Transcription
While everyone around is discussing which model will break the market next month, the real battle has already begun right now. GPT5, CL 4.1, GI 3. These are not just names, these are three different ways of thinking, three approaches to solving problems. And whoever first figures out the difference will hit the jackpot. In the next few minutes, you will understand which model will give you the maximum effect for your specific tasks. But for this, it is important not to get distracted, because each subsequent minute will change how you see opportunities. And if we are not yet familiar, I am the digital avatar of Yaroslav Gumirov. Subscribe to our Telegram channel and YouTube if you haven't already. Why have all companies suddenly decided to release their flagships simultaneously? Is it a coincidence, or do they have a plan that no one is openly talking about yet? Now I will show you the internal logic behind this decision, and then we will analyze what exactly is hidden inside each of the new models and how quickly you can benefit from them. First on the list is GPT5 from OpenAI. Look carefully, many expect GPT5 to be just smarter and bigger than GPT4. But this is not the case. OpenAI is making a completely different move, and it could change the entire game. Instead of one universal model, we now have three options: Bulbasaur, Squirtle, and Charmander. Yes, it sounds funny, but believe me, there is no joke inside. Each version is tailored to its own level of task complexity. Simple tasks: go to Bulbasaur. Need a bit more brainpower: Squirtle. And finally, Charmander – a monster for deep analysis and strategic tasks. Why did they do this? The reason is simple. They want to give you a choice, how to pay less and get more. Now, instead of an expensive universal solution, you choose the mode you need for a specific task. Want cheap and fast, get the light model. Need serious analytics and powerful reasoning, launch Charmander. And you know what else is curious? Test builds of these models are already circulating on closed platforms like Winsorf and Open Router. But the most interesting thing is not that. The most interesting thing is why OpenAI suddenly opened access to them, when before they always kept models under lock and key. I'll tell you in a minute, but for now, think about it. Isn't it strange that the world's largest company is suddenly giving away its trump cards so easily to competitors? Keep this question in mind, because the second model on the list is CL 4.1 from Anthropic. Many consider Claude to be just another alternative to ChatGPT, but they don't understand how the market works. Claude is a model for those who are not ready to make mistakes, who cannot afford to be sloppy. The new Claude 4.1 is an AI that is currently undergoing a brutal internal stress test, Red Teaming, to become absolutely reliable. No hallucinations, errors, or ambiguous conclusions. And while you might think this is not important, major corporate clients are already testing it and calculating savings in millions of dollars just by reducing errors and speeding up decisions. Perhaps you are thinking: "So what? GPT is also accurate enough." And this is where the most interesting part begins. The fact is that Claude 4.1 can do more than just answer questions. Its secret lies in its ability to structure complex tasks, break them down into stages, and provide not just text, but a clear algorithm of actions. Why this is important and why it will decide everything in business? We'll talk about that in a minute. But now we need to touch upon the third giant, Gemini 3 from Google. This is perhaps the most mysterious of all upcoming models. Google has not officially confirmed anything, but the community and developers have already found quite a few clues. Most importantly, in the summer of 2025, traces of two new names appeared in GitHub commits and Google AI Studio: Gemini Beta 3.0 Pro and King Fall. The latter is the codename for an experimental version that testers managed to spot for 20 minutes before it was removed from the studio. According to recent leaks, King Fall demonstrated unusually high accuracy in programming and even created a clone of Minecraft in a single HTML file. The main expectations from Gemini 3 are not just an expansion of the context window. Fantastic estimates up to 21 million tokens have appeared in several discussions, but in practice, a range of 1 million is expected, and even deeper multimodality. New leaks feature integration with video models VO. Real scenarios of processing video, images, audio, and texts simultaneously. A separate feature is the Deep Thinking mode, now always on. The model simultaneously analyzes multiple hypotheses, compares options, and selects the optimal solution path. On Reddit and X, it is predicted that the performance increase of Gemini 3 to 2 Pro could be 10-15%. And generation will be faster, up to 1,000 tokens per second. Emphasis on automating complex tasks. From processing large arrays of texts to autonomous work with Google Workspace, YouTube, and Vertex AI tools. When to expect it? Most forums and leaks converge on either autumn of this year or December, following Google's tradition. It is quite possible that an experimental version for a limited number of developers will appear even earlier, which is confirmed by individual Redditors who have spotted internal notifications about the launch of Gemini 3 Flash. Why is this important for the market? Everything coincided with the acceleration of releases by OpenAI, GPT5, Anthropic, Claude 4.1, 1+, XAI, and Chinese Deepseek. So Gemini 3 is no longer a follower, but Google's attempt to set its own standard in both reasoning and multimodality, and efficiency. The most interesting thing is that all forecasts indicate that Google is betting on the simplest possible scaling. New TPU V5P accelerators, instant responses, integration into Android 16, Workspace, Vertex AI. Everything is tailored to make Gemini 3 not just the biggest, but the most flexible and deeply integrated model of the year. If you already feel your perception of what's next is being turned upside down, wait, this is just the beginning. I will show you why it no longer matters who wins the model race and what exactly will be the basis of advantage in the coming months. And now let's return to the main question. Why did companies decide to release their models simultaneously? Why now? The fact is that the AI world will never be the same, and those who manage to adapt first will reap the biggest rewards. OpenAI is rolling out three models as part of GPT5 so that you can more accurately choose an approach for specific tasks. Anthropic is launching Claude 4.1 to give businesses not just a smart chat, but a reliable decision-making machine. Google with Gemini 3 plans to solve several key tasks in one model. They are not competing with each other. They are trying to catch up before the market is filled with powerful open-source alternatives from China that are breathing down their necks. That's the real reason for the rush. Now, a question specifically for you. Why is this important to you? It's simple. If you still don't have a clear strategy for using these models, you are already falling behind. Today you will hear the news, and in a month your competitors will start getting real results. And here it's worth stopping for a minute and thinking about what exactly will change when you can use these models. Let's take GPT5. Imagine what your work will look like if you don't waste money on power you don't need. If you're doing mailings or content, you don't need Charmander. The light one, Bulbasaur, will suffice. The savings in this solution are already obvious. Now think about how you use the freed-up budget. Maybe for analytics or for building new automated funnels. But if the task becomes more serious, you can simply switch to Squirtle or Charmander. And now even deep competitor analysis, complex market research, or building multi-step strategies will take minutes, not days or weeks. How many hours and money will this save, and how many new clients will it bring? Claude 4.1 solves completely different problems. Imagine a situation. You need to build a system of automated responses for customer support, sales, legal consultations. What happens if your AI makes a mistake in the details of a contract or in financial advice? The damage can be enormous, but if you connect Claude 4.1 instead of GPT, which is currently undergoing the toughest red teaming in the industry, you gain confidence that there will be no mistakes. This is what it means in practice. You no longer double-check every answer, you don't spend hours on documents, you don't lose clients due to minor slip-ups. The time for approvals, legal checks, and quality of consultations is reduced many times over, and customer trust grows. Now Gemini 3. Imagine you are launching a new product, and you need to quickly gather and process a huge amount of information: customer feedback, market analytics, competitor data, huge text documents and presentations, videos and images, which would previously have been separate tasks and taken weeks, is now handled by one model: in a matter of hours, or for example, content creation. If you need material that relies on a vast amount of data and precise details, you no longer need to hire an army of analysts and copywriters. You launch Gemini 3, upload arrays of information, videos, screenshots, reviews, reports, and after an hour you get a completely ready content package that will definitely hit the mark. And now the main thing. If you already feel how your work and results are changing just from hearing this, then a real bomb awaits you. Because it's no longer about which model is better – that's a question of the past. Today, the winner is the one who first understands how to use several AIs simultaneously to solve specific tasks. Now I will tell you what will happen when these models start to be combined and why this will be the biggest advantage in the coming months. Here's the main insight. The real breakthrough happens not when you choose one smartest model, but when you know how to combine several solutions at once. This is not just a nice statement, this is what we are already seeing from market leaders. Take a specific example. Suppose you need to quickly launch a new marketing campaign. You take GPT5 Bulbasaur to brainstorm light, catchy content, quickly gather ideas and tests. Then you connect Claude 4.1 to check the accuracy of offers, to ensure there are no errors in legal wording, contracts, and presentations. And finally, you use Gemini 3 to process a huge amount of information and identify insights that your competitors have missed. What do we get in the end? While competitors are playing with one model and waiting for it to work miracles, you quickly and efficiently assemble a solution from the best parts of different systems. As a result, you get a product that is an order of magnitude faster and higher quality than what is on the market. And this is not fantasy, but how companies that have understood the logic of the new approach are already working. That's why market leaders don't bet everything on one model. Because that's an outdated strategy. Today, your competitiveness is determined not by whom you chose, but by how quickly you integrate the necessary models for specific tasks. But there is one important point here that no one else will tell you. I see many people trying to implement AI, but then they get disappointed. They seem to be doing everything right, but they get plastic texts, lifeless mailings, automation for the sake of automation. Familiar, right? The reason this happens is surprisingly simple. Most people still believe that it's enough to just write the right prompt, and GPT will do everything for them. But this is a trap. From the very beginning, you were taught the wrong approach. You try to ask, and AI cannot fulfill requests, it simply copies templates that are already on the internet. It does not create meaning. It cannot invent what does not exist. This is what actually works. AI becomes truly effective only when it has a specific engineering architecture, when you stop asking and start building a system. Only in this case, GPT or other models start to produce not just something, but something that truly brings results. And right now, we have reached the main point of this video. In a minute, I will show you just two steps that completely change how you use AI, two steps after which you will understand why everything was wrong before and what to do now to finally get real results from neural networks. But first, answer yourself a question: are you ready to continue to be content with random results from AI, or do you want to finally integrate it into a system that works always and everywhere without randomness and endless experiments, because what follows will completely change your understanding of neural network capabilities. You will see why your attempts have failed so far, and what exactly needs to be done now to turn AI into a real tool that works for you, and not the other way around. If you haven't subscribed to the channel yet, now is the time, because what you started watching this video for is ahead. So, here are these two steps that completely change the rules of the game with AI. Step one: stop perceiving a neural network as a regular browser or search engine. Most people today still use GPT or Gemini as another Google, asking questions and waiting for a miracle. They don't know how else AI can be used, they don't know how to build clear, repeatable mechanics, and as a result, they get a vague, mediocre result. That's why it seems like AI doesn't work, because the approach was wrong from the start. Step two. Stop looking for a magic prompt and instead integrate neural networks into ready-made micro-automations that provide instant, predictable, and strong results. Here's how it works in practice. You take an already proven mechanic, for example, creating an offer, warming up, packaging a product, or a series of content, and launch it using micro-automation in one click. You don't go through templates and don't ask AI to invent something new. You simply repeat a proven algorithm, getting results immediately, without torment and guesswork. This is exactly what we do within our two closed clubs: NeuroBuilder and Club VA. NeuroBuilders is a place for those who want maximum opportunities. It contains dozens of micro-automations that instantly solve any marketing, content, sales, and analysis tasks. It's a whole infrastructure where you don't have to guess, but can simply connect a proven mechanic and get results immediately. Package a product in an evening, create a warm-up in an hour, write a series of posts in a few minutes. Everything is already inside, without guesswork, based on real cases and real results. Club VA is for those who value a quick start. These are the simplest and most powerful micro-automations that you can implement immediately, without extra settings, without complex explanations. Need a content plan in 5 minutes? You have it. Need an offer structure or packaging in half an hour? Done. Just press a button and get what you used to spend weeks and thousands of dollars on specialists for. This is the main difference between how you used AI before and how it happens within our clubs. Instead of guesswork, randomness, and futile attempts, clear, understandable, and proven solutions. AI stops being an unpredictable toy and becomes your reliable tool. And you have a choice right now. You can continue searching for answers on forums, spending hours on experiments, hoping for a random breakthrough. Or you can connect to an infrastructure in one evening that has already delivered results to hundreds of our participants. If you are tired of mediocre texts, weak content, ineffective offers, and lack of systemization, just follow the link in the description and see what's inside. All the information is there without water and unnecessary words. You will immediately understand how quickly you can get real results with our micro-automations. But note, we have specifically limited entry to maintain quality and provide maximum support. Therefore, you should join right now, before the price goes up and there is still space. Link in the description. Go and see for yourself. Now for the final conclusion, which will completely change your understanding of AI. The most important thing in the new era of neural networks is not the models themselves. Only how you use them matters. And today, this has become the biggest competitive advantage. Thank you for your attention.