The future of neural networks: why neural network aggregators appeared
EJust a year and a half ago, the workflow of a content designer looked quite simple. You need to write text - open ChatGPT. If you need a picture, you go to Midjourney. If you need a video, you launch Runway or Kling. If you want voice acting, you are looking for another service. For music - another site. For presentations - the next one.
CAt first, this arrangement seemed quite convenient. New models were not released every week, and the transition between services was perceived as an inevitable part of the work. But then the market literally exploded.
K In 2025, the number of generative models has increased significantly. OpenAI, Google, Anthropic, xAI, Runway, Pika, Kling, MiniMax, Ideogram, Flux, Recraft, Suno, Udio, ElevenLabs... Almost every month new AI models appeared, each of which promised to become the best in its field. At some point, it became clear that the problem of a modern specialist is no longer a lack of opportunities, but an overabundance of them.
PImagine a typical morning for a marketer.
CHe started by opening a neural network to come up with a concept for an advertising campaign. Then he copies the result to another model, because it writes selling texts better. After that, it switches to the image generator, then to the video creation service. Then he goes to a separate model for dubbing the video. After this, it returns to the text model again, because it is necessary to rewrite the prompt, which was not suitable for generating an image.
And so - dozens of times a day. Work begins to resemble not a creative process, but an endless journey between browser tabs. In response to this chaos, the first neural network aggregators.
Aaggregator is, in simple words, a single platform that combines dozens of different AI models in one interface. The user stops thinking about where exactly the required function is located. It simply selects a task, rather than a separate neural network. This approach has proven to be especially in demand among those who work with content on a daily basis.
Community of neural networks or one universal model?
KWhen the first conversations appeared about the need to somehow facilitate the simultaneous work with several AIs, many expected the emergence of one ideal neural network that would be able to do absolutely everything. And hope was in ChatGPT. But practice has shown the opposite. Some models are excellent at writing long analytical texts. Others create photorealistic images. Still others generate videos. The fourth ones do a better job with voice acting. The fifth ones know how to write music.
PIt turns out that the universal solution is a kind of community of neural networks, where each is responsible for its own strengths. It is this idea that the largest aggregators are developing today. They do not try to replace the best models on the market with their own developments. Instead, they combine the strengths of different technologies into a single ecosystem.
Nfor example, in the Syntx aggregator (Syntx) the user can consistently work with text, images, video, voiceover and AI agents in one interface, without switching between dozens of sites. This is especially convenient for content makers, marketers and designers who daily have to create materials in several formats at once. And this is just the beginning of a new trend.
EIf previously the neural network was a separate tool, today it is gradually becoming part of a single working environment. It is very likely that it is precisely these ecosystems that the market will develop in the coming years.
___________________________________________
Try the new Syntx aggregator for free
PAYBACK - promotional code for a 15% discount on payment
___________________________________________
How to make money on neural networks: why the AI aggregator turned out to be more important than the models themselves
KWhen the conversation turns to making money from artificial intelligence, most people imagine a person who writes a couple of requests in ChatGPT and receives money literally out of thin air. In practice, everything looks much more interesting and a little more complicated. Today, money is brought in not by the neural network itself, but by the speed of content production. The faster a person is able to produce quality articles, advertisements, images or videos, the higher his income.
And this is where aggregators unexpectedly became one of the main market drivers.
By data Microsoft Work Trend Index, employees who regularly use generative AI are saving several hours a week on routine tasks, and companies are increasingly redesigning processes around AI tools. McKinsey Research also shows that AI brings the greatest economic value to marketing, sales, product development, and content creation. In other words, exactly where a person has to switch between texts, images, videos and analytics every day.
The best AI options for content makers in 2026: what is really needed and what is useless
How to make money on neural networks if the entire market is already working through modern AI
KAs we understand, a new profession has already appeared - AI creator. One person, thanks to AI technologies, can replace a small production team. For example, in order to release a commercial for a new product, today it is no longer necessary to hire a scriptwriter, designer, photographer, videographer, announcer and editor. One creator can do the entire cycle independently:
ncome up with an idea;
nwrite a script;
withcreate characters;
withgenerate illustrations;
obring images to life;
withmake AI videos online;
dadd music;
othe video will sound in AI voice;
withmount the final version.
RPreviously, a similar project would take several weeks. Now it can be assembled in one or two days.
And here many people begin to worry about one question: does a neural network work for free as well as a paid subscripti
Why, without AI aggregators, cartoons would not have become widespread at all
OhThe changes in the market can be seen very clearly in the example of AI cartoons. In 2024-2026, TikTok, YouTube Shorts and Reels were literally filled with short mini-series created entirely by neural networks. Users daily publish stories with regular characters that receive hundreds of thousands and even millions of views.
NFew people think about how many different technologies are hidden behind one minute of such a video. Let's take a simple example, which clearly shows that in separate neural networks such a series cannot be made so high quality, as in a single hub. The main character of an animated series must appear the same in each new episode.
DTo do this, you first need to write a script.
ZThen create a description of the hero.
PAfter this, come up with prompts for generating his appearance.
PThen generate several images.
ZThen turn pictures into videos.
PAfter this, write new lines.
Ohsound characters.
Dadd music.
Cpick up installation.
And if in the third episode the hero suddenly suddenly began to look ten years younger or got a different eye color, he would have to go back almost to the very beginning of the process.
AndThis is why many early AI projects literally fell apart after just a few episodes. They were made in different neurons, and external uniformity was not achieved.
LThe hero's face was constantly changing.
CThe style and quality of the image "jumped".
Ohthe light became different.
Pcharacters lost recognition.
Rworking simultaneously on five or six different services was so inconvenient that most authors simply abandoned their projects. This is where aggregators turned out to be a real salvation.
Tfrom the same Syntx (Syntx) allows you to assemble almost the entire production cycle within one workspace. In one interface, you can use language models for scripting, image generators for creating characters, video models for animation, voice-over services, and music models. Thanks to this, the author does not lose the context of the project and does not waste time constantly switching between dozens of tabs.
NIn practice, this means a much more stable result and significantly fewer errors.
___________________________________________
Try the new Syntx aggregator for free
PAYBACK - promotional code for a 15% discount on payment
___________________________________________
The neural network specialist no longer works with just one model
EThere is an interesting pattern. The more experienced an AI user becomes, the less often he says the name of a particular neural network. Instead, he begins to speak in the language of tasks. Not “I’ll do it in GPT.” A: “Now I’ll quickly put together the structure of the article.” Not “I’ll go to Kling”, but: “Now I’ll make a commercial”
TThere is a specialist who gradually stops thinking in models. He begins to think in processes. That is why training in working with neural networks today is increasingly built around working scenarios, rather than around individual services.
CA modern specialist already knows the main principle of working with a neural network: it is not the specific model that is important, but the correct sequence of actions.
XA good analogy is Photoshop. When a designer opens Photoshop, he doesn’t think: “Now I’ll use brush number 17.” He thinks that he needs to remove the background and selects the appropriate tool automatically.
RThe AI market is moving in the same direction. After a few years, most users will most likely stop being interested in model names altogether. They will open the aggregator and simply select the desired task:
Nwrite the text.
Cmake an image.
Ccreate a video.
Cgenerate voiceovers.
Andwhether to conduct an AI photo shoot.
KWhich model will do the work inside will be a secondary issue for most users. This is why today the best neural network aggregators are developing so quickly. They are gradually turning not into a catalog of models, but into a full-fledged operating system for working with artificial intelligence.
Neurosphere, Syntx, Aidentika: comparing aggregators and professional neural networks
All neural networks in one place: why is an aggregator more profitable for companies than dozens of individual subscriptions
KWhen it comes to introducing artificial intelligence into a company, many people imagine purchasing one subscription to ChatGPT. In practice, everything turns out to be much more complicated. Modern work with AI is rarely limited to one service.
BLet's take three real professions.
PAn interesting picture emerges. Even one person has to keep several services open at once. What if there are five employees in the department?
Keveryone works differently.
Everyone has their own accounts.
Chowl prompts.
Cyour subscriptions.
CWhat are your ways to store projects?
AndIt is at this moment that the manager begins to understand that the problem is not at all in the cost of subscriptions, but in some kind of sheer chaos.
_____________________________________________________
>>> Clean up your company for free:
>>> Web version
>>> Tg - bot
>>> PAYBACK - promo code for 15% discount on first payment
_____________________________________________________
Syntx aggregator as a single workspace
AndThis is why more and more companies are switching to aggregators. If you look at how it's structured Syntx, it becomes clear why this format is beginning to displace the use of individual services.
FIn fact, this is a single workspace where more than ninety modern artificial intelligence models are collected.
NThe main idea is not quantity at all. She's in the work scenarios. Imagine a typical launch of an advertising campaign. A marketer receives the task of introducing a new product.
PThe first thing he does is ask the language model to conduct audience and competitor research.
ZThen, within the same interface, it generates several options for an advertising concept.
PAfter this, it creates routines for generating images.
Creceives ready-made illustrations with ice.
Zthen turns them into promotional videos.
Dadds professional voice acting.
Pturns on music if necessary.
And if you need to repeat the style of a previous campaign, the AI agent already knows the brand’s features and helps maintain a unified visual language.
BThe choice of text models is quite large:
LIt's easy to generate a script:
Cdo a quick start to describe the hero and generate the first frames for the future video in any style we decide to set:
BWe select the appropriate option, transfer the picture to the adjacent “Video” tab and set the task to create a video:
And then - for the voice acting:
After this, a stable video with voice acting will be ready in a minute. The best part is that the user does not have to re-explain the context of the future video for each new neural network, but simply transfers the message, picture, video to adjacent tabs. The AI agent inside Syntx remembers everything and controls the stability of the characters and the script, like a real director.
_____________________________________________________
>>> Make your first video in Syntx for free:
>>> Web version
>>> Tg - bot
>>> PAYBACK - promo code for 15% discount on first payment
_____________________________________________________
Modern AI goes further: what can we learn from the Neurosphere
AndIt is interesting that the aggregator market is developing very quickly, and today each major service is looking for its own specialization. A good example is Neurosphere.
Eif Syntx relies on combining a large number of advanced models in one workflow, then Neurosphere takes the next step - it tries to shorten the user’s path to the finished result as much as possible..
Here's how it works
TBut more and more often, a person does not even have to come up with a prompt on his own. For example, if you open the sections of the platform, it becomes noticeable that many functions are already designed as ready-made scripts. Instead of the empty “Enter a request” field, the user sees clear tasks:
NYou can create a viral video - there are ready-made trend templates.
XIf you want to do a fashionable AI photo shoot - you don’t need to look for prompts on the Internet, but just choose the desired style.
Pa new viral effect has appeared on social networks - very often it is one of the first to appear within the platform.
PExamples of photo scenarios that can only be repeated by uploading your photo:
PExamples of templates for trending AI videos:
AndToday this is becoming one of the main advantages of the Neurosphere. While most users are just starting to look for fresh industrial products, ready-made generation scripts are already appearing inside the aggregator.
FIn fact, developers constantly monitor what photo, video and audio trends begin to spread on the Internet, and turn them into ready-made tools.
PIt turns out to be an interesting evolution. If previously the user learned to write complex queries, now more and more often it is enough to simply press a button.
Take a trending photo or video for free
The neural network for content turns into a professional assistant
EAnother strength of the Neurosphere is its specialized tools. This is especially noticeable when working with marketplaces. If universal models can create images or write text, then ready-made assistants for specific business tasks appear here.
For example:
ggeneration of product cards;
withcreating infographics;
ndescription of product advantages;
SEO descriptions;
nselection of keywords;
aanalysis of competitors;
gsales agents for working with Wildberries and Ozon.
Kcategories of tools in Neurosphere PRO:
PIt turns out that the user is no longer just working with a neural network. He interacts virtually with a digital employee who understands the specifics of a particular profession.
PAn interesting picture emerges: even aggregators are already focusing on collecting suitable sets of neural networks for different professions.
Syntx Great for those who daily have to create content in a variety of formats - from articles and images to videos, voiceovers and AI agents.
Nneurosphere relies on ready-made scenarios, trends and specialized tools for specific professions, allowing even those who are just starting to work with artificial intelligence to get results faster.
Ethey are no longer competitors in the classical sense. Rather, they are three different approaches to using AI.
>>> Make an AI photo shoot or AI advertising video in Syntx for free
>>> Repeat a trend or card for Ozon on a turnkey basis in the Neurosphere for free
The future of neural networks is already here
EJust a few years ago we were choosing between separate neural networks. Today we increasingly choose a working system. In a few years, most likely, most specialists will stop thinking about which model wrote the text, drew the image, or edited the video. Just like today, no one thinks about exactly what algorithm Photoshop uses to cut out an object from a photograph. Only one thing will be important - how quickly you managed to get a high-quality result.
AndThis is why the future seems to belong to aggregators.
OhThey are gradually turning dozens of disparate services into a single ecosystem, where text, images, video, music, voice acting, analytics and specialized AI agents work as one team.
And if earlier the question sounded like this: “Which neural network to choose?”, then today they are increasingly asking another: “Which aggregator will allow me to close all my work in one place?”
CJudging by the way the market is developing, this question will become the main question in the coming years for marketers, designers, content makers, entrepreneurs and anyone who plans to build a career around artificial intelligence.
___________________________________________________
Syntx web version
Tg - Syntx bot
PAYBACK - promo code for 15% discount on first payment
_________
Neurosphere about free
For registration they give 10,000 tokens for text and three generations of any media