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    Rohan Paul Says AI Hardware Could End Smartphone Attention Demands

    Bengaluru machine learning engineer and newsletter writer shares changing tech purchase standards.

    RP
    1 Source, 27d ago, first seen 27d ago

    TLDR

    Rohan Paul, a Bengaluru-based machine learning engineer, Kaggle Master, YouTuber, and daily AI newsletter author, posted that his criteria for buying technology have shifted over time. He described the core limitation of smartphones as their constant demand for attention, where nearly every task requires pulling out the device, unlocking it, locating content, and interacting with a screen. Paul stated that AI hardware has the potential to break this pattern if a device can operate without those steps.

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    1 Source, first seen 27d ago

    Combined views

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    1 Source, first seen 27d ago

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    1 Source

    @rohanpaul_aiMy criteria for buying tech have changed a lot over the years. The biggest limitation of the smartphone might be that it requires your attention. Almost every useful action begins the same way: pull it out, unlock it, find something, interact with a screen. AI hardware has a chance to break that loop. If a device can understand enough context to know when to listen, capture, surface, or act, the screen stops being the center of the product. Also, a device doesn't need hundreds of apps if it understands the one environment it was built for. That makes specialization much more economically interesting than it was ten years ago. Instead of asking, “How many things can this device do?” I want to ask, “How little interaction does this one job require?” That’s also why something like HojoAI/Hojo-ASR-Multi-V1 from @hojoHQ is worth looking at through a hardware lens. An open multilingual speech layer potentially changes what you can build around a very narrow use case.

    1 Source

    @rohanpaul_aiMy criteria for buying tech have changed a lot over the years. The biggest limitation of the smartphone might be that it requires your attention. Almost every useful action begins the same way: pull it out, unlock it, find something, interact with a screen. AI hardware has a chance to break that loop. If a device can understand enough context to know when to listen, capture, surface, or act, the screen stops being the center of the product. Also, a device doesn't need hundreds of apps if it understands the one environment it was built for. That makes specialization much more economically interesting than it was ten years ago. Instead of asking, “How many things can this device do?” I want to ask, “How little interaction does this one job require?” That’s also why something like HojoAI/Hojo-ASR-Multi-V1 from @hojoHQ is worth looking at through a hardware lens. An open multilingual speech layer potentially changes what you can build around a very narrow use case.