Box CEO Aaron Levie on Why AI Agents Won’t Take Your Job

AI is filled with CEOs predicting the end of white collar work. So I talked to one of the few CEOs who has a vision of what the world will look like where AI works for us instead of replacing us. As

long as as there's still a three-dimensional world out there that we have to go and participate in, we're going to have much more signal, much more context than the AI will. And

that's going to just keep us in jobs, keeps us doing things for as long as we can we can kind of look out right now.

That's Box CEO Aaron Levy. He's been

running the company for almost two decades. They have 2,000 employees and

decades. They have 2,000 employees and almost overnight, he's made the company AI first, and that's taught him a lot about what the future's going to look like over the next few years. If you

care about the skills you're going to need in an economy filled with AI

agents, this is the interview to watch.

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Aaron, welcome to the show.

Hey, thanks for having me.

Thanks for coming on. So, for people who don't know, you are the CEO of Box. Uh,

you are a longtime ex poster, P O A S T E R. Um, and you have turned yourself

E R. Um, and you have turned yourself into, I think, a really interesting thinker on AI, which is not surprising, but I think, uh, for someone running running a big company, it seems like you've really gotten your hands in it

and really understand it in a deep way, which is awesome.

Yeah. No. Um, I I think my PO days are uh a little bit behind me, but um I've uh I I'm I'm handing that off to the next generation. Um, and now I'm just,

next generation. Um, and now I'm just, you know, just going to tweet about AI agents uh till uh uh till till the the whole uh the whole thing's over. But um

yeah, so it's it's been a super exciting journey. Um I think for all of us that

journey. Um I think for all of us that have been deep in the space um probably the most exciting technology in and at least my my lifetime um and uh and so just having a lot of fun with it. So,

one of the one of the places I want to start is I feel like you have a pretty particular perspective on why jobs aren't going away because of AI agents.

Do you want to talk about that?

Yeah. So, um you know, I I still leave open a 5% chance that I'm like totally obscenely wrong. Um

obscenely wrong. Um as you should.

Yeah. So, so this is sort of like a high confidence with with room for for debate and and you know some some internal doubts here and there but for the most part um and I think it's sort of

somewhat empirical when you use these tools um uh I'm a big believer in and you know thousands of people have talked about this at this point but but you know jobs are not tasks jobs are a

collection of tasks and AI is very good at automating tasks and uh obviously that that the the definition of a task is expanding dramatically based on now what agents can do. But at the end of

the day, there still is, you know, we still need a human to incorporate whatever the task that was executed into a broader workflow into a broader business process into actually, you

know, some form of value creation. And

so um because you you can't ever get rid of that person um that means we eventually will still have some degree of specialization of of of what people you know end up owning from a you know a

completion of all the relevant tasks in their in their domain. Um and so you know let's just go through the list. Um

you know even uh you know when an engineer needs to uh develop some some form of software they're going to go to an AI agent and and have it go work on part of the codebase. But then they're going to have to make a decision of do I

ship that feature? Do I like that code?

Do I, you know, I have to go talk to a product manager to make sure it's the right thing? Um, I have to incorporate

right thing? Um, I have to incorporate into a broader project or a broader system. And so no matter what, you're

system. And so no matter what, you're still going to need people for all of that. So, so that's that's sort of, you

that. So, so that's that's sort of, you know, why why jobs as a as a general matter don't really go away. Then the

question is, so what do you do if if you know work you can, you know, output 20 or 50 or 100% more in any given job? a

lawyer can review double the amount of, you know, legal briefings. An engineer

can can generate, you know, twox the amount of code or 5x the amount of code.

Well, shouldn't that that sort of, you know, reduce jobs in in some kind of commensurate way? And my view on that

commensurate way? And my view on that one is really just I think actually it turns out that that we're we are uh doing way less work than what is

actually economically useful. Um, and we are merely just constrained by how much time we have in the day and the cost of labor to be able to go and do that work.

And um, you know, and so I look at examples throughout our own company. And

if I could have uh lawyers go and review legal, you know, documents and and contracts two times faster, I would not have half the number of lawyers. I would

actually the throughput of our of that particular bottleneck in the organization would just go way faster and it would it would be reduced. And so

we would have we would employ the same number of lawyers, but we would be we would be reviewing all of our contracts at 2x the speed, which would actually probably in most cases mean that that

there's some feedback loop where we're growing sales incrementally faster and we're getting back to customers, you know, more quickly. So we have higher kind of customer satisfaction rates that would actually even in some cases lead

to more revenue or or better business results which ironically then would lead to hiring more people in those functions that that could drive more growth. So

similarly in product uh in in engineering if if we could ship two times the amount of code what's likely going to happen is we're just going to expand the footprint of our features.

We're going to build even more software that will then create even new that will create newer bottlenecks in the organization that cause us to hire more people. in the areas that now are um are

people. in the areas that now are um are are involved in that. And so in most cases I'm I'm I'm more finding that um that that we are just actually not at uh

capacity and we have not reached the point of of supply demand equilibrium where where we are doing just the perfect amount of work in the economy in any in any given function. If we can make it more efficient, we'll actually

do more of it. And that that kind of ties to the the Jevans paradox idea.

Javin's paradox, you know, I think mostly is applied to inanimate, you know, kind of resources and and, you know, AI systems or or, you know, rail

uh, you know, trains and and, you know, energy consumption, but you could actually apply it to people too. And the

idea would be if you could have a lawyer that could do 30% you know more output or 50% more output of again legal reviews um the demand actually would go

up for for legal work because it becomes incrementally cheaper to then go and do that legal work which means you open up a new trunch of use cases for that kind of work to get executed. So so I just

think all of the evidence right now is pointing more toward we're just going to do more. We're going to ship more. We're

do more. We're going to ship more. We're

going to you know better serve customers. We're going to have more

customers. We're going to have more marketing campaigns. We're going to

marketing campaigns. We're going to build more software. We're going to get better health care. We're going to, you know, have more tutoring and education.

But all of those things still then drive jobs in the economy.

I agree with you. I think the lawyer example is such a good one. I have

caught like hundreds of thousands of dollars worth of legal mistakes just by putting my contracts into GBT5. Um, and

I couldn't have hired a lawyer for that before because it would have taken too much time. And I already had a lawyer

much time. And I already had a lawyer draft the contracts and they're the ones that made the mistake. So like the second level of legal review is a thing I couldn't have paid for before but now is a job that either a lawyer assisted

with an AI or a legal firm could offer via you know CHBT or whatever or CHBT just offers that now in a way that I couldn't have bought before.

Yeah. I think I think if you think about it like what are all the things today that you're not doing because the the price of entry to doing that thing is I

have to hire a person or go and pay and procure us an external service like you know the the the minimum amount of money that you can that that you can spend just to even start to talk to a lawyer

is thousands of dollars right the minimum amount of money you can spend to prototype an idea with an ad agency is tens of thousands of dollars so what happens in a world where you go and do

that for $5 or $100 or $1,000, you're just going to do way more of those types of activities. And then again, kind of

of activities. And then again, kind of ironically, by doing more of those activities, you might actually find then these scenarios where you want to now bring an expert back into that workflow

that you wouldn't have had before. So

there are areas within our company where we start to do something purely as a as a test case or a prototype or or just you know kind of an ideation with an AI

system and it works just enough that we say now let's actually go do this in the real way and let's go you know pay somebody or hire somebody or put somebody on that project but we wouldn't have even got it started before if AI

didn't exist because we would not have we would not have have even thought that we should go you know sort of light up that project if we couldn't have prototyped it in the first place. And so

this is sort of the the part that that again no economist has any any way to to estimate how much of the economy is going to grow as a result of that. It's

impossible for your brains to kind of get around well how many new things get lit up because AI lowered the barrier to entry to cause then more people to get involved in that particular you know

task. But that's actually going to be

task. But that's actually going to be probably a substantial amount of work in the future.

How do you think about the future? So,

you know, you're someone uh you're running a big company. Uh you're running a a company that came up in the cloud era. Uh seeing a new technology wave

era. Uh seeing a new technology wave come through. There's probably a maybe

come through. There's probably a maybe like a little bit of a sickening moment like, "Oh my god, am I going to have to change everything or how does this affect my business, right?" Um and your job is to figure out where the future's

going and to and to start to understand, okay, is this going to be good for us?

Is this going to be bad for us? How is

it going to change jobs? All that all that kind of stuff. and and you're approaching it from a perspective that seems to, you know, you seem to have a pretty informed perspective on where it's going and I'm curious like how you

form that yourself. How do you how do you go through all the possibilities to understand what's coming next?

Yeah. I mean, I think um so to some extent I you know I'm kind of working through analogy and working through the the the fact that I've seen you know a couple of these major shifts and so you

have that you have that as a as a background um uh you know kind of experience that informs a lot of what's going to happen next and everyone kind

of can debate you know is you know what what are the most sort of relevant platform shifts that we've experienced that then AI relates to but at a minimum you can kind Think about it as okay, we

went from the mainframe to the PC. We

went from PC to mobile. We went from on-prem to cloud. Like these are these are platform level shifts. We kind of know how they work. You you have the early adopters start to play around with

new technologies. They adopt these tools

new technologies. They adopt these tools and then and then there are breakthrough use cases that cause um you know more of the kind of mainstream pragmatic you know buyers of technology to adopt

those. Then that kind of you know sort

those. Then that kind of you know sort of accelerates and then you eventually have the lagards. We we see this pattern every single time a new technology emerges and it it happens at the big macro technologies like cloud and and

mobile and then it happens at the micro technologies like you know any any subservice in in cloud or mobile you know experiences the same thing. So so

AI actually has a very similar curve.

It's it's going through the exact same sort of you know typical bell curve of of adoption patterns. One thing that's different is it's happening in a compressed fashion. So where cloud may

compressed fashion. So where cloud may have taken you know 10 years to reach you know um complete mainstream adoption by every company that that is relevant

AI is probably doing that in two years.

Um but each individual of technology within AI still has again a very similar curve. So um obviously you know those of

curve. So um obviously you know those of us spending too much time on X you know we're seeing AI agents encoding before the rest of the world but you kind of know exactly what's going to happen next over the next you know two years.

everybody's going to adopt AI coding agents. It's just a guarantee because

agents. It's just a guarantee because because the efficiency and the productivity gain is so massive that this will ripple through the economy. Um

and so so I think by having a lens into both what the prior trends have been and just by being a very active user of these technologies myself I I can kind of see you know where where you know

things are such a breakthrough that they will most likely again ripple through through the economy versus which things are maybe more incremental and so maybe it won't won't be that that impactful um uh you know that that that ends up

helping inform this and then I think maybe the third factor is because we were a startup right at the early days the cloud. I kind of, you know, I felt

the cloud. I kind of, you know, I felt that shift in the in, you know, deeply in the inside of a company that had to go through um executing on a a large technology shift that was happening. And

so to some extent, I'm kind of pulling from those memories as much as possible and saying, let's I kind of have to do that again. Now there's obviously more

that again. Now there's obviously more people, you know, we we have we have new risks, we have new opportunities. Um,

but it's very much like hardcore startup mode of, you know, I often just ask myself like quite literally what would we do if we were starting the company from scratch and it was just, you know, 10 people, how would we operate? What

would how would we execute? What would

we be building? What features would we be creating? And so if if we were

be creating? And so if if we were starting the company over in an AI first world, what would that look like? And so

again, you know, I'm benefiting from the fact that I I I saw that, you know, front row seat on the cloud wave and and we're trying to again that that's informing me and and informing the company, I think, on what what this

should look like. So what are some specific things that you remember from that cloud wave? Because I think um you you know the the term digital transformation was the big was the big

thing you know like 10 years ago and everyone everyone needed a digital transformation strategy a cloud strategy and some people probably did it well and but a lot of people I think they just

know they need to say those words and and there's a big difference between companies that say the words and are like yeah we have a cloud strategy and companies that actually ended up effectively doing it and I'm curious

like what your memories are of uh of how that works and who who does it well and who doesn't and then how you think that applies in this era.

Yeah. Yeah, you know what's interesting is um this this is going to be much bigger than that because in the in the cloud transformation and the digital transformation

f first of all it was a little bit um it was always a little bit um abstract as to and I think you're kind of getting at that in the question like it was always a little bit of an abstract concept of

like you know when United Airlines does digital transformation what does that what does that really mean that means that they should probably have like a really good mobile app a really good website, you know, the customer support

should be should be intelligent and kind of relevant to you. But at the end of the day, and with all respect to United Airlines, if you look at your flying experience from 15 years ago to today,

like basically nothing changed. Um, so

pre and post digital, it's probably a little worse.

It might be it might be worse. Um,

so, so, so the reality is is that for as much work that that as kind of went into that digital transformation process and and I'm sure that behind the scenes, lots of lots of really interesting

technology got used, lots of of um, you know, new ways that they're operating in their data centers changed. The the

actual day-to-day experience as a customer did not meaningfully change, meaningfully jump, you know, probably some better designed website, etc. And I think that's kind of probably felt

across a significant portion of the economy pre and post digital transformation. Now there's more extreme

transformation. Now there's more extreme examples. So if you look at Disney as

examples. So if you look at Disney as just a random example, I think they would probably say, well, we had to become a digital company in the form of our product has to be now fully digital.

We can't rely on movies on the the movie theaters. We're going to go to direct.

theaters. We're going to go to direct.

And that that's that's a more significant business model disruption that that uh that had to occur. probably

some banks maybe land in that category.

But but you know you were kind of on this continuum in in the AI transformation. The reason why this is

transformation. The reason why this is going to be so different and so much more impactful is it it is it changes how every single employer in your company operates. Again, the the daily

company operates. Again, the the daily experience of that United Airlines employee 15 years ago to today, their tools are a little bit more modern.

They're, you know, they're probably getting real-time data feeds where it used to be a little bit more asynchronous. uh that that was that was

asynchronous. uh that that was that was sort of a contained level of transformation in your daily experience as that employee. With AI, there's there's not going to be any going back

to the way things used to be and how we worked. It's just not possible because

worked. It's just not possible because the efficiency gain between the company that uses AI versus the one that doesn't is just is just too insurmountable to try and make up for it if you're not if

you're not using these technologies. And

the way that we will work at the end of that transition will be so different that that it will it will you'll just fundamentally feel it again in in your daily work in your daily tasks. And so

so maybe that would be the biggest difference is between maybe digital transformation and and you know this sort of you know AI era that we're entering is is the way that we work is going to be so fundamentally altered

that um that that you will again just experience this in your in your daily life as an employee in any one of these you know companies. some jobs will entirely change and be entirely shifted

and then other jobs again the daily activities will just be so different. So

let's take engineering you know space obviously that we're we're following if you if you talk to a you know very kind of you know clued in online engineer

right now and you say how are you developing software today versus even one year ago. It is the probably the biggest shift in any period in history of almost any knowledge worker job

that's ever occurred, right? Like one

year ago, you were typing into an IDE, maybe you were having some autocomplete, you know, technology like GitHub copilot, maybe you were asking a question of an AI system, getting some suggestion back. That was that was sort

suggestion back. That was that was sort of one year ago. Obviously, three years ago, none of that even existed. Today,

you're you're prompting an agent that's going to go off and do a large amount of work. it's going to come back with that

work. it's going to come back with that work product and you're going to go and review it. That is like a completely

review it. That is like a completely different job than you know within a one-year period. If you even if not

one-year period. If you even if not every job changes as much as that, if you kind of look at how that's going to ripple through knowledge work and you apply that to almost every form of knowledge work, it will mean that that

every all of our daily workflows, if you're generating marketing assets and building marketing campaigns, if you are in sales and you're in and you're, you know, supporting a customer, um if you're in, you know, research and life

sciences, every single one of our jobs is going to look completely different in the next, let's say, five, you know, plus or minus years. Um and that will be why it's so different than let's say even digital transformation was.

So do you think then the better metaphor or a more apt analogy is uh like the shift to using computers at all like when we first started using Visical and spreadsheets or something like that. I I

think I would be it would rank at that level um uh in terms of the amount of change, right? So the paper to digital

change, right? So the paper to digital process uh is was a a fundamental form factor change in in how you worked, right? Everything about the the workflow

right? Everything about the the workflow of a company, you know, well, it's probably even more significant. It's

probably from paper to to digital plus the internet. Um uh because you know I

the internet. Um uh because you know I think what we what we you know we we sort of did this skumorphic thing in the first in the first phase where we just kind of took the the paperbased you know

desk workers set of tools and we put them into a digital screen. That was a shift but but but it was then an even bigger shift once you could connect those systems you could collaborate in real time. So we're kind of compressing

real time. So we're kind of compressing that level of shift in a again a one or two year period. Um but uh but very much akin to that even the even the shift

from kind of on-prem to cloud. It it it sort of was uh was more impacted the aesthetics of software and it impacted the the fact that you know when I

chatted with you you got the you know you got the response faster and and it was queued up differently and the way we collaborated was we didn't use version control we just you know worked together

in real time. That was a very big deal.

But we already kind of understood the the general structure of how we would work together and how we would communicate together kind of pre and postcloud. the the shift from pre and

postcloud. the the shift from pre and post AI is again fundamentally different because you know what what I think we're seeing is is that the the job of an in uh individual contributor

really begins to change because you are now a manager of agents and that is a completely change in the kind of work that you do and and we don't again we that that's a that's a very different

level step function shift than what we've seen previously 100%. Um yeah, the the way I've been

100%. Um yeah, the the way I've been writing about it for a couple years is um thinking about us moving from a knowledge economy to an allocation economy where your job is to allocate

intelligence. Um even as a even as an

intelligence. Um even as a even as an IC. Um but that's that's a key point,

IC. Um but that's that's a key point, right? So so managers jobs are really

right? So so managers jobs are really about prioritization. They're about

about prioritization. They're about allocation. They're about uh using

allocation. They're about uh using judgment um a ac across a set of of of kind of tasks and projects that are happening and and that that effectively

becomes the new IC job in the future.

Totally. Um one thing I want to get into though is uh you said you're in startup mode and you've been running Box for a while. Um and I'm curious what that has

while. Um and I'm curious what that has been like for you. Are you like, you seem energetic, but are you were you like, "Oh, [ __ ] I can't believe I have to go back to startup mode." Or were you

like, "Uh, this is great. Finally, I get to, you know, feel like I'm back to the ground floor again or some mix." And you can be honest. This is safe space, but I'm really curious.

Yeah. Um, okay. I I'll try and be as honest as as as possible on this one.

Um, so the uh I'm going have to be introspective for 5 seconds. So, I'd say it's I'd say it's 80 to 90%

um very excited, 10% 20% anxiety. So, so

on the 80 90% um I I just I I love technology so much.

Uh and I mean, you know, you sort of ha you have to be, you know, to to be in this industry, you know, um and to be, you know, trying to build a company for for as long as we've been kind of

working on this. So, so I would say that the this is sort of me in my happy happy place. Uh, which is some something is is

place. Uh, which is some something is is you know some some major technology event is happening and you can get your hands on it and and you know ideally it impacts you in some way that you can

make it make sure that that you have to respond to something and do something like that kind of appeals to my ADD you know instincts. There was probably a

know instincts. There was probably a period, you know, three or four years ago where I was like, "Huh, maybe I should start to pick up some hobbies.

Industry was kind of settling down. We

kind of knew that like all the different landscape, we knew cloud, we knew mobile, we knew how everything was going to work." Um, and so this is like way

to work." Um, and so this is like way better because at 10 p.m. instead of

doing some arbitrary hobby, uh, I'm on a, you know, Zoom call with somebody about AI or playing around with a new feature or building something. And that

that is like that is so much better for me from an emotional, you know, excitement standpoint.

I don't know. I mean, I feel like the world needs more of your pottery or woodworking. Yes, I uh I the at the very

woodworking. Yes, I uh I the at the very tail end of tail end of COVID I I started picking up guitar just because like I had I had some free time and u uh

and like that's clearly a sign that like the industry had was was kind of cresting and there was wasn't a lot of change going on if I have time to to learn guitar. Um and uh and now now it's

learn guitar. Um and uh and now now it's just yeah we're we're in kind of full full crank mode and it's incredibly exciting because something changes every single day that you have to respond to.

Um and that that's definitely you know exactly what I where I like to be.

We are so back. We're so back.

We're so back. Yes.

Uh but one thing that I think you've done really impressively is you were one of the first CEOs to really start the we're a AI first wave. like you sent the memo and you were like, you know, we're

we're transitioning to being a company that takes this really really seriously and if you're not in, you're out. And

I'm curious, I think a lot of companies are thinking about doing this right now or trying to do it with very varying levels of success. And I'm curious how that has gone for you and what you've

learned about the way to do this well and to really be able to start from ground zero inside of an ex existing company. Like that's so [ __ ] hard. So

company. Like that's so [ __ ] hard. So

yeah, I'm curious uh what you've learned.

Yeah, it's extremely hard and and I would uh I would say um still still totally on that journey and we are not um you know we're not yet uh I can't you know kind of put the mission

accomplished flag in the ground and and say that like we are the case study. Uh

we're we are cranking on this every single day. Um the you know few quick

single day. Um the you know few quick lessons. One thing that we tried to do

lessons. One thing that we tried to do was was just be very clear that this is not about replacing jobs or or spending less money as a company. This is this is

purely about how do we get output to increase? How do we move faster as a

increase? How do we move faster as a company? How do we do more? And how do

company? How do we do more? And how do we better serve customers? So, the first thing that I wanted to do is just make sure that that this was not some threatening technology. Um, but this is

threatening technology. Um, but this is something actually that that we should be on the the forefront of because it's going to let us work better. It's going

to let us actually do more as a as an organization. That was kind of I think

organization. That was kind of I think important to lay out up front. Um the

next is just making sure that that everybody's using it every single day in some capacity and then constantly showing each other how we're all using it. Um so we do this thing every single

it. Um so we do this thing every single Friday. It's our internal all hands. We

Friday. It's our internal all hands. We

have somebody demo uh how they're using in our case box AI for different use cases. So they created an agent to

cases. So they created an agent to automate some sales workflow. They

create create an agent to automate a compliance workflow. And so we want to

compliance workflow. And so we want to constantly just have everybody learn from each other on on what you know what what this technology is, how it works, how it helps uh in your in your daily

work and then how you can go off and and do it yourself. Um you know we we've we haven't quite systematized this but but I think increasingly I'm I'm at least asking and I'm hearing more people ask

hey why can't we do that faster? um you

know you look at a project timeline that comes back and it's 3 weeks or you know four weeks and you say well you know I don't know why we can't do that in two days like like if you really just thought about it from a first principal

standpoint you're really just trying to create this thing or build that like why can't we dramatically compress that timeline and that's causing people to say okay maybe actually I should relook at this maybe there is a technology out

there that uh that we can go and leverage to make that happen um and uh and so that's starting to kind of create a a flywheel but I think again the end result is companies are just going to move far faster. They're going to get way more done. They're going to be able

to better serve their customers as a result of this. And I'm seeing plenty of examples of all of that happening. The

one asterisk that I'll say is the one thing we can't yet do, um, and this is maybe me being defensive, maybe we actually could if if I was just like so like burn the bridges kind of right now.

Um when I talk to five or 10 or 20 person startups with no existing process with complete you know clean sheet of paper and how they operate uh I am

seeing them be so differently wired than than you can be once you have existing workflows or processes that it is causing me to think like you know do I have to start to maybe go and find you

know areas where from a completely fresh start we go and re-engineer something and it's it's because these startups start with again nothing that they can kind of think about their engineering

workflows more in this modern way which is the workflow is actually you're prompting an agent, it's operating in the background, you're reviewing the code of that agent, you're you're very

documentation driven, you're very spec driven, you're prompt driven. Um, and

then you're letting the agent go off and and do lots of the work and then you're reviewing all of that. Um, and that that you can afford to do when you're not putting AI into an existing workflow, but when you're again kind of

reinventing the workflow from scratch.

And I do think there's areas where I want to do that much more in the organization. Yeah, I mean we have that

organization. Yeah, I mean we have that we run four software products internally and we're 15 people and I commit code to those which is I should not be able to um

and yeah totally would be totally impossible like without cloud code and codec cli and all those kinds of things and are it just totally changes the engineering process because it's yeah it's about the plan and if the plan's up

to date and who's reviewing the plan and what work has been done and and the actual code doesn't matter as much.

Yeah. And I think the, you know, when you have years and years of of highly, you know, tuned tribal knowledge on how to build things and, you know, in-person

code reviews and all of the internal workflows. Um, you know, it's sort of

workflows. Um, you know, it's sort of harder to do the let's just start let's invent this whole thing from scratch.

But we we are going through that journey. We will the way we build

journey. We will the way we build software will, you know, already looks very different than it did two years ago. And I I think it'll look vastly

ago. And I I think it'll look vastly more different in a year from now than the combination of of you know the past, you know, couple years.

Yeah, I'm totally with you. One thing

that's been on my mind is uh and I I agree on the there's way more demand than than can be served and you're generally just going to want to do more work as a company. You know, we've been

in a nonrecessionary environment for a long long long time. I'm curious, do you think that changes in a recession? I

think it changes but but again we don't know the counterfactual where where usually in a recession you you unfortunately have job reduction regardless. So so now you you would

regardless. So so now you you would probably still have job reduction um in a recession but but the companies are still able to drive more output because they can again they get more leverage

from AI. So, so you know, maybe

from AI. So, so you know, maybe optimistically I'm totally making this up. Maybe you get out of the recession

up. Maybe you get out of the recession faster because because you haven't totally decimated your your productivity levels as an organization. Um, but but I I I would say that that you know, I

could totally contemplate a scenario where you have a very bad economic environment that would lead to job reductions as it as it kind of always has. Unfortunately, that would right now

has. Unfortunately, that would right now probably be blamed on AI because that would be you'd still see you'd still see people doing work with AI. Um when again it's one of these it's a counterfactual

which is well again in a recession you know prior to AI you also had unfortunately job cuts that you have to make in those situations. So you can't really know what would have happened in the nonAI scenario. So I do think we

have to kind of watch out for that. But

but the but I I really think about this as again I'm not seeing any evidence to the to the contrary. I'm I think about this as as just the next kind of era of

of knowledge, work, technology that we we have always had these boosts in capability and productivity and um and you you sort of you when you're in the

moment of that transformation, it's easy to kind of look at it kind of myopically and say, well, oh my gosh, this is going to be, you know, this is going to totally reduce the number of jobs in this area or impact us in this area. And

then you look at it 10, 20, 30 years later and you realize, wow, actually it turned out the the the demand for that type of work was way bigger than we had imagined. And if we had never made it

imagined. And if we had never made it more efficient, we would never have actually gone and been able to to capitalize on that. I mean, if you just look at like I'm I'm I'm totally this is fanfiction, but like if you look at

probably, you know, what a graphic designer 30 or 40 years ago would have said when they saw Photoshop, right? and you're

like, "Wait a second. Right now, this project takes like a week to go and and design this poster for this client and you're going to make it take five

hours." Well, you know, h how is it not

hours." Well, you know, h how is it not going to reduce graphic design jobs by, you know, 10x? And today we have vastly more graphic designers in the world than

we did, you know, 30 or 40 years ago. or

um you know you look at at you know all of the you know stories of accounting when we went from um you know any kind of paperbased uh you know kind of you

know methods to to the PC and Excel and and Visyal and into it and we have way more accountants today than ever before.

So what is it about digitization that actually causes increases in in these jobs? It's because we finally make the

jobs? It's because we finally make the function efficient enough that way more people can actually go acquire those services and AI is is again for everything that I'm seeing is just going

to do the same thing again for a number of fields.

Hoping that um uh the the one negative impact of technology is more accountants than ever before. So hoping that doesn't continue.

love my accountant but the well one thing's for sure like you can imagine you know again in your example of AI reviewing your contract now imagine when you start doing that to

kind of everything in your organization from a legal standpoint like you're going to be hiring way more lawyers uh as a result because you're going to say oh I found this thing now I have to go talk to somebody and and you're still

it's still going to be bottlenecked by that human so so I I don't see a lot of these like in these large job categories getting reduced What do you think is overhyped in AI right now?

Uh this will this is going to you know certainly show my my uh bias on this topic. Um I I don't know if I can think

topic. Um I I don't know if I can think of something. Um uh

of something. Um uh uh I think I think if we if we look at where we are uh and this is again this is maybe one of the examples of this is

one of the examples of of having been through the cloud wave. I could I and again I might extrapolate too much but I remember 15 years ago you know being like gosh I can't imagine this ex SAS

category getting 10 times larger or five times larger and you zoom out and you're like oh my god like we were actually like we were just at the very small period of the of the curve at that point

and like I I I was like in shock that AWS was still you know doubling 10 years ago and you know it's probably you know two orders of magnitude bigger uh today

than it was. And and and and so I think there I don't know of a category where if I look at it, I say it's not going to be 10 times larger in in five years from now. I don't I don't think I can find

now. I don't I don't think I can find that. What would what would you what

that. What would what would you what would you argue?

What do I think is the the most overhyped thing in AI right now? I mean,

I think that we just uh people tend to o just generally with with AI, they tend to oscillate between like it's utopia and we're going to like everything's

going to be solved and and you know it's uh free room service and teleportation for everybody.

Um or we're all going to die. Um, and

uh, that's why I like talking to people like you, cuz I think you have a more grounded perspective on well, it's going to change it's going to change a lot of the way that we work and also the world

is going to continue more or less, you know, like we're still going to have problems. The bottlenecks just move somewhere else. And I think that's

somewhere else. And I think that's actually a much more interesting perspective and much more a much better way to talk about the future. I'm I'm

totally fine with the utopian people assuming they're they're you know driving positive progress in the technology on that. Um I don't think I I I don't think you end up in this

scenario of of that that probably people imagine on that front simply because um every step along the way some problem emerges that the AI is not good enough

to handle that humans have to play the kind of stop gap on and that I think is a rolling process as far as far as the eye can see and um

and I I think that each new technology breakthrough just leads to a new bottle neck somewhere else that people have to kind of you know we we play the role of of the duct tape on and and you know uh

I I think that's probably actually been the story of of technological progress for you know 150 years is we thought we were going to automate x function and

and we we we kind of did by 80% and then people have to do the rest um and um and I don't I again I don't see AI meaningfully changing that. Um there's

just so much and this is this is sort of back to this thing of like why why does the engineering job still exist in the future etc. There's so much signal and so much context that you get by still

operating in the outside world that is necessary for these AI systems to know about but they can't glean on their own.

And um and and there's no there's no breakthrough uh that that we have any example of that that replaces that. you

know, my ability to to talk to a person down the hall that gives us that gives me an idea because they just talked to a customer. We can't replicate that in an

customer. We can't replicate that in an AI system right now. We don't know how.

Um, and and there's no technological breakthrough that we know of that will replicate that. So, as long as as

replicate that. So, as long as as there's still a three-dimensional world out there that we have to go and participate in, we're going to have much more signal, much more context than than

the AI will. Um, and that's going to just keep us keep us in jobs, keeps us doing things again for for as long as we can we can kind of look out right now.

Yeah, I agree with that. I think um even if we get there and you know people are working on robotics and continue learning and all that kind of stuff, it's easy to forget that there are all these weird like people collect

experiences and you learn from experiences. And so if you've spent a

experiences. And so if you've spent a long time in an industry, for example, for the last like 15 years, you have a lot of this like tacit knowledge in your neural networks that you can't you you

get feelings about, but uh and and even if we have an AGI, if it hasn't been there, it's not going to have the same perspective. Maybe it has an interesting

perspective. Maybe it has an interesting perspective that's useful, but there's still just your own personal experience thinking about you versus other humans is ma matters.

Yes. And and I don't think we want I don't think it'd like be that fun if you have to just prompt engineer everything in your life. Like like if I have to if you had to prompt me when we're getting

on this podcast and like okay you were doing a podcast right now and here is what you're going to it's like no that wouldn't it would be like it would be so draining that that we just we would completely halt the you know all of our

all of our interaction. So, so you do need like but people don't have to be prompted in that way constantly, right?

We can kind of pick up on the cue that lets us be like, okay, I can put this in this part of my memory and I'm not going to, you know, I'm not going to accidentally talk to you about healthcare right now because that's not

what we're talking about. And and so you like like people are just going to be better at that in until you know you you kind of look at the you you uh if you watch the Richard Sutton you know um uh

you know podcast um with Doresh like it's it's kind of it's exactly that which is you know these systems are not they they do not have any context they they don't have they don't have any

ability to truly predict the future.

Their next token prediction it's insanely valuable. There's an incredible

insanely valuable. There's an incredible amount of economic value in that, but they don't replace people because people we can go around the world and we can build a tremendous amount of context

that the AI will never be able to get and no amount of humanoid robots in the world uh will be able to still replicate that and we will deploy these as as uh

utilities for us as again we always have. We we we continue as a as a

have. We we we continue as a as a species to be able to deploy technology as a utility so we can get better things in life, better healthcare, you know,

better life expectancy, better food production, uh you know, better entertainment and and I I think this is again one of those technologies.

Why do you think that more AI CEOs don't talk like this?

Uh when you say AI CEOs, are you saying like Daario or Yeah, I think a lot of a lot of the CEOs of the big labs Yeah. don't have this don't talk publicly with this perspective.

I think Well, there's a reason I chose B2B software. Um I'm probably I'm

B2B software. Um I'm probably I'm probably on the more boring end of of uh of of this ecosystem. Um

I've read your Twitter. You're not on the boring end.

Okay. But but like you know there's there's a reason that I landed in enterprise software and not building an AI lab. So um so I I live in in reality

AI lab. So um so I I live in in reality with the practical you know implications and limitations of these of these tools.

Um I actually don't mind that you know Sam Alman or Daario or others you know either talk in the way they do or or have the ambition that they in the way that they do. I I believe by the time

the technology hits the real world, it will manifest just a bit differently and so thus the implications are a bit different. Um but but I I think it's

different. Um but but I I think it's kind of fun that we have different approaches to this. I think it would be actually very boring if if everybody was like hyper pragmatic and and practical.

Um you know, I think on the margin I' I'd rather have the more sort of you know, this is going to be a crazy utopian future than the dystopian, you know, angle that that some take. Um but

again, I think it's cool. we have a marketplace of ideas. There's lots of different opinions. Um, but you know,

different opinions. Um, but you know, when when you see things like, let's say, Daario says, you know, we're going to see this massive, you know, job dislocation or or 50% of jobs or I forget the exact stat, so I don't want

to misattribute um, you know, what what he said. Um I think the thing that that

he said. Um I think the thing that that that that just you know where where where it ends up being a little bit different than that point of view is again these jobs are a collection of

these tasks and and we have figured out how to automate tasks and the tasks are getting bigger but there still is an end point where a person has to come in intervene review something and execute

something and even in the cases and again this is where this is where I I'm informed by having now you know box has a few thousand employees So I'm informed by this, you know, as a result of that.

But but but even in the places where we say, you know what, we probably could have, you know, a smaller total number of folks in the company doing password

reset emails because we can just automate that. The way as as a as a

automate that. The way as as a as a resource allocator that I respond to that though is we put more resources in a different area of customer success that has always been underfunded

chronically because we didn't have enough budget to go apply there. And so

if I can make one job function more efficient, I I I gladly will take those dollars and reapply them into an area that is more strategic that we have not been able to automate and that I see no

sort of plans to be able to automate.

and and that's the much more dynamic nature of these organizations and of the economy generally um that I think sometimes doesn't get sort of brought into the you know big economic dislocation conversation.

How are you splitting your time and your focus between I have these two buckets that I I try to think about. So one is just um removing the current bottleneck for for any product I'm working on or

any company I'm working on. It's like we have a funnel like where is the biggest bottleneck in the funnel and like how do we make that better? How do you split your time between that and like magic moments? It's like, wow, we have there's

moments? It's like, wow, we have there's this new technology that does this crazy wizard [ __ ] and we can do something fundamentally new here. And how do you how do you split your time and attention and your company's time and attention

between those two things?

And to clarify, do you mean internally operationally or for the product experiences we're building?

I think both because I I assume the internal operations like lead to the building like magic magical product stuff.

Yeah. So sometimes they they are they they can be decoupled but but um I I think again as a pragmatist um probably 80% of the time is going into the

incremental bottleneck that we can deblock. Um even from a product roadmap

deblock. Um even from a product roadmap standpoint if you look at the AI that we are delivering a lot of it is just hyper practical like you have a lot of contracts you have a lot of invoices you

have a lot of research papers I want to extract data from that so I can put it into a database to automate a workflow that's where we're putting a lot of our energy because that's just a major pain point in the economy it turns out

there's like trillions of documents that all have important data in them that you would like to be able to extract automatically and we could then power you know workflows more efficiently as a

result of that. That that's where we spend a lot of our time. Equally though

we have a number of initiatives not a number we have a small number of initiatives that are much more like okay how do I go and automate the entire workflow of of you know generating a a

loan agreement or um or of doing a due diligence on an M&A you know um uh transaction and that's a longunning agent. It's going to it's going to do

agent. It's going to it's going to do you know hours worth of work. It's going

to read lots of documents. It's going to collate them. It's going to generate a

collate them. It's going to generate a report. Um, and that's much more like a

report. Um, and that's much more like a 10xstep function change in how that workflow works today. But again, we're going to pay the bills because we're going to do a very practical automation uh along the way. And so, you know, I

think internally that's probably how we we look as well, which is 80% of our time is is let's let's reduce that bottleneck. Let's make that more

bottleneck. Let's make that more efficient. Let's improve, you know, how

efficient. Let's improve, you know, how we respond to customers there. And then

20% is experimenting saying, you know, what would this workflow look like if we had cloud code go and just run it as a as a background agent um and do a lot more of the work as opposed to a you

know a um uh more of just a a you know a basic way of interacting with the agent in an ID.

Tell me about this automated due diligence uh research agent. Like is it working? Like what have you learned from

working? Like what have you learned from building it? Do you think it's going to

building it? Do you think it's going to work?

Um still very early. Um and um it will work. um uh simply because as we're

work. um uh simply because as we're finding it's just all a trade-off on how much compute you want to apply to the problem. Um you know we we always we we

problem. Um you know we we always we we have a lot of internal debates uh which are like you know like man we can make that thing happen in 10 seconds but we

know that the hit rate is 50% or you know 7030 or we can make it happen in 1 minute and we know it'll be 93%. I'm

making up all the numbers and and and then you know at at some point the customer is going to be like wow that wasn't very magical because it took a minute but you're like but you got the

right answer and the customers obviously would be way happier if they were in the 70% success rate in 10 seconds but they're not going to be happy if they're in the in the 30%. So, so all of these things are just product trade-offs,

which is how long is the customer willing to wait? Uh, do you give them the knobs to tune, you know, th those decisions? If you give them the knobs,

decisions? If you give them the knobs, can a regular person outside of Silicon Valley understand what those knobs even mean, or are they just now super confused and we're doing technose? Um,

and so those are that's like I mean but it's it's so much fun that because I we have we have spent this I can guarantee

we have spent more time on UX patterns that are completely um unprecedented UX patterns in the past year and a half

than probably the past 20 years of building a company because because for the most part over the past 20 years software didn't really change again

pre-post cloud like like we had buttons, we had tabs, we named the things, you could have a sidebar like like all of the UX patterns of software, you know, were exactly the same. We just got

better at at designing pixels in the in the past kind of 10-15 years. Figma

just, you know, made it so we could actually make everything look modern and have a good design system. But like

nobody really reinvented like fundamental we, you know, there would drop down menus. there was there was a you know a you know you know windows and

in inside of each other etc. In AI man like every single couple weeks we're like [ __ ] like like how should we expose the idea that this is going to be a longer running agent to to the person

and how much should be anthropomorphized as what you would do when you're interacting with a human versus no this should be actually like kind of behind the scenes and it's just software doing stuff. Um, and and that actually

stuff. Um, and and that actually obviously makes it so much fun because like we're designing a new form of software. The software is actually

software. The software is actually labor, you know, that you're interacting with in some form and that doesn't have the classic patterns of software. Uh,

and so we get to invent a whole new style of how we build these tools.

Mhm. One of the other interesting trade-offs there in on the product end is which model to use. Um, so you know, newer models, more expensive, usually

faster, but like you get much better results. Older models less expensive,

results. Older models less expensive, you know, maybe worse results. But

there's always that there's always that trade-off there where it's like, yeah, we could serve this to everyone and it' be amazing, but it would bankrupt us.

So, how do you think about that?

Um, in general, right now, I'm in the in the uh you should just always be using the best that there is. Um and you know in our case we're we're you know

fortunate enough where we we can we can afford to sort of say okay you know what we'll we'll spend a little bit less in that area to fund the subsid you know some some of the compute on this area we can move things around. I I'm I'm

certainly sympathetic to smaller startups maybe that don't have you know venture in them. you can't make those decisions as easily. But generally

speaking, right now, I think we're in a part of the curve where you kind of just want to always be betting on the better technology and um and and mostly because you will have a competition that does

and you will not be able to be the company that has an inferior product right now. Um and and then equally any

right now. Um and and then equally any company that is doing uh work to mitigate the quality issues of a of an inferior model relative to what you

could be getting from a better model that work is totally wasted relative to again you know real productive value creation. So, so you kind of and this is

creation. So, so you kind of and this is why it's, you know, you know, fortunately a lot of people are kind of building in public, but um so you get to kind of see these examples, but like this is why I think on a regular basis

every 3 or 6 months, you're kind of like moving up the cur up the stack from a scaffolding standpoint because you built scaffolding two years ago that mitigated, you know, context window length and now that's not an issue. You

know, for instance, we we built a lot of features. This is just to give you an

features. This is just to give you an example. We built not a lot. We built a

example. We built not a lot. We built a couple services internally because chatbt GBD 3.5 had a context window of I don't know like 6,000 tokens or 8,000 tokens or whatever. Well, that

scaffolding is irrelevant in a world of 100k tokens or 200k tokens, you know, effectively. And so, we could have like

effectively. And so, we could have like been like, no, let's, you know, let's really keep betting on that thing and the old model and but obviously you're like, no, we you should just instantly upgrade. Screw the software that you

upgrade. Screw the software that you built. Um, now let's just benefit from

built. Um, now let's just benefit from the model's capabilities. you know,

similarly as um as you have, you know, better reasoning capabilities, better multimodal modal experiences, more of these features will be compressed into one single model. And so, I think you kind of have to just bet on the

best-in-class models that are out there.

Unfortunately, cost be damned if you can if you can make it more efficient with like intent routing and whatnot on your own, great. Um, but but you cannot have

own, great. Um, but but you cannot have you cannot afford to have an inferior experience on any dimension right now and be competitive based on how fast the space is moving.

That's great. I totally agree. Um Aaron,

this is a pleasure. Thank you so much for joining.

Yeah, thanks for having me.

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