Developers want to get back to the fun of coding. Yet, they reportedly spend less than 30 percent of their time on it.
Software developers say they are increasingly pulled into time-consuming and repetitive tasks, such as maintaining, testing and securing existing code rather than writing new code or improving existing code. This means less time spent solving new and interesting problems.
What Are the Benefits of AI in Software Development?
- AI tools are making developers more productive, giving them more time to think creatively and solve complicated problems.
- AI automation can take care of entry-level tasks, so new professionals can do more impactful work sooner.
- AI tools make app building more efficient and accessible.
How to Balance AI and Humans in Development
Imagine how long it would take to get fast internet or cure disease if mathematicians had to manually solve their equations without a calculator.
Similarly, generative AI-powered coding assistants help developers build more complex innovations more quickly. The key word is “help,” because humans have — and always will — drive the evolution of technology.
For developers, AI streamlines nearly all workloads by eliminating the undifferentiated heavy lifting like writing boilerplate code, such as class files and basic operations. It also performs necessary but tedious tasks, such as writing unit tests, documenting what the code does and upgrading to the latest code language versions.
But that doesn’t mean we’ll leave everything to AI.
Areas such as security and responsible AI will never be fully automated, nor should they be. AI is already helping strengthen these practices by automating scans for hard-to-detect security vulnerabilities and filtering out biased, toxic or undesirable code across large volumes of code. Still, there must always be a human in the loop to verify AI’s outputs and use cases.
Developers also need to vet and deploy software and cloud services from providers with a long history of upholding security and privacy best practices. These high-stakes decisions will always be made by humans.
One of the best things that’s come out of AI’s popularization is the hyper-focus on innovating responsibly. Developers, who are closest to the work, are necessary for defining and deploying responsible AI strategy, producing policies, predicting and addressing new types of threats early and reassessing and improving development techniques to ensure security and privacy are at the forefront of innovation.
AI Gives Software Developers Time to Invent
AI-powered tools are increasing developer productivity, which makes experimentation, invention, refinement and innovation easier and faster.
For example, indications show that generative AI-powered work assistant Amazon Q (which is powered by Amazon Bedrock) could help employees be 80 percent more productive. And since Amazon Q Developer has the highest reported code acceptance rates in the industry, developers are 27 percent more likely to complete their tasks successfully.
Amazon Chief Executive Officer Andy Jassy recently shared during Q2 2024 earnings that Amazon developers used its agents for code transformation capability to upgrade more than 30,000 Java applications, saving 4,500 years of development work and $260 million annually from performance improvement.
Developers have more time to think creatively and strategically, which the human brain is particularly good at, and act on their ideas. This means a shorter time from idea to product creation to market, and that velocity is especially valuable to individuals, startups, small businesses and organizations with limited resources.
AI Means Fewer Bored Interns
With AI automating repetitive, low-stakes work that usually gets assigned to interns and entry-level employees, there’s opportunity for new talent to start doing more meaningful, inventive work sooner in their careers.
Developers still need to know how to manually code, and students need to gain this skill when first learning software development. But once the foundational understanding is there, manual coding becomes context for understanding how architectures, systems and applications function.
AI-powered development assistants will encourage deeper learning, as junior talent can ask a plethora of questions to the assistant and get answers and examples instantly. For instance, companies can privately connect their internal data and codebase to Amazon Q Developer so users can receive company-relevant responses for their code and have a conversation about that code.
By getting relevant responses sooner, quick-turn questions don’t block work, and employees are able to go to their managers confidently with more ideas and suggestions and have deeper conversations about their projects.
One’s ability to break in to their first development job will no longer depend on how well they can recall a specific line of code verbatim. Rather, it’ll depend on the creative solutions they can invent and how they use their technical knowledge as a tool to make those ideas reality.
By accelerating the work developers do, AI can also accelerate their career growth.
AI Makes Application Building More Accessible
AI’s influence on tools has expanded who can build applications and how they do it. When embedded in tools, AI advances the capabilities of high-code tools, enabling developers to create with increased complexity, speed, safeguards and alignment to business goals.
AI also reduces the learning curve of software development tools, especially no- and low-code tools like Amazon Q Apps and AWS App Studio, and working with new coding languages. Even natural language prompting is a new way of developing.
What Will AI Unlock for Developers Next?
The next big moments for AI will be customization and more advanced agents. Customizing an AI-powered developer assistant is the difference between a good developer and a tenured developer who knows the business’s specific way to write code.
And as agents become more advanced, AI will become more of a member of the team that developers can assign tasks to and work asynchronously with, whether that’s designing a product architecture, coding a feature, reviewing a pull request or monitoring a running application to make real-time enhancements.
When developers are more productive, they have more time to focus on the quality of their work, and they’re able to create better solutions much quicker, sparking a positive feedback loop of innovation.
AI is not changing what developers are trying to accomplish, but how they accomplish it.
