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AI companies are pivoting from creating gods to building products
Turning models into products runs into five challenges
OpenAI and Anthropic’s DIY approach meant that early adopters of LLMs disproportionately tended to be bad actors, since they are more invested in figuring out how to adapt new technologies for their purposes, whereas everyday users want easy-to-use products. If you think about the success stories of machine learning, like ad targeting or fraud detection or, more recently, weather forecasting, perfect accuracy isn’t the goal — as long as the system is better than the state of the art, it is useful. We caution against purely technical interpretations of privacy such as “the data never leaves the device.” Meredith Whittaker argues that on-device fraud detection normalizes always-on surveillance and that the infrastructure can be repurposed for more oppressive purposes.
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