The bias-variance framing here maps well to what I've observed building AI-assisted workflows.

In practice, systematic misalignment (bias) is relatively easy to fix - you identify the pattern and add it to your prompt/context. "Always use our internal auth library" works reliably once specified.

Variance-dominated failures are a different beast. The same prompt, same context, same model can produce wildly different quality outputs on complex tasks. I've seen this most acutely when asking models to maintain consistency across multi-file changes.

The paper's finding that "larger models + harder problems = more variance" explains something I couldn't quite articulate before: why Sonnet sometimes outperforms Opus on specific workflows. The "smarter" model attempts more sophisticated solutions, but the solution space it's exploring has more local minima where it can get stuck.

One practical takeaway: decomposing complex tasks into smaller, well-specified subtasks doesn't just help with context limits - it fundamentally changes the bias/variance profile of each inference call. You're trading one high-variance call for multiple lower-variance calls, which tends to be more predictable even if it requires more orchestration overhead.