Which programming jobs are likely to be replaced by AI?

Generative AI has rapidly evolved in recent years, from ChatGPT to Claude, PerplexityAI and DALL-E. Once considered safe from automation, the programming field is now experiencing its own wave of AI-driven change.

While this new wave of technology creates exciting new opportunities, it also brings uncertainty for certain programming roles. As AI systems become more sophisticated, they can handle tasks that were once the exclusive domain of human programmers.

From automating code generation to optimising software performance, AI makes its presence felt across the tech landscape. But what does this mean for the future of programming jobs? Are some roles more at risk than others?

Criteria for Determining AI-Replaceable Jobs

Not all roles are equally vulnerable—certain characteristics make some jobs more likely to be automated by AI. Here are the key criteria to consider:

1. Repetitiveness and Predictability: AI excels at handling tasks with clear rules and patterns, making it ideal for jobs that don’t require much variation in day-to-day activities.

2. Level of Creativity Required: While AI can generate code and solve problems based on existing data, it struggles with tasks that require outside-the-box thinking or creating something entirely new.

3. Interaction with Other Humans: These roles often require emotional intelligence, negotiation, and understanding and responding to complex human needs.

4. Dependence on Machine Learning and Automation: Jobs that already rely heavily on machine learning and automation tools may be more susceptible to AI replacement.

Top Programming Jobs Likely to Be Replaced by AI

1. Basic Front-End Development

Traditionally, front-end developers have been responsible for translating design mockups into code. However, the rise of AI-powered website builders and code generators has started automating many tasks.

Platforms like Wix’s ADI (Artificial Design Intelligence) and The Grid use AI to design and build entire websites based on a few preferences or inputs from the user. These tools can produce layouts, select colour schemes, and even adjust design elements on the fly.

Want my advice? Focus on more complex and dynamic web applications that require advanced JavaScript frameworks (like React, Angular, or Vue.js) to help maintain a competitive edge.

2. Quality Assurance (QA) and Testing

Quality Assurance (QA) and testing are crucial stages in the software development lifecycle. These roles have always required manual testing, where testers review the software and identify specific issues.

However, tools like Selenium, Appium, and Testim leverage AI to automate the testing process, from unit tests to more complex functional and regression tests.

These tools can simulate user interactions, identify patterns in the code that may lead to bugs, and even generate test cases based on previous data. AI also plays a role in predictive analysis, where it can forecast potential problem areas in the code that have historically caused issues.

From a manual QA tester, you should try and upskill yourself to develop and manage automated testing frameworks.

3. Data Entry and Simple Data Analysis

Data entry and basic data analysis tasks have long been manual, requiring individuals to input data, clean it up, and run simple analyses. However, AI tools like Google Sheets’ built-in AI functions and Python libraries such as Pandas can now automate these tasks.

AI can now handle large datasets, perform data cleaning, and generate basic reports, making it easier for businesses to process data without human intervention. This automation reduces the need for roles focused on repetitive data entry or simple analysis tasks.

For data professionals, the key is to move beyond basic tasks and develop advanced data analysis, data visualisation, and machine learning skills to stay relevant in the evolving job market.

4. Automated Code Review

Code reviews are essential for maintaining code quality, but they often involve repetitive checks that can be time-consuming.

AI-powered tools like Codacy, DeepCode, and SonarQube are making these reviews faster and more efficient by automatically identifying potential issues in the code, such as bugs, security vulnerabilities, and style inconsistencies.

While AI can handle routine checks, it still lacks the context and experience to fully understand complex code logic.

I recommend that you consider focusing on complex code design and architecture, areas where AI still struggles, to ensure your skills remain in demand.

5. Bug Fixing and Maintenance

AI is now stepping in to automate much of bug fixing and maintenance work, with tools like GitHub Copilot and AI-driven debugging systems that can suggest or even apply fixes to common coding issues.

These tools can quickly identify and correct errors, reducing developers’ time on maintenance tasks. However, AI cannot handle more complex bugs that require deep problem-solving and understanding of the entire system.

To stay competitive, developers should focus on mastering complex debugging, optimising system performance, and developing preventive maintenance and system design skills.

6. Basic Database Management

Basic database management tasks, such as routine data entry, query optimization, and backups, have long been part of a database administrator’s role. AI-driven database management systems like Google’s BigQuery and Microsoft’s Azure SQL Database can now automate these routine tasks quite easily.

These systems can automatically handle data indexing, perform query optimization, and even manage backups without much human intervention. This automation reduces the need for manual database management, especially for straightforward tasks.

To stay ahead, database professionals should focus on more complex areas like data architecture, database security, and performance tuning, where human expertise is still critical.

7. Simple API Integration

Integrating APIs is a common task in development, but it’s becoming increasingly automated. AI tools like Postman’s AI assistant and various API management platforms can streamline and automate connecting APIs to different systems.

These tools simplify integration by automatically generating boilerplate code, handling authentication, and testing endpoints. As a result, the need for manual coding of simple API integrations is decreasing.

Developers should concentrate on complex API development, custom integrations, and understanding the underlying business logic to maintain relevance in the job market.