Welcome to Tech Stuff, a production from iHeartRadio. Today we are witnessed to one of those rare moments in history, the rise of an innovative technology with the potential to radically transform business and society forever. That technology, of course, is artificial intelligence, and it's the central focus for this new season of Smart Talks with IBM. Join hosts from your favorite Pushkin podcasts as a talk with industry experts and leaders to explore how businesses can integrate AI into their workflows and help drive real change in this new era of AI. And of course, host Malcolm Gladwell will be there to guide you through the season and throw in his two cents as well. Look out for new episodes of Smart Talks with IBM every other week on the iHeartRadio app, Apple Podcasts, wherever you get your podcasts and learn more are at IBM dot com slash smart Talks. All Right, Welcome everybody, you guys excited, Here we go.

Welcome to Tech Stuff, a production from iHeartRadio. Today we are witnessed to one of those rare moments in history, the rise of an innovative technology with the potential to radically transform business and society forever. That technology, of course, is artificial intelligence, and it's the central focus for this new season of Smart Talks with IBM. Join hosts from your favorite Pushkin podcasts as a talk with industry experts and leaders to explore how businesses can integrate AI into their workflows and help drive real change in this new era of AI. And of course, host Malcolm Gladwell will be there to guide you through the season and throw in his two cents as well. Look out for new episodes of Smart Talks with IBM every other week on the iHeartRadio app, Apple Podcasts, wherever you get your podcasts and learn more are at IBM dot com slash smart Talks. All Right, Welcome everybody, you guys excited, Here we go.

Hello, Hello, Welcome to Smart Talks with IBM, a podcast from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. This season, we're continuing our conversations with new creators visionaries who are creatively applying technology and business to drive change, but with a focus on the transformative power of artificial intelligence and what it means to leverage AI as a game changing multiplier for your business. Today's episode is a bit different. I was recently joined on stage by Dario Gill for a conversation in front of a live audience at the iHeartMedia headquarters in Manhattan. Dario is the senior vice president and director of IBM Research, one of the world's largest and most influential corporate research labs. We discussed the rise of generative AI, what it means for business and society. He also explained how organizations that leverage AI to create value will dominate in the near future. Okay, let's get on to the conversation. Hello everyone, welcome, and I'm here with doctor Dario Gil and I wanted to say before we get started. This is something I said backstage that I feel very guilty today because you're the you know, you know, arguably one of the most important figures in AI research in the world, and we have taken you away from your job for a morning. It's like, if you know, Oppenheimer's wife in nineteen forty four said let's go and have a little getaway in the Bahamas. It's that kind of thing. You know, What do you say to your wife, I can't We have got to work on this thing I can't tell you about. She's like getting me out of Los Alamos. No, So I do feel guilty. We've set back AI research by by about four hours here. But I wanted to you've been up with with ibo for twenty years, twenty years this summer. So and how old were you when you Not to give away your age, but you were how old when you started?

Hello, Hello, Welcome to Smart Talks with IBM, a podcast from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. This season, we're continuing our conversations with new creators visionaries who are creatively applying technology and business to drive change, but with a focus on the transformative power of artificial intelligence and what it means to leverage AI as a game changing multiplier for your business. Today's episode is a bit different. I was recently joined on stage by Dario Gill for a conversation in front of a live audience at the iHeartMedia headquarters in Manhattan. Dario is the senior vice president and director of IBM Research, one of the world's largest and most influential corporate research labs. We discussed the rise of generative AI, what it means for business and society. He also explained how organizations that leverage AI to create value will dominate in the near future. Okay, let's get on to the conversation. Hello everyone, welcome, and I'm here with doctor Dario Gil and I wanted to say before we get started. This is something I said backstage that I feel very guilty today because you're the you know, you know, arguably one of the most important figures in AI research in the world, and we have taken you away from your job for a morning. It's like, if you know, Oppenheimer's wife in nineteen forty four said let's go and have a little getaway in the Bahamas. It's that kind of thing. You know, What do you say to your wife, I can't We have got to work on this thing I can't tell you about. She's like getting me out of Los Alamos. No, So I do feel guilty. We've set back AI research by by about four hours here. But I wanted to you've been up with with ibo for twenty years, twenty years this summer. So and how old were you when you Not to give away your age, but you were how old when you started?

I was twenty eight?

I was twenty eight?

Okay, yeah, So I want to go back to your twenty eight year old self. Now, if I asked you about artificial intelligence, I asked twenty eight year old Dario, what does the future hold for AI? How quickly will this new technology transform our world? Et cetera, et cetera. What would twenty eight year old Darigo have said?

Okay, yeah, So I want to go back to your twenty eight year old self. Now, if I asked you about artificial intelligence, I asked twenty eight year old Dario, what does the future hold for AI? How quickly will this new technology transform our world? Et cetera, et cetera. What would twenty eight year old Darigo have said?

Well, I think the first thing is that even though AI as a feel has been with us for a long time since the mid nineteen fifties, at that time, AI was not a very polite word to say, meaning within the scientific community, people didn't use sort of that term that would have said things like, you know, maybe I do things relate to machine learning, right, or statistical techniques in terms of classific fires and so on.

Well, I think the first thing is that even though AI as a feel has been with us for a long time since the mid nineteen fifties, at that time, AI was not a very polite word to say, meaning within the scientific community, people didn't use sort of that term that would have said things like, you know, maybe I do things relate to machine learning, right, or statistical techniques in terms of classific fires and so on.

But AI had a mixed.

But AI had a mixed.

Reputation, right, it had gone through different cycles of hype, and it's also if moments of you know, a lot of negativity towards it because of lack of success. And so I think that would be the first thing we probably say, like AI is like what is that?

Reputation, right, it had gone through different cycles of hype, and it's also if moments of you know, a lot of negativity towards it because of lack of success. And so I think that would be the first thing we probably say, like AI is like what is that?

Like, you know, respectable scientists are not working.

Like, you know, respectable scientists are not working.

On AI the fin as such, and that really changed over the last fifteen years. Only, right, I would say, with the advent of deep learning over the last decade is when that re enter again the lexicon of saying AI and that that was a legitimate thing to work on. So I would say that that's the first thing I think we would have noticed a contrast twenty years ago.

On AI the fin as such, and that really changed over the last fifteen years. Only, right, I would say, with the advent of deep learning over the last decade is when that re enter again the lexicon of saying AI and that that was a legitimate thing to work on. So I would say that that's the first thing I think we would have noticed a contrast twenty years ago.

Yeah, So what point in your twenty year tenure at IBM would you say you kind of snapped into present kind of wow mode.

Yeah, So what point in your twenty year tenure at IBM would you say you kind of snapped into present kind of wow mode.

I would say in a late two thousands, when IBM was working on the Jeopardy project and just seeing the demonstrations of what could be done in question answering.

I would say in a late two thousands, when IBM was working on the Jeopardy project and just seeing the demonstrations of what could be done in question answering.

It's literally Jeopardy is this crucial moment in the history of Yeah.

It's literally Jeopardy is this crucial moment in the history of Yeah.

You know, there had been a long and wonderful history inside IBM on AI. So so for example, like you know, in terms of like these grand challenges, at the very beginning of the field founding, which is this famous Dartmouth conference that actually IBM sponsored uh TO to create, there was an IBM and there called Nathaniel Rochester and there were a few others who right after that they started thinking about demonstrations of this field. And then for example, they created the first you know game to play checkers and to demonstrate that you could do machine learning on that. Obviously we saw later in the nineties like chess that was a very famous example of that, deep Blue with Deep Blue right and playing with Caspar and then but I think the moment that was really the ones felt like, you know, kind of like brute force anticipating, sort of like moves ahead. But this aspect of dealing with language and question answering felt different, and I think for for us internally and many others, was when a moment of saying like wow, you know, what are the possibilities here? And then soon after that connected to the sort of advancements in computing and with deep learning. The last decade has just been an all out, you know, sort of like front of advancements and that, and I just continued to be more and more impressed. And the last few years have been remarkable too.

You know, there had been a long and wonderful history inside IBM on AI. So so for example, like you know, in terms of like these grand challenges, at the very beginning of the field founding, which is this famous Dartmouth conference that actually IBM sponsored uh TO to create, there was an IBM and there called Nathaniel Rochester and there were a few others who right after that they started thinking about demonstrations of this field. And then for example, they created the first you know game to play checkers and to demonstrate that you could do machine learning on that. Obviously we saw later in the nineties like chess that was a very famous example of that, deep Blue with Deep Blue right and playing with Caspar and then but I think the moment that was really the ones felt like, you know, kind of like brute force anticipating, sort of like moves ahead. But this aspect of dealing with language and question answering felt different, and I think for for us internally and many others, was when a moment of saying like wow, you know, what are the possibilities here? And then soon after that connected to the sort of advancements in computing and with deep learning. The last decade has just been an all out, you know, sort of like front of advancements and that, and I just continued to be more and more impressed. And the last few years have been remarkable too.

Yeah, So I ask you three quick conceptual questions before we dig into it, just so I sort of get a we all get a feel for the shape of AI. Question Number one is where are we in the evolution of this? So you know the obvious question. We we're all suddenly aware of it, we're talking about it. Can you give us an analogy about where we are in the kind of likely evolution of this is a technology?

Yeah, So I ask you three quick conceptual questions before we dig into it, just so I sort of get a we all get a feel for the shape of AI. Question Number one is where are we in the evolution of this? So you know the obvious question. We we're all suddenly aware of it, we're talking about it. Can you give us an analogy about where we are in the kind of likely evolution of this is a technology?

So I think we're on a significant inflection point that it feels the equivalent of the first browsers when they appear and people imagine the possibilities of the Internet or more imagined experience the internet. The Internet had been around, right for quite a few decades. AI has been around for many decades. I think the moment we find ourselves is that people can touch it, and they can. Before they were I systems that were like behind the scenes, like your search results or translation systems, but they didn't have the experience of like, this is what it feels like to interact with this thing.

So I think we're on a significant inflection point that it feels the equivalent of the first browsers when they appear and people imagine the possibilities of the Internet or more imagined experience the internet. The Internet had been around, right for quite a few decades. AI has been around for many decades. I think the moment we find ourselves is that people can touch it, and they can. Before they were I systems that were like behind the scenes, like your search results or translation systems, but they didn't have the experience of like, this is what it feels like to interact with this thing.

So that's what I mean.

So that's what I mean.

I think maybe that analogy of the browser is appropriate because it's all of a sudden, it's like whoa, you know, these network of machines and content can be distributed and everybody can self publish, and there was a moment that we all remember that, and I think that that is what the world has experience over the last nine months or so on. So but fundamentally, also what is important is that this moment is where the ease of the number of people that can build and u use AI has skyrocketed. So over the last decade, you know, technology firms that had large research teams could build AI that worked really well, honestly, but when you went down into say hey, can everybody use it? Can a data science team in a bank, you know, go and develop these applications.

I think maybe that analogy of the browser is appropriate because it's all of a sudden, it's like whoa, you know, these network of machines and content can be distributed and everybody can self publish, and there was a moment that we all remember that, and I think that that is what the world has experience over the last nine months or so on. So but fundamentally, also what is important is that this moment is where the ease of the number of people that can build and u use AI has skyrocketed. So over the last decade, you know, technology firms that had large research teams could build AI that worked really well, honestly, but when you went down into say hey, can everybody use it? Can a data science team in a bank, you know, go and develop these applications.

It was like more complicated. Some could do it, but it was more the barrier of entry was high.

It was like more complicated. Some could do it, but it was more the barrier of entry was high.

Now it's very different because of foundation models and the implications that that has.

Now it's very different because of foundation models and the implications that that has.

For at the moment where the technology is being democratized.

For at the moment where the technology is being democratized.

In demarketized, frankly, it works better for classes of problems like programming and other things, is really incredibly impressive what it can do. So the accuracy and the performance of it is much better, and the ease of use and the number of use cases we can pursue it much bigger, So that democratization is a big difference.

In demarketized, frankly, it works better for classes of problems like programming and other things, is really incredibly impressive what it can do. So the accuracy and the performance of it is much better, and the ease of use and the number of use cases we can pursue it much bigger, So that democratization is a big difference.

But when you say, when you make it an analogy to the first browsers, if you if we do another one of these time travel questions back at the beginning of the first browsers, to say, many of the potential uses of the Internet and such we hadn't even begun. We couldn't even anticipate, right, right, So we're at the point where the future direction is largely unpredictable.

But when you say, when you make it an analogy to the first browsers, if you if we do another one of these time travel questions back at the beginning of the first browsers, to say, many of the potential uses of the Internet and such we hadn't even begun. We couldn't even anticipate, right, right, So we're at the point where the future direction is largely unpredictable.

Yeah, I think that that is right, because it's such a horizontal technology that the intersection of the horizontal capability, which is about expanding our productivity and tasks that we wouldn't be able to do efficiently without it, has to marry now the use cases that reflect the diversity of human experience, our institutional diversity. So as more and more institutions said, you know, I'm focused on agriculture, you know, to be able to improve seeds, you know, in these kinds of environments. They'll find their own context in which that matters that the creators of a I did not anticipate at the beginning. So I think that that is then the fruit of surprises will be like why I wouldn't even think that it could be.

Yeah, I think that that is right, because it's such a horizontal technology that the intersection of the horizontal capability, which is about expanding our productivity and tasks that we wouldn't be able to do efficiently without it, has to marry now the use cases that reflect the diversity of human experience, our institutional diversity. So as more and more institutions said, you know, I'm focused on agriculture, you know, to be able to improve seeds, you know, in these kinds of environments. They'll find their own context in which that matters that the creators of a I did not anticipate at the beginning. So I think that that is then the fruit of surprises will be like why I wouldn't even think that it could be.

Used for that?

Used for that?

And also clever people will create.

And also clever people will create.

New business models as associated with that, like it happened with the Internet of course as well, and that will be its own source of transformation, unchanged in its own right.

New business models as associated with that, like it happened with the Internet of course as well, and that will be its own source of transformation, unchanged in its own right.

So I think all of that is yet to unfold.

So I think all of that is yet to unfold.

Right.

Right.

What we're seeing is this catalyst moment of technology that works well enough and it can be democratized.

What we're seeing is this catalyst moment of technology that works well enough and it can be democratized.

What next sort of conceptual question?

What next sort of conceptual question?

You know?

You know?

We can loosely understand or categorize innovations in terms of their impact on the kind of balance of power between haves and have nots. Some innovations, you know, obviously favor those who already have will make the rich richer. Some some it's a rising tie the lift cell boats and some bias in the other direction, they close the gap between. Is it possible to say to predict which of those three categories AI might fall into.

We can loosely understand or categorize innovations in terms of their impact on the kind of balance of power between haves and have nots. Some innovations, you know, obviously favor those who already have will make the rich richer. Some some it's a rising tie the lift cell boats and some bias in the other direction, they close the gap between. Is it possible to say to predict which of those three categories AI might fall into.

It's a great question, you know.

It's a great question, you know.

A first observation I would make on your first two categories.

A first observation I would make on your first two categories.

Is that it will be both likely be true that the use.

Is that it will be both likely be true that the use.

Of AI will be highly demarketized, meaning the number of people that have access to its power to make improvements in terms of efficiency and so on will be fairly universal, and that the ones who are able to create AI UH may be quite concentrated. So if you look at it from the lens of who creates wealth and value over sustained periods of time, particularly it's saying a context like business, I think just being a user of AI technology is an insufficient strategy and UH. And the reason for that is, like, yes, you will get the immediate productivity boost of like just making API calls and you know that would be a new baseline for everybody, but you're not accruing value in terms of representing your data inside the AI in way that it.

Of AI will be highly demarketized, meaning the number of people that have access to its power to make improvements in terms of efficiency and so on will be fairly universal, and that the ones who are able to create AI UH may be quite concentrated. So if you look at it from the lens of who creates wealth and value over sustained periods of time, particularly it's saying a context like business, I think just being a user of AI technology is an insufficient strategy and UH. And the reason for that is, like, yes, you will get the immediate productivity boost of like just making API calls and you know that would be a new baseline for everybody, but you're not accruing value in terms of representing your data inside the AI in way that it.

Gives you a sustainable competitive advantage.

Gives you a sustainable competitive advantage.

So I always try to tell people is don't just be an AI user, be an you know, AI value creator. And I think that that will have a lot of consequences in terms of the haves and have nots as an example, and that will apply both to institutions and regions and countries, et cetera. So I think it would be kind of a mistake, right to just develop strategies that are just about.

So I always try to tell people is don't just be an AI user, be an you know, AI value creator. And I think that that will have a lot of consequences in terms of the haves and have nots as an example, and that will apply both to institutions and regions and countries, et cetera. So I think it would be kind of a mistake, right to just develop strategies that are just about.

Usage, but to come back to that question from them and to give you a specific suppose I'm a I'm an industrial farmer in Iowa with ten million dollars of equipment and move and I'm comparing it to a subsistence farmer somewhere in the developing world who's got a cell phone. Over the next five years, who's whose well being rises by a greater amount.

Usage, but to come back to that question from them and to give you a specific suppose I'm a I'm an industrial farmer in Iowa with ten million dollars of equipment and move and I'm comparing it to a subsistence farmer somewhere in the developing world who's got a cell phone. Over the next five years, who's whose well being rises by a greater amount.

Yeah, I think, I mean, it's a it's a good question, but it might be hard to do a one to one sort of like attribution to just one variable in this case, which is AI. But again, provided that you have access to a phone, right and some kind to you know, be able to be connected. I do think so for example, in that context we've developed, we don't work with NASA as an example, to build you spatial models using some of these new techniques, and I think for example, or ability to do flood prediction, I'll tell you an advantage of why it would be a democratization force in that context. Before to build a flowed model based on satellite imagery was actually so.

Yeah, I think, I mean, it's a it's a good question, but it might be hard to do a one to one sort of like attribution to just one variable in this case, which is AI. But again, provided that you have access to a phone, right and some kind to you know, be able to be connected. I do think so for example, in that context we've developed, we don't work with NASA as an example, to build you spatial models using some of these new techniques, and I think for example, or ability to do flood prediction, I'll tell you an advantage of why it would be a democratization force in that context. Before to build a flowed model based on satellite imagery was actually so.

Onerous and so complicated and difficult.

Onerous and so complicated and difficult.

That you would just target to very specific regions and then obviously countries prioritize their own right. But what we've demonstrated is actually you can extend that technique to have global coverage around that. So in that context, I would say it's a four stores democratization that everybody sort of would have access if you have some connectivity, as.

That you would just target to very specific regions and then obviously countries prioritize their own right. But what we've demonstrated is actually you can extend that technique to have global coverage around that. So in that context, I would say it's a four stores democratization that everybody sort of would have access if you have some connectivity, as.

Today, Iowa farmer might have a flood model. The guy in the developing world definitely didn't, and now he's a shot at getting one.

Today, Iowa farmer might have a flood model. The guy in the developing world definitely didn't, and now he's a shot at getting one.

Yeah, but now it has a shot of getting one.

Yeah, but now it has a shot of getting one.

So there's aspects of it that so long as we provide connectivity and access to it, that they can be democratization forces.

So there's aspects of it that so long as we provide connectivity and access to it, that they can be democratization forces.

But I'll give you.

But I'll give you.

Another example that that can be quite concerning, which is language. Right, So there's so much language in the you know, in English, and there is sort of like the reinforcement loop that happens that the more you concentrate because it has obvious benefits for global communication and standardization, the more you can enrich like base AI models based on that capability. If you have very resource cars languages, you tend to develop less powerful AI with those languages and so on, So one has to actually worry and focus on the ability to actually represent that in that case is language as a piece of culture. Also in the AI sets that everybody can benefit from it too. So there's a lot of considerations in terms of equity about the data and the data sets that we accrue and what problems are we trying to solve. I mean, you mentioned agriculture or healthcare and so on. If we only solve problems that are related to marketing as an example, that would be a less rich world in terms of opportunity that if we incorporate many many other broader server problems.

Another example that that can be quite concerning, which is language. Right, So there's so much language in the you know, in English, and there is sort of like the reinforcement loop that happens that the more you concentrate because it has obvious benefits for global communication and standardization, the more you can enrich like base AI models based on that capability. If you have very resource cars languages, you tend to develop less powerful AI with those languages and so on, So one has to actually worry and focus on the ability to actually represent that in that case is language as a piece of culture. Also in the AI sets that everybody can benefit from it too. So there's a lot of considerations in terms of equity about the data and the data sets that we accrue and what problems are we trying to solve. I mean, you mentioned agriculture or healthcare and so on. If we only solve problems that are related to marketing as an example, that would be a less rich world in terms of opportunity that if we incorporate many many other broader server problems.

Who do you think what do you think are the biggest impediments to the adoption of a as you would like as you think aire to be adopted. I mean, if you look, what are the sticking points that.

Who do you think what do you think are the biggest impediments to the adoption of a as you would like as you think aire to be adopted. I mean, if you look, what are the sticking points that.

You would look In the end, I'm going to give a non technological answer as a first one has to do with workflow, right, So even if the technology is very capable, the organizational change inside a company to incorporate into the natural workflow of people or how we work is It's a lesson we have learned over the last decade is hugely important.

You would look In the end, I'm going to give a non technological answer as a first one has to do with workflow, right, So even if the technology is very capable, the organizational change inside a company to incorporate into the natural workflow of people or how we work is It's a lesson we have learned over the last decade is hugely important.

So there's a lot of design considerations.

So there's a lot of design considerations.

There's a lot of how do people want to work right, how do they work today?

There's a lot of how do people want to work right, how do they work today?

And what is the natural entry points for AI? So that's like number one.

And what is the natural entry points for AI? So that's like number one.

And then the second one is, you know, for the broad value creation aspect of it is the understanding inside the companies of how you have to curate and create data to combine it with external data says that you can have powerful AI models that actually fit your need. And that aspect of what it takes to actually and curate the data for this modern AI, it's still working progress, right. I think part of the problem that happens very often when I talk to institutions is that they say yah, yeah, yeah, yeah, I'm doing it. I've been doing it for a long time. And the reality is that that answer can sometimes be a little of a cop out, right, is like, I know you were doing machine learning, you were doing some of these things.

And then the second one is, you know, for the broad value creation aspect of it is the understanding inside the companies of how you have to curate and create data to combine it with external data says that you can have powerful AI models that actually fit your need. And that aspect of what it takes to actually and curate the data for this modern AI, it's still working progress, right. I think part of the problem that happens very often when I talk to institutions is that they say yah, yeah, yeah, yeah, I'm doing it. I've been doing it for a long time. And the reality is that that answer can sometimes be a little of a cop out, right, is like, I know you were doing machine learning, you were doing some of these things.

But actually the leader's version of AI.

But actually the leader's version of AI.

What was happening with foundation models, Not only is it very new, it's very hard to do and honestly, if you haven't been you know, assembling very large teams and spending hundreds of millions of dollars of compute, and so you're probably not doing it right. You're doing something else that is in the broad category. And I think the lessons about what it means to make this transition to this new wave is still in early phases of understanding.

What was happening with foundation models, Not only is it very new, it's very hard to do and honestly, if you haven't been you know, assembling very large teams and spending hundreds of millions of dollars of compute, and so you're probably not doing it right. You're doing something else that is in the broad category. And I think the lessons about what it means to make this transition to this new wave is still in early phases of understanding.

So what would you say? I want to give you a couple of examples of people with kind of real work in real world positions of responsibility. Imagine I'm sitting right here, So imagine that I am the president of a small liberal arts college and I come to you and I say, Dario, I keep hearing about the AI my college has. You know, I don't make it. You know, I'm I'm I'm making this much money. If that every year my enrollments declining, I feel like this maybe is an opportunity. What is the opportunity for me? What would you say?

So what would you say? I want to give you a couple of examples of people with kind of real work in real world positions of responsibility. Imagine I'm sitting right here, So imagine that I am the president of a small liberal arts college and I come to you and I say, Dario, I keep hearing about the AI my college has. You know, I don't make it. You know, I'm I'm I'm making this much money. If that every year my enrollments declining, I feel like this maybe is an opportunity. What is the opportunity for me? What would you say?

So it's probably in a couple of segments around that. Right one has.

So it's probably in a couple of segments around that. Right one has.

To do is well, what is the implications of this technology inside the institution itself instead of the college, and how we operate and can we improve for example, efficiency, like if you have in very low levels of sort of margin to be able to reinvest, is you know you run it, You run you know, infrastructure, you run many things inside the college. What are the opportunities to increase the productivity or automate and drive savings such that you can reinvest that money so into the mission of education right as an example.

To do is well, what is the implications of this technology inside the institution itself instead of the college, and how we operate and can we improve for example, efficiency, like if you have in very low levels of sort of margin to be able to reinvest, is you know you run it, You run you know, infrastructure, you run many things inside the college. What are the opportunities to increase the productivity or automate and drive savings such that you can reinvest that money so into the mission of education right as an example.

So number one is operational efficiency.

So number one is operational efficiency.

Operational efficiencyage is is a big one. I think the second one is within the context of the college, there's implications for the educational mission on its own, right, how will you know how.

Operational efficiencyage is is a big one. I think the second one is within the context of the college, there's implications for the educational mission on its own, right, how will you know how.

Does a curriculum need to evolve or not?

Does a curriculum need to evolve or not?

What are acceptable use policies or of some of these AI I think we've all read a lot about like what can happen in terms of exams and so on, and cheating and not cheating, or what are the actually positive elements of it in terms of how curriculum should be developed and professions sustain around that. And then there's another third dimension, which is the outdoor oriented element of it, which is like prospect students right, so, which is frankly speaking, a big use case that is happening right now, which in the broader industry is called customer care or client care or citizen care. So in this question will be education, like you know, hey are you reaching the right students around that that may apply to the college. How can you create them, for example, an environment to interact with the college and answering questions that could be a chat bought or something like that to learn about it, and personalization.

What are acceptable use policies or of some of these AI I think we've all read a lot about like what can happen in terms of exams and so on, and cheating and not cheating, or what are the actually positive elements of it in terms of how curriculum should be developed and professions sustain around that. And then there's another third dimension, which is the outdoor oriented element of it, which is like prospect students right, so, which is frankly speaking, a big use case that is happening right now, which in the broader industry is called customer care or client care or citizen care. So in this question will be education, like you know, hey are you reaching the right students around that that may apply to the college. How can you create them, for example, an environment to interact with the college and answering questions that could be a chat bought or something like that to learn about it, and personalization.

So I would say there.

So I would say there.

Is like at least three lenses with which I would give advice right the positive.

Is like at least three lenses with which I would give advice right the positive.

Second, because it's really interesting. So I really can't as sign an essay anymore? Can I?

Second, because it's really interesting. So I really can't as sign an essay anymore? Can I?

Can I sign an essay?

Can I sign an essay?

Can I say, rend me a research paper and come back to being three? Can I do that anymore?

Can I say, rend me a research paper and come back to being three? Can I do that anymore?

I think you can?

I think you can?

How do I do that?

How do I do that?

And then you can that.

And then you can that.

Look, there's there's two questions around that. I think that if one goes.

Look, there's there's two questions around that. I think that if one goes.

And explains in the context like what is it?

And explains in the context like what is it?

Why are we here?

Why are we here?

Why are in this class? What is the purpose of this?

Why are in this class? What is the purpose of this?

And and one starts with assuming like an element of like decency and people are people are there like to learn and so on, and you just give it disclaimer. Look, I know that one option you have is like just you know, put the essay question and click go and like and give an answer. You know, but that is not why we're here, and that is not the intent of what we're trying to do. So first I would start with the sort of like the norms of intent and decency and appeal to those as step number one. Then we all know that there will be a distribution of use cases of people like that will come in one year and come one or the other and do that. And so for a subset of that, you know, I think the technology is going to have all in such a way that we will have more and more of the ability to discern right, you know, when that has been AI generated right and uncreated.

And and one starts with assuming like an element of like decency and people are people are there like to learn and so on, and you just give it disclaimer. Look, I know that one option you have is like just you know, put the essay question and click go and like and give an answer. You know, but that is not why we're here, and that is not the intent of what we're trying to do. So first I would start with the sort of like the norms of intent and decency and appeal to those as step number one. Then we all know that there will be a distribution of use cases of people like that will come in one year and come one or the other and do that. And so for a subset of that, you know, I think the technology is going to have all in such a way that we will have more and more of the ability to discern right, you know, when that has been AI generated right and uncreated.

It won't be perfect, right.

It won't be perfect, right.

But there's some elements that you can imagine in putting the essay and you say, hey, this is likely to be generated right around that. And for example, one way you can do that, just to give you an intuition, you could just have an essay that you write with pencil and paper. At the beginning, you get a baseline of what you're writing is like. And then later when you you know generated, there will be obvious differences around what kind of writing has been generated.

But there's some elements that you can imagine in putting the essay and you say, hey, this is likely to be generated right around that. And for example, one way you can do that, just to give you an intuition, you could just have an essay that you write with pencil and paper. At the beginning, you get a baseline of what you're writing is like. And then later when you you know generated, there will be obvious differences around what kind of writing has been generated.

On the other way, but you've turned it's everything you're describing makes sense. Put it greatly in this In this respect, at least it seems to greatly come complicate the life of the teacher, whereas the other two use cases seem to kind of clarify and simplify the role. Right suddenly, you know, reaching student perspective students, sounds like I can do that much more kind of efficienty a lot. Yeah, I can bring out administration costs, but the teaching thing is tricky.

On the other way, but you've turned it's everything you're describing makes sense. Put it greatly in this In this respect, at least it seems to greatly come complicate the life of the teacher, whereas the other two use cases seem to kind of clarify and simplify the role. Right suddenly, you know, reaching student perspective students, sounds like I can do that much more kind of efficienty a lot. Yeah, I can bring out administration costs, but the teaching thing is tricky.

Well, until we developed the new norms, right, I mean again, I mean, I know it's not abuse analogy, but calculators we deal. We've done with that too, right, And it says, well, calculator, what is the purpose of math?

Well, until we developed the new norms, right, I mean again, I mean, I know it's not abuse analogy, but calculators we deal. We've done with that too, right, And it says, well, calculator, what is the purpose of math?

How are we going to do this?

How are we going to do this?

And so can I tell you my dad's calculator story?

And so can I tell you my dad's calculator story?

Yes? Please.

Yes? Please.

My father was a mathematician, taught mathematics at University of Waterloo, Agada, and in the seventies when people started to get pocket calculators, his students demanded that they'd be able to use them, and he said no, and they took him to the administration and he lost. So he then changed completely throughout all of his old exams and introduced new exams where there was no calculation. It was all like deep think, you know, figure out the problem on a conceptual level and describe it to me. And they were all students deeply unhappy that he'd made their lives for complicated. But to your point, I mean he probably the result was probably a better education. He just removed the element that they could game with their pocket calculators. I suppose it's a version.

My father was a mathematician, taught mathematics at University of Waterloo, Agada, and in the seventies when people started to get pocket calculators, his students demanded that they'd be able to use them, and he said no, and they took him to the administration and he lost. So he then changed completely throughout all of his old exams and introduced new exams where there was no calculation. It was all like deep think, you know, figure out the problem on a conceptual level and describe it to me. And they were all students deeply unhappy that he'd made their lives for complicated. But to your point, I mean he probably the result was probably a better education. He just removed the element that they could game with their pocket calculators. I suppose it's a version.

Of I think it's a version of that, And so I think they will develop the equivalent of what your father did. And I think people say, you know what if like these kinds of things, everybody's doing it generically and none of us have any meaning because all you're doing is pressing buttons, and like the intent of this was something which was to teach you how to write or to think or something.

Of I think it's a version of that, And so I think they will develop the equivalent of what your father did. And I think people say, you know what if like these kinds of things, everybody's doing it generically and none of us have any meaning because all you're doing is pressing buttons, and like the intent of this was something which was to teach you how to write or to think or something.

There may be a variant of how we do all of this. I mean, obviously some.

There may be a variant of how we do all of this. I mean, obviously some.

Version of that that has happened is like Okay, we're all going to sit down and doing with pencil and paper and computers in their classroom. But there'll be other variants of creativity that people will put forth to say, you know what, you know, that's a way to solve that problem too.

Version of that that has happened is like Okay, we're all going to sit down and doing with pencil and paper and computers in their classroom. But there'll be other variants of creativity that people will put forth to say, you know what, you know, that's a way to solve that problem too.

But this is interesting because to stay on this analogy, we're really talking about a profound rethinking just using a college as an example, a real profound rethinking of the way there's no part of this college it's unaffected by aia B. In one case, I've made everyone's job easier. In one case, I've made I'm asking us to really rethink from the ground up what teaching means. In another case, I've automated systems that I didn't think of it. I mean, it's like, that's right, that's it's not that's a lot to ask someone who got a pH d in medieval language literature, you know, forty years ago.

But this is interesting because to stay on this analogy, we're really talking about a profound rethinking just using a college as an example, a real profound rethinking of the way there's no part of this college it's unaffected by aia B. In one case, I've made everyone's job easier. In one case, I've made I'm asking us to really rethink from the ground up what teaching means. In another case, I've automated systems that I didn't think of it. I mean, it's like, that's right, that's it's not that's a lot to ask someone who got a pH d in medieval language literature, you know, forty years ago.

Yeah, but you know, I'll tell you a positive sort of development that I'm seeing the sciences around this, which is you're seen as you see more and more examples of applying AI technology within the context of like historians to as an example, Right, you have archival and you know, and you have all these books and being able to actually help you as an assistant right around that, but not only with text now, but with diagrams, right. And I've seen it in anthropology too, write and archaeology with examples of engravings and translations and things that can happen. So so as you see in diverse fields people applying these techniques to advance and how to do physics or how to do chemistry. They inspire each other, right, and this said, you know, how does it apply actually to my area? So once as that happens, it becomes less of a chore of like, my god, you know, how do I have to deal with this? But actually it's triggered by Curiosity's triggered by you know, there'll be like, you know, faculty that will be like, you know what, you know, let me explore what this means for my area, and they will adapt it to the local context, to the local you know, language, and the professional itself. So I see that as a positive vector that is not all going to feel like homework.

Yeah, but you know, I'll tell you a positive sort of development that I'm seeing the sciences around this, which is you're seen as you see more and more examples of applying AI technology within the context of like historians to as an example, Right, you have archival and you know, and you have all these books and being able to actually help you as an assistant right around that, but not only with text now, but with diagrams, right. And I've seen it in anthropology too, write and archaeology with examples of engravings and translations and things that can happen. So so as you see in diverse fields people applying these techniques to advance and how to do physics or how to do chemistry. They inspire each other, right, and this said, you know, how does it apply actually to my area? So once as that happens, it becomes less of a chore of like, my god, you know, how do I have to deal with this? But actually it's triggered by Curiosity's triggered by you know, there'll be like, you know, faculty that will be like, you know what, you know, let me explore what this means for my area, and they will adapt it to the local context, to the local you know, language, and the professional itself. So I see that as a positive vector that is not all going to feel like homework.

You know, it's not going to feel like, oh my god, this is so overwhelming.

You know, it's not going to feel like, oh my god, this is so overwhelming.

But rather to be very practical to see what works, What have I seen others to do that is inspiring?

But rather to be very practical to see what works, What have I seen others to do that is inspiring?

And what am I inspired to do? You know what? What is how is this going to help my career?

And what am I inspired to do? You know what? What is how is this going to help my career?

I think that that's going to be an interesting question for you know, those faculty members, for the students.

I think that that's going to be an interesting question for you know, those faculty members, for the students.

The professionals. Sorry, I'm gonna stick with this example along because it's really interesting. I'm curious following up on what you just said, that one of the most persistent critiques of academic but also of many of many corporate institutions in recent UITs, has been siloing. Right, different parts of the of the organization are going off on their own and not speaking to each other. Is a potential is a real potential benefit to AI the kind of breaking down a simple tool for breaking down those kinds of barriers, is that a very is that an elegant way of sort of saying what.

The professionals. Sorry, I'm gonna stick with this example along because it's really interesting. I'm curious following up on what you just said, that one of the most persistent critiques of academic but also of many of many corporate institutions in recent UITs, has been siloing. Right, different parts of the of the organization are going off on their own and not speaking to each other. Is a potential is a real potential benefit to AI the kind of breaking down a simple tool for breaking down those kinds of barriers, is that a very is that an elegant way of sort of saying what.

I really think?

I really think?

And I was actually just having a conversation with Provos stuff and very much on this topic, very recently exactly on that, which is all these this you know, this appetite right to collaborate across disciplines. There's a lot of attempts stores a goal, right, creating interdisciplinary centers, creating dual degree programs or dual appointment programs. But actually in a lot of progress in academia happens by methodology too. Write like a new you know, when when some methodology gets adopted, I mean the most famous example of that is a scientific method as an example of that. But when you have a methodology that gets adopted, it also provides a way to speak to your colleagues across different disciplines. And I think what's happened in AI is linked to that that within the context of the scientific method as an example, the methodology about we about what we do discovery, the role of data, the role of these neural networks, of how we actually find proximity to concepts to one another is actually fundamentally different than how we've traditionally applied it. So as we see across more professions, people applying this methodology is also going to give some element of common language to each other. Right And in fact, you know, in this very high dimensional representation of information that is pressent to neural networks, we may find amazing adjacencies or connections of themes and topics in ways that the individual practitioners cannot describe, but yet will be latent.

And I was actually just having a conversation with Provos stuff and very much on this topic, very recently exactly on that, which is all these this you know, this appetite right to collaborate across disciplines. There's a lot of attempts stores a goal, right, creating interdisciplinary centers, creating dual degree programs or dual appointment programs. But actually in a lot of progress in academia happens by methodology too. Write like a new you know, when when some methodology gets adopted, I mean the most famous example of that is a scientific method as an example of that. But when you have a methodology that gets adopted, it also provides a way to speak to your colleagues across different disciplines. And I think what's happened in AI is linked to that that within the context of the scientific method as an example, the methodology about we about what we do discovery, the role of data, the role of these neural networks, of how we actually find proximity to concepts to one another is actually fundamentally different than how we've traditionally applied it. So as we see across more professions, people applying this methodology is also going to give some element of common language to each other. Right And in fact, you know, in this very high dimensional representation of information that is pressent to neural networks, we may find amazing adjacencies or connections of themes and topics in ways that the individual practitioners cannot describe, but yet will be latent.

In these large calural networks.

In these large calural networks.

We are going to suffer a little bit from causality, from the problem of like, hey, what's the root cause of that? Because I think one of the unsatisfying aspects that this methodology will provide is they may give you answers for which they don't give you good reasons for where the answers came from, and then there will be the traditional process of discovery of saying, if that is the answer, what are the reasons? So we're going to have to do this sort of hybrid way of understanding the world. But I do think that common layer of AI is a powerful new thing.

We are going to suffer a little bit from causality, from the problem of like, hey, what's the root cause of that? Because I think one of the unsatisfying aspects that this methodology will provide is they may give you answers for which they don't give you good reasons for where the answers came from, and then there will be the traditional process of discovery of saying, if that is the answer, what are the reasons? So we're going to have to do this sort of hybrid way of understanding the world. But I do think that common layer of AI is a powerful new thing.

Yeah, well, a couple of random questions. I couldn't manage you talk in the In the Writer's strike that just ended in Hollywood, one of the sticking points was how the studios and writers would treat AI generated content. Good writers get credit if their material with somehow the source for A but more broadly, did the writers need protections against the use of I could go on? You know what, I'll be worth familiar with all of this. Had you been, I don't know whether you were, but had either side called you in for advice? During that the writers, the writers called you and said, Daria, what should we do about AI? And how should we that should be reflect should that be reflected in our contract negotiations? What would you have told them?

Yeah, well, a couple of random questions. I couldn't manage you talk in the In the Writer's strike that just ended in Hollywood, one of the sticking points was how the studios and writers would treat AI generated content. Good writers get credit if their material with somehow the source for A but more broadly, did the writers need protections against the use of I could go on? You know what, I'll be worth familiar with all of this. Had you been, I don't know whether you were, but had either side called you in for advice? During that the writers, the writers called you and said, Daria, what should we do about AI? And how should we that should be reflect should that be reflected in our contract negotiations? What would you have told them?

The way I think about that is that I divided.

The way I think about that is that I divided.

I would divide it into two pieces.

I would divide it into two pieces.

First, is what's technically possible right and anticipate scenarios like you know, what can you do with voice cloning for example? You know, now, for example, it is possible this being dubbing right legis take that topic right around the world. There was all these folks that would dub people in other languages. Well, now you can do these incredible renderings. I mean, I know if you've seen them, where you know you match the lips is your original voice, but speaking any language that you want. As an example, so busy that has a set of implications around that. I mean, just to give an example, So I would say, create a taxonomy that describes technical capabilities that we know of today and applications to the industry and to examples of like hey, you know I could film you for five minutes and I could generate two hours of content of you and I don't have to you know, then if you'll get paid by the hour, obviously I'm not paying you for that other thing. So I would say technological capability and then map with their expertise consequences of how it changes the way they work or the way they interact or the way they negotiate and so on.

First, is what's technically possible right and anticipate scenarios like you know, what can you do with voice cloning for example? You know, now, for example, it is possible this being dubbing right legis take that topic right around the world. There was all these folks that would dub people in other languages. Well, now you can do these incredible renderings. I mean, I know if you've seen them, where you know you match the lips is your original voice, but speaking any language that you want. As an example, so busy that has a set of implications around that. I mean, just to give an example, So I would say, create a taxonomy that describes technical capabilities that we know of today and applications to the industry and to examples of like hey, you know I could film you for five minutes and I could generate two hours of content of you and I don't have to you know, then if you'll get paid by the hour, obviously I'm not paying you for that other thing. So I would say technological capability and then map with their expertise consequences of how it changes the way they work or the way they interact or the way they negotiate and so on.

So that would be one element of it, and then the.

So that would be one element of it, and then the.

Other one is like a non technology related matter, which is an element of almost distributed justices like who deserves what right and who has the power to get what? And then that's a completely different discussion. That is to say, well, if this is the scenario of what's possible, you know, what do we want and what are we able to get? And I think that that's a different discussion, which.

Other one is like a non technology related matter, which is an element of almost distributed justices like who deserves what right and who has the power to get what? And then that's a completely different discussion. That is to say, well, if this is the scenario of what's possible, you know, what do we want and what are we able to get? And I think that that's a different discussion, which.

Is which all that life? Which when do you do?

Is which all that life? Which when do you do?

First?

First?

I think it's very helpful to have an understanding of what's possible and how it changes a landscape.

I think it's very helpful to have an understanding of what's possible and how it changes a landscape.

As part of a broader.

As part of a broader.

Discussion, right, and a broad negotiation, because you also have to see the opportunities because there will be a lot of ground to say, actually, you know, if we can do it in this way and we can all be that much more efficient in getting these piece work done or these filming done, but we have a reasonable agreement about how we both sides benefit from it, right, then that's a.

Discussion, right, and a broad negotiation, because you also have to see the opportunities because there will be a lot of ground to say, actually, you know, if we can do it in this way and we can all be that much more efficient in getting these piece work done or these filming done, but we have a reasonable agreement about how we both sides benefit from it, right, then that's a.

Win win for everybody.

Win win for everybody.

Yeah.

Yeah.

Right, so that's I think that would be a golden triangle.

Right, so that's I think that would be a golden triangle.

Right.

Right.

Here's my reading, and I would like you to correct me if I'm wrong, and I'm likely to be wrong. When I looked at that strike, I said if they're worried about AI. The writers are worried about AI. That seems silly. It should be the studios who are worried about the economic impact of AI. Does it? In the long run AI put the studios out of business long before it puts the writers out of business. I only need the studio because the costs of production are as high as the sky, and the cost of production are overwhelming. And whereas if I don't, if I have a tool which brings introduces massive technological efficiencies to the production of movies, then why don't studio? Why would they the scared ones?

Here's my reading, and I would like you to correct me if I'm wrong, and I'm likely to be wrong. When I looked at that strike, I said if they're worried about AI. The writers are worried about AI. That seems silly. It should be the studios who are worried about the economic impact of AI. Does it? In the long run AI put the studios out of business long before it puts the writers out of business. I only need the studio because the costs of production are as high as the sky, and the cost of production are overwhelming. And whereas if I don't, if I have a tool which brings introduces massive technological efficiencies to the production of movies, then why don't studio? Why would they the scared ones?

Or maybe maybe you need like a different kind of studio or a different kind of different kind of studies.

Or maybe maybe you need like a different kind of studio or a different kind of different kind of studies.

But I mean the in the but in the in this strike the fright the frightened ones with the writers and the you know, with the studios. Wasn't that backwards?

But I mean the in the but in the in this strike the fright the frightened ones with the writers and the you know, with the studios. Wasn't that backwards?

I haven't thought about it.

I haven't thought about it.

Uh, it can be about the implications of it.

Uh, it can be about the implications of it.

It goes back to we're talking before the implications because are so horizontal, it is right to think about it, like what does.

It goes back to we're talking before the implications because are so horizontal, it is right to think about it, like what does.

It do to the studios as well? Right?

It do to the studios as well? Right?

Yeah?

Yeah?

But then you know the reason why that happens is that it's the order of either negotiations or or who first got concerned about it and did something about it, right, which is in the context of the strike. You know, I don't know what the equivalent conversations are going inside the studio and whether they have a war room saying what this is going to mean to us? Right, but it doesn't get exercise through a strike, but maybe through a task force inside you know, the companies about what are they going to do?

But then you know the reason why that happens is that it's the order of either negotiations or or who first got concerned about it and did something about it, right, which is in the context of the strike. You know, I don't know what the equivalent conversations are going inside the studio and whether they have a war room saying what this is going to mean to us? Right, but it doesn't get exercise through a strike, but maybe through a task force inside you know, the companies about what are they going to do?

Right?

Right?

Well, and to go back to your thing you said, the first thing you do is you make a list of what techological capabilities are. But don't technological capabilities change every I mean they do. You're racing ahead so fast, so you can't Can you have a contract? I'm sorry for begetting in the little weeds here, but this is interesting. Can you You can't have a five year contract if the contract is based on an assessment of technological capabilities in twenty twenty three, because by the time we get to twenty eight twenty three eight, it's totally different. Right.

Well, and to go back to your thing you said, the first thing you do is you make a list of what techological capabilities are. But don't technological capabilities change every I mean they do. You're racing ahead so fast, so you can't Can you have a contract? I'm sorry for begetting in the little weeds here, but this is interesting. Can you You can't have a five year contract if the contract is based on an assessment of technological capabilities in twenty twenty three, because by the time we get to twenty eight twenty three eight, it's totally different. Right.

Yeah, But like you know, I mean where I was going is like there are some abstractions around that is like, you know, whate can we do with my image? Right, Like, if I generally get the category that my image can be reproduced, generated contents and so on, it's like, let's talk about the abstract notion about who has rights to that or do we both get to benefit from that? If you get that straight, Yes, the nature of how the image gets alter created as something will change underneath, but the concept will stay the same. And so I think what's important is to get the categories right.

Yeah, But like you know, I mean where I was going is like there are some abstractions around that is like, you know, whate can we do with my image? Right, Like, if I generally get the category that my image can be reproduced, generated contents and so on, it's like, let's talk about the abstract notion about who has rights to that or do we both get to benefit from that? If you get that straight, Yes, the nature of how the image gets alter created as something will change underneath, but the concept will stay the same. And so I think what's important is to get the categories right.

Yeah. Yeah, if you had to you to think about the biggest technological revolutions of the post war era last seventy five years, you can all come up with a list. Actually, it's really fun to come up with a list. I was thinking about this when we were you know, containerized shipping is my favorite, the green revolution, the internet is Where is the I in that list?

Yeah. Yeah, if you had to you to think about the biggest technological revolutions of the post war era last seventy five years, you can all come up with a list. Actually, it's really fun to come up with a list. I was thinking about this when we were you know, containerized shipping is my favorite, the green revolution, the internet is Where is the I in that list?

So I would put it first in that context that you put forth over since World War Two, undoubtedly, like computing as a category is one of those trajectories that has reshaped.

So I would put it first in that context that you put forth over since World War Two, undoubtedly, like computing as a category is one of those trajectories that has reshaped.

Right or world.

Right or world.

And I think we think computing, I would say the role that semiconductors have had has been increwdly defining. I would say AI is the second example of that as a core architecture that is going to have an equivalent level of impact. And then the third leg I would put to that equation.

And I think we think computing, I would say the role that semiconductors have had has been increwdly defining. I would say AI is the second example of that as a core architecture that is going to have an equivalent level of impact. And then the third leg I would put to that equation.

Will be quantum and quantum information.

Will be quantum and quantum information.

And that's sort of like I like to summarize that the future of computing its bits, neurons, and cubits, and it is that idea of high precision computation, the world of neural networks and artificial intelligence, and the world of quantum and the combination of those things is going to be the defining force of the next hundred years in that category of computing.

And that's sort of like I like to summarize that the future of computing its bits, neurons, and cubits, and it is that idea of high precision computation, the world of neural networks and artificial intelligence, and the world of quantum and the combination of those things is going to be the defining force of the next hundred years in that category of computing.

But it makes the list for sure.

But it makes the list for sure.

If it's that high up on the list. This is a total hypothetical. Would you if you were starting over if you're starting IBM right now, would you say, oh, our AI operations actually should be way bigger, Like how many how many thousands of people working for you?

If it's that high up on the list. This is a total hypothetical. Would you if you were starting over if you're starting IBM right now, would you say, oh, our AI operations actually should be way bigger, Like how many how many thousands of people working for you?

So within the research division it's about like three thousand, five hundred scientists.

So within the research division it's about like three thousand, five hundred scientists.

So in a perfect world, would you if it's that big, isn't that too small as a group?

So in a perfect world, would you if it's that big, isn't that too small as a group?

Yeah, Well that's like in the ricer division. I mean IBM overall, but I mean, like.

Yeah, Well that's like in the ricer division. I mean IBM overall, but I mean, like.

So starting from first, so you have a we've got a technology that you're ranking with compute and you know, up there with as in terms of a world changer, are we So what I'm basically asking is are we underinvested in this huge you.

So starting from first, so you have a we've got a technology that you're ranking with compute and you know, up there with as in terms of a world changer, are we So what I'm basically asking is are we underinvested in this huge you.

Know, but so so yeah, it's a it's a good question. So like what I would say is that I think we should segment how many people do you need on the creation of the technology itself, and what is the right size of research and engineers and compute to do that, And how many people do you need in the sort of application of the technology to create better products, to deliver services and consulting and then ultimately to diffuse it through you know, sort of all spheres of society. And the numbers are very different, and that is not different than anywhere else. I mean, I mean, if you give examples of since you were talking about in context of World War two, how many people does it take to create you know, an atomic weapon as an example, it's a large number.

Know, but so so yeah, it's a it's a good question. So like what I would say is that I think we should segment how many people do you need on the creation of the technology itself, and what is the right size of research and engineers and compute to do that, And how many people do you need in the sort of application of the technology to create better products, to deliver services and consulting and then ultimately to diffuse it through you know, sort of all spheres of society. And the numbers are very different, and that is not different than anywhere else. I mean, I mean, if you give examples of since you were talking about in context of World War two, how many people does it take to create you know, an atomic weapon as an example, it's a large number.

I mean, it wasn't just loss animals.

I mean, it wasn't just loss animals.

There was a lot of people in Okay, it's a large number, but it wasn't a million people, right, So you could have highly concentrated teams of people that, with enough resources, can do extraordinary scientific and technological achievements, and that's always by definition, is going to be a fraction of like one percent compared to the total volume that is going to require to then.

There was a lot of people in Okay, it's a large number, but it wasn't a million people, right, So you could have highly concentrated teams of people that, with enough resources, can do extraordinary scientific and technological achievements, and that's always by definition, is going to be a fraction of like one percent compared to the total volume that is going to require to then.

Deal with it.

Deal with it.

But the application side is infinite almost.

But the application side is infinite almost.

That's exactly.

That's exactly.

So that is where like in the end, the bottleneck really is.

So that is where like in the end, the bottleneck really is.

So with you know, thousands of scientists and engineers, you can create world class AI, right, And so no, you don't need ten thousand to be able to create the large language model and the generatic model and some but you need thousands, and you need you know, very significant amount of computer and data.

So with you know, thousands of scientists and engineers, you can create world class AI, right, And so no, you don't need ten thousand to be able to create the large language model and the generatic model and some but you need thousands, and you need you know, very significant amount of computer and data.

You need that.

You need that.

The rest is okay, I build software, I build databases, or I build a software product that allows you to do inventory management, or I build you know, a photo editor and so on. Now that product incorporating the AI, modifying, expanding it and so on. Well, now you're talking about the entire software industries. So now you're talking about millions of people right who are neted, you know, who are required to bring AI into their product. Then you go on a step beyond the technology creators in terms of software and you say, well, okay, now what the skills to help organizations go undeployed in the department of you know, the interior, right, And then I said, okay, well, now you need like consultants and experts and people to work they are to integer into the workflow. So now you're talking into the many tens of millions of people around that. So I see it as these concentric circles of it. But to some degree in many of these core technology areas, just saying like, well, I need a team of like one hundred thousand people to create like AI or a or a new transistor or a new quantum computer. It's actually a diminished in return right in the end, like too many people connecting with each other.