Large language models (LLMs) have recently revolutionized many aspects of various sectors. Although most of these changes have been positive, resulting in streamlined processes and less time spent on mundane tasks, some have been a double-edged sword.
Falling into the latter category has been the use of AI to aid with coding. Just like Casinos.com seeks out the best sites and games for players, developers are always on the hunt for a better, more efficient way to develop software or code required for other uses.
While AI has helped achieve this, it also has certain downsides that are worth knowing and discussing. If left unaddressed, these could have significant implications for the current generation of developers in the years to come. Join us as we look into this phenomenon and why the convenience of AI may be causing a skill gap that will need to be filled.
Coding With AI
Although almost all of the currently available AI models can produce code to a degree, the three largest competitors in the market excel at it. Each can provide code in various languages and help create anything from a snippet for use on a website to the coding for an entire application.
Google Gemini
Google Gemini is best known for its reasoning capabilities and extensive knowledge bank. These two factors make it particularly adept at coding, which the AI can do in Python, Java, Javascript, and C++. Because of its access to information through the company’s search engine, it is also kept up to date with the latest coding practices, ensuring that all the code it writes follows current techniques.
OpenAI ChatGPT
The original LLM, ChatGPT, has also been able to generate code since shortly after its public launch. While it is better at providing snippets or completing existing functions, the AI can also write extensive code if necessary. Like Gemini, ChatGPT supports multiple coding languages but is primarily used to code in Python. The conversational style of the AI also helps make it more user-friendly for first-time coders or those with less experience.
Anthropic Claude
Claude is significantly less capable at coding than other AIs. However, one thing that attracts coders to the model is its ethos in terms of coding. Like all other factors, Claude sets out to be a responsible AI, meaning that all the code it develops aims to be safe for use and responsibly developed.
Although support is offered for languages like Java, Claude is best with Python—just like ChatGPT.
The Advantages of Coding with AI
Like we mentioned earlier, the influx of AI into various industries has led to many unique benefits and advantages. This is no different with coding. Using the above (or other) models to generate code has distinct perks that are difficult to find elsewhere.
Reduced Development Time
One of the most prominent drawbacks to creating new software or apps is the time it requires. Coding is a lengthy process, and regardless of available libraries, a simple Android app can take up to three months to code. However, this time can be significantly reduced through the use of AI.
Because modern models can generate hundreds (if not thousands) of lines of code quickly, the development times of even sophisticated apps can be dramatically reduced. This enables shorter wait times before launch and can help companies with novel app ideas to enter the market first.
Productivity
In tandem with shorter development times, AI can help increase productivity by automating certain development tasks and suggesting solutions to problems with pre-written code. It can also generate boilerplate code that can be reused in various scenarios.
Using AI to do these things can free up time for developers, allowing them to focus on the more essential aspects of development. It also completes most of the grunt work of programming, again leading to shorter development times.
Improved Quality
The final major advantage of using AI to generate code is the difference in quality between human and AI-generated code. While human-generated code can be of exceptional quality, it is prone to errors, and developers who have been coding a certain way for years may be slow to adopt changes to best practices.
Conversely, AI stays up to date on the latest best practices and requirements for various coding languages. As such, it is able to draft code according to the best option. AI is also less likely to make errors. These factors lead to better code consistency and, overall, better quality code.
The Double-Edged Sword
There is no denying the unique advantages that AI brings to coding. However, there are also disadvantages, some of which can be severe.
Debugging
The first disadvantage is AI’s occasionally questionable ability to debug code. Although some AI models can assist with this process, they may not notice bugs in pre-written code. Even code written by AI can have mistakes that the AI won’t be able to identify, or else the mistake wouldn’t exist in the first place.
Further to this, debugging code written by AI could be an immense task. Due to not understanding the base logic that the AI used when writing the code or because the code is written using new practices means that even an experienced developer may struggle to identify errors.
Security
With around 6.5 cyberattacks each day, the security and integrity of published code are paramount. While understood by AI, this risk may not be fully mitigated in the code it produces—with loopholes or vulnerabilities written into the code that could be exploited.
Relying solely on this code could raise security concerns for new software or apps. Finding these types of issues in code written by AI is also an extensive process, meaning that some developers may not look for them to avoid delays. This could result in businesses using software that potentially exposes them to threats.
Skill Gap
The most significant disadvantage associated with using AI to code is the creation of a skills gap. While this feature of modern LLMs means that almost anybody can create code, developers who rely too heavily on it could may not keep up with coding practices or changes to the code.
Not actively employing their skills in writing foundational code could also cause them to forget the basics and create gaps in their ability to write code. If compounded over a period, this could lead to a sector filled with developers who aren’t able to fully perform their duties, resulting in software that is buggy, insecure, or ultimately unusable.
