In a world where machines are increasingly entering the domain of human creativity, we are on the threshold of a revolution in software development. As in Isaac Asimov's visionary novel I, Robot, where machines gradually take over increasingly complex tasks, so in our reality, tools such as Coursor AI, Devin, GitHub Copilot, and Replit Agent are beginning to transform the programming landscape.

This idea, deeply rooted in our culture, is reminiscent of the biblical concept of the creation of man in the image and likeness of God. Today, standing on the threshold of a revolution in the field of artificial intelligence, especially in the context of coding tools, we are witnessing the realization of these eternal dreams. This both scares us and pushes us to further develop them.

The tools mentioned are not just ordinary programming assistants – they are the first signs of a coming era in which the software development process will undergo a fundamental transformation. Although many programmers are still skeptical about their capabilities, pointing to current limitations and errors, history shows that technological progress often surprises with its pace and scale.

Criticism of AI tools in programming often focuses on their current limitations. Developers point to bugs in the generated code, a lack of understanding of the broader context of the project, or an inability to navigate large code bases. These arguments, while valid today, may not take into account the dynamics of AI technology development.

My thesis is based on the belief that we are witnessing a fundamental transformation in the field of software development. Software development as we know it today will likely disappear in its current form, giving way to new paradigms of work.

Key aspects of this transformation are:

  1. Democratization of programming: AI tools will significantly lower the barrier to entry into the IT industry. Software development will become accessible to a wider group of people, not necessarily those with deep technical knowledge.
  2. Changing skill requirements: While traditional programming required extensive technical knowledge and analytical skills, the future may favor skills like effective communication with AI systems, creative problem-solving, and interdisciplinary approaches.
  3. Automation of routine tasks: AI will take over many repetitive aspects of coding, freeing developers to focus on more strategic and creative aspects of software development.
  4. The Evolution of the Programmer Role: Instead of writing code from scratch, programmers can become more of an “orchestrator” or “conductor” of software development processes, guiding and adapting AI-generated outputs.
  5. Faster development and prototyping: AI tools will enable faster prototyping and iteration, potentially accelerating the product development cycle.
  6. New challenges: New issues will arise, such as ensuring the quality of AI-generated code, integrating AI solutions with existing systems, and managing copyright for machine-generated code.

I have observed that many of us in the IT industry, myself included, have been skeptical of the idea that AI could significantly change the nature of programming. Perhaps it was an expression of our ego, or perhaps the egalitarianism so characteristic of the IT community. However, the words of Jensen Huang, CEO of NVIDIA, that machines will soon do our bidding, force us to reflect deeply on the future of our profession.

What caused my approach to change? First of all, the emergence and development of new tools. I decided to approach them with an open mind, testing their capabilities without prejudice. The results were surprising – these tools can already generate simple applications, identify potential errors, analyze the code in terms of standards, quality or even scalability of the solution. It helps significantly in generating mockups or scripts based on the provided documentation of a given library.

My perspective has also been influenced by conversations with people who look further into the future. They have forced me to reflect on the pace of technological progress.

It is worth recalling the revolution in image recognition. In the years 2010-2014 there was a breakthrough thanks to deep learning. ILSVRC competitions on the ImageNet dataset show how the algorithms' performance improved dramatically from 28.2% top-5 error in 2010 to 3.6% error (ResNet) in 2015, surpassing human performance.

This example shows how quickly technology can push boundaries that previously seemed impossible.

Another argument is the current situation on the IT market. After the lean years of 2022-2023 for the IT industry, there was no quick rebound after the pandemic. This may suggest that the industry is on the verge of fundamental changes, and traditional software development models may be giving way to new, AI-based paradigms.

In the face of this technological revolution, roles in the IT industry will undergo significant redefinition.

  1. Front-end Developers: The role of front-end developers will likely undergo the most rapid transformation. AI tools will be able to generate UI code from descriptions or sketches, significantly speeding up the development process and empowering less technical people. Front-end developers will need to develop skills in effectively communicating with AI, precisely describing expected results, and rapid prototyping. Their work will focus more on user experience design and optimization than on writing code from scratch.
  2. Backend Developers (CRUD): Basic CRUD (Create, Read, Update, Delete) operations will become largely automated. AI will be able to generate standard API endpoints and database logic based on specifications. The role of backend developers will evolve towards systems architects, focusing on designing complex, scalable solutions, optimizing performance, and integrating different systems. Skills in architecture design, managing data at scale, and ensuring security will become essential.
  3. Automation Testers: will be hit hard. Test automation will move to the next level. AI will generate comprehensive test suites based on specifications, documentation and source code. The test generation step will be able to take place at the CI/CD level and will be fully automated, human creativity and work will rather be needed to define the process itself, and provide conditions and constraints of the system.
  4. Manual Testers: The role of manual testers will become increasingly important. Their deep knowledge of how the system works and their ability to identify unusual usage scenarios will become invaluable. They will be key in creating complex test cases that will then serve as the basis for generating automated tests and the code itself. Their work will consist of constantly expanding the scope of tests, identifying potential problems from a user perspective, and providing valuable feedback to AI systems.
  5. Data Engineers: The traditional role of a data engineer may be shrinking due to the increasing automation of ETL and data management processes. However, a new role of the “AI Data Engineer” will emerge, focused on preparing and managing data specifically for AI systems, ensuring data quality, and optimizing data flows for machine learning models.
  6. ML Engineers and Researchers: These roles are evolving to become more strategic. ML engineers will need to focus on integrating AI solutions into existing systems, optimizing models for performance and scalability, and managing the lifecycle of AI models. ML researchers will use AI tools to accelerate the research process, synthesize information from different domains, and prototype new ideas faster. Skills in interpreting AI-generated results and identifying new avenues for research will become essential.
  7. Product Owners: The role of Product Owners will be significantly strengthened. AI tools will enable them to create prototypes and MVPs faster, which will speed up the process of validating business ideas. Their work will focus on defining high-level requirements and product vision, which will then be translated into specific solutions by AI systems. Strategic thinking, market analysis, and effective communication of the product vision will become key skills.

This transformation does not mean the end of the programming profession and other IT roles, but rather their evolution in a more strategic and creative direction. We must be willing to adapt, constantly learn and develop new skills. The future of programming may be more about skillfully managing and collaborating with AI systems than traditional coding from scratch.

We are at the threshold of an era where the line between human and machine in software development is becoming increasingly blurred. It is an exciting, if somewhat unsettling time. Our task now is not to defend the status quo but to actively shape this new reality, ensuring that the development of AI in programming serves humanity and expands our capabilities, not limits them.

P.S. The text was created using Claude Sonnet 3.5
Image prompt Dalle3: Two people looking behind the window from a small room and a dark room with computers are overseer thousands of robot software developers writing code in a long hall bellow, post apocalyptical vision, wide-angle view,