Levie Defends the Application Layer

Box CEO Aaron Levie pushes back on the idea that stronger frontier models make the application layer less valuable. He argues the opposite: enterprise AI’s biggest opportunity sits between models and real workflows, where companies still need domain-specific interfaces, data integration, change management, model routing, evaluations and industry-specific pricing. Those layers, he says, create differentiation no single model can replace.

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Aaron Levie

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@levie

As we’re seeing in case study after case study, it turns out that the amount of value that can be created between the AI model and the ultimate end-user workflow is far larger than many people assumed or realized. Model capability is obviously doing a lot of heavy lifting in agentic products, but there’s still a lot more work to diffuse AI into the enterprise. 1. Getting agents to work well (and alongside people) in mission critical workflows tends to need to be represented differently depending on the business process. Sometimes it’s a chat experience. Other times it’s a background agent running in a deterministic workflow. And dozens of other variants. This is a mix of needing a harness that’s tuned to specific domains of work, but also making it show up in the right product experience. 2. Different workflows connect into entirely different enterprise systems and need access to very different data. Working with that data -whether it’s life sciences, financial, legal, etc.- requires contextual approaches, understanding of the data, having the right user experience for data interaction, and more. 3. The need for domain-specific change management remains critical in most verticals. The way you talk and implement technology at a bank is very different from a law firm. Having the right talent with a singular mission ends up being extremely useful for something as complex as process reengineering. 4. The ability to work with a variety of models means you can tune the workflows to different cost and performance levels. And you can eventually post train models for specific tasks to tailor the outcomes and eke out gains that aren’t coming otherwise in frontier models. 5. Evals! AI is basically not useful if it can’t be evaluated. Domain-specific evals that let you dramatically improve the performance of your harness for specific workflows just has a crazy long tail given how many tasks there are in the economy. Nearly impossible for one system to be tuned for all of them. 6. Lots of verticals and domains require pricing models that reflect relevant abstractions on top of tokens alone. The ability to price in ways that work for your industry’s consumption model ends up mattering in a variety of spaces. This just touches on some of the things that go into the applied AI layer. But it all adds up to being a huge surface area for being able to sustainably innovate and differentiate.

3:45 AM · Aug 19, 2026113.4KViews

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