Obsolescence in an AI Era

0:00Welcome to If I Gladson, where the future isn't just imagined, it's felt, and I'm your co-host Peggy Smadley. Best trigger, your other co-host. Together we're flipping nostalgia on its head. That's right. I Gladson is more than a word. It's a mindset. It's about preferring the dreams of the future to the experiences of the past. In this show, we'll explore visionary ideas, challenges, assumptions, and spotlight the people who always like the spark, something new. These are the people who go where no one has gone before in all areas of life on Earth. From tech and transformation to recognition and legacy, this is where emotion meets innovation. So let's stop chasing normal and let's start designing the future worth remembering. So let's go. So Dennis, why don't you tell us, why don't you start off by maybe helping us

1:00define obsolescence? I think in this era of AI, why don't you give us some feedback on your thoughts? Because I think when we talk a lot about it, we talk about systems failing, is it people failing? What's your take on it? Because I have a very strong opinion on this, but I would love to get your take on it and let's kind of go from there. So just defining what obsolescence means, is that what you're asking? I think if listeners are right now, in their mind, I'm sure they would love from your perspective, what would be, if we wanted to take an overarching, what is the definition of obsolescence? Before we talk about human obsolescence, let's just define obsolescence and then let's kind of define maybe human obsolescence. I mean, maybe we've got to split the baby in half or not or split the definitions in half. I'm not sure, but we really need to help the listener understand because I think we kind of mix up when we talk about machine obsolescence and human

2:05obsolescence and just obsolescence in general. I think they all get intertwined and I think we maybe need to make sure we're all talking the same thing and I think that's a very important way of distinguishing and defining so that people aren't too confused, especially when we talk about re-skilling and skilling and upskilling and in this era of speed and transformation, I think everything starts overlapping. Yeah, well, and of course, that's kind of the nature of the tech industry. The tech industry has always been, it's always considered humans second. And so it's always been focused on progress and favoring progress over human dignity. And so when we talk about obsolescence, technology obsolescence, we're talking about things that are no longer used. It's been made obsolete. And when we talk about human obsolescence, I don't know. I don't really talk about human

3:11obsolescence myself, mainly because my whole focus is human dignity. And so the idea that humans can be made obsolete is, I think, repugnant. But defining human obsolescence basically means we no longer need human resources. And so the economy is running without employees. And so what exactly does that mean? I mean, the implications of that is that people who are going to still make money in an era of human obsolescence basically implies that we're going to have to start our own, everybody's got to be an entrepreneur. That's what it boils down to. And so we've got a refocus from how do I get a good job to how do I accumulate my own wealth for myself, whether that's through making YouTube videos or it's through building a business that relies on

4:16on robots to build things or whatever from there. So as you make that distinction, and I think the word repugnant is a very strong one. And I think maybe the question then happens is, do we go to the next point of this conversation of saying, what really is the discussion? Humans aren't tools, right? So we need to have that tools become obsolete. And it's the idea that humans adapt, they reinterpret, they reskill. Those are all of the things that we need to be talking about. And I think it goes back historically to I think even when we think about the industrial revolution, which I started out in the opening kind of talking about that, you know, we didn't make our artisians obsolete. It made, you know, the guild structures obsolete. We have to talk about that. I even think about in the industrial revolution, we didn't make librarians obsolete. You know, we made card catalogs obsolete. I mean, if we think

5:18about what was obsolete, we have to really frame that properly. And I think AI won't make workers obsolete. It will make rigid job, I think, architectures obsolete. But I think as we say this, I think what we have to do is talk about what exactly is going to happen is that AI accelerates perceived obsolescence. And it's a perceived mindset. And I think that word is what. And again, the word repugnant is a very interesting word to use for that. And it's very strong. But I think I'm very concerned about what happens to cognitive tasks that we have, you know, AI is performing these cognitive tasks and not just physical ones. But are we now becoming people? What happens

6:19when people stop learning and rely on machines? That's the question now I throw back to you is to say what happens now we need to ask and ask and answer that question. And on some level, I think so human obsolescence in my view is kind of a it's a red herring. It's a diversion because for all the reasons that you've just described, right? And so in the same in a very similar sense, I think the idea of us offloading all of our learning onto machines is also a little bit of a diversion as well. Don't get me wrong. I'm sure a lot of kids are going to grow up over relying on on large language models and other forms of AI to the point that they kind of lose the plot and have to be probably have to go back to a school in order to learn how to learn because they'll be relying on faulty information because one way or another,

7:24machines still provide faults and they're not perfect. They still make mistakes and whether those mistakes are actually result of human incompetence or not is irrelevant to the point that they still make mistakes. There's the prime example probably the biggest example at the moment of course is the horizon scandal in the UK where a bunch of business owners, both postal business owners were accused of embezzling money that never actually existed but was showing in their in their software. It was showing in their accounting software but it never actually existed. And so these these business owners are accused of stealing money when in actuality there was no there was no actual cash to be stolen and it was all due to software error but the UK government didn't recognize that software makes mistakes and did not recognize the

8:28software error and so they progressed with accusing these human beings. And so this is a primary example or it's the biggest example at the moment of where machines still make mistakes and we cannot rely on them to the point that we lose our personal responsibility for our own output and our personal responsibility. We still have to take personal responsibility for whatever output we put out so whatever we get a machine to generate we still have to go through it we still have to verify it we still have to validate it in whatever way before we submit it to our our jobs whether that's marketing or or consulting report there's of course then let me understand this what you just described in my mind and tell me if I'm misunderstanding you you just described cultural narratives so whatever was put out there

9:28spread faster than institutional reforms. So now people are being accused of things reputations are being ruined couldn't be costing them so much money to have to defend themselves whatever the case may be and leaders are often misunderstanding what AI is actually capable of doing good AI versus in that case bad AI that's what you just described. Yeah yeah exactly and so this this is where again it comes down to personal responsibility so instead of relying on automation to handle everything we need to be actually looking at how the automation is working we need to be investigating it periodically to make sure it's actually doing what it's supposed to be doing and doing it the right way and questioning to make sure that whatever mistake whatever error is happening is not a result is we're putting the blame in the

10:31right place but I don't know if I'm explaining that quite correctly and largely because what's going on in in the UK with this horizon scandal the horizon is the name of the software that these business owners were using what's going on there is actually really really gets to the heart of where we are now with with AI I mean this this scandal has been going on since the year to 99 2000 I think that's when this software went into place and it's also around the time that these errors started popping up I think the first person to be I think the first legal action was taken in 2002 2003 if I remember right something of that nature so this has been going on for years and it's a very different type of technology from the large language models that are out now but we already know the large language models make a lot of mistakes even when they're made to only

11:35summarize information even when they're made to be exact and they're made to be accurate they still make mistakes they still hallucinate in various ways and so this is the difficulty with the over reliance on AI because AI another cultural narrative is this idea that AI exists AI doesn't exist on some level AI we do not understand and tell it so the way that AI was defined by the person who actually coined the term so his name was John McCarthy he started a Dartmouth workshop I won't get into the whole history the point is that how he defined it when he coined the term is that we would understand intelligence to such an extent that we can replicate it in machines we don't understand intelligence well enough to replicate it in machines we're still trying to understand it at a biological level at a philosophical level what role does

12:40consciousness play an intelligence what role does intuition however you want to define intuition these are all terms that are the definitions of which are a bit murky and so trying to say that we can replicate that in machines well we haven't done that yet and there's little evidence to suggest that we can largely because we still can't even come up with definitions for them that we can all agree on that there's no scientific consensus on what consciousness is there's no scientific consensus on what intelligence is and like I say intelligence relies quite heavily on consciousness of some sort on some level so then the deeper what you're saying from a philosophical level then is what is it uniquely to be human from that very thing and okay we're going back to the I think we should have a drinking game here repugnant because I think I'm back to that and I

13:42think anyone listening today should play the drinking game because how many times I say that they have to have a shot but I guess it goes back to this if you think about it because it now I think what I'm hearing you say is now historically humans have been defined by things like their strength their memory their ability to do those things you know judgment and all these things creativity empathy all the things we say if we're trying to say AI can't do that but in some ways we want to say can AI eventually do that what's the narrative what's the purpose well we need humans but yet we're saying AI has made decisions that are now putting humans in jeopardy and decisions are being made that force humans to have to defend themselves against the decision of machinists is made well how is what's then goes back to the deeper philosophical question that you just mentioned is intelligence are we relying on a machine that's making poor intellectual decisions that a human

14:45has to defend themselves against and what that boils down to ultimately is who's taking responsibility who's taking responsibility for the machine because we've and ultimately this is the difficulty so you're asking about what happens when we offload our learning to machines we sacrifice personal responsibility and we do so because having too much responsibility is incredibly stressful and we can't get a lot done when we are focused on all the different responsibilities that we have to handle in a day especially when you're a parent and you're also an employee and a high ranking employee and you're trying to balance everything and you've also something else that I'm experiencing myself being a parent as well as having to take care of my their grandparents my parents you know it's a lot of responsibility so we try to offload that responsibility onto machines and we've done it to the point that and in the UK at the at a legal level they've done it to a point

15:51that machines are like you say they're accused they're putting people into situations that they should not be in they're being accused of things by humans based on the things that the machines are doing so humanity needs to be taking responsibility for what happens and we need to stop offloading our responsibility on the machines we need to keep using machines as tools and stop looking for some sort of silver bullets that will help us to do our jobs without having to actually take responsibility for our jobs is the bigger question as I hear you talk I think about the idea of autonomous vehicles if we have driverless vehicles and they get into a car accident who's responsible is it going to be that's what comes to mind you say we don't want machines to be able to be the ones but at the same point we're going to have this issue at what point who's responsible in the

16:53are we going to actually have to come to mind to see all of this and I think that's going to be the challenge that we're all going to have to kind of figure out is it the insurance companies are going to have to say well as it was at the carmaker was it you know was it the sensor I mean there's all kinds of challenging issues are going to be here because nobody's driving the car and whose responsibility was it which car was it was it you know the the street light go off was it the infrastructure I mean it's going to be all kinds of you know and then we're we're going to say again whose responsible is it machine know is it going to be the person who own the car while they weren't because they weren't driving it well it's it's just going to be a mess yeah and and ultimately quite frankly it it comes down to give an accident happens is probably because of the car and if it's because of the car then it's probably the manufacturer of the car they're the ones who are maintaining it oh no it's not going to be that we know the responsibility for this let's

17:54be serious we know it's going to trickle down just like we've been having this conversation that's just the way it's going to be it's like the tires on a tire blows it's not the tire company it's it falls back to the car the owner of the car I mean we know it's not going to be those big companies I mean that's interrupt you but we the reality is it doesn't work like that we wish it would but it's not and this is the difficulty corporations want to be individuals they want their corporations to be individuals but they can't actually take individual responsibility and this is ultimately the point is as consumers as users of these machines if we're going to use ultimately responsibility if if you're in a driverless car you're complicit and whatever and the company is complicit the software developers are complicit which basically they're part of the company right so it's we're all everybody's on some on some level is complicit in what's

18:55going on and this is the issue is where do we as a society where do we actually stay I want to get off right like you look at the Cambridge Analytica scandal back in what was it when Trump was running for president the first time right there was a great scandal about Cambridge Analytica actually influencing people to vote for Trump and of course there were a lot of questions about whether a similar company was doing the same thing for the Democrats as well of course because the both sides seem to be having a lot of a lot of social media attention that did not seem to be warranted so my point in this is that bringing up Cambridge Analytica it's we haven't stepped away from social media we're still society is still using social media in a variety of ways we haven't really stopped using social media we're not even using social media any differently than we did before and so the question is did we learn anything as a society when Cambridge Analytica came to

20:02light that they were influencing votes that they were influencing people's opinions and people's perceptions of reality nothing really changed afterwards a lot of people got upset that Trump was in office and a lot of people got upset that people were upset that Trump was in office right both sides had a lot of emotions invested in what one company was influencing them to care about and yet nothing's really changed we've just gone on with these systems so where does society decide maybe this is enough maybe we've had enough technology maybe we need to re-evaluate what we use technology for because of course social media it's a great driver of growth for a lot of companies it's a great vehicle for marketing but driverless cars I'm not sure where the driver for growth

21:03in driverless cars is going to be so let's take a step back then and take it back to AI and what's triggering our initial conversation about obsolescent so is the fear of becoming obsolete like is is ancient or the idea that trigger changes triggers this anxiety because we've seen unprecedented levels of anxiety into humans and the idea being right now that AI it's the pace of change is the real uncomfortable truth maybe that's what we have to say it's the pace of change to which AI is competing that it's forcing the human to feel and again we're going back to feel obsolescence and human disengagement as a result because they can't keep up with the pace of technological change that's causing them to feel this way whether that's not the truth but do we have to worry about what's actually happening because we're going back to that they can't

22:04and AI is making more mistakes because they can't catch them because things are happening at a rate of change that they can't keep up and and how do they frame what's happening in front of them to their leaders to the management to the to the workforce around them to the community around them and everything's happening and it's just this pace is just enough where people are starting to burn out and burnout leads to disengagement this engagement leads to a society that says I'm out I just I don't want I just can't do it anymore but are they lazy or is it just burnout because they say look I just can't keep up this pace anymore yeah well see a large part of this I like you you started off the question of is it is it is an ancient thing or is it actually something that's that's new because we're we're scaling at the moment in a in a very different way I mean we're looking at global work forces rather than a workforce in England

23:06that's you know leading a Luddite rebellion it's these kinds of this kind of burnout is no longer localized it's it's become global and partially through social media of course it's not just the employees they're taking their burnout and kind of regurgitating it on on social media so that the overall social climate kind of has a taste of their burnout as well the the difficulty I think is so putting things in perspective we we talk about a lot about re-skilling so in the past in and I do specify in the past so in the past the typical pattern that we see when when automation comes through is that automation is introduced somewhere that local area where it's introduced has a dip

24:13in income but then income comes up in in a matter of years a couple of years a short a short comparatively short period of time now one of the questions of course is what's happened to the actual individuals in that community whether that community is actually whether the individuals in the community have found new work and have re-skilled and and gone into other things that's something that I haven't seen a lot of studies on tracking individuals in areas where automation is introduced but at a at an economic level that's what typically happens think the income dips and then it rebounds suggesting that automation is ultimately good for the economy but whether it's good for individuals is the real key question and this of course gets to how much responsibility

25:19do organizations need to take for the people that they employ a WS is let go of what'd you say 15,000 16,000 people now and a large part of that is robotics more than AI's per se but it's still all part of technological unemployment one of the key things we also have to remember though when we talk about anything going on in the news is that it is global news so this is it's not like this never happened previously right obviously Detroit used to be a much different city than it is now before automation took hold and and so the city itself is quite different than it was but a lot of those individuals spread out and went into other work and whether they have better lives or worse lives or just simply different lives may depend as much on the individuals themselves as it

26:20does on what happened to them the so we're running out of time let's let's do this I'm going to end with the comment I would love to hear yours because we're going to continue this conversation in April if I hear everything you've you've shared with me obsolescence is not about it's not about just the systems failing to adapt it's not about people failing to try we're dealing with a lot of moving parts at once it's it's so we have to reframe our thinking so I think next month what I'd love for us to start a conversation is what happens when people stop learning and rely on machines because I think if you and I start that conversation what actually are we talking about because I think we have to stop talking about it being human obsolescence because again we'll go back with that being repugnant against the drinking yep I love that but I think what we have to do is start

27:21talking and reframing it and I'd love for you to wrap up with your final thoughts on this part of the conversation right now the largely so my main point ultimately is that technological unemployment caused by technology is something that's been philosophized for millennia so for thousands of years we've been philosophizing about the idea of machines actually putting humans out of work ancient Greek philosophers discussed it it's also something that if we've been anticipating full technological unemployment where everybody's able to go out of employment for at least the past hundred years and actually probably longer than that keenes actually wrote a paper about it the big economist the big English economist keenes wrote a paper about full unemployment that was quite interesting and very little has changed about the narrative of full unemployment of effectively human up what what we also call human

28:25obsolescence humans are no longer needed in the economy and he had a lot of different things to say about that but obviously he had a very positive feel for it as well and like I say very little of that narrative has changed so I think in a lot of ways that narrative has driven a lot of the technological innovations that we're seeing now how can we get to a point where people no longer have to work and what does that look like well I don't know is I think a lot of people really dislike that idea I think a large part of what is what is full technological unemployment look like really depends on how much money you have to begin with right if you're lower class now and we're going to stop working well what's going to happen to them then if they don't have to if nobody else is working either do they stay lower class or do they get to progress up the

29:29ladder somehow and of course if you're if you're wealthy I think there's a lot of concerns there as well if we go into full unemployment because that only highlights the inequality in the system if if if somebody's wealthy and nobody's actually working so then it becomes a question it becomes a very society becomes very egalitarian if we all have the same equal access to employment because nobody's working so it's it's not really an I it's not really a utopia for the upper class or the lower class it's really only a bourgeois utopia a middle class utopia and so that's ultimately where where I kind of probably want to leave that is do we want that utopia and what is what is it what's it going to cost to achieve that kind of utopia

30:32that's it for today's journey into eyeglasses where the future is crafted with heart and emotion remember legacy isn't what we leave behind it's what we build looking forward thanks for joining us as always share your thoughts with us on x at connected world or follow us on linkedin and continue the conversation there remember we broadcast live every a Tuesday at 12 p.m. central please check out our website at connectoworld.com or show website until next time keep innovating boldly for the promise of what's to come