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Since November 2022, when ChatGPT 3.5 hit the market, generative artificial intelligence (GenAI) has become topic number one. People are truly impressed by its ability to maintain human-like interaction, write comprehensive texts, and even create code snippets. But, like with any new groundbreaking technology, people are getting worried about future employment. Will AI replace software engineers in the near future or will it actually create new jobs? In this article, we’ll discuss the limitations of GenAI and explore how this technology will redefine the future of software engineering.
In the words of one of the greatest minds of the 21st century Stephen Hawking, “Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks.”But how high are the risks of generative AI models replacing software engineers in reality?
Let’s begin with defining generative artificial intelligence models. Generative AI is a technology capable of creating text, images, audio, and video content. Tools like ChatGPT, Claude, Gemini, StableDiffusion, and many others are already being employed by professionals across domains to streamline their workload.
Programmers can rely on transformer models like Copilot that generate text and code based on the prompt. Using natural language processing (NLP) and machine learning (ML) techniques, these large language models (LLMs) can create human-like interactions for users. However, LLMs lack the reasoning and contextualization to produce error-free code. It can forget to track variables, declare idle variables, and neglect to properly define them or define them at all.
Can AI write code? As of right now, AI tools can assist software engineers in writing code faster and more effectively:
Task Automation: According to GitHub research, programmers who use Copilot in their work perform up to 55% faster than their non-AI counterparts and complete more tasks. While auto-complete features make writing code easier, basic debugging functions allow for higher-quality output. The emerging trend is forgoing repetitive and mundane tasks and focusing on more important big-picture issues.
Intent Recognition: NLP technology is what allows us to “speak” with the LLM model and get the desired results. By understanding the intent behind the prompt, GenAI can create the corresponding output. In the words of Andrej Karpathy, ex-OpenAI researcher, “… my ‘programming’ [language] is now writing English”.
Language Evolution: The aforementioned NLP can converge the way humans think and code, making developing more accessible to wider audiences. Programming languages continuously evolve and strengthen through the use of libraries, frameworks, and other cutting-edge tools.
So, will AI replace programmers? As time goes forward and AI technology improves, GenAI will serve as a partner in helping experts generate code faster and better. Even now, programmers can write more code more efficiently—instead of handcrafting each line, they can quickly generate batches of code. In other words, engineers will utilize their expertise to read the generated code snippets and enhance them as opposed to manually typing them out.
Even though AI already assists developers with its broad capabilities, as mentioned above, this tool still has a lot of limitations. Moreover, this is a crucial aspect to consider when answering the question, “Will AI replace programmers?” Although these shortages might be solved in the future, right now, they’re obstructing fully AI-automated software development. To answer the question of whether AI will replace programmers, let’s discuss the most significant limitations of generative AI in coding.
The most important aspect of writing code is functionality and accuracy. With current AI capabilities, you cannot fully rely on AI tools to generate lines of code. Without the supervision of a trained software specialist, the system is likely to produce low-quality code riddled with errors and bugs.
Moreover, AI systems lack contextual understanding and critical thinking skills that humans naturally possess. All they do is find the next most appropriate word in a series of words to satisfy the request. This quality makes them exceptional at content writing, but when faced with new conditions, they are helpless. Similarly, current AI models cannot grasp the full scope of the project, which makes them inefficient in autonomously creating larger apps or building optimal architecture.
One of the reasons we have generative AI is the vast amounts of data that were fed to these systems to learn how humans think. But data is not endless and researchers nowadays are struggling to find more high-quality data to make the machines better. Training LLMs on low-quality datasets leads to hallucinations and meaningless text, rendering AI systems unusable.
Additionally, training large language models requires immense energy resources, which is a disadvantage to smaller research centers and companies. Furthermore, this limitless energy consumption has already created a significant negative impact on our environment, and further AI development may exacerbate it further.
If coders start to over-rely on generative AI, there’s a risk of them losing the ability to manually write code. Newer generations of programmers especially will be taught to collaborate with AI, reducing the complexity of their professional training. In the long run, this trend can stagnate the programming industry and jeopardize its quality and creativity.
Much like any other technology, AI can be used for good and for bad. Unfortunately, the impressive capabilities of generative AI also aid malicious actors in creating deepfakes, spreading misinformation, and generating new hacking tools.
On top of that, a large portion of AI’s coding abilities come from learning from GitHub and other unmoderated coding communities. This means that AI could have learned erroneous information in the first place, making it even more unreliable for autonomous programming. Not only can an AI system generate a faulty line of code, but it can also fail to explain its thought process, complicating the debugging for the end user.
As Co-Director of the Stanford Institute for Human-Centered Artificial Intelligence and IT Professor at the Graduate School of Business Fei-Fei Li stated, “Artificial intelligence is not a substitute for human intelligence; it is a tool to amplify human creativity and ingenuity.” While AI is a powerful technology, it’s not going to completely replace human software engineers, but only streamline their performances.
Still, many people remain worried about the potential mass job displacement in the tech industry. Although some tasks can be and are being delegated to LLMs, programming isn’t simply about writing code. AI is taking on simple and repetitive tasks while coders focus on more complex and creative activities, making their jobs more satisfying and rewarding.
In reality, the landscape of programming jobs is changing, allowing businesses to build more innovative products faster and at a lower cost. In fact, the freed-up human resources can be used to tackle more ambitious projects and create products that couldn’t have been made before.
So, will AI replace software engineers? The most vulnerable areas of the software development life cycle are predominantly junior-level tasks including basic web development, template-based code writing and maintenance, standard API integrations, and simple debugging. In return, entry-level coding jobs may experience a decline making it harder for younger specialists to break into the profession.
AI is not likely to replace software engineers when it comes to technical jobs requiring system design and architecture, complex problem-solving, and leadership. Additionally, programmers need in-depth domain expertise to properly adopt AI in business. Security implication tasks also aren’t transferable to AI models, requiring an understanding of human behavior and the creation of new legislation to prevent misuse.
The creator of Unified Modeling Language (UML) Grady Booch, said that “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.” To stay competitive in this new era of technological progress, software engineers need to develop new essential skills. In this next part, we’ll take a look at the most crucial things they need to learn to remain relevant.
A steep learning curve is one of the reasons why some software engineers aren’t utilizing artificial intelligence in mobile apps and web software development. Learning prompt engineering can help bridge that gap and empower coders to build faster and better-performing solutions. To receive the most relevant and useful results, programmers need to excel at prompt engineering by writing clear and specific queries, experimenting with wording, and performing continuous validation. Additionally, it’s recommended to familiarize yourself with prompt optimization frameworks such as DSPy, Ell, and others.
To maximize the efficacy of AI adoption in software engineering, coders should develop a habit of thorough and ongoing code evaluation. Despite AI’s impressive capabilities, it does make mistakes, including logic flaws and syntax errors. To prevent these mistakes from going into production, programmers need to develop superior code review skills.
Machine learning is the foundation of GenAI’s ability to grow and evolve. Learning about ML algorithms and modeling is a critical step in securing the future of the software engineering profession. In addition to ML, it’s important to be proficient in NLP and deep learning.
Besides deepening hard skills, future software engineers will need to hone their soft skills, including communication, empathy, and critical thinking. The lead of Google AI Jeff Dean, said the following about the importance of soft skills: “AI can be a powerful tool for programmers, assisting them in writing better code and accelerating software development cycles. Is AI replacing programmers? No, because it still lacks creativity and problem-solving skills.”
Among the largest limitations of AI is its inability to comprehend complex contexts and employ critical thinking when making assertions. Unlike machines, people are naturally good at problem-solving and analytical thinking, in fact, we prefer these tasks to routine activities. When collaborating with an AI-powered coding assistant, it’s crucial to ask questions and doubt the results to make sure the thought behind the output has sound logic.
More importantly, working with AI tools can present unexpected bottlenecks and challenges that require a decent level of adaptability. The industry is currently going through massive changes, which are likely to persist, making the ability to quickly adapt to the new methodologies and techniques essential for a long-lasting successful career.
Whether you’re using AI in cybersecurity, healthcare, or education, you need to have a solid grasp of the field of study. Each industry comes with its own set of challenges, nuances, and opportunities that need to be taken into account. Having a deeper understanding of the domain allows software developers to make better decisions and address industry-specific limitations.
Communication skills are essential in both prompt engineering and team collaboration. From understanding the client’s needs and industry specifics to working together with data scientists and other software developers, improving your communication skills is crucial for your future in the field.
Another skill that AI systems inherently lack is empathy. AI models simply regurgitate data it was trained on, without understanding the deeper context behind it. This is where software engineers can shine and use their ethical thinking to truly understand users’ needs and requirements. Especially when it comes to inclusivity and accessibility, humans have a unique insight into other people’s challenges and limitations that they can rely on when designing and building applications.
We’ve established that the present-day GenAI capabilities aren’t sufficient to take programming jobs. But what about the future? When will AI replace programmers?
Over the next years or maybe even decades, AI will continue to reshape the software engineering industry. It can help businesses lower the costs of software production, automate repetitive tasks, and improve overall efficiency. Through AI-assisted code generation and automated testing and debugging tools, organizations will be able to do more with less.
While some entry-level jobs might become obsolete, the industry is likely to create new roles. To optimize and accelerate this AI transformation, companies will require prompt engineers, AI-generated code reviewers, AI-assisted software developers, and AI modeling experts. Consider enhancing your skills in these areas to ensure employment in the future.
The use of AI will also empower smaller companies and startups to innovate and enter the market sooner. By accelerating research and coding, even smaller players can remain competitive in the new environment. However, larger organizations will certainly have an unfair advantage over smaller players. Having more computing power at their disposal, they’ll be able to yield even greater benefits of AI by implementing it into their workflow and restructuring existing processes.
While AI’s abilities are quite limited, the next breakthrough can potentially render software developers useless, namely artificial general intelligence (AGI). By definition, AGI is indistinguishable from the human mind and can perform any of the actions at the professional human level or better. However, the potential emergence of AGI is probably decades ahead, if clear at all.
Although some experts argue that AGI will be delivered by 2061, the majority of respondents believe it won’t occur until 100 years from now. A small portion of experts believe that AGI is not possible. Either way, AGI is not coming tomorrow, so software engineers are guaranteed stable employment for at least a few decades.
So, will software engineers be replaced by AI in the foreseeable future? According to the vast majority of experts, it’s not likely to happen any time soon. However, the era of AI revolution is here and is going to permeate across industries. Consider advancing your skills further, learning about foundational AI technologies, and adopting leading tools. More importantly, hone in on your soft skills to provide unique insight into how humans think and function to make your final product better.
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