The role of a software developer is in flux — and it’s all because of the impact of artificial intelligence (AI). It is now clear that generative AI models and assistants, such as OpenAI’s GPT-4 and Microsoft’s Copilot, are adept at churning out code for any purpose, in any language, almost instantly.
This technology-enabled capacity means software developers will face layoffs. The main debate, at the moment, is “how much?”
Current verdict from industry watchers: So far, so good.
But there is mixed feedback on whether this will help developers succeed or displace many of their roles
It may even serve to smooth the path to application modernization.
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“Generative AI is dramatically transforming the way developers interact with their roles, ushering in a revolution in productivity,” said Joe Welch, its principal and technology leader. Start consultinga division Planet Group. “By incorporating GitHub Copilot into VS Code for a recent project, we’ve seen programmers reduce ten-minute tasks, such as writing a small function, to 30 seconds to write a comment explaining the function. The actual code is because the functions are written by Copilot, and often these functions are Works out of the box with no modifications required. Game changer hard to understate.”
While generative AI tools can replace much of the head-down grunt work of developers, the rise of this technology also opens up opportunities to enhance their role within organizations. In short, layoffs may not be a bad thing in the age of AI and automation — and may lead to new, more interesting roles.
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Right now, the industry is buzzing with the power and productivity that generated AI platforms are bringing to the software development profession. “For many developers, generative AI will become the most valuable coding partner they’ll ever know.” Report. Report Consultant is from KMPG. The technology could ultimately help overworked and stressed IT professionals abstract the more mundane aspects of their jobs and focus on bigger issues more relevant to their business.
At a basic level, this means the ability to deliver larger volumes of project work. The growing use of AI will “make developers more ubiquitous across frameworks, platforms, products and systems of record,” the KPMG report authors indicate. “Generative AI will provide them with the scaffolding and guidance they need to work on a wider range of projects than they would normally be able to handle.”
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But increasing productivity is only the starting point for the future impact of AI and automation on the workplace. Increased adoption of generative AI means developers are expected to work in higher-level roles, integrating AI-distributed resources to map business requirements. “What will become increasingly important is for developers to be able to clearly explain how they want a piece of code to perform,” he says. Mahesh SaptarshiChief Technology Officer of Motorola Solutions.
“A good user story should give the AI the right information to get a desired answer, where to ask the question and report the test results,” Saptarishi said. “Agile methods must adapt as the pace of translating a user story into a feature or product increases. In many ways, the description of what the software should do in the form of a user story can become the new code.”
This shift in emphasis will lead to a layoff that means the actual programming role will decrease and more business-focused developers will focus on integrating the capabilities needed for specific applications.
As technology evolves, “I believe human programming skills will become redundant and eventually be replaced by human-prompted engineers,” Duncan AngoveCEO of Blue Yonder, Forecast.
For its part, Angove reduces the actual programming role, and more business-focused developers assemble the capabilities needed for specific applications. As technology evolves, “I believe human programming skills will necessarily fade away, and eventually be replaced by human-prompted engineers,” he predicts.
“Business analysts and product managers will be the new prompt engineers, translating business needs into prompts that produce the code we need. In the short term, we’ll still need programmers to verify code quality, but over time that too will fade.”
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Of course, some perspective on the scale of these cuts is also important. Developers won’t use AI to write entire applications overnight, Saptarishi said: “AI will help developers do their work faster and make fewer mistakes, and over time, AI will play a bigger role in app development. In a more AI-intensive environment, IT professionals’ creativity, problem-solving skills, and ability to train and explain concepts to others will still play a key role in their success.”
A potential show-stopper for the actual generation of code – versus helping developers be more productive doing it – is the legal ramifications of freely using code that was originally designed elsewhere. “Intellectual property issues around generative AI remain unresolved,” the KPMG authors warn. “These models are trained on open-source code, with a variety of licenses, and it remains to be seen what happens if the software they produce is deemed too similar to open-source code.”
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While the kinds of layoffs for developer roles are highly debated, Welch at Launch holds many positive impacts on developers’ ability to deliver results more quickly and efficiently for their ever-demanding businesses:
- As a recommendation engine: A key benefit would be “integrating AI recommendations into the code development process or providing AI recommendations on code check-in,” he said. “GitHub Copilot is a great example of this and provides recommendations and advice to developers as a type. Developers can also indicate the code they’re trying to write in a specially formatted comment, and Copilot will provide a sample implementation of that function.”
- Creating documentation for existing code to help onboard new developers: “We used AI to provide a top-level summary of the subsystems and then a more detailed description of the individual modules,” Welch said. “After reading these overviews, developers can then directly interact with the AI chatbot to ask detailed questions about usage-specific functions or sections of code. This can greatly reduce the overall time to understand a new codebase.”
- Updated deprecated libraries: “One of our ongoing challenges is updating third-party libraries to supported versions according to appropriate security guidelines,” Welch said “Often, this clears up the level of risk involved in upgrading these libraries. Generative AI helps predict the overall effort, identify specific code patterns that need to be fixed, and ensure that these libraries and frameworks are kept up to date. The least amount of effort and business risk.”
- Migrating applications from legacy languages: “AI can migrate a large codebase from an older language like Cobol to a more modern language like Java or C#,” Welch said. “These migrations can often be challenging because they require developers who are fluent in both the old language and the new language.”
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But let’s be clear: layoffs in software development roles are already underway in the age of AI and automation. Ultimately, opportunities will abound for developers and other IT professionals “to do things that can’t be easily copied or taught,” Angov predicts. “Think about what big language models can’t do, and do it. The value of new thinking becomes even more valuable. Develop skills that help build tools — LLMs themselves — versus now-free applications.”