*Keep this article’s publish date in mind as the capabilities of AI are likely to evolve.
From voice assistants to full-blown autonomous vehicles, artificial intelligence has grown over the years. Now, your developers can use AI tooling to be more productive. Did you know that 88% of developers stated increased productivity using GitHub’s Copilot, an AI pair programmer?
Sure, all these things sound great, but to what extent can AI tooling help you and your teams? To what extent might these tools be inadvertently outsourcing critical thinking? To set realistic expectations around the use of AI development tooling, we have broken down what AI development tooling can and can’t do for you.
AI development tooling CAN’T do the following:
- Generate new ideas
- Replace human decision-making
- Solve all development challenges
This is because AI models can only create content based on their training data and can’t come up with completely new ideas on their own. They can mix existing ideas in new ways, but they can’t generate truly unique ideas because they don’t have personal feelings or experiences. Also, they don’t understand emotions or cultural context, which are important parts of how humans come up with new ideas and get creative.
Benefits
Despite its limitations, your teams CAN use AI development tooling to…
Save Time and Improve Productivity
Your teams could use AI to automate repetitive tasks and generate templated or boilerplate code. Some tools help developers write code faster due to code autocomplete capabilities. This can help your developers concentrate on more important tasks and problem-solving by freeing them up from simple and repetitive work.
Accelerate Learning and Onboarding
Using AI tools can help your teams gain a better understanding of the code they are working on. By asking AI to summarize or explain code snippets or entire files, developers can quickly become familiar with a codebase. This makes onboarding easier for new teammates.
Gotchas
Keep in mind, there are some things to look out for when allowing your teams to use AI, such as:
Plagiarism and Copyright Infringement
Since AI can only generate content based on their training data, your teams may be at risk of plagiarism and copyright infringement. It is important to be cautious and check for plagiarism when using AI tooling. We suggest using helpful tools like Copyleaks.
Data Security and Privacy
AI can’t sign an NDA as an individual can. It is important to remind your teams to remove any confidential information before using AI tools like ChatGPT. Copilot, on the other hand, does not retain code snippet data for training purposes according to their privacy statement, making it a safer option for data privacy.
Keep in mind, most development teams use tools like code linters, vulnerability scanners, cloud providers, and more. The data/code leak risks and security concerns associated with commonly used third-party development tools are similar to the use of AI tools. In both cases, you’re sending potentially sensitive data and/or code to third-party servers. When integrating AI tools, take the same level of caution as you would with third-party apps.
Biased and Unreliable Data
If trained on biased and unreliable data, you may receive inaccurate results. Be sure to have your teams fact-check any code or information generated by AI. We also recommend avoiding the use of AI when writing automated tests. Tests are designed to enforce how code is supposed to behave. If a human isn’t involved in the details of this specification, it’s possible to enforce incorrect (in nuanced and hard to detect ways) behavior.
Key Takeaway
AI is a hot topic right now. Don’t let the excitement of this tech’s potential allow its limitations to hold your organization back. If your development teams have a clear understanding of AI’s benefits and gotchas, they’ll be able to get the most out of the technology.