Aaron Levie

This sounds extreme and I wouldn’t have believed it even a year ago, but we’re starting to see plenty of examples of productivity gains that are an order of magnitude or two above output levels pre-AI.

With the right harness, a deep understanding of your domain of what you want to build, likely a relatively new codebase (with some exceptions), a fluency of how to work with multiple agents at once, and a setup that makes it so you can test and deploy with limited friction, outputs on this level feel realistic in coding.

What’s crazy is this gap doesn’t appear to be diminishing over time. You’d think that we’d see a convergence in the lines because of AI, but it turns out the more depth you had before -and willingness now to take a different approach- gives you crazy leverage. And that is only compounding as the models can take on larger work and harder problems.

Right now this is most pronounced in software engineering for the obvious reasons. But there’s no reason we don’t start to see this in a number of other computer-use-sensitive domains like other scientific fields, AEC, finance, cyber, and more. Going to get quite wild.

The interesting thing about AI agents is that as a theoretical matter, their inference can kind of run anywhere. AI is one of the few areas of technology that isn’t *as* sensitive to latency back to the user.

Long running agents are now going to be the norm, which means it’s ok to send off work to an AI factory somewhere and that work can essentially be largely done within that remote data center and a final result spit back to the user. The data can pass into these systems in an encrypted manner, be combed through by agents, and then disregarded right after.

And because the economic value of the task is so high relative to the latency sensitivity (eg getting back $500 in value from an automated task, but it takes an extra second to return), you don’t have to have the inference to be particularly close to the user.

Also, because inference is a temporary process with no sensitive data needing to be stored long term where the inference is happening, you can effectively treat AI factories as ephemeral systems, changing some of the data residency dynamics slightly.

Combined, this means you have more flexibility in where you can begin to deploy AI data centers. This is both a huge economic opportunity for the states or countries (or space) that want to take advantage of this fact. And it’s a loss to the states or countries that decide to forgo the opportunity.

The future is files. And GPUs.

We’re going to have 100X more agents in the enterprise working with our data and systems than even people do today. Every AI agent deployment comes down to how successfully they can get access to the critical context to make a decision, do research, or execute tasks.

90% of the data in an enterprise is unstructured data, which means this critical context is living inside of our documents, contracts, marketing assets, research materials, financial plans, design files, and more. Which means we need effective ways for agents to get access to this critical enterprise content to other work.

We’re excited to partner with NVIDIA to deliver advanced context retrieval -combining lexical and semantic search in Box- to ensure that AI agents can find the most relevant information they’re looking for. All in a way that adheres to their enterprise security, access controls, and data governance policies, so agents only work with the data they’re allowed to.

Great to work with @NVIDIAAI and @JensenHuang on this partnership. Files 🤝 GPUs.