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TheBadPlaceNBC News Top Stories | Anyone can code with AI. But it might come with a hidden cost. by Jared Perlo
Anyone can code with AI. But it might come with a hidden cost. Apps and platforms allow novice and veteran coders to generate more code more easily, presenting significant quality and security tradeoffs. Over the past year, AI systems have become so advanced that users without significant coding or computer science experience can now spin up websites or apps simply by giving instructions to a chatbot. Yet with the rise of AI systems powerful enough to translate the instructions into tomes of code, experts and software engineers are torn over whether the technology will lead to an explosion of bloated, error-riddled software or instead supercharge security efforts by reviewing code faster and more effectively than humans.
AI systems don’t make typos in the way we make typos,” said David Loker, head of AI for CodeRabbit, a company that helps software engineers and organizations review and improve the quality of their code. “But they make a lot of mistakes across the board, with readability and maintainability of the code chief among them.” Coding has long been an art and a science. Since the days of coding computer systems by punch cards in the mid-20th century, conveying computing instructions has been a challenge of elegance and efficiency for computer scientists. But inside today’s leading AI companies, most coding is performed by AI systems themselves, with human software engineers functioning more as coaches or high-level architects rather than in-the-weeds mechanics. Anthropic’s head of Claude Code, Boris Cherny, said on X that AI has written 100% of his code since at least December. “I don’t even make small edits by hand,” Cherny said.
The rise of AI-assisted coding — also called vibe coding — is simultaneously allowing people who have never coded before to unleash their creativity and enabling experienced software engineers to dramatically expand the amount of code they write. “The initial push of all this was developer productivity,” Loker told NBC News. “It was about increasing the throughput in terms of feature generation, the ability to build fast and ship things.” Though AI-coding systems have become significantly more capable even since November, they often fail to understand entire repositories of code as fully as experienced human developers. For example, Loker said, “AI coding systems might duplicate functionality in multiple different locations because they didn’t find that that function already existed, so they re-create it over and over and over again.” Now you end up with a sprawling problem. If you update a function in one spot and you don’t update it in the other, you have different business logic in different areas that don’t line up. You’re left wondering what’s going on. With AI coding systems supercharging the amount of code being created, experts wonder whether code will be the next victim of the AI slop onslaught. The concept of AI slop was originally popularized in 2024 as AI systems became capable and pervasive enough to start churning out volumes of low-quality, unwanted AI outputs — from AI-generated photos to unhelpful AI-powered search results. On one hand, AI coding systems are producing vast amounts of serviceable but imperfect code. On the other hand, those same systems are quickly getting better at reviewing their own code and finding security vulnerabilities.
Read more: https://www.nbcnews.com/tech/security/ai-code-vibe-claude-openai-chatgpt-rcna258807
#davidloker #coderabbit #aisystems #softwareengineers #aislop
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