Model Commoditization
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The concept of model commoditization in AI is discussed in several ways:
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Continuous Improvement: mentions that to prevent neural net models from becoming mere commodities, ongoing progress and innovation are crucial. Companies need to keep improving their models to make them more reliable and trustworthy. Additionally, specializing in certain areas can be a response to commoditization by offering unique strengths in those fields
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Data and Hardware: touches on the idea that as more sophisticated AI models become available, the two main resources needed—data and GPUs—are becoming commodities themselves. The vast amount of data on the internet and the availability of powerful GPUs enable widespread access to AI capabilities, which could lead to commoditization of models. He also notes the potential for organizations to limit access to proprietary data, which may challenge this trend
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These insights highlight that while commoditization pressures exist, continuous innovation, specialization, and access to unique data can differentiate AI models from one another.
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