Aaron Levie and Steven Sinofsky on the AI-Worker Future

What exactly is an AI agent, and how will agents change the way we work? In this episode, a16z general partners Erik Torenberg and Martin Casado sit down with Aaron Levie (CEO, Box) and Steven Sinofsky (a16z board partner; former Microsoft exec) to unpack one of the hottest debates in AI right now. They cover: - Competing definitions of an “agent,” from background tasks to autonomous interns - Why today’s agents look less like a single AGI and more like networks of specialized sub-agents - The technical challenges of long-running, self-improving systems - How agent-driven workflows could reshape coding, productivity, and enterprise software - What history — from the early PC era to the rise of the internet — tells us about platform shifts like this one The conversation moves from deep technical questions to big-picture implications for founders, enterprises, and the future of work. Timecodes: 0:00 Introduction: The Evolution of AI Agents 0:36 Defining Agency and Autonomy 1:39 Long-Running Agents and Feedback Loops 4:27 Specialization and Task Division in AI 6:04 Anthropomorphizing AI and Economic Impact 9:10 Predictions, Progress, and Platform Shifts 11:31 Recursive Self-Improvement and Technical Challenges 13: 13 Hallucinations, Verification, and Expert Productivity 16:16 The Role of Experts and Tool Adoption 22:14 Changing Workflows: Agents Reshaping Work Patterns 45:55 Division of Labor, Specialization, and New Roles 48:47 Verticalization, Applied AI, and the Future of Agents 54:44 Platform Competition and the Application Layer Resources: Find Aaron on X: https://x.com/levie Find Martin on X: https://x.com/martin_casado Find Steven on X: https://x.com/stevesi Stay Updated: Let us know what you think: https://ratethispodcast.com/a16z Find a16z on Twitter: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Subscribe on your favorite podcast app: https://a16z.simplecast.com/ Follow our host: https://x.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details, please see a16z.com/disclosures.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Aaron Levie?

  We thought that we were looking at the form factor of AI, which is you're talking back and forth or something. The real ultimate end state of AI and thus AI agents is these are autonomous things that run in the background on your behalf and executing real work for you. The more work that it's doing without you having to intervene, the more agentic it's becoming. Somehow it produces output that it feeds back into itself.

• 0:22Steven Sinofsky?

  It's literally just the ampersand in Linux, which is it's a background task. And it's like the worst assistant in the world. And agentification is just hiring a lot of these really bad interns.

• 0:36Speaker 02

  What exactly is an agent? And how will agents change the way we work? To unpack this question, we brought together three people with deep but very different advantage points. Aaron Levie, co-founder and CEO of Box. Steven Sinofsky, former Microsoft exec and A16Z board partner. And Martin Casado, Jarno partner here at A16Z. From the old school idea of agents as just background tasks to today's vision of fully autonomous systems, we'll explore what this means for coding, enterprise workflows, and how whole industries might reorganize around agents. Let's get into it.

• 1:13Erik Torenberg?

  I thought I'd start this wide-ranging podcast by asking the very simple but very provocative question, what is an agent?

• 1:19Aaron Levie?

  Oh, boy. To who?

• 1:21Erik Torenberg?

  Steven.

• 1:21Aaron Levie?

  Okay.

• 1:24Steven Sinofsky?

  So I actually have a very old person view of what an agent is, which is it's literally just the ampersand in Linux, which is it's a background task. Because like you type something into O3 and then it's like, hey, I'm trying this out. Oh, wait, I need a password. Can't do that. And it's like the worst assistant in the world. And really, it's just because they need to entertain you while it's taking a long time to answer your prompt. And so that's my old person view of what an agent. And agentification is just hiring a lot of these really bad interns.

• 1:57Aaron Levie?

  The intern, they're getting better.

• 1:58Steven Sinofsky?

  They're getting better, but they still don't remember if I have a password to nature.

• 2:03Aaron Levie?

  Is it possible you guys just had bad interns in like the 80s and 90s?

• 2:06Steven Sinofsky?

  We had terrible interns.

• 2:07Aaron Levie?

  I have like a very high esteem for interns.

• 2:10Steven Sinofsky?

  But now a real answer.

• 2:12Aaron Levie?

  No, I mean, I think collectively, we're seeing what these are becoming. So if you think about two years ago, the post chat should be T moment. We thought that we were looking at the form factor of AI, which is you're talking back and forth or something. And I think to Stephen's point, the real ultimate end state of AI and thus AI agents is these are autonomous things that run in the background on your behalf and executing real work for you. And you're ideally in an ideal world, interacting with them actually relatively little relative to the amount of value that they're creating. And so there's some kind of metric where the more work that it's doing without you having to intervene, the more agentic it's becoming. And I think that's sort of the paradigm that we're seeing.

• 2:49Martin Casado?

  The only addition I'd have in addition to long running, which I agree, is that somehow it produces output that it feeds back into itself as input, which you can actually do long running inference. Like you can make a video that's really long running, but it's just basically a single shot video and you just throw more compute at it. I think there's like tactical limitations if you start feeding the input back in, because we're not quite sure how to contain that too. And so I think you can measure things based on how long they run. And you could also measure it by how many times it's actually taken its own, which would be kind of more of an agency.

• 3:21Steven Sinofsky?

  Yeah, because I do think it's important that in this transition, look, we are, what Aaron describes is where we're going to be. It's just that what are the interesting steps that happen along the way? Because we are going to need it for the time being it to stop and say, am I heading in the right direction or not? Because putting aside all the horror stories about, you know, taking action without consent and using accounts and data or whatever. There is this thing where you just don't want to waste your time on the clock while it's churning away way off in the wrong direction.

• 3:52Martin Casado?

  Yeah. So the question is to what extent do they have their own agency? Which to me means they've spit something out and they've kind of consumed it back up again, and it's still a sensible thing. Which, by the way, as you start thinking of these things in distribution, it's actually a very difficult thing to do because it doesn't know if it's going to be spitting something out that's still in distribution when it brings it back in. Like, they don't have that self-reflection. So I think there's actually a very kind of technical question here of to what extent we can make these things have independent agency. But we can make them long run pretty easily.

• 4:17Aaron Levie?

  Yeah, yeah, we're good at the long run.

• 4:19Martin Casado?

  The long run.

• 4:19Aaron Levie?

  What you get back is, yeah. Yeah, I mean, I think the interesting thing is how the ecosystem is sort of solving or mitigating then the issues. Like, you're seeing sort of this logical division of the agents. So they might be long running, but they're not actually trying to do everything.

• 4:35Martin Casado?

  Yeah.

• 4:35Aaron Levie?

  And so, so the more that you subdivide the tasks out, then actually the more that they can go pretty far on a single task without getting kind of totally lost on what they're working on.

• 4:45Steven Sinofsky?

  Well, Unix is going to prove to be right, which is like, you're going to want to break things up into much smaller granularity and tools. And I think to other points that you've made on X, like, you're going to want to divide things up so that it's like an expert in this thing.

• 4:58Aaron Levie?

  Yeah.

• 4:58Steven Sinofsky?

  And then it might be a different, let's just say, body of code where you go and ask, you know, are you good at this thing? Let me get your answer on this part of the problem.

• 5:06Aaron Levie?

  Yeah, it's kind of interesting. I don't know how much you've plotted this, but like, the conversation on AGI has sort of evolved very clearly in the past like six months. And I think that the consensus was, maybe not even consensus, what some of the view was, let's say, two years ago, was is this sort of monolithic system that's just super intelligent and it solves out all things. And now if you kind of fast forward to today and let's say whatever we agree kind of state of the art is, it's sort of looking like that's probably not going to work for a variety of reasons, at least in today's architecture. So then what do you have is maybe a system of many agents and those agents have to become very, very deep experts in a particular set of tasks. And then somehow you're orchestrating those agents together. And then now you have two different types of problems. One has to go deep, the other has to be really good at orchestration.

• 5:51Aaron Levie?

  And that maybe is how you end up solving some of these issues over the long run.

• 5:54Martin Casado?

  I just think it's very difficult to think cleanly about this. Like, I've still yet to see a system where you, they perform very well and you don't draw a circle. That doesn't have a human being in it somewhere.

• 6:03Steven Sinofsky?

  Oh, yeah.

• 6:03Martin Casado?

  So in a sense, like, the G, like, often seems to be coming from, like, the general seems to get.

• 6:07Steven Sinofsky?

  Yeah.

• 6:07Martin Casado?

  So, like, I just, listen, these things are tremendously good at increasing productivity of humans. At some point, maybe they'll increase productivity without humans, but until then, it's just very hard.

• 6:16Steven Sinofsky?

  Yeah.

• 6:17Martin Casado?

  For me to actually talk.

• 6:18Steven Sinofsky?

  Well, and it's so important for people to get past sort of the anthropomorphization of AI, because that's what's holding everybody back. Like, AGI is about robot.

• 6:27Martin Casado?

  Right.

• 6:27Steven Sinofsky?

  Fantasyland, and that leads to all the nonsense about destroying jobs and blah, blah, blah. And none of that is helpful, because you have to, then you dig yourself out of that hole to just explain, wow, it's really, really good at writing a case study.

• 6:39Martin Casado?

  Right, right.

• 6:40Steven Sinofsky?

  Which, it writes a better case study than all the people that work for it. But it doesn't know who to write it about, it doesn't know what necessarily you want to emphasize, it doesn't know what the budget is, what's needed, how many words.

• 6:50Martin Casado?

  But it also turns out, like, AGI just does an awful lot of work. So, for example, someone asked me recently, and they say, well, are you worried that if we have AGI, then you'll no longer be investing in software companies? I'm like, well, I mean, you're AGI.

• 7:06Martin Casado?

  I'm still investing in software companies, right? And so, like, just because you're AGI says nothing about economic equilibrium or economic feasibility, et cetera. So, like, just the term AGI does basically infinite work for every kind of fear we have, and maybe every hope that we have. And the moment we tie it down to, like, not only it solves a class of problems, but the economics pencil out yes or no, we can actually have a more sensible discussion, which I actually, I think, is finally entering the discourse. I think we're actually talking a lot more sensibly now than we were a year ago.

• 7:33Erik Torenberg?

  And so, when people say things, or the AI 2027 paper, when they talk about sort of automated research, recursive self-improvement, does that feel like fiction or fantasy? Or does it feel like, or is thinking that even with those things, we're, you know, sort of nowhere near peak software, and there would just be unlimited sort of demand?

• 7:50Aaron Levie?

  I think you gotta go first for each question. I think you're gonna anchor us in reality, and then, and then we can deviate.

• 7:58Steven Sinofsky?

  Well, I, like, I think that, first, I'm just not a fan right now of buying into anything by year, because whatever year you want to buy into, in 2027, we're just gonna be having a fight over what we meant by,

• 8:10Martin Casado?

  Right, right, right.

• 8:11Steven Sinofsky?

  The metrics, and it just turns into, like, OKRs for an industry, which is just like a ridiculous place to be.

• 8:16Martin Casado?

  That's really funny.

• 8:17Steven Sinofsky?

  But I think that everything takes 10 years, but you can't predict anything in 10 years. So how do you even reconcile that? And I think that- You just have to recognize that we're on an exponential curve. So no one's predictive powers work.

• 8:30Aaron Levie?

  Right.

• 8:31Steven Sinofsky?

  And it's just going to keep happening. It's not going to plateau. It's not going to, you know, all of a sudden we're done. And that's what makes this a different kind of platform shift. If you just, you look at the progress and that's the same that went through with storage, that went through with bandwidth, that went through with productivity on computing, on connectivity around the world.

• 8:49Aaron Levie?

  Yeah.

• 8:49Steven Sinofsky?

  Like because it's exponential, you can't predict it. And it's just folly to sit around and try to predict. Now, you could do science fiction.

• 8:55Aaron Levie?

  Yeah.

• 8:55Steven Sinofsky?

  And you could say in the future, when we all have our personal AI with all this other stuff, and then that's great, but then you say it's going to happen in 2029.

• 9:02Aaron Levie?

  Yes.

• 9:03Steven Sinofsky?

  You're an idiot.

• 9:03Aaron Levie?

  Yes.

• 9:04Steven Sinofsky?

  And so.

• 9:05Aaron Levie?

  That sounds totally correct, right? Because basically, three years ago, you would not have been able to conceive of Claude Code, or Cursor, or name your background agent writing code. So it's like, what is the point of having some date at which you're naming something? And so we've actually seen probably vastly more progress in the past just two years of actual applied AI than we would have thought. And yet, does it matter that one or two of the predictions didn't play out? No. So I think it's probably more interesting to think about, like, where is the technology from more of a classic Moore's Law standpoint? Like, how much compute do we have? How much data are we working through? How powerful these models?

• 9:41Steven Sinofsky?

  Let me ask you, like, as semi-old, well, I mean, like.

• 9:44Aaron Levie?

  Yucky.

• 9:45Steven Sinofsky?

  Like, nobody, after AI collapsed and machine translation and machine vision failed.

• 9:52Aaron Levie?

  Yeah.

• 9:52Steven Sinofsky?

  You couldn't find anybody who thought that those would become solved problems.

• 9:56Aaron Levie?

  Totally.

• 9:56Steven Sinofsky?

  Or after neural nets imploded and like, literally, you were teaching.

• 10:00Aaron Levie?

  Or expert systems.

• 10:01Steven Sinofsky?

  Or expert systems. But you were teaching, and if you tried to teach neural nets, like, the students would rebel because you were wasting everybody's time. In 1999, like, Hinton couldn't get funded trying to do neural nets. Grad school was this three-volume history of artificial intelligence thing. Neural nets was like eight pages.

• 10:19Aaron Levie?

  You know, ironically, I remember when ML was the cool thing and neural nets was the old thing. And now, like, you know, ML is like the old thing and neural nets are the cool thing.

• 10:28Steven Sinofsky?

  Right, or NLP. And so, so the fact that, so we will return to all of these problems that couldn't be solved, like, even, like. Like this, everyone's favorite one, oh, it doesn't understand math.

• 10:38Martin Casado?

  Right.

• 10:39Steven Sinofsky?

  Like, okay, that is a solvable problem because math is solvable. Like, there's just, no one put the math layer in to understand what a number was and to, you know, hard code it and just build in an expert system for math, which is actually a well-understood thing because we've had Maxima since like 1975.

• 10:56Martin Casado?

  I think it's important to like, maybe for us to describe how hard it is to predict anything, right? So let's take recursive self-improvement. This is one of my favorite ones. So the theory recursive self-improvement is you have a graph or you have a box which is the thing. And then there's an arrow that goes back to the box which says improve. And then, of course, you look at that and you're like,

• 11:15Steven Sinofsky?

  Works.

• 11:16Martin Casado?

  So I guess, you know, like from an intuitive lay perspective, every time you have a box with an arrow back in it, you're like, okay, we're done, right? But like, if you know anything about nonlinear control theory, answering that question is one of the most difficult question that we know in all of technical sciences, right? Like, does it converge? Does it diverge? Like, does it asymptote, right? So, for example, you could recursively self-improve if you're doing basic search, but you asymptote, right? And so like, saying recursive self-improvement from like a deeply technical perspective says almost nothing. It says, but unfortunately, because we tend to anthropomorphize AI, we say recursive self-improvement. All of a sudden we're like, and then it like, overcomes energy boundaries and human intelligence.

• 11:59Steven Sinofsky?

  Well, that's how it goes from being a toddler to being like an eight-year-old. It's just because it figured out how to learn.

• 12:04Martin Casado?

  Recuriously self-improves, right? And so, I mean, the reality is like nonlinear control systems, which are feedback loops that are adaptive, we don't even have the math for relatively simple system to understand what happens. You have to actually know the distributions that come out and go into them. And so, these things are going to improve, they're going to continue to improve. Maybe they'll improve themselves, but just because they do improve themselves doesn't mean they can continue to do it. And this is kind of part of this entire journey is we're learning about these systems. Again, the good news is I think we're talking a lot more sensibly now than we were a year ago. And hopefully that will continue. Hopefully, hopefully the discourse can recursively self-improve, so we're just more sensible.

• 12:39Steven Sinofsky?

  Well, the good news is that's involving humans, so we don't actually have to worry. But I think that, I mean, you must be seeing this even with customers. I mean, like, take the conversation about like hallucinations and things like that. How dramatically that's altered in just the past two years, say?

• 12:54Aaron Levie?

  Yeah, on two dimensions, actually. So on one dimension, the problem of hallucinations has improved. So as the models get better, as our understanding of how do you, you know, whether it's RAG or whatever, even the problem of actually the efficacy of the context window has improved. So you have the technical improvements, you know, kind of across the stack. And equally, you have a kind of a cultural understanding to some degree within the enterprise as to like, okay, actually, no, these are these are non deterministic systems, they're probabilistic. So you're starting to see almost a culture shift, which is, okay, you can you can actually implement AI in essentially more and more critical

• 13:39Aaron Levie?

  use cases, because the employees that are using those systems understand that they do actually have to do the work to verify it. And then the only question is, is what is that ratio of time it took to verify versus if I had done it myself and how much efficiency gained for whatever that workflow is. But we are, we're going from probably like, two and a half years ago, where there was, you know, this instant excitement as to, oh, my God, this is going to be the greatest thing of all time, to a reality check within three to six months, because everybody was like hallucination is going to be the massive, you know, kind of problem to now, a couple of years later after that, which is like, okay, like, we're seeing the hallucination rates shrink, we're seeing the quality of the outputs increase, and we understand that you do have to go and review the work

• 14:22Aaron Levie?

  that these AI, you know, agents are doing. And that that takes on a different form depending on the use case. So in the form of coding, that means just like, you just had to go review the code in the

• 14:31Steven Sinofsky?

  Which you had to do anyway.

• 14:33Aaron Levie?

  You had to do

• 14:33Steven Sinofsky?

  People seem to be forgetting.

• 14:35Aaron Levie?

  You had to do anyway, but but like, there was probably at least a little bit of like, theory as to like, what part you you should go review with extra level of detail, because you kind of knew the person you were working with.

• 14:44Martin Casado?

  It also implicitly limits the value of AI, which people are uncomfortable with.

• 14:48Aaron Levie?

  Right.

• 14:49Martin Casado?

  Which it just basically says it helps people that will know more than the AI does.

• 14:52Aaron Levie?

  Yes.

• 14:52Martin Casado?

  And as soon as it knows more than, you know, like, it starts to actually kind of bisect the utility.

• 14:56Aaron Levie?

  Yeah, yeah, basically, it's super interesting, which is the experts are now becoming, the productivity of an expert is is outpacing everything else, which is Which was this, you know, I think we could have probably predicted it based on historical events, and I think you've got some good theories about how, you know, the skill, the type of skills that are, that, you know, that kind of, the right user for these models for the kind of use case. So we're seeing that, you know, where the expert engineers are like, like, I don't mind that it's a slot machine where I'm pulling it and I see what comes out because I know I can, I can still get 10x productivity.

• 15:27Martin Casado?

  It gives me good ideas.

• 15:28Aaron Levie?

  Yeah, and I get it good enough that it's worth that productivity gain. Whereas, if you were like, not an expert engineer and you do this slot machine, you probably would try and go and deploy, you know, all the ones that were also wrong.

• 15:38Martin Casado?

  And you don't actually don't know which lever to pull. Which is, a big thing is like, literally knowing like, what to ask for and what language to use will get you a better response.

• 15:46Steven Sinofsky?

  Well, that, I think that this is just an incredibly important point that you're making, and it really gets to the heart of what it means to use a tool. Like, you know, you put me in front of like, a 12-inch chop saw and say, like, "Go fix the fence." Really, really bad idea. I mean, I could go buy one. I could cruise the whole.

• 16:04Martin Casado?

  That's a real.

• 16:05Steven Sinofsky?

  And I'm like, "Ooh dang, man, I don't have it to wall." And I could buy it, but it's really not a particularly good idea.

• 16:14Martin Casado?

  Right.

• 16:14Steven Sinofsky?

  And, and I think that how these platform shifts happen, and why there's so much excitement over coding, is that, well, the best way for a platform shift to take hold is, it's the, the experts that are, the closest you have to an expert in the new platform is who becomes the most enthusiastic and the biggest users overall. Like, I've been practicing yoga over at the Coverly Community Center in Palo Alto, because the studio is closed for remodel. But, but what's neat is that was like, the OG place for computer clubs.

• 16:43Martin Casado?

  Oh, nice.

• 16:44Steven Sinofsky?

  Like, in the early 1990s and the late 80s, like, if you ever wanted to meet the computer club, and you would go, and like, this is like halt and catch fire. Like, and it's like, like a bunch of people with soldering irons and shit. And like, they're, that's who, and, and you know, when it, when it didn't work, when something was broken, that wasn't like, "Oh, man, these things are terrible, I'm wasting all my time." That was like, the whole meeting.

• 17:06Martin Casado?

  Right.

• 17:06Steven Sinofsky?

  Was like, who could get, like, one of these new discrete graphics cards to actually work and debug the driver? Just, can anyone print? Is there anyone in this room who can print? In this new thing called Postscript. And I think that's what's really happening right now. And so first, it's obvious it should happen with development and coding first. Because they're the most forgiving. And the most understanding of like, what's a bug? What's a thing that can never get fixed? And the thing to watch for is no one is saying that coding can't get fixed.

• 17:35Martin Casado?

  Right.

• 17:35Steven Sinofsky?

  Like whatever it's been generating that's bad for like a 2X coder rather than a 10X coder. No one is saying, well, that'll never be fixed.

• 17:43Martin Casado?

  Right.

• 17:43Steven Sinofsky?

  And then the next thing that's gonna happen is gonna be what I think is just gonna be like the the creation of words, like the the marketing document, the positioning document, all of this long form stuff where if you're really good at that job, you you can, you know, the right questions to ask. You know what looks good. And then you can, you get really domain specific, like on the next, you know, the next level is like, oh, I need to understand like a competitor. Which then is using real information from the internet in real time, not just statistical. And then you're like, well, they already know what the competitor does.

• 18:14Martin Casado?

  Right.

• 18:15Steven Sinofsky?

  Like they're there. And then my favorite scenario is the one that just constantly just has these aha moments is attack this thing I just wrote.

• 18:23Martin Casado?

  Yep.

• 18:23Steven Sinofsky?

  I'm not interested in you getting adding M dashes and making it a little bit better. I just want to know, what did I miss? You said one, I think, recently on those last one about like, here's my earnings statement.

• 18:34Martin Casado?

  Yeah.

• 18:35Steven Sinofsky?

  The the for people, that's the thing you read that you read after the to the analyst. That now like attack it like an analyst. And there's like 6,000 hours per company of analyst questions. It knows what they're gonna, they only ask like three questions anyway. Ooh, expense line, you know?

• 18:51Martin Casado?

  Right.

• 18:52Steven Sinofsky?

  And I feel like this is the thing that

• 18:54Martin Casado?

  Do not watch this if you're an analyst.

• 18:57Steven Sinofsky?

  This is not any advice about being an analyst. But I, this is what what's really gonna happen with with writing. And then it's gonna happen with PowerPoint and slides. And then it's gonna happen with video and it's

• 19:10Martin Casado?

  It's really important to call out, which is you're getting the consensus mean response. And so in the limit, it's offloading a lot of Kind of busy work if you're a professional. Like, if you're a professional, you actually know all of these things. You just don't have the time to go through all of it.

• 19:24Aaron Levie?

  Yeah.

• 19:24Martin Casado?

  And you may not remember it. So, so in a way, it's, it's, it's productivity helpful, but it's not, you know, solving some problems of where, you know, you are a particular expert in. And this is maybe why for those that are non-expert, it's a little bit more threatening because it can do that job.

• 19:39Aaron Levie?

  Yeah, well, the, the, maybe to, to bridge a view and probably throw in a different tangent, like, so, so Stephen, you're asking, like, so where is the enterprise now? So, so that was the coding piece. I think where you're seeing this is, you know, kind of clear understanding, which is, okay, what I'm going to get out will be correlated to what I put in. So, how precise I put the prompt, what, what, like, like, I think prompting doesn't go away anytime soon, simply because the leverage you get on the set of instructions you're going to give the AI at the start is still going to be massive. So we see-

• 20:08Martin Casado?

  Wait, what, what, what would, the prompting went away, what would you end up with?

• 20:12Aaron Levie?

  Well, I mean, two years ago, I think that, like, people were like, like, you'll just tell the AGI what you want it to produce.

• 20:18Martin Casado?

  Oh, just, yeah, I'm, I'm gonna get my- There's just one prompt, like, you unbox it and you say, go do something.

• 20:22Aaron Levie?

  My agent-

• 20:23Martin Casado?

  Be a software engineer.

• 20:24Aaron Levie?

  Right, no, literally, that was, like, that was like an open debate. And it was like, no, you're probably missing the fact that what is in my head is going to be unbelievably germane to the thing that I'm trying to produce. And like, I have to somehow give you that context. Like, there's no world where you have that context without me telling it to you. And now you're seeing it, like, you're seeing these incredibly unhinged prompts, which are like pages long.

• 20:43Martin Casado?

  Yeah.

• 20:44Aaron Levie?

  And the output you're getting from that is actually like way better than if you didn't give it that context. So I think there's a clear understanding of, of that side on the enterprise use cases and then a clear understanding that you've got to go and review it. And then, and then on, on this point about like, well, you know, what, what is-

• 20:59Martin Casado?

  Wait, I just have to say, we forget that formal languages came out of natural languages for a reason. We didn't stop, we didn't like start with like, we didn't start with like formal ages, like, oh, it's much easier to speak in English, just speak in English. It's just the opposite. It was like, we had this natural language, we're like, it's very tough to convey the information that I want to.

• 21:15Aaron Levie?

  Right.

• 21:15Martin Casado?

  You and I are both experts. We understand the solution space, so let's communicate more efficiently.

• 21:19Aaron Levie?

  Right.

• 21:19Martin Casado?

  So to think that this somehow wouldn't happen-

• 21:21Aaron Levie?

  And that's what jargon is.

• 21:23Martin Casado?

  Of course.

• 21:23Aaron Levie?

  Jargon is just a formalized way that people who have domain expertise talk to each other.

• 21:27Martin Casado?

  That's exactly right, yeah.

• 21:28Aaron Levie?

  So the thing that, that is kind of, kind of the most like fun to kind of think about right now at least is, and, and maybe you give us a little history lesson on this in, in kind of interesting parallels. So when does the style of work- Change because of the tool versus the tool sort of adapted to the style of work. And so what I'm starting, we're like only in day one of this, but what I'm starting to see kind of some, some patterns emerge, which is we thought agents would go and learn how we work and then automate that. And then the question, and so basically agents conform to how we work. The question is, when is the moment when we conform to how agents are best used? And you're, you're seeing this in a couple of areas. So you're seeing this in engineering to start with, which is like, people are saying, okay,

• 22:13Aaron Levie?

  I'm going to have agents and then sub agents for parts of the code base. And then I'm going to give them kind of read me files that the agents read. And then, and then I'm going to actually optimize my code base for the agent as opposed to the other way around in other forms of knowledge work. So within how we use box with, with our AI product, like you're starting to see people like basically tell the agent like it's complete, you know, job. And the, the workflow is now starting to be almost like the agent is almost dictating the workflow in the future as opposed to it's just mapping to the existing workflow. So I don't know like what the history is on this of like, when does, when does the work pattern itself shift because of what the technology is capable of. But I think, I think probably where this goes has to be some version of that, which is, which is it's not going to just be that agents just plop into how we currently do

• 22:58Aaron Levie?

  our work and then, and then just automate everything. I do think you start to change what we actually, what the work is itself. And then, and then agents actually go in and accelerate that.

• 23:07Steven Sinofsky?

  Well, as important as that is, it's actually more important.

• 23:10Aaron Levie?

  Okay.

• 23:10Steven Sinofsky?

  Like, because what, what happens is where there's, to reuse the word in a different, this anthropomorphization of work, what happens in is that the first tools actually anthropomorphize the work. And so like, if you go back, this is every single evolution of computing. I mean, like, how long did it take for Steve Jobs to get rid of the number buttons on his smartphone? Like, like they, they still had number buttons or like you look at, at cars and until Elon got rid of all the controls, everybody kept all of the controls. I don't want to get in that fight. But, but like what happened with every technology shift is, you know, if you, if you were to look at what accounting software looked like in the sixties before IBM said, stop.

• 23:54Steven Sinofsky?

  We all use double entry, but we need to have people skilled in how computers can do the accounting, not how people can, because we're never going to figure out how to close the books.

• 24:02Martin Casado?

  Right.

• 24:03Steven Sinofsky?

  If we have to automate this whole room of people with green eye shades.

• 24:07Martin Casado?

  Right.

• 24:07Steven Sinofsky?

  That have a manual process based on how far apart the desks were.

• 24:11Martin Casado?

  Right.

• 24:11Steven Sinofsky?

  And, and everything that happened with the rise of PCs and personal productivity started off, and I always use this example because I've watched it happen like five times now, which is the, the first PCs that did word processing, the biggest request was, how do I fill in like expense reports? And so the whole, this whole world grew up of tractor fed paper that was pre-printed with the expense report. And so then software, we wrote all of this code, like, are you using an Avery 2942 expense report? Or is it a New England Business Systems A397? And, and like, you know, and then you had like these adjustments in the print dialogue, like 0.208 inches and you, you moved little things and then you would print out like,

• 24:54Steven Sinofsky?

  ate dinner at $22 and that was all you printed. And then someone said, you know, we could use the computer to actually print the whole thing.

• 25:04Martin Casado?

  Right.

• 25:05Steven Sinofsky?

  And then like fast forward and finally Concur said, you know, why just take a picture, why not just take a picture of the receipt? And then we could do all of it. And so then the whole thing gets inverted and, and every single business process ended up being like that. And, and then there are things that really, really do change the tools. Like when email came along, you know, it used to be to prepare an agenda for a meeting, somebody would open up Word and type in all the things and then print it out. And everybody would show up the meeting with this very well format. And now, and then like email came out and that whole use case for Word just evaporated. And, and then an email agenda became no formatting, nothing, just like, here are the eight things we're going to talk about. And you show up and everybody's like, did you get the agenda?

• 25:49Martin Casado?

  You know, what's interesting about the AI one is it's kind of, it's like, we're seeing the same thing, but vis a vis AI. So nobody really predicted the generative stuff. And we've had AI for a very long time. So we had chatbots, we've had. You know, and so you had these kind of like AI shaped holes in the enterprise for a long time. And a lot of the mistakes that we see today is people are taking the generative stuff and trying to kind of cram it into the old models with like really a new behavior that's emerging. That's very much more like it used to be you'd centrally sell, you know, AI to some platform team and then they would kind of try to get the NLP thing to work or the voice to work for like talking to people on the phone for support. And it was this kind of very central. A lot of the adoption that we see is like much more individual, for example. And so I just think that there is a bit of a mixed match that we're seeing now that is getting ironed out, too.

• 26:34Aaron Levie?

  Well, and so I think the question is, is yeah, are we in the phase where we're trying to graft the agents and work in basically the what we've been doing for 30, 40 years of software? And is this going to be actually like a like the first real step function shift we've seen in what the workflow itself should look like?

• 26:52Steven Sinofsky?

  Oh, we but we are. I mean, like you, if you, you know, remember people like I tried to jam the internet into Office.

• 26:58Aaron Levie?

  Right.

• 26:59Steven Sinofsky?

  And it

• 27:00Aaron Levie?

  And it was fun to watch.

• 27:01Steven Sinofsky?

  But you were not watching. But but like, but but everybody around was trying to jam the internet into their product because that's the only way you could envision it.

• 27:13Aaron Levie?

  Right.

• 27:13Steven Sinofsky?

  And it didn't really like you. You were like, well, where else would the internet go? Like, there's no word processor on the internet.

• 27:20Aaron Levie?

  Right.

• 27:20Steven Sinofsky?

  Like, there's no spreadsheet on the internet. And and then the other people would be like, well, let me just try to implement Excel using these seven HTML tags with no script. That turned out to not be a really good idea either. The best was like, let's do PowerPoint. Well, how do you do it? You give them five edit controls, tell them their bullet points, and then we'll generate a GIF on the backend and send it back to you as the slide.

• 27:39Aaron Levie?

  Yeah.

• 27:39Steven Sinofsky?

  Okay, that that that was not. And so there was that whole like

• 27:43Aaron Levie?

  I think actually, maybe the the main point is just the durability of Office. It transcends all

• 27:47Steven Sinofsky?

  It does. I like to get pretty much rises above everything.

• 27:50Aaron Levie?

  Yeah, exactly.

• 27:51Steven Sinofsky?

  But the thing is, is that that's where we are now is everybody and, you know, like even

• 27:56Martin Casado?

  Do you but do you think, no, I mean, just to dig a little bit. So do you think this is similar to the internet and that it's a consumption layer change? So I always viewed the internet very much a consumption layer change. Like, I go to, you know, instead of going to my computer, I go to the internet. But otherwise, things kind of were the same where AI has got this weird quirk, which for the first time I can recall. Programs are abdicating logic to a third party. Like, we've always abdicated resources.

• 28:20Steven Sinofsky?

  Yeah.

• 28:21Martin Casado?

  Like, so we'd be like, okay, I'll use your disks or whatever, but like, I'm writing the logic. But this time, it feels like we're changing the consumption layer. So like, you know, when my son, you know, talks to an AI character and, you know, he's not going to Wells Fargo dot com. He's going to an AI character. And so like, that's changing kind of how we're interacting with the computer. But also, these programs are no longer kind of written by a human in the same way. So I feel like the change is maybe a bit more sophisticated.

• 28:47Steven Sinofsky?

  Oh, I think, but this is the, this is why it's a platform shift and not just an application shift. Like where, where each, each platform shift changes the abstraction layer with which you interact with computing. But what that also does is it changes what you write the programs to.

• 29:04Martin Casado?

  Do you, do you remember ever abdicating logic?

• 29:07Steven Sinofsky?

  Oh, here's a great, here's an example of how disruptive this can be. The, the first word processors in, in, in the DOS era, the character mode era, they all implemented their own print drivers and clipboard. So if you were Lotus and you wanted to put a chart into a memo, you, you, you couldn't because you didn't have a word. You didn't sell a word processor. So you actually made a separate program to make something that the leading word processor could consume. And if you were Perfect, your ads said, we support 1,700 printers. Like, and you won reviews because you had 7000 and Microsoft had 1,200. And so then a long.

• 29:44Martin Casado?

  That's a great one.

• 29:45Steven Sinofsky?

  And then so Windows comes along and, and if you were, and if you were trying to enter the word processing business, step one, I need to hire a team of 17 people to build device drivers for Epson and Okidata and Canon printers. Because you can't get them anywhere. Microsoft came along and for Windows, built print drivers and a clipboard. And all of a sudden, and also Macintosh did it. All of a sudden, you, there was a way that two applications that had no a priori knowledge of each other.

• 30:11Martin Casado?

  Good deal.

• 30:11Steven Sinofsky?

  But of course, if you were Word Perfect or Lotus, that's a, you got creamed by that. Because your ability to control your information. And so, and what happened was a bunch of developers were like, wow, this is cool. Because now I'm just by my, when we did C++ for Windows. Like, we were like, where the demo, in fact, at that Cumberley Community Center, I would go and I would show brand-new Windows programmers in 1990, like, "Hey, you don't have to write print drivers and use the clipboard." And like, literally, standing ovation of, you know, 10 people at the thing. And, but, but they were like, more than happy

• 30:47Aaron Levie?

  Yeah.

• 30:47Steven Sinofsky?

  to let data interchange between products. Because they were like, "That's nothing but opportunity for me."

• 30:52Aaron Levie?

  Can we, you, like, they, they probably, from an emotional standpoint, felt exactly the same way as like a vibe coder does today. Which is like, "You've just given me the platform." And it was just a print driver.

• 31:03Steven Sinofsky?

  The writing code for Windows book was like, this big. But the writing a device driver for an Epson printer was this big. Writing it for a Canon printer was this big.

• 31:11Martin Casado?

  But I'm just actually trying to think of like, but the paradigm shift is the same, which is, there's been many times where we've reduced the amount of work a developer takes. But I just don't remember ever where the programmer abdicates logic. Like, so for example,

• 31:24Steven Sinofsky?

  SDM didn't?

• 31:26Martin Casado?

  Not logic. Like, I, I would always say what is correct and what's not correct.

• 31:30Steven Sinofsky?

  I think you undersold it, though. No, this is the thing, by the way, everybody that Martine invented and worked on. That's no, like, but, but it's a big deal.

• 31:39Aaron Levie?

  Maybe we should postmortem your pitch at the time if you're not pitching this, yes.

• 31:44Martin Casado?

  Well, no, no, let me, like, logic specifically, which is, I am writing an app. My app is, whatever, some vertical SaaS app for a certain customer base. The answer the app gives is based on logic that I've written historically, right? Like, if I run it on the cloud, the cloud is not producing an answer, it's providing resources. If I'm using your device driver, it's, you know, providing access to a device, resources. But if I'm like, "Hey, large model, tell me the answer here." You're actually abdicating application logic. Maybe you're right, maybe this logic feels...

• 32:16Steven Sinofsky?

  I think what you're, you're, you're almost playing like incumbent in, in the sense of trying to, no, trying to like, decide, "This is abdicating the logic and this isn't." When in fact, like, it, it really was like a huge competitive advantage for WordPerfect. And, and they didn't want to give it up. And they, they fought against it. And the number of people who didn't want to do like, Great clip. And the next example, of course, is the browser.

• 32:41Martin Casado?

  I think.

• 32:41Steven Sinofsky?

  Where people literally gave up, like you in a in Windows or in Mac, you could rasterize anything you wanted. You wanted a button that you pushed and it spun and animated like a rainbow. You could do that in your product. But then the web came along and you're like, wow, I have to use a gray button that says submit. And and that was like.

• 33:01Martin Casado?

  Yeah, I guess I guess it's a point. We do use a bunch of third party things.

• 33:04Steven Sinofsky?

  Well, but it took a long time for those to show up. And so early in the Internet, the magazines in particular, and and the printed media were the ones who absolutely wouldn't go to the Internet because they would not give up their ability to format. And they and this is is another part about the the tooling and where where what happened, what's going to happen with AI is that, like, a huge amount of the productivity software space today is like the preparation of output. Like Office is basically a format debugger.

• 33:32Martin Casado?

  Right.

• 33:32Steven Sinofsky?

  Like, all it is is like 7,000 commands for how to do kerning and bold and italic. And like, it turns out AI not only doesn't care.

• 33:40Martin Casado?

  Yeah.

• 33:40Steven Sinofsky?

  You could ask it to make whatever you want. Like, you could just say, I'd like this to be a double indexed pie chart thing. That's not a thing I just but but you you can do that and it will just figure out something that looks like that and you'll go, oh, cool.

• 33:53Martin Casado?

  Yeah.

• 33:53Steven Sinofsky?

  And this was where to this disempowering and experts and who's not an expert. When when productivity software arose, the big thing about it was that there were people who figured out how to like make like killer charts.

• 34:04Martin Casado?

  Yeah.

• 34:05Steven Sinofsky?

  Like Benedict Evans, like killer chart guy.

• 34:07Martin Casado?

  Yes.

• 34:07Steven Sinofsky?

  And there were people who were like, every meeting started with, how did you make that chart? Like, I could be on an airplane and somebody would be like making a shitty chart.

• 34:15Martin Casado?

  Interesting. So like, in this case, the abdication is like, actually, what's the way to visually represent the data, which is absolutely.

• 34:21Steven Sinofsky?

  Right. And it turns out, well, because like 90% of the people never really got to be expert at doing that task, even though 90% of the tool is about like to even. So what happens each generation.

• 34:31Martin Casado?

  But the programmer didn't abdicate the logic in this case. This is the user.

• 34:34Steven Sinofsky?

  But you know, what's the user, what's the programmer in that? And, and in fact, what the programmer was doing was like, like we would invent a thing called wizards, or whatever, you know, and that would make a whole bunch of choices for you, style sheets, or whatever. And so in a sense, we were making a bunch of choices for the user, which to the experts looked like disempowering the experts who were tweaking all. And so I, this is all like this, there, there's some Steve Jobs quote that he loves about Schopenhauer, how if you've seen the conjurer.

• 35:03Martin Casado?

  The grand continuum of CS.

• 35:05Steven Sinofsky?

  If you've seen the conjurer, it's not a trick anymore. And I really feel like this is like the third or fourth time that this has happened just in, in my lifetime of watching this.

• 35:13Martin Casado?

  Well, so something that's really caught my attention, because it's the most senior people I know, um, is that a lot of very senior developers are spinning up a lot of background agents, like code agents, and they're interfacing at like the GitHub PR level.

• 35:25Steven Sinofsky?

  Right.

• 35:25Martin Casado?

  And so it's not obvious to me why you do a bunch as opposed to one. And it's not obvious to me why you wouldn't interact directly. So it feels like something's going on here, but I'm not quite sure what, and I would love your thoughts.

• 35:35Aaron Levie?

  Well, the, the, my read on it, um, uh, and then the, the, I guess I would kind of sort of throw out, like what then happens next as a result of this, because, because to me, it's actually a little bit of an epiphany on what the future work design could look like in this world. Cause engineers, back to the prior conversation, are just the first to experience this. But I think what, what my, my read from, from talking to kind of similar folks that are like all in on this is, um, is this mix of basically, uh, the, effectively the context rot problem, which is, you know, the more that we put in the context window, uh, the, the more it gets confused, the, the lossier the answers get. And so you have to have some kind of way to partition what, what an agent should work on.

• 36:16Aaron Levie?

  And we see this in building agents internally, which is, you know, the, the panacea that I think we maybe would have hoped for is like, well, you just put a million tokens into the context window and then obviously.

• 36:26Martin Casado?

  Oh, so you're saying this is almost like a counter trend to the AGI is almost like the, is like the opposite.

• 36:32Aaron Levie?

  It, it's the opposite, but it, it's, it's, it only works because the models are so good.

• 36:36Martin Casado?

  Yeah, but you're giving more things, more specific tasks rather than one thing, less specific tasks.

• 36:40Aaron Levie?

  Right. And so, so, so, but like, I think this is why it's happening. So basically the, the craziest version of this is I was talking to somebody who. Who is in startup land and they have, they have, to your point, they have all these subagents. But what's amazing is it maps one to one to each microservice in their codebase. And so they have an agent per microservice. They have effectively a read me for the agent and that agent owns the microservice. And they, I don't know the, the, the specific number, but let's just say you could have, you know, dozens or hundreds of these things going on. And you're, you're effectively mitigating this issue, which is if you just said, here's my entire codebase, you know, go run wild, you know, to one agent. It's, it will just, you know, produce worse and worse code over time. Because it's going to have context rot.

• 37:23Aaron Levie?

  It's not going to know exactly what, what, what you're trying to do in that one area of the microservice, but the subagent model seems to be working for that paradigm.

• 37:31Steven Sinofsky?

  I love this counter pattern. Cause everybody is like, they're going to like, you know, models will be get, you know, smarter and you'll give them higher level tasks and they'll do things longer.

• 37:39Aaron Levie?

  Yes.

• 37:39Steven Sinofsky?

  This is a counter one. I want to tweet that, but you have more Twitter followers.

• 37:42Aaron Levie?

  Oh wow, we can, we can collectively do it. But the, but then, so then the question is, okay, so let's just assume this works in engineering. You, you have this interesting dynamic, which is, well, then that means that like, some of the coding practices will be pretty different in the future. We've talked about this idea of, you know, the individual engineer becomes the manager of agents. So that was already kind of, I think a well-understood path. This is like a supercharger of that, of that concept. And then the, then the question is like, how does that translate to almost every form of work? Because if I am now, you know, the lawyer and working on cases and I can have 20 subagents that all, you know, do a different case and then basically, you know, come back in some kind of task queue that I'm going through, like, obviously one, just the, the sheer leverage now you get is, is going to be insane.

• 38:26Aaron Levie?

  But I do think the way that you, you know, might, might even organize the work and, and what the, what the, you know, workflows within an organization are, are, you know, inevitably going to change as a result of that.

• 38:37Steven Sinofsky?

  Oh, but I mean, I think, I think this just breaks, goes, gets to the, you know, essentially that, that the, the flow in the workflow has been serialized or linearized based sometimes on knowledge, but other times on tooling. And so what happens when, when the tooling changes is you just get this realignment of It's truly serial and what's not like if you're if you're planning an event for a company.

• 39:03Martin Casado?

  Right.

• 39:03Steven Sinofsky?

  Which is still going to keep happening, you know, like, oh, I have to book the venue. I have to invite all these people. We have to create all these materials. Well, they're actually not particularly gated on each other.

• 39:12Martin Casado?

  Right.

• 39:13Steven Sinofsky?

  But if you have an events person.

• 39:15Martin Casado?

  Right.

• 39:15Steven Sinofsky?

  They're gated.

• 39:16Martin Casado?

  Right.

• 39:16Steven Sinofsky?

  And so now an events person can start spinning up all of these these different elements. And then they're going to come back like I've gotten as far as I can on collateral until I get a logo for this event.

• 39:28Martin Casado?

  Right.

• 39:28Steven Sinofsky?

  Like I've gotten as far as I can on invites until I get the date and the time and the venue.

• 39:33Martin Casado?

  Right.

• 39:33Steven Sinofsky?

  And I think there's no, there's no reason why you can't spin off all those in parallel. Because of course, how does that happen today? Well, if you're a company and you use Box and you've done, this is your 58th event. You know, you have a folder called event.

• 39:46Martin Casado?

  Right.

• 39:46Steven Sinofsky?

  And people take the folder and go event 59 and they they make a copy of it and all the stuff in it. And, and well, if you think about that workflow, that's exactly what a series of of different background tasks.

• 39:59Martin Casado?

  Right.

• 39:59Steven Sinofsky?

  Or agents could go do. And so I think the reason that you could be doing all that in coding is, well, there's a, there was a natural. There's a natural way to break that up because there's a bunch of programming.

• 40:11Martin Casado?

  Right, but there's the other side, but there's also a bit of an indictment on the ability of you to give it a high level. You know, it kind of suggests that the human being needs to be, you know, giving them more granular orders. Otherwise, you know, to start a company, you'd issue one prompt, you'd go to the beach for six months. Right, right. Back and you'd have a full company.

• 40:27Aaron Levie?

  Which is a, which is this almost re-anthemorphizing effect, which is, which is like, it turns out we, we did. We did kind of figure out division of labor. We, we figured it out in the context of, of, of a lot of physical, you know, kind of analog limits that we, that we clearly had. That agents won't have, but we now, you know, there, there's no kind of, you know, total free lunch. So you have this context rot issue, which is, which is that you do actually have to subdivide the tasks at some point. So then the question is like, what are the right?

• 40:56Martin Casado?

  I mean, it may not be a context. Like the Occam's razor here is you need to give them specific instructions for, for specific tasks.

• 41:04Aaron Levie?

  Right.

• 41:04Martin Casado?

  And if you give them higher level instructions, independent of context.

• 41:06Aaron Levie?

  Right.

• 41:07Martin Casado?

  They just don't know what you want.

• 41:08Steven Sinofsky?

  Right, and this gets to the formal language part.

• 41:10Martin Casado?

  Yeah.

• 41:10Steven Sinofsky?

  Like at some point, if you tried to use like the Uber. Frontier to get the whole thing done, you have to tell it the whole thing.

• 41:18Aaron Levie?

  Yeah, exactly.

• 41:19Steven Sinofsky?

  And that just seems like a lot of work. Whereas if you have to tell it less because the part of the model you're using knows more.

• 41:27Aaron Levie?

  Right.

• 41:27Steven Sinofsky?

  It's basically a different way of thinking about templates or a different way of thinking about starting artifacts or scoping the context in a generic word.

• 41:36Aaron Levie?

  Well, but then there's this, I mean, it might though be the right architecture in general if you assume that, that, you know, there's, we're never going to get to a point where the model is just 100% perfect, right? And so it might also be the right kind of architecture design because at some point you're going to have, you don't want an agent or a set of agents to go so far down a path when there was a step that it needed to check in with you on.

• 41:59Steven Sinofsky?

  Right, right.

• 42:00Aaron Levie?

  Because there's just the compounding effect of that. So you do need to kind of subdivide the work also because if you do have gating, you know, moments that are going to have a bunch of dependencies, the agent does need to know like, at what point should I roll that back up to the user?

• 42:12Martin Casado?

  Yeah, again, against the, the, the common narrative, now that I think about it, it seems that the trend is, is prompts are getting more complex.

• 42:21Steven Sinofsky?

  Yes.

• 42:22Martin Casado?

  Not less.

• 42:23Steven Sinofsky?

  Right.

• 42:23Martin Casado?

  And we're seeing more agents, not less, doing more narrow tasks, which is almost this kind of counter AGI narrative. It's almost like these are much more specialized and much more deep.

• 42:31Steven Sinofsky?

  Yes.

• 42:32Martin Casado?

  Working with much more specific instructions.

• 42:33Steven Sinofsky?

  And there's like a sort of a history of this, this, wow, maybe we can actually solve it if we're specialized a little more. Like if you take expert systems, at first they thought expert systems would just be experts and they would just know. And then like, by the time you got to the actual published research, like at Stanford, it was like, this is an expert system in deciding on what type of infectious disease you have, as long as you have one of these seven.

• 42:57Martin Casado?

  No, literally, there was, there was a paper, it was like, there's just one digestive disorder that actually is a metabolic disorder.

• 43:02Aaron Levie?

  I do, I do want to though, because you wouldn't want, like, there, there is one big difference, which is somehow the model itself is packing in the inherent intelligence or capability to solve all of these, like, like, like we are benefiting from the fact that, that at least you can build these all on Claude 4 and GPT-5.

• 43:21Martin Casado?

  And that all on a computer too.

• 43:23Steven Sinofsky?

  Let me try to show, like, demonstrate this one with an old person example on this one, which was like, early in the PC era, the, there were words processors and spreadsheets and graphics and databases. And a lot of people were like, why are there these four programs? There should only be one program. And my answer to that, which often involved screaming, was have you been to an office supply store? Because if you go to an office supply store, there's like paper with numbers, and then there's blanks, rectangles of paper, and then there's transparency paper. And like, this has been around a really long time. There's some reason that these are different.

• 44:01Martin Casado?

  Human context.

• 44:01Aaron Levie?

  How many minutes did it take you for you to know Google Wave wasn't going to work?

• 44:05Steven Sinofsky?

  Zero.

• 44:06Aaron Levie?

  Okay, okay. It was instantaneous.

• 44:08Steven Sinofsky?

  It was instant. No, I mean, but this was the thing. There was a product, ancient Mac product, that was lauded by the industry called Claris Works, which was like, oh, it does, you could have a spreadsheet inside of a processor. And my first reaction is, have you seen a person use a spreadsheet? Because their monitor can't be big enough. So they just want as many cells as you could possibly have. And you're sitting there saying it has to fit on an eight and a half by 11 sheet of paper on a Mac. And I think that one of the things that happens is that these lenses that humans bring to specialization, like, really, really matter. And if you think about the medical profession, and you think about going from a GP to the radiologist, to a specialist, to a nurse practitioner, through the whole series, they're

• 44:52Steven Sinofsky?

  each going to look at and use AI in a different way.

• 44:55Aaron Levie?

  So then the only thing would be, okay, so that was, that level of specialization and division of labor emerged over a hundred year period. Right. Alongside tools, but also with, driven by a lot of the physical constraints and realities of how organizations emerged. So the only question would be, in a post-agent world, in 10 years from now, do those divisions of labor look exactly the same? Or do those shift also because the agents collapse, you know, some of the functions? And is there some blurring? And then is there just a new set of roles? Like, clearly, there's a role in a bunch of organizations emerging, which is like, no, I'm just like, my role is like, I'm the AI productivity person. And like, I just like, have a way of, you know, creating all new forms of productivity in the organization with AI.

• 45:40Aaron Levie?

  So like, clearly, we'll have a bunch of new roles, but is our current division of labor going to also collapse in some interesting ways because of AI?

• 45:46Steven Sinofsky?

  Well, I think that like if you actually stick with the medical example, we're just gonna wake up and there's gonna be way more people with way more specialties.

• 45:54Aaron Levie?

  Right.

• 45:54Steven Sinofsky?

  And AI will have created more jobs. And in the interim.

• 45:59Aaron Levie?

  Do you think AI causes more specialization over time?

• 46:01Steven Sinofsky?

  Absolutely, because everyone's good, every human is gonna be way better.

• 46:04Aaron Levie?

  Right.

• 46:04Steven Sinofsky?

  And more knowledge will amount. And I think this is a thing that has really happened with computing that people forget. Like, there used to just be like this morass of marketing.

• 46:16Aaron Levie?

  Right.

• 46:16Steven Sinofsky?

  And R and D.

• 46:17Aaron Levie?

  Right.

• 46:17Steven Sinofsky?

  And all of a sudden, like, just, and there used to just be coding. And then there was coding and testing and design and product management and program management, and usability and research and all of these specialties. And all of those had their own tools. Go to a construction site. I remember growing up, our neighbors built a house. We lived in an apartment and they built a house and there was Clem, the carpenter. And you built a house with a guy named Clem who used all the tools and everything. And now, like, you build a house and it's like this 20-person list of subcontractors, all who have whole companies that do nothing but like put in pavers, you know? And that's what it's gonna be.

• 46:52Martin Casado?

  I mean, there's been a long disaggregation in the history of IT, right? Like, everything in the same sheet metal, then disaggregate the OS and the hardware, then you disaggregate the apps.

• 47:02Steven Sinofsky?

  Right.

• 47:04Martin Casado?

  And then, it was kind of interesting, like, in the last 15 years, we saw the app and like independent functions got disaggregated, right? It's like almost everything became like, like an API would become a company, right? You'd have like the Tutorials, like Auth became a company, like PubSub became a company, et cetera. And so it may very well be the case that every agent becomes like a whole new vertical and a whole new specialization. And then, and then you can actually build a company around it. Like, it may be the case that today, just like with APIs, one company will have a whole bunch of agents. It may be the case in the future that a third party will provide that agent as an independent company.

• 47:37Steven Sinofsky?

  Well, it's so, it's the opportunity, to your point, is really there for that.

• 47:42Aaron Levie?

  Yeah.

• 47:43Steven Sinofsky?

  Because, like.

• 47:45Steven Sinofsky?

  It used to be like the impedance to creating a company and distributing was infinite.

• 47:50Aaron Levie?

  It used to be ridiculous to think that a single API like Auth could become a company. But then, you know, of course.

• 47:56Steven Sinofsky?

  Or it used to be ridiculous to think you could build a whole company out of signing documents.

• 48:00Aaron Levie?

  Right.

• 48:00Steven Sinofsky?

  And like, not just the whole company, but then all of a sudden you realize, wow, the addressable market for that is huge. And it's way bigger than signing because of all the stuff that got done that was just

• 48:11Aaron Levie?

  Right, surrounding.

• 48:12Steven Sinofsky?

  baked into a company, causing headcount and waste and fraud and abuse.

• 48:16Aaron Levie?

  Well, I think you can kind of underwrite thousands of these companies emerging. So Jared Freeman had a tweet about basically like go deep on a workflow. You know, basically do the job of some part of the economy payroll specialist and then build an agent for that. And it's not obvious that there's not literally like a thousand of those. So by every vertical and every line of department.

• 48:42Martin Casado?

  I just love this because this is like literally the anti AGA. It's basically following like the long arc of computer science where as the market grows, the level, the granularity can create a company.

• 48:50Steven Sinofsky?

  Well, it's also economic growth. Like take that payroll example. Like today, just like Salesforce, which is always my favorite example, like the idea of having a productive Salesforce used to just be a consultancy.

• 49:00Aaron Levie?

  Right.

• 49:01Steven Sinofsky?

  And the only way you could ever fix it was hiring a consultancy to show up and analyze whatever he does and then do a report that says this is how you need to reorg and it usually meant go the opposite of whatever you have. And then they would leave. And then, you know, people tried, but there was no cloud. So to build like CRM, you had to do all that consulting work and then roll it out. And then it was static and you couldn't maintain it.

• 49:25Aaron Levie?

  Right.

• 49:25Steven Sinofsky?

  And then all of a sudden there's like, oh, here's Marc Benioff and here's a whole way to do all this. And not only that, the people actually like it.

• 49:32Aaron Levie?

  Right.

• 49:32Steven Sinofsky?

  And they think they're better at selling, selling because they're, they're using their phone and they're putting in a few notes about this client, which helps everybody. And I think that's what's really going to happen with all this. And so suddenly something that looks really, really small.

• 49:46Aaron Levie?

  Right.

• 49:47Steven Sinofsky?

  Becomes like a whole thing because there's no problem with distribution. There's no problem with customization. You know, we'll actually have ways to solve security and privacy. See, just like we solved reliability and things like that. And I think it's just, I mean, look at, you know, the stuff that you're a world expert in, in the stack of internet technology, of networking technologies. I mean, you would have asked me 15 years ago, would CDN be companies? I never would have, I'm like, I don't see make any sense. Like, how could you have a company that's a cash?

• 50:13Aaron Levie?

  Yeah, I think that people are probably way too afraid of the model providers kind of eating them. And I think it was, I think it was basically a phenomenon in the first wave, which was if you were just doing like basic like, like if you had figured out that you could do something on GPT, you know, two and three, where it was a text interface that produced more text, like, yes, ChatGPT ate you. Like, like that, that clearly happened.

• 50:38Steven Sinofsky?

  Yeah.

• 50:39Aaron Levie?

  But basically, since then, most enterprises want kind of applied use cases for AI and AI agents. And so, so it's not obvious that the current crop of companies, if you're doing AI for healthcare, if you're doing AI for life sciences, if you're doing AI for financial services, if you're doing AI for coding, at the right parts of the stack, AI for coding may be the one asterisk area, which will be hyper competitive, simply because the model companies like don't want to use somebody else's product to build their own models. And so that kind of almost forces them to get really good at AI for coding. But with that as the one kind of, you know, exception, I think basically, we're just in a five-year period right now where you're going to have to build agents for every vertical, every domain. And there's a playbook that's starting to emerge of what that needs to look like.

• 51:21Martin Casado?

  I mean, so I think there was kind of a technical head shake that happened early on, which was pre-training. So the pre-training really was a 10 out of 10 technical innovation. I can't tell you like two years ago, if somebody was like, I had a friend that was building like their own aging model, post-training aging model, like we're going to make it so good at aging. Like, you know, this is a, this is a text to image model. And they wanted to make it so like old people looked really good at it. And then, of course, the next version of like mid journey, whatever comes out and it does a better job of it. And the thing with pre-training was you're just kind of consuming all of the world's existing data, you're draining all of that energy and it perfectly generalized, right? But it feels like technically that's passed and now we're more in post-training and RL, which is a lot more domain specific. So

• 52:05Aaron Levie?

  Well, in the moment that you have access to some set of data that is only

• 52:09Martin Casado?

  Exactly.

• 52:09Aaron Levie?

  Just for that enterprise. And so who gets permission to access that data? Who gets permission to do the workflow on it? It's going to be applied companies.

• 52:15Martin Casado?

  Yeah. So yeah, it is. If we had an infinite number of tokens, then the models would just continue to generalize. But it's pretty clear that that's not happening. And so now we're going into which we all understand very well, which is now companies have to choose which domains to go into. And they've got to solve the long tail of problems there and get access to the data, etc.

• 52:32Steven Sinofsky?

  And I also think that there's that the shadow having been the shadow, the shadow cast by large companies over, we're going to put you out of business and stomp you. It's ridiculous. And it has never in any technology wave lived up to the fear that people have. Look, if you built a new word processor in 1995, you were an idiot. Like, that was not the thing to go build.

• 52:53Aaron Levie?

  Yeah.

• 52:54Steven Sinofsky?

  You know, and, and, but, you know, there was a time just 10 years earlier where like companies built standalone spell checkers. Like, it was just a thing. You went to the store and you bought a spell checker. And, and like it had more words than the other spell checker. And, and so the, the thing is that that's not being said now, which we should do a whole one on is like, what is the actual platform?

• 53:15Aaron Levie?

  Yeah.

• 53:15Steven Sinofsky?

  Because you like, it's very, it's all well and good to say that the, the large models will go subsume every application. The thing is, the minute they start doing that, Nolan will be in their platform.

• 53:26Aaron Levie?

  Right.

• 53:26Steven Sinofsky?

  Because like, like no developer is going to sit around and say, if you're going to subsume me.

• 53:30Aaron Levie?

  Right.

• 53:31Steven Sinofsky?

  Then, and, and this is, there's a phrase that it's Sherlocking on the Mac and the Apple world.

• 53:36Aaron Levie?

  Yeah.

• 53:36Steven Sinofsky?

  To this thing. And so it does, it has a real chilling effect. And that's one of the things all the model people are going to learn very, very quickly.

• 53:42Aaron Levie?

  There's a chilling effect, but there's also just, I think there really is just a problem of like, it's hard to go deep in 50 categories. Like you just, you just can't.

• 53:49Martin Casado?

  Model pre-training, I think everybody's scared because pre-training was actually the one thing that was good at that. And then now they have to actually, yeah, I agree.

• 53:56Aaron Levie?

  You do have to like, at some point it becomes purely just an execution issue, which is like, I like, I, I don't know how anybody would set up a company to be, to be able to beat 50 startups across 50 different domains.

• 54:07Steven Sinofsky?

  No, it's ridiculous. And in fact, like, it's only good. Because the, what happens is, is that the, the, the big company raises, the, the big company raises the awareness of a whole category. And then you just swoop in and you go. To them, I'm just a feature.

• 54:23Speaker 02

  Right.

• 54:24Steven Sinofsky?

  But to you, this is my whole life.

• 54:26Speaker 02

  Right.

• 54:27Steven Sinofsky?

  And you're gonna... I always come back. There's a whole company that just signs things.

• 54:31Speaker 02

  Right.

• 54:31Steven Sinofsky?

  Like, I can't... I cannot believe there's a whole company that just signs things.

• 54:36Martin Casado?

  I have so much to say about this topic. I mean, even minimally, if you graph like the cost to produce... So the willingness to pay for an inference versus the cost to serve it. Something like, for most companies, for most spaces, 20% of the inference is or 80% of the cost. So like, actually, the problem of the application is just to choose those ones on, which tend to be more domain-specific.

• 54:58Steven Sinofsky?

  Yeah, yeah.

• 54:59Martin Casado?

  This is the problem of inviting the three of us on here, which is like...

• 55:02Steven Sinofsky?

  We just opened up like the next 10 hours.

• 55:04Martin Casado?

  It's just getting us to shut up is the trick.

• 55:06Speaker 02

  Yeah. Guys, thank you so much for coming on. This is fantastic.

• 55:12Speaker 02

  Thanks for listening to the A16Z Podcast. If you enjoyed the episode, let us know by leaving a review at ratethispodcast.com/a16z. We've got more great conversations coming your way. See you next time.

• 55:25Speaker 02

  As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a16z.com/disclosures.