You'll read a lot of press that basically says AI is coming for our jobs is most of the press is not inside of big companies, seeing how much time we spend on useless activities that are necessary but not strategic.
There's a very, very long list of things that software never did before, that AI agents are perfectly primed to go do now.
And that's basically the opportunity set.
Now is the moment this window will end, in this window between a year ago and three years or so plus or minus from now.
This is when the next hundreds of great companies will get started.
So Aaron and I go way back.
I don't know if you remember this.
I went and searched my old bump.
So bump was my startup.
I searched my old bump email for Aaron at box.net.
Yes.
And I found very old emails where we would coordinate in Mountain View times to meet up with three founders.
Yes.
You, me and this guy named Sam Altman.
Yeah.
And we would coordinate to try to like do brunch or lunch or whatever.
And then I saw emails between us saying like, oh yeah well, Sam's going to probably like not show up again.
So that's how we go up.
No, he would always remember this.
You started Box probably before many of the people in the audience were alive.
Okay.
Let me just show off hands.
Who's like below 20 years old?
Wow.
Okay.
Great.
Yes.
Then accurate.
Very accurate.
Maybe to start.
Yes.
We're going to talk about AI a lot.
But to start, you went through another major transformation.
Yes.
See, I use the word transformation.
Yes.
Thank you.
Digital transformation.
Yes.
Around cloud, going to cloud.
Maybe just walk us through what that looked like at a very high level.
Maybe what is different or similar about the transition to AI now.
We started the company in 2005 and this was a time where you have to literally go back 20 years and think about a world where the internet was much slower, browsers were way worse, we didn't have the iPhone, we didn't have Android, Chrome didn't exist.
Everything was just way worse on every dimension.
We basically had an initial idea that, as the internet got faster, as you worked on more mobile devices, You'd want to be able to access your data from anywhere.
And that was the original idea of Box, where we said you'd go between different computers, you'd access your files, you'd share them, you collaborate.
So we launched the company.
It was initially focused on the consumer market or consumers slash, just kind of prosumer anybody that wanted to sign up.
We started to get a little bit of traction.
And by a little bit, we're talking like 10 people signed up in the first week or something.
So it was just very, very slow and steady growth.
And what happened was we got a little bit more growth.
We got some early funding from Mark Cuban and some angel investors.
We then dropped out of college.
You know, sort of more of an upswing.
We had a freemium business model, so we let people sign up for free and start to use the product.
And then one day we kind of ran into this fork in the road which was do we stay the consumer, go down the consumer path, or do we pivot to the enterprise?
And calculus was.
We felt like it was going to be way too hard to compete with all of the consumer technology platforms that would give away storage for free.
They'd sort of embed it into their operating system or their social network or whatnot.
It would be way too hard to go and monetize this.
So we decided to pivot to the enterprise where we could be cheaper faster, easier than a lot of the big incumbents at the time.
So we pivoted to the enterprise and we got extremely lucky on the timing because we rode this growth wave of mobile and cloud that we're sort of working in tandem to effectively create a new IT architecture within enterprises.
And so for us.
We got to ride this wave where, once we had better security, more functionality than a lot of the incumbent services, As companies moved to the cloud, they needed a way to share their data, access their information.
And so we became an increasingly obvious choice.
So that was the cloud wave.
And that kind of propelled us to where we're at today.
And there's a lot of similarities to the early days of cloud and the early days of AI, with maybe one big difference, which is the early days of cloud.
We were having to go convince people that the cloud was going to be this big deal.
And we had to go tell everybody that the future is going to be cloud computing.
It's totally safe to trust this with your data.
A lot of people didn't believe us.
And so that meant we just couldn't win deals in entire segments of customers.
So conversely with AI, you're no longer really having to convince people that AI is the future.
Everybody tends to be bought in in the enterprise segment.
There's a lot of still slowness in adoption in large enterprises, but it's not because people aren't convinced that AI is the future.
It's just because there's lots of natural sort of pace of change that an enterprise has to go through.
Why are they convinced?
Is it just that they themselves personally have used Chechi BT?
Yeah.
Is that the main driver?
Because I'm not aware of a lot of AI solutions that are deployed to enterprises that have really made a difference.
Yeah, I think it's maybe unlike cloud.
Cloud didn't have decades and decades of societal level conversation about cloud.
It just emerged one day, and it was like, this seems kind of cool and efficient.
But if you're in an IT department, the cloud was actually very scary because you're taking your servers that you manage, you can see them, you manage all the software for, and you're relying on AWS or Microsoft or Google to manage that infrastructure.
And so there was a real big shift on IT and the CEO or the head of marketing, the head of sales, they didn't really care how the infrastructure was delivered.
So you didn't have anybody kind of pushing on the IT org saying we have to go to the cloud, like nobody really cared.
AI, totally different situation.
We've had science fiction for probably 100 years.
That has basically said you're going to have robots, you're going to have artificial intelligence.
It's been in the zeitgeist self-driving cars, watching Watson on Jeopardy, using early products like Siri and Alexa.
It's been pervasive that at some point AI is going to get good enough that it's going to be this helpful aid for us.
And now that you have the chat chibi tea moment, where the head of marketing can go and play with chat chibi tea and be like wow, this seems to write marketing copy, maybe better than even my own marketing people.
You don't need to sell them anymore that AI is like clearly the future.
Now it's actually just about like how can you go implement something that's going to be safe reliable, works with your data?
You can trust it, which is now the new set of changes that all these companies have to go through.
Got it.
Cool.
So, Box started as basically like a folder in the cloud, effectively.
Yeah.
And then you added a bunch more stuff to that, but that is still kind of the core of it.
AI seems to be able to like completely change what you can do.
Yeah.
Let me just help us understand what are those cool things that you can now do for big companies.
Yeah, so for us, the exciting thing is that AI agents basically thrive on unstructured data.
So if you think about it, there's basically two data types that really matter in the world.
They're structured data.
This is what goes into a database.
You know, if you launch an app tomorrow, you're going to start with a database and the stuff that's going to go in the database are like customer names and IDs and user IDs and all that.
If you go to a big company, the stuff that goes into a database is all of the invoice numbers and the the client record numbers and the amount of revenue they generate and their distribution partner names.
That's what's in their database.
Then they have a lot of unstructured data and that's all of their documents, it's their contracts, it's their invoices, it's their marketing assets, it's their presentations, all of that data.
The vast majority of data in the enterprise is that content.
It's all of this unstructured data, and it's called unstructured because basically it can be totally free form text.
There's no inherent kind of, you know, kind of computer structure to it.
And so the problem is is all of the data that goes into something like box?
Historically, you've never been able to really automate anything about it.
You know, if you just think about two years ago, you can't go to your your sort of all of your files and ask them a question.
You can ask a question in your database.
You can, you know, say, please find me all of the records above the, you know, following value.
You can't do that in your files because the computer doesn't know how to read all those documents and understand what's in them.
AI agents basically changes this.
So all of a sudden, all the data that's inside those folders becomes immensely valuable to companies, because now they can ask all that data questions.
You can begin to automate workflows around that data.
Our whole vision is basically what if you turned all of this information into this new kind of corporate asset or set of knowledge that companies can operate off of?
And that's where I think there's going to be immense startup opportunity is a world of how do you have AI agents for almost every task or job function in the enterprise.
Let's talk about that then.
Let's say this world emerges.
And we have AI agents that do a bunch of jobs.
I think a lot of people are worried like oh, that means that we don't need the humans to do those jobs anymore.
And I know you have like a strong perspective that like no, actually it will go the exact other way.
Yeah.
Tell us why you believe that.
And you sort of assess how valuable is all of that work that's getting done, how valuable is every email you send and all of the time you spend going and finding information, or all the manual work it takes to read data, kind of look at that document, extract information from it.
Versus the time, that really is the high impact stuff.
You're with a customer.
You're coming up with a product breakthrough.
You're supporting a customer to use more of your product.
And you kind of did a ratio of that time.
The vast majority of time inside of a company is on the stuff that really is not strategic.
It's sort of necessary work, but it's not strategic to get done.
So, when you think about that ratio, if you could free up a company to work on the stuff that's strategic and not the basically un-strategic stuff, that doesn't differentiate them Most companies actually have a large set of things they would go do at their time.
They would spend more time on breakthrough innovation.
They would spend more time with customers.
They would launch more marketing campaigns.
They would proactively support their customers instead of just being reactive.
The reason why I think the press gets this wrong and you'll read a lot of press that basically says AI is coming for our jobs is most of the press is not inside of big companies seeing how much time we spend on useless activities that are necessary but not strategic.
And so when I go talk to companies and I say, what if you had AI agents do all of this kind of work, they instantly their eyes light up because they realize well, now I can actually free up my time and my employees time to go do much more interesting things.
Or They start to have this list of all of this work that would be much more strategic if it got done, if AI agents could go and do it, as opposed to the work that never gets done because it's too unaffordable and it's just economically not viable to go in there.
Yeah, this is like the backlog of stuff in your company that you're like all right, if I had more people I could go do those things.
Exactly.
And basically there's an entire category of work where, if you just did like pure microeconomics, I could pay for the labor to do that work.
If I knew that it would produce enough value to pay for that labor.
But the threshold of starting that work is too high.
I can never even try and see if it's useful.
So I would.
I would argue that literally the like if we go 10 years into the future, The vast majority of work that gets done in 10 years from now will be work that today is in that category.
It's the work that, like right now, we can't even attack because we're like I'm not gonna hire somebody.
Pay them 120000 a year to just see if that thing produces value.
So I'm never gonna get around to it.
And then, in 10 years from now, when you just deploy AI agents everywhere to go do those things, we will be doing so much more as a company.
When we launch an ad campaign internally, We translated into like three to five languages.
Our top markets, that's about all we have time for.
Because it's just too expensive.
It sort of just hurts your brain to think about doing it across every segment of the market, in every region.
When an AI agent just takes an ad copy, translates it into 100 languages, Our company will just grow more.
We will just be in more markets.
We will serve more customers and agents will be the reason that we were able to do that, where previously we were bound by people time and we would never have been able to justify getting that work done previously.
Makes a lot of sense.
And yet today, Amazon announces that you should expect that they have fewer headcount over the next few years because of AI.
I totally agree with what everything you just said, but then the press sees these announcements.
What are they to make of that?
To be fair, I only saw that snippet literally one hour ago.
So I didn't see the full memo.
I'm sure Andy Jassy had some other thoughtful points.
This is why startups are in such an incredible position.
I think if you're at the point where I don't know the corporate headcount of Amazon, but let's say the total headcount is in the hundreds of thousands to low millions just across every delivery function, et cetera.
I could totally see the scenario where for them, they're like okay, given the markets that we're in, given the things we do.
You know, if we can't get this done with hundreds of thousands of people and AI agents, don't just augment that like we're probably running the company wrong.
I'm just picturing that's the internal kind of corporate meeting.
But now imagine the 50-person company where all of a sudden they can act like a 500-person company.
Then you just have to ask yourself if the 50-person company can act like a 500-person company because of AI, will that company become a 100-person company more quickly than pre-AI?
And then that basically tells you, does this thing create jobs or not?
And my argument would be that the 50-person company that is in more markets, serving their customers better, doing better research on their customers.
They're more armed with the next feature they should build.
They can build that feature faster because of, you know, cursor, windsurf, et cetera, a replica.
Will that company grow more quickly in a post-AI world on the human side?
I would argue yes, because they need to get themselves into more markets.
They get more done.
So I think it's more of a case of you're going to read headlines about the biggest companies Amazon, et cetera.
And I think there's a case being made where AI is an efficiency gain for them.
But now the hundreds of thousands of startups and small businesses, or millions of startups and smallest businesses, I think it becomes an economy where they can get so much more leverage than ever before.
Tell you about startups.
I think maybe a lot of folks in this room look at the B2B SaaS companies or the enterprise SaaS companies and just think oh, every problem has been solved.
There is a big company incumbent.
You are one of those big company incumbents.
How should they think about starting a company that could one day take down a company like yours?
Not your specific, like the other guys.
I'm not going to give you any advice on taking me down, but I'll give you advice on everybody else.
So interestingly, it's a very fascinating proposition in question.
So, starting with consumer for a second, three years ago I was having these kind of like, not like existential questions, but like deeply, like deep philosophical questions.
What year did you join YC?
2022.
Okay.
Actually, so great timing.
So around 2022, I kind of made this list of like nouns and verbs just as like a just a fun kind of mental thought experiment of like think about all the nouns and verbs of like what we do in our life.
We eat, we sleep, we travel, we watch something, we are entertained or whatever, and I went through that list.
And, you know, the list is not a thousand words, right?
It's like 50 or something.
And basically, down that entire list, I tried to plug, has that problem been solved?
Relative to like 15 years ago, just choosing an arbitrary point in time.
If we had this conversation in 2008 and we said you know music travel entertainment hospitality food, all of these things, we would basically sit around and be like all of these things kind of suck.
It kind of sucks to get food.
It kind of sucks to listen to music, get to download illegal music.
Everything was painful.
Fast forward to 2022, we've solved a lot of problems.
Like, when I want food, it comes in 20 minutes from DoorDash.
When I want to listen to music, it's on Spotify.
When I want to watch a movie, I got Netflix or YouTube or whatever.
So it was a tough environment for startups.
Because you're kind of like wow, now we're really only able to do derivative things because the core nouns and verbs have been solved.
And coincidentally, YC basically created half of them and or more.
And so basically, we had this period of 2008 to 2014 where every noun, every verb just got solved.
The same was largely true in the enterprise.
So now do the nouns and verbs in the enterprise.
Payroll, CRM, email, calendar.
You just go through all of those things and basically every problem.
I had some kind of incumbent or like at scale startup, which is very bad for startups because you basically had this era of companies that knew how to build a modern technology and they were solving these problems.
You don't really want to compete with gusto, because gusto is still a modern, really good payroll system.
There's not a lot of vectors into competing with gusto.
So that was three years ago.
Today.
It's the first period in probably about a decade where I'm extremely confident that there's now a new set of nouns and verbs where startups are in the right position to go and create the next set of solutions for because AI has created enough of a change in the landscape to create those opportunities.
They're not going to be always the most obvious things that you start out with.
Like it won't just be like Oh, it's CRM, but with AI, because Mark Benny, often sales force, is going to do CRM with AI.
Like, like he's going to figure out a way to do that.
He's they're very good at executing.
Like that will happen.
But there's a very, very long list of things that software never did before, that AI agents are perfectly primed to go do now.
And that's basically the opportunity set.
Which is, what categories of professional services or work is there no incumbent technology for that?
AI agents are basically finally able to go and solve.
And there will be 100 startups that get created between last year and in three years from now that all become Five 10, 20 billion dollar companies because they're able to find the next set of nouns and verbs, or a mixture of nouns and verbs that are like okay, it is legal work for this thing.
And there's an agent and for first time in history, you can go and deliver that by a software, as opposed to it used to only be able to be delivered by people.
And that's the opportunity that I think everybody has.
Very cool.
YC's next batch is now taking applications.
Got a startup in you?
Apply at ycombinator.com slash apply.
It's never too early and filling out the app will level up your idea.
Okay, back to the video.
When you create one of these new nouns or verbs, a lot of them don't look like software in the sense that we think about software today.
Where you like, sell a company, access to some number of seats of the software and the humans click the buttons and type the keys.
How will business models need to change or will they change?
How will companies charge for these things?
Yeah.
So if you were building a SaaS company again prior to literally this year or last year, your only real monetization strategy was how many humans are there that need licenses to buy software?
And in the SaaS world, we call those seats.
And basically, how many people need a seat of that software?
And you were maxed out based on the demographic size of that particular category.
So if I sold software for lawyers and I go to a company, I can only sell the amount of licenses as that company has lawyers, which is like a huge you know limiter to the addressable market size of your company.
And so agents basically completely blows that up, because all of a sudden you can have AI agents that effectively contain the labor of that job function in the software itself.
So you can go to a company and you can say I know you only have three lawyers, But my agents could do the amount of work of basically unlimited lawyers, which means you're obviously no longer going to sell based on the number of humans in that company related to legal work.
You're going to sell based on some approximation of the amount of volume of work that has to get done related to legal work.
And that's the new monetization strategy that I think we all have, which is let's make the example of you're doing some kind of legal review of some set of contracts.
And you basically say OK, previously a human would cost 5 or 10 per contract to review based on human time.
AI agents, you don't tell them this, but AI agents, let's say, can do this for $0.10.
So then you charge a customer $2.
And all of a sudden, they're like, wow, this is incredible.
You've just saved me 80%.
And you're now extracting, obviously, a very meaningful profit from that.
And there's no particular limiter to how much they're going to pay you.
It's just going to be how many contracts do they have to go through the system?
And so every company I think every space is going to have a slightly different version of that business model.
But the new business model is some form of consumption.
The only concern that you have to have of going of overly veering on consumption is the recurring nature of the revenue.
You generally want to be in a position where you have some kind of subscription fee for your software, as opposed to only being paid the moment that it happens, because then you run into this problem where the customer plows through your system and then next year they don't show up because they use what they need.
They've reviewed all their, So you have to figure out how you basically keep some kind of ongoing recurring revenue stream.
But besides that, you're going to see more of a consumption orientation with AI.
And I guess what you're saying is you think the prices these AI companies will be able to charge per unit of work let's call it or outcome will be a fraction of the human cost, as opposed to what we would maybe consider more of like a software cost.
Because if it costs 010 to do the job, are people really going to be willing to pay 2 for a thing that they know costs 010?
Well, the question is basically, how much software do you have to build on top of the AI tokens?
And it's very obvious.
Imagine a continuum where there's almost no software.
You will get price compressed down into 2x max of the token cost versus a world where there's like, a tremendous amount of software.
You could probably support 80 or 90 gross margins, which means a 5x plus 8 or 9x plus increase over the tokens.
I'm not going to tell you guys the number because it's proprietary.
But if I told you the amount that we spend at box on storage of storing files, You would be surprised.
Because you would say, well, I thought you were in the storage business.
But the reality is what customers are paying for is all of the software above the storage.
So eventually we're going to get to a point where customers are no longer going to just be paying for the intelligence tokens.
They're going to be paying for the workflow software that goes on top of the tokens themselves.
They're going to be paying for your ability to build AI agents that have a unique set of context and connections and capabilities and access to data that can command a meaningful premium on the underlying AI token.
Totally.
Yeah, I mean, I saw this at Google Photos.
And when we proposed that we should build Google Photos, a lot of people at Google were like why would you do that?
You can never make money.
It's a commoditized market.
It's just storage.
There's Amazon.
You can just store your photos there.
And it turns out it's like a 90-plus percent margin.
It's really great.
And this is what's amazing about we are all incredibly lucky to be in, effectively.
I'm sure there's other industries, but let's just say one of the top slash of the top industry that has deflationary economics on the supply side.
The reason why that matters is because it means that that over time, your raw materials will get cheaper.
You know, you don't have to basically raise your prices in perpetuity like many other industries.
You can actually just get more efficiency gains over time.
And so you know.
I don't know the latest price on Google Photos, but let's just say it's 10 or 20 bucks a month, right?
If you told anybody, we'll just store all of your photos ever created for 10 or 20 a month or whatever the number is.
You'd be like, yeah, that's fine.
That customer doesn't need to show up and say no, I'm only going to do it for 8, because I know your costs are going down.
They're like, fine to pay $10 for all their photos to be stored.
Yet every year, you know your underlying costs are going down.
That's what's going to happen in AI.
As long as you can find how to not be so greedy that you're pricing, it's kind of offensive.
You know, look at like again windsurf replet, cursor et cetera, like we're at like non-offensive levels of pricing.
It's 20 bucks a month, it's 50 bucks a month.
But we know that in 10 years from now they'll probably be able to drive down their raw materials lower, but we won't be able to command lower prices for those things because it's just like within a reasonable amount of spend.
And so you always want to be in a technology category where that is happening, and probably by being here today, you've effectively chosen to be in that kind of category.
Yeah, and I think that's true so long as there's not infinite competition driving prices down.
Okay, but here's what's amazing.
So let's just take, we haven't been in this war directly for a long time.
We pivoted from consumer to enterprise, but let's just say Google photos for a second.
Everybody knows Dropbox.
Would you agree that Dropbox has been in a infinitely competitive war for 10 years?
Yeah.
Okay.
And the company generates somewhere on the order of a billion or so in cash a year.
No economist in history would be able to understand this.
They would be able to say, wait a second, storage is getting commoditized.
How is it that people still pay 10 a month for something that has basically limited switching costs and other choices in the market?
And people build familiarity.
There actually are switching costs because there's some data network effects.
Sure.
There's user experience things you get used to.
So even in a world where of hyper-competitive thing, as long as again, you're not getting too greedy on your pricing, then you can usually land in a spot where people will stay with you as long as you're innovating.
Makes a lot of sense.
Okay, one of the topics I've heard from folks in the audience throughout the last couple of days is if you believe that AI is going to really keep growing for the next five years, which I think most of us do, are we not going to be in a world where companies just build all of their own software internally?
Instead of hiring box to do a job for me, won't I just command my AI agents to go write the software that emulates?
What box would have done?
What's your take on it?
The reason why I'm not afraid of it is there's a concept that I think Jeffrey Moore, if you read, I'll leave it with a couple of books that you should definitely read.
This book called Crossing the Chasm is probably one of the top five business books ever and you should definitely read it.
And right after you read it, read something called Innovators Dilemma, which is basically the number one business book of all time.
But in this book not this book, but the author Jeffrey Moore came up with this idea of core versus context.
And the premise of Coreverse Context is every company has to decide what is core to their business, with context to their business.
If you are Disney, core to your business is like, you know, designing amazing IP and characters.
Context to your business is your HR system.
So what does Disney need to do?
They need to get really, really good at technology to make Pixar insanely powerful.
They don't need to get really, really good at technology to run their HR department.
The Disney value proposition doesn't relate to, you know, did they pay their people on time or not?
They just need to pay their people on time.
They don't need to, you know, they don't need to innovate on that.
Every company has a choice of what time they're spending on innovation versus again like.
I just want that to be an autopilot.
And so this is more where, just because you can do something, the vast majority of the world doesn't end up doing something.
And coding your own custom software for every single bespoke need in your business tends to be in that category.
Because most people basically say, you know what?
Cool, I can build an HR system and I can go negotiate with Workday or whatever to lower my pricing.
But here's the problem.
Three years from now there's going to be a bug.
That bug is going to pay people the wrong amount of money.
I don't want to have to go and call my IT team in the middle of the night to be like shit, you have to go fix this bug that paid everybody the wrong amount of money.
I want to be able to go to a company that I know that I can sue if they fuck up or switch to a competitor.
So you don't want to be having that liability for things that are context.
You don't get the upside of getting really, really good at that.
That's how most companies work and operate.
I read these things from Klarna and others where they built their own systems.
And I think it's fun to read about.
I think it's very novel.
I'm glad that they're doing it because it lets us have this conversation.
I think it's going to be basically useless.
And other than for being cool fireside fodder, most companies won't do it.
Now I'm still very bullish on custom software because there are lots of things inside businesses that are custom software for the core where actually the company can't get around to building custom software for the core parts of their business.
And so having things like replica cursor or Windsor for whatever is actually very useful, because now they can go and work on software for those things.
And that actually I think ends up being very powerful.
Okay, we're going to open it up to audience QA, but before we do that, while you guys queue up, my last question is just this is an audience of you know college students, grad students, recent grads.
A lot of them want to do a startup one day.
What's your advice?
What should they do in this new world?
Read innovators dilemma, read crossing the chasm, read blue ocean strategy.
Those are the three books you have to read.
If you do what's in those books and you are going after the B2B market, I guarantee you you will be 10 times better off than any other startup.
That is just starting from scratch.
You will have a way to think about markets disruption.
What incumbents are vulnerable?
Which ones aren't?
If you really deeply internalize them, you will be so much better off.
That's the first thing.
Second thing is have an incredible founding team.
I'm, I mean, I know solo founders that will happen for sure, but just try and grab one friend.
They could be like the least technical friend of all time.
Just be in the grind with somebody just because you're going to have more fun.
You're going to see through more difficult times together.
So have a team that you really are excited to work with to kind of get through anything.
Do not underestimate the need for tailwinds in your market.
So make sure that you're going after a market where I'm just going to assume everything at this event is AI obviously, but if your market is not truly transformed by AI, don't touch it.
It's just not worth it because you're going to be fighting against a headwind that is just unnecessary to fight against.
Go after markets where AI fundamentally changes, like the very economics or or you know actual process of that thing.
So you always want to, want to ride a tailwind.
So ride a tailwind, have a great team, build a big vision.
Now is the moment this window will end.
It'll, it'll be over in two or three years from now.
You're in the window right now where, you know, maybe it won't be your first attempt.
Maybe it won't be your second attempt.
Maybe it won't be your third attempt.
But in this window between a year ago and three years or so plus or minus from now, This is when the next hundreds of great companies will get started.
So be ambitious because these windows don't come.
But for more than every 10 to 20 years.
So I would exploit that, take advantage of that.
In five years from now, you can be less ambitious.
But in the next four years you've got to go big, because these are these windows that give you that opportunity.
Love it.
All right.
Start over here.
Well, so I have two questions which probably have the same answer.
Oh, cool.
Very efficient.
I like this guy.
But yeah, so I want to know like in the core of your business that is storage like only in the core.
Do you see a space for AI to manage anything or help with anything?
And the second is in the storage space.
Are there any new age startups that perhaps like use some emergent technology that can better storage?
Or is it a solve problem?
Yeah, I think if you're if I take your question very literally at like the, do you mean like the literal hard drives of storing the data?
Anything like software.
Yeah.
Level behind sharing, storing.
Yeah.
I think it's, and I invite anybody to try and come up with a new thing.
I think the storing of the data is a pretty solved problem.
The thing that I think AI could augment is like just to get so boring is things like lifecycle management of the data.
In our business, Google Photos, you tend to have this curve, which is the most active data.
It needs to be in basically the part of your servers and in the regions that are most fast throughput and fast access, versus the stuff that nobody ever sees.
You can kind of store in some archive.
AI will probably help with that because it can kind of predict what data people want to access.
But higher up on the stack, that's where the transformation will be.
What do people do with their data now?
Not just the storage of it, but how do you turn that data into something that's much more valuable than just a document?
How do you turn that document into a new type of intellectual property or value for that company?
My name is Charlie.
I'm a game designer here in the city working at a startup.
I graduated from USC last year in computer science and game design, which is the best major ever.
And my question for you is what is the meaning of life?
Wait for reals?
Yeah.
Oh, man.
And you're 24, right?
Yes.
Yeah, I think you won't fully understand it, but you're in a period right now where you're just in grind mode and I would just recommend just do the grind thing.
This is not a period where you have got to do meaning of life stuff.
Over time I think you start to Better have a sense of okay, like you're on earth for some amount of years, you know like you obviously want to try and have as much of an impact as possible on something.
So can you help society in some way?
Like that's the, that's one part of fulfillment.
And then and then you have another set of more personal things.
You know kids' family, that side that has to be fulfilled.
But again, like you're in your 20s, so like I was just heads down grinding, not overly worried about the meaning of life.
So I'd say like, put a pin in that, be nice to everybody, like help the world as much as possible.
Check back in in about five years.
Like now is your window for just being super commercial.
Okay.
So thanks so much for being here today, Aaron.
My name is Gary and my question is about, like enterprise products and their relationship with design.
So earlier this afternoon, like Dylan Field joined it, was on stage talking about the growing value of designers, especially as AI makes the development process faster.
Yes.
And how that might imply, how craft and like great design, becomes a bigger differentiator for SaaS companies.
But when I think about like enterprise products and their motivations to just like plainly deliver value, at least for some of these companies, how do you think about like craft and great product design when building enterprise products?
And how does that maybe change as box has developed?
I mean Enterprise Software for sure, historically has not had good design, because the people purchasing the software tend to not really care about the design of it.
They just need to solve a particular kind of utilitarian task.
So it's actually been more voluntary that the companies decide to have really good design.
So over the years, companies like Slack Figma, certainly others, have said we're going to prioritize great design, even if Figma had to because of its demographic.
But we're going to prioritize great design because we should just have better software that people can use.
So I think this has been a trend over time.
I would highly recommend building just great looking, feeling, experience enterprise software.
Even if the customer doesn't incrementally value that from you, it just makes it much more fun to build software.
And so you absolutely raise the bar and try and build amazing software.
And some customers will care, some won't, but you'll feel a lot better about what you're producing.
Thank you.
Yeah.
We have time for maybe one or two more.
OK.
Hi Aaron, my name is Orion and I have actually built a unicorn startup in China for the past 10 years.
Wow.
And planning to come to the US and start something new here.
I know number one step is to join VIC for sure, but I want to know your answer.
I know you touched on this a bit in the previous question that So if we do things in HR in AI, how should I answer the question about like competition from Workday in the future?
Since it has a massive amount of the user data about like what the user like, about the candidates, about the performance, all that.
Since you're sort of incumbent in the data storage like space.
And I know Workday launches a lot of agents this recently.
I don't know if it's mostly PR or it's actually product.
But I want to know your perspective on that.
And I guess a second question is what's your view on clean knowledge management?
I think it's always good to to overestimate your competitors capabilities and then figure out your strategy in a world where they have they have those capabilities.
So what I would I would do is I would I would assume all of Workday's agents are amazing and then figure out what strategy would be competitive with them.
So, you know, a couple examples.
One, you can go after parts of the market that they're just not selling into.
Workday only has somewhere in the order of, let's say, 10,000 customers.
Well, there's like 10 million businesses globally that would be relevant for HR related agents.
So at a minimum, go sell to everybody else.
That's not a workday customer.
Then there's certainly going to be use cases where they're not the natural provider of an HR agent.
And that will also be then the right opportunity for a set of startups.
But this is why I'm very bullish on startups right now, because I think incumbents are only going to be the agent providers for their existing install base, which means that there's going to be tons of opportunity for so many more agents that those incumbents aren't already selling into.
On Glean.
I think I like them, but I think there's going to still be lots of different approaches to enterprise knowledge management.
All right.
I think we're out of time.
Awesome.
Everybody, give it up for Aaron.
Thank you.
