Why Every Agent Needs a Box — Aaron Levie, Box | Latent Space

Like you don't write code, you talk to an agent and it goes and does it for you and you maybe at best review it.

That's even probably like, like largely not even what you're doing.

What's happening is we are changing our work to make the agents effective in that model.

The agent didn't really adapt to how we work.

We basically adapted to how the agent works.

All of the economy has to go through that exact same evolution.

Right now.

It's a huge asset and an advantage for the teams that do it early and that are kind of wired into doing this, because you'll see compounding returns.

But that's just going to take a while for most companies to actually go and get this deployed.

Welcome to the Latent Space Pod.

We're back in the Chroma Studio with Chroma CEO, Jeff Hoover.

Welcome, returning guest, but now guest host.

It's a pleasure.

Wow, how'd you get upgraded to that? because he's like the perfect guy to be guest host for you.

That makes sense, actually.

We love context.

We both really love context.

We really do.

And we're here with Aaron Levy.

Welcome.

Thank you.

Good to be here.

Yeah, so we've all met offline and chatted a little bit, but it's always nice to get these things in person in conversation.

You just started off with so much energy.

You're super excited about agents.

I love agents.

Yeah.

OpenClaw just got bought by OpenAI.

Not bought, but you know what I mean.

Some, you know, acquihire-y.

Executive hire.

Executive hire, okay.

Hey, that's my term.

What are you pounding the table on on agents?

You have so many insightful tweets.

Well, the thing that we get super excited about that I think is probably you know should be relatively obvious is we've built a platform to help enterprises manage their files and their corporate files and the permissions of who has access to those files and the sharing and collaboration of those files.

And all of those files contain really, really important information for the enterprise.

It might have your contracts, it might have your research materials, might have marketing information, might have your memos.

All that data obviously has predominantly been used by humans.

But there's been one really interesting problem, which is that humans only really work with their files during an active engagement with them and they kind of go away and you don't really see them for a long time.

And all of a sudden, with the power of AI and AI agents, all of that data becomes extremely relevant as this ongoing source of answers to new questions, of data that will transform into something else that produces value in your organization.

It contains the answer to the new employee that's onboarding that needs to ramp up on a project.

It contains the answer to the right thing to sell a customer when you're having a conversation with them.

Contains the roadmap information that's going to produce the next feature.

So all that data that previously we've been just sort of storing and occasionally forgetting about because we're only working on the new active stuff

All of that information becomes valuable to the enterprise.

And it's going to become extremely valuable to end users because now they can have agents go find what they're looking for and produce new, new value and new data on that information.

And it's going to become incredibly valuable to agents, because agents can roam around and do a bunch of work and they're going to need access to that data as well.

And you know, sometimes that will be an agent that is sort of working on behalf of you and effectively as you and they are kind of accessing all of the same information that you have access to and operating as you in the system.

And then sometimes there's going to be agents that are just effectively autonomous and kind of run on their own and you're going to collaborate and work with them kind of like you did another person.

OpenClaw being the most recent and maybe first real sort of you know kind of you know updating everybody's views of this landscape version of what that could look like, which is okay.

I have an agent, it's on its own system, it's on its own computer, it has access to its own tools.

I probably don't give it access to my entire life.

I probably communicate with it like I would an assistant or a colleague, and then it sort of has this sandbox environment.

So all of that has massive implications for a platform that manage that enterprise data.

We think it's going to just transform how we work with all of the enterprise content that we work with, and we just have to make sure we're building the right platform to support that.

The sort of shorthand I put.

It is as people build agents, everybody's just realizing that every agent needs a box.

And it's nice to be called box and just give everyone a box.

You know, if we can make that go viral, like I think that terminology.

I think that's the tagline, every agent needs a box.

Every agent needs a box.

If we can make the headline of this, I'm fine with this.

That's the billboard.

Yeah, exactly.

Every agent needs a box.

I like it.

Can we ship this?

Like, okay.

My work here is done and I got the value I needed out of this podcast.

But you know.

So the thing that we kind of think about is whether you think the number's 10x or 100x or whatever the number is, we're going to have some order of magnitude more agents than people.

That's inevitable.

It has to happen.

So then the question is what is the infrastructure that's needed to make all those agents effective in the enterprise?

Make sure that they are well-governed.

Make sure they're only doing safe things on your information.

Make sure that they're not getting exposed to data that they shouldn't have access to.

There's going to be just incredibly, spectacularly crazy security incidents that will happen with agents, because you'll prompt inject an agent and sort of find your way through the CRM system and pull out data that you shouldn't have access to.

I mean, this is going to happen all over the place, right?

So then the thing is is how do you make sure you have the right security, the permissions, the access controls, the data governance?

We actually don't yet exactly know in many cases how we're going to regulate some of these agents, right?

If you think about an agent in financial services, does it have the exact same financial sort of requirements that a human did, or is the risk fully on the human that was interacting or created the agent?

All open questions.

But, no matter what, there's going to need to be a layer that manages the data they have access to, the workflows that they're involved in pulling up data from multiple systems.

This is the new infrastructure opportunity in the era of agents.

You have a piece on agent identities, which I think was today, which I think a lot of security people are talking about right.

Basically, I always think of this as like well, you need the human you, and then you need the agent you.

And well, I don't know if this is that simple, but is Box going to have an opinion on that?

Or you're just going to be like well, we're just sort of the storage layer.

Let Okta Officerial handle that.

I think we're going to have an opinion and we will work with generally wherever the contours of the market end up.

And the reason that we're going to have an opinion more than other topics probably is because one of the biggest use cases for why your agent might need an identity is for file system access.

So, so thus we have to kind of think about this pretty deeply.

And I think, unless you're, like in our world, thinking about this particular problem all day long, it might be, you know, like why is this such a big deal?

And the reason why it's a really big deal is because sometimes sort of say well, just give the agent an account on the system and it just treats.

Treat it like every other type of user on the system.

I, as Aaron, don't really have any responsibility over anybody else's box account in our organization.

I can't see the box account of any other employee that I work with.

I am not liable for anything that they do.

And I have strict privacy requirements on everything that they work on.

Agents don't have those properties.

The person who creates the agent probably is going to, for the foreseeable future, take on a lot of the liability of what that agent does.

That agent doesn't deserve any privacy because it can't fully be autonomously operated and it doesn't have any legal responsibility.

So thus You can't just be like oh, I'll just create a bunch of accounts and then I'll kind of work with that agent and I'll talk to it occasionally.

You need oversight of that.

And so then the question is how do you have a world where the agent sometimes you have oversight of?

But what if that agent goes and works with other people and that person over there is collaborating with the agent on something you shouldn't have access to what they're doing?

So we have all of these new boundaries that we're going to have to figure out of.

You know, it's really really easy.

So far, we've been in easy mode.

We've hit the easy button with AI, which is the agent just is you.

And when you're in Cloud Code and you're in Cursor and you're in Codex, the agent is you.

You're offing into your services.

It can do everything you can do.

That's the easy mode.

The hard mode is agents are kind of running on their own.

People check in with them occasionally.

They're doing things autonomously.

How do you give them access to resources in the enterprise and not dramatically increase the security risk and the risk that you might expose the wrong thing to somebody?

These are all the new problems that we have to get solved.

I like the identity layer and identity vendors as being a solution to that, but we'll need some opinions as well, because so many of the use cases are these collaborative file system use cases, which is how do I give an agent a subset of my data and give it its own workspace as well, because it's going to need to store off its own information that would be relevant for it?

And how do I have the right oversight into that?

One thing which I think is kind of interesting about that, you know, how humans work, right?

Like, I may not also just like give you access to the whole file.

I might like sit next to you and like scroll to this like one part of the file and just show you that one part.

Partial file access?

I'm just saying, I think RBAC does seem to be dead.

If you want to say something is dead, probably RBAC is dead.

The auth story to me seems incredibly unsolved and unaddressed by the existing state of AI vendors.

Yeah, I think we're.

I mean you're taking obviously really to the limit that we probably need to solve for.

And we built an access control system that was kind of like you know its own little world for a long time.

And the idea was this it's a many to many collaboration system where I can give you any part of the file system And it's a waterfall model.

So if I give you higher up in the system, you get everything below.

And that kind of created immense flexibility, because I can kind of point you to any layer in the tree, but then you're going to get access to everything kind of below it.

And that mostly is working in this world.

But you do have to manage this issue, which is how do I create an agent that has access to some of my stuff and somebody else's stuff as well?

And which parts do I get to look at as the creator of the agent?

And these are just brand new problems.

And humans, when there was a human there, that was really easy to do.

Like if the three of us were all sharing, there'd be a Venn diagram where we'd have an overlapping set of things we've shared, but then we'd have our own ways that we shared with each other.

But in an agent world, somebody needs to take responsibility for what that agent has access to and what they're working on.

These are, like some of the most probably boring problems for 98 of people on the internet, but they will be the problems that are the difference between can you actually have autonomous agents in an enterprise context that are not leaking your data constantly?

I mean, I run a very, very small company for my conference and we already have data sensitivity issues and some of my team members cannot see the others.

And I can't imagine what it's like to run a Fortune 500 and you have to worry about this.

I'm just kind of curious, you talk to 70, 80% of the Fortune 500 customers.

67%, just so we're being very SEC precise.

Okay, okay.

Something, I'm rounding up.

I appreciate the roundup, but for the government.

I'm projecting to the end of the year.

Okay, thank you, there we go.

Yeah, you do make it sound like, well, we've got to be on this.

We're taking way too long. to get to 80%.

I mean, so how are they approaching it, right?

Because you don't have a final answer yet.

Well, okay.

So this is actually, this is the stark reality that like unfortunately, is the kind of like pouring the water on the party a little bit.

Yes.

We all in Silicon Valley are like, have the absolute best conditions possible for ai ever, and i think we all saw the dworkash, you know kind of dario podcast and this idea of ai coding.

Why is that taken off and and we're not yet fully seeing it everywhere else?

Well like, if you just like, enumerated the list of properties that AI coding has and then compared it to other knowledge work.

Let's just go through a few of them.

Generally speaking, you bring on a new engineer, they have access to a large swath of the code base.

Like there's like very like you just like new engineer comes on, they can just go and find the stuff that they need to work with.

It's a fully text in text out, you know, medium.

It's only it's just going to be text at the end of the day.

So it's like really great from from just, you know, kind of what the agent can work with.

Obviously, the models are super trained on that data set.

The labs themselves have a really strong kind of self-reinforcing positive flywheel of why they need to do agentic coding deeply.

So then you get just better tooling, better services.

The actual developers of the AI are daily users of the thing that they're working in, versus like the.

You know probably there's only like seven clod co-work legal plugin users at anthropic any given day, but there's like a couple thousand clod code and you know users every single day.

So just like think about which one are they getting more feedback on all day long?

So you just go through this list.

Everybody who's a developer by definition is technical, so they can go install the latest thing.

We're all generally online or at least kind of the weird ones are and we're all talking to each other, sharing best practices.

That's like already eight differences versus the rest of the economy.

Every other part of the economy has like six to seven headwinds relative to that list.

You go into a company, you're a banker in financial services, you have access to like a tiny little subset of the total data that's going to be relevant to do your job.

And you have to start to go and talk to a bunch of people to get the right data to do your job, because Sally didn't add you to that deal room folder.

And the information is actually in a completely different organization that you now have to go in and sort of run into.

And it's like you have this endless list of access controls and security.

As you talked about, you have a medium which is not it's not just text, right?

You have you have a Zoom call that you're getting all of the requirements from the customer.

You have a lot of in-person conversations and you're doing in-person sales.

And like, how do you ever digitize all of that information?

You know, I think a lot of people got upset with this idea that the code base has all the context.

That I don't know if you follow.

You know, did you follow some of that conversation that went viral is like.

You know, it's not that simple that the code base doesn't have all the knowledge, but like it's a lot.

You're a lot better off than you are with other areas of knowledge work.

We have documentation practices.

You write specifications.

Those things don't exist for like 80% of work that happens in the enterprise.

That's the divide that we have, which is AI coding has just fully.

We've reached escape velocity of how powerful this stuff is.

Then we're gonna have to find a way to bring that same energy and momentum but to all these other areas of knowledge work where the tools aren't there, the data is not set up to be there.

The access controls don't make it that easy.

The context engineering is an incredibly hard problem because again, you have access control challenges.

You have different data formats.

You have end users that are going to need to kind of be kind of trained through this as opposed to they're adopting these tools in their free time.

That's where the Fortune 500 is.

And so we, I think you know, have to be prepared as an industry, where we are going to be on a multi-year march, to be able to bring agents to the enterprise for these workflows.

And I think probably the thing that we've learned most in coding that the rest of the world is not yet ready for I mean, they'll have to be ready for it because it's just going to inevitably happen is, I think, in coding what's interesting is if you think about the practice of coding today versus two years ago.

Yeah, it's probably the most changed workflow in maybe the history of time, from the amount of time it's changed right.

Like has any workflow in the entire economy changed that quickly In terms of the amount of change?

I just you know, at least in any knowledge worker workflow, there's like very rarely been an event where one piece of technology and work practice has so fundamentally changed what you do.

Like you don't write code, you talk to an agent and it goes and does it for you and you maybe at best review it.

And even that's even probably like, like largely not even what you're doing.

What's happening is we are changing our work to make the agents effective in that model.

The agent didn't really adapt to how we work.

We basically adapted to how the agent works.

All of the economy has to go through that exact same evolution.

The rest of the economy is going to have to update its workflows to make agents effective and to give agents the context that they need and to actually figure out what kind of prompting works and to figure out how do you ensure that the agent has the right access to information to be able to execute on its work.

This is not the panacea that people were hoping for of the agent drops in, just automates your life.

You have to basically re-engineer your workflow to get the most out of agents.

And that's just going to take multiple years across the economy.

Right now.

It's a huge asset and an advantage for the teams that do it early and that are kind of wired into doing this, because you'll see compounding returns.

But that's just going to take a while for most companies to actually go and get this deployed.

I love pushing back.

I think that that is what a lot of technology consultants love to hear this sort of stuff.

Yeah, yeah, yeah.

First to embrace the AI.

To get to the promised land, you must pay me so much money to adopt the prescribed way of conforming to the agents.

And I worry that you will be eclipsed by someone else who says no, come as you are and we'll meet you where you are.

And what was the thing that went viral a week ago?

OpenAI probably is hiring FDEs to go into the enterprise and then Anthropic is embedded at Goldman Sachs.

So if the labs are having to do this, If the labs have decided that they need to hire FTE and professional services, then I think that's a pretty clear indication that there's no easy mode of workflow transformation.

So to your point, I think actually this is a market opportunity, for you know new professional services and consulting firms that are like agent-pilled and they kind of, you know, go into organizations and they figure out how to re-engineer your workflows to make them more agent-ready and get your data into the right format and, you know, reconstruct your business process.

So you're not doing most of the work.

You're telling agents how to do the work and then you're reviewing it, But I haven't seen the thing that can just drop in and kind of let you not go through those changes.

I don't know how that kind of sales pitch goes over.

You're saying things like well, in my sort of nice beautiful walled garden, here's this beautiful box account that has everything.

And I'm like.

Well, most real life is extremely messy and poorly named and there's duplicates and there's outdated shit.

100%.

And so this is actually no.

So this is I mean we agree that getting to the beautiful garden is going to be tough.

There's also the other end of the spectrum where I just like it's a technical impossibility to solve.

The agent truly cannot get enough context to make the right decision in the incredibly messy land.

There's no AGI that will solve that.

So we're going to have to kind of land in somewhere in between, which is like we all collectively get better at documentation practices and having authoritative, relatively up-to-date information and putting it in the right place.

Agents will certainly cause us to be much better organized around how we work with our information, simply because the severity of the agent pulling the wrong data will be too high and the productivity gain that you'll miss out on by not doing this will be too high as well, that your competition will just do it and they'll just have higher velocity.

And we see this a lot firsthand.

So we build a series of agents internally that they can kind of have access to your full box account and go off and you give it a task and it can go find whatever information you're looking for and work with.

And thank God for the model progress.

But if you gave that task to an agent nine months ago, you're just going to get lots of bogus answers because it's going to say hey, here are five, you know, documents that all kind of smell like the right thing.

And I'm going to, but I bet you're you're putting me on the clock because my system prompt says, like you know, be pretty smart, but also try and respond to the user and it's going to respond.

And it's like, ah, it got the wrong document.

And then you do that once or twice as a knowledge worker.

And you just never again, you never again, you're just like done with the system.

Yeah.

It doesn't work.

And so you know Opus 46 and Gemini 31 Pro and you know whatever the latest 53 GPT will be like, those things are getting better and better and it's using better judgment and this sort of like the all of these updates to the agentic tool and search systems are we're seeing.

We're seeing very real progress where the agent kind of can, can almost smell some things a little bit fishy when it's getting.

You know, we have this process where we we have it go fan out to a bunch of searches, pull up a bunch of data and then it has to sort of do its own ranking of you know what are the right documents that it should be working with.

And again, like you know, the intelligence level of a model six months ago would be just throwing a dart at like I'm just.

I'm going to grab these seven files.

And I hope that that's the right answer.

And something like an Opus first four or five and now four or six is like oh, it's like no, that one doesn't seem right relative to this question, because I'm seeing some signal that is contradicting the document, where it would normally be in the tree and who should have access.

It's doing all of that kind of work for you, but it still doesn't work if you just have a total wasteland of data.

It's just not possible, partly because a human wouldn't even be able to do it.

So basically, if a really really smart human could not do that task in five or 10 minutes for a search retrieval type task, your agent is not going to be able to do it any better.

You see this all day long.

This touches on a thing that Jeff's passionate about, which is context engineering.

I'm just going to let you ramble or riff on context engineering if there's anything.

You did really good work on context rot, which has really taken over as the term that people use and the reference.

100%.

All we think about is the context rot problem.

Yeah, there's certainly a lot of ranking considerations.

Gentic surgery is incredibly promising.

Yeah, I was trying to generate a question, though.

I think I have a question right now, Swix.

Yeah no, but I think there was this moment, I don't know, two years ago, before we knew where the gotchas were going to be in AI.

And I think someone was like, well, infinite context windows will just solve all of these problems.

Because you'll just give the context window all the data.

And It's just like, OK, I mean, maybe in 2035, like this is a viable solution.

First of all, it would just it would just simply cost too much.

Like we just can't give the model, like the 5000 documents that might be relevant, and it's going to read them all.

And I've seen enough to start believing in crazy stuff.

So I'm willing to just say, sure, in 10 years from now.

Never say never.

In 10 years from now, we'll have infinite context windows at a thousandth of the price of today.

Let's just believe that that's possible.

But we're in reality today.

So today, we have a context engineering problem, which is I got 200,000 tokens that I can work with.

I don't even know what the latest graph is before like massive degradation.

Okay, 60,000 tokens that I get to work with where I'm going to get accurate information.

That's not a lot of tokens for a corpus of 10 million documents that a knowledge worker might have across all of the teams and all the projects and all the people they work with.

I have 10 million documents. which maybe is times five pages per document or something like that.

I'm at 50 million pages of information and I have 60,000 tokens.

Like holy shit, this is like how do I bridge the 50 million pages of information with, you know, the couple hundred that I get to work with in that, in that token window?

This is like this is like such an interesting problem.

And that's why actually so much work is actually like, just like search systems and the databases, and that layer has to just get so locked in.

But models getting better and importantly, knowing when they've done a search, they found the wrong thing, they go back, they check their work, they find a way to balance sort of appeasing the user versus double checking.

We have this one.

We have this one test case where We ask the agent to go find 10 pieces of information.

Is this the complex work eval?

This is actually not in the eval.

This is sort of just like we have a bunch of internal benchmark kind of scenarios every time we update our agent.

We have one, which is I ask it to find all of our office addresses and I give it the list of 10 offices that we have.

And there's not one document that has this.

Maybe there should be.

That would be a great example of the kind of thing that like, maybe over time, companies start to, you know, have these sort of like what are the canonical, you know, kind of key areas of knowledge that we need to have.

We don't seem to have this one document that says, here are all of our offices.

We have a bunch of documents that have like, here's the New York office and whatever.

So you'd ask this agent and you say, I need the addresses for these 10 offices, okay?

And, by the way, if you do this on any public chat model, the same outcome is going to happen, but for a different kind of query.

You give it, you say, I need these 10 addresses.

How many times should the agent go and do its search before it decides whether or not?

There's just no answer to this question?

Often, and especially the, let's say, lower tier models, it'll come back and it'll give you six of the 10 addresses.

And it'll just say, I couldn't find the other four.

It doesn't know what it doesn't know.

So the model is just like, when should it stop?

When should it stop doing like, should it do that task for literally an hour and just keep cranking through?

Maybe I actually made up an office location and it doesn't know that I made it up.

And I didn't even know that I made it up.

Like, should it just keep?

Should it read every single file in your entire Box account until it exhausts every single piece of information?

Expensive.

These are the new problems that we have.

So you know something like, let's say, a new Opus model is sort of like okay, I'm going to try these types of queries.

I didn't get exactly what I wanted.

I'm going to try again.

At some point I'm going to stop searching, because I've determined that no amount of searching is going to solve this problem.

I'm just not able to do it.

And that judgment is like a really new thing that the model needs to be able to have.

It's like, when should it give up on a task?

Because you just can't find the thing.

That's the real world of knowledge work problems.

And this is the stuff that the coding agents don't have to deal with.

It just doesn't.

You're not usually asking it about.

You're always creating net new information coming right out of the model for the most part.

Obviously, it has to know about your code base and your specs and your documentation.

But when you deploy an agent on all of your data, now you have all of these new problems that you're dealing with.

Our follow-up research to ContextRide is actually on agentic search.

And we've stress-tested frontier models and their ability to search.

And they are not actually that good at searching.

So you're sort of highlighting this explore exploit.

You're just a Debbie Donovan.

Everything doesn't work.

Somebody has to be.

Can I just throw out one more thing that is different from coding and the rest of the knowledge work that I failed to mention.

So one other kind of key point is that, at the end of the day, whether you believe we're in a slotpocalypse or whatever, at the end of the day, If you, if you build a working product, at the end of if you, if you've built a working solution, that is ultimately what the customer is paying for.

Like whether I have a lot of slop, a little slop or whatever.

I'm sure there's lots of code bases we could go into in enterprise software companies where it's like just crazy slop that humans did over a 20 year period.

But the end customer just gets this little interface.

They can, they can type into it.

It does its thing.

Knowledge work doesn't have that property.

If I have an AI model, go generate a contract and I generate a contract 20 times and all 20 times.

It's just 3 different.

And that kind of slop introduces all new kinds of risk for my organization that the code version of that slop didn't introduce.

And so how do you constrain these models to just the part that you want them to work on and just do the thing that you want them to do?

And in engineering, you can't be disbarred as an engineer.

You could be disbarred as a lawyer.

Like you can do the wrong medical thing in healthcare.

There's no equivalent to that of engineering.

Do you want there to be?

Because I've considered- Oh, is that?

Civil engineering there is, right?

Civil engineering, sure.

Oh yeah, for sure.

But, like in any of our companies, you're like.

You know you'll be forgiven if you took down the site and we will do a rollback and you'll be in a meeting, but you have not been disbarred.

As an engineer, we don't change your, you know, your computer science degree.

Yeah, exactly, exactly.

So now maybe we collectively as an industry need to figure out like, what are you liable for?

Not legally but like in a management sense of these agents all sorts of interesting problems that have to come out.

But in knowledge work, That's the real hostile environments that we're operating in.

I do think a lot of the last year's 2025 story was the rise of coding agents.

And I think 2026 story is definitely knowledge work.

Yes, 100%.

And I think open cloud and cloud work are just the beginning.

The next one is just going to be absolute craziness.

It is and it's going to be.

I mean again, this is going to be this wave where we are going to try and bring as many of the practices from coding, because that will clearly be the forefront, which is tell an agent to go do something.

It has an access to a set of resources.

You need to be responsible for reviewing it at the end of the process.

That to me is the kind of template that I just think goes across knowledge work.

And odd coworker is a great example.

OpenClaw is a great example.

You know, you can kind of sort of see what Codex could become over time.

These are some really interesting kind of platforms that are emerging.

Okay, I wanted to, we touched on evals a little bit.

You had the report that you're going to go bring up and then I was going to go into, like the boxes, evals.

But go ahead, talk about your agentic search thing, a few of the insights.

It's like everyone frontier model is not good at search.

Humans have this natural explore, exploit trade-off where we kind of understand like when to stop doing something.

Also, humans are pretty good at like forgetting, actually like pruning their own context, whereas agents are not and actually an agent in their kind of context history.

If they knew something was bad and even you could see in the trace the reasoning choice hey, that probably wasn't a good idea.

If it's still in the trace, still in the context, they'll still do it again.

And so like I think pruning is also going to be like a, really it's already becoming a thing, right.

But like letting myself prune the context windows.

Yeah.

So don't leave the mistake.

Don't leave the mistake in there.

Cut out the mistake, but tell it that you made a mistake in the past.

And so it doesn't repeat it.

Yeah, but like cut it out so it doesn't get like distracted by it again.

Because really, you know what is.

So it will repeat its mistake just because it's been.

It's in the context.

It's in the context so much.

That's a few shot example.

Even if it knows... It's like, oh, this is a great thing to go try.

Even if it doesn't work.

Yeah, exactly.

So there's like a bunch of stuff there.

Oh my God, just Groundhog's Day inside these models.

Okay.

I'm going to go keep doing the same wrong thing.

It kind of makes sense, right?

I feel like you know some crude analogy.

You're trying to like fit a manifold in latent space, which kind of is doing great program synthesis, which is kind of one way to think about how long you're doing right.

Like, you know, certain facts might be like sort of overly pitting it.

There are certain, you know, sectors of latent space.

And so like plug latent space.

Yeah, exactly.

Our editor adds a bell every time you say that.

You have to say that.

You have to remove those links.

You have to remove those links to give it the freedom to do what you need to do.

We'll release more soon.

That's awesome.

That'll be cool.

We're a cerebral podcast that people listen to us and sort of think really deep.

So we try to keep it subtle.

You guys do have you talked about your office thing?

But you've been also promoting Apex agents and complex work.

Whatever you want to take this just how you-.

Apex is obviously in our course kind of agent eval.

We supported that by sort of opening up some data for them around how we kind of see these data workspaces in in the you know kind of regular economy.

So how do lawyers have a workspace?

How do investment bankers have a workspace?

What kind of data goes into those?

And so we, we partnered with them on their, their Apex eval.

Our own eval is, it's actually relatively straightforward.

We have a set of documents in a range of industries.

We give the agent, previously did this as a one-shot test of just purely the model.

And then we just realized we need to, based on where everything's going.

It's just got to be more agentic.

So now it's a bit more of a test of both our harness and the model.

And we have a rubric of a set of things that has to get right and we score it.

And you're just seeing.

You know these incredible jumps in almost every single model in its own family of.

You know Opus 4.

You know Sonnet 46 versus Sonnet 45.

Yeah, we have this up on screen.

Okay, cool.

So you're seeing it somewhere like I forget the.

It was like 15 point jump, I think.

On the main, on the overall,

And it's just like, you know, these incredible leaps that are starting to happen.

And Anthopic doesn't know like any, it's completely held out from Anthopic.

This is not in any, there's no public data, which has benefits.

And this is just a private eval that we do.

And then we just happened to show it to the world.

So you can't train against it.

And I think it's just as representative of You know, it's obviously reasoning capabilities.

What it's doing at you know, kind of test time, compute capabilities, thinking levels, all like the context rot issues.

So many interesting, you know, kind of capabilities that are now improving.

One sector that you have.

That's interesting.

People are roughly familiar with healthcare and legal, but you have public sector in there.

Yeah.

What's that like?

What is that?

Yeah.

And we actually test against, I don't know, maybe 10 industries.

We end up usually just cutting a few that we think have interesting gains.

So OXXR has won a lot of like government type documents.

What is that?

What is a government type documents?

Like a tax return?

Probably not tax returns.

It would be more of what would the government be using as data.

So think about research, that type of data sets.

And then we have financial services for things like data rooms and what would be an investment prospectus.

That one you can dog food.

Yeah, exactly, exactly.

Yes, yes.

So we run the models in now more of an agent mode, but still with kind of limited capacity, and just try and see, like on a like for like basis.

What are the improvements?

And again, we just continue to be blown away by how good these models are getting.

Yeah.

I mean, I think every serious AI company needs something like that, where like well, this is the work we do.

Here's our company eval.

And if you don't have it, well, you're not a serious AI company. there's two dimensions, right?

So there's like, how are the models improving?

And so which model should you either recommend a customer use?

Which one should you adopt?

But then every single day we're making changes to our agents and you need to know- If you regressed.

Yeah.

You know, I've been fully convinced that the whole agent observability and eval space is going to be a massive space.

I'm super excited for what Braintrust is doing, excited for, you know, Langsmith, all the things.

And I think what you're going to.

I mean, this is like every enter, like literally every enterprise, But right now it's like the AI companies are the customers of these tools.

Every enterprise will have this.

You'll just have to have an eval of all of your work.

And like, you'll have an eval of your RFP generation.

You'll have an eval of your sales material creation.

You'll have an eval of your, invoice processing.

And, as you, you know, buy or use new agentic systems, you are going to need to know like, what's the quality of your pipeline?

So huge, huge market with agent evals.

Yeah, and I'm going to shout out your team a bit.

Your CTO Ben, did a great talk with us last year and he's going to come back again for World's Fair.

Just talk about your team, brag a little bit.

I think people take these eval numbers and pretty charts for granted, but no, there's lots of really smart people at work doing all this.

Biggest shout out is we have a couple of folks, Aditya, Siddharth, that kind of run this.

They're like a kind of tag team duo on our evals.

Ben, our CTO, heavily involved, Yasha, head of AI, a bunch of folks.

And evals is one part of the story.

And then just like the full kind of AI an agent team is core to this whole effort.

There's probably I don't know maybe a few dozen people that are the epicenter and then you just have layers and layers of of kind of concentric circles of okay.

Then there's a search team that supports them and an infrastructure team that supports them and it's starting to ripple through the entire company.

But there's that kind of core agent team.

Um, that's a pretty pretty close, uh close-knit group.

The search team is separate from the infer team.

I mean, we have like every layer of the stack we have to kind of do, except for just pure public cloud.

But you know we store I don't even know what our public numbers are in, you know, but like you can just think about it as like a lot of data is stored in Box.

And so you have every layer of the stack of how do you manage the data, the file system, the metadata system, the search system, just all of those components.

And then they all are having to understand that now you've got this new customer, which is the agent.

And they've been building for two types of customers in the past.

They've been building for users and they've been building for applications and now you've got this new agent user and it comes in with a different set of properties, sometimes like hey, maybe sometimes we should do embeddings, an embedding-based kind of search, versus your typical semantics.

It's just like you have to build the capabilities to support all of this and we're testing stuff, throwing things away, something doesn't work and not relevant.

It's like just total chaos.

But all of those teams are supporting the agent team that is kind of coming up with its requirements of what do we need?

Yeah, we just came from a fireside chat where you did, and you talked about how you're doing this.

It's kind of like an internal startup within the broader company.

And the broader company is like 3,000 people.

But this is a core team of like, well, here's the innovation center.

And like every company kind of is run this way.

I want to be sensitive.

I don't call it the innovation center only because I think everybody has to do innovation.

There's a part of the company that is sort of do or die for the agent wave.

Yeah.

And it only happens to be more of my focus simply because it's existential that we get it right.

Yeah, all of the supporting systems are necessary.

All of the surrounding adjacent capabilities are necessary.

The only reason we get to be a platform where you'd run an agent is because we have a security feature or a compliance feature or a governance feature that some team is working on.

But that's not going to be the make or break of of whether we get agents right.

Like that already exists and we need to keep innovating there.

I don't know what the right exact, precise number is, but it's not a thousand people and it's not ten people.

There's a number of people that are like the the kind of, like you know, startup within the company, that are the make or break on everything related to AI agents, you know, leveraging our platform and letting you work with your data.

And that's where I spend a lot of my time and Ben and Josh and Diego and Thierry you know these are just, you know, people that that you know kind of across the team are working.

Yeah.

Amazing.

How do you, how do you think about I mean, you talked a lot about like kind of read workflows over your box data, like you know genetic search questions queries, et cetera but like, what about like write or like authoring workflows?

Yes.

I've already probably revealed too much actually, now that I think about it.

So I've talked about- It's just us.

It's just us.

So I guess I would just I'll make it a little bit conceptual because again, I've already said things that are not even GA, but we've kind of like danced around it publicly.

Yeah.

Okay.

Just like, hopefully nobody watches this episode.

It's tidbits for the highly engaged to go figure out, like what exactly you know is your sort of line of thinking.

They can connect the dots.

Yeah.

So I would say that we you know, as a place where you have your enterprise content.

There's a use case where I want to, you know, have an agent read that data and answer questions for me.

And then there's a use case where I want the agent to create something and use the file system to create something or store off data that it's working on or be able to have, you know, various files that it's writing to about the work it's doing.

So we do see it as a total read-write.

The harder problem has so far been the read only because because again you have that kind of like 10 million to one ratio problem, whereas rights are a lot of that's just going to come from the model and and we just like we'll just put it in the file system and kind of use it.

So it's a little bit of a technically easier problem.

But the only part that's like not gonna say technically hard, it's just like it's not yet perfected in the state of the ecosystem is, you know, building a beautiful PowerPoint presentation is still a hard problem for these models.

Like, like we still, you know, like, like these formats are just, we're not built for working on it.

They're working on it.

Everybody's working on it.

Everybody launches like, well, we can do a PowerPoint now.

Yeah, getting a lot better each time.

But then you'll do this thing where you'll ask update one slide and all of a sudden the fonts will be just a little bit different on two of the slides.

Or it moved some shape over to the left a little bit.

And again, these are the kinds of things that like in code, obviously you could really care about.

If you really care about you know how beautiful is the code but at the end user doesn't notice all those problems.

In file creation, the end user instantly sees it.

You're like, ah, but like paragraph three, like you literally just changed the font on me.

Like it's totally different font and like midway through the document.

Those are the kind of things that you run into a lot of in the content creation side.

So we are going to have native agents that do all of those things.

They'll be powered by the leading kind of models and labs.

But the thing that I think is probably going to be a much bigger idea over time is any agent on any system again using Box as a file system for its work.

And in that kind of scenario, we don't necessarily care what it's putting in the file system.

It could put its memory files.

It could put its you know specification, you know documents.

It could put you know whatever its markdown files are.

Or it could, you know, generate PDFs.

It's just like, it's a workspace that is sort of sandboxed off for its work.

People can collaborate into it.

It can share with other people.

And so we're thinking a lot about what's the right kind of way to deliver that at scale.

I wanted to come into sort of these sort of AI transformation or AI sort of operations things.

One of the tweets that you wanted to talk about.

This is just me going through your tweets, by the way.

You're the easiest guest to prep for because you already have like, this is what I'm interested in.

I'm like, okay, well, Are we going to get to like February, January or something?

Where are we in the timeline?

How far back are we going?

Can you describe Box as a set of skills?

That's like one of the extremes of like.

Well, if you just turn everything into a markdown file, then your agent can run your company.

You just have to find the right sequence of words to to do it.

So I think the question is like what if we documented everything the way that you exactly said, like let's get all the Fortune 500s prepared for agents and, like you know, everything's in golden and nicely filed away and everything?

What's missing?

Like what's left, right?

Like you've run your company for a decade.

I think the challenge is that that information changes a week later.

And because something happened in the market for that customer or us as a company that now has to go get updated.

And so these systems are living and breathing and they have to experience reality and updates to reality, which right now is probably going to be humans know, kind of giving those, giving them the updates.

And you know there is this piece about context graphs as uh, that kind of went very viral yeah, and i, i was like a i, i thought it was super provocative.

I agreed with many parts of it.

I disagree with a few parts around.

You know, it's not going to be as easy as as just if we just have the agent traces, then we can finally do that work, because there's just like there's so much more other stuff that that's happening that that we haven't been able to capture and digitize.

And I think they actually represented that in the piece to be clear.

But like there's just a lot of work, you know that that has to.

You just can't have only skills files you know for your company, because it's just going to be like there's gonna be a lot of other stuff that happens change over time.

Yeah.

Most companies are practically apprenticeships.

Every new employee who joins the team, you spend one to three months ramping them up.

All that casted knowledge is not written down.

It would have to be if you wanted to give it to an agent.

That seems to me to be One is I think you're going to see again a premium on companies that can document this.

There'll be a huge premium on that, because can you shorten that three-month ramp cycle to a two-week ramp cycle?

That's an instant productivity gain.

Can you dramatically reduce rework in the organization because you've documented where all the stuff is and where the answers are?

Can you make your average employee as good as your 90th percentile employee because you've captured the knowledge that's sort of in the heads of those top employees and make that available.

So you can see some very clear productivity benefits if you had a company culture of making sure you know your information was captured digitized, put in a format that was agent ready and then made available to agents to work with.

And then you just again have this reality of like at a 10000 person company.

Mapping that to the access structure of the company is just a hard problem.

It's like, yeah, but not every piece of information that's digitized can be shared to everybody.

And so now you have to organize that in a way that actually works.

There was a pretty good piece.

This, this, this piece called your company as a file is a file system.

I don't know.

Did you see that one?

Nope.

Yes.

You saw it.

Yeah.

And, and I actually be curious your thoughts on it.

Like an interesting kind of like.

We, we agree with it because, because that's how we see the world and

We have it up on screen.

Okay.

But it's all about basically, like you know, we're already organized in this kind of, like you know, permission structure way.

And these are the kind of, you know, natural ways that agents can now work with data.

So it's kind of like this, you know, kind of interesting metaphor.

But I do think companies will have to start to think about how they start to digitize more of that data.

What was your take?

Yeah.

I mean like the company is probably like an acid compliant file system, which I'm guessing boxes.

Right.

So yeah.

Yeah. which you have a great piece on, but.

Yeah, well, my direction is a little bit like, I want to rewind a little bit to the graph word.

You said that that's a magic trigger word for us.

I always ask, what's your take on knowledge graphs?

Because every, especially every data database person, I just want to see what they think.

There's been knowledge graphs, type cycles, and you've seen it all, so.

I actually am not the expert in knowledge graphs.

So you might need to- You don't need to be an expert.

I think it's just like, well, how seriously do people take it?

Is there a lot of potential in the HOBI?

Well, can I understand?

First, is this a loaded question in the sense of are you super pro, super anti medium?

I see pros and cons, but I think your opinion should be independent of mine.

Yeah, no, no, totally.

I just wanted to see what I'm stepping into.

And it's a huge trigger word for a lot of people in our audience.

And they're trying to figure out.

Why is this such a hot item for them?

Because a lot of people get graph religion.

And they're like, everything's a graph.

Of course you have to represent it as a graph.

Well, how do you solve your knowledge changing over time?

Well, it's a graph.

And I think there's that line of work.

And then there's a lot of people who are like, well, you don't need it.

And both are right.

And what do the people who say you don't need it, what are they arguing for?

Markdown files.

Simplicity.

It's structure versus less structure, right?

I think the tricky thing is again when this gets met with real humans.

They're just going to their computer.

They're just working with some people on Slack or Teams.

They're just sharing some data through a collaborative file system and Google Docs or Box or whatever.

I certainly like the vision of most knowledge graph, you know kind of futuristic kind of ways of thinking about it.

It's just like, you know, it's 2026.

We haven't seen it yet kind of play out as, as I mean, I remember, Do you remember in?

Like actually I don't even know how old you guys are, but to show my age, I remember 17 years ago everybody thought enterprises would just run on wikis.

And not even I mean Confluence actually took off for engineering for sure, like unquestionably, but like this was like everything would be in the wiki.

And I think, based on our general style of what we were building, we were just like I don't know.

People just want a workspace.

They're going to collaborate with other people.

Exactly.

So you were anti-knowledge graph.

Not anti, not anti.

You were not non-knowledge graph.

I'm not into it because I think your search system.

I just think these are two systems that probably I'm not in any religious war.

I don't want to be in anybody's YouTube comments on this.

This is not a fight for me.

We love your two comments.

Get in the comments.

But like, it's mostly just a virtue of what we built and we just continued down that path.

And that was what we pursued, but I'm not, this is not a, you know, kind of, this is not a-

We're happy to plug into somebody else's graph.

We're happy to feed data into it.

We're happy for agents to talk to multiple systems.

Not our fight.

But I need your answer.

Graphs are nerd snipes.

It's a very effective nerd snipe.

See, this is one opinion and then I've.

I think that the actual graph structure is emergent in the mind of the agent, in the same way that it is in the mind of the human.

And that's a more powerful graph because it actually can evolve over time.

Don't tell me how to graph.

I'll figure it out myself.

Exactly.

Okay, all right.

And what's yours?

I like the wiki approach.

I'm actually like, you know, obviously I spend so much time in cognition, which you know very well.

And they've had a lot of success with deep wiki.

It empowers a lot of dev and brain.

Super powerful.

And it's useful for humans, but it's, oh my God, it's useful for agents.

Yes.

Tell me if you think I'm wrong on this, but not much of an access control structure issue.

It's like the whole.

You get the whole code base and everybody gets-.

Before I speak too much, there may be some enterprise controls on the enterprise deep wiki offering that I'm not familiar with.

But yeah, I don't have any- anything on the public side.

But yeah, I think, like almost like every agent should have its own Wiki that it's updating and that's persistent memory.

And that is a very weak knowledge graph and you could strengthen it if you want more structure, but you may not need it.

Markdown files having links and Wiki style.

Right.

Yep.

Very effective.

Right.

Lindy.

Yep.

I like that as a, as a general pattern.

Okay, so last couple of questions, but feel free to jump on in or if you want any rants.

I see you as a very interesting and unusual founder, where you've been in a business and you're of two worlds.

You're of Silicon Valley, but you're also of the Fortune 500s.

And I feel like your kind of founder mode is very different from the Brian Chesky founder mode.

And I'm just kind of curious if you have reflections on how you operate as a founder.

What would his banner mode be?

Don't delegate.

Ah, right.

And how would you put me?

You do delegate.

Ah, okay.

I see.

I think that I don't know that Brian and I would be that far removed from each other when you get to the specifics.

Okay.

So there's a whole bunch that I delegate.

90 of the work that happens at Box is fully know, fully delegated.

We've got great leaders running, running all that stuff.

It's just too much for my brain to handle.

And probably 70 of the work.

I'm going to make up all the numbers here.

Probably 70 of the work at box or 70 to 80 of the working box.

I only need to really look at about 5 of that for like, some high leverage decisions to be involved in.

You know what's the marketing message that we think is going to resonate with with customers.

So that's a little bit of high leverage thing, that that that we do in marketing.

But most of marketing activities I don't get involved in.

What's our sales pitch?

Maybe I'll be involved in that a little bit, or like what's roughly the investments or push we're going to do in certain verticals.

You know that's about 5 of like the total bandwidth of you know this, the key areas of sales or go to market.

Okay.

70, 80% of the company, I can just do about 5%.

And then just like operationally, we've got great leaders and they're going to execute on that.

And we collaborate on the 5% anyway.

It's not like I'm just like making up a decision and saying to go and do it.

Then there's this part that is like the existential part of the business, which is if we don't do this right, We're out of business.

And by virtue of just being a founder, you get kind of sucked into that part of the work because you can feel it like.

This is like like you can just see how the tsunami could wipe you out.

If you make just two three, four or five wrong decisions in this space, like a couple wrong architecture decisions, a couple wrong AI feature decisions, couple wrong API platform decisions, and you might be out of the game in a year from now.

And you just feel it in your bones.

You know this?

It's just like, we feel this all day long in this space, given what's happening.

And so in that area, You can't kind of delegate in a classic sense.

You still need to make sure you've got great leaders and strong hires and people that have high agency because they want to be able to own part of the strategy and the roadmap, or else you can't hire good people.

But you know there's going to be a lot of little micro forks in the road that they will compound to determine whether you succeed or fail.

And so your kind of founder energy, just like, automatically draws you into those, because they are the determining decisions of your company's future.

And that's kind of where I spend my time.

And you have to, kind of you know, do it in a collaborative way again, because if you are only dictatorial and just you know, you just won't eventually be able to hire the best people because they won't want to work on that environment.

But you also just can't like abdicate all the responsibility, because the risks are just simply too high.

And so you have to somehow obviously add some value.

And so the value I add is I've seen 20 years of this business.

So I think I can kind of piece together what I expect the value propositions are going to be and how customers will react to certain things.

So that's what I can bring to the table.

And then you have this kind of existential fear of, if I get it wrong, it's all on me anyway.

I don't get to blame the engineer that was working on that project.

It's my fault, right?

At the end of the day, it'll be my fault if it doesn't work.

By virtue of that liability responsibility, you just get pulled into needing to make sure like it's all going according to kind of how you think it needs to end up.

I don't know how Brian would answer that, I guess, but like, yeah.

It's a long essay.

It's an interesting essay.

People should go and compare and contrast your answer versus his.

I do think that systems have a way of letting entropy get to them.

And if you step away for too long, you need to have a way to check in and go.

Well, do I need to come back in, or are we good?

And people are going to tell you things are good, but they're not good.

Yes.

100%.

Yeah.

And that's actually, I'm a fan of actually process for that 70% to 80%.

So that 70 to 80 the process is.

You're going to do a quarterly business review and you're going to have a brand check-in and you're going to do those.

You're going to make sure that you're seeing all the right episodes of what's changing and how it's evolving and make sure it's going the right direction.

And then there's some areas where it's just like, no, it's 24-7.

I guarantee, after this podcast, at 11 pm, I'll be doing a Zoom with Ben and probably some other people, because we're going to be talking about agents and new platform features.

And like that's, you're just in the cauldron, you know, kind of grinding on that side.

Yeah.

That's extremely realistic as to what it's like.

And I just want to have people hear your perspective on what it is.

And this is this like you read the post about, you know everybody having agents running in the weekend.

And it's like you know, you just, I mean first of all anybody crazy enough to come to Silicon Valley.

Like we don't bring good news about the sort of like healthiness of our environment right now.

Like, you have to- and you have to know what you're signing up for.

But like, you know, there's a real issue, which is like, shoot, do I have enough agents running?

And- Oh yeah, I made a meme that was like semi-viral for me.

Yes, that was incredible.

You can't even enjoy a party these days because you're working with your tokens.

You just compute out there that you're not utilizing.

What the hell?

I paid for the $200.

I'm going to spend the $200.

I'm going to spend $6,000 out of the $200.

We need to make Anthropic very unprofitable.

Yeah, we're not doing a good enough job.

Cool.

I have a closing question.

I have a question.

I've asked this question in private before, but I'll ask it again.

It's a question that Tyler Cowen asks his guests on his podcast, which is what is the Aaron Levy production function?

I love this question because there are so few people that I think are good at both executing but also like distilling and like just putting good ideas into the ether.

You put a lot of good ideas into ether.

And so, like what is the Aaron Levy production function that allows you to do that versus others?

How do I get that information?

Or- I can give you a variant which is what goes into Airbnb and what goes out and how does it turn inside.

I'm just trying to think of cause.

I mean, you know, there's some very like.

I just read a lot of Twitter as well.

And so like, I just- And you've spent a lot of effort too.

You don't see like great mini essays from Brian Chesky every day.

But you do from you.

And you're kind of weird in that way.

So why?

Maybe he's healthier than me, actually.

We should just text him to see if he's got a- I think he does work out.

He's got bigger muscles.

Well, that's the thing.

I work out less than him.

And I tweet more than him.

So that's how we're balancing things out.

Mostly, the way I just think about it is just there's lots of work that's happening in the business.

I'm getting to see all the problems that we are running into constantly.

And I'm trying to be a little bit of a create a flywheel between what we're doing internally.

What then?

We talk about getting a feedback loop on that and seeing other people's you know experiences of what they're doing.

Bring that back into the business.

And so I just see like my job is, as you know, hopefully being able to kind of connect the dots of what's going on in the world with what's going on in Box.

And then I just happened to tweet about that along the way.

Because it's all you and there's no editor.

There's no, yeah.

I got.

It was a funny.

I tried to get an internship between freshman and sophomore year of this company.

And it was a film kind of production company in New York.

And I got the internship and then I emailed my liaison kind of guy who sponsored me for the internship.

And I said hey, I'd like to do a blog of my summer internship where I blog about being an intern at a production company in New York.

And about like a half a day day later, they emailed me back saying they've rescinded the internship.

No uh yeah because, because i showed a lack of judgment on, you know professionalism, you know, or whatever, like like, just even the, the idea that i would ask that question, red flags went up of like who the is this guy?

So anyway, i only say that to say that, like like to me, just like you know, building in public is just like a natural, is a natural thing.

And so I just, you know, go through the day.

We deal with interesting problems.

I tweet about them.

I get information back in the process.

I see your work.

You know, I see a bunch of folks and try and, you know, kind of incorporate that back into Box.

My job is to try and connect all these things together. and make it useful.

And you're, I mean, you're the number one spokesperson, right?

So you do have to be out there.

Yeah.

But I kind of would be doing it, whether or not, like it's like, I don't really think of it as a job requirement as much as like I just like I like social media.

You're so good at it.

Yeah.

It's still hard to believe.

So like, Do you get up at 5 a.m. with coffee?

Is that your secret?

Do you actually do this in the back of Waymos?

Do you do it that way?

How do you do this?

Right?

It's mostly that, though.

It's mostly there's a.

You know I have a commute home each night.

I try and see, you know, my kids most weekdays before I have to hop back online.

So there's like a 20 minute window there where I can kind of like distill the information that's happened and be like ah, is there anything I learned today that would be interesting to throw out there, or anything that I saw.

And then probably somewhere between like 730 and 9 pm, I finally get a chance to like look through the feed and see like, did anything crazy happen in AI?

And then that's, that will also kind of catalyze, you know, something.

As like, that's the best I can kind of, you know.

Yeah, okay, thanks.

Now I know your cutoff is 8 p.m.

I will try to get AI News out before 8 p.m. so I can help him do his thing.

Basically, if I don't see it before 8 to 8.30, I'm not going to be able to quote tweet or something.

Because then I'm back on Zoom after that.

I wasn't going to plan, kind of asking this, but you've mentioned yeah, you mentioned the film stuff.

And I know from, one of my favorite parts of doing research on you was that you got the idea for Box from like the Paramount lot pushing paper.

Are you a film guy?

You're big.

I would say I used to be more of a film guy.

Yeah.

What's your, what are your favorites if you have, you want to list off any?

Kind of the classic wannabe film student classics.

Are we talking Scorsese, Tarantino?

Pulp Fiction, Magnolia, Requiem for a Dream.

Basically like if there was an art house film in the 90s to early 2000s, that was my genre.

That got me into like, wow, wouldn't it be cool to do you know, you know, film.

And then I thought maybe I could connect digital into it.

Like, could you could you do film online?

That just seemed too hard from a licensing standpoint.

And then obviously Netflix kind of existed.

So I never quite was able to fully connect the dots on these things.

But the internship at Paramount was one kind of catalyst for starting Box, because we were using just traditional enterprise software.

And I was like wow, it's like really hard to share data, you know, just like files going back and forth.

But the same thing was happening in school as well.

And so that all led to, led to Box basically.

Well, A24 is kind of giving back this sort of resurgence of the independent film, I guess.

100%.

In the face of all the Marvel slop.

You know, I was thinking about this the other day and A24 is, you know, certainly the best example, I'm sure, of this today.

But you know they just don't.

You know it's hard to make a film like you know, No Country for Old Men or There Will Be Blood.

Like, what is that movie today?

Yeah.

Like, what is a brand new movie? that is just like original.

You just watch it and you're like, what did I just watch?

My, you know, sixes movie bench is Forrest Gump.

Okay.

Which iconic in its time.

Yeah, 100%.

Never again.

Yeah.

We don't know how to make Forrest Gump anymore.

We'll try it with the sequel, though, at some point.

For sure.

Forrest Gump 2 in 30 years.

I would be fine with it.

No, Forrest Gump has a kid.

He's still writing.

Yeah, he's still writing.

I think Forrest Gump has a grandkid would be like a good movie.

Like, what is the grandkid of Forrest Gump doing in?

Uh, in 2026 goes tropical yeah, but um yeah, i definitely let's.

I want to see.

Good, i want to see more movies out there.

You know, i'm a little bit conflicted on ai and film because oh, let's see that.

Well, because the world does not need more slop on AI entertainment.

But I'm kind of like in a mode where I think that AI is going to be, you know, generally a pure positive, because if I was me,

25 years ago in high school, for sure I would be making a full production film that had explosions and car chases.

But then there'd be people that would show up there.

So I think that ability to just you get to be Spielberg is completely amazing and democratizing that is incredible.

And i, you know, i'm i'm concerned about like, how do you make sure that we still get pt anderson along the way and and can we make sure that those those guys exist?

And then interestingly, i never and i never saw it, but darren aronofsky, i believe, has either put out or gonna put out a, an ai film.

Even some of the best artists are starting to adopt this.

But yeah, I definitely don't want to.

What I don't want to do is just be in this TikTok feed of just films.

And it's just like, oh, this film about the car chase that does this thing.

And it's just like, we don't need that.

Like like, like.

This should be a form of entertainment and art, and let's use AI to accelerate the production process.

Do the really hard CG work that that you just you had to spend way too much money on previously to do the you know kind of like let's.

Let's use it to test out all new kind of plot ideas.

Yeah.

Previs.

Yeah, exactly.

And that's incredible.

And all those things are super incredible.

I still like the.

It's very nostalgic but I still like the idea of like.

This is a camera and a person, and a person that says you know action.

And then, and let's hopefully like surround AI around that.

But we'll see how that plays out.

Yeah, I think you know.

So one of the things that stability AI made an impression on me was like well, you know, at least now we can remix Game of Thrones season eight.

And I can, you know, like it was meant to be, not rushed.

And then you watch.

I have a six and a half year old and I, you know, you see a lot of these kid movies and you're like yeah, that probably will be AI.

I don't totally know the job math because I don't know how many animators there are today, but I actually think weirdly, I think we could be producing more high quality, maybe even slightly educational kids entertainment, And so maybe that's a positive, is like we could just have like more, like you could just have a Pixar for like you know things where kids learn stuff.

And it used to be these like very, you know, lo-fi, you know, kind of lesson things.

I mean, we had Teletubbies, you know.

That was so slow.

So we could have way more of that, and maybe every animator that today is making a Pixar film is now.

You know we're like we fragment that out and but now they're responsible for more content and they've got AI agents running.

So like, So I think there's some optimistic scenarios on the entertainment side.

There's a lot of great use cases for being able to do generative media.

Edutainment as well.

I guess one question.

It's kind of like a self-serving one and almost like an advice side of the question.

One of the things I really enjoyed researching you was that Michael Arrington had some influence in the box journey because he went to his house party.

Yes.

And that's how you got funding.

Yes.

One of Leighton Space's, that's a deep cut, right?

Yeah, very deep cut.

That's a 06 deep cut.

Yeah.

I mean, do you want to tell that story?

I don't know if you've told it very much.

It's not even much of a story.

It's like a random intro, right?

Well, it was just, he used to have house parties.

TechCrunch had these house parties and it was probably no different than somebody's doing a house party in SF.

Just go?

Yeah, you just go and... you meet the VCs and founders and like, I'm going to make up examples.

So I don't want to like you know there'd be like Chad Hurley over there pitching his you know YouTube to people and like like that's just like how it worked.

And it was just like, wow, like that was this era where all these new companies were, were emerging.

And I met our first investor in Silicon Valley at one of these house parties, Emily Melton, who then brought us into DFJ that became our Series A.

So that was all because of Arrington's backyard party.

One of my aspirations for Latent Space is to be as helpful influential whatever, as TechCrunch was back in the day.

What would a new TechCrunch today look like?

What should I do?

There used to be TechCrunch disrupt.

I could do that with my conference, but I haven't done it yet.

Is that useful?

I don't know.

Well, you know, actually interestingly I would, I would argue, Disrupt came after the period that was the.

Was that deep cut period?

So so I think Disrupt, you know, ended up being, you know, you know, catalyzing.

I don't even I think Cloudflare launched Disrupt.

Is that the story, right?

Okay.

So like so, like.

I think anytime, anytime you can be in a launch pad is just great because it draws in people that are in that creative moment.

And whether it needs to be a contest or just like everybody gets like five minutes and you're fundraising.

I mean, who knows?

But I mean for what it's worth.

Like I don't have that much advice because I think you're already doing it effectively.

I just watch the YouTube videos late at night from the events.

I haven't been to one of your events, but from the camera angles, it looks like everybody's there.

What's great is that people are going to be in the audience as two random people, and they'll be like the next.

The next big AI company will come from.

You know people coming to a meetup because they were like I came in from Chicago and I'm from you know Poland, and let's go do a startup like that's the magic of the valley.

Dex Harthe found his co-founder at AIE.

And I know of at least one marriage that's- Wow, you have marriages already?

I never heard that about TechCrunch.

That's my favorite KPI.

Wow, we have AI marriages at the AI engineer conferences.

These are really humans.

That's a very good clarification.

I like that you have to check.

That's a very good clarification.

I think you're an insightful business leader with a lot of thoughts on media, so I just figured I would

I mean media is such an interesting space right now because with the Go Direct model, every company is going to have to be a media company.

You are the OG Go Direct.

Yeah, but you know we're still like I think what you guys are doing and I don't even know all the overlapping relationships, but like I watch your guys' videos of your events.

Watch your event videos.

But like, it's clearly like, this is the new format, right?

Companies have to become channels to communicate with audiences.

I think the resurgence resurgence maybe is a bad word because it implies a decline, but like DevRel is hot, like the hottest thing of all time right now, like if you could produce a fricking factory of DevRel people, like there's just like unlimited jobs right now.

On the other end of that,

Cause we're going to, everybody needs their services and APIs to be used by agents.

And so we have to all find a way to like, Like, hey, look at me.

Please come over here, agent.

And that's a content game.

Like, how do you get the agents to see your stuff and know your APIs?

And this is like a new world that we are in.

And it's going to be a...

It's going to completely be a digital marketing, you know, kind of world that we're in.

Yeah.

For what it's worth.

I'm trying to help by doing little writing bootcamps and basically turn into a DevRel bootcamp where you know well it's a demand and supply problem.

There's this huge demand.

There's no supply.

Wow.

Why is there no supply?

The really good ones work for themselves.

Creator economy screwed you over.

So I see.

So Substack and YouTube payouts.

They're making Patreon.

The most talented guys are making millions and just working for themselves.

We don't want them to make that much money.

We need to be able to hire people.

I mean, I think, like you know, do what some companies are doing, you know, not saying it's my situation exactly, but like give them equity and, like you know, it probably would be worth more, just like sort of helping them out.

Well, they are getting, oh, sorry, as full-time employees or not?

I'm part-time.

You need full-time.

I'm part-time.

Yeah, but you're NF1.

We also people that are full-time.

My classic joke or observation was this was when HubSpot bought their.

They bought a newsletter business.

And then they bought the My First Million, like the podcast.

Dharmesh, you must know Dharmesh Shah.

So he's like obsessed with this guy.

So my conclusion was like, every company must either build or buy a media company, right?

And until you, unless you realize that you have to take it that seriously, that you are running a media business in your company, you will never be good at it.

Yes, 100%.

Yeah.

Yeah no, we're very much taking that seriously, but still, and yet DevRel, I mean I got to do one plug.

We're hiring in DevRel.

No, we're all engineers here.

And I just said every agent needs a box.

Let's go.

No, that's the headline.

We're hiring DevRel to make that happen.

But yeah, I think DevRel is like the future job.

So we're all just gonna be doing DevRel in some form.

Okay, yeah.

I mean, what is FDE?

Developers are ruling the earth, yeah.

What is FDE?

I don't know.

No, it's DevRel.

Yeah?

Okay.

Isn't it just like glorified consulting?

That's the downside.

Sure.

I mean, I guess nobody can like actually you know fully define this, but I think it's micro dev rel.

Like you're in the company, you're helping them with the services.

You're doing a little bit extra implementation.

But yeah, so I think we're all you know.

The thing that's going to happen on the ledger of software is we're going to produce far more output of code and thus features per dollar.

But on the other end of this, we're going to actually end up spending probably just as much on how do you get all of that stuff to the customer.

And that's going to create a new set of roles that we are all doing.

Partly because, either because there's so much choice now you have to kind of fight for attention there, or because the stuff is just changing so quickly that you have to technically help your customers along the journey.

This is why I always laugh when people say you don't need to be an engineer, don't do computer science.

I actually think that is still one of the most protected job categories, because things are only getting more technical.

Things are only going to get harder.

And anybody in a technical position is in the best position to get agents deployed, get them built, get them adopted, build the custom code software for the IT system, all of that.

My classic founding story of why I picked AI Engineer as a title and as a theme for this podcast as a theme for my conference, was back in early 2023.

Someone non-technical came to me and said I'm all in on AI.

What should I do?

And I was like, I just looked at her.

I was like, God damn it.

There's nothing you can do.

Like engineers are about to get so much more powerful than you don't even understand.

Tell me this, did she go and then learn?

No, I didn't say any of that to her.

I'm not that honest.

I hope somewhere out there she went to some online academy.

Exactly, learn to code.

But there's a lot of people who believe AI too much and then they're like well, you don't need to learn to code, so I won't learn to code.

And then there's like there's a bunch of us who are just in that sweet spot of we can code and we can build AI a thousand times more effectively than you can.

And well, who's going to win here?

I think this was another tweet, but it was the observation that really software engineering for the past 30 years was the primary career track for technical high agency people that wanted to have a large outsized impact on the world.

And software was a means to do that, right, effectively.

And so, yeah, with AI, is it like that?

AI is going to eat software engineering or is software engineering going to eat all their domains and disciplines?

Those same principles then, get applied to every other.

You know function.

Yeah, exactly.

Well, this is the.

You know, anybody who believes that an enterprise and I'm mixed on the, I'm mixed on this is.

But if you believe that an enterprise is going to build its own software for all of its problems, then you must be the most long on computer science as a discipline of all time.

Because guess what?

Most of the economy does not have enough engineers to then maintain all those systems, to update to all those systems, to figure out the relationship between the business problem and what the code needs to do to go and actually manage that.

And so that's a very pro engineering job argument of what the future is going to look like.

I'm still going to go back and forth on like are you going to really build all these things versus no prepackaged software?

But no matter what, there's going to be 10 to 100 times more code.

So I think you can be very long.

Engineering right now, as just a you know, purely on the dimension of, of software, is going to become increasingly more important.

Once agents are are, you know, turning everything into software.

Yeah.

All right.

Three software guys say software.

Okay.

Aaron, your inspiration.

It's such a pleasure.

All right.

Good to be here.

[The Agentic Evolution: Infrastructure, Security, and the Future of Enterprise Data]-[Why Every Agent Needs a Box — Aaron Levie, Box]

📋 Summary

The Future of Enterprise: Every Agent Needs a Box

The Shift from Coding to Orchestration

The Hard Mode: Governance, Security, and Identity

The New Infrastructure Opportunity

Conclusion: The Long Road Ahead

🎯Key Sentences

📝Key Phrases

📖 Transcript

[The Agentic Evolution: Infrastructure, Security, and the Future of Enterprise Data]-[Why Every Agent Needs a Box — Aaron Levie, Box]

📋 Summary

The Future of Enterprise: Every Agent Needs a Box

The Shift from Coding to Orchestration

The Hard Mode: Governance, Security, and Identity

The New Infrastructure Opportunity

Conclusion: The Long Road Ahead

Deep Learning with ListenLeap

🎯Key Sentences

📝Key Phrases

📖 Transcript

ListenLeap Brings You Into Real Context Learning