Physical AI Era: A Multimodal GEO Content Guide

On September 14, 2026, PhysBrain 1.5, a Chinese physical-AI large model, topped the global open-source leaderboard, with its spatial intelligence capability widely regarded as on par with GPT-6 Astra (source: QbitAI, September 14, 2026). The same month, at the Bund Summit in Shanghai, investors and founders debated the "boundaries, data and commercialization of Physical AI" (source: Cailianshe, September 2026), while semiconductor maker ADI went so far as to declare 2026 the "Year of Physical Intelligence" (source: ADI press release, February 2026). Equally significant that day: Alibaba Cloud's Zhenwu chip super-node completed Qwen3.8 adaptation and went live on the Bailian platform, delivering up to 1.5x performance gains in agentic reasoning scenarios (source: QbitAI, September 14, 2026). The deep coupling of domestic compute and models is rapidly driving down the cost of multimodal inference.

For brands practicing GEO (Generative Engine Optimization), these two stories point to the same trend: AI engines are moving from "reading text" to "seeing the world," and AI search answers are evolving from plain text into multimodal combinations of images, video and even spatial information. When a user asks whether a company is trustworthy, the engine will no longer retrieve web text alone — it will also pull product photos, facility videos and spatial data to corroborate the answer. This article breaks down four impacts of the Physical AI era on AI search, and lays out a five-step roadmap for upgrading brand content for multimodal GEO.

1. Physical AI and Spatial Intelligence: Engines Are Learning to "See"

Physical AI refers to models that understand the laws of the physical world, spatial relationships and object properties; spatial intelligence is its core capability — the model does not merely recognize "what" is in an image, but understands "where" objects are, "in what state" and "how they interact." PhysBrain 1.5 topping the open-source leaderboard means such capabilities are becoming open-source and low-cost. Domestic engines such as Doubao and Qwen have already rolled out image and video understanding capabilities, so multimodal is no longer a lab concept.

The direct consequence for AI search: the engine's "source understanding" is expanding from pure text to images, video and 3D data. Previously, a brand only needed well-written website copy for AI to cite it. Now engines parse image content, video frames and structured spatial data at the same time — whoever's content assets are "visible" is more likely to be written into the answer. QuestMobile's 2026 AI Platform Development Research Report (published September 8, 2026) describes this stage as AI platforms developing toward "controlled anthropomorphism and tool-based utility," with multimodal understanding serving as the technical foundation of that anthropomorphism.

2. The Changing Answer Format: From Text Summaries to Mixed-Media Cards

AI search answers are getting richer. Early Doubao and DeepSeek replies were mostly text summaries; today's mainstream engines interleave images, video cards and entity information cards — ask about a warehouse service provider and the answer may include facility photos, warehouse videos and address coordinates. Brands that optimize only the text layer will systematically lose position in the multimodal source competition.

Three changes deserve attention. First, video is a new source format: engines extract video frames and subtitles to judge content, so voice-over scripts, captions and chapter titles become retrievable text layers, revaluing brand explainer videos and facility footage. Second, images become semantic: alt text, EXIF data and surrounding copy jointly determine how the engine understands an image, so brand image assets need systematic management (see our Doubao multimodal GEO guide). Third, spatial and entity information rises: structured spatial data such as store coordinates, floor layouts and warehouse areas are becoming the information source for "entity cards," cross-validating textual content (for the underlying mechanism, see structured data and LLM inclusion).

3. A Five-Step Roadmap for Multimodal GEO Upgrade

A multimodal content upgrade is not simply "shoot more videos." We recommend working through five steps:

Step 1: Inventory content assets. Take stock of the image, video and copy assets across your official site, WeChat official account, Channels and Douyin, tagging each item as "machine-readable" or "human-only." Real photos, facility footage and product demos belong to the former; marketing posters are mostly the latter.

Step 2: Add the text layer. Add subtitles and voice-over transcripts to every video, standard alt text to every image, and time/place entity metadata to every piece of content. AI retrieves "textualized video" and "semanticized images" — this step decides whether multimodal assets can be cited.

Step 3: Structured markup. Use JSON-LD to add VideoObject, ImageObject and LocalBusiness structured data for videos, images and business information, so engines can read content attributes directly instead of guessing.

Step 4: Entity consistency. Brand names, addresses, warehouse areas and license numbers must be consistent across platforms, eliminating entity confusion during AI cross-validation — this is the dividing line between "scattered content" and "trusted brand" in the multimodal era.

Step 5: Monitor and iterate. Add "multimodal mention rate" to your monitoring system: sample Doubao, Qwen, DeepSeek and Yuanbao answers to core brand questions monthly and record how often image/video cards appear (see our brand AI mention rate guide).

4. Timing: Falling Inference Costs and the Expanding Domestic Engine Base

Why now? Two macro variables are accelerating the shift. First, inference costs are falling: with the Zhenwu chip super-node live on Bailian, domestic large-model inference is getting cheaper (source: QbitAI, September 14, 2026), so multimodal features will reach the full user base faster and multimodal answers will keep growing in AI search. Second, the user base: the 57th CNNIC report shows China's generative-AI users have reached 602 million, a penetration rate of 42.8% (source: CNNIC, published February 2026) — a massive population is forming multimodal search habits. Third, the open-source ecosystem: open models such as PhysBrain 1.5 accelerate industry iteration, and engine capabilities re-rank brands in answers every few months (for the competitive landscape and adaptation points of domestic open-source models, see our earlier article on GEO strategy for China's open-source large models).

For brands, this is a low-cost window to close the multimodal content gap: the competitive landscape is unsettled and engine rules are not yet fixed. Companies that finish the "text layer + structured data + entity consistency" work first will take the first-mover position in the next round of AI search traffic distribution.

FAQ: Physical AI and Multimodal GEO

What does Physical AI have to do with brand GEO? Physical AI determines whether engines can "understand" images, video and spatial information — and therefore whether AI search answers can cite brand content as image and video cards. That is exactly what multimodal GEO solves.

Must brands produce videos? Not necessarily. Start with the text layer: add subtitles to existing videos, alt text to images, and structured data to pages — low cost, fast results. Produce video within your means, prioritizing real footage.

Does multimodal GEO replace traditional GEO? No — it stacks on top of it. Text-based source building remains the foundation; multimodal is the differentiator. Both share the same entity and source system, so plan them together.

How should a small business with a limited budget start? Start with structured website copy and images: standard alt text and descriptions for product photos, facility shots and license certificates, then add video subtitles. Monthly cost stays controllable.

How do you measure multimodal results? Add the appearance rate of image and video cards in Doubao/Qwen/DeepSeek/Yuanbao answers to your monthly sampling, and read it together with text mention rates. Effects typically show within one to three months.

Make Your Brand Seen by AI

AI search is moving from text to multimodal, and the Physical AI era is the last low-cost window for brands to build visual content assets. Zheming Digital Communication Research Institute provides GEO optimization and AI search brand visibility services covering multimodal content upgrades, structured data and engine citation monitoring. Free consultation: +86 18917757529 / jaysun@widesight.cn — request a brand multimodal content audit report.

Related reading:

• Doubao phone launch: AI search enters the hardware era
• Zero-click search: how brands should respond

This article was written by Zheming Digital Communication Research Institute. Industry data sources and dates are marked throughout (QbitAI/Cailianshe/QuestMobile/CNNIC/ADI). Data updated to 2026.