Pod Insights The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch 20VC: Everyone is Wrong; We Will Have More Developers in Five Years | Why Frontier Labs Will Be Way More Valuable Than They Are Today | Are SaaS Companies Cooked: Which Thrive & Which Die with Aaron Levie, Founder at Box About this episode Aaron Levie is one of the most forward-thinking public company CEOs when it comes to enterprise adoption of AI. Aaron is the CEO of Box, the enterprise storage company that does over $1BN in revenue but only has a market cap of $3.2BN. Something we discuss today… AGENDA: 00:00 — Why the Experts are DEAD WRONG About the US-China AI Race 10:55 — Everyone is Wrong About Labour Markets: You Will Not Lose Your Job 13:10 — What Role Does Not Exist Today But Will Be So Common in 5 Years 16:45 — Is Your SaaS Tool Actually a Valueless Database in an Agentic World? 20:50 — The Cybersecurity Tsunami: Why Agents are Your Biggest Threat 25:50 — Token Maxing: What Every Company Needs to Know About Budgeting Tokens 34:55 — Is Silicon Valley Secretly Being Powered by Open-Source CCP Models? 39:15 — The Brutal Truth: Is This Generation of CEOs Too Low-IQ for AI? 46:55 — Frontier Labs: Why Aaron is Still Betting Everything on the Labs
Transcript 11,154 words · 2 speakers
Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Speaker 00 I think I would still be probably loading up on all of the frontier rounds. These numbers could continue to get much larger. What we are in is a commercial and economic race. We haven't removed humans from the loop. We've just changed where they enter the loop. Everybody is so myopic about this. I want to just like shake the industry. There are going to be more lawyers in the next five years than we have today. The workflow needs to be redesigned for agents, not for people. The budget of tokens will have to move out of IT spend and into regular kind of OpEx spend.
0:29 Harry Stebbings? Is your job harder than ever?
0:31 Speaker 00 Yes, if you're in software or infrastructure or building agents, it's a year of complete, unrelenting execution.
0:37 Harry Stebbings? This is 20 VC with me, Harry Stebbings. Now, I think Aaron Levie is one of the luminaries on AI pervading into enterprise. And he did a viral tweet the other night and I messaged him and I said, dude, we've got to do a show on this and I'm so thrilled that he agreed to do it. So this is a show specifically on how AI will impact the biggest enterprises in the world, how agents will be introduced into the large enterprises. And we couldn't have anyone better than Aaron, founder and CEO of Box, one of the public companies of the last decade. This is an incredible discussion. But before we dive into the show today, did you know the industry average for booking a business trip is 45 minutes? That's a massive waste of your team's time.
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4:17 Speaker 00 You have now arrived at your destination.
4:20 Harry Stebbings? Aaron, dude, it's so lovely to have you on the show. You know that we have Rory on every week and he's just like, Aaron is the greatest. I'm not going to do his accent because I suck at them. He's like the greatest.
4:31 Speaker 00 You can't easily do an Irish accent?
4:34 Harry Stebbings? Well, you know, I can't really do the Irish accent so well. That's why, exactly. But you basically, I'm sure, bought Rory one of his houses. So no wonder he's grateful.
4:44 Speaker 00 Rory, we got more out of Rory than he got out of us. So.
4:48 Harry Stebbings? Exceptional man. But I wanted to start, and we were just chatting, I was running around the pot, listening to the Dwarsh and Jensen episode. And I was like, I don't think Jensen came out very well. Do you agree with me that Jensen didn't come out very well from that episode?
5:02 Speaker 00 I think this is like the greatest Rorschach test of all time of where somebody is mentally on AI. So I happened to see a bunch of the tweets before I watched it. And so I was a little bit, obviously biased in advance. But if I hadn't seen any of the commentary and I had just watched it, I would have been very confused by the by the commentary post, you know, post interview. And to be clear, I kind of jumped to the more salacious part of China and that topic. But I'm almost probably 80% with Jensen. My sort of way of thinking through the logic actually works much closer to Jensen. The idea that we're in some kind of existential race where a month or two of advantage is going to, you know, change
5:43 Speaker 00 the total outcome of AI progress and whatever body does between us and China, I just don't agree with. I think what we are in is a commercial and economic race. Obviously with safety built into that, there's no question. And I think we actually have a lot more power globally if it's our technology stack that's powering AI. And so I kind of am more in the camp of Jensen on his lines of logic. And, you know, Dwarsh kind of oversimplified a few components. You know, he said with Mythos, if we get early access to that, that we can go and upgrade all of our systems. And, you know, with, again, great respect to Dwarsh, it's like, upgrading software is a multi-year effort. So unless they somehow keep Mythos closed for the next decade, there's not like some magical moment where you can just secure
6:26 Speaker 00 everything. This is an ongoing, endless, till the end of time. You're always in this sort of leapfrogging, you know, between the defensive side and the offensive side. And so I just don't think these things are as binary. And so I actually more am inclined to Jensen's view of that. And then Jensen had a really key point that was, didn't go viral yet, so maybe you could kick it off. But he had this little small vignette, about 90 seconds in the whole conversation where he said, you know, we're going to do ourselves a disservice if we scare people out of engineering, if we scare people out of radiology, if we scare people out of healthcare, because they think all these jobs are going to get eliminated with AI. That is not helping us. That is, it's doing a disservice to the next generation.
7:06 Speaker 00 It's doing a disservice to society as a whole. Like, we don't yet know any way to use AI in a capacity other than augmenting our work, where we still eventually have to go and review the work in some, in some form. Maybe you don't have to review the tiny little parts of it anymore. You can review a bigger, you know, part of the work product that happens. But we haven't removed humans from the loop. We've just changed where they enter the loop. And, and I think that Jensen has a more pragmatic view of the technology. We should be very thoughtful about how we make these systems safe. But I much more land in Jensen's camp on the overall kind of contours of the debate.
7:42 Harry Stebbings? Okay, first, a disservice by discouraging people to go into categories like radiology or engineering. Do you think you will have more engineers at box in five years' time?
7:53 Speaker 00 We will. Everybody is so myopic about this. I want to just like shake the industry. We are so myopic and self-interested and, and we think that the entire industry is the tech industry. And when you go around the country or world and you go and talk to a tractor company and a bank and a pharma company and you ask them, do you think you have enough engineers to go and automate what is going to happen in your industry going forward? They absolutely unequivocally universally always say no. And so what the breakthroughs of Claude Code or Codex or others are doing is it's making it so those companies now can actually do the same kind of engineering that Silicon Valley has been able to do. And so we are myopic because we think that tech is the only use of engineers.
8:34 Speaker 00 And tech is only, I don't know what the right number is, eight, 10, 12, 15% of GDP in the economy. What happens when 85% of the economy now gets access to engineering tech has always had? That is what will happen. And so, yes, maybe if you're graduating, you know, name your computer science school today, you don't go immediately to Google. You go to literally John Deere or Caterpillar or Eli Lilly. But the skills that you have are going to be just as relevant in just a different domain. You're not going to be building a little app with little buttons. You're going to be automating pharmaceutical research. You're going to be doing AI for the future of farming and industrial equipment. So we're just too myopic about about how this works. And you can already start to see this sort of playing out.
9:17 Speaker 00 There was a really funny FT article, which is lawyers are being inundated by all of these kind of AI responses that they're now getting from their clients, saying, hey, can you review this contractor? Can you review this memo? Or can you look at this this case? Well, guess what happens when everybody thinks that they're a lawyer? Do you know what the ultimate constraint is? The ultimate constraint is the actual number of lawyers that actually are able to go and review all of this stuff being produced. So I would take the other side. I'd rather like there are going to be more lawyers in the next five years than we have today because we've made it easy to generate legal content. But it has not gotten any easier to actually get any of that approved by any court system or file a patent or any of the things that law actually ends up relating to.
9:59 Speaker 00 So these are, again, this is where I I just differ from the rest of the industry.
10:03 Harry Stebbings? Do you really think so? With the greatest of respects, we are seeing the eradication of kind of lower ranking legal positions.
10:10 Speaker 00 And that is a different issue, which is how do you do the next generation of mentorship and apprenticeship when AI does automate the maybe traditional tasks that those workers are doing? A big question facing every bank in the world, every law firm in the world, anybody who had a sort of an apprenticeship model, I don't doubt that that's a real issue. But that's different from the constraints that all of this work ends up resulting in that you still have not been able to automate. We had a Customer conversation two weeks ago and this is just going to sit with me forever. I mean, I always have this example. They've automated or they're working on automating patient referrals. When you know, when you want to go and see the radiologist or the high end doctor for whatever issue you have. They're automating that, which is awesome. So now you don't have to be on the phone for, you know, a week or whatever.
10:53 Speaker 00 Well, guess what? You can automate anything, but if it still is 18 months out before an appointment is available. Your ultimate constraint is still the healthcare institution and the amount of doctors we have. And actually, the amount of real labor we have across those organizations. So yes, maybe you don't want to, you know, stake your career on being a frontline, you know, customer service rep in healthcare right now. But first of all, that same person will have a lot of other types of jobs that they'll have access to. But you still will end up having all of these other constraints that eventually we will need to produce more and more jobs to go and resolve. So automation is going to actually just force us to see the next set of bottlenecks that are in all of these industries that we didn't perceive that we had before because everything was so slow and manual.
11:36 Harry Stebbings? What job title does not exist today that will be incredibly prominent in five years' time?
11:42 Speaker 00 So I'm workshopping and a bunch of people are doing this. So this is like not my invention, but I'm workshopping.
11:47 Harry Stebbings? Aaron, you've got to take attribution. As a venture investor, it's all about coining a term. Okay? This was your original thought. Okay? Had in the shower, Aaron Levie's. Share it with me.
11:59 Speaker 00 I've been influenced by nothing I've seen online. This is all from me. So there's some kind of, and who knows if this sustains as a full-time role or where it gets diffused into. I'm not, I'm not a hundred percent clear on that, but there is a hundred percent a role right now that there's going to be 500,000, a million jobs created for. And it's basically some kind of agent operator. And this person is actually going to be needing to be somewhat technical. They're going to have to like be deep in the AI world. They're going to have to understand MCPs and CLIs. And they're going to have to know how to write skills. They're going to have to understand agents.md files. It's going to be this group of people that will know how to go into your marketing team or your legal team or your operations team or your life sciences research team.
12:44 Speaker 00 And this is the person that is basically. Going to enable that function to get leverage from agents. And and the problem that the real world has that startups and frankly, many of your guests don't understand. Is that when when you start a company from scratch, you've got like, you know, the world is your oyster, right? You can design your workflows however you want. There's really no risk if you if something goes wrong because you don't have much scale to begin with. There's no real regulator that that is sort of calling on you to say, hey, you know, are you doing things the right way? It's effectively infinite upside in white space. When you go into a Fortune 1000 pharma company or bank or, you know, consultancy, that's just not the case, right? These guys have they're regulated, they have data fragmented across their organization.
13:26 Speaker 00 They have employees that are that are sort of wired to do workflows a particular way. So there needs to be somebody that can basically say, hey, if we actually want to get real leverage from automation, we need to start to redesign the workflow that we're doing. And the workflow needs to be redesigned for agents, not for people. So what do you do when you reimagine a business process? Where the agent is now doing much more of the work than what the human used to do in that process? And that just means it's a it's a very different sort of implementation cycle. There's real change management. You've got to get data organized in the right way. You've got to connect up systems in the right way. Guess what? The second a new model drops, your workflow probably breaks. Because the way you prompt that agent now is different. There's a different way that that it wants its syntax to be to be handled.
14:10 Speaker 00 So it just requires care and feeding and and and a real level of kind of technical and business process acumen. So I think we're going to create an untold amount of jobs that look like that. Some of those people will come from IT. Some of those people will come from operations. Some of them will come from engineering. If you're in a maybe more technically inclined company where it's like the next generation of. There's a a limit to, again, the number of software you need to build that looks like an app on your phone. There's an unlimited amount of software you need to build that looks like a background system process that's connecting different data sources, automating workflows. That's where the the work is going to go.
14:45 Harry Stebbings? Well, this was really going to be one of my main questions, which is Jensen very clearly said AI won't kill software. It will explode the amount of software needed. And when I thought about that, the thesis there is obviously you have this core AI that crawls over 15 SaaS tools. And they really become databases that agents crawl on top of. Is that what it looks like and are they not just valueless SaaS tools then?
15:07 Speaker 00 I think that I'm sympathetic to that argument in some categories. I think there's some software where because the person was the user of the software and they were clicking all the buttons. That your ratio of buttons to underlying APIs was more in favor of buttons. And I'm oversimplifying, but there are some tools where you open it up and there's like 93 features. That you're clicking around on and the user has been so accustomed to exactly how to do that. That the software's value proposition was correlated to roughly that sort of mass. In a world of APIs and a world of agents being able to do more of the work that you used to do on clicking those buttons. Then again, the value goes more to the API layer. So then the question is, how many APIs do you have?
15:51 Speaker 00 Not in like a, you just need a thousand APIs, but like how robust and useful and proprietary? And how much business logic is embedded in those APIs versus it's just calling a database and pulling a record? Does the API surround a set of business logic of like, no, it actually secures the data? Or it knows exactly what person each piece of attribute should have access to inside the organization? That's, at the end of the day, all software has a database behind it. So you could oversimplify it and be quite reductive to that. But there's a lot of business logic in the layer above the database that software players have. Like if you're an ERP system, you're way more than a database at this point. Because you've written a tremendous amount of business logic of how your supply chain should be automated and work and how you should do accounting. None of that goes away.
16:32 Speaker 00 So then the question is, what changes is the user interface that either the user or the workflow is interacting with. The user interface might be now you're just chatting with an agent. I think increasingly the right way to do this is there's some kind of agent in the background that's connecting multiple systems. So you're not even like, the user's maybe not even seeing half the value that's happening. But the agent is sort of working across an ERP system, a CRM system, an HR system. A document repository and then doing work across those systems, which means that the value proposition has to be how good are your APIs? How well designed are they? Are they ready for agents? And then can you monetize that in some way that makes sense? And we are treating software too much like one gigantic sort of monolithic industry.
17:15 Speaker 00 And it'd probably be better to have some kind of two by two, which is like, how much business logic is there? How much sort of human to agent collaboration does there need to be? The reason I bring that up is the moment you have human and agent collaboration, you need some kind of, usually you need something that the user can pop into to experience the work that the agent did. And that probably doesn't go away so much. And then, you know, when more agents are working on the software, which parts of software do the agents need those APIs even more than humans ever did? And I think there's a lot of categories of software where actually agents using the tools is a massive boon for the technology as opposed to a dilemma.
17:52 Harry Stebbings? Where will agents use the tools more than humans do? And those API calls become much more frequent?
17:57 Speaker 00 Yeah, so like an easy one is just unstructured data. Agents are going to be this incredible consumer and creator of your unstructured data. They're going to read through every one of your contracts and generate all of your contracts. They're going to generate marketing assets. They're going to write reports for you. And so when it becomes trivially easy for you to generate all this new information or have agents review it all, well, guess what? You still need a backbone that kind of manages and coordinates and creates the guardrails of those workflows and all the agents doing that work. So we're about to see an explosion of unstructured data as an example.
18:30 Harry Stebbings? With the greatest of respect, Aaron, can I just interject?
18:32 Speaker 00 Hundred percent.
18:33 Harry Stebbings? Does that increase the value of your business? When I think about that, I asked Aaron from Monday, if you become a data repository which agents crawl on top of, how do you retain value in that?
18:44 Speaker 00 Yeah, hundred percent. It's the question on the mind of every investor on the planet right now. So we're used to it and it's not a scary question. One thing that helps us is we've always had an API, maybe not first, but equal strategy. If I told you the number of API calls we did last year, or you guessed first, you'd probably be off by an order of magnitude. The volume of API usage on our system is already enormous and already is outsized relative to any of the end user interactions in the system. And that's just a virtue of you use content in a variety of applications and workflows that far exceed what what people kind of open up their finder and upload a document into. Like an ERP system generates files, a wealth management portal, you have clients uploading documents into the portal and they never see Box.
19:30 Speaker 00 You have workflows of invoice processing that's happening behind the scenes. So the headless version of Box has been alive and well for, you know, almost since the day we started the company. And so agents, to me, just again, represent a force multiplier on that. So it's it's actually an exciting proposition for us. We already know how to monetize it. The question is like, will the exact dollar and cents be the same between an agent user and a previous application user? We don't know, but we do know that if the number goes up by 100x or 1,000x, that's actually more opportunity for us in the future. Now, that's not all the same for all software providers, but for where we sit in the workflow, where you just you generate a document, it needs to go somewhere, you have to secure that, you have to protect it, you have to govern it over the long run. That's just more data going into our platform and that's, you know, so that's why it's just all upside for us.
20:16 Harry Stebbings? You said secure and protect it. We mentioned our mutual love for Rory O'Driscoll. I do a show with Rory and Jason every week. Jason's bluntly said that this will be the golden age for cybersecurity because the security threats are going through the roof. Are you concerned with the system vulnerabilities and the security threats that are coming with AI? And what do we not know about security that we should know?
20:36 Speaker 00 I am concerned, but not in any kind of like new concern sense. This, this to me was kind of priced in the moment that we were generating code with AI. So if you can generate code, you have two problems. One, you're going to generate way more code than anybody's ability to review that code. So starting with GitHub Copilot six years ago, or whatever the date was, five years ago, like that was just priced in, which is as soon as as AI writes most of the code or and then like 90% of the code and then 95% of the code. Then by volume, we're just going to produce this unbelievable amount of code and and any, any change in a system. You know, everybody kind of thinks about security as like, you know, is there a Zero day where there was an unpatched component of your technology,
21:19 Speaker 00 or somebody found a clever new package that you could kind of slip into. Every time you ship a new feature, you have a chance of a security vulnerability because the AI could have written in, "Oh, you know, we want to actually open up that port in the system because we need to do something," and maybe that was the wrong decision for the agent to go and do. So we're going to be living in this new world of cyber risk in the form of using agents more. And then on the other side, obviously, if you have the offensive side able to use AI, probably more in the form of open models and whatnot, then they can find more vulnerabilities because they can scan across the internet far faster than before. So you actually have two new forms of risk in the development process, and you only have one benefit, which is agents can also review the code and try and keep it secure.
22:04 Speaker 00 So it's going to be a very dynamic period. I think, you know, for better or worse, agents are the solution to the problem that agents have caused, and that's why there's going to be a lot of money made in agentic security as well.
22:16 Harry Stebbings? You said agents are the solution to the problem that agents have caused. It almost reminded me of when Janssen went on TV and was like, "Oh, every engineer should be spending," I can't remember the amount. I think it was either 250 or 500,000.
22:28 Speaker 00 Yeah, or like half the salary, essentially.
22:30 Harry Stebbings? Yeah, yeah, yeah. And it's kind of like, you know, drug dealer, you should buy drugs. Well, okay, no shit.
22:35 Speaker 00 Again, I'm, you know, obviously, Jen, I mean, listen, we love Jensen for that level of, you know, grandiosity and charisma. So I actually, I bet, you know, whether he's off by half or not, I mean, directionally, the idea is actually pretty salient, which is you're going to be spending more on compute per person in the future than you ever thought, and that you certainly are today.
22:56 Harry Stebbings? What percentage of salary are you going to spend on compute inbox in five years?
23:01 Speaker 00 Great question. I don't think we've modeled that out in five years. And obviously, the joy of being public.
23:06 Harry Stebbings? But this is your chance.
23:07 Speaker 00 No, no, totally. I, you know, I was told not to model long-term financial projections on podcasts. So, yeah, it's a weird SEC financial thing.
23:15 Harry Stebbings? It's so boring.
23:17 Speaker 00 Don't ever go public if you don't want to model on podcasts, so.
23:21 Harry Stebbings? This is why the Collisons don't. Everything else is great. They just didn't. Cheeky pineapple.
23:26 Speaker 00 I don't know if you'd be able to pin Patrick or John on the same question for their five year view, but, but you know, it'll be a larger number for sure than it is today.
23:35 Harry Stebbings? I'll smash them with four tequilas and then ask them.
23:39 Harry Stebbings? You said one of your observations in your very viral tweet was about token maxing and kind of token allocations within enterprises. I'm really intrigued. How do you think about advising CIOs on token allocation, token maxing, what we should know that we don't know? How do you think about that?
23:54 Speaker 00 This one's tough. It's, you know, the general advice will end up sounding kind of like, you know, kind of generic by definition. It, you know, usually, I mean, it's going to have, it's going to have something to do with your tokens will have to correlate to where there is the most amount of, you know, value generated for your company, like, like most bland statement of all time, but just obviously has to be true. In the software industry, we're into token maxing because guess what? Like generally, the value proposition of your company will correlate to how much software can you produce? And so if you're trying to drive a lot of change and you want to make sure everybody's shipping lots of software and you want to be able to teach the best practices faster, then token maxing and leaderboards are an interesting way to do that. You know, it's not obvious that you're going to see that across every industry. We've seen a couple of interesting examples. One company had this sort of like Shark Tank Pitch-a-thon type thing, which is teams have
24:39 Speaker 00 to show up and they have to go pitch for compute, you know, token budget. And then you kind of allocate it in some central fashion like a VC would. And then you sort of, you know, I don't know their exact interval, but I would imagine you review that three months, six months in being like, okay, did you get the upside that you thought on that token usage? So that, that's an interesting one. Another company had a kind of a view of like, it's some kind of like natural stratification of 5% of your users are doing the most valuable things. 20% are doing the next tier of most valuable things. And then everybody else is sort of doing general productivity and making up their numbers. But the idea would then be like, well, for that five or 10%, give them the best models with unlimited capacity. For the next 20%, have some limits. Maybe it's a little bit more efficient of a model.
25:23 Speaker 00 And for everybody else, it's sort of like, we're going to just use the cheapest thing on the market. It's not going to be like the game changer of the employee productivity. And so I think everybody's kind of working their way through this. The, the part that, back to Silicon Valley. Again, kind of, you know, sometimes more, let's just say like, like positively naive view is, is like, real world, they have like budgets and they have like annual budget planning cycles because they have EPS numbers they, they commit to Wall Street. And so you don't get to just be like, oh, we're going to token max across the enterprise, where everybody gets unlimited token budgets, because obviously then that company would just miss their earnings throughout the year. So you have to like wait for the budget cycle. You have to figure out what teams are most interested and have the best use cases. That's a, that's a natural journey.
26:05 Speaker 00 Well, in front of Mark, that I think is, is well understood now at this point, is the budget of tokens will have to move out of IT spend and into regular kind of OpEx spend. This can't be treated like a, oh, I'm going to trade off between Salesforce licenses or compute tokens. Like, it's going to more be, I'm going to trade off this next marketing campaign, and instead I'm going to go and drive more automation in our, in our marketing engine. Like, it's, it's going to be that kind of, of set of trade offs.
26:31 Harry Stebbings? What happens to that token budget when it transitions to that different spend category?
26:36 Speaker 00 Well, first of all, it goes up because IT spend as a percentage of revenue of, of large enterprises is, is-
26:42 Harry Stebbings? But is this the same as the kind of classic VC blog post, which every firm has written, which is like, AI, it's moving from software budgets to labor budgets. And every partner goes and likes the tweet and there's like, no fucking shit. Really?
26:58 Speaker 00 I mean, if you do it in that voice, it sounds, it sounds kind of like, you know, simple, but like, yeah, that, that, but like, that's just like a very big deal in technology. We've never had, there's never slash rarely been a technology that you could sell into an enterprise where you weren't capped by that company's corporate IT budget. And so now for the first time ever, you have a technology where you can go into the line of business and you can say, I can now offer you a new tool in the form of an agent that will augment a workflow that will make you 50% or 100% more productive. And so maybe I should be able to get 5% of your OpEx budget this year to go and do that. That is a new budget to tap into. And I don't think it like 10x's the size of, of IT spend or, or technology spend globally, but it certainly doubles it.
27:40 Harry Stebbings? I mean, current enterprise technology spend is estimated between 10 and 12%. To see that going to like 20%, as you said there, it's like, I think relatively feasible. You said about companies being like based on earnings per share and actually having budgets that they have to adhere to. Very strange not to have venture funded companies.
27:57 Speaker 00 Yeah, they don't have a limited VC to go and solve this.
28:00 Harry Stebbings? Can't we just go to our venture investor and ask for more money? The one thing that I worry about is we see this insane demand side pull. Every company in the world needs an AI story. Everyone wants to kick the tires with something. And I think we project the same demand side pull and extrapolate it continuously. Do you worry that we are in a momentary 18 month period on the demand side pull? And that may not always be lasting.
28:25 Speaker 00 It's very possible I should be more sensitive to that, but I would take the opposite side of that particular wager at the moment. Partly because I already saw one diffusion cycle with cloud and actually how long that ended up taking. And the kind of spiky early nature, you would have just been like, oh my God, this is, this is on fire. It's, and how could this last? And 20 years later, it lasted and got way bigger than we ever realized. If it works, the market's always larger than you ever think. The only part why 18 months is like not even a relevant window to me is I think diffusion is going to take longer than Silicon Valley thinks. And it's back to the very first kind of that new role idea.
29:01 Speaker 00 When you go to most companies, they can't yet just deploy an agent to do, you know, financial proposals for all of their, their clients without a human reviewing the thing. And because the SEC will just show up and be like, hey, you just gave this person bad financial advice and you're going to lose your license. Like that will just start to happen kind of across the board. And so that's why, you know, people take time. That's why there's a lot of regulatory controls and compliance teams, security teams have to figure this out. That just takes time in the economy.
29:29 Harry Stebbings? I had, I think Matt Fitzpatrick from Invisible, which is like a Turing or a McCullough competitor. And he said, you cannot sell into enterprise without an FDE model. It is impossible.
29:40 Speaker 00 I mean, it rounds to being true.
29:42 Harry Stebbings? Super interesting to hear that because we're seeing like the rise of, oh, we go PLG and then we like seep up into enterprise.
29:49 Speaker 00 Well, I don't think of those as mutually exclusive for what it's worth.
29:53 Harry Stebbings? I guess what I'm saying is when you think about adoption within the largest enterprises.
29:57 Speaker 00 Yeah.
29:57 Harry Stebbings? Are AI services companies the best positioned companies of the next five years?
30:02 Speaker 00 As in, you're saying like traditional professional services?
30:05 Harry Stebbings? Yeah, I'm saying Accenture's AI team that come into Bank of America.
30:09 Speaker 00 No, 100%, these spaces are going to be, again, both bigger and more sustainable and robust than people realize. We are always so, back to the myopic thing, we're so myopic, we're like, AI will replace all of this stuff because it just does it for you. And it's like, I'm trying to think of, you know, maybe my most recent experience with the best models in the world. I probably had to go and change 15% of the thing that was the output. We're nowhere near eliminating the human from the workflow. And so in a world where you don't eliminate the human, then there's a lot of like real change management of like, where should the human enter that business process? How would you want to review that work output? How do you wire up your systems to make them effective for the agent and human collaboration? How do you connect all of these data sources together?
30:52 Speaker 00 One thing that we see is, you know, if you wanted an agent right now in a Fortune 500 company to go and give you an answer to where is the most risk you have in your upcoming renewals for your contracts? That agent might find 10 different systems that contain contracts in them. And half those systems will be like legacy technologies that don't work well with the agent. They're kind of low throughput, or maybe you can't even wire them up. They're on network file shares, they're in legacy document management systems. So first of all, half your data state is not even ready to work with the agent. The other half of the data state is probably fragmented because you have two decades of employees bringing in their own tools. And so the agent will just go and find the wrong document, or the wrong contract, or the wrong piece of data.
31:35 Speaker 00 Because you never really cared to have some kind of standardized system for your contracts because people could just always go and find what they were looking for. Agents can't do that. They, I mean, they'll find what they're looking for, but they'll just as often find the wrong thing as the right thing. So they have to be targeted, they have to have that information get curated. They need to understand the context of what is the process that they're doing. What I just described right now is 10 years of work for Accenture in every enterprise on the planet. Or the next-gen Accenture that does this in particular industries or workflows. Like, we have to go upgrade your systems, we have to start to understand and organize your data in the right way. We have to start to describe these workflows to the agent itself. We have to figure out where the human is in the process. That is just real change management that every organization will have to go through.
32:20 Harry Stebbings? We also have to have someone to blame. This is why a lot of these industries last, which is I have lawyers, not because I can't necessarily ride an NDA, it's because it's your fricking fault if anything goes wrong.
32:33 Speaker 00 Yes, no, literally. And we don't know, like, I promise you, you're not going to be able to blame Anthropic when something goes wrong. And so if you can't blame Anthropic when something goes wrong, then at some point, it doesn't really work to tell your customer, well, that sort of system that we set up screwed up your data, or it automated something the wrong way, or created a security vulnerability, because the company will just say, well, I'm never working with you again. So then you have to have some accountability in your own organization for who is liable when something goes wrong. And the moment you have to have any liability, you have to have some amount of ownership and accountability. And people have to have, they have to roll up to somebody who has more liability and more ownership and more accountability.
33:12 Speaker 00 Like, this hasn't really changed the fundamental pattern of human behavior and contract law and the regulatory regimes that everybody's a part of. We've just sort of given our computers a machine gun to go generate way more information and work with all of our data.
33:27 Harry Stebbings? You said before, when I've tried the latest model, it's got like 85% of the way there. I speak to many of the best early-stage and more mature west coast-based companies, and they say, hey, we use frontier models to set where we can be. And then we use open-source Chinese models to get as close as we can to that frontier benchmark. Is Silicon Valley being funded by a generation of open CCP-funded open models?
33:53 Speaker 00 I mean, that must be kind of empirically true. I don't have the same kind of like, oh, that's so scary, you know, kind of element. Now, obviously, again, holding out some element of risk of some backdoor weights that can get triggered at some moment or some parameters, but like, that's not how I'm perceiving it. But also, that's, I would say that's kind of orthogonal to my point about like, the best frontier model still will go and do the wrong thing. And so thus, I have to be in the workflow loop to make sure that I review its work.
34:21 Harry Stebbings? You know, as a venture investor, I specialize in making bold statements with little substantive evidence.
34:28 Speaker 00 It worked for the greats, so.
34:29 Harry Stebbings? Do you know what? I'm just following their lead. Jason Lemkin, my dear friend, says, "Why has no public company created any good agent product? Everyone creates 60% shit agents." But he's like, "The one person who's done it is Palantir, and no other public company has created a sufficiently good agent product." Why is that?
34:50 Speaker 00 I guess, you know, I don't know that I can fully endorse the point, but I can give you the, because I would argue our agent is sort of the best agent for working with content. You know, this is a very fast-moving space, and you have to be kind of wired in at a level that I don't think you've ever had to be wired in in tech. And the information sources aren't the classic ones. It's not the roll-up review two weeks later from your traditional news publication that is going to give you any kind of alpha. It's the practitioner who's the, you know, literally the engineer at the agent sandbox company, and their long-form article on how they are handling, you know, memory and the harness. Like, if you're not wired into that ecosystem,
35:32 Speaker 00 it's very hard to then have your team be at the kind of forefront of all of what is happening. And so it just is, it's a different pattern than what we've ever had to do. Like, COVID was pretty crazy. Like, we all had to kind of like hunker down and be paying attention to daily news cycles on COVID-y stuff. But it wasn't like a tech problem. Like, it wasn't hard technologically. But there's not been a moment before where the speed of change and responsiveness you have to have is quite literally on a multi, multiple-times-a-week cycle.
36:03 Harry Stebbings? Is your job harder than ever?
36:04 Speaker 00 Yes.
36:05 Harry Stebbings? Because of that speed of transience of superiority of technology?
36:09 Speaker 00 Yes. You basically have this component of, one, there's a tsunami of change that you can just feel. And so you're like, "Okay, we gotta like run faster than ever before." And then there's just like the pure technical underpinnings, which some of it has business and strategy implications, some of it has product implications, some of it has partner ecosystem implications. Because of that tsunami, that you have to very quickly kind of wire up what you are doing about that shift and where the market is going. At the exact same time, you have to also be like, you know, find a way to be a bridge for your customers that also don't want to get crashed into by the tsunami, and they want to be able to have a bridge into the future. And so there's just, you're juggling a lot right now.
36:48 Harry Stebbings? You said your agent product is the best product. Again, Jason and Rory said this, and Rory might kill me for this because he gets a little bit more sensitive about when I quote him or misquote him more appropriately. But like, he basically says, and this is Jason again, if you can't like, charge way more for your agent product, Wall Street doesn't give a shit. That you have to re-accelerate revenue with agent products. Can you charge significantly more for an agent product?
37:11 Speaker 00 The answer is yes, but there's a little bit of nuance, which is our business model is we have a new plan tier that we just introduced last year. That basically houses our best workflow capabilities, our business automation, you know, our application development capabilities. And then the agent is sort of central to that because it's going to help you automate the work that you're actually doing with your content. So it'll read a document and extract metadata from it. It'll process information inside of a workflow. So that is actually causing a re-acceleration of our revenue growth. Last year, we saw an inflection in our revenue growth. And so that, it's already happening in our business. And so we are, we are doing the thing that I think Rory is sort of probably saying is the new benchmark.
37:52 Speaker 00 Now, to be fair to what's, what's happening though, is I think Wall Street still is sort of saying, we kind of need to just step back and see where everybody lands in this because of how much change there is. So this is very much a year where if you're in software or infrastructure or building agents, it's a year of complete unrelenting execution.
38:12 Harry Stebbings? Do you look at the ticker?
38:14 Speaker 00 Yeah.
38:14 Harry Stebbings? Every day?
38:15 Speaker 00 Yeah.
38:18 Speaker 00 I was like a day trader.
38:19 Harry Stebbings? I've never met a public company CEO who hasn't. The Navan CEO was on the other day and he's like, multiple times a day, multiple, multiple.
38:26 Speaker 00 Yeah, no, 100%. But like, but like partly I just, I have like ADHD or something. And so I just need like, I'm like, ah, is there?
38:33 Harry Stebbings? Will we look back on this period and be like, what the fuck? Companies trading at three times cash flow? Way over exaggerated or not?
38:40 Speaker 00 Well, three, three times cash flow is very much over exaggerated. I would say that we're in a period right now where basically the market is being treated roughly as a kind of indiscriminately, you know, kind of bucketed sector. And the next year, two years or whatnot, you'll start to see some separation. And parsing between the companies because as I noted in the beginning, agents will be really good for some parts of software and agents will put pressure on other parts of software. So and it'll mean some companies have to fully pivot and some companies can just sort of ride the wave. And if they respond, you know, effectively, clearly three X free cash flow. That seems like aggressively low territory, but I also think that at times in software,
39:19 Speaker 00 things have been aggressively overvalued beyond the realm of likely what the terminal value is of particular, you know, category or company as well. So I think there's just a pendulum that needs to kind of find its equilibrium right now. And that that'll play out over the next year.
39:34 Harry Stebbings? Do you think this generation of CEOs that you have around you though, is equipped for the AI transformation that is ahead? Cause I don't like, I think I'm not blowing smoke up your arse. You are, you're so versed in this, you're so fluid. But a lot are like, and one said to me the other day, no, we don't have the AI chops in house. We might need to bring it in.
39:53 Speaker 00 This one's hard. I think you still have a lot of kind of founder led or tech, you know, forward, whether they were an engineer or just they're just very technical category of folks that are pretty dialed in. And like I have Slack channels and WhatsApp groups where people on the weekend are just like working with Claude Code or Codex building stuff and they're public company CEOs. So they are clearly wired in tapped in, they can feel the technology and they are not going to let their company lose. You know, assuming that as a, as a category, they're in a spot where there's a lot of upside. So yeah, but like every, every technology wave, there's winners and losers. I don't know that the, this won't be any different. You just have to be super dialed in and work through it.
40:34 Harry Stebbings? Hard one before we do a quick fire. Who has the world turned their back on who you think should be much more appreciated?
40:41 Speaker 00 Oh boy, um, you know, I'll give maybe a shout out to like Atlassian as an example. I think that that feels like oversold territory, you know,
40:50 Harry Stebbings? You think 78% is a bit harsh?
40:52 Speaker 00 I think possibly and you know it's in the category of they've been fighting this this narrative engineering gets commoditized and so like where in the stack was was their engineering you know revenue generation and again with my headset I'm like no there's gonna be more engineers and so now does that mean that Elastian's product set will look exactly like it does today no like obviously it's got to evolve and whatnot but but I think if you're like a company selling infrastructure for engineering to be more automated that seems like a good spot to be in and you look at what linear is doing and it's it's fantastic and it's awesome to watch but I think there'll be multiple plays in that space just given how big the market is I think right now this is a moment where you need to be deep in the workflow and you need to have data you have to have data in your platform and you have to be the
41:37 Speaker 00 best place for that data to go and you have to be the best place where agents want to work with that data that's like the mandate right now is if you are not the best place that an agent would intentionally choose for working with data of that particular category or automating the workflow in that particular you know area that's a tough spot to be in and that that's the the job for all of us you know if you're building software.
41:59 Harry Stebbings? And the best place where agents want to work is defined by great API.
42:02 Speaker 00 Great APIs, great pricing models, the surrounding features to the API. So if you were to say hey I want to be able to wire up a workflow where this is a you know the box sales pitch I want to be able to wire up a workflow where an agent is interacting with FINRA compliant documents you know FINRA compliant document means the things that gets generated or seen or shared with a customer and it can't ever be deleted and and removed after you know for a certain amount of time then on one hand the APIs have to be super clean for the agent on the other hand you have to have a bunch of surrounding capabilities to ensure that that company can go to their regulator or auditor and say yeah we are complying with FINRA so that combination is what makes it so you would build that kind of agent on something like box and that that persists across a variety of industries.
42:44 Harry Stebbings? I'm gonna do a quick fire round with you. You have to go and be a public company CEO, I know. So what have you changed your mind on in the last 12 months most significantly?
42:54 Speaker 00 I do think that I've become more convinced that software is headless in the past year than I was maybe three years ago. And it's because of the the level of agentic capabilities on tool calling and searching across systems and the accuracy of that. And that that has happened faster than I I would have perceived. So two to three years ago, if you were to kind of, you know, wire up an agent and tell it, hey, go work inside of Box and find a document to work with and do some process, it would basically almost always find the wrong document and it wouldn't be able to handle actually like cracking open the file and reading through it. And so thus, you know, going headless wasn't sort of the most urgent priority
43:34 Speaker 00 from an agentic standpoint. And in the past year, those capabilities have just absolutely accelerated to the point where I'm fully convinced that you have to be, you know, headless first as a software platform.
43:45 Harry Stebbings? What acquisition did you not make that you wish you had made over the Box journey? Jensen said in the show, oh, I wish we'd invest in frontier models. That was my big mistake. What acquisition did you not make that you wish you had done?
43:57 Speaker 00 I honestly don't, I don't think I have any M and A regrets. I actually, it's the deals that I wanted to do that we ended up not doing that I don't regret is probably more the the situation.
44:09 Harry Stebbings? Which one is that?
44:10 Speaker 00 I'm not going to tell you those, but but there are some where left in my own devices I would have done. And I look back and I'm like, oh, thank God that there was, there was more rational logic in the process.
44:21 Harry Stebbings? Who is going to win the enterprise race, OpenAI or Anthropic?
44:24 Speaker 00 Oh, God, that's impossible. Back to the cloud piece, I think it's it's totally fair to think about it as a race. And certainly, if you're if you're in either of those companies, you have to treat it like a race because because, you know, like, you obviously want 80% market share, not 55% market share. So like, you have to treat this as a, you know, we got to dominate. That's exactly how they should be executing that way. Everything is going according to plan. If you compare it to other areas of compute, and I ran this analysis recently, in 2010, not, you know, maybe you were 12, but like, the rest of us, we were just like in companies doing things. In 2010, AWS made $500 million in revenue. Azure had just launched and GCP Was called Google App Engine and it has a little like a turbine logo with like wings or something.
45:09 Speaker 00 So that was the state of cloud. Fast forward to this year and it's a couple hundred billion dollar a year revenue ecosystem. So in 15 years, right? So, and we were in that moment being like, who's gonna win? AWS or Azure or GCP? What's, how's this all gonna play out? And it just turns out the market was so large. Obviously, it was due to their execution that they kept it going and kept it large and the competition kept up. But it, it just didn't really matter. Like everybody, everybody kind of won. And so I, I sort of think of AI in a similar fashion, which is I can't predict if it's gonna be OpenAI 60% and Anthropic 40% or it gets flipped, or I'm off by another 10% here or there. But no matter what, these markets are just fantastically large. Companies are, are going to adopt multiple of these systems. They don't want to have a single vendor in this stack.
45:54 Speaker 00 One service goes down or one changes its APIs or one has a new commercial model. You're gonna, it's gonna be a multi-vendor, multi-AI world. And so that's why it's very hard to kind of, like, call it at this stage.
46:05 Harry Stebbings? What does everyone think they know about enterprise adoption with AI that they get totally wrong?
46:10 Speaker 00 What they think is that, is that the outcomes that you're seeing in AI coding will quickly come for other areas of knowledge work. And that is a slight misread on the other areas of knowledge work. And some of it is the idiosyncrasies of coding. And some of it is the broad kind of just elements of the rest of work and how it happens.
46:31 Harry Stebbings? If you were a venture investor today, which category would you be most excited to invest in? Obviously, I'm just hypothetically.
46:38 Speaker 00 God, I would, I, I think I would still be probably loading up on all of the, the frontier rounds. It's, yeah, like, these numbers could, could continue to get much larger.
46:46 Harry Stebbings? Could they get, I mean, much larger. I mean, like, this is where am I? At 850 billion, you've got like a three acts to like a 2.1 trillion style.
46:54 Speaker 00 You know, I, I always think it's hard because I kind of have said that on the way up of many companies. Like, like, like, you know, just like.
47:02 Harry Stebbings? I did it with crypto. Like, how much further can it go? It's like.
47:05 Speaker 00 Yeah. Well, well, that one, I'm gonna put in a different category because that's, that can just sort of be memed to life. I'm just.
47:11 Harry Stebbings? Well, no, but I actually think, to the point on Atlassian and bluntly you and your whole category is the casinoization of the stock markets, which is like, if you're a momentum trader today, you still buy Palantir because the market's a casino right now.
47:26 Speaker 00 Yeah, well, to be a little bit more fair, I think you have some one-off companies that have done amazing job capturing the zeitgeist on that. I think the broad story right now is the sector rotation story. Hey, this AI thing's happening. Right now, I can get a higher return if I get closer to the semi-stack and the kind of where the workloads are going, where the data center buildout is happening. And I get less of a return if I'm in software with kind of pure licensing. And so I think that that is probably more of the color of what we're seeing. Now, some of the data center and infra names maybe have been memified also, and so that's kind of helping the case. But it's just a really weird time overall that's hard to think through.
48:09 Harry Stebbings? So you would not buy Allbirds as an AI company?
48:13 Speaker 00 I mean, maybe you would because of that exact point. So I think that will be in the kind of one-off category. So Newbird AI or whatever it's called. But I think, no, I hear, this is a generic statement. There will still be a lot of money to be made in the companies that can take the innovation that we're seeing in Silicon Valley and in the labs and apply it to the real world work that happens inside of enterprises. And whether that looks like vertical AI, whether that looks like the new kinds of tooling that companies will need. There's a company and a new category emerging on agent observability and evaluations. I'll give a shout out to Brain Trust as an example, not an investor, where I can just kind of sit back and be like, shit. Like, we thought that agent builders were going to need evals. So that's like a Silicon Valley TAM.
48:57 Speaker 00 And then I'm like, oh, actually, everybody on the entire planet, if you're putting agents into an enterprise workflow, needs evals because you need to know if all of a sudden your agent just stopped producing loan origination documents the right way. And so that's a category where it's probably not going to be owned by one of the labs. You kind of want it to work across all the labs. It's a very relevant kind of new form of infrastructure for an agentic enterprise. I think you're going to see a dozen, two dozen, five dozen of things like that that start to emerge.
49:28 Harry Stebbings? I've known you for a while now, and you put up with me for multiple different sessions. So I want to finish on something a bit off script, but you're a phenomenal CEO. You're a public company CEO, the pressure that you have on you is intense. You're also married and have a great relationship. Biggest advice on marriage? When it's super stressful, it's hard, and you also have to show up and be a great husband. What was the advice on marriage?
49:50 Speaker 00 It feels dangerous if I actually acknowledge the great husband piece and other other parts that were embedded in that. That feels like you need a full 360 eval. I will, I'll just say from my perspective, and I'm very lucky to have an amazing wife and family and, you know, you are, you're in a grind in one of these roles. And so obviously having a strong support base, you know, helps a ton. We try and make time, you know, for for the fun side of life as much as possible, but obviously that gets constrained in the kind of window that we're in. But I've been with my wife for, I don't know, 15 years or so, 16 years. And so she's seen the whole, the whole grind all the way. And she has her
50:32 Speaker 00 own set of grind in her business. And so it's, it's just lots of fun.
50:36 Harry Stebbings? Dude, you're my hero. I want to be you when I grow up. Thank you for being so great. I really appreciate it. And I was 14 in 2010.
50:43 Speaker 00 Okay, all right. So I almost called it.
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Previous 20VC: Jake Paul on Why Traditional VC is Toast and Attention is More Valuable Than Cash | Politics: Will Jake Paul Actually Run for President? | Inside the Payday of Fighting Anthony Joshua and Mike Tyson | with Geoffrey Wu, Co-Founder at Anti-Fund Next 20VC: Cursor Acquired for $60BN by xAI | Anthropic Hits $1TRN in Secondary Markets | Did Anthropic Just Kill Figma, Adobe and Canva | Rippling Hits $1BN in ARR | Salesforce Goes Headless: Smart or Stupid | Cerebras IPO 2.0 Page updated October 26, 2026 · transcript and summary are machine-generated
