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Is There a Future for Software Engineers? The Impact of AI [2024]

Last updated on
June 12, 2024



Is There a Future for Software Engineers? The Impact of AI [2024]


We live in times of change. AI is still flourishing, and although it often falls short at the moment, there is no doubt that this technology will continue to develop and improve. What is certain is that AI is increasingly changing our reality - including having a growing impact on the daily lives of many employees, including programmers.

AI is becoming a part of software development, making life easier for programmers in various ways by taking over tedious tasks and streamlining work. As a result, products are being created faster.

It has also been used for years in the form of Machine Learning - and in this area, it has already helped create new job positions.

What lies ahead? For now, it looks like programmers will still be needed, but the nature of their work, the required skills, and the areas they focus on will change somewhat.

Let's analyze this topic and check what the experts are saying.

Growing worries

According to a team of researchers at the US Department of Energy’s Oak Ridge National Laboratory, there’s a high chance that AI will replace software developers as early as 2040.

Programming trends suggest that software development will undergo a radical change in the future: the combination of machine learning, artificial intelligence, natural language processing, and code generation technologies will improve in such a way that machines, instead of humans, will write most of their own code by 2040, state the researchers.

Software developers are understandably worried. In fact, nearly 30 percent of the 550 software developers surveyed by Evans Data Corporation, a California-based market research firm that specializes in software development, believe that their development efforts will be replaced by artificial intelligence in the foreseeable future.

Current picture: How AI changes the software development world today

AI has significantly impacted the software development landscape in recent years, bringing about several notable changes and advancements. Programmers are using AI to generate code, automate repetitive and boring tasks, detect bugs easier, and make the DevOps process more efficient.

Automation of some tasks

With the help of AI, developers can automate some types of software development tasks, which boosts their productivity and efficiency. For example, AI-powered tools can automatically generate code snippets, perform code refactoring, and assist in bug detection and fixing. That helps to save time and focus on more complex and critical aspects of software development.

Code generation

AI tools can generate code based on existing patterns and examples. This includes auto-complete suggestions in integrated development environments (IDEs) and AI-generated code snippets for specific tasks. AI can also optimize code by identifying redundant or inefficient parts and suggesting improvements.

However: Even if you're tempted, we don't recommend relying solely on AI tools in terms of code generation. Created code should be checked by a programmer. AI can still write it a bit sloppy; the code will need some improvements and cleaning. However, it will still boost effectiveness and, most likely, decrease boredom.

Assistance in bug detection

AI also influenced the testing and quality assurance processes. AI-based testing tools can analyze code, identify potential vulnerabilities, and automatically generate test cases. Machine learning techniques are employed to learn from past test results and predict areas of code that are more likely to contain bugs. This helps developers identify and fix issues early in the development cycle, leading to higher-quality software.

DevOps process evolution

AI has contributed to the evolution of DevOps practices and continuous integration/continuous delivery (CI/CD) pipelines. AI techniques can analyze code changes, test results, and production metrics to provide insights on performance, quality, and potential issues. This helps streamline the software development lifecycle, improve deployment processes, and enhance overall software quality.

AI won’t replace programmers, but it will become an essential tool in their arsenal. It’s about empowering humans to do more, not do less. - Satya Nadella, CEO of Microsoft

New use cases

NLP, a subfield of AI, has made significant strides in understanding and processing human language. NLP technologies have influenced software development through the development of chatbots, virtual assistants, and voice-activated interfaces. These applications allow users to interact with software systems using natural language, enhancing user experiences and opening up new avenues for software development.

AI algorithms analyze vast amounts of data to make intelligent recommendations and personalize software experiences. For example, AI-powered recommendation systems are used in e-commerce applications to suggest products based on user preferences and browsing history. Similarly, AI can personalize user interfaces, content, and features based on individual user behavior and patterns.

AI can be a powerful tool for programmers, assisting them in writing better code and accelerating development cycles. AI still lacks creativity and problem-solving skills, so it won’t replace programmers. - Jeff Dean, Senior Fellow at Google AI

Tools to replace... or rather support software developers

There are several AI tools and frameworks that are commonly used in software development to facilitate work and enable the development of AI-driven applications. Here are some of the most popular ones:


TensorFlow is an open-source library developed by Google that is widely used for building and training deep learning models. It provides a flexible and comprehensive ecosystem for developing various AI applications and supports a wide range of platforms and devices.

How it supports developers:

  • Offers extensive libraries and tools for all machine learning stages.
  • Supports both high-level APIs (like Keras) and low-level APIs for custom models. Scalable across CPUs, GPUs, and TPUs.
  • Enables development on desktops, servers, mobile, and embedded systems via TensorFlow Lite.
  • Large community, resources like TensorFlow Extended (TFX), TensorFlow Hub, and TensorBoard.
  • Works well with Google Cloud services and popular libraries like NumPy and Pandas.


  • High-level APIs make prototyping and building models easier.
  • Optimized for hardware accelerators, enabling faster training and inference.
  • Regular updates and rigorous testing ensure robustness.
  • Supports a wide range of machine learning and deep learning tasks.
  • Tools for deploying, monitoring, and managing models at scale.

Potential flaws:

  • Steep learning curve, especially with low-level APIs.
  • Requires significant computational resources for training complex models.
  • Some backward compatibility issues across versions.
  • Debugging can be intricate, despite tools like TensorBoard.
  • Improper usage or suboptimal configurations can lead to bottlenecks.


PyTorch is another popular open-source deep learning framework known for its dynamic computational graph, which makes it easier to debug and experiment with models. It has gained popularity for its simplicity and is commonly used for research and rapid prototyping of AI models.

How it supports developers:

  • Uses dynamic computation graphs (define-by-run), allowing developers to modify the graph on the fly, making debugging and experimentation easier.
  • Intuitive and Pythonic API that integrates seamlessly with Python, making it accessible and straightforward for developers.
  • Includes tools and libraries like torchvision for computer vision, torchtext for natural language processing, and torchaudio for audio processing.
  • Supported by a robust community with extensive tutorials, forums, and third-party resources.
  • Works well with other Python libraries like NumPy and SciPy, and supports integration with C++ for performance-critical components.


  • Dynamic graphs enable flexibility and ease of use, particularly for research and experimentation.
  • Optimized for performance with support for GPUs and distributed training.
  • Ideal for interactive computing environments like Jupyter Notebooks, enhancing the developer experience.
  • Easier to understand and debug models due to the dynamic nature of the framework.
  • Increasingly adopted by researchers and industry professionals, leading to a growing ecosystem and support network.

Potential Flaws:

  • While extensive, the quality and clarity of documentation can vary, potentially posing a challenge for new users.
  • High computational requirements for training large models can be a constraint.
  • Compared to TensorFlow, some advanced deployment and production features are less mature.
  • Occasionally faces backward compatibility issues with updates, similar to TensorFlow.
  • Requires careful tuning and understanding of underlying mechanics to fully leverage performance benefits.

GitHub Copilot

GitHub Copilot is an AI-powered code completion tool developed by GitHub in collaboration with OpenAI. It uses machine learning models trained on a vast amount of code from open-source repositories to suggest code snippets and completions as developers write. Copilot aims to assist developers in writing code faster and more efficiently by providing context-aware suggestions directly within their coding environment.

How it supports developers:

  • Provides real-time code suggestions based on the context of the current file and project, helping to write code faster.
  • Autocompletes entire lines or blocks of code, reducing the amount of manual typing and minimizing syntax errors.
  • Supports a wide range of programming languages and frameworks, making it versatile for different development environments.
  • Understands the context from comments, variable names, and the surrounding code to provide relevant suggestions.
  • Integrates seamlessly with popular code editors like Visual Studio Code, making it easy to incorporate into existing workflows.


  • Speeds up coding by providing quick, context-aware code suggestions, allowing developers to focus on more complex tasks.
  • Helps reduce syntax and logical errors by suggesting correct code patterns and best practices.
  • Acts as an educational tool, helping developers learn new programming languages and frameworks through suggested code snippets.
  • Promotes consistent coding standards across projects by suggesting standardized code patterns.
  • Allows developers to focus more on problem-solving and logic, rather than boilerplate code and syntax.

Potential flaws:

  • Developers might become overly reliant on suggestions, potentially hindering their problem-solving and coding skills.
  • Uses machine learning models trained on public code repositories, raising potential privacy and intellectual property concerns.
  • While often accurate, suggestions may sometimes be irrelevant or incorrect, requiring careful review by developers.
  • May struggle with understanding complex project-specific contexts, leading to less useful suggestions in some cases.
  • Currently best integrated with Visual Studio Code, which might limit usability for developers using other IDEs.

Gartner's report highlights that by 2027, 50% of software engineering organizations will utilize software engineering intelligence platforms to measure and increase developer productivity. This shift is a significant increase from 5% in 2024, indicating a strong trend towards integrating intelligent platforms in software development.

Bridging the skill gap

While AI has the potential to automate many programming tasks, up to 80% of programming jobs will remain human-centric. McKinsey&CO

To successfully bridge the skill gap that exists within the software development industry, software developers themselves must realize that their skill sets will have to change.

According to a report from job search site Indeed, the three most in-demand AI jobs on the market are data scientist, software engineer, and machine learning engineer. The demand for these and other AI-related roles has more than doubled over the past three years, and it’s expected to keep growing at a similar pace.

The skills that software developers need to be proficient on AI projects include math, algebra, calculus, statistics, big data, data mining, data science, machine learning, MLOps, cognitive computing, text analytics, natural language processing, R, Hadoop, Spark, and many others.

Crucial skills for software developers in the age of AI

AI is going to fundamentally change what it means to be a programmer. It won’t eliminate programmers, but it will require them to develop new skills and work in new ways. - Grady Booch, American software engineer, best known for developing the Unified Modeling Language

In the age of AI, developers can enhance their skills to stay relevant and take advantage of the opportunities presented by AI. Here are some crucial skills for developers in the AI era.

Technical skills

Machine Learning (ML) and Data Science

Understanding the principles and techniques of machine learning is essential. Developers should learn about different ML algorithms, data preprocessing, feature engineering, model evaluation, and deployment. Additionally, gaining knowledge in data science, including data visualization, exploratory data analysis, and statistical analysis, can provide a solid foundation for working with AI systems.

Neural Networks and Deep Learning

Deep learning has revolutionized AI and is widely used in various applications. Developers should familiarize themselves with neural networks, including convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) for sequence data. Learning frameworks like TensorFlow and PyTorch can be valuable in building and training deep learning models.

Natural Language Processing (NLP)

NLP focuses on understanding and processing human language. Developers can learn about techniques such as text preprocessing, sentiment analysis, named entity recognition, and language generation. Knowledge of libraries like NLTK, spaCy, and transformers can be beneficial in working with NLP tasks.

Technical debt management

Developers need to know how to cope with various types of a growing technical debt. Managing technical debt is essential for ensuring that the software can grow and evolve without significant rework. Software engineers must balance the immediate benefits of quick fixes against the long-term impacts on productivity.

Skills needed are, for example, technical debt prioritization, technical debt metrics, coping with technical debt taking business goals into account.

Soft skills

Domain knowledge

AI is applied in various domains, such as healthcare, finance, robotics, and autonomous systems. Developers should acquire domain-specific knowledge to understand the challenges, requirements, and constraints of specific industries.

Lifelong learning and adaptability

The field of AI is evolving rapidly, so developers must embrace lifelong learning. They should stay updated with the latest research, techniques, and advancements in AI. Keeping up with online courses, attending conferences, participating in AI communities, and exploring open-source projects can help developers stay ahead in this dynamic field.

Critical thinking and problem solving

Strong problem-solving skills, critical thinking, and the ability to break down complex problems into manageable components are crucial in designing effective AI solutions.

Interdisciplinary collaboration

Working with data scientists, domain experts, and other stakeholders to develop AI solutions that meet diverse needs. Effective collaboration requires good communication and teamwork skills.

Business understanding

When software engineers are able to understand the business side of a project, they can make better decisions and precise prioritization.

Because we act as partners we also notice some things that the client couldn’t notice themselves sometimes. We are able to advise on a small change that can have a huge impact on the business as well. - Agata Szopa, .NET Software Engineer at Brainhub

The art of choosing

Clearly, it would be virtually impossible for most software developers to master each and every AI-related skill, especially considering the breakneck speed at which the field of AI is moving forward. That’s why software developers who want to stay relevant in the age of AI should see themselves as expert-generalists and treat learning new skills as an ongoing process.

Having a breadth of knowledge makes it far easier to acquire deep expertise in one particular area based on the current market demand. “[Those who will be successful will be the developers that have the best understanding [of] the essential complexity of their domains: which data are important [and] the impact of uncertainty on decision making, etc.,” says Todd Schiller, head of engineering at MOKA, a disruptive technologies advisory firm.

Software developers won’t have to know the intricate details of the latest machine learning algorithms or possess excellent command of the trendiest programming language to work on AI projects, but not being able to navigate the AI landscape and learn new skills at the speed of business won’t be equally optional.

Will software engineers be still needed in the future?

AI will not replace programmers but will fundamentally change the development landscape, making human creativity and problem-solving essential. - Janel Garvin, CEO of Evans Data Corporation

After analyzing all these opinions and data, the answer seems to be "yes", don't you think?

Software engineering involves solving complex problems, designing algorithms, and developing intricate software systems. While AI can assist in automating certain repetitive tasks and optimizing processes, it still requires human expertise to conceptualize, architect, and design complex software solutions that meet specific requirements.

Creativity and innovation are also indispensable. These qualities are needed while designing user interfaces, creating unique user experiences, and developing novel algorithms. AI can generate solutions based on existing patterns and data, but it currently lacks the ability to match human creativity and intuition.

Software engineers also possess contextual understanding and the ability to adapt to changing requirements. They work closely with stakeholders to gather requirements, understand business needs, and create software solutions that align with specific contexts. While AI can help them with data analysis and pattern recognition, but human software engineers are better equipped to understand complex contexts, make informed decisions, and adapt software systems accordingly.

What's coming up

"A large portion of programmers of tomorrow do not maintain complex software repositories, write intricate programs, or analyze their running times,” believes Andrej Karpathy, a former research scientist at OpenAI who now serves as Director of AI at Tesla. “They collect, clean, manipulate, label, analyze and visualize data that feeds neural networks."

Karpathy has proposed a new software development process for the age of AI, called Software 2.0, and its key components include problem and goal definition, data collection, data preparation, model learning, model deployment and integration, and model management. Software developers of the future will source and compose large data sets to train applications to be smart, instead of hard-coding the desired capabilities.

Artificial intelligence will radically reshape software development and force software developers to acquire new skills in order to stay relevant. Those who will adapt most successfully to the coming era will get to enjoy an abundance of work opportunities, but the process will require a different mindset than many software developers have today.

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Marcin Dryka
Software Engineer

Full-stack software developer with 17 years of professional experience.

Bianka Pluszczewska
Tech Editor

Software development enthusiast with 8 years of professional experience in this industry.

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