Can Generative AI Write Code?

GenAI coding tools like GitHub Copilot, ChatGPT, and Amazon CodeWhisperer help write, debug, and optimize code. They automate repetitive tasks, boost efficiency, and let developers focus on solving complex problems. But can AI replace human programmers? Or is it just an advanced assistant that enhances creativity and decision-making rather than replacing them? 

Generative AI in software development is advancing rapidly, with new tools and models emerging frequently. The goal isn’t to eliminate human developers but to support them by handling time-consuming tasks. AI can generate boilerplate code, identify errors, and even suggest optimizations. However, human oversight is still essential to ensure code quality, security, and maintainability. Developers must adapt to this new landscape, learning to work alongside AI instead of resisting it. The question is no longer whether GenAI coding can mimic human work, (it can!), but how developers can best integrate it into their workflows. 

Top genAI coding tools

GitHub Copilot 

GitHub Copilot, developed by OpenAI and GitHub, is a generative AI-powered coding assistant that suggests code snippets and even full functions as you type. It integrates with popular code editors like Visual Studio Code, Neovim, and JetBrains. Copilot supports multiple programming languages, helping developers speed up their workflow. Pricing: £8/month for individuals, £16/month for businesses. 

This tool is handy for working on large projects where efficiency is key. Instead of manually typing out repetitive code structures, Copilot provides intelligent suggestions based on context. It learns from the codebase and adapts, making it a valuable asset for beginners and experienced developers. While it streamlines development, it isn’t perfect—sometimes, its suggestions may be inaccurate or inefficient. That’s why developers must carefully review AI-generated code before implementation. 

Amazon CodeWhisperer 

Amazon CodeWhisperer provides smart code suggestions based on context. It supports languages like Python, Java, and JavaScript. Its integration with AWS services makes it especially useful for cloud-based development. It can even suggest security best practices. It is free for individuals; enterprise plans start at £15 per user per month. 

A significant advantage of CodeWhisperer is its deep AWS integration, making it ideal for developers building serverless applications, cloud services, or infrastructure-as-code solutions. Security is another primary consideration—AWS includes security recommendations to help developers write more secure code. However, like all AI tools, it requires human validation to prevent errors and vulnerabilities. 

OpenAI Codex 

OpenAI Codex powers GitHub Copilot and can generate code from natural language instructions. Developers can describe what they want in plain English, and Codex generates working code. It supports multiple programming paradigms and helps both beginners and experts. Requires API access, starting at £0.02 per 1,000 tokens. 

Codex represents a significant step forward in natural language processing for coding. It allows non-technical users to generate functional scripts without prior programming knowledge. However, it also raises questions about dependency—if developers rely too much on AI-generated code, they may struggle with understanding complex concepts and debugging issues. Generative AI can assist with coding, but a solid foundation in programming remains essential. 

Tabnine 

Tabnine is an AI-powered autocomplete tool focused on efficiency and privacy. Unlike cloud-based solutions, it runs locally, keeping your code secure. It supports over 20 languages and learns from your codebase, making suggestions that match your style. A free plan is available; a pro plan costs £10/month. 

Tabnine’s local processing provides a significant advantage for teams working on proprietary or confidential projects. Developers don’t have to worry about transmitting sensitive data to external servers. The downside is that local models may not always be as powerful as cloud-based alternatives, but the added privacy is worth the trade-off for many organizations. 

CodeT5 

Developed by Salesforce Research, CodeT5 is an open-source generative AI model for code generation and summarization. It helps with tasks like auto-completion, code translation, and generating comments. Since it’s open-source, developers can fine-tune it to suit their needs. Free and customizable. 

Open-source AI models like CodeT5 offer flexibility that proprietary tools don’t. Developers can train and modify them according to specific project requirements. However, using open-source AI requires more technical expertise and resources, making it more suitable for experienced teams. 

Boost your AI skills with top certifications

AI certifications are becoming increasingly valuable for developers who want to stand out in the job market. Many companies now prefer candidates who have experience with AI-powered tools and machine learning models. As AI evolves, professionals with AI expertise will be in high demand. 

• Microsoft AI-900: Azure AI Fundamentals 
  Covers core AI concepts and Azure AI services. Ideal for developers looking to integrate AI into applications. Cost: £69. 

• IBM AI Engineering Professional Certificate 
  Offers hands-on experience with AI tools, deep learning, and natural language processing. Available on Coursera, starting at £39/month. 

• Google TensorFlow Developer Certificate 
  Focuses on AI model development using TensorFlow and is helpful for developers working with AI-driven code. Costs £80. 

• AWS Certified Machine Learning – Specialty 
  Covers machine learning fundamentals and AI development on AWS. Ideal for AI automation and cloud applications. Cost: £300. 

Challenges and risks of adopting genAI coding  

While Generative AI is helpful, it has limitations. AI lacks deep contextual understanding, so it might generate code that works but doesn’t fit project needs. Security risks are another concern since AI is trained on public code, which may introduce vulnerabilities. There’s also a risk of developers relying too much on AI, weakening their problem-solving skills. Legal issues can arise if AI suggests copyrighted code. Developers must review and refine AI-generated code to ensure security, compliance, and quality. 

Another significant risk is bias in AI models. AI learns from existing code repositories, meaning it can inherit biases, inefficiencies, or security flaws in the training data. AI-generated code must always be evaluated critically to avoid perpetuating these issues. 

The Future of Generative AI in Development 

Generative AI is transforming software development by automating repetitive tasks and boosting productivity. However, it won’t replace human developers. AI enhances efficiency, but creativity, problem-solving, and decision-making require human expertise. The future of AI coding tools will likely focus on improving context awareness, reducing security risks, and offering more personalized suggestions. Developers who embrace AI while maintaining strong critical thinking skills will be best equipped for the future. 

Distilled 

Generative AI is reshaping how developers write and manage code. The industry is adapting fast, from AI-powered tools like GitHub Copilot to certifications that validate AI expertise. But genAI coding should be seen as an assistant, not a replacement. Developers who use AI to enhance their work—while staying sharp and adaptable—will thrive in the evolving tech landscape.