Scene 2
The code is different.
AI-generated work has a signature. Competent, characterless. This code has opinions. The error handling goes beyond standard spec. The naming conventions suggest someone who cares about readability. Between two functions, a comment: // This is the part where we trust the numbers. The kind of thing a human writes at 2 AM.
As Marcus works, something loosens in his chest. For the first time in months his brain is doing what it was built for.
The task is straightforward. Aion sent him a codebase and the dataset it processes—standard for a code audit. The system is a logistics reconciliation engine. It takes input data about quantities shipped and generates reports confirming everything reconciles at the destination. The data is anonymized—origin codes, destination codes, material categories, quantities. No company names, no industry context. Just branching paths of logic and math. Even the material categories are encoded. Unusual but not an obstacle.
He moves through the codebase the way a mechanic moves through an engine—architecture, logic, edge cases. Clean. Remarkably clean.
“Nice,” he says to an empty room.
He runs the reconciliation module against the dataset. Everything balances. Green across the board.
Marcus doesn’t stop there. His experience with software bugs and cybersecurity taught him the hard lesson that outputs that look right aren’t always what they seem. So he goes deeper and builds his own comparison. Raw inputs against final outputs.
Most fields match to the decimal. One doesn’t.
A single field—anonymized with no label he can interpret—shows a consistent discrepancy. The input quantities say one thing. The final report says something lower. Roughly seventeen percent lower. A process in the code, documented as “normalization protocols,” adjusts the numbers downward before the report is generated. Every other category passes through untouched. This one gets quietly shaved.
The report says everything balances. The raw data says seventeen percent of something isn’t going where it’s supposed to.
Probably a bug. Autonomous systems optimize for clean outputs. If the numbers don’t match, they smooth the numbers instead of raising a flag.
“This is why you need humans in the loop,” he comments to no one.
Satisfied with his work, he writes up the finding and submits it. Ninety seconds later, another notification from the app. Five hundred dollars delivered to his account. He stares at the screen so long, it takes him a full minute just to realize that he’s smiling.