MCP, Agents & the $40M Bet on Multiplayer AI

Stanislas Polu is Co-Founder & CTO of Dust — the enterprise AI agent platform used by 51,000 workers at 3,000+ companies. Before Dust, he spent three years on OpenAI's research team under Ilya Sutskever, working on mathematical reasoning in language models, and prior to that was an engineer at Stripe. He brings a rare combination of frontier AI research and product-building experience to the enterprise agent space.MCP, Agents & the $40M Bet on Multiplayer AI // MLOps Podcast #384 with Stanislas Polu, Co-Founder & CTO of Dust🤖 What is Dust? — How Dust enables teams to build and deploy AI agents powered by internal company data, and why the "multiplayer AI" model is winning in enterprise.🧠 From OpenAI Research to Startup Founder — Stanislas's journey from studying mathematical reasoning in LLMs under Ilya Sutskever to co-founding an enterprise AI company in Paris with Gabriel Hubert.🔗 MCP & Standardization — Why the Model Context Protocol matters, what's trivial vs. what's transformative about MCP, and how Dust integrates MCP-compatible servers for enterprise workflows.🚀 The $40M Series B — What Dust is building with fresh funding, the bet on human-agent collaboration as the future of work, and what "multiplayer AI" actually means in practice.🔄 The Outer-Loop Era — Stanislas's framework for thinking about where AI agents create the most value: not just automating tasks, but rewiring how work gets done across entire organizations.⚠️ What Most Enterprise AI Gets Wrong — The biggest mistakes companies make when deploying AI agents, why adoption fails, and how Dust achieves 70%+ weekly adoption rates.📊 Building Reliable Agent Infrastructure — Lessons from scaling to thousands of companies: observability, governance, data security, and why enterprise AI is harder than it looks.🛠️ Horizontal vs. Vertical AI Platforms — Why Dust chose to build a horizontal enterprise agent platform and how that decision shapes product, go-to-market, and technical architecture.This episode is essential for AI/ML engineers, enterprise AI leads, and anyone building or deploying AI agents at scale inside organizations.🔗 Links & Resources:• Dust: https://dust.tt• Stanislas Polu on X/Twitter: https://x.com/spolu• Dust on LinkedIn: https://www.linkedin.com/company/dust-tt• Dust $40M Series B announcement: https://dust.tt/blog• "The Outer-Loop Era" talk by Stanislas (dotconferences): https://www.youtube.com/watch?v=_outer_loop• Dust + Stripe MCP integration: https://stripe.com/customers/dust• Dust + Datadog observability case study: https://datadoghq.com/case-studies/dust⏱️ Timestamps [00:00] Future of Work[00:19] Dust Scaling Lessons[04:44] Human-Agent Collaboration[14:24] Pod as Workspace[22:30] Work Flow Optimization[29:37] Multiplayer Collaboration Vision[39:55] Token Economics and Inference[47:20] AI Pricing Challenges[52:36] Dust vs Co-work[57:06] Agentic Work Infrastructure[1:04:23] Stateful Sandbox Challenges[1:09:58] Product Use Case Discussion[1:14:05] Agent Data Interaction Needs[1:20:09] Wrap up#EnterpriseAI #AIAgents #Dust