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What Model, Where, At What Cost — The Three Decisions That Define Your AI Stack
Instead of the usual news roundup, this episode walks through the three decisions every technology leader deploying AI in 2026 needs to articulate: which model, where to run it, and which harness wraps it.
The model landscape now includes 7+ serious contenders across 4 countries, with a 36x price spread between frontier and budget tiers. The inference provider market has fragmented into four tiers — direct API, custom silicon (Groq, Cerebras, SambaNova), GPU-optimized (Fireworks, Together), and self-hosted. And the most important finding in AI tooling this year: harness design drives 22% of performance variance, while model selection drives just 1%.
Three worked scenarios show how these decisions compound: AI coding assistants, customer-facing agents, and batch processing pipelines — with real pricing and architecture trade-offs for each.
The episode splits at the 40-minute mark. The first half is the framework for your next board meeting or leadership discussion. The second half is detailed data — model-by-model pricing, provider-by-provider throughput, tool-by-tool comparison — for the technical leads on your team who need to build the evaluation.
Plus: GTC preview, Oracle's $50B infrastructure raise, defense AI hiring data, and the 90-day trajectory for multi-model routing, custom silicon adoption, and harness convergence.
38 sources cited. Full source list in show notes.
AI Disclosure: This episode was produced with AI assistance. Research synthesis and script writing used Claude (Anthropic) under human editorial direction. Audio narration by Microsoft Edge TTS (en-US-AndrewNeural voice).
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By Kris MooreInstead of the usual news roundup, this episode walks through the three decisions every technology leader deploying AI in 2026 needs to articulate: which model, where to run it, and which harness wraps it.
The model landscape now includes 7+ serious contenders across 4 countries, with a 36x price spread between frontier and budget tiers. The inference provider market has fragmented into four tiers — direct API, custom silicon (Groq, Cerebras, SambaNova), GPU-optimized (Fireworks, Together), and self-hosted. And the most important finding in AI tooling this year: harness design drives 22% of performance variance, while model selection drives just 1%.
Three worked scenarios show how these decisions compound: AI coding assistants, customer-facing agents, and batch processing pipelines — with real pricing and architecture trade-offs for each.
The episode splits at the 40-minute mark. The first half is the framework for your next board meeting or leadership discussion. The second half is detailed data — model-by-model pricing, provider-by-provider throughput, tool-by-tool comparison — for the technical leads on your team who need to build the evaluation.
Plus: GTC preview, Oracle's $50B infrastructure raise, defense AI hiring data, and the 90-day trajectory for multi-model routing, custom silicon adoption, and harness convergence.
38 sources cited. Full source list in show notes.
AI Disclosure: This episode was produced with AI assistance. Research synthesis and script writing used Claude (Anthropic) under human editorial direction. Audio narration by Microsoft Edge TTS (en-US-AndrewNeural voice).