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The Good Question Podcast
SuperCreativity in an AI World James Taylor On Human-AI Collaboration & Innovation
00:32:38
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- [00:01]
A
You're listening to the Good Question podcast with Richard Jacobs. Our goal is to make each of our guests exclaim, hmm, that's a good question. I don't know the answer. Because when that happens, it means you, the listener, may be inspired to learn more beyond the interview and to ask great questions yourself that lead to new insights. In this podcast, we cover historical and current anthropology, comparative religion and history. Welcome. And let's get started.
- [00:36]
B
Hello, this is Richard Jacobs with the Good Question podcast. I guess. This is James Taylor. He's an author of what's called Super Creativity Accelerating Innovation in the Age of Artificial Intelligence. He's an award winning keynote speaker on creativity, innovation and AI. So I think this would be a very good call. I use AI. Sometimes I feel like it's a great help, sometimes I feel like it's useless. I kind of go back and forth on it. So I'm always looking for people that are utilizing it in really cool and innovative ways. So. So welcome, James.
- [01:03]
C
Well, thank you, Rich. It's nice to be on the show.
- [01:05]
B
Yeah. Well, tell me a bit about your background and then what was your like, first experience with AI and you know, then we'll get into how you're using it today.
- [01:12]
C
Yeah. So as you were saying today I'm, I'm primarily a keynote speaker on creativity, innovation and artificial intelligence, but I actually started my, my career as a manager of music artists. I've been working with rock stars and different music artists and did that for the first kind of 10 years or so of my career. I had a lot of fun with it. And then I, I had an A to move to the Bay Area, to Silicon Valley kind of area and to work in technology. That was around 2010, so I moved over. As you could tell, I'm not a Californian with this accent. So I moved over to the States and kind of worked in technology. And while I was there, I got really interested in the AI side of things, but also more generally about how technology can augment the creativity in each and every one of us. So. So it's a mix between the creative creativity side, the kind of human side, the team side, and also what we can do with things like AI and robotics as well.
- [02:07]
B
Yeah, I've noticed I'm pretty good with writing and expressing ideas that way. So when I use like ChatGPT or these programs, I get really, really good outputs, like great, great stuff. But in terms of pictures and video and all that, I'm terrible. So when I try to use like nano banana or whatever it is, I Get garbage out of it. And I think that depending on someone's skill set, that's what's required to really get a lot out of AI. Like you. One more example. So I have a degree in chemical Eng and I had an idea for a long time about, you know, having to do with cars. So I put all the information into ChatGPT and it gave me a really good answer, answered my question, which I had been wondering about for years, but only because I understood the underlying formulas and dynamics and that's why the answer made sense. So I guess my first question is like, in order to use AI really well, do you feel like you have to have some level of skill to begin with to get good results?
- [02:55]
C
I think probably the key mindset to go in it, and obviously you, you kind of express this in the work you do and all the guests you have is just a sense of curiosity, this, this sense of wonder, of being able to ask questions. Obviously there's different types of questions we had different qualities of questions that we can ask as well. But I think it really kind of starts with that sense of curiosity that, that, you know, wanting to know how things work, wanting to understand, imagine if what is possible. So it begins with that. And then depending on what your objectives are and what your goals are, which industries you're in as well, has a. As a big factor in that is how you use these tools in different ways. And they are, they are really tools, you know, like, like anything else. And different people tend. Some people, I call this expression, they use them like cyborgs. So it's almost like it becomes part of who they are. They constantly intertwine what they're doing with artificial intelligence. Like some of those guitarists I used to work with many, many years ago, where almost that the guitar was an extension of themselves and then you have other people tends to be obviously more leaders, managers and organization. You mentioned your background in chemistry and, and a few days ago I was speaking in Istanbul for a company in the polymers industry, so in chemicals and, and for many of them they use it more like a. What I describe as a centaur. So they decide, okay, this is, this is the idea. This is kind of what I want to achieve or this is my goal. And for some of these things, it's going to require you to give some of that work to different AI or agentic AI, different agents. And other times it's actually working with other humans in different ways. So the book itself, this idea of, this idea of super creativity is, is really how we can augment our creativity by collaborating more deeply, not just with artificial intelligence, but with, with other people as well. And I think it's that, that combination of the human and machine I'm really interested in.
- [04:37]
B
So what, yeah, what are some things that you've done with. So, all right, so first of all, when you mentioned Centaur, I was reminded of chess players. So he VD blue. But then, you know, recently what's happened is this like the best players will use stockfish to become grandmasters really quickly, but then they'll also use it as a companion. So now the best players is not necessarily just the engines, but engine plus human. You know, they're working together a centaur. So what does that look like for AI, for the average person?
- [05:03]
C
Well, I think, you know, I remember starting to see a few years ago with coders and this idea of augmented coding. And I think today, I mean, you go to a lot of companies I work with and 40% or more of the code that's been written is being written by AI. And some companies actually it's much, much higher than that as well. So it kind of comes back. You know, one of the questions that often comes from that is, well, what does that leave for us? You know, you mentioned the idea of the kaspara of the famous game there with IBM. And then obviously a few years later you had the idea of Google and Deep seek and sorry, DeepMind. And that was another type of game, a different game, game of Go, much more complicated than chess. And as you said, what started to happen was those Go players who were kind of working alongside AI, they were kind of developing their skills by using AI, they became much higher in terms of the quality of the work they were able to produce. So I see that happening lots and lots of places. I see really as a, as a way of augmentation of us as humans. I think different industries are being affected in different ways. Different jobs are being affected in ways not all for the good. For example, I was just doing an event recently talking with people that are in the customer service industry. And in the past, you know, a customer service person would go into work and have a hundred queries tickets that they were dealing with. 80 of them were pretty simple to solve and 20 of them were slightly more complicated. But today, using a lot of agents and AI agents on a lot of support tickets, the AI is taking care of a lot of that. The 80, the 80% of those inquiries coming in, that means that the human, every day they're going into work, they're just dealing with really difficult stuff. So that has a slight cognitive load on people as well. But I mean, that's an example in terms of where it's. It's obviously proving a little bit more challenging. Where I'm seeing most people sit at the moment is they're just kind of going into using these tools, that kind of playing with these tools, trying different tools. And that was probably three years ago. Last year it was really, you know, the acceleration of that a bit more. And today I think you're just seeing in all kinds of industries just kind of blossoming and obviously the impact that has on jobs and also the impact that has on us as humans and what our role in society is.
- [07:16]
B
So how do you see people using it successfully? Like, what's an example of someone using it in a way that helps them versus, you know, I don't know, some other company or person tried to use it. They. They kind of gave up. They just didn't know how to have it help them.
- [07:29]
C
So one of the ideas I talk about in the book is this idea of imaginary masterminds and using it as a way of kind of building your team of mentors, your ideal board of advisors. You know, in the past, let's say a, you had a business plan you're working on and you could read lots of books by name, the entrepreneur in question, and you could think about how would Richard Branson deal with this, how would Elon Musk deal with this, how would XYZ deal with this issue, and what questions would they have? Today, obviously, with large language models, that's so much simpler to do, I can go on to any of the main chatgpts or copilots or whatever your tool is, and build out my own virtual imaginary board of advisors. And then I can give it problems, challenges. I'm trying to kind of work and ask for it to ask me kind of the questions. And this is, this is also a very interesting thing, is that one of the challenges with, I'm seeing with a lot of younger people starting to use AI is their critical thinking skills can become a little bit weaker if you're not too careful. So the way I like to use a lot of these tools is to use it as a way to find my cognitive biases and help it ask me some of those difficult questions I might not be looking at. So some of my blind spots that we have, and you're asking about examples. And there's one I talk about in the book, which is company I was, I was speaking for recently, and I was talking to one of the executives there, and he said, yeah, what he's done is he's built out this virtual, this imaginary mastermind using AI agents. It recognizes that these five individuals understands how they think, how they feel. He can put in proposals to it, and then it can give him feedback. It can ask him questions, maybe things he's not thinking about, but he's actually taking it one, almost like one step further from that. And he actually created a digital twin of his boss. So this means before he ever goes and presents an idea to his boss, he runs it through the digital twin of his boss first. That anticipates the kind of questions that his boss is likely to ask him. The weak points in his. In his proposal or his idea, for example, he can then go and integrate that back into his business plan again. So when he has that meeting with his boss is at a much higher level. The questions, he doesn't have to cover some of the more basic ground in that. In that proposal as well. So that's just, you know, simple examples of how you can use this to really amplify the creativity of us as individuals.
- [09:39]
B
And Lucia's boss reacted once he started doing that?
- [09:42]
C
Well, actually, his boss was quite happy. Yeah, after. After the shock of it wore off. And like, okay, you've. You've created this a twin of me. They were asking, well, how can I do this of my boss? And you kind of start to build out that as well. And it's. I mean, it's interesting. I was just. I was just doing event recently in. In the former Yugoslavian place of. Of Croatia and nearby, nearby Albania, they don't just have a minister of AI, they have an AI minister. And this AI minister is overseeing procurement contracts. So every procurement contract that comes through the government has to go through the AI minister first. And this is to. To kind of deal with things like fraud and other areas as well. So it's just. It's very interesting where you start to think about where you can augment yourself, whether you're a solopreneur that's listening to this just now, or whether you're a researcher, a scientist, or a musician or a creative of some sort.
- [10:34]
B
How did this guy make a digital twin of his boss? What did he use?
- [10:37]
C
So it's actually pretty straightforward just now. I mean, at its most basic, all you need is about a thousand words that someone's spoken or written, and you can create what we call a psychometric profile of that individual. So you can understand, like, the 74, four different facts of how this person thinks, how they make decisions, how they, what their value system is broadly. But obviously as you have more and more email conversations, transcripts from Zoom calls with your boss, for example, you can feed that all into the model as well. And it really builds up a bigger picture of that individual, how they make decisions, what their, what their weak points are, where their, their cognitive biases are, perhaps. I mean, I do this before I ever jump on a sales call, for example, is I will use an AI to essentially understand the psychometrics of that individual or those people. I'm going to be on that call. So I know that if I'm giving a proposal, I know that, well, this person really values practicality much more highly. So I'm going to give practice an example or this person is, is a bit of a contrarian. So I'm going to lean into that a little bit more in a conversation with them. So once again, it doesn't, you know, do the end work for me, but it hopefully does make me a better communicator of my ideas.
- [11:41]
B
This will be interesting. The idea just came to my mind of sales calls. If you have a, you know, let's say it takes like at least two calls to close somebody and you have the initial sales call with them and you have it transcribed, you feed that in and then you do a mock follow up call and have it voice the concerns that it can tell the person might have in the first transcript. That might be very helpful.
- [11:59]
C
Exactly. I mean, and you can also do it on a broader scale. I mean, my main job is as a keynote speaker. And so I'll, I'll give 50 to 100 keynotes a year all around the world. And one of the things you're always trying to factor in is you have a call, pre event calls with a client and they're telling you what their objectives, but really what you want to know is what is in the minds of the audience, those 500 people or a thousand people in the room. So what you can start to do is I often ask the client, can you give me the, the list, the attendee list, even just the job titles is actually fine most of the time and the companies they represent. And I will feed that to the model and it will give me almost like the psychometric profile of the room and it will tell me maybe like the four, here are the four or five key avatars in this room. And so when I do my first draft of a presentation, I will then run it through the AI and say, which of the avatars am I Not speaking to as much. Who am I missing out here? Who is going to be most aligned with this? What case studies could I be talking about that is really going to connect with that person who fits in that particular avatar as well. So that's just an example. Once again, it's not writing my presentation for me, but it hopefully is just making something more skilled in presenting to that audience.
- [13:06]
B
That's very smart as a marketer that I followed for a long time, Jan Kennedy. And he would speak to, you know, they used to be like these cosmetics distributors called like Mary KS or something like that or whatever it was. And they always wore like pink suits or, you know, their color was pink. So when he spoke to them live, he made sure he wore a pink suit dressed like the. And the examples he brought up from his bio, his credentials were ones that they would appreciate. And he did a lot of tailoring. You know, there was no AI back. What's coming to my mind is you could use AI to do the exact same thing.
- [13:35]
C
Yeah, I mean, I think smart. I think you see it also, you know, great salespeople or great communicators more broadly, they do this almost instinctively. You know, it might not go to the level of wearing the same suit as that person or same color as that person, but you can see them very subtly, perhaps matching in tone or speed or anchoring that person in different ways. And, you know, it can also be done really badly. And it looks very kind of clumsy and clunky. But I think that this is where the. And I talk a lot about this in the book where it is. I'm very keen on just focusing on how this helps us develop our art or our craft and whatever our profession is. I mean, I see. I have. I have a lot of clients and I know you have a lot of guests on. In terms of the medical field. And I look at this in terms of how in the medical field they're starting to use both AI and kind of robotic, robotic surgery to do absolutely incredible thing. But it still comes back to. To that. That human that's there as well. And there is a danger. Now. I've seen this happening. There's been. I saw some recent things coming out about this, is that some of the surgeons who are. They're almost leaning a little bit too hard on some of the robotic surgery tools and their. Their critical thinking skills. Some of their other skills kind of can deplete if they're not careful. And you probably see this if you see you. Maybe you've got kids or Children that are using these, these tools and certain generations, younger generations, they almost assume that whatever these AI tools are spitting out, that must be correct. But if you've been in any game but more longer, you know that that doesn't quite feel right. That doesn't quite sound right.
- [15:04]
B
Exactly.
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C
And it's that sense of touch that I think as well. In fact, the Japanese, they have a word for it, they call them takumis. So Malcolm Gladwell talks about the 10,000 hour rule. These are people that done 40,000 hour rules in their chosen profession. And these people are so skilled and they work in these factories alongside some of the most advanced robotics, some of the most advanced technology because they have a sense of touch and feel that even the AI is not able to fully replicate.
- [15:29]
B
Yeah, that makes sense. I use thinking models. So have you ever heard of the five thinking hats or six thinking hats?
- [15:37]
C
Edward de Bono and always great work in the 80s and 90s.
- [15:40]
B
Well, what I've done is I'll have an idea and then I'll say, all right, critique this from the standpoint of Edward de Bono, the six thinking hats. So it'll do. That was the upside, was the downside a little. And then I'll run it through like another thinking model and it'll give me more stuff. Use a SWOT analysis or use the Alexander technique, or look at this through the lens of 8020. And it's really cool. It's like when you mentioned having a board of advisors through AI, that's the way in which I've created that I haven't critiqued it from different angles. Is there any other things you've done?
- [16:11]
C
Yeah, you're almost conducting like a pre mortem on ideas by doing it that way as well. And you have to kind of go into it with that sense of curiosity, I think, because it can be something a bit downhearted when you, you, when it comes back to you say, you know, here are all the, the errors or here all the mistakes in it as well. But if you truly believe that you're using these tools to improve the work you're doing and at the end of it it's going to become more solid, then then that's wonderful. And I mean, I'll give you an example. I was just working with a pharmaceutical company in India who make a lot of generics and they have a couple of manufacturing plants in the US and labor costs in the US are much higher than they are in India. And they were thinking, well, how could we turn these manufacturing plants producing pharmaceuticals into More, more like dark labs or semi dark labs as an example. So there's less humans actually working, there's more robotics and different things. And, and so that's a really good example where you can say, okay, imagine you are these five or six individuals with these different skill sets. What should I be thinking about there? So you can say, what kind of questions would Satya Nadella have? What questions would someone with like a real great expertise in building out dark labs, what would they be asking for? And it just helps you see some of these gaps. And for me, I mean, I mean, if anyone's listening to just this just now, who are solopreneurs, as we start now, getting into the area of more agentic AI, this is incredibly exciting because you can build an entire army, you know, to support you and your ideas and your work and your business. And, you know, I remember a friend of mine, Elaine Pofeld, a few years ago wrote a book called the Million Dollar One Person Business. And recently I had a conversation with her. She said, I thought too small. It should be the billion dollar one person business. And people like Sam Altman from OpenAI say we're going to get to this point where we are talk about one person. We're using lots of different AI agents and different agentic forms of, of, of technology to create businesses that generate over a billion dollars. I mean, there's already examples of businesses generating over $100 million per employee. So as these things start to scale, it's. You can start to move to that place.
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B
Sure. That's amazing. You. Do you remember any of the examples you saw?
- [18:10]
C
Yeah, you know, actually I do remember one of the examples was only fans was an example because I think that's, that's $96 million per employee. I seem to remember was the signal or telegram was pretty, pretty similar to that. I remember there was a few years ago, actually not about six months ago, there was a young, I'm gonna say Israeli coder who built a Vibe coding tool and sold it for some like, unbelievable number. It wasn't quite a billion dollars, but it was like pushing up into the hundreds of million. And that was something that was a business that was created over six months using Vibe coding and different AI agents to be able to build out that tool.
- [18:49]
B
That's crazy, huh? And also, you know, one thing you mentioned is, let's say I'll have a couple of ideas for something. I'll come up with five different ways to, I don't know, to do xyz, then I'll put my thoughts into, you know, one of the AI engines and say anything else, and a lot of times it'll come up with maybe one more. Okay, great. You know, I put it to another engine, they would have come up with one more. So it's a way to increase your, your effect by even 10, 20% just by doing that. But always use your intellect first, always use your thought process first and don't depend on it to think for yourself. You. And in that way, then you want to shrink your brain and capacity and you can expand it instead.
- [19:24]
C
Yeah, I mean, you know, probably two years ago or so, everyone, or when the large language models first started getting well known, everyone's talking about prompt engineering, but it actually works the other way around as well, where the AI can prompt you as the human. So it's not the human necessarily prompting the AI. And I've seen some things recently where what a number of people are starting to do is they're kind of building out almost a board of different AIs, so they will give a, let's say, a proposal or something that they're looking to do or an idea, and they will use a tool to basically be able to give that task or that concept to six completely different AI, different large language. So Gemini, maybe Kimmy K2 in, in China, different tools, because they all work, they've all been trained on different data sets, and some are better at doing some things than others. And that's really interesting. And then you actually can ask the AIs to have a conversation between themselves to come up with the best suggestion for you. That's, that's where you can, you get to see some pretty interesting things.
- [20:24]
B
Any other uses for it that blow your mind that you're like, oh my gosh, that's stupid. How is someone using AI in this way?
- [20:30]
C
There's so many just now, and I think most of my clients are large multinational companies. And one of the things that I talk about in the book is that if you look at a lot of organizations as they look to deploy AI, they think it's all on the technology side, and they kind of lean in purely into that. But actually, companies say, Boston Consulting Group, show that the reason that a lot of, of organizations fail and really deploying AI and getting value from AI for themselves and their shareholders and their customers and clients is only 20% of the reason it fails is the technology, and 80% of the reason is the people and the playbooks that they're running in their head. And so a lot of things actually kind of come down to also to culture in an organization as well. And you know, we can actually use AI to help us building. That's that culture. And in psychology terms we call it like a sense of psychological safety that everyone in the organization is empowered to put forward ideas but also challenge ideas as well. So a really good example, there is one I mentioned in the book, which is a Hewlett Packard enterprise. And I was having a conversation with Emilio Bidenbaum, who's the heads the innovation Lab on the legal side. What they decided to do was to create a very simple app, a very simple tool that anyone in the organization can put forward ideas and also volunteer for ideas in the organization and projects and initiatives. No manager approval, no regulation tape required for this. And it works a little bit like modern dating apps like Tinder, for example, except for ideas, you know, swipe left, swipe right. But the AI that sits underneath it also recognizes what is the perfect combination of skill sets of different people and also their psychology, their, their mindsets in order to make a project a success. So for example, let's imagine someone in that company has an idea for improving the sustainability of some of their the server farms, for example, that, and that person is a data scientist. But that idea might be more like to be shown someone with a sustainability background, for example on the team or let's say someone from the marketing team has an idea and the AI recognizes that it needs someone on the team that has, is very good in terms of finance or data or some other area or whatever the area is. And it kind of helps show those ideas to those people. And it also ensures that when they're putting together the teams for these ideas and what it's showing, it also has the right mix of people because you want people that are really good at just generating ideas and love just that. But you'd like, you were Talking about that 6 thinking hats as well earlier with De Bono. You also want the black hats, the completers as well. People are going to challenge ideas and that's how ideas become stronger. So that's an idea there that's called Idea Matchmaker, which is Hewlett Packard's enterprise. And it's a really nice example about how you, you use AI to quickly kind of build teams around ideas or initiatives or projects and understanding like the weak signals sometimes in people. And you know, the big takeaway is will all these ideas end up becoming real reality? Absolutely not. But it does give everyone a sense of that sense of psychological safety. They have value in not just putting forward ideas, but also Challenging and developing other people's ideas.
- [23:32]
B
Yeah, excellent. Have you seen organizations where every position is using AI, at least in some part to augment themselves? Or is it, you know, only certain people in an organization use it. Like when you see it in action, when does it work? When does it not work?
- [23:46]
C
Yeah. So I think, I mean, I'll give you an example. I have a client in the Silicon Valley silicon chip industry. Let's say that that area and what they decided to do is even though on the actual designs of the products, the chips and things, they were using AI, they've been using machine learning for many, many years for doing that. But across the rest of the organization in roles like finance and HR and procurement, they actually weren't really using AI much. Really, some of them weren't using AI at all. So one particular department within that, the procurement department said, do you know what? We're going to figure out how to use AI to get rid of some of the DUP that we currently do to get rid of some of the silos within the organization to improve efficiency and flow into the organization. And I was having, I was just doing an event for them in Singapore recently, this company, and they, they now are saying that 95% of the procurement process, so someone, let's say ordering that laptop and recognizing that it needs this particular chip and then all the things that go into building that chip or the component parts, the, the finding out the best pricing for those parts, having those ship, those parts shipped so it can all be put together. 95% of that entire workflow now in procurement is being done by AI agents. Only 5% is being done by humans. Now that was something that 100% was being done by humans only 12 months ago. So you can imagine how that speeds up things and then you can decide how do we want to deploy those humans in our team? Because we don't need as many people. And some companies are just deciding, you know, we're going to, we have a natural attrition rate of say 10% leave the company every year. Rather than replace those individuals with other people, other humans, what we're going to do is replace those, those tasks or those jobs, different AI agent. And then as a result of that, the people that stay within the business, their remuneration, their salary increases because they get, capture some of those rewards.
- [25:35]
B
What are you able to say what parts were replaced by AI and which ones the humans had to do? Any sense of that again?
- [25:41]
C
Yeah, so often the, the, the, the, some of the, in this particular organization, the humans came in in terms of looking for things like fraud, for example, just checking, you know, the, the AIs would do a number of things. Let's say a supplier was offering this particular part at. Maybe the AI would go and find out, you know, the company records of that, of that business and how many years they've been in business and what other companies it was supplying. But maybe it would have just a final human doing kind of final check at the end of that, or maybe it was when we had, you know, a final payments had to be made for certain things. The human would be doing that or actually the 5%. A lot of them, what they were doing is actually building out these workflows, reimagining these workflows, improving those different workflows as well. So almost acting like a, an arc, an architect. And the way I describe about it in the book is it's almost like someone becoming a conductor of an orchestra and they're conducting this, this mix. And some of them are human and some of them are AI agents, but they're being that conductor, that person that kind of sits above it all and is able to understand like this is what we want. This is the feel, this is the vibe, this is the end result I want to be able to create. And then thinking about, well, what do we need to do? Who do we need to have involved, which agents, which platforms, which tools are we going to have to make that happen?
- [26:55]
B
So they were able to replace 95% of the process with AI. Amazing.
- [26:59]
C
Great. Yeah.
- [27:00]
B
Sheesh. What's your thought, I mean, in general of the. Sure. Will a lot of jobs become centaurs? Or will a lot of jobs go away and just AI will run it?
- [27:08]
C
Yeah, I mean, I think a lot of jobs will, will, will disappear like they have in every other time, but obviously lots of new jobs will be created. I mean, I know some people on my team, you know, those job titles didn't, or those types of jobs didn't really exist even a few years ago. But I, I saw a. Later next week I'm speaking in Dubai in the Institute of the Future there, which is this amazing building and organization in Dubai. They published study not so long ago ago where they anticipated that in 10 years time the children of today will be doing jobs that haven't even 85% of those children be doing jobs that haven't even been invented yet. Because we're going to be spinning up entirely new jobs. And we saw this a little bit, you know, obviously with the online kind of happening Internet, then we Saw again social media and I think about some of the people I interact with and in terms of their job titles, I thought I could never imagine that job title even three years ago. And so I think there'll be lots of new jobs created where I think is going to be more, everyone thinks it's going to be the, you know, the jobs that will be going at the lower jobs in the workforce, often the blue collar jobs. I actually don't think that is necessarily the case in some industries. And when you think about health care, I know you have a lot of health care. You think about a doctor, like a lot of the roles that, like a general Maryland as you call them in the US Is doing. A lot of that work is, you know, we can be done by AI frankly, you know, it's recognizing what the thing is. But think about the job that a nurse does. That nurse has to pick someone up. They have to, to take measurements, they have to take, you know, do tests, they have to have conversations with this, you know, bedside manner. They're doing a lot of different things as well. So I think some industries, some jobs will be affected in different ways. It's not going to be very balanced or even, I know, for example, in the US where you're based, you know, there's, there's a lot of, you know, a lot of factories and kind of, there's a lot of reshoring kind of going up. The question is always going to be is that, okay, new factories will be opening up, let's say in the U.S. and, or reopening in the U.S. the U.S. but will they actually have anyone in them working in those jobs? You go to a lot of, some of the most advanced manufacturing sites today in places like, like China for example, or Vietnam. And there's a lot of robots doing those jobs now. So you could, and this is going to, goes to a wider thing. We're going to see a lot of gains from artificial intelligence. Who is going to, how will the gains from that, that technology be spread out? You know, some companies, if you're in Silicon Valley, a lot of them like the idea of a universal basic income. Other people are not so keen on that idea. Other countries think slightly differently about it. So that's really big job of policymakers. And I, I don't think most policymakers, most politicians around the world, with perhaps a few exceptions, are really thinking deeply about this, you know, in terms of what happens to some of those jobs. What is the reskilling, the upskilling? That's, that's Required.
- [29:51]
B
It might come down to that. Yeah what's you know if, if AI can do a very large percentage of the work in certain areas and there's a lot less jobs than yeah. How are those people going to live and exist?
- [30:02]
C
It's a common question I often get you know in the Q and A sections of of keynotes and I talk about it a lot in the book that asked me well I have children, I have college age kids just now what should they be developing in themselves or skill and often they think I'm going to say hard skill like coding or something like that and actually that's not usually you know what I advise and I usually advise what people like Yuval Noah harari comes like SAP, Cisco talk about which is the four Cs of you know really developing their creativity, their ability to collaborate, their critical thinking skills and their communication skills. Those and it's not just me there's in the World Economics Forum for example talking about creative thinking will become like the most important skill over the next few years because machines will take away much of the non creative tasks that we actually do. But you still need people to be generating ideas to coming up combining ideas together. You could have be using some amazing AI tools but if you can't communicate to other people why they should be investing in your startup, why they should be buying your product then that's, that's going to be a challenge. So those four Cs and I talk about them a lot in the book as well and primarily I talk about creativity and collaboration to those those C's those are the things I think people should be investing in and really spending time and developing in themselves and their children.
- [31:17]
B
Very good. So where can people you know watch some of your speeches and find out more about your thoughts and what you're working on.
- [31:23]
C
So if they go to James Taylor me James JamesTaylor me they can find more information about the myself the keynotes that I do but also about the super creativity book which is just about is coming out and that kind of really goes a lot more in depth into some of these these areas and also in a talk talks about the the human and a human type of creative collaboration. We've spoken mostly today about the the human and machine collaboration but there's a whole other side in terms of how people creatively collaborate together. So that's the best place go to JamesTaylor me okay, very good.
- [31:54]
B
Well James, thanks so much for coming and giving all your you know, these really interesting ideas. I really appreciate it.
- [31:59]
C
Thank you Richard. Great being a guest on the show.
- [32:01]
B
If you like this podcast, please click the link in the description to subscribe and review us on our itunes.
- [32:09]
A
Thank you for listening to the Good Question podcast. Please email support thegoodquestionpodcast.com if you have any referrals to great guests for us to interview. Visit thegoodquestionpodcast.com to hear more interviews. And please help us spread the word by rating and reviewing us on Apple podcasts, iTunes, Spotify, YouTube or wherever you listen to this podcast.