How to Be Indispensable Not Obsolete in An AI World with Alastair McDermott #100

AI is changing everything and faster than most professionals are prepared for. As knowledge work becomes increasingly automated, the critical question isn’t if your role will be affected, but how you’ll stay relevant. Will you be replaced or elevated?

In this milestone 100th episode, we dive into the tension between human adaptability and machine efficiency. From the quiet disappearance of entry-level roles to the rise of AI  across industries and white collar roles, we explore how leaders and knowledge workers can retain their edge, not by outworking machines, but by learning to work with them.

Discover how to thrive, not just survive, in the age of AI.

GUEST BIO:

Alastair McDermott helps business leaders implement human-first AI strategies that drive extraordinary efficiency without cutting jobs. His approach has slashed processing times by 92%, cut operational costs by 90% and increased capacity 12x, while preserving the team and culture. Author of the Expert Authority Builder series - including An Absolute Beginner’s Guide to Using AI and the forthcoming "Use A.I. Stay Human",  Alastair speaks and podcasts on practical AI implementation that amplifies human potential.

TOPICS EXPLORED:

• Why roles built around repeatable, robotic tasks are most vulnerable to AI replacement
• The limitations of AI orchestrators vs. the value of human intuition, judgment, and implicit understanding
• Why McDermott still hires a human VA despite building agentic AI systems
• Four key opportunities AI offers: productivity, capability expansion, better decision-making, and accelerated learning
• The rise of the “Chief Robot Officer” mindset: managing workflows between humans and AI
• Case study: using AI to simulate beta readers for rapid book development and iteration
• The importance of AI literacy as a compounding skill in any profession
• How curiosity, experimentation, and play are essential to meaningful AI adoption

RESOURCES:
Download Alastair McDermott's "Complete AI Toolkit" for free at HumanSpark.ai/toolkit:

Alastair's new book available for pre-order on Amazon: Use A.I. Stay Human.: A Survival Guide for Experts Who Want to Stay Relevant, Authentic, and Indispensable in the Age of AI. (Or UK version)

HumanSpark Podcast: https://www.youtube.com/@AIPoweredBiz

McDermott's webiste: https://humanspark.ai/

CONNECT:
LinkedIn: https://www.linkedin.com/in/alastairmcdermott/

If you're interested in more visit ▶ https://almcbride.com/minicourse
for a free email minicourse on how to gain the psychological edge in your negotiations and critical conversations along with a helpful negotiation prep cheat sheet.

If you enjoyed this episode of Dealing with Goliath Podcast, hit subscribe to hear about our latest episodes.

Transcript

[01:00:00:02 - 01:02:16:03]
Welcome to the Dealing with Goliath podcast. Now we're not going to do our usual intro, because this isn't quite our usual episode. This is episode 100. I want to do something a little bit different. So the theme in this episode is about AI and the psychological edge. So for many of you who know me, maybe you have my book already, it's called Goliath, similar to the podcast name, Dealing with Goliath, psychological edge in gaining the psychological edge in negotiation. And I think there's a huge paradigm shift happening at the moment. Some people are well aware of it. Some people think it's overblown. And this is something with AI, changing everything. And this is something that I want to discuss with my good friend, an AI expert, coach and trainer, Mr. Alistair McDermott. So Alistair, welcome back on the show. Great to have you here. Great to be here. And congratulations on 100 episodes. Yeah, you got there well before me. I started before you were far more disciplined about your output. But here we are. Yeah. So look, as you know, I'm big into this idea of the psychological edge. But there's a huge amount of disruption already happening. We're starting to see it in hundreds of different ways, not least a huge amount of companies, software companies, legal companies, financial companies, suddenly, quite significantly reducing an awful lot of their new hires. This is predominantly in the States yet. It doesn't seem to have caught on in Ireland as much, a little bit less in the UK on the initial stats so far. But this is something for a lot of industries that's coming down the pipeline. That question of why should I hire you when I can just ask you actually PT. So let's dive in there. Let's let's talk about that. So if you don't mind, we can start on that one. I saw in one of your blog posts, you said, and it's a nice quote, if you work like a robot, AI will replace you. Can you talk us around that as to how people should be thinking about how they need to pivot or adjust their work or their work practices?

[01:02:17:14 - 01:04:41:00]
Yeah, so this goes back to scientific management, which was created by a guy called Taylor in I think the 1870s or so. I remember when I was 16, Bizorg class, yeah, Taylor and the scientific matter. Yeah, exactly. Exactly. Yeah. And it was management by stopwatch. They literally timed people doing all these things. And it worked really well for Henry Ford, who created the first moving production line, where the the components that we're building moves rather than the people and all of this, like McDonald's has this down to a fine art as well, where they've got SOPs for everything started operating procedures and and and you do everything exactly the same everywhere. And everything is in the same place, the same labels. And so you've got these kind of repeatable simple tasks, where we asked people to follow the exact script. And people don't really work great like that. That's not what we like to do. But actually, AI is really good at that. AI is really good at following the exact script and doing this small repeatable task really well. So it's kind of ironic, we've actually now built a system, which will now make those people who are doing that kind of work obsolete. Now, it's going to not hit McDonald's first, it's going to hit knowledge workers who are doing the virtual knowledge equivalent of that. If like, if you're, if you're taking a piece of information and copying it from one spreadsheet to another, and then sending an email, and that's all you're doing every day, that kind of simple, basic robotic admin work is perfect for AI to replace. So that's what I mean by if you work like a robot AI will replace you. The more simple and kind of granular those tasks are, the more likely it is, the more creative and the more complex that the less likely it is that AI will come along and replace you at least not immediately, it may be able to at some point in the future will probably almost certainly at some point in the future. But we don't know what that timeline looks like. Some people are very aggressive with their with their predictions and some people are saying it'll never happen. And I'm kind of somewhere in the middle. I think we don't really know yet.

[01:04:42:05 - 01:04:47:22]
Okay, very interesting. And it's very interesting. You're talking about that, as I said, we in so many industry delivery

[01:04:48:23 - 01:07:44:02]
successfully made humans act like robots. Which of course, this is this is one of these fallacies that you know, the particularly the current we're not going to get too political with the current American administration. Like I'm or low them are trying to bring jobs back to America. And they what pointed out subtly and not enough, is that if that factory comes back from Vietnam, or China or Mexico, it doesn't doesn't employ 5000 people like it did when it left in 1978 or 1984. It employs about 50. Because most of the work is done by high tech robots. Now you see the robot arms doing all the stuff that humans did, sort of 30 years ago, right? So that's kind of the example in that manufacturing way. But as we know, it's not so much the blue collar worker that's in danger of being wiped out by AI anymore. It's because they've had that they've actually gone over that that hump in many ways. It's as you say, the white collar worker. And this is what we're talking about that an awful lot of industries aren't needing those people to do what you might call the grunt work or those repeatable, more simple tasks. Because software, whether it's AI or just more advanced software can do it. And so talk to me about this, because just on a different tack on the same sort of area, you know, you were asked in a recent interview, which I found fascinating, you're asked by the inferior, would you still hire a human VA, a virtual assistant, and you emphatically immediately answered, yes. So can you explain why that is? Because you're Mr. AI. So I'm thinking you're going to say no, I have agents and I'm creating all these agent experiments. And I know they're not foolproof yet. But they've again, like everything in AI comes on the leaps and bounds every couple of months. So I was interested why you and faculty said yes, you still want a human, which is a low to a co by the way, I go is a real person. It's kind of ironic because your name starts with AI. Yeah. But yeah, so I spent a lot of time looking for somebody who had a name with AI in it. No, I was working with I go long before we do the AI. Okay, so let me let me go back one level and just explain what I'm talking about just just so that people understand the context here. So we have AI tools that can do one task. And they're really good at doing one task. And then above that, we have these kind of what we call a genetic systems. And that's where they can do more than one task at a time before they come back to you. So it can go and read a website, and then make a decision to read another website, and then come back and write a report and then you give you the report. So it's done three different things there. It's not just done one. And so at the higher level, the manager AI, we call it an orchestrator.

[01:07:45:03 - 01:08:47:23]
And so an AI orchestrator is really good at managing all of these sub tasks. And that's what's really interesting about where AI is going is when we can have these agentic systems where they can work in a way that doesn't need continuous human intervention. So what I mean by that is we can give a task. So because I remember and just give a super practical example of this and tell me if this is what it is. This is where someone can set their own assistant AI, book me a hair appointment. But you know my calendar and when I like to do that. So it'll ring their favorite salon and try and book the appointment. And if it's not available, we'll go to another one and then to another one until they get a time slot that works for the individual. The point is, it's actually still, it's going out on a loop getting feedback. If it's no, then it tries a different place. If it's yes, then it's booked into the calendar, right? I mean, Google used use this as an example, a few years, like a year or two ago, right? Yeah,

[01:08:49:11 - 01:15:07:07]
it was completely. Yeah. And they were talking in voice as well. Like, you know, it wasn't kind of machine language renting, they were just having an English language conversation. Yeah. So so the we have these orchestrators, which are not the technical term that people use, don't know why they chose to use that. But you know, I think of like a conductor in an orchestra, where you're telling everybody else what what they're doing and when they're doing it. So we have these orchestrators, these AI orchestrators, and they're pretty good, they can do a lot of stuff. The problem is, there are certain things that human orchestrators and the human orchestrators just a human doing a job in the same way. So when we're making decisions, and we're like some of you self employed, you have to make a decision as to what you're going to do. And then you have to put on the working hat and go and do it. And then you take off the working hat and you decide, okay, now what do I do next? And so human or like we're orchestrating ourselves there. And some of us do better than others. But when you have a human orchestrator, which is what I co is, like she's she's my assistant, but she's a human orchestrator. So she's able to figure out when I give her a task, she's able to read intent and implicit instructions that are not that I didn't explicitly give her, whereas an AI orchestrator will execute the command literally. And that's the problem. That's the problem is, is it doesn't know how to infer. And I think that they will get better at this over time. And again, this is this balance this trade off of when we're using AI of privacy versus functionality, the less privacy that we have personally, the more functional the AI will be because we've given it more context that it can make decisions in. But it'll make decisions based on data. Whereas a human orchestrator can navigate these kind of unwritten rules, and unwritten context and infer things. And it's, yeah, sorry, go ahead. Yeah, and a human will also do relationships better. It'll understand context of relationships. And it can also, human orchestrator can also solve problems creatively. And this is something where an AI orchestrator typically will be quite brittle. So if we go to the idea of brittle or fragile, and, and robust or creative problem solving, that's that's where AI falls down currently. And the humans will be much better. And this is kind of Yeah, yeah. And then another thing is this kind of judgment. A human can make a judgment call. And AI just can't do that, or at least not yet. So there's a few interesting things there. One is that, as I said, there's the inferences, there's knowing someone. So when they say this, you kind of have that extra context from knowing that person that maybe is somewhat unspoken, but is an impression gained over some time. As is, as I said, the creative problem solving, it's just very interesting going putting it back into negotiation, or high impact conversations, building rapport with clients with suppliers with our own staff, that these things are right there. And for me, one of those great, as I said, the psychological edge, when you look at my book, my program, my whole stuff that I'm all about, is all about that is like clarifying one's own values, being able to connect, being able to open up to the other side, being able to have them feel like they can open up to you. So you have that greater context for the creative problem solving ability, but it's that adaptability. That's absolutely key. So it's really interesting that you're saying that, in many ways, that's where AI still falls down and by the sounds of it will for quite some time. I think so. Like, I don't see. I don't see that part happening very quickly. Like AI is advancing quickly in an awful lot of capabilities. And I think it's incredibly useful. But I don't see it fully replacing people in certain roles. I do see it replacing people who are in robotic roles, and who are in very basic admin grunt work type roles where they're not really adding a whole lot of value except moving bits around. And if you're in that kind of role, then you are in trouble. And like, there's a lot of people who are quite happy to do that kind of work and who like doing work that is kind of not that taxing because you know, maybe they've got a very active life outside of work. So they don't want to have these jobs where it's it's particularly taxing. But I think the problem is AI is going to come along and take away a lot of that. And, and there's one reason why people say that AI may disproportionately impact on women because a lot more women are in admin jobs for various reasons, historically. So like there's all sorts of different factors going on there. Another issue is where are senior people going to come from later on? Because if we're replacing junior people with AI, because AI will do the research or do the grunt work, then we won't have kind of junior people coming up through the ranks. And so where did the next senior people come from when the current senior people retire? This is the thing. I mean, we talked about this, I'm actually trying to, at the moment, get together and experiment to, in my opinion, AI is that causing that problem can also solve that problem to a certain extent, in that if it can do the tasks, that the human can do faster, better, cheaper than it can train someone to do those tasks to an expert level, faster and through examples. And we kind of talked about this in a slightly different way that I've actually been using it to learn a whole new field myself attached to negotiation. So at the moment, for the last few months, I've been learning all about what they call micro mergers and acquisitions, M&A work. So companies half a million to 5 million, that sort of territory.

[01:15:08:14 - 01:17:09:14]
And, you know, I got a GPT, I had tons of I had several courses on it with vast amounts of information, but I was just lost in it. And I was like, I'm not using this at all. This is terrible. Why am I not learning this stuff? Because it maybe didn't quite know how to work through it and all of that sort of stuff. Instead, having talked to another guest actually on the show a few weeks back, Scott Shigori, he was talking about how using AI and how an issue some initial reports say it's it is making people a little bit less creative, less intelligent potentially. Whereas other uses of it actually improve your learning, your thinking, your creative problem solving, and all sorts of other facets. And the difference being how you use it. So it came from how Oxford University advised their students to use it, to use AI, and it's to use it as a tutor have a challenge you have a point out, tell me what you understand about blah, and then you sort of basically tell it and then it asks you questions and points out where you're incorrect or where you're missing subtleties are major factors. So this is kind of how I built GPT with your help, which loaded up with all the my favourite experts on this stuff. And then basically have it give me case studies, I tell it how I'd approach this sort of deal how I do this, the before the deal, the first 90 days and so on. And it tells me you did this really well. And you're like, yeah, stop giving me praise. But then it says you missed this or you underplayed that this is a huge red flag that you missed. This is often shows this as a problem. So in learning through, basically being in the sandbox playing with this thing, you know, you're doing it, it's not for real, but you are effectively doing the thing and it giving you live, personalised, individualised feedback, hugely powerful. Great fun too, by the way. But yeah, like the

[01:17:10:14 - 01:17:11:20]
learning curve, you know?

[01:17:13:03 - 01:27:19:09]
Yeah, so I'm sorry, I cut you off there, you say like an increase to the learning curve massively. And so I think so I see four pillars of opportunity for AI and maybe there are more but these are the four that I see. I've written about this in a free white paper, my website, it's, it's at humanspark.ai slash toolkit. And you can download that without any email address or anything. But the four pillars that I see are productivity. So this is kind of table stakes. Everybody talks about productivity and like we are seeing huge productivity increases. So there's been a lot of different studies in different industries and it varies from from industry to industry. But we're seeing around a 30% increase in productivity for knowledge workers, approximately. Then apart from productivity, I like the other ones a bit more because there's there's, there's a lot more than we can do then just become more productive. We can, we can transform so we can increase our capabilities. So capabilities is the second one. And that's when that we as a person or as an organization have the capability of doing something that we just couldn't before. So like for example, there's a lot of people now who are able to build software who could not build software a year ago. What they call the vibe coding rate. Yeah. And go and tell it to do stuff. For example, there are some organizations where they say, okay, we don't want you to come to us with a proposal for a new campaign. We want you to create a, you know, a mockup prototype, and bring us that and show us what it'll actually look like. Don't just describe it to us actually build it and bring it to us. And, and that moves the conversation forward because then somebody who didn't have that capability now has that capability. For example, I can now create designs, whereas I couldn't design before. I like I'm like the guy who does the music degree but can't actually play any music. I know a lot about design, but I couldn't actually do it. Whereas now I have the capabilities of doing design because of the tools that are available. So those are the first two productivity capabilities. Next one is decision making. So we can actually do smarter decision making in because we can use AI to process data, vast amounts of data, find patterns and help us to make better decisions. We can also train AI and this is a free to interject on you there just one second, but on the decision making is very interesting because what you can also do is tell us I'm thinking of doing this, this and this. Am I falling into some cognitive bias traps? I'm into some thinking errors and it can point out the errors potential flows in your thinking. So sorry, just just to double down on that that it can help you avoid some of those human, human error approaches. Like there's loads of different decision making frameworks, which I didn't know about. But there are many, many different decision making frameworks. So I built the tool in AI and free tool in chat GPT. And it it it knows about all the different decision making frameworks. So I can say, I need to make a decision. Here's some context. And it says, Okay, let's apply this decision making framework because it's the most appropriate for this context. And it's able to help me make a better, smarter decision based on the data that I have as well. And so we're able to make better decisions, we can also now look at, you know, we can get, you know, industry trends, we can look at historical data, we can feed all of this into the machine and the machine can process it and help us make decisions based on that data. So that's, that's the third one. So we have productivity capabilities, decision making. And the last one is accelerated learning. And this is what we were just talking about. And it's massive, like it can really help us to learn things very quickly. If, if we apply it in the way that you're doing, which I think is really good, where we're, you know, going back and forth. Now, I think that education is a field where there's really struggling with AI right now, because there's people getting kicked out of college for plagiarism, because AI wrote the thing for them. And then there's other people who are seeing huge advances in learning with AI. And it all depends on your approach to it and how you use it. And I think there needs to be more discussion about using it properly. And not this kind of road copy paste, which a lot of people are doing just, just to get get their, to get their work done, you know, in some ways, it's that's it's that age old question of, are you learning or doing this thing for the exam and the accreditation? Are you doing it to know the thing? And when you get that clear, Dennis, like you're being lazy and copying pasting, and you know nothing about this, versus I'm actually learning this to learn like, I used to be able to debate in German about, you know, Abdaglo Szekeik, like homelessness or mass tourism or all these topics, then I go to Germany could barely have a conversation. So, so in a way, I was kind of had all of the vocabulary that was almost completely useful in the real world. So there's a parallel there about, you know, learning for the exam or studying cramming for the exam versus being able to do the actual thing. And getting one's priorities correct on that, you know, the other thing is, people who are graduating college now are going to need to know how to use AI, because otherwise, they won't get a job because the jobs will now require that you're able to use AI to do something. So it's like, it, there's, there's an awful lot like if you unpack that whole AI and education thing, there's, you know, there's, there's so much in there to discuss and to think about. But what I would just say as kind of a guy as a high level overview, is AI can help people learn more. And it can help our brains to be more creative. And it can help us to raise our IQ or at least maintain it. Contrary to what some studies are claiming to show, you know, so there's like, there's nuance to this, these gray areas, and it certainly can be highly damaging. And it is being highly damaging in many ways. But there's also ways that it's highly positive. There's, it's very interesting you talk about that, because in many ways, for example, one of the guests I had on last year, I think it was was Dr. Dale Whealahan, who is one of the main advocates of this movement of the four day work week. So it's not the Tim Ferriss or our work week. It's a four day work week. And they've done, they've run pilot programs in all, nearly every continent and all sorts of businesses, different industries, they're usually mid sized businesses. But what's fascinating is they don't just sort of cut a day off, like everybody gets Friday off, hurrah, they train them in far more efficient methodology. So this isn't even without talking about AI. There are systems to operate more efficiently that most businesses like 99 point something percent aren't using very well. And what the deal is, I think usually they have six months as an experiment. And if they're as productive or more with the four days than the five, they get to keep the four days. Right. Now, as you can imagine, a lot of people like to work in these companies, right? Because they keep the five days of pay, by the way. Right? Yeah, sometimes it's two half days, sometimes it's different things for cover, you know, different people have different days and so on and so forth. But the point is the same is that there are already systems that exist. So one of the ways I'm thinking about a lot of this AI stuff is that idea of it's just a really powerful tool to do the thing that needs doing but which tool is necessary. So maybe you don't even need AI. You know, maybe it's just a piece of software that's not actually AI. It's not really, as in the software, if it's an algorithm will give you the same answer every time AI won't. Yeah, right. So maybe you need something slightly more dependable. But this goes back to what you're talking about with Ico with the idea of the orchestrator. I you know, it's something Perry Marshall, we're both a fan of talks about quite a lot this notion of the chief robot officer, that we are one of our roles now even as a solopreneur or in a small business that we're leading is or that we're just given a set of tasks is that we're chief robot officer. So which parts am I gonna is a human going to do you break down the task flow, which parts are you ever going to do which parts can various AIs do and then you're the one as you said orchestrating it putting it all together. What are your thoughts on that? Is that basically where you see everybody nearly managing their time as every will all become sort of chief robot officers to a certain extent? I think so. So there's there's so much to this again. Let me let me give you some blurred out some thoughts and then you can think about this. One thing is that an expert using AI expertly. So what I mean by that is somebody who is a subject matter expert in their own area, and is also an expert in using AI is suddenly has superpowers. Because they're able to get so much work done so quickly, they're able to delegate, they're able to build systems, they're able to abstract themselves and become the orchestrator rather than the worker. And can we just give a quick example here? Because often people are talking these grand terms. So let's actually just, and that's great. We're doing an overview. But let's go just deep. So because a lot of people don't really get how powerful some of these tools are. Can you give an example? Like maybe one of them is, for example, I know you built a custom program, which actually beta reads one of your books, which you are updating.

[01:27:20:14 - 01:30:02:11]
Would that be a useful example? Okay, yeah. So let me explain that system. And it's fairly fairly, I guess that's pretty simple. So I have a book called an absolute beginner's guide to using AI, which I wrote and published last year as an ebook only. And this year, I decided I wanted to update it because you know, things change in AI very quickly. So I wanted to release a second edition. And so I took the original and I set it up on my computer as where every, every chapter is a separate file. And I showed AI how to read it. And then I said, Okay, I want you to create 5% zonas for five potential beta readers for this. So five completely different people who might find the book useful. And so if you're if you're writing a book, having feedback from beta readers is incredibly useful, and is also very difficult to get because you have to go and find people get them to read some of your book, give you specific actionable feedback on it. And it takes weeks or possibly months. When it was like one of the many reasons why writing and publishing a book take a long time is the kind of the feedback loops. So I created these these virtual beta readers five avatars. And I got them to read every chapter. And I had feedback from them on every chapter from five different perspectives. And then I was able to say, Okay, I like this feedback, I don't care about that feedback. Let's now update the chapter based on the feedback. And so I was able to process it and triage all the feedback. And I was able to do all of this in you know, an hour, instead of you know, a three month process. So that's that's what I mean by huge jump, just speed up these things incredibly. Now I know that they they aren't human beta readers. But it's still good enough. And this is the thing like when is it good enough? Or when is it not? It's still able to give me feedback that is good enough to move it forward and make the product better. I hear that just just to go in a slightly different tack on that I know it with many of your several of your client companies where you've gone in. Maybe it's not the first stage, but I was really impressed and quite captivated actually, that you very much took the teacher person to fish rather than handling them a fish. So you weren't just showing Oh, here's how to use Gemini or chat to be here. Here's how to prompt better. You're doing a bit of that, of course, for the basics, right? But what blew me away was you're training them on how to think about seeing problems or difficulties or challenges how they could apply AI to help solve or improve on those situations or problems. Can you talk about that for a minute?

[01:30:03:12 - 01:33:12:06]
Yeah, so one of the things that I do is I do co building sessions. And so what this is, is where I will actually build a tool to solve a specific problem that that is a pain point for my client. So I'll say, Okay, let's let's discuss the problem and see can we build a tool to to fix this or to solve this or automate it. And then I share my screen and I start building it with them. And I get their feedback and we literally build it live. So at the end of you know, 60 or 90 minutes, they have a tool that they have that actually fixes that problem. Now I run these as usually six months where we meet up once or twice a month. And so we're building out lots and lots of systems. Now what I found is that those folks who are now on their fourth or fifth or sixth session with me, they're now saying actually, let me share my screen and let me do the work and you give me feedback on it. So they've learned by watching me and then taking one of built and experimented and they've gone away and now like one of one of my clients, Brad, I know that he doesn't mind me I mentioned his name. Brad is actually using a coding tool. And he's not a coder, but he's now using a coding tool to build these systems on his desktop and these multi agent systems. And like he, he has gone from being like an absolute basic AI user to being an advanced user through just working together and experimenting then. But I think this comes back to a really core thing. You have to have a mindset of curiosity and experimentation. And one of the reasons why that's so important is because it's there's like there's no rulebook out there. There's no manual. Now like people like me are trying to write books about this and show people like the basics. But how AI applies to your specific task, your specific job, your specific role, your specific business, and nobody else out there has done that yet. Like probably. So you are, if you're using AI and applying AI to your role, your task, your job, you're probably one of the first people in the world to do that, at least right now. And so like the opportunity is massive, but it also means there's not so many kind of guidance. There's not so much guidance out there. It's more of a sense of, okay, I know we can probably do this. Let's experiment and test and be curious and see if we can do that. Now what what I want to do is I want to raise literacy in AI at to the point where people feel confident in doing that and confident in experimenting. So that's kind of the aspect that I'm coming out of from. I think that and that goes back to like why I work that way is because I think that I don't have the knowledge, the domain knowledge, the subject matter knowledge to help create a system. But I can certainly teach them how to think about that and so that they can do it themselves. And then how to actually use the tools exactly. And yeah, to get those results. Absolutely. I love that though, that curiosity

[01:33:13:09 - 01:36:03:18]
meeting and doing an experiment. Doing an experiment is often something I repeat to so many clients in so many different arenas, because it takes so much of the pressure of getting things right off. It creates that idea of play, of having a hypothesis. How are we going to use this? And it doesn't have to be right. You can iterate and iterate or totally pivot, drop it, try a totally different experiment. And it means that as you said, people are actually learning the tool, as well as actually hopefully sooner or later getting a result that that's really, really useful. So we're running training workshops in person. I give people permission to leave the room. For example, you feel free to walk around, feel free to use your device, use your phone. One of the permissions that I like to give people is the permission to feel overwhelmed. And it's because, like, like for somebody learning about AI, I am overwhelmed all the time. There's just so much it's it's drinking from the firehose, as the Americans say, you know, it's it's, it's just this kind of deluge of information and updates and changes and, oh, look, there's a new button and chat, GBT that wasn't there yesterday. What does that do? You know, all of that kind of stuff. And that's why I think it's important to, to do experiment and press the new button when it comes along and see like, just have permission to be curious about things. And also have permission to feel, okay, this is a bit overwhelming. Let me just focus on getting better at the one thing that I'm working on right now. You know, very good, very good. So to kind of turn it back to AI and the psychological edge. You know, my thinking is very much that it's actually making us that bit more almost to use part of the word bipolar, that it's pushing us toward each pole, one where we can be much more ruthless with those repetitive tasks that we can use AI for to be incredibly productive amongst many of the other things that you're talking about, as well as all that problem solving creative, creative thinking. But for me, it's also that frees us off. I know this isn't a new concept, but it's being much clearer on where should I double down on as a human actor as a human with agency, where would actually serve me. And this is what I never quite understand is, yeah, of course, my clients get it to come to me and go, you know, I want to improve negotiation or be a better communicator because they can see that those gains compound its compound interest being able to communicate better with everyone in every conversation in your life, not just obviously the deals that you're making or those agreements that you're getting. But I'm just interested in your point of view as goes how those two things come together.

[01:36:06:15 - 01:36:25:14]
Yeah, so, okay, like this is a big topic. I think that I think that AI can help us to to improve our thinking. I think it can help us to improve our our creativity and our

[01:36:26:16 - 01:38:47:17]
communication, for example. But I think that can also damage all of those things. So absolutely. Yeah. Like, that's, I think that's the like, that's the crooks of it is like, it can be incredibly positive for these things, or it can be incredibly negative, depending on how you use it, which I think it's important that you that you learn how to use it. I think that this is also, by the way, a compounding skill, just like you mentioned, and having AI literacy and fluency is a compounding skill, because then you can use AI more and more. And as it gets better, and as new tools come out, you're going to be on top of them quicker. And that's going to give you this compounding impact as well. So that's, that's one aspect. Okay, ask me a specific question. Sorry, I'm losing my train of thought. Well, you know, we'll loop back to a specific question. I suppose that the principle I'm kind of working off is, you can outsource a lot of stuff. And that's outsourcing, whether it's a human VA, whether that's an AI, right, there are certain things that you don't have to do so much anymore, if you choose not to, right. But as the old phrase goes, you can't outsource your pushups. Right? Yeah, if you want to be stronger and healthier doing pull ups, push ups, you can't pay someone else to do them for you. Right. So it gets back to that question as to what are then the what are then the things that one can learn or do or get better at, like say, push ups or pull ups in the GM example, that we can't really outsource. And for me, being a better, more effective, and more rapport building, creative thinking communicator and negotiator is one of those key, I suppose you suppose it is a force multiplier. You know, if you were 5% better than the other person or 10% better than average, that those little gains compound over time, those little, you know, that person is that little bit happier to deal with you again, that little bit more loyal to you than the other person who's attracting them with a better price or whatever, that deal, that agreement you have with that person, that relationship is less transactional, more actual, based on an understanding than the other than people who are trying to to usurp you. And for me, that that's kind of the key thing. So just wondering about your opinion on that.

[01:38:49:09 - 01:39:18:23]
Yeah, I don't know. I don't know if I have an opinion directly on that, because, like, I think that this is I'll go back to what I said before is I do think this is a compounding skill. I think that humans are going to need to be in the loop. And that humans are are going to have certain things like in person meetings, I think are going to be become bigger in person

[01:39:20:07 - 01:40:19:04]
meetups, like group meetings, events, because it's got we're going to get to the point where you can send a virtual avatar to a meeting. And it will be indistinguishable from a real person. So we'll start to like we're already at the point where we can't be trust stuff that we see in terms of video and audio and photographs. So it has its own problems. That has a lot of problems. I'm not a lot of problems. Does that answer your question? It does. Yeah, with the more increase of AI generated content, the less valuable that content is and the more valuable the in person that somebody made an effort to see you in person shake your hand say hello, sit down with you becomes a whole different level of of gravity there. And it's something again, Perry Marshall pointed out that even just speaking on a Zoom call and answering questions on the fly means that you can actually give people insights. That is huge value compared to

[01:40:20:21 - 01:43:49:13]
you know, writing content on your website that says this stuff, but that could have been generated by AI in four seconds, as you say. So one of the things I was thinking about, you know, was that AI was already used in negotiation, you know, like the more county negotiators I know have done, you know, your chat TVT deep research where it's finding thousands of sources, hundreds of sources to push back with actual evidence that the price that they're demanding is too high compared to market value or whatever. I know for even several years now people have used, you know, the transcripts of conversations for word analysis for intent, like how collaborative are they versus, you know, by the choice of words and the certain psychological studies behind that that they use. And I think I don't know if it's there yet, but soon they'll at least highly likely to be examining tone and the influence of tone, like what's that mean, you know, as to what sort of is it particularly aggressive or the moments of need for collaboration or validation or all these sort of things that we can infer from people's tone to give an edge in the negotiation. But what concerns me is actually, you know, at the end of that, there's still a human negotiating with another human across the table, or a team of humans and another team of humans depending on the scale, which is great. But one of my clients from several years ago had this problem, they were doing deals with teams, you know, and there were hundreds and hundreds of millions, like I think one of their smallest was like 220 million euro or pounds or whatever. And they had this problem, they were saying that they built six months of rapport with the other side, really getting to grips with their needs and why they needed a thing and why they needed to do this and why they so they could give better solutions to their problems. And they were really like, this is going to be great, blah, blah, blah. Of course, like a lot of these companies, it was then given to a procurement people, or a closer was the other one where a new guy is suddenly parachuted in, who doesn't have any of this context. But the reason he was put, parachuted in was there's no rapport, so it can be absolutely ruthless. That was the thinking, right, to get this price down by x. And of course, that's where that guy was incentivized by. So his holding is I need to get this number down and that number up or whatever. Careful what you incentivize, right, for unintended consequences. And of course, the other side are like, well, if the price goes down, you're not getting the same thing, we have to remove something. So they went, well, we'll top out that and put that down by 50%. No, we'll be fine. Walks out like a rock star, right? And we get this massive bonus. Okay, except guess what? Nine times out of 10, those deals need to be renegotiated within six months. Why? Because the thing is no longer fit for purpose, because they hacked the original agreement, which was fit for purpose, they hacked it to pieces for a small gain. Now, imagine humans doing that. And then the corporation going, oh, well, we need to save money on this. And instead of a human being in that last stage, they get an AI in who's ruthless and has clear rules that they're not going to break. It's the same thing. I need you get this down by 4%, or half a percent or these numbers up by 12%, or whatever. It's the same thing. Why? Because humans are still making stupid decisions that aren't based on what they actually want, but what they think they want. So they're incentivizing the wrong thing. So I think that's not AI's fault, though, is it? You see what I mean? It's the human problem then transferred into the robot.

[01:43:51:02 - 01:45:50:20]
Yeah, and I think that if anybody wants to hear some fun discussion about procurement in particular, listen to David C. Baker and Blair Enns on their two Bob's podcast, they talk about that a bit. They do. They're experts as is. Yeah, a lot of your favorite guys are very anti procurement. Procurement, people aren't people. We just need to know how to work with them better. Yes, or not at all, as the case may be. Indeed. I mean, one of the other quick points that I'd make on that is really just that barbell strategy, you know, going extreme, don't stay in the middle, be far end to one end of the scale and far end the other. What's can be done by AI, push it to AI, what can be done by humans better push it to the human end. I mean, years ago, I spoke to a lawyer friend of mine, who's doing exceptionally well. And I said to him many years ago, when he was kind of starting out, I said, look, the robots are coming. And this is, you know, 1520 years ago, I said, the robots are going, I said, but in by that, I mean, right now, what can you do that's harder and harder, further and further from a robot or an AI replacing you. And it was this idea now that he brings to heart, where he says to a client, okay, if you want, like, you want to be done with this as fast as possible, this is your option. This is your path to go down. And we can do that. If you want to be the guy who's somewhere in the middle, doesn't want to be, you know, leaving too soon, but doesn't want this to drag on for ages. There's another path. And then there's this other path where if you want to absolutely hurt them, and you will go to the ends of the earth to do this to get your justice, as you see it, then there's this path, which version of you do you want to be so it's again getting into that psychology of how you want to turn up and understanding that that's the client, the psychology of the client is key here, the emotional lead, because we're all of those people, you know, and yeah, that's really interesting. The extension of that is why he one of the reasons why is so successful.

[01:45:52:03 - 01:45:55:11]
Yeah, I love the fact that he's giving them the choice there.

[01:45:56:12 - 01:46:09:19]
Because I think that some lawyers would not that they would just take their personality into it and say, this is the way I operate. Exactly. On that note, if people want to learn more about you, want to learn more about your services,

[01:46:11:00 - 01:47:01:03]
go to human spark.ai. And I'm also very active on LinkedIn. So I love to talk with you on LinkedIn. Yeah, reach out on there the human spark and Alistair McDermott that's a la ST AR rather than a li ST like I suppose. Yes, reach out to my LinkedIn. Very active, some great blog posts. They're really interesting reading. Continually nearly on a daily basis. I was about to say, I don't know how you're so productive, but I do. Yep. But it is valuable content. It is very, very valuable and insightful stuff. And all right, any last closing comments or thoughts? I'm sure. Just stay curious and experiment. I think that's the most important thing. What a brilliant, what a brilliant idea. Stay curious and experiment couldn't agree more.