AI Code, Here to Stay

cowboat
July 27, 2025

It’s Here

Code written by large language models is showing up more commonly in company codebases. AI-generated code is no longer a novelty or experimental tool, but is becoming a regular part of how software gets built. From GitHub Copilot autocompletions to ChatGPT-assisted debugging, and now teams using Cursor or Claude Code to have AI architect and code entire applications, developers are increasingly incorporating AI assistance into their daily workflows. What started as occasional help with boilerplate code has evolved into AI contributing substantial portions of many codebases, often without explicit tracking or documentation of its origin.

It’s Easy to Spot (For Now)

Experienced developers can often spot AI-generated code at a glance. It tends to follow certain patterns: verbose variable names, extensive comments, generic error handling, and a preference for well-established libraries and frameworks. These hallmarks are not necessarily bad, since they tend to resemble best practices. At the same time, AI code is often applied more like a blunt instrument than a precision tool. As a result, it often lacks the idiosyncratic shortcuts, domain-specific optimizations, and stylistic quirks that characterize human-written code.

Furthermore, as AI code becomes more prevalent, the mark of software produced by it may itself become obvious to end users. On the positive side, this may manifest itself through generic UX patterns, predictable feature sets, and standardized approaches to common problems. On the negative side, this may look like bland or uninspired interfaces, lack of comprehensive support for edge cases, and lack of depth in features or capabilities.

It Introduces New Risks

While most development teams utilizing AI-generated code require human review before it reaches production, how effective those reviews are can vary. Code reviews often focus on functionality and obvious bugs rather than deep architectural concerns or subtle security implications. Reviewers may be less thorough when examining code that “looks right” and passes tests, even if they know it’s AI-generated. The sheer volume of AI-assisted code can overwhelm traditional review processes, leading to rubber-stamp approvals and accumulated technical debt. Additionally, even well-reviewed code introduces risks to codebases and the teams that manage them due to the homogeneity and qualities of code produced by an LLM.

Common Patterns Create Vulnerabilities

When large language models generate similar solutions to common problems, they create systematic vulnerabilities that attackers can exploit at scale. If thousands of applications use the same AI-generated authentication pattern with the same subtle flaw, a single exploit can compromise numerous systems simultaneously. Even in code that does not have apparent flaws, homogenization of code patterns makes the entire software ecosystem more fragile and predictable to malicious actors.

Usability and Accessibility May Suffer

AI-generated code tends toward generic implementations that work for the most common cases but fail at the edges. This can result in software that feels generic, lacks thoughtful user experience design, and poorly serves users with accessibility needs or edge-case requirements.

Support and Maintenance Challenges

As codebases fill with AI-generated rather than human-written code, deep familiarity with solutions declines and institutional knowledge about why certain decisions were made begins to erode. When bugs emerge or requirements change, teams may struggle to understand and modify code that no human wrote or fully comprehends. This can lead to more expensive maintenance cycles, increased debugging time, and a tendency to replace rather than repair problematic code sections.

Be the Engineer Your Team Needs

Whether we like it or not, AI is here to stay, and it’s bringing major changes to our profession. The risks it presents are real, but engineers shouldn’t fear or reject it. The greater risk is to the quality and output of our profession, not the security of our jobs. We have a generational opportunity to shape the future of our industry for the better, and we should take it seriously. Engineers should focus on developing expertise in AI code review, establishing standards and best practices for AI-assisted development, and creating policies that ensure responsible adoption. This includes developing better prompting techniques, understanding the limitations and biases of different AI coding tools, and creating processes that maintain code quality and security in an AI-augmented environment.

Rather than viewing AI as a threat, engineers should position themselves as the essential human element in an increasingly AI-assisted development process. This means becoming experts in code quality assessment, developing strong architectural judgment, and focusing on the uniquely human aspects of software development: understanding user needs, making strategic technical decisions, and ensuring that software serves its intended purpose safely and effectively. The future belongs to engineers who can effectively collaborate with AI tools while maintaining the critical thinking and domain expertise that only humans can provide.