Try for free
The Private AI Lab
The Private AI Lab

#005 - The Why, What, and How of MCP with Maxime Colomès

08 January 2026 1:02:34 Johan van Amersfoort

Listen to episode

About this episode

In this episode of The Private AI Lab, Johan van Amersfoort talks with Maxime Colomès about the Model Context Protocol (MCP)—one of the most important emerging standards in AI today.


MCP is often described as the USB-C of AI: a universal way for AI models to connect to tools, data sources, and real-world systems. Maxime explains what MCP is, how it works, and why its recent donation to the Linux Foundation is such a major milestone for the AI ecosystem.


They explore real-world enterprise use cases, MCP security considerations, private AI architectures, and how MCP integrates with platforms like OpenShift AI. The conversation also touches on developer productivity, AI agents that can take action, and the future of personal, privacy-preserving AI assistants.


Key topics


  • What the Model Context Protocol (MCP) is and why it matters

  • MCP vs traditional APIs and plugin systems

  • Enterprise MCP architectures and gateways

  • MCP and private AI / data sovereignty

  • OpenShift AI and MLOps workflows

  • Security risks and best practices with MCP

  • Community MCP servers and registries

  • Future MCP use cases and predictions

More AI podcast episodes

Browse all →

Want to find AI jobs?

Join thousands of AI professionals finding their next opportunity

We respect your inbox. Unsubscribe at any time.

© 2026 The Private AI Lab. All rights reserved.

Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.

Still have questions?

Contact Support