If 2025 was AI’s vibe check, 2026 is the comedown.
The industry is sobering up from "scale solves everything" and putting its energy into architectures, agents and hardware that actually work in production. The shift is away from megamodel theater and toward usable systems that slot into real workflows, devices and networks.
Here’s what that pivot looks like.
From the age of scaling to the age of research
The last decade was defined by a simple idea: bigger is better.
- 2012: AlexNet showed GPUs could brute-force image recognition by training on millions of images.
- 2020: OpenAI’s GPT-3 proved that making models ~100x bigger could unlock coding and reasoning without task-specific training.
Kian Katanforoosh, CEO and founder of agent platform Workera, calls that the "age of scaling" — a period when more compute, more data and ever-larger transformers were treated as the default path to progress.
That narrative is cracking.
Meta’s former chief AI scientist Yann LeCun has spent years arguing that over-reliance on scaling is a dead end and that the field needs better architectures. Ilya Sutskever has also said current models are plateauing and pre‑training gains are flattening, a strong hint that simple scale-ups are running out of road.
"Most likely in the next five years, we are going to find a better architecture that is a significant improvement on transformers," Katanforoosh told TechCrunch. "And if we don’t, we can’t expect much improvement on the models."
In other words: 2026 looks less like a compute arms race and more like a return to hard research problems.
Small language models grow up
Enterprises are quietly discovering that smaller language models (SLMs) are often good enough — and much cheaper — for the work they actually need done.
Andy Markus, AT&T’s chief data officer, expects fine‑tuned SLMs to become a staple for mature AI deployments in 2026. The cost and performance profile is hard to ignore: when tuned on domain data, he says, SLMs can match larger general-purpose models on accuracy for many business workloads while beating them on speed and price.
We’ve already seen this argument from French startup Mistral, which claims its small, open‑weight models can outperform larger rivals on several benchmarks once fine‑tuned.
Jon Knisley, an AI strategist at ABBYY, calls out three reasons SLMs are getting traction:
- Efficiency: Less compute, lower latency.
- Cost‑effectiveness: You can run more instances closer to users.
- Adaptability: Narrow, high‑precision tasks are easier to nail.
Knisley also points to a second-order effect: small models are easier to deploy on local and edge devices. As edge computing improves, that makes SLMs the natural fit for on-device copilots and agents.
World models go from lab demo to business plan
Large language models only understand the world as text. World models try to learn how the world actually moves and interacts in 3D.
That’s where a lot of frontier research money is now heading.
- Yann LeCun left Meta to build a dedicated world-model lab and is reportedly targeting a $5 billion valuation.
- Google DeepMind has been iterating on Genie, its real-time, interactive world models.
- Fei‑Fei Li’s World Labs launched its first commercial world model, Marble.
- Startup General Intuition raised a $134 million seed round in October to teach agents spatial reasoning.
- Runway, the video generation company, released its first world model, GWM‑1, in December.
The first obvious application isn’t robotics or self‑driving cars — it’s games.
PitchBook estimates the market for world models in gaming could jump from $1.2 billion between 2022 and 2025 to $276 billion by 2030, powered by AI-generated interactive worlds and more lifelike NPCs.
Pim de Witte, founder of General Intuition, told TechCrunch that rich virtual environments won’t just change how games look and feel; they’ll also become critical testbeds for the next wave of foundation models.
Agents finally get their missing connector: MCP
AI agents were overhyped in 2025. The problem wasn’t the idea — it was the plumbing.
Most agents couldn’t reliably touch the systems where real work lives: CRMs, ticketing tools, ERPs, proprietary databases. Without secure, standardized access to tools and context, pilots stayed stuck as flashy demos.
Anthropic’s Model Context Protocol (MCP) changed that narrative.
MCP is basically a "USB‑C for AI" — a standard way for agents to talk to external tools like databases, search engines and APIs. In 2025 it quietly became the connective tissue the ecosystem was missing.
Key moves:
- OpenAI and Microsoft publicly embraced MCP.
- Anthropic donated MCP to the Linux Foundation’s new Agentic AI Foundation, signaling it wants this to be an open standard.
- Google began rolling out managed MCP servers to connect agents to its services.
With integration friction dropping, 2026 is set up as the year when agentic workflows move from hacky scripts to production systems.
Rajeev Dham, a partner at Sapphire Ventures, expects "agent‑first" products to take on system‑of‑record roles. As voice and software agents handle more end‑to‑end tasks like intake and customer communication, he argues they’ll start to look less like add‑ons and more like the core systems for:
- Home services and proptech
- Healthcare
- Horizontal functions like sales, IT and support
2026 is "the year of the humans"
More capable agents usually trigger one fear: layoffs.
Katanforoosh doesn’t buy that narrative for 2026. He expects the year to be defined less by full automation and more by augmentation.
The promised fully autonomous workflows simply aren’t there yet. And in a shaky macro environment, selling "we’re going to delete jobs" is bad politics and bad optics.
Instead, he expects companies to lean into:
- AI copilots that speed up human work rather than replace it
- New roles in AI governance, transparency, safety and data management
He even told TechCrunch he’s bullish on unemployment averaging under 4% next year.
Pim de Witte framed the cultural pressure more bluntly: "People want to be above the API, not below it," he said, arguing that 2026 will be a key year for defining where humans sit in agentic workflows.
Physical AI hits the mainstream
The last piece of the 2026 puzzle is hardware.
Smaller models, world models and better edge compute stack neatly into a single trend: AI that doesn’t just live in the cloud, but in physical devices.
"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," Vikram Taneja, head of AT&T Ventures, told TechCrunch.
Robotics and autonomous vehicles will keep getting attention, but they’re expensive to train and deploy. Wearables are the cheaper wedge.
Examples already shipping or emerging:
- Smart glasses like Meta’s Ray‑Ban line that can answer questions about what you’re looking at.
- AI‑powered health rings and smartwatches that normalize always‑on, on‑body inference.
All of that puts quiet but real pressure on telecoms and infrastructure providers.
"Connectivity providers will work to optimize their network infrastructure to support this new wave of devices," Taneja said, adding that those with flexible offerings will be best positioned.
From vibes to value
Put together, 2026 looks less like the year of another viral chatbot and more like a grind year:
- New architectures instead of blind faith in scaling laws
- Small, tuned models instead of one model to rule them all
- World models moving from papers to products
- Agents wired into real systems via MCP
- Physical devices that make AI tangible
The party isn’t over. But AI’s center of gravity is shifting — from hype cycles to hard integration work, and from grand promises of autonomy to the more pragmatic goal of making humans, workflows and devices meaningfully better at what they already do.
