After a period of hot growth and inflated expectations, artificial intelligence is entering 2026 with a different state when it is more alert, more realistic and closely linked to human needs.
The focus of the AI industry is clearly shifting. Instead of racing to build increasingly large language models, researchers and businesses focus on the difficult problem of making AI easy to deploy, easy to use and truly useful.
That means using smaller models in suitable places, integrating artificial intelligence into physical devices and integrating AI into human working processes in a seamless manner.
The history of AI development shows that major breakthroughs are often associated with expansion. From the breakthrough of ImageNet in 2012 to the birth of GPT-3 in 2020, the belief that "the larger the model, the smarter" has dominated the entire industry.
The era of scaling up with more data, more GPUs has once brought impressive results.
However, many scientists believe that this approach is gradually reaching its limits. Yann LeCun, former head of Meta's AI science group, has long warned of excessive dependence on scaling up.
Ilya Sutskever (a Canadian computer scientist of Israeli origin specializing in machine learning) also admitted that current models are showing signs of stagnation, requiring completely new architectures.
According to Kian Katanforoosh, CEO of Workera, if another approach is not found, the AI industry can hardly expect breakthroughs just by zooming in on the existing model.
Less but more effective
Another prominent trend of 2026 is the rise of small language models (SLM). Instead of using giant general models, businesses are increasingly prioritizing models that are refined for specific fields, both saving costs and ensuring accuracy.
Andy Markus, Data Director at AT&T, believes that SLM will become an essential tool for mature AI businesses thanks to its high performance and low cost.
This view is also supported by many companies such as Mistral or ABBYY, especially in the context of strong border computing development, allowing AI to run directly on local devices.
Model of the world and learning from experience
Not only stopping at language, AI is aiming to understand the world through experience. World models, which are systems that learn how objects move and interact in 3D space, are expected to create a new turning point.
2026 may witness the explosion of this field when a series of big names and startups participate, from DeepMind, Fei-Fei Li's World Labs to companies like Runway or General Intuition.
In the immediate future, the clearest impact may appear in the game industry, where virtual worlds are increasingly vivid and interacting in real time.
AI agent awaits transformation thanks to MCP connectivity standard
After performances that were not as expected in 2025, AI agents are having the opportunity to "transform" thanks to new connection standards. Anthropic's model context (MCP) protocol is likened to "USB-C for AI", helping agents access tools, data and real systems.
With the support of OpenAI, Microsoft and Google, MCP can become a platform for workflows based on actors to enter daily applications, from customer service, real estate to healthcare and IT.
AI enhances, but does not replace humans
Contrary to the worry of comprehensive automation, many experts believe that 2026 will be "the year of the human". AI is expected to play a supporting role, strengthening capacity instead of replacing labor. New positions in management, safety, transparency and AI data management may appear more.
Along with that, artificial intelligence in terms of physics from robots, self-driving cars to smart wearable devices will gradually penetrate the market. In which, wearable devices are considered a feasible direction thanks to low costs and high consumer acceptance.
After the fever and promises, AI is entering its mature stage. 2026 may no longer have spectacular shocks, but it is the time when this technology truly proves its sustainable value.
