Beyond ‘Shift Left’: Improving AI Training Data

The software development world is grappling with a new “engineering productivity paradox.” On one hand, AI-powered coding assistants are generating a staggering volume of code. For example, Google has said that 30% of its code uses AI-generated suggestions. However, the engineering velocity has not seen a proportional jump, with productivity gains being estimated at 10%.

This discrepancy highlights a critical bottleneck: All that AI-generated code must be reviewed, verified and often fixed by human developers. The core issue isn’t the…