In February of this year, Open Router did a study that showed that the token consumption by agents has now exceeded that of humans. And since February, from February until now, you have seen the increase of token consumption go up 14x for the agents. So, not only did we actually get into this phase of agents pretty fast, agents have now surpassed humans in its consumption of tokens and are doing most of the work related to AI.
Every task you hand to an AI agent comes with a management decision. What do you want accomplished? How much is it worth? And how much freedom should that agent have to get it done? And as the consumption of Agentic AI accelerates, those decisions carry real consequences for your budget, for your infrastructure, and your business. Because a growing token bill tells you something about how much AI you're using, it takes a different kind of judgment to know whether that work is making your organization better or more profitable. On today's episode, Cisco President and Chief Product Officer G2 Patel shares his view for how enterprise AI can move from AI adoption to agentic delegation and why leaders need to get comfortable managing that shift. In the conversation, we explore how to choose work worth delegating, give teams room to experiment within a budget, and secure agents whose actions can reach across the business. G2 also offers a challenge that stuck with me personally: use AI in ways that stretch your thinking. That means asking more of it than faster output and asking more of ourselves than simply passing that output along. This is the AI Proving Ground podcast from Worldwide Technology. Let's jump in.
Mr. G2 Patel, how are you today? Welcome to the AI Proving Ground podcast.
It's good to see you, man. How are you doing?
I am doing just well. We had you on the show about a year ago and at the time you said we were entering the second phase of AI. The first being chat bots that intelligently answer questions for a user and the second being agents that are conducting tasks and jobs fully autonomously on our behalf or near autonomously. And you know, 12 months later that prognostication looks mostly true. But you know, where do you see the industry right now? Now, are we starting to creep into a third phase even or are we still in that second agentic phase?
I think you'll have the second and third be parallel. So, it'll be physical AI will be the third phase. But I think on the second phase, it's a really interesting time because when we talked a year ago, we were talking about just entering into the agentic era. In February of this year, Open Router did a study that showed that the token consumption by agents has now exceeded that of humans. And since February, from February until now, you have seen the increase of token consumption go up 14x for the agents. So not only did we actually get into this phase of agents pretty fast, agents have now surpassed humans in its consumption of tokens and are doing most of the work related to AI. And that's, you know, like so what you essentially have right now is we're in this infrastructure super cycle and the infrastructure super cycle basically is completely in full effect with less than like a few percentage of humans using AI in power user mode, right? With agents, imagine what happens when 10%, 20%, 30%, 50%, 70% of the humans are using AI in power user mode. Like what kind of infrastructure is going to be needed to go out and fulfill the needs of AI? Like it is going to be an enormously supply constrained world for a long, long time.
Yeah, it's pretty crazy to think about. You talk about the AI super cycle and you know, the one that we're entering. I know you're bullish and you've talked a lot about how this most recent AI super cycle is going to be more defined by or less defined by you know, how many people are using AI and more about what those people will actually delegate to AI. Starts to get into agents of course. Explain to me that shift of thinking and what the implications are for enterprise leaders.
So I think like every human, we are now comfortably in the phase where every person entering into the workforce today will likely manage agents before they manage humans. And so all of us are going to be managers of agents. And as it pertains like what do we delegate to these agents? You'll essentially start to learn to delegate most things that we do to agents either partially or fully. But there's going to be very little that you're going to do without the help of agents. And that'll take a while because it's like it's almost like the thing that is the technology there today to help you with anything? Yes. But it's your instinct and your muscle memory is not quite there where you actually think about using AI for everything. And it takes a while. It's kind of like when you know, when the internet first came about, there was the first few months where I would ask a buddy of mine questions and he still works with me but I'd ask him questions and he'd be like, I don't know the answer of it. Let's look it up. And that notion of let's look it up was a very instinctive thing that happened to us over time. But initially it wasn't that instinctive because like you didn't really know what lookup was. That's not how you thought. I think it's going to be the same thing. It's going to be like let's make sure that we can delegate to an agent. Let's make sure that we can have the agent do this for us as a first pass. And so that will just get more and more kind of prominent in the way that we work. And so if it's going to be less about user adoption, people adoption, I mean certainly more and more people will begin or will start using AI, but the amount of agents that they utilize is going to extrapolate by quite a lot.
I'm curious from your standpoint right now and certainly still early innings so to speak, what qualifies as a good task or workflow or initiative that is, you know, ripe for that delegation? How should users be thinking about what they should versus what they should not be delegating?
I think you should think about virtually everything that you do not starting from a blank sheet of paper and saying how do I get a jump start on whatever I do. Like for example, I tend to delegate a lot of the research for the learning that I need to do on a day-to-day basis to AI, right? And I have I tend to delegate AI actually consolidating and pulling together news from different news sources for me. I tend to delegate aspects of AI where I need to create content, but I want to get a head start on the content so I can actually have my brain start working because otherwise you might experience writer's block at times. All of those things tend to be superbly valuable. I tend to use AI to delegate editing, but what I don't do is I try not to delegate something to AI that makes me intellectually lazy. Like I try not to say I'm not going to think, I'm just going to have AI think and then do that. That doesn't make me smarter. That makes me dumber. And so what I tend to do is say what's going to make me smarter and what's going to stretch my thinking. Anything that stretches my thinking by delegating, I love to delegate. Anything that doesn't stretch my thinking by delegating, I tend to, you know, hold off on delegating.
And that's a good rubric for anybody out there that's looking to jump in or continue their maturity with AI. But there's a whole kind of other side of the story here, which is enterprise adoption. If we're starting to delegate some of these tasks or workflows or making something more, you know, agentically forward, how should leaders think about what's creating actual value versus what's just, you know, consuming tokens or consuming your time on AI because you can get lost on that ladder.
You can get lost and I think your time actually matters more even than the time of the tokens. Like most people don't realize that sometimes you can just it can be this thing that sucks you in and then before you know it like hours have gone by and you haven't really accomplished much. You've just been going back and forth and back and forth. And so I think knowing how to get the most out of AI in the most efficient way possible is going to be a superpower that people are going to have to learn how to develop because just like you learned how to get a lot from your people that worked for you and got very efficient with how you managed them and got very thoughtful about the kind of work that you delegated to them. I think AI is going to be no different. Like what I found at times that started happening when Claude really was starting to take off with Claude co-work, either Claude or OpenAI for creating content and they never even read it. They would just start sending. So I started getting like volumes of documents over documents over documents. I'm like, there's no chance I'm going to be able to read all this and there's no point in me now paying for tokens to summarize all this and not even read it. Like, what's the point? Like, but have you read it? And what I found was a lot of the people that were sending me these documents themselves hadn't read it closely enough. Like, no. Well, I didn't read it closely enough. I figured I'd send it to you. Like, no. Actually, in a time like this when you can generate unlimited amounts of content, what you choose not to generate and how precise you are in what you generate actually conveying a point might end up taking just as much time if not longer. However, you might create output that's at least 10x better. And so the goal should be create output that's 10x better rather than just I am in this mad rush to just save time. You know, do something that wasn't possible before. Create original insights that weren't accessible to us before. I think those things tend to be far more useful and additive to society than the other way around.
How do you account for the fact that you know a good idea with AI? We encounter this all the time at Worldwide Technology. I'm sure you all have the same kind of situation, which is, you know, an idea or a dashboard or something that you whipped up in Codex or Claude Code. It's not working one day, but then two, three days later, a week later, whatever your time frame might be, it can work because of model updates. That seems like it's a real gap there to not necessarily put something out to pasture, but put it on the shelf, don't lose sight of it, and make sure that you revisit it. How do you all at Cisco strategize about that so that you don't lose any good ideas that your employees are bringing to you?
Brian, this is such an important question that you just asked because one of the things that we tell our teams is just because it doesn't work today, you should not assume that it's not going to work two weeks from now. In fact, what you should assume is that it's going to work two to three weeks from now and assuming that it works two to three weeks from now, four weeks from now, whatever, pick a time period, but it's not a year. It's a short enough amount of time. What you have to do is assuming it works, you have to start planning for it right now because your planning cycle will take longer than AI's fixing cycle. And so it changes the mental model of like speed is the superpower and you have to be, in my mind, speed is the moat and being early to market as a matter of habit and moving at a very fast speed is fundamental to long-term durable success.
So we're out here delegating now to agents. How do you account for the sprawl that may or may not be happening with Cisco, but it's certainly happening at other organizations around the world? I'll give you an example. I was reading a Reuters article that found, you know, one enterprise in an audit found over 400 agents when their CIO thought that they were only going to have 40. How do you harness all of this creativity, innovation? It seems like it's something that's hard to wrangle together at a time when everybody's just trying to move very quickly.
I think what you have to do is you have to teach people how to do things by making sure that you're optimizing for the right metrics. So, I would not focus on how many agents they can spin up or not spin up. I would focus on giving them a budget and saying you have this budget within which you have to get this job done. And so have at it. If you want to use 400 agents and expend your budget that way, that's up to you. If you don't want to use 400 agents but use three agents or two agents or one agent, that's up to you. How many tokens you generate is up to you based on the kind of model you want to use. You need to have some kind of intelligent routing layer that knows which query goes to what model. But don't just like we don't want to micromanage people. Like I'll give you an example. There are times when companies will say, hey, you know what, I'm going to micromanage every single nuance detail about budgets because I'm trying to do cost control. The alternative plan could be you just give the people a budget and say just stay within this budget and then you have autonomy to work within that budget the way that you want. But what that does is it creates leaders. The opposite actually creates a huge amount of micromanagement.
