If 2025 was the year AI got a vibe check, 2026 will be the year the technology gets practical. The focus is already shifting away from building ever-larger language models and toward the harder work of making AI usable. In practice, that involves deploying smaller models where they fit, embedding intelligence into physical devices, and designing systems that integrate cleanly into human workflows. Experts see 2026 as a year of transition, one that evolves from brute-force scaling to researching new architectures, from flashy demos to targeted deployments, and from agents that promise autonomy to ones that actually augment how people work. The party is not over, but the industry is starting to sober up.
Scaling laws will not cut it forever. In 2012, a pivotal paper showed how AI systems could learn to recognize objects in pictures by looking at millions of examples. That approach culminated around 2020 with models like GPT-3, which demonstrated that simply making a model much larger could unlock new abilities. This began an age of scaling, defined by the belief that more computing power, more data, and larger models would inevitably drive the next major breakthroughs.
Today, many researchers think the industry is beginning to exhaust the limits of scaling laws and will transition back into an age of research. Leading figures have argued against over-reliance on scaling, stressing the need for better architectures. They note that current models are plateauing, indicating a need for new ideas. As one expert stated, the next five years will likely see a significantly improved architecture emerge, because without it, model improvement will stall.
Sometimes less is more. Large language models are great at generalizing knowledge, but many experts say the next wave of enterprise AI adoption will be driven by smaller, more agile models that can be fine-tuned for specific tasks. Fine-tuned small language models are predicted to become a staple for mature AI enterprises in 2026, due to their cost and performance advantages. These smaller models can match larger models in accuracy for specific business applications while being superior in terms of speed and cost. Their efficiency also makes them ideal for deployment on local devices, a trend accelerated by advancements in edge computing.
Learning through experience is another frontier. Humans do not just learn through language; we learn by experiencing how the world works. Current language models predict the next word, but they do not truly understand the world. This is why many researchers believe the next big leap will come from world models: AI systems that learn how things move and interact in three-dimensional spaces so they can make predictions and take actions.
Signs point to 2026 being a big year for world models. Several prominent researchers have moved to start new ventures focused on this technology, and major companies and startups alike are releasing and developing their own world models. While researchers see long-term potential in fields like robotics, the near-term impact is likely to be seen first in video games, where the technology can generate interactive worlds and more life-like characters. Virtual environments may also become critical testing grounds for the next generation of AI models.
The development of agents is progressing. Agents failed to live up to the hype in 2025 largely because it was difficult to connect them to the systems where work actually happens. A key protocol has emerged as a standard to let AI agents talk to external tools like databases and APIs, acting as a universal connector. With this friction reduced, 2026 is likely to be the year agentic workflows move from demos into day-to-day practice. These advancements could lead to agent-first solutions taking on core operational roles across various industries, from home services to healthcare.
This shift is leaning toward augmentation, not automation. The conversation is moving away from AI replacing jobs and toward how it can augment human workflows. The technology is not yet capable of full autonomy, and the focus is now on how AI can assist rather than replace. This could lead to new roles in AI governance, safety, and data management. The prevailing sentiment is that people want to be in control of the technology, not replaced by it.
Finally, AI is getting physical. Advancements in small models, world models, and edge computing will enable more physical applications of machine learning. Physical AI is predicted to hit the mainstream in 2026 as new categories of AI-powered devices, including robotics, autonomous vehicles, drones, and wearables, enter the market. While autonomous vehicles and robotics will continue to grow, wearables like smart glasses and health rings provide a more immediate consumer pathway, normalizing always-on, on-body AI inference. Network providers will work to optimize infrastructure to support this new wave of connected devices.