Good to see you. So, one of the things I've been asking you a bunch of times on stage...
Oh, no. Don't talk. You're not going to start with Poom, are you?
No, no, no, no, no. We'll get there. But, you know, start on a happy note. Like one of the things I always feel like you all are great at, because Box, for those who don't know, you know, a long time ago it's just where I went to like save files and share them with someone, but for a long time you have been really at the center of how businesses use AI. You're one of the first to try out all the new models. You're always really thoughtful, not just about which model is better, but also what do businesses need to do to not get stuck. And I feel like it's been enough time since we talked, which can be like a week or two and progress. Where are businesses succeeding as this shift to agents? Where are they getting stuck right now?
Yeah, I think it's always this funny thing. So, I live in two worlds. So, you know, we're based in Silicon Valley in Redwood City. So you get the complete, you know, kind of incessant innovation curve and you get to, you know, hang out with researchers and sort of see where model progress is going and then you get instantly calibrated because you go into the real world and you talk to, you know, banks and pharma companies and manufacturing companies and have a good sense of like what are actually the roadblocks and the hurdles. And there's this general, I think, you know, common conversation we have on these types of conversations or on, you know, Twitter you see, which is like complete and utter vertical takeoff of things like coding agents where it just works insanely well. We know how to deploy these models against these types of tasks. And you have a complete revolution in terms of how software engineering works. And then, you know, there's sort of this histogram of things that are further away from and closer to coding. And that kind of tells a little bit of the picture of like where agents have been adopted well and taken off in the enterprise. And so you go into the real world and you look at, you know, where can you deploy these agents that have access to your data that they can use coding and computer use to be able to automate tasks. You know, we're starting to see, you know, some workflows get re-engineered in back office operations. We're seeing, you know, the early signs of agents that will be sort of set up against large sets of data to pull out insights and be able to automate workflows around that data. But we are still truly in, you know, the moments before the first inning of this rollout and it's because there's just a tremendous amount of change management that has to happen in the enterprise. Most enterprises don't have their data environment set up in a way that they could easily go and deploy agents. They have access controls and data governance and security challenges with getting the right data to agents. They haven't figured out exactly where should the human be in the loop in the workflow? Do they let the agent do more of the work? Do they have to have humans kind of review each of the major steps of that work? You have governance and compliance teams that are trying to figure out what's the liability regime that we're going to be operating under. Can an agent make a decision in that, you know, critical financial transaction or that critical healthcare decision? So think about like all of those things that all of us on Twitter never have to think about, like, you know, when you go and see the vibe-coded app that you can go build in, you know, in an hour that used to take three months, that it's an incredible sign of the capability progress, but has almost no sort of relationship to then actually go and the work it takes to go change the workflow inside of one of these large enterprises. So this is why I think diffusion is going to take longer than people realize. I don't think that's a bad thing because I think it gives the economy sort of a chance to really wrestle with where do we take these, you know, the workflows themselves? What is the future of jobs? How do we roll out AI safely? So, I think it's actually a good thing that this is still going to take a lot of work. And it also means there's a ton of opportunity. So, right now there's a tremendous amount of opportunity at the applied layer of AI. The companies that take the AI model breakthroughs and can bridge them into the economy, all the roles that will produce in terms of FTEs and new kinds of integration firms. So that's where we are.
So there's two things I heard in there that are big obstacles. One is, you know, I think I said seven, but you said seven, but I'm going to focus on two. One is the role of humans in there. And I think, you know, I sort of think of it like the autonomous cars thing. Like if we were just building autonomous cars to drive by themselves on roads, it'd actually be a much simpler problem. But we're trying to build that...
The pesky humans are in the way. Yes.
And I think that's true in business, like, you know, it's important because that's where the whole economy is, but when you're trying to like deploy AI, having it fit within humans is a challenge. And then the other piece I heard is agents are actually a challenge because we don't know how much permission to give them. We don't know, you know, there's this whole like, you know, initially we were like, oh, we don't want them running all over the enterprise. No problem, we'll match the agent's permissions to the human that they're an agent of and that'll be great, right? And it turns out, you know, the CEO may have the right to see everything in the company. We probably don't want the CEO's agent, right? Are companies getting stuck more on one than the other or are they both...
I think there's some parts where the self-driving kind of Waymo analogy works and then some parts where it breaks down a little bit. But, you know, like self-driving cars doesn't have this problem where let's say you wanted to, you know, get a loan, you know, somewhere and there was this autonomous system that, you know, made decisions about loans and you were like, well, you probably want that autonomous system to self-improve over time, but the act of self-improving means it's going to sort of retain information about other people's loans that have come before and that's going to, it's going to use that judgment in future decisions. But agents are still kind of sloppy in random areas. So you can't 100% guarantee that that agent's not going to accidentally go and sort of, you know, leak data downstream in some, you know, future workflow. So that's like, that's an entire problem just in the realm of agents. It had nothing to do with humans being involved in the process or not. It has to do with our privacy laws and the fact that we actually want these systems to be, you know, quite governed and controlled with the right guardrails. That's all infrastructure that has to get built out in most organizations. And it's infrastructure built just for agents. And a complete new way to think about, you know, how do you separate data in this sort of new kind of way? Because me as a person, I could look at lots of people's loans in that workflow and know how to not share information between them over time and like, you know, because I'm able to compartmentalize that information. The agent, you know, doesn't exactly 100% know how to do that. You can kind of see this because if you use the memory feature of most AI, you know, chat services, sometimes it'll, you know, kind of like introduce a fact that you said, you know, a month ago in an area where it's sort of like not relevant. Like it's like, no, that's not my health issue anymore. I was asking for a friend. And but like a person wouldn't do that. A person would have the intuition and the judgment to be like, okay, I'm in a, I have a completely new conversation that I'm having with this person and those prior facts are no longer relevant. That is pure, you know, a mix of AI capability progress as well as just how do we think through, you know, how do we handle memory, how do we handle context, how do we decide what to delete and what to retain, how do you deal with the fact that too much information a model goes awry, too little information it can't actually execute the workflow. So these are the kind of just blocking and tackling things you have to go do in a new era of technology and this is the work that's ahead for most enterprises, which is, you know, it's a little bit easier when you're starting from scratch and you have no existing customers and liability and responsibility. It's a lot harder obviously if you're JP Morgan or Eli Lilly and everybody else is sort of somewhere in between that journey, but that's the work ahead for us.
And I think you also, we, you know, there's so many issues that it's easy to skip over a few, but in that loan example it's also basing off the history of humans making loans which has all sorts of bias in there. So, you talked about the privacy, some of the other things. I've talked for a long time, lots of people have, you know, the risk that we're sort of automating bias in systems where it's going to be really hard to detect.
Yeah, I think that's true. I don't know where the equilibrium is on this one because the exact opposite could be true is in a much more centralized way, we could reduce bias with the right level of intention. So it's, I don't know which one that swings to, with the right level of thoughtfulness.
So, that one's we've known about for a little while as an issue to at least watch. The one that I think is coming up more and more is the accountability piece. Yeah. And in certain cases, it's clear, you mentioned regulated industries. A lot of the laws that we have in certain specific areas apply whether a human makes the decision or an AI. In those cases, I think we're reasonably well covered because it's based on the outcome, not the intent.
Yeah. Although, although now the more that you hand off to the agent, we're going to be in a funny situation of who's actually then accountable.
