How developers and app factories are thriving in the AI application era
Large, basic model producers have been appealing to the market since the second half of last year. They say that instead of engaging in intense competition at the model level, developers with limited resources would be better off developing AI applications. The founder of Baidu, Robin Li, has emphasized time and again that millions of AI-native applications are needed in the AI-native era to support the industry’s rapid expansion. The future is excitingly outlined in IDC’s prognosis. More than 500 million new apps are predicted to be created globally by 2024, which is nearly equivalent to the entire number of applications that have been created over the previous 40 years. But with all of this excitement over huge models, we can’t help but wonder: Will this bring unprecedented “inflation” to AI apps?
We have to acknowledge that this “inflation” has been made feasible by the lowering of the bar for developing AI applications. Even when Apple introduced the App Store in 2008, creators of applications still had to master difficult programming languages like Objective-C. Large model makers have made it feasible for people who do not understand code to join the ranks of developers through the advent of natural language programming. The developer community has definitely grown significantly as a result of this shift, which has also enabled the rapid expansion of AI applications.
On the other hand, the rapid advancement of AI technology has also increased the unpredictability of the application life cycle. Enbase Technology’s principal scientist, Chen Shukai, put it bluntly: “Some current AI applications may be quickly eliminated whenever a new model with stronger capabilities, like ChatGPT5, appears.” Although this phenomenon is uncommon in the age of mobile Internet access, it is already commonplace in the AI industry. This implies that a lot of AI applications might only gain traction temporarily before going extinct.
Big IT businesses have put effort into this wave of AI-native applications. AI applications are being aggressively launched by Baidu, ByteDance, Alibaba, and other large enterprises in a variety of industries, from social networking and painting to medical care and other areas. The introduction of these apps shows the technological prowess of IT businesses as well as their goals and aspirations for the AI industry. This “crazy horse racing” strategy does, however, come with certain drawbacks. Users may experience confusion and annoyance when using certain AI programs because they are not fully integrated with the main program and are instead released as stand-alone programs or PC plug-ins. However, there’s a chance that this development approach will result in task duplication and internal resource waste for the organization.
Users’ interest in AI solutions is growing in the C-end sector as well. The demand for independent apps is still rising, and there are an astounding 50 million active users of top apps, according to QuestMobile’s insight report. The quick rise and fall of AI applications, however, is the “dark side” of this epidemic. A lot of AI apps gained a lot of traction fast, but their use quickly declined as a result of market rivalry, technological advancements, and other factors. This behavior confuses and unnerves investors and users, in addition to frustrating developers.
Technology businesses must focus more on the sustainability and creativity of AI applications in order to meet this challenge. They must continue to advance technology and introduce AI applications that are more intelligent and tailored to the needs of users. Simultaneously, they must consistently improve the features and functionality of their products and focus more on user experience and user input. They can only become noticeable in intense market competition and gain the confidence and favor of users in this way.
To put it briefly, we are entering the “inflation” phase of AI applications. We must maintain our composure and reason in the midst of this chaos and recognize the risks and challenges in addition to the chances and hopes. We can only navigate this shift, establish our own path, and support the further growth of the AI sector in this manner.
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