Are SaaS Companies Cooked: Which Thrive & Which Die | Aaron Levie

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 5 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 OPEX 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. Ready to go.

Well, you know, I can't really do the Irish accent so well, but that's right. Exactly. But you basically, I'm sure, bought Rory one of his houses. So, no wonder he's grateful.

I think this is like the greatest Rorschach test of all time of where somebody is mentally on AI. 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-interview. 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 and my sort of kind 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 what 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 you know, Dario kind of oversimplified a few components. He said, well, with Mythos, if we get early access to that, then we can go and upgrade all of our systems. And with great respect to Dario, 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 everything. This is an ongoing endless thing till the end of time. You're always in this sort of leapfrogging 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 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. It's doing a disservice to the next generation. It's doing a disservice to society as a whole. 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 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 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.

We will and I think the part that everybody misses is that everybody is so myopic about this I want to just like shake the industry. 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 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 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 like tech has always had? That is what will happen. So yes, maybe if you're graduating from 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 contract or 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. There are going to be more lawyers in the next 5 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.

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. Big question, 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'm going to always have this example. They've automated or they're working on automating patient referrals 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 a week or whatever. Well guess what you can automate anything but if it still is 18 months out before an appointment is available. What 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 stake your career on being a frontline customer service 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.

Yeah. So I'm workshopping and a bunch of people are doing this. So this is like not my invention, but I'm workshopping.

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 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 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.mmd 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 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 because 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 pharma company or bank or consultancy that's just not the case. These guys are regulated, they have data fragmented across their organization, they have employees that are 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. And 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 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 it wants its syntax to be handled. So that 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 if there's a limit to 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.

Yeah, I mean 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 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? 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 like 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 you know 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. So then the question is what changes is the user interface that the 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 is 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 and it'd probably be better to have some kind of 2x2 which is like how much business logic is there, how much sort of human-to-agent collaboration does there need to be. And so 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.

Yeah, I mean 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.

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 sort of maybe not first but equal strategy. If I told you the number of API calls we did last year, you'd and or you guessed first, you'd probably be 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 into. 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 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 a thousand x that's actually more opportunity for us in the future. Now that's not the same for all software providers. For where we sit in the workflow where you just 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.

I am concerned but not in any kind of new concerned 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 Copilot six years ago or whatever the date was, that was just priced in which is as soon as AI writes most of the code 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 a system everybody kind of thinks about security as like 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, 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. So it's going to be a very dynamic period. I think 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.

Yeah. Or like half the salary essentially.

I again I'm you know obviously Jensen I mean listen we love Jensen for that level of grandiosity and charisma. So I actually 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 you ever thought. And that you certainly are today.

Great question. I don't think we've modeled that out in five years and obviously the joy of being public.

No, no, totally. I was told not to model long-term financial projections on podcasts. So let me... Yeah, it's a weird SEC financial thing.

Don't ever go public if you don't want to model on podcasts. This is why the Collisons don't. Everything else is great. They just didn't podcast.

This one's tough. The general advice will end up sounding kind of generic by definition. Usually I mean it's going to have something to do with your tokens will have to correlate to where there is the most amount of value generated for your company. Like most bland statement of all time, but just obviously has to be true. So in the software industry, we're into token maxing because guess what? 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. 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 token budget and then you kind of allocate it in some central fashion like a VC would and then you sort of I don't know their exact interval but I would imagine you review that 3 months, 6 months in being like okay did you get the upside that you thought on that token usage. So that's an interesting one. I think you have another company had a kind of a view of like it's some kind of 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 I'm 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's again kind of sometimes more positively naive view is like real world they have budgets and they have 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 wait for the earnings. You have to wait for the budget cycle. You have to figure out what teams make are most interested and have the best use cases. That's a natural journey. One final bookmark 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 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 marketing engine. Like it's going to be that kind of set of trade-offs.

Yeah. I mean if you do it in that voice it sounds kind of like maybe simple but like yeah 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 10x's the size of IT spend or technology spend globally, but it certainly doubles it.

Yeah. They don't have a limited VC to go and solve this.

You know, 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 because partly because we 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, 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. And then 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 full 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 kind of 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 mean it rounds to being true. So yeah.

Well I don't sorry I wouldn't 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 the largest enterprises, aren't AI services companies the best positioned companies of the next 5 years?

Yeah, I'm saying Accenture's AI team that come into Bank of America.

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 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. And so like 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 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 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 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 contract 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. That is like what I just described right now is 10 years of work for Accenture in every enterprise on the planet or the next 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.

100%. This is why a lot of these industries last, which is like I have lawyers, not because I can't necessarily write an NDA. It's because it's your freaking fault if anything goes wrong.