5G Network Capabilities
With the expansion of 5G network coverage, mobile applications are likely to include more sophisticated features and capabilities, such as improved streaming services, instant content downloads, and a more efficient use of cloud services.

AR/VR visualization and gaming
Augmented Reality (AR): Apple Vision Pro
Virtual Reality (VR): Meta
Other…

Security
With the increasing amount of mobile devices, users become more vulnerable to a growing number of safety and security threats.
This is why the users’ security and privacy becomes a priority in mobile application development.

Internet of Things (IoT)
Mobile applications will be increasingly used on IoT devices, providing real-time control, monitoring and analytics.

Geolocation Services
The need to quickly identify one's location existed long before the advent of mobile devices, and has become one of the basic user needs in mobile development.

Health Care and Sport Activities
Wearable devices that can read physical parameters and health tracking data via sensors.

CrossPlatform and MultiPlatform development
With the development of such techs as Flutter and React Native, the trend to development of multi-platform applications that provide a sufficient user experience on various devices and operating systems will continue to grow.

AI & ML integration
AI and ML: The use of artificial intelligence and machine learning in mobile applications will be expanded. This may include improved personalized tips, smart chatbots, and automated content moderation.

Automatization of code writing
In fact, there has been always a demand for automatic code generation. But this demand was limited technically. It's not an easy thing to invent new code reuse and code generation practices, and it's a way harder to initiate their use globally.

Automatization of code writing is a major trend in modern software development.
In real time, preferably
Not only in Mobile Development, almost in any sphere
Not just autocompletion, but a context analysis

How did we come to that conclusion? We often deal with common requirements for all IT projects, for instance:

1. Automatization of typical tasks.
2. The need to get visible results as fast as possible
3. Covering as many platforms as possible
4. Saving time for both, the customer and the developer

All of the common requirements refer to different kinds of code reuse and code generation practices.

Also, in iOS and Android development we see a shift of big tech companies to Declarative paradigm. For instance: SwiftUI, SwiftData and Jetpack Compose and others.

These approaches are more suitable for code generation compared to the old ones or even can generate code under the hood.

Generative Neural Networks (GNN)
GNNs capable of responding to human language requests as an input and its successful processing were developed a couple of years ago. Such tools as Static Analyzers and Smart Code completion existed earlier and they continue to evolve; however, they were not as great as GNN. Generative Neurone Networks also reuse the code. But they do it in an absolutely different way: in short, they use all available code bases and learn it using the probabilistic approach. These networks actually can create a code good enough for production.

How we use GNN in Mobile Development
It can create a working code almost online from the prompts.

• Almost any kind of text can be created
• Resources can also be created with GNN like Midjorney or Kandinsky
• Marketing texts and styles can be generated, as well

Our experience of using Chat GPT is more than successful

We used ChatGPT and CoPilot for the following real tasks:

• Creation of screens of Mobile App “On the Fly”
• Code transformation from one language to another
• Legacy code base migration to modern code style
• Transition of the code base to another platform
• Unit-tests writing
• Instructions writing

What we knew from using GNN in practice:

• Though not a revolution, but it is definitely a huge step forward in coding
• We do expect wide implementation of GNN in coding process in IT-projects
• We expect this not only in Mobile Development, but also in all activities relating to the text creation
• Bugs count reduced
• Greater number of platforms covered
• Higher development rate (Less time to market)

NLP and GNN prospects for the nearest future
NLP and GNN will become essential part of professional IDEs
Visual Studio already has the required plugins but we expect deeper integrations

• Any person will use NLP in his/her everyday life like Google Search is used now
• Humans will not be substituted by GNN and their productivity will be increased with GNN

Rise of AI Agents…
AI agent is an intermediate agent between the humans and GNN networks designed to reach a goal set by the human.

• AutoGPT: can get data from Internet
• LangChain: a framework that can build products based on GNN API
• Transformers Agents: can select the most appropriate GNN for human requests and build projects on it’s API