GPTunneL and the Forbes Trend: Why AI-Superapps Are Becoming the New Growth Driver for the Market

The generative AI market is rapidly shifting from isolated models to AI superapp format, where users come not for "an answer from a chatbot," but to complete an entire task: write text, process a file, generate an image, analyze data, or assemble a working workflow from multiple tools. The GPTunneL team, which grew from 1.5 to 2 million users in just a few months, believes this shift best explains why superapps are becoming not a fashionable label, but new market logic.

As external confirmation of this trend, the team points to February's Forbes forecast, which named AI superapps one of the main directions for 2026. But more important than the forecast itself is applied statistics: in 2025, traffic to multimodal tools within GPTunneL tripled. This means the audience increasingly abandons text-only queries and more often combines modes of operation — text, images, documents, voice scenarios, and automation.

At the same time, the nature of requests is changing. If users previously "asked" the model questions, they now formulate tasks: prepare a presentation, analyze a document, create a visual, rewrite a letter, draft a report. For a product, this is a fundamental difference, because value shifts from model access to the quality of the final action.

On the global market, this shift looks different, but the overall direction is the same. In China, the superapp idea is long familiar to users, so AI services naturally integrate into broader digital ecosystems. In the US, competition centers on universal work interfaces that combine multiple models, agents, and content types in a single window.

In Turkey and other rapidly growing markets, demand is visible for more applied products, where localization, price of entry, and clear usage scenarios matter more than technological "purity" of the chosen model. Against this backdrop, the AI superapp becomes a compromise between the complexity of modern AI infrastructure and the user's expectation of getting one clear service instead of a scattered set of tools. For Russia, this transition is complicated by several factors.

First, the market increasingly looks toward sovereign models and local control over critical infrastructure. Second, major players already deploy enterprise solutions, including GigaChat Enterprise, accelerating business demand not for "another chat," but for integrable corporate products. Third, GPU shortage remains a hard constraint on scaling, especially if a company wants not just to resell third-party API access, but to build its own technological layer.

It is within this logic that GPTunneL develops its GROM model: not as an image project, but as an attempt to gain more control over quality, cost, and application scenarios. Notably, token resale accounts for less than 20% of revenue, according to the team. This is an important signal: the economics of such services are increasingly less tied to simple markup on the model and increasingly tied to product packaging, interfaces, tool orchestration, and industry scenarios.

The main takeaway from this story is that the AI market is maturing faster than the usual language around it is changing. A user may still say they "use a model," but what they're buying is not a model—it's a result: a document, an analysis, automation, a ready-made workflow. For startups, this means building not a showcase of dozens of LLMs, but a coherent product with its own value layer.

For corporations—transitioning from experiments to implementation in business processes. And for the Russian market—additional pressure toward its own infrastructure, computing power, and local models. If this vector persists, in 2026 those with the longest list of connected neural networks won't win, but those who best package them into a single work tool.