Marc Andreessen discusses AI's 80-year technical trajectory, scaling laws, reasoning models, agents, and edge inference in a long-form conversation. Key technical insights include his perspectives on agents as a Unix-like architecture, edge AI economics, open-source models, and why software bottlenecks may matter more than model improvements going forward.

Comprehensive overview of inference-time scaling techniques for LLMs, covering methods like chain-of-thought prompting, self-consistency, best-of-N ranking, and rejection sampling with verifiers. The author shares practical experimentation results (achieving 15% to 52% accuracy improvement) and categorizes approaches from both academic literature and proprietary LLM implementations, making it directly applicable to deployed systems.