What Happens When AI Can Write Code But Not Explain It?
SMRTR summary
A comment buried in a public GitHub repository says it all: "TODO: Fix the Mess Gemini Created." It's a small, darkly funny artifact of a much larger problem unfolding across the software industry.
AI tools are now generating code at staggering speed. Y Combinator reports that 25% of startups in its Winter 2025 batch had codebases that were 95% AI-generated. But understanding that code? That's falling behind fast.
Google engineering lead Addy Osmani has a name for it: "comprehension debt," which he describes as "the growing gap between how much code exists in your system and how much of it any human being genuinely understands."
The numbers are sobering. Pull requests jumped 20% with AI assistance, but incidents per pull request rose 23.5%. And 45% of AI-generated code introduced known security vulnerabilities.
As former Google engineer Kelsey Hightower puts it: "Automation is the serialization of understanding." The developers who can build with AI and explain what they built, it turns out, may be the most valuable people in any engineering organization.
SMRTR provides this summary for quick context. The original article belongs to Hacker Noon.
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