Building on open source AI? Hugging Face’s policy chief on the costs, risks, and trade-offs
Open source models are becoming more mainstream as enterprises, governments and startups alike rely on the promise of lower costs, more control, and more flexibility. But as increasingly capable models become more freely available, how do companies capture value? And with Chinese open models gaining ground, can American players keep up? On this episode of
Open source models are becoming more mainstream as enterprises, governments and startups alike rely on the promise of lower costs, more control, and more flexibility. But as increasingly capable models become more freely available, how do companies capture value? And with Chinese open models gaining ground, can American players keep up?
On this episode of TechCrunch’s Equity podcast, Rebecca Bellan caught up with Irene Solaiman, Chief Policy Officer and SVP of Strategy at Hugging Face, about what startups need to know before building on open models, from choosing the right tools and infrastructure to navigating the trade-offs shaping the future of open AI.
Listen to the full episode to hear about:
- Why startups, enterprises, and governments are switching to open AI models to cut costs, and when it makes less sense to use the most powerful models.
- Who makes money when the models are free to use, and whether the companies building them can capture the value they create.
- Why the U.S. may need deep-pocketed players like Nvidia to build an open frontier model that can compete with China’s, and what’s at stake if Chinese models become the global default.
- How recent attacks on open AI platforms are raising a difficult question: how do you keep an open ecosystem secure without closing it off?
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