Is This the Cheapest Way to Use AI in 2026? (65+ Models Tested) | Iskander Malikov, Trioma

The Three Layers of Trioma

0:00Something that would have taken months if not years of development. Trioma did it in like 10 minutes. It is exciting and scary. There are a lot of these companies out right now that are offering multi-model access with a single subscription. Trioma does something a little bit more. Most people actually want certain things to get done. If that's what users want, why not let them tell us what they want by simply entering a prompt or recording their voice and letting us know what they want. And then the orchestration engine will figure out the best way to get there.

0:33We live in a world where so many new applications will emerge because now everyone have tools to build something truly unique. Trioma is a vertically integrated AI ecosystem where there are three separate layers. The top layer, which we call the interface. It includes the web platform, the desktop application, as well as iOS and Android apps that are coming soon. And then there's the mid layer, where we have 65 different AI models, as well as the orchestration layer that connects them and makes them work together.

1:05And then there's the bottom layer, which is GPU, essentially chips that power that compute. And all together, we call it Trioma as we have, yeah, three layers, essentially.

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Orchestration Over Chatbots

3:13There are a lot of these companies out right now that are offering multi-model access with a single subscription. Trioma does something a little bit more rather than the user picking the model. Trioma routes the model to the appropriate model depending on the task. Is that right? Can you talk about why this is the new model for access and who is the user?

3:52I think the old model, the original behavior was something similar to a chatbot where most users were utilizing AI the same way they were sort of utilizing Google search or any other search. They were asking a question and expecting a response. Over time, that response has shifted towards an actual result, whether it's an image, an audio file, a video like initially was a brief video. Now we can do like proper long videos, but it's still a question and answer type of interaction.

4:23What we've realized is that most people actually want certain things to get done. So when they interact with AI, they are mostly interested in the result itself, not so much the process, the reasoning, how the AI is processing your specific prompt. And we thought, OK, if that is what users want to us, users always come first. So we thought, OK, if that's what users want, why not let them tell us what they want by simply entering a prompt or recording your voice and letting us know what they want.

4:54And then the orchestration engine will figure out the best way to get there. And it could be a combination of different AI models. It could be a single model. It could be an orchestration engine, again, that utilizes different models to pick the most optimal and efficient model to get the job done. But what we thought about is that they need the end result. And to answer a question like, who is this for? That is something that we've been thinking long and hard about. Like, who are we actually building this for?

5:24Like, do we want to go with a specific audience, let's say, content creators? Or do we want to go with, say, coders? Or do we want to build something for just, you know, average people like stay at home moms, for example, to let them handle their like household stuff, automate their certain things that they do at home. And we thought that we should not be limiting our product to a specific group of people. Instead, we should go wide and we should let everyone use AI. Because one of our core principles is that AI has been trained by using universal knowledge.

5:58And therefore, everyone should be able to fully leverage that knowledge to be able to gain access to the intelligence that has been combined into those models that was, you know, the intelligence that was trained to build those models. So that's one of our core concepts. So to answer your question, it's for everyone. But consumer as opposed to enterprise? Or are you looking at both? Both. We're seeing different use cases. So consumers are mostly interested in things like content creation, coding.

6:28They leverage agents. They leverage the agentic workflow where different models work hand in hand when the orchestration kicks in. But the same thing applies to businesses as well, as many of them actually need agents in their businesses to build websites, to automate certain workflows, to automate certain processes, whether it's customer support, compliance, all sorts of things. So we're seeing demand for both. Most of our business customers are interested in the agentic workflow where they automate the entire flow.

7:00Consumers, on the other hand, they do a bit of both agentic as well as sort of question and answer type of interaction. And the combining, the orchestrator, it's more than routing, right? You're not just deciding, oh, this task should go to Gemini or this task should go to ChatGPT. Does Trioma break down tasks into subtasks and use an ensemble of models to get the task done?

7:32Absolutely. And that is one of the core functionalities of our engine. Essentially, it understands, first of all, it analyzes your prompt. So this is actually some of the feedback that we've been receiving from customers. They're saying, oh, like when I use a Frontier AI model, it's instant, right? So they're saying, oh, like if I'm using AI, it should be instant. Well, our response is like, it could be instant. But if you really wanted to understand deeply what you're trying to do, especially if your prompt is not something that is simple, but something more sophisticated, it takes time to analyze it and to understand which model or like which models should be utilized to get the job done.

8:10And that includes both orchestration that uses and leverages models that are picked based on efficiency, but also models that can get the job done. What I mean by that is certain models can get can be trained to perform certain tasks, but they could be censored not to do certain things, especially this. I mean, this this is oftentimes the case with some more US based private and closed models. And that is where orchestration shines, because it literally picks the different model to work on that particular task where censorship is stopping it from completing it.

8:48And then it goes back to the original model, which is more efficient, for example. So everything happens under the hood. The customer doesn't need to worry or the business doesn't need to worry about what's happening. What the customer needs to get is the end result. And all of that orchestration is essentially required to deliver the best result and consume the least amount of tokens for the customer or for the business.

Token Costs and GPU Networks

9:09And on the token consumption, this has become a real issue, particularly for enterprise, because while, you know, these models and particularly the agentic workflows can get a lot of stuff done. And they can also burn a lot of tokens. As these foundation models grow, there's a certain amount of cost arbitrage involved in your platform, right? You're you're able to obviously, otherwise it wouldn't be a business, charge more than than your spending in tokens.

9:49How do you do that? I mean, do you do you get a discount from the foundation model companies as as effectively a reseller? Or can you explain how that works and what does that mean for foundation companies? If people start using third party interfaces? Yeah, a couple of things. So first of all, we have direct relationships with many of our partners, including Frontier AI Labs, Research Labs, some of the big names in the industry, which I'm sure everyone on your podcast are familiar with.

10:29We have enterprise deals with them, meaning that we get a discount, kind of like a bulk order, so to speak, where we are able to bring in volumes and get a discount in return. At the end of the day, these are commercial organizations, right? So they're interested in making profit. And so are we. And to them, we are a reliable and stable customer because we are a business that consumes millions and billions of tokens. And they understand that it makes sense to have a long term contract with us versus having millions of contracts with individual consumers who can decide to just, you know, cancel their subscription.

11:08I mean, the churn rate with these SaaS companies is oftentimes insane. So it's a different business, right? So it has its ups and downs, but it's a different business. When you're dealing with retail, when you're dealing with consumer, there are a lot of factors you need to keep in mind. And this is coming from me as I was building and scaling several consumer applications from trading, from charting. So I understand how sometimes difficult consumer apps can be. We, on the other hand, are a pretty clear business to them, to our enterprise partners.

11:43And they're interested in having a long term relationships with us because, in a way, we are purchasing these tokens in bulks through an API integration. And we are the distribution layer for them. And why this is beneficial to both us and them is because we're building the community. We're building the actual user base. And we're distributing these models. But we're also enhancing them in a sense that we combine different models and make sure that customers get the best of everything. So they get the combinations, like a bit of a cocktail of different AI models that work in tandem, or sometimes three, four, five different models.

12:22And the end result is always better than what they would otherwise get if they were to work with a single company. That's one thing. The other thing is something that we've realized, and we actually thought about this enterprise consumption. And you're absolutely right. Companies today are consuming a lot of tokens. And if you remember earlier this year, there was this concept of token maxing. And a lot of corporations were actually encouraging their employees to sort of consume a lot of tokens. I think that's changing now, which is great, because I think a lot of that spend was probably not optimal, so to speak.

12:58And we thought about this. And we're like, OK, if there is so much demand for an AI token, how do we make sure that our clients, both individuals and businesses, are set so that they don't have to overspend on tokens and they can build their agentic workflows and automate their workflows and not think too much of an O-like. At some point, if the price of tokens keeps increasing, they will figure out that it's cheaper for me to hire someone versus letting the agent do the work.

13:29And we're kind of seeing that in some emerging economies already. So it's actually cheaper to hire a person versus paying an agent through tokens. So what we've built is something that we call TGN, is the Trioma GPU network, which allows our customers, both businesses and consumers, to participate in that GPU build out. Essentially, they're able to buy a seat. It's a software license that they can participate in. And that software license allows them to generate tests, which is the energy stream.

14:01And that energy stream can be swapped for AI credits. So they decide what they want to do over time. They want to participate in that network build out and potentially profit from that, or if they want to spend it on their AI agents, for example. And we're seeing different results from different cohorts of users. Some of them are essentially token maxing, and they're mostly spending it on their agentic workflows, and they're swapping it for AI. And some of them are just simply interested in seeing that growth in the AI industry, because they understand that this is the hottest sector on the planet, and they want a piece of it, basically.

14:33Wow. Explain that. Break that down a little bit, because that went by pretty fast. So you're building out this GPU network. Is that through virtual private clouds, or do you have your own data centers, or how are you doing that? Currently, we're renting GPUs. We work with cloud companies, so we rent them. But we are slowly moving towards ownership. We understand that the margins are better.

15:04We're able to do a lot more when we own the actual chips. And one of our core principles is actually decentralization and AI sovereignty. And we can't really be fully sovereign if we don't own the actual chips, the actual GPUs. So we're getting there. But to answer your question, the way it works is, essentially, we purchase the GPUs. The GPUs are then being rented to companies, and they're also being utilized internally by Trioma. As we deploy open-weight models, and we're able to improve our margins, and we as a business are able to essentially become more efficient.

15:41And that margin, that efficiency is then being passed to our customers. So this is how customers benefit, and this is how customers are able to get additional models, and how they're able to participate in that build-out. And what's the meter or the mechanism for passing that along to customers? So the energy, the Trioma energy. So this is why we call it energy. It's literally a metric for demand of compute. And as we're seeing today, demand for AI computers is basically skyrocketing.

16:16And a lot of people want to participate because of that. But essentially, yeah, so our users, both businesses and individuals, are able to decide what they want to do with that compute. They can swap it for AI credits, or they can keep it. So it's up to them to decide how they want to interact with AI. But we give access to AI ecosystem build-out to individuals. And the beauty of that is that they're also able to see the actual product, how that compute is being utilized. So it's not very often where you see a company that can let you participate in the underlying chip that powers everything.

16:52And the interface is just the front-end layer that they interact with. But the actual compute happens at the bottom layer, which is GPU. Yeah. So if I'm a customer and I want to participate in the build-out, what do I do? You just buy TGN, just participate in our TGN infrastructure. So we have a section called TGN, Trioma GPU Network. And anyone can participate. I think the minimum purchase amount is something like $30 or $50.

17:23I'm not sure. And we support all payment methods, whether it's bank transfer, credit card, stable coins. Yeah. So once people realize the potential and how they are able to participate in this, they get excited very, very quickly. And so first of all, I have my subscription to Trioma. And then I buy in the Trioma GPU network a certain amount of energy. Is that right? Yeah. You buy TGN, which is your slot, basically.

17:57You participate in that GPU infrastructure build-out. And that generates energy. So your GPU generates energy. And then you decide what to do with energy. You can swap it for AI credits, or you can withdraw it if you no longer want to participate. And is this a way to get a discount, or does this actually generate revenue at some level? Both. We encourage our customers to swap their energy for AI credits, because obviously we want them to use AI.

18:32And this is our way of making AI more accessible to them. But if they no longer wish to use AI, for some reason, they can always withdraw and not use it on Trioma. I see. So it's a discounting method. They're not withdrawing revenue from TGN. If they no longer wish to participate, they can withdraw through stable coins. But so far, the behavior that we're observing is most of them are either just buying more because they see this hype around AI, or they're simply using it for AI credits.

19:04And we're seeing a lot of heavy usage on our platform, quite a lot of power users who are consuming, as you said, many of these tokens. And to them, TGN is a perfect solution, because they pay for these tokens anyways, through Frontier AI models or through aggregators. And they realize that on Trioma, they can purchase their slot once, and they no longer have to think about it constantly, as long as the test generation satisfies their token needs, so to speak.

Subscription Limits and Arbitration

19:33When OpenClaw was out, there was a bit of controversy, because it was essentially doing an arbitrage on the Anthropic models, I guess. And Anthropic shut that down. What was that about, and is that something you have to worry about? Well, I mean, we have to understand that all of these companies, they are commercial organizations, first of all. And they understand that this whole subscription model is something that is a little bit unnatural to AI companies.

20:12And the reason is very simple. Normally, when we think of software or SaaS products, like software as a service products, we think of a company or an organization that has spent some time, built something, and then scales this and releases this to the max amount of people. The cost of extra user is tiny. You're basically paying the servers, you're covering your server and infrastructure costs, but you don't really have extra spend because of hundreds or thousands or millions of new users. With AI, it's different, right, because every single user generates something, whether it's text or images or content, and every single generation spends actual compute, right?

20:52It doesn't just come out of nowhere. There's an actual chip that does the actual processing. So as you scale, as you have more people and they're fully utilizing and maxing their subscriptions, very quickly, you realize that you're probably not running a very profitable operation here because your subscriptions may end up giving more tokens than the actual cost of that subscriptions. And as companies realize that, and as more and more organizations and coders realize that gap, they start arbitraging the tokens that they're getting through those subscriptions and the actual cost of that subscription.

21:30And this is why we're seeing all of these limits that are being introduced by Frontier AI Labs. And I think very quickly, we'll see more limits. And eventually, in my opinion, we're just going to shift towards AI usage. Simple as that. At Trioma, we've realized that very, very quickly. And we thought, OK, if someone is using AI on a regular basis and they want extra value, they're able to buy our subscriptions. And our subscriptions are very, very straightforward. They just give you extra AI credits, basically, as we value these customers and we give them some extra incentives if they know that they're going to spend at least $20 or $100 a month.

22:08But if they are power users and they don't really know how much they end up spending, if it's going to be $500 or 5,000 users, these are API customers. And I think many of these Frontier AI Labs are realizing that as well. And they're starting to limit those subscriptions. And they have to be careful because they have millions of customers, right, that they can't just disappoint them all at once. So they're introducing these things. And also, there's a lot of competition coming in from different parts of the world where people are realizing that maybe I don't need the best model.

22:40Maybe I don't need to follow the latest sort of PR stunt or marketing trend and use the latest model just because it has been promoted to me. Perhaps for my particular task, an average model is just as fine, but it costs like 10 times less. So people are getting smart about this. And I think this is why it's so important to ensure that people get access to all of these models and they're not tied to a single model. Because at the end of the day, we should be able to give people a freedom of choosing what model they're comfortable with.

23:10I mean, there are companies that offer multiple models and offer routing to the appropriate model and even offer ensembling where they put together, you know, they split the task between different models. Is cost becoming the competitive edge as more companies do this? I would say cost becomes an edge, but not in terms of token price or price per millions of tokens, as we usually count it, but the cost of the actual result.

23:51So you're no longer looking at, oh, like this company charges X amount of dollars for like a million tokens. You're looking at, oh, how many iterations it took me to get to a certain result to get to a result that is that I'm satisfied with. And if you realize that you have a, let's say you're, you're working on a single platform and it, it costs you like 20 or $30 to get a, say a document done or an image edited. And, and it took you like 15 iterations. Whereas on another platform, it took you like you've one shot at basically, you did a one shot with a single prompt and then maybe like had an additional prompt just to fine tune a little bit.

24:28And it costs you like 10 times less, you realize that the price of token is meaningless if the end result is like 10 times cheaper. So I think people are getting very knowledgeable about pricing. The, the, the arbitrage opportunities are, are, are starting to disappear. And, and, and this is, I think it's, it's just normal, like as, as AI proliferates. And as we see many of these new models emerging, it's just normal that at, at some point, eventually, um, I think, uh, most people will shift towards the result, um, and rather than picking a specific model based on price or some other parameters.

Launch and Global Model Access

25:06What just launched something just launched, uh, yesterday, I think. We did our actual launch yesterday. Uh, so before that. Yeah. So we were, um, um, um, building our products since January. Um, and we have a, a, a pretty, uh, say hardworking group of individuals, coders who've been building the product. Uh, and we've been collecting a lot of user feedback from, from day one. And, uh, uh, yesterday was our official launch, uh, where we're fully like live and we users were able to sort of use Trioma since August.

25:42But I think yesterday we, we had like a proper launch and now we're, again, like we have, uh, quite a few users, quite a lot of demand coming in and we're collecting that feedback to see what people are mostly interested in. Whether it's content creation or coding, uh, or API usage for businesses, et cetera. So we're letting people decide where Trioma should go. So we, we really want it to become a community driven platform so that people can tell us, uh, which direction we should be heading. And you mentioned access to open weight models.

26:14Does that include Chinese models, the GLM and Quinn and all of these other powerful, uh, Chinese models? Yeah, we have, uh, we have models based out of China. We have GLM. We have a few other Chinese models. Our job is to pick the most capable and official models to get the job done, right? We're not in the business of having 300 models just for the sake of having them, right? If no one's going to use them, what's the point of having them? Uh, first of all, it's not going to be used in orchestration and it doesn't really give you any benefits other than some bragging rights maybe.

26:47But again, that's not what users care about. Users care about results. So we only add models that are likely to be utilized through our orchestration engine. But yes, to answer your question, we don't discriminate. Us being a UAE based company, uh, we're happy to work with both, uh, uh, Chinese models, open weight models, as well as U.S. models and European models as well. And obviously, Trioma is a global product. Uh, are you accessible in all markets or are there some markets that, uh, don't want you playing?

27:21Uh, we, uh, do not provide services in certain restricted markets, um, mainly because our partners that we work with, uh, do not provide services in those countries. And, uh, and we work with quite a few partners and, uh, and we're talking about the sort of the usual suspects, so to speak. Sure. Some of these regions are restricted. We're talking about the North Korea and Iran and maybe a few other countries. And, uh, according to our terms and conditions, we're not, uh, able to provide services in those countries. But other than that, we're trying to be as global, uh, as possible because in our opinion, uh, AI, uh, should be used by everyone.

27:59We should not be limiting people. We should not be limiting, uh, their, uh, their usage. We should, however, give them the tools and warn them about the responsibility that they should be taking when they're using those tools. Because these tools are becoming very, very capable today and those tools in wrong hands, uh, can be misused or used by bad actors. So we're being very careful about who actually uses this. Uh, and that's a very fine line, right? You, you actually want people to benefit, but at the same time, you don't want bad actors to, uh, uh, misuse your tools.

28:31We view it as individual responsibility of the person who uses these tools, but there are certain limitations that we have to follow, uh, due to certain agreements.

Pricing Transparency and Routing

28:41And partnerships that we have, you know, I use, uh, perplexity, uh, but it gets very expensive, uh, to, to, to use all of their routing and, and, uh, orchestration. I think, uh, that's their max level, which I don't pay for. Uh, do you match all of their capabilities, uh, at just at a cheaper price?

29:13I mean, how, how do you compete with the behemoth like perplexity? When we set the pricing, we set a, a very reasonable margin to ensure that our customers get the best value. So, um, if you look at the, uh, sheer token price, it may be on par, if not lower, uh, than some of these, uh, larger companies, because we're in the business of proliferating AI and making sure that AI is usable by consumers, by individuals.

29:44And the only way you can, you can make that product huge if, if, if, if you keep your prices low. So we're in, we're in the business of obviously keeping them as low as possible. At the same time, we also understand that consumers are looking at the actual product and how much it costs them to get a certain job done. So if we can optimize and let them use, because people generally don't, uh, uh, always know, and, and, and there's nothing wrong with that, uh, what model to use for a particular task, because they normally pick a single model and then they start doing some work with it.

30:16Expecting that this model is, maybe they understand it may not be optimal and ideal for all tasks, but maybe they don't want to switch constantly back and forth. So they're just picking a certain model and they're doing the work. We, on the other, on the other hand, understand that different models charge are charged differently. And if we're able to let our users spend less money, it doesn't really matter that much, uh, whether like our, uh, tokens are like 10% or 10, 20% cheaper or more expensive. What matters is how many tokens you've actually used at the end of the day and what models have been used.

30:49Uh, so it's the end result and the total price that matters versus individual token price. And, and we're being very transparent about this. Our users are literally able to check their dashboard and see how much they've spent from every single model. Uh, and they can compare like our token prices to our competitors. They usually don't do that because they're happy with the result they're getting and how much they end up paying for it. But if they want to, they can always compare as we're being very, very transparent about our pricing. So one of the advantages is you don't need, as I do, uh, five different subscriptions and manually switch between tabs on your computer or, or apps.

31:30Uh, but the other advantages that, uh, whereas there are other companies that are offering these orchestrations of multiple models, it's just cheaper or, or in the end, it's cheaper if, uh, if you're more efficient in finding the best model for the task.

Prompt Engines and Desktop Coding

31:52Can you talk about the orchestra that seems to be kind of the, the heart of the offer? Uh, how is it built? Uh, presumably it's an LLM or an ensemble of LLMs. Uh, yeah. Can you talk about that? I think our CTO would be the best person to ask this question. Uh, all I know is it is constantly being updated as we add new models and there are certain weights and parameters that are constantly changing, but, um, we have a team of, uh, brilliant engineers and they keep, uh, sort of fine tuning this engine as we, uh, as we speak.

32:31I wish I could tell you more, but basically it's the, uh, it's the, uh, the, so on one hand you have the, the actual engine that utilizes different models and routes, uh, based on, uh, particular models and, uh, tries to pick the most optimal model for a particular task. On the other hand, you also have, uh, sort of an internal translator, so to speak, that understands your prompt and tries to get the job done, uh, based on your prompt.

33:05And if you remember in the early days, we had this term called prompt engineering as people were supposed to learn how to talk to AI to get the best, to get the best possible result. So we thought from day one that this is, this thing should not exist. People should not learn how to talk to AI. People should just talk like, like they normally do. And the whole point of AI is to make their lives easier and simpler and better. So they should talk to AI the way they talk normally. Uh, and this is what we've been working on. And it's part of our engine.

33:35Actually, it recognizes and understands normal speech, normal chats. Uh, it analyzes those prompts and gets the job done. It's part of that engine actually. Uh, because if you look at some of our platforms that focus on content creation, they have a whole bunch of presets and a whole bunch of complicated prompts that are insanely difficult to reproduce. Like a normal person would never be able to like describe all of that. Um, and here at Trioma, we understand that this is not how normal people interact with AI.

34:07They just literally tell what they feel. Uh, they tell what, what they want to, uh, see at the end. And it's the, the AI's job to understand that and to get the best possible result. So we've been building that for, since, since day one. And on the coding side, um, if, if somebody wants to use Trioma to code, uh, you have access to the underlying coding models. Yeah, I think on the coding side, uh, we are mostly utilizing our desktop application.

34:40Coders love it. It gives them everything they want. It gives them their, their space. They're able to, uh, use the terminal if they want to. They're able to leverage different models. They're, they're able to use orchestration. So desktop application is something that our coders are very much interested in. Uh, most of the feedback that we collect from, from our coders is around desktop. They love it. They just want to like extra features. Uh, we haven't really seen a lot of, uh, web platform usage from coders, although like they can, they can use that too.

35:14But, uh, just, uh, the, the capabilities of desktop applications are far superior to web platforms based on the specific privacy settings of your web browser. It is simply not able to access certain folders and applications on your computer. Whereas, uh, desktop really gives you the, the, like the autonomy and, um, and the full power of your local machine where desktop applications installed. And so far, I mean, I know it's very early days. Do you see usage clustering in, in, in a certain domain?

35:50I mean, is it, uh, just research or agentic workflows or, uh, coding? I mean, what would, do you see any early indications of where users are going? We saw a bit of a shift actually. Initially, when we just launched, we saw most people using AI, like they're using, uh, Google search. Like they're asking questions, et cetera. Then we saw two cohorts of users, mostly content creators. And this is why we've built a studio for them because of the demand that we've had for, for those types of tools and products that they wanted to utilize.

36:25So we're seeing a lot of demand from, uh, content creators and coders. Currently, uh, what is changing is that people are now trying to autumn. And, and, and this is mostly coming from, uh, more like advanced and sophisticated users as they go through this learning curve, as they sort of mature in their understanding of AI and capabilities of AI, they no longer wish to simply ask questions and get the result. They now are trying to automate this. And we're seeing a lot of this agentic workflow picking up.

36:57And because of that agentic workflow, there, many of them are now switching towards the desktop application to be able to automate these processes and workflows. So this is the behavior we've been observing lately.

Vibe Coding and the Death of SaaS

37:09Yeah.

Vibe Coding and the Death of SaaS

37:10So it basically started as a chat bot, as a search, uh, engine. Then it sort of shifted towards, I ask a question, I get a response. And now it's, it's all about agents basically. Yeah, I've been talking to people about, uh, the future of SAS generally because, and I'm not necessarily talking about the big, very complicated, uh, SAS offerings, but with this, uh, agentic coding, uh, you can build an application.

37:41I mean, I was, uh, uh, I was, uh, talking to a company the other day and I wanted an integration with, uh, Hey, Jen, you know, the, uh, voice generation, uh, company. And they said, well, we don't have it yet, but you can build that with your, uh, coding agent. And, uh, you know, so a lot of work, or I've seen people talk about Photoshop, you know, that you can build, uh, essentially a Photoshop application using coding agents that doesn't have the myriad of options that confuse everybody that is built really for your use case.

38:30How do you, how do you, how do you see that developing now that these models are becoming so capable and does that touch you guys at all? It does. Absolutely. First of all, that's bad news for SAS companies. I think, uh, uh, as we, as we're seeing many of these SAS companies are struggling, uh, for, for the exact reason you've just mentioned. If you remember in the early days, uh, internet was all about like emails and if you, it was mostly communication, it was a channel to transfer data.

39:03Uh, sometimes at the, in the early days, we thought as just a more efficient way of transferring emails, for example, oh, like it's better than fax or like, like you're able to search. You can look, look something up. Then we started filling internet with data and that's where the social media, uh, social networks, uh, came along, et cetera. And then SAS was really shining in, uh, like 10, 15 years ago, uh, mainly due to, uh, the capabilities of modern browsers as HTML5 became available as a technology.

39:33You're now able all of a sudden to transform a lot of heavy desktop applications into the cloud, into the browser. And I was part of it, right? When we were building and scaling trading, that's exactly what we did. We tried to use the analogy of, you know, uh, Microsoft Word and Excel versus Google Docs. Uh, even today, like Google Docs is not as powerful in terms of capabilities as your good old desktop application, but it's good enough for, for most people. And they're like perfectly fine. But the, the fact that you don't have to install anything and you can easily share your files, uh, is, uh, very convenient for a lot of people.

40:07And this is why many people are not even using these desktop applications any longer. So that was SAS, right? That was the beauty of SAS. It was giving the tools to people, uh, and they were able to do everything from their browser. They didn't have to install anything. It was cross-platform. It doesn't matter if you're on a Mac or on a Windows. It was just great. But now you no longer have to do the work. You literally tell AI what you want to do. So the whole concept, the whole notion of SAS was around giving the tools to people, giving like sophisticated, powerful tools, but they have to do the job, you know, with their own hands.

40:39But now you just tell AI to do it for you. So if you look at tools such as Figma or Photoshop, why would you do it by yourself or hire someone when you can just tell your AI to draw it and model it and build it for you? Um, and, and that impacts many of these companies and including your use case. We live in a pretty weird age, I'd say, where it's easier to vibe code an app than to, uh, look it up or find it and download it.

41:10Um, so we're seeing a lot of that, um, uh, uh, lately, uh, and Trioma is actually, uh, benefiting from that to be completely honest, uh, because, uh, many of our users are, uh, experimenting, uh, with different models with the orchestration and they understand that their capabilities are increasing as we add more models because now they can, uh, you know, pick and choose or think you just leverage the auto mode, the orchestration mode, uh, through desktop. And to give you some interesting examples, my co-founder the other day, uh, vibe coded his own messenger app and it took him like 10 minutes.

41:46And he was like, Oh, like get on this meeting. I was like, Oh, like, are you sending me a zoom link? I was like, no, like I'm sending you this invite. I was like, Oh, interesting. I thought it was going to be like this clunky little thing that will be buggy as hell, but no, like it had like proper audio, video, webcam support chats. I was able to create like groups and everything. It literally looked like a proper application, something that would have taken months, if not years of development. And, uh, Trioma did it in like 10 minutes. Um, so it is exciting and scary, but also again, exciting in a sense that we live in a world where so many new applications will emerge because now everyone have tools to, uh, to, to, to build something truly unique.

42:28Um, development is no longer a scarce resource. What is important now is the actual product that you're building for people. Like, what are you trying to build? Like, how are you planning to, uh, to service, uh, consumers, businesses? So the, the community and, uh, and people is becoming more important than ever because now you can no longer just focus on building something and expecting that people will come. If your product is good. No, now you have to focus a lot more on the people. And I think we live in a beautiful age now because, uh, we're going to see a lot of amazing applications.

43:03So Trioma, you were saying there are three elements in the, the, the make it try. Can you go through there? There's, uh, uh, orchestration, Trioma. Yeah. The reason why we call it the three is because we have the three elements, which is the TGN, Trioma GPU network. Then there's TESS, Trioma energy stream. And then there's TIE, Trioma AI credits. So TGN is the GPU infrastructure to build out. Uh, so people can participate in that. TESS is the energy stream.

43:34What essentially TGN generates. And TIE is AI credits. What we spend on prompts, what we spend on AI credits. Together, they form this loop, uh, which we call Trioma.

Video Production and Global Communities

43:47So where, where do you see Trioma going? Where do you see the industry going, uh, as things are changing so fast? And are you able to keep up with the model releases? So do you have the latest anthropic models or the latest open AL models? How long does it take for you to integrate those into the platform? Our devs are usually pretty quick. As soon as these models are released through an API, um, we add them in a matter of days, sometimes hours.

44:22Um, so I'd say we are pretty quick on that front. Uh, what takes some time is optimizing the engine. So it's not like, just like adding a model itself does not take a lot of resources. Making sure that model is properly utilized within that engine is what takes time and what takes a lot of development resources and effort. Um, again, as we're focusing on getting the best results for our consumers or businesses, we make sure that new model that we've integrated is properly integrated into that engine is only utilized when it's needed.

44:55So the capabilities of that model, uh, are being leveraged for a particular task, which that model is very good at. And what we're observing today is that there is no single model that is good at everything. Even, uh, even some of these models that make bold claims and statements that are like, they're multimodal. They have different capabilities in terms of generating content, in terms of, uh, text, copy creation, et cetera. Uh, they still have certain limitations because at the end of the day, they were trained by people.

45:25They were trained by certain intelligence. So there are certain limitations. Uh, however, when you combine these models, you multiply that intelligence. And that's what we're trying to build. We're literally giving access to people, uh, and we're, we're, we're letting them use the combined intelligence of all of these models. And have you, presumably you have, uh, done these tests, uh, which I've done, uh, I'll pick a model through somebody that provides multiple models. I mean, I'll say perplexity, but there are others.

45:58So I'll pick, uh, you know, fable in perplexity and I'll run a prompt. Uh, and then I'll go to anthropic and run the prompt, uh, through fable directly. And I get different and I get different and I'm not talking about the probability of, of answers. You get a slightly different answer every time you ask any model, but, uh, there seemed to be a difference in the capability of the model when I was doing it directly.

46:34And through, uh, through, uh, perplexity. Is there, is there any difference, uh, in running a prompt through a model within the Trioma stack and running it through the foundation model directly? Yeah, absolutely. Uh, a couple of things. First of all, um, when we interact with, uh, Frontier AI labs, research labs, uh, we don't always know what they actually utilize, uh, under the hood.

47:07So it's, it's a bit of a black box to a lot of, uh, users, um, as they have their own orchestration and we don't really know what model was used for what, because like we can pick a certain model, but it doesn't mean that that specific model was utilized. And it was utilized all the time. We have no idea what was actually used. It's a bit of a black box. And, and secondly, on Trioma, because of the orchestration, uh, you may, uh, select the auto mode on Trioma, or maybe you can even select a specific model and also select that same model on the, uh, the, um, uh, the Frontier AI lab website.

47:46And your result could be different because they have an orchestration as well. So basically everyone has it. It's just a matter of, but, but they would be limited to their own models, but we would be able to offer all kinds of models that we have integrated. So the result is always going to be slightly different based on the prompt. If it's a simple prompt, if you're asking, uh, Trioma to build a website and the Frontier AI lab to build a website, the result could be completely different. Um, because again, because of the orchestration, if you turn on the auto mode on Trioma, then the result is going to be way different because then the orchestration,

48:21the orchestration truly kicks in and now you're leveraging these different models, uh, for like audio, video images, copy, et cetera. You guys, and this is a, uh, selfish question. Uh, you know, I do these interviews, uh, live and I end up with four video streams and an audio stream, you know, two cameras, four cameras, and then audio. So, and I use a couple of different programs to try and manage that, but it's, uh, it's a lot of compute and, you know, to try and, uh, sync the videos and then edit switch between camera views and stuff like that.

49:08Would Trioma be able to handle something like that? Or is that too specialized? No, absolutely. We actually have a real film director on the team. So, um, I would encourage you to, uh, check out our studio, uh, on the web platform, uh, as well as desktop application. Um, I'd be surprised if it would not be able to handle your specific task. It literally has everything you've just described. Um, we're actually able to generate up to 60 minute videos on Trioma. Wow. Even though in the model itself has a limitation of 30 seconds.

49:39So yeah, uh, I'd encourage you to give it a try. Yeah. Okay. Uh, so you, you guys have just launched this, um, and where do you go next? I mean, this is a massive market. As I've said, there are other companies doing similar things, but the market is so huge. Uh, do you pick a geography or pick, uh, uh, a particular use case, or as you were saying at the beginning, decide whether this is a consumer or enterprise, uh, application.

50:17I mean, where do you go from this from here?

Sovereignty and the Future of Work

50:20Yeah.

Sovereignty and the Future of Work

50:21I think the beauty, the beauty of AI is that it's a truly global thing. So we're not limiting ourselves to a specific geography. Uh, we're seeing a lot of users pretty much everywhere, to be honest. I think U S is probably leading the way in terms of consumer apps and, uh, content creation, but Asia also is, uh, creating a lot of content, especially East Asia. Um, so I'd say this is a truly global platform, but to answer your question right now, we're focusing on distribution on users.

50:50We want Trioma to be accessible by everyone while doing so. So we understand that users are everything we're building our community. We're making sure our community is happy. Uh, so our number one goal is to let people know what Trioma is capable of doing, uh, and essentially spread the message. Uh, but at the same time, fully participate in this global, uh, GPU network build out as that is an essential part of our core principles for us to be fully sovereign and to be able to,

51:22uh, deploy our open weight models. This is something where, uh, we have a strong belief in. We think that AI should be available to everyone, that this is global universal intelligence that should be available to everyone. And we'll also think that, uh, AI should be sovereign, meaning we should be able to control what our AI models are producing. And in order for us to do that, we need to have our own GPU infrastructure. So that is something that we're actively working on to enable our users to produce amazing results

51:53and not get limited by certain factors, whether it's a certain model restriction or geographical restriction. So we're trying to proliferate the AI, but also one thing that is very important is again, community and education. The capabilities of these models today are superb. Like these models can do so much and a lot of people, uh, have heard of AI, but they don't really know how AI can benefit them personally. So this is why we're focusing a lot on community so that people trust other people, especially

52:25if it's all within the same community. And we want that community to help each other understand the capabilities of AI through education and, uh, internal communities and groups and chats. So we're very much focusing on the people because I think that will essentially define, uh, how AI is going to, uh, evolve as, as we grow as a company and both as the community of AI, uh, users. And when you say community or focus on the people, you're talking about a relationship between

52:56Trioma and its users, or are you talking about building a user community that communicates among itself? Both to us, it's the same thing. The users who use Trioma and users who talk to each other while they use Trioma to us, that it's the same community. So we want them to talk to each other, to share their ideas, share their thoughts, be creative and help each other. But at the same time, we want to educate them, to tell them what's possible today and how AI can benefit them.

53:27Because there's a lot of, uh, fear today that because of AI, a lot of people are going to lose their jobs. And some of that fear, to be honest, is justified. I mean, AI is actually creating a lot of threats, uh, to entry-level jobs, right? Uh, if we're talking entry-level customer support, compliance, um, many of like legal, like, uh, a lot of, many of these jobs are under a lot of stress right now, especially imagine if someone is a, just freshly graduated and now is thinking about starting his or her career

53:59and you have AI now, which basically is able to do a lot more because of that universal intelligence and, and for a, for a much cheaper price. So we think some of that, some of those, um, uh, concerns, some of those fears are justified. At the same time, AI really empowers people, right? It gives competent people access to knowledge and intelligence that they didn't have access to before. Uh, and, and they're now able to become a lot more competent and productive in what

54:30they do. Um, so in a way it gives extra leverage to competent people, extra power, so to speak. And they're now able to, uh, become a lot more productive people who are creative, um, and who are building things and creating content. AI gives endless opportunities. And we're observing a lot of this in content creation. I mean, there is so much new content being created. We're actually seeing proper series being done through AI. So I think we're going to see a lot of very interesting content, uh, in creative.

55:02Yeah. It's exciting time to be alive. Absolutely.