026

🎙️ Speed Is No Longer a Moat | Alex Galert, BRNZEveryone in AI is racing to ship faster. Alex Galert, CEO of BRNZ, says that race is already over, and speed was never the real advantage. In this conversation he breaks down why accountability, not velocity, is the new moat, and how his studio builds complete AI-powered companies for equity, with a 67% founder floor in writing and zero investors.In this episode:00:00 The thesis: why speed stopped being a moat 01:31 The Hero Founder and 10,000 hours in one industry 08:55 Business for equity: how BRNZ actually works 14:14 The anti-VC model: no fees, equity only by shipping 18:54 Inside DUDE.AI and the five levels of AI autonomy 26:08 The dirty secret of AI-generated code, and a real data hole 31:48 The failure that changed how Alex builds 45:31 Can a company run with no humans? 55:02 Five moves you can make Monday morningIf you build with AI, this one will change how you think about shipping. Like, subscribe, and drop a comment with the one move you are taking into Monday.🔗 Links & Resources Website: [brnz.com] | [DUDE.AI] Guest LinkedIn: [Alex Galert] (comment BUILD on the pinned post for the BRNZ Playbook)WhatsApp: message DIAGNOSIS to [wa.me/491711254074] for a Startup Loop Diagnosis ▶️ #ThinkAIPodcast #DaveGoyal #AI #AgenticAI #Startups

There are many founders, especially right now, they are experienced, they are experts, but they are trying to build startup in another niche, in another industry because started building right now with AI is easy, but this is not the case. Building is easy, selling is hard and it's getting harder and harder like probably every month. Welcome to the Think AI podcast. Each week we talk about the most exciting AI research, tools, case studies and more. I'm your host, Dave Goir, and I've been working behind the scene in data and AI for over 30 years, whether you are an AI expert, skeptic, or something in between, this podcast is for you. So today I'm sitting down with Alex Gallard. He has been

building company since 2006, and he has a habit of reinventing his whole strategy every 10 years. He has BRNZ platform pronounced as brains since 2016. Now due to an operating system for agentic work since 2006, he bills out of Cologne, Germany, and his eyes is on the U.S. market. Here's what Alex Worth next hour is studio brains builds complete AI-powered companies for experienced operators and takes equity only by shipping. The founder keeps the CEO's seat and 67% ownership floor in writing, no investors. The target he calls M12, a million in run rate by month 12, which is amazing.

Funded entirely by customers. But the line keeps coming back to me in this thesis, speed is no longer a mode. And I've been thinking a lot. So let's hold that thought about it. And then accountability is, Alex, welcome to the Think AI podcast. Thank you for hearing you. Hey. Awesome.

So let's get on with it. So let's jump directly into my curiosity. You say speed is no longer or more. Now I understand why you say that. But then that is the opposite of every AI pitch out there. Make the case why. Yeah. Like, you know, AI changed everything.

So we are all moving fast. Our agents are building code. We can build software 24-7 without sleep. And so many teams are doing it. But the main question is what are you building? And if everybody can build the software for your specific market, so what is your USB and how can you compete with all this different software providers? This is our experience for 20 years and according to our experience, you need to find

founder, which we are calling a hero founder, your founder spent probably 10,000 dollars in specific industry. He knows exactly how to build this specific product for the industry. He has already built his customer base audience and this is the main USP. This is how you're getting into the market fast. But the main point is you have to have the right product. And this is why it's not about the speed. It's about the building way.

It's more about quality. It's about security. It's about everything that is what you know from enterprise software development. And this is exactly the opposite of wipecoding, what everybody is doing now. This is why this year, as you mentioned, we've built a system what is called brains. This year we've built this system what is called Dute AI. This is an agentic work OS. And the main point of this system is to make the bots,

the agents which are building software, which are performing on other tasks accountable. I'm from Germany. We love rules and we see what agents need rules to run and build software the right way. And this is why the main point of the good is how to build the software that we can prove in like 10 years later.

It was a quality to build. We did this specific test, and IOS we performed on this acceptance criteria. And our audit cannot be changed by humans and agencies saved forever in our specific database. So you can use this software. Also for enterprise, customers for related and other industries. So it's all about evidence. It's all about evidence, it's all about accounting, acceptance, and in every loop,

even integrated a senior developer who is actually a bottleneck, actually the fact that is slowing down the system, but according to our experience, a JN Tech team in combination with a senior developer with reading the code who understands exactly what was built and by the agent team. This is the fastest way to build the product. Just one last startup of us, what we've launched beginning of this year. It's called tellotexting.com.

It's about sales recovery. And the whole build was about two weeks. And since then our founders scaling the startup, no issues, no worse. This is why he can focus on sales marketing goals. That's amazing. And I totally understand your point on speed is not the mode. And I think the key reason is more than ever,

I have built nine businesses, brand five of them. But it used to take time to build something. Now, due to all the agentic hardness, as different models, you can build something fairly quickly. When I say fairly quickly, literally in a weekend, but then the importance of having your strategy, having a correct design, where your customer base is, what's the competition out there, the whole setting of our business. And I've seen few clients of us failing miserably due to that.

Now you don't need half a million dollars to fail. you only need probably a cloud code account to fail, but nonetheless, you're going to fail. If you don't pay attention to how you position yourself, what's your product market fit, where it goes in the situation, what's your experience looks like out there working with different vendors, who just wants to build a product out there

rather than seeing the demand, seeing the product market and other things. Yeah, a very important question, and more or less my first question about the project idea, startup founder is always a validation. Guys, you want to build this specific product. What is your validation? What did you do? How many customer interviews did you have? And this is interesting point.

Here, founder, this guy who spent like five years, 10,000 dollars in specific industry. he can sell the product without having this product because he knows exactly what's going on. He knows exactly which customers will buy this product. And this is why this guy, he can just write like a small one pageer. We know exactly what to do, how to build it. And after this build is done, in most cases, it never changes. We are running one company, what's called Music to Beas. He in Germany, it's like a sporty music for businesses, like for gyms, holders and so on.

And this founder, he was from the music industry, so he could just build this document, provide it to us, and we knew exactly how to build the software. We built this software 2012 and it still ran. So nothing was changed. It was for sure extended, but this here of founder, he knows exactly how to build it, how to sell it. And this is why the validation phase is in most cases soft by real revenue in this, by this forms.

And this AI, this validation is getting even faster and simpler. We have a founder right now who is building AI assessment system and she's here from Cologne. And her idea was, hey, I don't want to have like a real build. I will build it myself by using lovable. And you guys, after the validation phases over and customers are ready to start the corporation, you will rebuild this lovable build, this MVP, not even my people prototype. You will rebuild it and, yeah, transform it in real product.

That's amazing. And I'm also going deeper into one of the things you mentioned. So you talked about the brain's model. And that definitely intrigues me in positive and both in negativity. I'm not going to lie. So walk me through how brains actually works. Business for equity. Interesting concept. And I've seen a few people here in Southern California doing that. A 67 founder floor in writing, right? And equity earned only by shipping. So the risk is yours because it did. close the business. That's the negative thing I was thinking. But then you're saying 12 target of a million in run rate by month 12. So I guess you're qualifying the companies who have a run rate by 12 months. Is this how it's work? Break it down for me. Yeah. And we are building startups for equity, like business for equity, since 2006. So this was from the beginning, our model. And the first 10 years, we just built different software products. And as you know, 90% of them were not successful.

So it was also in our case. So huge amount of waste code, time, resources. And this is why 2016, I decided to build one platform what can be the base for different companies. The only way, the only reason to build this company was to just save the time. And even if we have a startup with our success, we can still, like, we use the code because the startup doesn't need this call.

Because whatever, it all start operations. And this was 2016, so we built Brains. And the first business case of Brains was building of automated AI-based commerce brands. So we built in our own warehouse, like 20 different brands. And 2016, we've built AI, what was running on TensorFlow, own training model. There's for sure no open AI clothes and anything. So, yeah, you had to build a scratch. And this is what we've seen. Okay, brains make actually sense. So first, in ecommerce space, We've built these brands when we extended the platforms.

So we could build different fintech applications, different software service tools in the e-commerce space and some others. We've tried different systems. Our point was what is the maximum motivation of the founder? We've tried different equity splits. We've tried like 50-50, 60-40, 10-90, and we've seen what like 67, 33, this is like the sweet spot. And this is why it's, yeah, it's like fast you probably can see it on our website on our LinkedIn page and so on. And the model is actually pretty simple. We can build like any type of software in round the

about 1,000 human hours, but it's not this time. It's just our orientation because this is this is time where you can build product that can make money in the market. And this 1,000 hours, human hours are super compressed by AI. This is what you can build very, very fast because we are not building from scratch. Brains is more or less like Amazon Web Services for building startups. You have your infrastructure, you have your

your security for sure. Everything about payments, integrations with blockchain, all the frontier models where just existing. You can just take them and use it. The only point we have to build for every startup. What is unique is branding UIUX design. And this is why we can ship these startups very, very fast. And to accelerate this process,

we've built this new product called dute.a.ai and duet.a.i knows exactly how brains is built and how brains must be extended if you have to build this specific case and this is what is making

developers even faster. So we are not building from stretch. We are using an existing core system and in most cases 60% of everything what this specific startup needs is already there. We can just use it, probably customize and integrate in the solution.

And 40% are built by agents verified by our security, agents approved by human, senior developers, and then you're right to run. Well, that's pretty amazing. But I'm still intrigued with that idea and concept. And then, you know, my business redars is saying, So how do you manage the risk with the customers? Are you charging anything to them when you're building it for them? This is exactly this point and probably a USB of our system.

We are not charging customers at all. We are covering development costs from our already existing companies and existing their workforce. This is why this here of founder, who is the industry and insider and industry expert, knows exactly how to build the software, how to sell the software. He is not paying at all. We are completely on the equity strategy and this equity is invested in months in most cases.

So we are getting equity for shipping the product. So if something is not working, if we cannot provide this product and so on, we are not getting equity at all. This is the model. Yeah, no, that's perfect, actually. Come to think of it. But I'm sure you are funneling them, you are filtering them based on seeing somebody

serious and interested for long run because, again, your skin is in the game and they need to have something in the game too. Yeah, 100%. And we win only if the company wins and according to our experience from probably like 100 projects in this space, like risk funding, without funding. this exits with result success. We've seen what the founder might be full-time founder. This is like the first point.

This is why it's so easy to what a data are you running this company full-time or part-time because you have your whatever full-time draw. And you cannot focus on this. An exit point if you will. Yeah, yeah, exactly. This is why it never works around. So you can say, hey, I'm running this company right now, part time and I'll switch to full time.

Then I, whatever, raise money or genuine revenues. Just not working. So yeah, full time is the first point and the second point. As you know, building startups is slow. So the founder, he must have the ability to like, at least like survive 12 months of operation. and be focused not on making money, like for living,

but on building the company, the product, marketing sales actually cross. And this is why this is the second the second term that is very, very important for the startup building. And yeah, the third point, as I already mentioned, 10,000 hours in this specific industry of the startup because,

There are many founders, especially right now, they are experience, they are experts, but they are trying to build a startup in another niche, in another industry, because startup building right now with AI is easy, but this is not the case. Building is easy, selling is hard, and it's getting harder and harder like probably every month. This is why my favorite book, from zero to one, from Peter to this, which is exactly this, this first step. from zero to one.

This is what I love. This is what is hard. And scaling from one to end, this is totally possible by AI. This is what you can totally put on the agents and they will just help you to run it. Yeah. Well said. And, you know,

one of my parallel story about jumping from jobs to business, I always have that mindset to create. I'm a founder, innovator and a builder just like you. And I was never good in. but I knew how to build stuff. So now how about I build for others is what the thinking was when we created this business. My son was born.

I was working with the large hair care company, but I still decided to jump in. And if you're not committed, then you're not committed. If you put yours, put in two boards, you're going to sing. It's basically as simple as that. And yeah, I think that's your criteria to bring people and that's great. Let me switch gears. So I love everything about brands. But then you also talked about the whole thesis about accountability.

And you know, you have talked about few points on it, like identity, evidence, review, audit, metric, and memory. Unpack a few of them. And how does this AI worker looks like inside dew.a. I in production going with that philosophy. Yeah. The main goal of duty AI is actually autonomous building of startups. So we defined five different levels of autonomy.

And as I mentioned before, our startup size, like NBP size, is 1,000 human hours for building this first version of the product. And the maximum autonomy of Due.a. It will be if we can just tell this system to our agentic product owner, hey, this is my idea. This is what I want to build and it will just build and provide us a ready to run. So this is autonomy number level number five. And right now we are the autonomy level number three. This is why humans are involved and they are part of the loop. They are doing a code review and everything.

How we are building due to do this building actually itself. Because this level five autonomy is defined as like main KPI. We are calling this agenting delivery intelligence. And every week we collect the data from the dialogues, like dialogues between the agents, between humans and agents, and when we collect source code and what we produced during this week, and different are the KPI. So dude can understand, okay, where are the bottlenecks,

what I have to solve, how I have to improve this metric, improve this metrics and it's even like forecasting when it will achieve this autonomy level five. How we are building it, we defined a three level of these KPIs, now like main KPIs I mentioned is Argentic Tel Avivate, Talentgence and it's split it to software code quality, security, stability and so on and so and so. And the system is doing following. You have a startup that is in build right now. and during the daytime, when people are working, then people are providing tasks.

The system is working on these tasks. And during the nighttime, the system is doing self-optimizing in close loops to optimize this one specific startup KPI. So we have different KPIs, and these KPIs can be defined even, we can even define different KPIs for one specific startup because it has different work spaces.

And our idea is, or the main question is not to build a company, but to build a closed loop as a company, self-optimizing company, what learns and understands exactly what's going on, collecting data, in the best case, or revenue data, customers' dialogues and so on and so.

And based on this data, it can optimize itself. So in just this example, so you understand ADI is for due. This is more less clear. But if we would talk about teletexting, teletexting is in the area of sales recovery. We are working with around about 30 e-commerce brands and we are using iMessaging for recovery. This is why we can collect from iMessaging dialogues between our agents and users, everything sales, discounts, and so on. And then we have also financial data from Shopify shops.

And for sure, we have different other KPIs. So on our side, we can create, or we've created new KPI, what is called, revenue intelligence on our side. And we can help these brands to increase their revenue intelligence because we are tracking all these thousands of dialogues of brands. We can say, tell them the shop owner, hey, win off exactly because 50 people told us your subscription is not working. You have this specific box in your shop.

We know exactly if specific influence is profitable for you or not. We can create a future market data. We can create a playbook for this specific shop, for this specific product owner, how he can increase his revenues even without spending more. And using these KPIs, yeah, as I mentioned before, we are trying to create a close-loop self-form. optimizing company in every space.

And this is why we are talking about revenue intelligence, agentic delivery intelligence. New startup photo scamming here for KLO is doing pricing intelligence and so on. By using KPIs, you are providing like the North Star, like the goal for the system, for the agentic system. And in the best case, agentic team will achieve it itself without any human health

of the management. Not possible for now but yeah, our vision. Very interesting and I must say you've got the agent TKI done right. On the accountability point I have a parallel story to tell so I had a client who won't fully autonomous document system, no human at all

and then we actually walked them to one edge case where the system flexed the wrong vendor payment and they decided then to keep a human at the 95% confidence threshold. And the technology was not the problem. It's the accountability framework was. And that needs to be thing through more than ever in this era

where you just cannot leave everything in the hands of AI. Why, AI can help you in thousand ways. I have a whole team of C-suite for my own, what I call AI crews for my multiple businesses. And, you know, they have daily stand up. And I see that in my notion and I see what they're talking about. I have this whole task database where they are creating different tasks. And then they are also assigning it to human in making sure that that gets done and being executed upon.

And then my focus in my second brain is just top three. But then it makes mistakes. And you got to know and acknowledge that. and the accountability framework is meant to do that. I do want to switch gears. So we did talk about, you know, how you are building startups, the profitability model,

you know, shifting the risk on you so that they can freely build their idea. And then, you know, everybody has their own benefits around it. Then you also talked about the accountability and self-building AI using dude. But then all this brings, with some dirty secrets on security.

So you talk about the dirty secrets of AI generated code. Tell me about the real one. And the customer data whole you found and closed in a partner's project. And what security gates actually you apply? Yeah, actually very interesting, very interesting question. A couple of months ago,

we've seen what AI coding, like why coding is not really working, and this is why we started to build our own, even like a security startup built, but never publicly launched. So this is why it's just our internal security system. But we've seen like 40 or 50% of all this vibe coded projects have security issues. So you can go to many of the different. new projects which are probably started this year or last year and run your security audience and you will see a huge amount of security issues which can be used to steal the data and get access and so on and this is why as I mentioned we built a system

what is now part of Deut AI what is integrating duty AI software development process and this is an agentic security orchestrator running first the analysis of the code so it understands okay what this specific startup is doing what is the product and so on and then it decides which of 40 integrated open source security tools it should run after this is defined we are doing deep and deep scan using these tools and then we know exactly what's happening. There are the issues of what we have to fix before we go live and so on. And to do this, we are collecting every four hours.

Actually, available CVE databases, which have already like 400,000 CBEs, which are existing in the market in our database, two hours, every of our, process before we go live against this Cvine database and see, okay, there are no critical vulnerabilities so we can launch the code and run it. And this is exactly the system what we

actually just as a help run against a partner project and we found out what there was nexus, what was not somehow was protected by a password and without any administrator you could just download all the customer's data, user's data. So yeah, we reached out the department, helped him to close it and yeah, it was removed.

But this showing me what security is one of the main point. This is why this agent, this security agent is part of every of our process in software development. This is exactly one of the main components right now, like also integrate senior developer who understands what software is built. No, that's really good. And, you know, I'm sure you are paying a deeper attention to that. I have a little bit of different story on this topic.

I work with the client who discovered that they had about 40 AI tools running across the org. Obviously, there is no security audit and things that is out there. And IT only knew about three. The CFO was expensing a dozen, just a CFO on his personal card. And the security exposure was real. So it's not just about the money, by the way, as you can imagine. It's also about running in your organization, what security hosts it's placing. And so the first thing they did is they went backwards, you know, a solidifying

their infrastructure, finding out what security hosts they already have in their organization, which is allowing all these AI tools to embrace harness and grow in the organization. And the shift completely went towards, you know, it actually retracted them from AI solution, which is a little bit of a sad story, but at the same time, it's the right thing to do

at that point in time to bring some governance into it. So security, compliance, and AI works together and along with the data. But those four pillars is where it takes you to this newer of agentic engineering software development and things of that nature. I also want to switch gears on. So I am big on two things, actually three, leadership, motivation, and technology innovation. And motivation is, and also leadership is very well. connected with failures. I just mentioned about nine businesses, me also being a disabled

entrepreneur, being a dyslexic kid and some of those things. So I figured out my own way to overcome my own issues and at the same time how I can encourage and embrace others to do that. What failures you have actually seen, you know, your personal, professional and this tech life and how those failures have changed how you build. Okay. Interesting question. And as I mentioned, 20 years in building, building startups like building companies,

we had different different stories, one story what was what had like bad and good sides. was a company in the area of project management, this huge customers here in Germany, like Wunderman, what is a part of WPP holding from New York, this young Ruby camp and some other brands. We built a project management system

that was used by Microsoft, Matzda, Ford, and so on, for planning for their as campaigns. And this system was always built for equity, so we've been board equity partner and after it was launched the main partner, like the hero founder

who brought us this expertise he actually died on cancer two weeks a couple of weeks later after the launch and we've seen

what what you have to for the future to protect also your equity against in such cases because in his specific case, the equity went to the government and we had to handle everything. This is the government just to continue to run this operations, continue to run this company. And the main point, this was what I mentioned, the good side. software was running for another five to seven years in these different companies. They supported it, but yeah, this was the point.

So everything about the bad sides in the business, what happens if the partner dies, what happens if he wants to exit, what happens if he's not performing anymore? This is what you have to manage before you start the company. This is very difficult to discuss after it's always a lot. running after generating revenues. This is why all of this must be done before the start. And

as I mentioned, also during the whole way we've been trying to decrease wasting of time, energy, money for building software, for the startups. And actually, for instance, you, this is the system where we can say

we will probably even build more startups wasting less time because we can focus on choosing the right partners, actually. Yeah, as you mentioned, validation and the specific criteria, founder criteria. And having these new tools, hopefully you are not building much more, but you are building the right things. This is what you can really define right now before you start decoding, before you start the programming. So just a small example.

A couple of years ago, it was always how I built MVP, but it changed now. The first question is, I want to start this company. What is the design system? What is the brand? What are the main colors? How can I define all this point? So I don't have to rework my product.

So it looks from the beginning, perfect. My landing page, my product. emails and so on, so on. And as you know, we are in the attention economy. This is what allows you to get this attention. So design first, coding later. This is the main role. No, that's really amazing.

And also a lot of times, you know, I have talked to my customers as well that when they're building their teams, I've spent my life building data teams. and then do not hire the data scientists before the data pipeline. And that thinking of fast cash instead of a strategy kind of failure in so many ways. And if you skip the foundation, exciting part is the model. So people actually see, oh, yeah, I will just go and build something.

I'm a builder. But strategy comes first with the builder. What works, what doesn't work. And then it always comes back around. And your stories makes perfect sense to the people who are seriously thinking about building but have no experience on building. And I think you have correct the formula. So kudos to you on what you have been doing so far. I want to go back to, you know, the rejection.

A big chunk of audience that people I am connected as either solo entrepreneurs or jumping into the business of AI. They are either AI curious, AI skeptic even, or AI intelligence. is. So they may attract based on what you are doing on brains and dut.a.i. But then you run tech for equity model company. And obviously there's a gate to get in. So which kind of founders do you reject and why? Yeah. Actually, we have this free criteria, what I already told to you. I remember. Yeah. And the main point is full time, 10,000 hours in specific industry. and in this specific industry should be the startup and runway.

Yeah, like 12 in the best case, 18 months of runway should be safe for the founder, not to pay us or to pay whatever infrastructure. This is everything what we are providing. It's all about his living. And sorry to interject there. The reason why I push back onto that is so that's a good base level criteria. Yeah, but then there is always this industry knowledge, a particular line and segment. A lot of times people are just having an aspiration. And they'll say, oh, yeah, I will do that.

What experience you have? So I'm working with a founder right now. He wants to be a founder. He wants to build a tech in, you know, 3D imaging. But then, so he's experienced there. But there is no experience. This is for medical industry. That's a different ballgame altogether.

when it comes to US due to compliance. And if they don't bring the knowledge, the risk is on you. So are you looking into those elements as well when you are eliminating them or funneling them? This is why they cannot do any type of startup. You're completely right. But they cannot do, at least right now,

like robotics and probably like deep tech systems, platforms, because we have to finance this. first MVP and this first version of product. So this is why we are always talking about what is possible to build in like 1,000 human dollars as a product, but people would pay for this. So it's probably more as like MVP, but it's not it's not like a huge, I don't know, search engine like Google or whatever startup and in your case,

3D or hardware startups, they take much more space. So we want always to understand what is needed for you found to validate your idea. Is it something that we can provide? Is it somehow already existing on our site, but we can just like reprint, customize, and give it to you so you can provided probably even like a free or a small amount of money to your existing audience, to your existing customer pays. Because this first feedback, the product development starts.

Everything before is just guesses. Everything before is like just an idea and probably hobby. But this is the first customer feedback. You started this a real product development. And we need to understand what is needed to get to get to this first. feedback. The interesting point about different countries in Europe, people cannot really think

in MVP. People are thinking I want to change the world. I want to build this huge company with I don't know, thousands of people and we will achieve something that was never done before and so on and so on. And US founders, especially a hero founders,

they always think in MVP. What is the minimal viable what I can ship in four weeks and six weeks, in eight weeks, to get to the customer, to get the first feedback, to optimize it,

create the first revenue and scale on business. This is more or less why our main focus is on the US market. No, that's amazing. And you just mentioned something, which is a lot of philosophical value I have, too, which is, you know, you build early and fail early,

rather than you keep building, building, building, because your customers will tell you, you know, you fail and you succeed in the long run. So that tells you, that's the philosophy US definitely runs on. I want to go back to that point

because it's pretty interesting. You work or you build out of Cologne, Germany, and then your eyes is on the U.S. market you just mentioned. And then, you know, our holy grail is Silicon really, although now there are pockets across the United States like

New York, Silicon, L,E, Denver, Nashville, so many places are evolving right now. And I have evolved. But Germany versus Silicon Valley, what does each side get wrong about AI? So good, I understood. But what is wrong in each of the side? And I've also dealt with India. So I know a lot about what is good there and what is bad there.

Why do you call Kolo a feature, not a buck? You being over there is a feature, not a buck. Yeah. We've seen what these agents you can do whatever is possible right now. Our understanding, German understanding, is everybody needs rules. And this is the main and the core idea of a dude. Every agent has his own personal ID, you know exactly what this agent is doing, which tasks it's working, which test's criteria, acceptance criteria, it has to perform, to

process and all of this is not really existing in the systems which are used by the most people like cloud like codex because all of the systems are getting PRDs and producing software at the end agents to think you are yes I'm ready it's done but there's no evidence there's no real evaluation there's no structured process just how you can prove what was done, how you can show your results which you achieved and these are real results and not hallucinations and so on. And this is exactly what we see. Okay, we take this power from Silicon Valley and bring it on our rules, on our framework,

on our governance, evidence, and then we can control this, when we can build closed loops we can build self-optimizing companies, but only if this is under control right now. Otherwise, it will be just huge, probably explosion of actions, but no real results. And this is not something that I've learned from the books. This is how we started also to build air companies and seen and made this experience. and this was the beginning of Dioad. So our main point of Dioid is all about controlling governance of AI power. And as you know, all frontier models are getting better and better every week.

And this is why to update our models for six hours later and get this maximum power into the system. but yeah, result controlled it just cannot work because we are not building ideas, hobbies and so on

they are building companies and these companies have completely other criteria as when you just want to build some small product for your personal use

or for your team but a deal never be exposed to or hackers, cyber attacks and so. That's pretty good.

And you just mentioned building companies. So let me put you on spot. Can a company run with no humans? And let me set the preface here. So give me the honest answer, not the pitch. How far a business can actually run today with

no humans in it at all? And where is that line? Yes. This is an interesting question and we are writing experiments on our side. As I mentioned to you, it's all about autonomy. Autonomy level five of dudes would mean but we can just scale, build a huge amount of companies partly so, people involved. I mean on the technical side.

Yeah. Everything about business process, business idea, validation, this is still human. This is what we don't see, at least right now what you can replace by agents. This is why it's not even like this small experience we are talking

always about 10,000 hours. Because this is where you are starting to understand exactly what's going on in this industry. You know exactly the insights, how to build the product, how to sell the products, how to scale it, which partners can help you to build to build whatever

network and other scaling options and all this information is available all this information is existing in the AI and you can totally ask how can I scale this company

and probably will get the answer but we don't see what it's working right now. So strategy and business model is on human, and this is exactly the point from 0 to 1, and from 1 to end, this is

agentic, AI-based and fasking. Yeah, hold your thought on 0 to 1. I have a question. We want to geek out on that for a minute. But going back to the autonomous challenge here, do you think, is it more

technical, legal? So strategy, I totally understand. But which areas can be fully automated or fully autonomous, they're fully autonomous. You know, a good example. I drive Tesla, being a disabled person, my wife and I both. We love it.

We drove 3,000 miles across the country in Midwest this summer, and we were very happy about it and had a lot of fun, pretty relaxed vacation. But then again, it makes mistakes from time to time. So you have to be extra cautious over there. So where you see or where you see the potential of fully autoism, There are certain areas, and I have a philosophy there, which is anything that is connected with emotion cannot be given to AI. That is your own.

If you want to go play golf, you can prepare through AI, you know, the robots and things like that. You can improve your stats. You can improve your drives and everything of that. But the feeling you get, the emotion you get through playing rather than putting a humanite there is completely different. So anything connected to emotion is there. But in business, it works differently.

Is there any area you see potentially which can be completely autonomous? I'm talking right now about 2026. And the markets and technology is moving very fast. And according to our forecast, what I'm looking right now, and due to updating this forecast every week, full autonomy, level 5 autonomy will be beginning of 2028. I would I would really say it will be

a pariet or distribution so 20, 80% of the companies and businesses you probably can run them completely autonomously and 20% probably not and this number

will change with AI and super intelligence what is coming and so on So, no. Now, that's interesting, and thanks for answering that. I want to geek out on the book 0 to 1,

and you already have briefed a little bit about it, but let's expand on it. Let's build upon it. So your twist is that AI made one to end basically free, as long as the value is into, or I should say, so the value move from 0 to 1. Walk me through what that means for anyone building right now. The first point is about probably laugh, because this is exactly what I love.

From zero to one, this is what I'm doing for 20 years already and this is, this is, excites me as most, not the scaling the company, but from building like from nothing, something what's working and what can code, you know, retrieving is and so on and so on. So this is my personal opinion and this is what makes me happy in my job. And this is what I probably never stopped to do in my life. So this is the point. And the second about your question, all talking about pains and needs and validation. But every company, every serious company has an MTP.

a massive transformative goal. And our transformative goal is to say they are these guys operators which know exactly how to build a business, how to sell, how to grow. But in most cases, they are not on the cap table. In most cases, they are not getting all this rewards.

And especially exit, they are probably not even included in the exit. This is the MTV. of brains. We want to make operators to all us. We want to reach

the goal of what every operator, every hero founder who has this experience, can just build his company. We will help him to build the right infrastructure, right, product, scale the company and so on. And this is exactly this is exactly this space

from zero to one where it is here founder and operator sees he can feel his industry. He sees this specific problem, what is not solved. And probably he's even solving it. And this is a company as an employee, but has no resources to get out of the company and to start building his own stuff. This is our offer to the guys who are

who want to stop building companies of other people and want to build their own. That's amazing. And it should motivate a lot of young people who have ideas, who can imagine and have the ability to execute, like you said, operate. And building is one part.

They can forget about it, connecting and partnering with your team and focus on what's the idea, how do they operate. And, you know, scaling will come free like you're saying because once you build that zero to one the foundation of it using

the governed AI, the scaling will become automatic is what you're saying and that's where you are moving the era on this agentic startup and product development and kudos to you. I have a parallel story so I worked a lot with Microsoft

represented them in my past life and every decision we made at the very start was zero to one moment. If you see Microsoft growth path, it's always about that, right? They set up the Office 365, you know, in the competitive world with Google. They set up the, I've done data and AI for 30 years. I have a patent in AI as well, but Microsoft came into the game pretty late.

But then they did everything right, you know, targeted the mid-size and small businesses. They can make use of data. A lot of foundation is being built on that data platform. If you don't have it, AI doesn't exist for you. I can only do the things that it can read from internet, but what about your own data? And that's where Microsoft did things right. And that actually taught me something that your protocol layer matters the most.

You know, the foundation layer matters the most. Then you can build upon it as the foundation of the house, as we would say. And then you can build X number of, n number of buildings on top. And that's something what I can relate to. it. I'm coming towards the closer. I have one thing where the audience can make use of it. So last thing, five concrete moves a listener can make Monday morning to start building the accountable way. Go. First point, you have really to define what is the final result for the agent.

Because with this final result, the result is acceptance criteria. you will never understand if it was built the right way or not. So this is how we start building every whole project. They are starting this acceptance criteria. This is what must be done in every task. The next one is, as I mentioned, decide. Design system for your whole product, for your whole startup is the must. You will just save huge amount of hours for River Rock.

So build a design for us before you start coding, and it will save you time, money, and energy. The third point is all about evidence. How can you prove what this specific task was finished by this agent? Five years later, ten years later. What is the audit file where you can check how this software where this huge product was built, which tests scenarios you have formed on it, and they saved forever, so nobody can change it.

The next point is security. If you want to build AI-based software, always integrate your security agent in the project, because otherwise you are just one of these, like 40% of startups, which are building this AI and have security. issues in production code and you are just on risk. In the last point, never trust the agent who is telling you it's done. You have to see the evidence. Our agents, an example, they are saving screenshots of every functionality of what is done.

Later is functionality is approved by our senior developers and then we know exactly okay. This is what was finished, our guess is this, and this is what can launch live. Otherwise, it will be just a fight against the bucks and great five pillars you've given to them to get started right this Monday and be motivated. Alex, thank you for this 60 minutes. The line I keep coming is that speed is no longer a vote. It entices me. And accountability is if you want to follow Alex's work, go to BRNZ.com. do.a.ai and also find him on LinkedIn. He has a couple of things for you there, which we will put in the show notes as well. Subscribe to ThinkiI podcast wherever you listen. We have real stories, real systems, and real AI. I'm Dave Goyle. See you next time. Thank you, Dave.

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