State of Enterprise AI 2026: Aaron Levie on Tokenmaxxing, Rise of Headless, and AI-Proofing Your Job

25:37And I think Dario had a great conversation on this. Like the code base has so much of the context in coding, whereas in the rest of knowledge work, the context lives across like 20 different things, some digital and some very not digital, you know, kind of mediums. And then this is kind of a kind of a relatively boring one, but it's like it's going to be probably the most important, which is which is access controls in your code base. like I can go to most teams in engineering and they have access to the entire kind of portion of work that they need to be working on. Conversely, you go to knowledge work and, and like you constantly are running into either like, Oh, like Bob actually had too much access to something or Sally had too little access to something.

26:19So Sally has to go ask for something or Bob should actually like have less access. And in both those cases, the agent equivalent that would have been doing coding that just can consume all of the code base that it needs and generate whatever it needs, that agent and knowledge work is going to either bounce up against an entitlement issue immediately and it's not going to have access to a resource or it'll have access to too much in terms of resources and then start to answer questions with data that it shouldn't have because the company didn't have a clean environment for access controls. So you've got five or six reasons that AI coding looks very different from the rest of knowledge work.

26:55And so what the implications of this are is that it's basically like diffusion is going to take time. And we have increasingly the right kinds of applications for this, like Cloud Coworker is awesome. You know, obviously Codex as a super app is emerging as this powerful workhorse. Gemini with, I think, Spark and whatnot. You know, I think there's like rumors that Cursor might, you know, try and evolve based on the SpaceX relationship. So I think the tools are increasingly coming and or there. Now you have the hard part of like, how do I deploy this in my organization in a way that is safe, in a way that is reliable, in a way that my employees aren't going to kind of create some crazy blast radius of security challenges.

27:38In a way where employees sort of know like what is the right way to go wire up this workflow that ends up being useful for them. So you have this huge tech, you have this huge AI kind of diffusion challenge. So it's a much more technical problem than I think we sort of got used to with the chat paradigm. Because chat was like, basically, it could do two things. It could access search and it could access the LLM. And that was amazing. But guess what? Neither of those things has a permission problem. Neither of those things required wiring up some other system where you could have massive data leakage.

28:13Yeah, it's just a DLP problem at horse. Just a DLP problem. And honestly, like in many ways, not that different from somebody going to Google to say, like, I want to go research this customer versus going to chat to BT and say, I want to reach this. It's kind of like almost nothing has changed about the security paradigm of that enterprise. So maybe the prompt could include a little bit more IP, but like the work you were doing was not like that. The blast radius of that work was kind of quite contained. Conversely, I go to an agent and I happen to have access to the Salesforce MCP server. and it's actually incredible.

28:48And it's actually one of the reasons why I totally believe in headless software. But I can do a lot of work with that and I could pull out a lot of data and I can ask a lot of very powerful questions and a company is going to have to say, well, should every employee have the same level of access? And how should we make sure that we've cleaned up our access controls for that? And how do we tell people again, like what types of queries should they be doing that are going to have different kind of cost profiles? And now you have to do that for each of your software vendors. and applications. And then you have to figure out like, what is the new workflow on the other end of this?

29:21Do you really want employees prompting, you know, their way through the workday across lots of stuff? Or do you want some standardized best practices? And then you're like, okay, well, now I have to build skills internally, like, you know, capital S skills. And I have to have, or I have to have, you know, various kind of knowledge graph or other ways of getting agents to the right information and the right kind of context. All of that is highly technical work that is going to take one, two, three, five years of building out across most organizations. The really good news for, I think, 90 % of people, maybe other than like the super AI accelerationists, is that that work means tons of opportunity.

29:59It means actually there's a lot of opportunity for startups. It means there's a lot of opportunity for like new kinds of roles. One of the hottest topics also is like, you know, we have a lot of customers asking us, what is this new internal FDE role or external FDE role? Like, what is the technical talent I need to go and actually help me deploy these types of systems. So we're in for years of this kind of diffusion, and it's just non-trivial, and every company has to go through it one by one. And this is the kind of journey that we are all now on. Do you think it could be 10 years? The cloud took much longer than everybody expected, and that was ultimately an IT problem, not an enterprise-wide problem.

30:40Do you think this could be just taking, I don't know, over a decade? Apparently, I don't know how we define it and this because I think it's actually a continuous, it'll be a continuous sort of evolution. And not to play semantics, but it's more like, what do we think the end state is? And again, I think the AI sort of either doomer or accelerationists think there is some end state. I actually don't think there's an end state. I think this is a substrate of how work happens. And it will just constantly get better. And we will have to constantly move up abstraction layers. and like it's not even obvious to me like what the end is it's just like it's a new way to to basically execute work and some areas that will be a 5x productivity gain other areas will be a 10 % productivity gain and that will roll out and then in five years from now we'll find the next version of that and I think it's this always kind of evolving landscape but but I think that we should totally be thinking on the order of 10 years um as like a as a rough type scale um uh for whatever it and this might be.

31:41If you want to be like, when does Coca-Cola or Procter & Gamble have agents running around doing every single task in the enterprise across every crevice of the organization hyper-successfully, that's a multi-year kind of transformation. And I'm making up an example. Maybe they're already there in particular, but this is just what's going to happen. Now, a funny thing, our industry is actually like, some of this is actually weirdly a byproduct of the industry. So we have this incredible capability overhang, which is like, let's just say you took GBD55 or Opus and you just snap the line right now.

32:15You could probably do this diffusion in like two years or three years. And we could probably all do the change management like collectively as an ecosystem. The problem is, is the breakthroughs keep happening faster than the customer can implement any kind of standard architecture. And those breakthroughs oftentimes basically undo or make obsolete the last thing you implemented. So it's this really bittersweet thing, which is like the technology is getting so advanced that it makes obsolete the prior thing that you implemented, which actually means that the rollout takes longer because we have no stable, there's no stable environment to roll things out in.

32:53If you went to an enterprise right now, it's actually a period of maybe the least amount of consistency I've ever seen in IT of like the following question. I want to go deploy an agent to do client onboarding or to review some set of knowledge work in the enterprise. I could probably lay out up to 10 to 15 reference architectures to all solve that problem. That means that every systems integrator, every software startup, every lab is pitching a customer 10 to 15 different variants of what they should do to solve that one problem. And so what that actually leads to, ironically, is more lengthy sales cycles, more kind of complexity in decision making because you're like, man, that anthropic managed agency looks incredible.

33:46this is really awesome. Like that, that's exactly how we should do it. And then you're like, Oh, this, you know, open AI frontier is, is really good. And then you're like, Oh, but this startup is actually pitching me something that means I'm neutral to either of those. And then you're like, Oh no, actually my workflow vendor can now do this. And it's, it is a madhouse on that front right now if you're, if you're a CIO. And so like one of the memes is nobody's signing up for more than like one year deals with the labs. And, and part of that is because of the pace of innovation that's happening.

34:15And so it's a byproduct of actually how much innovation we are seeing. But that means diffusion ends up taking longer than I think most people think. Fascinating. What do you recommend that people do in your conversations, given this litany of things that need to happen and the space of innovation, all of it, but at the same time, you mentioned internal FDs. That's super interesting. We can talk about external FDs, which I think is a better understood thing. Where should people start or how do they accelerate? So the one part where I'm just like, you know, I'm a hammer looking for nails is I see most things as a data problem and data with associated things like access controls and like how well defined is the workflow, et cetera.

34:53So most agentic challenges, I think, are kind of inversions of basically like you have a data challenge. Like the agent can't get access to the right information to do the work. Maybe they have access to too much information, in which case then they're just going to like roam around and do the wrong thing. Or they have access to too little information, in which case obviously they're not going to work. Or they don't have enough context to be able to execute the task, which means they need more information surrounding the task. So we see data problems everywhere that we look. And so I think one of the first steps is like your enterprise just needs to be prepared from a data standpoint and from a kind of a core architecture.

35:35And I think we for 20 to 30 years in IT, it was sort of OK to sort of have all these systems, some redundant, some not well managed. You could kind of throw humans at the problem and just sort of say, yeah, like the data science team knows like where the bodies are buried in the database. And they know what table to use and what table not to use. And they know how to go and kind of like work through that particular sort of data model. And so when the business asks a question, the business goes to their analytics team or data science team and they say, hey, tell us our attrition rate or tell us our growth in Spain or tell us our upsell rate of this product.

36:17The data science team is this kind of constrained centralized function is maybe 10 people or 100 people, but it's not every employee. And they go and they know how to kind of like work the numbers and they have another spreadsheet that's living on top of Tableau. And then they're moving some stuff in there and they're doing some calculations and then they give you the answer. Now, all of a sudden, you're like, oh, well, I want to go democratize that to everybody. And now I want to MCP into whatever the data store is of that thing. And then guess what? Everybody's getting a different definition to their query.

36:49Because actually, the way that company calculated things is like, no, they do an FX adjusted number, or they measure their net retention rate differently than what the model was trained on. And so all of this stuff where you now actually weirdly have a data problem and a data integrity problem and an access control problem, that actually becomes one of the more meaningful kind of projects ahead. I think you're smiling way too much, which means either you funded something here or you're seeing it or I don't know. It's just I'm smiling at the, you know, all problems are new again. And then effectively, we're talking about a semantic layer, which, you know, I guess is getting rebranded as an ontology.

37:27And that's a new, new thing when in reality, it's been the same problem for 20 years plus. Oh, 100%. It's been the same problem for 20 years. But again, we could throw people at the problem before. Like at the end of the day, when I had a question about data, I know exactly the person to go ask. And I didn't ever have to worry about it, like as Aaron, you know, in a company, because the data science team had to worry about it. Now, if somebody gives me access to that data as a resource and I start asking questions, boom, that's a way bigger problem because I might go to somebody and be like, hey, why did we like grow, you know, 13 % in that one area?

38:02And they're like, well, your data is wrong. Like it's actually it was 16%. You just didn't adjust for FX or whatever. It's like much bigger problem now when everybody can go and do that. So and that's just the structured data. Think about all the unstructured data. You know, most enterprises have five different places where their contracts are being stored. Their roadmaps are across 30 different locations inside of their data environment. That's obviously the space that we see day in and day out. So if you're going to have a world of agents and you want to have some flexibility on what agentic platform you deploy and what type, do you deploy coworker, do you deploy managed agents or do you deploy codecs, then you need to get your data into a format that is going to work within that kind of agentic ecosystem.

38:42So I think a lot of the work to be done is sort of blocking and tackling in the enterprise on IT, which is like, how do I get agents that context? How can they make sure they have access to the right information with the right security levels, with the right entitlements? And that is a big chunk of work ahead to ensure that agents are going to work properly. To do that, that's where the kind of internal FDE motion comes in. So we are seeing this increase. Some of it is sort of repositioned internal IT people or software engineers. Some of it is just straight up hiring new kinds of people and talent for the organization.

39:16But I do think this is a highly technical skill. It's a highly technical role, which is, do you have technical people in your organization that you can say, I'm going to have you go sit next to the business or within the business. And your job is to understand the patterns of how these people work and make sure that they have the ability to use agents to go in and do that work. And some of that will be agents for people that are prompting. And some of it will be agents that kind of are just working in the background and automatically producing value for that knowledge worker. But your job is to go understand the workflow, understand the process, and then marry that with the full potential of where technology is going and make sure that the data is set up the right way, the instructions for the agents are set up the right way.

40:00You have the right sort of human in the loop elements of doing that work. That's just a ton. That's a lot of work for most organizations. And it's going to be one of the... Except if you add meta and you do that by putting software on everybody's desktop. Yes. Yeah. Yeah. I think that might be an end of one situation. So, you know, for mere moral companies, you're going to have people going and doing this. And those people are going to look like the next generation of a software engineer or kind of IT engineer. I think it's actually incredibly exciting work because you get to go and transform, like, how does a life sciences company run?

40:37How does an industrial giant, you know, operate? How do marketing campaigns get produced? So it's actually like very, I think, exciting technical work. But a lot of companies don't have this talent right now. So they're going to actually have to go and hire people out of CS programs or be able to pivot engineers into these kinds of functions. And as an asterisk, it's actually why the doomers are also wrong about jobs, because this is actually going to be a very real sustaining job that is not like a one time you implement the agent and you upgrade the system and then it kind of works forever. It's like, no, like once the model changes, there's another set of work to be done.

41:12You have to make sure, like, did you get the gains of that model improvement or do you have to leave behind some scaffolding that you had to build for the prior model? Like lots and lots of work to be done in this area. Super interesting. So do you think that the external FD position is here to stay as well? So internal FD being within the enterprise, external FD being within the vendors, the slightly cynical version of FDs in startups or larger tech companies right now is that, well, none of this really works. Therefore, you need to deploy a chunk of humans to come on premise at the customer and make it work.

41:51But I think what you're saying is more profound and that this is going to be a fixture rather than a temporary thing. Yeah, it's so funny because the AI super accelerationists, which sometimes actually end up in the same quadrant of their views of the doomers and the, let's say, I don't know, skeptics as another kind of end of the continuum. They land in the same spot in this particular topic. They're like, man, I can't believe we have to have people go and do this. It's like it proves the skeptics, you know, right. And somehow the doomers and the accelerationists are like, oh, man, like it's not happening the way that we thought.

42:28And then it's sort of the cynical thing. And what's funny is, is like you have people like me that are like, I just know enterprises. And it's like this was obviously 100 percent going to happen. Like you guys are all crazy if you didn't think this was going to happen. And it neither proves that the technology is not amazing, nor does it prove that it obviously had to play out this way. Why did it have to play out this way? It's because we built this insane technology that's incredible at using computers, incredibly at using software, incredibly at writing code, incredible at using tools. But guess what?

43:06It has a fixed amount of memory. It has a fixed amount of context it can work with. It can do totally dramatically crazy stuff with your data. Like, so obviously it has to be like implemented by somebody hyper-technical. Obviously it has to be like implemented in a way that drives change management in an appropriate way, like for that organization. So like, it's like, to me, this was a hundred percent priced in and, and the market just took way longer to get there than, than I think anybody would have realized. Like, like you could just feel this the moment you saw agents be real. you're like, yeah, this is amazing.

43:40And it's totally going to take a lot of work for enterprises to go and implement this. You mentioned headless software. Is that inevitable in your opinion and clearly the future? I think the headless conversation ends up usually in the same kind of spot as almost every other technology kind of trend in history where you're like, you always think that the next medium fully eradicates the prior medium. And then you're just like, you're like, oh no, actually, I do have an iPad and a MacBook and an iPhone. And for some reason, I don't just like use my iPad as my phone and I, and with a computer, it's like, no, I have three devices.

44:12They all do something different. And so, so I think, I think it's going to just be one of those things, which is, which is if I'm going to go and do a complex query that involves box data, Salesforce data work day, and it's got to triage a bunch of stuff. I'm going to do that fully headlessly inside of coworker codecs or something else, like unquestionably. If I want to go and like work on a set of documents and build a data room and go and make sure that I'm sharing all my contracts the right way, at some point, like doing that via text is sort of slower than just doing that in a graphical user interface and with all the knobs that I know how to interact with.

44:49And I get a lot more leverage that way. So I think it's just going to be this sort of dual model with the one nuance being probably by like, you know, database queries, headless will just be 100 times larger than the interface driven, you know, way of doing work. And so we'll just have to understand that like by volume, agents are going to be banging on these systems far more than humans ever did. The human will probably land as an end user seat within that piece of software, and they'll get a certain amount of allocation of usage as that end user seat. And then I believe that they should have a right to use that software and that data via agents up to a certain amount.

45:33And that certain amount will be different based on the vendor, depending on how compute intensive is that workload. And then past that certain amount or when it's fully just an agent, then it'll be a consumption model. So I think any enterprise software company in three years from now that gets through this AI transformation period, it will have a seat business model, assuming it has an end user component, and it'll have a consumption business model. And that consumption business model in some businesses might be bigger than the seat model and some might be smaller just because the seat still takes up so much set of the work.

46:04But I don't believe that we move fully to consumption and fully to headless because I think there's a lot of reasons why you still want to go into the interface and poke around for a bunch of, you know, kind of reasons. And do you think it's necessarily humans have a seat and agents have consumption? Or would there be an argument for saying that agents in some way are not that dissimilar from humans, although they'll be doing a lot more with a lot more volume of data? And therefore, there should be some kind of like seat based pricing for agents. I think this is sort of a tougher category because it all depends on the agentic use case.

46:39So I can totally see a world where we already have some customers playing around this idea of like, should agents have a box seat? Because why? Because they actually need to store data that gets retained and governed over a long period of time. And you want to be able to track it and manage it just like a person. But it's got to be stateful. And so that kind of makes sense as like we have to give it a name and a thing in our system to make that work. Do we charge the same as a regular end user seat? Probably not. Probably it's got to be cheaper. But then there's a lot of situations where the agent doesn't need an ongoing seat.

47:13They just need to be doing a lot of operations, in which case it's probably just pure consumption. So I think it really depends on where does your software category land on? Is there a reason why you'd have an agent be stateful in that organization and kind of take on an identity and take on ongoing work? versus it's a thing that just every employee calls on demand. And that would probably determine what that business model looks like. What does headless mean at Box? How do you guys go about it? We kind of think about it as everything you would ever want to do with your enterprise content, you should be able to do via an external agentic interface.

47:50And so the examples are, let's say you want an agent to go and read through 100 contracts or review a data room that you've created for risks in a client. We just launched this example with the Cloud for Legal Solutions announcement. So you can put all your contracts in a folder and then the agent within, you know, Cloud Cowork can go and kind of work through all of that data and use it as a knowledge repository for its work. You could do, you know, a client onboarding process where the client has to upload a bunch of documentation. It's got to get stored somewhere and then processed. All of those are situations where Box will be the backend sort of behind the scenes for some kind of agentic work that's happening, whether the user is sort of interacting with the agent or the agent is just kind of running, you know, on some kind of deterministic or non-deterministic event that happens.

48:39And what's, you know, we kind of like conveniently have not had to do like massive, you know, kind of crazy transformations of the model because we've always had an API, basically like almost on day one of the business, we had an API. So for us, whether it's a headless agentic user or a headless system machine application user is kind of, eventually it talks to our system in roughly the same way. There are some nuances, which is like, headless users might want to sign up for the service on their own. And so we've had to think about account provisioning differently, or they might use our search in a different way where they need more context than what a platform deterministic use case would have looked like.

49:22They're going to use our search tool very aggressively. So we have to inform them as they're doing their searches how to think about this certain context that's inside these files. So there's a lot of work that we are doing to make our system better for agents, but the concept of being headless and the concept of being API-first is kind of wired into our DNA. How do you think org charts evolve? So we're going to have agents, we're going to have internal FDs. How does the rest of the organization evolve? I'm sure that must be a key real concern when you talk to global 2000 companies, right? Like the whole, but partly, you know, AI is taking my job kind of thing.

50:03This sort of relates to the AI coding versus the rest of knowledge work. And, you know, I kind of set it up at the very beginning on obviously this diffusion thing. But the reason why I'm less concerned about the job part and more optimistic is when we most get fearful of jobs, we look at coding as the example. And again, back to this coding issue, coding has this other unique property that's kind of different from a lot of the rest of knowledge work, which is if I write code and it's like super sloppy because the agent is writing this code, it kind of at the end of the day doesn't matter short of like a security risk or maybe like it's using extra memory that it shouldn't use or whatnot if the software just runs.

50:43Like if it's like I could throw, I could, I could have, you know, an application that has a hundred thousand lines of code or a thousand lines of code. If it's doing the thing that it needs to do, it really doesn't matter. And so, so this is sort of why you're seeing a little bit more like hands off the steering wheel emerge in coding. And it's like, we're just going to throw agents on agents on agents. And, and then that's going to go and solve the problem. Take, take, you know, the other, maybe most topical category is like legal as a, as an alternative. In legal, you can't do that. I can't have online 2004 of the contract.

51:18It sort of like adjusts the liability rate slightly because I had an agent go and write this thing whole cloth. It doesn't matter. There's no way for me to verify the – I mean I can layer on agents and agents and agents and they review each other and then they review each other again. And I can get kind of down to smaller and smaller percentages of risk. But at the end of the day, you're still going to have some lawyer that has to basically say, I believe that this is 100 % valid and I can put this up for my client or I can go and ship this. And so this last mile of agentic work, I think, is going to remain in a much broader set of knowledge work areas than I think we realize.

51:59And there was, I mentioned this a little bit in the past, but there was this funny article from the Financial Times like three weeks ago of like lawyers being inundated with all of these contracts that their client created. Or the client went to Chachapiti and asked a bunch of legal questions that now the lawyer has to go and adjudicate and provide answers on. And I think it's a microcosm of the real life application of AI, which is it can accelerate one thing massively. I can review the contract far faster and I can go get to the risky areas or can generate a contract much faster. But in both those scenarios, there's still a lawyer on either end doing real work.

52:41And so I've removed one part of the bottleneck, still unconstrained by another part of the bottleneck. And so that's just why the jobs don't get eliminated as sort of the first thing. But the second thing is, we've talked about this, and the market is, I think, fully beaten over the head on Jevons Paradox, but nobody ever factors in the Jevons Paradox thing. And I mentioned this with designers, but designers are kind of like a, you know, kind of a maybe minor example relative to all of the areas where this is going to show up, which is if you go to Caterpillar or Eli Lilly or Johnson & Johnson, you know, John Deere, I'm just naming like big industrial companies, just like not in Silicon Valley.

53:19These companies forever, they want the top engineers like everybody else. They are working on incredibly mission critical, you know, areas of, you know, creating a new drug, building autonomous industrial equipment. So they need top engineers like everybody else. They have to go and sign up for similar level scale projects as everybody else. But those engineers have largely sort of seen that, no, like you go to CS and then you go to Google or you go to Meta, et cetera. So what's going to happen now with agents is all of a sudden that all of those other companies are going to light up far more technical projects and technical work in their organizations.

53:55because for the first time ever, one of their engineers now has the capacity of three or five or 10 or whatever metric you want. And so that's going to get them to sign up for way bigger projects than they would have been able to afford, which means that they now have a greater demand for that engineering capacity. And then you throw in one more category, which is every basically small business on the planet is going to be able to go in and augment any of their functions that they wouldn't have had internally before with agents. And each of those functions back to the human in a loop component often will need some human to be going and doing the extra work that it takes to make that agent actually effective.

54:33So let's say you want to do the marketing campaign agent and you're like a solo entrepreneur, maybe it's a three person team and like you're doing it and you're moonlighting. But then you're like, oh, this is actually really effective. This marketing campaign is working. Probably the next thing I'm going to do is go hire a marketing person to go and manage these agents to go and do this at scale. So I'm like a complete Jevons paradox-pilled person because, first of all, I see it in our own business, I see it in customers, and I see it in small startups where these startups are hiring as fast as possible because they have all these job functions that their productivity gains are causing them to need to hire for.

55:07So this is sort of, you know, you can kind of pick your argument, but there's like multiple reasons why the job argument ends up falling on its face once you start to see actually how AI is rolling out in a lot of organizations. I mean, so, you know, for us as an N of one example. I mean, we continue to hire in a decent percentage of the functions that we've always had. Just the work that those functions are doing just is going to look entirely different in the future because they should be augmented, you know, meaningfully by agents doing additional work for them. It's certainly changing what we can invest in and what we tilt toward.

55:44But some of that is actually just a byproduct of our business evolution and where we're seeing demand in the market. But, you know, we're hiring people in marketing. We're hiring people and we're hiring engineers quite actively. We're hiring people in IT to build these agents. We're hiring sales reps. So, like, those contours aren't shifting as much as one would expect at, like, the, you know, name of the job title. If I had to, you know, really, let's say, lean into the future scenario, I think what happens is there's some kind of embedded AI IT capacity in most functions. There will be an AI person slash team in sales and an AI person slash team in marketing and at different subsections of marketing.

56:30And they already exist in engineering because engineering has been going through these sort of productivity gains. And I would assume that that person slash team, their job should be looking at like, hey, what do you do every day as like a demand gen person? And how can I bring automation to that? So we could be testing five times the number of campaign ideas and keywords. And we could be integrating one part of the design process to a campaign lifecycle much faster. so so you have a kind of a technical sort of person kind of wired up next to the business and do over time like in 20 years from now is that maybe just the new expectation of one of those business people it could very well be and then and then you'd kind of compress that which is like the new job like it might be that in 10 years from now if you go into marketing you also you know basically are going to be a cs minor equivalent of whatever agents are doing and your job is like, you better know how to wire up a fully agentic marketing workflow.

57:29Not in like, again, I chatted with ChattoBT, but like I could deploy a full end-to-end marketing campaign, you know, as one of the tasks in marketing. Right now, that doesn't really exist in most areas of knowledge work that might change. Again, I think, you know, feels like it's every function augmented by agents. And then in some companies, I can totally see the scenario of where the perceived risk is. So in some companies are like, I had 10 designers, but if I had an agent next to my top five designers, they would just do all of the stuff. I think that's very, very plausible. But there's going to be then equally 20 companies that say, I can now do design, you know, for the first time ever in a very kind of high quality way.

58:14And those people will just take those five designers and employ them for the first time. So on a net jobs basis, this is why I'm kind of largely unworried is I think what happens is, you know, there are definitely some companies that reached saturation of their particular demand of a function already with humans. And so agents coming in, they don't have more work to do. But I think that's like true of maybe 10 % of the economy. And the rest of the economy is like, oh my gosh, like actually, if I could have one designer that now does the work of 10 designers, then that's the first time I can go and hire that designer because now they can be doing websites and campaigns and videos.

58:53And so I think you're going to see some collapsing of obviously like all of the micro adjacencies of functions, but not so far that it breaks and collapses like entire domains of work. I think that there are people that have an eye for design. And I think the people that have an eye for design will just be both designers and managers of agents doing design. I don't think that means you take a copywriter and you make them a world-class designer. Just as in engineering where we're already seeing obviously this kind of like tension, like I think we're already coming to the other end of it. There was this period which is like, oh, the product manager can ship production code.

59:33And it's like, okay, but it's probably going to be slop. Or the engineer doesn't need the PM because they can like write their specs. And it's like, okay, but who's going to get on the call with the next 20 customers when you want feedback on that feature? Do you really want your engineer taking from their engineering capacity time to go do that? And it's like, no, those actually make sense as specialist jobs. Like Adam Smith figured this out a long time ago. Like division of labor is like a really powerful thing. Agents haven't fundamentally changed the concept of division of labor. There might be some new definitions of where the divisions fall.

1:00:07But like you probably want your designers being really good at design. You probably want your sales reps really good at selling. You don't want them having to do lead generation as a side project. You don't want your product manager trying to figure out how to become a designer. I think that there's less collapse than the super, again, hype train is on right now. But there's probably incrementally more collapse than what we would have thought 10 years ago. If I'm an employee in a large company, so again, G, Procter & Gamble type companies, how do I future-proof myself? What do I need to do today so that I'm not caught flat-footed?

1:00:48Not to get too kind of like paternalistic on this, but I do think that companies owe the employees and broadly society some help in this regard. I think there is a kind of a social contract, which is like you probably do want like the next generation to be having jobs. And you probably do want like people to not have complete and utter fear when they're leaving college of like, are there jobs on the other end of this? Or am I moving into this kind of ruthless dystopian environment? Yeah. And booing famous CEOs at graduation speeches. Oh, totally. And that's just like the beginning, right? Of the, of the issues.

1:01:29So, so I think like, I mean, I'm, I'm, I'm like, I'm the most deeply kind of pro AI, pro innovation, pro acceleration generation person you'll find up to the one point, which is, which is if you, if you stop caring about the overall, you know, sort of societal impact and people side, then, then like, I'm not even worried about like the revolts of like, you know, you know, we're going to get socialism or whatever as like a political matter. It's just like, it's just like society works really well when like people want to work at companies and, and, and they, and they can, you know, they can feed their families And you don't want to blow that up just because you wanted like one extra point of operating margin.

1:02:11So I do think companies owe their employees and the future employees a real shot at upgrading their skills and upgrading their talent. So like some percentage of this is on the company themselves for the upscaling, for the training, for the enablement, for all of that. Now, once you've done all of that, and as a hedge, as an employee, I'd be doing this no matter what, because I'm relatively like to go on my own and do everything. So it's very easy for me to say. But like as an employee, I would be spending 5 % of my time, 10 % of my time, whatever you can kind of carve out of just getting really good at this stuff.

1:02:46Like, I mean, your podcast alone probably could would probably add like 30 percent extra knowledge to every person on the planet if they were just listening to your your average episode, maybe minus this one. Careful. I may I may clip this and play it on repeat. OK, OK. But like they should just be doing this and they should just like there's and there's five other podcasts that that that do this. But not as well. Not nearly as well, because mostly it's just, you know, fighting with Jensen. So first of all, everybody should be consuming some percentage of this content and having a fluency. You have to use the tools.

1:03:26There's no way around that. I mean, you should just try and find a way to spend$100,$50 a month, some number. Like stop your, you know, turn off one of your cable subscriptions to do this and just start to use agents a lot. Use codex, use co-work, use perplexity computer, use, you know, cursor if you're semi-technical and just figure out what these things are doing, how they work. connect it up to a couple systems, try it out on a personal workflow, have some fluency, and then let your mind kind of wander a little bit of like, well, what would I do if I had this sort of, you know, everybody has a slightly different analogy for it.

1:04:10Like one of the best ones, I guess, that's emerging is like, what if I just did have a chief of staff that I could throw any task to and I could go and do all of that stuff and then come back? What would you give an unlimited chief of staff to kind of work on? And that kind of opens up your mind a little bit of, oh, this is actually the power. And how would you rewire that workflow in your organization? So I think there's a lot that you can do. It doesn't require insanely high agency to do this. You don't have to be a YC startup founder to do anything that I just said. It's available to every knowledge worker.

1:04:44You should give it a shot or use the free tools that are out there. Please use the VC subsidies to your advantage as much as possible and start playing with these tools. And, and, and like, even I have had to rethink my way of thinking about work multiple times in the past year. Shout out to, you know, one fun shout out, perplexity computer, I find does a better job than any other computer based agent for just being a workhorse for going through websites and, and doing search related things where you have to, you have to click on the page and you have to read the page and all that. And so I give it these tasks where I start to think like, man, actually, like if I did have an agent that was always running kind of like ongoing and it was doing X, Y, Z thing, you know, these are like maybe a sales workflow.

1:05:33I could probably very quickly be like, like, like make a lot of extra money by doing that on an ongoing basis. And so, but like, I wouldn't have known that if I didn't, you know, 11 PM one night go in and start a project that, that I actually just like pushed the limits of this thing. And then the other end of it, I'm like, oh, this is incredibly powerful. Now, fun asterisk at the end of that project, after doing it, my conclusion was, man, I don't ever want to do that again. Personally, I'd rather hire somebody to go do that for me. And so, and so another example of like the job creation thing is like, is like, I have multiple tasks where if I hired a person to go and use agents to do something for me, I could easily pay for that person overnight, but I'm not going to myself go and do all the wiring up and all the prompting.

1:06:17And so you will actually see, interestingly, if you're like an executive and you start to do this, you'll see lots of areas actually where you should hire more people because you're like, oh my God, this thing is spitting out incredible goldmine of value, but who's going to go and run with that? What are you going to do next with all that value that was created? That's the next set of jobs. So I think as an employee, you got to be using the tools and pushing your kind of thinking on this. So as we get towards the end of this conversation, I'm curious about your thoughts on the market structure for lack of a better term.

1:06:47Obviously, we are heading towards extraordinary IPOs and we've seen companies that are compounding faster than ever. Where do you think that leaves startups, including vertical startups? Where do you see the opportunities? Are we in a world where everybody is ultimately either an OpenAI or Anthropic employee or in a service industry supporting them? Or is there room for lots of people to do lots of different things? I remain pretty confident and optimistic on the need for a kind of a bridge layer from the AI capability to the end user workflow. And some might sort of say that this gets kind of bitter lessened out, which is, you know, oh, these things are wrappers on the model.

1:07:30And at some point there's a training run where it just like is the final training run that renders the kind of vertical app or function specific app not as useful. And I think that is a little bit too much of an accelerationist view of what people are doing with the tool, which is like, it's not just like what the model is spitting out or the model's ability to review information. It is how is the thing wired up into the business workflow? How did it get the context that it needed to be useful? I think if you're in an industry or a line of business, there's a heavy amount of kind of integration with data sets, heavy amount of kind of bespoke workflows that that company does.

1:08:09That usually means that there's going to be a need for change management, implementation, ongoing support, ongoing expertise. And unless the labs build out literally the equivalent of hundreds or thousands of people for every single vertical and every single line of business, that means that there's actually a lot of opportunity in that kind of bridge area of the work. Now, what is the exact mix and makeup of what that work looks like and what those opportunities are? I think that's ongoing. And I think this is sort of one of the big kind of questions. Now, there's this interesting thing that I'm trying to think through and workshop a little bit, which is, you know, the labs are obviously going to keep moving up into the applied use cases.

1:08:54And they're going to do some well and some not well. And we're seeing the announcements all the time. And I think, you know, there are some announcements where like I'm now using the lab for that thing as opposed to using the vertical application because it was so good. And there's some where it's like, no, that was still like the kind of poor man's version of that. And so you still need the vertical application. And there's a mix of all of these. I do think at some point, maybe things will settle out where the labs will kind of have to decide, do you want these things to be plug-in in intelligence for applied use cases?

1:09:26Do you want everything to kind of, you know, orbit within your application. I think we're going to have to kind of see where the tension ends up landing on this. Some of it is, to some extent, an account control issue. If you're a lab, you don't want necessarily to have a vendor above you that can swap you out at any moment per the token cost point earlier. So it's like very strategic. It makes sense, which is like, I don't want, if I, if like, I don't want you to go and be able to swap me for another model, the moment that, that you find one tweak that could make that more efficient. So I, I need to, I need control of that account, but obviously by virtue of having control of that account now, now there's sort of less to be done in that vertical, you know, layer.

1:10:10And so we have to kind of figure that out. I think there's, I think we're very early in, in where that lands, but I could see some world where maybe there's a kind of a, of a, you know, know, a peace treaty, which is like, if you bring in the intelligence from this lab, then, then, you know, X happens inside this product. You know, very, very hazy. The hyperscalers actually had to figure this out. Interestingly enough, where they basically had to figure out like, where are they going to compete in the applied layer versus where are they going to partner and kind of be a pull through mechanism?

1:10:43You can see like things like the AWS marketplace be, I think, a very successful project on their end and they are pulling through tons of products that they might otherwise normally compete with because the bigger prize for them is the most amount of infrastructure. And so the labs might equally kind of think about this as, OK, well, actually, the biggest prize is the ultimate amount of inference. And so we do need to make sure that there's a balance of that ecosystem. So I think we're just in the early stages of how these kind of things land. And the great thing is, is like, you know, capitalism is very good at this, which is Like if some companies lean too heavily in a non-ecosystem approach, then somebody else emerges and you kind of can balance it out that way.

1:11:24But I still remain very bullish on a lot of the applied layer of AI simply because the level of focused kind of approaches you need for these things tends to be much more intense than I think people realize. Like the difference between us doing a prompt with AI, seeing this incredible outcome and we're like, oh, my God, like obviously that thing could completely destroy this one application to then the ongoing daily sort of mechanics of that product, the implementation of it in a workflow, the knowledge worker that doesn't have time for any of this stuff. Like they don't want to know like where was the skills file stored in their file system.

1:12:04They're like, no, I just I just need to like move on with my day. Like the compression of all of that into applied use cases is, I think, where the vertical players, etc., are going to have a, you know, that's where they'll have their opportunity to compete. Okay, so we are concluding on capitalism fixes all ills.

1:12:26That feels like a wonderful place to live it. Thank you so much, Aaron. This was fantastic. Really appreciate it. Thanks, Matt. Appreciate it. Hi, it's Matt Turk again. And thanks for listening to this episode of the Mad Podcast. If you enjoyed it, we'd be very grateful if you would consider subscribing if you haven't already or leaving a positive review or comment on whichever platform you're watching this or listening to this episode from. This really helps us build a podcast and get great guests. Thanks and see you at the next episode.