Chip emphasizes that post-training—such as reinforcement learning with human feedback (RLHF)—is now more critical than pre-training for differentiating AI models, especially as foundational data sources become saturated. She highlights that fine-tuning should be a last resort, and that evaluation (evals) is essential for refining model behavior, particularly in high-stakes or complex domains. Retrieval-augmented generation (RAG) effectiveness hinges on data quality, not just vector database choice. Contrary to assumptions, top engineers gain the most from AI coding tools like Cursor due to their ability to integrate them strategically. Adoption remains uneven, however, with senior engineers often resistant despite productivity gains. The future of engineering lies in system-level thinking, as AI struggles with holistic context. Ultimately, most AI challenges are not technical but rooted in UX and organizational alignment, requiring cross-functional collaboration and better measurement frameworks to realize true impact.