# AI Automation and the Fallacy of Job Exposure Scores

**Podcast:** Another Podcast
**Published:** 2026-04-16

## Transcript

Hi, I'm Tony Karen Brown.
And I'm Benedict Evans.
That hasn't changed.
That's good.
Yeah, not that.
Yeah, a week to get a month to go.
Um the thing I sort of sort of talking about was um telling me trying to predict what jobs, what industries, what companies will be affected by AI.
Because Anthropoc does this thing, and OpenAI have done these things based on sort of US Census data where they try and put like a numeric score job by job of exposure to AI, which seems to me just absolutely ludicrous as an exercise in sort of self-deception, that like you can't possibly know this.
And then there's a bunch of other people kind of proposing different kinds of two by two frameworks, like Sequoia did one and Costa did one in the last couple of weeks.
And it struck me it's just kind of interesting to talk about like what you can know, what you can't know.
And in particular, to say, well, imagine you've been doing this in 1997 with the internet, what would you have got, and what would you not have got?
And the funny thing about that is like we now forget what wasn't obvious back then.
It's so obvious to us now.
And actually, before we dive into that question for you, that I was thinking as you were saying that did we fret so much about what jobs were going to be replaced by technology, whether that was the web or the mobile back in the day, the same way that we are fretting so much about AI.
It's interesting, no, because there was there was less the the first obvious thing that you can do with this is automating existing jobs.
Yeah.
Which wasn't quite so much the case with with with the web.
It wasn't like the first obvious thing is that this will automate X and Y and Z.
So it's the automation piece that has ever because I have don't feel like I've ever heard so many people focused so heavily on the fact that these jobs or their job or the neighbour's job is going to get completely erased, whether it's right or wrong.
Yeah, because it's it's just a different character of the technology.
Um, although you know, you go back and look, that is kind of what happened with the internet and with PCs, just not in the same way.
Um, and so the thing I was sort of thinking was like, you know, as I started my career as an internet as an analyst in 1999, the spring of 99, just as they like the the the bubble was really starting to to inflate.
And so you would look at e-commerce, you know, imagine you're clever and hardworking, and you know, you'd look at well, I am, you know, but you know, you what would you do?
Well, you look at retail and you'd say, I think the framework that went around was what's high touch and low touch.
This is a sensible way of looking at it.
And that would tell you that like consumer electronics would go much more quickly than high fashion, which was more or less true.
Um, there were some interesting wrinkles within that.
So, like it was not clear that like makeup would be easy to do online, for example.
Um, and then you would kind of And then funnily enough, this stuff with like clothes that we're still struggling today with return and exchanges and women buying clothes that actually fits them, which is fascinating.
30 years later, we're still struggling with that.
So here we are 25 years, 20 we're we're 25 years later, and people are still trying to work out how this works.
Um, and 30 years later, and people are still trying to work out how this works.
So that's one piece.
Um, and then you would kind of go and look at industries and you would say, Well, who is about information arbitrage?
And where are their physical assets that used to matter and don't matter anymore?
And so you would look at that and you would say, you know, and then but and then the interesting thing is to kind of go and back test it.
So the thing I point I've made in meetings recently is to say, well, imagine you were doing travel.
So you would say, Well, obviously, travel agents have got a serious problem.
And if you're a hotel or an airline, then you need to think about price liquidity and price transparency, and maybe you need to think about loyalty programs, but in the end, your business is about owning a physical asset.
And that was true.
And then Airbnb came along 15 years later and said, No, maybe it isn't about owning a physical asset.
The same thing for um taxis.
You would have not nobody's nobody realized that Uber was coming.
And even when it happened, nobody understood that this made any kind of sense.
And people still kind of sometimes argue about it.
So there's a sort of an Uber test to all of these frameworks, which was what happens?
How much are you like doing linear extrapolation from the current market?
How much are you just presuming that you'll use this new thing to do the old thing but better?
Um, there was a valuation professor who notoriously did an analysis of Uber kind of 10 years ago, where he said, you know, the valuation of Uber should be based on the fact that it's TAM is the size of the taxi market.
And this is I'm gonna work out the size of the taxi market, and therefore Uber will get X percent, and therefore that's that's what Uber's worth.
And of course, this was completely wrong because Uber's TAM was not the taxi market, it was something else.
It was much bigger than the taxi market.
Um, and so you get this sort of sense of well, you can do the linear extrapolation and you can have your framework.
And that framework will generally be directionally correct, but every now and then someone will come along and pull the whole thing inside out and say, I'm going to make it, and suddenly your framework is going to be the wrong way of looking at the question.
And you know, go back and read it, look at retail, like most of the time it's right.
Trying to put a numeric score on it, it is idiotic.
You know, people now trying to say, well, you know, accountants have got exposure of 72 and bookkeepers have got an exposure of 84.
This is just insane.
You know, it's like there's an old joke about the physicists who are asked to um to predict which horse is going to win a horse race.
And the physicist says, first we presume that each horse is a perfect sphere.
Um trying to simplify and to get to get to a degree of precision that's impossible.
It is crazy as humans that we like a good number regardless, that makes us feel good, even though it could be completely wrong.
Uh, but we're just like, oh, but that 72% helps me wrap my head around it.
Does it, even if it's complete bullshit?
Okay.
Yeah, I mean, I'm subject to this too.
You know, sometimes I make a slide where I'm making kind of making a rhetorical point and I'm doing it with a chart, but we all kind of know the point.
The chart is just illustrating it, even when the numbers aren't that important.
So this is kind of the thing that you can come up with these frameworks.
So you can say, you know, is AI more about information retrieval or is it more about intelligence?
Is about is it about knowledge or is it about intelligence?
Or this is your point about reframing the questions, rethinking.
Yes, every now and then, and those things are always kind of useful, but there was going to be but but there's if you if but there's gonna be one that's wrong.
And the thing that I was sort of thinking about, as maybe getting to a high level of abstraction here, is to say, you know, the Clay Christensen phrase, what is the job to be done?
What is the actual thing that the customer is buying from you?
And how do you map that against what is the thing that this new technology is changing?
And so you can look at um and and where is the point of leverage?
And so simple example would be, you know, airlines.
Okay, the online flight booking is all going to change, and that's going to change a bunch of stuff about your pricing and maybe some of your margins.
But in the end, the booking isn't the product.
The product is a plane that takes you from A to B and back, and the plane isn't being changed by this thing.
On the other hand, if you think about retail, say maybe there's a there's a bigger continuum here, say there are some retailers where the business is to be the most efficient endpoint to a logistics system.
And if internet shipping is a more efficient endpoint to that logistics system, then you're out of business.
Um, there are some cases where internet is not more efficient, like groceries, which is why Walmart is still a giant business, because it's actually more efficient to drive to the supermarket than it is to deliver all of that product with the cold chain and the stuff that bruises and everything else and deliver that to everybody's home.
And you know, here we are, Amazon is still you know poking away at it after 25, 30 years, but you know, grocery is different.
But for everything else, if your retail proposition is to be the endpoint to a logistics chain, the internet would do it better.
It wasn't clear that Amazon would do all of that and not just books in 1997, but it was clear that like the internet was going to be this fundamentally new endpoint to a logistics chain.
On the other hand, if you're a retailer whose proposition was not being the efficient endpoint to a logistics chain, but something else, like if it like um experience, service, suggestion, curation, the word everyone has suddenly started using is taste.
Um, if it's about opinion rather than we have every widget, then the internet wasn't much of an issue, which is what happened in luxury goods, the extreme case.
But also fascinatingly in book bookstores.
Some of book purchasing is I want that book now, and I know what book I want.
But a whole other part of book purchasing is I don't know what I want, and bookshopping is a leisure activity.
I'm gonna go into the bookshop for an hour and I'll walk out with three books I didn't want to do.
And I want recommendation, and I want to your point someone with taste to understand what I'm looking for and that can give me good advice.
And the recommendation, in a sense, is even if it's just that you've only got room for 5,000 books, that is a constraint that drives recommendation.
Whereas Amazon has seven, eight, nine hundred Amazon has seven, eight or nine hundred million scooes, so they can't do recommendation.
Um and so that but that's a kind of the question: what's the point of leverage?
And newspapers, I think, are kind of an interesting midpoint in that you know you can make a joke, which is may even be true, that newspapers looked at the internet and thought this is going to be great because our printing bills will go down.
And but you know, to take that at that point kind of seriously, you know, what is a newspaper?
Well, you are everything encompassed by the word journalism, like recommendation and suggestion and curation and investigation and journalism, and then you are trucking and light manufacturing company.
And clearly the internet means you won't need the trucking and light manufacturing, but that's not what we're buying.
Customer isn't buying pieces of paper from you, they're buying the journalism from you.
So the first run at this framing would be, well, well, but we'll but we are fine, just as Chanel is fine, because that's not what people are buying.
People are buying the store, they're buying the product, and now we can deliver that product in a different way.
The problem was that the physical asset was also your barrier to entry, and it was also what protected you.
Um, so you've got this kind of split is like you've got you know, the internet splits these two things apart.
You've got this physical asset, and you've got the product.
And where is the point of leverage?
Or maybe they're both points of leverage in different ways.
And so if those get broken apart, and then you don't need one, is that better for you, or is that a catastrophe for you, or did it not make any difference?
And I think we saw that from the journalistic perspective.
I mean, you and I have spoken about this before, but with something like a Substack where the barrier to entry now is any journalist or anyone with an opinion and a following can create a newsletter that because the barrier to entry is now so low and anyone can do it.
What I found myself finding myself, the situation that I found myself with is overwhelmed by the amount of people who are putting things out there in the world.
And what I really need is someone to tell me, okay, there are 20 people writing about LLMs.
This is these are the two people of substance who actually have an opinion that's founded in experience or expertise.
And Benedict.
Like they're not.
I wasn't joking, it's a serious person.
But it is, but it it what is shifting is interesting because what we initially thought, oh, this is, and we saw it during the pandemic: the amount of journalists who are just like, I'm out, I no longer want to be associated with a big media entity, I want my freedom.
I and now the barrier to entry from a tech perspective is so low, and then realizing oh, it's actually hard getting people to give you money for being a single entity and being a sole writer.
Yeah.
What well that's I mean, the the other case here would be I put publisher.
So there was this brief moment when people thought that authors would just go direct, and of course that isn't what happened at all.
So anyway, so so so you know, but the the the thing that the point of the framing is like you've got you know generalise it from the internet, you are doing this thing that customers are paying you for, and then there's a thing that you have to do in order to deliver that, which is having a store or printing paper or something, and maybe doing that thing was actually what people were paying.
Maybe the thing that the internet automates, it was actually what your business was and it's gone, or maybe that was a barrier to entry and you have a problem, or maybe actually that wasn't really anything to do with what your your actual business was, and so not much changes, which would be the case of the airline.
Or maybe it's both, it's the end product and the experience, or the ease of the experience, or the delivery or the ease of the delivery method, and you're just like the sum total of those two is worth more.
Or maybe even better.
Now, but but the point is you've got those two separate things.
You've got the actual thing people are coming to you for, and then you've got the way you're doing it.
Are those separate?
If they are separate, what happens if the way you're doing it gets automated away?
Is that much better for you, or does that create new intermediaries like the new OTAs, or is it much worse for you?
Uh like newspapers.
And it seems to me you can apply exactly the same framing to AI.
Well, which is to say, like, okay, you are a law firm.
Now you can make the documents much quicker.
Okay.
Is that why people were coming to you?
Is that what people were paying for?
Now you are um a software development company, you're a software company, you make vertical SaaS.
Now people can write the code much quicker and easier.
Okay, was that the product?
Was were people is the reason that XYZ SAS company doesn't have competition or has little competition that you'd have to write this many hundreds of thousands of lines of code to do it.
You know, I was at Andrees and Horace for six years.
You were, I don't know, five or five or six years a nation builder.
I don't feel like the reason it's hard to compete with X or Y or Z company is how long it would take you to write the code to replicate the product, is almost never the problem.
It's ever the problem, the hard part is everything else.
It's working out what the product should actually be and what the code should be doing, and how you're going to insert this into the industry and persuade people to use it, and executing the sales force and getting the right go-to-market and the right pricing and working out how you coexist with people in the industry already in the industry and who you don't compete with and who you do, it's all the other stuff.
It's not writing the code, you've got to turn your notifications off.
You're giving me bubbles of thumbs up.
Um it's the um it's all the other stuff.
Yeah.
Now, of course, you know, that said, you know, there are I'm sure there are software companies where writing the code is the hard part for the sake of argument.
Like if you're if your business is maintaining COBOL code for regional banks in the USA, then yes, you've got an existential problem there.
Not next year, but you know, clearly this is going to fundamentally change your business.
Um, but if you are DoorDash, the hard part of DoorDash is not writing the code.
This was the asinine part of that email that went around a couple of weeks ago.
Like, oh, you'll just get the agent to write the code, and then the agent will just go and sign up the retailers and the the restaurants and the drivers like tell me you know nothing about how marketplaces work.
But the point is that's not the hard part.
The code isn't the hard part, and sometimes the code is a hard part.
The same thing with professional services.
Which part of what you go to to a lawyer for or an accountant for is a thing that's now going to be automated and which isn't.
Um, and the thing I was always going to do to kind of a pair of charts here is, or two pairs of charts.
The first pair of charts is something I've often talked about and made the centerpiece of my last presentation, which is elevator attendance.
It used to be that elevators were manually operated, they have an accelerator and a break, and there were like a hundred thousand people in America who employed as elevator attendance.
And then that gets automated, it turns into button that you press, and then you don't need an elevator attended anymore.
The person's job is to be a button, and that job is automated away.
So nothing except in a few apartment buildings in Manhattan where it continues as a sort of a as a as an amenity.
The other side is to look at accountants.
Like the number of accountants in the USA increased every single decade in the 20th century.
And you go through adding machines and um computers and mainframes and PCs and Excel and ERPs and SaaS, you get wave after wave of automation and the number of accountants keeps going up.
Which should tell you that the job wasn't adding up the numbers and putting them on a piece of paper, it was something else.
It wasn't counting the numbers and making sure they match the numbers on the other piece of paper.
That wasn't the job.
That was just how you were doing it.
And there's a sort of um, I'm sort of thinking out loud here, but there's a sort of a pricey elasticity question in here.
And this is another slide that I made last year and I'm using again now, which is if you make it cheaper and easier to do something, um, do you do that more or do you that do you do that?
Do you do the same amount for less money, or do you do more for the same amount of money?
Like if you make it cheaper and easier to build spreadsheets, do you do more spreadsheets?
Or do you do the same number of spreadsheets for fewer people?
But there's a step two is is having to have all the people to do that your barrier to entry, which is, and both of these are points apply to media, because you know, we made it much cheaper and easier to distribute content.
So the result is that we have massively more content, and the barrier to doing the and the and needing to have the physical infrastructure was your barrier to entry, so that's gone away.
So you have massively, so the newspapers are completely screwed, the magazines are screwed, there's massively more start more content created.
But there's a step three, which is what are the things that you couldn't do?
Because the only way to do that would be to have like a million people on say on payroll.
Um, and these the example, like the extreme example I gave with, we're sorry, I'm on logging, but like the extreme example I gave was like if you wanted to have an express train that went from London to Scotland in 1800, it wouldn't matter how many horses you bought, you still couldn't do it.
You could put 10,000 horses on the front of this thing, and you still wouldn't be able to go from London to Scotland in eight hours.
You could not run a quant fund if you had a million people working for you in no computers.
You just like that wouldn't work.
And so there's like the automation, and then there's a barrier to entry, and then there's what just wasn't possible without this before.
And I also think there's another piece here, which is what are we putting?
How to say this.
It used to be that we got really excited and impressed by the technology that was available, and then it felt like, and I've already felt this like 10-15 years ago, and then the human element was sort of less important.
And I think I saw this when we were selling SaaS.
It was like who had the better tech and who cares about the salesperson or the implementation team that you had.
And it feels almost like there's a shift now that our focus and what we're more excited by is actually what does the human element and human implementation of all of this look like, because we're less impressed by the tech because we're realizing that the tech that we're gonna be able to be to build is we unfathomable.
Like we know that we can create incredible tech.
And so I think what I'm getting more excited about is how are the how are the humans going to implement this tech?
And I we saw this in SAS when I remember having this tension with our founder where he was like, We sell software, we don't sell human services.
And I was just like, Yes, but you need the human services consultancy element if you want to close the hundred million dollar deal.
No one's gonna pay a hundred million dollars just for the technology that you can buy off the shelf.
They need to understand how they can map this to the needs that they have.
That is the consultancy element.
People still want to sit in a room with someone who's incredibly smart and who can tell you, I worked on Obama's campaign, this is what worked for us.
Yes, here's you've bought the right tech, but here's what we've learned, and how we want we think we can reproduce this for a European campaign, for example.
And I think that I don't know, I I feel like we we're less and less impressed by the tech, and we're gonna be more impressed by what the humans can do with it, or maybe that's completely wrong.
It's funny.
I'm just I'm imagining like the Drake meme of like no and yes, and it's like the Drake is saying, you know, professional services company.
No, for deployed engineers, yes.
But I I it's it almost feels like we're we're sort of we we're it's so hard to comprehend today what the tech is capable of doing, partly because of everything that you've talked about, we just don't know yet.
That I think I'm and I had this argument with my husband, I was asking him something, he's like, Have you asked Claude ChatGPT?
And I'm like, Oh, for God's sake, I actually want to have a human conversation with my husband about something.
I don't want to sit in front of my laptop.
Like the the reason that you know you get married is to problem solve together.
So the fact that that is being, you know, referred to me now just like, have you asked Chat GPT yet?
And I'm like, oh my god, no.
What are we doing here?
This is, I'm not like have you all stopped.
Have you asked Dr.
Google yet?
But so there's something there.
I'm just like, I actually, I don't know.
I I wonder if if our point of leverage or or what what we're gonna focus on more.
We've talked about this before, like Neta Porte and just their extreme, you know, their approach to e-commerce is very much a white gloved experience.
Um they can get here as fast as an Amazon delivery, but you're paying for something more than that.
I don't know.
I just feel like I'm having a hard time wrapping my head about the velocity of this next generation of tools and technology.
Well, some of this is also, I mean, there's a there's a pricey elasticity point.
I saw someone say, like, I think in the dot-com bubble, like somebody sort of said this point that like there's a big difference between making something very cheap and making it free.
There's a sort of a binary difference between making it very cheap and making it when you make it free, you unlock all sorts of stuff that you didn't have when it was just cheaper.
I think there's there's there's another bit here, which is something I keep coming back to thinking about is is LVMH and the other luxury ghost conglomerates.
And where you're selling mass-produced, mass-retailed, mass manufactured, mass merchandised individuality and uniqueness.
Um, but you're also selling people something other than this is just the thing that's in the store.
It's about the experience and the curation and the taste, you know, the word at the moment.
Um, but there's this is a I mean, that's a specific set of cases where that might be the difference, or that might be the point of the leverage.
But I think the c the real question here is like you're going to automate this thing now.
And that will vary.
The impact of that will vary a great deal by industry, by company, and it will vary in like weird, unpredictable ways.
There will be stuff where it didn't occur to anybody that you could automate that thing and now you can, or it didn't occur to anybody that people would want that automated and now you can.
I mean, this is part of the story, you know, the story of Amazon in the last 25 years is on the one hand converting stuff that people thought needed high touch into stuff that you turned out you didn't.
I mean, it's like, you know, I'm trying to buy a pair of shoes right now, and I bought three sizes from Mr.
Porter, rather than hunting around who in New York stocks this particular brand and has this and saying, I'm just fucking, you know, hell with that, just buy three sizes, and they'll arrive in the course of three or four days from different retailers, and I'll return the two the two that I don't like.
And so we've had this process of converting high touch into low touch.
Um, and what we had with um the internet was, you know, it turned out that you know, Instagram substitutes for celebrity magazines.
And Instagram is not a magazine, it's some it's not even journalism, it's some whole other thing that substitutes for, you know, it's like you're kind of pushing down mass size hierarchy, defining low and level, low and low different levels of abstraction of how you would think about what this is, which is why, back to what I said at the beginning, you think you've made this great framework, and then there will be something on your framework where it turns out that it's just not that at all, and someone turns it into the opposite thing.
But the framework to think about is okay, we've got this automation thing here.
It's the thing that it can automate the thing that you do, or is it just the way that you do the thing that you do, and what you're actually selling is something else?
So that sounds like that Tom Hanks maybe.
Um, sorry, that was not deliberate.
But what is it that people are actually covering to you for?
Um, and is this automation going to change that, or is this automation sort of incidental to that?
And as I said, there's two parts to that, because it may be that you are, you know, it may be that you are the bit that's getting automated.
Three parts.
It may be you are the thing that's being automated.
You are the electronic store.
It may be that you are the newspaper where that isn't really what people were buying from you, but that was your barrier to entry.
And it may be that you're the airline where in the end that whole part of the value chain was kind of incidental, and credit cards are a much bigger deal.
It's funny, I spent some time on this a couple of weeks ago trying to make a chart of audit costs.
You know, what I came up with was a chart of how many people were employed as accountants since 1900, and the number has gone up every decade.
But I was wondering is is there data on what the average company pays for an audit?
And it turns out it is, there is, and it hasn't changed since about 2003.
And then there's data I've had like other studies that went back through the 80s, but the the the interesting part of it is that you get these 30 page academic studies on audit costs, and they talk about all sorts of stuff, and they never mention computers.
There's all this other stuff that's going on in the industry that's changing audit costs.
And this is actually only one of five or ten things that's on your list.
I remember when I was at Andrews and Horwicks, I remember having a meeting um with a big utility company, a European utility company, and the CEO listened to my presentation about what we then call the AI, which is now just machine learning.
And he said, This is very interesting, very interesting.
I think maybe innovation will be one of our top five priorities next year.
Which, if you're in the valley, sounds completely insane.
And then you think, but this is a water company.
Like maybe they probably do have other priorities, like there's lead in the pipes and there's an earthquake and a drought and regulation, and like they're worried about the Russians blowing stuff up.
And like there's all sorts of other stuff that's more important than innovation if you're a water company.
Like you dig holes in the ground and they're there for 50 years.
Um I had a conversation with somebody who was who was then became the head of one of the big um global lab networks, it's like 2016, 2017.
Like, what are you worried about?
And he said, Well, obviously, you know, I'm worried about AI and I'm worried about social and Google and so on.
I'm also worried that one of our countries doesn't have a good head of creative.
There's like there's all the other stuff.
It's like the line, you know, what do you do when in in an emergency flying an airplane, first of all, fly the airplane with all the other creatures?
And by the way, I'm seeing this firsthand with a company like um Apple, who's just got the broadcast media rights for for Formula One.
Their first priority is can you know, before all the innovation, before or looking at all of the original content is can this succeed?
Can we make sure that the product doesn't change and that fans tuning in can actually see the race in decent quality?
Um, and and so it's that short-term gain versus the long-term you know stuff that they want to work on.
Um, and it is fascinating to your point, but it's but it's also interesting to see what fans are looking for versus what the consumer, sorry, is looking for.
And you're never gonna satisfy them at the same time.
If the if the broadcasting wasn't as good as what it previously was, they would have gone up in arms.
And at the same time, these same consumers are going, oh, but so nothing has changed.
So Apple has an input, innovative, and you're like, we're on race two.
What?
Yeah, what have you done for me lately?
Um, because again, if they hadn't to focused on the immediacy of just to your point, what are the priorities here?
The well, the priorities are making sure that fans tune in and don't see a difference, then we want them to be wowed and see a difference.
There's a um, I mean, I can't remember who it is that said that all models are wrong, but all useful, and this kind of goes back to my point.
I mean, you know, you could, you know, this is you know, open AI anthropic model of like we're gonna score jobs, we're gonna do GPT Eval, we're gonna score jobs by exposure to AI.
Uh my my fiance was looking at this and she was saying, like, but accountants have been audited, be people have been ordered.
This says that accountants have got really high exposure.
We've been auditing or automating accountants for 125, 150 years, and there's more every year.
So you might want to revisit your assumptions here.
And meanwhile, you've also said that fitness instructors are going to be really, really safe.
And have you thought about what happens if I put my lean my phone again, put my phone on a chair and point it at me while I do my exercises and connect to an AI agent that analyzes what I'm doing?
Maybe that the there'll be this is the Uber example, the Uber test.
There'll be a bunch of things that you think your framework tells you are completely safe, and it turns out someone will work out a way to turn that into into automation.
Um, and meanwhile, there'll be stuff where like people have been trying to automate that thing for 150 years and it doesn't work.
And so those frameworks, that framework in particular, the the the thing that really drives me crazy about that framework is the numeric accuracy of it.
It's like we're going to give a percentage score.
Like you have no idea.
You can most you can do is say probably higher, probably lower.
Um, but I always say this is more general point of like it's not physicality that's the question.
You have to go, maybe this is what I've been groping towards as I've been rambling and monologuing for the last half hour.
It's like the question isn't physicality, is your product physical or not?
The question is, does physicality matter?
It's not how easy is it to automate that job with a computer, it's this thing that you automate the actual job.
You know, I mean, I swear to god, I look at these scoring systems, it's like, well, a lawyer spends 32% of their day on phone calls, and AI can do voice, therefore, AI can automate that.
The map that's not the maths, yeah.
That's not what the job is.
That's not the job.
I like that.
That that like that, that's not the actual.
We're realizing we actually don't know what the job of most people is to do.
What are they actually paying?
Which comes to you know, it comes to the McKinsey thing.
What do you pay McKinsey for?
It's not the slides, it's it's other things.
Sometimes it is.
Sometimes you pay you bought you paid for a private equity due diligence deal.
Sometimes you pay for them to validate this so they they the strategy you've already decided on, and they give you a bunch of slides that tell you what you you already knew.
But that's not what you do as a as a partner or senior partner at Bain BCG McKinsey all day.
That's not the job.
And to your point, everyone is providing the same software or the same type of PowerPoint decks.
What's the differentiator here?
And the differentiator most of the time is the human that you have in front of you.
Well, this is also the question is you know, the typical big company today has four to five hundred and SAS comp SAS apps, and all those are is a bit of business logic wrapped around some SQL.
And so why is it that um um Zillow and Airbnb and Tinder all exist and they're not and there aren't 50 of each of them?
Well, because it's not just per Bit of SQL and some business logic and a UI, it's something else.
There we are.
That's a good place.
It's harder than you look, it's harder than it looks.
Yes, it is.
Um another good conversation.
See you next week.
Yep, good to chat.
Bye.
See you next week.
Bye.