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Percona CEO Urges Distinction Between 'Open Weight' and 'Open Source' in AI
Key points
- Percona CEO Peter Farkas spoke at Open Source Summit Europe.
- Farkas argues 'open weight' is not equivalent to 'open source'.
- Open weights provide model parameters, not source code or training data.
- True open source allows trust, improvement, and building upon the model.
Clarifying AI Terminology
At Open Source Summit Europe, Peter Farkas, CEO of Percona and co-creator of FerretDB, requested that the terms 'open weight' and 'open source' not be used interchangeably within the AI industry. He stated that preserving the meaning of 'open source' is crucial, as open weights do not offer the same freedoms.
The Rise of Open-Weight Models
The distinction between these terms is becoming more relevant as open-weight models gain prominence in production AI. In August, these models accounted for 56% of tokens processed via Vercel’s AI Gateway and 60% of US-originating token consumption on OpenRouter, with Chinese-developed models forming the majority.
Understanding Model Weights
An AI model's weights are numerical parameters generated during training, which encode the patterns learned by the model. Releasing these weights allows developers to run a model on their own infrastructure, even if the creator has not released the source code or training data used to produce it. Farkas questions calling this 'open source' when only the output is provided.
The Difference in Trust and Development
James Landay, director at Stanford’s Institute for Human-Centered AI (HAI), supports this distinction. He noted that while open weights answer whether a model can be run, true open source answers whether it can be trusted, improved, and built upon. Landay suggests that major AI labs currently address the former but fall short on the latter.
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Reporting from
Peter Farkas, CEO of Percona, stated that 'open weight' and 'open source' should not be used interchangeably in AI, emphasizing that open weights do not provide the same freedoms as open source. This distinction is important as open-weight models are increasingly used in production AI, yet they lack the transparency of true open-source projects.