AI Agents Are Entering Studios, but the Grid Is Not Built Yet

Introduction

At the same technology event, two very different descriptions of AI agents appeared. In Beijing, industry practitioners said AI is already moving from content generation to world building, even bringing parts of Hengdian’s physical film sets into online 3D production environments. In Hangzhou, researchers compared today’s agents to the first electric light bulb: useful, but far from the full power grid that would transform everything around it.

The contrast is less a contradiction than a difference in perspective. The industry track asked what today’s AI can already deliver in real workflows. The academic track asked when agents will become reliable systems that can learn, verify, and improve themselves.

Key points

• Industry is rebuilding production pipelines. Orca Entertainment discussed how AI can enter film and TV workflows, but stressed that quality production is not just about generating impressive clips. It still requires screenwriting taste, visual design, directing judgment, editing, and sound.
• Software is shifting from tools to outcomes. Meitu described a move from feature-heavy software to agent-driven products. Users state goals, while agent teams decide which models, tools, and skills to call for planning, design, retouching, and business-oriented delivery.
• Video generation needs spatial consistency. Many video models learn mainly from 2D pixels, which can lead to inconsistent rooms, props, and camera views. Kujiale’s approach is to build a 3D scene first, then let video models handle aesthetics and language control. The idea is to create once and reuse across shots.
• Academics remain cautious. Researchers noted that coding agents and digital-task agents are already useful, but embodied agents, open-world learning, long-term memory, and safety remain unresolved.
• The missing piece is a closed loop. A mature agent must execute tasks, judge whether results are correct, identify errors, revise its plan, and preserve useful experience as memory, skills, or model updates.

Why it matters

This split captures the current state of AI adoption. In bounded scenarios such as commercial images, virtual sets, or controlled content pipelines, companies can already make individual “lights” shine. But a broader agentic infrastructure still needs low-latency foundation models, better harnesses, reinforcement learning loops, learnable long-term memory, and safety mechanisms.

That is why both claims can be true: AI can already build usable worlds in specific production settings, while general-purpose self-evolving agents remain closer to the light-bulb stage than to a mature electrical grid.

Source: QbitAI