Same skills, new tools: why developer fundamentals endure in the AI era | usagoldmines.com

GenAI is being hailed as a revolutionary coding tool. Yes, it creates enormous opportunities for development teams, but we must remember that AI is a junior developer, not an engineer.

The idea that AI will take over app development overlooks a core aspect of a developer’s job. There’s a reason we call them developers or engineers and not code typists. Writing commands has never been the hard part. The challenge lies in ensuring the code solves the problem at hand within the product’s constraints and domain. The current generation of GenAI doesn’t accomplish this.

The developer role is not dead — it’s evolving. With AI assisting with code generation, human developers’ creativity, strategic thinking and contextual understanding will be even more crucial in shaping successful software solutions.

GenAI’s limitations in software development

Developers have long used code templates, generators and auto-complete to expedite programming. GenAI can take these tools one step further by writing entire functions or blocks of code from natural language prompts. However, AI does not fully understand logic and lacks context on business problems and the software’s purpose, resulting in mediocre code.

For example, GenAI can create a code that calculates total sales revenue. However, the output may fail to account for organization-specific variables, such as including returns and rebates in the equation and formatting results to meet reporting requirements. The code technically works, but it does not actually solve the problem.

Additionally, GenAI tools often generate bad code. The training data for the large language models (LLMs) contains both high and low quality data, and the algorithm cannot decipher the difference. Research from Bilkent University measured performance in terms of code quality metrics and found that ChatGPT only wrote correct code 65% of the time, with GitHub Copilot and Amazon CodeWhisperer performing even worse.

AI-generated code can also introduce vulnerabilities and compromise data security by neglecting to follow security protocols. This risk is made more dangerous by many developers’ misplaced confidence in the algorithms.

A Stanford University study found that developers who used AI to write code were more likely to believe it was secure when, in fact, it was less so than teams that were not using an AI tool. These results suggest that programmers may become less vigilant in reviewing their work as a result of relying on AI. More than 90% of security leaders have concerns about using AI in coding, but less than half have policies in place to ensure its safe use.

In light of these challenges, experienced human developers will always be necessary in application development.

What does the developer of the future look like?

Gartner projects that 90% of enterprise software engineers will use AI code assistants by 2028, shifting developers into strategic advisory roles. However, developers’ core responsibilities — maintaining code quality, strategically adapting systems to changing environments and meeting specific project demands — will remain essential.

Developers and engineers will increasingly act as architects who specify high-level requirements and constraints while AI fills in the detailed coding​. This means developers must focus less on writing low-context, low-value code and more on understanding business requirements, system architecture, edge cases and performance testing.

The cooperative relationship between AI and humans could resemble pair programming. AI will play the role of a less-experienced partner performing basic tasks, leaving developers to spend more time guiding and suggesting code improvements.

AI integration might push dev teams to shift further left on traditional code review practices like linting, testing and compliance checks. Since GenAI can produce functional but contextually inaccurate or insecure code, incorporating checks earlier in development allows teams to catch issues proactively. This approach enhances code quality, reduces the risk of errors and maintains consistency.

While GenAI can deliver many benefits, it presents a conundrum for the professional pipeline. With AI functioning as a junior developer, companies may need to hire fewer entry-level developers. This situation limits opportunities for human employees to advance their skills, which results in fewer people equipped to oversee code quality. This scenario remains a problem without a solution — one that needs to be answered soon.

Developer fundamentals will endure

A developer’s value lies in understanding the broader purpose and structure of code, not just in the act of writing it. Fundamentally, GenAI will not alter the skills required for this job, though developers may spend less time with their hands on the keyboard. Critical thinking and adaptability will become even more essential for success. With AI managing the bulk of the tedious tasks, developers must master the skills to instruct and correct AI to achieve the desired outcome.

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