The article discusses the complex dynamics between enterprise trust and data ownership in the context of AI, particularly among closed and open-source models. It highlights how companies like OpenAI create confusion with their data usage policies, emphasizing that while users own their output, their inputs can be restricted. Steve O'Grady from RedMonk outlines enterprises' hesitance to share internal data due to trust issues with vendors. Ultimately, companies that build strong trust will succeed, and it suggests that enterprises prioritize data management over licensing matters, citing AWS and Microsoft as successful examples.
This isn't really different from Meta's Llama being open to use-unless you're competing at scale.
The heart of the issue is trust and customer control, not open source versus closed source.
Enterprises recognize that to maximize the benefit from AI, they need to be able to grant access to their own internal data.
The vendors that will end up winning will be those that earn customers' trust.
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