Vicki Boykis: LLMs Are the Compressed Average of Public Code — Without Fundamentals, You Let the Agent Drive

Machine learning engineer Vicki Boykis made a specific judgment in an interview in October 2026: in a world where agents can generate code, explain papers, and complete most intermediate steps, the reason to learn machine learning fundamentals has changed — not because you will do everything by hand in the future, but because you need to judge whether the agent is doing things correctly and when to stop it.

Her core mechanism is this: LLM training data is primarily public code, and public code accounts for roughly only twenty percent of the world's software output. The internal code that large companies run for distributed workloads, the architecture decisions refined over years — she believes these are most likely not in the training set, though she acknowledges there may be exceptions. So model output is 'the compressed average of what we think good software should be.' If you lack fundamentals, you cannot recognize this average, and therefore cannot judge when you need to deviate from it.

Vicki used a recurring metaphor: are you driving, or is the agent driving? She described her actual workflow — at Malachyte, a New York startup doing real-time search and personalization for e-commerce, she insists on sketching out complicated features on a whiteboard or paper first, then hand-writing entry-point code, and only then letting the agent fill in details. She requires the agent to show its plan without pushing code, reviews piece by piece, and commits herself. She says doing this is 'very hard,' but it is friction she deliberately creates for herself.