AI tools often produce code that compiles and runs, but contains subtle bugs, security vulnerabilities, or inefficient implementations that may not surface until production. AI systems also lack a true understanding of business logic. They often create solutions that seem to work - but hide issues that aren't found until later. As developers are building solutions, the AI will most frequently cover common solutions but fail on edge cases.
Cybersecurity company Guardio is taking aim at a fresh market born amid this flux: finding malicious code written using AI tools. The company says it has found that with AI tools, malicious actors now find it easier than ever to build scam and phishing sites as well as the infrastructure needed to run them. Now, Guardio is leveraging its experience building browser extensions and apps that scan for malicious and phishing sites.
This isn't hypothetical. In a survey of 450 security leaders, engineers, and developers across the U.S. and Europe, 1 in 5 organizations said they had already suffered a serious cybersecurity incident tied to AI-generated code, and more than two-thirds (69%) had uncovered flaws created by AI. Mistakes made by a machine, rather than by a human, are directly linked to breaches that are already causing real financial, reputational, or operational damage. Yet artificial intelligence isn't going away.
But many engineering teams are noticing a trend: even as individual developers produce code faster, overall project delivery timelines are not shortening. This isn't just a feeling. A recent METR study found that AI coding assistants decreased experienced software developers' productivity by 19%. "After completing the study, developers estimate that allowing AI reduced completion time by 20%," the report noted. "Surprisingly, we find that allowing AI actually increases completion time by 19%-AI tooling slowed developers down."
She called vibe coding a beautiful, endless cocktail napkin on which one can perpetually sketch ideas. But dealing with AI-generated code that one hopes to use in production can be "worse than babysitting," she said, as these AI models can mess up work in ways that are hard to predict. She had turned to AI coding in a need for speed with her startup, as is the promise of AI tools.
I've been for a while now filing issues in the GitHub Community feedback area when Copilot intrudes on my GitHub usage, I deeply resent that on top of Copilot seemingly training itself on my GitHub-posted code in violation of my licenses, GitHub wants me to look at (effectively) ads for this project I will never touch
Nearly 100% of engineers at Robinhood are using AI code editors, making it hard to distinguish between human-written and AI-generated code, according to CEO Vlad Tenev.
