In the secondary market for trendy toys, Labubu has long transcended being just a doll; it has become a set of scarce IP assets repeatedly validated by the market.

The original price of 599 yuan for a Vans collaboration skyrocketed to 30,000 yuan in the secondary market. The world's only first-generation mint-colored Labubu sculpture sold for 1.08 million yuan at auction. Regular hidden editions commonly command tens of times their original price. Labubu continues to generate immense trading activity in the secondary market, even sparking a 'Labubunomics' collecting craze overseas. It is globally recognized as a phenomenal trendy toy IP.

On one side, traditional trendy toy giants maintain their solid premium myths and community influence. On the other, capital is flooding into the AI trendy toy sector. Currently, many entrepreneurs and investors hope to use intelligent interactions to reshape the trendy toy industry, attempting to replicate or even surpass Labubu's commercial success.

However, real feedback from the secondary market shows that relying solely on AI technology dividends is still insufficient to bridge the enormous gaps in IP accumulation, market recognition, and community value.

Technology can reshape the toy experience but cannot yet replicate the commercial barriers of a top-tier trendy toy IP that transcends cycles.

AI Trendy Toys Have Technological Appeal, But That's Not Enough

In the current primary market, AI trendy toys are becoming a hot cross-sector label between cultural innovation consumption and hard technology.

Leveraging advantages such as large model interactions, digital operations, and sustainable commercialization models, many startup projects have quickly secured financing and seen their valuations rise. Capital views them as a new variable poised to disrupt traditional static trendy toys.

However, stripping away the hype around AI concepts, the industry's shortcomings become clearer: most AI trendy toys possess technological advantages but consistently lack the core IP wealth-creation capabilities of the trendy toy industry.

Comparing them to the mature product barriers, emotional storytelling, and fan ecosystem of Pop Mart's Labubu, today's popular AI trendy toys resemble intelligent toy hardware rather than cultural IPs capable of long-term compounding value. This has become the core bottleneck restricting their sustained valuation growth.

Against the backdrop of traditional trendy toys entering a phase of Stock internal competition (translated as 'market saturation and internal competition'), the capital advantages of AI trendy toys are extremely scarce. The established trendy toy market has solidified, with leading brands like Pop Mart and 52TOYS dominating mainstream channels and user mindshare. New brands face long super-IP incubation cycles and single-dimensional business models, relying solely on new product launches and limited-edition premiums for profit with no sustained appreciation potential.

AI trendy toys, however, have broken free from the traditional cultural innovation framework, becoming a benchmark for the combined sector of consumer cultural innovation and intelligent hardware. Leveraging the dual trends of companion economy and AI large models, they are highly favored by the primary market.

Compared to the one-time sales model of traditional trendy toys, AI trendy toys have established a dual revenue system of hardware sales and software subscriptions. They can iteratively update storylines, voice content, and interaction methods through OTA updates, generating continuous cash flow from member services and exclusive digital content, thereby reducing reliance on new product iterations alone.

Simultaneously, AI trendy toys can accumulate behavioral, preference, and emotional data through continuous user interactions, building refined user personas and achieving digital user asset accumulation. This not only drives product iteration but also supports long-term brand operations and precision marketing—a core advantage unattainable by static trendy toys.

Coupled with policy dividends and fund preferences for the hard technology sector, the financing speed and valuation ceilings of AI trendy toy startups far exceed those of new traditional trendy toy brands.

However, capital enthusiasm cannot obscure the industry's fundamental shortcomings. The core of trendy toys has never been hardware and functions but rather the IP value that can be accumulated, empathized with, and compounded. Labubu's commercial success validates the ultimate barrier in the trendy toy industry, which is precisely the core capability currently lacking in most AI trendy toys.

On one hand, the lack of product distinctiveness is the biggest flaw of AI trendy toys.

Labubu has created an iconic 'ugly-cute' visual symbol by blending Nordic elf legends with Eastern mythical creatures, breaking away from the sweet, homogeneous aesthetics of traditional dolls. It precisely targets young people's emotional needs to reject conformity and pursue individuality, becoming a symbolic social icon within the community and supporting high premiums and secondary market liquidity.

In contrast, the AI trendy toy industry overwhelmingly relies on standardized public hardware templates, with pre-locked shells, structures, and sensors. Brands cannot conduct deep customization, leading to highly similar product designs in the market. Without exclusive visual IP symbols, AI trendy toys remain merely functional smart toys, unable to form user mindshare labels, and naturally struggle to achieve premium pricing and mainstream appeal.

On the other hand, material and craftsmanship homogenization further erodes the collectible value of AI trendy toys.

Labubu employs a composite material of vinyl bodies with plush accessories, balancing high-precision shape restoration with soft tactile sensations, creating rich textural layers. Through exclusive techniques like random tie-dyeing and pupil shimmer, each product becomes unique, endow (translated as 'conferring') scarcity and collectible attributes. This supports a full price range layout from affordable blind boxes to high-end limited-edition large dolls.

In contrast, AI trendy toys, constrained by public hardware cost-control logic, generally use ordinary plastic materials with thin tactile and visual textures and no differentiated material combinations. Production processes are highly uniform, losing the core scarcity and collectible attributes of trendy toys. They remain trapped in the mid-range price band with relatively limited gross margins and appreciation potential.

More critically, AI trendy toys universally lack IP storytelling, failing to establish deep emotional connections.

Currently, most AI trendy toys focus on basic voice Q&A and simple motion feedback, with relatively mechanical and rigid interaction modes. They cannot perceive user emotions and lack flexible companionship capabilities. Simultaneously, brands generally fail to build exclusive worldviews and character story systems, making it difficult for users to form long-term emotional bonds. User retention and repurchase rates naturally suffer.

The absence of content ecosystems and private community networks further locks away the IP compounding potential of AI trendy toys. Pop Mart, for example, has built a comprehensive content and community system for Labubu, continuously enriching its IP connotations through comics, themed exhibitions, and design sketch exposures. Relying on blind box machines, the Paqu APP, and tens of thousands of WeChat private communities, it has established a highly sticky fan ecosystem.

The valuation logic in the primary market is returning to rationality: AI technology is a value-add for trendy toys, but IP barriers are the ultimate defensive moat.

Fatal Shortcomings in Building Long-Term IPs

Public hardware limitations refer to manufacturers adopting standardized generic hardware solutions and then using technical means like firmware and underlying permission restrictions to lock down hardware modifications, performance tuning, and external expansion capabilities. This fixes the computational upper limits and functional boundaries of products from the factory, making it difficult for brands to independently upgrade or transform them.

Public graphics cards in the consumer electronics sector serve as a typical reference: manufacturers lock down GPU BIOS to unify quality control and reduce compatibility failures, blocking custom adjustments to core frequencies and memory parameters. This directly stifles hardware overclocking and personalized tuning possibilities.

This standardized locking logic is now replicated in the AI trendy toy sector, gradually becoming a shackle restricting the industry's IP-driven development.

Currently, the vast majority of AI trendy toy startups procure generic voice modules, standardized sensors, and public main controllers from the market to build their products. These hardware components can only support basic voice dialogues or simple sensor interactions, with inherently low performance ceilings.

More critically, the underlying drivers and software interaction interfaces for the entire hardware suite are pre-packaged and solidified by solution providers. Brand owners lack permissions for underlying optimization, hardware modifications, or architectural upgrades. They can only adjust superficial dialogue scripts and exterior prints. Any upgrade needs involving interaction precision, computational efficiency, or expanded hardware are directly blocked by the hardware's underlying limitations.

In the short term, public hardware may lower R&D and mass production thresholds, facilitating rapid project launches and revenue generation. However, from the perspective of long-term IP operations, hardware limitations create comprehensive and irreversible developmental constraints.

First, hardware limitations directly erase brand technological differentiation, cutting off the pathway for IP mindshare accumulation at the source.

Competition in the trendy toy sector has long moved beyond basic functional comparisons; sustained hardware and experience innovations are key to differentiating brands. However, constrained by locked public hardware, AI trendy toys struggle to achieve breakthrough hardware upgrades: they cannot integrate high-precision emotion sensors to enhance human-like interaction nuance, nor can they reconstruct hardware architectures to unlock higher computational power for supporting intelligent models.

Ultimately, similar products in the market may converge in interaction logic, response sensitivity, and perception capabilities, lacking distinctive brand-specific experiences. If consumers cannot perceive unique brand value, they struggle to develop exclusive brand recognition and long-term loyalty, eroding the foundation for IPs to continuously accumulate user assets.

Second, rigid hardware underpinnings severely limit functional iteration space, continuously depleting user freshness and undermining IP lifespans.

Young consumers' demands for trendy toys keep evolving. Simple Q&A interactions no longer satisfy emotional companionship needs; complex emotion recognition, character-specific storylines, and multi-scenario Linkage interaction (translated as 'linked interactions') have become market necessities.

However, public hardware limitations create two insurmountable pain points: first, closed hardware interfaces prevent connecting new sensor, audio-visual, or motion modules; second, fixed computational thresholds make it impossible to smoothly run complex emotional interaction and personalized generation algorithms.

Many brands have planned deep human-like companionship functions but ultimately shelved them due to insufficient hardware capacity. If product functions remain singular and static without iterative content or innovative interaction experiences, user freshness quickly fades. Retention and repurchase rates suffer, making it impossible to sustain brand IP heat (translated as 'popularity').

Labubu's ability to maintain sustained community popularity and high secondary market premiums year-round stems from its core advantage: the brand retains full autonomy over the entire product lifecycle. It can continuously iterate materials, craftsmanship, characters, and gameplay around the IP worldview, consistently delivering new content to maintain fan engagement and consumption enthusiasm.

In contrast, AI trendy toys, constrained by public hardware limitations, have their innovation capabilities firmly bound. They cannot continuously update differentiated hardware experiences nor implement higher-order interaction functions that align with IP personas. Without high-frequency iterations to sustain user enthusiasm, they remain stuck at the 'voice-enabled plastic toy' stage, unable to cultivate deep community emotional identification or build mature IPs with long-term premium pricing and fan-driven Spread attributes (translated as ' Spread attributes ' or 'spreadability'). Replicating Labubu's commercial influence and secondary market value becomes exceedingly difficult.

Under the Current Hype, How Can AI Trendy Toys Break Through Ceilings?

The primary market enthusiasm for AI trendy toys continues to rise. Large model empowerment, dual hardware-software monetization, and companion economy dividends convince countless entrepreneurs and investors that this represents a new sector poised to replace traditional static trendy toys.

However, when it comes to consumer adoption and the secondary market, the industry remains trapped in a core dilemma: most AI trendy toys possess only smart hardware attributes, not IP asset attributes.

The true breakthrough point for the industry has never been about piling on AI algorithms or adding voice functions. Instead, it requires first breaking through the rigid underpinnings of public hardware and then anchoring value through exclusive IPs. This is the indispensable path for AI trendy toys to evolve from smart toys into sustainable trendy toy IPs.

Rather than blindly investing in heavy-asset full-stack hardware R&D, a more practical optimization path at this stage involves deep collaboration between AI trendy toy brands and upstream chip/sensor supply chains. This collaboration should focus on creating targeted hardware co-customization and scenario-based innovations around emotional companionship, human-like interactions, and other trendy toy-specific scenarios.

Unlike general consumer electronics prioritizing universality and stability, AI trendy toys derive core value from emotional companionship and human-like interactions. This means generic voice modules and standardized sensor hardware often fail to meet nuanced emotional interaction needs.

The industry might achieve breakthroughs through upstream-downstream joint R&D to customize exclusive hardware modules adaptation (translated as ' adaptation ' or 'optimized for') multimodal interaction logic. This could enhance precision and fluidity in touch perception, emotion recognition, and voice responses, aligning hardware capabilities more closely with trendy toys' user experience logic.

Additionally, the rigid architecture and computational power allocation logic of public hardware partially limit the experience ceilings of AI trendy toys. General-purpose hardware relies heavily on cloud computational feedback, introducing high latency and struggling to support real-time human-like interactions.

In this context, the industry might explore introducing lightweight edge computing architectures to decentralize computational power for scenario generation, emotion judgment, and real-time responses to terminal devices. This adjustment could reduce reliance on public hardware's fixed computational frameworks and cloud services, further unlocking terminal products' interaction potential and enabling hardware architectures to flexibly adapt and iterate based on IP personas and interaction scenarios.

Of course, breaking through hardware bottlenecks is just the foundation. The ultimate breakthrough core for AI trendy toys lies in building irreplaceable original IP systems atop differentiated hardware capabilities.

A long-standing industry misconception holds AI as the core competitiveness (translated as 'competitive edge') of trendy toys. However, judging from Labubu's success logic and the essence of the trendy toy sector, technology merely serves as an experience tool. IP emotional value, cultural storytelling, and community identity likely represent the barriers that transcend market cycles.

Currently, most AI trendy toys concentrate content supply on shallow forms like children's songs, basic Q&A, and generic stories. This fragmented and highly substitutable content struggles to support long-term IP mindshare accumulation. Referring to the mature strategies of leading traditional trendy toys, the industry's future optimization direction will likely require starting from foundational storytelling to shape exclusive character traits, backstories, and growth worldviews for products.

Furthermore, user experience and community operations represent critical variables for AI trendy toys to solidify IP value and achieve commercial closure, as trendy toy consumption fundamentally leans toward emotional and community-driven spending. Labubu's ability to command extremely high secondary market premiums stems not merely from product design but also from the collector, customizer, sharer, and trader ecosystem spontaneously form (translated as 'spontaneously formed') by fans—a high-stickiness community continuously empowering IP value.

In contrast, traditional static trendy toys excel in mature cultural IPs and community emotional systems but are constrained by static experiences, unable to achieve continuous interaction and personalized iteration. Meanwhile, most current AI trendy toys wield technological and business model advantages but suffer long-term from hardware homogenization and content hollowness.

It can thus be predicted that projects relying solely on AI concept hype or public hardware assembly will likely see their valuation bubbles gradually deflate amid industry shakeouts.

In the future, players capable of balancing hardware autonomy, original IP storytelling, and long-term user operations may hold greater long-term potential. Such brands could merge traditional trendy toys' cultural barriers with AI products' intelligent experience advantages to forge a new growth path, progressively unlocking the long-term commercial value of the AI trendy toy sector.

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