In the final trading week before the Lunar New Year, AI applications once again ignited sentiment in the capital markets. This time, it was not agents, search, or office tools that burned the brightest, but rather the subsector of animation.

On the news front, ByteDance's Seedance 2.0 video generation model recently completed internal testing and went live on the Jimeng platform, quickly breaking through to widespread attention across the internet. #Seedance2.0# trended on hot searches, with a particularly viral AI-generated video depicting a fight between Stephen Chow and Bruce Lee. The footage was smooth, the camera work natural, and the audio-visual synchronization highly precise, with AI traces deliberately minimized.

After testing it, Feng Ji, the father of 'Black Myth: Wukong,' directly commented: 'This is undoubtedly the strongest video generation model on the market today, bar none.' The market's response was equally direct, with China Literature Group's stock surging by over 26% in just two trading days.

So, the question arises: Is this an industrial inflection point, or merely another valuation projection driven by technological sentiment? To answer this, we must dissect three layers: the true position of technological evolution, the distribution of control within the industrial chain, and the financial reality behind the so-called 'cost-reduction myth.'

Technology, Capital, and Form Resonate: Has the 'Inflection Point' for AI Animation Arrived?

To understand this wave of AI animation, we must first acknowledge a fact: Seedance 2.0 represents a landmark product.

Over the past few years, AI video generation models have emerged in droves, but they have largely remained at the demo stage. Fragmented footage, unstable characters, jerky camera transitions, and disjointed audio-visual elements have relegated these works to mere showcases of capability rather than true entrants into the content industrial system.

The breakthrough of Seedance 2.0 lies in its systematic resolution of the three long-standing pain points in AI video generation: coherence, controllability, and audio-visual synchronization. The cumulative effect of these capabilities significantly narrows the gap between 'generating a scene' and 'delivering a complete, deliverable piece of content.'

This step holds immense industrial significance. True content production demands the stable delivery of complete narratives, requiring unwavering character consistency, continuous camera work, and seamless emotional progression. The value of Seedance 2.0 lies in its nascent 'directorial capabilities.'

Furthermore, according to industry estimates cited by BOC International, Seedance 2.0 achieves a 90% usability rate for generating 15-second videos, far surpassing the industry average of around 20%. This shift not only implies cost savings but also enhances the planability of content production.

Breakthroughs at the tool level must ultimately pass through the commercial funnel for validation. So, why has AI animation become the first application scenario to catch fire after systemic relief of technological constraints?

The underlying logic is simple: its content form is highly compatible. Animation is neither traditional animation nor short drama but an industrially downgraded form Between comics 、 Between animation and short videos (already translated above as 'a form Between comics 、 Between animation and short videos ') that straddles comics, animation, and short videos. Essentially, it is a 'low-frame-rate narrative industrial product.' It inherently features short cycles, low barriers to entry, fast-paced content, divisibility, and replicability, making it highly suitable for platform-based, large-scale production.

In other words, animation represents an excellent transitional form for the AI-ification of the video content industry. The technological evolution of Seedance 2.0 has endowed this cost-sensitive content type with greater scalability potential.

Meanwhile, the external environment continues to strengthen the industry's overall support capabilities. The interconnection of computing infrastructure and the gradual maturation of open-source ecosystems for large models are transforming AI capabilities from proprietary tools for leading players into public goods for the industry. This time, the release of technological dividends will outpace any previous evolution in content forms.

With technological constraints being overcome, content forms aligning, and infrastructure providing support, the simultaneous loosening of these three production conditions has forcefully shifted the supply curve of AI animation to the right. A new content cycle is being unleashed by technology.

Smart Capital Bets on Control

In this rally, China Literature Group has emerged as the most direct positive feedback target for the market. However, this is not because it is more adept at using AI but because it stands at the most certain end of the content industrial chain.

The commercial essence of AI animation does not lie in production but in the distribution of control.

Within the entire chain, two types of entities truly hold pricing power: upstream players possessing IP copyrights and platforms controlling distribution and monetization entrances. The midstream production side and creators are relatively more squeezed, especially as AI technology accelerates toward democratization, making this trend even clearer.

China Literature Group, with the largest reserve of online literature IPs in the network and a portfolio of highly valuable and nationally recognized headline IPs, clearly falls into the former category. In animation alone, according to the latest official disclosures, since its comprehensive layout in October last year, ten of its works have surpassed 100 million views, and over a hundred have exceeded 10 million views.

Leveraging its resource barriers upstream, the company is also accelerating vertical integration. Midstream, it continues to strengthen its adaptation, incubation, and industrialization capabilities; downstream, it establishes stable distribution and monetization channels. To date, China Literature Group has constructed a full-link ecosystem spanning online literature, film and television, animation, games, derivatives, and overseas markets.

The intervene of AI has not disrupted this structure but amplified its value. As production costs decline, the frequency of IP development can increase, the turnover rate of IP assets can rise, and theoretically, the value density of IPs can escalate.

However, when viewed from an industry-wide perspective, can the theoretical 'increase in value density' quickly translate into widespread profit growth across the industry? The reality may be harsher.

In fact, once AI triggers an explosion in animation supply, platform traffic will become even more fragmented. At this point, an increase in the number of IP adaptations does not necessarily equate to a rise in revenue per IP. For instance, while 2025 may indeed be the 'gold rush' year for AI animation, few have truly struck gold, with 99% of works merely accompanying the race.

Therefore, the recent surge in China Literature Group's stock reflects a clear market logic: AI-driven cost reductions → accelerated IP development → ultimate cash flow release. However, the effectiveness of this chain currently concentrates almost entirely on leading companies with top-tier IP reserves. For the numerous mid-to-lower-tier IPs or emerging creators, the business model remains far from viable, and the industry as a whole is still in the early stages of concept validation.

Capital's pursuit, rather than confirming industry-wide adoption, is more akin to another vote for 'leader premium.'

The Truth Behind '90% Cost Reduction': Costs Haven't Disappeared, They've Just Shifted in Financial Reports

From a more objective standpoint, the narrative of cost reduction and efficiency gains in AI animation still harbors significant real-world cracks.

In investment bank research reports and industry roadshows, AI animation is repeatedly touted as a paradigm of 'cost reduction and efficiency gains.' According to calculations by Soochow Securities, production costs could plummet by 90%. However, this figure primarily reflects the compression of quantifiable links rather than a refactor (already translated above as 'restructuring') of the overall production function.

At a superficial level, AI has indeed reshaped the animation production process. Traditional high-cost procedures such as original artwork, coloring, storyboarding, and frame repair have been replaced by algorithms, significantly shrinking production team sizes and compressing per-episode production cycles. These changes are not illusory.

However, AI generation also creates a more difficult-to-quantify cost black hole. In real-world production, AI generation is not a one-shot process but highly dependent on repeated trials and selections. Every seemingly smooth shot often accompanies a large volume of ineffective generations. These failures do not directly appear in financial reports but genuinely consume manpower, time, and computing power.

Take the newly launched Seedance 2.0, for example. This iteration has greatly improved the usability rate of generated content, but achieving creators' ideal demands still necessitates extensive testing. This process still requires human intervention to complete prompt-driven, lottery-like generations.

Creation has shifted from controllable execution to probabilistic exploration. In a sense, AI has not eliminated certain foundational labor tasks but created a labor form that is harder to standardize and manage; costs have not vanished but shifted from explicit expenditures to implicit consumption of computing power and trial-and-error efforts.

A more pressing issue is that when the industry rushes in with the goal of 'ubiquitous hits,' survivorship bias becomes infinitely amplified, while failed samples are collectively ignored. Ultimately, the market sees screenshots of hits and cost myths, while practitioners bear the brunt of uncontrollable labor intensity and highly unstable output returns.

At this stage, small and medium-sized teams lacking scalability advantages are highly likely to become trapped in computing cost quagmires. The industry's overall narrative of commercial cost reduction still requires continuous evolution.

Beyond cost misallocation lies an even deeper risk: trust.

Video, once one of the most difficult media to forge, is now becoming increasingly vulnerable to fabrication. The impact on the content industry extends far beyond copyright disputes, directly striking at the core of social trust structures. This implies that during the industry's high-growth phase, regulatory backlash is inevitable. Seedance 2.0's capabilities in voice replication and character restoration, for instance, are highly prone to controversy.

In 2026, AI animation is highly likely to achieve rare synchronous acceleration across technology, capital, and content industrial dimensions. However, precisely because of this synchronicity, the structural risks it conceals become even easier to overlook.

Conclusion

AI animation is not a false proposition; it is destined to become part of the content industry. However, at this stage, it more closely resembles a tool efficiency upgrade than an industrial paradigm revolution.

The breakthrough of Seedance 2.0 is akin to the progression of large language models from GPT-3 to GPT-4—significant capability improvements, but the business model remains unreconstructed. The surge in China Literature Group's stock is supported by industrial logic but also contains an emotional premium.

The true inflection point for the industry lies in three metrics: Can AI animation form a stable Paid model (already translated above as 'payment model')? Does IP turnover rate translate into net profit margin growth? Does the surge in content supply lead to a decline in marginal returns on traffic?

Until these questions are validated one by one, AI animation remains more of a high-elasticity theme than a Deterministic mainline (already translated above as 'certain main trend'); it is still trapped in a relatively contradictory intermediate state, appearing incredibly efficient while accumulating internal frictions, manufacturing hit illusions while amplifying survivorship bias.

Source: Hong Kong Stocks Research Society