# AI Reshapes Enterprise Software Economics

**Podcast:** Another Podcast
**Published:** 2026-02-23

## Transcript

Hi, I'm Tony Cam Brown.
And I'm Benedict Evans, and this time I'm recording.
Great.
So that's that's first step into recording a podcast.
Um AI changing software.
Is that what we're doing today?
I think so, yeah.
Um as I was saying earlier, it felt like last week, everyone who works in enterprise software and knows anything about it, kind of put their head in their hands and said, Oh my god, do I actually have to explain why this is done?
Um, is you know, you'll use Claude instead of SAP.
Um, fine.
If you need me to explain this, I can't help you.
So let's talk about something else.
Um and it struck me that there's sort of a couple of more interesting building blocks to talk about here.
The first of them is like I started my career in the dot-com bubble, and you had a period of irrational optimism where people said, No, you don't understand, everything's gonna change, and it's gonna change tomorrow.
This is just like right now.
And they were right, but not for five and ten years.
And it wasn't five or ten years because you had to wait for people to build ball band either.
It just takes time to build all that stuff and get people to do that stuff.
And then on the other side, after the spring of 2020, 2000, spring of 2000, there was irrational pessimism.
And in particular, what would happen is you'd have a story that everyone would just knew this is ridiculous, and the stock would go down 15% or 20%.
And the best part was a month or two months later, there would be another version of the same story and the stock would go down 15 or 20% again, and people would say, but but we just did that.
And the answer was stuff can be in the price, and then it can be in the price again.
And a lot of the sentiment at the moment is look, the big four companies, platform companies, spent 400 billion dollars on CapEx last year.
This year they've said they'll spend 650 more or less.
Microsoft hasn't been given folio guidance.
But Meta and Google and Amazon have more or less doubled their cap, well, will say they're more or less double this cap their capex this year.
And so you're going from, apart from Amazon, these were companies that did not have a lot of CapEx, and suddenly they're talking about 30, 40, 50% of their revenue going on CapEx.
Whereas the telco spends 20%, 15 to 20%.
So everyone is sitting and going, whoa, whoa, whoa, whoa, whoa, whoa.
What where is the return from this going to come from?
Are these going to be structurally lower margin companies?
You know, how long is the is the business going to come from this?
So you get this super nervousness about Microsoft results where Microsoft is a mess, but Amazon's results, you know, is OpenAI really going to get that investment from NVIDIA.
And then you get this silly thing of Claude having a skill for legal and like all the more software, legal software companies go up 20%.
And then they're back up to where they started at the end of the day.
So there's a lot of nervousness around the just the amount of money involved here.
I think there's also nervousness around the fact that, you know, with the dot-com vote, boom, this was about creating new stuff.
When it wasn't obvious to everybody this was going to be hugely destructive to a bunch of other industries, and in fact it wasn't, like newspapers and travel and a few others.
Whereas here, the first wave of all of this is to take a bunch of cost out.
And so that's revenue that could go, which makes people nervous.
There's a big piece in the FT this morning about how much money the private equity industry put into buying software companies in the last 10 or 15 years, and a whole amount of debt, like half a billion dollars, half a trillion dollars of debt around that.
And everyone's thinking, Whoa, wait a minute, we bought these software companies, and now what?
So you've got all this kind of financial wall streety stuff that makes and a lot of this sort of euphoria on the other side of 25-year-olds who've never worked in another industry and have never seen a financial financial uh never seen an industry cycle and just think everything always goes up, and they were born with iPhones in their hands and don't really understand that like stuff changes before stuff has changed before.
Do you just a question there?
Do you think if there wasn't that amount of money being poured into this that people would be a little less nervous?
It's it's interesting in in San Francisco hearing people who are in their you know, 50s, 60s, 70s saying, I feel like with this is like the fourth wave of AI, and this time it's actually sticking, and it's crazy that I've been working on artificial intelligence and LLMs for 40 plus years, but with there's definitely a different moment right now.
But I'm curious where your head is at on that.
Oh, so I think I'm but there's there's unpicking different things within that.
Um, yes, this is a breakout.
I mean, yeah, the last wave of AI was machine learning 10, 15 years ago now, 10 years ago, really.
That was not transformative across everything.
It was a new bunch of stuff that everybody could build, and there were a bunch of new companies with it, but it wasn't the same scale of change that we had.
It was additive and and a nice evolution, but that's about it.
Yeah, um, you know, it was more like I don't know, it was like going to GUIs rather than the invention of the PC or the invention of the web.
It was a you know, a a um a smaller wave rather than a big wave.
Um, and I think you know, we should preface all of this by saying everything I'm about to say is going to be a bit skeptical, but like, no, this is an enormously big deal, this is hugely important.
I still see people who say, no, it's just like a stochastic parody, it doesn't do causation, it doesn't work very well, it's got hallucinations, it's all useless.
These people are morphs, like they are just idiots, but they're idiots in the sense of somebody who looked at the internet in 1999 and said nobody is going to use this.
Not in the sense of people who looked at it in 1999 and said, This is going to change absolutely everything, but that company's too expensive.
Uh, which in the end was was kind of the right view to take.
So that's a kind of a that's a kind of the bubbly conversation.
I think then you actually sit and sort of sit and look, well, well, what does AI mean for software?
And there's kind of two or three or four different, like fairly obvious building blocks and put on the table.
The first of them is, well, clearly, this is an order of magnitude, many maybe several orders of magnitude cheaper to make in any given piece of software.
Piece of software that you might have thought of 10 years ago that has nothing to do with AI, you can use AI to make that thing much more quickly, much more cheaply.
And so that means by if it weren't for any other conversation, that would mean a way more software, way more problems being automated that weren't automated before, way more stuff getting done inside enterprises, way more jobs that maybe needed people now being turning into turned into software, just software that runs on a database because you couldn't have made a database for that before because it was too expensive.
Um, and of course, that means competition for the existing companies and you know, churn within the industry and so on.
Secondly, this software can do something radically different, as we've all discussed at great length.
So there's another whole class of stuff that will get automated and changed, including stuff that today is a big giant company.
Thirdly, then you get to the the kind of the delusional idea, which is you know, um, Dwight and Fat Keith and Gareth from the office will come in one morning and say, Hey, you know, SAP doesn't work very well, I'm gonna make my own instead.
Where I mean I saw a a post on social media from someone posting the picture of Samuel L.
Jackson saying, Ink say English again, say say say what again, say what again.
Don't say system of record again, I dare you, or double dare you.
But like you've got 25,000 people in your company, and you all need to be on the all need to be on the same database, and you need to have the same processes and the same flow and the same buttons on the screen.
No, you can't just have one random people making their own doors.
What's much more interesting, and you know, we could talk about that for hours, and we kind of have to have talked about that for hours.
What's more interesting to me, and then I'll stop monologuing, is was to kind of scratch my head and think, well, today you might you've got the stuff that's systematized and institutionalized and turned into a rigid process in SAP, like a general purpose enterprise software.
And then you've also got hundreds of individual single purpose, special purpose vertical pieces of enterprise software, like you know, your counts payable system or you know, the thing that cracks graduate recruiting or and so on.
And then you've got the middle case of Excel and Google Sheets and email, where like we don't haven't got a dedicated tool to do that thing.
Maybe we don't do it often enough.
Maybe it's just come up today.
You're kind of improvising solutions, and sometimes those improvised solutions will turn into a SaaS company, particularly because of the two previous things I've just said, but often they won't.
It's like I need to do answer this question, I'm gonna export a CSV and find out the answer because I can't do it in Salesforce, or I can't do it in SAP or whatever it is.
So you've got this kind of improvised middle space.
And you know, there's a joke that everything that you see in the file on the screen in Excel times into a company.
Um now the answer might be or maybe I'll use AI instead.
Maybe you'll say to AI, hey, look at this thing and do this analysis.
Hey, look at this thing and answer that question for me.
And that's a different way of saying this thing's making tools for you than saying you're going to tell Claude to write code for you and then get you an AWS account so you can run it, which is you know purely delusional.
Um there's a sort of sorry, I was gonna said I was gonna stop.
I've something else I just wanted to occur to me to say is there's a recurring delusion throughout the history of software that firstly, people always think there's going to be a general purpose abstraction layer that will just do everything.
And secondly, people always think um that everyone who doesn't write code will write code.
And we don't make it a little bit easier, then everyone will write code, and those are both just completely wrong.
I mean, there's also an element that most people don't there was an interesting video the other day of um of someone just reminding everyone that AI is about writing code, like that is what it is.
It writes code, and I had that realization also of just like there's still a lot of people out there who have no design to write code.
Well, generalizes to put that slightly differently about most people do not sit and think about how they could optimize their job.
And even if they did, there's a huge difference between saying it's kind of a pain in the arse to do this thing and working out the right way of fixing it.
Which is why you see very often as this, I think Paul Graham had this phrase, the tar pit, where there are these things that people have tried and tried and tried to make a piece of software to solve that problem and failed over and over again because it's just really hard to get all the incentives aligned.
The underlying point though is I think the hard part of making software is almost never writing the code.
There are some things where you're solving some hard technical problem.
But mostly what you're doing the problem is working out is realizing that the problem in the company even existed, and then working out what would be the right way of solving that, and then working out how you would get everybody across the industry to use it.
And that's not something that you know a random middle manager or person at one part of that ecosystem can solve without going off and making that all they're going to do for the next five years of their lives.
Yeah, there's a complexity element there that's really interesting that I think we talked about briefly at some point of just like when we moved from um the complexity of being on-prem versus in the cloud and how people were thinking about that as a step change as well, which is the same thing.
Well, the the on-prem thing is the cloud thing is kind of interesting because this is something that starts in, you know, you could date it to Salesforce if you like.
You could argue about when exactly it starts.
But you know, it's been around 20, 25 years.
It's still only sort of a third of enterprise workflows.
So it takes a long time to to replatform.
Um but then there's stuff that comes with cloud.
So you go from um when you don't have to do an installation in the client's data center, so then it becomes you can have way more software because it's much easier to deploy it.
It's continuous deployment, so you don't do a version and then do another version 18 months or two or three years later.
You don't have different clients.
It's a lot of different clients who are on different versions of your software.
You have continuous deployment, you know, it's just a website with a URL.
And then of course you have to build a whole ecosystem around that of single sign-on of security and everything else.
Um but that meant a you got way more software automating way more things.
And the people who'd been in the old wave either got left behind or got made marginalized.
Ironically, Oracle is back in the headlines for the first time in 25 years because Oracle never made the jump to cloud, or you know, never became relevant in the cloud.
And so they've been losing market share in the enterprise um for 10 or 20 years.
Um now Larry Allison wants to go to parties again, so he's he's gone out and built a cloud business, borrowed a lot, an enormous amount of money, 50 billion dollars of bonus this year, um, to of capital raising this yeah.
Um, but there's a sort of again, there's like a structural point here, which is that the cloud wasn't just well, now the app is a website.
The cloud was, but now all this other stuff becomes possible.
And now many more problems can get solved and get automated.
And again, and it's changed the whole structure of the industry.
It also means it goes from the software goes from being CapEx to OPEX.
Um, and there's a lot of, and and that of course is the reason why companies don't buy it because they haven't depreciated the stuff they bought five years ago.
They're expecting to put it through the earnings for another 10 years as depreciation and now you need to do a write down um say no we won't buy this yet because there's all sorts of like second order questions that came out of the shift to cloud and you could imagine a lot of sort of second order questions that come here.
One of the things that I sort of think about as I as I you know talk to companies about this is um you know sort of classic framing I've used talking about platform shift is like step one is you use it to automate the obvious stuff you're doing that you can already see you have this problems like call centers.
Step two is you invent new stuff that you couldn't have done before.
Step three is well wait you automate and you want to grow at scale isn't it those but both hand in hand there no well no it's like you know you've got you've got this problem of you have a bunch of invoices coming in and you need to map them against your payments and now an LLM means that it will do that radically easier and quicker.
So you automate existing processes and then step two is um you know same thing back in the late 90s you know, you put your catalogue on the internet yeah and then step two is that's cheaper than mailing people a catalogue.
Well, that wasn't the end of the conversation.
Step two is people you get new revenue, you know, new businesses, people unbundle you, you unbundle other people, new kinds of competition.
But then step three is sometimes somebody kind of completely redefines the market and like actually changes the whole nature of what this thing is and how you think about that question.
And one of the the framings that I was sort of thinking about here for for for a big company is um, you know, imagine you're the manager of a Walmart somewhere in Jersey, and there's a big, you know, once in a 30 year storm coming through.
So I live in New York, we had this enormous storm.
Um, okay, so step one is I need to find that data.
Step two, step one point five is maybe what data should I be looking for?
Steps and that will that will do is that will find you ask the LLM and it will find you the thing in Salesforce or SAP or whatever the data system that you're using is.
And maybe it'll find it across 10 or 20 different systems and it'll put them all together for you in a dashboard.
And yes, you could have done that with SnapLogic or all these other orchestration project pop up tools 10 years ago.
Um, and maybe SnapLogic will use those kind of software, will use an LLM in order to help you build this.
So then step two is build me the dashboard, find me all this information so I don't have to go and build it, pull it all together.
But what you're seeing there is a deterministic screen.
You're seeing a screen of data feeds that come from your deterministic systems.
So you've got this kind of mix of probabilistic and deterministic systems.
Um but then step three is hey, I manage a Walmart in Jersey, and there's a once every 30 year storm coming through.
What questions should I be asking?
What should I be worried about?
Show build me a dashboard around that.
Now that's a different way of solving those kinds of problems.
And you're stepping up to a different level of abstraction in what you could be asking for.
I mean, there's a sort of an e-commerce equivalent here, which is you know, it's the difference between, I don't know, you take a picture of a coat and you say, suggest coats like this, and you say, Have a look at my Instagram and tell me what coat I would like.
Those are very different types of questions.
You know, you remember do you remember the old XKCD cartoon about image recognition?
So the SK CD cartoon where the boss says to you is in this is in like 2010.
The boss says to the engineer, um, I want to flag every picture if it's taken a national park.
And the engineer says, Okay, latitude, longitude look up, encoded in the metadata, I'll map it against a GIS database.
I can do that softening.
And then the boss says, also it should say if the picture contains a bird.
And the engineer says, Hmm, I'll need a research group in five years.
Feels it can feel like the same question, two fundamentally different asks.
And it looks like the same question, it's not the same question.
And of course, the answer five years later, like today, that's an API call and it's 10 minutes.
You know, does this contain a bird?
Do an API call to an image recognition model on AWS.
Um, and there's this sort of again, there's this sort of sense of like what kind of questions should I be asking my data?
Yeah, and you get these kind of dumb enterprise software ads where they say, Imagine having a conversation with your data.
But and there's a sort of, you know, there's a middle point here of no, what should actually happen is that the LLM, that the enterprise software should be saying, hmm, this guy is a manager in Jersey, and that looks like there's a big storm coming up.
And you shouldn't have to have coded the is there a big storm, it should just be looking at everything and seeing, well, there's a storm and it's a big storm.
Hmm.
That sounds like it might be good, but big deal for a retailer.
Let me look that up.
It should be doing that level of predictive analysis.
Um, and so you shift the layers of abstraction in how you're using your your systems of record.
There's there's that word again, the system of record.
The database is still there, and the database is a database and it's storing the data in the same way.
But what's happening around it?
How are you getting stuff in and out?
What are you asking?
What can you ask?
How can you use that?
How can you extend what kind of insights you can get out of that?
That to me is a lot more interesting than explaining why no people aren't going to use Claude to make their own SAP.
It's a new way of thinking and approaching and problem solving that I think most people are just not used to.
Well, it's not used to, but also there's a fuzzy gap of is that your job?
We haven't talked about that.
Yeah.
Should you be thinking about optimizing your work?
Is it your job to optimize your actual job?
At a certain point, if you're if you're being, I mean, if you're being paid half a million dollars and running a Walmart with a hundred employees, then it it kind of is your job.
Um, but there's a big fuzzy question of how much should you, as a normal person working for a company, have to be thinking about what the software could be doing for you, which I think gets you into this fallacy of like we're going to make everybody use AI, which to me is very like, you know, being in 1998 and saying we're going to make everybody use um the internet.
Or indeed, it's actually what happened with um with Facebook in the late 2000s, where they said we're going to make everybody use mobile, we're going to force all of our app engineers to use Androids because they've all got their thousand dollar you know, $700 or $600 iPhone, and that works great.
But most of your users have a hundred dollar Android, and your app is your Android app is terrible, but you've never used the Android app, and certainly not on a hundred dollar Android.
And so there is a layer of like you can just brute force people over the hump to get them into the new thing.
But that only works.
That works if you're Facebook.
I'm not or you know, I'm not sure that works if you are, you know, there's an interesting point though of like is it your job to optimize your job?
But there's also a flip side to that, which is shouldn't we all want to have a look at what we're doing today and optimize it and be excited by the fact that we could do be doing more with less and in less time and it requires less skills and it's less complicated.
I don't know.
I think that product management is hard, and working out what the tool should be and how you should be doing this is hard.
And there is a whole profession of thinking, well, what should the product be?
I mean, this is, you know, it's this sort of, you know, it's another sort of idiots on social media.
Nobody asked for this app.
Well, nobody asked for anything, nobody asked for cars or airplanes or electricity.
Nobody asked for half the time we don't know what we need to do.
Yes, it's not your job to invent everything that you that you haven't find useful in your life.
You shouldn't have to.
You know, there is a whole field.
But we should be inclined to be curious.
But there's a whole field in a career of thinking, you know, hey, this would be an interesting thing to do, and maybe people might like this, and maybe this is useful.
And that's a different set of skills.
I mean, you know, just sort of thinking back to a company we I remember looking at when I was at A16Z, which is frame.io.
And the the model for frame.io is you're working, say you're making a TV commercial or music video or something.
How many people in how many different organizations need to see that?
Five, 10 organizations, 20, 30 people, of which some subset are people who are actually doing stuff.
And there's a just different people doing different things.
You know, there's the people doing the color and the editor and the sound and all sorts of different things going on in that.
So how do you manage that?
Well, you're FedExing hard disks around the country, or maybe you've got a Google Drive if the files aren't too big and you've got enough bandwidth, and then there's a Google Sheet with time codes and comments and a whole bunch of email.
And somebody's whole job is to go into the email and put stuff into the Google timesheets and then take stuff out of the Google timesheets and email people.
And frame.io says no, we're gonna make Google Docs a video.
It's not literally Google Docs, and you can't actually edit the video, but it's everything around that.
It takes all of that, and as soon as I kind of start saying this, you kind of imagine all the stuff you'd want, like you be you, and then I say, Oh, but and also you can draw around that thing on that frame.
And as I say that, you think, oh, of course.
Would you have thought of that?
Would I have thought of that if I hadn't the product didn't have that?
And then it's six months or a year or two years as people actually systematically thinking, okay, what should every screen be?
What is the workflow?
What are the problems here?
How is it that we build this?
What's the right way of doing this?
What are the right trade-offs?
How do we get everybody to use this?
And now imagine you are managing the colour color grading of that video, and you're thinking, hey, it's a massive pen in the ass for me to share these videos.
Are you gonna build that?
Is that what you is that your skill?
Is that how you think?
Is that you do you want that to be the next two years of your life?
Is that what you do?
That's that's a making working, seeing the problem exists and thinking that that of how to solve it is not about the code.
That's not the hard part of any of this.
I say this as somebody who hasn't written code except for Excel in 20 years, so like Jonathan Krugger syndrome in action, but oh my girl, I'm an idiot.
But I don't think the hard part of that is the code.
Listening to you talk about this, there's something that strikes me as well as like, no, I don't think I haven't heard of anyone be particularly annoyed or angry at the idea that AI is going to change software and change the way we code SaaS products.
It's interesting to me that every time people do get very emotionally invested in AI, and where people do get mad is in the creative space, in the things where there isn't actually a right or wrong, which I find absolutely fascinating.
It's people get mad at you know AI-generated art or music when the reality is that's the stuff we should actually be trying to enjoy, and we don't need to worry about too much worry.
I'm putting this in inverted commas, um, not from a jobs perspective, but we don't need to worry about if it's right or wrong and what I'm ingesting or what I'm consuming is gonna put me down the wrong track of just like don't be an idiot that what you know it's it's completely hallucinating here, and it is in some way, but the fact that it may be wrong is just you don't like the art.
And I just find there's something really interesting of what people are getting angry at and where we're spending our emotional capacity, is actually the stuff that we should just be taking in and appreciating.
Well, yeah, I mean, we were we we were chatting about this earlier, and my my sort of pithy Twitter-y sort of framing of this was that the fields where AI is actually works best right now, are the fields where people are really angry about it and sure it's useless.
And it's not actually solving a problem, it's just offering us.
And vice versa.
Whereas the fields where it doesn't really doesn't quite work or needs an awful lot of other stuff around it, are the fields where people are sure that it works right now, and you're like, and that is completely wrong.
You actually had another when we were chatting a couple of days ago, you had a really interesting layer attached to that as well, which was this question about authenticity, like canned music versus Taylor Swift.
And I think that's a maybe that's a nice way of closing out this conversation because I think that question of authenticity actually does tie into the software piece and what people are getting angry about versus what they aren't.
Yeah, well, thanks for reminding me.
I mean, this is actually kind of a long way from enterprise software at one level.
But the thing that I was sort of thinking about was um, so I heard I was listening to a podcast about the history of copyright, and it mentioned that John Phillips Caesar hated recorded music, he called it canned music.
And it did st has struck me often that the people who are most upset about AI image generation appear to have no conception that there are things called cameras.
Like, but it's just a machine, you press the button and it makes the art.
Well, yeah.
Every photographer enjoys that.
And and yet somehow I can buy the same camera as Cartier Bressan and not get the same pictures.
And like I go and look at people who are doing interesting stuff with LLMs, and I think, but I have no, I wouldn't be able to get get get get get Imogen to make that.
I mean, you and I were saying we wouldn't even know where to start in our prompt to get the result that they are that we are looking at right now.
I wouldn't even know what words to use, what sentences to string together, what yeah, what vocabulary.
Yeah, exactly.
So so they they but the interesting thing here is is that you can have photography that is purely mechanistic purpose, like make me pictures of my kids, maybe take me a picture of that that product and photography that's art.
And what is it that makes it art?
Well, it's something to do with it's not really about the aesthetics, it's to do with the intent.
And the the point that we were making about authenticity is like, why do I want that picture?
Do I want that picture because I value the artistic integrity, the artist's journey, the vision behind it, the message that it conveys, or do I just want a nice picture?
Do I just want a nice poster for my my teenage bedroom?
Do I want music that expresses the musician's artistic vision?
Or do I just want 10 hours of soft jazz while I drive to drive Uber all day?
Um the you know, back to your point, you know, the really unkind thing one could say is that the people who are most upset about AI art are the people who are making stuff that has no particular authenticity or originality.
I mean, if you if you've made if you if you wrote 10 um vampire romance books last year and you're upset that somebody else is using AI to make 50 romance vampire romance novels, then I'm not quite sure how to explain this.
What the problem here might be.
Um of course, you know, the the the more needful way to put this is that you've got very different conceptions of what artisticity, uh authenticity, and art artistic value actually are.
Um but the underlying kind of puzzle here is, and this is a kind of a point about you know, what does AI do to software industries, is you know, what is it that you're getting here?
What are you buying?
Are you buying software?
Are you buying some code or are you buying somebody who's worked out what this should be?
Are you paying them for some SQL and some C and an AWS account that's already set up for you?
Or are you paying them for having worked out what this problem is and how it should get solved so that you don't have to do that?
I mean, it's a very funny way, it's the same as a set of you know, the idiots on LinkedIn or Twitter who say, Oh, you know, this new model can make slides, say McKinsey is out of business.
No, what McKinsey does.
You have no idea what McKinsey does or what the clients are paying for.
They're not paying for the floods.
Because I I see this in the creator space, especially in in a space like Formula One, where the barrier now is so low that already in a sport like Formula One, well, in sports in general, we have a lot of armchair experts.
Um, and you see this during the Olympics where you're looking at someone, you know, perfecting an art that they have.
Are you suggesting that middle-aged men in bars have opinions about Formula One?
Yes, and that yes, absolutely, and suggesting that I'm one of those persons who will look at someone do a triple somersault and go, how hard can that be?
I think I could have done that.
Um, that's how it's disappointing.
What do you mean?
What's that landing?
What do you mean both feet are not in the end?
But what's interesting with this that I've noticed is the amount of people who will now call themselves a Formula One 10X, you know, tech expert, because they are using Chat GPT to help them write a few prompts about a few pieces that they've got, and you look at it and go, This I actually enjoy reading this because it shows to me all the things that you will never be able to write because you are not standing in the paddock watching a mechanic work hand in hand with the engineer to figure out airflow on a Formula One car, and it's that to your point, it is that knowledge, it is that experience, it is that human element attached to it that is just so invaluable that I just look at this and go, I'm not worried.
You know, Formula One journalists are not going to disappear anytime soon.
Do they have to evolve in the way that they're commenting and documenting the sport?
Absolutely.
But to your point about McKenzie, that's not what people at McKenzie do.
Well there's it's I mean I think I've I've said before my my you know one of my the ways I would think about what I'm writing is I've written this is this what anybody else would say is this just saying what everybody knows or everyone thinks well then I won't publish it.
And now I just say well is this what chat GPT would have said.
Mm-hmm.
In which case it's generic and average and it's saying what everyone knows.
And there's no up there's actually no opinion to it.
And it's interesting because I again I've said this like I'm not going to compete to people who've been writing about Formula One for five decades.
What I can do is I can talk about the political element of it or the business case of it or what I'm seeing from a tech perspective or SaaS software.
Like I'm loving right now that there are so many AI tools being used by Formula One teams because actually SAS and SAS deployment is something that I used to work in.
So I look at that and go I'm interested to see how the team is going to adopt this piece piece of tool.
But I can't write about cars going around on track.
But to I like that.
But I like that and I I hadn't thought of that, by the way.
That analogy of is this what chat GPT would have written?
Great, then I have nothing to add this it's not something I would want to write.
Yes, I like the um apropos nothing else of the this this whole thing now.
Forward deployed engineers.
And you're you remember the joke that a a a machine learning scientist is a is a um statistician who lives in San Francisco.
And and it's now, you know, a forward deployed engineer is um uh an extension consultant who lives in the game.
Um but that feels like a good uh a good way to wrap up this episode about AI and and SaaS.
And I like that we've added that element of authenticity and well again, what do you what is it that you're buying here?
Are you buying the code?
Are you buying the PowerPoint deck?
Are you just buying the widget?
Do you just need it to be delivered?
Did you just go to Amazon, you knew what you wanted, you press buy, it arrived.
Or did you want something else?
Did you need opinion?
Did you need judgment?
Did you need imagination?
Did you need somebody have sat and stared at a wall for a week to work out what they should be?
As opposed to, oh well, that's the obvious sense.
Um and yeah, if you've got a piece of software, all you all you're really doing is wrapping up SQL and putting a car logo on it instead of a bike logo on it so that it's car software instead of bike software.
Yeah, you're screwed.
That's what happened with cloud too.
Um the question is what are you doing that isn't just well, I could get Chat CPT to do that.
That's great.
Um feels like a good way to wrap up.
Uh thank you for that.
And I'll speak to you soon.
Bye.
Speak to you soon.