About this episode
The episode discusses the competitive landscape between open and closed AI models and the complexities surrounding their development and funding.
We’re living through the period of time when we’ll learn if open models can keep up with closed labs. The obvious answer is that no, they won’t. This answer is a form of saying they won’t keep up in every area . This framing closes off a popular prediction where the open models completely catch up , as in all models saturate and open and closed models only become increasingly similar. In living through this, it’s evidently very unclear when the longer-term stable balance of capabilities will solidify. This is a very complex dynamic, where the core point we monitor is a capability gap between models . At the same time, this gap is intertwined with evolving dynamics in the funding of open models, who builds open models, how techniques like distillation that enable fast-following translate through new application domains, potential regulation hampering the open-source AI ecosystem, and of course who actually uses open models. The capabilities gap is one signal in a complex sea of forces, pushing supply and demand into different shapes. In many cases the demand — where obviously tons of individuals, organizations, and sovereigns want, or need, open models — is largely separated from…
People in this episode
Host: Nathan Lambert
Topics covered
- open models
- AI capabilities
- closed labs
- funding dynamics
- business strategies
Keywords
- open models
- closed labs
- capabilities gap
- AI ecosystem
- business strategies
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