Your competitor's AI and your AI use the same brain. That's about to change. In this episode, Stephen Forte unpacks Mistral Forge, the new platform that lets enterprises train custom AI models on their own proprietary data — and why the future of competitive advantage may not be your data, but the model you build on it.

The AI customization spectrum: Off-the-shelf → RAG → fine-tuning → custom training, and why most companies conflate the levels
What Mistral Forge is: A full-lifecycle training platform using Mistral's own production recipes — pre-training, RLHF, synthetic data, MoE architectures, agent-first design
Who's using it: ASML, Ericsson, European Space Agency, DSO Singapore
What it costs today: $160K–$1M+ for implementation, $500K–$5M for a meaningfully custom model. But enterprises report $1M–$50M/year in savings
Cost trajectory: Infrastructure costs dropped 280-fold. Inference declining 10x annually. Today's $1M could be $100K in 2–3 years
The competitive moat: A model that reasons like your best people vs. one that looks things up. That gap compounds over time
The Westlaw analogy: Two firms, same database — but one trained a model on every case they've ever argued

Sources: Mistral AI, TechCrunch, CIO.com, Forbes, BCG, Galileo AI, Counterpoint Research, AeoLogic Technologies