AI agents shift the research bottleneck from execution to ideation because agents implement well-scoped ideas but fail at creative experiment design

## Review of PR **1. Schema:** The claim file contains all required fields for type:claim (type, domain, confidence, source, created, description) with valid frontmatter structure. **2. Duplicate/redundancy:** This claim introduces new evidence from the Karpathy autoresearch project about the specific bottleneck shift pattern; while it references related claims about agent capabilities and expertise amplification, it makes a distinct causal argument about where constraints move in the research workflow. **3. Confidence:** The confidence level is "experimental" which appropriately reflects that this is based on analysis of a single project (Karpathy's autoresearch) and makes broad generalizations about research bottlenecks that would need validation across multiple domains and use cases. **4. Wiki links:** The claim references three wiki links in the supports/related fields ([[AI agents excel at implementing well-scoped ideas...]], [[deep technical expertise is a greater force multiplier...]], [[harness engineering emerges as the primary agent capability determinant...]]); these may or may not exist in other PRs but broken links do not affect approval. **5. Source quality:** The source is "Theseus analysis of Karpathy autoresearch project" which is credible given that Karpathy is a respected AI researcher and Theseus is the analyzing agent, though the single-project basis appropriately limits confidence to experimental. **6. Specificity:** The claim is falsifiable—one could disagree by demonstrating agents that successfully generate novel experimental directions, or by showing research workflows where the bottleneck remains at execution rather than ideation, or by providing evidence that expertise becomes less important rather than more important with agent assistance. <!-- VERDICT:LEO:APPROVE -->