How AI Is Changing Enterprise

The Lightcone hosts sit down with Aaron Levie, the co-founder & CEO of Box, to hear reports from the front of how large enterprise and Fortune 500 companies are adapting to the AI age.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Wait a second. If we could use AI to automate more, we can build more. If we could build more, we could lower the cost of things. If we can lower the cost of things, then we can actually lift up anybody's lifestyle right now.

• 0:10Garry Tan?

  I think that we're in the middle of the revolution and the revolution does not have to be Black Mirror. It could be something that is driven by Jevon's paradox, driven by abundance for everyone. And that's certainly the timeline we want to be on. So.

• 0:25Speaker 00

  That's the future I'm betting on.

• 0:33Garry Tan?

  Welcome back to another episode of The Light Cone. I'm Gary. This is Jared, Harge, and Diana. We're partners at YC and collectively we funded companies worth hundreds of billions of dollars. And today we have a really awesome guest, Aaron Levie of Box.

• 0:53Speaker 00

  Love that intro.

• 0:55Garry Tan?

  Aaron, you're one of the best product CEOs out there, public company as well.

• 0:59Speaker 00

  That's what I write in my Wikipedia.

• 1:01Garry Tan?

  Yeah, that's how I classify you. We're in the middle of the AI revolution. So how are you feeling?

• 1:08Speaker 00

  Oh, pretty good. It's a good time to be in software right now. So yeah, feeling pretty good today.

• 1:15Harj Taggar?

  Something we've been speaking about for a while, which I think we probably agree on, is that the chat GPT wrapper was like a bad meme and that actually there's like lots of value and always has been in building apps on top of these foundation model companies.

• 1:27Speaker 00

  In fact, the opposite might be true.

• 1:28Harj Taggar?

  Yeah. Which is going to be more valuable in 10 years' time.

• 1:31Speaker 00

  Yeah, I think so. So it was interesting. So there's, there's like probably 2% truth in the meme and then 90% not truth. And so, I mean, PG, you know, with the sort of wedge theory is like, actually, you do want something that is sort of a simple product that finds a little wedge and then you expand from there. You know, in the early days of cloud, if you were to be building software that let you manage documents and data, you would have been like, well, that's a wrapper on Amazon. And it was a total misunderstanding of the entire scale of software you have to build to make the storage bucket be useful in a particular application. So on the wrapper conversation, the exact same thing is true, which is how much software do you need around? On the workflow and the proprietary, you know, sort of business logic and the data that the customer brings.

• 2:15Speaker 00

  That's actually the value, not the, not just like, what is the, the set of tokens that are coming out. Where it's a little bit true, why, why startups should be, you know, at least think a couple of steps ahead is you probably don't want to be something that just ChatGPT would incorporate. So it's less that the model will incorporate your value proposition. It's more if there is, you know, if the model provider also has a consumer scale application. Like you don't want to be right in the way of something that ChatGPT will just fold in directly into its functionality. So in that case, I think you have to be, you know, sensitive to being kind of, you know, a quote unquote rapper.

• 2:46Harj Taggar?

  How do you separate out stuff that's going to get incorporated into the model from stuff that won't? Cause I feel like the hard part with that often is we don't know what the next models are going to be capable of. And there's this general sense of, oh, well, like anything could theoretically be incorporated into the next model. If it's powerful and approaches some version of generalized intelligence.

• 3:04Speaker 00

  Yeah. I mean, I, I look at all this through just the B two B lens, which I know that then probably you just lost half the podcast listeners.

• 3:13Harj Taggar?

  B two B sales is cool these days, actually. Yeah.

• 3:15Speaker 00

  On the B two B front, it, to me, it's a, it's a, it's a simpler question because an enterprise doesn't want a model. It wants an outcome. It wants an outcome of customer support, you know, conversations being answered or, you know, healthcare, you know, transcription going into an, an EHR system or an automated workflow of reading documents and contracts and plugging that into a, a contract workflow. So the model getting more intelligence is actually usually a better thing for anybody building software in those use cases. Cause then you're doing less in terms of, of, of, you know, hacking your way into the model because it's sort of insufficient at, at solving that particular problem. But what the customer actually wants to buy is like, I need software that will plug into, you know, my ERP system

• 3:58Speaker 00

  that will plug into my support system that will power the workflow that, that lets the customer do a password reset. Like that's actually the, what the customer wants to buy. And what the model is doing is, is sort of, you know, really abstracted from the ultimate customer value proposition. So I think as long as you're, you're building, you know, software that, that really can deliver that full outcome to the customer. And, and, you know, two years ago. The initial wave of these use cases started to emerge. And I think the companies that will do best are ones that realize that you need to abstract the model away as much as possible from the ultimate value proposition. And then you just incorporate all the model updates as quickly as possible for your customer. And again, they just buy the outcome of customer support. But it's just getting better and better every time there's a model improvement. So.

• 4:43Diana Hu?

  I wonder, maybe one good analogy is when you were building box for your customers. They didn't care what was the underlying database or cloud or what was the networking gear or all the hard drives that were running. It was all about the end user experience at the software level.

• 4:59Speaker 00

  Yeah.

• 4:59Diana Hu?

  And the analogy to today is the end users of B two B AI workflows don't care whatever model is or how it does it.

• 5:06Speaker 00

  Yeah.

• 5:07Diana Hu?

  But that ultimately does the workflow.

• 5:09Speaker 00

  Yes. Yeah, I think that is absolutely the conclusion. You'll often get some idiosyncrasies in different organizations where they do care. Okay, where is your data center hosted? Or what's your infrastructure provider? But that's a small minority. In AI, I think we're going to go through a temporary period where you do see differences in the models for anybody that has a discerning set of skills on this front. So you can see this in like cursor with Anthropic, right? Like people like that combination and they can sense the differences of the output. But if you fast forward five years, I think you'll see a convergence of basically models and intelligence to the point where you wouldn't really distinguish the quality levels that

• 5:52Speaker 00

  much for 90% of business use cases.

• 5:55Harj Taggar?

  It's definitely interesting to see how especially developers have developed different preferences for different models. Like I remember at our AI retreat a few weeks ago, Anthropic and Claude has also emerged as like the preferred LLM to orchestrate your agents. Like if you have multiple agents and you want the LLM to intelligently call the right ones, people seem to prefer Claude for that. What do you think is going to happen to the model companies themselves in this world, though?

• 6:17Speaker 00

  Probably everybody needs to update their understanding of what a model company is, just in general. I actually think there's very few model companies. There are sort of AI companies that have model frontier model labs, but. Increasingly, they're selling software to either consumers or businesses. Like, I don't even know who I would consider to be a pure play model company at this point. You know, Anthropic, if you look at their their software revenue, um, you know, it's it's effectively an API business for for enterprises. I'm sure they have, you know, some some large scale consumer kind of cloud business, but you're really paying for the security, the compliance, the governance, the privacy, the uptime, the SLAs, talking to somebody that that, you know, manages your account.

• 6:59Speaker 00

  And the the model just continues to sort of switch out underneath all that. If you look at OpenAI's revenue, anything that's been kind of leaked publicly, it's very clearly a software company at this point that has AI models that that power its underlying software. Google, obviously, you know, is just GCP. And then and then Meta doesn't need to monetize it because they can just open source it. So maybe XAI is almost the closest thing to now a model company, but that will show up in Grok, et cetera. So, you know, what you probably wouldn't want to do right now is is start a pure play model company expecting you're going to have like licensing, you know, revenue by just selling your model to to people to go and and use. If you don't have enough other kind of surrounding value proposition that that, you know, again,

• 7:42Speaker 00

  lets you get incorporated into into enterprises or has a large scale consumer application where you have some degree of kind of traffic that that, you know, keeps people within your ecosystem. I think it'd be very bad to be just a pure play model company in at this moment, just because you have you have enough different business models that have emerged now in AI where where it's going to be pretty hard if if your pure play business model is just pure AI tokens, because, you know, you always have Meta that will will always create a counterbalance by just open sourcing, you know, a frontier model that that kind of wipes you out. And now, yeah. And now DeepSeek. And so, and and and that's what's amazing is like now that like you can basically guarantee Meta has to do anything DeepSeek does, because it obviously has to stay in the game on the open source front.

• 8:27Speaker 00

  And so we always have, uh, there's enough dynamism in this industry that that basically ensures to to Gary's opening point that like the cost of intelligence is going to go to zero. Like, it's just like absolutely guaranteed.

• 8:37Diana Hu?

  So extrapolating, what does it mean for startups as intelligence becomes commodity, basically?

• 8:44Speaker 00

  Well, the good news is we kind of know, we kind of know the playbook on this. With one X factor, which is like AGI and like, what is the ultimate, you know, kind of

• 8:52Diana Hu?

  X factor.

• 8:53Speaker 00

  Yeah, exactly. A little bit of an X factor of like wiping out all of, all of relevant business models and we don't even have money in the future. But like, if you, if you put that to the side, I think these, these companies need to look like software companies. And, and it is, it's sort of back to basics, which is, you know, we used to have an API into a database. We had an API into storage. We had an API into compute. Now we have an API into intelligence. That intelligence is, it should be, you know, basically the cost of that intelligence will go down to the cost of the bare metal. So whatever the underlying cost of the GPU is, that's what you're going to pay. You know, with a little bit of margin from a hyperscaler, but the, the cost of the actual tokens, you know, will converge at zero. And so then it's all about, you know, do you build software that takes, you know, this

• 9:36Speaker 00

  complicated technology and delivers it to customers to solve real world problems? And so, you know, you guys have talked a lot about vertical, you know, AI. I think that's a massive play. I think there's going to be certainly a whole layer of AI software that, that kind of stitches together different AI systems. So you have horizontal plays, you have vertical plays. I think the idea of every single industry and every job function probably will have some degree of new startups and agents that they get built out for those, those, those like slots. I don't know if you guys have like a whiteboard of every industry and every job, but like, but like you can just basically play, you know, bingo on that. And then, and then until it's fully filled in, like there's probably still opportunity left within AI. We figured out the first wave of SaaS of, of how to do this.

• 10:21Speaker 00

  You know, YC was obviously a big part of, of a number of major kind of category killers in SaaS. And I think we'll see the same playbook happen in AI.

• 10:27Harj Taggar?

  One of the many interesting things about DeepSeek was specifically is the first open source reasoning model. Like in the short term, do you think there's like new ideas in the enterprise in particular that are going to come out because now we have open source reasoning models?

• 10:40Speaker 00

  So what we've seen is, so we, we do a number of benchmarking, a kind of exercise internally for, for the reasoning. Models versus versus, you know, kind of let's say non reasoning models and some things that they're actually better at, some things they're weirdly worse at. And I don't think we've even discovered why they're they're worse at these things. Maybe they overthink a problem. In general, I would I would kind of just argue that anything that directionally improves intelligence. You will see B two B use cases, you'll see the value of those use cases go up because, you know, you can begin to reasonably chain together more. More agents working together. You can get more agentic workflows happening. Like anytime we can get the intelligence factor to go up.

• 11:25Speaker 00

  I can now reliably introduce this for a more important business process. And so, you know, in the enterprise, you can almost think about it as there's some probably, you know, either two by two or chart. I don't know if anybody's made it, but like, kind of like mission criticality of the workflow, AI level of intelligence. And and and, you know, kind of there's an element of like, you can't introduce it to, you know, closed banking, you know, that like, you know, a banking systems, you know, sort of, you know, end of day data. You know, yet because it's it's not particularly deterministic. It's, you know, we don't sort of, you know, know all the the answers that it's going to give. But it could write a summary for for, you know, a new product launch at a bank. Or it could help answer, you know, banking, you know, product questions if you're a consumer.

• 12:07Speaker 00

  So there's a continuum there. And as as we get every degree of intelligence going up, we get more use cases that we can now implement this for. And then there's another axis, which is like, how many of those use cases, you know, can you string together to to complete like the full, the full workflow of that business process? And that's yet another, I think, axis that that that we're early in. But like, you know, I was, I was in New York a couple of weeks ago, meeting lots of banks and, you know, just generally what you think of like the New York industries in in enterprise. And I would say we're like 10% of the way into the adoption of, let's say, just like general chat. So like assistance. And like 1% of the way into adoption of anything we would all call agents, like, and that's maybe even inflated numbers.

• 12:47Harj Taggar?

  When you're in the room with like the banks, the 14 500s, all the people making their decisions in enterprise. Do they really have zero interest in the underlying models? Like is deep sea coming out just like a total nothing burger for them? And they just care about what you're pitching them and offering them? Or do they, do they have interest in the actual underlying tech?

• 13:05Speaker 00

  There are people like us and the people listening at every company in the planet. And so those people care. By the time you get to the, let's say, line of business, so I'm the head of wealth management at a bank. They don't care. So, but the CTO cares and the head of AI cares and the, and the, you know, the, the IT folks that dabble and they're hanging out on hacker news, like those people, they care because they're, they're using cursor and they're using, they're seeing the differences between, between Anthropic and, and OpenAI, you know, tokens within, within that. But when it goes to, to talking to, you know, an executive in the business or the daily end user, they, they have no interest. It's all, it's all, you know, a foreign language to them. And I think that will remain the, the, you know, the way forever. I think more of the expectation is, is that again, these things will converge.

• 13:50Speaker 00

  And what's amazing about AI is because of the, I don't want to call these the models fungible, but, but directionally fungible. Because they're somewhat fungible, you will see characteristics that we've seen in, in other areas of compute, which is any best in class model eventually has to be, you know, match the price of anybody that beats them in pricing. Because, because you can just switch to a slightly inferior model, even if it's like inferior by 1%. The risk is that you could switch to that and be, and find it acceptable for, for, you know, 80% of your use cases, which then by definition means whoever is at the frontier actually still has to match the pricing of somebody just slightly worse than them. Because they could just, you know. The end user actually doesn't care. And their business could evaporate if, if they don't do that, which means ironically,

• 14:35Speaker 00

  you could actually stay on one of the providers, you could just pick a provider and you kind of know that your, your tokens will become as cheap as the second or third, you know, cheapest option. Because that, that first, you know, whatever that first provider is, the marginal, the next marginal customer doesn't have to choose them. They could choose the second or third player. So you, which eventually then, you know, you, you run that experiment out, you know, 10 years, you converge on the basically the same pricing, which is what we've seen, which is like the difference in pricing of, of, you know, uh, storage buckets, you know, between the top three or four hyperscalers are, are not so different to drive business model, you know, fundamentally different business models in the software stack, similar to compute, et cetera. So really you're making a choice based on. You know, some other set of reasons, like how much data do I have in the system?

• 15:20Speaker 00

  What are my workflows that I've built in the system? And and then I think that again, the price of the AI eventually becomes largely the same.

• 15:26Diana Hu?

  Actually, I think what you're saying applies to what we're seeing for startups. I've done a number of office hours with AI startups that are selling to enterprises. And a particular story is this company that scaled to 12 million revenue within a year.

• 15:40Speaker 00

  Yeah.

• 15:41Diana Hu?

  They actually switched models underneath.

• 15:43Speaker 00

  Yeah.

• 15:44Diana Hu?

  A number of times and the end customers, which are these big enterprises, didn't care.

• 15:49Speaker 00

  Yeah.

• 15:49Diana Hu?

  What they care was that ultimately the contract and expectation was that just get the workflow done with this level of accuracy. Done.

• 15:56Speaker 00

  Yeah.

• 15:57Diana Hu?

  And as the cost per token has been cheaper, they actually have been increasing their margins. I think when they started launching, I think their margins were around like 30%.

• 16:06Speaker 00

  Yeah.

• 16:06Diana Hu?

  The next cycle of iterations, middle of last year with all the model releases, it got to 60%. And I think now they're like 80%.

• 16:13Speaker 00

  That sounds like file storage. Yeah, exactly. We love that.

• 16:15Diana Hu?

  So that's kind of what happened. I have a complete example for this company that done office hours and exactly.

• 16:21Speaker 00

  I mean, your analogy is actually quite quite literal. So we so right now publicly, we have 81% gross margin. If you had said, you know, let's say when we started the company in 05, that a a business that was perceived to be storing data would have 80% margin, you'd be like, no, that doesn't make any sense because because like. Just commoditized. People are paying for the storage and it's like, no, we have, I mean, you know, we have nearly 1,000 engineers at Box. One to 2% of them are working on what we would call file storage. So what is everybody else doing? We're building software that that is the abstraction layer of of compute and storage and databases to produce workflow and data governance and automation and insights on data.

• 17:05Speaker 00

  So the storage is is now, you know, a small fraction of what we overall provide. So similar to tokens, as as, you know, the tokens go down as a ratio of what you're really delivering as value is that software stack. So I think, you know, probably a one way to think about it as a heuristic is like, how much software is necessary on top of the output of the tokens for your value proposition to work, you know, success. Successfully for the customer, the less software there is there, probably the higher risk you'd have to either more competition or commoditization. The more software there is, where the tokens are just one, you know, kind of contained component of the full thing. Then you're probably, you know, in a position where you can, you know, you can build a moat, you can get stickier, you can then, you know, solve more of that customer problem. But you might get to the point where the customer pays for for a discrete outcome.

• 17:50Speaker 00

  This is like one of the big open questions. I'm sure you're seeing it in the batches, but like, what is your pricing model? Do you do you pay, you know, let's say you're a startup that does AI lead generation. Do you pay per lead, right? That's that's a fairly obvious, you know, kind of, you know, thing that you'd expect. And then and then basically that company now could be, you know, or do you even pay for like qualified lead? That the customer sort of says is is actually successful. So so like, there's a whole continuum of like. I pay for the a successful outcome, I pay for any outcome, or I pay for the underlying kind of resource utilization. Which we also see in like coding startups, you know, where it's like, okay, I want to buy some unit of compute measurement that goes into useful work. But the cool thing is we're going to see, you know, a mix of all new business models and software that we haven't seen.

• 18:34Harj Taggar?

  This has been one of the biggest changes in the advice I give to the startups during the batch is I feel like. It was really hardwired into me when I come, when a startup comes, we're like, we're getting like a pilot or they're going to pay us as we go or something like that to like say. That's like not a real customer, like you have to go back and you have to get them to sign like an annual contract and like lock in revenue. Otherwise, you're just kind of wasting your time with someone who's sort of one foot in, one foot out. But over the last year in particular, it's when I look at the most successful companies, essentially often they're like replacing a BPO or some sort of service like that. The customer actually wants usage based. And the like, the revenues just keep going up and up and up. So I'm no longer like, oh, you have to sign an annual contract.

• 19:12Speaker 00

  Yeah, yeah, I mean, we, you know, for a lot of areas where we're sort of, you know, there's a direct. Relationship between the the thing that the company is selling and then, you know, let's say labor on the other end. You do need, you know, a lot, you know, like you need, you need long term contracts, so you have to hire people. There's a lot of infrastructure you have to build out. The great thing about AI is it's entirely elastic. So it. That's, I mean, we, we're gonna have to, you know, imagine a new world where all of a sudden I have elastic resources for things that otherwise used to be very operationally intensive. So one day I can just say, hey, I wanna, I want 10,000 leads generated. Go run, you know, AI to go do that. And in the, in, you know, in a traditional way of doing that, that might take months of

• 19:56Speaker 00

  hiring and staffing and building out teams. Now it's, you know, a week later, you're, you're off to the races, you're generating those leads, you know? You can kind of go through any analogy of the business. Um, and, and that becomes possible. So totally different relationship between, uh, you know, the company and getting, getting out outcomes and output, totally different relationship also than for the software provider and what their, what their business model is with that customer.

• 20:18Jared Friedman?

  Aaron, can we go back to your trip to New York City?

• 20:21Speaker 00

  Yes. Just any of my, any of my trips?

• 20:26Jared Friedman?

  Like, you spend a lot of time talking to senior execs of Fortune 500 companies about their technology and AI strategy, probably more than almost anyone in the world. And I'm, I was really curious what those people are thinking about AI. Are they focused on it? What do they think it's gonna mean for their business? Are they building AI initiatives internally? Are they trying to buy products from other people? What's happening?

• 20:49Speaker 00

  Yeah, I mean, definitely all the above. Did you see this, this thing that went viral like two weeks ago? David Solomon, uh, CEO of Goldman Sachs. The S1. Yeah, the S1. He, he basically had this quote at, at a, at an AI event at Cisco. They're doing projects internally where, where AI is writing an S1 in like 10 minutes or something. And it used to be a team of, of six people that work on that, et cetera. The exact same quote, uh, just parallel universe quote, 15 years ago, let's say in the early days of cloud, just as a, as a useful kind of comparison. I'll probably keep coming back to the cloud thing. It probably would have been a banking CEO saying, we'll never go to the cloud. Like, we don't trust the cloud. We, we don't, and, and now the exact opposite. Which happened, right? Didn't Jamie Dimon? Jamie Dimon did. I think he's, he's sort of evolved his thinking, but you had that kind of across the board. And these were these famous moments. It was like, we'll never be a cloud company.

• 21:34Speaker 00

  We, you know, we don't trust it. We don't want to move. I mean, Amazon, it's a bookstore, like, like that was the, that was the refrain. And it made sense. I mean, I, I even said that when I saw S3. Like, the bookstore is gonna power, like, what? So think about how different of a world it is that. The CEO of Goldman Sachs is basically saying like, this is now what's possible. He wasn't saying that in like a, we shouldn't do it way. He was saying that in like, we need to open our, our eyes, you know, up to all of the, the potential use cases that AI is going to have in the business. And he was saying it as a way that they're leaning in and starting to try out all these use cases. So for that to happen for a top five bank in the world at this early in the cycle, you know, it only goes kind of, it only goes more aggressive from there. Because he's in the most regulated of all, all the businesses in the, in the most important financial market in the world.

• 22:19Speaker 00

  And he's already leaning in.

• 22:20Harj Taggar?

  Is that because he's like a particular early adopter? Or are you just seeing this across the board?

• 22:25Speaker 00

  Yeah, I mean, also, he's like a DJ.

• 22:29Speaker 00

  Yeah, maybe he's hanging out with EDM people that are just like really into AI music. Okay, so, you know, 10 years ago, we'd host these dinners, 15, you know, CIOs from different industries, heavily financial services, let's say if it's New York. And, and it's like the, the, you know, we're going to try cloud for this one tiny part of our business. We don't really think we can scale. The idea of being cloud first is like, it would be like totally in, you know, an anomaly. Like you would never, like a bank would never say they're cloud first 10 or 15 years ago. Today, it is, it is sort of like, we're trying this in as many areas as possible. Everybody's still insanely early because you've got privacy councils, compliance councils, regulatory bodies that have to look at everything.

• 23:12Speaker 00

  But everybody understands how, how big of a tidal wave this is going to be in their business. On a few dimensions, one, they know that the workforce is going to completely change. They, I think there's a recognition, this kind of hit me maybe a year ago in some of these conversations. There's a recognition that, that, that basically if you're entering the workforce today, you've had now a couple of years of ChatGPT of, of like college. Like you don't like. They're native. They're native. It's an AI native, you know, era of, of the workforce. And, you know, we could make some jokes about it maybe two years ago, like, oh my God, the writing essays, I can't believe it. But like, I, I basically almost don't search the internet anymore. Like, I only know how to use AI to find information. And guess what? Like, I'm, I'm, I find 10 times more, more information as a result of that. So, so actually in many respects.

• 23:56Speaker 00

  The AI native, you know, people will be smarter on the topics that they decide to go in on than than than the prior generation of whatever that is.

• 24:03Harj Taggar?

  And so what does that mean? Like these Fortune 500 companies are changing how they hire?

• 24:07Speaker 00

  So I don't necessarily know how they hire, but but it'll become clear that if you don't have AI, if you're not an AI first bank or media company.

• 24:13Speaker 00

  So what's your AI strategy? Literally, what is your AI strategy? Because because why would it?

• 24:16Harj Taggar?

  Even to the customers or internally?

• 24:18Speaker 00

  The the enterprise will will basically realize that they can't actually hire the next generation. You can't go from all of a sudden I have this AI native way of of operating in college or high school to going to a company that makes me use, you know, the equivalent of a fax machine, you know, level of technology. It's just like they won't be able to hire people. And then their their competition will have more output. They'll they'll they'll do more investment banking deals. They'll onboard customers faster. They'll get better financial advice to their clients than the company that that that that sort of doesn't do that. And so everybody's sort of realizing this is actually a competitive issue. Cloud didn't really have that. Cloud was a purely an efficiency story. It was like, it was like, ah, yeah, I don't really want to have to be building data centers as much.

• 25:01Speaker 00

  Elastic capacity sounds pretty good. I want to be able to test this new product faster. It was not like my customer is going to experience a different thing about my about the output of my company, whether I'm cloud or not cloud. Now, I think we all believe that that was that that that there is a difference there. There was not tangible to the buying side that cloud was going to make your company five times more competitive in a way that AI is is very clearly, I think, resonating that actually your company's competition will now be at risk if you're not AI native.

• 25:29Harj Taggar?

  Yeah, that's a really interesting point. Because like with cloud, early on, a lot of the benefits were actually to the startups who pushed it forward. So they just wanted an easy way to like get set up and not have to deal with hosting. But you're saying it might be the opposite where with AI productivity tools, startups don't really need them.

• 25:43Diana Hu?

  But actually, maybe the the counterpoint, to me, what this sounds like is actually way more opportunities for startups. It's just all the office hours that we're doing. I think it's the fastest I've seen B2B SaaS AI companies get enterprise deals. And I think you painted a very good picture of what's the vibe shift.

• 25:59Speaker 00

  Yeah, if I can just slip in one funny anecdote. I went to this banking conference eight or nine years ago and I did this little keynote at this banking conference. And it was all about like, we have to be cloud first and enterprises have to modernize how they operate with the cloud. And I think I've never been, I think I've never bored an audience more than that keynote. Like, I remember getting off the stage and nobody caring. Like, it was just like, because it was like, why are you talking about backend infrastructure? Like, like, nobody cares. Like, yeah, whatever, we could be in the cloud, we could not be in the cloud. You know, at our levels of budget, if we're spending like $500 million or a billion dollars a year on IT, who cares if we save 100 million because some of it is elastic or not? Like, it's like, not that big of a deal.

• 26:42Speaker 00

  If you were to do the same thing, but an AI first to non-AI first enterprise, people would be like, oh shit, like, actually, I probably can't run my business anymore, not AI first. Because you would just show people like, like, do you know the productivity gains of somebody using, let's say, Cursor? It's like, you will be blown out of the water competitively if you do not know how to build an AI first company right now. And yeah.

• 27:03Harj Taggar?

  How has Box invested in any internal AI tools to speed up how you run the company?

• 27:08Speaker 00

  Yeah, so a few categories. So one, we've been rolling out AI for, on the coding side. And we're trying everything.

• 27:18Harj Taggar?

  Like, internal tools for your engineers to?

• 27:20Speaker 00

  Yeah, yeah, so basically, just how do we make the engineering kind of more productive? And that's obviously what, one of the biggest X factors of our business is can we output more code? That is obviously useful and aligned to our product roadmap. So we will be, you know, fully AI first in terms of how we develop. You know, this year, it's, it's sort of the big year for all the change management on that. We are incrementally rolling out AI for different customer-facing things. Just again, can we solve the customer ticket problem? Can we, can we improve the rate of response? And then as an AI provider, a lot of the knowledge management use cases, we sort of now do ourselves with AI. So, you know, if an employee has a, has an HR question or a benefits question, we have a feature that lets you talk to all your HR data and all of the. Internal knowledge management. And so what became this breakthrough for us was all of a sudden,

• 28:05Speaker 00

  all the things that were inside your documents before become useful for now just interrogating with questions as opposed to reading documents. And so there's a lot of just embedded productivity that we focus on from that standpoint.

• 28:16Jared Friedman?

  What things do you think they're going to do internally? And what things do you think they're going to buy solutions for?

• 28:22Speaker 00

  I would basically guess that, again, it kind of looks pretty similar to kind of historical ways of thinking about this. I think Jeff Moore created this, but if I'm getting it wrong again, please, Jeff Moore, like pop into the comments. I'm in the right name.

• 28:39Speaker 00

  And the idea was, was sort of context is all this stuff that is sort of like, you have to do, it's necessary, but it's not kind of like, it's not going to make your business better than your competitor. And so that's your HR system, that's your ERP system. Like, you have to have it. It's important. It has to be done extremely well. But like, your version of the HR system, you know, is not going to be radically different than, than

• 29:01Garry Tan?

  Box checking. Any of the box checking things.

• 29:02Speaker 00

  You had to check the box, but it's got to be a good box and whatnot. And then there's core, which is, this is like literally your value proposition. Like, you sell, you know, wealth management to, you sell wealth management services to people. And, and that is something you own. If your thing looks exactly like your competitors, then you have no, there's no sort of, you know, profit, you know, margin that you'd be able to, you know, you wouldn't be able to get reasonable profits. You have to have something that's unique. And I think companies need to really understand which category is which, partly because if you get the, if you put the core in the context, then you'll probably be at a long-term disadvantage. And if you put the context in the core, then you're wasting a tremendous amount of time and energy. And this

• 29:43Speaker 00

  is why I really enjoy the Klarna announcements or whatever. It's fun to read, but I also think it's sort of misunderstanding the context versus core thing. Like, you don't need to build your own HR system. I'm glad they're doing it. I think it's provocative. Like, it's fun to see different approaches.

• 29:58Garry Tan?

  Gets the people going.

• 29:59Speaker 00

  You know, it gets our, like, we're like, our juices are flowing, but like, the average bank is just like, not, it's like, not a priority for them to like reinvent their HR system now. So I think, I think whatever, whatever is that for every industry. You know, in life sciences, you probably really want to understand like, how are you doing drug development? And you should probably have a very strong AI team working on that problem because that's something that sounds like it's going to be IP for you. But the automation of the clinical trial process, that's probably context for you because everybody's going to want to be doing that as quickly as possible. It probably doesn't involve a tremendous amount of proprietary data. And then, and then obviously your CRM system, your HR system and so on.

• 30:38Jared Friedman?

  So that actually sounds like they're going to buy a lot of things externally because most functions in a business are actually context.

• 30:44Speaker 00

  Yes. Yeah, I think most, most of the way AI will show up to a knowledge worker in 2030 will be from a, what we would have thought of as an ISV 10 years prior.

• 30:54Garry Tan?

  What's an ISV for the people watching?

• 30:55Speaker 00

  Yeah, it's just basically a software provider. The CRM system will still come from Salesforce or X competitor. It won't be that they built a homegrown AI generated CRM system. They might talk to that CRM system through also a new vendor or something that they build internally. It's interesting, there's actually a lot of chat pods being built internally by companies right now. I'm sensitive that I think it might be a temporary phenomenon and.

• 31:18Garry Tan?

  I like UX better than chat.

• 31:20Speaker 00

  Yeah, yeah, okay, yeah. I think we're going to, we're going to see a hybrid of these two things. I think the GUI is not as dead as, as, as people think. You do see a lot of kind of chat interfaces where you're like, you're like, I think you just did way more work than just going to the dashboard. Like, like, I'm 90% sure you just probably took all these savings from AI gains and efficiency and, and, and then just spent it on figuring out a prompt that like a dashboard would have solved. But I think we'll have a universe of both those things. But I do think ultimately most will come from software as like a, you know, on a percentage of like, like time that a, a knowledge worker spends inside of technology. But some of the most valuable things absolutely will be, will be homegrown. The algorithm you use for discovering the thing or personalizing the medicine or personalizing

• 32:05Speaker 00

  the wealth, you know, data or, you know, Netflix's recommendation engine, like those will be homegrown things. Maybe still using a model from, from a proprietary player, but like the scaffolding there will be, I think largely built by, built internally.

• 32:19Garry Tan?

  Interesting. So I guess the mental model. For people watching might be that there's inside the house and outside the house. Outside is context, which are just checkboxes you have to check. And those might be, you know, end to end things that just do the thing like Salesforce.

• 32:34Speaker 00

  Yeah, I think I think that's right. And I think the maybe another way to reverse engineering is if you were the customer of this company, would you care, would you care what technology they used for for that category of thing?

• 32:44Garry Tan?

  Or is it like, just get the get the box checked and I'm good?

• 32:46Speaker 00

  Yeah, as a customer of Netflix, I literally don't care what their ERP system is.

• 32:50Garry Tan?

  And then on the inside, like the things that are core that you really need to do. Maybe the opportunity there is infra and, you know, dev tools that allow an internal IT team or internal engineering or AI team to run really fast, but then create exactly what that business needs.

• 33:08Speaker 00

  Yeah, yeah, yeah, exactly. And then there's still, there's a little squishiness in there. It doesn't be very little about it because like you also probably don't want to do homegrown dev tools. Like, so, so you still might procure those. And then there's this layer between the dev tool and the customer experience, which is what am I doing with the output of these tokens? That's something I need proprietary software built on like, and, and so what is in that universe? But I think that like, like, for instance, I think there's going to be amazing, you know, amazing dev tool opportunities coming from, from startups right now, because it's a whole new stack that you have to build out for, for managing AI within your, your enterprise.

• 33:39Harj Taggar?

  That in between bit is actually, we see open source a lot.

• 33:41Speaker 00

  Yeah.

• 33:43Harj Taggar?

  Have you seen any renewed interest or any trends on open source interest in the enterprise?

• 33:48Speaker 00

  I've selfishly, I love open source because we don't have to pay for, you know, what we're using.

• 33:53Garry Tan?

  We should give grants.

• 33:54Speaker 00

  Well, well, it's actually, I've seen.

• 33:55Harj Taggar?

  You don't pay for the hosted versions all the time?

• 33:58Speaker 00

  Exactly. Sometimes we do, but, but I was actually thinking about this. I think, I think a, a thing that we cool to see tech people do more often is, is just like start large scale open source projects and fund them. I think more open source software for the common good of, of just like lots of different services, I think is a huge net positive. I'd love to see more, I'd love to see more open source in general right now. It's such a great symbiotic, you know, model because, because, you know, the hacker community or the startup community, they want to move fast, they don't want to, they can't, they can't often afford the licensing of whatever that thing is. The large enterprise community, you know, they want support. They want to have people that are experts in managing this stuff. And so it's this great relationship, which is like you have this great good for for society.

• 34:41Speaker 00

  And you still actually can be building a successful commercial business because lots of people want to pay for for the commercial version of that.

• 34:48Jared Friedman?

  Yeah, Aaron, in the in the early days of this cycle, there was a lot of concerns from enterprise about the security implications of using hosted models. Like a whole bunch of companies like ban chat GPT use internally. What's happening now? Have they gotten comfortable with the idea of having all of their data? Go to OpenAI and Anthropic, or did they still, are they sort of really worried about that and trying to host open source models and things like that on prem?

• 35:11Speaker 00

  Yeah, I I think you'll see a different different categories by industry. So a lot of times they'll go into, you know, a bank and they'll say, and they'll be proud of this and as they maybe should be, they'll they'll say, you know, we have our own kind of enclave version of of X model. And then we built out a wrapper on top of that, you know, for for deploying it to employees. And I think that that you'll always have some percentage of the market do that, you know, 10% of of the market. Because you know, you still have a lot of people that have on prem systems for a similar reason. That will always be there. But the comfort level is absolutely increasing. That tends to always happen in if if the industry or the the company takes it very seriously. So OpenAI has taken security, privacy, compliance, you know, regulatory controls very seriously.

• 35:53Speaker 00

  And so then that builds trust over time. So then more people can can can go and can can experience those, you know, those use cases. So anytime a software category matures and and becomes more enterprise grade or military grade, you know, you'll see a correlation of the the amount of trust and in putting data into those systems. And I think AI is no different than than against software and kind of cloud in that respect.

• 36:16Diana Hu?

  Maybe part of it is just a lot of the behavior is from all these enterprises. They got in a lot more comfortable because of what you pay with cloud, right? They kind of already have preferences on how to do this hosted and, okay, we know how this looks. So it's a lot harder for you to sell on that first cycle. This cycle, you kind of compounded it for this next generation of founders. So thank you.

• 36:36Speaker 00

  People need to be really thanking me. I am not, actually, you know, it's funny that you put it that way. I don't think I've. Ever been thanked for the hard work.

• 36:43Garry Tan?

  Consider yourself thanked.

• 36:44Speaker 00

  The years of dinners, all the CIOs that made it possible for your AI startup to get sold in the enterprise. I'd like a big thank you.

• 36:54Speaker 00

  I have not been respected. That is not, that has not shown up in a lot of ways, but I appreciate it.

• 36:59Garry Tan?

  Every single white hair is a dinner that you had to do to teach people.

• 37:02Speaker 00

  Literally.

• 37:03Garry Tan?

  That you should buy this.

• 37:04Speaker 00

  It's, uh, this is the only way to get that. I would like a plaque at the office. I don't know why I don't have that yet. Cause like, none of you have gray hair. So like, this is, this is, and I'm, I'm responsible for, for AI working now. Yeah, it's, but it's an interesting question, right? Cause, and this is, this is what's so fun about, about the compounding nature of technology. AI could not have happened in 2005. Let's just pretend the breakthroughs actually happened. Let's, let's pretend an alternative universe.

• 37:31Garry Tan?

  Okay.

• 37:32Speaker 00

  Where, where the exact same somehow like Jensen had made GPUs as powerful as they were now in oh five, but everything else was the same. Okay. Weird world, but let's just say. And you're, imagine going to a company and you're like, okay, we know that all of your infrastructure is on prem. And we know that all of your software is on prem and we know you're on C-Bol and we know you're on, on people's soft. And we know that all of your data is in this system, but if you just move it to this other thing that you have no control over. It's in a data center you don't know about, you know, you have no, no ability to kind of go in and look at the data center and, and look at the, the, the hard drives. If you just move everything over to this, think about all the intelligence. I mean, it would never, it would be DOA. Like this industry would just be over. It wouldn't matter what the tokens are coming out with.

• 38:14Speaker 00

  So you actually, it was a requirement as a prereq to have cloud have happened, to have SaaS have happened. And then now AI happens building on that. Um, and then, and then even like, even the consumerization piece is, is so interesting. Imagine a world where, where we were only in enterprise using technology. We didn't have the kind of consumer access to technology that, that eventually happened with mobile, et cetera. You wouldn't have people seeing ChatGPT and perplexity and Grok, et cetera, in their personal lives. And then saying, wait a second, why are my IT systems feel so archaic by, by, you know, comparison? That's what's causing all of this pull also. So you needed. We needed consumer adoption of technology and then and then the fact that now a billion people, billions of people can use AI to then pull that into the enterprises.

• 38:59Speaker 00

  You need the enterprise's infrastructure to be modern enough. So it's it's incredible Cambrian explosion of now opportunity because we're in a moment where enough of the the the the kind of core plumbing has been built out.

• 39:10Harj Taggar?

  Also, enterprises just became used to using like different software solutions versus just relying on like the

• 39:15Speaker 00

  Three vendors, exactly. Yeah, 100% like they like they now there's there. Now, some of them don't like this, but their IT stack is now hundreds of vendors versus a dozen. And it's a and so they can accept, okay, I'll take a meeting with Anthropic like this is totally it's it's, you know, it's only now 1% in increase in my total kind of IT. You know, vendor universe versus, you know, 15 years ago, be like, no, I can't bring in something that's not Oracle.

• 39:39Harj Taggar?

  Yeah.

• 39:40Diana Hu?

  We'll keep accelerating.

• 39:42Speaker 00

  Please just keep keep driving the startups that that causes to happen.

• 39:45Jared Friedman?

  You had this front row seat to the transition from on prem to cloud. And now we're at the dawn of the next transition from cloud to AI. How do you think it's going to play out similarly? How do you think it's going to play out differently?

• 39:58Harj Taggar?

  And and how do you think that relates to the TAM for software going forward?

• 40:02Speaker 00

  Yeah, so if I could, if I could try and merge these, actually, there's there's a there's a cool connection point. So the probably single biggest bear reason why people didn't invest in SaaS in the mid 2000s was was they they they thought the market sizes would be basically the same size as the on prem software company. And and so if it was the same size of the on prem software company, but also the in the software company that's already there is the incumbent. You know, it's like, how do you squeeze out enough enough money to to kind of make the business really, really, you know, make make it really interesting? And what everybody basically got wrong was it turned out that the Tams were probably about 10 times larger and

• 40:43Jared Friedman?

  Why did the TAM grow so much?

• 40:45Speaker 00

  Because just to like bore everybody, if you wanted to buy a CRM system in 2020, sorry, in 1999.

• 40:54Speaker 00

  You had to be like, okay, I'm gonna go to the systems integrator. I'm gonna get a data center. I'm gonna buy a bunch of servers from people. I'm gonna install some software. I have to manage the network of that. And like, you know, lo and behold, two years later, you might have a CRM system. And you probably spent five, $10 million on the full project to do that. So think about who is the market that then can implement a best in class CRM system? It's the world's largest enterprises. You know, 5,000 companies, 10,000 companies. Salesforce comes out and they're like, for three seats online with your credit card, you have a CRM system as good as Siebel. Obviously, there'll be some nuance because it didn't have as much functionality. But like, for that company, that was as powerful as Siebel, you know, getting started. Now, all of a sudden, your TAM is basically every business on the planet.

• 41:35Speaker 00

  So you go from a market that had maybe 10,000 customers, 20,000 addressable customers, to now a market that has 5 million, 10 million potential customers. It is a totally different scale, like, you know, two or three orders of magnitude more scale that you can now go and serve. We had a similar experience, which was, you know, the industry we were disrupting was like legacy enterprise document management, enterprise content management systems. Same exact thing with Siebel in terms of, like, we would read the S1 of our biggest incumbent competitor, and they were talking about like 1,000 customers or a couple thousand customers. And literally, you know, now we have 115,000, but like, but like, but at the time, we had, we had, I don't know, five or 10,000 when we started thinking about disrupting this. And like, the scale was just completely different. So, so that meant the market sizes were so much larger.

• 42:20Speaker 00

  You know, ServiceNow today, I don't know, the exact latest market cap, maybe it's $150, $175 billion. Their incumbent competitor, when they, when they were first growing and disrupting the market, today is worth maybe about 5 or 10 billion. So, so if you had looked at this company 20 years ago, you'd be like, at best, ServiceNow should be a 5 or 10 billion dollar company if it just was like a better version than the current thing. And it turns out it's, it's 15 times larger than, than, than what you would have thought. Salesforce did the exact same thing and so on. AI, I think, has a similar dynamic because you're basically increasing the total spend on software. So it's not so much that a new set of companies will buy software the first, for the first time. It'll be all companies use software.

• 43:04Speaker 00

  To do things that software didn't do before and that will take from budget that that previously was sort of untapped, you know, from software. So the budget will be from a variety of things, but often because now the software is doing useful work for you, you can now afford to spend even more on that software because the alternative was a much more expensive sort of proposition. Here's where I think people kind of get it wrong, though. They think about it as zero sum from well, then then all you can do is sort of take from the labor side of that spend. But it actually just turns out most companies aren't even spending on the labor side. They're just not doing the thing. So so, you know, most companies are like, just like globally are not like spending time to translate their advertising into a different language. So it's not that that, oh, the

• 43:48Speaker 00

  market for the translation services are this big and we're just going to digitize it. It's like, no, 100 times more people will do translation. You know, in our business, like we have, you know, software now that reads your contract and pulls out the critical data from that. So you can automate a contract workflow. And like the number of people globally that are reviewing contracts and pulling out that data, maybe it's, you know, 10,000, 50,000 people. I don't know the exact number, but a very small percentage of companies are doing that with their contracts. So they will now decide to prioritize automating a thing that they didn't automate before. You know, cursor, you know, the as just a, you know, back to that example, or Replit, or Devon, or whatever. There's probably not a single dollar that's being spent on that technology that comes from from takes away from what people are currently doing. It's purely additive because now it's expanding

• 44:33Speaker 00

  the use cases that that software can can can kind of tie into. So I think we're in for a potential scenario where the the size of software, now you have to include AI in that, could be five times larger in in the next decade because it just it supplies the actual underlying work that you actually bought the software for in the first place. And that just changes everything because now we're you're going to be paying for work as a paying for for a tool that enables other people to do work.

• 45:01Garry Tan?

  I think that's such a powerful AI white pill, actually.

• 45:03Speaker 00

  Yeah.

• 45:04Garry Tan?

  It's not merely, you know, zero sum, we're converting payroll into software revenue.

• 45:09Speaker 00

  Yeah.

• 45:09Garry Tan?

  And haha, that's it.

• 45:10Speaker 00

  Right.

• 45:10Garry Tan?

  It's actually, we're gonna do things that enterprises should be doing, would have been doing.

• 45:15Speaker 00

  They never got around.

• 45:16Garry Tan?

  And then, you know, actually, the people who are the consumers on the other end, they're gonna have better products, better services, like, the thing will actually just be better for...

• 45:24Speaker 00

  I haven't yet read like the full like economic study on this, but where economists always get this stuff wrong is, you know, they do probably by default tend to have a kind of a zero, I mean, you wouldn't have like, you know, Jevon's paradox if economists always knew, you know, how to anticipate these things. But like, what I think they often get wrong is they look at the total amount of market labor in a category, and they're like, well, shoot, if AI automates that, that's now gone. Look how many jobs that is. You know, I think we should debate it, and we should talk about it, because it's a very, you know, serious thing. But what they don't actually ever think about is the micro, the more microeconomic impact of this. So if I'm a company, and it could be Box, or it could be a 20% company, and I use AI to, let's say, code faster. Okay, well, why am I coding faster? Because I'm gonna, I want to build a better product for my customers.

• 46:09Speaker 00

  If I'm building a better product for my customers, my revenue should be growing faster. If my revenue is growing faster, I probably then am hiring people to go and do things to drive that revenue growth. Maybe it's people selling the software, maybe it's customer support, maybe it's HR people to help scale the operation. And eventually, I'll get to a point where I say, should I reuse those dollars that helped me grow faster to hire more engineers to grow even faster and to build more of that roadmap? And if we were in a market that wasn't competitive, maybe you could say, you know what? I actually just want to take the profit and be happy. But we're in a competitive market. So if you're the one company that decides to sit on the profits that AI generated, and just, and just live off of higher profit margin, 20%, 30%, 40% profit margin as a company,

• 46:51Speaker 00

  you'll just have somebody come into the space and say, no, actually, we'll just, I'm fine to have 20% profits and do that same thing. And then that company will eat into your lunch. So you actually then reinvest those dollars back into the things that are helping you grow faster. And that's actually like the microeconomic outcome of automation, is you decide... That you take that efficiency gain and you redeploy it into the business in something that will make you more competitive or grow faster or better serve your customers because you're in a competitive ecosystem. And that's why I think as, you know, over time, you'll, yes, you'll have some displacement in different categories, but over time, this is why it generally just looks like an upgrade to just how we tend to work, you know, in the world. Like,

• 47:32Speaker 00

  we just tend to use tools to work faster, to make better decisions, to build better customer products. The customer gets a better result out of that, but we reinvest those dollars back into the businesses because we're in competitive ecosystems.

• 47:44Garry Tan?

  And then the ultimate winner is the consumer.

• 47:46Speaker 00

  Consumer always wins on this stuff.

• 47:47Garry Tan?

  Right.

• 47:47Speaker 00

  Right? Now, with one X factor, just like bringing home to SF, like, like, assuming we have a regular regulatory environment where those winnings can actually turn into surplus, right? So if those, if it turns out that we take those winnings and then regulate, you know, the ability to build housing, then all of a sudden, everything's still just as expensive. But like, like, the total utopian, which is sort of the abundance thing, is like, wait a second, if we could use AI to automate more, we can build more. If we could build more, we could lower the cost of things. If we can lower the cost of things, then we can actually lift up anybody, anybody's lifestyle right now. You know, the, the, the 10 year old in an underserved community is now all of a sudden has access to the world's intelligence in the form of an AI agent. So they can, they, they now, you know, are able to be educated better. If we can lower

• 48:32Speaker 00

  the cost of delivering services to people, then you get, you know, better healthcare. That's like the ultimate utopian state is we use this automation to actually deliver better outcomes for, for the world. And that will require tons of jobs as a result of that.

• 48:46Garry Tan?

  Yep, we can be a society again.

• 48:48Speaker 00

  Yeah.

• 48:48Garry Tan?

  Because of AI.

• 48:49Speaker 00

  There you go.

• 48:50Garry Tan?

  Aaron, thank you so much.

• 48:51Speaker 00

  Thank you.

• 48:52Garry Tan?

  Thank you so much for being with us.

• 48:54Speaker 00

  Great to hear.

• 48:54Garry Tan?

  I think that that's a great place to end, just because, you know, to be continued. Like, you know, I, I think that we're in the middle of the revolution and the revolution does not have to be a Black Mirror. It could be something that is driven by Jevon's paradox, driven by abundance for everyone. And that's certainly the, the timeline we want to be on. So let's do it.

• 49:14Speaker 00

  That's the future I'm betting on.