If 2025 was the year AI got a vibe check, 2026 will be the year the tech 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.
The experts TechCrunch spoke to 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 isn’t over, but the industry is starting to sober up.
Scaling laws won’t cut it
Image Credits:Amazon
In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton’s AlexNet paper showed how AI systems could “learn” to recognize objects in pictures by looking at millions of examples. The approach was computationally expensive, but made possible with GPUs. The result? A decade of hardcore AI research as scientists worked to invent new architectures for different tasks.
That culminated around 2020 when OpenAI launched GPT-3, which showed how simply making the model 100 times bigger unlocks abilities like coding and reasoning without requiring explicit training. This marked the transition into what Kian Katanforoosh, CEO and founder of AI agent platform Workera, calls the “age of scaling”: a period defined by the belief that more compute, more data, and larger transformer models would inevitably drive the next major breakthroughs in AI.
Today, many researchers think the AI industry is beginning to exhaust the limits of scaling laws and will once again transition into an age of research.
Yann LeCun, Meta’s former chief AI scientist, has long argued against the over-reliance on scaling, and stressed the need to develop better architectures. And Sutskever said in a recent interview that current models are plateauing and pre-training results have flattened, indicating a need for new ideas.
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“I think most likely in the next five years, we are going to find a better architecture that is a significant improvement on transformers,” Katanforoosh said. “And if we don’t, we can’t expect much improvement on the models.”
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 language models that can be fine-tuned for domain-specific solutions.
“Fine-tuned SLMs will be the big trend and become a staple used by mature AI enterprises in 2026, as the cost and performance advantages will drive usage over out-of-the-box LLMs,” Andy Markus, AT&T’s chief data officer, told TechCrunch. “We’ve already seen businesses increasingly rely on SLMs because, if fine-tuned properly, they match the larger, generalized models in accuracy for enterprise business applications, and are superb in terms of cost and speed.”
We’ve seen this argument before from French open-weight AI startup Mistral: it argues 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 precision is paramount,” said Jon Knisley, an AI strategist at ABBYY, an Austin-based enterprise AI company.
While Markus thinks SLMs will be key in the agentic era …