As AI continues to evolve and deepen, the once-popular large models are now entering a new stage of fruition. Whether it is the implementation of large models by giants in their own ecological scenarios or the entry of emerging large model players into the capital market, becoming the "first global large model stock," we are beginning to see that large models are now transitioning from their initial, aggressive developmental stages to an objective and rational one. For future large models, the test will no longer be about Forward looking layout (forward-looking layout , replaced with descriptive term) layout , but rather more about their commercialization capabilities.

Recently, players represented by internet giants have started to integrate large models more and more with their own business scenarios, as well as deeply fusing AI with real-world commercialization scenarios, which serves as a very clear example. In a sense, with the help of AI, internet players have achieved a renewal of their own businesses and found a new second growth curve. For startup AI large model players, how to maintain their first-mover advantage while making up for the shortcomings in integrating their large models with commercialization may be the key to ensuring they can enter a new stage.

If one word were to describe the current large model market, it would be "fruition." So, in such a new stage, what truly tests large model players? For every player who wants to make a difference in this stage, how should they adjust their strategies to continue developing?

Technological Iteration and Updates Remain the Primary Driving Force

Although the development of large models has entered a relatively mature stage, as a relatively foundational existence, large models still need to continuously enhance their technological advantages and strengths to maximize their capabilities. This can be seen from the version updates of large models by leading players. Whether for giant players or startup large model players, only by continuously strengthening their technological iteration and update capabilities can they continuously gain momentum for their development.

One important reason why technological iteration and updates become the driving force for large model players is that each technological iteration and evolution of large models is inevitably manifested externally as an expansion and improvement of their own capabilities. Looking back at previous large models, they could only engage in simple chat conversations, whereas current large models can not only do that but also understand long texts, invoke programs, and possess many other new capabilities. Without technological iteration and updates, this would be completely unattainable.

After large models enter the stage of fruition, it is precisely their technological iteration and update capabilities that truly create gaps and allow players to stand out. For every player, only by continuously strengthening their technological iteration and update capabilities, evolving their large models from single functions to composite ones, and using such functions to better achieve commercial implementation of their large models can they continue to develop in the new stage.

Commercial Implementation and Application Remain the Primary Direction

For many large model players, during the initial stages of large models, they focused more on the invocation volume of large models. Ultimately, at this stage, players were more concerned with the connection between their own large models and real-world traffic. Against a backdrop where large models were not yet heavily integrated with real-world commerce, viewing large models themselves merely as a point of traffic acquisition was not wrong. However, as the development of the large model industry enters a new stage, especially when large models begin to enter the commercialization stage, if large models are still seen merely as an entry point for traffic, this greatly limits their ability to acquire traffic.

The reason is that large models are merely a very foundational existence. They should face a variety of application scenarios and industries. After entering the stage of fruition, what truly tests large model players is how to deeply and comprehensively integrate large models with real-world commerce and scenarios, how to immerse large models into various industries, and use these industries to connect with traffic, thereby elevating the traffic of large models to a new level.

Additionally, by applying large models to various industries, besides increasing the "contact area" between large models and traffic, another important reason is that it can also find a way and method for large models to monetize, making large models no longer a money-burning existence but one that achieves a virtuous cycle in commerce.

Therefore, whether from the perspective of acquiring traffic or commercial monetization, for large model players at this stage, commercial implementation and application remain the mainstream direction. For players, how to deeply and comprehensively integrate their own large models with various industries represented by real-world commerce and industries is the key to their continued development on a new stage.

Industrial Transformation and Integration Remain the Primary Litmus Test

Whether it is the industry transformation triggered by the application of AI in mobile phones or the industry transformation triggered by the application of AI in autonomous driving, we can see that the true litmus test for the success of large models still lies in whether they have triggered industry transformation and innovation. As the development of large models enters the stage of fruition, the true litmus test for the success of large models still lies in whether they have triggered industry transformation and integration and whether they have achieved industrial leapfrogging and upgrading.

One important reason why those internet giants we see can catch up from behind is that they have achieved renewal and upgrading of their traditional businesses with the help of large models, thereby subjecting their large models to real-world commercial scrutiny. Through this approach, internet technology has not only achieved the integration of digital and real, virtual and real but also deeply penetrated into various industries, achieving further industry transformation and innovation.

This is merely a "positive feedback" flow. Besides this "positive feedback," industrial transformation and integration also bring about a "negative feedback" effect on the development of the large model industry. Through such industrial feedback, large models can achieve better evolution, accumulate new capabilities for their technological iteration and updates, and thus promote continuous technological iteration and updates.

Therefore, the industrial transformation and integration triggered by large models are the true litmus test for them. For every player who wants to make a difference in the stage of fruition for large models, only by continuously strengthening their influence on industries and making good use of the reactive force of industries can they truly bring their development to a new stage. When industrial transformation and integration achieve a virtuous interaction with the iteration and updates of large models, the two can create a more harmonious symphony.

In Conclusion

With internet giants continuously delving deeper into the implementation and application of large models and emerging new generation players beginning to bring large models into larger capital markets, more and more signs indicate that the development of large models has entered a new stage. At this stage, large models are no longer tested on their first-mover advantage but more on their sustainability and commercial advantages. For any player who wants to make a difference at this stage, continuously building their comprehensive strength and transforming themselves from a "pioneer" into a "versatile warrior" may be the key to victory.