Will AI replace programmers? Why a computer science degree still matters

If AI can already write code, you may be wondering whether learning to programme is still worth your time. 

It’s a fair question. AI coding tools can now do much more than suggest the next line of code. They can generate and review code, help debug problems and take on parts of the development process. 

But being able to generate code isn’t the same as understanding the problem it needs to solve. You still need to know whether the code works and whether it’s the right solution. 

So, rather than only asking, “Will AI replace programmers?”, a more useful question may be: how is AI changing the skills programmers need? 

Understanding that shift can help you decide whether studying computer science and artificial intelligence remains valuable as AI becomes a larger part of software development. 

Can AI already write code? 

Yes, and the range of tasks AI coding tools can handle is growing. 

For example, Gemini Code Assist can generate code and unit tests, explain code and help with debugging. Google also warns that its AI can produce output that appears plausible but is factually incorrect and recommends checking it before use.  

GitHub Copilot can also take on larger coding tasks. Its coding agent can take on delegated tasks such as fixing bugs and adding tests before producing a pull request for review. GitHub also notes that AI-generated code can introduce security risks, including vulnerable patterns and accidentally committed secrets.  

AI can produce code. You still need to know whether that code is correct, secure and suitable for what you're trying to build. 

So, will AI replace programmers? 

There isn't a reliable way to predict exactly how programming jobs will change as AI develops. 

We can already see AI taking on parts of the coding process. That may change how programmers spend their time, but writing code is only one part of software development. 

Developers may also need to: 

• understand what users need 
• turn requirements into technical solutions 
• decide how a system should be designed 
• review and test code 
• find and fix errors 
• consider security 
• maintain existing software 
• check AI-generated output 
• explain technical choices to colleagues or clients. 

AI may change how you approach these tasks, but you still need to understand the problem you're solving. 

Knowing how to produce code is useful. Understanding how software works helps you question, test and improve what AI produces. 

How is AI changing software development? 

If you're considering a computer science degree, focus on how programming is changing rather than trying to predict which jobs may disappear. 

Here are a few changes already visible in today's development tools. 

AI can give programmers a starting point 

Starting with a blank screen isn't always necessary. 

Gemini Code Assist can generate functions or code blocks, while OpenAI Codex supports writing and editing code.  

A programmer might start by reviewing and adapting AI-generated code rather than writing every line from scratch. That only helps if you can understand and assess the code it produces. 

AI can help find and explain problems 

AI can also help you debug code. 

Gemini Code Assist includes debugging and code explanation among its capabilities, while OpenAI Codex is designed to help developers write, review and debug code.  

For someone learning to programme, that can be useful. But there's a difference between receiving a suggested fix and understanding why something went wrong. 

Developing your own debugging skills means you can investigate why something failed, not just apply a suggested fix. 

AI can take on larger development tasks 

AI coding tools aren't limited to answering individual questions. 

Claude Code can work across multiple files and run commands, while GitHub Copilot's coding agent can handle delegated tasks such as bug fixes and test additions. 

Future programmers will need to know how to review AI-generated work as well as create and debug their own code. 

Why learn programming when AI can generate code? 

Think about using a calculator. 

A calculator can give you an answer much faster than working it out yourself. But if you don't understand the maths behind the calculation, it can be difficult to recognise when you've entered the wrong numbers or used the wrong formula. 

AI-generated code creates a similar challenge. 

If AI gives you a piece of code, could you explain what it does? Could you change it? Could you identify a bug? Would you recognise a potential security issue? 

Learning programming helps you develop the knowledge to answer those questions. 

You can learn how instructions produce an outcome, how algorithms solve problems and how data is organised. You can also develop practical skills in testing, debugging and software design. 

AI can help you complete certain tasks, while your computer science knowledge helps you understand and assess the result. 

Why does computer science knowledge still matter? 

Programming languages and AI tools will continue to change. 

Learning computer science isn't only about mastering whichever tool happens to be widely used today. It's also about understanding the principles behind the technology. 

That includes learning how to: 

• break a complex problem into manageable steps 
• understand how software components work together 
• evaluate different technical solutions 
• use programming concepts across different languages and tools 
• test whether software behaves as intended 
• identify technical limitations and risks. 

Those skills matter when working with computer science and AI because an AI-generated answer isn't automatically the right answer. 

Google, for example, explicitly advises developers to validate output from Gemini Code Assist before using it.  

To validate AI-generated code, you need the technical knowledge to understand and test it. 

How should you use AI while learning to programme? 

You don't have to choose between learning programming and using AI. Instead, think about how AI can support your learning without doing the thinking for you. 

Here are a few practical ways to approach it: 

• Learn the fundamentals first. Understand programming concepts rather than relying entirely on generated solutions. 
• Read the code AI gives you. Don't copy and paste something you can't explain. 
• Test the result. Check whether AI-generated code does what you need it to do. 
• Practise debugging yourself. When code doesn't work, investigate the problem before immediately asking AI for the answer. 
• Consider security and privacy. Think about the risks associated with the code, data and tools you're using. 
• Explain your decisions. Ask yourself why one approach makes more sense than another. 

Used this way, AI becomes another tool in your development process rather than a substitute for understanding the subject. 

What should you study if you're interested in AI? 

If you're wondering what you need to study to learn about artificial intelligence, there isn't a single answer. Entry requirements and curricula vary between universities and programmes. 

Programming, algorithms, data and mathematics can all give you useful foundations for studying AI. 

 You don't necessarily need a degree with ‘artificial intelligence’ in its title to start building relevant skills. 

A broader computer science programme can introduce you to different areas of computing while giving you opportunities to explore AI-related topics. 

For example, Aston University Online's BSc Computer Science includes programming alongside AI, machine learning and web development. The programme is BCS accredited, takes four years part-time and is delivered through flexible distance learning.  

So, if you're asking, “How can I study artificial intelligence?”, look beyond the degree title. Check the modules and skills you'll study and consider how they match what you want to learn. 

Is a computer science degree still worth it in the age of AI? 

If you're considering a degree, the answer depends on what you want to get from it. 

If your only goal were to learn how to type lines of code, AI's growing capabilities might understandably make you question the investment. 

But computer science is broader than code generation. 

For a career changer, studying computer science can provide a structured introduction to an unfamiliar technical field. If you've taught yourself to programme, a degree can help you develop knowledge beyond the tools and languages you've already used. And if you're completely new to computing, you can build your knowledge without needing to have your eventual specialism mapped out from day one. 

Aston University Online's BSc Computer Science combines programming with areas including AI, machine learning and web development, and its current programme information states that no prior coding experience is required.  

A degree can't guarantee what your career will look like as AI develops. What it can do is give you a structured way to learn the computing concepts behind the technology and develop the skills to keep learning as the tools change. 

Build your computer science knowledge alongside AI 

AI is changing how developers write, review and debug code. For someone considering computer science, that doesn't mean competing with AI by writing code faster. 

It means learning enough to understand the problem you're solving, make informed technical decisions and judge whether the solution you're given works. 

If you're a beginner, career changer, or self-taught programmer looking to develop your knowledge, Aston University Online's BSc Computer Science offers part-time online study that covers programming alongside AI and machine learning. 

Explore the online BSc Computer Science