If 2025 Was a Vibe Check, 2026 Will Be a Real-World Revolution for AI

While 2025 was about AI’s vibe check, 2026 promises to transform the tech landscape into something far more practical. The spotlight is shifting from building larger language models to making AI genuinely usable. This means deploying smaller, efficient models, integrating AI into physical devices, and designing systems that fit seamlessly into human workflows.

Experts believe that 2026 will be a pivotal year of transition—from brute-force scaling to innovative research, from flashy demos to precise applications, and from self-proclaimed autonomous agents to those that genuinely enhance human productivity.

The party isn’t over, but the industry is starting to get serious.

Scaling Laws: Time for a Rethink

In 2012, the groundbreaking AlexNet paper showcased how AI could learn to recognize objects by analyzing millions of images. This computationally intensive process paved the way for a decade of AI advancements.

The launch of GPT-3 in 2020 marked a new era, suggesting that simply scaling up models could unlock capabilities like coding and reasoning. Kian Katanforoosh, CEO of AI agent platform Workera, calls this the “age of scaling,” where larger compute resources were thought to drive the next wave of advances.

However, researchers now believe the AI field is nearing the end of its scaling potential and may shift back into a phase of exploration.

Yann LeCun, Meta’s former chief AI scientist, has long warned against an over-reliance on scaling and advocates for developing innovative architectures. Meanwhile, Ilya Sutskever highlighted in a recent interview that current models are plateauing, underscoring the necessity for fresh ideas.

“I believe that in the next five years, we will discover a superior architecture that significantly advances beyond transformers,” Katanforoosh stated. “If we fail to do so, improvements in model performance may stagnate.”

Embracing Smaller Models: Less Can Be More

While large language models excel at generalization, experts predict that the next wave of enterprise AI will favor smaller, agile models customized for specific applications.

“Fine-tuning smaller language models will emerge as a major trend for established AI enterprises in 2026, driven by their cost-effectiveness and performance,” noted Andy Markus, AT&T’s chief data officer. “Businesses are increasingly turning to smaller models because, when fine-tuned, they can match larger models in accuracy and are far superior in terms of cost and speed.”

French AI startup Mistral has likewise argued for its small models’ enhanced performance after fine-tuning, suggesting they may outshine larger counterparts.

“SLMs’ efficiency, cost-effectiveness, and adaptability make them ideal for tailored applications where precision is crucial,” added Jon Knisley, an AI strategist at ABBYY.

Markus believes that SLMs will play a vital role in the forthcoming agentic era, while Knisley sees their smaller size as beneficial for local device deployment, a trend bolstered by advancements in edge computing.

Learning Through Experience: The Next Frontier

Unlike humans, who learn through experiences, language models primarily predict what comes next. Researchers believe that the next breakthrough will stem from “world models”—AI systems that understand movement and interactions in a 3D space.

Signs that 2026 will focus on world models are growing. LeCun has left Meta to establish his own world model lab and is reportedly seeking a $5 billion valuation. Google’s DeepMind has been developing Genie, a model that offers real-time interactive capabilities, while startups like Decart and Odyssey are also making strides in this space.

The potential impact of world models will likely become apparent first within the gaming industry. Reports suggest this sector could see growth from $1.2 billion between 2022 and 2025 to an astounding $276 billion by 2030.

As presented by Pim de Witte, founder of General Intuition, virtual environments may redefine gaming and serve as essential testing grounds for next-generation foundation models.

The Rise of Agentic AI Solutions

While agents didn’t meet expectations in 2025, their limitation stemmed from difficulty in accessing the tools necessary for real-work applications.

Anthropic’s Model Context Protocol (MCP) acts as the “USB-C for AI,” enabling seamless communication between AI agents and external resources, and is quickly becoming the industry standard. OpenAI and Microsoft have adopted MCP, with Anthropic recently contributing it to the Linux Foundation’s new Agentic AI Foundation.

With MCP simplifying agent integration, 2026 appears poised for a shift of agentic workflows from pilot projects to everyday business operations.

Rajeev Dham, a partner at Sapphire Ventures, predicts that advancements will see agent-first solutions become integral to numerous industries. “Voice agents will increasingly manage end-to-end tasks, laying the groundwork for core operational functions,” said Dham.

Emphasizing Augmentation Over Automation

Although increased agentic workflows may raise fears of layoffs, Katanforoosh of Workera believes the narrative will shift in 2026.

“Next year will truly be about the humans,” he asserts, noting that AI has yet to operate as independently as once predicted. Contrary to the anticipated rhetoric of job automation, the focus will shift to how AI augments, rather than replaces, human roles.

“Many businesses will begin hiring again,” Katanforoosh forecasts, as there will be increased demand for roles in AI governance, transparency, safety, and data management, leading to a projected unemployment rate of under 4% next year.

“People want to work with AI, not for it, and 2026 will be crucial for this paradigm shift,” noted de Witte.

Physical AI: Merging the Digital and Tangible Worlds

2026 is set to herald the rise of physical AI, with advancements in small models, world models, and edge computing paving the way for practical applications.

“Physical AI will hit the mainstream as a new wave of AI-powered devices, including robotics, autonomous vehicles, drones, and wearables, enters the market,” stated Vikram Taneja, head of AT&T Ventures.

While autonomous vehicles and robotics represent significant advances, the costly training and deployment remains a hurdle. Wearables, however, are more accessible and currently gaining traction. Smart glasses, like Meta’s Ray-Bans, are featuring assistants capable of providing contextual information, while devices like AI health rings and smartwatches encourage ongoing inference.

“Network providers are poised to optimize their infrastructures to accommodate this new array of devices, benefiting those offering flexible connectivity solutions,” Taneja concluded.