In 2026, artificial intelligence will move from hype to reality

If 2025 is the year AI is tested, then 2026 will be the year the technology becomes practical. The focus is already shifting away from building ever-larger language models and toward the harder work of making AI usable. In practice, this includes deploying smaller models where they fit, embedding intelligence into physical devices, and designing systems that integrate cleanly with human workflow.

Experts at TechCrunch spoke to see 2026 as a year of transition, one that evolves from brute-force scaling to the search for new architectures, from flashy demos to targeted deployments, and from agents that promise autonomy to those that actually enhance how people work.

The party isn’t over yet, but the industry is waking up.

Expansion laws won’t cut it

In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton’s research at ImageNet showed how AI systems can “learn” how to recognize objects in images by looking at millions of examples. This approach was computationally expensive, but was made possible using GPUs. The result? A decade of serious research in the field of artificial intelligence as scientists worked to invent new architectures for different tasks.

This culminated around 2020 when OpenAI launched GPT-3, which showed how simply making a model 100 times larger unlocks capabilities like programming and reasoning without requiring explicit training. This marked the transition into what Kian Katanforoush, CEO and founder of AI agent platform Workera, calls the “age of scale”: a period defined by the belief that more compute, more data, and larger transformer models will inevitably drive the next major breakthroughs in AI.

Today, many researchers believe that the AI ​​industry is beginning to exhaust the limits of scaling laws and will once again move into the search era.

Yann LeCun, former chief AI scientist at Meta, has long argued against over-reliance on measurement, and stressed the need to develop better architectures. Current models are stabilizing, and pre-training results have stabilized, suggesting a need for new ideas, Sutskever said in a recent interview.

“I think that probably in the next five years, we will find a better architecture that is a huge improvement in transformers,” Katanforos said. “If we don’t do that, we can’t expect much improvement in the models.”

Sometimes less is more

Large language models are great for generalizing knowledge, but many experts say the next wave of enterprise AI adoption will be driven by smaller, more flexible language models that can be fine-tuned for domain-specific solutions.

“Fine-tuned SLMs will be the big trend and will become a staple used by mature AI organizations in 2026, as cost and performance advantages will drive increased usage over off-the-shelf LLMs,” Andy Marcus, chief data officer at AT&T, told TechCrunch. “We have already seen companies increasingly rely on SLM technologies because, if properly tuned, they match larger, generalized models in terms of accuracy for business applications, and are great for cost and speed.”

We’ve seen this argument before from French open-weight AI startup Mistral: it says its small models actually perform better than larger models on several benchmarks after fine-tuning.

“The efficiency, cost-effectiveness and adaptability of SLMs make them ideal for tailored applications where accuracy is critical,” said John Knisley, an AI strategist at ABBYY, an Austin-based artificial intelligence company.

While Marcus believes SLM will be key in the age of agents, Knisley says the nature of small models means they are better for deployment on local machines, “a trend accelerated by advances in edge computing.”

Learning through experience

Humans do not learn through language alone; We learn through experience how the world works. But LLMs don’t really understand the world; They just predict the next word or idea. That’s why many researchers believe the next big leap will come from universal models: artificial intelligence systems that learn how things move and interact in 3D spaces so they can make predictions and take action.

Evidence is mounting that 2026 will be a big year for global supermodels. LeCun left Meta to start his own global modeling lab and is reportedly seeking a $5 billion valuation. Google’s DeepMind program has started working with Genie, and in August launched its latest model that builds interactive, general-purpose global models in real time. Along with demos from startups like Decart and Odyssey, Fei-Fei Li’s World Labs launched its first global commercial model, Marble. Newcomers like General Intuition in October secured a $134 million seed round to teach customers spatial thinking, and video generation startup Runway in December released its first global model, the GWM-1.

While researchers see long-term potential in robotics and autonomy, the near-term impact will likely appear first in video games. PitchBook expects the global model market in games to grow from $1.2 billion between 2022 and 2025 to $276 billion by 2030, driven by technology’s ability to create interactive worlds and more realistic non-player characters.

Virtual environments may not only reshape gaming, but also become a crucial proving ground for the next generation of core models, Pim de Witte, founder of General Intuition, told TechCrunch.

Agent nation

Agents fail to live up to the hype in 2025, but the main reason for this is that they are difficult to connect to the systems where the work actually happens. Without a way to access tools and context, most agents fell into the trap of the experimental workflow.

Anthropic’s Modular Context Protocol (MCP), a “USB-C for AI” that allows AI agents to talk to external tools such as databases, search engines, and APIs, proved the missing connective tissue and quickly became 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 setting 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 will likely be the year that agent workflows finally move from demos to everyday practice.

These developments will lead to first-agent solutions that take on “system of record roles” across industries, says Rajeev Dham, partner at Sapphire Ventures.

“As voice agents handle more end-to-end tasks like ingestion and customer communication, they will also begin to shape the underlying platforms,” Daham said. “We will see this in a variety of sectors such as home services, proptech, and healthcare, as well as horizontal functions such as sales, IT, and support.”

Augmentation, not automation

While more agent workflows may raise concerns about possible layoffs, Workera’s Katanforoush isn’t so sure that’s the message: “2026 will be the year of humans,” he says.

In 2024, every AI company predicts that it will automate jobs to eliminate the need for humans. But the technology isn’t there yet, and in an unstable economy, that’s not really popular rhetoric. In the coming year, we will realize that “AI is not operating as autonomously as we thought,” says Katanforush, and the conversation will focus more on how AI can be used to enhance human workflows, rather than replace them.

“And I think a lot of companies will start hiring,” he added, noting that he expects there will be new roles in artificial intelligence governance, transparency, safety, and data management. “I’m very optimistic about unemployment averaging below 4% next year.”

“People want to be above the API, not below it, and I think 2026 is an important year for that,” De Wit added.

Get physical

Experts say advances in technologies such as small models, global 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 robots, autonomous vehicles, drones and wearables, begin to enter the market,” Vikram Taneja, president of AT&T Ventures, told TechCrunch.

While autonomous vehicles and robotics are obvious use cases for physical AI that will undoubtedly continue to grow in 2026, the training and deployment required are still expensive. On the other hand, wearables offer a less expensive wedge with consumer acceptance. Smart glasses like the Ray-Ban Meta are starting to ship with assistants that can answer questions about what you’re looking at, and new form factors like AI-powered health rings and smartwatches are normalizing constant body heuristics.

“Communication service providers will improve their network infrastructure to support this new wave of devices, and those who are flexible in how they provide connectivity will be better positioned,” Taneja said.