1 Oct 2026 · 27m · Adi Ignatius, Alison Beard, Jeetu Patel · 19 quoted facts
Cisco's Jeetu Patel argues that AI makes building cheap and fast, so the scarce resource becomes human judgment and taste. When everyone has the same tools, advantage comes from deciding what to build and how to deploy people and agents, not from raw speed or output.
The central tension is speed versus control. Patel describes Cisco building in nine months what was expected to take years, and rolling out 90,000 agents. He also warns that agents behave like reckless teenagers, so they need machine-scale monitoring, human accountability for code, and a culture that keeps experimenting.
When code is nearly unlimited, the risk is producing "slop". Patel says the bottleneck shifts from coding to code review, and then to deciding what deserves building.
Cisco wanted one management plane across products gathered through about 250 acquisitions. Patel says teams estimated seven to nine years, but AI-assisted coding and a small focused team finished in nine months.
Patel expects trillions of agents, far too many for humans to oversee. He proposes runtime interception and agents and models that check one another.
Patel saw conversations get worse when people presented AI-generated documents they hadn't absorbed. He wants AI to deepen people's thinking, and he wants human owners accountable for AI-written code.
Patel says his first six months pushing AI use at Cisco were a struggle. He urges people to retry tools that failed, since capabilities improve within months.
Patel says advantage comes from how well a company directs its resources, which he sees as a matter of human judgment, intuition and focus. He compares it to two 1,000-person companies in the same market that end up with very different results.
Patel says agents are very intelligent but have little fear of consequences and often make poor decisions. He argues they need guardrails and monitoring, much like a new driver given a fast car.
Patel says Cisco has rolled out 90,000 agents, one per employee, with its own security harness around them. He uses his agent for weekly financial summaries and for tracking commitments he made in meetings and messages.
Patel says almost every job will be reconfigured, some will be lost and new ones will appear, as in past technology shifts. He expects Cisco to have more engineers, not fewer, by the end of next year.
Patel says companies need security tools that intercept agents at runtime, meaning while they act rather than afterward. He expects other agents and models to monitor them at machine scale, with models checking each other and publishing the results.
Stated by the speakers. Each quote matches the transcript, which shows what was said, not that it is true.
Jeetu Patel said Cisco has 32,000 engineers coding with AI and expects 70% of its products to be entirely AI-written by the end of next year.
“32,000 engineers are coding with AI. We will actually, by the end of next year, have 70% of our products that are 100% written with AI.”
Jeetu Patel said Cisco expects to have more engineers, not fewer, at the end of next year despite heavy AI coding.
“Interestingly enough, we will have more engineers than less at the end of next year at Cisco.”
Jeetu Patel said every piece of code an AI agent writes should be tied back to a human who owns responsibility for it.
“every piece of code that an AI agent writes should have human provenance to it, but it's actually tied back to a person that owns the responsibility of that.”
Jeetu Patel said Cisco has rolled out 90,000 AI agents, one for every employee.
“We have, uh, 90,000 agents that we've rolled out to every employee. There's an agent for every employee”
Jeetu Patel said Cisco made 250 acquisitions while reaching $63 billion in revenue, leaving it with many differently designed products to manage.
“over time, uh, $63 billion in revenue, we made 250 acquisitions. The way in which you manage all this infrastructure was multiple different products that all looked and felt different.”
Cisco decided to become an integrated platform company with a single management plane for all of its products, according to Jeetu Patel.
“We decided to become an integrated platform company. So we said we're gonna have one management plane for all of our products”
Jeetu Patel said Cisco built its unified management plane in nine months, a project the team had estimated would take seven to nine years.
“Something that was gonna take seven, eight, nine years, we were able to build within nine months because we were able to move at a pace”
Jeetu Patel said AI hallucination has dropped materially since ChatGPT first came out in 2022, though public sentiment has not improved as much.
“The hallucination factor with AI has gone down quite materially over the course of the past since 2022, when ChatGPT first came out.”
Jeetu Patel estimated that fewer than 2% of people worldwide use AI agents in any substantive way.
“Right now, my guess is sub 2% of humans are using agents in any kind of substantive capacity in the world.”
Jeetu Patel said Elon Musk had suggested that AI models test one another for safety and security, with results published and testing harnesses open sourced.
“have each one of these models test the other model for safety and security and then make sure that you publish the results and have the harnesses be open sourced.”
Jeetu Patel said coding has become largely automated, which makes code review the next bottleneck.
“Coding is largely a solved problem at this point and has gotten automated quite a bit. Code review becomes a bottleneck.”
Jeetu Patel said he struggled for the first six months to get Cisco employees to start using AI.
“at Cisco, the first six months, I really struggled with getting everyone to start using it.”
Jeetu Patel said companies need security technology that intercepts AI agents at runtime, while they act, rather than after the fact.
“technologies that can intercept at what they call runtime, which is at the time that these things are happening rather than post facto, so that you can stop an agent.”
Jeetu Patel predicted there will be trillions of AI agents, too many for human oversight, so they will need to be monitored by other agents.
“You're gonna have trillions of agents in the world. These trillions of agents are gonna need some kind of monitoring ability that is also gonna happen with agents”
Jeetu Patel described AI alignment as whether AI does what the human wants and follows the human's instructions.
“alignment, basically, is a fancy way of saying, me, the human, what I want out of AI”
Jeetu Patel said AI is moving from chatbots to agents that can carry out tasks and jobs almost fully autonomously.
“We're moving from chatbots to these agents that can intelligently answer our questions and conduct tasks and jobs for us almost fully autonomously.”
Jeetu Patel said almost every job will be reconfigured by AI, with some jobs lost and new ones created.
“almost every job will get reconfigured. Some jobs will be lost. An entirely new set of jobs will get created.”
Jeetu Patel said the quality of conversations declined when people presented AI-generated documents whose concepts they were not familiar with.
“What I found is the quality of conversations were starting to go down because even the people who had authored the the document weren't the ones who were intimately familiar with every single concept in the document”
Jeetu Patel said that each big improvement in one area of AI-assisted work creates a human bottleneck in another area.
“every single time there's a step function improvement in one dimension, there is a human bottleneck that has emerged in another dimension”
Patel argues that when building is cheap, the hard part is choosing what to build. He pushes back on the idea that humans will have nothing left to do.
“choosing what not to build is actually a far more important decision”
Patel criticizes pasting AI-generated slide decks without absorbing them. He says AI should deepen thinking, not just raise output.
“to not degrade our thinking, but to enhance our thinking”
Patel says the technology is improving faster than public confidence. He describes agents as brilliant but reckless and in need of guardrails.
“agents are like teenagers. They're supremely intelligent, they have no fear of consequence”
Patel says companies need runtime protection and agent-based monitoring because humans can't oversee trillions of agents.
“You're gonna have trillions of agents in the world.”
Patel describes Cisco's 90,000 agents as digital coworkers. He says every employee becomes a manager of agents.
“We have, uh, 90,000 agents that we've rolled out to every employee.”
Patel says advantage comes from how well resources are deployed, not from owning them. Agents are one more resource to deploy well.
“That is what creates competitive advantage, not the ability to have the resource.”
A concrete Cisco example shows how AI-assisted coding compressed a multi-year project. Patel notes this speed is cognitively disorienting.
“we were able to build within nine months”
Patel says people give up on AI too early, and he urges them to retry since capabilities improve within months.
“Hardest to change is culture.”
I'm Adi Ignatius.
I'm Allison Beard, and this is the HBR IdeaCast.
So, Allison, we are at week three of our special series on AI, and after diving deep on talent and communication, we are looking today at innovation.
I'm excited about this one because there's no question that generative AI is itself the most important technological innovation we've seen in decades, maybe our lifetimes, and that it's supercharging innovation in every other realm of business, from cancer treatments to space exploration. But I guess there's still a question if all of the innovation is that impressive or differentiating when everyone has access to the same tools.
Yeah, so that that's a great question. Look, I'm still trying to understand what an AI-powered company really is, you know, how the technology can create not just efficiencies, but can transform businesses and allow them to innovate at a scale that would have once been unimaginable. So to help explain all of this, my guest today is Jeetu Patel, who's leading AI strategy at Cisco, the company that is helping to build the infrastructure for the AI era. He has a lot to say about how AI can help drive innovation and how companies need to think about processes, about security, and about preserving human qualities like judgment.
Here's our conversation. AI is dramatically reducing the time and the cost required to build, test, iterate on ideas. You know, in your mind, does that fundamentally change what innovation is, you know, from a from a scarce activity to something that is we should think about as almost continuous?
When there's an element of abundance where you can actually start thinking and prosecuting ideas at a very fast pace, the rationing that you have to do is making sure that you don't create slop. In a world where you can write unlimited amounts of code, choosing what not to build is actually a far more important decision than deciding what to build. And from that perspective, I do feel like the need for exercising human judgment and taste and intuition and instinct is actually more important now than ever before.
And so, like I, I always find it interesting when people say, well, you know, like, all humans are gonna lose their jobs and we're not gonna have anything to do. I just don't even understand that because there's gonna be so many areas where human instinct is gonna be more required than less as we move forward.
Well, let's talk about processes a little bit, cause our companies have spent decades creating and perfecting, you know, this whole process for innovation, roadmaps, planning, you know, approval processes. Which of these processes should become obsolete when you can go from an idea to a prototype in hours and minutes instead of months?
The way that I think about processes in general is you have to be not pedantic about the process, and you have to be far more intellectually honest about the level of rigor that is done to go out and drive to a certain outcome and that you're sweating the details. Let me give you a very simple example. Have you seen someone create a PowerPoint in Claude or OpenAI and then start to present it to you? I find those those to be less effective than when someone has actually taken the time to build the PowerPoint.
Something must happen in the human brain where people are so eager on hitting send without actually going through and saying, is there thoughtful analysis done? Is the point being made in the right way? What I found is the quality of conversations were starting to go down because even the people who had authored the the document weren't the ones who were intimately familiar with every single concept in the document because they were they were just kind of regurgitating what someone else had created.
I think that's a bad way to do things. The goal of AI is to not degrade our thinking, but to enhance our thinking and allow us to think at a broader and higher aperture than what we were able to do before. And I don't think that that's something that we have adjusted to. The the most important thing we need to do is, can we just improve? If if there were three things that we were doing in a day, sure, one thing is guy can do ten things in a day, but that doesn't really change the world as much as saying those three things that I'm doing, can I do them a hundred x better than what I was doing before?
And that would actually create a much better outcome. We're moving from chatbots to these agents that can intelligently answer our questions and conduct tasks and jobs for us almost fully autonomously. But it was very hard to go out and use agents. What is now starting to happen is these personal agents, like Grokbot, Muse, and Instinct, are gonna democratize that quite dramatically. And if you leave aside the fear on safety and security, which we should talk about.
And if you said I can enrich those agents with the ultimate amount of context about me, can that agent really, in a meaningful way, lower my burden and help me out and actually make me feel like I can produce better quality thinking for myself because of the agent aiding me? I think the answer is categorically yes. And and and that is an area that I feel is gonna be the next very exciting phase in AI, is these personal agents that we're using.
That'll then bleed into the business agents as well.
You know, at this point, is AI generally for for companies, for teams, you know, still essentially making things more efficient, or is it changing the kinds of problems that we can choose to tackle? I mean, you know, is AI changing how we even think about problems we can solve?
I think that's the more imp- interesting part rather than the efficiency part. The efficiency will come. But the thing that is truly the force multiplier is original insights that get generated by AI that you would have never been able to generate without it. That then allow you to expand your thinking and your perspectives, and then that allows you to have the ambition and the credibility to be able to solve problems that you could never even dream of solving before.
Especially in the in the United States, we've gotten very negative on the narrative on AI, and I actually don't like that because I think this is a tremendous technology that can really help in solving problems across a multitude of dimensions that are gonna be really great. And we should also, of course, be focused on mitigating the risk. This constant level of negativity around AI, I don't think, is accurately reflective of the potential of the technology. We are using AI now more than any time before in our history.
32,000 engineers are coding with AI. We will actually, by the end of next year, have 70% of our products that are 100% written with AI. Interestingly enough, we will have more engineers than less at the end of next year at Cisco. You know, like you, you will you will need more people to think differently about things that we will be able to do. And, by the way, every piece of code that an AI agent writes should have human provenance to it, but it's actually tied back to a person that owns the responsibility of that.
Don't just delegate it to AI and say, let's see what happens.
Well, but that's an interesting question, cause if, you know, in my mind, if AI is gonna achieve these sort of remarkable, you know, step change, uh, innovations, at a certain point, we have to trust the output, right? What's your, you know, suggestion to to when we could have a comfort level with AI making consequential decisions and we don't have to check everything?
I think it depends on the domain. There's a technological reality, and then there's a psychological reality. The hallucination factor with AI has gone down quite materially over the course of the past since 2022, when ChatGPT first came out. The public sentiment on hallucination has not improved as much as the technology has improved.
First impressions?
Right? Part of this is you will actually need to make sure that you see the the degree of improvement that happens technologically. We will need to make sure that that actually is effectively reflected in the human psychology of it. And when both of those things happen, that is when society will be ready to say, I'm gonna give it. I'm gonna be ready to give it consequential decisions. Today, we're not. We're not 100% in either of those.
There's a lot of risks. I think there's a lot of talk around these agents going rogue or doing things in ways that might actually exercise poor judgment. The way I I've always talked about this, like, you know, agents are like teenagers. They're supremely intelligent, they have no fear of consequence, and they exercise poor decisions far more frequently than you and I would like.
Especially we learned when you put them in a room together.
That's right, that's right. So I do feel like we need to make sure that we actually solve those problems and we have the right level of guardrails and we've got the right level of behavior monitoring and we make sure that, uh, we can determine when something is wrong because there's lack of alignment with what we wanna do, which is alignment, basically, is a fancy way of saying, me, the human, what I want out of AI, does AI is it is it gonna follow my instructions to do what I wanted to do?
We are in the early days of getting those problems solved, and we need to make sure that we work on it fast. But do you have to know that these technologies move exponentially and they move much faster than we might think? And second is the psychological issue that we have to solve, which is are people feeling a level of comfort with this as the technology gets better, and we have to make sure that we actually get people educated as the technology is getting better.
So, on the security issue, you know, the idea of rogue agents doing scary things really went from, sort of, worst-case scenario sci-fi to, oh, it's it's happening, and and, uh, most, if not all, of the frontier models have experienced this. So, we could say, look, we hope the frontier models clean up their game, uh, we hope governments, you know, c- come up with sensible regulation. But for for companies, for executives, what can they, what should they do internally to mitigate the the, you know, the security risks that that we've been talking about?
I think you have to have a very, very kind of thoughtful, and diligent security practice within every company because these can be business-ending events. And you have to make sure that you are assessing the risks, and you've actually got technologies that can intercept at what they call runtime, which is at the time that these things are happening rather than post facto, so that you can stop an agent. Like, it's it's kind of like, um, going back to the teenager analogy, you would not let a thirteen-year-old or a sixteen-year-old who's just learned to drive be given a very fast sports car to say, go do whatever you want.
And, uh, you know, if if you wanna go out and drink and then come back home driving, everything is okay, you would never do that. You would actually have guardrails on them. You would make sure that your life three sixty can track how your kid, how fast your kid is going. And if you start to see drift from the expected behavior that is not aligned with you, you would actually take corrective action right away because it's your child.
I don't think AI is that much different from that. You have to manage these agents, not by humans overseeing it. You're gonna have to have that be happen at machine scale, because it's not gonna happen at human scale, because the volume's gonna be so large. You're gonna have trillions of agents in the world. These trillions of agents are gonna need some kind of monitoring ability that is also gonna happen with agents, and you'll have a check and balance between agents.
You will have models that'll be checked for safety and security by other models, and you will see partnerships that are happening among competitors because they will all kind of keep each other honest. And you might have, uh, Elon had suggested this, and it was a really good idea, is have each one of these models test the other model for safety and security and then make sure that you publish the results and have the harnesses be open sourced.
And then that'll actually start to see a created, you know, benefits in the way that is good for humanity as a whole.
I'm still trying to imagine, you know, a company that is genuinely AI native, you know, that not just using co-pilots and achieving efficiency, but one in which, I don't know, hundreds of thousands of agents are continually experimenting, building, analyzing, acting. You know, what does the management system of that company look like, and what is that company suddenly able to to do?
Well, I'll I'll give you our example. We have, uh, 90,000 agents that we've rolled out to every employee. There's an agent for every employee, and that agent, it has access to my email, it has access to my messages. We've built our own harness for security around that agent. We monitor how the agents are behaving. On a weekly basis, I give have the agent just give me, rather than me going out and actively looking at systems for how our numbers are doing, I tell the agent to say, give me a, um, give me a summary of how we did in, um, in our financials last quarter on both revenue and top line as well as on the budget.
I ask the agent to say, if I have, during my course of the day, if I have committed things to other people, give me a list of the things I've committed to at the end of the day. By listening to my meetings, by making sure that you look at my messages, um, so that I know what I can actually, what I have to make sure that I come through for for for delivering on those commitments. And so it actually becomes this aid and an assistant for me.
I brainstorm with it, I learn with it, I actually do deep research with it. It's fantastic. You have to think about these agents like digital coworkers that have been augmented to your team, and you just have to make sure that all of us are gonna be manage- uh, are gonna be managers of agents, and we have to learn how to use that agent force more effectively than the way that we have used it in the past.
Because you're gonna have a human force and you're gonna have an agent force, and the combination of those two will allow you to get from point A to point B.
All right, so so if we all have access to those tools, uh, you know, if if experimentation becomes, you know, easy, nearly free, what is competitive advantage? I mean, you know, you said judgment, and and that's something, but it seems it's it's y- you need an organizational ability to, you know, to test and learn and scale, you know, faster than anyone. You know, I I how do you think about competitive advantage in a world where everyone has these amazing tools?
What is competitive advantage? Let's say that there's two companies with thousand people. They're making the exact same product for the exact same market. Why is it that one company does better than the other? What do they do? The decisions they make on how to resource and focus the thousand people really matters. Two companies can have entirely different outcomes being in the same business. Agents are no different. Just because we have access to these resources doesn't mean that we are gonna utilize them in the most effective way possible.
That still requires human instinct and judgment and intuition. And so, competitive advantage is how effectively can you deploy the resources available to you to go produce an outcome that is gonna be a superior outcome in the market that is highly differentiated. That is what defines competitive advantage. That doesn't change with the fact that you have availability of agents. That just means that now you have another resource to tap into so that you can go out and deliver an outcome that is meaningfully differentiated, that cre- is value creating, that can allow people to do things that they weren't thinking of doing before, that can help solve problems that are important for people.
That is what creates competitive advantage, not the ability to have the resource.
So what happens then to the traditional innovation team? You know, if if if marketers, engineers, designers, planners, whatever, can all use agents to prototype and build, do the do the boundaries between those roles start to disappear? Do we need to rethink the roles that we even have within a corporation?
Firstly, all of them get supercharged is the way to think about it. And two is almost every job will get reconfigured. Some jobs will be lost. An entirely new set of jobs will get created. By the way, this is no different from any other technological revolution that's been had in the past. However, this one seems a little bit more uncomfortable because of the speed and pace at which things are moving. You know, like, I think we live in the bubble in Silicon Valley, and it's it's so profoundly insular in some ways because everyone believes in Silicon Valley that whatever you are doing on a daily basis is the norm all around the world.
And it so isn't, you know? That's why I find it superbly valuable to travel and go to different parts of the world and experience what people are experiencing there and saying, okay, so what is the gap and the delta, and what do we need to do to make sure that this technology gets diffused evenly? But in my mind, the overarching kind of thing that we have to keep in mind is we now have intelligence that is made available to us with no level of scarcity.
And it is up to us to make sure that we direct the intelligence in the right way so that we can get the right levels of outcomes from it. But that directing of the intelligence is actually a nontrivial task that I think is gonna take a long time to get automated.
As this is all rolling out, as the potential is sort of there to to grab, what's the biggest leadership mistake that you see companies making in terms of of AI's capability, in terms of of the risk of AI?
I think one of the big mistakes that people make is thinking that just because you have some tools, that all of a sudden you will just be able to ten x your throughput. It's not about ten xing your throughput. You will have to make sure that you actually create better outcomes, but it is gonna require every single time there's a step function improvement in one dimension, there is a human bottleneck that has emerged in another dimension, and that's what I'm seeing so far.
Coding is largely a solved problem at this point and has gotten automated quite a bit. Code review becomes a bottleneck. You will actually eventually solve code review as a bottleneck, and then deciding what to build and what not to build is gonna become a bottleneck. There's this kind of magical thing that happens when there's a light bulb that goes off because of serendipity or because you actually talk to people and there's a spark of insight that comes out.
Now, what's gonna happen is those sparks of insight will be more frequent because you will be able to do that not just when you're physically with someone, but you'll also be able to do it in a very, very kind of intentional way with AI. But it requires that you you actually incorporate that into your day-to-day workflow. And if you don't incorporate it, then you don't get any benefit from it. Then whatever it's giving you, uh, this is why I say, like, you know, asking AI to do a bunch of work for you and then not reviewing the work and not internalizing the work doesn't really help any.
The whole point is for it to do something that you can then consume and do something with, and when you can make that happen, that's when magic starts to occur.
So I think, if, you know, I think it's Cisco, uh, under the leadership of of you and others is, you know, very AI forward. Talk about what, like, what has actually changed inside Cisco, you know, whether it's budgeting, product development, incentives, decision rights, aimed at increasing the pace of innovation at this moment.
And I think it's important to also un- understand what's not changed. And so what's changed? The way in which we think about building products has completely changed, the way in which we position and market products has completely changed, the way in which we, uh, think about adding value to customers in a customer meeting and selling products has completely changed. The way in which we manage and operate the business has completely started to change. Those things have changed quite dramatically and quite materially.
The way in which we learn as individuals, uh, and collectively as teams, has changed, and the velocity has increased quite a bit. The way in which we do research has changed, uh, the way in which certain workflows get carried out, uh, and get automated has completely changed. What has not changed is the desire for people to wanna go out and contribute and add value, the desire for people to succeed, the need for status, the need for, you know, recognition.
Those things haven't changed in people. The people are still the same, like, they still those things. And I think that's a pretty important dynamic to keep in mind as we, um, as we think through this. The core foundation of what humans want, those basics, I I think, are gonna be remarkably similar twenty years from now. Like, people are still gonna feel like they're loved, they're gonna feel like they're being valued, they're gonna wanna feel like they're engaged in something purposeful and larger than themselves in life.
They wanna feel like they have a comfortable living and a comfortable life. They wanna feel like they've actually progressed and, um, you know, done well in society. They've contributed. All of those things will remain the same.
You know, is there something Cisco can now build in, say, a week that would have taken a year, two, three years ago?
I'll give you a very concrete example. We had one of our biggest problems that we had was we, you know, over time, uh, $63 billion in revenue, we made 250 acquisitions. The way in which you manage all this infrastructure was multiple different products that all looked and felt different. Really felt like a holding company. We decided to become an integrated platform company. So we said we're gonna have one management plane for all of our products, which is like a it was an absurd ambition to start with, completely absurd ambition to start with.
And so when I brought this up to the team, they said, well, G2, this would take the way that you're describing it, probably, I don't know, six, seven years to go out and build in the right way, and you'd need all of these resources. We built it with a small but mighty team and made it everyone's priority to help that team by adding to it within a matter of nine months, right? Something that was gonna take seven, eight, nine years, we were able to build within nine months because we were able to move at a pace that was just not even imaginable that we would be able to move at.
And so I think that's just an example, you know, the clock speed of a business changes dramatically. And by the way, it's very hard to get adjusted to that clock speed of the business because it's cognitively disorienting. Because, like, what are you talking about? Like, that's that can't be, and it's not gonna happen that fast. There must be some some kind of catch to this. And it's like, no, it's just you've got machines now writing code, and that's largely gotten automated.
And as a result, you can instruct it to write any code, and it'll probably write it for you pretty fast.
So you talked about bottlenecks to innovation, you know? So what has proven hardest to change? You know, something that that needs to evolve, but it's hard, whether it is for these human reasons or or maybe technological reasons.
Hardest to change is culture. I feel like the cultural aspects of this, of getting people to feel safe about learning and experimentation, getting people to make sure that they can be, um, they can be creative in the way in which they go about doing these pieces. Um, I think that takes a while in organizations. At first, I'll tell you this at Cisco, the first six months, I really struggled with getting everyone to start using it. Um, and then a light bulb went off with a few people, and they started, and the technology matured during that time quite materially as well.
What the one thing that's a failure state that happens in AI is, let's say you use AI and you don't find a great experience. I I let's say that you get started and you use it today, and you don't really find it to be earth-shattering. The typical, um, behavior that a human would have is saying, Okay, I tried it out, it didn't work. I'm gonna put it away for a while. That I'm gonna put it away for a while.
It can tend to be six months, can be tend to be twelve months, and you're like, Yeah, it didn't really work for me. The reality is you have to assume that the things that didn't work for you will actually start working for you in the next two to three months. And as a result, you have to assume that if that worked, would that be a really, really good thing for you? And you have to start planning while it's not working, that it is eventually gonna work.
And I feel like that is the hardest kind of mental model shift that people have because they're like, Yeah, I tried it out, it didn't work because they but but this thing is moving at an exponential curve. The exponential curve is almost a vertical line. That's how fast this thing is moving. And so from the time that you think about something to the time that that thing actually got incorporated into the product is a matter of minutes.
And so try again next week, and you might actually find yourself surprised. Right now, my guess is sub 2% of humans are using agents in any kind of substantive capacity in the world. Imagine what happens when 10%, 20%, 30%, 40%, 50% of humanity starts using it. You could start to see a very, very different level of productivity ratio. Standards of living will go up, and we will become smarter beings as a result of AI being around us.
And we will therefore be able to solve problems, cure diseases, you know, reduce poverty, um, you know, um, diffuse education in a much more broad way, in ways that we would never have imagined we would be able to do.
That's great. G2, thank you for being on IdeaCast.
Thank you for having me.
That was Jeetu Patel, chief product officer at Cisco, a company that is helping to build the infrastructure for AI. We'll be back with a regular episode on Tuesday, and then check back in your feed on Thursday as we conclude our AI series with a conversation with Wharton's Ethan Mollick on AI and strategy. If you found this episode helpful, please share it with a colleague and be sure to subscribe and rate IdeaCast in Apple Podcasts, Spotify, or wherever you listen.
If you want to help leaders move the world forward, please consider subscribing to Harvard Business Review. You'll get access to the HBR mobile app, the weekly exclusive Insider newsletter, and unlimited access to HBR Online. Just head to hbr.org/subscribe. Thanks to our team, senior producer Mary Dew, and senior production editor Kristen Murphy Romano, and thanks to you for listening to the HBR IdeaCast. We'll be back with a new, regular episode on Tuesday. I'm Adi Ignatius.