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    The gap between AI models and enterprise workflows

    Box CEO Aaron Levie argues that the opportunity for AI application companies lies in connecting models to business processes, company data and human oversight.

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    TLDR

    Box CEO Aaron Levie argues that improving AI models won’t eliminate the need for software tailored to business workflows. He points to process redesign, gathering relevant data, human oversight, testing for specific business domains, and managing security and governance. As models enable more complex tasks, he says, that software layer may become even more important and useful.

    Combined views

    465K

    2 Sources, first seen 15d ago

    Combined views

    465K

    2 Sources, first seen 15d ago

    430 likes
    15d ago
    first seen 15d ago
    430 likes
    78 comments
    499 saves
    53 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    78 comments
    499 saves
    53 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    2 Sources

    @sonyatweetybirdWell.. @levie and I filmed this episode of Training Data a week or two ago, when the “current thing” was Doug Leone’s novacaine root canals instead of pacing the frontier… Simpler times! But Aaron’s advice on reinventing yourself and your company for AI is timeless. Aaron founded @Box 20 years ago. It sits on hundreds of billions of enterprise files, and he's bet the company on agents that can read every one of them. He's also one of the most wired-in people in AI, on every cap table and, by his own admission, 95% Twitter-educated. He’s the rare CEO who can straddle both the internet AND has the ear of CIOs. His core argument: (1) the gap between what a model can do and what an enterprise workflow actually needs is vast, and closing it is a lot of software; (2) diffusion of AI outside of coding will take far longer than Silicon Valley thinks, and that slowness is exactly where the applied layer's value comes from. The conversation covers: — why application companies are the hottest neolabs, and why the LLM-wrapper thesis is finally working — the fox-guarding-the-henhouse problem with letting model providers route your tokens — work slop, and why we accept AI-written code but flinch at AI-written decks — how Box built its agentic harness and why it beats raw API access on accuracy and latency — the open-weights paradox: closed labs and open models both growing exponentially at once — what continual learning has to solve before it works for a lawyer with five matters and a Chinese wall — why 90% of enterprise tokens in five years will come from tasks no human kicked off — the mandate for founders right now: whoever gets it to the customer wins 0:00 – Introduction 1:55 – Are application companies the hottest neolabs? 6:56 – Will the labs move up the stack? 12:34 – Box and betting the company on AI 16:50 – Hero use cases: reading a million contracts and long-running agents 18:42 – Work slop: why AI code is embraced but AI content isn't 24:08 – Building Box's agentic harness and the evals that matter 27:23 – The state of the model race 29:25 – Open-weight model adoption in the enterprise 32:34 – Memory, continual learning, and what belongs in the weights 37:29 – Box Labs and systems of record in a world of agents 44:55 – Will chat be the dominant UI for enterprise AI? 48:00 – Why coding diffused fast and the rest of knowledge work hasn't 54:31 – Staying wired in, making a company AI-first, and what it takes to win
    @levieThere’s a massive chasm between the power of AI models and the ultimate workflows that enterprises are trying to automate. This gap is the opportunity for the applied AI layer to fill. You need to connect the intelligence to workflows, often reengineer processes, aggregate the right context and data, allow for the right human in the loop experiences, drive change management, do domain specific evals, manage the security and governance of the data and process, and much more. We’re going to see this layer emerge in every vertical and horizontal category. And ironically, even as models improve at incredible rates, this layer still must exist - and may become even more important and useful. Greater capability enables even more complex tasks to be tackled, amplifying the challenges if you don’t do this well. Was super fun chatting with @sonyatweetybird on all the things going into AI diffusion.

    2 Sources

    @sonyatweetybirdWell.. @levie and I filmed this episode of Training Data a week or two ago, when the “current thing” was Doug Leone’s novacaine root canals instead of pacing the frontier… Simpler times! But Aaron’s advice on reinventing yourself and your company for AI is timeless. Aaron founded @Box 20 years ago. It sits on hundreds of billions of enterprise files, and he's bet the company on agents that can read every one of them. He's also one of the most wired-in people in AI, on every cap table and, by his own admission, 95% Twitter-educated. He’s the rare CEO who can straddle both the internet AND has the ear of CIOs. His core argument: (1) the gap between what a model can do and what an enterprise workflow actually needs is vast, and closing it is a lot of software; (2) diffusion of AI outside of coding will take far longer than Silicon Valley thinks, and that slowness is exactly where the applied layer's value comes from. The conversation covers: — why application companies are the hottest neolabs, and why the LLM-wrapper thesis is finally working — the fox-guarding-the-henhouse problem with letting model providers route your tokens — work slop, and why we accept AI-written code but flinch at AI-written decks — how Box built its agentic harness and why it beats raw API access on accuracy and latency — the open-weights paradox: closed labs and open models both growing exponentially at once — what continual learning has to solve before it works for a lawyer with five matters and a Chinese wall — why 90% of enterprise tokens in five years will come from tasks no human kicked off — the mandate for founders right now: whoever gets it to the customer wins 0:00 – Introduction 1:55 – Are application companies the hottest neolabs? 6:56 – Will the labs move up the stack? 12:34 – Box and betting the company on AI 16:50 – Hero use cases: reading a million contracts and long-running agents 18:42 – Work slop: why AI code is embraced but AI content isn't 24:08 – Building Box's agentic harness and the evals that matter 27:23 – The state of the model race 29:25 – Open-weight model adoption in the enterprise 32:34 – Memory, continual learning, and what belongs in the weights 37:29 – Box Labs and systems of record in a world of agents 44:55 – Will chat be the dominant UI for enterprise AI? 48:00 – Why coding diffused fast and the rest of knowledge work hasn't 54:31 – Staying wired in, making a company AI-first, and what it takes to win
    @levieThere’s a massive chasm between the power of AI models and the ultimate workflows that enterprises are trying to automate. This gap is the opportunity for the applied AI layer to fill. You need to connect the intelligence to workflows, often reengineer processes, aggregate the right context and data, allow for the right human in the loop experiences, drive change management, do domain specific evals, manage the security and governance of the data and process, and much more. We’re going to see this layer emerge in every vertical and horizontal category. And ironically, even as models improve at incredible rates, this layer still must exist - and may become even more important and useful. Greater capability enables even more complex tasks to be tackled, amplifying the challenges if you don’t do this well. Was super fun chatting with @sonyatweetybird on all the things going into AI diffusion.