Steve Sinofsky (3:04)

I mean, I think you're right. It makes sense in a theoretical way, but in a practical way. We have to be really careful in that the way to say it is algorithmic thinking is really, really, really hard for the vast majority of people who have jobs.

Aaron Levy (3:21)

Yeah.

Steve Sinofsky (3:21)

And so the easiest way to think about it is if you were to go into any person and ask them to create a flowchart for a particular thing that they have to go do, they would probably fail at producing that flowchart.

Aaron Levy (3:33)

Yep.

Steve Sinofsky (3:34)

So within any organization, say, doing a marketing plan, and there's 50 marketing people working on a giant product line, one person probably understands and could document the flowchart 100%. So if you put one of these agents or you put this tool, this co working tool in front of people to create these things, their ability to explain to it what to do is really, really limited.

Aaron Levy (3:58)

But what if that becomes the new. This is the new way you have to interface with computers.

Steve Sinofsky (4:03)

Well.

Aaron Levy (4:03)

And you just have to cycle that through.

Steve Sinofsky (4:05)

Well, then you're basically just developing the next abstraction layer for how people interact.

Aaron Levy (4:10)

Yeah.

Steve Sinofsky (4:10)

And the developing an abstraction layer has historically, at each level of the abstraction layer, been a highly skilled, very specific individual within an organization developing that.

Aaron Levy (4:21)

Yes.

Steve Sinofsky (4:22)

And then the little parts that they build just become little toollets in the world of people doing particular tasks. And some people are able to stitch them together and some can't. But that happens with paperclips and thumbtacks.

Aaron Levy (4:34)

Oh, yeah, sure.

Steve Sinofsky (4:35)

And it's going to happen with whatever we do next.

Aaron Levy (4:38)

I think basically the timeless part is the job just moves up a rung and you learn a new set of skills. And that's why I actually don't think anything about this is any different. It's just now the leverage you get is obviously fantastic. There was this viral kind of tweet that went around which was the anthropic growth marketer. Do you guys see this? It's basically one person, and he was using cloud code at the time to basically more or less automate what maybe five or ten people would have done in various kind of siloed jobs. And I think the reason why it's interesting is you had to have been a systems thinker to be able to accomplish that. So, like, clearly he already was technical enough to be able to pull that off. But it did kind of represent what would each of these jobs look like if you had, like, imagine you had, you know, X job in the economy, and right next to that person was an infinite pool of engineers that could automate whatever that person wanted. And what would that job look like in the future as a result of that automation that now is possible? Yes. I agree that you'd have to find a way to think through your job as a system to be able to pull that off. Maybe the agent gets better and better over time at being able to, like, nudge you in that direction, but it does sort of stand a reason that, like, you will start to try and automate a lot of that kind of work. Of like, well, why don't I take, like, the keywords that are working in this, in Google, AdWords, and then Port them over to Facebook and make sure that those are replicated and then take in the new signal from what's happening in the market?

Steve Sinofsky (5:57)

That's a big leap.

Aaron Levy (5:57)

Yeah.

Steve Sinofsky (5:57)

So one thing first.

Aaron Levy (5:58)

I almost had you. You were nodding a little bit, and then I said something that went too far.

Steve Sinofsky (6:02)

The anthropic growth person is an example. That's a job.

Aaron Levy (6:05)

That is the rest of work.

Steve Sinofsky (6:06)

Yeah, I could do that job.

Aaron Levy (6:07)

Everybody's gonna be like, anthropolog, like, when

Steve Sinofsky (6:11)

demand is infinite and frankly, supply is infinite, this is not a difficult job. And so let's.

Martin Casado (6:17)

Let's say guy that runs the Petrol Pup in Australia right now is amazing.

Aaron Levy (6:21)

Right, Right.

Steve Sinofsky (6:22)

So, like, be. Instead be the $600pc marketing person and see how you can do against the Neo that.

Aaron Levy (6:28)

All right, we need a better example.

Steve Sinofsky (6:30)

But there is. I mean, it is really interesting. Like, I. Here, let me do an old example. An old person example. Like my cousin, mba elite school, joined her first job, she's a little older than me, joined right on the cusp of computing. Like, she actually didn't use a spreadsheet in grad school. And then spreadsheet showed up, but she wasn't a spreadsheet person. So instead they told her, hire as many interns as you want. And so her first year on the job, she, like, supervised, like, essentially a whole room of agents.

Aaron Levy (7:01)

Yeah.

Steve Sinofsky (7:02)

And the kids who was me, not literally, but they were in college, came and just did all the Spreadsheeting.

Aaron Levy (7:08)

Yeah.

Steve Sinofsky (7:08)

But then what happened sort of magically over the next couple years was she and her cohort all became the spreadsheet people.

Aaron Levy (7:17)

Yeah.

Steve Sinofsky (7:17)

And then this idea that you being a manager in a bank or just a two years in meant you had a cadre of people doing this for, you know, the whole abstraction layer moved up and the old job before those interns was you just sat there with basically calculators and an HP calculator, figuring out the model for some M and a deal or whatever. And you only got to do like two iterations before you had to put out the pitch deck or just go to the customer or the client or whatever. And then all of a sudden they're doing 30 iterations themselves. But they. And so I think where we are with agents is just at this step where you think you need 50. And the abstraction layer is such that we're dividing up in these really small pieces with one super smart person coordinating them all. And pretty soon that whole thing is just going to. They're all going to collapse on each other and there is just going to be like a skill set amount of code. Call it an agent that is like marketing. Ish.

Aaron Levy (8:12)

Yes.

Steve Sinofsky (8:12)

And you'll be able to ask it marketing stuff. Yeah. And then the next step will be and have it go do things. I'm a little skeptical of the. Until the whole like non reproducible, non random element of this AI stuff goes away, the doing stuff is going to get very costly. And. And so then you get into the human in a loop discussion and all of that. But I feel like when I talk to people trying to do stuff that we're right. I feel like I'm at Thanksgiving dinner talking to my cousin, six months in her job when I'm using a spreadsheet already. And I'm like, I don't know why this is so hard. You should just use one. And then two years later she's doing it. And I think this right now, you have to be an absolute. You have to be a rocket scientist and the growth marketing person to create 42 agents and spin them all up and do all of this stuff. But the rocket science part of it just is going to evaporate in very short order. And then you're talking about, wow, there's a giant chunk of domain expertise.

Martin Casado (9:06)

Yeah.

Aaron Levy (9:06)

Goes back to the domain expert.

Martin Casado (9:07)

So I actually think something that you said, I'll take the other side of which is I think it's very tempting to be like, these agents are going to code and do X. Yeah. But I think we're going the opposite way. So I think actually where we started was we'd take like a piece of SaaS software and we'd add AI.

Aaron Levy (9:21)

Yeah.

Martin Casado (9:22)

And then that's like the new kind of like AI enabled. So that's like the extreme version of using code for these types of things. But now what are we actually doing? We're like, okay, the SaaS software is still SaaS software and the agent uses it as a computer because, like, it's actually very good at that. So I'd say, like, we started with code, then we went to the terminal, which is actually less code.

Aaron Levy (9:41)

Yeah.

Martin Casado (9:42)

And now this year is going to be the year of computer use. So it's almost like they're much more like humans using computers than them generating code. And that feels like very much like this mezzanine step.

Aaron Levy (9:53)

Yeah.

Martin Casado (9:53)

And I actually come from like the generating code type of the world. Like, I would argue that that's happening less, not more.

Aaron Levy (9:58)

Yeah, I think so. To me, whether it's computer used, API use, or writing code on the fly, I kind of maybe erroneously put that all in one blank category.

Martin Casado (10:08)

They're very different.

Aaron Levy (10:09)

They're very different. But we have an agent that we're working on where it just makes a determination whether it should use an existing skill, is it using an existing tool from Box, or it should write code to solve that problem. And its ability to do any one of those three at any moment ends up being incredibly useful. Because sometimes there's just some specific operation you want to be able to do where writing code to be able to do that operation is just faster. And we don't have to. We can't possibly, you know, kind of pre plan for every thing that anybody would ever want to do on their documents. And so the fact that the model is good enough to also write code on the fly for that use case ends up just being like an amazing property. Even though maybe 90% of the things that it's going to do should just be using an existing over time.

Martin Casado (10:51)

Pareto takes over. And over time there's like literally like seven apps on her iPhone. There's seven SaaS apps. We end up like, over time, these things tend to consolidate.

Aaron Levy (11:00)

But the seven apps on the iPhone is a issue of humans don't want to learn these things over and over again. And so I, as a human, I don't have the mental bandwidth to learn that many apps. But an agent that is going to use tools and APIs and be able to code things doesn't have any of the same constraints that we have. So I, I don't know, like, I, I don't mind.

Martin Casado (11:19)

Well, you could argue that there's just so many things to do and you can make interfaces sufficiently general.

Aaron Levy (11:25)

Yeah, fine.

Steve Sinofsky (11:25)

Well, fair.

Martin Casado (11:26)

I.

Steve Sinofsky (11:26)

Let me. I think I like what, I like what you said then, because.

Aaron Levy (11:29)

Oh, I'm back.

Steve Sinofsky (11:30)

We're back. Okay, we're aligned.

Aaron Levy (11:33)

We're aligned.

Steve Sinofsky (11:33)

No, but, but I think there's something super interesting here which I do really, really like, which is that where software has evolved, you know, Like, I use SAP all day, I work in finance, I have to go and generate all these reports. And then somebody shows up and says, I want a report that does this view slice this way. And I'm like, oh God, I don't know how to make that right. And like, now let me go wade through the SAP help system and try to find it. One thing that, let's just say AI could be very good at is it actually can navigate that surface area much, much better. You know, the help is all there. And so it's a matter of finding it. Mapping language and humans have been a bottleneck in tapping the past 25 years of software capabilities. I mean, like, I spent my life, my life with sitting next to people on airplanes saying, how can I make PowerPoint do X and just go to the ribbon? And you know, it was because it hurt, physically hurt to watch somebody suffering with bullets and numbering and word or trying to figure out, you know, like, oh, let me just make a two, a two axis graph in Excel, which like, is rocket science. Like almost no one can do that, but yet it's super common. And so people are like, have not. And so that impedance mismatch was a human user interface design.

Martin Casado (12:49)

I totally buy it on the consumption layer. I totally buy it, which is like the perfectly fluid like UI or consumption layer. I just feel the back end, like the systems of record.

Aaron Levy (13:01)

Yeah, oh yeah.

Martin Casado (13:02)

It'll probably converge into like some database, like some generic set of APIs like that they'll connect to. And like, that seems to be the direction it's going.

Steve Sinofsky (13:09)

I agree. I think you go ahead.

Aaron Levy (13:10)

Sorry.

Martin Casado (13:10)

Well, like, so I spent all weekend like implementing my Nanoclaw bot. And when you first start out, it's like you're building an integration for everything. Nanoclaw's very like, like openclaw has all of the integrations. Nanoclaw has very few of them. And so you have it build all of its own tools. But after, you know, two or three days of These like, you know, you kind of have the tool integrations that you need and. Yeah, you know, like.

Aaron Levy (13:31)

Yeah, but back to the essay. I mean, we're talking about personal productivity. Probably like, like organizing your life or something.

Martin Casado (13:37)

Well, it's. It's work productivity.

Aaron Levy (13:38)

Okay, fine. Work productivity. And then an SAP system and like. And like.

Martin Casado (13:41)

And so.

Aaron Levy (13:42)

So there's like an infinite, like, there's an infinite amount of complexity when you get to, okay, some company that has a global supply chain and they're dealing with 75 pieces of information across, you know, 30 different systems that does require a certain amount of. Of horsepower from the agent that is just. We have. I mean, we just haven't been able to get from. From any architecture up until now.

Steve Sinofsky (14:03)

But like, take. But that.

Aaron Levy (14:04)

It.

Steve Sinofsky (14:05)

What you just described is literally been doing for 50 years and will continue to do, which is, you know, I have a friend who was the CIO of. Of the va, and he spent. All he spent his time on was gluing those 75 VA systems together. And it's all just integration, redundancy.

Martin Casado (14:25)

Perfect for integrate. Yeah, this I totally.

Aaron Levy (14:27)

Okay, great for integration.

Martin Casado (14:29)

These things are the best. But. But it's integration, right? It's literally how do I stitch these two systems?

Aaron Levy (14:33)

But it's in it. But now the thing that I think is happening is it's kind of like integration on demand. It's my new queried in the system that the IT team didn't pre wire. Now I need it to happen at runtime.

Steve Sinofsky (14:46)

Let me get off my lawn.

Aaron Levy (14:48)

Okay.

Steve Sinofsky (14:49)

Okay.

Aaron Levy (14:49)

So.

Martin Casado (14:49)

Okay.

Steve Sinofsky (14:50)

So the reason I just was in a room filled with a bunch of CFOs and CIOs, and they all looked at me when I said something along these lines, although not as optimistic as you can imagine, but they just.

Aaron Levy (15:02)

More realism was.

Steve Sinofsky (15:03)

No, it caused like six of them to come running up afterwards and say, you're insane. You've lost all credibility with me.

Martin Casado (15:09)

Because it's what specifically? That the. The agents are going to do integration.

Aaron Levy (15:14)

That.

Steve Sinofsky (15:14)

That. This. That the integration is a problem that will get a lot easier.

Aaron Levy (15:18)

Yes, they were against that.

Steve Sinofsky (15:20)

No, they're. No one's against.

Aaron Levy (15:21)

I know, but they're.

Steve Sinofsky (15:22)

I think it's practical, but their. Their fear is like unleashing not just the agents themselves, but humans to do integration. Because you put people creating new integrations and you just say, please break my system of record.

Aaron Levy (15:35)

Oh, yeah.

Steve Sinofsky (15:36)

And so this idea that you just create like a new. Between, you know, system 27 and system 38, and then you're that might be fine for a report because if that person wants to be wrong, that's their business. But you're not.

Aaron Levy (15:49)

I think we have a read only version of this for a number of

Steve Sinofsky (15:52)

years before where N is very large.

Aaron Levy (15:55)

Yeah.

Martin Casado (15:56)

And a lot of it's just a consumption layer where the consumer is a human being.

Steve Sinofsky (15:59)

Right.

Martin Casado (15:59)

It really feels right. Now a lot of the AI stuff

Aaron Levy (16:01)

is consumption layer, but yeah, I mean it's on, you know, we, we actually have. So we just rolled out the official box cli. Thank you for the liking the tweet on that.

Steve Sinofsky (16:11)

I, I, I used it, I have some feedback. We'll talk about it.

Aaron Levy (16:14)

I'll take, I'll take all the feedback, but it's a really interesting thing. So we, we had these, all these debates internally of like, okay, you give Claude code the box CLI and you can now interact with your entire box system via natural language. And you get the horsepower of Opus 4:6 being the orchestrator of doing a bunch of operations. And it's like, like, it's like, you know, blows your mind. I guess I'll get some feedback, but it blows your mind in some ways because you could just be like, upload this entire folder from my desktop into box and it'll work or process all these documents in this folder and it'll work. And it's amazing. And then we started thinking through like, well, let's say you were a company with, you know, 5,000 employees and everybody had access to some shared repository, like you know, engineering documentation and you know, marketing assets or whatever, and everybody had Claude code or Codex, you know, running with the cli.

Steve Sinofsky (17:03)

Wow.

Aaron Levy (17:03)

We now have some really interesting new challenges, which is like, how do you coordinate possibly the fact that you might be hitting the system like 10,000 times an hour or something. Not from a performance standpoint, but just like how do you make sure that people didn't move like a file from one thing accidentally from one folder to another folder while the other person was trying to do a write operation and somebody else was trying to delete something? Because you have these agents running wild. This is, this is going to be like the new big question that every cfo, cio, et cetera, is running around trying to with their hair on fire.

Steve Sinofsky (17:36)

Well, there's just, that's exactly what I ran into, which is I played around with your example, which is create the video example, which is create like a marketing plan directory or something. And like all of a sudden I'm like in some loop creating directories.

Aaron Levy (17:48)

Yes. Yeah. Like, go on as long as it can.

Steve Sinofsky (17:51)

Right? And I was like, I wonder what the limit is on box finesse directories, because I'm about to hit it.

Aaron Levy (17:56)

Actually, we're going to find out too. Yeah, yeah, ye.

Martin Casado (17:59)

But it does feel to me that a lot of the intuition is to build a new layer of controls and whatever, but what's actually happening on the ground is the opposite. So I'll give you an example. When we all picked up a lot of these personal agents, we would give them our API keys, we would give them our email addresses and then they would kind of access those things. They're like, oh, but how can I stop it from whatever? And so what everybody's doing now is you give it its own phone number. I actually gave my Nanoclaw its own credit card came in.

Steve Sinofsky (18:31)

Hopefully just a Visa debit card that

Aaron Levy (18:33)

you bought at cvs all the money. No, no, no.

Martin Casado (18:37)

But then, but then I give it its own Gmail account which you can log into. And then Gmail actually has all of these RBAC permissions that you have to use. So you could make an argument that like, you know, we've actually built in a lot of these permission systems. You have to treat it like a human, as a separate human. And then instead of like building another auth layer, building another.

Aaron Levy (18:54)

Okay, now can I instantly take, do a takedown of, of, of this, this element that we're going to run into, please?

Martin Casado (18:59)

Yeah.

Aaron Levy (18:59)

Okay, so that is fantastic for personal productivity.

Martin Casado (19:03)

Yes.

Aaron Levy (19:03)

And the question that we're going to run into is in an enterprise, let's say I have, let's just make a simple example. I have a 50 person team of something. Should everybody also basically, will we have a hun, will we have a hundred people now collaborate? I mean basically 50 humans and then 50 credit cards and then 50 agents in that same shared space. And do I have, I obviously have complete oversight over my agent, but what if my agent collaborates with somebody else and then accidentally gets access to some resource because they were sharing with that other person and I'm not supposed to have access to that resource. And now this autonomous sort of stateful, you know, agent is running around working on somebody else's information.

Martin Casado (19:46)

Default end to end argument is you treat them like human beings.

Aaron Levy (19:49)

It doesn't work. So you can't fully treat them like humans. Because here's the thing, and with regular humans, you don't get to look at the slack channel of the person that, that is working with you or working for you. You don't get to log in as Them, you don't get to oversee them. You are, they are accountable for their own set of execution in the real world. You don't get penalized for what, how they screw up the agent. You have all the liability of whatever they're doing. You do have complete oversight and you're probably going to need to have that complete oversight. They have no right to privacy. So, so there's gonna be these, some of these breakdowns that aren't as clean as. Just treat them like a person. Because I need to be able to kind of, I need to be able to give access to something to them, but I also need to be able to like log in as them at some point and be like, no, no, you fucked up the whole thing and I need to undo it all. But if I can log in as them, how could they have operated in the real world working with other people and keeping anything, you know, confidential or secure or whatever. So it really is still an extension of you. It's like almost impossible to get around them being an extension of you. So now the thing that we're thinking through that we're not going to be

Martin Casado (20:50)

able to do anytime soon, it doesn't logically follow.

Aaron Levy (20:53)

Yeah, maybe.

Martin Casado (20:54)

But for example, for my employees, I can log in as them.

Aaron Levy (20:58)

You don't though. You don't, you don't.

Martin Casado (20:59)

I can get access to their email.

Aaron Levy (21:01)

Yeah, no, in like, if you get like sued, you're not logging in. You're not logging in as them on a regular basis because they sent one email.

Martin Casado (21:08)

Isn't the right operating model with an agent the same thing?

Aaron Levy (21:11)

The risk is like a thousand times greater. Like these people, like they will just leak your information whenever they want. Like they will happily just go and send some email to somebody because they got prompted.

Martin Casado (21:20)

I think the terminal state is that these things are still these sloppy computers and therefore they will always.

Aaron Levy (21:24)

I don't like the word sloppy unless we're saying it very in a colloquial sense.

Martin Casado (21:29)

But like they, they'll never be able to contain information. They'll never.

Aaron Levy (21:33)

So we're like, I think the ability for you to keep the something in the context window a secret secret, like as in like you tell it, do not reveal X thing in the context window. I think that's a very hard problem to solve.

Steve Sinofsky (21:46)

Let's do.

Aaron Levy (21:46)

And so then, so then thus, if anything can ever enter that context window because they have access to a resource, then in theory you should assume it can be, you know, prompt ejected out of the pro of the context window. And I Don't know that we know of a way to solve that at the moment. Like, that's like. And so if I know your new agent's, you know, email address and I email it like it's an assistant, but like I can, I can social engineer it 10 times easier than a human. Like, it'll be hard for you to pull off that that agent is now also has access to your like M and A documents and stuff.

Martin Casado (22:17)

But isn't this like literally all of AI right now?

Aaron Levy (22:20)

Which part?

Martin Casado (22:20)

I mean, the fact that we've got these shared systems that we use the intelligence for that have shared context.

Aaron Levy (22:26)

But what do you mean by it's all of AI?

Martin Casado (22:28)

Well, I'm just saying, like right now, when we use AI internally and agents internally, this is exactly how we use.

Aaron Levy (22:33)

But this is why you are, they're working as, as you effectively right now, and we don't yet know how to make them not work as you.

Steve Sinofsky (22:40)

Let me offer an example. Let me, let me offer an example

Aaron Levy (22:43)

and then solving this problem though, like, like the, like, like the issue will be, will be like you will just be able to trick the agent to reveal information. So then, so then they, that's why, like having them have access to their own resources where they can fully make their own decisions is not yet something that we've been able to pull off.

Steve Sinofsky (23:03)

There's a perfect example for solving your problem, which is we already lived through this with open source.

Martin Casado (23:08)

Yes.

Steve Sinofsky (23:08)

The model for open source was it's all there and you just use it and you pick and choose. And then like, nobody debated it because the world was much smaller then and we weren't all on X doing podcasts when this was all happening. But then quickly everybody realized all the problems you were just talking about. Like, if you're running a big company, you can't have some person just go copy in a bunch of source code from open source into your commercial product like that. There was a whole licensing problem, a whole quality, a whole bunch of stuff. And so all these norms got developed. The debate that we're happening that's happening right now is just is this really interesting modern artifact of how new technologies develop, which is this is all happening in real time during open source. Like, we met in a conference room this big and debated how much open source we could use in Windows or Office. And nobody on the Internet knew we were having this debate. It was a very. And it, I think it's just so interesting that not just this, the debate about specifics, but this whole notion of where is this heading is happening in writ large and everybody is just trying to get to the end state. Like way, way more like in a, in a sense more quickly than we can actually reach the end state. And so what really needs to happen is people just need to go build.

Aaron Levy (24:26)

Winnie Sanders. What we just need Susanna.

Martin Casado (24:28)

No, I think we've got different intuitions on the end stage.

Aaron Levy (24:31)

No, no, we.

Steve Sinofsky (24:32)

You don't want my intuition.

Martin Casado (24:34)

But like one could make an end to end argument that these things actually converge on the same type of reliability as a human being. Which is exactly how we view like self driving.

Aaron Levy (24:42)

Yeah.

Martin Casado (24:43)

And in that case you use the exact same mechanisms that we use to protect with human beings. Like you consider insider threat. You consider the fact that people can be bought off. You consider the fact that people make mistakes.

Aaron Levy (24:53)

Yep.

Martin Casado (24:53)

And you.

Aaron Levy (24:54)

And that's a risk and that's operational processes.

Martin Casado (24:56)

So, so one intuition is like that will be the end state. Yeah, there's another intuition.

Aaron Levy (25:01)

I'm just saying, I'm talking about where we're at now. I actually, I don't know that we disagree on the end state.

Martin Casado (25:05)

Okay.

Aaron Levy (25:05)

And, and, and by the way like strategically we're hedging because we're going to build, we're gonna, we're gonna build agent users and reg. And we're like. So we're like. I love the idea of openclaw having a box account and it, it operates and uses.

Martin Casado (25:18)

Yeah.

Steve Sinofsky (25:18)

You just like twice as many accounts.

Aaron Levy (25:19)

Yeah, exactly.

Martin Casado (25:19)

This is great.

Aaron Levy (25:20)

Double the seats. No, no, I, I love it. I'm. I'm just saying thing on the ground right now, we don't yet know how to give it an M and a data room to fully securely be able to.

Steve Sinofsky (25:29)

But that. Yeah, but it's actually, it is harder

Aaron Levy (25:31)

than that though because the, the threat. He's a skeptic.

Martin Casado (25:35)

I'm good.

Steve Sinofsky (25:35)

The threat vectors are going to be way more sophisticated. So we do have a cat and a mouse game going on where you can't just assume that the agent acts like a human does today because it's going to be the fastest, most thoughtful, craziest ass human that ever existed. Trying to actually leak the information because. Got injected in some way.

Aaron Levy (25:56)

Yeah.

Steve Sinofsky (25:56)

And so there. Part of what's going to happen is you're. We're going to go through this phase where like the enterprise customers are just going to like close everything off until there's some sense of sanity in all of this and then, but in the meantime the individual and specifically the developers

Aaron Levy (26:14)

we're going to have Such a big.

Steve Sinofsky (26:15)

Are going to. And that's going to be. That I think is the most exciting tension.

Aaron Levy (26:19)

Yes.

Steve Sinofsky (26:20)

That's going to happen is that. That the enterprises are going to be, are going to get left behind by these sort of advanced individuals, which will then start to look like the startups.

Aaron Levy (26:29)

Yes.

Steve Sinofsky (26:30)

And the startups will start to move much, much faster than enterprises because they just don't have any of these problems. And, you know, you could end up with like the agent going rogue in a startup and doing that because you

Martin Casado (26:41)

had no employees that go rogue routinely in the startup.

Steve Sinofsky (26:45)

Yeah, yeah. Well, it'll just be an episode of Silicon Valley. And so, you know, big deal.

Aaron Levy (26:49)

I agree with you on like the. Okay, it's people, et cetera, the same risk. I think there's a couple, you know, differences though, in the sense that, that I can't really threaten, you know, the like, Claude code, that it's just. I'm gonna pull the plug on it. In the same way that you do have that thread as a regular employee is like, you, at least, like 95 of people are not, you know, trying to do bad stuff, you know, within

Steve Sinofsky (27:12)

an organization, but they're, they, they are, they're not trying, but the ability to inadvertently do bad stuff.

Aaron Levy (27:17)

Yeah.

Steve Sinofsky (27:17)

To your point about it, still not

Aaron Levy (27:19)

having that stuff, I would argue that, that it's a lot easier to have people not share, let's say, files with somebody outside the company in a, in a, in a wrong way more than it is for an agent right now to have that same set of instructions.

Steve Sinofsky (27:33)

And also you have the tools so that you, you can basically stop that at a whole different level of abstraction,

Aaron Levy (27:37)

which is why you have to build this into software and a lot of. But I do think actually if you were to like, if you were to like, put a bow around your last point, that a lot of this is actually why the diffusion of AI capability is going to take longer than people in Silicon Valley realize. Because what's happening is like, we see startups that can start from the ground up without any of the risks that we're talking about, because they have nothing to blow up. And, and so we look at that as the trajectory that we're on. And then you go to like, JP Morgan and you're like, how are you going to set up Nanoclaw to, to be able to, to actually, like, you know, automate your business anytime soon? And it's like, oh, okay, there's going to be like a little bit of a gap there.

Steve Sinofsky (28:14)

Yeah, well, what do you Guys think here's. I think that that opens up a pretty interesting problem which is this split between big and small, startup and enterprise, which is just that, that the, the Enterprise, the current SaaS vendors who are all struggling in this SaaS apocalypse weirdness that I don't really agree with, but they are struggling with this problem that they, they don't really sell the line of business data. They actually sell this intelligence and domain expertise in this whole system and the agent side of things wants to only buy the data now and they only want to license the data and they want to have unlimited access to the data but they've actually never really enabled that. Like that's never been their business and it's been a long standing tension point with the likes of Workday and SAP and stuff like how much API access to have. I mean Salesforce went through three different massive platform redesigns. You know, it's. I think that that's a particularly interesting problem. Not for the same reason that Wall street does. Wall Street's all wrong about the economics and the problem and all that stuff. But from a technology perspective, what does system of record mean in the face of people wanting to access the data when the data. For training or for. Well, they, they are.

Martin Casado (29:30)

You're talking about for like day to day operation.

Steve Sinofsky (29:32)

I think of it as executing the day to day operations. Their concern is that somebody that they want to do the training layer on, on your data like I' customer, they want to do the. My vendor wants to build a training

Aaron Levy (29:44)

actually even, even if you don't even get a training. They're concerned because. Oh yeah, because, because like monetizing, you know, you know sending a little bit over the Internet versus like you're in my ui.

Steve Sinofsky (29:55)

All of it's a very different level

Aaron Levy (29:57)

of monetization initially that you.

Steve Sinofsky (29:59)

But that's, that's sort of. That monetization part is the Wall street point.

Martin Casado (30:01)

I.

Steve Sinofsky (30:02)

Because I think like look, there is so much domain stuff in, in an SAP just to pick an example, not to pick on them or anything but like they're not going anywhere. Like it's ridiculous. It's just absurd to think you going to vibe code your way to like SAP. But also all of that, those, all of that domain knowledge. It's not so it's not just represented in some well orchestrated data layer as much as they tried. There's like a whole bunch in the, in the ui there's a whole bunch in middle tiers, there's a whole bunch and just how you use it and so I, I'm really unsure how this thing evolves because SAP isn't going anywhere. So then that's going to slow the diffusion of AI on that particular data source independent of whether or not it's agentified AI that's doing stuff or just read only reporting on stuff. So where do you come down on, where do you think that's going to go?

Aaron Levy (30:52)

I'm afraid of saying something that, okay,

Steve Sinofsky (30:57)

like that's, otherwise you're not going to get invited back. So say something good.

Aaron Levy (31:01)

I'm, I'm, I think I've, I think I'm, I've drunk the Kool Aid on, on build Something agents want. So, so this kind of, the Paul Graham term kind of like emerged on, you know, the past year on this topic, which is just like, like event I, I think we would actually, then I fully agree on this, which is at some point you do enough sort of iterations of this and at some point the agent is largely in charge of what tools it wants to implement and use and, and whatnot. And yes, it can't, the agent is not going to be able to change out an enterprise system. But like again, enough generations later, the agent might, might just run into so many walls with your software that it's just going to say you need to finally rip out your legacy HR system or I'm not going to be able to automate this workflow for you.

Steve Sinofsky (31:47)

Yeah.

Aaron Levy (31:47)

So I do think you have this really interesting dynamic which is back to this whole point of imagine that there's 100 or a thousand times more agent volume on software than people. You do that enough times and eventually the software stacks that agents talk to has to be built for them. And maybe there'll be a couple holdouts, maybe a couple ERP systems are like the final holdouts that don't do that. But everything else you basically like your business, your business performance will correlate to how well your agents can get access to the information they need to do their work. And so thus your enterprise IT stack has to be set up in such a way to support that. And so agents are kind of in charge because basically your software has to support those agents being affected. And then that's going to mean everybody that built a SaaS business or a software business is like the game is can you build really, really high quality APIs? Can you have a way of monetizing that? You know, do you have a way of handling the identities and all of the access controls for agents? And like, like that becomes the new problem you have to solve if you're building a software company. And so, yeah, like, and then, and then how you monetize it? Like, do you monetize it? Like, does workday charge a penny for every HR record it pulls? Like, we'll figure that out.

Martin Casado (32:59)

Out.

Aaron Levy (33:00)

I do think that in some businesses it could mean less revenue, and then in other businesses it can mean a lot more revenue. Like, the thing we get excited by is, like, every agent really loves working with files, so there'll probably be more files in the future than. Than there was going to be before. And so, you know, can we build a platform that, like, makes it really easy for agents to work with that data? You know, we, we. We're betting that that's actually a really optimistic outcome for, for, you know, our, our kind of business model. There might be some business models that are, like, more constrained because, like, the age agent is doing more of the value than the software is in that kind of future scenario. And then there'll be everything in between.

Martin Casado (33:31)

Can I quibble with one thing?

Aaron Levy (33:32)

You're equivalent with that. I thought that was, like, so not controversial.

Steve Sinofsky (33:35)

No, no, we're here to quibble.

Martin Casado (33:38)

No, no, no. But there's one thing I think, like Paul Graham and many actually gloss over, which is they focus on the interface. They'll say things like, you build something for the agents. And I actually think that's exactly wrong.

Aaron Levy (33:48)

Okay. In the sense, and to be fair to Paul Graham, he didn't.

Steve Sinofsky (33:52)

He had been extrapolated.

Aaron Levy (33:53)

Yeah, exactly. I brought Paul Graham into this.

Martin Casado (33:58)

So, okay, let me talk about something. People in the abstract say things like, now you're marketing to agents. The most important thing is to being like, whatever. You're like an API. You've got a good idea. I actually think that's almost exactly wrong,

Steve Sinofsky (34:10)

which is, wow, that's.

Aaron Levy (34:11)

This is breaking podcast news.

Martin Casado (34:13)

That's the one thing agents are really good at.

Aaron Levy (34:15)

Oh, okay. Is finding their way through.

Martin Casado (34:16)

And at the end of the day, like, it's the semantics that end up mattering a lot more. Right. And so, like, the agents in my recollection or in my experience, are very, very good at picking the right backend for whatever they're doing. So they're not like, oh, the interface for this is very good. The document. It's none of that. They're like the cost parameters of this, the durability of that, and so they actually have the collective wisdom of our experience using these platforms. Let's take cloud platforms. There's a bunch of cloud platforms out there. And whenever I Ask an agent to choose a platform. It's actually using meaningful stuff, not interface stuff. So I think as an industry we're so focused on these interf. Like, oh, you need to like market to agents this and that.

Aaron Levy (34:56)

Yeah.

Martin Casado (34:56)

When really I think that we're going to be pushed to actually build better systems and that's what's going to be chosen.

Aaron Levy (35:00)

Okay. Actually, so then there's probably no quibbling. I think we're actually fully aligned. I'm sorry to ruin the quibble thing. I don't treat this as like a, you know, kind of a marketing, you know, esque thing. I more mean like if your tool is closed off to the agent, the agent eventually will find a better tool for that company to go use. And so, and so what will happen is it used to be that you would go to like Gartner to be like, like tell me what, tell me what to do, tell me what system to use or whatnot. At, at some point with enough iterations the, the agent is going to say you should probably use this kind of database for this type of operation. And if you're not in, if you're not in there, then you're. It's your doa.

Martin Casado (35:35)

And, and I, I think we should actually be celebrating this because agents are actually pretty smart at choosing the right technology. In the past, I really think it was a lot of the other things that, that caused people to buy it which was like.

Aaron Levy (35:44)

But, but don't worry, we will. In Silicon Valley we will ruin the meritocracy of this very quickly because you'll just like I'm going to outsp.

Steve Sinofsky (35:53)

They'll bring an API to incent the agent. But you know, there's a. The marketing. The marketing agent at workday. Well, the marketing agent at workday will have the ability to purchase the recommendations.

Aaron Levy (36:04)

Find a way to replicate steak dinners for agents. Yeah, like what is the.

Steve Sinofsky (36:08)

There is a. But here's a real, here's a thing that again that, that happened with the web sort of internally like internal. Like just pick internal sites. Like every company had file share with like the best documentation, the best slideshows, the best financial models for any department or working area. And people sort of got familiar with that. And then when they didn't find the one they wanted, they created a new one. And many organizations sort of operated like that was essentially a free market in fact because before the world of box like it didn't if it was in a file, they just didn't care.

Aaron Levy (36:40)

Right.

Steve Sinofsky (36:41)

They only cared about if it was in SQL. And so one of the risks with the model you're describing is that the agents themselves will spin up what becomes like a de facto new system of revenue.

Aaron Levy (36:52)

Oh, they're going to fragment the heck

Steve Sinofsky (36:53)

out of what the IT people think of as some middleware end user BS area. And I think that that is a, is a real risk is that like in a sense like the macros end up running the corporation. And so I think that they've seen this move movie and they've seen what happens when you let marketing just go buy a website on the Internet to do an event and then it's like a huge security vulnerability and the mailing list is leaked and the whole company gets sued and all. And so I think there's a lot more real world tension in this dynamic than, than we just let on.

Aaron Levy (37:31)

Yeah.

Steve Sinofsky (37:31)

But I also think it's one of these ones where you, you, you know, their organizations are going to run at different paces and JP Morgan is going to be the slowest at doing this and the startups are going to be the fastest. But the delta is huge. But even the startup one is a little far off because even startups do need some systems of record at some point and they are going to all start with some SaaS and they're not going to replace it very quickly. So I think it's a little trickier.

Martin Casado (37:58)

So it feels like there's two very competing viewpoints on this one. And like Elon said, it was like, okay, we're going to issue a prompt and it's going to spit out machine code and that's basically the collapsing of layers view like whatever existing interfaces and layers that we've created in the past are all going to go away and it's literally like prompt a machine code. The other argument, like the history of systems is layers never go away, they just get layered. Right. And because a lot of the layers are actually more of like organizational boundaries or like state boundaries or, or compatibility,

Steve Sinofsky (38:28)

they're just, they stay for compatibility. Right.

Martin Casado (38:30)

So the, the other argument is, is like we've actually evolved these layers very specifically and up because of like more human and organizational needs and they're not going to change and the agents are going to go ahead and map to those. And I, I tend to be in that latter camp. Like I don't think that we're, I think like systems are going to continue to be used in fairly similar ways. Maybe there agents using them, I don't think they're going to evolve as much.

Aaron Levy (38:50)

Elon might be back in the like anthropic category of the anthropic growth marketer, which, which is like he, he, like, you know, over the years, when you kind of like study the various it, you know, departments of his companies, like they are the most, I mean, he could do that. He could do it. He's the most homegrown. Like, everything is visible.

Martin Casado (39:11)

Elon AI would do that. The Elon AI would like to.

Steve Sinofsky (39:14)

But also it's.

Aaron Levy (39:15)

And then, and then from your mortal portals, you're like, yeah, we kind of just want a CRM system that like.

Martin Casado (39:18)

No.

Aaron Levy (39:19)

Kind of works the same way every time.

Steve Sinofsky (39:20)

I mean, this is not, this is not. It also hasn't been been not tried before. Like you. If you were to look at an ERP system from first principles, you know, well, in 1970, whatever, when SAP started, there were a bunch of different assumptions. And today you would start from a different set of assumptions about what's important.

Aaron Levy (39:38)

Right.

Steve Sinofsky (39:38)

And you would architect a thing completely differently, but then it would still Only last like 10 years until you thought, wow, that was a broken decision. And I, and so I think that, that, that there's intentionality in layers. But you, you. But there's also this first principles thing. And you, you know, there's. That always will exist because the decisions you can make at first principles at any given time mandate a whole bunch of different stuff. And so even if you don't go with LiDAR, which made total sense 10 years ago, you still need 10 or 15 years to get to where LiDAR not having LiDAR worked. And then now there's going to be a whole bunch of other things that you're like, wow, we could have done that completely different. And so I feel like, like this is again, like this discussion about trying to race to an end point.

Aaron Levy (40:21)

Yeah.

Steve Sinofsky (40:22)

But let's see a first example of what you described happening. And I think that that's going to be the real tell because I think that there were just. Companies will figure all this out and I think that they will just fall back on layers and architectural models because it's the only way we know how

Martin Casado (40:38)

to think about it. For policy, we know how to think about it. For security, we know how to think about it.

Steve Sinofsky (40:40)

But it's also the only way to build a system.

Martin Casado (40:43)

Yeah.

Steve Sinofsky (40:44)

Otherwise you're just building an app. And if you're building an app to do one thing, we don't need all of this a whole different way to do it.

Aaron Levy (40:51)

The, the thing that I'm pretty fascinated by is, and I don't even have any amazing like data points or anecdotes. But the, the, at least the notion of these sort of companies that are emerging in this, in these kind of services categories from the ground up from the pure first principles approach, which is like, okay, well if I could start a marketing agency or consultant, you know, engineering consulting company or I don't know, maybe somebody's doing this for law firm.

Steve Sinofsky (41:16)

Construction worker.

Aaron Levy (41:17)

Yeah, like, well, maybe because it's design.

Martin Casado (41:19)

Construction. Design.

Steve Sinofsky (41:19)

Architecture.

Aaron Levy (41:20)

Design, Exactly. Architecture, design. Anything that would be like a knowledge worker kind of services company. Because you could kind of build your company pretty differently if you had no constraints of. I have no information barriers and boundaries of what people should have access to. I can give the agent just all the context it needs to do its work. I can write software on the fly for particular things. Like, like, I do think that will be relatively disruptive, you know, for some time until the bigger incumbents can kind of, you know, get out of the way on this. And that will at least create, you know, some precedent or case studies of what, what this new sort of corporation could look like. But I do, you know, over time they'll still run into the same exact problems of every other corporation which.

Steve Sinofsky (41:59)

Well, they'll run into the, run into geography or market segments, you know, or distribution challenges. Yeah, like those, those things. Anything outside your little walls.

Aaron Levy (42:08)

Yes.

Steve Sinofsky (42:09)

You will run into the physical world. Right.

Aaron Levy (42:11)

I do kind of like the idea, idea that there are some new business models that open up now.

Steve Sinofsky (42:17)

Oh, of course, yeah, yeah, yeah, yeah.

Aaron Levy (42:18)

Because like there's so much either information or software that, that basically goes underutilized by like 100x relative to like what, what its economic value is simply because like nobody wants to pay $0.05 for accessing a, you know, piece of data or use a tool for $1 once. But like you do give these agents, you know, a budget and a protocol to work with and all of a sudden you're like, oh, like on the fly they can go get medical research for some deep research tasks they're doing and I'll pay like $3 for that. And the agent is able to go and transact. Like it kind of opens up a whole new world of business models for the Internet.

Steve Sinofsky (42:53)

Let me. Oh, I'm going to. You're. That was too nice.

Aaron Levy (42:55)

Okay. No, no, you're going to go farther.

Steve Sinofsky (42:57)

No, that one is one where that's actually the biggest. I think that the biggest sort of in the air problem right now is everybody is trying to figure out the economics of all of this. This when they're off by at least an order of magnitude on how big the opportunity is. Okay, because the new models that people will come up with, that nobody knows what they are right now, but they will absolutely come out with new models because that's what happens with every new technology. And the thing that holds back the sort of discussion now is you basically have a bunch of finance and Wall street people trying to justify GPUs and tokens and things like as if we're in some old world. And they're, they're so, they're, they're viewing the world of revenue as sort of this linear step, literally linear growth curve and something justify all the, all the expense when people are going to create. Like this was the problem with PCs. People viewed PCs as a finite market because they just viewed the consumption of mips as some finite thing. And they didn't think what would happen if we put all those mips on every desktop. And in particular, particular people thought software just came with the mips and nobody thought, oh well, they'll just sell the software. One guy did. And, and it turns out that was like a really good idea. And the same Bill and Paul and, and the same thing happened, but the same thing happened with the cloud, which was people looked at the cloud and they said, oh, we're going to take all of the, the server business, which was like literally like 60,000 units a year, right. And we're just going to move it to someone else's data center.

Aaron Levy (44:28)

And that's the, and that would be the business.

Steve Sinofsky (44:30)

And then we'll divide up the price, right? And nobody went, oh, they're going to, people are going to use a thousand times as much. Right. Of the resource leveling if we move it there. And that's exactly, I mean that's the thing that I, it just drives me absolutely bonkers that the Wall street models have this fixed revenue pie, zero sum thinking and it's, it's this weird zero sum where they, they just think that the amount of money that a company is going to spend and like this was the problem with, with Salesforce that they think when you were starting too. But like Mark was, was just blazing the trail, which was like the CRM business was 2 billion a year and it was 2 billion in like you had to go buy all these servers and these Oracle licenses and this huge headache and years of deployment and consulting when if you could just get salespeople to sign up individually, they all will sign up with no friction. And that's, that is exact, There is No, no doubt that that is what's going to happen with AI.

Martin Casado (45:22)

Let me give you an example of this. So I, you know, I've been in for investing for 10 years now. I probably have a portfolio of 240 companies I work with. So I have visibility to, let's say into 50 of them. These are all infrastructure companies. Some historically have done well, some not so well. Every single one of them has gone asymptotic in the last six months. And you're like, okay, why is this? It just turns out there's so much more software being written now than ever

Steve Sinofsky (45:44)

has been, of course.

Martin Casado (45:46)

And so it's like, and it's not because they've got enterprise customers, you know, it's just because there's just so much consumption of the infrastructure layer right now. And so with more software, with more agents, there's gonna be a lot more consumption of computer resources. So certainly in the case of the computer side of things, we see a mess.

Steve Sinofsky (46:03)

Well, we haven't even gotten to the point yet where everyone's phone is a huge consumer of AI, right? So once everybody's phone and on device, like once your phone on device is consuming AI, the amount of it is gonna go up by a billion.

Aaron Levy (46:16)

So do you like the micropayment piece?

Steve Sinofsky (46:19)

All of them. The micropayments. There's a little bit of micropayments that has come with every technology where they always think that like you'll be able to get like a fraction of a pen. But in the end, especially in the enterprise, like people are just gonna consume things. It's just cheaper and easier to buy like a bulk license for a bunch of stuff.

Aaron Levy (46:36)

Yeah, you want some predictability on that.

Steve Sinofsky (46:37)

Well, you want predictability and you just want like to not have to think about it.

Aaron Levy (46:41)

I just, I like the idea that it is the first time that you could like there's just the agent doesn't care about the friction of a small transaction. And so it's the first time that you could have resources behind a paywall that something will actually be willing to pay for that resource.

Steve Sinofsky (46:54)

The world has built up the infrastructure to aggregate those payments into something efficient for a customer, a service.

Martin Casado (47:00)

Right. And, and because tokens are such a significant part of cogs right now, it is pushing the industry to do usage base in a way that we had like, I remember when we went from like perpetual to recurring and that required like a bunch of huge changes. Like we're like, we're going through the exact same change right now towards usage base. And usage base is pretty granular and it actually allows. I mean again, you will have a contract with like, you know, we went

Steve Sinofsky (47:23)

through this with, with aws. Like people learn earned to do the. To do. And we went through the phase where like people were like so terrified of Cloud Compute that they were like, we need companies in the middle to help us find the cheapest and to arbitrate it all.

Aaron Levy (47:38)

Okay, well now you write tokens into this and I don't see how we possibly have time in this, in this conversation, but as long as you guys can. Okay, but like, like the, the engineering compute budget conversation to me is going to be just like the most wild one in the next couple years. It's just like how much did you allocate. Allocate of your engineering expense to tokens? And, and it's like you, you know, depending on who you read on Twitter, it could be 1% and the other and the other side could be 100.

Steve Sinofsky (48:04)

And it's like, yeah, but this stuff. No, no.

Aaron Levy (48:07)

CFOs have to. Literally, they actually have to know the answer.

Steve Sinofsky (48:10)

I understand. They have to know. Okay. CFOs always want to know the answers to things that don't have answers.

Aaron Levy (48:15)

Wall street is going to make them know the answer.

Steve Sinofsky (48:17)

No, no. Wall street is going to make them come up with some number and hold them to it. Then they'll get fired and then it'll. But it.

Aaron Levy (48:22)

Okay, okay, I hear you. R D is C. Somewhere between 14 to 30% of revenue of any public company. Let's just say. Okay, the difference between compute being 2x the cost of your engineering team or 3% more is like that's all your EPS.

Steve Sinofsky (48:41)

I get it.

Aaron Levy (48:42)

We will have to know the.

Steve Sinofsky (48:43)

I'm perfectly willing to sacrifice A few CFOs at the altar of this. I want that.

Aaron Levy (48:48)

That's a good clip, by the way.

Steve Sinofsky (48:50)

But the reason is because again, this is. This is trying to know what we just don't know right now. Yeah. And. And this has happened with Internet bandwidth. This has happened.

Aaron Levy (49:00)

No, this is not even close to Internet bandwidth.

Steve Sinofsky (49:02)

Oh no, no, no.

Aaron Levy (49:03)

I beg to differ.

Steve Sinofsky (49:05)

Like, like people were free. It happened with vacuum tubes. It happened with transistors. It has happened with every technology. There was this. Oh my God. It happened with programmers. There was a. There, there was a time when programmers were going to swallow every company.

Aaron Levy (49:18)

Yeah.

Steve Sinofsky (49:19)

And that's not. It was in my lifetime. Not some made up. Weird. Yeah.

Aaron Levy (49:22)

But I don't think we've ever had a point where every end user in an organization has. Has sort of a completely elastic ability to spin up a resource on their behalf.

Martin Casado (49:32)

Well it's certainly.

Aaron Levy (49:33)

That actually is actually in many cases very valid for them to go spin it up.

Martin Casado (49:36)

But it certainly, it certainly rhymes with what happened in the early 2000s with Cloud. I remember very similar discussions when we went from capex to OPEX and then unlimited spend.

Steve Sinofsky (49:44)

Oh no.

Martin Casado (49:45)

And they were.

Steve Sinofsky (49:45)

Remember there were companies who the CFOs would sit in our briefing center here and say you don't. We are like we are agriculture.

Aaron Levy (49:52)

I can see the rhyming.

Steve Sinofsky (49:53)

I can see we are an agriculture company. We. We only know capex. We have no or no, we're sold through this. Right.

Aaron Levy (50:00)

Right.

Steve Sinofsky (50:01)

No we both or like oh no. We are an OPEX based company. So if you tell us we love the cloud because we just shipped everything, shifted everything to opex and so all of this stuff like the rules of accounting work out also don't I keep thinking do not disappoint. Count the local compute engine as being a release valve for all of this.

Aaron Levy (50:20)

When's that going to happen?

Steve Sinofsky (50:21)

Well the question is it's not when does it just happen with today's view of the technology but how all of a sudden wow, there's a.

Martin Casado (50:27)

Has that historically ever gone that direction?

Aaron Levy (50:29)

Yeah, exactly.

Martin Casado (50:30)

It goes the opposite. Right?

Steve Sinofsky (50:31)

No, it went all to the client.

Martin Casado (50:33)

Well, okay. And then go back to the 80s.

Aaron Levy (50:35)

Yes. No, that's most of the examples that we're hearing so far.

Steve Sinofsky (50:38)

Whoa, that was uncalled for.

Martin Casado (50:43)

Vacuum tubes.

Aaron Levy (50:44)

He's talking about vacuum tubes.

Steve Sinofsky (50:45)

But I do those examples because you can't argue with them and it's easier that way.

Aaron Levy (50:48)

I. You're right.

Steve Sinofsky (50:49)

I can't prosecute it back but it's only been, you know, 10 or 15 years that it's all that it moved back to all cloud. And then what has happened recently with that A lot of people wake up in the morning and they say oh, we're moving back to doing some critical but stationary workflows on on prem and with AI.

Martin Casado (51:09)

That's true.

Aaron Levy (51:09)

Dude, you wrote the blog post, man. Don't make me go through the archives. I had to deal with so many Wall street questions on that one, by the way.

Steve Sinofsky (51:17)

Well because you're also because you're comparing competitor what went. Went back to replace.

Martin Casado (51:22)

We're talking about two very different. I agree, I agree with like building your own data center is. I'm talking about this, this notion of edge computing where things go to devices like that seems to be.

Aaron Levy (51:33)

I, I'M more, I, I'm more in the like cloud maximalist camp. But, but, sorry. So you just don't think, you don't even think for like one second that it matters whether like how you're supposed to be an engineering leader right now managing the compute budget of the engineering engineering team?

Martin Casado (51:47)

No, of course it matters. I just think in the long term this thing will get.

Steve Sinofsky (51:50)

Oh sure.

Aaron Levy (51:51)

Oh, at long term, all of them. What do we. Who cares?

Steve Sinofsky (51:53)

We don't even need podcasts in the world. Here's what I think. Let me.

Martin Casado (51:56)

Here's a pragmatic.

Steve Sinofsky (51:57)

But here's a rule, here's a rule of thumb first, like the startups are going to burn through available capital pretending like it's not a problem.

Aaron Levy (52:05)

Yeah.

Steve Sinofsky (52:05)

And they are going to do that.

Aaron Levy (52:06)

Yeah.

Martin Casado (52:06)

A lot of do that anyway.

Steve Sinofsky (52:07)

Right, right. And a lot of big companies are going to be so terrified they're just going to freeze and not do anything and then people are going to actually start buying it on their own and they're going to do all the things that companies do do when they're big, have a lot of money but don't want to spend it. And in the middle we are going to see like if you pick a category of product or go to market or something, there are going to be people who are willing to make the bet for whatever reasons that they can because of their financials and they are going to go ahead and they are going to become the people who lead in the space so long as they can maintain the financials. Now they might do it in, they might say, oh, we're going to just do it here in this particular application space or here in this particular usage space. Space. But this, this idea that there are. That nobody is going to go in and because they're so terrified that the CFO is going to get fired or something.

Aaron Levy (52:55)

No, no.

Steve Sinofsky (52:56)

Is just crazy.

Aaron Levy (52:57)

Yeah.

Steve Sinofsky (52:58)

And. But then there are going to be CFOs who make a mistake and like, okay, everybody gets a little. Yes, well if they do that, that's a complete fail.

Aaron Levy (53:03)

Yes.

Steve Sinofsky (53:04)

Because.

Aaron Levy (53:04)

But also. Or like you get you like you. There is a really interesting like you know, finesse here which is like you don't really want your engineers right now having to think about computer budget because we're still developing the. Oh, okay. So that sent you over.

Martin Casado (53:20)

Feel like we've been having the discussion for 15 years when it comes to clag.

Aaron Levy (53:24)

This is totally new. Only, only like 10, only like 10% of your engineering had to think about cloud infrastructure.

Martin Casado (53:31)

Spend in 2016 and 2018 timeframe. There was a whole set of companies that was basically like, like the dashboard for what was it called?

Aaron Levy (53:40)

Finops, where the developers is very cool right now because of developers would have,

Martin Casado (53:44)

would developers have cloud spends were getting out of control and API spin were getting out of control. And so it was like, you know, here's your Twilio spin, here's.

Aaron Levy (53:52)

But, but you know, it's, it's pretty different and I'm going to wait for all the comments to come in on YouTube to call you out of this. Like it's, it's like you can get into a conference room and just be like, hey, can you make that one, you know, kind of algorithm a little bit more efficient so you don't use as much, you know, of our cluster at this time of night or whatever. And then you get out of the meeting, somebody go improves it and you're good. This is like every single prompt that every engineer is doing, doing. Like, do you, like, you have to decide like do you want that to be a long running prompt? Do you want to be a long running agent? Do you want to parallelize that? Like, like, do you want like what is your comfort level of wasted tokens? Like for me right now I'm like, yeah, we should probably waste a lot of tokens because that means that we're like trying new things and like, like should your head of engineering be happy if, if you run 10 experiments in parallel and thus you're obviously going to be wasting 90% of the token tokens but you're going to choose one of the successful paths or do you want to tell the team no before you go do that, make sure to really go and design the perfect system. We actually have a whole bunch of open questions that are going to start to happen literally as of this recording time. People are freaking out right now on the new Claude code Max plan because they're getting blocked after three prompts. So this is going to be a very real topic until we can actually find a way to build data center capacity.

Steve Sinofsky (55:09)

Oh, that's a different problem. Okay, no, because no one is well, assuming, well wait, no, you can assume that if we build more capacity the price will drop because there is more capacity and we're priced now based on limited capacity, whatever. But like this is just going to get worked out and I feel bad for the those that have to make a decision immediately about which 17 people get no more tokens this week or, or whatever and that the whole company is walking around with like A token card and the person in the lunch line.

Aaron Levy (55:38)

Yeah.

Steve Sinofsky (55:39)

Like it's the person in the L line is punching their card every time they do something. But, but like I, you know, I, I, I don't know. Like somebody we were talking about today about performance and how like you know, we used to write command line tools that spit out the time it took after you ran a command line just so you knew and if you knew you were getting better or worse. And you know, but it, you. The thing is is this is all going to go away. There's absolutely no doubt.

Aaron Levy (56:05)

Yeah.

Steve Sinofsky (56:05)

That this just goes away.

Aaron Levy (56:06)

I think on the 10 year time frame. 100%.

Steve Sinofsky (56:08)

And the biggest reason it's does is, is because you, you have to do the Benioff kind of math which is if you're paying an enterprise salesperson, you know, a million dollars a year, you have to ask how much is their tool worth?

Aaron Levy (56:20)

Yeah.

Steve Sinofsky (56:21)

And if you're paying an engineer X dollars a year. Well, at some point their, their tooling

Aaron Levy (56:27)

is worth, it's absolutely worth it.

Steve Sinofsky (56:29)

And it's not going to even be an issue.

Aaron Levy (56:31)

And, and yeah, yeah. I don't think it's, I think and

Steve Sinofsky (56:35)

so if there's a capacity thing in the short term.

Aaron Levy (56:36)

Yeah.

Steve Sinofsky (56:37)

That's a different, that, that is TR. A different problem driving the price than this. Just we're going to forever have to be in some budgeting exercise.

Aaron Levy (56:43)

I think, I think law of large numbers. Solve this because, because eventually you have enough engineers using this much compute. But like we're in a transition phase where like most people thought you know, the two year ago level of spend on AI which is a guy, it's a chatbot and.

Steve Sinofsky (56:57)

Yeah. Yeah.

Aaron Levy (56:57)

But they were wrong. Yeah.

Steve Sinofsky (56:58)

Right.

Aaron Levy (56:58)

Okay.

Steve Sinofsky (56:59)

But they were wrong. But they were wrong.

Aaron Levy (57:01)

We tried to warn them.

Steve Sinofsky (57:02)

No, but they were wrong because they saw it as, as this, this particular use case.

Aaron Levy (57:07)

Yes.

Steve Sinofsky (57:07)

And, but again like you, you know like the vacuum tube thing you made fun of. Yeah, but like there was a time when they thought that the, that like whole like all of the Dakotas would be covered in vacuum tube warehouses and people on roller skates would be running up and down the aisles replacing vacuum tubes just so we could fight World War II. And then like that was how that was the. And they thought that and then someone said hey, how about a transistor? And like we are going to have a transistor moment with all of this. It might just be more supply the way we think of it, but it also might be an actual algorithmic fundamental change it could be a change in the hardware. There's a lot of stuff that can happen that changes this particular moment in time. It's just this I think it's particularly weird that everybody has just gotten to token. Yeah, which is the same thing that happened with IBM and mainframes. People were on mips and then one day the reality was IBM was selling more mips for fewer dollars every year and didn't even realize it. And they were still pricing their mainframes by MIPS until it got pointed out to them that they were on a decreasing curve because they were making mips faster than they can charge for. And that's what's going to happen. Guaranteed. I just said that in a hardcore way.

Martin Casado (58:23)

I think that was great.

Steve Sinofsky (58:24)

Like sounds really great to sound like I know what I'm making. Guaranteed.

Aaron Levy (58:27)

Oh, I actually probably believe it

Podcast Host (58:32)

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 X16Z and subscribe to our substack@a1616z.substack.com thanks again for listening and I'll see you in the next episode. 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 details including a link to our investments, please see a16z.com disclosures.