This episode unpacks a decisive shift away from “bigger is always better” toward smarter orchestration. We break down DeepSeek Math v2 — an open‑source mixture‑of‑experts that hit IMO gold using generator‑verifier self‑correction — and explain why step‑by‑step auditing (generator + verifier) matters more than raw scale for reliable reasoning. Then we map Nvidia/University of Hong Kong’s Tool Orchestra case: an 8B orchestrator that delegates to specialists and beats much larger LLMs while cutting compute and latency. On the risk side we surface real operational lessons: vendor breaches (Mixpanel → OpenAI API profiles), the hidden tax of wasted tokens (nearly 18% in some ecosystems), and why single‑vendor, monolithic deployments leak cost and security. Practical wins and workflows follow — from NanoBanana‑style focused image generators to narrow prompting recipes (the songwriter example) and modular “skill” zip files that make brand‑safe automation possible. For marketers and AI practitioners the implications are immediate: prioritize orchestration frameworks, invest in small specialist models and skill packaging, harden vendor contracts and provenance, and measure token efficiency not just raw model accuracy. The central question we leave you with: are you architecting for the giant brain or building the conductor that will actually deliver dependable, auditable outcomes?