In recent years, the application of language models to programming has transformed the day-to-day of developers. Tools such as GitHub Copilot, ChatGPT and Replit Ghostwriter have increased productivity by suggesting code snippets, automating repetitive tasks and even generating complete solutions from natural language descriptions.However, recent gains are already incremental, indicating that LLMs, by their textual nature, have reached a structural limit.
LLMs were designed to interpret natural language and subsequently adapted to handle code. This adaptation has brought expressive results but faces limitations where code is not only text but also logic, dependency and behavior. Interpreting it requires algorithmic reasoning, structural coherence and understanding of broad contexts, skills that generalist LLMs were not designed to offer.
Brazilian micro, small and medium-sized enterprises (MSMEs) have a positive perspective on the potentials of Artificial Intelligence (AI), with 77% of decision makers considering that AI streamlines the processes of their companies. This is what the “IA research in micro, small and medium-sized enterprises reveals: trends, challenges and opportunities“, commissioned by Microsoft to Edelman Communication.
According to the study, 75% of the companies interviewed say they are optimistic about the impact of Artificial Intelligence (AI) on their work and this is reflected in the investment plans of the companies, which say that they will continue to invest or invest for the first time in AI (73%), and 61% of them already have an action plan or specific goals related to this technology.
However, to overcome these constraints, the need arises for a native AI code, a system designed from the beginning to treat code as a first language. This approach requires a new transformer architecture, capable of deeply understanding semantics, logic and complex software structures, going beyond the simple self-completion of snippets.
Key competencies of this new generation of AI include deep semantic understanding of code, logical and algorithmic reasoning, extended context maintenance on complex bases, understanding dependencies and libraries, ability to test and validate code, and interpretation of ambiguous requirements.The conjunction of these skills would allow AI to act autonomously, reliably, and consistently.
The development of this architecture will require new datasets, specific algorithms and changes in the way we conceive the act of programming. It is a basic transformation, which transcends incremental adjustments and redefines what it means to create software with the aid of AI. The expectation is that, in a five-year horizon, we can witness systems capable of acting as complete software engineers.
The current phase of generalist LLMs demonstrates that productivity has increased, but autonomy is still limited. Future evolution will depend on the creation of native code models, capable of handling complexity, dependencies and logical reasoning in an integrated manner, paving the way for more strategic, scalable and reliable programming.
Instead of acting only as a command executor, the professional will become an architect and supervisor of intelligent systems, guiding AI to transform abstract specifications into complete and functional solutions. The programming revolution with AI is just beginning. The next generation will not be limited to optimizing tasks, it promises to redesign the very concept of software development, making AI systems full technical partners, able to understand, create and iterate on complex solutions with autonomy and contextual intelligence.
