At the Bug Future Show 2024, our CEO, Martin Morava, sparked quite a conversation with his presentation titled "Farewell to Traditional Coders." Sounds dramatic, doesn’t it? True to form, the bold statement raised a flurry of discussions in the comment section after the event.
The question of "AI replace software engineers" has been a hot topic as AI's role in the IT industry continues to expand.
In this blog post, let's go over how artificial intelligence is reshaping the landscape of software development and why the core of programming, thinking, and problem-solving means the future of the profession is safe.
Beginning of automation in software development
Automation in software development dates back to the 1950s when assembly languages were created. These languages simplified machine code into more human-understandable forms, marking the beginning of reducing manual coding efforts.
The discussion about AI replacing software developers began even in the 1960s, so do not think this is a new idea. Initially, it was just speculation centered around AI’s potential to automate tasks traditionally performed by humans. This conversation became more relevant in the 1980s with the arrival of expert systems and rule-based AI, which started to automate complex tasks in the development process.
Today, AI technologies like generative AI and natural language processing have, in a way, changed the dialogue from whether AI can replace programmers to how it can augment their productivity and creativity.
Is there a distinction between coders and programmers?
There is a great video on YouTube by the Applicable Programming channel about the difference between coders, programmers, developers, software engineers, and architects. The video illustrates that coders are like musicians playing to compose notes given to them.
In other words, a coder would typically be someone who writes code based on clear specifications provided to them.
In the context of this blog, I would emphasize that the distinction is in mindset. I do not believe when someone is saying coders will become extinct they are categorizing people. This can sound alarming, but the underlying message is more about the approach to work, the passion involved, the desire to solve problems, and how one utilizes AI tools effectively. It's not merely about the tasks they perform but how they engage with and adapt to evolving technologies and challenges in the field.
The current state of AI tools in software engineering
Most of our programmers at Devōt use GitHub Copilot. It uses the Codex model, a descendant of OpenAI’s GPT-3, designed to understand and generate human-like code. code completion tool developed by GitHub in collaboration with OpenAI.
The primary function of GitHub Copilot is to suggest whole lines or blocks of code as developers type, essentially acting as a pair programmer. The AI draws from its training data to offer contextually relevant recommendations, significantly speeding up the coding process and potentially introducing developers to new libraries and frameworks that increase productivity.
There are many more AI tools used in software engineering, but Codium AI is also one of the most popular. This platform provides real-time feedback and suggestions to improve code quality and efficiency. By using machine learning techniques, Codium AI helps developers detect and correct potential issues early in the development process.
We need to talk about Devin
Introduced as an AI software engineer, Devin is designed to function not just as a coding assistant but as a full-fledged member of the development team. Leveraging advanced machine learning algorithms and natural language processing, Devin can understand project requirements, write substantial amounts of code, and even debug existing codebases with human-like intuition.
What sets Devin apart is its ability to interact with developers using human language. This makes it accessible to professionals without deep technical expertise in AI, freeing up human programmers to focus on more strategic and creative aspects of their projects.
At least, this is what they say. Will Devin live up to the hype or fall short of expectations? Currently, Devin AI is in its beta testing phase and is accessible to a select group of users through a request-only system.
AI and its limitations in software engineering
As with everything, AI has its limitations, especially as it gains popularity and is still in its early stages.
1. Don't forget that AI is replicating human errors
Since AI systems are trained on vast datasets that often include code written by humans, they inherit the biases and mistakes present within that data.
This means that AI might continue to create code with problems, such as security risks, slow algorithms, or poor coding habits. Developers have to be careful and check AI-created code as thoroughly as they would code written by people to make sure it’s good and correct.
2. Are you arguing with your AI?
Whether you are a programmer or in a non-tech job, if you have been using GPTs, you have probably spent your time correcting its answers.
While AI tools like code generators can significantly speed up the development process by automating routine tasks, they are not always precise or contextually appropriate. The time saved in initial code generation can sometimes be offset by the time required to refine this code to meet specific project standards or to integrate seamlessly into existing systems.
3. Not much of a side-kick in innovative problem-solving
AI's capability for innovative problem-solving is also limited. While it can execute defined tasks and optimize existing solutions, AI lacks the ability to perform the deep, abstract thinking necessary to devise truly innovative or out-of-the-box solutions.
It struggles with tasks that require understanding the subtle aspects of human behavior or the specific details of certain problems. Developers can understand the broader impacts of innovating and solve complex problems that AI currently cannot handle.
Can AI replicate human creativity?
AI systems (especially those using machine learning and generative AI) are proficient at analyzing vast amounts of data, identifying patterns, and applying learned information to generate new content. These systems can create music, art, and even write code. However, AI's approach to these tasks is different from human creativity.
AI lacks the ability to experience emotions, derive meaning from cultural contexts, or engage in the kind of subjective thinking that often inspires human creativity. While AI can produce novel combinations based on existing information, its creations are limited in the data it has been trained on. This training limits AI to what is already known and reduces its capacity for true innovation.
Many of our software engineers said that AI should stay out of the art, but here we are also talking about creativity in programming. Creativity is not just about writing new code but also about solving problems in unique ways, optimizing systems, and foreseeing future needs that may arise as technologies and markets evolve. Programmers often draw upon their experiences, intuitions, and personal insights, which are irreplicable by AI.
What are the real benefits of AI in programming?
1. It is a tool that helps in productivity
One of AI's primary benefits in programming is its ability to significantly enhance productivity. AI tools can automate many repetitive tasks, from code generation and error detection to testing and deployment.
This automation frees developers to focus on more complex and creative aspects of software development. AI can also suggest optimizations and improvements, further speeding the development cycle and enabling developers to accomplish more in less time.
2. Easier switching between the languages
AI also supports developers in becoming more versatile with different programming languages. AI-powered tools can help translate concepts and logic from one language to another, making it easier for developers to switch between languages or learn new ones.
3. It's a form of education, especially for juniors
Once there was Google, now we have ChatGPT. While it's true that AI, like GPT, can make mistakes (I've encountered a few myself, so fact-checking is always recommended), it also serves as a valuable coding assistant and educational platform.
For junior developers, especially those without access to mentors, AI can be a quick and convenient Q&A tool. It provides a way to understand complex concepts and seek guidance in real-time.
According to the Knowledge 2024 conference:
“Junior developers who are able to understand and adapt to their new roles will be best prepared to thrive in the AI-driven workforce of the future.”
The core of software development remains unchanged
Despite technological advances and the integration of AI tools, the core of software development remains focused on problem-solving and human insight.
At its heart, software development is much more than just writing code; it is about solving problems. Developers are tasked with creating solutions that address specific user needs, streamline business processes, or enhance system functionality.
While AI can assist by automating some coding tasks or suggesting code snippets, it lacks the ability to fully grasp the complexities of user requirements and the broader context of business needs. Developers bring a deep understanding of these subtle details, using their problem-solving skills to devise innovative, effective solutions.
Human insight is crucial in programming for understanding user behavior, making ethical choices, and considering cultural factors in software. AI, relying solely on data and algorithms, lacks the empathy and ethical judgment needed for complex decisions.
Why adaptability is key to surviving the AI shift
I read the comment on Reddit that said: "If AI comes for your job... good! Adapt and keep learning."
Programming is a large field; it is always changing, and you must keep up with it. As in any job, developers will update their skills and methods to integrate AI tools effectively if the situation requires it.
Think about it this way: as AI takes over routine tasks, it opens up opportunities for more complex roles. Developers who can adapt easily can take advantage of these new roles, using AI to tackle bigger challenges and drive innovation.
Adapting also involves improving soft skills like communication and teamwork (we talked a lot about soft skills in our blog, "Challenges of being a tech lead," which remain crucial as technical demands evolve.
It should be a collaboration rather than a replacement
AI tools bring immense potential to the software development process by automating routine tasks, suggesting optimizations, and even generating code. However, these technologies excel when used to augment the capabilities of human programmers, not replace them. Developers can use AI to handle mundane aspects of programming, freeing themselves to focus on more complex tasks.
By using AI's ability to handle data with human creativity and smart planning, development teams can accomplish more together than they could by themselves. This synergy allows for higher productivity, better problem-solving, and more innovative solutions in software development. Embracing this collaborative approach requires both a shift in mindset and a commitment to continuous learning.
In conclusion, the future of software development hinges not on choosing between human expertise and AI but on how well we can integrate the two. If we consider this more like collaboration, we can use the strengths of both human and artificial intelligence.
