The Data Exchange with Ben Lorica How a Public-Benefit Startup Plans to Make Open Source the Default for Serious AI
Jun 5, 2025
Manos Koukoumidis, CEO of Oumi Labs and former tech lead at Microsoft, Meta, and Google, shares his vision for unconditionally open foundation models in AI. He argues for transparency in data, code, and processes as essential for trustworthy technology. Discussions include the evolution of community-driven AI similar to Linux, the importance of a robust evaluation system in open-source contributions, and the innovative Halloumi tool for verifying AI claims. Manos emphasizes balancing innovation with safety as a pathway to reliable and accessible AI.
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Defining Unconditionally Open AI
- Truly open AI means sharing data, code, and model weights transparently and reproducibly.
- Open collaboration ease reproducing, extending, and improving foundation models collectively.
Focus on Post-Training Innovation
- Post-training improvements can be the immediate focus for open AI community innovation.
- Pre-training large models collaboratively is a longer-term goal requiring wider cooperation.
Collaboration via Standardized Platforms
- Adopt standardized platforms and benchmarks to evaluate and integrate open contributions effectively.
- Validate that improvements enhance capabilities without regressing safety or other key aspects.