Pete Soderling’s Post

All AI problems eventually become search problems, and all search problems ultimately become ranking problems.   This insight from David Karam suggests a fundamental reframing of where real bottlenecks live in your AI/ML systems.   When model quality plateaus, most teams look to obvious solutions: scale up with bigger models, more data, more compute. David argues this misses the mark. The real constraint might exist in your ranking function: are you optimizing for the right things? Are your feedback loops meaningful? And most critically, have you actually seen enough of your domain to represent it well?   David spent a decade at Google architecting massive-scale search & AI systems and these days he’s building modular scoring infra at pi-labs.ai…. safe to say he knows search and AI. His full argument:   https://lnkd.in/gYzzz6TZ