SHENZHEN, China, Aug. 7, 2024 /PRNewswire/ — AI video creation is a key development focus in the generative AI industry. Recently, Kling Video AI Model, published by Kwai, took the lead in launching large-scale open user testing, triggering user trials and a surge of “creative-oriented” AI video content on content platforms. Users’ response has been positive, injecting a sense of novelty among platform users. The Lumière brothers’ silent black-and-white film, The Arrival of a Train, premiered a century ago, creating a stunning effect on audiences. Today, a century later, with the support of AI video models, AI will pave a path for “realizing imagination” in content development. A new wave of “AI Short Videos” development may emerge, highlighting the convergence of conventional and unconventional, virtual and real, and traditional and modern approaches. Given the diminishing marginal utility of traditional content transformation, this could become a new direction for content creation and provide significant opportunities for AI to break through.
I. AI Video Models’ Delayed Arrival, Kwai Kling Takes the Lead To Reach a New Level
As a track in generative AI, AI video creation has garnered attention and significant investment from various sectors. According to incomplete statistics, over 30 AI video models and tools exist in China alone. However, despite the vast potential demand for AI text-to-video, many products are still in the early stages of technical exploration, with the usability of deployed models generally needing improvement. At the beginning of the year, Sora unveiled AI-generated videos that were up to one minute long, igniting market and user enthusiasm. However, the delayed opening of user testing led to a downturn in market sentiment towards AI text-to-video. This includes evaluations from some foreign producers and media professionals during Sora trials, causing the market to adopt a wait-and-see attitude towards text-to-video technology.
Additionally, some available AI video models face high trial costs, reducing user interest in continuous experimentation. For instance, Runaway’s latest Gen3 version, despite its internationally leading generation quality, comes at a steep price of $12/month for just 125 seconds of qualified output per month. Against this backdrop, achieving low-cost usage thresholds, minute-level output, and basically usable generation quality remains a significant market expectation for the generative AI industry.
Amidst the notable development pain points in AI video generation, Kwai Kling emerged, taking the lead in large-scale user trials. Launched at the beginning of last month, the Kwai Kling provides both web and mobile access points, with the mobile version requiring users to complete tasks within the Kwaiying App. It opened application-based testing, with numerous users already granted trial access and actively engaging in content creation. The model supports various AI video creation methods, including text-to-video, image-to-video, and video continuation, focusing more on user experience. According to data on SimilarWeb, after opening on June 6, the overall website UV for the Kling showed an upward trend. Entering July, the web UV continued to increase, with peak values approaching 100,000 DAU. On the mobile side, since both application and generation model are completed within the Kwaiying App, MoonFox iApp data reveals that from the second week of June, the weekly average DAU of Kwaiying App increased significantly. Particularly after the first week of July, the weekly average DAU increased by about 1 million compared to early June, indicating strong market enthusiasm for Kling
Changes in Monthly Active Users of Kwai Kling – Web Version in June-July (Partial)
Date
Weekly Average DAU
Note
6.06
41043
Kwai Kling AI Video Model opens for testing
6.13
43028
6.25
56508
Kwai Kling AI Video Model starts trending on social platforms
7.06
84554
Data Source: SimilarWeb; Data period: June 2024
Changes in Weekly Average DAU of Kwaiying App in June – July
Date
Weekly Average DAU
Note
10-16 June
5.401 (million)
Kwai Kling AI Video Model opens for testing
17-23 June
5.421 (million)
24-30 June
5.884 (million)
1-6 July
6.450 (million)
Data Source: MoonFox iApp; Data Period: June 2024 – July 2024
II. Empowered by AI Video Models, Young Generations Spark a New Wave of “AI Creative-Generated” Content, Injecting a Novel Experience for Users
Following the launch of the Kling AI Video Model, “AI Creative-Generated” videos have recently gained popularity on video platforms. For instance, various themed AI video content has appeared on the Bilibili platform, with some videos surpassing millions of views and thousands of likes. These contents are mostly highly imaginative creations. The users contributing to these creative AI productions will likely be predominantly from younger user groups. According to data on SimilarWeb, over 50% of web users of Kling AI Video Model are under 34 years old, with the 18-24 age group accounting for nearly 20%.
User Composition of Kwai Kling AI Video Model – Web Version
Gender Distribution
Percentage
Male
72.7 %
Female
27.3 %
Age Distribution
Aged 18-24
19.0 %
Aged 25-34
36.0 %
Aged 35-44
21.0 %
45 years old and above
24.0 %
Data Source: SimilarWeb; Data Period: June 2024
From the user feedback and evaluations perspective, enthusiasm for the Kling AI Video Model is currently high. We analyzed social media user comment data related to the Kling AI Video Model, excluding official accounts, influencers, and KOLs, retaining only ordinary user comment data. We found that overall, users hold a positive attitude towards the current Kling AI Video Model. User emotions are mainly concentrated in four categories: curiosity, excitement, anticipation, and satisfaction. Users exhibit high levels of pleasure and arousal towards the Kling AI Video Model. The high arousal level also indicates that compared to current content types, the Kling AI Video Model is more capable of activating users’ psychological enthusiasm and willingness to act, suggesting that more users will be willing to try using and viewing related content in the future.
Emotional Analysis of Kwai Kling AI Video Model User Evaluations
Emotion Type
Percentage
Curiosity
45.0 %
Excitement
31.8 %
Anticipation
12.7 %
Satisfaction
5.5 %
Wait-and-see
9.1 %
Data Source: MoonFox Big Data Analysis; Data Period: June 2024
III. The Concept of “AI-Generated Short Videos” May Break Through in the Future, Presenting a Different Development Approach from Traditional Content Creation
How AI generation can achieve true implementation has been a key concern across various sectors. Due to factors such as hallucination issues, many current AI applications still face retention problems. As a subdivision within this field, we believe AI video creation has the potential to gain further user recognition on the usage end in the future.
Drawing from the experience of the previous round of PC and internet development, the focus on AI is not just about the efficiency revolution but also about realizing imagination. Taking QQ as an example, the internet-era QQ provided a vast space for imagination, such as QQ Space, QQ Show, emoticons, and other creative designs. Compared to traditional content at the time, the experience was quite exceptional. Behind online social interaction, there was actually a large number of novel content experiences supporting it, not just simple efficiency improvements. Similarly, current AI virtual social interaction applications with daily user engagement times of several dozen minutes follow the same principle. On the other hand, entertainment content, compared to efficiency tools, inherently has broader coverage and stronger user stickiness in terms of experience.
However, in the AI era, we believe that the “novelty” to be embodied in video content creation differs from traditional interpretations. In a situation where user interests are relatively stable and user interest circles are very distinct, breakthroughs are necessary to refresh the experience. Specifically, we believe there might be three aspects: The fusion of conventional and unconventional, the integration of virtual and real, and the blend of traditional and modern, thereby integrating with the real world to capture user interest. Secondly, while the current high cost of AI video creation is a fact, using AI for large-scale generation of long videos may not be the short-term implementation direction. However, generating short videos or integrating with existing video segments is quite possible. AI short videos might become a direction where AI video models can be practically implemented in the near future.
A century ago, the earliest silent black-and-white films were very limited in duration due to cost constraints at the time. For example, the Lumière brothers’ The Arrival of a Train was only 50 seconds long. However, when it premiered, its realistic portrayal left a deep impression on the entire audience, thus propelling a massive film industry. A century later, the AI era may potentially guide creators to explore more ideas that are difficult to realize with traditional content, opening up a new approach to video creation, which could have profound implications for future user content consumption.
MoonFox Data is a leading expert in all-scenario data insights and analytics services. With a comprehensive, stable, secure and compliant mobile big data foundation, as well as professional and precise data analysis technology and AI algorithms, MoonFox Data has launched iAPP, iBrand, iMarketing, Alternative Data and professional research and consulting services of MoonFox Research, aiming to help companies gain insights into market growth and make accurate business decisions.
About us: Aurora Mobile (NASDAQ: JG) established in 2011, is a leading customer engagement and marketing technology service provider in China. Its business includes notification services, marketing growth, development tools, and data products. As its sub-brand, MoonFox Data is a leading expert in data insights and analysis services across all scenarios, aiming to help companies gain market insights and empower precise decision-making.
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