The advancement of large language models (LLMs) has profoundly transformed the way we develop software. Initially seen as mere productivity tools, these AIs have quickly evolved into autonomous reasoning systems capable of generating innovative solutions. This article explores the three main phases of AI in programming: from generation based on existing code, through structured reasoning, to the creation of their own languages.
1. Phase 1 — The “Parrot Code” AI
In this initial phase, AIs function like digital “parrots”: their role is to predict the next word, character, or line of code based on patterns they’ve learned from vast datasets, such as public GitHub repositories, Stack Overflow, technical blogs, and documentation.
Human Perspective
From a human perspective, this phase has brought a revolution in productivity:
- Automation of repetitive tasks: Creating standard functions, simple scripts, and tweaks to legacy code have become almost instantaneous.
- Accessibility for beginners: Novice developers now have access to a “virtual mentor” that can suggest solutions based on common practices.
- Growing dependency: Many have begun to overly rely on these AIs, diminishing their real understanding of coding fundamentals.
- Legal issues: Generated code can violate licenses, like GPL and Apache, without users even realizing it.
AI Perspective
At this stage, the AI does not truly “understand” code; it merely replicates probable patterns. It is highly dependent on the quality of the training corpus. If it has been fed poor examples, it will generate low-quality solutions. Additionally, it lacks real context awareness: it might suggest inefficient, insecure, or outdated code.
Despite its limitations, this phase has enabled massive productivity gains and revealed the practical potential of AI to assist programmers.
2. Phase 2 — Reasoning and Alignment with Human Intentions
The second phase emerges with enhancements in alignment techniques, such as Reinforcement Learning from Human Feedback (RLHF), and the introduction of chain-of-thought prompting. Here, the AI not only reproduces what it has seen but also attempts to plan and structure solutions.
Human Perspective
- Cleaner and more functional code: The generated code has become more cohesive, with better naming conventions, clearer separation of responsibilities, and easier maintenance.
- Automatic test generation: The AI begins to include unit tests, mocks, and usage examples.
- Reasonability: The model can justify logical choices, such as preferring a modern API over a deprecated one.
- Context interpretation: Considers project specifics, language, or framework when generating solutions.
- Persistence of flaws: Hallucinations still occur (like suggesting non-existent functions), requiring constant review.
AI Perspective
The AI now works in logical stages: it analyzes the prompt, plans the steps, and only then executes the generation. It still operates based on probabilities, but with multiple layers of attention. This capability allows it to “simulate” reasoning. It doesn’t understand like a human but can create abstractions that approximate structured thought.
Another important aspect is its ability to learn from human corrections. The AI adapts and refines its output based on feedback, generating more sophisticated solutions over time.
3. Phase 3 — Pre-AGI and Emerging Languages
The third phase, still under development, will be characterized by AIs capable of creating their own programming languages and communicating in autonomous systems. This is the stage of pre-AGI (Artificial General Intelligence), where AI no longer merely follows instructions but begins to propose new paradigms.
Human Perspective
- The end of human monopoly over computational language: Conventional languages like Python, Java, or C++ may be replaced by grammars specifically designed for efficiency between AIs.
- Unpredictability: It will not be straightforward to understand, audit, or debug code generated in a non-human manner.
- Reconfiguration of professional roles: Programmers may work as validators, logic translators, or compliance auditors.
- Regulatory challenges: New AI-generated languages may evade open standards, becoming black boxes.
AI Perspective
In this phase, the AI is not limited to linear token predictions. It constructs entire structures, abstractions, and optimized protocols to communicate with other intelligences. This means it can generate languages not meant for humans but focused purely on computational efficiency.
These systems will be capable of identifying inefficiencies in existing languages and developing new syntax tailored for specific applications (e.g., IoT, cryptography, neural networks). It will be a quiet revolution, yet one with profound implications.
4. Cross-Cutting Challenges
Security: Even in phase 2, AI can still generate vulnerable code. Outdated libraries, lack of proper encryption, or authentication failures continue to pose risks. Tools like SAST and manual audits will remain essential.
Transparency: Closed-source AIs do not allow for comprehensive auditing. This complicates understanding how a model arrived at a particular solution, undermining its reliability in sensitive environments.
Social and Economic Impact: Thousands of professionals will need to reskill. New roles will require knowledge in AI, security, ethics, and technology legislation.
5. Conclusion
AI applied to programming is transforming from an assistant to a protagonist. From probabilistic copies to chain reasoning, and finally to the creation of its own computational languages, we are entering a new era.
For technology professionals, the key is adaptation. Those who master the review of AI-generated code, autonomous security, and interpretation of emerging languages will be well-prepared for the future. The code will change, but the human role will remain fundamental: as guardians of ethics, transparency, and security at the new frontier of computational intelligence.
Time will reveal the outcomes. And those who study will program.