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. There's me, Stephen Sowski and Martin Casado who will wave and say hi.
Hi Martin.
And Aaron Levie who is working on the elevation of his hair today. So we're excited about that.
It just keeps getting more vertical and I thought I could kind of tame it but it didn't work.
And is that just a token issue or a parameter number of parameters issue?
Too many parameters. Too many parameters.
Okay. I have the same thing but in reverse.
Hey, listen. You have a distilled model.
There you go. I run local. So we had a lot of there's been a busy week of things, but we want to bubble it up a little bit and just start talking about where things are heading. But I'll let I just kick it to you, Aaron, 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've shared a lot on X, but I think you're the most in the trenches CEO who is really talking to customers every single day in the enterprise, which is what the three of us tend to look at the most.
Yeah, I think it feels like my job these days is just bring reality to the valley and then bring the valley to reality as much as possible. And it is a kind of a crazy divide that exists at the moment.
Let's take a step back. I actually think it's super interesting. What is it? What's the gap caused by?
Gap caused by. Yeah. Well, I think the gap, and Martin, I'm sure you see this, but I think the gap is caused by the styles of work that exist in Silicon Valley and in engineering roles versus sort of the rest of the world. So, and we've talked about this a couple times in different forms, but you know, the technical aptitude of an engineer is just like insanely high. The level of wiredness to what's going on on the internet is insanely high. The ability to use your own tools and make your own choices is insanely high. And when things go wrong with the systems that you choose, you can just quickly debug them and then make them sort of work for you. And then obviously you have all the benefits of just the models are really good at code and the work is verifiable. So you have like five or 10 things that make agents work in an enterprise context for engineering or at least even a startup context for engineering that tend to be there. There tends to be a gulf between the way you work that way in engineering and the rest of sort of knowledge work. And so a lot of what I see is trying to figure out how do we kind of bottle up all of the greatness that is what we are seeing from coding agents, what we're seeing from agents that can use computers, to how do you bring that into the enterprise where the workflows are quite different, the users are less technical, the data is much more fragmented, the systems are much more legacy. And so that tends to be the divide. So it's not even that we're like talking past each other like in one of those kind of classic like government versus industry. It's just literally like there is just a pure workflow and technology set divide and that's why it's going to be a number of years for this sort of diffusion to roll from what we're seeing in Silicon Valley, what we're seeing as tech startups all around the world, into the rest of knowledge work.
Martin just to build on that you have a ton of experience in big companies. One of the other issues though is scale and the way the difference in scale that Silicon Valley operates at at the startup level versus everyone else.
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 ChatGPT very effectively. 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'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. And so I don't you know when we say scale often 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 age-old processes that have been worked on for a decade around data and governance and operations and compliance etc. That's kind of right now where I think like Aaron is right between the secular trend and the organizational decision body. And this is something that we actually track very closely because we're starting to see now I would say in the last few months finally some real kind of inroads into the enterprise but it's tepid because and the last thing I'll say of this one of the reasons is there's a lot of skepticism because the board wants AI, CEO AI failures have created some amount of bruising which is requiring these companies to get past it in order to do kind of the second go at it and so I think this is exactly where we are.
Yeah, I 100% agree with that, which is it's good to start with agreements because we know how quickly that fade.
We'll disagree the rest of the show.
Exactly. Exactly. That's the only time we're going to agree. I think maybe one more point on the board for agreements. Maybe you guys would agree. There's also this very interesting dynamic. I would say this is a minor one relative to everything else is probably 5% of the problem. I think it'd be more fun to talk about the real problem, but there is a fun kind of as an aside, there's a fun dynamic where, you know, you go to an engineering team classically for the past, and Stephen, you can take us back 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 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. Because of the pace of change in AI, you actually have this incredible dynamic where the labs are obviously leapfrogging each other so frequently but with not the exact same paradigm of how you should deploy agents and how they will work and is the agent harness in the computer? Is it outside the computer? Do you run it in your cloud? Is it hosted? What tools does it have access to? Like we are like this is not a point where these are completely fungible technologies and so that actually creates a bit of paralysis because now as an enterprise architecture team in the real world you're like man like what horse do I want to kind of get behind and which architecture path do I want to get behind because I've been burned by doing the wrong thing in AI maybe three or four years ago and I went down some path that now is deprecated or not the right strategy anymore. So to some extent the speed of our change in tech actually reduces the ability for the tech to get diffused into the really really important workflows because now you have a lot of paralysis in just making decisions. So I actually think it's kind of fine because there's still so much upgrade work people need to do in their infrastructure and their systems and their data. But this is kind of an interesting dynamic where I'll go have conversations with CIOs and their AI teams and I'll say, Hey what are you using for your chat system or your core agent orchestration? and they'll say, Yeah, we're in the middle of a debate between these two or three paradigms. And you hear that across almost every single customer because there is a little bit of a nervousness of like who do you get in bed with and how much do you sort of fully lock yourself into one particular path and we also know that if you don't lock yourself into a path it's always then you're building for this sort of duality which is also takes a lot of work architecturally.
Oh, actually I sorry I hate to jump in, Steve. I just like there's a like so Aaron is totally correct and there's a very specific instance of this playing out in product companies right now. And I'll tell you what it is. So software product companies circa six months ago they viewed integrating AI was like you're actually integrating it into the product right so everybody was like adding like whatever this chat feature or like 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 right so instead like take your product make it a CLI tool and then have the AI be an agent that actually uses it. So you're not fusing the two. You're just making it more useful for AI. This is a very very significant architectural and mental shift, right? And so we started as pure product and then we didn't quite know what the end thing looked like. So we created this 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 be consumed by that and that's the actual modality but within a year now you've had to rearchitect your software twice and so I think no matter many places that you look in the industry is having this dilemma of actually trying to figure out what the final form looks like and Stephen, you will remember all the hybrid versions of cloud like remember like remote desktop and all these things like I think we're kind of like speed running that evolution to the final form.
Right and I think 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 your job history is you five two-year stints at startups that went from seed to series A to acqui-hire or something.
Yeah, he didn't learn anything.
You never you don't your frame of reference is not picking an accounts payable system that's going to last consequences.
Yeah. I actually I have like all these visual aids today. So here's like the ultimate engineer if you're in Silicon Valley is Gilfoil and Gilfoil is like I don't want to talk to anyone. And I will just write the code and you go do your thing. And the thing is that you have people in enterprises that are saying I'm going to use the model and do my thing. But they're only they're going to hit a wall at integration and the thing that's not different about AI and that agents don't fix that nothing fix is that any enterprise of a thousand people or more or that's older than 10 years is just a mass of stuff that's sitting there waiting to be integrated and you can't just say it's going to integrate. AI actually doesn't help to integrate anything. And so even if you change everything, the people say, Oh, no. If you make it an agent, then it can just go ahead and 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 talking about payment, not reservations. And so like what I think is so exciting is that now we have proof of this technology that everybody likes it. I mean, you see all the people who don't like AI are saying, Look at what's happening in law firms because people are seeing hallucinations and it's ruining legal cases and all this. And the reason that's happening is because the 25-year-old associate is the one using AI successfully already and had been using it for a year.
Well, 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 spoke to someone yesterday who worked for one of these large companies that famously does this and he's like me and my co-workers have agents do useless tasks just so that we can I'm no joke in.
No, totally. Well, you get whatever you measure.
Yeah. Yeah, that's right. So like it's like the extreme form of what you're saying, Stephen. Yeah. People that are like being fake productive and producing a lot of you know problematic artifacts just because they're using these models.
When the internet happened, all of a sudden every company needed websites. And so like a very famous moment in time was not too long ago when every internal team had like a team website and they went out and they got like a vendor to write HTML and to create their site and then there was a team. But like there's nothing dumber than having a team website at a large company because a team gets reorganized like six months later. And so companies were just filled with thousands of these dead webs is what the expression was.
Go ahead. No, but we should drill into your integration point because I do think this is something for, you know, sort of some reality to settle in in the valley on the real world's sort of journey to fully being identified and what that's going to take and what that's going to look like and 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 a different set of access controls that that human has. And if an agent can sort of bypass any of those steps, then that's how you instantly get the security risks that you need to kind of pass through those steps so that way you don't accidentally get to the wrong piece of information and there's verification. And so there's a lot that you need to kind of build out for agents to be able to go and work with all these systems. And we've talked about this, but like most legacy environments don't have the most authoritative access controls. So you're always as a human going and saying, Hey, Sally, can you share that thing with me that I don't have access to? Or, Hey, Bob, what's the number inside your data system for this question? And so if agents just get the exact same permissions that you had, then they'll just run into these walls everywhere and they won't be able to complete the process. And unlike a human, they're not going to know to go talk to Sally or ask the question of Bob. So, they're going to just be kind of stuck. So, what's going to happen is you're going to have a lot of agents that don't have access to the right data. They're kind of working through systems that are not the real sources of truth for the information. They're getting the wrong number. They're getting the wrong document. So, this is the real work that enterprises have to go through right now. The good news is that it's actually a great time again if you're a startup because you can just you get to know all the problems right out of the gate. So you can design your organizations to try and avoid this. But for big companies, there's real work that goes into how do I upgrade my systems? How do I modernize my technology environment? How do I make sure that agents will have access to the right data, the right documents, the right context to be able to do their work. That's sort of the work ahead. And there was this headline of OpenAI working with in Codex working with Accenture, Deloitte, all the major system integrators and there were some kind of snarky comments online around it that I was fascinated by because it sort of showed how maybe great that divide is from the rest of the world versus those in tech because to me it was like the most obvious announcement of all time which is a large enterprise is going to have to go through the change management, the systems implementation, the integration of technology for these agents to be able to go and work. And so there was this sort of people thought it was somewhat ironic that, oh, we need people to implement the agents that are going to go automate the people. And it's like, no, that's exactly how it works, right? Like you actually do need to do lots and lots of work to be able to be in a position where agents can actually go and help you do any of the automation. So that and that's going to be there's going to be businesses that are doing that for decades. Like it's going to be an incredible opportunity for this kind of next generation set of firms as well as existing ones that lean into that.
Let me throw this out there. Well, first I think the other thing that people shouldn't celebrate when those fail because they will fail because they're as Martin was describing they're a lot of them are going to be these sort of top-down mandates where they pick like the most acute problem in the company and think oh AI is going to go solve that and the IT people are going to be like oh god that's the worst. Yeah, don't do that. That's the worst system to try to do that. But the CEO or CFO or whatever is going to be obsessed with solving or the most likely the customer service person will be obsessed. But I do think if I were advising a startup specifically in order to enter the enterprise space in that way definitely would be thinking about not just like building a company that step one I only work with all the headless SaaS software that's out there because there just won't be any like the but the thing you can do is structure the value that you offer and also this applies to what you go do in a company is it's really a fork and the fork is is this an agent that is seeking information and presenting it to some human or is this an agent that's supposed to go act and do something like is it acquiring or is it doing because it turns out that's what happened with the internet. The internet got very very valuable when the first step was just providing access to things to people.
Yes. And like all of a sudden all the sites that were like that literally did integration like hey I need expense reports but viewed by department or I need to see our current inventory status across like the two companies we've acquired all of a sudden the web became the integration point and so I do think that if you just show up first and just say hey we can actually use agents to learn stuff about what's going on in a company and in particular because you're here Aaron like learning across the file systems becomes way more possible than it ever was before. In fact, AI might be the first time that inside a company search can actually provide immediate value because the web just wasn't structured to deliver those results and then you start to think once you could bring them all together then you can add like an agent that has an approve button or a reject button or something like that.
Let me just try and provide finally the point where I get to disagree.
No no you can get invited back. You're invited back. So good.
No no no. I think this is a very legit view but it's not the only view. And in light of AI I think it's not the only kind of compelling view. So here's the other so let me just try and rephrase. So the current view is we've got like AI is software. It works in a different way. We have a current set of systems and we have to integrate this new type of software with our existing systems so that it can get access to data it can do things but in a safe way right so here's the kind of end-to-end argument of why this isn't about evolving software systems the end-to-end argument is these LLMs are non-deterministic they are smart they deal with the long tail of complexity and it turns out those are all things humans do too and we've spent 40 years building interfaces, processes, and design to deal with messy humans. And you know, we know who to access and we have access control. And so if you have the mindset that an agent is more like a human and you hire the agent, you give it its own email address. It can access documents like humans can. It can log in. It can request the things that it needs. Then it will be drafting on all of the process that we've put in place for humans, not for software. And so I would just encourage us as we have this discussion like listen I grew up like you guys in software. I always think of every system like software but these models don't integrate well with software actually. I think it turns out and what we're learning as an industry is if you view them more like humans and you draft on the mechanisms we put in place for humans they are much easier to integrate.
Well and I think I LOVE THAT.
I think we agree with that for sure. I think the issue is humans have a bunch of extra benefits that the agent doesn't have. The human has a lot of context that it gets for that they get that we get for free by virtue of we can keep track of the myriad relationships that we've built in our organization and the person to tap on the shoulder when we need something done or we need to get information that's not documented in a company yet in a way that the agent can just sort of draft on. And so I mean I think we all would agree that you can't treat this like software. You treat these as people accessing systems and tools, but they are at a they're both at a massive advantage that they can work in parallel at infinite scale. And they're at a disadvantage in that they don't know who to tap on the shoulders.
Hey, I listen, Aaron, I am all for agent onboarding. Like, you know, the agent comes and it goes to orientation and then the CEO gives it the culture discussion and then every department does their pitch like this is what we do and like I mean I actually honestly think given the technical nature of these agents and how much entropy they have and kind of how unruly they are, we're going to have to go through the processes that we've refined around humans because humans have all of those things. And so I just, you know, it's more about providing schools for them than somehow building some fancy index database.
Totally totally agreed. I mean, of course, what I love about that is you just keep going with the analogy because what that is is it's the same argument that humanoid robots will be the best kind of robot, which is we have a whole world designed for humans. And I saw at the Consumer Electronic Show, I saw this robot go into an elevator and then there was a button pushing robot on the elevator. So because the robot was a tiny little thing that like a Roomba on the floor, it couldn't push the button. So the same company that invented that robot invented a device you buy for the elevator that pushes the button. But then I asked, why did you need a device to push the button? And it was very interesting. They said because the elevators don't have systems that they can hook into as a robot like there's no Wi-Fi press the button in the elevator capability.
Yeah. So there's no API for that.
There's no headless version of the elevator.
And that's actually a great metaphor for like the problem that I think that we're actually solving in the enterprise with these agents which is we just have two types of systems those for humans and those for software. And these tend to be more like humans. So we should draft on those as much as possible rather than try to retrofit them as well. And so the big news last week was I think you know Salesforce I don't know if they surprised people or not but based on the reaction it seemed like it was maybe a surprise 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. And I see that as a little bit of a bellwether because I think as Salesforce goes, so does a lot of enterprise software. And I think a lot of people are going to try and, you know, have to figure out what is the new business model in this headless world. You know, do you charge a little bit of a small just API tax? Is there a seat for the agent? So, there's obviously some work to do with that. And Stephen, I saw one of your tweets on, you know, some of the complications there. But I think as a moment it's a big deal because I think it's a recognition that software will be running in the background. It always has for machine users and applications and now it is for these sort of probabilistic machine users and or non-deterministic machine users. And what's cool and where I think this gets pretty exciting is, you know, as soon as I saw that announcement, like I had like five to 10 personal use cases where I would need, you know, the headless version of Salesforce because I'm always doing just a tremendous amount of customer related intelligence work. I'm going into a meeting. I need some information. I need to do I'm going into a city, who should I be meeting with? And so if you imagine being able to run compute in the form of agents across all of your data systems like the use cases become pretty wild around what that opens up. So I think this gives a lot of software platforms all new use cases that they can tap into that where again you were normally constrained by the number of people on these platforms but now the headless user can be 100 or thousandx the scale of those human users. So this is I think an exciting moment because as you have more of these agents running around and the headless software modes you just have way more use cases for these tools.
I also think I just think on this one what's so super cool is of course the first step is doing exactly what you described which is just looking stuff up. And so the most interesting thing is using this notion that an agent is just an entity. It's incredibly obvious to me that it's another license. Now, it might have a different license model, but it has to have an identity. Like when you go look something up in the Box CRM system, I don't know if it's Salesforce or not. When you use the Box CRM system, it has to be a person like with a certain amount of access rights. And you presumably as CEO, you might have access to a bunch of stuff, but also there's a lot of ways that they actually don't want you to have the rights at the right time. Like you might be able to look and see who is on the account, but you don't need the up-to-date quota of those salespeople and stuff and that might be HR sensitive and you should probably have some other level to go see that. But as you go down the org, the agent is never going to have more permissions than the person who's getting it to go do something. And in fact, it's just going to be like a peer to somebody else in an organization because otherwise you have all of these issues where a human can just say, Oh, get me the super smart agent that knows everything that I'm not allowed to know.
And to the in the IT architecture sense, what's so fascinating about that is you have to build you can't let the agent get the results and then try to figure out what works or not. Because of all the points that Martin made about the LLM stochastic model which is you're not going to be able to figure out it's not like a record in a SQL table that you could just apply a filter to. It's actually like it could be words in a sentence or just the number that shows up. And so I actually think that whole discussion about headless for me made the SaaS apocalypse seem even dumber than it was already. And it was already dumb. So, like it was like at first it was dumb and then I'm like, Oh my god, it's actually much dumber than I thought it was in the first place because you're just going to have this explosion. Now, someone might come with a very clever pricing scheme and that agents somehow cost less because maybe for the first five years they're read only or they're always tied to a person or something. But it is another seat. There is no way around it. And like if you're a SaaS company, you're crazy to try to say, Oh, just use the credentials of another human. Be like that's just that would be like bad security practice from the get-go.
Exactly. So actually in fact this is playing out in many domains. You can even make the argument that like a headless SaaS doesn't make sense. And here's the argument. The argument is well let me give you an example. So if you use OpenClaw, do you know why you use a Mac Mini with OpenClaw?
Well, is that what you're getting at?
It's not. It's two things. It's number one for iMessage.
Right. Right. It's for the integration. Yeah. Yeah. Yeah.
Because there is no headless version. So, you're just going to like use it.
And then the second one is very interesting, which is if you've tried to use headless browsers with agents, the problem is all of the websites have anti-scraping measures, so they don't work. And so the reason you use a back mini is so it can actually use Safari proper. So to do anything headless kind of assumes that the entire internet is going to go headless when I think all of these models, all of the data is humans working on the actual apps that are not headless. That's all of the data anyway. So I think these models are going to be very good at just using apps like they are today. And we're already seeing this happen. And rather than the headless versions, the non-headless versions are what's actually being used. So you could argue that it's just Salesforce, not headless, it will go to a...
Wait, but wait actually just to clarify. Do you literally mean the agent goes to the browser? And I'm taking the other side on that one big time.
Yeah. No, but let me just simplify the argument so we could actually have it. So today if you use an agent like Nanoclaw or OpenClaw, you could use a headless browser. Let's say I wanted to look up the value of my house on Zillow. The headless browser simply doesn't work because Zillow's so tired of people scraping it. So it will detect headless browsers.
So the thing that works is you pop up Safari and it uses a proper Safari directly, right? And so then all of a sudden it works. And so no, but I think any software that has a good API, the agent would absolutely prefer to use the API and then you pop into the browser the moment that you run into some execution problem.
I mean, and you know, Stephen is a fantastic long-term computer science software guy. However, these models are trained on data and RL environments from existing software that didn't have those APIs.
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 a program would do. So maybe you're right, but that's not what we're seeing. And you can honestly make the end-to-end argument when it comes to data and all of the controls in the internet. To Stephen's point, all the existing controls to just be like these are going to actually have the same actions as humans.
