Steven Sinofsky: 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'll 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. The funniest concept that the more code we write, the less we would need engineers. It' be the opposite 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 what how do I fix that problem or when there's a security incident. I mean, we're just getting started with the jobs on this front.

Martin Casado: They're going to hit a wall at integration and 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 and you can't just say it's going to integrate. AI actually doesn't help to integrate anything.

Steven Sinofsky: Hey, we are here monitoring the situation live and we're very excited to talk about a bunch of AI stuff and we have three of us are here today. Uh there's me, Stevenski and Martin Casado who will wave and say hi. I'm Martin and and Aaron Levy who is is working on the elevation of his hair today. So, we're excited about that.

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

Steven Sinofsky: And is that just a token issue or a parameter number of parameters issue?

Aaron Levie: It was too many parameters. Too many parameters.

Steven Sinofsky: Okay. I have the same thing but in reverse. Okay. So, you listen, you have a distilled model. There you go. My I run local. So, we had a lot of there's been a busy week of things, but we're we want to bubble it up a little bit and just start talking about where where things are are heading. Um, but I'll I'll let I just kick it to you, Erin, and you start where you are the most excited this moment because you have visited a ton of customers this week and have learned a lot. You share 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 Levie: Yeah, I think my uh it feels like my job these days is just bring reality to the valley and then bring the valley to reality as uh as much as possible. And it's a it is a kind of a crazy divide that that that exists at the moment. Caused by Yeah. Well, I think the gap is and Martine, 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 you know, the the technical aptitude of an engineer is just like insanely high. The level of wired inness to what's going on on the internet is insanely high. The 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 like 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 and the work is verifiable. So you have like, you know, five or 10 things that make agents work in an enterprise context for engineering or at least even a shortcut context for engineering that that tend to be uh there tends to be a gulf between the way you work that way in engineering and the rest of of sort of knowledge work. And so and so a lot of what I see is trying to figure out how do we kind of you know bottle up all of the greatness that is you know what we are seeing from coding agents what we're seeing from agents that can use computers um to how do you bring that into the enterprise where the workflows are are you know quite different the users are less technical the data is much more fragmented the systems are much more legacy and so that that tends to be the divide it's not even that we're like talking past each other like in a in one of those kind of those kind of classic like government versus industry It's it's just literally like there is just a pure workflow and and and technology set divide and and that's why it's going to be you know a number of years for this sort of diffusion to to roll from what we're seeing in Silicon Valley. What we're seeing is tech shortcuts all around the world into the rest of knowledge work.

Martin Casado: 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 is using chat GPT very effectively. What what they really should be saying is you know whatever like I listen I sit in these boards too. So the board goes to the CEO and 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's 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 processes that have been you know worked on on for a decade around you know data and governance and operations and compliance etc. That's kind of right now where I think like Aaron is right between the secular trend and the the organizational decision body. uh 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 it's it's tepid because and 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 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 Levie: Yeah, I I 100% agree with that. there's also this very interesting um dynamic. you go to an engineering team classically for the past, you know, and you know, Stephen, you can take us back in in history on this one. And one of like the easiest ways to stall a project was just getting the architecture, you know, kind of the the fights on, you know, 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. um because of the pace of change in AI, um you actually have this incredible dynamic where the the labs uh you know are are obviously leaprogging each other so frequently but with with not the exact same paradigm of how you should deploy agents and how they will work and is the is the 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 a a point where these are completely fungeable technologies and so that actually creates a 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 and which architecture path do I want to get behind because I've been burned by doing the wrong thing in AI maybe 3 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 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 in just making decisions.

Martin Casado: I actually 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 um you know circa 6 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 they 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 like just view it as a user so instead like take your product make it 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? And 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 can consumed by that and like that's the actual modality but you know within a year now you've had to rearchitect your software twice.

Steven Sinofsky: 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 five two-year stints at startups that went from seed to series A to aqua hire or something. 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 consequences. Yeah. I I actually I have like all these visual aids today. you have people in 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 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 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 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.

Martin Casado: Well, Ste 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. Oh, yeah. Yeah. Right. And so I'm not going to say the name of the I I spoke to someone yesterday who works 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 I'm no joke in No totally. Well you get whatever you measure. So yeah that's right. So like it's like the extreme form of what you're saying, Stephen. You have people that are like being fake productive and producing a lot of, you know, you could say perhaps problematic artifacts just because they're they're using these models.

Aaron Levie: 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 6 months later and so companies were just filled with like with thousands of these dead web is what what the expression was. your point about being passed to the different human, you know, based on the role that you needed to interact with. you know, agents basically don't have any there's no real exception yet for the agent having the same problem because you basically, you know, as you pass through a different human, it's it's a different set of access controls that that that human has. And if an agent can sort of bypass any of those steps, then then that's how you instantly get the security risks that that like you you need to kind of pass through those steps so that way you don't accidentally, you know, get to the wrong piece of information and there's verification. And so there's a lot that that you need to kind of build out for agents to be able to go and and work with all these systems. And and we've talked about this, but like most legacy environments don't have the most authoritative, you know, access controls. So you're always as a human going and saying, "Hey, Sally, can you share that thing with me that 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, you know, stuck.

Aaron Levie: and so so the big news last week was uh was I I 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 said you know like we want to be used everywhere across all of our all the different agents. Um, and I see that as a a little bit of a bellweather because I think as Salesforce goes, so does a lot of of of enterprise software. And I think a lot of people are going to try and, you know, have to figure out what is the new business model in this headless world. You know, do you do you charge a little bit of a a small just API tax? Is there a seat for the agents? So, there's obviously some work to do with that.

Steven Sinofsky: I also think I just I think on this one 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 it has to have an identity. 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 a super smart agent that knows everything that I'm not allowed to know.

Martin Casado: Exactly. So actually in in fact um so this is playing out in many domains. You can even make the argument that like a a a headless SAS doesn't make sense. And and here's the argument. The argument is let me give you an example. So um if you use openclaw, do you know why you use a Mac Mini with openclaw? It's number one for iMessage. 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 is all of the websites um 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.

Aaron Levie: Oh no I'm taking the other side on that one big time. Yeah. Yeah. No, but but let me just let let me let me simplify the argument so we could actually have it. h I mean uh I would just say that that that any software that has a good API the agent would absolutely prefer to use the API and then you and then you pop into the browser the moment that you run into some execution problem

Martin Casado: However, these models are trained on data and RL environments from existing software that didn't have those APIs. And and and 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.

Aaron Levie: But I I just think to me it's it's more of just an inefficiency of of navigating through pixels versus just you know you you can just you can just

Steven Sinofsky: an addage in systems Aaron is that is that layers never go away. You just get layered. Clicking through a 100% right. But but but but the but to to have support the point like the new codecs the computer use that on the desktop is is you know just insane and I I mean Stephen obviously knows you know everything about how it would work. I 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: now we're going to have 10,000 new PE 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 the first order because it wasn't architected for that volume.

Martin Casado: I just feel like we understand like whatever if it's readonly data you cache it you know like all the state issues are around mutable mutable globally shared state we understand the limits of those. when you code um 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. I would say anecdotally watching companies struggle with AI coding... I don't think we know how to do that yet.

Aaron Levie: Well, I I love that point because that gets back to where we started, which is the difference between scale and not scale and why it's perfectly rational for big company people to be like, "No freaking way is this coming into our company because a big company is about to the wheels are going to come off a big company... and I I feel this is so critical Steve... 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 mounds of extra work to do. 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 guard rails in place... AI built probably 80 to 90% of the feature and the the thing that slowed down the release of it was we have to do a full security review. I do think it's a 2 to 3x gain maybe across the board. By by the way, this is actually why I remain unbelievably optimistic on jobs because I don't think that you like I just think we've gotten it wrong on on thinking you know all the places where you're going to remove humans from this.

Steven Sinofsky: this was a book in the 80s called the end of work. sorry it was in the '90s. 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. 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 Levie: it's like to me it's like the funniest concept that the more code we write the less we would need engineers. to 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. you sort of forget, well, like John Deere is trying to make automated tractors and Caterpillar is trying to have AI systems and Eli Liy is trying to design even more pharmaceutical therapeutics. Mark and Dre predicted this you know 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... but you still need then an expert or a semi-expert... So, so all of the, you know, predictions on don't go into coding and don't go into software engineering, I think, will be proven quite quite wrong.