In 2026, AI Will Move from Hype to Pragmatism: What to Expect in the Tech World
If 2025 was the year AI got a reality check, 2026 will be the year technology gets practical. The tech industry is shifting away from building ever-larger language models toward making AI usable and valuable in real-world applications.
The End of Scaling Laws
The era of simply making AI models bigger is coming to an end. After years of believing that more compute, more data, and larger transformer models would inevitably drive breakthroughs, researchers are hitting the limits of scaling laws. Yann LeCun, Meta’s former chief AI scientist, has long argued against overreliance on scaling, emphasizing the need for better architectures. Ilya Sutskever, co-founder of OpenAI, has also noted that current models are plateauing and pretraining results have flattened.
“I think most likely in the next five years, we’re going to find a better architecture that is a significant improvement on transformers,” says Kian Katanforoosh, CEO and founder of AI agent platform Workera. “And if we don’t, we can’t expect much improvement on the models.”
Small Models, Big Impact
Large language models excel at generalizing knowledge, but experts predict that enterprise AI adoption will be driven by smaller, more agile language models fine-tuned for domain-specific solutions. These small language models (SLMs) offer significant cost and performance advantages over out-of-the-box LLMs.
“Fine-tuned SLMs will be a big trend and become a staple used by mature AI enterprises in 2026,” says Andy Markus, AT&T’s chief data officer. “We’ve already seen businesses increasingly rely on SLMs because, if fine-tuned properly, they match larger, generalized models in accuracy for enterprise business applications, and are superb in terms of cost and speed.”
The efficiency, cost-effectiveness, and adaptability of SLMs make them ideal for tailored applications where precision is paramount.
— Jon Knisley, AI Strategist at ABBYY
World Models: Understanding Physical Reality
Humans don’t just learn through language; we learn by experiencing how the world works. LLMs don’t truly understand the world—they just predict the next word or idea. That’s why many researchers believe the next big leap will come from world models: AI systems that learn how things move and interact in 3D spaces to make predictions and take actions.
Signs that 2026 will be a big year for world models are multiplying. LeCun left Meta to start his own world model lab and is reportedly seeking a $5 billion valuation. Google’s DeepMind has been working on Genie and launched its latest model that builds real-time interactive general-purpose world models. Alongside demos by startups like Decart and Odyssey, Fei-Fei Li’s World Labs has launched its first commercial world model, Marble.
While researchers see long-term potential in robotics and autonomy, the near-term impact will likely be seen first in video games. PitchBook predicts the market for world models in gaming could grow from $1.2 billion between 2022 and 2025 to $276 billion by 2030, driven by the technology’s ability to generate interactive worlds and more lifelike non-player characters.
The Rise of Agentic Workflows
AI agents failed to live up to the hype in 2025, largely because it was difficult to connect them to systems where work actually happens. Without access to tools and context, most agents were trapped in pilot workflows. Enter Anthropic’s Model Context Protocol (MCP)—described as “USB-C for AI”—which lets AI agents talk to external tools like databases, search engines, and APIs.
MCP proved to be the missing connective tissue and is quickly becoming the standard. OpenAI and Microsoft have publicly embraced MCP, and Anthropic recently donated it to the Linux Foundation’s new Agentic AI Foundation, which aims to help standardize open source agentic tools. Google has also begun standing up its own managed MCP servers to connect AI agents to its products and services.
With MCP reducing the friction of connecting agents to real systems, 2026 is likely to be the year agentic workflows finally move from demos into day-to-day practice. “We’ll see agent-first solutions taking on ‘system-of-record’ roles across industries,” says Rajeev Dham, a partner at Sapphire Ventures. “As voice agents handle more end-to-end tasks such as intake and customer communication, they’ll also begin to form the underlying core systems.”
AI Augmentation, Not Automation
While more agentic workflows might raise concerns about job displacement, experts don’t see automation as the primary message for 2026. “2026 will be the year of humans,” says Katanforoosh of Workera. In 2024, every AI company predicted they would automate jobs out of needing humans. But the technology isn’t there yet, and in an unstable economy, that’s not a popular narrative.
“I think a lot of companies are going to start hiring,” Katanforoosh added, noting that he expects there to be new roles in AI governance, transparency, safety, and data management. “I’m pretty bullish on unemployment averaging under 4% next year.”
People want to be above the API, not below it, and I think 2026 is an important year for this.
— Pim de Witte, Founder of General Intuition
Physical AI Goes Mainstream
Advancements in technologies like small models, world models, and edge computing will enable more physical applications of machine learning. “Physical AI will hit the mainstream in 2026 as new categories of AI-powered devices, including robotics, AVs, drones, and wearables start to enter the market,” says Vikram Taneja, head of AT&T Ventures.
While autonomous vehicles and robotics are obvious use cases for physical AI that will continue to grow in 2026, the training and deployment required are still expensive. Wearables, on the other hand, provide a less expensive entry point with consumer buy-in. Smart glasses like Ray-Ban Meta are starting to ship assistants that can answer questions about what you’re looking at, and new form factors like AI-powered health rings and smartwatches are normalizing always-on, on-body inference.
What This Means for Developers and Businesses
The shift from hype to pragmatism has significant implications for developers and businesses. Rather than chasing the latest large language model, organizations should focus on:
- Evaluating specific use cases: Determine where AI can provide real business value
- Choosing the right model size: Small, fine-tuned models may outperform larger ones for specific tasks
- Building agentic infrastructure: Implement systems that can connect AI to real tools and workflows
- Investing in AI governance: Establish frameworks for transparency, safety, and compliance
- Preparing for physical AI: Explore opportunities in robotics, wearables, and edge computing
Looking Ahead
2026 represents a crucial turning point for AI and technology more broadly. The industry is moving from a period of experimentation and hype to one of practical implementation and real-world impact. While the headlines may be less dramatic than in previous years, the changes will be more meaningful—transforming how businesses operate, how people work, and how technology integrates into daily life.
The party isn’t over, but the industry is starting to sober up. And that’s actually a good thing. Practical, reliable AI that solves real problems is far more valuable than impressive demos that never make it into production. Welcome to the era of pragmatism.