State of Enterprise AI 2026: Aaron Levie on Tokenmaxxing, Rise of Headless, and AI-Proofing Your Job

Let's just say you took GPT-5.5 or Opus and you just snapped the line right now. You could probably do this diffusion in like two years or three years and we could probably all do the change management collectively as an ecosystem. The problem is the breakthroughs keep happening faster than the customer can implement any kind of standard architecture and those breakthroughs often times make obsolete the last thing you implemented. It's this really bittersweet thing which is like the technology is getting so advanced that it makes obsolete the prior thing that you implemented which actually means that the rollout takes longer.

Hey, good to be back. Appreciate it.

Interestingly, we've always sort of played this role and if I had to distill maybe the singular concept we've always tried to think about is basically our job is to take super advanced technology breakthroughs and bridge them to the real world. So at the very early stages of cloud computing, it was like, oh, we can now move infinite storage into the cloud. Well, to get that in the hands of most businesses, you would need a simple interface and you need advanced security. So we've always sort of been this kind of bridge and it lets us kind of have a foot in both worlds. One world being like the super advanced, everything is moving a million miles a second, and then the rest of the world which is like there's change management and there's systems that have to be upgraded and all that. So we saw it with the cloud and now we're definitely seeing another version of that with AI. Again, I think there's one tiny little asterisk which is it's sort of not just Silicon Valley versus everybody else. It's sort of Silicon Valley engineering versus everybody else. And then engineering though to be clear, I would say, if you're tapped into AI right now and you're at one of those names of companies and you're in engineering, you probably could look fairly similar to Silicon Valley. So the bigger question is like Silicon Valley versus sort of non-engineering knowledge work. And that's like the big question right now which is what is this sort of path from AI coding agents that we know have totally reached escape velocity to now agentic work in the rest of the organization and what does that rollout look like? What are the use cases going to be? How do these get implemented? So, needless to say, this is the number one conversation every single customer that we talk to has at this point. And I think I have a decent kind of sample size, probably a couple hundred CIOs just this year, across the kind of Fortune 500, Global 2000 type of cohorts. And I would say this is the singular conversation dominating every single engagement that we have with an enterprise. It's the main and primary thing that every enterprise is trying to figure out. We are still incredibly early. So that's like the probably the really big TL;DR. And what's interesting and the reason why we're early even though it's like okay well we've been in the AI wave for three to four years let's say is everybody just started figuring out like their final rollout plans for like the chat system in the enterprise. And people have very appropriately been proud and happy that they finally got that thing out. And that's still rolling out in some organizations to be clear like we still have five years of growth of like the chat system for knowledge work but right as that happens then the capability has been extended even further. And so then everybody's sitting around saying, 'Okay, as we move from chat, which is like I ask a question, I get an answer back.' And so the productivity gain is sort of rate limited by the human's ability to like have a conversation. Now I actually want to go deploy an agent that's going to be doing things and producing real work in the enterprise. Maybe handling tasks. Maybe you kick it off via chat, but or maybe it's just running in a stateful way. And I'm pinging it or it's kicking off in a workflow. So now everybody is sort of saying, 'Okay, we think we know how the chat thing works.' And by the way, even though they know how that works, I'd say that it's still a changing dynamic in market share. You're having a lot of changes of what people are rolling out there. Now everybody's saying, 'Okay, we're going to go deploy agents. These are going to be much more advanced, much more capable.' And that's like the big conversation. I would say like we're in day one of that as an industry.

I would say if I did like the broadest sample, I think the mood would veer statistically more optimistic than maybe the framing that I think maybe you landed on like a few extra cynical CIOs in that. I think because what's happening is the CIO and our main audience is the CIO. When we talk to the CIO, they know that their engineering teams are using Claude Code and Codex and Cursor and they're seeing the productivity gains come out of those teams and they're like, 'Yeah, like my teams are just building apps way faster. They're being able to tackle IT projects much more quickly. Like we're doing security reviews faster.' Like they're seeing the productivity gains in their function. And I think they're often saying, 'How do I bring those same IT productivity gains to the non-IT parts of the organization?' and they're having the business pull them and say, 'I want access to Claude Code. I want access to Codex. I want access to these tools as well.' So, there's actually a certain kind of sex appeal to these tools right now where the business is sort of demanding, 'I want to be on the agentic train because I'm seeing all these great use cases.' And so I think the tone is actually remarkably optimistic and excited and positive as opposed to, you know, there's a sort of typical trough of disillusionment from Gartner and the hype cycle or whatnot. I think people are eyes wide open on there's no free lunch in this. It's not going to just immediately transform our productivity. Like we're no longer in that part of the conversation and maybe we were like two to three years ago. I think everybody is sort of like very firmly aware this thing doesn't just get deployed and magically it goes and transforms the business but at the same time they are having their employees say actually like I would like this thing to be able to go review all my documents for me I would like to go accelerate our client onboarding process I would like to be able to generate digital assets in our marketing campaign process so I think the demand is coming from the business and the IT organization is seeing the gains happening in coding. And now it's a bit more of a practical, tactical, you know, issues. And I'm sure we'll get into some of them. It's like it's the token cost thing. It's the how do you actually roll this out? It's do you have the talent to go and deploy these things. So I'm finding the conversations to be fairly positive and increasingly ambitious, but with a sense and strong dose of reality of none of this stuff is coming for free, you know, beyond even the cost side, but like free in a deployment, you know, sort of manner.

Yeah. Although to be fair I think the Microsoft one probably got spun in some interesting ways because that was probably much more of a reflection on they want to move to Copilot versus Codex. Yes, it's like they're going to be spending on the tokens like it's literally going to ride on their infrastructure. So I think the press really liked to modify the sort of story on that one.

I don't know if I have good recommendations yet actually. But I would say the token cost when we go and talk to organizations right now about where they are with agents, tokens, the cost of tokens and budgeting and budget planning and all of this probably is at least one-third of the hottest button issues that relate to AI. And it might even be like tied for number one like half the time because what they've seen is this move from, you know, everybody's sort of calling it like we were doing subsidization as an industry. I don't really think about it like that. I would say that the costs were just low enough that these things were included like Cursor just included a lot of usage. And maybe it was subsidization, but it was actually just like they could model that under their subscription fee in a fairly clean way. And then all of a sudden, what happened was these agents just can do way more work. Their context windows are way way larger. The cost of inference is way more because they have way more parameters and their capabilities way better. So we've just gone from a pricing model of a chatbot or like type-ahead functionality in GitHub Copilot to that pricing model no longer working when one coding agent could be consuming $1,000 of compute on a single task. So clearly like you can't lump that all into a $20 per user per month fee. So that's really the jump that's happened and it's all only happened in one year maybe less than a year. I mean it basically all correlates to you can just look at like the Anthropic revenue curve and that is the period of time where everything has sort of been flipped on its head on the sort of cost modeling and token budgeting side.

Yes, 100%. But I think the one nuance is I'm not 100% sure it's just the subsidization change as much as like no like these models are just way bigger and the hardware is not getting any cheaper anytime soon and we have a capacity constraint and so you've got a few atypical patterns from normal computing which is like usually like there's economies of scale and you don't have the same kind of shortage and so then as you build out everything gets cheaper and you have more Moore's Law and like we've compressed normally what should happen in like 10 years of rollout into like 18 months. And so lo and behold, the data center providers, the labs, etc. have pricing power. They don't need to lower their prices on anything. So you're not seeing the typical things that drive down the cost of compute. I'm highly optimistic that that happens over the next five to ten years, but it's just clearly not happening yet. So we're not seeing the curve that you should see in a normal like 10-year cycle of compute because that 10 years is now happening in 12 months. So what's happening is enterprises are saying okay I'm quite surprised by these bills and it's like it's a surprise that is sort of like an uncomfortable acceptance surprise as opposed to like I'm not doing this anymore surprise because they're empirically getting the productivity gains or they just wouldn't be paying the bills. It's just now they are saying oh okay this is a very real expense in the business. This is not the kind of expense that we just sort of add on 20 bucks per user in our headcount and now it has solved the problem. So one of the nuance sort of shift that's going to happen is I think the first two to three years of AI the IT budget could kind of consume the AI costs and this would show up as okay the company upgraded to Microsoft Copilot or they added on the AI product of XYZ vendor they could kind of get the Cursor licenses within the IT spend and as you know like IT spend is basically somewhere between like 3 to 7% of corporate revenue in a company. Sometimes lower, sometimes higher, but like it's kind of trapped at that. So then the question is like, well, where's the other 60, 70, 80% of revenue in an organization? It's opex and it's just like general purpose opex across the business. And so if AI is truly adding this productivity gain to your engineering team or your client onboarding process or your marketing team, then clearly you don't want to be trapped by this sort of 3 to 7% in the business. It's going to escape that and it's going to move to the line of business budgets. And so this is actually on one hand, it's actually good for the AI industry because you're no longer going to be constrained by IT spend budgets in an organization. On the other hand, you have all these now interesting downstream questions which is like the line of business doesn't necessarily know how to budget for compute. This is not a they don't have FinOps for the marketing team. They don't have FinOps for the sales team. That was something that the cloud people had and the IT team could kind of go and be confined to. So I think what's going to happen is first of all what's interesting is that you're going to have this tussle between kind of like the finance team, the line of business, the IT team that's going to be this interesting kind of how do you triage all of this. You are going to to some extent have to centralize the management of the IT systems management of what do you procure but then you also kind of have to decentralize the decision-making of how do you use these things because really like the CMO should decide do they want to spend a million dollars of compute or do they want to spend a million dollars in doing marketing events or something else like that that kind of can only come down to the business owner that is driving these decisions. So, and that is again a new type of format of how do you manage a compute budget in your marketing budget and in your sales budget and in your global manufacturing budget. So that's a whole thing that now people have to go figure out. One of the things that we don't have tooling for is like how do you measure the ROI on the tokens? And it's like it's, you know, I think it's kind of like absurd and hilarious to already be talking about ROI this early in the cycle, but I see it as actually it's like pretty practical. Like there are some things I could do on my computer right now that would cost the same amount of money as the lunch that my company provides me and I could do it. I could press one button and it could cost me the free lunch that I get. So clearly a company's not going to be like, 'Oh, let's just deploy a whole bunch of tools that people can just like willy-nilly press a bunch of buttons and have the equivalent of 10 lunches in 10 seconds without knowing like did were you doing something that actually produced value for the organization?' And that's something that like nobody has tooling for. Employees don't actually really know what the cost of compute is. So they're going to go about using these systems as freely as possible, not knowing that, yeah, this is actually that one little task you gave to that agent could cost $200 because you just happen to structure the query wrong. And now it's going to go fan out across a bunch of systems and it's going to read each document or each piece of data in your system and then it's going to go and compute it all. Like that one structure of that prompt is the difference between again like your entire benefits for a month at that company. So how do we handle all this? I actually have no solutions like this is going to be one of the most interesting questions and some mix of employee training, some mix of centralized capacity planning with decentralized sort of decisions of how do you roll that out. You're going to need new pieces of software. Probably there's probably a $5 billion startup waiting to happen just in like ERP for your AI compute. Which is just like how do I decide that all of this stuff is being used in the right way? How do I measure the value that it's being produced? How do I make sure that it rolls out to the right teams? So I think that's all up in the air right now. And I think this is so new that we're very short on best practices at the moment.

Wait, is there like a do you have any royalty approach to this or how does this work?

In my long diatribe on all the problems, I mean a few things that will inevitably happen. So one thing that will inevitably happen is OpenAI has a great program which is kind of this dedicated capacity which is okay if you kind of know your workload we're able to lock in certain pricing that helps support that. Like that's one way that you could kind of protect your costs. I think another thing that's going to happen is you're going to see this divergence as opposed to again maybe two years ago I would have predicted a convergence but let's go with the opposite now. Frontier AI model capabilities get applied to coding and advanced life sciences and like your contract process and your financial planning process and so that's where you apply GPT-5.5 High and Opus 4.7 and whatever the model but then once you have a task that is sort of like you can now perform that task reliably. You can sort of that once that capability gets saturated and you can perform that task in a reliable way then you can peel that off to a lower-cost model and sort of run that on an ongoing basis and we just don't have a lot of maturity in doing that because really until maybe the past 6, 12 months the models couldn't do any of our tasks that reliably. So, as this starts to happen, you can kind of say, 'Yeah, for that one customer service interaction, I can now cap that at 50 cents per million tokens and it will never go higher than that. In fact, it'll only go lower because I might swap it out with an OSS model, etc.' But for my coding, I still actually want the highest capability. And so, I think what's going to happen is you're going to have a mosaic of models in the enterprise. I think the average enterprise will certainly be using half a dozen models in their organization. You're not going to throw everything at the kind of Ferrari model from a performance standpoint. So companies will have to get better at that. You'll need to have some kind of deeper wherewithal on how do you shift tasks to different levels of compute. We're going to have again new ways of measuring all this back to the kind of startup idea. There's some use cases I think companies will eventually kind of realize oh actually maybe like that's not something that I even need an agent for. Where it's like, oh, I just need like software to get deployed into that. And actually, software is actually cheaper because it's going to just run on a CPU.

Yeah, software. It turns out that maybe you don't want your agent to re-render a UI every single morning. And that costs $30 per day of using the interface. So I think there's going to be a lot of mixed solutions on this, not to mention just like good competition in the market that says, you know what, why don't we have some cheaper models that get produced? How do we start to like I think the market will sort of work as you'd expect which is somebody will say oh there's actually an innovation opportunity here and go attack certain parts of the market.

More vendors. They need way more vendors. The answer is always more vendors.

100% 100%. But ideally subsidized VC backed vendors. So,

There was a tweet I mean this has come up probably multiple times which is like write as much code as you humanly can right now while some of these products are still subsidized. And it's actually kind of like a funny concept because like if you were really savvy, there's probably some parts of the market where you could be like, 'Oh, I could somehow use this LP capital to do work for me as my startup.' And there's like a window where you can find those exploits.

Just a DLP problem. And honestly like in many ways not that different from somebody going to Google to say like I want to go research this customer versus going to ChatGPT and say I want to research this like almost nothing has changed about the security paradigm of that enterprise. So maybe the prompt could include a little bit more IP but like the work you were doing was not like the blast radius of that work was quite contained. Conversely, I go to an agent and I happen to have access to the Salesforce MCP server and it's actually incredible and it's actually one of the reasons why I totally believe in headless software. But I can do a lot of work with that and I could pull out a lot of data and I can ask a lot of very powerful questions and a company is going to have to say well should every employee have the same level of access and how should we make sure that we've cleaned up our access controls for that and how do we tell people again like what types of queries should they be doing that are going to have different kind of cost profiles and now you have to do that for each of your software vendors and applications and then you have to figure out like what is the new workflow on the other end of this. Do you really want employees prompting their way through the workday across lots of stuff or do you want some standardized best practices and then you're like okay well now I have to build skills internally like capital S skills and I have to have or I have to have various kind of knowledge graph or other ways of getting agents to the right information and the right kind of context. All of that is highly technical work that is going to take one, two, three, five years of building out across most organizations. The really good news for I think 90% of people maybe other than like the super AI accelerationists is that that work means tons of opportunity. It means actually there's a lot of opportunity for startups. It means there's a lot of opportunity for like new kinds of roles. One of the hottest topics also is like we have a lot of customers asking us what is this new internal FDE role or external FDE role like what is the technical talent I need to go and actually help me deploy these types of systems. So we are in for years of this kind of diffusion and it's just non-trivial and every company has to go through it one by one and this is the kind of journey that we are all now on.

Partly I don't know how we define like it and this because I think it's actually a continuous it'll be a continuous sort of evolution and not to play semantics but like it's more like what do we think the end state is and again I think the AI sort of either doomer or accelerationists think there is some end state. I actually don't think there's an end state. I think this is a substrate of how work happens and it will just constantly get better and we will have to constantly move up abstraction layers and like it's not even obvious to me like what the end is. It's just like it's a new way to basically execute work and some areas that will be a 5x productivity gain. Other areas it'll be a 10% productivity gain and that will roll out and then in five years from now we'll find the next version of that. And I think it's this always kind of evolving landscape. But I think that we should totally be thinking on the order of 10 years as a rough type scale for like whatever and this might be like if you want to be like when does Coca-Cola or Procter & Gamble have agents running around doing every single task in the enterprise like across every crevice of the organization hyper successfully. That's a multi-year transformation. And I'm making up an example. Maybe they're already there in particular, but this is just what's going to happen. Now, a funny thing, our industry is actually like some of this is actually weirdly a byproduct of the industry. So, we have this incredible capability overhang, which is like let's just say you took GPT-5.5 or Opus and you just snapped the line right now. You could probably do this diffusion in like two years or three years and we could probably all do the change management collectively as an ecosystem. The problem is the breakthroughs keep happening faster than the customer can implement any kind of standard architecture and those breakthroughs often times basically undo or make obsolete the last thing you implemented. So it's this really bittersweet thing which is like the technology is getting so advanced that it makes obsolete the prior thing that you implemented which actually means that the rollout takes longer because we have no stable there's no stable environment to roll things out in. If you went to an enterprise right now it's actually a period of maybe the least amount of consistency I've ever seen in IT of like the following question. I want to go deploy an agent to do client onboarding or to review some set of knowledge work in the enterprise. I could probably lay out up to 10 to 15 reference architectures to all solve that problem. That means that every systems integrator, every software startup, every lab is pitching a customer 10 to 15 different variants of what they should do to solve that one problem. And so what that actually leads to ironically is more lengthy sales cycles, more complexity in decision-making because you're like, man, that Anthropic managed agents thing looks incredible. This is really awesome. Like that's exactly how we should do it. And then you're like, 'Oh, this OpenAI frontier is really good.' And then you're like, 'Oh, but this startup is actually pitching me something that means I'm neutral to either of those.' And then you're like, 'Oh, no. Actually, my workflow vendor can now do this.' And it is a madhouse on that front right now if you're a CIO. And so, like, one of the memes is nobody's signing up for more than like one-year deals with the labs. And part of that is because of the pace of innovation that's happening. And so it's a byproduct of actually how much innovation we are seeing but that means diffusion ends up taking longer than I think most people think.