Artificial Intelligence (AI) is not just limited to households. Today, it also helps people with their businesses and jobs. From automation to idea generation, AI is increasingly common in various fields. However, there is one field where AI is an upcoming supporter: coding.
Once considered a very tough job, AI is now greatly simplifying it. Software engineers are using AI not only to get coding support but also to simplify the process. There are several questions surrounding it, though, starting with the impact of AI and AI-generated code on jobs.
So, let us read this guide to know how AI-generated codes impact software engineering jobs.
How AI is Assisting Software Engineers
AI has now been working long enough in support of software engineers. Using this, you can speed up work and boost your time too. This also avoids errors and flaws. But that is just the start. Using AI, you can find bugs, errors, and loopholes quickly. This way, you can save time and ensure a better final outcome.
Here are the top ways AI is assisting software engineers:
- AI tools are acting as a hub of prewritten code.
- Engineers can use written code by making changes as needed.
- With repetitive tasks, AI can save time.
- When there is a bug, AI can detect it faster and offer a resolution that can save time and money.
- At times, AI can provide creative ideas that can boost efficiency.
- AI can help engineers study a task and find alternative code that works better.
Now, this is how AI assists software engineers. Does this mean AI-generated code is a threat to software engineering jobs, though?
Impact of AI-generated Code on Software Engineering Jobs
It is a common idea that AI may negatively impact jobs. But is that true? In most cases, AI is here to help and support humans rather than replace them. Here is how:
1. Shift from Coding to Problem-Solving
Software engineers are no longer just tasked with coding. They can now also focus on problem-solving. These experts can look at a problem from different angles and use AI to provide a holistic and improved solution. At the same time, engineers can invest more time in higher-level thinking, such as designing systems, improving performance, and solving complex logic problems.
2. Betterment in Entry-Level Opportunities
Most basic tasks, like bug fixing or basic feature development, can now be assisted with AI. This will allow even entry-level applicants to work on these issues, gain insights, and pursue better opportunities. They will thus have more time to learn and grow their portfolio, pushing new developers to upskill faster.
3. More Emphasis on Code Review and Validation
Since the AI-generated code is already there, the software engineers can now divert their time to other tasks. They can work on authenticating and proving whether the code is working as needed or not. Human review of the code can allow for evaluation in real-time, finding better solutions as required. This approach can help ensure code quality by keeping the code clean, safe, and aligned with project goals.
4. Faster Prototyping and Innovation Cycles
As a software engineer, you need to create new and innovative solutions to meet customers’ changing needs. With AI-generated code to help them, software engineers can invest their time in finding innovative solutions. They can create prototypes and get AI assistance to simplify the process.
5. Increased Demand for Hybrid Roles
AI-assisted code is semi-automated by nature, requiring engineers who know AI/ML (Machine Learning) for its development. While this can be a good thing, existing software engineers need to broaden their skill set so that they understand AI and code.
In the near future, more skills may be required. For example, engineers who understand both tech and business may have an edge.
6. Greater Focus on Ethics and Security
Various guidelines need to be followed, including GDPR, HIPAA, and more. Using AI-generated codes may impact these. The software engineer is now required to focus more on these aspects and ensure everything is streamlined. For example, engineers may need to ensure ethical practices, secure coding, and compliance.
7. Need for New Skills
A software engineer requires various skills. However, with AI on the rise, they must also know how to use AI tools. Skills like prompt engineering, AI tool usage, and integration practices are becoming important. Software engineers with such skills are now more in demand and in a position to stay at a company for a long time.
8. Enhanced Productivity Expectations
AI is here to boost productivity. When the code is automatically generated and the only need is to implement and use it, software engineers are expected to speed up their work. This shift demands that people become more agile, opening doors for those who can manage tools well and deliver quality work.
New Skills Engineers Will Need
It’s a rapidly shifting landscape, but at the moment, it looks like every software engineer should invest in certain skills, old and new. These include:
- Knowledge of AI tools and platforms like GitHub Copilot, Tabnine, and others.
- Understanding the prompts and writing them for precise results.
- Aligning code with the client’s/employer’s demands.
- Debugging skills.
- Critical thinking on how to scale, amend, or innovate.
- Ensuring compliance with standards.
- Creating code that is ethical, secure, and ensures privacy.
- Skills in prompt engineering and understanding of AL and ML.
- Efficiency in working in hybrid environments.
- Ability to learn and upskill in this fast-growing environment.
Challenges and Limitations of AI-Generated Code
While AI-generated code is here to help software engineers, there are also a few limitations. These limitations impact not just the work, but also the final outcome if not adhered to. Given how early we are in the AI revolution, here are some common limitations you should be aware of:
- AI-generated codes can have poor quality or accuracy.
- There is a safety risk if you are using open-platform AI tools.
- AI is still learning and evolving, so there is an inherent risk of investing in a soon-to-be-obsolete technology.
- If the developer is more likely to use AI, there is a chance of skill degradation.
- There are chances that some AI-generated code is licensed, so using it can negatively impact your work.
- The chances of being creative and generating new formats of codes may be more limited if you use AI.
- When errors arise, it may be difficult to trace the logic that was auto-generated by a model.
A Double-Edged Sword
AI-generated code is the new reality, and every software engineer needs to know how to use it. From writing snippets to handling entire functions, AI is speeding up development and shifting the role of software engineers. To effectively use AI, however, software engineers must also acquire some necessary skills.
At the same time, one should not just rely on AI-generated code but use it in a supporting role. Used properly, AI-generated code can not only improve performance but also save engineers a lot of time. This presents them with a golden opportunity to go beyond the generic and work on something innovative.
Engineers who view this as an opportunity to upskill and develop an innovative idea have a clear path to success. Those who fail to adapt may fall behind. At the end of the day, however, the future of software engineering is not man vs machine. It’s man and machine, i.e., how well the two can work together. Those who can do so are the ones who will succeed in unprecedented ways!
This is a guest post by Harikrishna Kundariya, a marketer, developer, IoT, Cloud, and AWS savvy co-founder and Director of eSparkBiz, a Software Development Company. His 14+ years of experience let him provide digital solutions to new start-ups based on IoT and SaaS applications.