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The popularity of Sora also ignited A shares, and Sora concept stocks rose collectively that day. Shensi Electronics, Jincai Internet, Hanwang Technology and many other stocks have daily limit, Hongruan Technology and Danghong Technology have risen over 9%, and Geling Shentong, Lingyun Guang and Tuoersi have risen over 6%.The popularity of Sora also ignited A shares, and Sora concept stocks rose collectively that day. Shensi Electronics, Jincai Internet, Hanwang Technology and many other stocks have daily limit, Hongruan Technology and Danghong Technology have risen over 9%, and Geling Shentong, Lingyun Guang and Tuoersi have risen over 6%.Sora is available! Concept stocks collectively rose by multiple daily limit.

[All kinds of themes are dazzling. Next year, the opportunity is on the main board! 】[All kinds of themes are dazzling. Next year, the opportunity is on the main board! 】In the early morning of December 10, Beijing time, OpenAI officially opened the AI video generation model Sora to users. At this time, it has been about 10 months since OpenAI first publicly displayed Sora. Time did not let Sora heat decay, users flocked to the official website, and the instantaneous traffic surged until it collapsed, and the registration and login service was suspended for a time.

Debon Securities said that Sora, as a leader in the field of video generation, plays the role of catfish. According to APPSO WeChat WeChat official account, no less than ten companies at home and abroad have released or updated video generation models since Sora was first publicly displayed. From the performance point of view, the generation effect of large video models at home and abroad has been significantly improved, and the video duration, resolution, screen ratio switching and other functions or performance indicators have narrowed the gap with Sora, and some functions have been overtaken. The official release of Sora is expected to become a new benchmark in the field of global video generation and accelerate the iteration and application of multi-modal large models.