Theo Priestley FUTURIST

Ever wondered what a futurist does? Well, it's not all time machines and crystal balls. Renowned futurist, best-selling author, and technologist, Theo Priestly, joins us to challenge your perception; it's less about predicting the future and more about preparing for it. He unveils his fascinating career trajectory from banking to technology and how this shift has informed his approach to futurism, emphasizing the need to ponder who bears the brunt of our technological decisions.

As our conversation progresses, we look at the compelling concept of cross-pollination of ideas between industries. Using game development as an intriguing case study, we demonstrate how data-driven insights can dramatically impact decision-making in fields as diverse as architecture, urban design, and engineering. We also explore the idea of leveraging technology to fuel both incremental growth and genuine innovation. But what does this mean for the decision-making process at the CEO level? Theo argues every choice must be dissected under the microscope of who benefits - shareholders, customers, or employees?

Finally, we dig into the complex minefield of AI ethics and data bias. Overlaying ethical data policies onto historically biased data is a task fraught with challenges; we delve deep into this topic. We also consider the role of consultants in shaping the corporate technology environment and how they navigate the introduction of new technologies and products in established companies. And just when you thought you had it all figured out, Theo introduces a paradigm-shifting idea - a device that could access the past. As we grapple with the implications of living in a 'post-truth' world, we ponder how technology could be used to access the source of truth. Join us for an engaging discourse that promises to disrupt your thinking and offer a fresh perspective on the future of technology and its ethical implications.

Contact the Future Construct Podcast Produced by BIM Designs, Inc!

• BIM Designs, Inc.: minority-owned, US-based, union-signatory preconstruction technology firm, offering turnkey BIM modeling, laser scanning, coordination management, and other VDC solutions to the AEC industry.
• Schedule a free consultation: sales@bimdesigns.net.
• Subscribe to our weekly blog and our Future Construct Podcast
• Suggest a podcast guest

Chapters

• 0:11 Futurist's Role in Technology
• 12:05 Cross-Pollination and Future Thinking in Business
• 23:23 AI Ethics and Data Bias Challenges
• 34:22 Search for Truth, Time Travel Solution

Transcript

Speaker 1: 0:11

Hi everyone. Welcome to the Future Construct podcast. I am your host, Amy Peck. I am super excited for our guest today a futurist technology, futurist keynote speaker, bestselling author and artificial intelligence and future trends and all around generally cool person, theo Priestly. Welcome, theo, hi.

Speaker 2: 0:34

Hi, how are you doing?

Speaker 1: 0:36

I'm good, good. So we met in Split, croatia, which is a random place to meet, but I got to hear you speak on a number of topics during that particular event and actually I think we're even on a panel at one point. We certainly had plenty of discussions together, yeah yeah, which was great and really enjoyable, and I love the way you think about things. So you know, the term futurist means a lot of different things to different people. So, first, if you could just talk about what really the role of a futurist is. But you've had a really interesting history, so it'd be great to also kind of go back and talk about how you got there.

Speaker 2: 1:21

Okay. Well, you're right about one thing If you ask 100 people what a futurist is, you'll get 100 different answers. For me, futurist isn't about actually predicting. You know the people say you'll predict me the next week's lottery numbers, for example. I see it more as, rather than prediction, but more as preparation, because the whole idea around the future for me is to extrapolate what are the possible futures, what is probable, what can be some of the decisions in the past taken to get to these futures and the steps involved to prepare you for those futures as well. So it's never really just about the end point and saying, oh, in five years we're going to have AI assistance, blah, blah, blah. It's really about well, if we're predicting AI assistance in five years, what does that mean for us now? What does it mean for us generally from year one, year two, year three, year four, year five, and we finally get them? And how do we adapt our lives and our business and society, et cetera, to get there? And is it the right steps to take as well for a start? So a lot of the time we feel like we don't have the agency to make those decisions, because they've been made for us, because the big technology companies and the government are basically saying, yes, we must head this way because it's good for everybody, but that's in their sense. Everybody is not 99% of the population who wants to see something different. So for me, as a futurist, I spend a lot of time looking at yes, this is great, but what if we take the wrong decision here? Who is this actually benefiting? And if it is indeed the right decision, then how do we get there and how do we prepare people to get there?

Speaker 2: 3:12

My career, in a sense, as a futurist has been a bit eclectic, because I started many, many, many years ago. If we want to go all the way back, I started bouncing checks in a bank. That's how I started my career in a sense. So I started in banking and I bounced checks and I got bored of that, as you would, and I got into mainframe development and that started my career in tech and through that because that was also definitely boring in a sense I did program management, I did business and technology change, managed some large scale projects. But all the time I was thinking about there's reading lots of reports and they were all saying technology does this business does that. This is how it's working and I'm seeing it from the ground floor, thinking, well, this isn't how it's actually happening.

Speaker 2: 4:09

So I started writing a blog, basically around technology, trends, what was coming around the corner, what businesses should be thinking about, which are the exciting vendors. So I acted almost like a weird hybrid industry analyst who is a freelance but also as a futurist in that sense, by projecting forward and thinking five, 10 years ahead, and I didn't really take on the role or the title as futurist. It was kind of sort of given to me by people when they heard me speak and they were like, oh, you're a bit of a futurist and I said I don't know, I like reading science fiction, but I'll take your title and I'll run with it, kind of thing. So that's that. You know. I've been writing about trends, speaking about trends, for the best part of 10, 15 years now and it's just really exciting to see what and come around the corner, but also trying to predict the positives and the negative impacts of every, every trend that comes around.

Speaker 1: 5:11

Yeah, and I think you know, when we met, in some of the conversations we had, I think I was very taken with the fact that you also, you know, you understand the stack, you understand the infrastructure and the architecture of technology, which you know, in this age of what I like to call and I do take credit for this term the Sinfluencer you know it's everyone who has, you know, wi-fi and any kind of personal brand now suddenly is an expert in all manner of things. And we've seen the meme with Homer Simpson who's like oh, I'm a metaverse expert and he just appears back into the bushes and like I'm an AI expert and you know, and so, and even some of the some of our interactions afterwards, like you are thinking very deeply about not just the what is going to happen, but it's like how are we going to get there? And so you know, how did that? How did the technology? Like the interest, because you have a kind of a deep love for all of this stuff and kind of like messing around with that right and deconstructing and reconstructing.

Speaker 1: 6:24

Where did that come from? Like what were the triggers? Like when you were a kid? No, you know, I don't know that there are kids who are like I'm going to grow up and I'm going to be a futurist. What was that journey that led you here?

Speaker 2: 6:38

I read a lot of comic books and science fiction and that's literally what it all stems down to. I mean, I've got several bookshelves filled with science fiction old sort of pulp, 1950s, 1960s type stuff from a lot of the sort of greats like Predict poll and Steven Baxter and Isaac Hasimov and everything else like that. And I collected Analog Magazine as well, or this small form factor sort of pulp magazine that had lots of science fiction stories in them and I would voraciously read these because they were just full of wild ideas about the future and things like that. And the way I look, the way my head works, is like there's 1000 cats with 2000 balls of string and they're all fighting each other, but every now and again there'll be a cat that grabs some random disparate strings and pulls them together and I try and connect those dots. So when you talk about infrastructure and trying to sort of see how things work, and I look at the conceptual level first, which is, if we took a bit of this technology and a bit of that and we actually connected the dots, would it actually work together and would it create something that's actually bigger than the silos that they're in just now? And that's kind of how I look at things.

Speaker 2: 8:04

I spend a lot of time reading about all these stacks and who's building what on top of X, y, z.

Speaker 2: 8:10

The latest stuff with spatial computing and AI, for example, is all trying to sort of.

Speaker 2: 8:14

I spend a lot of time trying to understand as best as I can how it works and then how it can be applied elsewhere, not just how it's being applied now, but if I like. I say, if I took a component of it and mixed it with something else, what would happen? And that's kind of how I try and sort of visualize steps forward for every trend, but also trying to sort of get the best out of every technology that people don't think about, because we all like when we were at Split, for example, we spoke about Web 3, we spoke about Metaverse, we spoke about AI and traditionally everybody talks about them in their particular buckets, so nobody can connect AI to Metaverse, to IoT, the big data to cloud, to Web 3 and decentralized pockets of data and stuff like that and data ownership, whereas I just look at them all as this whole kit bag of things that we can do lots of exciting stuff with if we just mix them together. So that's kind of how my head works.

Speaker 1: 9:18

How you got there.

Speaker 1: 9:18

Well, you just touched on something that I think is very relevant to what we're doing here on the podcast, because we our audience is largely in the architecture, engineering and construction industry We've been talking about that for the last couple of years and so, because I'm out there on the road as you are, meeting people like you and these kind of extraordinary big thinkers, I decided with the team that we should have this future a series, which is what this is part of now, driven by that exact thesis that we shouldn't just be looking in each industry, not only what our industry is doing, but what each technology is doing individually.

Speaker 1: 10:04

And the other thing you mentioned which I'd like to pull apart a little bit is people are very stuck on this notion of predicting the future and following trends and where we're going. But I would pause it and I'd love your take on this that we have this interesting moment in time where actually we can be much more prescriptive and proactive and really design the future. In fact, our mutual friend, Mike Powell, who was on the show last week, wrote a book called Designing the Future, and is there a proactive element that companies can start thinking about, where it's really kind of leapfrogging from where we are now and fundamentally changing the way we do business using technology.

Speaker 2: 10:52

Yeah, I mean, I think traditionally businesses wait for the technology to happen and then follow what happened. You know they have to adopt the technology and I think it's funnily enough I actually wrote about this and I dug out these quotes from an old Forbes article about eight years ago and I did kind of pause it back then that you know, especially for AI, that businesses should actually be starting to look at what kind of clever acquisitions they could make. Eight years ago that would actually have leapfrogged open AI in a sense, because that's where that's. You know there's no, there's nothing, there's nothing prescribed that says businesses have to wait for something to happen to in order to adopt it and then run with it. And this is the problem that I see is that a lot of you know a lot of traditional industries like finance, like manufacturing, etc. Etc. They don't invest enough the leapfrog even their own industry by taking examples from other industries and learning from them. I mean, we could learn so much from the games industry or being steeped in doing clever things with AI and with algorithms and all sorts of different ways of working and agile sort of ways of working in project management. But I know also the talent.

Speaker 2: 12:07

You've got to look at the talent in some of these industries as well.

Speaker 2: 12:10

Nobody seems to want to cross pollinate talent, you know, because it's all very again, prescriptive.

Speaker 2: 12:15

But in the wrong sense it's prescriptive, as in siloed thinking and mentality, which is you know, I've got my set of technologies and I must stick with this. I've got my set of people and I can't have anyone outside the industry coming in and bringing fresh ideas because they don't know the industry, and I think that's the completely wrong way to look at business nowadays, which is, you know, if we want to build the futures that we, that the businesses, want to envision for themselves, they're going to have to start thinking outside of their own industry and their own little sort of echo chamber, in a sense. So you know, if you want to invent the future for your industry, you've got to invest in that properly, not invest in tools that you can buy off the shelf from another vendor, actually invest in something that doesn't actually exist, and that's quite hard, I think, for businesses to take that risk. It's almost like creating a proper startup in your business, knowing that you could actually build something brand new and then sell it, as you know, as his own licensed product. For example.

Speaker 2: 13:19

But I don't think businesses think that way.

Speaker 1: 13:22

No, no, I think you're right. We're going to take a little break and hear from our sponsors, but we'll be right back in a moment. All right, we are back with futurist EO. Priestly, so you mentioned something interesting about game developers, and what I think people don't understand is that game developers the good ones really understand how their players move through space and they're constantly analyzing that, and so you know how can we look at game design and some of the ways that these gaming environments would apply, you know, especially in the AEC industry.

Speaker 2: 14:18

I think there's a huge misconception around, or certainly a misunderstanding around, the amount of data or how data driven game development really is, especially in terms of tweaking an initial game design to actually understand how players move through the game, through the game environment, their behavior. You know, when we were building Metanomic, which was a startup I had a couple of years ago, we were looking at behavioral player behavior as a data driven platform and it was really to understand the intent rather than just the behavior itself in terms of moving through was actually the intent in terms of why is a player doing this? Why is a player collecting all these items when there's lots of missions sitting there in this bucket waiting to be done? You know, why does he want to kill everything when there's lovely scenery to look at instead? So, understanding the intent of a player as they move through the environment and move through the game is really important Because of that data.

Speaker 2: 15:14

You can also look at it in terms of well, if I look at architecture and design and urban design and spatial computing, for example, and VR, you know how can I use that kind of data in terms of natural movement space? You know, because architects understand the space that they're building and if you combine that with the data in terms of behavior and behavioral economics and behavioral design and just basically how people perceive space, then you can start to understand what you can bring game development practices into architecture, into urban design, into anything to do with manufacturing and engineering and construction. And I think this goes back to my earlier point, before the break around cross-pollinisation of talent, of ideas, of existing systems, algorithms, etc. Borrow from other industries and learn how they do it in their particular niche and see if it applies to them. So I think we can all learn from each other, even though naturally we don't talk to each other because we're in completely different styles and buckets.

Speaker 1: 16:25

Yeah, that's so true and I have been promoting this idea that it's not, especially during the height of the whole metaverse hype cycle. I was saying for business, it's digital twin. You don't have to say how do we build a metaverse, it's digital twin. What level of digital twin do you need for what kind of data? And I think digital twin is an interesting example, because digital twin combined with AI, iot, sensor, data, analytics, that starts to move to this idea of from smart buildings to smart city and then kind of linking into this notion of an AR cloud, which is a digital twin of the entire planet, which we're hoping to get to a version of. But is that just too big? Like it's? You know, I think the challenge that all of us have working with large companies is they're going quarter to quarter. You're like I need to have a really positive earnings call in 90 days.

Speaker 1: 17:38

And so how do they have? You know? I think it's a parallel path, right, you have this incremental growth using technology, but that truly innovative path may be leapfrogging, investing, as you mentioned, in the future. Practically, how can companies do that? And I believe it starts from the CEO level. But where do they start?

Speaker 2: 18:03

Yeah, I think the biggest question that they have to start with is why do I need this for my business? And then what is it going to give me back? And you know, do I need what is it going to give me, as a company, back to my shareholders? And then what is it going to do for my customers and what is it going to do for my employees? You know, but again it's I think it's called the seven whys anyway, and I literally have to question every single decision that you make. I'm going to do this why? Well, because it's for my shareholders. Why? Why do they care?

Speaker 2: 18:29

You know that kind of thing and you get down to the real nitty gritty root of the problem, which is either it needs to be a competitive advantage and we need to be there, or it's actually going to become, it will become part of the fabric of society and civilization.

Speaker 2: 18:46

Having this digital twin and in which case it helps us understand how we interact and also prediction as well, is just that extra level.

Speaker 2: 18:53

You know, if we talk about futurism, having a digital twin allows us to make specific decisions based on data and analytics and at a world scale that we never had before, and you can make those decisions without actually having any material impact on your business.

Speaker 2: 19:11

So if I say I'm going to invest 500k or 5 million in seed thing and then you use that data to basically predict and scale out what's going to happen to my business, how's it going to impact local communities, how's it going to impact customers or my clients, etc. You can actually map that all out World scale as a digital twin at a decision making level and then start to make macro and macro decisions based on that. You know globally, regionally, whatever, and watch those play out. And this is especially good for like governmental and local authority and engineering and urban design and stuff like that. And you don't impact anything because it's simulation, but you get to see what the impact or potential impact could be and obviously the cost, the carbon footprint, the human factors, all of that kind of sort of thing. And that thinking at that scale is beyond a lot of people, I think you know I don't mean to be disparaging to everybody who's listening?

Speaker 2: 20:21

but you know, at that kind of scale you can't comprehend that if I make it like the butterfly effect, if I make one change to my strategy, you know I can chart it on a P and L for sure but then what does that actually look like in the wild? As a digital twin I can actually plan that out and actually see the ripple effect. And for some businesses it's not really necessary. But for others, like engineering, construction, healthcare, you know ESG, you know all of that kind of sort of thing, you know this is actually going to be really powerful to actually get comprehend.

Speaker 1: 20:57

Yeah. So the other thing that we hear a ton about is the fear factor, especially around AI. It's going to, you know, unseat people and you know you've got the pro people. I think, like us, they're like, no, it's a tool, and others are like no, it's stealing our jobs, it's evil. You know, the truth always lies somewhere in between.

Speaker 1: 21:19

But, you know, with AI it feels to me like it. I think it's allowing an understanding of how the convergence of technologies work. Right, because there are very few people who are using AI kind of in a silo now, except for people doing, you know, chat, gpt, for very specific items. But I think now people are starting to see, oh, you can use AI to optimize. You know this, you know technology, probably for you know, with an IoT, for example, in running simulations. But but do you see some challenges with AI in the near term? You know, around things like unconscious bias, you know, is there a way to build some trust in the algorithms? I think a lot of companies are caught by surprise by how quickly this this came about and haven't had time to to build AI policies, let alone a ethical AI policies. What do you see as some of the potential pitfalls with, specifically around AI?

Speaker 2: 22:23

So companies, companies are going to have to start thinking about specific AI strategies within their business, outside of the context of outsourcing their data to something like OpenAI or Microsoft or whatever to give them the answers back. And we've received that with Bloomberg GPT, which basically they took 40 years of financial analysis and because of the structure of the data, it was very you know, it's fairly simple for them to take that stick in a large language model trainer and I actually have Bloomberg GPT available to all their analysts in the firm and start analyzing the market and asking it questions. Now you could argue that financial data is relatively unbiased. You know it's essentially you know plus and minus on a balance sheet. But for a lot of businesses who have made decisions policy decisions like banking, for example, we've made credit lending decisions, etc. Etc. Based on people's backgrounds I think they're going to.

Speaker 2: 23:23

I think they're going to really struggle in terms of rewinding those ethical or overlaying new ethical data policies on top of things, on top of historical biased data that they will have to train large language models and an AI on, and that's going to be really hard and that's going to trip people up like you wouldn't, adam and Eve. I don't think they're prepared for that. I think what they'll do is they'll do a tick box exercise and say well, we've got a new ethical, you know, we've hired a chief AI ethics person, they've written a nice 40 page document and we're going to adhere to this. But unfortunately we've got 120 years worth of historically biased data that we have to train and you can't unwind that bias because you don't even know where it is.

Speaker 2: 24:14

Yeah, who designed it? Who made the decisions? So it's going to be really hard and we're going to see a lot of not only mistakes but a lot of lawsuits coming, I think, over the next five years, where people have not really understood the true nature and state of their data. And then they just throw it into the machine, rank the black box and see what happens next. And then, of course, people are going to ask it, customers are going to use it. You know people with employees are going to use it and they're going to be making decisions based on the data that is coming back out. They expect it to have been cleansed and it's all nice, and it's not going to happen that way at all.

Speaker 1: 25:05

Yeah, I think that's one of the challenges and I think, you know, I think we don't understand also as humans, just in the fundamental sense of how we created these algorithms in the first place that, as humans we are, it's impossible for us to think in purely analytical terms, right?

Speaker 2: 25:27

So the way these.

Speaker 1: 25:27

A lot of the algorithms I believe have been constructed have a belief system around the way we think and that we're trying to replicate that we don't even have a firm handle on.

Speaker 1: 25:41

So it's a little bit of, you know, the inmates running the asylum, but I still have a very, very positive belief system around it, which may be completely delusional, who knows. So I read a great quote which is on your website which made me chuckle a nod, and a little bit that I've had the pleasure of knowing you, and the quote is his opinions on industry trends and market activity are well respected and despised at the same time, making him a true industry influencer. And I would say you're much more elevated than an influencer, I think whoever wrote that that's. I think you're better than an influencer. I think you're well beyond influencer and you're actually an actor of positive change. But you know, being controversial is a challenge within a corporate construct and you know, as a consultant, when you get hired, there's always someone who there's a bull's eye on your head, right, because you are telling them things they don't want to hear. How do you navigate that?

Speaker 2: 26:45

Great difficulty actually, because obviously it's like I'm paying you to tell me what I want to hear, not what I don't want to hear, and you shouldn't be challenging me. And it's like, well, you're not paying me for the right reasons, then, or you clearly misunderstood what I'm all about and you know I've been doing this for you know, like I say, 10, 15 years. I mean it's you know, I've got a very public profile in terms of, you know, if you look at what, how I write, how I speak, it should be plainly obvious that I'm going to peel back the veneer and tell you what's actually happening under the hood, and I will stand in front of people like McKinsey's and Gartner's and KPMG's, etc. And I'll lock swords with them, and I have done in the past, because I don't believe in some of the things that they say or their analytics are, you know, are written in such a way that it gets the you know, the board member or the C-suite excited.

Speaker 2: 27:37

So it opens a checkbook and I'm there to basically say, hang on a minute, pause, slow down and let's, let's actually peel back some of this and see one. Do you need to do this? Whatever, it is that you're paying these people to tell you to do so. It's almost like I'm this very sarcastic or very cynical Jimmy Cricket with a Scottish accent that sits on their shoulder and kind of plays devil's advocate, not because you know I need to or because it's fashionable to, but I think it's. I think it's necessary in this industry to essentially act as a voice of reason in a sense.

Speaker 1: 28:17

Yeah, no, and I loved your example I. It took me back to the days when my kids were toddlers. You know why. Why? Why seven layers of why? Which would you know that drives anybody crazy? But the reason is probably by the time you get to about level four or level five, you run out of steam. You know you can't really justify it, but I was.

Speaker 1: 28:39

I always find that interesting that there's this notion that you bring in a consultant and they're going to help you solve your problems. But the sentiment is generally we want you to just make us a little bit better and kind of agree with the policies we've already put in place, because the unraveling of it is much harder. But I think that that again you're going to start to see, you know, young, limber companies. You know this is another Netflix blockbuster moment where you know companies see these solutions out there. They see these smaller companies coming with a completely different construct of how to do business. You see any either on an industry level. We're not going to name names of specific companies, but do you see some of the sort of larger, lumbering, older, established companies you know who have an opportunity now to to enact some change? Are there some new technologies and sort of products and services that are that could really potentially unseat an industry or a set of companies.

Speaker 2: 29:49

Well, I mean it will take AI. I mean, obviously, chat you, pt or any sort of AI type model that's actually been developed. I don't think in the next 20 years that we'd need enterprise stack vendors at all. You know, if you look at some of these big guys, for example, you know we don't need to name names, but most of them have three letter acronyms, for example, or three letter TLA's. You know these, these systems run on data, but so does the AI. Now, these systems also run on huge amounts of screens. You know they're, they're they're processing large amounts of information, large amounts of information. But I'm having to physically set a screen, pick a box, fill in a form you know, pull down, a drop down, etc. Etc. And these are old ways of working and in 20 years time, you know, people from behind us, in terms of generations, are not wanting to, are not wanting to interface with that kind of thing anymore.

Speaker 2: 30:48

You know and I see AI and even spatial computing, for example, is another layer of a different type of interactivity, that that just sits on top of data. They are pure and simple and we won't need the. The level of I don't know complete. It really is complexity All these things are.

Speaker 2: 31:08

I remember a CEO of another funnily enough a stack vendor calling him Frankenstacks. You know he was basically slagging off one of his competitors and he says, oh, this is a Frankenstack, and it is. It's just literally stuff that is bolted on, but it gets ever more complex. It's ever harder to actually unpick if you ever want to become more nimble in your organization or go or avoid locking vendor locking, for example and I think AI and spatial computing is actually a way to avoid vendor locking in the future, because it just sits on top of data and it presents in a completely different way that you interact with the data, rather than having to have massive amounts of screens and drop downs. You know that kind of thing and it'll feel more natural the way I'm talking to you and waving my hands around.

Speaker 1: 31:54

Yeah, yeah.

Speaker 2: 31:56

If you can imagine doing really mundane, boring processing work exactly like this, as if you were talking on a podcast and it's done at the end of that. That to me, is far more natural as a human being than it is sitting down, moving a mouse, typing in something in a form pressing that it gets sent to you. You have to look at it and authorize it and then send it back to me, to basically send back to the customer, and I think that's going to be dead and I think that vendors are going to struggle with this because they've got 60 years worth of legacy to try and unpick rather than stick something on the top again. Frankenstein, yeah, yeah. But I would love to see businesses actually tackle this themselves and actually understand that their data estate is all they need and they can actually just build the systems on top, build the spatial computing systems and the AI on top of that and actually free themselves from a lot of really bad vendor walking and legacy. Yeah.

Speaker 1: 33:01

And create their own interfaces for whatever business line needs to access it. No, that is definitely a brave new world. So, sadly, we're coming to a close, but before you go, I'm going to ask you the question I ask everyone, which is if you could project yourself 20, 25 years in the future which you know yeah, you obviously do on a daily basis and you could bring with you any product or services that just makes you personally happy or makes your life better in some way. What would it be and what would it do?

Speaker 2: 33:33

Oh God, well, if we're going to go wild and wacky, I mean, I remember reading an Arthur C Clark book I think it's called Time's Eye, and basically everybody was walking around with essentially a miniature black hole in their head, a black hole device that allowed them to tap into the past and see and make decisions based on real historical events, basically go back in time and draw information out and things like that. And I would love something like that which allows me to understand historical context and information and information from truth. And I think, yeah, if we go down this path, then obviously you know where we are right now. We're in a very post truth precarious situation where can't believe everything we see, can't believe everything we read. All the AI and the algorithms are actually feeding off each other, so they're subverting what's actually written out in terms of content, which is being generated and then used again to train themselves. So that compounds the problem.

Speaker 2: 34:47

I would love to be walking around with a device that actually allows me to peer back and actually find out what the real source of truth is by watching the event itself, and I think Arthur C Clark was onto something there. So you know if I can have a physical device or even something embedded that would allow me to actually drill back into the past and see the historical event in some kind of weird, timey, whiny time machine kind of tar. This way, that would be. That would be. Yeah, I love that.

Speaker 1: 35:15

I love that, I love that. And then eventually, as we deconstruct, time it'll be, you know, can go back, it can go forward or backward. Yeah, excellent, theo, it's been an absolute pleasure speaking with you today. Thanks so much for joining us.

Speaker 2: 35:32

Thanks for having me.