How AI is Reshaping the Developer Role—and Why It Matters for Hiring

The rapid rise of AI-powered tools—ChatGPT, Claude, GitHub Copilot, Cursor—has fundamentally changed how developers work. These tools aren’t just productivity boosters; they’re becoming an essential part of modern software engineering.

For hiring teams, this raises an urgent question: How do you fairly and effectively evaluate technical talent in a world where AI is embedded in their workflows? Let’s break it down: how developers actually work, how AI is reshaping their toolset, and what that means for hiring.

Developers Value in an Organization – Problem Solving

Very broadly, software engineers are employed in almost every industry in the world and spend their time building products.  Software engineers work across industries, building products that can involve back-end systems (data processing, integrations), front-end interfaces (user experience), or a mix of both, as full-stack developers. They may also focus on infrastructure, scaling software to meet demand, or newer areas like prompt engineering for AI products.

Developers can also be focused on data processing, or software infrastructure where they focus on building the infrastructure for software to scale with demand.  More recently we’ve seen some software development roles focused on prompt engineering as well – where they spend a significant amount of their time developing prompts for AI products to provide responses to then roll up into software products.

Of course, this is not an exhaustive list, however, it should give you a sense for the breadth of domains a software engineer can operate in.

Exactly how they spend their time varies from role to role and organization to organization, but your generic software developer spends the bulk of their time in these areas: 

• Designing solutions to business problems—the core of great engineering
• Writing and testing code—transforming solutions into functioning software
• Code review and collaboration—ensuring quality through peer feedback
• Planning and architecture—balancing technical strategy with business needs

As their career progresses they likely spend more of their time on design, architecture and planning, and team mentoring, and less time writing code.

The most valuable part of a software engineer’s role in any organization is designing plans, strategies, and algorithms to solve problems directly related to the business goals.  The most fun part about working in software engineering is trying to maximize the amount of time your team spends in this area, because it is the highest value for your users and therefore your business.

Tooling for software engineers has evolved so dramatically over the last 20 years that they spend less and less time working on just getting software to work, test correctly, and deploying it.  The most valuable aspect of their role is solving business problems through thoughtful design and strategy. Advancements in developer tools have reduced the time spent on basic functionality, testing, and deployment—allowing engineers to focus more on impactful problem-solving. Ultimately, the goal is to maximize the time engineers spend driving business value rather than wrestling with technical barriers.

What Tools Do Developers Use to Do Their Job Now?

Over the past 20 years, the software engineer’s toolkit has evolved dramatically — and it will continue to do so. This ongoing evolution is exciting because it allows developers to spend more time solving meaningful problems and less time on repetitive tasks.

Broadly speaking, here are the essential tools most developers use daily:

• An IDE. Integrated development environment.  This is where developers write, compile + run, test, and debug code.  This includes a lot of creature comforts like auto-completing, syntax highlighting, and other things that make developers’ lives much easier.  Most IDEs also include some integration into version control (see below) and can also have build + deploy hooks depending on what tech stack they’re using.
  
  If developers don’t use an IDE – they use more old school tools like vim or emacs to write their code which has less creature comforts but still has features that are similar to IDEs.
• Version control system.  The most popular of these are based on git, either GitHub or GitLab.  This is where developers spend time code reviewing their team’s work, pushing code to be staged for deployment, and in some cases actually instrument deployment.
• Search Engines.  This might come as a surprise, but developers spend a good amount of time researching solutions for problems they are either unfamiliar with or are stuck with on the internet.  Before the advent of the internet (I know, a long time ago), the only refuge developers had was tomes of language references – I still have these and dust them off from time to time for nostalgia.

If any software engineers are reading this, before you come at me with pitchforks this is not meant to be an exhaustive list.  This is meant to be the tools that are most commonly used by all engineers regardless of specialization.

AI Is Augmenting the Developer Toolset

AI is the latest evolution in a long line of developer productivity tools. Just as IDEs improved coding efficiency and GitHub streamlined collaboration, AI is now transforming how engineers approach daily tasks.

Here’s how AI is being woven into the existing developer toolkit:

AI in IDEs: The Rise of AI-Assisted Coding

Some of the most exciting developments in this area is the advent of AI products directly into IDEs.  There are tons of options in the market but my personal favorites (and most popular in the market) are CoPilot by GitHub and Cursor.

GitHub Copilot and Cursor have changed how developers write code by offering intelligent autocompletion, code suggestions, and inline debugging.

• Copilot: Integrates directly into IDEs like VS Code, suggesting code in real-time, reducing boilerplate, and even offering refactoring recommendations.
• Cursor: Expands on Copilot by allowing developers to ask AI inline questions and receive instant modifications to their code.

These tools don’t replace developers—they enhance productivity by eliminating repetitive tasks and helping engineers focus on higher-order problem solving.

AI as a Search Engine Alternative

This is where most AI products are used in the software development process.  

AI tools, especially large language models (LLMs), have become invaluable in software development. Engineers often spend significant time online, researching solutions and consulting documentation. LLMs streamline this process by offering more intelligent, context-aware responses compared to traditional search engines.

By providing context to an LLM, developers can receive aggregated insights from vast online knowledge, reducing the time spent digging through documentation and message boards. This allows teams to focus more on solving complex engineering problems.

For myself and my teams, these products have driven down the amount of time one actually has to poke around the internet researching things.  This then leaves that time you would be spending going through esoteric documentation and message boards to solve hard engineering problems, which is fantastic. However, LLMs aren’t perfect. They generalize information, which can sometimes strip away nuance or lead to inaccuracies. In such cases, traditional research may still be necessary — but LLMs offer a powerful starting point, accelerating the discovery of useful information.

AI as a Data Processing and Query Mechanism

LLMs are powerful tools for processing large data sets and extracting insights. This capability reduces the time engineers spend manually sifting through data, accelerating decision-making and discovery. Some of the most exciting work that’s going on in industry (and also internally at CoderPad) is being able to reduce the amount of work engineers have to do to pull meaningful insights out of large bodies of data.

This functionality extends beyond engineering — we’re already seeing products emerge that leverage LLMs for data processing; some companies are already making products available in the market to do exactly this. A related concept is “prompt engineering,” where crafting precise prompts helps guide LLMs to deliver actionable insights. We’ll explore this topic more in future articles.

How Does a Developer’s Job Change as a Result of AI Powered Technology?

The answer might be obvious.

AI doesn’t drastically change a developer’s core role but enhances it in key areas. It speeds up workflows by integrating into existing toolsets, streamlining search and research, and unlocks new opportunities for data processing, data analysis, and insight gathering. These advancements allow developers to focus more on solving complex, high-value problems.

No, AI Won’t Replace Developers—But It’s Changing How You Should Hire Them

Despite industry hype, AI isn’t eliminating software engineering jobs—rather it’s adding more tools to the developer toolset and potentially makes some repetitive tasks quicker to develop.

These tools still need human oversight, creativity, and strategic thinking to be effective. While AI can generate code, it lacks the critical thinking, strategic problem-solving, and collaboration that real-world development demands.

That said, AI’s influence is reshaping how companies should assess developer candidates, emphasizing adaptability and higher-level thinking.

Developer Toolsets Are Changing – Technical Hiring Needs to Adapt

As AI becomes a standard part of the developer workflow, hiring processes must evolve to reflect this shift. The most effective interviews let candidates use the tools they rely on daily, solving problems relevant to the role.

CoderPad is on the forefront of making sure you can account for these evolving toolsets risks and not miss any critical signals about how candidates will perform on the job. Get a demo and see how we’re integrating AI into our product, as well as enabling candidates and hiring managers to explore AI skills.