The Co-Intelligence Revolution: How Humans and AI Co-Create New Value | Talks at Google | YouTube Summarizer

Transcript

00:00

Good morning everyone. Uh welcome to talks at Google.

Uh I'm Sharath Bulusu. I work on the Google Pay product in India.

Um and I've been working at Google since 2005 on different things,

00:19

did stuff outside Google for a while as well, but on and off. I've been associated with Google for about 11 years now.

And over the course of these years, we've seen a lot of technological shifts. I went from being a PM who did not have to worry about small mobile screens to worrying about them

00:36

uh not worrying about cloud to worrying about uh things happening in the cloud. Thinking about AI and ML that happens somewhere in a corner to it being right in the middle of almost everything that we do.

Uh so with this context I'm actually really really excited that we have

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two brilliant authors here today who've been thinking about how the latest technological shift changes the world. I have with me here professor Venkat Ramaswamy and uh we also have Krishnan Narayanan.

Uh they're co-authors of a new book called the co-intelligence revolution

01:12

uh that came out just earlier this year. Um, and it's fascinating that they've not only thought about what does the change that's being driven by artificial intelligence do, they're also thinking about what does this mean for people, what does it mean for organizations, what does it mean for

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society. So what I was struck by as I looked at the book was not just that it addresses questions like where could this technology take us and think through some examples of real world impact that's been created in different companies uh including some of the time they spent here at Google

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uh during their research phase talking to Googlers about how this stuff has come together but a little bit about the authors before we before we uh go ahead u professor Venkat is a distinguished professor at the University of Michigan's Ross School of Business. Um, award-winning author who

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uh in 2004 wrote a book about the future of competition and since then his later books have focused on the concept of co-creation and I think it's it's very beautiful how that arc continues now to him talking about not just co-creation between people and organizations but also between

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people, organizations and machine intelligence. Um Krishnan also is uh an award-winning author. Uh he's co-founder and president of Ithasa Research and Digital. Uh and he's been studying how technology has evolved in India.

Uh and one of his books against all odds the IT story is a

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great recounting of how uh the beginning of the IT industry and its progress in India shaped a lot of what has happened here. uh many of us who are involved with different aspects of uh digital public infrastructure in India know what some of the personalities from that space have done

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uh and so it's been fascinating to see that and also his later book on empowering India. Uh today we we're exploring this whole idea of what happens if you bring humans and computers together in this completely new paradigm.

Um I want to start off with a few questions for the authors and

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then we'll take uh questions from the audience as well. Um I think the the first thing that I would probably start about start talking about and maybe a question for you but Krishnan please chime in as well.

You talk about this shift from artificial intelligence which is still the

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hot topic the the word that dominates a lot of headlines to talking about co-intelligence in your book. uh maybe it's helpful for our audience to understand what you mean by co-intelligence and that the premise of the book is centered around that.

So so maybe we start there. Sure.

So first

03:53

uh thanks Sharath and thanks to all the folks uh at talks at Google uh for this opportunity uh to share our thoughts based on the book. Uh actually our starting point therefore is uh natural intelligence which we humans are endowed with. Uh and uh the way we approach uh the book

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is that uh you know humans uh have had enormous creative capacities. If you go back in history uh we have created amazing things right including AI.

And uh so for us the starting point is uh that

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creative capacity that we as humans have but also the fact that we subjectively experience the world around us. We are sitting here you know looking at uh the room around us uh and uh that context of that world experience which is highly subjective uh and uh we have our own life world experiences

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uh that's very important to recognize because that brings the kind of context uh in terms of how we interact and engage the with the world right both at an individual level you know psychologically socially culturally right uh and you know there are various uh you know aspects to that subjective

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experience. So uh what we find fascinating is that uh uh we are now at a point where you know as we are engaging with the world suddenly now we have on the other side uh AI systems that can actually engage with us.

I just want to underscore that part of engage with us because um this book

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really started postgenerative AI. I mean that's where it's placed.

uh that was the starting point because where AI systems for the first time could understand us in our natural language. We think that's just amazing. It never happened in humanity where you have systems of intelligence if

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you want to use that word. It's a different kind of intelligence.

That's the way we look at it in the book. Um and so for the first time um you know you didn't need to understand computer languages, right?

Uh we all grew up with learning programming languages but in some sense now the programming language we argue is the human experience. We can just talk to it based on where we are in our you

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know lives like because what we want to accomplish how we want to engage the world and bring that sensibility and aspiration and desires and the way in which we want the system to engage with us so that it creates value in the way in which we think about value. For a long time the value was created

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in our goods and services that were delivered to us. If you go back like over 100 years right you have factories that produce goods and services. So we had this exchange paradigm where through the process of exchange value gets created.

We think we're now into this new paradigm where value gets interactively created. It is like enacted.

It is emergent from all of the interactions that

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we have in the real world, the physical world, but where now these systems are engaging with us uh both in the digital realm, you know, the digital intelligence but increasingly getting embodied uh in in in our physical world. So the co-intelligence to now answer your question is

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this kind of synergy between these uh intelligent AI systems which bring that different intelligence but that is that can engage and co-create with human intelligence. Uh so that's the uh uh the coal that's very interesting as as you were speaking I jotted down this phrase uh life

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experiences because you talk about that a lot in the book uh and you also introduce this concept of a life expverse. Now it's interesting that the book at least my impression as I read through it was I started by thinking it was about technology and then I realized more and more and more that

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it was about human centric concepts. It is about how the technology might affect us but more more of the book felt like you know you're talking about how does the ecosystem change because of this humanentric thing.

Now again life experts is it's it's a phrase you have come up with.

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I'd love to hear from you both what what do you mean by it and how should people think about it as they're thinking about applying this kind of technology for innovation. So maybe what I'll do so I'll just take an example and then invite Krishnan to you know jump in.

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uh so we actually kick off the book uh with the example of something called jugal bundi which uh is actually uh an Indian word which means creative improvisation which actually beautifully captures the the idea of co-intelligence and uh just to give you the backstory on juggle bundi

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uh so about a week to 10 days after chat GPT was released on November 30th 2022 uh some volunteer developers in India in Bengaluru um some of whom actually were working on you know language

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translation uh there was a government service called Bashinhi so some of them had that expertise uh basically took uh chat GPT and then along with Bashini uh built an application inside of WhatsApp

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uh and invoking uh Azure open AI services to create this application for farmers uh and they did this pilot in a in a village in India in Bwan in Hariana and what happened there was that uh farmers could speak in the natural language and this was essentially um grounded in

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the government's public uh benefit schemes which a lot of the farmers aren't even aware that they exist and whether they qualify etc. Now in India for those that have been kind of following the kind of the uh uh digital India story uh there's a a digital public infrastructure that has been

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built over the past decade and uh as one of the foundational layers there is an authentication of your identity. uh so that's important because that helps this application you know identify farmers right and since then a lot of KYC uh know your customer type applications are being built but in

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this context the farmer can just speak to it uh in their own natural language and there are you know over 22 uh official languages in India with lots of dialects and so on and so that's where that uh pashini came in but the point is a farmer can just be himself and just say hey you know am I eligible

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for any benefit schemes I mean just literally talk like that national language and then the system uh has the intelligence to then interpret what he's saying and then see whether he's eligible based on where he is and so on and what is interesting there is the system let's say says you're eligible

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and it actually came back to the farmer and said yes you're eligible and he said okay so you know how do I get the money now thanks to the digital public infrastructure those rails already exist in terms of you know if you qualify for the money to be transferred but now there is a process here where you have to fill out forms right so so go ahead and fill these forms you know maybe in PDF

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and maybe the farmer doesn't even know what PDF is right so the story actually is the farmer said you know go do it for me now today we're in the world of what we call agentic AI we have agents that do it for us and actually this application 3 years later uh we'll do it for the farmer but I think that's amazing because all of a sudden you unlock uh at population scale just the ability for

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uh you know a farmer in a remote village in India to now uh participate in this revolution on the one hand but actually now the the the government now can actually directly uh identify from their perspective create a value for the farmer and then just to finish the story uh it then evolved from

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this uh initial demo to a pilot to actually uh uh a government app called PM Kissan Kissan meaning farmer uh and then now uh there is an additional set of apps that have come in uh which now provide agricultural service to farmer. started with these government public benefit schemes uh but now the

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let's say the farmer now has the money okay hey I can now utilize that to enhance my uh income and so there are now various platforms being built on top of it especially by the private sector and one of the examples we feature is ITC it's agri business so this just unlocks

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uh all this value space uh which we really haven't tapped into which is in this thing we call the life exorce which is a neoism which we basically say is uh a combination of physical, digital, virtual realms, but it's situated in the kind of natural, societal, economic uh ecosystems

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uh that you know we inhabit like this life world, right? So uh thanks Sharath and thanks Google for inviting us.

So you know so I just want to give you a little backstory before I come to this life experts because you know I come from a from a tech world in the sense I atasa we deal with uh

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AI research and so on and so so if you look at the sort of the the demand side and the supply side of the of this equation right I come from the supply side the the AI side but then we've heard the story about uh you know we have to put humanity ahead of technology right and so I had that phrase

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with me as something which I knew people said but then this process of uh of co-creation with wanker it really and this book answers that question what does that mean right and so life experts for me I

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mean you you take any any kind of uh situation uh it could be uh uh you know beauty context uh user wants some particular kind of uh product foundation or a lipstick for a particular context.

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That's one life experience. Uh a a context it could be a uh worker in a factory.

Uh there's some problem going on and in the flow of this work at this moment I need some solution for this problem. That's another uh you know element of the life exit.

could be a teacher in a in a school

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discovering that the child has not learned well and now saying okay what can I do now to create a unique uh learning plan for the for the child that's another example in this life expverse and so how can the uh the AI system now come in the flow of the work not have the the human go outside

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away from the flow of the work so to speak, right? And in the flow of the work, how do we uh how do we now involve the the person as a creative experiencer?

And uh so that's the that's the the the thing that I want to want us to think when we think of the life exverse. No, this this

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is fascinating and I I think we'll come back to this notion of life experiences and life explorers uh as we go ahead in the conversation. But I also wanted to touch on the fact that you you made references to digital public infrastructure.

you spoke about the fact that there are certain types

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of systems that make this possible, right? I mean, for something like PM Kissan to happen, number of pieces had to fall into place, not just the AI that was used, uh, communication medium and so on.

And talking about infrastructure, you extend that concept. You talk about shared digitalized

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uh, intelligence and you talk about tokenized digital intelligence. I want to understand a little more about what you meant by that and uh you know how we should think about that as we're thinking about all the infrastructure that that permeates.

Sure. So uh let me get started and you

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know again uh Krishnan feel free to add. So so if you go back to the story I I asked I I basically shared the question you can ask there is like how did this all suddenly happen right this chat GBD

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moment. So now we have to bring in another kind of uh uh entity into the story and that's Nvidia. So as we know you know it was because of Nvidia and accelerated computing and actually uh Jensen Huang going to open AI presenting uh the DGX1 uh uh as we started now uh building out uh these

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uh you know electronic neural networks right uh which is which is part of the magic there right in terms of these transform models which came out of Google right so I think that's important for people to recognize But I think the way in which I think most people can try to understand it is

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that we are now for the first time also building AI factories which kind of produce these tokens of uh intelligence right. So think of it as you know in the industrial revolution we used a lots of uh uh raw materials to you actually even generate electricity right which then spurred uh a lot of

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further innovations uh in the revolution. Now electricity is kind of like the input and the output are tokens.

So we're using energy is coming in and then tokens are coming out. But these tokens which we experience right as text, image, video, audio files or even uh uh in the case of the recent Nobel Prize right you know protein structures right uh alpha fall Google

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um that's very remarkable the fact that uh you can uh essentially uh use floatingoint numbers uh to actually build these kind of representations which we find useful but the point is they are like raw materials right now they are things that we now bring into like we saying earlier

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our context of using it to bring value for us in terms of our engagements with the world around us uh in in this life experts. So uh I'm glad you pointed out that the these tokens of digitalized intelligence are very important because uh they are the units of intelligence that we have to kind

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of compose with uh to create various new forms of value and and therefore uh the infrastructure the the AI infrastructure now uh which obviously incorporates all of this what we're finding is in that example uh it's very important uh to have what we call a shared digitalized infrastructure

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because no one person is going to be able to build it. Even if you're a private company, uh uh we need to kind of if we go back to the industrial revolution, you know, highways were built uh and then you know the uh the public sector participate in that process.

So there's a different kind of engagement between like the public, private and the plural sectors that is

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happening like in that example which allows us to build this foundational infrastructure not just in terms of the foundational AI models but what you mean is this intelligence infrastructure which actually makes all this possible on top of which all these engagements are taking place and values getting enacted which in turn drives lots of impacts right at speed at

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scale you know in various uh in in in various arenas like Krishnan was mentioning all those uh what we call use case applications right uh and trying to do that sustainably because there's also this question about we're using enormous amounts of energy so to kind of summarize what

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we're seeing here is that we're we're unlocking this new value space but absolutely that's being driven by this ability to actually bring in this tokenized digital intelligence which we have to think about how does it parlay into various offerings in terms of what we're creating but

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also how we create you know all across the value chain and so that's like foundational to this uh co-creation of value and if I can give one one sort of example take the beauty thing that I that I talked about okay so uh and and in the book we talk about L'Oreal as an example and and so you

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know they have a number of offerings uh but let's look at a couple of them there's something called the beauty genius where somebody would have a conversation a consumer may have a conversation with beauty genius now there are words which they describe saying look I've I've just come back uh I

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feel dry I you know whatever I I need something now all these words get translated in some way for the the system to make a recommendation right and so that's one one aspect of that uh how the TDIs get into action but there is something more because TDIs also get into a

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an actionable intelligence if you will and so they they have another product called Perso Perso is uh uh you know actually a physical factory if you will right the AI factory in the back end is now capturing this information uh it might even say look here's a picture of my face

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and so that's another piece of information that's coming in there are some sensors it'll it'll sense the humidity in the place so a user using pursu in Bangalore versus using in Chennai the output that you want, you you you require that to be different because the humidity levels are are

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different in these two places. And so all these information now are translated in the form of TDIs for you know it could be we we kind of call it the hydration TDI if you will but that's what L'Oreal is now translating all this information that is captured to now create a a specific

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foundation or a lipstick for for you. So that's an example of a TDI in action for instance.

No, this is fascinating. you know as a product person I approach this with a lot of optimism but at the same time what I'm finding personally even though I I see the positive story in the examples that

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you've given both of you across uh the last few minutes of our conversation I also worry a little bit about a lot of our intuition for how products have worked and the impact they've had is breaking right uh this happened at many different technological revolutions right our our intuition

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for what happens when the speed of transport uh for goods goes down the speed with which people can move the speed with which I can communicate the ease with which they can communicate breaks in this case as you talk about co-intelligence personally I'm finding it still quite hard to

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develop my intuition for the space and at Google we talk often about a lot of the products that we build how do we build them responsibly uh we are aware that what we're building is very powerful we care about it doing good for the world. When you approach this, how how do you view

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the risks side of this? What do you worry about? How do you how do you prepare for that world in which you you do this more responsibly?

Yeah. So, so uh there are a couple of things. Let me just unpack your question.

So, the first one you said you talked about intuition and so on. So,

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absolutely. So, I think this is why we we call it the core intelligence revolution. We use the word purposefully because there is a revolution because in the traditional model if you go back to the industrial revolution the model was you had a value chain right which you control and that gave us the quality revolution because you know when I give you going back to the example

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a lot product right you give you what somebody else we want to ensure they're the same quality so we had quality six sigma all of that but we control the quality of the product and the process right because we our focus was on creating that that offering that came out of this value chain

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Right? And so we could control that.

Now we're seeing something very different because now we saying the starting point is is really not here in the sense that yes we have we have the setup right we have this when he said this uh uh the L'Oreal the the physical factory what he actually meant was that it is sitting in my uh home right in the bathroom let's say right that's

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it's a little device it's got cartridges and so on right um and now I'm actually talking to it through this interface I have the L'Oreal has the app and it understands kind of my needs Right and like you said brings in all this information. So there's a different kind of interface here where

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I'm interacting with it and it's figuring out therefore you know what it should be dispensing to me. Now there is still a value chain in creating that but if you if you look at it now what I've done is at this point of exchange now so just me giving you that fixed product right and then

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I I I use it um now the product itself in this context right uh changes as a function of how the user wants the product that particular day so it's a joint creation between the human and this and absolutely this is uh very uh difficult and challenging because all of a sudden

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uh I have just induced a lot of variability. If you go back to the heart of the quality and six sigma revolution, variability is the enemy, right? You want to root out variability.

But variability in terms of the quality of the product or process that still is there because you want you want the

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product to be of good quality. But what we are saying here is a value is now a function of the quality of the experience I create interactively with it.

Right? Uh so that space uh Absolutely.

It's it's it's highly variable because everyone wants a very different kind of personalized experience, right? And I can't it's not something I control.

In fact, it's the exact

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opposite. I need to embrace variability because everyone will interact with it in various ways. It opens up.

It's almost like anti-ex sigma in terms of the that interaction space. So, we do need new new ways by which we can now think about how the interaction will take place.

But

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it's not clear to me. Therefore, the intuition that served us well uh in terms of uh how we uh how we actually went about uh uh creating our offerings in the past still works because

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uh in in in the old world of the um uh the way in which you um develop these products uh you brought intuition through your own research. Maybe you you got some you know input feedback from customers.

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You had your own things in terms of what you want to bring to the table. What I'm saying is uh we need to and that may still exist but we need to kind of understand that we need to have that intuition is going to be co-developed right and so we need to actually build tools where the

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uh customers and the users uh can actually uh continue to inform us right not when we want to build the products but when they're having their ongoing experiences so I think that's going to be a challenge but actually we're now building AI tools where you can analyze for example a lot of

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uh sorry a lot of uh uh the uh you know people's voices at scale right so people can give uh voice feedback uh like the farmer can give voice feedback right the uh uh the person who's using this consumer can continue to give feedback but it's not like feedback to like market research

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questions we ask or you know it's just they can just share you know what worked what didn't work like the person can ask just like chachi pd saying hey you know I made this for you yesterday what was it like right and the person can come back and share what worked not oh it was good can just have that chat but we can extract from it and intuit it from it right uh what you know what that all means

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and that's why I think AI can also help us into it I think that's very important to understand along with all of this stuff so it it is it does open up this other space but absolutely in terms of risk the last part to unpack your question uh we are we are just creating more risk. So as we move into

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the space by definition we say risk is the other side of the coin. So you have to create riskmanage value is a phrase we use that is you have to now think about what are the new types of risk form of risk absolutely uh because before we control all of them this is a whole new territory right

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uh and so what are the different types of risk all the way from uh obviously privacy risk security risk those are the obvious ones but also other forms of risk that might uh uh uh now you have to kind of think about uh like in terms of creating that quality of the experience and in fact just

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just leading on from that from that point in fact NIST NIST in fact identifies these 12 risks which are particularly accentuated by generative AI right and we and so we we we there's in

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fact an entire section about how we manage risk how other companies manage risks and so on um I mean take take something like uh Adobe's Firefly okay now now the way we we featured that in the

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uh I'm saying the way uh way they managed the way they trained the that uh text to image generation engine was using licensed uh uh images right and so the copyright aspect is one one example from

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there for for instance where as a as a end user if you want to be assured of the copyright that's that's a way to handle uh I mean we do talk about Google buying mandandy in itself you know as a as a means of uh managing the cyber security risks and so on but this is another another place and

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I I'll just give one more example to to illustrate this one of the ways of managing this risk is now to you know the the the code is the law like Larry you know like so an example would be like the depa

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framework in India where it's codified or in web 3 where uh smart contract contracts now ensure that the uh you know you you do a deal and then if the things are fulfilled the the contract is automatically uh uh you know executed if you will. So these are examples where of managing

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such uh such risks which are very dynamic and emergent. Yeah, it was interesting but uh when you were answering the first part of the question you also spoke about the way people interact uh with AI.

Now the easy ones for people to get are you know the basic modes of communication. I

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not only can type in a question, I can speak it. I can give it an image or a video. But I think the next higher order is as you're co-creating though it is not just about you know I gave one input and got some other regular output back. How do you see human AI interaction changing?

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uh is that you know is the intuition that you think about it as a replacement for some of the things that exist today kind of meta is right uh there's a risk that you're going to outsource your thinking to the GPD right so let's take uh the business that at least I'm in right uh education

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uh this is a huge risk right where right now the current uh uh thinking about that risk is well as students use GPT more and more you Are they just outsourcing the thinking? Because one of the things we want the students to develop are critical thinking skills, right? We want them to be creative. We want them to collaborative.

We want them to also develop these

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critical thinking skills. So that poses a very interesting challenge because on the one hand, you know, in the traditional paradigm, we kind of delivered courses to them, right?

But they were fairly passive. They were not participating in the value that gets created.

But as you move from that model to a learn learn learner based model right uh where and how they want to learn uh you would

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imagine that hey you know chachi is great right because it can be your personal tutor it can help you learn and you know obviously we have to design it in different ways we give the example of like kigo for instance where they've designed it for active learning it doesn't give you the answer it it it kind of works with you until you get the answer and the and the teacher might want

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to design it but you know in terms of how long should that go before maybe you reveal the answer sometimes s we also learn from the answers and so on. So there's a lot of uh uh science and art that goes into designing these these systems and ways in which you know the student uh feels

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empowered to learn and it's actually engaged and is learning. But that's a very different process because in in the old model as a teacher it's very difficult for me to personalize the way I want to describe something so you understand it because I don't know what will will click with you.

But

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that's what the AI does very well, right? You can ask it to explain something like give me a sports analogy or you know help me understand it like you know in this context.

Uh so its ability to uh actually uh change the way something is described is phenomenal. Now now if you're

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if you're a teacher you know how do you look at this? It's it's a risk.

It seems like I'm going to be replaced right? uh but if you actually see the world that we're moving into we want to be able to train students to actually be able to work you know engage with the AI systems because in the

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workplace presumably now their task is to manage design you know and enhance these systems even as it enhance augments their own uh capabilities right but if you keep that as the goal yes it comes with challenges and risks but now it's very interesting what it does is it forces you to think

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about hey how do therefore evaluate students and to me and I've just started experimenting in my class where they all use CH GPS is so so so what what is my role now well actually I have to design it in a way in which uh they enhance the learning but the the focus shifts from my

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grading like answers because chibi gives answers to things to like how well do you question so do you you know how well do you craft questions how well do you use the system to frame problems individually working chach or you know, working in teams, right? So, it shifts the nature of the way

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uh you think about assignments, right? And and so because they're now interacting and so you're evaluating how well they interact because in the workplace presumably that's what they're going to be doing in the future.

So, in a way, you're saying don't show me what you've done, show me how you've thought about it. Yeah.

Yeah. But then you need to evaluate that, right? uh and

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so that is I think now that's where things are evolving now uh in terms of being able to uh uh allow them to learn you know at that pace in the way in which they want to allow them to creatively express their agency in the process. Uh but then our role is to actually build this architecturally

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to actually facilitate that co-intelligent you know co-creation of their learning experience. I mean and once again just thinking out aloud in terms of how and and presumably I mean in in Google you must be working on these ideas but we we briefly touch upon it in the book but things

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like a a world model does it understand like like a baby understands from just looking at the world and getting a sense of the of the world. How does the AI understand the world model in in how it uh you know responds to a situation?

that could be the next form of of interaction. some I mean

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I think even even for the AI system to say I don't know I think that's the next level of uh development right I mean I I like there is no clear objective function right now I don't know what to do here and so for the for the system to come back to the user saying now tell me what

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you think and having that dialogue that's another form of evolution if you will for the uh for the AI system in in forms of uh you know potential dialogue ogue that it could have with humans to understand. So maybe just pushing a little more in this direction.

We've spoken a bit about how

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humans whether they are students or educators or uh you know business professionals interact with AI but I'm assuming as this goes along this is going to have a lot of implications for how our organizations are designed how they interact with each other and these organizations are not

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necessarily only you know for-profit businesses. This could have implications for governments, for universities, for a whole range of types of organizations. How do you think about the impact of co-intelligence and that becoming a more natural normal way of working,

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way of doing things? How does that impact the design of organizations, how we manage them, how we run them?

Yes. So in in the book, we introduced the idea of the organization being thought of as a living system.

So we call it a co-creative living system organization, right? Because if you go back

36:39

to kind of where we started, what we are saying is that you know if you take our biological systems, we are also very adapted, right? We are living systems that we we interact and engage with the world.

So while the organization may have let's say a digital brain, however the the organization kind of processes a lot of this intelligence, right? And uh engages back with people.

The point

36:58

is that uh we need to we need to build uh the management systems in ways in which they function like living systems. So what does that mean?

Uh clearly uh the systems have to be very adaptive and very responsive at the individual level right in teams. So we have already you know things like

37:16

co-pilot and so on right in in in the flow of work. Uh but that's just in terms of designing workflows right and work artifacts and so on. But if you were to step back and say and going back to risk management, right?

How do we govern these systems? Uh so it puts a lot of emphasis on governance not just strategy and executing strategy.

Uh so how do we build these governance

37:36

uh you know architectures and management systems? Uh and remember again going back to if you look at product management systems right so now you have to worry about experience quality. So what we do in the book is to say depending on the context if you're looking at supply chain management like what changes there?

If you're looking at product management what changes there? If you're looking

37:53

at marketing, sales, service, what changes there? If you're looking at talent management, you know, what changes there? So, I think if you look at each set of like management activities, we say, okay, how are the interactions changing there?

Because we're moving from just the the activity itself to how people engage with AI in that activity. So we need to think through

38:12

at that kind of granular level at that micro level to really say you know what what changes need to be made because that ultimately gets then reflected in the various management systems and processes right so it's it's not like a top- down approach as much as uh this bottom up uh

38:30

redesign of management systems but in a very co-creative way in fact the systems itself are getting co-created and so the platforms that we uh build right in terms of whether it's performance management or various aspects of strateg Y management all those uh will change and so we take different types of management activities in the book and we feature examples of people who

38:50

uh with respect to that context are making that shift in terms of redesigning their management system and if you take like all of these things then we're really talking about what we call a co-intelligent enterprise of the future right uh but it's it's it's not something that

39:08

uh we can like define up front uh it it it it like works with co- intelligence you know all across uh uh its uh uh offering system its value chain system and its management activities uh so that's the way we see it right I mean I I just want to add two other points to the to what

39:27

Wret talked about uh for me one of the important things in this new way of working if you will is the is the the value creation in the flow of work, right? Every moment of interaction becomes becomes

39:43

important. And so the role of the manager as a creative experiencer in in maximizing that value creation in every moment of engagement.

So you need to give that capability to that that employee

40:02

uh in that thing. So as an organization do you have that kind of a a a core diligence knowledge environment?

So we we argue that you need to think about it. You need to create that kind of an environment.

So uh supply chain manager you you have now discovered that there is a flood

40:20

in some some place and so now what do you do at this moment? Is there a a knowledge environment available for you to maybe do some simulations of uh alternate uh availability?

What kind of time frames? what kind of you know and so maybe you will now discover that look uh only uh 80% of the

40:42

orders I can fulfill with these two but that's a business decision that you can now take you can go back to your you know your higherups and say this is what I simulated and here's so I'm saying this this ability for uh for providing that capability to the manager uh not just as a as just a passive

41:02

user but as a creative experiencer to participate in that, engage with the system, share certain inputs and then create certain, you know, simulate some scenarios. That is one very important uh requirement in this in this new world, right? Are you creating that kind of environment?

And if

41:18

I could actually build on that, what you're saying is we're moving to real-time value creation, right? Uh so that's what you know, so so if you actually take uh what he was describing, we have now the ability he used the word simulation uh to simulate like a digital twin of the entire

41:34

enterprise. So one of the things we are seeing and we feature in the book is that digital twins will become very pervasive in this life experts right because it's a representation of the interactions you have in the real world with the system environment but this time it's different because these digital twins are not digital twins have existed before you would go you simulate

41:53

some things and then you make some policy changes strate changes you implement that and you see how you know what happens but these digital twins are actually linked to the real world because uh increasingly everything is becoming software defined Right. So if you take a factory today uh the intelligence exists in the physical factory via all of the sensors and you know controllers

42:11

and so on and we give the example of seammens for instance and so the seaman's digital twin uh is actually connected to that real world. It's not just a mere representation and and you do a simulation.

So using tools like nvidia ombry words and so on uh you can actually simulate changes

42:27

in this let's say you know a factory that you're building. So you have the AI factory, you actually have a virtual representation of the factory built on top and then you have the real factory.

So there are three layers but these are connected. So as you make uh changes you can see what effects it might have and go going back to your risk point you can actually manage risks much better.

42:46

which we we feature in the book lots of examples like that. But the beauty is once we have decided this is the decision we're going to take looked at all the pros and cons and you know making informed decisions you can hit a button and you actually see the uh real factory floor change accordingly

43:04

because it's software defined because it changes the uh software in the real factory. That is huge uh because one of the benefits we found is that in designing the factory things become much more collaborative because you can invite people because it's less risk and so they said there's

43:20

also this thing called uh they're building it for engineers and they call it immersive engineering where now if I put on goggles and now if I bring in the AR VR aspect into that my managerial life experts in that moment uh I can engage with it and I can speak in my natural language I can

43:37

speak German you can speak French and so actually for the first time you can really truly tap into the power of this interactive collaboration where we are not just having like a Google meet or in a zoom call. We're actually looking at the factory 4 and someone can move things around

43:55

uh and we can see what effects it has and they may have some opinions about it and we can really have these very rich immersive conversations. uh and going back to your point you made earlier about uh intuition we can bring our collective intuition right but I think the key here is we

44:12

must have the customers part of this which is what they were saying in that case the factory flow of the customers are the people who are the employees and the supervisors so they make sure they are also part of it and not just the manager level people so when you extend this to making it more inclusive uh suddenly uh you are able to you know create value that actually people feel is is

44:32

getting realized for So that the shop floor person has a better experience, right? The managers who manage those systems have a better experience.

So in some sense we are saying that we can enhance the experiences of everyone involved. That's the that's what the opportunity is before us while we

44:47

risk manage it you know effectively especially with digital twins. This this is interesting. When we were talking earlier, I was describing, you know, when I was in college and studying industrial engineering, this kind of simulation that you're talking about to develop an intuition

45:03

for what happens when you make decisions in a factory was a mathematical simulation tool, right? And I remember when using mat lab or something.

Exactly. And and these were built using systems like that.

And there are a few universities that actually built like these little what they called virtual factories but basically they were mathematical simulations

45:20

along they were not really they were not really connected to a real world shop floor. I remember in one of my early jobs in industry I used to work for a railroad as an operations research analyst. We tried simulating the operating plan uh to try and understand where various risks lie right

45:37

uh because the forecast has variance all of these things but again like you said it's not it wasn't connected to the real world. So I think in in what you're describing there's a lot of excitement in some ways because I could imagine future generations of could be engineers could be economists etc coming out whose learning even though it's still in the academic space has a lot

45:57

more of a deeper connection to reality. So yeah absolutely this is absolutely fascinating.

Um, I'm also mindful of time, but I also think that given that we've been talking about uh, you know, co-intelligence and uh, you know, you've written books on co-creation, this is a chance to

46:13

co-create something. Sure.

By uh, by throwing a question back at me and Google while while I pull up some of the questions that Googlers have prepared. We were actually planning on doing that.

So, no. So, so Shahed, you know, last time when we came and spoke to you, of course, we we heard a little we heard a bit about your work when you are an the architect of the the the GP story

46:34

in India right now. So if you were to having read the book and having had some discussions about uh the co-intelligence if you were to sort of apply that to the to the GP ecosystem in India fintech ecosystem in India what might be some two three two three new things that you that that you would

46:53

like to do I mean at this point there's a lot of stuff racing through my mind but I think the the two things that I'll probably point out that that have stood out from this talk for me that apply to

47:09

this question are one this notion of understanding life life interactions a lot better and the second one is uh where you're talking about having the customer involved in the co-creation as well that

47:25

that L'Oreal example of the person almost being able to uh to produce the specific kind of product that they need if I try to find those parallels in fintech What I would say is that we've up until now generally been used to the idea of creating products where we tell people look you're looking

47:42

for a particular function or you're looking for uh um a particular product and there's a process that you go through to get it. So if you want to make a payment, you go choose a method of payment, you authenticate yourself, uh you say where that payment is going, all of that stuff.

48:02

and there's a particular flow that you go through. That flow looks somewhat similar for all of us, right? Whether it is Google pay or many other applications.

Uh if you took something more complex like a I want to get access to credit, we put them through a longer flow. Say not only is

48:18

it important enough to authenticate yourself, but you know I need details like your name, date of birth, some identifiers that let me pull a credit report. If that isn't available, give me alternate information that helps me underwrite and so on.

But imagine now that the user comes in talking about what they want to do and it is not just that we're able to tailor the

48:38

process. We could actually even create the right experience for them on the fly.

So I could have a user who from your example if it's the farmer in let's say Maharashtra and prefers an interface that works in Marathi is someone who has not taken a loan before is not completely aware of

48:56

various schemes they might be eligible for from the government. We do a more elaborate simple interface for them in Marati.

But if it was one of you who is a more tech-savvy individual who's comfortable with financial products etc. we might even speed up that process by introducing some

49:13

of the jargon that brings efficiency because you probably understand some of those things right and therefore there's no point in putting you through what would appear to be a very long uh experience. I think between these two the the thing that I don't have a good answer for and which goes back to this question of risk is at the same time to ensure that there's a level playing field

49:32

uh that you know we're not discriminating against people as they get into uh access to products uh to ensure that uh because it's a financial product we're making the right disclosures so that people know what they're getting into how do I maintain that baseline of making sure there is sufficient

49:50

transparency in the process that your privacy is respected that you have a secure experience uh and that it happens all of this happens in a timely fashion and and that is a harder one to solve for and that is where I think you know I'd go back to this question of life experiences because you need

50:07

to have that intuition put yourself in that user's shoes and say why are they trying to but if I may if you actually think of it as a life experience ecosystem uh then you know when you asked the question we said fintech ecosystem but that's just slotting you in terms of uh fintech. So one of the

50:24

things as you're talking as reminded up was my recent lived experience where uh you know since I came in from the US and my mother lives here uh she wanted to make my my my favorite dish for me which involves some kind of spinach and so there's a vegetable vendor uh whom she gets the spinach

50:40

from and it's amazing how she knows you know what kind of spinach and she brings exactly the right quantity so she does demand forecasting very well uh but she's got a limited uh physical space right in the cart so I was Uh and and and and by the way, so payment is so easy here. I just, you know,

50:57

scan the QR code and use GPA. I'm done.

And for her, that's great. I was talking to her.

She said she doesn't carry cash around. It's much more secure.

And you get all the stuff. But then when I was saying, hey, you know, if if uh you know, it's like Aladdin in the magic lamp.

If you had a wish, you know, I could grab what would that be? It's very interesting.

Uh she wanted to have another

51:16

car. She wants to grow her business, right?

I mean, she wouldn't use the words like growing business. She said, "Oh, it'd be nice if I could this because it's only so much I can do.

I wish I could have another card." But if you actually uh so so going back to what you're saying, there are two things. One is how how can she express that intent?

So if you think of it as just a financial

51:34

app, okay, it's doing the transaction. No, but what if you had something there, a chat saying, "Hey, you know, is there something somewhere, you know, how would you like to improve your your your your life, right, your livelihood?" And the person says, "Well, I would like to, you know, have another cart." what she's implicitly saying perhaps for you might be hey I I might

51:53

need another loan to actually get a card but she may not ask for a loan so that's one thing that struck me the other the other one is as you start then thinking about that what she's saying is hey uh I need another card now are you in the business of building carts not necessarily but it means

52:08

that from your ecosystem perspective you could create other kinds of services right that that can kind of plug into it like going back to the farmer example now you know ITC has built this platform it's almost like a marketplace place right where different people provide f you know fertilizers pesticides you know other things in a way in which the farmer wants to

52:26

utilize them right in order to grow his income. So similarly here it basically means going outside of the kind of the the narrow confines of the fintech ecosystem right it might involve uh other interconnected ecosystems so I think it also allows it brings a lot of opportunity but we need to really broaden our notion of what the offering is. Yeah, this this is fascinating.

52:46

I think lifting it from the question of a user who knows they want a loan to a user who may not even be able to express it in that form or not necessarily not able to but maybe hasn't thought yet to the point of whether they want to do it through a loan or something else but start from the actual need of saying you know I want to grow my business and the way I think about it is if I

53:06

had one more cart that I could rent out to someone else I've got a new uh new source of income. It's it's fascinating to think about.

There's a lot of opportunity small and medium enterprises if you were to just generalize with an example which is totally untapped. Yeah.

And and this also probably ties back to what you're saying about shared digitalized infrastructures because when you

53:25

get these more general kind of you know needs that people express. I may not have all of the building blocks to be able to build the solution for them. But if there is a way for me to connect other pieces and build that and I think that's that's part of the magic of what's happening in India

53:42

with a lot of the digital public infrastructure. Uh but I think if we can build on top of that then it becomes a lot easier to components from other ecosystems. Exactly.

Exactly. Now this this is fascinating.

I I do want to uh also bring up a question that someone uh from our team had

53:59

uh had submitted um and they were looking at your book and they went you know your premise is fascinating. Do you regard co-creation as a way out of the current human versus robot dilemma uh that surrounds all the AI narratives that we see today?

And and what do you envision when you think

54:15

about co-intelligence in the context of healthcare and life sciences? Um I'm sorry I didn't capture the name of the person submitted the question but came from other Googlers.

So I thought I should bring that up. uh I mean if you could have an interactive interaction with the person then we could clarify the question but uh so I I don't know if I'm interpreting the question right uh but

54:36

yeah in terms of human and and and machine right yes I mean broadly speaking I think yes because uh we we are thinking in either or terms when you say of uh machines replacing us humans so what if machines you know what if AI is not here to replace us humans but our premise is

54:53

what is there if it's there to co-create with us But that's up to us right uh in terms of how we view the these AI systems. So I think it's it in some sense there's a lot of the narrative uh which is looking as AI replacing humans and yes it will do several tasks better than us humans but

55:09

then the point is uh in human history we have always elevated ourselves in terms of how we how we build these systems. I mean like I said we have built these AI systems and granted now the AI systems has the potential to selfbuild itself.

Uh but keeping that aside and as to when and how it

55:25

might happen and and and also as humans we have a role to play in ensuring uh you know in speaking of risk and guardrailing it in ways in which you know uh we can steer it in ways in which it actually serves uh human humanity and not the other way around us serving AI right so from that

55:44

perspective absolutely uh co-creation is a way out to go back to the question because uh it's both in in creating these systems and also So creating through it right uh how it affects and being able to understand those effects faster uh like we said right using all of the inputs from people's lived

56:01

experiences of engaging with AI. So you need all of these pieces not just that infrastructure and platforms and focusing on the flow of engagements but also once they've had experiences how do you bring those experiences back uh which goes beyond reinforcement learning where we are trying to improve the system but actually letting the system understand that hey there we as humans have a uh a

56:23

lived experience of the world that is subjective right which the the the AI system doesn't and I think that's the role we have to play to teach it uh what uh our lived life experience are all out uh and so that going back to what he said that world model right and representing

56:38

that and getting it to understand the world uh is important. The last thing I would say is that when we think sometimes we conflate two things.

The way a robot perceives the world is not how us humans perceive the world. Right?

So I think sometimes we conflate the two. Uh for instance, you know,

56:56

when I came into uh for this talk at Google, if you were to ask me, hey, you know, what is the color of the wall you just passed by, etc., I wouldn't know. But if if I was a robot walking in, you can go and you can look further.

It tell you the color. It may even tell you where that material came from, etc.

But that's not our part of our human intelligence. Right?

So in some sense

57:14

as we are we operate in the world and there's the system one and system two of conam and one is you know automatic and this and we're still trying to understand the human brain right how we cognize the world and engage with it. So I think all of that will inform us in terms of what is unique

57:29

about our perception of how we engage with the world and so in some sense they're complimentary right because that can give you a lot of detail. uh and so if we can think from that perspective then together we can we can co-create new value. If I can just add one and I want to intentionally

57:46

take a more sort of a a forceful view the see if you if you accept uh that we have to move to this experiencecentric world okay then co-creation is is not like a choice okay we could do co-creation

58:01

but we could also do other mechanisms we are saying no like if I want to create a shhat experience I cannot produce it beforehand and keep it ready with me when Shhat wants I I I give it to you. It must be co-created.

It must be co-created. So I'm saying the answer is it it is it has to be

58:21

the in the experiencecentric world you have to co-create and because it's lifecentric human is at the at the helm of it and human co-creates with the AI to deliver that experience and Christian since you gave us some examples about different companies whether it's L'Oreal or others um the

58:39

second part of the question where this person is also asking you know how does this co-intelligence revolution affect specifically healthcare and life sciences yes are there any examples that we've studied that that true in fact let me let me just offer a a couple of different uh

58:55

scenarios I mean there is one that we talk about as open evidence which I think in the US is a at least 30 40% of the doctors have started using that as a uh in a way it's it's a more fine-tuned uh you know uh chatbot but but trained on uh medical journals and and so on. So more authentic

59:18

information impossible for humans to keep pace with the amount of knowledge that exists but using the open evidence the doctors can take a more uh informed decision about uh treatment protocols and so on. That's that's one example at the other end of the spectrum in terms of medical research and

59:37

so on. I mean there's a fascinating example that we that we start off with at uh you know at IIT Madras with the Sudha Gopal Krishnan Brain Center. They have now created uh they've just released

59:52

something called Dhani which is a database of uh uh human fetal brain. Okay.

So this is the world's largest collection of human fetal brain. So that means they are actually mapping fetal brains the whole brains uh they have released it now but this is all pabyte kind of information.

00:14

So they are now forcing uh organizations like Nvidia to come in and say how can we now use uh AI in a much more uh effective way to deal with such kind of information. I produced this data

00:30

but how will now doctors use this to make certain certain decisions. So that's the frontier in terms of saying how it's forcing new kinds of uh uh you know AI capabilities to be built to process this. Now it's definitely fascinating and while this is not my area of expertise I definitely see people

00:49

at Google working on areas like Medma there been people who have tried to map the connectto so a lot of what you're describing but also in terms of healthcare I think uh so this is on in terms of medical research side I just wanted to also add that there's this whole area and we give different examples where you have lots of uh for example helping nurse practitioners uh engage better with

01:08

uh patients like who might have dementia or people who who have mental health issues uh those are very very complex but with these things people can speak in the natural language right so you can better understand what they're going through right or what anxieties they have what you know what symptoms they experience in terms of pain or you know mental well-being and

01:27

so on because those are all very soft issues and they may express themselves through words images and so on so so I think there the the practitioners who are taking care of them can better connect with their lived experience I think that's where uh also the of power so that then you can adjust the way in which you know a certain uh treatment

01:48

plan works uh because before that was missing it was very hard for them to kind of share that right in terms of actually affecting the protocol uh in terms of the delivery of the healthcare I I think we also have some questions from Googlers here so I'll request someone to hand a mic there

02:11

so hi I'm Jasmine And my question is based on managing risks in co- intelligence systems. So what's one thing that you believe is often overlooked uh like a subtle risk that's often overlooked in uh organizations adopting AI? So you're saying that uh so I understood your

02:30

question right which is the risk that is often overlooked. Okay.

So that's a very interesting question actually. Um so if I were to think about all all of the uh uh risks I think one of the

02:45

uh biggest risks that is overlooked is actually uh the risk of not following through on uh you know people's engagements with it because uh a lot of the time uh we dismiss when someone let's say

03:03

fails to utilize something in in a certain way Right. It's it's it's it's almost like we have to invert that and see it in a positive way. It's like seeing the glasses half full.

Right. Uh and I think it's actually it's very important here because when people engage with the system,

03:20

for example, most people in the use GPD, they use it in a one-shot way. They hit it, they think they answer, oh, it's not.

But they what they fail to see is that they need to further engage with it, right? Have that dialogue with it.

But maybe they're not they don't know that. And of course now we give some prompts right uh and so on. Uh so actually just understanding how people

03:40

can better engage and if they don't engage why they don't engage right and u or maybe it's not evident to them how to engage with AI. So in other words it's almost like I'm flipping your question on its head and saying what are the risks they're seeing right uh or what are they fearful

03:55

of because of which they don't engage. So it's almost like like that I'm calling as a risk that is overlooked because if you understand that then you can make it easier for them to engage with uh you know it's uh it's a little bit more subtle notion of thinking about risk.

Yeah good question.

04:17

Hi this is Shanmuki. Uh the idea of this code intelligence is very fascinating that like which can answer the question does do AI replace humans? I felt that uh but in present world uh especially students are very much habituated to relying on AI like using chat GPT to answer everything.

04:38

So what do you think like how should students approach this u foundational getting foundational knowledge and critical thinking when AI can answer everything that they want? Yeah.

So that's a great question being an educator. Let me tackle that because uh I teach a course on innovation.

04:56

I've been now experimenting with the use of child GPD. You pose the question in terms of the students.

I think the first the implications for us as educators right first uh because uh so yes from students perspective I think we talked about the fact that you don't want to outsource your thinking but that may happen right so in a classroom setting right if I'm the educator the

05:18

question is that how do I ensure that you're not just outsourcing your thinking getting answers I think that is also part of your question. So one of the experiments that I've done is basically saying if you use chat GBD and I I briefly talked about it uh I really want to ensure that you're

05:35

able to have that interactive conversation right and build your skills in doing that but that's not something that I can just teach put up like a slide right it's something it comes through practice so in some sense uh I have to spend a lot of time which is what I found changing the nature of my assignments so in the old model I would have some assignments I'd give you something

05:53

and some questions But then you may you can cut and paste it into chat GPD and you'll get some answers right and then you may work on that but is that really what I now should be doing this world like probably not right so therefore I have to change like what the nature of the assignment is if it is to improve your thinking then it should be like how well I give you a situation how well

06:12

are you defining a problem then you may use GPT maybe somebody else in your team uses it well then talk with each other hey what did what did you get as outputs right what did you get oh what how did you prompt it with or how did you approach it? So it's almost like there is a conversation you had.

06:27

So you may share your you know chat with the other person. the other person may share the chat back which by the way is what I asked them to do and then I ask them to then share it and say hey you know is there something that you learned from how you interacted right so that they build those oh I didn't know oh I didn't know I could ask it that way I didn't know if I did this way it would come

06:45

back in this way you know because sometimes when it comes back with uh something that you think is incorrect you you might just say hey you can do better this is not correct and you'll say oh and you know and here's why so that interactive back and forth is very important but It's very

07:01

important to design uh the assignments in that way so that you can enhance that uh that capacity of people right if I can add I mean I I do do a lot of research in the space in AI and education at the center for responsibility at IT Madras. So I I'll answer this in two perspectives. One

07:21

uh what I what I recommend to students okay so and of course this will vary depending on the age of the student and so on. I mean there's there's a certain sense of maturity with age that you hope uh they develop and they understand the risk. But this is what I would say.

I think you should

07:36

you should keep using it on a daily basis to get that habit right and as you use more you you learn how to use it and engage with the system better. Uh I do know do I do know that uh that kids love to take the shortcut right? There's there's this beautiful answer right in

07:53

front of you. Question is already asked.

there's a tendency to just copy and paste it. Uh which they will do.

Uh so it's it's the responsibility of the of the teacher to figure out what else right. So we not just the answer but maybe they we should ask them the question but that's not

08:09

on the student. The student wants to make their life easy.

My my summation to them is play around with it. Build habit uh and as you do I mean the other very important thing is not to trust it.

uh despite despite a lot of efforts by by technology companies to to get it right right I mean uh the

08:31

agentic systems etc there is but I I would start with a sense of don't trust everything that the thing says you know you have to go and verify for yourself and uh and build it that's on the student on the uh organization that is the uh the school or the the educational institution I think they

08:52

have a a very clear responsibility to develop more thoughtful systems which means you can't have you know answers like you ask question it's spitting out answers you can't design systems like that you need to build guard rails in terms of uh uh you know reducing the amount of uh uh hallucinations

09:15

that it does so how how to maximize those systems that's another so I think there are lots of responsibility on uh educational institutions to design the systems uh more thoughtfully. Yeah.

And just to add uh even in terms of like for example what courses you should take. So I think sometimes

09:33

we conflate the fact that you know the chachi is allowing us to talk in a natural language that's one part of it but the product itself right the content so imagine if you're uh as a university we provide courses students are still clueless about like you know what courses you should take because we haven't designed that interface in a way in which uh it tries to understand you know

09:52

what your career path might be right based on that saying you know which courses might be uh more suitable for you what you have taken what your goals are and craft you know a very unique personalized learning path for you and then based on your experience of one course you know what you

10:07

should take so I think right now it's like it's it's very coursecentric you have dropped on box different types of courses but right there you've lost me because that's not how I want to engage with it I want it going back to everything we've been discussing like the the vegetable vendor you

10:22

know here's kind of what my aspirations are what I want to do like in that in that finance example right and then saying use that intelligence to go through all of the stuff there and say Hey, these are the courses that uh you know you might want to consider. That's very different, right?

Uh in that system helping you and then of course within that what you're saying is

10:39

then how does the learning take place? But I think there's this whole other set of things in terms of uh what does it mean to have like you know a set of offerings that connect with you as a student in terms of your uh uh career path.

So we jokingly in the book say like we have Corsera but

10:55

you know maybe we should move to learn era or maybe growth era right where it's about the the growth of the individual and and the the these AI systems are intelligent enough to take your inputs and to be able to craft uh that that learning path for you and I think there are lots of

11:13

u startups and various entities uh having this kind of frame of reference in terms of designing these systems future. So when they get designed I think inside of it the way learning will occur will also be different.

Thank you. This is this is fascinating and I think we could go on for a long

11:29

time on this topic but I I know we are at time. I I did want to say that um my experience of uh reading the book talking to you uh it definitely feels like yeah this is not a point of evolution. I I think that you're on to something when you call this the fourth industrial revolution

11:48

uh in the book and you said there's a massive massive change that we should be better prepared for the co- intelligence revolution. Yeah.

The co-intelligence revolution actually. Yeah.

Um and I I think the other thing that also stood out was when you answered the question about uh what is the subtle risk that that people overlook. It almost sounded like you were saying look more than

12:06

the risk there's the opportunity cost. Yes.

The risk is that we overlook the fact that there's a huge opportunity cost to not being involved in this at this stage. Uh I love the fact that as you brought out some of the examples it's clear that if we follow this path we actually end up creating

12:23

a much more inclusive world because it is not just that you know as the product manager working with a set of engineers and designers and various other professionals I create a thing that tries as hard as possible to meet your need but still doesn't do enough unless you can get involved in

12:41

it and say hey this is what I want in the moment and the entire product can adapt. to it and that reality is in our reach.

There may still be a few steps that we have to get to to get there, but it looks like that's the reality we're working towards. And that that's almost very very positive because if you think of it, more inclusion is what we're shooting for as we're creating technology,

13:00

putting it in the hands of more people, making it cheaper and so on. And underlying all of this, it sounds like you're also making the case that this is not just about, hey, go build a product based on these principles or go change this one thing.

Could be education, could be health,

13:16

could be something else. You're saying there's an entire ecosystem to build here that can that can have all the components working with each each other in an intelligent way.

Um, and for me, the the the last thing that stands out is a little bit of that adage of, you know, the more things change, they the more they remain the same. I think the if we've always told people that the

13:38

higher order skill that we want people to develop is critical thinking. We've always said that as long as you have empathy for another human.

A lot of the other skills of designing things will will follow through. Those are things that can be learned, they'll change.

Uh but it sounds like

13:54

the importance of critical thinking and empathy for our fellow human in many ways are are things that will still remain the same. And that's a big part of what will help people become part of this co- intelligence revol revolution in a in a meaningful way.

Absolutely. Thank you.

Thank

14:10

you. Thank you, Venkat.

Thank you, Krishnan. This was an absolutely fascinating fantastic way that you summarized the the key learnings. Fantastic. All the best.

Thank you very much.

The Co-Intelligence Revolution: How Humans and AI Co-Create New Value | Talks at Google

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