Aaron Levy (2:28)

It just keeps getting more vertical and I thought I could kind of tame it, but it didn't work.

Steven Sinofsky (2:33)

And is that just token issue or a parameter number of parameters issue.

Aaron Levy (2:38)

Too many parameters. Too many parameters.

Steven Sinofsky (2:39)

Okay. I have the same thing but in reverse.

Aaron Levy (2:42)

Okay, so you listen, you have a distilled model there.

Steven Sinofsky (2:46)

Yeah, my, I run local so we had a lot of. There's been a busy week of things but we want to bubble it up a little bit and just start talking about where, where things are heading. But I'll, I'll let. I just kick it to you Aaron, and you start where you are. Are the most excited this moment because you have visited a ton of customers this week and have learned a lot, you've shared a lot on X, but I think you're the most in the trenches CEO who is really talking to customers every single day in the enterprise, which is what the three of us tend to look at the most.

Aaron Levy (3:24)

Yeah, I think my, it feels like my job these days is just bring reality to the valley and then bring the valley to reality as as much as possible. And it's a, it is a kind of a crazy divide that, that exists at the moment.

Steven Sinofsky (3:38)

Step back. I actually think it's a super interesting. What, what is it? What's the gap caused by?

Aaron Levy (3:45)

What's caused by. Yeah, well, I think the gap and Martin, I'm sure you see this, but I think the gap is caused by the styles of work that exist in Silicon Valley and in engineering roles versus sort of the rest of the world. So, and we've talked about this a couple times in, in different forms. But, but the technical aptitude of an engineer is just insanely high. The level of wired in ness to what's going on on the Internet is insanely high. The ability to use your own tools and make your own choices is insanely high. And when things go wrong with the systems that you choose, you can just quickly debug them and then make them sort of work for you. And then obviously you have all the benefits of just the models are really good at code and the work is verifiable. So you have like five or ten things that make agents work in an enterprise context for engineering or at least even a startup context for engineering. There tends to be a gulf between the way you work that way in engineering and the rest of knowledge work. And so a lot of what I see is trying to figure out how do we kind of bottle up all of the greatness that is what we are seeing from coding agents or we're seeing from agents that can use computers to how do you bring that into the enterprise where the workflows are quite different, the users are less technical, the data is much more fragmented, the systems are much more legacy. And so that tends to be the divide. So it's not even that we're talking past each other's kind of classic government versus industry. It's just literally like there is just a pure workflow and technology set divide. And that's why it's going to be a number of years for this sort of diffusion to roll from what we're seeing in Silicon Valley. What we're seeing is tech startups all around the world into the rest of knowledge work.

Steven Sinofsky (5:29)

Martin, just to build on that, you have a ton of experience in big companies. One of the other issues though is scale and the way the difference in scale that Silicon Valley operates at the startup level versus everyone else.

Martin Casado (5:43)

Yeah, I also think that, I mean these secular trends like the Internet was like this actually start with individuals and big companies tend to make decisions centrally and this is one of the fastest growing secular trends. So like there's probably a lot of individuals in big companies that are doing it where like the big companies themselves don't know even how to think about it. And so when you hear stats like oh like MIT had this stat like 95% of AI efforts in big companies fail, like that's clearly silly because I am sure everybody's using ChatGPT very effectively. What they really should be saying is, you know, whatever, like I said in these boards too, so the board goes to the CEO. What does the board say? We need more AI. And what does the CEO said? Oh, okay, I will get like a consultant to do more AI. And then they have some centralized project that nobody knows how it works, they haven't aligned their operations and those things will fail. And so I don't, you know, when we say scale often we, we think about things like system scale or number of people. I think the secular trend is scaling wonderfully which is being reflected in the numbers of these companies. But organizations don't know how to adjust these kind of, you know, age old process that have been worked on for a decade around data and governance and operations and compliance, etc. That's kind of right now where I think Aaron is right between the secular trend and the organizational decision body. And this is something that we actually track very closely because we're starting to see now, I would say in the last few months, finally some real kind of inroads into the Enterprise. But it's tepid because. And the last thing I'll say of this, one of the reasons is there's a lot of skepticism because the board wants AI CEO. AI failures have created some amount of bruising, which is, you know, requiring these companies get past it in order to do kind of the second go at it. And so I think this is exactly where we are.

Aaron Levy (7:39)

Yeah, I 100% agree with that, which is that it's good to start with agreements, because we know how quickly that fades.

Martin Casado (7:46)

We'll disagree the rest of the show.

Aaron Levy (7:47)

Exactly, exactly, exactly. That's the only time we're going to agree. I think maybe one more point on the board for agreements, maybe you guys would agree. There's also this very interesting dynamic. I would say this is a minor one relative to everything else. It's probably 5% of the problem. I think it'd be more fun to talk about the real problem. But there is a fun kind of. As an aside, there's a fun dynamic where you go to an engineering team classically for the past, and, Steven, you can take us back in history on this one. And one of the easiest ways to stall a project was just getting the architecture fights on, what language to use, what architecture path to go down. That could take months and months to kind of work through as your teams work through that. Because of the pace of change in AI, you actually have this incredible dynamic where the labs are obviously leapfrogging each other so frequently, but with not the exact same paradigm of how you should deploy agents and how they will work. And is the agent harness in the computer? Is it outside the computer? Do you run it in your cloud? Is it hosted? What tools does it have access to? Like, we are like, this is not a point where these are completely fungible technologies. And so that actually creates a bit of paralysis, because now, as an enterprise architecture team in the real world, you're like, man, like, what. What horse do I want to, you know, kind of get behind? And which architecture path do I want to get behind? Because I've been burned by doing the wrong thing in AI maybe three or four years ago, and I went down some path that now is deprecated or not the right strategy anymore. So. So to some extent, the speed of our change in tech actually reduces the ability for the tech to get diffused into the really, really important workflows, because now you have a lot of paralysis in just making decisions. So I actually think it's kind of fine because there's still so much upgrade work. People need to do in their infrastructure and their systems and their data. But this is kind of an interesting dynamic where I'll go have conversations with CIOs and their AI teams and I'll say, hey, what are you using for your chat system or your, you know, core agent orchestration? And they'll say, yeah, we're in the middle of a debate between these two or three paradigms and it's, and, and you, and you hear that across almost every single customer because there is a little bit of a nervousness of like who do you get in bed with and how, how much do you sort of, you know, fully lock yourself into one particular path? And we also know that, that if you don't lock yourself into a path, it's always then, then you're building for this sort of duality which is, you know, also takes a lot of work architecturally.

Steven Sinofsky (10:14)

Oh, sorry.

Martin Casado (10:15)

Actually, I'm sorry, I, I, I hate to jump in Steve. I just like there's a, like so Aaron is totally correct and there's a, there's a very specific instance of this playing out in product companies right now. And I'll tell you what it is. So, so, so software product companies, you know, circa six months ago, they viewed integrating AI was like you're actually integrating it into the product, right? So everybody was like adding like whatever this chat feature or like the, you know, and so it's kind of like this fusion or this hybrid model. What we're seeing instead is instead of viewing AI as software, just view it as a user. So instead take your product, make it a CLI tool and then have the AI be an agent that actually uses it. So you're not fusing the two, you're just making it more useful for AI. This is a very, very significant architectural and mental shift, right? So we started as pure product and then we didn't quite know what the end thing looked like. So we created this, you know, this, you know, AI software hybrid that hasn't worked. And now we're kind of going to the agentic model which basically means the agent is going to be whatever, it's going to be cloud code or whatever. And then my product now just should be something that consumed by that and like that's the actual modality. But you know, within a year now you've had to re architect your software twice. And so I think no matter many places that you look in the industry is having this dilemma of actually trying to figure out what the final form looks like. And Stephen, you will remember, remember all the hybrid Versions of cloud, remember like, you know, like remote desktop and all these things. Like, I think we're kind of like speed running that evolution to the final form.

Steven Sinofsky (11:51)

Right. And I, and I think that, that people in Silicon Valley don't quite appreciate when a big company says well, we have to map out our bet that we're going to make because like that just seems stupid. And you know, if you have, if your job history is, you know, five two year stints at startups that went from seed to series A to acqui hire or something.

Martin Casado (12:16)

Yeah. He didn't learn anything.

Steven Sinofsky (12:17)

You, well you, you never, you, you don't, your frame of reference is not, you know, picking an accounts payable system that's going to last 40 years. Yeah, I actually, I have like all these visual aids today. So here's like the ultimate, the ultimate engineer if you're in Silicon Valley is lower, lower, lower, lower, lower, lower.

Aaron Levy (12:38)

Yeah.

Steven Sinofsky (12:39)

Is Guilfoyle. And Guilfoyle is like, I, I don't want to talk to anyone.

Aaron Levy (12:47)

Yes.

Steven Sinofsky (12:47)

And I will just write the code and you go do your thing. And the, the thing is, is that you, you have people in enterprises that are saying I'm going to use the model and do my thing, but they're only, they're going to hit a wall at integration. And this, the thing that's not different about AI and that agents don't fix that nothing fix is that any enterprise of a thousand people or more or that's older than 10 years is just a mass of stuff that's sitting there waiting to be integrated. And you can't just say it's going to integrate. AI actually doesn't help to integrate anything. And so even if you change everything, the people say, oh no, if you make it an agent, then it can just go ahead and, and, and be a user. But if you're a user, like if you've ever called customer service for something like literally you get bounced to another human if the system that you're talking to doesn't work. And they're like, well that's a manager or no, you're, you're talking about payment, not reservations. And, and so like we're, we're, what I think is so exciting is that now we have proof of this technology that everybody likes it. I mean, you see all the people who don't like AI are saying, look at what's happening in law firms because people are seeing hallucinations and is ruining legal cases and all this. And, and the reason that's happening is because the 25 year old associate is the one using AI successfully already and had been using it for a year.

Martin Casado (14:19)

Steve, it's actually. Steve, it's actually a little worse than that. Where is right now many companies are incentivizing people to use AI by counting tokens.

Steven Sinofsky (14:28)

Oh yeah, yeah, right.

Martin Casado (14:29)

And so I'm not going to say the name of the guy. I spoke to someone yesterday who worked for one of these large companies that famously does this and he's like me and my co workers have agents do useless tasks just so that we can. No joke. No, no, no, totally.

Aaron Levy (14:43)

Well, you get whatever you measure so.

Martin Casado (14:44)

Yeah, yeah, that's right. So like it's like the extreme form of what you're saying, Steve.

Steven Sinofsky (14:48)

Oh yeah, yeah, yeah.

Martin Casado (14:49)

People that are like being fake productive and producing a lot of, you know. Yeah, you could say perhaps problematic artifacts just because they're, they're using these models.

Steven Sinofsky (15:03)

When the Internet happened, all of a sudden every company needed websites. And so like a very famous moment in time was not too long ago when every internal team had like a team website and they went out and they got like a vendor to write HTML and to create their site and then there was a team. But like there's nothing dumber than having a team website at a large company because a team gets reorganized like six months later. And so companies were just filled with like, with thousands of these. Dead web is what the expression was. But I, I think. Go ahead, go ahead.

Aaron Levy (15:37)

No, no, but we should, we should drill into your integration point because I do think this is something for, you know, sort of some reality to settle in, in, in the valley on, on the real world's sort of journey to fully being identified and what take and what that's going to look like. And your point about being passed to the different human based on the role that you needed to interact with. Agents basically don't have any. There's no real exception yet for the agent having the same problem because basically as you pass through a different human, it's a different set of access controls that that human has. And if an agent can sort of bypass any of those steps, then that's how you instantly get the security risks that you need to kind of pass through those steps so that way you don't accidentally get to the wrong piece of information and verification. And so there's a lot that you need to kind of build out for agents to be able to go and work with all these systems. And we've talked about this, but like most legacy environments don't have the most Authoritative access controls. So you're always as a human going and saying, hey, Sally, can you share that thing with me that I don't have access to? Or hey, Bob, what's the number inside your data system for this question? And so if agents just get the exact same permissions that you had, then they'll just run into these walls everywhere and they won't be able to complete the process. And unlike a human, they're not going to know to go talk to Sally or ask the question of Bob. So they're going to just be kind of stuck. So what's going to happen is you're going to have a lot of agents that don't have access to the right data. They're kind of working through systems that are not the real sources of truth for the information. They're getting the wrong number, they're getting the wrong document. So this is the real work that enterprises have to go through right now. The good news is that it's actually a great time again if you're a startup because you can just, you get to know all the problems right out of the gate so you can design your organizations, you know, to try and avoid this. But for big companies, there's real work that goes into how do I upgrade my systems, how do I modernize my technology environment, how do I make sure that, you know, agents will have access to the right data, the right documents, the right context to be able to do their work. That's sort of the work ahead. And there was this kind of headline of OpenAI working with in Codex, working with Accenture, Deloitte, all the major system integrators. And there were some kind of snarky comments online around it that I was fascinated by because it sort of showed how maybe great that divide is from the rest of the world versus those in tech. Because to me, it was like the most obvious announcement of all time, which is a large enterprise is going to have to go through the change management, the systems implementation, the integration of technology for these agents to be able to go and work. And so there was this sort of like, you know, people thought it was somewhat ironic that, oh, we need people to implement the agents that are going to go automate the people. And it's like, no, that's exactly how it works.

Steven Sinofsky (18:31)

Right, right.

Aaron Levy (18:32)

You actually do need to do lots and lots of work to be able to be in a position where agents can actually go and help you do, you know, any of the automation. So, so that is. And that's going to be. There's going to be businesses that are doing that for decades. Like it's going to be an incredible opportunity for this kind of next generation set of firms as well as existing ones that lean into that.

Steven Sinofsky (18:52)

Let me throw this out there. Well, first, I think the other thing that people shouldn't celebrate when those fail because they will fail because they're, as Martin was describing, they're a lot of them are going to be these sort of top down mandates where they pick like the most acute problem in the company and think oh, AI is going to go solve that and the IT people are going to be like, oh God, that's yeah, don't do that. That's, that that's the worst system to try to do that. But the CEO or CFO or whatever is going to be obsessed with solving or the most likely the customer service person will be obsessed. But, but I do think if I were, if I were advising a startup specifically in order to sort of enter the enterprise space in that way, definitely would be thinking about not just like building a company that step one, I only work with all the headless SaaS software that's out there because there just won't be any like the, but the, the thing you can do is structure the value that you offer. And also this applies to what you go do in a company is it's really a fork and the fork is, is this an agent that is seeking information and presenting it to an, to some human or is this an agent that's supposed to go act and do something? Like is it, is it acquiring or is it doing? Because if it's, it turns out that's how what happened with the Internet, the Internet got very, very valuable when the first step was just providing access to things to people.

Aaron Levy (20:16)

Yes.

Steven Sinofsky (20:16)

And like all of a sudden all the sites that were like that literally did integration, like hey, I need expense reports but viewed by department or I need to see our current inventory status across like the two companies we've acquired. All of a sudden the web became the integration point. And so I do think that if you just show up first and just say, hey, we can actually use agents to learn stuff about what's going on in a company. And in particular because you're here, Aaron, like learning across the files becomes way more possible than it ever was before. In fact, AI might be the first time that inside a company search can actually provide immediate value because the web just wasn't structured to deliver those results. And then you start to think once you could bring them all together then you can add like an agent that has an approve button or a reject button or something like that.

Martin Casado (21:12)

Let me just try and provide finally the point where I get to disagree.

Steven Sinofsky (21:16)

Oh, we're in trouble now.

Martin Casado (21:18)

No, no, no.

Steven Sinofsky (21:19)

Well, you can get invited back. You're invited back. So good.

Martin Casado (21:21)

No, no. I think this is a very legit view, but it's not the only view. And in light of AI, I think it's not the only kind of compelling view. So here's, Let me just try and rephrase. So the current view is we've got like AI is software, it works in a different way. We have a current set of systems and we have to integrate this new type of software with our existing system so that it can get access to data, it can do things, but in a safe way. Right. So here's to the kind of end to end argument of why this isn't about evolving software systems. The end to endom argument is these LLMs are non deterministic, they are smart, they deal with the long tail of complexity. And it turns out those are all things humans do too. And we've spent 40 years building interfaces, processes and design to deal with messy humans. And we know who to access and we have access control. And so if you have the mindset that an agent is more like a human and you hire the agent, you give it its own email address, it can access documents like humans can, it can log in, it can request the things that it needs, needs, then it will be drafting on all of the process that we've put in place for humans, not for software. And so I would just encourage us as we have this discussion. Like, listen, I grew up like you guys in software. I always think of every system like software. But these models don't integrate well with software. Actually, I think it turns out, and what we're learning as an industry is if you view them more like humans and you draft on the mechanisms we put in place for humans, they're much easier to integrate.

Steven Sinofsky (22:57)

Well, that's.

Martin Casado (22:57)

And I think we're seeing, I love that point.

Aaron Levy (22:59)

I think we agree with that for sure. I think the issue is humans have a bunch of extra benefits that the agent doesn't have. The human has a lot of context that it gets for that they get that we get for free by virtue of we can keep track of the myriad relationships that we've built in our organization and the person to tap on the shoulder when we need something done or we need to get information that's not documented in a company yet in a way that the Agent can just sort of draft on. And so, so I think we all would agree that you can't treat this like software. You treat these as people accessing systems and tools. But they are at a, they're both at a massive advantage that they can work in parallel at infinite scale. And they're at a disadvantage in that they don't know who to tap on the shoulder.

Martin Casado (23:39)

Hey, listen, Aaron, I am all for agent onboarding. Like, you know, Agent comes and it goes to orientation and then the CEO gives it the culture discussion. And then every. I'm not kidding.

Aaron Levy (23:52)

No, you're probably right.

Martin Casado (23:54)

Every department does their pitch. Like this is what we do. I mean, I think, I actually honestly think given given the technical nature of these agents and how much entropy they have and kind of how unruly they are, we're gonna have to go through the processes that we've refined around humans. Yeah, 100%, because humans have all of those things. And so I just, you know, it's more about providing schools for them than somehow building some, you know, fancy index database.

Aaron Levy (24:23)

Totally agreed.

Steven Sinofsky (24:24)

I mean, of course what I love about, about that is you just keep going with the analogy because what that is, is it's the same argument that humanoid robots will, will be the best kind of robot, which is we have a whole world designed for humans. And I, like, I, I saw at the Consumer Electronics Show, I saw this robot go into an elevator and then there was a, a button pushing robot on the elevator. So because the, the robot was a tiny little thing that like a Roomba on the floor, it couldn't push the button. So the same company that invented that robot invented a device you buy for the elevator that pushes the button. But then I asked why did you need a device to push the button? And, and it was very interesting. They said because the elevators don't have systems that they can hook into as a robot. Like, there's no WI fi, press the button in the elevator capability.

Aaron Levy (25:17)

Yeah. So no API for that.

Steven Sinofsky (25:18)

There's no there, there is no headless version of the elevator.

Aaron Levy (25:22)

Yeah.

Martin Casado (25:22)

And that's actually a great metaphor for like the problem that I think that we're actually solving in the enterprise with these agents, which is we just, you know, we have two types of systems, those for humans and those for software. And these tend to be more like humans. So we should draft on those as much as possible rather than try to retrofit them.

Aaron Levy (25:39)

Well, well, and so, so the big news last week was, was, I think, you know, Salesforce, I don't know if they surprised people or not. But, but I mean based on the reaction, it seemed like it was maybe a surprise. They, they went full headless and they basically want to be used everywhere across all of our, all of the different agents. And I see that as a little bit of a bellwether because I think as Salesforce goes, so does a lot of enterprise software. And I think a lot of people are going to try and have to figure out what is the new business model in this headless world. Do you charge a little bit of a small, just API tax? Is there a seat for the agent? So there's obviously some work to do with that. And Steven, I saw one of your tweets on some of the complications there. But, but I think as a moment it's a big deal because I think it's a recognition that software will be running in the background. It always has for machine users and applications and now it is for these sort of probabilistic machine users or non deterministic machine users. And what's cool and where I think this gets pretty exciting is as soon as I saw that announcement, I had five to 10 personal use cases where I would need, you know, the headless version of Salesforce, because I'm always doing just a tremendous amount of customer related intelligence work. I'm going into a meeting, I need some information I need to do, I'm going into a city, who should I be meeting with? And so if you imagine, you know, being able to run compute in the form of agents across all of your data systems, like the use cases become, you know, pretty wild around what that opens up. So I think this gives, I think this gives a lot of software platforms, all new use cases that they can tap into where again you were normally constrained by the number of people on these platforms, but now the headless user can be, you know, 100 or a thousand x the scale of those human users. So this is I think an exciting moment because as you have more of these agents running around and the headless software modes, you just have, you know, way more use cases for these tools.

Steven Sinofsky (27:34)

I also think, I think on this one.

Martin Casado (27:36)

Please, please.

Steven Sinofsky (27:36)

What's so, what's so super cool is, is that of course the first step is doing exactly what you described, which is just looking stuff up. And so the, the most interesting thing is using this notion that an agent is just a, an entity. It's mo. It's incredibly obvious to me that it's another license. Now it might have a different license model but it actually, it has to have an identity. Like when you go look something up in, in the box CRM system, I, I don't know if it's salesforce or not. When you use the box CRM system it has to be a person like with a certain amount of access rights and you, presumably as CEO you might have access to a bunch of stuff but also there's a lot of ways that they actually don't want you to have rights at the right time. Like you might be able to look and see who is on the account but you don't need the up to date quota of those salespeople and stuff. And that might be HR sensitive and you should probably have some other level to go see that. But as you go down the org, the agent is never going to have more permissions than the person who's getting it to go do something. And in fact it's just going to be like a peer to somebody else in an organization because otherwise you have all of these issues where the peer, where a human can just say oh get me the super smart agent that knows everything that I'm not allowed to know. And to the, in the mod, in the IT architecture sense, what's so fascinating about that is you have to build, you, you can't let the agent get the results and then try to figure out what works or not. But all the points that Martin made about, about who, about the, the LLM stochastic model, which is you're not gonna be able to figure out. It's not like a record in a SQL table that you could just apply ACLs to. It's, it's actually like it could be words in a sentence or just the number that shows up. And so I actually think that whole discussion about headless for me made the SaaS apocalypse seem even dumber than it was already. And it was already dumb. So like it was like at first it was dumb and then I'm like oh my God, it's actually much dumber than I thought it was in the first place because you're just going to have this explosion. Now someone might come up with a very clever pricing scheme and that agents, you know, somehow cost less because maybe for the first five years they're read only or they, they're always tied to a person or something. But it is another seat, there is no way around it. And like if you're a SaaS company, you're crazy to try to say oh just use the credentials of another human. Like that's just, that would be like bad security practice from the get go.

Martin Casado (30:07)

Exactly. So actually in fact, so this is playing out in many domains. You can even make the argument that like a headless SaaS doesn't make sense. And here's the argument, the argument is, let me give you an example. So if you use openclaw, do you know why you use a Mac Mini with openclaw?

Steven Sinofsky (30:26)

Well,

Martin Casado (30:28)

for two reasons.

Steven Sinofsky (30:31)

Is that what you're getting at?

Martin Casado (30:32)

It's not, it's two things. It's number one for iMessage.

Steven Sinofsky (30:37)

Right, right.

Martin Casado (30:38)

It's for the integration because there is no headless version. So you're just going to like use it. And then the second one is very interesting, which is if you've tried to use headless browsers with agents, the problem is all of the websites have anti scraping measures, so they don't work. And so the reason you use a Mac Mini so it can actually use Safari proper. So to do anything headless kind of assumes that like the entire Internet is going to go headless. When I think all of these models, like all of the data is humans working on the actual apps that are not headless. Like that's all of the data anyway. So I think these models are going to be very good at just using apps like they are today. And we're already seeing this happen. And rather than the headless versions, the non headless versions are what's actually being used. So you could argue that it's just Salesforce like not a headless, like. Yeah, it will go to a.

Aaron Levy (31:30)

Wait. Actually, just to clarify, do you literally mean the agent goes to the browser?

Martin Casado (31:34)

Yes.

Aaron Levy (31:35)

Oh no, I'm taking the other side on that one big time.

Martin Casado (31:37)

Yeah, yeah, but he just let me, let me simplify the argument so we could actually have it. So today if you use an agent like nanoclaw or openclaw, you could use a headless browser. Let's say I wanted to look up the value of my house on Zillow. The headless browser simply doesn't work because Zillow's so tired of people scraping it. So it will detect headless browsers. Totally. So the thing that works is you pop up Safari and it uses a proper Safari directly. Right. And so then all of a sudden it works. And so.

Aaron Levy (32:08)

No, but I think, I mean I would just say that software that has a good API, the agent would absolutely prefer to use the API and then you pop into the browser the moment that you run into some execution problem with it.

Martin Casado (32:27)

Set is a fantastic long term computer science software guy. However, these models are trained on data and RL environments from existing software that didn't have those APIs.

Aaron Levy (32:38)

Yeah.

Martin Casado (32:39)

And right now, if you actually look at the adoption and the use of these agents, they look far more like what a human would do than what like a program would do. So maybe you're right. But a, that's not what we're seeing. And you can honestly make the end to end argument when it comes to data and all of the controls in the Internet, to Steven's points, all the existing controls to just be like, these are going to actually have the same actions as humans.

Aaron Levy (33:04)

Well, the APIs, most that, I mean, I think the APIs of any software provider will follow the same access controls of, of whatever the, whatever the user is that. That is.

Martin Casado (33:14)

Right. But they have to rebuild. But they have to re. They have to rebuild it. I mean, it's like, it's like you've got this existing app and all the, the models trained on all of the people using the app.

Aaron Levy (33:23)

Well, well, on, on that point, and it's a totally fair point, but I would, I would guess over time you're going to have really, you're going to have very, you know, kind of accurate, rigorous data sets for, you know, for models to be trained against the MCPS of every SaaS platform. The APIs of every SaaS platform. Already it's in the, you know, they're already training against all of our documentation on our, on our products and our APIs. But, but I just think to me it's more of just an inefficiency of navigating through pixels versus just, you know, you can just, you can just add it. Yeah.

Martin Casado (34:01)

And adage in Systems, Aaron, is that layers never go away. They just get layered.

Aaron Levy (34:09)

I'll support your point. 50%. Oh, well, there you go.

Martin Casado (34:12)

Yeah.

Aaron Levy (34:14)

I just think if you need to do a search for a document, our search API is going to be a faster way to do it than clicking through.

Martin Casado (34:22)

Oh, 100%.

Podcast Host/Narrator (34:23)

Yeah.

Steven Sinofsky (34:23)

Right.

Aaron Levy (34:25)

But to half support the point, like the new codecs, the computer use that on the desktop is just insane. And I mean, Stephen obviously knows everything about how it would work. When I saw my ability to move a mouse and then this other sort of mouse moving and clicking things, I was like, I don't understand computers anymore.

Steven Sinofsky (34:43)

Right, right.

Aaron Levy (34:44)

So there is a pretty. And to your point, Martin, my first instinct was to use it for something where I know there's no available API, so I did actually use it right away for a thing that I don't have access to, the API and an agent over Time is going to probably have to figure out is there an easy MCP or CLI for this action. And if not, then I'm going to pop into some kind of cloud browser or cloud computer or maybe local thing that I can sort of parallel track and then go and execute that. So that does seem like a reasonable architecture. But I still think that I'm going to pound the Salesforce API massively in headless mode, just because that'll be an efficient way to go look up reference.

Steven Sinofsky (35:24)

Yeah, yeah, I mean it. I. I think that you're. You're sort of. I think you're both saying the same thing, but there's just a time to. But, no, but there's a time dimension. And I, I think like, there was a moment at the Internet that, that I really was thinking about when, when you guys were. When I was seeing the timescale difference. What. Which was suddenly the 8 million quillion pages of how to use Word in Excel that we had written over the years that we posted on the Internet. We had used to ship them with the product. And people would have them on their hard drive, not connected to anything, and they would say, like, how do I make a nice chart? Or whatever. And it never worked. They could never find the thing that they wanted. But what happened with the Internet was the net result of everybody finding. It caused us to make better documentation, but it also caused Google search to be better at finding the information that it needed, which then completely changed the way that we thought about doing documentation. And I think that with Headless, especially for the kind that's just finding things, it's going to really change the. The way that information is exposed. And so the way that Salesforce sees today of exposing a headless API is I'm almost certain if I were to go look at it, it's going to look like the developer API in front of a. Behind a clique, and it's going to look a lot like that. But in fact, that's not at all how humans using Salesforce interact. As a human trying to solve, like I'm standing in the elevator waiting to go see a customer, what is the stuff I need to know? Like that that mapping is completely different. And so that API is going to really change as a result over time.

Aaron Levy (37:03)

Yes, I think the API changes for sure. I agree that. But I do think that unlike the humanoid kind of comparison, where sort of the physical world has interesting physics issues that you eventually run into, the digital world doesn't. And so at some point your agent can run in parallel 500 times. And I'm going into, I want to do a market map of customers across the Fortune 500. That agent can fan out and do that work in a way that I can't as a person in a browser. So to some extent agents get to let you sort of bend the laws of normal human based workflows. And so then that's why. And I think that means the APIs maybe eventually evolve, but not obviously in the direction of the end user product, but maybe more toward an agentic sort of set of workflows of what is that agent looking to do?

Steven Sinofsky (38:02)

Well, but Martin I think would jump in and just say, wait, you didn't describe anything new. You described an architectural. No, but you described an architectural problem with today's software, which is it's, it's API and performance gate was based on how much I could type, which, which is sort of the point I was making, which was our help system was designed on how much we could ship on one CD and had no data about what it is that people were trying to do and no context. But it didn't change like the problem, which is I needed to make a chart. Yes.

Aaron Levy (38:33)

Yeah, exactly. One real example of this, we launched a box agent that has a bunch more capabilities built into it and one of the capabilities is that it searches across your whole box environment. But it doesn't have the same limitations of a human based search where you type in one query, you get back a set of results, you look through them, it fans out, does multiple queries, it can look through hundreds of results instantly and do its own re ranking of that. And so that's just like, you know, again, you wouldn't want to be rate limited by the same process that a human went through, which is where the humanoid robot is. You know, you're kind of willing to be like, okay, the humanoid is still going to walk into the elevator, it's still going to press the button. When actually, you know, in an agent world you're like, no, no, I just want you to go and instantly press the floor that I'm going to.

Martin Casado (39:20)

Yeah, yeah, but we should just be very clear, like by the way, I very much agree, but we need to make a distinction between like would you ever build an indexing that's only for AI and not for a. And I think that's less obvious.

Aaron Levy (39:34)

Yeah.

Martin Casado (39:34)

So clearly there's like performance gains based on automation. We've got to evolve our architectures for those. But if you find a great way to index documents and you don't expose it to a human, I think.

Aaron Levy (39:44)

Yeah, you got it. Yeah, exactly. 100%. Well, well, this is, I mean, it's actually, I think this kind of probably reinforces some of Steven's, you know, kind of Internet analogy on documentation. There is this really interesting thing where, you know, it started out where we, as we've been building our, our next set of agents, we first gave it the current set of tools, we saw how it used those, and then eventually we realized, oh, there's actually even better way that the agent could do it. So we improve the underlying scaffolding. And oh, by the way, that will actually help the end user also. So it does let you sort of contribute back into the mothership of technology improvement. That does, you know, sort of lift all the boats of your users.

Steven Sinofsky (40:24)

Let me, let me ask this. It occurred to me as you were saying it, like, I sort of got all tense when, when the idea became no, like, oh, we have 10,000 people hitting our, hitting our SaaS system today and we've got it all working and it's all great, but now we're going to have 10,000 new people, which are the agents for each of those 10,000 employees. And they're, they're actually hitting it 500 times as much. Okay, so that SAS product will collapse. So like that's the first order because it wasn't architected for that volume. Like we saw this with, with all the BI tools. Like when all the BI tools came out, all of a sudden they were looking at the SAP data and trying to snapshot it and absorb the whole thing every night for a new kind of set of slices and dice that your view across all 500.

Aaron Levy (41:16)

Yes.

Steven Sinofsky (41:17)

And like all the people making ERP were like, well, we don't do that. And so they had to go build all of this themselves because they had the knowledge of the data. Their API just couldn't, was not designed for that kind of lookup. So my, my sort of thing to throw out there and fight about is what is it? What is the change management look like in a company? Because you, you can't let loose an agent that hits the system at 500x the humans. And it's not a token thing, it's an actual like, wow, we don't have the network bandwidth and the throughput to handle 500x for any one of our customers. So what happens?

Martin Casado (41:57)

So I've got a provocative adjacency which you guys can tell me if I'm doing too much on a tangent here, but here's my Provocative adjacency, which is. I don't know if having more agents is that big of an architectural shift. I just feel like we understand, like, whatever, if it's read only data, you cache it. You know, like all the state issues are around mutable, globally shared state. We understand the limits of those. We know how to architect around those. We had to tackle all of those things when we went to the Internet. And so if you built your system not to handle it, like, you suck at building the system and you deserve to go down and just go build a system that doesn't suck. And I just feel like this is kind of standard computer science. However, I do think agents do introduce something that organizations technically have to deal with. And let me just give the analogy

Aaron Levy (42:43)

in code, which is, I think, Stephen, this is what we call mogging on,

Martin Casado (42:47)

I don't know, not logging.

Steven Sinofsky (42:49)

Question mog.

Martin Casado (42:50)

I just questioned.

Steven Sinofsky (42:51)

I have no idea what he just did, but I'm just looking forward to how he magically made the problem go away.

Martin Casado (42:57)

But no, the problem is there. I just think we know how to go from 10 users.

Steven Sinofsky (43:03)

It's there for stupid people. We just got rid of the stupid people, so now everybody is smart.

Martin Casado (43:09)

No, no. Okay, so let me give you an example for coding. So this is where I actually think there's a shift in how work gets done. So when you code with AI, your code kind of gets worse over time, pretty materially. And so it's almost like you're introducing as many problems as you are solutions. And I don't think we've actually figured out how to manage that. Does this make sense?

Steven Sinofsky (43:33)

The whole world right now? Yeah.

Martin Casado (43:36)

I mean, like, this is this kind of reasonable question is, you know, if you're using AI, yes, you're productive, but are you creating more problems than you've actually solved for solutions? And I do think that there's this actually, you know, open question when it comes to using agents on existing systems for creating things, which is like, do we know how to wrap the growing set of entropy around that? And I would say anecdotally watching companies struggle with AI coding, which of course, listen, I'm very close to many AI coding companies. I'm clearly very bullish on it. I don't think we know how to do that yet. And so the agents on a system, I think we can tackle those with known techniques, using agents for long running things organizationally where like, you know, you know, the universe is kind of as clean as it was three days after then you started. I'm not Actually quite sure we know how to do that at all.

Steven Sinofsky (44:30)

Well, I love that point because that gets back to where we started, which is the difference between scale and not scale.

Martin Casado (44:37)

Yes.

Steven Sinofsky (44:37)

And why it's perfectly rational for big company people to be like, no fricking way is this coming into our company. Because a big company is about to. The wheels are going to come off a big company or a division in a big company or a product in a big company at any minute. Like if you're Marcy. We were both giant company executives. Like, literally, we woke up every morning thinking, oh, the wheels are coming off today. This is the end of it. I'm getting fired by the five o'. Clock. And whatever started what I left yesterday thinking we were three months late and it's. We're now nine months late. And that's a typical day. And so. But the reason that that doesn't happen is because you put constraints all over the place, which is exactly why Guilfoyle can't work at a big company, because he, he thinks he knows. And it's also why all the, the one shotting vibe coding kind of people have no problem saying it's fine because they've never had to live in an environment where the constraint was to prevent the whole thing from imploding.

Martin Casado (45:41)

And I feel this is so critical, Steve, that you're like, so again, this is going to sound like a little tangential, but it feeds into this, which is, I feel like core technologies kind of cater to like some human need. Like the Internet catered to like connectivity and social networking kind of catered to vanity. And I feel like AI caters to our need to be productive. So I feel like we feel like we're being very productive and we do all of these things, but we may actually be creating like mounts of extra work to do and.

Steven Sinofsky (46:12)

Well, well, Aaron, you're deploying AI right now. Like, box is all in. So tell us. Tell share a story, like, of the wheels coming off or not coming off.

Aaron Levy (46:22)

Yeah, well, well, I, I think, I think we're probably in the more pragmatic part of the continuum, so. So which is why we don't claim that it's a 10x productivity game to our engineering team. It's like, no, no, because we have a lot of guardrails in place that create these constraints automatically in our system. We still rely heavily on code reviews. We still rely heavily on security reviews.

Steven Sinofsky (46:45)

So are you guys coding with, like a rock and a chisel and stuff?

Aaron Levy (46:49)

We have chalkboards and like. But, no, but like we had this new feature that we launched and I was like, go, go, go, go, go. And AI built probably 80 to 90% of the feature. And, and the thing that slowed down the release of it was we have to do a full security review because we can't let there be any accidental code injection into the thing that we created. So there's a lot of stuff where you kind of go super fast, but then you get still rate limited or constrained by some other part of the process. And I think that's sort of relatively natural until we figure out then that other part of the process security review being one, or the actual code review being one, or just even your pipeline for getting things into production being another one. So we're doing quite a bit of retooling of the whole product development life cycle, but I don't think that it's a 5 to 10x gain. I do think it's a 2 to 3x gain. Maybe across the board you are still rate limited by how quickly can you review this stuff and check on the work. I do think that Martin's sort of pointing at though a thing that is the big open topic across enterprises and to some extent engineers will face it first and will find the right equilibrium. The harder part still remains in the rest of knowledge work. This is why if you're in accounting, we don't quite yet know when you can take your hands off the wheel doing a full accounting audit because of AI. What you can do is have the AI go and comb through unlimited amounts of data to find anomalies that maybe are that would alert your accounting team to oh, we actually have to go dig into this. That's awesome. Because that's only net new level of visibility versus the part of the accounting process where you're doing a fine tooth comb on making sure every single number is accurate. That's probably still humans right now. So I think the key is where do you find the productivity gains? And I do think that if you are a CEO or a board of directors or a management team, you're kind of trying to figure out and you're also getting confused because Silicon Valley is telling you all the things. And so you have to sort of figure out where is the productivity most potent. Where I actually can get the gain, I can get the success with less of the downside. And I think as an industry we're all sort of figuring it out. By the way, this is actually why I remain unbelievably optimistic on jobs, because I don't think I just Think we've gotten it wrong on thinking all the places where you're going to remove humans from this, because you still need a human in that, you know, somewhere in the loop, maybe the abstraction is a little bit higher and you don't need the human in a loop at every single stage that you needed a year ago, but you do need a human sort of kicking off the process, reviewing the process and incorporating whatever the work was. And so that creates just, still a tremendous amount of opportunity and jobs across these organizations.

Steven Sinofsky (49:35)

Oh, let me. I have to jump in because I have a whole bunch of like, visual aids I brought today to make it exciting. You got a bunch of comments on, on the MTS Live thing about people agreeing with you. So I don't want to let that slide because. Okay, you know, we complain about not agreeing with you, but. But like here, to your point, this was a book in the 80s called the End of Work.

Aaron Levy (49:56)

Yeah.

Steven Sinofsky (49:57)

And I. This. So actually, sorry, it was in the 90s. It came out like six months before the Internet hit. And the whole thesis was the technology revolution was a complete bust and we got no gains in productivity, but now there's going to be no more jobs because the economy is stagnant. And it just. And this was a guy, he called himself a futurist. And. And like, so the whole notion that it. That's like one of the neat things about this whole AI moment is like, the number of things that when you hear them the first time you think they're stupid. And then you go back and think about it, you're like, oh my God, it's way stupider. And this idea that like, AI just gets rid of jobs, it's as ancient as, like, you talked about the accountant. Like, one of the things people thought was that computers would get rid of accountants.

Aaron Levy (50:45)

Yes.

Steven Sinofsky (50:46)

And. And that was like IBM's pitch in like 1965. But what it actually did was like, oh my God, we could do so much more with accounting now that they're not like, literally just adding numbers all day.

Aaron Levy (50:57)

Yes.

Martin Casado (50:58)

And.

Steven Sinofsky (50:58)

And I think when you look at like, just the notion of like, like creating information, synthesizing and all that, like AI is, is an accelerant for that for a person who knows what they're doing. And companies are suddenly going to want more of those people creating more of that information. Not to mention the fact that if AI is creating valuable information and there's more of it, then more people will need to consume it to do something. And the idea, the essence of a company is acting on Information. And, and so this idea that information is just going to get produced easily and be in surplus and not used makes no sen. Because, as you know, like in the unstructured information world, the problem is that you can make it, but the consumption of it effectively is the gating factor. And that's the gating factor.

Martin Casado (51:43)

Now,

Aaron Levy (51:46)

I think I had a conversation with one of our board members who's chair of our audit committee. And so he's a cpa and he was telling us kind of early in his career, I can't even retell it because it felt so manual, but so I don't even know how the world worked. I don't know how the world worked before all of the modern technology. But he was explaining the CPA's process and I was like, yeah, it seemed like the most manual thing of all time. And Steven, I think this right out of your book is it was actually quite simple in sort of the amount of things you could do because of how undigitized and relatively manual the whole thing was. And computers actually only made it more complicated, more comprehensive, and thus created even more jobs because of that complexity that we introduced. And you can just sort of see how easy this is to show up in so many areas of work is like, we can just now we can afford to make things more complex. And so if you make things more complex, then actually you eventually still run into now new constraints of who can understand that complexity. And so to me, it's the funniest concept that the more code we write, the less we would need. Engineers would be the opposite because. Because now your systems are even more complex than before, which means that you're going to be running into even more challenges of when you need to do a system upgrade or when there's downtime and you have to figure out like, how do I fix that problem? Or when there's a security incident. So, yeah, I mean, this is like we're just getting started with the jobs on this front, right?

Martin Casado (53:16)

Listen, we're a few years actually into this and you can actually look a bit at the data too, right? Like, what are the companies that are hiring the fastest, like the AI native companies, and they're hiring like crazy. But not only that, like I remember there's this early prognostication which is AI writing code will get rid of infrastructure, like it's going to commoditize infrastructure. And like, which is this kind of very strange prediction given the fact that there's more software than ever before been written and sitting on the board. Of a bunch of infrastructure companies, some that have been flat for a while. They're all doing fantastic because there's so much software, there's so much more software out there now. And so listen, if you look at the data on the ground from the companies, it's more software. The AI native companies are hiring the, the, the, the, the most. And so it's very clear to me that we're in an expansion phase.

Aaron Levy (54:02)

And the, maybe just my, my only final point on this one at least is, is I think people, we, we have a little bit of a myopic view in Silicon Valley on, on thinking that, you know, engineering jobs are, you go to work at Google or name your, your, you know, tech company and startup and that that's an engineering job. And, and look, we're so wired into that because that's obviously the ecosystem that, that we're all a part of. And then you sort of forget, well, like John Deere is trying to make automated tractors and Caterpillar is trying to have AI systems and Eli Lilly is trying to design even more pharmaceutical kind of therapeutics. And just you can go through 5,000 other companies, they're going to now have the next set of engineers that are going to use Claude code and codecs and cursor to be able to automate even more of their businesses and be able to design and develop even more software for their workflows and their systems. And so, so it just might be that you don't go and work on a social network and improve the social network algorithm. You go work at John Deere and you improve the intelligent farming algorithm. And we just have to, I mean this is sort of like completely like Mark Andreessen predicted this 15 years ago of like software is going to eat the world. And what that means though is that everybody's going to have lots of software. And this gives everybody the ability to finally have lots of software. But you still need then an expert or a semi expert to be actually going and prompting the agent on what to do, reviewing its work and managing the system that it builds. So all of the predictions on don't go into coding and don't go into software engineering, I think will be proven quite wrong.

Steven Sinofsky (55:41)

That was super good, Aaron. And I think that the base case of all of this is just that there's too many people out there right now that don't like technology and have a static view of the world. So when they look to whatever it is that they think AI is going to do and people hear automation, they just assume it's going to take things away. Like, here's.

Aaron Levy (56:01)

Well, we have a lot of people who like technology, though, that are also creating that.

Martin Casado (56:05)

Right, right.

Steven Sinofsky (56:06)

So here's like, this is article fighting the paper chase. Well, I'm looking at a feed.

Aaron Levy (56:12)

Even lower.

Steven Sinofsky (56:12)

I'm looking at a feed. What are they doing?

Aaron Levy (56:14)

Oh, you're looking at a feed. Okay. Oh, sorry.

Martin Casado (56:15)

Okay, okay, okay.

Steven Sinofsky (56:16)

Oh, I see. Oh, you're looking at my Mac camera. Camera. And.

Aaron Levy (56:19)

Yeah.

Steven Sinofsky (56:19)

Oh, that's why.

Aaron Levy (56:20)

Oh, you're fancy. You're fancy. Right.

Steven Sinofsky (56:23)

So this is like Time magazine. Every kid in high school read it, 1981.

Martin Casado (56:27)

And.

Steven Sinofsky (56:27)

But the whole view of what computers would do would be they would automate the paper in a company. And so the idea, like the whole first generation of computing was literally taking paper forms and turning them into something on a screen, then printing them out and then making it all easier. And you fast forward and it's all of these things that you just said, Aaron. Like, like, you know, there was an era when lawyers didn't type. And so what happened was they just, they, they had people who were legal assistants. They called them paralegals, and they did all the typing. And then like some students at Harvard, they brought a computer into the classroom. And so this is, I'm lowering it so you guys can see.

Aaron Levy (57:05)

Yeah, yeah.

Steven Sinofsky (57:05)

So they brought. That's a original laptop in there in the early 1980s, and they brought, they brought this computer into the classroom and then they got thrown out. Out for using it. And. But they were literally, they used to, used to do law school, and you'd write the essays in longhand in a book, and then the professor would have to read them. And now, of course, you just type them and you have access to the database of all the citations. But that's exactly. Like, nobody deals with a lawyer who isn't in track changes with their, with your contract.

Aaron Levy (57:32)

Right.

Steven Sinofsky (57:33)

And, and I last I checked, there are way more lawyers today than there were 30 years ago. And they all are. Every human lawyer you talk to is a computerized lawyer. Their citations come from the Internet, their information. And the brief comes and they type.

Aaron Levy (57:49)

And I think kind of going back to the myopic approach, I think we maybe over, like, I mean, as a big lover of technology, I wish this was true, but I think we just over assume that, like, everybody's job is just. They're just inside of Microsoft Word and they're just typing a Word document. It's like, I mean, most of the time with lawyers, I'm like, strategizing something, or they're working through a complex analysis of a situation and it's not I could go to an AI for advice, but that would probably only increase the chance that I go and then call a lawyer to say, hey, what do you think about this situation that we're dealing with? And so a lot of these jobs just have a lot of context that aren't sitting just, just literally on the computer doing all the work. They do have to kind of touch grass as a part of the job. And so then AI, yeah, AI will help automate the creation and production of the content and the review of the information, but then it still has to be incorporated into the real world of real value production.

Steven Sinofsky (58:54)

I feel like we're live and we're supposed to end at 4. So what I'm going to do is just say we're live and it's four. And I guess that means we just stop and some lights fade or something. None of us have done this before. We don't know what's supposed to happen, but someone is waving at me and smiling saying, yes, I think you're right.

Aaron Levy (59:13)

The smile means stop talking.

Steven Sinofsky (59:14)

Okay, all right, well, it was great to see everybody. Bye everyone.

Podcast Host/Narrator (59:19)

Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes go to YouTube, Apple Podcasts, and Spotify. Follow us on x1.6z and subscribe to our substack@a16z.substack.com thanks again for listening and I'll see you in the next episode. This information is for educational purposes only and is not a recommendation to buy, hold, or sell any investment or financial product. This podcast has been produced by a third party and may include paid promotional advertisements. Other company references in individuals unaffiliated with A16 such advertisements, companies and individuals are not endorsed by AH Capital Management, LLC, A16Z or any of its affiliates. Information is from sources deemed reliable on the date of publication, but A16Z does not guarantee its accuracy.