In 2026, AI will move from hype to pragmatism

If 2025 was the year AI got a vibe check, 2026 will be the year the technology becomes practical. The focus is already shifting from building larger and larger language models to the harder work of making AI usable. In practice, this means deploying smaller models where they fit, embedding intelligence into physical devices, and designing systems that integrate neatly into human workflows.

TechCrunch’s experts saw 2026 as a year of transition, one that moves from brute-force scaling to exploring new architectures, from flashy demos to targeted deployments, and from agents that promise autonomy to agents that actually improve the way people work.

The party isn’t over yet, but the industry is starting to sober up.

Scaling laws are not enough

In 2012, Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton’s ImageNet paper showed how AI systems could ‘learn’ to recognize objects in images by looking at millions of examples. The approach was computationally expensive, but was made possible with GPUs. The result? A decade of hardcore AI research as scientists worked to devise new architectures for various tasks.

That culminated around 2020 when OpenAI launched GPT-3, which showed how simply making the model 100 times bigger unlocks skills like coding and reasoning without the need for explicit training. This marked the transition into what Kian Katanforoosh, CEO and founder of AI agent platform Workera, calls the “age of scale”: a period defined by the belief that more computing power, more data, and bigger transformer models would inevitably drive the next big breakthroughs in AI.

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

Yann LeCun, Meta’s former chief AI scientist, has long argued against overreliance on scaling and emphasized the need to develop better architectures. And Sutskever said in a recent one interview that current models are reaching a plateau and pre-training results have leveled off, indicating a need for new ideas.

“I think in the next five years we will most likely find a better architecture that is a significant improvement over transformers,” Katanforoosh said. “And if we don’t do that, we can’t expect much improvement in the models.”

Sometimes less is more

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

“Sophisticated SLMs will be the big trend and become a staple of mature AI companies by 2026 as the cost and performance benefits will drive adoption over off-the-shelf LLMs,” AT&T Chief Data Officer Andy Markus told TechCrunch. “We’ve already seen companies increasingly rely on SLMs because, when properly tuned, they match the larger, common enterprise application models in accuracy, and are excellent in terms of cost and speed.”

We’ve seen this argument before with French open-weight AI startup Mistral: it claims that after refinement, its small models even outperform larger models on several benchmarks.

“The efficiency, cost-effectiveness and adaptability of SLMs make them ideal for custom applications where precision is paramount,” says Jon Knisley, an AI strategist at ABBYY, an Austin-based AI company.

While Markus thinks SLMs will be critical in the agentic age, Knisley says the nature of small models means they are better suited for deployment to local devices, “a trend accelerated by advances in edge computing.”

Learning through experience

People don’t just learn through language; we learn by experiencing how the world works. But LLMs don’t really understand the world; they only 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 so they can make predictions and take action.

Signs are mounting that 2026 will be a big year for global modeling. LeCun left Meta to start his own global modeling lab and is reportedly aiming for a $5 billion valuation. Google’s DeepMind has joined Genie, launching its latest model in August that builds real-time interactive general-purpose world models. In addition to demos from startups like Decart and Odyssey, Fei-Fei Li’s World Labs has launched its first commercial world model: Marble. Newcomers like General Intuition scored a $134 million seed round in October to teach agents spatial reasoning, and video generation startup Runway released its first global model, GWM-1, in December.

While researchers see long-term potential in robotics and autonomy, the short-term impact will likely be seen first in video games. PitchBook predicts that 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.

Pim de Witte, founder of General Intuition, told TechCrunch that virtual environments can not only reshape gaming, but also become critical testing grounds for the next generation of base models.

Agentic nation

Agents failed to live up to the hype in 2025, but a major reason for this is that it is difficult to connect them to the systems where work actually takes place. Without access to tools and context, most agents were stuck in pilot workflows.

Anthropic’s Model 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 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 started setting up its own managed MCP servers to connect AI agents to its products and services.

With MCP reducing the friction in connecting agents to real systems, 2026 will likely be the year agentic workflows finally move from demos to everyday practice.

Rajeev Dham, a partner at Sapphire Ventures, says these developments will lead to agent-first solutions taking on a system-of-record role across industries.

“As voice agents perform more end-to-end tasks such as intake and customer communications, they will also form the underlying core systems,” Dham said. “We will see this across sectors such as home services, proptech and healthcare, as well as across horizontal functions such as sales, IT and support.”

Augmentation, not automation

While more agentic workflows may raise concerns about layoffs, Workera’s Katanforoosh isn’t so sure that’s the message: “2026 will be the year of the people,” he said.

By 2024, every AI company predicted that they would automate jobs because they no longer needed humans. But the technology isn’t there yet, and in an unstable economy that’s not exactly popular rhetoric. Katanforoosh says that next year we will realize that “AI hasn’t worked as autonomously as we thought,” and the conversation will focus more on how AI is being used to augment, rather than replace, human workflows.

“And I think a lot of companies are going to be hiring,” he added, noting that he expects new roles to emerge in AI governance, transparency, security and data management. “I am quite optimistic that unemployment will average below 4% next year.”

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

Get physical

Advances in technologies such as small models, world models and edge computing will enable more physical applications of machine learning, experts say.

“Physical AI will become mainstream in 2026 as new categories of AI-powered devices, including robotics, AVs, drones and wearables, enter the market,” Vikram Taneja, head 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. Wearables, on the other hand, offer a cheaper wedge to consumer buy-in. Smart glasses like the Ray-Ban Meta are starting to deliver assistants that can answer questions about what you’re looking at, and new form factors like AI-powered health rings And smart watches normalize the ‘always on’, ‘on-body’ inference.

“Connectivity providers will work to optimize their network infrastructure to support this new wave of devices, and those with flexibility in how they can deliver connectivity will be best positioned,” Taneja said.