Much like flying cars and teleportation machines, the idea of AI has always been synonymous with the future.
But unlike the first two examples, AI exists right now, bringing futuristic technology into the present day, and it’s shaking up industries in ways we never thought possible.
From art to music to writing, artificial intelligence is touching areas that historically have been reserved for the creative spirit of humanity.
Building things with code falls under a similar umbrella—so what about AI for coding? Can you use AI to write code? Or can AI code generation tools help you learn to code yourself?
And a potentially more scary question: is AI going to replace programmers? Will software engineers be needed in the future at all, if robots can just write the code for us?
Well, to put your mind at ease right off the bat, the robots aren’t replacing us and the future of software engineering is safe. Using AI to write code isn’t a job-destroyer, it’s just another tool that programmers can put in their arsenal.
In this post, we’ll give you an overview of how AI coding tools work and how you can use AI for coding your own projects and learning new skills!
Disclosure: I’m a proud affiliate for some of the resources mentioned in this article. If you buy a product through my links on this page, I may get a small commission for referring you. Thanks!
Can AI Write Code?
If you just give it the right prompts, can ChatGPT write code? Or are there any other AI code generation tools where you can just plug in what you want and have it spit out a ready-to-deploy product?
Right now, it isn’t an all-or-nothing answer. Can AI write code? Yes and no.
Using AI to write code isn’t a perfect solution. You can use AI to write code in the sense that you can weave the technology into your process, but it won’t do the whole job for you.
Given the current state of AI, it does have many capabilities that can assist programmers. Let’s learn about some of the top uses of AI for coding in the current landscape.
What is AI currently capable of when it comes to writing code?
As of the time of writing, these are some of the top uses of AI in coding:
1. Code autocompletion
AI-powered code editors use machine learning algorithms to analyze patterns in code. They can predict and suggest code snippets for you, making your coding process faster and more efficient.
This feature not only increases productivity, but can also help you learn best practices and coding conventions as you go.
2. Automated code generation
AI can also generate code snippets or even entire functions based on user prompts. This can accelerate the development process, especially for repetitive or boilerplate code.
3. Code refactoring
AI tools can analyze code and suggest improvements for better readability, performance, or adherence to coding standards. This assists programmers in maintaining clean and efficient codebases.
4. Bug detection and fixes
AI-powered tools can identify and even fix bugs in the code. It can detect potential issues before runtime, helping developers catch and resolve bugs early in the development cycle.
Start coding now
Stop waiting and start learning! Get my 10 tips on teaching yourself how to code.
Will AI replace programmers?
You may have heard claims that AI can generate whole apps for you—which begs the question, will software engineers be replaced by AI? In the near (or far) future, will ChatGPT replace programmers entirely?
Definitely not! Just like factories have machines but still need workers to operate them, AI will likely always need human guidance.
This Redditor sums it up: “Programming is really not about being able to type code. Programming is about being able to mentally deconstruct problems into well-defined algorithmic steps, decide on correct data structures that will model the problem and then turn all this into working code.”
While AI can write some basic code, it can’t write anything super complex, and it can’t think like a programmer.
That’s also why you can’t just jump into using AI for coding before you understand how CS and programming works yourself.
This Redditor explains: “Think of AI as a force multiplier. You have to learn to code first before you can use AI to help you.”
How Do AI Coding Assistants Work?
AI coding assistants work by leveraging machine learning algorithms to analyze and understand programming patterns, languages, and structures.
These tools aim to assist programmers in various coding tasks like the ones mentioned above—completing code automatically, suggesting improvements, and even generating code snippets.
How do programmers use them?
Programmers typically integrate AI coding assistants into their integrated development environments (IDEs), code editors, or the command line. The assistants often work in real-time, providing suggestions, feedback, and autocompletion as programmers write code.
The goal is to enhance productivity, reduce errors, and improve the overall coding experience.
Ultimately, these tools are designed to complement the skills of programmers, rather than replace them.
Should You Use AI to Write Code?
Before you use any new technology, it’s important to educate yourself on its benefits—but also consider any limitations and potential drawbacks. This will help you get clarity on how to use AI to write code, and how not to use it.
Benefits of AI for coding
Let’s start with some of the reasons why using AI to write code can be a good idea for programmers.
⏩ 1. Speeds up the coding process
AI-powered code generation can significantly speed up the development process, automating repetitive tasks and allowing developers to focus on more complex aspects of their projects. You won’t have to spend time typing everything out, looking up exact syntax, etc.
By having AI handle the “busywork,” you can become a more productive coder.
💥 2. Sparks new ideas
When you use AI for coding, the creative process can become a collaboration between humans and machines. By analyzing vast datasets and recognizing patterns, AI can quickly propose innovative solutions that may not have been immediately apparent to human developers.
(However, it’s important that you study and understand these ideas yourself before you implement them!)
🔎 3. Can help you find bugs quickly
One of the most irritating things for developers is not understanding why your code isn’t working… and eventually figuring out you were just missing a comma or a colon or another small detail.
AI tools excel at identifying bugs and errors in code, offering real-time suggestions and corrections. This not only enhances the overall quality of the code but also reduces the time spent on debugging.
🕵️ 4. Helps you explore code more efficiently
AI-powered tools often come with features that allow you to explore and understand new libraries, frameworks, or APIs.
This is especially helpful for anyone learning to code, since it can allow you to dive deeper into tools and functions that are still new to you and understand how to use them.
It can help you make more informed decisions and optimize your code. If you get stuck, you can ask an AI how or why something works (as long as you’re also prepared to verify those facts through outside sources).
Limitations & potential issues with AI for coding
All those benefits sound great—but there are plenty of reasons to be cautious in your use of AI for coding, too.
Here are some of the potential drawbacks devs should be aware of before looping these tools into their workflow.
❌ 1. It can be wrong
AI is trained on human-generated code, so can be flawed, biased, or even flat-out wrong. However, as anyone who has experimented with AI will tell you, it can be wrong with utter confidence. Don’t let that sway you from always testing and verifying the AI’s results.
Plus, AI models like ChatGPT are trained on data that may be a couple years behind. Since tech evolves quickly, that kind of delay can lead to incorrect or subpar solutions.
🤔 2. There’s temptation to use code you don’t understand
While AI can generate code swiftly, that comes with a potential drawback: the temptation to use that code without fully understanding it.
Relying too heavily on AI-generated solutions may lead to developers having only a superficial grasp of the underlying logic.
It’s important to resist the allure of quick fixes and make sure you understand the code before you use it. That also helps to ensure its reliability, maintainability, and adaptability in the long run as you work to maintain or upgrade the software you’re working on.
🔒 3. It can compromise your code’s security
The speed and convenience offered by AI in code generation can inadvertently compromise code security.
Automated tools might prioritize efficiency over robust security practices, introducing vulnerabilities that could be exploited by malicious actors.
In fact, a recent study showed that developers who used coding assistants wrote less secure code, but believed their code was more secure. 😬
That said, you can still write secure code and reap the benefits of AI-assisted coding as long as you don’t blindly trust the AI.
Developers who use AI to write code should remain vigilant, employing additional security measures and conducting thorough code reviews to safeguard against potential risks. Engage thoughtfully with your prompts, making tweaks as needed to improve the results.
👥 4. You may unwittingly plagiarize code
AI models trained on large datasets may learn common code patterns and structures. As a result, the generated code may resemble snippets found in online forums, repositories, or documentation.
Programmers using AI coding assistants might inadvertently accept or use generated code that closely resembles existing solutions without even being aware of the similarity.
Whether accidental or not, this can lead to legal and financial ramifications for the companies involved.
Cross-referencing your AI-generated code with existing code repositories can help you ensure that you’re building an original product.
🐢 5. Incomplete information can lead to inefficient code
AI, though powerful, lacks the nuanced understanding of context and project-specific requirements that human developers possess. Depending solely on AI may result in code that—while it may be functional—lacks optimization for the specific needs of a project.
Devs should focus on using AI-generated code as a starting point and a helpful asset rather than a final solution, to prevent inefficiencies stemming from incomplete contextual awareness.
😕 6. It can become a career crutch
Over-reliance on AI tools can quickly become a career crutch for developers.
While these tools can enhance productivity, you want to avoid becoming so dependent on them that you can’t stand on your own as a programmer.
Especially since if you can’t pass a technical interview (where you probably won’t be allowed to use AI tools), you’ll struggle to get a job in the first place!
It is crucial for developers to strike a healthy balance, using AI as a supportive tool while continually honing their manual coding abilities for a well-rounded and sustainable career in software development.
💔 7. AI tools can crash or break
No technology is infallible, and AI tools are no exception. They can crash or break unexpectedly (unless you’re self-hosting, e.g. Code Llama), which can completely throw off your process if you rely on them.
To mitigate these risks, you should always have contingency plans in place so you aren’t overly affected by technical glitches or failures in AI tools. As long as you know how to write the code and find the bugs yourself, you’ll be in good shape.
How Can You Use AI to Help You Learn to Code?
Besides assisting you with the coding process itself, how can you use AI to help you learn to code? Here are some ideas.
Have it teach you tech concepts
Are you struggling to get a particular concept to click in your brain?
You can ask ChatGPT to teach you a concept in a particular way that works for you, e.g. using a sports analogy or ELI5 (explain like I’m 5—aka super simple!). Or it can summarize guides and help docs for you to extract the information you actually need.
When you read code you don’t understand, paste it in and ask it to explain it to you.
Beyond the popular chat tools like GPT, there are also AI-powered code-learning platforms that use interactive courses and tutorials to help you learn.
Have it give you feedback and test you
AI-driven assessment tools can evaluate your code, pinpointing errors and offering constructive feedback in real-time.
When you get an error, tell it what you’re trying to do and the tech stack you’re using, paste your code in, and ask it what’s wrong. It can help you troubleshoot.
Additionally, these tools can create coding challenges and quizzes, allowing you to test your knowledge and reinforce your understanding of programming principles.
Start coding now
Stop waiting and start learning! Get my 10 tips on teaching yourself how to code.
Have it advise your learning strategy
AI can simulate this experience! Create a coding mentor GPT (or use an existing one) to help you make decisions, e.g. which tech stack to use or which language to learn next. Tell it your goals and ask it to create a learning roadmap and timetable for your journey.
As you dive into creating your own code, your virtual mentor can suggest improvements, alternative solutions, and best practices to smooth out your learning curve.
Have it give you ideas
Last but not least, you can include AI not just in the technical side of the process, but the creative side too! For instance…
- Ask it to generate project or side gig or portfolio ideas for you
- Ask it to generate dummy data you can experiment with (e.g. if you’re learning data analysis)
- Ask it to act as a pretend freelance client, giving you project specs to follow and maybe even coming up with mock complaints for you to address
- Ask it to give you a practice technical interview (you can use ChatGPT’s new voice features to make it feel authentic!)
And if you run out of your own plans, you can even ask the AI to give you more ideas about how to use it to learn to code!
What Does the Future of Programming With AI Look Like?
The future of programming with AI is still a big question mark, but there’s no question that it’s going to become a bigger part of coders’ lives as we move into the future.
It’ll help you write code faster by giving smart suggestions, handling simple busywork code, fixing bugs, and eventually maybe even crafting entire apps based on your ideas.
Learning to code will be more personalized—like having your own coding coach that explains concepts and tailors challenges just for you.
Overall, AI is poised to become a big player in the coding world, making development more efficient, creative, and accessible.
As long as you’re determined to keep learning, keep working, and using it as an assistant rather than relying on it as a crutch, using AI for coding can make you a better, more productive programmer.