The Mastering Portrait Photography Podcast

I recorded this episode in my second favourite place on earth - the local pub - with a pint in hand and a genuinely fascinating guest across the table. David Finch spent most of his career in marketing and creative agencies, most recently selling Purple Frog, a marketing consultancy. He now runs Thinking In Fields, which focuses on decision architectures - helping businesses bring AI into their operations in a coherent, orchestrated way rather than a scatter-gun approach.

David introduced me to two ideas I hadn't heard before, and I was quietly furious about that. The first is the Book of Remarkability - a framework for understanding what makes your business distinctive. The spine of the book is your emotional hook, the cover unpacks it, and the bulk of the content is 24 short stories from clients describing the value you created for them, in their words. Only six pages are yours to explain how you do it. It's based on the Canterbury Tales, and it's a genius analogy.

The second is the swimming pool with five lanes - customers in lane one, your people in lane five, your processes in lane three. AI sits in lane two (improving the customer journey) and lane four (improving internal efficiency). Most businesses focus on lane four and quietly destroy the customer experience. Some nail lane two but then drown their team with demand they can't fulfil. The whole point is keeping the business moving coherently through all five lanes at once.

We talked about AI as a team member rather than a tool - the most intelligent five-year-old you've ever met! Knows everything, but doesn't know what to do with it. David's view is you want to train it to be a teenager and never let it become an adult, because a teenager still has a bit of creative spark and a spiky opinion. Train it to full adulthood and it homogenises everything - it's a probability engine, and probability gives you 80% of the answer, perfectly averaged.

The conversation moved into what AI genuinely can't do - and that comes down to human experience. It has never had a first kiss, lost someone it loved, or stood in a field at a beer festival. Any task that depends on that kind of felt, embodied knowledge is still ours. The challenge is that a lot of what we thought required that - writing, design, commercial photography, music composition - turns out not to need it as much as we assumed.

For photographers specifically, David's view is that weddings and portraits are relatively safe for now, because the human interface is the whole point. Commercial photography is more exposed - brands are bringing production in-house, and that overcapacity will push talented commercial photographers into our market. The answer isn't to chase efficiency. It's to charge for value, not hours. Ask clients what they genuinely value, attach a price to that, and let the production tasks flow through the tools that do them best.

David ended with a metaphor I loved - the future of creative business isn't a pyramid or even an obelisk. It's going to be full of jazz bands. Highly talented people jamming together, creating something that no algorithm could have predicted. I hope he's right.

If you have enjoyed the episode, please do subscribe wherever it is that you get your podcasts!

 

Transcript

Paul: [00:00:00] Let's hit record both channels. Recording. At which point I put my podcast voice on. You've gotta forgive me. Alright.

David: Okay.

Paul: Uh, alright, well cheers David.

David: Cheers.

Paul: Um, well today instead of being in the Land Rover, I thought I'd come and frequent my second favorite place on earth. Uh, the local pub. So I'm sitting here with a really interesting guy I've known for years, um, who will introduce himself in a moment. But if you can hear background noises, that's because there ARE background noises.

David and I are sitting in a corner with two pints of beer , and we are gonna chat all things to do with the direction of travel in creative industries. I'm Paul and this is a very pub bound Mastering Portrait Photography Podcast.

Uh, So David, thank you [00:01:00] for agreeing to come in and chatting with me. Um, this has been a conversation a long time in the making ever since the thought occurred to me.

I think we crashed into each other at the end of a wedding. I think, I mean, I've known you for a long time, but I think it occurred to me at a wedding where you were in the back garden of the pub and it's like, yeah, you are the guy I want to talk to. So, before we get any further, could you to the people who don't know you, introduce yourself and give a little bit of background?

David: Sure. Yeah. I'm So David Finch, my current, uh, incarnation in business is Thinking In Fields, which is, um, all about decision architecture. So. Having spent most of my life in the, in the marketing and creative sector,

they recently sold Purple Frog. Yeah. Which was a, a marketing consultancy and prior to that, owned various other sales promotion companies and [00:02:00] quite, seems quite a long time ago now, but 20 years ago, a print group of all things. Yeah, I remember. Yeah. That was, uh, quite frightening. So, so yeah, so I, I sold that and I've sort of, I can't retire so the brain won't stop.

So I've moved into "Thinking In Fields", which came, came about based initially on the fact that, as you well know, I walk a lot now.

Paul: Yep.

David: Um. I do a lot of thinking and talk to my phone on frequent basis to, to, to write things or di dictate things, which I then, which I then, I used to write from the actual listening to it.

Now I give it to this strange thing called chat, GPT, and it, it transcribes it for me and, and it helps me assimilate thoughts. So, so that's what I do. But it is about decision architecture. So it's about actually helping, helping businesses understand how they, how they bring things like AI in, into a business.[00:03:00]

And if you think about intelligence, humans are got intelligence, AI's got intelligence. So how do you bring them together coherently,

Paul: right?

David: So that's what the, that's what the business does and helps, helps CFOs and leadership teams understand how they bring in ai.

In an orchestrated manner.

Paul: Right. And was, is that when you set up Thinking And Fields, did you think it was going to be about ai or did, because you, you've stepped out of the agency world for a while now, and AI really has only erupted properly in the past 18 months. I'm gonna guess, you know, the latest ChatGPT and Claude and some of these big LLMs and processing models slightly more complex. I always see the word thinking, but they're not thinking. But

David: they don't, they don't think No, they'll tend,

Paul: they don't

David: think, they don't think,

yeah.

No.

Paul: But you can force 'em into a pseudo analytical mode.

David: Yeah.

Paul: Um, prompting, but was it AI that drew you to create Thinking In Fields or [00:04:00] has that come about since going into Thinking In Fields?

David: Since, since So when I, so when, so when I started Thinking In Fields, it was, it was related quite a lot to. To what Purple Frog did.

Paul: Yeah.

David: It wasn't exactly the same. It was associated to it. So it was, it was about remarkability and how'd you create a decent value proposition and a value statement, which interestingly is, is focused on aligning your customer value proposition with your employee value proposition.

Paul: Right.

David: So that's what I've been working on with, with, with some clients. But what I found was that in order to be able to, for them to be able to own, own, the thinking themselves is either they can spend a lot of time with me and I'll board them to tears, or what we did is we've created, I've got the mortar creator, a chat, GPT, and a project where we stored all the information we were talking about, which enabled them and I, and I'm sure I must have mentioned this before, the Booker[00:05:00] Remarkability, have I ever mentioned the Booker Remarkability to you?

You shake your head with disappointment.

Paul: So, sorry,

I should be more open to the things you mentioned to me. I, yeah. I don't think you've ever meant it to me.

David: No, it's, so, the book of Remarkability is, it's really, it's really a concept of how you dis distinguish your yourself from, from other people. So it's really interesting in the photography field is how, what, what is you looking at, what value you bring to those you serve and, and what value you bring.

Is, is, is really by asking them how you make them feel.

Paul: Yeah.

David: What the, what emotional resonances you have with people. But the idea of the book of Remarkability very simply is that you create a spine which is compelling, which is emotion based.

Yeah.

And imagine people walk into the British library

Paul: Yeah.

David: And they're gonna find a photographer wedding, or for photographer for portraits or commercial photographer, they're gonna go to the section and there's 200 books.

Paul: Yeah. [00:06:00]

David: You're gonna be one of three. They pick out.

Paul: Yeah.

David: You better have a good spine. Yeah. Yeah. So that's the concept of the spine.

Paul: Yeah.

David: You unpack that with the cover.

Paul: Yeah.

David: And it's compelling enough to get people to want to read further. And then your standard Harvard Business Review, um, value proposition is your prologue. So we do this for these people by doing this. Right. That's your, your prologue, so it gets people in.

Paul: Yeah.

David: But the real answer of the book of Remarkability is it's,

Paul: how have I never heard of this?

David: Yeah, there you go. You don't remember these years. But the art of the book of Remarkability is, it's based on the concept of the Canterbury Tales. So, so there's 30 short stories. 24,

Paul: yeah.

David: Are your customers and clients speaking about speaking about the value you created?

Paul: Yeah.

David: In their eyes. Well, the value you think you created.

Paul: Yeah.

David: The value you truly [00:07:00] created as they describe it.

Paul: Right.

David: Then you get six pages to do or six short stories to describe how you do it. So, so basically the whole construct is that you are, the value proposition is based around what people are talking about.

Paul: Right.

David: And that's what makes you remarkable.

Paul: Right.

David: Just like you are, Paul,

Paul: I dunno about remarkable, slightly surprised have, have I not heard of that? It's a genius analogy. It's really clever.

Yeah.

Because this wasn't meant to be that podcast. It's not meant to be me discovering new stuff. It's meant to be me digging into a particular topic. However, I'm loving that.

Um, but the, I mean, I've dunno where you've kind of thrown me sideways a little bit and that wasn't what I was expecting, but, so this style of thinking, that's what Thinking In Fields was due to be. Yes. No.

David: Yes. So, so, so it was about, so it, it was taking. Small business owners through a process [00:08:00] that they could own the thinking themselves.

Yeah. So imagine, you know, bigger businesses go out. They, they employ an agency, creative agency, they give them a brand, they tell them the brand voice. Sadly, it never really gets properly used. 'cause master marketing team might understand it. The sales team don't, the production team certainly don't.

Paul: Yeah.

David: And there's that disconnect in the business.

Paul: Yeah.

David: So it was this disconnect in the business that then got me thinking, well how do you, how'd you get this to work properly? Right. So that's where I brought, brought in using AI and chat GPT to create. Um, and I hate to say it 'cause if any of my clients listening, they'll go, no, surely not, but a mini me.

Paul: Right. Okay. Excellent. That's just what you need in your computer is David Finch.

David: Yes. Tapping

Paul: on the screen.

David: So basically what it did that is, is it, it unpacks much more detail. The, like in a project where you give it all instructions and files, it has all that information, plus all the conversations we've had.

When I've been helping 'em go through it, plus all the calls that I would make to their clients to [00:09:00] under, to get the, the understanding of the value they're creating.

Paul: Yeah. '

David: cause it's easier if I do the calls because they never want to tell you yourself, if that makes sense. Yeah,

Paul: yeah, yeah.

David: But then actually work with them so they build their own voice, their own brand, their own value proposition without it being done by an agency.

So that, and the value of that is, it's what I call ownership. So it's, it becomes owned in the business by the people in the business, and that's just not imposed on them. Okay. So the thinking in fields morphed from that into, um, decision architecture because one of the things that I, I found with, with lots of businesses is it doesn't matter whether they're large or small in your, in honesty, they don't.

A lot of people are playing with ai. A lot of people are doing a lot of things. Do you mind if I share another.

Paul: Okay. No, if, if you're gonna blow my mind, you might as well just do it over and over .

David: So I would like you to [00:10:00] imagine a swimming pool with five lanes.

So in lane one, are your customers, your clients?

Paul: Yeah.

David: In lane five are your people, if you're just a photographer on your own, it's just you. But if you've got assistants and you've got production teams and whatever, sure. People are in lane five. In lane three are the processes, the systems and processes you use.

So how you go about delivering perfect wedding shots. Yeah. The, yeah, the Photoshop, the whatever, all the stuff that you've got and the process you go through to deliver to a client. When I, with AI coming in, what you've got is the, the challenge of Lane four being AI you put in to make your business more efficient.

And Lane two is the AI you put in to improve the customer journey or continue to add extra value to the customer,

Paul: right?

David: Yeah.

Paul: Yeah.

David: Now what most businesses do is they focus a lot on lane four [00:11:00] and destroy the customer journey.

And then some have very successfully focused on lane two, and this is where it gets a bit bit dark, sorry.

But That's right. They then drown the people in lane five because they haven't got, they've got too much work coming in and not enough ability to in capacity to deliver it.

The concept of that is, is basically keeping the business in motion.

So it's been a coherent business as you go through.

And then the decision architecture is how do you build the decision processes in order to be able to make sure you put AI in, in a coherent manner. .

And not scatter gun approach.

Paul: That I completely appreciate. Um, never heard it called a swimming pool before, but I totally understand.

David: Yeah.

Paul: Where we are.

What's curious is, well, it's curious. Obviously I'm in the creative industry, so for me, there are two distinct tranches of excitement, sadly for me in ai. One is the fact that it can do, um, [00:12:00] knowledge based tasks really pretty well if you're good with prompts and putting in background information. And the other is, it's actually pretty good at what I would've called day-to-day creative if you know prompts and can get it, get the data into it.

So we have a, an, we are in an interesting space right now, which is broadly speaking my industry. And I'm gonna guess by extension, agency side, designers, songwriters, lyricists, you know, all these people of. Built their skillset on being able to do something that up until now was unique and a unique ability to the people with that mindset has now suddenly become something that if you're good at prompting, which is a very technical thing, actually, you can get AI to do remarkably well provided, and there's a, there's a proviso to it provided it's not dependent on the human experience.

And by that I simply mean [00:13:00] AI cannot have ever felt anything. It, it, it's, it's just simply not possible. It didn't have children. It hasn't lost a loved one. It hasn't had the excitement of standing on a field at the very first Haddenham beer festival. You know, these are not things that AI could have done.

So provided the tasks you are asking are not prefaced on human emotion. AI is not only remarkably good at it in a lot of instances, it's a damsight better than humans. Which leaves us with this, what do we do conundrum. And what I originally wanted this conversation to be, these, I mean, the two things you told me already, I could wrap the podcast up now and go make a note of these and I will see you in a, in six months.

Thanks for the free consulting. but where, what I wanted to explore, um, and I had no idea actually I didn't know about all of this stuff in detail. I knew you'd said in passing that you were looking at these things, which is heads of the podcast is I get where we are here and now I [00:14:00] understand, you know, I love chat.

DPT, chat d we put stuff in. It says, oh my God, oh my God, Paul, you are amazing. That wasn't just, that wasn't just a workshop he ran. That was an experience. 'cause ache, BT is like that. And I can put the same thing into Claude and Claude simply goes, p he just, ah, there so many things you could do better. You know, it's like these two worlds love them both, by the way.

Um, I use them both all the time. Um. But we are basically dealing with a toddler of an industry, a toddler of a technology. You know, my PhD's in it 30 years ago, we were essentially looking at, I guess, the embryos and 30 years before that, they were looking at the seeds of it. Yeah. Um, now we are with this tiny child and this is precocious, so look how good it's, it's gonna grow up and it's gonna grow up fast.

How do you see that progressing? Where do you see the whole of these economies, all of these economies that have built[00:15:00] around, um, either creativity and or knowledge? How do you see them progressing?

David: I think it's, I think it's a really, gosh, if I had to had that crystal ball, I'd be,

Paul: well, yeah.

David: Welling with this now, wouldn't I?

But I think the, I think there's loads of, there's loads of parallels in, in the past. There's a difference with ai, it's just going at quantum speed.

Paul: Yeah.

David: Compared to everything else we've ever come across. So the change is happening really, really quick.

Paul: Yeah.

David: The, the challenge I think is, is that people treat it, we always talk about AI as a tool or most people talk about AI as a tool.

When did you come across a plumber whose spanner could think a little bit when you do Yeah. Could reason with you. The builder's drill could actually do that. They're tools, they're inanimate objects that we use in order to improve something[00:16:00] we're doing. This isn't a blunt edge tool. This is something which can almost reason with you.

And actually, you know, and I love the way you described the, the difference between chat g, Tim, and Claude. And you can get 'em to argue with each other perfectly. Yeah, I do. 'cause spend the whole day just having the leg, the, and I actually do it. Yeah. So, but, but to me this is about people sitting and thinking.

And you also use the, the thing about it being a toddler. So I always talk about don't think if it is a tool, this is a team member. Doesn't matter how big your business is, how small your business is, you've now got a team member and it comes without the national insurance surcharge. So it's probably quite cool.

Um, but you've got a team member and what you're gonna do with the team member is, and I always say treat it like a 5-year-old, like the most intelligent 5-year-old you've ever, ever come across. It knows everything.

Paul: Yeah. [00:17:00]

David: But it knows nothing.

Paul: Yeah.

David: So a 5-year-old child is really inquisitive, curious, has lots of knowledge, but doesn't know what to do with it.

'cause you think from a little baby how much information that that child's stored in that five years, that's enormous. But it's only got what it knows and fun enough so. Great. A large language model only got what it knows what the art is, is to train your team member to be a teenager and never to let it become an adult.

Paul: Interesting.

David: And the reason to say that is a teenager is a little bit narky, knows lots, has a bit of a spiky opinion of itself. But it's actually, it was, like you said, it's always really trying to deep down please you.

Paul: Yeah,

David: yeah, yeah, yeah. Because that's the way that's, that's what they're, it just, yep. But [00:18:00] what you've created as an advantage of a teenager is the way you create is you leave a bit of creativity in there and, 'cause we're gonna talk about creative industry that Yeah, that's, I mean, creativity is the creativity and the way it responds to you.

Because if you trade it to be an adult, it will, it will. Just to my it. It's, it's a large language model. It, it works on probability, therefore everything's gonna be homogenized.

Paul: Yeah.

David: So it will do everything for 80% of the stuff perfectly and all the stuff around the edges. And this is the bit where you talk about humans and interaction with humans and this customer journey bit, you know, the customer, the customer wants something which is out the ordinary.

If it becomes an adult, it won't respond in the, in the same way that human built beings write. Um, we write processes like the one that always bugs me, so I'll share it. It's tenders. So a tender is written in order to take human emotion out of a buying decision. By a very [00:19:00] nature of taking human emotion outta a buying decision, it actually means you rarely get a good decision.

You always get the same sort of decision, but it's rarely a good decision. Yeah. Yeah. It's predictably 80% of the problem solved.

Paul: Yeah.

David: Probably buy a company that's just ticked a couple of boxes that nobody else could manage to do.

Paul: Yeah, yeah. Yeah.

David: So I

Paul: remember my business.

David: So that's a process

Paul: criteria, matrices.

David: Yeah. Yeah. So that's a process and what AI does is a fantastic process.

Paul: Yeah.

David: But what it, what it's really good at, if you really set it up properly, is, is yanking in human judgment when it requires it. And it's really good at it. And this is the bit where it learns. So if you prompted it correctly, and I love the way you described prompting and, and being able to be really good prompt, you know, once you've created something like a project in and, and whatever, you just ask it to create.

If you wanna build a chat, a GPT, you do a project, put the learners in there and they say, build me A GPT and it'll build it for[00:20:00] you. Yep. Yeah. It's, yeah,

Paul: I've done them.

David: Yeah. It is brilliant.

Paul: Yeah. Yeah.

David: And, but equally then you can also set up the counter so you can, you can always set up a project to be.

Almost your, I will give you the counterpoint on everything. So like, it always wants to agree. You can make it, always disagree with it. Yes

you

Paul: can.

David: Yeah. So

Paul: the think

David: can, there's so many things bt yeah, there's so many things you can do. But if you, if people think about, right, I've got a, I've got an assistant who knows more than I do knowledge base wise.

So don't try and compete on knowledge. Try and learn, try and remember some of the knowledge because it's like a human being in that respect is you'll have told it loads

and then it forgets.

Paul: Yeah.

David: So you have to, you have to remind it.

Paul: Yeah. Yeah. You do

David: in the, in this thread, blah, blah, blah we talked about. And then it will check back and it will start reframing it. But equally it can forget the information. [00:21:00] But that's no different than an employee. Just 'cause you told me what to do six months ago, Paul doesn't mean I remember to do it today.

Yeah. But

Paul: an employee doesn't normally forget it. 10 paragraphs later.

David: No, correct. But, but it, but it's really interesting 'cause it's store, the way it's now store memory is, is completely different. So it's, it, it starts to build. So in your, like yours will call you Paul mine calls me David. Mine knows all about thinking in fields.

It gets certain things wrong, but it, but even if I start a, a thread, which has now got nothing to do with my business, it'll quite often go down the route of, and you are Thinking In Fields voice and I have to go. No, this is just something I'm just researching privately.

Paul: Yeah, yeah, yeah. I have, I have scratch projects where it says clearly at the top, this is for scratching only.

Do not include this in any of the normal, um, projects because otherwise my sketch ideas start to appear in things where I don't need that sort of analysis. And also it's not great to have that stuff appearing.

David: [00:22:00] Yes,

Paul: agreed. You know, I'm trying things out or trying different project ideas and

, What's curious about air? So when I studied ai, I was looking at essentially the structure of the brain. I mean, that's the stuff we were looking at. I was looking at neural networks, how we create topologies in a data space so that you can sort of add with a limited number of nodes, data points, understand a vast amount of data, and condense it down into something where you could usually interpolate an answer with a degree of risk back then, extrapolate an answer.

And that's still true today, by the way. It's just that the data space is so vast that the extrapolations are now less significant than they were when we were doing it. Um, but there is a curiosity in the interacting with these um, models. Certainly, certainly chat, TPT and OpenAI, I think made a business decision to do this.

In the end had to wind it back a bit. The sycophant, you know, the sycophan [00:23:00] model. Was it 4.1? Was it? Um, but actually, you know, when you, when you start to interact with, certainly with Chatt PT, less so with Anthropics models, uh, Gemini, somewhere in between is they are addictive. You, you know, I mean, there's kind of a side thread really, but you have to remember, well, I think you have to remember when using all of these, the current generation of AI models, is that the people behind them are still generating the business.

They're still building the business. They are squillions of dollars in debt. It's all investors' money and they're trying to you, it is the old, um, you don't need to outrun the, you don't have to outrun the bandits. You just have to outrun your friend. 'cause the bandits will catch him. And that's where they're just running as fast as they can last man standing wins.

But you know, chatt PT for instance, you get to the end of any task and it will. Would you like me to continue? I could develop that. I can turn that into a PDF. I can turn that into a downloadable thing. Would you like me to create a report for you? Can we do develop this idea further? [00:24:00] You know, chat, TPT never stops.

It's literally a fruit machine

David: that's have the last word.

Paul: It's, it is literally a fruit machine of knowledge. You know, every time you think you close to a win, it'll spin them up and you'll put another pound in.

Um, what's your view on that sort of emotional response to them? And I'm not gonna go a long way down this road, it was just the curiosity. Um, particularly creative people.

David: Yeah. I think, I think the interesting thing is if you become, if you become very good at, and so I'm not even use the word prompting.

If you become very good at talking to it, it's very good at talking back to you.

Paul: Yeah.

David: So what it does, and I can be a bit like you, I, I can go down rabbit holes with it just because I starting the conversation and a bit like we could sit here and chat and chat. I'm just doing the same thing. It's it, and what you do is.

And I think what's interesting is even when you then think, well, I've had enough, and you close the laptop and you go[00:25:00] on whatever, you know, have some dinner or go for a pint without your computer, and yes, I have got it as an app on my phone as well. So that's a bit of a nightmare. Yeah. But it's, you're still thinking about the conversation you had with it.

Paul: Yeah.

David: And that's, oh yeah. So that's the interesting thing. So what I, so what I think is really good at, if it's used properly, is prompting, almost prompting human thought. And then if you, so let's, let's think about this one. So a thought is a moment in time. We all, we have lots of thoughts all the time.

Yeah. Every day.

Paul: Yeah.

David: So thought is a moment in time.

Paul: Yeah.

David: When you think, you unpack the thought. And what we do with most of our thoughts is we bin them.

Paul: Yeah.

David: Partly because we can't make head of tail of the thinking. It becomes too complex, whatever. What. These models do is they, if you, if you're using them, they've started to help you [00:26:00] unpack that thought.

You might still bin it at the end, but then it generally ends up creating another thought. And that's what I find is so, even though I went down this route with it, and I often use use, I do use words, and I think when I think about it now is I, I will type something in or I'll dictate something to it and I have, I've given an opinion.

Um, and then I go, so what's your thoughts on it?

Paul: Yeah, I do the same. Yeah.

David: And, and it's really interesting if you ask it what its thoughts are, it gives a different answer than if you ask it what it thinks. 'cause it doesn't think it'll tell you. It can't think as in what we call thinking. Yeah, it analyzes.

Paul: Yeah.

David: It, it's, it's, yeah. But that's why it's really good, I think, because the way, I mean, you're technically more gifted at this than me, but the way. It responds to a thought to when you ask it. 'cause it says, doesn't it? A little flash goes up thinking.

Paul: Yeah.

David: And it's not really, it's [00:27:00] analyzing in a, in a different way.

Paul: Yeah. But here's, here's what's really interesting to me. And this, I mean, I say we're a little bit off topic, but it's fascinating is that I, for me personally, right? I'm not a psychologist, I'm not a psychiatrist. So self-analysis is not a wise decision. But I think I, broadly speaking, I have two modes of processing.

Um, linguistics, I mean, quite literally. You've talked about talking, you know, in your head you, I think in English. 'cause I'm a first, my, my first, well my only language, I spoke a little Welsh. I've ruined a little French, uh, but I speak English. So when I'm thinking, I'm thinking in language, when you actually think about it, you think using words, it's a linguistic process.

Correct. And then I feel. Okay. In a different way, I feel through body chemistry, I feel through, you know, uh, adrenaline for instance. It did, you know, a very funny thing for me, [00:28:00] it took me a while to figure it out, is I know when I'm shooting. Well, uh, some people call it getting into the flow, don't they?

Flow state is, I know I'm shooting, or at least I'm, I'm in that zone where my nose its, and it turns out it's an adrenaline response and my body's reacting to how I'm feeling. It's not a language based thing. No language is involved. It's all chemical. And that's to do with my experiences, the things I've processed in my life.

The excitement of seeing a sunrise, excitement of taking for me a portrait. 'cause that's my particular thing. Um, and what I find curious is how far we can get with emotional processing using language and language alone. And actually you can get a damned long way if you have enough life's experiences to be able to articulate.

How you are feeling. And that's basically the job of a counselor, right? How do you feel? How does that make you feel? Have you, what strategies do you have for processing this? And that's all linguistics. So the curiosity for me is, at the moment, AI models are [00:29:00] not sentient. They have no sense of being.

They can't, but the vast amount of linguistic acrobatics that they're capable of doing can make it feel to us that way. It's, it's a,

David: because it, because it's using that language. It's That's right. Yeah. So what it hasn't got, so this is, this is the, this is humans in reality. We have two brains.

Did you know that we have two brains?

Paul: Well, I know there's one in the stomach, isn't it?

David: That's right. So, and gut feeling

Paul: That's right.

David: Yeah. So it doesn't have that either. And this is the point we're gonna make. So we, we call that intuition or sixth sense, or whatever it is.

It's something we have, which is contextualized to where, where we are. So if, you know, sometimes if you are, if you are in a, in a room and you're talking about something, and suddenly you might get butterflies about bomb bit and, and there's no reason why you've got butterflies other than something reacted to [00:30:00] in your stomach churns, or, or, yeah.

And it hasn't got that. I suppose the only, I don't suppose it'll ever have the. Now who knows whether they'll have the gut reaction or not. Eventually. The interesting thing about the senses as in sight, smell, touch here, it's getting those, because robots will be linked into the same environment, so a robot can touch, they'll have robots that do recipes and it'll smell things.

So those things are coming.

Paul: Yeah.

David: So we start having those senses and start to understand those. But it's never, unless it starts eating food and I the need to have an enlarged and small intestine, I'm not sure it's gonna get a gut feeling.

Paul: Yeah. But the thing is, a gut feeling is it is not actually about the brain, it's about the experiences you've had.

David: Yes.

Paul: Which, so that on the one side of it, you can argue, I can argue that these systems as they are, you know, it's what I[00:31:00] said when we walked into the pub, you know, they can never, they've never had the birth of their first child, never had their first kiss, their first loss. You know, these are not things that it's even possible for them to do.

David: So this is an interesting thing. So if you think where it started and it was out reading whatever it was pointed out to read on the, on the worldwide web and other information that it was fed. Now we're all talking to it.

Paul: Yeah.

David: So we're all telling it our experiences. It, it's already got lots of people using it to understand medical Yeah.

Information. Mm-hmm. Yeah. So , the GP down the road would always tell you, don't use Dr. Google.

Paul: Yeah.

David: Well, Dr. Google was one thing. You had to go searching for information then you weren't quite f Sure.

Whether you found the right thing.

Paul: Yeah.

David: Chat, GPT or Claude give you and Gemini. Wow. The whole. Chapter in verse.

Paul: Yeah.

David: And they give you the latest thinking [00:32:00] about what it is. And I always say that, that it is not, you need to go and see a doctor, but we've suddenly got knowledge that we didn't have before.

Paul: Yes.

David: Now sometimes that's a good thing. 'cause too much knowledge is great and other times too much knowledge is a bad thing, isn't it? So a little bit of knowledge is good and too much knowledge and things you haven't got a clue about. Mm. Yeah. Bit, not bit like me trying to do DIY that's another story. I, I know how to hold these bits and do whatever, but it never comes out good.

Paul: No, I, well, yeah, I'm, I'm good at DIY but sadly not great at DIY, which leaves rough edges. Yeah. You know, I'm a rustic DI yer. Um, I mean, I'm fascinated by in, and the parallel I've got, and I'm, I'm always a bit cautious of it, um, in the sense that I don't know whether I've ever met a psychopath or sociopath. I have no idea. And the reason I have no idea is I, I don't work in those fields. But also people, when I've researched [00:33:00] those particular personality traits is people who don't naturally feel emotional, read emotion, learn how to replicate it, and we wouldn't know any different.

Correct.

And that's how I sort of envisage the way AI will progress is it will think we're getting emotion from them, but obviously

David: Yeah.

Well no different than an nectar.

Paul: Yes. Yeah.

David: A play part.

Paul: Yeah.

David: And not necessary. That same character in real life had you, how many up there all action packed characters have been that when you meet them in real life?

Paul: I've not,

David: I've not that

Paul: many. I've not met that many in

David: real life. No. But shy person who sits in a corner.

Paul: Well, yeah, I've, I've met a few like that. I've one actor come to the studio to do head shots. It was great. And he walked in, he said, I'm terrified. I have no idea what to do. And I said, we'll have a cup of tea and we'll chat. And I picked up the camera and he said, what do you want me to do? And I'm like, I've, your agent sent me because they wanted you to be natural.

And he said, I dunno how to [00:34:00] do that. So I gave him another cup of tea and we chatted and eventually course he relaxed and it was fine. Um, it was a lot of fun, but it was, I think I was more terrified than him in the end because like, I'm not a director, that's not my skillset. My skillset is to make someone relaxed.

Um, that was a slight aside, I guess in the sense that, I mean, certainly at this stage, you know, I mean, you know, one thing I love to do, for instance, I love writing for some of the, um, industry press. Um, that will be a moot point I think in a couple of years because the sheer amount of knowledge and ability to articulate that these models will have begs the question for what role will there be for people who write or people who.

Great photographs. I mean, I think there will be certain things that will be profoundly important and won't change. Someone who can cook probably is gonna stay absolutely where it is because, um, there's something very physical about that. Um, [00:35:00] someone who can play music live. So, uh, one of my team is a big fan of going to concerts.

So her view has been that AI will never be able to write music. And my view is do you know who wrote the music of the bands you've loved to see? And she can't answer that, in which case AI can write just as much music and will do very well at it. But what we like to see as humans, as we like to see a band perform.

David: Yep.

Paul: Um, and so my daughter went to see the Abba Voyage Voyage, whatever it is, and she was actually, and this is real disappointed that ABBA didn't show up, that's another story. Um, and she said it just wasn't real. It wasn't, didn't feel real. It was, I mean, it was a big. And so I think anywhere where there's a, a human interface, I think AI will be a supporting act.

It'll be an enabler, it'll make things easier, it'll give us tools that we didn't have before, as long as that interface is important.[00:36:00] But if the interface isn't important, I'm curious as to where you think that's gonna go.

David: I think that's, um,

I think it comes back to the fact that we, we interact as human beings with each other and whatever walk of life we're in, it's, it's human to human. And we've built things throughout the history of mankind that sit between us to make, in many respects that interaction easier. You know, telephone, we just uses that as, as a very simple example.

We couldn't talk to each other when we were miles away. Now we can

Paul: Yeah.

David: But it, it's still a, um. An object. You know, industrialization changed the way that, that people work. And you know, people didn't go to a factory or an office 200 years ago.

Paul: Yeah.

David: I mean, they'd be they people 200 years. They'd be going, what a strange way to live your life.

Paul: Yeah.

David: And I think all we're seeing here is a change that's coming. And this is the point. It's more condensed. So there's gonna be people [00:37:00] who in their lifetime, see, and we've already sort of done it with social media and we've seen it with, with the.com boom and so forth. We've seen things change far quicker than we expected.

But even then, that was five, maybe 10 years.

Paul: Yeah.

David: So your human brain had time to sort of adapt to, oh, this is how it is today. You know, you, you, you look at my, my mother-in-law has her mobile phone. I don't know if this is an ar but it is, the thing is, she has her mobile phone and then so that when she goes walking and she's 86, so when she goes walking, if she has an accident, she can, she can give a call.

It's got an alarm button on it. Yeah. And it ring God knows how many phones in the family. Yeah.

Paul: Hopefully with a GPS.

David: Yeah. The problem is, is she forgets to switch it on.

Paul: Oh yeah.

David: Oh yeah. But I don't like to switch it on 'cause I'm not sure what to do with it. And, and so the, the point I'm making with [00:38:00] this is that's because these technologies take, and the older you get, the, the harder it is on average to want to absorb new technologies.

The interesting thing with chat GPT is, and so this is a statistic from last April, but uh, it was doing 1.8 billion searches a day compared to. Google doing God knows how many billion.

Paul: Yeah.

David: But it was still a small percentage.

Paul: Yeah.

David: But the majority of the demographic using chat GPT for searches was, uh, baby boomers.

Paul: Was it?

David: Yeah. So actually the old, what's coming gonna be the, the generation of the older people are actually more attuned to technology

Paul: Right. Than,

David: than they have been in the past.

Paul: Yeah.

David: But again, we're still gonna go and I'm one of those baby boomers basically gonna go through [00:39:00] that point of, wow, how fast is this going?

To the point of Yep. But what you've now got as 20 year olds coming into the, hopefully coming into the workplace, 'cause that's another interesting angle to this, but 20 year olds coming in who just, they're, they're going to see the world adapt. And when they're young, really quickly, but they'll be able to absorb it.

Whereas are we, even though I think I can take a lot of this and absorb it, at what point does it suddenly pass me by? If that makes sense.

Paul: Yeah.

David: Because I, whereas my, and the reason I was using my mother-in-law, she said 20 years to think about using a mobile phone, she still doesn't particularly, and I only have three years to work out what's coming next.

Paul: But I mean, what's interesting with ai, I mean, I remember when the researchers turned into mobile phones. I remember, I remember thinking, I can't remember all of these numbers anymore. And I used to remember them all.

David: Yeah.

Paul: And there's a, I think it's called, I [00:40:00] think it's called Transactional Memory, I think, I'm hoping I've got that phrase right, which is essentially, as you offload onto devices, your brain is filled with other stuff.

David: Yeah.

Paul: And you just use the devices to do the job you need 'em to do, so you no longer need to remember,

David: so you know your number and you don't know. Yeah,

Paul: that's right. I, you

David: probably don't even know Sarah's number. I don. No, I dunno. Michelle's, so that's the way it is.

Paul: Yeah.

David: See there's a button on my phone.

Paul: That's right. That's exactly right. And I think it's called Transactional Memory. Um, and if these AI models become, I mean, to be honest, this is true now is I think probably as long as I've got a subscription to chat GPT, probably I don't need to keep up in the way that the generations before us have needed to do because actually I can offload a lot of that thinking to the very thing that we are fearing or get away from us.

David: Correct.

Paul: So it's an interesting space because it will become like a, a second part of me. In fact, it's already for some of the work we're developing is already becoming a second part of me.

Um, [00:41:00] and you know, there, there, there are things I simply cannot keep track of anymore. It's not possible. But I know that it's all sat there stored Yeah. In various conversations. Provided I keep my subscriptions as well. You can't help me if I ever, and if

David: only you start charging you 2000 pound a month, then yeah,

Paul: well of course that's the business model.

Right? Get us addicted and then charge us more money. I mean it's, you know, crack cocaine for the techie.

David: Yeah.

Paul: Um, and that's exactly what's gonna happen because last man standing can charge what they like. Um, but in the meantime, while they're all competing for, for users, I think we're okay for a year or two.

I'm gonna ask you a couple of questions. Um, one is your view of the bias of these large language models. 'cause predominantly that the, or rather the pre domination of, um, documents that they've learn on is pre 2020.

English written words.

David: Yep.

Paul: And that basically gives us a, a time in history when, [00:42:00] um, the patriarchy is still very much in dominance. Um, it's white and it's western now obviously the amount of stuff that's being generated now will gradually even that out. But the actual founding, the foundational learning of these language systems has been, broadly speaking, the English speaking western world with all of its biases, all of its of nastiness really.

How do you see that shaping the way we're moving forward? Because at the end of the day, you and I are both doing the same thing. We're using AI for advice. We're using AI to build knowledge models. We're using AI to do tasks that are gonna walk, walk us forward into the future. But that's all founded. On things that have been of the past decade or so shifted.

One of the challenges with neural networks is that relearning, or [00:43:00] sorry, iterative learning is not easy to get a neur network or to get these large angles and wants to train really well on the whole, you crash it and start again with a bigger data set. Um, now I know there's a huge amount of research in this area and it's, I, I'm, I'm, I just remember my bit of it a long time ago, but even the documentation I read recently suggests that's still a challenge.

Incremental learning is hard, which says that everything they've learned so far is ingrained and it's gonna take a little while to flush that out and make things a bit more 21st century. So I was just looking for your view on that and how we, how we know that that's going on and offset against it.

David: Gosh. Um, that is a bit of a. Inspect that question.

Paul: Sorry.

David: That's fine. That's why

Paul: I brought you down. I brought

your

David: pin. Yeah, so my thinking with that is that in the same way that, so I'm not a computer person. I don't under, I mean, I understand what neural network is and [00:44:00] I sort of done a bit of background on the human brain over the years

and the way I think and the way I act and the things I do and the biases I had when I was 20 are completely different now.

Paul: Yeah.

David: Now that's taken me a lifetime of my one experience in life and learning from other people to amend those. If I was a large language model and I'm learning from thousands of people all the time, and I can go back.

So I can go back to 2016, I suppose it was 2017. So I had a client who demoed a chatbot to, to one of my other clients who I won't mention 'cause they're a very large multinational, PLC. Um, but they, [00:45:00] they wanted some, they wanted some help with how did they get information out throughout the whole world in multiple languages and save all these people there and asked all the same questions in their support center all the time.

So these guys turned up where they, they only asked them seven questions and it was just on a little laptop. I dunno what they connected it to otherwise, but it was on a little laptop. And then they had a room of these people and they were all the service engineers and they said, you've asked seven questions within an hour.

We reckon we can get this answer in. More than just the seven questions. In fact, we probably get it to answer 80% of the queries you ever get within an hour. And they did by, from starting with seven questions. 'cause what it did is if you didn't know the answer, it asked one of the agents. The agents told it.

Paul: Yeah.

David: Then it started to relate the two together. Okay. Yep. So, so the point I'm making there is that's a, a, [00:46:00] a data set which was specific to that one client. So it's not quite, it's a small language model, but, but the principle is the same. Yeah. The fact it learn quite quickly. So I would be thinking, and we do know that the, the guys who've started all this, some of them haven't got a clue how it learns some of the stuff now.

Paul: Yeah, yeah, yeah.

David: No, I know. Yeah. So, so there's an element in, in my mind it's as it starts learning all these other, and if, if chat GPS only ever used by White Western people, then it won't learn will it? But if chat GP PT is I sure being rolled out into other areas of the world, which it is, and it's in other languages.

Then it will start to understand culturally how different things work and how different. So I think it will, I actually think it, it learns so quickly that that actually will be a problem that disappears. That the challenge, I'd say will be ones where, where you get the, the split. So you're saying this is about the last man [00:47:00] standing.

You know, look, Microsoft and Apple sort of still hang around and Android and the operating systems, and there's gonna be more than one. The difference is, is how do they, how do they Yeah. Fit into the ecosystem together. But what you may add that with is one, one frog gonna say deep seek, which is just sitting over there.

In a completely different mindset.

Paul: Yeah.

David: So there's a book called Nexus. Um, I can't mental block suddenly. See, I've just done a chat. GPT and I can't remember the name of the author. Funny. You

Paul: could just ask your phone. I know,

David: but it's, it's Nexus. He, he, um, it'll come back to me in a bit, but he, he, but it's all about large language models and if they get in the, in the, in the hands of, uh, nefarious dictators, bad

Paul: actors, I think

David: bad actors', they, yeah.

But it was really interesting, yeah. About how it can manage data [00:48:00] and, and all the, the stuff, it's, it is actually a good, it's actually a good read, but I think, um, he did a book called Homo Sapiens as well. So it's, it's really, I, I think, I think it will learn and it will adjust itself.

Paul: Yeah.

David: I think it's gonna be ahead of where we think it's gonna be.

Paul: Okay.

David: So that's the bit I think is. The spooky thing, I think with it is where we, we dunno what it's going to morph into on its own.

Paul: True.

David: And then when you say, well, you can switch it off 'cause it's a computer. Yes. The data center possibly, but there's lots of them so you switch them all off. Yeah,

Paul: yeah, yeah.

David: And actually it's in loads of things, isn't it? Yeah. It's in your car

Paul: in all the gadgets.

David: Yeah, it is everywhere.

Paul: Yeah. Yeah.

David: It's omnipresent.

Paul: And in a very real sense, um, I remember, you know, when we were doing the research, if you understood, if you could articulate what it was doing, it's not artificial intelligence. That's an [00:49:00] algorithm. If you don't know what it's doing, then there's a better chance that it's an artificial intelligence. Um, anyway, that's an slight an aside.

Um, I'm gonna kind of draw towards the end of the, of, of the podcast and again, come back to really where it started. Um, and by the way, all of this is fascinating. Um, and obviously, you know, swim lanes and, uh, remarkability and the book of Remarkability, I mean, these are. Now things that I'm taking away, whether I have a listener pay attention or not.

Genius, I bought you a pint and got free consulting. Um, I guess very specific to photographers and associated industries, photographers, retouches, I guess consumers of photography. Um, I'd like your comment, I guess on the short to medium term, long term, who knows, I mean, none of us can predict that. I'm not gonna ask that.

But short to medium term, where do you see it going and how do you think photographers can protect [00:50:00] themselves by, sorry, protectionism is not really the right word. How can they make the best of the situation we're in? How can we still have thriving businesses, still have clients, um, human clients, hopefully.

How do we get that

David: to be human clients? Hopefully?

Paul: Well, at some point you've gotta be

David: Yes. I, yeah, you'll be taking,

Paul: well, I'm already getting emails and you know that AI emails, because very often they're the same but from different companies. So you know that there's models out there who will give me repeater sales messages.

David: Yes.

Paul: You haven't emailed me, you haven't emailed me back. I'd love to hear from you. You still haven't emailed me back. And I'm getting the same wording in three or four different companies. You know, AI is quite literally pitching for work.

David: So I, I think what you're gonna find is, is so what AI does is it, it, it homogenizes the world.

It's, it's a probability engine. So correct me if I'm wrong on that, but to me what it does is it, it it knows the next probable word. That's what it calculates. So it's a probability engine. Yeah. So if everybody, [00:51:00] if everybody uses ai, so if you think of photographers, um, if everybody uses AI to do exactly the same thing, to become more efficient and, and, and provide a faster service and a more cost effective service, we're gonna use for the precise moment

Paul: Yeah.

David: To their clients.

Paul: Yeah.

David: They're all. And this is a bit where I'll, I'll just go a bit negative. Yeah. Because I don't want this to be negative, but they're, they're, they're, they're, they're, they're the ones that are gonna struggle to survive this and the, the best power I can give. And right at the beginning, I did introduce that.

I had a print group.

Paul: Yep.

David: Um, back in, in the, in the nineties, machinery in print industry got faster and faster and faster. The time it took to prepare and do the graphics got shrunk dramatically.

Paul: Yeah.

David: And we went from being able to turn a, uh, turn a job round from switching the machine off to replacing it, put another one for two to [00:52:00] two hours, two and a half hours, sometimes to 10 minutes.

Paul: Yeah.

David: And the machines run running at 15,000 an hour on the floor, not 5,000 an hour. Game changer. Now what I'll tell you is the print industry. Died because everybody followed the same route.

Paul: Yeah.

David: So there was not enough capacity, there was more capacity than there was demand.

Paul: Yeah. Of

David: course. Then the internet came along.

Of

Paul: course, yeah.

David: Then the internet came along and the demand collapsed.

Paul: Yeah.

David: And which we didn't mention at the beginning when you started your photography business. I had one as well.

Paul: I'd forgotten that. I had genuinely forgotten that.

David: Yes. So, and we were in commercial photography. Yeah. And the digital, the digital revolution meant that our clients, which were high end and we did just mainly did a lot of reflective work.

Paul: Yeah.

David: Um, tableware, guns and things for the, you know, shooting guns or whatever.

Paul: Yeah.

David: It was, they were willing to pay us a lot of money to not have reflections [00:53:00] because it was worth the paying a lot more than paying somebody a lot of money for with reflections. When they found they could do it themselves, they didn't care about the reflections.

Paul: Funny that

David: it did. So what you get and the point are gonna make with this is what you get with the, with the buyers is they, they, they experiment and accept a lower quality for a period of time. They always do. In everything that's happened that, that I've seen, they experiment and then what they do is they then come back because it doesn't achieve the objective they wanted.

Background: Right.

David: Now, again, we're assuming this is fairly short term with AI and there's a lot of businesses out there we keep panicking about are people using it? So in the, in the creative sector, there are big businesses are busy in housing.

Paul: Yeah.

David: So they they're bringing everything in house. Yeah. So there's gonna be over capacity in the marketplace.

So if you are, if you are there today and you are [00:54:00] doing commercial. Based work.

Paul: Yeah.

David: You know, people aren't gonna do their own wedding photography.

Paul: No,

David: they're not. So on that basis, wedding photography for the moment is, is pretty safe. But what you are, what you're gonna get in people who are doing commercial photography and a lot of videography and that sort of stuff where they're, they're engaging with, with brands is that's gonna be taken in-house..

For a period of time. So to survive it, you're gonna have to pivot your business and the thinking. So this is the challenges that people have got is this isn't Chase efficiency is chase this. And it comes back to, which is quite nice. We started with the book of Remarkability. Yeah. Because you're coming back to

Paul: it.

I can feel it.

David: Yeah. But because it's actually where can you add value over and above the production process that you currently do? So post-production, when we were doing, even the year ago, we were paying for, for a reasonably decent shit. We were paying. Almost the same for [00:55:00] post-production as we were for, for, for, for the shoe.

Paul: Yeah.

David: So if you are, if that can be halved, you're just not making money on post post-production. So don't try and compete on that. Almost outsource it. Don't try and do it. It's, it is that mindset. And the other bit is, and I don't know, I don't know how nowadays how most photographers charge, but in the most of the creative industry, it's hour based.

Paul: Yes.

David: Right. So our hourly rates have gone, if your mindset today is I don't charge by the hour. .

Your, you are now starting to think correctly.

This is about charging for the value you create .

in the eyes of the person Yeah. That you are, you are serving.

Paul: Yeah.

David: And so the real challenge is understanding how you work out, what the value is.

So ask, and this sounds really weird. So the one of the, so you wanna make a decision that I don't want to do a [00:56:00] job for less than a thousand pound or less than 5,000 pound or whatever it is in your world. But what you're gonna do is you're gonna be asking clients what are, what, what not, what are you willing to pay?

Is how much, what do you really value?

Paul: Yeah.

David: And how would you like me to go about delivering that value?

And so once you know what they really value, then you can, you can attach a price to it.

.

David: But the price doesn't relate to how many hours it's gonna take you. It relates to the skill set that you've got.

.

David: Because some of the hours that you used to do, you're just going to

.

David: Pass it through a, a, a chat to bt or, or one of the specialist for photography packages.

Paul: Yeah. I, this is so, you know, my observation with it is. That for a long time now. So, I mean, I mean, I've worked with you commercially and I've, you know, I've, I've worked with most of our clients on a, we euphemistically in the business, we call it social photography, which is weddings, portraits, families, those kinds of things.

Yeah.

And we jump the line all the time. Um, and I come back to something I said [00:57:00] earlier, which is at the face, at that interface bit, if that moment is personal, um, irre, irreplaceable, non repeatable, though, those are where we add real value in not just capturing those, but capturing those in a way that adds to the experience.

If it's a sort of a nameless product to go, or a nameless scene to go on a book cover, a nameless model to go on a magazine cover. I mean, the irony, did you see that one or was it Cosmopolitan? I can't remember who the brand was, but they published a full AI model, um, you know, single A four.

David: Yeah.

Paul: All ai. Um, and I kid you not.

Alister models came out in protest that it was an unachievable level of beauty. Oh, the irony. 'cause that's something that we've been shouting about forever, isn't it? That unachievable thing that's going to go, because AI models are gonna do that incredibly well from now on. Forwards. They're [00:58:00] not, that's not gonna slow down.

David: No, of course it's not.

Paul: Um, I think part of the challenge there in our industry isn't going to be directly that weddings and families are gonna switch to ai. They might expect more just as people who understood Photoshop, we, what can you do with Photoshop? You know, you can remove that and post, can't you?

That kind of quote. Um, and it's the same thing as DTP when DTP arrived.

David: Yeah.

Paul: We went through a period of a myriad fonts on every single flyer until eventually people realized the talent wasn't the package. The talent was the user

David: was, was the understanding of

Paul: That's right.

David: Typography. That,

Paul: that's

David: right.

Design. Yes.

Paul: Um, however, there is a real risk to the industry, the parts industry that we, I sit in. Which is all those people have thriving businesses, doing the commercial photography. That's probably now going to be in decline, at least in the short term. All this equipment, all of these skills, and they're looking around, where's there still money to be made?

Probably are gonna have an eye on the markets we are in. So it's going to, there is, there is an indirect challenge, [00:59:00] I think from AI in our sector. However, you know, I've just written down value, value based proposition, which is something that all my life in commercial, I've understood. And you've basically just articulated it.

It's a, it's what you are proposing, right? And it's the value that you bring. And that hasn't changed. It's not going to change.

David: And what we've done is we've created, so the a so if you go back to the 1960s, um, not that I was working in the 1960s, but the, the film, the, the program, mad Man, mad Men.

Paul: Yes.

David: Yeah, yeah,

Paul: yeah, yeah.

David: The, so when that first came about, there was no alley rate. They, they didn't, I mean, I, we, we worked in the nineties with the music industry. Learn virtual, our business was mu music industry related. Yeah. They did no concept of of of hours and pricing and whatever. It was all, no, they value, but they knew what they valued and then they were willing to pay for it.

Paul: Yeah.

David: And, and I think that's the bit that people have gotta get their head round now. [01:00:00] And let's look on the positive side. If, if it means that human beings haven't gotta work in factories and be production, you know, and talk about the design sector when we, you know, we refer to juniors as Mac monkeys.

Yeah, yeah. I mean, come on. It was cheap labor to churn out, to charge the client more.

Paul: Yeah.

David: If those days have gone, and I, and I think the, the, the, the analogy, the metaphor, whatever I would use for this is businesses are very, have been very hierarchical. They've been very pyramid. And somebody wrote, oh, the pyramids are gone.

It's an obelisk. There's an article about this, about like the big consulting firms and the big agencies where the juniors are gonna go and there's gonna be very narrow obelisk. That's a lot of rubbish. What's it gonna be? And, 'cause you're gonna love this, I know all these people listening, I'm having one of this, the world's gonna be full of jazz [01:01:00] bands.

And what you're going to get is people who are highly talented bring in and jamming with other people who are highly talented to create great harmonies and great photos, great music, great sounds. It's, that's exactly where it's gonna go. And businesses are gonna flatten their hierarchy and be full of lots of different types of jazz bands.

Paul: Man, on, on that note, on two fronts, I hope you're right. 'cause um, I use the music analogy all the time. Um, for being a photographer because the processes are remarkably similar.

David: Correct.

Paul: So on that note, David Finch, thank you very much. That's one pint down a lot of conversations, uh, the book of Remarkability in the swimming pool analogy. Well, you know, I'll, if nobody else listens, I will take those away. Thank you so much for your time, um, to anyone who's listening, and I hope you've all enjoyed that.

Um, please do head across to mastering portrait photography.com where I'll give links to anything we can think of that would go well with this. [01:02:00] Uh, of course there's a ton of art of articles. All about this wonderful art of ours, which is the business, the love, and the joy of portrait photography. And of course, it's the spiritual home of this particular podcast.

If you've enjoyed it, please do subscribe wherever it is that you listen to your podcast. Until next time, whatever else from David and I in a very noisy pub, now it's started. So quiet. Uh, this is the Mastering Portrait Photography podcast. And remember, be kind to yourself. Take care.