# Box CEO on Enterprise AI Trends No One is Talking About Yet

Markdown copy of https://realalpha.app/v/g9E7iCnYOoI/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

Aaron Levie says enterprise AI’s biggest opportunities lie in workflow automation and data-rich applications, not just consumer tools.

- Show: The Logan Bartlett Show
- Hosts: [Logan Bartlett](https://realalpha.app/people/logan-bartlett/)
- Guest: [Aaron Levie](https://realalpha.app/people/aaron-levie/) is cofounder and CEO of Box.
- Published: Mar 28, 2025
- Length: 56 min

## The best minutes

- [13:09](https://realalpha.app/v/g9E7iCnYOoI/?t=789) [AI agents can expand software into service budgets](https://realalpha.app/s/ai-agents-can-expand-software-into-service-budgets/) (4 min)
- [26:56](https://realalpha.app/v/g9E7iCnYOoI/?t=1616) [Enterprise AI demand is unlike early cloud adoption](https://realalpha.app/s/enterprise-ai-demand-is-unlike-early-cloud-adoption/) (4 min)
- [38:45](https://realalpha.app/v/g9E7iCnYOoI/?t=2325) [AI-generated software does not remove customer responsibility](https://realalpha.app/s/ai-generated-software-does-not-remove-customer/) (5 min)

## Also worth watching

- [2:06](https://realalpha.app/v/g9E7iCnYOoI/?t=126) [Standalone model vendors face a pricing squeeze](https://realalpha.app/s/standalone-model-vendors-face-a-pricing-squeeze/) (4 min)
- [8:28](https://realalpha.app/v/g9E7iCnYOoI/?t=508) [Business agents have clearer product-market fit than consumer agents](https://realalpha.app/s/business-agents-have-clearer-product-market-fit-than/) (3 min)
- [18:21](https://realalpha.app/v/g9E7iCnYOoI/?t=1101) [AI products can build switching costs through accumulated learning](https://realalpha.app/s/ai-products-can-build-switching-costs-through-accumulated/) (8 min)
- [34:45](https://realalpha.app/v/g9E7iCnYOoI/?t=2085) [Fast-moving AI layers should stay replaceable](https://realalpha.app/s/fast-moving-ai-layers-should-stay-replaceable/) (3 min)
- [53:28](https://realalpha.app/v/g9E7iCnYOoI/?t=3208) [Topic-specific hubs give enterprise AI a clearer source of truth](https://realalpha.app/s/topic-specific-hubs-give-enterprise-ai-a-clearer-source-of/) (3 min)

Skip the early frontier-model discussion, the extended competition-and-pricing discussion, and the rapid-fire AI outlook.

## Full analysis: Aaron Levie says agents could grow contract software to $20 billion

- He put contract software at a couple billion dollars and floated $20 billion as a possible market.
- That is a hypothetical expansion, not evidence vendors will capture the spend or retain software margins.
- Founders should measure completed work, review costs and switching friction before budgeting around agent revenue.

Its 6 sections, what it means for you and what to watch are on [the page](https://realalpha.app/v/g9E7iCnYOoI/#analysis), free with an account:

- [16:25](https://realalpha.app/v/g9E7iCnYOoI/?t=985) Agents could price software against labor budgets
- [22:02](https://realalpha.app/v/g9E7iCnYOoI/?t=1322) Market expansion does not settle the margin question
- [29:46](https://realalpha.app/v/g9E7iCnYOoI/?t=1786) Enterprise urgency now outruns implementation readiness
- [40:30](https://realalpha.app/v/g9E7iCnYOoI/?t=2430) Agent-built software leaves liability with the customer
- [25:02](https://realalpha.app/v/g9E7iCnYOoI/?t=1502) Accumulated learning can make hidden workflows sticky
- [54:12](https://realalpha.app/v/g9E7iCnYOoI/?t=3252) Box narrows the source and keeps models swappable

## Summary

Aaron Levie is the cofounder and CEO of Box. He discusses Box’s AI work, including Hubs and document data extraction.

Levie argues that AI can expand software markets by automating work and capturing some of the budgets now spent on labor and services. He says enterprise adoption is moving faster than cloud adoption did, although data access, governance, accuracy and review processes still constrain deployment. He expects familiar software moats—data, workflows, integrations and customer references—to matter, while advising companies to keep parts of their AI stack flexible. He also sees agentic workflows as a major enterprise opportunity and highlights Box Hubs and document extraction as practical applications.

### Minute by minute

- [0:57](https://realalpha.app/v/g9E7iCnYOoI/?t=57) **Standalone model businesses face pressure.** Aaron Levie distinguishes companies that sell models from AI companies that also offer products, APIs and services, and says it is hard to build a large business by selling models alone. He argues hyperscalers will keep pushing prices down and model quality up; Box’s tests found Google’s open-weight Gemma only a couple of points behind Gemini on accurate enterprise document extraction.
- [5:27](https://realalpha.app/v/g9E7iCnYOoI/?t=327) **Frontier models reinforce products.** Levie says he would keep investing in frontier models because they support consumer, business and agent products. He sees the work as a reinforcing cycle, not separate bets. For many common consumer questions, models have converged, but he says harder tasks such as booking a flight still need substantial improvement.
- [8:28](https://realalpha.app/v/g9E7iCnYOoI/?t=508) **Business agents show early fit.** Levie points to 11X for sales lead generation, coding tools such as Cursor, Windsurf and Replit, and Box’s document extraction workflows as examples of business use cases with product-market fit. He says customers want more document-to-database and workflow automation than Box can currently deliver. He sees less evidence of consumer agent fit and thinks the enterprise opportunity is underappreciated.
- [11:29](https://realalpha.app/v/g9E7iCnYOoI/?t=689) **Agents expand software budgets.** Levie compares AI pricing with the cloud shift from large upfront costs to subscriptions, but says agents can also sell software as a form of labor. He contrasts contract management, a couple-billion-dollar global category, with legal services, a couple-hundred-billion-dollar category; capturing 10% of that spend could make the software category a $20 billion market. He also sees AI enabling product outcomes that were previously out of reach.
- [18:21](https://realalpha.app/v/g9E7iCnYOoI/?t=1101) **Data and workflows support durable value.** Bartlett raises the risk that competition will limit how much AI companies can charge. Levie says AI does not change ordinary market dynamics: competition constrains prices, while better products, reference customers and learning can help a company pull ahead. He sees data, workflows and integrations as familiar software moats, and says labor-linked AI pricing may eventually move toward typical software margins.
- [26:56](https://realalpha.app/v/g9E7iCnYOoI/?t=1616) **AI adoption differs from cloud.** Levie says cloud mainly improved IT operations and efficiency, while AI could change how companies make decisions, serve customers and build products. He says enterprise customers are already asking how to adopt AI, rather than resisting it as they often did with cloud. He estimates companies have moved five to seven years faster into implementation planning than they did with cloud.
- [31:25](https://realalpha.app/v/g9E7iCnYOoI/?t=1885) **Readiness remains a practical hurdle.** Levie says some eager customers leap toward full automation before their data or the technology is ready. Over-provisioned access can expose sensitive information, while AI-generated code raises questions about review and production approval. He cautions that the customers he hears from are already leaning in, while many others are still unsure where to start.
- [34:45](https://realalpha.app/v/g9E7iCnYOoI/?t=2085) **Keep AI architectures flexible.** Levie says Box separates stable parts of its stack from areas where technology may change, such as vector search and RAG. For fast-moving components, including models, he favors modularity and the option to switch providers; Box uses multiple model providers within some workflows. He says teams should decide which interfaces to lock down and where to preserve choice.
- [37:45](https://realalpha.app/v/g9E7iCnYOoI/?t=2265) **AI will not erase most SaaS.** Levie calls Klarna’s agentic-workflow experiment provocative, but says it does not make him rethink all of SaaS. Most companies do not want responsibility for building and maintaining systems such as HR software, and AI providers will not assume liability for generated code. He sees more room for short-lived, nonstrategic micro-apps than for replacing core enterprise systems.
- [43:35](https://realalpha.app/v/g9E7iCnYOoI/?t=2615) **AI could broaden roles and flatten teams.** Levie answers that AI probably will not reduce average knowledge-worker hours over the next five to ten years, but says it could flatten organizations. He argues AI lets people take on adjacent work—for example, designers writing front-end code or copywriters producing full white papers—reducing some handoffs and making roles broader.
- [46:48](https://realalpha.app/v/g9E7iCnYOoI/?t=2808) **Research and design tools save time.** Levie says deep research saved him an hour and a half on a task and that he now uses it five to ten times a week, checking some of its answers. He is also excited by generating front-end designs from a screenshot or prompt, which can replace a request to an engineer or designer. He notes that repeated exposure has made new AI capabilities less surprising.
- [50:26](https://realalpha.app/v/g9E7iCnYOoI/?t=3026) **Agentic workflows offer major upside.** Levie says perfectly executing agentic workflows would be the enterprise holy grail. He imagines agents researching clinical trials, assessing a new region for expansion, or synthesizing product feedback into a roadmap. He sees this as extending job-like compute tasks into much more of corporate knowledge work.
- [52:08](https://realalpha.app/v/g9E7iCnYOoI/?t=3128) **Open weights can follow breakthroughs quickly.** Levie expects an open-source version of a closed-source model breakthrough to appear within three months. Because he expects users not to give up much in quality, he thinks open-weight models could account for a large share of model traffic and use.
- [53:28](https://realalpha.app/v/g9E7iCnYOoI/?t=3208) **Box Hubs focus enterprise search.** Levie says Box Hubs let users group files and folders by topic, then ask questions within that knowledge area. He says this gives users a clearer sense of what the AI can access and can reduce hallucinations by grounding answers in authoritative documents. He also highlights extracting information from contracts, invoices, resumes and financial reports.

## Transcript

The Logan Bartlett Show, hosted by Logan Bartlett, with Aaron Levie.

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[0:01](https://realalpha.app/v/g9E7iCnYOoI/?t=1) On this episode, what you're going to hear is a conversation I had with co-founder and CEO of Fox, Aaron Levy. We cover a bunch of ground related to the state of the enterprise today and what he and his customers are thinking about artificial intelligence. We talked through a bunch of different topics related to AI, what different people should be thinking about, his views on closed source versus open source, and the opportunity that presents going forward, as well as some of the considerations related to the business model change that seems to be occurring with usage-based pricing. A really fun conversation that you'll hear with Aaron now. Aaron, thanks for doing this. Thanks for having me.

[1:10](https://realalpha.app/v/g9E7iCnYOoI/?t=70) What in your mind is a model company in today's parlance as we sit here, you know, March of 2025? Wow. Okay. That's just jumping right in. What is a model company? Well, I mean, I think, i don't think you actually even have many model only companies um and so maybe the only distinction is is you have companies that have models and then other things um so so if i'm being like extremely technical uh the technical about the definition uh there's probably not not that many like model companies i think you have you have ai product companies that have research divisions that, you know, build models and then they have APIs into those models.

[1:56](https://realalpha.app/v/g9E7iCnYOoI/?t=116) So like, I mean, probably to this point, like it's like Mistral is probably the only, you know, like true model company where most, you know, OpenAI, I would, you know, I would not call a model company. I wouldn't call Anthropic a model company. I'd say those are, you know, AI, you know, foundation, frontier lab companies that have products and models and services and just like all the things. Do you think that serving enterprises with some type of model offering, but ultimately selling whatever the core things that enterprises mostly buy, do you think a company can build long-term value around that?

[2:39](https://realalpha.app/v/g9E7iCnYOoI/?t=159) Or do you think that will ultimately, whatever that is, resides within an AWS or a Microsoft Azure or a GCP or something like that? I think it would be hard to underwrite that alone as a massive business because you basically are dealing with this issue where all of the hyperscalers at a minimum and then a few other companies that aren't kind of natural hyperscalers, but they're adjacent enough to the hyperscalers. They need to make sure their models get used at an unbelievable scale because they have to win an infrastructure war. They need the compute to come to them. They have other software they have to sell.

[3:21](https://realalpha.app/v/g9E7iCnYOoI/?t=201) So it is so existential to them to have their models get used at massive scale that they will constantly drive down the price of AI and the quality up in AI. that you would just get squeezed into such a difficult position that I'm not sure what your value proposition would be. Like, we ran these tests on Gemma from two weeks ago and the new open source model from Google. Yeah, not to be confused with the severance character, Gemma. Yes, yes, exactly.

[3:59](https://realalpha.app/v/g9E7iCnYOoI/?t=239) **Aaron Levie:** Um, so, so we ran these tests and it actually performed only like a couple of points worse than Gemini on, on our evals for, for accurate, accurate data extraction, uh, from, from enterprise

[4:13](https://realalpha.app/v/g9E7iCnYOoI/?t=253) documents and data. And so the fact that you have an open weights model from Google that is almost at par with their commercial model that is only was already kind of state of the art two, three months prior as a set of breakthroughs means that like, you know, you're As an enterprise, you're going to have a choice between an open weights model, you're going to have a choice between a commercial model, they're going to all perform extremely well, and you're really only going to be making a choice of like, do I want all of the scaffolding managed for me or do I want to just run this in my own data center or GPUs or my own cloud?

[4:46](https://realalpha.app/v/g9E7iCnYOoI/?t=286) But, like, imagine being a kind of a complete standalone company trying to do that when you have, you know, Alphabet and Meta and, you know, Microsoft and then OpenAI and Anthropic and X, like, everybody's doing that. I don't know how you would, I don't know how you'd sort of slip in there. So, you know, there's a case to be made for obviously an extreme, like, deep vertical, you know, use case or some, you know, kind of niche. But I'm taking your question literally. I think you'd still eventually then need to build the software around it. You'd have to get really good at all of the technology that surrounds delivering that AI model to the customer in a way that lets them actually use it, as opposed to just selling tokens back and forth.

[5:32](https://realalpha.app/v/g9E7iCnYOoI/?t=332) I don't think there's a business model for kind of an independent player at scale to just own kind of monetizing tokens. If you were running OpenAI, would you keep pushing the boundaries of frontier models? Or does that just feel like such diminishing returns? And why not redirect all that energy to the consumer product that is ChatGVT? Yeah, so I would definitely do it. And I don't have that much, I don't have that much kind of critical feedback for OpenAI or kind of any of the major players at the moment. You know, the areas of critical, you know, issue are, you know, what does Google do about core search? Like, that's a super interesting question. What does Apple do about, you know, kind of the Siri problem? And like, how much do they need to own themselves versus, you know, kind of let other players, you know, kind of, you know, power some of that functionality.

[6:27](https://realalpha.app/v/g9E7iCnYOoI/?t=387) So, you know, there's some interesting strategic questions that I think emerge. But in terms of OpenAI, their execution, I think it's exactly what probably I'd be doing, which is let's kind of, you know, it's not even hedging your bets. There's a nice flywheel. So they're not like different work streams. It's like, let's have the best models in the world. Let's then incorporate that into a consumer and, you know, prosumer and then business set of products. And then there's now a new layer, which is like, we're going to let's make sure that we have like applied use cases in these agents. the rumored engineering agent and all this stuff. And so they can kind of, I think they can have their cake and eat it too in a lot of this. And, but it's, it's critical that they are always at the, at the forefront of the state of the art models, because that's the, that's the calling card into the business conversation, which is, we also have, you know, the, the, the best performing models on these, on these dimensions. And if there was only, you know, the chat GBT play, I'm not sure that would be, you know, that is not why their brand got to where they are.

[7:29](https://realalpha.app/v/g9E7iCnYOoI/?t=449) And I think you want both those things to kind of be interlocked. Now, there was a there was an interesting thought experiment that somebody mentioned recently, which was, you know, for at least the consumer use case. If tomorrow you swapped in, you know, Gemini or DeepSeek, you know, just like and just, you know, had a person go to ChatGPT, would they notice the difference? The answer is, you know, obviously, probably not at this point. Like we've reached, we've reached a convergence on model quality for most consumer use cases to be satisfied for, for, for, but, but most as in like, as in like, you know, the majority, but not necessarily the most important ones.

[8:06](https://realalpha.app/v/g9E7iCnYOoI/?t=486) And so, and so there's going to be an 80-20 rule, which is we probably have solved like 80% of, of like, Hey, what's the score to the sports game? Tell me about World War II. You know, what should I wear in this meeting? Like, like we, we, we, we've solved that. but the the 20 is going to still be like you know orders of magnitude more more quality that we need which is like go out and you know do the classic i need to book a flight type problem. And those were nowhere close to solving. So we kind of got to the 80% really quickly. And now the 20% is going to take years and years to kind of keep cranking through. I get the feeling you're excited about agents if I read your tweets. Is there an agent that you think has product market fit today? Yes. But a couple of things. I think that I don't know if everybody's equally defining agents in the same way, but I'll give you like three or four.

[9:06](https://realalpha.app/v/g9E7iCnYOoI/?t=546) So, you know, one company that I happen to be an investor in, but I think they just independently, you'd probably agree, is this sort of space around outbound selling. So one company is 11X. I think there's others that are good. And I think they've tapped into, you know, maybe the product market fit right now is like lead gen. And then it kind of expands from there. But for all intents and purposes, people are using this to augment, you know, their sales capacity, you know, generate more demand. That seems to be working. I think, like, writ large coding agents, you know, seem to be working. Again, maybe we could define different levels of agenticness.

[9:45](https://realalpha.app/v/g9E7iCnYOoI/?t=585) Um, uh, but, but what you're seeing in, you know, cursor, windsurf, uh, replit, you know, level of bolt, et cetera, like obviously, you know, probably different use cases, like probably 80% of the vibe coding is more fun and it's interesting to watch and not going into production, but I could see a lot of, you know, product market fit for, Hey, I have this internal it tool. It's not, you know, not the most strategic thing we have to do. I can have an agent kind of write it or modify it. and then boom, I've just solved that problem that used to maybe go to low-code tools or you'd either never solve or you might hire a contract firm and it would take six months to go build.

[10:23](https://realalpha.app/v/g9E7iCnYOoI/?t=623) So I'd say that's product market fit. On our end, we have one of our first kind of agentic use cases in just the fact that it's not kind of a single one-shot in the model is just we extract data from documents so then you can say, hey, here's a contract, here's an invoice. I want to read the document, put that into a database, and then automate a workflow around it. That's clearly got, at least in our world, product market fit.

[10:49](https://realalpha.app/v/g9E7iCnYOoI/?t=649) **Aaron Levie:** Mostly our problem is we can't keep up with all the range of use cases customers want.

[10:55](https://realalpha.app/v/g9E7iCnYOoI/?t=655) And so there's more demand than the ability to satisfy everything that the customers are asking for. But those are a few. On the consumer side, I'd say probably not a lot of PMF on agents. But on business, you know, it's my bet was this two years ago. My bet will probably be it in five years from now. We are probably, you know, we're probably over indexed on on the flashiest parts of AI in the consumer world and then under indexed on the things that will just make, you know, hundreds of billions of dollars in B2B. And like, I'm sure like Nikita is like. You're talking my book here. I guess one question, the business model of cloud, SaaS, if you will, I think has been historically probably underappreciated versus obviously the delivery mechanism and all that.

[11:59](https://realalpha.app/v/g9E7iCnYOoI/?t=719) I think people have a lot of appreciation for, but just purely how the pricing was able to disrupt a lot of things, be it Salesforce or whoever. I'm curious, do you think that usage pricing is an opportunity with AI to have that similar effect or is it something different in your mind? for like uh the the broad transformation or disruption or or which on which axis Yeah, I guess as you think about like who your historical competitors were, I think historically people underestimated the on-prem to SaaS transition and the number of customers you could reach was probably one of the big things. But there was a lot of innovation that ended up happening around seek-based pricing and elements of fair use pricing like Slack was early in that.

[12:56](https://realalpha.app/v/g9E7iCnYOoI/?t=776) And I think all of that stuff, there was a bunch of different elements of the SaaS transition that ended up being, you know, user based in some way that I think was an accelerant of this transformation and transition that ended up playing out. I guess, do you think usage has a similar opportunity for existing businesses or is it a little orthogonal and ultimately it might not be as broadly applicable as? I think it's, I think it's, I think it's very relevant. And then, you know, so my, my characterization of SaaS in cloud from a business model disruption was it took something that you used to spend upfront, like maybe millions of dollars on to now you spent thousands of dollars on.

[13:40](https://realalpha.app/v/g9E7iCnYOoI/?t=820) The CapEx to OPEX transition. CapEx to OPEX, sort of like perpetual license to, you know, buy the drip, you know, anything that was just like, okay, I have to wrap my head around this major outlay of capital, system integrator fees, infrastructure I have to build out, team, like, you know, if you, this is more anecdotal, but if you were like to deploy a CRM system in the 90s, you know, just think about what had to go into that. like, like data centers, system integration, like, uh, you bought software for the, for the CRM system, but you still had to buy hardware, you had to buy all the servers, you had to have a network. So like, think about how many steps from the moment you said, I want a CRM system to the day that you're actually going to like log into it with your data, I don't know, a year, year and a half, you know, millions of dollars Salesforce shows up and they're like, literally like tomorrow, you can just have a CRM system. And Oh, by the way, you could use it for 20 people in your company. And there's no, you don't need some kind of like critical mass to make the economics work, like just like start using it. And so, so, you know, you multiply that over two decades or two and a half decades. And it's like every SMB on the planet can do it. Every small

[14:52](https://realalpha.app/v/g9E7iCnYOoI/?t=892) team can do it. And these, and the TAMs of these markets just basically grow because now everybody can afford a CRM system that couldn't. So, so that was, it was because it was a seat base, it was because it was subscription. It was also just because literally like, like your day one upfront bill was just a hundredth of what it would have been, you know, because you don't have all the other, all the other things, you know, wrapped around it. Cloud did the same thing, obviously more on an elastic basis with compute. So I think what agents do is, is that I think there's going to be two big, two big kind of areas of upside. One is you're doing that now for effectively a form of labor. And so that kind of has this interesting impact of it expands the TAM of software from only being able to sell the software to people on the other end, so you were kind of capped by the number of seats, to now you have kind of like, you know, like I can think of categories where the category of AI agent spend will be an order of magnitude more than the spend of the software in a kind of a non-AI world. So take, you know, a relatively simple example

[15:54](https://realalpha.app/v/g9E7iCnYOoI/?t=954) **Aaron Levie:** like contract management software. Contract management software as a category is like a couple billion dollars, like globally in the world. Legal services is a couple hundred billion dollar category. And so now can software take 10% of that spend through AI and pull from the

[16:16](https://realalpha.app/v/g9E7iCnYOoI/?t=976) legal services spend or expand the legal services spend? And then all of a sudden this category that was relatively niche, couldn't make that much money because the amount of people that wanted to buy contracts. management software is kind of low, could that now be a $20 billion category as opposed to a couple billion dollar category? So you could do that probably 20, 50 times across software and already underwrite master TAM expansion after going after a whole bunch of new use cases that used to be kind of labor essentially. And then the other part is AI has a way of just completing the things that people have kind of always wanted to do in the products themselves, but just were never possible, which then also unlocks a whole bunch of new budget as well.

[16:59](https://realalpha.app/v/g9E7iCnYOoI/?t=1019) So I think you're going to see this is the new version of disruption is it just will, it makes, you know, it lets you go and complete the outcome that the customer was looking for in a way that they are not required to go and deliver it themselves with their people. And then that means that you, you know, more of them, that value accrues to you. And so that's the usage base or outcome pricing I think we're going to see in AI. When you extrapolate that service budget there forward and think about the value to the end customer, totally makes sense that they'd be willing to pay. Hey, there was this person that I had to do this, and now I can get software to reach things that weren't even possible or do it more.

[17:46](https://realalpha.app/v/g9E7iCnYOoI/?t=1066) I worry about the deflationary element a little bit of like the competition. And at the end of the day, are you beholden to what your next competitor is willing to charge? And so we might not be able to actually tap into the service budget quite as much, not because the opportunity is not there, but because there's tons of venture companies popping up and competing with one another. So I guess, one, we'd love to get your reaction to that. Two, how do you think about durable value creation? Because I think everyone agrees, for example, Zoom, I would pay anything for it, basically, to work if there was no other alternative. But there's a lot of other alternatives. So there's an anchor on what I'm actually willing to pay. Yeah, so I don't think that any law of market dynamics, you know, changes because of AI. So, you know, but I would say that has probably been true in, you know, venture startup land kind of forever, which is, you know, the CRM, I bet there was a CRM market map from Gartner in 96, that was like 47 companies that did CRM and somehow only Siebel and like one or two others made it to the other end. And so, you know, did they temporarily cause a deflationary impact on the price of maybe what Siebel could

[19:06](https://realalpha.app/v/g9E7iCnYOoI/?t=1146) have charged if they were left alone, for sure, to still create a great, you know, outcome for Siebel. And, you know, you know, there's a version of the world where it doesn't sell the Oracle and they, you know, pivot the company and now it's a, you know, $100 billion company. So I guess, I guess it's 100% true, but nothing about AI seems to follow a different law of how these things play out, which is competition will suppress, you know, what you could like maximally charge. And it keeps kind of all the players in check. But eventually you get like some degree of network effects, some degree of like, you know, reference customers that tell each other, you know, that you can all use, you know, X thing and, and, and so on. And, you know, like just apropos the timing of this conversation, like my, my, my dad was asking like, what, how did Wiz get sold for, you know, the $30 billion and we're Wiz customer. And I think the technology is fantastic um how does that happen in a world where like obviously there's like 500 security startups like well how does one break out and it's there's a little flywheel where like your product's just 20 better then you get a few more reference customers and then and

[20:16](https://realalpha.app/v/g9E7iCnYOoI/?t=1216) then you learn faster and then your product is another 20 better faster and then you get slightly better you know people working on it and and it kind of spins out and then all of a sudden you have one with escape velocity, you know, outpacing the rest of the category, but yet probably there's still a second and third and fourth player that are pretty good and they'll probably also grow and then they'll get acquired. So I think AI, I mean, I don't, I don't see a different dynamic playing out in AI for any reason. And so, and so then the things you probably want to do to like maximize your moats are, you know, again, the same lessons probably from 35 years ago in enterprise software, which is, you know, get data, do workflows, you know, make sure you've got, make sure you've got a, you know, kind of enough happening within your ecosystem that keeps customers to have some kind of, there's like a, like a compounding value, more data that comes into the system, you know, the next run of the AI agent, you know, gets just a little bit smarter.

[21:11](https://realalpha.app/v/g9E7iCnYOoI/?t=1271) You know, there's going to be a lot of network effects on the embeddings of your data. So you could, as a single programmer, you could probably switch between Cursor, Windsurf, and, you know, Replit and a few others. But as a network of 500 engineers in a company, and I've done embeddings on the whole code base, and I'm talking to all the different, you know, systems it's wired up to, wow, change management on that, you know, becomes a lot harder. So I think we're going to see a lot of very similar, maybe this will be bad news for everybody in AI, but it's going to look a lot like SaaS and the lessons are going to look a lot like SaaS.

[21:49](https://realalpha.app/v/g9E7iCnYOoI/?t=1309) And so you want data, you want workflows, you want strong customer references and you want to build a network effect around your customers. And I think it's going to play out like that. And then maybe the one thing that we just have to be prepared for is there might be some pricing dynamics right now in play in AI that won't necessarily last forever, which is like right now you can kind of comp it to labor in some areas. And that probably feels like a little bit of a temporary advantage that you might have when ultimately it'll probably be more comp to like typical software margin structures. And that'll just be a journey that, you know, the industry goes through as we kind of land to the terminal business model.

[22:30](https://realalpha.app/v/g9E7iCnYOoI/?t=1350) Yeah, I guess we don't need to belabor this point. The two things that I do wonder about with AI specifically is, are the solutions that are maybe less workflow UI centric in some ways, there's like the, the SaaS world. Traditionally, we had all these screens and logins and, you know, the workflow that existed on top. And so maybe security in that way is actually the right analogy because there's definitely dashboards and integration points and all that with security, but ultimately the work is getting done oftentimes outside of those screens, right? It's pulling in all the data in some ways. And so, yeah, it's just something we've been noodling internally.

[23:11](https://realalpha.app/v/g9E7iCnYOoI/?t=1391) And what do you conclude because of that? I think the things that are just tapping into the work automation, be it customer support or SDR or whatever it is, ultimately, because there's less workflow embedded into it, the change from one to the next, I think there's less friction associated with that change. And so you can swap it out. You see, it's almost like a meme on the internet of people going from, you know, cursor to Codium or whatever it is. And that's not the right analogy because that actually has some workflow around it. But it does feel like people are a little more willing to switch than at least I had historically experienced.

[23:50](https://realalpha.app/v/g9E7iCnYOoI/?t=1430) Where, I mean, in truth, what's the difference between Asana and Monday and Trello or whatever? Well, the difference is like, you got to switch your entire team onto something different in a meaningful way versus when the work is actually being done outside of the UI. It might just be one-click change, which just leads to a little bit, I don't know, more ability to price shop, more ability to do other stuff. But I guess that it ties back to the integration, similar to security. Yeah, yeah. So that's the thing. So the question is, so it used to be the network, the stickiness was I had 100 people that all learned how to use it.

[24:25](https://realalpha.app/v/g9E7iCnYOoI/?t=1465) to use the buttons and, and then there was data behind the buttons. And so, man, that's a lot of, that's a lot of change. But the question would be like, is there going to be an analogy where, you know, it's inverted where you still, the AI, you know, it's, it's APIs, it's APIs into your other systems. It's the APIs into your e-commerce thing. It's then the logic around when to make the decision about the e-commerce thing. So I think. And it's self-reinforcing and learning, right? And so like, at some extent you get all the data, get all the integrations. But then there's ultimately some algorithm or whatever, some AI thing on top that's learned on top of itself.

[25:00](https://realalpha.app/v/g9E7iCnYOoI/?t=1500) So sure, you could switch, but then it's a cold start to start over. Because I could underwrite the exact opposite of this point, which is that when you have a learning system, that learning system will probably perform better than your people did. and so previously you could change out you could swap out software and and there wasn't like so much ip in the heads of the people about how that software worked because we're we're just like okay click the next button and whatever but like but like the ai is going to track every event it's done in history and and you know and and just get a little bit better and and the signal will just get a little bit better and it'll it'll you know have a little bit more fine-tuning or it'll have a little bit better instructions to the model. And then that becomes some opaque data source to you, the customer that is inside this AI product that just makes the thing always perform five or 10% better in a way where you can't just go to people and say, here's the new sheet of paper for how to use this new product we just implemented. You almost have now created your own, you know, for all intents and purposes, model for doing customer support or coding in your

[26:14](https://realalpha.app/v/g9E7iCnYOoI/?t=1574) **Aaron Levie:** product. So, you know, so the more black box or obfuscated or non-UI oriented it is, to me, doesn't necessarily correlate to its stickiness. And in fact, maybe it could be

[26:27](https://realalpha.app/v/g9E7iCnYOoI/?t=1587) the opposite. I heard you say that cloud was pure efficiency. And maybe there goes back to one talk you were giving at some big bank that everyone's eyes were glazing over. I think it was like a conference or something that maybe the most boring or least interested you've had a crowd. But it wasn't like tangibly output based in some way that like the rank and file within an organization didn't care about the backend infrastructure. And ultimately, you're speaking about some elements of it uh can you give that quick anecdote but then also talk about how ai is is different and how that translates to the enterprise and the conversations you're having yeah so uh if i recall the anecdote properly it was i think i was just referencing that like in the peak of cloud adoption i i did this keynote on like the future of of you know running your bank or you know financial services and and it was it was mostly just the pitch cloud and and it was you know the the the i i find it very exciting uh but the audience didn't because it was because it was just like yep cool we get it man like the servers are not in our data center anymore they're in now a cloud data center like really like super cool and um uh and and so so that that that to me

[27:50](https://realalpha.app/v/g9E7iCnYOoI/?t=1670) was kind of the it kind of captured the cloud transition you know at an industry level in a nutshell which was like it was it made your it made your it operations faster it meant you could store more data so you can make better decisions. It makes your employees more efficient because they have better software, but it didn't radically transform like entire industries or sectors. And AI, you know, conversely has the opportunity to actually radically, you know, transform how you run your business, how you make decisions, you know, how you, you know, serve your customers, how you build products. And, you know, I think it's, I think it's always useful to learn, you know, figure out like what things will be similar to a prior technical.

[28:30](https://realalpha.app/v/g9E7iCnYOoI/?t=1710) technology transition, which things will be different. I think the thing that will be similar is that adopting AI versus cloud will take all the same change management. It'll take all the same meetings. It'll take all of the same privacy and policy and governance and compliance review people. It's going to be all of that. But I do notice and see one big distinction in the two waves, having kind of been at the similar timeframes for both, in the very early days of cloud, you would go into a meeting with a bank, a pharma, a government agency, and you'd say, you know, I'd love to talk to you about, you know, moving your data to the cloud.

[29:10](https://realalpha.app/v/g9E7iCnYOoI/?t=1750) And it was sort of like, like, not interested, shock, like, definitely not going to happen, not going to happen here. Maybe, maybe there'll be some small little workload that's like a dev test workload. Maybe some department or fringe group will use it. And so it was mostly like years and years of just like selling and selling and selling and trying to get you to understand what was so much better and all of this. Conversely, and so we're only about two and a half years into Chachapiti, let's say. So that would easily describe two and a half years into cloud if I go back to the late 2000s, let's say.

[29:46](https://realalpha.app/v/g9E7iCnYOoI/?t=1786) If I look at AI right now, that exact same meeting with a customer or set of peer customers

[29:52](https://realalpha.app/v/g9E7iCnYOoI/?t=1792) **Aaron Levie:** is like, is like, we know this is going to change everything. Everybody's clamoring for it. Every employee needs it. The new generation coming into the workforce is asking for it. My boss is asking

[30:02](https://realalpha.app/v/g9E7iCnYOoI/?t=1802) for it. The board is asking for it. I need to figure out a strategy. And, and like, it's not when it's, it's how, um, it's not, sorry, it's not if it's, it's when it's how it's like, what do we, what can we do? I need to do more yesterday. And so totally different universes. Um, and, And it is, so basically what that means is that you've bypassed all of the normal, like, resistance to this happening that normally, you know, happens with one of these big changes. And now it's purely in the mode of, like, how do we make it happen? Which functions are going to use it? What is the impact to, you know, how do we make decisions when an AI generated the response?

[30:43](https://realalpha.app/v/g9E7iCnYOoI/?t=1843) How much human in the loop do we need? So we've jumped all into just now that mode. five to seven years faster than we did with cloud. Now, that doesn't mean that the adoption will follow a different trajectory because again, it's really people, it's change management, it's systems, but the reluctance and emotional kind of consternation is very different this time. And so what is the resistance when you're going into the organization or how would you describe beyond the enthusiasm and trying to figure out the how and the when? Like, what are the long poles in the tent? How much are on these organizations versus how much are on the commercial vendors?

[31:26](https://realalpha.app/v/g9E7iCnYOoI/?t=1886) Yeah, I would say, actually, maybe also this might abuse the analogy too much, but if I compare to cloud, cloud could do more than what customers were asking for because they were resisting it. So you could have always implemented cloud 50% more than you actually were at the time or hundreds of percent more. Maybe actually the reverse is true, which is I'd say most conversations now, the customer is actually like, they're jumping like three steps ahead once they see the demo and they're like, oh, that's great. Like, we'll just like totally automate that entire thing. And you're like, okay, well, well, let's, let's, your data is not yet in the environment that it's going to need to be to do that use case. Or, you know, you know, we're still waiting on some reasoning model breakthroughs from OpenAI or Cloud or something to just get that extra 3% of accuracy that you're going to need. So in this case, I think customers have pretty much jumped ahead to way closer to the end state, which is like, yeah, great. Let's give all the employees a productivity boost. Let's figure out where we can deploy.

[32:30](https://realalpha.app/v/g9E7iCnYOoI/?t=1950) to the organization. Now, you know, I want to do a massive asterisk. I'm talking to the people that are talking to us, which means that like, by definition, they're leaning in, they're a little bit more forward thinking. So some of the anecdotes I'm sharing are from those types of customers. I think there's still an 80% of customers that are like, whoa, like my head's exploding. Where should I start? Where should I go first? But there's a lot of, there are a lot of customers where they're now ahead of the technology based on the set of use cases that they have. Um, and, uh, and so I wouldn't say it's, it's, it's like the hurdles unfortunately are, are much more like practical considerations. Like literally like I can't just, you know, turn on an AI thing that looks at all my data. And then an employee can ask the question because guess what? It's all of a sudden going to reveal, you know, secrets in the organization that they shouldn't have had access to, but there were over provision for access like five years ago.

[33:18](https://realalpha.app/v/g9E7iCnYOoI/?t=1998) Or, hey, you know, AI agents are really good at doing code, you know, auto-completion, but, like, am I ready to put an AI agent in production for, like, generating a full, you know, multi-file, you know, output? And then how do I review that when the human actually wasn't along the ride for the actual, you know, editing process? Are they going to really understand the code? Are they allowed to press submit and that goes into production? like like that's going to be that like these are like the big questions that people are dealing with i mean imagine imagine how unhinged we are online where we're like oh man we just made a video game in three you know three hours and it's like and it does all this stuff and then you're like you're you're an engineer in like a banking system like are you supposed to go implement that right now like like is that like are we ready for like are we ready for like your bank account to be driven by vibe coding? We don't know. So these are the kind of things that I think every enterprise is trying to figure out like, okay, so what pace should I implement this stuff? Is my data ready?

[34:25](https://realalpha.app/v/g9E7iCnYOoI/?t=2065) Are my systems ready? A lot of it is about, wow, the industry is moving so fast. Ironically, the industry is moving so fast. How do I make decisions right now that I'm going to be stuck with in a fast changing space? So a lot of it is actually like, can you get your architecture set up for like optionality in the future, because maybe the vendor you work with today is not going to be the one that's the best in the future. You need to swap out different parts. These are the kind of conversation that we have. To that end, you cautioned, I forget if it was a blog post or a tweet or something, but that being early to AI in some ways can be a liability if you don't set up the architecture with the right abstractions in some ways. I guess as you think about the applicability of this for Box as an organization and therefore the advice you might have for others. How do you think about being flexible with this pace of change? And is it more about being model agnostic or modular in elements of the design? What would you share on that?

[35:30](https://realalpha.app/v/g9E7iCnYOoI/?t=2130) Yeah, all of the above. So there's how we build and then how we even implement AI internally. how we build is, is basically we try and figure out the parts of the stack that there's probably a few different categories and I'm making up some of the, maybe the nomenclature on the fly, but like there'll be parts of the stack that just like, we'll never change, you know, a model breakthrough will happen as it doesn't matter. Like we're not going to change that particular interface, you know, with our system so that you can just like lock and load and start executing. There's other parts where you're like, you know, we think there's a best in class technology now.

[36:09](https://realalpha.app/v/g9E7iCnYOoI/?t=2169) You know, maybe we're kind of like, we're shaky on it. You know, we probably prefer there to be an open source version. So that's like a spot where you're, you know, you probably need to be really thoughtful about, you know, how much do you lean into a particular... particular part of the architecture. I'm thinking like your vector search, your rag kind of execution. These are spaces where there's a bunch of first movers, people started adopting them. It's not exactly clear where it all ends up in five years from now. So you kind of want to be thoughtful about that. And then there's the final part on your modular point, which is like, you know, this space is changing every three minutes. You, you, you need to decide, do you want, do you want like maximum flexibility? So these are the model providers. Do you want to

[37:01](https://realalpha.app/v/g9E7iCnYOoI/?t=2221) **Aaron Levie:** be able to have a way of, of, you know, swapping in different parts? There's multiple parts of box where we use multiple model providers for the same workflow. Um, uh, and so you just need

[37:11](https://realalpha.app/v/g9E7iCnYOoI/?t=2231) to then have like a high degree of optionality, a high degree of flexibility in that part of the stack. And, um, uh, and so that, that, you know, so you, and then you have different like abstraction layers between those different interfaces. But I think basically anybody in AI at this point has been building with that pattern. You go to perplexity, you change your model, you go to cursor, you change your model. I think we're all realizing, okay, you probably want to let the user change their model, or at least you as the developer have some degree of optionality there because we know how fast that space moves. And then you have to decide which other parts of the stack to just kind of, you know, lock in on and don't worry about again.

[37:58](https://realalpha.app/v/g9E7iCnYOoI/?t=2278) Klarna, Sebastian, who we've had on before and a good, a good guy, great CEO has been, I think he tweeted something recently. I think he accidentally made news in his telling of like consolidating SaaS vendors and moving more things to a Gentic workflow. And I'll tell you, It set off a little bit of a firestorm internally to see if this is like a long term thing that we need to be existentially worried about investing in software because there was going to agentic workflows and systems were just going to do away with the need for the interface. And therefore, everything could be in a CRUD database in some ways.

[38:41](https://realalpha.app/v/g9E7iCnYOoI/?t=2321) I'm curious your perspective on that as we zoom out and maybe is what they're doing, you think, broadly applicable to other organizations? Or is this a one of one, maybe exceptional case that they're testing something and on the early frontier? Yeah, I mean, I found it very fascinating and I enjoyed that it was happening. And I mean, I enjoy that it's still theoretically happening. I think some of his updates were that, like, they didn't actually do as much on the customer support side or they have some more, you know, kind of premium agent, you know, human agents that they now have. But but so I thought it was great from a provocativeness standpoint.

[39:20](https://realalpha.app/v/g9E7iCnYOoI/?t=2360) I think we do need people testing the boundaries. um um i think it's the kind of thing which is it didn't cause me to rethink all of sass because because the the you know uh uh i don't remember the first time i heard this but maybe maybe it was like 15 years ago or something i was uh i was meeting with um an it you know leader at tesla and and they were like building i'm gonna make up 80 of the story but they were building their own ERP system. And I was like, holy crap, I cannot believe that you're doing this. And maybe this is the future. And then you quickly wake up that like, okay, no, this is sort of an Elon special where he needed to control end to end the whole thing. And they needed their own, they didn't want any of the off the shelf stuff. And the amount of fortitude and just sheer entrepreneurialness you need to have to even want to attempt that is like kind of insane.

[40:18](https://realalpha.app/v/g9E7iCnYOoI/?t=2418) And so I think the Clarn example is more in that category. Like it's just like if you go to the average company and you're like, do you want to be responsible for running your own HR system? Like the average enterprise. is not going to say yes to that. And no matter how agentic, you know, something is, it does mean

[40:36](https://realalpha.app/v/g9E7iCnYOoI/?t=2436) **Aaron Levie:** that you're responsible for the thing. Like, like this, the AI provider that has made the agent that wrote your, you know, your HR system is not taking responsibility for the HR system code that was generated. So that means by definition, you, the customer are now responsible for whatever the

[40:53](https://realalpha.app/v/g9E7iCnYOoI/?t=2453) thing is that it produced. It's not going to be the AI models. You know, Claude is not, Anthropic is not taking any responsibility for how your HR system works. And if it leaks data, and you get sued by the EU, because there's some kind of, you know, data privacy thing, like, that's not Claude. Like, that's Workday. Workday will do that, not, not Anthropic. So I think you'd have to be like, you know, which parts of software are people so unhappy about that they would do themselves? Or which parts of software just like are so needed to be customized? Or which parts software are so unstrategic that you don't mind whipping up these like micro apps for like one off things. And so so you don't you're fine that it is built by this, you know, an agent that you can just deploy quickly and turn it off. And it's kind of, you know, it's a three month, you know, it needs to live in the world for only three months, those for the foreseeable future next two years, let's say, that would be the only thing I think could be disrupted. But I don't think your HR system is going away. I don't think your CRM system is going away. I don't think that really changes. To your point about deflationary impact, I do think that there's a chance that

[41:59](https://realalpha.app/v/g9E7iCnYOoI/?t=2519) you could see, you know, you could see a new crop of like, I'm going to just like, like YOLO it and build like the AI, you know, like, I'm going to build Oracle with AI agents and just like give it a shot. But that would still be a software company that emerges from that. And you're really using AI purely to just, you know, add engineering augmentation. I don't think the customer wants to be responsible for that software at the end of the day. That was my conclusion as well, which I think is good for people investing in software companies. Yeah. Now, big asterisk, like in 10 years, do I want to be on the record as saying this?

[42:38](https://realalpha.app/v/g9E7iCnYOoI/?t=2558) Let's see. But like, but like, just I just don't think companies want to be responsible for every problem in the world. Like you want like the Jeffrey Moore came up with this idea. I think it was Jeffrey Moore that came up with it, but, but maybe he credited somebody else of core versus context. And, and like context is just like shit that like, like you just want done for you. You don't want, you don't want to worry about it. Core obviously being like, this is like existential to my business. And for most people, like, like their core business is not their HR system. And so thus you hire other people to do your HR system. And so that's why you don't really want, you know, you don't, you don't want your internal IT team being responsible for rebuilding something that you can get off the shelf, then just it's done for you.

[43:21](https://realalpha.app/v/g9E7iCnYOoI/?t=2601) That's the way I feel whenever technical founders want to reinvent sales comp. And it's just like, you're not, it's hard enough to differentiate on one vector. You know, this isn't going to be the make or break. Yeah, we, you know, we can go through all the books and talk about it. All right, I'm going to give you some quick ones and you give me your gut reaction on some of these things. Ready? Do you think the net of AI in some reasonable period of time, maybe five to 10 years, decreases the number of hours worked by your average knowledge worker? Well, how many years? Five to 10. I'm going to stick with no for now.

[44:07](https://realalpha.app/v/g9E7iCnYOoI/?t=2647) Yeah, I think I agree. Do you think AI will flatten org structures in any meaningful way? Example, fewer middle managers, faster approval, more self-service, knowledge work. This one I could buy. I could buy. I think actually layers have gotten pretty crazy over the past couple of decades. So so I think I think, yes, I think, yes. And part of it is because I think that. AI kind of lets you, it lets functions, my latest kind of conclusion is that it kind of lets functions sometimes do their adjacent function. And so it lets the designer write the front end code, it lets the back end developer write the front end, you know, code, it lets the copywriter, you know, generate the full white paper.

[44:57](https://realalpha.app/v/g9E7iCnYOoI/?t=2697) Or so, so I think what that means is that you will have, have sort of more full stack workers almost like, like we don't really think about this outside of engineering, but I think you'll probably, I think we've gotten like maybe overly disciplined in, in job functions in some parts of, of the economy. And I think AI kind of lets you have a little bit more expansive responsibility. So then that probably means that that's a little bit of a flattening when you don't have to hop through and around as many orgs to get things done. So, but we'll see. Did you ever read the David Epstein book, Range, Why Generalists Triumph?

[45:36](https://realalpha.app/v/g9E7iCnYOoI/?t=2736) No. It's a good one. It's a good one. It's a good one. Yeah, kind of. It sort of talks about, like, they give the example of Roger Federer growing up and playing multiple sports versus Tiger Woods being specialized in one. And how many breakthroughs in society have occurred from people outside of a core field or domain, leveraging the knowledge they have of that and going into another one. And so it goes through scientific breakthroughs or company foundings or all this, where when you get too verticalized in some ways, you just don't have the context of the way other people do things. And so oftentimes breakthroughs come outside.

[46:06](https://realalpha.app/v/g9E7iCnYOoI/?t=2766) I think I think work has probably gotten like a little bit to Adam Smith. And we've we've like, you know, we're all just doing our little part of the paperclip factory or whatever the, you know, or the needle. I think it was needle factory or. Yeah, the paperclip. We've all Andrew, what is it, Aaron Ross or whatever, predictable revenue. We've done that across every element of society at this point. Yeah. So, so I think, I think probably AI relieves us a little bit of that, like division of labor, maybe, maybe got just a little bit too granular over, over time. And so I think maybe we get, we get a little bit of a chance of a reset, which is like, no, I can like just throw a little bit more of a general problem at somebody and they've got the ability to kind of go and fully solve it, which, which frankly, I think is a huge, hugely exciting thing and much more fulfilling probably for, for the knowledge worker kind of class.

[47:01](https://realalpha.app/v/g9E7iCnYOoI/?t=2821) um, uh, that, that I think has a lot of upside. What's the coolest, uh, or most mind blowing demo you've seen in the last, call it three to six months where, where you were like, wow, I can't believe that's actually possible that we can do this. Yeah. Um, I'm having a, a funny journey because like, it's like, it's like you do, you do start to now, unfortunately, like build such high tolerance and desensitization. It's like the hedonic treadmill in some ways, right? You're just like, I always reference now with AI, the Louis C.K. bit about airplane and Wi-Fi. And you're just like, you're flying through the sky and you have access to the internet.

[47:42](https://realalpha.app/v/g9E7iCnYOoI/?t=2862) And that's the way I feel about a lot of the AI things where deep research hallucinates a little bit. And I'm like, well, this is bullshit. It only saved me seven hours. Was that 15 hours of work you would have done otherwise prepping for this interview? Yes. you know? Yeah. Um, so, so it's, it's tough because I, like, I do wish I could, I wish I could have my pre chat to BT, like wiring for every individual breakthrough, because I think I would just be like, holy shit, this thing is witchcraft like every day. Um, like I remember, I remember like yelling at a friend who was like deep in LLMs, like, you know, a week into chat to BT. And I was like, I was like, I'm sorry, I literally don't understand how did it write the business strategy on a thing it's never scanned from the internet and and like i was just like obviously an idiot at that point on on on all this stuff and um And like, but I, I want that, I want that like, like energy again, where I'm just like surprised by everything.

[48:42](https://realalpha.app/v/g9E7iCnYOoI/?t=2922) So, um, so I, you know, I'm, I'm, I'm like, I think from a pure utilitarian standpoint, I still love the, you know, my, you know, it's the exact example you just did, which is, which is, I try and force myself as many times as possible to be like, okay, deep research exists, deep research exists. Like, like, don't, don't do the thing where you open seven tabs and you go through all this stuff. it's just like, just send the query, go back to it. I had a, I had a thing 48 hours ago. It saved me an hour and a half. Um, and, um, and like, I, but I had to catch myself cause I was like about to just go to do my research, my normal, like, you know, I've been doing this for 20 years.

[49:21](https://realalpha.app/v/g9E7iCnYOoI/?t=2961) You know, I know how to research things, did deep research and lo and behold, like as far as I can tell, no hallucination. I, I, I checked a couple of the answers. Fantastic. Because it's just like, boom, solve the problem for me. Like, so, you know, I'm now doing that probably five to 10 times a week on something, um, which is just an awesome productivity boost. I, um, I still am, um, I'm, I'm still probably incrementally, uh, surprised by and excited by front end design creation with AI. So, so, you know, I'll, I'll pop in a screenshot into cursor and it codes it, or I'll pop in a prompt into, you know, V0 Replit and it produces something that I would have like had to ping an engineer or a designer about and be like, can you go work on this?

[50:06](https://realalpha.app/v/g9E7iCnYOoI/?t=3006) And so like, you know, unfortunately like my browser history kind of looks a little unhinged because it's like 11.30 PM on Saturday. I'm like on V0 doing designs. So that's, you know, but that still is pretty exciting, I think, to me. Yeah. What do you think is more likely to be impactful in the enterprise for AI, multimodal capabilities like image, text, code, voice, or the agentic workflows that we kind of talked about earlier? If you have perfectly executing agentic workflows, that's the holy grail. So the amount of the, you know, my workshop analogy is like, you know, in data science as an example, and anything obviously that you put into a structured database, you know, we've had, we have, we've always had this ability to be like, okay, I'm going to go compute some large set of things, like go run the analysis on this customer base in this region to do a thing.

[51:08](https://realalpha.app/v/g9E7iCnYOoI/?t=3068) And like, we used to, you know, we still do, but we like, we would talk about it, like, okay, I have a job running and, and I'm going to, you know, I'll check back in in an hour and figure out what the answer was. Like imagine in, in all forms of unstructured knowledge work, a world where you just have jobs running, where you're just like, I just sent these 10 agents to go and like, figure out and synthesize all of this clinical drug trial research to give me insights like you're it's like, we were only wired to think about sending off jobs and compute tasks for like, like 3% of all corporate work. Now imagine if you do that for the other 97% or at least 80% of the 90%. And you're just like, yeah, like I sent out a job to go research this new region I want to enter as a business. I sent out a job to go and, and, you know, figure out what, what all of my, you know, product feedback was, and then build me a roadmap that I should go look at and decide if it's, you know, validated or right. Like, like, it's just going to be crazy when you can actually do full agentic workflows, whether it's deep research or executing a task or automating a customer experience, that continues to probably be the biggest area of upside.

[52:21](https://realalpha.app/v/g9E7iCnYOoI/?t=3141) Two years, five years, 10 years. Do you think the best model in those periods will be closed source, proprietary, or open source? Maybe go through each, like in two years, you say closed source? open source? I think for lack of having any imagination on this, I think that I would mostly be in the camp of, it almost doesn't matter because within three months of the breakthrough, you'll have an open source version. So I think it kind of doesn't matter whether the open source version did it first, you know, wow, China did it, or that OpenAI did it, Because I think within three months, we can expect that you'll have the open source version of the closed source breakthrough.

[53:12](https://realalpha.app/v/g9E7iCnYOoI/?t=3192) So then almost by definition, you can almost still expect then a large amount of the traffic and the use will be open source because you know you're not trading off that much because you'll still get the open weight version of what just emerged. What's the most exciting Box AI feature, application, workflow that you've rolled out that you think is underappreciated by your existing user base and everyone should really be thinking more about adopting? There's two features we're most excited about. The first is we have this product called Hubs. And what Hubs does is if you go to Box and you go to any file system, you create a folder, you put files in the folder, you share that with people.

[53:53](https://realalpha.app/v/g9E7iCnYOoI/?t=3233) You have this problem, which is, well, like folders are kind of like unintuitive for the recipient because they're like, Like, I don't know, like, which, you know, which third subfolder down did you put the, you know, the sales material that I'm looking for to use for my, you know, customer pitch. So we created hubs, which is an overlay onto folders. So you can put as many of your files and folders into a hub. And then you can go and just ask questions of the hub. And so this solves actually a very, very nuanced, highly important thing, which was if you go to a general purpose, just AI chat window, you have to do a lot of work to guess what stuff is on the other end of this chat window.

[54:36](https://realalpha.app/v/g9E7iCnYOoI/?t=3276) **Aaron Levie:** If you go to a hub, in our case, you kind of know by hub, what does it have access to?

[54:41](https://realalpha.app/v/g9E7iCnYOoI/?t=3281) So we have a sales hub, which means by definition that has all the sales information. We have an HR hub, which means it has all your HR information. So you go to the HR hub and you ask the HR question, and then we're basically doing rag on your documents for all of your HR documents. And we're doing rag on on all your sales, you know, you know, presentations. We're doing rag on all your customer support, product documentation. So that basically gives you these micro knowledge portals for every topic that you want. And so that that's a game changer because because you just again, the user instantly knows what topic is going to be covered in this thing.

[55:15](https://realalpha.app/v/g9E7iCnYOoI/?t=3315) so you you dramatically reduce the hallucination you you dramatically increase the authoritative content that will be in the hub so that's pretty cool um and then the final one that that is is just working out kind of way better than we we had hoped initially like you know a year and a half ago let's say uh is just ai data extraction from documents um it sounds like way too straightforward for anybody listening but it's insanely powerful if all your job is is like review contracts review invoices, pull out data from resumes, standardize my financial reporting documentation. We can just now automate basically most of that. That's great. Well, Aaron, thanks for doing this. I think we covered a lot of ground, so really appreciate it.

[56:03](https://realalpha.app/v/g9E7iCnYOoI/?t=3363) Thank you, Logan. Good luck on the valuations. Thank you. Yeah, it's good. We'll be right back.