From “Igniter” to “Screen”: The Harsh Coming-of-Age Ceremony That the AI Toy Industry Is Going Through

Looking back at the rapid development of AI toys over the past two years, the real engine that truly propelled the industry from 0 to 1 was not the brands in the spotlight, nor the channel providers with huge funds, but rather those unknown solution companies. It was they who managed to squeeze the large models, algorithms, and codes that were originally floating in the clouds into the small shells of plush toys and desktop robots. Without this crucial “technical translation”, AI toys might still be stuck on the concept diagrams on the PPT, unable to produce even a decent sample. The solution companies undoubtedly were the initial ignition sources of this industry.

However, as the narrative of the industry moved from the excited period of “can it be made” to the calm period of “can it be sold”, a huge gap began to emerge. At various exhibitions and roadshows, we saw more and more “seemingly good” products: they could have smooth conversations, could sense emotions, and even had certain memory capabilities. But strangely, when these shiny samples were actually put on the market, they rarely sparked sustained sales waves, and even the most basic repeat purchase rate was difficult to maintain. This leads to a highly ironic situation: at exhibitions, there are more and more products; in the market, there are fewer and fewer best-selling items.

Here, we need to clarify a concept that has been long confused. For solution companies, the sole purpose of a Demo is to prove “I have the ability to implement”. Before a client chooses a partner, they must confirm that you can turn your ideas into physical objects, which is completely reasonable in business logic. But the ultimate judge of the market, the market itself, is never concerned with “can you do it”, but “is it worth doing”. These two questions are separated by a gap called “market laws”. The tragedy of many AI toys lies in that they are perfectly valid in the “display scenarios”, but completely fail in the “consumption scenarios”. At the exhibition site, a minute of interaction is enough to trigger novelty, a few witty conversations can make people feel “quite interesting”. But when brought back to the real flow of life, will users use it every day? Will they develop an emotional dependence on it? Is it worth having it occupy the precious bedside table or office desk space? These questions were rarely seriously addressed during the Demo stage. Therefore, what we see is not “bad products”, but a large number of “products that have not been fully defined”, they are like exquisite shells without souls, merely having a surface appearance, but unable to find a foothold in users’ daily lives.

If the responsibility is completely attributed to solution companies, it is somewhat unfair. Because in the current AI toy ecosystem, what is truly lacking is not technical ability, but a higher-level role – a product definitioner. This role is far from the traditional product manager; he must be a group of more forward-looking and more essential question answerers: for whom is this product? What specific emotions or needs does it solve? In what specific scenarios will it be used? Through what channels should it be sold? Why do the channels want to promote it? When these questions remain unresolved, solution companies can only helplessly use their most proficient technical abilities to fill the gap. Thus, the industry naturally slides towards a result: using “what can be done” to replace “what should be done”.

We are witnessing a very clear industry shift: the competitive barriers of the AI toy industry are upgrading from “solution capabilities” to “sales verification capabilities”. Here, “sales verification capabilities” are not simply selling skills, but an extremely valuable “preliminary judgment ability” – before the product is mass-produced, through logical deduction and small-scale tests, it is known why it can sell. This ability at least includes three indispensable aspects. Firstly, there is user verification. This product must correspond to a real existing need, rather than an imagined concept. Does it solve a specific problem related to loneliness, companionship, or social interaction? If there is no clear “purchase reason”, even the most advanced AI capabilities are just useless add-ons. Secondly, there is scenario verification. When is this product used? Is it before bedtime, at the desk, or during commutes? The frequency of use directly determines the long-term value of the product. If a product is only “useful when displayed”, but has no place in “real life”, it is destined to fail to become a true commodity. Finally, there is channel verification. This is the most easily overlooked but most fatal link. Whether a product can be sold depends not only on end consumers, but also on a vast network of middlemen. Do distributors have the motivation to promote it? Do stores are willing to give it a prime shelf position? Is it easy to explain and demonstrate? Many AI toys die not because users don’t like them, but because no one is willing to sell them for you. When these three layers of verification are missing, no matter how advanced the technology is, the result will only be: a “realizable product”, not a “sellable product”.

This by no means implies that the solution companies will be eliminated. On the contrary, this represents a huge upgrade opportunity. Future more valuable solution companies will complete the transformation from “technical outsourcing” to “product co-creators”. They will no longer merely create demos to showcase functions, but directly address specific consumption scenarios, allowing customers to visually understand “how this thing can be sold”. They will no longer passively receive clients’ briefs, but based on a profound understanding of users’ psychology and channel characteristics, propose more likely viable product directions in reverse. They will actively participate in small-scale trials and channel tests, using real market feedback to refine the product, rather than cutting off contact at the moment of delivery. When solution companies possess these capabilities, their role will undergo a qualitative change. And those “technical outsourcing” companies lacking the ability to incubate hit products will gradually lose their bargaining power.

Every new industry, in its early stage, is driven by technology because “being able to do it” is itself a scarcity. But the industries that truly scale up and gain momentum will eventually shift to being driven by consumption. AI toys are approaching this critical point. Next, what determines the industry landscape will no longer be whose AI model parameters are larger, but who earlier and more accurately establishes the “sales validation ability” for users, scenarios, and channels. Because history has already proven that the ones that ultimately survive are never those “products that can be made”, but those that are brought home by consumers and repeatedly sold by channels. This evolution from “igniter” to “screen” is precisely the cruel yet necessary coming-of-age ceremony that the AI toy industry is undergoing.