The role of artificial intelligence (AI) technology in software engineering is more important than ever before.
Below, I have shared a Google Trends chart that shows the peak demand for the search term ‘AI in software engineering’ after the rise of AI tools.
So, to answer whether will AI replace software engineers or not, I have asked tech experts in the IT field and also did extensive research to highlight data points.
TL;DR
No, AI is not completely replacing software engineers in the near future. However, it will obviously replace some tasks of software engineers and improve the overall efficiency of the software developers team.
Here’s what tech experts are saying when I asked them about AI as a threat to software engineers:
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1. AI is an Aid, Not a Threat
Honestly, I don’t feel that AI is a threat to software engineering. Even with AI, there’ll be a need for people behind the curtain to manage it. AI will also free software engineers to shift their focus to innovative solutions they otherwise didn’t have the bandwidth to think up.
In the end, AI should be an aid and not a crutch, and if we’re wise in our ethical use of it, software engineers will partner with it and not let it be the cause of their extinction.
AI is unlikely to fully replace software engineers in the near future. Despite the significant advancements in AI, human creativity continues to drive innovation.
In a previous startup, an AI tool was developed to automate simple coding tasks. This tool was effective in handling routine functions, allowing engineers to concentrate on more complex problem-solving.
Current AI, while intelligent, lacks the human judgment, intuition, and abstract thinking necessary for breakthroughs. Humans can bend rules, while AI strictly follows them. Engineers understand when it’s appropriate to break “best practices”, a concept machines have yet to comprehend.
It’s possible that AI could eventually achieve this level of discernment, but there’s still a significant journey ahead before AI can make the creative leaps and judgment calls that are inherent in pioneering technology.
The field of software engineering values innovative thinking. Until AI can philosophize like Plato and compose like Mozart, innovative engineering will continue to be a deeply human endeavor.
3. Productivity Booster, Not Replacement
AI can be seen as a productivity booster. This means that it can fully replace some mundane tasks, destroying some jobs in the process. However, if we look back in history, we can see that automation has usually been a net creator of job opportunities.
This leads me to believe that AI, while it may automate some aspects of software engineering, will ultimately enhance the developer experience, making us more productive.
If we look at jobs that were automated during the Industrial Revolution, like car manufacturing, it’s the repetitive tasks that were automated the most. Creative tasks experienced lesser automation.
Since software development is a very creative task that not only involves coding but also gathering requirements and designing solutions, some aspects of our jobs will likely be automated, but the bulk of it looks safe for now.
4. AI will partially replace software engineers, not fully
The question of whether AI will replace software engineers is more complex than a simple yes or no.
AI technologies have the potential to automate certain tasks that developers currently perform, thereby altering the nature of a developer’s work rather than replacing the role completely.
AI-based tools are already helping developers to manage and manipulate large data sets, streamline code debugging, and induce more efficiency into routine back-end tasks.
A smart tool, like GitHub’s co-pilot, can suggest codes but the role of a developer is still crucial for building a bigger picture i.e., a full-blown product, and weaving individual components into it.
Put simply, software developers don’t just write code. They analyse problems, design solutions, and manage projects, which require human qualities such as empathy, creativity, and perspective-taking.
For instance, understanding the user experience and converting it into an effective application flow is a developer’s job which AI lacks.
So, instead of visualizing AI as a substitute, it would be more appropriate to see it as an augmentation tool, helping developers to increase their productivity, decide better, and reduce errors. The fusion of human intelligence with AI will lead to technology in the future.
Changing Skill Requirements
However, one cannot dismiss the fact that AI advancements will change the skill requirements of a software engineer. Much akin to the Industrial Revolution, this could lead to a situation where certain roles will become obsolete while new roles are created. This suggests the need for professionals in the field to continuously update their skills and adapt to the evolving landscape.
Hence, AI and software engineers will co-exist, foster, and infuse a new wave of technological advancement with a steep and continuous learning curve.
Insight from Abhi Bavishi, Growth and automation expert
5. AI improved but doesn’t replace human software engineers
AI excels at repetitive and routine tasks, streamlining processes and aiding in code generation. However, software engineering involves creativity, problem-solving, and a deep understanding of human needs. Engineers design solutions, ensure scalability, and adapt to evolving requirements – tasks that require nuanced human judgment.
AI’s current limitations lie in comprehending context, ethical decision-making, and navigating unforeseen challenges. The dynamic and evolving nature of technology demands engineers who can learn, adapt, and innovate in real-time.
Key takeaway: AI is a powerful tool that can augment and enhance software engineering processes, but it cannot replicate the holistic skills, creativity, and adaptability that human engineers bring to the table. The future likely holds a collaborative landscape where engineers leverage AI to their advantage.
6. AI will accelerate software developers’ productivity, not replace them
I’m a firm believer that AI won’t fully replace developers. Instead, it will accelerate their productivity potential. If you develop without AI, you’re going to quickly fall behind.
I think the future will revolve around AIOps – the process of building step-by-step alongside AI. Developers will need to take a problem, and break it down into bite-sized steps while determining the input/output requirements of every step. Then, they’ll need to work with AI to provide those requirements, evaluate the generated code to ensure requirements are met, and then string together all of the components in a production environment. Tests for the code can also be developed automatically using AI tools.
Here are two more experiments that I ran on a data orchestration platform that focuses on workflow automation.
Experiment 1: Proof of concept for code interpreter link sharing and automated code deployment
Since OpenAI launched Code Interpreter (now Advanced Data Analysis), we realized that non-technical people will now have the ability to share conversations with generated code through a link. Our platform lets teams deploy and automate code.
We built out a proof of concept that can “import from OpenAI” where the user provides a link and we extract the final code and package requirements that need to be installed. We used Code Interpreter ourselves to build out the entire proof of concept in 6 hours, instead of the likely 20 hours it would have taken.
Experiment 2: Transforming descriptions into usable YAML configurations
Over a year ago, we built out a proof of concept that we called “Words to Workflows”. The idea was that a user could provide a description of the workflow they wanted to build and we could turn this into our YAML configuration file that could be understood by the application.
For example, a user might say “I want to download the last 7 days of orders from Snowflake and send the finance team an email with the data every morning at 6“.
We would have to turn that into a usable workflow.
This resulted in us having to break down the problem by identifying:
Individual tasks desired from the description
If the task could be accomplished with our existing low-code Blueprints, or if they needed to have custom code written
What both experiments have taught us is that to work with AI effectively, you have to chunk up the work into as few steps as possible that can be easily, programmatically verified. Anything else is a gamble.
7. AI will replace entry-level and repetitive tasks but not skilled software engineers
In my opinion, there are still a couple of decades left for AI to replace software engineers, that also if things progress without any anomaly.
Now, the accuracy of my opinion depends on a bunch of conditions. For example, which level of software engineers are we talking about? If we look at entry-level engineers who do basic and repetitive tasks, then yes, AI can replace them in a couple of years.
But if it comes to software engineers who are working on more critical and untouched areas, then there is no need for them to worry about being replaced by AI.
Besides, as much as Chat GPT and other generative AI models are gaining popularity, we shouldn’t forget that the overall demand for software is also spreading across sectors we never thought of before. We can see a big difference in the number of edTech platforms in pre and post-pandemic settings. So, for now, software engineers can see AI as a helping hand to be more productive.
Now, let’s try to understand the basics of whether AI will replace software engineers and how AI will collaborate with software engineers in the future.
What is Artificial intelligence? What can it do?
AI, or Artificial Intelligence, is a branch of computer science that aims to create machines and systems capable of performing tasks that typically require human intelligence, such as problem-solving, learning, and decision-making.
AI technologies include machine learning, natural language processing, and computer vision that aid in analyzing data, recognizing patterns, and making informed decisions.
Overview of software engineering roles
Software engineers hold critical roles in the development, maintenance, and enhancement of software applications and to do so, they write code, fix problems in the software, and make sure it works well and is safe.
It comprises various roles like front-end and back-end development, testing, and system architecture design, demanding proficiency in programming, algorithms, and problem-solving.
AI is really good at doing things automatically and making tasks easier, but software engineering is about solving creative problems, designing user-friendly stuff, and understanding projects.
The reason this question matters is that we’re trying to figure out how AI can work together with software engineering, not replace it completely— This means we need to adapt and work together.
The Current State of AI in Software Development
1. AI in Code Generation
AI tools like OpenAI’s GPT-3, and GitHub bot can assist in generating code snippets based on natural language descriptions. For instance, developers can describe a specific task, and the AI model can provide code to perform that task, saving time and effort.
2. Automated Testing and Debugging
AI-powered testing platforms like Testim and Applitools use computer vision and machine learning to automate the testing of user interfaces, helping developers find and fix bugs more efficiently.
3. AI-Powered Project Management Tools
Tools like Monday.com and Trello use AI to analyze project data, predict potential delays, and suggest changes to how we use resources.
They can also do everyday project jobs, like giving out tasks and keeping an eye on how things are going.
4. Impact on Repetitive Tasks
Robotic Process Automation (RPA) software, like UiPath and Automation Anywhere, can handle repetitive, rule-based tasks such as data entry, file manipulation, and report generation.
This automation frees software engineers to focus on more creative and complex aspects of software development.
5. Code Explainers
AI in software development has a cool feature that can explain code line by line. This is really helpful when working with code that you’re not familiar with. It can make it easier to understand and work with the code, even if you’re new to it.
Advantages of AI in Software Development
1. Improve Productivity
AI, when part of the development environment (IDE), makes work faster and easier for developers. It helps by suggesting methods, filling in parameters, and preventing syntax errors. This means less time and effort for software developers.
2. Enhanced Code Quality
AI tools that check code, like CodeClimate and SonarQube, can find problems with code quality, security, and how fast it works. This makes sure software engineers can create better code with fewer mistakes.
3. Reduction of Human Error
AI reduces the risk of human error by automating repetitive tasks and performing them accurately whenever required. This is important in tasks like data entry, so we know the data is right and reliable.
4. Enhanced Architectural Decision-Making with AI
When software engineers use AI in the design phase, they get better guidance when deciding how to build things. It helps them understand the pros and cons of different choices more clearly, so they can make smarter decisions and create better software.
Limitations of AI in Replacing Software Engineers
1. Creativity and Problem-Solving
For example, AI can automate repetitive coding tasks or data analysis but may struggle to innovate and design unique user experiences that require creative thinking and an understanding of user preferences.
2. Interpersonal Skills and Collaboration
A recent Harvard Business Review article extensively discussed this matter, emphasizing the importance of effective teamwork in software development. It pointed out that AI lacks the interpersonal skills needed to comprehend the desires of team members, clients, or users, potentially resulting in issues and dissatisfaction among stakeholders.
3. Ethical Considerations
AI systems can accidentally make unfair decisions, like favouring certain groups, as seen in hiring algorithms. Human software engineers are better at spotting and fixing these problems to make sure technology is fair and responsible.
Most AI capabilities work well with small pieces of code, but they struggle with larger ones. AI models, like GPT-3.5 and GPT-4, take a task, such as rewriting code, and produce new code, but they can only handle a limited amount of information due to a context window.
For instance, if you have a file with 2,000 lines of code, it could exceed the limits of the GPT-4 8k model. Even the larger GPT-4 32k model may not handle such a codebase if there are more files like it.
The Future of AI in Software Engineering (Emerging AI Trends)
For example, reinforcement learning algorithms can optimize software configurations for better performance, while GANs can generate realistic synthetic data for testing and training machine learning models.
AI-powered DevOps tools, like AIOps, are anticipated to enhance software development workflows by automating processes and providing insights for better decision-making.
1. Potential AI-Software Engineer Collaboration
AI-based code analysis tools can identify code quality issues and suggest improvements, allowing engineers to focus on refining the architecture and solving complex problems.
2. Continuous Learning and Improvement
AI in software engineering can continuously learn from data such as code repositories, documentation, and user feedback. This ongoing learning process will allow AI systems to enhance their capabilities and become more knowledgeable in assisting developers. As a result, AI remains relevant and effective in the ever-evolving landscape of software development.
Key takeaway
To answer the question we started with: No, AI won’t destroy the programming industry. It will help programmers handle harder problems and push us forward. It’s a great time to be in this field.
Throughout the history of programming, there’s been a progression of abstraction layers, making problem-solving easier. From punch cards to assembly language, and then high-level languages like COBOL and C, programmers have had more powerful tools. AI is the next step in this evolution, set to simplify programming further and address even more complex challenges.
As AI evolves, software engineers need to learn new skills and work with AI as a helpful partner in their jobs. They should be ready to use new technologies, team up with AI systems, and keep learning about new trends to succeed in the ever-changing world of software engineering. The future looks bright for those who mix their own creative ideas with the power of AI.
Tata Sai Nandini is an IT Professional with experience in the Python and Data Science domain. With expertise in Statistical Analysis, Exploratory Data Analysis, Data Visualization, Data Extraction, Data Wrangling, Image Processing, Text Processing, and the creation of Machine Learning and Deep Learning models, she is a versatile and skilled practitioner in the field. Tata Sai Nandini’s skillset encompasses a wide range of tools and technologies, including Python, C++, C, various IDEs, Python libraries, data visualization tools, and machine learning and deep learning frameworks.
