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
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The discussion centers on a pragmatic view of AI's impact, arguing it is primarily a commercial and economic race where technological leadership confers global influence. Contrary to fears of job displacement, AI is seen as augmenting human labor, exposing new bottlenecks in sectors like healthcare and law, which will actually increase demand for skilled professionals such as engineers and lawyers. A significant new role—agent operators—is predicted to emerge, involving individuals who technically redesign workflows for AI agents within regulated, complex enterprises. The value of software will evolve, with emphasis shifting from user interfaces to robust APIs and business logic that agents can leverage, though many existing software systems will retain importance due to their embedded logic. The conversation advocates against discouraging careers in fields like engineering, highlighting broader opportunities across the economy as AI democratizes technical capabilities.
I think I'd 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. They're 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 op-ex spend. Is your job harder than ever? Yes. If you're in software or infrastructure or building agents, it's a year of complete unrelenting execution. This is 20 VC with me, Harry Stabbings. Now, I think Alan Levy is one of the luminaries on AI-povading and prize. 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 enterprise in the world, how agents will be introduced into the large-dand prices. And we couldn't have anyone better than Alan, found a ANSI 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. Or would an Avan your employees can book a trip in just seven on average? Avan is the AI-powered travel and expense platform, designed for companies that value efficiency. It drives real business impact through high employee adoption and automated policy control. Now, the built-in AI approves in policy bookings and blocks the rest automatically. This allows finance teams to stop chasing receipts and skip the month and chaos. And you get this real-time visibility that can save your company up to 15% on your travel budget. 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So no one, he's grateful. But Rory, we got more out of Rory than he got out of us. So exceptional man. But I wanted to start on. We were just chatting. I was running around the pot listening to the Dwakash and Jensen episode. And I was like, I didn't think Jensen came out very well. Do you agree with me that Jensen didn't come out very well from that episode? 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 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 change the total outcome of AI progress and whatever everybody 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 Dwakash kind of oversimplified a few components. And he said with Mithos, 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 Dwakash, it's like upgrading software is a multi-year effort. So unless they somehow keep Mithos closed for the next decade, there's not like some magical moment where you can just secure 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 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 health care, 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. 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 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. And I think that the Jensen has a more pragmatic view of the technology. We should be very thoughtful about how we make these systems safe, but I'm much more land in Jensen's camp on the overall kind of contours of the debate. Okay, first, a disservice by discouraging people to go into categories, light radiology, or engineering. Do you think he will have more engineers at Box in five years' time? We will. We are so myopic and self-interested. 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 Cloud 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's been able to do. And so we are myopic because we think that tech is the only use of engineers. And tech is only, I don't know what the right number is, 8, 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 how this works. And you can already start to see this sort of playing out. 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 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, they're 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.
So these are, again, this is where I just differ from the rest of the industry. - Do you really think so with the greatest of respect? We are seeing the eradication of kind of low-arranging legal positions. - 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 gonna sit with me forever and I always have this example. They have automated, or they're working on automating patient referrals when you wanna 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 a week or whatever, well guess what? You can automate anything, but if it still is 18 months out before an appointment is available, whether 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. - What job title does not exist today that will be incredibly prominent in five years' time? - So I'm workshopping and a bunch of people are doing this, so this is like not my invention, but I'm workshopping. - I'm out of town, you've got to take attribution as a venture investor, it's all about coining a term, okay? This was your original thought, okay? How did the shower, our in levies, share it with me? - 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 100% clear on that, but there is 100% a role right now that there's gonna be 500,000, a million jobs created for, and it's basically some kind of agent operator. And this person is actually gonna be needing to be somewhat technical. They're gonna have to be deep in the AI world. They're gonna have to understand MCPs and CLIs, and they're gonna have to know how to write skills. They're gonna have to understand agents.md files. It's gonna 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. And this is the person that is basically going to enable that function to get leverage from agents. And the problem that the real world has that startups and frankly many of your guests don't understand is that when you start a company from scratch, you've got like, the world is your oyster, right? You can design your workflows however you want. There's really no risk if something goes wrong 'cause you don't have much scale to begin with. There's no real regulator that is sort of calling on you to say, hey, are you doing things the right way? It's effectively infinite upside in whitespace. When you go into a Fortune 1000 farm company or bank or consultancy, that's not the case, right? These guys have, they're regulated, they have data fragmented across their organization. They have employees that are sort of wired to do workflows in 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 very different sort of implementations like, oh, 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. You're workflow probably breaks because the way you prompt that agent now is different. There's a different way that it wants it syntax to be handled. So it just requires care and feeding 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 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 work is going to go. Well, this was really going to be one of my main questions, which is, you know, Jensen very clearly said, AI won't kill software. It will explode the amount of software needed. And when I thought about that, you know, the thesis there is obviously kind of, you have this kind of 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 value SaaS tools then? 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 sort of ratio of buttons to underlying APIs was like 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 kind of 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 in 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, 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 know, 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. 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 users, 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, too much like one gigantic sort of monolithic industry. 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 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. Where will agents use the tools more than humans do? And those API tools become much more frequent? 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 guard rails of those workflows and all the agents doing that work. So we're about to see an explosion of unstructured data as an example. With the greatest of respect, I can actually imagine 100%. It's not increased the value of your business. When I think about that, I ask Aaron from Monday, if you become a data repository, which agents cruel on top of, how do you retain value in that? Yeah, 100%. 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 off by an order of magnitude. So the volume of API usage on our system is already enormous and already is outsized relative to any of the end user interactions on the system. And that's just a virtue of, you use content in a variety of applications and workflows that far exceed what people kind of open up their finder and upload a document to like an ERP system generates files, a wealth management portal, you have clients uploading documents into the portal and they never see box. You have workflows of invoice processing that's happening behind the scenes. So the headless version of box has been alive and well for almost since the day we started the company. And so agents, to me, just again, represent a force multiplier on that. So it's actually an exciting proposition for us. We already know how to monetize it. The question is, 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 1000x, 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 and 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. You said secure and protect it. We mentioned on mutual love for royal dress school. I do a show with Rory and Jason every week. Jason's bluntly said that this will be the golden age for cyber security 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? I am concerned but not in any kind of like new concern sense. 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 co-pilot six years ago or whatever the date was five years ago, like that was just priced in which 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 any change in the 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 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 what not, then they can find more vulnerabilities because they can scan across the internet far faster than before. So you actually have to do 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. 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 and that's why there's going to be a lot of money made in agent security as well. You said agents of 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. Yeah, I was like half the salary essentially. Yeah, yeah, and it's kind of like, you know, drug dealer, you should buy drugs. Well, no shit. Again, I'm, you know, obviously, gen, I mean, listen, we love Jensen for that level of, you know, grandiosity and charisma. So I actually, I, I, but 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, than you ever thought and that you certainly are today. What percentage of salary you go into spend on compute in box in five years? Great question. I don't think we've modeled that out in five years and obviously the joy of being public is your chance. 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. I was so boring. I see it's true. You won't ever go public if you don't want to model on, on, on podcasts. So this is why the Colossans don't. Everything else is great. They just didn't pop. Yeah, cheeky pine. I don't know if you, 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. It's smashed them with four tequila's than I asked them. 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? 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. We'll 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 it 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 leader boards 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 pitchathon type thing, which is teams have 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'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 5 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. 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 part that back to Silicon Valley is again, kind of, you know, sometimes more, let's just say like, like, positively naive view is like real world, they have like budgets and they have like annual budget planning cycles because they have EPS numbers, 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 yet to figure out what teams are most interested and have the best use cases. That's a natural journey. Well, in front of our work that I think 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 op expend. 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 marketing engine. Like, it's going to be that kind of set of tradeoffs. What happens to that token budget when it transitions to that different spend category? Well, first of all, it goes up because IT spend as a percentage of revenue of large enterprises is. 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. I mean, if you do that voice, it sounds, it sounds kind of like, you know, simple. But like, yeah, that's just like a very big deal in technology. We've never had, there's never a slash railing 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 op-ex budget this year to go and do that. That is a new budget to tap into. And I don't think it like 10X is the size of IT spend or technology spend globally, but it certainly doubles it. I mean, Tony, enterprise technology spend is estimated between 10 and 12% to see that going to like 20%. As you said, there is like, I think relatively feasible. You said about kind of companies being like based on earnings per share and actually having budgets that they have to adhere to very strange not to have venture funding companies. They didn't even limited VC to go and solve this. 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, demon 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 demon side pull and extrapolate it continuously. Do you worry that we are in a momentary 18 month period on the demon side pull? And that may not always be lasting. It's very possible I should be more sensitive to that, but I would take the opposite side of that particular wage or 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 on fire. It's in 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 when you go to most companies, they can't yet just deploy an agent to do financial proposals for all of 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. That will just start to happen across the board. And so that's why 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. I had, I think Matt Fitzpatrick from Invisible, which is like a churring or a McCall competitor. And he said, you cannot sell into enterprise without an FD model. It is impossible. I mean, it rounds to being true. Super interesting to hear that because we're seeing like the rise of, oh, we go PLG and then we see up into enterprise. Well, I don't think of those as mutually exclusive for what it's worth. I guess what I'm saying is when you think about adoption within in the largest enterprises or AI services companies. The
best position companies of the next five years. - As you were saying, traditional professional services? - Yeah, I'm saying Accentures AI team that come into Bank of America. - 100% these bases are gonna 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 'cause it just does it for you. And it's like, I'm trying to think of, maybe my most recent experience with the best models in the world that 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? 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 estate 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 bring 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 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'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 genic center 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. We also have to have someone to blame. Right? No, this is why a lot of these industries lost, which is like, I have lawyers, not because I can't necessarily write an NDA. It's because it's your fucking fault if anything goes wrong. Yes, no, literally. And we don't know, like I promise you, you're not going to be able to blame and thropic with something goes wrong. And so if you can't blame and thropic 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 automated something the wrong way or create 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 in 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. 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 and machine gun to go generate way more information and work with all of our data. - You said before, when I've tried the latest model, it's got like 85% of the way there. I speak 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? - 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. - You know, as a venture investor, I specialize in making bold statements with little substantive evidence. - It's worked for the greats. So, do you know what I'm just following their lead? Jason Lampkin, 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 his palanter and no other public company has created a sufficiently good agent product. Why is that? - 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 and 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 gonna 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, 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 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 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. - Is your job harder than ever? - Yes. - Because of that speed of transience of superior or state of technology? - Yes. - Basically, I have this component of one, there's a tsunami of change that you can just feel. And so, you're like, okay, we gotta 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 is 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 wanna get crashing to by the tsunami. And they wanna be able to have a bridge into the future. And so, you're juggling a lot right now. - You said your agent product is the best product. Again, Jason Amoroi said this, and Rory might kill me for this 'cause 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, will shoot doesn't give a shit. But you have to re-exaggerate revenue with agent products. Can you charge significantly more for an agent product?" - 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 gonna 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-exceleration 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're doing the thing that I think Rory is sort of probably saying is the new benchmark. Now to be fair to 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. - Do you look at the ticker? - Yeah. - Every day. - Yeah. (laughing) - But I was like a day-trigger. - I never met a public company CEO who hasn't, I haven't met an avancier who was on the other end. He's like multiple times a day, multiple, multiple. - Yeah, no, 100%. But like, partly I just, I have like 80 HD or something. And so I just need, like, I'm like, I'm like, oh, it's a lot of, well, we look back on this period and be like, what the fuck? Companies trading at three times cash flow way over exaggerated or not. - Well, 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 their 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, 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'll play out over the next year. Do you think this generation of CEOs that you have around you though is equipped the AI transformation that is ahead? 'Cause I don't, like I think, I'm not blowing smoke up your eyes, you are. You're so vast in this, you're so fluid. But a lot of like, one said to me the other day, no, we don't have the AI chops in house, we might need to bring it in. This one's hard. I think you still have a lot of kind of founder led or tech forward, whether it's the right way to go.
they were an engineer or just they're just very technical category 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 Cloud Code or Codex building stuff and they're public company CEOs. So they are clearly wired in, tapped in, they can field the technology and they are not going to let their company lose. You know, assuming that as a category, they're in a spot where there's a lot of upside. So yeah, but like every technology wave, there's winners and losers. I don't know that this won't be any different. You just have to be super dialed in and work through it. A hard one before we do a quick fire. Okay. Who has the well turned that back on who you think should be much more appreciated? 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, 1878 cents bit harsh. 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 going to be more engineers. And so now does that mean that Atlassian'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 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's the job for all of us, you know, if you're building software. And the best place where agents want to work is defined by great API. 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 the, 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 the customer and it can't ever be deleted 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. I'm going to do a quick fire round with you. You have to go and be a public company CEO. I know. So if you change your mind on in the last 12 months, most significantly, I do think that I become more convinced that software is headless in the past year than I was maybe three years ago. And it's because of the level of agent capabilities on tool calling and searching across systems and the accuracy of that. And that has happened faster than 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 that's, you know, going headless wasn't sort of the most urgent priority from an agentics 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. 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 have messed in front of models. That was my big mistake. What acquisition did you not make that you wish you had done? I honestly don't, I don't think I have any M&A regrets. I actually, it's the deals that I wanted to do that we end up not doing that I don't regret is probably more that the situation. Which one is that? I'm not going to take those. But there are somewhere 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. Who is going to win the enterprise race? Open AI or Anthropic? Oh God, that's impossible. Back to the cloud piece. I think it's totally fair to think about it as a race. And certainly if you're in either of those companies, you have to treat it like a race. Because, because, you know, you like, you obviously want 80% market share or not 55% market share. 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. 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 going to win AWS or Azure or GCP? What's how is this all going to 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 just didn't really matter. Like everybody, everybody kind of won. And so I sort of think of AI in a similar fashion, which is I can't predict if it's going to be open AI 60% and then drop it 40% or it gets flipped or I'm off by another 10% tier there. But no matter what these markets are just fantastically large, companies are going to adopt multiple of these systems. They don't want to single vendor in this stack. One service goes down or one changes as APIs or one as a new commercial model. You're going to it's going to be a multi vendor multi AI world. And so that's why it's very hard to kind of call it at this stage. What does everyone think they know about enterprise adoption with AI that they get totally wrong? 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. If you were a venture investor today, which category would you be most excited to invest in? Obviously, I'm just, I pathetic. Yeah, right. God, I think I would still be probably loading up on all of the frontier rounds. It's like these numbers could continue to get much larger. So they get much larger. I mean, I, this is where I'm at 850 billion. You've got like a three acts to like a 2.1 trillion style. You know, I always think it's hard because I kind of have said that on the way up of many companies, like, like, you know, just like I did it with crypto, like how much further can it go? It's like, yeah. Well, that one, I'm going to put in a different category because that can just sort of be meamed the life. I'm just, I actually, no, but I actually think to the point on it last year 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. Yeah. Well, to be a little bit more fair, I think you have some one off companies that have done a, you know, 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, you know, where the workloads are going and where the data center build out is happening. And I get less of a return if I'm in software, you know, with, 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 meamed also. And so that's kind of helping the case, but it's just a really weird time, you know, overall that's, you know, hard to think through. So you would not buy all buds as an AI company? I mean, maybe you would because of that exact point. So I think you'll have that will be in the kind of one off category. So new bird 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, you know, 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, you know, a company and a new category emerging on agent observability and evaluations. I'll give a shout out to BrainTrust 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. 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, you know, 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. And kind of wanted to work across all the labs. It's a very relevant kind of new form of infrastructure for an agent enterprise. I think you're going to see a dozen, two dozen, five dozen of things like that that start to emerge. 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 using tents. You're also married and have a great relationship. I think it's to buy some marriage when it's super stressful, it's hard and you also have to drop a great husband. What was the advice on marriage? It feels dangerous if I actually acknowledge the great husband piece and 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 helps a ton. We try and make time, you know, 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 out of 15 years or so, and so she's seen the whole grind all the way. And she has her own set of grind in her business. And so it's, it's just lots of fun. Dude, you're my hair. I want to be when I grow up. Thank you for being so great. I really appreciate it. And I was 14 in 2010. OK, all right. So almost got it. Almost got it. But before we leave you today, did you know the industry average for booking a business trip is 45 minutes? Navan is the AI-powered travel and expense platform designed for companies that value efficiency. This allows finance teams to stop chasing receipts and skip the month than chaos. And you get this real time visibility that can save your company up to 15% on your travel budget. And that's why leaders like Visa, Stripe, Figma and even Anthropic rely on Navan these days. Go to navan.com/20VC today to see for yourself. Head over to navan.com/20VC now. What's navan? You raise VC funding and you're scaling globally and it's no longer about shipping products. The most exciting part for me, they're heavily investing in agente finance. If you're scaling globally, you need a banking and finance platform that's borderless, real time and intelligent. Check out Air Wallets today and see how they're helping thousands of businesses like Canva, McLean and Deal. Scale at airwollets.com/20VC. Well that's Vanta. For startups, low on time and resources, Vanta becomes your first security hire, using AI and automation to get you compliant, fast and unblock really big deals. And if your big enterprises, Vanta is your AI-powered hub for compliance and risk, bringing together data from across your business and automating workflows so you can prove trust at any moment. That's why top companies from startups like Cursor to enterprises like Snowflake choose Vanta. That's vanta.com/20VC. for $1,000 off Vanta.
Podcast Summary
Key Points:
The current AI landscape is viewed as a commercial and economic race, not an existential one, where global influence is gained by having the dominant technology stack.
AI is seen as augmenting human work rather than eliminating jobs; it will expose new bottlenecks in industries, creating demand for more skilled professionals like engineers and lawyers.
A new job category, "agent operators," will emerge to redesign business workflows for AI agents, requiring technical and process expertise.
The value of enterprise software will shift toward robust APIs and embedded business logic that agents can utilize, rather than user-facing features.
Summary:
The discussion centers on a pragmatic view of AI's impact, arguing it is primarily a commercial and economic race where technological leadership confers global influence. Contrary to fears of job displacement, AI is seen as augmenting human labor, exposing new bottlenecks in sectors like healthcare and law, which will actually increase demand for skilled professionals such as engineers and lawyers. A significant new role—agent operators—is predicted to emerge, involving individuals who technically redesign workflows for AI agents within regulated, complex enterprises.
The value of software will evolve, with emphasis shifting from user interfaces to robust APIs and business logic that agents can leverage, though many existing software systems will retain importance due to their embedded logic. The conversation advocates against discouraging careers in fields like engineering, highlighting broader opportunities across the economy as AI democratizes technical capabilities.
FAQs
No, AI is more likely to augment human work rather than replace it entirely. There will still be a need for human oversight and review in many fields, and demand for skilled professionals may even increase as automation reveals new bottlenecks.
Yes, AI makes it easier to generate legal content, but the actual review and approval by qualified lawyers remains a constraint. This could lead to an increased demand for lawyers to handle the growing volume of AI-generated materials.
A role like 'agent operator' is emerging, requiring technical skills to design and manage AI agents within business workflows. These professionals will help organizations redesign processes specifically for automation and ensure systems integrate effectively.
AI is expected to increase the need for software, particularly for APIs and background systems that agents can interact with. The focus shifts from user-facing applications to robust, agent-friendly interfaces and business logic embedded in software.
The primary constraint is often the availability of human labor and expertise, not the automation technology itself. For example, in healthcare, automating referrals doesn't solve appointment backlogs if there aren't enough doctors available.
Workflows need to be reimagined for agents, not people, focusing on how agents can perform tasks previously done by humans. This involves organizing data, connecting systems, and managing change to leverage automation effectively.
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