Artificial intelligence is no longer just a supplementary tool for developers — it is actively shaping a new approach to software creation. Code automation, intelligent suggestions, function and test generation — all of this is becoming part of everyday practice thanks to tools like GitHub Copilot and ChatGPT. This is particularly relevant in Europe, where startups and large companies are looking for ways to speed up development without compromising on quality.
Hungary, as one of the region’s technologically advanced centers, is closely watching these changes — not only in the field of traditional programming but also in dynamic sectors such as fintech, web, and online entertainment, including online casino.
The Code Writing Revolution: From Autocomplete to Code Generation
Until recently, the main assistants for programmers were IDEs and suggestion plugins. Today, they are being replaced by full-fledged “cognitive coding”, where AI doesn’t just complete a line — it generates entire functional blocks, including documentation and unit tests.
GitHub Copilot, built on OpenAI’s Codex, became the first widely adopted tool that offers real-time code suggestions based on context. A user writes a comment — Copilot offers a ready-made implementation. This technology saves hours, especially in routine work, and allows teams to focus on architecture and business logic.
ChatGPT, in turn, extends capabilities beyond the IDE. It can explain unclear code, assist with debugging, adapt old fragments to new platforms, or even act as a coach, offering best practices. In Hungarian IT companies, especially in agile teams, this has become a popular form of internal support that doesn’t require involving an external consultant.
How AI Is Changing the Development Approach
A new trend is generating software code based on natural language task descriptions. AI, when given a request like “create a REST API for user management with authorization”, can not only suggest the code but also structure the project, propose a data model, connect a database, and generate routes.
This brings development closer to the so-called “no-code” future — with an important distinction: control still remains with the engineer. This is particularly crucial in sectors with strict security and compliance requirements, such as financial technologies or online gaming.
For example, platforms offering online casino with real money must comply not only with national regulations but also with international security protocols. In this context, automatic code generation requires manual review, and AI acts more like a co-author than an executor.
The Role of Copilot and ChatGPT: Differences and Intersections
Although both tools are built on OpenAI technologies, their behavior and use cases differ.
Copilot is deeply integrated into the code editor. It instantly responds to the developer’s actions, offering short and context-aware snippets. This makes it convenient for writing new functions, especially in a familiar environment.
ChatGPT is effective as a universal assistant working “in dialogue.” It helps not only to generate code but also to analyze bugs, suggest architectural solutions, and compare frameworks. ChatGPT is especially popular among beginner developers in Budapest and other Hungarian cities, where it is used as an alternative to educational courses and reference materials.
Interestingly, many professionals today use both tools in parallel. One — for productive work in the editor, the other — for clarifying concepts and exploring approaches. This combination is becoming standard among full-stack developers and DevOps engineers.
Perspectives: Generating Entire Applications
One of the most ambitious trends is the generation of complete applications. We’re not just talking about code but the creation of entire systems: with front-end, back-end, databases, CI/CD, and even Docker configurations. AI-powered solutions are already available that can generate a web application and automatically publish it on a hosting platform based on a simple description.
However, these solutions are still far from perfect. Deep refinement is often necessary, especially if the project requires non-standard solutions or deals with sensitive data. Nevertheless, the future is clear: AI is increasingly involved in the technical implementation of ideas, allowing developers to focus on high-level tasks.
This is precisely where AI assistants can transform the approach in entire industries. Take digital platforms for online casino, for example, where it is crucial to quickly adapt to changing jurisdictional rules, currencies, and payment methods. Thanks to AI-driven development, the release of new features can be accelerated — from adding new providers to creating a localized interface for Hungarian users with support for their language and currency.
A Future with AI: Learning, Control, Ethics
Despite the obvious advantages, widespread AI implementation in development comes with new challenges. One is the potential skill loss among newcomers. If a developer relies on Copilot or ChatGPT from the very beginning, they may not learn the fundamental principles of programming.
Another issue is quality control. Generated code may contain vulnerabilities, logical errors, or fail to meet company standards. That’s why it’s crucial to train teams not just to use AI but to critically assess its output.
And finally, ethics. Who is responsible for errors in AI-generated code? How should such fragments be documented? Where is the line between original work and machine generation?
The answers to these questions are still forming, but one thing is clear: artificial intelligence has already become part of the programming culture. It is changing the way we work, the structure of teams, and in some cases — the professions themselves.
Conclusion
AI tools like GitHub Copilot and ChatGPT don’t just speed up development — they are shaping a new way of thinking. In an environment of growing demand for fast and high-quality software, especially in sectors sensitive to security and time-to-market, such as online gaming and financial services, they become indispensable allies. Hungarian developers are already using these technologies to solve real-world problems — and increasingly, the question isn’t “should we use it?” but “how do we use it best?”