AI 2026: What We Expect From the World of AI

Through 2025 we watched together as AI became a daily part of development and tech products. What comes next? Which technologies and approaches proved themselves, and which turned out to be a dead end? What changes should companies expect in 2026? In this article we sum up our predictions for the coming year.

By late 2025, Chinese open-source models like DeepSeek-V3 or Qwen 2.5-Max were already reaching results comparable to GPT-4o or Claude 3.5 Sonnet. The performance gap has practically closed, and the Chinese models are far cheaper to build. For perspective: DeepSeek-V3 was created on a budget under 6 million dollars, while American models routinely exceed 50 million.

What does this mean in practice?

Companies will care far more about the results of the model itself than about the "brand". Open-source models from China will become an increasingly common choice for companies that want to test AI quickly, scale it cheaply, and keep control over deployment.

Tools like Claude Artifacts or v0.dev can already generate whole components in real time. By 2025 they are commonly used for prototyping, simple tools, or visualizations. The logical next step? Bring this ability into production systems. Think of it like "vibecoding", but right inside the app. It will not be a change from one year to the next; 2026 will be more of a year of first real attempts.

What does this mean in practice?

Faster prototypes, faster idea testing, less code written from scratch. Gradually we get to a point where the app's UI is assembled at runtime, much like today's ChatGPT apps. The model generates a specific widget (a UI element) dynamically the moment the user needs it, instead of having it prepared in the code in advance.

AI as a UI engine saves developers time, speeds up product design, and opens more room to experiment. It is a step toward LLMs, or AI, as a new kind of operating system.

MCP will remain a popular tool, and some of its security concerns will likely get resolved. We expect the big AI players to keep supporting it, but 2026 will also bring other paths to take, such as UTCP or agent-to-agent communication protocols.

What does this mean in practice?

The hype around MCP will hold, but companies should not bet everything on one card. 2026 will be about experiments and finding the right way to roll out communication between agents and tools safely and at scale.

2026 will bring a mass spread of small language models. Their development is speeding up, their capabilities are growing, and so is the number of real deployments. In performance they are getting close to ChatGPT-4, for example, but without needing a cloud connection and with full control over data.

What does this mean in practice?

AI will become a normal part of work even for less technical teams. Loading company documents, analyzing data, or generating text can be handled by a small model right on the device, without complex integration and without worrying about data leaks.

2026 will bring a stronger emphasis on practicality, scalability, and control over AI tools. Companies will start rethinking what they really need. Not the best marketing, but the best performance for a given investment.

AI will tie even more closely into everyday development, and whoever cannot integrate it effectively will fall behind.

If you want to know which news shaped 2025, you will find a summary in our monthly AI news roundups.