In 2026, AI will move from hype to pragmatism | TechCrunch

"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 towards 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."

"Scaling laws won't cut it 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."

2026 marks a shift from brute-force model scaling toward practical, usable AI. The focus moves from ever-larger language models to deploying smaller, task-specific models where they fit and embedding intelligence into physical devices. Systems will be designed to integrate cleanly into human workflows and to augment human work rather than replace it. Research emphasis will pivot to inventing new architectures and compute-efficient approaches as scaling laws show diminishing returns. Targeted deployments and engineering for usability will take precedence over flashy demos, driving careful, measurable real-world integration.

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