As applications emerge as the new focal point for AI practitioners, AI’s influence is increasingly felt across diverse industries and scenarios. Beyond integrating AI into their core business operations, AI practitioners are also venturing into areas beyond their traditional domains. In this pursuit, some opt for investments, others employ technological strategies, and still others leverage industrial and supply chains for strategic布局 (deployment). Regardless of the approach, it is clear that AI practitioners are building a new ecosystem centered around AI.

This trend is reminiscent of the internet era, where we witnessed a similar transformation. During that time, we not only saw the 'Internet Plus' approach revolutionize various industries and scenarios but also observed the rise of an internet-based infrastructure ecosystem. Now, as the AI era unfolds, history seems to be repeating itself.

It is foreseeable that as AI penetrates deeper, especially as its implementation and applications become ubiquitous in our production and daily lives, competition among practitioners will shift from mere parameter comparisons to a broader test of ecosystem synergy, industrial collaboration, and scenario integration. This is why ecosystems are emerging as AI’s next destination.

Firstly, this is dictated by the foundational nature of AI.

From the outset, AI’s emergence has given rise to a plethora of large models, a continuous stream of AI assistants, and AI-powered robots. The proliferation of AI-derived products and services underscores AI’s deep-rooted presence in the underlying infrastructure.

As a new type of infrastructure, AI’s full potential can only be unlocked when its applications extend across various scenarios. The key lies in how extensively AI can be implemented and how a new ecosystem, with AI as its capillary network, can be constructed.

This is why we observe more and more AI practitioners, beyond applying AI within their own business systems, extending their reach into related fields and scenarios. This trend is particularly pronounced among large, super-ecosystem players.

By harnessing AI, they not only transform their traditional industries but also discover new growth engines. In this process, AI transcends its role as a mere catalyst for self-renewal; it becomes a pivotal tool for unlocking new growth and further expanding their ecosystems. It is the foundational nature of AI that drives more practitioners to focus on ecosystems and choose them as the starting point for building their own.

Secondly, this is determined by the synergistic nature of AI.

The new industrial upgrade initiated by AI is not confined to specific industries or scenarios but permeates every facet of people’s production and lives. Industries such as AI-powered office work, social networking, retail, and content creation are emerging and gaining acceptance among a growing user base.

At the same time, following the internet era, the era of incremental growth has given way to the era of existing assets. In this context, retaining users within one’s ecosystem has become a paramount concern for every practitioner. Therefore, breaking down barriers and silos between different AI scenarios and enabling seamless user switching across scenarios have become essential considerations. Ultimately, it is about moving away from closed AI applications and instead creating a large ecosystem to foster new growth for users in the era of existing assets.

Achieving synergy across different industries and scenarios not only allows AI to unleash greater functionality and utility but also, more importantly, provides users with a seamless experience, reducing user churn and enabling new growth in the era of existing assets.

Thirdly, this is shaped by the development patterns of AI.

We know that the current landscape of large models encompasses both general-purpose and vertical models. This scenario, reminiscent of the internet era, is now unfolding in the AI era. On one hand, it showcases AI’s rapid development; on the other hand, it reveals AI’s development patterns, namely, its continuous deepening and comprehensive presence, evolving from singularity to diversity.

At the same time, we should also note that as AI matures, its impact has shifted from the early consumer (C) end to the business (B) end and even to large-scale service (S) providers. As AI’s influence becomes deeper and more comprehensive, it is no longer confined to a single or a few scenarios but extends to a wider range of scenarios.

In the future, what truly determines the imagination space for practitioners will no longer be parameter and benchmark comparisons but how to apply AI to more scenarios and achieve synergy among them. In this process, building a large ecosystem to maximize AI’s functionality and utility will become a crucial consideration for every practitioner.

As AI competition intensifies, practitioners’ competition has evolved from single-point breakthroughs to multi-dimensional synergy development. In this context, building a large ecosystem and achieving synergy among AI scenarios have become essential considerations for every practitioner, especially the large ones. The success of ecosystem competition not only determines the outcome of market competition among practitioners but also affects whether AI’s effectiveness can be maximized. Anchoring on ecosystems to define the next direction of AI will become the new choice for more and more practitioners.