From Creators to Correctors: AI,Platform Power,and Animation Work in Small and Medium Studios

China’s Animation Industry and Artificial Intelligence

From 1949 to the early 1980s, animation production in China was primarily organized under a state-led studio system, with state-owned film studios handling production. The most representative among them was the Shanghai Animation Film Studio. Under this system, animation was typically created through collective efforts within the studio. The main goals of the production were cultural dissemination, artistic exploration, and educational purposes, rather than focusing on commercial markets (Lent, 2001).

After the Reform and Opening-Up in 1978, especially during the mid-to-late 1980s, China gradually became part of the global animation production network (Lent, 2001). In 1985, Jade Animation, backed by Hong Kong’s TVB, was established in Shenzhen and became the first professional animation company on the mainland. Later, processing companies like Pacific Animation, Cailing Animation, Anli Animation, Zhuhai Chisheng Cartoon, and Jing Hongye Animation were set up in the Pearl River Delta, drawing in animation talent from across the country (Niu, 2011).

In 1986, Taiwan-funded Hongying Animation was established and later moved to mainland China in 1992, where it founded Suzhou Hongying. In 1988, the first private animation company in mainland China, Dalian Avanti International Animation, was created, initially doing outsourcing work for Europe and Japan (Niu, 2011).

By the 1990s, the industrial landscape had expanded to two main regions: the Pearl River Delta and the Yangtze River Delta. Along the southeastern coast, there were 5–6 large animation companies, loosely spread out, and over 80 smaller ones, with the two largest being Taiwan-funded companies Wang Film Productions and Hongying. These businesses mainly focused on labor-intensive tasks like in-between drawing and coloring, forming the basic industry structure where large international companies led, while small domestic studios were scattered throughout the industry (The evolution of Chinese animation outsourcing, 2013).

China subsequently replaced South Korea and Taiwan as the world’s leading animation outsourcing hub. Over 70% of Japanese animation and many European and American animated productions completed their intermediate stages in China. Jade Animation contributed to Hollywood productions such as The Lion King and Mulan (Niu, 2011).

In 2004, the animation industry was officially included in the national cultural industry development strategy agenda. Animation output then increased rapidly, rising from about 12,000 minutes in 2003 to approximately 220,000 minutes in 2010, and in 2006 it surpassed Japan to become the country with the highest animation production output in the world (Su, 2022).

Since the mid-2010s, with the rise of digital platforms, China’s animation industry has undergone another structural shift. Online video platforms have increasingly become essential infrastructures for producing cultural content. By investing in animation projects, developing IP, and integrating distribution channels, they have significantly shaped the content creation process and continuously strengthened their role within the industry value chain (Cunningham & Craig, 2019; Keane, 2013; Oreate, 2026). Industry reports show that leading platforms have become the main distribution channels for domestic animation, with iQIYI, Tencent Video, and Youku collectively accounting for 87.8% of the market share (Vzkoo, 2023).

Within this platform-driven production network, although several large animation companies—such as Xuanji Technology, Rocen Digital, Haoliners Animation, YHKT Entertainment, and Phantom Digital—have emerged and work with platforms or IP rights holders on a project basis to produce content, it is the numerous small and medium-sized production teams that actually handle most domestic animation tasks. These teams usually subcontract for larger companies or platforms, making them a vital force within the platform-led model.

This division of labor, where “platforms control the channels while small teams handle production,” combined with the animation industry’s long-term reliance on many labor-intensive, repetitive tasks, makes it one of the sectors most affected by artificial intelligence (Barber et al., 2016; Torrejon et al., 2020). With advances in technologies such as machine learning and deep learning, reports indicate that AI platforms now manage the entire process from concept design to 3D rendering, including sketch generation, intelligent in-betweening, and automatic rendering (Anantrasirichai & Bull, 2022; IvyPanda, 2018; Tang et al., 2025). This also shows that AI technology is systematically transforming the animation industry. Industry reports show that about 70% of small and medium-sized animation studios have already adopted AI tools for asset creation, and the adoption of AI animation tools has cut the average production cycle by roughly 30% (SuperAGI Research, 2025; WiFiTalents, 2025).

This technological shift has created new opportunities for small- and medium-sized animation teams in China, which often struggle with limited resources. A clear example is the Chinese AIGC-based animated series “Tomorrow Is Monday.” This project was completed by a team of just 10 people in 45 days and produced 50 episodes, with the cost per minute dropping significantly from the traditional 30,000–50,000 RMB to only 2,000–3,000 RMB. (Sina Finance, 2025).

Platformization, Technological Monopoly and Artificial Intelligence

Platformization theory examines how digital platforms influence and reshape content creation and value sharing through technological structures, algorithmic systems, and rule frameworks (Helmond, 2015). Platforms are not neutral tools but governance systems with defined power dynamics. They first bring various participants into established rules via programmable architectures, using this as a basis for expanding capital and extracting data. Then, they serve as “curators” through algorithmic systems in content distribution and visibility, further shaping the norms of cultural production (Gillespie, 2018).

Although some studies highlight the positive role of platforms in resource sharing and innovation diffusion (Chen et al., 2022; Dahmani & Ben Youssef, 2023; Jiang & Sun, 2025), critical perspectives point out that such sharing often depends on platforms monopolizing data profits. This unequal power dynamic has been well documented in the distribution aspects of creative industries. Research on Spotify shows that algorithmic recommendation systems strip creators of control over their work’s exposure, forcing them to passively adapt to algorithmic rules (Prey, 2020). Similarly, research on the Webtoon platform indicates that algorithmic ranking and crowdsourcing mechanisms enable platforms to define value, resulting in a high concentration of revenue among top creators, while most creators become marginalized and passive recipients of platform rules (Kim & Yu, 2019).

In modern research, AI is widely seen as the next phase of platformization. By controlling essential resources like data, models, and computing power, AI has significantly changed how creative work is organized and how value is distributed (Berlinski et al., 2024; Fox, 2024). Optimistic studies emphasize how AI transforms the relationship between platforms and creators, arguing that AI tools lower the barriers to creation and enable more small teams and individual creators to join platforms and participate in content production, opening new opportunities for creators (Capraro et al., 2024). However, some research also highlights that this kind of technological monopoly can actually strengthen platform control over infrastructure and shift platforms from relatively open innovation systems to highly centralized power structures (Verdegem, 2024).

In this paper, “technological monopoly” refers to platform-based control over production tools, data infrastructures, and algorithmic systems, through which platforms extract, standardize, and reappropriate creators’ practices as data for model training. The idea of technological monopoly goes beyond market dominance in economics and is more deeply expressed as cultural and ideological control (Illich & Lang, 1973; Postman, 2011). Langdon Winner (2020) noted that technological artifacts are not neutral; specific technological designs reflect particular power preferences. Neil Postman (2011) described this as the point where a technology becomes dominant in an industry, shifting from an optional device to the only standard for defining “progress,” “efficiency,” and even “truth.”

In the digital era, the most common practitioners of technological monopoly are digital platforms that combine computing power, data, and distribution channels (UK Competition and Markets Authority, 2015; Zuboff, 2023). For creative industries, leading platforms create insurmountable knowledge barriers by controlling underlying algorithms, training data, and computing resources, forming what is known as an “Obligatory Passage Point” (Verdegem, 2024), making the platform the essential technological gateway through which creative work must pass. Nick Srnicek (2017) observed that platforms establish a new monopoly over technological infrastructure by controlling data and user connections. Zuboff (2019) further described this as a “Radical Monopoly,” where small actors lacking such technological capabilities are deprived of legitimacy in the market. Nieborg and Poell (2018) uncovered the concrete operation of this power structure from the perspective of creators: platforms monopolize content circulation by controlling algorithms and distribution tools, and creators must follow platform rules to gain visibility. This exclusive control over access to audiences raises technology from a mere tool to a form of power, locking creators into a dependency structure.

The Ecological Impact of AI on the Animation Industry

The current animation industry in China displays a typical “pyramid” structure. At the top are leading platform companies such as Tencent Animation and Bilibili, which control key distribution channels and IP resources and hold a dominant market position. In the middle are large animation production companies like Xuanji Technology, Original Force Animation, Light Chaser Animation, and Fantawild, which have full-process production capabilities and consistent output. At the base are numerous small- and medium-sized studios—estimated to exceed 6,000—most of which depend on outsourced projects to survive (Liu, 2014, 2025; Xiong, 2025).

Unlike the first two tiers, small and medium-sized studios usually do not engage in core IP development or full-process production. Instead, they focus on modular tasks like scene asset creation, visual effects rendering, and in-between frame drawing, leveraging their specialized skills. They embed themselves into platform-led production networks on a project basis, acting as the “capillaries” of the industry chain (Li, 2011). In terms of collaboration, platforms connect large production companies and small studios through equity investment and project-based cooperation (Li, 2011; Liu, 2014). These small studios handle most labor-intensive tasks, such as rendering and in-betweening. For example, in Ne Zha 2, among 138 participating companies, small- and medium-sized teams accounted for over 87% and completed most of the film’s production workload (Wu & Wu, 2025).

This division of labor, in which small and medium-sized studios serve as the main workers, has led to many repetitive, standardized, and labor-intensive tasks persisting in animation production. These tasks are now the first to be affected and changed by artificial intelligence. AI is now entering China’s animation industry at an unprecedented pace, with the changes at the workflow level being immediate and highly noticeable.

Repetitive and detailed tasks that once needed a lot of human effort can now be done efficiently by algorithms, and features like “text-to-storyboard” are changing the early steps of animation production. However, behind what technological optimists call “new opportunities” for entering the industry (Capraro et al., 2024), a more complicated reality is starting to show: the heart of creative work—the artistic intuition built up over many years, the aesthetic judgment honed through practice, and the unique style developed through repeated refinement—is being reshaped by technology.

First, on a technical level, by streamlining workflows, AI tools help small and medium-sized teams boost production efficiency and lower the costs associated with trial-and-error from early-stage design through later-stage rendering. For creators, this efficiency gain is more noticeable as a shift in labor. The studio where Oliver works has already achieved full-process AI-assisted animation generation by building workflows on an AI platform, and her primary work has shifted from “drawing” to “image correction.” She explained: “Basically, there is no need to draw. The main issue is that AI-generated content contains errors, such as four fingers or similar mistakes. We need to review them and modify the prompts to correct the mistakes.”

It is worth noting that the “relief” AI has provided has not truly freed human workers. The interviewed animators and storyboard artists both stated: “The workload has become smaller, but the work intensity has in fact intensified.” The reason is that although the process from creative idea to completed work has been shortened, labor intensity has not decreased accordingly. As the animator Alice remarked, “Before (as an animator), we drew about 3 to 4 images per week. In the studio, each person must produce at least 5 images per day. Hand drawing is no longer required, but when you use AI, the company’s expectations for your output become much higher.” It can therefore be argued that the time saved by technology has not been turned into more personal time for workers; instead, it has been redirected to meet higher productivity demands.

Another animation storyboard artist, Jimmy, added, “When we were hand-drawing before, the process was actually more flexible. Once you finished drawing, you were done. Now, every day you’re tied to the workstation. You’re either writing prompts or repeatedly modifying prompts based on the images produced by AI—adjust the prompt, generate an image, it doesn’t work, change another set of keywords, generate it again. AI can produce an image in a few seconds, but getting a usable one may take dozens of iterations. On top of that, bosses think AI is efficient, that one person can replace several people, so the number of assigned tasks keeps doubling. There’s scarcely any breathing space.”

This feeling isn’t unique to Jimmy. For many creative workers, this process increasingly positions them as correctors, whose work involves continuous prompt adjustments, error corrections, and iterative refinement. Increasingly, people are experiencing the overwhelming sensation of being “dragged along by machines and unable to stop even for a moment.” On the surface, technological progress has made image generation faster, but in reality, the workload is quietly growing—constantly writing prompts, fixing errors repeatedly, and making continuous revisions—all of which become additional burdens that individuals must silently carry.

Even more hidden is that in the process of repeatedly correcting AI errors, creators contribute not only physical effort and aesthetic judgment but also provide training data for AI models without compensation, using their own labor to help machines become “smarter,” while they themselves fall into an even more intensive cycle of work. These changes in the individual work content of laborers are not merely isolated shifts in professional experience but signal a fundamental restructuring of the production model of small and medium-sized teams. The former “workshop-style” production, which relied on internal knowledge transmission and accumulated experience within teams, is gradually being replaced by “standardized interface-style” production defined by platform AI tools. Core team members shift from “creators” to “correctors,” while the production rhythm, output standards, and even the team’s creative direction are shaped by the technological frameworks set by platform tools.

Changes in workflow have also directly influenced the industry’s occupational structure, with entry-level and repetitive roles among the first to be affected. In animation production, AI technology has predominantly integrated into tasks that are relatively standardized and easy to automate, such as bulk generation of scene assets or assistance with in-between frame creation, demonstrating a clear focus on cost efficiency. This trend was supported by several interviewees: Eleanor mentioned that the number of employees in the same role has decreased, while senior lead animator Oliver noted that the impact has been concentrated on “entry-level positions.” Likewise, Samuel, who works in design, reported that the promotional poster department has been reduced from 10 people to two, plus AI.

Contrary to the common expectation that many “prompt engineers” would emerge, new types of roles have not appeared. Instead, people in their current jobs need to learn new skills to stay up to date. For example, John, the technical manager of Alibaba Youku’s AI storyboard team, said, “In the past, when we hired people, we mainly looked at professional ability. But now it’s different—we prefer people who not only have a solid professional background but can also skillfully use AI tools.”

The impact of AI on the animation industry has two sides. On one side, it boosts production efficiency and cuts costs. On the other side, it quietly reshapes the core of creative work—artistic intuition built up by creators over many years, aesthetic judgment refined through practice, and drawing skills internalized through repeated effort are being redefined by technology in every act of “correction” and “selection” during daily work. These two changes happen at the same time and together form the structural context faced by small and medium-sized teams in the age of AI.

Constraints of Small and Medium-Sized Teams: Mitigation or Intensification?

Before artificial intelligence transformed the animation production process, small and medium-sized animation teams in China had long faced structural challenges in their path toward industrialization. Due to limited control over upstream core IP and downstream distribution channels, these teams have historically been pushed into the low-profit middle segment of the industry chain, mainly handling outsourced or commissioned work, with their survival space and bargaining power continuously constrained (Pan, 2024; Toubao Research Institute, 2020).

With the rise of streaming platforms, platform capital has further strengthened its dominance over production by controlling distribution channels and traffic allocation. Although small teams have gained more opportunities for exposure, they are also compelled to adapt to the platforms’ algorithmic logic and traffic rules. Consequently, their creative direction increasingly compromises with market demands, while the gap between value contribution and economic return continues to widen (Xu, 2024).

Under the influence of AI, a notable trend is that while small and medium-sized teams use platform AI tools to boost production efficiency, they may also face the risk of gradually losing their core competitiveness. In traditional animation production, small and medium-sized teams build experience, technology, and aesthetic preferences through project practice, transforming these into reusable internal knowledge assets that are crucial for their competitiveness. When AI simplifies work steps, the project execution process bypasses the repeated refinements that were once necessary, depriving team members of opportunities to gain experience and develop personal aesthetics through continuous revision.

A senior designer deeply felt this change: “In the past, most of the day was spent struggling with design drafts and prototypes; now it might take only 2 hours to get it done.” In his view, this is not because people have become lazier, but because the rhythm of production has been completely changed by AI. This also means that when the process of repeated refinement is removed, creators lose the opportunity to accumulate experience and develop personal aesthetics through continuous modification.

The transformation of work steps has also led to the devaluation of creators’ labor. Betty, a screenwriter, wondered, “Now my work basically involves writing prompts based on tasks and filtering the results. This makes me think: what is my unique value in this creative process?” Betty’s confusion is not an isolated case. When creative work is reduced to two stages—“writing prompts” and “filtering results”—the artistic intuition built up by creators over many years is displaced. Related research indicates that artificial intelligence capitalism reinforces the logic of efficiency and scale, and may alter the system of value assessment in cultural production (Verdegem, 2024). Professional aesthetic judgment shifts from that of a creator to that of an inspector, thereby reducing the value attributed to creative contribution in the production process. This devaluation occurs because AI has reshaped the foundations of value in animation, moving the focus from originality and artistry to adaptability and efficiency.

Based on the resource-based view (Barney, 1991), the real competitive advantage of small teams should be creative outputs that are “difficult to imitate.” In the traditional model, this advantage was built by teams gradually gaining experience, technology, and aesthetic preferences through long-term practice, turning them into internal knowledge assets. However, AI as a tool is dissolving this mechanism.

In the traditional model, production tools were fairly neutral. Over time, teams could gradually develop their own technical habits and aesthetic preferences. However, today’s AI tools are different: they are mostly standardized services offered by platforms, and everyone uses the same models and similar parameters. When tools no longer allow teams to incorporate their own experience into the creative process, the advantages they once relied on become harder to leverage, and maintaining uniqueness becomes more difficult.

Several interviewees confirmed this trend. Robert pointed out that AI tools generally tend to produce “template-style aesthetics.” Paul, an independent animator with many years of experience, reflected: “Bosses are eager to use AI to cut costs and boost efficiency. The work is indeed done faster, but the outputs may eventually look the same.” The 29-year-old animator Judy described her daily work like this: “Hand drawing used to be the skill I relied on to make a living. How the lines move and how the shapes are captured were all in my own hands. Now I no longer need to draw by hand—animation generation relies entirely on Stable Diffusion.”

Judy’s observation is not just about replacing tools. Instead, optimizing production tools and platforms encourages creators to abandon their long-held skills and rely heavily on platform-specific tools. While this may seem empowering, it results in a form of hidden technological dependence. As creators increasingly rely on platform infrastructures for both production and distribution, cultural creation becomes gradually integrated into the system of platformization (Nieborg & Poell, 2018). In such a setup, when technological tools become the primary means of solving problems, creators’ reliance on a particular technological system can evolve into a technological monopoly (Illich & Lang, 1973).

Judy’s personal experience highlights a broader collective problem: when team members stop developing new skills, the organization’s core knowledge assets lose their ability to regenerate. From a resource-based view, the real competitive edge for small and medium-sized teams should come from “tacit knowledge” embedded in members’ practices, which is hard for the market to copy. But AI tools standardize and clarify the creative process, turning experiences that were once hidden within teams into explicit data that platform algorithms can capture. What the team loses is not just individual skills but also the organization’s ability to learn and its unique knowledge reserves.

A manager of a medium-sized animation studio, Kim, said: “We do feel that something is a bit strange, but we can only use their tools. We basically have no say in it. After all, the platform provides them for us to use for free, and we don’t really have any position to refuse.” From Kim’s words, we see that promoting platform AI tools is not just about creators relying on them; it’s about deliberately deploying tools at the production end. Creators are aware of this, but because of their disadvantaged position, they have no direct way to oppose the platform’s embedding of these tools.

By monopolizing production tools, platforms absorb creators’ aesthetic knowledge and work as AI training data, transforming their labor into the core resource that drives model iteration. Creators contribute cognition and creativity but do not receive a proportional return. Meanwhile, platforms continue collecting data and expanding their control over production, further solidifying their monopolistic dominance.

A manager of a medium-sized animation studio stated, “We often cooperate with the platform to test some of the AI tools they develop. We give them a lot of feedback, which is also considered part of our work, but there is no payment.” In the Youku AI storyboard project observed in this study, project managers frequently invited small- and medium-sized studios to test AI tools for free and used their feedback to guide subsequent modifications. Additionally, part of the model’s training data comes from the internet, while another part comes from works created by small and medium-sized teams, and the project team has never paid copyright fees to either party.

The data engineer, Robert, explained, “Other teams (other AI tool development teams) are doing the same thing. Since this is for storyboarding, the outputs generated by AI will not retain traces of the original content, so we are not considering copyright issues at the moment.” Another storyboard artist, Amy, added, “We have never considered income from this aspect. Since we cooperate with the platform, it would not be good to fall out over such a small matter. We just treat it as something done along the way.”

When faced with the platform’s data extraction, small and medium-sized studios and creators experience a clear compromise and helplessness. Creators are personally training and refining the AI tools that might replace them someday or further diminish their bargaining power. By monopolizing production tools, platforms absorb creators’ surplus value into their models, making the platforms increasingly dominant while the individual value of creators continues to be diluted.

The Survival Strategies of Small and Medium-Sized Creators Amid Emerging Trends

The introduction of AI technology has greatly increased the complexity of the bargaining relationship between small teams and platforms. Previously, platforms controlled traffic distribution, and small teams depended on them to reach audiences, mainly negotiating over IP ownership and revenue sharing (Nieborg & Poell, 2018; Poell et al., 2021). AI has changed this dynamic, shifting small teams from a simple reliance on distribution channels to a dual dependence on both “channels and production tools.” This shift makes the situation more delicate for small teams—they must rely on platform tools to boost efficiency while also actively minimizing the “AI traces” in their work, aiming to showcase their creative value by maintaining a sense of craftsmanship and emphasizing personal style.

To meet platform requirements for AI tools, most small and medium-sized teams interviewed adopt a production strategy that combines “manual refinement” and “AI generation,” with the manual-to-AI ratio roughly 4:6. Using the team where animator Peter works as an example, he explained: “Even when clients require the use of AI, we still try in actual production to ‘minimize visible AI traces as much as possible’.” For these teams, this isn’t just an artistic stance but also a survival strategy in the market. By emphasizing manual refinement and the personal style of contracted animators, they aim to distinguish between “pure AI generation” and “partial AI participation” in the marketplace. In doing so, they try to establish a different position, marked by a “sense of craftsmanship,” beyond the platform logic that prioritizes efficiency above all else.

The reason teams dedicate significant effort to “manual refinement” of fine details is to show the platform that high-quality results come not from the machine’s automatic output but from the team’s judgment and craftsmanship—elements that algorithms cannot replace. This careful manual refinement, across many details, becomes a bargaining chip that demonstrates the team’s value to the platform. Labor process theory highlights that under “deskilling” pressure, workers attempt to regain control through their labor traces, which also serve as resources for showcasing their value to capital (Burawoy, 2012; Edwards, 1982). At the team level, this principle is reflected in how teams turn the individual skills and labor traces of contracted animators into differentiated competitive advantages at the organizational level.

It is crucial to understand that the bargaining relationship between small- and medium-sized teams and platforms exists within a larger dynamic. On one side, teams depend on their animators to resist standardization through “manual refinement,” aiming to showcase the team’s unique value through independent creation. On the other side, platforms do not suppress this autonomy but instead turn it into a key resource for production—every instance of “manual refinement” by animators, including operational traces, modification records, and creative decisions, becomes training data for platform algorithms. This means that the more teams rely on animators to demonstrate their irreplaceability through “manual refinement,” the more they provide platforms with examples of how these skills can be duplicated or replaced. The very tool teams use to combat standardization, while serving as leverage, also accelerates the digital breakdown and reproduction of the craft itself.

Animation planner Lindy offered supporting evidence: “Now, some clients do actively request that the work ‘retain a certain hand-drawn feeling’ or ‘not have too strong an AI flavor’. They worry that work generated entirely by AI may appear cheap.” When platforms produce promotional materials, they often require production teams to submit process-oriented materials, such as hand-drawn sketches and modeling assets. These are included in the publicity content. A liaison from one platform explained: “It’s more about showcasing a kind of craftsmanship, but it can’t be used as an excuse for delaying deadlines. Our requirement is always to ensure product quality while meeting delivery schedules.” Capital thus demands that creators maintain efficiency while simultaneously conveying a sense of craft. This dual pressure—of “efficiency” and “human touch”—amounts to a form of double extraction. Animators, under severely compressed timelines, must labor intensively to erase traces of the machine.

Hesmondhalgh and Baker (2013) argue that creative work typically offers a high level of autonomy, enabling workers to express themselves, experience a sense of internal achievement, and gain recognition from peers. When faced with a loss of a sense of achievement due to skill deterioration that is hard to reverse in their work, creative workers shift their creative focus to their leisure time. This phenomenon, called leisure crafting, acts as a psychological compensation and self-protection mechanism that creative workers instinctively develop when their work becomes “deskilling.” To some degree, this mechanism fulfills creative cultural workers’ need for achievement and helps prevent the decline of their core original skills (Petrou & Bakker, 2016; Petrou et al., 2024). Several interviewed animators stated: “Daytime is work, after work is life. When I go home, I draw things I like, and it actually gives me a sense of control over life.” Since high-intensity, low-skilled routine labor during the day no longer provides a sense of achievement or aesthetic satisfaction, creative workers actively pursue highly autonomous, challenging creative activities after hours to counteract the feelings of meaninglessness from their daytime jobs. One animator said, “I definitely can’t spend my whole life being this kind of technical laborer. Drawing something after work is also a way of exploring the direction of my next job, to see whether there might be other opportunities.” Another animator believed: “AI animation is too low-end and has little value; it will definitely be eliminated. After work, I still keep drawing to maintain my skills. Hand-drawn animation will sooner or later become mainstream again.”

Unlike established animation teams, some newly formed teams try to adopt AI with optimism in the early stages of their creative process. For them, AI is a way to boost revenue and cut costs. However, without support from platform capital, these startup teams often face high subscription fees for AI tools. One interviewed animation director mentioned that they use strategies like “sharing accounts” or “seeking alternative AI tools,” and control costs by “making the most of each use and choosing only the most valuable parts.”

In our research, we also found that as barriers to using AI tools decrease, many people with no previous experience have started experimenting with AI to create animation. One hobbyist interviewee said: “Now that AI is so advanced, I create AI animation partly because I’m worried about losing my job in middle age and want to learn more technical skills, and partly out of personal interest.” However, these individual AI animation accounts often update slowly. Several interviewees stopped updating entirely after two to 3 months, and some respondents mentioned that “it’s not interesting and there’s no way to make money” or “work schedules do not allow it” as the main reasons for giving up.

In summary, small and medium-sized teams adopt dual survival strategies. They rely on AI tools from platforms to maintain production efficiency while also trying to demonstrate their unique value by emphasizing manual work. However, all of this occurs within the rules established by platforms. The more teams showcase their individuality, the more they provide training samples that help platforms standardize and automate these traits. Therefore, efforts by teams to resist and by platforms to absorb happen simultaneously in the reality of AI for small- and medium-sized creative actors.